-h\" for help',\n Color.BRIGHT_YELLOW)","label":0},{"cid":"conans/client/command.py::Command._show_help.check_all_commands_listed","text":"conans/client/command.py::Command._show_help.check_all_commands_listed\n def check_all_commands_listed():\n \"\"\"Keep updated the main directory, raise if don't\"\"\"\n all_commands = self._commands()\n all_in_grps = [command for _, command_list in grps for command in command_list]\n if set(all_in_grps) != set(all_commands):\n diff = set(all_commands) - set(all_in_grps)\n raise Exception(\"Some command is missing in the main help: %s\" % \",\".join(diff))\n return all_commands","label":0},{"cid":"conans/client/command.py::Command._commands","text":"conans/client/command.py::Command._commands\n def _commands(self):\n \"\"\" returns a list of available commands\n \"\"\"\n result = {}\n for m in inspect.getmembers(self, predicate=inspect.ismethod):\n method_name = m[0]\n if not method_name.startswith('_'):\n if \"export_pkg\" == method_name:\n method_name = \"export-pkg\"\n method = m[1]\n if method.__doc__ and not method.__doc__.startswith('HIDDEN'):\n result[method_name] = method\n return result","label":0},{"cid":"conans/client/command.py::Command._check_query_parameter_and_get_reference","text":"conans/client/command.py::Command._check_query_parameter_and_get_reference\n @staticmethod\n def _check_query_parameter_and_get_reference(pattern, query):\n reference = None\n if pattern:\n try:\n reference = ConanFileReference.loads(pattern)\n except ConanException:\n if query is not None:\n raise ConanException(\"-q parameter only allowed with a valid recipe \"\n \"reference as search pattern. e.g conan search \"\n \"MyPackage/1.2@user/channel -q \\\"os=Windows\\\"\")\n return reference","label":0},{"cid":"conans/client/command.py::Command.run","text":"conans/client/command.py::Command.run\n def run(self, *args):\n \"\"\"HIDDEN: entry point for executing commands, dispatcher to class\n methods\n \"\"\"\n errors = False\n try:\n try:\n command = args[0][0]\n commands = self._commands()\n method = commands[command]\n except KeyError as exc:\n if command in [\"-v\", \"--version\"]:\n self._user_io.out.success(\"Conan version %s\" % client_version)\n return False\n self._show_help()\n if command in [\"-h\", \"--help\"]:\n return False\n raise ConanException(\"Unknown command %s\" % str(exc))\n except IndexError: # No parameters\n self._show_help()\n return False\n method(args[0][1:])\n except KeyboardInterrupt as exc:\n logger.error(exc)\n errors = True\n except SystemExit as exc:\n if exc.code != 0:\n logger.error(exc)\n self._user_io.out.error(\"Exiting with code: %d\" % exc.code)\n errors = exc.code\n except ConanException as exc:\n errors = True\n msg = exception_message_safe(exc)\n self._user_io.out.error(msg)\n except Exception as exc:\n import traceback\n print(traceback.format_exc())\n errors = True\n msg = exception_message_safe(exc)\n self._user_io.out.error(msg)\n\n return errors","label":0},{"cid":"conans/client/command.py::get_reference_fields","text":"conans/client/command.py::get_reference_fields\ndef get_reference_fields(arg_reference):\n \"\"\"\n :param arg_reference: String with a complete reference, or only user/channel\n :return: name, version, user and channel, in a tuple\n \"\"\"\n\n if not arg_reference:\n return None, None, None, None\n\n try:\n name_version, user_channel = arg_reference.split(\"@\")\n name, version = name_version.split(\"/\")\n user, channel = user_channel.split(\"/\")\n except ValueError:\n name, version = None, None\n try:\n user, channel = arg_reference.split(\"/\")\n except ValueError:\n raise ConanException(\"Invalid parameter '%s', specify the full reference or \"\n \"user/channel\" % arg_reference)\n\n return name, version, user, channel","label":0},{"cid":"conans/client/command.py::_add_manifests_arguments","text":"conans/client/command.py::_add_manifests_arguments\ndef _add_manifests_arguments(parser):\n parser.add_argument(\"-m\", \"--manifests\", const=default_manifest_folder, nargs=\"?\",\n help='Install dependencies manifests in folder for later verify.'\n ' Default folder is .conan_manifests, but can be changed',\n action=OnceArgument)\n parser.add_argument(\"-mi\", \"--manifests-interactive\", const=default_manifest_folder,\n nargs=\"?\",\n help='Install dependencies manifests in folder for later verify, '\n 'asking user for confirmation. '\n 'Default folder is .conan_manifests, but can be changed',\n action=OnceArgument)\n parser.add_argument(\"-v\", \"--verify\", const=default_manifest_folder, nargs=\"?\",\n help='Verify dependencies manifests against stored ones',\n action=OnceArgument)","label":0},{"cid":"conans/client/command.py::_add_common_install_arguments","text":"conans/client/command.py::_add_common_install_arguments\ndef _add_common_install_arguments(parser, build_help):\n parser.add_argument(\"-u\", \"--update\", action='store_true', default=False,\n help=\"check updates exist from upstream remotes\")\n parser.add_argument(\"-pr\", \"--profile\", default=None, action=OnceArgument,\n help='Apply the specified profile to the install command')\n parser.add_argument(\"-r\", \"--remote\", help='look in the specified remote server',\n action=OnceArgument)\n parser.add_argument(\"-o\", \"--options\",\n help='Define options values, e.g., -o Pkg:with_qt=true',\n nargs=1, action=Extender)\n parser.add_argument(\"-s\", \"--settings\",\n help='Settings to build the package, overwriting the defaults. e.g., '\n '-s compiler=gcc',\n nargs=1, action=Extender)\n parser.add_argument(\"-e\", \"--env\",\n help='Environment variables that will be set during the package build, '\n '-e CXX=/usr/bin/clang++',\n nargs=1, action=Extender)\n if build_help:\n parser.add_argument(\"-b\", \"--build\", action=Extender, nargs=\"*\", help=build_help)","label":0},{"cid":"conans/client/command.py::main","text":"conans/client/command.py::main\ndef main(args):\n \"\"\" main entry point of the conan application, using a Command to\n parse parameters\n\n Exit codes for conan command:\n\n 0: Success (done)\n 1: General ConanException error (done)\n 2: Migration error\n 3: Ctrl+C\n 4: Ctrl+Break\n \"\"\"\n try:\n conan_api, client_cache, user_io = Conan.factory()\n except ConanException: # Error migrating\n sys.exit(2)\n\n outputer = CommandOutputer(user_io, client_cache)\n command = Command(conan_api, client_cache, user_io, outputer)\n current_dir = os.getcwd()\n try:\n import signal\n\n def ctrl_c_handler(_, __):\n print('You pressed Ctrl+C!')\n sys.exit(3)\n\n def ctrl_break_handler(_, __):\n print('You pressed Ctrl+Break!')\n sys.exit(4)\n\n signal.signal(signal.SIGINT, ctrl_c_handler)\n if sys.platform == 'win32':\n signal.signal(signal.SIGBREAK, ctrl_break_handler)\n error = command.run(args)\n finally:\n os.chdir(current_dir)\n sys.exit(error)","label":0},{"cid":"conans/client/conan_api.py::get_basic_requester","text":"conans/client/conan_api.py::get_basic_requester\ndef get_basic_requester(client_cache):\n requester = requests.Session()\n proxies = client_cache.conan_config.proxies\n if proxies:\n # Account for the requests NO_PROXY env variable, not defined as a proxy like http=\n no_proxy = proxies.pop(\"no_proxy\", None)\n if no_proxy:\n os.environ[\"NO_PROXY\"] = no_proxy\n requester.proxies = proxies\n return requester","label":0},{"cid":"conans/client/conan_api.py::api_method","text":"conans/client/conan_api.py::api_method\ndef api_method(f):\n def wrapper(*args, **kwargs):\n the_self = args[0]\n try:\n log_command(f.__name__, kwargs)\n with tools.environment_append(the_self._client_cache.conan_config.env_vars):\n # Patch the globals in tools\n return f(*args, **kwargs)\n except Exception as exc:\n msg = exception_message_safe(exc)\n try:\n log_exception(exc, msg)\n except:\n pass\n raise\n\n return wrapper","label":0},{"cid":"conans/client/conan_api.py::api_method.wrapper","text":"conans/client/conan_api.py::api_method.wrapper\n def wrapper(*args, **kwargs):\n the_self = args[0]\n try:\n log_command(f.__name__, kwargs)\n with tools.environment_append(the_self._client_cache.conan_config.env_vars):\n # Patch the globals in tools\n return f(*args, **kwargs)\n except Exception as exc:\n msg = exception_message_safe(exc)\n try:\n log_exception(exc, msg)\n except:\n pass\n raise","label":0},{"cid":"conans/client/conan_api.py::_make_abs_path","text":"conans/client/conan_api.py::_make_abs_path\ndef _make_abs_path(path, cwd=None, default=None):\n \"\"\"convert 'path' to absolute if necessary (could be already absolute)\n if not defined (empty, or None), will return 'default' one or 'cwd'\n \"\"\"\n cwd = cwd or os.getcwd()\n if not path:\n return default or cwd\n if os.path.isabs(path):\n return path\n return os.path.normpath(os.path.join(cwd, path))","label":0},{"cid":"conans/client/conan_api.py::_get_conanfile_path","text":"conans/client/conan_api.py::_get_conanfile_path\ndef _get_conanfile_path(path, cwd, py):\n \"\"\"\n param py= True: Must be .py, False: Must be .txt, None: Try .py, then .txt\n \"\"\"\n path = _make_abs_path(path, cwd)\n\n if os.path.isdir(path): # Can be a folder\n if py:\n path = os.path.join(path, \"conanfile.py\")\n elif py is False:\n path = os.path.join(path, \"conanfile.txt\")\n else:\n path_py = os.path.join(path, \"conanfile.py\")\n if os.path.exists(path_py):\n path = path_py\n else:\n path = os.path.join(path, \"conanfile.txt\")\n\n if not os.path.isfile(path): # Must exist\n raise ConanException(\"Conanfile not found: %s\" % path)\n\n if py and not path.endswith(\".py\"):\n raise ConanException(\"A conanfile.py is needed (not valid conanfile.txt)\")\n\n return path","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.factory","text":"conans/client/conan_api.py::ConanAPIV1.factory\n @staticmethod\n def factory():\n \"\"\"Factory\"\"\"\n\n def instance_remote_manager(client_cache):\n requester = get_basic_requester(client_cache)\n # Verify client version against remotes\n version_checker_req = VersionCheckerRequester(requester, Version(client_version),\n Version(MIN_SERVER_COMPATIBLE_VERSION),\n out)\n # To handle remote connections\n put_headers = client_cache.read_put_headers()\n rest_api_client = RestApiClient(out, requester=version_checker_req,\n put_headers=put_headers)\n # To store user and token\n localdb = LocalDB(client_cache.localdb)\n # Wraps RestApiClient to add authentication support (same interface)\n auth_manager = ConanApiAuthManager(rest_api_client, user_io, localdb)\n # Handle remote connections\n remote_manager = RemoteManager(client_cache, auth_manager, out)\n return remote_manager\n\n use_color = get_env(\"CONAN_COLOR_DISPLAY\", 1)\n if use_color and hasattr(sys.stdout, \"isatty\") and sys.stdout.isatty():\n import colorama\n colorama.init()\n color = True\n else:\n color = False\n out = ConanOutput(sys.stdout, color)\n user_io = UserIO(out=out)\n\n try:\n user_home = get_conan_user_home()\n client_cache = migrate_and_get_client_cache(user_home, out)\n sys.path.append(os.path.join(user_home, \"python\"))\n except Exception as e:\n out.error(str(e))\n raise\n\n with tools.environment_append(client_cache.conan_config.env_vars):\n # Adjust CONAN_LOGGING_LEVEL with the env readed\n conans.util.log.logger = configure_logger()\n\n # Get the","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.factory.instance_remote_manager","text":"conans/client/conan_api.py::ConanAPIV1.factory.instance_remote_manager\n def instance_remote_manager(client_cache):\n requester = get_basic_requester(client_cache)\n # Verify client version against remotes\n version_checker_req = VersionCheckerRequester(requester, Version(client_version),\n Version(MIN_SERVER_COMPATIBLE_VERSION),\n out)\n # To handle remote connections\n put_headers = client_cache.read_put_headers()\n rest_api_client = RestApiClient(out, requester=version_checker_req,\n put_headers=put_headers)\n # To store user and token\n localdb = LocalDB(client_cache.localdb)\n # Wraps RestApiClient to add authentication support (same interface)\n auth_manager = ConanApiAuthManager(rest_api_client, user_io, localdb)\n # Handle remote connections\n remote_manager = RemoteManager(client_cache, auth_manager, out)\n return remote_manager","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.__init__","text":"conans/client/conan_api.py::ConanAPIV1.__init__\n def __init__(self, client_cache, user_io, runner, remote_manager, search_manager,\n settings_preprocessor):\n assert isinstance(user_io, UserIO)\n assert isinstance(client_cache, ClientCache)\n self._client_cache = client_cache\n self._user_io = user_io\n self._runner = runner\n self._remote_manager = remote_manager\n self._manager = ConanManager(client_cache, user_io, runner, remote_manager, search_manager,\n settings_preprocessor)\n # Patch the tools module with a good requester and user_io\n set_global_instances(self._user_io.out, get_basic_requester(self._client_cache))","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.new","text":"conans/client/conan_api.py::ConanAPIV1.new\n @api_method\n def new(self, name, header=False, pure_c=False, test=False, exports_sources=False, bare=False,\n cwd=None, visual_versions=None, linux_gcc_versions=None, linux_clang_versions=None,\n osx_clang_versions=None, shared=None, upload_url=None, gitignore=None,\n gitlab_gcc_versions=None, gitlab_clang_versions=None):\n from conans.client.cmd.new import cmd_new\n cwd = os.path.abspath(cwd or os.getcwd())\n files = cmd_new(name, header=header, pure_c=pure_c, test=test,\n exports_sources=exports_sources, bare=bare,\n visual_versions=visual_versions,\n linux_gcc_versions=linux_gcc_versions,\n linux_clang_versions=linux_clang_versions,\n osx_clang_versions=osx_clang_versions, shared=shared,\n upload_url=upload_url, gitignore=gitignore,\n gitlab_gcc_versions=gitlab_gcc_versions,\n gitlab_clang_versions=gitlab_clang_versions)\n\n save_files(cwd, files)\n for f in sorted(files):\n self._user_io.out.success(\"File saved: %s\" % f)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.test","text":"conans/client/conan_api.py::ConanAPIV1.test\n @api_method\n def test(self, path, reference, profile_name=None, settings=None, options=None, env=None,\n remote=None, update=False, build_modes=None, cwd=None, test_build_folder=None):\n\n settings = settings or []\n options = options or []\n env = env or []\n\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\n cwd = cwd or os.getcwd()\n profile = profile_from_args(profile_name, settings, options, env, cwd,\n self._client_cache)\n reference = ConanFileReference.loads(reference)\n pt = PackageTester(self._manager, self._user_io)\n pt.install_build_and_test(conanfile_path, reference, profile, remote,\n update, build_modes=build_modes,\n test_build_folder=test_build_folder)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.create","text":"conans/client/conan_api.py::ConanAPIV1.create\n @api_method\n def create(self, conanfile_path, name=None, version=None, user=None, channel=None,\n profile_name=None, settings=None,\n options=None, env=None, test_folder=None, not_export=False,\n build_modes=None,\n keep_source=False, keep_build=False, verify=None,\n manifests=None, manifests_interactive=None,\n remote=None, update=False, cwd=None, test_build_folder=None):\n \"\"\"\n API method to create a conan package\n\n :param test_folder: default None - looks for default 'test' or 'test_package' folder),\n string - test_folder path\n False - disabling tests\n \"\"\"\n settings = settings or []\n options = options or []\n env = env or []\n\n cwd = cwd or os.getcwd()\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\n\n if not name or not version:\n conanfile = load_conanfile_class(conanfile_path)\n name, version = conanfile.name, conanfile.version\n if not name or not version:\n raise ConanException(\"conanfile.py doesn't declare package name or version\")\n\n reference = ConanFileReference(name, version, user, channel)\n scoped_output = ScopedOutput(str(reference), self._user_io.out)\n # Make sure keep_source is set for keep_build\n if keep_build:\n keep_source = True\n # Forcing an export!\n if not not_export:\n scoped_output.highlight(\"Exporting package recipe\")\n self._manager.export(conanfile_path, name, version, user, channel, keep_source)\n\n if build_modes is None: # Not specified, force build the tested library\n build_modes = [name]\n\n manifests = _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd)\n manifest_folder, manifest_inter","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.create.get_test_conanfile_path","text":"conans/client/conan_api.py::ConanAPIV1.create.get_test_conanfile_path\n def get_test_conanfile_path(tf):\n \"\"\"Searches in the declared test_folder or in the standard locations\"\"\"\n\n if tf is False:\n # Look up for testing conanfile can be disabled if tf (test folder) is False\n return None\n\n test_folders = [tf] if tf else [\"test_package\", \"test\"]\n base_folder = os.path.dirname(conanfile_path)\n for test_folder_name in test_folders:\n test_folder = os.path.join(base_folder, test_folder_name)\n test_conanfile_path = os.path.join(test_folder, \"conanfile.py\")\n if os.path.exists(test_conanfile_path):\n return test_conanfile_path\n else:\n if tf:\n raise ConanException(\"test folder '%s' not available, \"\n \"or it doesn't have a conanfile.py\" % tf)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.export_pkg","text":"conans/client/conan_api.py::ConanAPIV1.export_pkg\n @api_method\n def export_pkg(self, conanfile_path, name, channel, source_folder=None, build_folder=None,\n package_folder=None, install_folder=None, profile_name=None, settings=None,\n options=None, env=None, force=False, user=None, version=None, cwd=None):\n\n settings = settings or []\n options = options or []\n env = env or []\n cwd = cwd or os.getcwd()\n\n # Checks that info files exists if the install folder is specified\n if install_folder and not existing_info_files(_make_abs_path(install_folder, cwd)):\n raise ConanException(\"The specified install folder doesn't contain '%s' and '%s' \"\n \"files\" % (CONANINFO, BUILD_INFO))\n\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\n\n if package_folder:\n if build_folder or source_folder:\n raise ConanException(\"package folder definition incompatible with build and source folders\")\n package_folder = _make_abs_path(package_folder, cwd)\n\n build_folder = _make_abs_path(build_folder, cwd)\n install_folder = _make_abs_path(install_folder, cwd, default=build_folder)\n source_folder = _make_abs_path(source_folder, cwd, default=os.path.dirname(conanfile_path))\n\n # Checks that no both settings and info files are specified\n if install_folder and existing_info_files(install_folder) and \\\n (profile_name or settings or options or env):\n raise ConanException(\"%s and %s are found, at '%s' folder, so specifying profile, \"\n \"settings, options or env is not allowed\" % (CONANINFO, BUILD_INFO,\n install_folder))\n\n infos_present = existing_info_files(install_folder)\n if not infos_present:\n profile = profile_from_args(profil","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.download","text":"conans/client/conan_api.py::ConanAPIV1.download\n @api_method\n def download(self, reference, remote=None, package=None):\n # Install packages without settings (fixed ids or all)\n conan_ref = ConanFileReference.loads(reference)\n self._manager.download(conan_ref, package, remote=remote)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.install_reference","text":"conans/client/conan_api.py::ConanAPIV1.install_reference\n @api_method\n def install_reference(self, reference, settings=None, options=None, env=None,\n remote=None, verify=None, manifests=None,\n manifests_interactive=None, build=None, profile_name=None,\n update=False, generators=None, install_folder=None, cwd=None):\n\n cwd = cwd or os.getcwd()\n install_folder = _make_abs_path(install_folder, cwd)\n\n manifests = _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd)\n manifest_folder, manifest_interactive, manifest_verify = manifests\n\n profile = profile_from_args(profile_name, settings, options, env, cwd,\n self._client_cache)\n\n if not generators: # We don't want the default txt\n generators = False\n\n mkdir(install_folder)\n self._manager.install(reference=reference, install_folder=install_folder, remote=remote,\n profile=profile, build_modes=build, update=update,\n manifest_folder=manifest_folder,\n manifest_verify=manifest_verify,\n manifest_interactive=manifest_interactive,\n generators=generators,\n install_reference=True)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.install","text":"conans/client/conan_api.py::ConanAPIV1.install\n @api_method\n def install(self, path=\"\", settings=None, options=None, env=None,\n remote=None, verify=None, manifests=None,\n manifests_interactive=None, build=None, profile_name=None,\n update=False, generators=None, no_imports=False, install_folder=None, cwd=None):\n\n cwd = cwd or os.getcwd()\n install_folder = _make_abs_path(install_folder, cwd)\n conanfile_path = _get_conanfile_path(path, cwd, py=None)\n\n manifests = _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd)\n manifest_folder, manifest_interactive, manifest_verify = manifests\n\n profile = profile_from_args(profile_name, settings, options, env, cwd,\n self._client_cache)\n\n self._manager.install(reference=conanfile_path,\n install_folder=install_folder,\n remote=remote,\n profile=profile,\n build_modes=build,\n update=update,\n manifest_folder=manifest_folder,\n manifest_verify=manifest_verify,\n manifest_interactive=manifest_interactive,\n generators=generators,\n no_imports=no_imports)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.config_get","text":"conans/client/conan_api.py::ConanAPIV1.config_get\n @api_method\n def config_get(self, item):\n config_parser = ConanClientConfigParser(self._client_cache.conan_conf_path)\n self._user_io.out.info(config_parser.get_item(item))\n return config_parser.get_item(item)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.config_set","text":"conans/client/conan_api.py::ConanAPIV1.config_set\n @api_method\n def config_set(self, item, value):\n config_parser = ConanClientConfigParser(self._client_cache.conan_conf_path)\n config_parser.set_item(item, value)\n self._client_cache.invalidate()","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.config_rm","text":"conans/client/conan_api.py::ConanAPIV1.config_rm\n @api_method\n def config_rm(self, item):\n config_parser = ConanClientConfigParser(self._client_cache.conan_conf_path)\n config_parser.rm_item(item)\n self._client_cache.invalidate()","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.config_install","text":"conans/client/conan_api.py::ConanAPIV1.config_install\n @api_method\n def config_install(self, item, verify_ssl):\n from conans.client.conf.config_installer import configuration_install\n return configuration_install(item, self._client_cache, self._user_io.out, self._runner, verify_ssl)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1._info_get_profile","text":"conans/client/conan_api.py::ConanAPIV1._info_get_profile\n def _info_get_profile(self, reference, install_folder, profile_name, settings, options, env):\n cwd = os.getcwd()\n try:\n reference = ConanFileReference.loads(reference)\n except ConanException:\n reference = _get_conanfile_path(reference, cwd=None, py=None)\n if install_folder or not (profile_name or settings or options or env):\n # When not install folder is specified but neither any setting, we try to read the\n # info from cwd\n install_folder = _make_abs_path(install_folder, cwd)\n if existing_info_files(install_folder):\n return reference, read_conaninfo_profile(install_folder)\n\n return reference, profile_from_args(profile_name, settings, options, env=env,\n cwd=cwd, client_cache=self._client_cache)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.info_build_order","text":"conans/client/conan_api.py::ConanAPIV1.info_build_order\n @api_method\n def info_build_order(self, reference, settings=None, options=None, env=None,\n profile_name=None, remote=None, build_order=None, check_updates=None,\n install_folder=None):\n reference, profile = self._info_get_profile(reference, install_folder, profile_name, settings,\n options, env)\n graph = self._manager.info_build_order(reference, profile, build_order, remote, check_updates)\n return graph","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.info_nodes_to_build","text":"conans/client/conan_api.py::ConanAPIV1.info_nodes_to_build\n @api_method\n def info_nodes_to_build(self, reference, build_modes, settings=None, options=None, env=None,\n profile_name=None, remote=None, check_updates=None, install_folder=None):\n reference, profile = self._info_get_profile(reference, install_folder, profile_name, settings,\n options, env)\n ret = self._manager.info_nodes_to_build(reference, profile, build_modes, remote,\n check_updates)\n ref_list, project_reference = ret\n return ref_list, project_reference","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.info_get_graph","text":"conans/client/conan_api.py::ConanAPIV1.info_get_graph\n @api_method\n def info_get_graph(self, reference, remote=None, settings=None, options=None, env=None,\n profile_name=None, update=False, install_folder=None):\n reference, profile = self._info_get_profile(reference, install_folder, profile_name, settings,\n options, env)\n ret = self._manager.info_get_graph(reference, remote=remote, profile=profile,\n check_updates=update)\n deps_graph, graph_updates_info, project_reference = ret\n return deps_graph, graph_updates_info, project_reference","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.build","text":"conans/client/conan_api.py::ConanAPIV1.build\n @api_method\n def build(self, conanfile_path, source_folder=None, package_folder=None, build_folder=None,\n install_folder=None, cwd=None):\n\n cwd = cwd or os.getcwd()\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\n build_folder = _make_abs_path(build_folder, cwd)\n install_folder = _make_abs_path(install_folder, cwd, default=build_folder)\n source_folder = _make_abs_path(source_folder, cwd, default=os.path.dirname(conanfile_path))\n default_pkg_folder = os.path.join(build_folder, \"package\")\n package_folder = _make_abs_path(package_folder, cwd, default=default_pkg_folder)\n\n self._manager.build(conanfile_path, source_folder, build_folder, package_folder,\n install_folder)","label":1},{"cid":"conans/client/conan_api.py::ConanAPIV1.package","text":"conans/client/conan_api.py::ConanAPIV1.package\n @api_method\n def package(self, path, build_folder, package_folder, source_folder=None, install_folder=None, cwd=None):\n cwd = cwd or os.getcwd()\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\n build_folder = _make_abs_path(build_folder, cwd)\n install_folder = _make_abs_path(install_folder, cwd, default=build_folder)\n source_folder = _make_abs_path(source_folder, cwd, default=os.path.dirname(conanfile_path))\n default_pkg_folder = os.path.join(build_folder, \"package\")\n package_folder = _make_abs_path(package_folder, cwd, default=default_pkg_folder)\n\n self._manager.local_package(package_folder, conanfile_path, build_folder, source_folder,\n install_folder)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.source","text":"conans/client/conan_api.py::ConanAPIV1.source\n @api_method\n def source(self, path, source_folder=None, info_folder=None, cwd=None):\n cwd = cwd or os.getcwd()\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\n source_folder = _make_abs_path(source_folder, cwd)\n info_folder = _make_abs_path(info_folder, cwd)\n\n mkdir(source_folder)\n if not os.path.exists(info_folder):\n raise ConanException(\"Specified info-folder doesn't exist\")\n\n self._manager.source(conanfile_path, source_folder, info_folder)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.imports","text":"conans/client/conan_api.py::ConanAPIV1.imports\n @api_method\n def imports(self, path, dest=None, info_folder=None, cwd=None):\n \"\"\"\n :param path: Path to the conanfile\n :param dest: Dir to put the imported files. (Abs path or relative to cwd)\n :param info_folder: Dir where the conaninfo.txt and conanbuildinfo.txt files are\n :param cwd: Current working directory\n :return: None\n \"\"\"\n cwd = cwd or os.getcwd()\n info_folder = _make_abs_path(info_folder, cwd)\n dest = _make_abs_path(dest, cwd)\n\n mkdir(dest)\n conanfile_abs_path = _get_conanfile_path(path, cwd, py=None)\n self._manager.imports(conanfile_abs_path, dest, info_folder)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.imports_undo","text":"conans/client/conan_api.py::ConanAPIV1.imports_undo\n @api_method\n def imports_undo(self, manifest_path):\n cwd = os.getcwd()\n manifest_path = _make_abs_path(manifest_path, cwd)\n self._manager.imports_undo(manifest_path)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.export","text":"conans/client/conan_api.py::ConanAPIV1.export\n @api_method\n def export(self, path, name, version, user, channel, keep_source=False, cwd=None):\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\n self._manager.export(conanfile_path, name, version, user, channel, keep_source)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remove","text":"conans/client/conan_api.py::ConanAPIV1.remove\n @api_method\n def remove(self, pattern, query=None, packages=None, builds=None, src=False, force=False,\n remote=None, outdated=False):\n self._manager.remove(pattern, package_ids_filter=packages, build_ids=builds,\n src=src, force=force, remote=remote, packages_query=query,\n outdated=outdated)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.copy","text":"conans/client/conan_api.py::ConanAPIV1.copy\n @api_method\n def copy(self, reference, user_channel, force=False, packages=None):\n \"\"\"\n param packages: None=No binaries, True=All binaries, else list of IDs\n \"\"\"\n from conans.client.cmd.copy import cmd_copy\n # FIXME: conan copy does not support short-paths in Windows\n cmd_copy(reference, user_channel, packages, self._client_cache,\n self._user_io, self._remote_manager, force=force)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.user","text":"conans/client/conan_api.py::ConanAPIV1.user\n @api_method\n def user(self, name=None, clean=False, remote=None, password=None):\n if clean:\n localdb = LocalDB(self._client_cache.localdb)\n localdb.init(clean=True)\n self._user_io.out.success(\"Deleted user data\")\n return\n self._manager.user(remote, name, password)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.search_recipes","text":"conans/client/conan_api.py::ConanAPIV1.search_recipes\n @api_method\n def search_recipes(self, pattern, remote=None, case_sensitive=False):\n search = Search(self._client_cache, self._remote_manager, self._user_io)\n return search.search_recipes(pattern, remote, case_sensitive)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.search_packages","text":"conans/client/conan_api.py::ConanAPIV1.search_packages\n @api_method\n def search_packages(self, reference, query=None, remote=None, outdated=False):\n search = Search(self._client_cache, self._remote_manager, self._user_io)\n return search.search_packages(reference, remote, query=query,\n outdated=outdated)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.upload","text":"conans/client/conan_api.py::ConanAPIV1.upload\n @api_method\n def upload(self, pattern, package=None, remote=None, all_packages=False, force=False,\n confirm=False, retry=2, retry_wait=5, skip_upload=False, integrity_check=False):\n \"\"\" Uploads a package recipe and the generated binary packages to a specified remote\n \"\"\"\n uploader = CmdUpload(self._client_cache, self._user_io, self._remote_manager,\n remote)\n return uploader.upload(pattern, package, all_packages, force, confirm, retry,\n retry_wait, skip_upload, integrity_check)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_list","text":"conans/client/conan_api.py::ConanAPIV1.remote_list\n @api_method\n def remote_list(self):\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\n return registry.remotes","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_add","text":"conans/client/conan_api.py::ConanAPIV1.remote_add\n @api_method\n def remote_add(self, remote, url, verify_ssl=True, insert=None):\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\n return registry.add(remote, url, verify_ssl, insert)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_remove","text":"conans/client/conan_api.py::ConanAPIV1.remote_remove\n @api_method\n def remote_remove(self, remote):\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\n return registry.remove(remote)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_update","text":"conans/client/conan_api.py::ConanAPIV1.remote_update\n @api_method\n def remote_update(self, remote, url, verify_ssl=True, insert=None):\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\n return registry.update(remote, url, verify_ssl, insert)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_list_ref","text":"conans/client/conan_api.py::ConanAPIV1.remote_list_ref\n @api_method\n def remote_list_ref(self):\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\n return registry.refs","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_add_ref","text":"conans/client/conan_api.py::ConanAPIV1.remote_add_ref\n @api_method\n def remote_add_ref(self, reference, remote):\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\n return registry.add_ref(reference, remote)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_remove_ref","text":"conans/client/conan_api.py::ConanAPIV1.remote_remove_ref\n @api_method\n def remote_remove_ref(self, reference):\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\n return registry.remove_ref(reference)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_update_ref","text":"conans/client/conan_api.py::ConanAPIV1.remote_update_ref\n @api_method\n def remote_update_ref(self, reference, remote):\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\n return registry.update_ref(reference, remote)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.profile_list","text":"conans/client/conan_api.py::ConanAPIV1.profile_list\n @api_method\n def profile_list(self):\n return cmd_profile_list(self._client_cache.profiles_path, self._user_io.out)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.create_profile","text":"conans/client/conan_api.py::ConanAPIV1.create_profile\n @api_method\n def create_profile(self, profile_name, detect=False):\n return cmd_profile_create(profile_name, self._client_cache.profiles_path,\n self._user_io.out, detect)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.update_profile","text":"conans/client/conan_api.py::ConanAPIV1.update_profile\n @api_method\n def update_profile(self, profile_name, key, value):\n return cmd_profile_update(profile_name, key, value, self._client_cache.profiles_path)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.get_profile_key","text":"conans/client/conan_api.py::ConanAPIV1.get_profile_key\n @api_method\n def get_profile_key(self, profile_name, key):\n return cmd_profile_get(profile_name, key, self._client_cache.profiles_path)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.delete_profile_key","text":"conans/client/conan_api.py::ConanAPIV1.delete_profile_key\n @api_method\n def delete_profile_key(self, profile_name, key):\n return cmd_profile_delete_key(profile_name, key, self._client_cache.profiles_path)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.read_profile","text":"conans/client/conan_api.py::ConanAPIV1.read_profile\n @api_method\n def read_profile(self, profile=None):\n p, _ = read_profile(profile, os.getcwd(), self._client_cache.profiles_path)\n return p","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.get_path","text":"conans/client/conan_api.py::ConanAPIV1.get_path\n @api_method\n def get_path(self, reference, package_id=None, path=None, remote=None):\n reference = ConanFileReference.loads(str(reference))\n return self._manager.get_path(reference, package_id, path, remote)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.export_alias","text":"conans/client/conan_api.py::ConanAPIV1.export_alias\n @api_method\n def export_alias(self, reference, target_reference):\n reference = ConanFileReference.loads(str(reference))\n target_reference = ConanFileReference.loads(str(target_reference))\n return self._manager.export_alias(reference, target_reference)","label":0},{"cid":"conans/client/conan_api.py::_parse_manifests_arguments","text":"conans/client/conan_api.py::_parse_manifests_arguments\ndef _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd):\n if manifests and manifests_interactive:\n raise ConanException(\"Do not specify both manifests and \"\n \"manifests-interactive arguments\")\n if verify and (manifests or manifests_interactive):\n raise ConanException(\"Do not specify both 'verify' and \"\n \"'manifests' or 'manifests-interactive' arguments\")\n manifest_folder = verify or manifests or manifests_interactive\n if manifest_folder:\n if not os.path.isabs(manifest_folder):\n if not cwd:\n raise ConanException(\"'cwd' should be defined if the manifest folder is relative.\")\n manifest_folder = os.path.join(cwd, manifest_folder)\n manifest_verify = verify is not None\n manifest_interactive = manifests_interactive is not None\n else:\n manifest_verify = manifest_interactive = False\n\n return manifest_folder, manifest_interactive, manifest_verify","label":0},{"cid":"conans/client/conan_api.py::get_conan_runner","text":"conans/client/conan_api.py::get_conan_runner\ndef get_conan_runner():\n print_commands_to_output = get_env(\"CONAN_PRINT_RUN_COMMANDS\", False)\n generate_run_log_file = get_env(\"CONAN_LOG_RUN_TO_FILE\", False)\n log_run_to_output = get_env(\"CONAN_LOG_RUN_TO_OUTPUT\", True)\n runner = ConanRunner(print_commands_to_output, generate_run_log_file, log_run_to_output)\n return runner","label":0},{"cid":"conans/client/conan_api.py::migrate_and_get_client_cache","text":"conans/client/conan_api.py::migrate_and_get_client_cache\ndef migrate_and_get_client_cache(base_folder, out, storage_folder=None):\n # Init paths\n client_cache = ClientCache(base_folder, storage_folder, out)\n\n # Migration system\n migrator = ClientMigrator(client_cache, Version(client_version), out)\n migrator.migrate()\n\n return client_cache","label":0},{"cid":"conans/client/installer.py::build_id","text":"conans/client/installer.py::build_id\ndef build_id(conan_file):\n if hasattr(conan_file, \"build_id\"):\n # construct new ConanInfo\n build_id_info = conan_file.info.copy()\n conan_file.info_build = build_id_info\n # effectively call the user function to change the package values\n with conanfile_exception_formatter(str(conan_file), \"build_id\"):\n conan_file.build_id()\n # compute modified ID\n return build_id_info.package_id()\n return None","label":0},{"cid":"conans/client/installer.py::_ConanPackageBuilder.__init__","text":"conans/client/installer.py::_ConanPackageBuilder.__init__\n def __init__(self, conan_file, package_reference, client_cache, output):\n self._client_cache = client_cache\n self._conan_file = conan_file\n self._out = output\n self._package_reference = package_reference\n self._conan_ref = self._package_reference.conan\n self._skip_build = False # If build_id()\n\n new_id = build_id(self._conan_file)\n self.build_reference = PackageReference(self._conan_ref, new_id) if new_id else package_reference\n self.build_folder = self._client_cache.build(self.build_reference,\n self._conan_file.short_paths)\n self.package_folder = self._client_cache.package(self._package_reference,\n self._conan_file.short_paths)\n self.source_folder = self._client_cache.source(self._conan_ref, self._conan_file.short_paths)","label":0},{"cid":"conans/client/installer.py::_ConanPackageBuilder.prepare_build","text":"conans/client/installer.py::_ConanPackageBuilder.prepare_build\n def prepare_build(self):\n if self.build_reference != self._package_reference and \\\n os.path.exists(self.build_folder) and hasattr(self._conan_file, \"build_id\"):\n self._skip_build = True\n return\n\n # build_id is not caching the build folder, so actually rebuild the package\n _handle_system_requirements(self._conan_file, self._package_reference,\n self._client_cache, self._out)\n\n export_folder = self._client_cache.export(self._conan_ref)\n export_source_folder = self._client_cache.export_sources(self._conan_ref,\n self._conan_file.short_paths)\n\n try:\n rmdir(self.build_folder)\n rmdir(self.package_folder)\n except OSError as e:\n raise ConanException(\"%s\\n\\nCouldn't remove folder, might be busy or open\\n\"\n \"Close any app using it, and retry\" % str(e))\n\n self._out.info('Building your package in %s' % self.build_folder)\n config_source(export_folder, export_source_folder, self.source_folder,\n self._conan_file, self._out)\n self._out.info('Copying sources to build folder')\n\n if getattr(self._conan_file, 'no_copy_source', False):\n mkdir(self.build_folder)\n self._conan_file.source_folder = self.source_folder\n else:\n if platform.system() == \"Windows\" and os.getenv(\"CONAN_USER_HOME_SHORT\") != \"None\":\n from conans.util.windows import ignore_long_path_files\n ignore = ignore_long_path_files(self.source_folder, self.build_folder, self._out)\n else:\n ignore = None\n\n shutil.copytree(self.source_folder, self.build_folder, symlinks=True, ignore=ignore)\n logger.debug(\"Copied to %s\", self.build_folder)\n logger.de","label":0},{"cid":"conans/client/installer.py::_ConanPackageBuilder.build","text":"conans/client/installer.py::_ConanPackageBuilder.build\n def build(self):\n \"\"\"Calls the conanfile's build method\"\"\"\n if self._skip_build:\n return\n with get_env_context_manager(self._conan_file):\n self._build_package()","label":0},{"cid":"conans/client/installer.py::_ConanPackageBuilder.package","text":"conans/client/installer.py::_ConanPackageBuilder.package\n def package(self):\n \"\"\"Generate the info txt files and calls the conanfile package method.\n \"\"\"\n\n # FIXME: Is weak to assign here the recipe_hash\n manifest = self._client_cache.load_manifest(self._conan_ref)\n self._conan_file.info.recipe_hash = manifest.summary_hash\n\n # Creating ***info.txt files\n save(os.path.join(self.build_folder, CONANINFO), self._conan_file.info.dumps())\n self._out.info(\"Generated %s\" % CONANINFO)\n save(os.path.join(self.build_folder, BUILD_INFO), TXTGenerator(self._conan_file).content)\n self._out.info(\"Generated %s\" % BUILD_INFO)\n\n os.chdir(self.build_folder)\n\n if getattr(self._conan_file, 'no_copy_source', False):\n source_folder = self.source_folder\n else:\n source_folder = self.build_folder\n with get_env_context_manager(self._conan_file):\n install_folder = self.build_folder # While installing, the infos goes to build folder\n create_package(self._conan_file, source_folder, self.build_folder, self.package_folder,\n install_folder, self._out)\n\n if get_env(\"CONAN_READ_ONLY_CACHE\", False):\n make_read_only(self.package_folder)","label":0},{"cid":"conans/client/installer.py::_ConanPackageBuilder._build_package","text":"conans/client/installer.py::_ConanPackageBuilder._build_package\n def _build_package(self):\n \"\"\" calls the imports + conanfile.build() method\n \"\"\"\n os.chdir(self.build_folder)\n self._conan_file.build_folder = self.build_folder\n self._conan_file.package_folder = self.package_folder\n # In local cache, install folder always is build_folder\n self._conan_file.install_folder = self.build_folder\n\n # Read generators from conanfile and generate the needed files\n logger.debug(\"Writing generators\")\n write_generators(self._conan_file, self.build_folder, self._out)\n logger.debug(\"Files copied after generators %s\", os.listdir(self.build_folder))\n\n # Build step might need DLLs, binaries as protoc to generate source files\n # So execute imports() before build, storing the list of copied_files\n from conans.client.importer import run_imports\n copied_files = run_imports(self._conan_file, self.build_folder, self._out)\n\n try:\n # This is necessary because it is different for user projects\n # than for packages\n logger.debug(\"Call conanfile.build() with files in build folder: %s\",\n os.listdir(self.build_folder))\n self._out.highlight(\"Calling build()\")\n with conanfile_exception_formatter(str(self._conan_file), \"build\"):\n self._conan_file.build()\n\n self._out.success(\"Package '%s' built\" % self._conan_file.info.package_id())\n self._out.info(\"Build folder %s\" % self.build_folder)\n except Exception as exc:\n self._out.writeln(\"\")\n self._out.error(\"Package '%s' build failed\" % self._conan_file.info.package_id())\n self._out.warn(\"Build folder %s\" % self.build_folder)\n if isinstance(exc, ConanExceptionInUserConanfileMethod):\n raise exc\n raise ConanException(exc)\n finally:\n # Now r","label":0},{"cid":"conans/client/installer.py::_raise_package_not_found_error","text":"conans/client/installer.py::_raise_package_not_found_error\ndef _raise_package_not_found_error(conan_file, conan_ref, package_id, out):\n settings_text = \", \".join(conan_file.info.full_settings.dumps().splitlines())\n options_text = \", \".join(conan_file.info.full_options.dumps().splitlines())\n\n out.warn('''Can't find a '%s' package for the specified options and settings:\n- Settings: %s\n- Options: %s\n- Package ID: %s\n''' % (conan_ref, settings_text, options_text, package_id))\n\n raise ConanException('''Missing prebuilt package for '%s'\nTry to build it from sources with \"--build %s\"\nOr read \"http://docs.conan.io/en/latest/faq/troubleshooting.html#error-missing-prebuilt-package\"\n''' % (conan_ref, conan_ref.name))","label":0},{"cid":"conans/client/installer.py::_handle_system_requirements","text":"conans/client/installer.py::_handle_system_requirements\ndef _handle_system_requirements(conan_file, package_reference, client_cache, out):\n \"\"\" check first the system_reqs/system_requirements.txt existence, if not existing\n check package/sha1/\n\n Used after remote package retrieving and before package building\n \"\"\"\n if \"system_requirements\" not in type(conan_file).__dict__:\n return\n\n system_reqs_path = client_cache.system_reqs(package_reference.conan)\n system_reqs_package_path = client_cache.system_reqs_package(package_reference)\n if os.path.exists(system_reqs_path) or os.path.exists(system_reqs_package_path):\n return\n\n ret = call_system_requirements(conan_file, out)\n\n try:\n ret = str(ret or \"\")\n except:\n out.warn(\"System requirements didn't return a string\")\n ret = \"\"\n if getattr(conan_file, \"global_system_requirements\", None):\n save(system_reqs_path, ret)\n else:\n save(system_reqs_package_path, ret)","label":0},{"cid":"conans/client/installer.py::call_system_requirements","text":"conans/client/installer.py::call_system_requirements\ndef call_system_requirements(conanfile, output):\n try:\n return conanfile.system_requirements()\n except Exception as e:\n output.error(\"while executing system_requirements(): %s\" % str(e))\n raise ConanException(\"Error in system requirements\")","label":0},{"cid":"conans/client/installer.py::ConanInstaller.__init__","text":"conans/client/installer.py::ConanInstaller.__init__\n def __init__(self, client_cache, output, remote_proxy, build_mode, build_requires):\n self._client_cache = client_cache\n self._out = output\n self._remote_proxy = remote_proxy\n self._build_requires = build_requires\n self._build_mode = build_mode\n self._built_packages = set() # To avoid re-building twice the same package reference","label":0},{"cid":"conans/client/installer.py::ConanInstaller.install","text":"conans/client/installer.py::ConanInstaller.install\n def install(self, deps_graph, profile_build_requires, keep_build=False):\n \"\"\" given a DepsGraph object, build necessary nodes or retrieve them\n \"\"\"\n t1 = time.time()\n # order by levels and separate the root node (conan_ref=None) from the rest\n nodes_by_level = deps_graph.by_levels()\n root_level = nodes_by_level.pop()\n _, root_conanfile = root_level[0]\n logger.debug(\"Install-Process buildinfo %s\", (time.time() - t1))\n t1 = time.time()\n skip_private_nodes = self._compute_private_nodes(deps_graph)\n logger.debug(\"Install-Process private %s\", (time.time() - t1))\n t1 = time.time()\n # Get the nodes in order and if we have to build them\n nodes_to_process = self._get_nodes(nodes_by_level, skip_private_nodes)\n self._build(nodes_to_process, deps_graph, skip_private_nodes, profile_build_requires, keep_build,\n root_conanfile)\n logger.debug(\"Install-build %s\", (time.time() - t1))","label":0},{"cid":"conans/client/installer.py::ConanInstaller._compute_private_nodes","text":"conans/client/installer.py::ConanInstaller._compute_private_nodes\n def _compute_private_nodes(self, deps_graph):\n \"\"\" computes a list of nodes that are not required to be built, as they are\n private requirements of already available shared libraries as binaries.\n\n If the package requiring a private node has an up to date binary package,\n the private node is not retrieved nor built\n \"\"\"\n skip_nodes = set() # Nodes that require private packages but are already built\n for node in deps_graph.nodes:\n conan_ref, conanfile = node\n if not [r for r in conanfile.requires.values() if r.private]:\n continue\n\n if conan_ref:\n build_forced = self._build_mode.forced(conanfile, conan_ref)\n if build_forced:\n continue\n\n package_id = conanfile.info.package_id()\n package_reference = PackageReference(conan_ref, package_id)\n check_outdated = self._build_mode.outdated\n\n if self._remote_proxy.package_available(package_reference,\n conanfile.short_paths,\n check_outdated):\n skip_nodes.add(node)\n\n # Get the private nodes\n skippable_private_nodes = deps_graph.private_nodes(skip_nodes)\n return skippable_private_nodes","label":0},{"cid":"conans/client/installer.py::ConanInstaller.nodes_to_build","text":"conans/client/installer.py::ConanInstaller.nodes_to_build\n def nodes_to_build(self, deps_graph):\n \"\"\"Called from info command when a build policy is used in build_order parameter\"\"\"\n # Get the nodes in order and if we have to build them\n nodes_by_level = deps_graph.by_levels()\n nodes_by_level.pop() # Remove latest one, consumer node with conan_ref=None\n skip_private_nodes = self._compute_private_nodes(deps_graph)\n nodes = self._get_nodes(nodes_by_level, skip_private_nodes)\n return [(PackageReference(conan_ref, package_id), conan_file)\n for conan_ref, package_id, conan_file, build in nodes if build]","label":0},{"cid":"conans/client/installer.py::ConanInstaller._build","text":"conans/client/installer.py::ConanInstaller._build\n def _build(self, nodes_to_process, deps_graph, skip_nodes, profile_build_requires, keep_build,\n root_conanfile):\n \"\"\" The build assumes an input of conans ordered by degree, first level\n should be independent from each other, the next-second level should have\n dependencies only to first level conans.\n param nodes_by_level: list of lists [[nodeA, nodeB], [nodeC], [nodeD, ...], ...]\n \"\"\"\n inverse = deps_graph.inverse_levels()\n flat = []\n\n for level in inverse:\n level = sorted(level, key=lambda x: x.conan_ref)\n flat.extend(n for n in level if n not in skip_nodes)\n\n for conan_ref, package_id, conan_file, build_needed in nodes_to_process:\n output = ScopedOutput(str(conan_ref), self._out)\n package_ref = PackageReference(conan_ref, package_id)\n package_folder = self._client_cache.package(package_ref,\n conan_file.short_paths)\n\n if build_needed and (conan_ref, package_id) not in self._built_packages:\n self._build_package(conan_file, conan_ref, package_id, package_ref, output,\n keep_build, profile_build_requires, flat, deps_graph)\n else:\n # Get the package, we have a not outdated remote package\n with self._client_cache.package_lock(package_ref):\n self._get_remote_package(conan_file, package_ref, output, package_folder)\n self._propagate_info(conan_file, conan_ref, flat, deps_graph)\n\n # Call the info method\n self._call_package_info(conan_file, package_folder)\n\n # Finally, propagate information to root node (conan_ref=None)\n self._propagate_info(root_conanfile, None, flat, deps_graph)","label":0},{"cid":"conans/client/installer.py::ConanInstaller._build_package","text":"conans/client/installer.py::ConanInstaller._build_package\n def _build_package(self, conan_file, conan_ref, package_id, package_ref, output, keep_build,\n profile_build_requires, flat, deps_graph):\n build_allowed = self._build_mode.allowed(conan_file, conan_ref)\n if not build_allowed:\n _raise_package_not_found_error(conan_file, conan_ref, package_id, output)\n\n skip_build = conan_file.develop and keep_build\n if skip_build:\n output.info(\"Won't be built as specified by --keep-build\")\n else:\n if conan_file.build_policy_missing:\n output.info(\"Building package from source as defined by build_policy='missing'\")\n elif self._build_mode.forced(conan_file, conan_ref):\n output.warn('Forced build from source')\n\n if not skip_build:\n self._build_requires.install(conan_ref, conan_file, self,\n profile_build_requires, output)\n\n # It is important that it is done AFTER build_requires install\n self._propagate_info(conan_file, conan_ref, flat, deps_graph)\n\n t1 = time.time()\n builder = _ConanPackageBuilder(conan_file, package_ref, self._client_cache, output)\n\n if skip_build:\n if not os.path.exists(builder.build_folder):\n raise ConanException(\"--keep-build specified, but build folder not found\")\n else:\n with self._client_cache.conanfile_write_lock(conan_ref):\n self._remote_proxy.get_recipe_sources(conan_ref, conan_file.short_paths)\n builder.prepare_build()\n\n with self._client_cache.conanfile_read_lock(conan_ref):\n with self._client_cache.package_lock(builder.build_reference):\n if not skip_build:\n builder.build()\n builder.package()\n\n self._remote_proxy.handle_package_manifest(package_ref, installed=True)\n\n ","label":0},{"cid":"conans/client/installer.py::ConanInstaller._get_remote_package","text":"conans/client/installer.py::ConanInstaller._get_remote_package\n def _get_remote_package(self, conan_file, package_reference, output, package_folder):\n \"\"\"Get remote package. It won't check if it's outdated\"\"\"\n # Compute conan_file package from local (already compiled) or from remote\n # If already exists do not dirt the output, the common situation\n # is that package is already installed and OK. If don't, the proxy\n # will print some other message about it\n if not os.path.exists(package_folder):\n self._out.info(\"Retrieving package %s\" % package_reference.package_id)\n\n if self._remote_proxy.get_package(package_reference,\n short_paths=conan_file.short_paths):\n _handle_system_requirements(conan_file, package_reference,\n self._client_cache, output)\n if get_env(\"CONAN_READ_ONLY_CACHE\", False):\n make_read_only(package_folder)\n return True\n\n _raise_package_not_found_error(conan_file, package_reference.conan,\n package_reference.package_id, output)","label":0},{"cid":"conans/client/installer.py::ConanInstaller._log_built_package","text":"conans/client/installer.py::ConanInstaller._log_built_package\n def _log_built_package(self, build_folder, package_ref, duration):\n log_file = os.path.join(build_folder, RUN_LOG_NAME)\n log_file = log_file if os.path.exists(log_file) else None\n log_package_built(package_ref, duration, log_file)","label":0},{"cid":"conans/client/installer.py::ConanInstaller._propagate_info","text":"conans/client/installer.py::ConanInstaller._propagate_info\n @staticmethod\n def _propagate_info(conan_file, conan_ref, flat, deps_graph):\n # Get deps_cpp_info from upstream nodes\n node_order = deps_graph.ordered_closure((conan_ref, conan_file), flat)\n for n in node_order:\n conan_file.deps_cpp_info.update(n.conanfile.cpp_info, n.conan_ref.name)\n conan_file.deps_env_info.update(n.conanfile.env_info, n.conan_ref.name)\n conan_file.deps_user_info[n.conan_ref.name] = n.conanfile.user_info\n\n # Update the info but filtering the package values that not apply to the subtree\n # of this current node and its dependencies.\n subtree_libnames = [ref.name for (ref, _) in node_order]\n for package_name, env_vars in conan_file._env_values.data.items():\n for name, value in env_vars.items():\n if not package_name or package_name in subtree_libnames or \\\n package_name == conan_file.name:\n conan_file.info.env_values.add(name, value, package_name)","label":0},{"cid":"conans/client/installer.py::ConanInstaller._call_package_info","text":"conans/client/installer.py::ConanInstaller._call_package_info\n @staticmethod\n def _call_package_info(conanfile, package_folder):\n conanfile.cpp_info = CppInfo(package_folder)\n conanfile.cpp_info.version = conanfile.version\n conanfile.cpp_info.description = conanfile.description\n conanfile.env_info = EnvInfo()\n conanfile.user_info = UserInfo()\n\n # Get deps_cpp_info from upstream nodes\n public_deps = [name for name, req in conanfile.requires.items() if not req.private]\n conanfile.cpp_info.public_deps = public_deps\n # Once the node is build, execute package info, so it has access to the\n # package folder and artifacts\n with tools.chdir(package_folder):\n with conanfile_exception_formatter(str(conanfile), \"package_info\"):\n conanfile.package_folder = package_folder\n conanfile.source_folder = None\n conanfile.build_folder = None\n conanfile.install_folder = None\n conanfile.package_info()","label":0},{"cid":"conans/client/installer.py::ConanInstaller._get_nodes","text":"conans/client/installer.py::ConanInstaller._get_nodes\n def _get_nodes(self, nodes_by_level, skip_nodes):\n \"\"\"Compute a list of (conan_ref, package_id, conan_file, build_node)\n defining what to do with each node\n \"\"\"\n\n nodes_to_build = []\n # Now build each level, starting from the most independent one\n package_references = set()\n for level in nodes_by_level:\n for node in level:\n if node in skip_nodes:\n continue\n conan_ref, conan_file = node\n build_node = False\n logger.debug(\"Processing node %s\", repr(conan_ref))\n package_id = conan_file.info.package_id()\n package_reference = PackageReference(conan_ref, package_id)\n # Avoid processing twice the same package reference\n if package_reference not in package_references:\n package_references.add(package_reference)\n check_outdated = self._build_mode.outdated\n if self._build_mode.forced(conan_file, conan_ref):\n build_node = True\n else:\n available = self._remote_proxy.package_available(package_reference,\n conan_file.short_paths,\n check_outdated)\n build_node = not available\n\n nodes_to_build.append((conan_ref, package_id, conan_file, build_node))\n\n # A check to be sure that if introduced a pattern, something is going to be built\n if self._build_mode.patterns:\n to_build = [str(n[0].name) for n in nodes_to_build if n[3]]\n self._build_mode.check_matches(to_build)\n\n return nodes_to_build","label":0},{"cid":"conans/client/manager.py::BuildMode.__init__","text":"conans/client/manager.py::BuildMode.__init__\n def __init__(self, params, output):\n self._out = output\n self.outdated = False\n self.missing = False\n self.never = False\n self.patterns = []\n self._unused_patterns = []\n self.all = False\n if params is None:\n return\n\n assert isinstance(params, list)\n if len(params) == 0:\n self.all = True\n else:\n for param in params:\n if param == \"outdated\":\n self.outdated = True\n elif param == \"missing\":\n self.missing = True\n elif param == \"never\":\n self.never = True\n else:\n self.patterns.append(\"%s\" % param)\n\n if self.never and (self.outdated or self.missing or self.patterns):\n raise ConanException(\"--build=never not compatible with other options\")\n self._unused_patterns = list(self.patterns)","label":0},{"cid":"conans/client/manager.py::BuildMode.forced","text":"conans/client/manager.py::BuildMode.forced\n def forced(self, conan_file, reference):\n if self.never:\n return False\n if self.all:\n return True\n\n if conan_file.build_policy_always:\n out = ScopedOutput(str(reference), self._out)\n out.info(\"Building package from source as defined by build_policy='always'\")\n return True\n\n ref = reference.name\n # Patterns to match, if package matches pattern, build is forced\n force_build = any([fnmatch.fnmatch(ref, pattern) for pattern in self.patterns])\n return force_build","label":0},{"cid":"conans/client/manager.py::BuildMode.allowed","text":"conans/client/manager.py::BuildMode.allowed\n def allowed(self, conan_file, reference):\n return (self.missing or self.outdated or self.forced(conan_file, reference) or\n conan_file.build_policy_missing)","label":0},{"cid":"conans/client/manager.py::BuildMode.check_matches","text":"conans/client/manager.py::BuildMode.check_matches\n def check_matches(self, references):\n for pattern in list(self._unused_patterns):\n matched = any(fnmatch.fnmatch(ref, pattern) for ref in references)\n if matched:\n self._unused_patterns.remove(pattern)","label":0},{"cid":"conans/client/manager.py::BuildMode.report_matches","text":"conans/client/manager.py::BuildMode.report_matches\n def report_matches(self):\n for pattern in self._unused_patterns:\n self._out.error(\"No package matching '%s' pattern\" % pattern)","label":0},{"cid":"conans/client/manager.py::ConanManager.__init__","text":"conans/client/manager.py::ConanManager.__init__\n def __init__(self, client_cache, user_io, runner, remote_manager, search_manager,\n settings_preprocessor):\n assert isinstance(user_io, UserIO)\n assert isinstance(client_cache, ClientCache)\n self._client_cache = client_cache\n self._user_io = user_io\n self._runner = runner\n self._remote_manager = remote_manager\n self._search_manager = search_manager\n self._settings_preprocessor = settings_preprocessor","label":0},{"cid":"conans/client/manager.py::ConanManager._load_consumer_conanfile","text":"conans/client/manager.py::ConanManager._load_consumer_conanfile\n def _load_consumer_conanfile(self, conanfile_path, info_folder, output,\n deps_info_required=False):\n \"\"\"loads a conanfile for local flow: source, imports, package, build\n \"\"\"\n profile = read_conaninfo_profile(info_folder) or self._client_cache.default_profile\n loader = self.get_loader(profile, local=True)\n if conanfile_path.endswith(\".py\"):\n conanfile = loader.load_conan(conanfile_path, output, consumer=True, local=True)\n else:\n conanfile = loader.load_conan_txt(conanfile_path, output)\n\n _load_deps_info(info_folder, conanfile, required=deps_info_required)\n\n return conanfile","label":0},{"cid":"conans/client/manager.py::ConanManager._load_install_conanfile","text":"conans/client/manager.py::ConanManager._load_install_conanfile\n def _load_install_conanfile(self, loader, reference_or_path):\n \"\"\"loads a conanfile for installation: install, info\n \"\"\"\n if isinstance(reference_or_path, ConanFileReference):\n conanfile = loader.load_virtual([reference_or_path])\n else:\n output = ScopedOutput(\"PROJECT\", self._user_io.out)\n if reference_or_path.endswith(\".py\"):\n conanfile = loader.load_conan(reference_or_path, output, consumer=True)\n else:\n conanfile = loader.load_conan_txt(reference_or_path, output)\n return conanfile","label":0},{"cid":"conans/client/manager.py::ConanManager.get_loader","text":"conans/client/manager.py::ConanManager.get_loader\n def get_loader(self, profile, local=False):\n \"\"\" When local=True it means that the state is being recovered from installed files\n conaninfo.txt, conanbuildinfo.txt, and only local methods as build() are being executed.\n Thus, it is necessary to restore settings from that info, as configure() is not called,\n being necessary to remove those settings that doesn't have a value\n \"\"\"\n cache_settings = self._client_cache.settings.copy()\n cache_settings.values = profile.settings_values\n if local:\n cache_settings.remove_undefined()\n else:\n self._settings_preprocessor.preprocess(cache_settings)\n return ConanFileLoader(self._runner, cache_settings, profile)","label":0},{"cid":"conans/client/manager.py::ConanManager.export","text":"conans/client/manager.py::ConanManager.export\n def export(self, conanfile_path, name, version, user, channel, keep_source=False):\n cmd_export(conanfile_path, name, version, user, channel, keep_source,\n self._user_io.out, self._client_cache)","label":0},{"cid":"conans/client/manager.py::ConanManager.export_pkg","text":"conans/client/manager.py::ConanManager.export_pkg\n def export_pkg(self, reference, source_folder, build_folder, package_folder, install_folder, profile, force):\n\n conan_file_path = self._client_cache.conanfile(reference)\n if not os.path.exists(conan_file_path):\n raise ConanException(\"Package recipe '%s' does not exist\" % str(reference))\n\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager,\n remote_name=None, update=False, check_updates=False,\n manifest_manager=None)\n\n loader = self.get_loader(profile)\n conanfile = loader.load_virtual([reference], scope_options=True)\n if install_folder and existing_info_files(install_folder):\n _load_deps_info(install_folder, conanfile, required=True)\n\n graph_builder = self._get_graph_builder(loader, False, remote_proxy)\n deps_graph = graph_builder.load(conanfile)\n\n # this is a bit tricky, but works. The loading of a cache package makes the referenced\n # one, the first of the first level, always existing\n nodes = deps_graph.direct_requires()\n _, conanfile = nodes[0]\n pkg_id = conanfile.info.package_id()\n self._user_io.out.info(\"Packaging to %s\" % pkg_id)\n pkg_reference = PackageReference(reference, pkg_id)\n dest_package_folder = self._client_cache.package(pkg_reference,\n short_paths=conanfile.short_paths)\n\n if os.path.exists(dest_package_folder):\n if force:\n rmdir(dest_package_folder)\n else:\n raise ConanException(\"Package already exists. Please use --force, -f to \"\n \"overwrite it\")\n\n recipe_hash = self._client_cache.load_manifest(reference).summary_hash\n conanfile.info.recipe_hash = recipe_hash\n conanfile.develop = True\n package_","label":0},{"cid":"conans/client/manager.py::ConanManager.download","text":"conans/client/manager.py::ConanManager.download\n def download(self, reference, package_ids, remote):\n \"\"\" Download conanfile and specified packages to local repository\n @param reference: ConanFileReference\n @param package_ids: Package ids or empty for download all\n @param remote: install only from that remote\n \"\"\"\n assert(isinstance(reference, ConanFileReference))\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote)\n remote, _ = remote_proxy._get_remote()\n package = self._remote_manager.search_recipes(remote, reference, None)\n if not package: # Search the reference first, and raise if it doesn't exist\n raise ConanException(\"'%s' not found in remote\" % str(reference))\n\n # First of all download package recipe\n remote_proxy.get_recipe(reference)\n\n if package_ids:\n remote_proxy.download_packages(reference, package_ids)\n else:\n self._user_io.out.info(\"Getting the complete package list \"\n \"from '%s'...\" % str(reference))\n packages_props = self._remote_manager.search_packages(remote, reference, None)\n if not packages_props:\n output = ScopedOutput(str(reference), self._user_io.out)\n output.warn(\"No remote binary packages found in remote\")\n else:\n remote_proxy.download_packages(reference, list(packages_props.keys()))","label":0},{"cid":"conans/client/manager.py::ConanManager._inject_require","text":"conans/client/manager.py::ConanManager._inject_require\n @staticmethod\n def _inject_require(conanfile, inject_require):\n \"\"\" test_package functionality requires injecting the tested package as requirement\n before running the install\n \"\"\"\n require = conanfile.requires.get(inject_require.name)\n if require:\n require.conan_reference = require.range_reference = inject_require\n else:\n conanfile.requires(str(inject_require))\n conanfile._user = inject_require.user\n conanfile._channel = inject_require.channel","label":0},{"cid":"conans/client/manager.py::ConanManager._get_graph_builder","text":"conans/client/manager.py::ConanManager._get_graph_builder\n def _get_graph_builder(self, loader, update, remote_proxy):\n local_search = self._search_manager\n resolver = RequireResolver(self._user_io.out, local_search, remote_proxy, update=update)\n graph_builder = DepsGraphBuilder(remote_proxy, self._user_io.out, loader, resolver)\n return graph_builder","label":0},{"cid":"conans/client/manager.py::ConanManager._get_deps_graph","text":"conans/client/manager.py::ConanManager._get_deps_graph\n def _get_deps_graph(self, reference, profile, remote_proxy):\n loader = self.get_loader(profile)\n conanfile = self._load_install_conanfile(loader, reference)\n graph_builder = self._get_graph_builder(loader, False, remote_proxy)\n deps_graph = graph_builder.load(conanfile)\n return deps_graph, graph_builder, conanfile","label":0},{"cid":"conans/client/manager.py::ConanManager.info_build_order","text":"conans/client/manager.py::ConanManager.info_build_order\n def info_build_order(self, reference, profile, build_order, remote, check_updates):\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote,\n update=False, check_updates=check_updates)\n deps_graph, _, _ = self._get_deps_graph(reference, profile, remote_proxy)\n result = deps_graph.build_order(build_order)\n return result","label":0},{"cid":"conans/client/manager.py::ConanManager.info_nodes_to_build","text":"conans/client/manager.py::ConanManager.info_nodes_to_build\n def info_nodes_to_build(self, reference, profile, build_modes, remote, check_updates):\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote,\n update=False, check_updates=check_updates)\n deps_graph, _, conanfile = self._get_deps_graph(reference, profile, remote_proxy)\n build_mode = BuildMode(build_modes, self._user_io.out)\n installer = ConanInstaller(self._client_cache, self._user_io.out, remote_proxy, build_mode,\n None)\n nodes = installer.nodes_to_build(deps_graph)\n counter = Counter(ref.conan.name for ref, _ in nodes)\n ret = [ref if counter[ref.conan.name] > 1 else str(ref.conan) for ref, _ in nodes]\n return ret, self._get_project_reference(reference, conanfile)","label":0},{"cid":"conans/client/manager.py::ConanManager._get_project_reference","text":"conans/client/manager.py::ConanManager._get_project_reference\n def _get_project_reference(self, reference, conanfile):\n if isinstance(reference, ConanFileReference):\n project_reference = None\n else:\n project_reference = str(conanfile)\n\n return project_reference","label":0},{"cid":"conans/client/manager.py::ConanManager.info_get_graph","text":"conans/client/manager.py::ConanManager.info_get_graph\n def info_get_graph(self, reference, profile, remote=None, check_updates=False):\n \"\"\" Fetch and build all dependencies for the given reference\n @param reference: ConanFileReference or path to user space conanfile\n @param current_path: where the output files will be saved\n @param remote: install only from that remote\n @param profile: Profile object with both the -s introduced options and profile readed values\n @param build_modes: List of build_modes specified\n \"\"\"\n\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote,\n update=False, check_updates=check_updates)\n\n deps_graph, graph_builder, conanfile = self._get_deps_graph(reference, profile, remote_proxy)\n\n if check_updates:\n graph_updates_info = graph_builder.get_graph_updates_info(deps_graph)\n else:\n graph_updates_info = {}\n\n return deps_graph, graph_updates_info, self._get_project_reference(reference, conanfile)","label":0},{"cid":"conans/client/manager.py::ConanManager.install","text":"conans/client/manager.py::ConanManager.install\n def install(self, reference, install_folder, profile, remote=None, build_modes=None,\n update=False, manifest_folder=None, manifest_verify=False,\n manifest_interactive=False, generators=None, no_imports=False, inject_require=None,\n install_reference=False, keep_build=False):\n \"\"\" Fetch and build all dependencies for the given reference\n @param reference: ConanFileReference or path to user space conanfile\n @param install_folder: where the output files will be saved\n @param remote: install only from that remote\n @param profile: Profile object with both the -s introduced options and profile read values\n @param build_modes: List of build_modes specified\n @param update: Check for updated in the upstream remotes (and update)\n @param manifest_folder: Folder to install the manifests\n @param manifest_verify: Verify dependencies manifests against stored ones\n @param manifest_interactive: Install deps manifests in folder for later verify, asking user\n for confirmation\n @param generators: List of generators from command line. If False, no generator will be\n written\n @param no_imports: Install specified packages but avoid running imports\n @param inject_require: Reference to add as a requirement to the conanfile\n \"\"\"\n if generators is not False:\n generators = set(generators) if generators else set()\n generators.add(\"txt\") # Add txt generator by default\n\n manifest_manager = ManifestManager(manifest_folder, user_io=self._user_io,\n client_cache=self._client_cache,\n verify=manifest_verify,\n interactive=manifest_interactive) if manifest_folder else None\n remote_proxy = ConanProxy(self._client_cache, self._user_io, ","label":0},{"cid":"conans/client/manager.py::ConanManager.source","text":"conans/client/manager.py::ConanManager.source\n def source(self, conanfile_path, source_folder, info_folder):\n \"\"\"\n :param conanfile_path: Absolute path to a conanfile\n :param source_folder: Absolute path where to put the files\n :param info_folder: Absolute path where to read the info files\n :param package_folder: Absolute path to the package_folder, only to have the var present\n :return:\n \"\"\"\n output = ScopedOutput(\"PROJECT\", self._user_io.out)\n # only infos if exist\n conanfile = self._load_consumer_conanfile(conanfile_path, info_folder, output)\n config_source_local(source_folder, conanfile, output)","label":0},{"cid":"conans/client/manager.py::ConanManager.imports_undo","text":"conans/client/manager.py::ConanManager.imports_undo\n def imports_undo(self, current_path):\n undo_imports(current_path, self._user_io.out)","label":0},{"cid":"conans/client/manager.py::ConanManager.imports","text":"conans/client/manager.py::ConanManager.imports\n def imports(self, conan_file_path, dest_folder, info_folder):\n \"\"\"\n :param conan_file_path: Abs path to a conanfile\n :param dest_folder: Folder where to put the files\n :param info_folder: Folder containing the conaninfo/conanbuildinfo.txt files\n :return:\n \"\"\"\n\n output = ScopedOutput(\"PROJECT\", self._user_io.out)\n conanfile = self._load_consumer_conanfile(conan_file_path, info_folder,\n output, deps_info_required=True)\n\n run_imports(conanfile, dest_folder, output)","label":0},{"cid":"conans/client/manager.py::ConanManager.local_package","text":"conans/client/manager.py::ConanManager.local_package\n def local_package(self, package_folder, conanfile_path, build_folder, source_folder,\n install_folder):\n if package_folder == build_folder:\n raise ConanException(\"Cannot 'conan package' to the build folder. \"\n \"--build-folder and package folder can't be the same\")\n output = ScopedOutput(\"PROJECT\", self._user_io.out)\n conanfile = self._load_consumer_conanfile(conanfile_path, install_folder, output,\n deps_info_required=True)\n packager.create_package(conanfile, source_folder, build_folder, package_folder,\n install_folder, output, local=True, copy_info=True)","label":0},{"cid":"conans/client/manager.py::ConanManager.build","text":"conans/client/manager.py::ConanManager.build\n def build(self, conanfile_path, source_folder, build_folder, package_folder, install_folder,\n test=False):\n \"\"\" Call to build() method saved on the conanfile.py\n param conanfile_path: path to a conanfile.py\n \"\"\"\n logger.debug(\"Building in %s\" % build_folder)\n logger.debug(\"Conanfile in %s\" % conanfile_path)\n\n try:\n # Append env_vars to execution environment and clear when block code ends\n output = ScopedOutput((\"%s (test package)\" % test) if test else \"Project\",\n self._user_io.out)\n conan_file = self._load_consumer_conanfile(conanfile_path, install_folder, output,\n deps_info_required=True)\n except NotFoundException:\n # TODO: Auto generate conanfile from requirements file\n raise ConanException(\"'%s' file is needed for build.\\n\"\n \"Create a '%s' and move manually the \"\n \"requirements and generators from '%s' file\"\n % (CONANFILE, CONANFILE, CONANFILE_TXT))\n\n if test:\n try:\n conan_file.requires.add(test)\n except ConanException:\n pass\n\n try:\n mkdir(build_folder)\n os.chdir(build_folder)\n conan_file.build_folder = build_folder\n conan_file.source_folder = source_folder\n conan_file.package_folder = package_folder\n conan_file.install_folder = install_folder\n with get_env_context_manager(conan_file):\n output.highlight(\"Running build()\")\n with conanfile_exception_formatter(str(conan_file), \"build\"):\n conan_file.build()\n if test:\n output.highlight(\"Running test()\")\n with conanfile_exception_format","label":1},{"cid":"conans/client/manager.py::ConanManager.remove","text":"conans/client/manager.py::ConanManager.remove\n def remove(self, pattern, src=False, build_ids=None, package_ids_filter=None, force=False,\n remote=None, packages_query=None, outdated=False):\n \"\"\" Remove conans and/or packages\n @param pattern: string to match packages\n @param src: Remove src folder\n @param package_ids_filter: list of ids or [] for all list\n @param build_ids: list of ids or [] for all list\n @param remote: search on another origin to get packages info\n @param force: if True, it will be deleted without requesting anything\n @param packages_query: Only if src is a reference. Query settings and options\n \"\"\"\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote)\n remover = ConanRemover(self._client_cache, self._remote_manager, self._user_io, remote_proxy)\n remover.remove(pattern, remote, src, build_ids, package_ids_filter, force=force,\n packages_query=packages_query, outdated=outdated)","label":0},{"cid":"conans/client/manager.py::ConanManager.user","text":"conans/client/manager.py::ConanManager.user\n def user(self, remote=None, name=None, password=None):\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote)\n if password == \"\":\n if not remote:\n remote = remote_proxy.registry.default_remote.name\n name, password = self._user_io.request_login(remote_name=remote, username=name)\n return remote_proxy.authenticate(name, password)","label":0},{"cid":"conans/client/manager.py::ConanManager.get_path","text":"conans/client/manager.py::ConanManager.get_path\n def get_path(self, reference, package_id=None, path=None, remote=None):\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote)\n if not path and not package_id:\n path = \"conanfile.py\"\n elif not path and package_id:\n path = \"conaninfo.txt\"\n return remote_proxy.get_path(reference, package_id, path), path","label":0},{"cid":"conans/client/manager.py::ConanManager.export_alias","text":"conans/client/manager.py::ConanManager.export_alias\n def export_alias(self, reference, target_reference):\n\n conanfile = \"\"\"\nfrom conans import ConanFile\n\nclass AliasConanfile(ConanFile):\n alias = \"%s\"\n\"\"\" % str(target_reference)\n\n export_path = self._client_cache.export(reference)\n mkdir(export_path)\n save(os.path.join(export_path, CONANFILE), conanfile)\n mkdir(self._client_cache.export_sources(reference))\n digest = FileTreeManifest.create(export_path)\n save(os.path.join(export_path, CONAN_MANIFEST), str(digest))","label":0},{"cid":"conans/client/manager.py::_load_deps_info","text":"conans/client/manager.py::_load_deps_info\ndef _load_deps_info(current_path, conanfile, required):\n\n def get_forbidden_access_object(field_name):\n class InfoObjectNotDefined(object):\n def __getitem__(self, item):\n raise ConanException(\"self.%s not defined. If you need it for a \"\n \"local command run 'conan install'\" % field_name)\n __getattr__ = __getitem__\n\n return InfoObjectNotDefined()\n\n if not current_path:\n return\n info_file_path = os.path.join(current_path, BUILD_INFO)\n try:\n deps_cpp_info, deps_user_info, deps_env_info = TXTGenerator.loads(load(info_file_path))\n conanfile.deps_cpp_info = deps_cpp_info\n conanfile.deps_user_info = deps_user_info\n conanfile.deps_env_info = deps_env_info\n except IOError:\n if required:\n raise ConanException(\"%s file not found in %s\\nIt is required for this command\\n\"\n \"You can generate it using 'conan install'\"\n % (BUILD_INFO, current_path))\n conanfile.deps_cpp_info = get_forbidden_access_object(\"deps_cpp_info\")\n conanfile.deps_user_info = get_forbidden_access_object(\"deps_user_info\")\n except ConanException:\n raise ConanException(\"Parse error in '%s' file in %s\" % (BUILD_INFO, current_path))","label":0},{"cid":"conans/client/manager.py::_load_deps_info.get_forbidden_access_object","text":"conans/client/manager.py::_load_deps_info.get_forbidden_access_object\n def get_forbidden_access_object(field_name):\n class InfoObjectNotDefined(object):\n def __getitem__(self, item):\n raise ConanException(\"self.%s not defined. If you need it for a \"\n \"local command run 'conan install'\" % field_name)\n __getattr__ = __getitem__\n\n return InfoObjectNotDefined()","label":0},{"cid":"conans/client/manager.py::_load_deps_info.get_forbidden_access_object.InfoObjectNotDefined.__getitem__","text":"conans/client/manager.py::_load_deps_info.get_forbidden_access_object.InfoObjectNotDefined.__getitem__\n def __getitem__(self, item):\n raise ConanException(\"self.%s not defined. If you need it for a \"\n \"local command run 'conan install'\" % field_name)","label":0},{"cid":"conans/client/manager.py::existing_info_files","text":"conans/client/manager.py::existing_info_files\ndef existing_info_files(folder):\n return os.path.exists(os.path.join(folder, CONANINFO)) and \\\n os.path.exists(os.path.join(folder, BUILD_INFO))","label":0},{"cid":"conans/model/conan_file.py::create_options","text":"conans/model/conan_file.py::create_options\ndef create_options(conanfile):\n try:\n package_options = PackageOptions(getattr(conanfile, \"options\", None))\n options = Options(package_options)\n\n default_options = getattr(conanfile, \"default_options\", None)\n if default_options:\n if isinstance(default_options, (list, tuple)):\n default_values = OptionsValues(default_options)\n elif isinstance(default_options, str):\n default_values = OptionsValues.loads(default_options)\n else:\n raise ConanException(\"Please define your default_options as list or \"\n \"multiline string\")\n options.values = default_values\n return options\n except Exception as e:\n raise ConanException(\"Error while initializing options. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::create_requirements","text":"conans/model/conan_file.py::create_requirements\ndef create_requirements(conanfile):\n try:\n # Actual requirements of this package\n if not hasattr(conanfile, \"requires\"):\n return Requirements()\n else:\n if not conanfile.requires:\n return Requirements()\n if isinstance(conanfile.requires, tuple):\n return Requirements(*conanfile.requires)\n else:\n return Requirements(conanfile.requires, )\n except Exception as e:\n raise ConanException(\"Error while initializing requirements. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::create_settings","text":"conans/model/conan_file.py::create_settings\ndef create_settings(conanfile, settings, local):\n try:\n defined_settings = getattr(conanfile, \"settings\", None)\n if isinstance(defined_settings, str):\n defined_settings = [defined_settings]\n current = defined_settings or {}\n settings.constraint(current, raise_undefined_field=not local)\n return settings\n except Exception as e:\n raise ConanException(\"Error while initializing settings. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::create_exports","text":"conans/model/conan_file.py::create_exports\ndef create_exports(conanfile):\n if not hasattr(conanfile, \"exports\"):\n return None\n else:\n if isinstance(conanfile.exports, str):\n return (conanfile.exports, )\n return conanfile.exports","label":0},{"cid":"conans/model/conan_file.py::create_exports_sources","text":"conans/model/conan_file.py::create_exports_sources\ndef create_exports_sources(conanfile):\n if not hasattr(conanfile, \"exports_sources\"):\n return None\n else:\n if isinstance(conanfile.exports_sources, str):\n return (conanfile.exports_sources, )\n return conanfile.exports_sources","label":0},{"cid":"conans/model/conan_file.py::_env_and_python","text":"conans/model/conan_file.py::_env_and_python\n@contextmanager\ndef _env_and_python(conanfile):\n with environment_append(conanfile.env):\n with pythonpath(conanfile):\n yield","label":0},{"cid":"conans/model/conan_file.py::get_env_context_manager","text":"conans/model/conan_file.py::get_env_context_manager\ndef get_env_context_manager(conanfile, without_python=False):\n if not conanfile.apply_env:\n return no_op()\n if without_python:\n return environment_append(conanfile.env)\n return _env_and_python(conanfile)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.__init__","text":"conans/model/conan_file.py::ConanFile.__init__\n def __init__(self, output, runner, settings, user=None, channel=None, local=None):\n # User defined generators\n self.generators = self.generators if hasattr(self, \"generators\") else [\"txt\"]\n if isinstance(self.generators, str):\n self.generators = [self.generators]\n\n # User defined options\n self.options = create_options(self)\n self.requires = create_requirements(self)\n self.settings = create_settings(self, settings, local)\n try:\n if self.settings.os_build and self.settings.os:\n output.writeln(\"*\"*60, front=Color.BRIGHT_RED)\n output.writeln(\" This package defines both 'os' and 'os_build' \",\n front=Color.BRIGHT_RED)\n output.writeln(\" Please use 'os' for libraries and 'os_build'\",\n front=Color.BRIGHT_RED)\n output.writeln(\" only for build-requires used for cross-building\",\n front=Color.BRIGHT_RED)\n output.writeln(\"*\"*60, front=Color.BRIGHT_RED)\n except ConanException:\n pass\n self.exports = create_exports(self)\n self.exports_sources = create_exports_sources(self)\n # needed variables to pack the project\n self.cpp_info = None # Will be initialized at processing time\n self.deps_cpp_info = DepsCppInfo()\n\n # environment variables declared in the package_info\n self.env_info = None # Will be initialized at processing time\n self.deps_env_info = DepsEnvInfo()\n\n # user declared variables\n self.user_info = None\n # Keys are the package names, and the values a dict with the vars\n self.deps_user_info = DepsUserInfo()\n\n self.copy = None # initialized at runtime\n self.copy_deps = None # initialized at runtime\n\n # an output stream (writeln, info, warn error)\n self.ou","label":1},{"cid":"conans/model/conan_file.py::ConanFile.env","text":"conans/model/conan_file.py::ConanFile.env\n @property\n def env(self):\n \"\"\"Apply the self.deps_env_info into a copy of self._env_values (will prioritize the\n self._env_values, user specified from profiles or -e first, then inherited)\"\"\"\n # Cannot be lazy cached, because it's called in configure node, and we still don't have\n # the deps_env_info objects available\n tmp_env_values = self._env_values.copy()\n tmp_env_values.update(self.deps_env_info)\n\n ret, multiple = tmp_env_values.env_dicts(self.name)\n ret.update(multiple)\n return ret","label":0},{"cid":"conans/model/conan_file.py::ConanFile.channel","text":"conans/model/conan_file.py::ConanFile.channel\n @property\n def channel(self):\n if not self._channel:\n self._channel = os.getenv(\"CONAN_CHANNEL\")\n if not self._channel:\n raise ConanException(\"CONAN_CHANNEL environment variable not defined, \"\n \"but self.channel is used in conanfile\")\n return self._channel","label":0},{"cid":"conans/model/conan_file.py::ConanFile.user","text":"conans/model/conan_file.py::ConanFile.user\n @property\n def user(self):\n if not self._user:\n self._user = os.getenv(\"CONAN_USERNAME\")\n if not self._user:\n raise ConanException(\"CONAN_USERNAME environment variable not defined, \"\n \"but self.user is used in conanfile\")\n return self._user","label":0},{"cid":"conans/model/conan_file.py::ConanFile.collect_libs","text":"conans/model/conan_file.py::ConanFile.collect_libs\n def collect_libs(self, folder=\"lib\"):\n self.output.warn(\"Use 'self.collect_libs' is deprecated, \"\n \"use tools.collect_libs(self) instead\")\n return tools.collect_libs(self, folder=folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_policy_missing","text":"conans/model/conan_file.py::ConanFile.build_policy_missing\n @property\n def build_policy_missing(self):\n return self.build_policy == \"missing\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_policy_always","text":"conans/model/conan_file.py::ConanFile.build_policy_always\n @property\n def build_policy_always(self):\n return self.build_policy == \"always\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.source","text":"conans/model/conan_file.py::ConanFile.source\n def source(self):\n pass","label":0},{"cid":"conans/model/conan_file.py::ConanFile.system_requirements","text":"conans/model/conan_file.py::ConanFile.system_requirements\n def system_requirements(self):\n \"\"\" this method can be overwritten to implement logic for system package\n managers, as apt-get\n\n You can define self.global_system_requirements = True, if you want the installation\n to be for all packages (not depending on settings/options/requirements)\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.config_options","text":"conans/model/conan_file.py::ConanFile.config_options\n def config_options(self):\n \"\"\" modify options, probably conditioned to some settings. This call is executed\n before config_settings. E.g.\n if self.settings.os == \"Windows\":\n del self.options.shared # shared/static not supported in win\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.configure","text":"conans/model/conan_file.py::ConanFile.configure\n def configure(self):\n \"\"\" modify settings, probably conditioned to some options. This call is executed\n after config_options. E.g.\n if self.options.header_only:\n self.settings.clear()\n This is also the place for conditional requirements\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build","text":"conans/model/conan_file.py::ConanFile.build\n def build(self):\n self.output.warn(\"This conanfile has no build step\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package","text":"conans/model/conan_file.py::ConanFile.package\n def package(self):\n self.output.warn(\"This conanfile has no package step\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_info","text":"conans/model/conan_file.py::ConanFile.package_info\n def package_info(self):\n \"\"\" define cpp_build_info, flags, etc\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.run","text":"conans/model/conan_file.py::ConanFile.run\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True):\n if not win_bash:\n retcode = self._runner(command, output, os.path.abspath(RUN_LOG_NAME), cwd)\n else:\n retcode = tools.run_in_windows_bash(self, bashcmd=command, cwd=cwd, subsystem=subsystem,\n msys_mingw=msys_mingw)\n\n if retcode != 0:\n raise ConanException(\"Error %d while executing %s\" % (retcode, command))\n\n return retcode","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_id","text":"conans/model/conan_file.py::ConanFile.package_id\n def package_id(self):\n \"\"\" modify the conans info, typically to narrow values\n eg.: conaninfo.package_references = []\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.test","text":"conans/model/conan_file.py::ConanFile.test\n def test(self):\n raise ConanException(\"You need to create a method 'test' in your test/conanfile.py\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.__repr__","text":"conans/model/conan_file.py::ConanFile.__repr__\n def __repr__(self):\n if self.name and self.version and self._channel and self._user:\n return \"%s/%s@%s/%s\" % (self.name, self.version, self.user, self.channel)\n elif self.name and self.version:\n return \"%s/%s@PROJECT\" % (self.name, self.version)\n else:\n return \"PROJECT\"","label":0}],"string":"[\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.__init__\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.__init__\\n def __init__(self, conanfile, win_bash=False, include_rpath_flags=False):\\n \\\"\\\"\\\"\\n FIXME: include_rpath_flags CONAN 2.0 to default True? Could break many packages in center\\n \\\"\\\"\\\"\\n self._conanfile = conanfile\\n self._win_bash = win_bash\\n self._include_rpath_flags = include_rpath_flags\\n self.subsystem = OSInfo().detect_windows_subsystem() if self._win_bash else None\\n self._deps_cpp_info = conanfile.deps_cpp_info\\n self._arch = conanfile.settings.get_safe(\\\"arch\\\")\\n self._build_type = conanfile.settings.get_safe(\\\"build_type\\\")\\n self._compiler = conanfile.settings.get_safe(\\\"compiler\\\")\\n self._compiler_version = conanfile.settings.get_safe(\\\"compiler.version\\\")\\n self._libcxx = conanfile.settings.get_safe(\\\"compiler.libcxx\\\")\\n self._cppstd = conanfile.settings.get_safe(\\\"cppstd\\\")\\n\\n # Set the generic objects before mapping to env vars to let the user\\n # alter some value\\n self.libs = copy.copy(self._deps_cpp_info.libs)\\n self.include_paths = copy.copy(self._deps_cpp_info.include_paths)\\n self.library_paths = copy.copy(self._deps_cpp_info.lib_paths)\\n\\n self.defines = self._configure_defines()\\n # Will go to CFLAGS and CXXFLAGS [\\\"-m64\\\" \\\"-m32\\\", \\\"-g\\\", \\\"-s\\\"]\\n self.flags = self._configure_flags()\\n # Only c++ flags [-stdlib, -library], will go to CXXFLAGS\\n self.cxx_flags = self._configure_cxx_flags()\\n # cpp standard\\n self.cppstd_flag = cppstd_flag(self._compiler, self._compiler_version, self._cppstd)\\n # Not -L flags, [\\\"-m64\\\" \\\"-m32\\\"]\\n self.link_flags = self._configure_link_flags() # TEST!\\n # Not declared by default\\n self.fpic = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_host_build_target_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_host_build_target_flags\\n def _get_host_build_target_flags(self, arch_detected, os_detected):\\n \\\"\\\"\\\"Based on google search for build/host triplets, it could need a lot\\n and complex verification\\\"\\\"\\\"\\n if not cross_building(self._conanfile.settings, os_detected, arch_detected):\\n return False, False, False\\n\\n arch_setting = self._conanfile.settings.get_safe(\\\"arch\\\")\\n os_setting = self._conanfile.settings.get_safe(\\\"os\\\")\\n\\n if os_detected == \\\"Windows\\\" and os_setting != \\\"Windows\\\":\\n # Don't know what to do with these, even exists? its only for configure\\n return None, None, None\\n\\n # Building FOR windows\\n if os_setting == \\\"Windows\\\":\\n build = \\\"i686-w64-mingw32\\\" if arch_detected == \\\"x86\\\" else \\\"x86_64-w64-mingw32\\\"\\n host = \\\"i686-w64-mingw32\\\" if arch_setting == \\\"x86\\\" else \\\"x86_64-w64-mingw32\\\"\\n else: # Building for Linux or Android\\n build = \\\"%s-%s\\\" % (arch_detected, {\\\"Linux\\\": \\\"linux-gnu\\\",\\n \\\"Darwin\\\": \\\"apple-darwin\\\"}.get(os_detected,\\n os_detected.lower()))\\n if arch_setting == \\\"x86\\\":\\n host_arch = \\\"i686\\\"\\n elif arch_setting == \\\"armv8\\\":\\n host_arch = \\\"aarch64\\\"\\n else:\\n host_arch = \\\"arm\\\" if \\\"arm\\\" in arch_setting else arch_setting\\n\\n host = \\\"%s%s\\\" % (host_arch, {\\\"Linux\\\": \\\"-linux-gnu\\\",\\n \\\"Android\\\": \\\"-linux-android\\\",\\n \\\"Macos\\\": \\\"-apple-darwin\\\",\\n \\\"iOS\\\": \\\"-apple-darwin\\\",\\n \\\"watchOS\\\": \\\"-apple-darwin\\\",\\n \\\"tvOS\\\": \\\"-apple-darwin\\\"}.get(os_setting, \\\"\\\"))\\n\\n if os_setting in (\\\"Linux\\\", \\\"Android\\\"):\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure\\n def configure(self, configure_dir=None, args=None, build=None, host=None, target=None,\\n pkg_config_paths=None):\\n \\\"\\\"\\\"\\n :param pkg_config_paths: Optional paths to locate the *.pc files\\n :param configure_dir: Absolute or relative path to the configure script\\n :param args: Optional arguments to pass to configure.\\n :param build: In which system the program will be built. \\\"False\\\" skips the --build flag\\n :param host: In which system the generated program will run. \\\"False\\\" skips the --host flag\\n :param target: This option is only used to build a cross-compiling toolchain.\\n \\\"False\\\" skips the --target flag\\n When the tool chain generates executable program, in which target system\\n the program will run.\\n :return: None\\n\\n http://jingfenghanmax.blogspot.com.es/2010/09/configure-with-host-target-and-build.html\\n https://gcc.gnu.org/onlinedocs/gccint/Configure-Terms.html\\n\\n \\\"\\\"\\\"\\n if configure_dir:\\n configure_dir = configure_dir.rstrip(\\\"/\\\")\\n else:\\n configure_dir = \\\".\\\"\\n auto_build, auto_host, auto_target = None, None, None\\n if build is None or host is None or target is None:\\n flags = self._get_host_build_target_flags(detected_architecture(), platform.system())\\n auto_build, auto_host, auto_target = flags\\n triplet_args = []\\n\\n if build is not False: # Skipped by user\\n if build or auto_build: # User specified value or automatic\\n triplet_args.append(\\\"--build=%s\\\" % (build or auto_build))\\n\\n if host is not False: # Skipped by user\\n if host or auto_host: # User specified value or automatic\\n triplet_args.append(\\\"--host=%s\\\" % (host or auto_host))\\n\\n if target is not False: # Skipped by user\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._adjust_path\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._adjust_path\\n def _adjust_path(self, path):\\n if self._win_bash:\\n path = unix_path(path, path_flavor=self.subsystem)\\n return '\\\"%s\\\"' % path if \\\" \\\" in path else path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.make\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.make\\n def make(self, args=\\\"\\\", make_program=None, target=None):\\n make_program = os.getenv(\\\"CONAN_MAKE_PROGRAM\\\") or make_program or \\\"make\\\"\\n with environment_append(self.vars):\\n str_args = args_to_string(args)\\n cpu_count_option = (\\\"-j%s\\\" % cpu_count()) if \\\"-j\\\" not in str_args else None\\n self._conanfile.run(\\\"%s\\\" % join_arguments([make_program, target, str_args,\\n cpu_count_option]),\\n win_bash=self._win_bash, subsystem=self.subsystem)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags\\n def _configure_link_flags(self):\\n \\\"\\\"\\\"Not the -L\\\"\\\"\\\"\\n ret = copy.copy(self._deps_cpp_info.sharedlinkflags)\\n ret.extend(self._deps_cpp_info.exelinkflags)\\n arch_flag = architecture_flag(compiler=self._compiler, arch=self._arch)\\n if arch_flag:\\n ret.append(arch_flag)\\n\\n sysf = sysroot_flag(self._deps_cpp_info.sysroot, win_bash=self._win_bash,\\n subsystem=self.subsystem,\\n compiler=self._compiler)\\n if sysf:\\n ret.append(sysf)\\n\\n if self._include_rpath_flags:\\n the_os = self._conanfile.settings.get_safe(\\\"os_build\\\") or \\\\\\n self._conanfile.settings.get_safe(\\\"os\\\")\\n ret.extend(rpath_flags(the_os, self._compiler, self._deps_cpp_info.lib_paths))\\n\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags\\n def _configure_flags(self):\\n ret = copy.copy(self._deps_cpp_info.cflags)\\n arch_flag = architecture_flag(compiler=self._compiler, arch=self._arch)\\n if arch_flag:\\n ret.append(arch_flag)\\n btf = build_type_flag(compiler=self._compiler, build_type=self._build_type)\\n if btf:\\n ret.append(btf)\\n srf = sysroot_flag(self._deps_cpp_info.sysroot, win_bash=self._win_bash,\\n subsystem=self.subsystem,\\n compiler=self._compiler)\\n if srf:\\n ret.append(srf)\\n\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_cxx_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_cxx_flags\\n def _configure_cxx_flags(self):\\n ret = copy.copy(self._deps_cpp_info.cppflags)\\n cxxf = libcxx_flag(compiler=self._compiler, libcxx=self._libcxx)\\n if cxxf:\\n ret.append(cxxf)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_defines\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_defines\\n def _configure_defines(self):\\n # requires declared defines\\n ret = copy.copy(self._deps_cpp_info.defines)\\n\\n # Debug definition for GCC\\n btf = build_type_define(build_type=self._build_type)\\n if btf:\\n ret.append(btf)\\n\\n # CXX11 ABI\\n abif = libcxx_define(compiler=self._compiler, libcxx=self._libcxx)\\n if abif:\\n ret.append(abif)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars\\n def _get_vars(self):\\n def append(*args):\\n ret = []\\n for arg in args:\\n if arg:\\n if isinstance(arg, list):\\n ret.extend(arg)\\n else:\\n ret.append(arg)\\n return ret\\n\\n lib_paths = format_library_paths(self.library_paths, win_bash=self._win_bash,\\n subsystem=self.subsystem, compiler=self._compiler)\\n include_paths = format_include_paths(self.include_paths, win_bash=self._win_bash,\\n subsystem=self.subsystem, compiler=self._compiler)\\n\\n ld_flags = append(self.link_flags, lib_paths)\\n cpp_flags = append(include_paths, format_defines(self.defines, self._compiler))\\n libs = format_libraries(self.libs, compiler=self._compiler)\\n\\n tmp_compilation_flags = copy.copy(self.flags)\\n if self.fpic:\\n tmp_compilation_flags.append(pic_flag(self._compiler))\\n\\n cxx_flags = append(tmp_compilation_flags, self.cxx_flags, self.cppstd_flag)\\n c_flags = tmp_compilation_flags\\n\\n return ld_flags, cpp_flags, libs, cxx_flags, c_flags\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars.append\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars.append\\n def append(*args):\\n ret = []\\n for arg in args:\\n if arg:\\n if isinstance(arg, list):\\n ret.extend(arg)\\n else:\\n ret.append(arg)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict\\n @property\\n def vars_dict(self):\\n\\n ld_flags, cpp_flags, libs, cxx_flags, c_flags = self._get_vars()\\n\\n if os.environ.get(\\\"CPPFLAGS\\\", None):\\n cpp_flags.append(os.environ.get(\\\"CPPFLAGS\\\", None))\\n\\n if os.environ.get(\\\"CXXFLAGS\\\", None):\\n cxx_flags.append(os.environ.get(\\\"CXXFLAGS\\\", None))\\n\\n if os.environ.get(\\\"CFLAGS\\\", None):\\n c_flags.append(os.environ.get(\\\"CFLAGS\\\", None))\\n\\n if os.environ.get(\\\"LDFLAGS\\\", None):\\n ld_flags.append(os.environ.get(\\\"LDFLAGS\\\", None))\\n\\n if os.environ.get(\\\"LIBS\\\", None):\\n libs.append(os.environ.get(\\\"LIBS\\\", None))\\n\\n ret = {\\\"CPPFLAGS\\\": cpp_flags,\\n \\\"CXXFLAGS\\\": cxx_flags,\\n \\\"CFLAGS\\\": c_flags,\\n \\\"LDFLAGS\\\": ld_flags,\\n \\\"LIBS\\\": libs,\\n }\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars\\n @property\\n def vars(self):\\n\\n ld_flags, cpp_flags, libs, cxx_flags, c_flags = self._get_vars()\\n\\n cpp_flags = \\\" \\\".join(cpp_flags) + _environ_value_prefix(\\\"CPPFLAGS\\\")\\n cxx_flags = \\\" \\\".join(cxx_flags) + _environ_value_prefix(\\\"CXXFLAGS\\\")\\n cflags = \\\" \\\".join(c_flags) + _environ_value_prefix(\\\"CFLAGS\\\")\\n ldflags = \\\" \\\".join(ld_flags) + _environ_value_prefix(\\\"LDFLAGS\\\")\\n libs = \\\" \\\".join(libs) + _environ_value_prefix(\\\"LIBS\\\")\\n\\n ret = {\\\"CPPFLAGS\\\": cpp_flags.strip(),\\n \\\"CXXFLAGS\\\": cxx_flags.strip(),\\n \\\"CFLAGS\\\": cflags.strip(),\\n \\\"LDFLAGS\\\": ldflags.strip(),\\n \\\"LIBS\\\": libs.strip(),\\n }\\n\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::_environ_value_prefix\",\n \"text\": \"conans/client/build/autotools_environment.py::_environ_value_prefix\\ndef _environ_value_prefix(var_name, prefix=\\\" \\\"):\\n if os.environ.get(var_name, \\\"\\\"):\\n return \\\"%s%s\\\" % (prefix, os.environ.get(var_name, \\\"\\\"))\\n else:\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::_get_env_cmake_system_name\",\n \"text\": \"conans/client/build/cmake.py::_get_env_cmake_system_name\\ndef _get_env_cmake_system_name():\\n env_system_name = get_env(\\\"CONAN_CMAKE_SYSTEM_NAME\\\", \\\"\\\")\\n return {\\\"False\\\": False, \\\"True\\\": True, \\\"\\\": None}.get(env_system_name, env_system_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.__init__\",\n \"text\": \"conans/client/build/cmake.py::CMake.__init__\\n def __init__(self, conanfile, generator=None, cmake_system_name=True,\\n parallel=True, build_type=None, toolset=None, make_program=None, set_cmake_flags=False):\\n \\\"\\\"\\\"\\n :param settings_or_conanfile: Conanfile instance (or settings for retro compatibility)\\n :param generator: Generator name to use or none to autodetect\\n :param cmake_system_name: False to not use CMAKE_SYSTEM_NAME variable,\\n True for auto-detect or directly a string with the system name\\n :param parallel: Try to build with multiple cores if available\\n :param build_type: Overrides default build type comming from settings\\n :param toolset: Toolset name to use (such as llvm-vs2014) or none for default one,\\n applies only to certain generators (e.g. Visual Studio)\\n :param set_cmake_flags: whether or not to set CMake flags like CMAKE_CXX_FLAGS, CMAKE_C_FLAGS, etc.\\n it's vital to set for certain projects (e.g. using CMAKE_SIZEOF_VOID_P or CMAKE_LIBRARY_ARCHITECTURE)\\n \\\"\\\"\\\"\\n if not isinstance(conanfile, ConanFile):\\n raise ConanException(\\\"First argument of CMake() has to be ConanFile. Use CMake(self)\\\")\\n\\n self._settings = conanfile.settings\\n self._conanfile = conanfile\\n\\n self._os = self._settings.get_safe(\\\"os\\\")\\n self._os_build, _, self._os_host, _ = get_cross_building_settings(self._settings)\\n\\n self._compiler = self._settings.get_safe(\\\"compiler\\\")\\n self._compiler_version = self._settings.get_safe(\\\"compiler.version\\\")\\n self._arch = self._settings.get_safe(\\\"arch\\\")\\n self._op_system_version = self._settings.get_safe(\\\"os.version\\\")\\n self._libcxx = self._settings.get_safe(\\\"compiler.libcxx\\\")\\n self._runtime = self._settings.get_safe(\\\"compiler.runtime\\\")\\n self._build_type = self._settings.get_safe(\\\"build_type\\\")\\n self._cppstd = self._settings.get_safe(\\\"cppstd\\\")\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_folder\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_folder\\n @property\\n def build_folder(self):\\n return self.build_dir\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_folder\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_folder\\n @build_folder.setter\\n def build_folder(self, value):\\n self.build_dir = value\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_type\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_type\\n @property\\n def build_type(self):\\n return self._build_type\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_type\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_type\\n @build_type.setter\\n def build_type(self, build_type):\\n settings_build_type = self._settings.get_safe(\\\"build_type\\\")\\n if build_type != settings_build_type:\\n self._conanfile.output.warn(\\n 'Set CMake build type \\\"%s\\\" is different than the settings build_type \\\"%s\\\"'\\n % (build_type, settings_build_type))\\n self._build_type = build_type\\n self.definitions.update(self._build_type_definition())\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.flags\",\n \"text\": \"conans/client/build/cmake.py::CMake.flags\\n @property\\n def flags(self):\\n return defs_to_string(self.definitions)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._generator\",\n \"text\": \"conans/client/build/cmake.py::CMake._generator\\n def _generator(self):\\n if \\\"CONAN_CMAKE_GENERATOR\\\" in os.environ:\\n return os.environ[\\\"CONAN_CMAKE_GENERATOR\\\"]\\n\\n if not self._compiler or not self._compiler_version or not self._arch:\\n raise ConanException(\\\"You must specify compiler, compiler.version and arch in \\\"\\n \\\"your settings to use a CMake generator\\\")\\n\\n if self._compiler == \\\"Visual Studio\\\":\\n _visuals = {'8': '8 2005',\\n '9': '9 2008',\\n '10': '10 2010',\\n '11': '11 2012',\\n '12': '12 2013',\\n '14': '14 2015',\\n '15': '15 2017'}\\n base = \\\"Visual Studio %s\\\" % _visuals.get(self._compiler_version,\\n \\\"UnknownVersion %s\\\" % self._compiler_version)\\n if self._arch == \\\"x86_64\\\":\\n return base + \\\" Win64\\\"\\n elif \\\"arm\\\" in self._arch:\\n return base + \\\" ARM\\\"\\n else:\\n return base\\n\\n # The generator depends on the build machine, not the target\\n if self._os_build == \\\"Windows\\\":\\n return \\\"MinGW Makefiles\\\" # it is valid only under Windows\\n\\n return \\\"Unix Makefiles\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._toolset\",\n \"text\": \"conans/client/build/cmake.py::CMake._toolset\\n def _toolset(self, toolset=None):\\n if toolset:\\n return toolset\\n elif self._settings.get_safe(\\\"compiler\\\") == \\\"Visual Studio\\\":\\n subs_toolset = self._settings.get_safe(\\\"compiler.toolset\\\")\\n if subs_toolset:\\n return subs_toolset\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._cmake_compiler_options\",\n \"text\": \"conans/client/build/cmake.py::CMake._cmake_compiler_options\\n def _cmake_compiler_options(self):\\n cmake_definitions = OrderedDict()\\n\\n if str(self._os).lower() == \\\"macos\\\":\\n if self._arch == \\\"x86\\\":\\n cmake_definitions[\\\"CMAKE_OSX_ARCHITECTURES\\\"] = \\\"i386\\\"\\n return cmake_definitions\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._cmake_cross_build_defines\",\n \"text\": \"conans/client/build/cmake.py::CMake._cmake_cross_build_defines\\n def _cmake_cross_build_defines(self):\\n\\n ret = OrderedDict()\\n os_ver = get_env(\\\"CONAN_CMAKE_SYSTEM_VERSION\\\", self._op_system_version)\\n toolchain_file = get_env(\\\"CONAN_CMAKE_TOOLCHAIN_FILE\\\", \\\"\\\")\\n\\n if toolchain_file != \\\"\\\":\\n logger.info(\\\"Setting Cross build toolchain file: %s\\\" % toolchain_file)\\n ret[\\\"CMAKE_TOOLCHAIN_FILE\\\"] = toolchain_file\\n return ret\\n\\n if self._cmake_system_name is False:\\n return ret\\n\\n if self._cmake_system_name is not True: # String not empty\\n ret[\\\"CMAKE_SYSTEM_NAME\\\"] = self._cmake_system_name\\n ret[\\\"CMAKE_SYSTEM_VERSION\\\"] = os_ver\\n else: # detect if we are cross building and the system name and version\\n if cross_building(self._conanfile.settings): # We are cross building\\n if self._os != self._os_build:\\n if self._os: # the_os is the host (regular setting)\\n ret[\\\"CMAKE_SYSTEM_NAME\\\"] = \\\"Darwin\\\" if self._os in [\\\"iOS\\\", \\\"tvOS\\\",\\n \\\"watchOS\\\"] else self._os\\n if os_ver:\\n ret[\\\"CMAKE_SYSTEM_VERSION\\\"] = os_ver\\n else:\\n ret[\\\"CMAKE_SYSTEM_NAME\\\"] = \\\"Generic\\\"\\n\\n if ret: # If enabled cross compile\\n for env_var in [\\\"CONAN_CMAKE_SYSTEM_PROCESSOR\\\",\\n \\\"CONAN_CMAKE_FIND_ROOT_PATH\\\",\\n \\\"CONAN_CMAKE_FIND_ROOT_PATH_MODE_PROGRAM\\\",\\n \\\"CONAN_CMAKE_FIND_ROOT_PATH_MODE_LIBRARY\\\",\\n \\\"CONAN_CMAKE_FIND_ROOT_PATH_MODE_INCLUDE\\\"]:\\n\\n value = os.getenv(env_var, None)\\n if value:\\n ret[env_var] = value\\n\\n if self._conanfile and self._conanfile.deps_cpp_info.sysroot:\\n sysroot_path = self._conanf\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.is_multi_configuration\",\n \"text\": \"conans/client/build/cmake.py::CMake.is_multi_configuration\\n @property\\n def is_multi_configuration(self):\\n \\\"\\\"\\\" some IDEs are multi-configuration, as Visual. Makefiles or Ninja are single-conf\\n \\\"\\\"\\\"\\n if \\\"Visual\\\" in self.generator or \\\"Xcode\\\" in self.generator:\\n return True\\n # TODO: complete logic\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.command_line\",\n \"text\": \"conans/client/build/cmake.py::CMake.command_line\\n @property\\n def command_line(self):\\n args = [\\n '-G \\\"%s\\\"' % self.generator,\\n self.flags,\\n '-Wno-dev'\\n ]\\n if self.toolset:\\n args.append('-T \\\"%s\\\"' % self.toolset)\\n return join_arguments(args)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._build_type_definition\",\n \"text\": \"conans/client/build/cmake.py::CMake._build_type_definition\\n def _build_type_definition(self):\\n if self._build_type and not self.is_multi_configuration:\\n return {'CMAKE_BUILD_TYPE': self._build_type}\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.runtime\",\n \"text\": \"conans/client/build/cmake.py::CMake.runtime\\n @property\\n def runtime(self):\\n return defs_to_string(self._runtime_definition())\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._runtime_definition\",\n \"text\": \"conans/client/build/cmake.py::CMake._runtime_definition\\n def _runtime_definition(self):\\n if self._runtime:\\n return {\\\"CONAN_LINK_RUNTIME\\\": \\\"/%s\\\" % self._runtime}\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_config\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_config\\n @property\\n def build_config(self):\\n \\\"\\\"\\\" cmake --build tool have a --config option for Multi-configuration IDEs\\n \\\"\\\"\\\"\\n if self._build_type and self.is_multi_configuration:\\n return \\\"--config %s\\\" % self._build_type\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._get_cmake_definitions\",\n \"text\": \"conans/client/build/cmake.py::CMake._get_cmake_definitions\\n def _get_cmake_definitions(self):\\n def add_cmake_flag(cmake_flags, name, flag):\\n \\\"\\\"\\\"\\n appends compiler linker flags (if already present), or just sets\\n \\\"\\\"\\\"\\n if flag:\\n if name not in cmake_flags:\\n cmake_flags[name] = flag\\n else:\\n cmake_flags[name] = ' ' + flag\\n return cmake_flags\\n\\n ret = OrderedDict()\\n ret.update(self._build_type_definition())\\n ret.update(self._runtime_definition())\\n ret.update(self._cmake_compiler_options())\\n ret.update(self._cmake_cross_build_defines())\\n ret.update(self._get_cpp_standard_vars())\\n\\n ret[\\\"CONAN_EXPORTED\\\"] = \\\"1\\\"\\n if self._compiler:\\n ret[\\\"CONAN_COMPILER\\\"] = self._compiler\\n if self._compiler_version:\\n ret[\\\"CONAN_COMPILER_VERSION\\\"] = str(self._compiler_version)\\n\\n # Force compiler flags -- TODO: give as environment/setting parameter?\\n arch_flag = architecture_flag(compiler=self._compiler, arch=self._arch)\\n ret = add_cmake_flag(ret, 'CONAN_CXX_FLAGS', arch_flag)\\n ret = add_cmake_flag(ret, 'CONAN_SHARED_LINKER_FLAGS', arch_flag)\\n ret = add_cmake_flag(ret, 'CONAN_C_FLAGS', arch_flag)\\n if self._set_cmake_flags:\\n ret = add_cmake_flag(ret, 'CMAKE_CXX_FLAGS', arch_flag)\\n ret = add_cmake_flag(ret, 'CMAKE_SHARED_LINKER_FLAGS', arch_flag)\\n ret = add_cmake_flag(ret, 'CMAKE_C_FLAGS', arch_flag)\\n\\n if self._libcxx:\\n ret[\\\"CONAN_LIBCXX\\\"] = self._libcxx\\n\\n # Shared library\\n try:\\n ret[\\\"BUILD_SHARED_LIBS\\\"] = \\\"ON\\\" if self._conanfile.options.shared else \\\"OFF\\\"\\n except ConanException:\\n pass\\n\\n # Install to package folder\\n try:\\n if self._conanfile.package_folder:\\n ret[\\\"CMAKE_INSTALL_PREFIX\\\"] = self._conanfile.package_fo\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._get_cmake_definitions.add_cmake_flag\",\n \"text\": \"conans/client/build/cmake.py::CMake._get_cmake_definitions.add_cmake_flag\\n def add_cmake_flag(cmake_flags, name, flag):\\n \\\"\\\"\\\"\\n appends compiler linker flags (if already present), or just sets\\n \\\"\\\"\\\"\\n if flag:\\n if name not in cmake_flags:\\n cmake_flags[name] = flag\\n else:\\n cmake_flags[name] = ' ' + flag\\n return cmake_flags\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._get_dirs\",\n \"text\": \"conans/client/build/cmake.py::CMake._get_dirs\\n def _get_dirs(self, source_folder, build_folder, source_dir, build_dir, cache_build_folder):\\n if (source_folder or build_folder) and (source_dir or build_dir):\\n raise ConanException(\\\"Use 'build_folder'/'source_folder' arguments\\\")\\n\\n def get_dir(folder, origin):\\n if folder:\\n if os.path.isabs(folder):\\n return folder\\n return os.path.join(origin, folder)\\n return origin\\n\\n if source_dir or build_dir: # OLD MODE\\n build_ret = build_dir or self.build_dir or self._conanfile.build_folder\\n source_ret = source_dir or self._conanfile.source_folder\\n else:\\n build_ret = get_dir(build_folder, self._conanfile.build_folder)\\n source_ret = get_dir(source_folder, self._conanfile.source_folder)\\n\\n if self._conanfile.in_local_cache and cache_build_folder:\\n build_ret = get_dir(cache_build_folder, self._conanfile.build_folder)\\n\\n return source_ret, build_ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._get_dirs.get_dir\",\n \"text\": \"conans/client/build/cmake.py::CMake._get_dirs.get_dir\\n def get_dir(folder, origin):\\n if folder:\\n if os.path.isabs(folder):\\n return folder\\n return os.path.join(origin, folder)\\n return origin\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.configure\",\n \"text\": \"conans/client/build/cmake.py::CMake.configure\\n def configure(self, args=None, defs=None, source_dir=None, build_dir=None,\\n source_folder=None, build_folder=None, cache_build_folder=None):\\n\\n # TODO: Deprecate source_dir and build_dir in favor of xxx_folder\\n args = args or []\\n defs = defs or {}\\n source_dir, self.build_dir = self._get_dirs(source_folder, build_folder,\\n source_dir, build_dir,\\n cache_build_folder)\\n mkdir(self.build_dir)\\n arg_list = join_arguments([\\n self.command_line,\\n args_to_string(args),\\n defs_to_string(defs),\\n args_to_string([source_dir])\\n ])\\n command = \\\"cd %s && cmake %s\\\" % (args_to_string([self.build_dir]), arg_list)\\n if platform.system() == \\\"Windows\\\" and self.generator == \\\"MinGW Makefiles\\\":\\n with tools.remove_from_path(\\\"sh\\\"):\\n self._conanfile.run(command)\\n else:\\n self._conanfile.run(command)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build\",\n \"text\": \"conans/client/build/cmake.py::CMake.build\\n def build(self, args=None, build_dir=None, target=None):\\n args = args or []\\n build_dir = build_dir or self.build_dir or self._conanfile.build_folder\\n if target is not None:\\n args = [\\\"--target\\\", target] + args\\n\\n if self.parallel:\\n if \\\"Makefiles\\\" in self.generator and \\\"NMake\\\" not in self.generator:\\n if \\\"--\\\" not in args:\\n args.append(\\\"--\\\")\\n args.append(\\\"-j%i\\\" % cpu_count())\\n elif \\\"Visual Studio\\\" in self.generator and \\\\\\n self._compiler_version and Version(self._compiler_version) >= \\\"10\\\":\\n if \\\"--\\\" not in args:\\n args.append(\\\"--\\\")\\n args.append(\\\"/m:%i\\\" % cpu_count())\\n\\n arg_list = join_arguments([\\n args_to_string([build_dir]),\\n self.build_config,\\n args_to_string(args)\\n ])\\n command = \\\"cmake --build %s\\\" % arg_list\\n self._conanfile.run(command)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.install\",\n \"text\": \"conans/client/build/cmake.py::CMake.install\\n def install(self, args=None, build_dir=None):\\n mkdir(self._conanfile.package_folder)\\n if not self.definitions.get(\\\"CMAKE_INSTALL_PREFIX\\\"):\\n raise ConanException(\\\"CMAKE_INSTALL_PREFIX not defined for 'cmake.install()'\\\\n\\\"\\n \\\"Make sure 'package_folder' is defined\\\")\\n self.build(args=args, build_dir=build_dir, target=\\\"install\\\")\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.test\",\n \"text\": \"conans/client/build/cmake.py::CMake.test\\n def test(self, args=None, build_dir=None, target=None):\\n if not target:\\n target = \\\"RUN_TESTS\\\" if self.is_multi_configuration else \\\"test\\\"\\n self.build(args=args, build_dir=build_dir, target=target)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.verbose\",\n \"text\": \"conans/client/build/cmake.py::CMake.verbose\\n @property\\n def verbose(self):\\n try:\\n verbose = self.definitions[\\\"CMAKE_VERBOSE_MAKEFILE\\\"]\\n return get_bool_from_text(str(verbose))\\n except KeyError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.verbose\",\n \"text\": \"conans/client/build/cmake.py::CMake.verbose\\n @verbose.setter\\n def verbose(self, value):\\n self.definitions[\\\"CMAKE_VERBOSE_MAKEFILE\\\"] = \\\"ON\\\" if value else \\\"OFF\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.patch_config_paths\",\n \"text\": \"conans/client/build/cmake.py::CMake.patch_config_paths\\n def patch_config_paths(self):\\n \\\"\\\"\\\"\\n changes references to the absolute path of the installed package in\\n exported cmake config files to the appropriate conan variable. This makes\\n most (sensible) cmake config files portable.\\n\\n For example, if a package foo installs a file called \\\"fooConfig.cmake\\\" to\\n be used by cmake's find_package method, normally this file will contain\\n absolute paths to the installed package folder, for example it will contain\\n a line such as:\\n\\n SET(Foo_INSTALL_DIR /home/developer/.conan/data/Foo/1.0.0/...)\\n\\n This will cause cmake find_package() method to fail when someone else\\n installs the package via conan.\\n\\n This function will replace such mentions to\\n\\n SET(Foo_INSTALL_DIR ${CONAN_FOO_ROOT})\\n\\n which is a variable that is set by conanbuildinfo.cmake, so that find_package()\\n now correctly works on this conan package.\\n\\n If the install() method of the CMake object in the conan file is used, this\\n function should be called _after_ that invocation. For example:\\n\\n def build(self):\\n cmake = CMake(self)\\n cmake.configure()\\n cmake.build()\\n cmake.install()\\n cmake.patch_config_paths()\\n \\\"\\\"\\\"\\n\\n if not self._conanfile.name:\\n raise ConanException(\\\"cmake.patch_config_paths() can't work without package name. \\\"\\n \\\"Define name in your recipe\\\")\\n pf = self.definitions.get(\\\"CMAKE_INSTALL_PREFIX\\\")\\n replstr = \\\"${CONAN_%s_ROOT}\\\" % self._conanfile.name.upper()\\n allwalk = chain(os.walk(self._conanfile.build_folder), os.walk(self._conanfile.package_folder))\\n for root, _, files in allwalk:\\n for f in files:\\n if f.endswith(\\\".cmake\\\"):\\n tools.replace_in_file(os.path.join(root, f), pf,\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._get_cpp_standard_vars\",\n \"text\": \"conans/client/build/cmake.py::CMake._get_cpp_standard_vars\\n def _get_cpp_standard_vars(self):\\n if not self._cppstd:\\n return {}\\n\\n ret = {}\\n if self._cppstd.startswith(\\\"gnu\\\"):\\n ret[\\\"CONAN_CMAKE_CXX_STANDARD\\\"] = self._cppstd[3:]\\n ret[\\\"CONAN_CMAKE_CXX_EXTENSIONS\\\"] = \\\"ON\\\"\\n else:\\n ret[\\\"CONAN_CMAKE_CXX_STANDARD\\\"] = self._cppstd\\n ret[\\\"CONAN_CMAKE_CXX_EXTENSIONS\\\"] = \\\"OFF\\\"\\n\\n ret[\\\"CONAN_STD_CXX_FLAG\\\"] = cppstd_flag(self._compiler, self._compiler_version,\\n self._cppstd)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/meson.py::Meson.__init__\",\n \"text\": \"conans/client/build/meson.py::Meson.__init__\\n def __init__(self, conanfile, backend=None, build_type=None):\\n \\\"\\\"\\\"\\n :param conanfile: Conanfile instance (or settings for retro compatibility)\\n :param backend: Generator name to use or none to autodetect.\\n Possible values: ninja,vs,vs2010,vs2015,vs2017,xcode\\n :param build_type: Overrides default build type comming from settings\\n \\\"\\\"\\\"\\n self._conanfile = conanfile\\n self._settings = conanfile.settings\\n\\n self._os = self._settings.get_safe(\\\"os\\\")\\n self._compiler = self._settings.get_safe(\\\"compiler\\\")\\n self._compiler_version = self._settings.get_safe(\\\"compiler.version\\\")\\n self._build_type = self._settings.get_safe(\\\"build_type\\\")\\n\\n self.backend = backend or \\\"ninja\\\" # Other backends are poorly supported, not default other.\\n self.build_dir = None\\n self.definitions = {}\\n if build_type and build_type != self._build_type:\\n # Call the setter to warn and update the definitions if needed\\n self.build_type = build_type\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/meson.py::Meson.build_type\",\n \"text\": \"conans/client/build/meson.py::Meson.build_type\\n @property\\n def build_type(self):\\n return self._build_type\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/meson.py::Meson.build_type\",\n \"text\": \"conans/client/build/meson.py::Meson.build_type\\n @build_type.setter\\n def build_type(self, build_type):\\n settings_build_type = self._settings.get_safe(\\\"build_type\\\")\\n if build_type != settings_build_type:\\n self._conanfile.output.warn(\\n 'Set build type \\\"%s\\\" is different than the settings build_type \\\"%s\\\"'\\n % (build_type, settings_build_type))\\n self._build_type = build_type\\n self.definitions.update(self._build_type_definition())\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/meson.py::Meson.build_folder\",\n \"text\": \"conans/client/build/meson.py::Meson.build_folder\\n @property\\n def build_folder(self):\\n return self.build_dir\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/meson.py::Meson.build_folder\",\n \"text\": \"conans/client/build/meson.py::Meson.build_folder\\n @build_folder.setter\\n def build_folder(self, value):\\n self.build_dir = value\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/meson.py::Meson._get_dir\",\n \"text\": \"conans/client/build/meson.py::Meson._get_dir\\n @staticmethod\\n def _get_dir(folder, origin):\\n if folder:\\n if os.path.isabs(folder):\\n return folder\\n return os.path.join(origin, folder)\\n return origin\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/meson.py::Meson._get_dirs\",\n \"text\": \"conans/client/build/meson.py::Meson._get_dirs\\n def _get_dirs(self, source_folder, build_folder, source_dir, build_dir, cache_build_folder):\\n if (source_folder or build_folder) and (source_dir or build_dir):\\n raise ConanException(\\\"Use 'build_folder'/'source_folder'\\\")\\n\\n if source_dir or build_dir: # OLD MODE\\n build_ret = build_dir or self.build_dir or self._conanfile.build_folder\\n source_ret = source_dir or self._conanfile.source_folder\\n else:\\n build_ret = self._get_dir(build_folder, self._conanfile.build_folder)\\n source_ret = self._get_dir(source_folder, self._conanfile.source_folder)\\n\\n if self._conanfile.in_local_cache and cache_build_folder:\\n build_ret = self._get_dir(cache_build_folder, self._conanfile.build_folder)\\n\\n return source_ret, build_ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/meson.py::Meson.configure\",\n \"text\": \"conans/client/build/meson.py::Meson.configure\\n def configure(self, args=None, defs=None, source_dir=None, build_dir=None,\\n pkg_config_paths=None, cache_build_folder=None,\\n build_folder=None, source_folder=None):\\n args = args or []\\n defs = defs or {}\\n\\n source_dir, self.build_dir = self._get_dirs(source_folder, build_folder,\\n source_dir, build_dir,\\n cache_build_folder)\\n\\n if pkg_config_paths:\\n pc_paths = os.pathsep.join(self._get_dir(f, self._conanfile.install_folder)\\n for f in pkg_config_paths)\\n else:\\n pc_paths = self._conanfile.install_folder\\n\\n mkdir(self.build_dir)\\n build_type = (\\\"--buildtype=%s\\\" % self.build_type if self.build_type else \\\"\\\").lower()\\n arg_list = join_arguments([\\n \\\"--backend=%s\\\" % self.backend,\\n args_to_string(args),\\n defs_to_string(defs),\\n build_type\\n ])\\n command = 'meson \\\"%s\\\" \\\"%s\\\" %s' % (source_dir, self.build_dir, arg_list)\\n command = self._append_vs_if_needed(command)\\n with tools.environment_append({\\\"PKG_CONFIG_PATH\\\": pc_paths}):\\n self._conanfile.run(command)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/meson.py::Meson._append_vs_if_needed\",\n \"text\": \"conans/client/build/meson.py::Meson._append_vs_if_needed\\n def _append_vs_if_needed(self, command):\\n if self._compiler == \\\"Visual Studio\\\" and self.backend == \\\"ninja\\\":\\n command = \\\"%s && %s\\\" % (tools.vcvars_command(self._conanfile.settings), command)\\n return command\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/meson.py::Meson.build\",\n \"text\": \"conans/client/build/meson.py::Meson.build\\n def build(self, args=None, build_dir=None, targets=None):\\n if self.backend != \\\"ninja\\\":\\n raise ConanException(\\\"Build only supported with 'ninja' backend\\\")\\n\\n args = args or []\\n build_dir = build_dir or self.build_dir or self._conanfile.build_folder\\n\\n arg_list = join_arguments([\\n '-C \\\"%s\\\"' % build_dir,\\n args_to_string(args),\\n args_to_string(targets)\\n ])\\n command = \\\"ninja %s\\\" % arg_list\\n command = self._append_vs_if_needed(command)\\n self._conanfile.run(command)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/command.py::Extender.__call__\",\n \"text\": \"conans/client/command.py::Extender.__call__\\n def __call__(self, parser, namespace, values, option_strings=None): # @UnusedVariable\\n # Need None here incase `argparse.SUPPRESS` was supplied for `dest`\\n dest = getattr(namespace, self.dest, None)\\n if not hasattr(dest, 'extend') or dest == self.default:\\n dest = []\\n setattr(namespace, self.dest, dest)\\n # if default isn't set to None, this method might be called\\n # with the default as `values` for other arguments which\\n # share this destination.\\n parser.set_defaults(**{self.dest: None})\\n\\n try:\\n dest.extend(values)\\n except ValueError:\\n dest.append(values)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::OnceArgument.__call__\",\n \"text\": \"conans/client/command.py::OnceArgument.__call__\\n def __call__(self, parser, namespace, values, option_string=None):\\n if getattr(namespace, self.dest) is not None and self.default is None:\\n msg = '{o} can only be specified once'.format(o=option_string)\\n raise argparse.ArgumentError(None, msg)\\n setattr(namespace, self.dest, values)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.__init__\",\n \"text\": \"conans/client/command.py::Command.__init__\\n def __init__(self, conan_api, client_cache, user_io, outputer):\\n assert isinstance(conan_api, Conan)\\n self._conan = conan_api\\n self._client_cache = client_cache\\n self._user_io = user_io\\n self._outputer = outputer\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.help\",\n \"text\": \"conans/client/command.py::Command.help\\n def help(self, *args):\\n \\\"\\\"\\\"Show help of a specific commmand.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.help.__doc__, prog=\\\"conan help\\\")\\n parser.add_argument(\\\"command\\\", help='command', nargs=\\\"?\\\")\\n args = parser.parse_args(*args)\\n if not args.command:\\n self._show_help()\\n return\\n try:\\n commands = self._commands()\\n method = commands[args.command]\\n method([\\\"--help\\\"])\\n except KeyError:\\n raise ConanException(\\\"Unknown command '%s'\\\" % args.command)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.new\",\n \"text\": \"conans/client/command.py::Command.new\\n def new(self, *args):\\n \\\"\\\"\\\"Creates a new package recipe template with a 'conanfile.py'.\\n And optionally, 'test_package' package testing files.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.new.__doc__, prog=\\\"conan new\\\")\\n parser.add_argument(\\\"name\\\", help='Package name, e.g.: \\\"Poco/1.7.3\\\" or complete reference'\\n ' for CI scripts: \\\"Poco/1.7.3@conan/stable\\\"')\\n parser.add_argument(\\\"-t\\\", \\\"--test\\\", action='store_true', default=False,\\n help='Create test_package skeleton to test package')\\n parser.add_argument(\\\"-i\\\", \\\"--header\\\", action='store_true', default=False,\\n help='Create a headers only package template')\\n parser.add_argument(\\\"-c\\\", \\\"--pure-c\\\", action='store_true', default=False,\\n help='Create a C language package only package, '\\n 'deleting \\\"self.settings.compiler.libcxx\\\" setting '\\n 'in the configure method')\\n parser.add_argument(\\\"-s\\\", \\\"--sources\\\", action='store_true', default=False,\\n help='Create a package with embedded sources in \\\"src\\\" folder, '\\n 'using \\\"exports_sources\\\" instead of retrieving external code with '\\n 'the \\\"source()\\\" method')\\n parser.add_argument(\\\"-b\\\", \\\"--bare\\\", action='store_true', default=False,\\n help='Create the minimum package recipe, without build() method'\\n 'Useful in combination with \\\"export-pkg\\\" command')\\n parser.add_argument(\\\"-cis\\\", \\\"--ci-shared\\\", action='store_true',\\n default=False,\\n help='Package will have a \\\"shared\\\" option to be used in CI')\\n parser.add_argument(\\\"-cilg\\\", \\\"--ci-travis-gcc\\\", action='store_true',\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.test\",\n \"text\": \"conans/client/command.py::Command.test\\n def test(self, *args):\\n \\\"\\\"\\\" Test a package, consuming it with a conanfile recipe with a test() method.\\n This command installs the conanfile dependencies (including the tested\\n package), calls a \\\"conan build\\\" to build test apps, and finally executes\\n the test() method.\\n The testing recipe is not a package, does not require name/version,\\n neither define package() or package_info() methods.\\n The package to be tested must exist in the local cache or any configured remote.\\n To create and test a binary package use the 'conan create' command.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.test.__doc__, prog=\\\"conan test\\\")\\n parser.add_argument(\\\"-tbf\\\", \\\"--test-build-folder\\\", action=OnceArgument,\\n help=\\\"Optional. Working directory of the build process.\\\")\\n parser.add_argument(\\\"path\\\", help='path to the \\\"testing\\\" folder containing a recipe '\\n '(conanfile.py) with a test() method or to a recipe file, '\\n 'e.g. conan test_package/conanfile.py pkg/version@user/channel')\\n parser.add_argument(\\\"reference\\\",\\n help='a full package reference pkg/version@user/channel, of the '\\n 'package to be tested')\\n\\n _add_common_install_arguments(parser, build_help=_help_build_policies)\\n args = parser.parse_args(*args)\\n return self._conan.test(args.path, args.reference, args.profile, args.settings,\\n args.options, args.env, args.remote, args.update,\\n build_modes=args.build, test_build_folder=args.test_build_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.create\",\n \"text\": \"conans/client/command.py::Command.create\\n def create(self, *args):\\n \\\"\\\"\\\" Builds a binary package for recipe (conanfile.py) located in current dir.\\n Uses the specified configuration in a profile or in -s settings, -o options etc.\\n If a 'test_package' folder (the name can be configured with -tf) is found, the command will\\n run the consumer project to ensure that the package has been created correctly. Check the\\n 'conan test' command to know more about the 'test_folder' project.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.create.__doc__,\\n prog=\\\"conan create\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"reference\\\", help='user/channel, or a full package reference'\\n ' (Pkg/version@user/channel), if name and version '\\n ' are not declared in the recipe (conanfile.py)')\\n parser.add_argument(\\\"-ne\\\", \\\"--not-export\\\", default=False, action='store_true',\\n help='Do not export the conanfile')\\n parser.add_argument(\\\"-tf\\\", \\\"--test-folder\\\", action=OnceArgument,\\n help='alternative test folder name, by default is \\\"test_package\\\". '\\n '\\\"None\\\" if test stage needs to be disabled')\\n parser.add_argument(\\\"-tbf\\\", \\\"--test-build-folder\\\", action=OnceArgument,\\n help='Optional. Working directory for the build of the test project.')\\n parser.add_argument('-k', '--keep-source', default=False, action='store_true',\\n help='Optional. Do not remove the source folder in local cache. '\\n 'Use for testing purposes only')\\n parser.add_argument('-kb', '--keep-build', default=False, action='store_true',\\n help='Optional. Do not remove the build folder in local ca\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.download\",\n \"text\": \"conans/client/command.py::Command.download\\n def download(self, *args):\\n \\\"\\\"\\\"Downloads recipe and binaries to the local cache, without using settings.\\n It works specifying the recipe reference and package ID to be installed.\\n Not transitive, requirements of the specified reference will be retrieved.\\n Useful together with 'conan copy' to automate the promotion of\\n packages to a different user/channel. If only a reference is specified, it will download\\n all packages in the specified remote.\\n If no remote is specified will search sequentially in the available configured remotes.\\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(description=self.download.__doc__, prog=\\\"conan download\\\")\\n parser.add_argument(\\\"reference\\\",\\n help='package recipe reference e.g., MyPackage/1.2@user/channel')\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", nargs=1, action=Extender,\\n help='Force install specified package ID (ignore settings/options)')\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", help='look in the specified remote server',\\n action=OnceArgument)\\n\\n args = parser.parse_args(*args)\\n\\n return self._conan.download(reference=args.reference, package=args.package, remote=args.remote)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.install\",\n \"text\": \"conans/client/command.py::Command.install\\n def install(self, *args):\\n \\\"\\\"\\\"Installs the requirements specified in a conanfile (.py or .txt).\\n If any requirement is not found in the local cache it will retrieve the recipe from a\\n remote, looking for it sequentially in the available configured remotes.\\n When the recipes have been downloaded it will try to download a binary package matching\\n the specified settings, only from the remote from which the recipe was retrieved.\\n If no binary package is found you can build the package from sources using the '--build'\\n option.\\n When the package is installed, Conan will write the files for the specified generators.\\n It can also be used to install a concrete recipe/package specifying a reference in the\\n \\\"path\\\" parameter.\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.install.__doc__, prog=\\\"conan install\\\")\\n parser.add_argument(\\\"path\\\", help=\\\"path to a folder containing a recipe\\\"\\n \\\" (conanfile.py or conanfile.txt) or to a recipe file. e.g., \\\"\\n \\\"./my_project/conanfile.txt. It could also be a reference\\\")\\n parser.add_argument(\\\"-g\\\", \\\"--generator\\\", nargs=1, action=Extender,\\n help='Generators to use')\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help='Use this directory as the directory where to put the generator'\\n 'files, conaninfo/conanbuildinfo.txt etc.')\\n\\n _add_manifests_arguments(parser)\\n\\n parser.add_argument(\\\"--no-imports\\\", action='store_true', default=False,\\n help='Install specified packages but avoid running imports')\\n\\n _add_common_install_arguments(parser, build_help=_help_build_policies)\\n\\n args = parser.parse_args(*args)\\n\\n try:\\n reference = ConanFileRefe\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.config\",\n \"text\": \"conans/client/command.py::Command.config\\n def config(self, *args):\\n \\\"\\\"\\\"Manages configuration. Edits the conan.conf or installs config files.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.config.__doc__, prog=\\\"conan config\\\")\\n\\n subparsers = parser.add_subparsers(dest='subcommand', help='sub-command help')\\n rm_subparser = subparsers.add_parser('rm', help='rm an existing config element')\\n set_subparser = subparsers.add_parser('set', help='set/add value')\\n get_subparser = subparsers.add_parser('get', help='get the value of existing element')\\n install_subparser = subparsers.add_parser('install',\\n help='install a full configuration from a zip '\\n 'file, local or remote')\\n\\n rm_subparser.add_argument(\\\"item\\\", help=\\\"item to remove\\\")\\n get_subparser.add_argument(\\\"item\\\", nargs=\\\"?\\\", help=\\\"item to print\\\")\\n set_subparser.add_argument(\\\"item\\\", help=\\\"key=value to set\\\")\\n install_subparser.add_argument(\\\"item\\\", nargs=\\\"?\\\", help=\\\"configuration file to use\\\")\\n\\n install_subparser.add_argument(\\\"--verify-ssl\\\", nargs=\\\"?\\\", default=\\\"True\\\",\\n help='Verify SSL connection when downloading file')\\n\\n args = parser.parse_args(*args)\\n\\n if args.subcommand == \\\"set\\\":\\n try:\\n key, value = args.item.split(\\\"=\\\", 1)\\n except ValueError:\\n raise ConanException(\\\"Please specify key=value\\\")\\n return self._conan.config_set(key, value)\\n elif args.subcommand == \\\"get\\\":\\n return self._conan.config_get(args.item)\\n elif args.subcommand == \\\"rm\\\":\\n return self._conan.config_rm(args.item)\\n elif args.subcommand == \\\"install\\\":\\n verify_ssl = get_bool_from_text(args.verify_ssl)\\n return self._conan.config_install(args.item, verify_ssl)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.info\",\n \"text\": \"conans/client/command.py::Command.info\\n def info(self, *args):\\n \\\"\\\"\\\"Gets information about the dependency graph of a recipe.\\n You can use it for your current project, by passing a path to a conanfile.py as the\\n reference, or for any existing package in your local cache.\\n \\\"\\\"\\\"\\n\\n info_only_options = [\\\"id\\\", \\\"build_id\\\", \\\"remote\\\", \\\"url\\\", \\\"license\\\", \\\"requires\\\", \\\"update\\\",\\n \\\"required\\\", \\\"date\\\", \\\"author\\\", \\\"None\\\"]\\n path_only_options = [\\\"export_folder\\\", \\\"build_folder\\\", \\\"package_folder\\\", \\\"source_folder\\\"]\\n str_path_only_options = \\\", \\\".join(['\\\"%s\\\"' % field for field in path_only_options])\\n str_only_options = \\\", \\\".join(['\\\"%s\\\"' % field for field in info_only_options])\\n\\n parser = argparse.ArgumentParser(description=self.info.__doc__, prog=\\\"conan info\\\")\\n parser.add_argument(\\\"reference\\\", help=\\\"path to a folder containing a recipe\\\"\\n \\\" (conanfile.py or conanfile.txt) or to a recipe file. e.g., \\\"\\n \\\"./my_project/conanfile.txt. It could also be a reference\\\")\\n parser.add_argument(\\\"-n\\\", \\\"--only\\\", nargs=1, action=Extender,\\n help='show the specified fields only from: '\\n '%s or use --paths with options %s. Use --only None to show only '\\n 'references.'\\n % (str_only_options, str_path_only_options))\\n parser.add_argument(\\\"--paths\\\", action='store_true', default=False,\\n help='Show package paths in local cache')\\n parser.add_argument(\\\"--package-filter\\\", nargs='?',\\n help='print information only for packages that match the filter'\\n 'e.g., MyPackage/1.2@user/channel or MyPackage*')\\n parser.add_argument(\\\"-bo\\\", \\\"--build-order\\\",\\n help='given a modified reference, return an ordered list to build (CI)'\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.source\",\n \"text\": \"conans/client/command.py::Command.source\\n def source(self, *args):\\n \\\"\\\"\\\" Calls your local conanfile.py 'source()' method.\\n I.e., downloads and unzip the package source.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.source.__doc__, prog=\\\"conan source\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"-sf\\\", \\\"--source-folder\\\", action=OnceArgument,\\n help='Destination directory. Defaulted to current directory')\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=\\\"Optional. Local folder containing the conaninfo.txt and \\\"\\n \\\"conanbuildinfo.txt \\\"\\n \\\"files (from a previous conan install execution). Defaulted to the \\\"\\n \\\"current directory. Optional, source method will run without the \\\"\\n \\\"information retrieved from the conaninfo.txt and conanbuildinfo.txt, \\\"\\n \\\"only required when using conditional source() based on settings, \\\"\\n \\\"options, env_info and user_info \\\")\\n args = parser.parse_args(*args)\\n\\n try:\\n if \\\"@\\\" in args.path and ConanFileReference.loads(args.path):\\n raise ArgumentError(None,\\n \\\"'conan source' doesn't accept a reference anymore. \\\"\\n \\\"If you were using it as a concurrency workaround, \\\"\\n \\\"you can call 'conan install' simultaneously from several \\\"\\n \\\"different processes, the concurrency is now natively supported\\\"\\n \\\". The path parameter should be a folder containing a \\\"\\n \\\"conanfile.py file.\\\")\\n except ConanException:\\n pass\\n\\n return self._conan.source(args.path, args.source_folder, args.in\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.build\",\n \"text\": \"conans/client/command.py::Command.build\\n def build(self, *args):\\n \\\"\\\"\\\" Calls your local conanfile.py 'build()' method.\\n The recipe will be built in the local directory specified by --build-folder,\\n reading the sources from --source-folder. If you are using a build helper, like CMake(), the\\n --package-folder will be configured as destination folder for the install step.\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(description=self.build.__doc__, prog=\\\"conan build\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"-sf\\\", \\\"--source-folder\\\", action=OnceArgument,\\n help=\\\"local folder containing the sources. Defaulted to the directory \\\"\\n \\\"of the conanfile. A relative path can also be specified \\\"\\n \\\"(relative to the current directory)\\\")\\n parser.add_argument(\\\"-bf\\\", \\\"--build-folder\\\", action=OnceArgument,\\n help=\\\"build folder, working directory of the build process. Defaulted \\\"\\n \\\"to the current directory. A relative path can also be specified \\\"\\n \\\"(relative to the current directory)\\\")\\n parser.add_argument(\\\"-pf\\\", \\\"--package-folder\\\", action=OnceArgument,\\n help=\\\"folder to install the package (when the build system or build() \\\"\\n \\\"method does it). Defaulted to the '{build_folder}/package' folder\\\"\\n \\\". A relative path can be specified, relative to the current \\\"\\n \\\" folder. Also an absolute path is allowed.\\\")\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=\\\"Optional. Local folder containing the conaninfo.txt and \\\"\\n \\\"conanbuildinfo.txt files (from a previous conan install \\\"\\n \\\"executio\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/command.py::Command.package\",\n \"text\": \"conans/client/command.py::Command.package\\n def package(self, *args):\\n \\\"\\\"\\\" Calls your local conanfile.py 'package()' method.\\n\\n This command works locally, in the user space, and it will copy artifacts from the\\n --build-folder and --source-folder folder to the --package-folder one.\\n\\n It won't create a new package in the local cache, if you want to do it, use 'create' or use\\n 'export-pkg' after a 'build' command.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.package.__doc__, prog=\\\"conan package\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"-sf\\\", \\\"--source-folder\\\", action=OnceArgument,\\n help=\\\"local folder containing the sources. Defaulted to the directory \\\"\\n \\\"of the conanfile. A relative path can also be specified \\\"\\n \\\"(relative to the current directory)\\\")\\n parser.add_argument(\\\"-bf\\\", \\\"--build-folder\\\", action=OnceArgument,\\n help=\\\"build folder, working directory of the build process. Defaulted \\\"\\n \\\"to the current directory. A relative path can also be specified \\\"\\n \\\"(relative to the current directory)\\\")\\n parser.add_argument(\\\"-pf\\\", \\\"--package-folder\\\", action=OnceArgument,\\n help=\\\"folder to install the package. Defaulted to the \\\"\\n \\\"'{build_folder}/package' folder. A relative path can be specified\\\"\\n \\\" (relative to the current directory). Also an absolute path\\\"\\n \\\" is allowed.\\\")\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=\\\"Optional. Local folder containing the conaninfo.txt and \\\"\\n \\\"conanbuildinfo.txt files (from a previous conan install \\\"\\n \\\"ex\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.imports\",\n \"text\": \"conans/client/command.py::Command.imports\\n def imports(self, *args):\\n \\\"\\\"\\\" Calls your local conanfile.py or conanfile.txt 'imports' method.\\n It requires to have been previously installed and have a conanbuildinfo.txt generated file\\n in the --install-folder (defaulted to current directory).\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.imports.__doc__, prog=\\\"conan imports\\\")\\n parser.add_argument(\\\"path\\\",\\n help=\\\"path to a recipe (conanfile.py). e.g., ./my_project/\\\"\\n \\\"With --undo option, this parameter is the folder \\\"\\n \\\"containing the conan_imports_manifest.txt file generated in a previous\\\"\\n \\\"execution. e.j: conan imports ./imported_files --undo \\\")\\n parser.add_argument(\\\"-imf\\\", \\\"--import-folder\\\", action=OnceArgument,\\n help=\\\"Directory to copy the artifacts to. By default it will be the\\\"\\n \\\" current directory\\\")\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=\\\"local folder containing the conaninfo.txt and conanbuildinfo.txt \\\"\\n \\\"files (from a previous conan install execution)\\\")\\n parser.add_argument(\\\"-u\\\", \\\"--undo\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Undo imports. Remove imported files\\\")\\n args = parser.parse_args(*args)\\n\\n if args.undo:\\n return self._conan.imports_undo(args.path)\\n\\n try:\\n if \\\"@\\\" in args.path and ConanFileReference.loads(args.path):\\n raise ArgumentError(None, \\\"Parameter 'path' cannot be a reference,\\\"\\n \\\" but a folder containing a conanfile.py or conanfile.txt\\\"\\n \\\" file.\\\")\\n except ConanException:\\n pass\\n\\n return self._conan.imports(args.path, args.imp\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.export_pkg\",\n \"text\": \"conans/client/command.py::Command.export_pkg\\n def export_pkg(self, *args):\\n \\\"\\\"\\\"Exports a recipe & creates a package with given files calling 'package'.\\n It executes the package() method applied to the local folders '--source-folder' and\\n '--build-folder' and creates a new package in the local cache for the specified\\n 'reference' and for the specified '--settings', '--options' and or '--profile'.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.export_pkg.__doc__,\\n prog=\\\"conan export-pkg .\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"reference\\\", help='user/channel, or a full package reference'\\n ' (Pkg/version@user/channel), if name and version '\\n ' are not declared in the recipe (conanfile.py)')\\n parser.add_argument(\\\"-sf\\\", \\\"--source-folder\\\", action=OnceArgument,\\n help=\\\"local folder containing the sources. Defaulted to the directory \\\"\\n \\\"of the conanfile. A relative path can also be specified \\\"\\n \\\"(relative to the current directory)\\\")\\n parser.add_argument(\\\"-bf\\\", \\\"--build-folder\\\", action=OnceArgument,\\n help=\\\"build folder, working directory of the build process. Defaulted \\\"\\n \\\"to the current directory. A relative path can also be specified \\\"\\n \\\"(relative to the current directory)\\\")\\n parser.add_argument(\\\"-pf\\\", \\\"--package-folder\\\", action=OnceArgument,\\n help=\\\"folder containing a locally created package. \\\"\\n \\\"If a value is giving, it won't call the recipe 'package()' \\\"\\n \\\"method, and will run a copy of the provided folder.\\\")\\n parser.add_argument(\\\"-if\\\", \\\"--ins\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.export\",\n \"text\": \"conans/client/command.py::Command.export\\n def export(self, *args):\\n \\\"\\\"\\\" Copies the recipe (conanfile.py & associated files) to your local cache.\\n Use the 'reference' param to specify a user and channel where to export.\\n Once the recipe is in the local cache it can be shared and reused. It can be uploaded\\n to any remote with the \\\"conan upload\\\" command.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.export.__doc__, prog=\\\"conan export\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"reference\\\", help='user/channel, or a full package reference'\\n ' (Pkg/version@user/channel), if name and version '\\n ' are not declared in the recipe (conanfile.py)')\\n parser.add_argument('-k', '--keep-source', default=False, action='store_true',\\n help='Optional. Do not remove the source folder in the local cache. '\\n 'Use for testing purposes only')\\n args = parser.parse_args(*args)\\n name, version, user, channel = get_reference_fields(args.reference)\\n\\n return self._conan.export(path=args.path,\\n name=name, version=version, user=user, channel=channel,\\n keep_source=args.keep_source)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.remove\",\n \"text\": \"conans/client/command.py::Command.remove\\n def remove(self, *args):\\n \\\"\\\"\\\"Removes packages or binaries matching pattern from local cache or remote.\\n It can also be used to remove temporary source or build folders in the local conan cache.\\n If no remote is specified, the removal will be done by default in the local conan cache.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.remove.__doc__, prog=\\\"conan remove\\\")\\n parser.add_argument('pattern', help='Pattern name, e.g., openssl/*')\\n parser.add_argument('-p', '--packages',\\n help='By default, remove all the packages or select one, '\\n 'specifying the package ID',\\n nargs=\\\"*\\\", action=Extender)\\n parser.add_argument('-b', '--builds',\\n help='By default, remove all the build folders or select one, '\\n 'specifying the package ID',\\n nargs=\\\"*\\\", action=Extender)\\n\\n parser.add_argument('-s', '--src', default=False, action=\\\"store_true\\\",\\n help='Remove source folders')\\n parser.add_argument('-f', '--force', default=False,\\n action='store_true', help='Remove without requesting a confirmation')\\n parser.add_argument('-r', '--remote', help='Will remove from the specified remote',\\n action=OnceArgument)\\n parser.add_argument('-q', '--query', default=None, action=OnceArgument,\\n help='Packages query: \\\"os=Windows AND '\\n '(arch=x86 OR compiler=gcc)\\\".'\\n ' The \\\"pattern\\\" parameter '\\n 'has to be a package recipe '\\n 'reference: MyPackage/1.2'\\n '@user/channel')\\n parser.add_argument(\\\"-o\\\", \\\"--outdated\\\", help=\\\"Remove only outdated\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.copy\",\n \"text\": \"conans/client/command.py::Command.copy\\n def copy(self, *args):\\n \\\"\\\"\\\" Copies conan recipes and packages to another user/channel.\\n Useful to promote packages (e.g. from \\\"beta\\\" to \\\"stable\\\").\\n Also for moving packages from one user to another.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.copy.__doc__, prog=\\\"conan copy\\\")\\n parser.add_argument(\\\"reference\\\", default=\\\"\\\",\\n help='package reference. e.g., MyPackage/1.2@user/channel')\\n parser.add_argument(\\\"user_channel\\\", default=\\\"\\\",\\n help='Destination user/channel. '\\n 'e.g., lasote/testing')\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", nargs=1, action=Extender,\\n help='copy specified package ID')\\n parser.add_argument(\\\"--all\\\", action='store_true',\\n default=False,\\n help='Copy all packages from the specified package recipe')\\n parser.add_argument(\\\"--force\\\", action='store_true',\\n default=False,\\n help='Override destination packages and the package recipe')\\n args = parser.parse_args(*args)\\n if args.all and args.package:\\n raise ConanException(\\\"Cannot specify both --all and --package\\\")\\n\\n return self._conan.copy(reference=args.reference, user_channel=args.user_channel,\\n force=args.force,\\n packages=args.package or args.all)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.user\",\n \"text\": \"conans/client/command.py::Command.user\\n def user(self, *parameters):\\n \\\"\\\"\\\" Authenticates against a remote with user/pass, caching the auth token.\\n Useful to avoid the user and password being requested later.\\n e.g. while you're uploading a package.\\n You can have more than one user (one per remote). Changing the user, or introducing the\\n password is only necessary to upload packages to a remote.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.user.__doc__, prog=\\\"conan user\\\")\\n parser.add_argument(\\\"name\\\", nargs='?', default=None,\\n help='Username you want to use. '\\n 'If no name is provided it will show the current user.')\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", help='look in the specified remote server',\\n action=OnceArgument)\\n parser.add_argument('-c', '--clean', default=False,\\n action='store_true', help='Remove user and tokens for all remotes')\\n parser.add_argument(\\\"-p\\\", \\\"--password\\\", nargs='?', const=\\\"\\\", type=str,\\n action=OnceArgument,\\n help='User password. Use double quotes if password with spacing, '\\n 'and escape quotes if existing. If empty, the password is '\\n 'requested interactively (not exposed)')\\n args = parser.parse_args(*parameters) # To enable -h\\n return self._conan.user(name=args.name, clean=args.clean, remote=args.remote,\\n password=args.password)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.search\",\n \"text\": \"conans/client/command.py::Command.search\\n def search(self, *args):\\n \\\"\\\"\\\" Searches package recipes and binaries in the local cache or in a remote.\\n\\n If you provide a pattern, then it will search for existing package recipes matching that pattern.\\n If a full and complete package reference is provided, like Pkg/0.1@user/channel, then the existing\\n binary packages for that reference will be displayed.\\n You can search in a remote or in the local cache, if nothing is specified, the local conan cache is\\n assumed.\\n Search is case sensitive, exact case has to be used. For case insensitive file systems, like Windows,\\n case sensitive search can be forced with the --case-sensitive argument\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.search.__doc__, prog=\\\"conan search\\\")\\n parser.add_argument('pattern', nargs='?', help='Pattern name, e.g. openssl/* or package'\\n ' recipe reference if \\\"-q\\\" is used. e.g. '\\n 'MyPackage/1.2@user/channel')\\n parser.add_argument('--case-sensitive', default=False,\\n action='store_true', help='Make a case-sensitive search. '\\n 'Use it to guarantee case-sensitive '\\n 'search in Windows or other case-insensitive filesystems')\\n parser.add_argument('-r', '--remote', help='Remote origin. `all` searches all remotes', action=OnceArgument)\\n parser.add_argument('--raw', default=False, action='store_true',\\n help='Print just the list of recipes')\\n parser.add_argument('--table', action=OnceArgument,\\n help='Outputs html file with a table of binaries. Only valid if '\\n '\\\"pattern\\\" is a package recipe reference')\\n parser.add_argument('-q', '--query', default\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.upload\",\n \"text\": \"conans/client/command.py::Command.upload\\n def upload(self, *args):\\n \\\"\\\"\\\" Uploads a recipe and binary packages to a remote.\\n\\n If you use the --force variable, it won't check the package date. It will override\\n the remote with the local package.\\n If you use a pattern instead of a conan recipe reference you can use the -c or\\n --confirm option to upload all the matching recipes.\\n If you use the --retry option you can specify how many times should conan try to upload\\n the packages in case of failure. The default is 2.\\n With --retry_wait you can specify the seconds to wait between upload attempts.\\n If no remote is specified, the first configured remote (by default conan-center, use\\n 'conan remote list' to list the remotes) will be used.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.upload.__doc__,\\n prog=\\\"conan upload\\\")\\n parser.add_argument('pattern', help='Pattern or package recipe reference, '\\n 'e.g., \\\"openssl/*\\\", \\\"MyPackage/1.2@user/channel\\\"')\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", default=None, help='package ID to upload',\\n action=OnceArgument)\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", help='upload to this specific remote',\\n action=OnceArgument)\\n parser.add_argument(\\\"--all\\\", action='store_true',\\n default=False, help='Upload both package recipe and packages')\\n parser.add_argument(\\\"--skip-upload\\\", action='store_true',\\n default=False, help='Do not upload anything, just run '\\n 'the checks and the compression.')\\n parser.add_argument(\\\"--force\\\", action='store_true',\\n default=False,\\n help='Do not check conan recipe date, \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.remote\",\n \"text\": \"conans/client/command.py::Command.remote\\n def remote(self, *args):\\n \\\"\\\"\\\" Manages the remote list and the package recipes associated to a remote.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.remote.__doc__, prog=\\\"conan remote\\\")\\n subparsers = parser.add_subparsers(dest='subcommand', help='sub-command help')\\n\\n # create the parser for the \\\"a\\\" command\\n subparsers.add_parser('list', help='list current remotes')\\n parser_add = subparsers.add_parser('add', help='add a remote')\\n parser_add.add_argument('remote', help='name of the remote')\\n parser_add.add_argument('url', help='url of the remote')\\n parser_add.add_argument('verify_ssl',\\n help='Verify SSL certificated. Default True',\\n default=\\\"True\\\", nargs=\\\"?\\\")\\n parser_add.add_argument(\\\"-i\\\", \\\"--insert\\\", nargs=\\\"?\\\", const=0, type=int,\\n help=\\\"insert remote at specific index\\\", action=OnceArgument)\\n parser_rm = subparsers.add_parser('remove', help='remove a remote')\\n parser_rm.add_argument('remote', help='name of the remote')\\n parser_upd = subparsers.add_parser('update', help='update the remote url')\\n parser_upd.add_argument('remote', help='name of the remote')\\n parser_upd.add_argument('url', help='url')\\n parser_upd.add_argument('verify_ssl',\\n help='Verify SSL certificated. Default True',\\n default=\\\"True\\\", nargs=\\\"?\\\")\\n parser_upd.add_argument(\\\"-i\\\", \\\"--insert\\\", nargs=\\\"?\\\", const=0, type=int,\\n help=\\\"insert remote at specific index\\\",\\n action=OnceArgument)\\n\\n subparsers.add_parser('list_ref',\\n help='list the package recipes and its associated remotes')\\n parser_padd = subparsers.add_parser('add_ref',\\n help=\\\"as\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.profile\",\n \"text\": \"conans/client/command.py::Command.profile\\n def profile(self, *args):\\n \\\"\\\"\\\" Lists profiles in the '.conan/profiles' folder, or shows profile details.\\n The 'list' subcommand will always use the default user 'conan/profiles' folder. But the\\n 'show' subcommand is able to resolve absolute and relative paths, as well as to map names to\\n '.conan/profiles' folder, in the same way as the '--profile' install argument.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.profile.__doc__, prog=\\\"conan profile\\\")\\n subparsers = parser.add_subparsers(dest='subcommand')\\n\\n # create the parser for the \\\"profile\\\" command\\n subparsers.add_parser('list', help='List current profiles')\\n parser_show = subparsers.add_parser('show', help='Show the values defined for a profile')\\n parser_show.add_argument('profile', help=\\\"name of the profile in the '.conan/profiles' \\\"\\n \\\"folder or path to a profile file\\\")\\n\\n parser_new = subparsers.add_parser('new', help='Creates a new empty profile')\\n parser_new.add_argument('profile', help=\\\"name for the profile in the '.conan/profiles' \\\"\\n \\\"folder or path and name for a profile file\\\")\\n parser_new.add_argument(\\\"--detect\\\", action='store_true',\\n default=False,\\n help='Autodetect settings and fill [settings] section')\\n\\n parser_update = subparsers.add_parser('update', help='Update a profile with desired value')\\n parser_update.add_argument('item', help='key=\\\"value to set\\\", e.j: settings.compiler=gcc')\\n parser_update.add_argument('profile', help=\\\"name of the profile in the '.conan/profiles' \\\"\\n \\\"folder or path to a profile file\\\")\\n\\n parser_get = subparsers.add_parser('get', help='Get a profile key')\\n parser_get.add_argument('item'\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.get\",\n \"text\": \"conans/client/command.py::Command.get\\n def get(self, *args):\\n \\\"\\\"\\\" Gets a file or list a directory of a given reference or package.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.get.__doc__,\\n prog=\\\"conan get\\\")\\n parser.add_argument('reference', help='package recipe reference')\\n parser.add_argument('path',\\n help='Path to the file or directory. If not specified will get the '\\n 'conanfile if only a reference is specified and a conaninfo.txt '\\n 'file contents if the package is also specified',\\n default=None, nargs=\\\"?\\\")\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", default=None, help='package ID',\\n action=OnceArgument)\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", help='Get from this specific remote',\\n action=OnceArgument)\\n parser.add_argument(\\\"-raw\\\", \\\"--raw\\\", help='Do not decorate the text', default=False,\\n action='store_true')\\n args = parser.parse_args(*args)\\n\\n ret, path = self._conan.get_path(args.reference, args.package, args.path, args.remote)\\n if isinstance(ret, list):\\n self._outputer.print_dir_list(ret, path, args.raw)\\n else:\\n self._outputer.print_file_contents(ret, path, args.raw)\\n\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.alias\",\n \"text\": \"conans/client/command.py::Command.alias\\n def alias(self, *args):\\n \\\"\\\"\\\"Creates and exports an 'alias package recipe'. An \\\"alias\\\" package is a\\n symbolic name (reference) for another package (target). When some\\n package depends on an alias, the target one will be retrieved and used\\n instead, so the alias reference, the symbolic name, does not appear\\n in the final dependency graph.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.alias.__doc__,\\n prog=\\\"conan alias\\\")\\n parser.add_argument('reference', help='Alias reference. e.j: mylib/1.X@user/channel')\\n parser.add_argument('target', help='Target reference. e.j: mylib/1.12@user/channel')\\n args = parser.parse_args(*args)\\n\\n self._conan.export_alias(args.reference, args.target)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._show_help\",\n \"text\": \"conans/client/command.py::Command._show_help\\n def _show_help(self):\\n \\\"\\\"\\\" prints a summary of all commands\\n \\\"\\\"\\\"\\n grps = [(\\\"Consumer commands\\\", (\\\"install\\\", \\\"config\\\", \\\"get\\\", \\\"info\\\", \\\"search\\\")),\\n (\\\"Creator commands\\\", (\\\"new\\\", \\\"create\\\", \\\"upload\\\", \\\"export\\\", \\\"export-pkg\\\", \\\"test\\\")),\\n (\\\"Package development commands\\\", (\\\"source\\\", \\\"build\\\", \\\"package\\\")),\\n (\\\"Misc commands\\\", (\\\"profile\\\", \\\"remote\\\", \\\"user\\\", \\\"imports\\\", \\\"copy\\\", \\\"remove\\\",\\n \\\"alias\\\", \\\"download\\\", \\\"help\\\"))]\\n\\n def check_all_commands_listed():\\n \\\"\\\"\\\"Keep updated the main directory, raise if don't\\\"\\\"\\\"\\n all_commands = self._commands()\\n all_in_grps = [command for _, command_list in grps for command in command_list]\\n if set(all_in_grps) != set(all_commands):\\n diff = set(all_commands) - set(all_in_grps)\\n raise Exception(\\\"Some command is missing in the main help: %s\\\" % \\\",\\\".join(diff))\\n return all_commands\\n\\n commands = check_all_commands_listed()\\n max_len = max((len(c) for c in commands)) + 1\\n fmt = ' %-{}s'.format(max_len)\\n\\n for group_name, comm_names in grps:\\n self._user_io.out.writeln(group_name, Color.BRIGHT_MAGENTA)\\n for name in comm_names:\\n # future-proof way to ensure tabular formatting\\n self._user_io.out.write(fmt % name, Color.GREEN)\\n self._user_io.out.writeln(commands[name].__doc__.split('\\\\n', 1)[0].strip())\\n\\n self._user_io.out.writeln(\\\"\\\")\\n self._user_io.out.writeln('Conan commands. Type \\\"conan <command> -h\\\" for help',\\n Color.BRIGHT_YELLOW)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._show_help.check_all_commands_listed\",\n \"text\": \"conans/client/command.py::Command._show_help.check_all_commands_listed\\n def check_all_commands_listed():\\n \\\"\\\"\\\"Keep updated the main directory, raise if don't\\\"\\\"\\\"\\n all_commands = self._commands()\\n all_in_grps = [command for _, command_list in grps for command in command_list]\\n if set(all_in_grps) != set(all_commands):\\n diff = set(all_commands) - set(all_in_grps)\\n raise Exception(\\\"Some command is missing in the main help: %s\\\" % \\\",\\\".join(diff))\\n return all_commands\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._commands\",\n \"text\": \"conans/client/command.py::Command._commands\\n def _commands(self):\\n \\\"\\\"\\\" returns a list of available commands\\n \\\"\\\"\\\"\\n result = {}\\n for m in inspect.getmembers(self, predicate=inspect.ismethod):\\n method_name = m[0]\\n if not method_name.startswith('_'):\\n if \\\"export_pkg\\\" == method_name:\\n method_name = \\\"export-pkg\\\"\\n method = m[1]\\n if method.__doc__ and not method.__doc__.startswith('HIDDEN'):\\n result[method_name] = method\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._check_query_parameter_and_get_reference\",\n \"text\": \"conans/client/command.py::Command._check_query_parameter_and_get_reference\\n @staticmethod\\n def _check_query_parameter_and_get_reference(pattern, query):\\n reference = None\\n if pattern:\\n try:\\n reference = ConanFileReference.loads(pattern)\\n except ConanException:\\n if query is not None:\\n raise ConanException(\\\"-q parameter only allowed with a valid recipe \\\"\\n \\\"reference as search pattern. e.g conan search \\\"\\n \\\"MyPackage/1.2@user/channel -q \\\\\\\"os=Windows\\\\\\\"\\\")\\n return reference\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.run\",\n \"text\": \"conans/client/command.py::Command.run\\n def run(self, *args):\\n \\\"\\\"\\\"HIDDEN: entry point for executing commands, dispatcher to class\\n methods\\n \\\"\\\"\\\"\\n errors = False\\n try:\\n try:\\n command = args[0][0]\\n commands = self._commands()\\n method = commands[command]\\n except KeyError as exc:\\n if command in [\\\"-v\\\", \\\"--version\\\"]:\\n self._user_io.out.success(\\\"Conan version %s\\\" % client_version)\\n return False\\n self._show_help()\\n if command in [\\\"-h\\\", \\\"--help\\\"]:\\n return False\\n raise ConanException(\\\"Unknown command %s\\\" % str(exc))\\n except IndexError: # No parameters\\n self._show_help()\\n return False\\n method(args[0][1:])\\n except KeyboardInterrupt as exc:\\n logger.error(exc)\\n errors = True\\n except SystemExit as exc:\\n if exc.code != 0:\\n logger.error(exc)\\n self._user_io.out.error(\\\"Exiting with code: %d\\\" % exc.code)\\n errors = exc.code\\n except ConanException as exc:\\n errors = True\\n msg = exception_message_safe(exc)\\n self._user_io.out.error(msg)\\n except Exception as exc:\\n import traceback\\n print(traceback.format_exc())\\n errors = True\\n msg = exception_message_safe(exc)\\n self._user_io.out.error(msg)\\n\\n return errors\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::get_reference_fields\",\n \"text\": \"conans/client/command.py::get_reference_fields\\ndef get_reference_fields(arg_reference):\\n \\\"\\\"\\\"\\n :param arg_reference: String with a complete reference, or only user/channel\\n :return: name, version, user and channel, in a tuple\\n \\\"\\\"\\\"\\n\\n if not arg_reference:\\n return None, None, None, None\\n\\n try:\\n name_version, user_channel = arg_reference.split(\\\"@\\\")\\n name, version = name_version.split(\\\"/\\\")\\n user, channel = user_channel.split(\\\"/\\\")\\n except ValueError:\\n name, version = None, None\\n try:\\n user, channel = arg_reference.split(\\\"/\\\")\\n except ValueError:\\n raise ConanException(\\\"Invalid parameter '%s', specify the full reference or \\\"\\n \\\"user/channel\\\" % arg_reference)\\n\\n return name, version, user, channel\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::_add_manifests_arguments\",\n \"text\": \"conans/client/command.py::_add_manifests_arguments\\ndef _add_manifests_arguments(parser):\\n parser.add_argument(\\\"-m\\\", \\\"--manifests\\\", const=default_manifest_folder, nargs=\\\"?\\\",\\n help='Install dependencies manifests in folder for later verify.'\\n ' Default folder is .conan_manifests, but can be changed',\\n action=OnceArgument)\\n parser.add_argument(\\\"-mi\\\", \\\"--manifests-interactive\\\", const=default_manifest_folder,\\n nargs=\\\"?\\\",\\n help='Install dependencies manifests in folder for later verify, '\\n 'asking user for confirmation. '\\n 'Default folder is .conan_manifests, but can be changed',\\n action=OnceArgument)\\n parser.add_argument(\\\"-v\\\", \\\"--verify\\\", const=default_manifest_folder, nargs=\\\"?\\\",\\n help='Verify dependencies manifests against stored ones',\\n action=OnceArgument)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::_add_common_install_arguments\",\n \"text\": \"conans/client/command.py::_add_common_install_arguments\\ndef _add_common_install_arguments(parser, build_help):\\n parser.add_argument(\\\"-u\\\", \\\"--update\\\", action='store_true', default=False,\\n help=\\\"check updates exist from upstream remotes\\\")\\n parser.add_argument(\\\"-pr\\\", \\\"--profile\\\", default=None, action=OnceArgument,\\n help='Apply the specified profile to the install command')\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", help='look in the specified remote server',\\n action=OnceArgument)\\n parser.add_argument(\\\"-o\\\", \\\"--options\\\",\\n help='Define options values, e.g., -o Pkg:with_qt=true',\\n nargs=1, action=Extender)\\n parser.add_argument(\\\"-s\\\", \\\"--settings\\\",\\n help='Settings to build the package, overwriting the defaults. e.g., '\\n '-s compiler=gcc',\\n nargs=1, action=Extender)\\n parser.add_argument(\\\"-e\\\", \\\"--env\\\",\\n help='Environment variables that will be set during the package build, '\\n '-e CXX=/usr/bin/clang++',\\n nargs=1, action=Extender)\\n if build_help:\\n parser.add_argument(\\\"-b\\\", \\\"--build\\\", action=Extender, nargs=\\\"*\\\", help=build_help)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::main\",\n \"text\": \"conans/client/command.py::main\\ndef main(args):\\n \\\"\\\"\\\" main entry point of the conan application, using a Command to\\n parse parameters\\n\\n Exit codes for conan command:\\n\\n 0: Success (done)\\n 1: General ConanException error (done)\\n 2: Migration error\\n 3: Ctrl+C\\n 4: Ctrl+Break\\n \\\"\\\"\\\"\\n try:\\n conan_api, client_cache, user_io = Conan.factory()\\n except ConanException: # Error migrating\\n sys.exit(2)\\n\\n outputer = CommandOutputer(user_io, client_cache)\\n command = Command(conan_api, client_cache, user_io, outputer)\\n current_dir = os.getcwd()\\n try:\\n import signal\\n\\n def ctrl_c_handler(_, __):\\n print('You pressed Ctrl+C!')\\n sys.exit(3)\\n\\n def ctrl_break_handler(_, __):\\n print('You pressed Ctrl+Break!')\\n sys.exit(4)\\n\\n signal.signal(signal.SIGINT, ctrl_c_handler)\\n if sys.platform == 'win32':\\n signal.signal(signal.SIGBREAK, ctrl_break_handler)\\n error = command.run(args)\\n finally:\\n os.chdir(current_dir)\\n sys.exit(error)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::get_basic_requester\",\n \"text\": \"conans/client/conan_api.py::get_basic_requester\\ndef get_basic_requester(client_cache):\\n requester = requests.Session()\\n proxies = client_cache.conan_config.proxies\\n if proxies:\\n # Account for the requests NO_PROXY env variable, not defined as a proxy like http=\\n no_proxy = proxies.pop(\\\"no_proxy\\\", None)\\n if no_proxy:\\n os.environ[\\\"NO_PROXY\\\"] = no_proxy\\n requester.proxies = proxies\\n return requester\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::api_method\",\n \"text\": \"conans/client/conan_api.py::api_method\\ndef api_method(f):\\n def wrapper(*args, **kwargs):\\n the_self = args[0]\\n try:\\n log_command(f.__name__, kwargs)\\n with tools.environment_append(the_self._client_cache.conan_config.env_vars):\\n # Patch the globals in tools\\n return f(*args, **kwargs)\\n except Exception as exc:\\n msg = exception_message_safe(exc)\\n try:\\n log_exception(exc, msg)\\n except:\\n pass\\n raise\\n\\n return wrapper\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::api_method.wrapper\",\n \"text\": \"conans/client/conan_api.py::api_method.wrapper\\n def wrapper(*args, **kwargs):\\n the_self = args[0]\\n try:\\n log_command(f.__name__, kwargs)\\n with tools.environment_append(the_self._client_cache.conan_config.env_vars):\\n # Patch the globals in tools\\n return f(*args, **kwargs)\\n except Exception as exc:\\n msg = exception_message_safe(exc)\\n try:\\n log_exception(exc, msg)\\n except:\\n pass\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::_make_abs_path\",\n \"text\": \"conans/client/conan_api.py::_make_abs_path\\ndef _make_abs_path(path, cwd=None, default=None):\\n \\\"\\\"\\\"convert 'path' to absolute if necessary (could be already absolute)\\n if not defined (empty, or None), will return 'default' one or 'cwd'\\n \\\"\\\"\\\"\\n cwd = cwd or os.getcwd()\\n if not path:\\n return default or cwd\\n if os.path.isabs(path):\\n return path\\n return os.path.normpath(os.path.join(cwd, path))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::_get_conanfile_path\",\n \"text\": \"conans/client/conan_api.py::_get_conanfile_path\\ndef _get_conanfile_path(path, cwd, py):\\n \\\"\\\"\\\"\\n param py= True: Must be .py, False: Must be .txt, None: Try .py, then .txt\\n \\\"\\\"\\\"\\n path = _make_abs_path(path, cwd)\\n\\n if os.path.isdir(path): # Can be a folder\\n if py:\\n path = os.path.join(path, \\\"conanfile.py\\\")\\n elif py is False:\\n path = os.path.join(path, \\\"conanfile.txt\\\")\\n else:\\n path_py = os.path.join(path, \\\"conanfile.py\\\")\\n if os.path.exists(path_py):\\n path = path_py\\n else:\\n path = os.path.join(path, \\\"conanfile.txt\\\")\\n\\n if not os.path.isfile(path): # Must exist\\n raise ConanException(\\\"Conanfile not found: %s\\\" % path)\\n\\n if py and not path.endswith(\\\".py\\\"):\\n raise ConanException(\\\"A conanfile.py is needed (not valid conanfile.txt)\\\")\\n\\n return path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.factory\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.factory\\n @staticmethod\\n def factory():\\n \\\"\\\"\\\"Factory\\\"\\\"\\\"\\n\\n def instance_remote_manager(client_cache):\\n requester = get_basic_requester(client_cache)\\n # Verify client version against remotes\\n version_checker_req = VersionCheckerRequester(requester, Version(client_version),\\n Version(MIN_SERVER_COMPATIBLE_VERSION),\\n out)\\n # To handle remote connections\\n put_headers = client_cache.read_put_headers()\\n rest_api_client = RestApiClient(out, requester=version_checker_req,\\n put_headers=put_headers)\\n # To store user and token\\n localdb = LocalDB(client_cache.localdb)\\n # Wraps RestApiClient to add authentication support (same interface)\\n auth_manager = ConanApiAuthManager(rest_api_client, user_io, localdb)\\n # Handle remote connections\\n remote_manager = RemoteManager(client_cache, auth_manager, out)\\n return remote_manager\\n\\n use_color = get_env(\\\"CONAN_COLOR_DISPLAY\\\", 1)\\n if use_color and hasattr(sys.stdout, \\\"isatty\\\") and sys.stdout.isatty():\\n import colorama\\n colorama.init()\\n color = True\\n else:\\n color = False\\n out = ConanOutput(sys.stdout, color)\\n user_io = UserIO(out=out)\\n\\n try:\\n user_home = get_conan_user_home()\\n client_cache = migrate_and_get_client_cache(user_home, out)\\n sys.path.append(os.path.join(user_home, \\\"python\\\"))\\n except Exception as e:\\n out.error(str(e))\\n raise\\n\\n with tools.environment_append(client_cache.conan_config.env_vars):\\n # Adjust CONAN_LOGGING_LEVEL with the env readed\\n conans.util.log.logger = configure_logger()\\n\\n # Get the\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.factory.instance_remote_manager\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.factory.instance_remote_manager\\n def instance_remote_manager(client_cache):\\n requester = get_basic_requester(client_cache)\\n # Verify client version against remotes\\n version_checker_req = VersionCheckerRequester(requester, Version(client_version),\\n Version(MIN_SERVER_COMPATIBLE_VERSION),\\n out)\\n # To handle remote connections\\n put_headers = client_cache.read_put_headers()\\n rest_api_client = RestApiClient(out, requester=version_checker_req,\\n put_headers=put_headers)\\n # To store user and token\\n localdb = LocalDB(client_cache.localdb)\\n # Wraps RestApiClient to add authentication support (same interface)\\n auth_manager = ConanApiAuthManager(rest_api_client, user_io, localdb)\\n # Handle remote connections\\n remote_manager = RemoteManager(client_cache, auth_manager, out)\\n return remote_manager\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.__init__\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.__init__\\n def __init__(self, client_cache, user_io, runner, remote_manager, search_manager,\\n settings_preprocessor):\\n assert isinstance(user_io, UserIO)\\n assert isinstance(client_cache, ClientCache)\\n self._client_cache = client_cache\\n self._user_io = user_io\\n self._runner = runner\\n self._remote_manager = remote_manager\\n self._manager = ConanManager(client_cache, user_io, runner, remote_manager, search_manager,\\n settings_preprocessor)\\n # Patch the tools module with a good requester and user_io\\n set_global_instances(self._user_io.out, get_basic_requester(self._client_cache))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.new\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.new\\n @api_method\\n def new(self, name, header=False, pure_c=False, test=False, exports_sources=False, bare=False,\\n cwd=None, visual_versions=None, linux_gcc_versions=None, linux_clang_versions=None,\\n osx_clang_versions=None, shared=None, upload_url=None, gitignore=None,\\n gitlab_gcc_versions=None, gitlab_clang_versions=None):\\n from conans.client.cmd.new import cmd_new\\n cwd = os.path.abspath(cwd or os.getcwd())\\n files = cmd_new(name, header=header, pure_c=pure_c, test=test,\\n exports_sources=exports_sources, bare=bare,\\n visual_versions=visual_versions,\\n linux_gcc_versions=linux_gcc_versions,\\n linux_clang_versions=linux_clang_versions,\\n osx_clang_versions=osx_clang_versions, shared=shared,\\n upload_url=upload_url, gitignore=gitignore,\\n gitlab_gcc_versions=gitlab_gcc_versions,\\n gitlab_clang_versions=gitlab_clang_versions)\\n\\n save_files(cwd, files)\\n for f in sorted(files):\\n self._user_io.out.success(\\\"File saved: %s\\\" % f)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.test\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.test\\n @api_method\\n def test(self, path, reference, profile_name=None, settings=None, options=None, env=None,\\n remote=None, update=False, build_modes=None, cwd=None, test_build_folder=None):\\n\\n settings = settings or []\\n options = options or []\\n env = env or []\\n\\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\\n cwd = cwd or os.getcwd()\\n profile = profile_from_args(profile_name, settings, options, env, cwd,\\n self._client_cache)\\n reference = ConanFileReference.loads(reference)\\n pt = PackageTester(self._manager, self._user_io)\\n pt.install_build_and_test(conanfile_path, reference, profile, remote,\\n update, build_modes=build_modes,\\n test_build_folder=test_build_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.create\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.create\\n @api_method\\n def create(self, conanfile_path, name=None, version=None, user=None, channel=None,\\n profile_name=None, settings=None,\\n options=None, env=None, test_folder=None, not_export=False,\\n build_modes=None,\\n keep_source=False, keep_build=False, verify=None,\\n manifests=None, manifests_interactive=None,\\n remote=None, update=False, cwd=None, test_build_folder=None):\\n \\\"\\\"\\\"\\n API method to create a conan package\\n\\n :param test_folder: default None - looks for default 'test' or 'test_package' folder),\\n string - test_folder path\\n False - disabling tests\\n \\\"\\\"\\\"\\n settings = settings or []\\n options = options or []\\n env = env or []\\n\\n cwd = cwd or os.getcwd()\\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\\n\\n if not name or not version:\\n conanfile = load_conanfile_class(conanfile_path)\\n name, version = conanfile.name, conanfile.version\\n if not name or not version:\\n raise ConanException(\\\"conanfile.py doesn't declare package name or version\\\")\\n\\n reference = ConanFileReference(name, version, user, channel)\\n scoped_output = ScopedOutput(str(reference), self._user_io.out)\\n # Make sure keep_source is set for keep_build\\n if keep_build:\\n keep_source = True\\n # Forcing an export!\\n if not not_export:\\n scoped_output.highlight(\\\"Exporting package recipe\\\")\\n self._manager.export(conanfile_path, name, version, user, channel, keep_source)\\n\\n if build_modes is None: # Not specified, force build the tested library\\n build_modes = [name]\\n\\n manifests = _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd)\\n manifest_folder, manifest_inter\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.create.get_test_conanfile_path\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.create.get_test_conanfile_path\\n def get_test_conanfile_path(tf):\\n \\\"\\\"\\\"Searches in the declared test_folder or in the standard locations\\\"\\\"\\\"\\n\\n if tf is False:\\n # Look up for testing conanfile can be disabled if tf (test folder) is False\\n return None\\n\\n test_folders = [tf] if tf else [\\\"test_package\\\", \\\"test\\\"]\\n base_folder = os.path.dirname(conanfile_path)\\n for test_folder_name in test_folders:\\n test_folder = os.path.join(base_folder, test_folder_name)\\n test_conanfile_path = os.path.join(test_folder, \\\"conanfile.py\\\")\\n if os.path.exists(test_conanfile_path):\\n return test_conanfile_path\\n else:\\n if tf:\\n raise ConanException(\\\"test folder '%s' not available, \\\"\\n \\\"or it doesn't have a conanfile.py\\\" % tf)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.export_pkg\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.export_pkg\\n @api_method\\n def export_pkg(self, conanfile_path, name, channel, source_folder=None, build_folder=None,\\n package_folder=None, install_folder=None, profile_name=None, settings=None,\\n options=None, env=None, force=False, user=None, version=None, cwd=None):\\n\\n settings = settings or []\\n options = options or []\\n env = env or []\\n cwd = cwd or os.getcwd()\\n\\n # Checks that info files exists if the install folder is specified\\n if install_folder and not existing_info_files(_make_abs_path(install_folder, cwd)):\\n raise ConanException(\\\"The specified install folder doesn't contain '%s' and '%s' \\\"\\n \\\"files\\\" % (CONANINFO, BUILD_INFO))\\n\\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\\n\\n if package_folder:\\n if build_folder or source_folder:\\n raise ConanException(\\\"package folder definition incompatible with build and source folders\\\")\\n package_folder = _make_abs_path(package_folder, cwd)\\n\\n build_folder = _make_abs_path(build_folder, cwd)\\n install_folder = _make_abs_path(install_folder, cwd, default=build_folder)\\n source_folder = _make_abs_path(source_folder, cwd, default=os.path.dirname(conanfile_path))\\n\\n # Checks that no both settings and info files are specified\\n if install_folder and existing_info_files(install_folder) and \\\\\\n (profile_name or settings or options or env):\\n raise ConanException(\\\"%s and %s are found, at '%s' folder, so specifying profile, \\\"\\n \\\"settings, options or env is not allowed\\\" % (CONANINFO, BUILD_INFO,\\n install_folder))\\n\\n infos_present = existing_info_files(install_folder)\\n if not infos_present:\\n profile = profile_from_args(profil\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.download\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.download\\n @api_method\\n def download(self, reference, remote=None, package=None):\\n # Install packages without settings (fixed ids or all)\\n conan_ref = ConanFileReference.loads(reference)\\n self._manager.download(conan_ref, package, remote=remote)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.install_reference\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.install_reference\\n @api_method\\n def install_reference(self, reference, settings=None, options=None, env=None,\\n remote=None, verify=None, manifests=None,\\n manifests_interactive=None, build=None, profile_name=None,\\n update=False, generators=None, install_folder=None, cwd=None):\\n\\n cwd = cwd or os.getcwd()\\n install_folder = _make_abs_path(install_folder, cwd)\\n\\n manifests = _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd)\\n manifest_folder, manifest_interactive, manifest_verify = manifests\\n\\n profile = profile_from_args(profile_name, settings, options, env, cwd,\\n self._client_cache)\\n\\n if not generators: # We don't want the default txt\\n generators = False\\n\\n mkdir(install_folder)\\n self._manager.install(reference=reference, install_folder=install_folder, remote=remote,\\n profile=profile, build_modes=build, update=update,\\n manifest_folder=manifest_folder,\\n manifest_verify=manifest_verify,\\n manifest_interactive=manifest_interactive,\\n generators=generators,\\n install_reference=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.install\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.install\\n @api_method\\n def install(self, path=\\\"\\\", settings=None, options=None, env=None,\\n remote=None, verify=None, manifests=None,\\n manifests_interactive=None, build=None, profile_name=None,\\n update=False, generators=None, no_imports=False, install_folder=None, cwd=None):\\n\\n cwd = cwd or os.getcwd()\\n install_folder = _make_abs_path(install_folder, cwd)\\n conanfile_path = _get_conanfile_path(path, cwd, py=None)\\n\\n manifests = _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd)\\n manifest_folder, manifest_interactive, manifest_verify = manifests\\n\\n profile = profile_from_args(profile_name, settings, options, env, cwd,\\n self._client_cache)\\n\\n self._manager.install(reference=conanfile_path,\\n install_folder=install_folder,\\n remote=remote,\\n profile=profile,\\n build_modes=build,\\n update=update,\\n manifest_folder=manifest_folder,\\n manifest_verify=manifest_verify,\\n manifest_interactive=manifest_interactive,\\n generators=generators,\\n no_imports=no_imports)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.config_get\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.config_get\\n @api_method\\n def config_get(self, item):\\n config_parser = ConanClientConfigParser(self._client_cache.conan_conf_path)\\n self._user_io.out.info(config_parser.get_item(item))\\n return config_parser.get_item(item)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.config_set\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.config_set\\n @api_method\\n def config_set(self, item, value):\\n config_parser = ConanClientConfigParser(self._client_cache.conan_conf_path)\\n config_parser.set_item(item, value)\\n self._client_cache.invalidate()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.config_rm\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.config_rm\\n @api_method\\n def config_rm(self, item):\\n config_parser = ConanClientConfigParser(self._client_cache.conan_conf_path)\\n config_parser.rm_item(item)\\n self._client_cache.invalidate()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.config_install\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.config_install\\n @api_method\\n def config_install(self, item, verify_ssl):\\n from conans.client.conf.config_installer import configuration_install\\n return configuration_install(item, self._client_cache, self._user_io.out, self._runner, verify_ssl)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1._info_get_profile\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1._info_get_profile\\n def _info_get_profile(self, reference, install_folder, profile_name, settings, options, env):\\n cwd = os.getcwd()\\n try:\\n reference = ConanFileReference.loads(reference)\\n except ConanException:\\n reference = _get_conanfile_path(reference, cwd=None, py=None)\\n if install_folder or not (profile_name or settings or options or env):\\n # When not install folder is specified but neither any setting, we try to read the\\n # info from cwd\\n install_folder = _make_abs_path(install_folder, cwd)\\n if existing_info_files(install_folder):\\n return reference, read_conaninfo_profile(install_folder)\\n\\n return reference, profile_from_args(profile_name, settings, options, env=env,\\n cwd=cwd, client_cache=self._client_cache)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.info_build_order\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.info_build_order\\n @api_method\\n def info_build_order(self, reference, settings=None, options=None, env=None,\\n profile_name=None, remote=None, build_order=None, check_updates=None,\\n install_folder=None):\\n reference, profile = self._info_get_profile(reference, install_folder, profile_name, settings,\\n options, env)\\n graph = self._manager.info_build_order(reference, profile, build_order, remote, check_updates)\\n return graph\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.info_nodes_to_build\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.info_nodes_to_build\\n @api_method\\n def info_nodes_to_build(self, reference, build_modes, settings=None, options=None, env=None,\\n profile_name=None, remote=None, check_updates=None, install_folder=None):\\n reference, profile = self._info_get_profile(reference, install_folder, profile_name, settings,\\n options, env)\\n ret = self._manager.info_nodes_to_build(reference, profile, build_modes, remote,\\n check_updates)\\n ref_list, project_reference = ret\\n return ref_list, project_reference\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.info_get_graph\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.info_get_graph\\n @api_method\\n def info_get_graph(self, reference, remote=None, settings=None, options=None, env=None,\\n profile_name=None, update=False, install_folder=None):\\n reference, profile = self._info_get_profile(reference, install_folder, profile_name, settings,\\n options, env)\\n ret = self._manager.info_get_graph(reference, remote=remote, profile=profile,\\n check_updates=update)\\n deps_graph, graph_updates_info, project_reference = ret\\n return deps_graph, graph_updates_info, project_reference\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.build\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.build\\n @api_method\\n def build(self, conanfile_path, source_folder=None, package_folder=None, build_folder=None,\\n install_folder=None, cwd=None):\\n\\n cwd = cwd or os.getcwd()\\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\\n build_folder = _make_abs_path(build_folder, cwd)\\n install_folder = _make_abs_path(install_folder, cwd, default=build_folder)\\n source_folder = _make_abs_path(source_folder, cwd, default=os.path.dirname(conanfile_path))\\n default_pkg_folder = os.path.join(build_folder, \\\"package\\\")\\n package_folder = _make_abs_path(package_folder, cwd, default=default_pkg_folder)\\n\\n self._manager.build(conanfile_path, source_folder, build_folder, package_folder,\\n install_folder)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.package\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.package\\n @api_method\\n def package(self, path, build_folder, package_folder, source_folder=None, install_folder=None, cwd=None):\\n cwd = cwd or os.getcwd()\\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\\n build_folder = _make_abs_path(build_folder, cwd)\\n install_folder = _make_abs_path(install_folder, cwd, default=build_folder)\\n source_folder = _make_abs_path(source_folder, cwd, default=os.path.dirname(conanfile_path))\\n default_pkg_folder = os.path.join(build_folder, \\\"package\\\")\\n package_folder = _make_abs_path(package_folder, cwd, default=default_pkg_folder)\\n\\n self._manager.local_package(package_folder, conanfile_path, build_folder, source_folder,\\n install_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.source\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.source\\n @api_method\\n def source(self, path, source_folder=None, info_folder=None, cwd=None):\\n cwd = cwd or os.getcwd()\\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\\n source_folder = _make_abs_path(source_folder, cwd)\\n info_folder = _make_abs_path(info_folder, cwd)\\n\\n mkdir(source_folder)\\n if not os.path.exists(info_folder):\\n raise ConanException(\\\"Specified info-folder doesn't exist\\\")\\n\\n self._manager.source(conanfile_path, source_folder, info_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.imports\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.imports\\n @api_method\\n def imports(self, path, dest=None, info_folder=None, cwd=None):\\n \\\"\\\"\\\"\\n :param path: Path to the conanfile\\n :param dest: Dir to put the imported files. (Abs path or relative to cwd)\\n :param info_folder: Dir where the conaninfo.txt and conanbuildinfo.txt files are\\n :param cwd: Current working directory\\n :return: None\\n \\\"\\\"\\\"\\n cwd = cwd or os.getcwd()\\n info_folder = _make_abs_path(info_folder, cwd)\\n dest = _make_abs_path(dest, cwd)\\n\\n mkdir(dest)\\n conanfile_abs_path = _get_conanfile_path(path, cwd, py=None)\\n self._manager.imports(conanfile_abs_path, dest, info_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.imports_undo\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.imports_undo\\n @api_method\\n def imports_undo(self, manifest_path):\\n cwd = os.getcwd()\\n manifest_path = _make_abs_path(manifest_path, cwd)\\n self._manager.imports_undo(manifest_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.export\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.export\\n @api_method\\n def export(self, path, name, version, user, channel, keep_source=False, cwd=None):\\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\\n self._manager.export(conanfile_path, name, version, user, channel, keep_source)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remove\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remove\\n @api_method\\n def remove(self, pattern, query=None, packages=None, builds=None, src=False, force=False,\\n remote=None, outdated=False):\\n self._manager.remove(pattern, package_ids_filter=packages, build_ids=builds,\\n src=src, force=force, remote=remote, packages_query=query,\\n outdated=outdated)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.copy\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.copy\\n @api_method\\n def copy(self, reference, user_channel, force=False, packages=None):\\n \\\"\\\"\\\"\\n param packages: None=No binaries, True=All binaries, else list of IDs\\n \\\"\\\"\\\"\\n from conans.client.cmd.copy import cmd_copy\\n # FIXME: conan copy does not support short-paths in Windows\\n cmd_copy(reference, user_channel, packages, self._client_cache,\\n self._user_io, self._remote_manager, force=force)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.user\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.user\\n @api_method\\n def user(self, name=None, clean=False, remote=None, password=None):\\n if clean:\\n localdb = LocalDB(self._client_cache.localdb)\\n localdb.init(clean=True)\\n self._user_io.out.success(\\\"Deleted user data\\\")\\n return\\n self._manager.user(remote, name, password)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.search_recipes\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.search_recipes\\n @api_method\\n def search_recipes(self, pattern, remote=None, case_sensitive=False):\\n search = Search(self._client_cache, self._remote_manager, self._user_io)\\n return search.search_recipes(pattern, remote, case_sensitive)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.search_packages\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.search_packages\\n @api_method\\n def search_packages(self, reference, query=None, remote=None, outdated=False):\\n search = Search(self._client_cache, self._remote_manager, self._user_io)\\n return search.search_packages(reference, remote, query=query,\\n outdated=outdated)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.upload\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.upload\\n @api_method\\n def upload(self, pattern, package=None, remote=None, all_packages=False, force=False,\\n confirm=False, retry=2, retry_wait=5, skip_upload=False, integrity_check=False):\\n \\\"\\\"\\\" Uploads a package recipe and the generated binary packages to a specified remote\\n \\\"\\\"\\\"\\n uploader = CmdUpload(self._client_cache, self._user_io, self._remote_manager,\\n remote)\\n return uploader.upload(pattern, package, all_packages, force, confirm, retry,\\n retry_wait, skip_upload, integrity_check)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_list\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_list\\n @api_method\\n def remote_list(self):\\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\\n return registry.remotes\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_add\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_add\\n @api_method\\n def remote_add(self, remote, url, verify_ssl=True, insert=None):\\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\\n return registry.add(remote, url, verify_ssl, insert)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_remove\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_remove\\n @api_method\\n def remote_remove(self, remote):\\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\\n return registry.remove(remote)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_update\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_update\\n @api_method\\n def remote_update(self, remote, url, verify_ssl=True, insert=None):\\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\\n return registry.update(remote, url, verify_ssl, insert)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_list_ref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_list_ref\\n @api_method\\n def remote_list_ref(self):\\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\\n return registry.refs\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_add_ref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_add_ref\\n @api_method\\n def remote_add_ref(self, reference, remote):\\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\\n return registry.add_ref(reference, remote)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_remove_ref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_remove_ref\\n @api_method\\n def remote_remove_ref(self, reference):\\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\\n return registry.remove_ref(reference)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_update_ref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_update_ref\\n @api_method\\n def remote_update_ref(self, reference, remote):\\n registry = RemoteRegistry(self._client_cache.registry, self._user_io.out)\\n return registry.update_ref(reference, remote)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.profile_list\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.profile_list\\n @api_method\\n def profile_list(self):\\n return cmd_profile_list(self._client_cache.profiles_path, self._user_io.out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.create_profile\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.create_profile\\n @api_method\\n def create_profile(self, profile_name, detect=False):\\n return cmd_profile_create(profile_name, self._client_cache.profiles_path,\\n self._user_io.out, detect)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.update_profile\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.update_profile\\n @api_method\\n def update_profile(self, profile_name, key, value):\\n return cmd_profile_update(profile_name, key, value, self._client_cache.profiles_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.get_profile_key\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.get_profile_key\\n @api_method\\n def get_profile_key(self, profile_name, key):\\n return cmd_profile_get(profile_name, key, self._client_cache.profiles_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.delete_profile_key\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.delete_profile_key\\n @api_method\\n def delete_profile_key(self, profile_name, key):\\n return cmd_profile_delete_key(profile_name, key, self._client_cache.profiles_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.read_profile\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.read_profile\\n @api_method\\n def read_profile(self, profile=None):\\n p, _ = read_profile(profile, os.getcwd(), self._client_cache.profiles_path)\\n return p\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.get_path\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.get_path\\n @api_method\\n def get_path(self, reference, package_id=None, path=None, remote=None):\\n reference = ConanFileReference.loads(str(reference))\\n return self._manager.get_path(reference, package_id, path, remote)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.export_alias\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.export_alias\\n @api_method\\n def export_alias(self, reference, target_reference):\\n reference = ConanFileReference.loads(str(reference))\\n target_reference = ConanFileReference.loads(str(target_reference))\\n return self._manager.export_alias(reference, target_reference)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::_parse_manifests_arguments\",\n \"text\": \"conans/client/conan_api.py::_parse_manifests_arguments\\ndef _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd):\\n if manifests and manifests_interactive:\\n raise ConanException(\\\"Do not specify both manifests and \\\"\\n \\\"manifests-interactive arguments\\\")\\n if verify and (manifests or manifests_interactive):\\n raise ConanException(\\\"Do not specify both 'verify' and \\\"\\n \\\"'manifests' or 'manifests-interactive' arguments\\\")\\n manifest_folder = verify or manifests or manifests_interactive\\n if manifest_folder:\\n if not os.path.isabs(manifest_folder):\\n if not cwd:\\n raise ConanException(\\\"'cwd' should be defined if the manifest folder is relative.\\\")\\n manifest_folder = os.path.join(cwd, manifest_folder)\\n manifest_verify = verify is not None\\n manifest_interactive = manifests_interactive is not None\\n else:\\n manifest_verify = manifest_interactive = False\\n\\n return manifest_folder, manifest_interactive, manifest_verify\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::get_conan_runner\",\n \"text\": \"conans/client/conan_api.py::get_conan_runner\\ndef get_conan_runner():\\n print_commands_to_output = get_env(\\\"CONAN_PRINT_RUN_COMMANDS\\\", False)\\n generate_run_log_file = get_env(\\\"CONAN_LOG_RUN_TO_FILE\\\", False)\\n log_run_to_output = get_env(\\\"CONAN_LOG_RUN_TO_OUTPUT\\\", True)\\n runner = ConanRunner(print_commands_to_output, generate_run_log_file, log_run_to_output)\\n return runner\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::migrate_and_get_client_cache\",\n \"text\": \"conans/client/conan_api.py::migrate_and_get_client_cache\\ndef migrate_and_get_client_cache(base_folder, out, storage_folder=None):\\n # Init paths\\n client_cache = ClientCache(base_folder, storage_folder, out)\\n\\n # Migration system\\n migrator = ClientMigrator(client_cache, Version(client_version), out)\\n migrator.migrate()\\n\\n return client_cache\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::build_id\",\n \"text\": \"conans/client/installer.py::build_id\\ndef build_id(conan_file):\\n if hasattr(conan_file, \\\"build_id\\\"):\\n # construct new ConanInfo\\n build_id_info = conan_file.info.copy()\\n conan_file.info_build = build_id_info\\n # effectively call the user function to change the package values\\n with conanfile_exception_formatter(str(conan_file), \\\"build_id\\\"):\\n conan_file.build_id()\\n # compute modified ID\\n return build_id_info.package_id()\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_ConanPackageBuilder.__init__\",\n \"text\": \"conans/client/installer.py::_ConanPackageBuilder.__init__\\n def __init__(self, conan_file, package_reference, client_cache, output):\\n self._client_cache = client_cache\\n self._conan_file = conan_file\\n self._out = output\\n self._package_reference = package_reference\\n self._conan_ref = self._package_reference.conan\\n self._skip_build = False # If build_id()\\n\\n new_id = build_id(self._conan_file)\\n self.build_reference = PackageReference(self._conan_ref, new_id) if new_id else package_reference\\n self.build_folder = self._client_cache.build(self.build_reference,\\n self._conan_file.short_paths)\\n self.package_folder = self._client_cache.package(self._package_reference,\\n self._conan_file.short_paths)\\n self.source_folder = self._client_cache.source(self._conan_ref, self._conan_file.short_paths)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_ConanPackageBuilder.prepare_build\",\n \"text\": \"conans/client/installer.py::_ConanPackageBuilder.prepare_build\\n def prepare_build(self):\\n if self.build_reference != self._package_reference and \\\\\\n os.path.exists(self.build_folder) and hasattr(self._conan_file, \\\"build_id\\\"):\\n self._skip_build = True\\n return\\n\\n # build_id is not caching the build folder, so actually rebuild the package\\n _handle_system_requirements(self._conan_file, self._package_reference,\\n self._client_cache, self._out)\\n\\n export_folder = self._client_cache.export(self._conan_ref)\\n export_source_folder = self._client_cache.export_sources(self._conan_ref,\\n self._conan_file.short_paths)\\n\\n try:\\n rmdir(self.build_folder)\\n rmdir(self.package_folder)\\n except OSError as e:\\n raise ConanException(\\\"%s\\\\n\\\\nCouldn't remove folder, might be busy or open\\\\n\\\"\\n \\\"Close any app using it, and retry\\\" % str(e))\\n\\n self._out.info('Building your package in %s' % self.build_folder)\\n config_source(export_folder, export_source_folder, self.source_folder,\\n self._conan_file, self._out)\\n self._out.info('Copying sources to build folder')\\n\\n if getattr(self._conan_file, 'no_copy_source', False):\\n mkdir(self.build_folder)\\n self._conan_file.source_folder = self.source_folder\\n else:\\n if platform.system() == \\\"Windows\\\" and os.getenv(\\\"CONAN_USER_HOME_SHORT\\\") != \\\"None\\\":\\n from conans.util.windows import ignore_long_path_files\\n ignore = ignore_long_path_files(self.source_folder, self.build_folder, self._out)\\n else:\\n ignore = None\\n\\n shutil.copytree(self.source_folder, self.build_folder, symlinks=True, ignore=ignore)\\n logger.debug(\\\"Copied to %s\\\", self.build_folder)\\n logger.de\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_ConanPackageBuilder.build\",\n \"text\": \"conans/client/installer.py::_ConanPackageBuilder.build\\n def build(self):\\n \\\"\\\"\\\"Calls the conanfile's build method\\\"\\\"\\\"\\n if self._skip_build:\\n return\\n with get_env_context_manager(self._conan_file):\\n self._build_package()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_ConanPackageBuilder.package\",\n \"text\": \"conans/client/installer.py::_ConanPackageBuilder.package\\n def package(self):\\n \\\"\\\"\\\"Generate the info txt files and calls the conanfile package method.\\n \\\"\\\"\\\"\\n\\n # FIXME: Is weak to assign here the recipe_hash\\n manifest = self._client_cache.load_manifest(self._conan_ref)\\n self._conan_file.info.recipe_hash = manifest.summary_hash\\n\\n # Creating ***info.txt files\\n save(os.path.join(self.build_folder, CONANINFO), self._conan_file.info.dumps())\\n self._out.info(\\\"Generated %s\\\" % CONANINFO)\\n save(os.path.join(self.build_folder, BUILD_INFO), TXTGenerator(self._conan_file).content)\\n self._out.info(\\\"Generated %s\\\" % BUILD_INFO)\\n\\n os.chdir(self.build_folder)\\n\\n if getattr(self._conan_file, 'no_copy_source', False):\\n source_folder = self.source_folder\\n else:\\n source_folder = self.build_folder\\n with get_env_context_manager(self._conan_file):\\n install_folder = self.build_folder # While installing, the infos goes to build folder\\n create_package(self._conan_file, source_folder, self.build_folder, self.package_folder,\\n install_folder, self._out)\\n\\n if get_env(\\\"CONAN_READ_ONLY_CACHE\\\", False):\\n make_read_only(self.package_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_ConanPackageBuilder._build_package\",\n \"text\": \"conans/client/installer.py::_ConanPackageBuilder._build_package\\n def _build_package(self):\\n \\\"\\\"\\\" calls the imports + conanfile.build() method\\n \\\"\\\"\\\"\\n os.chdir(self.build_folder)\\n self._conan_file.build_folder = self.build_folder\\n self._conan_file.package_folder = self.package_folder\\n # In local cache, install folder always is build_folder\\n self._conan_file.install_folder = self.build_folder\\n\\n # Read generators from conanfile and generate the needed files\\n logger.debug(\\\"Writing generators\\\")\\n write_generators(self._conan_file, self.build_folder, self._out)\\n logger.debug(\\\"Files copied after generators %s\\\", os.listdir(self.build_folder))\\n\\n # Build step might need DLLs, binaries as protoc to generate source files\\n # So execute imports() before build, storing the list of copied_files\\n from conans.client.importer import run_imports\\n copied_files = run_imports(self._conan_file, self.build_folder, self._out)\\n\\n try:\\n # This is necessary because it is different for user projects\\n # than for packages\\n logger.debug(\\\"Call conanfile.build() with files in build folder: %s\\\",\\n os.listdir(self.build_folder))\\n self._out.highlight(\\\"Calling build()\\\")\\n with conanfile_exception_formatter(str(self._conan_file), \\\"build\\\"):\\n self._conan_file.build()\\n\\n self._out.success(\\\"Package '%s' built\\\" % self._conan_file.info.package_id())\\n self._out.info(\\\"Build folder %s\\\" % self.build_folder)\\n except Exception as exc:\\n self._out.writeln(\\\"\\\")\\n self._out.error(\\\"Package '%s' build failed\\\" % self._conan_file.info.package_id())\\n self._out.warn(\\\"Build folder %s\\\" % self.build_folder)\\n if isinstance(exc, ConanExceptionInUserConanfileMethod):\\n raise exc\\n raise ConanException(exc)\\n finally:\\n # Now r\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_raise_package_not_found_error\",\n \"text\": \"conans/client/installer.py::_raise_package_not_found_error\\ndef _raise_package_not_found_error(conan_file, conan_ref, package_id, out):\\n settings_text = \\\", \\\".join(conan_file.info.full_settings.dumps().splitlines())\\n options_text = \\\", \\\".join(conan_file.info.full_options.dumps().splitlines())\\n\\n out.warn('''Can't find a '%s' package for the specified options and settings:\\n- Settings: %s\\n- Options: %s\\n- Package ID: %s\\n''' % (conan_ref, settings_text, options_text, package_id))\\n\\n raise ConanException('''Missing prebuilt package for '%s'\\nTry to build it from sources with \\\"--build %s\\\"\\nOr read \\\"http://docs.conan.io/en/latest/faq/troubleshooting.html#error-missing-prebuilt-package\\\"\\n''' % (conan_ref, conan_ref.name))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_handle_system_requirements\",\n \"text\": \"conans/client/installer.py::_handle_system_requirements\\ndef _handle_system_requirements(conan_file, package_reference, client_cache, out):\\n \\\"\\\"\\\" check first the system_reqs/system_requirements.txt existence, if not existing\\n check package/sha1/\\n\\n Used after remote package retrieving and before package building\\n \\\"\\\"\\\"\\n if \\\"system_requirements\\\" not in type(conan_file).__dict__:\\n return\\n\\n system_reqs_path = client_cache.system_reqs(package_reference.conan)\\n system_reqs_package_path = client_cache.system_reqs_package(package_reference)\\n if os.path.exists(system_reqs_path) or os.path.exists(system_reqs_package_path):\\n return\\n\\n ret = call_system_requirements(conan_file, out)\\n\\n try:\\n ret = str(ret or \\\"\\\")\\n except:\\n out.warn(\\\"System requirements didn't return a string\\\")\\n ret = \\\"\\\"\\n if getattr(conan_file, \\\"global_system_requirements\\\", None):\\n save(system_reqs_path, ret)\\n else:\\n save(system_reqs_package_path, ret)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::call_system_requirements\",\n \"text\": \"conans/client/installer.py::call_system_requirements\\ndef call_system_requirements(conanfile, output):\\n try:\\n return conanfile.system_requirements()\\n except Exception as e:\\n output.error(\\\"while executing system_requirements(): %s\\\" % str(e))\\n raise ConanException(\\\"Error in system requirements\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::ConanInstaller.__init__\",\n \"text\": \"conans/client/installer.py::ConanInstaller.__init__\\n def __init__(self, client_cache, output, remote_proxy, build_mode, build_requires):\\n self._client_cache = client_cache\\n self._out = output\\n self._remote_proxy = remote_proxy\\n self._build_requires = build_requires\\n self._build_mode = build_mode\\n self._built_packages = set() # To avoid re-building twice the same package reference\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::ConanInstaller.install\",\n \"text\": \"conans/client/installer.py::ConanInstaller.install\\n def install(self, deps_graph, profile_build_requires, keep_build=False):\\n \\\"\\\"\\\" given a DepsGraph object, build necessary nodes or retrieve them\\n \\\"\\\"\\\"\\n t1 = time.time()\\n # order by levels and separate the root node (conan_ref=None) from the rest\\n nodes_by_level = deps_graph.by_levels()\\n root_level = nodes_by_level.pop()\\n _, root_conanfile = root_level[0]\\n logger.debug(\\\"Install-Process buildinfo %s\\\", (time.time() - t1))\\n t1 = time.time()\\n skip_private_nodes = self._compute_private_nodes(deps_graph)\\n logger.debug(\\\"Install-Process private %s\\\", (time.time() - t1))\\n t1 = time.time()\\n # Get the nodes in order and if we have to build them\\n nodes_to_process = self._get_nodes(nodes_by_level, skip_private_nodes)\\n self._build(nodes_to_process, deps_graph, skip_private_nodes, profile_build_requires, keep_build,\\n root_conanfile)\\n logger.debug(\\\"Install-build %s\\\", (time.time() - t1))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::ConanInstaller._compute_private_nodes\",\n \"text\": \"conans/client/installer.py::ConanInstaller._compute_private_nodes\\n def _compute_private_nodes(self, deps_graph):\\n \\\"\\\"\\\" computes a list of nodes that are not required to be built, as they are\\n private requirements of already available shared libraries as binaries.\\n\\n If the package requiring a private node has an up to date binary package,\\n the private node is not retrieved nor built\\n \\\"\\\"\\\"\\n skip_nodes = set() # Nodes that require private packages but are already built\\n for node in deps_graph.nodes:\\n conan_ref, conanfile = node\\n if not [r for r in conanfile.requires.values() if r.private]:\\n continue\\n\\n if conan_ref:\\n build_forced = self._build_mode.forced(conanfile, conan_ref)\\n if build_forced:\\n continue\\n\\n package_id = conanfile.info.package_id()\\n package_reference = PackageReference(conan_ref, package_id)\\n check_outdated = self._build_mode.outdated\\n\\n if self._remote_proxy.package_available(package_reference,\\n conanfile.short_paths,\\n check_outdated):\\n skip_nodes.add(node)\\n\\n # Get the private nodes\\n skippable_private_nodes = deps_graph.private_nodes(skip_nodes)\\n return skippable_private_nodes\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::ConanInstaller.nodes_to_build\",\n \"text\": \"conans/client/installer.py::ConanInstaller.nodes_to_build\\n def nodes_to_build(self, deps_graph):\\n \\\"\\\"\\\"Called from info command when a build policy is used in build_order parameter\\\"\\\"\\\"\\n # Get the nodes in order and if we have to build them\\n nodes_by_level = deps_graph.by_levels()\\n nodes_by_level.pop() # Remove latest one, consumer node with conan_ref=None\\n skip_private_nodes = self._compute_private_nodes(deps_graph)\\n nodes = self._get_nodes(nodes_by_level, skip_private_nodes)\\n return [(PackageReference(conan_ref, package_id), conan_file)\\n for conan_ref, package_id, conan_file, build in nodes if build]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::ConanInstaller._build\",\n \"text\": \"conans/client/installer.py::ConanInstaller._build\\n def _build(self, nodes_to_process, deps_graph, skip_nodes, profile_build_requires, keep_build,\\n root_conanfile):\\n \\\"\\\"\\\" The build assumes an input of conans ordered by degree, first level\\n should be independent from each other, the next-second level should have\\n dependencies only to first level conans.\\n param nodes_by_level: list of lists [[nodeA, nodeB], [nodeC], [nodeD, ...], ...]\\n \\\"\\\"\\\"\\n inverse = deps_graph.inverse_levels()\\n flat = []\\n\\n for level in inverse:\\n level = sorted(level, key=lambda x: x.conan_ref)\\n flat.extend(n for n in level if n not in skip_nodes)\\n\\n for conan_ref, package_id, conan_file, build_needed in nodes_to_process:\\n output = ScopedOutput(str(conan_ref), self._out)\\n package_ref = PackageReference(conan_ref, package_id)\\n package_folder = self._client_cache.package(package_ref,\\n conan_file.short_paths)\\n\\n if build_needed and (conan_ref, package_id) not in self._built_packages:\\n self._build_package(conan_file, conan_ref, package_id, package_ref, output,\\n keep_build, profile_build_requires, flat, deps_graph)\\n else:\\n # Get the package, we have a not outdated remote package\\n with self._client_cache.package_lock(package_ref):\\n self._get_remote_package(conan_file, package_ref, output, package_folder)\\n self._propagate_info(conan_file, conan_ref, flat, deps_graph)\\n\\n # Call the info method\\n self._call_package_info(conan_file, package_folder)\\n\\n # Finally, propagate information to root node (conan_ref=None)\\n self._propagate_info(root_conanfile, None, flat, deps_graph)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::ConanInstaller._build_package\",\n \"text\": \"conans/client/installer.py::ConanInstaller._build_package\\n def _build_package(self, conan_file, conan_ref, package_id, package_ref, output, keep_build,\\n profile_build_requires, flat, deps_graph):\\n build_allowed = self._build_mode.allowed(conan_file, conan_ref)\\n if not build_allowed:\\n _raise_package_not_found_error(conan_file, conan_ref, package_id, output)\\n\\n skip_build = conan_file.develop and keep_build\\n if skip_build:\\n output.info(\\\"Won't be built as specified by --keep-build\\\")\\n else:\\n if conan_file.build_policy_missing:\\n output.info(\\\"Building package from source as defined by build_policy='missing'\\\")\\n elif self._build_mode.forced(conan_file, conan_ref):\\n output.warn('Forced build from source')\\n\\n if not skip_build:\\n self._build_requires.install(conan_ref, conan_file, self,\\n profile_build_requires, output)\\n\\n # It is important that it is done AFTER build_requires install\\n self._propagate_info(conan_file, conan_ref, flat, deps_graph)\\n\\n t1 = time.time()\\n builder = _ConanPackageBuilder(conan_file, package_ref, self._client_cache, output)\\n\\n if skip_build:\\n if not os.path.exists(builder.build_folder):\\n raise ConanException(\\\"--keep-build specified, but build folder not found\\\")\\n else:\\n with self._client_cache.conanfile_write_lock(conan_ref):\\n self._remote_proxy.get_recipe_sources(conan_ref, conan_file.short_paths)\\n builder.prepare_build()\\n\\n with self._client_cache.conanfile_read_lock(conan_ref):\\n with self._client_cache.package_lock(builder.build_reference):\\n if not skip_build:\\n builder.build()\\n builder.package()\\n\\n self._remote_proxy.handle_package_manifest(package_ref, installed=True)\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::ConanInstaller._get_remote_package\",\n \"text\": \"conans/client/installer.py::ConanInstaller._get_remote_package\\n def _get_remote_package(self, conan_file, package_reference, output, package_folder):\\n \\\"\\\"\\\"Get remote package. It won't check if it's outdated\\\"\\\"\\\"\\n # Compute conan_file package from local (already compiled) or from remote\\n # If already exists do not dirt the output, the common situation\\n # is that package is already installed and OK. If don't, the proxy\\n # will print some other message about it\\n if not os.path.exists(package_folder):\\n self._out.info(\\\"Retrieving package %s\\\" % package_reference.package_id)\\n\\n if self._remote_proxy.get_package(package_reference,\\n short_paths=conan_file.short_paths):\\n _handle_system_requirements(conan_file, package_reference,\\n self._client_cache, output)\\n if get_env(\\\"CONAN_READ_ONLY_CACHE\\\", False):\\n make_read_only(package_folder)\\n return True\\n\\n _raise_package_not_found_error(conan_file, package_reference.conan,\\n package_reference.package_id, output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::ConanInstaller._log_built_package\",\n \"text\": \"conans/client/installer.py::ConanInstaller._log_built_package\\n def _log_built_package(self, build_folder, package_ref, duration):\\n log_file = os.path.join(build_folder, RUN_LOG_NAME)\\n log_file = log_file if os.path.exists(log_file) else None\\n log_package_built(package_ref, duration, log_file)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::ConanInstaller._propagate_info\",\n \"text\": \"conans/client/installer.py::ConanInstaller._propagate_info\\n @staticmethod\\n def _propagate_info(conan_file, conan_ref, flat, deps_graph):\\n # Get deps_cpp_info from upstream nodes\\n node_order = deps_graph.ordered_closure((conan_ref, conan_file), flat)\\n for n in node_order:\\n conan_file.deps_cpp_info.update(n.conanfile.cpp_info, n.conan_ref.name)\\n conan_file.deps_env_info.update(n.conanfile.env_info, n.conan_ref.name)\\n conan_file.deps_user_info[n.conan_ref.name] = n.conanfile.user_info\\n\\n # Update the info but filtering the package values that not apply to the subtree\\n # of this current node and its dependencies.\\n subtree_libnames = [ref.name for (ref, _) in node_order]\\n for package_name, env_vars in conan_file._env_values.data.items():\\n for name, value in env_vars.items():\\n if not package_name or package_name in subtree_libnames or \\\\\\n package_name == conan_file.name:\\n conan_file.info.env_values.add(name, value, package_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::ConanInstaller._call_package_info\",\n \"text\": \"conans/client/installer.py::ConanInstaller._call_package_info\\n @staticmethod\\n def _call_package_info(conanfile, package_folder):\\n conanfile.cpp_info = CppInfo(package_folder)\\n conanfile.cpp_info.version = conanfile.version\\n conanfile.cpp_info.description = conanfile.description\\n conanfile.env_info = EnvInfo()\\n conanfile.user_info = UserInfo()\\n\\n # Get deps_cpp_info from upstream nodes\\n public_deps = [name for name, req in conanfile.requires.items() if not req.private]\\n conanfile.cpp_info.public_deps = public_deps\\n # Once the node is build, execute package info, so it has access to the\\n # package folder and artifacts\\n with tools.chdir(package_folder):\\n with conanfile_exception_formatter(str(conanfile), \\\"package_info\\\"):\\n conanfile.package_folder = package_folder\\n conanfile.source_folder = None\\n conanfile.build_folder = None\\n conanfile.install_folder = None\\n conanfile.package_info()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::ConanInstaller._get_nodes\",\n \"text\": \"conans/client/installer.py::ConanInstaller._get_nodes\\n def _get_nodes(self, nodes_by_level, skip_nodes):\\n \\\"\\\"\\\"Compute a list of (conan_ref, package_id, conan_file, build_node)\\n defining what to do with each node\\n \\\"\\\"\\\"\\n\\n nodes_to_build = []\\n # Now build each level, starting from the most independent one\\n package_references = set()\\n for level in nodes_by_level:\\n for node in level:\\n if node in skip_nodes:\\n continue\\n conan_ref, conan_file = node\\n build_node = False\\n logger.debug(\\\"Processing node %s\\\", repr(conan_ref))\\n package_id = conan_file.info.package_id()\\n package_reference = PackageReference(conan_ref, package_id)\\n # Avoid processing twice the same package reference\\n if package_reference not in package_references:\\n package_references.add(package_reference)\\n check_outdated = self._build_mode.outdated\\n if self._build_mode.forced(conan_file, conan_ref):\\n build_node = True\\n else:\\n available = self._remote_proxy.package_available(package_reference,\\n conan_file.short_paths,\\n check_outdated)\\n build_node = not available\\n\\n nodes_to_build.append((conan_ref, package_id, conan_file, build_node))\\n\\n # A check to be sure that if introduced a pattern, something is going to be built\\n if self._build_mode.patterns:\\n to_build = [str(n[0].name) for n in nodes_to_build if n[3]]\\n self._build_mode.check_matches(to_build)\\n\\n return nodes_to_build\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::BuildMode.__init__\",\n \"text\": \"conans/client/manager.py::BuildMode.__init__\\n def __init__(self, params, output):\\n self._out = output\\n self.outdated = False\\n self.missing = False\\n self.never = False\\n self.patterns = []\\n self._unused_patterns = []\\n self.all = False\\n if params is None:\\n return\\n\\n assert isinstance(params, list)\\n if len(params) == 0:\\n self.all = True\\n else:\\n for param in params:\\n if param == \\\"outdated\\\":\\n self.outdated = True\\n elif param == \\\"missing\\\":\\n self.missing = True\\n elif param == \\\"never\\\":\\n self.never = True\\n else:\\n self.patterns.append(\\\"%s\\\" % param)\\n\\n if self.never and (self.outdated or self.missing or self.patterns):\\n raise ConanException(\\\"--build=never not compatible with other options\\\")\\n self._unused_patterns = list(self.patterns)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::BuildMode.forced\",\n \"text\": \"conans/client/manager.py::BuildMode.forced\\n def forced(self, conan_file, reference):\\n if self.never:\\n return False\\n if self.all:\\n return True\\n\\n if conan_file.build_policy_always:\\n out = ScopedOutput(str(reference), self._out)\\n out.info(\\\"Building package from source as defined by build_policy='always'\\\")\\n return True\\n\\n ref = reference.name\\n # Patterns to match, if package matches pattern, build is forced\\n force_build = any([fnmatch.fnmatch(ref, pattern) for pattern in self.patterns])\\n return force_build\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::BuildMode.allowed\",\n \"text\": \"conans/client/manager.py::BuildMode.allowed\\n def allowed(self, conan_file, reference):\\n return (self.missing or self.outdated or self.forced(conan_file, reference) or\\n conan_file.build_policy_missing)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::BuildMode.check_matches\",\n \"text\": \"conans/client/manager.py::BuildMode.check_matches\\n def check_matches(self, references):\\n for pattern in list(self._unused_patterns):\\n matched = any(fnmatch.fnmatch(ref, pattern) for ref in references)\\n if matched:\\n self._unused_patterns.remove(pattern)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::BuildMode.report_matches\",\n \"text\": \"conans/client/manager.py::BuildMode.report_matches\\n def report_matches(self):\\n for pattern in self._unused_patterns:\\n self._out.error(\\\"No package matching '%s' pattern\\\" % pattern)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.__init__\",\n \"text\": \"conans/client/manager.py::ConanManager.__init__\\n def __init__(self, client_cache, user_io, runner, remote_manager, search_manager,\\n settings_preprocessor):\\n assert isinstance(user_io, UserIO)\\n assert isinstance(client_cache, ClientCache)\\n self._client_cache = client_cache\\n self._user_io = user_io\\n self._runner = runner\\n self._remote_manager = remote_manager\\n self._search_manager = search_manager\\n self._settings_preprocessor = settings_preprocessor\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager._load_consumer_conanfile\",\n \"text\": \"conans/client/manager.py::ConanManager._load_consumer_conanfile\\n def _load_consumer_conanfile(self, conanfile_path, info_folder, output,\\n deps_info_required=False):\\n \\\"\\\"\\\"loads a conanfile for local flow: source, imports, package, build\\n \\\"\\\"\\\"\\n profile = read_conaninfo_profile(info_folder) or self._client_cache.default_profile\\n loader = self.get_loader(profile, local=True)\\n if conanfile_path.endswith(\\\".py\\\"):\\n conanfile = loader.load_conan(conanfile_path, output, consumer=True, local=True)\\n else:\\n conanfile = loader.load_conan_txt(conanfile_path, output)\\n\\n _load_deps_info(info_folder, conanfile, required=deps_info_required)\\n\\n return conanfile\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager._load_install_conanfile\",\n \"text\": \"conans/client/manager.py::ConanManager._load_install_conanfile\\n def _load_install_conanfile(self, loader, reference_or_path):\\n \\\"\\\"\\\"loads a conanfile for installation: install, info\\n \\\"\\\"\\\"\\n if isinstance(reference_or_path, ConanFileReference):\\n conanfile = loader.load_virtual([reference_or_path])\\n else:\\n output = ScopedOutput(\\\"PROJECT\\\", self._user_io.out)\\n if reference_or_path.endswith(\\\".py\\\"):\\n conanfile = loader.load_conan(reference_or_path, output, consumer=True)\\n else:\\n conanfile = loader.load_conan_txt(reference_or_path, output)\\n return conanfile\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.get_loader\",\n \"text\": \"conans/client/manager.py::ConanManager.get_loader\\n def get_loader(self, profile, local=False):\\n \\\"\\\"\\\" When local=True it means that the state is being recovered from installed files\\n conaninfo.txt, conanbuildinfo.txt, and only local methods as build() are being executed.\\n Thus, it is necessary to restore settings from that info, as configure() is not called,\\n being necessary to remove those settings that doesn't have a value\\n \\\"\\\"\\\"\\n cache_settings = self._client_cache.settings.copy()\\n cache_settings.values = profile.settings_values\\n if local:\\n cache_settings.remove_undefined()\\n else:\\n self._settings_preprocessor.preprocess(cache_settings)\\n return ConanFileLoader(self._runner, cache_settings, profile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.export\",\n \"text\": \"conans/client/manager.py::ConanManager.export\\n def export(self, conanfile_path, name, version, user, channel, keep_source=False):\\n cmd_export(conanfile_path, name, version, user, channel, keep_source,\\n self._user_io.out, self._client_cache)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.export_pkg\",\n \"text\": \"conans/client/manager.py::ConanManager.export_pkg\\n def export_pkg(self, reference, source_folder, build_folder, package_folder, install_folder, profile, force):\\n\\n conan_file_path = self._client_cache.conanfile(reference)\\n if not os.path.exists(conan_file_path):\\n raise ConanException(\\\"Package recipe '%s' does not exist\\\" % str(reference))\\n\\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager,\\n remote_name=None, update=False, check_updates=False,\\n manifest_manager=None)\\n\\n loader = self.get_loader(profile)\\n conanfile = loader.load_virtual([reference], scope_options=True)\\n if install_folder and existing_info_files(install_folder):\\n _load_deps_info(install_folder, conanfile, required=True)\\n\\n graph_builder = self._get_graph_builder(loader, False, remote_proxy)\\n deps_graph = graph_builder.load(conanfile)\\n\\n # this is a bit tricky, but works. The loading of a cache package makes the referenced\\n # one, the first of the first level, always existing\\n nodes = deps_graph.direct_requires()\\n _, conanfile = nodes[0]\\n pkg_id = conanfile.info.package_id()\\n self._user_io.out.info(\\\"Packaging to %s\\\" % pkg_id)\\n pkg_reference = PackageReference(reference, pkg_id)\\n dest_package_folder = self._client_cache.package(pkg_reference,\\n short_paths=conanfile.short_paths)\\n\\n if os.path.exists(dest_package_folder):\\n if force:\\n rmdir(dest_package_folder)\\n else:\\n raise ConanException(\\\"Package already exists. Please use --force, -f to \\\"\\n \\\"overwrite it\\\")\\n\\n recipe_hash = self._client_cache.load_manifest(reference).summary_hash\\n conanfile.info.recipe_hash = recipe_hash\\n conanfile.develop = True\\n package_\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.download\",\n \"text\": \"conans/client/manager.py::ConanManager.download\\n def download(self, reference, package_ids, remote):\\n \\\"\\\"\\\" Download conanfile and specified packages to local repository\\n @param reference: ConanFileReference\\n @param package_ids: Package ids or empty for download all\\n @param remote: install only from that remote\\n \\\"\\\"\\\"\\n assert(isinstance(reference, ConanFileReference))\\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote)\\n remote, _ = remote_proxy._get_remote()\\n package = self._remote_manager.search_recipes(remote, reference, None)\\n if not package: # Search the reference first, and raise if it doesn't exist\\n raise ConanException(\\\"'%s' not found in remote\\\" % str(reference))\\n\\n # First of all download package recipe\\n remote_proxy.get_recipe(reference)\\n\\n if package_ids:\\n remote_proxy.download_packages(reference, package_ids)\\n else:\\n self._user_io.out.info(\\\"Getting the complete package list \\\"\\n \\\"from '%s'...\\\" % str(reference))\\n packages_props = self._remote_manager.search_packages(remote, reference, None)\\n if not packages_props:\\n output = ScopedOutput(str(reference), self._user_io.out)\\n output.warn(\\\"No remote binary packages found in remote\\\")\\n else:\\n remote_proxy.download_packages(reference, list(packages_props.keys()))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager._inject_require\",\n \"text\": \"conans/client/manager.py::ConanManager._inject_require\\n @staticmethod\\n def _inject_require(conanfile, inject_require):\\n \\\"\\\"\\\" test_package functionality requires injecting the tested package as requirement\\n before running the install\\n \\\"\\\"\\\"\\n require = conanfile.requires.get(inject_require.name)\\n if require:\\n require.conan_reference = require.range_reference = inject_require\\n else:\\n conanfile.requires(str(inject_require))\\n conanfile._user = inject_require.user\\n conanfile._channel = inject_require.channel\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager._get_graph_builder\",\n \"text\": \"conans/client/manager.py::ConanManager._get_graph_builder\\n def _get_graph_builder(self, loader, update, remote_proxy):\\n local_search = self._search_manager\\n resolver = RequireResolver(self._user_io.out, local_search, remote_proxy, update=update)\\n graph_builder = DepsGraphBuilder(remote_proxy, self._user_io.out, loader, resolver)\\n return graph_builder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager._get_deps_graph\",\n \"text\": \"conans/client/manager.py::ConanManager._get_deps_graph\\n def _get_deps_graph(self, reference, profile, remote_proxy):\\n loader = self.get_loader(profile)\\n conanfile = self._load_install_conanfile(loader, reference)\\n graph_builder = self._get_graph_builder(loader, False, remote_proxy)\\n deps_graph = graph_builder.load(conanfile)\\n return deps_graph, graph_builder, conanfile\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.info_build_order\",\n \"text\": \"conans/client/manager.py::ConanManager.info_build_order\\n def info_build_order(self, reference, profile, build_order, remote, check_updates):\\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote,\\n update=False, check_updates=check_updates)\\n deps_graph, _, _ = self._get_deps_graph(reference, profile, remote_proxy)\\n result = deps_graph.build_order(build_order)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.info_nodes_to_build\",\n \"text\": \"conans/client/manager.py::ConanManager.info_nodes_to_build\\n def info_nodes_to_build(self, reference, profile, build_modes, remote, check_updates):\\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote,\\n update=False, check_updates=check_updates)\\n deps_graph, _, conanfile = self._get_deps_graph(reference, profile, remote_proxy)\\n build_mode = BuildMode(build_modes, self._user_io.out)\\n installer = ConanInstaller(self._client_cache, self._user_io.out, remote_proxy, build_mode,\\n None)\\n nodes = installer.nodes_to_build(deps_graph)\\n counter = Counter(ref.conan.name for ref, _ in nodes)\\n ret = [ref if counter[ref.conan.name] > 1 else str(ref.conan) for ref, _ in nodes]\\n return ret, self._get_project_reference(reference, conanfile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager._get_project_reference\",\n \"text\": \"conans/client/manager.py::ConanManager._get_project_reference\\n def _get_project_reference(self, reference, conanfile):\\n if isinstance(reference, ConanFileReference):\\n project_reference = None\\n else:\\n project_reference = str(conanfile)\\n\\n return project_reference\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.info_get_graph\",\n \"text\": \"conans/client/manager.py::ConanManager.info_get_graph\\n def info_get_graph(self, reference, profile, remote=None, check_updates=False):\\n \\\"\\\"\\\" Fetch and build all dependencies for the given reference\\n @param reference: ConanFileReference or path to user space conanfile\\n @param current_path: where the output files will be saved\\n @param remote: install only from that remote\\n @param profile: Profile object with both the -s introduced options and profile readed values\\n @param build_modes: List of build_modes specified\\n \\\"\\\"\\\"\\n\\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote,\\n update=False, check_updates=check_updates)\\n\\n deps_graph, graph_builder, conanfile = self._get_deps_graph(reference, profile, remote_proxy)\\n\\n if check_updates:\\n graph_updates_info = graph_builder.get_graph_updates_info(deps_graph)\\n else:\\n graph_updates_info = {}\\n\\n return deps_graph, graph_updates_info, self._get_project_reference(reference, conanfile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.install\",\n \"text\": \"conans/client/manager.py::ConanManager.install\\n def install(self, reference, install_folder, profile, remote=None, build_modes=None,\\n update=False, manifest_folder=None, manifest_verify=False,\\n manifest_interactive=False, generators=None, no_imports=False, inject_require=None,\\n install_reference=False, keep_build=False):\\n \\\"\\\"\\\" Fetch and build all dependencies for the given reference\\n @param reference: ConanFileReference or path to user space conanfile\\n @param install_folder: where the output files will be saved\\n @param remote: install only from that remote\\n @param profile: Profile object with both the -s introduced options and profile read values\\n @param build_modes: List of build_modes specified\\n @param update: Check for updated in the upstream remotes (and update)\\n @param manifest_folder: Folder to install the manifests\\n @param manifest_verify: Verify dependencies manifests against stored ones\\n @param manifest_interactive: Install deps manifests in folder for later verify, asking user\\n for confirmation\\n @param generators: List of generators from command line. If False, no generator will be\\n written\\n @param no_imports: Install specified packages but avoid running imports\\n @param inject_require: Reference to add as a requirement to the conanfile\\n \\\"\\\"\\\"\\n if generators is not False:\\n generators = set(generators) if generators else set()\\n generators.add(\\\"txt\\\") # Add txt generator by default\\n\\n manifest_manager = ManifestManager(manifest_folder, user_io=self._user_io,\\n client_cache=self._client_cache,\\n verify=manifest_verify,\\n interactive=manifest_interactive) if manifest_folder else None\\n remote_proxy = ConanProxy(self._client_cache, self._user_io, \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.source\",\n \"text\": \"conans/client/manager.py::ConanManager.source\\n def source(self, conanfile_path, source_folder, info_folder):\\n \\\"\\\"\\\"\\n :param conanfile_path: Absolute path to a conanfile\\n :param source_folder: Absolute path where to put the files\\n :param info_folder: Absolute path where to read the info files\\n :param package_folder: Absolute path to the package_folder, only to have the var present\\n :return:\\n \\\"\\\"\\\"\\n output = ScopedOutput(\\\"PROJECT\\\", self._user_io.out)\\n # only infos if exist\\n conanfile = self._load_consumer_conanfile(conanfile_path, info_folder, output)\\n config_source_local(source_folder, conanfile, output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.imports_undo\",\n \"text\": \"conans/client/manager.py::ConanManager.imports_undo\\n def imports_undo(self, current_path):\\n undo_imports(current_path, self._user_io.out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.imports\",\n \"text\": \"conans/client/manager.py::ConanManager.imports\\n def imports(self, conan_file_path, dest_folder, info_folder):\\n \\\"\\\"\\\"\\n :param conan_file_path: Abs path to a conanfile\\n :param dest_folder: Folder where to put the files\\n :param info_folder: Folder containing the conaninfo/conanbuildinfo.txt files\\n :return:\\n \\\"\\\"\\\"\\n\\n output = ScopedOutput(\\\"PROJECT\\\", self._user_io.out)\\n conanfile = self._load_consumer_conanfile(conan_file_path, info_folder,\\n output, deps_info_required=True)\\n\\n run_imports(conanfile, dest_folder, output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.local_package\",\n \"text\": \"conans/client/manager.py::ConanManager.local_package\\n def local_package(self, package_folder, conanfile_path, build_folder, source_folder,\\n install_folder):\\n if package_folder == build_folder:\\n raise ConanException(\\\"Cannot 'conan package' to the build folder. \\\"\\n \\\"--build-folder and package folder can't be the same\\\")\\n output = ScopedOutput(\\\"PROJECT\\\", self._user_io.out)\\n conanfile = self._load_consumer_conanfile(conanfile_path, install_folder, output,\\n deps_info_required=True)\\n packager.create_package(conanfile, source_folder, build_folder, package_folder,\\n install_folder, output, local=True, copy_info=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.build\",\n \"text\": \"conans/client/manager.py::ConanManager.build\\n def build(self, conanfile_path, source_folder, build_folder, package_folder, install_folder,\\n test=False):\\n \\\"\\\"\\\" Call to build() method saved on the conanfile.py\\n param conanfile_path: path to a conanfile.py\\n \\\"\\\"\\\"\\n logger.debug(\\\"Building in %s\\\" % build_folder)\\n logger.debug(\\\"Conanfile in %s\\\" % conanfile_path)\\n\\n try:\\n # Append env_vars to execution environment and clear when block code ends\\n output = ScopedOutput((\\\"%s (test package)\\\" % test) if test else \\\"Project\\\",\\n self._user_io.out)\\n conan_file = self._load_consumer_conanfile(conanfile_path, install_folder, output,\\n deps_info_required=True)\\n except NotFoundException:\\n # TODO: Auto generate conanfile from requirements file\\n raise ConanException(\\\"'%s' file is needed for build.\\\\n\\\"\\n \\\"Create a '%s' and move manually the \\\"\\n \\\"requirements and generators from '%s' file\\\"\\n % (CONANFILE, CONANFILE, CONANFILE_TXT))\\n\\n if test:\\n try:\\n conan_file.requires.add(test)\\n except ConanException:\\n pass\\n\\n try:\\n mkdir(build_folder)\\n os.chdir(build_folder)\\n conan_file.build_folder = build_folder\\n conan_file.source_folder = source_folder\\n conan_file.package_folder = package_folder\\n conan_file.install_folder = install_folder\\n with get_env_context_manager(conan_file):\\n output.highlight(\\\"Running build()\\\")\\n with conanfile_exception_formatter(str(conan_file), \\\"build\\\"):\\n conan_file.build()\\n if test:\\n output.highlight(\\\"Running test()\\\")\\n with conanfile_exception_format\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.remove\",\n \"text\": \"conans/client/manager.py::ConanManager.remove\\n def remove(self, pattern, src=False, build_ids=None, package_ids_filter=None, force=False,\\n remote=None, packages_query=None, outdated=False):\\n \\\"\\\"\\\" Remove conans and/or packages\\n @param pattern: string to match packages\\n @param src: Remove src folder\\n @param package_ids_filter: list of ids or [] for all list\\n @param build_ids: list of ids or [] for all list\\n @param remote: search on another origin to get packages info\\n @param force: if True, it will be deleted without requesting anything\\n @param packages_query: Only if src is a reference. Query settings and options\\n \\\"\\\"\\\"\\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote)\\n remover = ConanRemover(self._client_cache, self._remote_manager, self._user_io, remote_proxy)\\n remover.remove(pattern, remote, src, build_ids, package_ids_filter, force=force,\\n packages_query=packages_query, outdated=outdated)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.user\",\n \"text\": \"conans/client/manager.py::ConanManager.user\\n def user(self, remote=None, name=None, password=None):\\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote)\\n if password == \\\"\\\":\\n if not remote:\\n remote = remote_proxy.registry.default_remote.name\\n name, password = self._user_io.request_login(remote_name=remote, username=name)\\n return remote_proxy.authenticate(name, password)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.get_path\",\n \"text\": \"conans/client/manager.py::ConanManager.get_path\\n def get_path(self, reference, package_id=None, path=None, remote=None):\\n remote_proxy = ConanProxy(self._client_cache, self._user_io, self._remote_manager, remote)\\n if not path and not package_id:\\n path = \\\"conanfile.py\\\"\\n elif not path and package_id:\\n path = \\\"conaninfo.txt\\\"\\n return remote_proxy.get_path(reference, package_id, path), path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.export_alias\",\n \"text\": \"conans/client/manager.py::ConanManager.export_alias\\n def export_alias(self, reference, target_reference):\\n\\n conanfile = \\\"\\\"\\\"\\nfrom conans import ConanFile\\n\\nclass AliasConanfile(ConanFile):\\n alias = \\\"%s\\\"\\n\\\"\\\"\\\" % str(target_reference)\\n\\n export_path = self._client_cache.export(reference)\\n mkdir(export_path)\\n save(os.path.join(export_path, CONANFILE), conanfile)\\n mkdir(self._client_cache.export_sources(reference))\\n digest = FileTreeManifest.create(export_path)\\n save(os.path.join(export_path, CONAN_MANIFEST), str(digest))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::_load_deps_info\",\n \"text\": \"conans/client/manager.py::_load_deps_info\\ndef _load_deps_info(current_path, conanfile, required):\\n\\n def get_forbidden_access_object(field_name):\\n class InfoObjectNotDefined(object):\\n def __getitem__(self, item):\\n raise ConanException(\\\"self.%s not defined. If you need it for a \\\"\\n \\\"local command run 'conan install'\\\" % field_name)\\n __getattr__ = __getitem__\\n\\n return InfoObjectNotDefined()\\n\\n if not current_path:\\n return\\n info_file_path = os.path.join(current_path, BUILD_INFO)\\n try:\\n deps_cpp_info, deps_user_info, deps_env_info = TXTGenerator.loads(load(info_file_path))\\n conanfile.deps_cpp_info = deps_cpp_info\\n conanfile.deps_user_info = deps_user_info\\n conanfile.deps_env_info = deps_env_info\\n except IOError:\\n if required:\\n raise ConanException(\\\"%s file not found in %s\\\\nIt is required for this command\\\\n\\\"\\n \\\"You can generate it using 'conan install'\\\"\\n % (BUILD_INFO, current_path))\\n conanfile.deps_cpp_info = get_forbidden_access_object(\\\"deps_cpp_info\\\")\\n conanfile.deps_user_info = get_forbidden_access_object(\\\"deps_user_info\\\")\\n except ConanException:\\n raise ConanException(\\\"Parse error in '%s' file in %s\\\" % (BUILD_INFO, current_path))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::_load_deps_info.get_forbidden_access_object\",\n \"text\": \"conans/client/manager.py::_load_deps_info.get_forbidden_access_object\\n def get_forbidden_access_object(field_name):\\n class InfoObjectNotDefined(object):\\n def __getitem__(self, item):\\n raise ConanException(\\\"self.%s not defined. If you need it for a \\\"\\n \\\"local command run 'conan install'\\\" % field_name)\\n __getattr__ = __getitem__\\n\\n return InfoObjectNotDefined()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::_load_deps_info.get_forbidden_access_object.InfoObjectNotDefined.__getitem__\",\n \"text\": \"conans/client/manager.py::_load_deps_info.get_forbidden_access_object.InfoObjectNotDefined.__getitem__\\n def __getitem__(self, item):\\n raise ConanException(\\\"self.%s not defined. If you need it for a \\\"\\n \\\"local command run 'conan install'\\\" % field_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::existing_info_files\",\n \"text\": \"conans/client/manager.py::existing_info_files\\ndef existing_info_files(folder):\\n return os.path.exists(os.path.join(folder, CONANINFO)) and \\\\\\n os.path.exists(os.path.join(folder, BUILD_INFO))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_options\",\n \"text\": \"conans/model/conan_file.py::create_options\\ndef create_options(conanfile):\\n try:\\n package_options = PackageOptions(getattr(conanfile, \\\"options\\\", None))\\n options = Options(package_options)\\n\\n default_options = getattr(conanfile, \\\"default_options\\\", None)\\n if default_options:\\n if isinstance(default_options, (list, tuple)):\\n default_values = OptionsValues(default_options)\\n elif isinstance(default_options, str):\\n default_values = OptionsValues.loads(default_options)\\n else:\\n raise ConanException(\\\"Please define your default_options as list or \\\"\\n \\\"multiline string\\\")\\n options.values = default_values\\n return options\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing options. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_requirements\",\n \"text\": \"conans/model/conan_file.py::create_requirements\\ndef create_requirements(conanfile):\\n try:\\n # Actual requirements of this package\\n if not hasattr(conanfile, \\\"requires\\\"):\\n return Requirements()\\n else:\\n if not conanfile.requires:\\n return Requirements()\\n if isinstance(conanfile.requires, tuple):\\n return Requirements(*conanfile.requires)\\n else:\\n return Requirements(conanfile.requires, )\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing requirements. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_settings\",\n \"text\": \"conans/model/conan_file.py::create_settings\\ndef create_settings(conanfile, settings, local):\\n try:\\n defined_settings = getattr(conanfile, \\\"settings\\\", None)\\n if isinstance(defined_settings, str):\\n defined_settings = [defined_settings]\\n current = defined_settings or {}\\n settings.constraint(current, raise_undefined_field=not local)\\n return settings\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing settings. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_exports\",\n \"text\": \"conans/model/conan_file.py::create_exports\\ndef create_exports(conanfile):\\n if not hasattr(conanfile, \\\"exports\\\"):\\n return None\\n else:\\n if isinstance(conanfile.exports, str):\\n return (conanfile.exports, )\\n return conanfile.exports\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_exports_sources\",\n \"text\": \"conans/model/conan_file.py::create_exports_sources\\ndef create_exports_sources(conanfile):\\n if not hasattr(conanfile, \\\"exports_sources\\\"):\\n return None\\n else:\\n if isinstance(conanfile.exports_sources, str):\\n return (conanfile.exports_sources, )\\n return conanfile.exports_sources\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::_env_and_python\",\n \"text\": \"conans/model/conan_file.py::_env_and_python\\n@contextmanager\\ndef _env_and_python(conanfile):\\n with environment_append(conanfile.env):\\n with pythonpath(conanfile):\\n yield\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::get_env_context_manager\",\n \"text\": \"conans/model/conan_file.py::get_env_context_manager\\ndef get_env_context_manager(conanfile, without_python=False):\\n if not conanfile.apply_env:\\n return no_op()\\n if without_python:\\n return environment_append(conanfile.env)\\n return _env_and_python(conanfile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.__init__\",\n \"text\": \"conans/model/conan_file.py::ConanFile.__init__\\n def __init__(self, output, runner, settings, user=None, channel=None, local=None):\\n # User defined generators\\n self.generators = self.generators if hasattr(self, \\\"generators\\\") else [\\\"txt\\\"]\\n if isinstance(self.generators, str):\\n self.generators = [self.generators]\\n\\n # User defined options\\n self.options = create_options(self)\\n self.requires = create_requirements(self)\\n self.settings = create_settings(self, settings, local)\\n try:\\n if self.settings.os_build and self.settings.os:\\n output.writeln(\\\"*\\\"*60, front=Color.BRIGHT_RED)\\n output.writeln(\\\" This package defines both 'os' and 'os_build' \\\",\\n front=Color.BRIGHT_RED)\\n output.writeln(\\\" Please use 'os' for libraries and 'os_build'\\\",\\n front=Color.BRIGHT_RED)\\n output.writeln(\\\" only for build-requires used for cross-building\\\",\\n front=Color.BRIGHT_RED)\\n output.writeln(\\\"*\\\"*60, front=Color.BRIGHT_RED)\\n except ConanException:\\n pass\\n self.exports = create_exports(self)\\n self.exports_sources = create_exports_sources(self)\\n # needed variables to pack the project\\n self.cpp_info = None # Will be initialized at processing time\\n self.deps_cpp_info = DepsCppInfo()\\n\\n # environment variables declared in the package_info\\n self.env_info = None # Will be initialized at processing time\\n self.deps_env_info = DepsEnvInfo()\\n\\n # user declared variables\\n self.user_info = None\\n # Keys are the package names, and the values a dict with the vars\\n self.deps_user_info = DepsUserInfo()\\n\\n self.copy = None # initialized at runtime\\n self.copy_deps = None # initialized at runtime\\n\\n # an output stream (writeln, info, warn error)\\n self.ou\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.env\",\n \"text\": \"conans/model/conan_file.py::ConanFile.env\\n @property\\n def env(self):\\n \\\"\\\"\\\"Apply the self.deps_env_info into a copy of self._env_values (will prioritize the\\n self._env_values, user specified from profiles or -e first, then inherited)\\\"\\\"\\\"\\n # Cannot be lazy cached, because it's called in configure node, and we still don't have\\n # the deps_env_info objects available\\n tmp_env_values = self._env_values.copy()\\n tmp_env_values.update(self.deps_env_info)\\n\\n ret, multiple = tmp_env_values.env_dicts(self.name)\\n ret.update(multiple)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.channel\",\n \"text\": \"conans/model/conan_file.py::ConanFile.channel\\n @property\\n def channel(self):\\n if not self._channel:\\n self._channel = os.getenv(\\\"CONAN_CHANNEL\\\")\\n if not self._channel:\\n raise ConanException(\\\"CONAN_CHANNEL environment variable not defined, \\\"\\n \\\"but self.channel is used in conanfile\\\")\\n return self._channel\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.user\",\n \"text\": \"conans/model/conan_file.py::ConanFile.user\\n @property\\n def user(self):\\n if not self._user:\\n self._user = os.getenv(\\\"CONAN_USERNAME\\\")\\n if not self._user:\\n raise ConanException(\\\"CONAN_USERNAME environment variable not defined, \\\"\\n \\\"but self.user is used in conanfile\\\")\\n return self._user\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.collect_libs\",\n \"text\": \"conans/model/conan_file.py::ConanFile.collect_libs\\n def collect_libs(self, folder=\\\"lib\\\"):\\n self.output.warn(\\\"Use 'self.collect_libs' is deprecated, \\\"\\n \\\"use tools.collect_libs(self) instead\\\")\\n return tools.collect_libs(self, folder=folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_policy_missing\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_policy_missing\\n @property\\n def build_policy_missing(self):\\n return self.build_policy == \\\"missing\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_policy_always\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_policy_always\\n @property\\n def build_policy_always(self):\\n return self.build_policy == \\\"always\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.source\",\n \"text\": \"conans/model/conan_file.py::ConanFile.source\\n def source(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.system_requirements\",\n \"text\": \"conans/model/conan_file.py::ConanFile.system_requirements\\n def system_requirements(self):\\n \\\"\\\"\\\" this method can be overwritten to implement logic for system package\\n managers, as apt-get\\n\\n You can define self.global_system_requirements = True, if you want the installation\\n to be for all packages (not depending on settings/options/requirements)\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.config_options\",\n \"text\": \"conans/model/conan_file.py::ConanFile.config_options\\n def config_options(self):\\n \\\"\\\"\\\" modify options, probably conditioned to some settings. This call is executed\\n before config_settings. E.g.\\n if self.settings.os == \\\"Windows\\\":\\n del self.options.shared # shared/static not supported in win\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.configure\",\n \"text\": \"conans/model/conan_file.py::ConanFile.configure\\n def configure(self):\\n \\\"\\\"\\\" modify settings, probably conditioned to some options. This call is executed\\n after config_options. E.g.\\n if self.options.header_only:\\n self.settings.clear()\\n This is also the place for conditional requirements\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build\\n def build(self):\\n self.output.warn(\\\"This conanfile has no build step\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package\\n def package(self):\\n self.output.warn(\\\"This conanfile has no package step\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_info\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_info\\n def package_info(self):\\n \\\"\\\"\\\" define cpp_build_info, flags, etc\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.run\",\n \"text\": \"conans/model/conan_file.py::ConanFile.run\\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True):\\n if not win_bash:\\n retcode = self._runner(command, output, os.path.abspath(RUN_LOG_NAME), cwd)\\n else:\\n retcode = tools.run_in_windows_bash(self, bashcmd=command, cwd=cwd, subsystem=subsystem,\\n msys_mingw=msys_mingw)\\n\\n if retcode != 0:\\n raise ConanException(\\\"Error %d while executing %s\\\" % (retcode, command))\\n\\n return retcode\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_id\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_id\\n def package_id(self):\\n \\\"\\\"\\\" modify the conans info, typically to narrow values\\n eg.: conaninfo.package_references = []\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.test\",\n \"text\": \"conans/model/conan_file.py::ConanFile.test\\n def test(self):\\n raise ConanException(\\\"You need to create a method 'test' in your test/conanfile.py\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.__repr__\",\n \"text\": \"conans/model/conan_file.py::ConanFile.__repr__\\n def __repr__(self):\\n if self.name and self.version and self._channel and self._user:\\n return \\\"%s/%s@%s/%s\\\" % (self.name, self.version, self.user, self.channel)\\n elif self.name and self.version:\\n return \\\"%s/%s@PROJECT\\\" % (self.name, self.version)\\n else:\\n return \\\"PROJECT\\\"\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":49,"cells":{"instance_id":{"kind":"string","value":"pantsbuild__pants-14169"},"repo":{"kind":"string","value":"pantsbuild/pants"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Add line number to unowned_dependency_behavior warn/error\n**Is your feature request related to a problem? Please describe.**\r\nThe output now is:\r\n\r\n```\r\n08:27:12.67 [ERROR] The following imports in path/to/file.py:address have no owners:\r\n\r\n * mod.name\r\n\r\n```\r\n\r\n**Describe the solution you'd like**\r\n```\r\n08:27:12.67 [ERROR] The following imports in path/to/file.py:address have no owners:\r\n\r\n * mod.name (line 255)\r\n\r\n```\r\n\r\nor if we want editors to allow auto-click:\r\n\r\n```\r\n08:27:12.67 [ERROR] The following imports in path/to/file.py:address have no owners:\r\n\r\n * mod.name (path/to/file.py:255)\r\n\r\n```\r\n\r\n**Describe alternatives you've considered**\r\nN/A\r\n\r\n**Additional context**\r\nN/A\n"},"gold_files":{"kind":"list like","value":["src/python/pants/backend/python/dependency_inference/parse_python_imports.py","src/python/pants/backend/python/dependency_inference/rules.py","src/python/pants/backend/python/dependency_inference/scripts/import_parser.py"],"string":"[\n \"src/python/pants/backend/python/dependency_inference/parse_python_imports.py\",\n \"src/python/pants/backend/python/dependency_inference/rules.py\",\n \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["src/python/pants/backend/python/dependency_inference/parse_python_imports.py::<module>","src/python/pants/backend/python/dependency_inference/parse_python_imports.py::parse_python_imports","src/python/pants/backend/python/dependency_inference/rules.py::infer_python_dependencies_via_imports","src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::<module>","src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.__init__","src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt","src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt.consume_until","src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.maybe_add_string_import","src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_Call","src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::main"],"string":"[\n \"src/python/pants/backend/python/dependency_inference/parse_python_imports.py::<module>\",\n \"src/python/pants/backend/python/dependency_inference/parse_python_imports.py::parse_python_imports\",\n \"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_dependencies_via_imports\",\n \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::<module>\",\n \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.__init__\",\n \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt\",\n \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt.consume_until\",\n \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.maybe_add_string_import\",\n \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_Call\",\n \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::main\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"src/python/pants/backend/python/dependency_inference/parse_python_imports.py","text":"src/python/pants/backend/python/dependency_inference/parse_python_imports.py\nclass ParsedPythonImports # All the discovered imports from a Python source file.\nclass ParsePythonImportsRequest\ndef parse_python_imports(request: ParsePythonImportsRequest)\ndef rules()","label":1},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py","text":"src/python/pants/backend/python/dependency_inference/rules.py\nclass UnownedDependencyError # The inferred dependency does not have any owner.\nclass UnownedDependencyUsage # What action to take when an inferred dependency is unowned.\nclass PythonInferSubsystem\n def register_options(cls, register)\n def imports(self)\n def string_imports(self)\n def string_imports_min_dots(self)\n def inits(self)\n def conftests(self)\n def entry_points(self)\n def unowned_dependency_behavior(self)\nclass InferPythonImportDependencies\ndef infer_python_dependencies_via_imports(request: InferPythonImportDependencies, python_infer_subsystem: PythonInferSubsystem, python_setup: PythonSetup)\nclass InferInitDependencies\ndef infer_python_init_dependencies(request: InferInitDependencies, python_infer_subsystem: PythonInferSubsystem)\nclass InferConftestDependencies\ndef infer_python_conftest_dependencies(request: InferConftestDependencies, python_infer_subsystem: PythonInferSubsystem)\ndef import_rules()\ndef rules()","label":1},{"cid":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py","text":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py\nclass AstVisitor\n def __init__(self, package_parts, contents)\n def maybe_add_string_import(self, s)\n def _is_pragma_ignored(line)\n def _visit_import_stmt(self, node, import_prefix)\n def consume_until(string)\n def visit_Import(self, node)\n def visit_ImportFrom(self, node)\n def visit_Call(self, node)\ndef main(filename)","label":1},{"cid":"src/python/pants/backend/python/target_types_rules.py","text":"src/python/pants/backend/python/target_types_rules.py\nclass GenerateTargetsFromPythonTests\ndef generate_targets_from_python_tests(request: GenerateTargetsFromPythonTests, files_not_found_behavior: FilesNotFoundBehavior, python_infer: PythonInferSubsystem, union_membership: UnionMembership)\nclass GenerateTargetsFromPythonSources\ndef generate_targets_from_python_sources(request: GenerateTargetsFromPythonSources, python_infer: PythonInferSubsystem, files_not_found_behavior: FilesNotFoundBehavior, union_membership: UnionMembership)\nclass GenerateTargetsFromPythonTestUtils\ndef generate_targets_from_python_test_utils(request: GenerateTargetsFromPythonTestUtils, python_infer: PythonInferSubsystem, files_not_found_behavior: FilesNotFoundBehavior, union_membership: UnionMembership)\nclass GenerateTargetsFromPexBinaries\ndef generate_targets_from_pex_binaries(request: GenerateTargetsFromPexBinaries, union_membership: UnionMembership)\n def create_pex_binary(entry_point_spec: str)\ndef resolve_pex_entry_point(request: ResolvePexEntryPointRequest)\nclass InjectPexBinaryEntryPointDependency\ndef inject_pex_binary_entry_point_dependency(request: InjectPexBinaryEntryPointDependency, python_infer_subsystem: PythonInferSubsystem)\ndef _classify_entry_points(all_entry_points: _EntryPointsDictType) # Looks at each entry point to see if it is a target address or not.\ndef resolve_python_distribution_entry_points(request: ResolvePythonDistributionEntryPointsRequest)\nclass InjectPythonDistributionDependencies\ndef inject_python_distribution_dependencies(request: InjectPythonDistributionDependencies, python_infer_subsystem: PythonInferSubsystem) # Inject dependencies that we can infer from entry points in the distribution.\ndef rules()","label":0},{"cid":"src/python/pants/backend/shell/dependency_inference.py","text":"src/python/pants/backend/shell/dependency_inference.py\nclass AllShellTargets\ndef find_all_shell_targets(all_tgts: AllTargets)\nclass ShellMapping # A mapping of Shell file names to their owning file address.\ndef map_shell_files(tgts: AllShellTargets)\nclass ParsedShellImports\nclass ParseShellImportsRequest\ndef parse_shell_imports(request: ParseShellImportsRequest, shellcheck: Shellcheck)\nclass InferShellDependencies\ndef infer_shell_dependencies(request: InferShellDependencies, shell_mapping: ShellMapping, shell_setup: ShellSetup)\ndef rules()","label":0},{"cid":"src/python/pants/option/global_options.py","text":"src/python/pants/option/global_options.py\nclass FilesNotFoundBehavior # What to do when globs do not match in BUILD files.\n def to_glob_match_error_behavior(self)\nclass OwnersNotFoundBehavior # What to do when a file argument cannot be mapped to an owning target.\n def to_glob_match_error_behavior(self)\nclass RemoteCacheWarningsBehavior\nclass AuthPluginState\nclass AuthPluginResult # The return type for a function specified via `--remote-auth-plugin`.\n def __post_init__(self)\n def assert_valid_address(addr: str | None, field_name: str)\n def is_available(self)\nclass DynamicRemoteOptions # Options related to remote execution of processes which are computed dynamically.\n def disabled(cls)\n def from_options(cls, full_options: Options, env: CompleteEnvironment, prior_result: AuthPluginResult | None=None)\nclass ExecutionOptions # A collection of all options related to (remote) execution of processes.\n def from_options(cls, bootstrap_options: OptionValueContainer, dynamic_remote_options: DynamicRemoteOptions)\nclass LocalStoreOptions # A collection of all options related to the local store.\n def target_total_size_bytes(self) # Returns the target total size of all of the stores.\n def from_options(cls, options: OptionValueContainer)\nclass GlobalOptions\n def register_bootstrap_options(cls, register) # Register bootstrap options.\n def register_options(cls, register) # Register options not tied to any particular task or subsystem.\n def validate_instance(cls, opts) # Validates an instance of global options for cases that are not prohibited via\n def validate_remote_address(opt_name: str)\n def validate_remote_headers(opt_name: str)\n def create_py_executor(bootstrap_options: OptionValueContainer)\n def compute_pants_ignore(buildroot, global_options) # Computes the merged value of the `--pants-ignore` flag.\n def add(absolute_path, include=False)\n def compute_pantsd_invalidation_globs(buildroot: str, bootstrap_options: OptionValueContainer) # Computes the merged value of the `--pantsd-invalidation-globs` option.\n def get_options_flags(cls)\nclass GlobalOptionsFlags\n def create(cls, GlobalOptionsType: type[GlobalOptions])\n def capture_the_flags(*args: str, **kwargs)\nclass ProcessCleanupOption # A wrapper around the global option `process_cleanup`.","label":0}],"string":"[\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/parse_python_imports.py\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/parse_python_imports.py\\nclass ParsedPythonImports # All the discovered imports from a Python source file.\\nclass ParsePythonImportsRequest\\ndef parse_python_imports(request: ParsePythonImportsRequest)\\ndef rules()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py\\nclass UnownedDependencyError # The inferred dependency does not have any owner.\\nclass UnownedDependencyUsage # What action to take when an inferred dependency is unowned.\\nclass PythonInferSubsystem\\n def register_options(cls, register)\\n def imports(self)\\n def string_imports(self)\\n def string_imports_min_dots(self)\\n def inits(self)\\n def conftests(self)\\n def entry_points(self)\\n def unowned_dependency_behavior(self)\\nclass InferPythonImportDependencies\\ndef infer_python_dependencies_via_imports(request: InferPythonImportDependencies, python_infer_subsystem: PythonInferSubsystem, python_setup: PythonSetup)\\nclass InferInitDependencies\\ndef infer_python_init_dependencies(request: InferInitDependencies, python_infer_subsystem: PythonInferSubsystem)\\nclass InferConftestDependencies\\ndef infer_python_conftest_dependencies(request: InferConftestDependencies, python_infer_subsystem: PythonInferSubsystem)\\ndef import_rules()\\ndef rules()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py\\nclass AstVisitor\\n def __init__(self, package_parts, contents)\\n def maybe_add_string_import(self, s)\\n def _is_pragma_ignored(line)\\n def _visit_import_stmt(self, node, import_prefix)\\n def consume_until(string)\\n def visit_Import(self, node)\\n def visit_ImportFrom(self, node)\\n def visit_Call(self, node)\\ndef main(filename)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py\\nclass GenerateTargetsFromPythonTests\\ndef generate_targets_from_python_tests(request: GenerateTargetsFromPythonTests, files_not_found_behavior: FilesNotFoundBehavior, python_infer: PythonInferSubsystem, union_membership: UnionMembership)\\nclass GenerateTargetsFromPythonSources\\ndef generate_targets_from_python_sources(request: GenerateTargetsFromPythonSources, python_infer: PythonInferSubsystem, files_not_found_behavior: FilesNotFoundBehavior, union_membership: UnionMembership)\\nclass GenerateTargetsFromPythonTestUtils\\ndef generate_targets_from_python_test_utils(request: GenerateTargetsFromPythonTestUtils, python_infer: PythonInferSubsystem, files_not_found_behavior: FilesNotFoundBehavior, union_membership: UnionMembership)\\nclass GenerateTargetsFromPexBinaries\\ndef generate_targets_from_pex_binaries(request: GenerateTargetsFromPexBinaries, union_membership: UnionMembership)\\n def create_pex_binary(entry_point_spec: str)\\ndef resolve_pex_entry_point(request: ResolvePexEntryPointRequest)\\nclass InjectPexBinaryEntryPointDependency\\ndef inject_pex_binary_entry_point_dependency(request: InjectPexBinaryEntryPointDependency, python_infer_subsystem: PythonInferSubsystem)\\ndef _classify_entry_points(all_entry_points: _EntryPointsDictType) # Looks at each entry point to see if it is a target address or not.\\ndef resolve_python_distribution_entry_points(request: ResolvePythonDistributionEntryPointsRequest)\\nclass InjectPythonDistributionDependencies\\ndef inject_python_distribution_dependencies(request: InjectPythonDistributionDependencies, python_infer_subsystem: PythonInferSubsystem) # Inject dependencies that we can infer from entry points in the distribution.\\ndef rules()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/shell/dependency_inference.py\",\n \"text\": \"src/python/pants/backend/shell/dependency_inference.py\\nclass AllShellTargets\\ndef find_all_shell_targets(all_tgts: AllTargets)\\nclass ShellMapping # A mapping of Shell file names to their owning file address.\\ndef map_shell_files(tgts: AllShellTargets)\\nclass ParsedShellImports\\nclass ParseShellImportsRequest\\ndef parse_shell_imports(request: ParseShellImportsRequest, shellcheck: Shellcheck)\\nclass InferShellDependencies\\ndef infer_shell_dependencies(request: InferShellDependencies, shell_mapping: ShellMapping, shell_setup: ShellSetup)\\ndef rules()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py\",\n \"text\": \"src/python/pants/option/global_options.py\\nclass FilesNotFoundBehavior # What to do when globs do not match in BUILD files.\\n def to_glob_match_error_behavior(self)\\nclass OwnersNotFoundBehavior # What to do when a file argument cannot be mapped to an owning target.\\n def to_glob_match_error_behavior(self)\\nclass RemoteCacheWarningsBehavior\\nclass AuthPluginState\\nclass AuthPluginResult # The return type for a function specified via `--remote-auth-plugin`.\\n def __post_init__(self)\\n def assert_valid_address(addr: str | None, field_name: str)\\n def is_available(self)\\nclass DynamicRemoteOptions # Options related to remote execution of processes which are computed dynamically.\\n def disabled(cls)\\n def from_options(cls, full_options: Options, env: CompleteEnvironment, prior_result: AuthPluginResult | None=None)\\nclass ExecutionOptions # A collection of all options related to (remote) execution of processes.\\n def from_options(cls, bootstrap_options: OptionValueContainer, dynamic_remote_options: DynamicRemoteOptions)\\nclass LocalStoreOptions # A collection of all options related to the local store.\\n def target_total_size_bytes(self) # Returns the target total size of all of the stores.\\n def from_options(cls, options: OptionValueContainer)\\nclass GlobalOptions\\n def register_bootstrap_options(cls, register) # Register bootstrap options.\\n def register_options(cls, register) # Register options not tied to any particular task or subsystem.\\n def validate_instance(cls, opts) # Validates an instance of global options for cases that are not prohibited via\\n def validate_remote_address(opt_name: str)\\n def validate_remote_headers(opt_name: str)\\n def create_py_executor(bootstrap_options: OptionValueContainer)\\n def compute_pants_ignore(buildroot, global_options) # Computes the merged value of the `--pants-ignore` flag.\\n def add(absolute_path, include=False)\\n def compute_pantsd_invalidation_globs(buildroot: str, bootstrap_options: OptionValueContainer) # Computes the merged value of the `--pantsd-invalidation-globs` option.\\n def get_options_flags(cls)\\nclass GlobalOptionsFlags\\n def create(cls, GlobalOptionsType: type[GlobalOptions])\\n def capture_the_flags(*args: str, **kwargs)\\nclass ProcessCleanupOption # A wrapper around the global option `process_cleanup`.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"src/python/pants/backend/python/dependency_inference/parse_python_imports.py::parse_python_imports","text":"src/python/pants/backend/python/dependency_inference/parse_python_imports.py::parse_python_imports\n@rule\nasync def parse_python_imports(request: ParsePythonImportsRequest) -> ParsedPythonImports:\n script = pkgutil.get_data(__name__, \"scripts/import_parser.py\")\n assert script is not None\n python_interpreter, script_digest, stripped_sources = await MultiGet(\n Get(PythonExecutable, InterpreterConstraints, request.interpreter_constraints),\n Get(Digest, CreateDigest([FileContent(\"__parse_python_imports.py\", script)])),\n Get(StrippedSourceFiles, SourceFilesRequest([request.source])),\n )\n\n # We operate on PythonSourceField, which should be one file.\n assert len(stripped_sources.snapshot.files) == 1\n file = stripped_sources.snapshot.files[0]\n\n input_digest = await Get(\n Digest, MergeDigests([script_digest, stripped_sources.snapshot.digest])\n )\n process_result = await Get(\n ProcessResult,\n Process(\n argv=[\n python_interpreter.path,\n \"./__parse_python_imports.py\",\n file,\n ],\n input_digest=input_digest,\n description=f\"Determine Python imports for {request.source.address}\",\n env={\n \"STRING_IMPORTS\": \"y\" if request.string_imports else \"n\",\n \"MIN_DOTS\": str(request.string_imports_min_dots),\n },\n level=LogLevel.DEBUG,\n ),\n )\n # See above for where we explicitly encoded as utf8. Even though utf8 is the\n # default for decode(), we make that explicit here for emphasis.\n return ParsedPythonImports(process_result.stdout.decode(\"utf8\").strip().splitlines())","label":1},{"cid":"src/python/pants/backend/python/dependency_inference/parse_python_imports.py::rules","text":"src/python/pants/backend/python/dependency_inference/parse_python_imports.py::rules\ndef rules():\n return collect_rules()","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.register_options","text":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.register_options\n @classmethod\n def register_options(cls, register):\n super().register_options(register)\n register(\n \"--imports\",\n default=True,\n type=bool,\n help=(\n \"Infer a target's imported dependencies by parsing import statements from sources.\"\n ),\n )\n register(\n \"--string-imports\",\n default=False,\n type=bool,\n help=(\n \"Infer a target's dependencies based on strings that look like dynamic \"\n \"dependencies, such as Django settings files expressing dependencies as strings. \"\n \"To ignore any false positives, put `!{bad_address}` in the `dependencies` field \"\n \"of your target.\"\n ),\n )\n register(\n \"--string-imports-min-dots\",\n default=2,\n type=int,\n help=(\n \"If --string-imports is True, treat valid-looking strings with at least this many \"\n \"dots in them as potential dynamic dependencies. E.g., `'foo.bar.Baz'` will be \"\n \"treated as a potential dependency if this option is set to 2 but not if set to 3.\"\n ),\n )\n register(\n \"--inits\",\n default=False,\n type=bool,\n help=(\n \"Infer a target's dependencies on any `__init__.py` files in the packages \"\n \"it is located in (recursively upward in the directory structure).\\n\\nEven if this \"\n \"is disabled, Pants will still include any ancestor `__init__.py` files, only they \"\n \"will not be 'proper' dependencies, e.g. they will not show up in \"\n \"`./pants dependencies` and their own dependencies will not be used.\\n\\nIf you \"\n \"have empty `__init__.py` files, it's safe to leave","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.imports","text":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.imports\n @property\n def imports(self) -> bool:\n return cast(bool, self.options.imports)","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.string_imports","text":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.string_imports\n @property\n def string_imports(self) -> bool:\n return cast(bool, self.options.string_imports)","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.string_imports_min_dots","text":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.string_imports_min_dots\n @property\n def string_imports_min_dots(self) -> int:\n return cast(int, self.options.string_imports_min_dots)","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.inits","text":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.inits\n @property\n def inits(self) -> bool:\n return cast(bool, self.options.inits)","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.conftests","text":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.conftests\n @property\n def conftests(self) -> bool:\n return cast(bool, self.options.conftests)","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.entry_points","text":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.entry_points\n @property\n def entry_points(self) -> bool:\n return cast(bool, self.options.entry_points)","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.unowned_dependency_behavior","text":"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.unowned_dependency_behavior\n @property\n def unowned_dependency_behavior(self) -> UnownedDependencyUsage:\n return cast(UnownedDependencyUsage, self.options.unowned_dependency_behavior)","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_dependencies_via_imports","text":"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_dependencies_via_imports\n@rule(desc=\"Inferring Python dependencies by analyzing imports\")\nasync def infer_python_dependencies_via_imports(\n request: InferPythonImportDependencies,\n python_infer_subsystem: PythonInferSubsystem,\n python_setup: PythonSetup,\n) -> InferredDependencies:\n if not python_infer_subsystem.imports:\n return InferredDependencies([])\n\n wrapped_tgt = await Get(WrappedTarget, Address, request.sources_field.address)\n explicitly_provided_deps, detected_imports = await MultiGet(\n Get(ExplicitlyProvidedDependencies, DependenciesRequest(wrapped_tgt.target[Dependencies])),\n Get(\n ParsedPythonImports,\n ParsePythonImportsRequest(\n cast(PythonSourceField, request.sources_field),\n InterpreterConstraints.create_from_targets([wrapped_tgt.target], python_setup),\n string_imports=python_infer_subsystem.string_imports,\n string_imports_min_dots=python_infer_subsystem.string_imports_min_dots,\n ),\n ),\n )\n\n owners_per_import = await MultiGet(\n Get(PythonModuleOwners, PythonModule(imported_module))\n for imported_module in detected_imports\n )\n\n merged_result: set[Address] = set()\n unowned_imports: set[str] = set()\n address = wrapped_tgt.target.address\n for owners, imp in zip(owners_per_import, detected_imports):\n merged_result.update(owners.unambiguous)\n explicitly_provided_deps.maybe_warn_of_ambiguous_dependency_inference(\n owners.ambiguous,\n address,\n import_reference=\"module\",\n context=f\"The target {address} imports `{imp}`\",\n )\n maybe_disambiguated = explicitly_provided_deps.disambiguated(owners.ambiguous)\n if maybe_disambiguated:\n merged_result.add(maybe_disambiguated)\n\n if not owners.unambiguous and imp.split(\".\")[0] not in ","label":1},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_init_dependencies","text":"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_init_dependencies\n@rule(desc=\"Inferring dependencies on `__init__.py` files\")\nasync def infer_python_init_dependencies(\n request: InferInitDependencies, python_infer_subsystem: PythonInferSubsystem\n) -> InferredDependencies:\n if not python_infer_subsystem.inits:\n return InferredDependencies([])\n\n fp = request.sources_field.file_path\n assert fp is not None\n init_files = await Get(\n AncestorFiles,\n AncestorFilesRequest(input_files=(fp,), requested=(\"__init__.py\", \"__init__.pyi\")),\n )\n owners = await MultiGet(Get(Owners, OwnersRequest((f,))) for f in init_files.snapshot.files)\n return InferredDependencies(itertools.chain.from_iterable(owners))","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_conftest_dependencies","text":"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_conftest_dependencies\n@rule(desc=\"Inferring dependencies on `conftest.py` files\")\nasync def infer_python_conftest_dependencies(\n request: InferConftestDependencies,\n python_infer_subsystem: PythonInferSubsystem,\n) -> InferredDependencies:\n if not python_infer_subsystem.conftests:\n return InferredDependencies([])\n\n fp = request.sources_field.file_path\n assert fp is not None\n conftest_files = await Get(\n AncestorFiles,\n AncestorFilesRequest(input_files=(fp,), requested=(\"conftest.py\",)),\n )\n owners = await MultiGet(\n # NB: Because conftest.py files effectively always have content, we require an\n # owning target.\n Get(Owners, OwnersRequest((f,), OwnersNotFoundBehavior.error))\n for f in conftest_files.snapshot.files\n )\n return InferredDependencies(itertools.chain.from_iterable(owners))","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::import_rules","text":"src/python/pants/backend/python/dependency_inference/rules.py::import_rules\ndef import_rules():\n return [\n infer_python_dependencies_via_imports,\n *pex.rules(),\n *parse_python_imports.rules(),\n *module_mapper.rules(),\n *stripped_source_files.rules(),\n SubsystemRule(PythonInferSubsystem),\n SubsystemRule(PythonSetup),\n UnionRule(InferDependenciesRequest, InferPythonImportDependencies),\n ]","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/rules.py::rules","text":"src/python/pants/backend/python/dependency_inference/rules.py::rules\ndef rules():\n return [\n *import_rules(),\n infer_python_init_dependencies,\n infer_python_conftest_dependencies,\n *ancestor_files.rules(),\n UnionRule(InferDependenciesRequest, InferInitDependencies),\n UnionRule(InferDependenciesRequest, InferConftestDependencies),\n ]","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.__init__","text":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.__init__\n def __init__(self, package_parts, contents):\n self._package_parts = package_parts\n self._contents_lines = contents.decode(errors=\"ignore\").splitlines()\n self.imports = set()","label":1},{"cid":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.maybe_add_string_import","text":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.maybe_add_string_import\n def maybe_add_string_import(self, s):\n if STRING_IMPORT_REGEX.match(s):\n self.imports.add(s)","label":1},{"cid":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._is_pragma_ignored","text":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._is_pragma_ignored\n @staticmethod\n def _is_pragma_ignored(line):\n return \"# pants: ignore\" in line","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt","text":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt\n def _visit_import_stmt(self, node, import_prefix):\n # N.B. We only add imports whose line doesn't contain \"# pants: ignore\"\n # However, `ast` doesn't expose the exact lines each specific import is on,\n # so we are forced to tokenize the import statement to tease out which imported\n # name is on which line so we can check for the ignore pragma.\n node_lines_iter = itertools.islice(self._contents_lines, node.lineno - 1, None)\n token_iter = tokenize.generate_tokens(lambda: next(node_lines_iter))\n\n def consume_until(string):\n return list(itertools.takewhile(lambda t: t[1] != string, token_iter))\n\n consume_until(\"import\")\n\n # N.B. The names in this list are in the same order as the import statement\n for alias in node.names:\n consume_until(alias.name.split(\".\")[-1])\n\n # N.B. Keep consuming lines while they end in a line-continuation\n # (unfortunately `tokenize` doesn't capture this)\n line = \"\\\\\"\n while line.endswith(\"\\\\\"):\n token = next(token_iter)\n line = token[4]\n\n if not self._is_pragma_ignored(line):\n self.imports.add(import_prefix + alias.name)\n if alias.asname and token[1] != alias.asname:\n consume_until(alias.asname)","label":1},{"cid":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt.consume_until","text":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt.consume_until\n def consume_until(string):\n return list(itertools.takewhile(lambda t: t[1] != string, token_iter))","label":1},{"cid":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_Import","text":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_Import\n def visit_Import(self, node):\n self._visit_import_stmt(node, \"\")","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_ImportFrom","text":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_ImportFrom\n def visit_ImportFrom(self, node):\n if node.level:\n # Relative import.\n rel_module = node.module\n abs_module = \".\".join(\n self._package_parts[0 : len(self._package_parts) - node.level + 1]\n + ([] if rel_module is None else [rel_module])\n )\n else:\n abs_module = node.module\n self._visit_import_stmt(node, abs_module + \".\")","label":0},{"cid":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_Call","text":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_Call\n def visit_Call(self, node):\n # Handle __import__(\"string_literal\"). This is commonly used in __init__.py files,\n # to explicitly mark namespace packages. Note that we don't handle more complex\n # uses, such as those that set `level`.\n if isinstance(node.func, ast.Name) and node.func.id == \"__import__\" and len(node.args) == 1:\n name = None\n if sys.version_info[0:2] < (3, 8) and isinstance(node.args[0], ast.Str):\n name = node.args[0].s\n elif isinstance(node.args[0], ast.Constant):\n name = str(node.args[0].value)\n\n if name is not None:\n if not self._is_pragma_ignored(self._contents_lines[node.args[0].lineno - 1]):\n self.imports.add(name)\n return\n\n self.generic_visit(node)","label":1},{"cid":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::main","text":"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::main\ndef main(filename):\n with open(filename, \"rb\") as f:\n content = f.read()\n try:\n tree = ast.parse(content, filename=filename)\n except SyntaxError:\n return\n\n package_parts = os.path.dirname(filename).split(os.path.sep)\n visitor = AstVisitor(package_parts, content)\n visitor.visit(tree)\n\n # We have to be careful to set the encoding explicitly and write raw bytes ourselves.\n # See below for where we explicitly decode.\n buffer = sys.stdout if sys.version_info[0:2] == (2, 7) else sys.stdout.buffer\n buffer.write(\"\\n\".join(sorted(visitor.imports)).encode(\"utf8\"))","label":1},{"cid":"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_tests","text":"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_tests\n@rule\nasync def generate_targets_from_python_tests(\n request: GenerateTargetsFromPythonTests,\n files_not_found_behavior: FilesNotFoundBehavior,\n python_infer: PythonInferSubsystem,\n union_membership: UnionMembership,\n) -> GeneratedTargets:\n sources_paths = await Get(\n SourcesPaths, SourcesPathsRequest(request.generator[PythonTestsGeneratingSourcesField])\n )\n\n all_overrides = {}\n overrides_field = request.generator[OverridesField]\n if overrides_field.value:\n _all_override_paths = await MultiGet(\n Get(Paths, PathGlobs, path_globs)\n for path_globs in overrides_field.to_path_globs(files_not_found_behavior)\n )\n all_overrides = overrides_field.flatten_paths(\n dict(zip(_all_override_paths, overrides_field.value.values()))\n )\n\n return generate_file_level_targets(\n PythonTestTarget,\n request.generator,\n sources_paths.files,\n union_membership,\n add_dependencies_on_all_siblings=not python_infer.imports,\n overrides=all_overrides,\n )","label":0},{"cid":"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_sources","text":"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_sources\n@rule\nasync def generate_targets_from_python_sources(\n request: GenerateTargetsFromPythonSources,\n python_infer: PythonInferSubsystem,\n files_not_found_behavior: FilesNotFoundBehavior,\n union_membership: UnionMembership,\n) -> GeneratedTargets:\n sources_paths = await Get(\n SourcesPaths, SourcesPathsRequest(request.generator[PythonSourcesGeneratingSourcesField])\n )\n\n all_overrides = {}\n overrides_field = request.generator[OverridesField]\n if overrides_field.value:\n _all_override_paths = await MultiGet(\n Get(Paths, PathGlobs, path_globs)\n for path_globs in overrides_field.to_path_globs(files_not_found_behavior)\n )\n all_overrides = overrides_field.flatten_paths(\n dict(zip(_all_override_paths, overrides_field.value.values()))\n )\n\n return generate_file_level_targets(\n PythonSourceTarget,\n request.generator,\n sources_paths.files,\n union_membership,\n add_dependencies_on_all_siblings=not python_infer.imports,\n overrides=all_overrides,\n )","label":0},{"cid":"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_test_utils","text":"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_test_utils\n@rule\nasync def generate_targets_from_python_test_utils(\n request: GenerateTargetsFromPythonTestUtils,\n python_infer: PythonInferSubsystem,\n files_not_found_behavior: FilesNotFoundBehavior,\n union_membership: UnionMembership,\n) -> GeneratedTargets:\n sources_paths = await Get(\n SourcesPaths, SourcesPathsRequest(request.generator[PythonTestUtilsGeneratingSourcesField])\n )\n\n all_overrides = {}\n overrides_field = request.generator[OverridesField]\n if overrides_field.value:\n _all_override_paths = await MultiGet(\n Get(Paths, PathGlobs, path_globs)\n for path_globs in overrides_field.to_path_globs(files_not_found_behavior)\n )\n all_overrides = overrides_field.flatten_paths(\n dict(zip(_all_override_paths, overrides_field.value.values()))\n )\n\n return generate_file_level_targets(\n PythonSourceTarget,\n request.generator,\n sources_paths.files,\n union_membership,\n add_dependencies_on_all_siblings=not python_infer.imports,\n overrides=all_overrides,\n )","label":0},{"cid":"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_pex_binaries","text":"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_pex_binaries\n@rule\nasync def generate_targets_from_pex_binaries(\n request: GenerateTargetsFromPexBinaries,\n union_membership: UnionMembership,\n) -> GeneratedTargets:\n generator_addr = request.generator.address\n entry_points_field = request.generator[PexEntryPointsField].value or []\n overrides = request.generator[OverridesField].flatten()\n\n # Note that we don't check for overlap because it seems unlikely to be a problem.\n # If it does, we should add this check. (E.g. `path.to.app` and `path/to/app.py`)\n\n def create_pex_binary(entry_point_spec: str) -> PexBinary:\n target_fields = {\n field.alias: field.value\n for field in request.generator.field_values.values()\n if not isinstance(field, (PexEntryPointsField, OverridesField))\n }\n target_fields.update(overrides.pop(entry_point_spec, {}))\n\n return PexBinary(\n {PexEntryPointField.alias: entry_point_spec, **target_fields},\n # \":\" is a forbidden character in target names\n generator_addr.create_generated(entry_point_spec.replace(\":\", \"-\")),\n union_membership,\n residence_dir=generator_addr.spec_path,\n )\n\n pex_binaries = [create_pex_binary(entry_point) for entry_point in entry_points_field]\n\n if overrides:\n raise InvalidFieldException(\n f\"Unused key in the `overrides` field for {request.generator.address}: \"\n f\"{sorted(overrides)}\"\n f\"Tip: if you'd like to override a field's value for every `{PexBinary.alias}` target \"\n \"generated by this target, change the field directly on this target rather than using \"\n \"the `overrides` field.\"\n )\n\n return GeneratedTargets(request.generator, pex_binaries)","label":0},{"cid":"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_pex_binaries.create_pex_binary","text":"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_pex_binaries.create_pex_binary\n def create_pex_binary(entry_point_spec: str) -> PexBinary:\n target_fields = {\n field.alias: field.value\n for field in request.generator.field_values.values()\n if not isinstance(field, (PexEntryPointsField, OverridesField))\n }\n target_fields.update(overrides.pop(entry_point_spec, {}))\n\n return PexBinary(\n {PexEntryPointField.alias: entry_point_spec, **target_fields},\n # \":\" is a forbidden character in target names\n generator_addr.create_generated(entry_point_spec.replace(\":\", \"-\")),\n union_membership,\n residence_dir=generator_addr.spec_path,\n )","label":0},{"cid":"src/python/pants/backend/python/target_types_rules.py::resolve_pex_entry_point","text":"src/python/pants/backend/python/target_types_rules.py::resolve_pex_entry_point\n@rule(desc=\"Determining the entry point for a `pex_binary` target\", level=LogLevel.DEBUG)\nasync def resolve_pex_entry_point(request: ResolvePexEntryPointRequest) -> ResolvedPexEntryPoint:\n ep_val = request.entry_point_field.value\n if ep_val is None:\n return ResolvedPexEntryPoint(None, file_name_used=False)\n address = request.entry_point_field.address\n\n # We support several different schemes:\n # 1) `path.to.module` => preserve exactly.\n # 2) `path.to.module:func` => preserve exactly.\n # 3) `app.py` => convert into `path.to.app`.\n # 4) `app.py:func` => convert into `path.to.app:func`.\n\n # If it's already a module (cases #1 and #2), simply use that. Otherwise, convert the file name\n # into a module path (cases #3 and #4).\n if not ep_val.module.endswith(\".py\"):\n return ResolvedPexEntryPoint(ep_val, file_name_used=False)\n\n # Use the engine to validate that the file exists and that it resolves to only one file.\n full_glob = os.path.join(address.spec_path, ep_val.module)\n entry_point_paths = await Get(\n Paths,\n PathGlobs(\n [full_glob],\n glob_match_error_behavior=GlobMatchErrorBehavior.error,\n description_of_origin=f\"{address}'s `{request.entry_point_field.alias}` field\",\n ),\n )\n # We will have already raised if the glob did not match, i.e. if there were no files. But\n # we need to check if they used a file glob (`*` or `**`) that resolved to >1 file.\n if len(entry_point_paths.files) != 1:\n raise InvalidFieldException(\n f\"Multiple files matched for the `{request.entry_point_field.alias}` \"\n f\"{ep_val.spec!r} for the target {address}, but only one file expected. Are you using \"\n f\"a glob, rather than a file name?\\n\\n\"\n f\"All matching files: {list(entry_point_paths.files)}.\"\n )\n entry_point_path = entry_point_paths","label":0},{"cid":"src/python/pants/backend/python/target_types_rules.py::inject_pex_binary_entry_point_dependency","text":"src/python/pants/backend/python/target_types_rules.py::inject_pex_binary_entry_point_dependency\n@rule(desc=\"Inferring dependency from the pex_binary `entry_point` field\")\nasync def inject_pex_binary_entry_point_dependency(\n request: InjectPexBinaryEntryPointDependency, python_infer_subsystem: PythonInferSubsystem\n) -> InjectedDependencies:\n if not python_infer_subsystem.entry_points:\n return InjectedDependencies()\n original_tgt = await Get(WrappedTarget, Address, request.dependencies_field.address)\n entry_point_field = original_tgt.target.get(PexEntryPointField)\n if entry_point_field.value is None:\n return InjectedDependencies()\n\n explicitly_provided_deps, entry_point = await MultiGet(\n Get(ExplicitlyProvidedDependencies, DependenciesRequest(original_tgt.target[Dependencies])),\n Get(ResolvedPexEntryPoint, ResolvePexEntryPointRequest(entry_point_field)),\n )\n if entry_point.val is None:\n return InjectedDependencies()\n owners = await Get(PythonModuleOwners, PythonModule(entry_point.val.module))\n address = original_tgt.target.address\n explicitly_provided_deps.maybe_warn_of_ambiguous_dependency_inference(\n owners.ambiguous,\n address,\n # If the entry point was specified as a file, like `app.py`, we know the module must\n # live in the pex_binary's directory or subdirectory, so the owners must be ancestors.\n owners_must_be_ancestors=entry_point.file_name_used,\n import_reference=\"module\",\n context=(\n f\"The pex_binary target {address} has the field \"\n f\"`entry_point={repr(entry_point_field.value.spec)}`, which \"\n f\"maps to the Python module `{entry_point.val.module}`\"\n ),\n )\n maybe_disambiguated = explicitly_provided_deps.disambiguated(\n owners.ambiguous, owners_must_be_ancestors=entry_point.file_name_used\n )\n unambiguous_owners = owners.unambiguous or (\n (maybe_disambiguated,) if maybe_disambigu","label":0},{"cid":"src/python/pants/backend/python/target_types_rules.py::_classify_entry_points","text":"src/python/pants/backend/python/target_types_rules.py::_classify_entry_points\ndef _classify_entry_points(\n all_entry_points: _EntryPointsDictType,\n) -> Generator[Tuple[bool, str, str, str], None, None]:\n \"\"\"Looks at each entry point to see if it is a target address or not.\n\n Yields tuples: is_target, category, name, entry_point_str.\n \"\"\"\n for category, entry_points in all_entry_points.items():\n for name, entry_point_str in entry_points.items():\n yield (\n entry_point_str.startswith(\":\") or \"/\" in entry_point_str,\n category,\n name,\n entry_point_str,\n )","label":0},{"cid":"src/python/pants/backend/python/target_types_rules.py::resolve_python_distribution_entry_points","text":"src/python/pants/backend/python/target_types_rules.py::resolve_python_distribution_entry_points\n@rule(desc=\"Determining the entry points for a `python_distribution` target\", level=LogLevel.DEBUG)\nasync def resolve_python_distribution_entry_points(\n request: ResolvePythonDistributionEntryPointsRequest,\n) -> ResolvedPythonDistributionEntryPoints:\n if request.entry_points_field:\n if request.entry_points_field.value is None:\n return ResolvedPythonDistributionEntryPoints()\n address = request.entry_points_field.address\n all_entry_points = cast(_EntryPointsDictType, request.entry_points_field.value)\n\n elif request.provides_field:\n address = request.provides_field.address\n provides_field_value = cast(\n _EntryPointsDictType, request.provides_field.value.kwargs.get(\"entry_points\") or {}\n )\n\n if provides_field_value:\n all_entry_points = provides_field_value\n else:\n return ResolvedPythonDistributionEntryPoints()\n else:\n return ResolvedPythonDistributionEntryPoints()\n\n classified_entry_points = list(_classify_entry_points(all_entry_points))\n\n # Pick out all target addresses up front, so we can use MultiGet later.\n #\n # This calls for a bit of trickery however (using the \"y_by_x\" mapping dicts), so we keep track\n # of which address belongs to which entry point. I.e. the `address_by_ref` and\n # `binary_entry_point_by_address` variables.\n\n target_refs = [\n entry_point_str for is_target, _, _, entry_point_str in classified_entry_points if is_target\n ]\n\n # Intermediate step, as Get(Targets) returns a deduplicated set.. which breaks in case of\n # multiple input refs that maps to the same target.\n target_addresses = await Get(\n Addresses, UnparsedAddressInputs(target_refs, owning_address=address)\n )\n address_by_ref = dict(zip(target_refs, target_addresses))\n targets = await Get(Targets, Addresses, target_addresses","label":0},{"cid":"src/python/pants/backend/python/target_types_rules.py::inject_python_distribution_dependencies","text":"src/python/pants/backend/python/target_types_rules.py::inject_python_distribution_dependencies\n@rule\nasync def inject_python_distribution_dependencies(\n request: InjectPythonDistributionDependencies, python_infer_subsystem: PythonInferSubsystem\n) -> InjectedDependencies:\n \"\"\"Inject dependencies that we can infer from entry points in the distribution.\"\"\"\n if not python_infer_subsystem.entry_points:\n return InjectedDependencies()\n\n original_tgt = await Get(WrappedTarget, Address, request.dependencies_field.address)\n explicitly_provided_deps, distribution_entry_points, provides_entry_points = await MultiGet(\n Get(ExplicitlyProvidedDependencies, DependenciesRequest(original_tgt.target[Dependencies])),\n Get(\n ResolvedPythonDistributionEntryPoints,\n ResolvePythonDistributionEntryPointsRequest(\n entry_points_field=original_tgt.target[PythonDistributionEntryPointsField]\n ),\n ),\n Get(\n ResolvedPythonDistributionEntryPoints,\n ResolvePythonDistributionEntryPointsRequest(\n provides_field=original_tgt.target[PythonProvidesField]\n ),\n ),\n )\n\n address = original_tgt.target.address\n all_module_entry_points = [\n (category, name, entry_point)\n for category, entry_points in chain(\n distribution_entry_points.explicit_modules.items(),\n provides_entry_points.explicit_modules.items(),\n )\n for name, entry_point in entry_points.items()\n ]\n all_module_owners = iter(\n await MultiGet(\n Get(PythonModuleOwners, PythonModule(entry_point.module))\n for _, _, entry_point in all_module_entry_points\n )\n )\n module_owners: OrderedSet[Address] = OrderedSet()\n for (category, name, entry_point), owners in zip(all_module_entry_points, all_module_owners):\n field_str = repr({category: {name: entry_point.spec}})\n explicitly_provided_deps.","label":0},{"cid":"src/python/pants/backend/python/target_types_rules.py::rules","text":"src/python/pants/backend/python/target_types_rules.py::rules\ndef rules():\n return (\n *collect_rules(),\n *import_rules(),\n UnionRule(GenerateTargetsRequest, GenerateTargetsFromPythonTests),\n UnionRule(GenerateTargetsRequest, GenerateTargetsFromPythonSources),\n UnionRule(GenerateTargetsRequest, GenerateTargetsFromPythonTestUtils),\n UnionRule(GenerateTargetsRequest, GenerateTargetsFromPexBinaries),\n UnionRule(InjectDependenciesRequest, InjectPexBinaryEntryPointDependency),\n UnionRule(InjectDependenciesRequest, InjectPythonDistributionDependencies),\n )","label":0},{"cid":"src/python/pants/backend/shell/dependency_inference.py::find_all_shell_targets","text":"src/python/pants/backend/shell/dependency_inference.py::find_all_shell_targets\n@rule(desc=\"Find all Shell targets in project\", level=LogLevel.DEBUG)\ndef find_all_shell_targets(all_tgts: AllTargets) -> AllShellTargets:\n return AllShellTargets(tgt for tgt in all_tgts if tgt.has_field(ShellSourceField))","label":0},{"cid":"src/python/pants/backend/shell/dependency_inference.py::map_shell_files","text":"src/python/pants/backend/shell/dependency_inference.py::map_shell_files\n@rule(desc=\"Creating map of Shell file names to Shell targets\", level=LogLevel.DEBUG)\ndef map_shell_files(tgts: AllShellTargets) -> ShellMapping:\n files_to_addresses: dict[str, Address] = {}\n files_with_multiple_owners: DefaultDict[str, set[Address]] = defaultdict(set)\n for tgt in tgts:\n fp = tgt[ShellSourceField].file_path\n if fp in files_to_addresses:\n files_with_multiple_owners[fp].update({files_to_addresses[fp], tgt.address})\n else:\n files_to_addresses[fp] = tgt.address\n\n # Remove files with ambiguous owners.\n for ambiguous_f in files_with_multiple_owners:\n files_to_addresses.pop(ambiguous_f)\n\n return ShellMapping(\n mapping=FrozenDict(sorted(files_to_addresses.items())),\n ambiguous_modules=FrozenDict(\n (k, tuple(sorted(v))) for k, v in sorted(files_with_multiple_owners.items())\n ),\n )","label":0},{"cid":"src/python/pants/backend/shell/dependency_inference.py::parse_shell_imports","text":"src/python/pants/backend/shell/dependency_inference.py::parse_shell_imports\n@rule\nasync def parse_shell_imports(\n request: ParseShellImportsRequest, shellcheck: Shellcheck\n) -> ParsedShellImports:\n # We use Shellcheck to parse for us by running it against each file in isolation, which means\n # that all `source` statements will error. Then, we can extract the problematic paths from the\n # JSON output.\n downloaded_shellcheck = await Get(\n DownloadedExternalTool, ExternalToolRequest, shellcheck.get_request(Platform.current)\n )\n input_digest = await Get(Digest, MergeDigests([request.digest, downloaded_shellcheck.digest]))\n process_result = await Get(\n FallibleProcessResult,\n Process(\n # NB: We do not load up `[shellcheck].{args,config}` because it would risk breaking\n # determinism of dependency inference in an unexpected way.\n [downloaded_shellcheck.exe, \"--format=json\", request.fp],\n input_digest=input_digest,\n description=f\"Detect Shell imports for {request.fp}\",\n level=LogLevel.DEBUG,\n # We expect this to always fail, but it should still be cached because the process is\n # deterministic.\n cache_scope=ProcessCacheScope.ALWAYS,\n ),\n )\n\n try:\n output = json.loads(process_result.stdout)\n except json.JSONDecodeError:\n logger.error(\n f\"Parsing {request.fp} for dependency inference failed because Shellcheck's output \"\n f\"could not be loaded as JSON. Please open a GitHub issue at \"\n f\"https://github.com/pantsbuild/pants/issues/new with this error message attached.\\n\\n\"\n f\"\\nshellcheck version: {shellcheck.version}\\n\"\n f\"process_result.stdout: {process_result.stdout.decode()}\"\n )\n return ParsedShellImports()\n\n paths = set()\n for error in output:\n if not error.get(\"code\", \"\") == 1091:\n continue\n msg = error","label":0},{"cid":"src/python/pants/backend/shell/dependency_inference.py::infer_shell_dependencies","text":"src/python/pants/backend/shell/dependency_inference.py::infer_shell_dependencies\n@rule(desc=\"Inferring Shell dependencies by analyzing imports\")\nasync def infer_shell_dependencies(\n request: InferShellDependencies, shell_mapping: ShellMapping, shell_setup: ShellSetup\n) -> InferredDependencies:\n if not shell_setup.dependency_inference:\n return InferredDependencies([])\n\n address = request.sources_field.address\n wrapped_tgt = await Get(WrappedTarget, Address, address)\n explicitly_provided_deps, hydrated_sources = await MultiGet(\n Get(ExplicitlyProvidedDependencies, DependenciesRequest(wrapped_tgt.target[Dependencies])),\n Get(HydratedSources, HydrateSourcesRequest(request.sources_field)),\n )\n assert len(hydrated_sources.snapshot.files) == 1\n\n detected_imports = await Get(\n ParsedShellImports,\n ParseShellImportsRequest(\n hydrated_sources.snapshot.digest, hydrated_sources.snapshot.files[0]\n ),\n )\n result: OrderedSet[Address] = OrderedSet()\n for import_path in detected_imports:\n unambiguous = shell_mapping.mapping.get(import_path)\n ambiguous = shell_mapping.ambiguous_modules.get(import_path)\n if unambiguous:\n result.add(unambiguous)\n elif ambiguous:\n explicitly_provided_deps.maybe_warn_of_ambiguous_dependency_inference(\n ambiguous,\n address,\n import_reference=\"file\",\n context=f\"The target {address} sources `{import_path}`\",\n )\n maybe_disambiguated = explicitly_provided_deps.disambiguated(ambiguous)\n if maybe_disambiguated:\n result.add(maybe_disambiguated)\n return InferredDependencies(sorted(result))","label":0},{"cid":"src/python/pants/backend/shell/dependency_inference.py::rules","text":"src/python/pants/backend/shell/dependency_inference.py::rules\ndef rules():\n return (*collect_rules(), UnionRule(InferDependenciesRequest, InferShellDependencies))","label":0},{"cid":"src/python/pants/option/global_options.py::FilesNotFoundBehavior.to_glob_match_error_behavior","text":"src/python/pants/option/global_options.py::FilesNotFoundBehavior.to_glob_match_error_behavior\n def to_glob_match_error_behavior(self) -> GlobMatchErrorBehavior:\n return GlobMatchErrorBehavior(self.value)","label":0},{"cid":"src/python/pants/option/global_options.py::OwnersNotFoundBehavior.to_glob_match_error_behavior","text":"src/python/pants/option/global_options.py::OwnersNotFoundBehavior.to_glob_match_error_behavior\n def to_glob_match_error_behavior(self) -> GlobMatchErrorBehavior:\n return GlobMatchErrorBehavior(self.value)","label":0},{"cid":"src/python/pants/option/global_options.py::AuthPluginResult.__post_init__","text":"src/python/pants/option/global_options.py::AuthPluginResult.__post_init__\n def __post_init__(self) -> None:\n def assert_valid_address(addr: str | None, field_name: str) -> None:\n valid_schemes = [f\"{scheme}://\" for scheme in (\"grpc\", \"grpcs\")]\n if addr and not any(addr.startswith(scheme) for scheme in valid_schemes):\n raise ValueError(\n f\"Invalid `{field_name}` in `AuthPluginResult` returned from \"\n f\"`--remote-auth-plugin`. Must start with `grpc://` or `grpcs://`, but was \"\n f\"{addr}.\"\n )\n\n assert_valid_address(self.store_address, \"store_address\")\n assert_valid_address(self.execution_address, \"execution_address\")","label":0},{"cid":"src/python/pants/option/global_options.py::AuthPluginResult.__post_init__.assert_valid_address","text":"src/python/pants/option/global_options.py::AuthPluginResult.__post_init__.assert_valid_address\n def assert_valid_address(addr: str | None, field_name: str) -> None:\n valid_schemes = [f\"{scheme}://\" for scheme in (\"grpc\", \"grpcs\")]\n if addr and not any(addr.startswith(scheme) for scheme in valid_schemes):\n raise ValueError(\n f\"Invalid `{field_name}` in `AuthPluginResult` returned from \"\n f\"`--remote-auth-plugin`. Must start with `grpc://` or `grpcs://`, but was \"\n f\"{addr}.\"\n )","label":0},{"cid":"src/python/pants/option/global_options.py::AuthPluginResult.is_available","text":"src/python/pants/option/global_options.py::AuthPluginResult.is_available\n @property\n def is_available(self) -> bool:\n return self.state == AuthPluginState.OK","label":0},{"cid":"src/python/pants/option/global_options.py::DynamicRemoteOptions.disabled","text":"src/python/pants/option/global_options.py::DynamicRemoteOptions.disabled\n @classmethod\n def disabled(cls) -> DynamicRemoteOptions:\n return DynamicRemoteOptions(\n execution=False,\n cache_read=False,\n cache_write=False,\n instance_name=None,\n store_address=None,\n execution_address=None,\n store_headers={},\n execution_headers={},\n parallelism=DEFAULT_EXECUTION_OPTIONS.process_execution_remote_parallelism,\n store_rpc_concurrency=DEFAULT_EXECUTION_OPTIONS.remote_store_rpc_concurrency,\n cache_rpc_concurrency=DEFAULT_EXECUTION_OPTIONS.remote_cache_rpc_concurrency,\n execution_rpc_concurrency=DEFAULT_EXECUTION_OPTIONS.remote_execution_rpc_concurrency,\n )","label":0},{"cid":"src/python/pants/option/global_options.py::DynamicRemoteOptions.from_options","text":"src/python/pants/option/global_options.py::DynamicRemoteOptions.from_options\n @classmethod\n def from_options(\n cls,\n full_options: Options,\n env: CompleteEnvironment,\n prior_result: AuthPluginResult | None = None,\n ) -> tuple[DynamicRemoteOptions, AuthPluginResult | None]:\n bootstrap_options = full_options.bootstrap_option_values()\n assert bootstrap_options is not None\n execution = cast(bool, bootstrap_options.remote_execution)\n cache_read = cast(bool, bootstrap_options.remote_cache_read)\n cache_write = cast(bool, bootstrap_options.remote_cache_write)\n store_address = cast(\"str | None\", bootstrap_options.remote_store_address)\n execution_address = cast(\"str | None\", bootstrap_options.remote_execution_address)\n instance_name = cast(\"str | None\", bootstrap_options.remote_instance_name)\n execution_headers = cast(\"dict[str, str]\", bootstrap_options.remote_execution_headers)\n store_headers = cast(\"dict[str, str]\", bootstrap_options.remote_store_headers)\n parallelism = cast(int, bootstrap_options.process_execution_remote_parallelism)\n store_rpc_concurrency = cast(int, bootstrap_options.remote_store_rpc_concurrency)\n cache_rpc_concurrency = cast(int, bootstrap_options.remote_cache_rpc_concurrency)\n execution_rpc_concurrency = cast(int, bootstrap_options.remote_execution_rpc_concurrency)\n\n if bootstrap_options.remote_oauth_bearer_token_path:\n oauth_token = (\n Path(bootstrap_options.remote_oauth_bearer_token_path).resolve().read_text().strip()\n )\n if set(oauth_token).intersection({\"\\n\", \"\\r\"}):\n raise OptionsError(\n f\"OAuth bearer token path {bootstrap_options.remote_oauth_bearer_token_path} \"\n \"must not contain multiple lines.\"\n )\n token_header = {\"authorization\": f\"Bearer {oauth_token}\"}\n execution_","label":0},{"cid":"src/python/pants/option/global_options.py::ExecutionOptions.from_options","text":"src/python/pants/option/global_options.py::ExecutionOptions.from_options\n @classmethod\n def from_options(\n cls,\n bootstrap_options: OptionValueContainer,\n dynamic_remote_options: DynamicRemoteOptions,\n ) -> ExecutionOptions:\n return cls(\n # Remote execution strategy.\n remote_execution=dynamic_remote_options.execution,\n remote_cache_read=dynamic_remote_options.cache_read,\n remote_cache_write=dynamic_remote_options.cache_write,\n # General remote setup.\n remote_instance_name=dynamic_remote_options.instance_name,\n remote_ca_certs_path=bootstrap_options.remote_ca_certs_path,\n # Process execution setup.\n process_cleanup=bootstrap_options.process_cleanup,\n local_cache=bootstrap_options.local_cache,\n process_execution_local_parallelism=bootstrap_options.process_execution_local_parallelism,\n process_execution_remote_parallelism=dynamic_remote_options.parallelism,\n process_execution_cache_namespace=bootstrap_options.process_execution_cache_namespace,\n process_execution_local_enable_nailgun=bootstrap_options.process_execution_local_enable_nailgun,\n # Remote store setup.\n remote_store_address=dynamic_remote_options.store_address,\n remote_store_headers=dynamic_remote_options.store_headers,\n remote_store_chunk_bytes=bootstrap_options.remote_store_chunk_bytes,\n remote_store_chunk_upload_timeout_seconds=bootstrap_options.remote_store_chunk_upload_timeout_seconds,\n remote_store_rpc_retries=bootstrap_options.remote_store_rpc_retries,\n remote_store_rpc_concurrency=dynamic_remote_options.store_rpc_concurrency,\n remote_store_batch_api_size_limit=bootstrap_options.remote_store_batch_api_size_limit,\n # Remote cache setup.\n remote_cache_eager_fetch=bootstrap_options.remote_cache_eager_fetch,\n ","label":0},{"cid":"src/python/pants/option/global_options.py::LocalStoreOptions.target_total_size_bytes","text":"src/python/pants/option/global_options.py::LocalStoreOptions.target_total_size_bytes\n def target_total_size_bytes(self) -> int:\n \"\"\"Returns the target total size of all of the stores.\n\n The `max_size` values are caps on the total size of each store: the \"target\" size\n is the size that garbage collection will attempt to shrink the stores to each time\n it runs.\n\n NB: This value is not currently configurable, but that could be desirable in the future.\n \"\"\"\n max_total_size_bytes = (\n self.processes_max_size_bytes\n + self.files_max_size_bytes\n + self.directories_max_size_bytes\n )\n return max_total_size_bytes // 10","label":0},{"cid":"src/python/pants/option/global_options.py::LocalStoreOptions.from_options","text":"src/python/pants/option/global_options.py::LocalStoreOptions.from_options\n @classmethod\n def from_options(cls, options: OptionValueContainer) -> LocalStoreOptions:\n return cls(\n store_dir=str(Path(options.local_store_dir).resolve()),\n processes_max_size_bytes=options.local_store_processes_max_size_bytes,\n files_max_size_bytes=options.local_store_files_max_size_bytes,\n directories_max_size_bytes=options.local_store_directories_max_size_bytes,\n shard_count=options.local_store_shard_count,\n )","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptions.register_bootstrap_options","text":"src/python/pants/option/global_options.py::GlobalOptions.register_bootstrap_options\n @classmethod\n def register_bootstrap_options(cls, register):\n \"\"\"Register bootstrap options.\n\n \"Bootstrap options\" are the set of options necessary to create a Scheduler. If an option is\n not consumed during creation of a Scheduler, it should be in `register_options` instead.\n\n Bootstrap option values can be interpolated into the config file, and can be referenced\n programmatically in registration code, e.g., as register.bootstrap.pants_workdir.\n\n Note that regular code can also access these options as normal global-scope options. Their\n status as \"bootstrap options\" is only pertinent during option registration.\n \"\"\"\n buildroot = get_buildroot()\n default_distdir_name = \"dist\"\n default_rel_distdir = f\"/{default_distdir_name}/\"\n\n register(\n \"--backend-packages\",\n advanced=True,\n type=list,\n default=[],\n help=(\n \"Register functionality from these backends.\\n\\nThe backend packages must be \"\n \"present on the PYTHONPATH, typically because they are in the Pants core dist, in a \"\n \"plugin dist, or available as sources in the repo.\"\n ),\n )\n register(\n \"--plugins\",\n advanced=True,\n type=list,\n help=(\n \"Allow backends to be loaded from these plugins (usually released through PyPI). \"\n \"The default backends for each plugin will be loaded automatically. Other backends \"\n \"in a plugin can be loaded by listing them in `backend_packages` in the \"\n \"`[GLOBAL]` scope.\"\n ),\n )\n register(\n \"--plugins-force-resolve\",\n advanced=True,\n type=bool,\n default=False,\n help=\"Re-resolve plugins, even if previously resolv","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptions.register_options","text":"src/python/pants/option/global_options.py::GlobalOptions.register_options\n @classmethod\n def register_options(cls, register):\n \"\"\"Register options not tied to any particular task or subsystem.\"\"\"\n # The bootstrap options need to be registered on the post-bootstrap Options instance, so it\n # won't choke on them on the command line, and also so we can access their values as regular\n # global-scope options, for convenience.\n cls.register_bootstrap_options(register)\n\n register(\n \"--colors\",\n type=bool,\n default=sys.stdout.isatty(),\n help=(\n \"Whether Pants should use colors in output or not. This may also impact whether \"\n \"some tools Pants runs use color.\\n\\nWhen unset, this value defaults based on \"\n \"whether the output destination supports color.\"\n ),\n )\n register(\n \"--dynamic-ui\",\n type=bool,\n default=((\"CI\" not in os.environ) and sys.stderr.isatty()),\n help=\"Display a dynamically-updating console UI as Pants runs. This is true by default \"\n \"if Pants detects a TTY and there is no 'CI' environment variable indicating that \"\n \"Pants is running in a continuous integration environment.\",\n )\n\n register(\n \"--tag\",\n type=list,\n metavar=\"[+-]tag1,tag2,...\",\n help=(\n \"Include only targets with these tags (optional '+' prefix) or without these \"\n f\"tags ('-' prefix). See {doc_url('advanced-target-selection')}.\"\n ),\n )\n register(\n \"--exclude-target-regexp\",\n type=list,\n default=[],\n metavar=\"<regexp>\",\n help=\"Exclude targets that match these regexes. This does not impact file arguments.\",\n )\n\n register(\n \"--files-not-found-behavior\",\n advanced=True,\n ","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptions.validate_instance","text":"src/python/pants/option/global_options.py::GlobalOptions.validate_instance\n @classmethod\n def validate_instance(cls, opts):\n \"\"\"Validates an instance of global options for cases that are not prohibited via\n registration.\n\n For example: mutually exclusive options may be registered by passing a `mutually_exclusive_group`,\n but when multiple flags must be specified together, it can be necessary to specify post-parse\n checks.\n\n Raises pants.option.errors.OptionsError on validation failure.\n \"\"\"\n if opts.rule_threads_core < 2:\n # TODO: This is a defense against deadlocks due to #11329: we only run one `@goal_rule`\n # at a time, and a `@goal_rule` will only block one thread.\n raise OptionsError(\n \"--rule-threads-core values less than 2 are not supported, but it was set to \"\n f\"{opts.rule_threads_core}.\"\n )\n\n if opts.remote_execution and (opts.remote_cache_read or opts.remote_cache_write):\n raise OptionsError(\n \"`--remote-execution` cannot be set at the same time as either \"\n \"`--remote-cache-read` or `--remote-cache-write`.\\n\\nIf remote execution is \"\n \"enabled, it will already use remote caching.\"\n )\n\n if opts.remote_execution and not opts.remote_execution_address:\n raise OptionsError(\n \"The `--remote-execution` option requires also setting \"\n \"`--remote-execution-address` to work properly.\"\n )\n if opts.remote_execution_address and not opts.remote_store_address:\n raise OptionsError(\n \"The `--remote-execution-address` option requires also setting \"\n \"`--remote-store-address`. Often these have the same value.\"\n )\n\n if opts.remote_cache_read and not opts.remote_store_address:\n raise OptionsError(\n \"The `--remote-cache-r","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptions.validate_instance.validate_remote_address","text":"src/python/pants/option/global_options.py::GlobalOptions.validate_instance.validate_remote_address\n def validate_remote_address(opt_name: str) -> None:\n valid_schemes = [f\"{scheme}://\" for scheme in (\"grpc\", \"grpcs\")]\n address = getattr(opts, opt_name)\n if address and not any(address.startswith(scheme) for scheme in valid_schemes):\n raise OptionsError(\n f\"The `{opt_name}` option must begin with one of {valid_schemes}, but \"\n f\"was {address}.\"\n )","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptions.validate_instance.validate_remote_headers","text":"src/python/pants/option/global_options.py::GlobalOptions.validate_instance.validate_remote_headers\n def validate_remote_headers(opt_name: str) -> None:\n command_line_opt_name = f\"--{opt_name.replace('_', '-')}\"\n for k, v in getattr(opts, opt_name).items():\n if not k.isascii():\n raise OptionsError(\n f\"All values in `{command_line_opt_name}` must be ASCII, but the key \"\n f\"in `{k}: {v}` has non-ASCII characters.\"\n )\n if not v.isascii():\n raise OptionsError(\n f\"All values in `{command_line_opt_name}` must be ASCII, but the value in \"\n f\"`{k}: {v}` has non-ASCII characters.\"\n )","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptions.create_py_executor","text":"src/python/pants/option/global_options.py::GlobalOptions.create_py_executor\n @staticmethod\n def create_py_executor(bootstrap_options: OptionValueContainer) -> PyExecutor:\n rule_threads_max = (\n bootstrap_options.rule_threads_max\n if bootstrap_options.rule_threads_max\n else 4 * bootstrap_options.rule_threads_core\n )\n return PyExecutor(\n core_threads=bootstrap_options.rule_threads_core, max_threads=rule_threads_max\n )","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptions.compute_pants_ignore","text":"src/python/pants/option/global_options.py::GlobalOptions.compute_pants_ignore\n @staticmethod\n def compute_pants_ignore(buildroot, global_options):\n \"\"\"Computes the merged value of the `--pants-ignore` flag.\n\n This inherently includes the workdir and distdir locations if they are located under the\n buildroot.\n \"\"\"\n pants_ignore = list(global_options.pants_ignore)\n\n def add(absolute_path, include=False):\n # To ensure that the path is ignored regardless of whether it is a symlink or a directory, we\n # strip trailing slashes (which would signal that we wanted to ignore only directories).\n maybe_rel_path = fast_relpath_optional(absolute_path, buildroot)\n if maybe_rel_path:\n rel_path = maybe_rel_path.rstrip(os.path.sep)\n prefix = \"!\" if include else \"\"\n pants_ignore.append(f\"{prefix}/{rel_path}\")\n\n add(global_options.pants_workdir)\n add(global_options.pants_distdir)\n add(global_options.pants_subprocessdir)\n\n return pants_ignore","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptions.compute_pants_ignore.add","text":"src/python/pants/option/global_options.py::GlobalOptions.compute_pants_ignore.add\n def add(absolute_path, include=False):\n # To ensure that the path is ignored regardless of whether it is a symlink or a directory, we\n # strip trailing slashes (which would signal that we wanted to ignore only directories).\n maybe_rel_path = fast_relpath_optional(absolute_path, buildroot)\n if maybe_rel_path:\n rel_path = maybe_rel_path.rstrip(os.path.sep)\n prefix = \"!\" if include else \"\"\n pants_ignore.append(f\"{prefix}/{rel_path}\")","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptions.compute_pantsd_invalidation_globs","text":"src/python/pants/option/global_options.py::GlobalOptions.compute_pantsd_invalidation_globs\n @staticmethod\n def compute_pantsd_invalidation_globs(\n buildroot: str, bootstrap_options: OptionValueContainer\n ) -> tuple[str, ...]:\n \"\"\"Computes the merged value of the `--pantsd-invalidation-globs` option.\n\n Combines --pythonpath and --pants-config-files files that are in {buildroot} dir with those\n invalidation_globs provided by users.\n \"\"\"\n invalidation_globs: OrderedSet[str] = OrderedSet()\n\n # Globs calculated from the sys.path and other file-like configuration need to be sanitized\n # to relative globs (where possible).\n potentially_absolute_globs = (\n *sys.path,\n *bootstrap_options.pythonpath,\n *bootstrap_options.pants_config_files,\n )\n for glob in potentially_absolute_globs:\n # NB: We use `relpath` here because these paths are untrusted, and might need to be\n # normalized in addition to being relativized.\n glob_relpath = os.path.relpath(glob, buildroot)\n if glob_relpath == \".\" or glob_relpath.startswith(\"..\"):\n logger.debug(\n f\"Changes to {glob}, outside of the buildroot, will not be invalidated.\"\n )\n else:\n invalidation_globs.update([glob_relpath, glob_relpath + \"/**\"])\n\n # Explicitly specified globs are already relative, and are added verbatim.\n invalidation_globs.update(\n (\n \"!*.pyc\",\n \"!__pycache__/\",\n \".gitignore\",\n # TODO: This is a bandaid for https://github.com/pantsbuild/pants/issues/7022:\n # macros should be adapted to allow this dependency to be automatically detected.\n \"requirements.txt\",\n \"3rdparty/**/requirements.txt\",\n \"pyproject.toml\",\n \"3rdparty/**/pyproject.to","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptions.get_options_flags","text":"src/python/pants/option/global_options.py::GlobalOptions.get_options_flags\n @memoized_classmethod\n def get_options_flags(cls) -> GlobalOptionsFlags:\n return GlobalOptionsFlags.create(cast(\"Type[GlobalOptions]\", cls))","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptionsFlags.create","text":"src/python/pants/option/global_options.py::GlobalOptionsFlags.create\n @classmethod\n def create(cls, GlobalOptionsType: type[GlobalOptions]) -> GlobalOptionsFlags:\n flags = set()\n short_flags = set()\n\n def capture_the_flags(*args: str, **kwargs) -> None:\n for arg in args:\n flags.add(arg)\n if len(arg) == 2:\n short_flags.add(arg)\n elif kwargs.get(\"type\") == bool:\n flags.add(f\"--no-{arg[2:]}\")\n\n GlobalOptionsType.register_bootstrap_options(capture_the_flags)\n return cls(FrozenOrderedSet(flags), FrozenOrderedSet(short_flags))","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptionsFlags.create.capture_the_flags","text":"src/python/pants/option/global_options.py::GlobalOptionsFlags.create.capture_the_flags\n def capture_the_flags(*args: str, **kwargs) -> None:\n for arg in args:\n flags.add(arg)\n if len(arg) == 2:\n short_flags.add(arg)\n elif kwargs.get(\"type\") == bool:\n flags.add(f\"--no-{arg[2:]}\")","label":0}],"string":"[\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/parse_python_imports.py::parse_python_imports\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/parse_python_imports.py::parse_python_imports\\n@rule\\nasync def parse_python_imports(request: ParsePythonImportsRequest) -> ParsedPythonImports:\\n script = pkgutil.get_data(__name__, \\\"scripts/import_parser.py\\\")\\n assert script is not None\\n python_interpreter, script_digest, stripped_sources = await MultiGet(\\n Get(PythonExecutable, InterpreterConstraints, request.interpreter_constraints),\\n Get(Digest, CreateDigest([FileContent(\\\"__parse_python_imports.py\\\", script)])),\\n Get(StrippedSourceFiles, SourceFilesRequest([request.source])),\\n )\\n\\n # We operate on PythonSourceField, which should be one file.\\n assert len(stripped_sources.snapshot.files) == 1\\n file = stripped_sources.snapshot.files[0]\\n\\n input_digest = await Get(\\n Digest, MergeDigests([script_digest, stripped_sources.snapshot.digest])\\n )\\n process_result = await Get(\\n ProcessResult,\\n Process(\\n argv=[\\n python_interpreter.path,\\n \\\"./__parse_python_imports.py\\\",\\n file,\\n ],\\n input_digest=input_digest,\\n description=f\\\"Determine Python imports for {request.source.address}\\\",\\n env={\\n \\\"STRING_IMPORTS\\\": \\\"y\\\" if request.string_imports else \\\"n\\\",\\n \\\"MIN_DOTS\\\": str(request.string_imports_min_dots),\\n },\\n level=LogLevel.DEBUG,\\n ),\\n )\\n # See above for where we explicitly encoded as utf8. Even though utf8 is the\\n # default for decode(), we make that explicit here for emphasis.\\n return ParsedPythonImports(process_result.stdout.decode(\\\"utf8\\\").strip().splitlines())\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/parse_python_imports.py::rules\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/parse_python_imports.py::rules\\ndef rules():\\n return collect_rules()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.register_options\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.register_options\\n @classmethod\\n def register_options(cls, register):\\n super().register_options(register)\\n register(\\n \\\"--imports\\\",\\n default=True,\\n type=bool,\\n help=(\\n \\\"Infer a target's imported dependencies by parsing import statements from sources.\\\"\\n ),\\n )\\n register(\\n \\\"--string-imports\\\",\\n default=False,\\n type=bool,\\n help=(\\n \\\"Infer a target's dependencies based on strings that look like dynamic \\\"\\n \\\"dependencies, such as Django settings files expressing dependencies as strings. \\\"\\n \\\"To ignore any false positives, put `!{bad_address}` in the `dependencies` field \\\"\\n \\\"of your target.\\\"\\n ),\\n )\\n register(\\n \\\"--string-imports-min-dots\\\",\\n default=2,\\n type=int,\\n help=(\\n \\\"If --string-imports is True, treat valid-looking strings with at least this many \\\"\\n \\\"dots in them as potential dynamic dependencies. E.g., `'foo.bar.Baz'` will be \\\"\\n \\\"treated as a potential dependency if this option is set to 2 but not if set to 3.\\\"\\n ),\\n )\\n register(\\n \\\"--inits\\\",\\n default=False,\\n type=bool,\\n help=(\\n \\\"Infer a target's dependencies on any `__init__.py` files in the packages \\\"\\n \\\"it is located in (recursively upward in the directory structure).\\\\n\\\\nEven if this \\\"\\n \\\"is disabled, Pants will still include any ancestor `__init__.py` files, only they \\\"\\n \\\"will not be 'proper' dependencies, e.g. they will not show up in \\\"\\n \\\"`./pants dependencies` and their own dependencies will not be used.\\\\n\\\\nIf you \\\"\\n \\\"have empty `__init__.py` files, it's safe to leave\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.imports\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.imports\\n @property\\n def imports(self) -> bool:\\n return cast(bool, self.options.imports)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.string_imports\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.string_imports\\n @property\\n def string_imports(self) -> bool:\\n return cast(bool, self.options.string_imports)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.string_imports_min_dots\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.string_imports_min_dots\\n @property\\n def string_imports_min_dots(self) -> int:\\n return cast(int, self.options.string_imports_min_dots)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.inits\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.inits\\n @property\\n def inits(self) -> bool:\\n return cast(bool, self.options.inits)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.conftests\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.conftests\\n @property\\n def conftests(self) -> bool:\\n return cast(bool, self.options.conftests)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.entry_points\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.entry_points\\n @property\\n def entry_points(self) -> bool:\\n return cast(bool, self.options.entry_points)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.unowned_dependency_behavior\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::PythonInferSubsystem.unowned_dependency_behavior\\n @property\\n def unowned_dependency_behavior(self) -> UnownedDependencyUsage:\\n return cast(UnownedDependencyUsage, self.options.unowned_dependency_behavior)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_dependencies_via_imports\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_dependencies_via_imports\\n@rule(desc=\\\"Inferring Python dependencies by analyzing imports\\\")\\nasync def infer_python_dependencies_via_imports(\\n request: InferPythonImportDependencies,\\n python_infer_subsystem: PythonInferSubsystem,\\n python_setup: PythonSetup,\\n) -> InferredDependencies:\\n if not python_infer_subsystem.imports:\\n return InferredDependencies([])\\n\\n wrapped_tgt = await Get(WrappedTarget, Address, request.sources_field.address)\\n explicitly_provided_deps, detected_imports = await MultiGet(\\n Get(ExplicitlyProvidedDependencies, DependenciesRequest(wrapped_tgt.target[Dependencies])),\\n Get(\\n ParsedPythonImports,\\n ParsePythonImportsRequest(\\n cast(PythonSourceField, request.sources_field),\\n InterpreterConstraints.create_from_targets([wrapped_tgt.target], python_setup),\\n string_imports=python_infer_subsystem.string_imports,\\n string_imports_min_dots=python_infer_subsystem.string_imports_min_dots,\\n ),\\n ),\\n )\\n\\n owners_per_import = await MultiGet(\\n Get(PythonModuleOwners, PythonModule(imported_module))\\n for imported_module in detected_imports\\n )\\n\\n merged_result: set[Address] = set()\\n unowned_imports: set[str] = set()\\n address = wrapped_tgt.target.address\\n for owners, imp in zip(owners_per_import, detected_imports):\\n merged_result.update(owners.unambiguous)\\n explicitly_provided_deps.maybe_warn_of_ambiguous_dependency_inference(\\n owners.ambiguous,\\n address,\\n import_reference=\\\"module\\\",\\n context=f\\\"The target {address} imports `{imp}`\\\",\\n )\\n maybe_disambiguated = explicitly_provided_deps.disambiguated(owners.ambiguous)\\n if maybe_disambiguated:\\n merged_result.add(maybe_disambiguated)\\n\\n if not owners.unambiguous and imp.split(\\\".\\\")[0] not in \",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_init_dependencies\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_init_dependencies\\n@rule(desc=\\\"Inferring dependencies on `__init__.py` files\\\")\\nasync def infer_python_init_dependencies(\\n request: InferInitDependencies, python_infer_subsystem: PythonInferSubsystem\\n) -> InferredDependencies:\\n if not python_infer_subsystem.inits:\\n return InferredDependencies([])\\n\\n fp = request.sources_field.file_path\\n assert fp is not None\\n init_files = await Get(\\n AncestorFiles,\\n AncestorFilesRequest(input_files=(fp,), requested=(\\\"__init__.py\\\", \\\"__init__.pyi\\\")),\\n )\\n owners = await MultiGet(Get(Owners, OwnersRequest((f,))) for f in init_files.snapshot.files)\\n return InferredDependencies(itertools.chain.from_iterable(owners))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_conftest_dependencies\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::infer_python_conftest_dependencies\\n@rule(desc=\\\"Inferring dependencies on `conftest.py` files\\\")\\nasync def infer_python_conftest_dependencies(\\n request: InferConftestDependencies,\\n python_infer_subsystem: PythonInferSubsystem,\\n) -> InferredDependencies:\\n if not python_infer_subsystem.conftests:\\n return InferredDependencies([])\\n\\n fp = request.sources_field.file_path\\n assert fp is not None\\n conftest_files = await Get(\\n AncestorFiles,\\n AncestorFilesRequest(input_files=(fp,), requested=(\\\"conftest.py\\\",)),\\n )\\n owners = await MultiGet(\\n # NB: Because conftest.py files effectively always have content, we require an\\n # owning target.\\n Get(Owners, OwnersRequest((f,), OwnersNotFoundBehavior.error))\\n for f in conftest_files.snapshot.files\\n )\\n return InferredDependencies(itertools.chain.from_iterable(owners))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::import_rules\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::import_rules\\ndef import_rules():\\n return [\\n infer_python_dependencies_via_imports,\\n *pex.rules(),\\n *parse_python_imports.rules(),\\n *module_mapper.rules(),\\n *stripped_source_files.rules(),\\n SubsystemRule(PythonInferSubsystem),\\n SubsystemRule(PythonSetup),\\n UnionRule(InferDependenciesRequest, InferPythonImportDependencies),\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/rules.py::rules\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/rules.py::rules\\ndef rules():\\n return [\\n *import_rules(),\\n infer_python_init_dependencies,\\n infer_python_conftest_dependencies,\\n *ancestor_files.rules(),\\n UnionRule(InferDependenciesRequest, InferInitDependencies),\\n UnionRule(InferDependenciesRequest, InferConftestDependencies),\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.__init__\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.__init__\\n def __init__(self, package_parts, contents):\\n self._package_parts = package_parts\\n self._contents_lines = contents.decode(errors=\\\"ignore\\\").splitlines()\\n self.imports = set()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.maybe_add_string_import\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.maybe_add_string_import\\n def maybe_add_string_import(self, s):\\n if STRING_IMPORT_REGEX.match(s):\\n self.imports.add(s)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._is_pragma_ignored\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._is_pragma_ignored\\n @staticmethod\\n def _is_pragma_ignored(line):\\n return \\\"# pants: ignore\\\" in line\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt\\n def _visit_import_stmt(self, node, import_prefix):\\n # N.B. We only add imports whose line doesn't contain \\\"# pants: ignore\\\"\\n # However, `ast` doesn't expose the exact lines each specific import is on,\\n # so we are forced to tokenize the import statement to tease out which imported\\n # name is on which line so we can check for the ignore pragma.\\n node_lines_iter = itertools.islice(self._contents_lines, node.lineno - 1, None)\\n token_iter = tokenize.generate_tokens(lambda: next(node_lines_iter))\\n\\n def consume_until(string):\\n return list(itertools.takewhile(lambda t: t[1] != string, token_iter))\\n\\n consume_until(\\\"import\\\")\\n\\n # N.B. The names in this list are in the same order as the import statement\\n for alias in node.names:\\n consume_until(alias.name.split(\\\".\\\")[-1])\\n\\n # N.B. Keep consuming lines while they end in a line-continuation\\n # (unfortunately `tokenize` doesn't capture this)\\n line = \\\"\\\\\\\\\\\"\\n while line.endswith(\\\"\\\\\\\\\\\"):\\n token = next(token_iter)\\n line = token[4]\\n\\n if not self._is_pragma_ignored(line):\\n self.imports.add(import_prefix + alias.name)\\n if alias.asname and token[1] != alias.asname:\\n consume_until(alias.asname)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt.consume_until\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor._visit_import_stmt.consume_until\\n def consume_until(string):\\n return list(itertools.takewhile(lambda t: t[1] != string, token_iter))\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_Import\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_Import\\n def visit_Import(self, node):\\n self._visit_import_stmt(node, \\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_ImportFrom\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_ImportFrom\\n def visit_ImportFrom(self, node):\\n if node.level:\\n # Relative import.\\n rel_module = node.module\\n abs_module = \\\".\\\".join(\\n self._package_parts[0 : len(self._package_parts) - node.level + 1]\\n + ([] if rel_module is None else [rel_module])\\n )\\n else:\\n abs_module = node.module\\n self._visit_import_stmt(node, abs_module + \\\".\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_Call\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::AstVisitor.visit_Call\\n def visit_Call(self, node):\\n # Handle __import__(\\\"string_literal\\\"). This is commonly used in __init__.py files,\\n # to explicitly mark namespace packages. Note that we don't handle more complex\\n # uses, such as those that set `level`.\\n if isinstance(node.func, ast.Name) and node.func.id == \\\"__import__\\\" and len(node.args) == 1:\\n name = None\\n if sys.version_info[0:2] < (3, 8) and isinstance(node.args[0], ast.Str):\\n name = node.args[0].s\\n elif isinstance(node.args[0], ast.Constant):\\n name = str(node.args[0].value)\\n\\n if name is not None:\\n if not self._is_pragma_ignored(self._contents_lines[node.args[0].lineno - 1]):\\n self.imports.add(name)\\n return\\n\\n self.generic_visit(node)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::main\",\n \"text\": \"src/python/pants/backend/python/dependency_inference/scripts/import_parser.py::main\\ndef main(filename):\\n with open(filename, \\\"rb\\\") as f:\\n content = f.read()\\n try:\\n tree = ast.parse(content, filename=filename)\\n except SyntaxError:\\n return\\n\\n package_parts = os.path.dirname(filename).split(os.path.sep)\\n visitor = AstVisitor(package_parts, content)\\n visitor.visit(tree)\\n\\n # We have to be careful to set the encoding explicitly and write raw bytes ourselves.\\n # See below for where we explicitly decode.\\n buffer = sys.stdout if sys.version_info[0:2] == (2, 7) else sys.stdout.buffer\\n buffer.write(\\\"\\\\n\\\".join(sorted(visitor.imports)).encode(\\\"utf8\\\"))\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_tests\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_tests\\n@rule\\nasync def generate_targets_from_python_tests(\\n request: GenerateTargetsFromPythonTests,\\n files_not_found_behavior: FilesNotFoundBehavior,\\n python_infer: PythonInferSubsystem,\\n union_membership: UnionMembership,\\n) -> GeneratedTargets:\\n sources_paths = await Get(\\n SourcesPaths, SourcesPathsRequest(request.generator[PythonTestsGeneratingSourcesField])\\n )\\n\\n all_overrides = {}\\n overrides_field = request.generator[OverridesField]\\n if overrides_field.value:\\n _all_override_paths = await MultiGet(\\n Get(Paths, PathGlobs, path_globs)\\n for path_globs in overrides_field.to_path_globs(files_not_found_behavior)\\n )\\n all_overrides = overrides_field.flatten_paths(\\n dict(zip(_all_override_paths, overrides_field.value.values()))\\n )\\n\\n return generate_file_level_targets(\\n PythonTestTarget,\\n request.generator,\\n sources_paths.files,\\n union_membership,\\n add_dependencies_on_all_siblings=not python_infer.imports,\\n overrides=all_overrides,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_sources\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_sources\\n@rule\\nasync def generate_targets_from_python_sources(\\n request: GenerateTargetsFromPythonSources,\\n python_infer: PythonInferSubsystem,\\n files_not_found_behavior: FilesNotFoundBehavior,\\n union_membership: UnionMembership,\\n) -> GeneratedTargets:\\n sources_paths = await Get(\\n SourcesPaths, SourcesPathsRequest(request.generator[PythonSourcesGeneratingSourcesField])\\n )\\n\\n all_overrides = {}\\n overrides_field = request.generator[OverridesField]\\n if overrides_field.value:\\n _all_override_paths = await MultiGet(\\n Get(Paths, PathGlobs, path_globs)\\n for path_globs in overrides_field.to_path_globs(files_not_found_behavior)\\n )\\n all_overrides = overrides_field.flatten_paths(\\n dict(zip(_all_override_paths, overrides_field.value.values()))\\n )\\n\\n return generate_file_level_targets(\\n PythonSourceTarget,\\n request.generator,\\n sources_paths.files,\\n union_membership,\\n add_dependencies_on_all_siblings=not python_infer.imports,\\n overrides=all_overrides,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_test_utils\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_python_test_utils\\n@rule\\nasync def generate_targets_from_python_test_utils(\\n request: GenerateTargetsFromPythonTestUtils,\\n python_infer: PythonInferSubsystem,\\n files_not_found_behavior: FilesNotFoundBehavior,\\n union_membership: UnionMembership,\\n) -> GeneratedTargets:\\n sources_paths = await Get(\\n SourcesPaths, SourcesPathsRequest(request.generator[PythonTestUtilsGeneratingSourcesField])\\n )\\n\\n all_overrides = {}\\n overrides_field = request.generator[OverridesField]\\n if overrides_field.value:\\n _all_override_paths = await MultiGet(\\n Get(Paths, PathGlobs, path_globs)\\n for path_globs in overrides_field.to_path_globs(files_not_found_behavior)\\n )\\n all_overrides = overrides_field.flatten_paths(\\n dict(zip(_all_override_paths, overrides_field.value.values()))\\n )\\n\\n return generate_file_level_targets(\\n PythonSourceTarget,\\n request.generator,\\n sources_paths.files,\\n union_membership,\\n add_dependencies_on_all_siblings=not python_infer.imports,\\n overrides=all_overrides,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_pex_binaries\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_pex_binaries\\n@rule\\nasync def generate_targets_from_pex_binaries(\\n request: GenerateTargetsFromPexBinaries,\\n union_membership: UnionMembership,\\n) -> GeneratedTargets:\\n generator_addr = request.generator.address\\n entry_points_field = request.generator[PexEntryPointsField].value or []\\n overrides = request.generator[OverridesField].flatten()\\n\\n # Note that we don't check for overlap because it seems unlikely to be a problem.\\n # If it does, we should add this check. (E.g. `path.to.app` and `path/to/app.py`)\\n\\n def create_pex_binary(entry_point_spec: str) -> PexBinary:\\n target_fields = {\\n field.alias: field.value\\n for field in request.generator.field_values.values()\\n if not isinstance(field, (PexEntryPointsField, OverridesField))\\n }\\n target_fields.update(overrides.pop(entry_point_spec, {}))\\n\\n return PexBinary(\\n {PexEntryPointField.alias: entry_point_spec, **target_fields},\\n # \\\":\\\" is a forbidden character in target names\\n generator_addr.create_generated(entry_point_spec.replace(\\\":\\\", \\\"-\\\")),\\n union_membership,\\n residence_dir=generator_addr.spec_path,\\n )\\n\\n pex_binaries = [create_pex_binary(entry_point) for entry_point in entry_points_field]\\n\\n if overrides:\\n raise InvalidFieldException(\\n f\\\"Unused key in the `overrides` field for {request.generator.address}: \\\"\\n f\\\"{sorted(overrides)}\\\"\\n f\\\"Tip: if you'd like to override a field's value for every `{PexBinary.alias}` target \\\"\\n \\\"generated by this target, change the field directly on this target rather than using \\\"\\n \\\"the `overrides` field.\\\"\\n )\\n\\n return GeneratedTargets(request.generator, pex_binaries)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_pex_binaries.create_pex_binary\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py::generate_targets_from_pex_binaries.create_pex_binary\\n def create_pex_binary(entry_point_spec: str) -> PexBinary:\\n target_fields = {\\n field.alias: field.value\\n for field in request.generator.field_values.values()\\n if not isinstance(field, (PexEntryPointsField, OverridesField))\\n }\\n target_fields.update(overrides.pop(entry_point_spec, {}))\\n\\n return PexBinary(\\n {PexEntryPointField.alias: entry_point_spec, **target_fields},\\n # \\\":\\\" is a forbidden character in target names\\n generator_addr.create_generated(entry_point_spec.replace(\\\":\\\", \\\"-\\\")),\\n union_membership,\\n residence_dir=generator_addr.spec_path,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py::resolve_pex_entry_point\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py::resolve_pex_entry_point\\n@rule(desc=\\\"Determining the entry point for a `pex_binary` target\\\", level=LogLevel.DEBUG)\\nasync def resolve_pex_entry_point(request: ResolvePexEntryPointRequest) -> ResolvedPexEntryPoint:\\n ep_val = request.entry_point_field.value\\n if ep_val is None:\\n return ResolvedPexEntryPoint(None, file_name_used=False)\\n address = request.entry_point_field.address\\n\\n # We support several different schemes:\\n # 1) `path.to.module` => preserve exactly.\\n # 2) `path.to.module:func` => preserve exactly.\\n # 3) `app.py` => convert into `path.to.app`.\\n # 4) `app.py:func` => convert into `path.to.app:func`.\\n\\n # If it's already a module (cases #1 and #2), simply use that. Otherwise, convert the file name\\n # into a module path (cases #3 and #4).\\n if not ep_val.module.endswith(\\\".py\\\"):\\n return ResolvedPexEntryPoint(ep_val, file_name_used=False)\\n\\n # Use the engine to validate that the file exists and that it resolves to only one file.\\n full_glob = os.path.join(address.spec_path, ep_val.module)\\n entry_point_paths = await Get(\\n Paths,\\n PathGlobs(\\n [full_glob],\\n glob_match_error_behavior=GlobMatchErrorBehavior.error,\\n description_of_origin=f\\\"{address}'s `{request.entry_point_field.alias}` field\\\",\\n ),\\n )\\n # We will have already raised if the glob did not match, i.e. if there were no files. But\\n # we need to check if they used a file glob (`*` or `**`) that resolved to >1 file.\\n if len(entry_point_paths.files) != 1:\\n raise InvalidFieldException(\\n f\\\"Multiple files matched for the `{request.entry_point_field.alias}` \\\"\\n f\\\"{ep_val.spec!r} for the target {address}, but only one file expected. Are you using \\\"\\n f\\\"a glob, rather than a file name?\\\\n\\\\n\\\"\\n f\\\"All matching files: {list(entry_point_paths.files)}.\\\"\\n )\\n entry_point_path = entry_point_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py::inject_pex_binary_entry_point_dependency\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py::inject_pex_binary_entry_point_dependency\\n@rule(desc=\\\"Inferring dependency from the pex_binary `entry_point` field\\\")\\nasync def inject_pex_binary_entry_point_dependency(\\n request: InjectPexBinaryEntryPointDependency, python_infer_subsystem: PythonInferSubsystem\\n) -> InjectedDependencies:\\n if not python_infer_subsystem.entry_points:\\n return InjectedDependencies()\\n original_tgt = await Get(WrappedTarget, Address, request.dependencies_field.address)\\n entry_point_field = original_tgt.target.get(PexEntryPointField)\\n if entry_point_field.value is None:\\n return InjectedDependencies()\\n\\n explicitly_provided_deps, entry_point = await MultiGet(\\n Get(ExplicitlyProvidedDependencies, DependenciesRequest(original_tgt.target[Dependencies])),\\n Get(ResolvedPexEntryPoint, ResolvePexEntryPointRequest(entry_point_field)),\\n )\\n if entry_point.val is None:\\n return InjectedDependencies()\\n owners = await Get(PythonModuleOwners, PythonModule(entry_point.val.module))\\n address = original_tgt.target.address\\n explicitly_provided_deps.maybe_warn_of_ambiguous_dependency_inference(\\n owners.ambiguous,\\n address,\\n # If the entry point was specified as a file, like `app.py`, we know the module must\\n # live in the pex_binary's directory or subdirectory, so the owners must be ancestors.\\n owners_must_be_ancestors=entry_point.file_name_used,\\n import_reference=\\\"module\\\",\\n context=(\\n f\\\"The pex_binary target {address} has the field \\\"\\n f\\\"`entry_point={repr(entry_point_field.value.spec)}`, which \\\"\\n f\\\"maps to the Python module `{entry_point.val.module}`\\\"\\n ),\\n )\\n maybe_disambiguated = explicitly_provided_deps.disambiguated(\\n owners.ambiguous, owners_must_be_ancestors=entry_point.file_name_used\\n )\\n unambiguous_owners = owners.unambiguous or (\\n (maybe_disambiguated,) if maybe_disambigu\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py::_classify_entry_points\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py::_classify_entry_points\\ndef _classify_entry_points(\\n all_entry_points: _EntryPointsDictType,\\n) -> Generator[Tuple[bool, str, str, str], None, None]:\\n \\\"\\\"\\\"Looks at each entry point to see if it is a target address or not.\\n\\n Yields tuples: is_target, category, name, entry_point_str.\\n \\\"\\\"\\\"\\n for category, entry_points in all_entry_points.items():\\n for name, entry_point_str in entry_points.items():\\n yield (\\n entry_point_str.startswith(\\\":\\\") or \\\"/\\\" in entry_point_str,\\n category,\\n name,\\n entry_point_str,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py::resolve_python_distribution_entry_points\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py::resolve_python_distribution_entry_points\\n@rule(desc=\\\"Determining the entry points for a `python_distribution` target\\\", level=LogLevel.DEBUG)\\nasync def resolve_python_distribution_entry_points(\\n request: ResolvePythonDistributionEntryPointsRequest,\\n) -> ResolvedPythonDistributionEntryPoints:\\n if request.entry_points_field:\\n if request.entry_points_field.value is None:\\n return ResolvedPythonDistributionEntryPoints()\\n address = request.entry_points_field.address\\n all_entry_points = cast(_EntryPointsDictType, request.entry_points_field.value)\\n\\n elif request.provides_field:\\n address = request.provides_field.address\\n provides_field_value = cast(\\n _EntryPointsDictType, request.provides_field.value.kwargs.get(\\\"entry_points\\\") or {}\\n )\\n\\n if provides_field_value:\\n all_entry_points = provides_field_value\\n else:\\n return ResolvedPythonDistributionEntryPoints()\\n else:\\n return ResolvedPythonDistributionEntryPoints()\\n\\n classified_entry_points = list(_classify_entry_points(all_entry_points))\\n\\n # Pick out all target addresses up front, so we can use MultiGet later.\\n #\\n # This calls for a bit of trickery however (using the \\\"y_by_x\\\" mapping dicts), so we keep track\\n # of which address belongs to which entry point. I.e. the `address_by_ref` and\\n # `binary_entry_point_by_address` variables.\\n\\n target_refs = [\\n entry_point_str for is_target, _, _, entry_point_str in classified_entry_points if is_target\\n ]\\n\\n # Intermediate step, as Get(Targets) returns a deduplicated set.. which breaks in case of\\n # multiple input refs that maps to the same target.\\n target_addresses = await Get(\\n Addresses, UnparsedAddressInputs(target_refs, owning_address=address)\\n )\\n address_by_ref = dict(zip(target_refs, target_addresses))\\n targets = await Get(Targets, Addresses, target_addresses\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py::inject_python_distribution_dependencies\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py::inject_python_distribution_dependencies\\n@rule\\nasync def inject_python_distribution_dependencies(\\n request: InjectPythonDistributionDependencies, python_infer_subsystem: PythonInferSubsystem\\n) -> InjectedDependencies:\\n \\\"\\\"\\\"Inject dependencies that we can infer from entry points in the distribution.\\\"\\\"\\\"\\n if not python_infer_subsystem.entry_points:\\n return InjectedDependencies()\\n\\n original_tgt = await Get(WrappedTarget, Address, request.dependencies_field.address)\\n explicitly_provided_deps, distribution_entry_points, provides_entry_points = await MultiGet(\\n Get(ExplicitlyProvidedDependencies, DependenciesRequest(original_tgt.target[Dependencies])),\\n Get(\\n ResolvedPythonDistributionEntryPoints,\\n ResolvePythonDistributionEntryPointsRequest(\\n entry_points_field=original_tgt.target[PythonDistributionEntryPointsField]\\n ),\\n ),\\n Get(\\n ResolvedPythonDistributionEntryPoints,\\n ResolvePythonDistributionEntryPointsRequest(\\n provides_field=original_tgt.target[PythonProvidesField]\\n ),\\n ),\\n )\\n\\n address = original_tgt.target.address\\n all_module_entry_points = [\\n (category, name, entry_point)\\n for category, entry_points in chain(\\n distribution_entry_points.explicit_modules.items(),\\n provides_entry_points.explicit_modules.items(),\\n )\\n for name, entry_point in entry_points.items()\\n ]\\n all_module_owners = iter(\\n await MultiGet(\\n Get(PythonModuleOwners, PythonModule(entry_point.module))\\n for _, _, entry_point in all_module_entry_points\\n )\\n )\\n module_owners: OrderedSet[Address] = OrderedSet()\\n for (category, name, entry_point), owners in zip(all_module_entry_points, all_module_owners):\\n field_str = repr({category: {name: entry_point.spec}})\\n explicitly_provided_deps.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/target_types_rules.py::rules\",\n \"text\": \"src/python/pants/backend/python/target_types_rules.py::rules\\ndef rules():\\n return (\\n *collect_rules(),\\n *import_rules(),\\n UnionRule(GenerateTargetsRequest, GenerateTargetsFromPythonTests),\\n UnionRule(GenerateTargetsRequest, GenerateTargetsFromPythonSources),\\n UnionRule(GenerateTargetsRequest, GenerateTargetsFromPythonTestUtils),\\n UnionRule(GenerateTargetsRequest, GenerateTargetsFromPexBinaries),\\n UnionRule(InjectDependenciesRequest, InjectPexBinaryEntryPointDependency),\\n UnionRule(InjectDependenciesRequest, InjectPythonDistributionDependencies),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/shell/dependency_inference.py::find_all_shell_targets\",\n \"text\": \"src/python/pants/backend/shell/dependency_inference.py::find_all_shell_targets\\n@rule(desc=\\\"Find all Shell targets in project\\\", level=LogLevel.DEBUG)\\ndef find_all_shell_targets(all_tgts: AllTargets) -> AllShellTargets:\\n return AllShellTargets(tgt for tgt in all_tgts if tgt.has_field(ShellSourceField))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/shell/dependency_inference.py::map_shell_files\",\n \"text\": \"src/python/pants/backend/shell/dependency_inference.py::map_shell_files\\n@rule(desc=\\\"Creating map of Shell file names to Shell targets\\\", level=LogLevel.DEBUG)\\ndef map_shell_files(tgts: AllShellTargets) -> ShellMapping:\\n files_to_addresses: dict[str, Address] = {}\\n files_with_multiple_owners: DefaultDict[str, set[Address]] = defaultdict(set)\\n for tgt in tgts:\\n fp = tgt[ShellSourceField].file_path\\n if fp in files_to_addresses:\\n files_with_multiple_owners[fp].update({files_to_addresses[fp], tgt.address})\\n else:\\n files_to_addresses[fp] = tgt.address\\n\\n # Remove files with ambiguous owners.\\n for ambiguous_f in files_with_multiple_owners:\\n files_to_addresses.pop(ambiguous_f)\\n\\n return ShellMapping(\\n mapping=FrozenDict(sorted(files_to_addresses.items())),\\n ambiguous_modules=FrozenDict(\\n (k, tuple(sorted(v))) for k, v in sorted(files_with_multiple_owners.items())\\n ),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/shell/dependency_inference.py::parse_shell_imports\",\n \"text\": \"src/python/pants/backend/shell/dependency_inference.py::parse_shell_imports\\n@rule\\nasync def parse_shell_imports(\\n request: ParseShellImportsRequest, shellcheck: Shellcheck\\n) -> ParsedShellImports:\\n # We use Shellcheck to parse for us by running it against each file in isolation, which means\\n # that all `source` statements will error. Then, we can extract the problematic paths from the\\n # JSON output.\\n downloaded_shellcheck = await Get(\\n DownloadedExternalTool, ExternalToolRequest, shellcheck.get_request(Platform.current)\\n )\\n input_digest = await Get(Digest, MergeDigests([request.digest, downloaded_shellcheck.digest]))\\n process_result = await Get(\\n FallibleProcessResult,\\n Process(\\n # NB: We do not load up `[shellcheck].{args,config}` because it would risk breaking\\n # determinism of dependency inference in an unexpected way.\\n [downloaded_shellcheck.exe, \\\"--format=json\\\", request.fp],\\n input_digest=input_digest,\\n description=f\\\"Detect Shell imports for {request.fp}\\\",\\n level=LogLevel.DEBUG,\\n # We expect this to always fail, but it should still be cached because the process is\\n # deterministic.\\n cache_scope=ProcessCacheScope.ALWAYS,\\n ),\\n )\\n\\n try:\\n output = json.loads(process_result.stdout)\\n except json.JSONDecodeError:\\n logger.error(\\n f\\\"Parsing {request.fp} for dependency inference failed because Shellcheck's output \\\"\\n f\\\"could not be loaded as JSON. Please open a GitHub issue at \\\"\\n f\\\"https://github.com/pantsbuild/pants/issues/new with this error message attached.\\\\n\\\\n\\\"\\n f\\\"\\\\nshellcheck version: {shellcheck.version}\\\\n\\\"\\n f\\\"process_result.stdout: {process_result.stdout.decode()}\\\"\\n )\\n return ParsedShellImports()\\n\\n paths = set()\\n for error in output:\\n if not error.get(\\\"code\\\", \\\"\\\") == 1091:\\n continue\\n msg = error\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/shell/dependency_inference.py::infer_shell_dependencies\",\n \"text\": \"src/python/pants/backend/shell/dependency_inference.py::infer_shell_dependencies\\n@rule(desc=\\\"Inferring Shell dependencies by analyzing imports\\\")\\nasync def infer_shell_dependencies(\\n request: InferShellDependencies, shell_mapping: ShellMapping, shell_setup: ShellSetup\\n) -> InferredDependencies:\\n if not shell_setup.dependency_inference:\\n return InferredDependencies([])\\n\\n address = request.sources_field.address\\n wrapped_tgt = await Get(WrappedTarget, Address, address)\\n explicitly_provided_deps, hydrated_sources = await MultiGet(\\n Get(ExplicitlyProvidedDependencies, DependenciesRequest(wrapped_tgt.target[Dependencies])),\\n Get(HydratedSources, HydrateSourcesRequest(request.sources_field)),\\n )\\n assert len(hydrated_sources.snapshot.files) == 1\\n\\n detected_imports = await Get(\\n ParsedShellImports,\\n ParseShellImportsRequest(\\n hydrated_sources.snapshot.digest, hydrated_sources.snapshot.files[0]\\n ),\\n )\\n result: OrderedSet[Address] = OrderedSet()\\n for import_path in detected_imports:\\n unambiguous = shell_mapping.mapping.get(import_path)\\n ambiguous = shell_mapping.ambiguous_modules.get(import_path)\\n if unambiguous:\\n result.add(unambiguous)\\n elif ambiguous:\\n explicitly_provided_deps.maybe_warn_of_ambiguous_dependency_inference(\\n ambiguous,\\n address,\\n import_reference=\\\"file\\\",\\n context=f\\\"The target {address} sources `{import_path}`\\\",\\n )\\n maybe_disambiguated = explicitly_provided_deps.disambiguated(ambiguous)\\n if maybe_disambiguated:\\n result.add(maybe_disambiguated)\\n return InferredDependencies(sorted(result))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/shell/dependency_inference.py::rules\",\n \"text\": \"src/python/pants/backend/shell/dependency_inference.py::rules\\ndef rules():\\n return (*collect_rules(), UnionRule(InferDependenciesRequest, InferShellDependencies))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::FilesNotFoundBehavior.to_glob_match_error_behavior\",\n \"text\": \"src/python/pants/option/global_options.py::FilesNotFoundBehavior.to_glob_match_error_behavior\\n def to_glob_match_error_behavior(self) -> GlobMatchErrorBehavior:\\n return GlobMatchErrorBehavior(self.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::OwnersNotFoundBehavior.to_glob_match_error_behavior\",\n \"text\": \"src/python/pants/option/global_options.py::OwnersNotFoundBehavior.to_glob_match_error_behavior\\n def to_glob_match_error_behavior(self) -> GlobMatchErrorBehavior:\\n return GlobMatchErrorBehavior(self.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::AuthPluginResult.__post_init__\",\n \"text\": \"src/python/pants/option/global_options.py::AuthPluginResult.__post_init__\\n def __post_init__(self) -> None:\\n def assert_valid_address(addr: str | None, field_name: str) -> None:\\n valid_schemes = [f\\\"{scheme}://\\\" for scheme in (\\\"grpc\\\", \\\"grpcs\\\")]\\n if addr and not any(addr.startswith(scheme) for scheme in valid_schemes):\\n raise ValueError(\\n f\\\"Invalid `{field_name}` in `AuthPluginResult` returned from \\\"\\n f\\\"`--remote-auth-plugin`. Must start with `grpc://` or `grpcs://`, but was \\\"\\n f\\\"{addr}.\\\"\\n )\\n\\n assert_valid_address(self.store_address, \\\"store_address\\\")\\n assert_valid_address(self.execution_address, \\\"execution_address\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::AuthPluginResult.__post_init__.assert_valid_address\",\n \"text\": \"src/python/pants/option/global_options.py::AuthPluginResult.__post_init__.assert_valid_address\\n def assert_valid_address(addr: str | None, field_name: str) -> None:\\n valid_schemes = [f\\\"{scheme}://\\\" for scheme in (\\\"grpc\\\", \\\"grpcs\\\")]\\n if addr and not any(addr.startswith(scheme) for scheme in valid_schemes):\\n raise ValueError(\\n f\\\"Invalid `{field_name}` in `AuthPluginResult` returned from \\\"\\n f\\\"`--remote-auth-plugin`. Must start with `grpc://` or `grpcs://`, but was \\\"\\n f\\\"{addr}.\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::AuthPluginResult.is_available\",\n \"text\": \"src/python/pants/option/global_options.py::AuthPluginResult.is_available\\n @property\\n def is_available(self) -> bool:\\n return self.state == AuthPluginState.OK\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::DynamicRemoteOptions.disabled\",\n \"text\": \"src/python/pants/option/global_options.py::DynamicRemoteOptions.disabled\\n @classmethod\\n def disabled(cls) -> DynamicRemoteOptions:\\n return DynamicRemoteOptions(\\n execution=False,\\n cache_read=False,\\n cache_write=False,\\n instance_name=None,\\n store_address=None,\\n execution_address=None,\\n store_headers={},\\n execution_headers={},\\n parallelism=DEFAULT_EXECUTION_OPTIONS.process_execution_remote_parallelism,\\n store_rpc_concurrency=DEFAULT_EXECUTION_OPTIONS.remote_store_rpc_concurrency,\\n cache_rpc_concurrency=DEFAULT_EXECUTION_OPTIONS.remote_cache_rpc_concurrency,\\n execution_rpc_concurrency=DEFAULT_EXECUTION_OPTIONS.remote_execution_rpc_concurrency,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::DynamicRemoteOptions.from_options\",\n \"text\": \"src/python/pants/option/global_options.py::DynamicRemoteOptions.from_options\\n @classmethod\\n def from_options(\\n cls,\\n full_options: Options,\\n env: CompleteEnvironment,\\n prior_result: AuthPluginResult | None = None,\\n ) -> tuple[DynamicRemoteOptions, AuthPluginResult | None]:\\n bootstrap_options = full_options.bootstrap_option_values()\\n assert bootstrap_options is not None\\n execution = cast(bool, bootstrap_options.remote_execution)\\n cache_read = cast(bool, bootstrap_options.remote_cache_read)\\n cache_write = cast(bool, bootstrap_options.remote_cache_write)\\n store_address = cast(\\\"str | None\\\", bootstrap_options.remote_store_address)\\n execution_address = cast(\\\"str | None\\\", bootstrap_options.remote_execution_address)\\n instance_name = cast(\\\"str | None\\\", bootstrap_options.remote_instance_name)\\n execution_headers = cast(\\\"dict[str, str]\\\", bootstrap_options.remote_execution_headers)\\n store_headers = cast(\\\"dict[str, str]\\\", bootstrap_options.remote_store_headers)\\n parallelism = cast(int, bootstrap_options.process_execution_remote_parallelism)\\n store_rpc_concurrency = cast(int, bootstrap_options.remote_store_rpc_concurrency)\\n cache_rpc_concurrency = cast(int, bootstrap_options.remote_cache_rpc_concurrency)\\n execution_rpc_concurrency = cast(int, bootstrap_options.remote_execution_rpc_concurrency)\\n\\n if bootstrap_options.remote_oauth_bearer_token_path:\\n oauth_token = (\\n Path(bootstrap_options.remote_oauth_bearer_token_path).resolve().read_text().strip()\\n )\\n if set(oauth_token).intersection({\\\"\\\\n\\\", \\\"\\\\r\\\"}):\\n raise OptionsError(\\n f\\\"OAuth bearer token path {bootstrap_options.remote_oauth_bearer_token_path} \\\"\\n \\\"must not contain multiple lines.\\\"\\n )\\n token_header = {\\\"authorization\\\": f\\\"Bearer {oauth_token}\\\"}\\n execution_\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::ExecutionOptions.from_options\",\n \"text\": \"src/python/pants/option/global_options.py::ExecutionOptions.from_options\\n @classmethod\\n def from_options(\\n cls,\\n bootstrap_options: OptionValueContainer,\\n dynamic_remote_options: DynamicRemoteOptions,\\n ) -> ExecutionOptions:\\n return cls(\\n # Remote execution strategy.\\n remote_execution=dynamic_remote_options.execution,\\n remote_cache_read=dynamic_remote_options.cache_read,\\n remote_cache_write=dynamic_remote_options.cache_write,\\n # General remote setup.\\n remote_instance_name=dynamic_remote_options.instance_name,\\n remote_ca_certs_path=bootstrap_options.remote_ca_certs_path,\\n # Process execution setup.\\n process_cleanup=bootstrap_options.process_cleanup,\\n local_cache=bootstrap_options.local_cache,\\n process_execution_local_parallelism=bootstrap_options.process_execution_local_parallelism,\\n process_execution_remote_parallelism=dynamic_remote_options.parallelism,\\n process_execution_cache_namespace=bootstrap_options.process_execution_cache_namespace,\\n process_execution_local_enable_nailgun=bootstrap_options.process_execution_local_enable_nailgun,\\n # Remote store setup.\\n remote_store_address=dynamic_remote_options.store_address,\\n remote_store_headers=dynamic_remote_options.store_headers,\\n remote_store_chunk_bytes=bootstrap_options.remote_store_chunk_bytes,\\n remote_store_chunk_upload_timeout_seconds=bootstrap_options.remote_store_chunk_upload_timeout_seconds,\\n remote_store_rpc_retries=bootstrap_options.remote_store_rpc_retries,\\n remote_store_rpc_concurrency=dynamic_remote_options.store_rpc_concurrency,\\n remote_store_batch_api_size_limit=bootstrap_options.remote_store_batch_api_size_limit,\\n # Remote cache setup.\\n remote_cache_eager_fetch=bootstrap_options.remote_cache_eager_fetch,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::LocalStoreOptions.target_total_size_bytes\",\n \"text\": \"src/python/pants/option/global_options.py::LocalStoreOptions.target_total_size_bytes\\n def target_total_size_bytes(self) -> int:\\n \\\"\\\"\\\"Returns the target total size of all of the stores.\\n\\n The `max_size` values are caps on the total size of each store: the \\\"target\\\" size\\n is the size that garbage collection will attempt to shrink the stores to each time\\n it runs.\\n\\n NB: This value is not currently configurable, but that could be desirable in the future.\\n \\\"\\\"\\\"\\n max_total_size_bytes = (\\n self.processes_max_size_bytes\\n + self.files_max_size_bytes\\n + self.directories_max_size_bytes\\n )\\n return max_total_size_bytes // 10\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::LocalStoreOptions.from_options\",\n \"text\": \"src/python/pants/option/global_options.py::LocalStoreOptions.from_options\\n @classmethod\\n def from_options(cls, options: OptionValueContainer) -> LocalStoreOptions:\\n return cls(\\n store_dir=str(Path(options.local_store_dir).resolve()),\\n processes_max_size_bytes=options.local_store_processes_max_size_bytes,\\n files_max_size_bytes=options.local_store_files_max_size_bytes,\\n directories_max_size_bytes=options.local_store_directories_max_size_bytes,\\n shard_count=options.local_store_shard_count,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptions.register_bootstrap_options\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptions.register_bootstrap_options\\n @classmethod\\n def register_bootstrap_options(cls, register):\\n \\\"\\\"\\\"Register bootstrap options.\\n\\n \\\"Bootstrap options\\\" are the set of options necessary to create a Scheduler. If an option is\\n not consumed during creation of a Scheduler, it should be in `register_options` instead.\\n\\n Bootstrap option values can be interpolated into the config file, and can be referenced\\n programmatically in registration code, e.g., as register.bootstrap.pants_workdir.\\n\\n Note that regular code can also access these options as normal global-scope options. Their\\n status as \\\"bootstrap options\\\" is only pertinent during option registration.\\n \\\"\\\"\\\"\\n buildroot = get_buildroot()\\n default_distdir_name = \\\"dist\\\"\\n default_rel_distdir = f\\\"/{default_distdir_name}/\\\"\\n\\n register(\\n \\\"--backend-packages\\\",\\n advanced=True,\\n type=list,\\n default=[],\\n help=(\\n \\\"Register functionality from these backends.\\\\n\\\\nThe backend packages must be \\\"\\n \\\"present on the PYTHONPATH, typically because they are in the Pants core dist, in a \\\"\\n \\\"plugin dist, or available as sources in the repo.\\\"\\n ),\\n )\\n register(\\n \\\"--plugins\\\",\\n advanced=True,\\n type=list,\\n help=(\\n \\\"Allow backends to be loaded from these plugins (usually released through PyPI). \\\"\\n \\\"The default backends for each plugin will be loaded automatically. Other backends \\\"\\n \\\"in a plugin can be loaded by listing them in `backend_packages` in the \\\"\\n \\\"`[GLOBAL]` scope.\\\"\\n ),\\n )\\n register(\\n \\\"--plugins-force-resolve\\\",\\n advanced=True,\\n type=bool,\\n default=False,\\n help=\\\"Re-resolve plugins, even if previously resolv\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptions.register_options\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptions.register_options\\n @classmethod\\n def register_options(cls, register):\\n \\\"\\\"\\\"Register options not tied to any particular task or subsystem.\\\"\\\"\\\"\\n # The bootstrap options need to be registered on the post-bootstrap Options instance, so it\\n # won't choke on them on the command line, and also so we can access their values as regular\\n # global-scope options, for convenience.\\n cls.register_bootstrap_options(register)\\n\\n register(\\n \\\"--colors\\\",\\n type=bool,\\n default=sys.stdout.isatty(),\\n help=(\\n \\\"Whether Pants should use colors in output or not. This may also impact whether \\\"\\n \\\"some tools Pants runs use color.\\\\n\\\\nWhen unset, this value defaults based on \\\"\\n \\\"whether the output destination supports color.\\\"\\n ),\\n )\\n register(\\n \\\"--dynamic-ui\\\",\\n type=bool,\\n default=((\\\"CI\\\" not in os.environ) and sys.stderr.isatty()),\\n help=\\\"Display a dynamically-updating console UI as Pants runs. This is true by default \\\"\\n \\\"if Pants detects a TTY and there is no 'CI' environment variable indicating that \\\"\\n \\\"Pants is running in a continuous integration environment.\\\",\\n )\\n\\n register(\\n \\\"--tag\\\",\\n type=list,\\n metavar=\\\"[+-]tag1,tag2,...\\\",\\n help=(\\n \\\"Include only targets with these tags (optional '+' prefix) or without these \\\"\\n f\\\"tags ('-' prefix). See {doc_url('advanced-target-selection')}.\\\"\\n ),\\n )\\n register(\\n \\\"--exclude-target-regexp\\\",\\n type=list,\\n default=[],\\n metavar=\\\"<regexp>\\\",\\n help=\\\"Exclude targets that match these regexes. This does not impact file arguments.\\\",\\n )\\n\\n register(\\n \\\"--files-not-found-behavior\\\",\\n advanced=True,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptions.validate_instance\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptions.validate_instance\\n @classmethod\\n def validate_instance(cls, opts):\\n \\\"\\\"\\\"Validates an instance of global options for cases that are not prohibited via\\n registration.\\n\\n For example: mutually exclusive options may be registered by passing a `mutually_exclusive_group`,\\n but when multiple flags must be specified together, it can be necessary to specify post-parse\\n checks.\\n\\n Raises pants.option.errors.OptionsError on validation failure.\\n \\\"\\\"\\\"\\n if opts.rule_threads_core < 2:\\n # TODO: This is a defense against deadlocks due to #11329: we only run one `@goal_rule`\\n # at a time, and a `@goal_rule` will only block one thread.\\n raise OptionsError(\\n \\\"--rule-threads-core values less than 2 are not supported, but it was set to \\\"\\n f\\\"{opts.rule_threads_core}.\\\"\\n )\\n\\n if opts.remote_execution and (opts.remote_cache_read or opts.remote_cache_write):\\n raise OptionsError(\\n \\\"`--remote-execution` cannot be set at the same time as either \\\"\\n \\\"`--remote-cache-read` or `--remote-cache-write`.\\\\n\\\\nIf remote execution is \\\"\\n \\\"enabled, it will already use remote caching.\\\"\\n )\\n\\n if opts.remote_execution and not opts.remote_execution_address:\\n raise OptionsError(\\n \\\"The `--remote-execution` option requires also setting \\\"\\n \\\"`--remote-execution-address` to work properly.\\\"\\n )\\n if opts.remote_execution_address and not opts.remote_store_address:\\n raise OptionsError(\\n \\\"The `--remote-execution-address` option requires also setting \\\"\\n \\\"`--remote-store-address`. Often these have the same value.\\\"\\n )\\n\\n if opts.remote_cache_read and not opts.remote_store_address:\\n raise OptionsError(\\n \\\"The `--remote-cache-r\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptions.validate_instance.validate_remote_address\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptions.validate_instance.validate_remote_address\\n def validate_remote_address(opt_name: str) -> None:\\n valid_schemes = [f\\\"{scheme}://\\\" for scheme in (\\\"grpc\\\", \\\"grpcs\\\")]\\n address = getattr(opts, opt_name)\\n if address and not any(address.startswith(scheme) for scheme in valid_schemes):\\n raise OptionsError(\\n f\\\"The `{opt_name}` option must begin with one of {valid_schemes}, but \\\"\\n f\\\"was {address}.\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptions.validate_instance.validate_remote_headers\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptions.validate_instance.validate_remote_headers\\n def validate_remote_headers(opt_name: str) -> None:\\n command_line_opt_name = f\\\"--{opt_name.replace('_', '-')}\\\"\\n for k, v in getattr(opts, opt_name).items():\\n if not k.isascii():\\n raise OptionsError(\\n f\\\"All values in `{command_line_opt_name}` must be ASCII, but the key \\\"\\n f\\\"in `{k}: {v}` has non-ASCII characters.\\\"\\n )\\n if not v.isascii():\\n raise OptionsError(\\n f\\\"All values in `{command_line_opt_name}` must be ASCII, but the value in \\\"\\n f\\\"`{k}: {v}` has non-ASCII characters.\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptions.create_py_executor\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptions.create_py_executor\\n @staticmethod\\n def create_py_executor(bootstrap_options: OptionValueContainer) -> PyExecutor:\\n rule_threads_max = (\\n bootstrap_options.rule_threads_max\\n if bootstrap_options.rule_threads_max\\n else 4 * bootstrap_options.rule_threads_core\\n )\\n return PyExecutor(\\n core_threads=bootstrap_options.rule_threads_core, max_threads=rule_threads_max\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptions.compute_pants_ignore\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptions.compute_pants_ignore\\n @staticmethod\\n def compute_pants_ignore(buildroot, global_options):\\n \\\"\\\"\\\"Computes the merged value of the `--pants-ignore` flag.\\n\\n This inherently includes the workdir and distdir locations if they are located under the\\n buildroot.\\n \\\"\\\"\\\"\\n pants_ignore = list(global_options.pants_ignore)\\n\\n def add(absolute_path, include=False):\\n # To ensure that the path is ignored regardless of whether it is a symlink or a directory, we\\n # strip trailing slashes (which would signal that we wanted to ignore only directories).\\n maybe_rel_path = fast_relpath_optional(absolute_path, buildroot)\\n if maybe_rel_path:\\n rel_path = maybe_rel_path.rstrip(os.path.sep)\\n prefix = \\\"!\\\" if include else \\\"\\\"\\n pants_ignore.append(f\\\"{prefix}/{rel_path}\\\")\\n\\n add(global_options.pants_workdir)\\n add(global_options.pants_distdir)\\n add(global_options.pants_subprocessdir)\\n\\n return pants_ignore\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptions.compute_pants_ignore.add\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptions.compute_pants_ignore.add\\n def add(absolute_path, include=False):\\n # To ensure that the path is ignored regardless of whether it is a symlink or a directory, we\\n # strip trailing slashes (which would signal that we wanted to ignore only directories).\\n maybe_rel_path = fast_relpath_optional(absolute_path, buildroot)\\n if maybe_rel_path:\\n rel_path = maybe_rel_path.rstrip(os.path.sep)\\n prefix = \\\"!\\\" if include else \\\"\\\"\\n pants_ignore.append(f\\\"{prefix}/{rel_path}\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptions.compute_pantsd_invalidation_globs\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptions.compute_pantsd_invalidation_globs\\n @staticmethod\\n def compute_pantsd_invalidation_globs(\\n buildroot: str, bootstrap_options: OptionValueContainer\\n ) -> tuple[str, ...]:\\n \\\"\\\"\\\"Computes the merged value of the `--pantsd-invalidation-globs` option.\\n\\n Combines --pythonpath and --pants-config-files files that are in {buildroot} dir with those\\n invalidation_globs provided by users.\\n \\\"\\\"\\\"\\n invalidation_globs: OrderedSet[str] = OrderedSet()\\n\\n # Globs calculated from the sys.path and other file-like configuration need to be sanitized\\n # to relative globs (where possible).\\n potentially_absolute_globs = (\\n *sys.path,\\n *bootstrap_options.pythonpath,\\n *bootstrap_options.pants_config_files,\\n )\\n for glob in potentially_absolute_globs:\\n # NB: We use `relpath` here because these paths are untrusted, and might need to be\\n # normalized in addition to being relativized.\\n glob_relpath = os.path.relpath(glob, buildroot)\\n if glob_relpath == \\\".\\\" or glob_relpath.startswith(\\\"..\\\"):\\n logger.debug(\\n f\\\"Changes to {glob}, outside of the buildroot, will not be invalidated.\\\"\\n )\\n else:\\n invalidation_globs.update([glob_relpath, glob_relpath + \\\"/**\\\"])\\n\\n # Explicitly specified globs are already relative, and are added verbatim.\\n invalidation_globs.update(\\n (\\n \\\"!*.pyc\\\",\\n \\\"!__pycache__/\\\",\\n \\\".gitignore\\\",\\n # TODO: This is a bandaid for https://github.com/pantsbuild/pants/issues/7022:\\n # macros should be adapted to allow this dependency to be automatically detected.\\n \\\"requirements.txt\\\",\\n \\\"3rdparty/**/requirements.txt\\\",\\n \\\"pyproject.toml\\\",\\n \\\"3rdparty/**/pyproject.to\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptions.get_options_flags\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptions.get_options_flags\\n @memoized_classmethod\\n def get_options_flags(cls) -> GlobalOptionsFlags:\\n return GlobalOptionsFlags.create(cast(\\\"Type[GlobalOptions]\\\", cls))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptionsFlags.create\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptionsFlags.create\\n @classmethod\\n def create(cls, GlobalOptionsType: type[GlobalOptions]) -> GlobalOptionsFlags:\\n flags = set()\\n short_flags = set()\\n\\n def capture_the_flags(*args: str, **kwargs) -> None:\\n for arg in args:\\n flags.add(arg)\\n if len(arg) == 2:\\n short_flags.add(arg)\\n elif kwargs.get(\\\"type\\\") == bool:\\n flags.add(f\\\"--no-{arg[2:]}\\\")\\n\\n GlobalOptionsType.register_bootstrap_options(capture_the_flags)\\n return cls(FrozenOrderedSet(flags), FrozenOrderedSet(short_flags))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptionsFlags.create.capture_the_flags\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptionsFlags.create.capture_the_flags\\n def capture_the_flags(*args: str, **kwargs) -> None:\\n for arg in args:\\n flags.add(arg)\\n if len(arg) == 2:\\n short_flags.add(arg)\\n elif kwargs.get(\\\"type\\\") == bool:\\n flags.add(f\\\"--no-{arg[2:]}\\\")\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":50,"cells":{"instance_id":{"kind":"string","value":"wagtail__wagtail-7821"},"repo":{"kind":"string","value":"wagtail/wagtail"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Wagtail 2.15 Bulk Publish for Inherited Page\nFound a bug? Please fill out the sections below. 👍\r\n\r\n### Issue Summary\r\nAssume we have two classes:\r\nclass A(Page):\r\n name = models.CharField(max_length=255)\r\n\r\nclass B(A):\r\n nickname = models.CharField(max_length=255)\r\n\r\nWhen do bulk action of publishing the pages with B, an error will occur:\r\n\r\n\r\nThe existing solution I think is to customize bulk action according to https://docs.wagtail.io/en/stable/extending/custom_bulk_actions.html#custom-bulk-actions\r\nBut I would like to know if there is other solution?\n"},"gold_files":{"kind":"list like","value":["wagtail/admin/views/pages/bulk_actions/page_bulk_action.py","wagtail/admin/views/pages/bulk_actions/publish.py"],"string":"[\n \"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py\",\n \"wagtail/admin/views/pages/bulk_actions/publish.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::<module>","wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.execute_action"],"string":"[\n \"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::<module>\",\n \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.execute_action\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"wagtail/admin/action_menu.py","text":"wagtail/admin/action_menu.py\ndef requires_request_arg(method) # Helper function to handle deprecation of old ActionMenuItem API where get_url, i\nclass ActionMenuItem # Defines an item in the actions drop-up on the page creation/edit view\n def __init__(self, order=None)\n def get_user_page_permissions_tester(self, context)\n def is_shown(self, *args) # Whether this action should be shown on this request; permission checks etc shoul\n def get_context(self, request, parent_context)\n def get_context_data(self, parent_context) # Defines context for the template, overridable to use more data\n def get_url(self, *args)\n def render_html(self, *args)\nclass PublishMenuItem\n def is_shown(self, context)\n def get_context_data(self, parent_context)\nclass SubmitForModerationMenuItem\n def is_shown(self, context)\n def get_context_data(self, parent_context)\nclass WorkflowMenuItem\n def __init__(self, name, label, launch_modal, *args, **kwargs)\n def get_context_data(self, parent_context)\n def is_shown(self, context)\nclass RestartWorkflowMenuItem\n def is_shown(self, context)\nclass CancelWorkflowMenuItem\n def is_shown(self, context)\nclass UnpublishMenuItem\n def is_shown(self, context)\n def get_url(self, context)\nclass DeleteMenuItem\n def is_shown(self, context)\n def get_url(self, context)\nclass LockMenuItem\n def is_shown(self, context)\n def get_url(self, context)\n def get_context_data(self, parent_context)\nclass UnlockMenuItem\n def is_shown(self, context)\n def get_url(self, context)\nclass SaveDraftMenuItem\n def get_context_data(self, parent_context)\nclass PageLockedMenuItem\n def is_shown(self, context)\n def get_context_data(self, parent_context)\ndef _get_base_page_action_menu_items() # Retrieve the global list of menu items for the page action menu,\nclass PageActionMenu\n def __init__(self, request, **kwargs)\n def render_html(self)\n def media(self)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py","text":"wagtail/admin/templatetags/wagtailadmin_tags.py\ndef menu_search(context)\ndef main_nav(context)\ndef explorer_breadcrumb(context, page, include_self=True, trailing_arrow=False)\ndef search_other(context, current=None)\ndef main_nav_js()\ndef main_nav_css()\ndef ellipsistrim(value, max_length)\ndef fieldtype(bound_field)\ndef widgettype(bound_field)\ndef _get_user_page_permissions(context)\ndef page_permissions(context, page) # Usage: {% page_permissions page as page_perms %}\ndef test_collection_is_public(context, collection) # Usage: {% test_collection_is_public collection as is_public %}\ndef test_page_is_public(context, page) # Usage: {% test_page_is_public page as is_public %}\ndef hook_output(hook_name) # Example: {% hook_output 'insert_editor_css' %}\ndef usage_count_enabled()\ndef base_url_setting()\ndef allow_unicode_slugs()\ndef auto_update_preview()\nclass EscapeScriptNode\n def __init__(self, nodelist)\n def render(self, context)\n def handle(cls, parser, token)\ndef render_with_errors(bound_field) # Usage: {{ field|render_with_errors }} as opposed to {{ field }}.\ndef has_unrendered_errors(bound_field) # Return true if this field has errors that were not accounted for by render_with_\ndef cautious_slugify(value)\ndef querystring(context, **kwargs) # Print out the current querystring. Any keyword arguments to this template\ndef page_table_header_label(context, label=None, parent_page_title=None, **kwargs) # Wraps table_header_label to add a title attribute based on the parent page title\ndef table_header_label(context, label=None, sortable=True, ordering=None, sort_context_var='ordering', sort_param='ordering', sort_field=None, ascending_title_text=None, descending_title_text=None) # A label to go in a table header cell, optionally with a 'sort' link that alterna\ndef pagination_querystring(context, page_number, page_key='p') # Print out a querystring with an updated page number:\ndef paginate(context, page, base_url='', page_key='p', classnames='') # Print pagination previous/next links, and the page count. Take the\ndef page_listing_buttons(context, page, page_perms, is_parent=False)\ndef bulk_action_choices(context, app_label, model_name)\ndef message_tags(message)\ndef _abs(val)\ndef admin_urlquote(value)\ndef avatar_url(user, size=50, gravatar_only=False) # A template tag that receives a user and size and return\ndef js_translation_strings()\ndef notification_static(path) # Variant of the {% static %}` tag for use in notification emails - tries to form\ndef versioned_static(path) # Wrapper for Django's static file finder to append a cache-busting query paramete\ndef icon(name=None, class_name='icon', title=None, wrapped=False) # Abstracts away the actual icon implementation.\ndef timesince_simple(d) # Returns a simplified timesince:\ndef timesince_last_update(last_update, time_prefix='', use_shorthand=True) # Returns:\ndef format_collection(coll: Collection, min_depth: int=2) # Renders a given Collection's name as a formatted string that display","label":0},{"cid":"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py","text":"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py\nclass DefaultPageForm\nclass PageBulkAction\n def get_all_objects_in_listing_query(self, parent_id)\n def get_context_data(self, **kwargs)\n def get_execution_context(self)","label":1},{"cid":"wagtail/admin/views/pages/bulk_actions/publish.py","text":"wagtail/admin/views/pages/bulk_actions/publish.py\nclass PublishBulkAction\n def check_perm(self, page)\n def object_context(self, obj)\n def get_context_data(self, **kwargs)\n def get_execution_context(self)\n def execute_action(cls, objects, include_descendants=False, user=None, **kwargs)\n def get_success_message(self, num_parent_objects, num_child_objects)","label":1},{"cid":"wagtail/core/migrations/0001_initial.py","text":"wagtail/core/migrations/0001_initial.py\ndef set_page_path_collation(apps, schema_editor) # Treebeard's path comparison logic can fail on certain locales such as sk_SK, whi\nclass Migration","label":0},{"cid":"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py","text":"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py\ndef initial_data(apps, schema_editor)\ndef remove_initial_data(apps, schema_editor) # This function does nothing. The below code is commented out together\ndef set_page_path_collation(apps, schema_editor) # Treebeard's path comparison logic can fail on certain locales such as sk_SK, whi\nclass Migration","label":0},{"cid":"wagtail/core/migrations/0014_add_verbose_name.py","text":"wagtail/core/migrations/0014_add_verbose_name.py\nclass Migration","label":0},{"cid":"wagtail/images/migrations/0016_deprecate_rendition_filter_relation.py","text":"wagtail/images/migrations/0016_deprecate_rendition_filter_relation.py\nclass Migration","label":0},{"cid":"wagtail/images/models.py","text":"wagtail/images/models.py\nclass SourceImageIOError # Custom exception to distinguish IOErrors that were thrown while opening the sour\nclass ImageQuerySet\ndef get_upload_to(instance, filename) # Obtain a valid upload path for an image file.\ndef get_rendition_upload_to(instance, filename) # Obtain a valid upload path for an image rendition file.\nclass ImageFileMixin\n def is_stored_locally(self) # Returns True if the image is hosted on the local filesystem\n def get_file_size(self)\n def open_file(self)\n def get_willow_image(self)\nclass AbstractImage\n def _set_file_hash(self, file_contents)\n def get_file_hash(self)\n def get_upload_to(self, filename)\n def get_usage(self)\n def usage_url(self)\n def __str__(self)\n def get_rect(self)\n def get_focal_point(self)\n def has_focal_point(self)\n def set_focal_point(self, rect)\n def get_suggested_focal_point(self)\n def get_rendition_model(cls) # Get the Rendition model for this Image model\n def get_rendition(self, filter)\n def is_portrait(self)\n def is_landscape(self)\n def filename(self)\n def default_alt_text(self)\n def is_editable_by_user(self, user)\n class Meta\nclass Image\n class Meta\nclass Filter # Represents one or more operations that can be applied to an Image to produce a r\n def __init__(self, spec=None)\n def operations(self)\n def transform_operations(self)\n def filter_operations(self)\n def get_transform(self, image, size=None) # Returns an ImageTransform with all the transforms in this filter applied.\n def run(self, image, output)\n def get_cache_key(self, image)\nclass AbstractRendition\n def url(self)\n def alt(self)\n def attrs(self) # The src, width, height, and alt attributes for an <img> tag, as a HTML\n def attrs_dict(self) # A dict of the src, width, height, and alt attributes for an <img> tag.\n def full_url(self)\n def filter(self)\n def focal_point(self)\n def background_position_style(self) # Returns a `background-position` rule to be put in the inline style of an element\n def img_tag(self, extra_attributes={})\n def __html__(self)\n def get_upload_to(self, filename)\n def check(cls, **kwargs)\n def construct_cache_key(image_id, filter_cache_key, filter_spec)\n def purge_from_cache(self)\n class Meta\nclass Rendition\n class Meta\nclass UploadedImage # Temporary storage for images uploaded through the multiple image uploader, when ","label":0}],"string":"[\n {\n \"cid\": \"wagtail/admin/action_menu.py\",\n \"text\": \"wagtail/admin/action_menu.py\\ndef requires_request_arg(method) # Helper function to handle deprecation of old ActionMenuItem API where get_url, i\\nclass ActionMenuItem # Defines an item in the actions drop-up on the page creation/edit view\\n def __init__(self, order=None)\\n def get_user_page_permissions_tester(self, context)\\n def is_shown(self, *args) # Whether this action should be shown on this request; permission checks etc shoul\\n def get_context(self, request, parent_context)\\n def get_context_data(self, parent_context) # Defines context for the template, overridable to use more data\\n def get_url(self, *args)\\n def render_html(self, *args)\\nclass PublishMenuItem\\n def is_shown(self, context)\\n def get_context_data(self, parent_context)\\nclass SubmitForModerationMenuItem\\n def is_shown(self, context)\\n def get_context_data(self, parent_context)\\nclass WorkflowMenuItem\\n def __init__(self, name, label, launch_modal, *args, **kwargs)\\n def get_context_data(self, parent_context)\\n def is_shown(self, context)\\nclass RestartWorkflowMenuItem\\n def is_shown(self, context)\\nclass CancelWorkflowMenuItem\\n def is_shown(self, context)\\nclass UnpublishMenuItem\\n def is_shown(self, context)\\n def get_url(self, context)\\nclass DeleteMenuItem\\n def is_shown(self, context)\\n def get_url(self, context)\\nclass LockMenuItem\\n def is_shown(self, context)\\n def get_url(self, context)\\n def get_context_data(self, parent_context)\\nclass UnlockMenuItem\\n def is_shown(self, context)\\n def get_url(self, context)\\nclass SaveDraftMenuItem\\n def get_context_data(self, parent_context)\\nclass PageLockedMenuItem\\n def is_shown(self, context)\\n def get_context_data(self, parent_context)\\ndef _get_base_page_action_menu_items() # Retrieve the global list of menu items for the page action menu,\\nclass PageActionMenu\\n def __init__(self, request, **kwargs)\\n def render_html(self)\\n def media(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py\\ndef menu_search(context)\\ndef main_nav(context)\\ndef explorer_breadcrumb(context, page, include_self=True, trailing_arrow=False)\\ndef search_other(context, current=None)\\ndef main_nav_js()\\ndef main_nav_css()\\ndef ellipsistrim(value, max_length)\\ndef fieldtype(bound_field)\\ndef widgettype(bound_field)\\ndef _get_user_page_permissions(context)\\ndef page_permissions(context, page) # Usage: {% page_permissions page as page_perms %}\\ndef test_collection_is_public(context, collection) # Usage: {% test_collection_is_public collection as is_public %}\\ndef test_page_is_public(context, page) # Usage: {% test_page_is_public page as is_public %}\\ndef hook_output(hook_name) # Example: {% hook_output 'insert_editor_css' %}\\ndef usage_count_enabled()\\ndef base_url_setting()\\ndef allow_unicode_slugs()\\ndef auto_update_preview()\\nclass EscapeScriptNode\\n def __init__(self, nodelist)\\n def render(self, context)\\n def handle(cls, parser, token)\\ndef render_with_errors(bound_field) # Usage: {{ field|render_with_errors }} as opposed to {{ field }}.\\ndef has_unrendered_errors(bound_field) # Return true if this field has errors that were not accounted for by render_with_\\ndef cautious_slugify(value)\\ndef querystring(context, **kwargs) # Print out the current querystring. Any keyword arguments to this template\\ndef page_table_header_label(context, label=None, parent_page_title=None, **kwargs) # Wraps table_header_label to add a title attribute based on the parent page title\\ndef table_header_label(context, label=None, sortable=True, ordering=None, sort_context_var='ordering', sort_param='ordering', sort_field=None, ascending_title_text=None, descending_title_text=None) # A label to go in a table header cell, optionally with a 'sort' link that alterna\\ndef pagination_querystring(context, page_number, page_key='p') # Print out a querystring with an updated page number:\\ndef paginate(context, page, base_url='', page_key='p', classnames='') # Print pagination previous/next links, and the page count. Take the\\ndef page_listing_buttons(context, page, page_perms, is_parent=False)\\ndef bulk_action_choices(context, app_label, model_name)\\ndef message_tags(message)\\ndef _abs(val)\\ndef admin_urlquote(value)\\ndef avatar_url(user, size=50, gravatar_only=False) # A template tag that receives a user and size and return\\ndef js_translation_strings()\\ndef notification_static(path) # Variant of the {% static %}` tag for use in notification emails - tries to form\\ndef versioned_static(path) # Wrapper for Django's static file finder to append a cache-busting query paramete\\ndef icon(name=None, class_name='icon', title=None, wrapped=False) # Abstracts away the actual icon implementation.\\ndef timesince_simple(d) # Returns a simplified timesince:\\ndef timesince_last_update(last_update, time_prefix='', use_shorthand=True) # Returns:\\ndef format_collection(coll: Collection, min_depth: int=2) # Renders a given Collection's name as a formatted string that display\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py\",\n \"text\": \"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py\\nclass DefaultPageForm\\nclass PageBulkAction\\n def get_all_objects_in_listing_query(self, parent_id)\\n def get_context_data(self, **kwargs)\\n def get_execution_context(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/admin/views/pages/bulk_actions/publish.py\",\n \"text\": \"wagtail/admin/views/pages/bulk_actions/publish.py\\nclass PublishBulkAction\\n def check_perm(self, page)\\n def object_context(self, obj)\\n def get_context_data(self, **kwargs)\\n def get_execution_context(self)\\n def execute_action(cls, objects, include_descendants=False, user=None, **kwargs)\\n def get_success_message(self, num_parent_objects, num_child_objects)\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/core/migrations/0001_initial.py\",\n \"text\": \"wagtail/core/migrations/0001_initial.py\\ndef set_page_path_collation(apps, schema_editor) # Treebeard's path comparison logic can fail on certain locales such as sk_SK, whi\\nclass Migration\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py\",\n \"text\": \"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py\\ndef initial_data(apps, schema_editor)\\ndef remove_initial_data(apps, schema_editor) # This function does nothing. The below code is commented out together\\ndef set_page_path_collation(apps, schema_editor) # Treebeard's path comparison logic can fail on certain locales such as sk_SK, whi\\nclass Migration\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/migrations/0014_add_verbose_name.py\",\n \"text\": \"wagtail/core/migrations/0014_add_verbose_name.py\\nclass Migration\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/migrations/0016_deprecate_rendition_filter_relation.py\",\n \"text\": \"wagtail/images/migrations/0016_deprecate_rendition_filter_relation.py\\nclass Migration\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py\",\n \"text\": \"wagtail/images/models.py\\nclass SourceImageIOError # Custom exception to distinguish IOErrors that were thrown while opening the sour\\nclass ImageQuerySet\\ndef get_upload_to(instance, filename) # Obtain a valid upload path for an image file.\\ndef get_rendition_upload_to(instance, filename) # Obtain a valid upload path for an image rendition file.\\nclass ImageFileMixin\\n def is_stored_locally(self) # Returns True if the image is hosted on the local filesystem\\n def get_file_size(self)\\n def open_file(self)\\n def get_willow_image(self)\\nclass AbstractImage\\n def _set_file_hash(self, file_contents)\\n def get_file_hash(self)\\n def get_upload_to(self, filename)\\n def get_usage(self)\\n def usage_url(self)\\n def __str__(self)\\n def get_rect(self)\\n def get_focal_point(self)\\n def has_focal_point(self)\\n def set_focal_point(self, rect)\\n def get_suggested_focal_point(self)\\n def get_rendition_model(cls) # Get the Rendition model for this Image model\\n def get_rendition(self, filter)\\n def is_portrait(self)\\n def is_landscape(self)\\n def filename(self)\\n def default_alt_text(self)\\n def is_editable_by_user(self, user)\\n class Meta\\nclass Image\\n class Meta\\nclass Filter # Represents one or more operations that can be applied to an Image to produce a r\\n def __init__(self, spec=None)\\n def operations(self)\\n def transform_operations(self)\\n def filter_operations(self)\\n def get_transform(self, image, size=None) # Returns an ImageTransform with all the transforms in this filter applied.\\n def run(self, image, output)\\n def get_cache_key(self, image)\\nclass AbstractRendition\\n def url(self)\\n def alt(self)\\n def attrs(self) # The src, width, height, and alt attributes for an <img> tag, as a HTML\\n def attrs_dict(self) # A dict of the src, width, height, and alt attributes for an <img> tag.\\n def full_url(self)\\n def filter(self)\\n def focal_point(self)\\n def background_position_style(self) # Returns a `background-position` rule to be put in the inline style of an element\\n def img_tag(self, extra_attributes={})\\n def __html__(self)\\n def get_upload_to(self, filename)\\n def check(cls, **kwargs)\\n def construct_cache_key(image_id, filter_cache_key, filter_spec)\\n def purge_from_cache(self)\\n class Meta\\nclass Rendition\\n class Meta\\nclass UploadedImage # Temporary storage for images uploaded through the multiple image uploader, when \",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"wagtail/admin/action_menu.py::requires_request_arg","text":"wagtail/admin/action_menu.py::requires_request_arg\ndef requires_request_arg(method):\n \"\"\"\n Helper function to handle deprecation of old ActionMenuItem API where get_url, is_show,\n get_context and render_html all accepted both 'request' and 'parent_context' as arguments\n \"\"\"\n try:\n # see if this is a pre-2.15 get_url method that takes both request and context kwargs\n inspect.signature(method).bind({})\n except TypeError:\n return True\n else:\n return False","label":0},{"cid":"wagtail/admin/action_menu.py::ActionMenuItem.__init__","text":"wagtail/admin/action_menu.py::ActionMenuItem.__init__\n def __init__(self, order=None):\n if order is not None:\n self.order = order","label":0},{"cid":"wagtail/admin/action_menu.py::ActionMenuItem.get_user_page_permissions_tester","text":"wagtail/admin/action_menu.py::ActionMenuItem.get_user_page_permissions_tester\n def get_user_page_permissions_tester(self, context):\n if 'user_page_permissions_tester' in context:\n return context['user_page_permissions_tester']\n return context['user_page_permissions'].for_page(context['page'])","label":0},{"cid":"wagtail/admin/action_menu.py::ActionMenuItem.is_shown","text":"wagtail/admin/action_menu.py::ActionMenuItem.is_shown\n def is_shown(self, *args):\n # accepts both is_shown(request, context) (pre-2.15 signature)\n # and is_shown(context) (signature as of 2.15)\n # to allow for pre-2.15 ActionMenuItem subclasses calling super().\n # RemovedInWagtail217Warning: signature should become is_shown(self, context)\n \"\"\"\n Whether this action should be shown on this request; permission checks etc should go here.\n By default, actions are shown for unlocked pages, hidden for locked pages\n\n context = dictionary containing at least:\n 'request' = the current request object\n 'view' = 'create', 'edit' or 'revisions_revert'\n 'page' (if view = 'edit' or 'revisions_revert') = the page being edited\n 'parent_page' (if view = 'create') = the parent page of the page being created\n 'user_page_permissions' = a UserPagePermissionsProxy for the current user, to test permissions against\n may also contain:\n 'user_page_permissions_tester' = a PagePermissionTester for the current user and page\n \"\"\"\n if len(args) == 2:\n warn(\n \"ActionMenuItem.is_shown no longer takes a 'request' argument. \"\n \"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\",\n category=RemovedInWagtail217Warning, stacklevel=2\n )\n request, context = args\n else:\n context, = args\n\n return (\n context['view'] == 'create'\n or not self.get_user_page_permissions_tester(context).page_locked()\n )","label":0},{"cid":"wagtail/admin/action_menu.py::ActionMenuItem.get_context","text":"wagtail/admin/action_menu.py::ActionMenuItem.get_context\n def get_context(self, request, parent_context):\n # Obsolete - included here for pre-2.15 subclasses that override this and call super().\n # RemovedInWagtail217Warning\n return self.get_context_data(parent_context)","label":0},{"cid":"wagtail/admin/action_menu.py::ActionMenuItem.get_context_data","text":"wagtail/admin/action_menu.py::ActionMenuItem.get_context_data\n def get_context_data(self, parent_context):\n \"\"\"Defines context for the template, overridable to use more data\"\"\"\n context = parent_context.copy()\n\n if requires_request_arg(self.get_url):\n warn(\n \"%s.get_url should no longer take a 'request' argument. \"\n \"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\" % type(self).__name__,\n category=RemovedInWagtail217Warning\n )\n url = self.get_url(parent_context['request'], parent_context)\n else:\n url = self.get_url(parent_context)\n\n context.update({\n 'label': self.label,\n 'url': url,\n 'name': self.name,\n 'classname': self.classname,\n 'icon_name': self.icon_name,\n 'request': parent_context['request'],\n })\n return context","label":0},{"cid":"wagtail/admin/action_menu.py::ActionMenuItem.get_url","text":"wagtail/admin/action_menu.py::ActionMenuItem.get_url\n def get_url(self, *args):\n # accepts both get_url(request, parent_context) (pre-2.15 signature)\n # and get_url(parent_context) (signature as of 2.15)\n # to allow for pre-2.15 ActionMenuItem subclasses calling super().\n # RemovedInWagtail217Warning: signature should become get_url(self, parent_context)\n return None","label":0},{"cid":"wagtail/admin/action_menu.py::ActionMenuItem.render_html","text":"wagtail/admin/action_menu.py::ActionMenuItem.render_html\n def render_html(self, *args):\n # accepts both render_html(request, parent_context) (pre-2.15 signature)\n # and render_html(parent_context) (signature as of 2.15)\n # to allow for pre-2.15 ActionMenuItem subclasses calling super().\n # RemovedInWagtail217Warning: signature should become render_html(self, parent_context)\n\n if len(args) == 2:\n warn(\n \"ActionMenuItem.render_html no longer takes a 'request' argument. \"\n \"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\",\n category=RemovedInWagtail217Warning, stacklevel=2\n )\n request, parent_context = args\n else:\n parent_context, = args\n\n if not getattr(self.get_context, 'is_base_method', False):\n # get_context has been overridden, so call it instead of get_context_data\n warn(\n \"%s should define get_context_data(self, parent_context) instead of get_context(self, request, get_context_data). \"\n \"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\" % type(self).__name__,\n category=RemovedInWagtail217Warning\n )\n context_data = self.get_context(parent_context['request'], parent_context)\n else:\n context_data = self.get_context_data(parent_context)\n\n if self.template:\n warn(\n \"%s should define template_name instead of template. \"\n \"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\" % type(self).__name__,\n category=RemovedInWagtail217Warning\n )\n template_name = self.template\n else:\n template_name = self.template_name\n\n template = get_template(template_name)\n return template.render(context_data)","label":0},{"cid":"wagtail/admin/action_menu.py::PublishMenuItem.is_shown","text":"wagtail/admin/action_menu.py::PublishMenuItem.is_shown\n def is_shown(self, context):\n if context['view'] == 'create':\n return context['user_page_permissions'].for_page(context['parent_page']).can_publish_subpage()\n else: # view == 'edit' or 'revisions_revert'\n perms_tester = self.get_user_page_permissions_tester(context)\n return (\n not perms_tester.page_locked()\n and perms_tester.can_publish()\n )","label":0},{"cid":"wagtail/admin/action_menu.py::PublishMenuItem.get_context_data","text":"wagtail/admin/action_menu.py::PublishMenuItem.get_context_data\n def get_context_data(self, parent_context):\n context = super().get_context_data(parent_context)\n context['is_revision'] = (context['view'] == 'revisions_revert')\n return context","label":0},{"cid":"wagtail/admin/action_menu.py::SubmitForModerationMenuItem.is_shown","text":"wagtail/admin/action_menu.py::SubmitForModerationMenuItem.is_shown\n def is_shown(self, context):\n if not getattr(settings, 'WAGTAIL_MODERATION_ENABLED', True):\n return False\n\n if context['view'] == 'create':\n return context['parent_page'].has_workflow\n\n if context['view'] == 'edit':\n perms_tester = self.get_user_page_permissions_tester(context)\n return perms_tester.can_submit_for_moderation() and not perms_tester.page_locked()\n # context == revisions_revert\n return False","label":0},{"cid":"wagtail/admin/action_menu.py::SubmitForModerationMenuItem.get_context_data","text":"wagtail/admin/action_menu.py::SubmitForModerationMenuItem.get_context_data\n def get_context_data(self, parent_context):\n context = super().get_context_data(parent_context)\n page = context.get('page')\n workflow_state = page.current_workflow_state if page else None\n if workflow_state and workflow_state.status == workflow_state.STATUS_NEEDS_CHANGES:\n context['label'] = _(\"Resubmit to {}\").format(workflow_state.current_task_state.task.name)\n elif page:\n workflow = page.get_workflow()\n if workflow:\n context['label'] = _(\"Submit to {}\").format(workflow.name)\n return context","label":0},{"cid":"wagtail/admin/action_menu.py::WorkflowMenuItem.__init__","text":"wagtail/admin/action_menu.py::WorkflowMenuItem.__init__\n def __init__(self, name, label, launch_modal, *args, **kwargs):\n self.name = name\n self.label = label\n self.launch_modal = launch_modal\n\n if kwargs.get('icon_name'):\n self.icon_name = kwargs.pop('icon_name')\n\n super().__init__(*args, **kwargs)","label":0},{"cid":"wagtail/admin/action_menu.py::WorkflowMenuItem.get_context_data","text":"wagtail/admin/action_menu.py::WorkflowMenuItem.get_context_data\n def get_context_data(self, parent_context):\n context = super().get_context_data(parent_context)\n context['launch_modal'] = self.launch_modal\n context['current_task_state'] = context['page'].current_workflow_task_state\n return context","label":0},{"cid":"wagtail/admin/action_menu.py::WorkflowMenuItem.is_shown","text":"wagtail/admin/action_menu.py::WorkflowMenuItem.is_shown\n def is_shown(self, context):\n if context['view'] == 'edit':\n perms_tester = self.get_user_page_permissions_tester(context)\n return not perms_tester.page_locked()","label":0},{"cid":"wagtail/admin/action_menu.py::RestartWorkflowMenuItem.is_shown","text":"wagtail/admin/action_menu.py::RestartWorkflowMenuItem.is_shown\n def is_shown(self, context):\n if not getattr(settings, 'WAGTAIL_MODERATION_ENABLED', True):\n return False\n elif context['view'] == 'edit':\n workflow_state = context['page'].current_workflow_state\n perms_tester = self.get_user_page_permissions_tester(context)\n return (\n perms_tester.can_submit_for_moderation()\n and not perms_tester.page_locked()\n and workflow_state\n and workflow_state.user_can_cancel(context['request'].user)\n )\n else:\n return False","label":0},{"cid":"wagtail/admin/action_menu.py::CancelWorkflowMenuItem.is_shown","text":"wagtail/admin/action_menu.py::CancelWorkflowMenuItem.is_shown\n def is_shown(self, context):\n if context['view'] == 'edit':\n workflow_state = context['page'].current_workflow_state\n return workflow_state and workflow_state.user_can_cancel(context['request'].user)\n return False","label":0},{"cid":"wagtail/admin/action_menu.py::UnpublishMenuItem.is_shown","text":"wagtail/admin/action_menu.py::UnpublishMenuItem.is_shown\n def is_shown(self, context):\n if context['view'] == 'edit':\n perms_tester = self.get_user_page_permissions_tester(context)\n return (\n not perms_tester.page_locked()\n and perms_tester.can_unpublish()\n )","label":0},{"cid":"wagtail/admin/action_menu.py::UnpublishMenuItem.get_url","text":"wagtail/admin/action_menu.py::UnpublishMenuItem.get_url\n def get_url(self, context):\n return reverse('wagtailadmin_pages:unpublish', args=(context['page'].id,))","label":0},{"cid":"wagtail/admin/action_menu.py::DeleteMenuItem.is_shown","text":"wagtail/admin/action_menu.py::DeleteMenuItem.is_shown\n def is_shown(self, context):\n if context['view'] == 'edit':\n perms_tester = self.get_user_page_permissions_tester(context)\n return (\n not perms_tester.page_locked()\n and perms_tester.can_delete()\n )","label":0},{"cid":"wagtail/admin/action_menu.py::DeleteMenuItem.get_url","text":"wagtail/admin/action_menu.py::DeleteMenuItem.get_url\n def get_url(self, context):\n return reverse('wagtailadmin_pages:delete', args=(context['page'].id,))","label":0},{"cid":"wagtail/admin/action_menu.py::LockMenuItem.is_shown","text":"wagtail/admin/action_menu.py::LockMenuItem.is_shown\n def is_shown(self, context):\n return (\n context['view'] == 'edit'\n and not context['page'].locked\n and self.get_user_page_permissions_tester(context).can_lock()\n )","label":0},{"cid":"wagtail/admin/action_menu.py::LockMenuItem.get_url","text":"wagtail/admin/action_menu.py::LockMenuItem.get_url\n def get_url(self, context):\n return reverse('wagtailadmin_pages:lock', args=(context['page'].id,))","label":0},{"cid":"wagtail/admin/action_menu.py::LockMenuItem.get_context_data","text":"wagtail/admin/action_menu.py::LockMenuItem.get_context_data\n def get_context_data(self, parent_context):\n context = super().get_context_data(parent_context)\n context['aria_label'] = self.aria_label\n return context","label":0},{"cid":"wagtail/admin/action_menu.py::UnlockMenuItem.is_shown","text":"wagtail/admin/action_menu.py::UnlockMenuItem.is_shown\n def is_shown(self, context):\n return (\n context['view'] == 'edit'\n and context['page'].locked\n and self.get_user_page_permissions_tester(context).can_unlock()\n )","label":0},{"cid":"wagtail/admin/action_menu.py::UnlockMenuItem.get_url","text":"wagtail/admin/action_menu.py::UnlockMenuItem.get_url\n def get_url(self, context):\n return reverse('wagtailadmin_pages:unlock', args=(context['page'].id,))","label":0},{"cid":"wagtail/admin/action_menu.py::SaveDraftMenuItem.get_context_data","text":"wagtail/admin/action_menu.py::SaveDraftMenuItem.get_context_data\n def get_context_data(self, parent_context):\n context = super().get_context_data(parent_context)\n context['is_revision'] = (context['view'] == 'revisions_revert')\n return context","label":0},{"cid":"wagtail/admin/action_menu.py::PageLockedMenuItem.is_shown","text":"wagtail/admin/action_menu.py::PageLockedMenuItem.is_shown\n def is_shown(self, context):\n return (\n 'page' in context\n and self.get_user_page_permissions_tester(context).page_locked()\n )","label":0},{"cid":"wagtail/admin/action_menu.py::PageLockedMenuItem.get_context_data","text":"wagtail/admin/action_menu.py::PageLockedMenuItem.get_context_data\n def get_context_data(self, parent_context):\n context = super().get_context_data(parent_context)\n context['is_revision'] = (context['view'] == 'revisions_revert')\n return context","label":0},{"cid":"wagtail/admin/action_menu.py::_get_base_page_action_menu_items","text":"wagtail/admin/action_menu.py::_get_base_page_action_menu_items\ndef _get_base_page_action_menu_items():\n \"\"\"\n Retrieve the global list of menu items for the page action menu,\n which may then be customised on a per-request basis\n \"\"\"\n global BASE_PAGE_ACTION_MENU_ITEMS\n\n if BASE_PAGE_ACTION_MENU_ITEMS is None:\n BASE_PAGE_ACTION_MENU_ITEMS = [\n SaveDraftMenuItem(order=0),\n DeleteMenuItem(order=10),\n LockMenuItem(order=15),\n UnlockMenuItem(order=15),\n UnpublishMenuItem(order=20),\n PublishMenuItem(order=30),\n CancelWorkflowMenuItem(order=40),\n RestartWorkflowMenuItem(order=50),\n SubmitForModerationMenuItem(order=60),\n PageLockedMenuItem(order=10000),\n ]\n for hook in hooks.get_hooks('register_page_action_menu_item'):\n action_menu_item = hook()\n if action_menu_item:\n BASE_PAGE_ACTION_MENU_ITEMS.append(action_menu_item)\n\n return BASE_PAGE_ACTION_MENU_ITEMS","label":0},{"cid":"wagtail/admin/action_menu.py::PageActionMenu.__init__","text":"wagtail/admin/action_menu.py::PageActionMenu.__init__\n def __init__(self, request, **kwargs):\n self.request = request\n self.context = kwargs\n self.context['request'] = request\n page = self.context.get('page')\n user_page_permissions = UserPagePermissionsProxy(self.request.user)\n self.context['user_page_permissions'] = user_page_permissions\n if page:\n self.context['user_page_permissions_tester'] = user_page_permissions.for_page(page)\n\n self.menu_items = []\n\n if page:\n task = page.current_workflow_task\n current_workflow_state = page.current_workflow_state\n is_final_task = current_workflow_state and current_workflow_state.is_at_final_task\n if task:\n actions = task.get_actions(page, request.user)\n workflow_menu_items = []\n for name, label, launch_modal in actions:\n icon_name = 'edit'\n if name == \"approve\":\n if is_final_task and not getattr(settings, 'WAGTAIL_WORKFLOW_REQUIRE_REAPPROVAL_ON_EDIT', False):\n label = _(\"%(label)s and Publish\") % {'label': label}\n icon_name = 'success'\n\n item = WorkflowMenuItem(name, label, launch_modal, icon_name=icon_name)\n\n if requires_request_arg(item.is_shown):\n warn(\n \"%s.is_shown should no longer take a 'request' argument. \"\n \"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\" % type(item).__name__,\n category=RemovedInWagtail217Warning\n )\n is_shown = item.is_shown(self.request, self.context)\n else:\n is_shown = item.is_shown(self.context)\n\n if is_shown:\n workflow_menu","label":0},{"cid":"wagtail/admin/action_menu.py::PageActionMenu.render_html","text":"wagtail/admin/action_menu.py::PageActionMenu.render_html\n def render_html(self):\n rendered_menu_items = []\n for menu_item in self.menu_items:\n if requires_request_arg(menu_item.render_html):\n warn(\n \"%s.render_html should no longer take a 'request' argument. \"\n \"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\" % type(menu_item).__name__,\n category=RemovedInWagtail217Warning\n )\n rendered_menu_items.append(menu_item.render_html(self.request, self.context))\n else:\n rendered_menu_items.append(menu_item.render_html(self.context))\n\n if requires_request_arg(self.default_item.render_html):\n warn(\n \"%s.render_html should no longer take a 'request' argument. \"\n \"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\" % type(self.default_item).__name__,\n category=RemovedInWagtail217Warning\n )\n rendered_default_item = self.default_item.render_html(self.request, self.context)\n else:\n rendered_default_item = self.default_item.render_html(self.context)\n\n return render_to_string(self.template, {\n 'default_menu_item': rendered_default_item,\n 'show_menu': bool(self.menu_items),\n 'rendered_menu_items': rendered_menu_items,\n }, request=self.request)","label":0},{"cid":"wagtail/admin/action_menu.py::PageActionMenu.media","text":"wagtail/admin/action_menu.py::PageActionMenu.media\n @cached_property\n def media(self):\n media = Media()\n for item in self.menu_items:\n media += item.media\n return media","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::menu_search","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::menu_search\n@register.simple_tag(takes_context=True)\ndef menu_search(context):\n request = context['request']\n\n search_areas = admin_search_areas.search_items_for_request(request)\n if not search_areas:\n return ''\n search_area = search_areas[0]\n\n return render_to_string('wagtailadmin/shared/menu_search.html', {\n 'search_url': search_area.url,\n })","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav\n@register.inclusion_tag('wagtailadmin/shared/main_nav.html', takes_context=True)\ndef main_nav(context):\n request = context['request']\n\n return {\n 'menu_html': admin_menu.render_html(request),\n 'request': request,\n }","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::explorer_breadcrumb","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::explorer_breadcrumb\n@register.inclusion_tag('wagtailadmin/shared/breadcrumb.html', takes_context=True)\ndef explorer_breadcrumb(context, page, include_self=True, trailing_arrow=False):\n user = context['request'].user\n\n # find the closest common ancestor of the pages that this user has direct explore permission\n # (i.e. add/edit/publish/lock) over; this will be the root of the breadcrumb\n cca = get_explorable_root_page(user)\n if not cca:\n return {'pages': Page.objects.none()}\n\n return {\n 'pages': page.get_ancestors(inclusive=include_self).descendant_of(cca, inclusive=True).specific(),\n 'trailing_arrow': trailing_arrow\n }","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::search_other","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::search_other\n@register.inclusion_tag('wagtailadmin/shared/search_other.html', takes_context=True)\ndef search_other(context, current=None):\n request = context['request']\n\n return {\n 'options_html': admin_search_areas.render_html(request, current),\n 'request': request,\n }","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav_js","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav_js\n@register.simple_tag\ndef main_nav_js():\n if slim_sidebar_enabled():\n return Media(\n js=[\n versioned_static('wagtailadmin/js/telepath/telepath.js'),\n versioned_static('wagtailadmin/js/sidebar.js'),\n ]\n )\n\n else:\n return Media(\n js=[\n versioned_static('wagtailadmin/js/sidebar-legacy.js')\n ]\n ) + admin_menu.media['js']","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav_css","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav_css\n@register.simple_tag\ndef main_nav_css():\n if slim_sidebar_enabled():\n return Media(\n css={\n 'all': [\n versioned_static('wagtailadmin/css/sidebar.css')\n ]\n }\n )\n\n else:\n # Legacy sidebar CSS in core.css\n return admin_menu.media['css']","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::ellipsistrim","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::ellipsistrim\n@register.filter(\"ellipsistrim\")\ndef ellipsistrim(value, max_length):\n if len(value) > max_length:\n truncd_val = value[:max_length]\n if not len(value) == (max_length + 1) and value[max_length + 1] != \" \":\n truncd_val = truncd_val[:truncd_val.rfind(\" \")]\n return truncd_val + \"…\"\n return value","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::fieldtype","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::fieldtype\n@register.filter\ndef fieldtype(bound_field):\n try:\n return camelcase_to_underscore(bound_field.field.__class__.__name__)\n except AttributeError:\n try:\n return camelcase_to_underscore(bound_field.__class__.__name__)\n except AttributeError:\n return \"\"","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::widgettype","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::widgettype\n@register.filter\ndef widgettype(bound_field):\n try:\n return camelcase_to_underscore(bound_field.field.widget.__class__.__name__)\n except AttributeError:\n try:\n return camelcase_to_underscore(bound_field.widget.__class__.__name__)\n except AttributeError:\n return \"\"","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::_get_user_page_permissions","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::_get_user_page_permissions\ndef _get_user_page_permissions(context):\n # Create a UserPagePermissionsProxy object to represent the user's global permissions, and\n # cache it in the context for the duration of the page request, if one does not exist already\n if 'user_page_permissions' not in context:\n context['user_page_permissions'] = UserPagePermissionsProxy(context['request'].user)\n\n return context['user_page_permissions']","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_permissions","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_permissions\n@register.simple_tag(takes_context=True)\ndef page_permissions(context, page):\n \"\"\"\n Usage: {% page_permissions page as page_perms %}\n Sets the variable 'page_perms' to a PagePermissionTester object that can be queried to find out\n what actions the current logged-in user can perform on the given page.\n \"\"\"\n return _get_user_page_permissions(context).for_page(page)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::test_collection_is_public","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::test_collection_is_public\n@register.simple_tag(takes_context=True)\ndef test_collection_is_public(context, collection):\n \"\"\"\n Usage: {% test_collection_is_public collection as is_public %}\n Sets 'is_public' to True iff there are no collection view restrictions in place\n on this collection.\n Caches the list of collection view restrictions in the context, to avoid repeated\n DB queries on repeated calls.\n \"\"\"\n if 'all_collection_view_restrictions' not in context:\n context['all_collection_view_restrictions'] = CollectionViewRestriction.objects.select_related('collection').values_list(\n 'collection__name', flat=True\n )\n\n is_private = collection.name in context['all_collection_view_restrictions']\n\n return not is_private","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::test_page_is_public","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::test_page_is_public\n@register.simple_tag(takes_context=True)\ndef test_page_is_public(context, page):\n \"\"\"\n Usage: {% test_page_is_public page as is_public %}\n Sets 'is_public' to True iff there are no page view restrictions in place on\n this page.\n Caches the list of page view restrictions on the request, to avoid repeated\n DB queries on repeated calls.\n \"\"\"\n if not hasattr(context[\"request\"], \"all_page_view_restriction_paths\"):\n context['request'].all_page_view_restriction_paths = PageViewRestriction.objects.select_related('page').values_list(\n 'page__path', flat=True\n )\n\n is_private = any([\n page.path.startswith(restricted_path)\n for restricted_path in context[\"request\"].all_page_view_restriction_paths\n ])\n\n return not is_private","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::hook_output","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::hook_output\n@register.simple_tag\ndef hook_output(hook_name):\n \"\"\"\n Example: {% hook_output 'insert_editor_css' %}\n Whenever we have a hook whose functions take no parameters and return a string, this tag can be used\n to output the concatenation of all of those return values onto the page.\n Note that the output is not escaped - it is the hook function's responsibility to escape unsafe content.\n \"\"\"\n snippets = [fn() for fn in hooks.get_hooks(hook_name)]\n return mark_safe(''.join(snippets))","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::usage_count_enabled","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::usage_count_enabled\n@register.simple_tag\ndef usage_count_enabled():\n return getattr(settings, 'WAGTAIL_USAGE_COUNT_ENABLED', False)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::base_url_setting","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::base_url_setting\n@register.simple_tag\ndef base_url_setting():\n return getattr(settings, 'BASE_URL', None)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::allow_unicode_slugs","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::allow_unicode_slugs\n@register.simple_tag\ndef allow_unicode_slugs():\n return getattr(settings, 'WAGTAIL_ALLOW_UNICODE_SLUGS', True)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::auto_update_preview","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::auto_update_preview\n@register.simple_tag\ndef auto_update_preview():\n return getattr(settings, 'WAGTAIL_AUTO_UPDATE_PREVIEW', False)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.__init__","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.__init__\n def __init__(self, nodelist):\n super().__init__()\n self.nodelist = nodelist","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.render","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.render\n def render(self, context):\n out = self.nodelist.render(context)\n return escape_script(out)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.handle","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.handle\n @classmethod\n def handle(cls, parser, token):\n nodelist = parser.parse(('end' + EscapeScriptNode.TAG_NAME,))\n parser.delete_first_token()\n return cls(nodelist)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::render_with_errors","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::render_with_errors\n@register.filter\ndef render_with_errors(bound_field):\n \"\"\"\n Usage: {{ field|render_with_errors }} as opposed to {{ field }}.\n If the field (a BoundField instance) has errors on it, and the associated widget implements\n a render_with_errors method, call that; otherwise, call the regular widget rendering mechanism.\n \"\"\"\n widget = bound_field.field.widget\n if bound_field.errors and hasattr(widget, 'render_with_errors'):\n return widget.render_with_errors(\n bound_field.html_name,\n bound_field.value(),\n attrs={'id': bound_field.auto_id},\n errors=bound_field.errors\n )\n else:\n return bound_field.as_widget()","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::has_unrendered_errors","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::has_unrendered_errors\n@register.filter\ndef has_unrendered_errors(bound_field):\n \"\"\"\n Return true if this field has errors that were not accounted for by render_with_errors, because\n the widget does not support the render_with_errors method\n \"\"\"\n return bound_field.errors and not hasattr(bound_field.field.widget, 'render_with_errors')","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::cautious_slugify","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::cautious_slugify\n@register.filter(is_safe=True)\n@stringfilter\ndef cautious_slugify(value):\n return _cautious_slugify(value)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::querystring","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::querystring\n@register.simple_tag(takes_context=True)\ndef querystring(context, **kwargs):\n \"\"\"\n Print out the current querystring. Any keyword arguments to this template\n tag will be added to the querystring before it is printed out.\n\n <a href=\"/page/{% querystring key='value' %}\">\n\n Will result in something like:\n\n <a href=\"/page/?foo=bar&key=value\">\n \"\"\"\n request = context['request']\n querydict = request.GET.copy()\n # Can't do querydict.update(kwargs), because QueryDict.update() appends to\n # the list of values, instead of replacing the values.\n for key, value in kwargs.items():\n if value is None:\n # Remove the key if the value is None\n querydict.pop(key, None)\n else:\n # Set the key otherwise\n querydict[key] = str(value)\n\n return '?' + querydict.urlencode()","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_table_header_label","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_table_header_label\n@register.simple_tag(takes_context=True)\ndef page_table_header_label(context, label=None, parent_page_title=None, **kwargs):\n \"\"\"\n Wraps table_header_label to add a title attribute based on the parent page title and the column label\n \"\"\"\n if label:\n translation_context = {'parent': parent_page_title, 'label': label}\n ascending_title_text = _(\"Sort the order of child pages within '%(parent)s' by '%(label)s' in ascending order.\") % translation_context\n descending_title_text = _(\"Sort the order of child pages within '%(parent)s' by '%(label)s' in descending order.\") % translation_context\n else:\n ascending_title_text = None\n descending_title_text = None\n\n return table_header_label(context, label=label, ascending_title_text=ascending_title_text, descending_title_text=descending_title_text, **kwargs)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::table_header_label","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::table_header_label\n@register.simple_tag(takes_context=True)\ndef table_header_label(\n context, label=None, sortable=True, ordering=None,\n sort_context_var='ordering', sort_param='ordering', sort_field=None,\n ascending_title_text=None, descending_title_text=None\n):\n \"\"\"\n A label to go in a table header cell, optionally with a 'sort' link that alternates between\n forward and reverse sorting\n\n label = label text\n ordering = current active ordering. If not specified, we will fetch it from the template context variable\n given by sort_context_var. (We don't fetch it from the URL because that wouldn't give the view method\n the opportunity to set a default)\n sort_param = URL parameter that indicates the current active ordering\n sort_field = the value for sort_param that indicates that sorting is currently on this column.\n For example, if sort_param='ordering' and sort_field='title', then a URL parameter of\n ordering=title indicates that the listing is ordered forwards on this column, and a URL parameter\n of ordering=-title indicated that the listing is ordered in reverse on this column\n ascending_title_text = title attribute to use on the link when the link action will sort in ascending order\n descending_title_text = title attribute to use on the link when the link action will sort in descending order\n\n To disable sorting on this column, set sortable=False or leave sort_field unspecified.\n \"\"\"\n if not sortable or not sort_field:\n # render label without a sort link\n return label\n\n if ordering is None:\n ordering = context.get(sort_context_var)\n reverse_sort_field = \"-%s\" % sort_field\n\n if ordering == sort_field:\n # currently ordering forwards on this column; link should change to reverse ordering\n attrs = {\n 'href': querystring(context, **{sort_param: reverse_sort_field}),\n 'class': \"i","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::pagination_querystring","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::pagination_querystring\n@register.simple_tag(takes_context=True)\ndef pagination_querystring(context, page_number, page_key='p'):\n \"\"\"\n Print out a querystring with an updated page number:\n\n {% if page.has_next_page %}\n <a href=\"{% pagination_link page.next_page_number %}\">Next page</a>\n {% endif %}\n \"\"\"\n return querystring(context, **{page_key: page_number})","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::paginate","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::paginate\n@register.inclusion_tag(\"wagtailadmin/pages/listing/_pagination.html\",\n takes_context=True)\ndef paginate(context, page, base_url='', page_key='p',\n classnames=''):\n \"\"\"\n Print pagination previous/next links, and the page count. Take the\n following arguments:\n\n page\n The current page of results. This should be a Django pagination `Page`\n instance\n\n base_url\n The base URL of the next/previous page, with no querystring.\n This is optional, and defaults to the current page by just printing the\n querystring for the next/previous page.\n\n page_key\n The name of the page variable in the query string. Defaults to 'p'.\n\n classnames\n Extra classes to add to the next/previous links.\n \"\"\"\n request = context['request']\n return {\n 'base_url': base_url,\n 'classnames': classnames,\n 'request': request,\n 'page': page,\n 'page_key': page_key,\n 'paginator': page.paginator,\n }","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_listing_buttons","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_listing_buttons\n@register.inclusion_tag(\"wagtailadmin/pages/listing/_buttons.html\",\n takes_context=True)\ndef page_listing_buttons(context, page, page_perms, is_parent=False):\n next_url = context.request.path\n button_hooks = hooks.get_hooks('register_page_listing_buttons')\n\n buttons = []\n for hook in button_hooks:\n buttons.extend(hook(page, page_perms, is_parent, next_url))\n\n buttons.sort()\n\n for hook in hooks.get_hooks('construct_page_listing_buttons'):\n hook(buttons, page, page_perms, is_parent, context)\n\n return {'page': page, 'buttons': buttons}","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::bulk_action_choices","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::bulk_action_choices\n@register.inclusion_tag(\"wagtailadmin/pages/listing/_buttons.html\",\n takes_context=True)\ndef bulk_action_choices(context, app_label, model_name):\n\n bulk_actions_list = list(bulk_action_registry.get_bulk_actions_for_model(app_label, model_name))\n bulk_actions_list.sort(key=lambda x: x.action_priority)\n\n bulk_action_more_list = []\n if len(bulk_actions_list) > 4:\n bulk_action_more_list = bulk_actions_list[4:]\n bulk_actions_list = bulk_actions_list[:4]\n\n next_url = get_valid_next_url_from_request(context['request'])\n if not next_url:\n next_url = context['request'].path\n\n bulk_action_buttons = [\n PageListingButton(\n action.display_name,\n reverse('wagtail_bulk_action', args=[app_label, model_name, action.action_type]) + '?' + urlencode({'next': next_url}),\n attrs={'aria-label': action.aria_label},\n priority=action.action_priority,\n classes=action.classes | {'bulk-action-btn'},\n ) for action in bulk_actions_list\n ]\n\n if bulk_action_more_list:\n more_button = ButtonWithDropdown(\n label=_(\"More\"),\n attrs={\n 'title': _(\"View more bulk actions\")\n },\n classes={'bulk-actions-more', 'dropup'},\n button_classes={'button', 'button-small'},\n buttons_data=[{\n 'label': action.display_name,\n 'url': reverse('wagtail_bulk_action', args=[app_label, model_name, action.action_type]) + '?' + urlencode({'next': next_url}),\n 'attrs': {'aria-label': action.aria_label},\n 'priority': action.action_priority,\n 'classes': {'bulk-action-btn'},\n } for action in bulk_action_more_list]\n )\n more_button.is_parent = True\n bulk_action_buttons.append(more_button)\n\n return {'buttons': bulk_action_buttons}","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::message_tags","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::message_tags\n@register.simple_tag\ndef message_tags(message):\n level_tag = MESSAGE_TAGS.get(message.level)\n if message.extra_tags and level_tag:\n return message.extra_tags + ' ' + level_tag\n elif message.extra_tags:\n return message.extra_tags\n elif level_tag:\n return level_tag\n else:\n return ''","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::_abs","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::_abs\n@register.filter('abs')\ndef _abs(val):\n return abs(val)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::admin_urlquote","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::admin_urlquote\n@register.filter\ndef admin_urlquote(value):\n return quote(value)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::avatar_url","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::avatar_url\n@register.simple_tag\ndef avatar_url(user, size=50, gravatar_only=False):\n \"\"\"\n A template tag that receives a user and size and return\n the appropriate avatar url for that user.\n Example usage: {% avatar_url request.user 50 %}\n \"\"\"\n\n if not gravatar_only and hasattr(user, 'wagtail_userprofile') and user.wagtail_userprofile.avatar:\n return user.wagtail_userprofile.avatar.url\n\n if hasattr(user, 'email'):\n gravatar_url = get_gravatar_url(user.email, size=size)\n if gravatar_url is not None:\n return gravatar_url\n\n return versioned_static_func('wagtailadmin/images/default-user-avatar.png')","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::js_translation_strings","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::js_translation_strings\n@register.simple_tag\ndef js_translation_strings():\n return mark_safe(json.dumps(get_js_translation_strings()))","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::notification_static","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::notification_static\n@register.simple_tag\ndef notification_static(path):\n \"\"\"\n Variant of the {% static %}` tag for use in notification emails - tries to form\n a full URL using BASE_URL if the static URL isn't already a full URL.\n \"\"\"\n return urljoin(base_url_setting(), static(path))","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::versioned_static","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::versioned_static\n@register.simple_tag\ndef versioned_static(path):\n \"\"\"\n Wrapper for Django's static file finder to append a cache-busting query parameter\n that updates on each Wagtail version\n \"\"\"\n return versioned_static_func(path)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::icon","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::icon\n@register.inclusion_tag(\"wagtailadmin/shared/icon.html\", takes_context=False)\ndef icon(name=None, class_name='icon', title=None, wrapped=False):\n \"\"\"\n Abstracts away the actual icon implementation.\n\n Usage:\n {% load wagtailadmin_tags %}\n ...\n {% icon name=\"cogs\" class_name=\"icon--red\" title=\"Settings\" %}\n\n :param name: the icon name/id, required (string)\n :param class_name: default 'icon' (string)\n :param title: accessible label intended for screen readers (string)\n :return: Rendered template snippet (string)\n \"\"\"\n if not name:\n raise ValueError(\"You must supply an icon name\")\n\n return {\n 'name': name,\n 'class_name': class_name,\n 'title': title,\n 'wrapped': wrapped\n }","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_simple","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_simple\n@register.filter()\ndef timesince_simple(d):\n \"\"\"\n Returns a simplified timesince:\n 19 hours, 48 minutes ago -> 19 hours ago\n 1 week, 1 day ago -> 1 week ago\n 0 minutes ago -> just now\n \"\"\"\n time_period = timesince(d).split(',')[0]\n if time_period == avoid_wrapping(_('0 minutes')):\n return _(\"Just now\")\n return _(\"%(time_period)s ago\") % {'time_period': time_period}","label":0},{"cid":"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_all_objects_in_listing_query","text":"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_all_objects_in_listing_query\n def get_all_objects_in_listing_query(self, parent_id):\n listing_objects = self.model.objects.all()\n\n if parent_id is not None:\n listing_objects = listing_objects.get(id=parent_id).get_children()\n\n listing_objects = listing_objects.values_list('pk', flat=True)\n\n if 'q' in self.request.GET:\n q = self.request.GET.get('q', '')\n\n listing_objects = page_filter_search(q, listing_objects)[0].results()\n\n return listing_objects","label":0},{"cid":"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_context_data","text":"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_context_data\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context['items_with_no_access'] = [\n {'item': page, 'can_edit': page.permissions_for_user(self.request.user).can_edit()} for page in context['items_with_no_access']\n ]\n return context","label":0},{"cid":"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_execution_context","text":"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_execution_context\n def get_execution_context(self):\n return {\n 'user': self.request.user\n }","label":0},{"cid":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.check_perm","text":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.check_perm\n def check_perm(self, page):\n return page.permissions_for_user(self.request.user).can_publish()","label":0},{"cid":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.object_context","text":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.object_context\n def object_context(self, obj):\n context = super().object_context(obj)\n context['draft_descendant_count'] = context['item'].get_descendants().not_live().count()\n return context","label":0},{"cid":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_context_data","text":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_context_data\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context['has_draft_descendants'] = any(map(lambda x: x['draft_descendant_count'], context['items']))\n return context","label":0},{"cid":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_execution_context","text":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_execution_context\n def get_execution_context(self):\n return {\n **super().get_execution_context(),\n 'include_descendants': self.cleaned_form.cleaned_data['include_descendants'],\n }","label":0},{"cid":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.execute_action","text":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.execute_action\n @classmethod\n def execute_action(cls, objects, include_descendants=False, user=None, **kwargs):\n num_parent_objects, num_child_objects = 0, 0\n for page in objects:\n page = page.specific\n revision = page.save_revision(user=user)\n revision.publish(user=user)\n num_parent_objects += 1\n\n if include_descendants:\n for draft_descendant_page in page.get_descendants().not_live().defer_streamfields().specific():\n if user is None or draft_descendant_page.permissions_for_user(user).can_publish():\n revision = draft_descendant_page.save_revision(user=user)\n revision.publish(user=user)\n num_child_objects += 1\n return num_parent_objects, num_child_objects","label":1},{"cid":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_success_message","text":"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_success_message\n def get_success_message(self, num_parent_objects, num_child_objects):\n include_descendants = self.cleaned_form.cleaned_data['include_descendants']\n if num_parent_objects == 1:\n if include_descendants:\n if num_child_objects == 0:\n success_message = _(\"1 page has been published\")\n else:\n success_message = ngettext(\n \"1 page and %(num_child_objects)d child page have been published\",\n \"1 page and %(num_child_objects)d child pages have been published\",\n num_child_objects\n ) % {\n 'num_child_objects': num_child_objects\n }\n else:\n success_message = _(\"1 page has been published\")\n else:\n if include_descendants:\n if num_child_objects == 0:\n success_message = _(\"%(num_parent_objects)d pages have been published\") % {'num_parent_objects': num_parent_objects}\n else:\n success_message = ngettext(\n \"%(num_parent_objects)d pages and %(num_child_objects)d child page have been published\",\n \"%(num_parent_objects)d pages and %(num_child_objects)d child pages have been published\",\n num_child_objects\n ) % {\n 'num_child_objects': num_child_objects,\n 'num_parent_objects': num_parent_objects\n }\n else:\n success_message = _(\"%(num_parent_objects)d pages have been published\") % {'num_parent_objects': num_parent_objects}\n return success_message","label":0},{"cid":"wagtail/core/migrations/0001_initial.py::set_page_path_collation","text":"wagtail/core/migrations/0001_initial.py::set_page_path_collation\ndef set_page_path_collation(apps, schema_editor):\n \"\"\"\n Treebeard's path comparison logic can fail on certain locales such as sk_SK, which\n sort numbers after letters. To avoid this, we explicitly set the collation for the\n 'path' column to the (non-locale-specific) 'C' collation.\n\n See: https://groups.google.com/d/msg/wagtail/q0leyuCnYWI/I9uDvVlyBAAJ\n \"\"\"\n if schema_editor.connection.vendor == 'postgresql':\n schema_editor.execute(\"\"\"\n ALTER TABLE wagtailcore_page ALTER COLUMN path TYPE VARCHAR(255) COLLATE \"C\"\n \"\"\")","label":0},{"cid":"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::initial_data","text":"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::initial_data\ndef initial_data(apps, schema_editor):\n ContentType = apps.get_model('contenttypes.ContentType')\n Group = apps.get_model('auth.Group')\n Page = apps.get_model('wagtailcore.Page')\n Site = apps.get_model('wagtailcore.Site')\n GroupPagePermission = apps.get_model('wagtailcore.GroupPagePermission')\n\n # Create page content type\n page_content_type, created = ContentType.objects.get_or_create(\n model='page',\n app_label='wagtailcore'\n )\n\n # Create root page\n root = Page.objects.create(\n title=\"Root\",\n slug='root',\n content_type=page_content_type,\n path='0001',\n depth=1,\n numchild=1,\n url_path='/',\n )\n\n # Create homepage\n homepage = Page.objects.create(\n title=\"Welcome to your new Wagtail site!\",\n slug='home',\n content_type=page_content_type,\n path='00010001',\n depth=2,\n numchild=0,\n url_path='/home/',\n )\n\n # Create default site\n Site.objects.get_or_create(\n hostname='localhost',\n root_page_id=homepage.id,\n is_default_site=True\n )\n\n # Create auth groups\n moderators_group, created = Group.objects.get_or_create(name='Moderators')\n editors_group, created = Group.objects.get_or_create(name='Editors')\n\n # Create group permissions\n GroupPagePermission.objects.create(\n group=moderators_group,\n page=root,\n permission_type='add',\n )\n GroupPagePermission.objects.create(\n group=moderators_group,\n page=root,\n permission_type='edit',\n )\n GroupPagePermission.objects.create(\n group=moderators_group,\n page=root,\n permission_type='publish',\n )\n\n GroupPagePermission.objects.create(\n group=editors_group,\n page=root,\n permission_type='add',\n )\n GroupPagePermission.objects.create(\n group=editors_","label":0},{"cid":"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::remove_initial_data","text":"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::remove_initial_data\ndef remove_initial_data(apps, schema_editor):\n \"\"\"This function does nothing. The below code is commented out together\n with an explanation of why we don't need to bother reversing any of the\n initial data\"\"\"\n pass","label":0},{"cid":"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::set_page_path_collation","text":"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::set_page_path_collation\ndef set_page_path_collation(apps, schema_editor):\n \"\"\"\n Treebeard's path comparison logic can fail on certain locales such as sk_SK, which\n sort numbers after letters. To avoid this, we explicitly set the collation for the\n 'path' column to the (non-locale-specific) 'C' collation.\n\n See: https://groups.google.com/d/msg/wagtail/q0leyuCnYWI/I9uDvVlyBAAJ\n \"\"\"\n if schema_editor.connection.vendor == 'postgresql':\n schema_editor.execute(\"\"\"\n ALTER TABLE wagtailcore_page ALTER COLUMN path TYPE VARCHAR(255) COLLATE \"C\"\n \"\"\")","label":0},{"cid":"wagtail/images/models.py::get_upload_to","text":"wagtail/images/models.py::get_upload_to\ndef get_upload_to(instance, filename):\n \"\"\"\n Obtain a valid upload path for an image file.\n\n This needs to be a module-level function so that it can be referenced within migrations,\n but simply delegates to the `get_upload_to` method of the instance, so that AbstractImage\n subclasses can override it.\n \"\"\"\n return instance.get_upload_to(filename)","label":0},{"cid":"wagtail/images/models.py::get_rendition_upload_to","text":"wagtail/images/models.py::get_rendition_upload_to\ndef get_rendition_upload_to(instance, filename):\n \"\"\"\n Obtain a valid upload path for an image rendition file.\n\n This needs to be a module-level function so that it can be referenced within migrations,\n but simply delegates to the `get_upload_to` method of the instance, so that AbstractRendition\n subclasses can override it.\n \"\"\"\n return instance.get_upload_to(filename)","label":0},{"cid":"wagtail/images/models.py::ImageFileMixin.is_stored_locally","text":"wagtail/images/models.py::ImageFileMixin.is_stored_locally\n def is_stored_locally(self):\n \"\"\"\n Returns True if the image is hosted on the local filesystem\n \"\"\"\n try:\n self.file.path\n\n return True\n except NotImplementedError:\n return False","label":0},{"cid":"wagtail/images/models.py::ImageFileMixin.get_file_size","text":"wagtail/images/models.py::ImageFileMixin.get_file_size\n def get_file_size(self):\n if self.file_size is None:\n try:\n self.file_size = self.file.size\n except Exception as e:\n # File not found\n #\n # Have to catch everything, because the exception\n # depends on the file subclass, and therefore the\n # storage being used.\n raise SourceImageIOError(str(e))\n\n self.save(update_fields=['file_size'])\n\n return self.file_size","label":0},{"cid":"wagtail/images/models.py::ImageFileMixin.open_file","text":"wagtail/images/models.py::ImageFileMixin.open_file\n @contextmanager\n def open_file(self):\n # Open file if it is closed\n close_file = False\n try:\n image_file = self.file\n\n if self.file.closed:\n # Reopen the file\n if self.is_stored_locally():\n self.file.open('rb')\n else:\n # Some external storage backends don't allow reopening\n # the file. Get a fresh file instance. #1397\n storage = self._meta.get_field('file').storage\n image_file = storage.open(self.file.name, 'rb')\n\n close_file = True\n except IOError as e:\n # re-throw this as a SourceImageIOError so that calling code can distinguish\n # these from IOErrors elsewhere in the process\n raise SourceImageIOError(str(e))\n\n # Seek to beginning\n image_file.seek(0)\n\n try:\n yield image_file\n finally:\n if close_file:\n image_file.close()","label":0},{"cid":"wagtail/images/models.py::ImageFileMixin.get_willow_image","text":"wagtail/images/models.py::ImageFileMixin.get_willow_image\n @contextmanager\n def get_willow_image(self):\n with self.open_file() as image_file:\n yield WillowImage.open(image_file)","label":0},{"cid":"wagtail/images/models.py::AbstractImage._set_file_hash","text":"wagtail/images/models.py::AbstractImage._set_file_hash\n def _set_file_hash(self, file_contents):\n self.file_hash = hashlib.sha1(file_contents).hexdigest()","label":0},{"cid":"wagtail/images/models.py::AbstractImage.get_file_hash","text":"wagtail/images/models.py::AbstractImage.get_file_hash\n def get_file_hash(self):\n if self.file_hash == '':\n with self.open_file() as f:\n self._set_file_hash(f.read())\n\n self.save(update_fields=['file_hash'])\n\n return self.file_hash","label":0},{"cid":"wagtail/images/models.py::AbstractImage.get_upload_to","text":"wagtail/images/models.py::AbstractImage.get_upload_to\n def get_upload_to(self, filename):\n folder_name = 'original_images'\n filename = self.file.field.storage.get_valid_name(filename)\n\n # do a unidecode in the filename and then\n # replace non-ascii characters in filename with _ , to sidestep issues with filesystem encoding\n filename = \"\".join((i if ord(i) < 128 else '_') for i in string_to_ascii(filename))\n\n # Truncate filename so it fits in the 100 character limit\n # https://code.djangoproject.com/ticket/9893\n full_path = os.path.join(folder_name, filename)\n if len(full_path) >= 95:\n chars_to_trim = len(full_path) - 94\n prefix, extension = os.path.splitext(filename)\n filename = prefix[:-chars_to_trim] + extension\n full_path = os.path.join(folder_name, filename)\n\n return full_path","label":0},{"cid":"wagtail/images/models.py::AbstractImage.get_usage","text":"wagtail/images/models.py::AbstractImage.get_usage\n def get_usage(self):\n return get_object_usage(self)","label":0},{"cid":"wagtail/images/models.py::AbstractImage.usage_url","text":"wagtail/images/models.py::AbstractImage.usage_url\n @property\n def usage_url(self):\n return reverse('wagtailimages:image_usage',\n args=(self.id,))","label":0},{"cid":"wagtail/images/models.py::AbstractImage.__str__","text":"wagtail/images/models.py::AbstractImage.__str__\n def __str__(self):\n return self.title","label":0},{"cid":"wagtail/images/models.py::AbstractImage.get_rect","text":"wagtail/images/models.py::AbstractImage.get_rect\n def get_rect(self):\n return Rect(0, 0, self.width, self.height)","label":0},{"cid":"wagtail/images/models.py::AbstractImage.get_focal_point","text":"wagtail/images/models.py::AbstractImage.get_focal_point\n def get_focal_point(self):\n if self.focal_point_x is not None and \\\n self.focal_point_y is not None and \\\n self.focal_point_width is not None and \\\n self.focal_point_height is not None:\n return Rect.from_point(\n self.focal_point_x,\n self.focal_point_y,\n self.focal_point_width,\n self.focal_point_height,\n )","label":0},{"cid":"wagtail/images/models.py::AbstractImage.has_focal_point","text":"wagtail/images/models.py::AbstractImage.has_focal_point\n def has_focal_point(self):\n return self.get_focal_point() is not None","label":0},{"cid":"wagtail/images/models.py::AbstractImage.set_focal_point","text":"wagtail/images/models.py::AbstractImage.set_focal_point\n def set_focal_point(self, rect):\n if rect is not None:\n self.focal_point_x = rect.centroid_x\n self.focal_point_y = rect.centroid_y\n self.focal_point_width = rect.width\n self.focal_point_height = rect.height\n else:\n self.focal_point_x = None\n self.focal_point_y = None\n self.focal_point_width = None\n self.focal_point_height = None","label":0},{"cid":"wagtail/images/models.py::AbstractImage.get_suggested_focal_point","text":"wagtail/images/models.py::AbstractImage.get_suggested_focal_point\n def get_suggested_focal_point(self):\n with self.get_willow_image() as willow:\n faces = willow.detect_faces()\n\n if faces:\n # Create a bounding box around all faces\n left = min(face[0] for face in faces)\n top = min(face[1] for face in faces)\n right = max(face[2] for face in faces)\n bottom = max(face[3] for face in faces)\n focal_point = Rect(left, top, right, bottom)\n else:\n features = willow.detect_features()\n if features:\n # Create a bounding box around all features\n left = min(feature[0] for feature in features)\n top = min(feature[1] for feature in features)\n right = max(feature[0] for feature in features)\n bottom = max(feature[1] for feature in features)\n focal_point = Rect(left, top, right, bottom)\n else:\n return None\n\n # Add 20% to width and height and give it a minimum size\n x, y = focal_point.centroid\n width, height = focal_point.size\n\n width *= 1.20\n height *= 1.20\n\n width = max(width, 100)\n height = max(height, 100)\n\n return Rect.from_point(x, y, width, height)","label":0},{"cid":"wagtail/images/models.py::AbstractImage.get_rendition_model","text":"wagtail/images/models.py::AbstractImage.get_rendition_model\n @classmethod\n def get_rendition_model(cls):\n \"\"\" Get the Rendition model for this Image model \"\"\"\n return cls.renditions.rel.related_model","label":0},{"cid":"wagtail/images/models.py::AbstractImage.get_rendition","text":"wagtail/images/models.py::AbstractImage.get_rendition\n def get_rendition(self, filter):\n if isinstance(filter, str):\n filter = Filter(spec=filter)\n\n cache_key = filter.get_cache_key(self)\n Rendition = self.get_rendition_model()\n\n try:\n rendition_caching = True\n cache = caches['renditions']\n rendition_cache_key = Rendition.construct_cache_key(\n self.id,\n cache_key,\n filter.spec\n )\n cached_rendition = cache.get(rendition_cache_key)\n if cached_rendition:\n return cached_rendition\n except InvalidCacheBackendError:\n rendition_caching = False\n\n try:\n rendition = self.renditions.get(\n filter_spec=filter.spec,\n focal_point_key=cache_key,\n )\n except Rendition.DoesNotExist:\n # Generate the rendition image\n try:\n logger.debug(\n \"Generating '%s' rendition for image %d\",\n filter.spec,\n self.pk,\n )\n\n start_time = time.time()\n generated_image = filter.run(self, BytesIO())\n\n logger.debug(\n \"Generated '%s' rendition for image %d in %.1fms\",\n filter.spec,\n self.pk,\n (time.time() - start_time) * 1000\n )\n except: # noqa:B901,E722\n logger.debug(\"Failed to generate '%s' rendition for image %d\", filter.spec, self.pk)\n raise\n\n # Generate filename\n input_filename = os.path.basename(self.file.name)\n input_filename_without_extension, input_extension = os.path.splitext(input_filename)\n\n # A mapping of image formats to extensions\n FORMAT_EXTENSIONS = {\n 'jpeg': '.jpg',\n 'png': '.png'","label":0},{"cid":"wagtail/images/models.py::AbstractImage.is_portrait","text":"wagtail/images/models.py::AbstractImage.is_portrait\n def is_portrait(self):\n return (self.width < self.height)","label":0},{"cid":"wagtail/images/models.py::AbstractImage.is_landscape","text":"wagtail/images/models.py::AbstractImage.is_landscape\n def is_landscape(self):\n return (self.height < self.width)","label":0},{"cid":"wagtail/images/models.py::AbstractImage.filename","text":"wagtail/images/models.py::AbstractImage.filename\n @property\n def filename(self):\n return os.path.basename(self.file.name)","label":0},{"cid":"wagtail/images/models.py::AbstractImage.default_alt_text","text":"wagtail/images/models.py::AbstractImage.default_alt_text\n @property\n def default_alt_text(self):\n # by default the alt text field (used in rich text insertion) is populated\n # from the title. Subclasses might provide a separate alt field, and\n # override this\n return self.title","label":0},{"cid":"wagtail/images/models.py::AbstractImage.is_editable_by_user","text":"wagtail/images/models.py::AbstractImage.is_editable_by_user\n def is_editable_by_user(self, user):\n from wagtail.images.permissions import permission_policy\n return permission_policy.user_has_permission_for_instance(user, 'change', self)","label":0},{"cid":"wagtail/images/models.py::Filter.__init__","text":"wagtail/images/models.py::Filter.__init__\n def __init__(self, spec=None):\n # The spec pattern is operation1-var1-var2|operation2-var1\n self.spec = spec","label":0},{"cid":"wagtail/images/models.py::Filter.operations","text":"wagtail/images/models.py::Filter.operations\n @cached_property\n def operations(self):\n # Search for operations\n registered_operations = {}\n for fn in hooks.get_hooks('register_image_operations'):\n registered_operations.update(dict(fn()))\n\n # Build list of operation objects\n operations = []\n for op_spec in self.spec.split('|'):\n op_spec_parts = op_spec.split('-')\n\n if op_spec_parts[0] not in registered_operations:\n raise InvalidFilterSpecError(\"Unrecognised operation: %s\" % op_spec_parts[0])\n\n op_class = registered_operations[op_spec_parts[0]]\n operations.append(op_class(*op_spec_parts))\n return operations","label":0},{"cid":"wagtail/images/models.py::Filter.transform_operations","text":"wagtail/images/models.py::Filter.transform_operations\n @property\n def transform_operations(self):\n return [\n operation for operation in self.operations\n if isinstance(operation, TransformOperation)\n ]","label":0},{"cid":"wagtail/images/models.py::Filter.filter_operations","text":"wagtail/images/models.py::Filter.filter_operations\n @property\n def filter_operations(self):\n return [\n operation for operation in self.operations\n if isinstance(operation, FilterOperation)\n ]","label":0},{"cid":"wagtail/images/models.py::Filter.get_transform","text":"wagtail/images/models.py::Filter.get_transform\n def get_transform(self, image, size=None):\n \"\"\"\n Returns an ImageTransform with all the transforms in this filter applied.\n\n The ImageTransform is an object with two attributes:\n - .size - The size of the final image\n - .matrix - An affine transformation matrix that combines any\n transform/scale/rotation operations that need to be applied to the image\n \"\"\"\n\n if not size:\n size = (image.width, image.height)\n\n transform = ImageTransform(size)\n for operation in self.transform_operations:\n transform = operation.run(transform, image)\n return transform","label":0},{"cid":"wagtail/images/models.py::Filter.run","text":"wagtail/images/models.py::Filter.run\n def run(self, image, output):\n with image.get_willow_image() as willow:\n original_format = willow.format_name\n\n # Fix orientation of image\n willow = willow.auto_orient()\n\n # Transform the image\n transform = self.get_transform(image, (willow.image.width, willow.image.height))\n willow = willow.crop(transform.get_rect().round())\n willow = willow.resize(transform.size)\n\n # Apply filters\n env = {\n 'original-format': original_format,\n }\n for operation in self.filter_operations:\n willow = operation.run(willow, image, env) or willow\n\n # Find the output format to use\n if 'output-format' in env:\n # Developer specified an output format\n output_format = env['output-format']\n else:\n # Convert bmp and webp to png by default\n default_conversions = {\n 'bmp': 'png',\n 'webp': 'png',\n }\n\n # Convert unanimated GIFs to PNG as well\n if not willow.has_animation():\n default_conversions['gif'] = 'png'\n\n # Allow the user to override the conversions\n conversion = getattr(settings, 'WAGTAILIMAGES_FORMAT_CONVERSIONS', {})\n default_conversions.update(conversion)\n\n # Get the converted output format falling back to the original\n output_format = default_conversions.get(\n original_format, original_format)\n\n if output_format == 'jpeg':\n # Allow changing of JPEG compression quality\n if 'jpeg-quality' in env:\n quality = env['jpeg-quality']\n else:\n quality = getattr(settings, 'WAGTAILIMAGES_JPEG_QUALITY', 85)\n\n # If t","label":0},{"cid":"wagtail/images/models.py::Filter.get_cache_key","text":"wagtail/images/models.py::Filter.get_cache_key\n def get_cache_key(self, image):\n vary_parts = []\n\n for operation in self.operations:\n for field in getattr(operation, 'vary_fields', []):\n value = getattr(image, field, '')\n vary_parts.append(str(value))\n\n vary_string = '-'.join(vary_parts)\n\n # Return blank string if there are no vary fields\n if not vary_string:\n return ''\n\n return hashlib.sha1(vary_string.encode('utf-8')).hexdigest()[:8]","label":0},{"cid":"wagtail/images/models.py::AbstractRendition.url","text":"wagtail/images/models.py::AbstractRendition.url\n @property\n def url(self):\n return self.file.url","label":0},{"cid":"wagtail/images/models.py::AbstractRendition.alt","text":"wagtail/images/models.py::AbstractRendition.alt\n @property\n def alt(self):\n return self.image.default_alt_text","label":0},{"cid":"wagtail/images/models.py::AbstractRendition.attrs","text":"wagtail/images/models.py::AbstractRendition.attrs\n @property\n def attrs(self):\n \"\"\"\n The src, width, height, and alt attributes for an <img> tag, as a HTML\n string\n \"\"\"\n return flatatt(self.attrs_dict)","label":0},{"cid":"wagtail/images/models.py::AbstractRendition.attrs_dict","text":"wagtail/images/models.py::AbstractRendition.attrs_dict\n @property\n def attrs_dict(self):\n \"\"\"\n A dict of the src, width, height, and alt attributes for an <img> tag.\n \"\"\"\n return OrderedDict([\n ('src', self.url),\n ('width', self.width),\n ('height', self.height),\n ('alt', self.alt),\n ])","label":0},{"cid":"wagtail/images/models.py::AbstractRendition.full_url","text":"wagtail/images/models.py::AbstractRendition.full_url\n @property\n def full_url(self):\n url = self.url\n if hasattr(settings, 'BASE_URL') and url.startswith(\"/\"):\n url = settings.BASE_URL + url\n return url","label":0},{"cid":"wagtail/images/models.py::AbstractRendition.filter","text":"wagtail/images/models.py::AbstractRendition.filter\n @property\n def filter(self):\n return Filter(self.filter_spec)","label":0},{"cid":"wagtail/images/models.py::AbstractRendition.focal_point","text":"wagtail/images/models.py::AbstractRendition.focal_point\n @cached_property\n def focal_point(self):\n image_focal_point = self.image.get_focal_point()\n if image_focal_point:\n transform = self.filter.get_transform(self.image)\n return image_focal_point.transform(transform)","label":0},{"cid":"wagtail/images/models.py::AbstractRendition.background_position_style","text":"wagtail/images/models.py::AbstractRendition.background_position_style\n @property\n def background_position_style(self):\n \"\"\"\n Returns a `background-position` rule to be put in the inline style of an element which uses the rendition for its background.\n\n This positions the rendition according to the value of the focal point. This is helpful for when the element does not have\n the same aspect ratio as the rendition.\n\n For example:\n\n {% image page.image fill-1920x600 as image %}\n <div style=\"background-image: url('{{ image.url }}'); {{ image.background_position_style }}\">\n </div>\n \"\"\"\n focal_point = self.focal_point\n if focal_point:\n horz = int((focal_point.x * 100) // self.width)\n vert = int((focal_point.y * 100) // self.height)\n return 'background-position: {}% {}%;'.format(horz, vert)\n else:\n return 'background-position: 50% 50%;'","label":0},{"cid":"wagtail/images/models.py::AbstractRendition.img_tag","text":"wagtail/images/models.py::AbstractRendition.img_tag\n def img_tag(self, extra_attributes={}):\n attrs = self.attrs_dict.copy()\n attrs.update(extra_attributes)\n return mark_safe('<img{}>'.format(flatatt(attrs)))","label":0}],"string":"[\n {\n \"cid\": \"wagtail/admin/action_menu.py::requires_request_arg\",\n \"text\": \"wagtail/admin/action_menu.py::requires_request_arg\\ndef requires_request_arg(method):\\n \\\"\\\"\\\"\\n Helper function to handle deprecation of old ActionMenuItem API where get_url, is_show,\\n get_context and render_html all accepted both 'request' and 'parent_context' as arguments\\n \\\"\\\"\\\"\\n try:\\n # see if this is a pre-2.15 get_url method that takes both request and context kwargs\\n inspect.signature(method).bind({})\\n except TypeError:\\n return True\\n else:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::ActionMenuItem.__init__\",\n \"text\": \"wagtail/admin/action_menu.py::ActionMenuItem.__init__\\n def __init__(self, order=None):\\n if order is not None:\\n self.order = order\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::ActionMenuItem.get_user_page_permissions_tester\",\n \"text\": \"wagtail/admin/action_menu.py::ActionMenuItem.get_user_page_permissions_tester\\n def get_user_page_permissions_tester(self, context):\\n if 'user_page_permissions_tester' in context:\\n return context['user_page_permissions_tester']\\n return context['user_page_permissions'].for_page(context['page'])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::ActionMenuItem.is_shown\",\n \"text\": \"wagtail/admin/action_menu.py::ActionMenuItem.is_shown\\n def is_shown(self, *args):\\n # accepts both is_shown(request, context) (pre-2.15 signature)\\n # and is_shown(context) (signature as of 2.15)\\n # to allow for pre-2.15 ActionMenuItem subclasses calling super().\\n # RemovedInWagtail217Warning: signature should become is_shown(self, context)\\n \\\"\\\"\\\"\\n Whether this action should be shown on this request; permission checks etc should go here.\\n By default, actions are shown for unlocked pages, hidden for locked pages\\n\\n context = dictionary containing at least:\\n 'request' = the current request object\\n 'view' = 'create', 'edit' or 'revisions_revert'\\n 'page' (if view = 'edit' or 'revisions_revert') = the page being edited\\n 'parent_page' (if view = 'create') = the parent page of the page being created\\n 'user_page_permissions' = a UserPagePermissionsProxy for the current user, to test permissions against\\n may also contain:\\n 'user_page_permissions_tester' = a PagePermissionTester for the current user and page\\n \\\"\\\"\\\"\\n if len(args) == 2:\\n warn(\\n \\\"ActionMenuItem.is_shown no longer takes a 'request' argument. \\\"\\n \\\"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\\\",\\n category=RemovedInWagtail217Warning, stacklevel=2\\n )\\n request, context = args\\n else:\\n context, = args\\n\\n return (\\n context['view'] == 'create'\\n or not self.get_user_page_permissions_tester(context).page_locked()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::ActionMenuItem.get_context\",\n \"text\": \"wagtail/admin/action_menu.py::ActionMenuItem.get_context\\n def get_context(self, request, parent_context):\\n # Obsolete - included here for pre-2.15 subclasses that override this and call super().\\n # RemovedInWagtail217Warning\\n return self.get_context_data(parent_context)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::ActionMenuItem.get_context_data\",\n \"text\": \"wagtail/admin/action_menu.py::ActionMenuItem.get_context_data\\n def get_context_data(self, parent_context):\\n \\\"\\\"\\\"Defines context for the template, overridable to use more data\\\"\\\"\\\"\\n context = parent_context.copy()\\n\\n if requires_request_arg(self.get_url):\\n warn(\\n \\\"%s.get_url should no longer take a 'request' argument. \\\"\\n \\\"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\\\" % type(self).__name__,\\n category=RemovedInWagtail217Warning\\n )\\n url = self.get_url(parent_context['request'], parent_context)\\n else:\\n url = self.get_url(parent_context)\\n\\n context.update({\\n 'label': self.label,\\n 'url': url,\\n 'name': self.name,\\n 'classname': self.classname,\\n 'icon_name': self.icon_name,\\n 'request': parent_context['request'],\\n })\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::ActionMenuItem.get_url\",\n \"text\": \"wagtail/admin/action_menu.py::ActionMenuItem.get_url\\n def get_url(self, *args):\\n # accepts both get_url(request, parent_context) (pre-2.15 signature)\\n # and get_url(parent_context) (signature as of 2.15)\\n # to allow for pre-2.15 ActionMenuItem subclasses calling super().\\n # RemovedInWagtail217Warning: signature should become get_url(self, parent_context)\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::ActionMenuItem.render_html\",\n \"text\": \"wagtail/admin/action_menu.py::ActionMenuItem.render_html\\n def render_html(self, *args):\\n # accepts both render_html(request, parent_context) (pre-2.15 signature)\\n # and render_html(parent_context) (signature as of 2.15)\\n # to allow for pre-2.15 ActionMenuItem subclasses calling super().\\n # RemovedInWagtail217Warning: signature should become render_html(self, parent_context)\\n\\n if len(args) == 2:\\n warn(\\n \\\"ActionMenuItem.render_html no longer takes a 'request' argument. \\\"\\n \\\"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\\\",\\n category=RemovedInWagtail217Warning, stacklevel=2\\n )\\n request, parent_context = args\\n else:\\n parent_context, = args\\n\\n if not getattr(self.get_context, 'is_base_method', False):\\n # get_context has been overridden, so call it instead of get_context_data\\n warn(\\n \\\"%s should define get_context_data(self, parent_context) instead of get_context(self, request, get_context_data). \\\"\\n \\\"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\\\" % type(self).__name__,\\n category=RemovedInWagtail217Warning\\n )\\n context_data = self.get_context(parent_context['request'], parent_context)\\n else:\\n context_data = self.get_context_data(parent_context)\\n\\n if self.template:\\n warn(\\n \\\"%s should define template_name instead of template. \\\"\\n \\\"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\\\" % type(self).__name__,\\n category=RemovedInWagtail217Warning\\n )\\n template_name = self.template\\n else:\\n template_name = self.template_name\\n\\n template = get_template(template_name)\\n return template.render(context_data)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::PublishMenuItem.is_shown\",\n \"text\": \"wagtail/admin/action_menu.py::PublishMenuItem.is_shown\\n def is_shown(self, context):\\n if context['view'] == 'create':\\n return context['user_page_permissions'].for_page(context['parent_page']).can_publish_subpage()\\n else: # view == 'edit' or 'revisions_revert'\\n perms_tester = self.get_user_page_permissions_tester(context)\\n return (\\n not perms_tester.page_locked()\\n and perms_tester.can_publish()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::PublishMenuItem.get_context_data\",\n \"text\": \"wagtail/admin/action_menu.py::PublishMenuItem.get_context_data\\n def get_context_data(self, parent_context):\\n context = super().get_context_data(parent_context)\\n context['is_revision'] = (context['view'] == 'revisions_revert')\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::SubmitForModerationMenuItem.is_shown\",\n \"text\": \"wagtail/admin/action_menu.py::SubmitForModerationMenuItem.is_shown\\n def is_shown(self, context):\\n if not getattr(settings, 'WAGTAIL_MODERATION_ENABLED', True):\\n return False\\n\\n if context['view'] == 'create':\\n return context['parent_page'].has_workflow\\n\\n if context['view'] == 'edit':\\n perms_tester = self.get_user_page_permissions_tester(context)\\n return perms_tester.can_submit_for_moderation() and not perms_tester.page_locked()\\n # context == revisions_revert\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::SubmitForModerationMenuItem.get_context_data\",\n \"text\": \"wagtail/admin/action_menu.py::SubmitForModerationMenuItem.get_context_data\\n def get_context_data(self, parent_context):\\n context = super().get_context_data(parent_context)\\n page = context.get('page')\\n workflow_state = page.current_workflow_state if page else None\\n if workflow_state and workflow_state.status == workflow_state.STATUS_NEEDS_CHANGES:\\n context['label'] = _(\\\"Resubmit to {}\\\").format(workflow_state.current_task_state.task.name)\\n elif page:\\n workflow = page.get_workflow()\\n if workflow:\\n context['label'] = _(\\\"Submit to {}\\\").format(workflow.name)\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::WorkflowMenuItem.__init__\",\n \"text\": \"wagtail/admin/action_menu.py::WorkflowMenuItem.__init__\\n def __init__(self, name, label, launch_modal, *args, **kwargs):\\n self.name = name\\n self.label = label\\n self.launch_modal = launch_modal\\n\\n if kwargs.get('icon_name'):\\n self.icon_name = kwargs.pop('icon_name')\\n\\n super().__init__(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::WorkflowMenuItem.get_context_data\",\n \"text\": \"wagtail/admin/action_menu.py::WorkflowMenuItem.get_context_data\\n def get_context_data(self, parent_context):\\n context = super().get_context_data(parent_context)\\n context['launch_modal'] = self.launch_modal\\n context['current_task_state'] = context['page'].current_workflow_task_state\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::WorkflowMenuItem.is_shown\",\n \"text\": \"wagtail/admin/action_menu.py::WorkflowMenuItem.is_shown\\n def is_shown(self, context):\\n if context['view'] == 'edit':\\n perms_tester = self.get_user_page_permissions_tester(context)\\n return not perms_tester.page_locked()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::RestartWorkflowMenuItem.is_shown\",\n \"text\": \"wagtail/admin/action_menu.py::RestartWorkflowMenuItem.is_shown\\n def is_shown(self, context):\\n if not getattr(settings, 'WAGTAIL_MODERATION_ENABLED', True):\\n return False\\n elif context['view'] == 'edit':\\n workflow_state = context['page'].current_workflow_state\\n perms_tester = self.get_user_page_permissions_tester(context)\\n return (\\n perms_tester.can_submit_for_moderation()\\n and not perms_tester.page_locked()\\n and workflow_state\\n and workflow_state.user_can_cancel(context['request'].user)\\n )\\n else:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::CancelWorkflowMenuItem.is_shown\",\n \"text\": \"wagtail/admin/action_menu.py::CancelWorkflowMenuItem.is_shown\\n def is_shown(self, context):\\n if context['view'] == 'edit':\\n workflow_state = context['page'].current_workflow_state\\n return workflow_state and workflow_state.user_can_cancel(context['request'].user)\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::UnpublishMenuItem.is_shown\",\n \"text\": \"wagtail/admin/action_menu.py::UnpublishMenuItem.is_shown\\n def is_shown(self, context):\\n if context['view'] == 'edit':\\n perms_tester = self.get_user_page_permissions_tester(context)\\n return (\\n not perms_tester.page_locked()\\n and perms_tester.can_unpublish()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::UnpublishMenuItem.get_url\",\n \"text\": \"wagtail/admin/action_menu.py::UnpublishMenuItem.get_url\\n def get_url(self, context):\\n return reverse('wagtailadmin_pages:unpublish', args=(context['page'].id,))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::DeleteMenuItem.is_shown\",\n \"text\": \"wagtail/admin/action_menu.py::DeleteMenuItem.is_shown\\n def is_shown(self, context):\\n if context['view'] == 'edit':\\n perms_tester = self.get_user_page_permissions_tester(context)\\n return (\\n not perms_tester.page_locked()\\n and perms_tester.can_delete()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::DeleteMenuItem.get_url\",\n \"text\": \"wagtail/admin/action_menu.py::DeleteMenuItem.get_url\\n def get_url(self, context):\\n return reverse('wagtailadmin_pages:delete', args=(context['page'].id,))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::LockMenuItem.is_shown\",\n \"text\": \"wagtail/admin/action_menu.py::LockMenuItem.is_shown\\n def is_shown(self, context):\\n return (\\n context['view'] == 'edit'\\n and not context['page'].locked\\n and self.get_user_page_permissions_tester(context).can_lock()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::LockMenuItem.get_url\",\n \"text\": \"wagtail/admin/action_menu.py::LockMenuItem.get_url\\n def get_url(self, context):\\n return reverse('wagtailadmin_pages:lock', args=(context['page'].id,))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::LockMenuItem.get_context_data\",\n \"text\": \"wagtail/admin/action_menu.py::LockMenuItem.get_context_data\\n def get_context_data(self, parent_context):\\n context = super().get_context_data(parent_context)\\n context['aria_label'] = self.aria_label\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::UnlockMenuItem.is_shown\",\n \"text\": \"wagtail/admin/action_menu.py::UnlockMenuItem.is_shown\\n def is_shown(self, context):\\n return (\\n context['view'] == 'edit'\\n and context['page'].locked\\n and self.get_user_page_permissions_tester(context).can_unlock()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::UnlockMenuItem.get_url\",\n \"text\": \"wagtail/admin/action_menu.py::UnlockMenuItem.get_url\\n def get_url(self, context):\\n return reverse('wagtailadmin_pages:unlock', args=(context['page'].id,))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::SaveDraftMenuItem.get_context_data\",\n \"text\": \"wagtail/admin/action_menu.py::SaveDraftMenuItem.get_context_data\\n def get_context_data(self, parent_context):\\n context = super().get_context_data(parent_context)\\n context['is_revision'] = (context['view'] == 'revisions_revert')\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::PageLockedMenuItem.is_shown\",\n \"text\": \"wagtail/admin/action_menu.py::PageLockedMenuItem.is_shown\\n def is_shown(self, context):\\n return (\\n 'page' in context\\n and self.get_user_page_permissions_tester(context).page_locked()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::PageLockedMenuItem.get_context_data\",\n \"text\": \"wagtail/admin/action_menu.py::PageLockedMenuItem.get_context_data\\n def get_context_data(self, parent_context):\\n context = super().get_context_data(parent_context)\\n context['is_revision'] = (context['view'] == 'revisions_revert')\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::_get_base_page_action_menu_items\",\n \"text\": \"wagtail/admin/action_menu.py::_get_base_page_action_menu_items\\ndef _get_base_page_action_menu_items():\\n \\\"\\\"\\\"\\n Retrieve the global list of menu items for the page action menu,\\n which may then be customised on a per-request basis\\n \\\"\\\"\\\"\\n global BASE_PAGE_ACTION_MENU_ITEMS\\n\\n if BASE_PAGE_ACTION_MENU_ITEMS is None:\\n BASE_PAGE_ACTION_MENU_ITEMS = [\\n SaveDraftMenuItem(order=0),\\n DeleteMenuItem(order=10),\\n LockMenuItem(order=15),\\n UnlockMenuItem(order=15),\\n UnpublishMenuItem(order=20),\\n PublishMenuItem(order=30),\\n CancelWorkflowMenuItem(order=40),\\n RestartWorkflowMenuItem(order=50),\\n SubmitForModerationMenuItem(order=60),\\n PageLockedMenuItem(order=10000),\\n ]\\n for hook in hooks.get_hooks('register_page_action_menu_item'):\\n action_menu_item = hook()\\n if action_menu_item:\\n BASE_PAGE_ACTION_MENU_ITEMS.append(action_menu_item)\\n\\n return BASE_PAGE_ACTION_MENU_ITEMS\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::PageActionMenu.__init__\",\n \"text\": \"wagtail/admin/action_menu.py::PageActionMenu.__init__\\n def __init__(self, request, **kwargs):\\n self.request = request\\n self.context = kwargs\\n self.context['request'] = request\\n page = self.context.get('page')\\n user_page_permissions = UserPagePermissionsProxy(self.request.user)\\n self.context['user_page_permissions'] = user_page_permissions\\n if page:\\n self.context['user_page_permissions_tester'] = user_page_permissions.for_page(page)\\n\\n self.menu_items = []\\n\\n if page:\\n task = page.current_workflow_task\\n current_workflow_state = page.current_workflow_state\\n is_final_task = current_workflow_state and current_workflow_state.is_at_final_task\\n if task:\\n actions = task.get_actions(page, request.user)\\n workflow_menu_items = []\\n for name, label, launch_modal in actions:\\n icon_name = 'edit'\\n if name == \\\"approve\\\":\\n if is_final_task and not getattr(settings, 'WAGTAIL_WORKFLOW_REQUIRE_REAPPROVAL_ON_EDIT', False):\\n label = _(\\\"%(label)s and Publish\\\") % {'label': label}\\n icon_name = 'success'\\n\\n item = WorkflowMenuItem(name, label, launch_modal, icon_name=icon_name)\\n\\n if requires_request_arg(item.is_shown):\\n warn(\\n \\\"%s.is_shown should no longer take a 'request' argument. \\\"\\n \\\"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\\\" % type(item).__name__,\\n category=RemovedInWagtail217Warning\\n )\\n is_shown = item.is_shown(self.request, self.context)\\n else:\\n is_shown = item.is_shown(self.context)\\n\\n if is_shown:\\n workflow_menu\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::PageActionMenu.render_html\",\n \"text\": \"wagtail/admin/action_menu.py::PageActionMenu.render_html\\n def render_html(self):\\n rendered_menu_items = []\\n for menu_item in self.menu_items:\\n if requires_request_arg(menu_item.render_html):\\n warn(\\n \\\"%s.render_html should no longer take a 'request' argument. \\\"\\n \\\"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\\\" % type(menu_item).__name__,\\n category=RemovedInWagtail217Warning\\n )\\n rendered_menu_items.append(menu_item.render_html(self.request, self.context))\\n else:\\n rendered_menu_items.append(menu_item.render_html(self.context))\\n\\n if requires_request_arg(self.default_item.render_html):\\n warn(\\n \\\"%s.render_html should no longer take a 'request' argument. \\\"\\n \\\"See https://docs.wagtail.io/en/stable/releases/2.15.html#template-components-2-15\\\" % type(self.default_item).__name__,\\n category=RemovedInWagtail217Warning\\n )\\n rendered_default_item = self.default_item.render_html(self.request, self.context)\\n else:\\n rendered_default_item = self.default_item.render_html(self.context)\\n\\n return render_to_string(self.template, {\\n 'default_menu_item': rendered_default_item,\\n 'show_menu': bool(self.menu_items),\\n 'rendered_menu_items': rendered_menu_items,\\n }, request=self.request)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/action_menu.py::PageActionMenu.media\",\n \"text\": \"wagtail/admin/action_menu.py::PageActionMenu.media\\n @cached_property\\n def media(self):\\n media = Media()\\n for item in self.menu_items:\\n media += item.media\\n return media\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::menu_search\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::menu_search\\n@register.simple_tag(takes_context=True)\\ndef menu_search(context):\\n request = context['request']\\n\\n search_areas = admin_search_areas.search_items_for_request(request)\\n if not search_areas:\\n return ''\\n search_area = search_areas[0]\\n\\n return render_to_string('wagtailadmin/shared/menu_search.html', {\\n 'search_url': search_area.url,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav\\n@register.inclusion_tag('wagtailadmin/shared/main_nav.html', takes_context=True)\\ndef main_nav(context):\\n request = context['request']\\n\\n return {\\n 'menu_html': admin_menu.render_html(request),\\n 'request': request,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::explorer_breadcrumb\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::explorer_breadcrumb\\n@register.inclusion_tag('wagtailadmin/shared/breadcrumb.html', takes_context=True)\\ndef explorer_breadcrumb(context, page, include_self=True, trailing_arrow=False):\\n user = context['request'].user\\n\\n # find the closest common ancestor of the pages that this user has direct explore permission\\n # (i.e. add/edit/publish/lock) over; this will be the root of the breadcrumb\\n cca = get_explorable_root_page(user)\\n if not cca:\\n return {'pages': Page.objects.none()}\\n\\n return {\\n 'pages': page.get_ancestors(inclusive=include_self).descendant_of(cca, inclusive=True).specific(),\\n 'trailing_arrow': trailing_arrow\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::search_other\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::search_other\\n@register.inclusion_tag('wagtailadmin/shared/search_other.html', takes_context=True)\\ndef search_other(context, current=None):\\n request = context['request']\\n\\n return {\\n 'options_html': admin_search_areas.render_html(request, current),\\n 'request': request,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav_js\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav_js\\n@register.simple_tag\\ndef main_nav_js():\\n if slim_sidebar_enabled():\\n return Media(\\n js=[\\n versioned_static('wagtailadmin/js/telepath/telepath.js'),\\n versioned_static('wagtailadmin/js/sidebar.js'),\\n ]\\n )\\n\\n else:\\n return Media(\\n js=[\\n versioned_static('wagtailadmin/js/sidebar-legacy.js')\\n ]\\n ) + admin_menu.media['js']\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav_css\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::main_nav_css\\n@register.simple_tag\\ndef main_nav_css():\\n if slim_sidebar_enabled():\\n return Media(\\n css={\\n 'all': [\\n versioned_static('wagtailadmin/css/sidebar.css')\\n ]\\n }\\n )\\n\\n else:\\n # Legacy sidebar CSS in core.css\\n return admin_menu.media['css']\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::ellipsistrim\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::ellipsistrim\\n@register.filter(\\\"ellipsistrim\\\")\\ndef ellipsistrim(value, max_length):\\n if len(value) > max_length:\\n truncd_val = value[:max_length]\\n if not len(value) == (max_length + 1) and value[max_length + 1] != \\\" \\\":\\n truncd_val = truncd_val[:truncd_val.rfind(\\\" \\\")]\\n return truncd_val + \\\"…\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::fieldtype\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::fieldtype\\n@register.filter\\ndef fieldtype(bound_field):\\n try:\\n return camelcase_to_underscore(bound_field.field.__class__.__name__)\\n except AttributeError:\\n try:\\n return camelcase_to_underscore(bound_field.__class__.__name__)\\n except AttributeError:\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::widgettype\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::widgettype\\n@register.filter\\ndef widgettype(bound_field):\\n try:\\n return camelcase_to_underscore(bound_field.field.widget.__class__.__name__)\\n except AttributeError:\\n try:\\n return camelcase_to_underscore(bound_field.widget.__class__.__name__)\\n except AttributeError:\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::_get_user_page_permissions\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::_get_user_page_permissions\\ndef _get_user_page_permissions(context):\\n # Create a UserPagePermissionsProxy object to represent the user's global permissions, and\\n # cache it in the context for the duration of the page request, if one does not exist already\\n if 'user_page_permissions' not in context:\\n context['user_page_permissions'] = UserPagePermissionsProxy(context['request'].user)\\n\\n return context['user_page_permissions']\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_permissions\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_permissions\\n@register.simple_tag(takes_context=True)\\ndef page_permissions(context, page):\\n \\\"\\\"\\\"\\n Usage: {% page_permissions page as page_perms %}\\n Sets the variable 'page_perms' to a PagePermissionTester object that can be queried to find out\\n what actions the current logged-in user can perform on the given page.\\n \\\"\\\"\\\"\\n return _get_user_page_permissions(context).for_page(page)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::test_collection_is_public\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::test_collection_is_public\\n@register.simple_tag(takes_context=True)\\ndef test_collection_is_public(context, collection):\\n \\\"\\\"\\\"\\n Usage: {% test_collection_is_public collection as is_public %}\\n Sets 'is_public' to True iff there are no collection view restrictions in place\\n on this collection.\\n Caches the list of collection view restrictions in the context, to avoid repeated\\n DB queries on repeated calls.\\n \\\"\\\"\\\"\\n if 'all_collection_view_restrictions' not in context:\\n context['all_collection_view_restrictions'] = CollectionViewRestriction.objects.select_related('collection').values_list(\\n 'collection__name', flat=True\\n )\\n\\n is_private = collection.name in context['all_collection_view_restrictions']\\n\\n return not is_private\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::test_page_is_public\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::test_page_is_public\\n@register.simple_tag(takes_context=True)\\ndef test_page_is_public(context, page):\\n \\\"\\\"\\\"\\n Usage: {% test_page_is_public page as is_public %}\\n Sets 'is_public' to True iff there are no page view restrictions in place on\\n this page.\\n Caches the list of page view restrictions on the request, to avoid repeated\\n DB queries on repeated calls.\\n \\\"\\\"\\\"\\n if not hasattr(context[\\\"request\\\"], \\\"all_page_view_restriction_paths\\\"):\\n context['request'].all_page_view_restriction_paths = PageViewRestriction.objects.select_related('page').values_list(\\n 'page__path', flat=True\\n )\\n\\n is_private = any([\\n page.path.startswith(restricted_path)\\n for restricted_path in context[\\\"request\\\"].all_page_view_restriction_paths\\n ])\\n\\n return not is_private\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::hook_output\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::hook_output\\n@register.simple_tag\\ndef hook_output(hook_name):\\n \\\"\\\"\\\"\\n Example: {% hook_output 'insert_editor_css' %}\\n Whenever we have a hook whose functions take no parameters and return a string, this tag can be used\\n to output the concatenation of all of those return values onto the page.\\n Note that the output is not escaped - it is the hook function's responsibility to escape unsafe content.\\n \\\"\\\"\\\"\\n snippets = [fn() for fn in hooks.get_hooks(hook_name)]\\n return mark_safe(''.join(snippets))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::usage_count_enabled\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::usage_count_enabled\\n@register.simple_tag\\ndef usage_count_enabled():\\n return getattr(settings, 'WAGTAIL_USAGE_COUNT_ENABLED', False)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::base_url_setting\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::base_url_setting\\n@register.simple_tag\\ndef base_url_setting():\\n return getattr(settings, 'BASE_URL', None)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::allow_unicode_slugs\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::allow_unicode_slugs\\n@register.simple_tag\\ndef allow_unicode_slugs():\\n return getattr(settings, 'WAGTAIL_ALLOW_UNICODE_SLUGS', True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::auto_update_preview\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::auto_update_preview\\n@register.simple_tag\\ndef auto_update_preview():\\n return getattr(settings, 'WAGTAIL_AUTO_UPDATE_PREVIEW', False)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.__init__\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.__init__\\n def __init__(self, nodelist):\\n super().__init__()\\n self.nodelist = nodelist\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.render\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.render\\n def render(self, context):\\n out = self.nodelist.render(context)\\n return escape_script(out)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.handle\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.handle\\n @classmethod\\n def handle(cls, parser, token):\\n nodelist = parser.parse(('end' + EscapeScriptNode.TAG_NAME,))\\n parser.delete_first_token()\\n return cls(nodelist)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::render_with_errors\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::render_with_errors\\n@register.filter\\ndef render_with_errors(bound_field):\\n \\\"\\\"\\\"\\n Usage: {{ field|render_with_errors }} as opposed to {{ field }}.\\n If the field (a BoundField instance) has errors on it, and the associated widget implements\\n a render_with_errors method, call that; otherwise, call the regular widget rendering mechanism.\\n \\\"\\\"\\\"\\n widget = bound_field.field.widget\\n if bound_field.errors and hasattr(widget, 'render_with_errors'):\\n return widget.render_with_errors(\\n bound_field.html_name,\\n bound_field.value(),\\n attrs={'id': bound_field.auto_id},\\n errors=bound_field.errors\\n )\\n else:\\n return bound_field.as_widget()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::has_unrendered_errors\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::has_unrendered_errors\\n@register.filter\\ndef has_unrendered_errors(bound_field):\\n \\\"\\\"\\\"\\n Return true if this field has errors that were not accounted for by render_with_errors, because\\n the widget does not support the render_with_errors method\\n \\\"\\\"\\\"\\n return bound_field.errors and not hasattr(bound_field.field.widget, 'render_with_errors')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::cautious_slugify\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::cautious_slugify\\n@register.filter(is_safe=True)\\n@stringfilter\\ndef cautious_slugify(value):\\n return _cautious_slugify(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::querystring\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::querystring\\n@register.simple_tag(takes_context=True)\\ndef querystring(context, **kwargs):\\n \\\"\\\"\\\"\\n Print out the current querystring. Any keyword arguments to this template\\n tag will be added to the querystring before it is printed out.\\n\\n <a href=\\\"/page/{% querystring key='value' %}\\\">\\n\\n Will result in something like:\\n\\n <a href=\\\"/page/?foo=bar&key=value\\\">\\n \\\"\\\"\\\"\\n request = context['request']\\n querydict = request.GET.copy()\\n # Can't do querydict.update(kwargs), because QueryDict.update() appends to\\n # the list of values, instead of replacing the values.\\n for key, value in kwargs.items():\\n if value is None:\\n # Remove the key if the value is None\\n querydict.pop(key, None)\\n else:\\n # Set the key otherwise\\n querydict[key] = str(value)\\n\\n return '?' + querydict.urlencode()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_table_header_label\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_table_header_label\\n@register.simple_tag(takes_context=True)\\ndef page_table_header_label(context, label=None, parent_page_title=None, **kwargs):\\n \\\"\\\"\\\"\\n Wraps table_header_label to add a title attribute based on the parent page title and the column label\\n \\\"\\\"\\\"\\n if label:\\n translation_context = {'parent': parent_page_title, 'label': label}\\n ascending_title_text = _(\\\"Sort the order of child pages within '%(parent)s' by '%(label)s' in ascending order.\\\") % translation_context\\n descending_title_text = _(\\\"Sort the order of child pages within '%(parent)s' by '%(label)s' in descending order.\\\") % translation_context\\n else:\\n ascending_title_text = None\\n descending_title_text = None\\n\\n return table_header_label(context, label=label, ascending_title_text=ascending_title_text, descending_title_text=descending_title_text, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::table_header_label\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::table_header_label\\n@register.simple_tag(takes_context=True)\\ndef table_header_label(\\n context, label=None, sortable=True, ordering=None,\\n sort_context_var='ordering', sort_param='ordering', sort_field=None,\\n ascending_title_text=None, descending_title_text=None\\n):\\n \\\"\\\"\\\"\\n A label to go in a table header cell, optionally with a 'sort' link that alternates between\\n forward and reverse sorting\\n\\n label = label text\\n ordering = current active ordering. If not specified, we will fetch it from the template context variable\\n given by sort_context_var. (We don't fetch it from the URL because that wouldn't give the view method\\n the opportunity to set a default)\\n sort_param = URL parameter that indicates the current active ordering\\n sort_field = the value for sort_param that indicates that sorting is currently on this column.\\n For example, if sort_param='ordering' and sort_field='title', then a URL parameter of\\n ordering=title indicates that the listing is ordered forwards on this column, and a URL parameter\\n of ordering=-title indicated that the listing is ordered in reverse on this column\\n ascending_title_text = title attribute to use on the link when the link action will sort in ascending order\\n descending_title_text = title attribute to use on the link when the link action will sort in descending order\\n\\n To disable sorting on this column, set sortable=False or leave sort_field unspecified.\\n \\\"\\\"\\\"\\n if not sortable or not sort_field:\\n # render label without a sort link\\n return label\\n\\n if ordering is None:\\n ordering = context.get(sort_context_var)\\n reverse_sort_field = \\\"-%s\\\" % sort_field\\n\\n if ordering == sort_field:\\n # currently ordering forwards on this column; link should change to reverse ordering\\n attrs = {\\n 'href': querystring(context, **{sort_param: reverse_sort_field}),\\n 'class': \\\"i\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::pagination_querystring\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::pagination_querystring\\n@register.simple_tag(takes_context=True)\\ndef pagination_querystring(context, page_number, page_key='p'):\\n \\\"\\\"\\\"\\n Print out a querystring with an updated page number:\\n\\n {% if page.has_next_page %}\\n <a href=\\\"{% pagination_link page.next_page_number %}\\\">Next page</a>\\n {% endif %}\\n \\\"\\\"\\\"\\n return querystring(context, **{page_key: page_number})\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::paginate\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::paginate\\n@register.inclusion_tag(\\\"wagtailadmin/pages/listing/_pagination.html\\\",\\n takes_context=True)\\ndef paginate(context, page, base_url='', page_key='p',\\n classnames=''):\\n \\\"\\\"\\\"\\n Print pagination previous/next links, and the page count. Take the\\n following arguments:\\n\\n page\\n The current page of results. This should be a Django pagination `Page`\\n instance\\n\\n base_url\\n The base URL of the next/previous page, with no querystring.\\n This is optional, and defaults to the current page by just printing the\\n querystring for the next/previous page.\\n\\n page_key\\n The name of the page variable in the query string. Defaults to 'p'.\\n\\n classnames\\n Extra classes to add to the next/previous links.\\n \\\"\\\"\\\"\\n request = context['request']\\n return {\\n 'base_url': base_url,\\n 'classnames': classnames,\\n 'request': request,\\n 'page': page,\\n 'page_key': page_key,\\n 'paginator': page.paginator,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_listing_buttons\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_listing_buttons\\n@register.inclusion_tag(\\\"wagtailadmin/pages/listing/_buttons.html\\\",\\n takes_context=True)\\ndef page_listing_buttons(context, page, page_perms, is_parent=False):\\n next_url = context.request.path\\n button_hooks = hooks.get_hooks('register_page_listing_buttons')\\n\\n buttons = []\\n for hook in button_hooks:\\n buttons.extend(hook(page, page_perms, is_parent, next_url))\\n\\n buttons.sort()\\n\\n for hook in hooks.get_hooks('construct_page_listing_buttons'):\\n hook(buttons, page, page_perms, is_parent, context)\\n\\n return {'page': page, 'buttons': buttons}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::bulk_action_choices\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::bulk_action_choices\\n@register.inclusion_tag(\\\"wagtailadmin/pages/listing/_buttons.html\\\",\\n takes_context=True)\\ndef bulk_action_choices(context, app_label, model_name):\\n\\n bulk_actions_list = list(bulk_action_registry.get_bulk_actions_for_model(app_label, model_name))\\n bulk_actions_list.sort(key=lambda x: x.action_priority)\\n\\n bulk_action_more_list = []\\n if len(bulk_actions_list) > 4:\\n bulk_action_more_list = bulk_actions_list[4:]\\n bulk_actions_list = bulk_actions_list[:4]\\n\\n next_url = get_valid_next_url_from_request(context['request'])\\n if not next_url:\\n next_url = context['request'].path\\n\\n bulk_action_buttons = [\\n PageListingButton(\\n action.display_name,\\n reverse('wagtail_bulk_action', args=[app_label, model_name, action.action_type]) + '?' + urlencode({'next': next_url}),\\n attrs={'aria-label': action.aria_label},\\n priority=action.action_priority,\\n classes=action.classes | {'bulk-action-btn'},\\n ) for action in bulk_actions_list\\n ]\\n\\n if bulk_action_more_list:\\n more_button = ButtonWithDropdown(\\n label=_(\\\"More\\\"),\\n attrs={\\n 'title': _(\\\"View more bulk actions\\\")\\n },\\n classes={'bulk-actions-more', 'dropup'},\\n button_classes={'button', 'button-small'},\\n buttons_data=[{\\n 'label': action.display_name,\\n 'url': reverse('wagtail_bulk_action', args=[app_label, model_name, action.action_type]) + '?' + urlencode({'next': next_url}),\\n 'attrs': {'aria-label': action.aria_label},\\n 'priority': action.action_priority,\\n 'classes': {'bulk-action-btn'},\\n } for action in bulk_action_more_list]\\n )\\n more_button.is_parent = True\\n bulk_action_buttons.append(more_button)\\n\\n return {'buttons': bulk_action_buttons}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::message_tags\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::message_tags\\n@register.simple_tag\\ndef message_tags(message):\\n level_tag = MESSAGE_TAGS.get(message.level)\\n if message.extra_tags and level_tag:\\n return message.extra_tags + ' ' + level_tag\\n elif message.extra_tags:\\n return message.extra_tags\\n elif level_tag:\\n return level_tag\\n else:\\n return ''\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::_abs\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::_abs\\n@register.filter('abs')\\ndef _abs(val):\\n return abs(val)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::admin_urlquote\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::admin_urlquote\\n@register.filter\\ndef admin_urlquote(value):\\n return quote(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::avatar_url\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::avatar_url\\n@register.simple_tag\\ndef avatar_url(user, size=50, gravatar_only=False):\\n \\\"\\\"\\\"\\n A template tag that receives a user and size and return\\n the appropriate avatar url for that user.\\n Example usage: {% avatar_url request.user 50 %}\\n \\\"\\\"\\\"\\n\\n if not gravatar_only and hasattr(user, 'wagtail_userprofile') and user.wagtail_userprofile.avatar:\\n return user.wagtail_userprofile.avatar.url\\n\\n if hasattr(user, 'email'):\\n gravatar_url = get_gravatar_url(user.email, size=size)\\n if gravatar_url is not None:\\n return gravatar_url\\n\\n return versioned_static_func('wagtailadmin/images/default-user-avatar.png')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::js_translation_strings\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::js_translation_strings\\n@register.simple_tag\\ndef js_translation_strings():\\n return mark_safe(json.dumps(get_js_translation_strings()))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::notification_static\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::notification_static\\n@register.simple_tag\\ndef notification_static(path):\\n \\\"\\\"\\\"\\n Variant of the {% static %}` tag for use in notification emails - tries to form\\n a full URL using BASE_URL if the static URL isn't already a full URL.\\n \\\"\\\"\\\"\\n return urljoin(base_url_setting(), static(path))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::versioned_static\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::versioned_static\\n@register.simple_tag\\ndef versioned_static(path):\\n \\\"\\\"\\\"\\n Wrapper for Django's static file finder to append a cache-busting query parameter\\n that updates on each Wagtail version\\n \\\"\\\"\\\"\\n return versioned_static_func(path)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::icon\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::icon\\n@register.inclusion_tag(\\\"wagtailadmin/shared/icon.html\\\", takes_context=False)\\ndef icon(name=None, class_name='icon', title=None, wrapped=False):\\n \\\"\\\"\\\"\\n Abstracts away the actual icon implementation.\\n\\n Usage:\\n {% load wagtailadmin_tags %}\\n ...\\n {% icon name=\\\"cogs\\\" class_name=\\\"icon--red\\\" title=\\\"Settings\\\" %}\\n\\n :param name: the icon name/id, required (string)\\n :param class_name: default 'icon' (string)\\n :param title: accessible label intended for screen readers (string)\\n :return: Rendered template snippet (string)\\n \\\"\\\"\\\"\\n if not name:\\n raise ValueError(\\\"You must supply an icon name\\\")\\n\\n return {\\n 'name': name,\\n 'class_name': class_name,\\n 'title': title,\\n 'wrapped': wrapped\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_simple\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_simple\\n@register.filter()\\ndef timesince_simple(d):\\n \\\"\\\"\\\"\\n Returns a simplified timesince:\\n 19 hours, 48 minutes ago -> 19 hours ago\\n 1 week, 1 day ago -> 1 week ago\\n 0 minutes ago -> just now\\n \\\"\\\"\\\"\\n time_period = timesince(d).split(',')[0]\\n if time_period == avoid_wrapping(_('0 minutes')):\\n return _(\\\"Just now\\\")\\n return _(\\\"%(time_period)s ago\\\") % {'time_period': time_period}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_all_objects_in_listing_query\",\n \"text\": \"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_all_objects_in_listing_query\\n def get_all_objects_in_listing_query(self, parent_id):\\n listing_objects = self.model.objects.all()\\n\\n if parent_id is not None:\\n listing_objects = listing_objects.get(id=parent_id).get_children()\\n\\n listing_objects = listing_objects.values_list('pk', flat=True)\\n\\n if 'q' in self.request.GET:\\n q = self.request.GET.get('q', '')\\n\\n listing_objects = page_filter_search(q, listing_objects)[0].results()\\n\\n return listing_objects\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_context_data\",\n \"text\": \"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_context_data\\n def get_context_data(self, **kwargs):\\n context = super().get_context_data(**kwargs)\\n context['items_with_no_access'] = [\\n {'item': page, 'can_edit': page.permissions_for_user(self.request.user).can_edit()} for page in context['items_with_no_access']\\n ]\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_execution_context\",\n \"text\": \"wagtail/admin/views/pages/bulk_actions/page_bulk_action.py::PageBulkAction.get_execution_context\\n def get_execution_context(self):\\n return {\\n 'user': self.request.user\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.check_perm\",\n \"text\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.check_perm\\n def check_perm(self, page):\\n return page.permissions_for_user(self.request.user).can_publish()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.object_context\",\n \"text\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.object_context\\n def object_context(self, obj):\\n context = super().object_context(obj)\\n context['draft_descendant_count'] = context['item'].get_descendants().not_live().count()\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_context_data\",\n \"text\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_context_data\\n def get_context_data(self, **kwargs):\\n context = super().get_context_data(**kwargs)\\n context['has_draft_descendants'] = any(map(lambda x: x['draft_descendant_count'], context['items']))\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_execution_context\",\n \"text\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_execution_context\\n def get_execution_context(self):\\n return {\\n **super().get_execution_context(),\\n 'include_descendants': self.cleaned_form.cleaned_data['include_descendants'],\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.execute_action\",\n \"text\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.execute_action\\n @classmethod\\n def execute_action(cls, objects, include_descendants=False, user=None, **kwargs):\\n num_parent_objects, num_child_objects = 0, 0\\n for page in objects:\\n page = page.specific\\n revision = page.save_revision(user=user)\\n revision.publish(user=user)\\n num_parent_objects += 1\\n\\n if include_descendants:\\n for draft_descendant_page in page.get_descendants().not_live().defer_streamfields().specific():\\n if user is None or draft_descendant_page.permissions_for_user(user).can_publish():\\n revision = draft_descendant_page.save_revision(user=user)\\n revision.publish(user=user)\\n num_child_objects += 1\\n return num_parent_objects, num_child_objects\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_success_message\",\n \"text\": \"wagtail/admin/views/pages/bulk_actions/publish.py::PublishBulkAction.get_success_message\\n def get_success_message(self, num_parent_objects, num_child_objects):\\n include_descendants = self.cleaned_form.cleaned_data['include_descendants']\\n if num_parent_objects == 1:\\n if include_descendants:\\n if num_child_objects == 0:\\n success_message = _(\\\"1 page has been published\\\")\\n else:\\n success_message = ngettext(\\n \\\"1 page and %(num_child_objects)d child page have been published\\\",\\n \\\"1 page and %(num_child_objects)d child pages have been published\\\",\\n num_child_objects\\n ) % {\\n 'num_child_objects': num_child_objects\\n }\\n else:\\n success_message = _(\\\"1 page has been published\\\")\\n else:\\n if include_descendants:\\n if num_child_objects == 0:\\n success_message = _(\\\"%(num_parent_objects)d pages have been published\\\") % {'num_parent_objects': num_parent_objects}\\n else:\\n success_message = ngettext(\\n \\\"%(num_parent_objects)d pages and %(num_child_objects)d child page have been published\\\",\\n \\\"%(num_parent_objects)d pages and %(num_child_objects)d child pages have been published\\\",\\n num_child_objects\\n ) % {\\n 'num_child_objects': num_child_objects,\\n 'num_parent_objects': num_parent_objects\\n }\\n else:\\n success_message = _(\\\"%(num_parent_objects)d pages have been published\\\") % {'num_parent_objects': num_parent_objects}\\n return success_message\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/migrations/0001_initial.py::set_page_path_collation\",\n \"text\": \"wagtail/core/migrations/0001_initial.py::set_page_path_collation\\ndef set_page_path_collation(apps, schema_editor):\\n \\\"\\\"\\\"\\n Treebeard's path comparison logic can fail on certain locales such as sk_SK, which\\n sort numbers after letters. To avoid this, we explicitly set the collation for the\\n 'path' column to the (non-locale-specific) 'C' collation.\\n\\n See: https://groups.google.com/d/msg/wagtail/q0leyuCnYWI/I9uDvVlyBAAJ\\n \\\"\\\"\\\"\\n if schema_editor.connection.vendor == 'postgresql':\\n schema_editor.execute(\\\"\\\"\\\"\\n ALTER TABLE wagtailcore_page ALTER COLUMN path TYPE VARCHAR(255) COLLATE \\\"C\\\"\\n \\\"\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::initial_data\",\n \"text\": \"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::initial_data\\ndef initial_data(apps, schema_editor):\\n ContentType = apps.get_model('contenttypes.ContentType')\\n Group = apps.get_model('auth.Group')\\n Page = apps.get_model('wagtailcore.Page')\\n Site = apps.get_model('wagtailcore.Site')\\n GroupPagePermission = apps.get_model('wagtailcore.GroupPagePermission')\\n\\n # Create page content type\\n page_content_type, created = ContentType.objects.get_or_create(\\n model='page',\\n app_label='wagtailcore'\\n )\\n\\n # Create root page\\n root = Page.objects.create(\\n title=\\\"Root\\\",\\n slug='root',\\n content_type=page_content_type,\\n path='0001',\\n depth=1,\\n numchild=1,\\n url_path='/',\\n )\\n\\n # Create homepage\\n homepage = Page.objects.create(\\n title=\\\"Welcome to your new Wagtail site!\\\",\\n slug='home',\\n content_type=page_content_type,\\n path='00010001',\\n depth=2,\\n numchild=0,\\n url_path='/home/',\\n )\\n\\n # Create default site\\n Site.objects.get_or_create(\\n hostname='localhost',\\n root_page_id=homepage.id,\\n is_default_site=True\\n )\\n\\n # Create auth groups\\n moderators_group, created = Group.objects.get_or_create(name='Moderators')\\n editors_group, created = Group.objects.get_or_create(name='Editors')\\n\\n # Create group permissions\\n GroupPagePermission.objects.create(\\n group=moderators_group,\\n page=root,\\n permission_type='add',\\n )\\n GroupPagePermission.objects.create(\\n group=moderators_group,\\n page=root,\\n permission_type='edit',\\n )\\n GroupPagePermission.objects.create(\\n group=moderators_group,\\n page=root,\\n permission_type='publish',\\n )\\n\\n GroupPagePermission.objects.create(\\n group=editors_group,\\n page=root,\\n permission_type='add',\\n )\\n GroupPagePermission.objects.create(\\n group=editors_\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::remove_initial_data\",\n \"text\": \"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::remove_initial_data\\ndef remove_initial_data(apps, schema_editor):\\n \\\"\\\"\\\"This function does nothing. The below code is commented out together\\n with an explanation of why we don't need to bother reversing any of the\\n initial data\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::set_page_path_collation\",\n \"text\": \"wagtail/core/migrations/0001_squashed_0016_change_page_url_path_to_text_field.py::set_page_path_collation\\ndef set_page_path_collation(apps, schema_editor):\\n \\\"\\\"\\\"\\n Treebeard's path comparison logic can fail on certain locales such as sk_SK, which\\n sort numbers after letters. To avoid this, we explicitly set the collation for the\\n 'path' column to the (non-locale-specific) 'C' collation.\\n\\n See: https://groups.google.com/d/msg/wagtail/q0leyuCnYWI/I9uDvVlyBAAJ\\n \\\"\\\"\\\"\\n if schema_editor.connection.vendor == 'postgresql':\\n schema_editor.execute(\\\"\\\"\\\"\\n ALTER TABLE wagtailcore_page ALTER COLUMN path TYPE VARCHAR(255) COLLATE \\\"C\\\"\\n \\\"\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::get_upload_to\",\n \"text\": \"wagtail/images/models.py::get_upload_to\\ndef get_upload_to(instance, filename):\\n \\\"\\\"\\\"\\n Obtain a valid upload path for an image file.\\n\\n This needs to be a module-level function so that it can be referenced within migrations,\\n but simply delegates to the `get_upload_to` method of the instance, so that AbstractImage\\n subclasses can override it.\\n \\\"\\\"\\\"\\n return instance.get_upload_to(filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::get_rendition_upload_to\",\n \"text\": \"wagtail/images/models.py::get_rendition_upload_to\\ndef get_rendition_upload_to(instance, filename):\\n \\\"\\\"\\\"\\n Obtain a valid upload path for an image rendition file.\\n\\n This needs to be a module-level function so that it can be referenced within migrations,\\n but simply delegates to the `get_upload_to` method of the instance, so that AbstractRendition\\n subclasses can override it.\\n \\\"\\\"\\\"\\n return instance.get_upload_to(filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::ImageFileMixin.is_stored_locally\",\n \"text\": \"wagtail/images/models.py::ImageFileMixin.is_stored_locally\\n def is_stored_locally(self):\\n \\\"\\\"\\\"\\n Returns True if the image is hosted on the local filesystem\\n \\\"\\\"\\\"\\n try:\\n self.file.path\\n\\n return True\\n except NotImplementedError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::ImageFileMixin.get_file_size\",\n \"text\": \"wagtail/images/models.py::ImageFileMixin.get_file_size\\n def get_file_size(self):\\n if self.file_size is None:\\n try:\\n self.file_size = self.file.size\\n except Exception as e:\\n # File not found\\n #\\n # Have to catch everything, because the exception\\n # depends on the file subclass, and therefore the\\n # storage being used.\\n raise SourceImageIOError(str(e))\\n\\n self.save(update_fields=['file_size'])\\n\\n return self.file_size\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::ImageFileMixin.open_file\",\n \"text\": \"wagtail/images/models.py::ImageFileMixin.open_file\\n @contextmanager\\n def open_file(self):\\n # Open file if it is closed\\n close_file = False\\n try:\\n image_file = self.file\\n\\n if self.file.closed:\\n # Reopen the file\\n if self.is_stored_locally():\\n self.file.open('rb')\\n else:\\n # Some external storage backends don't allow reopening\\n # the file. Get a fresh file instance. #1397\\n storage = self._meta.get_field('file').storage\\n image_file = storage.open(self.file.name, 'rb')\\n\\n close_file = True\\n except IOError as e:\\n # re-throw this as a SourceImageIOError so that calling code can distinguish\\n # these from IOErrors elsewhere in the process\\n raise SourceImageIOError(str(e))\\n\\n # Seek to beginning\\n image_file.seek(0)\\n\\n try:\\n yield image_file\\n finally:\\n if close_file:\\n image_file.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::ImageFileMixin.get_willow_image\",\n \"text\": \"wagtail/images/models.py::ImageFileMixin.get_willow_image\\n @contextmanager\\n def get_willow_image(self):\\n with self.open_file() as image_file:\\n yield WillowImage.open(image_file)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage._set_file_hash\",\n \"text\": \"wagtail/images/models.py::AbstractImage._set_file_hash\\n def _set_file_hash(self, file_contents):\\n self.file_hash = hashlib.sha1(file_contents).hexdigest()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.get_file_hash\",\n \"text\": \"wagtail/images/models.py::AbstractImage.get_file_hash\\n def get_file_hash(self):\\n if self.file_hash == '':\\n with self.open_file() as f:\\n self._set_file_hash(f.read())\\n\\n self.save(update_fields=['file_hash'])\\n\\n return self.file_hash\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.get_upload_to\",\n \"text\": \"wagtail/images/models.py::AbstractImage.get_upload_to\\n def get_upload_to(self, filename):\\n folder_name = 'original_images'\\n filename = self.file.field.storage.get_valid_name(filename)\\n\\n # do a unidecode in the filename and then\\n # replace non-ascii characters in filename with _ , to sidestep issues with filesystem encoding\\n filename = \\\"\\\".join((i if ord(i) < 128 else '_') for i in string_to_ascii(filename))\\n\\n # Truncate filename so it fits in the 100 character limit\\n # https://code.djangoproject.com/ticket/9893\\n full_path = os.path.join(folder_name, filename)\\n if len(full_path) >= 95:\\n chars_to_trim = len(full_path) - 94\\n prefix, extension = os.path.splitext(filename)\\n filename = prefix[:-chars_to_trim] + extension\\n full_path = os.path.join(folder_name, filename)\\n\\n return full_path\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.get_usage\",\n \"text\": \"wagtail/images/models.py::AbstractImage.get_usage\\n def get_usage(self):\\n return get_object_usage(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.usage_url\",\n \"text\": \"wagtail/images/models.py::AbstractImage.usage_url\\n @property\\n def usage_url(self):\\n return reverse('wagtailimages:image_usage',\\n args=(self.id,))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.__str__\",\n \"text\": \"wagtail/images/models.py::AbstractImage.__str__\\n def __str__(self):\\n return self.title\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.get_rect\",\n \"text\": \"wagtail/images/models.py::AbstractImage.get_rect\\n def get_rect(self):\\n return Rect(0, 0, self.width, self.height)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.get_focal_point\",\n \"text\": \"wagtail/images/models.py::AbstractImage.get_focal_point\\n def get_focal_point(self):\\n if self.focal_point_x is not None and \\\\\\n self.focal_point_y is not None and \\\\\\n self.focal_point_width is not None and \\\\\\n self.focal_point_height is not None:\\n return Rect.from_point(\\n self.focal_point_x,\\n self.focal_point_y,\\n self.focal_point_width,\\n self.focal_point_height,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.has_focal_point\",\n \"text\": \"wagtail/images/models.py::AbstractImage.has_focal_point\\n def has_focal_point(self):\\n return self.get_focal_point() is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.set_focal_point\",\n \"text\": \"wagtail/images/models.py::AbstractImage.set_focal_point\\n def set_focal_point(self, rect):\\n if rect is not None:\\n self.focal_point_x = rect.centroid_x\\n self.focal_point_y = rect.centroid_y\\n self.focal_point_width = rect.width\\n self.focal_point_height = rect.height\\n else:\\n self.focal_point_x = None\\n self.focal_point_y = None\\n self.focal_point_width = None\\n self.focal_point_height = None\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.get_suggested_focal_point\",\n \"text\": \"wagtail/images/models.py::AbstractImage.get_suggested_focal_point\\n def get_suggested_focal_point(self):\\n with self.get_willow_image() as willow:\\n faces = willow.detect_faces()\\n\\n if faces:\\n # Create a bounding box around all faces\\n left = min(face[0] for face in faces)\\n top = min(face[1] for face in faces)\\n right = max(face[2] for face in faces)\\n bottom = max(face[3] for face in faces)\\n focal_point = Rect(left, top, right, bottom)\\n else:\\n features = willow.detect_features()\\n if features:\\n # Create a bounding box around all features\\n left = min(feature[0] for feature in features)\\n top = min(feature[1] for feature in features)\\n right = max(feature[0] for feature in features)\\n bottom = max(feature[1] for feature in features)\\n focal_point = Rect(left, top, right, bottom)\\n else:\\n return None\\n\\n # Add 20% to width and height and give it a minimum size\\n x, y = focal_point.centroid\\n width, height = focal_point.size\\n\\n width *= 1.20\\n height *= 1.20\\n\\n width = max(width, 100)\\n height = max(height, 100)\\n\\n return Rect.from_point(x, y, width, height)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.get_rendition_model\",\n \"text\": \"wagtail/images/models.py::AbstractImage.get_rendition_model\\n @classmethod\\n def get_rendition_model(cls):\\n \\\"\\\"\\\" Get the Rendition model for this Image model \\\"\\\"\\\"\\n return cls.renditions.rel.related_model\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.get_rendition\",\n \"text\": \"wagtail/images/models.py::AbstractImage.get_rendition\\n def get_rendition(self, filter):\\n if isinstance(filter, str):\\n filter = Filter(spec=filter)\\n\\n cache_key = filter.get_cache_key(self)\\n Rendition = self.get_rendition_model()\\n\\n try:\\n rendition_caching = True\\n cache = caches['renditions']\\n rendition_cache_key = Rendition.construct_cache_key(\\n self.id,\\n cache_key,\\n filter.spec\\n )\\n cached_rendition = cache.get(rendition_cache_key)\\n if cached_rendition:\\n return cached_rendition\\n except InvalidCacheBackendError:\\n rendition_caching = False\\n\\n try:\\n rendition = self.renditions.get(\\n filter_spec=filter.spec,\\n focal_point_key=cache_key,\\n )\\n except Rendition.DoesNotExist:\\n # Generate the rendition image\\n try:\\n logger.debug(\\n \\\"Generating '%s' rendition for image %d\\\",\\n filter.spec,\\n self.pk,\\n )\\n\\n start_time = time.time()\\n generated_image = filter.run(self, BytesIO())\\n\\n logger.debug(\\n \\\"Generated '%s' rendition for image %d in %.1fms\\\",\\n filter.spec,\\n self.pk,\\n (time.time() - start_time) * 1000\\n )\\n except: # noqa:B901,E722\\n logger.debug(\\\"Failed to generate '%s' rendition for image %d\\\", filter.spec, self.pk)\\n raise\\n\\n # Generate filename\\n input_filename = os.path.basename(self.file.name)\\n input_filename_without_extension, input_extension = os.path.splitext(input_filename)\\n\\n # A mapping of image formats to extensions\\n FORMAT_EXTENSIONS = {\\n 'jpeg': '.jpg',\\n 'png': '.png'\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.is_portrait\",\n \"text\": \"wagtail/images/models.py::AbstractImage.is_portrait\\n def is_portrait(self):\\n return (self.width < self.height)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.is_landscape\",\n \"text\": \"wagtail/images/models.py::AbstractImage.is_landscape\\n def is_landscape(self):\\n return (self.height < self.width)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.filename\",\n \"text\": \"wagtail/images/models.py::AbstractImage.filename\\n @property\\n def filename(self):\\n return os.path.basename(self.file.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.default_alt_text\",\n \"text\": \"wagtail/images/models.py::AbstractImage.default_alt_text\\n @property\\n def default_alt_text(self):\\n # by default the alt text field (used in rich text insertion) is populated\\n # from the title. Subclasses might provide a separate alt field, and\\n # override this\\n return self.title\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractImage.is_editable_by_user\",\n \"text\": \"wagtail/images/models.py::AbstractImage.is_editable_by_user\\n def is_editable_by_user(self, user):\\n from wagtail.images.permissions import permission_policy\\n return permission_policy.user_has_permission_for_instance(user, 'change', self)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::Filter.__init__\",\n \"text\": \"wagtail/images/models.py::Filter.__init__\\n def __init__(self, spec=None):\\n # The spec pattern is operation1-var1-var2|operation2-var1\\n self.spec = spec\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::Filter.operations\",\n \"text\": \"wagtail/images/models.py::Filter.operations\\n @cached_property\\n def operations(self):\\n # Search for operations\\n registered_operations = {}\\n for fn in hooks.get_hooks('register_image_operations'):\\n registered_operations.update(dict(fn()))\\n\\n # Build list of operation objects\\n operations = []\\n for op_spec in self.spec.split('|'):\\n op_spec_parts = op_spec.split('-')\\n\\n if op_spec_parts[0] not in registered_operations:\\n raise InvalidFilterSpecError(\\\"Unrecognised operation: %s\\\" % op_spec_parts[0])\\n\\n op_class = registered_operations[op_spec_parts[0]]\\n operations.append(op_class(*op_spec_parts))\\n return operations\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::Filter.transform_operations\",\n \"text\": \"wagtail/images/models.py::Filter.transform_operations\\n @property\\n def transform_operations(self):\\n return [\\n operation for operation in self.operations\\n if isinstance(operation, TransformOperation)\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::Filter.filter_operations\",\n \"text\": \"wagtail/images/models.py::Filter.filter_operations\\n @property\\n def filter_operations(self):\\n return [\\n operation for operation in self.operations\\n if isinstance(operation, FilterOperation)\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::Filter.get_transform\",\n \"text\": \"wagtail/images/models.py::Filter.get_transform\\n def get_transform(self, image, size=None):\\n \\\"\\\"\\\"\\n Returns an ImageTransform with all the transforms in this filter applied.\\n\\n The ImageTransform is an object with two attributes:\\n - .size - The size of the final image\\n - .matrix - An affine transformation matrix that combines any\\n transform/scale/rotation operations that need to be applied to the image\\n \\\"\\\"\\\"\\n\\n if not size:\\n size = (image.width, image.height)\\n\\n transform = ImageTransform(size)\\n for operation in self.transform_operations:\\n transform = operation.run(transform, image)\\n return transform\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::Filter.run\",\n \"text\": \"wagtail/images/models.py::Filter.run\\n def run(self, image, output):\\n with image.get_willow_image() as willow:\\n original_format = willow.format_name\\n\\n # Fix orientation of image\\n willow = willow.auto_orient()\\n\\n # Transform the image\\n transform = self.get_transform(image, (willow.image.width, willow.image.height))\\n willow = willow.crop(transform.get_rect().round())\\n willow = willow.resize(transform.size)\\n\\n # Apply filters\\n env = {\\n 'original-format': original_format,\\n }\\n for operation in self.filter_operations:\\n willow = operation.run(willow, image, env) or willow\\n\\n # Find the output format to use\\n if 'output-format' in env:\\n # Developer specified an output format\\n output_format = env['output-format']\\n else:\\n # Convert bmp and webp to png by default\\n default_conversions = {\\n 'bmp': 'png',\\n 'webp': 'png',\\n }\\n\\n # Convert unanimated GIFs to PNG as well\\n if not willow.has_animation():\\n default_conversions['gif'] = 'png'\\n\\n # Allow the user to override the conversions\\n conversion = getattr(settings, 'WAGTAILIMAGES_FORMAT_CONVERSIONS', {})\\n default_conversions.update(conversion)\\n\\n # Get the converted output format falling back to the original\\n output_format = default_conversions.get(\\n original_format, original_format)\\n\\n if output_format == 'jpeg':\\n # Allow changing of JPEG compression quality\\n if 'jpeg-quality' in env:\\n quality = env['jpeg-quality']\\n else:\\n quality = getattr(settings, 'WAGTAILIMAGES_JPEG_QUALITY', 85)\\n\\n # If t\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::Filter.get_cache_key\",\n \"text\": \"wagtail/images/models.py::Filter.get_cache_key\\n def get_cache_key(self, image):\\n vary_parts = []\\n\\n for operation in self.operations:\\n for field in getattr(operation, 'vary_fields', []):\\n value = getattr(image, field, '')\\n vary_parts.append(str(value))\\n\\n vary_string = '-'.join(vary_parts)\\n\\n # Return blank string if there are no vary fields\\n if not vary_string:\\n return ''\\n\\n return hashlib.sha1(vary_string.encode('utf-8')).hexdigest()[:8]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractRendition.url\",\n \"text\": \"wagtail/images/models.py::AbstractRendition.url\\n @property\\n def url(self):\\n return self.file.url\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractRendition.alt\",\n \"text\": \"wagtail/images/models.py::AbstractRendition.alt\\n @property\\n def alt(self):\\n return self.image.default_alt_text\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractRendition.attrs\",\n \"text\": \"wagtail/images/models.py::AbstractRendition.attrs\\n @property\\n def attrs(self):\\n \\\"\\\"\\\"\\n The src, width, height, and alt attributes for an <img> tag, as a HTML\\n string\\n \\\"\\\"\\\"\\n return flatatt(self.attrs_dict)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractRendition.attrs_dict\",\n \"text\": \"wagtail/images/models.py::AbstractRendition.attrs_dict\\n @property\\n def attrs_dict(self):\\n \\\"\\\"\\\"\\n A dict of the src, width, height, and alt attributes for an <img> tag.\\n \\\"\\\"\\\"\\n return OrderedDict([\\n ('src', self.url),\\n ('width', self.width),\\n ('height', self.height),\\n ('alt', self.alt),\\n ])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractRendition.full_url\",\n \"text\": \"wagtail/images/models.py::AbstractRendition.full_url\\n @property\\n def full_url(self):\\n url = self.url\\n if hasattr(settings, 'BASE_URL') and url.startswith(\\\"/\\\"):\\n url = settings.BASE_URL + url\\n return url\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractRendition.filter\",\n \"text\": \"wagtail/images/models.py::AbstractRendition.filter\\n @property\\n def filter(self):\\n return Filter(self.filter_spec)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractRendition.focal_point\",\n \"text\": \"wagtail/images/models.py::AbstractRendition.focal_point\\n @cached_property\\n def focal_point(self):\\n image_focal_point = self.image.get_focal_point()\\n if image_focal_point:\\n transform = self.filter.get_transform(self.image)\\n return image_focal_point.transform(transform)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractRendition.background_position_style\",\n \"text\": \"wagtail/images/models.py::AbstractRendition.background_position_style\\n @property\\n def background_position_style(self):\\n \\\"\\\"\\\"\\n Returns a `background-position` rule to be put in the inline style of an element which uses the rendition for its background.\\n\\n This positions the rendition according to the value of the focal point. This is helpful for when the element does not have\\n the same aspect ratio as the rendition.\\n\\n For example:\\n\\n {% image page.image fill-1920x600 as image %}\\n <div style=\\\"background-image: url('{{ image.url }}'); {{ image.background_position_style }}\\\">\\n </div>\\n \\\"\\\"\\\"\\n focal_point = self.focal_point\\n if focal_point:\\n horz = int((focal_point.x * 100) // self.width)\\n vert = int((focal_point.y * 100) // self.height)\\n return 'background-position: {}% {}%;'.format(horz, vert)\\n else:\\n return 'background-position: 50% 50%;'\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/models.py::AbstractRendition.img_tag\",\n \"text\": \"wagtail/images/models.py::AbstractRendition.img_tag\\n def img_tag(self, extra_attributes={}):\\n attrs = self.attrs_dict.copy()\\n attrs.update(extra_attributes)\\n return mark_safe('<img{}>'.format(flatatt(attrs)))\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":51,"cells":{"instance_id":{"kind":"string","value":"googleapis__google-cloud-python-5033"},"repo":{"kind":"string","value":"googleapis/google-cloud-python"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Release spanner 1.2.0\nBatch query support is merged.\r\n\r\nNote that `development_status` is still showing beta.\n"},"gold_files":{"kind":"list like","value":["spanner/setup.py"],"string":"[\n \"spanner/setup.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["spanner/setup.py::<module>"],"string":"[\n \"spanner/setup.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"bigquery_datatransfer/docs/conf.py","text":"bigquery_datatransfer/docs/conf.py","label":0},{"cid":"spanner/google/cloud/spanner_admin_database_v1/proto/spanner_database_admin_pb2.py","text":"spanner/google/cloud/spanner_admin_database_v1/proto/spanner_database_admin_pb2.py","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py\nclass SpannerStub # Cloud Spanner API\n def __init__(self, channel) # Constructor.\nclass SpannerServicer # Cloud Spanner API\n def CreateSession(self, request, context) # Creates a new session. A session can be used to perform\n def GetSession(self, request, context) # Gets a session. Returns `NOT_FOUND` if the session does not exist.\n def ListSessions(self, request, context) # Lists all sessions in a given database.\n def DeleteSession(self, request, context) # Ends a session, releasing server resources associated with it.\n def ExecuteSql(self, request, context) # Executes an SQL query, returning all rows in a single reply. This\n def ExecuteStreamingSql(self, request, context) # Like [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql], except returns the resu\n def Read(self, request, context) # Reads rows from the database using key lookups and scans, as a\n def StreamingRead(self, request, context) # Like [Read][google.spanner.v1.Spanner.Read], except returns the result set as a\n def BeginTransaction(self, request, context) # Begins a new transaction. This step can often be skipped:\n def Commit(self, request, context) # Commits a transaction. The request includes the mutations to be\n def Rollback(self, request, context) # Rolls back a transaction, releasing any locks it holds. It is a good\n def PartitionQuery(self, request, context) # Creates a set of partition tokens that can be used to execute a query\n def PartitionRead(self, request, context) # Creates a set of partition tokens that can be used to execute a read\ndef add_SpannerServicer_to_server(servicer, server)","label":0},{"cid":"spanner/setup.py","text":"spanner/setup.py","label":1}],"string":"[\n {\n \"cid\": \"bigquery_datatransfer/docs/conf.py\",\n \"text\": \"bigquery_datatransfer/docs/conf.py\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_admin_database_v1/proto/spanner_database_admin_pb2.py\",\n \"text\": \"spanner/google/cloud/spanner_admin_database_v1/proto/spanner_database_admin_pb2.py\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py\\nclass SpannerStub # Cloud Spanner API\\n def __init__(self, channel) # Constructor.\\nclass SpannerServicer # Cloud Spanner API\\n def CreateSession(self, request, context) # Creates a new session. A session can be used to perform\\n def GetSession(self, request, context) # Gets a session. Returns `NOT_FOUND` if the session does not exist.\\n def ListSessions(self, request, context) # Lists all sessions in a given database.\\n def DeleteSession(self, request, context) # Ends a session, releasing server resources associated with it.\\n def ExecuteSql(self, request, context) # Executes an SQL query, returning all rows in a single reply. This\\n def ExecuteStreamingSql(self, request, context) # Like [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql], except returns the resu\\n def Read(self, request, context) # Reads rows from the database using key lookups and scans, as a\\n def StreamingRead(self, request, context) # Like [Read][google.spanner.v1.Spanner.Read], except returns the result set as a\\n def BeginTransaction(self, request, context) # Begins a new transaction. This step can often be skipped:\\n def Commit(self, request, context) # Commits a transaction. The request includes the mutations to be\\n def Rollback(self, request, context) # Rolls back a transaction, releasing any locks it holds. It is a good\\n def PartitionQuery(self, request, context) # Creates a set of partition tokens that can be used to execute a query\\n def PartitionRead(self, request, context) # Creates a set of partition tokens that can be used to execute a read\\ndef add_SpannerServicer_to_server(servicer, server)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/setup.py\",\n \"text\": \"spanner/setup.py\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerStub.__init__","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerStub.__init__\n def __init__(self, channel):\n \"\"\"Constructor.\n\n Args:\n channel: A grpc.Channel.\n \"\"\"\n self.CreateSession = channel.unary_unary(\n '/google.spanner.v1.Spanner/CreateSession',\n request_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.CreateSessionRequest.SerializeToString,\n response_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.Session.FromString,\n )\n self.GetSession = channel.unary_unary(\n '/google.spanner.v1.Spanner/GetSession',\n request_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.GetSessionRequest.SerializeToString,\n response_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.Session.FromString,\n )\n self.ListSessions = channel.unary_unary(\n '/google.spanner.v1.Spanner/ListSessions',\n request_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ListSessionsRequest.SerializeToString,\n response_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ListSessionsResponse.FromString,\n )\n self.DeleteSession = channel.unary_unary(\n '/google.spanner.v1.Spanner/DeleteSession',\n request_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.DeleteSessionRequest.SerializeToString,\n response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString,\n )\n self.ExecuteSql = channel.unary_unary(\n '/google.spanner.v1.Spanner/ExecuteSql',\n request_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ExecuteSqlRequest.SerializeToString,\n response_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_result__set__pb2.ResultSet.FromString,\n )\n self.ExecuteStreamingSql = channel.unary_stream(\n '/google.spanner.v1.Spanner/ExecuteStreamingSql',\n requ","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.CreateSession","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.CreateSession\n def CreateSession(self, request, context):\n \"\"\"Creates a new session. A session can be used to perform\n transactions that read and/or modify data in a Cloud Spanner database.\n Sessions are meant to be reused for many consecutive\n transactions.\n\n Sessions can only execute one transaction at a time. To execute\n multiple concurrent read-write/write-only transactions, create\n multiple sessions. Note that standalone reads and queries use a\n transaction internally, and count toward the one transaction\n limit.\n\n Cloud Spanner limits the number of sessions that can exist at any given\n time; thus, it is a good idea to delete idle and/or unneeded sessions.\n Aside from explicit deletes, Cloud Spanner can delete sessions for which no\n operations are sent for more than an hour. If a session is deleted,\n requests to it return `NOT_FOUND`.\n\n Idle sessions can be kept alive by sending a trivial SQL query\n periodically, e.g., `\"SELECT 1\"`.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.GetSession","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.GetSession\n def GetSession(self, request, context):\n \"\"\"Gets a session. Returns `NOT_FOUND` if the session does not exist.\n This is mainly useful for determining whether a session is still\n alive.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ListSessions","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ListSessions\n def ListSessions(self, request, context):\n \"\"\"Lists all sessions in a given database.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.DeleteSession","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.DeleteSession\n def DeleteSession(self, request, context):\n \"\"\"Ends a session, releasing server resources associated with it.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ExecuteSql","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ExecuteSql\n def ExecuteSql(self, request, context):\n \"\"\"Executes an SQL query, returning all rows in a single reply. This\n method cannot be used to return a result set larger than 10 MiB;\n if the query yields more data than that, the query fails with\n a `FAILED_PRECONDITION` error.\n\n Queries inside read-write transactions might return `ABORTED`. If\n this occurs, the application should restart the transaction from\n the beginning. See [Transaction][google.spanner.v1.Transaction] for more details.\n\n Larger result sets can be fetched in streaming fashion by calling\n [ExecuteStreamingSql][google.spanner.v1.Spanner.ExecuteStreamingSql] instead.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ExecuteStreamingSql","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ExecuteStreamingSql\n def ExecuteStreamingSql(self, request, context):\n \"\"\"Like [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql], except returns the result\n set as a stream. Unlike [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql], there\n is no limit on the size of the returned result set. However, no\n individual row in the result set can exceed 100 MiB, and no\n column value can exceed 10 MiB.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Read","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Read\n def Read(self, request, context):\n \"\"\"Reads rows from the database using key lookups and scans, as a\n simple key/value style alternative to\n [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql]. This method cannot be used to\n return a result set larger than 10 MiB; if the read matches more\n data than that, the read fails with a `FAILED_PRECONDITION`\n error.\n\n Reads inside read-write transactions might return `ABORTED`. If\n this occurs, the application should restart the transaction from\n the beginning. See [Transaction][google.spanner.v1.Transaction] for more details.\n\n Larger result sets can be yielded in streaming fashion by calling\n [StreamingRead][google.spanner.v1.Spanner.StreamingRead] instead.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.StreamingRead","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.StreamingRead\n def StreamingRead(self, request, context):\n \"\"\"Like [Read][google.spanner.v1.Spanner.Read], except returns the result set as a\n stream. Unlike [Read][google.spanner.v1.Spanner.Read], there is no limit on the\n size of the returned result set. However, no individual row in\n the result set can exceed 100 MiB, and no column value can exceed\n 10 MiB.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.BeginTransaction","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.BeginTransaction\n def BeginTransaction(self, request, context):\n \"\"\"Begins a new transaction. This step can often be skipped:\n [Read][google.spanner.v1.Spanner.Read], [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql] and\n [Commit][google.spanner.v1.Spanner.Commit] can begin a new transaction as a\n side-effect.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Commit","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Commit\n def Commit(self, request, context):\n \"\"\"Commits a transaction. The request includes the mutations to be\n applied to rows in the database.\n\n `Commit` might return an `ABORTED` error. This can occur at any time;\n commonly, the cause is conflicts with concurrent\n transactions. However, it can also happen for a variety of other\n reasons. If `Commit` returns `ABORTED`, the caller should re-attempt\n the transaction from the beginning, re-using the same session.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Rollback","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Rollback\n def Rollback(self, request, context):\n \"\"\"Rolls back a transaction, releasing any locks it holds. It is a good\n idea to call this for any transaction that includes one or more\n [Read][google.spanner.v1.Spanner.Read] or [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql] requests and\n ultimately decides not to commit.\n\n `Rollback` returns `OK` if it successfully aborts the transaction, the\n transaction was already aborted, or the transaction is not\n found. `Rollback` never returns `ABORTED`.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.PartitionQuery","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.PartitionQuery\n def PartitionQuery(self, request, context):\n \"\"\"Creates a set of partition tokens that can be used to execute a query\n operation in parallel. Each of the returned partition tokens can be used\n by [ExecuteStreamingSql][google.spanner.v1.Spanner.ExecuteStreamingSql] to specify a subset\n of the query result to read. The same session and read-only transaction\n must be used by the PartitionQueryRequest used to create the\n partition tokens and the ExecuteSqlRequests that use the partition tokens.\n Partition tokens become invalid when the session used to create them\n is deleted or begins a new transaction.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.PartitionRead","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.PartitionRead\n def PartitionRead(self, request, context):\n \"\"\"Creates a set of partition tokens that can be used to execute a read\n operation in parallel. Each of the returned partition tokens can be used\n by [StreamingRead][google.spanner.v1.Spanner.StreamingRead] to specify a subset of the read\n result to read. The same session and read-only transaction must be used by\n the PartitionReadRequest used to create the partition tokens and the\n ReadRequests that use the partition tokens.\n Partition tokens become invalid when the session used to create them\n is deleted or begins a new transaction.\n \"\"\"\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","label":0},{"cid":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::add_SpannerServicer_to_server","text":"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::add_SpannerServicer_to_server\ndef add_SpannerServicer_to_server(servicer, server):\n rpc_method_handlers = {\n 'CreateSession': grpc.unary_unary_rpc_method_handler(\n servicer.CreateSession,\n request_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.CreateSessionRequest.FromString,\n response_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.Session.SerializeToString,\n ),\n 'GetSession': grpc.unary_unary_rpc_method_handler(\n servicer.GetSession,\n request_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.GetSessionRequest.FromString,\n response_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.Session.SerializeToString,\n ),\n 'ListSessions': grpc.unary_unary_rpc_method_handler(\n servicer.ListSessions,\n request_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ListSessionsRequest.FromString,\n response_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ListSessionsResponse.SerializeToString,\n ),\n 'DeleteSession': grpc.unary_unary_rpc_method_handler(\n servicer.DeleteSession,\n request_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.DeleteSessionRequest.FromString,\n response_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,\n ),\n 'ExecuteSql': grpc.unary_unary_rpc_method_handler(\n servicer.ExecuteSql,\n request_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ExecuteSqlRequest.FromString,\n response_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_result__set__pb2.ResultSet.SerializeToString,\n ),\n 'ExecuteStreamingSql': grpc.unary_stream_rpc_method_handler(\n servicer.ExecuteStreamingSql,\n request_deserializer=goo","label":0}],"string":"[\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerStub.__init__\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerStub.__init__\\n def __init__(self, channel):\\n \\\"\\\"\\\"Constructor.\\n\\n Args:\\n channel: A grpc.Channel.\\n \\\"\\\"\\\"\\n self.CreateSession = channel.unary_unary(\\n '/google.spanner.v1.Spanner/CreateSession',\\n request_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.CreateSessionRequest.SerializeToString,\\n response_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.Session.FromString,\\n )\\n self.GetSession = channel.unary_unary(\\n '/google.spanner.v1.Spanner/GetSession',\\n request_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.GetSessionRequest.SerializeToString,\\n response_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.Session.FromString,\\n )\\n self.ListSessions = channel.unary_unary(\\n '/google.spanner.v1.Spanner/ListSessions',\\n request_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ListSessionsRequest.SerializeToString,\\n response_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ListSessionsResponse.FromString,\\n )\\n self.DeleteSession = channel.unary_unary(\\n '/google.spanner.v1.Spanner/DeleteSession',\\n request_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.DeleteSessionRequest.SerializeToString,\\n response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString,\\n )\\n self.ExecuteSql = channel.unary_unary(\\n '/google.spanner.v1.Spanner/ExecuteSql',\\n request_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ExecuteSqlRequest.SerializeToString,\\n response_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_result__set__pb2.ResultSet.FromString,\\n )\\n self.ExecuteStreamingSql = channel.unary_stream(\\n '/google.spanner.v1.Spanner/ExecuteStreamingSql',\\n requ\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.CreateSession\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.CreateSession\\n def CreateSession(self, request, context):\\n \\\"\\\"\\\"Creates a new session. A session can be used to perform\\n transactions that read and/or modify data in a Cloud Spanner database.\\n Sessions are meant to be reused for many consecutive\\n transactions.\\n\\n Sessions can only execute one transaction at a time. To execute\\n multiple concurrent read-write/write-only transactions, create\\n multiple sessions. Note that standalone reads and queries use a\\n transaction internally, and count toward the one transaction\\n limit.\\n\\n Cloud Spanner limits the number of sessions that can exist at any given\\n time; thus, it is a good idea to delete idle and/or unneeded sessions.\\n Aside from explicit deletes, Cloud Spanner can delete sessions for which no\\n operations are sent for more than an hour. If a session is deleted,\\n requests to it return `NOT_FOUND`.\\n\\n Idle sessions can be kept alive by sending a trivial SQL query\\n periodically, e.g., `\\\"SELECT 1\\\"`.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.GetSession\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.GetSession\\n def GetSession(self, request, context):\\n \\\"\\\"\\\"Gets a session. Returns `NOT_FOUND` if the session does not exist.\\n This is mainly useful for determining whether a session is still\\n alive.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ListSessions\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ListSessions\\n def ListSessions(self, request, context):\\n \\\"\\\"\\\"Lists all sessions in a given database.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.DeleteSession\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.DeleteSession\\n def DeleteSession(self, request, context):\\n \\\"\\\"\\\"Ends a session, releasing server resources associated with it.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ExecuteSql\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ExecuteSql\\n def ExecuteSql(self, request, context):\\n \\\"\\\"\\\"Executes an SQL query, returning all rows in a single reply. This\\n method cannot be used to return a result set larger than 10 MiB;\\n if the query yields more data than that, the query fails with\\n a `FAILED_PRECONDITION` error.\\n\\n Queries inside read-write transactions might return `ABORTED`. If\\n this occurs, the application should restart the transaction from\\n the beginning. See [Transaction][google.spanner.v1.Transaction] for more details.\\n\\n Larger result sets can be fetched in streaming fashion by calling\\n [ExecuteStreamingSql][google.spanner.v1.Spanner.ExecuteStreamingSql] instead.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ExecuteStreamingSql\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.ExecuteStreamingSql\\n def ExecuteStreamingSql(self, request, context):\\n \\\"\\\"\\\"Like [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql], except returns the result\\n set as a stream. Unlike [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql], there\\n is no limit on the size of the returned result set. However, no\\n individual row in the result set can exceed 100 MiB, and no\\n column value can exceed 10 MiB.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Read\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Read\\n def Read(self, request, context):\\n \\\"\\\"\\\"Reads rows from the database using key lookups and scans, as a\\n simple key/value style alternative to\\n [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql]. This method cannot be used to\\n return a result set larger than 10 MiB; if the read matches more\\n data than that, the read fails with a `FAILED_PRECONDITION`\\n error.\\n\\n Reads inside read-write transactions might return `ABORTED`. If\\n this occurs, the application should restart the transaction from\\n the beginning. See [Transaction][google.spanner.v1.Transaction] for more details.\\n\\n Larger result sets can be yielded in streaming fashion by calling\\n [StreamingRead][google.spanner.v1.Spanner.StreamingRead] instead.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.StreamingRead\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.StreamingRead\\n def StreamingRead(self, request, context):\\n \\\"\\\"\\\"Like [Read][google.spanner.v1.Spanner.Read], except returns the result set as a\\n stream. Unlike [Read][google.spanner.v1.Spanner.Read], there is no limit on the\\n size of the returned result set. However, no individual row in\\n the result set can exceed 100 MiB, and no column value can exceed\\n 10 MiB.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.BeginTransaction\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.BeginTransaction\\n def BeginTransaction(self, request, context):\\n \\\"\\\"\\\"Begins a new transaction. This step can often be skipped:\\n [Read][google.spanner.v1.Spanner.Read], [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql] and\\n [Commit][google.spanner.v1.Spanner.Commit] can begin a new transaction as a\\n side-effect.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Commit\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Commit\\n def Commit(self, request, context):\\n \\\"\\\"\\\"Commits a transaction. The request includes the mutations to be\\n applied to rows in the database.\\n\\n `Commit` might return an `ABORTED` error. This can occur at any time;\\n commonly, the cause is conflicts with concurrent\\n transactions. However, it can also happen for a variety of other\\n reasons. If `Commit` returns `ABORTED`, the caller should re-attempt\\n the transaction from the beginning, re-using the same session.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Rollback\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.Rollback\\n def Rollback(self, request, context):\\n \\\"\\\"\\\"Rolls back a transaction, releasing any locks it holds. It is a good\\n idea to call this for any transaction that includes one or more\\n [Read][google.spanner.v1.Spanner.Read] or [ExecuteSql][google.spanner.v1.Spanner.ExecuteSql] requests and\\n ultimately decides not to commit.\\n\\n `Rollback` returns `OK` if it successfully aborts the transaction, the\\n transaction was already aborted, or the transaction is not\\n found. `Rollback` never returns `ABORTED`.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.PartitionQuery\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.PartitionQuery\\n def PartitionQuery(self, request, context):\\n \\\"\\\"\\\"Creates a set of partition tokens that can be used to execute a query\\n operation in parallel. Each of the returned partition tokens can be used\\n by [ExecuteStreamingSql][google.spanner.v1.Spanner.ExecuteStreamingSql] to specify a subset\\n of the query result to read. The same session and read-only transaction\\n must be used by the PartitionQueryRequest used to create the\\n partition tokens and the ExecuteSqlRequests that use the partition tokens.\\n Partition tokens become invalid when the session used to create them\\n is deleted or begins a new transaction.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.PartitionRead\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::SpannerServicer.PartitionRead\\n def PartitionRead(self, request, context):\\n \\\"\\\"\\\"Creates a set of partition tokens that can be used to execute a read\\n operation in parallel. Each of the returned partition tokens can be used\\n by [StreamingRead][google.spanner.v1.Spanner.StreamingRead] to specify a subset of the read\\n result to read. The same session and read-only transaction must be used by\\n the PartitionReadRequest used to create the partition tokens and the\\n ReadRequests that use the partition tokens.\\n Partition tokens become invalid when the session used to create them\\n is deleted or begins a new transaction.\\n \\\"\\\"\\\"\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::add_SpannerServicer_to_server\",\n \"text\": \"spanner/google/cloud/spanner_v1/proto/spanner_pb2_grpc.py::add_SpannerServicer_to_server\\ndef add_SpannerServicer_to_server(servicer, server):\\n rpc_method_handlers = {\\n 'CreateSession': grpc.unary_unary_rpc_method_handler(\\n servicer.CreateSession,\\n request_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.CreateSessionRequest.FromString,\\n response_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.Session.SerializeToString,\\n ),\\n 'GetSession': grpc.unary_unary_rpc_method_handler(\\n servicer.GetSession,\\n request_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.GetSessionRequest.FromString,\\n response_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.Session.SerializeToString,\\n ),\\n 'ListSessions': grpc.unary_unary_rpc_method_handler(\\n servicer.ListSessions,\\n request_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ListSessionsRequest.FromString,\\n response_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ListSessionsResponse.SerializeToString,\\n ),\\n 'DeleteSession': grpc.unary_unary_rpc_method_handler(\\n servicer.DeleteSession,\\n request_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.DeleteSessionRequest.FromString,\\n response_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,\\n ),\\n 'ExecuteSql': grpc.unary_unary_rpc_method_handler(\\n servicer.ExecuteSql,\\n request_deserializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_spanner__pb2.ExecuteSqlRequest.FromString,\\n response_serializer=google_dot_cloud_dot_spanner__v1_dot_proto_dot_result__set__pb2.ResultSet.SerializeToString,\\n ),\\n 'ExecuteStreamingSql': grpc.unary_stream_rpc_method_handler(\\n servicer.ExecuteStreamingSql,\\n request_deserializer=goo\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":52,"cells":{"instance_id":{"kind":"string","value":"ray-project__ray-7665"},"repo":{"kind":"string","value":"ray-project/ray"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"[Python] jsonschema included twice in setup.py requires list.\n<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->\r\n\r\n### What is the problem?\r\n\r\n`jsonschema` is included twice in the Python package [setup.py `requires` list](https://github.com/ray-project/ray/blob/master/python/setup.py#L176-L183). This is causing the usage of the Ray Python library within Bazel to fail during the analysis phase due to label duplication in the generated `py_library` target's `'deps'`:\r\n\r\n```\r\nERROR: .../external/requirements_py3_pypi__ray_0_9_0_dev0/BUILD:6:1: Label '@requirements_py3_pypi__jsonschema_3_2_0//:pkg' is duplicated in the 'deps' attribute of rule 'pkg'\r\n```\r\n\r\nThis bug was introduced in the [cluster json schema validator PR](https://github.com/ray-project/ray/pull/7261/files#diff-8cf6167d58ce775a08acafcfe6f40966).\r\n\r\n*Ray version and other system information (Python version, TensorFlow version, OS):*\r\n\r\nRay master commit 90b553ed058a546e036374cd0919e00604892514 (most recent commit as of this issue filing)\r\n\r\n\r\n### Reproduction (REQUIRED)\r\n\r\n\r\n\r\n- [x] I have verified my script runs in a clean environment and reproduces the issue.\r\n- [x] I have verified the issue also occurs with the [latest wheels](https://ray.readthedocs.io/en/latest/installation.html).\r\n\n"},"gold_files":{"kind":"list like","value":["python/setup.py"],"string":"[\n \"python/setup.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["python/setup.py::<module>"],"string":"[\n \"python/setup.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"doc/source/conf.py","text":"doc/source/conf.py\ndef feedback_form_url(project, page) # Create a URL for feedback on a particular page in a project.\ndef update_context(app, pagename, templatename, context, doctree) # Update the page rendering context to include ``feedback_form_url``.\ndef setup(app)","label":0},{"cid":"python/ray/services.py","text":"python/ray/services.py\ndef address(ip_address, port)\ndef new_port()\ndef include_java_from_redis(redis_client) # This is used for query include_java bool from redis.\ndef find_redis_address_or_die()\ndef get_address_info_from_redis_helper(redis_address, node_ip_address, redis_password=None)\ndef get_address_info_from_redis(redis_address, node_ip_address, num_retries=5, redis_password=None)\ndef get_webui_url_from_redis(redis_client)\ndef remaining_processes_alive() # See if the remaining processes are alive or not.\ndef validate_redis_address(address, redis_address) # Validates redis address parameter and splits it into host/ip components.\ndef address_to_ip(address) # Convert a hostname to a numerical IP addresses in an address.\ndef get_node_ip_address(address='8.8.8.8:53') # Determine the IP address of the local node.\ndef create_redis_client(redis_address, password=None) # Create a Redis client.\ndef start_ray_process(command, process_type, fate_share, env_updates=None, cwd=None, use_valgrind=False, use_gdb=False, use_valgrind_profiler=False, use_perftools_profiler=False, use_tmux=False, stdout_file=None, stderr_file=None, pipe_stdin=False) # Start one of the Ray processes.\n def preexec_fn()\ndef wait_for_redis_to_start(redis_ip_address, redis_port, password=None, num_retries=5) # Wait for a Redis server to be available.\ndef _compute_version_info() # Compute the versions of Python, and Ray.\ndef _put_version_info_in_redis(redis_client) # Store version information in Redis.\ndef check_version_info(redis_client) # Check if various version info of this process is correct.\ndef start_reaper(fate_share=None) # Start the reaper process.\ndef start_redis(node_ip_address, redirect_files, resource_spec, port=None, redis_shard_ports=None, num_redis_shards=1, redis_max_clients=None, redirect_worker_output=False, password=None, use_credis=None, include_java=False, fate_share=None) # Start the Redis global state store.\ndef _start_redis_instance(executable, modules, port=None, redis_max_clients=None, num_retries=20, stdout_file=None, stderr_file=None, password=None, redis_max_memory=None, fate_share=None) # Start a single Redis server.\ndef start_log_monitor(redis_address, logs_dir, stdout_file=None, stderr_file=None, redis_password=None, fate_share=None) # Start a log monitor process.\ndef start_reporter(redis_address, stdout_file=None, stderr_file=None, redis_password=None, fate_share=None) # Start a reporter process.\ndef start_dashboard(require_webui, host, redis_address, temp_dir, stdout_file=None, stderr_file=None, redis_password=None, fate_share=None) # Start a dashboard process.\ndef start_gcs_server(redis_address, stdout_file=None, stderr_file=None, redis_password=None, config=None, fate_share=None) # Start a gcs server.\ndef start_raylet(redis_address, node_ip_address, node_manager_port, raylet_name, plasma_store_name, worker_path, temp_dir, session_dir, resource_spec, object_manager_port=None, redis_password=None, use_valgrind=False, use_profile","label":0},{"cid":"python/ray/setup-dev.py","text":"python/ray/setup-dev.py\ndef do_link(package, force=False, local_path='')","label":0},{"cid":"python/setup.py","text":"python/setup.py\nclass build_ext\n def run(self)\n def walk_directory(self, directory)\n def move_file(self, filename)\nclass BinaryDistribution\n def has_ext_modules(self)\ndef find_version(*filepath)","label":1}],"string":"[\n {\n \"cid\": \"doc/source/conf.py\",\n \"text\": \"doc/source/conf.py\\ndef feedback_form_url(project, page) # Create a URL for feedback on a particular page in a project.\\ndef update_context(app, pagename, templatename, context, doctree) # Update the page rendering context to include ``feedback_form_url``.\\ndef setup(app)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py\",\n \"text\": \"python/ray/services.py\\ndef address(ip_address, port)\\ndef new_port()\\ndef include_java_from_redis(redis_client) # This is used for query include_java bool from redis.\\ndef find_redis_address_or_die()\\ndef get_address_info_from_redis_helper(redis_address, node_ip_address, redis_password=None)\\ndef get_address_info_from_redis(redis_address, node_ip_address, num_retries=5, redis_password=None)\\ndef get_webui_url_from_redis(redis_client)\\ndef remaining_processes_alive() # See if the remaining processes are alive or not.\\ndef validate_redis_address(address, redis_address) # Validates redis address parameter and splits it into host/ip components.\\ndef address_to_ip(address) # Convert a hostname to a numerical IP addresses in an address.\\ndef get_node_ip_address(address='8.8.8.8:53') # Determine the IP address of the local node.\\ndef create_redis_client(redis_address, password=None) # Create a Redis client.\\ndef start_ray_process(command, process_type, fate_share, env_updates=None, cwd=None, use_valgrind=False, use_gdb=False, use_valgrind_profiler=False, use_perftools_profiler=False, use_tmux=False, stdout_file=None, stderr_file=None, pipe_stdin=False) # Start one of the Ray processes.\\n def preexec_fn()\\ndef wait_for_redis_to_start(redis_ip_address, redis_port, password=None, num_retries=5) # Wait for a Redis server to be available.\\ndef _compute_version_info() # Compute the versions of Python, and Ray.\\ndef _put_version_info_in_redis(redis_client) # Store version information in Redis.\\ndef check_version_info(redis_client) # Check if various version info of this process is correct.\\ndef start_reaper(fate_share=None) # Start the reaper process.\\ndef start_redis(node_ip_address, redirect_files, resource_spec, port=None, redis_shard_ports=None, num_redis_shards=1, redis_max_clients=None, redirect_worker_output=False, password=None, use_credis=None, include_java=False, fate_share=None) # Start the Redis global state store.\\ndef _start_redis_instance(executable, modules, port=None, redis_max_clients=None, num_retries=20, stdout_file=None, stderr_file=None, password=None, redis_max_memory=None, fate_share=None) # Start a single Redis server.\\ndef start_log_monitor(redis_address, logs_dir, stdout_file=None, stderr_file=None, redis_password=None, fate_share=None) # Start a log monitor process.\\ndef start_reporter(redis_address, stdout_file=None, stderr_file=None, redis_password=None, fate_share=None) # Start a reporter process.\\ndef start_dashboard(require_webui, host, redis_address, temp_dir, stdout_file=None, stderr_file=None, redis_password=None, fate_share=None) # Start a dashboard process.\\ndef start_gcs_server(redis_address, stdout_file=None, stderr_file=None, redis_password=None, config=None, fate_share=None) # Start a gcs server.\\ndef start_raylet(redis_address, node_ip_address, node_manager_port, raylet_name, plasma_store_name, worker_path, temp_dir, session_dir, resource_spec, object_manager_port=None, redis_password=None, use_valgrind=False, use_profile\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/setup-dev.py\",\n \"text\": \"python/ray/setup-dev.py\\ndef do_link(package, force=False, local_path='')\",\n \"label\": 0\n },\n {\n \"cid\": \"python/setup.py\",\n \"text\": \"python/setup.py\\nclass build_ext\\n def run(self)\\n def walk_directory(self, directory)\\n def move_file(self, filename)\\nclass BinaryDistribution\\n def has_ext_modules(self)\\ndef find_version(*filepath)\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"doc/source/conf.py::feedback_form_url","text":"doc/source/conf.py::feedback_form_url\ndef feedback_form_url(project, page):\n \"\"\"Create a URL for feedback on a particular page in a project.\"\"\"\n return FEEDBACK_FORM_FMT.format(\n title=urllib.parse.quote(\n \"[docs] Issue on `{page}.rst`\".format(page=page)),\n body=urllib.parse.quote(\n \"# Documentation Problem/Question/Comment\\n\"\n \"<!-- Describe your issue/question/comment below. -->\\n\"\n \"<!-- If there are typos or errors in the docs, feel free to create a pull-request. -->\\n\"\n \"\\n\\n\\n\\n\"\n \"(Created directly from the docs)\\n\"))","label":0},{"cid":"doc/source/conf.py::update_context","text":"doc/source/conf.py::update_context\ndef update_context(app, pagename, templatename, context, doctree):\n \"\"\"Update the page rendering context to include ``feedback_form_url``.\"\"\"\n context['feedback_form_url'] = feedback_form_url(app.config.project,\n pagename)","label":0},{"cid":"doc/source/conf.py::setup","text":"doc/source/conf.py::setup\ndef setup(app):\n app.connect('html-page-context', update_context)\n # Custom directives\n app.add_directive('customgalleryitem', CustomGalleryItemDirective)","label":0},{"cid":"python/ray/services.py::address","text":"python/ray/services.py::address\ndef address(ip_address, port):\n return ip_address + \":\" + str(port)","label":0},{"cid":"python/ray/services.py::new_port","text":"python/ray/services.py::new_port\ndef new_port():\n return random.randint(10000, 65535)","label":0},{"cid":"python/ray/services.py::include_java_from_redis","text":"python/ray/services.py::include_java_from_redis\ndef include_java_from_redis(redis_client):\n \"\"\"This is used for query include_java bool from redis.\n\n Args:\n redis_client (StrictRedis): The redis client to GCS.\n\n Returns:\n True if this cluster backend enables Java worker.\n \"\"\"\n return redis_client.get(\"INCLUDE_JAVA\") == b\"1\"","label":0},{"cid":"python/ray/services.py::find_redis_address_or_die","text":"python/ray/services.py::find_redis_address_or_die\ndef find_redis_address_or_die():\n pids = psutil.pids()\n redis_addresses = set()\n for pid in pids:\n try:\n proc = psutil.Process(pid)\n # HACK: Workaround for UNIX idiosyncrasy\n # Normally, cmdline() is supposed to return the argument list.\n # But it in some cases (such as when setproctitle is called),\n # an arbitrary string resembling a command-line is stored in\n # the first argument.\n # Explanation: https://unix.stackexchange.com/a/432681\n # More info: https://github.com/giampaolo/psutil/issues/1179\n for arglist in proc.cmdline():\n # Given we're merely seeking --redis-address, we just split\n # every argument on spaces for now.\n for arg in arglist.split(\" \"):\n # TODO(ekl): Find a robust solution for locating Redis.\n if arg.startswith(\"--redis-address=\"):\n addr = arg.split(\"=\")[1]\n redis_addresses.add(addr)\n except psutil.AccessDenied:\n pass\n except psutil.NoSuchProcess:\n pass\n if len(redis_addresses) > 1:\n raise ConnectionError(\n \"Found multiple active Ray instances: {}. \".format(redis_addresses)\n + \"Please specify the one to connect to by setting `address`.\")\n sys.exit(1)\n elif not redis_addresses:\n raise ConnectionError(\n \"Could not find any running Ray instance. \"\n \"Please specify the one to connect to by setting `address`.\")\n return redis_addresses.pop()","label":0},{"cid":"python/ray/services.py::get_address_info_from_redis_helper","text":"python/ray/services.py::get_address_info_from_redis_helper\ndef get_address_info_from_redis_helper(redis_address,\n node_ip_address,\n redis_password=None):\n redis_ip_address, redis_port = redis_address.split(\":\")\n # For this command to work, some other client (on the same machine as\n # Redis) must have run \"CONFIG SET protected-mode no\".\n redis_client = create_redis_client(redis_address, password=redis_password)\n\n client_table = ray.state._parse_client_table(redis_client)\n if len(client_table) == 0:\n raise RuntimeError(\n \"Redis has started but no raylets have registered yet.\")\n\n relevant_client = None\n for client_info in client_table:\n client_node_ip_address = client_info[\"NodeManagerAddress\"]\n if (client_node_ip_address == node_ip_address\n or (client_node_ip_address == \"127.0.0.1\"\n and redis_ip_address == get_node_ip_address())):\n relevant_client = client_info\n break\n if relevant_client is None:\n raise RuntimeError(\n \"Redis has started but no raylets have registered yet.\")\n\n return {\n \"object_store_address\": relevant_client[\"ObjectStoreSocketName\"],\n \"raylet_socket_name\": relevant_client[\"RayletSocketName\"],\n \"node_manager_port\": relevant_client[\"NodeManagerPort\"]\n }","label":0},{"cid":"python/ray/services.py::get_address_info_from_redis","text":"python/ray/services.py::get_address_info_from_redis\ndef get_address_info_from_redis(redis_address,\n node_ip_address,\n num_retries=5,\n redis_password=None):\n counter = 0\n while True:\n try:\n return get_address_info_from_redis_helper(\n redis_address, node_ip_address, redis_password=redis_password)\n except Exception:\n if counter == num_retries:\n raise\n # Some of the information may not be in Redis yet, so wait a little\n # bit.\n logger.warning(\n \"Some processes that the driver needs to connect to have \"\n \"not registered with Redis, so retrying. Have you run \"\n \"'ray start' on this node?\")\n time.sleep(1)\n counter += 1","label":0},{"cid":"python/ray/services.py::get_webui_url_from_redis","text":"python/ray/services.py::get_webui_url_from_redis\ndef get_webui_url_from_redis(redis_client):\n webui_url = redis_client.hmget(\"webui\", \"url\")[0]\n return ray.utils.decode(webui_url) if webui_url is not None else None","label":0},{"cid":"python/ray/services.py::remaining_processes_alive","text":"python/ray/services.py::remaining_processes_alive\ndef remaining_processes_alive():\n \"\"\"See if the remaining processes are alive or not.\n\n Note that this ignores processes that have been explicitly killed,\n e.g., via a command like node.kill_raylet().\n\n Returns:\n True if the remaining processes started by ray.init() are alive and\n False otherwise.\n\n Raises:\n Exception: An exception is raised if the processes were not started by\n ray.init().\n \"\"\"\n if ray.worker._global_node is None:\n raise RuntimeError(\"This process is not in a position to determine \"\n \"whether all processes are alive or not.\")\n return ray.worker._global_node.remaining_processes_alive()","label":0},{"cid":"python/ray/services.py::validate_redis_address","text":"python/ray/services.py::validate_redis_address\ndef validate_redis_address(address, redis_address):\n \"\"\"Validates redis address parameter and splits it into host/ip components.\n\n We temporarily support both 'address' and 'redis_address', so both are\n handled here.\n\n Returns:\n redis_address: string containing the full <host:port> address.\n redis_ip: string representing the host portion of the address.\n redis_port: integer representing the port portion of the address.\n\n Raises:\n ValueError: if both address and redis_address were specified or the\n address was malformed.\n \"\"\"\n\n if redis_address == \"auto\":\n raise ValueError(\"auto address resolution not supported for \"\n \"redis_address parameter. Please use address.\")\n\n if address:\n if redis_address:\n raise ValueError(\n \"Both address and redis_address specified. Use only address.\")\n if address == \"auto\":\n address = find_redis_address_or_die()\n redis_address = address\n\n redis_address = address_to_ip(redis_address)\n\n redis_address_parts = redis_address.split(\":\")\n if len(redis_address_parts) != 2:\n raise ValueError(\"Malformed address. Expected '<host>:<port>'.\")\n redis_ip = redis_address_parts[0]\n try:\n redis_port = int(redis_address_parts[1])\n except ValueError:\n raise ValueError(\"Malformed address port. Must be an integer.\")\n if redis_port < 1024 or redis_port > 65535:\n raise ValueError(\"Invalid address port. Must \"\n \"be between 1024 and 65535.\")\n\n return redis_address, redis_ip, redis_port","label":0},{"cid":"python/ray/services.py::address_to_ip","text":"python/ray/services.py::address_to_ip\ndef address_to_ip(address):\n \"\"\"Convert a hostname to a numerical IP addresses in an address.\n\n This should be a no-op if address already contains an actual numerical IP\n address.\n\n Args:\n address: This can be either a string containing a hostname (or an IP\n address) and a port or it can be just an IP address.\n\n Returns:\n The same address but with the hostname replaced by a numerical IP\n address.\n \"\"\"\n address_parts = address.split(\":\")\n ip_address = socket.gethostbyname(address_parts[0])\n # Make sure localhost isn't resolved to the loopback ip\n if ip_address == \"127.0.0.1\":\n ip_address = get_node_ip_address()\n return \":\".join([ip_address] + address_parts[1:])","label":0},{"cid":"python/ray/services.py::get_node_ip_address","text":"python/ray/services.py::get_node_ip_address\ndef get_node_ip_address(address=\"8.8.8.8:53\"):\n \"\"\"Determine the IP address of the local node.\n\n Args:\n address (str): The IP address and port of any known live service on the\n network you care about.\n\n Returns:\n The IP address of the current node.\n \"\"\"\n ip_address, port = address.split(\":\")\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n try:\n # This command will raise an exception if there is no internet\n # connection.\n s.connect((ip_address, int(port)))\n node_ip_address = s.getsockname()[0]\n except OSError as e:\n node_ip_address = \"127.0.0.1\"\n # [Errno 101] Network is unreachable\n if e.errno == errno.ENETUNREACH:\n try:\n # try get node ip address from host name\n host_name = socket.getfqdn(socket.gethostname())\n node_ip_address = socket.gethostbyname(host_name)\n except Exception:\n pass\n finally:\n s.close()\n\n return node_ip_address","label":0},{"cid":"python/ray/services.py::create_redis_client","text":"python/ray/services.py::create_redis_client\ndef create_redis_client(redis_address, password=None):\n \"\"\"Create a Redis client.\n\n Args:\n The IP address, port, and password of the Redis server.\n\n Returns:\n A Redis client.\n \"\"\"\n redis_ip_address, redis_port = redis_address.split(\":\")\n # For this command to work, some other client (on the same machine\n # as Redis) must have run \"CONFIG SET protected-mode no\".\n return redis.StrictRedis(\n host=redis_ip_address, port=int(redis_port), password=password)","label":0},{"cid":"python/ray/services.py::start_ray_process","text":"python/ray/services.py::start_ray_process\ndef start_ray_process(command,\n process_type,\n fate_share,\n env_updates=None,\n cwd=None,\n use_valgrind=False,\n use_gdb=False,\n use_valgrind_profiler=False,\n use_perftools_profiler=False,\n use_tmux=False,\n stdout_file=None,\n stderr_file=None,\n pipe_stdin=False):\n \"\"\"Start one of the Ray processes.\n\n TODO(rkn): We need to figure out how these commands interact. For example,\n it may only make sense to start a process in gdb if we also start it in\n tmux. Similarly, certain combinations probably don't make sense, like\n simultaneously running the process in valgrind and the profiler.\n\n Args:\n command (List[str]): The command to use to start the Ray process.\n process_type (str): The type of the process that is being started\n (e.g., \"raylet\").\n fate_share: If true, the child will be killed if its parent (us) dies.\n True must only be passed after detection of this functionality.\n env_updates (dict): A dictionary of additional environment variables to\n run the command with (in addition to the caller's environment\n variables).\n cwd (str): The directory to run the process in.\n use_valgrind (bool): True if we should start the process in valgrind.\n use_gdb (bool): True if we should start the process in gdb.\n use_valgrind_profiler (bool): True if we should start the process in\n the valgrind profiler.\n use_perftools_profiler (bool): True if we should profile the process\n using perftools.\n use_tmux (bool): True if we should start the process in tmux.\n stdout_file: A file handle opened for writing to redirect stdout to. If\n no","label":0},{"cid":"python/ray/services.py::start_ray_process.preexec_fn","text":"python/ray/services.py::start_ray_process.preexec_fn\n def preexec_fn():\n import signal\n signal.pthread_sigmask(signal.SIG_BLOCK, {signal.SIGINT})\n if fate_share and sys.platform.startswith(\"linux\"):\n ray.utils.set_kill_on_parent_death_linux()","label":0},{"cid":"python/ray/services.py::wait_for_redis_to_start","text":"python/ray/services.py::wait_for_redis_to_start\ndef wait_for_redis_to_start(redis_ip_address,\n redis_port,\n password=None,\n num_retries=5):\n \"\"\"Wait for a Redis server to be available.\n\n This is accomplished by creating a Redis client and sending a random\n command to the server until the command gets through.\n\n Args:\n redis_ip_address (str): The IP address of the redis server.\n redis_port (int): The port of the redis server.\n password (str): The password of the redis server.\n num_retries (int): The number of times to try connecting with redis.\n The client will sleep for one second between attempts.\n\n Raises:\n Exception: An exception is raised if we could not connect with Redis.\n \"\"\"\n redis_client = redis.StrictRedis(\n host=redis_ip_address, port=redis_port, password=password)\n # Wait for the Redis server to start.\n counter = 0\n while counter < num_retries:\n try:\n # Run some random command and see if it worked.\n logger.debug(\n \"Waiting for redis server at {}:{} to respond...\".format(\n redis_ip_address, redis_port))\n redis_client.client_list()\n except redis.ConnectionError:\n # Wait a little bit.\n time.sleep(1)\n logger.info(\"Failed to connect to the redis server, retrying.\")\n counter += 1\n else:\n break\n if counter == num_retries:\n raise RuntimeError(\"Unable to connect to Redis. If the Redis instance \"\n \"is on a different machine, check that your \"\n \"firewall is configured properly.\")","label":0},{"cid":"python/ray/services.py::_compute_version_info","text":"python/ray/services.py::_compute_version_info\ndef _compute_version_info():\n \"\"\"Compute the versions of Python, and Ray.\n\n Returns:\n A tuple containing the version information.\n \"\"\"\n ray_version = ray.__version__\n python_version = \".\".join(map(str, sys.version_info[:3]))\n return ray_version, python_version","label":0},{"cid":"python/ray/services.py::_put_version_info_in_redis","text":"python/ray/services.py::_put_version_info_in_redis\ndef _put_version_info_in_redis(redis_client):\n \"\"\"Store version information in Redis.\n\n This will be used to detect if workers or drivers are started using\n different versions of Python, or Ray.\n\n Args:\n redis_client: A client for the primary Redis shard.\n \"\"\"\n redis_client.set(\"VERSION_INFO\", json.dumps(_compute_version_info()))","label":0},{"cid":"python/ray/services.py::check_version_info","text":"python/ray/services.py::check_version_info\ndef check_version_info(redis_client):\n \"\"\"Check if various version info of this process is correct.\n\n This will be used to detect if workers or drivers are started using\n different versions of Python, or Ray. If the version\n information is not present in Redis, then no check is done.\n\n Args:\n redis_client: A client for the primary Redis shard.\n\n Raises:\n Exception: An exception is raised if there is a version mismatch.\n \"\"\"\n redis_reply = redis_client.get(\"VERSION_INFO\")\n\n # Don't do the check if there is no version information in Redis. This\n # is to make it easier to do things like start the processes by hand.\n if redis_reply is None:\n return\n\n true_version_info = tuple(json.loads(ray.utils.decode(redis_reply)))\n version_info = _compute_version_info()\n if version_info != true_version_info:\n node_ip_address = get_node_ip_address()\n error_message = (\"Version mismatch: The cluster was started with:\\n\"\n \" Ray: \" + true_version_info[0] + \"\\n\"\n \" Python: \" + true_version_info[1] + \"\\n\"\n \"This process on node \" + node_ip_address +\n \" was started with:\" + \"\\n\"\n \" Ray: \" + version_info[0] + \"\\n\"\n \" Python: \" + version_info[1] + \"\\n\")\n if version_info[:2] != true_version_info[:2]:\n raise RuntimeError(error_message)\n else:\n logger.warning(error_message)","label":0},{"cid":"python/ray/services.py::start_reaper","text":"python/ray/services.py::start_reaper\ndef start_reaper(fate_share=None):\n \"\"\"Start the reaper process.\n\n This is a lightweight process that simply\n waits for its parent process to die and then terminates its own\n process group. This allows us to ensure that ray processes are always\n terminated properly so long as that process itself isn't SIGKILLed.\n\n Returns:\n ProcessInfo for the process that was started.\n \"\"\"\n # Make ourselves a process group leader so that the reaper can clean\n # up other ray processes without killing the process group of the\n # process that started us.\n try:\n os.setpgrp()\n except (AttributeError, OSError) as e:\n errcode = e.errno if isinstance(e, OSError) else None\n if errcode == errno.EPERM and os.getpgrp() == os.getpid():\n # Nothing to do; we're already a session leader.\n pass\n else:\n logger.warning(\"setpgrp failed, processes may not be \"\n \"cleaned up properly: {}.\".format(e))\n # Don't start the reaper in this case as it could result in killing\n # other user processes.\n return None\n\n reaper_filepath = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"ray_process_reaper.py\")\n command = [sys.executable, \"-u\", reaper_filepath]\n process_info = start_ray_process(\n command,\n ray_constants.PROCESS_TYPE_REAPER,\n pipe_stdin=True,\n fate_share=fate_share)\n return process_info","label":0},{"cid":"python/ray/services.py::start_redis","text":"python/ray/services.py::start_redis\ndef start_redis(node_ip_address,\n redirect_files,\n resource_spec,\n port=None,\n redis_shard_ports=None,\n num_redis_shards=1,\n redis_max_clients=None,\n redirect_worker_output=False,\n password=None,\n use_credis=None,\n include_java=False,\n fate_share=None):\n \"\"\"Start the Redis global state store.\n\n Args:\n node_ip_address: The IP address of the current node. This is only used\n for recording the log filenames in Redis.\n redirect_files: The list of (stdout, stderr) file pairs.\n resource_spec (ResourceSpec): Resources for the node.\n port (int): If provided, the primary Redis shard will be started on\n this port.\n redis_shard_ports: A list of the ports to use for the non-primary Redis\n shards.\n num_redis_shards (int): If provided, the number of Redis shards to\n start, in addition to the primary one. The default value is one\n shard.\n redis_max_clients: If this is provided, Ray will attempt to configure\n Redis with this maxclients number.\n redirect_worker_output (bool): True if worker output should be\n redirected to a file and false otherwise. Workers will have access\n to this value when they start up.\n password (str): Prevents external clients without the password\n from connecting to Redis if provided.\n use_credis: If True, additionally load the chain-replicated libraries\n into the redis servers. Defaults to None, which means its value is\n set by the presence of \"RAY_USE_NEW_GCS\" in os.environ.\n include_java (bool): If True, the raylet backend can also support\n Java worker.\n\n Returns:\n A tuple of the address for the primary Redis shard, a list of\n ","label":0},{"cid":"python/ray/services.py::_start_redis_instance","text":"python/ray/services.py::_start_redis_instance\ndef _start_redis_instance(executable,\n modules,\n port=None,\n redis_max_clients=None,\n num_retries=20,\n stdout_file=None,\n stderr_file=None,\n password=None,\n redis_max_memory=None,\n fate_share=None):\n \"\"\"Start a single Redis server.\n\n Notes:\n If \"port\" is not None, then we will only use this port and try\n only once. Otherwise, random ports will be used and the maximum\n retries count is \"num_retries\".\n\n Args:\n executable (str): Full path of the redis-server executable.\n modules (list of str): A list of pathnames, pointing to the redis\n module(s) that will be loaded in this redis server.\n port (int): If provided, start a Redis server with this port.\n redis_max_clients: If this is provided, Ray will attempt to configure\n Redis with this maxclients number.\n num_retries (int): The number of times to attempt to start Redis. If a\n port is provided, this defaults to 1.\n stdout_file: A file handle opened for writing to redirect stdout to. If\n no redirection should happen, then this should be None.\n stderr_file: A file handle opened for writing to redirect stderr to. If\n no redirection should happen, then this should be None.\n password (str): Prevents external clients without the password\n from connecting to Redis if provided.\n redis_max_memory: The max amount of memory (in bytes) to allow redis\n to use, or None for no limit. Once the limit is exceeded, redis\n will start LRU eviction of entries.\n\n Returns:\n A tuple of the port used by Redis and ProcessInfo for the process that\n was started. If a port is passed in","label":0},{"cid":"python/ray/services.py::start_log_monitor","text":"python/ray/services.py::start_log_monitor\ndef start_log_monitor(redis_address,\n logs_dir,\n stdout_file=None,\n stderr_file=None,\n redis_password=None,\n fate_share=None):\n \"\"\"Start a log monitor process.\n\n Args:\n redis_address (str): The address of the Redis instance.\n logs_dir (str): The directory of logging files.\n stdout_file: A file handle opened for writing to redirect stdout to. If\n no redirection should happen, then this should be None.\n stderr_file: A file handle opened for writing to redirect stderr to. If\n no redirection should happen, then this should be None.\n redis_password (str): The password of the redis server.\n\n Returns:\n ProcessInfo for the process that was started.\n \"\"\"\n log_monitor_filepath = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"log_monitor.py\")\n command = [\n sys.executable, \"-u\", log_monitor_filepath,\n \"--redis-address={}\".format(redis_address),\n \"--logs-dir={}\".format(logs_dir)\n ]\n if redis_password:\n command += [\"--redis-password\", redis_password]\n process_info = start_ray_process(\n command,\n ray_constants.PROCESS_TYPE_LOG_MONITOR,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n fate_share=fate_share)\n return process_info","label":0},{"cid":"python/ray/services.py::start_reporter","text":"python/ray/services.py::start_reporter\ndef start_reporter(redis_address,\n stdout_file=None,\n stderr_file=None,\n redis_password=None,\n fate_share=None):\n \"\"\"Start a reporter process.\n\n Args:\n redis_address (str): The address of the Redis instance.\n stdout_file: A file handle opened for writing to redirect stdout to. If\n no redirection should happen, then this should be None.\n stderr_file: A file handle opened for writing to redirect stderr to. If\n no redirection should happen, then this should be None.\n redis_password (str): The password of the redis server.\n\n Returns:\n ProcessInfo for the process that was started.\n \"\"\"\n reporter_filepath = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"reporter.py\")\n command = [\n sys.executable, \"-u\", reporter_filepath,\n \"--redis-address={}\".format(redis_address)\n ]\n if redis_password:\n command += [\"--redis-password\", redis_password]\n\n process_info = start_ray_process(\n command,\n ray_constants.PROCESS_TYPE_REPORTER,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n fate_share=fate_share)\n return process_info","label":0},{"cid":"python/ray/services.py::start_dashboard","text":"python/ray/services.py::start_dashboard\ndef start_dashboard(require_webui,\n host,\n redis_address,\n temp_dir,\n stdout_file=None,\n stderr_file=None,\n redis_password=None,\n fate_share=None):\n \"\"\"Start a dashboard process.\n\n Args:\n require_webui (bool): If true, this will raise an exception if we fail\n to start the webui. Otherwise it will print a warning if we fail\n to start the webui.\n host (str): The host to bind the dashboard web server to.\n redis_address (str): The address of the Redis instance.\n temp_dir (str): The temporary directory used for log files and\n information for this Ray session.\n stdout_file: A file handle opened for writing to redirect stdout to. If\n no redirection should happen, then this should be None.\n stderr_file: A file handle opened for writing to redirect stderr to. If\n no redirection should happen, then this should be None.\n redis_password (str): The password of the redis server.\n\n Returns:\n ProcessInfo for the process that was started.\n \"\"\"\n port = 8265 # Note: list(map(ord, \"RAY\")) == [82, 65, 89]\n while True:\n try:\n port_test_socket = socket.socket()\n port_test_socket.bind((\"127.0.0.1\", port))\n port_test_socket.close()\n break\n except socket.error:\n port += 1\n\n dashboard_filepath = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"dashboard/dashboard.py\")\n command = [\n sys.executable,\n \"-u\",\n dashboard_filepath,\n \"--host={}\".format(host),\n \"--port={}\".format(port),\n \"--redis-address={}\".format(redis_address),\n \"--temp-dir={}\".format(temp_dir),\n ]\n if redis_password:\n command += [\"--redis-password\", redis_password]\n\n webui_depen","label":0},{"cid":"python/ray/services.py::start_gcs_server","text":"python/ray/services.py::start_gcs_server\ndef start_gcs_server(redis_address,\n stdout_file=None,\n stderr_file=None,\n redis_password=None,\n config=None,\n fate_share=None):\n \"\"\"Start a gcs server.\n Args:\n redis_address (str): The address that the Redis server is listening on.\n stdout_file: A file handle opened for writing to redirect stdout to. If\n no redirection should happen, then this should be None.\n stderr_file: A file handle opened for writing to redirect stderr to. If\n no redirection should happen, then this should be None.\n redis_password (str): The password of the redis server.\n config (dict|None): Optional configuration that will\n override defaults in RayConfig.\n Returns:\n ProcessInfo for the process that was started.\n \"\"\"\n gcs_ip_address, gcs_port = redis_address.split(\":\")\n redis_password = redis_password or \"\"\n config = config or {}\n config_str = \",\".join([\"{},{}\".format(*kv) for kv in config.items()])\n command = [\n GCS_SERVER_EXECUTABLE,\n \"--redis_address={}\".format(gcs_ip_address),\n \"--redis_port={}\".format(gcs_port),\n \"--config_list={}\".format(config_str),\n ]\n if redis_password:\n command += [\"--redis_password={}\".format(redis_password)]\n process_info = start_ray_process(\n command,\n ray_constants.PROCESS_TYPE_GCS_SERVER,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n fate_share=fate_share)\n return process_info","label":0},{"cid":"python/ray/services.py::start_raylet","text":"python/ray/services.py::start_raylet\ndef start_raylet(redis_address,\n node_ip_address,\n node_manager_port,\n raylet_name,\n plasma_store_name,\n worker_path,\n temp_dir,\n session_dir,\n resource_spec,\n object_manager_port=None,\n redis_password=None,\n use_valgrind=False,\n use_profiler=False,\n stdout_file=None,\n stderr_file=None,\n config=None,\n include_java=False,\n java_worker_options=None,\n load_code_from_local=False,\n fate_share=None):\n \"\"\"Start a raylet, which is a combined local scheduler and object manager.\n\n Args:\n redis_address (str): The address of the primary Redis server.\n node_ip_address (str): The IP address of this node.\n node_manager_port(int): The port to use for the node manager. This must\n not be 0.\n raylet_name (str): The name of the raylet socket to create.\n plasma_store_name (str): The name of the plasma store socket to connect\n to.\n worker_path (str): The path of the Python file that new worker\n processes will execute.\n temp_dir (str): The path of the temporary directory Ray will use.\n session_dir (str): The path of this session.\n resource_spec (ResourceSpec): Resources for this raylet.\n object_manager_port: The port to use for the object manager. If this is\n None, then the object manager will choose its own port.\n redis_password: The password to use when connecting to Redis.\n use_valgrind (bool): True if the raylet should be started inside\n of valgrind. If this is True, use_profiler must be False.\n use_profiler (bool): True if the raylet should be started inside\n a profiler. If this is True, ","label":0},{"cid":"python/ray/services.py::get_ray_jars_dir","text":"python/ray/services.py::get_ray_jars_dir\ndef get_ray_jars_dir():\n \"\"\"Return a directory where all ray-related jars and\n their dependencies locate.\"\"\"\n current_dir = os.path.abspath(os.path.dirname(__file__))\n jars_dir = os.path.abspath(os.path.join(current_dir, \"jars\"))\n if not os.path.exists(jars_dir):\n raise RuntimeError(\"Ray jars is not packaged into ray. \"\n \"Please build ray with java enabled \"\n \"(set env var RAY_INSTALL_JAVA=1)\")\n return os.path.abspath(os.path.join(current_dir, \"jars\"))","label":0},{"cid":"python/ray/services.py::build_java_worker_command","text":"python/ray/services.py::build_java_worker_command\ndef build_java_worker_command(\n java_worker_options,\n redis_address,\n node_manager_port,\n plasma_store_name,\n raylet_name,\n redis_password,\n session_dir,\n):\n \"\"\"This method assembles the command used to start a Java worker.\n\n Args:\n java_worker_options (list): The command options for Java worker.\n redis_address (str): Redis address of GCS.\n plasma_store_name (str): The name of the plasma store socket to connect\n to.\n raylet_name (str): The name of the raylet socket to create.\n redis_password (str): The password of connect to redis.\n session_dir (str): The path of this session.\n Returns:\n The command string for starting Java worker.\n \"\"\"\n pairs = []\n if redis_address is not None:\n pairs.append((\"ray.redis.address\", redis_address))\n pairs.append((\"ray.raylet.node-manager-port\", node_manager_port))\n\n if plasma_store_name is not None:\n pairs.append((\"ray.object-store.socket-name\", plasma_store_name))\n\n if raylet_name is not None:\n pairs.append((\"ray.raylet.socket-name\", raylet_name))\n\n if redis_password is not None:\n pairs.append((\"ray.redis.password\", redis_password))\n\n pairs.append((\"ray.home\", RAY_HOME))\n pairs.append((\"ray.log-dir\", os.path.join(session_dir, \"logs\")))\n pairs.append((\"ray.session-dir\", session_dir))\n\n command = [\"java\"] + [\"-D{}={}\".format(*pair) for pair in pairs]\n\n command += [\"RAY_WORKER_RAYLET_CONFIG_PLACEHOLDER\"]\n\n # Add ray jars path to java classpath\n ray_jars = os.path.join(get_ray_jars_dir(), \"*\")\n if java_worker_options is None:\n options = []\n else:\n assert isinstance(java_worker_options, (tuple, list))\n options = list(java_worker_options)\n cp_index = -1\n for i in range(len(options)):\n option = options[i]\n if option == \"-cp\" or option == \"-classpath\":\n ","label":0},{"cid":"python/ray/services.py::determine_plasma_store_config","text":"python/ray/services.py::determine_plasma_store_config\ndef determine_plasma_store_config(object_store_memory,\n plasma_directory=None,\n huge_pages=False):\n \"\"\"Figure out how to configure the plasma object store.\n\n This will determine which directory to use for the plasma store. On Linux,\n we will try to use /dev/shm unless the shared memory file system is too\n small, in which case we will fall back to /tmp. If any of the object store\n memory or plasma directory parameters are specified by the user, then those\n values will be preserved.\n\n Args:\n object_store_memory (int): The objec store memory to use.\n plasma_directory (str): The user-specified plasma directory parameter.\n huge_pages (bool): The user-specified huge pages parameter.\n\n Returns:\n The plasma directory to use. If it is specified by the user, then that\n value will be preserved.\n \"\"\"\n system_memory = ray.utils.get_system_memory()\n\n # Determine which directory to use. By default, use /tmp on MacOS and\n # /dev/shm on Linux, unless the shared-memory file system is too small,\n # in which case we default to /tmp on Linux.\n if plasma_directory is None:\n if sys.platform == \"linux\" or sys.platform == \"linux2\":\n shm_avail = ray.utils.get_shared_memory_bytes()\n # Compare the requested memory size to the memory available in\n # /dev/shm.\n if shm_avail > object_store_memory:\n plasma_directory = \"/dev/shm\"\n else:\n plasma_directory = ray.utils.get_user_temp_dir()\n logger.warning(\n \"WARNING: The object store is using {} instead of \"\n \"/dev/shm because /dev/shm has only {} bytes available. \"\n \"This may slow down performance! You may be able to free \"\n \"up space by deleting files in /dev/shm or termina","label":0},{"cid":"python/ray/services.py::_start_plasma_store","text":"python/ray/services.py::_start_plasma_store\ndef _start_plasma_store(plasma_store_memory,\n use_valgrind=False,\n use_profiler=False,\n stdout_file=None,\n stderr_file=None,\n plasma_directory=None,\n huge_pages=False,\n socket_name=None,\n fate_share=None):\n \"\"\"Start a plasma store process.\n\n Args:\n plasma_store_memory (int): The amount of memory in bytes to start the\n plasma store with.\n use_valgrind (bool): True if the plasma store should be started inside\n of valgrind. If this is True, use_profiler must be False.\n use_profiler (bool): True if the plasma store should be started inside\n a profiler. If this is True, use_valgrind must be False.\n stdout_file: A file handle opened for writing to redirect stdout to. If\n no redirection should happen, then this should be None.\n stderr_file: A file handle opened for writing to redirect stderr to. If\n no redirection should happen, then this should be None.\n plasma_directory: A directory where the Plasma memory mapped files will\n be created.\n huge_pages: a boolean flag indicating whether to start the\n Object Store with hugetlbfs support. Requires plasma_directory.\n socket_name (str): If provided, it will specify the socket\n name used by the plasma store.\n\n Return:\n A tuple of the name of the plasma store socket and ProcessInfo for the\n plasma store process.\n \"\"\"\n if use_valgrind and use_profiler:\n raise ValueError(\"Cannot use valgrind and profiler at the same time.\")\n\n if huge_pages and not (sys.platform == \"linux\"\n or sys.platform == \"linux2\"):\n raise ValueError(\"The huge_pages argument is only supported on \"\n \"","label":0},{"cid":"python/ray/services.py::start_plasma_store","text":"python/ray/services.py::start_plasma_store\ndef start_plasma_store(resource_spec,\n stdout_file=None,\n stderr_file=None,\n plasma_directory=None,\n huge_pages=False,\n plasma_store_socket_name=None,\n fate_share=None):\n \"\"\"This method starts an object store process.\n\n Args:\n resource_spec (ResourceSpec): Resources for the node.\n stdout_file: A file handle opened for writing to redirect stdout\n to. If no redirection should happen, then this should be None.\n stderr_file: A file handle opened for writing to redirect stderr\n to. If no redirection should happen, then this should be None.\n plasma_directory: A directory where the Plasma memory mapped files will\n be created.\n huge_pages: Boolean flag indicating whether to start the Object\n Store with hugetlbfs support. Requires plasma_directory.\n\n Returns:\n ProcessInfo for the process that was started.\n \"\"\"\n assert resource_spec.resolved()\n object_store_memory = resource_spec.object_store_memory\n plasma_directory = determine_plasma_store_config(\n object_store_memory, plasma_directory, huge_pages)\n\n if object_store_memory < ray_constants.OBJECT_STORE_MINIMUM_MEMORY_BYTES:\n raise ValueError(\"Attempting to cap object store memory usage at {} \"\n \"bytes, but the minimum allowed is {} bytes.\".format(\n object_store_memory,\n ray_constants.OBJECT_STORE_MINIMUM_MEMORY_BYTES))\n\n # Print the object store memory using two decimal places.\n object_store_memory_str = (object_store_memory / 10**7) / 10**2\n logger.debug(\"Starting the Plasma object store with {} GB memory \"\n \"using {}.\".format(\n round(object_store_memory_str, 2), plasma_directory))\n # Start the Plasma sto","label":0},{"cid":"python/ray/services.py::start_worker","text":"python/ray/services.py::start_worker\ndef start_worker(node_ip_address,\n object_store_name,\n raylet_name,\n redis_address,\n worker_path,\n temp_dir,\n stdout_file=None,\n stderr_file=None,\n fate_share=None):\n \"\"\"This method starts a worker process.\n\n Args:\n node_ip_address (str): The IP address of the node that this worker is\n running on.\n object_store_name (str): The socket name of the object store.\n raylet_name (str): The socket name of the raylet server.\n redis_address (str): The address that the Redis server is listening on.\n worker_path (str): The path of the source code which the worker process\n will run.\n temp_dir (str): The path of the temp dir.\n stdout_file: A file handle opened for writing to redirect stdout to. If\n no redirection should happen, then this should be None.\n stderr_file: A file handle opened for writing to redirect stderr to. If\n no redirection should happen, then this should be None.\n\n Returns:\n ProcessInfo for the process that was started.\n \"\"\"\n command = [\n sys.executable, \"-u\", worker_path,\n \"--node-ip-address=\" + node_ip_address,\n \"--object-store-name=\" + object_store_name,\n \"--raylet-name=\" + raylet_name,\n \"--redis-address=\" + str(redis_address), \"--temp-dir=\" + temp_dir\n ]\n process_info = start_ray_process(\n command,\n ray_constants.PROCESS_TYPE_WORKER,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n fate_share=fate_share)\n return process_info","label":0},{"cid":"python/ray/services.py::start_monitor","text":"python/ray/services.py::start_monitor\ndef start_monitor(redis_address,\n stdout_file=None,\n stderr_file=None,\n autoscaling_config=None,\n redis_password=None,\n fate_share=None):\n \"\"\"Run a process to monitor the other processes.\n\n Args:\n redis_address (str): The address that the Redis server is listening on.\n stdout_file: A file handle opened for writing to redirect stdout to. If\n no redirection should happen, then this should be None.\n stderr_file: A file handle opened for writing to redirect stderr to. If\n no redirection should happen, then this should be None.\n autoscaling_config: path to autoscaling config file.\n redis_password (str): The password of the redis server.\n\n Returns:\n ProcessInfo for the process that was started.\n \"\"\"\n monitor_path = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"monitor.py\")\n command = [\n sys.executable, \"-u\", monitor_path,\n \"--redis-address=\" + str(redis_address)\n ]\n if autoscaling_config:\n command.append(\"--autoscaling-config=\" + str(autoscaling_config))\n if redis_password:\n command.append(\"--redis-password=\" + redis_password)\n process_info = start_ray_process(\n command,\n ray_constants.PROCESS_TYPE_MONITOR,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n fate_share=fate_share)\n return process_info","label":0},{"cid":"python/ray/services.py::start_raylet_monitor","text":"python/ray/services.py::start_raylet_monitor\ndef start_raylet_monitor(redis_address,\n stdout_file=None,\n stderr_file=None,\n redis_password=None,\n config=None,\n fate_share=None):\n \"\"\"Run a process to monitor the other processes.\n\n Args:\n redis_address (str): The address that the Redis server is listening on.\n stdout_file: A file handle opened for writing to redirect stdout to. If\n no redirection should happen, then this should be None.\n stderr_file: A file handle opened for writing to redirect stderr to. If\n no redirection should happen, then this should be None.\n redis_password (str): The password of the redis server.\n config (dict|None): Optional configuration that will\n override defaults in RayConfig.\n\n Returns:\n ProcessInfo for the process that was started.\n \"\"\"\n gcs_ip_address, gcs_port = redis_address.split(\":\")\n redis_password = redis_password or \"\"\n config = config or {}\n config_str = \",\".join([\"{},{}\".format(*kv) for kv in config.items()])\n command = [\n RAYLET_MONITOR_EXECUTABLE,\n \"--redis_address={}\".format(gcs_ip_address),\n \"--redis_port={}\".format(gcs_port),\n \"--config_list={}\".format(config_str),\n ]\n if redis_password:\n command += [\"--redis_password={}\".format(redis_password)]\n process_info = start_ray_process(\n command,\n ray_constants.PROCESS_TYPE_RAYLET_MONITOR,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n fate_share=fate_share)\n return process_info","label":0},{"cid":"python/ray/setup-dev.py::do_link","text":"python/ray/setup-dev.py::do_link\ndef do_link(package, force=False, local_path=\"\"):\n package_home = os.path.abspath(\n os.path.join(ray.__file__, \"../{}\".format(package)))\n local_home = os.path.abspath(\n os.path.join(__file__, local_path + \"../{}\".format(package)))\n if not os.path.isdir(package_home):\n print(\"{} does not exist. Continuing to link.\".format(package_home))\n assert os.path.isdir(local_home), local_home\n if not force and not click.confirm(\n \"This will replace:\\n {}\\nwith a symlink to:\\n {}\".format(\n package_home, local_home),\n default=True):\n return\n if os.access(os.path.dirname(package_home), os.W_OK):\n subprocess.check_call([\"rm\", \"-rf\", package_home])\n subprocess.check_call([\"ln\", \"-s\", local_home, package_home])\n else:\n print(\"You don't have write permission to {}, using sudo:\".format(\n package_home))\n subprocess.check_call([\"sudo\", \"rm\", \"-rf\", package_home])\n subprocess.check_call([\"sudo\", \"ln\", \"-s\", local_home, package_home])","label":0},{"cid":"python/setup.py::build_ext.run","text":"python/setup.py::build_ext.run\n def run(self):\n # Note: We are passing in sys.executable so that we use the same\n # version of Python to build packages inside the build.sh script. Note\n # that certain flags will not be passed along such as --user or sudo.\n # TODO(rkn): Fix this.\n command = [\"../build.sh\", \"-p\", sys.executable]\n if build_java:\n # Also build binaries for Java if the above env variable exists.\n command += [\"-l\", \"python,java\"]\n subprocess.check_call(command)\n\n # We also need to install pickle5 along with Ray, so make sure that the\n # relevant non-Python pickle5 files get copied.\n pickle5_files = self.walk_directory(\"./ray/pickle5_files/pickle5\")\n\n thirdparty_files = self.walk_directory(\"./ray/thirdparty_files\")\n\n files_to_include = ray_files + pickle5_files + thirdparty_files\n\n # Copy over the autogenerated protobuf Python bindings.\n for directory in generated_python_directories:\n for filename in os.listdir(directory):\n if filename[-3:] == \".py\":\n files_to_include.append(os.path.join(directory, filename))\n\n for filename in files_to_include:\n self.move_file(filename)\n\n # Try to copy over the optional files.\n for filename in optional_ray_files:\n try:\n self.move_file(filename)\n except Exception:\n print(\"Failed to copy optional file {}. This is ok.\"\n .format(filename))","label":0},{"cid":"python/setup.py::build_ext.walk_directory","text":"python/setup.py::build_ext.walk_directory\n def walk_directory(self, directory):\n file_list = []\n for (root, dirs, filenames) in os.walk(directory):\n for name in filenames:\n file_list.append(os.path.join(root, name))\n return file_list","label":0},{"cid":"python/setup.py::build_ext.move_file","text":"python/setup.py::build_ext.move_file\n def move_file(self, filename):\n # TODO(rkn): This feels very brittle. It may not handle all cases. See\n # https://github.com/apache/arrow/blob/master/python/setup.py for an\n # example.\n source = filename\n destination = os.path.join(self.build_lib, filename)\n # Create the target directory if it doesn't already exist.\n parent_directory = os.path.dirname(destination)\n if not os.path.exists(parent_directory):\n os.makedirs(parent_directory)\n if not os.path.exists(destination):\n print(\"Copying {} to {}.\".format(source, destination))\n shutil.copy(source, destination, follow_symlinks=True)","label":0},{"cid":"python/setup.py::BinaryDistribution.has_ext_modules","text":"python/setup.py::BinaryDistribution.has_ext_modules\n def has_ext_modules(self):\n return True","label":0},{"cid":"python/setup.py::find_version","text":"python/setup.py::find_version\ndef find_version(*filepath):\n # Extract version information from filepath\n here = os.path.abspath(os.path.dirname(__file__))\n with open(os.path.join(here, *filepath)) as fp:\n version_match = re.search(r\"^__version__ = ['\\\"]([^'\\\"]*)['\\\"]\",\n fp.read(), re.M)\n if version_match:\n return version_match.group(1)\n raise RuntimeError(\"Unable to find version string.\")","label":0}],"string":"[\n {\n \"cid\": \"doc/source/conf.py::feedback_form_url\",\n \"text\": \"doc/source/conf.py::feedback_form_url\\ndef feedback_form_url(project, page):\\n \\\"\\\"\\\"Create a URL for feedback on a particular page in a project.\\\"\\\"\\\"\\n return FEEDBACK_FORM_FMT.format(\\n title=urllib.parse.quote(\\n \\\"[docs] Issue on `{page}.rst`\\\".format(page=page)),\\n body=urllib.parse.quote(\\n \\\"# Documentation Problem/Question/Comment\\\\n\\\"\\n \\\"<!-- Describe your issue/question/comment below. -->\\\\n\\\"\\n \\\"<!-- If there are typos or errors in the docs, feel free to create a pull-request. -->\\\\n\\\"\\n \\\"\\\\n\\\\n\\\\n\\\\n\\\"\\n \\\"(Created directly from the docs)\\\\n\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/source/conf.py::update_context\",\n \"text\": \"doc/source/conf.py::update_context\\ndef update_context(app, pagename, templatename, context, doctree):\\n \\\"\\\"\\\"Update the page rendering context to include ``feedback_form_url``.\\\"\\\"\\\"\\n context['feedback_form_url'] = feedback_form_url(app.config.project,\\n pagename)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/source/conf.py::setup\",\n \"text\": \"doc/source/conf.py::setup\\ndef setup(app):\\n app.connect('html-page-context', update_context)\\n # Custom directives\\n app.add_directive('customgalleryitem', CustomGalleryItemDirective)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::address\",\n \"text\": \"python/ray/services.py::address\\ndef address(ip_address, port):\\n return ip_address + \\\":\\\" + str(port)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::new_port\",\n \"text\": \"python/ray/services.py::new_port\\ndef new_port():\\n return random.randint(10000, 65535)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::include_java_from_redis\",\n \"text\": \"python/ray/services.py::include_java_from_redis\\ndef include_java_from_redis(redis_client):\\n \\\"\\\"\\\"This is used for query include_java bool from redis.\\n\\n Args:\\n redis_client (StrictRedis): The redis client to GCS.\\n\\n Returns:\\n True if this cluster backend enables Java worker.\\n \\\"\\\"\\\"\\n return redis_client.get(\\\"INCLUDE_JAVA\\\") == b\\\"1\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::find_redis_address_or_die\",\n \"text\": \"python/ray/services.py::find_redis_address_or_die\\ndef find_redis_address_or_die():\\n pids = psutil.pids()\\n redis_addresses = set()\\n for pid in pids:\\n try:\\n proc = psutil.Process(pid)\\n # HACK: Workaround for UNIX idiosyncrasy\\n # Normally, cmdline() is supposed to return the argument list.\\n # But it in some cases (such as when setproctitle is called),\\n # an arbitrary string resembling a command-line is stored in\\n # the first argument.\\n # Explanation: https://unix.stackexchange.com/a/432681\\n # More info: https://github.com/giampaolo/psutil/issues/1179\\n for arglist in proc.cmdline():\\n # Given we're merely seeking --redis-address, we just split\\n # every argument on spaces for now.\\n for arg in arglist.split(\\\" \\\"):\\n # TODO(ekl): Find a robust solution for locating Redis.\\n if arg.startswith(\\\"--redis-address=\\\"):\\n addr = arg.split(\\\"=\\\")[1]\\n redis_addresses.add(addr)\\n except psutil.AccessDenied:\\n pass\\n except psutil.NoSuchProcess:\\n pass\\n if len(redis_addresses) > 1:\\n raise ConnectionError(\\n \\\"Found multiple active Ray instances: {}. \\\".format(redis_addresses)\\n + \\\"Please specify the one to connect to by setting `address`.\\\")\\n sys.exit(1)\\n elif not redis_addresses:\\n raise ConnectionError(\\n \\\"Could not find any running Ray instance. \\\"\\n \\\"Please specify the one to connect to by setting `address`.\\\")\\n return redis_addresses.pop()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::get_address_info_from_redis_helper\",\n \"text\": \"python/ray/services.py::get_address_info_from_redis_helper\\ndef get_address_info_from_redis_helper(redis_address,\\n node_ip_address,\\n redis_password=None):\\n redis_ip_address, redis_port = redis_address.split(\\\":\\\")\\n # For this command to work, some other client (on the same machine as\\n # Redis) must have run \\\"CONFIG SET protected-mode no\\\".\\n redis_client = create_redis_client(redis_address, password=redis_password)\\n\\n client_table = ray.state._parse_client_table(redis_client)\\n if len(client_table) == 0:\\n raise RuntimeError(\\n \\\"Redis has started but no raylets have registered yet.\\\")\\n\\n relevant_client = None\\n for client_info in client_table:\\n client_node_ip_address = client_info[\\\"NodeManagerAddress\\\"]\\n if (client_node_ip_address == node_ip_address\\n or (client_node_ip_address == \\\"127.0.0.1\\\"\\n and redis_ip_address == get_node_ip_address())):\\n relevant_client = client_info\\n break\\n if relevant_client is None:\\n raise RuntimeError(\\n \\\"Redis has started but no raylets have registered yet.\\\")\\n\\n return {\\n \\\"object_store_address\\\": relevant_client[\\\"ObjectStoreSocketName\\\"],\\n \\\"raylet_socket_name\\\": relevant_client[\\\"RayletSocketName\\\"],\\n \\\"node_manager_port\\\": relevant_client[\\\"NodeManagerPort\\\"]\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::get_address_info_from_redis\",\n \"text\": \"python/ray/services.py::get_address_info_from_redis\\ndef get_address_info_from_redis(redis_address,\\n node_ip_address,\\n num_retries=5,\\n redis_password=None):\\n counter = 0\\n while True:\\n try:\\n return get_address_info_from_redis_helper(\\n redis_address, node_ip_address, redis_password=redis_password)\\n except Exception:\\n if counter == num_retries:\\n raise\\n # Some of the information may not be in Redis yet, so wait a little\\n # bit.\\n logger.warning(\\n \\\"Some processes that the driver needs to connect to have \\\"\\n \\\"not registered with Redis, so retrying. Have you run \\\"\\n \\\"'ray start' on this node?\\\")\\n time.sleep(1)\\n counter += 1\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::get_webui_url_from_redis\",\n \"text\": \"python/ray/services.py::get_webui_url_from_redis\\ndef get_webui_url_from_redis(redis_client):\\n webui_url = redis_client.hmget(\\\"webui\\\", \\\"url\\\")[0]\\n return ray.utils.decode(webui_url) if webui_url is not None else None\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::remaining_processes_alive\",\n \"text\": \"python/ray/services.py::remaining_processes_alive\\ndef remaining_processes_alive():\\n \\\"\\\"\\\"See if the remaining processes are alive or not.\\n\\n Note that this ignores processes that have been explicitly killed,\\n e.g., via a command like node.kill_raylet().\\n\\n Returns:\\n True if the remaining processes started by ray.init() are alive and\\n False otherwise.\\n\\n Raises:\\n Exception: An exception is raised if the processes were not started by\\n ray.init().\\n \\\"\\\"\\\"\\n if ray.worker._global_node is None:\\n raise RuntimeError(\\\"This process is not in a position to determine \\\"\\n \\\"whether all processes are alive or not.\\\")\\n return ray.worker._global_node.remaining_processes_alive()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::validate_redis_address\",\n \"text\": \"python/ray/services.py::validate_redis_address\\ndef validate_redis_address(address, redis_address):\\n \\\"\\\"\\\"Validates redis address parameter and splits it into host/ip components.\\n\\n We temporarily support both 'address' and 'redis_address', so both are\\n handled here.\\n\\n Returns:\\n redis_address: string containing the full <host:port> address.\\n redis_ip: string representing the host portion of the address.\\n redis_port: integer representing the port portion of the address.\\n\\n Raises:\\n ValueError: if both address and redis_address were specified or the\\n address was malformed.\\n \\\"\\\"\\\"\\n\\n if redis_address == \\\"auto\\\":\\n raise ValueError(\\\"auto address resolution not supported for \\\"\\n \\\"redis_address parameter. Please use address.\\\")\\n\\n if address:\\n if redis_address:\\n raise ValueError(\\n \\\"Both address and redis_address specified. Use only address.\\\")\\n if address == \\\"auto\\\":\\n address = find_redis_address_or_die()\\n redis_address = address\\n\\n redis_address = address_to_ip(redis_address)\\n\\n redis_address_parts = redis_address.split(\\\":\\\")\\n if len(redis_address_parts) != 2:\\n raise ValueError(\\\"Malformed address. Expected '<host>:<port>'.\\\")\\n redis_ip = redis_address_parts[0]\\n try:\\n redis_port = int(redis_address_parts[1])\\n except ValueError:\\n raise ValueError(\\\"Malformed address port. Must be an integer.\\\")\\n if redis_port < 1024 or redis_port > 65535:\\n raise ValueError(\\\"Invalid address port. Must \\\"\\n \\\"be between 1024 and 65535.\\\")\\n\\n return redis_address, redis_ip, redis_port\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::address_to_ip\",\n \"text\": \"python/ray/services.py::address_to_ip\\ndef address_to_ip(address):\\n \\\"\\\"\\\"Convert a hostname to a numerical IP addresses in an address.\\n\\n This should be a no-op if address already contains an actual numerical IP\\n address.\\n\\n Args:\\n address: This can be either a string containing a hostname (or an IP\\n address) and a port or it can be just an IP address.\\n\\n Returns:\\n The same address but with the hostname replaced by a numerical IP\\n address.\\n \\\"\\\"\\\"\\n address_parts = address.split(\\\":\\\")\\n ip_address = socket.gethostbyname(address_parts[0])\\n # Make sure localhost isn't resolved to the loopback ip\\n if ip_address == \\\"127.0.0.1\\\":\\n ip_address = get_node_ip_address()\\n return \\\":\\\".join([ip_address] + address_parts[1:])\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::get_node_ip_address\",\n \"text\": \"python/ray/services.py::get_node_ip_address\\ndef get_node_ip_address(address=\\\"8.8.8.8:53\\\"):\\n \\\"\\\"\\\"Determine the IP address of the local node.\\n\\n Args:\\n address (str): The IP address and port of any known live service on the\\n network you care about.\\n\\n Returns:\\n The IP address of the current node.\\n \\\"\\\"\\\"\\n ip_address, port = address.split(\\\":\\\")\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n try:\\n # This command will raise an exception if there is no internet\\n # connection.\\n s.connect((ip_address, int(port)))\\n node_ip_address = s.getsockname()[0]\\n except OSError as e:\\n node_ip_address = \\\"127.0.0.1\\\"\\n # [Errno 101] Network is unreachable\\n if e.errno == errno.ENETUNREACH:\\n try:\\n # try get node ip address from host name\\n host_name = socket.getfqdn(socket.gethostname())\\n node_ip_address = socket.gethostbyname(host_name)\\n except Exception:\\n pass\\n finally:\\n s.close()\\n\\n return node_ip_address\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::create_redis_client\",\n \"text\": \"python/ray/services.py::create_redis_client\\ndef create_redis_client(redis_address, password=None):\\n \\\"\\\"\\\"Create a Redis client.\\n\\n Args:\\n The IP address, port, and password of the Redis server.\\n\\n Returns:\\n A Redis client.\\n \\\"\\\"\\\"\\n redis_ip_address, redis_port = redis_address.split(\\\":\\\")\\n # For this command to work, some other client (on the same machine\\n # as Redis) must have run \\\"CONFIG SET protected-mode no\\\".\\n return redis.StrictRedis(\\n host=redis_ip_address, port=int(redis_port), password=password)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_ray_process\",\n \"text\": \"python/ray/services.py::start_ray_process\\ndef start_ray_process(command,\\n process_type,\\n fate_share,\\n env_updates=None,\\n cwd=None,\\n use_valgrind=False,\\n use_gdb=False,\\n use_valgrind_profiler=False,\\n use_perftools_profiler=False,\\n use_tmux=False,\\n stdout_file=None,\\n stderr_file=None,\\n pipe_stdin=False):\\n \\\"\\\"\\\"Start one of the Ray processes.\\n\\n TODO(rkn): We need to figure out how these commands interact. For example,\\n it may only make sense to start a process in gdb if we also start it in\\n tmux. Similarly, certain combinations probably don't make sense, like\\n simultaneously running the process in valgrind and the profiler.\\n\\n Args:\\n command (List[str]): The command to use to start the Ray process.\\n process_type (str): The type of the process that is being started\\n (e.g., \\\"raylet\\\").\\n fate_share: If true, the child will be killed if its parent (us) dies.\\n True must only be passed after detection of this functionality.\\n env_updates (dict): A dictionary of additional environment variables to\\n run the command with (in addition to the caller's environment\\n variables).\\n cwd (str): The directory to run the process in.\\n use_valgrind (bool): True if we should start the process in valgrind.\\n use_gdb (bool): True if we should start the process in gdb.\\n use_valgrind_profiler (bool): True if we should start the process in\\n the valgrind profiler.\\n use_perftools_profiler (bool): True if we should profile the process\\n using perftools.\\n use_tmux (bool): True if we should start the process in tmux.\\n stdout_file: A file handle opened for writing to redirect stdout to. If\\n no\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_ray_process.preexec_fn\",\n \"text\": \"python/ray/services.py::start_ray_process.preexec_fn\\n def preexec_fn():\\n import signal\\n signal.pthread_sigmask(signal.SIG_BLOCK, {signal.SIGINT})\\n if fate_share and sys.platform.startswith(\\\"linux\\\"):\\n ray.utils.set_kill_on_parent_death_linux()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::wait_for_redis_to_start\",\n \"text\": \"python/ray/services.py::wait_for_redis_to_start\\ndef wait_for_redis_to_start(redis_ip_address,\\n redis_port,\\n password=None,\\n num_retries=5):\\n \\\"\\\"\\\"Wait for a Redis server to be available.\\n\\n This is accomplished by creating a Redis client and sending a random\\n command to the server until the command gets through.\\n\\n Args:\\n redis_ip_address (str): The IP address of the redis server.\\n redis_port (int): The port of the redis server.\\n password (str): The password of the redis server.\\n num_retries (int): The number of times to try connecting with redis.\\n The client will sleep for one second between attempts.\\n\\n Raises:\\n Exception: An exception is raised if we could not connect with Redis.\\n \\\"\\\"\\\"\\n redis_client = redis.StrictRedis(\\n host=redis_ip_address, port=redis_port, password=password)\\n # Wait for the Redis server to start.\\n counter = 0\\n while counter < num_retries:\\n try:\\n # Run some random command and see if it worked.\\n logger.debug(\\n \\\"Waiting for redis server at {}:{} to respond...\\\".format(\\n redis_ip_address, redis_port))\\n redis_client.client_list()\\n except redis.ConnectionError:\\n # Wait a little bit.\\n time.sleep(1)\\n logger.info(\\\"Failed to connect to the redis server, retrying.\\\")\\n counter += 1\\n else:\\n break\\n if counter == num_retries:\\n raise RuntimeError(\\\"Unable to connect to Redis. If the Redis instance \\\"\\n \\\"is on a different machine, check that your \\\"\\n \\\"firewall is configured properly.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::_compute_version_info\",\n \"text\": \"python/ray/services.py::_compute_version_info\\ndef _compute_version_info():\\n \\\"\\\"\\\"Compute the versions of Python, and Ray.\\n\\n Returns:\\n A tuple containing the version information.\\n \\\"\\\"\\\"\\n ray_version = ray.__version__\\n python_version = \\\".\\\".join(map(str, sys.version_info[:3]))\\n return ray_version, python_version\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::_put_version_info_in_redis\",\n \"text\": \"python/ray/services.py::_put_version_info_in_redis\\ndef _put_version_info_in_redis(redis_client):\\n \\\"\\\"\\\"Store version information in Redis.\\n\\n This will be used to detect if workers or drivers are started using\\n different versions of Python, or Ray.\\n\\n Args:\\n redis_client: A client for the primary Redis shard.\\n \\\"\\\"\\\"\\n redis_client.set(\\\"VERSION_INFO\\\", json.dumps(_compute_version_info()))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::check_version_info\",\n \"text\": \"python/ray/services.py::check_version_info\\ndef check_version_info(redis_client):\\n \\\"\\\"\\\"Check if various version info of this process is correct.\\n\\n This will be used to detect if workers or drivers are started using\\n different versions of Python, or Ray. If the version\\n information is not present in Redis, then no check is done.\\n\\n Args:\\n redis_client: A client for the primary Redis shard.\\n\\n Raises:\\n Exception: An exception is raised if there is a version mismatch.\\n \\\"\\\"\\\"\\n redis_reply = redis_client.get(\\\"VERSION_INFO\\\")\\n\\n # Don't do the check if there is no version information in Redis. This\\n # is to make it easier to do things like start the processes by hand.\\n if redis_reply is None:\\n return\\n\\n true_version_info = tuple(json.loads(ray.utils.decode(redis_reply)))\\n version_info = _compute_version_info()\\n if version_info != true_version_info:\\n node_ip_address = get_node_ip_address()\\n error_message = (\\\"Version mismatch: The cluster was started with:\\\\n\\\"\\n \\\" Ray: \\\" + true_version_info[0] + \\\"\\\\n\\\"\\n \\\" Python: \\\" + true_version_info[1] + \\\"\\\\n\\\"\\n \\\"This process on node \\\" + node_ip_address +\\n \\\" was started with:\\\" + \\\"\\\\n\\\"\\n \\\" Ray: \\\" + version_info[0] + \\\"\\\\n\\\"\\n \\\" Python: \\\" + version_info[1] + \\\"\\\\n\\\")\\n if version_info[:2] != true_version_info[:2]:\\n raise RuntimeError(error_message)\\n else:\\n logger.warning(error_message)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_reaper\",\n \"text\": \"python/ray/services.py::start_reaper\\ndef start_reaper(fate_share=None):\\n \\\"\\\"\\\"Start the reaper process.\\n\\n This is a lightweight process that simply\\n waits for its parent process to die and then terminates its own\\n process group. This allows us to ensure that ray processes are always\\n terminated properly so long as that process itself isn't SIGKILLed.\\n\\n Returns:\\n ProcessInfo for the process that was started.\\n \\\"\\\"\\\"\\n # Make ourselves a process group leader so that the reaper can clean\\n # up other ray processes without killing the process group of the\\n # process that started us.\\n try:\\n os.setpgrp()\\n except (AttributeError, OSError) as e:\\n errcode = e.errno if isinstance(e, OSError) else None\\n if errcode == errno.EPERM and os.getpgrp() == os.getpid():\\n # Nothing to do; we're already a session leader.\\n pass\\n else:\\n logger.warning(\\\"setpgrp failed, processes may not be \\\"\\n \\\"cleaned up properly: {}.\\\".format(e))\\n # Don't start the reaper in this case as it could result in killing\\n # other user processes.\\n return None\\n\\n reaper_filepath = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"ray_process_reaper.py\\\")\\n command = [sys.executable, \\\"-u\\\", reaper_filepath]\\n process_info = start_ray_process(\\n command,\\n ray_constants.PROCESS_TYPE_REAPER,\\n pipe_stdin=True,\\n fate_share=fate_share)\\n return process_info\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_redis\",\n \"text\": \"python/ray/services.py::start_redis\\ndef start_redis(node_ip_address,\\n redirect_files,\\n resource_spec,\\n port=None,\\n redis_shard_ports=None,\\n num_redis_shards=1,\\n redis_max_clients=None,\\n redirect_worker_output=False,\\n password=None,\\n use_credis=None,\\n include_java=False,\\n fate_share=None):\\n \\\"\\\"\\\"Start the Redis global state store.\\n\\n Args:\\n node_ip_address: The IP address of the current node. This is only used\\n for recording the log filenames in Redis.\\n redirect_files: The list of (stdout, stderr) file pairs.\\n resource_spec (ResourceSpec): Resources for the node.\\n port (int): If provided, the primary Redis shard will be started on\\n this port.\\n redis_shard_ports: A list of the ports to use for the non-primary Redis\\n shards.\\n num_redis_shards (int): If provided, the number of Redis shards to\\n start, in addition to the primary one. The default value is one\\n shard.\\n redis_max_clients: If this is provided, Ray will attempt to configure\\n Redis with this maxclients number.\\n redirect_worker_output (bool): True if worker output should be\\n redirected to a file and false otherwise. Workers will have access\\n to this value when they start up.\\n password (str): Prevents external clients without the password\\n from connecting to Redis if provided.\\n use_credis: If True, additionally load the chain-replicated libraries\\n into the redis servers. Defaults to None, which means its value is\\n set by the presence of \\\"RAY_USE_NEW_GCS\\\" in os.environ.\\n include_java (bool): If True, the raylet backend can also support\\n Java worker.\\n\\n Returns:\\n A tuple of the address for the primary Redis shard, a list of\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::_start_redis_instance\",\n \"text\": \"python/ray/services.py::_start_redis_instance\\ndef _start_redis_instance(executable,\\n modules,\\n port=None,\\n redis_max_clients=None,\\n num_retries=20,\\n stdout_file=None,\\n stderr_file=None,\\n password=None,\\n redis_max_memory=None,\\n fate_share=None):\\n \\\"\\\"\\\"Start a single Redis server.\\n\\n Notes:\\n If \\\"port\\\" is not None, then we will only use this port and try\\n only once. Otherwise, random ports will be used and the maximum\\n retries count is \\\"num_retries\\\".\\n\\n Args:\\n executable (str): Full path of the redis-server executable.\\n modules (list of str): A list of pathnames, pointing to the redis\\n module(s) that will be loaded in this redis server.\\n port (int): If provided, start a Redis server with this port.\\n redis_max_clients: If this is provided, Ray will attempt to configure\\n Redis with this maxclients number.\\n num_retries (int): The number of times to attempt to start Redis. If a\\n port is provided, this defaults to 1.\\n stdout_file: A file handle opened for writing to redirect stdout to. If\\n no redirection should happen, then this should be None.\\n stderr_file: A file handle opened for writing to redirect stderr to. If\\n no redirection should happen, then this should be None.\\n password (str): Prevents external clients without the password\\n from connecting to Redis if provided.\\n redis_max_memory: The max amount of memory (in bytes) to allow redis\\n to use, or None for no limit. Once the limit is exceeded, redis\\n will start LRU eviction of entries.\\n\\n Returns:\\n A tuple of the port used by Redis and ProcessInfo for the process that\\n was started. If a port is passed in\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_log_monitor\",\n \"text\": \"python/ray/services.py::start_log_monitor\\ndef start_log_monitor(redis_address,\\n logs_dir,\\n stdout_file=None,\\n stderr_file=None,\\n redis_password=None,\\n fate_share=None):\\n \\\"\\\"\\\"Start a log monitor process.\\n\\n Args:\\n redis_address (str): The address of the Redis instance.\\n logs_dir (str): The directory of logging files.\\n stdout_file: A file handle opened for writing to redirect stdout to. If\\n no redirection should happen, then this should be None.\\n stderr_file: A file handle opened for writing to redirect stderr to. If\\n no redirection should happen, then this should be None.\\n redis_password (str): The password of the redis server.\\n\\n Returns:\\n ProcessInfo for the process that was started.\\n \\\"\\\"\\\"\\n log_monitor_filepath = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"log_monitor.py\\\")\\n command = [\\n sys.executable, \\\"-u\\\", log_monitor_filepath,\\n \\\"--redis-address={}\\\".format(redis_address),\\n \\\"--logs-dir={}\\\".format(logs_dir)\\n ]\\n if redis_password:\\n command += [\\\"--redis-password\\\", redis_password]\\n process_info = start_ray_process(\\n command,\\n ray_constants.PROCESS_TYPE_LOG_MONITOR,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n fate_share=fate_share)\\n return process_info\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_reporter\",\n \"text\": \"python/ray/services.py::start_reporter\\ndef start_reporter(redis_address,\\n stdout_file=None,\\n stderr_file=None,\\n redis_password=None,\\n fate_share=None):\\n \\\"\\\"\\\"Start a reporter process.\\n\\n Args:\\n redis_address (str): The address of the Redis instance.\\n stdout_file: A file handle opened for writing to redirect stdout to. If\\n no redirection should happen, then this should be None.\\n stderr_file: A file handle opened for writing to redirect stderr to. If\\n no redirection should happen, then this should be None.\\n redis_password (str): The password of the redis server.\\n\\n Returns:\\n ProcessInfo for the process that was started.\\n \\\"\\\"\\\"\\n reporter_filepath = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"reporter.py\\\")\\n command = [\\n sys.executable, \\\"-u\\\", reporter_filepath,\\n \\\"--redis-address={}\\\".format(redis_address)\\n ]\\n if redis_password:\\n command += [\\\"--redis-password\\\", redis_password]\\n\\n process_info = start_ray_process(\\n command,\\n ray_constants.PROCESS_TYPE_REPORTER,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n fate_share=fate_share)\\n return process_info\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_dashboard\",\n \"text\": \"python/ray/services.py::start_dashboard\\ndef start_dashboard(require_webui,\\n host,\\n redis_address,\\n temp_dir,\\n stdout_file=None,\\n stderr_file=None,\\n redis_password=None,\\n fate_share=None):\\n \\\"\\\"\\\"Start a dashboard process.\\n\\n Args:\\n require_webui (bool): If true, this will raise an exception if we fail\\n to start the webui. Otherwise it will print a warning if we fail\\n to start the webui.\\n host (str): The host to bind the dashboard web server to.\\n redis_address (str): The address of the Redis instance.\\n temp_dir (str): The temporary directory used for log files and\\n information for this Ray session.\\n stdout_file: A file handle opened for writing to redirect stdout to. If\\n no redirection should happen, then this should be None.\\n stderr_file: A file handle opened for writing to redirect stderr to. If\\n no redirection should happen, then this should be None.\\n redis_password (str): The password of the redis server.\\n\\n Returns:\\n ProcessInfo for the process that was started.\\n \\\"\\\"\\\"\\n port = 8265 # Note: list(map(ord, \\\"RAY\\\")) == [82, 65, 89]\\n while True:\\n try:\\n port_test_socket = socket.socket()\\n port_test_socket.bind((\\\"127.0.0.1\\\", port))\\n port_test_socket.close()\\n break\\n except socket.error:\\n port += 1\\n\\n dashboard_filepath = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"dashboard/dashboard.py\\\")\\n command = [\\n sys.executable,\\n \\\"-u\\\",\\n dashboard_filepath,\\n \\\"--host={}\\\".format(host),\\n \\\"--port={}\\\".format(port),\\n \\\"--redis-address={}\\\".format(redis_address),\\n \\\"--temp-dir={}\\\".format(temp_dir),\\n ]\\n if redis_password:\\n command += [\\\"--redis-password\\\", redis_password]\\n\\n webui_depen\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_gcs_server\",\n \"text\": \"python/ray/services.py::start_gcs_server\\ndef start_gcs_server(redis_address,\\n stdout_file=None,\\n stderr_file=None,\\n redis_password=None,\\n config=None,\\n fate_share=None):\\n \\\"\\\"\\\"Start a gcs server.\\n Args:\\n redis_address (str): The address that the Redis server is listening on.\\n stdout_file: A file handle opened for writing to redirect stdout to. If\\n no redirection should happen, then this should be None.\\n stderr_file: A file handle opened for writing to redirect stderr to. If\\n no redirection should happen, then this should be None.\\n redis_password (str): The password of the redis server.\\n config (dict|None): Optional configuration that will\\n override defaults in RayConfig.\\n Returns:\\n ProcessInfo for the process that was started.\\n \\\"\\\"\\\"\\n gcs_ip_address, gcs_port = redis_address.split(\\\":\\\")\\n redis_password = redis_password or \\\"\\\"\\n config = config or {}\\n config_str = \\\",\\\".join([\\\"{},{}\\\".format(*kv) for kv in config.items()])\\n command = [\\n GCS_SERVER_EXECUTABLE,\\n \\\"--redis_address={}\\\".format(gcs_ip_address),\\n \\\"--redis_port={}\\\".format(gcs_port),\\n \\\"--config_list={}\\\".format(config_str),\\n ]\\n if redis_password:\\n command += [\\\"--redis_password={}\\\".format(redis_password)]\\n process_info = start_ray_process(\\n command,\\n ray_constants.PROCESS_TYPE_GCS_SERVER,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n fate_share=fate_share)\\n return process_info\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_raylet\",\n \"text\": \"python/ray/services.py::start_raylet\\ndef start_raylet(redis_address,\\n node_ip_address,\\n node_manager_port,\\n raylet_name,\\n plasma_store_name,\\n worker_path,\\n temp_dir,\\n session_dir,\\n resource_spec,\\n object_manager_port=None,\\n redis_password=None,\\n use_valgrind=False,\\n use_profiler=False,\\n stdout_file=None,\\n stderr_file=None,\\n config=None,\\n include_java=False,\\n java_worker_options=None,\\n load_code_from_local=False,\\n fate_share=None):\\n \\\"\\\"\\\"Start a raylet, which is a combined local scheduler and object manager.\\n\\n Args:\\n redis_address (str): The address of the primary Redis server.\\n node_ip_address (str): The IP address of this node.\\n node_manager_port(int): The port to use for the node manager. This must\\n not be 0.\\n raylet_name (str): The name of the raylet socket to create.\\n plasma_store_name (str): The name of the plasma store socket to connect\\n to.\\n worker_path (str): The path of the Python file that new worker\\n processes will execute.\\n temp_dir (str): The path of the temporary directory Ray will use.\\n session_dir (str): The path of this session.\\n resource_spec (ResourceSpec): Resources for this raylet.\\n object_manager_port: The port to use for the object manager. If this is\\n None, then the object manager will choose its own port.\\n redis_password: The password to use when connecting to Redis.\\n use_valgrind (bool): True if the raylet should be started inside\\n of valgrind. If this is True, use_profiler must be False.\\n use_profiler (bool): True if the raylet should be started inside\\n a profiler. If this is True, \",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::get_ray_jars_dir\",\n \"text\": \"python/ray/services.py::get_ray_jars_dir\\ndef get_ray_jars_dir():\\n \\\"\\\"\\\"Return a directory where all ray-related jars and\\n their dependencies locate.\\\"\\\"\\\"\\n current_dir = os.path.abspath(os.path.dirname(__file__))\\n jars_dir = os.path.abspath(os.path.join(current_dir, \\\"jars\\\"))\\n if not os.path.exists(jars_dir):\\n raise RuntimeError(\\\"Ray jars is not packaged into ray. \\\"\\n \\\"Please build ray with java enabled \\\"\\n \\\"(set env var RAY_INSTALL_JAVA=1)\\\")\\n return os.path.abspath(os.path.join(current_dir, \\\"jars\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::build_java_worker_command\",\n \"text\": \"python/ray/services.py::build_java_worker_command\\ndef build_java_worker_command(\\n java_worker_options,\\n redis_address,\\n node_manager_port,\\n plasma_store_name,\\n raylet_name,\\n redis_password,\\n session_dir,\\n):\\n \\\"\\\"\\\"This method assembles the command used to start a Java worker.\\n\\n Args:\\n java_worker_options (list): The command options for Java worker.\\n redis_address (str): Redis address of GCS.\\n plasma_store_name (str): The name of the plasma store socket to connect\\n to.\\n raylet_name (str): The name of the raylet socket to create.\\n redis_password (str): The password of connect to redis.\\n session_dir (str): The path of this session.\\n Returns:\\n The command string for starting Java worker.\\n \\\"\\\"\\\"\\n pairs = []\\n if redis_address is not None:\\n pairs.append((\\\"ray.redis.address\\\", redis_address))\\n pairs.append((\\\"ray.raylet.node-manager-port\\\", node_manager_port))\\n\\n if plasma_store_name is not None:\\n pairs.append((\\\"ray.object-store.socket-name\\\", plasma_store_name))\\n\\n if raylet_name is not None:\\n pairs.append((\\\"ray.raylet.socket-name\\\", raylet_name))\\n\\n if redis_password is not None:\\n pairs.append((\\\"ray.redis.password\\\", redis_password))\\n\\n pairs.append((\\\"ray.home\\\", RAY_HOME))\\n pairs.append((\\\"ray.log-dir\\\", os.path.join(session_dir, \\\"logs\\\")))\\n pairs.append((\\\"ray.session-dir\\\", session_dir))\\n\\n command = [\\\"java\\\"] + [\\\"-D{}={}\\\".format(*pair) for pair in pairs]\\n\\n command += [\\\"RAY_WORKER_RAYLET_CONFIG_PLACEHOLDER\\\"]\\n\\n # Add ray jars path to java classpath\\n ray_jars = os.path.join(get_ray_jars_dir(), \\\"*\\\")\\n if java_worker_options is None:\\n options = []\\n else:\\n assert isinstance(java_worker_options, (tuple, list))\\n options = list(java_worker_options)\\n cp_index = -1\\n for i in range(len(options)):\\n option = options[i]\\n if option == \\\"-cp\\\" or option == \\\"-classpath\\\":\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::determine_plasma_store_config\",\n \"text\": \"python/ray/services.py::determine_plasma_store_config\\ndef determine_plasma_store_config(object_store_memory,\\n plasma_directory=None,\\n huge_pages=False):\\n \\\"\\\"\\\"Figure out how to configure the plasma object store.\\n\\n This will determine which directory to use for the plasma store. On Linux,\\n we will try to use /dev/shm unless the shared memory file system is too\\n small, in which case we will fall back to /tmp. If any of the object store\\n memory or plasma directory parameters are specified by the user, then those\\n values will be preserved.\\n\\n Args:\\n object_store_memory (int): The objec store memory to use.\\n plasma_directory (str): The user-specified plasma directory parameter.\\n huge_pages (bool): The user-specified huge pages parameter.\\n\\n Returns:\\n The plasma directory to use. If it is specified by the user, then that\\n value will be preserved.\\n \\\"\\\"\\\"\\n system_memory = ray.utils.get_system_memory()\\n\\n # Determine which directory to use. By default, use /tmp on MacOS and\\n # /dev/shm on Linux, unless the shared-memory file system is too small,\\n # in which case we default to /tmp on Linux.\\n if plasma_directory is None:\\n if sys.platform == \\\"linux\\\" or sys.platform == \\\"linux2\\\":\\n shm_avail = ray.utils.get_shared_memory_bytes()\\n # Compare the requested memory size to the memory available in\\n # /dev/shm.\\n if shm_avail > object_store_memory:\\n plasma_directory = \\\"/dev/shm\\\"\\n else:\\n plasma_directory = ray.utils.get_user_temp_dir()\\n logger.warning(\\n \\\"WARNING: The object store is using {} instead of \\\"\\n \\\"/dev/shm because /dev/shm has only {} bytes available. \\\"\\n \\\"This may slow down performance! You may be able to free \\\"\\n \\\"up space by deleting files in /dev/shm or termina\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::_start_plasma_store\",\n \"text\": \"python/ray/services.py::_start_plasma_store\\ndef _start_plasma_store(plasma_store_memory,\\n use_valgrind=False,\\n use_profiler=False,\\n stdout_file=None,\\n stderr_file=None,\\n plasma_directory=None,\\n huge_pages=False,\\n socket_name=None,\\n fate_share=None):\\n \\\"\\\"\\\"Start a plasma store process.\\n\\n Args:\\n plasma_store_memory (int): The amount of memory in bytes to start the\\n plasma store with.\\n use_valgrind (bool): True if the plasma store should be started inside\\n of valgrind. If this is True, use_profiler must be False.\\n use_profiler (bool): True if the plasma store should be started inside\\n a profiler. If this is True, use_valgrind must be False.\\n stdout_file: A file handle opened for writing to redirect stdout to. If\\n no redirection should happen, then this should be None.\\n stderr_file: A file handle opened for writing to redirect stderr to. If\\n no redirection should happen, then this should be None.\\n plasma_directory: A directory where the Plasma memory mapped files will\\n be created.\\n huge_pages: a boolean flag indicating whether to start the\\n Object Store with hugetlbfs support. Requires plasma_directory.\\n socket_name (str): If provided, it will specify the socket\\n name used by the plasma store.\\n\\n Return:\\n A tuple of the name of the plasma store socket and ProcessInfo for the\\n plasma store process.\\n \\\"\\\"\\\"\\n if use_valgrind and use_profiler:\\n raise ValueError(\\\"Cannot use valgrind and profiler at the same time.\\\")\\n\\n if huge_pages and not (sys.platform == \\\"linux\\\"\\n or sys.platform == \\\"linux2\\\"):\\n raise ValueError(\\\"The huge_pages argument is only supported on \\\"\\n \\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_plasma_store\",\n \"text\": \"python/ray/services.py::start_plasma_store\\ndef start_plasma_store(resource_spec,\\n stdout_file=None,\\n stderr_file=None,\\n plasma_directory=None,\\n huge_pages=False,\\n plasma_store_socket_name=None,\\n fate_share=None):\\n \\\"\\\"\\\"This method starts an object store process.\\n\\n Args:\\n resource_spec (ResourceSpec): Resources for the node.\\n stdout_file: A file handle opened for writing to redirect stdout\\n to. If no redirection should happen, then this should be None.\\n stderr_file: A file handle opened for writing to redirect stderr\\n to. If no redirection should happen, then this should be None.\\n plasma_directory: A directory where the Plasma memory mapped files will\\n be created.\\n huge_pages: Boolean flag indicating whether to start the Object\\n Store with hugetlbfs support. Requires plasma_directory.\\n\\n Returns:\\n ProcessInfo for the process that was started.\\n \\\"\\\"\\\"\\n assert resource_spec.resolved()\\n object_store_memory = resource_spec.object_store_memory\\n plasma_directory = determine_plasma_store_config(\\n object_store_memory, plasma_directory, huge_pages)\\n\\n if object_store_memory < ray_constants.OBJECT_STORE_MINIMUM_MEMORY_BYTES:\\n raise ValueError(\\\"Attempting to cap object store memory usage at {} \\\"\\n \\\"bytes, but the minimum allowed is {} bytes.\\\".format(\\n object_store_memory,\\n ray_constants.OBJECT_STORE_MINIMUM_MEMORY_BYTES))\\n\\n # Print the object store memory using two decimal places.\\n object_store_memory_str = (object_store_memory / 10**7) / 10**2\\n logger.debug(\\\"Starting the Plasma object store with {} GB memory \\\"\\n \\\"using {}.\\\".format(\\n round(object_store_memory_str, 2), plasma_directory))\\n # Start the Plasma sto\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_worker\",\n \"text\": \"python/ray/services.py::start_worker\\ndef start_worker(node_ip_address,\\n object_store_name,\\n raylet_name,\\n redis_address,\\n worker_path,\\n temp_dir,\\n stdout_file=None,\\n stderr_file=None,\\n fate_share=None):\\n \\\"\\\"\\\"This method starts a worker process.\\n\\n Args:\\n node_ip_address (str): The IP address of the node that this worker is\\n running on.\\n object_store_name (str): The socket name of the object store.\\n raylet_name (str): The socket name of the raylet server.\\n redis_address (str): The address that the Redis server is listening on.\\n worker_path (str): The path of the source code which the worker process\\n will run.\\n temp_dir (str): The path of the temp dir.\\n stdout_file: A file handle opened for writing to redirect stdout to. If\\n no redirection should happen, then this should be None.\\n stderr_file: A file handle opened for writing to redirect stderr to. If\\n no redirection should happen, then this should be None.\\n\\n Returns:\\n ProcessInfo for the process that was started.\\n \\\"\\\"\\\"\\n command = [\\n sys.executable, \\\"-u\\\", worker_path,\\n \\\"--node-ip-address=\\\" + node_ip_address,\\n \\\"--object-store-name=\\\" + object_store_name,\\n \\\"--raylet-name=\\\" + raylet_name,\\n \\\"--redis-address=\\\" + str(redis_address), \\\"--temp-dir=\\\" + temp_dir\\n ]\\n process_info = start_ray_process(\\n command,\\n ray_constants.PROCESS_TYPE_WORKER,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n fate_share=fate_share)\\n return process_info\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_monitor\",\n \"text\": \"python/ray/services.py::start_monitor\\ndef start_monitor(redis_address,\\n stdout_file=None,\\n stderr_file=None,\\n autoscaling_config=None,\\n redis_password=None,\\n fate_share=None):\\n \\\"\\\"\\\"Run a process to monitor the other processes.\\n\\n Args:\\n redis_address (str): The address that the Redis server is listening on.\\n stdout_file: A file handle opened for writing to redirect stdout to. If\\n no redirection should happen, then this should be None.\\n stderr_file: A file handle opened for writing to redirect stderr to. If\\n no redirection should happen, then this should be None.\\n autoscaling_config: path to autoscaling config file.\\n redis_password (str): The password of the redis server.\\n\\n Returns:\\n ProcessInfo for the process that was started.\\n \\\"\\\"\\\"\\n monitor_path = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"monitor.py\\\")\\n command = [\\n sys.executable, \\\"-u\\\", monitor_path,\\n \\\"--redis-address=\\\" + str(redis_address)\\n ]\\n if autoscaling_config:\\n command.append(\\\"--autoscaling-config=\\\" + str(autoscaling_config))\\n if redis_password:\\n command.append(\\\"--redis-password=\\\" + redis_password)\\n process_info = start_ray_process(\\n command,\\n ray_constants.PROCESS_TYPE_MONITOR,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n fate_share=fate_share)\\n return process_info\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/services.py::start_raylet_monitor\",\n \"text\": \"python/ray/services.py::start_raylet_monitor\\ndef start_raylet_monitor(redis_address,\\n stdout_file=None,\\n stderr_file=None,\\n redis_password=None,\\n config=None,\\n fate_share=None):\\n \\\"\\\"\\\"Run a process to monitor the other processes.\\n\\n Args:\\n redis_address (str): The address that the Redis server is listening on.\\n stdout_file: A file handle opened for writing to redirect stdout to. If\\n no redirection should happen, then this should be None.\\n stderr_file: A file handle opened for writing to redirect stderr to. If\\n no redirection should happen, then this should be None.\\n redis_password (str): The password of the redis server.\\n config (dict|None): Optional configuration that will\\n override defaults in RayConfig.\\n\\n Returns:\\n ProcessInfo for the process that was started.\\n \\\"\\\"\\\"\\n gcs_ip_address, gcs_port = redis_address.split(\\\":\\\")\\n redis_password = redis_password or \\\"\\\"\\n config = config or {}\\n config_str = \\\",\\\".join([\\\"{},{}\\\".format(*kv) for kv in config.items()])\\n command = [\\n RAYLET_MONITOR_EXECUTABLE,\\n \\\"--redis_address={}\\\".format(gcs_ip_address),\\n \\\"--redis_port={}\\\".format(gcs_port),\\n \\\"--config_list={}\\\".format(config_str),\\n ]\\n if redis_password:\\n command += [\\\"--redis_password={}\\\".format(redis_password)]\\n process_info = start_ray_process(\\n command,\\n ray_constants.PROCESS_TYPE_RAYLET_MONITOR,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n fate_share=fate_share)\\n return process_info\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/setup-dev.py::do_link\",\n \"text\": \"python/ray/setup-dev.py::do_link\\ndef do_link(package, force=False, local_path=\\\"\\\"):\\n package_home = os.path.abspath(\\n os.path.join(ray.__file__, \\\"../{}\\\".format(package)))\\n local_home = os.path.abspath(\\n os.path.join(__file__, local_path + \\\"../{}\\\".format(package)))\\n if not os.path.isdir(package_home):\\n print(\\\"{} does not exist. Continuing to link.\\\".format(package_home))\\n assert os.path.isdir(local_home), local_home\\n if not force and not click.confirm(\\n \\\"This will replace:\\\\n {}\\\\nwith a symlink to:\\\\n {}\\\".format(\\n package_home, local_home),\\n default=True):\\n return\\n if os.access(os.path.dirname(package_home), os.W_OK):\\n subprocess.check_call([\\\"rm\\\", \\\"-rf\\\", package_home])\\n subprocess.check_call([\\\"ln\\\", \\\"-s\\\", local_home, package_home])\\n else:\\n print(\\\"You don't have write permission to {}, using sudo:\\\".format(\\n package_home))\\n subprocess.check_call([\\\"sudo\\\", \\\"rm\\\", \\\"-rf\\\", package_home])\\n subprocess.check_call([\\\"sudo\\\", \\\"ln\\\", \\\"-s\\\", local_home, package_home])\",\n \"label\": 0\n },\n {\n \"cid\": \"python/setup.py::build_ext.run\",\n \"text\": \"python/setup.py::build_ext.run\\n def run(self):\\n # Note: We are passing in sys.executable so that we use the same\\n # version of Python to build packages inside the build.sh script. Note\\n # that certain flags will not be passed along such as --user or sudo.\\n # TODO(rkn): Fix this.\\n command = [\\\"../build.sh\\\", \\\"-p\\\", sys.executable]\\n if build_java:\\n # Also build binaries for Java if the above env variable exists.\\n command += [\\\"-l\\\", \\\"python,java\\\"]\\n subprocess.check_call(command)\\n\\n # We also need to install pickle5 along with Ray, so make sure that the\\n # relevant non-Python pickle5 files get copied.\\n pickle5_files = self.walk_directory(\\\"./ray/pickle5_files/pickle5\\\")\\n\\n thirdparty_files = self.walk_directory(\\\"./ray/thirdparty_files\\\")\\n\\n files_to_include = ray_files + pickle5_files + thirdparty_files\\n\\n # Copy over the autogenerated protobuf Python bindings.\\n for directory in generated_python_directories:\\n for filename in os.listdir(directory):\\n if filename[-3:] == \\\".py\\\":\\n files_to_include.append(os.path.join(directory, filename))\\n\\n for filename in files_to_include:\\n self.move_file(filename)\\n\\n # Try to copy over the optional files.\\n for filename in optional_ray_files:\\n try:\\n self.move_file(filename)\\n except Exception:\\n print(\\\"Failed to copy optional file {}. This is ok.\\\"\\n .format(filename))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/setup.py::build_ext.walk_directory\",\n \"text\": \"python/setup.py::build_ext.walk_directory\\n def walk_directory(self, directory):\\n file_list = []\\n for (root, dirs, filenames) in os.walk(directory):\\n for name in filenames:\\n file_list.append(os.path.join(root, name))\\n return file_list\",\n \"label\": 0\n },\n {\n \"cid\": \"python/setup.py::build_ext.move_file\",\n \"text\": \"python/setup.py::build_ext.move_file\\n def move_file(self, filename):\\n # TODO(rkn): This feels very brittle. It may not handle all cases. See\\n # https://github.com/apache/arrow/blob/master/python/setup.py for an\\n # example.\\n source = filename\\n destination = os.path.join(self.build_lib, filename)\\n # Create the target directory if it doesn't already exist.\\n parent_directory = os.path.dirname(destination)\\n if not os.path.exists(parent_directory):\\n os.makedirs(parent_directory)\\n if not os.path.exists(destination):\\n print(\\\"Copying {} to {}.\\\".format(source, destination))\\n shutil.copy(source, destination, follow_symlinks=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/setup.py::BinaryDistribution.has_ext_modules\",\n \"text\": \"python/setup.py::BinaryDistribution.has_ext_modules\\n def has_ext_modules(self):\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"python/setup.py::find_version\",\n \"text\": \"python/setup.py::find_version\\ndef find_version(*filepath):\\n # Extract version information from filepath\\n here = os.path.abspath(os.path.dirname(__file__))\\n with open(os.path.join(here, *filepath)) as fp:\\n version_match = re.search(r\\\"^__version__ = ['\\\\\\\"]([^'\\\\\\\"]*)['\\\\\\\"]\\\",\\n fp.read(), re.M)\\n if version_match:\\n return version_match.group(1)\\n raise RuntimeError(\\\"Unable to find version string.\\\")\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":53,"cells":{"instance_id":{"kind":"string","value":"googleapis__google-cloud-python-603"},"repo":{"kind":"string","value":"googleapis/google-cloud-python"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Add documentation for storage.Blob's special methods (contains, next, etc)\nRight now it's not really clear how to check for a key's existence (do I need to do if `Key('name') in bucket`? or just `'name' in bucket`?\n\nWe need to make sure these special methods are documented with examples in the API docs for the Key class.\n\n"},"gold_files":{"kind":"list like","value":["gcloud/storage/bucket.py"],"string":"[\n \"gcloud/storage/bucket.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["gcloud/storage/bucket.py::<module>"],"string":"[\n \"gcloud/storage/bucket.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/conf.py","text":"docs/conf.py","label":0},{"cid":"gcloud/credentials.py","text":"gcloud/credentials.py\ndef get_credentials() # Gets credentials implicitly from the current environment.\ndef get_for_service_account_p12(client_email, private_key_path, scope=None) # Gets the credentials for a service account.\ndef _get_pem_key(credentials) # Gets RSA key for a PEM payload from a credentials object.\ndef _get_signed_query_params(credentials, expiration, signature_string) # Gets query parameters for creating a signed URL.\ndef _utcnow() # Returns current time as UTC datetime.\ndef _get_expiration_seconds(expiration) # Convert 'expiration' to a number of seconds in the future.\ndef generate_signed_url(credentials, resource, expiration, api_access_endpoint='', method='GET', content_md5=None, content_type=None) # Generate signed URL to provide query-string auth'n to a resource.","label":0},{"cid":"gcloud/datastore/demo/demo.py","text":"gcloud/datastore/demo/demo.py","label":0},{"cid":"gcloud/datastore/helpers.py","text":"gcloud/datastore/helpers.py\ndef entity_from_protobuf(pb) # Factory method for creating an entity based on a protobuf.\ndef key_from_protobuf(pb) # Factory method for creating a key based on a protobuf.\ndef _pb_attr_value(val) # Given a value, return the protobuf attribute name and proper value.\ndef _get_value_from_value_pb(value_pb) # Given a protobuf for a Value, get the correct value.\ndef _get_value_from_property_pb(property_pb) # Given a protobuf for a Property, get the correct value.\ndef _set_protobuf_value(value_pb, val) # Assign 'val' to the correct subfield of 'value_pb'.\ndef _prepare_key_for_request(key_pb) # Add protobuf keys to a request object.\ndef _add_keys_to_request(request_field_pb, key_pbs) # Add protobuf keys to a request object.\ndef _dataset_ids_equal(dataset_id1, dataset_id2) # Compares two dataset IDs for fuzzy equality.","label":0},{"cid":"gcloud/storage/acl.py","text":"gcloud/storage/acl.py\nclass _ACLEntity # Class representing a set of roles for an entity.\n def __init__(self, entity_type, identifier=None) # Entity constructor.\n def __str__(self)\n def __repr__(self)\n def get_roles(self) # Get the list of roles permitted by this entity.\n def grant(self, role) # Add a role to the entity.\n def revoke(self, role) # Remove a role from the entity.\n def grant_read(self) # Grant read access to the current entity.\n def grant_write(self) # Grant write access to the current entity.\n def grant_owner(self) # Grant owner access to the current entity.\n def revoke_read(self) # Revoke read access from the current entity.\n def revoke_write(self) # Revoke write access from the current entity.\n def revoke_owner(self) # Revoke owner access from the current entity.\nclass ACL # Container class representing a list of access controls.\n def __init__(self)\n def _ensure_loaded(self) # Load if not already loaded.\n def reset(self) # Remove all entities from the ACL, and clear the ``loaded`` flag.\n def __iter__(self)\n def entity_from_dict(self, entity_dict) # Build an _ACLEntity object from a dictionary of data.\n def has_entity(self, entity) # Returns whether or not this ACL has any entries for an entity.\n def get_entity(self, entity, default=None) # Gets an entity object from the ACL.\n def add_entity(self, entity) # Add an entity to the ACL.\n def entity(self, entity_type, identifier=None) # Factory method for creating an Entity.\n def user(self, identifier) # Factory method for a user Entity.\n def group(self, identifier) # Factory method for a group Entity.\n def domain(self, domain) # Factory method for a domain Entity.\n def all(self) # Factory method for an Entity representing all users.\n def all_authenticated(self) # Factory method for an Entity representing all authenticated users.\n def get_entities(self) # Get a list of all Entity objects.\n def reload(self) # Reload the ACL data from Cloud Storage.\n def save(self, acl=None) # A method to be overridden by subclasses.\n def clear(self) # Remove all entities from the ACL.\nclass BucketACL # An ACL specifically for a bucket.\n def __init__(self, bucket) # :type bucket: :class:`gcloud.storage.bucket.Bucket`\n def reload(self) # Reload the ACL data from Cloud Storage.\n def save(self, acl=None) # Save this ACL for the current bucket.\n def clear(self) # Remove all ACL entries.\nclass DefaultObjectACL # A class representing the default object ACL for a bucket.\nclass ObjectACL # An ACL specifically for a Cloud Storage object / blob.\n def __init__(self, blob)\n def reload(self) # Reload the ACL data from Cloud Storage.\n def save(self, acl=None) # Save the ACL data for this blob.\n def clear(self) # Remove all ACL rules from the blob.","label":0},{"cid":"gcloud/storage/bucket.py","text":"gcloud/storage/bucket.py\nclass _BlobIterator # An iterator listing blobs in a bucket\n def __init__(self, bucket, extra_params=None)\n def get_items_from_response(self, response) # Yield :class:`.storage.blob.Blob` items from response.\nclass Bucket # A class representing a Bucket on Cloud Storage.\n def __init__(self, connection=None, name=None, properties=None)\n def __repr__(self)\n def __iter__(self)\n def __contains__(self, blob)\n def exists(self) # Determines whether or not this bucket exists.\n def acl(self) # Create our ACL on demand.\n def default_object_acl(self) # Create our defaultObjectACL on demand.\n def connection(self) # Getter property for the connection to use with this Bucket.\n def path_helper(bucket_name) # Relative URL path for a bucket.\n def path(self) # The URL path to this bucket.\n def get_blob(self, blob) # Get a blob object by name.\n def get_all_blobs(self) # List all the blobs in this bucket.\n def iterator(self, prefix=None, delimiter=None, max_results=None, versions=None) # Return an iterator used to find blobs in the bucket.\n def new_blob(self, blob) # Given path name (or Blob), return a :class:`Blob` object.\n def delete(self, force=False) # Delete this bucket.\n def delete_blob(self, blob) # Deletes a blob from the current bucket.\n def delete_blobs(self, blobs, on_error=None) # Deletes a list of blobs from the current bucket.\n def copy_blob(self, blob, destination_bucket, new_name=None) # Copy the given blob to the given bucket, optionally with a new name.\n def upload_file(self, filename, blob=None) # Shortcut method to upload a file into this bucket.\n def upload_file_object(self, file_obj, blob=None) # Shortcut method to upload a file object into this bucket.\n def get_cors(self) # Retrieve CORS policies configured for this bucket.\n def update_cors(self, entries) # Update CORS policies configured for this bucket.\n def get_default_object_acl(self) # Get the current Default Object ACL rules.\n def etag(self) # Retrieve the ETag for the bucket.\n def id(self) # Retrieve the ID for the bucket.\n def get_lifecycle(self) # Retrieve lifecycle rules configured for this bucket.\n def update_lifecycle(self, rules) # Update CORS policies configured for this bucket.\n def get_logging(self) # Return info about access logging for this bucket.\n def enable_logging(self, bucket_name, object_prefix='') # Enable access logging for this bucket.\n def disable_logging(self) # Disable access logging for this bucket.\n def metageneration(self) # Retrieve the metageneration for the bucket.\n def owner(self) # Retrieve info about the owner of the bucket.\n def project_number(self) # Retrieve the number of the project to which the bucket is assigned.\n def self_link(self) # Retrieve the URI for the bucket.\n def storage_class(self) # Retrieve the storage class for the bucket.\n def time_created(self) # Retrieve the tim","label":1},{"cid":"gcloud/storage/connection.py","text":"gcloud/storage/connection.py\nclass Connection # A connection to Google Cloud Storage via the JSON REST API.\n def __init__(self, project, *args, **kwargs) # :type project: string\n def build_api_url(self, path, query_params=None, api_base_url=None, api_version=None, upload=False) # Construct an API url given a few components, some optional.\n def make_request(self, method, url, data=None, content_type=None, headers=None) # A low level method to send a request to the API.\n def api_request(self, method, path, query_params=None, data=None, content_type=None, api_base_url=None, api_version=None, expect_json=True) # Make a request over the HTTP transport to the Cloud Storage API.\n def get_all_buckets(self) # Get all buckets in the project.\n def get_bucket(self, bucket_name) # Get a bucket by name.\n def create_bucket(self, bucket_name) # Create a new bucket.\n def delete_bucket(self, bucket_name) # Delete a bucket.\nclass _BucketIterator # An iterator listing all buckets.\n def __init__(self, connection)\n def get_items_from_response(self, response) # Factory method which yields :class:`.Bucket` items from a response.","label":0},{"cid":"gcloud/storage/demo/demo.py","text":"gcloud/storage/demo/demo.py","label":0}],"string":"[\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/credentials.py\",\n \"text\": \"gcloud/credentials.py\\ndef get_credentials() # Gets credentials implicitly from the current environment.\\ndef get_for_service_account_p12(client_email, private_key_path, scope=None) # Gets the credentials for a service account.\\ndef _get_pem_key(credentials) # Gets RSA key for a PEM payload from a credentials object.\\ndef _get_signed_query_params(credentials, expiration, signature_string) # Gets query parameters for creating a signed URL.\\ndef _utcnow() # Returns current time as UTC datetime.\\ndef _get_expiration_seconds(expiration) # Convert 'expiration' to a number of seconds in the future.\\ndef generate_signed_url(credentials, resource, expiration, api_access_endpoint='', method='GET', content_md5=None, content_type=None) # Generate signed URL to provide query-string auth'n to a resource.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/demo/demo.py\",\n \"text\": \"gcloud/datastore/demo/demo.py\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py\",\n \"text\": \"gcloud/datastore/helpers.py\\ndef entity_from_protobuf(pb) # Factory method for creating an entity based on a protobuf.\\ndef key_from_protobuf(pb) # Factory method for creating a key based on a protobuf.\\ndef _pb_attr_value(val) # Given a value, return the protobuf attribute name and proper value.\\ndef _get_value_from_value_pb(value_pb) # Given a protobuf for a Value, get the correct value.\\ndef _get_value_from_property_pb(property_pb) # Given a protobuf for a Property, get the correct value.\\ndef _set_protobuf_value(value_pb, val) # Assign 'val' to the correct subfield of 'value_pb'.\\ndef _prepare_key_for_request(key_pb) # Add protobuf keys to a request object.\\ndef _add_keys_to_request(request_field_pb, key_pbs) # Add protobuf keys to a request object.\\ndef _dataset_ids_equal(dataset_id1, dataset_id2) # Compares two dataset IDs for fuzzy equality.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py\",\n \"text\": \"gcloud/storage/acl.py\\nclass _ACLEntity # Class representing a set of roles for an entity.\\n def __init__(self, entity_type, identifier=None) # Entity constructor.\\n def __str__(self)\\n def __repr__(self)\\n def get_roles(self) # Get the list of roles permitted by this entity.\\n def grant(self, role) # Add a role to the entity.\\n def revoke(self, role) # Remove a role from the entity.\\n def grant_read(self) # Grant read access to the current entity.\\n def grant_write(self) # Grant write access to the current entity.\\n def grant_owner(self) # Grant owner access to the current entity.\\n def revoke_read(self) # Revoke read access from the current entity.\\n def revoke_write(self) # Revoke write access from the current entity.\\n def revoke_owner(self) # Revoke owner access from the current entity.\\nclass ACL # Container class representing a list of access controls.\\n def __init__(self)\\n def _ensure_loaded(self) # Load if not already loaded.\\n def reset(self) # Remove all entities from the ACL, and clear the ``loaded`` flag.\\n def __iter__(self)\\n def entity_from_dict(self, entity_dict) # Build an _ACLEntity object from a dictionary of data.\\n def has_entity(self, entity) # Returns whether or not this ACL has any entries for an entity.\\n def get_entity(self, entity, default=None) # Gets an entity object from the ACL.\\n def add_entity(self, entity) # Add an entity to the ACL.\\n def entity(self, entity_type, identifier=None) # Factory method for creating an Entity.\\n def user(self, identifier) # Factory method for a user Entity.\\n def group(self, identifier) # Factory method for a group Entity.\\n def domain(self, domain) # Factory method for a domain Entity.\\n def all(self) # Factory method for an Entity representing all users.\\n def all_authenticated(self) # Factory method for an Entity representing all authenticated users.\\n def get_entities(self) # Get a list of all Entity objects.\\n def reload(self) # Reload the ACL data from Cloud Storage.\\n def save(self, acl=None) # A method to be overridden by subclasses.\\n def clear(self) # Remove all entities from the ACL.\\nclass BucketACL # An ACL specifically for a bucket.\\n def __init__(self, bucket) # :type bucket: :class:`gcloud.storage.bucket.Bucket`\\n def reload(self) # Reload the ACL data from Cloud Storage.\\n def save(self, acl=None) # Save this ACL for the current bucket.\\n def clear(self) # Remove all ACL entries.\\nclass DefaultObjectACL # A class representing the default object ACL for a bucket.\\nclass ObjectACL # An ACL specifically for a Cloud Storage object / blob.\\n def __init__(self, blob)\\n def reload(self) # Reload the ACL data from Cloud Storage.\\n def save(self, acl=None) # Save the ACL data for this blob.\\n def clear(self) # Remove all ACL rules from the blob.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py\",\n \"text\": \"gcloud/storage/bucket.py\\nclass _BlobIterator # An iterator listing blobs in a bucket\\n def __init__(self, bucket, extra_params=None)\\n def get_items_from_response(self, response) # Yield :class:`.storage.blob.Blob` items from response.\\nclass Bucket # A class representing a Bucket on Cloud Storage.\\n def __init__(self, connection=None, name=None, properties=None)\\n def __repr__(self)\\n def __iter__(self)\\n def __contains__(self, blob)\\n def exists(self) # Determines whether or not this bucket exists.\\n def acl(self) # Create our ACL on demand.\\n def default_object_acl(self) # Create our defaultObjectACL on demand.\\n def connection(self) # Getter property for the connection to use with this Bucket.\\n def path_helper(bucket_name) # Relative URL path for a bucket.\\n def path(self) # The URL path to this bucket.\\n def get_blob(self, blob) # Get a blob object by name.\\n def get_all_blobs(self) # List all the blobs in this bucket.\\n def iterator(self, prefix=None, delimiter=None, max_results=None, versions=None) # Return an iterator used to find blobs in the bucket.\\n def new_blob(self, blob) # Given path name (or Blob), return a :class:`Blob` object.\\n def delete(self, force=False) # Delete this bucket.\\n def delete_blob(self, blob) # Deletes a blob from the current bucket.\\n def delete_blobs(self, blobs, on_error=None) # Deletes a list of blobs from the current bucket.\\n def copy_blob(self, blob, destination_bucket, new_name=None) # Copy the given blob to the given bucket, optionally with a new name.\\n def upload_file(self, filename, blob=None) # Shortcut method to upload a file into this bucket.\\n def upload_file_object(self, file_obj, blob=None) # Shortcut method to upload a file object into this bucket.\\n def get_cors(self) # Retrieve CORS policies configured for this bucket.\\n def update_cors(self, entries) # Update CORS policies configured for this bucket.\\n def get_default_object_acl(self) # Get the current Default Object ACL rules.\\n def etag(self) # Retrieve the ETag for the bucket.\\n def id(self) # Retrieve the ID for the bucket.\\n def get_lifecycle(self) # Retrieve lifecycle rules configured for this bucket.\\n def update_lifecycle(self, rules) # Update CORS policies configured for this bucket.\\n def get_logging(self) # Return info about access logging for this bucket.\\n def enable_logging(self, bucket_name, object_prefix='') # Enable access logging for this bucket.\\n def disable_logging(self) # Disable access logging for this bucket.\\n def metageneration(self) # Retrieve the metageneration for the bucket.\\n def owner(self) # Retrieve info about the owner of the bucket.\\n def project_number(self) # Retrieve the number of the project to which the bucket is assigned.\\n def self_link(self) # Retrieve the URI for the bucket.\\n def storage_class(self) # Retrieve the storage class for the bucket.\\n def time_created(self) # Retrieve the tim\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/storage/connection.py\",\n \"text\": \"gcloud/storage/connection.py\\nclass Connection # A connection to Google Cloud Storage via the JSON REST API.\\n def __init__(self, project, *args, **kwargs) # :type project: string\\n def build_api_url(self, path, query_params=None, api_base_url=None, api_version=None, upload=False) # Construct an API url given a few components, some optional.\\n def make_request(self, method, url, data=None, content_type=None, headers=None) # A low level method to send a request to the API.\\n def api_request(self, method, path, query_params=None, data=None, content_type=None, api_base_url=None, api_version=None, expect_json=True) # Make a request over the HTTP transport to the Cloud Storage API.\\n def get_all_buckets(self) # Get all buckets in the project.\\n def get_bucket(self, bucket_name) # Get a bucket by name.\\n def create_bucket(self, bucket_name) # Create a new bucket.\\n def delete_bucket(self, bucket_name) # Delete a bucket.\\nclass _BucketIterator # An iterator listing all buckets.\\n def __init__(self, connection)\\n def get_items_from_response(self, response) # Factory method which yields :class:`.Bucket` items from a response.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/demo/demo.py\",\n \"text\": \"gcloud/storage/demo/demo.py\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"gcloud/credentials.py::get_credentials","text":"gcloud/credentials.py::get_credentials\ndef get_credentials():\n \"\"\"Gets credentials implicitly from the current environment.\n\n .. note::\n You should not need to use this function directly. Instead, use the\n helper method :func:`gcloud.datastore.__init__.get_connection`\n which uses this method under the hood.\n\n Checks environment in order of precedence:\n\n * Google App Engine (production and testing)\n * Environment variable GOOGLE_APPLICATION_CREDENTIALS pointing to\n a file with stored credentials information.\n * Stored \"well known\" file associated with ``gcloud`` command line tool.\n * Google Compute Engine production environment.\n\n The file referred to in GOOGLE_APPLICATION_CREDENTIALS is expected to\n contain information about credentials that are ready to use. This means\n either service account information or user account information with\n a ready-to-use refresh token::\n\n { {\n 'type': 'authorized_user', 'type': 'service_account',\n 'client_id': '...', 'client_id': '...',\n 'client_secret': '...', OR 'client_email': '...',\n 'refresh_token': '..., 'private_key_id': '...',\n } 'private_key': '...',\n }\n\n The second of these is simply a JSON key downloaded from the Google APIs\n console. The first is a close cousin of the \"client secrets\" JSON file\n used by ``oauth2client.clientsecrets`` but differs in formatting.\n\n :rtype: :class:`oauth2client.client.GoogleCredentials`,\n :class:`oauth2client.appengine.AppAssertionCredentials`,\n :class:`oauth2client.gce.AppAssertionCredentials`,\n :class:`oauth2client.service_account._ServiceAccountCredentials`\n :returns: A new credentials instance corresponding to the implicit\n environment.\n \"\"\"\n ","label":0},{"cid":"gcloud/credentials.py::get_for_service_account_p12","text":"gcloud/credentials.py::get_for_service_account_p12\ndef get_for_service_account_p12(client_email, private_key_path, scope=None):\n \"\"\"Gets the credentials for a service account.\n\n .. note::\n This method is not used by default, instead :func:`get_credentials`\n is used. This method is intended to be used when the environments is\n known explicitly and detecting the environment implicitly would be\n superfluous.\n\n :type client_email: string\n :param client_email: The e-mail attached to the service account.\n\n :type private_key_path: string\n :param private_key_path: The path to a private key file (this file was\n given to you when you created the service\n account). This file must be in P12 format.\n\n :type scope: string or tuple of string\n :param scope: The scope against which to authenticate. (Different services\n require different scopes, check the documentation for which\n scope is required for the different levels of access to any\n particular API.)\n\n :rtype: :class:`oauth2client.client.SignedJwtAssertionCredentials`\n :returns: A new ``SignedJwtAssertionCredentials`` instance with the\n needed service account settings.\n \"\"\"\n return client.SignedJwtAssertionCredentials(\n service_account_name=client_email,\n private_key=open(private_key_path, 'rb').read(),\n scope=scope)","label":0},{"cid":"gcloud/credentials.py::_get_pem_key","text":"gcloud/credentials.py::_get_pem_key\ndef _get_pem_key(credentials):\n \"\"\"Gets RSA key for a PEM payload from a credentials object.\n\n :type credentials: :class:`client.SignedJwtAssertionCredentials`,\n :class:`service_account._ServiceAccountCredentials`\n :param credentials: The credentials used to create an RSA key\n for signing text.\n\n :rtype: :class:`Crypto.PublicKey.RSA._RSAobj`\n :returns: An RSA object used to sign text.\n :raises: `TypeError` if `credentials` is the wrong type.\n \"\"\"\n if isinstance(credentials, client.SignedJwtAssertionCredentials):\n # Take our PKCS12 (.p12) key and make it into a RSA key we can use.\n pem_text = crypt.pkcs12_key_as_pem(credentials.private_key,\n credentials.private_key_password)\n elif isinstance(credentials, service_account._ServiceAccountCredentials):\n pem_text = credentials._private_key_pkcs8_text\n else:\n raise TypeError((credentials,\n 'not a valid service account credentials type'))\n\n return RSA.importKey(pem_text)","label":0},{"cid":"gcloud/credentials.py::_get_signed_query_params","text":"gcloud/credentials.py::_get_signed_query_params\ndef _get_signed_query_params(credentials, expiration, signature_string):\n \"\"\"Gets query parameters for creating a signed URL.\n\n :type credentials: :class:`client.SignedJwtAssertionCredentials`,\n :class:`service_account._ServiceAccountCredentials`\n :param credentials: The credentials used to create an RSA key\n for signing text.\n\n :type expiration: int or long\n :param expiration: When the signed URL should expire.\n\n :type signature_string: string\n :param signature_string: The string to be signed by the credentials.\n\n :rtype: dict\n :returns: Query parameters matching the signing credentials with a\n signed payload.\n \"\"\"\n pem_key = _get_pem_key(credentials)\n # Sign the string with the RSA key.\n signer = PKCS1_v1_5.new(pem_key)\n signature_hash = SHA256.new(signature_string)\n signature_bytes = signer.sign(signature_hash)\n signature = base64.b64encode(signature_bytes)\n\n if isinstance(credentials, client.SignedJwtAssertionCredentials):\n service_account_name = credentials.service_account_name\n elif isinstance(credentials, service_account._ServiceAccountCredentials):\n service_account_name = credentials._service_account_email\n # We know one of the above must occur since `_get_pem_key` fails if not.\n return {\n 'GoogleAccessId': service_account_name,\n 'Expires': str(expiration),\n 'Signature': signature,\n }","label":0},{"cid":"gcloud/credentials.py::_utcnow","text":"gcloud/credentials.py::_utcnow\ndef _utcnow(): # pragma: NO COVER testing replaces\n \"\"\"Returns current time as UTC datetime.\n\n NOTE: on the module namespace so tests can replace it.\n \"\"\"\n return datetime.datetime.utcnow()","label":0},{"cid":"gcloud/credentials.py::_get_expiration_seconds","text":"gcloud/credentials.py::_get_expiration_seconds\ndef _get_expiration_seconds(expiration):\n \"\"\"Convert 'expiration' to a number of seconds in the future.\n\n :type expiration: int, long, datetime.datetime, datetime.timedelta\n :param expiration: When the signed URL should expire.\n\n :rtype: int\n :returns: a timestamp as an absolute number of seconds.\n \"\"\"\n # If it's a timedelta, add it to `now` in UTC.\n if isinstance(expiration, datetime.timedelta):\n now = _utcnow().replace(tzinfo=pytz.utc)\n expiration = now + expiration\n\n # If it's a datetime, convert to a timestamp.\n if isinstance(expiration, datetime.datetime):\n # Make sure the timezone on the value is UTC\n # (either by converting or replacing the value).\n if expiration.tzinfo:\n expiration = expiration.astimezone(pytz.utc)\n else:\n expiration = expiration.replace(tzinfo=pytz.utc)\n\n # Turn the datetime into a timestamp (seconds, not microseconds).\n expiration = int(calendar.timegm(expiration.timetuple()))\n\n if not isinstance(expiration, six.integer_types):\n raise TypeError('Expected an integer timestamp, datetime, or '\n 'timedelta. Got %s' % type(expiration))\n return expiration","label":0},{"cid":"gcloud/credentials.py::generate_signed_url","text":"gcloud/credentials.py::generate_signed_url\ndef generate_signed_url(credentials, resource, expiration,\n api_access_endpoint='',\n method='GET', content_md5=None,\n content_type=None):\n \"\"\"Generate signed URL to provide query-string auth'n to a resource.\n\n :type credentials: :class:`oauth2client.appengine.AppAssertionCredentials`\n :param credentials: Credentials object with an associated private key to\n sign text.\n\n :type resource: string\n :param resource: A pointer to a specific resource\n (typically, ``/bucket-name/path/to/blob.txt``).\n\n :type expiration: int, long, datetime.datetime, datetime.timedelta\n :param expiration: When the signed URL should expire.\n\n :type api_access_endpoint: string\n :param api_access_endpoint: Optional URI base. Defaults to empty string.\n\n :type method: string\n :param method: The HTTP verb that will be used when requesting the URL.\n\n :type content_md5: string\n :param content_md5: The MD5 hash of the object referenced by\n ``resource``.\n\n :type content_type: string\n :param content_type: The content type of the object referenced by\n ``resource``.\n\n :rtype: string\n :returns: A signed URL you can use to access the resource\n until expiration.\n \"\"\"\n expiration = _get_expiration_seconds(expiration)\n\n # Generate the string to sign.\n signature_string = '\\n'.join([\n method,\n content_md5 or '',\n content_type or '',\n str(expiration),\n resource])\n\n # Set the right query parameters.\n query_params = _get_signed_query_params(credentials,\n expiration,\n signature_string)\n\n # Return the built URL.\n return '{endpoint}{resource}?{querystring}'.format(\n endpoint=api_access_endpoint, resource=reso","label":0},{"cid":"gcloud/datastore/helpers.py::entity_from_protobuf","text":"gcloud/datastore/helpers.py::entity_from_protobuf\ndef entity_from_protobuf(pb):\n \"\"\"Factory method for creating an entity based on a protobuf.\n\n The protobuf should be one returned from the Cloud Datastore\n Protobuf API.\n\n :type pb: :class:`gcloud.datastore._datastore_v1_pb2.Entity`\n :param pb: The Protobuf representing the entity.\n\n :rtype: :class:`gcloud.datastore.entity.Entity`\n :returns: The entity derived from the protobuf.\n \"\"\"\n key = key_from_protobuf(pb.key)\n entity_props = {}\n exclude_from_indexes = []\n\n for property_pb in pb.property:\n value = _get_value_from_property_pb(property_pb)\n entity_props[property_pb.name] = value\n\n # Check if property_pb.value was indexed. Lists need to be\n # special-cased and we require all `indexed` values in a list agree.\n if isinstance(value, list):\n indexed_values = set(value_pb.indexed\n for value_pb in property_pb.value.list_value)\n if len(indexed_values) != 1:\n raise ValueError('For a list_value, subvalues must either all '\n 'be indexed or all excluded from indexes.')\n\n if not indexed_values.pop():\n exclude_from_indexes.append(property_pb.name)\n else:\n if not property_pb.value.indexed:\n exclude_from_indexes.append(property_pb.name)\n\n entity = Entity(key=key, exclude_from_indexes=exclude_from_indexes)\n entity.update(entity_props)\n return entity","label":0},{"cid":"gcloud/datastore/helpers.py::key_from_protobuf","text":"gcloud/datastore/helpers.py::key_from_protobuf\ndef key_from_protobuf(pb):\n \"\"\"Factory method for creating a key based on a protobuf.\n\n The protobuf should be one returned from the Cloud Datastore\n Protobuf API.\n\n :type pb: :class:`gcloud.datastore._datastore_v1_pb2.Key`\n :param pb: The Protobuf representing the key.\n\n :rtype: :class:`gcloud.datastore.key.Key`\n :returns: a new `Key` instance\n \"\"\"\n path_args = []\n for element in pb.path_element:\n path_args.append(element.kind)\n if element.HasField('id'):\n path_args.append(element.id)\n # This is safe: we expect proto objects returned will only have\n # one of `name` or `id` set.\n if element.HasField('name'):\n path_args.append(element.name)\n\n dataset_id = None\n if pb.partition_id.HasField('dataset_id'):\n dataset_id = pb.partition_id.dataset_id\n namespace = None\n if pb.partition_id.HasField('namespace'):\n namespace = pb.partition_id.namespace\n\n return Key(*path_args, namespace=namespace, dataset_id=dataset_id)","label":0},{"cid":"gcloud/datastore/helpers.py::_pb_attr_value","text":"gcloud/datastore/helpers.py::_pb_attr_value\ndef _pb_attr_value(val):\n \"\"\"Given a value, return the protobuf attribute name and proper value.\n\n The Protobuf API uses different attribute names based on value types\n rather than inferring the type. This function simply determines the\n proper attribute name based on the type of the value provided and\n returns the attribute name as well as a properly formatted value.\n\n Certain value types need to be coerced into a different type (such\n as a `datetime.datetime` into an integer timestamp, or a\n `gcloud.datastore.key.Key` into a Protobuf representation. This\n function handles that for you.\n\n .. note::\n Values which are \"text\" ('unicode' in Python2, 'str' in Python3) map\n to 'string_value' in the datastore; values which are \"bytes\"\n ('str' in Python2, 'bytes' in Python3) map to 'blob_value'.\n\n For example:\n\n >>> _pb_attr_value(1234)\n ('integer_value', 1234)\n >>> _pb_attr_value('my_string')\n ('string_value', 'my_string')\n\n :type val: `datetime.datetime`, :class:`gcloud.datastore.key.Key`,\n bool, float, integer, string\n :param val: The value to be scrutinized.\n\n :returns: A tuple of the attribute name and proper value type.\n \"\"\"\n\n if isinstance(val, datetime.datetime):\n name = 'timestamp_microseconds'\n # If the datetime is naive (no timezone), consider that it was\n # intended to be UTC and replace the tzinfo to that effect.\n if not val.tzinfo:\n val = val.replace(tzinfo=pytz.utc)\n # Regardless of what timezone is on the value, convert it to UTC.\n val = val.astimezone(pytz.utc)\n # Convert the datetime to a microsecond timestamp.\n value = int(calendar.timegm(val.timetuple()) * 1e6) + val.microsecond\n elif isinstance(val, Key):\n name, value = 'key', val.to_protobuf()\n elif isinstance(val, bool):\n name, value = 'boolean', val\n elif isinstance(val, flo","label":0},{"cid":"gcloud/datastore/helpers.py::_get_value_from_value_pb","text":"gcloud/datastore/helpers.py::_get_value_from_value_pb\ndef _get_value_from_value_pb(value_pb):\n \"\"\"Given a protobuf for a Value, get the correct value.\n\n The Cloud Datastore Protobuf API returns a Property Protobuf which\n has one value set and the rest blank. This function retrieves the\n the one value provided.\n\n Some work is done to coerce the return value into a more useful type\n (particularly in the case of a timestamp value, or a key value).\n\n :type value_pb: :class:`gcloud.datastore._datastore_v1_pb2.Value`\n :param value_pb: The Value Protobuf.\n\n :returns: The value provided by the Protobuf.\n \"\"\"\n result = None\n if value_pb.HasField('timestamp_microseconds_value'):\n microseconds = value_pb.timestamp_microseconds_value\n naive = (datetime.datetime.utcfromtimestamp(0) +\n datetime.timedelta(microseconds=microseconds))\n result = naive.replace(tzinfo=pytz.utc)\n\n elif value_pb.HasField('key_value'):\n result = key_from_protobuf(value_pb.key_value)\n\n elif value_pb.HasField('boolean_value'):\n result = value_pb.boolean_value\n\n elif value_pb.HasField('double_value'):\n result = value_pb.double_value\n\n elif value_pb.HasField('integer_value'):\n result = value_pb.integer_value\n\n elif value_pb.HasField('string_value'):\n result = value_pb.string_value\n\n elif value_pb.HasField('blob_value'):\n result = value_pb.blob_value\n\n elif value_pb.HasField('entity_value'):\n result = entity_from_protobuf(value_pb.entity_value)\n\n elif value_pb.list_value:\n result = [_get_value_from_value_pb(x) for x in value_pb.list_value]\n\n return result","label":0},{"cid":"gcloud/datastore/helpers.py::_get_value_from_property_pb","text":"gcloud/datastore/helpers.py::_get_value_from_property_pb\ndef _get_value_from_property_pb(property_pb):\n \"\"\"Given a protobuf for a Property, get the correct value.\n\n The Cloud Datastore Protobuf API returns a Property Protobuf which\n has one value set and the rest blank. This function retrieves the\n the one value provided.\n\n Some work is done to coerce the return value into a more useful type\n (particularly in the case of a timestamp value, or a key value).\n\n :type property_pb: :class:`gcloud.datastore._datastore_v1_pb2.Property`\n :param property_pb: The Property Protobuf.\n\n :returns: The value provided by the Protobuf.\n \"\"\"\n return _get_value_from_value_pb(property_pb.value)","label":0},{"cid":"gcloud/datastore/helpers.py::_set_protobuf_value","text":"gcloud/datastore/helpers.py::_set_protobuf_value\ndef _set_protobuf_value(value_pb, val):\n \"\"\"Assign 'val' to the correct subfield of 'value_pb'.\n\n The Protobuf API uses different attribute names based on value types\n rather than inferring the type.\n\n Some value types (entities, keys, lists) cannot be directly\n assigned; this function handles them correctly.\n\n :type value_pb: :class:`gcloud.datastore._datastore_v1_pb2.Value`\n :param value_pb: The value protobuf to which the value is being assigned.\n\n :type val: `datetime.datetime`, boolean, float, integer, string,\n :class:`gcloud.datastore.key.Key`,\n :class:`gcloud.datastore.entity.Entity`,\n :param val: The value to be assigned.\n \"\"\"\n if val is None:\n value_pb.Clear()\n return\n\n attr, val = _pb_attr_value(val)\n if attr == 'key_value':\n value_pb.key_value.CopyFrom(val)\n elif attr == 'entity_value':\n e_pb = value_pb.entity_value\n e_pb.Clear()\n key = val.key\n if key is not None:\n e_pb.key.CopyFrom(key.to_protobuf())\n for item_key, value in val.items():\n p_pb = e_pb.property.add()\n p_pb.name = item_key\n _set_protobuf_value(p_pb.value, value)\n elif attr == 'list_value':\n l_pb = value_pb.list_value\n for item in val:\n i_pb = l_pb.add()\n _set_protobuf_value(i_pb, item)\n else: # scalar, just assign\n setattr(value_pb, attr, val)","label":0},{"cid":"gcloud/datastore/helpers.py::_prepare_key_for_request","text":"gcloud/datastore/helpers.py::_prepare_key_for_request\ndef _prepare_key_for_request(key_pb):\n \"\"\"Add protobuf keys to a request object.\n\n :type key_pb: :class:`gcloud.datastore._datastore_v1_pb2.Key`\n :param key_pb: A key to be added to a request.\n\n :rtype: :class:`gcloud.datastore._datastore_v1_pb2.Key`\n :returns: A key which will be added to a request. It will be the\n original if nothing needs to be changed.\n \"\"\"\n if key_pb.partition_id.HasField('dataset_id'):\n # We remove the dataset_id from the protobuf. This is because\n # the backend fails a request if the key contains un-prefixed\n # dataset ID. The backend fails because requests to\n # /datastore/.../datasets/foo/...\n # and\n # /datastore/.../datasets/s~foo/...\n # both go to the datastore given by 's~foo'. So if the key\n # protobuf in the request body has dataset_id='foo', the\n # backend will reject since 'foo' != 's~foo'.\n new_key_pb = datastore_pb.Key()\n new_key_pb.CopyFrom(key_pb)\n new_key_pb.partition_id.ClearField('dataset_id')\n key_pb = new_key_pb\n return key_pb","label":0},{"cid":"gcloud/datastore/helpers.py::_add_keys_to_request","text":"gcloud/datastore/helpers.py::_add_keys_to_request\ndef _add_keys_to_request(request_field_pb, key_pbs):\n \"\"\"Add protobuf keys to a request object.\n\n :type request_field_pb: `RepeatedCompositeFieldContainer`\n :param request_field_pb: A repeated proto field that contains keys.\n\n :type key_pbs: list of :class:`gcloud.datastore._datastore_v1_pb2.Key`\n :param key_pbs: The keys to add to a request.\n \"\"\"\n for key_pb in key_pbs:\n key_pb = _prepare_key_for_request(key_pb)\n request_field_pb.add().CopyFrom(key_pb)","label":0},{"cid":"gcloud/datastore/helpers.py::_dataset_ids_equal","text":"gcloud/datastore/helpers.py::_dataset_ids_equal\ndef _dataset_ids_equal(dataset_id1, dataset_id2):\n \"\"\"Compares two dataset IDs for fuzzy equality.\n\n Each may be prefixed or unprefixed (but not null, since dataset ID\n is required on a key). The only allowed prefixes are 's~' and 'e~'.\n\n Two identical prefixed match\n\n >>> 's~foo' == 's~foo'\n >>> 'e~bar' == 'e~bar'\n\n while non-identical prefixed don't\n\n >>> 's~foo' != 's~bar'\n >>> 's~foo' != 'e~foo'\n\n As for non-prefixed, they can match other non-prefixed or\n prefixed:\n\n >>> 'foo' == 'foo'\n >>> 'foo' == 's~foo'\n >>> 'foo' == 'e~foo'\n >>> 'foo' != 'bar'\n >>> 'foo' != 's~bar'\n\n (Ties are resolved since 'foo' can only be an alias for one of\n s~foo or e~foo in the backend.)\n\n :type dataset_id1: string\n :param dataset_id1: A dataset ID.\n\n :type dataset_id2: string\n :param dataset_id2: A dataset ID.\n\n :rtype: boolean\n :returns: Boolean indicating if the IDs are the same.\n \"\"\"\n if dataset_id1 == dataset_id2:\n return True\n\n if dataset_id1.startswith('s~') or dataset_id1.startswith('e~'):\n # If `dataset_id1` is prefixed and not matching, then the only way\n # they can match is if `dataset_id2` is unprefixed.\n return dataset_id1[2:] == dataset_id2\n elif dataset_id2.startswith('s~') or dataset_id2.startswith('e~'):\n # Here we know `dataset_id1` is unprefixed and `dataset_id2`\n # is prefixed.\n return dataset_id1 == dataset_id2[2:]\n\n return False","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.__init__","text":"gcloud/storage/acl.py::_ACLEntity.__init__\n def __init__(self, entity_type, identifier=None):\n \"\"\"Entity constructor.\n\n :type entity_type: string\n :param entity_type: The type of entity (ie, 'group' or 'user').\n\n :type identifier: string\n :param identifier: The ID or e-mail of the entity. For the special\n entity types (like 'allUsers') this is optional.\n \"\"\"\n self.identifier = identifier\n self.roles = set([])\n self.type = entity_type","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.__str__","text":"gcloud/storage/acl.py::_ACLEntity.__str__\n def __str__(self):\n if not self.identifier:\n return str(self.type)\n else:\n return '{acl.type}-{acl.identifier}'.format(acl=self)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.__repr__","text":"gcloud/storage/acl.py::_ACLEntity.__repr__\n def __repr__(self):\n return '<ACL Entity: {acl} ({roles})>'.format(\n acl=self, roles=', '.join(self.roles))","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.get_roles","text":"gcloud/storage/acl.py::_ACLEntity.get_roles\n def get_roles(self):\n \"\"\"Get the list of roles permitted by this entity.\n\n :rtype: list of strings\n :returns: The list of roles associated with this entity.\n \"\"\"\n return self.roles","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.grant","text":"gcloud/storage/acl.py::_ACLEntity.grant\n def grant(self, role):\n \"\"\"Add a role to the entity.\n\n :type role: string\n :param role: The role to add to the entity.\n\n :rtype: :class:`_ACLEntity`\n :returns: The entity class.\n \"\"\"\n self.roles.add(role)\n return self","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.revoke","text":"gcloud/storage/acl.py::_ACLEntity.revoke\n def revoke(self, role):\n \"\"\"Remove a role from the entity.\n\n :type role: string\n :param role: The role to remove from the entity.\n\n :rtype: :class:`_ACLEntity`\n :returns: The entity class.\n \"\"\"\n if role in self.roles:\n self.roles.remove(role)\n return self","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.grant_read","text":"gcloud/storage/acl.py::_ACLEntity.grant_read\n def grant_read(self):\n \"\"\"Grant read access to the current entity.\"\"\"\n\n return self.grant(_ACLEntity.READER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.grant_write","text":"gcloud/storage/acl.py::_ACLEntity.grant_write\n def grant_write(self):\n \"\"\"Grant write access to the current entity.\"\"\"\n\n return self.grant(_ACLEntity.WRITER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.grant_owner","text":"gcloud/storage/acl.py::_ACLEntity.grant_owner\n def grant_owner(self):\n \"\"\"Grant owner access to the current entity.\"\"\"\n\n return self.grant(_ACLEntity.OWNER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.revoke_read","text":"gcloud/storage/acl.py::_ACLEntity.revoke_read\n def revoke_read(self):\n \"\"\"Revoke read access from the current entity.\"\"\"\n\n return self.revoke(_ACLEntity.READER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.revoke_write","text":"gcloud/storage/acl.py::_ACLEntity.revoke_write\n def revoke_write(self):\n \"\"\"Revoke write access from the current entity.\"\"\"\n\n return self.revoke(_ACLEntity.WRITER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.revoke_owner","text":"gcloud/storage/acl.py::_ACLEntity.revoke_owner\n def revoke_owner(self):\n \"\"\"Revoke owner access from the current entity.\"\"\"\n\n return self.revoke(_ACLEntity.OWNER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::ACL.__init__","text":"gcloud/storage/acl.py::ACL.__init__\n def __init__(self):\n self.entities = {}","label":0},{"cid":"gcloud/storage/acl.py::ACL._ensure_loaded","text":"gcloud/storage/acl.py::ACL._ensure_loaded\n def _ensure_loaded(self):\n \"\"\"Load if not already loaded.\"\"\"\n if not self.loaded:\n self.reload()","label":0},{"cid":"gcloud/storage/acl.py::ACL.reset","text":"gcloud/storage/acl.py::ACL.reset\n def reset(self):\n \"\"\"Remove all entities from the ACL, and clear the ``loaded`` flag.\"\"\"\n self.entities.clear()\n self.loaded = False","label":0},{"cid":"gcloud/storage/acl.py::ACL.__iter__","text":"gcloud/storage/acl.py::ACL.__iter__\n def __iter__(self):\n self._ensure_loaded()\n\n for entity in self.entities.values():\n for role in entity.get_roles():\n if role:\n yield {'entity': str(entity), 'role': role}","label":0},{"cid":"gcloud/storage/acl.py::ACL.entity_from_dict","text":"gcloud/storage/acl.py::ACL.entity_from_dict\n def entity_from_dict(self, entity_dict):\n \"\"\"Build an _ACLEntity object from a dictionary of data.\n\n An entity is a mutable object that represents a list of roles\n belonging to either a user or group or the special types for all\n users and all authenticated users.\n\n :type entity_dict: dict\n :param entity_dict: Dictionary full of data from an ACL lookup.\n\n :rtype: :class:`_ACLEntity`\n :returns: An Entity constructed from the dictionary.\n \"\"\"\n entity = entity_dict['entity']\n role = entity_dict['role']\n\n if entity == 'allUsers':\n entity = self.all()\n\n elif entity == 'allAuthenticatedUsers':\n entity = self.all_authenticated()\n\n elif '-' in entity:\n entity_type, identifier = entity.split('-', 1)\n entity = self.entity(entity_type=entity_type,\n identifier=identifier)\n\n if not isinstance(entity, _ACLEntity):\n raise ValueError('Invalid dictionary: %s' % entity_dict)\n\n return entity.grant(role)","label":0},{"cid":"gcloud/storage/acl.py::ACL.has_entity","text":"gcloud/storage/acl.py::ACL.has_entity\n def has_entity(self, entity):\n \"\"\"Returns whether or not this ACL has any entries for an entity.\n\n :type entity: :class:`_ACLEntity`\n :param entity: The entity to check for existence in this ACL.\n\n :rtype: boolean\n :returns: True of the entity exists in the ACL.\n \"\"\"\n self._ensure_loaded()\n return str(entity) in self.entities","label":0},{"cid":"gcloud/storage/acl.py::ACL.get_entity","text":"gcloud/storage/acl.py::ACL.get_entity\n def get_entity(self, entity, default=None):\n \"\"\"Gets an entity object from the ACL.\n\n :type entity: :class:`_ACLEntity` or string\n :param entity: The entity to get lookup in the ACL.\n\n :type default: anything\n :param default: This value will be returned if the entity\n doesn't exist.\n\n :rtype: :class:`_ACLEntity`\n :returns: The corresponding entity or the value provided\n to ``default``.\n \"\"\"\n self._ensure_loaded()\n return self.entities.get(str(entity), default)","label":0},{"cid":"gcloud/storage/acl.py::ACL.add_entity","text":"gcloud/storage/acl.py::ACL.add_entity\n def add_entity(self, entity):\n \"\"\"Add an entity to the ACL.\n\n :type entity: :class:`_ACLEntity`\n :param entity: The entity to add to this ACL.\n \"\"\"\n self._ensure_loaded()\n self.entities[str(entity)] = entity","label":0},{"cid":"gcloud/storage/acl.py::ACL.entity","text":"gcloud/storage/acl.py::ACL.entity\n def entity(self, entity_type, identifier=None):\n \"\"\"Factory method for creating an Entity.\n\n If an entity with the same type and identifier already exists,\n this will return a reference to that entity. If not, it will\n create a new one and add it to the list of known entities for\n this ACL.\n\n :type entity_type: string\n :param entity_type: The type of entity to create\n (ie, ``user``, ``group``, etc)\n\n :type identifier: string\n :param identifier: The ID of the entity (if applicable).\n This can be either an ID or an e-mail address.\n\n :rtype: :class:`_ACLEntity`\n :returns: A new Entity or a reference to an existing identical entity.\n \"\"\"\n entity = _ACLEntity(entity_type=entity_type, identifier=identifier)\n if self.has_entity(entity):\n entity = self.get_entity(entity)\n else:\n self.add_entity(entity)\n return entity","label":0},{"cid":"gcloud/storage/acl.py::ACL.user","text":"gcloud/storage/acl.py::ACL.user\n def user(self, identifier):\n \"\"\"Factory method for a user Entity.\n\n :type identifier: string\n :param identifier: An id or e-mail for this particular user.\n\n :rtype: :class:`_ACLEntity`\n :returns: An Entity corresponding to this user.\n \"\"\"\n return self.entity('user', identifier=identifier)","label":0},{"cid":"gcloud/storage/acl.py::ACL.group","text":"gcloud/storage/acl.py::ACL.group\n def group(self, identifier):\n \"\"\"Factory method for a group Entity.\n\n :type identifier: string\n :param identifier: An id or e-mail for this particular group.\n\n :rtype: :class:`_ACLEntity`\n :returns: An Entity corresponding to this group.\n \"\"\"\n return self.entity('group', identifier=identifier)","label":0},{"cid":"gcloud/storage/acl.py::ACL.domain","text":"gcloud/storage/acl.py::ACL.domain\n def domain(self, domain):\n \"\"\"Factory method for a domain Entity.\n\n :type domain: string\n :param domain: The domain for this entity.\n\n :rtype: :class:`_ACLEntity`\n :returns: An entity corresponding to this domain.\n \"\"\"\n return self.entity('domain', identifier=domain)","label":0},{"cid":"gcloud/storage/acl.py::ACL.all","text":"gcloud/storage/acl.py::ACL.all\n def all(self):\n \"\"\"Factory method for an Entity representing all users.\n\n :rtype: :class:`_ACLEntity`\n :returns: An entity representing all users.\n \"\"\"\n return self.entity('allUsers')","label":0},{"cid":"gcloud/storage/acl.py::ACL.all_authenticated","text":"gcloud/storage/acl.py::ACL.all_authenticated\n def all_authenticated(self):\n \"\"\"Factory method for an Entity representing all authenticated users.\n\n :rtype: :class:`_ACLEntity`\n :returns: An entity representing all authenticated users.\n \"\"\"\n return self.entity('allAuthenticatedUsers')","label":0},{"cid":"gcloud/storage/acl.py::ACL.get_entities","text":"gcloud/storage/acl.py::ACL.get_entities\n def get_entities(self):\n \"\"\"Get a list of all Entity objects.\n\n :rtype: list of :class:`_ACLEntity` objects\n :returns: A list of all Entity objects.\n \"\"\"\n self._ensure_loaded()\n return list(self.entities.values())","label":0},{"cid":"gcloud/storage/acl.py::ACL.reload","text":"gcloud/storage/acl.py::ACL.reload\n def reload(self):\n \"\"\"Reload the ACL data from Cloud Storage.\n\n :rtype: :class:`ACL`\n :returns: The current ACL.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"gcloud/storage/acl.py::ACL.save","text":"gcloud/storage/acl.py::ACL.save\n def save(self, acl=None):\n \"\"\"A method to be overridden by subclasses.\n\n :type acl: :class:`gcloud.storage.acl.ACL`, or a compatible list.\n :param acl: The ACL object to save. If left blank, this will save\n current entries.\n\n :raises: NotImplementedError\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"gcloud/storage/acl.py::ACL.clear","text":"gcloud/storage/acl.py::ACL.clear\n def clear(self):\n \"\"\"Remove all entities from the ACL.\"\"\"\n raise NotImplementedError","label":0},{"cid":"gcloud/storage/acl.py::BucketACL.__init__","text":"gcloud/storage/acl.py::BucketACL.__init__\n def __init__(self, bucket):\n \"\"\"\n :type bucket: :class:`gcloud.storage.bucket.Bucket`\n :param bucket: The bucket to which this ACL relates.\n \"\"\"\n super(BucketACL, self).__init__()\n self.bucket = bucket","label":0},{"cid":"gcloud/storage/acl.py::BucketACL.reload","text":"gcloud/storage/acl.py::BucketACL.reload\n def reload(self):\n \"\"\"Reload the ACL data from Cloud Storage.\n\n :rtype: :class:`gcloud.storage.acl.BucketACL`\n :returns: The current ACL.\n \"\"\"\n self.entities.clear()\n\n url_path = '%s/%s' % (self.bucket.path, self._URL_PATH_ELEM)\n found = self.bucket.connection.api_request(method='GET', path=url_path)\n self.loaded = True\n for entry in found['items']:\n self.add_entity(self.entity_from_dict(entry))\n\n return self","label":0},{"cid":"gcloud/storage/acl.py::BucketACL.save","text":"gcloud/storage/acl.py::BucketACL.save\n def save(self, acl=None):\n \"\"\"Save this ACL for the current bucket.\n\n If called without arguments, this will save the entries\n currently stored on this ACL::\n\n >>> acl.save()\n\n You can also provide a specific ACL to save instead of the one\n currently set on the Bucket object::\n\n >>> acl.save(acl=my_other_acl)\n\n You can use this to set access controls to be consistent from\n one bucket to another::\n\n >>> bucket1 = connection.get_bucket(bucket1_name)\n >>> bucket2 = connection.get_bucket(bucket2_name)\n >>> bucket2.acl.save(bucket1.acl)\n\n :type acl: :class:`gcloud.storage.acl.ACL`, or a compatible list.\n :param acl: The ACL object to save. If left blank, this will save\n current entries.\n\n :rtype: :class:`gcloud.storage.acl.BucketACL`\n :returns: The current ACL.\n \"\"\"\n if acl is None:\n acl = self\n save_to_backend = acl.loaded\n else:\n save_to_backend = True\n\n if save_to_backend:\n result = self.bucket.connection.api_request(\n method='PATCH', path=self.bucket.path,\n data={self._URL_PATH_ELEM: list(acl)},\n query_params={'projection': 'full'})\n self.entities.clear()\n for entry in result[self._URL_PATH_ELEM]:\n self.add_entity(self.entity_from_dict(entry))\n self.loaded = True\n\n return self","label":0},{"cid":"gcloud/storage/acl.py::BucketACL.clear","text":"gcloud/storage/acl.py::BucketACL.clear\n def clear(self):\n \"\"\"Remove all ACL entries.\n\n Note that this won't actually remove *ALL* the rules, but it\n will remove all the non-default rules. In short, you'll still\n have access to a bucket that you created even after you clear\n ACL rules with this method.\n\n For example, imagine that you granted access to this bucket to a\n bunch of coworkers::\n\n >>> acl.user('coworker1@example.org').grant_read()\n >>> acl.user('coworker2@example.org').grant_read()\n >>> acl.save()\n\n Now they work in another part of the company and you want to\n 'start fresh' on who has access::\n\n >>> acl.clear()\n\n At this point all the custom rules you created have been removed.\n\n :rtype: :class:`gcloud.storage.acl.BucketACL`\n :returns: The current ACL.\n \"\"\"\n return self.save([])","label":0},{"cid":"gcloud/storage/acl.py::ObjectACL.__init__","text":"gcloud/storage/acl.py::ObjectACL.__init__\n def __init__(self, blob):\n super(ObjectACL, self).__init__()\n self.blob = blob","label":0},{"cid":"gcloud/storage/acl.py::ObjectACL.reload","text":"gcloud/storage/acl.py::ObjectACL.reload\n def reload(self):\n \"\"\"Reload the ACL data from Cloud Storage.\n\n :rtype: :class:`ObjectACL`\n :returns: The current ACL.\n \"\"\"\n self.entities.clear()\n\n url_path = '%s/acl' % self.blob.path\n found = self.blob.connection.api_request(method='GET', path=url_path)\n self.loaded = True\n for entry in found['items']:\n self.add_entity(self.entity_from_dict(entry))\n\n return self","label":0},{"cid":"gcloud/storage/acl.py::ObjectACL.save","text":"gcloud/storage/acl.py::ObjectACL.save\n def save(self, acl=None):\n \"\"\"Save the ACL data for this blob.\n\n :type acl: :class:`gcloud.storage.acl.ACL`\n :param acl: The ACL object to save. If left blank, this will\n save the entries set locally on the ACL.\n \"\"\"\n if acl is None:\n acl = self\n save_to_backend = acl.loaded\n else:\n save_to_backend = True\n\n if save_to_backend:\n result = self.blob.connection.api_request(\n method='PATCH', path=self.blob.path, data={'acl': list(acl)},\n query_params={'projection': 'full'})\n self.entities.clear()\n for entry in result['acl']:\n self.add_entity(self.entity_from_dict(entry))\n self.loaded = True\n\n return self","label":0},{"cid":"gcloud/storage/acl.py::ObjectACL.clear","text":"gcloud/storage/acl.py::ObjectACL.clear\n def clear(self):\n \"\"\"Remove all ACL rules from the blob.\n\n Note that this won't actually remove *ALL* the rules, but it\n will remove all the non-default rules. In short, you'll still\n have access to a blob that you created even after you clear ACL\n rules with this method.\n \"\"\"\n return self.save([])","label":0},{"cid":"gcloud/storage/bucket.py::_BlobIterator.__init__","text":"gcloud/storage/bucket.py::_BlobIterator.__init__\n def __init__(self, bucket, extra_params=None):\n self.bucket = bucket\n self.prefixes = ()\n super(_BlobIterator, self).__init__(\n connection=bucket.connection, path=bucket.path + '/o',\n extra_params=extra_params)","label":0},{"cid":"gcloud/storage/bucket.py::_BlobIterator.get_items_from_response","text":"gcloud/storage/bucket.py::_BlobIterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Yield :class:`.storage.blob.Blob` items from response.\n\n :type response: dict\n :param response: The JSON API response for a page of blobs.\n \"\"\"\n self.prefixes = tuple(response.get('prefixes', ()))\n for item in response.get('items', []):\n yield Blob(None, properties=item, bucket=self.bucket)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.__init__","text":"gcloud/storage/bucket.py::Bucket.__init__\n def __init__(self, connection=None, name=None, properties=None):\n if name is None and properties is not None:\n name = properties.get('name')\n super(Bucket, self).__init__(name=name, properties=properties)\n self._connection = connection","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.__repr__","text":"gcloud/storage/bucket.py::Bucket.__repr__\n def __repr__(self):\n return '<Bucket: %s>' % self.name","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.__iter__","text":"gcloud/storage/bucket.py::Bucket.__iter__\n def __iter__(self):\n return iter(self._iterator_class(bucket=self))","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.__contains__","text":"gcloud/storage/bucket.py::Bucket.__contains__\n def __contains__(self, blob):\n return self.get_blob(blob) is not None","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.exists","text":"gcloud/storage/bucket.py::Bucket.exists\n def exists(self):\n \"\"\"Determines whether or not this bucket exists.\n\n :rtype: boolean\n :returns: True if the bucket exists in Cloud Storage.\n \"\"\"\n try:\n self.connection.get_bucket(self.name)\n return True\n except NotFound:\n return False","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.acl","text":"gcloud/storage/bucket.py::Bucket.acl\n @property\n def acl(self):\n \"\"\"Create our ACL on demand.\"\"\"\n if self._acl is None:\n self._acl = BucketACL(self)\n return self._acl","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.default_object_acl","text":"gcloud/storage/bucket.py::Bucket.default_object_acl\n @property\n def default_object_acl(self):\n \"\"\"Create our defaultObjectACL on demand.\"\"\"\n if self._default_object_acl is None:\n self._default_object_acl = DefaultObjectACL(self)\n return self._default_object_acl","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.connection","text":"gcloud/storage/bucket.py::Bucket.connection\n @property\n def connection(self):\n \"\"\"Getter property for the connection to use with this Bucket.\n\n :rtype: :class:`gcloud.storage.connection.Connection`\n :returns: The connection to use.\n \"\"\"\n return self._connection","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.path_helper","text":"gcloud/storage/bucket.py::Bucket.path_helper\n @staticmethod\n def path_helper(bucket_name):\n \"\"\"Relative URL path for a bucket.\n\n :type bucket_name: string\n :param bucket_name: The bucket name in the path.\n\n :rtype: string\n :returns: The relative URL path for ``bucket_name``.\n \"\"\"\n return '/b/' + bucket_name","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.path","text":"gcloud/storage/bucket.py::Bucket.path\n @property\n def path(self):\n \"\"\"The URL path to this bucket.\"\"\"\n if not self.name:\n raise ValueError('Cannot determine path without bucket name.')\n\n return self.path_helper(self.name)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_blob","text":"gcloud/storage/bucket.py::Bucket.get_blob\n def get_blob(self, blob):\n \"\"\"Get a blob object by name.\n\n This will return None if the blob doesn't exist::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> print bucket.get_blob('/path/to/blob.txt')\n <Blob: my-bucket, /path/to/blob.txt>\n >>> print bucket.get_blob('/does-not-exist.txt')\n None\n\n :type blob: string or :class:`gcloud.storage.blob.Blob`\n :param blob: The name of the blob to retrieve.\n\n :rtype: :class:`gcloud.storage.blob.Blob` or None\n :returns: The blob object if it exists, otherwise None.\n \"\"\"\n # Coerce this -- either from a Blob or a string.\n blob = self.new_blob(blob)\n\n try:\n response = self.connection.api_request(method='GET',\n path=blob.path)\n return Blob(None, bucket=self, properties=response)\n except NotFound:\n return None","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_all_blobs","text":"gcloud/storage/bucket.py::Bucket.get_all_blobs\n def get_all_blobs(self):\n \"\"\"List all the blobs in this bucket.\n\n This will **not** retrieve all the data for all the blobs, it\n will only retrieve the blob paths.\n\n This is equivalent to::\n\n blobs = [blob for blob in bucket]\n\n :rtype: list of :class:`gcloud.storage.blob.Blob`\n :returns: A list of all the Blob objects in this bucket.\n \"\"\"\n return list(self)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.iterator","text":"gcloud/storage/bucket.py::Bucket.iterator\n def iterator(self, prefix=None, delimiter=None, max_results=None,\n versions=None):\n \"\"\"Return an iterator used to find blobs in the bucket.\n\n :type prefix: string or None\n :param prefix: optional prefix used to filter blobs.\n\n :type delimiter: string or None\n :param delimiter: optional delimter, used with ``prefix`` to\n emulate hierarchy.\n\n :type max_results: integer or None\n :param max_results: maximum number of blobs to return.\n\n :type versions: boolean or None\n :param versions: whether object versions should be returned as\n separate blobs.\n\n :rtype: :class:`_BlobIterator`\n \"\"\"\n extra_params = {}\n\n if prefix is not None:\n extra_params['prefix'] = prefix\n\n if delimiter is not None:\n extra_params['delimiter'] = delimiter\n\n if max_results is not None:\n extra_params['maxResults'] = max_results\n\n if versions is not None:\n extra_params['versions'] = versions\n\n return self._iterator_class(self, extra_params=extra_params)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.new_blob","text":"gcloud/storage/bucket.py::Bucket.new_blob\n def new_blob(self, blob):\n \"\"\"Given path name (or Blob), return a :class:`Blob` object.\n\n This is really useful when you're not sure if you have a ``Blob``\n instance or a string path name. Given either of those types, this\n returns the corresponding ``Blob``.\n\n :type blob: string or :class:`gcloud.storage.blob.Blob`\n :param blob: A path name or actual blob object.\n\n :rtype: :class:`gcloud.storage.blob.Blob`\n :returns: A Blob object with the path provided.\n \"\"\"\n if isinstance(blob, Blob):\n return blob\n\n if isinstance(blob, six.string_types):\n return Blob(bucket=self, name=blob)\n\n raise TypeError('Invalid blob: %s' % blob)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.delete","text":"gcloud/storage/bucket.py::Bucket.delete\n def delete(self, force=False):\n \"\"\"Delete this bucket.\n\n The bucket **must** be empty in order to submit a delete request. If\n ``force=True`` is passed, this will first attempt to delete all the\n objects / blobs in the bucket (i.e. try to empty the bucket).\n\n If the bucket doesn't exist, this will raise\n :class:`gcloud.exceptions.NotFound`. If the bucket is not empty\n (and ``force=False``), will raise :class:`gcloud.exceptions.Conflict`.\n\n If ``force=True`` and the bucket contains more than 256 objects / blobs\n this will cowardly refuse to delete the objects (or the bucket). This\n is to prevent accidental bucket deletion and to prevent extremely long\n runtime of this method.\n\n :type force: boolean\n :param force: If True, empties the bucket's objects then deletes it.\n\n :raises: :class:`ValueError` if ``force`` is ``True`` and the bucket\n contains more than 256 objects / blobs.\n \"\"\"\n if force:\n blobs = list(self.iterator(\n max_results=self._MAX_OBJECTS_FOR_BUCKET_DELETE + 1))\n if len(blobs) > self._MAX_OBJECTS_FOR_BUCKET_DELETE:\n message = (\n 'Refusing to delete bucket with more than '\n '%d objects. If you actually want to delete '\n 'this bucket, please delete the objects '\n 'yourself before calling Bucket.delete().'\n ) % (self._MAX_OBJECTS_FOR_BUCKET_DELETE,)\n raise ValueError(message)\n\n # Ignore 404 errors on delete.\n self.delete_blobs(blobs, on_error=lambda blob: None)\n\n self.connection.delete_bucket(self.name)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.delete_blob","text":"gcloud/storage/bucket.py::Bucket.delete_blob\n def delete_blob(self, blob):\n \"\"\"Deletes a blob from the current bucket.\n\n If the blob isn't found, raise a\n :class:`gcloud.exceptions.NotFound`.\n\n For example::\n\n >>> from gcloud.exceptions import NotFound\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> print bucket.get_all_blobs()\n [<Blob: my-bucket, my-file.txt>]\n >>> bucket.delete_blob('my-file.txt')\n >>> try:\n ... bucket.delete_blob('doesnt-exist')\n ... except NotFound:\n ... pass\n\n\n :type blob: string or :class:`gcloud.storage.blob.Blob`\n :param blob: A blob name or Blob object to delete.\n\n :rtype: :class:`gcloud.storage.blob.Blob`\n :returns: The blob that was just deleted.\n :raises: :class:`gcloud.exceptions.NotFound` (to suppress\n the exception, call ``delete_blobs``, passing a no-op\n ``on_error`` callback, e.g.::\n\n >>> bucket.delete_blobs([blob], on_error=lambda blob: None)\n \"\"\"\n blob = self.new_blob(blob)\n self.connection.api_request(method='DELETE', path=blob.path)\n return blob","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.delete_blobs","text":"gcloud/storage/bucket.py::Bucket.delete_blobs\n def delete_blobs(self, blobs, on_error=None):\n \"\"\"Deletes a list of blobs from the current bucket.\n\n Uses :func:`Bucket.delete_blob` to delete each individual blob.\n\n :type blobs: list of string or :class:`gcloud.storage.blob.Blob`\n :param blobs: A list of blob names or Blob objects to delete.\n\n :type on_error: a callable taking (blob)\n :param on_error: If not ``None``, called once for each blob raising\n :class:`gcloud.exceptions.NotFound`;\n otherwise, the exception is propagated.\n\n :raises: :class:`gcloud.exceptions.NotFound` (if\n `on_error` is not passed).\n \"\"\"\n for blob in blobs:\n try:\n self.delete_blob(blob)\n except NotFound:\n if on_error is not None:\n on_error(blob)\n else:\n raise","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.copy_blob","text":"gcloud/storage/bucket.py::Bucket.copy_blob\n def copy_blob(self, blob, destination_bucket, new_name=None):\n \"\"\"Copy the given blob to the given bucket, optionally with a new name.\n\n :type blob: string or :class:`gcloud.storage.blob.Blob`\n :param blob: The blob to be copied.\n\n :type destination_bucket: :class:`gcloud.storage.bucket.Bucket`\n :param destination_bucket: The bucket into which the blob should be\n copied.\n\n :type new_name: string\n :param new_name: (optional) the new name for the copied file.\n\n :rtype: :class:`gcloud.storage.blob.Blob`\n :returns: The new Blob.\n \"\"\"\n if new_name is None:\n new_name = blob.name\n new_blob = destination_bucket.new_blob(new_name)\n api_path = blob.path + '/copyTo' + new_blob.path\n self.connection.api_request(method='POST', path=api_path)\n return new_blob","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.upload_file","text":"gcloud/storage/bucket.py::Bucket.upload_file\n def upload_file(self, filename, blob=None):\n \"\"\"Shortcut method to upload a file into this bucket.\n\n Use this method to quickly put a local file in Cloud Storage.\n\n For example::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> bucket.upload_file('~/my-file.txt', 'remote-text-file.txt')\n >>> print bucket.get_all_blobs()\n [<Blob: my-bucket, remote-text-file.txt>]\n\n If you don't provide a blob value, we will try to upload the file\n using the local filename as the blob (**not** the complete\n path)::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> bucket.upload_file('~/my-file.txt')\n >>> print bucket.get_all_blobs()\n [<Blob: my-bucket, my-file.txt>]\n\n :type filename: string\n :param filename: Local path to the file you want to upload.\n\n :type blob: string or :class:`gcloud.storage.blob.Blob`\n :param blob: The blob (either an object or a remote path) of where\n to put the file. If this is blank, we will try to\n upload the file to the root of the bucket with the\n same name as on your local file system.\n\n :rtype: :class:`Blob`\n :returns: The updated Blob object.\n \"\"\"\n if blob is None:\n blob = os.path.basename(filename)\n blob = self.new_blob(blob)\n blob.upload_from_filename(filename)\n return blob","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.upload_file_object","text":"gcloud/storage/bucket.py::Bucket.upload_file_object\n def upload_file_object(self, file_obj, blob=None):\n \"\"\"Shortcut method to upload a file object into this bucket.\n\n Use this method to quickly put a local file in Cloud Storage.\n\n For example::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> bucket.upload_file(open('~/my-file.txt'), 'remote-text-file.txt')\n >>> print bucket.get_all_blobs()\n [<Blob: my-bucket, remote-text-file.txt>]\n\n If you don't provide a blob value, we will try to upload the file\n using the local filename as the blob (**not** the complete\n path)::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> bucket.upload_file(open('~/my-file.txt'))\n >>> print bucket.get_all_blobs()\n [<Blob: my-bucket, my-file.txt>]\n\n :type file_obj: file\n :param file_obj: A file handle open for reading.\n\n :type blob: string or :class:`gcloud.storage.blob.Blob`\n :param blob: The blob (either an object or a remote path) of where\n to put the file. If this is blank, we will try to\n upload the file to the root of the bucket with the\n same name as on your local file system.\n\n :rtype: :class:`Blob`\n :returns: The updated Blob object.\n \"\"\"\n if blob:\n blob = self.new_blob(blob)\n else:\n blob = self.new_blob(os.path.basename(file_obj.name))\n blob.upload_from_file(file_obj)\n return blob","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_cors","text":"gcloud/storage/bucket.py::Bucket.get_cors\n def get_cors(self):\n \"\"\"Retrieve CORS policies configured for this bucket.\n\n See: http://www.w3.org/TR/cors/ and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: list(dict)\n :returns: A sequence of mappings describing each CORS policy.\n \"\"\"\n return [policy.copy() for policy in self.properties.get('cors', ())]","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.update_cors","text":"gcloud/storage/bucket.py::Bucket.update_cors\n def update_cors(self, entries):\n \"\"\"Update CORS policies configured for this bucket.\n\n See: http://www.w3.org/TR/cors/ and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :type entries: list(dict)\n :param entries: A sequence of mappings describing each CORS policy.\n \"\"\"\n self._patch_properties({'cors': entries})","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_default_object_acl","text":"gcloud/storage/bucket.py::Bucket.get_default_object_acl\n def get_default_object_acl(self):\n \"\"\"Get the current Default Object ACL rules.\n\n If the acl isn't available locally, this method will reload it from\n Cloud Storage.\n\n :rtype: :class:`gcloud.storage.acl.DefaultObjectACL`\n :returns: A DefaultObjectACL object for this bucket.\n \"\"\"\n if not self.default_object_acl.loaded:\n self.default_object_acl.reload()\n return self.default_object_acl","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.etag","text":"gcloud/storage/bucket.py::Bucket.etag\n @property\n def etag(self):\n \"\"\"Retrieve the ETag for the bucket.\n\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n \"\"\"\n return self.properties['etag']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.id","text":"gcloud/storage/bucket.py::Bucket.id\n @property\n def id(self):\n \"\"\"Retrieve the ID for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n \"\"\"\n return self.properties['id']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_lifecycle","text":"gcloud/storage/bucket.py::Bucket.get_lifecycle\n def get_lifecycle(self):\n \"\"\"Retrieve lifecycle rules configured for this bucket.\n\n See: https://cloud.google.com/storage/docs/lifecycle and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: list(dict)\n :returns: A sequence of mappings describing each lifecycle rule.\n \"\"\"\n info = self.properties.get('lifecycle', {})\n return [rule.copy() for rule in info.get('rule', ())]","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.update_lifecycle","text":"gcloud/storage/bucket.py::Bucket.update_lifecycle\n def update_lifecycle(self, rules):\n \"\"\"Update CORS policies configured for this bucket.\n\n See: https://cloud.google.com/storage/docs/lifecycle and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :type rules: list(dict)\n :param rules: A sequence of mappings describing each lifecycle rule.\n \"\"\"\n self._patch_properties({'lifecycle': {'rule': rules}})","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_logging","text":"gcloud/storage/bucket.py::Bucket.get_logging\n def get_logging(self):\n \"\"\"Return info about access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#status\n\n :rtype: dict or None\n :returns: a dict w/ keys, ``logBucket`` and ``logObjectPrefix``\n (if logging is enabled), or None (if not).\n \"\"\"\n info = self.properties.get('logging')\n if info is not None:\n return info.copy()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.enable_logging","text":"gcloud/storage/bucket.py::Bucket.enable_logging\n def enable_logging(self, bucket_name, object_prefix=''):\n \"\"\"Enable access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#delivery\n\n :type bucket_name: string\n :param bucket_name: name of bucket in which to store access logs\n\n :type object_prefix: string\n :param object_prefix: prefix for access log filenames\n \"\"\"\n info = {'logBucket': bucket_name, 'logObjectPrefix': object_prefix}\n self._patch_properties({'logging': info})","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.disable_logging","text":"gcloud/storage/bucket.py::Bucket.disable_logging\n def disable_logging(self):\n \"\"\"Disable access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#disabling\n \"\"\"\n self._patch_properties({'logging': None})","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.metageneration","text":"gcloud/storage/bucket.py::Bucket.metageneration\n @property\n def metageneration(self):\n \"\"\"Retrieve the metageneration for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: integer\n \"\"\"\n return self.properties['metageneration']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.owner","text":"gcloud/storage/bucket.py::Bucket.owner\n @property\n def owner(self):\n \"\"\"Retrieve info about the owner of the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: dict\n :returns: mapping of owner's role/ID.\n \"\"\"\n return self.properties['owner'].copy()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.project_number","text":"gcloud/storage/bucket.py::Bucket.project_number\n @property\n def project_number(self):\n \"\"\"Retrieve the number of the project to which the bucket is assigned.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: integer\n \"\"\"\n return self.properties['projectNumber']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.self_link","text":"gcloud/storage/bucket.py::Bucket.self_link\n @property\n def self_link(self):\n \"\"\"Retrieve the URI for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n \"\"\"\n return self.properties['selfLink']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.storage_class","text":"gcloud/storage/bucket.py::Bucket.storage_class\n @property\n def storage_class(self):\n \"\"\"Retrieve the storage class for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets and\n https://cloud.google.com/storage/docs/durable-reduced-availability\n\n :rtype: string\n :returns: Currently one of \"STANDARD\", \"DURABLE_REDUCED_AVAILABILITY\"\n \"\"\"\n return self.properties['storageClass']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.time_created","text":"gcloud/storage/bucket.py::Bucket.time_created\n @property\n def time_created(self):\n \"\"\"Retrieve the timestamp at which the bucket was created.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n :returns: timestamp in RFC 3339 format.\n \"\"\"\n return self.properties['timeCreated']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.versioning_enabled","text":"gcloud/storage/bucket.py::Bucket.versioning_enabled\n @property\n def versioning_enabled(self):\n \"\"\"Is versioning enabled for this bucket?\n\n See: https://cloud.google.com/storage/docs/object-versioning for\n details.\n\n :rtype: boolean\n :returns: True if enabled, else False.\n \"\"\"\n versioning = self.properties.get('versioning', {})\n return versioning.get('enabled', False)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.versioning_enabled","text":"gcloud/storage/bucket.py::Bucket.versioning_enabled\n @versioning_enabled.setter\n def versioning_enabled(self, value):\n \"\"\"Enable versioning for this bucket.\n\n See: https://cloud.google.com/storage/docs/object-versioning for\n details.\n\n :type value: convertible to boolean\n :param value: should versioning be anabled for the bucket?\n \"\"\"\n self._patch_properties({'versioning': {'enabled': bool(value)}})","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.configure_website","text":"gcloud/storage/bucket.py::Bucket.configure_website\n def configure_website(self, main_page_suffix=None, not_found_page=None):\n \"\"\"Configure website-related properties.\n\n See: https://developers.google.com/storage/docs/website-configuration\n\n .. note::\n This (apparently) only works\n if your bucket name is a domain name\n (and to do that, you need to get approved somehow...).\n\n If you want this bucket to host a website, just provide the name\n of an index page and a page to use when a blob isn't found::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project)\n >>> bucket = connection.get_bucket(bucket_name)\n >>> bucket.configure_website('index.html', '404.html')\n\n You probably should also make the whole bucket public::\n\n >>> bucket.make_public(recursive=True, future=True)\n\n This says: \"Make the bucket public, and all the stuff already in\n the bucket, and anything else I add to the bucket. Just make it\n all public.\"\n\n :type main_page_suffix: string\n :param main_page_suffix: The page to use as the main page\n of a directory.\n Typically something like index.html.\n\n :type not_found_page: string\n :param not_found_page: The file to use when a page isn't found.\n \"\"\"\n data = {\n 'website': {\n 'mainPageSuffix': main_page_suffix,\n 'notFoundPage': not_found_page,\n },\n }\n return self._patch_properties(data)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.disable_website","text":"gcloud/storage/bucket.py::Bucket.disable_website\n def disable_website(self):\n \"\"\"Disable the website configuration for this bucket.\n\n This is really just a shortcut for setting the website-related\n attributes to ``None``.\n \"\"\"\n return self.configure_website(None, None)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.make_public","text":"gcloud/storage/bucket.py::Bucket.make_public\n def make_public(self, recursive=False, future=False):\n \"\"\"Make a bucket public.\n\n :type recursive: boolean\n :param recursive: If True, this will make all blobs inside the bucket\n public as well.\n\n :type future: boolean\n :param future: If True, this will make all objects created in the\n future public as well.\n \"\"\"\n self.acl.all().grant_read()\n self.acl.save()\n\n if future:\n doa = self.get_default_object_acl()\n doa.all().grant_read()\n doa.save()\n\n if recursive:\n for blob in self:\n blob.acl.all().grant_read()\n blob.save_acl()","label":0},{"cid":"gcloud/storage/connection.py::Connection.__init__","text":"gcloud/storage/connection.py::Connection.__init__\n def __init__(self, project, *args, **kwargs):\n \"\"\":type project: string\n\n :param project: The project name to connect to.\n \"\"\"\n super(Connection, self).__init__(*args, **kwargs)\n self.project = project","label":0},{"cid":"gcloud/storage/connection.py::Connection.build_api_url","text":"gcloud/storage/connection.py::Connection.build_api_url\n def build_api_url(self, path, query_params=None, api_base_url=None,\n api_version=None, upload=False):\n \"\"\"Construct an API url given a few components, some optional.\n\n Typically, you shouldn't need to use this method.\n\n :type path: string\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\n\n :type query_params: dict\n :param query_params: A dictionary of keys and values to insert into\n the query string of the URL.\n\n :type api_base_url: string\n :param api_base_url: The base URL for the API endpoint.\n Typically you won't have to provide this.\n\n :type api_version: string\n :param api_version: The version of the API to call.\n Typically you shouldn't provide this and instead\n use the default for the library.\n\n :type upload: boolean\n :param upload: True if the URL is for uploading purposes.\n\n :rtype: string\n :returns: The URL assembled from the pieces provided.\n \"\"\"\n api_base_url = api_base_url or self.API_BASE_URL\n if upload:\n api_base_url += '/upload'\n\n url = self.API_URL_TEMPLATE.format(\n api_base_url=(api_base_url or self.API_BASE_URL),\n api_version=(api_version or self.API_VERSION),\n path=path)\n\n query_params = query_params or {}\n query_params.update({'project': self.project})\n url += '?' + urlencode(query_params)\n\n return url","label":0},{"cid":"gcloud/storage/connection.py::Connection.make_request","text":"gcloud/storage/connection.py::Connection.make_request\n def make_request(self, method, url, data=None, content_type=None,\n headers=None):\n \"\"\"A low level method to send a request to the API.\n\n Typically, you shouldn't need to use this method.\n\n :type method: string\n :param method: The HTTP method to use in the request.\n\n :type url: string\n :param url: The URL to send the request to.\n\n :type data: string\n :param data: The data to send as the body of the request.\n\n :type content_type: string\n :param content_type: The proper MIME type of the data provided.\n\n :type headers: dict\n :param headers: A dictionary of HTTP headers to send with the request.\n\n :rtype: tuple of ``response`` (a dictionary of sorts)\n and ``content`` (a string).\n :returns: The HTTP response object and the content of the response.\n \"\"\"\n headers = headers or {}\n headers['Accept-Encoding'] = 'gzip'\n\n if data:\n content_length = len(str(data))\n else:\n content_length = 0\n\n headers['Content-Length'] = content_length\n\n if content_type:\n headers['Content-Type'] = content_type\n\n headers['User-Agent'] = self.USER_AGENT\n\n return self.http.request(uri=url, method=method, headers=headers,\n body=data)","label":0},{"cid":"gcloud/storage/connection.py::Connection.api_request","text":"gcloud/storage/connection.py::Connection.api_request\n def api_request(self, method, path, query_params=None,\n data=None, content_type=None,\n api_base_url=None, api_version=None,\n expect_json=True):\n \"\"\"Make a request over the HTTP transport to the Cloud Storage API.\n\n You shouldn't need to use this method, but if you plan to\n interact with the API using these primitives, this is the\n correct one to use...\n\n :type method: string\n :param method: The HTTP method name (ie, ``GET``, ``POST``, etc).\n Required.\n\n :type path: string\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\n Required.\n\n :type query_params: dict\n :param query_params: A dictionary of keys and values to insert into\n the query string of the URL. Default is\n empty dict.\n\n :type data: string\n :param data: The data to send as the body of the request. Default is\n the empty string.\n\n :type content_type: string\n :param content_type: The proper MIME type of the data provided. Default\n is None.\n\n :type api_base_url: string\n :param api_base_url: The base URL for the API endpoint.\n Typically you won't have to provide this.\n Default is the standard API base URL.\n\n :type api_version: string\n :param api_version: The version of the API to call. Typically\n you shouldn't provide this and instead use\n the default for the library. Default is the\n latest API version supported by\n gcloud-python.\n\n :type expect_json: boolean\n :param expect_json: If True, this method will try to parse the\n ","label":0},{"cid":"gcloud/storage/connection.py::Connection.get_all_buckets","text":"gcloud/storage/connection.py::Connection.get_all_buckets\n def get_all_buckets(self):\n \"\"\"Get all buckets in the project.\n\n This will not populate the list of blobs available in each\n bucket.\n\n You can also iterate over the connection object, so these two\n operations are identical::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project)\n >>> for bucket in connection.get_all_buckets():\n >>> print bucket\n\n :rtype: list of :class:`gcloud.storage.bucket.Bucket` objects.\n :returns: All buckets belonging to this project.\n \"\"\"\n return iter(_BucketIterator(connection=self))","label":0},{"cid":"gcloud/storage/connection.py::Connection.get_bucket","text":"gcloud/storage/connection.py::Connection.get_bucket\n def get_bucket(self, bucket_name):\n \"\"\"Get a bucket by name.\n\n If the bucket isn't found, this will raise a\n :class:`gcloud.storage.exceptions.NotFound`.\n\n For example::\n\n >>> from gcloud import storage\n >>> from gcloud.exceptions import NotFound\n >>> connection = storage.get_connection(project)\n >>> try:\n >>> bucket = connection.get_bucket('my-bucket')\n >>> except NotFound:\n >>> print 'Sorry, that bucket does not exist!'\n\n :type bucket_name: string\n :param bucket_name: The name of the bucket to get.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The bucket matching the name provided.\n :raises: :class:`gcloud.exceptions.NotFound`\n \"\"\"\n bucket = Bucket(connection=self, name=bucket_name)\n response = self.api_request(method='GET', path=bucket.path)\n return Bucket(properties=response, connection=self)","label":0},{"cid":"gcloud/storage/connection.py::Connection.create_bucket","text":"gcloud/storage/connection.py::Connection.create_bucket\n def create_bucket(self, bucket_name):\n \"\"\"Create a new bucket.\n\n For example::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project)\n >>> bucket = connection.create_bucket('my-bucket')\n >>> print bucket\n <Bucket: my-bucket>\n\n :type bucket_name: string\n :param bucket_name: The bucket name to create.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The newly created bucket.\n :raises: :class:`gcloud.exceptions.Conflict` if\n there is a confict (bucket already exists, invalid name, etc.)\n \"\"\"\n response = self.api_request(method='POST', path='/b',\n data={'name': bucket_name})\n return Bucket(properties=response, connection=self)","label":0},{"cid":"gcloud/storage/connection.py::Connection.delete_bucket","text":"gcloud/storage/connection.py::Connection.delete_bucket\n def delete_bucket(self, bucket_name):\n \"\"\"Delete a bucket.\n\n You can use this method to delete a bucket by name.\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project)\n >>> connection.delete_bucket('my-bucket')\n\n If the bucket doesn't exist, this will raise a\n :class:`gcloud.exceptions.NotFound`::\n\n >>> from gcloud.exceptions import NotFound\n >>> try:\n >>> connection.delete_bucket('my-bucket')\n >>> except NotFound:\n >>> print 'That bucket does not exist!'\n\n If the bucket still has objects in it, this will raise a\n :class:`gcloud.exceptions.Conflict`::\n\n >>> from gcloud.exceptions import Conflict\n >>> try:\n >>> connection.delete_bucket('my-bucket')\n >>> except Conflict:\n >>> print 'That bucket is not empty!'\n\n :type bucket_name: string\n :param bucket_name: The bucket name to delete.\n \"\"\"\n bucket_path = Bucket.path_helper(bucket_name)\n self.api_request(method='DELETE', path=bucket_path)","label":0},{"cid":"gcloud/storage/connection.py::_BucketIterator.__init__","text":"gcloud/storage/connection.py::_BucketIterator.__init__\n def __init__(self, connection):\n super(_BucketIterator, self).__init__(connection=connection, path='/b')","label":0},{"cid":"gcloud/storage/connection.py::_BucketIterator.get_items_from_response","text":"gcloud/storage/connection.py::_BucketIterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Factory method which yields :class:`.Bucket` items from a response.\n\n :type response: dict\n :param response: The JSON API response for a page of buckets.\n \"\"\"\n for item in response.get('items', []):\n yield Bucket(properties=item, connection=self.connection)","label":0}],"string":"[\n {\n \"cid\": \"gcloud/credentials.py::get_credentials\",\n \"text\": \"gcloud/credentials.py::get_credentials\\ndef get_credentials():\\n \\\"\\\"\\\"Gets credentials implicitly from the current environment.\\n\\n .. note::\\n You should not need to use this function directly. Instead, use the\\n helper method :func:`gcloud.datastore.__init__.get_connection`\\n which uses this method under the hood.\\n\\n Checks environment in order of precedence:\\n\\n * Google App Engine (production and testing)\\n * Environment variable GOOGLE_APPLICATION_CREDENTIALS pointing to\\n a file with stored credentials information.\\n * Stored \\\"well known\\\" file associated with ``gcloud`` command line tool.\\n * Google Compute Engine production environment.\\n\\n The file referred to in GOOGLE_APPLICATION_CREDENTIALS is expected to\\n contain information about credentials that are ready to use. This means\\n either service account information or user account information with\\n a ready-to-use refresh token::\\n\\n { {\\n 'type': 'authorized_user', 'type': 'service_account',\\n 'client_id': '...', 'client_id': '...',\\n 'client_secret': '...', OR 'client_email': '...',\\n 'refresh_token': '..., 'private_key_id': '...',\\n } 'private_key': '...',\\n }\\n\\n The second of these is simply a JSON key downloaded from the Google APIs\\n console. The first is a close cousin of the \\\"client secrets\\\" JSON file\\n used by ``oauth2client.clientsecrets`` but differs in formatting.\\n\\n :rtype: :class:`oauth2client.client.GoogleCredentials`,\\n :class:`oauth2client.appengine.AppAssertionCredentials`,\\n :class:`oauth2client.gce.AppAssertionCredentials`,\\n :class:`oauth2client.service_account._ServiceAccountCredentials`\\n :returns: A new credentials instance corresponding to the implicit\\n environment.\\n \\\"\\\"\\\"\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/credentials.py::get_for_service_account_p12\",\n \"text\": \"gcloud/credentials.py::get_for_service_account_p12\\ndef get_for_service_account_p12(client_email, private_key_path, scope=None):\\n \\\"\\\"\\\"Gets the credentials for a service account.\\n\\n .. note::\\n This method is not used by default, instead :func:`get_credentials`\\n is used. This method is intended to be used when the environments is\\n known explicitly and detecting the environment implicitly would be\\n superfluous.\\n\\n :type client_email: string\\n :param client_email: The e-mail attached to the service account.\\n\\n :type private_key_path: string\\n :param private_key_path: The path to a private key file (this file was\\n given to you when you created the service\\n account). This file must be in P12 format.\\n\\n :type scope: string or tuple of string\\n :param scope: The scope against which to authenticate. (Different services\\n require different scopes, check the documentation for which\\n scope is required for the different levels of access to any\\n particular API.)\\n\\n :rtype: :class:`oauth2client.client.SignedJwtAssertionCredentials`\\n :returns: A new ``SignedJwtAssertionCredentials`` instance with the\\n needed service account settings.\\n \\\"\\\"\\\"\\n return client.SignedJwtAssertionCredentials(\\n service_account_name=client_email,\\n private_key=open(private_key_path, 'rb').read(),\\n scope=scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/credentials.py::_get_pem_key\",\n \"text\": \"gcloud/credentials.py::_get_pem_key\\ndef _get_pem_key(credentials):\\n \\\"\\\"\\\"Gets RSA key for a PEM payload from a credentials object.\\n\\n :type credentials: :class:`client.SignedJwtAssertionCredentials`,\\n :class:`service_account._ServiceAccountCredentials`\\n :param credentials: The credentials used to create an RSA key\\n for signing text.\\n\\n :rtype: :class:`Crypto.PublicKey.RSA._RSAobj`\\n :returns: An RSA object used to sign text.\\n :raises: `TypeError` if `credentials` is the wrong type.\\n \\\"\\\"\\\"\\n if isinstance(credentials, client.SignedJwtAssertionCredentials):\\n # Take our PKCS12 (.p12) key and make it into a RSA key we can use.\\n pem_text = crypt.pkcs12_key_as_pem(credentials.private_key,\\n credentials.private_key_password)\\n elif isinstance(credentials, service_account._ServiceAccountCredentials):\\n pem_text = credentials._private_key_pkcs8_text\\n else:\\n raise TypeError((credentials,\\n 'not a valid service account credentials type'))\\n\\n return RSA.importKey(pem_text)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/credentials.py::_get_signed_query_params\",\n \"text\": \"gcloud/credentials.py::_get_signed_query_params\\ndef _get_signed_query_params(credentials, expiration, signature_string):\\n \\\"\\\"\\\"Gets query parameters for creating a signed URL.\\n\\n :type credentials: :class:`client.SignedJwtAssertionCredentials`,\\n :class:`service_account._ServiceAccountCredentials`\\n :param credentials: The credentials used to create an RSA key\\n for signing text.\\n\\n :type expiration: int or long\\n :param expiration: When the signed URL should expire.\\n\\n :type signature_string: string\\n :param signature_string: The string to be signed by the credentials.\\n\\n :rtype: dict\\n :returns: Query parameters matching the signing credentials with a\\n signed payload.\\n \\\"\\\"\\\"\\n pem_key = _get_pem_key(credentials)\\n # Sign the string with the RSA key.\\n signer = PKCS1_v1_5.new(pem_key)\\n signature_hash = SHA256.new(signature_string)\\n signature_bytes = signer.sign(signature_hash)\\n signature = base64.b64encode(signature_bytes)\\n\\n if isinstance(credentials, client.SignedJwtAssertionCredentials):\\n service_account_name = credentials.service_account_name\\n elif isinstance(credentials, service_account._ServiceAccountCredentials):\\n service_account_name = credentials._service_account_email\\n # We know one of the above must occur since `_get_pem_key` fails if not.\\n return {\\n 'GoogleAccessId': service_account_name,\\n 'Expires': str(expiration),\\n 'Signature': signature,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/credentials.py::_utcnow\",\n \"text\": \"gcloud/credentials.py::_utcnow\\ndef _utcnow(): # pragma: NO COVER testing replaces\\n \\\"\\\"\\\"Returns current time as UTC datetime.\\n\\n NOTE: on the module namespace so tests can replace it.\\n \\\"\\\"\\\"\\n return datetime.datetime.utcnow()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/credentials.py::_get_expiration_seconds\",\n \"text\": \"gcloud/credentials.py::_get_expiration_seconds\\ndef _get_expiration_seconds(expiration):\\n \\\"\\\"\\\"Convert 'expiration' to a number of seconds in the future.\\n\\n :type expiration: int, long, datetime.datetime, datetime.timedelta\\n :param expiration: When the signed URL should expire.\\n\\n :rtype: int\\n :returns: a timestamp as an absolute number of seconds.\\n \\\"\\\"\\\"\\n # If it's a timedelta, add it to `now` in UTC.\\n if isinstance(expiration, datetime.timedelta):\\n now = _utcnow().replace(tzinfo=pytz.utc)\\n expiration = now + expiration\\n\\n # If it's a datetime, convert to a timestamp.\\n if isinstance(expiration, datetime.datetime):\\n # Make sure the timezone on the value is UTC\\n # (either by converting or replacing the value).\\n if expiration.tzinfo:\\n expiration = expiration.astimezone(pytz.utc)\\n else:\\n expiration = expiration.replace(tzinfo=pytz.utc)\\n\\n # Turn the datetime into a timestamp (seconds, not microseconds).\\n expiration = int(calendar.timegm(expiration.timetuple()))\\n\\n if not isinstance(expiration, six.integer_types):\\n raise TypeError('Expected an integer timestamp, datetime, or '\\n 'timedelta. Got %s' % type(expiration))\\n return expiration\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/credentials.py::generate_signed_url\",\n \"text\": \"gcloud/credentials.py::generate_signed_url\\ndef generate_signed_url(credentials, resource, expiration,\\n api_access_endpoint='',\\n method='GET', content_md5=None,\\n content_type=None):\\n \\\"\\\"\\\"Generate signed URL to provide query-string auth'n to a resource.\\n\\n :type credentials: :class:`oauth2client.appengine.AppAssertionCredentials`\\n :param credentials: Credentials object with an associated private key to\\n sign text.\\n\\n :type resource: string\\n :param resource: A pointer to a specific resource\\n (typically, ``/bucket-name/path/to/blob.txt``).\\n\\n :type expiration: int, long, datetime.datetime, datetime.timedelta\\n :param expiration: When the signed URL should expire.\\n\\n :type api_access_endpoint: string\\n :param api_access_endpoint: Optional URI base. Defaults to empty string.\\n\\n :type method: string\\n :param method: The HTTP verb that will be used when requesting the URL.\\n\\n :type content_md5: string\\n :param content_md5: The MD5 hash of the object referenced by\\n ``resource``.\\n\\n :type content_type: string\\n :param content_type: The content type of the object referenced by\\n ``resource``.\\n\\n :rtype: string\\n :returns: A signed URL you can use to access the resource\\n until expiration.\\n \\\"\\\"\\\"\\n expiration = _get_expiration_seconds(expiration)\\n\\n # Generate the string to sign.\\n signature_string = '\\\\n'.join([\\n method,\\n content_md5 or '',\\n content_type or '',\\n str(expiration),\\n resource])\\n\\n # Set the right query parameters.\\n query_params = _get_signed_query_params(credentials,\\n expiration,\\n signature_string)\\n\\n # Return the built URL.\\n return '{endpoint}{resource}?{querystring}'.format(\\n endpoint=api_access_endpoint, resource=reso\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::entity_from_protobuf\",\n \"text\": \"gcloud/datastore/helpers.py::entity_from_protobuf\\ndef entity_from_protobuf(pb):\\n \\\"\\\"\\\"Factory method for creating an entity based on a protobuf.\\n\\n The protobuf should be one returned from the Cloud Datastore\\n Protobuf API.\\n\\n :type pb: :class:`gcloud.datastore._datastore_v1_pb2.Entity`\\n :param pb: The Protobuf representing the entity.\\n\\n :rtype: :class:`gcloud.datastore.entity.Entity`\\n :returns: The entity derived from the protobuf.\\n \\\"\\\"\\\"\\n key = key_from_protobuf(pb.key)\\n entity_props = {}\\n exclude_from_indexes = []\\n\\n for property_pb in pb.property:\\n value = _get_value_from_property_pb(property_pb)\\n entity_props[property_pb.name] = value\\n\\n # Check if property_pb.value was indexed. Lists need to be\\n # special-cased and we require all `indexed` values in a list agree.\\n if isinstance(value, list):\\n indexed_values = set(value_pb.indexed\\n for value_pb in property_pb.value.list_value)\\n if len(indexed_values) != 1:\\n raise ValueError('For a list_value, subvalues must either all '\\n 'be indexed or all excluded from indexes.')\\n\\n if not indexed_values.pop():\\n exclude_from_indexes.append(property_pb.name)\\n else:\\n if not property_pb.value.indexed:\\n exclude_from_indexes.append(property_pb.name)\\n\\n entity = Entity(key=key, exclude_from_indexes=exclude_from_indexes)\\n entity.update(entity_props)\\n return entity\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::key_from_protobuf\",\n \"text\": \"gcloud/datastore/helpers.py::key_from_protobuf\\ndef key_from_protobuf(pb):\\n \\\"\\\"\\\"Factory method for creating a key based on a protobuf.\\n\\n The protobuf should be one returned from the Cloud Datastore\\n Protobuf API.\\n\\n :type pb: :class:`gcloud.datastore._datastore_v1_pb2.Key`\\n :param pb: The Protobuf representing the key.\\n\\n :rtype: :class:`gcloud.datastore.key.Key`\\n :returns: a new `Key` instance\\n \\\"\\\"\\\"\\n path_args = []\\n for element in pb.path_element:\\n path_args.append(element.kind)\\n if element.HasField('id'):\\n path_args.append(element.id)\\n # This is safe: we expect proto objects returned will only have\\n # one of `name` or `id` set.\\n if element.HasField('name'):\\n path_args.append(element.name)\\n\\n dataset_id = None\\n if pb.partition_id.HasField('dataset_id'):\\n dataset_id = pb.partition_id.dataset_id\\n namespace = None\\n if pb.partition_id.HasField('namespace'):\\n namespace = pb.partition_id.namespace\\n\\n return Key(*path_args, namespace=namespace, dataset_id=dataset_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_pb_attr_value\",\n \"text\": \"gcloud/datastore/helpers.py::_pb_attr_value\\ndef _pb_attr_value(val):\\n \\\"\\\"\\\"Given a value, return the protobuf attribute name and proper value.\\n\\n The Protobuf API uses different attribute names based on value types\\n rather than inferring the type. This function simply determines the\\n proper attribute name based on the type of the value provided and\\n returns the attribute name as well as a properly formatted value.\\n\\n Certain value types need to be coerced into a different type (such\\n as a `datetime.datetime` into an integer timestamp, or a\\n `gcloud.datastore.key.Key` into a Protobuf representation. This\\n function handles that for you.\\n\\n .. note::\\n Values which are \\\"text\\\" ('unicode' in Python2, 'str' in Python3) map\\n to 'string_value' in the datastore; values which are \\\"bytes\\\"\\n ('str' in Python2, 'bytes' in Python3) map to 'blob_value'.\\n\\n For example:\\n\\n >>> _pb_attr_value(1234)\\n ('integer_value', 1234)\\n >>> _pb_attr_value('my_string')\\n ('string_value', 'my_string')\\n\\n :type val: `datetime.datetime`, :class:`gcloud.datastore.key.Key`,\\n bool, float, integer, string\\n :param val: The value to be scrutinized.\\n\\n :returns: A tuple of the attribute name and proper value type.\\n \\\"\\\"\\\"\\n\\n if isinstance(val, datetime.datetime):\\n name = 'timestamp_microseconds'\\n # If the datetime is naive (no timezone), consider that it was\\n # intended to be UTC and replace the tzinfo to that effect.\\n if not val.tzinfo:\\n val = val.replace(tzinfo=pytz.utc)\\n # Regardless of what timezone is on the value, convert it to UTC.\\n val = val.astimezone(pytz.utc)\\n # Convert the datetime to a microsecond timestamp.\\n value = int(calendar.timegm(val.timetuple()) * 1e6) + val.microsecond\\n elif isinstance(val, Key):\\n name, value = 'key', val.to_protobuf()\\n elif isinstance(val, bool):\\n name, value = 'boolean', val\\n elif isinstance(val, flo\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_get_value_from_value_pb\",\n \"text\": \"gcloud/datastore/helpers.py::_get_value_from_value_pb\\ndef _get_value_from_value_pb(value_pb):\\n \\\"\\\"\\\"Given a protobuf for a Value, get the correct value.\\n\\n The Cloud Datastore Protobuf API returns a Property Protobuf which\\n has one value set and the rest blank. This function retrieves the\\n the one value provided.\\n\\n Some work is done to coerce the return value into a more useful type\\n (particularly in the case of a timestamp value, or a key value).\\n\\n :type value_pb: :class:`gcloud.datastore._datastore_v1_pb2.Value`\\n :param value_pb: The Value Protobuf.\\n\\n :returns: The value provided by the Protobuf.\\n \\\"\\\"\\\"\\n result = None\\n if value_pb.HasField('timestamp_microseconds_value'):\\n microseconds = value_pb.timestamp_microseconds_value\\n naive = (datetime.datetime.utcfromtimestamp(0) +\\n datetime.timedelta(microseconds=microseconds))\\n result = naive.replace(tzinfo=pytz.utc)\\n\\n elif value_pb.HasField('key_value'):\\n result = key_from_protobuf(value_pb.key_value)\\n\\n elif value_pb.HasField('boolean_value'):\\n result = value_pb.boolean_value\\n\\n elif value_pb.HasField('double_value'):\\n result = value_pb.double_value\\n\\n elif value_pb.HasField('integer_value'):\\n result = value_pb.integer_value\\n\\n elif value_pb.HasField('string_value'):\\n result = value_pb.string_value\\n\\n elif value_pb.HasField('blob_value'):\\n result = value_pb.blob_value\\n\\n elif value_pb.HasField('entity_value'):\\n result = entity_from_protobuf(value_pb.entity_value)\\n\\n elif value_pb.list_value:\\n result = [_get_value_from_value_pb(x) for x in value_pb.list_value]\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_get_value_from_property_pb\",\n \"text\": \"gcloud/datastore/helpers.py::_get_value_from_property_pb\\ndef _get_value_from_property_pb(property_pb):\\n \\\"\\\"\\\"Given a protobuf for a Property, get the correct value.\\n\\n The Cloud Datastore Protobuf API returns a Property Protobuf which\\n has one value set and the rest blank. This function retrieves the\\n the one value provided.\\n\\n Some work is done to coerce the return value into a more useful type\\n (particularly in the case of a timestamp value, or a key value).\\n\\n :type property_pb: :class:`gcloud.datastore._datastore_v1_pb2.Property`\\n :param property_pb: The Property Protobuf.\\n\\n :returns: The value provided by the Protobuf.\\n \\\"\\\"\\\"\\n return _get_value_from_value_pb(property_pb.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_set_protobuf_value\",\n \"text\": \"gcloud/datastore/helpers.py::_set_protobuf_value\\ndef _set_protobuf_value(value_pb, val):\\n \\\"\\\"\\\"Assign 'val' to the correct subfield of 'value_pb'.\\n\\n The Protobuf API uses different attribute names based on value types\\n rather than inferring the type.\\n\\n Some value types (entities, keys, lists) cannot be directly\\n assigned; this function handles them correctly.\\n\\n :type value_pb: :class:`gcloud.datastore._datastore_v1_pb2.Value`\\n :param value_pb: The value protobuf to which the value is being assigned.\\n\\n :type val: `datetime.datetime`, boolean, float, integer, string,\\n :class:`gcloud.datastore.key.Key`,\\n :class:`gcloud.datastore.entity.Entity`,\\n :param val: The value to be assigned.\\n \\\"\\\"\\\"\\n if val is None:\\n value_pb.Clear()\\n return\\n\\n attr, val = _pb_attr_value(val)\\n if attr == 'key_value':\\n value_pb.key_value.CopyFrom(val)\\n elif attr == 'entity_value':\\n e_pb = value_pb.entity_value\\n e_pb.Clear()\\n key = val.key\\n if key is not None:\\n e_pb.key.CopyFrom(key.to_protobuf())\\n for item_key, value in val.items():\\n p_pb = e_pb.property.add()\\n p_pb.name = item_key\\n _set_protobuf_value(p_pb.value, value)\\n elif attr == 'list_value':\\n l_pb = value_pb.list_value\\n for item in val:\\n i_pb = l_pb.add()\\n _set_protobuf_value(i_pb, item)\\n else: # scalar, just assign\\n setattr(value_pb, attr, val)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_prepare_key_for_request\",\n \"text\": \"gcloud/datastore/helpers.py::_prepare_key_for_request\\ndef _prepare_key_for_request(key_pb):\\n \\\"\\\"\\\"Add protobuf keys to a request object.\\n\\n :type key_pb: :class:`gcloud.datastore._datastore_v1_pb2.Key`\\n :param key_pb: A key to be added to a request.\\n\\n :rtype: :class:`gcloud.datastore._datastore_v1_pb2.Key`\\n :returns: A key which will be added to a request. It will be the\\n original if nothing needs to be changed.\\n \\\"\\\"\\\"\\n if key_pb.partition_id.HasField('dataset_id'):\\n # We remove the dataset_id from the protobuf. This is because\\n # the backend fails a request if the key contains un-prefixed\\n # dataset ID. The backend fails because requests to\\n # /datastore/.../datasets/foo/...\\n # and\\n # /datastore/.../datasets/s~foo/...\\n # both go to the datastore given by 's~foo'. So if the key\\n # protobuf in the request body has dataset_id='foo', the\\n # backend will reject since 'foo' != 's~foo'.\\n new_key_pb = datastore_pb.Key()\\n new_key_pb.CopyFrom(key_pb)\\n new_key_pb.partition_id.ClearField('dataset_id')\\n key_pb = new_key_pb\\n return key_pb\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_add_keys_to_request\",\n \"text\": \"gcloud/datastore/helpers.py::_add_keys_to_request\\ndef _add_keys_to_request(request_field_pb, key_pbs):\\n \\\"\\\"\\\"Add protobuf keys to a request object.\\n\\n :type request_field_pb: `RepeatedCompositeFieldContainer`\\n :param request_field_pb: A repeated proto field that contains keys.\\n\\n :type key_pbs: list of :class:`gcloud.datastore._datastore_v1_pb2.Key`\\n :param key_pbs: The keys to add to a request.\\n \\\"\\\"\\\"\\n for key_pb in key_pbs:\\n key_pb = _prepare_key_for_request(key_pb)\\n request_field_pb.add().CopyFrom(key_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_dataset_ids_equal\",\n \"text\": \"gcloud/datastore/helpers.py::_dataset_ids_equal\\ndef _dataset_ids_equal(dataset_id1, dataset_id2):\\n \\\"\\\"\\\"Compares two dataset IDs for fuzzy equality.\\n\\n Each may be prefixed or unprefixed (but not null, since dataset ID\\n is required on a key). The only allowed prefixes are 's~' and 'e~'.\\n\\n Two identical prefixed match\\n\\n >>> 's~foo' == 's~foo'\\n >>> 'e~bar' == 'e~bar'\\n\\n while non-identical prefixed don't\\n\\n >>> 's~foo' != 's~bar'\\n >>> 's~foo' != 'e~foo'\\n\\n As for non-prefixed, they can match other non-prefixed or\\n prefixed:\\n\\n >>> 'foo' == 'foo'\\n >>> 'foo' == 's~foo'\\n >>> 'foo' == 'e~foo'\\n >>> 'foo' != 'bar'\\n >>> 'foo' != 's~bar'\\n\\n (Ties are resolved since 'foo' can only be an alias for one of\\n s~foo or e~foo in the backend.)\\n\\n :type dataset_id1: string\\n :param dataset_id1: A dataset ID.\\n\\n :type dataset_id2: string\\n :param dataset_id2: A dataset ID.\\n\\n :rtype: boolean\\n :returns: Boolean indicating if the IDs are the same.\\n \\\"\\\"\\\"\\n if dataset_id1 == dataset_id2:\\n return True\\n\\n if dataset_id1.startswith('s~') or dataset_id1.startswith('e~'):\\n # If `dataset_id1` is prefixed and not matching, then the only way\\n # they can match is if `dataset_id2` is unprefixed.\\n return dataset_id1[2:] == dataset_id2\\n elif dataset_id2.startswith('s~') or dataset_id2.startswith('e~'):\\n # Here we know `dataset_id1` is unprefixed and `dataset_id2`\\n # is prefixed.\\n return dataset_id1 == dataset_id2[2:]\\n\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.__init__\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.__init__\\n def __init__(self, entity_type, identifier=None):\\n \\\"\\\"\\\"Entity constructor.\\n\\n :type entity_type: string\\n :param entity_type: The type of entity (ie, 'group' or 'user').\\n\\n :type identifier: string\\n :param identifier: The ID or e-mail of the entity. For the special\\n entity types (like 'allUsers') this is optional.\\n \\\"\\\"\\\"\\n self.identifier = identifier\\n self.roles = set([])\\n self.type = entity_type\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.__str__\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.__str__\\n def __str__(self):\\n if not self.identifier:\\n return str(self.type)\\n else:\\n return '{acl.type}-{acl.identifier}'.format(acl=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.__repr__\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.__repr__\\n def __repr__(self):\\n return '<ACL Entity: {acl} ({roles})>'.format(\\n acl=self, roles=', '.join(self.roles))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.get_roles\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.get_roles\\n def get_roles(self):\\n \\\"\\\"\\\"Get the list of roles permitted by this entity.\\n\\n :rtype: list of strings\\n :returns: The list of roles associated with this entity.\\n \\\"\\\"\\\"\\n return self.roles\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.grant\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.grant\\n def grant(self, role):\\n \\\"\\\"\\\"Add a role to the entity.\\n\\n :type role: string\\n :param role: The role to add to the entity.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: The entity class.\\n \\\"\\\"\\\"\\n self.roles.add(role)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.revoke\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.revoke\\n def revoke(self, role):\\n \\\"\\\"\\\"Remove a role from the entity.\\n\\n :type role: string\\n :param role: The role to remove from the entity.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: The entity class.\\n \\\"\\\"\\\"\\n if role in self.roles:\\n self.roles.remove(role)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.grant_read\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.grant_read\\n def grant_read(self):\\n \\\"\\\"\\\"Grant read access to the current entity.\\\"\\\"\\\"\\n\\n return self.grant(_ACLEntity.READER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.grant_write\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.grant_write\\n def grant_write(self):\\n \\\"\\\"\\\"Grant write access to the current entity.\\\"\\\"\\\"\\n\\n return self.grant(_ACLEntity.WRITER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.grant_owner\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.grant_owner\\n def grant_owner(self):\\n \\\"\\\"\\\"Grant owner access to the current entity.\\\"\\\"\\\"\\n\\n return self.grant(_ACLEntity.OWNER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.revoke_read\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.revoke_read\\n def revoke_read(self):\\n \\\"\\\"\\\"Revoke read access from the current entity.\\\"\\\"\\\"\\n\\n return self.revoke(_ACLEntity.READER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.revoke_write\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.revoke_write\\n def revoke_write(self):\\n \\\"\\\"\\\"Revoke write access from the current entity.\\\"\\\"\\\"\\n\\n return self.revoke(_ACLEntity.WRITER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.revoke_owner\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.revoke_owner\\n def revoke_owner(self):\\n \\\"\\\"\\\"Revoke owner access from the current entity.\\\"\\\"\\\"\\n\\n return self.revoke(_ACLEntity.OWNER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.__init__\",\n \"text\": \"gcloud/storage/acl.py::ACL.__init__\\n def __init__(self):\\n self.entities = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL._ensure_loaded\",\n \"text\": \"gcloud/storage/acl.py::ACL._ensure_loaded\\n def _ensure_loaded(self):\\n \\\"\\\"\\\"Load if not already loaded.\\\"\\\"\\\"\\n if not self.loaded:\\n self.reload()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.reset\",\n \"text\": \"gcloud/storage/acl.py::ACL.reset\\n def reset(self):\\n \\\"\\\"\\\"Remove all entities from the ACL, and clear the ``loaded`` flag.\\\"\\\"\\\"\\n self.entities.clear()\\n self.loaded = False\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.__iter__\",\n \"text\": \"gcloud/storage/acl.py::ACL.__iter__\\n def __iter__(self):\\n self._ensure_loaded()\\n\\n for entity in self.entities.values():\\n for role in entity.get_roles():\\n if role:\\n yield {'entity': str(entity), 'role': role}\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.entity_from_dict\",\n \"text\": \"gcloud/storage/acl.py::ACL.entity_from_dict\\n def entity_from_dict(self, entity_dict):\\n \\\"\\\"\\\"Build an _ACLEntity object from a dictionary of data.\\n\\n An entity is a mutable object that represents a list of roles\\n belonging to either a user or group or the special types for all\\n users and all authenticated users.\\n\\n :type entity_dict: dict\\n :param entity_dict: Dictionary full of data from an ACL lookup.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An Entity constructed from the dictionary.\\n \\\"\\\"\\\"\\n entity = entity_dict['entity']\\n role = entity_dict['role']\\n\\n if entity == 'allUsers':\\n entity = self.all()\\n\\n elif entity == 'allAuthenticatedUsers':\\n entity = self.all_authenticated()\\n\\n elif '-' in entity:\\n entity_type, identifier = entity.split('-', 1)\\n entity = self.entity(entity_type=entity_type,\\n identifier=identifier)\\n\\n if not isinstance(entity, _ACLEntity):\\n raise ValueError('Invalid dictionary: %s' % entity_dict)\\n\\n return entity.grant(role)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.has_entity\",\n \"text\": \"gcloud/storage/acl.py::ACL.has_entity\\n def has_entity(self, entity):\\n \\\"\\\"\\\"Returns whether or not this ACL has any entries for an entity.\\n\\n :type entity: :class:`_ACLEntity`\\n :param entity: The entity to check for existence in this ACL.\\n\\n :rtype: boolean\\n :returns: True of the entity exists in the ACL.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n return str(entity) in self.entities\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.get_entity\",\n \"text\": \"gcloud/storage/acl.py::ACL.get_entity\\n def get_entity(self, entity, default=None):\\n \\\"\\\"\\\"Gets an entity object from the ACL.\\n\\n :type entity: :class:`_ACLEntity` or string\\n :param entity: The entity to get lookup in the ACL.\\n\\n :type default: anything\\n :param default: This value will be returned if the entity\\n doesn't exist.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: The corresponding entity or the value provided\\n to ``default``.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n return self.entities.get(str(entity), default)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.add_entity\",\n \"text\": \"gcloud/storage/acl.py::ACL.add_entity\\n def add_entity(self, entity):\\n \\\"\\\"\\\"Add an entity to the ACL.\\n\\n :type entity: :class:`_ACLEntity`\\n :param entity: The entity to add to this ACL.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n self.entities[str(entity)] = entity\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.entity\",\n \"text\": \"gcloud/storage/acl.py::ACL.entity\\n def entity(self, entity_type, identifier=None):\\n \\\"\\\"\\\"Factory method for creating an Entity.\\n\\n If an entity with the same type and identifier already exists,\\n this will return a reference to that entity. If not, it will\\n create a new one and add it to the list of known entities for\\n this ACL.\\n\\n :type entity_type: string\\n :param entity_type: The type of entity to create\\n (ie, ``user``, ``group``, etc)\\n\\n :type identifier: string\\n :param identifier: The ID of the entity (if applicable).\\n This can be either an ID or an e-mail address.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: A new Entity or a reference to an existing identical entity.\\n \\\"\\\"\\\"\\n entity = _ACLEntity(entity_type=entity_type, identifier=identifier)\\n if self.has_entity(entity):\\n entity = self.get_entity(entity)\\n else:\\n self.add_entity(entity)\\n return entity\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.user\",\n \"text\": \"gcloud/storage/acl.py::ACL.user\\n def user(self, identifier):\\n \\\"\\\"\\\"Factory method for a user Entity.\\n\\n :type identifier: string\\n :param identifier: An id or e-mail for this particular user.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An Entity corresponding to this user.\\n \\\"\\\"\\\"\\n return self.entity('user', identifier=identifier)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.group\",\n \"text\": \"gcloud/storage/acl.py::ACL.group\\n def group(self, identifier):\\n \\\"\\\"\\\"Factory method for a group Entity.\\n\\n :type identifier: string\\n :param identifier: An id or e-mail for this particular group.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An Entity corresponding to this group.\\n \\\"\\\"\\\"\\n return self.entity('group', identifier=identifier)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.domain\",\n \"text\": \"gcloud/storage/acl.py::ACL.domain\\n def domain(self, domain):\\n \\\"\\\"\\\"Factory method for a domain Entity.\\n\\n :type domain: string\\n :param domain: The domain for this entity.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An entity corresponding to this domain.\\n \\\"\\\"\\\"\\n return self.entity('domain', identifier=domain)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.all\",\n \"text\": \"gcloud/storage/acl.py::ACL.all\\n def all(self):\\n \\\"\\\"\\\"Factory method for an Entity representing all users.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An entity representing all users.\\n \\\"\\\"\\\"\\n return self.entity('allUsers')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.all_authenticated\",\n \"text\": \"gcloud/storage/acl.py::ACL.all_authenticated\\n def all_authenticated(self):\\n \\\"\\\"\\\"Factory method for an Entity representing all authenticated users.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An entity representing all authenticated users.\\n \\\"\\\"\\\"\\n return self.entity('allAuthenticatedUsers')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.get_entities\",\n \"text\": \"gcloud/storage/acl.py::ACL.get_entities\\n def get_entities(self):\\n \\\"\\\"\\\"Get a list of all Entity objects.\\n\\n :rtype: list of :class:`_ACLEntity` objects\\n :returns: A list of all Entity objects.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n return list(self.entities.values())\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.reload\",\n \"text\": \"gcloud/storage/acl.py::ACL.reload\\n def reload(self):\\n \\\"\\\"\\\"Reload the ACL data from Cloud Storage.\\n\\n :rtype: :class:`ACL`\\n :returns: The current ACL.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.save\",\n \"text\": \"gcloud/storage/acl.py::ACL.save\\n def save(self, acl=None):\\n \\\"\\\"\\\"A method to be overridden by subclasses.\\n\\n :type acl: :class:`gcloud.storage.acl.ACL`, or a compatible list.\\n :param acl: The ACL object to save. If left blank, this will save\\n current entries.\\n\\n :raises: NotImplementedError\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.clear\",\n \"text\": \"gcloud/storage/acl.py::ACL.clear\\n def clear(self):\\n \\\"\\\"\\\"Remove all entities from the ACL.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::BucketACL.__init__\",\n \"text\": \"gcloud/storage/acl.py::BucketACL.__init__\\n def __init__(self, bucket):\\n \\\"\\\"\\\"\\n :type bucket: :class:`gcloud.storage.bucket.Bucket`\\n :param bucket: The bucket to which this ACL relates.\\n \\\"\\\"\\\"\\n super(BucketACL, self).__init__()\\n self.bucket = bucket\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::BucketACL.reload\",\n \"text\": \"gcloud/storage/acl.py::BucketACL.reload\\n def reload(self):\\n \\\"\\\"\\\"Reload the ACL data from Cloud Storage.\\n\\n :rtype: :class:`gcloud.storage.acl.BucketACL`\\n :returns: The current ACL.\\n \\\"\\\"\\\"\\n self.entities.clear()\\n\\n url_path = '%s/%s' % (self.bucket.path, self._URL_PATH_ELEM)\\n found = self.bucket.connection.api_request(method='GET', path=url_path)\\n self.loaded = True\\n for entry in found['items']:\\n self.add_entity(self.entity_from_dict(entry))\\n\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::BucketACL.save\",\n \"text\": \"gcloud/storage/acl.py::BucketACL.save\\n def save(self, acl=None):\\n \\\"\\\"\\\"Save this ACL for the current bucket.\\n\\n If called without arguments, this will save the entries\\n currently stored on this ACL::\\n\\n >>> acl.save()\\n\\n You can also provide a specific ACL to save instead of the one\\n currently set on the Bucket object::\\n\\n >>> acl.save(acl=my_other_acl)\\n\\n You can use this to set access controls to be consistent from\\n one bucket to another::\\n\\n >>> bucket1 = connection.get_bucket(bucket1_name)\\n >>> bucket2 = connection.get_bucket(bucket2_name)\\n >>> bucket2.acl.save(bucket1.acl)\\n\\n :type acl: :class:`gcloud.storage.acl.ACL`, or a compatible list.\\n :param acl: The ACL object to save. If left blank, this will save\\n current entries.\\n\\n :rtype: :class:`gcloud.storage.acl.BucketACL`\\n :returns: The current ACL.\\n \\\"\\\"\\\"\\n if acl is None:\\n acl = self\\n save_to_backend = acl.loaded\\n else:\\n save_to_backend = True\\n\\n if save_to_backend:\\n result = self.bucket.connection.api_request(\\n method='PATCH', path=self.bucket.path,\\n data={self._URL_PATH_ELEM: list(acl)},\\n query_params={'projection': 'full'})\\n self.entities.clear()\\n for entry in result[self._URL_PATH_ELEM]:\\n self.add_entity(self.entity_from_dict(entry))\\n self.loaded = True\\n\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::BucketACL.clear\",\n \"text\": \"gcloud/storage/acl.py::BucketACL.clear\\n def clear(self):\\n \\\"\\\"\\\"Remove all ACL entries.\\n\\n Note that this won't actually remove *ALL* the rules, but it\\n will remove all the non-default rules. In short, you'll still\\n have access to a bucket that you created even after you clear\\n ACL rules with this method.\\n\\n For example, imagine that you granted access to this bucket to a\\n bunch of coworkers::\\n\\n >>> acl.user('coworker1@example.org').grant_read()\\n >>> acl.user('coworker2@example.org').grant_read()\\n >>> acl.save()\\n\\n Now they work in another part of the company and you want to\\n 'start fresh' on who has access::\\n\\n >>> acl.clear()\\n\\n At this point all the custom rules you created have been removed.\\n\\n :rtype: :class:`gcloud.storage.acl.BucketACL`\\n :returns: The current ACL.\\n \\\"\\\"\\\"\\n return self.save([])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ObjectACL.__init__\",\n \"text\": \"gcloud/storage/acl.py::ObjectACL.__init__\\n def __init__(self, blob):\\n super(ObjectACL, self).__init__()\\n self.blob = blob\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ObjectACL.reload\",\n \"text\": \"gcloud/storage/acl.py::ObjectACL.reload\\n def reload(self):\\n \\\"\\\"\\\"Reload the ACL data from Cloud Storage.\\n\\n :rtype: :class:`ObjectACL`\\n :returns: The current ACL.\\n \\\"\\\"\\\"\\n self.entities.clear()\\n\\n url_path = '%s/acl' % self.blob.path\\n found = self.blob.connection.api_request(method='GET', path=url_path)\\n self.loaded = True\\n for entry in found['items']:\\n self.add_entity(self.entity_from_dict(entry))\\n\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ObjectACL.save\",\n \"text\": \"gcloud/storage/acl.py::ObjectACL.save\\n def save(self, acl=None):\\n \\\"\\\"\\\"Save the ACL data for this blob.\\n\\n :type acl: :class:`gcloud.storage.acl.ACL`\\n :param acl: The ACL object to save. If left blank, this will\\n save the entries set locally on the ACL.\\n \\\"\\\"\\\"\\n if acl is None:\\n acl = self\\n save_to_backend = acl.loaded\\n else:\\n save_to_backend = True\\n\\n if save_to_backend:\\n result = self.blob.connection.api_request(\\n method='PATCH', path=self.blob.path, data={'acl': list(acl)},\\n query_params={'projection': 'full'})\\n self.entities.clear()\\n for entry in result['acl']:\\n self.add_entity(self.entity_from_dict(entry))\\n self.loaded = True\\n\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ObjectACL.clear\",\n \"text\": \"gcloud/storage/acl.py::ObjectACL.clear\\n def clear(self):\\n \\\"\\\"\\\"Remove all ACL rules from the blob.\\n\\n Note that this won't actually remove *ALL* the rules, but it\\n will remove all the non-default rules. In short, you'll still\\n have access to a blob that you created even after you clear ACL\\n rules with this method.\\n \\\"\\\"\\\"\\n return self.save([])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::_BlobIterator.__init__\",\n \"text\": \"gcloud/storage/bucket.py::_BlobIterator.__init__\\n def __init__(self, bucket, extra_params=None):\\n self.bucket = bucket\\n self.prefixes = ()\\n super(_BlobIterator, self).__init__(\\n connection=bucket.connection, path=bucket.path + '/o',\\n extra_params=extra_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::_BlobIterator.get_items_from_response\",\n \"text\": \"gcloud/storage/bucket.py::_BlobIterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Yield :class:`.storage.blob.Blob` items from response.\\n\\n :type response: dict\\n :param response: The JSON API response for a page of blobs.\\n \\\"\\\"\\\"\\n self.prefixes = tuple(response.get('prefixes', ()))\\n for item in response.get('items', []):\\n yield Blob(None, properties=item, bucket=self.bucket)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__init__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__init__\\n def __init__(self, connection=None, name=None, properties=None):\\n if name is None and properties is not None:\\n name = properties.get('name')\\n super(Bucket, self).__init__(name=name, properties=properties)\\n self._connection = connection\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__repr__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__repr__\\n def __repr__(self):\\n return '<Bucket: %s>' % self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__iter__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__iter__\\n def __iter__(self):\\n return iter(self._iterator_class(bucket=self))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__contains__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__contains__\\n def __contains__(self, blob):\\n return self.get_blob(blob) is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.exists\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.exists\\n def exists(self):\\n \\\"\\\"\\\"Determines whether or not this bucket exists.\\n\\n :rtype: boolean\\n :returns: True if the bucket exists in Cloud Storage.\\n \\\"\\\"\\\"\\n try:\\n self.connection.get_bucket(self.name)\\n return True\\n except NotFound:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.acl\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.acl\\n @property\\n def acl(self):\\n \\\"\\\"\\\"Create our ACL on demand.\\\"\\\"\\\"\\n if self._acl is None:\\n self._acl = BucketACL(self)\\n return self._acl\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.default_object_acl\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.default_object_acl\\n @property\\n def default_object_acl(self):\\n \\\"\\\"\\\"Create our defaultObjectACL on demand.\\\"\\\"\\\"\\n if self._default_object_acl is None:\\n self._default_object_acl = DefaultObjectACL(self)\\n return self._default_object_acl\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.connection\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.connection\\n @property\\n def connection(self):\\n \\\"\\\"\\\"Getter property for the connection to use with this Bucket.\\n\\n :rtype: :class:`gcloud.storage.connection.Connection`\\n :returns: The connection to use.\\n \\\"\\\"\\\"\\n return self._connection\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.path_helper\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.path_helper\\n @staticmethod\\n def path_helper(bucket_name):\\n \\\"\\\"\\\"Relative URL path for a bucket.\\n\\n :type bucket_name: string\\n :param bucket_name: The bucket name in the path.\\n\\n :rtype: string\\n :returns: The relative URL path for ``bucket_name``.\\n \\\"\\\"\\\"\\n return '/b/' + bucket_name\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.path\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"The URL path to this bucket.\\\"\\\"\\\"\\n if not self.name:\\n raise ValueError('Cannot determine path without bucket name.')\\n\\n return self.path_helper(self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_blob\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_blob\\n def get_blob(self, blob):\\n \\\"\\\"\\\"Get a blob object by name.\\n\\n This will return None if the blob doesn't exist::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> print bucket.get_blob('/path/to/blob.txt')\\n <Blob: my-bucket, /path/to/blob.txt>\\n >>> print bucket.get_blob('/does-not-exist.txt')\\n None\\n\\n :type blob: string or :class:`gcloud.storage.blob.Blob`\\n :param blob: The name of the blob to retrieve.\\n\\n :rtype: :class:`gcloud.storage.blob.Blob` or None\\n :returns: The blob object if it exists, otherwise None.\\n \\\"\\\"\\\"\\n # Coerce this -- either from a Blob or a string.\\n blob = self.new_blob(blob)\\n\\n try:\\n response = self.connection.api_request(method='GET',\\n path=blob.path)\\n return Blob(None, bucket=self, properties=response)\\n except NotFound:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_all_blobs\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_all_blobs\\n def get_all_blobs(self):\\n \\\"\\\"\\\"List all the blobs in this bucket.\\n\\n This will **not** retrieve all the data for all the blobs, it\\n will only retrieve the blob paths.\\n\\n This is equivalent to::\\n\\n blobs = [blob for blob in bucket]\\n\\n :rtype: list of :class:`gcloud.storage.blob.Blob`\\n :returns: A list of all the Blob objects in this bucket.\\n \\\"\\\"\\\"\\n return list(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.iterator\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.iterator\\n def iterator(self, prefix=None, delimiter=None, max_results=None,\\n versions=None):\\n \\\"\\\"\\\"Return an iterator used to find blobs in the bucket.\\n\\n :type prefix: string or None\\n :param prefix: optional prefix used to filter blobs.\\n\\n :type delimiter: string or None\\n :param delimiter: optional delimter, used with ``prefix`` to\\n emulate hierarchy.\\n\\n :type max_results: integer or None\\n :param max_results: maximum number of blobs to return.\\n\\n :type versions: boolean or None\\n :param versions: whether object versions should be returned as\\n separate blobs.\\n\\n :rtype: :class:`_BlobIterator`\\n \\\"\\\"\\\"\\n extra_params = {}\\n\\n if prefix is not None:\\n extra_params['prefix'] = prefix\\n\\n if delimiter is not None:\\n extra_params['delimiter'] = delimiter\\n\\n if max_results is not None:\\n extra_params['maxResults'] = max_results\\n\\n if versions is not None:\\n extra_params['versions'] = versions\\n\\n return self._iterator_class(self, extra_params=extra_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.new_blob\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.new_blob\\n def new_blob(self, blob):\\n \\\"\\\"\\\"Given path name (or Blob), return a :class:`Blob` object.\\n\\n This is really useful when you're not sure if you have a ``Blob``\\n instance or a string path name. Given either of those types, this\\n returns the corresponding ``Blob``.\\n\\n :type blob: string or :class:`gcloud.storage.blob.Blob`\\n :param blob: A path name or actual blob object.\\n\\n :rtype: :class:`gcloud.storage.blob.Blob`\\n :returns: A Blob object with the path provided.\\n \\\"\\\"\\\"\\n if isinstance(blob, Blob):\\n return blob\\n\\n if isinstance(blob, six.string_types):\\n return Blob(bucket=self, name=blob)\\n\\n raise TypeError('Invalid blob: %s' % blob)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.delete\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.delete\\n def delete(self, force=False):\\n \\\"\\\"\\\"Delete this bucket.\\n\\n The bucket **must** be empty in order to submit a delete request. If\\n ``force=True`` is passed, this will first attempt to delete all the\\n objects / blobs in the bucket (i.e. try to empty the bucket).\\n\\n If the bucket doesn't exist, this will raise\\n :class:`gcloud.exceptions.NotFound`. If the bucket is not empty\\n (and ``force=False``), will raise :class:`gcloud.exceptions.Conflict`.\\n\\n If ``force=True`` and the bucket contains more than 256 objects / blobs\\n this will cowardly refuse to delete the objects (or the bucket). This\\n is to prevent accidental bucket deletion and to prevent extremely long\\n runtime of this method.\\n\\n :type force: boolean\\n :param force: If True, empties the bucket's objects then deletes it.\\n\\n :raises: :class:`ValueError` if ``force`` is ``True`` and the bucket\\n contains more than 256 objects / blobs.\\n \\\"\\\"\\\"\\n if force:\\n blobs = list(self.iterator(\\n max_results=self._MAX_OBJECTS_FOR_BUCKET_DELETE + 1))\\n if len(blobs) > self._MAX_OBJECTS_FOR_BUCKET_DELETE:\\n message = (\\n 'Refusing to delete bucket with more than '\\n '%d objects. If you actually want to delete '\\n 'this bucket, please delete the objects '\\n 'yourself before calling Bucket.delete().'\\n ) % (self._MAX_OBJECTS_FOR_BUCKET_DELETE,)\\n raise ValueError(message)\\n\\n # Ignore 404 errors on delete.\\n self.delete_blobs(blobs, on_error=lambda blob: None)\\n\\n self.connection.delete_bucket(self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.delete_blob\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.delete_blob\\n def delete_blob(self, blob):\\n \\\"\\\"\\\"Deletes a blob from the current bucket.\\n\\n If the blob isn't found, raise a\\n :class:`gcloud.exceptions.NotFound`.\\n\\n For example::\\n\\n >>> from gcloud.exceptions import NotFound\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> print bucket.get_all_blobs()\\n [<Blob: my-bucket, my-file.txt>]\\n >>> bucket.delete_blob('my-file.txt')\\n >>> try:\\n ... bucket.delete_blob('doesnt-exist')\\n ... except NotFound:\\n ... pass\\n\\n\\n :type blob: string or :class:`gcloud.storage.blob.Blob`\\n :param blob: A blob name or Blob object to delete.\\n\\n :rtype: :class:`gcloud.storage.blob.Blob`\\n :returns: The blob that was just deleted.\\n :raises: :class:`gcloud.exceptions.NotFound` (to suppress\\n the exception, call ``delete_blobs``, passing a no-op\\n ``on_error`` callback, e.g.::\\n\\n >>> bucket.delete_blobs([blob], on_error=lambda blob: None)\\n \\\"\\\"\\\"\\n blob = self.new_blob(blob)\\n self.connection.api_request(method='DELETE', path=blob.path)\\n return blob\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.delete_blobs\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.delete_blobs\\n def delete_blobs(self, blobs, on_error=None):\\n \\\"\\\"\\\"Deletes a list of blobs from the current bucket.\\n\\n Uses :func:`Bucket.delete_blob` to delete each individual blob.\\n\\n :type blobs: list of string or :class:`gcloud.storage.blob.Blob`\\n :param blobs: A list of blob names or Blob objects to delete.\\n\\n :type on_error: a callable taking (blob)\\n :param on_error: If not ``None``, called once for each blob raising\\n :class:`gcloud.exceptions.NotFound`;\\n otherwise, the exception is propagated.\\n\\n :raises: :class:`gcloud.exceptions.NotFound` (if\\n `on_error` is not passed).\\n \\\"\\\"\\\"\\n for blob in blobs:\\n try:\\n self.delete_blob(blob)\\n except NotFound:\\n if on_error is not None:\\n on_error(blob)\\n else:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.copy_blob\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.copy_blob\\n def copy_blob(self, blob, destination_bucket, new_name=None):\\n \\\"\\\"\\\"Copy the given blob to the given bucket, optionally with a new name.\\n\\n :type blob: string or :class:`gcloud.storage.blob.Blob`\\n :param blob: The blob to be copied.\\n\\n :type destination_bucket: :class:`gcloud.storage.bucket.Bucket`\\n :param destination_bucket: The bucket into which the blob should be\\n copied.\\n\\n :type new_name: string\\n :param new_name: (optional) the new name for the copied file.\\n\\n :rtype: :class:`gcloud.storage.blob.Blob`\\n :returns: The new Blob.\\n \\\"\\\"\\\"\\n if new_name is None:\\n new_name = blob.name\\n new_blob = destination_bucket.new_blob(new_name)\\n api_path = blob.path + '/copyTo' + new_blob.path\\n self.connection.api_request(method='POST', path=api_path)\\n return new_blob\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.upload_file\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.upload_file\\n def upload_file(self, filename, blob=None):\\n \\\"\\\"\\\"Shortcut method to upload a file into this bucket.\\n\\n Use this method to quickly put a local file in Cloud Storage.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> bucket.upload_file('~/my-file.txt', 'remote-text-file.txt')\\n >>> print bucket.get_all_blobs()\\n [<Blob: my-bucket, remote-text-file.txt>]\\n\\n If you don't provide a blob value, we will try to upload the file\\n using the local filename as the blob (**not** the complete\\n path)::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> bucket.upload_file('~/my-file.txt')\\n >>> print bucket.get_all_blobs()\\n [<Blob: my-bucket, my-file.txt>]\\n\\n :type filename: string\\n :param filename: Local path to the file you want to upload.\\n\\n :type blob: string or :class:`gcloud.storage.blob.Blob`\\n :param blob: The blob (either an object or a remote path) of where\\n to put the file. If this is blank, we will try to\\n upload the file to the root of the bucket with the\\n same name as on your local file system.\\n\\n :rtype: :class:`Blob`\\n :returns: The updated Blob object.\\n \\\"\\\"\\\"\\n if blob is None:\\n blob = os.path.basename(filename)\\n blob = self.new_blob(blob)\\n blob.upload_from_filename(filename)\\n return blob\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.upload_file_object\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.upload_file_object\\n def upload_file_object(self, file_obj, blob=None):\\n \\\"\\\"\\\"Shortcut method to upload a file object into this bucket.\\n\\n Use this method to quickly put a local file in Cloud Storage.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> bucket.upload_file(open('~/my-file.txt'), 'remote-text-file.txt')\\n >>> print bucket.get_all_blobs()\\n [<Blob: my-bucket, remote-text-file.txt>]\\n\\n If you don't provide a blob value, we will try to upload the file\\n using the local filename as the blob (**not** the complete\\n path)::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> bucket.upload_file(open('~/my-file.txt'))\\n >>> print bucket.get_all_blobs()\\n [<Blob: my-bucket, my-file.txt>]\\n\\n :type file_obj: file\\n :param file_obj: A file handle open for reading.\\n\\n :type blob: string or :class:`gcloud.storage.blob.Blob`\\n :param blob: The blob (either an object or a remote path) of where\\n to put the file. If this is blank, we will try to\\n upload the file to the root of the bucket with the\\n same name as on your local file system.\\n\\n :rtype: :class:`Blob`\\n :returns: The updated Blob object.\\n \\\"\\\"\\\"\\n if blob:\\n blob = self.new_blob(blob)\\n else:\\n blob = self.new_blob(os.path.basename(file_obj.name))\\n blob.upload_from_file(file_obj)\\n return blob\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_cors\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_cors\\n def get_cors(self):\\n \\\"\\\"\\\"Retrieve CORS policies configured for this bucket.\\n\\n See: http://www.w3.org/TR/cors/ and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: list(dict)\\n :returns: A sequence of mappings describing each CORS policy.\\n \\\"\\\"\\\"\\n return [policy.copy() for policy in self.properties.get('cors', ())]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.update_cors\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.update_cors\\n def update_cors(self, entries):\\n \\\"\\\"\\\"Update CORS policies configured for this bucket.\\n\\n See: http://www.w3.org/TR/cors/ and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :type entries: list(dict)\\n :param entries: A sequence of mappings describing each CORS policy.\\n \\\"\\\"\\\"\\n self._patch_properties({'cors': entries})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_default_object_acl\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_default_object_acl\\n def get_default_object_acl(self):\\n \\\"\\\"\\\"Get the current Default Object ACL rules.\\n\\n If the acl isn't available locally, this method will reload it from\\n Cloud Storage.\\n\\n :rtype: :class:`gcloud.storage.acl.DefaultObjectACL`\\n :returns: A DefaultObjectACL object for this bucket.\\n \\\"\\\"\\\"\\n if not self.default_object_acl.loaded:\\n self.default_object_acl.reload()\\n return self.default_object_acl\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.etag\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.etag\\n @property\\n def etag(self):\\n \\\"\\\"\\\"Retrieve the ETag for the bucket.\\n\\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['etag']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.id\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.id\\n @property\\n def id(self):\\n \\\"\\\"\\\"Retrieve the ID for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['id']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_lifecycle\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_lifecycle\\n def get_lifecycle(self):\\n \\\"\\\"\\\"Retrieve lifecycle rules configured for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/lifecycle and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: list(dict)\\n :returns: A sequence of mappings describing each lifecycle rule.\\n \\\"\\\"\\\"\\n info = self.properties.get('lifecycle', {})\\n return [rule.copy() for rule in info.get('rule', ())]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.update_lifecycle\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.update_lifecycle\\n def update_lifecycle(self, rules):\\n \\\"\\\"\\\"Update CORS policies configured for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/lifecycle and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :type rules: list(dict)\\n :param rules: A sequence of mappings describing each lifecycle rule.\\n \\\"\\\"\\\"\\n self._patch_properties({'lifecycle': {'rule': rules}})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_logging\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_logging\\n def get_logging(self):\\n \\\"\\\"\\\"Return info about access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#status\\n\\n :rtype: dict or None\\n :returns: a dict w/ keys, ``logBucket`` and ``logObjectPrefix``\\n (if logging is enabled), or None (if not).\\n \\\"\\\"\\\"\\n info = self.properties.get('logging')\\n if info is not None:\\n return info.copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.enable_logging\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.enable_logging\\n def enable_logging(self, bucket_name, object_prefix=''):\\n \\\"\\\"\\\"Enable access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#delivery\\n\\n :type bucket_name: string\\n :param bucket_name: name of bucket in which to store access logs\\n\\n :type object_prefix: string\\n :param object_prefix: prefix for access log filenames\\n \\\"\\\"\\\"\\n info = {'logBucket': bucket_name, 'logObjectPrefix': object_prefix}\\n self._patch_properties({'logging': info})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.disable_logging\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.disable_logging\\n def disable_logging(self):\\n \\\"\\\"\\\"Disable access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#disabling\\n \\\"\\\"\\\"\\n self._patch_properties({'logging': None})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.metageneration\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.metageneration\\n @property\\n def metageneration(self):\\n \\\"\\\"\\\"Retrieve the metageneration for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['metageneration']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.owner\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.owner\\n @property\\n def owner(self):\\n \\\"\\\"\\\"Retrieve info about the owner of the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: dict\\n :returns: mapping of owner's role/ID.\\n \\\"\\\"\\\"\\n return self.properties['owner'].copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.project_number\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.project_number\\n @property\\n def project_number(self):\\n \\\"\\\"\\\"Retrieve the number of the project to which the bucket is assigned.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['projectNumber']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.self_link\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.self_link\\n @property\\n def self_link(self):\\n \\\"\\\"\\\"Retrieve the URI for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['selfLink']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.storage_class\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.storage_class\\n @property\\n def storage_class(self):\\n \\\"\\\"\\\"Retrieve the storage class for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets and\\n https://cloud.google.com/storage/docs/durable-reduced-availability\\n\\n :rtype: string\\n :returns: Currently one of \\\"STANDARD\\\", \\\"DURABLE_REDUCED_AVAILABILITY\\\"\\n \\\"\\\"\\\"\\n return self.properties['storageClass']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.time_created\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.time_created\\n @property\\n def time_created(self):\\n \\\"\\\"\\\"Retrieve the timestamp at which the bucket was created.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n :returns: timestamp in RFC 3339 format.\\n \\\"\\\"\\\"\\n return self.properties['timeCreated']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\\n @property\\n def versioning_enabled(self):\\n \\\"\\\"\\\"Is versioning enabled for this bucket?\\n\\n See: https://cloud.google.com/storage/docs/object-versioning for\\n details.\\n\\n :rtype: boolean\\n :returns: True if enabled, else False.\\n \\\"\\\"\\\"\\n versioning = self.properties.get('versioning', {})\\n return versioning.get('enabled', False)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\\n @versioning_enabled.setter\\n def versioning_enabled(self, value):\\n \\\"\\\"\\\"Enable versioning for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/object-versioning for\\n details.\\n\\n :type value: convertible to boolean\\n :param value: should versioning be anabled for the bucket?\\n \\\"\\\"\\\"\\n self._patch_properties({'versioning': {'enabled': bool(value)}})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.configure_website\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.configure_website\\n def configure_website(self, main_page_suffix=None, not_found_page=None):\\n \\\"\\\"\\\"Configure website-related properties.\\n\\n See: https://developers.google.com/storage/docs/website-configuration\\n\\n .. note::\\n This (apparently) only works\\n if your bucket name is a domain name\\n (and to do that, you need to get approved somehow...).\\n\\n If you want this bucket to host a website, just provide the name\\n of an index page and a page to use when a blob isn't found::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project)\\n >>> bucket = connection.get_bucket(bucket_name)\\n >>> bucket.configure_website('index.html', '404.html')\\n\\n You probably should also make the whole bucket public::\\n\\n >>> bucket.make_public(recursive=True, future=True)\\n\\n This says: \\\"Make the bucket public, and all the stuff already in\\n the bucket, and anything else I add to the bucket. Just make it\\n all public.\\\"\\n\\n :type main_page_suffix: string\\n :param main_page_suffix: The page to use as the main page\\n of a directory.\\n Typically something like index.html.\\n\\n :type not_found_page: string\\n :param not_found_page: The file to use when a page isn't found.\\n \\\"\\\"\\\"\\n data = {\\n 'website': {\\n 'mainPageSuffix': main_page_suffix,\\n 'notFoundPage': not_found_page,\\n },\\n }\\n return self._patch_properties(data)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.disable_website\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.disable_website\\n def disable_website(self):\\n \\\"\\\"\\\"Disable the website configuration for this bucket.\\n\\n This is really just a shortcut for setting the website-related\\n attributes to ``None``.\\n \\\"\\\"\\\"\\n return self.configure_website(None, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.make_public\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.make_public\\n def make_public(self, recursive=False, future=False):\\n \\\"\\\"\\\"Make a bucket public.\\n\\n :type recursive: boolean\\n :param recursive: If True, this will make all blobs inside the bucket\\n public as well.\\n\\n :type future: boolean\\n :param future: If True, this will make all objects created in the\\n future public as well.\\n \\\"\\\"\\\"\\n self.acl.all().grant_read()\\n self.acl.save()\\n\\n if future:\\n doa = self.get_default_object_acl()\\n doa.all().grant_read()\\n doa.save()\\n\\n if recursive:\\n for blob in self:\\n blob.acl.all().grant_read()\\n blob.save_acl()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.__init__\",\n \"text\": \"gcloud/storage/connection.py::Connection.__init__\\n def __init__(self, project, *args, **kwargs):\\n \\\"\\\"\\\":type project: string\\n\\n :param project: The project name to connect to.\\n \\\"\\\"\\\"\\n super(Connection, self).__init__(*args, **kwargs)\\n self.project = project\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.build_api_url\",\n \"text\": \"gcloud/storage/connection.py::Connection.build_api_url\\n def build_api_url(self, path, query_params=None, api_base_url=None,\\n api_version=None, upload=False):\\n \\\"\\\"\\\"Construct an API url given a few components, some optional.\\n\\n Typically, you shouldn't need to use this method.\\n\\n :type path: string\\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\\n\\n :type query_params: dict\\n :param query_params: A dictionary of keys and values to insert into\\n the query string of the URL.\\n\\n :type api_base_url: string\\n :param api_base_url: The base URL for the API endpoint.\\n Typically you won't have to provide this.\\n\\n :type api_version: string\\n :param api_version: The version of the API to call.\\n Typically you shouldn't provide this and instead\\n use the default for the library.\\n\\n :type upload: boolean\\n :param upload: True if the URL is for uploading purposes.\\n\\n :rtype: string\\n :returns: The URL assembled from the pieces provided.\\n \\\"\\\"\\\"\\n api_base_url = api_base_url or self.API_BASE_URL\\n if upload:\\n api_base_url += '/upload'\\n\\n url = self.API_URL_TEMPLATE.format(\\n api_base_url=(api_base_url or self.API_BASE_URL),\\n api_version=(api_version or self.API_VERSION),\\n path=path)\\n\\n query_params = query_params or {}\\n query_params.update({'project': self.project})\\n url += '?' + urlencode(query_params)\\n\\n return url\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.make_request\",\n \"text\": \"gcloud/storage/connection.py::Connection.make_request\\n def make_request(self, method, url, data=None, content_type=None,\\n headers=None):\\n \\\"\\\"\\\"A low level method to send a request to the API.\\n\\n Typically, you shouldn't need to use this method.\\n\\n :type method: string\\n :param method: The HTTP method to use in the request.\\n\\n :type url: string\\n :param url: The URL to send the request to.\\n\\n :type data: string\\n :param data: The data to send as the body of the request.\\n\\n :type content_type: string\\n :param content_type: The proper MIME type of the data provided.\\n\\n :type headers: dict\\n :param headers: A dictionary of HTTP headers to send with the request.\\n\\n :rtype: tuple of ``response`` (a dictionary of sorts)\\n and ``content`` (a string).\\n :returns: The HTTP response object and the content of the response.\\n \\\"\\\"\\\"\\n headers = headers or {}\\n headers['Accept-Encoding'] = 'gzip'\\n\\n if data:\\n content_length = len(str(data))\\n else:\\n content_length = 0\\n\\n headers['Content-Length'] = content_length\\n\\n if content_type:\\n headers['Content-Type'] = content_type\\n\\n headers['User-Agent'] = self.USER_AGENT\\n\\n return self.http.request(uri=url, method=method, headers=headers,\\n body=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.api_request\",\n \"text\": \"gcloud/storage/connection.py::Connection.api_request\\n def api_request(self, method, path, query_params=None,\\n data=None, content_type=None,\\n api_base_url=None, api_version=None,\\n expect_json=True):\\n \\\"\\\"\\\"Make a request over the HTTP transport to the Cloud Storage API.\\n\\n You shouldn't need to use this method, but if you plan to\\n interact with the API using these primitives, this is the\\n correct one to use...\\n\\n :type method: string\\n :param method: The HTTP method name (ie, ``GET``, ``POST``, etc).\\n Required.\\n\\n :type path: string\\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\\n Required.\\n\\n :type query_params: dict\\n :param query_params: A dictionary of keys and values to insert into\\n the query string of the URL. Default is\\n empty dict.\\n\\n :type data: string\\n :param data: The data to send as the body of the request. Default is\\n the empty string.\\n\\n :type content_type: string\\n :param content_type: The proper MIME type of the data provided. Default\\n is None.\\n\\n :type api_base_url: string\\n :param api_base_url: The base URL for the API endpoint.\\n Typically you won't have to provide this.\\n Default is the standard API base URL.\\n\\n :type api_version: string\\n :param api_version: The version of the API to call. Typically\\n you shouldn't provide this and instead use\\n the default for the library. Default is the\\n latest API version supported by\\n gcloud-python.\\n\\n :type expect_json: boolean\\n :param expect_json: If True, this method will try to parse the\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.get_all_buckets\",\n \"text\": \"gcloud/storage/connection.py::Connection.get_all_buckets\\n def get_all_buckets(self):\\n \\\"\\\"\\\"Get all buckets in the project.\\n\\n This will not populate the list of blobs available in each\\n bucket.\\n\\n You can also iterate over the connection object, so these two\\n operations are identical::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project)\\n >>> for bucket in connection.get_all_buckets():\\n >>> print bucket\\n\\n :rtype: list of :class:`gcloud.storage.bucket.Bucket` objects.\\n :returns: All buckets belonging to this project.\\n \\\"\\\"\\\"\\n return iter(_BucketIterator(connection=self))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.get_bucket\",\n \"text\": \"gcloud/storage/connection.py::Connection.get_bucket\\n def get_bucket(self, bucket_name):\\n \\\"\\\"\\\"Get a bucket by name.\\n\\n If the bucket isn't found, this will raise a\\n :class:`gcloud.storage.exceptions.NotFound`.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> from gcloud.exceptions import NotFound\\n >>> connection = storage.get_connection(project)\\n >>> try:\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> except NotFound:\\n >>> print 'Sorry, that bucket does not exist!'\\n\\n :type bucket_name: string\\n :param bucket_name: The name of the bucket to get.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The bucket matching the name provided.\\n :raises: :class:`gcloud.exceptions.NotFound`\\n \\\"\\\"\\\"\\n bucket = Bucket(connection=self, name=bucket_name)\\n response = self.api_request(method='GET', path=bucket.path)\\n return Bucket(properties=response, connection=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.create_bucket\",\n \"text\": \"gcloud/storage/connection.py::Connection.create_bucket\\n def create_bucket(self, bucket_name):\\n \\\"\\\"\\\"Create a new bucket.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project)\\n >>> bucket = connection.create_bucket('my-bucket')\\n >>> print bucket\\n <Bucket: my-bucket>\\n\\n :type bucket_name: string\\n :param bucket_name: The bucket name to create.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The newly created bucket.\\n :raises: :class:`gcloud.exceptions.Conflict` if\\n there is a confict (bucket already exists, invalid name, etc.)\\n \\\"\\\"\\\"\\n response = self.api_request(method='POST', path='/b',\\n data={'name': bucket_name})\\n return Bucket(properties=response, connection=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.delete_bucket\",\n \"text\": \"gcloud/storage/connection.py::Connection.delete_bucket\\n def delete_bucket(self, bucket_name):\\n \\\"\\\"\\\"Delete a bucket.\\n\\n You can use this method to delete a bucket by name.\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project)\\n >>> connection.delete_bucket('my-bucket')\\n\\n If the bucket doesn't exist, this will raise a\\n :class:`gcloud.exceptions.NotFound`::\\n\\n >>> from gcloud.exceptions import NotFound\\n >>> try:\\n >>> connection.delete_bucket('my-bucket')\\n >>> except NotFound:\\n >>> print 'That bucket does not exist!'\\n\\n If the bucket still has objects in it, this will raise a\\n :class:`gcloud.exceptions.Conflict`::\\n\\n >>> from gcloud.exceptions import Conflict\\n >>> try:\\n >>> connection.delete_bucket('my-bucket')\\n >>> except Conflict:\\n >>> print 'That bucket is not empty!'\\n\\n :type bucket_name: string\\n :param bucket_name: The bucket name to delete.\\n \\\"\\\"\\\"\\n bucket_path = Bucket.path_helper(bucket_name)\\n self.api_request(method='DELETE', path=bucket_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::_BucketIterator.__init__\",\n \"text\": \"gcloud/storage/connection.py::_BucketIterator.__init__\\n def __init__(self, connection):\\n super(_BucketIterator, self).__init__(connection=connection, path='/b')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::_BucketIterator.get_items_from_response\",\n \"text\": \"gcloud/storage/connection.py::_BucketIterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Factory method which yields :class:`.Bucket` items from a response.\\n\\n :type response: dict\\n :param response: The JSON API response for a page of buckets.\\n \\\"\\\"\\\"\\n for item in response.get('items', []):\\n yield Bucket(properties=item, connection=self.connection)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":54,"cells":{"instance_id":{"kind":"string","value":"googleapis__google-cloud-python-4015"},"repo":{"kind":"string","value":"googleapis/google-cloud-python"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Google cloud spanner client fails when reading large columns\nConsider following table:\r\n\r\n```\r\nCREATE TABLE big_arrays (\r\n key INT64 NOT NULL,\r\n a_big_array ARRAY<TIMESTAMP> NOT NULL,\r\n) PRIMARY KEY (key)\r\n```\r\n\r\nWhich gets populated by 100 rows each containing a large array of 1000 timestamps.\r\n\r\n```\r\nfrom google.cloud import spanner\r\nimport datetime\r\nimport random\r\nspanner_client = spanner.Client(project='my-project')\r\ninstance = spanner_client.instance('my-instance')\r\ndb = instance.database('my-db')\r\n\r\ndef random_timestamp():\r\n return datetime.datetime.utcfromtimestamp(random.uniform(1e9, 2e9))\r\ndef random_big_array(size=100):\r\n return [random_timestamp() for i in range(1000)]\r\n\r\nbatch_size = 10\r\nbatches = 10\r\nfor batch_index in range(batches):\r\n with db.batch() as batch:\r\n batch.insert_or_update(\r\n table='big_arrays',\r\n columns=['key', 'a_big_array'],\r\n values = [(key, random_big_array()) for key in range(batch_index*batch_size, (batch_index+1)*batch_size)]\r\n )\r\n```\r\nTrying to fetch these rows with `db.execute_sql('select * from big_arrays limit 100').consume_all()` results in following error and stacktrace:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"Untitled5.py\", line 60, in <module>\r\n db.execute_sql(\"SELECT * FROM big_arrays limit 100\").consume_all()\r\n File \"/Users/myname/Repos/transformations/venv/lib/python2.7/site-packages/google/cloud/spanner/streamed.py\", line 159, in consume_all\r\n self.consume_next()\r\n File \"/Users/myname/Repos/transformations/venv/lib/python2.7/site-packages/google/cloud/spanner/streamed.py\", line 148, in consume_next\r\n values[0] = self._merge_chunk(values[0])\r\n File \"/Users/myname/Repos/transformations/venv/lib/python2.7/site-packages/google/cloud/spanner/streamed.py\", line 108, in _merge_chunk\r\n merged = _merge_by_type(self._pending_chunk, value, field.type)\r\n File \"/Users/myname/Repos/transformations/venv/lib/python2.7/site-packages/google/cloud/spanner/streamed.py\", line 272, in _merge_by_type\r\n return merger(lhs, rhs, type_)\r\n File \"/Users/myname/Repos/transformations/venv/lib/python2.7/site-packages/google/cloud/spanner/streamed.py\", line 233, in _merge_array\r\n merged = _merge_by_type(last, first, element_type)\r\n File \"/Users/myname/Repos/transformations/venv/lib/python2.7/site-packages/google/cloud/spanner/streamed.py\", line 271, in _merge_by_type\r\n merger = _MERGE_BY_TYPE[type_.code]\r\nKeyError: 4\r\n```\r\nRunning the above query with the gcloud cli works as intended\r\n`gcloud spanner databases execute-sql my-db --instance=my-instance --sql='select * from big_arrays limit 100' >/dev/null`\r\n\nadd merging methods for date and timestamp chunks\nFix for https://github.com/GoogleCloudPlatform/google-cloud-python/issues/3998 \n"},"gold_files":{"kind":"list like","value":["spanner/google/cloud/spanner/streamed.py"],"string":"[\n \"spanner/google/cloud/spanner/streamed.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["spanner/google/cloud/spanner/streamed.py::<module>"],"string":"[\n \"spanner/google/cloud/spanner/streamed.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py\nclass Cursor # DB-API Cursor to Google BigQuery.\n def __init__(self, connection)\n def close(self) # No-op.\n def _set_description(self, schema) # Set description from schema.\n def _set_rowcount(self, query_results) # Set the rowcount from query results.\n def execute(self, operation, parameters=None, job_id=None) # Prepare and execute a database operation.\n def executemany(self, operation, seq_of_parameters) # Prepare and execute a database operation multiple times.\n def _try_fetch(self, size=None) # Try to start fetching data, if not yet started.\n def fetchone(self) # Fetch a single row from the results of the last ``execute*()`` call.\n def fetchmany(self, size=None) # Fetch multiple results from the last ``execute*()`` call.\n def fetchall(self) # Fetch all remaining results from the last ``execute*()`` call.\n def setinputsizes(self, sizes) # No-op.\n def setoutputsize(self, size, column=None) # No-op.\ndef _format_operation_list(operation, parameters) # Formats parameters in operation in the way BigQuery expects.\ndef _format_operation_dict(operation, parameters) # Formats parameters in operation in the way BigQuery expects.\ndef _format_operation(operation, parameters=None) # Formats parameters in operation in way BigQuery expects.","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py","text":"bigtable/google/cloud/bigtable/row_filters.py\nclass RowFilter # Basic filter to apply to cells in a row.\nclass _BoolFilter # Row filter that uses a boolean flag.\n def __init__(self, flag)\n def __eq__(self, other)\n def __ne__(self, other)\nclass SinkFilter # Advanced row filter to skip parent filters.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass PassAllFilter # Row filter equivalent to not filtering at all.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass BlockAllFilter # Row filter that doesn't match any cells.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass _RegexFilter # Row filter that uses a regular expression.\n def __init__(self, regex)\n def __eq__(self, other)\n def __ne__(self, other)\nclass RowKeyRegexFilter # Row filter for a row key regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass RowSampleFilter # Matches all cells from a row with probability p.\n def __init__(self, sample)\n def __eq__(self, other)\n def __ne__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass FamilyNameRegexFilter # Row filter for a family name regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ColumnQualifierRegexFilter # Row filter for a column qualifier regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass TimestampRange # Range of time with inclusive lower and exclusive upper bounds.\n def __init__(self, start=None, end=None)\n def __eq__(self, other)\n def __ne__(self, other)\n def to_pb(self) # Converts the :class:`TimestampRange` to a protobuf.\nclass TimestampRangeFilter # Row filter that limits cells to a range of time.\n def __init__(self, range_)\n def __eq__(self, other)\n def __ne__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ColumnRangeFilter # A row filter to restrict to a range of columns.\n def __init__(self, column_family_id, start_column=None, end_column=None, inclusive_start=None, inclusive_end=None)\n def __eq__(self, other)\n def __ne__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ValueRegexFilter # Row filter for a value regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ValueRangeFilter # A range of values to restrict to in a row filter.\n def __init__(self, start_value=None, end_value=None, inclusive_start=None, inclusive_end=None)\n def __eq__(self, other)\n def __ne__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass _CellCountFilter # Row filter that uses an integer count of cells.\n def __init__(self, num_cells)\n def __eq__(self, other)\n def __ne__(self, other)\nclass CellsRowOffsetFilter # Row filter to skip cells in a row.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass CellsRowLimitFilter # Row filter to lim","label":0},{"cid":"spanner/google/cloud/spanner/database.py","text":"spanner/google/cloud/spanner/database.py\nclass Database # Representation of a Cloud Spanner Database.\n def __init__(self, database_id, instance, ddl_statements=(), pool=None)\n def from_pb(cls, database_pb, instance, pool=None) # Creates an instance of this class from a protobuf.\n def name(self) # Database name used in requests.\n def ddl_statements(self) # DDL Statements used to define database schema.\n def spanner_api(self) # Helper for session-related API calls.\n def __eq__(self, other)\n def __ne__(self, other)\n def create(self) # Create this database within its instance\n def exists(self) # Test whether this database exists.\n def reload(self) # Reload this database.\n def update_ddl(self, ddl_statements) # Update DDL for this database.\n def drop(self) # Drop this database.\n def session(self) # Factory to create a session for this database.\n def snapshot(self, **kw) # Return an object which wraps a snapshot.\n def batch(self) # Return an object which wraps a batch.\n def run_in_transaction(self, func, *args, **kw) # Perform a unit of work in a transaction, retrying on abort.\nclass BatchCheckout # Context manager for using a batch from a database.\n def __init__(self, database)\n def __enter__(self) # Begin ``with`` block.\n def __exit__(self, exc_type, exc_val, exc_tb) # End ``with`` block.\nclass SnapshotCheckout # Context manager for using a snapshot from a database.\n def __init__(self, database, **kw)\n def __enter__(self) # Begin ``with`` block.\n def __exit__(self, exc_type, exc_val, exc_tb) # End ``with`` block.\ndef _check_ddl_statements(value) # Validate DDL Statements used to define database schema.","label":0},{"cid":"spanner/google/cloud/spanner/session.py","text":"spanner/google/cloud/spanner/session.py\nclass Session # Representation of a Cloud Spanner Session.\n def __init__(self, database)\n def __lt__(self, other)\n def session_id(self) # Read-only ID, set by the back-end during :meth:`create`.\n def name(self) # Session name used in requests.\n def create(self) # Create this session, bound to its database.\n def exists(self) # Test for the existence of this session.\n def delete(self) # Delete this session.\n def snapshot(self, **kw) # Create a snapshot to perform a set of reads with shared staleness.\n def read(self, table, columns, keyset, index='', limit=0, resume_token=b'') # Perform a ``StreamingRead`` API request for rows in a table.\n def execute_sql(self, sql, params=None, param_types=None, query_mode=None, resume_token=b'') # Perform an ``ExecuteStreamingSql`` API request.\n def batch(self) # Factory to create a batch for this session.\n def transaction(self) # Create a transaction to perform a set of reads with shared staleness.\n def run_in_transaction(self, func, *args, **kw) # Perform a unit of work in a transaction, retrying on abort.\ndef _delay_until_retry(exc, deadline) # Helper for :meth:`Session.run_in_transaction`.\ndef _get_retry_delay(cause) # Helper for :func:`_delay_until_retry`.","label":0},{"cid":"spanner/google/cloud/spanner/snapshot.py","text":"spanner/google/cloud/spanner/snapshot.py\nclass _SnapshotBase # Base class for Snapshot.\n def _make_txn_selector(self) # Helper for :meth:`read` / :meth:`execute_sql`.\n def read(self, table, columns, keyset, index='', limit=0, resume_token=b'') # Perform a ``StreamingRead`` API request for rows in a table.\n def execute_sql(self, sql, params=None, param_types=None, query_mode=None, resume_token=b'') # Perform an ``ExecuteStreamingSql`` API request for rows in a table.\nclass Snapshot # Allow a set of reads / SQL statements with shared staleness.\n def __init__(self, session, read_timestamp=None, min_read_timestamp=None, max_staleness=None, exact_staleness=None, multi_use=False)\n def _make_txn_selector(self) # Helper for :meth:`read`.\n def begin(self) # Begin a read-only transaction on the database.","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py","text":"spanner/google/cloud/spanner/streamed.py\nclass StreamedResultSet # Process a sequence of partial result sets into a single set of row data.\n def __init__(self, response_iterator, source=None)\n def rows(self) # Fully-processed rows.\n def fields(self) # Field descriptors for result set columns.\n def metadata(self) # Result set metadata\n def stats(self) # Result set statistics\n def resume_token(self) # Token for resuming interrupted read / query.\n def _merge_chunk(self, value) # Merge pending chunk with next value.\n def _merge_values(self, values) # Merge values into rows.\n def consume_next(self) # Consume the next partial result set from the stream.\n def consume_all(self) # Consume the streamed responses until there are no more.\n def __iter__(self)\n def one(self) # Return exactly one result, or raise an exception.\n def one_or_none(self) # Return exactly one result, or None if there are no results.\nclass Unmergeable # Unable to merge two values.\n def __init__(self, lhs, rhs, type_)\ndef _unmergeable(lhs, rhs, type_) # Helper for '_merge_by_type'.\ndef _merge_float64(lhs, rhs, type_) # Helper for '_merge_by_type'.\ndef _merge_string(lhs, rhs, type_) # Helper for '_merge_by_type'.\ndef _merge_array(lhs, rhs, type_) # Helper for '_merge_by_type'.\ndef _merge_struct(lhs, rhs, type_) # Helper for '_merge_by_type'.\ndef _merge_by_type(lhs, rhs, type_) # Helper for '_merge_chunk'.","label":1}],"string":"[\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py\\nclass Cursor # DB-API Cursor to Google BigQuery.\\n def __init__(self, connection)\\n def close(self) # No-op.\\n def _set_description(self, schema) # Set description from schema.\\n def _set_rowcount(self, query_results) # Set the rowcount from query results.\\n def execute(self, operation, parameters=None, job_id=None) # Prepare and execute a database operation.\\n def executemany(self, operation, seq_of_parameters) # Prepare and execute a database operation multiple times.\\n def _try_fetch(self, size=None) # Try to start fetching data, if not yet started.\\n def fetchone(self) # Fetch a single row from the results of the last ``execute*()`` call.\\n def fetchmany(self, size=None) # Fetch multiple results from the last ``execute*()`` call.\\n def fetchall(self) # Fetch all remaining results from the last ``execute*()`` call.\\n def setinputsizes(self, sizes) # No-op.\\n def setoutputsize(self, size, column=None) # No-op.\\ndef _format_operation_list(operation, parameters) # Formats parameters in operation in the way BigQuery expects.\\ndef _format_operation_dict(operation, parameters) # Formats parameters in operation in the way BigQuery expects.\\ndef _format_operation(operation, parameters=None) # Formats parameters in operation in way BigQuery expects.\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py\\nclass RowFilter # Basic filter to apply to cells in a row.\\nclass _BoolFilter # Row filter that uses a boolean flag.\\n def __init__(self, flag)\\n def __eq__(self, other)\\n def __ne__(self, other)\\nclass SinkFilter # Advanced row filter to skip parent filters.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass PassAllFilter # Row filter equivalent to not filtering at all.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass BlockAllFilter # Row filter that doesn't match any cells.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass _RegexFilter # Row filter that uses a regular expression.\\n def __init__(self, regex)\\n def __eq__(self, other)\\n def __ne__(self, other)\\nclass RowKeyRegexFilter # Row filter for a row key regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass RowSampleFilter # Matches all cells from a row with probability p.\\n def __init__(self, sample)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass FamilyNameRegexFilter # Row filter for a family name regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ColumnQualifierRegexFilter # Row filter for a column qualifier regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass TimestampRange # Range of time with inclusive lower and exclusive upper bounds.\\n def __init__(self, start=None, end=None)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def to_pb(self) # Converts the :class:`TimestampRange` to a protobuf.\\nclass TimestampRangeFilter # Row filter that limits cells to a range of time.\\n def __init__(self, range_)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ColumnRangeFilter # A row filter to restrict to a range of columns.\\n def __init__(self, column_family_id, start_column=None, end_column=None, inclusive_start=None, inclusive_end=None)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ValueRegexFilter # Row filter for a value regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ValueRangeFilter # A range of values to restrict to in a row filter.\\n def __init__(self, start_value=None, end_value=None, inclusive_start=None, inclusive_end=None)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass _CellCountFilter # Row filter that uses an integer count of cells.\\n def __init__(self, num_cells)\\n def __eq__(self, other)\\n def __ne__(self, other)\\nclass CellsRowOffsetFilter # Row filter to skip cells in a row.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass CellsRowLimitFilter # Row filter to lim\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py\",\n \"text\": \"spanner/google/cloud/spanner/database.py\\nclass Database # Representation of a Cloud Spanner Database.\\n def __init__(self, database_id, instance, ddl_statements=(), pool=None)\\n def from_pb(cls, database_pb, instance, pool=None) # Creates an instance of this class from a protobuf.\\n def name(self) # Database name used in requests.\\n def ddl_statements(self) # DDL Statements used to define database schema.\\n def spanner_api(self) # Helper for session-related API calls.\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def create(self) # Create this database within its instance\\n def exists(self) # Test whether this database exists.\\n def reload(self) # Reload this database.\\n def update_ddl(self, ddl_statements) # Update DDL for this database.\\n def drop(self) # Drop this database.\\n def session(self) # Factory to create a session for this database.\\n def snapshot(self, **kw) # Return an object which wraps a snapshot.\\n def batch(self) # Return an object which wraps a batch.\\n def run_in_transaction(self, func, *args, **kw) # Perform a unit of work in a transaction, retrying on abort.\\nclass BatchCheckout # Context manager for using a batch from a database.\\n def __init__(self, database)\\n def __enter__(self) # Begin ``with`` block.\\n def __exit__(self, exc_type, exc_val, exc_tb) # End ``with`` block.\\nclass SnapshotCheckout # Context manager for using a snapshot from a database.\\n def __init__(self, database, **kw)\\n def __enter__(self) # Begin ``with`` block.\\n def __exit__(self, exc_type, exc_val, exc_tb) # End ``with`` block.\\ndef _check_ddl_statements(value) # Validate DDL Statements used to define database schema.\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py\",\n \"text\": \"spanner/google/cloud/spanner/session.py\\nclass Session # Representation of a Cloud Spanner Session.\\n def __init__(self, database)\\n def __lt__(self, other)\\n def session_id(self) # Read-only ID, set by the back-end during :meth:`create`.\\n def name(self) # Session name used in requests.\\n def create(self) # Create this session, bound to its database.\\n def exists(self) # Test for the existence of this session.\\n def delete(self) # Delete this session.\\n def snapshot(self, **kw) # Create a snapshot to perform a set of reads with shared staleness.\\n def read(self, table, columns, keyset, index='', limit=0, resume_token=b'') # Perform a ``StreamingRead`` API request for rows in a table.\\n def execute_sql(self, sql, params=None, param_types=None, query_mode=None, resume_token=b'') # Perform an ``ExecuteStreamingSql`` API request.\\n def batch(self) # Factory to create a batch for this session.\\n def transaction(self) # Create a transaction to perform a set of reads with shared staleness.\\n def run_in_transaction(self, func, *args, **kw) # Perform a unit of work in a transaction, retrying on abort.\\ndef _delay_until_retry(exc, deadline) # Helper for :meth:`Session.run_in_transaction`.\\ndef _get_retry_delay(cause) # Helper for :func:`_delay_until_retry`.\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/snapshot.py\",\n \"text\": \"spanner/google/cloud/spanner/snapshot.py\\nclass _SnapshotBase # Base class for Snapshot.\\n def _make_txn_selector(self) # Helper for :meth:`read` / :meth:`execute_sql`.\\n def read(self, table, columns, keyset, index='', limit=0, resume_token=b'') # Perform a ``StreamingRead`` API request for rows in a table.\\n def execute_sql(self, sql, params=None, param_types=None, query_mode=None, resume_token=b'') # Perform an ``ExecuteStreamingSql`` API request for rows in a table.\\nclass Snapshot # Allow a set of reads / SQL statements with shared staleness.\\n def __init__(self, session, read_timestamp=None, min_read_timestamp=None, max_staleness=None, exact_staleness=None, multi_use=False)\\n def _make_txn_selector(self) # Helper for :meth:`read`.\\n def begin(self) # Begin a read-only transaction on the database.\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py\\nclass StreamedResultSet # Process a sequence of partial result sets into a single set of row data.\\n def __init__(self, response_iterator, source=None)\\n def rows(self) # Fully-processed rows.\\n def fields(self) # Field descriptors for result set columns.\\n def metadata(self) # Result set metadata\\n def stats(self) # Result set statistics\\n def resume_token(self) # Token for resuming interrupted read / query.\\n def _merge_chunk(self, value) # Merge pending chunk with next value.\\n def _merge_values(self, values) # Merge values into rows.\\n def consume_next(self) # Consume the next partial result set from the stream.\\n def consume_all(self) # Consume the streamed responses until there are no more.\\n def __iter__(self)\\n def one(self) # Return exactly one result, or raise an exception.\\n def one_or_none(self) # Return exactly one result, or None if there are no results.\\nclass Unmergeable # Unable to merge two values.\\n def __init__(self, lhs, rhs, type_)\\ndef _unmergeable(lhs, rhs, type_) # Helper for '_merge_by_type'.\\ndef _merge_float64(lhs, rhs, type_) # Helper for '_merge_by_type'.\\ndef _merge_string(lhs, rhs, type_) # Helper for '_merge_by_type'.\\ndef _merge_array(lhs, rhs, type_) # Helper for '_merge_by_type'.\\ndef _merge_struct(lhs, rhs, type_) # Helper for '_merge_by_type'.\\ndef _merge_by_type(lhs, rhs, type_) # Helper for '_merge_chunk'.\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.__init__","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.__init__\n def __init__(self, connection):\n self.connection = connection\n self.description = None\n # Per PEP 249: The attribute is -1 in case no .execute*() has been\n # performed on the cursor or the rowcount of the last operation\n # cannot be determined by the interface.\n self.rowcount = -1\n # Per PEP 249: The arraysize attribute defaults to 1, meaning to fetch\n # a single row at a time.\n self.arraysize = 1\n self._query_data = None\n self._query_results = None","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.close","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.close\n def close(self):\n \"\"\"No-op.\"\"\"","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._set_description","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._set_description\n def _set_description(self, schema):\n \"\"\"Set description from schema.\n\n :type schema: Sequence[google.cloud.bigquery.schema.SchemaField]\n :param schema: A description of fields in the schema.\n \"\"\"\n if schema is None:\n self.description = None\n return\n\n self.description = tuple([\n Column(\n name=field.name,\n type_code=field.field_type,\n display_size=None,\n internal_size=None,\n precision=None,\n scale=None,\n null_ok=field.is_nullable)\n for field in schema])","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._set_rowcount","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._set_rowcount\n def _set_rowcount(self, query_results):\n \"\"\"Set the rowcount from query results.\n\n Normally, this sets rowcount to the number of rows returned by the\n query, but if it was a DML statement, it sets rowcount to the number\n of modified rows.\n\n :type query_results:\n :class:`~google.cloud.bigquery.query.QueryResults`\n :param query_results: results of a query\n \"\"\"\n total_rows = 0\n num_dml_affected_rows = query_results.num_dml_affected_rows\n\n if (query_results.total_rows is not None\n and query_results.total_rows > 0):\n total_rows = query_results.total_rows\n if num_dml_affected_rows is not None and num_dml_affected_rows > 0:\n total_rows = num_dml_affected_rows\n self.rowcount = total_rows","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.execute","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.execute\n def execute(self, operation, parameters=None, job_id=None):\n \"\"\"Prepare and execute a database operation.\n\n .. note::\n When setting query parameters, values which are \"text\"\n (``unicode`` in Python2, ``str`` in Python3) will use\n the 'STRING' BigQuery type. Values which are \"bytes\" (``str`` in\n Python2, ``bytes`` in Python3), will use using the 'BYTES' type.\n\n A `~datetime.datetime` parameter without timezone information uses\n the 'DATETIME' BigQuery type (example: Global Pi Day Celebration\n March 14, 2017 at 1:59pm). A `~datetime.datetime` parameter with\n timezone information uses the 'TIMESTAMP' BigQuery type (example:\n a wedding on April 29, 2011 at 11am, British Summer Time).\n\n For more information about BigQuery data types, see:\n https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types\n\n ``STRUCT``/``RECORD`` and ``REPEATED`` query parameters are not\n yet supported. See:\n https://github.com/GoogleCloudPlatform/google-cloud-python/issues/3524\n\n :type operation: str\n :param operation: A Google BigQuery query string.\n\n :type parameters: Mapping[str, Any] or Sequence[Any]\n :param parameters:\n (Optional) dictionary or sequence of parameter values.\n\n :type job_id: str\n :param job_id: (Optional) The job_id to use. If not set, a job ID\n is generated at random.\n \"\"\"\n self._query_data = None\n self._query_results = None\n client = self.connection._client\n if job_id is None:\n job_id = str(uuid.uuid4())\n\n # The DB-API uses the pyformat formatting, since the way BigQuery does\n # query parameters was not one of the standard options. Convert both\n # the query and the parameters to the format expected by","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.executemany","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.executemany\n def executemany(self, operation, seq_of_parameters):\n \"\"\"Prepare and execute a database operation multiple times.\n\n :type operation: str\n :param operation: A Google BigQuery query string.\n\n :type seq_of_parameters: Sequence[Mapping[str, Any] or Sequence[Any]]\n :param parameters: Sequence of many sets of parameter values.\n \"\"\"\n for parameters in seq_of_parameters:\n self.execute(operation, parameters)","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._try_fetch","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._try_fetch\n def _try_fetch(self, size=None):\n \"\"\"Try to start fetching data, if not yet started.\n\n Mutates self to indicate that iteration has started.\n \"\"\"\n if self._query_results is None:\n raise exceptions.InterfaceError(\n 'No query results: execute() must be called before fetch.')\n\n if size is None:\n size = self.arraysize\n\n if self._query_data is None:\n self._query_data = iter(\n self._query_results.fetch_data(max_results=size))","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchone","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchone\n def fetchone(self):\n \"\"\"Fetch a single row from the results of the last ``execute*()`` call.\n\n :rtype: tuple\n :returns:\n A tuple representing a row or ``None`` if no more data is\n available.\n :raises: :class:`~google.cloud.bigquery.dbapi.InterfaceError`\n if called before ``execute()``.\n \"\"\"\n self._try_fetch()\n try:\n return six.next(self._query_data)\n except StopIteration:\n return None","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchmany","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchmany\n def fetchmany(self, size=None):\n \"\"\"Fetch multiple results from the last ``execute*()`` call.\n\n .. note::\n The size parameter is not used for the request/response size.\n Set the ``arraysize`` attribute before calling ``execute()`` to\n set the batch size.\n\n :type size: int\n :param size:\n (Optional) Maximum number of rows to return. Defaults to the\n ``arraysize`` property value.\n\n :rtype: List[tuple]\n :returns: A list of rows.\n :raises: :class:`~google.cloud.bigquery.dbapi.InterfaceError`\n if called before ``execute()``.\n \"\"\"\n if size is None:\n size = self.arraysize\n\n self._try_fetch(size=size)\n rows = []\n\n for row in self._query_data:\n rows.append(row)\n if len(rows) >= size:\n break\n\n return rows","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchall","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchall\n def fetchall(self):\n \"\"\"Fetch all remaining results from the last ``execute*()`` call.\n\n :rtype: List[tuple]\n :returns: A list of all the rows in the results.\n :raises: :class:`~google.cloud.bigquery.dbapi.InterfaceError`\n if called before ``execute()``.\n \"\"\"\n self._try_fetch()\n return [row for row in self._query_data]","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.setinputsizes","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.setinputsizes\n def setinputsizes(self, sizes):\n \"\"\"No-op.\"\"\"","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.setoutputsize","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.setoutputsize\n def setoutputsize(self, size, column=None):\n \"\"\"No-op.\"\"\"","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation_list","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation_list\ndef _format_operation_list(operation, parameters):\n \"\"\"Formats parameters in operation in the way BigQuery expects.\n\n The input operation will be a query like ``SELECT %s`` and the output\n will be a query like ``SELECT ?``.\n\n :type operation: str\n :param operation: A Google BigQuery query string.\n\n :type parameters: Sequence[Any]\n :param parameters: Sequence of parameter values.\n\n :rtype: str\n :returns: A formatted query string.\n :raises: :class:`~google.cloud.bigquery.dbapi.ProgrammingError`\n if a parameter used in the operation is not found in the\n ``parameters`` argument.\n \"\"\"\n formatted_params = ['?' for _ in parameters]\n\n try:\n return operation % tuple(formatted_params)\n except TypeError as exc:\n raise exceptions.ProgrammingError(exc)","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation_dict","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation_dict\ndef _format_operation_dict(operation, parameters):\n \"\"\"Formats parameters in operation in the way BigQuery expects.\n\n The input operation will be a query like ``SELECT %(namedparam)s`` and\n the output will be a query like ``SELECT @namedparam``.\n\n :type operation: str\n :param operation: A Google BigQuery query string.\n\n :type parameters: Mapping[str, Any]\n :param parameters: Dictionary of parameter values.\n\n :rtype: str\n :returns: A formatted query string.\n :raises: :class:`~google.cloud.bigquery.dbapi.ProgrammingError`\n if a parameter used in the operation is not found in the\n ``parameters`` argument.\n \"\"\"\n formatted_params = {}\n for name in parameters:\n escaped_name = name.replace('`', r'\\`')\n formatted_params[name] = '@`{}`'.format(escaped_name)\n\n try:\n return operation % formatted_params\n except KeyError as exc:\n raise exceptions.ProgrammingError(exc)","label":0},{"cid":"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation","text":"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation\ndef _format_operation(operation, parameters=None):\n \"\"\"Formats parameters in operation in way BigQuery expects.\n\n :type: str\n :param operation: A Google BigQuery query string.\n\n :type: Mapping[str, Any] or Sequence[Any]\n :param parameters: Optional parameter values.\n\n :rtype: str\n :returns: A formatted query string.\n :raises: :class:`~google.cloud.bigquery.dbapi.ProgrammingError`\n if a parameter used in the operation is not found in the\n ``parameters`` argument.\n \"\"\"\n if parameters is None:\n return operation\n\n if isinstance(parameters, collections.Mapping):\n return _format_operation_dict(operation, parameters)\n\n return _format_operation_list(operation, parameters)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__init__","text":"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__init__\n def __init__(self, flag):\n self.flag = flag","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__eq__","text":"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return other.flag == self.flag","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__ne__","text":"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__ne__\n def __ne__(self, other):\n return not self == other","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::SinkFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::SinkFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(sink=self.flag)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::PassAllFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::PassAllFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(pass_all_filter=self.flag)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::BlockAllFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::BlockAllFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(block_all_filter=self.flag)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__init__","text":"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__init__\n def __init__(self, regex):\n self.regex = _to_bytes(regex)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__eq__","text":"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return other.regex == self.regex","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__ne__","text":"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__ne__\n def __ne__(self, other):\n return not self == other","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(row_key_regex_filter=self.regex)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__init__","text":"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__init__\n def __init__(self, sample):\n self.sample = sample","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__eq__","text":"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return other.sample == self.sample","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__ne__","text":"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__ne__\n def __ne__(self, other):\n return not self == other","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(row_sample_filter=self.sample)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(family_name_regex_filter=self.regex)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(column_qualifier_regex_filter=self.regex)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__init__","text":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__init__\n def __init__(self, start=None, end=None):\n self.start = start\n self.end = end","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__eq__","text":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return (other.start == self.start and\n other.end == self.end)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__ne__","text":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__ne__\n def __ne__(self, other):\n return not self == other","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.to_pb\n def to_pb(self):\n \"\"\"Converts the :class:`TimestampRange` to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.TimestampRange`\n :returns: The converted current object.\n \"\"\"\n timestamp_range_kwargs = {}\n if self.start is not None:\n timestamp_range_kwargs['start_timestamp_micros'] = (\n _microseconds_from_datetime(self.start))\n if self.end is not None:\n timestamp_range_kwargs['end_timestamp_micros'] = (\n _microseconds_from_datetime(self.end))\n return data_v2_pb2.TimestampRange(**timestamp_range_kwargs)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__init__","text":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__init__\n def __init__(self, range_):\n self.range_ = range_","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__","text":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return other.range_ == self.range_","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__ne__","text":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__ne__\n def __ne__(self, other):\n return not self == other","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n First converts the ``range_`` on the current object to a protobuf and\n then uses it in the ``timestamp_range_filter`` field.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(\n timestamp_range_filter=self.range_.to_pb())","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__init__","text":"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__init__\n def __init__(self, column_family_id, start_column=None, end_column=None,\n inclusive_start=None, inclusive_end=None):\n self.column_family_id = column_family_id\n\n if inclusive_start is None:\n inclusive_start = True\n elif start_column is None:\n raise ValueError('Inclusive start was specified but no '\n 'start column was given.')\n self.start_column = start_column\n self.inclusive_start = inclusive_start\n\n if inclusive_end is None:\n inclusive_end = True\n elif end_column is None:\n raise ValueError('Inclusive end was specified but no '\n 'end column was given.')\n self.end_column = end_column\n self.inclusive_end = inclusive_end","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__","text":"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return (other.column_family_id == self.column_family_id and\n other.start_column == self.start_column and\n other.end_column == self.end_column and\n other.inclusive_start == self.inclusive_start and\n other.inclusive_end == self.inclusive_end)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__ne__","text":"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__ne__\n def __ne__(self, other):\n return not self == other","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n First converts to a :class:`.data_v2_pb2.ColumnRange` and then uses it\n in the ``column_range_filter`` field.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n column_range_kwargs = {'family_name': self.column_family_id}\n if self.start_column is not None:\n if self.inclusive_start:\n key = 'start_qualifier_closed'\n else:\n key = 'start_qualifier_open'\n column_range_kwargs[key] = _to_bytes(self.start_column)\n if self.end_column is not None:\n if self.inclusive_end:\n key = 'end_qualifier_closed'\n else:\n key = 'end_qualifier_open'\n column_range_kwargs[key] = _to_bytes(self.end_column)\n\n column_range = data_v2_pb2.ColumnRange(**column_range_kwargs)\n return data_v2_pb2.RowFilter(column_range_filter=column_range)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::ValueRegexFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::ValueRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(value_regex_filter=self.regex)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__init__","text":"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__init__\n def __init__(self, start_value=None, end_value=None,\n inclusive_start=None, inclusive_end=None):\n if inclusive_start is None:\n inclusive_start = True\n elif start_value is None:\n raise ValueError('Inclusive start was specified but no '\n 'start value was given.')\n self.start_value = start_value\n self.inclusive_start = inclusive_start\n\n if inclusive_end is None:\n inclusive_end = True\n elif end_value is None:\n raise ValueError('Inclusive end was specified but no '\n 'end value was given.')\n self.end_value = end_value\n self.inclusive_end = inclusive_end","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__eq__","text":"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return (other.start_value == self.start_value and\n other.end_value == self.end_value and\n other.inclusive_start == self.inclusive_start and\n other.inclusive_end == self.inclusive_end)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__ne__","text":"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__ne__\n def __ne__(self, other):\n return not self == other","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n First converts to a :class:`.data_v2_pb2.ValueRange` and then uses\n it to create a row filter protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n value_range_kwargs = {}\n if self.start_value is not None:\n if self.inclusive_start:\n key = 'start_value_closed'\n else:\n key = 'start_value_open'\n value_range_kwargs[key] = _to_bytes(self.start_value)\n if self.end_value is not None:\n if self.inclusive_end:\n key = 'end_value_closed'\n else:\n key = 'end_value_open'\n value_range_kwargs[key] = _to_bytes(self.end_value)\n\n value_range = data_v2_pb2.ValueRange(**value_range_kwargs)\n return data_v2_pb2.RowFilter(value_range_filter=value_range)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__init__","text":"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__init__\n def __init__(self, num_cells):\n self.num_cells = num_cells","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__eq__","text":"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return other.num_cells == self.num_cells","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__ne__","text":"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__ne__\n def __ne__(self, other):\n return not self == other","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(\n cells_per_row_offset_filter=self.num_cells)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(cells_per_row_limit_filter=self.num_cells)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(\n cells_per_column_limit_filter=self.num_cells)","label":0},{"cid":"bigtable/google/cloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb","text":"bigtable/google/cloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(strip_value_transformer=self.flag)","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.__init__","text":"spanner/google/cloud/spanner/database.py::Database.__init__\n def __init__(self, database_id, instance, ddl_statements=(), pool=None):\n self.database_id = database_id\n self._instance = instance\n self._ddl_statements = _check_ddl_statements(ddl_statements)\n self._local = threading.local()\n\n if pool is None:\n pool = BurstyPool()\n\n self._pool = pool\n pool.bind(self)","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.from_pb","text":"spanner/google/cloud/spanner/database.py::Database.from_pb\n @classmethod\n def from_pb(cls, database_pb, instance, pool=None):\n \"\"\"Creates an instance of this class from a protobuf.\n\n :type database_pb:\n :class:`google.spanner.v2.spanner_instance_admin_pb2.Instance`\n :param database_pb: A instance protobuf object.\n\n :type instance: :class:`~google.cloud.spanner.instance.Instance`\n :param instance: The instance that owns the database.\n\n :type pool: concrete subclass of\n :class:`~google.cloud.spanner.pool.AbstractSessionPool`.\n :param pool: (Optional) session pool to be used by database.\n\n :rtype: :class:`Database`\n :returns: The database parsed from the protobuf response.\n :raises ValueError:\n if the instance name does not match the expected format\n or if the parsed project ID does not match the project ID\n on the instance's client, or if the parsed instance ID does\n not match the instance's ID.\n \"\"\"\n match = _DATABASE_NAME_RE.match(database_pb.name)\n if match is None:\n raise ValueError('Database protobuf name was not in the '\n 'expected format.', database_pb.name)\n if match.group('project') != instance._client.project:\n raise ValueError('Project ID on database does not match the '\n 'project ID on the instance\\'s client')\n instance_id = match.group('instance_id')\n if instance_id != instance.instance_id:\n raise ValueError('Instance ID on database does not match the '\n 'Instance ID on the instance')\n database_id = match.group('database_id')\n\n return cls(database_id, instance, pool=pool)","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.name","text":"spanner/google/cloud/spanner/database.py::Database.name\n @property\n def name(self):\n \"\"\"Database name used in requests.\n\n .. note::\n\n This property will not change if ``database_id`` does not, but the\n return value is not cached.\n\n The database name is of the form\n\n ``\"projects/../instances/../databases/{database_id}\"``\n\n :rtype: str\n :returns: The database name.\n \"\"\"\n return self._instance.name + '/databases/' + self.database_id","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.ddl_statements","text":"spanner/google/cloud/spanner/database.py::Database.ddl_statements\n @property\n def ddl_statements(self):\n \"\"\"DDL Statements used to define database schema.\n\n See\n cloud.google.com/spanner/docs/data-definition-language\n\n :rtype: sequence of string\n :returns: the statements\n \"\"\"\n return self._ddl_statements","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.spanner_api","text":"spanner/google/cloud/spanner/database.py::Database.spanner_api\n @property\n def spanner_api(self):\n \"\"\"Helper for session-related API calls.\"\"\"\n if self._spanner_api is None:\n credentials = self._instance._client.credentials\n if isinstance(credentials, google.auth.credentials.Scoped):\n credentials = credentials.with_scopes((SPANNER_DATA_SCOPE,))\n self._spanner_api = SpannerClient(\n lib_name='gccl',\n lib_version=__version__,\n credentials=credentials,\n )\n return self._spanner_api","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.__eq__","text":"spanner/google/cloud/spanner/database.py::Database.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return NotImplemented\n return (other.database_id == self.database_id and\n other._instance == self._instance)","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.__ne__","text":"spanner/google/cloud/spanner/database.py::Database.__ne__\n def __ne__(self, other):\n return not self == other","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.create","text":"spanner/google/cloud/spanner/database.py::Database.create\n def create(self):\n \"\"\"Create this database within its instance\n\n Inclues any configured schema assigned to :attr:`ddl_statements`.\n\n See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.DatabaseAdmin.CreateDatabase\n\n :rtype: :class:`~google.api.core.operation.Operation`\n :returns: a future used to poll the status of the create request\n :raises Conflict: if the database already exists\n :raises NotFound: if the instance owning the database does not exist\n :raises GaxError:\n for errors other than ``ALREADY_EXISTS`` returned from the call\n \"\"\"\n api = self._instance._client.database_admin_api\n options = _options_with_prefix(self.name)\n db_name = self.database_id\n if '-' in db_name:\n db_name = '`%s`' % (db_name,)\n\n try:\n future = api.create_database(\n parent=self._instance.name,\n create_statement='CREATE DATABASE %s' % (db_name,),\n extra_statements=list(self._ddl_statements),\n options=options,\n )\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.ALREADY_EXISTS:\n raise Conflict(self.name)\n elif exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound('Instance not found: {name}'.format(\n name=self._instance.name,\n ))\n raise\n\n return future","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.exists","text":"spanner/google/cloud/spanner/database.py::Database.exists\n def exists(self):\n \"\"\"Test whether this database exists.\n\n See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.DatabaseAdmin.GetDatabaseDDL\n\n :rtype: bool\n :returns: True if the database exists, else false.\n :raises GaxError:\n for errors other than ``NOT_FOUND`` returned from the call\n \"\"\"\n api = self._instance._client.database_admin_api\n options = _options_with_prefix(self.name)\n\n try:\n api.get_database_ddl(self.name, options=options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n return False\n raise\n return True","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.reload","text":"spanner/google/cloud/spanner/database.py::Database.reload\n def reload(self):\n \"\"\"Reload this database.\n\n Refresh any configured schema into :attr:`ddl_statements`.\n\n See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.DatabaseAdmin.GetDatabaseDDL\n\n :raises NotFound: if the database does not exist\n :raises GaxError:\n for errors other than ``NOT_FOUND`` returned from the call\n \"\"\"\n api = self._instance._client.database_admin_api\n options = _options_with_prefix(self.name)\n\n try:\n response = api.get_database_ddl(self.name, options=options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(self.name)\n raise\n self._ddl_statements = tuple(response.statements)","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.update_ddl","text":"spanner/google/cloud/spanner/database.py::Database.update_ddl\n def update_ddl(self, ddl_statements):\n \"\"\"Update DDL for this database.\n\n Apply any configured schema from :attr:`ddl_statements`.\n\n See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.DatabaseAdmin.UpdateDatabase\n\n :rtype: :class:`google.api.core.operation.Operation`\n :returns: an operation instance\n :raises NotFound: if the database does not exist\n :raises GaxError:\n for errors other than ``NOT_FOUND`` returned from the call\n \"\"\"\n client = self._instance._client\n api = client.database_admin_api\n options = _options_with_prefix(self.name)\n\n try:\n future = api.update_database_ddl(\n self.name, ddl_statements, '', options=options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(self.name)\n raise\n\n return future","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.drop","text":"spanner/google/cloud/spanner/database.py::Database.drop\n def drop(self):\n \"\"\"Drop this database.\n\n See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.DatabaseAdmin.DropDatabase\n \"\"\"\n api = self._instance._client.database_admin_api\n options = _options_with_prefix(self.name)\n\n try:\n api.drop_database(self.name, options=options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(self.name)\n raise","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.session","text":"spanner/google/cloud/spanner/database.py::Database.session\n def session(self):\n \"\"\"Factory to create a session for this database.\n\n :rtype: :class:`~google.cloud.spanner.session.Session`\n :returns: a session bound to this database.\n \"\"\"\n return Session(self)","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.snapshot","text":"spanner/google/cloud/spanner/database.py::Database.snapshot\n def snapshot(self, **kw):\n \"\"\"Return an object which wraps a snapshot.\n\n The wrapper *must* be used as a context manager, with the snapshot\n as the value returned by the wrapper.\n\n See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.TransactionOptions.ReadOnly\n\n :type kw: dict\n :param kw:\n Passed through to\n :class:`~google.cloud.spanner.snapshot.Snapshot` constructor.\n\n :rtype: :class:`~google.cloud.spanner.database.SnapshotCheckout`\n :returns: new wrapper\n \"\"\"\n return SnapshotCheckout(self, **kw)","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.batch","text":"spanner/google/cloud/spanner/database.py::Database.batch\n def batch(self):\n \"\"\"Return an object which wraps a batch.\n\n The wrapper *must* be used as a context manager, with the batch\n as the value returned by the wrapper.\n\n :rtype: :class:`~google.cloud.spanner.database.BatchCheckout`\n :returns: new wrapper\n \"\"\"\n return BatchCheckout(self)","label":0},{"cid":"spanner/google/cloud/spanner/database.py::Database.run_in_transaction","text":"spanner/google/cloud/spanner/database.py::Database.run_in_transaction\n def run_in_transaction(self, func, *args, **kw):\n \"\"\"Perform a unit of work in a transaction, retrying on abort.\n\n :type func: callable\n :param func: takes a required positional argument, the transaction,\n and additional positional / keyword arguments as supplied\n by the caller.\n\n :type args: tuple\n :param args: additional positional arguments to be passed to ``func``.\n\n :type kw: dict\n :param kw: optional keyword arguments to be passed to ``func``.\n If passed, \"timeout_secs\" will be removed and used to\n override the default timeout.\n\n :rtype: :class:`datetime.datetime`\n :returns: timestamp of committed transaction\n \"\"\"\n # Sanity check: Is there a transaction already running?\n # If there is, then raise a red flag. Otherwise, mark that this one\n # is running.\n if getattr(self._local, 'transaction_running', False):\n raise RuntimeError('Spanner does not support nested transactions.')\n self._local.transaction_running = True\n\n # Check out a session and run the function in a transaction; once\n # done, flip the sanity check bit back.\n try:\n with SessionCheckout(self._pool) as session:\n return session.run_in_transaction(func, *args, **kw)\n finally:\n self._local.transaction_running = False","label":0},{"cid":"spanner/google/cloud/spanner/database.py::BatchCheckout.__init__","text":"spanner/google/cloud/spanner/database.py::BatchCheckout.__init__\n def __init__(self, database):\n self._database = database\n self._session = self._batch = None","label":0},{"cid":"spanner/google/cloud/spanner/database.py::BatchCheckout.__enter__","text":"spanner/google/cloud/spanner/database.py::BatchCheckout.__enter__\n def __enter__(self):\n \"\"\"Begin ``with`` block.\"\"\"\n session = self._session = self._database._pool.get()\n batch = self._batch = Batch(session)\n return batch","label":0},{"cid":"spanner/google/cloud/spanner/database.py::BatchCheckout.__exit__","text":"spanner/google/cloud/spanner/database.py::BatchCheckout.__exit__\n def __exit__(self, exc_type, exc_val, exc_tb):\n \"\"\"End ``with`` block.\"\"\"\n try:\n if exc_type is None:\n self._batch.commit()\n finally:\n self._database._pool.put(self._session)","label":0},{"cid":"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__init__","text":"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__init__\n def __init__(self, database, **kw):\n self._database = database\n self._session = None\n self._kw = kw","label":0},{"cid":"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__enter__","text":"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__enter__\n def __enter__(self):\n \"\"\"Begin ``with`` block.\"\"\"\n session = self._session = self._database._pool.get()\n return Snapshot(session, **self._kw)","label":0},{"cid":"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__exit__","text":"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__exit__\n def __exit__(self, exc_type, exc_val, exc_tb):\n \"\"\"End ``with`` block.\"\"\"\n self._database._pool.put(self._session)","label":0},{"cid":"spanner/google/cloud/spanner/database.py::_check_ddl_statements","text":"spanner/google/cloud/spanner/database.py::_check_ddl_statements\ndef _check_ddl_statements(value):\n \"\"\"Validate DDL Statements used to define database schema.\n\n See\n https://cloud.google.com/spanner/docs/data-definition-language\n\n :type value: list of string\n :param value: DDL statements, excluding the 'CREATE DATABSE' statement\n\n :rtype: tuple\n :returns: tuple of validated DDL statement strings.\n :raises ValueError:\n if elements in ``value`` are not strings, or if ``value`` contains\n a ``CREATE DATABASE`` statement.\n \"\"\"\n if not all(isinstance(line, six.string_types) for line in value):\n raise ValueError(\"Pass a list of strings\")\n\n if any('create database' in line.lower() for line in value):\n raise ValueError(\"Do not pass a 'CREATE DATABASE' statement\")\n\n return tuple(value)","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.__init__","text":"spanner/google/cloud/spanner/session.py::Session.__init__\n def __init__(self, database):\n self._database = database","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.__lt__","text":"spanner/google/cloud/spanner/session.py::Session.__lt__\n def __lt__(self, other):\n return self._session_id < other._session_id","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.session_id","text":"spanner/google/cloud/spanner/session.py::Session.session_id\n @property\n def session_id(self):\n \"\"\"Read-only ID, set by the back-end during :meth:`create`.\"\"\"\n return self._session_id","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.name","text":"spanner/google/cloud/spanner/session.py::Session.name\n @property\n def name(self):\n \"\"\"Session name used in requests.\n\n .. note::\n\n This property will not change if ``session_id`` does not, but the\n return value is not cached.\n\n The session name is of the form\n\n ``\"projects/../instances/../databases/../sessions/{session_id}\"``\n\n :rtype: str\n :returns: The session name.\n :raises ValueError: if session is not yet created\n \"\"\"\n if self._session_id is None:\n raise ValueError('No session ID set by back-end')\n return self._database.name + '/sessions/' + self._session_id","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.create","text":"spanner/google/cloud/spanner/session.py::Session.create\n def create(self):\n \"\"\"Create this session, bound to its database.\n\n See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.Spanner.CreateSession\n\n :raises: :exc:`ValueError` if :attr:`session_id` is already set.\n \"\"\"\n if self._session_id is not None:\n raise ValueError('Session ID already set by back-end')\n api = self._database.spanner_api\n options = _options_with_prefix(self._database.name)\n session_pb = api.create_session(self._database.name, options=options)\n self._session_id = session_pb.name.split('/')[-1]","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.exists","text":"spanner/google/cloud/spanner/session.py::Session.exists\n def exists(self):\n \"\"\"Test for the existence of this session.\n\n See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.Spanner.GetSession\n\n :rtype: bool\n :returns: True if the session exists on the back-end, else False.\n :raises GaxError:\n for errors other than ``NOT_FOUND`` returned from the call\n \"\"\"\n if self._session_id is None:\n return False\n api = self._database.spanner_api\n options = _options_with_prefix(self._database.name)\n try:\n api.get_session(self.name, options=options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n return False\n raise\n else:\n return True","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.delete","text":"spanner/google/cloud/spanner/session.py::Session.delete\n def delete(self):\n \"\"\"Delete this session.\n\n See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.Spanner.GetSession\n\n :raises ValueError: if :attr:`session_id` is not already set.\n :raises NotFound: if the session does not exist\n :raises GaxError:\n for errors other than ``NOT_FOUND`` returned from the call\n \"\"\"\n if self._session_id is None:\n raise ValueError('Session ID not set by back-end')\n api = self._database.spanner_api\n options = _options_with_prefix(self._database.name)\n try:\n api.delete_session(self.name, options=options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(self.name)\n raise","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.snapshot","text":"spanner/google/cloud/spanner/session.py::Session.snapshot\n def snapshot(self, **kw):\n \"\"\"Create a snapshot to perform a set of reads with shared staleness.\n\n See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.TransactionOptions.ReadOnly\n\n :type kw: dict\n :param kw: Passed through to\n :class:`~google.cloud.spanner.snapshot.Snapshot` ctor.\n\n :rtype: :class:`~google.cloud.spanner.snapshot.Snapshot`\n :returns: a snapshot bound to this session\n :raises ValueError: if the session has not yet been created.\n \"\"\"\n if self._session_id is None:\n raise ValueError(\"Session has not been created.\")\n\n return Snapshot(self, **kw)","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.read","text":"spanner/google/cloud/spanner/session.py::Session.read\n def read(self, table, columns, keyset, index='', limit=0,\n resume_token=b''):\n \"\"\"Perform a ``StreamingRead`` API request for rows in a table.\n\n :type table: str\n :param table: name of the table from which to fetch data\n\n :type columns: list of str\n :param columns: names of columns to be retrieved\n\n :type keyset: :class:`~google.cloud.spanner.keyset.KeySet`\n :param keyset: keys / ranges identifying rows to be retrieved\n\n :type index: str\n :param index: (Optional) name of index to use, rather than the\n table's primary key\n\n :type limit: int\n :param limit: (Optional) maxiumn number of rows to return\n\n :type resume_token: bytes\n :param resume_token: token for resuming previously-interrupted read\n\n :rtype: :class:`~google.cloud.spanner.streamed.StreamedResultSet`\n :returns: a result set instance which can be used to consume rows.\n \"\"\"\n return self.snapshot().read(\n table, columns, keyset, index, limit, resume_token)","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.execute_sql","text":"spanner/google/cloud/spanner/session.py::Session.execute_sql\n def execute_sql(self, sql, params=None, param_types=None, query_mode=None,\n resume_token=b''):\n \"\"\"Perform an ``ExecuteStreamingSql`` API request.\n\n :type sql: str\n :param sql: SQL query statement\n\n :type params: dict, {str -> column value}\n :param params: values for parameter replacement. Keys must match\n the names used in ``sql``.\n\n :type param_types:\n dict, {str -> :class:`google.spanner.v1.type_pb2.TypeCode`}\n :param param_types: (Optional) explicit types for one or more param\n values; overrides default type detection on the\n back-end.\n\n :type query_mode:\n :class:`google.spanner.v1.spanner_pb2.ExecuteSqlRequest.QueryMode`\n :param query_mode: Mode governing return of results / query plan. See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.ExecuteSqlRequest.QueryMode1\n\n :type resume_token: bytes\n :param resume_token: token for resuming previously-interrupted query\n\n :rtype: :class:`~google.cloud.spanner.streamed.StreamedResultSet`\n :returns: a result set instance which can be used to consume rows.\n \"\"\"\n return self.snapshot().execute_sql(\n sql, params, param_types, query_mode, resume_token)","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.batch","text":"spanner/google/cloud/spanner/session.py::Session.batch\n def batch(self):\n \"\"\"Factory to create a batch for this session.\n\n :rtype: :class:`~google.cloud.spanner.batch.Batch`\n :returns: a batch bound to this session\n :raises ValueError: if the session has not yet been created.\n \"\"\"\n if self._session_id is None:\n raise ValueError(\"Session has not been created.\")\n\n return Batch(self)","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.transaction","text":"spanner/google/cloud/spanner/session.py::Session.transaction\n def transaction(self):\n \"\"\"Create a transaction to perform a set of reads with shared staleness.\n\n :rtype: :class:`~google.cloud.spanner.transaction.Transaction`\n :returns: a transaction bound to this session\n :raises ValueError: if the session has not yet been created.\n \"\"\"\n if self._session_id is None:\n raise ValueError(\"Session has not been created.\")\n\n if self._transaction is not None:\n self._transaction._rolled_back = True\n del self._transaction\n\n txn = self._transaction = Transaction(self)\n return txn","label":0},{"cid":"spanner/google/cloud/spanner/session.py::Session.run_in_transaction","text":"spanner/google/cloud/spanner/session.py::Session.run_in_transaction\n def run_in_transaction(self, func, *args, **kw):\n \"\"\"Perform a unit of work in a transaction, retrying on abort.\n\n :type func: callable\n :param func: takes a required positional argument, the transaction,\n and additional positional / keyword arguments as supplied\n by the caller.\n\n :type args: tuple\n :param args: additional positional arguments to be passed to ``func``.\n\n :type kw: dict\n :param kw: optional keyword arguments to be passed to ``func``.\n If passed, \"timeout_secs\" will be removed and used to\n override the default timeout.\n\n :rtype: Any\n :returns: The return value of ``func``.\n\n :raises Exception:\n reraises any non-ABORT execptions raised by ``func``.\n \"\"\"\n deadline = time.time() + kw.pop(\n 'timeout_secs', DEFAULT_RETRY_TIMEOUT_SECS)\n\n while True:\n if self._transaction is None:\n txn = self.transaction()\n else:\n txn = self._transaction\n if txn._transaction_id is None:\n txn.begin()\n try:\n return_value = func(txn, *args, **kw)\n except (GaxError, GrpcRendezvous) as exc:\n _delay_until_retry(exc, deadline)\n del self._transaction\n continue\n except Exception:\n txn.rollback()\n raise\n\n try:\n txn.commit()\n except GaxError as exc:\n _delay_until_retry(exc, deadline)\n del self._transaction\n else:\n return return_value","label":0},{"cid":"spanner/google/cloud/spanner/session.py::_delay_until_retry","text":"spanner/google/cloud/spanner/session.py::_delay_until_retry\ndef _delay_until_retry(exc, deadline):\n \"\"\"Helper for :meth:`Session.run_in_transaction`.\n\n Detect retryable abort, and impose server-supplied delay.\n\n :type exc: :class:`google.gax.errors.GaxError`\n :param exc: exception for aborted transaction\n\n :type deadline: float\n :param deadline: maximum timestamp to continue retrying the transaction.\n \"\"\"\n if isinstance(exc, GrpcRendezvous): # pragma: NO COVER see #3663\n cause = exc\n else:\n cause = exc.cause\n\n if exc_to_code(cause) != StatusCode.ABORTED:\n raise\n\n now = time.time()\n\n if now >= deadline:\n raise\n\n delay = _get_retry_delay(cause)\n if delay is not None:\n\n if now + delay > deadline:\n raise\n\n time.sleep(delay)","label":0},{"cid":"spanner/google/cloud/spanner/session.py::_get_retry_delay","text":"spanner/google/cloud/spanner/session.py::_get_retry_delay\ndef _get_retry_delay(cause):\n \"\"\"Helper for :func:`_delay_until_retry`.\n\n :type exc: :class:`google.gax.errors.GaxError`\n :param exc: exception for aborted transaction\n\n :rtype: float\n :returns: seconds to wait before retrying the transaction.\n \"\"\"\n metadata = dict(cause.trailing_metadata())\n retry_info_pb = metadata.get('google.rpc.retryinfo-bin')\n if retry_info_pb is not None:\n retry_info = RetryInfo()\n retry_info.ParseFromString(retry_info_pb)\n nanos = retry_info.retry_delay.nanos\n return retry_info.retry_delay.seconds + nanos / 1.0e9","label":0},{"cid":"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase._make_txn_selector","text":"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase._make_txn_selector\n def _make_txn_selector(self): # pylint: disable=redundant-returns-doc\n \"\"\"Helper for :meth:`read` / :meth:`execute_sql`.\n\n Subclasses must override, returning an instance of\n :class:`transaction_pb2.TransactionSelector`\n appropriate for making ``read`` / ``execute_sql`` requests\n\n :raises: NotImplementedError, always\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase.read","text":"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase.read\n def read(self, table, columns, keyset, index='', limit=0,\n resume_token=b''):\n \"\"\"Perform a ``StreamingRead`` API request for rows in a table.\n\n :type table: str\n :param table: name of the table from which to fetch data\n\n :type columns: list of str\n :param columns: names of columns to be retrieved\n\n :type keyset: :class:`~google.cloud.spanner.keyset.KeySet`\n :param keyset: keys / ranges identifying rows to be retrieved\n\n :type index: str\n :param index: (Optional) name of index to use, rather than the\n table's primary key\n\n :type limit: int\n :param limit: (Optional) maxiumn number of rows to return\n\n :type resume_token: bytes\n :param resume_token: token for resuming previously-interrupted read\n\n :rtype: :class:`~google.cloud.spanner.streamed.StreamedResultSet`\n :returns: a result set instance which can be used to consume rows.\n :raises ValueError:\n for reuse of single-use snapshots, or if a transaction ID is\n already pending for multiple-use snapshots.\n \"\"\"\n if self._read_request_count > 0:\n if not self._multi_use:\n raise ValueError(\"Cannot re-use single-use snapshot.\")\n if self._transaction_id is None:\n raise ValueError(\"Transaction ID pending.\")\n\n database = self._session._database\n api = database.spanner_api\n options = _options_with_prefix(database.name)\n transaction = self._make_txn_selector()\n\n iterator = api.streaming_read(\n self._session.name, table, columns, keyset.to_pb(),\n transaction=transaction, index=index, limit=limit,\n resume_token=resume_token, options=options)\n\n self._read_request_count += 1\n\n if self._multi_use:\n return StreamedResultSet(iterator, source=self)\n ","label":0},{"cid":"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase.execute_sql","text":"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase.execute_sql\n def execute_sql(self, sql, params=None, param_types=None, query_mode=None,\n resume_token=b''):\n \"\"\"Perform an ``ExecuteStreamingSql`` API request for rows in a table.\n\n :type sql: str\n :param sql: SQL query statement\n\n :type params: dict, {str -> column value}\n :param params: values for parameter replacement. Keys must match\n the names used in ``sql``.\n\n :type param_types: dict\n :param param_types:\n (Optional) maps explicit types for one or more param values;\n required if parameters are passed.\n\n :type query_mode:\n :class:`google.cloud.proto.spanner.v1.ExecuteSqlRequest.QueryMode`\n :param query_mode: Mode governing return of results / query plan. See\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.ExecuteSqlRequest.QueryMode1\n\n :type resume_token: bytes\n :param resume_token: token for resuming previously-interrupted query\n\n :rtype: :class:`~google.cloud.spanner.streamed.StreamedResultSet`\n :returns: a result set instance which can be used to consume rows.\n :raises ValueError:\n for reuse of single-use snapshots, or if a transaction ID is\n already pending for multiple-use snapshots.\n \"\"\"\n if self._read_request_count > 0:\n if not self._multi_use:\n raise ValueError(\"Cannot re-use single-use snapshot.\")\n if self._transaction_id is None:\n raise ValueError(\"Transaction ID pending.\")\n\n if params is not None:\n if param_types is None:\n raise ValueError(\n \"Specify 'param_types' when passing 'params'.\")\n params_pb = Struct(fields={\n key: _make_value_pb(value) for key, value in params.items()})\n else:\n params_pb","label":0},{"cid":"spanner/google/cloud/spanner/snapshot.py::Snapshot.__init__","text":"spanner/google/cloud/spanner/snapshot.py::Snapshot.__init__\n def __init__(self, session, read_timestamp=None, min_read_timestamp=None,\n max_staleness=None, exact_staleness=None, multi_use=False):\n super(Snapshot, self).__init__(session)\n opts = [\n read_timestamp, min_read_timestamp, max_staleness, exact_staleness]\n flagged = [opt for opt in opts if opt is not None]\n\n if len(flagged) > 1:\n raise ValueError(\"Supply zero or one options.\")\n\n if multi_use:\n if min_read_timestamp is not None or max_staleness is not None:\n raise ValueError(\n \"'multi_use' is incompatible with \"\n \"'min_read_timestamp' / 'max_staleness'\")\n\n self._strong = len(flagged) == 0\n self._read_timestamp = read_timestamp\n self._min_read_timestamp = min_read_timestamp\n self._max_staleness = max_staleness\n self._exact_staleness = exact_staleness\n self._multi_use = multi_use","label":0},{"cid":"spanner/google/cloud/spanner/snapshot.py::Snapshot._make_txn_selector","text":"spanner/google/cloud/spanner/snapshot.py::Snapshot._make_txn_selector\n def _make_txn_selector(self):\n \"\"\"Helper for :meth:`read`.\"\"\"\n if self._transaction_id is not None:\n return TransactionSelector(id=self._transaction_id)\n\n if self._read_timestamp:\n key = 'read_timestamp'\n value = _datetime_to_pb_timestamp(self._read_timestamp)\n elif self._min_read_timestamp:\n key = 'min_read_timestamp'\n value = _datetime_to_pb_timestamp(self._min_read_timestamp)\n elif self._max_staleness:\n key = 'max_staleness'\n value = _timedelta_to_duration_pb(self._max_staleness)\n elif self._exact_staleness:\n key = 'exact_staleness'\n value = _timedelta_to_duration_pb(self._exact_staleness)\n else:\n key = 'strong'\n value = True\n\n options = TransactionOptions(\n read_only=TransactionOptions.ReadOnly(**{key: value}))\n\n if self._multi_use:\n return TransactionSelector(begin=options)\n else:\n return TransactionSelector(single_use=options)","label":0},{"cid":"spanner/google/cloud/spanner/snapshot.py::Snapshot.begin","text":"spanner/google/cloud/spanner/snapshot.py::Snapshot.begin\n def begin(self):\n \"\"\"Begin a read-only transaction on the database.\n\n :rtype: bytes\n :returns: the ID for the newly-begun transaction.\n :raises ValueError:\n if the transaction is already begun, committed, or rolled back.\n \"\"\"\n if not self._multi_use:\n raise ValueError(\"Cannot call 'begin' single-use snapshots\")\n\n if self._transaction_id is not None:\n raise ValueError(\"Read-only transaction already begun\")\n\n if self._read_request_count > 0:\n raise ValueError(\"Read-only transaction already pending\")\n\n database = self._session._database\n api = database.spanner_api\n options = _options_with_prefix(database.name)\n txn_selector = self._make_txn_selector()\n response = api.begin_transaction(\n self._session.name, txn_selector.begin, options=options)\n self._transaction_id = response.id\n return self._transaction_id","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.__init__","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.__init__\n def __init__(self, response_iterator, source=None):\n self._response_iterator = response_iterator\n self._rows = [] # Fully-processed rows\n self._counter = 0 # Counter for processed responses\n self._metadata = None # Until set from first PRS\n self._stats = None # Until set from last PRS\n self._resume_token = None # To resume from last received PRS\n self._current_row = [] # Accumulated values for incomplete row\n self._pending_chunk = None # Incomplete value\n self._source = source # Source snapshot","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.rows","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.rows\n @property\n def rows(self):\n \"\"\"Fully-processed rows.\n\n :rtype: list of row-data lists.\n :returns: list of completed row data, from proceesd PRS responses.\n \"\"\"\n return self._rows","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.fields","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.fields\n @property\n def fields(self):\n \"\"\"Field descriptors for result set columns.\n\n :rtype: list of :class:`~google.cloud.proto.spanner.v1.type_pb2.Field`\n :returns: list of fields describing column names / types.\n \"\"\"\n return self._metadata.row_type.fields","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.metadata","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.metadata\n @property\n def metadata(self):\n \"\"\"Result set metadata\n\n :rtype: :class:`~.result_set_pb2.ResultSetMetadata`\n :returns: structure describing the results\n \"\"\"\n return self._metadata","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.stats","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.stats\n @property\n def stats(self):\n \"\"\"Result set statistics\n\n :rtype:\n :class:`~google.cloud.proto.spanner.v1.result_set_pb2.ResultSetStats`\n :returns: structure describing status about the response\n \"\"\"\n return self._stats","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.resume_token","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.resume_token\n @property\n def resume_token(self):\n \"\"\"Token for resuming interrupted read / query.\n\n :rtype: bytes\n :returns: token from last chunk of results.\n \"\"\"\n return self._resume_token","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet._merge_chunk","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet._merge_chunk\n def _merge_chunk(self, value):\n \"\"\"Merge pending chunk with next value.\n\n :type value: :class:`~google.protobuf.struct_pb2.Value`\n :param value: continuation of chunked value from previous\n partial result set.\n\n :rtype: :class:`~google.protobuf.struct_pb2.Value`\n :returns: the merged value\n \"\"\"\n current_column = len(self._current_row)\n field = self.fields[current_column]\n merged = _merge_by_type(self._pending_chunk, value, field.type)\n self._pending_chunk = None\n return merged","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet._merge_values","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet._merge_values\n def _merge_values(self, values):\n \"\"\"Merge values into rows.\n\n :type values: list of :class:`~google.protobuf.struct_pb2.Value`\n :param values: non-chunked values from partial result set.\n \"\"\"\n width = len(self.fields)\n for value in values:\n index = len(self._current_row)\n field = self.fields[index]\n self._current_row.append(_parse_value_pb(value, field.type))\n if len(self._current_row) == width:\n self._rows.append(self._current_row)\n self._current_row = []","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.consume_next","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.consume_next\n def consume_next(self):\n \"\"\"Consume the next partial result set from the stream.\n\n Parse the result set into new/existing rows in :attr:`_rows`\n \"\"\"\n response = six.next(self._response_iterator)\n self._counter += 1\n self._resume_token = response.resume_token\n\n if self._metadata is None: # first response\n metadata = self._metadata = response.metadata\n\n source = self._source\n if source is not None and source._transaction_id is None:\n source._transaction_id = metadata.transaction.id\n\n if response.HasField('stats'): # last response\n self._stats = response.stats\n\n values = list(response.values)\n if self._pending_chunk is not None:\n values[0] = self._merge_chunk(values[0])\n\n if response.chunked_value:\n self._pending_chunk = values.pop()\n\n self._merge_values(values)","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.consume_all","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.consume_all\n def consume_all(self):\n \"\"\"Consume the streamed responses until there are no more.\"\"\"\n while True:\n try:\n self.consume_next()\n except StopIteration:\n break","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.__iter__","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.__iter__\n def __iter__(self):\n iter_rows, self._rows[:] = self._rows[:], ()\n while True:\n if not iter_rows:\n self.consume_next() # raises StopIteration\n iter_rows, self._rows[:] = self._rows[:], ()\n while iter_rows:\n yield iter_rows.pop(0)","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.one","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.one\n def one(self):\n \"\"\"Return exactly one result, or raise an exception.\n\n :raises: :exc:`NotFound`: If there are no results.\n :raises: :exc:`ValueError`: If there are multiple results.\n :raises: :exc:`RuntimeError`: If consumption has already occurred,\n in whole or in part.\n \"\"\"\n answer = self.one_or_none()\n if answer is None:\n raise exceptions.NotFound('No rows matched the given query.')\n return answer","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.one_or_none","text":"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.one_or_none\n def one_or_none(self):\n \"\"\"Return exactly one result, or None if there are no results.\n\n :raises: :exc:`ValueError`: If there are multiple results.\n :raises: :exc:`RuntimeError`: If consumption has already occurred,\n in whole or in part.\n \"\"\"\n # Sanity check: Has consumption of this query already started?\n # If it has, then this is an exception.\n if self._metadata is not None:\n raise RuntimeError('Can not call `.one` or `.one_or_none` after '\n 'stream consumption has already started.')\n\n # Consume the first result of the stream.\n # If there is no first result, then return None.\n iterator = iter(self)\n try:\n answer = next(iterator)\n except StopIteration:\n return None\n\n # Attempt to consume more. This should no-op; if we get additional\n # rows, then this is an error case.\n try:\n next(iterator)\n raise ValueError('Expected one result; got more.')\n except StopIteration:\n return answer","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::Unmergeable.__init__","text":"spanner/google/cloud/spanner/streamed.py::Unmergeable.__init__\n def __init__(self, lhs, rhs, type_):\n message = \"Cannot merge %s values: %s %s\" % (\n type_pb2.TypeCode.Name(type_.code), lhs, rhs)\n super(Unmergeable, self).__init__(message)","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::_unmergeable","text":"spanner/google/cloud/spanner/streamed.py::_unmergeable\ndef _unmergeable(lhs, rhs, type_):\n \"\"\"Helper for '_merge_by_type'.\"\"\"\n raise Unmergeable(lhs, rhs, type_)","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::_merge_float64","text":"spanner/google/cloud/spanner/streamed.py::_merge_float64\ndef _merge_float64(lhs, rhs, type_): # pylint: disable=unused-argument\n \"\"\"Helper for '_merge_by_type'.\"\"\"\n lhs_kind = lhs.WhichOneof('kind')\n if lhs_kind == 'string_value':\n return Value(string_value=lhs.string_value + rhs.string_value)\n rhs_kind = rhs.WhichOneof('kind')\n array_continuation = (\n lhs_kind == 'number_value' and\n rhs_kind == 'string_value' and\n rhs.string_value == '')\n if array_continuation:\n return lhs\n raise Unmergeable(lhs, rhs, type_)","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::_merge_string","text":"spanner/google/cloud/spanner/streamed.py::_merge_string\ndef _merge_string(lhs, rhs, type_): # pylint: disable=unused-argument\n \"\"\"Helper for '_merge_by_type'.\"\"\"\n return Value(string_value=lhs.string_value + rhs.string_value)","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::_merge_array","text":"spanner/google/cloud/spanner/streamed.py::_merge_array\ndef _merge_array(lhs, rhs, type_):\n \"\"\"Helper for '_merge_by_type'.\"\"\"\n element_type = type_.array_element_type\n if element_type.code in _UNMERGEABLE_TYPES:\n # Individual values cannot be merged, just concatenate\n lhs.list_value.values.extend(rhs.list_value.values)\n return lhs\n lhs, rhs = list(lhs.list_value.values), list(rhs.list_value.values)\n first = rhs.pop(0)\n if first.HasField('null_value'): # can't merge\n lhs.append(first)\n else:\n last = lhs.pop()\n try:\n merged = _merge_by_type(last, first, element_type)\n except Unmergeable:\n lhs.append(last)\n lhs.append(first)\n else:\n lhs.append(merged)\n return Value(list_value=ListValue(values=(lhs + rhs)))","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::_merge_struct","text":"spanner/google/cloud/spanner/streamed.py::_merge_struct\ndef _merge_struct(lhs, rhs, type_):\n \"\"\"Helper for '_merge_by_type'.\"\"\"\n fields = type_.struct_type.fields\n lhs, rhs = list(lhs.list_value.values), list(rhs.list_value.values)\n candidate_type = fields[len(lhs) - 1].type\n first = rhs.pop(0)\n if (first.HasField('null_value') or\n candidate_type.code in _UNMERGEABLE_TYPES):\n lhs.append(first)\n else:\n last = lhs.pop()\n lhs.append(_merge_by_type(last, first, candidate_type))\n return Value(list_value=ListValue(values=lhs + rhs))","label":0},{"cid":"spanner/google/cloud/spanner/streamed.py::_merge_by_type","text":"spanner/google/cloud/spanner/streamed.py::_merge_by_type\ndef _merge_by_type(lhs, rhs, type_):\n \"\"\"Helper for '_merge_chunk'.\"\"\"\n merger = _MERGE_BY_TYPE[type_.code]\n return merger(lhs, rhs, type_)","label":0}],"string":"[\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.__init__\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.__init__\\n def __init__(self, connection):\\n self.connection = connection\\n self.description = None\\n # Per PEP 249: The attribute is -1 in case no .execute*() has been\\n # performed on the cursor or the rowcount of the last operation\\n # cannot be determined by the interface.\\n self.rowcount = -1\\n # Per PEP 249: The arraysize attribute defaults to 1, meaning to fetch\\n # a single row at a time.\\n self.arraysize = 1\\n self._query_data = None\\n self._query_results = None\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.close\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.close\\n def close(self):\\n \\\"\\\"\\\"No-op.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._set_description\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._set_description\\n def _set_description(self, schema):\\n \\\"\\\"\\\"Set description from schema.\\n\\n :type schema: Sequence[google.cloud.bigquery.schema.SchemaField]\\n :param schema: A description of fields in the schema.\\n \\\"\\\"\\\"\\n if schema is None:\\n self.description = None\\n return\\n\\n self.description = tuple([\\n Column(\\n name=field.name,\\n type_code=field.field_type,\\n display_size=None,\\n internal_size=None,\\n precision=None,\\n scale=None,\\n null_ok=field.is_nullable)\\n for field in schema])\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._set_rowcount\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._set_rowcount\\n def _set_rowcount(self, query_results):\\n \\\"\\\"\\\"Set the rowcount from query results.\\n\\n Normally, this sets rowcount to the number of rows returned by the\\n query, but if it was a DML statement, it sets rowcount to the number\\n of modified rows.\\n\\n :type query_results:\\n :class:`~google.cloud.bigquery.query.QueryResults`\\n :param query_results: results of a query\\n \\\"\\\"\\\"\\n total_rows = 0\\n num_dml_affected_rows = query_results.num_dml_affected_rows\\n\\n if (query_results.total_rows is not None\\n and query_results.total_rows > 0):\\n total_rows = query_results.total_rows\\n if num_dml_affected_rows is not None and num_dml_affected_rows > 0:\\n total_rows = num_dml_affected_rows\\n self.rowcount = total_rows\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.execute\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.execute\\n def execute(self, operation, parameters=None, job_id=None):\\n \\\"\\\"\\\"Prepare and execute a database operation.\\n\\n .. note::\\n When setting query parameters, values which are \\\"text\\\"\\n (``unicode`` in Python2, ``str`` in Python3) will use\\n the 'STRING' BigQuery type. Values which are \\\"bytes\\\" (``str`` in\\n Python2, ``bytes`` in Python3), will use using the 'BYTES' type.\\n\\n A `~datetime.datetime` parameter without timezone information uses\\n the 'DATETIME' BigQuery type (example: Global Pi Day Celebration\\n March 14, 2017 at 1:59pm). A `~datetime.datetime` parameter with\\n timezone information uses the 'TIMESTAMP' BigQuery type (example:\\n a wedding on April 29, 2011 at 11am, British Summer Time).\\n\\n For more information about BigQuery data types, see:\\n https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types\\n\\n ``STRUCT``/``RECORD`` and ``REPEATED`` query parameters are not\\n yet supported. See:\\n https://github.com/GoogleCloudPlatform/google-cloud-python/issues/3524\\n\\n :type operation: str\\n :param operation: A Google BigQuery query string.\\n\\n :type parameters: Mapping[str, Any] or Sequence[Any]\\n :param parameters:\\n (Optional) dictionary or sequence of parameter values.\\n\\n :type job_id: str\\n :param job_id: (Optional) The job_id to use. If not set, a job ID\\n is generated at random.\\n \\\"\\\"\\\"\\n self._query_data = None\\n self._query_results = None\\n client = self.connection._client\\n if job_id is None:\\n job_id = str(uuid.uuid4())\\n\\n # The DB-API uses the pyformat formatting, since the way BigQuery does\\n # query parameters was not one of the standard options. Convert both\\n # the query and the parameters to the format expected by\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.executemany\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.executemany\\n def executemany(self, operation, seq_of_parameters):\\n \\\"\\\"\\\"Prepare and execute a database operation multiple times.\\n\\n :type operation: str\\n :param operation: A Google BigQuery query string.\\n\\n :type seq_of_parameters: Sequence[Mapping[str, Any] or Sequence[Any]]\\n :param parameters: Sequence of many sets of parameter values.\\n \\\"\\\"\\\"\\n for parameters in seq_of_parameters:\\n self.execute(operation, parameters)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._try_fetch\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor._try_fetch\\n def _try_fetch(self, size=None):\\n \\\"\\\"\\\"Try to start fetching data, if not yet started.\\n\\n Mutates self to indicate that iteration has started.\\n \\\"\\\"\\\"\\n if self._query_results is None:\\n raise exceptions.InterfaceError(\\n 'No query results: execute() must be called before fetch.')\\n\\n if size is None:\\n size = self.arraysize\\n\\n if self._query_data is None:\\n self._query_data = iter(\\n self._query_results.fetch_data(max_results=size))\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchone\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchone\\n def fetchone(self):\\n \\\"\\\"\\\"Fetch a single row from the results of the last ``execute*()`` call.\\n\\n :rtype: tuple\\n :returns:\\n A tuple representing a row or ``None`` if no more data is\\n available.\\n :raises: :class:`~google.cloud.bigquery.dbapi.InterfaceError`\\n if called before ``execute()``.\\n \\\"\\\"\\\"\\n self._try_fetch()\\n try:\\n return six.next(self._query_data)\\n except StopIteration:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchmany\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchmany\\n def fetchmany(self, size=None):\\n \\\"\\\"\\\"Fetch multiple results from the last ``execute*()`` call.\\n\\n .. note::\\n The size parameter is not used for the request/response size.\\n Set the ``arraysize`` attribute before calling ``execute()`` to\\n set the batch size.\\n\\n :type size: int\\n :param size:\\n (Optional) Maximum number of rows to return. Defaults to the\\n ``arraysize`` property value.\\n\\n :rtype: List[tuple]\\n :returns: A list of rows.\\n :raises: :class:`~google.cloud.bigquery.dbapi.InterfaceError`\\n if called before ``execute()``.\\n \\\"\\\"\\\"\\n if size is None:\\n size = self.arraysize\\n\\n self._try_fetch(size=size)\\n rows = []\\n\\n for row in self._query_data:\\n rows.append(row)\\n if len(rows) >= size:\\n break\\n\\n return rows\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchall\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.fetchall\\n def fetchall(self):\\n \\\"\\\"\\\"Fetch all remaining results from the last ``execute*()`` call.\\n\\n :rtype: List[tuple]\\n :returns: A list of all the rows in the results.\\n :raises: :class:`~google.cloud.bigquery.dbapi.InterfaceError`\\n if called before ``execute()``.\\n \\\"\\\"\\\"\\n self._try_fetch()\\n return [row for row in self._query_data]\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.setinputsizes\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.setinputsizes\\n def setinputsizes(self, sizes):\\n \\\"\\\"\\\"No-op.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.setoutputsize\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::Cursor.setoutputsize\\n def setoutputsize(self, size, column=None):\\n \\\"\\\"\\\"No-op.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation_list\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation_list\\ndef _format_operation_list(operation, parameters):\\n \\\"\\\"\\\"Formats parameters in operation in the way BigQuery expects.\\n\\n The input operation will be a query like ``SELECT %s`` and the output\\n will be a query like ``SELECT ?``.\\n\\n :type operation: str\\n :param operation: A Google BigQuery query string.\\n\\n :type parameters: Sequence[Any]\\n :param parameters: Sequence of parameter values.\\n\\n :rtype: str\\n :returns: A formatted query string.\\n :raises: :class:`~google.cloud.bigquery.dbapi.ProgrammingError`\\n if a parameter used in the operation is not found in the\\n ``parameters`` argument.\\n \\\"\\\"\\\"\\n formatted_params = ['?' for _ in parameters]\\n\\n try:\\n return operation % tuple(formatted_params)\\n except TypeError as exc:\\n raise exceptions.ProgrammingError(exc)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation_dict\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation_dict\\ndef _format_operation_dict(operation, parameters):\\n \\\"\\\"\\\"Formats parameters in operation in the way BigQuery expects.\\n\\n The input operation will be a query like ``SELECT %(namedparam)s`` and\\n the output will be a query like ``SELECT @namedparam``.\\n\\n :type operation: str\\n :param operation: A Google BigQuery query string.\\n\\n :type parameters: Mapping[str, Any]\\n :param parameters: Dictionary of parameter values.\\n\\n :rtype: str\\n :returns: A formatted query string.\\n :raises: :class:`~google.cloud.bigquery.dbapi.ProgrammingError`\\n if a parameter used in the operation is not found in the\\n ``parameters`` argument.\\n \\\"\\\"\\\"\\n formatted_params = {}\\n for name in parameters:\\n escaped_name = name.replace('`', r'\\\\`')\\n formatted_params[name] = '@`{}`'.format(escaped_name)\\n\\n try:\\n return operation % formatted_params\\n except KeyError as exc:\\n raise exceptions.ProgrammingError(exc)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation\",\n \"text\": \"bigquery/google/cloud/bigquery/dbapi/cursor.py::_format_operation\\ndef _format_operation(operation, parameters=None):\\n \\\"\\\"\\\"Formats parameters in operation in way BigQuery expects.\\n\\n :type: str\\n :param operation: A Google BigQuery query string.\\n\\n :type: Mapping[str, Any] or Sequence[Any]\\n :param parameters: Optional parameter values.\\n\\n :rtype: str\\n :returns: A formatted query string.\\n :raises: :class:`~google.cloud.bigquery.dbapi.ProgrammingError`\\n if a parameter used in the operation is not found in the\\n ``parameters`` argument.\\n \\\"\\\"\\\"\\n if parameters is None:\\n return operation\\n\\n if isinstance(parameters, collections.Mapping):\\n return _format_operation_dict(operation, parameters)\\n\\n return _format_operation_list(operation, parameters)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__init__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__init__\\n def __init__(self, flag):\\n self.flag = flag\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__eq__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return NotImplemented\\n return other.flag == self.flag\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__ne__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::_BoolFilter.__ne__\\n def __ne__(self, other):\\n return not self == other\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::SinkFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::SinkFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(sink=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::PassAllFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::PassAllFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(pass_all_filter=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::BlockAllFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::BlockAllFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(block_all_filter=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__init__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__init__\\n def __init__(self, regex):\\n self.regex = _to_bytes(regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__eq__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return NotImplemented\\n return other.regex == self.regex\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__ne__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::_RegexFilter.__ne__\\n def __ne__(self, other):\\n return not self == other\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(row_key_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__init__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__init__\\n def __init__(self, sample):\\n self.sample = sample\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__eq__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return NotImplemented\\n return other.sample == self.sample\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__ne__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.__ne__\\n def __ne__(self, other):\\n return not self == other\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::RowSampleFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(row_sample_filter=self.sample)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(family_name_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(column_qualifier_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__init__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__init__\\n def __init__(self, start=None, end=None):\\n self.start = start\\n self.end = end\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__eq__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return NotImplemented\\n return (other.start == self.start and\\n other.end == self.end)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__ne__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.__ne__\\n def __ne__(self, other):\\n return not self == other\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRange.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the :class:`TimestampRange` to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.TimestampRange`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n timestamp_range_kwargs = {}\\n if self.start is not None:\\n timestamp_range_kwargs['start_timestamp_micros'] = (\\n _microseconds_from_datetime(self.start))\\n if self.end is not None:\\n timestamp_range_kwargs['end_timestamp_micros'] = (\\n _microseconds_from_datetime(self.end))\\n return data_v2_pb2.TimestampRange(**timestamp_range_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__init__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__init__\\n def __init__(self, range_):\\n self.range_ = range_\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return NotImplemented\\n return other.range_ == self.range_\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__ne__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__ne__\\n def __ne__(self, other):\\n return not self == other\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n First converts the ``range_`` on the current object to a protobuf and\\n then uses it in the ``timestamp_range_filter`` field.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(\\n timestamp_range_filter=self.range_.to_pb())\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__init__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__init__\\n def __init__(self, column_family_id, start_column=None, end_column=None,\\n inclusive_start=None, inclusive_end=None):\\n self.column_family_id = column_family_id\\n\\n if inclusive_start is None:\\n inclusive_start = True\\n elif start_column is None:\\n raise ValueError('Inclusive start was specified but no '\\n 'start column was given.')\\n self.start_column = start_column\\n self.inclusive_start = inclusive_start\\n\\n if inclusive_end is None:\\n inclusive_end = True\\n elif end_column is None:\\n raise ValueError('Inclusive end was specified but no '\\n 'end column was given.')\\n self.end_column = end_column\\n self.inclusive_end = inclusive_end\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return NotImplemented\\n return (other.column_family_id == self.column_family_id and\\n other.start_column == self.start_column and\\n other.end_column == self.end_column and\\n other.inclusive_start == self.inclusive_start and\\n other.inclusive_end == self.inclusive_end)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__ne__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__ne__\\n def __ne__(self, other):\\n return not self == other\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n First converts to a :class:`.data_v2_pb2.ColumnRange` and then uses it\\n in the ``column_range_filter`` field.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n column_range_kwargs = {'family_name': self.column_family_id}\\n if self.start_column is not None:\\n if self.inclusive_start:\\n key = 'start_qualifier_closed'\\n else:\\n key = 'start_qualifier_open'\\n column_range_kwargs[key] = _to_bytes(self.start_column)\\n if self.end_column is not None:\\n if self.inclusive_end:\\n key = 'end_qualifier_closed'\\n else:\\n key = 'end_qualifier_open'\\n column_range_kwargs[key] = _to_bytes(self.end_column)\\n\\n column_range = data_v2_pb2.ColumnRange(**column_range_kwargs)\\n return data_v2_pb2.RowFilter(column_range_filter=column_range)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::ValueRegexFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::ValueRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(value_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__init__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__init__\\n def __init__(self, start_value=None, end_value=None,\\n inclusive_start=None, inclusive_end=None):\\n if inclusive_start is None:\\n inclusive_start = True\\n elif start_value is None:\\n raise ValueError('Inclusive start was specified but no '\\n 'start value was given.')\\n self.start_value = start_value\\n self.inclusive_start = inclusive_start\\n\\n if inclusive_end is None:\\n inclusive_end = True\\n elif end_value is None:\\n raise ValueError('Inclusive end was specified but no '\\n 'end value was given.')\\n self.end_value = end_value\\n self.inclusive_end = inclusive_end\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__eq__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return NotImplemented\\n return (other.start_value == self.start_value and\\n other.end_value == self.end_value and\\n other.inclusive_start == self.inclusive_start and\\n other.inclusive_end == self.inclusive_end)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__ne__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.__ne__\\n def __ne__(self, other):\\n return not self == other\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::ValueRangeFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n First converts to a :class:`.data_v2_pb2.ValueRange` and then uses\\n it to create a row filter protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n value_range_kwargs = {}\\n if self.start_value is not None:\\n if self.inclusive_start:\\n key = 'start_value_closed'\\n else:\\n key = 'start_value_open'\\n value_range_kwargs[key] = _to_bytes(self.start_value)\\n if self.end_value is not None:\\n if self.inclusive_end:\\n key = 'end_value_closed'\\n else:\\n key = 'end_value_open'\\n value_range_kwargs[key] = _to_bytes(self.end_value)\\n\\n value_range = data_v2_pb2.ValueRange(**value_range_kwargs)\\n return data_v2_pb2.RowFilter(value_range_filter=value_range)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__init__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__init__\\n def __init__(self, num_cells):\\n self.num_cells = num_cells\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__eq__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return NotImplemented\\n return other.num_cells == self.num_cells\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__ne__\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::_CellCountFilter.__ne__\\n def __ne__(self, other):\\n return not self == other\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(\\n cells_per_row_offset_filter=self.num_cells)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(cells_per_row_limit_filter=self.num_cells)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(\\n cells_per_column_limit_filter=self.num_cells)\",\n \"label\": 0\n },\n {\n \"cid\": \"bigtable/google/cloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb\",\n \"text\": \"bigtable/google/cloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(strip_value_transformer=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.__init__\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.__init__\\n def __init__(self, database_id, instance, ddl_statements=(), pool=None):\\n self.database_id = database_id\\n self._instance = instance\\n self._ddl_statements = _check_ddl_statements(ddl_statements)\\n self._local = threading.local()\\n\\n if pool is None:\\n pool = BurstyPool()\\n\\n self._pool = pool\\n pool.bind(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.from_pb\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.from_pb\\n @classmethod\\n def from_pb(cls, database_pb, instance, pool=None):\\n \\\"\\\"\\\"Creates an instance of this class from a protobuf.\\n\\n :type database_pb:\\n :class:`google.spanner.v2.spanner_instance_admin_pb2.Instance`\\n :param database_pb: A instance protobuf object.\\n\\n :type instance: :class:`~google.cloud.spanner.instance.Instance`\\n :param instance: The instance that owns the database.\\n\\n :type pool: concrete subclass of\\n :class:`~google.cloud.spanner.pool.AbstractSessionPool`.\\n :param pool: (Optional) session pool to be used by database.\\n\\n :rtype: :class:`Database`\\n :returns: The database parsed from the protobuf response.\\n :raises ValueError:\\n if the instance name does not match the expected format\\n or if the parsed project ID does not match the project ID\\n on the instance's client, or if the parsed instance ID does\\n not match the instance's ID.\\n \\\"\\\"\\\"\\n match = _DATABASE_NAME_RE.match(database_pb.name)\\n if match is None:\\n raise ValueError('Database protobuf name was not in the '\\n 'expected format.', database_pb.name)\\n if match.group('project') != instance._client.project:\\n raise ValueError('Project ID on database does not match the '\\n 'project ID on the instance\\\\'s client')\\n instance_id = match.group('instance_id')\\n if instance_id != instance.instance_id:\\n raise ValueError('Instance ID on database does not match the '\\n 'Instance ID on the instance')\\n database_id = match.group('database_id')\\n\\n return cls(database_id, instance, pool=pool)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.name\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Database name used in requests.\\n\\n .. note::\\n\\n This property will not change if ``database_id`` does not, but the\\n return value is not cached.\\n\\n The database name is of the form\\n\\n ``\\\"projects/../instances/../databases/{database_id}\\\"``\\n\\n :rtype: str\\n :returns: The database name.\\n \\\"\\\"\\\"\\n return self._instance.name + '/databases/' + self.database_id\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.ddl_statements\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.ddl_statements\\n @property\\n def ddl_statements(self):\\n \\\"\\\"\\\"DDL Statements used to define database schema.\\n\\n See\\n cloud.google.com/spanner/docs/data-definition-language\\n\\n :rtype: sequence of string\\n :returns: the statements\\n \\\"\\\"\\\"\\n return self._ddl_statements\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.spanner_api\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.spanner_api\\n @property\\n def spanner_api(self):\\n \\\"\\\"\\\"Helper for session-related API calls.\\\"\\\"\\\"\\n if self._spanner_api is None:\\n credentials = self._instance._client.credentials\\n if isinstance(credentials, google.auth.credentials.Scoped):\\n credentials = credentials.with_scopes((SPANNER_DATA_SCOPE,))\\n self._spanner_api = SpannerClient(\\n lib_name='gccl',\\n lib_version=__version__,\\n credentials=credentials,\\n )\\n return self._spanner_api\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.__eq__\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return NotImplemented\\n return (other.database_id == self.database_id and\\n other._instance == self._instance)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.__ne__\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.__ne__\\n def __ne__(self, other):\\n return not self == other\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.create\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.create\\n def create(self):\\n \\\"\\\"\\\"Create this database within its instance\\n\\n Inclues any configured schema assigned to :attr:`ddl_statements`.\\n\\n See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.DatabaseAdmin.CreateDatabase\\n\\n :rtype: :class:`~google.api.core.operation.Operation`\\n :returns: a future used to poll the status of the create request\\n :raises Conflict: if the database already exists\\n :raises NotFound: if the instance owning the database does not exist\\n :raises GaxError:\\n for errors other than ``ALREADY_EXISTS`` returned from the call\\n \\\"\\\"\\\"\\n api = self._instance._client.database_admin_api\\n options = _options_with_prefix(self.name)\\n db_name = self.database_id\\n if '-' in db_name:\\n db_name = '`%s`' % (db_name,)\\n\\n try:\\n future = api.create_database(\\n parent=self._instance.name,\\n create_statement='CREATE DATABASE %s' % (db_name,),\\n extra_statements=list(self._ddl_statements),\\n options=options,\\n )\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.ALREADY_EXISTS:\\n raise Conflict(self.name)\\n elif exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound('Instance not found: {name}'.format(\\n name=self._instance.name,\\n ))\\n raise\\n\\n return future\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.exists\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.exists\\n def exists(self):\\n \\\"\\\"\\\"Test whether this database exists.\\n\\n See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.DatabaseAdmin.GetDatabaseDDL\\n\\n :rtype: bool\\n :returns: True if the database exists, else false.\\n :raises GaxError:\\n for errors other than ``NOT_FOUND`` returned from the call\\n \\\"\\\"\\\"\\n api = self._instance._client.database_admin_api\\n options = _options_with_prefix(self.name)\\n\\n try:\\n api.get_database_ddl(self.name, options=options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n return False\\n raise\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.reload\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.reload\\n def reload(self):\\n \\\"\\\"\\\"Reload this database.\\n\\n Refresh any configured schema into :attr:`ddl_statements`.\\n\\n See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.DatabaseAdmin.GetDatabaseDDL\\n\\n :raises NotFound: if the database does not exist\\n :raises GaxError:\\n for errors other than ``NOT_FOUND`` returned from the call\\n \\\"\\\"\\\"\\n api = self._instance._client.database_admin_api\\n options = _options_with_prefix(self.name)\\n\\n try:\\n response = api.get_database_ddl(self.name, options=options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(self.name)\\n raise\\n self._ddl_statements = tuple(response.statements)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.update_ddl\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.update_ddl\\n def update_ddl(self, ddl_statements):\\n \\\"\\\"\\\"Update DDL for this database.\\n\\n Apply any configured schema from :attr:`ddl_statements`.\\n\\n See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.DatabaseAdmin.UpdateDatabase\\n\\n :rtype: :class:`google.api.core.operation.Operation`\\n :returns: an operation instance\\n :raises NotFound: if the database does not exist\\n :raises GaxError:\\n for errors other than ``NOT_FOUND`` returned from the call\\n \\\"\\\"\\\"\\n client = self._instance._client\\n api = client.database_admin_api\\n options = _options_with_prefix(self.name)\\n\\n try:\\n future = api.update_database_ddl(\\n self.name, ddl_statements, '', options=options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(self.name)\\n raise\\n\\n return future\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.drop\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.drop\\n def drop(self):\\n \\\"\\\"\\\"Drop this database.\\n\\n See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.DatabaseAdmin.DropDatabase\\n \\\"\\\"\\\"\\n api = self._instance._client.database_admin_api\\n options = _options_with_prefix(self.name)\\n\\n try:\\n api.drop_database(self.name, options=options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(self.name)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.session\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.session\\n def session(self):\\n \\\"\\\"\\\"Factory to create a session for this database.\\n\\n :rtype: :class:`~google.cloud.spanner.session.Session`\\n :returns: a session bound to this database.\\n \\\"\\\"\\\"\\n return Session(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.snapshot\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.snapshot\\n def snapshot(self, **kw):\\n \\\"\\\"\\\"Return an object which wraps a snapshot.\\n\\n The wrapper *must* be used as a context manager, with the snapshot\\n as the value returned by the wrapper.\\n\\n See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.TransactionOptions.ReadOnly\\n\\n :type kw: dict\\n :param kw:\\n Passed through to\\n :class:`~google.cloud.spanner.snapshot.Snapshot` constructor.\\n\\n :rtype: :class:`~google.cloud.spanner.database.SnapshotCheckout`\\n :returns: new wrapper\\n \\\"\\\"\\\"\\n return SnapshotCheckout(self, **kw)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.batch\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.batch\\n def batch(self):\\n \\\"\\\"\\\"Return an object which wraps a batch.\\n\\n The wrapper *must* be used as a context manager, with the batch\\n as the value returned by the wrapper.\\n\\n :rtype: :class:`~google.cloud.spanner.database.BatchCheckout`\\n :returns: new wrapper\\n \\\"\\\"\\\"\\n return BatchCheckout(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::Database.run_in_transaction\",\n \"text\": \"spanner/google/cloud/spanner/database.py::Database.run_in_transaction\\n def run_in_transaction(self, func, *args, **kw):\\n \\\"\\\"\\\"Perform a unit of work in a transaction, retrying on abort.\\n\\n :type func: callable\\n :param func: takes a required positional argument, the transaction,\\n and additional positional / keyword arguments as supplied\\n by the caller.\\n\\n :type args: tuple\\n :param args: additional positional arguments to be passed to ``func``.\\n\\n :type kw: dict\\n :param kw: optional keyword arguments to be passed to ``func``.\\n If passed, \\\"timeout_secs\\\" will be removed and used to\\n override the default timeout.\\n\\n :rtype: :class:`datetime.datetime`\\n :returns: timestamp of committed transaction\\n \\\"\\\"\\\"\\n # Sanity check: Is there a transaction already running?\\n # If there is, then raise a red flag. Otherwise, mark that this one\\n # is running.\\n if getattr(self._local, 'transaction_running', False):\\n raise RuntimeError('Spanner does not support nested transactions.')\\n self._local.transaction_running = True\\n\\n # Check out a session and run the function in a transaction; once\\n # done, flip the sanity check bit back.\\n try:\\n with SessionCheckout(self._pool) as session:\\n return session.run_in_transaction(func, *args, **kw)\\n finally:\\n self._local.transaction_running = False\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::BatchCheckout.__init__\",\n \"text\": \"spanner/google/cloud/spanner/database.py::BatchCheckout.__init__\\n def __init__(self, database):\\n self._database = database\\n self._session = self._batch = None\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::BatchCheckout.__enter__\",\n \"text\": \"spanner/google/cloud/spanner/database.py::BatchCheckout.__enter__\\n def __enter__(self):\\n \\\"\\\"\\\"Begin ``with`` block.\\\"\\\"\\\"\\n session = self._session = self._database._pool.get()\\n batch = self._batch = Batch(session)\\n return batch\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::BatchCheckout.__exit__\",\n \"text\": \"spanner/google/cloud/spanner/database.py::BatchCheckout.__exit__\\n def __exit__(self, exc_type, exc_val, exc_tb):\\n \\\"\\\"\\\"End ``with`` block.\\\"\\\"\\\"\\n try:\\n if exc_type is None:\\n self._batch.commit()\\n finally:\\n self._database._pool.put(self._session)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__init__\",\n \"text\": \"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__init__\\n def __init__(self, database, **kw):\\n self._database = database\\n self._session = None\\n self._kw = kw\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__enter__\",\n \"text\": \"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__enter__\\n def __enter__(self):\\n \\\"\\\"\\\"Begin ``with`` block.\\\"\\\"\\\"\\n session = self._session = self._database._pool.get()\\n return Snapshot(session, **self._kw)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__exit__\",\n \"text\": \"spanner/google/cloud/spanner/database.py::SnapshotCheckout.__exit__\\n def __exit__(self, exc_type, exc_val, exc_tb):\\n \\\"\\\"\\\"End ``with`` block.\\\"\\\"\\\"\\n self._database._pool.put(self._session)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/database.py::_check_ddl_statements\",\n \"text\": \"spanner/google/cloud/spanner/database.py::_check_ddl_statements\\ndef _check_ddl_statements(value):\\n \\\"\\\"\\\"Validate DDL Statements used to define database schema.\\n\\n See\\n https://cloud.google.com/spanner/docs/data-definition-language\\n\\n :type value: list of string\\n :param value: DDL statements, excluding the 'CREATE DATABSE' statement\\n\\n :rtype: tuple\\n :returns: tuple of validated DDL statement strings.\\n :raises ValueError:\\n if elements in ``value`` are not strings, or if ``value`` contains\\n a ``CREATE DATABASE`` statement.\\n \\\"\\\"\\\"\\n if not all(isinstance(line, six.string_types) for line in value):\\n raise ValueError(\\\"Pass a list of strings\\\")\\n\\n if any('create database' in line.lower() for line in value):\\n raise ValueError(\\\"Do not pass a 'CREATE DATABASE' statement\\\")\\n\\n return tuple(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.__init__\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.__init__\\n def __init__(self, database):\\n self._database = database\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.__lt__\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.__lt__\\n def __lt__(self, other):\\n return self._session_id < other._session_id\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.session_id\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.session_id\\n @property\\n def session_id(self):\\n \\\"\\\"\\\"Read-only ID, set by the back-end during :meth:`create`.\\\"\\\"\\\"\\n return self._session_id\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.name\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Session name used in requests.\\n\\n .. note::\\n\\n This property will not change if ``session_id`` does not, but the\\n return value is not cached.\\n\\n The session name is of the form\\n\\n ``\\\"projects/../instances/../databases/../sessions/{session_id}\\\"``\\n\\n :rtype: str\\n :returns: The session name.\\n :raises ValueError: if session is not yet created\\n \\\"\\\"\\\"\\n if self._session_id is None:\\n raise ValueError('No session ID set by back-end')\\n return self._database.name + '/sessions/' + self._session_id\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.create\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.create\\n def create(self):\\n \\\"\\\"\\\"Create this session, bound to its database.\\n\\n See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.Spanner.CreateSession\\n\\n :raises: :exc:`ValueError` if :attr:`session_id` is already set.\\n \\\"\\\"\\\"\\n if self._session_id is not None:\\n raise ValueError('Session ID already set by back-end')\\n api = self._database.spanner_api\\n options = _options_with_prefix(self._database.name)\\n session_pb = api.create_session(self._database.name, options=options)\\n self._session_id = session_pb.name.split('/')[-1]\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.exists\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.exists\\n def exists(self):\\n \\\"\\\"\\\"Test for the existence of this session.\\n\\n See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.Spanner.GetSession\\n\\n :rtype: bool\\n :returns: True if the session exists on the back-end, else False.\\n :raises GaxError:\\n for errors other than ``NOT_FOUND`` returned from the call\\n \\\"\\\"\\\"\\n if self._session_id is None:\\n return False\\n api = self._database.spanner_api\\n options = _options_with_prefix(self._database.name)\\n try:\\n api.get_session(self.name, options=options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n return False\\n raise\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.delete\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.delete\\n def delete(self):\\n \\\"\\\"\\\"Delete this session.\\n\\n See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.Spanner.GetSession\\n\\n :raises ValueError: if :attr:`session_id` is not already set.\\n :raises NotFound: if the session does not exist\\n :raises GaxError:\\n for errors other than ``NOT_FOUND`` returned from the call\\n \\\"\\\"\\\"\\n if self._session_id is None:\\n raise ValueError('Session ID not set by back-end')\\n api = self._database.spanner_api\\n options = _options_with_prefix(self._database.name)\\n try:\\n api.delete_session(self.name, options=options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(self.name)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.snapshot\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.snapshot\\n def snapshot(self, **kw):\\n \\\"\\\"\\\"Create a snapshot to perform a set of reads with shared staleness.\\n\\n See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.TransactionOptions.ReadOnly\\n\\n :type kw: dict\\n :param kw: Passed through to\\n :class:`~google.cloud.spanner.snapshot.Snapshot` ctor.\\n\\n :rtype: :class:`~google.cloud.spanner.snapshot.Snapshot`\\n :returns: a snapshot bound to this session\\n :raises ValueError: if the session has not yet been created.\\n \\\"\\\"\\\"\\n if self._session_id is None:\\n raise ValueError(\\\"Session has not been created.\\\")\\n\\n return Snapshot(self, **kw)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.read\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.read\\n def read(self, table, columns, keyset, index='', limit=0,\\n resume_token=b''):\\n \\\"\\\"\\\"Perform a ``StreamingRead`` API request for rows in a table.\\n\\n :type table: str\\n :param table: name of the table from which to fetch data\\n\\n :type columns: list of str\\n :param columns: names of columns to be retrieved\\n\\n :type keyset: :class:`~google.cloud.spanner.keyset.KeySet`\\n :param keyset: keys / ranges identifying rows to be retrieved\\n\\n :type index: str\\n :param index: (Optional) name of index to use, rather than the\\n table's primary key\\n\\n :type limit: int\\n :param limit: (Optional) maxiumn number of rows to return\\n\\n :type resume_token: bytes\\n :param resume_token: token for resuming previously-interrupted read\\n\\n :rtype: :class:`~google.cloud.spanner.streamed.StreamedResultSet`\\n :returns: a result set instance which can be used to consume rows.\\n \\\"\\\"\\\"\\n return self.snapshot().read(\\n table, columns, keyset, index, limit, resume_token)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.execute_sql\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.execute_sql\\n def execute_sql(self, sql, params=None, param_types=None, query_mode=None,\\n resume_token=b''):\\n \\\"\\\"\\\"Perform an ``ExecuteStreamingSql`` API request.\\n\\n :type sql: str\\n :param sql: SQL query statement\\n\\n :type params: dict, {str -> column value}\\n :param params: values for parameter replacement. Keys must match\\n the names used in ``sql``.\\n\\n :type param_types:\\n dict, {str -> :class:`google.spanner.v1.type_pb2.TypeCode`}\\n :param param_types: (Optional) explicit types for one or more param\\n values; overrides default type detection on the\\n back-end.\\n\\n :type query_mode:\\n :class:`google.spanner.v1.spanner_pb2.ExecuteSqlRequest.QueryMode`\\n :param query_mode: Mode governing return of results / query plan. See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.ExecuteSqlRequest.QueryMode1\\n\\n :type resume_token: bytes\\n :param resume_token: token for resuming previously-interrupted query\\n\\n :rtype: :class:`~google.cloud.spanner.streamed.StreamedResultSet`\\n :returns: a result set instance which can be used to consume rows.\\n \\\"\\\"\\\"\\n return self.snapshot().execute_sql(\\n sql, params, param_types, query_mode, resume_token)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.batch\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.batch\\n def batch(self):\\n \\\"\\\"\\\"Factory to create a batch for this session.\\n\\n :rtype: :class:`~google.cloud.spanner.batch.Batch`\\n :returns: a batch bound to this session\\n :raises ValueError: if the session has not yet been created.\\n \\\"\\\"\\\"\\n if self._session_id is None:\\n raise ValueError(\\\"Session has not been created.\\\")\\n\\n return Batch(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.transaction\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.transaction\\n def transaction(self):\\n \\\"\\\"\\\"Create a transaction to perform a set of reads with shared staleness.\\n\\n :rtype: :class:`~google.cloud.spanner.transaction.Transaction`\\n :returns: a transaction bound to this session\\n :raises ValueError: if the session has not yet been created.\\n \\\"\\\"\\\"\\n if self._session_id is None:\\n raise ValueError(\\\"Session has not been created.\\\")\\n\\n if self._transaction is not None:\\n self._transaction._rolled_back = True\\n del self._transaction\\n\\n txn = self._transaction = Transaction(self)\\n return txn\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::Session.run_in_transaction\",\n \"text\": \"spanner/google/cloud/spanner/session.py::Session.run_in_transaction\\n def run_in_transaction(self, func, *args, **kw):\\n \\\"\\\"\\\"Perform a unit of work in a transaction, retrying on abort.\\n\\n :type func: callable\\n :param func: takes a required positional argument, the transaction,\\n and additional positional / keyword arguments as supplied\\n by the caller.\\n\\n :type args: tuple\\n :param args: additional positional arguments to be passed to ``func``.\\n\\n :type kw: dict\\n :param kw: optional keyword arguments to be passed to ``func``.\\n If passed, \\\"timeout_secs\\\" will be removed and used to\\n override the default timeout.\\n\\n :rtype: Any\\n :returns: The return value of ``func``.\\n\\n :raises Exception:\\n reraises any non-ABORT execptions raised by ``func``.\\n \\\"\\\"\\\"\\n deadline = time.time() + kw.pop(\\n 'timeout_secs', DEFAULT_RETRY_TIMEOUT_SECS)\\n\\n while True:\\n if self._transaction is None:\\n txn = self.transaction()\\n else:\\n txn = self._transaction\\n if txn._transaction_id is None:\\n txn.begin()\\n try:\\n return_value = func(txn, *args, **kw)\\n except (GaxError, GrpcRendezvous) as exc:\\n _delay_until_retry(exc, deadline)\\n del self._transaction\\n continue\\n except Exception:\\n txn.rollback()\\n raise\\n\\n try:\\n txn.commit()\\n except GaxError as exc:\\n _delay_until_retry(exc, deadline)\\n del self._transaction\\n else:\\n return return_value\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::_delay_until_retry\",\n \"text\": \"spanner/google/cloud/spanner/session.py::_delay_until_retry\\ndef _delay_until_retry(exc, deadline):\\n \\\"\\\"\\\"Helper for :meth:`Session.run_in_transaction`.\\n\\n Detect retryable abort, and impose server-supplied delay.\\n\\n :type exc: :class:`google.gax.errors.GaxError`\\n :param exc: exception for aborted transaction\\n\\n :type deadline: float\\n :param deadline: maximum timestamp to continue retrying the transaction.\\n \\\"\\\"\\\"\\n if isinstance(exc, GrpcRendezvous): # pragma: NO COVER see #3663\\n cause = exc\\n else:\\n cause = exc.cause\\n\\n if exc_to_code(cause) != StatusCode.ABORTED:\\n raise\\n\\n now = time.time()\\n\\n if now >= deadline:\\n raise\\n\\n delay = _get_retry_delay(cause)\\n if delay is not None:\\n\\n if now + delay > deadline:\\n raise\\n\\n time.sleep(delay)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/session.py::_get_retry_delay\",\n \"text\": \"spanner/google/cloud/spanner/session.py::_get_retry_delay\\ndef _get_retry_delay(cause):\\n \\\"\\\"\\\"Helper for :func:`_delay_until_retry`.\\n\\n :type exc: :class:`google.gax.errors.GaxError`\\n :param exc: exception for aborted transaction\\n\\n :rtype: float\\n :returns: seconds to wait before retrying the transaction.\\n \\\"\\\"\\\"\\n metadata = dict(cause.trailing_metadata())\\n retry_info_pb = metadata.get('google.rpc.retryinfo-bin')\\n if retry_info_pb is not None:\\n retry_info = RetryInfo()\\n retry_info.ParseFromString(retry_info_pb)\\n nanos = retry_info.retry_delay.nanos\\n return retry_info.retry_delay.seconds + nanos / 1.0e9\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase._make_txn_selector\",\n \"text\": \"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase._make_txn_selector\\n def _make_txn_selector(self): # pylint: disable=redundant-returns-doc\\n \\\"\\\"\\\"Helper for :meth:`read` / :meth:`execute_sql`.\\n\\n Subclasses must override, returning an instance of\\n :class:`transaction_pb2.TransactionSelector`\\n appropriate for making ``read`` / ``execute_sql`` requests\\n\\n :raises: NotImplementedError, always\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase.read\",\n \"text\": \"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase.read\\n def read(self, table, columns, keyset, index='', limit=0,\\n resume_token=b''):\\n \\\"\\\"\\\"Perform a ``StreamingRead`` API request for rows in a table.\\n\\n :type table: str\\n :param table: name of the table from which to fetch data\\n\\n :type columns: list of str\\n :param columns: names of columns to be retrieved\\n\\n :type keyset: :class:`~google.cloud.spanner.keyset.KeySet`\\n :param keyset: keys / ranges identifying rows to be retrieved\\n\\n :type index: str\\n :param index: (Optional) name of index to use, rather than the\\n table's primary key\\n\\n :type limit: int\\n :param limit: (Optional) maxiumn number of rows to return\\n\\n :type resume_token: bytes\\n :param resume_token: token for resuming previously-interrupted read\\n\\n :rtype: :class:`~google.cloud.spanner.streamed.StreamedResultSet`\\n :returns: a result set instance which can be used to consume rows.\\n :raises ValueError:\\n for reuse of single-use snapshots, or if a transaction ID is\\n already pending for multiple-use snapshots.\\n \\\"\\\"\\\"\\n if self._read_request_count > 0:\\n if not self._multi_use:\\n raise ValueError(\\\"Cannot re-use single-use snapshot.\\\")\\n if self._transaction_id is None:\\n raise ValueError(\\\"Transaction ID pending.\\\")\\n\\n database = self._session._database\\n api = database.spanner_api\\n options = _options_with_prefix(database.name)\\n transaction = self._make_txn_selector()\\n\\n iterator = api.streaming_read(\\n self._session.name, table, columns, keyset.to_pb(),\\n transaction=transaction, index=index, limit=limit,\\n resume_token=resume_token, options=options)\\n\\n self._read_request_count += 1\\n\\n if self._multi_use:\\n return StreamedResultSet(iterator, source=self)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase.execute_sql\",\n \"text\": \"spanner/google/cloud/spanner/snapshot.py::_SnapshotBase.execute_sql\\n def execute_sql(self, sql, params=None, param_types=None, query_mode=None,\\n resume_token=b''):\\n \\\"\\\"\\\"Perform an ``ExecuteStreamingSql`` API request for rows in a table.\\n\\n :type sql: str\\n :param sql: SQL query statement\\n\\n :type params: dict, {str -> column value}\\n :param params: values for parameter replacement. Keys must match\\n the names used in ``sql``.\\n\\n :type param_types: dict\\n :param param_types:\\n (Optional) maps explicit types for one or more param values;\\n required if parameters are passed.\\n\\n :type query_mode:\\n :class:`google.cloud.proto.spanner.v1.ExecuteSqlRequest.QueryMode`\\n :param query_mode: Mode governing return of results / query plan. See\\n https://cloud.google.com/spanner/reference/rpc/google.spanner.v1#google.spanner.v1.ExecuteSqlRequest.QueryMode1\\n\\n :type resume_token: bytes\\n :param resume_token: token for resuming previously-interrupted query\\n\\n :rtype: :class:`~google.cloud.spanner.streamed.StreamedResultSet`\\n :returns: a result set instance which can be used to consume rows.\\n :raises ValueError:\\n for reuse of single-use snapshots, or if a transaction ID is\\n already pending for multiple-use snapshots.\\n \\\"\\\"\\\"\\n if self._read_request_count > 0:\\n if not self._multi_use:\\n raise ValueError(\\\"Cannot re-use single-use snapshot.\\\")\\n if self._transaction_id is None:\\n raise ValueError(\\\"Transaction ID pending.\\\")\\n\\n if params is not None:\\n if param_types is None:\\n raise ValueError(\\n \\\"Specify 'param_types' when passing 'params'.\\\")\\n params_pb = Struct(fields={\\n key: _make_value_pb(value) for key, value in params.items()})\\n else:\\n params_pb\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/snapshot.py::Snapshot.__init__\",\n \"text\": \"spanner/google/cloud/spanner/snapshot.py::Snapshot.__init__\\n def __init__(self, session, read_timestamp=None, min_read_timestamp=None,\\n max_staleness=None, exact_staleness=None, multi_use=False):\\n super(Snapshot, self).__init__(session)\\n opts = [\\n read_timestamp, min_read_timestamp, max_staleness, exact_staleness]\\n flagged = [opt for opt in opts if opt is not None]\\n\\n if len(flagged) > 1:\\n raise ValueError(\\\"Supply zero or one options.\\\")\\n\\n if multi_use:\\n if min_read_timestamp is not None or max_staleness is not None:\\n raise ValueError(\\n \\\"'multi_use' is incompatible with \\\"\\n \\\"'min_read_timestamp' / 'max_staleness'\\\")\\n\\n self._strong = len(flagged) == 0\\n self._read_timestamp = read_timestamp\\n self._min_read_timestamp = min_read_timestamp\\n self._max_staleness = max_staleness\\n self._exact_staleness = exact_staleness\\n self._multi_use = multi_use\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/snapshot.py::Snapshot._make_txn_selector\",\n \"text\": \"spanner/google/cloud/spanner/snapshot.py::Snapshot._make_txn_selector\\n def _make_txn_selector(self):\\n \\\"\\\"\\\"Helper for :meth:`read`.\\\"\\\"\\\"\\n if self._transaction_id is not None:\\n return TransactionSelector(id=self._transaction_id)\\n\\n if self._read_timestamp:\\n key = 'read_timestamp'\\n value = _datetime_to_pb_timestamp(self._read_timestamp)\\n elif self._min_read_timestamp:\\n key = 'min_read_timestamp'\\n value = _datetime_to_pb_timestamp(self._min_read_timestamp)\\n elif self._max_staleness:\\n key = 'max_staleness'\\n value = _timedelta_to_duration_pb(self._max_staleness)\\n elif self._exact_staleness:\\n key = 'exact_staleness'\\n value = _timedelta_to_duration_pb(self._exact_staleness)\\n else:\\n key = 'strong'\\n value = True\\n\\n options = TransactionOptions(\\n read_only=TransactionOptions.ReadOnly(**{key: value}))\\n\\n if self._multi_use:\\n return TransactionSelector(begin=options)\\n else:\\n return TransactionSelector(single_use=options)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/snapshot.py::Snapshot.begin\",\n \"text\": \"spanner/google/cloud/spanner/snapshot.py::Snapshot.begin\\n def begin(self):\\n \\\"\\\"\\\"Begin a read-only transaction on the database.\\n\\n :rtype: bytes\\n :returns: the ID for the newly-begun transaction.\\n :raises ValueError:\\n if the transaction is already begun, committed, or rolled back.\\n \\\"\\\"\\\"\\n if not self._multi_use:\\n raise ValueError(\\\"Cannot call 'begin' single-use snapshots\\\")\\n\\n if self._transaction_id is not None:\\n raise ValueError(\\\"Read-only transaction already begun\\\")\\n\\n if self._read_request_count > 0:\\n raise ValueError(\\\"Read-only transaction already pending\\\")\\n\\n database = self._session._database\\n api = database.spanner_api\\n options = _options_with_prefix(database.name)\\n txn_selector = self._make_txn_selector()\\n response = api.begin_transaction(\\n self._session.name, txn_selector.begin, options=options)\\n self._transaction_id = response.id\\n return self._transaction_id\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.__init__\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.__init__\\n def __init__(self, response_iterator, source=None):\\n self._response_iterator = response_iterator\\n self._rows = [] # Fully-processed rows\\n self._counter = 0 # Counter for processed responses\\n self._metadata = None # Until set from first PRS\\n self._stats = None # Until set from last PRS\\n self._resume_token = None # To resume from last received PRS\\n self._current_row = [] # Accumulated values for incomplete row\\n self._pending_chunk = None # Incomplete value\\n self._source = source # Source snapshot\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.rows\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.rows\\n @property\\n def rows(self):\\n \\\"\\\"\\\"Fully-processed rows.\\n\\n :rtype: list of row-data lists.\\n :returns: list of completed row data, from proceesd PRS responses.\\n \\\"\\\"\\\"\\n return self._rows\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.fields\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.fields\\n @property\\n def fields(self):\\n \\\"\\\"\\\"Field descriptors for result set columns.\\n\\n :rtype: list of :class:`~google.cloud.proto.spanner.v1.type_pb2.Field`\\n :returns: list of fields describing column names / types.\\n \\\"\\\"\\\"\\n return self._metadata.row_type.fields\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.metadata\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.metadata\\n @property\\n def metadata(self):\\n \\\"\\\"\\\"Result set metadata\\n\\n :rtype: :class:`~.result_set_pb2.ResultSetMetadata`\\n :returns: structure describing the results\\n \\\"\\\"\\\"\\n return self._metadata\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.stats\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.stats\\n @property\\n def stats(self):\\n \\\"\\\"\\\"Result set statistics\\n\\n :rtype:\\n :class:`~google.cloud.proto.spanner.v1.result_set_pb2.ResultSetStats`\\n :returns: structure describing status about the response\\n \\\"\\\"\\\"\\n return self._stats\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.resume_token\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.resume_token\\n @property\\n def resume_token(self):\\n \\\"\\\"\\\"Token for resuming interrupted read / query.\\n\\n :rtype: bytes\\n :returns: token from last chunk of results.\\n \\\"\\\"\\\"\\n return self._resume_token\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet._merge_chunk\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet._merge_chunk\\n def _merge_chunk(self, value):\\n \\\"\\\"\\\"Merge pending chunk with next value.\\n\\n :type value: :class:`~google.protobuf.struct_pb2.Value`\\n :param value: continuation of chunked value from previous\\n partial result set.\\n\\n :rtype: :class:`~google.protobuf.struct_pb2.Value`\\n :returns: the merged value\\n \\\"\\\"\\\"\\n current_column = len(self._current_row)\\n field = self.fields[current_column]\\n merged = _merge_by_type(self._pending_chunk, value, field.type)\\n self._pending_chunk = None\\n return merged\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet._merge_values\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet._merge_values\\n def _merge_values(self, values):\\n \\\"\\\"\\\"Merge values into rows.\\n\\n :type values: list of :class:`~google.protobuf.struct_pb2.Value`\\n :param values: non-chunked values from partial result set.\\n \\\"\\\"\\\"\\n width = len(self.fields)\\n for value in values:\\n index = len(self._current_row)\\n field = self.fields[index]\\n self._current_row.append(_parse_value_pb(value, field.type))\\n if len(self._current_row) == width:\\n self._rows.append(self._current_row)\\n self._current_row = []\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.consume_next\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.consume_next\\n def consume_next(self):\\n \\\"\\\"\\\"Consume the next partial result set from the stream.\\n\\n Parse the result set into new/existing rows in :attr:`_rows`\\n \\\"\\\"\\\"\\n response = six.next(self._response_iterator)\\n self._counter += 1\\n self._resume_token = response.resume_token\\n\\n if self._metadata is None: # first response\\n metadata = self._metadata = response.metadata\\n\\n source = self._source\\n if source is not None and source._transaction_id is None:\\n source._transaction_id = metadata.transaction.id\\n\\n if response.HasField('stats'): # last response\\n self._stats = response.stats\\n\\n values = list(response.values)\\n if self._pending_chunk is not None:\\n values[0] = self._merge_chunk(values[0])\\n\\n if response.chunked_value:\\n self._pending_chunk = values.pop()\\n\\n self._merge_values(values)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.consume_all\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.consume_all\\n def consume_all(self):\\n \\\"\\\"\\\"Consume the streamed responses until there are no more.\\\"\\\"\\\"\\n while True:\\n try:\\n self.consume_next()\\n except StopIteration:\\n break\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.__iter__\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.__iter__\\n def __iter__(self):\\n iter_rows, self._rows[:] = self._rows[:], ()\\n while True:\\n if not iter_rows:\\n self.consume_next() # raises StopIteration\\n iter_rows, self._rows[:] = self._rows[:], ()\\n while iter_rows:\\n yield iter_rows.pop(0)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.one\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.one\\n def one(self):\\n \\\"\\\"\\\"Return exactly one result, or raise an exception.\\n\\n :raises: :exc:`NotFound`: If there are no results.\\n :raises: :exc:`ValueError`: If there are multiple results.\\n :raises: :exc:`RuntimeError`: If consumption has already occurred,\\n in whole or in part.\\n \\\"\\\"\\\"\\n answer = self.one_or_none()\\n if answer is None:\\n raise exceptions.NotFound('No rows matched the given query.')\\n return answer\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.one_or_none\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::StreamedResultSet.one_or_none\\n def one_or_none(self):\\n \\\"\\\"\\\"Return exactly one result, or None if there are no results.\\n\\n :raises: :exc:`ValueError`: If there are multiple results.\\n :raises: :exc:`RuntimeError`: If consumption has already occurred,\\n in whole or in part.\\n \\\"\\\"\\\"\\n # Sanity check: Has consumption of this query already started?\\n # If it has, then this is an exception.\\n if self._metadata is not None:\\n raise RuntimeError('Can not call `.one` or `.one_or_none` after '\\n 'stream consumption has already started.')\\n\\n # Consume the first result of the stream.\\n # If there is no first result, then return None.\\n iterator = iter(self)\\n try:\\n answer = next(iterator)\\n except StopIteration:\\n return None\\n\\n # Attempt to consume more. This should no-op; if we get additional\\n # rows, then this is an error case.\\n try:\\n next(iterator)\\n raise ValueError('Expected one result; got more.')\\n except StopIteration:\\n return answer\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::Unmergeable.__init__\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::Unmergeable.__init__\\n def __init__(self, lhs, rhs, type_):\\n message = \\\"Cannot merge %s values: %s %s\\\" % (\\n type_pb2.TypeCode.Name(type_.code), lhs, rhs)\\n super(Unmergeable, self).__init__(message)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::_unmergeable\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::_unmergeable\\ndef _unmergeable(lhs, rhs, type_):\\n \\\"\\\"\\\"Helper for '_merge_by_type'.\\\"\\\"\\\"\\n raise Unmergeable(lhs, rhs, type_)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::_merge_float64\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::_merge_float64\\ndef _merge_float64(lhs, rhs, type_): # pylint: disable=unused-argument\\n \\\"\\\"\\\"Helper for '_merge_by_type'.\\\"\\\"\\\"\\n lhs_kind = lhs.WhichOneof('kind')\\n if lhs_kind == 'string_value':\\n return Value(string_value=lhs.string_value + rhs.string_value)\\n rhs_kind = rhs.WhichOneof('kind')\\n array_continuation = (\\n lhs_kind == 'number_value' and\\n rhs_kind == 'string_value' and\\n rhs.string_value == '')\\n if array_continuation:\\n return lhs\\n raise Unmergeable(lhs, rhs, type_)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::_merge_string\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::_merge_string\\ndef _merge_string(lhs, rhs, type_): # pylint: disable=unused-argument\\n \\\"\\\"\\\"Helper for '_merge_by_type'.\\\"\\\"\\\"\\n return Value(string_value=lhs.string_value + rhs.string_value)\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::_merge_array\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::_merge_array\\ndef _merge_array(lhs, rhs, type_):\\n \\\"\\\"\\\"Helper for '_merge_by_type'.\\\"\\\"\\\"\\n element_type = type_.array_element_type\\n if element_type.code in _UNMERGEABLE_TYPES:\\n # Individual values cannot be merged, just concatenate\\n lhs.list_value.values.extend(rhs.list_value.values)\\n return lhs\\n lhs, rhs = list(lhs.list_value.values), list(rhs.list_value.values)\\n first = rhs.pop(0)\\n if first.HasField('null_value'): # can't merge\\n lhs.append(first)\\n else:\\n last = lhs.pop()\\n try:\\n merged = _merge_by_type(last, first, element_type)\\n except Unmergeable:\\n lhs.append(last)\\n lhs.append(first)\\n else:\\n lhs.append(merged)\\n return Value(list_value=ListValue(values=(lhs + rhs)))\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::_merge_struct\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::_merge_struct\\ndef _merge_struct(lhs, rhs, type_):\\n \\\"\\\"\\\"Helper for '_merge_by_type'.\\\"\\\"\\\"\\n fields = type_.struct_type.fields\\n lhs, rhs = list(lhs.list_value.values), list(rhs.list_value.values)\\n candidate_type = fields[len(lhs) - 1].type\\n first = rhs.pop(0)\\n if (first.HasField('null_value') or\\n candidate_type.code in _UNMERGEABLE_TYPES):\\n lhs.append(first)\\n else:\\n last = lhs.pop()\\n lhs.append(_merge_by_type(last, first, candidate_type))\\n return Value(list_value=ListValue(values=lhs + rhs))\",\n \"label\": 0\n },\n {\n \"cid\": \"spanner/google/cloud/spanner/streamed.py::_merge_by_type\",\n \"text\": \"spanner/google/cloud/spanner/streamed.py::_merge_by_type\\ndef _merge_by_type(lhs, rhs, type_):\\n \\\"\\\"\\\"Helper for '_merge_chunk'.\\\"\\\"\\\"\\n merger = _MERGE_BY_TYPE[type_.code]\\n return merger(lhs, rhs, type_)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":55,"cells":{"instance_id":{"kind":"string","value":"ray-project__ray-9381"},"repo":{"kind":"string","value":"ray-project/ray"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"[tune] Handling exceptions for the analysis module when encountering nans\nHi, after running some experiments with tune I was trying to get the best configuration/log dir using `get_best_config` and `get_best_logdir` from the analysis module. Unfortunately, I had some `nan` values in the metric I was measuring, and both methods returned an error related to Pandas. After checking the code, I found that the line causing the error is in the function `_retrieve_rows`:\r\nhttps://github.com/ray-project/ray/blob/4da0e542d54c7977aae5bda5242cab15ab3f344b/python/ray/tune/analysis/experiment_analysis.py#L164\r\nIn particular, if there are `nan` values then the variable `idx` will be equal to `nan` and that will cause the following: `TypeError: Cannot index by location index with a non-integer key`.\r\nI was wondering if you could add an exception for this case, warning the user that the experiment returned some `nan` values. I had to spend some time understanding what was wrong.\n"},"gold_files":{"kind":"list like","value":["python/ray/tune/analysis/experiment_analysis.py"],"string":"[\n \"python/ray/tune/analysis/experiment_analysis.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["python/ray/tune/analysis/experiment_analysis.py::Analysis._retrieve_rows","python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_config","python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_logdir"],"string":"[\n \"python/ray/tune/analysis/experiment_analysis.py::Analysis._retrieve_rows\",\n \"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_config\",\n \"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_logdir\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"python/ray/tune/analysis/experiment_analysis.py","text":"python/ray/tune/analysis/experiment_analysis.py\nclass Analysis # Analyze all results from a directory of experiments.\n def __init__(self, experiment_dir)\n def dataframe(self, metric=None, mode=None) # Returns a pandas.DataFrame object constructed from the trials.\n def get_best_config(self, metric, mode='max') # Retrieve the best config corresponding to the trial.\n def get_best_logdir(self, metric, mode='max') # Retrieve the logdir corresponding to the best trial.\n def fetch_trial_dataframes(self)\n def get_all_configs(self, prefix=False) # Returns a list of all configurations.\n def get_trial_checkpoints_paths(self, trial, metric=TRAINING_ITERATION) # Gets paths and metrics of all persistent checkpoints of a trial.\n def _retrieve_rows(self, metric=None, mode=None)\n def _get_trial_paths(self)\n def trial_dataframes(self) # List of all dataframes of the trials.\nclass ExperimentAnalysis # Analyze results from a Tune experiment.\n def __init__(self, experiment_checkpoint_path, trials=None)\n def get_best_trial(self, metric, mode='max', scope='all') # Retrieve the best trial object.\n def get_best_config(self, metric, mode='max', scope='all') # Retrieve the best config corresponding to the trial.\n def get_best_logdir(self, metric, mode='max', scope='all') # Retrieve the logdir corresponding to the best trial.\n def stats(self) # Returns a dictionary of the statistics of the experiment.\n def runner_data(self) # Returns a dictionary of the TrialRunner data.\n def _get_trial_paths(self) # Overwrites Analysis to only have trials of one experiment.","label":1},{"cid":"python/ray/worker.py","text":"python/ray/worker.py\nclass ActorCheckpointInfo # Information used to maintain actor checkpoints.\n def __init__(self, num_tasks_since_last_checkpoint, last_checkpoint_timestamp, checkpoint_ids)\nclass Worker # A class used to define the control flow of a worker process.\n def __init__(self) # Initialize a Worker object.\n def connected(self)\n def node_ip_address(self)\n def load_code_from_local(self)\n def current_job_id(self)\n def actor_id(self)\n def current_task_id(self)\n def current_session_and_job(self) # Get the current session index and job id as pair.\n def mark_actor_init_failed(self, error) # Called to mark this actor as failed during initialization.\n def reraise_actor_init_error(self) # Raises any previous actor initialization error.\n def get_serialization_context(self, job_id=None) # Get the SerializationContext of the job that this worker is processing.\n def check_connected(self) # Check if the worker is connected.\n def set_mode(self, mode) # Set the mode of the worker.\n def put_object(self, value, object_id=None, pin_object=True) # Put value in the local object store with object id `objectid`.\n def deserialize_objects(self, data_metadata_pairs, object_ids)\n def get_objects(self, object_ids, timeout=None) # Get the values in the object store associated with the IDs.\n def run_function_on_all_workers(self, function, run_on_other_drivers=False) # Run arbitrary code on all of the workers.\n def main_loop(self) # The main loop a worker runs to receive and execute tasks.\n def sigterm_handler(signum, frame)\ndef get_gpu_ids() # Get the IDs of the GPUs that are available to the worker.\ndef get_resource_ids() # Get the IDs of the resources that are available to the worker.\ndef get_webui_url() # Get the URL to access the web UI.\ndef print_failed_task(task_status) # Print information about failed tasks.\ndef init(address=None, redis_address=None, redis_port=None, num_cpus=None, num_gpus=None, memory=None, object_store_memory=None, resources=None, driver_object_store_memory=None, redis_max_memory=None, log_to_driver=True, node_ip_address=ray_constants.NODE_DEFAULT_IP, object_id_seed=None, local_mode=False, redirect_worker_output=None, redirect_output=None, ignore_reinit_error=False, num_redis_shards=None, redis_max_clients=None, redis_password=ray_constants.REDIS_DEFAULT_PASSWORD, plasma_directory=None, huge_pages=False, include_java=False, include_dashboard=None, dashboard_host='localhost', dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT, job_id=None, configure_logging=True, logging_level=logging.INFO, logging_format=ray_constants.LOGGER_FORMAT, plasma_store_socket_name=None, raylet_socket_name=None, temp_dir=None, load_code_from_local=False, java_worker_options=None, use_pickle=True, _internal_config=None, lru_evict=False) # Connect to an existing Ray cluster or start one and connect to it.\ndef shutdown(exiting_interpreter=False) # Disconnect the worker, and terminate ","label":0}],"string":"[\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py\\nclass Analysis # Analyze all results from a directory of experiments.\\n def __init__(self, experiment_dir)\\n def dataframe(self, metric=None, mode=None) # Returns a pandas.DataFrame object constructed from the trials.\\n def get_best_config(self, metric, mode='max') # Retrieve the best config corresponding to the trial.\\n def get_best_logdir(self, metric, mode='max') # Retrieve the logdir corresponding to the best trial.\\n def fetch_trial_dataframes(self)\\n def get_all_configs(self, prefix=False) # Returns a list of all configurations.\\n def get_trial_checkpoints_paths(self, trial, metric=TRAINING_ITERATION) # Gets paths and metrics of all persistent checkpoints of a trial.\\n def _retrieve_rows(self, metric=None, mode=None)\\n def _get_trial_paths(self)\\n def trial_dataframes(self) # List of all dataframes of the trials.\\nclass ExperimentAnalysis # Analyze results from a Tune experiment.\\n def __init__(self, experiment_checkpoint_path, trials=None)\\n def get_best_trial(self, metric, mode='max', scope='all') # Retrieve the best trial object.\\n def get_best_config(self, metric, mode='max', scope='all') # Retrieve the best config corresponding to the trial.\\n def get_best_logdir(self, metric, mode='max', scope='all') # Retrieve the logdir corresponding to the best trial.\\n def stats(self) # Returns a dictionary of the statistics of the experiment.\\n def runner_data(self) # Returns a dictionary of the TrialRunner data.\\n def _get_trial_paths(self) # Overwrites Analysis to only have trials of one experiment.\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/worker.py\",\n \"text\": \"python/ray/worker.py\\nclass ActorCheckpointInfo # Information used to maintain actor checkpoints.\\n def __init__(self, num_tasks_since_last_checkpoint, last_checkpoint_timestamp, checkpoint_ids)\\nclass Worker # A class used to define the control flow of a worker process.\\n def __init__(self) # Initialize a Worker object.\\n def connected(self)\\n def node_ip_address(self)\\n def load_code_from_local(self)\\n def current_job_id(self)\\n def actor_id(self)\\n def current_task_id(self)\\n def current_session_and_job(self) # Get the current session index and job id as pair.\\n def mark_actor_init_failed(self, error) # Called to mark this actor as failed during initialization.\\n def reraise_actor_init_error(self) # Raises any previous actor initialization error.\\n def get_serialization_context(self, job_id=None) # Get the SerializationContext of the job that this worker is processing.\\n def check_connected(self) # Check if the worker is connected.\\n def set_mode(self, mode) # Set the mode of the worker.\\n def put_object(self, value, object_id=None, pin_object=True) # Put value in the local object store with object id `objectid`.\\n def deserialize_objects(self, data_metadata_pairs, object_ids)\\n def get_objects(self, object_ids, timeout=None) # Get the values in the object store associated with the IDs.\\n def run_function_on_all_workers(self, function, run_on_other_drivers=False) # Run arbitrary code on all of the workers.\\n def main_loop(self) # The main loop a worker runs to receive and execute tasks.\\n def sigterm_handler(signum, frame)\\ndef get_gpu_ids() # Get the IDs of the GPUs that are available to the worker.\\ndef get_resource_ids() # Get the IDs of the resources that are available to the worker.\\ndef get_webui_url() # Get the URL to access the web UI.\\ndef print_failed_task(task_status) # Print information about failed tasks.\\ndef init(address=None, redis_address=None, redis_port=None, num_cpus=None, num_gpus=None, memory=None, object_store_memory=None, resources=None, driver_object_store_memory=None, redis_max_memory=None, log_to_driver=True, node_ip_address=ray_constants.NODE_DEFAULT_IP, object_id_seed=None, local_mode=False, redirect_worker_output=None, redirect_output=None, ignore_reinit_error=False, num_redis_shards=None, redis_max_clients=None, redis_password=ray_constants.REDIS_DEFAULT_PASSWORD, plasma_directory=None, huge_pages=False, include_java=False, include_dashboard=None, dashboard_host='localhost', dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT, job_id=None, configure_logging=True, logging_level=logging.INFO, logging_format=ray_constants.LOGGER_FORMAT, plasma_store_socket_name=None, raylet_socket_name=None, temp_dir=None, load_code_from_local=False, java_worker_options=None, use_pickle=True, _internal_config=None, lru_evict=False) # Connect to an existing Ray cluster or start one and connect to it.\\ndef shutdown(exiting_interpreter=False) # Disconnect the worker, and terminate \",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"python/ray/tune/analysis/experiment_analysis.py::Analysis.__init__","text":"python/ray/tune/analysis/experiment_analysis.py::Analysis.__init__\n def __init__(self, experiment_dir):\n experiment_dir = os.path.expanduser(experiment_dir)\n if not os.path.isdir(experiment_dir):\n raise ValueError(\n \"{} is not a valid directory.\".format(experiment_dir))\n self._experiment_dir = experiment_dir\n self._configs = {}\n self._trial_dataframes = {}\n\n if not pd:\n logger.warning(\n \"pandas not installed. Run `pip install pandas` for \"\n \"Analysis utilities.\")\n else:\n self.fetch_trial_dataframes()","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::Analysis.dataframe","text":"python/ray/tune/analysis/experiment_analysis.py::Analysis.dataframe\n def dataframe(self, metric=None, mode=None):\n \"\"\"Returns a pandas.DataFrame object constructed from the trials.\n\n Args:\n metric (str): Key for trial info to order on.\n If None, uses last result.\n mode (str): One of [min, max].\n\n Returns:\n pd.DataFrame: Constructed from a result dict of each trial.\n \"\"\"\n rows = self._retrieve_rows(metric=metric, mode=mode)\n all_configs = self.get_all_configs(prefix=True)\n for path, config in all_configs.items():\n if path in rows:\n rows[path].update(config)\n rows[path].update(logdir=path)\n return pd.DataFrame(list(rows.values()))","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_config","text":"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_config\n def get_best_config(self, metric, mode=\"max\"):\n \"\"\"Retrieve the best config corresponding to the trial.\n\n Args:\n metric (str): Key for trial info to order on.\n mode (str): One of [min, max].\n \"\"\"\n rows = self._retrieve_rows(metric=metric, mode=mode)\n all_configs = self.get_all_configs()\n compare_op = max if mode == \"max\" else min\n best_path = compare_op(rows, key=lambda k: rows[k][metric])\n return all_configs[best_path]","label":1},{"cid":"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_logdir","text":"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_logdir\n def get_best_logdir(self, metric, mode=\"max\"):\n \"\"\"Retrieve the logdir corresponding to the best trial.\n\n Args:\n metric (str): Key for trial info to order on.\n mode (str): One of [min, max].\n \"\"\"\n df = self.dataframe(metric=metric, mode=mode)\n if mode == \"max\":\n return df.iloc[df[metric].idxmax()].logdir\n elif mode == \"min\":\n return df.iloc[df[metric].idxmin()].logdir","label":1},{"cid":"python/ray/tune/analysis/experiment_analysis.py::Analysis.fetch_trial_dataframes","text":"python/ray/tune/analysis/experiment_analysis.py::Analysis.fetch_trial_dataframes\n def fetch_trial_dataframes(self):\n fail_count = 0\n for path in self._get_trial_paths():\n try:\n self.trial_dataframes[path] = pd.read_csv(\n os.path.join(path, EXPR_PROGRESS_FILE))\n except Exception:\n fail_count += 1\n\n if fail_count:\n logger.debug(\n \"Couldn't read results from {} paths\".format(fail_count))\n return self.trial_dataframes","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_all_configs","text":"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_all_configs\n def get_all_configs(self, prefix=False):\n \"\"\"Returns a list of all configurations.\n\n Args:\n prefix (bool): If True, flattens the config dict\n and prepends `config/`.\n\n Returns:\n List[dict]: List of all configurations of trials,\n \"\"\"\n fail_count = 0\n for path in self._get_trial_paths():\n try:\n with open(os.path.join(path, EXPR_PARAM_FILE)) as f:\n config = json.load(f)\n if prefix:\n for k in list(config):\n config[CONFIG_PREFIX + k] = config.pop(k)\n self._configs[path] = config\n except Exception:\n fail_count += 1\n\n if fail_count:\n logger.warning(\n \"Couldn't read config from {} paths\".format(fail_count))\n return self._configs","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_trial_checkpoints_paths","text":"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_trial_checkpoints_paths\n def get_trial_checkpoints_paths(self, trial, metric=TRAINING_ITERATION):\n \"\"\"Gets paths and metrics of all persistent checkpoints of a trial.\n\n Args:\n trial (Trial): The log directory of a trial, or a trial instance.\n metric (str): key for trial info to return, e.g. \"mean_accuracy\".\n \"training_iteration\" is used by default.\n\n Returns:\n List of [path, metric] for all persistent checkpoints of the trial.\n \"\"\"\n if isinstance(trial, str):\n trial_dir = os.path.expanduser(trial)\n # Get checkpoints from logdir.\n chkpt_df = TrainableUtil.get_checkpoints_paths(trial_dir)\n\n # Join with trial dataframe to get metrics.\n trial_df = self.trial_dataframes[trial_dir]\n path_metric_df = chkpt_df.merge(\n trial_df, on=\"training_iteration\", how=\"inner\")\n return path_metric_df[[\"chkpt_path\", metric]].values.tolist()\n elif isinstance(trial, Trial):\n checkpoints = trial.checkpoint_manager.best_checkpoints()\n return [[c.value, c.result[metric]] for c in checkpoints]\n else:\n raise ValueError(\"trial should be a string or a Trial instance.\")","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::Analysis._retrieve_rows","text":"python/ray/tune/analysis/experiment_analysis.py::Analysis._retrieve_rows\n def _retrieve_rows(self, metric=None, mode=None):\n assert mode is None or mode in [\"max\", \"min\"]\n rows = {}\n for path, df in self.trial_dataframes.items():\n if mode == \"max\":\n idx = df[metric].idxmax()\n elif mode == \"min\":\n idx = df[metric].idxmin()\n else:\n idx = -1\n rows[path] = df.iloc[idx].to_dict()\n\n return rows","label":1},{"cid":"python/ray/tune/analysis/experiment_analysis.py::Analysis._get_trial_paths","text":"python/ray/tune/analysis/experiment_analysis.py::Analysis._get_trial_paths\n def _get_trial_paths(self):\n _trial_paths = []\n for trial_path, _, files in os.walk(self._experiment_dir):\n if EXPR_PROGRESS_FILE in files:\n _trial_paths += [trial_path]\n\n if not _trial_paths:\n raise TuneError(\"No trials found in {}.\".format(\n self._experiment_dir))\n return _trial_paths","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::Analysis.trial_dataframes","text":"python/ray/tune/analysis/experiment_analysis.py::Analysis.trial_dataframes\n @property\n def trial_dataframes(self):\n \"\"\"List of all dataframes of the trials.\"\"\"\n return self._trial_dataframes","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.__init__","text":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.__init__\n def __init__(self, experiment_checkpoint_path, trials=None):\n with open(experiment_checkpoint_path) as f:\n _experiment_state = json.load(f)\n self._experiment_state = _experiment_state\n\n if \"checkpoints\" not in _experiment_state:\n raise TuneError(\"Experiment state invalid; no checkpoints found.\")\n self._checkpoints = _experiment_state[\"checkpoints\"]\n self.trials = trials\n super(ExperimentAnalysis, self).__init__(\n os.path.dirname(experiment_checkpoint_path))","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_trial","text":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_trial\n def get_best_trial(self, metric, mode=\"max\", scope=\"all\"):\n \"\"\"Retrieve the best trial object.\n\n Compares all trials' scores on `metric`.\n\n Args:\n metric (str): Key for trial info to order on.\n mode (str): One of [min, max].\n scope (str): One of [all, last, avg, last-5-avg, last-10-avg].\n If `scope=last`, only look at each trial's final step for\n `metric`, and compare across trials based on `mode=[min,max]`.\n If `scope=avg`, consider the simple average over all steps\n for `metric` and compare across trials based on\n `mode=[min,max]`. If `scope=last-5-avg` or `scope=last-10-avg`,\n consider the simple average over the last 5 or 10 steps for\n `metric` and compare across trials based on `mode=[min,max]`.\n If `scope=all`, find each trial's min/max score for `metric`\n based on `mode`, and compare trials based on `mode=[min,max]`.\n \"\"\"\n if mode not in [\"max\", \"min\"]:\n raise ValueError(\n \"ExperimentAnalysis: attempting to get best trial for \"\n \"metric {} for mode {} not in [\\\"max\\\", \\\"min\\\"]\".format(\n metric, mode))\n if scope not in [\"all\", \"last\", \"avg\", \"last-5-avg\", \"last-10-avg\"]:\n raise ValueError(\n \"ExperimentAnalysis: attempting to get best trial for \"\n \"metric {} for scope {} not in [\\\"all\\\", \\\"last\\\", \\\"avg\\\", \"\n \"\\\"last-5-avg\\\", \\\"last-10-avg\\\"]\".format(metric, scope))\n best_trial = None\n best_metric_score = None\n for trial in self.trials:\n if metric not in trial.metric_analysis:\n continue\n\n if scope in [\"last\", \"avg\", \"last-5-avg\", \"last-10-avg\"]:\n metric_score = trial.metric_analysis[m","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_config","text":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_config\n def get_best_config(self, metric, mode=\"max\", scope=\"all\"):\n \"\"\"Retrieve the best config corresponding to the trial.\n\n Compares all trials' scores on `metric`.\n\n Args:\n metric (str): Key for trial info to order on.\n mode (str): One of [min, max].\n scope (str): One of [all, last, avg, last-5-avg, last-10-avg].\n If `scope=last`, only look at each trial's final step for\n `metric`, and compare across trials based on `mode=[min,max]`.\n If `scope=avg`, consider the simple average over all steps\n for `metric` and compare across trials based on\n `mode=[min,max]`. If `scope=last-5-avg` or `scope=last-10-avg`,\n consider the simple average over the last 5 or 10 steps for\n `metric` and compare across trials based on `mode=[min,max]`.\n If `scope=all`, find each trial's min/max score for `metric`\n based on `mode`, and compare trials based on `mode=[min,max]`.\n \"\"\"\n best_trial = self.get_best_trial(metric, mode, scope)\n return best_trial.config if best_trial else None","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_logdir","text":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_logdir\n def get_best_logdir(self, metric, mode=\"max\", scope=\"all\"):\n \"\"\"Retrieve the logdir corresponding to the best trial.\n\n Compares all trials' scores on `metric`.\n\n Args:\n metric (str): Key for trial info to order on.\n mode (str): One of [min, max].\n scope (str): One of [all, last, avg, last-5-avg, last-10-avg].\n If `scope=last`, only look at each trial's final step for\n `metric`, and compare across trials based on `mode=[min,max]`.\n If `scope=avg`, consider the simple average over all steps\n for `metric` and compare across trials based on\n `mode=[min,max]`. If `scope=last-5-avg` or `scope=last-10-avg`,\n consider the simple average over the last 5 or 10 steps for\n `metric` and compare across trials based on `mode=[min,max]`.\n If `scope=all`, find each trial's min/max score for `metric`\n based on `mode`, and compare trials based on `mode=[min,max]`.\n \"\"\"\n best_trial = self.get_best_trial(metric, mode, scope)\n return best_trial.logdir if best_trial else None","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.stats","text":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.stats\n def stats(self):\n \"\"\"Returns a dictionary of the statistics of the experiment.\"\"\"\n return self._experiment_state.get(\"stats\")","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.runner_data","text":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.runner_data\n def runner_data(self):\n \"\"\"Returns a dictionary of the TrialRunner data.\"\"\"\n return self._experiment_state.get(\"runner_data\")","label":0},{"cid":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis._get_trial_paths","text":"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis._get_trial_paths\n def _get_trial_paths(self):\n \"\"\"Overwrites Analysis to only have trials of one experiment.\"\"\"\n if self.trials:\n _trial_paths = [t.logdir for t in self.trials]\n else:\n logger.warning(\"No `self.trials`. Drawing logdirs from checkpoint \"\n \"file. This may result in some information that is \"\n \"out of sync, as checkpointing is periodic.\")\n _trial_paths = [\n checkpoint[\"logdir\"] for checkpoint in self._checkpoints\n ]\n if not _trial_paths:\n raise TuneError(\"No trials found.\")\n return _trial_paths","label":0},{"cid":"python/ray/worker.py::ActorCheckpointInfo.__init__","text":"python/ray/worker.py::ActorCheckpointInfo.__init__\n def __init__(self, num_tasks_since_last_checkpoint,\n last_checkpoint_timestamp, checkpoint_ids):\n self.num_tasks_since_last_checkpoint = num_tasks_since_last_checkpoint\n self.last_checkpoint_timestamp = last_checkpoint_timestamp\n self.checkpoint_ids = checkpoint_ids","label":0},{"cid":"python/ray/worker.py::Worker.__init__","text":"python/ray/worker.py::Worker.__init__\n def __init__(self):\n \"\"\"Initialize a Worker object.\"\"\"\n self.node = None\n self.mode = None\n self.cached_functions_to_run = []\n self.actor_init_error = None\n self.actors = {}\n # Information used to maintain actor checkpoints.\n self.actor_checkpoint_info = {}\n self.actor_task_counter = 0\n # When the worker is constructed. Record the original value of the\n # CUDA_VISIBLE_DEVICES environment variable.\n self.original_gpu_ids = ray.utils.get_cuda_visible_devices()\n self.memory_monitor = memory_monitor.MemoryMonitor()\n # A dictionary that maps from driver id to SerializationContext\n # TODO: clean up the SerializationContext once the job finished.\n self.serialization_context_map = {}\n self.function_actor_manager = FunctionActorManager(self)\n # This event is checked regularly by all of the threads so that they\n # know when to exit.\n self.threads_stopped = threading.Event()\n # Index of the current session. This number will\n # increment every time when `ray.shutdown` is called.\n self._session_index = 0\n # Functions to run to process the values returned by ray.get. Each\n # postprocessor must take two arguments (\"object_ids\", and \"values\").\n self._post_get_hooks = []","label":0},{"cid":"python/ray/worker.py::Worker.connected","text":"python/ray/worker.py::Worker.connected\n @property\n def connected(self):\n return self.node is not None","label":0},{"cid":"python/ray/worker.py::Worker.node_ip_address","text":"python/ray/worker.py::Worker.node_ip_address\n @property\n def node_ip_address(self):\n self.check_connected()\n return self.node.node_ip_address","label":0},{"cid":"python/ray/worker.py::Worker.load_code_from_local","text":"python/ray/worker.py::Worker.load_code_from_local\n @property\n def load_code_from_local(self):\n self.check_connected()\n return self.node.load_code_from_local","label":0},{"cid":"python/ray/worker.py::Worker.current_job_id","text":"python/ray/worker.py::Worker.current_job_id\n @property\n def current_job_id(self):\n if hasattr(self, \"core_worker\"):\n return self.core_worker.get_current_job_id()\n return JobID.nil()","label":0},{"cid":"python/ray/worker.py::Worker.actor_id","text":"python/ray/worker.py::Worker.actor_id\n @property\n def actor_id(self):\n if hasattr(self, \"core_worker\"):\n return self.core_worker.get_actor_id()\n return ActorID.nil()","label":0},{"cid":"python/ray/worker.py::Worker.current_task_id","text":"python/ray/worker.py::Worker.current_task_id\n @property\n def current_task_id(self):\n return self.core_worker.get_current_task_id()","label":0},{"cid":"python/ray/worker.py::Worker.current_session_and_job","text":"python/ray/worker.py::Worker.current_session_and_job\n @property\n def current_session_and_job(self):\n \"\"\"Get the current session index and job id as pair.\"\"\"\n assert isinstance(self._session_index, int)\n assert isinstance(self.current_job_id, ray.JobID)\n return self._session_index, self.current_job_id","label":0},{"cid":"python/ray/worker.py::Worker.mark_actor_init_failed","text":"python/ray/worker.py::Worker.mark_actor_init_failed\n def mark_actor_init_failed(self, error):\n \"\"\"Called to mark this actor as failed during initialization.\"\"\"\n\n self.actor_init_error = error","label":0},{"cid":"python/ray/worker.py::Worker.reraise_actor_init_error","text":"python/ray/worker.py::Worker.reraise_actor_init_error\n def reraise_actor_init_error(self):\n \"\"\"Raises any previous actor initialization error.\"\"\"\n\n if self.actor_init_error is not None:\n raise self.actor_init_error","label":0},{"cid":"python/ray/worker.py::Worker.get_serialization_context","text":"python/ray/worker.py::Worker.get_serialization_context\n def get_serialization_context(self, job_id=None):\n \"\"\"Get the SerializationContext of the job that this worker is processing.\n\n Args:\n job_id: The ID of the job that indicates which job to get\n the serialization context for.\n\n Returns:\n The serialization context of the given job.\n \"\"\"\n # This function needs to be protected by a lock, because it will be\n # called by`register_class_for_serialization`, as well as the import\n # thread, from different threads. Also, this function will recursively\n # call itself, so we use RLock here.\n if job_id is None:\n job_id = self.current_job_id\n with self.lock:\n if job_id not in self.serialization_context_map:\n self.serialization_context_map[\n job_id] = serialization.SerializationContext(self)\n return self.serialization_context_map[job_id]","label":0},{"cid":"python/ray/worker.py::Worker.check_connected","text":"python/ray/worker.py::Worker.check_connected\n def check_connected(self):\n \"\"\"Check if the worker is connected.\n\n Raises:\n Exception: An exception is raised if the worker is not connected.\n \"\"\"\n if not self.connected:\n raise RayConnectionError(\"Ray has not been started yet. You can \"\n \"start Ray with 'ray.init()'.\")","label":0},{"cid":"python/ray/worker.py::Worker.set_mode","text":"python/ray/worker.py::Worker.set_mode\n def set_mode(self, mode):\n \"\"\"Set the mode of the worker.\n\n The mode SCRIPT_MODE should be used if this Worker is a driver that is\n being run as a Python script or interactively in a shell. It will print\n information about task failures.\n\n The mode WORKER_MODE should be used if this Worker is not a driver. It\n will not print information about tasks.\n\n The mode LOCAL_MODE should be used if this Worker is a driver and if\n you want to run the driver in a manner equivalent to serial Python for\n debugging purposes. It will not send remote function calls to the\n scheduler and will instead execute them in a blocking fashion.\n\n Args:\n mode: One of SCRIPT_MODE, WORKER_MODE, and LOCAL_MODE.\n \"\"\"\n self.mode = mode","label":0},{"cid":"python/ray/worker.py::Worker.put_object","text":"python/ray/worker.py::Worker.put_object\n def put_object(self, value, object_id=None, pin_object=True):\n \"\"\"Put value in the local object store with object id `objectid`.\n\n This assumes that the value for `objectid` has not yet been placed in\n the local object store. If the plasma store is full, the worker will\n automatically retry up to DEFAULT_PUT_OBJECT_RETRIES times. Each\n retry will delay for an exponentially doubling amount of time,\n starting with DEFAULT_PUT_OBJECT_DELAY. After this, exception\n will be raised.\n\n Args:\n value: The value to put in the object store.\n object_id (object_id.ObjectID): The object ID of the value to be\n put. If None, one will be generated.\n pin_object: If set, the object will be pinned at the raylet.\n\n Returns:\n object_id.ObjectID: The object ID the object was put under.\n\n Raises:\n ray.exceptions.ObjectStoreFullError: This is raised if the attempt\n to store the object fails because the object store is full even\n after multiple retries.\n \"\"\"\n # Make sure that the value is not an object ID.\n if isinstance(value, ObjectID):\n raise TypeError(\n \"Calling 'put' on an ray.ObjectID is not allowed \"\n \"(similarly, returning an ray.ObjectID from a remote \"\n \"function is not allowed). If you really want to \"\n \"do this, you can wrap the ray.ObjectID in a list and \"\n \"call 'put' on it (or return it).\")\n\n if self.mode == LOCAL_MODE:\n assert object_id is None, (\"Local Mode does not support \"\n \"inserting with an objectID\")\n\n serialized_value = self.get_serialization_context().serialize(value)\n # This *must* be the first place that we construct this python\n # ObjectID because an entry with 0 local refer","label":0},{"cid":"python/ray/worker.py::Worker.deserialize_objects","text":"python/ray/worker.py::Worker.deserialize_objects\n def deserialize_objects(self, data_metadata_pairs, object_ids):\n context = self.get_serialization_context()\n return context.deserialize_objects(data_metadata_pairs, object_ids)","label":0},{"cid":"python/ray/worker.py::Worker.get_objects","text":"python/ray/worker.py::Worker.get_objects\n def get_objects(self, object_ids, timeout=None):\n \"\"\"Get the values in the object store associated with the IDs.\n\n Return the values from the local object store for object_ids. This will\n block until all the values for object_ids have been written to the\n local object store.\n\n Args:\n object_ids (List[object_id.ObjectID]): A list of the object IDs\n whose values should be retrieved.\n timeout (float): timeout (float): The maximum amount of time in\n seconds to wait before returning.\n \"\"\"\n # Make sure that the values are object IDs.\n for object_id in object_ids:\n if not isinstance(object_id, ObjectID):\n raise TypeError(\n \"Attempting to call `get` on the value {}, \"\n \"which is not an ray.ObjectID.\".format(object_id))\n\n timeout_ms = int(timeout * 1000) if timeout else -1\n data_metadata_pairs = self.core_worker.get_objects(\n object_ids, self.current_task_id, timeout_ms)\n return self.deserialize_objects(data_metadata_pairs, object_ids)","label":0},{"cid":"python/ray/worker.py::Worker.run_function_on_all_workers","text":"python/ray/worker.py::Worker.run_function_on_all_workers\n def run_function_on_all_workers(self, function,\n run_on_other_drivers=False):\n \"\"\"Run arbitrary code on all of the workers.\n\n This function will first be run on the driver, and then it will be\n exported to all of the workers to be run. It will also be run on any\n new workers that register later. If ray.init has not been called yet,\n then cache the function and export it later.\n\n Args:\n function (Callable): The function to run on all of the workers. It\n takes only one argument, a worker info dict. If it returns\n anything, its return values will not be used.\n run_on_other_drivers: The boolean that indicates whether we want to\n run this function on other drivers. One case is we may need to\n share objects across drivers.\n \"\"\"\n # If ray.init has not been called yet, then cache the function and\n # export it when connect is called. Otherwise, run the function on all\n # workers.\n if self.mode is None:\n self.cached_functions_to_run.append(function)\n else:\n # Attempt to pickle the function before we need it. This could\n # fail, and it is more convenient if the failure happens before we\n # actually run the function locally.\n pickled_function = pickle.dumps(function)\n\n function_to_run_id = hashlib.sha1(pickled_function).digest()\n key = b\"FunctionsToRun:\" + function_to_run_id\n # First run the function on the driver.\n # We always run the task locally.\n function({\"worker\": self})\n # Check if the function has already been put into redis.\n function_exported = self.redis_client.setnx(b\"Lock:\" + key, 1)\n if not function_exported:\n # In this case, the function has already b","label":0},{"cid":"python/ray/worker.py::Worker.main_loop","text":"python/ray/worker.py::Worker.main_loop\n def main_loop(self):\n \"\"\"The main loop a worker runs to receive and execute tasks.\"\"\"\n\n def sigterm_handler(signum, frame):\n shutdown(True)\n sys.exit(1)\n\n ray.utils.set_sigterm_handler(sigterm_handler)\n self.core_worker.run_task_loop()\n sys.exit(0)","label":0},{"cid":"python/ray/worker.py::Worker.main_loop.sigterm_handler","text":"python/ray/worker.py::Worker.main_loop.sigterm_handler\n def sigterm_handler(signum, frame):\n shutdown(True)\n sys.exit(1)","label":0},{"cid":"python/ray/worker.py::get_gpu_ids","text":"python/ray/worker.py::get_gpu_ids\ndef get_gpu_ids():\n \"\"\"Get the IDs of the GPUs that are available to the worker.\n\n If the CUDA_VISIBLE_DEVICES environment variable was set when the worker\n started up, then the IDs returned by this method will be a subset of the\n IDs in CUDA_VISIBLE_DEVICES. If not, the IDs will fall in the range\n [0, NUM_GPUS - 1], where NUM_GPUS is the number of GPUs that the node has.\n\n Returns:\n A list of GPU IDs.\n \"\"\"\n\n # TODO(ilr) Handle inserting resources in local mode\n all_resource_ids = global_worker.core_worker.resource_ids()\n assigned_ids = [\n resource_id for resource_id, _ in all_resource_ids.get(\"GPU\", [])\n ]\n # If the user had already set CUDA_VISIBLE_DEVICES, then respect that (in\n # the sense that only GPU IDs that appear in CUDA_VISIBLE_DEVICES should be\n # returned).\n if global_worker.original_gpu_ids is not None:\n assigned_ids = [\n global_worker.original_gpu_ids[gpu_id] for gpu_id in assigned_ids\n ]\n # Give all GPUs in local_mode.\n if global_worker.mode == LOCAL_MODE:\n max_gpus = global_worker.node.get_resource_spec().num_gpus\n return global_worker.original_gpu_ids[:max_gpus]\n\n return assigned_ids","label":0},{"cid":"python/ray/worker.py::get_resource_ids","text":"python/ray/worker.py::get_resource_ids\ndef get_resource_ids():\n \"\"\"Get the IDs of the resources that are available to the worker.\n\n Returns:\n A dictionary mapping the name of a resource to a list of pairs, where\n each pair consists of the ID of a resource and the fraction of that\n resource reserved for this worker.\n \"\"\"\n if _mode() == LOCAL_MODE:\n raise RuntimeError(\"ray.get_resource_ids() currently does not work in \"\n \"local_mode.\")\n\n return global_worker.core_worker.resource_ids()","label":0},{"cid":"python/ray/worker.py::get_webui_url","text":"python/ray/worker.py::get_webui_url\ndef get_webui_url():\n \"\"\"Get the URL to access the web UI.\n\n Note that the URL does not specify which node the web UI is on.\n\n Returns:\n The URL of the web UI as a string.\n \"\"\"\n if _global_node is None:\n raise RuntimeError(\"Ray has not been initialized/connected.\")\n return _global_node.webui_url","label":0},{"cid":"python/ray/worker.py::print_failed_task","text":"python/ray/worker.py::print_failed_task\ndef print_failed_task(task_status):\n \"\"\"Print information about failed tasks.\n\n Args:\n task_status (Dict): A dictionary containing the name, operationid, and\n error message for a failed task.\n \"\"\"\n logger.error(\"\"\"\n Error: Task failed\n Function Name: {}\n Task ID: {}\n Error Message: \\n{}\n \"\"\".format(task_status[\"function_name\"], task_status[\"operationid\"],\n task_status[\"error_message\"]))","label":0},{"cid":"python/ray/worker.py::init","text":"python/ray/worker.py::init\ndef init(address=None,\n redis_address=None,\n redis_port=None,\n num_cpus=None,\n num_gpus=None,\n memory=None,\n object_store_memory=None,\n resources=None,\n driver_object_store_memory=None,\n redis_max_memory=None,\n log_to_driver=True,\n node_ip_address=ray_constants.NODE_DEFAULT_IP,\n object_id_seed=None,\n local_mode=False,\n redirect_worker_output=None,\n redirect_output=None,\n ignore_reinit_error=False,\n num_redis_shards=None,\n redis_max_clients=None,\n redis_password=ray_constants.REDIS_DEFAULT_PASSWORD,\n plasma_directory=None,\n huge_pages=False,\n include_java=False,\n include_dashboard=None,\n dashboard_host=\"localhost\",\n dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT,\n job_id=None,\n configure_logging=True,\n logging_level=logging.INFO,\n logging_format=ray_constants.LOGGER_FORMAT,\n plasma_store_socket_name=None,\n raylet_socket_name=None,\n temp_dir=None,\n load_code_from_local=False,\n java_worker_options=None,\n use_pickle=True,\n _internal_config=None,\n lru_evict=False):\n \"\"\"\n Connect to an existing Ray cluster or start one and connect to it.\n\n This method handles two cases; either a Ray cluster already exists and we\n just attach this driver to it or we start all of the processes associated\n with a Ray cluster and attach to the newly started cluster.\n\n To start Ray and all of the relevant processes, use this as follows:\n\n .. code-block:: python\n\n ray.init()\n\n To connect to an existing Ray cluster, use this as follows (substituting\n in the appropriate address):\n\n .. code-block:: python\n\n ray.init(address=\"123.45.67.89:6379\")\n\n You can also define an environment variable called `RAY_ADDRESS` in\n the same for","label":0},{"cid":"python/ray/worker.py::shutdown","text":"python/ray/worker.py::shutdown\ndef shutdown(exiting_interpreter=False):\n \"\"\"Disconnect the worker, and terminate processes started by ray.init().\n\n This will automatically run at the end when a Python process that uses Ray\n exits. It is ok to run this twice in a row. The primary use case for this\n function is to cleanup state between tests.\n\n Note that this will clear any remote function definitions, actor\n definitions, and existing actors, so if you wish to use any previously\n defined remote functions or actors after calling ray.shutdown(), then you\n need to redefine them. If they were defined in an imported module, then you\n will need to reload the module.\n\n Args:\n exiting_interpreter (bool): True if this is called by the atexit hook\n and false otherwise. If we are exiting the interpreter, we will\n wait a little while to print any extra error messages.\n \"\"\"\n if exiting_interpreter and global_worker.mode == SCRIPT_MODE:\n # This is a duration to sleep before shutting down everything in order\n # to make sure that log messages finish printing.\n time.sleep(0.5)\n\n disconnect(exiting_interpreter)\n\n # We need to destruct the core worker here because after this function,\n # we will tear down any processes spawned by ray.init() and the background\n # IO thread in the core worker doesn't currently handle that gracefully.\n if hasattr(global_worker, \"core_worker\"):\n del global_worker.core_worker\n\n # Disconnect global state from GCS.\n ray.state.state.disconnect()\n\n # Shut down the Ray processes.\n global _global_node\n if _global_node is not None:\n _global_node.kill_all_processes(check_alive=False, allow_graceful=True)\n _global_node = None\n\n # TODO(rkn): Instead of manually resetting some of the worker fields, we\n # should simply set \"global_worker\" to equal \"None\" or something like that.\n global_worker.set_mode(None)\n global_worker._","label":0},{"cid":"python/ray/worker.py::sigterm_handler","text":"python/ray/worker.py::sigterm_handler\ndef sigterm_handler(signum, frame):\n sys.exit(signum)","label":0},{"cid":"python/ray/worker.py::custom_excepthook","text":"python/ray/worker.py::custom_excepthook\ndef custom_excepthook(type, value, tb):\n # If this is a driver, push the exception to GCS worker table.\n if global_worker.mode == SCRIPT_MODE:\n error_message = \"\".join(traceback.format_tb(tb))\n worker_id = global_worker.worker_id\n worker_type = ray.gcs_utils.DRIVER\n worker_info = {\"exception\": error_message}\n\n ray.state.state.add_worker(worker_id, worker_type, worker_info)\n # Call the normal excepthook.\n normal_excepthook(type, value, tb)","label":0},{"cid":"python/ray/worker.py::_set_log_file","text":"python/ray/worker.py::_set_log_file\ndef _set_log_file(file_name, worker_pid, old_obj, setter_func):\n # Line-buffer the output (mode 1).\n f = create_and_init_new_worker_log(file_name, worker_pid)\n\n # TODO (Alex): Python seems to always flush when writing. If that is no\n # longer true, then we need to manually flush the old buffer.\n # old_obj.flush()\n\n # TODO (Alex): Flush the c/c++ userspace buffers if necessary.\n # `fflush(stdout); cout.flush();`\n\n fileno = old_obj.fileno()\n\n # C++ logging requires redirecting the stdout file descriptor. Note that\n # dup2 will automatically close the old file descriptor before overriding\n # it.\n os.dup2(f.fileno(), fileno)\n\n # We also manually set sys.stdout and sys.stderr because that seems to\n # have an effect on the output buffering. Without doing this, stdout\n # and stderr are heavily buffered resulting in seemingly lost logging\n # statements. We never want to close the stdout file descriptor, dup2 will\n # close it when necessary and we don't want python's GC to close it.\n setter_func(open_log(fileno, closefd=False))\n\n return os.path.abspath(f.name)","label":0},{"cid":"python/ray/worker.py::set_log_file","text":"python/ray/worker.py::set_log_file\ndef set_log_file(stdout_name, stderr_name):\n \"\"\"Sets up logging for the current worker, creating the (fd backed) file and\n flushing buffers as is necessary.\n\n Args:\n stdout_name (str): The file name that stdout should be written to.\n stderr_name(str): The file name that stderr should be written to.\n\n Returns:\n (tuple) The absolute paths of the files that stdout and stderr will be\n written to.\n\n \"\"\"\n stdout_path = \"\"\n stderr_path = \"\"\n worker_pid = os.getpid()\n\n # lambda cannot contain assignment\n def stdout_setter(x):\n sys.stdout = x\n\n def stderr_setter(x):\n sys.stderr = x\n\n if stdout_name:\n _set_log_file(stdout_name, worker_pid, sys.stdout, stdout_setter)\n\n # The stderr case should be analogous to the stdout case\n if stderr_name:\n _set_log_file(stderr_name, worker_pid, sys.stderr, stderr_setter)\n\n return stdout_path, stderr_path","label":0},{"cid":"python/ray/worker.py::set_log_file.stdout_setter","text":"python/ray/worker.py::set_log_file.stdout_setter\n def stdout_setter(x):\n sys.stdout = x","label":0},{"cid":"python/ray/worker.py::set_log_file.stderr_setter","text":"python/ray/worker.py::set_log_file.stderr_setter\n def stderr_setter(x):\n sys.stderr = x","label":0},{"cid":"python/ray/worker.py::print_logs","text":"python/ray/worker.py::print_logs\ndef print_logs(redis_client, threads_stopped, job_id):\n \"\"\"Prints log messages from workers on all of the nodes.\n\n Args:\n redis_client: A client to the primary Redis shard.\n threads_stopped (threading.Event): A threading event used to signal to\n the thread that it should exit.\n job_id (JobID): The id of the driver's job\n\n \"\"\"\n pubsub_client = redis_client.pubsub(ignore_subscribe_messages=True)\n pubsub_client.subscribe(ray.gcs_utils.LOG_FILE_CHANNEL)\n localhost = services.get_node_ip_address()\n try:\n # Keep track of the number of consecutive log messages that have been\n # received with no break in between. If this number grows continually,\n # then the worker is probably not able to process the log messages as\n # rapidly as they are coming in.\n num_consecutive_messages_received = 0\n while True:\n # Exit if we received a signal that we should stop.\n if threads_stopped.is_set():\n return\n\n msg = pubsub_client.get_message()\n if msg is None:\n num_consecutive_messages_received = 0\n threads_stopped.wait(timeout=0.01)\n continue\n num_consecutive_messages_received += 1\n if (num_consecutive_messages_received % 100 == 0\n and num_consecutive_messages_received > 0):\n logger.warning(\n \"The driver may not be able to keep up with the \"\n \"stdout/stderr of the workers. To avoid forwarding logs \"\n \"to the driver, use 'ray.init(log_to_driver=False)'.\")\n\n data = json.loads(ray.utils.decode(msg[\"data\"]))\n\n # Don't show logs from other drivers.\n if data[\"job\"] and ray.utils.binary_to_hex(\n job_id.binary()) != data[\"job\"]:\n continue\n\n def color_for(data):\n if data","label":0},{"cid":"python/ray/worker.py::print_error_messages_raylet","text":"python/ray/worker.py::print_error_messages_raylet\ndef print_error_messages_raylet(task_error_queue, threads_stopped):\n \"\"\"Prints message received in the given output queue.\n\n This checks periodically if any un-raised errors occured in the background.\n\n Args:\n task_error_queue (queue.Queue): A queue used to receive errors from the\n thread that listens to Redis.\n threads_stopped (threading.Event): A threading event used to signal to\n the thread that it should exit.\n \"\"\"\n\n while True:\n # Exit if we received a signal that we should stop.\n if threads_stopped.is_set():\n return\n\n try:\n error, t = task_error_queue.get(block=False)\n except queue.Empty:\n threads_stopped.wait(timeout=0.01)\n continue\n # Delay errors a little bit of time to attempt to suppress redundant\n # messages originating from the worker.\n while t + UNCAUGHT_ERROR_GRACE_PERIOD > time.time():\n threads_stopped.wait(timeout=1)\n if threads_stopped.is_set():\n break\n if t < last_task_error_raise_time + UNCAUGHT_ERROR_GRACE_PERIOD:\n logger.debug(\"Suppressing error from worker: {}\".format(error))\n else:\n logger.error(\n \"Possible unhandled error from worker: {}\".format(error))","label":0},{"cid":"python/ray/worker.py::listen_error_messages_raylet","text":"python/ray/worker.py::listen_error_messages_raylet\ndef listen_error_messages_raylet(worker, task_error_queue, threads_stopped):\n \"\"\"Listen to error messages in the background on the driver.\n\n This runs in a separate thread on the driver and pushes (error, time)\n tuples to the output queue.\n\n Args:\n worker: The worker class that this thread belongs to.\n task_error_queue (queue.Queue): A queue used to communicate with the\n thread that prints the errors found by this thread.\n threads_stopped (threading.Event): A threading event used to signal to\n the thread that it should exit.\n \"\"\"\n worker.error_message_pubsub_client = worker.redis_client.pubsub(\n ignore_subscribe_messages=True)\n # Exports that are published after the call to\n # error_message_pubsub_client.subscribe and before the call to\n # error_message_pubsub_client.listen will still be processed in the loop.\n\n # Really we should just subscribe to the errors for this specific job.\n # However, currently all errors seem to be published on the same channel.\n error_pubsub_channel = str(\n ray.gcs_utils.TablePubsub.Value(\"ERROR_INFO_PUBSUB\")).encode(\"ascii\")\n worker.error_message_pubsub_client.subscribe(error_pubsub_channel)\n # worker.error_message_pubsub_client.psubscribe(\"*\")\n\n try:\n # Get the errors that occurred before the call to subscribe.\n error_messages = ray.errors()\n for error_message in error_messages:\n logger.error(error_message)\n\n while True:\n # Exit if we received a signal that we should stop.\n if threads_stopped.is_set():\n return\n\n msg = worker.error_message_pubsub_client.get_message()\n if msg is None:\n threads_stopped.wait(timeout=0.01)\n continue\n gcs_entry = ray.gcs_utils.GcsEntry.FromString(msg[\"data\"])\n assert len(gcs_entry.entries) == 1\n error","label":0},{"cid":"python/ray/worker.py::is_initialized","text":"python/ray/worker.py::is_initialized\ndef is_initialized():\n \"\"\"Check if ray.init has been called yet.\n\n Returns:\n True if ray.init has already been called and false otherwise.\n \"\"\"\n return ray.worker.global_worker.connected","label":0},{"cid":"python/ray/worker.py::connect","text":"python/ray/worker.py::connect\ndef connect(node,\n mode=WORKER_MODE,\n log_to_driver=False,\n worker=global_worker,\n driver_object_store_memory=None,\n job_id=None):\n \"\"\"Connect this worker to the raylet, to Plasma, and to Redis.\n\n Args:\n node (ray.node.Node): The node to connect.\n mode: The mode of the worker. One of SCRIPT_MODE, WORKER_MODE, and\n LOCAL_MODE.\n log_to_driver (bool): If true, then output from all of the worker\n processes on all nodes will be directed to the driver.\n worker: The ray.Worker instance.\n driver_object_store_memory: Limit the amount of memory the driver can\n use in the object store when creating objects.\n job_id: The ID of job. If it's None, then we will generate one.\n \"\"\"\n # Do some basic checking to make sure we didn't call ray.init twice.\n error_message = \"Perhaps you called ray.init twice by accident?\"\n assert not worker.connected, error_message\n assert worker.cached_functions_to_run is not None, error_message\n\n # Enable nice stack traces on SIGSEGV etc.\n try:\n if not faulthandler.is_enabled():\n faulthandler.enable(all_threads=False)\n except io.UnsupportedOperation:\n pass # ignore\n\n # Create a Redis client to primary.\n # The Redis client can safely be shared between threads. However,\n # that is not true of Redis pubsub clients. See the documentation at\n # https://github.com/andymccurdy/redis-py#thread-safety.\n worker.redis_client = node.create_redis_client()\n\n # Initialize some fields.\n if mode is WORKER_MODE:\n # We should not specify the job_id if it's `WORKER_MODE`.\n assert job_id is None\n job_id = JobID.nil()\n # TODO(qwang): Rename this to `worker_id_str` or type to `WorkerID`\n worker.worker_id = _random_string()\n else:\n # This is the code path of driver mode.\n if job_id is None:\n ","label":0},{"cid":"python/ray/worker.py::disconnect","text":"python/ray/worker.py::disconnect\ndef disconnect(exiting_interpreter=False):\n \"\"\"Disconnect this worker from the raylet and object store.\"\"\"\n # Reset the list of cached remote functions and actors so that if more\n # remote functions or actors are defined and then connect is called again,\n # the remote functions will be exported. This is mostly relevant for the\n # tests.\n worker = global_worker\n if worker.connected:\n # Shutdown all of the threads that we've started. TODO(rkn): This\n # should be handled cleanly in the worker object's destructor and not\n # in this disconnect method.\n worker.threads_stopped.set()\n if hasattr(worker, \"import_thread\"):\n worker.import_thread.join_import_thread()\n if hasattr(worker, \"listener_thread\"):\n worker.listener_thread.join()\n if hasattr(worker, \"printer_thread\"):\n worker.printer_thread.join()\n if hasattr(worker, \"logger_thread\"):\n worker.logger_thread.join()\n worker.threads_stopped.clear()\n worker._session_index += 1\n\n worker.node = None # Disconnect the worker from the node.\n worker.cached_functions_to_run = []\n worker.serialization_context_map.clear()\n try:\n ray_actor = ray.actor\n except AttributeError:\n ray_actor = None # This can occur during program termination\n if ray_actor is not None:\n ray_actor.ActorClassMethodMetadata.reset_cache()","label":0},{"cid":"python/ray/worker.py::_changeproctitle","text":"python/ray/worker.py::_changeproctitle\n@contextmanager\ndef _changeproctitle(title, next_title):\n setproctitle.setproctitle(title)\n try:\n yield\n finally:\n setproctitle.setproctitle(next_title)","label":0},{"cid":"python/ray/worker.py::register_custom_serializer","text":"python/ray/worker.py::register_custom_serializer\ndef register_custom_serializer(cls,\n serializer,\n deserializer,\n use_pickle=False,\n use_dict=False,\n class_id=None):\n \"\"\"Registers custom functions for efficient object serialization.\n\n The serializer and deserializer are used when transferring objects of\n `cls` across processes and nodes. This can be significantly faster than\n the Ray default fallbacks. Wraps `register_custom_serializer` underneath.\n\n Args:\n cls (type): The class that ray should use this custom serializer for.\n serializer: The custom serializer that takes in a cls instance and\n outputs a serialized representation. use_pickle and use_dict\n must be False if provided.\n deserializer: The custom deserializer that takes in a serialized\n representation of the cls and outputs a cls instance. use_pickle\n and use_dict must be False if provided.\n use_pickle: Deprecated.\n use_dict: Deprecated.\n class_id (str): Unique ID of the class. Autogenerated if None.\n \"\"\"\n worker = global_worker\n worker.check_connected()\n\n if use_pickle:\n raise DeprecationWarning(\n \"`use_pickle` is no longer a valid parameter and will be removed \"\n \"in future versions of Ray. If this breaks your application, \"\n \"see `SerializationContext.register_custom_serializer`.\")\n if use_dict:\n raise DeprecationWarning(\n \"`use_pickle` is no longer a valid parameter and will be removed \"\n \"in future versions of Ray. If this breaks your application, \"\n \"see `SerializationContext.register_custom_serializer`.\")\n assert serializer is not None and deserializer is not None\n context = global_worker.get_serialization_context()\n context.register_custom_serializer","label":0}],"string":"[\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.__init__\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.__init__\\n def __init__(self, experiment_dir):\\n experiment_dir = os.path.expanduser(experiment_dir)\\n if not os.path.isdir(experiment_dir):\\n raise ValueError(\\n \\\"{} is not a valid directory.\\\".format(experiment_dir))\\n self._experiment_dir = experiment_dir\\n self._configs = {}\\n self._trial_dataframes = {}\\n\\n if not pd:\\n logger.warning(\\n \\\"pandas not installed. Run `pip install pandas` for \\\"\\n \\\"Analysis utilities.\\\")\\n else:\\n self.fetch_trial_dataframes()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.dataframe\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.dataframe\\n def dataframe(self, metric=None, mode=None):\\n \\\"\\\"\\\"Returns a pandas.DataFrame object constructed from the trials.\\n\\n Args:\\n metric (str): Key for trial info to order on.\\n If None, uses last result.\\n mode (str): One of [min, max].\\n\\n Returns:\\n pd.DataFrame: Constructed from a result dict of each trial.\\n \\\"\\\"\\\"\\n rows = self._retrieve_rows(metric=metric, mode=mode)\\n all_configs = self.get_all_configs(prefix=True)\\n for path, config in all_configs.items():\\n if path in rows:\\n rows[path].update(config)\\n rows[path].update(logdir=path)\\n return pd.DataFrame(list(rows.values()))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_config\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_config\\n def get_best_config(self, metric, mode=\\\"max\\\"):\\n \\\"\\\"\\\"Retrieve the best config corresponding to the trial.\\n\\n Args:\\n metric (str): Key for trial info to order on.\\n mode (str): One of [min, max].\\n \\\"\\\"\\\"\\n rows = self._retrieve_rows(metric=metric, mode=mode)\\n all_configs = self.get_all_configs()\\n compare_op = max if mode == \\\"max\\\" else min\\n best_path = compare_op(rows, key=lambda k: rows[k][metric])\\n return all_configs[best_path]\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_logdir\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_best_logdir\\n def get_best_logdir(self, metric, mode=\\\"max\\\"):\\n \\\"\\\"\\\"Retrieve the logdir corresponding to the best trial.\\n\\n Args:\\n metric (str): Key for trial info to order on.\\n mode (str): One of [min, max].\\n \\\"\\\"\\\"\\n df = self.dataframe(metric=metric, mode=mode)\\n if mode == \\\"max\\\":\\n return df.iloc[df[metric].idxmax()].logdir\\n elif mode == \\\"min\\\":\\n return df.iloc[df[metric].idxmin()].logdir\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.fetch_trial_dataframes\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.fetch_trial_dataframes\\n def fetch_trial_dataframes(self):\\n fail_count = 0\\n for path in self._get_trial_paths():\\n try:\\n self.trial_dataframes[path] = pd.read_csv(\\n os.path.join(path, EXPR_PROGRESS_FILE))\\n except Exception:\\n fail_count += 1\\n\\n if fail_count:\\n logger.debug(\\n \\\"Couldn't read results from {} paths\\\".format(fail_count))\\n return self.trial_dataframes\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_all_configs\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_all_configs\\n def get_all_configs(self, prefix=False):\\n \\\"\\\"\\\"Returns a list of all configurations.\\n\\n Args:\\n prefix (bool): If True, flattens the config dict\\n and prepends `config/`.\\n\\n Returns:\\n List[dict]: List of all configurations of trials,\\n \\\"\\\"\\\"\\n fail_count = 0\\n for path in self._get_trial_paths():\\n try:\\n with open(os.path.join(path, EXPR_PARAM_FILE)) as f:\\n config = json.load(f)\\n if prefix:\\n for k in list(config):\\n config[CONFIG_PREFIX + k] = config.pop(k)\\n self._configs[path] = config\\n except Exception:\\n fail_count += 1\\n\\n if fail_count:\\n logger.warning(\\n \\\"Couldn't read config from {} paths\\\".format(fail_count))\\n return self._configs\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_trial_checkpoints_paths\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.get_trial_checkpoints_paths\\n def get_trial_checkpoints_paths(self, trial, metric=TRAINING_ITERATION):\\n \\\"\\\"\\\"Gets paths and metrics of all persistent checkpoints of a trial.\\n\\n Args:\\n trial (Trial): The log directory of a trial, or a trial instance.\\n metric (str): key for trial info to return, e.g. \\\"mean_accuracy\\\".\\n \\\"training_iteration\\\" is used by default.\\n\\n Returns:\\n List of [path, metric] for all persistent checkpoints of the trial.\\n \\\"\\\"\\\"\\n if isinstance(trial, str):\\n trial_dir = os.path.expanduser(trial)\\n # Get checkpoints from logdir.\\n chkpt_df = TrainableUtil.get_checkpoints_paths(trial_dir)\\n\\n # Join with trial dataframe to get metrics.\\n trial_df = self.trial_dataframes[trial_dir]\\n path_metric_df = chkpt_df.merge(\\n trial_df, on=\\\"training_iteration\\\", how=\\\"inner\\\")\\n return path_metric_df[[\\\"chkpt_path\\\", metric]].values.tolist()\\n elif isinstance(trial, Trial):\\n checkpoints = trial.checkpoint_manager.best_checkpoints()\\n return [[c.value, c.result[metric]] for c in checkpoints]\\n else:\\n raise ValueError(\\\"trial should be a string or a Trial instance.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis._retrieve_rows\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis._retrieve_rows\\n def _retrieve_rows(self, metric=None, mode=None):\\n assert mode is None or mode in [\\\"max\\\", \\\"min\\\"]\\n rows = {}\\n for path, df in self.trial_dataframes.items():\\n if mode == \\\"max\\\":\\n idx = df[metric].idxmax()\\n elif mode == \\\"min\\\":\\n idx = df[metric].idxmin()\\n else:\\n idx = -1\\n rows[path] = df.iloc[idx].to_dict()\\n\\n return rows\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis._get_trial_paths\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis._get_trial_paths\\n def _get_trial_paths(self):\\n _trial_paths = []\\n for trial_path, _, files in os.walk(self._experiment_dir):\\n if EXPR_PROGRESS_FILE in files:\\n _trial_paths += [trial_path]\\n\\n if not _trial_paths:\\n raise TuneError(\\\"No trials found in {}.\\\".format(\\n self._experiment_dir))\\n return _trial_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.trial_dataframes\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::Analysis.trial_dataframes\\n @property\\n def trial_dataframes(self):\\n \\\"\\\"\\\"List of all dataframes of the trials.\\\"\\\"\\\"\\n return self._trial_dataframes\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.__init__\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.__init__\\n def __init__(self, experiment_checkpoint_path, trials=None):\\n with open(experiment_checkpoint_path) as f:\\n _experiment_state = json.load(f)\\n self._experiment_state = _experiment_state\\n\\n if \\\"checkpoints\\\" not in _experiment_state:\\n raise TuneError(\\\"Experiment state invalid; no checkpoints found.\\\")\\n self._checkpoints = _experiment_state[\\\"checkpoints\\\"]\\n self.trials = trials\\n super(ExperimentAnalysis, self).__init__(\\n os.path.dirname(experiment_checkpoint_path))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_trial\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_trial\\n def get_best_trial(self, metric, mode=\\\"max\\\", scope=\\\"all\\\"):\\n \\\"\\\"\\\"Retrieve the best trial object.\\n\\n Compares all trials' scores on `metric`.\\n\\n Args:\\n metric (str): Key for trial info to order on.\\n mode (str): One of [min, max].\\n scope (str): One of [all, last, avg, last-5-avg, last-10-avg].\\n If `scope=last`, only look at each trial's final step for\\n `metric`, and compare across trials based on `mode=[min,max]`.\\n If `scope=avg`, consider the simple average over all steps\\n for `metric` and compare across trials based on\\n `mode=[min,max]`. If `scope=last-5-avg` or `scope=last-10-avg`,\\n consider the simple average over the last 5 or 10 steps for\\n `metric` and compare across trials based on `mode=[min,max]`.\\n If `scope=all`, find each trial's min/max score for `metric`\\n based on `mode`, and compare trials based on `mode=[min,max]`.\\n \\\"\\\"\\\"\\n if mode not in [\\\"max\\\", \\\"min\\\"]:\\n raise ValueError(\\n \\\"ExperimentAnalysis: attempting to get best trial for \\\"\\n \\\"metric {} for mode {} not in [\\\\\\\"max\\\\\\\", \\\\\\\"min\\\\\\\"]\\\".format(\\n metric, mode))\\n if scope not in [\\\"all\\\", \\\"last\\\", \\\"avg\\\", \\\"last-5-avg\\\", \\\"last-10-avg\\\"]:\\n raise ValueError(\\n \\\"ExperimentAnalysis: attempting to get best trial for \\\"\\n \\\"metric {} for scope {} not in [\\\\\\\"all\\\\\\\", \\\\\\\"last\\\\\\\", \\\\\\\"avg\\\\\\\", \\\"\\n \\\"\\\\\\\"last-5-avg\\\\\\\", \\\\\\\"last-10-avg\\\\\\\"]\\\".format(metric, scope))\\n best_trial = None\\n best_metric_score = None\\n for trial in self.trials:\\n if metric not in trial.metric_analysis:\\n continue\\n\\n if scope in [\\\"last\\\", \\\"avg\\\", \\\"last-5-avg\\\", \\\"last-10-avg\\\"]:\\n metric_score = trial.metric_analysis[m\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_config\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_config\\n def get_best_config(self, metric, mode=\\\"max\\\", scope=\\\"all\\\"):\\n \\\"\\\"\\\"Retrieve the best config corresponding to the trial.\\n\\n Compares all trials' scores on `metric`.\\n\\n Args:\\n metric (str): Key for trial info to order on.\\n mode (str): One of [min, max].\\n scope (str): One of [all, last, avg, last-5-avg, last-10-avg].\\n If `scope=last`, only look at each trial's final step for\\n `metric`, and compare across trials based on `mode=[min,max]`.\\n If `scope=avg`, consider the simple average over all steps\\n for `metric` and compare across trials based on\\n `mode=[min,max]`. If `scope=last-5-avg` or `scope=last-10-avg`,\\n consider the simple average over the last 5 or 10 steps for\\n `metric` and compare across trials based on `mode=[min,max]`.\\n If `scope=all`, find each trial's min/max score for `metric`\\n based on `mode`, and compare trials based on `mode=[min,max]`.\\n \\\"\\\"\\\"\\n best_trial = self.get_best_trial(metric, mode, scope)\\n return best_trial.config if best_trial else None\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_logdir\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.get_best_logdir\\n def get_best_logdir(self, metric, mode=\\\"max\\\", scope=\\\"all\\\"):\\n \\\"\\\"\\\"Retrieve the logdir corresponding to the best trial.\\n\\n Compares all trials' scores on `metric`.\\n\\n Args:\\n metric (str): Key for trial info to order on.\\n mode (str): One of [min, max].\\n scope (str): One of [all, last, avg, last-5-avg, last-10-avg].\\n If `scope=last`, only look at each trial's final step for\\n `metric`, and compare across trials based on `mode=[min,max]`.\\n If `scope=avg`, consider the simple average over all steps\\n for `metric` and compare across trials based on\\n `mode=[min,max]`. If `scope=last-5-avg` or `scope=last-10-avg`,\\n consider the simple average over the last 5 or 10 steps for\\n `metric` and compare across trials based on `mode=[min,max]`.\\n If `scope=all`, find each trial's min/max score for `metric`\\n based on `mode`, and compare trials based on `mode=[min,max]`.\\n \\\"\\\"\\\"\\n best_trial = self.get_best_trial(metric, mode, scope)\\n return best_trial.logdir if best_trial else None\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.stats\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.stats\\n def stats(self):\\n \\\"\\\"\\\"Returns a dictionary of the statistics of the experiment.\\\"\\\"\\\"\\n return self._experiment_state.get(\\\"stats\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.runner_data\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis.runner_data\\n def runner_data(self):\\n \\\"\\\"\\\"Returns a dictionary of the TrialRunner data.\\\"\\\"\\\"\\n return self._experiment_state.get(\\\"runner_data\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis._get_trial_paths\",\n \"text\": \"python/ray/tune/analysis/experiment_analysis.py::ExperimentAnalysis._get_trial_paths\\n def _get_trial_paths(self):\\n \\\"\\\"\\\"Overwrites Analysis to only have trials of one experiment.\\\"\\\"\\\"\\n if self.trials:\\n _trial_paths = [t.logdir for t in self.trials]\\n else:\\n logger.warning(\\\"No `self.trials`. Drawing logdirs from checkpoint \\\"\\n \\\"file. This may result in some information that is \\\"\\n \\\"out of sync, as checkpointing is periodic.\\\")\\n _trial_paths = [\\n checkpoint[\\\"logdir\\\"] for checkpoint in self._checkpoints\\n ]\\n if not _trial_paths:\\n raise TuneError(\\\"No trials found.\\\")\\n return _trial_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::ActorCheckpointInfo.__init__\",\n \"text\": \"python/ray/worker.py::ActorCheckpointInfo.__init__\\n def __init__(self, num_tasks_since_last_checkpoint,\\n last_checkpoint_timestamp, checkpoint_ids):\\n self.num_tasks_since_last_checkpoint = num_tasks_since_last_checkpoint\\n self.last_checkpoint_timestamp = last_checkpoint_timestamp\\n self.checkpoint_ids = checkpoint_ids\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.__init__\",\n \"text\": \"python/ray/worker.py::Worker.__init__\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a Worker object.\\\"\\\"\\\"\\n self.node = None\\n self.mode = None\\n self.cached_functions_to_run = []\\n self.actor_init_error = None\\n self.actors = {}\\n # Information used to maintain actor checkpoints.\\n self.actor_checkpoint_info = {}\\n self.actor_task_counter = 0\\n # When the worker is constructed. Record the original value of the\\n # CUDA_VISIBLE_DEVICES environment variable.\\n self.original_gpu_ids = ray.utils.get_cuda_visible_devices()\\n self.memory_monitor = memory_monitor.MemoryMonitor()\\n # A dictionary that maps from driver id to SerializationContext\\n # TODO: clean up the SerializationContext once the job finished.\\n self.serialization_context_map = {}\\n self.function_actor_manager = FunctionActorManager(self)\\n # This event is checked regularly by all of the threads so that they\\n # know when to exit.\\n self.threads_stopped = threading.Event()\\n # Index of the current session. This number will\\n # increment every time when `ray.shutdown` is called.\\n self._session_index = 0\\n # Functions to run to process the values returned by ray.get. Each\\n # postprocessor must take two arguments (\\\"object_ids\\\", and \\\"values\\\").\\n self._post_get_hooks = []\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.connected\",\n \"text\": \"python/ray/worker.py::Worker.connected\\n @property\\n def connected(self):\\n return self.node is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.node_ip_address\",\n \"text\": \"python/ray/worker.py::Worker.node_ip_address\\n @property\\n def node_ip_address(self):\\n self.check_connected()\\n return self.node.node_ip_address\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.load_code_from_local\",\n \"text\": \"python/ray/worker.py::Worker.load_code_from_local\\n @property\\n def load_code_from_local(self):\\n self.check_connected()\\n return self.node.load_code_from_local\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.current_job_id\",\n \"text\": \"python/ray/worker.py::Worker.current_job_id\\n @property\\n def current_job_id(self):\\n if hasattr(self, \\\"core_worker\\\"):\\n return self.core_worker.get_current_job_id()\\n return JobID.nil()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.actor_id\",\n \"text\": \"python/ray/worker.py::Worker.actor_id\\n @property\\n def actor_id(self):\\n if hasattr(self, \\\"core_worker\\\"):\\n return self.core_worker.get_actor_id()\\n return ActorID.nil()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.current_task_id\",\n \"text\": \"python/ray/worker.py::Worker.current_task_id\\n @property\\n def current_task_id(self):\\n return self.core_worker.get_current_task_id()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.current_session_and_job\",\n \"text\": \"python/ray/worker.py::Worker.current_session_and_job\\n @property\\n def current_session_and_job(self):\\n \\\"\\\"\\\"Get the current session index and job id as pair.\\\"\\\"\\\"\\n assert isinstance(self._session_index, int)\\n assert isinstance(self.current_job_id, ray.JobID)\\n return self._session_index, self.current_job_id\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.mark_actor_init_failed\",\n \"text\": \"python/ray/worker.py::Worker.mark_actor_init_failed\\n def mark_actor_init_failed(self, error):\\n \\\"\\\"\\\"Called to mark this actor as failed during initialization.\\\"\\\"\\\"\\n\\n self.actor_init_error = error\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.reraise_actor_init_error\",\n \"text\": \"python/ray/worker.py::Worker.reraise_actor_init_error\\n def reraise_actor_init_error(self):\\n \\\"\\\"\\\"Raises any previous actor initialization error.\\\"\\\"\\\"\\n\\n if self.actor_init_error is not None:\\n raise self.actor_init_error\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.get_serialization_context\",\n \"text\": \"python/ray/worker.py::Worker.get_serialization_context\\n def get_serialization_context(self, job_id=None):\\n \\\"\\\"\\\"Get the SerializationContext of the job that this worker is processing.\\n\\n Args:\\n job_id: The ID of the job that indicates which job to get\\n the serialization context for.\\n\\n Returns:\\n The serialization context of the given job.\\n \\\"\\\"\\\"\\n # This function needs to be protected by a lock, because it will be\\n # called by`register_class_for_serialization`, as well as the import\\n # thread, from different threads. Also, this function will recursively\\n # call itself, so we use RLock here.\\n if job_id is None:\\n job_id = self.current_job_id\\n with self.lock:\\n if job_id not in self.serialization_context_map:\\n self.serialization_context_map[\\n job_id] = serialization.SerializationContext(self)\\n return self.serialization_context_map[job_id]\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.check_connected\",\n \"text\": \"python/ray/worker.py::Worker.check_connected\\n def check_connected(self):\\n \\\"\\\"\\\"Check if the worker is connected.\\n\\n Raises:\\n Exception: An exception is raised if the worker is not connected.\\n \\\"\\\"\\\"\\n if not self.connected:\\n raise RayConnectionError(\\\"Ray has not been started yet. You can \\\"\\n \\\"start Ray with 'ray.init()'.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.set_mode\",\n \"text\": \"python/ray/worker.py::Worker.set_mode\\n def set_mode(self, mode):\\n \\\"\\\"\\\"Set the mode of the worker.\\n\\n The mode SCRIPT_MODE should be used if this Worker is a driver that is\\n being run as a Python script or interactively in a shell. It will print\\n information about task failures.\\n\\n The mode WORKER_MODE should be used if this Worker is not a driver. It\\n will not print information about tasks.\\n\\n The mode LOCAL_MODE should be used if this Worker is a driver and if\\n you want to run the driver in a manner equivalent to serial Python for\\n debugging purposes. It will not send remote function calls to the\\n scheduler and will instead execute them in a blocking fashion.\\n\\n Args:\\n mode: One of SCRIPT_MODE, WORKER_MODE, and LOCAL_MODE.\\n \\\"\\\"\\\"\\n self.mode = mode\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.put_object\",\n \"text\": \"python/ray/worker.py::Worker.put_object\\n def put_object(self, value, object_id=None, pin_object=True):\\n \\\"\\\"\\\"Put value in the local object store with object id `objectid`.\\n\\n This assumes that the value for `objectid` has not yet been placed in\\n the local object store. If the plasma store is full, the worker will\\n automatically retry up to DEFAULT_PUT_OBJECT_RETRIES times. Each\\n retry will delay for an exponentially doubling amount of time,\\n starting with DEFAULT_PUT_OBJECT_DELAY. After this, exception\\n will be raised.\\n\\n Args:\\n value: The value to put in the object store.\\n object_id (object_id.ObjectID): The object ID of the value to be\\n put. If None, one will be generated.\\n pin_object: If set, the object will be pinned at the raylet.\\n\\n Returns:\\n object_id.ObjectID: The object ID the object was put under.\\n\\n Raises:\\n ray.exceptions.ObjectStoreFullError: This is raised if the attempt\\n to store the object fails because the object store is full even\\n after multiple retries.\\n \\\"\\\"\\\"\\n # Make sure that the value is not an object ID.\\n if isinstance(value, ObjectID):\\n raise TypeError(\\n \\\"Calling 'put' on an ray.ObjectID is not allowed \\\"\\n \\\"(similarly, returning an ray.ObjectID from a remote \\\"\\n \\\"function is not allowed). If you really want to \\\"\\n \\\"do this, you can wrap the ray.ObjectID in a list and \\\"\\n \\\"call 'put' on it (or return it).\\\")\\n\\n if self.mode == LOCAL_MODE:\\n assert object_id is None, (\\\"Local Mode does not support \\\"\\n \\\"inserting with an objectID\\\")\\n\\n serialized_value = self.get_serialization_context().serialize(value)\\n # This *must* be the first place that we construct this python\\n # ObjectID because an entry with 0 local refer\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.deserialize_objects\",\n \"text\": \"python/ray/worker.py::Worker.deserialize_objects\\n def deserialize_objects(self, data_metadata_pairs, object_ids):\\n context = self.get_serialization_context()\\n return context.deserialize_objects(data_metadata_pairs, object_ids)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.get_objects\",\n \"text\": \"python/ray/worker.py::Worker.get_objects\\n def get_objects(self, object_ids, timeout=None):\\n \\\"\\\"\\\"Get the values in the object store associated with the IDs.\\n\\n Return the values from the local object store for object_ids. This will\\n block until all the values for object_ids have been written to the\\n local object store.\\n\\n Args:\\n object_ids (List[object_id.ObjectID]): A list of the object IDs\\n whose values should be retrieved.\\n timeout (float): timeout (float): The maximum amount of time in\\n seconds to wait before returning.\\n \\\"\\\"\\\"\\n # Make sure that the values are object IDs.\\n for object_id in object_ids:\\n if not isinstance(object_id, ObjectID):\\n raise TypeError(\\n \\\"Attempting to call `get` on the value {}, \\\"\\n \\\"which is not an ray.ObjectID.\\\".format(object_id))\\n\\n timeout_ms = int(timeout * 1000) if timeout else -1\\n data_metadata_pairs = self.core_worker.get_objects(\\n object_ids, self.current_task_id, timeout_ms)\\n return self.deserialize_objects(data_metadata_pairs, object_ids)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.run_function_on_all_workers\",\n \"text\": \"python/ray/worker.py::Worker.run_function_on_all_workers\\n def run_function_on_all_workers(self, function,\\n run_on_other_drivers=False):\\n \\\"\\\"\\\"Run arbitrary code on all of the workers.\\n\\n This function will first be run on the driver, and then it will be\\n exported to all of the workers to be run. It will also be run on any\\n new workers that register later. If ray.init has not been called yet,\\n then cache the function and export it later.\\n\\n Args:\\n function (Callable): The function to run on all of the workers. It\\n takes only one argument, a worker info dict. If it returns\\n anything, its return values will not be used.\\n run_on_other_drivers: The boolean that indicates whether we want to\\n run this function on other drivers. One case is we may need to\\n share objects across drivers.\\n \\\"\\\"\\\"\\n # If ray.init has not been called yet, then cache the function and\\n # export it when connect is called. Otherwise, run the function on all\\n # workers.\\n if self.mode is None:\\n self.cached_functions_to_run.append(function)\\n else:\\n # Attempt to pickle the function before we need it. This could\\n # fail, and it is more convenient if the failure happens before we\\n # actually run the function locally.\\n pickled_function = pickle.dumps(function)\\n\\n function_to_run_id = hashlib.sha1(pickled_function).digest()\\n key = b\\\"FunctionsToRun:\\\" + function_to_run_id\\n # First run the function on the driver.\\n # We always run the task locally.\\n function({\\\"worker\\\": self})\\n # Check if the function has already been put into redis.\\n function_exported = self.redis_client.setnx(b\\\"Lock:\\\" + key, 1)\\n if not function_exported:\\n # In this case, the function has already b\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.main_loop\",\n \"text\": \"python/ray/worker.py::Worker.main_loop\\n def main_loop(self):\\n \\\"\\\"\\\"The main loop a worker runs to receive and execute tasks.\\\"\\\"\\\"\\n\\n def sigterm_handler(signum, frame):\\n shutdown(True)\\n sys.exit(1)\\n\\n ray.utils.set_sigterm_handler(sigterm_handler)\\n self.core_worker.run_task_loop()\\n sys.exit(0)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.main_loop.sigterm_handler\",\n \"text\": \"python/ray/worker.py::Worker.main_loop.sigterm_handler\\n def sigterm_handler(signum, frame):\\n shutdown(True)\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::get_gpu_ids\",\n \"text\": \"python/ray/worker.py::get_gpu_ids\\ndef get_gpu_ids():\\n \\\"\\\"\\\"Get the IDs of the GPUs that are available to the worker.\\n\\n If the CUDA_VISIBLE_DEVICES environment variable was set when the worker\\n started up, then the IDs returned by this method will be a subset of the\\n IDs in CUDA_VISIBLE_DEVICES. If not, the IDs will fall in the range\\n [0, NUM_GPUS - 1], where NUM_GPUS is the number of GPUs that the node has.\\n\\n Returns:\\n A list of GPU IDs.\\n \\\"\\\"\\\"\\n\\n # TODO(ilr) Handle inserting resources in local mode\\n all_resource_ids = global_worker.core_worker.resource_ids()\\n assigned_ids = [\\n resource_id for resource_id, _ in all_resource_ids.get(\\\"GPU\\\", [])\\n ]\\n # If the user had already set CUDA_VISIBLE_DEVICES, then respect that (in\\n # the sense that only GPU IDs that appear in CUDA_VISIBLE_DEVICES should be\\n # returned).\\n if global_worker.original_gpu_ids is not None:\\n assigned_ids = [\\n global_worker.original_gpu_ids[gpu_id] for gpu_id in assigned_ids\\n ]\\n # Give all GPUs in local_mode.\\n if global_worker.mode == LOCAL_MODE:\\n max_gpus = global_worker.node.get_resource_spec().num_gpus\\n return global_worker.original_gpu_ids[:max_gpus]\\n\\n return assigned_ids\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::get_resource_ids\",\n \"text\": \"python/ray/worker.py::get_resource_ids\\ndef get_resource_ids():\\n \\\"\\\"\\\"Get the IDs of the resources that are available to the worker.\\n\\n Returns:\\n A dictionary mapping the name of a resource to a list of pairs, where\\n each pair consists of the ID of a resource and the fraction of that\\n resource reserved for this worker.\\n \\\"\\\"\\\"\\n if _mode() == LOCAL_MODE:\\n raise RuntimeError(\\\"ray.get_resource_ids() currently does not work in \\\"\\n \\\"local_mode.\\\")\\n\\n return global_worker.core_worker.resource_ids()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::get_webui_url\",\n \"text\": \"python/ray/worker.py::get_webui_url\\ndef get_webui_url():\\n \\\"\\\"\\\"Get the URL to access the web UI.\\n\\n Note that the URL does not specify which node the web UI is on.\\n\\n Returns:\\n The URL of the web UI as a string.\\n \\\"\\\"\\\"\\n if _global_node is None:\\n raise RuntimeError(\\\"Ray has not been initialized/connected.\\\")\\n return _global_node.webui_url\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::print_failed_task\",\n \"text\": \"python/ray/worker.py::print_failed_task\\ndef print_failed_task(task_status):\\n \\\"\\\"\\\"Print information about failed tasks.\\n\\n Args:\\n task_status (Dict): A dictionary containing the name, operationid, and\\n error message for a failed task.\\n \\\"\\\"\\\"\\n logger.error(\\\"\\\"\\\"\\n Error: Task failed\\n Function Name: {}\\n Task ID: {}\\n Error Message: \\\\n{}\\n \\\"\\\"\\\".format(task_status[\\\"function_name\\\"], task_status[\\\"operationid\\\"],\\n task_status[\\\"error_message\\\"]))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::init\",\n \"text\": \"python/ray/worker.py::init\\ndef init(address=None,\\n redis_address=None,\\n redis_port=None,\\n num_cpus=None,\\n num_gpus=None,\\n memory=None,\\n object_store_memory=None,\\n resources=None,\\n driver_object_store_memory=None,\\n redis_max_memory=None,\\n log_to_driver=True,\\n node_ip_address=ray_constants.NODE_DEFAULT_IP,\\n object_id_seed=None,\\n local_mode=False,\\n redirect_worker_output=None,\\n redirect_output=None,\\n ignore_reinit_error=False,\\n num_redis_shards=None,\\n redis_max_clients=None,\\n redis_password=ray_constants.REDIS_DEFAULT_PASSWORD,\\n plasma_directory=None,\\n huge_pages=False,\\n include_java=False,\\n include_dashboard=None,\\n dashboard_host=\\\"localhost\\\",\\n dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT,\\n job_id=None,\\n configure_logging=True,\\n logging_level=logging.INFO,\\n logging_format=ray_constants.LOGGER_FORMAT,\\n plasma_store_socket_name=None,\\n raylet_socket_name=None,\\n temp_dir=None,\\n load_code_from_local=False,\\n java_worker_options=None,\\n use_pickle=True,\\n _internal_config=None,\\n lru_evict=False):\\n \\\"\\\"\\\"\\n Connect to an existing Ray cluster or start one and connect to it.\\n\\n This method handles two cases; either a Ray cluster already exists and we\\n just attach this driver to it or we start all of the processes associated\\n with a Ray cluster and attach to the newly started cluster.\\n\\n To start Ray and all of the relevant processes, use this as follows:\\n\\n .. code-block:: python\\n\\n ray.init()\\n\\n To connect to an existing Ray cluster, use this as follows (substituting\\n in the appropriate address):\\n\\n .. code-block:: python\\n\\n ray.init(address=\\\"123.45.67.89:6379\\\")\\n\\n You can also define an environment variable called `RAY_ADDRESS` in\\n the same for\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::shutdown\",\n \"text\": \"python/ray/worker.py::shutdown\\ndef shutdown(exiting_interpreter=False):\\n \\\"\\\"\\\"Disconnect the worker, and terminate processes started by ray.init().\\n\\n This will automatically run at the end when a Python process that uses Ray\\n exits. It is ok to run this twice in a row. The primary use case for this\\n function is to cleanup state between tests.\\n\\n Note that this will clear any remote function definitions, actor\\n definitions, and existing actors, so if you wish to use any previously\\n defined remote functions or actors after calling ray.shutdown(), then you\\n need to redefine them. If they were defined in an imported module, then you\\n will need to reload the module.\\n\\n Args:\\n exiting_interpreter (bool): True if this is called by the atexit hook\\n and false otherwise. If we are exiting the interpreter, we will\\n wait a little while to print any extra error messages.\\n \\\"\\\"\\\"\\n if exiting_interpreter and global_worker.mode == SCRIPT_MODE:\\n # This is a duration to sleep before shutting down everything in order\\n # to make sure that log messages finish printing.\\n time.sleep(0.5)\\n\\n disconnect(exiting_interpreter)\\n\\n # We need to destruct the core worker here because after this function,\\n # we will tear down any processes spawned by ray.init() and the background\\n # IO thread in the core worker doesn't currently handle that gracefully.\\n if hasattr(global_worker, \\\"core_worker\\\"):\\n del global_worker.core_worker\\n\\n # Disconnect global state from GCS.\\n ray.state.state.disconnect()\\n\\n # Shut down the Ray processes.\\n global _global_node\\n if _global_node is not None:\\n _global_node.kill_all_processes(check_alive=False, allow_graceful=True)\\n _global_node = None\\n\\n # TODO(rkn): Instead of manually resetting some of the worker fields, we\\n # should simply set \\\"global_worker\\\" to equal \\\"None\\\" or something like that.\\n global_worker.set_mode(None)\\n global_worker._\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::sigterm_handler\",\n \"text\": \"python/ray/worker.py::sigterm_handler\\ndef sigterm_handler(signum, frame):\\n sys.exit(signum)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::custom_excepthook\",\n \"text\": \"python/ray/worker.py::custom_excepthook\\ndef custom_excepthook(type, value, tb):\\n # If this is a driver, push the exception to GCS worker table.\\n if global_worker.mode == SCRIPT_MODE:\\n error_message = \\\"\\\".join(traceback.format_tb(tb))\\n worker_id = global_worker.worker_id\\n worker_type = ray.gcs_utils.DRIVER\\n worker_info = {\\\"exception\\\": error_message}\\n\\n ray.state.state.add_worker(worker_id, worker_type, worker_info)\\n # Call the normal excepthook.\\n normal_excepthook(type, value, tb)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::_set_log_file\",\n \"text\": \"python/ray/worker.py::_set_log_file\\ndef _set_log_file(file_name, worker_pid, old_obj, setter_func):\\n # Line-buffer the output (mode 1).\\n f = create_and_init_new_worker_log(file_name, worker_pid)\\n\\n # TODO (Alex): Python seems to always flush when writing. If that is no\\n # longer true, then we need to manually flush the old buffer.\\n # old_obj.flush()\\n\\n # TODO (Alex): Flush the c/c++ userspace buffers if necessary.\\n # `fflush(stdout); cout.flush();`\\n\\n fileno = old_obj.fileno()\\n\\n # C++ logging requires redirecting the stdout file descriptor. Note that\\n # dup2 will automatically close the old file descriptor before overriding\\n # it.\\n os.dup2(f.fileno(), fileno)\\n\\n # We also manually set sys.stdout and sys.stderr because that seems to\\n # have an effect on the output buffering. Without doing this, stdout\\n # and stderr are heavily buffered resulting in seemingly lost logging\\n # statements. We never want to close the stdout file descriptor, dup2 will\\n # close it when necessary and we don't want python's GC to close it.\\n setter_func(open_log(fileno, closefd=False))\\n\\n return os.path.abspath(f.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::set_log_file\",\n \"text\": \"python/ray/worker.py::set_log_file\\ndef set_log_file(stdout_name, stderr_name):\\n \\\"\\\"\\\"Sets up logging for the current worker, creating the (fd backed) file and\\n flushing buffers as is necessary.\\n\\n Args:\\n stdout_name (str): The file name that stdout should be written to.\\n stderr_name(str): The file name that stderr should be written to.\\n\\n Returns:\\n (tuple) The absolute paths of the files that stdout and stderr will be\\n written to.\\n\\n \\\"\\\"\\\"\\n stdout_path = \\\"\\\"\\n stderr_path = \\\"\\\"\\n worker_pid = os.getpid()\\n\\n # lambda cannot contain assignment\\n def stdout_setter(x):\\n sys.stdout = x\\n\\n def stderr_setter(x):\\n sys.stderr = x\\n\\n if stdout_name:\\n _set_log_file(stdout_name, worker_pid, sys.stdout, stdout_setter)\\n\\n # The stderr case should be analogous to the stdout case\\n if stderr_name:\\n _set_log_file(stderr_name, worker_pid, sys.stderr, stderr_setter)\\n\\n return stdout_path, stderr_path\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::set_log_file.stdout_setter\",\n \"text\": \"python/ray/worker.py::set_log_file.stdout_setter\\n def stdout_setter(x):\\n sys.stdout = x\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::set_log_file.stderr_setter\",\n \"text\": \"python/ray/worker.py::set_log_file.stderr_setter\\n def stderr_setter(x):\\n sys.stderr = x\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::print_logs\",\n \"text\": \"python/ray/worker.py::print_logs\\ndef print_logs(redis_client, threads_stopped, job_id):\\n \\\"\\\"\\\"Prints log messages from workers on all of the nodes.\\n\\n Args:\\n redis_client: A client to the primary Redis shard.\\n threads_stopped (threading.Event): A threading event used to signal to\\n the thread that it should exit.\\n job_id (JobID): The id of the driver's job\\n\\n \\\"\\\"\\\"\\n pubsub_client = redis_client.pubsub(ignore_subscribe_messages=True)\\n pubsub_client.subscribe(ray.gcs_utils.LOG_FILE_CHANNEL)\\n localhost = services.get_node_ip_address()\\n try:\\n # Keep track of the number of consecutive log messages that have been\\n # received with no break in between. If this number grows continually,\\n # then the worker is probably not able to process the log messages as\\n # rapidly as they are coming in.\\n num_consecutive_messages_received = 0\\n while True:\\n # Exit if we received a signal that we should stop.\\n if threads_stopped.is_set():\\n return\\n\\n msg = pubsub_client.get_message()\\n if msg is None:\\n num_consecutive_messages_received = 0\\n threads_stopped.wait(timeout=0.01)\\n continue\\n num_consecutive_messages_received += 1\\n if (num_consecutive_messages_received % 100 == 0\\n and num_consecutive_messages_received > 0):\\n logger.warning(\\n \\\"The driver may not be able to keep up with the \\\"\\n \\\"stdout/stderr of the workers. To avoid forwarding logs \\\"\\n \\\"to the driver, use 'ray.init(log_to_driver=False)'.\\\")\\n\\n data = json.loads(ray.utils.decode(msg[\\\"data\\\"]))\\n\\n # Don't show logs from other drivers.\\n if data[\\\"job\\\"] and ray.utils.binary_to_hex(\\n job_id.binary()) != data[\\\"job\\\"]:\\n continue\\n\\n def color_for(data):\\n if data\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::print_error_messages_raylet\",\n \"text\": \"python/ray/worker.py::print_error_messages_raylet\\ndef print_error_messages_raylet(task_error_queue, threads_stopped):\\n \\\"\\\"\\\"Prints message received in the given output queue.\\n\\n This checks periodically if any un-raised errors occured in the background.\\n\\n Args:\\n task_error_queue (queue.Queue): A queue used to receive errors from the\\n thread that listens to Redis.\\n threads_stopped (threading.Event): A threading event used to signal to\\n the thread that it should exit.\\n \\\"\\\"\\\"\\n\\n while True:\\n # Exit if we received a signal that we should stop.\\n if threads_stopped.is_set():\\n return\\n\\n try:\\n error, t = task_error_queue.get(block=False)\\n except queue.Empty:\\n threads_stopped.wait(timeout=0.01)\\n continue\\n # Delay errors a little bit of time to attempt to suppress redundant\\n # messages originating from the worker.\\n while t + UNCAUGHT_ERROR_GRACE_PERIOD > time.time():\\n threads_stopped.wait(timeout=1)\\n if threads_stopped.is_set():\\n break\\n if t < last_task_error_raise_time + UNCAUGHT_ERROR_GRACE_PERIOD:\\n logger.debug(\\\"Suppressing error from worker: {}\\\".format(error))\\n else:\\n logger.error(\\n \\\"Possible unhandled error from worker: {}\\\".format(error))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::listen_error_messages_raylet\",\n \"text\": \"python/ray/worker.py::listen_error_messages_raylet\\ndef listen_error_messages_raylet(worker, task_error_queue, threads_stopped):\\n \\\"\\\"\\\"Listen to error messages in the background on the driver.\\n\\n This runs in a separate thread on the driver and pushes (error, time)\\n tuples to the output queue.\\n\\n Args:\\n worker: The worker class that this thread belongs to.\\n task_error_queue (queue.Queue): A queue used to communicate with the\\n thread that prints the errors found by this thread.\\n threads_stopped (threading.Event): A threading event used to signal to\\n the thread that it should exit.\\n \\\"\\\"\\\"\\n worker.error_message_pubsub_client = worker.redis_client.pubsub(\\n ignore_subscribe_messages=True)\\n # Exports that are published after the call to\\n # error_message_pubsub_client.subscribe and before the call to\\n # error_message_pubsub_client.listen will still be processed in the loop.\\n\\n # Really we should just subscribe to the errors for this specific job.\\n # However, currently all errors seem to be published on the same channel.\\n error_pubsub_channel = str(\\n ray.gcs_utils.TablePubsub.Value(\\\"ERROR_INFO_PUBSUB\\\")).encode(\\\"ascii\\\")\\n worker.error_message_pubsub_client.subscribe(error_pubsub_channel)\\n # worker.error_message_pubsub_client.psubscribe(\\\"*\\\")\\n\\n try:\\n # Get the errors that occurred before the call to subscribe.\\n error_messages = ray.errors()\\n for error_message in error_messages:\\n logger.error(error_message)\\n\\n while True:\\n # Exit if we received a signal that we should stop.\\n if threads_stopped.is_set():\\n return\\n\\n msg = worker.error_message_pubsub_client.get_message()\\n if msg is None:\\n threads_stopped.wait(timeout=0.01)\\n continue\\n gcs_entry = ray.gcs_utils.GcsEntry.FromString(msg[\\\"data\\\"])\\n assert len(gcs_entry.entries) == 1\\n error\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::is_initialized\",\n \"text\": \"python/ray/worker.py::is_initialized\\ndef is_initialized():\\n \\\"\\\"\\\"Check if ray.init has been called yet.\\n\\n Returns:\\n True if ray.init has already been called and false otherwise.\\n \\\"\\\"\\\"\\n return ray.worker.global_worker.connected\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::connect\",\n \"text\": \"python/ray/worker.py::connect\\ndef connect(node,\\n mode=WORKER_MODE,\\n log_to_driver=False,\\n worker=global_worker,\\n driver_object_store_memory=None,\\n job_id=None):\\n \\\"\\\"\\\"Connect this worker to the raylet, to Plasma, and to Redis.\\n\\n Args:\\n node (ray.node.Node): The node to connect.\\n mode: The mode of the worker. One of SCRIPT_MODE, WORKER_MODE, and\\n LOCAL_MODE.\\n log_to_driver (bool): If true, then output from all of the worker\\n processes on all nodes will be directed to the driver.\\n worker: The ray.Worker instance.\\n driver_object_store_memory: Limit the amount of memory the driver can\\n use in the object store when creating objects.\\n job_id: The ID of job. If it's None, then we will generate one.\\n \\\"\\\"\\\"\\n # Do some basic checking to make sure we didn't call ray.init twice.\\n error_message = \\\"Perhaps you called ray.init twice by accident?\\\"\\n assert not worker.connected, error_message\\n assert worker.cached_functions_to_run is not None, error_message\\n\\n # Enable nice stack traces on SIGSEGV etc.\\n try:\\n if not faulthandler.is_enabled():\\n faulthandler.enable(all_threads=False)\\n except io.UnsupportedOperation:\\n pass # ignore\\n\\n # Create a Redis client to primary.\\n # The Redis client can safely be shared between threads. However,\\n # that is not true of Redis pubsub clients. See the documentation at\\n # https://github.com/andymccurdy/redis-py#thread-safety.\\n worker.redis_client = node.create_redis_client()\\n\\n # Initialize some fields.\\n if mode is WORKER_MODE:\\n # We should not specify the job_id if it's `WORKER_MODE`.\\n assert job_id is None\\n job_id = JobID.nil()\\n # TODO(qwang): Rename this to `worker_id_str` or type to `WorkerID`\\n worker.worker_id = _random_string()\\n else:\\n # This is the code path of driver mode.\\n if job_id is None:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::disconnect\",\n \"text\": \"python/ray/worker.py::disconnect\\ndef disconnect(exiting_interpreter=False):\\n \\\"\\\"\\\"Disconnect this worker from the raylet and object store.\\\"\\\"\\\"\\n # Reset the list of cached remote functions and actors so that if more\\n # remote functions or actors are defined and then connect is called again,\\n # the remote functions will be exported. This is mostly relevant for the\\n # tests.\\n worker = global_worker\\n if worker.connected:\\n # Shutdown all of the threads that we've started. TODO(rkn): This\\n # should be handled cleanly in the worker object's destructor and not\\n # in this disconnect method.\\n worker.threads_stopped.set()\\n if hasattr(worker, \\\"import_thread\\\"):\\n worker.import_thread.join_import_thread()\\n if hasattr(worker, \\\"listener_thread\\\"):\\n worker.listener_thread.join()\\n if hasattr(worker, \\\"printer_thread\\\"):\\n worker.printer_thread.join()\\n if hasattr(worker, \\\"logger_thread\\\"):\\n worker.logger_thread.join()\\n worker.threads_stopped.clear()\\n worker._session_index += 1\\n\\n worker.node = None # Disconnect the worker from the node.\\n worker.cached_functions_to_run = []\\n worker.serialization_context_map.clear()\\n try:\\n ray_actor = ray.actor\\n except AttributeError:\\n ray_actor = None # This can occur during program termination\\n if ray_actor is not None:\\n ray_actor.ActorClassMethodMetadata.reset_cache()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::_changeproctitle\",\n \"text\": \"python/ray/worker.py::_changeproctitle\\n@contextmanager\\ndef _changeproctitle(title, next_title):\\n setproctitle.setproctitle(title)\\n try:\\n yield\\n finally:\\n setproctitle.setproctitle(next_title)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::register_custom_serializer\",\n \"text\": \"python/ray/worker.py::register_custom_serializer\\ndef register_custom_serializer(cls,\\n serializer,\\n deserializer,\\n use_pickle=False,\\n use_dict=False,\\n class_id=None):\\n \\\"\\\"\\\"Registers custom functions for efficient object serialization.\\n\\n The serializer and deserializer are used when transferring objects of\\n `cls` across processes and nodes. This can be significantly faster than\\n the Ray default fallbacks. Wraps `register_custom_serializer` underneath.\\n\\n Args:\\n cls (type): The class that ray should use this custom serializer for.\\n serializer: The custom serializer that takes in a cls instance and\\n outputs a serialized representation. use_pickle and use_dict\\n must be False if provided.\\n deserializer: The custom deserializer that takes in a serialized\\n representation of the cls and outputs a cls instance. use_pickle\\n and use_dict must be False if provided.\\n use_pickle: Deprecated.\\n use_dict: Deprecated.\\n class_id (str): Unique ID of the class. Autogenerated if None.\\n \\\"\\\"\\\"\\n worker = global_worker\\n worker.check_connected()\\n\\n if use_pickle:\\n raise DeprecationWarning(\\n \\\"`use_pickle` is no longer a valid parameter and will be removed \\\"\\n \\\"in future versions of Ray. If this breaks your application, \\\"\\n \\\"see `SerializationContext.register_custom_serializer`.\\\")\\n if use_dict:\\n raise DeprecationWarning(\\n \\\"`use_pickle` is no longer a valid parameter and will be removed \\\"\\n \\\"in future versions of Ray. If this breaks your application, \\\"\\n \\\"see `SerializationContext.register_custom_serializer`.\\\")\\n assert serializer is not None and deserializer is not None\\n context = global_worker.get_serialization_context()\\n context.register_custom_serializer\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":56,"cells":{"instance_id":{"kind":"string","value":"ray-project__ray-3711"},"repo":{"kind":"string","value":"ray-project/ray"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"[tune[ partial function cannot be registered as trainable\n### System information\r\n- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Ubuntu 18.04\r\n- **Ray installed from (source or binary)**: binary\r\n- **Ray version**: 0.6.1\r\n- **Python version**: 3.7\r\n- **Exact command to reproduce**:\r\n\r\nThe following code fails:\r\n```\r\ndef dummy_fn(c, a, b):\r\n print(\"Called\")\r\n\r\nfrom functools import partial\r\nfrom ray.tune import register_trainable\r\nregister_trainable(\"test\", partial(dummy_fn, c=None))\r\n\r\n```\r\n\r\nwhile the following code works:\r\n```\r\ndef dummy_fn(a, b):\r\n print(\"Called\")\r\n\r\nfrom functools import partial\r\nfrom ray.tune import register_trainable\r\nregister_trainable(\"test\", dummy_fn)\r\n\r\n```\r\n### Describe the problem\r\nThe first code sample does not work, despite the function (after the `partial`) fullfills all requirements to be properly registered.\r\n\r\n### Source code / logs\r\nTraceback:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/home/temp/schock/conda/envs/delira_new/lib/python3.7/site-packages/ray/tune/registry.py\", line 35, in register_trainable\r\n if not issubclass(trainable, Trainable):\r\nTypeError: issubclass() arg 1 must be a class\r\n```\r\n\n"},"gold_files":{"kind":"list like","value":["python/ray/tune/registry.py"],"string":"[\n \"python/ray/tune/registry.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["python/ray/tune/registry.py::<module>","python/ray/tune/registry.py::register_trainable"],"string":"[\n \"python/ray/tune/registry.py::<module>\",\n \"python/ray/tune/registry.py::register_trainable\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"doc/source/conf.py","text":"doc/source/conf.py","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py","text":"python/ray/cloudpickle/cloudpickle.py\ndef _make_cell_set_template_code() # Get the Python compiler to emit LOAD_FAST(arg); STORE_DEREF\n def inner(value)\ndef cell_set(cell, value) # Set the value of a closure cell.\ndef islambda(func)\ndef _builtin_type(name)\ndef _make__new__factory(type_)\n def _factory()\nclass CloudPickler\n def __init__(self, file, protocol=None)\n def dump(self, obj)\n def save_memoryview(self, obj)\n def save_unsupported(self, obj)\n def save_module(self, obj) # Save a module as an import\n def save_codeobject(self, obj) # Save a code object\n def save_function(self, obj, name=None) # Registered with the dispatch to handle all function types.\n def _save_subimports(self, code, top_level_dependencies) # Ensure de-pickler imports any package child-modules that\n def save_dynamic_class(self, obj) # Save a class that can't be stored as module global.\n def save_function_tuple(self, func) # Pickles an actual func object.\n def extract_code_globals(cls, co) # Find all globals names read or written to by codeblock co\n def extract_func_data(self, func) # Turn the function into a tuple of data necessary to recreate it:\n def save_builtin_function(self, obj)\n def save_global(self, obj, name=None, pack=struct.pack) # Save a \"global\".\n def save_instancemethod(self, obj)\n def save_inst(self, obj) # Inner logic to save instance. Based off pickle.save_inst\n def save_property(self, obj)\n def save_classmethod(self, obj)\n def save_itemgetter(self, obj) # itemgetter serializer (needed for namedtuple support)\n class Dummy\n def __getitem__(self, item)\n def save_attrgetter(self, obj) # attrgetter serializer\n class Dummy\n def __init__(self, attrs, index=None)\n def __getattribute__(self, item)\n def save_file(self, obj) # Save a file\n def save_ellipsis(self, obj)\n def save_not_implemented(self, obj)\n def save_weakset(self, obj)\n def save_logger(self, obj)\n def inject_addons(self) # Plug in system. Register additional pickling functions if modules already loaded\ndef is_tornado_coroutine(func) # Return whether *func* is a Tornado coroutine function.\ndef _rebuild_tornado_coroutine(func)\ndef dump(obj, file, protocol=None) # Serialize obj as bytes streamed into file\ndef dumps(obj, protocol=None) # Serialize obj as a string of bytes allocated in memory\ndef subimport(name)\ndef dynamic_subimport(name, vars)\ndef _restore_attr(obj, attr)\ndef _get_module_builtins()\ndef print_exec(stream)\ndef _modules_to_main(modList) # Force every module in modList to be placed into main\ndef _genpartial(func, args, kwds)\ndef _gen_ellipsis()\ndef _gen_not_implemented()\ndef _get_cell_contents(cell)\ndef instance(cls) # Create a new instance of a class.\nclass _empty_cell_value # sentinel for empty closures\n def __reduce__(cls)\ndef _fill_function(*args) # Fills in the rest of function data into the skeleton function object\ndef _make_empty_cell(","label":0},{"cid":"python/ray/function_manager.py","text":"python/ray/function_manager.py\nclass FunctionDescriptor # A class used to describe a python function.\n def __init__(self, module_name, function_name, class_name='', function_source_hash=b'')\n def __repr__(self)\n def from_bytes_list(cls, function_descriptor_list) # Create a FunctionDescriptor instance from list of bytes.\n def from_function(cls, function) # Create a FunctionDescriptor from a function instance.\n def from_class(cls, target_class) # Create a FunctionDescriptor from a class.\n def for_driver_task(cls) # Create a FunctionDescriptor instance for a driver task.\n def is_for_driver_task(self) # See whether this function descriptor is for a driver or not.\n def module_name(self) # Get the module name of current function descriptor.\n def class_name(self) # Get the class name of current function descriptor.\n def function_name(self) # Get the function name of current function descriptor.\n def function_hash(self) # Get the hash code of the function source code.\n def function_id(self) # Get the function id calculated from this descriptor.\n def _get_function_id(self) # Calculate the function id of current function descriptor.\n def get_function_descriptor_list(self) # Return a list of bytes representing the function descriptor.\nclass FunctionActorManager # A class used to export/load remote functions and actors.\n def __init__(self, worker)\n def increase_task_counter(self, driver_id, function_descriptor)\n def get_task_counter(self, driver_id, function_descriptor)\n def export_cached(self) # Export cached remote functions\n def reset_cache(self)\n def export(self, remote_function) # Export a remote function.\n def _do_export(self, remote_function) # Pickle a remote function and export it to redis.\n def fetch_and_register_remote_function(self, key) # Import a remote function.\n def f()\n def get_execution_info(self, driver_id, function_descriptor) # Get the FunctionExecutionInfo of a remote function.\n def _wait_for_function(self, function_descriptor, driver_id, timeout=10) # Wait until the function to be executed is present on this worker.\n def _publish_actor_class_to_key(self, key, actor_class_info) # Push an actor class definition to Redis.\n def export_actor_class(self, Class, actor_method_names, checkpoint_interval)\n def load_actor(self, driver_id, function_descriptor)\n def fetch_and_register_actor(self, actor_class_key) # Import an actor.\n class TemporaryActor\n def temporary_actor_method(*xs)\n def _make_actor_method_executor(self, method_name, method, actor_imported) # Make an executor that wraps a user-defined actor method.\n def actor_method_executor(dummy_return_id, actor, *args)","label":0},{"cid":"python/ray/tune/examples/tune_mnist_ray_hyperband.py","text":"python/ray/tune/examples/tune_mnist_ray_hyperband.py\ndef setupCNN(x) # setupCNN builds the graph for a deep net for classifying digits.\ndef conv2d(x, W) # conv2d returns a 2d convolution layer with full stride.\ndef max_pool_2x2(x) # max_pool_2x2 downsamples a feature map by 2X.\ndef weight_variable(shape) # weight_variable generates a weight variable of a given shape.\ndef bias_variable(shape) # bias_variable generates a bias variable of a given shape.\nclass TrainMNIST # Example MNIST trainable.\n def _setup(self, config)\n def _train(self)\n def _save(self, checkpoint_dir)\n def _restore(self, path)","label":0},{"cid":"python/ray/tune/experiment.py","text":"python/ray/tune/experiment.py\ndef _raise_deprecation_note(deprecated, replacement, soft=False) # User notification for deprecated parameter.\nclass Experiment # Tracks experiment specifications.\n def __init__(self, name, run, stop=None, config=None, resources_per_trial=None, num_samples=1, local_dir=None, upload_dir=None, trial_name_creator=None, custom_loggers=None, sync_function=None, checkpoint_freq=0, checkpoint_at_end=False, max_failures=3, restore=None, repeat=None, trial_resources=None)\n def from_json(cls, name, spec) # Generates an Experiment object from JSON.\n def _register_if_needed(cls, run_object) # Registers Trainable or Function at runtime.\ndef convert_to_experiment_list(experiments) # Produces a list of Experiment objects.","label":0},{"cid":"python/ray/tune/registry.py","text":"python/ray/tune/registry.py\ndef register_trainable(name, trainable) # Register a trainable function or class.\ndef register_env(name, env_creator) # Register a custom environment for use with RLlib.\ndef _make_key(category, key) # Generate a binary key for the given category and key.\nclass _Registry\n def __init__(self)\n def register(self, category, key, value)\n def contains(self, category, key)\n def get(self, category, key)\n def flush_values(self)","label":1}],"string":"[\n {\n \"cid\": \"doc/source/conf.py\",\n \"text\": \"doc/source/conf.py\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py\\ndef _make_cell_set_template_code() # Get the Python compiler to emit LOAD_FAST(arg); STORE_DEREF\\n def inner(value)\\ndef cell_set(cell, value) # Set the value of a closure cell.\\ndef islambda(func)\\ndef _builtin_type(name)\\ndef _make__new__factory(type_)\\n def _factory()\\nclass CloudPickler\\n def __init__(self, file, protocol=None)\\n def dump(self, obj)\\n def save_memoryview(self, obj)\\n def save_unsupported(self, obj)\\n def save_module(self, obj) # Save a module as an import\\n def save_codeobject(self, obj) # Save a code object\\n def save_function(self, obj, name=None) # Registered with the dispatch to handle all function types.\\n def _save_subimports(self, code, top_level_dependencies) # Ensure de-pickler imports any package child-modules that\\n def save_dynamic_class(self, obj) # Save a class that can't be stored as module global.\\n def save_function_tuple(self, func) # Pickles an actual func object.\\n def extract_code_globals(cls, co) # Find all globals names read or written to by codeblock co\\n def extract_func_data(self, func) # Turn the function into a tuple of data necessary to recreate it:\\n def save_builtin_function(self, obj)\\n def save_global(self, obj, name=None, pack=struct.pack) # Save a \\\"global\\\".\\n def save_instancemethod(self, obj)\\n def save_inst(self, obj) # Inner logic to save instance. Based off pickle.save_inst\\n def save_property(self, obj)\\n def save_classmethod(self, obj)\\n def save_itemgetter(self, obj) # itemgetter serializer (needed for namedtuple support)\\n class Dummy\\n def __getitem__(self, item)\\n def save_attrgetter(self, obj) # attrgetter serializer\\n class Dummy\\n def __init__(self, attrs, index=None)\\n def __getattribute__(self, item)\\n def save_file(self, obj) # Save a file\\n def save_ellipsis(self, obj)\\n def save_not_implemented(self, obj)\\n def save_weakset(self, obj)\\n def save_logger(self, obj)\\n def inject_addons(self) # Plug in system. Register additional pickling functions if modules already loaded\\ndef is_tornado_coroutine(func) # Return whether *func* is a Tornado coroutine function.\\ndef _rebuild_tornado_coroutine(func)\\ndef dump(obj, file, protocol=None) # Serialize obj as bytes streamed into file\\ndef dumps(obj, protocol=None) # Serialize obj as a string of bytes allocated in memory\\ndef subimport(name)\\ndef dynamic_subimport(name, vars)\\ndef _restore_attr(obj, attr)\\ndef _get_module_builtins()\\ndef print_exec(stream)\\ndef _modules_to_main(modList) # Force every module in modList to be placed into main\\ndef _genpartial(func, args, kwds)\\ndef _gen_ellipsis()\\ndef _gen_not_implemented()\\ndef _get_cell_contents(cell)\\ndef instance(cls) # Create a new instance of a class.\\nclass _empty_cell_value # sentinel for empty closures\\n def __reduce__(cls)\\ndef _fill_function(*args) # Fills in the rest of function data into the skeleton function object\\ndef _make_empty_cell(\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py\",\n \"text\": \"python/ray/function_manager.py\\nclass FunctionDescriptor # A class used to describe a python function.\\n def __init__(self, module_name, function_name, class_name='', function_source_hash=b'')\\n def __repr__(self)\\n def from_bytes_list(cls, function_descriptor_list) # Create a FunctionDescriptor instance from list of bytes.\\n def from_function(cls, function) # Create a FunctionDescriptor from a function instance.\\n def from_class(cls, target_class) # Create a FunctionDescriptor from a class.\\n def for_driver_task(cls) # Create a FunctionDescriptor instance for a driver task.\\n def is_for_driver_task(self) # See whether this function descriptor is for a driver or not.\\n def module_name(self) # Get the module name of current function descriptor.\\n def class_name(self) # Get the class name of current function descriptor.\\n def function_name(self) # Get the function name of current function descriptor.\\n def function_hash(self) # Get the hash code of the function source code.\\n def function_id(self) # Get the function id calculated from this descriptor.\\n def _get_function_id(self) # Calculate the function id of current function descriptor.\\n def get_function_descriptor_list(self) # Return a list of bytes representing the function descriptor.\\nclass FunctionActorManager # A class used to export/load remote functions and actors.\\n def __init__(self, worker)\\n def increase_task_counter(self, driver_id, function_descriptor)\\n def get_task_counter(self, driver_id, function_descriptor)\\n def export_cached(self) # Export cached remote functions\\n def reset_cache(self)\\n def export(self, remote_function) # Export a remote function.\\n def _do_export(self, remote_function) # Pickle a remote function and export it to redis.\\n def fetch_and_register_remote_function(self, key) # Import a remote function.\\n def f()\\n def get_execution_info(self, driver_id, function_descriptor) # Get the FunctionExecutionInfo of a remote function.\\n def _wait_for_function(self, function_descriptor, driver_id, timeout=10) # Wait until the function to be executed is present on this worker.\\n def _publish_actor_class_to_key(self, key, actor_class_info) # Push an actor class definition to Redis.\\n def export_actor_class(self, Class, actor_method_names, checkpoint_interval)\\n def load_actor(self, driver_id, function_descriptor)\\n def fetch_and_register_actor(self, actor_class_key) # Import an actor.\\n class TemporaryActor\\n def temporary_actor_method(*xs)\\n def _make_actor_method_executor(self, method_name, method, actor_imported) # Make an executor that wraps a user-defined actor method.\\n def actor_method_executor(dummy_return_id, actor, *args)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py\",\n \"text\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py\\ndef setupCNN(x) # setupCNN builds the graph for a deep net for classifying digits.\\ndef conv2d(x, W) # conv2d returns a 2d convolution layer with full stride.\\ndef max_pool_2x2(x) # max_pool_2x2 downsamples a feature map by 2X.\\ndef weight_variable(shape) # weight_variable generates a weight variable of a given shape.\\ndef bias_variable(shape) # bias_variable generates a bias variable of a given shape.\\nclass TrainMNIST # Example MNIST trainable.\\n def _setup(self, config)\\n def _train(self)\\n def _save(self, checkpoint_dir)\\n def _restore(self, path)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/experiment.py\",\n \"text\": \"python/ray/tune/experiment.py\\ndef _raise_deprecation_note(deprecated, replacement, soft=False) # User notification for deprecated parameter.\\nclass Experiment # Tracks experiment specifications.\\n def __init__(self, name, run, stop=None, config=None, resources_per_trial=None, num_samples=1, local_dir=None, upload_dir=None, trial_name_creator=None, custom_loggers=None, sync_function=None, checkpoint_freq=0, checkpoint_at_end=False, max_failures=3, restore=None, repeat=None, trial_resources=None)\\n def from_json(cls, name, spec) # Generates an Experiment object from JSON.\\n def _register_if_needed(cls, run_object) # Registers Trainable or Function at runtime.\\ndef convert_to_experiment_list(experiments) # Produces a list of Experiment objects.\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/registry.py\",\n \"text\": \"python/ray/tune/registry.py\\ndef register_trainable(name, trainable) # Register a trainable function or class.\\ndef register_env(name, env_creator) # Register a custom environment for use with RLlib.\\ndef _make_key(category, key) # Generate a binary key for the given category and key.\\nclass _Registry\\n def __init__(self)\\n def register(self, category, key, value)\\n def contains(self, category, key)\\n def get(self, category, key)\\n def flush_values(self)\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"python/ray/cloudpickle/cloudpickle.py::_make_cell_set_template_code","text":"python/ray/cloudpickle/cloudpickle.py::_make_cell_set_template_code\ndef _make_cell_set_template_code():\n \"\"\"Get the Python compiler to emit LOAD_FAST(arg); STORE_DEREF\n\n Notes\n -----\n In Python 3, we could use an easier function:\n\n .. code-block:: python\n\n def f():\n cell = None\n\n def _stub(value):\n nonlocal cell\n cell = value\n\n return _stub\n\n _cell_set_template_code = f()\n\n This function is _only_ a LOAD_FAST(arg); STORE_DEREF, but that is\n invalid syntax on Python 2. If we use this function we also don't need\n to do the weird freevars/cellvars swap below\n \"\"\"\n def inner(value):\n lambda: cell # make ``cell`` a closure so that we get a STORE_DEREF\n cell = value\n\n co = inner.__code__\n\n # NOTE: we are marking the cell variable as a free variable intentionally\n # so that we simulate an inner function instead of the outer function. This\n # is what gives us the ``nonlocal`` behavior in a Python 2 compatible way.\n if not PY3:\n return types.CodeType(\n co.co_argcount,\n co.co_nlocals,\n co.co_stacksize,\n co.co_flags,\n co.co_code,\n co.co_consts,\n co.co_names,\n co.co_varnames,\n co.co_filename,\n co.co_name,\n co.co_firstlineno,\n co.co_lnotab,\n co.co_cellvars, # this is the trickery\n (),\n )\n else:\n return types.CodeType(\n co.co_argcount,\n co.co_kwonlyargcount,\n co.co_nlocals,\n co.co_stacksize,\n co.co_flags,\n co.co_code,\n co.co_consts,\n co.co_names,\n co.co_varnames,\n co.co_filename,\n co.co_name,\n co.co_firstlineno,\n co.co_lnotab,\n co.co_cellvars, # this is the trickery\n (),\n )","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::_make_cell_set_template_code.inner","text":"python/ray/cloudpickle/cloudpickle.py::_make_cell_set_template_code.inner\n def inner(value):\n lambda: cell # make ``cell`` a closure so that we get a STORE_DEREF\n cell = value","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::cell_set","text":"python/ray/cloudpickle/cloudpickle.py::cell_set\ndef cell_set(cell, value):\n \"\"\"Set the value of a closure cell.\n \"\"\"\n return types.FunctionType(\n _cell_set_template_code,\n {},\n '_cell_set_inner',\n (),\n (cell,),\n )(value)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::islambda","text":"python/ray/cloudpickle/cloudpickle.py::islambda\ndef islambda(func):\n return getattr(func,'__name__') == '<lambda>'","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::_builtin_type","text":"python/ray/cloudpickle/cloudpickle.py::_builtin_type\ndef _builtin_type(name):\n return getattr(types, name)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::_make__new__factory","text":"python/ray/cloudpickle/cloudpickle.py::_make__new__factory\ndef _make__new__factory(type_):\n def _factory():\n return type_.__new__\n return _factory","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::_make__new__factory._factory","text":"python/ray/cloudpickle/cloudpickle.py::_make__new__factory._factory\n def _factory():\n return type_.__new__","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.__init__","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.__init__\n def __init__(self, file, protocol=None):\n if protocol is None:\n protocol = DEFAULT_PROTOCOL\n Pickler.__init__(self, file, protocol=protocol)\n # set of modules to unpickle\n self.modules = set()\n # map ids to dictionary. used to ensure that functions can share global env\n self.globals_ref = {}","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.dump","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.dump\n def dump(self, obj):\n self.inject_addons()\n try:\n return Pickler.dump(self, obj)\n except RuntimeError as e:\n if 'recursion' in e.args[0]:\n msg = \"\"\"Could not pickle object as excessively deep recursion required.\"\"\"\n raise pickle.PicklingError(msg)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_memoryview","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_memoryview\n def save_memoryview(self, obj):\n self.save(obj.tobytes())","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_unsupported","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_unsupported\n def save_unsupported(self, obj):\n raise pickle.PicklingError(\"Cannot pickle objects of type %s\" % type(obj))","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_module","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_module\n def save_module(self, obj):\n \"\"\"\n Save a module as an import\n \"\"\"\n mod_name = obj.__name__\n # If module is successfully found then it is not a dynamically created module\n if hasattr(obj, '__file__'):\n is_dynamic = False\n else:\n try:\n _find_module(mod_name)\n is_dynamic = False\n except ImportError:\n is_dynamic = True\n\n self.modules.add(obj)\n if is_dynamic:\n self.save_reduce(dynamic_subimport, (obj.__name__, vars(obj)), obj=obj)\n else:\n self.save_reduce(subimport, (obj.__name__,), obj=obj)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_codeobject","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_codeobject\n def save_codeobject(self, obj):\n \"\"\"\n Save a code object\n \"\"\"\n if PY3:\n args = (\n obj.co_argcount, obj.co_kwonlyargcount, obj.co_nlocals, obj.co_stacksize,\n obj.co_flags, obj.co_code, obj.co_consts, obj.co_names, obj.co_varnames,\n obj.co_filename, obj.co_name, obj.co_firstlineno, obj.co_lnotab, obj.co_freevars,\n obj.co_cellvars\n )\n else:\n args = (\n obj.co_argcount, obj.co_nlocals, obj.co_stacksize, obj.co_flags, obj.co_code,\n obj.co_consts, obj.co_names, obj.co_varnames, obj.co_filename, obj.co_name,\n obj.co_firstlineno, obj.co_lnotab, obj.co_freevars, obj.co_cellvars\n )\n self.save_reduce(types.CodeType, args, obj=obj)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_function","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_function\n def save_function(self, obj, name=None):\n \"\"\" Registered with the dispatch to handle all function types.\n\n Determines what kind of function obj is (e.g. lambda, defined at\n interactive prompt, etc) and handles the pickling appropriately.\n \"\"\"\n if obj in _BUILTIN_TYPE_CONSTRUCTORS:\n # We keep a special-cased cache of built-in type constructors at\n # global scope, because these functions are structured very\n # differently in different python versions and implementations (for\n # example, they're instances of types.BuiltinFunctionType in\n # CPython, but they're ordinary types.FunctionType instances in\n # PyPy).\n #\n # If the function we've received is in that cache, we just\n # serialize it as a lookup into the cache.\n return self.save_reduce(_BUILTIN_TYPE_CONSTRUCTORS[obj], (), obj=obj)\n\n write = self.write\n\n if name is None:\n name = obj.__name__\n try:\n # whichmodule() could fail, see\n # https://bitbucket.org/gutworth/six/issues/63/importing-six-breaks-pickling\n modname = pickle.whichmodule(obj, name)\n except Exception:\n modname = None\n # print('which gives %s %s %s' % (modname, obj, name))\n try:\n themodule = sys.modules[modname]\n except KeyError:\n # eval'd items such as namedtuple give invalid items for their function __module__\n modname = '__main__'\n\n if modname == '__main__':\n themodule = None\n\n try:\n lookedup_by_name = getattr(themodule, name, None)\n except Exception:\n lookedup_by_name = None\n\n if themodule:\n self.modules.add(themodule)\n if lookedup_by_name is obj:\n return self.save_global(obj, name)\n\n # a builtin_func","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler._save_subimports","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler._save_subimports\n def _save_subimports(self, code, top_level_dependencies):\n \"\"\"\n Ensure de-pickler imports any package child-modules that\n are needed by the function\n \"\"\"\n # check if any known dependency is an imported package\n for x in top_level_dependencies:\n if isinstance(x, types.ModuleType) and hasattr(x, '__package__') and x.__package__:\n # check if the package has any currently loaded sub-imports\n prefix = x.__name__ + '.'\n for name, module in sys.modules.items():\n # Older versions of pytest will add a \"None\" module to sys.modules.\n if name is not None and name.startswith(prefix):\n # check whether the function can address the sub-module\n tokens = set(name[len(prefix):].split('.'))\n if not tokens - set(code.co_names):\n # ensure unpickler executes this import\n self.save(module)\n # then discards the reference to it\n self.write(pickle.POP)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_dynamic_class","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_dynamic_class\n def save_dynamic_class(self, obj):\n \"\"\"\n Save a class that can't be stored as module global.\n\n This method is used to serialize classes that are defined inside\n functions, or that otherwise can't be serialized as attribute lookups\n from global modules.\n \"\"\"\n clsdict = dict(obj.__dict__) # copy dict proxy to a dict\n clsdict.pop('__weakref__', None)\n\n # On PyPy, __doc__ is a readonly attribute, so we need to include it in\n # the initial skeleton class. This is safe because we know that the\n # doc can't participate in a cycle with the original class.\n type_kwargs = {'__doc__': clsdict.pop('__doc__', None)}\n\n # If type overrides __dict__ as a property, include it in the type kwargs.\n # In Python 2, we can't set this attribute after construction.\n __dict__ = clsdict.pop('__dict__', None)\n if isinstance(__dict__, property):\n type_kwargs['__dict__'] = __dict__\n\n save = self.save\n write = self.write\n\n # We write pickle instructions explicitly here to handle the\n # possibility that the type object participates in a cycle with its own\n # __dict__. We first write an empty \"skeleton\" version of the class and\n # memoize it before writing the class' __dict__ itself. We then write\n # instructions to \"rehydrate\" the skeleton class by restoring the\n # attributes from the __dict__.\n #\n # A type can appear in a cycle with its __dict__ if an instance of the\n # type appears in the type's __dict__ (which happens for the stdlib\n # Enum class), or if the type defines methods that close over the name\n # of the type, (which is common for Python 2-style super() calls).\n\n # Push the rehydration function.\n save(_rehydrate_skeleton_class)\n\n # Mark the start of the args tuple for the rehydr","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_function_tuple","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_function_tuple\n def save_function_tuple(self, func):\n \"\"\" Pickles an actual func object.\n\n A func comprises: code, globals, defaults, closure, and dict. We\n extract and save these, injecting reducing functions at certain points\n to recreate the func object. Keep in mind that some of these pieces\n can contain a ref to the func itself. Thus, a naive save on these\n pieces could trigger an infinite loop of save's. To get around that,\n we first create a skeleton func object using just the code (this is\n safe, since this won't contain a ref to the func), and memoize it as\n soon as it's created. The other stuff can then be filled in later.\n \"\"\"\n if is_tornado_coroutine(func):\n self.save_reduce(_rebuild_tornado_coroutine, (func.__wrapped__,),\n obj=func)\n return\n\n save = self.save\n write = self.write\n\n code, f_globals, defaults, closure_values, dct, base_globals = self.extract_func_data(func)\n\n save(_fill_function) # skeleton function updater\n write(pickle.MARK) # beginning of tuple that _fill_function expects\n\n self._save_subimports(\n code,\n itertools.chain(f_globals.values(), closure_values or ()),\n )\n\n # create a skeleton function object and memoize it\n save(_make_skel_func)\n save((\n code,\n len(closure_values) if closure_values is not None else -1,\n base_globals,\n ))\n write(pickle.REDUCE)\n self.memoize(func)\n\n # save the rest of the func data needed by _fill_function\n state = {\n 'globals': f_globals,\n 'defaults': defaults,\n 'dict': dct,\n 'module': func.__module__,\n 'closure_values': closure_values,\n }\n if hasattr(func, '__qualname__'):\n state[","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.extract_code_globals","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.extract_code_globals\n @classmethod\n def extract_code_globals(cls, co):\n \"\"\"\n Find all globals names read or written to by codeblock co\n \"\"\"\n out_names = cls._extract_code_globals_cache.get(co)\n if out_names is None:\n try:\n names = co.co_names\n except AttributeError:\n # PyPy \"builtin-code\" object\n out_names = set()\n else:\n out_names = set(names[oparg]\n for op, oparg in _walk_global_ops(co))\n\n # see if nested function have any global refs\n if co.co_consts:\n for const in co.co_consts:\n if type(const) is types.CodeType:\n out_names |= cls.extract_code_globals(const)\n\n cls._extract_code_globals_cache[co] = out_names\n\n return out_names","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.extract_func_data","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.extract_func_data\n def extract_func_data(self, func):\n \"\"\"\n Turn the function into a tuple of data necessary to recreate it:\n code, globals, defaults, closure_values, dict\n \"\"\"\n code = func.__code__\n\n # extract all global ref's\n func_global_refs = self.extract_code_globals(code)\n\n # process all variables referenced by global environment\n f_globals = {}\n for var in func_global_refs:\n if var in func.__globals__:\n f_globals[var] = func.__globals__[var]\n\n # defaults requires no processing\n defaults = func.__defaults__\n\n # process closure\n closure = (\n list(map(_get_cell_contents, func.__closure__))\n if func.__closure__ is not None\n else None\n )\n\n # save the dict\n dct = func.__dict__\n\n base_globals = self.globals_ref.get(id(func.__globals__), {})\n self.globals_ref[id(func.__globals__)] = base_globals\n\n return (code, f_globals, defaults, closure, dct, base_globals)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_builtin_function","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_builtin_function\n def save_builtin_function(self, obj):\n if obj.__module__ == \"__builtin__\":\n return self.save_global(obj)\n return self.save_function(obj)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_global","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_global\n def save_global(self, obj, name=None, pack=struct.pack):\n \"\"\"\n Save a \"global\".\n\n The name of this method is somewhat misleading: all types get\n dispatched here.\n \"\"\"\n if obj.__module__ == \"__main__\":\n return self.save_dynamic_class(obj)\n\n try:\n return Pickler.save_global(self, obj, name=name)\n except Exception:\n if obj.__module__ == \"__builtin__\" or obj.__module__ == \"builtins\":\n if obj in _BUILTIN_TYPE_NAMES:\n return self.save_reduce(\n _builtin_type, (_BUILTIN_TYPE_NAMES[obj],), obj=obj)\n\n typ = type(obj)\n if typ is not obj and isinstance(obj, (type, types.ClassType)):\n return self.save_dynamic_class(obj)\n\n raise","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_instancemethod","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_instancemethod\n def save_instancemethod(self, obj):\n # Memoization rarely is ever useful due to python bounding\n if obj.__self__ is None:\n self.save_reduce(getattr, (obj.im_class, obj.__name__))\n else:\n if PY3:\n self.save_reduce(types.MethodType, (obj.__func__, obj.__self__), obj=obj)\n else:\n self.save_reduce(types.MethodType, (obj.__func__, obj.__self__, obj.__self__.__class__),\n obj=obj)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_inst","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_inst\n def save_inst(self, obj):\n \"\"\"Inner logic to save instance. Based off pickle.save_inst\"\"\"\n cls = obj.__class__\n\n # Try the dispatch table (pickle module doesn't do it)\n f = self.dispatch.get(cls)\n if f:\n f(self, obj) # Call unbound method with explicit self\n return\n\n memo = self.memo\n write = self.write\n save = self.save\n\n if hasattr(obj, '__getinitargs__'):\n args = obj.__getinitargs__()\n len(args) # XXX Assert it's a sequence\n pickle._keep_alive(args, memo)\n else:\n args = ()\n\n write(pickle.MARK)\n\n if self.bin:\n save(cls)\n for arg in args:\n save(arg)\n write(pickle.OBJ)\n else:\n for arg in args:\n save(arg)\n write(pickle.INST + cls.__module__ + '\\n' + cls.__name__ + '\\n')\n\n self.memoize(obj)\n\n try:\n getstate = obj.__getstate__\n except AttributeError:\n stuff = obj.__dict__\n else:\n stuff = getstate()\n pickle._keep_alive(stuff, memo)\n save(stuff)\n write(pickle.BUILD)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_property","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_property\n def save_property(self, obj):\n # properties not correctly saved in python\n self.save_reduce(property, (obj.fget, obj.fset, obj.fdel, obj.__doc__), obj=obj)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_classmethod","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_classmethod\n def save_classmethod(self, obj):\n orig_func = obj.__func__\n self.save_reduce(type(obj), (orig_func,), obj=obj)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_itemgetter","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_itemgetter\n def save_itemgetter(self, obj):\n \"\"\"itemgetter serializer (needed for namedtuple support)\"\"\"\n class Dummy:\n def __getitem__(self, item):\n return item\n items = obj(Dummy())\n if not isinstance(items, tuple):\n items = (items, )\n return self.save_reduce(operator.itemgetter, items)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_itemgetter.Dummy.__getitem__","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_itemgetter.Dummy.__getitem__\n def __getitem__(self, item):\n return item","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter\n def save_attrgetter(self, obj):\n \"\"\"attrgetter serializer\"\"\"\n class Dummy(object):\n def __init__(self, attrs, index=None):\n self.attrs = attrs\n self.index = index\n def __getattribute__(self, item):\n attrs = object.__getattribute__(self, \"attrs\")\n index = object.__getattribute__(self, \"index\")\n if index is None:\n index = len(attrs)\n attrs.append(item)\n else:\n attrs[index] = \".\".join([attrs[index], item])\n return type(self)(attrs, index)\n attrs = []\n obj(Dummy(attrs))\n return self.save_reduce(operator.attrgetter, tuple(attrs))","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter.Dummy.__init__","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter.Dummy.__init__\n def __init__(self, attrs, index=None):\n self.attrs = attrs\n self.index = index","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter.Dummy.__getattribute__","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter.Dummy.__getattribute__\n def __getattribute__(self, item):\n attrs = object.__getattribute__(self, \"attrs\")\n index = object.__getattribute__(self, \"index\")\n if index is None:\n index = len(attrs)\n attrs.append(item)\n else:\n attrs[index] = \".\".join([attrs[index], item])\n return type(self)(attrs, index)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_file","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_file\n def save_file(self, obj):\n \"\"\"Save a file\"\"\"\n try:\n import StringIO as pystringIO #we can't use cStringIO as it lacks the name attribute\n except ImportError:\n import io as pystringIO\n\n if not hasattr(obj, 'name') or not hasattr(obj, 'mode'):\n raise pickle.PicklingError(\"Cannot pickle files that do not map to an actual file\")\n if obj is sys.stdout:\n return self.save_reduce(getattr, (sys,'stdout'), obj=obj)\n if obj is sys.stderr:\n return self.save_reduce(getattr, (sys,'stderr'), obj=obj)\n if obj is sys.stdin:\n raise pickle.PicklingError(\"Cannot pickle standard input\")\n if obj.closed:\n raise pickle.PicklingError(\"Cannot pickle closed files\")\n if hasattr(obj, 'isatty') and obj.isatty():\n raise pickle.PicklingError(\"Cannot pickle files that map to tty objects\")\n if 'r' not in obj.mode and '+' not in obj.mode:\n raise pickle.PicklingError(\"Cannot pickle files that are not opened for reading: %s\" % obj.mode)\n\n name = obj.name\n\n retval = pystringIO.StringIO()\n\n try:\n # Read the whole file\n curloc = obj.tell()\n obj.seek(0)\n contents = obj.read()\n obj.seek(curloc)\n except IOError:\n raise pickle.PicklingError(\"Cannot pickle file %s as it cannot be read\" % name)\n retval.write(contents)\n retval.seek(curloc)\n\n retval.name = name\n self.save(retval)\n self.memoize(obj)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_ellipsis","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_ellipsis\n def save_ellipsis(self, obj):\n self.save_reduce(_gen_ellipsis, ())","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_not_implemented","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_not_implemented\n def save_not_implemented(self, obj):\n self.save_reduce(_gen_not_implemented, ())","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_weakset","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_weakset\n def save_weakset(self, obj):\n self.save_reduce(weakref.WeakSet, (list(obj),))","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_logger","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_logger\n def save_logger(self, obj):\n self.save_reduce(logging.getLogger, (obj.name,), obj=obj)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.inject_addons","text":"python/ray/cloudpickle/cloudpickle.py::CloudPickler.inject_addons\n def inject_addons(self):\n \"\"\"Plug in system. Register additional pickling functions if modules already loaded\"\"\"\n pass","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::is_tornado_coroutine","text":"python/ray/cloudpickle/cloudpickle.py::is_tornado_coroutine\ndef is_tornado_coroutine(func):\n \"\"\"\n Return whether *func* is a Tornado coroutine function.\n Running coroutines are not supported.\n \"\"\"\n if 'tornado.gen' not in sys.modules:\n return False\n gen = sys.modules['tornado.gen']\n if not hasattr(gen, \"is_coroutine_function\"):\n # Tornado version is too old\n return False\n return gen.is_coroutine_function(func)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::_rebuild_tornado_coroutine","text":"python/ray/cloudpickle/cloudpickle.py::_rebuild_tornado_coroutine\ndef _rebuild_tornado_coroutine(func):\n from tornado import gen\n return gen.coroutine(func)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::dump","text":"python/ray/cloudpickle/cloudpickle.py::dump\ndef dump(obj, file, protocol=None):\n \"\"\"Serialize obj as bytes streamed into file\n\n protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to\n pickle.HIGHEST_PROTOCOL. This setting favors maximum communication speed\n between processes running the same Python version.\n\n Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure\n compatibility with older versions of Python.\n \"\"\"\n CloudPickler(file, protocol=protocol).dump(obj)","label":0},{"cid":"python/ray/cloudpickle/cloudpickle.py::dumps","text":"python/ray/cloudpickle/cloudpickle.py::dumps\ndef dumps(obj, protocol=None):\n \"\"\"Serialize obj as a string of bytes allocated in memory\n\n protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to\n pickle.HIGHEST_PROTOCOL. This setting favors maximum communication speed\n between processes running the same Python version.\n\n Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure\n compatibility with older versions of Python.\n \"\"\"\n file = StringIO()\n try:\n cp = CloudPickler(file, protocol=protocol)\n cp.dump(obj)\n return file.getvalue()\n finally:\n file.close()","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.__init__","text":"python/ray/function_manager.py::FunctionDescriptor.__init__\n def __init__(self,\n module_name,\n function_name,\n class_name=\"\",\n function_source_hash=b\"\"):\n self._module_name = module_name\n self._class_name = class_name\n self._function_name = function_name\n self._function_source_hash = function_source_hash\n self._function_id = self._get_function_id()","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.__repr__","text":"python/ray/function_manager.py::FunctionDescriptor.__repr__\n def __repr__(self):\n return (\"FunctionDescriptor:\" + self._module_name + \".\" +\n self._class_name + \".\" + self._function_name + \".\" +\n binary_to_hex(self._function_source_hash))","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.from_bytes_list","text":"python/ray/function_manager.py::FunctionDescriptor.from_bytes_list\n @classmethod\n def from_bytes_list(cls, function_descriptor_list):\n \"\"\"Create a FunctionDescriptor instance from list of bytes.\n\n This function is used to create the function descriptor from\n backend data.\n\n Args:\n cls: Current class which is required argument for classmethod.\n function_descriptor_list: list of bytes to represent the\n function descriptor.\n\n Returns:\n The FunctionDescriptor instance created from the bytes list.\n \"\"\"\n assert isinstance(function_descriptor_list, list)\n if len(function_descriptor_list) == 0:\n # This is a function descriptor of driver task.\n return FunctionDescriptor.for_driver_task()\n elif (len(function_descriptor_list) == 3\n or len(function_descriptor_list) == 4):\n module_name = function_descriptor_list[0].decode()\n class_name = function_descriptor_list[1].decode()\n function_name = function_descriptor_list[2].decode()\n if len(function_descriptor_list) == 4:\n return cls(module_name, function_name, class_name,\n function_descriptor_list[3])\n else:\n return cls(module_name, function_name, class_name)\n else:\n raise Exception(\n \"Invalid input for FunctionDescriptor.from_bytes_list\")","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.from_function","text":"python/ray/function_manager.py::FunctionDescriptor.from_function\n @classmethod\n def from_function(cls, function):\n \"\"\"Create a FunctionDescriptor from a function instance.\n\n This function is used to create the function descriptor from\n a python function. If a function is a class function, it should\n not be used by this function.\n\n Args:\n cls: Current class which is required argument for classmethod.\n function: the python function used to create the function\n descriptor.\n\n Returns:\n The FunctionDescriptor instance created according to the function.\n \"\"\"\n module_name = function.__module__\n function_name = function.__name__\n class_name = \"\"\n\n function_source_hasher = hashlib.sha1()\n try:\n # If we are running a script or are in IPython, include the source\n # code in the hash.\n source = inspect.getsource(function).encode(\"ascii\")\n function_source_hasher.update(source)\n function_source_hash = function_source_hasher.digest()\n except (IOError, OSError, TypeError):\n # Source code may not be available:\n # e.g. Cython or Python interpreter.\n function_source_hash = b\"\"\n\n return cls(module_name, function_name, class_name,\n function_source_hash)","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.from_class","text":"python/ray/function_manager.py::FunctionDescriptor.from_class\n @classmethod\n def from_class(cls, target_class):\n \"\"\"Create a FunctionDescriptor from a class.\n\n Args:\n cls: Current class which is required argument for classmethod.\n target_class: the python class used to create the function\n descriptor.\n\n Returns:\n The FunctionDescriptor instance created according to the class.\n \"\"\"\n module_name = target_class.__module__\n class_name = target_class.__name__\n return cls(module_name, \"__init__\", class_name)","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.for_driver_task","text":"python/ray/function_manager.py::FunctionDescriptor.for_driver_task\n @classmethod\n def for_driver_task(cls):\n \"\"\"Create a FunctionDescriptor instance for a driver task.\"\"\"\n return cls(\"\", \"\", \"\", b\"\")","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.is_for_driver_task","text":"python/ray/function_manager.py::FunctionDescriptor.is_for_driver_task\n @property\n def is_for_driver_task(self):\n \"\"\"See whether this function descriptor is for a driver or not.\n\n Returns:\n True if this function descriptor is for driver tasks.\n \"\"\"\n return all(\n len(x) == 0\n for x in [self.module_name, self.class_name, self.function_name])","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.module_name","text":"python/ray/function_manager.py::FunctionDescriptor.module_name\n @property\n def module_name(self):\n \"\"\"Get the module name of current function descriptor.\n\n Returns:\n The module name of the function descriptor.\n \"\"\"\n return self._module_name","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.class_name","text":"python/ray/function_manager.py::FunctionDescriptor.class_name\n @property\n def class_name(self):\n \"\"\"Get the class name of current function descriptor.\n\n Returns:\n The class name of the function descriptor. It could be\n empty if the function is not a class method.\n \"\"\"\n return self._class_name","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.function_name","text":"python/ray/function_manager.py::FunctionDescriptor.function_name\n @property\n def function_name(self):\n \"\"\"Get the function name of current function descriptor.\n\n Returns:\n The function name of the function descriptor.\n \"\"\"\n return self._function_name","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.function_hash","text":"python/ray/function_manager.py::FunctionDescriptor.function_hash\n @property\n def function_hash(self):\n \"\"\"Get the hash code of the function source code.\n\n Returns:\n The bytes with length of ray_constants.ID_SIZE if the source\n code is available. Otherwise, the bytes length will be 0.\n \"\"\"\n return self._function_source_hash","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.function_id","text":"python/ray/function_manager.py::FunctionDescriptor.function_id\n @property\n def function_id(self):\n \"\"\"Get the function id calculated from this descriptor.\n\n Returns:\n The value of ray.ObjectID that represents the function id.\n \"\"\"\n return ray.ObjectID(self._function_id)","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor._get_function_id","text":"python/ray/function_manager.py::FunctionDescriptor._get_function_id\n def _get_function_id(self):\n \"\"\"Calculate the function id of current function descriptor.\n\n This function id is calculated from all the fields of function\n descriptor.\n\n Returns:\n bytes with length of ray_constants.ID_SIZE.\n \"\"\"\n if self.is_for_driver_task:\n return ray_constants.NIL_FUNCTION_ID.id()\n function_id_hash = hashlib.sha1()\n # Include the function module and name in the hash.\n function_id_hash.update(self.module_name.encode(\"ascii\"))\n function_id_hash.update(self.function_name.encode(\"ascii\"))\n function_id_hash.update(self.class_name.encode(\"ascii\"))\n function_id_hash.update(self._function_source_hash)\n # Compute the function ID.\n function_id = function_id_hash.digest()\n assert len(function_id) == ray_constants.ID_SIZE\n return function_id","label":0},{"cid":"python/ray/function_manager.py::FunctionDescriptor.get_function_descriptor_list","text":"python/ray/function_manager.py::FunctionDescriptor.get_function_descriptor_list\n def get_function_descriptor_list(self):\n \"\"\"Return a list of bytes representing the function descriptor.\n\n This function is used to pass this function descriptor to backend.\n\n Returns:\n A list of bytes.\n \"\"\"\n descriptor_list = []\n if self.is_for_driver_task:\n # Driver task returns an empty list.\n return descriptor_list\n else:\n descriptor_list.append(self.module_name.encode(\"ascii\"))\n descriptor_list.append(self.class_name.encode(\"ascii\"))\n descriptor_list.append(self.function_name.encode(\"ascii\"))\n if len(self._function_source_hash) != 0:\n descriptor_list.append(self._function_source_hash)\n return descriptor_list","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.__init__","text":"python/ray/function_manager.py::FunctionActorManager.__init__\n def __init__(self, worker):\n self._worker = worker\n self._functions_to_export = []\n self._actors_to_export = []\n # This field is a dictionary that maps a driver ID to a dictionary of\n # functions (and information about those functions) that have been\n # registered for that driver (this inner dictionary maps function IDs\n # to a FunctionExecutionInfo object. This should only be used on\n # workers that execute remote functions.\n self._function_execution_info = defaultdict(lambda: {})\n self._num_task_executions = defaultdict(lambda: {})\n # A set of all of the actor class keys that have been imported by the\n # import thread. It is safe to convert this worker into an actor of\n # these types.\n self.imported_actor_classes = set()","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.increase_task_counter","text":"python/ray/function_manager.py::FunctionActorManager.increase_task_counter\n def increase_task_counter(self, driver_id, function_descriptor):\n function_id = function_descriptor.function_id.id()\n self._num_task_executions[driver_id][function_id] += 1","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.get_task_counter","text":"python/ray/function_manager.py::FunctionActorManager.get_task_counter\n def get_task_counter(self, driver_id, function_descriptor):\n function_id = function_descriptor.function_id.id()\n return self._num_task_executions[driver_id][function_id]","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.export_cached","text":"python/ray/function_manager.py::FunctionActorManager.export_cached\n def export_cached(self):\n \"\"\"Export cached remote functions\n\n Note: this should be called only once when worker is connected.\n \"\"\"\n for remote_function in self._functions_to_export:\n self._do_export(remote_function)\n self._functions_to_export = None\n for info in self._actors_to_export:\n (key, actor_class_info) = info\n self._publish_actor_class_to_key(key, actor_class_info)","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.reset_cache","text":"python/ray/function_manager.py::FunctionActorManager.reset_cache\n def reset_cache(self):\n self._functions_to_export = []\n self._actors_to_export = []","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.export","text":"python/ray/function_manager.py::FunctionActorManager.export\n def export(self, remote_function):\n \"\"\"Export a remote function.\n\n Args:\n remote_function: the RemoteFunction object.\n \"\"\"\n if self._worker.mode is None:\n # If the worker isn't connected, cache the function\n # and export it later.\n self._functions_to_export.append(remote_function)\n return\n if self._worker.mode != ray.worker.SCRIPT_MODE:\n # Don't need to export if the worker is not a driver.\n return\n self._do_export(remote_function)","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager._do_export","text":"python/ray/function_manager.py::FunctionActorManager._do_export\n def _do_export(self, remote_function):\n \"\"\"Pickle a remote function and export it to redis.\n\n Args:\n remote_function: the RemoteFunction object.\n \"\"\"\n # Work around limitations of Python pickling.\n function = remote_function._function\n function_name_global_valid = function.__name__ in function.__globals__\n function_name_global_value = function.__globals__.get(\n function.__name__)\n # Allow the function to reference itself as a global variable\n if not is_cython(function):\n function.__globals__[function.__name__] = remote_function\n try:\n pickled_function = pickle.dumps(function)\n finally:\n # Undo our changes\n if function_name_global_valid:\n function.__globals__[function.__name__] = (\n function_name_global_value)\n else:\n del function.__globals__[function.__name__]\n\n check_oversized_pickle(pickled_function,\n remote_function._function_name,\n \"remote function\", self._worker)\n key = (b\"RemoteFunction:\" + self._worker.task_driver_id.id() + b\":\" +\n remote_function._function_descriptor.function_id.id())\n self._worker.redis_client.hmset(\n key, {\n \"driver_id\": self._worker.task_driver_id.id(),\n \"function_id\": remote_function._function_descriptor.\n function_id.id(),\n \"name\": remote_function._function_name,\n \"module\": function.__module__,\n \"function\": pickled_function,\n \"max_calls\": remote_function._max_calls\n })\n self._worker.redis_client.rpush(\"Exports\", key)","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_remote_function","text":"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_remote_function\n def fetch_and_register_remote_function(self, key):\n \"\"\"Import a remote function.\"\"\"\n (driver_id, function_id_str, function_name, serialized_function,\n num_return_vals, module, resources,\n max_calls) = self._worker.redis_client.hmget(key, [\n \"driver_id\", \"function_id\", \"name\", \"function\", \"num_return_vals\",\n \"module\", \"resources\", \"max_calls\"\n ])\n function_id = ray.ObjectID(function_id_str)\n function_name = decode(function_name)\n max_calls = int(max_calls)\n module = decode(module)\n\n # This is a placeholder in case the function can't be unpickled. This\n # will be overwritten if the function is successfully registered.\n def f():\n raise Exception(\"This function was not imported properly.\")\n\n self._function_execution_info[driver_id][function_id.id()] = (\n FunctionExecutionInfo(\n function=f, function_name=function_name, max_calls=max_calls))\n self._num_task_executions[driver_id][function_id.id()] = 0\n\n try:\n function = pickle.loads(serialized_function)\n except Exception:\n # If an exception was thrown when the remote function was imported,\n # we record the traceback and notify the scheduler of the failure.\n traceback_str = format_error_message(traceback.format_exc())\n # Log the error message.\n push_error_to_driver(\n self._worker,\n ray_constants.REGISTER_REMOTE_FUNCTION_PUSH_ERROR,\n traceback_str,\n driver_id=driver_id,\n data={\n \"function_id\": function_id.id(),\n \"function_name\": function_name\n })\n else:\n # The below line is necessary. Because in the driver process,\n # if the function is define","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_remote_function.f","text":"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_remote_function.f\n def f():\n raise Exception(\"This function was not imported properly.\")","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.get_execution_info","text":"python/ray/function_manager.py::FunctionActorManager.get_execution_info\n def get_execution_info(self, driver_id, function_descriptor):\n \"\"\"Get the FunctionExecutionInfo of a remote function.\n\n Args:\n driver_id: ID of the driver that the function belongs to.\n function_descriptor: The FunctionDescriptor of the function to get.\n\n Returns:\n A FunctionExecutionInfo object.\n \"\"\"\n function_id = function_descriptor.function_id.id()\n\n # Wait until the function to be executed has actually been\n # registered on this worker. We will push warnings to the user if\n # we spend too long in this loop.\n # The driver function may not be found in sys.path. Try to load\n # the function from GCS.\n with profiling.profile(\"wait_for_function\", worker=self._worker):\n self._wait_for_function(function_descriptor, driver_id)\n try:\n info = self._function_execution_info[driver_id][function_id]\n except KeyError as e:\n message = (\"Error occurs in get_execution_info: \"\n \"driver_id: %s, function_descriptor: %s. Message: %s\" %\n (binary_to_hex(driver_id), function_descriptor, e))\n raise KeyError(message)\n return info","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager._wait_for_function","text":"python/ray/function_manager.py::FunctionActorManager._wait_for_function\n def _wait_for_function(self, function_descriptor, driver_id, timeout=10):\n \"\"\"Wait until the function to be executed is present on this worker.\n\n This method will simply loop until the import thread has imported the\n relevant function. If we spend too long in this loop, that may indicate\n a problem somewhere and we will push an error message to the user.\n\n If this worker is an actor, then this will wait until the actor has\n been defined.\n\n Args:\n function_descriptor : The FunctionDescriptor of the function that\n we want to execute.\n driver_id (str): The ID of the driver to push the error message to\n if this times out.\n \"\"\"\n start_time = time.time()\n # Only send the warning once.\n warning_sent = False\n while True:\n with self._worker.lock:\n if (self._worker.actor_id == ray.worker.NIL_ACTOR_ID\n and (function_descriptor.function_id.id() in\n self._function_execution_info[driver_id])):\n break\n elif self._worker.actor_id != ray.worker.NIL_ACTOR_ID and (\n self._worker.actor_id in self._worker.actors):\n break\n if time.time() - start_time > timeout:\n warning_message = (\"This worker was asked to execute a \"\n \"function that it does not have \"\n \"registered. You may have to restart \"\n \"Ray.\")\n if not warning_sent:\n ray.utils.push_error_to_driver(\n self._worker,\n ray_constants.WAIT_FOR_FUNCTION_PUSH_ERROR,\n warning_message,\n drive","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager._publish_actor_class_to_key","text":"python/ray/function_manager.py::FunctionActorManager._publish_actor_class_to_key\n def _publish_actor_class_to_key(self, key, actor_class_info):\n \"\"\"Push an actor class definition to Redis.\n\n The is factored out as a separate function because it is also called\n on cached actor class definitions when a worker connects for the first\n time.\n\n Args:\n key: The key to store the actor class info at.\n actor_class_info: Information about the actor class.\n worker: The worker to use to connect to Redis.\n \"\"\"\n # We set the driver ID here because it may not have been available when\n # the actor class was defined.\n self._worker.redis_client.hmset(key, actor_class_info)\n self._worker.redis_client.rpush(\"Exports\", key)","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.export_actor_class","text":"python/ray/function_manager.py::FunctionActorManager.export_actor_class\n def export_actor_class(self, Class, actor_method_names,\n checkpoint_interval):\n function_descriptor = FunctionDescriptor.from_class(Class)\n # `task_driver_id` shouldn't be NIL, unless:\n # 1) This worker isn't an actor;\n # 2) And a previous task started a background thread, which didn't\n # finish before the task finished, and still uses Ray API\n # after that.\n assert not self._worker.task_driver_id.is_nil(), (\n \"You might have started a background thread in a non-actor task, \"\n \"please make sure the thread finishes before the task finishes.\")\n driver_id = self._worker.task_driver_id\n key = (b\"ActorClass:\" + driver_id.id() + b\":\" +\n function_descriptor.function_id.id())\n actor_class_info = {\n \"class_name\": Class.__name__,\n \"module\": Class.__module__,\n \"class\": pickle.dumps(Class),\n \"checkpoint_interval\": checkpoint_interval,\n \"driver_id\": driver_id.id(),\n \"actor_method_names\": json.dumps(list(actor_method_names))\n }\n\n check_oversized_pickle(actor_class_info[\"class\"],\n actor_class_info[\"class_name\"], \"actor\",\n self._worker)\n\n if self._worker.mode is None:\n # This means that 'ray.init()' has not been called yet and so we\n # must cache the actor class definition and export it when\n # 'ray.init()' is called.\n assert self._actors_to_export is not None\n self._actors_to_export.append((key, actor_class_info))\n # This caching code path is currently not used because we only\n # export actor class definitions lazily when we instantiate the\n # actor for the first time.\n assert False, \"This should be unreachable.\"\n else:\n ","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.load_actor","text":"python/ray/function_manager.py::FunctionActorManager.load_actor\n def load_actor(self, driver_id, function_descriptor):\n key = (b\"ActorClass:\" + driver_id + b\":\" +\n function_descriptor.function_id.id())\n # Wait for the actor class key to have been imported by the\n # import thread. TODO(rkn): It shouldn't be possible to end\n # up in an infinite loop here, but we should push an error to\n # the driver if too much time is spent here.\n while key not in self.imported_actor_classes:\n time.sleep(0.001)\n with self._worker.lock:\n self.fetch_and_register_actor(key)","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_actor","text":"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_actor\n def fetch_and_register_actor(self, actor_class_key):\n \"\"\"Import an actor.\n\n This will be called by the worker's import thread when the worker\n receives the actor_class export, assuming that the worker is an actor\n for that class.\n\n Args:\n actor_class_key: The key in Redis to use to fetch the actor.\n worker: The worker to use.\n \"\"\"\n actor_id_str = self._worker.actor_id\n (driver_id, class_name, module, pickled_class, checkpoint_interval,\n actor_method_names) = self._worker.redis_client.hmget(\n actor_class_key, [\n \"driver_id\", \"class_name\", \"module\", \"class\",\n \"checkpoint_interval\", \"actor_method_names\"\n ])\n\n class_name = decode(class_name)\n module = decode(module)\n checkpoint_interval = int(checkpoint_interval)\n actor_method_names = json.loads(decode(actor_method_names))\n\n # In Python 2, json loads strings as unicode, so convert them back to\n # strings.\n if sys.version_info < (3, 0):\n actor_method_names = [\n method_name.encode(\"ascii\")\n for method_name in actor_method_names\n ]\n\n # Create a temporary actor with some temporary methods so that if\n # the actor fails to be unpickled, the temporary actor can be used\n # (just to produce error messages and to prevent the driver from\n # hanging).\n class TemporaryActor(object):\n pass\n\n self._worker.actors[actor_id_str] = TemporaryActor()\n self._worker.actor_checkpoint_interval = checkpoint_interval\n\n def temporary_actor_method(*xs):\n raise Exception(\n \"The actor with name {} failed to be imported, \"\n \"and so cannot execute this method\".format(class_name))\n\n # Register the actor method executors.\n","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_actor.temporary_actor_method","text":"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_actor.temporary_actor_method\n def temporary_actor_method(*xs):\n raise Exception(\n \"The actor with name {} failed to be imported, \"\n \"and so cannot execute this method\".format(class_name))","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager._make_actor_method_executor","text":"python/ray/function_manager.py::FunctionActorManager._make_actor_method_executor\n def _make_actor_method_executor(self, method_name, method, actor_imported):\n \"\"\"Make an executor that wraps a user-defined actor method.\n\n The wrapped method updates the worker's internal state and performs any\n necessary checkpointing operations.\n\n Args:\n worker (Worker): The worker that is executing the actor.\n method_name (str): The name of the actor method.\n method (instancemethod): The actor method to wrap. This should be a\n method defined on the actor class and should therefore take an\n instance of the actor as the first argument.\n actor_imported (bool): Whether the actor has been imported.\n Checkpointing operations will not be run if this is set to\n False.\n\n Returns:\n A function that executes the given actor method on the worker's\n stored instance of the actor. The function also updates the\n worker's internal state to record the executed method.\n \"\"\"\n\n def actor_method_executor(dummy_return_id, actor, *args):\n # Update the actor's task counter to reflect the task we're about\n # to execute.\n self._worker.actor_task_counter += 1\n\n # If this is the first task to execute on the actor, try to resume\n # from a checkpoint.\n # Current __init__ will be called by default. So the real function\n # call will start from 2.\n if actor_imported and self._worker.actor_task_counter == 2:\n checkpoint_resumed = ray.actor.restore_and_log_checkpoint(\n self._worker, actor)\n if checkpoint_resumed:\n # NOTE(swang): Since we did not actually execute the\n # __init__ method, this will put None as the return value.\n # If the ","label":0},{"cid":"python/ray/function_manager.py::FunctionActorManager._make_actor_method_executor.actor_method_executor","text":"python/ray/function_manager.py::FunctionActorManager._make_actor_method_executor.actor_method_executor\n def actor_method_executor(dummy_return_id, actor, *args):\n # Update the actor's task counter to reflect the task we're about\n # to execute.\n self._worker.actor_task_counter += 1\n\n # If this is the first task to execute on the actor, try to resume\n # from a checkpoint.\n # Current __init__ will be called by default. So the real function\n # call will start from 2.\n if actor_imported and self._worker.actor_task_counter == 2:\n checkpoint_resumed = ray.actor.restore_and_log_checkpoint(\n self._worker, actor)\n if checkpoint_resumed:\n # NOTE(swang): Since we did not actually execute the\n # __init__ method, this will put None as the return value.\n # If the __init__ method is supposed to return multiple\n # values, an exception will be logged.\n return\n\n # Determine whether we should checkpoint the actor.\n checkpointing_on = (actor_imported\n and self._worker.actor_checkpoint_interval > 0)\n # We should checkpoint the actor if user checkpointing is on, we've\n # executed checkpoint_interval tasks since the last checkpoint, and\n # the method we're about to execute is not a checkpoint.\n save_checkpoint = (checkpointing_on\n and (self._worker.actor_task_counter %\n self._worker.actor_checkpoint_interval == 0\n and method_name != \"__ray_checkpoint__\"))\n\n # Execute the assigned method and save a checkpoint if necessary.\n try:\n if is_class_method(method):\n method_returns = method(*args)\n els","label":0},{"cid":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::setupCNN","text":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::setupCNN\ndef setupCNN(x):\n \"\"\"setupCNN builds the graph for a deep net for classifying digits.\n Args:\n x: an input tensor with the dimensions (N_examples, 784), where 784 is\n the number of pixels in a standard MNIST image.\n Returns:\n A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with\n values equal to the logits of classifying the digit into one of 10\n classes (the digits 0-9). keep_prob is a scalar placeholder for the\n probability of dropout.\n \"\"\"\n # Reshape to use within a convolutional neural net.\n # Last dimension is for \"features\" - there is only one here, since images\n # are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.\n with tf.name_scope('reshape'):\n x_image = tf.reshape(x, [-1, 28, 28, 1])\n\n # First convolutional layer - maps one grayscale image to 32 feature maps.\n with tf.name_scope('conv1'):\n W_conv1 = weight_variable([5, 5, 1, 32])\n b_conv1 = bias_variable([32])\n h_conv1 = activation_fn(conv2d(x_image, W_conv1) + b_conv1)\n\n # Pooling layer - downsamples by 2X.\n with tf.name_scope('pool1'):\n h_pool1 = max_pool_2x2(h_conv1)\n\n # Second convolutional layer -- maps 32 feature maps to 64.\n with tf.name_scope('conv2'):\n W_conv2 = weight_variable([5, 5, 32, 64])\n b_conv2 = bias_variable([64])\n h_conv2 = activation_fn(conv2d(h_pool1, W_conv2) + b_conv2)\n\n # Second pooling layer.\n with tf.name_scope('pool2'):\n h_pool2 = max_pool_2x2(h_conv2)\n\n # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image\n # is down to 7x7x64 feature maps -- maps this to 1024 features.\n with tf.name_scope('fc1'):\n W_fc1 = weight_variable([7 * 7 * 64, 1024])\n b_fc1 = bias_variable([1024])\n\n h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])\n h_fc1 = activation_fn(tf.matmul(h_pool2_flat, W_fc1)","label":0},{"cid":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::conv2d","text":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::conv2d\ndef conv2d(x, W):\n \"\"\"conv2d returns a 2d convolution layer with full stride.\"\"\"\n return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')","label":0},{"cid":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::max_pool_2x2","text":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::max_pool_2x2\ndef max_pool_2x2(x):\n \"\"\"max_pool_2x2 downsamples a feature map by 2X.\"\"\"\n return tf.nn.max_pool(\n x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')","label":0},{"cid":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::weight_variable","text":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::weight_variable\ndef weight_variable(shape):\n \"\"\"weight_variable generates a weight variable of a given shape.\"\"\"\n initial = tf.truncated_normal(shape, stddev=0.1)\n return tf.Variable(initial)","label":0},{"cid":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::bias_variable","text":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::bias_variable\ndef bias_variable(shape):\n \"\"\"bias_variable generates a bias variable of a given shape.\"\"\"\n initial = tf.constant(0.1, shape=shape)\n return tf.Variable(initial)","label":0},{"cid":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._setup","text":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._setup\n def _setup(self, config):\n global activation_fn\n\n self.timestep = 0\n\n # Import data\n for _ in range(10):\n try:\n self.mnist = input_data.read_data_sets(\n \"/tmp/mnist_ray_demo\", one_hot=True)\n break\n except Exception as e:\n print(\"Error loading data, retrying\", e)\n time.sleep(5)\n\n assert self.mnist\n\n self.x = tf.placeholder(tf.float32, [None, 784])\n self.y_ = tf.placeholder(tf.float32, [None, 10])\n\n activation_fn = getattr(tf.nn, config['activation'])\n\n # Build the graph for the deep net\n y_conv, self.keep_prob = setupCNN(self.x)\n\n with tf.name_scope('loss'):\n cross_entropy = tf.nn.softmax_cross_entropy_with_logits(\n labels=self.y_, logits=y_conv)\n cross_entropy = tf.reduce_mean(cross_entropy)\n\n with tf.name_scope('adam_optimizer'):\n train_step = tf.train.AdamOptimizer(\n config['learning_rate']).minimize(cross_entropy)\n\n self.train_step = train_step\n\n with tf.name_scope('accuracy'):\n correct_prediction = tf.equal(\n tf.argmax(y_conv, 1), tf.argmax(self.y_, 1))\n correct_prediction = tf.cast(correct_prediction, tf.float32)\n self.accuracy = tf.reduce_mean(correct_prediction)\n\n self.sess = tf.Session()\n self.sess.run(tf.global_variables_initializer())\n self.iterations = 0\n self.saver = tf.train.Saver()","label":0},{"cid":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._train","text":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._train\n def _train(self):\n for i in range(10):\n batch = self.mnist.train.next_batch(50)\n self.sess.run(\n self.train_step,\n feed_dict={\n self.x: batch[0],\n self.y_: batch[1],\n self.keep_prob: 0.5\n })\n\n batch = self.mnist.train.next_batch(50)\n train_accuracy = self.sess.run(\n self.accuracy,\n feed_dict={\n self.x: batch[0],\n self.y_: batch[1],\n self.keep_prob: 1.0\n })\n\n self.iterations += 1\n return {\"mean_accuracy\": train_accuracy}","label":0},{"cid":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._save","text":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._save\n def _save(self, checkpoint_dir):\n return self.saver.save(\n self.sess, checkpoint_dir + \"/save\", global_step=self.iterations)","label":0},{"cid":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._restore","text":"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._restore\n def _restore(self, path):\n return self.saver.restore(self.sess, path)","label":0},{"cid":"python/ray/tune/experiment.py::_raise_deprecation_note","text":"python/ray/tune/experiment.py::_raise_deprecation_note\ndef _raise_deprecation_note(deprecated, replacement, soft=False):\n \"\"\"User notification for deprecated parameter.\n\n Arguments:\n deprecated (str): Deprecated parameter.\n replacement (str): Replacement parameter to use instead.\n soft (bool): Fatal if True.\n \"\"\"\n error_msg = (\"`{deprecated}` is deprecated. Please use `{replacement}`. \"\n \"`{deprecated}` will be removed in future versions of \"\n \"Ray.\".format(deprecated=deprecated, replacement=replacement))\n if soft:\n logger.warning(error_msg)\n else:\n raise DeprecationWarning(error_msg)","label":0},{"cid":"python/ray/tune/experiment.py::Experiment.__init__","text":"python/ray/tune/experiment.py::Experiment.__init__\n def __init__(self,\n name,\n run,\n stop=None,\n config=None,\n resources_per_trial=None,\n num_samples=1,\n local_dir=None,\n upload_dir=None,\n trial_name_creator=None,\n custom_loggers=None,\n sync_function=None,\n checkpoint_freq=0,\n checkpoint_at_end=False,\n max_failures=3,\n restore=None,\n repeat=None,\n trial_resources=None):\n if sync_function:\n assert upload_dir, \"Need `upload_dir` if sync_function given.\"\n\n if repeat:\n _raise_deprecation_note(\"repeat\", \"num_samples\", soft=False)\n if trial_resources:\n _raise_deprecation_note(\n \"trial_resources\", \"resources_per_trial\", soft=True)\n resources_per_trial = trial_resources\n\n spec = {\n \"run\": Experiment._register_if_needed(run),\n \"stop\": stop or {},\n \"config\": config or {},\n \"resources_per_trial\": resources_per_trial,\n \"num_samples\": num_samples,\n \"local_dir\": os.path.expanduser(local_dir or DEFAULT_RESULTS_DIR),\n \"upload_dir\": upload_dir or \"\", # argparse converts None to \"null\"\n \"trial_name_creator\": trial_name_creator,\n \"custom_loggers\": custom_loggers,\n \"sync_function\": sync_function,\n \"checkpoint_freq\": checkpoint_freq,\n \"checkpoint_at_end\": checkpoint_at_end,\n \"max_failures\": max_failures,\n \"restore\": restore\n }\n\n self.name = name\n self.spec = spec","label":0},{"cid":"python/ray/tune/experiment.py::Experiment.from_json","text":"python/ray/tune/experiment.py::Experiment.from_json\n @classmethod\n def from_json(cls, name, spec):\n \"\"\"Generates an Experiment object from JSON.\n\n Args:\n name (str): Name of Experiment.\n spec (dict): JSON configuration of experiment.\n \"\"\"\n if \"run\" not in spec:\n raise TuneError(\"No trainable specified!\")\n\n # Special case the `env` param for RLlib by automatically\n # moving it into the `config` section.\n if \"env\" in spec:\n spec[\"config\"] = spec.get(\"config\", {})\n spec[\"config\"][\"env\"] = spec[\"env\"]\n del spec[\"env\"]\n\n spec = copy.deepcopy(spec)\n\n run_value = spec.pop(\"run\")\n try:\n exp = cls(name, run_value, **spec)\n except TypeError:\n raise TuneError(\"Improper argument from JSON: {}.\".format(spec))\n return exp","label":0},{"cid":"python/ray/tune/experiment.py::Experiment._register_if_needed","text":"python/ray/tune/experiment.py::Experiment._register_if_needed\n @classmethod\n def _register_if_needed(cls, run_object):\n \"\"\"Registers Trainable or Function at runtime.\n\n Assumes already registered if run_object is a string. Does not\n register lambdas because they could be part of variant generation.\n Also, does not inspect interface of given run_object.\n\n Arguments:\n run_object (str|function|class): Trainable to run. If string,\n assumes it is an ID and does not modify it. Otherwise,\n returns a string corresponding to the run_object name.\n\n Returns:\n A string representing the trainable identifier.\n \"\"\"\n\n if isinstance(run_object, six.string_types):\n return run_object\n elif isinstance(run_object, types.FunctionType):\n if run_object.__name__ == \"<lambda>\":\n logger.warning(\n \"Not auto-registering lambdas - resolving as variant.\")\n return run_object\n else:\n name = run_object.__name__\n register_trainable(name, run_object)\n return name\n elif isinstance(run_object, type):\n name = run_object.__name__\n register_trainable(name, run_object)\n return name\n else:\n raise TuneError(\"Improper 'run' - not string nor trainable.\")","label":0},{"cid":"python/ray/tune/experiment.py::convert_to_experiment_list","text":"python/ray/tune/experiment.py::convert_to_experiment_list\ndef convert_to_experiment_list(experiments):\n \"\"\"Produces a list of Experiment objects.\n\n Converts input from dict, single experiment, or list of\n experiments to list of experiments. If input is None,\n will return an empty list.\n\n Arguments:\n experiments (Experiment | list | dict): Experiments to run.\n\n Returns:\n List of experiments.\n \"\"\"\n exp_list = experiments\n\n # Transform list if necessary\n if experiments is None:\n exp_list = []\n elif isinstance(experiments, Experiment):\n exp_list = [experiments]\n elif type(experiments) is dict:\n exp_list = [\n Experiment.from_json(name, spec)\n for name, spec in experiments.items()\n ]\n\n # Validate exp_list\n if (type(exp_list) is list\n and all(isinstance(exp, Experiment) for exp in exp_list)):\n if len(exp_list) > 1:\n logger.warning(\"All experiments will be \"\n \"using the same SearchAlgorithm.\")\n else:\n raise TuneError(\"Invalid argument: {}\".format(experiments))\n\n return exp_list","label":0},{"cid":"python/ray/tune/registry.py::register_trainable","text":"python/ray/tune/registry.py::register_trainable\ndef register_trainable(name, trainable):\n \"\"\"Register a trainable function or class.\n\n Args:\n name (str): Name to register.\n trainable (obj): Function or tune.Trainable class. Functions must\n take (config, status_reporter) as arguments and will be\n automatically converted into a class during registration.\n \"\"\"\n\n from ray.tune.trainable import Trainable, wrap_function\n\n if isinstance(trainable, FunctionType):\n trainable = wrap_function(trainable)\n if not issubclass(trainable, Trainable):\n raise TypeError(\"Second argument must be convertable to Trainable\",\n trainable)\n _global_registry.register(TRAINABLE_CLASS, name, trainable)","label":1},{"cid":"python/ray/tune/registry.py::register_env","text":"python/ray/tune/registry.py::register_env\ndef register_env(name, env_creator):\n \"\"\"Register a custom environment for use with RLlib.\n\n Args:\n name (str): Name to register.\n env_creator (obj): Function that creates an env.\n \"\"\"\n\n if not isinstance(env_creator, FunctionType):\n raise TypeError(\"Second argument must be a function.\", env_creator)\n _global_registry.register(ENV_CREATOR, name, env_creator)","label":0},{"cid":"python/ray/tune/registry.py::_make_key","text":"python/ray/tune/registry.py::_make_key\ndef _make_key(category, key):\n \"\"\"Generate a binary key for the given category and key.\n\n Args:\n category (str): The category of the item\n key (str): The unique identifier for the item\n\n Returns:\n The key to use for storing a the value.\n \"\"\"\n return (b\"TuneRegistry:\" + category.encode(\"ascii\") + b\"/\" +\n key.encode(\"ascii\"))","label":0},{"cid":"python/ray/tune/registry.py::_Registry.__init__","text":"python/ray/tune/registry.py::_Registry.__init__\n def __init__(self):\n self._to_flush = {}","label":0},{"cid":"python/ray/tune/registry.py::_Registry.register","text":"python/ray/tune/registry.py::_Registry.register\n def register(self, category, key, value):\n if category not in KNOWN_CATEGORIES:\n from ray.tune import TuneError\n raise TuneError(\"Unknown category {} not among {}\".format(\n category, KNOWN_CATEGORIES))\n self._to_flush[(category, key)] = pickle.dumps(value)\n if _internal_kv_initialized():\n self.flush_values()","label":0},{"cid":"python/ray/tune/registry.py::_Registry.contains","text":"python/ray/tune/registry.py::_Registry.contains\n def contains(self, category, key):\n if _internal_kv_initialized():\n value = _internal_kv_get(_make_key(category, key))\n return value is not None\n else:\n return (category, key) in self._to_flush","label":0},{"cid":"python/ray/tune/registry.py::_Registry.get","text":"python/ray/tune/registry.py::_Registry.get\n def get(self, category, key):\n if _internal_kv_initialized():\n value = _internal_kv_get(_make_key(category, key))\n if value is None:\n raise ValueError(\n \"Registry value for {}/{} doesn't exist.\".format(\n category, key))\n return pickle.loads(value)\n else:\n return pickle.loads(self._to_flush[(category, key)])","label":0},{"cid":"python/ray/tune/registry.py::_Registry.flush_values","text":"python/ray/tune/registry.py::_Registry.flush_values\n def flush_values(self):\n for (category, key), value in self._to_flush.items():\n _internal_kv_put(_make_key(category, key), value, overwrite=True)\n self._to_flush.clear()","label":0}],"string":"[\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::_make_cell_set_template_code\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::_make_cell_set_template_code\\ndef _make_cell_set_template_code():\\n \\\"\\\"\\\"Get the Python compiler to emit LOAD_FAST(arg); STORE_DEREF\\n\\n Notes\\n -----\\n In Python 3, we could use an easier function:\\n\\n .. code-block:: python\\n\\n def f():\\n cell = None\\n\\n def _stub(value):\\n nonlocal cell\\n cell = value\\n\\n return _stub\\n\\n _cell_set_template_code = f()\\n\\n This function is _only_ a LOAD_FAST(arg); STORE_DEREF, but that is\\n invalid syntax on Python 2. If we use this function we also don't need\\n to do the weird freevars/cellvars swap below\\n \\\"\\\"\\\"\\n def inner(value):\\n lambda: cell # make ``cell`` a closure so that we get a STORE_DEREF\\n cell = value\\n\\n co = inner.__code__\\n\\n # NOTE: we are marking the cell variable as a free variable intentionally\\n # so that we simulate an inner function instead of the outer function. This\\n # is what gives us the ``nonlocal`` behavior in a Python 2 compatible way.\\n if not PY3:\\n return types.CodeType(\\n co.co_argcount,\\n co.co_nlocals,\\n co.co_stacksize,\\n co.co_flags,\\n co.co_code,\\n co.co_consts,\\n co.co_names,\\n co.co_varnames,\\n co.co_filename,\\n co.co_name,\\n co.co_firstlineno,\\n co.co_lnotab,\\n co.co_cellvars, # this is the trickery\\n (),\\n )\\n else:\\n return types.CodeType(\\n co.co_argcount,\\n co.co_kwonlyargcount,\\n co.co_nlocals,\\n co.co_stacksize,\\n co.co_flags,\\n co.co_code,\\n co.co_consts,\\n co.co_names,\\n co.co_varnames,\\n co.co_filename,\\n co.co_name,\\n co.co_firstlineno,\\n co.co_lnotab,\\n co.co_cellvars, # this is the trickery\\n (),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::_make_cell_set_template_code.inner\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::_make_cell_set_template_code.inner\\n def inner(value):\\n lambda: cell # make ``cell`` a closure so that we get a STORE_DEREF\\n cell = value\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::cell_set\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::cell_set\\ndef cell_set(cell, value):\\n \\\"\\\"\\\"Set the value of a closure cell.\\n \\\"\\\"\\\"\\n return types.FunctionType(\\n _cell_set_template_code,\\n {},\\n '_cell_set_inner',\\n (),\\n (cell,),\\n )(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::islambda\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::islambda\\ndef islambda(func):\\n return getattr(func,'__name__') == '<lambda>'\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::_builtin_type\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::_builtin_type\\ndef _builtin_type(name):\\n return getattr(types, name)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::_make__new__factory\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::_make__new__factory\\ndef _make__new__factory(type_):\\n def _factory():\\n return type_.__new__\\n return _factory\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::_make__new__factory._factory\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::_make__new__factory._factory\\n def _factory():\\n return type_.__new__\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.__init__\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.__init__\\n def __init__(self, file, protocol=None):\\n if protocol is None:\\n protocol = DEFAULT_PROTOCOL\\n Pickler.__init__(self, file, protocol=protocol)\\n # set of modules to unpickle\\n self.modules = set()\\n # map ids to dictionary. used to ensure that functions can share global env\\n self.globals_ref = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.dump\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.dump\\n def dump(self, obj):\\n self.inject_addons()\\n try:\\n return Pickler.dump(self, obj)\\n except RuntimeError as e:\\n if 'recursion' in e.args[0]:\\n msg = \\\"\\\"\\\"Could not pickle object as excessively deep recursion required.\\\"\\\"\\\"\\n raise pickle.PicklingError(msg)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_memoryview\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_memoryview\\n def save_memoryview(self, obj):\\n self.save(obj.tobytes())\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_unsupported\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_unsupported\\n def save_unsupported(self, obj):\\n raise pickle.PicklingError(\\\"Cannot pickle objects of type %s\\\" % type(obj))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_module\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_module\\n def save_module(self, obj):\\n \\\"\\\"\\\"\\n Save a module as an import\\n \\\"\\\"\\\"\\n mod_name = obj.__name__\\n # If module is successfully found then it is not a dynamically created module\\n if hasattr(obj, '__file__'):\\n is_dynamic = False\\n else:\\n try:\\n _find_module(mod_name)\\n is_dynamic = False\\n except ImportError:\\n is_dynamic = True\\n\\n self.modules.add(obj)\\n if is_dynamic:\\n self.save_reduce(dynamic_subimport, (obj.__name__, vars(obj)), obj=obj)\\n else:\\n self.save_reduce(subimport, (obj.__name__,), obj=obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_codeobject\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_codeobject\\n def save_codeobject(self, obj):\\n \\\"\\\"\\\"\\n Save a code object\\n \\\"\\\"\\\"\\n if PY3:\\n args = (\\n obj.co_argcount, obj.co_kwonlyargcount, obj.co_nlocals, obj.co_stacksize,\\n obj.co_flags, obj.co_code, obj.co_consts, obj.co_names, obj.co_varnames,\\n obj.co_filename, obj.co_name, obj.co_firstlineno, obj.co_lnotab, obj.co_freevars,\\n obj.co_cellvars\\n )\\n else:\\n args = (\\n obj.co_argcount, obj.co_nlocals, obj.co_stacksize, obj.co_flags, obj.co_code,\\n obj.co_consts, obj.co_names, obj.co_varnames, obj.co_filename, obj.co_name,\\n obj.co_firstlineno, obj.co_lnotab, obj.co_freevars, obj.co_cellvars\\n )\\n self.save_reduce(types.CodeType, args, obj=obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_function\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_function\\n def save_function(self, obj, name=None):\\n \\\"\\\"\\\" Registered with the dispatch to handle all function types.\\n\\n Determines what kind of function obj is (e.g. lambda, defined at\\n interactive prompt, etc) and handles the pickling appropriately.\\n \\\"\\\"\\\"\\n if obj in _BUILTIN_TYPE_CONSTRUCTORS:\\n # We keep a special-cased cache of built-in type constructors at\\n # global scope, because these functions are structured very\\n # differently in different python versions and implementations (for\\n # example, they're instances of types.BuiltinFunctionType in\\n # CPython, but they're ordinary types.FunctionType instances in\\n # PyPy).\\n #\\n # If the function we've received is in that cache, we just\\n # serialize it as a lookup into the cache.\\n return self.save_reduce(_BUILTIN_TYPE_CONSTRUCTORS[obj], (), obj=obj)\\n\\n write = self.write\\n\\n if name is None:\\n name = obj.__name__\\n try:\\n # whichmodule() could fail, see\\n # https://bitbucket.org/gutworth/six/issues/63/importing-six-breaks-pickling\\n modname = pickle.whichmodule(obj, name)\\n except Exception:\\n modname = None\\n # print('which gives %s %s %s' % (modname, obj, name))\\n try:\\n themodule = sys.modules[modname]\\n except KeyError:\\n # eval'd items such as namedtuple give invalid items for their function __module__\\n modname = '__main__'\\n\\n if modname == '__main__':\\n themodule = None\\n\\n try:\\n lookedup_by_name = getattr(themodule, name, None)\\n except Exception:\\n lookedup_by_name = None\\n\\n if themodule:\\n self.modules.add(themodule)\\n if lookedup_by_name is obj:\\n return self.save_global(obj, name)\\n\\n # a builtin_func\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler._save_subimports\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler._save_subimports\\n def _save_subimports(self, code, top_level_dependencies):\\n \\\"\\\"\\\"\\n Ensure de-pickler imports any package child-modules that\\n are needed by the function\\n \\\"\\\"\\\"\\n # check if any known dependency is an imported package\\n for x in top_level_dependencies:\\n if isinstance(x, types.ModuleType) and hasattr(x, '__package__') and x.__package__:\\n # check if the package has any currently loaded sub-imports\\n prefix = x.__name__ + '.'\\n for name, module in sys.modules.items():\\n # Older versions of pytest will add a \\\"None\\\" module to sys.modules.\\n if name is not None and name.startswith(prefix):\\n # check whether the function can address the sub-module\\n tokens = set(name[len(prefix):].split('.'))\\n if not tokens - set(code.co_names):\\n # ensure unpickler executes this import\\n self.save(module)\\n # then discards the reference to it\\n self.write(pickle.POP)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_dynamic_class\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_dynamic_class\\n def save_dynamic_class(self, obj):\\n \\\"\\\"\\\"\\n Save a class that can't be stored as module global.\\n\\n This method is used to serialize classes that are defined inside\\n functions, or that otherwise can't be serialized as attribute lookups\\n from global modules.\\n \\\"\\\"\\\"\\n clsdict = dict(obj.__dict__) # copy dict proxy to a dict\\n clsdict.pop('__weakref__', None)\\n\\n # On PyPy, __doc__ is a readonly attribute, so we need to include it in\\n # the initial skeleton class. This is safe because we know that the\\n # doc can't participate in a cycle with the original class.\\n type_kwargs = {'__doc__': clsdict.pop('__doc__', None)}\\n\\n # If type overrides __dict__ as a property, include it in the type kwargs.\\n # In Python 2, we can't set this attribute after construction.\\n __dict__ = clsdict.pop('__dict__', None)\\n if isinstance(__dict__, property):\\n type_kwargs['__dict__'] = __dict__\\n\\n save = self.save\\n write = self.write\\n\\n # We write pickle instructions explicitly here to handle the\\n # possibility that the type object participates in a cycle with its own\\n # __dict__. We first write an empty \\\"skeleton\\\" version of the class and\\n # memoize it before writing the class' __dict__ itself. We then write\\n # instructions to \\\"rehydrate\\\" the skeleton class by restoring the\\n # attributes from the __dict__.\\n #\\n # A type can appear in a cycle with its __dict__ if an instance of the\\n # type appears in the type's __dict__ (which happens for the stdlib\\n # Enum class), or if the type defines methods that close over the name\\n # of the type, (which is common for Python 2-style super() calls).\\n\\n # Push the rehydration function.\\n save(_rehydrate_skeleton_class)\\n\\n # Mark the start of the args tuple for the rehydr\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_function_tuple\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_function_tuple\\n def save_function_tuple(self, func):\\n \\\"\\\"\\\" Pickles an actual func object.\\n\\n A func comprises: code, globals, defaults, closure, and dict. We\\n extract and save these, injecting reducing functions at certain points\\n to recreate the func object. Keep in mind that some of these pieces\\n can contain a ref to the func itself. Thus, a naive save on these\\n pieces could trigger an infinite loop of save's. To get around that,\\n we first create a skeleton func object using just the code (this is\\n safe, since this won't contain a ref to the func), and memoize it as\\n soon as it's created. The other stuff can then be filled in later.\\n \\\"\\\"\\\"\\n if is_tornado_coroutine(func):\\n self.save_reduce(_rebuild_tornado_coroutine, (func.__wrapped__,),\\n obj=func)\\n return\\n\\n save = self.save\\n write = self.write\\n\\n code, f_globals, defaults, closure_values, dct, base_globals = self.extract_func_data(func)\\n\\n save(_fill_function) # skeleton function updater\\n write(pickle.MARK) # beginning of tuple that _fill_function expects\\n\\n self._save_subimports(\\n code,\\n itertools.chain(f_globals.values(), closure_values or ()),\\n )\\n\\n # create a skeleton function object and memoize it\\n save(_make_skel_func)\\n save((\\n code,\\n len(closure_values) if closure_values is not None else -1,\\n base_globals,\\n ))\\n write(pickle.REDUCE)\\n self.memoize(func)\\n\\n # save the rest of the func data needed by _fill_function\\n state = {\\n 'globals': f_globals,\\n 'defaults': defaults,\\n 'dict': dct,\\n 'module': func.__module__,\\n 'closure_values': closure_values,\\n }\\n if hasattr(func, '__qualname__'):\\n state[\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.extract_code_globals\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.extract_code_globals\\n @classmethod\\n def extract_code_globals(cls, co):\\n \\\"\\\"\\\"\\n Find all globals names read or written to by codeblock co\\n \\\"\\\"\\\"\\n out_names = cls._extract_code_globals_cache.get(co)\\n if out_names is None:\\n try:\\n names = co.co_names\\n except AttributeError:\\n # PyPy \\\"builtin-code\\\" object\\n out_names = set()\\n else:\\n out_names = set(names[oparg]\\n for op, oparg in _walk_global_ops(co))\\n\\n # see if nested function have any global refs\\n if co.co_consts:\\n for const in co.co_consts:\\n if type(const) is types.CodeType:\\n out_names |= cls.extract_code_globals(const)\\n\\n cls._extract_code_globals_cache[co] = out_names\\n\\n return out_names\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.extract_func_data\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.extract_func_data\\n def extract_func_data(self, func):\\n \\\"\\\"\\\"\\n Turn the function into a tuple of data necessary to recreate it:\\n code, globals, defaults, closure_values, dict\\n \\\"\\\"\\\"\\n code = func.__code__\\n\\n # extract all global ref's\\n func_global_refs = self.extract_code_globals(code)\\n\\n # process all variables referenced by global environment\\n f_globals = {}\\n for var in func_global_refs:\\n if var in func.__globals__:\\n f_globals[var] = func.__globals__[var]\\n\\n # defaults requires no processing\\n defaults = func.__defaults__\\n\\n # process closure\\n closure = (\\n list(map(_get_cell_contents, func.__closure__))\\n if func.__closure__ is not None\\n else None\\n )\\n\\n # save the dict\\n dct = func.__dict__\\n\\n base_globals = self.globals_ref.get(id(func.__globals__), {})\\n self.globals_ref[id(func.__globals__)] = base_globals\\n\\n return (code, f_globals, defaults, closure, dct, base_globals)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_builtin_function\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_builtin_function\\n def save_builtin_function(self, obj):\\n if obj.__module__ == \\\"__builtin__\\\":\\n return self.save_global(obj)\\n return self.save_function(obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_global\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_global\\n def save_global(self, obj, name=None, pack=struct.pack):\\n \\\"\\\"\\\"\\n Save a \\\"global\\\".\\n\\n The name of this method is somewhat misleading: all types get\\n dispatched here.\\n \\\"\\\"\\\"\\n if obj.__module__ == \\\"__main__\\\":\\n return self.save_dynamic_class(obj)\\n\\n try:\\n return Pickler.save_global(self, obj, name=name)\\n except Exception:\\n if obj.__module__ == \\\"__builtin__\\\" or obj.__module__ == \\\"builtins\\\":\\n if obj in _BUILTIN_TYPE_NAMES:\\n return self.save_reduce(\\n _builtin_type, (_BUILTIN_TYPE_NAMES[obj],), obj=obj)\\n\\n typ = type(obj)\\n if typ is not obj and isinstance(obj, (type, types.ClassType)):\\n return self.save_dynamic_class(obj)\\n\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_instancemethod\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_instancemethod\\n def save_instancemethod(self, obj):\\n # Memoization rarely is ever useful due to python bounding\\n if obj.__self__ is None:\\n self.save_reduce(getattr, (obj.im_class, obj.__name__))\\n else:\\n if PY3:\\n self.save_reduce(types.MethodType, (obj.__func__, obj.__self__), obj=obj)\\n else:\\n self.save_reduce(types.MethodType, (obj.__func__, obj.__self__, obj.__self__.__class__),\\n obj=obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_inst\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_inst\\n def save_inst(self, obj):\\n \\\"\\\"\\\"Inner logic to save instance. Based off pickle.save_inst\\\"\\\"\\\"\\n cls = obj.__class__\\n\\n # Try the dispatch table (pickle module doesn't do it)\\n f = self.dispatch.get(cls)\\n if f:\\n f(self, obj) # Call unbound method with explicit self\\n return\\n\\n memo = self.memo\\n write = self.write\\n save = self.save\\n\\n if hasattr(obj, '__getinitargs__'):\\n args = obj.__getinitargs__()\\n len(args) # XXX Assert it's a sequence\\n pickle._keep_alive(args, memo)\\n else:\\n args = ()\\n\\n write(pickle.MARK)\\n\\n if self.bin:\\n save(cls)\\n for arg in args:\\n save(arg)\\n write(pickle.OBJ)\\n else:\\n for arg in args:\\n save(arg)\\n write(pickle.INST + cls.__module__ + '\\\\n' + cls.__name__ + '\\\\n')\\n\\n self.memoize(obj)\\n\\n try:\\n getstate = obj.__getstate__\\n except AttributeError:\\n stuff = obj.__dict__\\n else:\\n stuff = getstate()\\n pickle._keep_alive(stuff, memo)\\n save(stuff)\\n write(pickle.BUILD)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_property\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_property\\n def save_property(self, obj):\\n # properties not correctly saved in python\\n self.save_reduce(property, (obj.fget, obj.fset, obj.fdel, obj.__doc__), obj=obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_classmethod\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_classmethod\\n def save_classmethod(self, obj):\\n orig_func = obj.__func__\\n self.save_reduce(type(obj), (orig_func,), obj=obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_itemgetter\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_itemgetter\\n def save_itemgetter(self, obj):\\n \\\"\\\"\\\"itemgetter serializer (needed for namedtuple support)\\\"\\\"\\\"\\n class Dummy:\\n def __getitem__(self, item):\\n return item\\n items = obj(Dummy())\\n if not isinstance(items, tuple):\\n items = (items, )\\n return self.save_reduce(operator.itemgetter, items)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_itemgetter.Dummy.__getitem__\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_itemgetter.Dummy.__getitem__\\n def __getitem__(self, item):\\n return item\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter\\n def save_attrgetter(self, obj):\\n \\\"\\\"\\\"attrgetter serializer\\\"\\\"\\\"\\n class Dummy(object):\\n def __init__(self, attrs, index=None):\\n self.attrs = attrs\\n self.index = index\\n def __getattribute__(self, item):\\n attrs = object.__getattribute__(self, \\\"attrs\\\")\\n index = object.__getattribute__(self, \\\"index\\\")\\n if index is None:\\n index = len(attrs)\\n attrs.append(item)\\n else:\\n attrs[index] = \\\".\\\".join([attrs[index], item])\\n return type(self)(attrs, index)\\n attrs = []\\n obj(Dummy(attrs))\\n return self.save_reduce(operator.attrgetter, tuple(attrs))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter.Dummy.__init__\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter.Dummy.__init__\\n def __init__(self, attrs, index=None):\\n self.attrs = attrs\\n self.index = index\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter.Dummy.__getattribute__\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_attrgetter.Dummy.__getattribute__\\n def __getattribute__(self, item):\\n attrs = object.__getattribute__(self, \\\"attrs\\\")\\n index = object.__getattribute__(self, \\\"index\\\")\\n if index is None:\\n index = len(attrs)\\n attrs.append(item)\\n else:\\n attrs[index] = \\\".\\\".join([attrs[index], item])\\n return type(self)(attrs, index)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_file\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_file\\n def save_file(self, obj):\\n \\\"\\\"\\\"Save a file\\\"\\\"\\\"\\n try:\\n import StringIO as pystringIO #we can't use cStringIO as it lacks the name attribute\\n except ImportError:\\n import io as pystringIO\\n\\n if not hasattr(obj, 'name') or not hasattr(obj, 'mode'):\\n raise pickle.PicklingError(\\\"Cannot pickle files that do not map to an actual file\\\")\\n if obj is sys.stdout:\\n return self.save_reduce(getattr, (sys,'stdout'), obj=obj)\\n if obj is sys.stderr:\\n return self.save_reduce(getattr, (sys,'stderr'), obj=obj)\\n if obj is sys.stdin:\\n raise pickle.PicklingError(\\\"Cannot pickle standard input\\\")\\n if obj.closed:\\n raise pickle.PicklingError(\\\"Cannot pickle closed files\\\")\\n if hasattr(obj, 'isatty') and obj.isatty():\\n raise pickle.PicklingError(\\\"Cannot pickle files that map to tty objects\\\")\\n if 'r' not in obj.mode and '+' not in obj.mode:\\n raise pickle.PicklingError(\\\"Cannot pickle files that are not opened for reading: %s\\\" % obj.mode)\\n\\n name = obj.name\\n\\n retval = pystringIO.StringIO()\\n\\n try:\\n # Read the whole file\\n curloc = obj.tell()\\n obj.seek(0)\\n contents = obj.read()\\n obj.seek(curloc)\\n except IOError:\\n raise pickle.PicklingError(\\\"Cannot pickle file %s as it cannot be read\\\" % name)\\n retval.write(contents)\\n retval.seek(curloc)\\n\\n retval.name = name\\n self.save(retval)\\n self.memoize(obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_ellipsis\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_ellipsis\\n def save_ellipsis(self, obj):\\n self.save_reduce(_gen_ellipsis, ())\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_not_implemented\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_not_implemented\\n def save_not_implemented(self, obj):\\n self.save_reduce(_gen_not_implemented, ())\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_weakset\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_weakset\\n def save_weakset(self, obj):\\n self.save_reduce(weakref.WeakSet, (list(obj),))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_logger\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.save_logger\\n def save_logger(self, obj):\\n self.save_reduce(logging.getLogger, (obj.name,), obj=obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.inject_addons\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::CloudPickler.inject_addons\\n def inject_addons(self):\\n \\\"\\\"\\\"Plug in system. Register additional pickling functions if modules already loaded\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::is_tornado_coroutine\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::is_tornado_coroutine\\ndef is_tornado_coroutine(func):\\n \\\"\\\"\\\"\\n Return whether *func* is a Tornado coroutine function.\\n Running coroutines are not supported.\\n \\\"\\\"\\\"\\n if 'tornado.gen' not in sys.modules:\\n return False\\n gen = sys.modules['tornado.gen']\\n if not hasattr(gen, \\\"is_coroutine_function\\\"):\\n # Tornado version is too old\\n return False\\n return gen.is_coroutine_function(func)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::_rebuild_tornado_coroutine\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::_rebuild_tornado_coroutine\\ndef _rebuild_tornado_coroutine(func):\\n from tornado import gen\\n return gen.coroutine(func)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::dump\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::dump\\ndef dump(obj, file, protocol=None):\\n \\\"\\\"\\\"Serialize obj as bytes streamed into file\\n\\n protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to\\n pickle.HIGHEST_PROTOCOL. This setting favors maximum communication speed\\n between processes running the same Python version.\\n\\n Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure\\n compatibility with older versions of Python.\\n \\\"\\\"\\\"\\n CloudPickler(file, protocol=protocol).dump(obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/cloudpickle/cloudpickle.py::dumps\",\n \"text\": \"python/ray/cloudpickle/cloudpickle.py::dumps\\ndef dumps(obj, protocol=None):\\n \\\"\\\"\\\"Serialize obj as a string of bytes allocated in memory\\n\\n protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to\\n pickle.HIGHEST_PROTOCOL. This setting favors maximum communication speed\\n between processes running the same Python version.\\n\\n Set protocol=pickle.DEFAULT_PROTOCOL instead if you need to ensure\\n compatibility with older versions of Python.\\n \\\"\\\"\\\"\\n file = StringIO()\\n try:\\n cp = CloudPickler(file, protocol=protocol)\\n cp.dump(obj)\\n return file.getvalue()\\n finally:\\n file.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.__init__\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.__init__\\n def __init__(self,\\n module_name,\\n function_name,\\n class_name=\\\"\\\",\\n function_source_hash=b\\\"\\\"):\\n self._module_name = module_name\\n self._class_name = class_name\\n self._function_name = function_name\\n self._function_source_hash = function_source_hash\\n self._function_id = self._get_function_id()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.__repr__\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.__repr__\\n def __repr__(self):\\n return (\\\"FunctionDescriptor:\\\" + self._module_name + \\\".\\\" +\\n self._class_name + \\\".\\\" + self._function_name + \\\".\\\" +\\n binary_to_hex(self._function_source_hash))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.from_bytes_list\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.from_bytes_list\\n @classmethod\\n def from_bytes_list(cls, function_descriptor_list):\\n \\\"\\\"\\\"Create a FunctionDescriptor instance from list of bytes.\\n\\n This function is used to create the function descriptor from\\n backend data.\\n\\n Args:\\n cls: Current class which is required argument for classmethod.\\n function_descriptor_list: list of bytes to represent the\\n function descriptor.\\n\\n Returns:\\n The FunctionDescriptor instance created from the bytes list.\\n \\\"\\\"\\\"\\n assert isinstance(function_descriptor_list, list)\\n if len(function_descriptor_list) == 0:\\n # This is a function descriptor of driver task.\\n return FunctionDescriptor.for_driver_task()\\n elif (len(function_descriptor_list) == 3\\n or len(function_descriptor_list) == 4):\\n module_name = function_descriptor_list[0].decode()\\n class_name = function_descriptor_list[1].decode()\\n function_name = function_descriptor_list[2].decode()\\n if len(function_descriptor_list) == 4:\\n return cls(module_name, function_name, class_name,\\n function_descriptor_list[3])\\n else:\\n return cls(module_name, function_name, class_name)\\n else:\\n raise Exception(\\n \\\"Invalid input for FunctionDescriptor.from_bytes_list\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.from_function\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.from_function\\n @classmethod\\n def from_function(cls, function):\\n \\\"\\\"\\\"Create a FunctionDescriptor from a function instance.\\n\\n This function is used to create the function descriptor from\\n a python function. If a function is a class function, it should\\n not be used by this function.\\n\\n Args:\\n cls: Current class which is required argument for classmethod.\\n function: the python function used to create the function\\n descriptor.\\n\\n Returns:\\n The FunctionDescriptor instance created according to the function.\\n \\\"\\\"\\\"\\n module_name = function.__module__\\n function_name = function.__name__\\n class_name = \\\"\\\"\\n\\n function_source_hasher = hashlib.sha1()\\n try:\\n # If we are running a script or are in IPython, include the source\\n # code in the hash.\\n source = inspect.getsource(function).encode(\\\"ascii\\\")\\n function_source_hasher.update(source)\\n function_source_hash = function_source_hasher.digest()\\n except (IOError, OSError, TypeError):\\n # Source code may not be available:\\n # e.g. Cython or Python interpreter.\\n function_source_hash = b\\\"\\\"\\n\\n return cls(module_name, function_name, class_name,\\n function_source_hash)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.from_class\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.from_class\\n @classmethod\\n def from_class(cls, target_class):\\n \\\"\\\"\\\"Create a FunctionDescriptor from a class.\\n\\n Args:\\n cls: Current class which is required argument for classmethod.\\n target_class: the python class used to create the function\\n descriptor.\\n\\n Returns:\\n The FunctionDescriptor instance created according to the class.\\n \\\"\\\"\\\"\\n module_name = target_class.__module__\\n class_name = target_class.__name__\\n return cls(module_name, \\\"__init__\\\", class_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.for_driver_task\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.for_driver_task\\n @classmethod\\n def for_driver_task(cls):\\n \\\"\\\"\\\"Create a FunctionDescriptor instance for a driver task.\\\"\\\"\\\"\\n return cls(\\\"\\\", \\\"\\\", \\\"\\\", b\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.is_for_driver_task\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.is_for_driver_task\\n @property\\n def is_for_driver_task(self):\\n \\\"\\\"\\\"See whether this function descriptor is for a driver or not.\\n\\n Returns:\\n True if this function descriptor is for driver tasks.\\n \\\"\\\"\\\"\\n return all(\\n len(x) == 0\\n for x in [self.module_name, self.class_name, self.function_name])\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.module_name\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.module_name\\n @property\\n def module_name(self):\\n \\\"\\\"\\\"Get the module name of current function descriptor.\\n\\n Returns:\\n The module name of the function descriptor.\\n \\\"\\\"\\\"\\n return self._module_name\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.class_name\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.class_name\\n @property\\n def class_name(self):\\n \\\"\\\"\\\"Get the class name of current function descriptor.\\n\\n Returns:\\n The class name of the function descriptor. It could be\\n empty if the function is not a class method.\\n \\\"\\\"\\\"\\n return self._class_name\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.function_name\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.function_name\\n @property\\n def function_name(self):\\n \\\"\\\"\\\"Get the function name of current function descriptor.\\n\\n Returns:\\n The function name of the function descriptor.\\n \\\"\\\"\\\"\\n return self._function_name\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.function_hash\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.function_hash\\n @property\\n def function_hash(self):\\n \\\"\\\"\\\"Get the hash code of the function source code.\\n\\n Returns:\\n The bytes with length of ray_constants.ID_SIZE if the source\\n code is available. Otherwise, the bytes length will be 0.\\n \\\"\\\"\\\"\\n return self._function_source_hash\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.function_id\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.function_id\\n @property\\n def function_id(self):\\n \\\"\\\"\\\"Get the function id calculated from this descriptor.\\n\\n Returns:\\n The value of ray.ObjectID that represents the function id.\\n \\\"\\\"\\\"\\n return ray.ObjectID(self._function_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor._get_function_id\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor._get_function_id\\n def _get_function_id(self):\\n \\\"\\\"\\\"Calculate the function id of current function descriptor.\\n\\n This function id is calculated from all the fields of function\\n descriptor.\\n\\n Returns:\\n bytes with length of ray_constants.ID_SIZE.\\n \\\"\\\"\\\"\\n if self.is_for_driver_task:\\n return ray_constants.NIL_FUNCTION_ID.id()\\n function_id_hash = hashlib.sha1()\\n # Include the function module and name in the hash.\\n function_id_hash.update(self.module_name.encode(\\\"ascii\\\"))\\n function_id_hash.update(self.function_name.encode(\\\"ascii\\\"))\\n function_id_hash.update(self.class_name.encode(\\\"ascii\\\"))\\n function_id_hash.update(self._function_source_hash)\\n # Compute the function ID.\\n function_id = function_id_hash.digest()\\n assert len(function_id) == ray_constants.ID_SIZE\\n return function_id\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionDescriptor.get_function_descriptor_list\",\n \"text\": \"python/ray/function_manager.py::FunctionDescriptor.get_function_descriptor_list\\n def get_function_descriptor_list(self):\\n \\\"\\\"\\\"Return a list of bytes representing the function descriptor.\\n\\n This function is used to pass this function descriptor to backend.\\n\\n Returns:\\n A list of bytes.\\n \\\"\\\"\\\"\\n descriptor_list = []\\n if self.is_for_driver_task:\\n # Driver task returns an empty list.\\n return descriptor_list\\n else:\\n descriptor_list.append(self.module_name.encode(\\\"ascii\\\"))\\n descriptor_list.append(self.class_name.encode(\\\"ascii\\\"))\\n descriptor_list.append(self.function_name.encode(\\\"ascii\\\"))\\n if len(self._function_source_hash) != 0:\\n descriptor_list.append(self._function_source_hash)\\n return descriptor_list\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.__init__\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.__init__\\n def __init__(self, worker):\\n self._worker = worker\\n self._functions_to_export = []\\n self._actors_to_export = []\\n # This field is a dictionary that maps a driver ID to a dictionary of\\n # functions (and information about those functions) that have been\\n # registered for that driver (this inner dictionary maps function IDs\\n # to a FunctionExecutionInfo object. This should only be used on\\n # workers that execute remote functions.\\n self._function_execution_info = defaultdict(lambda: {})\\n self._num_task_executions = defaultdict(lambda: {})\\n # A set of all of the actor class keys that have been imported by the\\n # import thread. It is safe to convert this worker into an actor of\\n # these types.\\n self.imported_actor_classes = set()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.increase_task_counter\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.increase_task_counter\\n def increase_task_counter(self, driver_id, function_descriptor):\\n function_id = function_descriptor.function_id.id()\\n self._num_task_executions[driver_id][function_id] += 1\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.get_task_counter\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.get_task_counter\\n def get_task_counter(self, driver_id, function_descriptor):\\n function_id = function_descriptor.function_id.id()\\n return self._num_task_executions[driver_id][function_id]\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.export_cached\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.export_cached\\n def export_cached(self):\\n \\\"\\\"\\\"Export cached remote functions\\n\\n Note: this should be called only once when worker is connected.\\n \\\"\\\"\\\"\\n for remote_function in self._functions_to_export:\\n self._do_export(remote_function)\\n self._functions_to_export = None\\n for info in self._actors_to_export:\\n (key, actor_class_info) = info\\n self._publish_actor_class_to_key(key, actor_class_info)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.reset_cache\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.reset_cache\\n def reset_cache(self):\\n self._functions_to_export = []\\n self._actors_to_export = []\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.export\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.export\\n def export(self, remote_function):\\n \\\"\\\"\\\"Export a remote function.\\n\\n Args:\\n remote_function: the RemoteFunction object.\\n \\\"\\\"\\\"\\n if self._worker.mode is None:\\n # If the worker isn't connected, cache the function\\n # and export it later.\\n self._functions_to_export.append(remote_function)\\n return\\n if self._worker.mode != ray.worker.SCRIPT_MODE:\\n # Don't need to export if the worker is not a driver.\\n return\\n self._do_export(remote_function)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager._do_export\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager._do_export\\n def _do_export(self, remote_function):\\n \\\"\\\"\\\"Pickle a remote function and export it to redis.\\n\\n Args:\\n remote_function: the RemoteFunction object.\\n \\\"\\\"\\\"\\n # Work around limitations of Python pickling.\\n function = remote_function._function\\n function_name_global_valid = function.__name__ in function.__globals__\\n function_name_global_value = function.__globals__.get(\\n function.__name__)\\n # Allow the function to reference itself as a global variable\\n if not is_cython(function):\\n function.__globals__[function.__name__] = remote_function\\n try:\\n pickled_function = pickle.dumps(function)\\n finally:\\n # Undo our changes\\n if function_name_global_valid:\\n function.__globals__[function.__name__] = (\\n function_name_global_value)\\n else:\\n del function.__globals__[function.__name__]\\n\\n check_oversized_pickle(pickled_function,\\n remote_function._function_name,\\n \\\"remote function\\\", self._worker)\\n key = (b\\\"RemoteFunction:\\\" + self._worker.task_driver_id.id() + b\\\":\\\" +\\n remote_function._function_descriptor.function_id.id())\\n self._worker.redis_client.hmset(\\n key, {\\n \\\"driver_id\\\": self._worker.task_driver_id.id(),\\n \\\"function_id\\\": remote_function._function_descriptor.\\n function_id.id(),\\n \\\"name\\\": remote_function._function_name,\\n \\\"module\\\": function.__module__,\\n \\\"function\\\": pickled_function,\\n \\\"max_calls\\\": remote_function._max_calls\\n })\\n self._worker.redis_client.rpush(\\\"Exports\\\", key)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_remote_function\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_remote_function\\n def fetch_and_register_remote_function(self, key):\\n \\\"\\\"\\\"Import a remote function.\\\"\\\"\\\"\\n (driver_id, function_id_str, function_name, serialized_function,\\n num_return_vals, module, resources,\\n max_calls) = self._worker.redis_client.hmget(key, [\\n \\\"driver_id\\\", \\\"function_id\\\", \\\"name\\\", \\\"function\\\", \\\"num_return_vals\\\",\\n \\\"module\\\", \\\"resources\\\", \\\"max_calls\\\"\\n ])\\n function_id = ray.ObjectID(function_id_str)\\n function_name = decode(function_name)\\n max_calls = int(max_calls)\\n module = decode(module)\\n\\n # This is a placeholder in case the function can't be unpickled. This\\n # will be overwritten if the function is successfully registered.\\n def f():\\n raise Exception(\\\"This function was not imported properly.\\\")\\n\\n self._function_execution_info[driver_id][function_id.id()] = (\\n FunctionExecutionInfo(\\n function=f, function_name=function_name, max_calls=max_calls))\\n self._num_task_executions[driver_id][function_id.id()] = 0\\n\\n try:\\n function = pickle.loads(serialized_function)\\n except Exception:\\n # If an exception was thrown when the remote function was imported,\\n # we record the traceback and notify the scheduler of the failure.\\n traceback_str = format_error_message(traceback.format_exc())\\n # Log the error message.\\n push_error_to_driver(\\n self._worker,\\n ray_constants.REGISTER_REMOTE_FUNCTION_PUSH_ERROR,\\n traceback_str,\\n driver_id=driver_id,\\n data={\\n \\\"function_id\\\": function_id.id(),\\n \\\"function_name\\\": function_name\\n })\\n else:\\n # The below line is necessary. Because in the driver process,\\n # if the function is define\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_remote_function.f\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_remote_function.f\\n def f():\\n raise Exception(\\\"This function was not imported properly.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.get_execution_info\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.get_execution_info\\n def get_execution_info(self, driver_id, function_descriptor):\\n \\\"\\\"\\\"Get the FunctionExecutionInfo of a remote function.\\n\\n Args:\\n driver_id: ID of the driver that the function belongs to.\\n function_descriptor: The FunctionDescriptor of the function to get.\\n\\n Returns:\\n A FunctionExecutionInfo object.\\n \\\"\\\"\\\"\\n function_id = function_descriptor.function_id.id()\\n\\n # Wait until the function to be executed has actually been\\n # registered on this worker. We will push warnings to the user if\\n # we spend too long in this loop.\\n # The driver function may not be found in sys.path. Try to load\\n # the function from GCS.\\n with profiling.profile(\\\"wait_for_function\\\", worker=self._worker):\\n self._wait_for_function(function_descriptor, driver_id)\\n try:\\n info = self._function_execution_info[driver_id][function_id]\\n except KeyError as e:\\n message = (\\\"Error occurs in get_execution_info: \\\"\\n \\\"driver_id: %s, function_descriptor: %s. Message: %s\\\" %\\n (binary_to_hex(driver_id), function_descriptor, e))\\n raise KeyError(message)\\n return info\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager._wait_for_function\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager._wait_for_function\\n def _wait_for_function(self, function_descriptor, driver_id, timeout=10):\\n \\\"\\\"\\\"Wait until the function to be executed is present on this worker.\\n\\n This method will simply loop until the import thread has imported the\\n relevant function. If we spend too long in this loop, that may indicate\\n a problem somewhere and we will push an error message to the user.\\n\\n If this worker is an actor, then this will wait until the actor has\\n been defined.\\n\\n Args:\\n function_descriptor : The FunctionDescriptor of the function that\\n we want to execute.\\n driver_id (str): The ID of the driver to push the error message to\\n if this times out.\\n \\\"\\\"\\\"\\n start_time = time.time()\\n # Only send the warning once.\\n warning_sent = False\\n while True:\\n with self._worker.lock:\\n if (self._worker.actor_id == ray.worker.NIL_ACTOR_ID\\n and (function_descriptor.function_id.id() in\\n self._function_execution_info[driver_id])):\\n break\\n elif self._worker.actor_id != ray.worker.NIL_ACTOR_ID and (\\n self._worker.actor_id in self._worker.actors):\\n break\\n if time.time() - start_time > timeout:\\n warning_message = (\\\"This worker was asked to execute a \\\"\\n \\\"function that it does not have \\\"\\n \\\"registered. You may have to restart \\\"\\n \\\"Ray.\\\")\\n if not warning_sent:\\n ray.utils.push_error_to_driver(\\n self._worker,\\n ray_constants.WAIT_FOR_FUNCTION_PUSH_ERROR,\\n warning_message,\\n drive\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager._publish_actor_class_to_key\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager._publish_actor_class_to_key\\n def _publish_actor_class_to_key(self, key, actor_class_info):\\n \\\"\\\"\\\"Push an actor class definition to Redis.\\n\\n The is factored out as a separate function because it is also called\\n on cached actor class definitions when a worker connects for the first\\n time.\\n\\n Args:\\n key: The key to store the actor class info at.\\n actor_class_info: Information about the actor class.\\n worker: The worker to use to connect to Redis.\\n \\\"\\\"\\\"\\n # We set the driver ID here because it may not have been available when\\n # the actor class was defined.\\n self._worker.redis_client.hmset(key, actor_class_info)\\n self._worker.redis_client.rpush(\\\"Exports\\\", key)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.export_actor_class\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.export_actor_class\\n def export_actor_class(self, Class, actor_method_names,\\n checkpoint_interval):\\n function_descriptor = FunctionDescriptor.from_class(Class)\\n # `task_driver_id` shouldn't be NIL, unless:\\n # 1) This worker isn't an actor;\\n # 2) And a previous task started a background thread, which didn't\\n # finish before the task finished, and still uses Ray API\\n # after that.\\n assert not self._worker.task_driver_id.is_nil(), (\\n \\\"You might have started a background thread in a non-actor task, \\\"\\n \\\"please make sure the thread finishes before the task finishes.\\\")\\n driver_id = self._worker.task_driver_id\\n key = (b\\\"ActorClass:\\\" + driver_id.id() + b\\\":\\\" +\\n function_descriptor.function_id.id())\\n actor_class_info = {\\n \\\"class_name\\\": Class.__name__,\\n \\\"module\\\": Class.__module__,\\n \\\"class\\\": pickle.dumps(Class),\\n \\\"checkpoint_interval\\\": checkpoint_interval,\\n \\\"driver_id\\\": driver_id.id(),\\n \\\"actor_method_names\\\": json.dumps(list(actor_method_names))\\n }\\n\\n check_oversized_pickle(actor_class_info[\\\"class\\\"],\\n actor_class_info[\\\"class_name\\\"], \\\"actor\\\",\\n self._worker)\\n\\n if self._worker.mode is None:\\n # This means that 'ray.init()' has not been called yet and so we\\n # must cache the actor class definition and export it when\\n # 'ray.init()' is called.\\n assert self._actors_to_export is not None\\n self._actors_to_export.append((key, actor_class_info))\\n # This caching code path is currently not used because we only\\n # export actor class definitions lazily when we instantiate the\\n # actor for the first time.\\n assert False, \\\"This should be unreachable.\\\"\\n else:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.load_actor\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.load_actor\\n def load_actor(self, driver_id, function_descriptor):\\n key = (b\\\"ActorClass:\\\" + driver_id + b\\\":\\\" +\\n function_descriptor.function_id.id())\\n # Wait for the actor class key to have been imported by the\\n # import thread. TODO(rkn): It shouldn't be possible to end\\n # up in an infinite loop here, but we should push an error to\\n # the driver if too much time is spent here.\\n while key not in self.imported_actor_classes:\\n time.sleep(0.001)\\n with self._worker.lock:\\n self.fetch_and_register_actor(key)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_actor\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_actor\\n def fetch_and_register_actor(self, actor_class_key):\\n \\\"\\\"\\\"Import an actor.\\n\\n This will be called by the worker's import thread when the worker\\n receives the actor_class export, assuming that the worker is an actor\\n for that class.\\n\\n Args:\\n actor_class_key: The key in Redis to use to fetch the actor.\\n worker: The worker to use.\\n \\\"\\\"\\\"\\n actor_id_str = self._worker.actor_id\\n (driver_id, class_name, module, pickled_class, checkpoint_interval,\\n actor_method_names) = self._worker.redis_client.hmget(\\n actor_class_key, [\\n \\\"driver_id\\\", \\\"class_name\\\", \\\"module\\\", \\\"class\\\",\\n \\\"checkpoint_interval\\\", \\\"actor_method_names\\\"\\n ])\\n\\n class_name = decode(class_name)\\n module = decode(module)\\n checkpoint_interval = int(checkpoint_interval)\\n actor_method_names = json.loads(decode(actor_method_names))\\n\\n # In Python 2, json loads strings as unicode, so convert them back to\\n # strings.\\n if sys.version_info < (3, 0):\\n actor_method_names = [\\n method_name.encode(\\\"ascii\\\")\\n for method_name in actor_method_names\\n ]\\n\\n # Create a temporary actor with some temporary methods so that if\\n # the actor fails to be unpickled, the temporary actor can be used\\n # (just to produce error messages and to prevent the driver from\\n # hanging).\\n class TemporaryActor(object):\\n pass\\n\\n self._worker.actors[actor_id_str] = TemporaryActor()\\n self._worker.actor_checkpoint_interval = checkpoint_interval\\n\\n def temporary_actor_method(*xs):\\n raise Exception(\\n \\\"The actor with name {} failed to be imported, \\\"\\n \\\"and so cannot execute this method\\\".format(class_name))\\n\\n # Register the actor method executors.\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_actor.temporary_actor_method\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager.fetch_and_register_actor.temporary_actor_method\\n def temporary_actor_method(*xs):\\n raise Exception(\\n \\\"The actor with name {} failed to be imported, \\\"\\n \\\"and so cannot execute this method\\\".format(class_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager._make_actor_method_executor\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager._make_actor_method_executor\\n def _make_actor_method_executor(self, method_name, method, actor_imported):\\n \\\"\\\"\\\"Make an executor that wraps a user-defined actor method.\\n\\n The wrapped method updates the worker's internal state and performs any\\n necessary checkpointing operations.\\n\\n Args:\\n worker (Worker): The worker that is executing the actor.\\n method_name (str): The name of the actor method.\\n method (instancemethod): The actor method to wrap. This should be a\\n method defined on the actor class and should therefore take an\\n instance of the actor as the first argument.\\n actor_imported (bool): Whether the actor has been imported.\\n Checkpointing operations will not be run if this is set to\\n False.\\n\\n Returns:\\n A function that executes the given actor method on the worker's\\n stored instance of the actor. The function also updates the\\n worker's internal state to record the executed method.\\n \\\"\\\"\\\"\\n\\n def actor_method_executor(dummy_return_id, actor, *args):\\n # Update the actor's task counter to reflect the task we're about\\n # to execute.\\n self._worker.actor_task_counter += 1\\n\\n # If this is the first task to execute on the actor, try to resume\\n # from a checkpoint.\\n # Current __init__ will be called by default. So the real function\\n # call will start from 2.\\n if actor_imported and self._worker.actor_task_counter == 2:\\n checkpoint_resumed = ray.actor.restore_and_log_checkpoint(\\n self._worker, actor)\\n if checkpoint_resumed:\\n # NOTE(swang): Since we did not actually execute the\\n # __init__ method, this will put None as the return value.\\n # If the \",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/function_manager.py::FunctionActorManager._make_actor_method_executor.actor_method_executor\",\n \"text\": \"python/ray/function_manager.py::FunctionActorManager._make_actor_method_executor.actor_method_executor\\n def actor_method_executor(dummy_return_id, actor, *args):\\n # Update the actor's task counter to reflect the task we're about\\n # to execute.\\n self._worker.actor_task_counter += 1\\n\\n # If this is the first task to execute on the actor, try to resume\\n # from a checkpoint.\\n # Current __init__ will be called by default. So the real function\\n # call will start from 2.\\n if actor_imported and self._worker.actor_task_counter == 2:\\n checkpoint_resumed = ray.actor.restore_and_log_checkpoint(\\n self._worker, actor)\\n if checkpoint_resumed:\\n # NOTE(swang): Since we did not actually execute the\\n # __init__ method, this will put None as the return value.\\n # If the __init__ method is supposed to return multiple\\n # values, an exception will be logged.\\n return\\n\\n # Determine whether we should checkpoint the actor.\\n checkpointing_on = (actor_imported\\n and self._worker.actor_checkpoint_interval > 0)\\n # We should checkpoint the actor if user checkpointing is on, we've\\n # executed checkpoint_interval tasks since the last checkpoint, and\\n # the method we're about to execute is not a checkpoint.\\n save_checkpoint = (checkpointing_on\\n and (self._worker.actor_task_counter %\\n self._worker.actor_checkpoint_interval == 0\\n and method_name != \\\"__ray_checkpoint__\\\"))\\n\\n # Execute the assigned method and save a checkpoint if necessary.\\n try:\\n if is_class_method(method):\\n method_returns = method(*args)\\n els\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::setupCNN\",\n \"text\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::setupCNN\\ndef setupCNN(x):\\n \\\"\\\"\\\"setupCNN builds the graph for a deep net for classifying digits.\\n Args:\\n x: an input tensor with the dimensions (N_examples, 784), where 784 is\\n the number of pixels in a standard MNIST image.\\n Returns:\\n A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with\\n values equal to the logits of classifying the digit into one of 10\\n classes (the digits 0-9). keep_prob is a scalar placeholder for the\\n probability of dropout.\\n \\\"\\\"\\\"\\n # Reshape to use within a convolutional neural net.\\n # Last dimension is for \\\"features\\\" - there is only one here, since images\\n # are grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.\\n with tf.name_scope('reshape'):\\n x_image = tf.reshape(x, [-1, 28, 28, 1])\\n\\n # First convolutional layer - maps one grayscale image to 32 feature maps.\\n with tf.name_scope('conv1'):\\n W_conv1 = weight_variable([5, 5, 1, 32])\\n b_conv1 = bias_variable([32])\\n h_conv1 = activation_fn(conv2d(x_image, W_conv1) + b_conv1)\\n\\n # Pooling layer - downsamples by 2X.\\n with tf.name_scope('pool1'):\\n h_pool1 = max_pool_2x2(h_conv1)\\n\\n # Second convolutional layer -- maps 32 feature maps to 64.\\n with tf.name_scope('conv2'):\\n W_conv2 = weight_variable([5, 5, 32, 64])\\n b_conv2 = bias_variable([64])\\n h_conv2 = activation_fn(conv2d(h_pool1, W_conv2) + b_conv2)\\n\\n # Second pooling layer.\\n with tf.name_scope('pool2'):\\n h_pool2 = max_pool_2x2(h_conv2)\\n\\n # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image\\n # is down to 7x7x64 feature maps -- maps this to 1024 features.\\n with tf.name_scope('fc1'):\\n W_fc1 = weight_variable([7 * 7 * 64, 1024])\\n b_fc1 = bias_variable([1024])\\n\\n h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])\\n h_fc1 = activation_fn(tf.matmul(h_pool2_flat, W_fc1)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::conv2d\",\n \"text\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::conv2d\\ndef conv2d(x, W):\\n \\\"\\\"\\\"conv2d returns a 2d convolution layer with full stride.\\\"\\\"\\\"\\n return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::max_pool_2x2\",\n \"text\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::max_pool_2x2\\ndef max_pool_2x2(x):\\n \\\"\\\"\\\"max_pool_2x2 downsamples a feature map by 2X.\\\"\\\"\\\"\\n return tf.nn.max_pool(\\n x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::weight_variable\",\n \"text\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::weight_variable\\ndef weight_variable(shape):\\n \\\"\\\"\\\"weight_variable generates a weight variable of a given shape.\\\"\\\"\\\"\\n initial = tf.truncated_normal(shape, stddev=0.1)\\n return tf.Variable(initial)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::bias_variable\",\n \"text\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::bias_variable\\ndef bias_variable(shape):\\n \\\"\\\"\\\"bias_variable generates a bias variable of a given shape.\\\"\\\"\\\"\\n initial = tf.constant(0.1, shape=shape)\\n return tf.Variable(initial)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._setup\",\n \"text\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._setup\\n def _setup(self, config):\\n global activation_fn\\n\\n self.timestep = 0\\n\\n # Import data\\n for _ in range(10):\\n try:\\n self.mnist = input_data.read_data_sets(\\n \\\"/tmp/mnist_ray_demo\\\", one_hot=True)\\n break\\n except Exception as e:\\n print(\\\"Error loading data, retrying\\\", e)\\n time.sleep(5)\\n\\n assert self.mnist\\n\\n self.x = tf.placeholder(tf.float32, [None, 784])\\n self.y_ = tf.placeholder(tf.float32, [None, 10])\\n\\n activation_fn = getattr(tf.nn, config['activation'])\\n\\n # Build the graph for the deep net\\n y_conv, self.keep_prob = setupCNN(self.x)\\n\\n with tf.name_scope('loss'):\\n cross_entropy = tf.nn.softmax_cross_entropy_with_logits(\\n labels=self.y_, logits=y_conv)\\n cross_entropy = tf.reduce_mean(cross_entropy)\\n\\n with tf.name_scope('adam_optimizer'):\\n train_step = tf.train.AdamOptimizer(\\n config['learning_rate']).minimize(cross_entropy)\\n\\n self.train_step = train_step\\n\\n with tf.name_scope('accuracy'):\\n correct_prediction = tf.equal(\\n tf.argmax(y_conv, 1), tf.argmax(self.y_, 1))\\n correct_prediction = tf.cast(correct_prediction, tf.float32)\\n self.accuracy = tf.reduce_mean(correct_prediction)\\n\\n self.sess = tf.Session()\\n self.sess.run(tf.global_variables_initializer())\\n self.iterations = 0\\n self.saver = tf.train.Saver()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._train\",\n \"text\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._train\\n def _train(self):\\n for i in range(10):\\n batch = self.mnist.train.next_batch(50)\\n self.sess.run(\\n self.train_step,\\n feed_dict={\\n self.x: batch[0],\\n self.y_: batch[1],\\n self.keep_prob: 0.5\\n })\\n\\n batch = self.mnist.train.next_batch(50)\\n train_accuracy = self.sess.run(\\n self.accuracy,\\n feed_dict={\\n self.x: batch[0],\\n self.y_: batch[1],\\n self.keep_prob: 1.0\\n })\\n\\n self.iterations += 1\\n return {\\\"mean_accuracy\\\": train_accuracy}\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._save\",\n \"text\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._save\\n def _save(self, checkpoint_dir):\\n return self.saver.save(\\n self.sess, checkpoint_dir + \\\"/save\\\", global_step=self.iterations)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._restore\",\n \"text\": \"python/ray/tune/examples/tune_mnist_ray_hyperband.py::TrainMNIST._restore\\n def _restore(self, path):\\n return self.saver.restore(self.sess, path)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/experiment.py::_raise_deprecation_note\",\n \"text\": \"python/ray/tune/experiment.py::_raise_deprecation_note\\ndef _raise_deprecation_note(deprecated, replacement, soft=False):\\n \\\"\\\"\\\"User notification for deprecated parameter.\\n\\n Arguments:\\n deprecated (str): Deprecated parameter.\\n replacement (str): Replacement parameter to use instead.\\n soft (bool): Fatal if True.\\n \\\"\\\"\\\"\\n error_msg = (\\\"`{deprecated}` is deprecated. Please use `{replacement}`. \\\"\\n \\\"`{deprecated}` will be removed in future versions of \\\"\\n \\\"Ray.\\\".format(deprecated=deprecated, replacement=replacement))\\n if soft:\\n logger.warning(error_msg)\\n else:\\n raise DeprecationWarning(error_msg)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/experiment.py::Experiment.__init__\",\n \"text\": \"python/ray/tune/experiment.py::Experiment.__init__\\n def __init__(self,\\n name,\\n run,\\n stop=None,\\n config=None,\\n resources_per_trial=None,\\n num_samples=1,\\n local_dir=None,\\n upload_dir=None,\\n trial_name_creator=None,\\n custom_loggers=None,\\n sync_function=None,\\n checkpoint_freq=0,\\n checkpoint_at_end=False,\\n max_failures=3,\\n restore=None,\\n repeat=None,\\n trial_resources=None):\\n if sync_function:\\n assert upload_dir, \\\"Need `upload_dir` if sync_function given.\\\"\\n\\n if repeat:\\n _raise_deprecation_note(\\\"repeat\\\", \\\"num_samples\\\", soft=False)\\n if trial_resources:\\n _raise_deprecation_note(\\n \\\"trial_resources\\\", \\\"resources_per_trial\\\", soft=True)\\n resources_per_trial = trial_resources\\n\\n spec = {\\n \\\"run\\\": Experiment._register_if_needed(run),\\n \\\"stop\\\": stop or {},\\n \\\"config\\\": config or {},\\n \\\"resources_per_trial\\\": resources_per_trial,\\n \\\"num_samples\\\": num_samples,\\n \\\"local_dir\\\": os.path.expanduser(local_dir or DEFAULT_RESULTS_DIR),\\n \\\"upload_dir\\\": upload_dir or \\\"\\\", # argparse converts None to \\\"null\\\"\\n \\\"trial_name_creator\\\": trial_name_creator,\\n \\\"custom_loggers\\\": custom_loggers,\\n \\\"sync_function\\\": sync_function,\\n \\\"checkpoint_freq\\\": checkpoint_freq,\\n \\\"checkpoint_at_end\\\": checkpoint_at_end,\\n \\\"max_failures\\\": max_failures,\\n \\\"restore\\\": restore\\n }\\n\\n self.name = name\\n self.spec = spec\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/experiment.py::Experiment.from_json\",\n \"text\": \"python/ray/tune/experiment.py::Experiment.from_json\\n @classmethod\\n def from_json(cls, name, spec):\\n \\\"\\\"\\\"Generates an Experiment object from JSON.\\n\\n Args:\\n name (str): Name of Experiment.\\n spec (dict): JSON configuration of experiment.\\n \\\"\\\"\\\"\\n if \\\"run\\\" not in spec:\\n raise TuneError(\\\"No trainable specified!\\\")\\n\\n # Special case the `env` param for RLlib by automatically\\n # moving it into the `config` section.\\n if \\\"env\\\" in spec:\\n spec[\\\"config\\\"] = spec.get(\\\"config\\\", {})\\n spec[\\\"config\\\"][\\\"env\\\"] = spec[\\\"env\\\"]\\n del spec[\\\"env\\\"]\\n\\n spec = copy.deepcopy(spec)\\n\\n run_value = spec.pop(\\\"run\\\")\\n try:\\n exp = cls(name, run_value, **spec)\\n except TypeError:\\n raise TuneError(\\\"Improper argument from JSON: {}.\\\".format(spec))\\n return exp\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/experiment.py::Experiment._register_if_needed\",\n \"text\": \"python/ray/tune/experiment.py::Experiment._register_if_needed\\n @classmethod\\n def _register_if_needed(cls, run_object):\\n \\\"\\\"\\\"Registers Trainable or Function at runtime.\\n\\n Assumes already registered if run_object is a string. Does not\\n register lambdas because they could be part of variant generation.\\n Also, does not inspect interface of given run_object.\\n\\n Arguments:\\n run_object (str|function|class): Trainable to run. If string,\\n assumes it is an ID and does not modify it. Otherwise,\\n returns a string corresponding to the run_object name.\\n\\n Returns:\\n A string representing the trainable identifier.\\n \\\"\\\"\\\"\\n\\n if isinstance(run_object, six.string_types):\\n return run_object\\n elif isinstance(run_object, types.FunctionType):\\n if run_object.__name__ == \\\"<lambda>\\\":\\n logger.warning(\\n \\\"Not auto-registering lambdas - resolving as variant.\\\")\\n return run_object\\n else:\\n name = run_object.__name__\\n register_trainable(name, run_object)\\n return name\\n elif isinstance(run_object, type):\\n name = run_object.__name__\\n register_trainable(name, run_object)\\n return name\\n else:\\n raise TuneError(\\\"Improper 'run' - not string nor trainable.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/experiment.py::convert_to_experiment_list\",\n \"text\": \"python/ray/tune/experiment.py::convert_to_experiment_list\\ndef convert_to_experiment_list(experiments):\\n \\\"\\\"\\\"Produces a list of Experiment objects.\\n\\n Converts input from dict, single experiment, or list of\\n experiments to list of experiments. If input is None,\\n will return an empty list.\\n\\n Arguments:\\n experiments (Experiment | list | dict): Experiments to run.\\n\\n Returns:\\n List of experiments.\\n \\\"\\\"\\\"\\n exp_list = experiments\\n\\n # Transform list if necessary\\n if experiments is None:\\n exp_list = []\\n elif isinstance(experiments, Experiment):\\n exp_list = [experiments]\\n elif type(experiments) is dict:\\n exp_list = [\\n Experiment.from_json(name, spec)\\n for name, spec in experiments.items()\\n ]\\n\\n # Validate exp_list\\n if (type(exp_list) is list\\n and all(isinstance(exp, Experiment) for exp in exp_list)):\\n if len(exp_list) > 1:\\n logger.warning(\\\"All experiments will be \\\"\\n \\\"using the same SearchAlgorithm.\\\")\\n else:\\n raise TuneError(\\\"Invalid argument: {}\\\".format(experiments))\\n\\n return exp_list\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/registry.py::register_trainable\",\n \"text\": \"python/ray/tune/registry.py::register_trainable\\ndef register_trainable(name, trainable):\\n \\\"\\\"\\\"Register a trainable function or class.\\n\\n Args:\\n name (str): Name to register.\\n trainable (obj): Function or tune.Trainable class. Functions must\\n take (config, status_reporter) as arguments and will be\\n automatically converted into a class during registration.\\n \\\"\\\"\\\"\\n\\n from ray.tune.trainable import Trainable, wrap_function\\n\\n if isinstance(trainable, FunctionType):\\n trainable = wrap_function(trainable)\\n if not issubclass(trainable, Trainable):\\n raise TypeError(\\\"Second argument must be convertable to Trainable\\\",\\n trainable)\\n _global_registry.register(TRAINABLE_CLASS, name, trainable)\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/tune/registry.py::register_env\",\n \"text\": \"python/ray/tune/registry.py::register_env\\ndef register_env(name, env_creator):\\n \\\"\\\"\\\"Register a custom environment for use with RLlib.\\n\\n Args:\\n name (str): Name to register.\\n env_creator (obj): Function that creates an env.\\n \\\"\\\"\\\"\\n\\n if not isinstance(env_creator, FunctionType):\\n raise TypeError(\\\"Second argument must be a function.\\\", env_creator)\\n _global_registry.register(ENV_CREATOR, name, env_creator)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/registry.py::_make_key\",\n \"text\": \"python/ray/tune/registry.py::_make_key\\ndef _make_key(category, key):\\n \\\"\\\"\\\"Generate a binary key for the given category and key.\\n\\n Args:\\n category (str): The category of the item\\n key (str): The unique identifier for the item\\n\\n Returns:\\n The key to use for storing a the value.\\n \\\"\\\"\\\"\\n return (b\\\"TuneRegistry:\\\" + category.encode(\\\"ascii\\\") + b\\\"/\\\" +\\n key.encode(\\\"ascii\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/registry.py::_Registry.__init__\",\n \"text\": \"python/ray/tune/registry.py::_Registry.__init__\\n def __init__(self):\\n self._to_flush = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/registry.py::_Registry.register\",\n \"text\": \"python/ray/tune/registry.py::_Registry.register\\n def register(self, category, key, value):\\n if category not in KNOWN_CATEGORIES:\\n from ray.tune import TuneError\\n raise TuneError(\\\"Unknown category {} not among {}\\\".format(\\n category, KNOWN_CATEGORIES))\\n self._to_flush[(category, key)] = pickle.dumps(value)\\n if _internal_kv_initialized():\\n self.flush_values()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/registry.py::_Registry.contains\",\n \"text\": \"python/ray/tune/registry.py::_Registry.contains\\n def contains(self, category, key):\\n if _internal_kv_initialized():\\n value = _internal_kv_get(_make_key(category, key))\\n return value is not None\\n else:\\n return (category, key) in self._to_flush\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/registry.py::_Registry.get\",\n \"text\": \"python/ray/tune/registry.py::_Registry.get\\n def get(self, category, key):\\n if _internal_kv_initialized():\\n value = _internal_kv_get(_make_key(category, key))\\n if value is None:\\n raise ValueError(\\n \\\"Registry value for {}/{} doesn't exist.\\\".format(\\n category, key))\\n return pickle.loads(value)\\n else:\\n return pickle.loads(self._to_flush[(category, key)])\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/tune/registry.py::_Registry.flush_values\",\n \"text\": \"python/ray/tune/registry.py::_Registry.flush_values\\n def flush_values(self):\\n for (category, key), value in self._to_flush.items():\\n _internal_kv_put(_make_key(category, key), value, overwrite=True)\\n self._to_flush.clear()\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":57,"cells":{"instance_id":{"kind":"string","value":"google__jax-509"},"repo":{"kind":"string","value":"google/jax"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"BatchNorm breaks for non-default axis values\nThe current BatchNorm implementation breaks if the `axis` argument takes values where the missing axis is not the last one, e.g. `axis=(0, 2, 3)`, because the axes are dropped for `gamma` and `beta` and not restored later.\r\nI guess this can be fixed by removing this line: https://github.com/google/jax/blob/7e93bff2b95e6f49be7e2cd310e8953bbbdcdc9d/jax/experimental/stax.py#L134\r\n(and adding `tuple` in the line before)\n"},"gold_files":{"kind":"list like","value":["jax/experimental/stax.py"],"string":"[\n \"jax/experimental/stax.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["jax/experimental/stax.py::BatchNorm.apply_fun","jax/experimental/stax.py::BatchNorm.init_fun"],"string":"[\n \"jax/experimental/stax.py::BatchNorm.apply_fun\",\n \"jax/experimental/stax.py::BatchNorm.init_fun\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"jax/api.py","text":"jax/api.py\ndef jit(fun, static_argnums=()) # Sets up `fun` for just-in-time compilation with XLA.\n def f_jitted(*args, **kwargs)\ndef disable_jit() # Context manager that disables `jit`.\ndef xla_computation(fun, static_argnums=())\n def pv_like(x)\n def computation_maker(*args, **kwargs)\ndef grad(fun, argnums=0, has_aux=False) # Creates a function which evaluates the gradient of `fun`.\n def grad_f(*args, **kwargs)\ndef value_and_grad(fun, argnums=0, has_aux=False) # Creates a function which evaluates both `fun` and the gradient of `fun`.\n def value_and_grad_f(*args, **kwargs)\ndef jacfwd(fun, argnums=0) # Jacobian of `fun` evaluated column-by-column using forward-mode AD.\n def jacfun(*args, **kwargs)\ndef jacrev(fun, argnums=0) # Jacobian of `fun` evaluated row-by-row using reverse-mode AD.\n def jacfun(*args, **kwargs)\ndef hessian(fun, argnums=0) # Hessian of `fun`.\ndef _std_basis(pytree)\ndef _unravel_array_into_pytree(pytree, axis, arr)\ndef _split(x, indices, axis)\ndef _dtype(x)\ndef vmap(fun, in_axes=0, out_axes=0) # Vectorizing map. Creates a function which maps `fun` over additional axes.\n def batched_fun(*args, **kwargs)\ndef pmap(fun, axis_name=None) # Set up SPMD function for JIT compilation and parallel execution with XLA.\n def f_jitted(*args, **kwargs)\ndef serial_pmap(fun, axis_name=None, in_axes=0, out_axes=0) # Vectorizing pseudo-map for single-program multiple-data (SPMD) functions.\n def map_fun(*args, **kwargs)\nclass _TempAxisName\n def __repr__(self)\ndef papply(fun, axis_size, in_axes=0, out_axes=0) # Apply a function using parallel computation by sharding inputs.\n def papply_fun(*args, **kwargs)\ndef jvp(fun, primals, tangents) # Computes a (forward-mode) Jacobian-vector product of `fun`.\n def trim_arg(primal, tangent)\ndef linearize(traceable, *primals)\ndef lift_linearized(jaxpr, consts, io_tree, out_pval, *py_args)\n def fun(*args)\ndef vjp(fun, *primals, **kwargs) # Compute a (reverse-mode) vector-Jacobian product of `fun`.\n def out_vjp_packed(cotangent_in)\ndef trace_to_jaxpr(traceable, py_pvals, **kwargs)\ndef lift_jaxpr(jaxpr, consts, io_tree, pvals, py_args)\n def fun(*args)\ndef make_jaxpr(fun) # Adapts `fun` to return its `jaxpr` program representation.\n def pv_like(x)\n def jaxpr_maker(*args, **kwargs)\ndef _argnums_partial(f, dyn_argnums, args)\ndef _argnums_partial_(dyn_argnums, fixed_args, *dyn_args)\ndef _check_args(args)\ndef _check_scalar(x)\ndef _primitive(fun)\n def traceable(*args, **kwargs)","label":0},{"cid":"jax/experimental/stax.py","text":"jax/experimental/stax.py\ndef relu(x)\ndef softplus(x)\ndef logsoftmax(x, axis=-1) # Apply log softmax to an array of logits, log-normalizing along an axis.\ndef softmax(x, axis=-1) # Apply softmax to an array of logits, exponentiating and normalizing along an axi\ndef fastvar(x, axis, keepdims) # A fast but less numerically-stable variance calculation than np.var.\ndef randn(stddev=0.01, rng=npr) # An initializer function for random normal coefficients.\n def init(shape)\ndef glorot(out_dim=0, in_dim=1, scale=onp.sqrt(2), rng=npr) # An initializer function for random Glorot-scaled coefficients.\n def init(shape)\ndef Dense(out_dim, W_init=glorot(), b_init=randn()) # Layer constructor function for a dense (fully-connected) layer.\n def init_fun(input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef GeneralConv(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=randn(1e-06)) # Layer construction function for a general convolution layer.\n def init_fun(input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-05, center=True, scale=True, beta_init=zeros, gamma_init=ones) # Layer construction function for a batch normalization layer.\n def init_fun(input_shape)\n def apply_fun(params, x, **kwargs)\ndef _elemwise_no_params(fun, **fun_kwargs)\ndef _pooling_layer(reducer, init_val, rescaler=None)\n def PoolingLayer(window_shape, strides=None, padding='VALID') # Layer construction function for a pooling layer.\n def init_fun(input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef _normalize_by_window_size(dims, strides, padding)\n def rescale(outputs, inputs)\ndef Flatten() # Layer construction function for flattening all but the leading dim.\n def init_fun(input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef Identity() # Layer construction function for an identity layer.\ndef FanOut(num) # Layer construction function for a fan-out layer.\ndef FanInSum() # Layer construction function for a fan-in sum layer.\ndef FanInConcat(axis=-1) # Layer construction function for a fan-in concatenation layer.\n def init_fun(input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef Dropout(rate, mode='train') # Layer construction function for a dropout layer with given rate.\n def init_fun(input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef serial(*layers) # Combinator for composing layers in serial.\n def init_fun(input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef parallel(*layers) # Combinator for composing layers in parallel.\n def init_fun(input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef shape_dependent(make_layer) # Combinator to delay layer constructor pair until input shapes are known.\n def init_fun(input_shape)\n def apply_fun(params, inputs, **kwargs)","label":1},{"cid":"jax/random.py","text":"jax/random.py\ndef PRNGKey(seed) # Create a pseudo-random number generator (PRNG) key given an integer seed.\ndef _is_prng_key(key)\ndef _make_rotate_left(dtype)\n def _rotate_left(x, d)\ndef _bit_stats(bits) # This is a debugging function to compute the statistics of bit fields.\ndef threefry_2x32(keypair, count) # Apply the Threefry 2x32 hash.\n def apply_round(v, rot)\ndef split(key, num=2) # Splits a PRNG key into `num` new keys by adding a leading axis.\ndef fold_in(key, data) # Folds in data to a PRNG key to form a new PRNG key.\ndef _random_bits(key, bit_width, shape) # Sample uniform random bits of given width and shape using PRNG key.\ndef uniform(key, shape, dtype=onp.float32, minval=0.0, maxval=1.0) # Sample uniform random values in [minval, maxval) with given shape/dtype.\ndef randint(key, shape, minval, maxval, dtype=onp.int32) # Sample uniform random values in [minval, maxval) with given shape/dtype.\ndef shuffle(key, x, axis=0) # Shuffle the elements of an array uniformly at random along an axis.\ndef normal(key, shape, dtype=onp.float32) # Sample standard normal random values with given shape and float dtype.\ndef bernoulli(key, mean=onp.float32(0.5), shape=()) # Sample Bernoulli random values with given shape and mean.","label":0}],"string":"[\n {\n \"cid\": \"jax/api.py\",\n \"text\": \"jax/api.py\\ndef jit(fun, static_argnums=()) # Sets up `fun` for just-in-time compilation with XLA.\\n def f_jitted(*args, **kwargs)\\ndef disable_jit() # Context manager that disables `jit`.\\ndef xla_computation(fun, static_argnums=())\\n def pv_like(x)\\n def computation_maker(*args, **kwargs)\\ndef grad(fun, argnums=0, has_aux=False) # Creates a function which evaluates the gradient of `fun`.\\n def grad_f(*args, **kwargs)\\ndef value_and_grad(fun, argnums=0, has_aux=False) # Creates a function which evaluates both `fun` and the gradient of `fun`.\\n def value_and_grad_f(*args, **kwargs)\\ndef jacfwd(fun, argnums=0) # Jacobian of `fun` evaluated column-by-column using forward-mode AD.\\n def jacfun(*args, **kwargs)\\ndef jacrev(fun, argnums=0) # Jacobian of `fun` evaluated row-by-row using reverse-mode AD.\\n def jacfun(*args, **kwargs)\\ndef hessian(fun, argnums=0) # Hessian of `fun`.\\ndef _std_basis(pytree)\\ndef _unravel_array_into_pytree(pytree, axis, arr)\\ndef _split(x, indices, axis)\\ndef _dtype(x)\\ndef vmap(fun, in_axes=0, out_axes=0) # Vectorizing map. Creates a function which maps `fun` over additional axes.\\n def batched_fun(*args, **kwargs)\\ndef pmap(fun, axis_name=None) # Set up SPMD function for JIT compilation and parallel execution with XLA.\\n def f_jitted(*args, **kwargs)\\ndef serial_pmap(fun, axis_name=None, in_axes=0, out_axes=0) # Vectorizing pseudo-map for single-program multiple-data (SPMD) functions.\\n def map_fun(*args, **kwargs)\\nclass _TempAxisName\\n def __repr__(self)\\ndef papply(fun, axis_size, in_axes=0, out_axes=0) # Apply a function using parallel computation by sharding inputs.\\n def papply_fun(*args, **kwargs)\\ndef jvp(fun, primals, tangents) # Computes a (forward-mode) Jacobian-vector product of `fun`.\\n def trim_arg(primal, tangent)\\ndef linearize(traceable, *primals)\\ndef lift_linearized(jaxpr, consts, io_tree, out_pval, *py_args)\\n def fun(*args)\\ndef vjp(fun, *primals, **kwargs) # Compute a (reverse-mode) vector-Jacobian product of `fun`.\\n def out_vjp_packed(cotangent_in)\\ndef trace_to_jaxpr(traceable, py_pvals, **kwargs)\\ndef lift_jaxpr(jaxpr, consts, io_tree, pvals, py_args)\\n def fun(*args)\\ndef make_jaxpr(fun) # Adapts `fun` to return its `jaxpr` program representation.\\n def pv_like(x)\\n def jaxpr_maker(*args, **kwargs)\\ndef _argnums_partial(f, dyn_argnums, args)\\ndef _argnums_partial_(dyn_argnums, fixed_args, *dyn_args)\\ndef _check_args(args)\\ndef _check_scalar(x)\\ndef _primitive(fun)\\n def traceable(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py\",\n \"text\": \"jax/experimental/stax.py\\ndef relu(x)\\ndef softplus(x)\\ndef logsoftmax(x, axis=-1) # Apply log softmax to an array of logits, log-normalizing along an axis.\\ndef softmax(x, axis=-1) # Apply softmax to an array of logits, exponentiating and normalizing along an axi\\ndef fastvar(x, axis, keepdims) # A fast but less numerically-stable variance calculation than np.var.\\ndef randn(stddev=0.01, rng=npr) # An initializer function for random normal coefficients.\\n def init(shape)\\ndef glorot(out_dim=0, in_dim=1, scale=onp.sqrt(2), rng=npr) # An initializer function for random Glorot-scaled coefficients.\\n def init(shape)\\ndef Dense(out_dim, W_init=glorot(), b_init=randn()) # Layer constructor function for a dense (fully-connected) layer.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef GeneralConv(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=randn(1e-06)) # Layer construction function for a general convolution layer.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-05, center=True, scale=True, beta_init=zeros, gamma_init=ones) # Layer construction function for a batch normalization layer.\\n def init_fun(input_shape)\\n def apply_fun(params, x, **kwargs)\\ndef _elemwise_no_params(fun, **fun_kwargs)\\ndef _pooling_layer(reducer, init_val, rescaler=None)\\n def PoolingLayer(window_shape, strides=None, padding='VALID') # Layer construction function for a pooling layer.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef _normalize_by_window_size(dims, strides, padding)\\n def rescale(outputs, inputs)\\ndef Flatten() # Layer construction function for flattening all but the leading dim.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef Identity() # Layer construction function for an identity layer.\\ndef FanOut(num) # Layer construction function for a fan-out layer.\\ndef FanInSum() # Layer construction function for a fan-in sum layer.\\ndef FanInConcat(axis=-1) # Layer construction function for a fan-in concatenation layer.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef Dropout(rate, mode='train') # Layer construction function for a dropout layer with given rate.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef serial(*layers) # Combinator for composing layers in serial.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef parallel(*layers) # Combinator for composing layers in parallel.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef shape_dependent(make_layer) # Combinator to delay layer constructor pair until input shapes are known.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, **kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/random.py\",\n \"text\": \"jax/random.py\\ndef PRNGKey(seed) # Create a pseudo-random number generator (PRNG) key given an integer seed.\\ndef _is_prng_key(key)\\ndef _make_rotate_left(dtype)\\n def _rotate_left(x, d)\\ndef _bit_stats(bits) # This is a debugging function to compute the statistics of bit fields.\\ndef threefry_2x32(keypair, count) # Apply the Threefry 2x32 hash.\\n def apply_round(v, rot)\\ndef split(key, num=2) # Splits a PRNG key into `num` new keys by adding a leading axis.\\ndef fold_in(key, data) # Folds in data to a PRNG key to form a new PRNG key.\\ndef _random_bits(key, bit_width, shape) # Sample uniform random bits of given width and shape using PRNG key.\\ndef uniform(key, shape, dtype=onp.float32, minval=0.0, maxval=1.0) # Sample uniform random values in [minval, maxval) with given shape/dtype.\\ndef randint(key, shape, minval, maxval, dtype=onp.int32) # Sample uniform random values in [minval, maxval) with given shape/dtype.\\ndef shuffle(key, x, axis=0) # Shuffle the elements of an array uniformly at random along an axis.\\ndef normal(key, shape, dtype=onp.float32) # Sample standard normal random values with given shape and float dtype.\\ndef bernoulli(key, mean=onp.float32(0.5), shape=()) # Sample Bernoulli random values with given shape and mean.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"jax/api.py::jit","text":"jax/api.py::jit\ndef jit(fun, static_argnums=()):\n \"\"\"Sets up `fun` for just-in-time compilation with XLA.\n\n Args:\n fun: Function to be jitted. Should be a pure function, as side-effects may\n only be executed once. Its positional arguments and return value should be\n arrays, scalars, or standard Python containers (tuple/list/dict) thereof.\n Keyword arguments and positional arguments specified by `static_argnums`\n can be anything at all. These are treated as static (see below).\n static_argnums: A tuple of ints. Specifies which arguments to treat as\n static (compile-time constant). Operations that only depend on static\n arguments will be constant-folded. Calling the jitted function with\n different values for these constants will trigger recompilation.\n\n Returns:\n A wrapped version of `fun`, set up for just-in-time compilation.\n\n In the following example, `selu` can be compiled into a single fused kernel by\n XLA:\n\n >>> @jax.jit\n >>> def selu(x, alpha=1.67, lmbda=1.05):\n >>> return lmbda * jax.numpy.where(x > 0, x, alpha * jax.numpy.exp(x) - alpha)\n >>>\n >>> key = jax.random.PRNGKey(0)\n >>> x = jax.random.normal(key, (10,))\n >>> selu(x)\n array([-0.54485154, 0.27744263, -0.29255125, -0.91421586, -0.62452525,\n -0.2474813 , -0.8574326 , -0.7823267 , 0.7682731 , 0.59566754],\n dtype=float32)\n\n \"\"\"\n @wraps(fun)\n def f_jitted(*args, **kwargs):\n if _jit_is_disabled or config.read('jax_disable_jit'):\n return fun(*args, **kwargs)\n f = lu.wrap_init(fun, kwargs)\n dyn_argnums = [i for i in range(len(args)) if i not in static_argnums]\n f, dyn_args = _argnums_partial(f, dyn_argnums, args)\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, dyn_args))\n _check_args(jaxtupletree_args)\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n jaxtupletree_out = xla.xla_call(jaxtree_fun, *jaxtupletree_args)\n return build_tree(out_tree(), jaxtupletree_out)","label":0},{"cid":"jax/api.py::jit.f_jitted","text":"jax/api.py::jit.f_jitted\n @wraps(fun)\n def f_jitted(*args, **kwargs):\n if _jit_is_disabled or config.read('jax_disable_jit'):\n return fun(*args, **kwargs)\n f = lu.wrap_init(fun, kwargs)\n dyn_argnums = [i for i in range(len(args)) if i not in static_argnums]\n f, dyn_args = _argnums_partial(f, dyn_argnums, args)\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, dyn_args))\n _check_args(jaxtupletree_args)\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n jaxtupletree_out = xla.xla_call(jaxtree_fun, *jaxtupletree_args)\n return build_tree(out_tree(), jaxtupletree_out)","label":0},{"cid":"jax/api.py::disable_jit","text":"jax/api.py::disable_jit\n@contextmanager\ndef disable_jit():\n \"\"\"Context manager that disables `jit`.\n\n For debugging purposes, it is useful to have a mechanism that disables `jit`\n everywhere in a block of code, namely the `disable_jit` decorator.\n\n Inside a `jit`-ted function the values flowing through\n traced code can be abstract (i.e., shaped arrays with an unknown values),\n instead of concrete (i.e., specific arrays with known values).\n\n For example:\n\n >>> @jax.jit\n >>> def f(x):\n >>> y = x *2\n >>> print(\"Value of y is\", y)\n >>> return y + 3\n >>>\n >>> print(f(jax.numpy.array([1, 2, 3])))\n Value of y is Traced<ShapedArray(int32[3]):JaxprTrace(level=-1/1)>\n [5 7 9]\n\n Here `y` has been abstracted by `jit` to a `ShapedArray`, which represents an\n array with a fixed shape and type but an arbitrary value. If we want to see a\n concrete values while debugging, we can use the `disable_jit` decorator, at\n the cost of slower code:\n\n >>> with jax.disable_jit():\n >>> print(f(np.array([1, 2, 3])))\n >>>\n Value of y is [2 4 6]\n [5 7 9]\n \"\"\"\n global _jit_is_disabled\n _jit_is_disabled, prev_val = True, _jit_is_disabled\n yield\n _jit_is_disabled = prev_val","label":0},{"cid":"jax/api.py::xla_computation","text":"jax/api.py::xla_computation\ndef xla_computation(fun, static_argnums=()):\n def pv_like(x):\n aval = xla.abstractify(x)\n return pe.PartialVal((aval, core.unit))\n\n wrapped = lu.wrap_init(fun)\n\n @wraps(fun)\n def computation_maker(*args, **kwargs):\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\n pvals = map(pv_like, jax_args)\n jaxpr, _, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\n return xla.build_jaxpr(jaxpr, consts, *map(xla.abstractify, args))\n\n return computation_maker","label":0},{"cid":"jax/api.py::xla_computation.pv_like","text":"jax/api.py::xla_computation.pv_like\n def pv_like(x):\n aval = xla.abstractify(x)\n return pe.PartialVal((aval, core.unit))","label":0},{"cid":"jax/api.py::xla_computation.computation_maker","text":"jax/api.py::xla_computation.computation_maker\n @wraps(fun)\n def computation_maker(*args, **kwargs):\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\n pvals = map(pv_like, jax_args)\n jaxpr, _, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\n return xla.build_jaxpr(jaxpr, consts, *map(xla.abstractify, args))","label":0},{"cid":"jax/api.py::grad","text":"jax/api.py::grad\ndef grad(fun, argnums=0, has_aux=False):\n \"\"\"Creates a function which evaluates the gradient of `fun`.\n\n Args:\n fun: Function to be differentiated. Its arguments at positions specified by\n `argnums` should be arrays, scalars, or standard Python containers. It\n should return a scalar (which includes arrays with shape `()` but not\n arrays with shape `(1,)` etc.)\n argnums: Optional, integer or tuple of integers. Specifies which positional\n argument(s) to differentiate with respect to (default 0).\n has_aux: Optional, bool. Indicates whether `fun` returns a pair where the\n first element is considered the output of the mathematical function to be\n differentiated and the second element is auxiliary data. Default False.\n\n Returns:\n A function with the same arguments as `fun`, that evaluates the gradient of\n `fun`. If `argnums` is an integer then the gradient has the same shape and\n type as the positional argument indicated by that integer. If argnums is a\n tuple of integers, the gradient is a tuple of values with the same shapes\n and types as the corresponding arguments. If `has_aux` is True then a pair\n of (gradient, auxiliary_data) is returned.\n\n For example:\n\n >>> grad_tanh = jax.grad(jax.numpy.tanh)\n >>> grad_tanh(0.2)\n array(0.961043, dtype=float32)\n\n \"\"\"\n value_and_grad_f = value_and_grad(fun, argnums, has_aux=has_aux)\n\n docstr = (\"Gradient of {fun} with respect to positional argument(s) \"\n \"{argnums}. Takes the same arguments as {fun} but returns the \"\n \"gradient, which has the same shape as the arguments at \"\n \"positions {argnums}.\")\n\n @wraps(fun, docstr=docstr, argnums=argnums)\n def grad_f(*args, **kwargs):\n if not has_aux:\n _, g = value_and_grad_f(*args, **kwargs)\n return g\n else:\n (_, aux), g = value_and_grad_f(*args, **kwargs)\n return g, aux\n\n return grad_f","label":0},{"cid":"jax/api.py::grad.grad_f","text":"jax/api.py::grad.grad_f\n @wraps(fun, docstr=docstr, argnums=argnums)\n def grad_f(*args, **kwargs):\n if not has_aux:\n _, g = value_and_grad_f(*args, **kwargs)\n return g\n else:\n (_, aux), g = value_and_grad_f(*args, **kwargs)\n return g, aux","label":0},{"cid":"jax/api.py::value_and_grad","text":"jax/api.py::value_and_grad\ndef value_and_grad(fun, argnums=0, has_aux=False):\n \"\"\"Creates a function which evaluates both `fun` and the gradient of `fun`.\n\n Args:\n fun: Function to be differentiated. Its arguments at positions specified by\n `argnums` should be arrays, scalars, or standard Python containers. It\n should return a scalar (which includes arrays with shape `()` but not\n arrays with shape `(1,)` etc.)\n argnums: Optional, integer or tuple of integers. Specifies which positional\n argument(s) to differentiate with respect to (default 0).\n has_aux: Optional, bool. Indicates whether `fun` returns a pair where the\n first element is considered the output of the mathematical function to be\n differentiated and the second element is auxiliary data. Default False.\n\n Returns:\n A function with the same arguments as `fun` that evaluates both `fun` and\n the gradient of `fun` and returns them as a pair (a two-element tuple). If\n `argnums` is an integer then the gradient has the same shape and type as the\n positional argument indicated by that integer. If argnums is a tuple of\n integers, the gradient is a tuple of values with the same shapes and types\n as the corresponding arguments.\n \"\"\"\n\n docstr = (\"Value and gradient of {fun} with respect to positional \"\n \"argument(s) {argnums}. Takes the same arguments as {fun} but \"\n \"returns a two-element tuple where the first element is the value \"\n \"of {fun} and the second element is the gradient, which has the \"\n \"same shape as the arguments at positions {argnums}.\")\n\n @wraps(fun, docstr=docstr, argnums=argnums)\n def value_and_grad_f(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n if not has_aux:\n ans, vjp_py = vjp(f_partial, *dyn_args)\n else:\n ans, vjp_py, aux = vjp(f_partial, *dyn_args, has_aux=True)\n _check_scalar(ans)\n g = vjp_py(onp.on","label":0},{"cid":"jax/api.py::value_and_grad.value_and_grad_f","text":"jax/api.py::value_and_grad.value_and_grad_f\n @wraps(fun, docstr=docstr, argnums=argnums)\n def value_and_grad_f(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n if not has_aux:\n ans, vjp_py = vjp(f_partial, *dyn_args)\n else:\n ans, vjp_py, aux = vjp(f_partial, *dyn_args, has_aux=True)\n _check_scalar(ans)\n g = vjp_py(onp.ones((), onp.result_type(ans)))\n g = g[0] if isinstance(argnums, int) else g\n if not has_aux:\n return ans, g\n else:\n return (ans, aux), g","label":0},{"cid":"jax/api.py::jacfwd","text":"jax/api.py::jacfwd\ndef jacfwd(fun, argnums=0):\n \"\"\"Jacobian of `fun` evaluated column-by-column using forward-mode AD.\n\n Args:\n fun: Function whose Jacobian is to be computed.\n argnums: Optional, integer or tuple of integers. Specifies which positional\n argument(s) to differentiate with respect to (default `0`).\n\n Returns:\n A function with the same arguments as `fun`, that evaluates the Jacobian of\n `fun` using forward-mode automatic differentiation.\n\n >>> def f(x):\n >>> return jax.numpy.asarray(\n >>> [x[0], 5*x[2], 4*x[1]**2 - 2*x[2], x[2] * jax.numpy.sin(x[0])])\n >>> jax.jacfwd(f)(np.array([1., 2., 3.]))\n array([[ 1. , 0. , 0. ],\n [ 0. , 0. , 5. ],\n [ 0. , 16. , -2. ],\n [ 1.6209068 , 0. , 0.84147096]], dtype=float32)\n \"\"\"\n\n def jacfun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n pushfwd = partial(jvp, f_partial, dyn_args)\n y, jac = vmap(pushfwd, out_axes=(None, -1))(_std_basis(dyn_args))\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\n return tree_map(partial(_unravel_array_into_pytree, example_args, -1), jac)\n\n return jacfun","label":0},{"cid":"jax/api.py::jacfwd.jacfun","text":"jax/api.py::jacfwd.jacfun\n def jacfun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n pushfwd = partial(jvp, f_partial, dyn_args)\n y, jac = vmap(pushfwd, out_axes=(None, -1))(_std_basis(dyn_args))\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\n return tree_map(partial(_unravel_array_into_pytree, example_args, -1), jac)","label":0},{"cid":"jax/api.py::jacrev","text":"jax/api.py::jacrev\ndef jacrev(fun, argnums=0):\n \"\"\"Jacobian of `fun` evaluated row-by-row using reverse-mode AD.\n\n Args:\n fun: Function whose Jacobian is to be computed.\n argnums: Optional, integer or tuple of integers. Specifies which positional\n argument(s) to differentiate with respect to (default `0`).\n\n Returns:\n A function with the same arguments as `fun`, that evaluates the Jacobian of\n `fun` using reverse-mode automatic differentiation.\n\n >>> def f(x):\n >>> return jax.numpy.asarray(\n >>> [x[0], 5*x[2], 4*x[1]**2 - 2*x[2], x[2] * jax.numpy.sin(x[0])])\n >>> jax.jacrev(f)(np.array([1., 2., 3.]))\n array([[ 1. , 0. , 0. ],\n [ 0. , 0. , 5. ],\n [ 0. , 16. , -2. ],\n [ 1.6209068 , 0. , 0.84147096]], dtype=float32)\n \"\"\"\n def jacfun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n y, pullback = vjp(f_partial, *dyn_args)\n jac = vmap(pullback)(_std_basis(y))\n jac = jac[0] if isinstance(argnums, int) else jac\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\n jac = tree_map(partial(_unravel_array_into_pytree, y, 0), jac)\n return tree_transpose(tree_structure(example_args), tree_structure(y), jac)\n\n return jacfun","label":0},{"cid":"jax/api.py::jacrev.jacfun","text":"jax/api.py::jacrev.jacfun\n def jacfun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n y, pullback = vjp(f_partial, *dyn_args)\n jac = vmap(pullback)(_std_basis(y))\n jac = jac[0] if isinstance(argnums, int) else jac\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\n jac = tree_map(partial(_unravel_array_into_pytree, y, 0), jac)\n return tree_transpose(tree_structure(example_args), tree_structure(y), jac)","label":0},{"cid":"jax/api.py::hessian","text":"jax/api.py::hessian\ndef hessian(fun, argnums=0):\n \"\"\"Hessian of `fun`.\n\n Args:\n fun: Function whose Hessian is to be computed.\n argnums: Optional, integer or tuple of integers. Specifies which positional\n argument(s) to differentiate with respect to (default `0`).\n\n Returns:\n A function with the same arguments as `fun`, that evaluates the Hessian of\n `fun`.\n\n >>> g = lambda(x): x[0]**3 - 2*x[0]*x[1] - x[1]**6\n >>> jax.hessian(g)(jax.numpy.array([1., 2.]))\n array([[ 6., -2.],\n [ -2., -480.]], dtype=float32)\n \"\"\"\n\n return jacfwd(jacrev(fun, argnums=argnums), argnums=argnums)","label":0},{"cid":"jax/api.py::_std_basis","text":"jax/api.py::_std_basis\ndef _std_basis(pytree):\n leaves, _ = tree_flatten(pytree)\n ndim = sum(map(onp.size, leaves))\n return _unravel_array_into_pytree(pytree, 1, onp.eye(ndim))","label":0},{"cid":"jax/api.py::_unravel_array_into_pytree","text":"jax/api.py::_unravel_array_into_pytree\ndef _unravel_array_into_pytree(pytree, axis, arr):\n leaves, treedef = tree_flatten(pytree)\n axis = axis % arr.ndim\n dtypes = map(_dtype, leaves)\n shapes = [arr.shape[:axis] + onp.shape(l) + arr.shape[axis+1:] for l in leaves]\n parts = _split(arr, onp.cumsum(map(onp.size, leaves[:-1])), axis)\n reshaped_parts = [onp.reshape(part.astype(dtype), shape)\n for part, dtype, shape in zip(parts, dtypes, shapes)]\n return tree_unflatten(treedef, reshaped_parts)","label":0},{"cid":"jax/api.py::_split","text":"jax/api.py::_split\ndef _split(x, indices, axis):\n if isinstance(x, onp.ndarray):\n return onp.split(x, indices, axis)\n else:\n return x.split(indices, axis)","label":0},{"cid":"jax/api.py::_dtype","text":"jax/api.py::_dtype\ndef _dtype(x):\n return canonicalize_dtype(onp.result_type(x))","label":0},{"cid":"jax/api.py::vmap","text":"jax/api.py::vmap\ndef vmap(fun, in_axes=0, out_axes=0):\n \"\"\"Vectorizing map. Creates a function which maps `fun` over additional axes.\n\n Args:\n fun: Function to be mapped over additional axes.\n in_axes: Specifies which input axes to map over. These may be integers,\n `None`, or (possibly nested) tuples of integers or `None`.\n out_axes: Specifies which output axes to map over. These may be integers,\n `None`, or (possibly nested) tuples of integers or `None`.\n\n Returns:\n Batched/vectorized version of `fun` with arguments that correspond to those\n of `fun`, but with extra array axes at positions indicated by `in_axes`, and\n a return value that corresponds to that of `fun`, but with extra array axes\n at positions indicated by `out_axes`.\n\n For example, we can implement a matrix-matrix product using a vector dot\n product:\n\n >>> vv = lambda x, y: np.vdot(x, y) # ([a], [a]) -> []\n >>> mv = vmap(vv, (0, None), 0) # ([a,b], [b]) -> [a]\n >>> mm = vmap(mv, (None, 1), 1) # ([a,b], [b,c]) -> [a,c]\n\n (`[a,b]` indicates an array with shape (a,b))\n \"\"\"\n\n docstr = (\"Vectorized version of {fun}. Takes similar arguments as {fun} \"\n \"but with additional array axes over which {fun} is mapped.\")\n\n if (not isinstance(in_axes, (list, tuple, type(None), int))\n or not isinstance(out_axes, (list, tuple, type(None), int))):\n msg = (\"vmap arguments in_axes and out_axes must each be an integer, None, \"\n \"or a (nested) tuple of those types, got {} and {} respectively.\")\n raise TypeError(msg.format(type(in_axes), type(out_axes)))\n\n @wraps(fun, docstr=docstr)\n def batched_fun(*args, **kwargs):\n if not isinstance(fun, lu.WrappedFun):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = ba","label":0},{"cid":"jax/api.py::vmap.batched_fun","text":"jax/api.py::vmap.batched_fun\n @wraps(fun, docstr=docstr)\n def batched_fun(*args, **kwargs):\n if not isinstance(fun, lu.WrappedFun):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = batching.batch(jaxtree_fun, in_flat, in_axes_, out_axes)\n return build_tree(out_tree(), out_flat)","label":0},{"cid":"jax/api.py::pmap","text":"jax/api.py::pmap\ndef pmap(fun, axis_name=None):\n \"\"\"Set up SPMD function for JIT compilation and parallel execution with XLA.\"\"\"\n axis_name = _TempAxisName() if axis_name is None else axis_name\n\n @wraps(fun)\n def f_jitted(*args, **kwargs):\n leaves, _ = tree_flatten(args)\n axis_sizes = set(onp.shape(leaf)[0] for leaf in leaves)\n if len(axis_sizes) != 1:\n msg = \"pmap requires all leading axes to have equal length, got {}.\"\n raise TypeError(msg.format(axis_sizes))\n axis_size = axis_sizes.pop()\n\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n _check_args(jaxtupletree_args)\n f = lu.wrap_init(fun, kwargs)\n f, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n jaxtupletree_out = pxla.xla_pcall(f, *jaxtupletree_args,\n axis_name=axis_name, axis_size=axis_size)\n return build_tree(out_tree(), jaxtupletree_out)\n\n namestr = \"pmap({}, axis_name={})\".format\n f_jitted.__name__ = namestr(f_jitted.__name__, axis_name)\n return f_jitted","label":0},{"cid":"jax/api.py::pmap.f_jitted","text":"jax/api.py::pmap.f_jitted\n @wraps(fun)\n def f_jitted(*args, **kwargs):\n leaves, _ = tree_flatten(args)\n axis_sizes = set(onp.shape(leaf)[0] for leaf in leaves)\n if len(axis_sizes) != 1:\n msg = \"pmap requires all leading axes to have equal length, got {}.\"\n raise TypeError(msg.format(axis_sizes))\n axis_size = axis_sizes.pop()\n\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n _check_args(jaxtupletree_args)\n f = lu.wrap_init(fun, kwargs)\n f, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n jaxtupletree_out = pxla.xla_pcall(f, *jaxtupletree_args,\n axis_name=axis_name, axis_size=axis_size)\n return build_tree(out_tree(), jaxtupletree_out)","label":0},{"cid":"jax/api.py::serial_pmap","text":"jax/api.py::serial_pmap\ndef serial_pmap(fun, axis_name=None, in_axes=0, out_axes=0):\n \"\"\"Vectorizing pseudo-map for single-program multiple-data (SPMD) functions.\"\"\"\n axis_name = _TempAxisName() if axis_name is None else axis_name\n\n def map_fun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = parallel.serial_pmap(jaxtree_fun, axis_name, in_flat, in_axes_, out_axes)\n return build_tree(out_tree(), out_flat)\n\n return map_fun","label":0},{"cid":"jax/api.py::serial_pmap.map_fun","text":"jax/api.py::serial_pmap.map_fun\n def map_fun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = parallel.serial_pmap(jaxtree_fun, axis_name, in_flat, in_axes_, out_axes)\n return build_tree(out_tree(), out_flat)","label":0},{"cid":"jax/api.py::_TempAxisName.__repr__","text":"jax/api.py::_TempAxisName.__repr__\n def __repr__(self):\n return '<temp axis {}>'.format(hex(id(self)))","label":0},{"cid":"jax/api.py::papply","text":"jax/api.py::papply\ndef papply(fun, axis_size, in_axes=0, out_axes=0):\n \"\"\"Apply a function using parallel computation by sharding inputs.\"\"\"\n axis_name = parallel.newvar()\n\n def papply_fun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n args_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = parallel.papply(jaxtree_fun, axis_name, args_flat, axis_size,\n in_axes_, out_axes)\n return build_tree(out_tree(), out_flat)\n\n return papply_fun, axis_name","label":0},{"cid":"jax/api.py::papply.papply_fun","text":"jax/api.py::papply.papply_fun\n def papply_fun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n args_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = parallel.papply(jaxtree_fun, axis_name, args_flat, axis_size,\n in_axes_, out_axes)\n return build_tree(out_tree(), out_flat)","label":0},{"cid":"jax/api.py::jvp","text":"jax/api.py::jvp\ndef jvp(fun, primals, tangents):\n \"\"\"Computes a (forward-mode) Jacobian-vector product of `fun`.\n\n Args:\n fun: Function to be differentiated. Its arguments should be arrays, scalars,\n or standard Python containers of arrays or scalars. It should return an\n array, scalar, or standard Python container of arrays or scalars.\n primals: The primal values at which the Jacobian of `fun` should be\n evaluated. Should be a tuple of arrays, scalar, or standard Python\n container thereof. The length of the tuple is equal to the number of\n positional parameters of `fun`.\n tangents: The tangent vector for which the Jacobian-vector product should be\n evaluated. Should be a tuple of arrays, scalar, or standard Python\n container thereof, with the same tree structure and array shapes as\n `primals`.\n\n Returns:\n A `(primals_out, tangents_out)` pair, where `primals_out` is\n `fun(*primals)`, and `tangents_out` is the Jacobian-vector product of\n `function` evaluated at `primals` with `tangents`. The `tangents_out` value\n has the same Python tree structure and shapes as `primals_out`.\n\n For example:\n\n >>> jax.jvp(jax.numpy.sin, (0.1,), (0.2,))\n (array(0.09983342, dtype=float32), array(0.19900084, dtype=float32))\n \"\"\"\n def trim_arg(primal, tangent):\n primal_jtuple, tree_def = pytree_to_jaxtupletree(primal)\n tangent_jtuple, tree_def_2 = pytree_to_jaxtupletree(tangent)\n assert tree_def == tree_def_2, (tree_def, tree_def_2)\n return primal_jtuple, tangent_jtuple, tree_def\n\n if not isinstance(fun, lu.WrappedFun):\n fun = lu.wrap_init(fun)\n ps_flat, ts_flat, in_trees = unzip3(map(trim_arg, primals, tangents))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\n out_primal, out_tangent = ad.jvp(jaxtree_fun).call_wrapped(ps_flat, ts_flat)\n return (build_tree(out_tree(), out_primal), build_tree(out_tree(), out_tangent))","label":0},{"cid":"jax/api.py::jvp.trim_arg","text":"jax/api.py::jvp.trim_arg\n def trim_arg(primal, tangent):\n primal_jtuple, tree_def = pytree_to_jaxtupletree(primal)\n tangent_jtuple, tree_def_2 = pytree_to_jaxtupletree(tangent)\n assert tree_def == tree_def_2, (tree_def, tree_def_2)\n return primal_jtuple, tangent_jtuple, tree_def","label":0},{"cid":"jax/api.py::linearize","text":"jax/api.py::linearize\ndef linearize(traceable, *primals):\n fun = lu.wrap_init(traceable)\n primals_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, primals))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\n out_primal, out_pval, jaxpr, consts = ad.linearize(jaxtree_fun, *primals_flat)\n out_tree = out_tree()\n out_primal_py = build_tree(out_tree, out_primal)\n lifted_jvp = partial(lift_linearized, jaxpr, consts, (in_trees, out_tree), out_pval)\n return out_primal_py, lifted_jvp","label":0},{"cid":"jax/api.py::lift_linearized","text":"jax/api.py::lift_linearized\ndef lift_linearized(jaxpr, consts, io_tree, out_pval, *py_args):\n def fun(*args):\n primals = pack(args) # doesn't matter what these are-they'll be ignored\n tangents = pack(args)\n _, ans = eval_jaxpr(jaxpr, consts, (), primals, tangents)\n return pe.merge_pvals(ans, out_pval)\n\n return apply_jaxtree_fun(fun, io_tree, *py_args)","label":0},{"cid":"jax/api.py::lift_linearized.fun","text":"jax/api.py::lift_linearized.fun\n def fun(*args):\n primals = pack(args) # doesn't matter what these are-they'll be ignored\n tangents = pack(args)\n _, ans = eval_jaxpr(jaxpr, consts, (), primals, tangents)\n return pe.merge_pvals(ans, out_pval)","label":0},{"cid":"jax/api.py::vjp","text":"jax/api.py::vjp\ndef vjp(fun, *primals, **kwargs):\n \"\"\"Compute a (reverse-mode) vector-Jacobian product of `fun`.\n\n `grad` is implemented as a special case of `vjp`.\n\n Args:\n fun: Function to be differentiated. Its arguments should be arrays, scalars,\n or standard Python containers of arrays or scalars. It should return an\n array, scalar, or standard Python container of arrays or scalars.\n primals: A sequence of primal values at which the Jacobian of `fun`\n should be evaluated. The length of `primals` should be equal to the number\n of positional parameters to `fun`. Each primal value should be a tuple of\n arrays, scalar, or standard Python containers thereof.\n has_aux: Optional, bool. Indicates whether `fun` returns a pair where the\n first element is considered the output of the mathematical function to be\n differentiated and the second element is auxiliary data. Default False.\n\n Returns:\n A `(primals_out, vjpfun)` pair, where `primals_out` is `fun(*primals)`.\n `vjpfun` is a function from a cotangent vector with the same shape as\n `primals_out` to a tuple of cotangent vectors with the same shape as\n `primals`, representing the vector-Jacobian product of `fun` evaluated at\n `primals`.\n\n >>> def f(x, y):\n >>> return jax.numpy.sin(x), jax.numpy.cos(y)\n >>> primals, g = jax.vjp(f, 0.5, 1.0)\n >>> g((-0.7, 0.3))\n (array(-0.61430776, dtype=float32), array(-0.2524413, dtype=float32))\n \"\"\"\n has_aux = kwargs.pop('has_aux', False)\n assert not kwargs\n if not isinstance(fun, lu.WrappedFun):\n fun = lu.wrap_init(fun)\n primals_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, primals))\n _check_args(primals_flat)\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\n if not has_aux:\n out_primal, out_vjp = ad.vjp(jaxtree_fun, primals_flat)\n else:\n out_primal, out_vjp, aux = ad.vjp(jaxtree_fun, primals_flat, has_aux=True)\n out_tree = out_tree()\n if has_aux:\n out_tree, aux_tree ","label":0},{"cid":"jax/api.py::vjp.out_vjp_packed","text":"jax/api.py::vjp.out_vjp_packed\n def out_vjp_packed(cotangent_in):\n return out_vjp(cotangent_in)","label":0},{"cid":"jax/api.py::trace_to_jaxpr","text":"jax/api.py::trace_to_jaxpr\ndef trace_to_jaxpr(traceable, py_pvals, **kwargs):\n fun = lu.wrap_init(traceable)\n pvals, in_trees = unzip2(map(tree_to_pval_tuples, py_pvals))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\n jaxpr, out_pval, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\n return jaxpr, consts, out_pval, (in_trees, out_tree())","label":0},{"cid":"jax/api.py::lift_jaxpr","text":"jax/api.py::lift_jaxpr\ndef lift_jaxpr(jaxpr, consts, io_tree, pvals, py_args):\n def fun(*args):\n ans = eval_jaxpr(jaxpr, consts, (), *args)\n return pe.merge_pvals(ans, pvals)\n return apply_jaxtree_fun(fun, io_tree, *py_args)","label":0},{"cid":"jax/api.py::lift_jaxpr.fun","text":"jax/api.py::lift_jaxpr.fun\n def fun(*args):\n ans = eval_jaxpr(jaxpr, consts, (), *args)\n return pe.merge_pvals(ans, pvals)","label":0},{"cid":"jax/api.py::make_jaxpr","text":"jax/api.py::make_jaxpr\ndef make_jaxpr(fun):\n \"\"\"Adapts `fun` to return its `jaxpr` program representation.\n\n Args:\n fun: The function whose `jaxpr` is to be computed. Its positional arguments\n and return value should be arrays, scalars, or standard Python containers\n (tuple/list/dict) thereof.\n\n Returns:\n A wrapped version of `fun`, set up to return a `jaxpr`.\n\n A `jaxpr` is JAX's intermediate representation for program traces. The `jaxpr`\n language is based on the simply-typed first-order lambda calculus with\n let-bindings. `make_jaxpr` adapts a function to return its `jaxpr`, which we\n can inspect to understand what JAX is doing internally.\n\n The `jaxpr` returned is a trace of `fun` abstracted to `ShapedArray` level.\n Other levels of abstraction exist internally.\n\n We do not describe the semantics of the `jaxpr` language in detail here, but\n instead give a few examples.\n\n >>> def f(x): return jax.numpy.sin(jax.numpy.cos(x))\n >>> f(3.0)\n array(-0.83602184, dtype=float32)\n >>> jax.make_jaxpr(f)(3.0)\n { lambda ; ; a.\n let b = cos a\n c = sin b\n in c }\n >>> jax.make_jaxpr(jax.grad(f))(3.0)\n { lambda b ; ; a.\n let c = pack a\n (d) = id c\n e = cos d\n f = cos e\n g = mul b f\n h = neg g\n i = sin d\n j = mul h i\n k = pack j\n (l) = id k\n in l }\n \"\"\"\n def pv_like(x):\n aval = xla.abstractify(x)\n return pe.PartialVal((aval, core.unit))\n\n wrapped = lu.wrap_init(fun)\n\n @wraps(fun)\n def jaxpr_maker(*args, **kwargs):\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\n pvals = map(pv_like, jax_args)\n jaxpr, _, _ = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\n return jaxpr\n\n jaxpr_maker.__name__ = \"make_jaxpr({})\".format(jaxpr_maker.__name__)\n return jaxpr_maker","label":0},{"cid":"jax/api.py::make_jaxpr.pv_like","text":"jax/api.py::make_jaxpr.pv_like\n def pv_like(x):\n aval = xla.abstractify(x)\n return pe.PartialVal((aval, core.unit))","label":0},{"cid":"jax/experimental/stax.py::relu","text":"jax/experimental/stax.py::relu\ndef relu(x): return np.maximum(x, 0.)","label":0},{"cid":"jax/experimental/stax.py::softplus","text":"jax/experimental/stax.py::softplus\ndef softplus(x): return np.logaddexp(x, 0.)","label":0},{"cid":"jax/experimental/stax.py::logsoftmax","text":"jax/experimental/stax.py::logsoftmax\ndef logsoftmax(x, axis=-1):\n \"\"\"Apply log softmax to an array of logits, log-normalizing along an axis.\"\"\"\n return x - logsumexp(x, axis, keepdims=True)","label":0},{"cid":"jax/experimental/stax.py::softmax","text":"jax/experimental/stax.py::softmax\ndef softmax(x, axis=-1):\n \"\"\"Apply softmax to an array of logits, exponentiating and normalizing along an axis.\"\"\"\n unnormalized = np.exp(x - x.max(axis, keepdims=True))\n return unnormalized / unnormalized.sum(axis, keepdims=True)","label":0},{"cid":"jax/experimental/stax.py::fastvar","text":"jax/experimental/stax.py::fastvar\ndef fastvar(x, axis, keepdims):\n \"\"\"A fast but less numerically-stable variance calculation than np.var.\"\"\"\n return np.mean(x**2, axis, keepdims=keepdims) - np.mean(x, axis, keepdims=keepdims)**2","label":0},{"cid":"jax/experimental/stax.py::randn","text":"jax/experimental/stax.py::randn\ndef randn(stddev=1e-2, rng=npr):\n \"\"\"An initializer function for random normal coefficients.\"\"\"\n def init(shape):\n return rng.normal(size=shape, scale=stddev).astype('float32')\n return init","label":0},{"cid":"jax/experimental/stax.py::randn.init","text":"jax/experimental/stax.py::randn.init\n def init(shape):\n return rng.normal(size=shape, scale=stddev).astype('float32')","label":0},{"cid":"jax/experimental/stax.py::glorot","text":"jax/experimental/stax.py::glorot\ndef glorot(out_dim=0, in_dim=1, scale=onp.sqrt(2), rng=npr):\n \"\"\"An initializer function for random Glorot-scaled coefficients.\"\"\"\n def init(shape):\n fan_in, fan_out = shape[in_dim], shape[out_dim]\n size = onp.prod(onp.delete(shape, [in_dim, out_dim]))\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\n return rng.normal(size=shape, scale=std).astype('float32')\n return init","label":0},{"cid":"jax/experimental/stax.py::glorot.init","text":"jax/experimental/stax.py::glorot.init\n def init(shape):\n fan_in, fan_out = shape[in_dim], shape[out_dim]\n size = onp.prod(onp.delete(shape, [in_dim, out_dim]))\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\n return rng.normal(size=shape, scale=std).astype('float32')","label":0},{"cid":"jax/experimental/stax.py::Dense","text":"jax/experimental/stax.py::Dense\ndef Dense(out_dim, W_init=glorot(), b_init=randn()):\n \"\"\"Layer constructor function for a dense (fully-connected) layer.\"\"\"\n def init_fun(input_shape):\n output_shape = input_shape[:-1] + (out_dim,)\n W, b = W_init((input_shape[-1], out_dim)), b_init((out_dim,))\n return output_shape, (W, b)\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return np.dot(inputs, W) + b\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Dense.init_fun","text":"jax/experimental/stax.py::Dense.init_fun\n def init_fun(input_shape):\n output_shape = input_shape[:-1] + (out_dim,)\n W, b = W_init((input_shape[-1], out_dim)), b_init((out_dim,))\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::Dense.apply_fun","text":"jax/experimental/stax.py::Dense.apply_fun\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return np.dot(inputs, W) + b","label":0},{"cid":"jax/experimental/stax.py::GeneralConv","text":"jax/experimental/stax.py::GeneralConv\ndef GeneralConv(dimension_numbers, out_chan, filter_shape,\n strides=None, padding='VALID', W_init=None, b_init=randn(1e-6)):\n \"\"\"Layer construction function for a general convolution layer.\"\"\"\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n one = (1,) * len(filter_shape)\n strides = strides or one\n W_init = W_init or glorot(rhs_spec.index('O'), rhs_spec.index('I'))\n def init_fun(input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_general_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n W, b = W_init(kernel_shape), b_init(bias_shape)\n return output_shape, (W, b)\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\n dimension_numbers) + b\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::GeneralConv.init_fun","text":"jax/experimental/stax.py::GeneralConv.init_fun\n def init_fun(input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_general_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n W, b = W_init(kernel_shape), b_init(bias_shape)\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::GeneralConv.apply_fun","text":"jax/experimental/stax.py::GeneralConv.apply_fun\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\n dimension_numbers) + b","label":0},{"cid":"jax/experimental/stax.py::BatchNorm","text":"jax/experimental/stax.py::BatchNorm\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-5, center=True, scale=True,\n beta_init=zeros, gamma_init=ones):\n \"\"\"Layer construction function for a batch normalization layer.\"\"\"\n _beta_init = lambda shape: beta_init(shape) if center else ()\n _gamma_init = lambda shape: gamma_init(shape) if scale else ()\n axis = (axis,) if np.isscalar(axis) else axis\n def init_fun(input_shape):\n shape = (1 if i in axis else d for i, d in enumerate(input_shape))\n shape = tuple(itertools.dropwhile(lambda x: x == 1, shape))\n beta, gamma = _beta_init(shape), _gamma_init(shape)\n return input_shape, (beta, gamma)\n def apply_fun(params, x, **kwargs):\n beta, gamma = params\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\n z = (x - mean) / np.sqrt(var + epsilon)\n if center and scale: return gamma * z + beta\n if center: return z + beta\n if scale: return gamma * z\n return z\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::BatchNorm.init_fun","text":"jax/experimental/stax.py::BatchNorm.init_fun\n def init_fun(input_shape):\n shape = (1 if i in axis else d for i, d in enumerate(input_shape))\n shape = tuple(itertools.dropwhile(lambda x: x == 1, shape))\n beta, gamma = _beta_init(shape), _gamma_init(shape)\n return input_shape, (beta, gamma)","label":1},{"cid":"jax/experimental/stax.py::BatchNorm.apply_fun","text":"jax/experimental/stax.py::BatchNorm.apply_fun\n def apply_fun(params, x, **kwargs):\n beta, gamma = params\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\n z = (x - mean) / np.sqrt(var + epsilon)\n if center and scale: return gamma * z + beta\n if center: return z + beta\n if scale: return gamma * z\n return z","label":1},{"cid":"jax/experimental/stax.py::_elemwise_no_params","text":"jax/experimental/stax.py::_elemwise_no_params\ndef _elemwise_no_params(fun, **fun_kwargs):\n init_fun = lambda input_shape: (input_shape, ())\n apply_fun = lambda params, inputs, **kwargs: fun(inputs, **fun_kwargs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer","text":"jax/experimental/stax.py::_pooling_layer\ndef _pooling_layer(reducer, init_val, rescaler=None):\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\n \"\"\"Layer construction function for a pooling layer.\"\"\"\n strides = strides or (1,) * len(window_shape)\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\n dims = (1,) + window_shape + (1,) # NHWC\n strides = (1,) + strides + (1,)\n def init_fun(input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\n return rescale(out, inputs) if rescale else out\n return init_fun, apply_fun\n return PoolingLayer","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\n \"\"\"Layer construction function for a pooling layer.\"\"\"\n strides = strides or (1,) * len(window_shape)\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\n dims = (1,) + window_shape + (1,) # NHWC\n strides = (1,) + strides + (1,)\n def init_fun(input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\n return rescale(out, inputs) if rescale else out\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\n def init_fun(input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\n return out_shape, ()","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\n return rescale(out, inputs) if rescale else out","label":0},{"cid":"jax/experimental/stax.py::_normalize_by_window_size","text":"jax/experimental/stax.py::_normalize_by_window_size\ndef _normalize_by_window_size(dims, strides, padding):\n def rescale(outputs, inputs):\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\n return outputs / window_sizes[..., np.newaxis]\n return rescale","label":0},{"cid":"jax/experimental/stax.py::_normalize_by_window_size.rescale","text":"jax/experimental/stax.py::_normalize_by_window_size.rescale\n def rescale(outputs, inputs):\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\n return outputs / window_sizes[..., np.newaxis]","label":0},{"cid":"jax/experimental/stax.py::Flatten","text":"jax/experimental/stax.py::Flatten\ndef Flatten():\n \"\"\"Layer construction function for flattening all but the leading dim.\"\"\"\n def init_fun(input_shape):\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\n return output_shape, ()\n def apply_fun(params, inputs, **kwargs):\n return np.reshape(inputs, (inputs.shape[0], -1))\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Flatten.init_fun","text":"jax/experimental/stax.py::Flatten.init_fun\n def init_fun(input_shape):\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\n return output_shape, ()","label":0},{"cid":"jax/experimental/stax.py::Flatten.apply_fun","text":"jax/experimental/stax.py::Flatten.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return np.reshape(inputs, (inputs.shape[0], -1))","label":0},{"cid":"jax/experimental/stax.py::Identity","text":"jax/experimental/stax.py::Identity\ndef Identity():\n \"\"\"Layer construction function for an identity layer.\"\"\"\n init_fun = lambda input_shape: (input_shape, ())\n apply_fun = lambda params, inputs, **kwargs: inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanOut","text":"jax/experimental/stax.py::FanOut\ndef FanOut(num):\n \"\"\"Layer construction function for a fan-out layer.\"\"\"\n init_fun = lambda input_shape: ([input_shape] * num, ())\n apply_fun = lambda params, inputs, **kwargs: [inputs] * num\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInSum","text":"jax/experimental/stax.py::FanInSum\ndef FanInSum():\n \"\"\"Layer construction function for a fan-in sum layer.\"\"\"\n init_fun = lambda input_shape: (input_shape[0], ())\n apply_fun = lambda params, inputs, **kwargs: sum(inputs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInConcat","text":"jax/experimental/stax.py::FanInConcat\ndef FanInConcat(axis=-1):\n \"\"\"Layer construction function for a fan-in concatenation layer.\"\"\"\n def init_fun(input_shape):\n ax = axis % len(input_shape[0])\n concat_size = sum(shape[ax] for shape in input_shape)\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n return np.concatenate(inputs, axis)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInConcat.init_fun","text":"jax/experimental/stax.py::FanInConcat.init_fun\n def init_fun(input_shape):\n ax = axis % len(input_shape[0])\n concat_size = sum(shape[ax] for shape in input_shape)\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\n return out_shape, ()","label":0},{"cid":"jax/experimental/stax.py::FanInConcat.apply_fun","text":"jax/experimental/stax.py::FanInConcat.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return np.concatenate(inputs, axis)","label":0},{"cid":"jax/experimental/stax.py::Dropout","text":"jax/experimental/stax.py::Dropout\ndef Dropout(rate, mode='train'):\n \"\"\"Layer construction function for a dropout layer with given rate.\"\"\"\n def init_fun(input_shape):\n return input_shape, ()\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.get('rng', None)\n if rng is None:\n msg = (\"Dropout layer requires apply_fun to be called with a PRNG key \"\n \"argument. That is, instead of `apply_fun(params, inputs)`, call \"\n \"it like `apply_fun(params, inputs, key)` where `key` is a \"\n \"jax.random.PRNGKey value.\")\n raise ValueError(msg)\n if mode == 'train':\n keep = random.bernoulli(rng, rate, inputs.shape)\n return np.where(keep, inputs / rate, 0)\n else:\n return inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Dropout.init_fun","text":"jax/experimental/stax.py::Dropout.init_fun\n def init_fun(input_shape):\n return input_shape, ()","label":0},{"cid":"jax/experimental/stax.py::Dropout.apply_fun","text":"jax/experimental/stax.py::Dropout.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.get('rng', None)\n if rng is None:\n msg = (\"Dropout layer requires apply_fun to be called with a PRNG key \"\n \"argument. That is, instead of `apply_fun(params, inputs)`, call \"\n \"it like `apply_fun(params, inputs, key)` where `key` is a \"\n \"jax.random.PRNGKey value.\")\n raise ValueError(msg)\n if mode == 'train':\n keep = random.bernoulli(rng, rate, inputs.shape)\n return np.where(keep, inputs / rate, 0)\n else:\n return inputs","label":0},{"cid":"jax/experimental/stax.py::serial","text":"jax/experimental/stax.py::serial\ndef serial(*layers):\n \"\"\"Combinator for composing layers in serial.\n\n Args:\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the serial\n composition of the given sequence of layers.\n \"\"\"\n nlayers = len(layers)\n init_funs, apply_funs = zip(*layers)\n def init_fun(input_shape):\n params = []\n for init_fun in init_funs:\n input_shape, param = init_fun(input_shape)\n params.append(param)\n return input_shape, params\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n for fun, param, rng in zip(apply_funs, params, rngs):\n inputs = fun(param, inputs, rng=rng, **kwargs)\n return inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::serial.init_fun","text":"jax/experimental/stax.py::serial.init_fun\n def init_fun(input_shape):\n params = []\n for init_fun in init_funs:\n input_shape, param = init_fun(input_shape)\n params.append(param)\n return input_shape, params","label":0},{"cid":"jax/experimental/stax.py::serial.apply_fun","text":"jax/experimental/stax.py::serial.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n for fun, param, rng in zip(apply_funs, params, rngs):\n inputs = fun(param, inputs, rng=rng, **kwargs)\n return inputs","label":0},{"cid":"jax/experimental/stax.py::parallel","text":"jax/experimental/stax.py::parallel\ndef parallel(*layers):\n \"\"\"Combinator for composing layers in parallel.\n\n The layer resulting from this combinator is often used with the FanOut and\n FanInSum layers.\n\n Args:\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the\n parallel composition of the given sequence of layers. In particular, the\n returned layer takes a sequence of inputs and returns a sequence of outputs\n with the same length as the argument `layers`.\n \"\"\"\n nlayers = len(layers)\n init_funs, apply_funs = zip(*layers)\n def init_fun(input_shape):\n return zip(*[init(shape) for init, shape in zip(init_funs, input_shape)])\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::parallel.init_fun","text":"jax/experimental/stax.py::parallel.init_fun\n def init_fun(input_shape):\n return zip(*[init(shape) for init, shape in zip(init_funs, input_shape)])","label":0},{"cid":"jax/experimental/stax.py::parallel.apply_fun","text":"jax/experimental/stax.py::parallel.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]","label":0},{"cid":"jax/experimental/stax.py::shape_dependent","text":"jax/experimental/stax.py::shape_dependent\ndef shape_dependent(make_layer):\n \"\"\"Combinator to delay layer constructor pair until input shapes are known.\n\n Args:\n make_layer: a one-argument function that takes an input shape as an argument\n (a tuple of positive integers) and returns an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the same\n layer as returned by `make_layer` but with its construction delayed until\n input shapes are known.\n \"\"\"\n def init_fun(input_shape):\n return make_layer(input_shape)[0](input_shape)\n def apply_fun(params, inputs, **kwargs):\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::shape_dependent.init_fun","text":"jax/experimental/stax.py::shape_dependent.init_fun\n def init_fun(input_shape):\n return make_layer(input_shape)[0](input_shape)","label":0},{"cid":"jax/experimental/stax.py::shape_dependent.apply_fun","text":"jax/experimental/stax.py::shape_dependent.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)","label":0},{"cid":"jax/random.py::PRNGKey","text":"jax/random.py::PRNGKey\ndef PRNGKey(seed):\n \"\"\"Create a pseudo-random number generator (PRNG) key given an integer seed.\n\n Args:\n seed: a 64- or 32-bit integer used as the value of the key.\n\n Returns:\n A PRNG key, which is modeled as an array of shape (2,) and dtype uint32. The\n key is constructed from a 64-bit seed by effectively bit-casting to a pair\n of uint32 values (or from a 32-bit seed by first padding out with zeros).\n \"\"\"\n if onp.shape(seed):\n raise TypeError(\"PRNGKey seed must be a scalar.\")\n convert = lambda k: lax.reshape(lax.convert_element_type(k, onp.uint32), [1])\n if isinstance(seed, (int, onp.ndarray)):\n # Special handling of raw integer values, which may have be 64bit even\n # when jax_enable_x64=False and we don't want to drop the top 32 bits\n k1 = convert(onp.bitwise_and(onp.right_shift(seed, 32), 0xFFFFFFFF))\n else:\n k1 = convert(lax.shift_right_logical(seed, 32))\n k2 = convert(lax.bitwise_and(seed, 0xFFFFFFFF))\n return lax.concatenate([k1, k2], 0)","label":0},{"cid":"jax/random.py::_is_prng_key","text":"jax/random.py::_is_prng_key\ndef _is_prng_key(key):\n try:\n return key.shape == (2,) and key.dtype == onp.uint32\n except AttributeError:\n return False","label":0},{"cid":"jax/random.py::_make_rotate_left","text":"jax/random.py::_make_rotate_left\ndef _make_rotate_left(dtype):\n if not onp.issubdtype(dtype, onp.integer):\n raise TypeError(\"_rotate_left only accepts integer dtypes.\")\n nbits = onp.array(onp.iinfo(dtype).bits, dtype)\n\n def _rotate_left(x, d):\n if lax._dtype(d) != lax._dtype(x):\n d = lax.convert_element_type(d, x.dtype)\n return (x << d) | lax.shift_right_logical(x, nbits - d)\n return _rotate_left","label":0},{"cid":"jax/random.py::_make_rotate_left._rotate_left","text":"jax/random.py::_make_rotate_left._rotate_left\n def _rotate_left(x, d):\n if lax._dtype(d) != lax._dtype(x):\n d = lax.convert_element_type(d, x.dtype)\n return (x << d) | lax.shift_right_logical(x, nbits - d)","label":0},{"cid":"jax/random.py::_bit_stats","text":"jax/random.py::_bit_stats\ndef _bit_stats(bits):\n \"\"\"This is a debugging function to compute the statistics of bit fields.\"\"\"\n return onp.array([list(map(int, onp.binary_repr(x, 64))) for x in bits]).mean(0)","label":0},{"cid":"jax/random.py::threefry_2x32","text":"jax/random.py::threefry_2x32\n@jit\ndef threefry_2x32(keypair, count):\n \"\"\"Apply the Threefry 2x32 hash.\n\n Args:\n keypair: a pair of 32bit unsigned integers used for the key.\n count: an array of dtype uint32 used for the counts.\n\n Returns:\n An array of dtype uint32 with the same shape as `count`.\n \"\"\"\n # Based on ThreeFry2x32 by phawkins@ in //.../xla/client/lib/prng.cc\n key1, key2 = keypair\n if not lax._dtype(key1) == lax._dtype(key2) == lax._dtype(count) == onp.uint32:\n msg = \"threefry_2x32 requires uint32 arguments, got {}\"\n raise TypeError(msg.format([lax._dtype(x) for x in [key1, key2, count]]))\n\n rotate_left = _make_rotate_left(lax._dtype(count))\n\n def apply_round(v, rot):\n v = v[:]\n v[0] = v[0] + v[1]\n v[1] = rotate_left(v[1], rot)\n v[1] = v[0] ^ v[1]\n return v\n\n odd_size = count.size % 2\n if odd_size:\n x = list(np.split(np.concatenate([count.ravel(), onp.uint32([0])]), 2))\n else:\n x = list(np.split(count.ravel(), 2))\n\n rotations = [13, 15, 26, 6, 17, 29, 16, 24]\n ks = [key1, key2, key1 ^ key2 ^ onp.uint32(0x1BD11BDA)]\n\n x[0] = x[0] + ks[0]\n x[1] = x[1] + ks[1]\n\n for r in rotations[:4]:\n x = apply_round(x, r)\n x[0] = x[0] + ks[1]\n x[1] = x[1] + ks[2] + onp.uint32(1)\n\n for r in rotations[4:]:\n x = apply_round(x, r)\n x[0] = x[0] + ks[2]\n x[1] = x[1] + ks[0] + onp.uint32(2)\n\n for r in rotations[:4]:\n x = apply_round(x, r)\n x[0] = x[0] + ks[0]\n x[1] = x[1] + ks[1] + onp.uint32(3)\n\n for r in rotations[4:]:\n x = apply_round(x, r)\n x[0] = x[0] + ks[1]\n x[1] = x[1] + ks[2] + onp.uint32(4)\n\n for r in rotations[:4]:\n x = apply_round(x, r)\n x[0] = x[0] + ks[2]\n x[1] = x[1] + ks[0] + onp.uint32(5)\n\n out = np.concatenate(x)\n assert out.dtype == onp.uint32\n return lax.reshape(out[:-1] if odd_size else out, count.shape)","label":0},{"cid":"jax/random.py::threefry_2x32.apply_round","text":"jax/random.py::threefry_2x32.apply_round\n def apply_round(v, rot):\n v = v[:]\n v[0] = v[0] + v[1]\n v[1] = rotate_left(v[1], rot)\n v[1] = v[0] ^ v[1]\n return v","label":0},{"cid":"jax/random.py::split","text":"jax/random.py::split\n@partial(jit, static_argnums=(1,))\ndef split(key, num=2):\n \"\"\"Splits a PRNG key into `num` new keys by adding a leading axis.\n\n Args:\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\n num: optional, a positive integer indicating the number of keys to produce\n (default 2).\n\n Returns:\n An array with shape (num, 2) and dtype uint32 representing `num` new keys.\n \"\"\"\n counts = lax.tie_in(key, lax.iota(onp.uint32, num * 2))\n return lax.reshape(threefry_2x32(key, counts), (num, 2))","label":0},{"cid":"jax/random.py::fold_in","text":"jax/random.py::fold_in\n@partial(jit, static_argnums=(1,))\ndef fold_in(key, data):\n \"\"\"Folds in data to a PRNG key to form a new PRNG key.\n\n Args:\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\n data: an integer representing data to be folded in to the key.\n\n Returns:\n A new PRNGKey that is a deterministic function of the inputs and is\n statistically safe for producing a stream of new pseudo-random values.\n \"\"\"\n key2 = lax.tie_in(key, PRNGKey(data))\n return threefry_2x32(key, key2)","label":0},{"cid":"jax/random.py::_random_bits","text":"jax/random.py::_random_bits\ndef _random_bits(key, bit_width, shape):\n \"\"\"Sample uniform random bits of given width and shape using PRNG key.\"\"\"\n if not _is_prng_key(key):\n raise TypeError(\"_random_bits got invalid prng key.\")\n if bit_width not in (32, 64):\n raise TypeError(\"requires 32- or 64-bit field width.\")\n max_count = (bit_width // 32) * onp.prod(shape)\n if max_count >= onp.iinfo(onp.uint32).max:\n # TODO(mattjj): just split the key here\n raise TypeError(\"requesting more random bits than a single call provides.\")\n\n counts = lax.tie_in(key, lax.iota(onp.uint32, max_count))\n bits = threefry_2x32(key, counts)\n if bit_width == 64:\n bits = [lax.convert_element_type(x, onp.uint64) for x in np.split(bits, 2)]\n bits = (bits[0] << onp.uint64(32)) | bits[1]\n return lax.reshape(bits, shape)","label":0},{"cid":"jax/random.py::uniform","text":"jax/random.py::uniform\n@partial(jit, static_argnums=(1, 2))\ndef uniform(key, shape, dtype=onp.float32, minval=0., maxval=1.):\n \"\"\"Sample uniform random values in [minval, maxval) with given shape/dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: a tuple of nonnegative integers representing the shape.\n dtype: optional, a float dtype for the returned values (default float32).\n minval: optional, a minimum (inclusive) value for the range (default 0).\n maxval: optional, a maximum (exclusive) value for the range (default 1).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n if not onp.issubdtype(dtype, onp.floating):\n raise TypeError(\"uniform only accepts floating point dtypes.\")\n\n dtype = xla_bridge.canonicalize_dtype(dtype)\n minval = lax.convert_element_type(minval, dtype)\n maxval = lax.convert_element_type(maxval, dtype)\n finfo = onp.finfo(dtype)\n nbits, nmant = finfo.bits, finfo.nmant\n\n if nbits not in (32, 64):\n raise TypeError(\"uniform only accepts 32- or 64-bit dtypes.\")\n\n bits = _random_bits(key, nbits, shape)\n\n # The strategy here is to randomize only the mantissa bits with an exponent of\n # 1 (after applying the bias), then shift and scale to the desired range. The\n # bit-level transformation we use relies on Numpy and XLA having bit-for-bit\n # equivalent float representations, which might not be true on all platforms.\n float_bits = lax.bitwise_or(\n lax.shift_right_logical(bits, onp.array(nbits - nmant, lax._dtype(bits))),\n onp.array(1., dtype).view(onp.uint32 if nbits == 32 else onp.uint64))\n floats = lax.bitcast_convert_type(float_bits, dtype) - onp.array(1., dtype)\n return lax.max(\n minval,\n lax.reshape(floats * (maxval - minval) + minval, shape))","label":0},{"cid":"jax/random.py::randint","text":"jax/random.py::randint\n@partial(jit, static_argnums=(1, 4))\ndef randint(key, shape, minval, maxval, dtype=onp.int32):\n \"\"\"Sample uniform random values in [minval, maxval) with given shape/dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: a tuple of nonnegative integers representing the shape.\n minval: optional, a minimum (inclusive) value for the range (default 0).\n maxval: optional, a maximum (exclusive) value for the range (default 1).\n dtype: optional, an int dtype for the returned values (default int32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n if not onp.issubdtype(dtype, onp.integer):\n raise TypeError(\"randint only accepts integer dtypes.\")\n\n dtype = xla_bridge.canonicalize_dtype(dtype)\n minval = lax.convert_element_type(minval, dtype)\n maxval = lax.convert_element_type(maxval, dtype)\n nbits = onp.iinfo(dtype).bits\n\n if nbits not in (32, 64):\n raise TypeError(\"randint only accepts 32- or 64-bit dtypes.\")\n\n # if we don't have minval < maxval, just always return minval\n # https://github.com/google/jax/issues/222\n maxval = lax.max(lax.add(minval, onp.array(1, dtype)), maxval)\n\n # This algorithm is biased whenever (maxval - minval) is not a power of 2.\n # We generate double the number of random bits required by the dtype so as to\n # reduce that bias.\n k1, k2 = split(key)\n rbits = lambda key: _random_bits(key, nbits, shape)\n higher_bits, lower_bits = rbits(k1), rbits(k2)\n\n unsigned_dtype = onp.uint32 if nbits == 32 else onp.uint64\n span = lax.convert_element_type(maxval - minval, unsigned_dtype)\n\n # To compute a remainder operation on an integer that might have twice as many\n # bits as we can represent in the native unsigned dtype, we compute a\n # multiplier equal to 2**nbits % span (using that nbits is 32 or 64).\n multiplier = lax.rem(onp.array(2**16, unsigned_dtype), span)\n multiplier = lax.rem(lax.mul(multiplier, multiplier), span)\n if nbits == 64:\n multiplier = lax.re","label":0},{"cid":"jax/random.py::shuffle","text":"jax/random.py::shuffle\n@partial(jit, static_argnums=(2,))\ndef shuffle(key, x, axis=0):\n \"\"\"Shuffle the elements of an array uniformly at random along an axis.\n\n Args:\n key: a PRNGKey used as the random key.\n x: the array to be shuffled.\n axis: optional, an int axis along which to shuffle (default 0).\n\n Returns:\n A shuffled version of x.\n \"\"\"\n # On parallel architectures, Fisher-Yates is more expensive than doing\n # multiple sorts. This algorithm is based on one developed and analyzed by\n # tjablin@. We sort according to randomly-generated 32bit keys, but those keys\n # may have collisions. If we repeat the process, using fresh 32bit keys for\n # each sort, then whenever all pairs of elements have been assigned distinct\n # keys at some iteration (or equivalently when the strings formed by\n # concatenating the successive keys for each element are all distinct) then we\n # are guaranteed to have a perfect sample (assuming that either the sort is\n # stable or that any bias is not value-dependent). Since checking uniqueness\n # at runtime may be expensive, we use a heuristic static stop criterion\n # developed by tjablin@. See tensorflow/compiler/tf2xla/random_ops.cc for more\n # info, and for the original implementation of this algorithm. See also\n # Section 2 of http://people.csail.mit.edu/costis/6896sp11/lec5s.pdf for\n # another analysis (where the keys are generated one bit at a time).\n exponent = 3 # see tjablin@'s analysis for explanation of this parameter\n uint32max = onp.iinfo(onp.uint32).max\n num_rounds = int(onp.ceil(exponent * onp.log(x.size) / onp.log(uint32max)))\n\n for _ in range(num_rounds):\n key, subkey = split(key)\n sort_keys = _random_bits(subkey, 32, x.shape)\n _, x = lax.sort_key_val(sort_keys, x, axis)\n\n return x","label":0},{"cid":"jax/random.py::normal","text":"jax/random.py::normal\n@partial(jit, static_argnums=(1, 2))\ndef normal(key, shape, dtype=onp.float32):\n \"\"\"Sample standard normal random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: a tuple of nonnegative integers representing the shape.\n dtype: optional, a float dtype for the returned values (default float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n lo = onp.nextafter(onp.array(-1., dtype), 0., dtype=dtype)\n hi = onp.array(1., dtype)\n u = uniform(key, shape, dtype, lo, hi)\n return onp.array(onp.sqrt(2), dtype) * lax.erf_inv(u)","label":0},{"cid":"jax/random.py::bernoulli","text":"jax/random.py::bernoulli\n@partial(jit, static_argnums=(2,))\ndef bernoulli(key, mean=onp.float32(0.5), shape=()):\n \"\"\"Sample Bernoulli random values with given shape and mean.\n\n Args:\n key: a PRNGKey used as the random key.\n mean: optional, an array-like broadcastable to `shape` for the mean of the\n random variables (default 0.5).\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n\n Returns:\n A random array with the specified shape and boolean dtype.\n \"\"\"\n shape = shape or onp.shape(mean)\n if not onp.issubdtype(lax._dtype(mean), onp.float32):\n mean = lax.convert_element_type(mean, onp.float32)\n if onp.shape(mean) != shape:\n mean = np.broadcast_to(mean, shape)\n return lax.lt(uniform(key, shape), mean)","label":0}],"string":"[\n {\n \"cid\": \"jax/api.py::jit\",\n \"text\": \"jax/api.py::jit\\ndef jit(fun, static_argnums=()):\\n \\\"\\\"\\\"Sets up `fun` for just-in-time compilation with XLA.\\n\\n Args:\\n fun: Function to be jitted. Should be a pure function, as side-effects may\\n only be executed once. Its positional arguments and return value should be\\n arrays, scalars, or standard Python containers (tuple/list/dict) thereof.\\n Keyword arguments and positional arguments specified by `static_argnums`\\n can be anything at all. These are treated as static (see below).\\n static_argnums: A tuple of ints. Specifies which arguments to treat as\\n static (compile-time constant). Operations that only depend on static\\n arguments will be constant-folded. Calling the jitted function with\\n different values for these constants will trigger recompilation.\\n\\n Returns:\\n A wrapped version of `fun`, set up for just-in-time compilation.\\n\\n In the following example, `selu` can be compiled into a single fused kernel by\\n XLA:\\n\\n >>> @jax.jit\\n >>> def selu(x, alpha=1.67, lmbda=1.05):\\n >>> return lmbda * jax.numpy.where(x > 0, x, alpha * jax.numpy.exp(x) - alpha)\\n >>>\\n >>> key = jax.random.PRNGKey(0)\\n >>> x = jax.random.normal(key, (10,))\\n >>> selu(x)\\n array([-0.54485154, 0.27744263, -0.29255125, -0.91421586, -0.62452525,\\n -0.2474813 , -0.8574326 , -0.7823267 , 0.7682731 , 0.59566754],\\n dtype=float32)\\n\\n \\\"\\\"\\\"\\n @wraps(fun)\\n def f_jitted(*args, **kwargs):\\n if _jit_is_disabled or config.read('jax_disable_jit'):\\n return fun(*args, **kwargs)\\n f = lu.wrap_init(fun, kwargs)\\n dyn_argnums = [i for i in range(len(args)) if i not in static_argnums]\\n f, dyn_args = _argnums_partial(f, dyn_argnums, args)\\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, dyn_args))\\n _check_args(jaxtupletree_args)\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n jaxtupletree_out = xla.xla_call(jaxtree_fun, *jaxtupletree_args)\\n return build_tree(out_tree(), jaxtupletree_out)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jit.f_jitted\",\n \"text\": \"jax/api.py::jit.f_jitted\\n @wraps(fun)\\n def f_jitted(*args, **kwargs):\\n if _jit_is_disabled or config.read('jax_disable_jit'):\\n return fun(*args, **kwargs)\\n f = lu.wrap_init(fun, kwargs)\\n dyn_argnums = [i for i in range(len(args)) if i not in static_argnums]\\n f, dyn_args = _argnums_partial(f, dyn_argnums, args)\\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, dyn_args))\\n _check_args(jaxtupletree_args)\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n jaxtupletree_out = xla.xla_call(jaxtree_fun, *jaxtupletree_args)\\n return build_tree(out_tree(), jaxtupletree_out)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::disable_jit\",\n \"text\": \"jax/api.py::disable_jit\\n@contextmanager\\ndef disable_jit():\\n \\\"\\\"\\\"Context manager that disables `jit`.\\n\\n For debugging purposes, it is useful to have a mechanism that disables `jit`\\n everywhere in a block of code, namely the `disable_jit` decorator.\\n\\n Inside a `jit`-ted function the values flowing through\\n traced code can be abstract (i.e., shaped arrays with an unknown values),\\n instead of concrete (i.e., specific arrays with known values).\\n\\n For example:\\n\\n >>> @jax.jit\\n >>> def f(x):\\n >>> y = x *2\\n >>> print(\\\"Value of y is\\\", y)\\n >>> return y + 3\\n >>>\\n >>> print(f(jax.numpy.array([1, 2, 3])))\\n Value of y is Traced<ShapedArray(int32[3]):JaxprTrace(level=-1/1)>\\n [5 7 9]\\n\\n Here `y` has been abstracted by `jit` to a `ShapedArray`, which represents an\\n array with a fixed shape and type but an arbitrary value. If we want to see a\\n concrete values while debugging, we can use the `disable_jit` decorator, at\\n the cost of slower code:\\n\\n >>> with jax.disable_jit():\\n >>> print(f(np.array([1, 2, 3])))\\n >>>\\n Value of y is [2 4 6]\\n [5 7 9]\\n \\\"\\\"\\\"\\n global _jit_is_disabled\\n _jit_is_disabled, prev_val = True, _jit_is_disabled\\n yield\\n _jit_is_disabled = prev_val\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::xla_computation\",\n \"text\": \"jax/api.py::xla_computation\\ndef xla_computation(fun, static_argnums=()):\\n def pv_like(x):\\n aval = xla.abstractify(x)\\n return pe.PartialVal((aval, core.unit))\\n\\n wrapped = lu.wrap_init(fun)\\n\\n @wraps(fun)\\n def computation_maker(*args, **kwargs):\\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\\n pvals = map(pv_like, jax_args)\\n jaxpr, _, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\\n return xla.build_jaxpr(jaxpr, consts, *map(xla.abstractify, args))\\n\\n return computation_maker\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::xla_computation.pv_like\",\n \"text\": \"jax/api.py::xla_computation.pv_like\\n def pv_like(x):\\n aval = xla.abstractify(x)\\n return pe.PartialVal((aval, core.unit))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::xla_computation.computation_maker\",\n \"text\": \"jax/api.py::xla_computation.computation_maker\\n @wraps(fun)\\n def computation_maker(*args, **kwargs):\\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\\n pvals = map(pv_like, jax_args)\\n jaxpr, _, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\\n return xla.build_jaxpr(jaxpr, consts, *map(xla.abstractify, args))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::grad\",\n \"text\": \"jax/api.py::grad\\ndef grad(fun, argnums=0, has_aux=False):\\n \\\"\\\"\\\"Creates a function which evaluates the gradient of `fun`.\\n\\n Args:\\n fun: Function to be differentiated. Its arguments at positions specified by\\n `argnums` should be arrays, scalars, or standard Python containers. It\\n should return a scalar (which includes arrays with shape `()` but not\\n arrays with shape `(1,)` etc.)\\n argnums: Optional, integer or tuple of integers. Specifies which positional\\n argument(s) to differentiate with respect to (default 0).\\n has_aux: Optional, bool. Indicates whether `fun` returns a pair where the\\n first element is considered the output of the mathematical function to be\\n differentiated and the second element is auxiliary data. Default False.\\n\\n Returns:\\n A function with the same arguments as `fun`, that evaluates the gradient of\\n `fun`. If `argnums` is an integer then the gradient has the same shape and\\n type as the positional argument indicated by that integer. If argnums is a\\n tuple of integers, the gradient is a tuple of values with the same shapes\\n and types as the corresponding arguments. If `has_aux` is True then a pair\\n of (gradient, auxiliary_data) is returned.\\n\\n For example:\\n\\n >>> grad_tanh = jax.grad(jax.numpy.tanh)\\n >>> grad_tanh(0.2)\\n array(0.961043, dtype=float32)\\n\\n \\\"\\\"\\\"\\n value_and_grad_f = value_and_grad(fun, argnums, has_aux=has_aux)\\n\\n docstr = (\\\"Gradient of {fun} with respect to positional argument(s) \\\"\\n \\\"{argnums}. Takes the same arguments as {fun} but returns the \\\"\\n \\\"gradient, which has the same shape as the arguments at \\\"\\n \\\"positions {argnums}.\\\")\\n\\n @wraps(fun, docstr=docstr, argnums=argnums)\\n def grad_f(*args, **kwargs):\\n if not has_aux:\\n _, g = value_and_grad_f(*args, **kwargs)\\n return g\\n else:\\n (_, aux), g = value_and_grad_f(*args, **kwargs)\\n return g, aux\\n\\n return grad_f\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::grad.grad_f\",\n \"text\": \"jax/api.py::grad.grad_f\\n @wraps(fun, docstr=docstr, argnums=argnums)\\n def grad_f(*args, **kwargs):\\n if not has_aux:\\n _, g = value_and_grad_f(*args, **kwargs)\\n return g\\n else:\\n (_, aux), g = value_and_grad_f(*args, **kwargs)\\n return g, aux\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::value_and_grad\",\n \"text\": \"jax/api.py::value_and_grad\\ndef value_and_grad(fun, argnums=0, has_aux=False):\\n \\\"\\\"\\\"Creates a function which evaluates both `fun` and the gradient of `fun`.\\n\\n Args:\\n fun: Function to be differentiated. Its arguments at positions specified by\\n `argnums` should be arrays, scalars, or standard Python containers. It\\n should return a scalar (which includes arrays with shape `()` but not\\n arrays with shape `(1,)` etc.)\\n argnums: Optional, integer or tuple of integers. Specifies which positional\\n argument(s) to differentiate with respect to (default 0).\\n has_aux: Optional, bool. Indicates whether `fun` returns a pair where the\\n first element is considered the output of the mathematical function to be\\n differentiated and the second element is auxiliary data. Default False.\\n\\n Returns:\\n A function with the same arguments as `fun` that evaluates both `fun` and\\n the gradient of `fun` and returns them as a pair (a two-element tuple). If\\n `argnums` is an integer then the gradient has the same shape and type as the\\n positional argument indicated by that integer. If argnums is a tuple of\\n integers, the gradient is a tuple of values with the same shapes and types\\n as the corresponding arguments.\\n \\\"\\\"\\\"\\n\\n docstr = (\\\"Value and gradient of {fun} with respect to positional \\\"\\n \\\"argument(s) {argnums}. Takes the same arguments as {fun} but \\\"\\n \\\"returns a two-element tuple where the first element is the value \\\"\\n \\\"of {fun} and the second element is the gradient, which has the \\\"\\n \\\"same shape as the arguments at positions {argnums}.\\\")\\n\\n @wraps(fun, docstr=docstr, argnums=argnums)\\n def value_and_grad_f(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n if not has_aux:\\n ans, vjp_py = vjp(f_partial, *dyn_args)\\n else:\\n ans, vjp_py, aux = vjp(f_partial, *dyn_args, has_aux=True)\\n _check_scalar(ans)\\n g = vjp_py(onp.on\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::value_and_grad.value_and_grad_f\",\n \"text\": \"jax/api.py::value_and_grad.value_and_grad_f\\n @wraps(fun, docstr=docstr, argnums=argnums)\\n def value_and_grad_f(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n if not has_aux:\\n ans, vjp_py = vjp(f_partial, *dyn_args)\\n else:\\n ans, vjp_py, aux = vjp(f_partial, *dyn_args, has_aux=True)\\n _check_scalar(ans)\\n g = vjp_py(onp.ones((), onp.result_type(ans)))\\n g = g[0] if isinstance(argnums, int) else g\\n if not has_aux:\\n return ans, g\\n else:\\n return (ans, aux), g\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jacfwd\",\n \"text\": \"jax/api.py::jacfwd\\ndef jacfwd(fun, argnums=0):\\n \\\"\\\"\\\"Jacobian of `fun` evaluated column-by-column using forward-mode AD.\\n\\n Args:\\n fun: Function whose Jacobian is to be computed.\\n argnums: Optional, integer or tuple of integers. Specifies which positional\\n argument(s) to differentiate with respect to (default `0`).\\n\\n Returns:\\n A function with the same arguments as `fun`, that evaluates the Jacobian of\\n `fun` using forward-mode automatic differentiation.\\n\\n >>> def f(x):\\n >>> return jax.numpy.asarray(\\n >>> [x[0], 5*x[2], 4*x[1]**2 - 2*x[2], x[2] * jax.numpy.sin(x[0])])\\n >>> jax.jacfwd(f)(np.array([1., 2., 3.]))\\n array([[ 1. , 0. , 0. ],\\n [ 0. , 0. , 5. ],\\n [ 0. , 16. , -2. ],\\n [ 1.6209068 , 0. , 0.84147096]], dtype=float32)\\n \\\"\\\"\\\"\\n\\n def jacfun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n pushfwd = partial(jvp, f_partial, dyn_args)\\n y, jac = vmap(pushfwd, out_axes=(None, -1))(_std_basis(dyn_args))\\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\\n return tree_map(partial(_unravel_array_into_pytree, example_args, -1), jac)\\n\\n return jacfun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jacfwd.jacfun\",\n \"text\": \"jax/api.py::jacfwd.jacfun\\n def jacfun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n pushfwd = partial(jvp, f_partial, dyn_args)\\n y, jac = vmap(pushfwd, out_axes=(None, -1))(_std_basis(dyn_args))\\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\\n return tree_map(partial(_unravel_array_into_pytree, example_args, -1), jac)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jacrev\",\n \"text\": \"jax/api.py::jacrev\\ndef jacrev(fun, argnums=0):\\n \\\"\\\"\\\"Jacobian of `fun` evaluated row-by-row using reverse-mode AD.\\n\\n Args:\\n fun: Function whose Jacobian is to be computed.\\n argnums: Optional, integer or tuple of integers. Specifies which positional\\n argument(s) to differentiate with respect to (default `0`).\\n\\n Returns:\\n A function with the same arguments as `fun`, that evaluates the Jacobian of\\n `fun` using reverse-mode automatic differentiation.\\n\\n >>> def f(x):\\n >>> return jax.numpy.asarray(\\n >>> [x[0], 5*x[2], 4*x[1]**2 - 2*x[2], x[2] * jax.numpy.sin(x[0])])\\n >>> jax.jacrev(f)(np.array([1., 2., 3.]))\\n array([[ 1. , 0. , 0. ],\\n [ 0. , 0. , 5. ],\\n [ 0. , 16. , -2. ],\\n [ 1.6209068 , 0. , 0.84147096]], dtype=float32)\\n \\\"\\\"\\\"\\n def jacfun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n y, pullback = vjp(f_partial, *dyn_args)\\n jac = vmap(pullback)(_std_basis(y))\\n jac = jac[0] if isinstance(argnums, int) else jac\\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\\n jac = tree_map(partial(_unravel_array_into_pytree, y, 0), jac)\\n return tree_transpose(tree_structure(example_args), tree_structure(y), jac)\\n\\n return jacfun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jacrev.jacfun\",\n \"text\": \"jax/api.py::jacrev.jacfun\\n def jacfun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n y, pullback = vjp(f_partial, *dyn_args)\\n jac = vmap(pullback)(_std_basis(y))\\n jac = jac[0] if isinstance(argnums, int) else jac\\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\\n jac = tree_map(partial(_unravel_array_into_pytree, y, 0), jac)\\n return tree_transpose(tree_structure(example_args), tree_structure(y), jac)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::hessian\",\n \"text\": \"jax/api.py::hessian\\ndef hessian(fun, argnums=0):\\n \\\"\\\"\\\"Hessian of `fun`.\\n\\n Args:\\n fun: Function whose Hessian is to be computed.\\n argnums: Optional, integer or tuple of integers. Specifies which positional\\n argument(s) to differentiate with respect to (default `0`).\\n\\n Returns:\\n A function with the same arguments as `fun`, that evaluates the Hessian of\\n `fun`.\\n\\n >>> g = lambda(x): x[0]**3 - 2*x[0]*x[1] - x[1]**6\\n >>> jax.hessian(g)(jax.numpy.array([1., 2.]))\\n array([[ 6., -2.],\\n [ -2., -480.]], dtype=float32)\\n \\\"\\\"\\\"\\n\\n return jacfwd(jacrev(fun, argnums=argnums), argnums=argnums)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::_std_basis\",\n \"text\": \"jax/api.py::_std_basis\\ndef _std_basis(pytree):\\n leaves, _ = tree_flatten(pytree)\\n ndim = sum(map(onp.size, leaves))\\n return _unravel_array_into_pytree(pytree, 1, onp.eye(ndim))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::_unravel_array_into_pytree\",\n \"text\": \"jax/api.py::_unravel_array_into_pytree\\ndef _unravel_array_into_pytree(pytree, axis, arr):\\n leaves, treedef = tree_flatten(pytree)\\n axis = axis % arr.ndim\\n dtypes = map(_dtype, leaves)\\n shapes = [arr.shape[:axis] + onp.shape(l) + arr.shape[axis+1:] for l in leaves]\\n parts = _split(arr, onp.cumsum(map(onp.size, leaves[:-1])), axis)\\n reshaped_parts = [onp.reshape(part.astype(dtype), shape)\\n for part, dtype, shape in zip(parts, dtypes, shapes)]\\n return tree_unflatten(treedef, reshaped_parts)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::_split\",\n \"text\": \"jax/api.py::_split\\ndef _split(x, indices, axis):\\n if isinstance(x, onp.ndarray):\\n return onp.split(x, indices, axis)\\n else:\\n return x.split(indices, axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::_dtype\",\n \"text\": \"jax/api.py::_dtype\\ndef _dtype(x):\\n return canonicalize_dtype(onp.result_type(x))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::vmap\",\n \"text\": \"jax/api.py::vmap\\ndef vmap(fun, in_axes=0, out_axes=0):\\n \\\"\\\"\\\"Vectorizing map. Creates a function which maps `fun` over additional axes.\\n\\n Args:\\n fun: Function to be mapped over additional axes.\\n in_axes: Specifies which input axes to map over. These may be integers,\\n `None`, or (possibly nested) tuples of integers or `None`.\\n out_axes: Specifies which output axes to map over. These may be integers,\\n `None`, or (possibly nested) tuples of integers or `None`.\\n\\n Returns:\\n Batched/vectorized version of `fun` with arguments that correspond to those\\n of `fun`, but with extra array axes at positions indicated by `in_axes`, and\\n a return value that corresponds to that of `fun`, but with extra array axes\\n at positions indicated by `out_axes`.\\n\\n For example, we can implement a matrix-matrix product using a vector dot\\n product:\\n\\n >>> vv = lambda x, y: np.vdot(x, y) # ([a], [a]) -> []\\n >>> mv = vmap(vv, (0, None), 0) # ([a,b], [b]) -> [a]\\n >>> mm = vmap(mv, (None, 1), 1) # ([a,b], [b,c]) -> [a,c]\\n\\n (`[a,b]` indicates an array with shape (a,b))\\n \\\"\\\"\\\"\\n\\n docstr = (\\\"Vectorized version of {fun}. Takes similar arguments as {fun} \\\"\\n \\\"but with additional array axes over which {fun} is mapped.\\\")\\n\\n if (not isinstance(in_axes, (list, tuple, type(None), int))\\n or not isinstance(out_axes, (list, tuple, type(None), int))):\\n msg = (\\\"vmap arguments in_axes and out_axes must each be an integer, None, \\\"\\n \\\"or a (nested) tuple of those types, got {} and {} respectively.\\\")\\n raise TypeError(msg.format(type(in_axes), type(out_axes)))\\n\\n @wraps(fun, docstr=docstr)\\n def batched_fun(*args, **kwargs):\\n if not isinstance(fun, lu.WrappedFun):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = ba\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::vmap.batched_fun\",\n \"text\": \"jax/api.py::vmap.batched_fun\\n @wraps(fun, docstr=docstr)\\n def batched_fun(*args, **kwargs):\\n if not isinstance(fun, lu.WrappedFun):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = batching.batch(jaxtree_fun, in_flat, in_axes_, out_axes)\\n return build_tree(out_tree(), out_flat)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::pmap\",\n \"text\": \"jax/api.py::pmap\\ndef pmap(fun, axis_name=None):\\n \\\"\\\"\\\"Set up SPMD function for JIT compilation and parallel execution with XLA.\\\"\\\"\\\"\\n axis_name = _TempAxisName() if axis_name is None else axis_name\\n\\n @wraps(fun)\\n def f_jitted(*args, **kwargs):\\n leaves, _ = tree_flatten(args)\\n axis_sizes = set(onp.shape(leaf)[0] for leaf in leaves)\\n if len(axis_sizes) != 1:\\n msg = \\\"pmap requires all leading axes to have equal length, got {}.\\\"\\n raise TypeError(msg.format(axis_sizes))\\n axis_size = axis_sizes.pop()\\n\\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n _check_args(jaxtupletree_args)\\n f = lu.wrap_init(fun, kwargs)\\n f, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n jaxtupletree_out = pxla.xla_pcall(f, *jaxtupletree_args,\\n axis_name=axis_name, axis_size=axis_size)\\n return build_tree(out_tree(), jaxtupletree_out)\\n\\n namestr = \\\"pmap({}, axis_name={})\\\".format\\n f_jitted.__name__ = namestr(f_jitted.__name__, axis_name)\\n return f_jitted\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::pmap.f_jitted\",\n \"text\": \"jax/api.py::pmap.f_jitted\\n @wraps(fun)\\n def f_jitted(*args, **kwargs):\\n leaves, _ = tree_flatten(args)\\n axis_sizes = set(onp.shape(leaf)[0] for leaf in leaves)\\n if len(axis_sizes) != 1:\\n msg = \\\"pmap requires all leading axes to have equal length, got {}.\\\"\\n raise TypeError(msg.format(axis_sizes))\\n axis_size = axis_sizes.pop()\\n\\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n _check_args(jaxtupletree_args)\\n f = lu.wrap_init(fun, kwargs)\\n f, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n jaxtupletree_out = pxla.xla_pcall(f, *jaxtupletree_args,\\n axis_name=axis_name, axis_size=axis_size)\\n return build_tree(out_tree(), jaxtupletree_out)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::serial_pmap\",\n \"text\": \"jax/api.py::serial_pmap\\ndef serial_pmap(fun, axis_name=None, in_axes=0, out_axes=0):\\n \\\"\\\"\\\"Vectorizing pseudo-map for single-program multiple-data (SPMD) functions.\\\"\\\"\\\"\\n axis_name = _TempAxisName() if axis_name is None else axis_name\\n\\n def map_fun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = parallel.serial_pmap(jaxtree_fun, axis_name, in_flat, in_axes_, out_axes)\\n return build_tree(out_tree(), out_flat)\\n\\n return map_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::serial_pmap.map_fun\",\n \"text\": \"jax/api.py::serial_pmap.map_fun\\n def map_fun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = parallel.serial_pmap(jaxtree_fun, axis_name, in_flat, in_axes_, out_axes)\\n return build_tree(out_tree(), out_flat)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::_TempAxisName.__repr__\",\n \"text\": \"jax/api.py::_TempAxisName.__repr__\\n def __repr__(self):\\n return '<temp axis {}>'.format(hex(id(self)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::papply\",\n \"text\": \"jax/api.py::papply\\ndef papply(fun, axis_size, in_axes=0, out_axes=0):\\n \\\"\\\"\\\"Apply a function using parallel computation by sharding inputs.\\\"\\\"\\\"\\n axis_name = parallel.newvar()\\n\\n def papply_fun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n args_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = parallel.papply(jaxtree_fun, axis_name, args_flat, axis_size,\\n in_axes_, out_axes)\\n return build_tree(out_tree(), out_flat)\\n\\n return papply_fun, axis_name\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::papply.papply_fun\",\n \"text\": \"jax/api.py::papply.papply_fun\\n def papply_fun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n args_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = parallel.papply(jaxtree_fun, axis_name, args_flat, axis_size,\\n in_axes_, out_axes)\\n return build_tree(out_tree(), out_flat)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jvp\",\n \"text\": \"jax/api.py::jvp\\ndef jvp(fun, primals, tangents):\\n \\\"\\\"\\\"Computes a (forward-mode) Jacobian-vector product of `fun`.\\n\\n Args:\\n fun: Function to be differentiated. Its arguments should be arrays, scalars,\\n or standard Python containers of arrays or scalars. It should return an\\n array, scalar, or standard Python container of arrays or scalars.\\n primals: The primal values at which the Jacobian of `fun` should be\\n evaluated. Should be a tuple of arrays, scalar, or standard Python\\n container thereof. The length of the tuple is equal to the number of\\n positional parameters of `fun`.\\n tangents: The tangent vector for which the Jacobian-vector product should be\\n evaluated. Should be a tuple of arrays, scalar, or standard Python\\n container thereof, with the same tree structure and array shapes as\\n `primals`.\\n\\n Returns:\\n A `(primals_out, tangents_out)` pair, where `primals_out` is\\n `fun(*primals)`, and `tangents_out` is the Jacobian-vector product of\\n `function` evaluated at `primals` with `tangents`. The `tangents_out` value\\n has the same Python tree structure and shapes as `primals_out`.\\n\\n For example:\\n\\n >>> jax.jvp(jax.numpy.sin, (0.1,), (0.2,))\\n (array(0.09983342, dtype=float32), array(0.19900084, dtype=float32))\\n \\\"\\\"\\\"\\n def trim_arg(primal, tangent):\\n primal_jtuple, tree_def = pytree_to_jaxtupletree(primal)\\n tangent_jtuple, tree_def_2 = pytree_to_jaxtupletree(tangent)\\n assert tree_def == tree_def_2, (tree_def, tree_def_2)\\n return primal_jtuple, tangent_jtuple, tree_def\\n\\n if not isinstance(fun, lu.WrappedFun):\\n fun = lu.wrap_init(fun)\\n ps_flat, ts_flat, in_trees = unzip3(map(trim_arg, primals, tangents))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\\n out_primal, out_tangent = ad.jvp(jaxtree_fun).call_wrapped(ps_flat, ts_flat)\\n return (build_tree(out_tree(), out_primal), build_tree(out_tree(), out_tangent))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jvp.trim_arg\",\n \"text\": \"jax/api.py::jvp.trim_arg\\n def trim_arg(primal, tangent):\\n primal_jtuple, tree_def = pytree_to_jaxtupletree(primal)\\n tangent_jtuple, tree_def_2 = pytree_to_jaxtupletree(tangent)\\n assert tree_def == tree_def_2, (tree_def, tree_def_2)\\n return primal_jtuple, tangent_jtuple, tree_def\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::linearize\",\n \"text\": \"jax/api.py::linearize\\ndef linearize(traceable, *primals):\\n fun = lu.wrap_init(traceable)\\n primals_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, primals))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\\n out_primal, out_pval, jaxpr, consts = ad.linearize(jaxtree_fun, *primals_flat)\\n out_tree = out_tree()\\n out_primal_py = build_tree(out_tree, out_primal)\\n lifted_jvp = partial(lift_linearized, jaxpr, consts, (in_trees, out_tree), out_pval)\\n return out_primal_py, lifted_jvp\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::lift_linearized\",\n \"text\": \"jax/api.py::lift_linearized\\ndef lift_linearized(jaxpr, consts, io_tree, out_pval, *py_args):\\n def fun(*args):\\n primals = pack(args) # doesn't matter what these are-they'll be ignored\\n tangents = pack(args)\\n _, ans = eval_jaxpr(jaxpr, consts, (), primals, tangents)\\n return pe.merge_pvals(ans, out_pval)\\n\\n return apply_jaxtree_fun(fun, io_tree, *py_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::lift_linearized.fun\",\n \"text\": \"jax/api.py::lift_linearized.fun\\n def fun(*args):\\n primals = pack(args) # doesn't matter what these are-they'll be ignored\\n tangents = pack(args)\\n _, ans = eval_jaxpr(jaxpr, consts, (), primals, tangents)\\n return pe.merge_pvals(ans, out_pval)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::vjp\",\n \"text\": \"jax/api.py::vjp\\ndef vjp(fun, *primals, **kwargs):\\n \\\"\\\"\\\"Compute a (reverse-mode) vector-Jacobian product of `fun`.\\n\\n `grad` is implemented as a special case of `vjp`.\\n\\n Args:\\n fun: Function to be differentiated. Its arguments should be arrays, scalars,\\n or standard Python containers of arrays or scalars. It should return an\\n array, scalar, or standard Python container of arrays or scalars.\\n primals: A sequence of primal values at which the Jacobian of `fun`\\n should be evaluated. The length of `primals` should be equal to the number\\n of positional parameters to `fun`. Each primal value should be a tuple of\\n arrays, scalar, or standard Python containers thereof.\\n has_aux: Optional, bool. Indicates whether `fun` returns a pair where the\\n first element is considered the output of the mathematical function to be\\n differentiated and the second element is auxiliary data. Default False.\\n\\n Returns:\\n A `(primals_out, vjpfun)` pair, where `primals_out` is `fun(*primals)`.\\n `vjpfun` is a function from a cotangent vector with the same shape as\\n `primals_out` to a tuple of cotangent vectors with the same shape as\\n `primals`, representing the vector-Jacobian product of `fun` evaluated at\\n `primals`.\\n\\n >>> def f(x, y):\\n >>> return jax.numpy.sin(x), jax.numpy.cos(y)\\n >>> primals, g = jax.vjp(f, 0.5, 1.0)\\n >>> g((-0.7, 0.3))\\n (array(-0.61430776, dtype=float32), array(-0.2524413, dtype=float32))\\n \\\"\\\"\\\"\\n has_aux = kwargs.pop('has_aux', False)\\n assert not kwargs\\n if not isinstance(fun, lu.WrappedFun):\\n fun = lu.wrap_init(fun)\\n primals_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, primals))\\n _check_args(primals_flat)\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\\n if not has_aux:\\n out_primal, out_vjp = ad.vjp(jaxtree_fun, primals_flat)\\n else:\\n out_primal, out_vjp, aux = ad.vjp(jaxtree_fun, primals_flat, has_aux=True)\\n out_tree = out_tree()\\n if has_aux:\\n out_tree, aux_tree \",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::vjp.out_vjp_packed\",\n \"text\": \"jax/api.py::vjp.out_vjp_packed\\n def out_vjp_packed(cotangent_in):\\n return out_vjp(cotangent_in)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::trace_to_jaxpr\",\n \"text\": \"jax/api.py::trace_to_jaxpr\\ndef trace_to_jaxpr(traceable, py_pvals, **kwargs):\\n fun = lu.wrap_init(traceable)\\n pvals, in_trees = unzip2(map(tree_to_pval_tuples, py_pvals))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\\n jaxpr, out_pval, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\\n return jaxpr, consts, out_pval, (in_trees, out_tree())\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::lift_jaxpr\",\n \"text\": \"jax/api.py::lift_jaxpr\\ndef lift_jaxpr(jaxpr, consts, io_tree, pvals, py_args):\\n def fun(*args):\\n ans = eval_jaxpr(jaxpr, consts, (), *args)\\n return pe.merge_pvals(ans, pvals)\\n return apply_jaxtree_fun(fun, io_tree, *py_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::lift_jaxpr.fun\",\n \"text\": \"jax/api.py::lift_jaxpr.fun\\n def fun(*args):\\n ans = eval_jaxpr(jaxpr, consts, (), *args)\\n return pe.merge_pvals(ans, pvals)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::make_jaxpr\",\n \"text\": \"jax/api.py::make_jaxpr\\ndef make_jaxpr(fun):\\n \\\"\\\"\\\"Adapts `fun` to return its `jaxpr` program representation.\\n\\n Args:\\n fun: The function whose `jaxpr` is to be computed. Its positional arguments\\n and return value should be arrays, scalars, or standard Python containers\\n (tuple/list/dict) thereof.\\n\\n Returns:\\n A wrapped version of `fun`, set up to return a `jaxpr`.\\n\\n A `jaxpr` is JAX's intermediate representation for program traces. The `jaxpr`\\n language is based on the simply-typed first-order lambda calculus with\\n let-bindings. `make_jaxpr` adapts a function to return its `jaxpr`, which we\\n can inspect to understand what JAX is doing internally.\\n\\n The `jaxpr` returned is a trace of `fun` abstracted to `ShapedArray` level.\\n Other levels of abstraction exist internally.\\n\\n We do not describe the semantics of the `jaxpr` language in detail here, but\\n instead give a few examples.\\n\\n >>> def f(x): return jax.numpy.sin(jax.numpy.cos(x))\\n >>> f(3.0)\\n array(-0.83602184, dtype=float32)\\n >>> jax.make_jaxpr(f)(3.0)\\n { lambda ; ; a.\\n let b = cos a\\n c = sin b\\n in c }\\n >>> jax.make_jaxpr(jax.grad(f))(3.0)\\n { lambda b ; ; a.\\n let c = pack a\\n (d) = id c\\n e = cos d\\n f = cos e\\n g = mul b f\\n h = neg g\\n i = sin d\\n j = mul h i\\n k = pack j\\n (l) = id k\\n in l }\\n \\\"\\\"\\\"\\n def pv_like(x):\\n aval = xla.abstractify(x)\\n return pe.PartialVal((aval, core.unit))\\n\\n wrapped = lu.wrap_init(fun)\\n\\n @wraps(fun)\\n def jaxpr_maker(*args, **kwargs):\\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\\n pvals = map(pv_like, jax_args)\\n jaxpr, _, _ = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\\n return jaxpr\\n\\n jaxpr_maker.__name__ = \\\"make_jaxpr({})\\\".format(jaxpr_maker.__name__)\\n return jaxpr_maker\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::make_jaxpr.pv_like\",\n \"text\": \"jax/api.py::make_jaxpr.pv_like\\n def pv_like(x):\\n aval = xla.abstractify(x)\\n return pe.PartialVal((aval, core.unit))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::relu\",\n \"text\": \"jax/experimental/stax.py::relu\\ndef relu(x): return np.maximum(x, 0.)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::softplus\",\n \"text\": \"jax/experimental/stax.py::softplus\\ndef softplus(x): return np.logaddexp(x, 0.)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::logsoftmax\",\n \"text\": \"jax/experimental/stax.py::logsoftmax\\ndef logsoftmax(x, axis=-1):\\n \\\"\\\"\\\"Apply log softmax to an array of logits, log-normalizing along an axis.\\\"\\\"\\\"\\n return x - logsumexp(x, axis, keepdims=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::softmax\",\n \"text\": \"jax/experimental/stax.py::softmax\\ndef softmax(x, axis=-1):\\n \\\"\\\"\\\"Apply softmax to an array of logits, exponentiating and normalizing along an axis.\\\"\\\"\\\"\\n unnormalized = np.exp(x - x.max(axis, keepdims=True))\\n return unnormalized / unnormalized.sum(axis, keepdims=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::fastvar\",\n \"text\": \"jax/experimental/stax.py::fastvar\\ndef fastvar(x, axis, keepdims):\\n \\\"\\\"\\\"A fast but less numerically-stable variance calculation than np.var.\\\"\\\"\\\"\\n return np.mean(x**2, axis, keepdims=keepdims) - np.mean(x, axis, keepdims=keepdims)**2\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::randn\",\n \"text\": \"jax/experimental/stax.py::randn\\ndef randn(stddev=1e-2, rng=npr):\\n \\\"\\\"\\\"An initializer function for random normal coefficients.\\\"\\\"\\\"\\n def init(shape):\\n return rng.normal(size=shape, scale=stddev).astype('float32')\\n return init\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::randn.init\",\n \"text\": \"jax/experimental/stax.py::randn.init\\n def init(shape):\\n return rng.normal(size=shape, scale=stddev).astype('float32')\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::glorot\",\n \"text\": \"jax/experimental/stax.py::glorot\\ndef glorot(out_dim=0, in_dim=1, scale=onp.sqrt(2), rng=npr):\\n \\\"\\\"\\\"An initializer function for random Glorot-scaled coefficients.\\\"\\\"\\\"\\n def init(shape):\\n fan_in, fan_out = shape[in_dim], shape[out_dim]\\n size = onp.prod(onp.delete(shape, [in_dim, out_dim]))\\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\\n return rng.normal(size=shape, scale=std).astype('float32')\\n return init\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::glorot.init\",\n \"text\": \"jax/experimental/stax.py::glorot.init\\n def init(shape):\\n fan_in, fan_out = shape[in_dim], shape[out_dim]\\n size = onp.prod(onp.delete(shape, [in_dim, out_dim]))\\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\\n return rng.normal(size=shape, scale=std).astype('float32')\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense\",\n \"text\": \"jax/experimental/stax.py::Dense\\ndef Dense(out_dim, W_init=glorot(), b_init=randn()):\\n \\\"\\\"\\\"Layer constructor function for a dense (fully-connected) layer.\\\"\\\"\\\"\\n def init_fun(input_shape):\\n output_shape = input_shape[:-1] + (out_dim,)\\n W, b = W_init((input_shape[-1], out_dim)), b_init((out_dim,))\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return np.dot(inputs, W) + b\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense.init_fun\",\n \"text\": \"jax/experimental/stax.py::Dense.init_fun\\n def init_fun(input_shape):\\n output_shape = input_shape[:-1] + (out_dim,)\\n W, b = W_init((input_shape[-1], out_dim)), b_init((out_dim,))\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Dense.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return np.dot(inputs, W) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv\",\n \"text\": \"jax/experimental/stax.py::GeneralConv\\ndef GeneralConv(dimension_numbers, out_chan, filter_shape,\\n strides=None, padding='VALID', W_init=None, b_init=randn(1e-6)):\\n \\\"\\\"\\\"Layer construction function for a general convolution layer.\\\"\\\"\\\"\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n one = (1,) * len(filter_shape)\\n strides = strides or one\\n W_init = W_init or glorot(rhs_spec.index('O'), rhs_spec.index('I'))\\n def init_fun(input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_general_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n W, b = W_init(kernel_shape), b_init(bias_shape)\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\\n dimension_numbers) + b\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv.init_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConv.init_fun\\n def init_fun(input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_general_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n W, b = W_init(kernel_shape), b_init(bias_shape)\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv.apply_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConv.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\\n dimension_numbers) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm\",\n \"text\": \"jax/experimental/stax.py::BatchNorm\\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-5, center=True, scale=True,\\n beta_init=zeros, gamma_init=ones):\\n \\\"\\\"\\\"Layer construction function for a batch normalization layer.\\\"\\\"\\\"\\n _beta_init = lambda shape: beta_init(shape) if center else ()\\n _gamma_init = lambda shape: gamma_init(shape) if scale else ()\\n axis = (axis,) if np.isscalar(axis) else axis\\n def init_fun(input_shape):\\n shape = (1 if i in axis else d for i, d in enumerate(input_shape))\\n shape = tuple(itertools.dropwhile(lambda x: x == 1, shape))\\n beta, gamma = _beta_init(shape), _gamma_init(shape)\\n return input_shape, (beta, gamma)\\n def apply_fun(params, x, **kwargs):\\n beta, gamma = params\\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\\n z = (x - mean) / np.sqrt(var + epsilon)\\n if center and scale: return gamma * z + beta\\n if center: return z + beta\\n if scale: return gamma * z\\n return z\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm.init_fun\",\n \"text\": \"jax/experimental/stax.py::BatchNorm.init_fun\\n def init_fun(input_shape):\\n shape = (1 if i in axis else d for i, d in enumerate(input_shape))\\n shape = tuple(itertools.dropwhile(lambda x: x == 1, shape))\\n beta, gamma = _beta_init(shape), _gamma_init(shape)\\n return input_shape, (beta, gamma)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm.apply_fun\",\n \"text\": \"jax/experimental/stax.py::BatchNorm.apply_fun\\n def apply_fun(params, x, **kwargs):\\n beta, gamma = params\\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\\n z = (x - mean) / np.sqrt(var + epsilon)\\n if center and scale: return gamma * z + beta\\n if center: return z + beta\\n if scale: return gamma * z\\n return z\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::_elemwise_no_params\",\n \"text\": \"jax/experimental/stax.py::_elemwise_no_params\\ndef _elemwise_no_params(fun, **fun_kwargs):\\n init_fun = lambda input_shape: (input_shape, ())\\n apply_fun = lambda params, inputs, **kwargs: fun(inputs, **fun_kwargs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer\\ndef _pooling_layer(reducer, init_val, rescaler=None):\\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\\n \\\"\\\"\\\"Layer construction function for a pooling layer.\\\"\\\"\\\"\\n strides = strides or (1,) * len(window_shape)\\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\\n dims = (1,) + window_shape + (1,) # NHWC\\n strides = (1,) + strides + (1,)\\n def init_fun(input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\\n return rescale(out, inputs) if rescale else out\\n return init_fun, apply_fun\\n return PoolingLayer\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer\\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\\n \\\"\\\"\\\"Layer construction function for a pooling layer.\\\"\\\"\\\"\\n strides = strides or (1,) * len(window_shape)\\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\\n dims = (1,) + window_shape + (1,) # NHWC\\n strides = (1,) + strides + (1,)\\n def init_fun(input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\\n return rescale(out, inputs) if rescale else out\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\\n def init_fun(input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\\n return out_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\\n return rescale(out, inputs) if rescale else out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_normalize_by_window_size\",\n \"text\": \"jax/experimental/stax.py::_normalize_by_window_size\\ndef _normalize_by_window_size(dims, strides, padding):\\n def rescale(outputs, inputs):\\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\\n return outputs / window_sizes[..., np.newaxis]\\n return rescale\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_normalize_by_window_size.rescale\",\n \"text\": \"jax/experimental/stax.py::_normalize_by_window_size.rescale\\n def rescale(outputs, inputs):\\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\\n return outputs / window_sizes[..., np.newaxis]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten\",\n \"text\": \"jax/experimental/stax.py::Flatten\\ndef Flatten():\\n \\\"\\\"\\\"Layer construction function for flattening all but the leading dim.\\\"\\\"\\\"\\n def init_fun(input_shape):\\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\\n return output_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n return np.reshape(inputs, (inputs.shape[0], -1))\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten.init_fun\",\n \"text\": \"jax/experimental/stax.py::Flatten.init_fun\\n def init_fun(input_shape):\\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\\n return output_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Flatten.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return np.reshape(inputs, (inputs.shape[0], -1))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Identity\",\n \"text\": \"jax/experimental/stax.py::Identity\\ndef Identity():\\n \\\"\\\"\\\"Layer construction function for an identity layer.\\\"\\\"\\\"\\n init_fun = lambda input_shape: (input_shape, ())\\n apply_fun = lambda params, inputs, **kwargs: inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanOut\",\n \"text\": \"jax/experimental/stax.py::FanOut\\ndef FanOut(num):\\n \\\"\\\"\\\"Layer construction function for a fan-out layer.\\\"\\\"\\\"\\n init_fun = lambda input_shape: ([input_shape] * num, ())\\n apply_fun = lambda params, inputs, **kwargs: [inputs] * num\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInSum\",\n \"text\": \"jax/experimental/stax.py::FanInSum\\ndef FanInSum():\\n \\\"\\\"\\\"Layer construction function for a fan-in sum layer.\\\"\\\"\\\"\\n init_fun = lambda input_shape: (input_shape[0], ())\\n apply_fun = lambda params, inputs, **kwargs: sum(inputs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat\",\n \"text\": \"jax/experimental/stax.py::FanInConcat\\ndef FanInConcat(axis=-1):\\n \\\"\\\"\\\"Layer construction function for a fan-in concatenation layer.\\\"\\\"\\\"\\n def init_fun(input_shape):\\n ax = axis % len(input_shape[0])\\n concat_size = sum(shape[ax] for shape in input_shape)\\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n return np.concatenate(inputs, axis)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat.init_fun\",\n \"text\": \"jax/experimental/stax.py::FanInConcat.init_fun\\n def init_fun(input_shape):\\n ax = axis % len(input_shape[0])\\n concat_size = sum(shape[ax] for shape in input_shape)\\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\\n return out_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat.apply_fun\",\n \"text\": \"jax/experimental/stax.py::FanInConcat.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return np.concatenate(inputs, axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout\",\n \"text\": \"jax/experimental/stax.py::Dropout\\ndef Dropout(rate, mode='train'):\\n \\\"\\\"\\\"Layer construction function for a dropout layer with given rate.\\\"\\\"\\\"\\n def init_fun(input_shape):\\n return input_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.get('rng', None)\\n if rng is None:\\n msg = (\\\"Dropout layer requires apply_fun to be called with a PRNG key \\\"\\n \\\"argument. That is, instead of `apply_fun(params, inputs)`, call \\\"\\n \\\"it like `apply_fun(params, inputs, key)` where `key` is a \\\"\\n \\\"jax.random.PRNGKey value.\\\")\\n raise ValueError(msg)\\n if mode == 'train':\\n keep = random.bernoulli(rng, rate, inputs.shape)\\n return np.where(keep, inputs / rate, 0)\\n else:\\n return inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout.init_fun\",\n \"text\": \"jax/experimental/stax.py::Dropout.init_fun\\n def init_fun(input_shape):\\n return input_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Dropout.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.get('rng', None)\\n if rng is None:\\n msg = (\\\"Dropout layer requires apply_fun to be called with a PRNG key \\\"\\n \\\"argument. That is, instead of `apply_fun(params, inputs)`, call \\\"\\n \\\"it like `apply_fun(params, inputs, key)` where `key` is a \\\"\\n \\\"jax.random.PRNGKey value.\\\")\\n raise ValueError(msg)\\n if mode == 'train':\\n keep = random.bernoulli(rng, rate, inputs.shape)\\n return np.where(keep, inputs / rate, 0)\\n else:\\n return inputs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial\",\n \"text\": \"jax/experimental/stax.py::serial\\ndef serial(*layers):\\n \\\"\\\"\\\"Combinator for composing layers in serial.\\n\\n Args:\\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the serial\\n composition of the given sequence of layers.\\n \\\"\\\"\\\"\\n nlayers = len(layers)\\n init_funs, apply_funs = zip(*layers)\\n def init_fun(input_shape):\\n params = []\\n for init_fun in init_funs:\\n input_shape, param = init_fun(input_shape)\\n params.append(param)\\n return input_shape, params\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n for fun, param, rng in zip(apply_funs, params, rngs):\\n inputs = fun(param, inputs, rng=rng, **kwargs)\\n return inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial.init_fun\",\n \"text\": \"jax/experimental/stax.py::serial.init_fun\\n def init_fun(input_shape):\\n params = []\\n for init_fun in init_funs:\\n input_shape, param = init_fun(input_shape)\\n params.append(param)\\n return input_shape, params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial.apply_fun\",\n \"text\": \"jax/experimental/stax.py::serial.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n for fun, param, rng in zip(apply_funs, params, rngs):\\n inputs = fun(param, inputs, rng=rng, **kwargs)\\n return inputs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel\",\n \"text\": \"jax/experimental/stax.py::parallel\\ndef parallel(*layers):\\n \\\"\\\"\\\"Combinator for composing layers in parallel.\\n\\n The layer resulting from this combinator is often used with the FanOut and\\n FanInSum layers.\\n\\n Args:\\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the\\n parallel composition of the given sequence of layers. In particular, the\\n returned layer takes a sequence of inputs and returns a sequence of outputs\\n with the same length as the argument `layers`.\\n \\\"\\\"\\\"\\n nlayers = len(layers)\\n init_funs, apply_funs = zip(*layers)\\n def init_fun(input_shape):\\n return zip(*[init(shape) for init, shape in zip(init_funs, input_shape)])\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel.init_fun\",\n \"text\": \"jax/experimental/stax.py::parallel.init_fun\\n def init_fun(input_shape):\\n return zip(*[init(shape) for init, shape in zip(init_funs, input_shape)])\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel.apply_fun\",\n \"text\": \"jax/experimental/stax.py::parallel.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent\",\n \"text\": \"jax/experimental/stax.py::shape_dependent\\ndef shape_dependent(make_layer):\\n \\\"\\\"\\\"Combinator to delay layer constructor pair until input shapes are known.\\n\\n Args:\\n make_layer: a one-argument function that takes an input shape as an argument\\n (a tuple of positive integers) and returns an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the same\\n layer as returned by `make_layer` but with its construction delayed until\\n input shapes are known.\\n \\\"\\\"\\\"\\n def init_fun(input_shape):\\n return make_layer(input_shape)[0](input_shape)\\n def apply_fun(params, inputs, **kwargs):\\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent.init_fun\",\n \"text\": \"jax/experimental/stax.py::shape_dependent.init_fun\\n def init_fun(input_shape):\\n return make_layer(input_shape)[0](input_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent.apply_fun\",\n \"text\": \"jax/experimental/stax.py::shape_dependent.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::PRNGKey\",\n \"text\": \"jax/random.py::PRNGKey\\ndef PRNGKey(seed):\\n \\\"\\\"\\\"Create a pseudo-random number generator (PRNG) key given an integer seed.\\n\\n Args:\\n seed: a 64- or 32-bit integer used as the value of the key.\\n\\n Returns:\\n A PRNG key, which is modeled as an array of shape (2,) and dtype uint32. The\\n key is constructed from a 64-bit seed by effectively bit-casting to a pair\\n of uint32 values (or from a 32-bit seed by first padding out with zeros).\\n \\\"\\\"\\\"\\n if onp.shape(seed):\\n raise TypeError(\\\"PRNGKey seed must be a scalar.\\\")\\n convert = lambda k: lax.reshape(lax.convert_element_type(k, onp.uint32), [1])\\n if isinstance(seed, (int, onp.ndarray)):\\n # Special handling of raw integer values, which may have be 64bit even\\n # when jax_enable_x64=False and we don't want to drop the top 32 bits\\n k1 = convert(onp.bitwise_and(onp.right_shift(seed, 32), 0xFFFFFFFF))\\n else:\\n k1 = convert(lax.shift_right_logical(seed, 32))\\n k2 = convert(lax.bitwise_and(seed, 0xFFFFFFFF))\\n return lax.concatenate([k1, k2], 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_is_prng_key\",\n \"text\": \"jax/random.py::_is_prng_key\\ndef _is_prng_key(key):\\n try:\\n return key.shape == (2,) and key.dtype == onp.uint32\\n except AttributeError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_make_rotate_left\",\n \"text\": \"jax/random.py::_make_rotate_left\\ndef _make_rotate_left(dtype):\\n if not onp.issubdtype(dtype, onp.integer):\\n raise TypeError(\\\"_rotate_left only accepts integer dtypes.\\\")\\n nbits = onp.array(onp.iinfo(dtype).bits, dtype)\\n\\n def _rotate_left(x, d):\\n if lax._dtype(d) != lax._dtype(x):\\n d = lax.convert_element_type(d, x.dtype)\\n return (x << d) | lax.shift_right_logical(x, nbits - d)\\n return _rotate_left\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_make_rotate_left._rotate_left\",\n \"text\": \"jax/random.py::_make_rotate_left._rotate_left\\n def _rotate_left(x, d):\\n if lax._dtype(d) != lax._dtype(x):\\n d = lax.convert_element_type(d, x.dtype)\\n return (x << d) | lax.shift_right_logical(x, nbits - d)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_bit_stats\",\n \"text\": \"jax/random.py::_bit_stats\\ndef _bit_stats(bits):\\n \\\"\\\"\\\"This is a debugging function to compute the statistics of bit fields.\\\"\\\"\\\"\\n return onp.array([list(map(int, onp.binary_repr(x, 64))) for x in bits]).mean(0)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::threefry_2x32\",\n \"text\": \"jax/random.py::threefry_2x32\\n@jit\\ndef threefry_2x32(keypair, count):\\n \\\"\\\"\\\"Apply the Threefry 2x32 hash.\\n\\n Args:\\n keypair: a pair of 32bit unsigned integers used for the key.\\n count: an array of dtype uint32 used for the counts.\\n\\n Returns:\\n An array of dtype uint32 with the same shape as `count`.\\n \\\"\\\"\\\"\\n # Based on ThreeFry2x32 by phawkins@ in //.../xla/client/lib/prng.cc\\n key1, key2 = keypair\\n if not lax._dtype(key1) == lax._dtype(key2) == lax._dtype(count) == onp.uint32:\\n msg = \\\"threefry_2x32 requires uint32 arguments, got {}\\\"\\n raise TypeError(msg.format([lax._dtype(x) for x in [key1, key2, count]]))\\n\\n rotate_left = _make_rotate_left(lax._dtype(count))\\n\\n def apply_round(v, rot):\\n v = v[:]\\n v[0] = v[0] + v[1]\\n v[1] = rotate_left(v[1], rot)\\n v[1] = v[0] ^ v[1]\\n return v\\n\\n odd_size = count.size % 2\\n if odd_size:\\n x = list(np.split(np.concatenate([count.ravel(), onp.uint32([0])]), 2))\\n else:\\n x = list(np.split(count.ravel(), 2))\\n\\n rotations = [13, 15, 26, 6, 17, 29, 16, 24]\\n ks = [key1, key2, key1 ^ key2 ^ onp.uint32(0x1BD11BDA)]\\n\\n x[0] = x[0] + ks[0]\\n x[1] = x[1] + ks[1]\\n\\n for r in rotations[:4]:\\n x = apply_round(x, r)\\n x[0] = x[0] + ks[1]\\n x[1] = x[1] + ks[2] + onp.uint32(1)\\n\\n for r in rotations[4:]:\\n x = apply_round(x, r)\\n x[0] = x[0] + ks[2]\\n x[1] = x[1] + ks[0] + onp.uint32(2)\\n\\n for r in rotations[:4]:\\n x = apply_round(x, r)\\n x[0] = x[0] + ks[0]\\n x[1] = x[1] + ks[1] + onp.uint32(3)\\n\\n for r in rotations[4:]:\\n x = apply_round(x, r)\\n x[0] = x[0] + ks[1]\\n x[1] = x[1] + ks[2] + onp.uint32(4)\\n\\n for r in rotations[:4]:\\n x = apply_round(x, r)\\n x[0] = x[0] + ks[2]\\n x[1] = x[1] + ks[0] + onp.uint32(5)\\n\\n out = np.concatenate(x)\\n assert out.dtype == onp.uint32\\n return lax.reshape(out[:-1] if odd_size else out, count.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::threefry_2x32.apply_round\",\n \"text\": \"jax/random.py::threefry_2x32.apply_round\\n def apply_round(v, rot):\\n v = v[:]\\n v[0] = v[0] + v[1]\\n v[1] = rotate_left(v[1], rot)\\n v[1] = v[0] ^ v[1]\\n return v\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::split\",\n \"text\": \"jax/random.py::split\\n@partial(jit, static_argnums=(1,))\\ndef split(key, num=2):\\n \\\"\\\"\\\"Splits a PRNG key into `num` new keys by adding a leading axis.\\n\\n Args:\\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\\n num: optional, a positive integer indicating the number of keys to produce\\n (default 2).\\n\\n Returns:\\n An array with shape (num, 2) and dtype uint32 representing `num` new keys.\\n \\\"\\\"\\\"\\n counts = lax.tie_in(key, lax.iota(onp.uint32, num * 2))\\n return lax.reshape(threefry_2x32(key, counts), (num, 2))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::fold_in\",\n \"text\": \"jax/random.py::fold_in\\n@partial(jit, static_argnums=(1,))\\ndef fold_in(key, data):\\n \\\"\\\"\\\"Folds in data to a PRNG key to form a new PRNG key.\\n\\n Args:\\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\\n data: an integer representing data to be folded in to the key.\\n\\n Returns:\\n A new PRNGKey that is a deterministic function of the inputs and is\\n statistically safe for producing a stream of new pseudo-random values.\\n \\\"\\\"\\\"\\n key2 = lax.tie_in(key, PRNGKey(data))\\n return threefry_2x32(key, key2)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_random_bits\",\n \"text\": \"jax/random.py::_random_bits\\ndef _random_bits(key, bit_width, shape):\\n \\\"\\\"\\\"Sample uniform random bits of given width and shape using PRNG key.\\\"\\\"\\\"\\n if not _is_prng_key(key):\\n raise TypeError(\\\"_random_bits got invalid prng key.\\\")\\n if bit_width not in (32, 64):\\n raise TypeError(\\\"requires 32- or 64-bit field width.\\\")\\n max_count = (bit_width // 32) * onp.prod(shape)\\n if max_count >= onp.iinfo(onp.uint32).max:\\n # TODO(mattjj): just split the key here\\n raise TypeError(\\\"requesting more random bits than a single call provides.\\\")\\n\\n counts = lax.tie_in(key, lax.iota(onp.uint32, max_count))\\n bits = threefry_2x32(key, counts)\\n if bit_width == 64:\\n bits = [lax.convert_element_type(x, onp.uint64) for x in np.split(bits, 2)]\\n bits = (bits[0] << onp.uint64(32)) | bits[1]\\n return lax.reshape(bits, shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::uniform\",\n \"text\": \"jax/random.py::uniform\\n@partial(jit, static_argnums=(1, 2))\\ndef uniform(key, shape, dtype=onp.float32, minval=0., maxval=1.):\\n \\\"\\\"\\\"Sample uniform random values in [minval, maxval) with given shape/dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: a tuple of nonnegative integers representing the shape.\\n dtype: optional, a float dtype for the returned values (default float32).\\n minval: optional, a minimum (inclusive) value for the range (default 0).\\n maxval: optional, a maximum (exclusive) value for the range (default 1).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n if not onp.issubdtype(dtype, onp.floating):\\n raise TypeError(\\\"uniform only accepts floating point dtypes.\\\")\\n\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n minval = lax.convert_element_type(minval, dtype)\\n maxval = lax.convert_element_type(maxval, dtype)\\n finfo = onp.finfo(dtype)\\n nbits, nmant = finfo.bits, finfo.nmant\\n\\n if nbits not in (32, 64):\\n raise TypeError(\\\"uniform only accepts 32- or 64-bit dtypes.\\\")\\n\\n bits = _random_bits(key, nbits, shape)\\n\\n # The strategy here is to randomize only the mantissa bits with an exponent of\\n # 1 (after applying the bias), then shift and scale to the desired range. The\\n # bit-level transformation we use relies on Numpy and XLA having bit-for-bit\\n # equivalent float representations, which might not be true on all platforms.\\n float_bits = lax.bitwise_or(\\n lax.shift_right_logical(bits, onp.array(nbits - nmant, lax._dtype(bits))),\\n onp.array(1., dtype).view(onp.uint32 if nbits == 32 else onp.uint64))\\n floats = lax.bitcast_convert_type(float_bits, dtype) - onp.array(1., dtype)\\n return lax.max(\\n minval,\\n lax.reshape(floats * (maxval - minval) + minval, shape))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::randint\",\n \"text\": \"jax/random.py::randint\\n@partial(jit, static_argnums=(1, 4))\\ndef randint(key, shape, minval, maxval, dtype=onp.int32):\\n \\\"\\\"\\\"Sample uniform random values in [minval, maxval) with given shape/dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: a tuple of nonnegative integers representing the shape.\\n minval: optional, a minimum (inclusive) value for the range (default 0).\\n maxval: optional, a maximum (exclusive) value for the range (default 1).\\n dtype: optional, an int dtype for the returned values (default int32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n if not onp.issubdtype(dtype, onp.integer):\\n raise TypeError(\\\"randint only accepts integer dtypes.\\\")\\n\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n minval = lax.convert_element_type(minval, dtype)\\n maxval = lax.convert_element_type(maxval, dtype)\\n nbits = onp.iinfo(dtype).bits\\n\\n if nbits not in (32, 64):\\n raise TypeError(\\\"randint only accepts 32- or 64-bit dtypes.\\\")\\n\\n # if we don't have minval < maxval, just always return minval\\n # https://github.com/google/jax/issues/222\\n maxval = lax.max(lax.add(minval, onp.array(1, dtype)), maxval)\\n\\n # This algorithm is biased whenever (maxval - minval) is not a power of 2.\\n # We generate double the number of random bits required by the dtype so as to\\n # reduce that bias.\\n k1, k2 = split(key)\\n rbits = lambda key: _random_bits(key, nbits, shape)\\n higher_bits, lower_bits = rbits(k1), rbits(k2)\\n\\n unsigned_dtype = onp.uint32 if nbits == 32 else onp.uint64\\n span = lax.convert_element_type(maxval - minval, unsigned_dtype)\\n\\n # To compute a remainder operation on an integer that might have twice as many\\n # bits as we can represent in the native unsigned dtype, we compute a\\n # multiplier equal to 2**nbits % span (using that nbits is 32 or 64).\\n multiplier = lax.rem(onp.array(2**16, unsigned_dtype), span)\\n multiplier = lax.rem(lax.mul(multiplier, multiplier), span)\\n if nbits == 64:\\n multiplier = lax.re\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::shuffle\",\n \"text\": \"jax/random.py::shuffle\\n@partial(jit, static_argnums=(2,))\\ndef shuffle(key, x, axis=0):\\n \\\"\\\"\\\"Shuffle the elements of an array uniformly at random along an axis.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n x: the array to be shuffled.\\n axis: optional, an int axis along which to shuffle (default 0).\\n\\n Returns:\\n A shuffled version of x.\\n \\\"\\\"\\\"\\n # On parallel architectures, Fisher-Yates is more expensive than doing\\n # multiple sorts. This algorithm is based on one developed and analyzed by\\n # tjablin@. We sort according to randomly-generated 32bit keys, but those keys\\n # may have collisions. If we repeat the process, using fresh 32bit keys for\\n # each sort, then whenever all pairs of elements have been assigned distinct\\n # keys at some iteration (or equivalently when the strings formed by\\n # concatenating the successive keys for each element are all distinct) then we\\n # are guaranteed to have a perfect sample (assuming that either the sort is\\n # stable or that any bias is not value-dependent). Since checking uniqueness\\n # at runtime may be expensive, we use a heuristic static stop criterion\\n # developed by tjablin@. See tensorflow/compiler/tf2xla/random_ops.cc for more\\n # info, and for the original implementation of this algorithm. See also\\n # Section 2 of http://people.csail.mit.edu/costis/6896sp11/lec5s.pdf for\\n # another analysis (where the keys are generated one bit at a time).\\n exponent = 3 # see tjablin@'s analysis for explanation of this parameter\\n uint32max = onp.iinfo(onp.uint32).max\\n num_rounds = int(onp.ceil(exponent * onp.log(x.size) / onp.log(uint32max)))\\n\\n for _ in range(num_rounds):\\n key, subkey = split(key)\\n sort_keys = _random_bits(subkey, 32, x.shape)\\n _, x = lax.sort_key_val(sort_keys, x, axis)\\n\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::normal\",\n \"text\": \"jax/random.py::normal\\n@partial(jit, static_argnums=(1, 2))\\ndef normal(key, shape, dtype=onp.float32):\\n \\\"\\\"\\\"Sample standard normal random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: a tuple of nonnegative integers representing the shape.\\n dtype: optional, a float dtype for the returned values (default float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n lo = onp.nextafter(onp.array(-1., dtype), 0., dtype=dtype)\\n hi = onp.array(1., dtype)\\n u = uniform(key, shape, dtype, lo, hi)\\n return onp.array(onp.sqrt(2), dtype) * lax.erf_inv(u)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::bernoulli\",\n \"text\": \"jax/random.py::bernoulli\\n@partial(jit, static_argnums=(2,))\\ndef bernoulli(key, mean=onp.float32(0.5), shape=()):\\n \\\"\\\"\\\"Sample Bernoulli random values with given shape and mean.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n mean: optional, an array-like broadcastable to `shape` for the mean of the\\n random variables (default 0.5).\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n\\n Returns:\\n A random array with the specified shape and boolean dtype.\\n \\\"\\\"\\\"\\n shape = shape or onp.shape(mean)\\n if not onp.issubdtype(lax._dtype(mean), onp.float32):\\n mean = lax.convert_element_type(mean, onp.float32)\\n if onp.shape(mean) != shape:\\n mean = np.broadcast_to(mean, shape)\\n return lax.lt(uniform(key, shape), mean)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":58,"cells":{"instance_id":{"kind":"string","value":"docker__compose-2492"},"repo":{"kind":"string","value":"docker/compose"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Stop warning about \".yaml\" file extension.\nCurrently compose outputs a warning when using extensions other than `.yml` for the config file:\n\n```\nWARNING: Please be aware that .yml is the expected extension in most cases, and using .yaml can cause compatibility issues in future.\n```\n\nHowever, the creators of YAML request that we use `.yaml` when possible. [[1]](http://www.yaml.org/faq.html)\n\nAdditionally, I don't think it's fruitful to warn users about this particular \"compatibility issue\"; things like which character is used for `\\n` in text files or using uppercase or non-ASCII characters in file names can also be \"compatibility issues\" when multiple platforms are involved, yet most programs don't force a user of a particular platform to use a convention which is compatible with other platforms.\n\nJust my two cents. If the devs agree that this can/should be removed, I'll make a PR.\n\n"},"gold_files":{"kind":"list like","value":["compose/config/config.py"],"string":"[\n \"compose/config/config.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["compose/config/config.py::get_default_config_files"],"string":"[\n \"compose/config/config.py::get_default_config_files\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"compose/cli/command.py","text":"compose/cli/command.py\ndef friendly_error_message()\ndef project_from_options(base_dir, options)\ndef get_config_path(file_option)\ndef get_client(verbose=False, version=None)\ndef get_project(base_dir, config_path=None, project_name=None, verbose=False, use_networking=False, network_driver=None)\ndef get_project_name(working_dir, project_name=None)\n def normalize_name(name)","label":0},{"cid":"compose/cli/docker_client.py","text":"compose/cli/docker_client.py\ndef docker_client(version=None) # Returns a docker-py client configured using environment variables","label":0},{"cid":"compose/cli/log_printer.py","text":"compose/cli/log_printer.py\nclass LogPrinter # Print logs from many containers to a single output stream.\n def __init__(self, containers, output=sys.stdout, monochrome=False)\n def run(self)\n def _make_log_generators(self, monochrome, prefix_width)\n def no_color(text)\ndef build_log_prefix(container, prefix_width)\ndef max_name_width(containers) # Calculate the maximum width of container names so we can make the log\ndef get_log_generator(container)\ndef build_no_log_generator(container, prefix, color_func) # Return a generator that prints a warning about logs and waits for\ndef build_log_generator(container, prefix, color_func)\ndef wait_on_exit(container)","label":0},{"cid":"compose/cli/main.py","text":"compose/cli/main.py\ndef main()\ndef setup_logging()\ndef setup_console_handler(handler, verbose)\ndef parse_doc_section(name, source)\nclass TopLevelCommand # Define and run multi-container applications with Docker.\n def docopt_options(self)\n def perform_command(self, options, handler, command_options)\n def build(self, project, options) # Build or rebuild services.\n def help(self, project, options) # Get help on a command.\n def kill(self, project, options) # Force stop service containers.\n def logs(self, project, options) # View output from containers.\n def pause(self, project, options) # Pause services.\n def port(self, project, options) # Print the public port for a port binding.\n def ps(self, project, options) # List containers.\n def pull(self, project, options) # Pulls images for services.\n def rm(self, project, options) # Remove stopped service containers.\n def run(self, project, options) # Run a one-off command on a service.\n def scale(self, project, options) # Set number of containers to run for a service.\n def start(self, project, options) # Start existing containers.\n def stop(self, project, options) # Stop running containers without removing them.\n def restart(self, project, options) # Restart running containers.\n def unpause(self, project, options) # Unpause services.\n def up(self, project, options) # Builds, (re)creates, starts, and attaches to containers for a service.\n def migrate_to_labels(self, project, _options) # Recreate containers to add labels\n def version(self, project, options) # Show version informations\ndef convergence_strategy_from_opts(options)\ndef run_one_off_container(container_options, project, service, options)\n def remove_container(force=False)\n def force_shutdown(signal, frame)\n def shutdown(signal, frame)\ndef build_log_printer(containers, service_names, monochrome)\ndef attach_to_logs(project, log_printer, service_names, timeout)\n def force_shutdown(signal, frame)\n def shutdown(signal, frame)\ndef set_signal_handler(handler)\ndef list_containers(containers)","label":0},{"cid":"compose/cli/utils.py","text":"compose/cli/utils.py\ndef yesno(prompt, default=None) # Prompt the user for a yes or no.\ndef call_silently(*args, **kwargs) # Like subprocess.call(), but redirects stdout and stderr to /dev/null.\ndef is_mac()\ndef is_ubuntu()\ndef get_version_info(scope)\ndef get_build_version()","label":0},{"cid":"compose/config/config.py","text":"compose/config/config.py\nclass ConfigDetails # :param working_dir: the directory to use for relative paths in the config\nclass ConfigFile # :param filename: filename of the config file\n def from_filename(cls, filename)\nclass ServiceConfig\n def with_abs_paths(cls, working_dir, filename, name, config)\ndef find(base_dir, filenames)\ndef get_default_config_files(base_dir)\ndef get_default_override_file(path)\ndef find_candidates_in_parent_dirs(filenames, path) # Given a directory path to start, looks for filenames in the\ndef load(config_details) # Load the configuration from a working directory and a list of\n def build_service(filename, service_name, service_dict)\n def build_services(config_file)\n def merge_services(base, override)\ndef process_config_file(config_file, service_name=None)\nclass ServiceExtendsResolver\n def __init__(self, service_config, already_seen=None)\n def signature(self)\n def detect_cycle(self)\n def run(self)\n def validate_and_construct_extends(self)\n def resolve_extends(self, extended_config_path, service_dict, service_name)\n def get_extended_config_path(self, extends_options) # Service we are extending either has a value for 'file' set, which we\ndef resolve_environment(service_dict) # Unpack any environment variables from an env_file, if set.\ndef validate_extended_service_dict(service_dict, filename, service)\ndef validate_ulimits(ulimit_config)\ndef process_service(service_config)\ndef finalize_service(service_config)\ndef merge_service_dicts_from_files(base, override) # When merging services from multiple files we need to merge the `extends`\ndef merge_service_dicts(base, override)\n def merge_field(field, merge_func, default=None)\ndef merge_image_or_build(base, override, output)\ndef merge_environment(base, override)\ndef parse_environment(environment)\ndef split_env(env)\ndef resolve_env_var(key, val)\ndef env_vars_from_file(filename) # Read in a line delimited file of environment variables.\ndef resolve_volume_paths(working_dir, service_dict)\ndef resolve_volume_path(working_dir, volume)\ndef resolve_build_path(working_dir, build_path)\ndef is_url(build_path)\ndef validate_paths(service_dict)\ndef merge_path_mappings(base, override)\ndef dict_from_path_mappings(path_mappings)\ndef path_mappings_from_dict(d)\ndef split_path_mapping(volume_path) # Ascertain if the volume_path contains a host path as well as a container\ndef join_path_mapping(pair)\ndef merge_labels(base, override)\ndef parse_labels(labels)\ndef split_label(label)\ndef expand_path(working_dir, path)\ndef merge_list_or_string(base, override)\ndef to_list(value)\ndef load_yaml(filename)","label":1},{"cid":"compose/config/validation.py","text":"compose/config/validation.py\ndef format_ports(instance)\ndef format_expose(instance)\ndef format_boolean_in_environment(instance) # Check if there is a boolean in the environment and display a warning.\ndef validate_top_level_service_objects(config_file) # Perform some high level validation of the service name and value.\ndef validate_top_level_object(config_file)\ndef validate_extends_file_path(service_name, extends_options, filename) # The service to be extended must either be defined in the config key 'file',\ndef get_unsupported_config_msg(service_name, error_key)\ndef anglicize_validator(validator)\ndef handle_error_for_schema_with_id(error, service_name)\ndef handle_generic_service_error(error, service_name)\ndef parse_key_from_error_msg(error)\ndef _parse_valid_types_from_validator(validator) # A validator value can be either an array of valid types or a string of\ndef _parse_oneof_validator(error) # oneOf has multiple schemas, so we need to reason about which schema, sub\ndef process_errors(errors, service_name=None) # jsonschema gives us an error tree full of information to explain what has\n def format_error_message(error, service_name)\ndef validate_against_fields_schema(config, filename)\ndef validate_against_service_schema(config, service_name)\ndef _validate_against_schema(config, schema_filename, format_checker=(), service_name=None, filename=None)","label":0},{"cid":"compose/legacy.py","text":"compose/legacy.py\ndef check_for_legacy_containers(client, project, services, allow_one_off=True) # Check if there are containers named using the old naming convention\nclass LegacyError\n def __unicode__(self)\nclass LegacyContainersError\n def __init__(self, names, one_off_names)\nclass LegacyOneOffContainersError\n def __init__(self, one_off_names)\ndef add_labels(project, container)\ndef migrate_project_to_labels(project)\ndef get_legacy_containers(client, project, services, one_off=False)\ndef _get_legacy_containers_iter(client, project, services, one_off=False)\ndef has_container(project, service, name, one_off=False)\ndef is_valid_name(name, one_off=False)\ndef parse_name(name)","label":0},{"cid":"compose/service.py","text":"compose/service.py\nclass BuildError\n def __init__(self, service, reason)\nclass NeedsBuildError\n def __init__(self, service)\nclass NoSuchImageError\nclass ConvergenceStrategy # Enumeration for all possible convergence strategies. Values refer to\n def allows_recreate(self)\nclass Service\n def __init__(self, name, client=None, project='default', use_networking=False, links=None, volumes_from=None, net=None, **options)\n def containers(self, stopped=False, one_off=False, filters={})\n def get_container(self, number=1) # Return a :class:`compose.container.Container` for this service. The\n def start(self, **options)\n def stop(self, **options)\n def pause(self, **options)\n def unpause(self, **options)\n def kill(self, **options)\n def restart(self, **options)\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT) # Adjusts the number of containers to the specified number and ensures\n def create_and_start(service, number)\n def create_container(self, one_off=False, do_build=True, previous_container=None, number=None, quiet=False, **override_options) # Create a container for this service. If the image doesn't exist, attempt to pull\n def ensure_image_exists(self, do_build=True)\n def image(self)\n def image_name(self)\n def convergence_plan(self, strategy=ConvergenceStrategy.changed)\n def _containers_have_diverged(self, containers)\n def execute_convergence_plan(self, plan, do_build=True, timeout=DEFAULT_TIMEOUT, detached=False)\n def recreate_container(self, container, do_build=False, timeout=DEFAULT_TIMEOUT, attach_logs=False) # Recreate a container.\n def start_container_if_stopped(self, container, attach_logs=False)\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT)\n def duplicate_containers(self)\n def config_hash(self)\n def config_dict(self)\n def get_dependency_names(self)\n def get_linked_service_names(self)\n def get_link_names(self)\n def get_volumes_from_names(self)\n def get_container_name(self, number, one_off=False)\n def _next_container_number(self, one_off=False)\n def _get_links(self, link_to_self)\n def _get_volumes_from(self)\n def _get_container_create_options(self, override_options, number, one_off=False, previous_container=None)\n def _get_container_host_config(self, override_options, one_off=False)\n def build(self, no_cache=False, pull=False, force_rm=False)\n def can_be_built(self)\n def full_name(self) # The tag to give to images built for this service.\n def labels(self, one_off=False)\n def custom_container_name(self)\n def specifies_host_port(self)\n def has_host_port(binding)\n def pull(self, ignore_pull_failures=False)\nclass Net # A `standard` network mode (ex: host, bridge)\n def __init__(self, net)\n def id(self)\nclass ContainerNet # A network mode that uses a container's network stack.\n def __init__(self, container)\n def id(self)\n def mode(self)\nclass ServiceNet # A network mode t","label":0},{"cid":"compose/utils.py","text":"compose/utils.py\ndef get_output_stream(stream)\ndef stream_as_text(stream) # Given a stream of bytes or text, if any of the items in the stream\ndef line_splitter(buffer, separator=u'\\n')\ndef split_buffer(stream, splitter=None, decoder=lambda a: a) # Given a generator which yields strings and a splitter function,\ndef json_splitter(buffer) # Attempt to parse a json object from a buffer. If there is at least one\ndef json_stream(stream) # Given a stream of text, return a stream of json objects.\ndef json_hash(obj)","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/command.py\",\n \"text\": \"compose/cli/command.py\\ndef friendly_error_message()\\ndef project_from_options(base_dir, options)\\ndef get_config_path(file_option)\\ndef get_client(verbose=False, version=None)\\ndef get_project(base_dir, config_path=None, project_name=None, verbose=False, use_networking=False, network_driver=None)\\ndef get_project_name(working_dir, project_name=None)\\n def normalize_name(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/docker_client.py\",\n \"text\": \"compose/cli/docker_client.py\\ndef docker_client(version=None) # Returns a docker-py client configured using environment variables\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py\",\n \"text\": \"compose/cli/log_printer.py\\nclass LogPrinter # Print logs from many containers to a single output stream.\\n def __init__(self, containers, output=sys.stdout, monochrome=False)\\n def run(self)\\n def _make_log_generators(self, monochrome, prefix_width)\\n def no_color(text)\\ndef build_log_prefix(container, prefix_width)\\ndef max_name_width(containers) # Calculate the maximum width of container names so we can make the log\\ndef get_log_generator(container)\\ndef build_no_log_generator(container, prefix, color_func) # Return a generator that prints a warning about logs and waits for\\ndef build_log_generator(container, prefix, color_func)\\ndef wait_on_exit(container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py\",\n \"text\": \"compose/cli/main.py\\ndef main()\\ndef setup_logging()\\ndef setup_console_handler(handler, verbose)\\ndef parse_doc_section(name, source)\\nclass TopLevelCommand # Define and run multi-container applications with Docker.\\n def docopt_options(self)\\n def perform_command(self, options, handler, command_options)\\n def build(self, project, options) # Build or rebuild services.\\n def help(self, project, options) # Get help on a command.\\n def kill(self, project, options) # Force stop service containers.\\n def logs(self, project, options) # View output from containers.\\n def pause(self, project, options) # Pause services.\\n def port(self, project, options) # Print the public port for a port binding.\\n def ps(self, project, options) # List containers.\\n def pull(self, project, options) # Pulls images for services.\\n def rm(self, project, options) # Remove stopped service containers.\\n def run(self, project, options) # Run a one-off command on a service.\\n def scale(self, project, options) # Set number of containers to run for a service.\\n def start(self, project, options) # Start existing containers.\\n def stop(self, project, options) # Stop running containers without removing them.\\n def restart(self, project, options) # Restart running containers.\\n def unpause(self, project, options) # Unpause services.\\n def up(self, project, options) # Builds, (re)creates, starts, and attaches to containers for a service.\\n def migrate_to_labels(self, project, _options) # Recreate containers to add labels\\n def version(self, project, options) # Show version informations\\ndef convergence_strategy_from_opts(options)\\ndef run_one_off_container(container_options, project, service, options)\\n def remove_container(force=False)\\n def force_shutdown(signal, frame)\\n def shutdown(signal, frame)\\ndef build_log_printer(containers, service_names, monochrome)\\ndef attach_to_logs(project, log_printer, service_names, timeout)\\n def force_shutdown(signal, frame)\\n def shutdown(signal, frame)\\ndef set_signal_handler(handler)\\ndef list_containers(containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/utils.py\",\n \"text\": \"compose/cli/utils.py\\ndef yesno(prompt, default=None) # Prompt the user for a yes or no.\\ndef call_silently(*args, **kwargs) # Like subprocess.call(), but redirects stdout and stderr to /dev/null.\\ndef is_mac()\\ndef is_ubuntu()\\ndef get_version_info(scope)\\ndef get_build_version()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py\",\n \"text\": \"compose/config/config.py\\nclass ConfigDetails # :param working_dir: the directory to use for relative paths in the config\\nclass ConfigFile # :param filename: filename of the config file\\n def from_filename(cls, filename)\\nclass ServiceConfig\\n def with_abs_paths(cls, working_dir, filename, name, config)\\ndef find(base_dir, filenames)\\ndef get_default_config_files(base_dir)\\ndef get_default_override_file(path)\\ndef find_candidates_in_parent_dirs(filenames, path) # Given a directory path to start, looks for filenames in the\\ndef load(config_details) # Load the configuration from a working directory and a list of\\n def build_service(filename, service_name, service_dict)\\n def build_services(config_file)\\n def merge_services(base, override)\\ndef process_config_file(config_file, service_name=None)\\nclass ServiceExtendsResolver\\n def __init__(self, service_config, already_seen=None)\\n def signature(self)\\n def detect_cycle(self)\\n def run(self)\\n def validate_and_construct_extends(self)\\n def resolve_extends(self, extended_config_path, service_dict, service_name)\\n def get_extended_config_path(self, extends_options) # Service we are extending either has a value for 'file' set, which we\\ndef resolve_environment(service_dict) # Unpack any environment variables from an env_file, if set.\\ndef validate_extended_service_dict(service_dict, filename, service)\\ndef validate_ulimits(ulimit_config)\\ndef process_service(service_config)\\ndef finalize_service(service_config)\\ndef merge_service_dicts_from_files(base, override) # When merging services from multiple files we need to merge the `extends`\\ndef merge_service_dicts(base, override)\\n def merge_field(field, merge_func, default=None)\\ndef merge_image_or_build(base, override, output)\\ndef merge_environment(base, override)\\ndef parse_environment(environment)\\ndef split_env(env)\\ndef resolve_env_var(key, val)\\ndef env_vars_from_file(filename) # Read in a line delimited file of environment variables.\\ndef resolve_volume_paths(working_dir, service_dict)\\ndef resolve_volume_path(working_dir, volume)\\ndef resolve_build_path(working_dir, build_path)\\ndef is_url(build_path)\\ndef validate_paths(service_dict)\\ndef merge_path_mappings(base, override)\\ndef dict_from_path_mappings(path_mappings)\\ndef path_mappings_from_dict(d)\\ndef split_path_mapping(volume_path) # Ascertain if the volume_path contains a host path as well as a container\\ndef join_path_mapping(pair)\\ndef merge_labels(base, override)\\ndef parse_labels(labels)\\ndef split_label(label)\\ndef expand_path(working_dir, path)\\ndef merge_list_or_string(base, override)\\ndef to_list(value)\\ndef load_yaml(filename)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/validation.py\",\n \"text\": \"compose/config/validation.py\\ndef format_ports(instance)\\ndef format_expose(instance)\\ndef format_boolean_in_environment(instance) # Check if there is a boolean in the environment and display a warning.\\ndef validate_top_level_service_objects(config_file) # Perform some high level validation of the service name and value.\\ndef validate_top_level_object(config_file)\\ndef validate_extends_file_path(service_name, extends_options, filename) # The service to be extended must either be defined in the config key 'file',\\ndef get_unsupported_config_msg(service_name, error_key)\\ndef anglicize_validator(validator)\\ndef handle_error_for_schema_with_id(error, service_name)\\ndef handle_generic_service_error(error, service_name)\\ndef parse_key_from_error_msg(error)\\ndef _parse_valid_types_from_validator(validator) # A validator value can be either an array of valid types or a string of\\ndef _parse_oneof_validator(error) # oneOf has multiple schemas, so we need to reason about which schema, sub\\ndef process_errors(errors, service_name=None) # jsonschema gives us an error tree full of information to explain what has\\n def format_error_message(error, service_name)\\ndef validate_against_fields_schema(config, filename)\\ndef validate_against_service_schema(config, service_name)\\ndef _validate_against_schema(config, schema_filename, format_checker=(), service_name=None, filename=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py\",\n \"text\": \"compose/legacy.py\\ndef check_for_legacy_containers(client, project, services, allow_one_off=True) # Check if there are containers named using the old naming convention\\nclass LegacyError\\n def __unicode__(self)\\nclass LegacyContainersError\\n def __init__(self, names, one_off_names)\\nclass LegacyOneOffContainersError\\n def __init__(self, one_off_names)\\ndef add_labels(project, container)\\ndef migrate_project_to_labels(project)\\ndef get_legacy_containers(client, project, services, one_off=False)\\ndef _get_legacy_containers_iter(client, project, services, one_off=False)\\ndef has_container(project, service, name, one_off=False)\\ndef is_valid_name(name, one_off=False)\\ndef parse_name(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py\",\n \"text\": \"compose/service.py\\nclass BuildError\\n def __init__(self, service, reason)\\nclass NeedsBuildError\\n def __init__(self, service)\\nclass NoSuchImageError\\nclass ConvergenceStrategy # Enumeration for all possible convergence strategies. Values refer to\\n def allows_recreate(self)\\nclass Service\\n def __init__(self, name, client=None, project='default', use_networking=False, links=None, volumes_from=None, net=None, **options)\\n def containers(self, stopped=False, one_off=False, filters={})\\n def get_container(self, number=1) # Return a :class:`compose.container.Container` for this service. The\\n def start(self, **options)\\n def stop(self, **options)\\n def pause(self, **options)\\n def unpause(self, **options)\\n def kill(self, **options)\\n def restart(self, **options)\\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT) # Adjusts the number of containers to the specified number and ensures\\n def create_and_start(service, number)\\n def create_container(self, one_off=False, do_build=True, previous_container=None, number=None, quiet=False, **override_options) # Create a container for this service. If the image doesn't exist, attempt to pull\\n def ensure_image_exists(self, do_build=True)\\n def image(self)\\n def image_name(self)\\n def convergence_plan(self, strategy=ConvergenceStrategy.changed)\\n def _containers_have_diverged(self, containers)\\n def execute_convergence_plan(self, plan, do_build=True, timeout=DEFAULT_TIMEOUT, detached=False)\\n def recreate_container(self, container, do_build=False, timeout=DEFAULT_TIMEOUT, attach_logs=False) # Recreate a container.\\n def start_container_if_stopped(self, container, attach_logs=False)\\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT)\\n def duplicate_containers(self)\\n def config_hash(self)\\n def config_dict(self)\\n def get_dependency_names(self)\\n def get_linked_service_names(self)\\n def get_link_names(self)\\n def get_volumes_from_names(self)\\n def get_container_name(self, number, one_off=False)\\n def _next_container_number(self, one_off=False)\\n def _get_links(self, link_to_self)\\n def _get_volumes_from(self)\\n def _get_container_create_options(self, override_options, number, one_off=False, previous_container=None)\\n def _get_container_host_config(self, override_options, one_off=False)\\n def build(self, no_cache=False, pull=False, force_rm=False)\\n def can_be_built(self)\\n def full_name(self) # The tag to give to images built for this service.\\n def labels(self, one_off=False)\\n def custom_container_name(self)\\n def specifies_host_port(self)\\n def has_host_port(binding)\\n def pull(self, ignore_pull_failures=False)\\nclass Net # A `standard` network mode (ex: host, bridge)\\n def __init__(self, net)\\n def id(self)\\nclass ContainerNet # A network mode that uses a container's network stack.\\n def __init__(self, container)\\n def id(self)\\n def mode(self)\\nclass ServiceNet # A network mode t\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py\",\n \"text\": \"compose/utils.py\\ndef get_output_stream(stream)\\ndef stream_as_text(stream) # Given a stream of bytes or text, if any of the items in the stream\\ndef line_splitter(buffer, separator=u'\\\\n')\\ndef split_buffer(stream, splitter=None, decoder=lambda a: a) # Given a generator which yields strings and a splitter function,\\ndef json_splitter(buffer) # Attempt to parse a json object from a buffer. If there is at least one\\ndef json_stream(stream) # Given a stream of text, return a stream of json objects.\\ndef json_hash(obj)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"compose/cli/command.py::friendly_error_message","text":"compose/cli/command.py::friendly_error_message\n@contextlib.contextmanager\ndef friendly_error_message():\n try:\n yield\n except SSLError as e:\n raise errors.UserError('SSL error: %s' % e)\n except ConnectionError:\n if call_silently(['which', 'docker']) != 0:\n if is_mac():\n raise errors.DockerNotFoundMac()\n elif is_ubuntu():\n raise errors.DockerNotFoundUbuntu()\n else:\n raise errors.DockerNotFoundGeneric()\n elif call_silently(['which', 'docker-machine']) == 0:\n raise errors.ConnectionErrorDockerMachine()\n else:\n raise errors.ConnectionErrorGeneric(get_client().base_url)","label":0},{"cid":"compose/cli/command.py::project_from_options","text":"compose/cli/command.py::project_from_options\ndef project_from_options(base_dir, options):\n return get_project(\n base_dir,\n get_config_path(options.get('--file')),\n project_name=options.get('--project-name'),\n verbose=options.get('--verbose'),\n use_networking=options.get('--x-networking'),\n network_driver=options.get('--x-network-driver'),\n )","label":0},{"cid":"compose/cli/command.py::get_config_path","text":"compose/cli/command.py::get_config_path\ndef get_config_path(file_option):\n if file_option:\n return file_option\n\n if 'FIG_FILE' in os.environ:\n log.warn('The FIG_FILE environment variable is deprecated.')\n log.warn('Please use COMPOSE_FILE instead.')\n\n config_file = os.environ.get('COMPOSE_FILE') or os.environ.get('FIG_FILE')\n return [config_file] if config_file else None","label":0},{"cid":"compose/cli/command.py::get_client","text":"compose/cli/command.py::get_client\ndef get_client(verbose=False, version=None):\n client = docker_client(version=version)\n if verbose:\n version_info = six.iteritems(client.version())\n log.info(get_version_info('full'))\n log.info(\"Docker base_url: %s\", client.base_url)\n log.info(\"Docker version: %s\",\n \", \".join(\"%s=%s\" % item for item in version_info))\n return verbose_proxy.VerboseProxy('docker', client)\n return client","label":0},{"cid":"compose/cli/command.py::get_project","text":"compose/cli/command.py::get_project\ndef get_project(base_dir, config_path=None, project_name=None, verbose=False,\n use_networking=False, network_driver=None):\n config_details = config.find(base_dir, config_path)\n\n api_version = '1.21' if use_networking else None\n return Project.from_dicts(\n get_project_name(config_details.working_dir, project_name),\n config.load(config_details),\n get_client(verbose=verbose, version=api_version),\n use_networking=use_networking,\n network_driver=network_driver)","label":0},{"cid":"compose/cli/command.py::get_project_name","text":"compose/cli/command.py::get_project_name\ndef get_project_name(working_dir, project_name=None):\n def normalize_name(name):\n return re.sub(r'[^a-z0-9]', '', name.lower())\n\n if 'FIG_PROJECT_NAME' in os.environ:\n log.warn('The FIG_PROJECT_NAME environment variable is deprecated.')\n log.warn('Please use COMPOSE_PROJECT_NAME instead.')\n\n project_name = (\n project_name or\n os.environ.get('COMPOSE_PROJECT_NAME') or\n os.environ.get('FIG_PROJECT_NAME'))\n if project_name is not None:\n return normalize_name(project_name)\n\n project = os.path.basename(os.path.abspath(working_dir))\n if project:\n return normalize_name(project)\n\n return 'default'","label":0},{"cid":"compose/cli/command.py::get_project_name.normalize_name","text":"compose/cli/command.py::get_project_name.normalize_name\n def normalize_name(name):\n return re.sub(r'[^a-z0-9]', '', name.lower())","label":0},{"cid":"compose/cli/docker_client.py::docker_client","text":"compose/cli/docker_client.py::docker_client\ndef docker_client(version=None):\n \"\"\"\n Returns a docker-py client configured using environment variables\n according to the same logic as the official Docker client.\n \"\"\"\n if 'DOCKER_CLIENT_TIMEOUT' in os.environ:\n log.warn('The DOCKER_CLIENT_TIMEOUT environment variable is deprecated. Please use COMPOSE_HTTP_TIMEOUT instead.')\n\n kwargs = kwargs_from_env(assert_hostname=False)\n kwargs['version'] = version or os.environ.get(\n 'COMPOSE_API_VERSION',\n DEFAULT_API_VERSION)\n kwargs['timeout'] = HTTP_TIMEOUT\n return Client(**kwargs)","label":0},{"cid":"compose/cli/log_printer.py::LogPrinter.__init__","text":"compose/cli/log_printer.py::LogPrinter.__init__\n def __init__(self, containers, output=sys.stdout, monochrome=False):\n self.containers = containers\n self.output = utils.get_output_stream(output)\n self.monochrome = monochrome","label":0},{"cid":"compose/cli/log_printer.py::LogPrinter.run","text":"compose/cli/log_printer.py::LogPrinter.run\n def run(self):\n if not self.containers:\n return\n\n prefix_width = max_name_width(self.containers)\n generators = list(self._make_log_generators(self.monochrome, prefix_width))\n for line in Multiplexer(generators).loop():\n self.output.write(line)\n self.output.flush()","label":0},{"cid":"compose/cli/log_printer.py::LogPrinter._make_log_generators","text":"compose/cli/log_printer.py::LogPrinter._make_log_generators\n def _make_log_generators(self, monochrome, prefix_width):\n def no_color(text):\n return text\n\n if monochrome:\n color_funcs = cycle([no_color])\n else:\n color_funcs = cycle(colors.rainbow())\n\n for color_func, container in zip(color_funcs, self.containers):\n generator_func = get_log_generator(container)\n prefix = color_func(build_log_prefix(container, prefix_width))\n yield generator_func(container, prefix, color_func)","label":0},{"cid":"compose/cli/log_printer.py::LogPrinter._make_log_generators.no_color","text":"compose/cli/log_printer.py::LogPrinter._make_log_generators.no_color\n def no_color(text):\n return text","label":0},{"cid":"compose/cli/log_printer.py::build_log_prefix","text":"compose/cli/log_printer.py::build_log_prefix\ndef build_log_prefix(container, prefix_width):\n return container.name_without_project.ljust(prefix_width) + ' | '","label":0},{"cid":"compose/cli/log_printer.py::max_name_width","text":"compose/cli/log_printer.py::max_name_width\ndef max_name_width(containers):\n \"\"\"Calculate the maximum width of container names so we can make the log\n prefixes line up like so:\n\n db_1 | Listening\n web_1 | Listening\n \"\"\"\n return max(len(container.name_without_project) for container in containers)","label":0},{"cid":"compose/cli/log_printer.py::get_log_generator","text":"compose/cli/log_printer.py::get_log_generator\ndef get_log_generator(container):\n if container.has_api_logs:\n return build_log_generator\n return build_no_log_generator","label":0},{"cid":"compose/cli/log_printer.py::build_no_log_generator","text":"compose/cli/log_printer.py::build_no_log_generator\ndef build_no_log_generator(container, prefix, color_func):\n \"\"\"Return a generator that prints a warning about logs and waits for\n container to exit.\n \"\"\"\n yield \"{} WARNING: no logs are available with the '{}' log driver\\n\".format(\n prefix,\n container.log_driver)\n yield color_func(wait_on_exit(container))","label":0},{"cid":"compose/cli/log_printer.py::build_log_generator","text":"compose/cli/log_printer.py::build_log_generator\ndef build_log_generator(container, prefix, color_func):\n # if the container doesn't have a log_stream we need to attach to container\n # before log printer starts running\n if container.log_stream is None:\n stream = container.attach(stdout=True, stderr=True, stream=True, logs=True)\n line_generator = split_buffer(stream)\n else:\n line_generator = split_buffer(container.log_stream)\n\n for line in line_generator:\n yield prefix + line\n yield color_func(wait_on_exit(container))","label":0},{"cid":"compose/cli/log_printer.py::wait_on_exit","text":"compose/cli/log_printer.py::wait_on_exit\ndef wait_on_exit(container):\n exit_code = container.wait()\n return \"%s exited with code %s\\n\" % (container.name, exit_code)","label":0},{"cid":"compose/cli/main.py::main","text":"compose/cli/main.py::main\ndef main():\n setup_logging()\n try:\n command = TopLevelCommand()\n command.sys_dispatch()\n except KeyboardInterrupt:\n log.error(\"\\nAborting.\")\n sys.exit(1)\n except (UserError, NoSuchService, ConfigurationError, legacy.LegacyError) as e:\n log.error(e.msg)\n sys.exit(1)\n except NoSuchCommand as e:\n commands = \"\\n\".join(parse_doc_section(\"commands:\", getdoc(e.supercommand)))\n log.error(\"No such command: %s\\n\\n%s\", e.command, commands)\n sys.exit(1)\n except APIError as e:\n log.error(e.explanation)\n sys.exit(1)\n except BuildError as e:\n log.error(\"Service '%s' failed to build: %s\" % (e.service.name, e.reason))\n sys.exit(1)\n except StreamOutputError as e:\n log.error(e)\n sys.exit(1)\n except NeedsBuildError as e:\n log.error(\"Service '%s' needs to be built, but --no-build was passed.\" % e.service.name)\n sys.exit(1)\n except ReadTimeout as e:\n log.error(\n \"An HTTP request took too long to complete. Retry with --verbose to obtain debug information.\\n\"\n \"If you encounter this issue regularly because of slow network conditions, consider setting \"\n \"COMPOSE_HTTP_TIMEOUT to a higher value (current value: %s).\" % HTTP_TIMEOUT\n )\n sys.exit(1)","label":0},{"cid":"compose/cli/main.py::setup_logging","text":"compose/cli/main.py::setup_logging\ndef setup_logging():\n root_logger = logging.getLogger()\n root_logger.addHandler(console_handler)\n root_logger.setLevel(logging.DEBUG)\n\n # Disable requests logging\n logging.getLogger(\"requests\").propagate = False","label":0},{"cid":"compose/cli/main.py::setup_console_handler","text":"compose/cli/main.py::setup_console_handler\ndef setup_console_handler(handler, verbose):\n if handler.stream.isatty():\n format_class = ConsoleWarningFormatter\n else:\n format_class = logging.Formatter\n\n if verbose:\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\n handler.setLevel(logging.DEBUG)\n else:\n handler.setFormatter(format_class())\n handler.setLevel(logging.INFO)","label":0},{"cid":"compose/cli/main.py::parse_doc_section","text":"compose/cli/main.py::parse_doc_section\ndef parse_doc_section(name, source):\n pattern = re.compile('^([^\\n]*' + name + '[^\\n]*\\n?(?:[ \\t].*?(?:\\n|$))*)',\n re.IGNORECASE | re.MULTILINE)\n return [s.strip() for s in pattern.findall(source)]","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.docopt_options","text":"compose/cli/main.py::TopLevelCommand.docopt_options\n def docopt_options(self):\n options = super(TopLevelCommand, self).docopt_options()\n options['version'] = get_version_info('compose')\n return options","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.perform_command","text":"compose/cli/main.py::TopLevelCommand.perform_command\n def perform_command(self, options, handler, command_options):\n setup_console_handler(console_handler, options.get('--verbose'))\n\n if options['COMMAND'] in ('help', 'version'):\n # Skip looking up the compose file.\n handler(None, command_options)\n return\n\n project = project_from_options(self.base_dir, options)\n with friendly_error_message():\n handler(project, command_options)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.build","text":"compose/cli/main.py::TopLevelCommand.build\n def build(self, project, options):\n \"\"\"\n Build or rebuild services.\n\n Services are built once and then tagged as `project_service`,\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\n contents of its build directory, you can run `docker-compose build` to rebuild it.\n\n Usage: build [options] [SERVICE...]\n\n Options:\n --force-rm Always remove intermediate containers.\n --no-cache Do not use cache when building the image.\n --pull Always attempt to pull a newer version of the image.\n \"\"\"\n project.build(\n service_names=options['SERVICE'],\n no_cache=bool(options.get('--no-cache', False)),\n pull=bool(options.get('--pull', False)),\n force_rm=bool(options.get('--force-rm', False)))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.help","text":"compose/cli/main.py::TopLevelCommand.help\n def help(self, project, options):\n \"\"\"\n Get help on a command.\n\n Usage: help COMMAND\n \"\"\"\n handler = self.get_handler(options['COMMAND'])\n raise SystemExit(getdoc(handler))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.kill","text":"compose/cli/main.py::TopLevelCommand.kill\n def kill(self, project, options):\n \"\"\"\n Force stop service containers.\n\n Usage: kill [options] [SERVICE...]\n\n Options:\n -s SIGNAL SIGNAL to send to the container.\n Default signal is SIGKILL.\n \"\"\"\n signal = options.get('-s', 'SIGKILL')\n\n project.kill(service_names=options['SERVICE'], signal=signal)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.logs","text":"compose/cli/main.py::TopLevelCommand.logs\n def logs(self, project, options):\n \"\"\"\n View output from containers.\n\n Usage: logs [options] [SERVICE...]\n\n Options:\n --no-color Produce monochrome output.\n \"\"\"\n containers = project.containers(service_names=options['SERVICE'], stopped=True)\n\n monochrome = options['--no-color']\n print(\"Attaching to\", list_containers(containers))\n LogPrinter(containers, monochrome=monochrome).run()","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pause","text":"compose/cli/main.py::TopLevelCommand.pause\n def pause(self, project, options):\n \"\"\"\n Pause services.\n\n Usage: pause [SERVICE...]\n \"\"\"\n project.pause(service_names=options['SERVICE'])","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.port","text":"compose/cli/main.py::TopLevelCommand.port\n def port(self, project, options):\n \"\"\"\n Print the public port for a port binding.\n\n Usage: port [options] SERVICE PRIVATE_PORT\n\n Options:\n --protocol=proto tcp or udp [default: tcp]\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n \"\"\"\n index = int(options.get('--index'))\n service = project.get_service(options['SERVICE'])\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n print(container.get_local_port(\n options['PRIVATE_PORT'],\n protocol=options.get('--protocol') or 'tcp') or '')","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.ps","text":"compose/cli/main.py::TopLevelCommand.ps\n def ps(self, project, options):\n \"\"\"\n List containers.\n\n Usage: ps [options] [SERVICE...]\n\n Options:\n -q Only display IDs\n \"\"\"\n containers = sorted(\n project.containers(service_names=options['SERVICE'], stopped=True) +\n project.containers(service_names=options['SERVICE'], one_off=True),\n key=attrgetter('name'))\n\n if options['-q']:\n for container in containers:\n print(container.id)\n else:\n headers = [\n 'Name',\n 'Command',\n 'State',\n 'Ports',\n ]\n rows = []\n for container in containers:\n command = container.human_readable_command\n if len(command) > 30:\n command = '%s ...' % command[:26]\n rows.append([\n container.name,\n command,\n container.human_readable_state,\n container.human_readable_ports,\n ])\n print(Formatter().table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pull","text":"compose/cli/main.py::TopLevelCommand.pull\n def pull(self, project, options):\n \"\"\"\n Pulls images for services.\n\n Usage: pull [options] [SERVICE...]\n\n Options:\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\n --allow-insecure-ssl Deprecated - no effect.\n \"\"\"\n if options['--allow-insecure-ssl']:\n log.warn(INSECURE_SSL_WARNING)\n\n project.pull(\n service_names=options['SERVICE'],\n ignore_pull_failures=options.get('--ignore-pull-failures')\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.rm","text":"compose/cli/main.py::TopLevelCommand.rm\n def rm(self, project, options):\n \"\"\"\n Remove stopped service containers.\n\n Usage: rm [options] [SERVICE...]\n\n Options:\n -f, --force Don't ask to confirm removal\n -v Remove volumes associated with containers\n \"\"\"\n all_containers = project.containers(service_names=options['SERVICE'], stopped=True)\n stopped_containers = [c for c in all_containers if not c.is_running]\n\n if len(stopped_containers) > 0:\n print(\"Going to remove\", list_containers(stopped_containers))\n if options.get('--force') \\\n or yesno(\"Are you sure? [yN] \", default=False):\n project.remove_stopped(\n service_names=options['SERVICE'],\n v=options.get('-v', False)\n )\n else:\n print(\"No stopped containers\")","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.run","text":"compose/cli/main.py::TopLevelCommand.run\n def run(self, project, options):\n \"\"\"\n Run a one-off command on a service.\n\n For example:\n\n $ docker-compose run web python manage.py shell\n\n By default, linked services will be started, unless they are already\n running. If you do not want to start linked services, use\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\n\n Usage: run [options] [-p PORT...] [-e KEY=VAL...] SERVICE [COMMAND] [ARGS...]\n\n Options:\n --allow-insecure-ssl Deprecated - no effect.\n -d Detached mode: Run container in the background, print\n new container name.\n --name NAME Assign a name to the container\n --entrypoint CMD Override the entrypoint of the image.\n -e KEY=VAL Set an environment variable (can be used multiple times)\n -u, --user=\"\" Run as specified username or uid\n --no-deps Don't start linked services.\n --rm Remove container after run. Ignored in detached mode.\n -p, --publish=[] Publish a container's port(s) to the host\n --service-ports Run command with the service's ports enabled and mapped\n to the host.\n -T Disable pseudo-tty allocation. By default `docker-compose run`\n allocates a TTY.\n \"\"\"\n service = project.get_service(options['SERVICE'])\n detach = options['-d']\n\n if IS_WINDOWS_PLATFORM and not detach:\n raise UserError(\n \"Interactive mode is not yet supported on Windows.\\n\"\n \"Please pass the -d flag when using `docker-compose run`.\"\n )\n\n if options['--allow-insecure-ssl']:\n log.warn(INSECURE_SSL_WARNING)\n\n if options['COMMAND']:\n ","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.scale","text":"compose/cli/main.py::TopLevelCommand.scale\n def scale(self, project, options):\n \"\"\"\n Set number of containers to run for a service.\n\n Numbers are specified in the form `service=num` as arguments.\n For example:\n\n $ docker-compose scale web=2 worker=3\n\n Usage: scale [options] [SERVICE=NUM...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\n\n for s in options['SERVICE=NUM']:\n if '=' not in s:\n raise UserError('Arguments to scale should be in the form service=num')\n service_name, num = s.split('=', 1)\n try:\n num = int(num)\n except ValueError:\n raise UserError('Number of containers for service \"%s\" is not a '\n 'number' % service_name)\n project.get_service(service_name).scale(num, timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.start","text":"compose/cli/main.py::TopLevelCommand.start\n def start(self, project, options):\n \"\"\"\n Start existing containers.\n\n Usage: start [SERVICE...]\n \"\"\"\n project.start(service_names=options['SERVICE'])","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.stop","text":"compose/cli/main.py::TopLevelCommand.stop\n def stop(self, project, options):\n \"\"\"\n Stop running containers without removing them.\n\n They can be started again with `docker-compose start`.\n\n Usage: stop [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\n project.stop(service_names=options['SERVICE'], timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.restart","text":"compose/cli/main.py::TopLevelCommand.restart\n def restart(self, project, options):\n \"\"\"\n Restart running containers.\n\n Usage: restart [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\n project.restart(service_names=options['SERVICE'], timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.unpause","text":"compose/cli/main.py::TopLevelCommand.unpause\n def unpause(self, project, options):\n \"\"\"\n Unpause services.\n\n Usage: unpause [SERVICE...]\n \"\"\"\n project.unpause(service_names=options['SERVICE'])","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.up","text":"compose/cli/main.py::TopLevelCommand.up\n def up(self, project, options):\n \"\"\"\n Builds, (re)creates, starts, and attaches to containers for a service.\n\n Unless they are already running, this command also starts any linked services.\n\n The `docker-compose up` command aggregates the output of each container. When\n the command exits, all containers are stopped. Running `docker-compose up -d`\n starts the containers in the background and leaves them running.\n\n If there are existing containers for a service, and the service's configuration\n or image was changed after the container's creation, `docker-compose up` picks\n up the changes by stopping and recreating the containers (preserving mounted\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\n flag.\n\n If you want to force Compose to stop and recreate all containers, use the\n `--force-recreate` flag.\n\n Usage: up [options] [SERVICE...]\n\n Options:\n --allow-insecure-ssl Deprecated - no effect.\n -d Detached mode: Run containers in the background,\n print new container names.\n --no-color Produce monochrome output.\n --no-deps Don't start linked services.\n --force-recreate Recreate containers even if their configuration and\n image haven't changed. Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate them.\n Incompatible with --force-recreate.\n --no-build Don't build an image, even if it's missing\n -t, --timeout TIMEOUT Use this timeout in seconds for container shutdown\n when attached or when containers are already\n running. (default:","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.migrate_to_labels","text":"compose/cli/main.py::TopLevelCommand.migrate_to_labels\n def migrate_to_labels(self, project, _options):\n \"\"\"\n Recreate containers to add labels\n\n If you're coming from Compose 1.2 or earlier, you'll need to remove or\n migrate your existing containers after upgrading Compose. This is\n because, as of version 1.3, Compose uses Docker labels to keep track\n of containers, and so they need to be recreated with labels added.\n\n If Compose detects containers that were created without labels, it\n will refuse to run so that you don't end up with two sets of them. If\n you want to keep using your existing containers (for example, because\n they have data volumes you want to preserve) you can migrate them with\n the following command:\n\n docker-compose migrate-to-labels\n\n Alternatively, if you're not worried about keeping them, you can\n remove them - Compose will just create new ones.\n\n docker rm -f myapp_web_1 myapp_db_1 ...\n\n Usage: migrate-to-labels\n \"\"\"\n legacy.migrate_project_to_labels(project)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.version","text":"compose/cli/main.py::TopLevelCommand.version\n def version(self, project, options):\n \"\"\"\n Show version informations\n\n Usage: version [--short]\n\n Options:\n --short Shows only Compose's version number.\n \"\"\"\n if options['--short']:\n print(__version__)\n else:\n print(get_version_info('full'))","label":0},{"cid":"compose/cli/main.py::convergence_strategy_from_opts","text":"compose/cli/main.py::convergence_strategy_from_opts\ndef convergence_strategy_from_opts(options):\n no_recreate = options['--no-recreate']\n force_recreate = options['--force-recreate']\n if force_recreate and no_recreate:\n raise UserError(\"--force-recreate and --no-recreate cannot be combined.\")\n\n if force_recreate:\n return ConvergenceStrategy.always\n\n if no_recreate:\n return ConvergenceStrategy.never\n\n return ConvergenceStrategy.changed","label":0},{"cid":"compose/cli/main.py::run_one_off_container","text":"compose/cli/main.py::run_one_off_container\ndef run_one_off_container(container_options, project, service, options):\n if not options['--no-deps']:\n deps = service.get_linked_service_names()\n if deps:\n project.up(\n service_names=deps,\n start_deps=True,\n strategy=ConvergenceStrategy.never)\n\n if project.use_networking:\n project.ensure_network_exists()\n\n try:\n container = service.create_container(\n quiet=True,\n one_off=True,\n **container_options)\n except APIError:\n legacy.check_for_legacy_containers(\n project.client,\n project.name,\n [service.name],\n allow_one_off=False)\n raise\n\n if options['-d']:\n container.start()\n print(container.name)\n return\n\n def remove_container(force=False):\n if options['--rm']:\n project.client.remove_container(container.id, force=True)\n\n def force_shutdown(signal, frame):\n project.client.kill(container.id)\n remove_container(force=True)\n sys.exit(2)\n\n def shutdown(signal, frame):\n set_signal_handler(force_shutdown)\n project.client.stop(container.id)\n remove_container()\n sys.exit(1)\n\n set_signal_handler(shutdown)\n dockerpty.start(project.client, container.id, interactive=not options['-T'])\n exit_code = container.wait()\n remove_container()\n sys.exit(exit_code)","label":0},{"cid":"compose/cli/main.py::run_one_off_container.remove_container","text":"compose/cli/main.py::run_one_off_container.remove_container\n def remove_container(force=False):\n if options['--rm']:\n project.client.remove_container(container.id, force=True)","label":0},{"cid":"compose/cli/main.py::run_one_off_container.force_shutdown","text":"compose/cli/main.py::run_one_off_container.force_shutdown\n def force_shutdown(signal, frame):\n project.client.kill(container.id)\n remove_container(force=True)\n sys.exit(2)","label":0},{"cid":"compose/cli/main.py::run_one_off_container.shutdown","text":"compose/cli/main.py::run_one_off_container.shutdown\n def shutdown(signal, frame):\n set_signal_handler(force_shutdown)\n project.client.stop(container.id)\n remove_container()\n sys.exit(1)","label":0},{"cid":"compose/cli/main.py::build_log_printer","text":"compose/cli/main.py::build_log_printer\ndef build_log_printer(containers, service_names, monochrome):\n if service_names:\n containers = [\n container\n for container in containers if container.service in service_names\n ]\n return LogPrinter(containers, monochrome=monochrome)","label":0},{"cid":"compose/cli/main.py::attach_to_logs","text":"compose/cli/main.py::attach_to_logs\ndef attach_to_logs(project, log_printer, service_names, timeout):\n\n def force_shutdown(signal, frame):\n project.kill(service_names=service_names)\n sys.exit(2)\n\n def shutdown(signal, frame):\n set_signal_handler(force_shutdown)\n print(\"Gracefully stopping... (press Ctrl+C again to force)\")\n project.stop(service_names=service_names, timeout=timeout)\n\n print(\"Attaching to\", list_containers(log_printer.containers))\n set_signal_handler(shutdown)\n log_printer.run()","label":0},{"cid":"compose/cli/main.py::attach_to_logs.force_shutdown","text":"compose/cli/main.py::attach_to_logs.force_shutdown\n def force_shutdown(signal, frame):\n project.kill(service_names=service_names)\n sys.exit(2)","label":0},{"cid":"compose/cli/main.py::attach_to_logs.shutdown","text":"compose/cli/main.py::attach_to_logs.shutdown\n def shutdown(signal, frame):\n set_signal_handler(force_shutdown)\n print(\"Gracefully stopping... (press Ctrl+C again to force)\")\n project.stop(service_names=service_names, timeout=timeout)","label":0},{"cid":"compose/cli/main.py::set_signal_handler","text":"compose/cli/main.py::set_signal_handler\ndef set_signal_handler(handler):\n signal.signal(signal.SIGINT, handler)\n signal.signal(signal.SIGTERM, handler)","label":0},{"cid":"compose/cli/main.py::list_containers","text":"compose/cli/main.py::list_containers\ndef list_containers(containers):\n return \", \".join(c.name for c in containers)","label":0},{"cid":"compose/cli/utils.py::yesno","text":"compose/cli/utils.py::yesno\ndef yesno(prompt, default=None):\n \"\"\"\n Prompt the user for a yes or no.\n\n Can optionally specify a default value, which will only be\n used if they enter a blank line.\n\n Unrecognised input (anything other than \"y\", \"n\", \"yes\",\n \"no\" or \"\") will return None.\n \"\"\"\n answer = input(prompt).strip().lower()\n\n if answer == \"y\" or answer == \"yes\":\n return True\n elif answer == \"n\" or answer == \"no\":\n return False\n elif answer == \"\":\n return default\n else:\n return None","label":0},{"cid":"compose/cli/utils.py::call_silently","text":"compose/cli/utils.py::call_silently\ndef call_silently(*args, **kwargs):\n \"\"\"\n Like subprocess.call(), but redirects stdout and stderr to /dev/null.\n \"\"\"\n with open(os.devnull, 'w') as shutup:\n try:\n return subprocess.call(*args, stdout=shutup, stderr=shutup, **kwargs)\n except WindowsError:\n # On Windows, subprocess.call() can still raise exceptions. Normalize\n # to POSIXy behaviour by returning a nonzero exit code.\n return 1","label":0},{"cid":"compose/cli/utils.py::is_mac","text":"compose/cli/utils.py::is_mac\ndef is_mac():\n return platform.system() == 'Darwin'","label":0},{"cid":"compose/cli/utils.py::is_ubuntu","text":"compose/cli/utils.py::is_ubuntu\ndef is_ubuntu():\n return platform.system() == 'Linux' and platform.linux_distribution()[0] == 'Ubuntu'","label":0},{"cid":"compose/cli/utils.py::get_version_info","text":"compose/cli/utils.py::get_version_info\ndef get_version_info(scope):\n versioninfo = 'docker-compose version {}, build {}'.format(\n compose.__version__,\n get_build_version())\n\n if scope == 'compose':\n return versioninfo\n if scope == 'full':\n return (\n \"{}\\n\"\n \"docker-py version: {}\\n\"\n \"{} version: {}\\n\"\n \"OpenSSL version: {}\"\n ).format(\n versioninfo,\n docker.version,\n platform.python_implementation(),\n platform.python_version(),\n ssl.OPENSSL_VERSION)\n\n raise ValueError(\"{} is not a valid version scope\".format(scope))","label":0},{"cid":"compose/cli/utils.py::get_build_version","text":"compose/cli/utils.py::get_build_version\ndef get_build_version():\n filename = os.path.join(os.path.dirname(compose.__file__), 'GITSHA')\n if not os.path.exists(filename):\n return 'unknown'\n\n with open(filename) as fh:\n return fh.read().strip()","label":0},{"cid":"compose/config/config.py::ConfigFile.from_filename","text":"compose/config/config.py::ConfigFile.from_filename\n @classmethod\n def from_filename(cls, filename):\n return cls(filename, load_yaml(filename))","label":0},{"cid":"compose/config/config.py::ServiceConfig.with_abs_paths","text":"compose/config/config.py::ServiceConfig.with_abs_paths\n @classmethod\n def with_abs_paths(cls, working_dir, filename, name, config):\n if not working_dir:\n raise ValueError(\"No working_dir for ServiceConfig.\")\n\n return cls(\n os.path.abspath(working_dir),\n os.path.abspath(filename) if filename else filename,\n name,\n config)","label":0},{"cid":"compose/config/config.py::find","text":"compose/config/config.py::find\ndef find(base_dir, filenames):\n if filenames == ['-']:\n return ConfigDetails(\n os.getcwd(),\n [ConfigFile(None, yaml.safe_load(sys.stdin))])\n\n if filenames:\n filenames = [os.path.join(base_dir, f) for f in filenames]\n else:\n filenames = get_default_config_files(base_dir)\n\n log.debug(\"Using configuration files: {}\".format(\",\".join(filenames)))\n return ConfigDetails(\n os.path.dirname(filenames[0]),\n [ConfigFile.from_filename(f) for f in filenames])","label":0},{"cid":"compose/config/config.py::get_default_config_files","text":"compose/config/config.py::get_default_config_files\ndef get_default_config_files(base_dir):\n (candidates, path) = find_candidates_in_parent_dirs(SUPPORTED_FILENAMES, base_dir)\n\n if not candidates:\n raise ComposeFileNotFound(SUPPORTED_FILENAMES)\n\n winner = candidates[0]\n\n if len(candidates) > 1:\n log.warn(\"Found multiple config files with supported names: %s\", \", \".join(candidates))\n log.warn(\"Using %s\\n\", winner)\n\n if winner == 'docker-compose.yaml':\n log.warn(\"Please be aware that .yml is the expected extension \"\n \"in most cases, and using .yaml can cause compatibility \"\n \"issues in future.\\n\")\n\n if winner.startswith(\"fig.\"):\n log.warn(\"%s is deprecated and will not be supported in future. \"\n \"Please rename your config file to docker-compose.yml\\n\" % winner)\n\n return [os.path.join(path, winner)] + get_default_override_file(path)","label":1},{"cid":"compose/config/config.py::get_default_override_file","text":"compose/config/config.py::get_default_override_file\ndef get_default_override_file(path):\n override_filename = os.path.join(path, DEFAULT_OVERRIDE_FILENAME)\n return [override_filename] if os.path.exists(override_filename) else []","label":0},{"cid":"compose/config/config.py::find_candidates_in_parent_dirs","text":"compose/config/config.py::find_candidates_in_parent_dirs\ndef find_candidates_in_parent_dirs(filenames, path):\n \"\"\"\n Given a directory path to start, looks for filenames in the\n directory, and then each parent directory successively,\n until found.\n\n Returns tuple (candidates, path).\n \"\"\"\n candidates = [filename for filename in filenames\n if os.path.exists(os.path.join(path, filename))]\n\n if not candidates:\n parent_dir = os.path.join(path, '..')\n if os.path.abspath(parent_dir) != os.path.abspath(path):\n return find_candidates_in_parent_dirs(filenames, parent_dir)\n\n return (candidates, path)","label":0},{"cid":"compose/config/config.py::load","text":"compose/config/config.py::load\ndef load(config_details):\n \"\"\"Load the configuration from a working directory and a list of\n configuration files. Files are loaded in order, and merged on top\n of each other to create the final configuration.\n\n Return a fully interpolated, extended and validated configuration.\n \"\"\"\n\n def build_service(filename, service_name, service_dict):\n service_config = ServiceConfig.with_abs_paths(\n config_details.working_dir,\n filename,\n service_name,\n service_dict)\n resolver = ServiceExtendsResolver(service_config)\n service_dict = process_service(resolver.run())\n\n # TODO: move to validate_service()\n validate_against_service_schema(service_dict, service_config.name)\n validate_paths(service_dict)\n\n service_dict = finalize_service(service_config._replace(config=service_dict))\n service_dict['name'] = service_config.name\n return service_dict\n\n def build_services(config_file):\n return sort_service_dicts([\n build_service(config_file.filename, name, service_dict)\n for name, service_dict in config_file.config.items()\n ])\n\n def merge_services(base, override):\n all_service_names = set(base) | set(override)\n return {\n name: merge_service_dicts_from_files(\n base.get(name, {}),\n override.get(name, {}))\n for name in all_service_names\n }\n\n config_file = process_config_file(config_details.config_files[0])\n for next_file in config_details.config_files[1:]:\n next_file = process_config_file(next_file)\n\n config = merge_services(config_file.config, next_file.config)\n config_file = config_file._replace(config=config)\n\n return build_services(config_file)","label":0},{"cid":"compose/config/config.py::load.build_service","text":"compose/config/config.py::load.build_service\n def build_service(filename, service_name, service_dict):\n service_config = ServiceConfig.with_abs_paths(\n config_details.working_dir,\n filename,\n service_name,\n service_dict)\n resolver = ServiceExtendsResolver(service_config)\n service_dict = process_service(resolver.run())\n\n # TODO: move to validate_service()\n validate_against_service_schema(service_dict, service_config.name)\n validate_paths(service_dict)\n\n service_dict = finalize_service(service_config._replace(config=service_dict))\n service_dict['name'] = service_config.name\n return service_dict","label":0},{"cid":"compose/config/config.py::load.build_services","text":"compose/config/config.py::load.build_services\n def build_services(config_file):\n return sort_service_dicts([\n build_service(config_file.filename, name, service_dict)\n for name, service_dict in config_file.config.items()\n ])","label":0},{"cid":"compose/config/config.py::load.merge_services","text":"compose/config/config.py::load.merge_services\n def merge_services(base, override):\n all_service_names = set(base) | set(override)\n return {\n name: merge_service_dicts_from_files(\n base.get(name, {}),\n override.get(name, {}))\n for name in all_service_names\n }","label":0},{"cid":"compose/config/config.py::process_config_file","text":"compose/config/config.py::process_config_file\ndef process_config_file(config_file, service_name=None):\n validate_top_level_object(config_file)\n processed_config = interpolate_environment_variables(config_file.config)\n validate_against_fields_schema(processed_config, config_file.filename)\n\n if service_name and service_name not in processed_config:\n raise ConfigurationError(\n \"Cannot extend service '{}' in {}: Service not found\".format(\n service_name, config_file.filename))\n\n return config_file._replace(config=processed_config)","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.__init__","text":"compose/config/config.py::ServiceExtendsResolver.__init__\n def __init__(self, service_config, already_seen=None):\n self.service_config = service_config\n self.working_dir = service_config.working_dir\n self.already_seen = already_seen or []","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.signature","text":"compose/config/config.py::ServiceExtendsResolver.signature\n @property\n def signature(self):\n return self.service_config.filename, self.service_config.name","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.detect_cycle","text":"compose/config/config.py::ServiceExtendsResolver.detect_cycle\n def detect_cycle(self):\n if self.signature in self.already_seen:\n raise CircularReference(self.already_seen + [self.signature])","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.run","text":"compose/config/config.py::ServiceExtendsResolver.run\n def run(self):\n self.detect_cycle()\n\n if 'extends' in self.service_config.config:\n service_dict = self.resolve_extends(*self.validate_and_construct_extends())\n return self.service_config._replace(config=service_dict)\n\n return self.service_config","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.validate_and_construct_extends","text":"compose/config/config.py::ServiceExtendsResolver.validate_and_construct_extends\n def validate_and_construct_extends(self):\n extends = self.service_config.config['extends']\n if not isinstance(extends, dict):\n extends = {'service': extends}\n\n config_path = self.get_extended_config_path(extends)\n service_name = extends['service']\n\n extended_file = process_config_file(\n ConfigFile.from_filename(config_path),\n service_name=service_name)\n service_config = extended_file.config[service_name]\n return config_path, service_config, service_name","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.resolve_extends","text":"compose/config/config.py::ServiceExtendsResolver.resolve_extends\n def resolve_extends(self, extended_config_path, service_dict, service_name):\n resolver = ServiceExtendsResolver(\n ServiceConfig.with_abs_paths(\n os.path.dirname(extended_config_path),\n extended_config_path,\n service_name,\n service_dict),\n already_seen=self.already_seen + [self.signature])\n\n service_config = resolver.run()\n other_service_dict = process_service(service_config)\n validate_extended_service_dict(\n other_service_dict,\n extended_config_path,\n service_name,\n )\n\n return merge_service_dicts(other_service_dict, self.service_config.config)","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.get_extended_config_path","text":"compose/config/config.py::ServiceExtendsResolver.get_extended_config_path\n def get_extended_config_path(self, extends_options):\n \"\"\"Service we are extending either has a value for 'file' set, which we\n need to obtain a full path too or we are extending from a service\n defined in our own file.\n \"\"\"\n filename = self.service_config.filename\n validate_extends_file_path(\n self.service_config.name,\n extends_options,\n filename)\n if 'file' in extends_options:\n return expand_path(self.working_dir, extends_options['file'])\n return filename","label":0},{"cid":"compose/config/config.py::resolve_environment","text":"compose/config/config.py::resolve_environment\ndef resolve_environment(service_dict):\n \"\"\"Unpack any environment variables from an env_file, if set.\n Interpolate environment values if set.\n \"\"\"\n env = {}\n for env_file in service_dict.get('env_file', []):\n env.update(env_vars_from_file(env_file))\n\n env.update(parse_environment(service_dict.get('environment')))\n return dict(resolve_env_var(k, v) for k, v in six.iteritems(env))","label":0},{"cid":"compose/config/config.py::validate_extended_service_dict","text":"compose/config/config.py::validate_extended_service_dict\ndef validate_extended_service_dict(service_dict, filename, service):\n error_prefix = \"Cannot extend service '%s' in %s:\" % (service, filename)\n\n if 'links' in service_dict:\n raise ConfigurationError(\n \"%s services with 'links' cannot be extended\" % error_prefix)\n\n if 'volumes_from' in service_dict:\n raise ConfigurationError(\n \"%s services with 'volumes_from' cannot be extended\" % error_prefix)\n\n if 'net' in service_dict:\n if get_service_name_from_net(service_dict['net']) is not None:\n raise ConfigurationError(\n \"%s services with 'net: container' cannot be extended\" % error_prefix)","label":0},{"cid":"compose/config/config.py::validate_ulimits","text":"compose/config/config.py::validate_ulimits\ndef validate_ulimits(ulimit_config):\n for limit_name, soft_hard_values in six.iteritems(ulimit_config):\n if isinstance(soft_hard_values, dict):\n if not soft_hard_values['soft'] <= soft_hard_values['hard']:\n raise ConfigurationError(\n \"ulimit_config \\\"{}\\\" cannot contain a 'soft' value higher \"\n \"than 'hard' value\".format(ulimit_config))","label":0},{"cid":"compose/config/config.py::process_service","text":"compose/config/config.py::process_service\ndef process_service(service_config):\n working_dir = service_config.working_dir\n service_dict = dict(service_config.config)\n\n if 'env_file' in service_dict:\n service_dict['env_file'] = [\n expand_path(working_dir, path)\n for path in to_list(service_dict['env_file'])\n ]\n\n if 'volumes' in service_dict and service_dict.get('volume_driver') is None:\n service_dict['volumes'] = resolve_volume_paths(working_dir, service_dict)\n\n if 'build' in service_dict:\n service_dict['build'] = resolve_build_path(working_dir, service_dict['build'])\n\n if 'labels' in service_dict:\n service_dict['labels'] = parse_labels(service_dict['labels'])\n\n if 'extra_hosts' in service_dict:\n service_dict['extra_hosts'] = parse_extra_hosts(service_dict['extra_hosts'])\n\n # TODO: move to a validate_service()\n if 'ulimits' in service_dict:\n validate_ulimits(service_dict['ulimits'])\n\n return service_dict","label":0},{"cid":"compose/config/config.py::finalize_service","text":"compose/config/config.py::finalize_service\ndef finalize_service(service_config):\n service_dict = dict(service_config.config)\n\n if 'environment' in service_dict or 'env_file' in service_dict:\n service_dict['environment'] = resolve_environment(service_dict)\n service_dict.pop('env_file', None)\n\n if 'volumes_from' in service_dict:\n service_dict['volumes_from'] = [\n VolumeFromSpec.parse(vf) for vf in service_dict['volumes_from']]\n\n if 'volumes' in service_dict:\n service_dict['volumes'] = [\n VolumeSpec.parse(v) for v in service_dict['volumes']]\n\n if 'restart' in service_dict:\n service_dict['restart'] = parse_restart_spec(service_dict['restart'])\n\n return service_dict","label":0},{"cid":"compose/config/config.py::merge_service_dicts_from_files","text":"compose/config/config.py::merge_service_dicts_from_files\ndef merge_service_dicts_from_files(base, override):\n \"\"\"When merging services from multiple files we need to merge the `extends`\n field. This is not handled by `merge_service_dicts()` which is used to\n perform the `extends`.\n \"\"\"\n new_service = merge_service_dicts(base, override)\n if 'extends' in override:\n new_service['extends'] = override['extends']\n return new_service","label":0},{"cid":"compose/config/config.py::merge_service_dicts","text":"compose/config/config.py::merge_service_dicts\ndef merge_service_dicts(base, override):\n d = {}\n\n def merge_field(field, merge_func, default=None):\n if field in base or field in override:\n d[field] = merge_func(\n base.get(field, default),\n override.get(field, default))\n\n merge_field('environment', merge_environment)\n merge_field('labels', merge_labels)\n merge_image_or_build(base, override, d)\n\n for field in ['volumes', 'devices']:\n merge_field(field, merge_path_mappings)\n\n for field in ['ports', 'expose', 'external_links']:\n merge_field(field, operator.add, default=[])\n\n for field in ['dns', 'dns_search', 'env_file']:\n merge_field(field, merge_list_or_string)\n\n already_merged_keys = set(d) | {'image', 'build'}\n for field in set(ALLOWED_KEYS) - already_merged_keys:\n if field in base or field in override:\n d[field] = override.get(field, base.get(field))\n\n return d","label":0},{"cid":"compose/config/config.py::merge_service_dicts.merge_field","text":"compose/config/config.py::merge_service_dicts.merge_field\n def merge_field(field, merge_func, default=None):\n if field in base or field in override:\n d[field] = merge_func(\n base.get(field, default),\n override.get(field, default))","label":0},{"cid":"compose/config/config.py::merge_image_or_build","text":"compose/config/config.py::merge_image_or_build\ndef merge_image_or_build(base, override, output):\n if 'image' in override:\n output['image'] = override['image']\n elif 'build' in override:\n output['build'] = override['build']\n elif 'image' in base:\n output['image'] = base['image']\n elif 'build' in base:\n output['build'] = base['build']","label":0},{"cid":"compose/config/config.py::merge_environment","text":"compose/config/config.py::merge_environment\ndef merge_environment(base, override):\n env = parse_environment(base)\n env.update(parse_environment(override))\n return env","label":0},{"cid":"compose/config/config.py::parse_environment","text":"compose/config/config.py::parse_environment\ndef parse_environment(environment):\n if not environment:\n return {}\n\n if isinstance(environment, list):\n return dict(split_env(e) for e in environment)\n\n if isinstance(environment, dict):\n return dict(environment)\n\n raise ConfigurationError(\n \"environment \\\"%s\\\" must be a list or mapping,\" %\n environment\n )","label":0},{"cid":"compose/config/config.py::split_env","text":"compose/config/config.py::split_env\ndef split_env(env):\n if isinstance(env, six.binary_type):\n env = env.decode('utf-8', 'replace')\n if '=' in env:\n return env.split('=', 1)\n else:\n return env, None","label":0},{"cid":"compose/config/config.py::resolve_env_var","text":"compose/config/config.py::resolve_env_var\ndef resolve_env_var(key, val):\n if val is not None:\n return key, val\n elif key in os.environ:\n return key, os.environ[key]\n else:\n return key, ''","label":0},{"cid":"compose/config/config.py::env_vars_from_file","text":"compose/config/config.py::env_vars_from_file\ndef env_vars_from_file(filename):\n \"\"\"\n Read in a line delimited file of environment variables.\n \"\"\"\n if not os.path.exists(filename):\n raise ConfigurationError(\"Couldn't find env file: %s\" % filename)\n env = {}\n for line in codecs.open(filename, 'r', 'utf-8'):\n line = line.strip()\n if line and not line.startswith('#'):\n k, v = split_env(line)\n env[k] = v\n return env","label":0},{"cid":"compose/config/config.py::resolve_volume_paths","text":"compose/config/config.py::resolve_volume_paths\ndef resolve_volume_paths(working_dir, service_dict):\n return [\n resolve_volume_path(working_dir, volume)\n for volume in service_dict['volumes']\n ]","label":0},{"cid":"compose/config/config.py::resolve_volume_path","text":"compose/config/config.py::resolve_volume_path\ndef resolve_volume_path(working_dir, volume):\n container_path, host_path = split_path_mapping(volume)\n\n if host_path is not None:\n if host_path.startswith('.'):\n host_path = expand_path(working_dir, host_path)\n host_path = os.path.expanduser(host_path)\n return u\"{}:{}\".format(host_path, container_path)\n else:\n return container_path","label":0},{"cid":"compose/config/config.py::resolve_build_path","text":"compose/config/config.py::resolve_build_path\ndef resolve_build_path(working_dir, build_path):\n if is_url(build_path):\n return build_path\n return expand_path(working_dir, build_path)","label":0},{"cid":"compose/config/config.py::is_url","text":"compose/config/config.py::is_url\ndef is_url(build_path):\n return build_path.startswith(DOCKER_VALID_URL_PREFIXES)","label":0},{"cid":"compose/config/config.py::validate_paths","text":"compose/config/config.py::validate_paths\ndef validate_paths(service_dict):\n if 'build' in service_dict:\n build_path = service_dict['build']\n if (\n not is_url(build_path) and\n (not os.path.exists(build_path) or not os.access(build_path, os.R_OK))\n ):\n raise ConfigurationError(\n \"build path %s either does not exist, is not accessible, \"\n \"or is not a valid URL.\" % build_path)","label":0},{"cid":"compose/config/config.py::merge_path_mappings","text":"compose/config/config.py::merge_path_mappings\ndef merge_path_mappings(base, override):\n d = dict_from_path_mappings(base)\n d.update(dict_from_path_mappings(override))\n return path_mappings_from_dict(d)","label":0},{"cid":"compose/config/config.py::dict_from_path_mappings","text":"compose/config/config.py::dict_from_path_mappings\ndef dict_from_path_mappings(path_mappings):\n if path_mappings:\n return dict(split_path_mapping(v) for v in path_mappings)\n else:\n return {}","label":0},{"cid":"compose/config/config.py::path_mappings_from_dict","text":"compose/config/config.py::path_mappings_from_dict\ndef path_mappings_from_dict(d):\n return [join_path_mapping(v) for v in d.items()]","label":0},{"cid":"compose/config/config.py::split_path_mapping","text":"compose/config/config.py::split_path_mapping\ndef split_path_mapping(volume_path):\n \"\"\"\n Ascertain if the volume_path contains a host path as well as a container\n path. Using splitdrive so windows absolute paths won't cause issues with\n splitting on ':'.\n \"\"\"\n # splitdrive has limitations when it comes to relative paths, so when it's\n # relative, handle special case to set the drive to ''\n if volume_path.startswith('.') or volume_path.startswith('~'):\n drive, volume_config = '', volume_path\n else:\n drive, volume_config = os.path.splitdrive(volume_path)\n\n if ':' in volume_config:\n (host, container) = volume_config.split(':', 1)\n return (container, drive + host)\n else:\n return (volume_path, None)","label":0},{"cid":"compose/config/config.py::join_path_mapping","text":"compose/config/config.py::join_path_mapping\ndef join_path_mapping(pair):\n (container, host) = pair\n if host is None:\n return container\n else:\n return \":\".join((host, container))","label":0},{"cid":"compose/config/config.py::merge_labels","text":"compose/config/config.py::merge_labels\ndef merge_labels(base, override):\n labels = parse_labels(base)\n labels.update(parse_labels(override))\n return labels","label":0},{"cid":"compose/config/config.py::parse_labels","text":"compose/config/config.py::parse_labels\ndef parse_labels(labels):\n if not labels:\n return {}\n\n if isinstance(labels, list):\n return dict(split_label(e) for e in labels)\n\n if isinstance(labels, dict):\n return dict(labels)","label":0},{"cid":"compose/config/config.py::split_label","text":"compose/config/config.py::split_label\ndef split_label(label):\n if '=' in label:\n return label.split('=', 1)\n else:\n return label, ''","label":0},{"cid":"compose/config/config.py::expand_path","text":"compose/config/config.py::expand_path\ndef expand_path(working_dir, path):\n return os.path.abspath(os.path.join(working_dir, os.path.expanduser(path)))","label":0},{"cid":"compose/config/config.py::merge_list_or_string","text":"compose/config/config.py::merge_list_or_string\ndef merge_list_or_string(base, override):\n return to_list(base) + to_list(override)","label":0},{"cid":"compose/config/config.py::to_list","text":"compose/config/config.py::to_list\ndef to_list(value):\n if value is None:\n return []\n elif isinstance(value, six.string_types):\n return [value]\n else:\n return value","label":0},{"cid":"compose/config/config.py::load_yaml","text":"compose/config/config.py::load_yaml\ndef load_yaml(filename):\n try:\n with open(filename, 'r') as fh:\n return yaml.safe_load(fh)\n except (IOError, yaml.YAMLError) as e:\n error_name = getattr(e, '__module__', '') + '.' + e.__class__.__name__\n raise ConfigurationError(u\"{}: {}\".format(error_name, e))","label":0},{"cid":"compose/config/validation.py::format_ports","text":"compose/config/validation.py::format_ports\n@FormatChecker.cls_checks(format=\"ports\", raises=ValidationError)\ndef format_ports(instance):\n try:\n split_port(instance)\n except ValueError as e:\n raise ValidationError(six.text_type(e))\n return True","label":0},{"cid":"compose/config/validation.py::format_expose","text":"compose/config/validation.py::format_expose\n@FormatChecker.cls_checks(format=\"expose\", raises=ValidationError)\ndef format_expose(instance):\n if isinstance(instance, six.string_types):\n if not re.match(VALID_EXPOSE_FORMAT, instance):\n raise ValidationError(\n \"should be of the format 'PORT[/PROTOCOL]'\")\n\n return True","label":0},{"cid":"compose/config/validation.py::format_boolean_in_environment","text":"compose/config/validation.py::format_boolean_in_environment\n@FormatChecker.cls_checks(format=\"bool-value-in-mapping\")\ndef format_boolean_in_environment(instance):\n \"\"\"\n Check if there is a boolean in the environment and display a warning.\n Always return True here so the validation won't raise an error.\n \"\"\"\n if isinstance(instance, bool):\n log.warn(\n \"There is a boolean value in the 'environment' key.\\n\"\n \"Environment variables can only be strings.\\n\"\n \"Please add quotes to any boolean values to make them string \"\n \"(eg, 'True', 'yes', 'N').\\n\"\n \"This warning will become an error in a future release. \\r\\n\"\n )\n return True","label":0},{"cid":"compose/config/validation.py::validate_top_level_service_objects","text":"compose/config/validation.py::validate_top_level_service_objects\ndef validate_top_level_service_objects(config_file):\n \"\"\"Perform some high level validation of the service name and value.\n\n This validation must happen before interpolation, which must happen\n before the rest of validation, which is why it's separate from the\n rest of the service validation.\n \"\"\"\n for service_name, service_dict in config_file.config.items():\n if not isinstance(service_name, six.string_types):\n raise ConfigurationError(\n \"In file '{}' service name: {} needs to be a string, eg '{}'\".format(\n config_file.filename,\n service_name,\n service_name))\n\n if not isinstance(service_dict, dict):\n raise ConfigurationError(\n \"In file '{}' service '{}' doesn\\'t have any configuration options. \"\n \"All top level keys in your docker-compose.yml must map \"\n \"to a dictionary of configuration options.\".format(\n config_file.filename,\n service_name))","label":0},{"cid":"compose/config/validation.py::validate_top_level_object","text":"compose/config/validation.py::validate_top_level_object\ndef validate_top_level_object(config_file):\n if not isinstance(config_file.config, dict):\n raise ConfigurationError(\n \"Top level object in '{}' needs to be an object not '{}'. Check \"\n \"that you have defined a service at the top level.\".format(\n config_file.filename,\n type(config_file.config)))\n validate_top_level_service_objects(config_file)","label":0},{"cid":"compose/config/validation.py::validate_extends_file_path","text":"compose/config/validation.py::validate_extends_file_path\ndef validate_extends_file_path(service_name, extends_options, filename):\n \"\"\"\n The service to be extended must either be defined in the config key 'file',\n or within 'filename'.\n \"\"\"\n error_prefix = \"Invalid 'extends' configuration for %s:\" % service_name\n\n if 'file' not in extends_options and filename is None:\n raise ConfigurationError(\n \"%s you need to specify a 'file', e.g. 'file: something.yml'\" % error_prefix\n )","label":0},{"cid":"compose/config/validation.py::get_unsupported_config_msg","text":"compose/config/validation.py::get_unsupported_config_msg\ndef get_unsupported_config_msg(service_name, error_key):\n msg = \"Unsupported config option for '{}' service: '{}'\".format(service_name, error_key)\n if error_key in DOCKER_CONFIG_HINTS:\n msg += \" (did you mean '{}'?)\".format(DOCKER_CONFIG_HINTS[error_key])\n return msg","label":0},{"cid":"compose/config/validation.py::anglicize_validator","text":"compose/config/validation.py::anglicize_validator\ndef anglicize_validator(validator):\n if validator in [\"array\", \"object\"]:\n return 'an ' + validator\n return 'a ' + validator","label":0},{"cid":"compose/config/validation.py::handle_error_for_schema_with_id","text":"compose/config/validation.py::handle_error_for_schema_with_id\ndef handle_error_for_schema_with_id(error, service_name):\n schema_id = error.schema['id']\n\n if schema_id == 'fields_schema.json' and error.validator == 'additionalProperties':\n return \"Invalid service name '{}' - only {} characters are allowed\".format(\n # The service_name is the key to the json object\n list(error.instance)[0],\n VALID_NAME_CHARS)\n\n if schema_id == '#/definitions/constraints':\n if 'image' in error.instance and 'build' in error.instance:\n return (\n \"Service '{}' has both an image and build path specified. \"\n \"A service can either be built to image or use an existing \"\n \"image, not both.\".format(service_name))\n if 'image' not in error.instance and 'build' not in error.instance:\n return (\n \"Service '{}' has neither an image nor a build path \"\n \"specified. Exactly one must be provided.\".format(service_name))\n if 'image' in error.instance and 'dockerfile' in error.instance:\n return (\n \"Service '{}' has both an image and alternate Dockerfile. \"\n \"A service can either be built to image or use an existing \"\n \"image, not both.\".format(service_name))\n\n if schema_id == '#/definitions/service':\n if error.validator == 'additionalProperties':\n invalid_config_key = parse_key_from_error_msg(error)\n return get_unsupported_config_msg(service_name, invalid_config_key)","label":0},{"cid":"compose/config/validation.py::handle_generic_service_error","text":"compose/config/validation.py::handle_generic_service_error\ndef handle_generic_service_error(error, service_name):\n config_key = \" \".join(\"'%s'\" % k for k in error.path)\n msg_format = None\n error_msg = error.message\n\n if error.validator == 'oneOf':\n msg_format = \"Service '{}' configuration key {} {}\"\n error_msg = _parse_oneof_validator(error)\n\n elif error.validator == 'type':\n msg_format = (\"Service '{}' configuration key {} contains an invalid \"\n \"type, it should be {}\")\n error_msg = _parse_valid_types_from_validator(error.validator_value)\n\n # TODO: no test case for this branch, there are no config options\n # which exercise this branch\n elif error.validator == 'required':\n msg_format = \"Service '{}' configuration key '{}' is invalid, {}\"\n\n elif error.validator == 'dependencies':\n msg_format = \"Service '{}' configuration key '{}' is invalid: {}\"\n config_key = list(error.validator_value.keys())[0]\n required_keys = \",\".join(error.validator_value[config_key])\n error_msg = \"when defining '{}' you must set '{}' as well\".format(\n config_key,\n required_keys)\n\n elif error.cause:\n error_msg = six.text_type(error.cause)\n msg_format = \"Service '{}' configuration key {} is invalid: {}\"\n\n elif error.path:\n msg_format = \"Service '{}' configuration key {} value {}\"\n\n if msg_format:\n return msg_format.format(service_name, config_key, error_msg)\n\n return error.message","label":0},{"cid":"compose/config/validation.py::parse_key_from_error_msg","text":"compose/config/validation.py::parse_key_from_error_msg\ndef parse_key_from_error_msg(error):\n return error.message.split(\"'\")[1]","label":0},{"cid":"compose/config/validation.py::_parse_valid_types_from_validator","text":"compose/config/validation.py::_parse_valid_types_from_validator\ndef _parse_valid_types_from_validator(validator):\n \"\"\"A validator value can be either an array of valid types or a string of\n a valid type. Parse the valid types and prefix with the correct article.\n \"\"\"\n if not isinstance(validator, list):\n return anglicize_validator(validator)\n\n if len(validator) == 1:\n return anglicize_validator(validator[0])\n\n return \"{}, or {}\".format(\n \", \".join([anglicize_validator(validator[0])] + validator[1:-1]),\n anglicize_validator(validator[-1]))","label":0},{"cid":"compose/config/validation.py::_parse_oneof_validator","text":"compose/config/validation.py::_parse_oneof_validator\ndef _parse_oneof_validator(error):\n \"\"\"oneOf has multiple schemas, so we need to reason about which schema, sub\n schema or constraint the validation is failing on.\n Inspecting the context value of a ValidationError gives us information about\n which sub schema failed and which kind of error it is.\n \"\"\"\n types = []\n for context in error.context:\n\n if context.validator == 'required':\n return context.message\n\n if context.validator == 'additionalProperties':\n invalid_config_key = parse_key_from_error_msg(context)\n return \"contains unsupported option: '{}'\".format(invalid_config_key)\n\n if context.path:\n invalid_config_key = \" \".join(\n \"'{}' \".format(fragment) for fragment in context.path\n if isinstance(fragment, six.string_types)\n )\n return \"{}contains {}, which is an invalid type, it should be {}\".format(\n invalid_config_key,\n context.instance,\n _parse_valid_types_from_validator(context.validator_value))\n\n if context.validator == 'uniqueItems':\n return \"contains non unique items, please remove duplicates from {}\".format(\n context.instance)\n\n if context.validator == 'type':\n types.append(context.validator_value)\n\n valid_types = _parse_valid_types_from_validator(types)\n return \"contains an invalid type, it should be {}\".format(valid_types)","label":0},{"cid":"compose/config/validation.py::process_errors","text":"compose/config/validation.py::process_errors\ndef process_errors(errors, service_name=None):\n \"\"\"jsonschema gives us an error tree full of information to explain what has\n gone wrong. Process each error and pull out relevant information and re-write\n helpful error messages that are relevant.\n \"\"\"\n def format_error_message(error, service_name):\n if not service_name and error.path:\n # field_schema errors will have service name on the path\n service_name = error.path.popleft()\n\n if 'id' in error.schema:\n error_msg = handle_error_for_schema_with_id(error, service_name)\n if error_msg:\n return error_msg\n\n return handle_generic_service_error(error, service_name)\n\n return '\\n'.join(format_error_message(error, service_name) for error in errors)","label":0},{"cid":"compose/config/validation.py::process_errors.format_error_message","text":"compose/config/validation.py::process_errors.format_error_message\n def format_error_message(error, service_name):\n if not service_name and error.path:\n # field_schema errors will have service name on the path\n service_name = error.path.popleft()\n\n if 'id' in error.schema:\n error_msg = handle_error_for_schema_with_id(error, service_name)\n if error_msg:\n return error_msg\n\n return handle_generic_service_error(error, service_name)","label":0},{"cid":"compose/config/validation.py::validate_against_fields_schema","text":"compose/config/validation.py::validate_against_fields_schema\ndef validate_against_fields_schema(config, filename):\n _validate_against_schema(\n config,\n \"fields_schema.json\",\n format_checker=[\"ports\", \"expose\", \"bool-value-in-mapping\"],\n filename=filename)","label":0},{"cid":"compose/config/validation.py::validate_against_service_schema","text":"compose/config/validation.py::validate_against_service_schema\ndef validate_against_service_schema(config, service_name):\n _validate_against_schema(\n config,\n \"service_schema.json\",\n format_checker=[\"ports\"],\n service_name=service_name)","label":0},{"cid":"compose/config/validation.py::_validate_against_schema","text":"compose/config/validation.py::_validate_against_schema\ndef _validate_against_schema(\n config,\n schema_filename,\n format_checker=(),\n service_name=None,\n filename=None):\n config_source_dir = os.path.dirname(os.path.abspath(__file__))\n\n if sys.platform == \"win32\":\n file_pre_fix = \"///\"\n config_source_dir = config_source_dir.replace('\\\\', '/')\n else:\n file_pre_fix = \"//\"\n\n resolver_full_path = \"file:{}{}/\".format(file_pre_fix, config_source_dir)\n schema_file = os.path.join(config_source_dir, schema_filename)\n\n with open(schema_file, \"r\") as schema_fh:\n schema = json.load(schema_fh)\n\n resolver = RefResolver(resolver_full_path, schema)\n validation_output = Draft4Validator(\n schema,\n resolver=resolver,\n format_checker=FormatChecker(format_checker))\n\n errors = [error for error in sorted(validation_output.iter_errors(config), key=str)]\n if not errors:\n return\n\n error_msg = process_errors(errors, service_name)\n file_msg = \" in file '{}'\".format(filename) if filename else ''\n raise ConfigurationError(\"Validation failed{}, reason(s):\\n{}\".format(\n file_msg,\n error_msg))","label":0},{"cid":"compose/legacy.py::check_for_legacy_containers","text":"compose/legacy.py::check_for_legacy_containers\ndef check_for_legacy_containers(\n client,\n project,\n services,\n allow_one_off=True):\n \"\"\"Check if there are containers named using the old naming convention\n and warn the user that those containers may need to be migrated to\n using labels, so that compose can find them.\n \"\"\"\n containers = get_legacy_containers(client, project, services, one_off=False)\n\n if containers:\n one_off_containers = get_legacy_containers(client, project, services, one_off=True)\n\n raise LegacyContainersError(\n [c.name for c in containers],\n [c.name for c in one_off_containers],\n )\n\n if not allow_one_off:\n one_off_containers = get_legacy_containers(client, project, services, one_off=True)\n\n if one_off_containers:\n raise LegacyOneOffContainersError(\n [c.name for c in one_off_containers],\n )","label":0},{"cid":"compose/legacy.py::LegacyError.__unicode__","text":"compose/legacy.py::LegacyError.__unicode__\n def __unicode__(self):\n return self.msg","label":0},{"cid":"compose/legacy.py::LegacyContainersError.__init__","text":"compose/legacy.py::LegacyContainersError.__init__\n def __init__(self, names, one_off_names):\n self.names = names\n self.one_off_names = one_off_names\n\n self.msg = ERROR_MESSAGE_FORMAT.format(\n names_list=\"\\n\".join(\" {}\".format(name) for name in names),\n rm_args=\" \".join(names),\n )\n\n if one_off_names:\n self.msg += ONE_OFF_ADDENDUM_FORMAT.format(rm_args=\" \".join(one_off_names))","label":0},{"cid":"compose/legacy.py::LegacyOneOffContainersError.__init__","text":"compose/legacy.py::LegacyOneOffContainersError.__init__\n def __init__(self, one_off_names):\n self.one_off_names = one_off_names\n\n self.msg = ONE_OFF_ERROR_MESSAGE_FORMAT.format(\n names_list=\"\\n\".join(\" {}\".format(name) for name in one_off_names),\n rm_args=\" \".join(one_off_names),\n )","label":0},{"cid":"compose/legacy.py::add_labels","text":"compose/legacy.py::add_labels\ndef add_labels(project, container):\n project_name, service_name, one_off, number = NAME_RE.match(container.name).groups()\n if project_name != project.name or service_name not in project.service_names:\n return\n service = project.get_service(service_name)\n service.recreate_container(container)","label":0},{"cid":"compose/legacy.py::migrate_project_to_labels","text":"compose/legacy.py::migrate_project_to_labels\ndef migrate_project_to_labels(project):\n log.info(\"Running migration to labels for project %s\", project.name)\n\n containers = get_legacy_containers(\n project.client,\n project.name,\n project.service_names,\n one_off=False,\n )\n\n for container in containers:\n add_labels(project, container)","label":0},{"cid":"compose/legacy.py::get_legacy_containers","text":"compose/legacy.py::get_legacy_containers\ndef get_legacy_containers(\n client,\n project,\n services,\n one_off=False):\n\n return list(_get_legacy_containers_iter(\n client,\n project,\n services,\n one_off=one_off,\n ))","label":0},{"cid":"compose/legacy.py::_get_legacy_containers_iter","text":"compose/legacy.py::_get_legacy_containers_iter\ndef _get_legacy_containers_iter(\n client,\n project,\n services,\n one_off=False):\n\n containers = client.containers(all=True)\n\n for service in services:\n for container in containers:\n if LABEL_VERSION in (container.get('Labels') or {}):\n continue\n\n name = get_container_name(container)\n if has_container(project, service, name, one_off=one_off):\n yield Container.from_ps(client, container)","label":0},{"cid":"compose/legacy.py::has_container","text":"compose/legacy.py::has_container\ndef has_container(project, service, name, one_off=False):\n if not name or not is_valid_name(name, one_off):\n return False\n container_project, container_service, _container_number = parse_name(name)\n return container_project == project and container_service == service","label":0},{"cid":"compose/legacy.py::is_valid_name","text":"compose/legacy.py::is_valid_name\ndef is_valid_name(name, one_off=False):\n match = NAME_RE.match(name)\n if match is None:\n return False\n if one_off:\n return match.group(3) == 'run_'\n else:\n return match.group(3) is None","label":0},{"cid":"compose/legacy.py::parse_name","text":"compose/legacy.py::parse_name\ndef parse_name(name):\n match = NAME_RE.match(name)\n (project, service_name, _, suffix) = match.groups()\n return (project, service_name, int(suffix))","label":0},{"cid":"compose/service.py::BuildError.__init__","text":"compose/service.py::BuildError.__init__\n def __init__(self, service, reason):\n self.service = service\n self.reason = reason","label":0},{"cid":"compose/service.py::NeedsBuildError.__init__","text":"compose/service.py::NeedsBuildError.__init__\n def __init__(self, service):\n self.service = service","label":0},{"cid":"compose/service.py::ConvergenceStrategy.allows_recreate","text":"compose/service.py::ConvergenceStrategy.allows_recreate\n @property\n def allows_recreate(self):\n return self is not type(self).never","label":0},{"cid":"compose/service.py::Service.__init__","text":"compose/service.py::Service.__init__\n def __init__(\n self,\n name,\n client=None,\n project='default',\n use_networking=False,\n links=None,\n volumes_from=None,\n net=None,\n **options\n ):\n self.name = name\n self.client = client\n self.project = project\n self.use_networking = use_networking\n self.links = links or []\n self.volumes_from = volumes_from or []\n self.net = net or Net(None)\n self.options = options","label":0},{"cid":"compose/service.py::Service.containers","text":"compose/service.py::Service.containers\n def containers(self, stopped=False, one_off=False, filters={}):\n filters.update({'label': self.labels(one_off=one_off)})\n\n containers = list(filter(None, [\n Container.from_ps(self.client, container)\n for container in self.client.containers(\n all=stopped,\n filters=filters)]))\n\n if not containers:\n check_for_legacy_containers(\n self.client,\n self.project,\n [self.name],\n )\n\n return containers","label":0},{"cid":"compose/service.py::Service.get_container","text":"compose/service.py::Service.get_container\n def get_container(self, number=1):\n \"\"\"Return a :class:`compose.container.Container` for this service. The\n container must be active, and match `number`.\n \"\"\"\n labels = self.labels() + ['{0}={1}'.format(LABEL_CONTAINER_NUMBER, number)]\n for container in self.client.containers(filters={'label': labels}):\n return Container.from_ps(self.client, container)\n\n raise ValueError(\"No container found for %s_%s\" % (self.name, number))","label":0},{"cid":"compose/service.py::Service.start","text":"compose/service.py::Service.start\n def start(self, **options):\n for c in self.containers(stopped=True):\n self.start_container_if_stopped(c, **options)","label":0},{"cid":"compose/service.py::Service.stop","text":"compose/service.py::Service.stop\n def stop(self, **options):\n for c in self.containers():\n log.info(\"Stopping %s\" % c.name)\n c.stop(**options)","label":0},{"cid":"compose/service.py::Service.pause","text":"compose/service.py::Service.pause\n def pause(self, **options):\n for c in self.containers(filters={'status': 'running'}):\n log.info(\"Pausing %s\" % c.name)\n c.pause(**options)","label":0},{"cid":"compose/service.py::Service.unpause","text":"compose/service.py::Service.unpause\n def unpause(self, **options):\n for c in self.containers(filters={'status': 'paused'}):\n log.info(\"Unpausing %s\" % c.name)\n c.unpause()","label":0},{"cid":"compose/service.py::Service.kill","text":"compose/service.py::Service.kill\n def kill(self, **options):\n for c in self.containers():\n log.info(\"Killing %s\" % c.name)\n c.kill(**options)","label":0},{"cid":"compose/service.py::Service.restart","text":"compose/service.py::Service.restart\n def restart(self, **options):\n for c in self.containers(stopped=True):\n log.info(\"Restarting %s\" % c.name)\n c.restart(**options)","label":0},{"cid":"compose/service.py::Service.scale","text":"compose/service.py::Service.scale\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT):\n \"\"\"\n Adjusts the number of containers to the specified number and ensures\n they are running.\n\n - creates containers until there are at least `desired_num`\n - stops containers until there are at most `desired_num` running\n - starts containers until there are at least `desired_num` running\n - removes all stopped containers\n \"\"\"\n if self.custom_container_name() and desired_num > 1:\n log.warn('The \"%s\" service is using the custom container name \"%s\". '\n 'Docker requires each container to have a unique name. '\n 'Remove the custom name to scale the service.'\n % (self.name, self.custom_container_name()))\n\n if self.specifies_host_port():\n log.warn('The \"%s\" service specifies a port on the host. If multiple containers '\n 'for this service are created on a single host, the port will clash.'\n % self.name)\n\n def create_and_start(service, number):\n container = service.create_container(number=number, quiet=True)\n container.start()\n return container\n\n running_containers = self.containers(stopped=False)\n num_running = len(running_containers)\n\n if desired_num == num_running:\n # do nothing as we already have the desired number\n log.info('Desired container number already achieved')\n return\n\n if desired_num > num_running:\n # we need to start/create until we have desired_num\n all_containers = self.containers(stopped=True)\n\n if num_running != len(all_containers):\n # we have some stopped containers, let's start them up again\n stopped_containers = sorted([c for c in all_containers if not c.is_running], key=attrgetter('number'))\n\n num","label":0},{"cid":"compose/service.py::Service.scale.create_and_start","text":"compose/service.py::Service.scale.create_and_start\n def create_and_start(service, number):\n container = service.create_container(number=number, quiet=True)\n container.start()\n return container","label":0},{"cid":"compose/service.py::Service.create_container","text":"compose/service.py::Service.create_container\n def create_container(self,\n one_off=False,\n do_build=True,\n previous_container=None,\n number=None,\n quiet=False,\n **override_options):\n \"\"\"\n Create a container for this service. If the image doesn't exist, attempt to pull\n it.\n \"\"\"\n self.ensure_image_exists(do_build=do_build)\n\n container_options = self._get_container_create_options(\n override_options,\n number or self._next_container_number(one_off=one_off),\n one_off=one_off,\n previous_container=previous_container,\n )\n\n if 'name' in container_options and not quiet:\n log.info(\"Creating %s\" % container_options['name'])\n\n return Container.create(self.client, **container_options)","label":0},{"cid":"compose/service.py::Service.ensure_image_exists","text":"compose/service.py::Service.ensure_image_exists\n def ensure_image_exists(self, do_build=True):\n try:\n self.image()\n return\n except NoSuchImageError:\n pass\n\n if self.can_be_built():\n if do_build:\n self.build()\n else:\n raise NeedsBuildError(self)\n else:\n self.pull()","label":0},{"cid":"compose/service.py::Service.image","text":"compose/service.py::Service.image\n def image(self):\n try:\n return self.client.inspect_image(self.image_name)\n except APIError as e:\n if e.response.status_code == 404 and e.explanation and 'No such image' in str(e.explanation):\n raise NoSuchImageError(\"Image '{}' not found\".format(self.image_name))\n else:\n raise","label":0},{"cid":"compose/service.py::Service.image_name","text":"compose/service.py::Service.image_name\n @property\n def image_name(self):\n if self.can_be_built():\n return self.full_name\n else:\n return self.options['image']","label":0},{"cid":"compose/service.py::Service.convergence_plan","text":"compose/service.py::Service.convergence_plan\n def convergence_plan(self, strategy=ConvergenceStrategy.changed):\n containers = self.containers(stopped=True)\n\n if not containers:\n return ConvergencePlan('create', [])\n\n if strategy is ConvergenceStrategy.never:\n return ConvergencePlan('start', containers)\n\n if (\n strategy is ConvergenceStrategy.always or\n self._containers_have_diverged(containers)\n ):\n return ConvergencePlan('recreate', containers)\n\n stopped = [c for c in containers if not c.is_running]\n\n if stopped:\n return ConvergencePlan('start', stopped)\n\n return ConvergencePlan('noop', containers)","label":0},{"cid":"compose/service.py::Service._containers_have_diverged","text":"compose/service.py::Service._containers_have_diverged\n def _containers_have_diverged(self, containers):\n config_hash = None\n\n try:\n config_hash = self.config_hash\n except NoSuchImageError as e:\n log.debug(\n 'Service %s has diverged: %s',\n self.name, six.text_type(e),\n )\n return True\n\n has_diverged = False\n\n for c in containers:\n container_config_hash = c.labels.get(LABEL_CONFIG_HASH, None)\n if container_config_hash != config_hash:\n log.debug(\n '%s has diverged: %s != %s',\n c.name, container_config_hash, config_hash,\n )\n has_diverged = True\n\n return has_diverged","label":0},{"cid":"compose/service.py::Service.execute_convergence_plan","text":"compose/service.py::Service.execute_convergence_plan\n def execute_convergence_plan(self,\n plan,\n do_build=True,\n timeout=DEFAULT_TIMEOUT,\n detached=False):\n (action, containers) = plan\n should_attach_logs = not detached\n\n if action == 'create':\n container = self.create_container(do_build=do_build)\n\n if should_attach_logs:\n container.attach_log_stream()\n\n container.start()\n\n return [container]\n\n elif action == 'recreate':\n return [\n self.recreate_container(\n container,\n do_build=do_build,\n timeout=timeout,\n attach_logs=should_attach_logs\n )\n for container in containers\n ]\n\n elif action == 'start':\n for container in containers:\n self.start_container_if_stopped(container, attach_logs=should_attach_logs)\n\n return containers\n\n elif action == 'noop':\n for c in containers:\n log.info(\"%s is up-to-date\" % c.name)\n\n return containers\n\n else:\n raise Exception(\"Invalid action: {}\".format(action))","label":0},{"cid":"compose/service.py::Service.recreate_container","text":"compose/service.py::Service.recreate_container\n def recreate_container(\n self,\n container,\n do_build=False,\n timeout=DEFAULT_TIMEOUT,\n attach_logs=False):\n \"\"\"Recreate a container.\n\n The original container is renamed to a temporary name so that data\n volumes can be copied to the new container, before the original\n container is removed.\n \"\"\"\n log.info(\"Recreating %s\" % container.name)\n\n container.stop(timeout=timeout)\n container.rename_to_tmp_name()\n new_container = self.create_container(\n do_build=do_build,\n previous_container=container,\n number=container.labels.get(LABEL_CONTAINER_NUMBER),\n quiet=True,\n )\n if attach_logs:\n new_container.attach_log_stream()\n new_container.start()\n container.remove()\n return new_container","label":0},{"cid":"compose/service.py::Service.start_container_if_stopped","text":"compose/service.py::Service.start_container_if_stopped\n def start_container_if_stopped(self, container, attach_logs=False):\n if not container.is_running:\n log.info(\"Starting %s\" % container.name)\n if attach_logs:\n container.attach_log_stream()\n container.start()\n return container","label":0},{"cid":"compose/service.py::Service.remove_duplicate_containers","text":"compose/service.py::Service.remove_duplicate_containers\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT):\n for c in self.duplicate_containers():\n log.info('Removing %s' % c.name)\n c.stop(timeout=timeout)\n c.remove()","label":0},{"cid":"compose/service.py::Service.duplicate_containers","text":"compose/service.py::Service.duplicate_containers\n def duplicate_containers(self):\n containers = sorted(\n self.containers(stopped=True),\n key=lambda c: c.get('Created'),\n )\n\n numbers = set()\n\n for c in containers:\n if c.number in numbers:\n yield c\n else:\n numbers.add(c.number)","label":0},{"cid":"compose/service.py::Service.config_hash","text":"compose/service.py::Service.config_hash\n @property\n def config_hash(self):\n return json_hash(self.config_dict())","label":0},{"cid":"compose/service.py::Service.config_dict","text":"compose/service.py::Service.config_dict\n def config_dict(self):\n return {\n 'options': self.options,\n 'image_id': self.image()['Id'],\n 'links': self.get_link_names(),\n 'net': self.net.id,\n 'volumes_from': [\n (v.source.name, v.mode) for v in self.volumes_from if isinstance(v.source, Service)\n ],\n }","label":0},{"cid":"compose/service.py::Service.get_dependency_names","text":"compose/service.py::Service.get_dependency_names\n def get_dependency_names(self):\n net_name = self.net.service_name\n return (self.get_linked_service_names() +\n self.get_volumes_from_names() +\n ([net_name] if net_name else []))","label":0},{"cid":"compose/service.py::Service.get_linked_service_names","text":"compose/service.py::Service.get_linked_service_names\n def get_linked_service_names(self):\n return [service.name for (service, _) in self.links]","label":0},{"cid":"compose/service.py::Service.get_link_names","text":"compose/service.py::Service.get_link_names\n def get_link_names(self):\n return [(service.name, alias) for service, alias in self.links]","label":0},{"cid":"compose/service.py::Service.get_volumes_from_names","text":"compose/service.py::Service.get_volumes_from_names\n def get_volumes_from_names(self):\n return [s.source.name for s in self.volumes_from if isinstance(s.source, Service)]","label":0},{"cid":"compose/service.py::Service.get_container_name","text":"compose/service.py::Service.get_container_name\n def get_container_name(self, number, one_off=False):\n # TODO: Implement issue #652 here\n return build_container_name(self.project, self.name, number, one_off)","label":0},{"cid":"compose/service.py::Service._next_container_number","text":"compose/service.py::Service._next_container_number\n def _next_container_number(self, one_off=False):\n containers = filter(None, [\n Container.from_ps(self.client, container)\n for container in self.client.containers(\n all=True,\n filters={'label': self.labels(one_off=one_off)})\n ])\n numbers = [c.number for c in containers]\n return 1 if not numbers else max(numbers) + 1","label":0},{"cid":"compose/service.py::Service._get_links","text":"compose/service.py::Service._get_links\n def _get_links(self, link_to_self):\n if self.use_networking:\n return []\n\n links = []\n for service, link_name in self.links:\n for container in service.containers():\n links.append((container.name, link_name or service.name))\n links.append((container.name, container.name))\n links.append((container.name, container.name_without_project))\n if link_to_self:\n for container in self.containers():\n links.append((container.name, self.name))\n links.append((container.name, container.name))\n links.append((container.name, container.name_without_project))\n for external_link in self.options.get('external_links') or []:\n if ':' not in external_link:\n link_name = external_link\n else:\n external_link, link_name = external_link.split(':')\n links.append((external_link, link_name))\n return links","label":0},{"cid":"compose/service.py::Service._get_volumes_from","text":"compose/service.py::Service._get_volumes_from\n def _get_volumes_from(self):\n volumes_from = []\n for volume_from_spec in self.volumes_from:\n volumes = build_volume_from(volume_from_spec)\n volumes_from.extend(volumes)\n\n return volumes_from","label":0},{"cid":"compose/service.py::Service._get_container_create_options","text":"compose/service.py::Service._get_container_create_options\n def _get_container_create_options(\n self,\n override_options,\n number,\n one_off=False,\n previous_container=None):\n add_config_hash = (not one_off and not override_options)\n\n container_options = dict(\n (k, self.options[k])\n for k in DOCKER_CONFIG_KEYS if k in self.options)\n container_options.update(override_options)\n\n if self.custom_container_name() and not one_off:\n container_options['name'] = self.custom_container_name()\n elif not container_options.get('name'):\n container_options['name'] = self.get_container_name(number, one_off)\n\n if 'detach' not in container_options:\n container_options['detach'] = True\n\n # If a qualified hostname was given, split it into an\n # unqualified hostname and a domainname unless domainname\n # was also given explicitly. This matches the behavior of\n # the official Docker CLI in that scenario.\n if ('hostname' in container_options\n and 'domainname' not in container_options\n and '.' in container_options['hostname']):\n parts = container_options['hostname'].partition('.')\n container_options['hostname'] = parts[0]\n container_options['domainname'] = parts[2]\n\n if 'ports' in container_options or 'expose' in self.options:\n ports = []\n all_ports = container_options.get('ports', []) + self.options.get('expose', [])\n for port_range in all_ports:\n internal_range, _ = split_port(port_range)\n for port in internal_range:\n port = str(port)\n if '/' in port:\n port = tuple(port.split('/'))\n ports.append(port)\n container_options['ports'] = ports\n\n override_options['binds'] = merge_volume_b","label":0},{"cid":"compose/service.py::Service._get_container_host_config","text":"compose/service.py::Service._get_container_host_config\n def _get_container_host_config(self, override_options, one_off=False):\n options = dict(self.options, **override_options)\n\n log_config = LogConfig(\n type=options.get('log_driver', \"\"),\n config=options.get('log_opt', None)\n )\n return self.client.create_host_config(\n links=self._get_links(link_to_self=one_off),\n port_bindings=build_port_bindings(options.get('ports') or []),\n binds=options.get('binds'),\n volumes_from=self._get_volumes_from(),\n privileged=options.get('privileged', False),\n network_mode=self.net.mode,\n devices=options.get('devices'),\n dns=options.get('dns'),\n dns_search=options.get('dns_search'),\n restart_policy=options.get('restart'),\n cap_add=options.get('cap_add'),\n cap_drop=options.get('cap_drop'),\n mem_limit=options.get('mem_limit'),\n memswap_limit=options.get('memswap_limit'),\n ulimits=build_ulimits(options.get('ulimits')),\n log_config=log_config,\n extra_hosts=options.get('extra_hosts'),\n read_only=options.get('read_only'),\n pid_mode=options.get('pid'),\n security_opt=options.get('security_opt'),\n ipc_mode=options.get('ipc'),\n cgroup_parent=options.get('cgroup_parent'),\n )","label":0},{"cid":"compose/service.py::Service.build","text":"compose/service.py::Service.build\n def build(self, no_cache=False, pull=False, force_rm=False):\n log.info('Building %s' % self.name)\n\n path = self.options['build']\n # python2 os.path() doesn't support unicode, so we need to encode it to\n # a byte string\n if not six.PY3:\n path = path.encode('utf8')\n\n build_output = self.client.build(\n path=path,\n tag=self.image_name,\n stream=True,\n rm=True,\n forcerm=force_rm,\n pull=pull,\n nocache=no_cache,\n dockerfile=self.options.get('dockerfile', None),\n )\n\n try:\n all_events = stream_output(build_output, sys.stdout)\n except StreamOutputError as e:\n raise BuildError(self, six.text_type(e))\n\n # Ensure the HTTP connection is not reused for another\n # streaming command, as the Docker daemon can sometimes\n # complain about it\n self.client.close()\n\n image_id = None\n\n for event in all_events:\n if 'stream' in event:\n match = re.search(r'Successfully built ([0-9a-f]+)', event.get('stream', ''))\n if match:\n image_id = match.group(1)\n\n if image_id is None:\n raise BuildError(self, event if all_events else 'Unknown')\n\n return image_id","label":0},{"cid":"compose/service.py::Service.can_be_built","text":"compose/service.py::Service.can_be_built\n def can_be_built(self):\n return 'build' in self.options","label":0},{"cid":"compose/service.py::Service.full_name","text":"compose/service.py::Service.full_name\n @property\n def full_name(self):\n \"\"\"\n The tag to give to images built for this service.\n \"\"\"\n return '%s_%s' % (self.project, self.name)","label":0},{"cid":"compose/utils.py::get_output_stream","text":"compose/utils.py::get_output_stream\ndef get_output_stream(stream):\n if six.PY3:\n return stream\n return codecs.getwriter('utf-8')(stream)","label":0},{"cid":"compose/utils.py::stream_as_text","text":"compose/utils.py::stream_as_text\ndef stream_as_text(stream):\n \"\"\"Given a stream of bytes or text, if any of the items in the stream\n are bytes convert them to text.\n\n This function can be removed once docker-py returns text streams instead\n of byte streams.\n \"\"\"\n for data in stream:\n if not isinstance(data, six.text_type):\n data = data.decode('utf-8', 'replace')\n yield data","label":0},{"cid":"compose/utils.py::line_splitter","text":"compose/utils.py::line_splitter\ndef line_splitter(buffer, separator=u'\\n'):\n index = buffer.find(six.text_type(separator))\n if index == -1:\n return None\n return buffer[:index + 1], buffer[index + 1:]","label":0},{"cid":"compose/utils.py::split_buffer","text":"compose/utils.py::split_buffer\ndef split_buffer(stream, splitter=None, decoder=lambda a: a):\n \"\"\"Given a generator which yields strings and a splitter function,\n joins all input, splits on the separator and yields each chunk.\n\n Unlike string.split(), each chunk includes the trailing\n separator, except for the last one if none was found on the end\n of the input.\n \"\"\"\n splitter = splitter or line_splitter\n buffered = six.text_type('')\n\n for data in stream_as_text(stream):\n buffered += data\n while True:\n buffer_split = splitter(buffered)\n if buffer_split is None:\n break\n\n item, buffered = buffer_split\n yield item\n\n if buffered:\n yield decoder(buffered)","label":0},{"cid":"compose/utils.py::json_splitter","text":"compose/utils.py::json_splitter\ndef json_splitter(buffer):\n \"\"\"Attempt to parse a json object from a buffer. If there is at least one\n object, return it and the rest of the buffer, otherwise return None.\n \"\"\"\n try:\n obj, index = json_decoder.raw_decode(buffer)\n rest = buffer[json.decoder.WHITESPACE.match(buffer, index).end():]\n return obj, rest\n except ValueError:\n return None","label":0},{"cid":"compose/utils.py::json_stream","text":"compose/utils.py::json_stream\ndef json_stream(stream):\n \"\"\"Given a stream of text, return a stream of json objects.\n This handles streams which are inconsistently buffered (some entries may\n be newline delimited, and others are not).\n \"\"\"\n return split_buffer(stream, json_splitter, json_decoder.decode)","label":0},{"cid":"compose/utils.py::json_hash","text":"compose/utils.py::json_hash\ndef json_hash(obj):\n dump = json.dumps(obj, sort_keys=True, separators=(',', ':'))\n h = hashlib.sha256()\n h.update(dump.encode('utf8'))\n return h.hexdigest()","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/command.py::friendly_error_message\",\n \"text\": \"compose/cli/command.py::friendly_error_message\\n@contextlib.contextmanager\\ndef friendly_error_message():\\n try:\\n yield\\n except SSLError as e:\\n raise errors.UserError('SSL error: %s' % e)\\n except ConnectionError:\\n if call_silently(['which', 'docker']) != 0:\\n if is_mac():\\n raise errors.DockerNotFoundMac()\\n elif is_ubuntu():\\n raise errors.DockerNotFoundUbuntu()\\n else:\\n raise errors.DockerNotFoundGeneric()\\n elif call_silently(['which', 'docker-machine']) == 0:\\n raise errors.ConnectionErrorDockerMachine()\\n else:\\n raise errors.ConnectionErrorGeneric(get_client().base_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::project_from_options\",\n \"text\": \"compose/cli/command.py::project_from_options\\ndef project_from_options(base_dir, options):\\n return get_project(\\n base_dir,\\n get_config_path(options.get('--file')),\\n project_name=options.get('--project-name'),\\n verbose=options.get('--verbose'),\\n use_networking=options.get('--x-networking'),\\n network_driver=options.get('--x-network-driver'),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::get_config_path\",\n \"text\": \"compose/cli/command.py::get_config_path\\ndef get_config_path(file_option):\\n if file_option:\\n return file_option\\n\\n if 'FIG_FILE' in os.environ:\\n log.warn('The FIG_FILE environment variable is deprecated.')\\n log.warn('Please use COMPOSE_FILE instead.')\\n\\n config_file = os.environ.get('COMPOSE_FILE') or os.environ.get('FIG_FILE')\\n return [config_file] if config_file else None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::get_client\",\n \"text\": \"compose/cli/command.py::get_client\\ndef get_client(verbose=False, version=None):\\n client = docker_client(version=version)\\n if verbose:\\n version_info = six.iteritems(client.version())\\n log.info(get_version_info('full'))\\n log.info(\\\"Docker base_url: %s\\\", client.base_url)\\n log.info(\\\"Docker version: %s\\\",\\n \\\", \\\".join(\\\"%s=%s\\\" % item for item in version_info))\\n return verbose_proxy.VerboseProxy('docker', client)\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::get_project\",\n \"text\": \"compose/cli/command.py::get_project\\ndef get_project(base_dir, config_path=None, project_name=None, verbose=False,\\n use_networking=False, network_driver=None):\\n config_details = config.find(base_dir, config_path)\\n\\n api_version = '1.21' if use_networking else None\\n return Project.from_dicts(\\n get_project_name(config_details.working_dir, project_name),\\n config.load(config_details),\\n get_client(verbose=verbose, version=api_version),\\n use_networking=use_networking,\\n network_driver=network_driver)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::get_project_name\",\n \"text\": \"compose/cli/command.py::get_project_name\\ndef get_project_name(working_dir, project_name=None):\\n def normalize_name(name):\\n return re.sub(r'[^a-z0-9]', '', name.lower())\\n\\n if 'FIG_PROJECT_NAME' in os.environ:\\n log.warn('The FIG_PROJECT_NAME environment variable is deprecated.')\\n log.warn('Please use COMPOSE_PROJECT_NAME instead.')\\n\\n project_name = (\\n project_name or\\n os.environ.get('COMPOSE_PROJECT_NAME') or\\n os.environ.get('FIG_PROJECT_NAME'))\\n if project_name is not None:\\n return normalize_name(project_name)\\n\\n project = os.path.basename(os.path.abspath(working_dir))\\n if project:\\n return normalize_name(project)\\n\\n return 'default'\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::get_project_name.normalize_name\",\n \"text\": \"compose/cli/command.py::get_project_name.normalize_name\\n def normalize_name(name):\\n return re.sub(r'[^a-z0-9]', '', name.lower())\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/docker_client.py::docker_client\",\n \"text\": \"compose/cli/docker_client.py::docker_client\\ndef docker_client(version=None):\\n \\\"\\\"\\\"\\n Returns a docker-py client configured using environment variables\\n according to the same logic as the official Docker client.\\n \\\"\\\"\\\"\\n if 'DOCKER_CLIENT_TIMEOUT' in os.environ:\\n log.warn('The DOCKER_CLIENT_TIMEOUT environment variable is deprecated. Please use COMPOSE_HTTP_TIMEOUT instead.')\\n\\n kwargs = kwargs_from_env(assert_hostname=False)\\n kwargs['version'] = version or os.environ.get(\\n 'COMPOSE_API_VERSION',\\n DEFAULT_API_VERSION)\\n kwargs['timeout'] = HTTP_TIMEOUT\\n return Client(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPrinter.__init__\",\n \"text\": \"compose/cli/log_printer.py::LogPrinter.__init__\\n def __init__(self, containers, output=sys.stdout, monochrome=False):\\n self.containers = containers\\n self.output = utils.get_output_stream(output)\\n self.monochrome = monochrome\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPrinter.run\",\n \"text\": \"compose/cli/log_printer.py::LogPrinter.run\\n def run(self):\\n if not self.containers:\\n return\\n\\n prefix_width = max_name_width(self.containers)\\n generators = list(self._make_log_generators(self.monochrome, prefix_width))\\n for line in Multiplexer(generators).loop():\\n self.output.write(line)\\n self.output.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPrinter._make_log_generators\",\n \"text\": \"compose/cli/log_printer.py::LogPrinter._make_log_generators\\n def _make_log_generators(self, monochrome, prefix_width):\\n def no_color(text):\\n return text\\n\\n if monochrome:\\n color_funcs = cycle([no_color])\\n else:\\n color_funcs = cycle(colors.rainbow())\\n\\n for color_func, container in zip(color_funcs, self.containers):\\n generator_func = get_log_generator(container)\\n prefix = color_func(build_log_prefix(container, prefix_width))\\n yield generator_func(container, prefix, color_func)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPrinter._make_log_generators.no_color\",\n \"text\": \"compose/cli/log_printer.py::LogPrinter._make_log_generators.no_color\\n def no_color(text):\\n return text\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_log_prefix\",\n \"text\": \"compose/cli/log_printer.py::build_log_prefix\\ndef build_log_prefix(container, prefix_width):\\n return container.name_without_project.ljust(prefix_width) + ' | '\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::max_name_width\",\n \"text\": \"compose/cli/log_printer.py::max_name_width\\ndef max_name_width(containers):\\n \\\"\\\"\\\"Calculate the maximum width of container names so we can make the log\\n prefixes line up like so:\\n\\n db_1 | Listening\\n web_1 | Listening\\n \\\"\\\"\\\"\\n return max(len(container.name_without_project) for container in containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::get_log_generator\",\n \"text\": \"compose/cli/log_printer.py::get_log_generator\\ndef get_log_generator(container):\\n if container.has_api_logs:\\n return build_log_generator\\n return build_no_log_generator\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_no_log_generator\",\n \"text\": \"compose/cli/log_printer.py::build_no_log_generator\\ndef build_no_log_generator(container, prefix, color_func):\\n \\\"\\\"\\\"Return a generator that prints a warning about logs and waits for\\n container to exit.\\n \\\"\\\"\\\"\\n yield \\\"{} WARNING: no logs are available with the '{}' log driver\\\\n\\\".format(\\n prefix,\\n container.log_driver)\\n yield color_func(wait_on_exit(container))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_log_generator\",\n \"text\": \"compose/cli/log_printer.py::build_log_generator\\ndef build_log_generator(container, prefix, color_func):\\n # if the container doesn't have a log_stream we need to attach to container\\n # before log printer starts running\\n if container.log_stream is None:\\n stream = container.attach(stdout=True, stderr=True, stream=True, logs=True)\\n line_generator = split_buffer(stream)\\n else:\\n line_generator = split_buffer(container.log_stream)\\n\\n for line in line_generator:\\n yield prefix + line\\n yield color_func(wait_on_exit(container))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::wait_on_exit\",\n \"text\": \"compose/cli/log_printer.py::wait_on_exit\\ndef wait_on_exit(container):\\n exit_code = container.wait()\\n return \\\"%s exited with code %s\\\\n\\\" % (container.name, exit_code)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::main\",\n \"text\": \"compose/cli/main.py::main\\ndef main():\\n setup_logging()\\n try:\\n command = TopLevelCommand()\\n command.sys_dispatch()\\n except KeyboardInterrupt:\\n log.error(\\\"\\\\nAborting.\\\")\\n sys.exit(1)\\n except (UserError, NoSuchService, ConfigurationError, legacy.LegacyError) as e:\\n log.error(e.msg)\\n sys.exit(1)\\n except NoSuchCommand as e:\\n commands = \\\"\\\\n\\\".join(parse_doc_section(\\\"commands:\\\", getdoc(e.supercommand)))\\n log.error(\\\"No such command: %s\\\\n\\\\n%s\\\", e.command, commands)\\n sys.exit(1)\\n except APIError as e:\\n log.error(e.explanation)\\n sys.exit(1)\\n except BuildError as e:\\n log.error(\\\"Service '%s' failed to build: %s\\\" % (e.service.name, e.reason))\\n sys.exit(1)\\n except StreamOutputError as e:\\n log.error(e)\\n sys.exit(1)\\n except NeedsBuildError as e:\\n log.error(\\\"Service '%s' needs to be built, but --no-build was passed.\\\" % e.service.name)\\n sys.exit(1)\\n except ReadTimeout as e:\\n log.error(\\n \\\"An HTTP request took too long to complete. Retry with --verbose to obtain debug information.\\\\n\\\"\\n \\\"If you encounter this issue regularly because of slow network conditions, consider setting \\\"\\n \\\"COMPOSE_HTTP_TIMEOUT to a higher value (current value: %s).\\\" % HTTP_TIMEOUT\\n )\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_logging\",\n \"text\": \"compose/cli/main.py::setup_logging\\ndef setup_logging():\\n root_logger = logging.getLogger()\\n root_logger.addHandler(console_handler)\\n root_logger.setLevel(logging.DEBUG)\\n\\n # Disable requests logging\\n logging.getLogger(\\\"requests\\\").propagate = False\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_console_handler\",\n \"text\": \"compose/cli/main.py::setup_console_handler\\ndef setup_console_handler(handler, verbose):\\n if handler.stream.isatty():\\n format_class = ConsoleWarningFormatter\\n else:\\n format_class = logging.Formatter\\n\\n if verbose:\\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\\n handler.setLevel(logging.DEBUG)\\n else:\\n handler.setFormatter(format_class())\\n handler.setLevel(logging.INFO)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::parse_doc_section\",\n \"text\": \"compose/cli/main.py::parse_doc_section\\ndef parse_doc_section(name, source):\\n pattern = re.compile('^([^\\\\n]*' + name + '[^\\\\n]*\\\\n?(?:[ \\\\t].*?(?:\\\\n|$))*)',\\n re.IGNORECASE | re.MULTILINE)\\n return [s.strip() for s in pattern.findall(source)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.docopt_options\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.docopt_options\\n def docopt_options(self):\\n options = super(TopLevelCommand, self).docopt_options()\\n options['version'] = get_version_info('compose')\\n return options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.perform_command\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.perform_command\\n def perform_command(self, options, handler, command_options):\\n setup_console_handler(console_handler, options.get('--verbose'))\\n\\n if options['COMMAND'] in ('help', 'version'):\\n # Skip looking up the compose file.\\n handler(None, command_options)\\n return\\n\\n project = project_from_options(self.base_dir, options)\\n with friendly_error_message():\\n handler(project, command_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.build\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.build\\n def build(self, project, options):\\n \\\"\\\"\\\"\\n Build or rebuild services.\\n\\n Services are built once and then tagged as `project_service`,\\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\\n contents of its build directory, you can run `docker-compose build` to rebuild it.\\n\\n Usage: build [options] [SERVICE...]\\n\\n Options:\\n --force-rm Always remove intermediate containers.\\n --no-cache Do not use cache when building the image.\\n --pull Always attempt to pull a newer version of the image.\\n \\\"\\\"\\\"\\n project.build(\\n service_names=options['SERVICE'],\\n no_cache=bool(options.get('--no-cache', False)),\\n pull=bool(options.get('--pull', False)),\\n force_rm=bool(options.get('--force-rm', False)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.help\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.help\\n def help(self, project, options):\\n \\\"\\\"\\\"\\n Get help on a command.\\n\\n Usage: help COMMAND\\n \\\"\\\"\\\"\\n handler = self.get_handler(options['COMMAND'])\\n raise SystemExit(getdoc(handler))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.kill\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.kill\\n def kill(self, project, options):\\n \\\"\\\"\\\"\\n Force stop service containers.\\n\\n Usage: kill [options] [SERVICE...]\\n\\n Options:\\n -s SIGNAL SIGNAL to send to the container.\\n Default signal is SIGKILL.\\n \\\"\\\"\\\"\\n signal = options.get('-s', 'SIGKILL')\\n\\n project.kill(service_names=options['SERVICE'], signal=signal)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.logs\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.logs\\n def logs(self, project, options):\\n \\\"\\\"\\\"\\n View output from containers.\\n\\n Usage: logs [options] [SERVICE...]\\n\\n Options:\\n --no-color Produce monochrome output.\\n \\\"\\\"\\\"\\n containers = project.containers(service_names=options['SERVICE'], stopped=True)\\n\\n monochrome = options['--no-color']\\n print(\\\"Attaching to\\\", list_containers(containers))\\n LogPrinter(containers, monochrome=monochrome).run()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pause\\n def pause(self, project, options):\\n \\\"\\\"\\\"\\n Pause services.\\n\\n Usage: pause [SERVICE...]\\n \\\"\\\"\\\"\\n project.pause(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.port\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.port\\n def port(self, project, options):\\n \\\"\\\"\\\"\\n Print the public port for a port binding.\\n\\n Usage: port [options] SERVICE PRIVATE_PORT\\n\\n Options:\\n --protocol=proto tcp or udp [default: tcp]\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n \\\"\\\"\\\"\\n index = int(options.get('--index'))\\n service = project.get_service(options['SERVICE'])\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n print(container.get_local_port(\\n options['PRIVATE_PORT'],\\n protocol=options.get('--protocol') or 'tcp') or '')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.ps\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.ps\\n def ps(self, project, options):\\n \\\"\\\"\\\"\\n List containers.\\n\\n Usage: ps [options] [SERVICE...]\\n\\n Options:\\n -q Only display IDs\\n \\\"\\\"\\\"\\n containers = sorted(\\n project.containers(service_names=options['SERVICE'], stopped=True) +\\n project.containers(service_names=options['SERVICE'], one_off=True),\\n key=attrgetter('name'))\\n\\n if options['-q']:\\n for container in containers:\\n print(container.id)\\n else:\\n headers = [\\n 'Name',\\n 'Command',\\n 'State',\\n 'Ports',\\n ]\\n rows = []\\n for container in containers:\\n command = container.human_readable_command\\n if len(command) > 30:\\n command = '%s ...' % command[:26]\\n rows.append([\\n container.name,\\n command,\\n container.human_readable_state,\\n container.human_readable_ports,\\n ])\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pull\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pull\\n def pull(self, project, options):\\n \\\"\\\"\\\"\\n Pulls images for services.\\n\\n Usage: pull [options] [SERVICE...]\\n\\n Options:\\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\\n --allow-insecure-ssl Deprecated - no effect.\\n \\\"\\\"\\\"\\n if options['--allow-insecure-ssl']:\\n log.warn(INSECURE_SSL_WARNING)\\n\\n project.pull(\\n service_names=options['SERVICE'],\\n ignore_pull_failures=options.get('--ignore-pull-failures')\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.rm\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.rm\\n def rm(self, project, options):\\n \\\"\\\"\\\"\\n Remove stopped service containers.\\n\\n Usage: rm [options] [SERVICE...]\\n\\n Options:\\n -f, --force Don't ask to confirm removal\\n -v Remove volumes associated with containers\\n \\\"\\\"\\\"\\n all_containers = project.containers(service_names=options['SERVICE'], stopped=True)\\n stopped_containers = [c for c in all_containers if not c.is_running]\\n\\n if len(stopped_containers) > 0:\\n print(\\\"Going to remove\\\", list_containers(stopped_containers))\\n if options.get('--force') \\\\\\n or yesno(\\\"Are you sure? [yN] \\\", default=False):\\n project.remove_stopped(\\n service_names=options['SERVICE'],\\n v=options.get('-v', False)\\n )\\n else:\\n print(\\\"No stopped containers\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.run\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.run\\n def run(self, project, options):\\n \\\"\\\"\\\"\\n Run a one-off command on a service.\\n\\n For example:\\n\\n $ docker-compose run web python manage.py shell\\n\\n By default, linked services will be started, unless they are already\\n running. If you do not want to start linked services, use\\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\\n\\n Usage: run [options] [-p PORT...] [-e KEY=VAL...] SERVICE [COMMAND] [ARGS...]\\n\\n Options:\\n --allow-insecure-ssl Deprecated - no effect.\\n -d Detached mode: Run container in the background, print\\n new container name.\\n --name NAME Assign a name to the container\\n --entrypoint CMD Override the entrypoint of the image.\\n -e KEY=VAL Set an environment variable (can be used multiple times)\\n -u, --user=\\\"\\\" Run as specified username or uid\\n --no-deps Don't start linked services.\\n --rm Remove container after run. Ignored in detached mode.\\n -p, --publish=[] Publish a container's port(s) to the host\\n --service-ports Run command with the service's ports enabled and mapped\\n to the host.\\n -T Disable pseudo-tty allocation. By default `docker-compose run`\\n allocates a TTY.\\n \\\"\\\"\\\"\\n service = project.get_service(options['SERVICE'])\\n detach = options['-d']\\n\\n if IS_WINDOWS_PLATFORM and not detach:\\n raise UserError(\\n \\\"Interactive mode is not yet supported on Windows.\\\\n\\\"\\n \\\"Please pass the -d flag when using `docker-compose run`.\\\"\\n )\\n\\n if options['--allow-insecure-ssl']:\\n log.warn(INSECURE_SSL_WARNING)\\n\\n if options['COMMAND']:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.scale\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.scale\\n def scale(self, project, options):\\n \\\"\\\"\\\"\\n Set number of containers to run for a service.\\n\\n Numbers are specified in the form `service=num` as arguments.\\n For example:\\n\\n $ docker-compose scale web=2 worker=3\\n\\n Usage: scale [options] [SERVICE=NUM...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\\n\\n for s in options['SERVICE=NUM']:\\n if '=' not in s:\\n raise UserError('Arguments to scale should be in the form service=num')\\n service_name, num = s.split('=', 1)\\n try:\\n num = int(num)\\n except ValueError:\\n raise UserError('Number of containers for service \\\"%s\\\" is not a '\\n 'number' % service_name)\\n project.get_service(service_name).scale(num, timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.start\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.start\\n def start(self, project, options):\\n \\\"\\\"\\\"\\n Start existing containers.\\n\\n Usage: start [SERVICE...]\\n \\\"\\\"\\\"\\n project.start(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.stop\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.stop\\n def stop(self, project, options):\\n \\\"\\\"\\\"\\n Stop running containers without removing them.\\n\\n They can be started again with `docker-compose start`.\\n\\n Usage: stop [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\\n project.stop(service_names=options['SERVICE'], timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.restart\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.restart\\n def restart(self, project, options):\\n \\\"\\\"\\\"\\n Restart running containers.\\n\\n Usage: restart [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\\n project.restart(service_names=options['SERVICE'], timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.unpause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.unpause\\n def unpause(self, project, options):\\n \\\"\\\"\\\"\\n Unpause services.\\n\\n Usage: unpause [SERVICE...]\\n \\\"\\\"\\\"\\n project.unpause(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.up\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.up\\n def up(self, project, options):\\n \\\"\\\"\\\"\\n Builds, (re)creates, starts, and attaches to containers for a service.\\n\\n Unless they are already running, this command also starts any linked services.\\n\\n The `docker-compose up` command aggregates the output of each container. When\\n the command exits, all containers are stopped. Running `docker-compose up -d`\\n starts the containers in the background and leaves them running.\\n\\n If there are existing containers for a service, and the service's configuration\\n or image was changed after the container's creation, `docker-compose up` picks\\n up the changes by stopping and recreating the containers (preserving mounted\\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\\n flag.\\n\\n If you want to force Compose to stop and recreate all containers, use the\\n `--force-recreate` flag.\\n\\n Usage: up [options] [SERVICE...]\\n\\n Options:\\n --allow-insecure-ssl Deprecated - no effect.\\n -d Detached mode: Run containers in the background,\\n print new container names.\\n --no-color Produce monochrome output.\\n --no-deps Don't start linked services.\\n --force-recreate Recreate containers even if their configuration and\\n image haven't changed. Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate them.\\n Incompatible with --force-recreate.\\n --no-build Don't build an image, even if it's missing\\n -t, --timeout TIMEOUT Use this timeout in seconds for container shutdown\\n when attached or when containers are already\\n running. (default:\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.migrate_to_labels\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.migrate_to_labels\\n def migrate_to_labels(self, project, _options):\\n \\\"\\\"\\\"\\n Recreate containers to add labels\\n\\n If you're coming from Compose 1.2 or earlier, you'll need to remove or\\n migrate your existing containers after upgrading Compose. This is\\n because, as of version 1.3, Compose uses Docker labels to keep track\\n of containers, and so they need to be recreated with labels added.\\n\\n If Compose detects containers that were created without labels, it\\n will refuse to run so that you don't end up with two sets of them. If\\n you want to keep using your existing containers (for example, because\\n they have data volumes you want to preserve) you can migrate them with\\n the following command:\\n\\n docker-compose migrate-to-labels\\n\\n Alternatively, if you're not worried about keeping them, you can\\n remove them - Compose will just create new ones.\\n\\n docker rm -f myapp_web_1 myapp_db_1 ...\\n\\n Usage: migrate-to-labels\\n \\\"\\\"\\\"\\n legacy.migrate_project_to_labels(project)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.version\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.version\\n def version(self, project, options):\\n \\\"\\\"\\\"\\n Show version informations\\n\\n Usage: version [--short]\\n\\n Options:\\n --short Shows only Compose's version number.\\n \\\"\\\"\\\"\\n if options['--short']:\\n print(__version__)\\n else:\\n print(get_version_info('full'))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::convergence_strategy_from_opts\",\n \"text\": \"compose/cli/main.py::convergence_strategy_from_opts\\ndef convergence_strategy_from_opts(options):\\n no_recreate = options['--no-recreate']\\n force_recreate = options['--force-recreate']\\n if force_recreate and no_recreate:\\n raise UserError(\\\"--force-recreate and --no-recreate cannot be combined.\\\")\\n\\n if force_recreate:\\n return ConvergenceStrategy.always\\n\\n if no_recreate:\\n return ConvergenceStrategy.never\\n\\n return ConvergenceStrategy.changed\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::run_one_off_container\",\n \"text\": \"compose/cli/main.py::run_one_off_container\\ndef run_one_off_container(container_options, project, service, options):\\n if not options['--no-deps']:\\n deps = service.get_linked_service_names()\\n if deps:\\n project.up(\\n service_names=deps,\\n start_deps=True,\\n strategy=ConvergenceStrategy.never)\\n\\n if project.use_networking:\\n project.ensure_network_exists()\\n\\n try:\\n container = service.create_container(\\n quiet=True,\\n one_off=True,\\n **container_options)\\n except APIError:\\n legacy.check_for_legacy_containers(\\n project.client,\\n project.name,\\n [service.name],\\n allow_one_off=False)\\n raise\\n\\n if options['-d']:\\n container.start()\\n print(container.name)\\n return\\n\\n def remove_container(force=False):\\n if options['--rm']:\\n project.client.remove_container(container.id, force=True)\\n\\n def force_shutdown(signal, frame):\\n project.client.kill(container.id)\\n remove_container(force=True)\\n sys.exit(2)\\n\\n def shutdown(signal, frame):\\n set_signal_handler(force_shutdown)\\n project.client.stop(container.id)\\n remove_container()\\n sys.exit(1)\\n\\n set_signal_handler(shutdown)\\n dockerpty.start(project.client, container.id, interactive=not options['-T'])\\n exit_code = container.wait()\\n remove_container()\\n sys.exit(exit_code)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::run_one_off_container.remove_container\",\n \"text\": \"compose/cli/main.py::run_one_off_container.remove_container\\n def remove_container(force=False):\\n if options['--rm']:\\n project.client.remove_container(container.id, force=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::run_one_off_container.force_shutdown\",\n \"text\": \"compose/cli/main.py::run_one_off_container.force_shutdown\\n def force_shutdown(signal, frame):\\n project.client.kill(container.id)\\n remove_container(force=True)\\n sys.exit(2)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::run_one_off_container.shutdown\",\n \"text\": \"compose/cli/main.py::run_one_off_container.shutdown\\n def shutdown(signal, frame):\\n set_signal_handler(force_shutdown)\\n project.client.stop(container.id)\\n remove_container()\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::build_log_printer\",\n \"text\": \"compose/cli/main.py::build_log_printer\\ndef build_log_printer(containers, service_names, monochrome):\\n if service_names:\\n containers = [\\n container\\n for container in containers if container.service in service_names\\n ]\\n return LogPrinter(containers, monochrome=monochrome)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::attach_to_logs\",\n \"text\": \"compose/cli/main.py::attach_to_logs\\ndef attach_to_logs(project, log_printer, service_names, timeout):\\n\\n def force_shutdown(signal, frame):\\n project.kill(service_names=service_names)\\n sys.exit(2)\\n\\n def shutdown(signal, frame):\\n set_signal_handler(force_shutdown)\\n print(\\\"Gracefully stopping... (press Ctrl+C again to force)\\\")\\n project.stop(service_names=service_names, timeout=timeout)\\n\\n print(\\\"Attaching to\\\", list_containers(log_printer.containers))\\n set_signal_handler(shutdown)\\n log_printer.run()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::attach_to_logs.force_shutdown\",\n \"text\": \"compose/cli/main.py::attach_to_logs.force_shutdown\\n def force_shutdown(signal, frame):\\n project.kill(service_names=service_names)\\n sys.exit(2)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::attach_to_logs.shutdown\",\n \"text\": \"compose/cli/main.py::attach_to_logs.shutdown\\n def shutdown(signal, frame):\\n set_signal_handler(force_shutdown)\\n print(\\\"Gracefully stopping... (press Ctrl+C again to force)\\\")\\n project.stop(service_names=service_names, timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::set_signal_handler\",\n \"text\": \"compose/cli/main.py::set_signal_handler\\ndef set_signal_handler(handler):\\n signal.signal(signal.SIGINT, handler)\\n signal.signal(signal.SIGTERM, handler)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::list_containers\",\n \"text\": \"compose/cli/main.py::list_containers\\ndef list_containers(containers):\\n return \\\", \\\".join(c.name for c in containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/utils.py::yesno\",\n \"text\": \"compose/cli/utils.py::yesno\\ndef yesno(prompt, default=None):\\n \\\"\\\"\\\"\\n Prompt the user for a yes or no.\\n\\n Can optionally specify a default value, which will only be\\n used if they enter a blank line.\\n\\n Unrecognised input (anything other than \\\"y\\\", \\\"n\\\", \\\"yes\\\",\\n \\\"no\\\" or \\\"\\\") will return None.\\n \\\"\\\"\\\"\\n answer = input(prompt).strip().lower()\\n\\n if answer == \\\"y\\\" or answer == \\\"yes\\\":\\n return True\\n elif answer == \\\"n\\\" or answer == \\\"no\\\":\\n return False\\n elif answer == \\\"\\\":\\n return default\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/utils.py::call_silently\",\n \"text\": \"compose/cli/utils.py::call_silently\\ndef call_silently(*args, **kwargs):\\n \\\"\\\"\\\"\\n Like subprocess.call(), but redirects stdout and stderr to /dev/null.\\n \\\"\\\"\\\"\\n with open(os.devnull, 'w') as shutup:\\n try:\\n return subprocess.call(*args, stdout=shutup, stderr=shutup, **kwargs)\\n except WindowsError:\\n # On Windows, subprocess.call() can still raise exceptions. Normalize\\n # to POSIXy behaviour by returning a nonzero exit code.\\n return 1\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/utils.py::is_mac\",\n \"text\": \"compose/cli/utils.py::is_mac\\ndef is_mac():\\n return platform.system() == 'Darwin'\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/utils.py::is_ubuntu\",\n \"text\": \"compose/cli/utils.py::is_ubuntu\\ndef is_ubuntu():\\n return platform.system() == 'Linux' and platform.linux_distribution()[0] == 'Ubuntu'\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/utils.py::get_version_info\",\n \"text\": \"compose/cli/utils.py::get_version_info\\ndef get_version_info(scope):\\n versioninfo = 'docker-compose version {}, build {}'.format(\\n compose.__version__,\\n get_build_version())\\n\\n if scope == 'compose':\\n return versioninfo\\n if scope == 'full':\\n return (\\n \\\"{}\\\\n\\\"\\n \\\"docker-py version: {}\\\\n\\\"\\n \\\"{} version: {}\\\\n\\\"\\n \\\"OpenSSL version: {}\\\"\\n ).format(\\n versioninfo,\\n docker.version,\\n platform.python_implementation(),\\n platform.python_version(),\\n ssl.OPENSSL_VERSION)\\n\\n raise ValueError(\\\"{} is not a valid version scope\\\".format(scope))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/utils.py::get_build_version\",\n \"text\": \"compose/cli/utils.py::get_build_version\\ndef get_build_version():\\n filename = os.path.join(os.path.dirname(compose.__file__), 'GITSHA')\\n if not os.path.exists(filename):\\n return 'unknown'\\n\\n with open(filename) as fh:\\n return fh.read().strip()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ConfigFile.from_filename\",\n \"text\": \"compose/config/config.py::ConfigFile.from_filename\\n @classmethod\\n def from_filename(cls, filename):\\n return cls(filename, load_yaml(filename))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceConfig.with_abs_paths\",\n \"text\": \"compose/config/config.py::ServiceConfig.with_abs_paths\\n @classmethod\\n def with_abs_paths(cls, working_dir, filename, name, config):\\n if not working_dir:\\n raise ValueError(\\\"No working_dir for ServiceConfig.\\\")\\n\\n return cls(\\n os.path.abspath(working_dir),\\n os.path.abspath(filename) if filename else filename,\\n name,\\n config)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::find\",\n \"text\": \"compose/config/config.py::find\\ndef find(base_dir, filenames):\\n if filenames == ['-']:\\n return ConfigDetails(\\n os.getcwd(),\\n [ConfigFile(None, yaml.safe_load(sys.stdin))])\\n\\n if filenames:\\n filenames = [os.path.join(base_dir, f) for f in filenames]\\n else:\\n filenames = get_default_config_files(base_dir)\\n\\n log.debug(\\\"Using configuration files: {}\\\".format(\\\",\\\".join(filenames)))\\n return ConfigDetails(\\n os.path.dirname(filenames[0]),\\n [ConfigFile.from_filename(f) for f in filenames])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::get_default_config_files\",\n \"text\": \"compose/config/config.py::get_default_config_files\\ndef get_default_config_files(base_dir):\\n (candidates, path) = find_candidates_in_parent_dirs(SUPPORTED_FILENAMES, base_dir)\\n\\n if not candidates:\\n raise ComposeFileNotFound(SUPPORTED_FILENAMES)\\n\\n winner = candidates[0]\\n\\n if len(candidates) > 1:\\n log.warn(\\\"Found multiple config files with supported names: %s\\\", \\\", \\\".join(candidates))\\n log.warn(\\\"Using %s\\\\n\\\", winner)\\n\\n if winner == 'docker-compose.yaml':\\n log.warn(\\\"Please be aware that .yml is the expected extension \\\"\\n \\\"in most cases, and using .yaml can cause compatibility \\\"\\n \\\"issues in future.\\\\n\\\")\\n\\n if winner.startswith(\\\"fig.\\\"):\\n log.warn(\\\"%s is deprecated and will not be supported in future. \\\"\\n \\\"Please rename your config file to docker-compose.yml\\\\n\\\" % winner)\\n\\n return [os.path.join(path, winner)] + get_default_override_file(path)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/config.py::get_default_override_file\",\n \"text\": \"compose/config/config.py::get_default_override_file\\ndef get_default_override_file(path):\\n override_filename = os.path.join(path, DEFAULT_OVERRIDE_FILENAME)\\n return [override_filename] if os.path.exists(override_filename) else []\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::find_candidates_in_parent_dirs\",\n \"text\": \"compose/config/config.py::find_candidates_in_parent_dirs\\ndef find_candidates_in_parent_dirs(filenames, path):\\n \\\"\\\"\\\"\\n Given a directory path to start, looks for filenames in the\\n directory, and then each parent directory successively,\\n until found.\\n\\n Returns tuple (candidates, path).\\n \\\"\\\"\\\"\\n candidates = [filename for filename in filenames\\n if os.path.exists(os.path.join(path, filename))]\\n\\n if not candidates:\\n parent_dir = os.path.join(path, '..')\\n if os.path.abspath(parent_dir) != os.path.abspath(path):\\n return find_candidates_in_parent_dirs(filenames, parent_dir)\\n\\n return (candidates, path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load\",\n \"text\": \"compose/config/config.py::load\\ndef load(config_details):\\n \\\"\\\"\\\"Load the configuration from a working directory and a list of\\n configuration files. Files are loaded in order, and merged on top\\n of each other to create the final configuration.\\n\\n Return a fully interpolated, extended and validated configuration.\\n \\\"\\\"\\\"\\n\\n def build_service(filename, service_name, service_dict):\\n service_config = ServiceConfig.with_abs_paths(\\n config_details.working_dir,\\n filename,\\n service_name,\\n service_dict)\\n resolver = ServiceExtendsResolver(service_config)\\n service_dict = process_service(resolver.run())\\n\\n # TODO: move to validate_service()\\n validate_against_service_schema(service_dict, service_config.name)\\n validate_paths(service_dict)\\n\\n service_dict = finalize_service(service_config._replace(config=service_dict))\\n service_dict['name'] = service_config.name\\n return service_dict\\n\\n def build_services(config_file):\\n return sort_service_dicts([\\n build_service(config_file.filename, name, service_dict)\\n for name, service_dict in config_file.config.items()\\n ])\\n\\n def merge_services(base, override):\\n all_service_names = set(base) | set(override)\\n return {\\n name: merge_service_dicts_from_files(\\n base.get(name, {}),\\n override.get(name, {}))\\n for name in all_service_names\\n }\\n\\n config_file = process_config_file(config_details.config_files[0])\\n for next_file in config_details.config_files[1:]:\\n next_file = process_config_file(next_file)\\n\\n config = merge_services(config_file.config, next_file.config)\\n config_file = config_file._replace(config=config)\\n\\n return build_services(config_file)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load.build_service\",\n \"text\": \"compose/config/config.py::load.build_service\\n def build_service(filename, service_name, service_dict):\\n service_config = ServiceConfig.with_abs_paths(\\n config_details.working_dir,\\n filename,\\n service_name,\\n service_dict)\\n resolver = ServiceExtendsResolver(service_config)\\n service_dict = process_service(resolver.run())\\n\\n # TODO: move to validate_service()\\n validate_against_service_schema(service_dict, service_config.name)\\n validate_paths(service_dict)\\n\\n service_dict = finalize_service(service_config._replace(config=service_dict))\\n service_dict['name'] = service_config.name\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load.build_services\",\n \"text\": \"compose/config/config.py::load.build_services\\n def build_services(config_file):\\n return sort_service_dicts([\\n build_service(config_file.filename, name, service_dict)\\n for name, service_dict in config_file.config.items()\\n ])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load.merge_services\",\n \"text\": \"compose/config/config.py::load.merge_services\\n def merge_services(base, override):\\n all_service_names = set(base) | set(override)\\n return {\\n name: merge_service_dicts_from_files(\\n base.get(name, {}),\\n override.get(name, {}))\\n for name in all_service_names\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::process_config_file\",\n \"text\": \"compose/config/config.py::process_config_file\\ndef process_config_file(config_file, service_name=None):\\n validate_top_level_object(config_file)\\n processed_config = interpolate_environment_variables(config_file.config)\\n validate_against_fields_schema(processed_config, config_file.filename)\\n\\n if service_name and service_name not in processed_config:\\n raise ConfigurationError(\\n \\\"Cannot extend service '{}' in {}: Service not found\\\".format(\\n service_name, config_file.filename))\\n\\n return config_file._replace(config=processed_config)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.__init__\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.__init__\\n def __init__(self, service_config, already_seen=None):\\n self.service_config = service_config\\n self.working_dir = service_config.working_dir\\n self.already_seen = already_seen or []\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.signature\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.signature\\n @property\\n def signature(self):\\n return self.service_config.filename, self.service_config.name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.detect_cycle\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.detect_cycle\\n def detect_cycle(self):\\n if self.signature in self.already_seen:\\n raise CircularReference(self.already_seen + [self.signature])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.run\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.run\\n def run(self):\\n self.detect_cycle()\\n\\n if 'extends' in self.service_config.config:\\n service_dict = self.resolve_extends(*self.validate_and_construct_extends())\\n return self.service_config._replace(config=service_dict)\\n\\n return self.service_config\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.validate_and_construct_extends\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.validate_and_construct_extends\\n def validate_and_construct_extends(self):\\n extends = self.service_config.config['extends']\\n if not isinstance(extends, dict):\\n extends = {'service': extends}\\n\\n config_path = self.get_extended_config_path(extends)\\n service_name = extends['service']\\n\\n extended_file = process_config_file(\\n ConfigFile.from_filename(config_path),\\n service_name=service_name)\\n service_config = extended_file.config[service_name]\\n return config_path, service_config, service_name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.resolve_extends\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.resolve_extends\\n def resolve_extends(self, extended_config_path, service_dict, service_name):\\n resolver = ServiceExtendsResolver(\\n ServiceConfig.with_abs_paths(\\n os.path.dirname(extended_config_path),\\n extended_config_path,\\n service_name,\\n service_dict),\\n already_seen=self.already_seen + [self.signature])\\n\\n service_config = resolver.run()\\n other_service_dict = process_service(service_config)\\n validate_extended_service_dict(\\n other_service_dict,\\n extended_config_path,\\n service_name,\\n )\\n\\n return merge_service_dicts(other_service_dict, self.service_config.config)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.get_extended_config_path\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.get_extended_config_path\\n def get_extended_config_path(self, extends_options):\\n \\\"\\\"\\\"Service we are extending either has a value for 'file' set, which we\\n need to obtain a full path too or we are extending from a service\\n defined in our own file.\\n \\\"\\\"\\\"\\n filename = self.service_config.filename\\n validate_extends_file_path(\\n self.service_config.name,\\n extends_options,\\n filename)\\n if 'file' in extends_options:\\n return expand_path(self.working_dir, extends_options['file'])\\n return filename\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_environment\",\n \"text\": \"compose/config/config.py::resolve_environment\\ndef resolve_environment(service_dict):\\n \\\"\\\"\\\"Unpack any environment variables from an env_file, if set.\\n Interpolate environment values if set.\\n \\\"\\\"\\\"\\n env = {}\\n for env_file in service_dict.get('env_file', []):\\n env.update(env_vars_from_file(env_file))\\n\\n env.update(parse_environment(service_dict.get('environment')))\\n return dict(resolve_env_var(k, v) for k, v in six.iteritems(env))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::validate_extended_service_dict\",\n \"text\": \"compose/config/config.py::validate_extended_service_dict\\ndef validate_extended_service_dict(service_dict, filename, service):\\n error_prefix = \\\"Cannot extend service '%s' in %s:\\\" % (service, filename)\\n\\n if 'links' in service_dict:\\n raise ConfigurationError(\\n \\\"%s services with 'links' cannot be extended\\\" % error_prefix)\\n\\n if 'volumes_from' in service_dict:\\n raise ConfigurationError(\\n \\\"%s services with 'volumes_from' cannot be extended\\\" % error_prefix)\\n\\n if 'net' in service_dict:\\n if get_service_name_from_net(service_dict['net']) is not None:\\n raise ConfigurationError(\\n \\\"%s services with 'net: container' cannot be extended\\\" % error_prefix)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::validate_ulimits\",\n \"text\": \"compose/config/config.py::validate_ulimits\\ndef validate_ulimits(ulimit_config):\\n for limit_name, soft_hard_values in six.iteritems(ulimit_config):\\n if isinstance(soft_hard_values, dict):\\n if not soft_hard_values['soft'] <= soft_hard_values['hard']:\\n raise ConfigurationError(\\n \\\"ulimit_config \\\\\\\"{}\\\\\\\" cannot contain a 'soft' value higher \\\"\\n \\\"than 'hard' value\\\".format(ulimit_config))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::process_service\",\n \"text\": \"compose/config/config.py::process_service\\ndef process_service(service_config):\\n working_dir = service_config.working_dir\\n service_dict = dict(service_config.config)\\n\\n if 'env_file' in service_dict:\\n service_dict['env_file'] = [\\n expand_path(working_dir, path)\\n for path in to_list(service_dict['env_file'])\\n ]\\n\\n if 'volumes' in service_dict and service_dict.get('volume_driver') is None:\\n service_dict['volumes'] = resolve_volume_paths(working_dir, service_dict)\\n\\n if 'build' in service_dict:\\n service_dict['build'] = resolve_build_path(working_dir, service_dict['build'])\\n\\n if 'labels' in service_dict:\\n service_dict['labels'] = parse_labels(service_dict['labels'])\\n\\n if 'extra_hosts' in service_dict:\\n service_dict['extra_hosts'] = parse_extra_hosts(service_dict['extra_hosts'])\\n\\n # TODO: move to a validate_service()\\n if 'ulimits' in service_dict:\\n validate_ulimits(service_dict['ulimits'])\\n\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::finalize_service\",\n \"text\": \"compose/config/config.py::finalize_service\\ndef finalize_service(service_config):\\n service_dict = dict(service_config.config)\\n\\n if 'environment' in service_dict or 'env_file' in service_dict:\\n service_dict['environment'] = resolve_environment(service_dict)\\n service_dict.pop('env_file', None)\\n\\n if 'volumes_from' in service_dict:\\n service_dict['volumes_from'] = [\\n VolumeFromSpec.parse(vf) for vf in service_dict['volumes_from']]\\n\\n if 'volumes' in service_dict:\\n service_dict['volumes'] = [\\n VolumeSpec.parse(v) for v in service_dict['volumes']]\\n\\n if 'restart' in service_dict:\\n service_dict['restart'] = parse_restart_spec(service_dict['restart'])\\n\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_service_dicts_from_files\",\n \"text\": \"compose/config/config.py::merge_service_dicts_from_files\\ndef merge_service_dicts_from_files(base, override):\\n \\\"\\\"\\\"When merging services from multiple files we need to merge the `extends`\\n field. This is not handled by `merge_service_dicts()` which is used to\\n perform the `extends`.\\n \\\"\\\"\\\"\\n new_service = merge_service_dicts(base, override)\\n if 'extends' in override:\\n new_service['extends'] = override['extends']\\n return new_service\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_service_dicts\",\n \"text\": \"compose/config/config.py::merge_service_dicts\\ndef merge_service_dicts(base, override):\\n d = {}\\n\\n def merge_field(field, merge_func, default=None):\\n if field in base or field in override:\\n d[field] = merge_func(\\n base.get(field, default),\\n override.get(field, default))\\n\\n merge_field('environment', merge_environment)\\n merge_field('labels', merge_labels)\\n merge_image_or_build(base, override, d)\\n\\n for field in ['volumes', 'devices']:\\n merge_field(field, merge_path_mappings)\\n\\n for field in ['ports', 'expose', 'external_links']:\\n merge_field(field, operator.add, default=[])\\n\\n for field in ['dns', 'dns_search', 'env_file']:\\n merge_field(field, merge_list_or_string)\\n\\n already_merged_keys = set(d) | {'image', 'build'}\\n for field in set(ALLOWED_KEYS) - already_merged_keys:\\n if field in base or field in override:\\n d[field] = override.get(field, base.get(field))\\n\\n return d\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_service_dicts.merge_field\",\n \"text\": \"compose/config/config.py::merge_service_dicts.merge_field\\n def merge_field(field, merge_func, default=None):\\n if field in base or field in override:\\n d[field] = merge_func(\\n base.get(field, default),\\n override.get(field, default))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_image_or_build\",\n \"text\": \"compose/config/config.py::merge_image_or_build\\ndef merge_image_or_build(base, override, output):\\n if 'image' in override:\\n output['image'] = override['image']\\n elif 'build' in override:\\n output['build'] = override['build']\\n elif 'image' in base:\\n output['image'] = base['image']\\n elif 'build' in base:\\n output['build'] = base['build']\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_environment\",\n \"text\": \"compose/config/config.py::merge_environment\\ndef merge_environment(base, override):\\n env = parse_environment(base)\\n env.update(parse_environment(override))\\n return env\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::parse_environment\",\n \"text\": \"compose/config/config.py::parse_environment\\ndef parse_environment(environment):\\n if not environment:\\n return {}\\n\\n if isinstance(environment, list):\\n return dict(split_env(e) for e in environment)\\n\\n if isinstance(environment, dict):\\n return dict(environment)\\n\\n raise ConfigurationError(\\n \\\"environment \\\\\\\"%s\\\\\\\" must be a list or mapping,\\\" %\\n environment\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::split_env\",\n \"text\": \"compose/config/config.py::split_env\\ndef split_env(env):\\n if isinstance(env, six.binary_type):\\n env = env.decode('utf-8', 'replace')\\n if '=' in env:\\n return env.split('=', 1)\\n else:\\n return env, None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_env_var\",\n \"text\": \"compose/config/config.py::resolve_env_var\\ndef resolve_env_var(key, val):\\n if val is not None:\\n return key, val\\n elif key in os.environ:\\n return key, os.environ[key]\\n else:\\n return key, ''\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::env_vars_from_file\",\n \"text\": \"compose/config/config.py::env_vars_from_file\\ndef env_vars_from_file(filename):\\n \\\"\\\"\\\"\\n Read in a line delimited file of environment variables.\\n \\\"\\\"\\\"\\n if not os.path.exists(filename):\\n raise ConfigurationError(\\\"Couldn't find env file: %s\\\" % filename)\\n env = {}\\n for line in codecs.open(filename, 'r', 'utf-8'):\\n line = line.strip()\\n if line and not line.startswith('#'):\\n k, v = split_env(line)\\n env[k] = v\\n return env\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_volume_paths\",\n \"text\": \"compose/config/config.py::resolve_volume_paths\\ndef resolve_volume_paths(working_dir, service_dict):\\n return [\\n resolve_volume_path(working_dir, volume)\\n for volume in service_dict['volumes']\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_volume_path\",\n \"text\": \"compose/config/config.py::resolve_volume_path\\ndef resolve_volume_path(working_dir, volume):\\n container_path, host_path = split_path_mapping(volume)\\n\\n if host_path is not None:\\n if host_path.startswith('.'):\\n host_path = expand_path(working_dir, host_path)\\n host_path = os.path.expanduser(host_path)\\n return u\\\"{}:{}\\\".format(host_path, container_path)\\n else:\\n return container_path\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_build_path\",\n \"text\": \"compose/config/config.py::resolve_build_path\\ndef resolve_build_path(working_dir, build_path):\\n if is_url(build_path):\\n return build_path\\n return expand_path(working_dir, build_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::is_url\",\n \"text\": \"compose/config/config.py::is_url\\ndef is_url(build_path):\\n return build_path.startswith(DOCKER_VALID_URL_PREFIXES)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::validate_paths\",\n \"text\": \"compose/config/config.py::validate_paths\\ndef validate_paths(service_dict):\\n if 'build' in service_dict:\\n build_path = service_dict['build']\\n if (\\n not is_url(build_path) and\\n (not os.path.exists(build_path) or not os.access(build_path, os.R_OK))\\n ):\\n raise ConfigurationError(\\n \\\"build path %s either does not exist, is not accessible, \\\"\\n \\\"or is not a valid URL.\\\" % build_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_path_mappings\",\n \"text\": \"compose/config/config.py::merge_path_mappings\\ndef merge_path_mappings(base, override):\\n d = dict_from_path_mappings(base)\\n d.update(dict_from_path_mappings(override))\\n return path_mappings_from_dict(d)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::dict_from_path_mappings\",\n \"text\": \"compose/config/config.py::dict_from_path_mappings\\ndef dict_from_path_mappings(path_mappings):\\n if path_mappings:\\n return dict(split_path_mapping(v) for v in path_mappings)\\n else:\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::path_mappings_from_dict\",\n \"text\": \"compose/config/config.py::path_mappings_from_dict\\ndef path_mappings_from_dict(d):\\n return [join_path_mapping(v) for v in d.items()]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::split_path_mapping\",\n \"text\": \"compose/config/config.py::split_path_mapping\\ndef split_path_mapping(volume_path):\\n \\\"\\\"\\\"\\n Ascertain if the volume_path contains a host path as well as a container\\n path. Using splitdrive so windows absolute paths won't cause issues with\\n splitting on ':'.\\n \\\"\\\"\\\"\\n # splitdrive has limitations when it comes to relative paths, so when it's\\n # relative, handle special case to set the drive to ''\\n if volume_path.startswith('.') or volume_path.startswith('~'):\\n drive, volume_config = '', volume_path\\n else:\\n drive, volume_config = os.path.splitdrive(volume_path)\\n\\n if ':' in volume_config:\\n (host, container) = volume_config.split(':', 1)\\n return (container, drive + host)\\n else:\\n return (volume_path, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::join_path_mapping\",\n \"text\": \"compose/config/config.py::join_path_mapping\\ndef join_path_mapping(pair):\\n (container, host) = pair\\n if host is None:\\n return container\\n else:\\n return \\\":\\\".join((host, container))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_labels\",\n \"text\": \"compose/config/config.py::merge_labels\\ndef merge_labels(base, override):\\n labels = parse_labels(base)\\n labels.update(parse_labels(override))\\n return labels\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::parse_labels\",\n \"text\": \"compose/config/config.py::parse_labels\\ndef parse_labels(labels):\\n if not labels:\\n return {}\\n\\n if isinstance(labels, list):\\n return dict(split_label(e) for e in labels)\\n\\n if isinstance(labels, dict):\\n return dict(labels)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::split_label\",\n \"text\": \"compose/config/config.py::split_label\\ndef split_label(label):\\n if '=' in label:\\n return label.split('=', 1)\\n else:\\n return label, ''\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::expand_path\",\n \"text\": \"compose/config/config.py::expand_path\\ndef expand_path(working_dir, path):\\n return os.path.abspath(os.path.join(working_dir, os.path.expanduser(path)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_list_or_string\",\n \"text\": \"compose/config/config.py::merge_list_or_string\\ndef merge_list_or_string(base, override):\\n return to_list(base) + to_list(override)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::to_list\",\n \"text\": \"compose/config/config.py::to_list\\ndef to_list(value):\\n if value is None:\\n return []\\n elif isinstance(value, six.string_types):\\n return [value]\\n else:\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load_yaml\",\n \"text\": \"compose/config/config.py::load_yaml\\ndef load_yaml(filename):\\n try:\\n with open(filename, 'r') as fh:\\n return yaml.safe_load(fh)\\n except (IOError, yaml.YAMLError) as e:\\n error_name = getattr(e, '__module__', '') + '.' + e.__class__.__name__\\n raise ConfigurationError(u\\\"{}: {}\\\".format(error_name, e))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_ports\",\n \"text\": \"compose/config/validation.py::format_ports\\n@FormatChecker.cls_checks(format=\\\"ports\\\", raises=ValidationError)\\ndef format_ports(instance):\\n try:\\n split_port(instance)\\n except ValueError as e:\\n raise ValidationError(six.text_type(e))\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_expose\",\n \"text\": \"compose/config/validation.py::format_expose\\n@FormatChecker.cls_checks(format=\\\"expose\\\", raises=ValidationError)\\ndef format_expose(instance):\\n if isinstance(instance, six.string_types):\\n if not re.match(VALID_EXPOSE_FORMAT, instance):\\n raise ValidationError(\\n \\\"should be of the format 'PORT[/PROTOCOL]'\\\")\\n\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_boolean_in_environment\",\n \"text\": \"compose/config/validation.py::format_boolean_in_environment\\n@FormatChecker.cls_checks(format=\\\"bool-value-in-mapping\\\")\\ndef format_boolean_in_environment(instance):\\n \\\"\\\"\\\"\\n Check if there is a boolean in the environment and display a warning.\\n Always return True here so the validation won't raise an error.\\n \\\"\\\"\\\"\\n if isinstance(instance, bool):\\n log.warn(\\n \\\"There is a boolean value in the 'environment' key.\\\\n\\\"\\n \\\"Environment variables can only be strings.\\\\n\\\"\\n \\\"Please add quotes to any boolean values to make them string \\\"\\n \\\"(eg, 'True', 'yes', 'N').\\\\n\\\"\\n \\\"This warning will become an error in a future release. \\\\r\\\\n\\\"\\n )\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_top_level_service_objects\",\n \"text\": \"compose/config/validation.py::validate_top_level_service_objects\\ndef validate_top_level_service_objects(config_file):\\n \\\"\\\"\\\"Perform some high level validation of the service name and value.\\n\\n This validation must happen before interpolation, which must happen\\n before the rest of validation, which is why it's separate from the\\n rest of the service validation.\\n \\\"\\\"\\\"\\n for service_name, service_dict in config_file.config.items():\\n if not isinstance(service_name, six.string_types):\\n raise ConfigurationError(\\n \\\"In file '{}' service name: {} needs to be a string, eg '{}'\\\".format(\\n config_file.filename,\\n service_name,\\n service_name))\\n\\n if not isinstance(service_dict, dict):\\n raise ConfigurationError(\\n \\\"In file '{}' service '{}' doesn\\\\'t have any configuration options. \\\"\\n \\\"All top level keys in your docker-compose.yml must map \\\"\\n \\\"to a dictionary of configuration options.\\\".format(\\n config_file.filename,\\n service_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_top_level_object\",\n \"text\": \"compose/config/validation.py::validate_top_level_object\\ndef validate_top_level_object(config_file):\\n if not isinstance(config_file.config, dict):\\n raise ConfigurationError(\\n \\\"Top level object in '{}' needs to be an object not '{}'. Check \\\"\\n \\\"that you have defined a service at the top level.\\\".format(\\n config_file.filename,\\n type(config_file.config)))\\n validate_top_level_service_objects(config_file)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_extends_file_path\",\n \"text\": \"compose/config/validation.py::validate_extends_file_path\\ndef validate_extends_file_path(service_name, extends_options, filename):\\n \\\"\\\"\\\"\\n The service to be extended must either be defined in the config key 'file',\\n or within 'filename'.\\n \\\"\\\"\\\"\\n error_prefix = \\\"Invalid 'extends' configuration for %s:\\\" % service_name\\n\\n if 'file' not in extends_options and filename is None:\\n raise ConfigurationError(\\n \\\"%s you need to specify a 'file', e.g. 'file: something.yml'\\\" % error_prefix\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::get_unsupported_config_msg\",\n \"text\": \"compose/config/validation.py::get_unsupported_config_msg\\ndef get_unsupported_config_msg(service_name, error_key):\\n msg = \\\"Unsupported config option for '{}' service: '{}'\\\".format(service_name, error_key)\\n if error_key in DOCKER_CONFIG_HINTS:\\n msg += \\\" (did you mean '{}'?)\\\".format(DOCKER_CONFIG_HINTS[error_key])\\n return msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::anglicize_validator\",\n \"text\": \"compose/config/validation.py::anglicize_validator\\ndef anglicize_validator(validator):\\n if validator in [\\\"array\\\", \\\"object\\\"]:\\n return 'an ' + validator\\n return 'a ' + validator\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::handle_error_for_schema_with_id\",\n \"text\": \"compose/config/validation.py::handle_error_for_schema_with_id\\ndef handle_error_for_schema_with_id(error, service_name):\\n schema_id = error.schema['id']\\n\\n if schema_id == 'fields_schema.json' and error.validator == 'additionalProperties':\\n return \\\"Invalid service name '{}' - only {} characters are allowed\\\".format(\\n # The service_name is the key to the json object\\n list(error.instance)[0],\\n VALID_NAME_CHARS)\\n\\n if schema_id == '#/definitions/constraints':\\n if 'image' in error.instance and 'build' in error.instance:\\n return (\\n \\\"Service '{}' has both an image and build path specified. \\\"\\n \\\"A service can either be built to image or use an existing \\\"\\n \\\"image, not both.\\\".format(service_name))\\n if 'image' not in error.instance and 'build' not in error.instance:\\n return (\\n \\\"Service '{}' has neither an image nor a build path \\\"\\n \\\"specified. Exactly one must be provided.\\\".format(service_name))\\n if 'image' in error.instance and 'dockerfile' in error.instance:\\n return (\\n \\\"Service '{}' has both an image and alternate Dockerfile. \\\"\\n \\\"A service can either be built to image or use an existing \\\"\\n \\\"image, not both.\\\".format(service_name))\\n\\n if schema_id == '#/definitions/service':\\n if error.validator == 'additionalProperties':\\n invalid_config_key = parse_key_from_error_msg(error)\\n return get_unsupported_config_msg(service_name, invalid_config_key)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::handle_generic_service_error\",\n \"text\": \"compose/config/validation.py::handle_generic_service_error\\ndef handle_generic_service_error(error, service_name):\\n config_key = \\\" \\\".join(\\\"'%s'\\\" % k for k in error.path)\\n msg_format = None\\n error_msg = error.message\\n\\n if error.validator == 'oneOf':\\n msg_format = \\\"Service '{}' configuration key {} {}\\\"\\n error_msg = _parse_oneof_validator(error)\\n\\n elif error.validator == 'type':\\n msg_format = (\\\"Service '{}' configuration key {} contains an invalid \\\"\\n \\\"type, it should be {}\\\")\\n error_msg = _parse_valid_types_from_validator(error.validator_value)\\n\\n # TODO: no test case for this branch, there are no config options\\n # which exercise this branch\\n elif error.validator == 'required':\\n msg_format = \\\"Service '{}' configuration key '{}' is invalid, {}\\\"\\n\\n elif error.validator == 'dependencies':\\n msg_format = \\\"Service '{}' configuration key '{}' is invalid: {}\\\"\\n config_key = list(error.validator_value.keys())[0]\\n required_keys = \\\",\\\".join(error.validator_value[config_key])\\n error_msg = \\\"when defining '{}' you must set '{}' as well\\\".format(\\n config_key,\\n required_keys)\\n\\n elif error.cause:\\n error_msg = six.text_type(error.cause)\\n msg_format = \\\"Service '{}' configuration key {} is invalid: {}\\\"\\n\\n elif error.path:\\n msg_format = \\\"Service '{}' configuration key {} value {}\\\"\\n\\n if msg_format:\\n return msg_format.format(service_name, config_key, error_msg)\\n\\n return error.message\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::parse_key_from_error_msg\",\n \"text\": \"compose/config/validation.py::parse_key_from_error_msg\\ndef parse_key_from_error_msg(error):\\n return error.message.split(\\\"'\\\")[1]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::_parse_valid_types_from_validator\",\n \"text\": \"compose/config/validation.py::_parse_valid_types_from_validator\\ndef _parse_valid_types_from_validator(validator):\\n \\\"\\\"\\\"A validator value can be either an array of valid types or a string of\\n a valid type. Parse the valid types and prefix with the correct article.\\n \\\"\\\"\\\"\\n if not isinstance(validator, list):\\n return anglicize_validator(validator)\\n\\n if len(validator) == 1:\\n return anglicize_validator(validator[0])\\n\\n return \\\"{}, or {}\\\".format(\\n \\\", \\\".join([anglicize_validator(validator[0])] + validator[1:-1]),\\n anglicize_validator(validator[-1]))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::_parse_oneof_validator\",\n \"text\": \"compose/config/validation.py::_parse_oneof_validator\\ndef _parse_oneof_validator(error):\\n \\\"\\\"\\\"oneOf has multiple schemas, so we need to reason about which schema, sub\\n schema or constraint the validation is failing on.\\n Inspecting the context value of a ValidationError gives us information about\\n which sub schema failed and which kind of error it is.\\n \\\"\\\"\\\"\\n types = []\\n for context in error.context:\\n\\n if context.validator == 'required':\\n return context.message\\n\\n if context.validator == 'additionalProperties':\\n invalid_config_key = parse_key_from_error_msg(context)\\n return \\\"contains unsupported option: '{}'\\\".format(invalid_config_key)\\n\\n if context.path:\\n invalid_config_key = \\\" \\\".join(\\n \\\"'{}' \\\".format(fragment) for fragment in context.path\\n if isinstance(fragment, six.string_types)\\n )\\n return \\\"{}contains {}, which is an invalid type, it should be {}\\\".format(\\n invalid_config_key,\\n context.instance,\\n _parse_valid_types_from_validator(context.validator_value))\\n\\n if context.validator == 'uniqueItems':\\n return \\\"contains non unique items, please remove duplicates from {}\\\".format(\\n context.instance)\\n\\n if context.validator == 'type':\\n types.append(context.validator_value)\\n\\n valid_types = _parse_valid_types_from_validator(types)\\n return \\\"contains an invalid type, it should be {}\\\".format(valid_types)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_errors\",\n \"text\": \"compose/config/validation.py::process_errors\\ndef process_errors(errors, service_name=None):\\n \\\"\\\"\\\"jsonschema gives us an error tree full of information to explain what has\\n gone wrong. Process each error and pull out relevant information and re-write\\n helpful error messages that are relevant.\\n \\\"\\\"\\\"\\n def format_error_message(error, service_name):\\n if not service_name and error.path:\\n # field_schema errors will have service name on the path\\n service_name = error.path.popleft()\\n\\n if 'id' in error.schema:\\n error_msg = handle_error_for_schema_with_id(error, service_name)\\n if error_msg:\\n return error_msg\\n\\n return handle_generic_service_error(error, service_name)\\n\\n return '\\\\n'.join(format_error_message(error, service_name) for error in errors)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_errors.format_error_message\",\n \"text\": \"compose/config/validation.py::process_errors.format_error_message\\n def format_error_message(error, service_name):\\n if not service_name and error.path:\\n # field_schema errors will have service name on the path\\n service_name = error.path.popleft()\\n\\n if 'id' in error.schema:\\n error_msg = handle_error_for_schema_with_id(error, service_name)\\n if error_msg:\\n return error_msg\\n\\n return handle_generic_service_error(error, service_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_against_fields_schema\",\n \"text\": \"compose/config/validation.py::validate_against_fields_schema\\ndef validate_against_fields_schema(config, filename):\\n _validate_against_schema(\\n config,\\n \\\"fields_schema.json\\\",\\n format_checker=[\\\"ports\\\", \\\"expose\\\", \\\"bool-value-in-mapping\\\"],\\n filename=filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_against_service_schema\",\n \"text\": \"compose/config/validation.py::validate_against_service_schema\\ndef validate_against_service_schema(config, service_name):\\n _validate_against_schema(\\n config,\\n \\\"service_schema.json\\\",\\n format_checker=[\\\"ports\\\"],\\n service_name=service_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::_validate_against_schema\",\n \"text\": \"compose/config/validation.py::_validate_against_schema\\ndef _validate_against_schema(\\n config,\\n schema_filename,\\n format_checker=(),\\n service_name=None,\\n filename=None):\\n config_source_dir = os.path.dirname(os.path.abspath(__file__))\\n\\n if sys.platform == \\\"win32\\\":\\n file_pre_fix = \\\"///\\\"\\n config_source_dir = config_source_dir.replace('\\\\\\\\', '/')\\n else:\\n file_pre_fix = \\\"//\\\"\\n\\n resolver_full_path = \\\"file:{}{}/\\\".format(file_pre_fix, config_source_dir)\\n schema_file = os.path.join(config_source_dir, schema_filename)\\n\\n with open(schema_file, \\\"r\\\") as schema_fh:\\n schema = json.load(schema_fh)\\n\\n resolver = RefResolver(resolver_full_path, schema)\\n validation_output = Draft4Validator(\\n schema,\\n resolver=resolver,\\n format_checker=FormatChecker(format_checker))\\n\\n errors = [error for error in sorted(validation_output.iter_errors(config), key=str)]\\n if not errors:\\n return\\n\\n error_msg = process_errors(errors, service_name)\\n file_msg = \\\" in file '{}'\\\".format(filename) if filename else ''\\n raise ConfigurationError(\\\"Validation failed{}, reason(s):\\\\n{}\\\".format(\\n file_msg,\\n error_msg))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::check_for_legacy_containers\",\n \"text\": \"compose/legacy.py::check_for_legacy_containers\\ndef check_for_legacy_containers(\\n client,\\n project,\\n services,\\n allow_one_off=True):\\n \\\"\\\"\\\"Check if there are containers named using the old naming convention\\n and warn the user that those containers may need to be migrated to\\n using labels, so that compose can find them.\\n \\\"\\\"\\\"\\n containers = get_legacy_containers(client, project, services, one_off=False)\\n\\n if containers:\\n one_off_containers = get_legacy_containers(client, project, services, one_off=True)\\n\\n raise LegacyContainersError(\\n [c.name for c in containers],\\n [c.name for c in one_off_containers],\\n )\\n\\n if not allow_one_off:\\n one_off_containers = get_legacy_containers(client, project, services, one_off=True)\\n\\n if one_off_containers:\\n raise LegacyOneOffContainersError(\\n [c.name for c in one_off_containers],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::LegacyError.__unicode__\",\n \"text\": \"compose/legacy.py::LegacyError.__unicode__\\n def __unicode__(self):\\n return self.msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::LegacyContainersError.__init__\",\n \"text\": \"compose/legacy.py::LegacyContainersError.__init__\\n def __init__(self, names, one_off_names):\\n self.names = names\\n self.one_off_names = one_off_names\\n\\n self.msg = ERROR_MESSAGE_FORMAT.format(\\n names_list=\\\"\\\\n\\\".join(\\\" {}\\\".format(name) for name in names),\\n rm_args=\\\" \\\".join(names),\\n )\\n\\n if one_off_names:\\n self.msg += ONE_OFF_ADDENDUM_FORMAT.format(rm_args=\\\" \\\".join(one_off_names))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::LegacyOneOffContainersError.__init__\",\n \"text\": \"compose/legacy.py::LegacyOneOffContainersError.__init__\\n def __init__(self, one_off_names):\\n self.one_off_names = one_off_names\\n\\n self.msg = ONE_OFF_ERROR_MESSAGE_FORMAT.format(\\n names_list=\\\"\\\\n\\\".join(\\\" {}\\\".format(name) for name in one_off_names),\\n rm_args=\\\" \\\".join(one_off_names),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::add_labels\",\n \"text\": \"compose/legacy.py::add_labels\\ndef add_labels(project, container):\\n project_name, service_name, one_off, number = NAME_RE.match(container.name).groups()\\n if project_name != project.name or service_name not in project.service_names:\\n return\\n service = project.get_service(service_name)\\n service.recreate_container(container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::migrate_project_to_labels\",\n \"text\": \"compose/legacy.py::migrate_project_to_labels\\ndef migrate_project_to_labels(project):\\n log.info(\\\"Running migration to labels for project %s\\\", project.name)\\n\\n containers = get_legacy_containers(\\n project.client,\\n project.name,\\n project.service_names,\\n one_off=False,\\n )\\n\\n for container in containers:\\n add_labels(project, container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::get_legacy_containers\",\n \"text\": \"compose/legacy.py::get_legacy_containers\\ndef get_legacy_containers(\\n client,\\n project,\\n services,\\n one_off=False):\\n\\n return list(_get_legacy_containers_iter(\\n client,\\n project,\\n services,\\n one_off=one_off,\\n ))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::_get_legacy_containers_iter\",\n \"text\": \"compose/legacy.py::_get_legacy_containers_iter\\ndef _get_legacy_containers_iter(\\n client,\\n project,\\n services,\\n one_off=False):\\n\\n containers = client.containers(all=True)\\n\\n for service in services:\\n for container in containers:\\n if LABEL_VERSION in (container.get('Labels') or {}):\\n continue\\n\\n name = get_container_name(container)\\n if has_container(project, service, name, one_off=one_off):\\n yield Container.from_ps(client, container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::has_container\",\n \"text\": \"compose/legacy.py::has_container\\ndef has_container(project, service, name, one_off=False):\\n if not name or not is_valid_name(name, one_off):\\n return False\\n container_project, container_service, _container_number = parse_name(name)\\n return container_project == project and container_service == service\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::is_valid_name\",\n \"text\": \"compose/legacy.py::is_valid_name\\ndef is_valid_name(name, one_off=False):\\n match = NAME_RE.match(name)\\n if match is None:\\n return False\\n if one_off:\\n return match.group(3) == 'run_'\\n else:\\n return match.group(3) is None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::parse_name\",\n \"text\": \"compose/legacy.py::parse_name\\ndef parse_name(name):\\n match = NAME_RE.match(name)\\n (project, service_name, _, suffix) = match.groups()\\n return (project, service_name, int(suffix))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::BuildError.__init__\",\n \"text\": \"compose/service.py::BuildError.__init__\\n def __init__(self, service, reason):\\n self.service = service\\n self.reason = reason\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::NeedsBuildError.__init__\",\n \"text\": \"compose/service.py::NeedsBuildError.__init__\\n def __init__(self, service):\\n self.service = service\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::ConvergenceStrategy.allows_recreate\",\n \"text\": \"compose/service.py::ConvergenceStrategy.allows_recreate\\n @property\\n def allows_recreate(self):\\n return self is not type(self).never\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.__init__\",\n \"text\": \"compose/service.py::Service.__init__\\n def __init__(\\n self,\\n name,\\n client=None,\\n project='default',\\n use_networking=False,\\n links=None,\\n volumes_from=None,\\n net=None,\\n **options\\n ):\\n self.name = name\\n self.client = client\\n self.project = project\\n self.use_networking = use_networking\\n self.links = links or []\\n self.volumes_from = volumes_from or []\\n self.net = net or Net(None)\\n self.options = options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.containers\",\n \"text\": \"compose/service.py::Service.containers\\n def containers(self, stopped=False, one_off=False, filters={}):\\n filters.update({'label': self.labels(one_off=one_off)})\\n\\n containers = list(filter(None, [\\n Container.from_ps(self.client, container)\\n for container in self.client.containers(\\n all=stopped,\\n filters=filters)]))\\n\\n if not containers:\\n check_for_legacy_containers(\\n self.client,\\n self.project,\\n [self.name],\\n )\\n\\n return containers\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_container\",\n \"text\": \"compose/service.py::Service.get_container\\n def get_container(self, number=1):\\n \\\"\\\"\\\"Return a :class:`compose.container.Container` for this service. The\\n container must be active, and match `number`.\\n \\\"\\\"\\\"\\n labels = self.labels() + ['{0}={1}'.format(LABEL_CONTAINER_NUMBER, number)]\\n for container in self.client.containers(filters={'label': labels}):\\n return Container.from_ps(self.client, container)\\n\\n raise ValueError(\\\"No container found for %s_%s\\\" % (self.name, number))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start\",\n \"text\": \"compose/service.py::Service.start\\n def start(self, **options):\\n for c in self.containers(stopped=True):\\n self.start_container_if_stopped(c, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.stop\",\n \"text\": \"compose/service.py::Service.stop\\n def stop(self, **options):\\n for c in self.containers():\\n log.info(\\\"Stopping %s\\\" % c.name)\\n c.stop(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.pause\",\n \"text\": \"compose/service.py::Service.pause\\n def pause(self, **options):\\n for c in self.containers(filters={'status': 'running'}):\\n log.info(\\\"Pausing %s\\\" % c.name)\\n c.pause(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.unpause\",\n \"text\": \"compose/service.py::Service.unpause\\n def unpause(self, **options):\\n for c in self.containers(filters={'status': 'paused'}):\\n log.info(\\\"Unpausing %s\\\" % c.name)\\n c.unpause()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.kill\",\n \"text\": \"compose/service.py::Service.kill\\n def kill(self, **options):\\n for c in self.containers():\\n log.info(\\\"Killing %s\\\" % c.name)\\n c.kill(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.restart\",\n \"text\": \"compose/service.py::Service.restart\\n def restart(self, **options):\\n for c in self.containers(stopped=True):\\n log.info(\\\"Restarting %s\\\" % c.name)\\n c.restart(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale\",\n \"text\": \"compose/service.py::Service.scale\\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT):\\n \\\"\\\"\\\"\\n Adjusts the number of containers to the specified number and ensures\\n they are running.\\n\\n - creates containers until there are at least `desired_num`\\n - stops containers until there are at most `desired_num` running\\n - starts containers until there are at least `desired_num` running\\n - removes all stopped containers\\n \\\"\\\"\\\"\\n if self.custom_container_name() and desired_num > 1:\\n log.warn('The \\\"%s\\\" service is using the custom container name \\\"%s\\\". '\\n 'Docker requires each container to have a unique name. '\\n 'Remove the custom name to scale the service.'\\n % (self.name, self.custom_container_name()))\\n\\n if self.specifies_host_port():\\n log.warn('The \\\"%s\\\" service specifies a port on the host. If multiple containers '\\n 'for this service are created on a single host, the port will clash.'\\n % self.name)\\n\\n def create_and_start(service, number):\\n container = service.create_container(number=number, quiet=True)\\n container.start()\\n return container\\n\\n running_containers = self.containers(stopped=False)\\n num_running = len(running_containers)\\n\\n if desired_num == num_running:\\n # do nothing as we already have the desired number\\n log.info('Desired container number already achieved')\\n return\\n\\n if desired_num > num_running:\\n # we need to start/create until we have desired_num\\n all_containers = self.containers(stopped=True)\\n\\n if num_running != len(all_containers):\\n # we have some stopped containers, let's start them up again\\n stopped_containers = sorted([c for c in all_containers if not c.is_running], key=attrgetter('number'))\\n\\n num\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale.create_and_start\",\n \"text\": \"compose/service.py::Service.scale.create_and_start\\n def create_and_start(service, number):\\n container = service.create_container(number=number, quiet=True)\\n container.start()\\n return container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.create_container\",\n \"text\": \"compose/service.py::Service.create_container\\n def create_container(self,\\n one_off=False,\\n do_build=True,\\n previous_container=None,\\n number=None,\\n quiet=False,\\n **override_options):\\n \\\"\\\"\\\"\\n Create a container for this service. If the image doesn't exist, attempt to pull\\n it.\\n \\\"\\\"\\\"\\n self.ensure_image_exists(do_build=do_build)\\n\\n container_options = self._get_container_create_options(\\n override_options,\\n number or self._next_container_number(one_off=one_off),\\n one_off=one_off,\\n previous_container=previous_container,\\n )\\n\\n if 'name' in container_options and not quiet:\\n log.info(\\\"Creating %s\\\" % container_options['name'])\\n\\n return Container.create(self.client, **container_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.ensure_image_exists\",\n \"text\": \"compose/service.py::Service.ensure_image_exists\\n def ensure_image_exists(self, do_build=True):\\n try:\\n self.image()\\n return\\n except NoSuchImageError:\\n pass\\n\\n if self.can_be_built():\\n if do_build:\\n self.build()\\n else:\\n raise NeedsBuildError(self)\\n else:\\n self.pull()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.image\",\n \"text\": \"compose/service.py::Service.image\\n def image(self):\\n try:\\n return self.client.inspect_image(self.image_name)\\n except APIError as e:\\n if e.response.status_code == 404 and e.explanation and 'No such image' in str(e.explanation):\\n raise NoSuchImageError(\\\"Image '{}' not found\\\".format(self.image_name))\\n else:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.image_name\",\n \"text\": \"compose/service.py::Service.image_name\\n @property\\n def image_name(self):\\n if self.can_be_built():\\n return self.full_name\\n else:\\n return self.options['image']\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.convergence_plan\",\n \"text\": \"compose/service.py::Service.convergence_plan\\n def convergence_plan(self, strategy=ConvergenceStrategy.changed):\\n containers = self.containers(stopped=True)\\n\\n if not containers:\\n return ConvergencePlan('create', [])\\n\\n if strategy is ConvergenceStrategy.never:\\n return ConvergencePlan('start', containers)\\n\\n if (\\n strategy is ConvergenceStrategy.always or\\n self._containers_have_diverged(containers)\\n ):\\n return ConvergencePlan('recreate', containers)\\n\\n stopped = [c for c in containers if not c.is_running]\\n\\n if stopped:\\n return ConvergencePlan('start', stopped)\\n\\n return ConvergencePlan('noop', containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._containers_have_diverged\",\n \"text\": \"compose/service.py::Service._containers_have_diverged\\n def _containers_have_diverged(self, containers):\\n config_hash = None\\n\\n try:\\n config_hash = self.config_hash\\n except NoSuchImageError as e:\\n log.debug(\\n 'Service %s has diverged: %s',\\n self.name, six.text_type(e),\\n )\\n return True\\n\\n has_diverged = False\\n\\n for c in containers:\\n container_config_hash = c.labels.get(LABEL_CONFIG_HASH, None)\\n if container_config_hash != config_hash:\\n log.debug(\\n '%s has diverged: %s != %s',\\n c.name, container_config_hash, config_hash,\\n )\\n has_diverged = True\\n\\n return has_diverged\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.execute_convergence_plan\",\n \"text\": \"compose/service.py::Service.execute_convergence_plan\\n def execute_convergence_plan(self,\\n plan,\\n do_build=True,\\n timeout=DEFAULT_TIMEOUT,\\n detached=False):\\n (action, containers) = plan\\n should_attach_logs = not detached\\n\\n if action == 'create':\\n container = self.create_container(do_build=do_build)\\n\\n if should_attach_logs:\\n container.attach_log_stream()\\n\\n container.start()\\n\\n return [container]\\n\\n elif action == 'recreate':\\n return [\\n self.recreate_container(\\n container,\\n do_build=do_build,\\n timeout=timeout,\\n attach_logs=should_attach_logs\\n )\\n for container in containers\\n ]\\n\\n elif action == 'start':\\n for container in containers:\\n self.start_container_if_stopped(container, attach_logs=should_attach_logs)\\n\\n return containers\\n\\n elif action == 'noop':\\n for c in containers:\\n log.info(\\\"%s is up-to-date\\\" % c.name)\\n\\n return containers\\n\\n else:\\n raise Exception(\\\"Invalid action: {}\\\".format(action))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.recreate_container\",\n \"text\": \"compose/service.py::Service.recreate_container\\n def recreate_container(\\n self,\\n container,\\n do_build=False,\\n timeout=DEFAULT_TIMEOUT,\\n attach_logs=False):\\n \\\"\\\"\\\"Recreate a container.\\n\\n The original container is renamed to a temporary name so that data\\n volumes can be copied to the new container, before the original\\n container is removed.\\n \\\"\\\"\\\"\\n log.info(\\\"Recreating %s\\\" % container.name)\\n\\n container.stop(timeout=timeout)\\n container.rename_to_tmp_name()\\n new_container = self.create_container(\\n do_build=do_build,\\n previous_container=container,\\n number=container.labels.get(LABEL_CONTAINER_NUMBER),\\n quiet=True,\\n )\\n if attach_logs:\\n new_container.attach_log_stream()\\n new_container.start()\\n container.remove()\\n return new_container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start_container_if_stopped\",\n \"text\": \"compose/service.py::Service.start_container_if_stopped\\n def start_container_if_stopped(self, container, attach_logs=False):\\n if not container.is_running:\\n log.info(\\\"Starting %s\\\" % container.name)\\n if attach_logs:\\n container.attach_log_stream()\\n container.start()\\n return container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.remove_duplicate_containers\",\n \"text\": \"compose/service.py::Service.remove_duplicate_containers\\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT):\\n for c in self.duplicate_containers():\\n log.info('Removing %s' % c.name)\\n c.stop(timeout=timeout)\\n c.remove()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.duplicate_containers\",\n \"text\": \"compose/service.py::Service.duplicate_containers\\n def duplicate_containers(self):\\n containers = sorted(\\n self.containers(stopped=True),\\n key=lambda c: c.get('Created'),\\n )\\n\\n numbers = set()\\n\\n for c in containers:\\n if c.number in numbers:\\n yield c\\n else:\\n numbers.add(c.number)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.config_hash\",\n \"text\": \"compose/service.py::Service.config_hash\\n @property\\n def config_hash(self):\\n return json_hash(self.config_dict())\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.config_dict\",\n \"text\": \"compose/service.py::Service.config_dict\\n def config_dict(self):\\n return {\\n 'options': self.options,\\n 'image_id': self.image()['Id'],\\n 'links': self.get_link_names(),\\n 'net': self.net.id,\\n 'volumes_from': [\\n (v.source.name, v.mode) for v in self.volumes_from if isinstance(v.source, Service)\\n ],\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_dependency_names\",\n \"text\": \"compose/service.py::Service.get_dependency_names\\n def get_dependency_names(self):\\n net_name = self.net.service_name\\n return (self.get_linked_service_names() +\\n self.get_volumes_from_names() +\\n ([net_name] if net_name else []))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_linked_service_names\",\n \"text\": \"compose/service.py::Service.get_linked_service_names\\n def get_linked_service_names(self):\\n return [service.name for (service, _) in self.links]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_link_names\",\n \"text\": \"compose/service.py::Service.get_link_names\\n def get_link_names(self):\\n return [(service.name, alias) for service, alias in self.links]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_volumes_from_names\",\n \"text\": \"compose/service.py::Service.get_volumes_from_names\\n def get_volumes_from_names(self):\\n return [s.source.name for s in self.volumes_from if isinstance(s.source, Service)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_container_name\",\n \"text\": \"compose/service.py::Service.get_container_name\\n def get_container_name(self, number, one_off=False):\\n # TODO: Implement issue #652 here\\n return build_container_name(self.project, self.name, number, one_off)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._next_container_number\",\n \"text\": \"compose/service.py::Service._next_container_number\\n def _next_container_number(self, one_off=False):\\n containers = filter(None, [\\n Container.from_ps(self.client, container)\\n for container in self.client.containers(\\n all=True,\\n filters={'label': self.labels(one_off=one_off)})\\n ])\\n numbers = [c.number for c in containers]\\n return 1 if not numbers else max(numbers) + 1\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_links\",\n \"text\": \"compose/service.py::Service._get_links\\n def _get_links(self, link_to_self):\\n if self.use_networking:\\n return []\\n\\n links = []\\n for service, link_name in self.links:\\n for container in service.containers():\\n links.append((container.name, link_name or service.name))\\n links.append((container.name, container.name))\\n links.append((container.name, container.name_without_project))\\n if link_to_self:\\n for container in self.containers():\\n links.append((container.name, self.name))\\n links.append((container.name, container.name))\\n links.append((container.name, container.name_without_project))\\n for external_link in self.options.get('external_links') or []:\\n if ':' not in external_link:\\n link_name = external_link\\n else:\\n external_link, link_name = external_link.split(':')\\n links.append((external_link, link_name))\\n return links\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_volumes_from\",\n \"text\": \"compose/service.py::Service._get_volumes_from\\n def _get_volumes_from(self):\\n volumes_from = []\\n for volume_from_spec in self.volumes_from:\\n volumes = build_volume_from(volume_from_spec)\\n volumes_from.extend(volumes)\\n\\n return volumes_from\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_container_create_options\",\n \"text\": \"compose/service.py::Service._get_container_create_options\\n def _get_container_create_options(\\n self,\\n override_options,\\n number,\\n one_off=False,\\n previous_container=None):\\n add_config_hash = (not one_off and not override_options)\\n\\n container_options = dict(\\n (k, self.options[k])\\n for k in DOCKER_CONFIG_KEYS if k in self.options)\\n container_options.update(override_options)\\n\\n if self.custom_container_name() and not one_off:\\n container_options['name'] = self.custom_container_name()\\n elif not container_options.get('name'):\\n container_options['name'] = self.get_container_name(number, one_off)\\n\\n if 'detach' not in container_options:\\n container_options['detach'] = True\\n\\n # If a qualified hostname was given, split it into an\\n # unqualified hostname and a domainname unless domainname\\n # was also given explicitly. This matches the behavior of\\n # the official Docker CLI in that scenario.\\n if ('hostname' in container_options\\n and 'domainname' not in container_options\\n and '.' in container_options['hostname']):\\n parts = container_options['hostname'].partition('.')\\n container_options['hostname'] = parts[0]\\n container_options['domainname'] = parts[2]\\n\\n if 'ports' in container_options or 'expose' in self.options:\\n ports = []\\n all_ports = container_options.get('ports', []) + self.options.get('expose', [])\\n for port_range in all_ports:\\n internal_range, _ = split_port(port_range)\\n for port in internal_range:\\n port = str(port)\\n if '/' in port:\\n port = tuple(port.split('/'))\\n ports.append(port)\\n container_options['ports'] = ports\\n\\n override_options['binds'] = merge_volume_b\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_container_host_config\",\n \"text\": \"compose/service.py::Service._get_container_host_config\\n def _get_container_host_config(self, override_options, one_off=False):\\n options = dict(self.options, **override_options)\\n\\n log_config = LogConfig(\\n type=options.get('log_driver', \\\"\\\"),\\n config=options.get('log_opt', None)\\n )\\n return self.client.create_host_config(\\n links=self._get_links(link_to_self=one_off),\\n port_bindings=build_port_bindings(options.get('ports') or []),\\n binds=options.get('binds'),\\n volumes_from=self._get_volumes_from(),\\n privileged=options.get('privileged', False),\\n network_mode=self.net.mode,\\n devices=options.get('devices'),\\n dns=options.get('dns'),\\n dns_search=options.get('dns_search'),\\n restart_policy=options.get('restart'),\\n cap_add=options.get('cap_add'),\\n cap_drop=options.get('cap_drop'),\\n mem_limit=options.get('mem_limit'),\\n memswap_limit=options.get('memswap_limit'),\\n ulimits=build_ulimits(options.get('ulimits')),\\n log_config=log_config,\\n extra_hosts=options.get('extra_hosts'),\\n read_only=options.get('read_only'),\\n pid_mode=options.get('pid'),\\n security_opt=options.get('security_opt'),\\n ipc_mode=options.get('ipc'),\\n cgroup_parent=options.get('cgroup_parent'),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.build\",\n \"text\": \"compose/service.py::Service.build\\n def build(self, no_cache=False, pull=False, force_rm=False):\\n log.info('Building %s' % self.name)\\n\\n path = self.options['build']\\n # python2 os.path() doesn't support unicode, so we need to encode it to\\n # a byte string\\n if not six.PY3:\\n path = path.encode('utf8')\\n\\n build_output = self.client.build(\\n path=path,\\n tag=self.image_name,\\n stream=True,\\n rm=True,\\n forcerm=force_rm,\\n pull=pull,\\n nocache=no_cache,\\n dockerfile=self.options.get('dockerfile', None),\\n )\\n\\n try:\\n all_events = stream_output(build_output, sys.stdout)\\n except StreamOutputError as e:\\n raise BuildError(self, six.text_type(e))\\n\\n # Ensure the HTTP connection is not reused for another\\n # streaming command, as the Docker daemon can sometimes\\n # complain about it\\n self.client.close()\\n\\n image_id = None\\n\\n for event in all_events:\\n if 'stream' in event:\\n match = re.search(r'Successfully built ([0-9a-f]+)', event.get('stream', ''))\\n if match:\\n image_id = match.group(1)\\n\\n if image_id is None:\\n raise BuildError(self, event if all_events else 'Unknown')\\n\\n return image_id\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.can_be_built\",\n \"text\": \"compose/service.py::Service.can_be_built\\n def can_be_built(self):\\n return 'build' in self.options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.full_name\",\n \"text\": \"compose/service.py::Service.full_name\\n @property\\n def full_name(self):\\n \\\"\\\"\\\"\\n The tag to give to images built for this service.\\n \\\"\\\"\\\"\\n return '%s_%s' % (self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::get_output_stream\",\n \"text\": \"compose/utils.py::get_output_stream\\ndef get_output_stream(stream):\\n if six.PY3:\\n return stream\\n return codecs.getwriter('utf-8')(stream)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::stream_as_text\",\n \"text\": \"compose/utils.py::stream_as_text\\ndef stream_as_text(stream):\\n \\\"\\\"\\\"Given a stream of bytes or text, if any of the items in the stream\\n are bytes convert them to text.\\n\\n This function can be removed once docker-py returns text streams instead\\n of byte streams.\\n \\\"\\\"\\\"\\n for data in stream:\\n if not isinstance(data, six.text_type):\\n data = data.decode('utf-8', 'replace')\\n yield data\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::line_splitter\",\n \"text\": \"compose/utils.py::line_splitter\\ndef line_splitter(buffer, separator=u'\\\\n'):\\n index = buffer.find(six.text_type(separator))\\n if index == -1:\\n return None\\n return buffer[:index + 1], buffer[index + 1:]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::split_buffer\",\n \"text\": \"compose/utils.py::split_buffer\\ndef split_buffer(stream, splitter=None, decoder=lambda a: a):\\n \\\"\\\"\\\"Given a generator which yields strings and a splitter function,\\n joins all input, splits on the separator and yields each chunk.\\n\\n Unlike string.split(), each chunk includes the trailing\\n separator, except for the last one if none was found on the end\\n of the input.\\n \\\"\\\"\\\"\\n splitter = splitter or line_splitter\\n buffered = six.text_type('')\\n\\n for data in stream_as_text(stream):\\n buffered += data\\n while True:\\n buffer_split = splitter(buffered)\\n if buffer_split is None:\\n break\\n\\n item, buffered = buffer_split\\n yield item\\n\\n if buffered:\\n yield decoder(buffered)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::json_splitter\",\n \"text\": \"compose/utils.py::json_splitter\\ndef json_splitter(buffer):\\n \\\"\\\"\\\"Attempt to parse a json object from a buffer. If there is at least one\\n object, return it and the rest of the buffer, otherwise return None.\\n \\\"\\\"\\\"\\n try:\\n obj, index = json_decoder.raw_decode(buffer)\\n rest = buffer[json.decoder.WHITESPACE.match(buffer, index).end():]\\n return obj, rest\\n except ValueError:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::json_stream\",\n \"text\": \"compose/utils.py::json_stream\\ndef json_stream(stream):\\n \\\"\\\"\\\"Given a stream of text, return a stream of json objects.\\n This handles streams which are inconsistently buffered (some entries may\\n be newline delimited, and others are not).\\n \\\"\\\"\\\"\\n return split_buffer(stream, json_splitter, json_decoder.decode)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::json_hash\",\n \"text\": \"compose/utils.py::json_hash\\ndef json_hash(obj):\\n dump = json.dumps(obj, sort_keys=True, separators=(',', ':'))\\n h = hashlib.sha256()\\n h.update(dump.encode('utf8'))\\n return h.hexdigest()\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":59,"cells":{"instance_id":{"kind":"string","value":"Qiskit__qiskit-10622"},"repo":{"kind":"string","value":"Qiskit/qiskit"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Create `layout` stage (trivial, dense, sabre, noise_adaptive) plugin\nCreate a plugin for the builtin layout stage methods. One particular oddity for this stage is it's not just the straight methods, but also a default method will be needed. The default stage will integrate vf2layout (and initial layout too for level 1).\n"},"gold_files":{"kind":"list like","value":["qiskit/transpiler/preset_passmanagers/builtin_plugins.py","qiskit/transpiler/preset_passmanagers/level0.py","qiskit/transpiler/preset_passmanagers/level1.py","qiskit/transpiler/preset_passmanagers/level2.py","qiskit/transpiler/preset_passmanagers/level3.py","qiskit/transpiler/preset_passmanagers/plugin.py","setup.py"],"string":"[\n \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py\",\n \"qiskit/transpiler/preset_passmanagers/level0.py\",\n \"qiskit/transpiler/preset_passmanagers/level1.py\",\n \"qiskit/transpiler/preset_passmanagers/level2.py\",\n \"qiskit/transpiler/preset_passmanagers/level3.py\",\n \"qiskit/transpiler/preset_passmanagers/plugin.py\",\n \"setup.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["qiskit/transpiler/preset_passmanagers/builtin_plugins.py::<module>","qiskit/transpiler/preset_passmanagers/builtin_plugins.py::DefaultSchedulingPassManager.pass_manager","qiskit/transpiler/preset_passmanagers/level0.py::<module>","qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager","qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager._choose_layout_condition","qiskit/transpiler/preset_passmanagers/level1.py::<module>","qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager","qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._choose_layout_condition","qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._layout_not_perfect","qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._vf2_match_not_found","qiskit/transpiler/preset_passmanagers/level2.py::<module>","qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager","qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._choose_layout_condition","qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._vf2_match_not_found","qiskit/transpiler/preset_passmanagers/level3.py::<module>","qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager","qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._choose_layout_condition","qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._vf2_match_not_found","qiskit/transpiler/preset_passmanagers/plugin.py::<module>","setup.py::<module>"],"string":"[\n \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::<module>\",\n \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::DefaultSchedulingPassManager.pass_manager\",\n \"qiskit/transpiler/preset_passmanagers/level0.py::<module>\",\n \"qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager\",\n \"qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager._choose_layout_condition\",\n \"qiskit/transpiler/preset_passmanagers/level1.py::<module>\",\n \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager\",\n \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._choose_layout_condition\",\n \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._layout_not_perfect\",\n \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._vf2_match_not_found\",\n \"qiskit/transpiler/preset_passmanagers/level2.py::<module>\",\n \"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager\",\n \"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._choose_layout_condition\",\n \"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._vf2_match_not_found\",\n \"qiskit/transpiler/preset_passmanagers/level3.py::<module>\",\n \"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager\",\n \"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._choose_layout_condition\",\n \"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._vf2_match_not_found\",\n \"qiskit/transpiler/preset_passmanagers/plugin.py::<module>\",\n \"setup.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"qiskit/compiler/transpiler.py","text":"qiskit/compiler/transpiler.py\ndef transpile(circuits: _CircuitT, backend: Optional[Backend]=None, basis_gates: Optional[List[str]]=None, inst_map: Optional[List[InstructionScheduleMap]]=None, coupling_map: Optional[Union[CouplingMap, List[List[int]]]]=None, backend_properties: Optional[BackendProperties]=None, initial_layout: Optional[Union[Layout, Dict, List]]=None, layout_method: Optional[str]=None, routing_method: Optional[str]=None, translation_method: Optional[str]=None, scheduling_method: Optional[str]=None, instruction_durations: Optional[InstructionDurationsType]=None, dt: Optional[float]=None, approximation_degree: Optional[float]=1.0, timing_constraints: Optional[Dict[str, int]]=None, seed_transpiler: Optional[int]=None, optimization_level: Optional[int]=None, callback: Optional[Callable[[BasePass, DAGCircuit, float, PropertySet, int], Any]]=None, output_name: Optional[Union[str, List[str]]]=None, unitary_synthesis_method: str='default', unitary_synthesis_plugin_config: Optional[dict]=None, target: Optional[Target]=None, hls_config: Optional[HLSConfig]=None, init_method: Optional[str]=None, optimization_method: Optional[str]=None, ignore_backend_supplied_default_methods: bool=False) # Transpile one or more circuits, according to some desired transpilation targets.\ndef _check_circuits_coupling_map(circuits, cmap, backend)\ndef _log_transpile_time(start_time, end_time)\ndef _parse_inst_map(inst_map, backend)\ndef _parse_coupling_map(coupling_map, backend)\ndef _parse_initial_layout(initial_layout)\ndef _parse_instruction_durations(backend, inst_durations, dt, circuit) # Create a list of ``InstructionDuration``s. If ``inst_durations`` is provided,\ndef _parse_approximation_degree(approximation_degree)\ndef _parse_output_name(output_name, circuits)\ndef _parse_timing_constraints(backend, timing_constraints)","label":0},{"cid":"qiskit/transpiler/passmanager_config.py","text":"qiskit/transpiler/passmanager_config.py\nclass PassManagerConfig # Pass Manager Configuration.\n def __init__(self, initial_layout=None, basis_gates=None, inst_map=None, coupling_map=None, layout_method=None, routing_method=None, translation_method=None, scheduling_method=None, instruction_durations=None, backend_properties=None, approximation_degree=None, seed_transpiler=None, timing_constraints=None, unitary_synthesis_method='default', unitary_synthesis_plugin_config=None, target=None, hls_config=None, init_method=None, optimization_method=None) # Initialize a PassManagerConfig object\n def from_backend(cls, backend, _skip_target=False, **pass_manager_options) # Construct a configuration based on a backend and user input.\n def __str__(self)","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/__init__.py","text":"qiskit/transpiler/preset_passmanagers/__init__.py\ndef generate_preset_pass_manager(optimization_level, backend=None, target=None, basis_gates=None, inst_map=None, coupling_map=None, instruction_durations=None, backend_properties=None, timing_constraints=None, initial_layout=None, layout_method=None, routing_method=None, translation_method=None, scheduling_method=None, approximation_degree=None, seed_transpiler=None, unitary_synthesis_method='default', unitary_synthesis_plugin_config=None, hls_config=None, init_method=None, optimization_method=None, *, _skip_target=False) # Generate a preset :class:`~.PassManager`","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py\nclass BasisTranslatorPassManager # Plugin class for translation stage with :class:`~.BasisTranslator`\n def pass_manager(self, pass_manager_config, optimization_level=None)\nclass UnrollerPassManager # Plugin class for translation stage with :class:`~.BasisTranslator`\n def pass_manager(self, pass_manager_config, optimization_level=None)\nclass UnitarySynthesisPassManager # Plugin class for translation stage with :class:`~.BasisTranslator`\n def pass_manager(self, pass_manager_config, optimization_level=None)\nclass BasicSwapPassManager # Plugin class for routing stage with :class:`~.BasicSwap`\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build routing stage PassManager.\nclass StochasticSwapPassManager # Plugin class for routing stage with :class:`~.StochasticSwap`\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build routing stage PassManager.\nclass LookaheadSwapPassManager # Plugin class for routing stage with :class:`~.LookaheadSwap`\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build routing stage PassManager.\nclass SabreSwapPassManager # Plugin class for routing stage with :class:`~.SabreSwap`\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build routing stage PassManager.\nclass NoneRoutingPassManager # Plugin class for routing stage with error on routing.\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build routing stage PassManager.\nclass AlapSchedulingPassManager # Plugin class for alap scheduling stage.\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build scheduling stage PassManager\nclass AsapSchedulingPassManager # Plugin class for alap scheduling stage.\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build scheduling stage PassManager\nclass DefaultSchedulingPassManager # Plugin class for alap scheduling stage.\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build scheduling stage PassManager","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/common.py","text":"qiskit/transpiler/preset_passmanagers/common.py\ndef _has_control_flow(property_set)\ndef _without_control_flow(property_set)\nclass _InvalidControlFlowForBackend\n def __init__(self, basis_gates=(), target=None)\n def message(self, property_set) # Create an error message for the given property set.\n def condition(self, property_set) # Checkable condition for the given property set.\ndef generate_control_flow_options_check(layout_method=None, routing_method=None, translation_method=None, optimization_method=None, scheduling_method=None, basis_gates=(), target=None) # Generate a pass manager that, when run on a DAG that contains control flow, fail\ndef generate_error_on_control_flow(message) # Get a pass manager that always raises an error if control flow is present in a g\ndef if_has_control_flow_else(if_present, if_absent) # Generate a pass manager that will run the passes in ``if_present`` if the given \ndef generate_unroll_3q(target, basis_gates=None, approximation_degree=None, unitary_synthesis_method='default', unitary_synthesis_plugin_config=None, hls_config=None) # Generate an unroll >3q :class:`~qiskit.transpiler.PassManager`\ndef generate_embed_passmanager(coupling_map) # Generate a layout embedding :class:`~qiskit.transpiler.PassManager`\ndef _layout_not_perfect(property_set) # Return ``True`` if the first attempt at layout has been checked and found to be \ndef _apply_post_layout_condition(property_set)\ndef generate_routing_passmanager(routing_pass, target, coupling_map=None, vf2_call_limit=None, backend_properties=None, seed_transpiler=None, check_trivial=False, use_barrier_before_measurement=True, vf2_max_trials=None) # Generate a routing :class:`~qiskit.transpiler.PassManager`\n def _run_post_layout_condition(property_set)\n def _swap_condition(property_set)\ndef generate_pre_op_passmanager(target=None, coupling_map=None, remove_reset_in_zero=False) # Generate a pre-optimization loop :class:`~qiskit.transpiler.PassManager`\ndef generate_translation_passmanager(target, basis_gates=None, method='translator', approximation_degree=None, coupling_map=None, backend_props=None, unitary_synthesis_method='default', unitary_synthesis_plugin_config=None, hls_config=None) # Generate a basis translation :class:`~qiskit.transpiler.PassManager`\ndef generate_scheduling(instruction_durations, scheduling_method, timing_constraints, inst_map, target=None) # Generate a post optimization scheduling :class:`~qiskit.transpiler.PassManager`\ndef get_vf2_call_limit(optimization_level: int, layout_method: Optional[str]=None, initial_layout: Optional[Layout]=None) # Get the vf2 call limit for vf2 based layout passes.\ndef get_vf2_limits(optimization_level: int, layout_method: Optional[str]=None, initial_layout: Optional[Layout]=None) # Get the VF2 limits for VF2-based layout passes.","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/level0.py","text":"qiskit/transpiler/preset_passmanagers/level0.py\ndef level_0_pass_manager(pass_manager_config: PassManagerConfig) # Level 0 pass manager: no explicit optimization other than mapping to backend.\n def _choose_layout_condition(property_set)","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level1.py","text":"qiskit/transpiler/preset_passmanagers/level1.py\ndef level_1_pass_manager(pass_manager_config: PassManagerConfig) # Level 1 pass manager: light optimization by simple adjacent gate collapsing.\n def _choose_layout_condition(property_set)\n def _layout_not_perfect(property_set) # Return ``True`` if the first attempt at layout has been checked and found to be\n def _vf2_match_not_found(property_set)\n def _opt_control(property_set)","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level2.py","text":"qiskit/transpiler/preset_passmanagers/level2.py\ndef level_2_pass_manager(pass_manager_config: PassManagerConfig) # Level 2 pass manager: medium optimization by initial layout selection and\n def _choose_layout_condition(property_set)\n def _vf2_match_not_found(property_set)\n def _opt_control(property_set)","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level3.py","text":"qiskit/transpiler/preset_passmanagers/level3.py\ndef level_3_pass_manager(pass_manager_config: PassManagerConfig) # Level 3 pass manager: heavy optimization by noise adaptive qubit mapping and\n def _choose_layout_condition(property_set)\n def _vf2_match_not_found(property_set)\n def _opt_control(property_set)","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/plugin.py","text":"qiskit/transpiler/preset_passmanagers/plugin.py\nclass PassManagerStagePlugin # A ``PassManagerStagePlugin`` is a plugin interface object for using custom\n def pass_manager(self, pass_manager_config: PassManagerConfig, optimization_level: Optional[int]=None) # This method is designed to return a :class:`~.PassManager` for the stage this im\nclass PassManagerStagePluginManager # Manager class for preset pass manager stage plugins.\n def __init__(self)\n def get_passmanager_stage(self, stage_name: str, plugin_name: str, pm_config: PassManagerConfig, optimization_level=None) # Get a stage\n def _build_pm(self, stage_obj: stevedore.ExtensionManager, stage_name: str, plugin_name: str, pm_config: PassManagerConfig, optimization_level: Optional[int]=None)\ndef list_stage_plugins(stage_name: str) # Get a list of installed plugins for a stage.\ndef passmanager_stage_plugins(stage: str) # Return a dict with, for each stage name, the class type of the plugin.","label":1},{"cid":"setup.py","text":"setup.py","label":1}],"string":"[\n {\n \"cid\": \"qiskit/compiler/transpiler.py\",\n \"text\": \"qiskit/compiler/transpiler.py\\ndef transpile(circuits: _CircuitT, backend: Optional[Backend]=None, basis_gates: Optional[List[str]]=None, inst_map: Optional[List[InstructionScheduleMap]]=None, coupling_map: Optional[Union[CouplingMap, List[List[int]]]]=None, backend_properties: Optional[BackendProperties]=None, initial_layout: Optional[Union[Layout, Dict, List]]=None, layout_method: Optional[str]=None, routing_method: Optional[str]=None, translation_method: Optional[str]=None, scheduling_method: Optional[str]=None, instruction_durations: Optional[InstructionDurationsType]=None, dt: Optional[float]=None, approximation_degree: Optional[float]=1.0, timing_constraints: Optional[Dict[str, int]]=None, seed_transpiler: Optional[int]=None, optimization_level: Optional[int]=None, callback: Optional[Callable[[BasePass, DAGCircuit, float, PropertySet, int], Any]]=None, output_name: Optional[Union[str, List[str]]]=None, unitary_synthesis_method: str='default', unitary_synthesis_plugin_config: Optional[dict]=None, target: Optional[Target]=None, hls_config: Optional[HLSConfig]=None, init_method: Optional[str]=None, optimization_method: Optional[str]=None, ignore_backend_supplied_default_methods: bool=False) # Transpile one or more circuits, according to some desired transpilation targets.\\ndef _check_circuits_coupling_map(circuits, cmap, backend)\\ndef _log_transpile_time(start_time, end_time)\\ndef _parse_inst_map(inst_map, backend)\\ndef _parse_coupling_map(coupling_map, backend)\\ndef _parse_initial_layout(initial_layout)\\ndef _parse_instruction_durations(backend, inst_durations, dt, circuit) # Create a list of ``InstructionDuration``s. If ``inst_durations`` is provided,\\ndef _parse_approximation_degree(approximation_degree)\\ndef _parse_output_name(output_name, circuits)\\ndef _parse_timing_constraints(backend, timing_constraints)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/passmanager_config.py\",\n \"text\": \"qiskit/transpiler/passmanager_config.py\\nclass PassManagerConfig # Pass Manager Configuration.\\n def __init__(self, initial_layout=None, basis_gates=None, inst_map=None, coupling_map=None, layout_method=None, routing_method=None, translation_method=None, scheduling_method=None, instruction_durations=None, backend_properties=None, approximation_degree=None, seed_transpiler=None, timing_constraints=None, unitary_synthesis_method='default', unitary_synthesis_plugin_config=None, target=None, hls_config=None, init_method=None, optimization_method=None) # Initialize a PassManagerConfig object\\n def from_backend(cls, backend, _skip_target=False, **pass_manager_options) # Construct a configuration based on a backend and user input.\\n def __str__(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/__init__.py\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/__init__.py\\ndef generate_preset_pass_manager(optimization_level, backend=None, target=None, basis_gates=None, inst_map=None, coupling_map=None, instruction_durations=None, backend_properties=None, timing_constraints=None, initial_layout=None, layout_method=None, routing_method=None, translation_method=None, scheduling_method=None, approximation_degree=None, seed_transpiler=None, unitary_synthesis_method='default', unitary_synthesis_plugin_config=None, hls_config=None, init_method=None, optimization_method=None, *, _skip_target=False) # Generate a preset :class:`~.PassManager`\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py\\nclass BasisTranslatorPassManager # Plugin class for translation stage with :class:`~.BasisTranslator`\\n def pass_manager(self, pass_manager_config, optimization_level=None)\\nclass UnrollerPassManager # Plugin class for translation stage with :class:`~.BasisTranslator`\\n def pass_manager(self, pass_manager_config, optimization_level=None)\\nclass UnitarySynthesisPassManager # Plugin class for translation stage with :class:`~.BasisTranslator`\\n def pass_manager(self, pass_manager_config, optimization_level=None)\\nclass BasicSwapPassManager # Plugin class for routing stage with :class:`~.BasicSwap`\\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build routing stage PassManager.\\nclass StochasticSwapPassManager # Plugin class for routing stage with :class:`~.StochasticSwap`\\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build routing stage PassManager.\\nclass LookaheadSwapPassManager # Plugin class for routing stage with :class:`~.LookaheadSwap`\\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build routing stage PassManager.\\nclass SabreSwapPassManager # Plugin class for routing stage with :class:`~.SabreSwap`\\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build routing stage PassManager.\\nclass NoneRoutingPassManager # Plugin class for routing stage with error on routing.\\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build routing stage PassManager.\\nclass AlapSchedulingPassManager # Plugin class for alap scheduling stage.\\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build scheduling stage PassManager\\nclass AsapSchedulingPassManager # Plugin class for alap scheduling stage.\\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build scheduling stage PassManager\\nclass DefaultSchedulingPassManager # Plugin class for alap scheduling stage.\\n def pass_manager(self, pass_manager_config, optimization_level=None) # Build scheduling stage PassManager\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py\\ndef _has_control_flow(property_set)\\ndef _without_control_flow(property_set)\\nclass _InvalidControlFlowForBackend\\n def __init__(self, basis_gates=(), target=None)\\n def message(self, property_set) # Create an error message for the given property set.\\n def condition(self, property_set) # Checkable condition for the given property set.\\ndef generate_control_flow_options_check(layout_method=None, routing_method=None, translation_method=None, optimization_method=None, scheduling_method=None, basis_gates=(), target=None) # Generate a pass manager that, when run on a DAG that contains control flow, fail\\ndef generate_error_on_control_flow(message) # Get a pass manager that always raises an error if control flow is present in a g\\ndef if_has_control_flow_else(if_present, if_absent) # Generate a pass manager that will run the passes in ``if_present`` if the given \\ndef generate_unroll_3q(target, basis_gates=None, approximation_degree=None, unitary_synthesis_method='default', unitary_synthesis_plugin_config=None, hls_config=None) # Generate an unroll >3q :class:`~qiskit.transpiler.PassManager`\\ndef generate_embed_passmanager(coupling_map) # Generate a layout embedding :class:`~qiskit.transpiler.PassManager`\\ndef _layout_not_perfect(property_set) # Return ``True`` if the first attempt at layout has been checked and found to be \\ndef _apply_post_layout_condition(property_set)\\ndef generate_routing_passmanager(routing_pass, target, coupling_map=None, vf2_call_limit=None, backend_properties=None, seed_transpiler=None, check_trivial=False, use_barrier_before_measurement=True, vf2_max_trials=None) # Generate a routing :class:`~qiskit.transpiler.PassManager`\\n def _run_post_layout_condition(property_set)\\n def _swap_condition(property_set)\\ndef generate_pre_op_passmanager(target=None, coupling_map=None, remove_reset_in_zero=False) # Generate a pre-optimization loop :class:`~qiskit.transpiler.PassManager`\\ndef generate_translation_passmanager(target, basis_gates=None, method='translator', approximation_degree=None, coupling_map=None, backend_props=None, unitary_synthesis_method='default', unitary_synthesis_plugin_config=None, hls_config=None) # Generate a basis translation :class:`~qiskit.transpiler.PassManager`\\ndef generate_scheduling(instruction_durations, scheduling_method, timing_constraints, inst_map, target=None) # Generate a post optimization scheduling :class:`~qiskit.transpiler.PassManager`\\ndef get_vf2_call_limit(optimization_level: int, layout_method: Optional[str]=None, initial_layout: Optional[Layout]=None) # Get the vf2 call limit for vf2 based layout passes.\\ndef get_vf2_limits(optimization_level: int, layout_method: Optional[str]=None, initial_layout: Optional[Layout]=None) # Get the VF2 limits for VF2-based layout passes.\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level0.py\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level0.py\\ndef level_0_pass_manager(pass_manager_config: PassManagerConfig) # Level 0 pass manager: no explicit optimization other than mapping to backend.\\n def _choose_layout_condition(property_set)\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level1.py\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level1.py\\ndef level_1_pass_manager(pass_manager_config: PassManagerConfig) # Level 1 pass manager: light optimization by simple adjacent gate collapsing.\\n def _choose_layout_condition(property_set)\\n def _layout_not_perfect(property_set) # Return ``True`` if the first attempt at layout has been checked and found to be\\n def _vf2_match_not_found(property_set)\\n def _opt_control(property_set)\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level2.py\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level2.py\\ndef level_2_pass_manager(pass_manager_config: PassManagerConfig) # Level 2 pass manager: medium optimization by initial layout selection and\\n def _choose_layout_condition(property_set)\\n def _vf2_match_not_found(property_set)\\n def _opt_control(property_set)\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level3.py\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level3.py\\ndef level_3_pass_manager(pass_manager_config: PassManagerConfig) # Level 3 pass manager: heavy optimization by noise adaptive qubit mapping and\\n def _choose_layout_condition(property_set)\\n def _vf2_match_not_found(property_set)\\n def _opt_control(property_set)\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/plugin.py\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/plugin.py\\nclass PassManagerStagePlugin # A ``PassManagerStagePlugin`` is a plugin interface object for using custom\\n def pass_manager(self, pass_manager_config: PassManagerConfig, optimization_level: Optional[int]=None) # This method is designed to return a :class:`~.PassManager` for the stage this im\\nclass PassManagerStagePluginManager # Manager class for preset pass manager stage plugins.\\n def __init__(self)\\n def get_passmanager_stage(self, stage_name: str, plugin_name: str, pm_config: PassManagerConfig, optimization_level=None) # Get a stage\\n def _build_pm(self, stage_obj: stevedore.ExtensionManager, stage_name: str, plugin_name: str, pm_config: PassManagerConfig, optimization_level: Optional[int]=None)\\ndef list_stage_plugins(stage_name: str) # Get a list of installed plugins for a stage.\\ndef passmanager_stage_plugins(stage: str) # Return a dict with, for each stage name, the class type of the plugin.\",\n \"label\": 1\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"qiskit/compiler/transpiler.py::transpile","text":"qiskit/compiler/transpiler.py::transpile\ndef transpile( # pylint: disable=too-many-return-statements\n circuits: _CircuitT,\n backend: Optional[Backend] = None,\n basis_gates: Optional[List[str]] = None,\n inst_map: Optional[List[InstructionScheduleMap]] = None,\n coupling_map: Optional[Union[CouplingMap, List[List[int]]]] = None,\n backend_properties: Optional[BackendProperties] = None,\n initial_layout: Optional[Union[Layout, Dict, List]] = None,\n layout_method: Optional[str] = None,\n routing_method: Optional[str] = None,\n translation_method: Optional[str] = None,\n scheduling_method: Optional[str] = None,\n instruction_durations: Optional[InstructionDurationsType] = None,\n dt: Optional[float] = None,\n approximation_degree: Optional[float] = 1.0,\n timing_constraints: Optional[Dict[str, int]] = None,\n seed_transpiler: Optional[int] = None,\n optimization_level: Optional[int] = None,\n callback: Optional[Callable[[BasePass, DAGCircuit, float, PropertySet, int], Any]] = None,\n output_name: Optional[Union[str, List[str]]] = None,\n unitary_synthesis_method: str = \"default\",\n unitary_synthesis_plugin_config: Optional[dict] = None,\n target: Optional[Target] = None,\n hls_config: Optional[HLSConfig] = None,\n init_method: Optional[str] = None,\n optimization_method: Optional[str] = None,\n ignore_backend_supplied_default_methods: bool = False,\n) -> _CircuitT:\n \"\"\"Transpile one or more circuits, according to some desired transpilation targets.\n\n Transpilation is potentially done in parallel using multiprocessing when ``circuits``\n is a list with > 1 :class:`~.QuantumCircuit` object depending on the local environment\n and configuration.\n\n Args:\n circuits: Circuit(s) to transpile\n backend: If set, the transpiler will compile the input circuit to this target\n device. If any other option is explicitly set (e.g., ``coupling_map``), it\n will override the backend's.\n","label":0},{"cid":"qiskit/compiler/transpiler.py::_check_circuits_coupling_map","text":"qiskit/compiler/transpiler.py::_check_circuits_coupling_map\ndef _check_circuits_coupling_map(circuits, cmap, backend):\n # Check circuit width against number of qubits in coupling_map(s)\n max_qubits = None\n if cmap is not None:\n max_qubits = cmap.size()\n elif backend is not None:\n backend_version = getattr(backend, \"version\", 0)\n if backend_version <= 1:\n if not backend.configuration().simulator:\n max_qubits = backend.configuration().n_qubits\n else:\n max_qubits = None\n else:\n max_qubits = backend.num_qubits\n for circuit in circuits:\n # If coupling_map is not None or num_qubits == 1\n num_qubits = len(circuit.qubits)\n if max_qubits is not None and (num_qubits > max_qubits):\n raise TranspilerError(\n f\"Number of qubits ({num_qubits}) in {circuit.name} \"\n f\"is greater than maximum ({max_qubits}) in the coupling_map\"\n )","label":0},{"cid":"qiskit/compiler/transpiler.py::_log_transpile_time","text":"qiskit/compiler/transpiler.py::_log_transpile_time\ndef _log_transpile_time(start_time, end_time):\n log_msg = \"Total Transpile Time - %.5f (ms)\" % ((end_time - start_time) * 1000)\n logger.info(log_msg)","label":0},{"cid":"qiskit/compiler/transpiler.py::_parse_inst_map","text":"qiskit/compiler/transpiler.py::_parse_inst_map\ndef _parse_inst_map(inst_map, backend):\n # try getting inst_map from user, else backend\n if inst_map is None:\n backend_version = getattr(backend, \"version\", 0)\n if backend_version <= 1:\n if hasattr(backend, \"defaults\"):\n inst_map = getattr(backend.defaults(), \"instruction_schedule_map\", None)\n else:\n inst_map = backend.target.instruction_schedule_map()\n return inst_map","label":0},{"cid":"qiskit/compiler/transpiler.py::_parse_coupling_map","text":"qiskit/compiler/transpiler.py::_parse_coupling_map\ndef _parse_coupling_map(coupling_map, backend):\n # try getting coupling_map from user, else backend\n if coupling_map is None:\n backend_version = getattr(backend, \"version\", 0)\n if backend_version <= 1:\n if getattr(backend, \"configuration\", None):\n configuration = backend.configuration()\n if hasattr(configuration, \"coupling_map\") and configuration.coupling_map:\n coupling_map = CouplingMap(configuration.coupling_map)\n else:\n coupling_map = backend.coupling_map\n\n # coupling_map could be None, or a list of lists, e.g. [[0, 1], [2, 1]]\n if coupling_map is None or isinstance(coupling_map, CouplingMap):\n return coupling_map\n if isinstance(coupling_map, list) and all(\n isinstance(i, list) and len(i) == 2 for i in coupling_map\n ):\n return CouplingMap(coupling_map)\n else:\n raise TranspilerError(\n \"Only a single input coupling map can be used with transpile() if you need to \"\n \"target different coupling maps for different circuits you must call transpile() \"\n \"multiple times\"\n )","label":0},{"cid":"qiskit/compiler/transpiler.py::_parse_initial_layout","text":"qiskit/compiler/transpiler.py::_parse_initial_layout\ndef _parse_initial_layout(initial_layout):\n # initial_layout could be None, or a list of ints, e.g. [0, 5, 14]\n # or a list of tuples/None e.g. [qr[0], None, qr[1]] or a dict e.g. {qr[0]: 0}\n if initial_layout is None or isinstance(initial_layout, Layout):\n return initial_layout\n if isinstance(initial_layout, dict):\n return Layout(initial_layout)\n if all(phys is None or isinstance(phys, Qubit) for phys in initial_layout):\n return Layout.from_qubit_list(initial_layout)\n else:\n return initial_layout","label":0},{"cid":"qiskit/compiler/transpiler.py::_parse_instruction_durations","text":"qiskit/compiler/transpiler.py::_parse_instruction_durations\ndef _parse_instruction_durations(backend, inst_durations, dt, circuit):\n \"\"\"Create a list of ``InstructionDuration``s. If ``inst_durations`` is provided,\n the backend will be ignored, otherwise, the durations will be populated from the\n backend. If any circuits have gate calibrations, those calibration durations would\n take precedence over backend durations, but be superceded by ``inst_duration``s.\n \"\"\"\n if not inst_durations:\n backend_version = getattr(backend, \"version\", 0)\n if backend_version <= 1:\n backend_durations = InstructionDurations()\n try:\n backend_durations = InstructionDurations.from_backend(backend)\n except AttributeError:\n pass\n else:\n backend_durations = backend.instruction_durations\n\n circ_durations = InstructionDurations()\n if not inst_durations:\n circ_durations.update(backend_durations, dt or backend_durations.dt)\n\n if circuit.calibrations:\n cal_durations = []\n for gate, gate_cals in circuit.calibrations.items():\n for (qubits, parameters), schedule in gate_cals.items():\n cal_durations.append((gate, qubits, parameters, schedule.duration))\n circ_durations.update(cal_durations, circ_durations.dt)\n\n if inst_durations:\n circ_durations.update(inst_durations, dt or getattr(inst_durations, \"dt\", None))\n\n return circ_durations","label":0},{"cid":"qiskit/compiler/transpiler.py::_parse_approximation_degree","text":"qiskit/compiler/transpiler.py::_parse_approximation_degree\ndef _parse_approximation_degree(approximation_degree):\n if approximation_degree is None:\n return None\n if approximation_degree < 0.0 or approximation_degree > 1.0:\n raise TranspilerError(\"Approximation degree must be in [0.0, 1.0]\")\n return approximation_degree","label":0},{"cid":"qiskit/compiler/transpiler.py::_parse_output_name","text":"qiskit/compiler/transpiler.py::_parse_output_name\ndef _parse_output_name(output_name, circuits):\n # naming and returning circuits\n # output_name could be either a string or a list\n if output_name is not None:\n if isinstance(output_name, str):\n # single circuit\n if len(circuits) == 1:\n return [output_name]\n # multiple circuits\n else:\n raise TranspilerError(\n \"Expected a list object of length equal \"\n + \"to that of the number of circuits \"\n + \"being transpiled\"\n )\n elif isinstance(output_name, list):\n if len(circuits) == len(output_name) and all(\n isinstance(name, str) for name in output_name\n ):\n return output_name\n else:\n raise TranspilerError(\n \"The length of output_name list \"\n \"must be equal to the number of \"\n \"transpiled circuits and the output_name \"\n \"list should be strings.\"\n )\n else:\n raise TranspilerError(\n \"The parameter output_name should be a string or a\"\n \"list of strings: %s was used.\" % type(output_name)\n )\n else:\n return [circuit.name for circuit in circuits]","label":0},{"cid":"qiskit/compiler/transpiler.py::_parse_timing_constraints","text":"qiskit/compiler/transpiler.py::_parse_timing_constraints\ndef _parse_timing_constraints(backend, timing_constraints):\n if isinstance(timing_constraints, TimingConstraints):\n return timing_constraints\n if backend is None and timing_constraints is None:\n timing_constraints = TimingConstraints()\n else:\n backend_version = getattr(backend, \"version\", 0)\n if backend_version <= 1:\n if timing_constraints is None:\n # get constraints from backend\n timing_constraints = getattr(backend.configuration(), \"timing_constraints\", {})\n timing_constraints = TimingConstraints(**timing_constraints)\n else:\n timing_constraints = backend.target.timing_constraints()\n return timing_constraints","label":0},{"cid":"qiskit/transpiler/passmanager_config.py::PassManagerConfig.__init__","text":"qiskit/transpiler/passmanager_config.py::PassManagerConfig.__init__\n def __init__(\n self,\n initial_layout=None,\n basis_gates=None,\n inst_map=None,\n coupling_map=None,\n layout_method=None,\n routing_method=None,\n translation_method=None,\n scheduling_method=None,\n instruction_durations=None,\n backend_properties=None,\n approximation_degree=None,\n seed_transpiler=None,\n timing_constraints=None,\n unitary_synthesis_method=\"default\",\n unitary_synthesis_plugin_config=None,\n target=None,\n hls_config=None,\n init_method=None,\n optimization_method=None,\n ):\n \"\"\"Initialize a PassManagerConfig object\n\n Args:\n initial_layout (Layout): Initial position of virtual qubits on\n physical qubits.\n basis_gates (list): List of basis gate names to unroll to.\n inst_map (InstructionScheduleMap): Mapping object that maps gate to schedule.\n coupling_map (CouplingMap): Directed graph represented a coupling\n map.\n layout_method (str): the pass to use for choosing initial qubit\n placement. This will be the plugin name if an external layout stage\n plugin is being used.\n routing_method (str): the pass to use for routing qubits on the\n architecture. This will be a plugin name if an external routing stage\n plugin is being used.\n translation_method (str): the pass to use for translating gates to\n basis_gates. This will be a plugin name if an external translation stage\n plugin is being used.\n scheduling_method (str): the pass to use for scheduling instructions. This will\n be a plugin name if an external scheduling stage plugin is being used.\n instruction_durations (InstructionDurations): Dictionary of duration\n ","label":0},{"cid":"qiskit/transpiler/passmanager_config.py::PassManagerConfig.from_backend","text":"qiskit/transpiler/passmanager_config.py::PassManagerConfig.from_backend\n @classmethod\n def from_backend(cls, backend, _skip_target=False, **pass_manager_options):\n \"\"\"Construct a configuration based on a backend and user input.\n\n This method automatically gererates a PassManagerConfig object based on the backend's\n features. User options can be used to overwrite the configuration.\n\n Args:\n backend (BackendV1): The backend that provides the configuration.\n pass_manager_options: User-defined option-value pairs.\n\n Returns:\n PassManagerConfig: The configuration generated based on the arguments.\n\n Raises:\n AttributeError: If the backend does not support a `configuration()` method.\n \"\"\"\n res = cls(**pass_manager_options)\n backend_version = getattr(backend, \"version\", 0)\n if not isinstance(backend_version, int):\n backend_version = 0\n if backend_version < 2:\n config = backend.configuration()\n if res.basis_gates is None:\n if backend_version < 2:\n res.basis_gates = getattr(config, \"basis_gates\", None)\n else:\n res.basis_gates = backend.operation_names\n if res.inst_map is None:\n if backend_version < 2:\n if hasattr(backend, \"defaults\"):\n defaults = backend.defaults()\n if defaults is not None:\n res.inst_map = defaults.instruction_schedule_map\n else:\n res.inst_map = backend.instruction_schedule_map\n if res.coupling_map is None:\n if backend_version < 2:\n cmap_edge_list = getattr(config, \"coupling_map\", None)\n if cmap_edge_list is not None:\n res.coupling_map = CouplingMap(cmap_edge_list)\n else:\n res.coupling_map = backend.coupling_map\n if res.instruction_duration","label":0},{"cid":"qiskit/transpiler/passmanager_config.py::PassManagerConfig.__str__","text":"qiskit/transpiler/passmanager_config.py::PassManagerConfig.__str__\n def __str__(self):\n newline = \"\\n\"\n newline_tab = \"\\n\\t\"\n if self.backend_properties is not None:\n backend_props = pprint.pformat(self.backend_properties.to_dict())\n backend_props = backend_props.replace(newline, newline_tab)\n else:\n backend_props = str(None)\n return (\n \"Pass Manager Config:\\n\"\n f\"\\tinitial_layout: {self.initial_layout}\\n\"\n f\"\\tbasis_gates: {self.basis_gates}\\n\"\n f\"\\tinst_map: {str(self.inst_map).replace(newline, newline_tab)}\\n\"\n f\"\\tcoupling_map: {self.coupling_map}\\n\"\n f\"\\tlayout_method: {self.layout_method}\\n\"\n f\"\\trouting_method: {self.routing_method}\\n\"\n f\"\\ttranslation_method: {self.translation_method}\\n\"\n f\"\\tscheduling_method: {self.scheduling_method}\\n\"\n f\"\\tinstruction_durations: {str(self.instruction_durations).replace(newline, newline_tab)}\\n\"\n f\"\\tbackend_properties: {backend_props}\\n\"\n f\"\\tapproximation_degree: {self.approximation_degree}\\n\"\n f\"\\tseed_transpiler: {self.seed_transpiler}\\n\"\n f\"\\ttiming_constraints: {self.timing_constraints}\\n\"\n f\"\\tunitary_synthesis_method: {self.unitary_synthesis_method}\\n\"\n f\"\\tunitary_synthesis_plugin_config: {self.unitary_synthesis_plugin_config}\\n\"\n f\"\\ttarget: {str(self.target).replace(newline, newline_tab)}\\n\"\n )","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/__init__.py::generate_preset_pass_manager","text":"qiskit/transpiler/preset_passmanagers/__init__.py::generate_preset_pass_manager\ndef generate_preset_pass_manager(\n optimization_level,\n backend=None,\n target=None,\n basis_gates=None,\n inst_map=None,\n coupling_map=None,\n instruction_durations=None,\n backend_properties=None,\n timing_constraints=None,\n initial_layout=None,\n layout_method=None,\n routing_method=None,\n translation_method=None,\n scheduling_method=None,\n approximation_degree=None,\n seed_transpiler=None,\n unitary_synthesis_method=\"default\",\n unitary_synthesis_plugin_config=None,\n hls_config=None,\n init_method=None,\n optimization_method=None,\n *,\n _skip_target=False,\n):\n \"\"\"Generate a preset :class:`~.PassManager`\n\n This function is used to quickly generate a preset pass manager. A preset pass\n manager are the default pass managers used by the :func:`~.transpile`\n function. This function provides a convenient and simple method to construct\n a standalone :class:`~.PassManager` object that mirrors what the transpile\n\n\n Args:\n optimization_level (int): The optimization level to generate a\n :class:`~.PassManager` for. This can be 0, 1, 2, or 3. Higher\n levels generate more optimized circuits, at the expense of\n longer transpilation time:\n\n * 0: no optimization\n * 1: light optimization\n * 2: heavy optimization\n * 3: even heavier optimization\n\n backend (Backend): An optional backend object which can be used as the\n source of the default values for the ``basis_gates``, ``inst_map``,\n ``couplig_map``, ``backend_properties``, ``instruction_durations``,\n ``timing_constraints``, and ``target``. If any of those other arguments\n are specified in addition to ``backend`` they will take precedence\n over the value contained in the backend.\n target (Target): The :class:`~.Tar","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::BasisTranslatorPassManager.pass_manager","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::BasisTranslatorPassManager.pass_manager\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\n return common.generate_translation_passmanager(\n pass_manager_config.target,\n basis_gates=pass_manager_config.basis_gates,\n method=\"translator\",\n approximation_degree=pass_manager_config.approximation_degree,\n coupling_map=pass_manager_config.coupling_map,\n backend_props=pass_manager_config.backend_properties,\n unitary_synthesis_method=pass_manager_config.unitary_synthesis_method,\n unitary_synthesis_plugin_config=pass_manager_config.unitary_synthesis_plugin_config,\n hls_config=pass_manager_config.hls_config,\n )","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::UnrollerPassManager.pass_manager","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::UnrollerPassManager.pass_manager\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\n return common.generate_translation_passmanager(\n pass_manager_config.target,\n basis_gates=pass_manager_config.basis_gates,\n method=\"unroller\",\n approximation_degree=pass_manager_config.approximation_degree,\n coupling_map=pass_manager_config.coupling_map,\n backend_props=pass_manager_config.backend_properties,\n unitary_synthesis_method=pass_manager_config.unitary_synthesis_method,\n unitary_synthesis_plugin_config=pass_manager_config.unitary_synthesis_plugin_config,\n hls_config=pass_manager_config.hls_config,\n )","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::UnitarySynthesisPassManager.pass_manager","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::UnitarySynthesisPassManager.pass_manager\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\n return common.generate_translation_passmanager(\n pass_manager_config.target,\n basis_gates=pass_manager_config.basis_gates,\n method=\"synthesis\",\n approximation_degree=pass_manager_config.approximation_degree,\n coupling_map=pass_manager_config.coupling_map,\n backend_props=pass_manager_config.backend_properties,\n unitary_synthesis_method=pass_manager_config.unitary_synthesis_method,\n unitary_synthesis_plugin_config=pass_manager_config.unitary_synthesis_plugin_config,\n hls_config=pass_manager_config.hls_config,\n )","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::BasicSwapPassManager.pass_manager","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::BasicSwapPassManager.pass_manager\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\n \"\"\"Build routing stage PassManager.\"\"\"\n seed_transpiler = pass_manager_config.seed_transpiler\n target = pass_manager_config.target\n coupling_map = pass_manager_config.coupling_map\n backend_properties = pass_manager_config.backend_properties\n if target is None:\n routing_pass = BasicSwap(coupling_map)\n else:\n routing_pass = BasicSwap(target)\n\n vf2_call_limit, vf2_max_trials = common.get_vf2_limits(\n optimization_level,\n pass_manager_config.layout_method,\n pass_manager_config.initial_layout,\n )\n if optimization_level == 0:\n return common.generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map=coupling_map,\n seed_transpiler=seed_transpiler,\n use_barrier_before_measurement=True,\n )\n if optimization_level == 1:\n return common.generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map,\n vf2_call_limit=vf2_call_limit,\n vf2_max_trials=vf2_max_trials,\n backend_properties=backend_properties,\n seed_transpiler=seed_transpiler,\n check_trivial=True,\n use_barrier_before_measurement=True,\n )\n if optimization_level == 2:\n return common.generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map=coupling_map,\n vf2_call_limit=vf2_call_limit,\n vf2_max_trials=vf2_max_trials,\n backend_properties=backend_properties,\n seed_transpiler=seed_transpiler,\n use_barrier_before","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::StochasticSwapPassManager.pass_manager","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::StochasticSwapPassManager.pass_manager\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\n \"\"\"Build routing stage PassManager.\"\"\"\n seed_transpiler = pass_manager_config.seed_transpiler\n target = pass_manager_config.target\n coupling_map = pass_manager_config.coupling_map\n coupling_map_routing = target\n if coupling_map_routing is None:\n coupling_map_routing = coupling_map\n backend_properties = pass_manager_config.backend_properties\n vf2_call_limit, vf2_max_trials = common.get_vf2_limits(\n optimization_level,\n pass_manager_config.layout_method,\n pass_manager_config.initial_layout,\n )\n if optimization_level == 3:\n routing_pass = StochasticSwap(coupling_map_routing, trials=200, seed=seed_transpiler)\n else:\n routing_pass = StochasticSwap(coupling_map_routing, trials=20, seed=seed_transpiler)\n\n if optimization_level == 0:\n return common.generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map=coupling_map,\n seed_transpiler=seed_transpiler,\n use_barrier_before_measurement=True,\n )\n if optimization_level == 1:\n return common.generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map,\n vf2_call_limit=vf2_call_limit,\n vf2_max_trials=vf2_max_trials,\n backend_properties=backend_properties,\n seed_transpiler=seed_transpiler,\n check_trivial=True,\n use_barrier_before_measurement=True,\n )\n if optimization_level in {2, 3}:\n return common.generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map=c","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::LookaheadSwapPassManager.pass_manager","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::LookaheadSwapPassManager.pass_manager\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\n \"\"\"Build routing stage PassManager.\"\"\"\n seed_transpiler = pass_manager_config.seed_transpiler\n target = pass_manager_config.target\n coupling_map = pass_manager_config.coupling_map\n coupling_map_routing = target\n if coupling_map_routing is None:\n coupling_map_routing = coupling_map\n backend_properties = pass_manager_config.backend_properties\n vf2_call_limit, vf2_max_trials = common.get_vf2_limits(\n optimization_level,\n pass_manager_config.layout_method,\n pass_manager_config.initial_layout,\n )\n if optimization_level == 0:\n routing_pass = LookaheadSwap(coupling_map_routing, search_depth=2, search_width=2)\n return common.generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map=coupling_map,\n seed_transpiler=seed_transpiler,\n use_barrier_before_measurement=True,\n )\n if optimization_level == 1:\n routing_pass = LookaheadSwap(coupling_map_routing, search_depth=4, search_width=4)\n return common.generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map,\n vf2_call_limit=vf2_call_limit,\n vf2_max_trials=vf2_max_trials,\n backend_properties=backend_properties,\n seed_transpiler=seed_transpiler,\n check_trivial=True,\n use_barrier_before_measurement=True,\n )\n if optimization_level == 2:\n routing_pass = LookaheadSwap(coupling_map_routing, search_depth=5, search_width=6)\n return common.generate_routing_passmanager(\n routing_pass,\n targe","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::SabreSwapPassManager.pass_manager","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::SabreSwapPassManager.pass_manager\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\n \"\"\"Build routing stage PassManager.\"\"\"\n seed_transpiler = pass_manager_config.seed_transpiler\n target = pass_manager_config.target\n coupling_map = pass_manager_config.coupling_map\n coupling_map_routing = target\n if coupling_map_routing is None:\n coupling_map_routing = coupling_map\n backend_properties = pass_manager_config.backend_properties\n vf2_call_limit, vf2_max_trials = common.get_vf2_limits(\n optimization_level,\n pass_manager_config.layout_method,\n pass_manager_config.initial_layout,\n )\n if optimization_level == 0:\n routing_pass = SabreSwap(\n coupling_map_routing,\n heuristic=\"basic\",\n seed=seed_transpiler,\n trials=5,\n )\n return common.generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map=coupling_map,\n seed_transpiler=seed_transpiler,\n use_barrier_before_measurement=True,\n )\n if optimization_level == 1:\n routing_pass = SabreSwap(\n coupling_map_routing,\n heuristic=\"decay\",\n seed=seed_transpiler,\n trials=5,\n )\n return common.generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map,\n vf2_call_limit=vf2_call_limit,\n vf2_max_trials=vf2_max_trials,\n backend_properties=backend_properties,\n seed_transpiler=seed_transpiler,\n check_trivial=True,\n use_barrier_before_measurement=True,\n )\n if optimization_level == 2:\n routin","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::NoneRoutingPassManager.pass_manager","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::NoneRoutingPassManager.pass_manager\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\n \"\"\"Build routing stage PassManager.\"\"\"\n seed_transpiler = pass_manager_config.seed_transpiler\n target = pass_manager_config.target\n coupling_map = pass_manager_config.coupling_map\n routing_pass = Error(\n msg=\"No routing method selected, but circuit is not routed to device. \"\n \"CheckMap Error: {check_map_msg}\",\n action=\"raise\",\n )\n return common.generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map=coupling_map,\n seed_transpiler=seed_transpiler,\n use_barrier_before_measurement=True,\n )","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::AlapSchedulingPassManager.pass_manager","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::AlapSchedulingPassManager.pass_manager\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\n \"\"\"Build scheduling stage PassManager\"\"\"\n\n instruction_durations = pass_manager_config.instruction_durations\n scheduling_method = pass_manager_config.scheduling_method\n timing_constraints = pass_manager_config.timing_constraints\n inst_map = pass_manager_config.inst_map\n target = pass_manager_config.target\n\n return common.generate_scheduling(\n instruction_durations, scheduling_method, timing_constraints, inst_map, target\n )","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::AsapSchedulingPassManager.pass_manager","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::AsapSchedulingPassManager.pass_manager\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\n \"\"\"Build scheduling stage PassManager\"\"\"\n\n instruction_durations = pass_manager_config.instruction_durations\n scheduling_method = pass_manager_config.scheduling_method\n timing_constraints = pass_manager_config.timing_constraints\n inst_map = pass_manager_config.inst_map\n target = pass_manager_config.target\n\n return common.generate_scheduling(\n instruction_durations, scheduling_method, timing_constraints, inst_map, target\n )","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::DefaultSchedulingPassManager.pass_manager","text":"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::DefaultSchedulingPassManager.pass_manager\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\n \"\"\"Build scheduling stage PassManager\"\"\"\n\n instruction_durations = pass_manager_config.instruction_durations\n scheduling_method = None\n timing_constraints = pass_manager_config.timing_constraints or TimingConstraints()\n inst_map = pass_manager_config.inst_map\n target = pass_manager_config.target\n\n return common.generate_scheduling(\n instruction_durations, scheduling_method, timing_constraints, inst_map, target\n )","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::_has_control_flow","text":"qiskit/transpiler/preset_passmanagers/common.py::_has_control_flow\ndef _has_control_flow(property_set):\n return any(property_set[f\"contains_{x}\"] for x in _CONTROL_FLOW_OP_NAMES)","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::_without_control_flow","text":"qiskit/transpiler/preset_passmanagers/common.py::_without_control_flow\ndef _without_control_flow(property_set):\n return not any(property_set[f\"contains_{x}\"] for x in _CONTROL_FLOW_OP_NAMES)","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.__init__","text":"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.__init__\n def __init__(self, basis_gates=(), target=None):\n if target is not None:\n self.unsupported = [op for op in _CONTROL_FLOW_OP_NAMES if op not in target]\n else:\n basis_gates = set(basis_gates) if basis_gates is not None else set()\n self.unsupported = [op for op in _CONTROL_FLOW_OP_NAMES if op not in basis_gates]","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.message","text":"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.message\n def message(self, property_set):\n \"\"\"Create an error message for the given property set.\"\"\"\n fails = [x for x in self.unsupported if property_set[f\"contains_{x}\"]]\n if len(fails) == 1:\n return f\"The control-flow construct '{fails[0]}' is not supported by the backend.\"\n return (\n f\"The control-flow constructs [{', '.join(repr(op) for op in fails)}]\"\n \" are not supported by the backend.\"\n )","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.condition","text":"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.condition\n def condition(self, property_set):\n \"\"\"Checkable condition for the given property set.\"\"\"\n return any(property_set[f\"contains_{x}\"] for x in self.unsupported)","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::generate_control_flow_options_check","text":"qiskit/transpiler/preset_passmanagers/common.py::generate_control_flow_options_check\ndef generate_control_flow_options_check(\n layout_method=None,\n routing_method=None,\n translation_method=None,\n optimization_method=None,\n scheduling_method=None,\n basis_gates=(),\n target=None,\n):\n \"\"\"Generate a pass manager that, when run on a DAG that contains control flow, fails with an\n error message explaining the invalid options, and what could be used instead.\n\n Returns:\n PassManager: a pass manager that populates the ``contains_x`` properties for each of the\n control-flow operations, and raises an error if any of the given options do not support\n control flow, but a circuit with control flow is given.\n \"\"\"\n bad_options = []\n message = \"Some options cannot be used with control flow.\"\n for stage, given in [\n (\"layout\", layout_method),\n (\"routing\", routing_method),\n (\"translation\", translation_method),\n (\"optimization\", optimization_method),\n (\"scheduling\", scheduling_method),\n ]:\n option = stage + \"_method\"\n method_states = _CONTROL_FLOW_STATES[option]\n if given is not None and given in method_states.not_working:\n if method_states.working:\n message += (\n f\" Got {option}='{given}', but valid values are {list(method_states.working)}.\"\n )\n else:\n message += (\n f\" Got {option}='{given}', but the entire {stage} stage is not supported.\"\n )\n bad_options.append(option)\n out = PassManager()\n out.append(ContainsInstruction(_CONTROL_FLOW_OP_NAMES, recurse=False))\n if bad_options:\n out.append(Error(message), condition=_has_control_flow)\n backend_control = _InvalidControlFlowForBackend(basis_gates, target)\n out.append(Error(backend_control.message), condition=backend_control.condition)\n return out","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::generate_error_on_control_flow","text":"qiskit/transpiler/preset_passmanagers/common.py::generate_error_on_control_flow\ndef generate_error_on_control_flow(message):\n \"\"\"Get a pass manager that always raises an error if control flow is present in a given\n circuit.\"\"\"\n out = PassManager()\n out.append(ContainsInstruction(_CONTROL_FLOW_OP_NAMES, recurse=False))\n out.append(Error(message), condition=_has_control_flow)\n return out","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::if_has_control_flow_else","text":"qiskit/transpiler/preset_passmanagers/common.py::if_has_control_flow_else\ndef if_has_control_flow_else(if_present, if_absent):\n \"\"\"Generate a pass manager that will run the passes in ``if_present`` if the given circuit\n has control-flow operations in it, and those in ``if_absent`` if it doesn't.\"\"\"\n if isinstance(if_present, PassManager):\n if_present = if_present.to_flow_controller()\n if isinstance(if_absent, PassManager):\n if_absent = if_absent.to_flow_controller()\n out = PassManager()\n out.append(ContainsInstruction(_CONTROL_FLOW_OP_NAMES, recurse=False))\n out.append(if_present, condition=_has_control_flow)\n out.append(if_absent, condition=_without_control_flow)\n return out","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::generate_unroll_3q","text":"qiskit/transpiler/preset_passmanagers/common.py::generate_unroll_3q\ndef generate_unroll_3q(\n target,\n basis_gates=None,\n approximation_degree=None,\n unitary_synthesis_method=\"default\",\n unitary_synthesis_plugin_config=None,\n hls_config=None,\n):\n \"\"\"Generate an unroll >3q :class:`~qiskit.transpiler.PassManager`\n\n Args:\n target (Target): the :class:`~.Target` object representing the backend\n basis_gates (list): A list of str gate names that represent the basis\n gates on the backend target\n approximation_degree (Optional[float]): The heuristic approximation degree to\n use. Can be between 0 and 1.\n unitary_synthesis_method (str): The unitary synthesis method to use. You can see\n a list of installed plugins with :func:`.unitary_synthesis_plugin_names`.\n unitary_synthesis_plugin_config (dict): The optional dictionary plugin\n configuration, this is plugin specific refer to the specified plugin's\n documentation for how to use.\n hls_config (HLSConfig): An optional configuration class to use for\n :class:`~qiskit.transpiler.passes.HighLevelSynthesis` pass.\n Specifies how to synthesize various high-level objects.\n\n Returns:\n PassManager: The unroll 3q or more pass manager\n \"\"\"\n unroll_3q = PassManager()\n unroll_3q.append(\n UnitarySynthesis(\n basis_gates,\n approximation_degree=approximation_degree,\n method=unitary_synthesis_method,\n min_qubits=3,\n plugin_config=unitary_synthesis_plugin_config,\n target=target,\n )\n )\n unroll_3q.append(\n HighLevelSynthesis(\n hls_config=hls_config, coupling_map=None, target=target, use_qubit_indices=False\n )\n )\n unroll_3q.append(Unroll3qOrMore(target=target, basis_gates=basis_gates))\n return unroll_3q","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::generate_embed_passmanager","text":"qiskit/transpiler/preset_passmanagers/common.py::generate_embed_passmanager\ndef generate_embed_passmanager(coupling_map):\n \"\"\"Generate a layout embedding :class:`~qiskit.transpiler.PassManager`\n\n This is used to generate a :class:`~qiskit.transpiler.PassManager` object\n that can be used to expand and apply an initial layout to a circuit\n\n Args:\n coupling_map (Union[CouplingMap, Target): The coupling map for the backend to embed\n the circuit to.\n Returns:\n PassManager: The embedding passmanager that assumes the layout property\n set has been set in earlier stages\n \"\"\"\n return PassManager([FullAncillaAllocation(coupling_map), EnlargeWithAncilla(), ApplyLayout()])","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::_layout_not_perfect","text":"qiskit/transpiler/preset_passmanagers/common.py::_layout_not_perfect\ndef _layout_not_perfect(property_set):\n \"\"\"Return ``True`` if the first attempt at layout has been checked and found to be imperfect.\n In this case, perfection means \"does not require any swap routing\".\"\"\"\n return property_set[\"is_swap_mapped\"] is not None and not property_set[\"is_swap_mapped\"]","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::_apply_post_layout_condition","text":"qiskit/transpiler/preset_passmanagers/common.py::_apply_post_layout_condition\ndef _apply_post_layout_condition(property_set):\n # if VF2 Post layout found a solution we need to re-apply the better\n # layout. Otherwise we can skip apply layout.\n return (\n property_set[\"VF2PostLayout_stop_reason\"] is not None\n and property_set[\"VF2PostLayout_stop_reason\"] is VF2PostLayoutStopReason.SOLUTION_FOUND\n )","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager","text":"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager\ndef generate_routing_passmanager(\n routing_pass,\n target,\n coupling_map=None,\n vf2_call_limit=None,\n backend_properties=None,\n seed_transpiler=None,\n check_trivial=False,\n use_barrier_before_measurement=True,\n vf2_max_trials=None,\n):\n \"\"\"Generate a routing :class:`~qiskit.transpiler.PassManager`\n\n Args:\n routing_pass (TransformationPass): The pass which will perform the\n routing\n target (Target): the :class:`~.Target` object representing the backend\n coupling_map (CouplingMap): The coupling map of the backend to route\n for\n vf2_call_limit (int): The internal call limit for the vf2 post layout\n pass. If this is ``None`` or ``0`` the vf2 post layout will not be\n run.\n backend_properties (BackendProperties): Properties of a backend to\n synthesize for (e.g. gate fidelities).\n seed_transpiler (int): Sets random seed for the stochastic parts of\n the transpiler.\n check_trivial (bool): If set to true this will condition running the\n :class:`~.VF2PostLayout` pass after routing on whether a trivial\n layout was tried and was found to not be perfect. This is only\n needed if the constructed pass manager runs :class:`~.TrivialLayout`\n as a first layout attempt and uses it if it's a perfect layout\n (as is the case with preset pass manager level 1).\n use_barrier_before_measurement (bool): If true (the default) the\n :class:`~.BarrierBeforeFinalMeasurements` transpiler pass will be run prior to the\n specified pass in the ``routing_pass`` argument.\n vf2_max_trials (int): The maximum number of trials to run VF2 when\n evaluating the vf2 post layout\n pass. If this is ``None`` or ``0`` the vf2 post layout will not be run.\n Returns:\n PassManager: ","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager._run_post_layout_condition","text":"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager._run_post_layout_condition\n def _run_post_layout_condition(property_set):\n # If we check trivial layout and the found trivial layout was not perfect also\n # ensure VF2 initial layout was not used before running vf2 post layout\n if not check_trivial or _layout_not_perfect(property_set):\n vf2_stop_reason = property_set[\"VF2Layout_stop_reason\"]\n if vf2_stop_reason is None or vf2_stop_reason != VF2LayoutStopReason.SOLUTION_FOUND:\n return True\n return False","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager._swap_condition","text":"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager._swap_condition\n def _swap_condition(property_set):\n return not property_set[\"routing_not_needed\"]","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::generate_pre_op_passmanager","text":"qiskit/transpiler/preset_passmanagers/common.py::generate_pre_op_passmanager\ndef generate_pre_op_passmanager(target=None, coupling_map=None, remove_reset_in_zero=False):\n \"\"\"Generate a pre-optimization loop :class:`~qiskit.transpiler.PassManager`\n\n This pass manager will check to ensure that directionality from the coupling\n map is respected\n\n Args:\n target (Target): the :class:`~.Target` object representing the backend\n coupling_map (CouplingMap): The coupling map to use\n remove_reset_in_zero (bool): If ``True`` include the remove reset in\n zero pass in the generated PassManager\n Returns:\n PassManager: The pass manager\n\n \"\"\"\n pre_opt = PassManager()\n if coupling_map:\n pre_opt.append(CheckGateDirection(coupling_map, target=target))\n\n def _direction_condition(property_set):\n return not property_set[\"is_direction_mapped\"]\n\n pre_opt.append([GateDirection(coupling_map, target=target)], condition=_direction_condition)\n if remove_reset_in_zero:\n pre_opt.append(RemoveResetInZeroState())\n return pre_opt","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::generate_translation_passmanager","text":"qiskit/transpiler/preset_passmanagers/common.py::generate_translation_passmanager\ndef generate_translation_passmanager(\n target,\n basis_gates=None,\n method=\"translator\",\n approximation_degree=None,\n coupling_map=None,\n backend_props=None,\n unitary_synthesis_method=\"default\",\n unitary_synthesis_plugin_config=None,\n hls_config=None,\n):\n \"\"\"Generate a basis translation :class:`~qiskit.transpiler.PassManager`\n\n Args:\n target (Target): the :class:`~.Target` object representing the backend\n basis_gates (list): A list of str gate names that represent the basis\n gates on the backend target\n method (str): The basis translation method to use\n approximation_degree (Optional[float]): The heuristic approximation degree to\n use. Can be between 0 and 1.\n coupling_map (CouplingMap): the coupling map of the backend\n in case synthesis is done on a physical circuit. The\n directionality of the coupling_map will be taken into\n account if pulse_optimize is True/None and natural_direction\n is True/None.\n unitary_synthesis_plugin_config (dict): The optional dictionary plugin\n configuration, this is plugin specific refer to the specified plugin's\n documentation for how to use.\n backend_props (BackendProperties): Properties of a backend to\n synthesize for (e.g. gate fidelities).\n unitary_synthesis_method (str): The unitary synthesis method to use. You can\n see a list of installed plugins with :func:`.unitary_synthesis_plugin_names`.\n hls_config (HLSConfig): An optional configuration class to use for\n :class:`~qiskit.transpiler.passes.HighLevelSynthesis` pass.\n Specifies how to synthesize various high-level objects.\n\n Returns:\n PassManager: The basis translation pass manager\n\n Raises:\n TranspilerError: If the ``method`` kwarg is not a valid value\n ","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::generate_scheduling","text":"qiskit/transpiler/preset_passmanagers/common.py::generate_scheduling\ndef generate_scheduling(\n instruction_durations, scheduling_method, timing_constraints, inst_map, target=None\n):\n \"\"\"Generate a post optimization scheduling :class:`~qiskit.transpiler.PassManager`\n\n Args:\n instruction_durations (dict): The dictionary of instruction durations\n scheduling_method (str): The scheduling method to use, can either be\n ``'asap'``/``'as_soon_as_possible'`` or\n ``'alap'``/``'as_late_as_possible'``\n timing_constraints (TimingConstraints): Hardware time alignment restrictions.\n inst_map (InstructionScheduleMap): Mapping object that maps gate to schedule.\n target (Target): The :class:`~.Target` object representing the backend\n\n Returns:\n PassManager: The scheduling pass manager\n\n Raises:\n TranspilerError: If the ``scheduling_method`` kwarg is not a valid value\n \"\"\"\n scheduling = PassManager()\n if inst_map and inst_map.has_custom_gate():\n scheduling.append(PulseGates(inst_map=inst_map, target=target))\n if scheduling_method:\n # Do scheduling after unit conversion.\n scheduler = {\n \"alap\": ALAPScheduleAnalysis,\n \"as_late_as_possible\": ALAPScheduleAnalysis,\n \"asap\": ASAPScheduleAnalysis,\n \"as_soon_as_possible\": ASAPScheduleAnalysis,\n }\n scheduling.append(TimeUnitConversion(instruction_durations, target=target))\n try:\n scheduling.append(scheduler[scheduling_method](instruction_durations, target=target))\n except KeyError as ex:\n raise TranspilerError(\"Invalid scheduling method %s.\" % scheduling_method) from ex\n elif instruction_durations:\n # No scheduling. But do unit conversion for delays.\n def _contains_delay(property_set):\n return property_set[\"contains_delay\"]\n\n scheduling.append(ContainsInstruction(\"delay\"))\n scheduling.append(\n ","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::get_vf2_call_limit","text":"qiskit/transpiler/preset_passmanagers/common.py::get_vf2_call_limit\n@deprecate_func(\n additional_msg=\"Instead, use :func:`~qiskit.transpiler.preset_passmanagers.common.get_vf2_limits`.\",\n since=\"0.25.0\",\n)\ndef get_vf2_call_limit(\n optimization_level: int,\n layout_method: Optional[str] = None,\n initial_layout: Optional[Layout] = None,\n) -> Optional[int]:\n \"\"\"Get the vf2 call limit for vf2 based layout passes.\"\"\"\n vf2_call_limit = None\n if layout_method is None and initial_layout is None:\n if optimization_level == 1:\n vf2_call_limit = int(5e4) # Set call limit to ~100ms with rustworkx 0.10.2\n elif optimization_level == 2:\n vf2_call_limit = int(5e6) # Set call limit to ~10 sec with rustworkx 0.10.2\n elif optimization_level == 3:\n vf2_call_limit = int(3e7) # Set call limit to ~60 sec with rustworkx 0.10.2\n return vf2_call_limit","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/common.py::get_vf2_limits","text":"qiskit/transpiler/preset_passmanagers/common.py::get_vf2_limits\ndef get_vf2_limits(\n optimization_level: int,\n layout_method: Optional[str] = None,\n initial_layout: Optional[Layout] = None,\n) -> VF2Limits:\n \"\"\"Get the VF2 limits for VF2-based layout passes.\n\n Returns:\n VF2Limits: An namedtuple with optional elements\n ``call_limit`` and ``max_trials``.\n \"\"\"\n limits = VF2Limits(None, None)\n if layout_method is None and initial_layout is None:\n if optimization_level == 1:\n limits = VF2Limits(\n int(5e4), # Set call limit to ~100ms with rustworkx 0.10.2\n 2500, # Limits layout scoring to < 600ms on ~400 qubit devices\n )\n elif optimization_level == 2:\n limits = VF2Limits(\n int(5e6), # Set call limit to ~10 sec with rustworkx 0.10.2\n 25000, # Limits layout scoring to < 6 sec on ~400 qubit devices\n )\n elif optimization_level == 3:\n limits = VF2Limits(\n int(3e7), # Set call limit to ~60 sec with rustworkx 0.10.2\n 250000, # Limits layout scoring to < 60 sec on ~400 qubit devices\n )\n return limits","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager","text":"qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager\ndef level_0_pass_manager(pass_manager_config: PassManagerConfig) -> StagedPassManager:\n \"\"\"Level 0 pass manager: no explicit optimization other than mapping to backend.\n\n This pass manager applies the user-given initial layout. If none is given, a trivial\n layout consisting of mapping the i-th virtual qubit to the i-th physical qubit is used.\n Any unused physical qubit is allocated as ancilla space.\n\n The pass manager then unrolls the circuit to the desired basis, and transforms the\n circuit to match the coupling map.\n\n Args:\n pass_manager_config: configuration of the pass manager.\n\n Returns:\n a level 0 pass manager.\n\n Raises:\n TranspilerError: if the passmanager config is invalid.\n \"\"\"\n plugin_manager = PassManagerStagePluginManager()\n basis_gates = pass_manager_config.basis_gates\n coupling_map = pass_manager_config.coupling_map\n initial_layout = pass_manager_config.initial_layout\n init_method = pass_manager_config.init_method\n layout_method = pass_manager_config.layout_method or \"trivial\"\n routing_method = pass_manager_config.routing_method or \"stochastic\"\n translation_method = pass_manager_config.translation_method or \"translator\"\n optimization_method = pass_manager_config.optimization_method\n scheduling_method = pass_manager_config.scheduling_method or \"default\"\n seed_transpiler = pass_manager_config.seed_transpiler\n backend_properties = pass_manager_config.backend_properties\n approximation_degree = pass_manager_config.approximation_degree\n unitary_synthesis_method = pass_manager_config.unitary_synthesis_method\n unitary_synthesis_plugin_config = pass_manager_config.unitary_synthesis_plugin_config\n target = pass_manager_config.target\n hls_config = pass_manager_config.hls_config\n\n # Choose an initial layout if not set by user (default: trivial layout)\n _given_layout = SetLayout(initial_lay","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager._choose_layout_condition","text":"qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager._choose_layout_condition\n def _choose_layout_condition(property_set):\n return not property_set[\"layout\"]","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager","text":"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager\ndef level_1_pass_manager(pass_manager_config: PassManagerConfig) -> StagedPassManager:\n \"\"\"Level 1 pass manager: light optimization by simple adjacent gate collapsing.\n\n This pass manager applies the user-given initial layout. If none is given,\n and a trivial layout (i-th virtual -> i-th physical) makes the circuit fit\n the coupling map, that is used.\n Otherwise, the circuit is mapped to the most densely connected coupling subgraph,\n and swaps are inserted to map. Any unused physical qubit is allocated as ancilla space.\n The pass manager then unrolls the circuit to the desired basis, and transforms the\n circuit to match the coupling map. Finally, optimizations in the form of adjacent\n gate collapse and redundant reset removal are performed.\n\n Args:\n pass_manager_config: configuration of the pass manager.\n\n Returns:\n a level 1 pass manager.\n\n Raises:\n TranspilerError: if the passmanager config is invalid.\n \"\"\"\n plugin_manager = PassManagerStagePluginManager()\n basis_gates = pass_manager_config.basis_gates\n coupling_map = pass_manager_config.coupling_map\n initial_layout = pass_manager_config.initial_layout\n init_method = pass_manager_config.init_method\n # Unlike other presets, the layout and routing defaults aren't set here because they change\n # based on whether the input circuit has control flow.\n layout_method = pass_manager_config.layout_method or \"sabre\"\n routing_method = pass_manager_config.routing_method or \"sabre\"\n translation_method = pass_manager_config.translation_method or \"translator\"\n optimization_method = pass_manager_config.optimization_method\n scheduling_method = pass_manager_config.scheduling_method or \"default\"\n seed_transpiler = pass_manager_config.seed_transpiler\n backend_properties = pass_manager_config.backend_properties\n approximation_degree = pass_manager_config.approxima","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._choose_layout_condition","text":"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._choose_layout_condition\n def _choose_layout_condition(property_set):\n return not property_set[\"layout\"]","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._layout_not_perfect","text":"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._layout_not_perfect\n def _layout_not_perfect(property_set):\n \"\"\"Return ``True`` if the first attempt at layout has been checked and found to be\n imperfect. In this case, perfection means \"does not require any swap routing\".\"\"\"\n return property_set[\"is_swap_mapped\"] is not None and not property_set[\"is_swap_mapped\"]","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._vf2_match_not_found","text":"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._vf2_match_not_found\n def _vf2_match_not_found(property_set):\n # If a layout hasn't been set by the time we run vf2 layout we need to\n # run layout\n if property_set[\"layout\"] is None:\n return True\n # if VF2 layout stopped for any reason other than solution found we need\n # to run layout since VF2 didn't converge.\n if (\n property_set[\"VF2Layout_stop_reason\"] is not None\n and property_set[\"VF2Layout_stop_reason\"] is not VF2LayoutStopReason.SOLUTION_FOUND\n ):\n return True\n return False","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._opt_control","text":"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._opt_control\n def _opt_control(property_set):\n return (not property_set[\"depth_fixed_point\"]) or (not property_set[\"size_fixed_point\"])","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager","text":"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager\ndef level_2_pass_manager(pass_manager_config: PassManagerConfig) -> StagedPassManager:\n \"\"\"Level 2 pass manager: medium optimization by initial layout selection and\n gate cancellation using commutativity rules.\n\n This pass manager applies the user-given initial layout. If none is given, a search\n for a perfect layout (i.e. one that satisfies all 2-qubit interactions) is conducted.\n If no such layout is found, qubits are laid out on the most densely connected subset\n which also exhibits the best gate fidelities.\n\n The pass manager then transforms the circuit to match the coupling constraints.\n It is then unrolled to the basis, and any flipped cx directions are fixed.\n Finally, optimizations in the form of commutative gate cancellation and redundant\n reset removal are performed.\n\n Args:\n pass_manager_config: configuration of the pass manager.\n\n Returns:\n a level 2 pass manager.\n\n Raises:\n TranspilerError: if the passmanager config is invalid.\n \"\"\"\n plugin_manager = PassManagerStagePluginManager()\n basis_gates = pass_manager_config.basis_gates\n coupling_map = pass_manager_config.coupling_map\n initial_layout = pass_manager_config.initial_layout\n init_method = pass_manager_config.init_method\n layout_method = pass_manager_config.layout_method or \"sabre\"\n routing_method = pass_manager_config.routing_method or \"sabre\"\n translation_method = pass_manager_config.translation_method or \"translator\"\n optimization_method = pass_manager_config.optimization_method\n scheduling_method = pass_manager_config.scheduling_method or \"default\"\n seed_transpiler = pass_manager_config.seed_transpiler\n backend_properties = pass_manager_config.backend_properties\n approximation_degree = pass_manager_config.approximation_degree\n unitary_synthesis_method = pass_manager_config.unitary_synthesis_method\n unitary_synthesis_plugi","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._choose_layout_condition","text":"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._choose_layout_condition\n def _choose_layout_condition(property_set):\n # layout hasn't been set yet\n return not property_set[\"layout\"]","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._vf2_match_not_found","text":"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._vf2_match_not_found\n def _vf2_match_not_found(property_set):\n # If a layout hasn't been set by the time we run vf2 layout we need to\n # run layout\n if property_set[\"layout\"] is None:\n return True\n # if VF2 layout stopped for any reason other than solution found we need\n # to run layout since VF2 didn't converge.\n if (\n property_set[\"VF2Layout_stop_reason\"] is not None\n and property_set[\"VF2Layout_stop_reason\"] is not VF2LayoutStopReason.SOLUTION_FOUND\n ):\n return True\n return False","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._opt_control","text":"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._opt_control\n def _opt_control(property_set):\n return (not property_set[\"depth_fixed_point\"]) or (not property_set[\"size_fixed_point\"])","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager","text":"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager\ndef level_3_pass_manager(pass_manager_config: PassManagerConfig) -> StagedPassManager:\n \"\"\"Level 3 pass manager: heavy optimization by noise adaptive qubit mapping and\n gate cancellation using commutativity rules and unitary synthesis.\n\n This pass manager applies the user-given initial layout. If none is given, a search\n for a perfect layout (i.e. one that satisfies all 2-qubit interactions) is conducted.\n If no such layout is found, and device calibration information is available, the\n circuit is mapped to the qubits with best readouts and to CX gates with highest fidelity.\n\n The pass manager then transforms the circuit to match the coupling constraints.\n It is then unrolled to the basis, and any flipped cx directions are fixed.\n Finally, optimizations in the form of commutative gate cancellation, resynthesis\n of two-qubit unitary blocks, and redundant reset removal are performed.\n\n Args:\n pass_manager_config: configuration of the pass manager.\n\n Returns:\n a level 3 pass manager.\n\n Raises:\n TranspilerError: if the passmanager config is invalid.\n \"\"\"\n plugin_manager = PassManagerStagePluginManager()\n basis_gates = pass_manager_config.basis_gates\n coupling_map = pass_manager_config.coupling_map\n initial_layout = pass_manager_config.initial_layout\n init_method = pass_manager_config.init_method\n layout_method = pass_manager_config.layout_method or \"sabre\"\n routing_method = pass_manager_config.routing_method or \"sabre\"\n translation_method = pass_manager_config.translation_method or \"translator\"\n optimization_method = pass_manager_config.optimization_method\n scheduling_method = pass_manager_config.scheduling_method or \"default\"\n seed_transpiler = pass_manager_config.seed_transpiler\n backend_properties = pass_manager_config.backend_properties\n approximation_degree = pass_manager_config.approximation_deg","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._choose_layout_condition","text":"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._choose_layout_condition\n def _choose_layout_condition(property_set):\n # layout hasn't been set yet\n return not property_set[\"layout\"]","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._vf2_match_not_found","text":"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._vf2_match_not_found\n def _vf2_match_not_found(property_set):\n # If a layout hasn't been set by the time we run vf2 layout we need to\n # run layout\n if property_set[\"layout\"] is None:\n return True\n # if VF2 layout stopped for any reason other than solution found we need\n # to run layout since VF2 didn't converge.\n if (\n property_set[\"VF2Layout_stop_reason\"] is not None\n and property_set[\"VF2Layout_stop_reason\"] is not VF2LayoutStopReason.SOLUTION_FOUND\n ):\n return True\n return False","label":1},{"cid":"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._opt_control","text":"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._opt_control\n def _opt_control(property_set):\n return not property_set[\"optimization_loop_minimum_point\"]","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePlugin.pass_manager","text":"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePlugin.pass_manager\n @abc.abstractmethod\n def pass_manager(\n self, pass_manager_config: PassManagerConfig, optimization_level: Optional[int] = None\n ) -> PassManager:\n \"\"\"This method is designed to return a :class:`~.PassManager` for the stage this implements\n\n Args:\n pass_manager_config: A configuration object that defines all the target device\n specifications and any user specified options to :func:`~.transpile` or\n :func:`~.generate_preset_pass_manager`\n optimization_level: The optimization level of the transpilation, if set this\n should be used to set values for any tunable parameters to trade off runtime\n for potential optimization. Valid values should be ``0``, ``1``, ``2``, or ``3``\n and the higher the number the more optimization is expected.\n \"\"\"\n pass","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager.__init__","text":"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager.__init__\n def __init__(self):\n super().__init__()\n self.init_plugins = stevedore.ExtensionManager(\n \"qiskit.transpiler.init\", invoke_on_load=True, propagate_map_exceptions=True\n )\n self.layout_plugins = stevedore.ExtensionManager(\n \"qiskit.transpiler.layout\", invoke_on_load=True, propagate_map_exceptions=True\n )\n self.routing_plugins = stevedore.ExtensionManager(\n \"qiskit.transpiler.routing\", invoke_on_load=True, propagate_map_exceptions=True\n )\n self.translation_plugins = stevedore.ExtensionManager(\n \"qiskit.transpiler.translation\", invoke_on_load=True, propagate_map_exceptions=True\n )\n self.optimization_plugins = stevedore.ExtensionManager(\n \"qiskit.transpiler.optimization\", invoke_on_load=True, propagate_map_exceptions=True\n )\n self.scheduling_plugins = stevedore.ExtensionManager(\n \"qiskit.transpiler.scheduling\", invoke_on_load=True, propagate_map_exceptions=True\n )","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager.get_passmanager_stage","text":"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager.get_passmanager_stage\n def get_passmanager_stage(\n self,\n stage_name: str,\n plugin_name: str,\n pm_config: PassManagerConfig,\n optimization_level=None,\n ) -> PassManager:\n \"\"\"Get a stage\"\"\"\n if stage_name == \"init\":\n return self._build_pm(\n self.init_plugins, stage_name, plugin_name, pm_config, optimization_level\n )\n elif stage_name == \"layout\":\n return self._build_pm(\n self.layout_plugins, stage_name, plugin_name, pm_config, optimization_level\n )\n elif stage_name == \"routing\":\n return self._build_pm(\n self.routing_plugins, stage_name, plugin_name, pm_config, optimization_level\n )\n elif stage_name == \"translation\":\n return self._build_pm(\n self.translation_plugins, stage_name, plugin_name, pm_config, optimization_level\n )\n elif stage_name == \"optimization\":\n return self._build_pm(\n self.optimization_plugins, stage_name, plugin_name, pm_config, optimization_level\n )\n elif stage_name == \"scheduling\":\n return self._build_pm(\n self.scheduling_plugins, stage_name, plugin_name, pm_config, optimization_level\n )\n else:\n raise TranspilerError(f\"Invalid stage name: {stage_name}\")","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager._build_pm","text":"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager._build_pm\n def _build_pm(\n self,\n stage_obj: stevedore.ExtensionManager,\n stage_name: str,\n plugin_name: str,\n pm_config: PassManagerConfig,\n optimization_level: Optional[int] = None,\n ):\n if plugin_name not in stage_obj:\n raise TranspilerError(f\"Invalid plugin name {plugin_name} for stage {stage_name}\")\n plugin_obj = stage_obj[plugin_name]\n return plugin_obj.obj.pass_manager(pm_config, optimization_level)","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/plugin.py::list_stage_plugins","text":"qiskit/transpiler/preset_passmanagers/plugin.py::list_stage_plugins\ndef list_stage_plugins(stage_name: str) -> List[str]:\n \"\"\"Get a list of installed plugins for a stage.\n\n Args:\n stage_name: The stage name to get the plugin names for\n\n Returns:\n plugins: The list of installed plugin names for the specified stages\n\n Raises:\n TranspilerError: If an invalid stage name is specified.\n \"\"\"\n plugin_mgr = PassManagerStagePluginManager()\n if stage_name == \"init\":\n return plugin_mgr.init_plugins.names()\n elif stage_name == \"layout\":\n return plugin_mgr.layout_plugins.names()\n elif stage_name == \"routing\":\n return plugin_mgr.routing_plugins.names()\n elif stage_name == \"translation\":\n return plugin_mgr.translation_plugins.names()\n elif stage_name == \"optimization\":\n return plugin_mgr.optimization_plugins.names()\n elif stage_name == \"scheduling\":\n return plugin_mgr.scheduling_plugins.names()\n else:\n raise TranspilerError(f\"Invalid stage name: {stage_name}\")","label":0},{"cid":"qiskit/transpiler/preset_passmanagers/plugin.py::passmanager_stage_plugins","text":"qiskit/transpiler/preset_passmanagers/plugin.py::passmanager_stage_plugins\ndef passmanager_stage_plugins(stage: str) -> Dict[str, PassManagerStagePlugin]:\n \"\"\"Return a dict with, for each stage name, the class type of the plugin.\n\n This function is useful for getting more information about a plugin:\n\n .. code-block:: python\n\n from qiskit.transpiler.preset_passmanagers.plugin import passmanager_stage_plugins\n routing_plugins = passmanager_stage_plugins('routing')\n basic_plugin = routing_plugins['basic']\n help(basic_plugin)\n\n .. code-block:: text\n\n Help on BasicSwapPassManager in module ...preset_passmanagers.builtin_plugins object:\n\n class BasicSwapPassManager(...preset_passmanagers.plugin.PassManagerStagePlugin)\n | Plugin class for routing stage with :class:`~.BasicSwap`\n |\n | Method resolution order:\n | BasicSwapPassManager\n | ...preset_passmanagers.plugin.PassManagerStagePlugin\n | abc.ABC\n | builtins.object\n ...\n\n Args:\n stage: The stage name to get\n\n Returns:\n dict: the key is the name of the plugin and the value is the class type for each.\n\n Raises:\n TranspilerError: If an invalid stage name is specified.\n \"\"\"\n plugin_mgr = PassManagerStagePluginManager()\n try:\n manager = getattr(plugin_mgr, f\"{stage}_plugins\")\n except AttributeError as exc:\n raise TranspilerError(f\"Passmanager stage {stage} not found\") from exc\n\n return {name: manager[name].obj for name in manager.names()}","label":0}],"string":"[\n {\n \"cid\": \"qiskit/compiler/transpiler.py::transpile\",\n \"text\": \"qiskit/compiler/transpiler.py::transpile\\ndef transpile( # pylint: disable=too-many-return-statements\\n circuits: _CircuitT,\\n backend: Optional[Backend] = None,\\n basis_gates: Optional[List[str]] = None,\\n inst_map: Optional[List[InstructionScheduleMap]] = None,\\n coupling_map: Optional[Union[CouplingMap, List[List[int]]]] = None,\\n backend_properties: Optional[BackendProperties] = None,\\n initial_layout: Optional[Union[Layout, Dict, List]] = None,\\n layout_method: Optional[str] = None,\\n routing_method: Optional[str] = None,\\n translation_method: Optional[str] = None,\\n scheduling_method: Optional[str] = None,\\n instruction_durations: Optional[InstructionDurationsType] = None,\\n dt: Optional[float] = None,\\n approximation_degree: Optional[float] = 1.0,\\n timing_constraints: Optional[Dict[str, int]] = None,\\n seed_transpiler: Optional[int] = None,\\n optimization_level: Optional[int] = None,\\n callback: Optional[Callable[[BasePass, DAGCircuit, float, PropertySet, int], Any]] = None,\\n output_name: Optional[Union[str, List[str]]] = None,\\n unitary_synthesis_method: str = \\\"default\\\",\\n unitary_synthesis_plugin_config: Optional[dict] = None,\\n target: Optional[Target] = None,\\n hls_config: Optional[HLSConfig] = None,\\n init_method: Optional[str] = None,\\n optimization_method: Optional[str] = None,\\n ignore_backend_supplied_default_methods: bool = False,\\n) -> _CircuitT:\\n \\\"\\\"\\\"Transpile one or more circuits, according to some desired transpilation targets.\\n\\n Transpilation is potentially done in parallel using multiprocessing when ``circuits``\\n is a list with > 1 :class:`~.QuantumCircuit` object depending on the local environment\\n and configuration.\\n\\n Args:\\n circuits: Circuit(s) to transpile\\n backend: If set, the transpiler will compile the input circuit to this target\\n device. If any other option is explicitly set (e.g., ``coupling_map``), it\\n will override the backend's.\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/compiler/transpiler.py::_check_circuits_coupling_map\",\n \"text\": \"qiskit/compiler/transpiler.py::_check_circuits_coupling_map\\ndef _check_circuits_coupling_map(circuits, cmap, backend):\\n # Check circuit width against number of qubits in coupling_map(s)\\n max_qubits = None\\n if cmap is not None:\\n max_qubits = cmap.size()\\n elif backend is not None:\\n backend_version = getattr(backend, \\\"version\\\", 0)\\n if backend_version <= 1:\\n if not backend.configuration().simulator:\\n max_qubits = backend.configuration().n_qubits\\n else:\\n max_qubits = None\\n else:\\n max_qubits = backend.num_qubits\\n for circuit in circuits:\\n # If coupling_map is not None or num_qubits == 1\\n num_qubits = len(circuit.qubits)\\n if max_qubits is not None and (num_qubits > max_qubits):\\n raise TranspilerError(\\n f\\\"Number of qubits ({num_qubits}) in {circuit.name} \\\"\\n f\\\"is greater than maximum ({max_qubits}) in the coupling_map\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/compiler/transpiler.py::_log_transpile_time\",\n \"text\": \"qiskit/compiler/transpiler.py::_log_transpile_time\\ndef _log_transpile_time(start_time, end_time):\\n log_msg = \\\"Total Transpile Time - %.5f (ms)\\\" % ((end_time - start_time) * 1000)\\n logger.info(log_msg)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/compiler/transpiler.py::_parse_inst_map\",\n \"text\": \"qiskit/compiler/transpiler.py::_parse_inst_map\\ndef _parse_inst_map(inst_map, backend):\\n # try getting inst_map from user, else backend\\n if inst_map is None:\\n backend_version = getattr(backend, \\\"version\\\", 0)\\n if backend_version <= 1:\\n if hasattr(backend, \\\"defaults\\\"):\\n inst_map = getattr(backend.defaults(), \\\"instruction_schedule_map\\\", None)\\n else:\\n inst_map = backend.target.instruction_schedule_map()\\n return inst_map\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/compiler/transpiler.py::_parse_coupling_map\",\n \"text\": \"qiskit/compiler/transpiler.py::_parse_coupling_map\\ndef _parse_coupling_map(coupling_map, backend):\\n # try getting coupling_map from user, else backend\\n if coupling_map is None:\\n backend_version = getattr(backend, \\\"version\\\", 0)\\n if backend_version <= 1:\\n if getattr(backend, \\\"configuration\\\", None):\\n configuration = backend.configuration()\\n if hasattr(configuration, \\\"coupling_map\\\") and configuration.coupling_map:\\n coupling_map = CouplingMap(configuration.coupling_map)\\n else:\\n coupling_map = backend.coupling_map\\n\\n # coupling_map could be None, or a list of lists, e.g. [[0, 1], [2, 1]]\\n if coupling_map is None or isinstance(coupling_map, CouplingMap):\\n return coupling_map\\n if isinstance(coupling_map, list) and all(\\n isinstance(i, list) and len(i) == 2 for i in coupling_map\\n ):\\n return CouplingMap(coupling_map)\\n else:\\n raise TranspilerError(\\n \\\"Only a single input coupling map can be used with transpile() if you need to \\\"\\n \\\"target different coupling maps for different circuits you must call transpile() \\\"\\n \\\"multiple times\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/compiler/transpiler.py::_parse_initial_layout\",\n \"text\": \"qiskit/compiler/transpiler.py::_parse_initial_layout\\ndef _parse_initial_layout(initial_layout):\\n # initial_layout could be None, or a list of ints, e.g. [0, 5, 14]\\n # or a list of tuples/None e.g. [qr[0], None, qr[1]] or a dict e.g. {qr[0]: 0}\\n if initial_layout is None or isinstance(initial_layout, Layout):\\n return initial_layout\\n if isinstance(initial_layout, dict):\\n return Layout(initial_layout)\\n if all(phys is None or isinstance(phys, Qubit) for phys in initial_layout):\\n return Layout.from_qubit_list(initial_layout)\\n else:\\n return initial_layout\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/compiler/transpiler.py::_parse_instruction_durations\",\n \"text\": \"qiskit/compiler/transpiler.py::_parse_instruction_durations\\ndef _parse_instruction_durations(backend, inst_durations, dt, circuit):\\n \\\"\\\"\\\"Create a list of ``InstructionDuration``s. If ``inst_durations`` is provided,\\n the backend will be ignored, otherwise, the durations will be populated from the\\n backend. If any circuits have gate calibrations, those calibration durations would\\n take precedence over backend durations, but be superceded by ``inst_duration``s.\\n \\\"\\\"\\\"\\n if not inst_durations:\\n backend_version = getattr(backend, \\\"version\\\", 0)\\n if backend_version <= 1:\\n backend_durations = InstructionDurations()\\n try:\\n backend_durations = InstructionDurations.from_backend(backend)\\n except AttributeError:\\n pass\\n else:\\n backend_durations = backend.instruction_durations\\n\\n circ_durations = InstructionDurations()\\n if not inst_durations:\\n circ_durations.update(backend_durations, dt or backend_durations.dt)\\n\\n if circuit.calibrations:\\n cal_durations = []\\n for gate, gate_cals in circuit.calibrations.items():\\n for (qubits, parameters), schedule in gate_cals.items():\\n cal_durations.append((gate, qubits, parameters, schedule.duration))\\n circ_durations.update(cal_durations, circ_durations.dt)\\n\\n if inst_durations:\\n circ_durations.update(inst_durations, dt or getattr(inst_durations, \\\"dt\\\", None))\\n\\n return circ_durations\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/compiler/transpiler.py::_parse_approximation_degree\",\n \"text\": \"qiskit/compiler/transpiler.py::_parse_approximation_degree\\ndef _parse_approximation_degree(approximation_degree):\\n if approximation_degree is None:\\n return None\\n if approximation_degree < 0.0 or approximation_degree > 1.0:\\n raise TranspilerError(\\\"Approximation degree must be in [0.0, 1.0]\\\")\\n return approximation_degree\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/compiler/transpiler.py::_parse_output_name\",\n \"text\": \"qiskit/compiler/transpiler.py::_parse_output_name\\ndef _parse_output_name(output_name, circuits):\\n # naming and returning circuits\\n # output_name could be either a string or a list\\n if output_name is not None:\\n if isinstance(output_name, str):\\n # single circuit\\n if len(circuits) == 1:\\n return [output_name]\\n # multiple circuits\\n else:\\n raise TranspilerError(\\n \\\"Expected a list object of length equal \\\"\\n + \\\"to that of the number of circuits \\\"\\n + \\\"being transpiled\\\"\\n )\\n elif isinstance(output_name, list):\\n if len(circuits) == len(output_name) and all(\\n isinstance(name, str) for name in output_name\\n ):\\n return output_name\\n else:\\n raise TranspilerError(\\n \\\"The length of output_name list \\\"\\n \\\"must be equal to the number of \\\"\\n \\\"transpiled circuits and the output_name \\\"\\n \\\"list should be strings.\\\"\\n )\\n else:\\n raise TranspilerError(\\n \\\"The parameter output_name should be a string or a\\\"\\n \\\"list of strings: %s was used.\\\" % type(output_name)\\n )\\n else:\\n return [circuit.name for circuit in circuits]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/compiler/transpiler.py::_parse_timing_constraints\",\n \"text\": \"qiskit/compiler/transpiler.py::_parse_timing_constraints\\ndef _parse_timing_constraints(backend, timing_constraints):\\n if isinstance(timing_constraints, TimingConstraints):\\n return timing_constraints\\n if backend is None and timing_constraints is None:\\n timing_constraints = TimingConstraints()\\n else:\\n backend_version = getattr(backend, \\\"version\\\", 0)\\n if backend_version <= 1:\\n if timing_constraints is None:\\n # get constraints from backend\\n timing_constraints = getattr(backend.configuration(), \\\"timing_constraints\\\", {})\\n timing_constraints = TimingConstraints(**timing_constraints)\\n else:\\n timing_constraints = backend.target.timing_constraints()\\n return timing_constraints\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/passmanager_config.py::PassManagerConfig.__init__\",\n \"text\": \"qiskit/transpiler/passmanager_config.py::PassManagerConfig.__init__\\n def __init__(\\n self,\\n initial_layout=None,\\n basis_gates=None,\\n inst_map=None,\\n coupling_map=None,\\n layout_method=None,\\n routing_method=None,\\n translation_method=None,\\n scheduling_method=None,\\n instruction_durations=None,\\n backend_properties=None,\\n approximation_degree=None,\\n seed_transpiler=None,\\n timing_constraints=None,\\n unitary_synthesis_method=\\\"default\\\",\\n unitary_synthesis_plugin_config=None,\\n target=None,\\n hls_config=None,\\n init_method=None,\\n optimization_method=None,\\n ):\\n \\\"\\\"\\\"Initialize a PassManagerConfig object\\n\\n Args:\\n initial_layout (Layout): Initial position of virtual qubits on\\n physical qubits.\\n basis_gates (list): List of basis gate names to unroll to.\\n inst_map (InstructionScheduleMap): Mapping object that maps gate to schedule.\\n coupling_map (CouplingMap): Directed graph represented a coupling\\n map.\\n layout_method (str): the pass to use for choosing initial qubit\\n placement. This will be the plugin name if an external layout stage\\n plugin is being used.\\n routing_method (str): the pass to use for routing qubits on the\\n architecture. This will be a plugin name if an external routing stage\\n plugin is being used.\\n translation_method (str): the pass to use for translating gates to\\n basis_gates. This will be a plugin name if an external translation stage\\n plugin is being used.\\n scheduling_method (str): the pass to use for scheduling instructions. This will\\n be a plugin name if an external scheduling stage plugin is being used.\\n instruction_durations (InstructionDurations): Dictionary of duration\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/passmanager_config.py::PassManagerConfig.from_backend\",\n \"text\": \"qiskit/transpiler/passmanager_config.py::PassManagerConfig.from_backend\\n @classmethod\\n def from_backend(cls, backend, _skip_target=False, **pass_manager_options):\\n \\\"\\\"\\\"Construct a configuration based on a backend and user input.\\n\\n This method automatically gererates a PassManagerConfig object based on the backend's\\n features. User options can be used to overwrite the configuration.\\n\\n Args:\\n backend (BackendV1): The backend that provides the configuration.\\n pass_manager_options: User-defined option-value pairs.\\n\\n Returns:\\n PassManagerConfig: The configuration generated based on the arguments.\\n\\n Raises:\\n AttributeError: If the backend does not support a `configuration()` method.\\n \\\"\\\"\\\"\\n res = cls(**pass_manager_options)\\n backend_version = getattr(backend, \\\"version\\\", 0)\\n if not isinstance(backend_version, int):\\n backend_version = 0\\n if backend_version < 2:\\n config = backend.configuration()\\n if res.basis_gates is None:\\n if backend_version < 2:\\n res.basis_gates = getattr(config, \\\"basis_gates\\\", None)\\n else:\\n res.basis_gates = backend.operation_names\\n if res.inst_map is None:\\n if backend_version < 2:\\n if hasattr(backend, \\\"defaults\\\"):\\n defaults = backend.defaults()\\n if defaults is not None:\\n res.inst_map = defaults.instruction_schedule_map\\n else:\\n res.inst_map = backend.instruction_schedule_map\\n if res.coupling_map is None:\\n if backend_version < 2:\\n cmap_edge_list = getattr(config, \\\"coupling_map\\\", None)\\n if cmap_edge_list is not None:\\n res.coupling_map = CouplingMap(cmap_edge_list)\\n else:\\n res.coupling_map = backend.coupling_map\\n if res.instruction_duration\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/passmanager_config.py::PassManagerConfig.__str__\",\n \"text\": \"qiskit/transpiler/passmanager_config.py::PassManagerConfig.__str__\\n def __str__(self):\\n newline = \\\"\\\\n\\\"\\n newline_tab = \\\"\\\\n\\\\t\\\"\\n if self.backend_properties is not None:\\n backend_props = pprint.pformat(self.backend_properties.to_dict())\\n backend_props = backend_props.replace(newline, newline_tab)\\n else:\\n backend_props = str(None)\\n return (\\n \\\"Pass Manager Config:\\\\n\\\"\\n f\\\"\\\\tinitial_layout: {self.initial_layout}\\\\n\\\"\\n f\\\"\\\\tbasis_gates: {self.basis_gates}\\\\n\\\"\\n f\\\"\\\\tinst_map: {str(self.inst_map).replace(newline, newline_tab)}\\\\n\\\"\\n f\\\"\\\\tcoupling_map: {self.coupling_map}\\\\n\\\"\\n f\\\"\\\\tlayout_method: {self.layout_method}\\\\n\\\"\\n f\\\"\\\\trouting_method: {self.routing_method}\\\\n\\\"\\n f\\\"\\\\ttranslation_method: {self.translation_method}\\\\n\\\"\\n f\\\"\\\\tscheduling_method: {self.scheduling_method}\\\\n\\\"\\n f\\\"\\\\tinstruction_durations: {str(self.instruction_durations).replace(newline, newline_tab)}\\\\n\\\"\\n f\\\"\\\\tbackend_properties: {backend_props}\\\\n\\\"\\n f\\\"\\\\tapproximation_degree: {self.approximation_degree}\\\\n\\\"\\n f\\\"\\\\tseed_transpiler: {self.seed_transpiler}\\\\n\\\"\\n f\\\"\\\\ttiming_constraints: {self.timing_constraints}\\\\n\\\"\\n f\\\"\\\\tunitary_synthesis_method: {self.unitary_synthesis_method}\\\\n\\\"\\n f\\\"\\\\tunitary_synthesis_plugin_config: {self.unitary_synthesis_plugin_config}\\\\n\\\"\\n f\\\"\\\\ttarget: {str(self.target).replace(newline, newline_tab)}\\\\n\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/__init__.py::generate_preset_pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/__init__.py::generate_preset_pass_manager\\ndef generate_preset_pass_manager(\\n optimization_level,\\n backend=None,\\n target=None,\\n basis_gates=None,\\n inst_map=None,\\n coupling_map=None,\\n instruction_durations=None,\\n backend_properties=None,\\n timing_constraints=None,\\n initial_layout=None,\\n layout_method=None,\\n routing_method=None,\\n translation_method=None,\\n scheduling_method=None,\\n approximation_degree=None,\\n seed_transpiler=None,\\n unitary_synthesis_method=\\\"default\\\",\\n unitary_synthesis_plugin_config=None,\\n hls_config=None,\\n init_method=None,\\n optimization_method=None,\\n *,\\n _skip_target=False,\\n):\\n \\\"\\\"\\\"Generate a preset :class:`~.PassManager`\\n\\n This function is used to quickly generate a preset pass manager. A preset pass\\n manager are the default pass managers used by the :func:`~.transpile`\\n function. This function provides a convenient and simple method to construct\\n a standalone :class:`~.PassManager` object that mirrors what the transpile\\n\\n\\n Args:\\n optimization_level (int): The optimization level to generate a\\n :class:`~.PassManager` for. This can be 0, 1, 2, or 3. Higher\\n levels generate more optimized circuits, at the expense of\\n longer transpilation time:\\n\\n * 0: no optimization\\n * 1: light optimization\\n * 2: heavy optimization\\n * 3: even heavier optimization\\n\\n backend (Backend): An optional backend object which can be used as the\\n source of the default values for the ``basis_gates``, ``inst_map``,\\n ``couplig_map``, ``backend_properties``, ``instruction_durations``,\\n ``timing_constraints``, and ``target``. If any of those other arguments\\n are specified in addition to ``backend`` they will take precedence\\n over the value contained in the backend.\\n target (Target): The :class:`~.Tar\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::BasisTranslatorPassManager.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::BasisTranslatorPassManager.pass_manager\\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\\n return common.generate_translation_passmanager(\\n pass_manager_config.target,\\n basis_gates=pass_manager_config.basis_gates,\\n method=\\\"translator\\\",\\n approximation_degree=pass_manager_config.approximation_degree,\\n coupling_map=pass_manager_config.coupling_map,\\n backend_props=pass_manager_config.backend_properties,\\n unitary_synthesis_method=pass_manager_config.unitary_synthesis_method,\\n unitary_synthesis_plugin_config=pass_manager_config.unitary_synthesis_plugin_config,\\n hls_config=pass_manager_config.hls_config,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::UnrollerPassManager.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::UnrollerPassManager.pass_manager\\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\\n return common.generate_translation_passmanager(\\n pass_manager_config.target,\\n basis_gates=pass_manager_config.basis_gates,\\n method=\\\"unroller\\\",\\n approximation_degree=pass_manager_config.approximation_degree,\\n coupling_map=pass_manager_config.coupling_map,\\n backend_props=pass_manager_config.backend_properties,\\n unitary_synthesis_method=pass_manager_config.unitary_synthesis_method,\\n unitary_synthesis_plugin_config=pass_manager_config.unitary_synthesis_plugin_config,\\n hls_config=pass_manager_config.hls_config,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::UnitarySynthesisPassManager.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::UnitarySynthesisPassManager.pass_manager\\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\\n return common.generate_translation_passmanager(\\n pass_manager_config.target,\\n basis_gates=pass_manager_config.basis_gates,\\n method=\\\"synthesis\\\",\\n approximation_degree=pass_manager_config.approximation_degree,\\n coupling_map=pass_manager_config.coupling_map,\\n backend_props=pass_manager_config.backend_properties,\\n unitary_synthesis_method=pass_manager_config.unitary_synthesis_method,\\n unitary_synthesis_plugin_config=pass_manager_config.unitary_synthesis_plugin_config,\\n hls_config=pass_manager_config.hls_config,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::BasicSwapPassManager.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::BasicSwapPassManager.pass_manager\\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\\n \\\"\\\"\\\"Build routing stage PassManager.\\\"\\\"\\\"\\n seed_transpiler = pass_manager_config.seed_transpiler\\n target = pass_manager_config.target\\n coupling_map = pass_manager_config.coupling_map\\n backend_properties = pass_manager_config.backend_properties\\n if target is None:\\n routing_pass = BasicSwap(coupling_map)\\n else:\\n routing_pass = BasicSwap(target)\\n\\n vf2_call_limit, vf2_max_trials = common.get_vf2_limits(\\n optimization_level,\\n pass_manager_config.layout_method,\\n pass_manager_config.initial_layout,\\n )\\n if optimization_level == 0:\\n return common.generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map=coupling_map,\\n seed_transpiler=seed_transpiler,\\n use_barrier_before_measurement=True,\\n )\\n if optimization_level == 1:\\n return common.generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map,\\n vf2_call_limit=vf2_call_limit,\\n vf2_max_trials=vf2_max_trials,\\n backend_properties=backend_properties,\\n seed_transpiler=seed_transpiler,\\n check_trivial=True,\\n use_barrier_before_measurement=True,\\n )\\n if optimization_level == 2:\\n return common.generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map=coupling_map,\\n vf2_call_limit=vf2_call_limit,\\n vf2_max_trials=vf2_max_trials,\\n backend_properties=backend_properties,\\n seed_transpiler=seed_transpiler,\\n use_barrier_before\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::StochasticSwapPassManager.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::StochasticSwapPassManager.pass_manager\\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\\n \\\"\\\"\\\"Build routing stage PassManager.\\\"\\\"\\\"\\n seed_transpiler = pass_manager_config.seed_transpiler\\n target = pass_manager_config.target\\n coupling_map = pass_manager_config.coupling_map\\n coupling_map_routing = target\\n if coupling_map_routing is None:\\n coupling_map_routing = coupling_map\\n backend_properties = pass_manager_config.backend_properties\\n vf2_call_limit, vf2_max_trials = common.get_vf2_limits(\\n optimization_level,\\n pass_manager_config.layout_method,\\n pass_manager_config.initial_layout,\\n )\\n if optimization_level == 3:\\n routing_pass = StochasticSwap(coupling_map_routing, trials=200, seed=seed_transpiler)\\n else:\\n routing_pass = StochasticSwap(coupling_map_routing, trials=20, seed=seed_transpiler)\\n\\n if optimization_level == 0:\\n return common.generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map=coupling_map,\\n seed_transpiler=seed_transpiler,\\n use_barrier_before_measurement=True,\\n )\\n if optimization_level == 1:\\n return common.generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map,\\n vf2_call_limit=vf2_call_limit,\\n vf2_max_trials=vf2_max_trials,\\n backend_properties=backend_properties,\\n seed_transpiler=seed_transpiler,\\n check_trivial=True,\\n use_barrier_before_measurement=True,\\n )\\n if optimization_level in {2, 3}:\\n return common.generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map=c\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::LookaheadSwapPassManager.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::LookaheadSwapPassManager.pass_manager\\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\\n \\\"\\\"\\\"Build routing stage PassManager.\\\"\\\"\\\"\\n seed_transpiler = pass_manager_config.seed_transpiler\\n target = pass_manager_config.target\\n coupling_map = pass_manager_config.coupling_map\\n coupling_map_routing = target\\n if coupling_map_routing is None:\\n coupling_map_routing = coupling_map\\n backend_properties = pass_manager_config.backend_properties\\n vf2_call_limit, vf2_max_trials = common.get_vf2_limits(\\n optimization_level,\\n pass_manager_config.layout_method,\\n pass_manager_config.initial_layout,\\n )\\n if optimization_level == 0:\\n routing_pass = LookaheadSwap(coupling_map_routing, search_depth=2, search_width=2)\\n return common.generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map=coupling_map,\\n seed_transpiler=seed_transpiler,\\n use_barrier_before_measurement=True,\\n )\\n if optimization_level == 1:\\n routing_pass = LookaheadSwap(coupling_map_routing, search_depth=4, search_width=4)\\n return common.generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map,\\n vf2_call_limit=vf2_call_limit,\\n vf2_max_trials=vf2_max_trials,\\n backend_properties=backend_properties,\\n seed_transpiler=seed_transpiler,\\n check_trivial=True,\\n use_barrier_before_measurement=True,\\n )\\n if optimization_level == 2:\\n routing_pass = LookaheadSwap(coupling_map_routing, search_depth=5, search_width=6)\\n return common.generate_routing_passmanager(\\n routing_pass,\\n targe\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::SabreSwapPassManager.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::SabreSwapPassManager.pass_manager\\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\\n \\\"\\\"\\\"Build routing stage PassManager.\\\"\\\"\\\"\\n seed_transpiler = pass_manager_config.seed_transpiler\\n target = pass_manager_config.target\\n coupling_map = pass_manager_config.coupling_map\\n coupling_map_routing = target\\n if coupling_map_routing is None:\\n coupling_map_routing = coupling_map\\n backend_properties = pass_manager_config.backend_properties\\n vf2_call_limit, vf2_max_trials = common.get_vf2_limits(\\n optimization_level,\\n pass_manager_config.layout_method,\\n pass_manager_config.initial_layout,\\n )\\n if optimization_level == 0:\\n routing_pass = SabreSwap(\\n coupling_map_routing,\\n heuristic=\\\"basic\\\",\\n seed=seed_transpiler,\\n trials=5,\\n )\\n return common.generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map=coupling_map,\\n seed_transpiler=seed_transpiler,\\n use_barrier_before_measurement=True,\\n )\\n if optimization_level == 1:\\n routing_pass = SabreSwap(\\n coupling_map_routing,\\n heuristic=\\\"decay\\\",\\n seed=seed_transpiler,\\n trials=5,\\n )\\n return common.generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map,\\n vf2_call_limit=vf2_call_limit,\\n vf2_max_trials=vf2_max_trials,\\n backend_properties=backend_properties,\\n seed_transpiler=seed_transpiler,\\n check_trivial=True,\\n use_barrier_before_measurement=True,\\n )\\n if optimization_level == 2:\\n routin\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::NoneRoutingPassManager.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::NoneRoutingPassManager.pass_manager\\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\\n \\\"\\\"\\\"Build routing stage PassManager.\\\"\\\"\\\"\\n seed_transpiler = pass_manager_config.seed_transpiler\\n target = pass_manager_config.target\\n coupling_map = pass_manager_config.coupling_map\\n routing_pass = Error(\\n msg=\\\"No routing method selected, but circuit is not routed to device. \\\"\\n \\\"CheckMap Error: {check_map_msg}\\\",\\n action=\\\"raise\\\",\\n )\\n return common.generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map=coupling_map,\\n seed_transpiler=seed_transpiler,\\n use_barrier_before_measurement=True,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::AlapSchedulingPassManager.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::AlapSchedulingPassManager.pass_manager\\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\\n \\\"\\\"\\\"Build scheduling stage PassManager\\\"\\\"\\\"\\n\\n instruction_durations = pass_manager_config.instruction_durations\\n scheduling_method = pass_manager_config.scheduling_method\\n timing_constraints = pass_manager_config.timing_constraints\\n inst_map = pass_manager_config.inst_map\\n target = pass_manager_config.target\\n\\n return common.generate_scheduling(\\n instruction_durations, scheduling_method, timing_constraints, inst_map, target\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::AsapSchedulingPassManager.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::AsapSchedulingPassManager.pass_manager\\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\\n \\\"\\\"\\\"Build scheduling stage PassManager\\\"\\\"\\\"\\n\\n instruction_durations = pass_manager_config.instruction_durations\\n scheduling_method = pass_manager_config.scheduling_method\\n timing_constraints = pass_manager_config.timing_constraints\\n inst_map = pass_manager_config.inst_map\\n target = pass_manager_config.target\\n\\n return common.generate_scheduling(\\n instruction_durations, scheduling_method, timing_constraints, inst_map, target\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::DefaultSchedulingPassManager.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/builtin_plugins.py::DefaultSchedulingPassManager.pass_manager\\n def pass_manager(self, pass_manager_config, optimization_level=None) -> PassManager:\\n \\\"\\\"\\\"Build scheduling stage PassManager\\\"\\\"\\\"\\n\\n instruction_durations = pass_manager_config.instruction_durations\\n scheduling_method = None\\n timing_constraints = pass_manager_config.timing_constraints or TimingConstraints()\\n inst_map = pass_manager_config.inst_map\\n target = pass_manager_config.target\\n\\n return common.generate_scheduling(\\n instruction_durations, scheduling_method, timing_constraints, inst_map, target\\n )\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::_has_control_flow\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::_has_control_flow\\ndef _has_control_flow(property_set):\\n return any(property_set[f\\\"contains_{x}\\\"] for x in _CONTROL_FLOW_OP_NAMES)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::_without_control_flow\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::_without_control_flow\\ndef _without_control_flow(property_set):\\n return not any(property_set[f\\\"contains_{x}\\\"] for x in _CONTROL_FLOW_OP_NAMES)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.__init__\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.__init__\\n def __init__(self, basis_gates=(), target=None):\\n if target is not None:\\n self.unsupported = [op for op in _CONTROL_FLOW_OP_NAMES if op not in target]\\n else:\\n basis_gates = set(basis_gates) if basis_gates is not None else set()\\n self.unsupported = [op for op in _CONTROL_FLOW_OP_NAMES if op not in basis_gates]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.message\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.message\\n def message(self, property_set):\\n \\\"\\\"\\\"Create an error message for the given property set.\\\"\\\"\\\"\\n fails = [x for x in self.unsupported if property_set[f\\\"contains_{x}\\\"]]\\n if len(fails) == 1:\\n return f\\\"The control-flow construct '{fails[0]}' is not supported by the backend.\\\"\\n return (\\n f\\\"The control-flow constructs [{', '.join(repr(op) for op in fails)}]\\\"\\n \\\" are not supported by the backend.\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.condition\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::_InvalidControlFlowForBackend.condition\\n def condition(self, property_set):\\n \\\"\\\"\\\"Checkable condition for the given property set.\\\"\\\"\\\"\\n return any(property_set[f\\\"contains_{x}\\\"] for x in self.unsupported)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_control_flow_options_check\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_control_flow_options_check\\ndef generate_control_flow_options_check(\\n layout_method=None,\\n routing_method=None,\\n translation_method=None,\\n optimization_method=None,\\n scheduling_method=None,\\n basis_gates=(),\\n target=None,\\n):\\n \\\"\\\"\\\"Generate a pass manager that, when run on a DAG that contains control flow, fails with an\\n error message explaining the invalid options, and what could be used instead.\\n\\n Returns:\\n PassManager: a pass manager that populates the ``contains_x`` properties for each of the\\n control-flow operations, and raises an error if any of the given options do not support\\n control flow, but a circuit with control flow is given.\\n \\\"\\\"\\\"\\n bad_options = []\\n message = \\\"Some options cannot be used with control flow.\\\"\\n for stage, given in [\\n (\\\"layout\\\", layout_method),\\n (\\\"routing\\\", routing_method),\\n (\\\"translation\\\", translation_method),\\n (\\\"optimization\\\", optimization_method),\\n (\\\"scheduling\\\", scheduling_method),\\n ]:\\n option = stage + \\\"_method\\\"\\n method_states = _CONTROL_FLOW_STATES[option]\\n if given is not None and given in method_states.not_working:\\n if method_states.working:\\n message += (\\n f\\\" Got {option}='{given}', but valid values are {list(method_states.working)}.\\\"\\n )\\n else:\\n message += (\\n f\\\" Got {option}='{given}', but the entire {stage} stage is not supported.\\\"\\n )\\n bad_options.append(option)\\n out = PassManager()\\n out.append(ContainsInstruction(_CONTROL_FLOW_OP_NAMES, recurse=False))\\n if bad_options:\\n out.append(Error(message), condition=_has_control_flow)\\n backend_control = _InvalidControlFlowForBackend(basis_gates, target)\\n out.append(Error(backend_control.message), condition=backend_control.condition)\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_error_on_control_flow\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_error_on_control_flow\\ndef generate_error_on_control_flow(message):\\n \\\"\\\"\\\"Get a pass manager that always raises an error if control flow is present in a given\\n circuit.\\\"\\\"\\\"\\n out = PassManager()\\n out.append(ContainsInstruction(_CONTROL_FLOW_OP_NAMES, recurse=False))\\n out.append(Error(message), condition=_has_control_flow)\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::if_has_control_flow_else\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::if_has_control_flow_else\\ndef if_has_control_flow_else(if_present, if_absent):\\n \\\"\\\"\\\"Generate a pass manager that will run the passes in ``if_present`` if the given circuit\\n has control-flow operations in it, and those in ``if_absent`` if it doesn't.\\\"\\\"\\\"\\n if isinstance(if_present, PassManager):\\n if_present = if_present.to_flow_controller()\\n if isinstance(if_absent, PassManager):\\n if_absent = if_absent.to_flow_controller()\\n out = PassManager()\\n out.append(ContainsInstruction(_CONTROL_FLOW_OP_NAMES, recurse=False))\\n out.append(if_present, condition=_has_control_flow)\\n out.append(if_absent, condition=_without_control_flow)\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_unroll_3q\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_unroll_3q\\ndef generate_unroll_3q(\\n target,\\n basis_gates=None,\\n approximation_degree=None,\\n unitary_synthesis_method=\\\"default\\\",\\n unitary_synthesis_plugin_config=None,\\n hls_config=None,\\n):\\n \\\"\\\"\\\"Generate an unroll >3q :class:`~qiskit.transpiler.PassManager`\\n\\n Args:\\n target (Target): the :class:`~.Target` object representing the backend\\n basis_gates (list): A list of str gate names that represent the basis\\n gates on the backend target\\n approximation_degree (Optional[float]): The heuristic approximation degree to\\n use. Can be between 0 and 1.\\n unitary_synthesis_method (str): The unitary synthesis method to use. You can see\\n a list of installed plugins with :func:`.unitary_synthesis_plugin_names`.\\n unitary_synthesis_plugin_config (dict): The optional dictionary plugin\\n configuration, this is plugin specific refer to the specified plugin's\\n documentation for how to use.\\n hls_config (HLSConfig): An optional configuration class to use for\\n :class:`~qiskit.transpiler.passes.HighLevelSynthesis` pass.\\n Specifies how to synthesize various high-level objects.\\n\\n Returns:\\n PassManager: The unroll 3q or more pass manager\\n \\\"\\\"\\\"\\n unroll_3q = PassManager()\\n unroll_3q.append(\\n UnitarySynthesis(\\n basis_gates,\\n approximation_degree=approximation_degree,\\n method=unitary_synthesis_method,\\n min_qubits=3,\\n plugin_config=unitary_synthesis_plugin_config,\\n target=target,\\n )\\n )\\n unroll_3q.append(\\n HighLevelSynthesis(\\n hls_config=hls_config, coupling_map=None, target=target, use_qubit_indices=False\\n )\\n )\\n unroll_3q.append(Unroll3qOrMore(target=target, basis_gates=basis_gates))\\n return unroll_3q\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_embed_passmanager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_embed_passmanager\\ndef generate_embed_passmanager(coupling_map):\\n \\\"\\\"\\\"Generate a layout embedding :class:`~qiskit.transpiler.PassManager`\\n\\n This is used to generate a :class:`~qiskit.transpiler.PassManager` object\\n that can be used to expand and apply an initial layout to a circuit\\n\\n Args:\\n coupling_map (Union[CouplingMap, Target): The coupling map for the backend to embed\\n the circuit to.\\n Returns:\\n PassManager: The embedding passmanager that assumes the layout property\\n set has been set in earlier stages\\n \\\"\\\"\\\"\\n return PassManager([FullAncillaAllocation(coupling_map), EnlargeWithAncilla(), ApplyLayout()])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::_layout_not_perfect\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::_layout_not_perfect\\ndef _layout_not_perfect(property_set):\\n \\\"\\\"\\\"Return ``True`` if the first attempt at layout has been checked and found to be imperfect.\\n In this case, perfection means \\\"does not require any swap routing\\\".\\\"\\\"\\\"\\n return property_set[\\\"is_swap_mapped\\\"] is not None and not property_set[\\\"is_swap_mapped\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::_apply_post_layout_condition\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::_apply_post_layout_condition\\ndef _apply_post_layout_condition(property_set):\\n # if VF2 Post layout found a solution we need to re-apply the better\\n # layout. Otherwise we can skip apply layout.\\n return (\\n property_set[\\\"VF2PostLayout_stop_reason\\\"] is not None\\n and property_set[\\\"VF2PostLayout_stop_reason\\\"] is VF2PostLayoutStopReason.SOLUTION_FOUND\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager\\ndef generate_routing_passmanager(\\n routing_pass,\\n target,\\n coupling_map=None,\\n vf2_call_limit=None,\\n backend_properties=None,\\n seed_transpiler=None,\\n check_trivial=False,\\n use_barrier_before_measurement=True,\\n vf2_max_trials=None,\\n):\\n \\\"\\\"\\\"Generate a routing :class:`~qiskit.transpiler.PassManager`\\n\\n Args:\\n routing_pass (TransformationPass): The pass which will perform the\\n routing\\n target (Target): the :class:`~.Target` object representing the backend\\n coupling_map (CouplingMap): The coupling map of the backend to route\\n for\\n vf2_call_limit (int): The internal call limit for the vf2 post layout\\n pass. If this is ``None`` or ``0`` the vf2 post layout will not be\\n run.\\n backend_properties (BackendProperties): Properties of a backend to\\n synthesize for (e.g. gate fidelities).\\n seed_transpiler (int): Sets random seed for the stochastic parts of\\n the transpiler.\\n check_trivial (bool): If set to true this will condition running the\\n :class:`~.VF2PostLayout` pass after routing on whether a trivial\\n layout was tried and was found to not be perfect. This is only\\n needed if the constructed pass manager runs :class:`~.TrivialLayout`\\n as a first layout attempt and uses it if it's a perfect layout\\n (as is the case with preset pass manager level 1).\\n use_barrier_before_measurement (bool): If true (the default) the\\n :class:`~.BarrierBeforeFinalMeasurements` transpiler pass will be run prior to the\\n specified pass in the ``routing_pass`` argument.\\n vf2_max_trials (int): The maximum number of trials to run VF2 when\\n evaluating the vf2 post layout\\n pass. If this is ``None`` or ``0`` the vf2 post layout will not be run.\\n Returns:\\n PassManager: \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager._run_post_layout_condition\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager._run_post_layout_condition\\n def _run_post_layout_condition(property_set):\\n # If we check trivial layout and the found trivial layout was not perfect also\\n # ensure VF2 initial layout was not used before running vf2 post layout\\n if not check_trivial or _layout_not_perfect(property_set):\\n vf2_stop_reason = property_set[\\\"VF2Layout_stop_reason\\\"]\\n if vf2_stop_reason is None or vf2_stop_reason != VF2LayoutStopReason.SOLUTION_FOUND:\\n return True\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager._swap_condition\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_routing_passmanager._swap_condition\\n def _swap_condition(property_set):\\n return not property_set[\\\"routing_not_needed\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_pre_op_passmanager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_pre_op_passmanager\\ndef generate_pre_op_passmanager(target=None, coupling_map=None, remove_reset_in_zero=False):\\n \\\"\\\"\\\"Generate a pre-optimization loop :class:`~qiskit.transpiler.PassManager`\\n\\n This pass manager will check to ensure that directionality from the coupling\\n map is respected\\n\\n Args:\\n target (Target): the :class:`~.Target` object representing the backend\\n coupling_map (CouplingMap): The coupling map to use\\n remove_reset_in_zero (bool): If ``True`` include the remove reset in\\n zero pass in the generated PassManager\\n Returns:\\n PassManager: The pass manager\\n\\n \\\"\\\"\\\"\\n pre_opt = PassManager()\\n if coupling_map:\\n pre_opt.append(CheckGateDirection(coupling_map, target=target))\\n\\n def _direction_condition(property_set):\\n return not property_set[\\\"is_direction_mapped\\\"]\\n\\n pre_opt.append([GateDirection(coupling_map, target=target)], condition=_direction_condition)\\n if remove_reset_in_zero:\\n pre_opt.append(RemoveResetInZeroState())\\n return pre_opt\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_translation_passmanager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_translation_passmanager\\ndef generate_translation_passmanager(\\n target,\\n basis_gates=None,\\n method=\\\"translator\\\",\\n approximation_degree=None,\\n coupling_map=None,\\n backend_props=None,\\n unitary_synthesis_method=\\\"default\\\",\\n unitary_synthesis_plugin_config=None,\\n hls_config=None,\\n):\\n \\\"\\\"\\\"Generate a basis translation :class:`~qiskit.transpiler.PassManager`\\n\\n Args:\\n target (Target): the :class:`~.Target` object representing the backend\\n basis_gates (list): A list of str gate names that represent the basis\\n gates on the backend target\\n method (str): The basis translation method to use\\n approximation_degree (Optional[float]): The heuristic approximation degree to\\n use. Can be between 0 and 1.\\n coupling_map (CouplingMap): the coupling map of the backend\\n in case synthesis is done on a physical circuit. The\\n directionality of the coupling_map will be taken into\\n account if pulse_optimize is True/None and natural_direction\\n is True/None.\\n unitary_synthesis_plugin_config (dict): The optional dictionary plugin\\n configuration, this is plugin specific refer to the specified plugin's\\n documentation for how to use.\\n backend_props (BackendProperties): Properties of a backend to\\n synthesize for (e.g. gate fidelities).\\n unitary_synthesis_method (str): The unitary synthesis method to use. You can\\n see a list of installed plugins with :func:`.unitary_synthesis_plugin_names`.\\n hls_config (HLSConfig): An optional configuration class to use for\\n :class:`~qiskit.transpiler.passes.HighLevelSynthesis` pass.\\n Specifies how to synthesize various high-level objects.\\n\\n Returns:\\n PassManager: The basis translation pass manager\\n\\n Raises:\\n TranspilerError: If the ``method`` kwarg is not a valid value\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_scheduling\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::generate_scheduling\\ndef generate_scheduling(\\n instruction_durations, scheduling_method, timing_constraints, inst_map, target=None\\n):\\n \\\"\\\"\\\"Generate a post optimization scheduling :class:`~qiskit.transpiler.PassManager`\\n\\n Args:\\n instruction_durations (dict): The dictionary of instruction durations\\n scheduling_method (str): The scheduling method to use, can either be\\n ``'asap'``/``'as_soon_as_possible'`` or\\n ``'alap'``/``'as_late_as_possible'``\\n timing_constraints (TimingConstraints): Hardware time alignment restrictions.\\n inst_map (InstructionScheduleMap): Mapping object that maps gate to schedule.\\n target (Target): The :class:`~.Target` object representing the backend\\n\\n Returns:\\n PassManager: The scheduling pass manager\\n\\n Raises:\\n TranspilerError: If the ``scheduling_method`` kwarg is not a valid value\\n \\\"\\\"\\\"\\n scheduling = PassManager()\\n if inst_map and inst_map.has_custom_gate():\\n scheduling.append(PulseGates(inst_map=inst_map, target=target))\\n if scheduling_method:\\n # Do scheduling after unit conversion.\\n scheduler = {\\n \\\"alap\\\": ALAPScheduleAnalysis,\\n \\\"as_late_as_possible\\\": ALAPScheduleAnalysis,\\n \\\"asap\\\": ASAPScheduleAnalysis,\\n \\\"as_soon_as_possible\\\": ASAPScheduleAnalysis,\\n }\\n scheduling.append(TimeUnitConversion(instruction_durations, target=target))\\n try:\\n scheduling.append(scheduler[scheduling_method](instruction_durations, target=target))\\n except KeyError as ex:\\n raise TranspilerError(\\\"Invalid scheduling method %s.\\\" % scheduling_method) from ex\\n elif instruction_durations:\\n # No scheduling. But do unit conversion for delays.\\n def _contains_delay(property_set):\\n return property_set[\\\"contains_delay\\\"]\\n\\n scheduling.append(ContainsInstruction(\\\"delay\\\"))\\n scheduling.append(\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::get_vf2_call_limit\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::get_vf2_call_limit\\n@deprecate_func(\\n additional_msg=\\\"Instead, use :func:`~qiskit.transpiler.preset_passmanagers.common.get_vf2_limits`.\\\",\\n since=\\\"0.25.0\\\",\\n)\\ndef get_vf2_call_limit(\\n optimization_level: int,\\n layout_method: Optional[str] = None,\\n initial_layout: Optional[Layout] = None,\\n) -> Optional[int]:\\n \\\"\\\"\\\"Get the vf2 call limit for vf2 based layout passes.\\\"\\\"\\\"\\n vf2_call_limit = None\\n if layout_method is None and initial_layout is None:\\n if optimization_level == 1:\\n vf2_call_limit = int(5e4) # Set call limit to ~100ms with rustworkx 0.10.2\\n elif optimization_level == 2:\\n vf2_call_limit = int(5e6) # Set call limit to ~10 sec with rustworkx 0.10.2\\n elif optimization_level == 3:\\n vf2_call_limit = int(3e7) # Set call limit to ~60 sec with rustworkx 0.10.2\\n return vf2_call_limit\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/common.py::get_vf2_limits\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/common.py::get_vf2_limits\\ndef get_vf2_limits(\\n optimization_level: int,\\n layout_method: Optional[str] = None,\\n initial_layout: Optional[Layout] = None,\\n) -> VF2Limits:\\n \\\"\\\"\\\"Get the VF2 limits for VF2-based layout passes.\\n\\n Returns:\\n VF2Limits: An namedtuple with optional elements\\n ``call_limit`` and ``max_trials``.\\n \\\"\\\"\\\"\\n limits = VF2Limits(None, None)\\n if layout_method is None and initial_layout is None:\\n if optimization_level == 1:\\n limits = VF2Limits(\\n int(5e4), # Set call limit to ~100ms with rustworkx 0.10.2\\n 2500, # Limits layout scoring to < 600ms on ~400 qubit devices\\n )\\n elif optimization_level == 2:\\n limits = VF2Limits(\\n int(5e6), # Set call limit to ~10 sec with rustworkx 0.10.2\\n 25000, # Limits layout scoring to < 6 sec on ~400 qubit devices\\n )\\n elif optimization_level == 3:\\n limits = VF2Limits(\\n int(3e7), # Set call limit to ~60 sec with rustworkx 0.10.2\\n 250000, # Limits layout scoring to < 60 sec on ~400 qubit devices\\n )\\n return limits\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager\\ndef level_0_pass_manager(pass_manager_config: PassManagerConfig) -> StagedPassManager:\\n \\\"\\\"\\\"Level 0 pass manager: no explicit optimization other than mapping to backend.\\n\\n This pass manager applies the user-given initial layout. If none is given, a trivial\\n layout consisting of mapping the i-th virtual qubit to the i-th physical qubit is used.\\n Any unused physical qubit is allocated as ancilla space.\\n\\n The pass manager then unrolls the circuit to the desired basis, and transforms the\\n circuit to match the coupling map.\\n\\n Args:\\n pass_manager_config: configuration of the pass manager.\\n\\n Returns:\\n a level 0 pass manager.\\n\\n Raises:\\n TranspilerError: if the passmanager config is invalid.\\n \\\"\\\"\\\"\\n plugin_manager = PassManagerStagePluginManager()\\n basis_gates = pass_manager_config.basis_gates\\n coupling_map = pass_manager_config.coupling_map\\n initial_layout = pass_manager_config.initial_layout\\n init_method = pass_manager_config.init_method\\n layout_method = pass_manager_config.layout_method or \\\"trivial\\\"\\n routing_method = pass_manager_config.routing_method or \\\"stochastic\\\"\\n translation_method = pass_manager_config.translation_method or \\\"translator\\\"\\n optimization_method = pass_manager_config.optimization_method\\n scheduling_method = pass_manager_config.scheduling_method or \\\"default\\\"\\n seed_transpiler = pass_manager_config.seed_transpiler\\n backend_properties = pass_manager_config.backend_properties\\n approximation_degree = pass_manager_config.approximation_degree\\n unitary_synthesis_method = pass_manager_config.unitary_synthesis_method\\n unitary_synthesis_plugin_config = pass_manager_config.unitary_synthesis_plugin_config\\n target = pass_manager_config.target\\n hls_config = pass_manager_config.hls_config\\n\\n # Choose an initial layout if not set by user (default: trivial layout)\\n _given_layout = SetLayout(initial_lay\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager._choose_layout_condition\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level0.py::level_0_pass_manager._choose_layout_condition\\n def _choose_layout_condition(property_set):\\n return not property_set[\\\"layout\\\"]\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager\\ndef level_1_pass_manager(pass_manager_config: PassManagerConfig) -> StagedPassManager:\\n \\\"\\\"\\\"Level 1 pass manager: light optimization by simple adjacent gate collapsing.\\n\\n This pass manager applies the user-given initial layout. If none is given,\\n and a trivial layout (i-th virtual -> i-th physical) makes the circuit fit\\n the coupling map, that is used.\\n Otherwise, the circuit is mapped to the most densely connected coupling subgraph,\\n and swaps are inserted to map. Any unused physical qubit is allocated as ancilla space.\\n The pass manager then unrolls the circuit to the desired basis, and transforms the\\n circuit to match the coupling map. Finally, optimizations in the form of adjacent\\n gate collapse and redundant reset removal are performed.\\n\\n Args:\\n pass_manager_config: configuration of the pass manager.\\n\\n Returns:\\n a level 1 pass manager.\\n\\n Raises:\\n TranspilerError: if the passmanager config is invalid.\\n \\\"\\\"\\\"\\n plugin_manager = PassManagerStagePluginManager()\\n basis_gates = pass_manager_config.basis_gates\\n coupling_map = pass_manager_config.coupling_map\\n initial_layout = pass_manager_config.initial_layout\\n init_method = pass_manager_config.init_method\\n # Unlike other presets, the layout and routing defaults aren't set here because they change\\n # based on whether the input circuit has control flow.\\n layout_method = pass_manager_config.layout_method or \\\"sabre\\\"\\n routing_method = pass_manager_config.routing_method or \\\"sabre\\\"\\n translation_method = pass_manager_config.translation_method or \\\"translator\\\"\\n optimization_method = pass_manager_config.optimization_method\\n scheduling_method = pass_manager_config.scheduling_method or \\\"default\\\"\\n seed_transpiler = pass_manager_config.seed_transpiler\\n backend_properties = pass_manager_config.backend_properties\\n approximation_degree = pass_manager_config.approxima\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._choose_layout_condition\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._choose_layout_condition\\n def _choose_layout_condition(property_set):\\n return not property_set[\\\"layout\\\"]\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._layout_not_perfect\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._layout_not_perfect\\n def _layout_not_perfect(property_set):\\n \\\"\\\"\\\"Return ``True`` if the first attempt at layout has been checked and found to be\\n imperfect. In this case, perfection means \\\"does not require any swap routing\\\".\\\"\\\"\\\"\\n return property_set[\\\"is_swap_mapped\\\"] is not None and not property_set[\\\"is_swap_mapped\\\"]\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._vf2_match_not_found\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._vf2_match_not_found\\n def _vf2_match_not_found(property_set):\\n # If a layout hasn't been set by the time we run vf2 layout we need to\\n # run layout\\n if property_set[\\\"layout\\\"] is None:\\n return True\\n # if VF2 layout stopped for any reason other than solution found we need\\n # to run layout since VF2 didn't converge.\\n if (\\n property_set[\\\"VF2Layout_stop_reason\\\"] is not None\\n and property_set[\\\"VF2Layout_stop_reason\\\"] is not VF2LayoutStopReason.SOLUTION_FOUND\\n ):\\n return True\\n return False\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._opt_control\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level1.py::level_1_pass_manager._opt_control\\n def _opt_control(property_set):\\n return (not property_set[\\\"depth_fixed_point\\\"]) or (not property_set[\\\"size_fixed_point\\\"])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager\\ndef level_2_pass_manager(pass_manager_config: PassManagerConfig) -> StagedPassManager:\\n \\\"\\\"\\\"Level 2 pass manager: medium optimization by initial layout selection and\\n gate cancellation using commutativity rules.\\n\\n This pass manager applies the user-given initial layout. If none is given, a search\\n for a perfect layout (i.e. one that satisfies all 2-qubit interactions) is conducted.\\n If no such layout is found, qubits are laid out on the most densely connected subset\\n which also exhibits the best gate fidelities.\\n\\n The pass manager then transforms the circuit to match the coupling constraints.\\n It is then unrolled to the basis, and any flipped cx directions are fixed.\\n Finally, optimizations in the form of commutative gate cancellation and redundant\\n reset removal are performed.\\n\\n Args:\\n pass_manager_config: configuration of the pass manager.\\n\\n Returns:\\n a level 2 pass manager.\\n\\n Raises:\\n TranspilerError: if the passmanager config is invalid.\\n \\\"\\\"\\\"\\n plugin_manager = PassManagerStagePluginManager()\\n basis_gates = pass_manager_config.basis_gates\\n coupling_map = pass_manager_config.coupling_map\\n initial_layout = pass_manager_config.initial_layout\\n init_method = pass_manager_config.init_method\\n layout_method = pass_manager_config.layout_method or \\\"sabre\\\"\\n routing_method = pass_manager_config.routing_method or \\\"sabre\\\"\\n translation_method = pass_manager_config.translation_method or \\\"translator\\\"\\n optimization_method = pass_manager_config.optimization_method\\n scheduling_method = pass_manager_config.scheduling_method or \\\"default\\\"\\n seed_transpiler = pass_manager_config.seed_transpiler\\n backend_properties = pass_manager_config.backend_properties\\n approximation_degree = pass_manager_config.approximation_degree\\n unitary_synthesis_method = pass_manager_config.unitary_synthesis_method\\n unitary_synthesis_plugi\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._choose_layout_condition\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._choose_layout_condition\\n def _choose_layout_condition(property_set):\\n # layout hasn't been set yet\\n return not property_set[\\\"layout\\\"]\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._vf2_match_not_found\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._vf2_match_not_found\\n def _vf2_match_not_found(property_set):\\n # If a layout hasn't been set by the time we run vf2 layout we need to\\n # run layout\\n if property_set[\\\"layout\\\"] is None:\\n return True\\n # if VF2 layout stopped for any reason other than solution found we need\\n # to run layout since VF2 didn't converge.\\n if (\\n property_set[\\\"VF2Layout_stop_reason\\\"] is not None\\n and property_set[\\\"VF2Layout_stop_reason\\\"] is not VF2LayoutStopReason.SOLUTION_FOUND\\n ):\\n return True\\n return False\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._opt_control\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level2.py::level_2_pass_manager._opt_control\\n def _opt_control(property_set):\\n return (not property_set[\\\"depth_fixed_point\\\"]) or (not property_set[\\\"size_fixed_point\\\"])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager\\ndef level_3_pass_manager(pass_manager_config: PassManagerConfig) -> StagedPassManager:\\n \\\"\\\"\\\"Level 3 pass manager: heavy optimization by noise adaptive qubit mapping and\\n gate cancellation using commutativity rules and unitary synthesis.\\n\\n This pass manager applies the user-given initial layout. If none is given, a search\\n for a perfect layout (i.e. one that satisfies all 2-qubit interactions) is conducted.\\n If no such layout is found, and device calibration information is available, the\\n circuit is mapped to the qubits with best readouts and to CX gates with highest fidelity.\\n\\n The pass manager then transforms the circuit to match the coupling constraints.\\n It is then unrolled to the basis, and any flipped cx directions are fixed.\\n Finally, optimizations in the form of commutative gate cancellation, resynthesis\\n of two-qubit unitary blocks, and redundant reset removal are performed.\\n\\n Args:\\n pass_manager_config: configuration of the pass manager.\\n\\n Returns:\\n a level 3 pass manager.\\n\\n Raises:\\n TranspilerError: if the passmanager config is invalid.\\n \\\"\\\"\\\"\\n plugin_manager = PassManagerStagePluginManager()\\n basis_gates = pass_manager_config.basis_gates\\n coupling_map = pass_manager_config.coupling_map\\n initial_layout = pass_manager_config.initial_layout\\n init_method = pass_manager_config.init_method\\n layout_method = pass_manager_config.layout_method or \\\"sabre\\\"\\n routing_method = pass_manager_config.routing_method or \\\"sabre\\\"\\n translation_method = pass_manager_config.translation_method or \\\"translator\\\"\\n optimization_method = pass_manager_config.optimization_method\\n scheduling_method = pass_manager_config.scheduling_method or \\\"default\\\"\\n seed_transpiler = pass_manager_config.seed_transpiler\\n backend_properties = pass_manager_config.backend_properties\\n approximation_degree = pass_manager_config.approximation_deg\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._choose_layout_condition\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._choose_layout_condition\\n def _choose_layout_condition(property_set):\\n # layout hasn't been set yet\\n return not property_set[\\\"layout\\\"]\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._vf2_match_not_found\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._vf2_match_not_found\\n def _vf2_match_not_found(property_set):\\n # If a layout hasn't been set by the time we run vf2 layout we need to\\n # run layout\\n if property_set[\\\"layout\\\"] is None:\\n return True\\n # if VF2 layout stopped for any reason other than solution found we need\\n # to run layout since VF2 didn't converge.\\n if (\\n property_set[\\\"VF2Layout_stop_reason\\\"] is not None\\n and property_set[\\\"VF2Layout_stop_reason\\\"] is not VF2LayoutStopReason.SOLUTION_FOUND\\n ):\\n return True\\n return False\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._opt_control\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/level3.py::level_3_pass_manager._opt_control\\n def _opt_control(property_set):\\n return not property_set[\\\"optimization_loop_minimum_point\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePlugin.pass_manager\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePlugin.pass_manager\\n @abc.abstractmethod\\n def pass_manager(\\n self, pass_manager_config: PassManagerConfig, optimization_level: Optional[int] = None\\n ) -> PassManager:\\n \\\"\\\"\\\"This method is designed to return a :class:`~.PassManager` for the stage this implements\\n\\n Args:\\n pass_manager_config: A configuration object that defines all the target device\\n specifications and any user specified options to :func:`~.transpile` or\\n :func:`~.generate_preset_pass_manager`\\n optimization_level: The optimization level of the transpilation, if set this\\n should be used to set values for any tunable parameters to trade off runtime\\n for potential optimization. Valid values should be ``0``, ``1``, ``2``, or ``3``\\n and the higher the number the more optimization is expected.\\n \\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager.__init__\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager.__init__\\n def __init__(self):\\n super().__init__()\\n self.init_plugins = stevedore.ExtensionManager(\\n \\\"qiskit.transpiler.init\\\", invoke_on_load=True, propagate_map_exceptions=True\\n )\\n self.layout_plugins = stevedore.ExtensionManager(\\n \\\"qiskit.transpiler.layout\\\", invoke_on_load=True, propagate_map_exceptions=True\\n )\\n self.routing_plugins = stevedore.ExtensionManager(\\n \\\"qiskit.transpiler.routing\\\", invoke_on_load=True, propagate_map_exceptions=True\\n )\\n self.translation_plugins = stevedore.ExtensionManager(\\n \\\"qiskit.transpiler.translation\\\", invoke_on_load=True, propagate_map_exceptions=True\\n )\\n self.optimization_plugins = stevedore.ExtensionManager(\\n \\\"qiskit.transpiler.optimization\\\", invoke_on_load=True, propagate_map_exceptions=True\\n )\\n self.scheduling_plugins = stevedore.ExtensionManager(\\n \\\"qiskit.transpiler.scheduling\\\", invoke_on_load=True, propagate_map_exceptions=True\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager.get_passmanager_stage\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager.get_passmanager_stage\\n def get_passmanager_stage(\\n self,\\n stage_name: str,\\n plugin_name: str,\\n pm_config: PassManagerConfig,\\n optimization_level=None,\\n ) -> PassManager:\\n \\\"\\\"\\\"Get a stage\\\"\\\"\\\"\\n if stage_name == \\\"init\\\":\\n return self._build_pm(\\n self.init_plugins, stage_name, plugin_name, pm_config, optimization_level\\n )\\n elif stage_name == \\\"layout\\\":\\n return self._build_pm(\\n self.layout_plugins, stage_name, plugin_name, pm_config, optimization_level\\n )\\n elif stage_name == \\\"routing\\\":\\n return self._build_pm(\\n self.routing_plugins, stage_name, plugin_name, pm_config, optimization_level\\n )\\n elif stage_name == \\\"translation\\\":\\n return self._build_pm(\\n self.translation_plugins, stage_name, plugin_name, pm_config, optimization_level\\n )\\n elif stage_name == \\\"optimization\\\":\\n return self._build_pm(\\n self.optimization_plugins, stage_name, plugin_name, pm_config, optimization_level\\n )\\n elif stage_name == \\\"scheduling\\\":\\n return self._build_pm(\\n self.scheduling_plugins, stage_name, plugin_name, pm_config, optimization_level\\n )\\n else:\\n raise TranspilerError(f\\\"Invalid stage name: {stage_name}\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager._build_pm\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/plugin.py::PassManagerStagePluginManager._build_pm\\n def _build_pm(\\n self,\\n stage_obj: stevedore.ExtensionManager,\\n stage_name: str,\\n plugin_name: str,\\n pm_config: PassManagerConfig,\\n optimization_level: Optional[int] = None,\\n ):\\n if plugin_name not in stage_obj:\\n raise TranspilerError(f\\\"Invalid plugin name {plugin_name} for stage {stage_name}\\\")\\n plugin_obj = stage_obj[plugin_name]\\n return plugin_obj.obj.pass_manager(pm_config, optimization_level)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/plugin.py::list_stage_plugins\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/plugin.py::list_stage_plugins\\ndef list_stage_plugins(stage_name: str) -> List[str]:\\n \\\"\\\"\\\"Get a list of installed plugins for a stage.\\n\\n Args:\\n stage_name: The stage name to get the plugin names for\\n\\n Returns:\\n plugins: The list of installed plugin names for the specified stages\\n\\n Raises:\\n TranspilerError: If an invalid stage name is specified.\\n \\\"\\\"\\\"\\n plugin_mgr = PassManagerStagePluginManager()\\n if stage_name == \\\"init\\\":\\n return plugin_mgr.init_plugins.names()\\n elif stage_name == \\\"layout\\\":\\n return plugin_mgr.layout_plugins.names()\\n elif stage_name == \\\"routing\\\":\\n return plugin_mgr.routing_plugins.names()\\n elif stage_name == \\\"translation\\\":\\n return plugin_mgr.translation_plugins.names()\\n elif stage_name == \\\"optimization\\\":\\n return plugin_mgr.optimization_plugins.names()\\n elif stage_name == \\\"scheduling\\\":\\n return plugin_mgr.scheduling_plugins.names()\\n else:\\n raise TranspilerError(f\\\"Invalid stage name: {stage_name}\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/transpiler/preset_passmanagers/plugin.py::passmanager_stage_plugins\",\n \"text\": \"qiskit/transpiler/preset_passmanagers/plugin.py::passmanager_stage_plugins\\ndef passmanager_stage_plugins(stage: str) -> Dict[str, PassManagerStagePlugin]:\\n \\\"\\\"\\\"Return a dict with, for each stage name, the class type of the plugin.\\n\\n This function is useful for getting more information about a plugin:\\n\\n .. code-block:: python\\n\\n from qiskit.transpiler.preset_passmanagers.plugin import passmanager_stage_plugins\\n routing_plugins = passmanager_stage_plugins('routing')\\n basic_plugin = routing_plugins['basic']\\n help(basic_plugin)\\n\\n .. code-block:: text\\n\\n Help on BasicSwapPassManager in module ...preset_passmanagers.builtin_plugins object:\\n\\n class BasicSwapPassManager(...preset_passmanagers.plugin.PassManagerStagePlugin)\\n | Plugin class for routing stage with :class:`~.BasicSwap`\\n |\\n | Method resolution order:\\n | BasicSwapPassManager\\n | ...preset_passmanagers.plugin.PassManagerStagePlugin\\n | abc.ABC\\n | builtins.object\\n ...\\n\\n Args:\\n stage: The stage name to get\\n\\n Returns:\\n dict: the key is the name of the plugin and the value is the class type for each.\\n\\n Raises:\\n TranspilerError: If an invalid stage name is specified.\\n \\\"\\\"\\\"\\n plugin_mgr = PassManagerStagePluginManager()\\n try:\\n manager = getattr(plugin_mgr, f\\\"{stage}_plugins\\\")\\n except AttributeError as exc:\\n raise TranspilerError(f\\\"Passmanager stage {stage} not found\\\") from exc\\n\\n return {name: manager[name].obj for name in manager.names()}\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":60,"cells":{"instance_id":{"kind":"string","value":"pandas-dev__pandas-23402"},"repo":{"kind":"string","value":"pandas-dev/pandas"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"pd.DataFrame.expanding bug with axis = 1\n```python\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndf = pd.DataFrame(np.ones((10, 20)))\r\ndf.expanding(3, axis = 1).sum()\r\n 0 1 2 3 4 5 6 7 8 9 10 11 12 13 \\\r\n0 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 10.0 10.0 10.0 10.0 \r\n1 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 10.0 10.0 10.0 10.0 \r\n2 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 10.0 10.0 10.0 10.0 \r\n3 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 10.0 10.0 10.0 10.0 \r\n4 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 10.0 10.0 10.0 10.0 \r\n5 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 10.0 10.0 10.0 10.0 \r\n6 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 10.0 10.0 10.0 10.0 \r\n7 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 10.0 10.0 10.0 10.0 \r\n8 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 10.0 10.0 10.0 10.0 \r\n9 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 10.0 10.0 10.0 10.0 \r\n\r\n 14 15 16 17 18 19 \r\n0 10.0 10.0 10.0 10.0 10.0 10.0 \r\n1 10.0 10.0 10.0 10.0 10.0 10.0 \r\n2 10.0 10.0 10.0 10.0 10.0 10.0 \r\n3 10.0 10.0 10.0 10.0 10.0 10.0 \r\n4 10.0 10.0 10.0 10.0 10.0 10.0 \r\n5 10.0 10.0 10.0 10.0 10.0 10.0 \r\n6 10.0 10.0 10.0 10.0 10.0 10.0 \r\n7 10.0 10.0 10.0 10.0 10.0 10.0 \r\n8 10.0 10.0 10.0 10.0 10.0 10.0 \r\n9 10.0 10.0 10.0 10.0 10.0 10.0 \r\n\r\n```\r\naxis = 1 on pd.DataFrame.expanding doesn't seem to be working as expected; a horizontal expanding window. Instead, the window size appears to be constrained to the number of rows.\r\n\r\naxis = 0 is working fine as a vertical expanding window\r\n\r\n#### Expected Output\r\n```python\r\ndf.T.expanding(3, axis = 0).sum().T\r\n\r\n 0 1 2 3 4 5 6 7 8 9 10 11 12 13 \\\r\n0 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 \r\n1 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 \r\n2 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 \r\n3 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 \r\n4 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 \r\n5 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 \r\n6 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 \r\n7 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 \r\n8 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 \r\n9 NaN NaN 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 \r\n\r\n 14 15 16 17 18 19 \r\n0 15.0 16.0 17.0 18.0 19.0 20.0 \r\n1 15.0 16.0 17.0 18.0 19.0 20.0 \r\n2 15.0 16.0 17.0 18.0 19.0 20.0 \r\n3 15.0 16.0 17.0 18.0 19.0 20.0 \r\n4 15.0 16.0 17.0 18.0 19.0 20.0 \r\n5 15.0 16.0 17.0 18.0 19.0 20.0 \r\n6 15.0 16.0 17.0 18.0 19.0 20.0 \r\n7 15.0 16.0 17.0 18.0 19.0 20.0 \r\n8 15.0 16.0 17.0 18.0 19.0 20.0 \r\n9 15.0 16.0 17.0 18.0 19.0 20.0 \r\n```\r\n#### Output of ``pd.show_versions()``\r\n<details>\r\nINSTALLED VERSIONS\r\n------------------\r\ncommit: None\r\npython: 3.7.1.final.0\r\npython-bits: 64\r\nOS: Linux\r\nOS-release: 4.17.19-1-MANJARO\r\nmachine: x86_64\r\nprocessor: \r\nbyteorder: little\r\nLC_ALL: None\r\nLANG: en_SG.UTF-8\r\nLOCALE: en_SG.UTF-8\r\n\r\npandas: 0.23.4\r\npytest: None\r\npip: 18.0\r\nsetuptools: 40.4.3\r\nCython: 0.29\r\nnumpy: 1.15.2\r\nscipy: 1.1.0\r\npyarrow: None\r\nxarray: None\r\nIPython: 6.5.0\r\nsphinx: None\r\npatsy: 0.5.0\r\ndateutil: 2.7.3\r\npytz: 2018.5\r\nblosc: None\r\nbottleneck: None\r\ntables: None\r\nnumexpr: None\r\nfeather: None\r\nmatplotlib: 2.2.3\r\nopenpyxl: None\r\nxlrd: None\r\nxlwt: None\r\nxlsxwriter: None\r\nlxml: None\r\nbs4: None\r\nhtml5lib: 1.0.1\r\nsqlalchemy: 1.2.12\r\npymysql: None\r\npsycopg2: 2.7.5 (dt dec pq3 ext lo64)\r\njinja2: 2.10\r\ns3fs: None\r\nfastparquet: None\r\npandas_gbq: None\r\npandas_datareader: None\r\n</details>\n"},"gold_files":{"kind":"list like","value":["pandas/core/window.py"],"string":"[\n \"pandas/core/window.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pandas/core/window.py::Expanding._get_window"],"string":"[\n \"pandas/core/window.py::Expanding._get_window\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"asv_bench/benchmarks/groupby.py","text":"asv_bench/benchmarks/groupby.py\nclass ApplyDictReturn\n def setup(self)\n def time_groupby_apply_dict_return(self)\nclass Apply\n def setup_cache(self)\n def time_scalar_function_multi_col(self, df)\n def time_scalar_function_single_col(self, df)\n def df_copy_function(g)\n def time_copy_function_multi_col(self, df)\n def time_copy_overhead_single_col(self, df)\nclass Groups\n def setup_cache(self)\n def setup(self, data, key)\n def time_series_groups(self, data, key)\nclass GroupManyLabels\n def setup(self, ncols)\n def time_sum(self, ncols)\nclass Nth\n def setup(self, dtype)\n def time_frame_nth_any(self, dtype)\n def time_groupby_nth_all(self, dtype)\n def time_frame_nth(self, dtype)\n def time_series_nth_any(self, dtype)\n def time_series_nth_all(self, dtype)\n def time_series_nth(self, dtype)\nclass DateAttributes\n def setup(self)\n def time_len_groupby_object(self)\nclass Int64\n def setup(self)\n def time_overflow(self)\nclass CountMultiDtype\n def setup_cache(self)\n def time_multi_count(self, df)\nclass CountMultiInt\n def setup_cache(self)\n def time_multi_int_count(self, df)\n def time_multi_int_nunique(self, df)\nclass AggFunctions\n def setup_cache()\n def time_different_str_functions(self, df)\n def time_different_numpy_functions(self, df)\n def time_different_python_functions_multicol(self, df)\n def time_different_python_functions_singlecol(self, df)\nclass GroupStrings\n def setup(self)\n def time_multi_columns(self)\nclass MultiColumn\n def setup_cache(self)\n def time_lambda_sum(self, df)\n def time_cython_sum(self, df)\n def time_col_select_lambda_sum(self, df)\n def time_col_select_numpy_sum(self, df)\nclass Size\n def setup(self)\n def time_multi_size(self)\n def time_dt_timegrouper_size(self)\n def time_category_size(self)\nclass GroupByMethods\n def setup(self, dtype, method, application)\n def time_dtype_as_group(self, dtype, method, application)\n def time_dtype_as_field(self, dtype, method, application)\nclass RankWithTies\n def setup(self, dtype, tie_method)\n def time_rank_ties(self, dtype, tie_method)\nclass Float32\n def setup(self)\n def time_sum(self)\nclass Categories\n def setup(self)\n def time_groupby_sort(self)\n def time_groupby_nosort(self)\n def time_groupby_ordered_sort(self)\n def time_groupby_ordered_nosort(self)\n def time_groupby_extra_cat_sort(self)\n def time_groupby_extra_cat_nosort(self)\nclass Datelike\n def setup(self, grouper)\n def time_sum(self, grouper)\nclass SumBools\n def setup(self)\n def time_groupby_sum_booleans(self)\nclass SumMultiLevel\n def setup(self)\n def time_groupby_sum_multiindex(self)\nclass Transform\n def setup(self)\n def time_transform_lambda_max(self)\n def time_transform_ufunc_max(self)\n def time_transform_multi_key1(self)\n def time_transform_multi_key2(self)\n def time_transform_multi_key3(self)\n def time_transform_multi_key4(self)\nclass Tra","label":0},{"cid":"pandas/core/accessor.py","text":"pandas/core/accessor.py\nclass DirNamesMixin\n def _dir_deletions(self) # delete unwanted __dir__ for this object\n def _dir_additions(self) # add additional __dir__ for this object\n def __dir__(self) # Provide method name lookup and completion\nclass PandasDelegate # an abstract base class for delegating methods/properties\n def _delegate_property_get(self, name, *args, **kwargs)\n def _delegate_property_set(self, name, value, *args, **kwargs)\n def _delegate_method(self, name, *args, **kwargs)\n def _add_delegate_accessors(cls, delegate, accessors, typ, overwrite=False) # add accessors to cls from the delegate class\n def _create_delegator_property(name)\n def _getter(self)\n def _setter(self, new_values)\n def _create_delegator_method(name)\n def f(self, *args, **kwargs)\ndef delegate_names(delegate, accessors, typ, overwrite=False) # Add delegated names to a class using a class decorator. This provides\n def add_delegate_accessors(cls)\nclass CachedAccessor # Custom property-like object (descriptor) for caching accessors.\n def __init__(self, name, accessor)\n def __get__(self, obj, cls)\ndef _register_accessor(name, cls)\n def decorator(accessor)\ndef register_dataframe_accessor(name)\ndef register_series_accessor(name)\ndef register_index_accessor(name)","label":0},{"cid":"pandas/core/reshape/tile.py","text":"pandas/core/reshape/tile.py\ndef cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise') # Bin values into discrete intervals.\ndef qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise') # Quantile-based discretization function. Discretize variable into\ndef _bins_to_cuts(x, bins, right=True, labels=None, precision=3, include_lowest=False, dtype=None, duplicates='raise')\ndef _trim_zeros(x)\ndef _coerce_to_type(x) # if the passed data is of datetime/timedelta type,\ndef _convert_bin_to_numeric_type(bins, dtype) # if the passed bin is of datetime/timedelta type,\ndef _convert_bin_to_datelike_type(bins, dtype) # Convert bins to a DatetimeIndex or TimedeltaIndex if the orginal dtype is\ndef _format_labels(bins, precision, right=True, include_lowest=False, dtype=None) # based on the dtype, return our labels\ndef _preprocess_for_cut(x) # handles preprocessing for cut where we convert passed\ndef _postprocess_for_cut(fac, bins, retbins, x_is_series, series_index, name, dtype) # handles post processing for the cut method where\ndef _round_frac(x, precision) # Round the fractional part of the given number\ndef _infer_precision(base_precision, bins) # Infer an appropriate precision for _round_frac","label":0},{"cid":"pandas/core/window.py","text":"pandas/core/window.py\nclass _Window\n def __init__(self, obj, window=None, min_periods=None, center=False, win_type=None, axis=0, on=None, closed=None, **kwargs)\n def _constructor(self)\n def is_datetimelike(self)\n def _on(self)\n def is_freq_type(self)\n def validate(self)\n def _convert_freq(self) # resample according to the how, return a new object\n def _create_blocks(self) # split data into blocks & return conformed data\n def _gotitem(self, key, ndim, subset=None) # sub-classes to define\n def __getattr__(self, attr)\n def _dir_additions(self)\n def _get_window(self, other=None)\n def _window_type(self)\n def __unicode__(self) # provide a nice str repr of our rolling object\n def __iter__(self)\n def _get_index(self, index=None) # Return index as ndarrays\n def _prep_values(self, values=None, kill_inf=True)\n def _wrap_result(self, result, block=None, obj=None) # wrap a single result\n def _wrap_results(self, results, blocks, obj) # wrap the results\n def _center_window(self, result, window) # center the result in the window\n def aggregate(self, arg, *args, **kwargs)\nclass Window # Provides rolling window calculations.\n def validate(self)\n def _prep_window(self, **kwargs) # provide validation for our window type, return the window\n def _apply_window(self, mean=True, **kwargs) # Applies a moving window of type ``window_type`` on the data.\n def aggregate(self, arg, *args, **kwargs)\n def sum(self, *args, **kwargs)\n def mean(self, *args, **kwargs)\nclass _GroupByMixin # provide the groupby facilities\n def __init__(self, obj, *args, **kwargs)\n def _apply(self, func, name, window=None, center=None, check_minp=None, **kwargs) # dispatch to apply; we are stripping all of the _apply kwargs and\n def f(x, name=name, *args)\nclass _Rolling\n def _constructor(self)\n def _apply(self, func, name=None, window=None, center=None, check_minp=None, **kwargs) # Rolling statistical measure using supplied function. Designed to be\nclass _Rolling_and_Expanding\n def count(self)\n def apply(self, func, raw=None, args=(), kwargs={})\n def f(arg, window, min_periods, closed)\n def sum(self, *args, **kwargs)\n def max(self, *args, **kwargs)\n def min(self, *args, **kwargs)\n def mean(self, *args, **kwargs)\n def median(self, **kwargs)\n def std(self, ddof=1, *args, **kwargs)\n def f(arg, *args, **kwargs)\n def var(self, ddof=1, *args, **kwargs)\n def skew(self, **kwargs)\n def kurt(self, **kwargs)\n def quantile(self, quantile, interpolation='linear', **kwargs)\n def f(arg, *args, **kwargs)\n def cov(self, other=None, pairwise=None, ddof=1, **kwargs)\n def _get_cov(X, Y)\n def corr(self, other=None, pairwise=None, **kwargs)\n def _get_corr(a, b)\nclass Rolling\n def is_datetimelike(self)\n def _on(self)\n def validate(self)\n def _validate_monotonic(self) # validate on is monotonic\n def _validate_freq(self) #","label":1}],"string":"[\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py\",\n \"text\": \"asv_bench/benchmarks/groupby.py\\nclass ApplyDictReturn\\n def setup(self)\\n def time_groupby_apply_dict_return(self)\\nclass Apply\\n def setup_cache(self)\\n def time_scalar_function_multi_col(self, df)\\n def time_scalar_function_single_col(self, df)\\n def df_copy_function(g)\\n def time_copy_function_multi_col(self, df)\\n def time_copy_overhead_single_col(self, df)\\nclass Groups\\n def setup_cache(self)\\n def setup(self, data, key)\\n def time_series_groups(self, data, key)\\nclass GroupManyLabels\\n def setup(self, ncols)\\n def time_sum(self, ncols)\\nclass Nth\\n def setup(self, dtype)\\n def time_frame_nth_any(self, dtype)\\n def time_groupby_nth_all(self, dtype)\\n def time_frame_nth(self, dtype)\\n def time_series_nth_any(self, dtype)\\n def time_series_nth_all(self, dtype)\\n def time_series_nth(self, dtype)\\nclass DateAttributes\\n def setup(self)\\n def time_len_groupby_object(self)\\nclass Int64\\n def setup(self)\\n def time_overflow(self)\\nclass CountMultiDtype\\n def setup_cache(self)\\n def time_multi_count(self, df)\\nclass CountMultiInt\\n def setup_cache(self)\\n def time_multi_int_count(self, df)\\n def time_multi_int_nunique(self, df)\\nclass AggFunctions\\n def setup_cache()\\n def time_different_str_functions(self, df)\\n def time_different_numpy_functions(self, df)\\n def time_different_python_functions_multicol(self, df)\\n def time_different_python_functions_singlecol(self, df)\\nclass GroupStrings\\n def setup(self)\\n def time_multi_columns(self)\\nclass MultiColumn\\n def setup_cache(self)\\n def time_lambda_sum(self, df)\\n def time_cython_sum(self, df)\\n def time_col_select_lambda_sum(self, df)\\n def time_col_select_numpy_sum(self, df)\\nclass Size\\n def setup(self)\\n def time_multi_size(self)\\n def time_dt_timegrouper_size(self)\\n def time_category_size(self)\\nclass GroupByMethods\\n def setup(self, dtype, method, application)\\n def time_dtype_as_group(self, dtype, method, application)\\n def time_dtype_as_field(self, dtype, method, application)\\nclass RankWithTies\\n def setup(self, dtype, tie_method)\\n def time_rank_ties(self, dtype, tie_method)\\nclass Float32\\n def setup(self)\\n def time_sum(self)\\nclass Categories\\n def setup(self)\\n def time_groupby_sort(self)\\n def time_groupby_nosort(self)\\n def time_groupby_ordered_sort(self)\\n def time_groupby_ordered_nosort(self)\\n def time_groupby_extra_cat_sort(self)\\n def time_groupby_extra_cat_nosort(self)\\nclass Datelike\\n def setup(self, grouper)\\n def time_sum(self, grouper)\\nclass SumBools\\n def setup(self)\\n def time_groupby_sum_booleans(self)\\nclass SumMultiLevel\\n def setup(self)\\n def time_groupby_sum_multiindex(self)\\nclass Transform\\n def setup(self)\\n def time_transform_lambda_max(self)\\n def time_transform_ufunc_max(self)\\n def time_transform_multi_key1(self)\\n def time_transform_multi_key2(self)\\n def time_transform_multi_key3(self)\\n def time_transform_multi_key4(self)\\nclass Tra\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py\",\n \"text\": \"pandas/core/accessor.py\\nclass DirNamesMixin\\n def _dir_deletions(self) # delete unwanted __dir__ for this object\\n def _dir_additions(self) # add additional __dir__ for this object\\n def __dir__(self) # Provide method name lookup and completion\\nclass PandasDelegate # an abstract base class for delegating methods/properties\\n def _delegate_property_get(self, name, *args, **kwargs)\\n def _delegate_property_set(self, name, value, *args, **kwargs)\\n def _delegate_method(self, name, *args, **kwargs)\\n def _add_delegate_accessors(cls, delegate, accessors, typ, overwrite=False) # add accessors to cls from the delegate class\\n def _create_delegator_property(name)\\n def _getter(self)\\n def _setter(self, new_values)\\n def _create_delegator_method(name)\\n def f(self, *args, **kwargs)\\ndef delegate_names(delegate, accessors, typ, overwrite=False) # Add delegated names to a class using a class decorator. This provides\\n def add_delegate_accessors(cls)\\nclass CachedAccessor # Custom property-like object (descriptor) for caching accessors.\\n def __init__(self, name, accessor)\\n def __get__(self, obj, cls)\\ndef _register_accessor(name, cls)\\n def decorator(accessor)\\ndef register_dataframe_accessor(name)\\ndef register_series_accessor(name)\\ndef register_index_accessor(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py\",\n \"text\": \"pandas/core/reshape/tile.py\\ndef cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise') # Bin values into discrete intervals.\\ndef qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise') # Quantile-based discretization function. Discretize variable into\\ndef _bins_to_cuts(x, bins, right=True, labels=None, precision=3, include_lowest=False, dtype=None, duplicates='raise')\\ndef _trim_zeros(x)\\ndef _coerce_to_type(x) # if the passed data is of datetime/timedelta type,\\ndef _convert_bin_to_numeric_type(bins, dtype) # if the passed bin is of datetime/timedelta type,\\ndef _convert_bin_to_datelike_type(bins, dtype) # Convert bins to a DatetimeIndex or TimedeltaIndex if the orginal dtype is\\ndef _format_labels(bins, precision, right=True, include_lowest=False, dtype=None) # based on the dtype, return our labels\\ndef _preprocess_for_cut(x) # handles preprocessing for cut where we convert passed\\ndef _postprocess_for_cut(fac, bins, retbins, x_is_series, series_index, name, dtype) # handles post processing for the cut method where\\ndef _round_frac(x, precision) # Round the fractional part of the given number\\ndef _infer_precision(base_precision, bins) # Infer an appropriate precision for _round_frac\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py\",\n \"text\": \"pandas/core/window.py\\nclass _Window\\n def __init__(self, obj, window=None, min_periods=None, center=False, win_type=None, axis=0, on=None, closed=None, **kwargs)\\n def _constructor(self)\\n def is_datetimelike(self)\\n def _on(self)\\n def is_freq_type(self)\\n def validate(self)\\n def _convert_freq(self) # resample according to the how, return a new object\\n def _create_blocks(self) # split data into blocks & return conformed data\\n def _gotitem(self, key, ndim, subset=None) # sub-classes to define\\n def __getattr__(self, attr)\\n def _dir_additions(self)\\n def _get_window(self, other=None)\\n def _window_type(self)\\n def __unicode__(self) # provide a nice str repr of our rolling object\\n def __iter__(self)\\n def _get_index(self, index=None) # Return index as ndarrays\\n def _prep_values(self, values=None, kill_inf=True)\\n def _wrap_result(self, result, block=None, obj=None) # wrap a single result\\n def _wrap_results(self, results, blocks, obj) # wrap the results\\n def _center_window(self, result, window) # center the result in the window\\n def aggregate(self, arg, *args, **kwargs)\\nclass Window # Provides rolling window calculations.\\n def validate(self)\\n def _prep_window(self, **kwargs) # provide validation for our window type, return the window\\n def _apply_window(self, mean=True, **kwargs) # Applies a moving window of type ``window_type`` on the data.\\n def aggregate(self, arg, *args, **kwargs)\\n def sum(self, *args, **kwargs)\\n def mean(self, *args, **kwargs)\\nclass _GroupByMixin # provide the groupby facilities\\n def __init__(self, obj, *args, **kwargs)\\n def _apply(self, func, name, window=None, center=None, check_minp=None, **kwargs) # dispatch to apply; we are stripping all of the _apply kwargs and\\n def f(x, name=name, *args)\\nclass _Rolling\\n def _constructor(self)\\n def _apply(self, func, name=None, window=None, center=None, check_minp=None, **kwargs) # Rolling statistical measure using supplied function. Designed to be\\nclass _Rolling_and_Expanding\\n def count(self)\\n def apply(self, func, raw=None, args=(), kwargs={})\\n def f(arg, window, min_periods, closed)\\n def sum(self, *args, **kwargs)\\n def max(self, *args, **kwargs)\\n def min(self, *args, **kwargs)\\n def mean(self, *args, **kwargs)\\n def median(self, **kwargs)\\n def std(self, ddof=1, *args, **kwargs)\\n def f(arg, *args, **kwargs)\\n def var(self, ddof=1, *args, **kwargs)\\n def skew(self, **kwargs)\\n def kurt(self, **kwargs)\\n def quantile(self, quantile, interpolation='linear', **kwargs)\\n def f(arg, *args, **kwargs)\\n def cov(self, other=None, pairwise=None, ddof=1, **kwargs)\\n def _get_cov(X, Y)\\n def corr(self, other=None, pairwise=None, **kwargs)\\n def _get_corr(a, b)\\nclass Rolling\\n def is_datetimelike(self)\\n def _on(self)\\n def validate(self)\\n def _validate_monotonic(self) # validate on is monotonic\\n def _validate_freq(self) #\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"asv_bench/benchmarks/groupby.py::ApplyDictReturn.setup","text":"asv_bench/benchmarks/groupby.py::ApplyDictReturn.setup\n def setup(self):\n self.labels = np.arange(1000).repeat(10)\n self.data = Series(np.random.randn(len(self.labels)))","label":0},{"cid":"asv_bench/benchmarks/groupby.py::ApplyDictReturn.time_groupby_apply_dict_return","text":"asv_bench/benchmarks/groupby.py::ApplyDictReturn.time_groupby_apply_dict_return\n def time_groupby_apply_dict_return(self):\n self.data.groupby(self.labels).apply(lambda x: {'first': x.values[0],\n 'last': x.values[-1]})","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Apply.setup_cache","text":"asv_bench/benchmarks/groupby.py::Apply.setup_cache\n def setup_cache(self):\n N = 10**4\n labels = np.random.randint(0, 2000, size=N)\n labels2 = np.random.randint(0, 3, size=N)\n df = DataFrame({'key': labels,\n 'key2': labels2,\n 'value1': np.random.randn(N),\n 'value2': ['foo', 'bar', 'baz', 'qux'] * (N // 4)\n })\n return df","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Apply.time_scalar_function_multi_col","text":"asv_bench/benchmarks/groupby.py::Apply.time_scalar_function_multi_col\n def time_scalar_function_multi_col(self, df):\n df.groupby(['key', 'key2']).apply(lambda x: 1)","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Apply.time_scalar_function_single_col","text":"asv_bench/benchmarks/groupby.py::Apply.time_scalar_function_single_col\n def time_scalar_function_single_col(self, df):\n df.groupby('key').apply(lambda x: 1)","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Apply.df_copy_function","text":"asv_bench/benchmarks/groupby.py::Apply.df_copy_function\n @staticmethod\n def df_copy_function(g):\n # ensure that the group name is available (see GH #15062)\n g.name\n return g.copy()","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Apply.time_copy_function_multi_col","text":"asv_bench/benchmarks/groupby.py::Apply.time_copy_function_multi_col\n def time_copy_function_multi_col(self, df):\n df.groupby(['key', 'key2']).apply(self.df_copy_function)","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Apply.time_copy_overhead_single_col","text":"asv_bench/benchmarks/groupby.py::Apply.time_copy_overhead_single_col\n def time_copy_overhead_single_col(self, df):\n df.groupby('key').apply(self.df_copy_function)","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Groups.setup_cache","text":"asv_bench/benchmarks/groupby.py::Groups.setup_cache\n def setup_cache(self):\n size = 10**6\n data = {'int64_small': Series(np.random.randint(0, 100, size=size)),\n 'int64_large': Series(np.random.randint(0, 10000, size=size)),\n 'object_small': Series(\n tm.makeStringIndex(100).take(\n np.random.randint(0, 100, size=size))),\n 'object_large': Series(\n tm.makeStringIndex(10000).take(\n np.random.randint(0, 10000, size=size)))}\n return data","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Groups.setup","text":"asv_bench/benchmarks/groupby.py::Groups.setup\n def setup(self, data, key):\n self.ser = data[key]","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Groups.time_series_groups","text":"asv_bench/benchmarks/groupby.py::Groups.time_series_groups\n def time_series_groups(self, data, key):\n self.ser.groupby(self.ser).groups","label":0},{"cid":"asv_bench/benchmarks/groupby.py::GroupManyLabels.setup","text":"asv_bench/benchmarks/groupby.py::GroupManyLabels.setup\n def setup(self, ncols):\n N = 1000\n data = np.random.randn(N, ncols)\n self.labels = np.random.randint(0, 100, size=N)\n self.df = DataFrame(data)","label":0},{"cid":"asv_bench/benchmarks/groupby.py::GroupManyLabels.time_sum","text":"asv_bench/benchmarks/groupby.py::GroupManyLabels.time_sum\n def time_sum(self, ncols):\n self.df.groupby(self.labels).sum()","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Nth.setup","text":"asv_bench/benchmarks/groupby.py::Nth.setup\n def setup(self, dtype):\n N = 10**5\n # with datetimes (GH7555)\n if dtype == 'datetime':\n values = date_range('1/1/2011', periods=N, freq='s')\n elif dtype == 'object':\n values = ['foo'] * N\n else:\n values = np.arange(N).astype(dtype)\n\n key = np.arange(N)\n self.df = DataFrame({'key': key, 'values': values})\n self.df.iloc[1, 1] = np.nan # insert missing data","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Nth.time_frame_nth_any","text":"asv_bench/benchmarks/groupby.py::Nth.time_frame_nth_any\n def time_frame_nth_any(self, dtype):\n self.df.groupby('key').nth(0, dropna='any')","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Nth.time_groupby_nth_all","text":"asv_bench/benchmarks/groupby.py::Nth.time_groupby_nth_all\n def time_groupby_nth_all(self, dtype):\n self.df.groupby('key').nth(0, dropna='all')","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Nth.time_frame_nth","text":"asv_bench/benchmarks/groupby.py::Nth.time_frame_nth\n def time_frame_nth(self, dtype):\n self.df.groupby('key').nth(0)","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Nth.time_series_nth_any","text":"asv_bench/benchmarks/groupby.py::Nth.time_series_nth_any\n def time_series_nth_any(self, dtype):\n self.df['values'].groupby(self.df['key']).nth(0, dropna='any')","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Nth.time_series_nth_all","text":"asv_bench/benchmarks/groupby.py::Nth.time_series_nth_all\n def time_series_nth_all(self, dtype):\n self.df['values'].groupby(self.df['key']).nth(0, dropna='all')","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Nth.time_series_nth","text":"asv_bench/benchmarks/groupby.py::Nth.time_series_nth\n def time_series_nth(self, dtype):\n self.df['values'].groupby(self.df['key']).nth(0)","label":0},{"cid":"asv_bench/benchmarks/groupby.py::DateAttributes.setup","text":"asv_bench/benchmarks/groupby.py::DateAttributes.setup\n def setup(self):\n rng = date_range('1/1/2000', '12/31/2005', freq='H')\n self.year, self.month, self.day = rng.year, rng.month, rng.day\n self.ts = Series(np.random.randn(len(rng)), index=rng)","label":0},{"cid":"asv_bench/benchmarks/groupby.py::DateAttributes.time_len_groupby_object","text":"asv_bench/benchmarks/groupby.py::DateAttributes.time_len_groupby_object\n def time_len_groupby_object(self):\n len(self.ts.groupby([self.year, self.month, self.day]))","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Int64.setup","text":"asv_bench/benchmarks/groupby.py::Int64.setup\n def setup(self):\n arr = np.random.randint(-1 << 12, 1 << 12, (1 << 17, 5))\n i = np.random.choice(len(arr), len(arr) * 5)\n arr = np.vstack((arr, arr[i]))\n i = np.random.permutation(len(arr))\n arr = arr[i]\n self.cols = list('abcde')\n self.df = DataFrame(arr, columns=self.cols)\n self.df['jim'], self.df['joe'] = np.random.randn(2, len(self.df)) * 10","label":0},{"cid":"asv_bench/benchmarks/groupby.py::Int64.time_overflow","text":"asv_bench/benchmarks/groupby.py::Int64.time_overflow\n def time_overflow(self):\n self.df.groupby(self.cols).max()","label":0},{"cid":"asv_bench/benchmarks/groupby.py::CountMultiDtype.setup_cache","text":"asv_bench/benchmarks/groupby.py::CountMultiDtype.setup_cache\n def setup_cache(self):\n n = 10000\n offsets = np.random.randint(n, size=n).astype('timedelta64[ns]')\n dates = np.datetime64('now') + offsets\n dates[np.random.rand(n) > 0.5] = np.datetime64('nat')\n offsets[np.random.rand(n) > 0.5] = np.timedelta64('nat')\n value2 = np.random.randn(n)\n value2[np.random.rand(n) > 0.5] = np.nan\n obj = np.random.choice(list('ab'), size=n).astype(object)\n obj[np.random.randn(n) > 0.5] = np.nan\n df = DataFrame({'key1': np.random.randint(0, 500, size=n),\n 'key2': np.random.randint(0, 100, size=n),\n 'dates': dates,\n 'value2': value2,\n 'value3': np.random.randn(n),\n 'ints': np.random.randint(0, 1000, size=n),\n 'obj': obj,\n 'offsets': offsets})\n return df","label":0},{"cid":"asv_bench/benchmarks/groupby.py::CountMultiDtype.time_multi_count","text":"asv_bench/benchmarks/groupby.py::CountMultiDtype.time_multi_count\n def time_multi_count(self, df):\n df.groupby(['key1', 'key2']).count()","label":0},{"cid":"asv_bench/benchmarks/groupby.py::CountMultiInt.setup_cache","text":"asv_bench/benchmarks/groupby.py::CountMultiInt.setup_cache\n def setup_cache(self):\n n = 10000\n df = DataFrame({'key1': np.random.randint(0, 500, size=n),\n 'key2': np.random.randint(0, 100, size=n),\n 'ints': np.random.randint(0, 1000, size=n),\n 'ints2': np.random.randint(0, 1000, size=n)})\n return df","label":0},{"cid":"asv_bench/benchmarks/groupby.py::CountMultiInt.time_multi_int_count","text":"asv_bench/benchmarks/groupby.py::CountMultiInt.time_multi_int_count\n def time_multi_int_count(self, df):\n df.groupby(['key1', 'key2']).count()","label":0},{"cid":"asv_bench/benchmarks/groupby.py::CountMultiInt.time_multi_int_nunique","text":"asv_bench/benchmarks/groupby.py::CountMultiInt.time_multi_int_nunique\n def time_multi_int_nunique(self, df):\n df.groupby(['key1', 'key2']).nunique()","label":0},{"cid":"asv_bench/benchmarks/groupby.py::AggFunctions.setup_cache","text":"asv_bench/benchmarks/groupby.py::AggFunctions.setup_cache\n def setup_cache():\n N = 10**5\n fac1 = np.array(['A', 'B', 'C'], dtype='O')\n fac2 = np.array(['one', 'two'], dtype='O')\n df = DataFrame({'key1': fac1.take(np.random.randint(0, 3, size=N)),\n 'key2': fac2.take(np.random.randint(0, 2, size=N)),\n 'value1': np.random.randn(N),\n 'value2': np.random.randn(N),\n 'value3': np.random.randn(N)})\n return df","label":0},{"cid":"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_str_functions","text":"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_str_functions\n def time_different_str_functions(self, df):\n df.groupby(['key1', 'key2']).agg({'value1': 'mean',\n 'value2': 'var',\n 'value3': 'sum'})","label":0},{"cid":"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_numpy_functions","text":"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_numpy_functions\n def time_different_numpy_functions(self, df):\n df.groupby(['key1', 'key2']).agg({'value1': np.mean,\n 'value2': np.var,\n 'value3': np.sum})","label":0},{"cid":"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_python_functions_multicol","text":"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_python_functions_multicol\n def time_different_python_functions_multicol(self, df):\n df.groupby(['key1', 'key2']).agg([sum, min, max])","label":0},{"cid":"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_python_functions_singlecol","text":"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_python_functions_singlecol\n def time_different_python_functions_singlecol(self, df):\n df.groupby('key1').agg([sum, min, max])","label":0},{"cid":"asv_bench/benchmarks/groupby.py::GroupStrings.setup","text":"asv_bench/benchmarks/groupby.py::GroupStrings.setup\n def setup(self):\n n = 2 * 10**5\n alpha = list(map(''.join, product(ascii_letters, repeat=4)))\n data = np.random.choice(alpha, (n // 5, 4), replace=False)\n data = np.repeat(data, 5, axis=0)\n self.df = DataFrame(data, columns=list('abcd'))\n self.df['joe'] = (np.random.randn(len(self.df)) * 10).round(3)\n self.df = self.df.sample(frac=1).reset_index(drop=True)","label":0},{"cid":"asv_bench/benchmarks/groupby.py::GroupStrings.time_multi_columns","text":"asv_bench/benchmarks/groupby.py::GroupStrings.time_multi_columns\n def time_multi_columns(self):\n self.df.groupby(list('abcd')).max()","label":0},{"cid":"asv_bench/benchmarks/groupby.py::MultiColumn.setup_cache","text":"asv_bench/benchmarks/groupby.py::MultiColumn.setup_cache\n def setup_cache(self):\n N = 10**5\n key1 = np.tile(np.arange(100, dtype=object), 1000)\n key2 = key1.copy()\n np.random.shuffle(key1)\n np.random.shuffle(key2)\n df = DataFrame({'key1': key1,\n 'key2': key2,\n 'data1': np.random.randn(N),\n 'data2': np.random.randn(N)})\n return df","label":0},{"cid":"asv_bench/benchmarks/groupby.py::MultiColumn.time_lambda_sum","text":"asv_bench/benchmarks/groupby.py::MultiColumn.time_lambda_sum\n def time_lambda_sum(self, df):\n df.groupby(['key1', 'key2']).agg(lambda x: x.values.sum())","label":0},{"cid":"asv_bench/benchmarks/groupby.py::MultiColumn.time_cython_sum","text":"asv_bench/benchmarks/groupby.py::MultiColumn.time_cython_sum\n def time_cython_sum(self, df):\n df.groupby(['key1', 'key2']).sum()","label":0},{"cid":"asv_bench/benchmarks/groupby.py::MultiColumn.time_col_select_lambda_sum","text":"asv_bench/benchmarks/groupby.py::MultiColumn.time_col_select_lambda_sum\n def time_col_select_lambda_sum(self, df):\n df.groupby(['key1', 'key2'])['data1'].agg(lambda x: x.values.sum())","label":0},{"cid":"pandas/core/accessor.py::DirNamesMixin._dir_deletions","text":"pandas/core/accessor.py::DirNamesMixin._dir_deletions\n def _dir_deletions(self):\n \"\"\" delete unwanted __dir__ for this object \"\"\"\n return self._accessors | self._deprecations","label":0},{"cid":"pandas/core/accessor.py::DirNamesMixin._dir_additions","text":"pandas/core/accessor.py::DirNamesMixin._dir_additions\n def _dir_additions(self):\n \"\"\" add additional __dir__ for this object \"\"\"\n rv = set()\n for accessor in self._accessors:\n try:\n getattr(self, accessor)\n rv.add(accessor)\n except AttributeError:\n pass\n return rv","label":0},{"cid":"pandas/core/accessor.py::DirNamesMixin.__dir__","text":"pandas/core/accessor.py::DirNamesMixin.__dir__\n def __dir__(self):\n \"\"\"\n Provide method name lookup and completion\n Only provide 'public' methods\n \"\"\"\n rv = set(dir(type(self)))\n rv = (rv - self._dir_deletions()) | self._dir_additions()\n return sorted(rv)","label":0},{"cid":"pandas/core/accessor.py::PandasDelegate._delegate_property_get","text":"pandas/core/accessor.py::PandasDelegate._delegate_property_get\n def _delegate_property_get(self, name, *args, **kwargs):\n raise TypeError(\"You cannot access the \"\n \"property {name}\".format(name=name))","label":0},{"cid":"pandas/core/accessor.py::PandasDelegate._delegate_property_set","text":"pandas/core/accessor.py::PandasDelegate._delegate_property_set\n def _delegate_property_set(self, name, value, *args, **kwargs):\n raise TypeError(\"The property {name} cannot be set\".format(name=name))","label":0},{"cid":"pandas/core/accessor.py::PandasDelegate._delegate_method","text":"pandas/core/accessor.py::PandasDelegate._delegate_method\n def _delegate_method(self, name, *args, **kwargs):\n raise TypeError(\"You cannot call method {name}\".format(name=name))","label":0},{"cid":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors","text":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors\n @classmethod\n def _add_delegate_accessors(cls, delegate, accessors, typ,\n overwrite=False):\n \"\"\"\n add accessors to cls from the delegate class\n\n Parameters\n ----------\n cls : the class to add the methods/properties to\n delegate : the class to get methods/properties & doc-strings\n acccessors : string list of accessors to add\n typ : 'property' or 'method'\n overwrite : boolean, default False\n overwrite the method/property in the target class if it exists\n \"\"\"\n\n def _create_delegator_property(name):\n\n def _getter(self):\n return self._delegate_property_get(name)\n\n def _setter(self, new_values):\n return self._delegate_property_set(name, new_values)\n\n _getter.__name__ = name\n _setter.__name__ = name\n\n return property(fget=_getter, fset=_setter,\n doc=getattr(delegate, name).__doc__)\n\n def _create_delegator_method(name):\n\n def f(self, *args, **kwargs):\n return self._delegate_method(name, *args, **kwargs)\n\n f.__name__ = name\n f.__doc__ = getattr(delegate, name).__doc__\n\n return f\n\n for name in accessors:\n\n if typ == 'property':\n f = _create_delegator_property(name)\n else:\n f = _create_delegator_method(name)\n\n # don't overwrite existing methods/properties\n if overwrite or not hasattr(cls, name):\n setattr(cls, name, f)","label":0},{"cid":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property","text":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property\n def _create_delegator_property(name):\n\n def _getter(self):\n return self._delegate_property_get(name)\n\n def _setter(self, new_values):\n return self._delegate_property_set(name, new_values)\n\n _getter.__name__ = name\n _setter.__name__ = name\n\n return property(fget=_getter, fset=_setter,\n doc=getattr(delegate, name).__doc__)","label":0},{"cid":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property._getter","text":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property._getter\n def _getter(self):\n return self._delegate_property_get(name)","label":0},{"cid":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property._setter","text":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property._setter\n def _setter(self, new_values):\n return self._delegate_property_set(name, new_values)","label":0},{"cid":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_method","text":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_method\n def _create_delegator_method(name):\n\n def f(self, *args, **kwargs):\n return self._delegate_method(name, *args, **kwargs)\n\n f.__name__ = name\n f.__doc__ = getattr(delegate, name).__doc__\n\n return f","label":0},{"cid":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_method.f","text":"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_method.f\n def f(self, *args, **kwargs):\n return self._delegate_method(name, *args, **kwargs)","label":0},{"cid":"pandas/core/accessor.py::delegate_names","text":"pandas/core/accessor.py::delegate_names\ndef delegate_names(delegate, accessors, typ, overwrite=False):\n \"\"\"\n Add delegated names to a class using a class decorator. This provides\n an alternative usage to directly calling `_add_delegate_accessors`\n below a class definition.\n\n Parameters\n ----------\n delegate : object\n the class to get methods/properties & doc-strings\n acccessors : Sequence[str]\n List of accessor to add\n typ : {'property', 'method'}\n overwrite : boolean, default False\n overwrite the method/property in the target class if it exists\n\n Returns\n -------\n callable\n A class decorator.\n\n Examples\n --------\n @delegate_names(Categorical, [\"categories\", \"ordered\"], \"property\")\n class CategoricalAccessor(PandasDelegate):\n [...]\n \"\"\"\n def add_delegate_accessors(cls):\n cls._add_delegate_accessors(delegate, accessors, typ,\n overwrite=overwrite)\n return cls\n\n return add_delegate_accessors","label":0},{"cid":"pandas/core/accessor.py::delegate_names.add_delegate_accessors","text":"pandas/core/accessor.py::delegate_names.add_delegate_accessors\n def add_delegate_accessors(cls):\n cls._add_delegate_accessors(delegate, accessors, typ,\n overwrite=overwrite)\n return cls","label":0},{"cid":"pandas/core/accessor.py::CachedAccessor.__init__","text":"pandas/core/accessor.py::CachedAccessor.__init__\n def __init__(self, name, accessor):\n self._name = name\n self._accessor = accessor","label":0},{"cid":"pandas/core/accessor.py::CachedAccessor.__get__","text":"pandas/core/accessor.py::CachedAccessor.__get__\n def __get__(self, obj, cls):\n if obj is None:\n # we're accessing the attribute of the class, i.e., Dataset.geo\n return self._accessor\n accessor_obj = self._accessor(obj)\n # Replace the property with the accessor object. Inspired by:\n # http://www.pydanny.com/cached-property.html\n # We need to use object.__setattr__ because we overwrite __setattr__ on\n # NDFrame\n object.__setattr__(obj, self._name, accessor_obj)\n return accessor_obj","label":0},{"cid":"pandas/core/accessor.py::_register_accessor","text":"pandas/core/accessor.py::_register_accessor\ndef _register_accessor(name, cls):\n def decorator(accessor):\n if hasattr(cls, name):\n warnings.warn(\n 'registration of accessor {!r} under name {!r} for type '\n '{!r} is overriding a preexisting attribute with the same '\n 'name.'.format(accessor, name, cls),\n UserWarning,\n stacklevel=2)\n setattr(cls, name, CachedAccessor(name, accessor))\n cls._accessors.add(name)\n return accessor\n return decorator","label":0},{"cid":"pandas/core/accessor.py::_register_accessor.decorator","text":"pandas/core/accessor.py::_register_accessor.decorator\n def decorator(accessor):\n if hasattr(cls, name):\n warnings.warn(\n 'registration of accessor {!r} under name {!r} for type '\n '{!r} is overriding a preexisting attribute with the same '\n 'name.'.format(accessor, name, cls),\n UserWarning,\n stacklevel=2)\n setattr(cls, name, CachedAccessor(name, accessor))\n cls._accessors.add(name)\n return accessor","label":0},{"cid":"pandas/core/accessor.py::register_dataframe_accessor","text":"pandas/core/accessor.py::register_dataframe_accessor\n@Appender(_doc % dict(klass=\"DataFrame\",\n others=(\"register_series_accessor, \"\n \"register_index_accessor\")))\ndef register_dataframe_accessor(name):\n from pandas import DataFrame\n return _register_accessor(name, DataFrame)","label":0},{"cid":"pandas/core/accessor.py::register_series_accessor","text":"pandas/core/accessor.py::register_series_accessor\n@Appender(_doc % dict(klass=\"Series\",\n others=(\"register_dataframe_accessor, \"\n \"register_index_accessor\")))\ndef register_series_accessor(name):\n from pandas import Series\n return _register_accessor(name, Series)","label":0},{"cid":"pandas/core/accessor.py::register_index_accessor","text":"pandas/core/accessor.py::register_index_accessor\n@Appender(_doc % dict(klass=\"Index\",\n others=(\"register_dataframe_accessor, \"\n \"register_series_accessor\")))\ndef register_index_accessor(name):\n from pandas import Index\n return _register_accessor(name, Index)","label":0},{"cid":"pandas/core/reshape/tile.py::cut","text":"pandas/core/reshape/tile.py::cut\ndef cut(x, bins, right=True, labels=None, retbins=False, precision=3,\n include_lowest=False, duplicates='raise'):\n \"\"\"\n Bin values into discrete intervals.\n\n Use `cut` when you need to segment and sort data values into bins. This\n function is also useful for going from a continuous variable to a\n categorical variable. For example, `cut` could convert ages to groups of\n age ranges. Supports binning into an equal number of bins, or a\n pre-specified array of bins.\n\n Parameters\n ----------\n x : array-like\n The input array to be binned. Must be 1-dimensional.\n bins : int, sequence of scalars, or pandas.IntervalIndex\n The criteria to bin by.\n\n * int : Defines the number of equal-width bins in the range of `x`. The\n range of `x` is extended by .1% on each side to include the minimum\n and maximum values of `x`.\n * sequence of scalars : Defines the bin edges allowing for non-uniform\n width. No extension of the range of `x` is done.\n * IntervalIndex : Defines the exact bins to be used.\n\n right : bool, default True\n Indicates whether `bins` includes the rightmost edge or not. If\n ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]``\n indicate (1,2], (2,3], (3,4]. This argument is ignored when\n `bins` is an IntervalIndex.\n labels : array or bool, optional\n Specifies the labels for the returned bins. Must be the same length as\n the resulting bins. If False, returns only integer indicators of the\n bins. This affects the type of the output container (see below).\n This argument is ignored when `bins` is an IntervalIndex.\n retbins : bool, default False\n Whether to return the bins or not. Useful when bins is provided\n as a scalar.\n precision : int, default 3\n The precision at which to store and display the bins labels.\n include_lowest : bool, defaul","label":0},{"cid":"pandas/core/reshape/tile.py::qcut","text":"pandas/core/reshape/tile.py::qcut\ndef qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'):\n \"\"\"\n Quantile-based discretization function. Discretize variable into\n equal-sized buckets based on rank or based on sample quantiles. For example\n 1000 values for 10 quantiles would produce a Categorical object indicating\n quantile membership for each data point.\n\n Parameters\n ----------\n x : 1d ndarray or Series\n q : integer or array of quantiles\n Number of quantiles. 10 for deciles, 4 for quartiles, etc. Alternately\n array of quantiles, e.g. [0, .25, .5, .75, 1.] for quartiles\n labels : array or boolean, default None\n Used as labels for the resulting bins. Must be of the same length as\n the resulting bins. If False, return only integer indicators of the\n bins.\n retbins : bool, optional\n Whether to return the (bins, labels) or not. Can be useful if bins\n is given as a scalar.\n precision : int, optional\n The precision at which to store and display the bins labels\n duplicates : {default 'raise', 'drop'}, optional\n If bin edges are not unique, raise ValueError or drop non-uniques.\n\n .. versionadded:: 0.20.0\n\n Returns\n -------\n out : Categorical or Series or array of integers if labels is False\n The return type (Categorical or Series) depends on the input: a Series\n of type category if input is a Series else Categorical. Bins are\n represented as categories when categorical data is returned.\n bins : ndarray of floats\n Returned only if `retbins` is True.\n\n Notes\n -----\n Out of bounds values will be NA in the resulting Categorical object\n\n Examples\n --------\n >>> pd.qcut(range(5), 4)\n ... # doctest: +ELLIPSIS\n [(-0.001, 1.0], (-0.001, 1.0], (1.0, 2.0], (2.0, 3.0], (3.0, 4.0]]\n Categories (4, interval[float64]): [(-0.001, 1.0] < (1.0, 2.0] ...\n\n >>> pd.qcut(range(5), 3, labels=[\"good\", ","label":0},{"cid":"pandas/core/reshape/tile.py::_bins_to_cuts","text":"pandas/core/reshape/tile.py::_bins_to_cuts\ndef _bins_to_cuts(x, bins, right=True, labels=None,\n precision=3, include_lowest=False,\n dtype=None, duplicates='raise'):\n\n if duplicates not in ['raise', 'drop']:\n raise ValueError(\"invalid value for 'duplicates' parameter, \"\n \"valid options are: raise, drop\")\n\n if isinstance(bins, IntervalIndex):\n # we have a fast-path here\n ids = bins.get_indexer(x)\n result = algos.take_nd(bins, ids)\n result = Categorical(result, categories=bins, ordered=True)\n return result, bins\n\n unique_bins = algos.unique(bins)\n if len(unique_bins) < len(bins) and len(bins) != 2:\n if duplicates == 'raise':\n raise ValueError(\"Bin edges must be unique: {bins!r}.\\nYou \"\n \"can drop duplicate edges by setting \"\n \"the 'duplicates' kwarg\".format(bins=bins))\n else:\n bins = unique_bins\n\n side = 'left' if right else 'right'\n ids = ensure_int64(bins.searchsorted(x, side=side))\n\n if include_lowest:\n # Numpy 1.9 support: ensure this mask is a Numpy array\n ids[np.asarray(x == bins[0])] = 1\n\n na_mask = isna(x) | (ids == len(bins)) | (ids == 0)\n has_nas = na_mask.any()\n\n if labels is not False:\n if labels is None:\n labels = _format_labels(bins, precision, right=right,\n include_lowest=include_lowest,\n dtype=dtype)\n else:\n if len(labels) != len(bins) - 1:\n raise ValueError('Bin labels must be one fewer than '\n 'the number of bin edges')\n if not is_categorical_dtype(labels):\n labels = Categorical(labels, categories=labels, ordered=True)\n\n np.putmask(ids, na_mask, 0)\n result = algos.take_nd(labels, ids - 1)\n\n else:\n result = ids - 1\n if has","label":0},{"cid":"pandas/core/reshape/tile.py::_trim_zeros","text":"pandas/core/reshape/tile.py::_trim_zeros\ndef _trim_zeros(x):\n while len(x) > 1 and x[-1] == '0':\n x = x[:-1]\n if len(x) > 1 and x[-1] == '.':\n x = x[:-1]\n return x","label":0},{"cid":"pandas/core/reshape/tile.py::_coerce_to_type","text":"pandas/core/reshape/tile.py::_coerce_to_type\ndef _coerce_to_type(x):\n \"\"\"\n if the passed data is of datetime/timedelta type,\n this method converts it to numeric so that cut method can\n handle it\n \"\"\"\n dtype = None\n\n if is_datetime64tz_dtype(x):\n dtype = x.dtype\n elif is_datetime64_dtype(x):\n x = to_datetime(x)\n dtype = np.datetime64\n elif is_timedelta64_dtype(x):\n x = to_timedelta(x)\n dtype = np.timedelta64\n\n if dtype is not None:\n # GH 19768: force NaT to NaN during integer conversion\n x = np.where(x.notna(), x.view(np.int64), np.nan)\n\n return x, dtype","label":0},{"cid":"pandas/core/reshape/tile.py::_convert_bin_to_numeric_type","text":"pandas/core/reshape/tile.py::_convert_bin_to_numeric_type\ndef _convert_bin_to_numeric_type(bins, dtype):\n \"\"\"\n if the passed bin is of datetime/timedelta type,\n this method converts it to integer\n\n Parameters\n ----------\n bins : list-like of bins\n dtype : dtype of data\n\n Raises\n ------\n ValueError if bins are not of a compat dtype to dtype\n \"\"\"\n bins_dtype = infer_dtype(bins)\n if is_timedelta64_dtype(dtype):\n if bins_dtype in ['timedelta', 'timedelta64']:\n bins = to_timedelta(bins).view(np.int64)\n else:\n raise ValueError(\"bins must be of timedelta64 dtype\")\n elif is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):\n if bins_dtype in ['datetime', 'datetime64']:\n bins = to_datetime(bins).view(np.int64)\n else:\n raise ValueError(\"bins must be of datetime64 dtype\")\n\n return bins","label":0},{"cid":"pandas/core/reshape/tile.py::_convert_bin_to_datelike_type","text":"pandas/core/reshape/tile.py::_convert_bin_to_datelike_type\ndef _convert_bin_to_datelike_type(bins, dtype):\n \"\"\"\n Convert bins to a DatetimeIndex or TimedeltaIndex if the orginal dtype is\n datelike\n\n Parameters\n ----------\n bins : list-like of bins\n dtype : dtype of data\n\n Returns\n -------\n bins : Array-like of bins, DatetimeIndex or TimedeltaIndex if dtype is\n datelike\n \"\"\"\n if is_datetime64tz_dtype(dtype) or is_datetime_or_timedelta_dtype(dtype):\n bins = Index(bins.astype(np.int64), dtype=dtype)\n return bins","label":0},{"cid":"pandas/core/reshape/tile.py::_format_labels","text":"pandas/core/reshape/tile.py::_format_labels\ndef _format_labels(bins, precision, right=True,\n include_lowest=False, dtype=None):\n \"\"\" based on the dtype, return our labels \"\"\"\n\n closed = 'right' if right else 'left'\n\n if is_datetime64tz_dtype(dtype):\n formatter = partial(Timestamp, tz=dtype.tz)\n adjust = lambda x: x - Timedelta('1ns')\n elif is_datetime64_dtype(dtype):\n formatter = Timestamp\n adjust = lambda x: x - Timedelta('1ns')\n elif is_timedelta64_dtype(dtype):\n formatter = Timedelta\n adjust = lambda x: x - Timedelta('1ns')\n else:\n precision = _infer_precision(precision, bins)\n formatter = lambda x: _round_frac(x, precision)\n adjust = lambda x: x - 10 ** (-precision)\n\n breaks = [formatter(b) for b in bins]\n labels = IntervalIndex.from_breaks(breaks, closed=closed)\n\n if right and include_lowest:\n # we will adjust the left hand side by precision to\n # account that we are all right closed\n v = adjust(labels[0].left)\n\n i = IntervalIndex([Interval(v, labels[0].right, closed='right')])\n labels = i.append(labels[1:])\n\n return labels","label":0},{"cid":"pandas/core/reshape/tile.py::_preprocess_for_cut","text":"pandas/core/reshape/tile.py::_preprocess_for_cut\ndef _preprocess_for_cut(x):\n \"\"\"\n handles preprocessing for cut where we convert passed\n input to array, strip the index information and store it\n separately\n \"\"\"\n x_is_series = isinstance(x, Series)\n series_index = None\n name = None\n\n if x_is_series:\n series_index = x.index\n name = x.name\n\n # Check that the passed array is a Pandas or Numpy object\n # We don't want to strip away a Pandas data-type here (e.g. datetimetz)\n ndim = getattr(x, 'ndim', None)\n if ndim is None:\n x = np.asarray(x)\n if x.ndim != 1:\n raise ValueError(\"Input array must be 1 dimensional\")\n\n return x_is_series, series_index, name, x","label":0},{"cid":"pandas/core/reshape/tile.py::_postprocess_for_cut","text":"pandas/core/reshape/tile.py::_postprocess_for_cut\ndef _postprocess_for_cut(fac, bins, retbins, x_is_series,\n series_index, name, dtype):\n \"\"\"\n handles post processing for the cut method where\n we combine the index information if the originally passed\n datatype was a series\n \"\"\"\n if x_is_series:\n fac = Series(fac, index=series_index, name=name)\n\n if not retbins:\n return fac\n\n bins = _convert_bin_to_datelike_type(bins, dtype)\n\n return fac, bins","label":0},{"cid":"pandas/core/reshape/tile.py::_round_frac","text":"pandas/core/reshape/tile.py::_round_frac\ndef _round_frac(x, precision):\n \"\"\"\n Round the fractional part of the given number\n \"\"\"\n if not np.isfinite(x) or x == 0:\n return x\n else:\n frac, whole = np.modf(x)\n if whole == 0:\n digits = -int(np.floor(np.log10(abs(frac)))) - 1 + precision\n else:\n digits = precision\n return np.around(x, digits)","label":0},{"cid":"pandas/core/reshape/tile.py::_infer_precision","text":"pandas/core/reshape/tile.py::_infer_precision\ndef _infer_precision(base_precision, bins):\n \"\"\"Infer an appropriate precision for _round_frac\n \"\"\"\n for precision in range(base_precision, 20):\n levels = [_round_frac(b, precision) for b in bins]\n if algos.unique(levels).size == bins.size:\n return precision\n return base_precision # default","label":0},{"cid":"pandas/core/window.py::_Window.__init__","text":"pandas/core/window.py::_Window.__init__\n def __init__(self, obj, window=None, min_periods=None,\n center=False, win_type=None, axis=0, on=None, closed=None,\n **kwargs):\n\n self.__dict__.update(kwargs)\n self.blocks = []\n self.obj = obj\n self.on = on\n self.closed = closed\n self.window = window\n self.min_periods = min_periods\n self.center = center\n self.win_type = win_type\n self.win_freq = None\n self.axis = obj._get_axis_number(axis) if axis is not None else None\n self.validate()","label":0},{"cid":"pandas/core/window.py::_Window._constructor","text":"pandas/core/window.py::_Window._constructor\n @property\n def _constructor(self):\n return Window","label":0},{"cid":"pandas/core/window.py::_Window.is_datetimelike","text":"pandas/core/window.py::_Window.is_datetimelike\n @property\n def is_datetimelike(self):\n return None","label":0},{"cid":"pandas/core/window.py::_Window._on","text":"pandas/core/window.py::_Window._on\n @property\n def _on(self):\n return None","label":0},{"cid":"pandas/core/window.py::_Window.is_freq_type","text":"pandas/core/window.py::_Window.is_freq_type\n @property\n def is_freq_type(self):\n return self.win_type == 'freq'","label":0},{"cid":"pandas/core/window.py::_Window.validate","text":"pandas/core/window.py::_Window.validate\n def validate(self):\n if self.center is not None and not is_bool(self.center):\n raise ValueError(\"center must be a boolean\")\n if (self.min_periods is not None and\n not is_integer(self.min_periods)):\n raise ValueError(\"min_periods must be an integer\")\n if (self.closed is not None and\n self.closed not in ['right', 'both', 'left', 'neither']):\n raise ValueError(\"closed must be 'right', 'left', 'both' or \"\n \"'neither'\")","label":0},{"cid":"pandas/core/window.py::_Window._convert_freq","text":"pandas/core/window.py::_Window._convert_freq\n def _convert_freq(self):\n \"\"\" resample according to the how, return a new object \"\"\"\n\n obj = self._selected_obj\n index = None\n return obj, index","label":0},{"cid":"pandas/core/window.py::_Window._create_blocks","text":"pandas/core/window.py::_Window._create_blocks\n def _create_blocks(self):\n \"\"\" split data into blocks & return conformed data \"\"\"\n\n obj, index = self._convert_freq()\n if index is not None:\n index = self._on\n\n # filter out the on from the object\n if self.on is not None:\n if obj.ndim == 2:\n obj = obj.reindex(columns=obj.columns.difference([self.on]),\n copy=False)\n blocks = obj._to_dict_of_blocks(copy=False).values()\n\n return blocks, obj, index","label":0},{"cid":"pandas/core/window.py::_Window._gotitem","text":"pandas/core/window.py::_Window._gotitem\n def _gotitem(self, key, ndim, subset=None):\n \"\"\"\n sub-classes to define\n return a sliced object\n\n Parameters\n ----------\n key : string / list of selections\n ndim : 1,2\n requested ndim of result\n subset : object, default None\n subset to act on\n \"\"\"\n\n # create a new object to prevent aliasing\n if subset is None:\n subset = self.obj\n self = self._shallow_copy(subset)\n self._reset_cache()\n if subset.ndim == 2:\n if is_scalar(key) and key in subset or is_list_like(key):\n self._selection = key\n return self","label":0},{"cid":"pandas/core/window.py::_Window.__getattr__","text":"pandas/core/window.py::_Window.__getattr__\n def __getattr__(self, attr):\n if attr in self._internal_names_set:\n return object.__getattribute__(self, attr)\n if attr in self.obj:\n return self[attr]\n\n raise AttributeError(\"%r object has no attribute %r\" %\n (type(self).__name__, attr))","label":0},{"cid":"pandas/core/window.py::_Window._dir_additions","text":"pandas/core/window.py::_Window._dir_additions\n def _dir_additions(self):\n return self.obj._dir_additions()","label":0},{"cid":"pandas/core/window.py::_Window._get_window","text":"pandas/core/window.py::_Window._get_window\n def _get_window(self, other=None):\n return self.window","label":0},{"cid":"pandas/core/window.py::_Window._window_type","text":"pandas/core/window.py::_Window._window_type\n @property\n def _window_type(self):\n return self.__class__.__name__","label":0},{"cid":"pandas/core/window.py::_Window.__unicode__","text":"pandas/core/window.py::_Window.__unicode__\n def __unicode__(self):\n \"\"\" provide a nice str repr of our rolling object \"\"\"\n\n attrs = [\"{k}={v}\".format(k=k, v=getattr(self, k))\n for k in self._attributes\n if getattr(self, k, None) is not None]\n return \"{klass} [{attrs}]\".format(klass=self._window_type,\n attrs=','.join(attrs))","label":0},{"cid":"pandas/core/window.py::_Window.__iter__","text":"pandas/core/window.py::_Window.__iter__\n def __iter__(self):\n url = 'https://github.com/pandas-dev/pandas/issues/11704'\n raise NotImplementedError('See issue #11704 {url}'.format(url=url))","label":0},{"cid":"pandas/core/window.py::_Window._get_index","text":"pandas/core/window.py::_Window._get_index\n def _get_index(self, index=None):\n \"\"\"\n Return index as ndarrays\n\n Returns\n -------\n tuple of (index, index_as_ndarray)\n \"\"\"\n\n if self.is_freq_type:\n if index is None:\n index = self._on\n return index, index.asi8\n return index, index","label":0},{"cid":"pandas/core/window.py::_Window._prep_values","text":"pandas/core/window.py::_Window._prep_values\n def _prep_values(self, values=None, kill_inf=True):\n\n if values is None:\n values = getattr(self._selected_obj, 'values', self._selected_obj)\n\n # GH #12373 : rolling functions error on float32 data\n # make sure the data is coerced to float64\n if is_float_dtype(values.dtype):\n values = ensure_float64(values)\n elif is_integer_dtype(values.dtype):\n values = ensure_float64(values)\n elif needs_i8_conversion(values.dtype):\n raise NotImplementedError(\"ops for {action} for this \"\n \"dtype {dtype} are not \"\n \"implemented\".format(\n action=self._window_type,\n dtype=values.dtype))\n else:\n try:\n values = ensure_float64(values)\n except (ValueError, TypeError):\n raise TypeError(\"cannot handle this type -> {0}\"\n \"\".format(values.dtype))\n\n if kill_inf:\n values = values.copy()\n values[np.isinf(values)] = np.NaN\n\n return values","label":0},{"cid":"pandas/core/window.py::_Window._wrap_result","text":"pandas/core/window.py::_Window._wrap_result\n def _wrap_result(self, result, block=None, obj=None):\n \"\"\" wrap a single result \"\"\"\n\n if obj is None:\n obj = self._selected_obj\n index = obj.index\n\n if isinstance(result, np.ndarray):\n\n # coerce if necessary\n if block is not None:\n if is_timedelta64_dtype(block.values.dtype):\n from pandas import to_timedelta\n result = to_timedelta(\n result.ravel(), unit='ns').values.reshape(result.shape)\n\n if result.ndim == 1:\n from pandas import Series\n return Series(result, index, name=obj.name)\n\n return type(obj)(result, index=index, columns=block.columns)\n return result","label":0},{"cid":"pandas/core/window.py::_Window._wrap_results","text":"pandas/core/window.py::_Window._wrap_results\n def _wrap_results(self, results, blocks, obj):\n \"\"\"\n wrap the results\n\n Parameters\n ----------\n results : list of ndarrays\n blocks : list of blocks\n obj : conformed data (may be resampled)\n \"\"\"\n\n from pandas import Series, concat\n from pandas.core.index import ensure_index\n\n final = []\n for result, block in zip(results, blocks):\n\n result = self._wrap_result(result, block=block, obj=obj)\n if result.ndim == 1:\n return result\n final.append(result)\n\n # if we have an 'on' column\n # we want to put it back into the results\n # in the same location\n columns = self._selected_obj.columns\n if self.on is not None and not self._on.equals(obj.index):\n\n name = self._on.name\n final.append(Series(self._on, index=obj.index, name=name))\n\n if self._selection is not None:\n\n selection = ensure_index(self._selection)\n\n # need to reorder to include original location of\n # the on column (if its not already there)\n if name not in selection:\n columns = self.obj.columns\n indexer = columns.get_indexer(selection.tolist() + [name])\n columns = columns.take(sorted(indexer))\n\n if not len(final):\n return obj.astype('float64')\n return concat(final, axis=1).reindex(columns=columns, copy=False)","label":0},{"cid":"pandas/core/window.py::_Window._center_window","text":"pandas/core/window.py::_Window._center_window\n def _center_window(self, result, window):\n \"\"\" center the result in the window \"\"\"\n if self.axis > result.ndim - 1:\n raise ValueError(\"Requested axis is larger then no. of argument \"\n \"dimensions\")\n\n offset = _offset(window, True)\n if offset > 0:\n if isinstance(result, (ABCSeries, ABCDataFrame)):\n result = result.slice_shift(-offset, axis=self.axis)\n else:\n lead_indexer = [slice(None)] * result.ndim\n lead_indexer[self.axis] = slice(offset, None)\n result = np.copy(result[tuple(lead_indexer)])\n return result","label":0},{"cid":"pandas/core/window.py::_Window.aggregate","text":"pandas/core/window.py::_Window.aggregate\n def aggregate(self, arg, *args, **kwargs):\n result, how = self._aggregate(arg, *args, **kwargs)\n if result is None:\n return self.apply(arg, raw=False, args=args, kwargs=kwargs)\n return result","label":0},{"cid":"pandas/core/window.py::Window.validate","text":"pandas/core/window.py::Window.validate\n def validate(self):\n super(Window, self).validate()\n\n window = self.window\n if isinstance(window, (list, tuple, np.ndarray)):\n pass\n elif is_integer(window):\n if window <= 0:\n raise ValueError(\"window must be > 0 \")\n try:\n import scipy.signal as sig\n except ImportError:\n raise ImportError('Please install scipy to generate window '\n 'weight')\n\n if not isinstance(self.win_type, compat.string_types):\n raise ValueError('Invalid win_type {0}'.format(self.win_type))\n if getattr(sig, self.win_type, None) is None:\n raise ValueError('Invalid win_type {0}'.format(self.win_type))\n else:\n raise ValueError('Invalid window {0}'.format(window))","label":0},{"cid":"pandas/core/window.py::Window._prep_window","text":"pandas/core/window.py::Window._prep_window\n def _prep_window(self, **kwargs):\n \"\"\"\n provide validation for our window type, return the window\n we have already been validated\n \"\"\"\n\n window = self._get_window()\n if isinstance(window, (list, tuple, np.ndarray)):\n return com.asarray_tuplesafe(window).astype(float)\n elif is_integer(window):\n import scipy.signal as sig\n\n # the below may pop from kwargs\n def _validate_win_type(win_type, kwargs):\n arg_map = {'kaiser': ['beta'],\n 'gaussian': ['std'],\n 'general_gaussian': ['power', 'width'],\n 'slepian': ['width']}\n if win_type in arg_map:\n return tuple([win_type] + _pop_args(win_type,\n arg_map[win_type],\n kwargs))\n return win_type\n\n def _pop_args(win_type, arg_names, kwargs):\n msg = '%s window requires %%s' % win_type\n all_args = []\n for n in arg_names:\n if n not in kwargs:\n raise ValueError(msg % n)\n all_args.append(kwargs.pop(n))\n return all_args\n\n win_type = _validate_win_type(self.win_type, kwargs)\n # GH #15662. `False` makes symmetric window, rather than periodic.\n return sig.get_window(win_type, window, False).astype(float)","label":0},{"cid":"pandas/core/window.py::Window._apply_window","text":"pandas/core/window.py::Window._apply_window\n def _apply_window(self, mean=True, **kwargs):\n \"\"\"\n Applies a moving window of type ``window_type`` on the data.\n\n Parameters\n ----------\n mean : boolean, default True\n If True computes weighted mean, else weighted sum\n\n Returns\n -------\n y : same type as input argument\n\n \"\"\"\n window = self._prep_window(**kwargs)\n center = self.center\n\n blocks, obj, index = self._create_blocks()\n results = []\n for b in blocks:\n try:\n values = self._prep_values(b.values)\n except TypeError:\n results.append(b.values.copy())\n continue\n\n if values.size == 0:\n results.append(values.copy())\n continue\n\n offset = _offset(window, center)\n additional_nans = np.array([np.NaN] * offset)\n\n def f(arg, *args, **kwargs):\n minp = _use_window(self.min_periods, len(window))\n return _window.roll_window(np.concatenate((arg,\n additional_nans))\n if center else arg, window, minp,\n avg=mean)\n\n result = np.apply_along_axis(f, self.axis, values)\n\n if center:\n result = self._center_window(result, window)\n results.append(result)\n\n return self._wrap_results(results, blocks, obj)","label":0},{"cid":"pandas/core/window.py::Window.aggregate","text":"pandas/core/window.py::Window.aggregate\n @Appender(_agg_doc)\n @Appender(_shared_docs['aggregate'] % dict(\n versionadded='',\n klass='Series/DataFrame',\n axis=''))\n def aggregate(self, arg, *args, **kwargs):\n result, how = self._aggregate(arg, *args, **kwargs)\n if result is None:\n\n # these must apply directly\n result = arg(self)\n\n return result","label":0},{"cid":"pandas/core/window.py::Window.sum","text":"pandas/core/window.py::Window.sum\n @Substitution(name='window')\n @Appender(_shared_docs['sum'])\n def sum(self, *args, **kwargs):\n nv.validate_window_func('sum', args, kwargs)\n return self._apply_window(mean=False, **kwargs)","label":0},{"cid":"pandas/core/window.py::Window.mean","text":"pandas/core/window.py::Window.mean\n @Substitution(name='window')\n @Appender(_shared_docs['mean'])\n def mean(self, *args, **kwargs):\n nv.validate_window_func('mean', args, kwargs)\n return self._apply_window(mean=True, **kwargs)","label":0},{"cid":"pandas/core/window.py::_GroupByMixin.__init__","text":"pandas/core/window.py::_GroupByMixin.__init__\n def __init__(self, obj, *args, **kwargs):\n parent = kwargs.pop('parent', None) # noqa\n groupby = kwargs.pop('groupby', None)\n if groupby is None:\n groupby, obj = obj, obj.obj\n self._groupby = groupby\n self._groupby.mutated = True\n self._groupby.grouper.mutated = True\n super(GroupByMixin, self).__init__(obj, *args, **kwargs)","label":0},{"cid":"pandas/core/window.py::_GroupByMixin._apply","text":"pandas/core/window.py::_GroupByMixin._apply\n def _apply(self, func, name, window=None, center=None,\n check_minp=None, **kwargs):\n \"\"\"\n dispatch to apply; we are stripping all of the _apply kwargs and\n performing the original function call on the grouped object\n \"\"\"\n\n def f(x, name=name, *args):\n x = self._shallow_copy(x)\n\n if isinstance(name, compat.string_types):\n return getattr(x, name)(*args, **kwargs)\n\n return x.apply(name, *args, **kwargs)\n\n return self._groupby.apply(f)","label":0},{"cid":"pandas/core/window.py::_GroupByMixin._apply.f","text":"pandas/core/window.py::_GroupByMixin._apply.f\n def f(x, name=name, *args):\n x = self._shallow_copy(x)\n\n if isinstance(name, compat.string_types):\n return getattr(x, name)(*args, **kwargs)\n\n return x.apply(name, *args, **kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling._constructor","text":"pandas/core/window.py::_Rolling._constructor\n @property\n def _constructor(self):\n return Rolling","label":0},{"cid":"pandas/core/window.py::_Rolling._apply","text":"pandas/core/window.py::_Rolling._apply\n def _apply(self, func, name=None, window=None, center=None,\n check_minp=None, **kwargs):\n \"\"\"\n Rolling statistical measure using supplied function. Designed to be\n used with passed-in Cython array-based functions.\n\n Parameters\n ----------\n func : string/callable to apply\n name : string, optional\n name of this function\n window : int/array, default to _get_window()\n center : boolean, default to self.center\n check_minp : function, default to _use_window\n\n Returns\n -------\n y : type of input\n \"\"\"\n if center is None:\n center = self.center\n if window is None:\n window = self._get_window()\n\n if check_minp is None:\n check_minp = _use_window\n\n blocks, obj, index = self._create_blocks()\n index, indexi = self._get_index(index=index)\n results = []\n for b in blocks:\n values = self._prep_values(b.values)\n\n if values.size == 0:\n results.append(values.copy())\n continue\n\n # if we have a string function name, wrap it\n if isinstance(func, compat.string_types):\n cfunc = getattr(_window, func, None)\n if cfunc is None:\n raise ValueError(\"we do not support this function \"\n \"in _window.{0}\".format(func))\n\n def func(arg, window, min_periods=None, closed=None):\n minp = check_minp(min_periods, window)\n # ensure we are only rolling on floats\n arg = ensure_float64(arg)\n return cfunc(arg,\n window, minp, indexi, closed, **kwargs)\n\n # calculation function\n if center:\n offset = _offset(window, center)\n additional_nans = np.array(","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.count","text":"pandas/core/window.py::_Rolling_and_Expanding.count\n def count(self):\n\n blocks, obj, index = self._create_blocks()\n # Validate the index\n self._get_index(index=index)\n\n window = self._get_window()\n window = min(window, len(obj)) if not self.center else window\n\n results = []\n for b in blocks:\n result = b.notna().astype(int)\n result = self._constructor(result, window=window, min_periods=0,\n center=self.center,\n closed=self.closed).sum()\n results.append(result)\n\n return self._wrap_results(results, blocks, obj)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.apply","text":"pandas/core/window.py::_Rolling_and_Expanding.apply\n def apply(self, func, raw=None, args=(), kwargs={}):\n from pandas import Series\n\n # TODO: _level is unused?\n _level = kwargs.pop('_level', None) # noqa\n window = self._get_window()\n offset = _offset(window, self.center)\n index, indexi = self._get_index()\n\n # TODO: default is for backward compat\n # change to False in the future\n if raw is None:\n warnings.warn(\n \"Currently, 'apply' passes the values as ndarrays to the \"\n \"applied function. In the future, this will change to passing \"\n \"it as Series objects. You need to specify 'raw=True' to keep \"\n \"the current behaviour, and you can pass 'raw=False' to \"\n \"silence this warning\", FutureWarning, stacklevel=3)\n raw = True\n\n def f(arg, window, min_periods, closed):\n minp = _use_window(min_periods, window)\n if not raw:\n arg = Series(arg, index=self.obj.index)\n return _window.roll_generic(\n arg, window, minp, indexi,\n closed, offset, func, raw, args, kwargs)\n\n return self._apply(f, func, args=args, kwargs=kwargs,\n center=False, raw=raw)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.apply.f","text":"pandas/core/window.py::_Rolling_and_Expanding.apply.f\n def f(arg, window, min_periods, closed):\n minp = _use_window(min_periods, window)\n if not raw:\n arg = Series(arg, index=self.obj.index)\n return _window.roll_generic(\n arg, window, minp, indexi,\n closed, offset, func, raw, args, kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.sum","text":"pandas/core/window.py::_Rolling_and_Expanding.sum\n def sum(self, *args, **kwargs):\n nv.validate_window_func('sum', args, kwargs)\n return self._apply('roll_sum', 'sum', **kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.max","text":"pandas/core/window.py::_Rolling_and_Expanding.max\n def max(self, *args, **kwargs):\n nv.validate_window_func('max', args, kwargs)\n return self._apply('roll_max', 'max', **kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.min","text":"pandas/core/window.py::_Rolling_and_Expanding.min\n def min(self, *args, **kwargs):\n nv.validate_window_func('min', args, kwargs)\n return self._apply('roll_min', 'min', **kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.mean","text":"pandas/core/window.py::_Rolling_and_Expanding.mean\n def mean(self, *args, **kwargs):\n nv.validate_window_func('mean', args, kwargs)\n return self._apply('roll_mean', 'mean', **kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.median","text":"pandas/core/window.py::_Rolling_and_Expanding.median\n def median(self, **kwargs):\n return self._apply('roll_median_c', 'median', **kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.std","text":"pandas/core/window.py::_Rolling_and_Expanding.std\n def std(self, ddof=1, *args, **kwargs):\n nv.validate_window_func('std', args, kwargs)\n window = self._get_window()\n index, indexi = self._get_index()\n\n def f(arg, *args, **kwargs):\n minp = _require_min_periods(1)(self.min_periods, window)\n return _zsqrt(_window.roll_var(arg, window, minp, indexi,\n self.closed, ddof))\n\n return self._apply(f, 'std', check_minp=_require_min_periods(1),\n ddof=ddof, **kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.std.f","text":"pandas/core/window.py::_Rolling_and_Expanding.std.f\n def f(arg, *args, **kwargs):\n minp = _require_min_periods(1)(self.min_periods, window)\n return _zsqrt(_window.roll_var(arg, window, minp, indexi,\n self.closed, ddof))","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.var","text":"pandas/core/window.py::_Rolling_and_Expanding.var\n def var(self, ddof=1, *args, **kwargs):\n nv.validate_window_func('var', args, kwargs)\n return self._apply('roll_var', 'var',\n check_minp=_require_min_periods(1), ddof=ddof,\n **kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.skew","text":"pandas/core/window.py::_Rolling_and_Expanding.skew\n def skew(self, **kwargs):\n return self._apply('roll_skew', 'skew',\n check_minp=_require_min_periods(3), **kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.kurt","text":"pandas/core/window.py::_Rolling_and_Expanding.kurt\n def kurt(self, **kwargs):\n return self._apply('roll_kurt', 'kurt',\n check_minp=_require_min_periods(4), **kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.quantile","text":"pandas/core/window.py::_Rolling_and_Expanding.quantile\n def quantile(self, quantile, interpolation='linear', **kwargs):\n window = self._get_window()\n index, indexi = self._get_index()\n\n def f(arg, *args, **kwargs):\n minp = _use_window(self.min_periods, window)\n if quantile == 1.0:\n return _window.roll_max(arg, window, minp, indexi,\n self.closed)\n elif quantile == 0.0:\n return _window.roll_min(arg, window, minp, indexi,\n self.closed)\n else:\n return _window.roll_quantile(arg, window, minp, indexi,\n self.closed, quantile,\n interpolation)\n\n return self._apply(f, 'quantile', quantile=quantile,\n **kwargs)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.quantile.f","text":"pandas/core/window.py::_Rolling_and_Expanding.quantile.f\n def f(arg, *args, **kwargs):\n minp = _use_window(self.min_periods, window)\n if quantile == 1.0:\n return _window.roll_max(arg, window, minp, indexi,\n self.closed)\n elif quantile == 0.0:\n return _window.roll_min(arg, window, minp, indexi,\n self.closed)\n else:\n return _window.roll_quantile(arg, window, minp, indexi,\n self.closed, quantile,\n interpolation)","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.cov","text":"pandas/core/window.py::_Rolling_and_Expanding.cov\n def cov(self, other=None, pairwise=None, ddof=1, **kwargs):\n if other is None:\n other = self._selected_obj\n # only default unset\n pairwise = True if pairwise is None else pairwise\n other = self._shallow_copy(other)\n\n # GH 16058: offset window\n if self.is_freq_type:\n window = self.win_freq\n else:\n window = self._get_window(other)\n\n def _get_cov(X, Y):\n # GH #12373 : rolling functions error on float32 data\n # to avoid potential overflow, cast the data to float64\n X = X.astype('float64')\n Y = Y.astype('float64')\n mean = lambda x: x.rolling(window, self.min_periods,\n center=self.center).mean(**kwargs)\n count = (X + Y).rolling(window=window,\n center=self.center).count(**kwargs)\n bias_adj = count / (count - ddof)\n return (mean(X * Y) - mean(X) * mean(Y)) * bias_adj\n\n return _flex_binary_moment(self._selected_obj, other._selected_obj,\n _get_cov, pairwise=bool(pairwise))","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.cov._get_cov","text":"pandas/core/window.py::_Rolling_and_Expanding.cov._get_cov\n def _get_cov(X, Y):\n # GH #12373 : rolling functions error on float32 data\n # to avoid potential overflow, cast the data to float64\n X = X.astype('float64')\n Y = Y.astype('float64')\n mean = lambda x: x.rolling(window, self.min_periods,\n center=self.center).mean(**kwargs)\n count = (X + Y).rolling(window=window,\n center=self.center).count(**kwargs)\n bias_adj = count / (count - ddof)\n return (mean(X * Y) - mean(X) * mean(Y)) * bias_adj","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.corr","text":"pandas/core/window.py::_Rolling_and_Expanding.corr\n def corr(self, other=None, pairwise=None, **kwargs):\n if other is None:\n other = self._selected_obj\n # only default unset\n pairwise = True if pairwise is None else pairwise\n other = self._shallow_copy(other)\n window = self._get_window(other)\n\n def _get_corr(a, b):\n a = a.rolling(window=window, min_periods=self.min_periods,\n center=self.center)\n b = b.rolling(window=window, min_periods=self.min_periods,\n center=self.center)\n\n return a.cov(b, **kwargs) / (a.std(**kwargs) * b.std(**kwargs))\n\n return _flex_binary_moment(self._selected_obj, other._selected_obj,\n _get_corr, pairwise=bool(pairwise))","label":0},{"cid":"pandas/core/window.py::_Rolling_and_Expanding.corr._get_corr","text":"pandas/core/window.py::_Rolling_and_Expanding.corr._get_corr\n def _get_corr(a, b):\n a = a.rolling(window=window, min_periods=self.min_periods,\n center=self.center)\n b = b.rolling(window=window, min_periods=self.min_periods,\n center=self.center)\n\n return a.cov(b, **kwargs) / (a.std(**kwargs) * b.std(**kwargs))","label":0},{"cid":"pandas/core/window.py::Rolling.is_datetimelike","text":"pandas/core/window.py::Rolling.is_datetimelike\n @cache_readonly\n def is_datetimelike(self):\n return isinstance(self._on,\n (ABCDatetimeIndex,\n ABCTimedeltaIndex,\n ABCPeriodIndex))","label":0},{"cid":"pandas/core/window.py::Rolling._on","text":"pandas/core/window.py::Rolling._on\n @cache_readonly\n def _on(self):\n\n if self.on is None:\n return self.obj.index\n elif (isinstance(self.obj, ABCDataFrame) and\n self.on in self.obj.columns):\n from pandas import Index\n return Index(self.obj[self.on])\n else:\n raise ValueError(\"invalid on specified as {0}, \"\n \"must be a column (if DataFrame) \"\n \"or None\".format(self.on))","label":0},{"cid":"pandas/core/window.py::Rolling.validate","text":"pandas/core/window.py::Rolling.validate\n def validate(self):\n super(Rolling, self).validate()\n\n # we allow rolling on a datetimelike index\n if ((self.obj.empty or self.is_datetimelike) and\n isinstance(self.window, (compat.string_types, ABCDateOffset,\n timedelta))):\n\n self._validate_monotonic()\n freq = self._validate_freq()\n\n # we don't allow center\n if self.center:\n raise NotImplementedError(\"center is not implemented \"\n \"for datetimelike and offset \"\n \"based windows\")\n\n # this will raise ValueError on non-fixed freqs\n self.win_freq = self.window\n self.window = freq.nanos\n self.win_type = 'freq'\n\n # min_periods must be an integer\n if self.min_periods is None:\n self.min_periods = 1\n\n elif not is_integer(self.window):\n raise ValueError(\"window must be an integer\")\n elif self.window < 0:\n raise ValueError(\"window must be non-negative\")\n\n if not self.is_datetimelike and self.closed is not None:\n raise ValueError(\"closed only implemented for datetimelike \"\n \"and offset based windows\")","label":0},{"cid":"pandas/core/window.py::Rolling._validate_monotonic","text":"pandas/core/window.py::Rolling._validate_monotonic\n def _validate_monotonic(self):\n \"\"\" validate on is monotonic \"\"\"\n if not self._on.is_monotonic:\n formatted = self.on or 'index'\n raise ValueError(\"{0} must be \"\n \"monotonic\".format(formatted))","label":0},{"cid":"pandas/core/window.py::Rolling._validate_freq","text":"pandas/core/window.py::Rolling._validate_freq\n def _validate_freq(self):\n \"\"\" validate & return window frequency \"\"\"\n from pandas.tseries.frequencies import to_offset\n try:\n return to_offset(self.window)\n except (TypeError, ValueError):\n raise ValueError(\"passed window {0} is not \"\n \"compatible with a datetimelike \"\n \"index\".format(self.window))","label":0},{"cid":"pandas/core/window.py::Rolling.aggregate","text":"pandas/core/window.py::Rolling.aggregate\n @Appender(_agg_doc)\n @Appender(_shared_docs['aggregate'] % dict(\n versionadded='',\n klass='Series/DataFrame',\n axis=''))\n def aggregate(self, arg, *args, **kwargs):\n return super(Rolling, self).aggregate(arg, *args, **kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.count","text":"pandas/core/window.py::Rolling.count\n @Substitution(name='rolling')\n @Appender(_shared_docs['count'])\n def count(self):\n\n # different impl for freq counting\n if self.is_freq_type:\n return self._apply('roll_count', 'count')\n\n return super(Rolling, self).count()","label":0},{"cid":"pandas/core/window.py::Rolling.apply","text":"pandas/core/window.py::Rolling.apply\n @Substitution(name='rolling')\n @Appender(_doc_template)\n @Appender(_shared_docs['apply'])\n def apply(self, func, raw=None, args=(), kwargs={}):\n return super(Rolling, self).apply(\n func, raw=raw, args=args, kwargs=kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.sum","text":"pandas/core/window.py::Rolling.sum\n @Substitution(name='rolling')\n @Appender(_shared_docs['sum'])\n def sum(self, *args, **kwargs):\n nv.validate_rolling_func('sum', args, kwargs)\n return super(Rolling, self).sum(*args, **kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.max","text":"pandas/core/window.py::Rolling.max\n @Substitution(name='rolling')\n @Appender(_doc_template)\n @Appender(_shared_docs['max'])\n def max(self, *args, **kwargs):\n nv.validate_rolling_func('max', args, kwargs)\n return super(Rolling, self).max(*args, **kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.min","text":"pandas/core/window.py::Rolling.min\n @Substitution(name='rolling')\n @Appender(_shared_docs['min'])\n def min(self, *args, **kwargs):\n nv.validate_rolling_func('min', args, kwargs)\n return super(Rolling, self).min(*args, **kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.mean","text":"pandas/core/window.py::Rolling.mean\n @Substitution(name='rolling')\n @Appender(_shared_docs['mean'])\n def mean(self, *args, **kwargs):\n nv.validate_rolling_func('mean', args, kwargs)\n return super(Rolling, self).mean(*args, **kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.median","text":"pandas/core/window.py::Rolling.median\n @Substitution(name='rolling')\n @Appender(_shared_docs['median'])\n def median(self, **kwargs):\n return super(Rolling, self).median(**kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.std","text":"pandas/core/window.py::Rolling.std\n @Substitution(name='rolling')\n @Appender(_shared_docs['std'])\n def std(self, ddof=1, *args, **kwargs):\n nv.validate_rolling_func('std', args, kwargs)\n return super(Rolling, self).std(ddof=ddof, **kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.var","text":"pandas/core/window.py::Rolling.var\n @Substitution(name='rolling')\n @Appender(_shared_docs['var'])\n def var(self, ddof=1, *args, **kwargs):\n nv.validate_rolling_func('var', args, kwargs)\n return super(Rolling, self).var(ddof=ddof, **kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.skew","text":"pandas/core/window.py::Rolling.skew\n @Substitution(name='rolling')\n @Appender(_doc_template)\n @Appender(_shared_docs['skew'])\n def skew(self, **kwargs):\n return super(Rolling, self).skew(**kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.kurt","text":"pandas/core/window.py::Rolling.kurt\n @Appender(_agg_doc)\n @Substitution(name='rolling')\n @Appender(_shared_docs['kurt'])\n def kurt(self, **kwargs):\n return super(Rolling, self).kurt(**kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.quantile","text":"pandas/core/window.py::Rolling.quantile\n @Substitution(name='rolling')\n @Appender(_shared_docs['quantile'])\n def quantile(self, quantile, interpolation='linear', **kwargs):\n return super(Rolling, self).quantile(quantile=quantile,\n interpolation=interpolation,\n **kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.cov","text":"pandas/core/window.py::Rolling.cov\n @Substitution(name='rolling')\n @Appender(_doc_template)\n @Appender(_shared_docs['cov'])\n def cov(self, other=None, pairwise=None, ddof=1, **kwargs):\n return super(Rolling, self).cov(other=other, pairwise=pairwise,\n ddof=ddof, **kwargs)","label":0},{"cid":"pandas/core/window.py::Rolling.corr","text":"pandas/core/window.py::Rolling.corr\n @Substitution(name='rolling')\n @Appender(_shared_docs['corr'])\n def corr(self, other=None, pairwise=None, **kwargs):\n return super(Rolling, self).corr(other=other, pairwise=pairwise,\n **kwargs)","label":0},{"cid":"pandas/core/window.py::RollingGroupby._constructor","text":"pandas/core/window.py::RollingGroupby._constructor\n @property\n def _constructor(self):\n return Rolling","label":0},{"cid":"pandas/core/window.py::RollingGroupby._gotitem","text":"pandas/core/window.py::RollingGroupby._gotitem\n def _gotitem(self, key, ndim, subset=None):\n\n # we are setting the index on the actual object\n # here so our index is carried thru to the selected obj\n # when we do the splitting for the groupby\n if self.on is not None:\n self._groupby.obj = self._groupby.obj.set_index(self._on)\n self.on = None\n return super(RollingGroupby, self)._gotitem(key, ndim, subset=subset)","label":0},{"cid":"pandas/core/window.py::RollingGroupby._validate_monotonic","text":"pandas/core/window.py::RollingGroupby._validate_monotonic\n def _validate_monotonic(self):\n \"\"\"\n validate that on is monotonic;\n we don't care for groupby.rolling\n because we have already validated at a higher\n level\n \"\"\"\n pass","label":0},{"cid":"pandas/core/window.py::Expanding.__init__","text":"pandas/core/window.py::Expanding.__init__\n def __init__(self, obj, min_periods=1, center=False, axis=0,\n **kwargs):\n super(Expanding, self).__init__(obj=obj, min_periods=min_periods,\n center=center, axis=axis)","label":0},{"cid":"pandas/core/window.py::Expanding._constructor","text":"pandas/core/window.py::Expanding._constructor\n @property\n def _constructor(self):\n return Expanding","label":0},{"cid":"pandas/core/window.py::Expanding._get_window","text":"pandas/core/window.py::Expanding._get_window\n def _get_window(self, other=None):\n obj = self._selected_obj\n if other is None:\n return (max(len(obj), self.min_periods) if self.min_periods\n else len(obj))\n return (max((len(obj) + len(obj)), self.min_periods)\n if self.min_periods else (len(obj) + len(obj)))","label":1},{"cid":"pandas/core/window.py::Expanding.aggregate","text":"pandas/core/window.py::Expanding.aggregate\n @Appender(_agg_doc)\n @Appender(_shared_docs['aggregate'] % dict(\n versionadded='',\n klass='Series/DataFrame',\n axis=''))\n def aggregate(self, arg, *args, **kwargs):\n return super(Expanding, self).aggregate(arg, *args, **kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.count","text":"pandas/core/window.py::Expanding.count\n @Substitution(name='expanding')\n @Appender(_shared_docs['count'])\n def count(self, **kwargs):\n return super(Expanding, self).count(**kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.apply","text":"pandas/core/window.py::Expanding.apply\n @Substitution(name='expanding')\n @Appender(_doc_template)\n @Appender(_shared_docs['apply'])\n def apply(self, func, raw=None, args=(), kwargs={}):\n return super(Expanding, self).apply(\n func, raw=raw, args=args, kwargs=kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.sum","text":"pandas/core/window.py::Expanding.sum\n @Substitution(name='expanding')\n @Appender(_shared_docs['sum'])\n def sum(self, *args, **kwargs):\n nv.validate_expanding_func('sum', args, kwargs)\n return super(Expanding, self).sum(*args, **kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.max","text":"pandas/core/window.py::Expanding.max\n @Substitution(name='expanding')\n @Appender(_doc_template)\n @Appender(_shared_docs['max'])\n def max(self, *args, **kwargs):\n nv.validate_expanding_func('max', args, kwargs)\n return super(Expanding, self).max(*args, **kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.min","text":"pandas/core/window.py::Expanding.min\n @Substitution(name='expanding')\n @Appender(_shared_docs['min'])\n def min(self, *args, **kwargs):\n nv.validate_expanding_func('min', args, kwargs)\n return super(Expanding, self).min(*args, **kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.mean","text":"pandas/core/window.py::Expanding.mean\n @Substitution(name='expanding')\n @Appender(_shared_docs['mean'])\n def mean(self, *args, **kwargs):\n nv.validate_expanding_func('mean', args, kwargs)\n return super(Expanding, self).mean(*args, **kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.median","text":"pandas/core/window.py::Expanding.median\n @Substitution(name='expanding')\n @Appender(_shared_docs['median'])\n def median(self, **kwargs):\n return super(Expanding, self).median(**kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.std","text":"pandas/core/window.py::Expanding.std\n @Substitution(name='expanding')\n @Appender(_shared_docs['std'])\n def std(self, ddof=1, *args, **kwargs):\n nv.validate_expanding_func('std', args, kwargs)\n return super(Expanding, self).std(ddof=ddof, **kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.var","text":"pandas/core/window.py::Expanding.var\n @Substitution(name='expanding')\n @Appender(_shared_docs['var'])\n def var(self, ddof=1, *args, **kwargs):\n nv.validate_expanding_func('var', args, kwargs)\n return super(Expanding, self).var(ddof=ddof, **kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.skew","text":"pandas/core/window.py::Expanding.skew\n @Substitution(name='expanding')\n @Appender(_doc_template)\n @Appender(_shared_docs['skew'])\n def skew(self, **kwargs):\n return super(Expanding, self).skew(**kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.kurt","text":"pandas/core/window.py::Expanding.kurt\n @Appender(_agg_doc)\n @Substitution(name='expanding')\n @Appender(_shared_docs['kurt'])\n def kurt(self, **kwargs):\n return super(Expanding, self).kurt(**kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.quantile","text":"pandas/core/window.py::Expanding.quantile\n @Substitution(name='expanding')\n @Appender(_shared_docs['quantile'])\n def quantile(self, quantile, interpolation='linear', **kwargs):\n return super(Expanding, self).quantile(quantile=quantile,\n interpolation=interpolation,\n **kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.cov","text":"pandas/core/window.py::Expanding.cov\n @Substitution(name='expanding')\n @Appender(_doc_template)\n @Appender(_shared_docs['cov'])\n def cov(self, other=None, pairwise=None, ddof=1, **kwargs):\n return super(Expanding, self).cov(other=other, pairwise=pairwise,\n ddof=ddof, **kwargs)","label":0},{"cid":"pandas/core/window.py::Expanding.corr","text":"pandas/core/window.py::Expanding.corr\n @Substitution(name='expanding')\n @Appender(_shared_docs['corr'])\n def corr(self, other=None, pairwise=None, **kwargs):\n return super(Expanding, self).corr(other=other, pairwise=pairwise,\n **kwargs)","label":0},{"cid":"pandas/core/window.py::ExpandingGroupby._constructor","text":"pandas/core/window.py::ExpandingGroupby._constructor\n @property\n def _constructor(self):\n return Expanding","label":0},{"cid":"pandas/core/window.py::EWM.__init__","text":"pandas/core/window.py::EWM.__init__\n def __init__(self, obj, com=None, span=None, halflife=None, alpha=None,\n min_periods=0, adjust=True, ignore_na=False,\n axis=0):\n self.obj = obj\n self.com = _get_center_of_mass(com, span, halflife, alpha)\n self.min_periods = min_periods\n self.adjust = adjust\n self.ignore_na = ignore_na\n self.axis = axis\n self.on = None","label":0},{"cid":"pandas/core/window.py::EWM._constructor","text":"pandas/core/window.py::EWM._constructor\n @property\n def _constructor(self):\n return EWM","label":0},{"cid":"pandas/core/window.py::EWM.aggregate","text":"pandas/core/window.py::EWM.aggregate\n @Appender(_agg_doc)\n @Appender(_shared_docs['aggregate'] % dict(\n versionadded='',\n klass='Series/DataFrame',\n axis=''))\n def aggregate(self, arg, *args, **kwargs):\n return super(EWM, self).aggregate(arg, *args, **kwargs)","label":0},{"cid":"pandas/core/window.py::EWM._apply","text":"pandas/core/window.py::EWM._apply\n def _apply(self, func, **kwargs):\n \"\"\"Rolling statistical measure using supplied function. Designed to be\n used with passed-in Cython array-based functions.\n\n Parameters\n ----------\n func : string/callable to apply\n\n Returns\n -------\n y : same type as input argument\n\n \"\"\"\n blocks, obj, index = self._create_blocks()\n results = []\n for b in blocks:\n try:\n values = self._prep_values(b.values)\n except TypeError:\n results.append(b.values.copy())\n continue\n\n if values.size == 0:\n results.append(values.copy())\n continue\n\n # if we have a string function name, wrap it\n if isinstance(func, compat.string_types):\n cfunc = getattr(_window, func, None)\n if cfunc is None:\n raise ValueError(\"we do not support this function \"\n \"in _window.{0}\".format(func))\n\n def func(arg):\n return cfunc(arg, self.com, int(self.adjust),\n int(self.ignore_na), int(self.min_periods))\n\n results.append(np.apply_along_axis(func, self.axis, values))\n\n return self._wrap_results(results, blocks, obj)","label":0},{"cid":"pandas/core/window.py::EWM.mean","text":"pandas/core/window.py::EWM.mean\n @Substitution(name='ewm')\n @Appender(_doc_template)\n def mean(self, *args, **kwargs):\n \"\"\"exponential weighted moving average\"\"\"\n nv.validate_window_func('mean', args, kwargs)\n return self._apply('ewma', **kwargs)","label":0},{"cid":"pandas/core/window.py::EWM.std","text":"pandas/core/window.py::EWM.std\n @Substitution(name='ewm')\n @Appender(_doc_template)\n @Appender(_bias_template)\n def std(self, bias=False, *args, **kwargs):\n \"\"\"exponential weighted moving stddev\"\"\"\n nv.validate_window_func('std', args, kwargs)\n return _zsqrt(self.var(bias=bias, **kwargs))","label":0},{"cid":"pandas/core/window.py::EWM.var","text":"pandas/core/window.py::EWM.var\n @Substitution(name='ewm')\n @Appender(_doc_template)\n @Appender(_bias_template)\n def var(self, bias=False, *args, **kwargs):\n \"\"\"exponential weighted moving variance\"\"\"\n nv.validate_window_func('var', args, kwargs)\n\n def f(arg):\n return _window.ewmcov(arg, arg, self.com, int(self.adjust),\n int(self.ignore_na), int(self.min_periods),\n int(bias))\n\n return self._apply(f, **kwargs)","label":0},{"cid":"pandas/core/window.py::EWM.var.f","text":"pandas/core/window.py::EWM.var.f\n def f(arg):\n return _window.ewmcov(arg, arg, self.com, int(self.adjust),\n int(self.ignore_na), int(self.min_periods),\n int(bias))","label":0},{"cid":"pandas/core/window.py::EWM.cov","text":"pandas/core/window.py::EWM.cov\n @Substitution(name='ewm')\n @Appender(_doc_template)\n @Appender(_pairwise_template)\n def cov(self, other=None, pairwise=None, bias=False, **kwargs):\n \"\"\"exponential weighted sample covariance\"\"\"\n if other is None:\n other = self._selected_obj\n # only default unset\n pairwise = True if pairwise is None else pairwise\n other = self._shallow_copy(other)\n\n def _get_cov(X, Y):\n X = self._shallow_copy(X)\n Y = self._shallow_copy(Y)\n cov = _window.ewmcov(X._prep_values(), Y._prep_values(), self.com,\n int(self.adjust), int(self.ignore_na),\n int(self.min_periods), int(bias))\n return X._wrap_result(cov)\n\n return _flex_binary_moment(self._selected_obj, other._selected_obj,\n _get_cov, pairwise=bool(pairwise))","label":0},{"cid":"pandas/core/window.py::EWM.cov._get_cov","text":"pandas/core/window.py::EWM.cov._get_cov\n def _get_cov(X, Y):\n X = self._shallow_copy(X)\n Y = self._shallow_copy(Y)\n cov = _window.ewmcov(X._prep_values(), Y._prep_values(), self.com,\n int(self.adjust), int(self.ignore_na),\n int(self.min_periods), int(bias))\n return X._wrap_result(cov)","label":0},{"cid":"pandas/core/window.py::EWM.corr","text":"pandas/core/window.py::EWM.corr\n @Substitution(name='ewm')\n @Appender(_doc_template)\n @Appender(_pairwise_template)\n def corr(self, other=None, pairwise=None, **kwargs):\n \"\"\"exponential weighted sample correlation\"\"\"\n if other is None:\n other = self._selected_obj\n # only default unset\n pairwise = True if pairwise is None else pairwise\n other = self._shallow_copy(other)\n\n def _get_corr(X, Y):\n X = self._shallow_copy(X)\n Y = self._shallow_copy(Y)\n\n def _cov(x, y):\n return _window.ewmcov(x, y, self.com, int(self.adjust),\n int(self.ignore_na),\n int(self.min_periods),\n 1)\n\n x_values = X._prep_values()\n y_values = Y._prep_values()\n with np.errstate(all='ignore'):\n cov = _cov(x_values, y_values)\n x_var = _cov(x_values, x_values)\n y_var = _cov(y_values, y_values)\n corr = cov / _zsqrt(x_var * y_var)\n return X._wrap_result(corr)\n\n return _flex_binary_moment(self._selected_obj, other._selected_obj,\n _get_corr, pairwise=bool(pairwise))","label":0},{"cid":"pandas/core/window.py::EWM.corr._get_corr","text":"pandas/core/window.py::EWM.corr._get_corr\n def _get_corr(X, Y):\n X = self._shallow_copy(X)\n Y = self._shallow_copy(Y)\n\n def _cov(x, y):\n return _window.ewmcov(x, y, self.com, int(self.adjust),\n int(self.ignore_na),\n int(self.min_periods),\n 1)\n\n x_values = X._prep_values()\n y_values = Y._prep_values()\n with np.errstate(all='ignore'):\n cov = _cov(x_values, y_values)\n x_var = _cov(x_values, x_values)\n y_var = _cov(y_values, y_values)\n corr = cov / _zsqrt(x_var * y_var)\n return X._wrap_result(corr)","label":0},{"cid":"pandas/core/window.py::EWM.corr._get_corr._cov","text":"pandas/core/window.py::EWM.corr._get_corr._cov\n def _cov(x, y):\n return _window.ewmcov(x, y, self.com, int(self.adjust),\n int(self.ignore_na),\n int(self.min_periods),\n 1)","label":0},{"cid":"pandas/core/window.py::_flex_binary_moment","text":"pandas/core/window.py::_flex_binary_moment\ndef _flex_binary_moment(arg1, arg2, f, pairwise=False):\n\n if not (isinstance(arg1, (np.ndarray, ABCSeries, ABCDataFrame)) and\n isinstance(arg2, (np.ndarray, ABCSeries, ABCDataFrame))):\n raise TypeError(\"arguments to moment function must be of type \"\n \"np.ndarray/Series/DataFrame\")\n\n if (isinstance(arg1, (np.ndarray, ABCSeries)) and\n isinstance(arg2, (np.ndarray, ABCSeries))):\n X, Y = _prep_binary(arg1, arg2)\n return f(X, Y)\n\n elif isinstance(arg1, ABCDataFrame):\n from pandas import DataFrame\n\n def dataframe_from_int_dict(data, frame_template):\n result = DataFrame(data, index=frame_template.index)\n if len(result.columns) > 0:\n result.columns = frame_template.columns[result.columns]\n return result\n\n results = {}\n if isinstance(arg2, ABCDataFrame):\n if pairwise is False:\n if arg1 is arg2:\n # special case in order to handle duplicate column names\n for i, col in enumerate(arg1.columns):\n results[i] = f(arg1.iloc[:, i], arg2.iloc[:, i])\n return dataframe_from_int_dict(results, arg1)\n else:\n if not arg1.columns.is_unique:\n raise ValueError(\"'arg1' columns are not unique\")\n if not arg2.columns.is_unique:\n raise ValueError(\"'arg2' columns are not unique\")\n with warnings.catch_warnings(record=True):\n warnings.simplefilter(\"ignore\", RuntimeWarning)\n X, Y = arg1.align(arg2, join='outer')\n X = X + 0 * Y\n Y = Y + 0 * X\n\n with warnings.catch_warnings(record=True):\n warnings.simplefilter(\"ignore\", RuntimeWarning)\n res_columns","label":0},{"cid":"pandas/core/window.py::_get_center_of_mass","text":"pandas/core/window.py::_get_center_of_mass\ndef _get_center_of_mass(comass, span, halflife, alpha):\n valid_count = com.count_not_none(comass, span, halflife, alpha)\n if valid_count > 1:\n raise ValueError(\"comass, span, halflife, and alpha \"\n \"are mutually exclusive\")\n\n # Convert to center of mass; domain checks ensure 0 < alpha <= 1\n if comass is not None:\n if comass < 0:\n raise ValueError(\"comass must satisfy: comass >= 0\")\n elif span is not None:\n if span < 1:\n raise ValueError(\"span must satisfy: span >= 1\")\n comass = (span - 1) / 2.\n elif halflife is not None:\n if halflife <= 0:\n raise ValueError(\"halflife must satisfy: halflife > 0\")\n decay = 1 - np.exp(np.log(0.5) / halflife)\n comass = 1 / decay - 1\n elif alpha is not None:\n if alpha <= 0 or alpha > 1:\n raise ValueError(\"alpha must satisfy: 0 < alpha <= 1\")\n comass = (1.0 - alpha) / alpha\n else:\n raise ValueError(\"Must pass one of comass, span, halflife, or alpha\")\n\n return float(comass)","label":0},{"cid":"pandas/core/window.py::_offset","text":"pandas/core/window.py::_offset\ndef _offset(window, center):\n if not is_integer(window):\n window = len(window)\n offset = (window - 1) / 2. if center else 0\n try:\n return int(offset)\n except TypeError:\n return offset.astype(int)","label":0},{"cid":"pandas/core/window.py::_require_min_periods","text":"pandas/core/window.py::_require_min_periods\ndef _require_min_periods(p):\n def _check_func(minp, window):\n if minp is None:\n return window\n else:\n return max(p, minp)\n\n return _check_func","label":0},{"cid":"pandas/core/window.py::_require_min_periods._check_func","text":"pandas/core/window.py::_require_min_periods._check_func\n def _check_func(minp, window):\n if minp is None:\n return window\n else:\n return max(p, minp)","label":0},{"cid":"pandas/core/window.py::_use_window","text":"pandas/core/window.py::_use_window\ndef _use_window(minp, window):\n if minp is None:\n return window\n else:\n return minp","label":0},{"cid":"pandas/core/window.py::_zsqrt","text":"pandas/core/window.py::_zsqrt\ndef _zsqrt(x):\n with np.errstate(all='ignore'):\n result = np.sqrt(x)\n mask = x < 0\n\n if isinstance(x, ABCDataFrame):\n if mask.values.any():\n result[mask] = 0\n else:\n if mask.any():\n result[mask] = 0\n\n return result","label":0},{"cid":"pandas/core/window.py::_prep_binary","text":"pandas/core/window.py::_prep_binary\ndef _prep_binary(arg1, arg2):\n if not isinstance(arg2, type(arg1)):\n raise Exception('Input arrays must be of the same type!')\n\n # mask out values, this also makes a common index...\n X = arg1 + 0 * arg2\n Y = arg2 + 0 * arg1\n\n return X, Y","label":0},{"cid":"pandas/core/window.py::rolling","text":"pandas/core/window.py::rolling\ndef rolling(obj, win_type=None, **kwds):\n if not isinstance(obj, (ABCSeries, ABCDataFrame)):\n raise TypeError('invalid type: %s' % type(obj))\n\n if win_type is not None:\n return Window(obj, win_type=win_type, **kwds)\n\n return Rolling(obj, **kwds)","label":0},{"cid":"pandas/core/window.py::expanding","text":"pandas/core/window.py::expanding\ndef expanding(obj, **kwds):\n if not isinstance(obj, (ABCSeries, ABCDataFrame)):\n raise TypeError('invalid type: %s' % type(obj))\n\n return Expanding(obj, **kwds)","label":0},{"cid":"pandas/core/window.py::ewm","text":"pandas/core/window.py::ewm\ndef ewm(obj, **kwds):\n if not isinstance(obj, (ABCSeries, ABCDataFrame)):\n raise TypeError('invalid type: %s' % type(obj))\n\n return EWM(obj, **kwds)","label":0}],"string":"[\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::ApplyDictReturn.setup\",\n \"text\": \"asv_bench/benchmarks/groupby.py::ApplyDictReturn.setup\\n def setup(self):\\n self.labels = np.arange(1000).repeat(10)\\n self.data = Series(np.random.randn(len(self.labels)))\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::ApplyDictReturn.time_groupby_apply_dict_return\",\n \"text\": \"asv_bench/benchmarks/groupby.py::ApplyDictReturn.time_groupby_apply_dict_return\\n def time_groupby_apply_dict_return(self):\\n self.data.groupby(self.labels).apply(lambda x: {'first': x.values[0],\\n 'last': x.values[-1]})\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Apply.setup_cache\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Apply.setup_cache\\n def setup_cache(self):\\n N = 10**4\\n labels = np.random.randint(0, 2000, size=N)\\n labels2 = np.random.randint(0, 3, size=N)\\n df = DataFrame({'key': labels,\\n 'key2': labels2,\\n 'value1': np.random.randn(N),\\n 'value2': ['foo', 'bar', 'baz', 'qux'] * (N // 4)\\n })\\n return df\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Apply.time_scalar_function_multi_col\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Apply.time_scalar_function_multi_col\\n def time_scalar_function_multi_col(self, df):\\n df.groupby(['key', 'key2']).apply(lambda x: 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Apply.time_scalar_function_single_col\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Apply.time_scalar_function_single_col\\n def time_scalar_function_single_col(self, df):\\n df.groupby('key').apply(lambda x: 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Apply.df_copy_function\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Apply.df_copy_function\\n @staticmethod\\n def df_copy_function(g):\\n # ensure that the group name is available (see GH #15062)\\n g.name\\n return g.copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Apply.time_copy_function_multi_col\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Apply.time_copy_function_multi_col\\n def time_copy_function_multi_col(self, df):\\n df.groupby(['key', 'key2']).apply(self.df_copy_function)\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Apply.time_copy_overhead_single_col\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Apply.time_copy_overhead_single_col\\n def time_copy_overhead_single_col(self, df):\\n df.groupby('key').apply(self.df_copy_function)\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Groups.setup_cache\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Groups.setup_cache\\n def setup_cache(self):\\n size = 10**6\\n data = {'int64_small': Series(np.random.randint(0, 100, size=size)),\\n 'int64_large': Series(np.random.randint(0, 10000, size=size)),\\n 'object_small': Series(\\n tm.makeStringIndex(100).take(\\n np.random.randint(0, 100, size=size))),\\n 'object_large': Series(\\n tm.makeStringIndex(10000).take(\\n np.random.randint(0, 10000, size=size)))}\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Groups.setup\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Groups.setup\\n def setup(self, data, key):\\n self.ser = data[key]\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Groups.time_series_groups\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Groups.time_series_groups\\n def time_series_groups(self, data, key):\\n self.ser.groupby(self.ser).groups\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::GroupManyLabels.setup\",\n \"text\": \"asv_bench/benchmarks/groupby.py::GroupManyLabels.setup\\n def setup(self, ncols):\\n N = 1000\\n data = np.random.randn(N, ncols)\\n self.labels = np.random.randint(0, 100, size=N)\\n self.df = DataFrame(data)\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::GroupManyLabels.time_sum\",\n \"text\": \"asv_bench/benchmarks/groupby.py::GroupManyLabels.time_sum\\n def time_sum(self, ncols):\\n self.df.groupby(self.labels).sum()\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Nth.setup\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Nth.setup\\n def setup(self, dtype):\\n N = 10**5\\n # with datetimes (GH7555)\\n if dtype == 'datetime':\\n values = date_range('1/1/2011', periods=N, freq='s')\\n elif dtype == 'object':\\n values = ['foo'] * N\\n else:\\n values = np.arange(N).astype(dtype)\\n\\n key = np.arange(N)\\n self.df = DataFrame({'key': key, 'values': values})\\n self.df.iloc[1, 1] = np.nan # insert missing data\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Nth.time_frame_nth_any\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Nth.time_frame_nth_any\\n def time_frame_nth_any(self, dtype):\\n self.df.groupby('key').nth(0, dropna='any')\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Nth.time_groupby_nth_all\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Nth.time_groupby_nth_all\\n def time_groupby_nth_all(self, dtype):\\n self.df.groupby('key').nth(0, dropna='all')\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Nth.time_frame_nth\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Nth.time_frame_nth\\n def time_frame_nth(self, dtype):\\n self.df.groupby('key').nth(0)\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Nth.time_series_nth_any\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Nth.time_series_nth_any\\n def time_series_nth_any(self, dtype):\\n self.df['values'].groupby(self.df['key']).nth(0, dropna='any')\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Nth.time_series_nth_all\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Nth.time_series_nth_all\\n def time_series_nth_all(self, dtype):\\n self.df['values'].groupby(self.df['key']).nth(0, dropna='all')\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Nth.time_series_nth\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Nth.time_series_nth\\n def time_series_nth(self, dtype):\\n self.df['values'].groupby(self.df['key']).nth(0)\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::DateAttributes.setup\",\n \"text\": \"asv_bench/benchmarks/groupby.py::DateAttributes.setup\\n def setup(self):\\n rng = date_range('1/1/2000', '12/31/2005', freq='H')\\n self.year, self.month, self.day = rng.year, rng.month, rng.day\\n self.ts = Series(np.random.randn(len(rng)), index=rng)\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::DateAttributes.time_len_groupby_object\",\n \"text\": \"asv_bench/benchmarks/groupby.py::DateAttributes.time_len_groupby_object\\n def time_len_groupby_object(self):\\n len(self.ts.groupby([self.year, self.month, self.day]))\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Int64.setup\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Int64.setup\\n def setup(self):\\n arr = np.random.randint(-1 << 12, 1 << 12, (1 << 17, 5))\\n i = np.random.choice(len(arr), len(arr) * 5)\\n arr = np.vstack((arr, arr[i]))\\n i = np.random.permutation(len(arr))\\n arr = arr[i]\\n self.cols = list('abcde')\\n self.df = DataFrame(arr, columns=self.cols)\\n self.df['jim'], self.df['joe'] = np.random.randn(2, len(self.df)) * 10\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::Int64.time_overflow\",\n \"text\": \"asv_bench/benchmarks/groupby.py::Int64.time_overflow\\n def time_overflow(self):\\n self.df.groupby(self.cols).max()\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::CountMultiDtype.setup_cache\",\n \"text\": \"asv_bench/benchmarks/groupby.py::CountMultiDtype.setup_cache\\n def setup_cache(self):\\n n = 10000\\n offsets = np.random.randint(n, size=n).astype('timedelta64[ns]')\\n dates = np.datetime64('now') + offsets\\n dates[np.random.rand(n) > 0.5] = np.datetime64('nat')\\n offsets[np.random.rand(n) > 0.5] = np.timedelta64('nat')\\n value2 = np.random.randn(n)\\n value2[np.random.rand(n) > 0.5] = np.nan\\n obj = np.random.choice(list('ab'), size=n).astype(object)\\n obj[np.random.randn(n) > 0.5] = np.nan\\n df = DataFrame({'key1': np.random.randint(0, 500, size=n),\\n 'key2': np.random.randint(0, 100, size=n),\\n 'dates': dates,\\n 'value2': value2,\\n 'value3': np.random.randn(n),\\n 'ints': np.random.randint(0, 1000, size=n),\\n 'obj': obj,\\n 'offsets': offsets})\\n return df\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::CountMultiDtype.time_multi_count\",\n \"text\": \"asv_bench/benchmarks/groupby.py::CountMultiDtype.time_multi_count\\n def time_multi_count(self, df):\\n df.groupby(['key1', 'key2']).count()\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::CountMultiInt.setup_cache\",\n \"text\": \"asv_bench/benchmarks/groupby.py::CountMultiInt.setup_cache\\n def setup_cache(self):\\n n = 10000\\n df = DataFrame({'key1': np.random.randint(0, 500, size=n),\\n 'key2': np.random.randint(0, 100, size=n),\\n 'ints': np.random.randint(0, 1000, size=n),\\n 'ints2': np.random.randint(0, 1000, size=n)})\\n return df\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::CountMultiInt.time_multi_int_count\",\n \"text\": \"asv_bench/benchmarks/groupby.py::CountMultiInt.time_multi_int_count\\n def time_multi_int_count(self, df):\\n df.groupby(['key1', 'key2']).count()\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::CountMultiInt.time_multi_int_nunique\",\n \"text\": \"asv_bench/benchmarks/groupby.py::CountMultiInt.time_multi_int_nunique\\n def time_multi_int_nunique(self, df):\\n df.groupby(['key1', 'key2']).nunique()\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::AggFunctions.setup_cache\",\n \"text\": \"asv_bench/benchmarks/groupby.py::AggFunctions.setup_cache\\n def setup_cache():\\n N = 10**5\\n fac1 = np.array(['A', 'B', 'C'], dtype='O')\\n fac2 = np.array(['one', 'two'], dtype='O')\\n df = DataFrame({'key1': fac1.take(np.random.randint(0, 3, size=N)),\\n 'key2': fac2.take(np.random.randint(0, 2, size=N)),\\n 'value1': np.random.randn(N),\\n 'value2': np.random.randn(N),\\n 'value3': np.random.randn(N)})\\n return df\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_str_functions\",\n \"text\": \"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_str_functions\\n def time_different_str_functions(self, df):\\n df.groupby(['key1', 'key2']).agg({'value1': 'mean',\\n 'value2': 'var',\\n 'value3': 'sum'})\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_numpy_functions\",\n \"text\": \"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_numpy_functions\\n def time_different_numpy_functions(self, df):\\n df.groupby(['key1', 'key2']).agg({'value1': np.mean,\\n 'value2': np.var,\\n 'value3': np.sum})\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_python_functions_multicol\",\n \"text\": \"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_python_functions_multicol\\n def time_different_python_functions_multicol(self, df):\\n df.groupby(['key1', 'key2']).agg([sum, min, max])\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_python_functions_singlecol\",\n \"text\": \"asv_bench/benchmarks/groupby.py::AggFunctions.time_different_python_functions_singlecol\\n def time_different_python_functions_singlecol(self, df):\\n df.groupby('key1').agg([sum, min, max])\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::GroupStrings.setup\",\n \"text\": \"asv_bench/benchmarks/groupby.py::GroupStrings.setup\\n def setup(self):\\n n = 2 * 10**5\\n alpha = list(map(''.join, product(ascii_letters, repeat=4)))\\n data = np.random.choice(alpha, (n // 5, 4), replace=False)\\n data = np.repeat(data, 5, axis=0)\\n self.df = DataFrame(data, columns=list('abcd'))\\n self.df['joe'] = (np.random.randn(len(self.df)) * 10).round(3)\\n self.df = self.df.sample(frac=1).reset_index(drop=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::GroupStrings.time_multi_columns\",\n \"text\": \"asv_bench/benchmarks/groupby.py::GroupStrings.time_multi_columns\\n def time_multi_columns(self):\\n self.df.groupby(list('abcd')).max()\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::MultiColumn.setup_cache\",\n \"text\": \"asv_bench/benchmarks/groupby.py::MultiColumn.setup_cache\\n def setup_cache(self):\\n N = 10**5\\n key1 = np.tile(np.arange(100, dtype=object), 1000)\\n key2 = key1.copy()\\n np.random.shuffle(key1)\\n np.random.shuffle(key2)\\n df = DataFrame({'key1': key1,\\n 'key2': key2,\\n 'data1': np.random.randn(N),\\n 'data2': np.random.randn(N)})\\n return df\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::MultiColumn.time_lambda_sum\",\n \"text\": \"asv_bench/benchmarks/groupby.py::MultiColumn.time_lambda_sum\\n def time_lambda_sum(self, df):\\n df.groupby(['key1', 'key2']).agg(lambda x: x.values.sum())\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::MultiColumn.time_cython_sum\",\n \"text\": \"asv_bench/benchmarks/groupby.py::MultiColumn.time_cython_sum\\n def time_cython_sum(self, df):\\n df.groupby(['key1', 'key2']).sum()\",\n \"label\": 0\n },\n {\n \"cid\": \"asv_bench/benchmarks/groupby.py::MultiColumn.time_col_select_lambda_sum\",\n \"text\": \"asv_bench/benchmarks/groupby.py::MultiColumn.time_col_select_lambda_sum\\n def time_col_select_lambda_sum(self, df):\\n df.groupby(['key1', 'key2'])['data1'].agg(lambda x: x.values.sum())\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::DirNamesMixin._dir_deletions\",\n \"text\": \"pandas/core/accessor.py::DirNamesMixin._dir_deletions\\n def _dir_deletions(self):\\n \\\"\\\"\\\" delete unwanted __dir__ for this object \\\"\\\"\\\"\\n return self._accessors | self._deprecations\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::DirNamesMixin._dir_additions\",\n \"text\": \"pandas/core/accessor.py::DirNamesMixin._dir_additions\\n def _dir_additions(self):\\n \\\"\\\"\\\" add additional __dir__ for this object \\\"\\\"\\\"\\n rv = set()\\n for accessor in self._accessors:\\n try:\\n getattr(self, accessor)\\n rv.add(accessor)\\n except AttributeError:\\n pass\\n return rv\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::DirNamesMixin.__dir__\",\n \"text\": \"pandas/core/accessor.py::DirNamesMixin.__dir__\\n def __dir__(self):\\n \\\"\\\"\\\"\\n Provide method name lookup and completion\\n Only provide 'public' methods\\n \\\"\\\"\\\"\\n rv = set(dir(type(self)))\\n rv = (rv - self._dir_deletions()) | self._dir_additions()\\n return sorted(rv)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::PandasDelegate._delegate_property_get\",\n \"text\": \"pandas/core/accessor.py::PandasDelegate._delegate_property_get\\n def _delegate_property_get(self, name, *args, **kwargs):\\n raise TypeError(\\\"You cannot access the \\\"\\n \\\"property {name}\\\".format(name=name))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::PandasDelegate._delegate_property_set\",\n \"text\": \"pandas/core/accessor.py::PandasDelegate._delegate_property_set\\n def _delegate_property_set(self, name, value, *args, **kwargs):\\n raise TypeError(\\\"The property {name} cannot be set\\\".format(name=name))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::PandasDelegate._delegate_method\",\n \"text\": \"pandas/core/accessor.py::PandasDelegate._delegate_method\\n def _delegate_method(self, name, *args, **kwargs):\\n raise TypeError(\\\"You cannot call method {name}\\\".format(name=name))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors\",\n \"text\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors\\n @classmethod\\n def _add_delegate_accessors(cls, delegate, accessors, typ,\\n overwrite=False):\\n \\\"\\\"\\\"\\n add accessors to cls from the delegate class\\n\\n Parameters\\n ----------\\n cls : the class to add the methods/properties to\\n delegate : the class to get methods/properties & doc-strings\\n acccessors : string list of accessors to add\\n typ : 'property' or 'method'\\n overwrite : boolean, default False\\n overwrite the method/property in the target class if it exists\\n \\\"\\\"\\\"\\n\\n def _create_delegator_property(name):\\n\\n def _getter(self):\\n return self._delegate_property_get(name)\\n\\n def _setter(self, new_values):\\n return self._delegate_property_set(name, new_values)\\n\\n _getter.__name__ = name\\n _setter.__name__ = name\\n\\n return property(fget=_getter, fset=_setter,\\n doc=getattr(delegate, name).__doc__)\\n\\n def _create_delegator_method(name):\\n\\n def f(self, *args, **kwargs):\\n return self._delegate_method(name, *args, **kwargs)\\n\\n f.__name__ = name\\n f.__doc__ = getattr(delegate, name).__doc__\\n\\n return f\\n\\n for name in accessors:\\n\\n if typ == 'property':\\n f = _create_delegator_property(name)\\n else:\\n f = _create_delegator_method(name)\\n\\n # don't overwrite existing methods/properties\\n if overwrite or not hasattr(cls, name):\\n setattr(cls, name, f)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property\",\n \"text\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property\\n def _create_delegator_property(name):\\n\\n def _getter(self):\\n return self._delegate_property_get(name)\\n\\n def _setter(self, new_values):\\n return self._delegate_property_set(name, new_values)\\n\\n _getter.__name__ = name\\n _setter.__name__ = name\\n\\n return property(fget=_getter, fset=_setter,\\n doc=getattr(delegate, name).__doc__)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property._getter\",\n \"text\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property._getter\\n def _getter(self):\\n return self._delegate_property_get(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property._setter\",\n \"text\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_property._setter\\n def _setter(self, new_values):\\n return self._delegate_property_set(name, new_values)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_method\",\n \"text\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_method\\n def _create_delegator_method(name):\\n\\n def f(self, *args, **kwargs):\\n return self._delegate_method(name, *args, **kwargs)\\n\\n f.__name__ = name\\n f.__doc__ = getattr(delegate, name).__doc__\\n\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_method.f\",\n \"text\": \"pandas/core/accessor.py::PandasDelegate._add_delegate_accessors._create_delegator_method.f\\n def f(self, *args, **kwargs):\\n return self._delegate_method(name, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::delegate_names\",\n \"text\": \"pandas/core/accessor.py::delegate_names\\ndef delegate_names(delegate, accessors, typ, overwrite=False):\\n \\\"\\\"\\\"\\n Add delegated names to a class using a class decorator. This provides\\n an alternative usage to directly calling `_add_delegate_accessors`\\n below a class definition.\\n\\n Parameters\\n ----------\\n delegate : object\\n the class to get methods/properties & doc-strings\\n acccessors : Sequence[str]\\n List of accessor to add\\n typ : {'property', 'method'}\\n overwrite : boolean, default False\\n overwrite the method/property in the target class if it exists\\n\\n Returns\\n -------\\n callable\\n A class decorator.\\n\\n Examples\\n --------\\n @delegate_names(Categorical, [\\\"categories\\\", \\\"ordered\\\"], \\\"property\\\")\\n class CategoricalAccessor(PandasDelegate):\\n [...]\\n \\\"\\\"\\\"\\n def add_delegate_accessors(cls):\\n cls._add_delegate_accessors(delegate, accessors, typ,\\n overwrite=overwrite)\\n return cls\\n\\n return add_delegate_accessors\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::delegate_names.add_delegate_accessors\",\n \"text\": \"pandas/core/accessor.py::delegate_names.add_delegate_accessors\\n def add_delegate_accessors(cls):\\n cls._add_delegate_accessors(delegate, accessors, typ,\\n overwrite=overwrite)\\n return cls\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::CachedAccessor.__init__\",\n \"text\": \"pandas/core/accessor.py::CachedAccessor.__init__\\n def __init__(self, name, accessor):\\n self._name = name\\n self._accessor = accessor\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::CachedAccessor.__get__\",\n \"text\": \"pandas/core/accessor.py::CachedAccessor.__get__\\n def __get__(self, obj, cls):\\n if obj is None:\\n # we're accessing the attribute of the class, i.e., Dataset.geo\\n return self._accessor\\n accessor_obj = self._accessor(obj)\\n # Replace the property with the accessor object. Inspired by:\\n # http://www.pydanny.com/cached-property.html\\n # We need to use object.__setattr__ because we overwrite __setattr__ on\\n # NDFrame\\n object.__setattr__(obj, self._name, accessor_obj)\\n return accessor_obj\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::_register_accessor\",\n \"text\": \"pandas/core/accessor.py::_register_accessor\\ndef _register_accessor(name, cls):\\n def decorator(accessor):\\n if hasattr(cls, name):\\n warnings.warn(\\n 'registration of accessor {!r} under name {!r} for type '\\n '{!r} is overriding a preexisting attribute with the same '\\n 'name.'.format(accessor, name, cls),\\n UserWarning,\\n stacklevel=2)\\n setattr(cls, name, CachedAccessor(name, accessor))\\n cls._accessors.add(name)\\n return accessor\\n return decorator\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::_register_accessor.decorator\",\n \"text\": \"pandas/core/accessor.py::_register_accessor.decorator\\n def decorator(accessor):\\n if hasattr(cls, name):\\n warnings.warn(\\n 'registration of accessor {!r} under name {!r} for type '\\n '{!r} is overriding a preexisting attribute with the same '\\n 'name.'.format(accessor, name, cls),\\n UserWarning,\\n stacklevel=2)\\n setattr(cls, name, CachedAccessor(name, accessor))\\n cls._accessors.add(name)\\n return accessor\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::register_dataframe_accessor\",\n \"text\": \"pandas/core/accessor.py::register_dataframe_accessor\\n@Appender(_doc % dict(klass=\\\"DataFrame\\\",\\n others=(\\\"register_series_accessor, \\\"\\n \\\"register_index_accessor\\\")))\\ndef register_dataframe_accessor(name):\\n from pandas import DataFrame\\n return _register_accessor(name, DataFrame)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::register_series_accessor\",\n \"text\": \"pandas/core/accessor.py::register_series_accessor\\n@Appender(_doc % dict(klass=\\\"Series\\\",\\n others=(\\\"register_dataframe_accessor, \\\"\\n \\\"register_index_accessor\\\")))\\ndef register_series_accessor(name):\\n from pandas import Series\\n return _register_accessor(name, Series)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/accessor.py::register_index_accessor\",\n \"text\": \"pandas/core/accessor.py::register_index_accessor\\n@Appender(_doc % dict(klass=\\\"Index\\\",\\n others=(\\\"register_dataframe_accessor, \\\"\\n \\\"register_series_accessor\\\")))\\ndef register_index_accessor(name):\\n from pandas import Index\\n return _register_accessor(name, Index)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::cut\",\n \"text\": \"pandas/core/reshape/tile.py::cut\\ndef cut(x, bins, right=True, labels=None, retbins=False, precision=3,\\n include_lowest=False, duplicates='raise'):\\n \\\"\\\"\\\"\\n Bin values into discrete intervals.\\n\\n Use `cut` when you need to segment and sort data values into bins. This\\n function is also useful for going from a continuous variable to a\\n categorical variable. For example, `cut` could convert ages to groups of\\n age ranges. Supports binning into an equal number of bins, or a\\n pre-specified array of bins.\\n\\n Parameters\\n ----------\\n x : array-like\\n The input array to be binned. Must be 1-dimensional.\\n bins : int, sequence of scalars, or pandas.IntervalIndex\\n The criteria to bin by.\\n\\n * int : Defines the number of equal-width bins in the range of `x`. The\\n range of `x` is extended by .1% on each side to include the minimum\\n and maximum values of `x`.\\n * sequence of scalars : Defines the bin edges allowing for non-uniform\\n width. No extension of the range of `x` is done.\\n * IntervalIndex : Defines the exact bins to be used.\\n\\n right : bool, default True\\n Indicates whether `bins` includes the rightmost edge or not. If\\n ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]``\\n indicate (1,2], (2,3], (3,4]. This argument is ignored when\\n `bins` is an IntervalIndex.\\n labels : array or bool, optional\\n Specifies the labels for the returned bins. Must be the same length as\\n the resulting bins. If False, returns only integer indicators of the\\n bins. This affects the type of the output container (see below).\\n This argument is ignored when `bins` is an IntervalIndex.\\n retbins : bool, default False\\n Whether to return the bins or not. Useful when bins is provided\\n as a scalar.\\n precision : int, default 3\\n The precision at which to store and display the bins labels.\\n include_lowest : bool, defaul\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::qcut\",\n \"text\": \"pandas/core/reshape/tile.py::qcut\\ndef qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise'):\\n \\\"\\\"\\\"\\n Quantile-based discretization function. Discretize variable into\\n equal-sized buckets based on rank or based on sample quantiles. For example\\n 1000 values for 10 quantiles would produce a Categorical object indicating\\n quantile membership for each data point.\\n\\n Parameters\\n ----------\\n x : 1d ndarray or Series\\n q : integer or array of quantiles\\n Number of quantiles. 10 for deciles, 4 for quartiles, etc. Alternately\\n array of quantiles, e.g. [0, .25, .5, .75, 1.] for quartiles\\n labels : array or boolean, default None\\n Used as labels for the resulting bins. Must be of the same length as\\n the resulting bins. If False, return only integer indicators of the\\n bins.\\n retbins : bool, optional\\n Whether to return the (bins, labels) or not. Can be useful if bins\\n is given as a scalar.\\n precision : int, optional\\n The precision at which to store and display the bins labels\\n duplicates : {default 'raise', 'drop'}, optional\\n If bin edges are not unique, raise ValueError or drop non-uniques.\\n\\n .. versionadded:: 0.20.0\\n\\n Returns\\n -------\\n out : Categorical or Series or array of integers if labels is False\\n The return type (Categorical or Series) depends on the input: a Series\\n of type category if input is a Series else Categorical. Bins are\\n represented as categories when categorical data is returned.\\n bins : ndarray of floats\\n Returned only if `retbins` is True.\\n\\n Notes\\n -----\\n Out of bounds values will be NA in the resulting Categorical object\\n\\n Examples\\n --------\\n >>> pd.qcut(range(5), 4)\\n ... # doctest: +ELLIPSIS\\n [(-0.001, 1.0], (-0.001, 1.0], (1.0, 2.0], (2.0, 3.0], (3.0, 4.0]]\\n Categories (4, interval[float64]): [(-0.001, 1.0] < (1.0, 2.0] ...\\n\\n >>> pd.qcut(range(5), 3, labels=[\\\"good\\\", \",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::_bins_to_cuts\",\n \"text\": \"pandas/core/reshape/tile.py::_bins_to_cuts\\ndef _bins_to_cuts(x, bins, right=True, labels=None,\\n precision=3, include_lowest=False,\\n dtype=None, duplicates='raise'):\\n\\n if duplicates not in ['raise', 'drop']:\\n raise ValueError(\\\"invalid value for 'duplicates' parameter, \\\"\\n \\\"valid options are: raise, drop\\\")\\n\\n if isinstance(bins, IntervalIndex):\\n # we have a fast-path here\\n ids = bins.get_indexer(x)\\n result = algos.take_nd(bins, ids)\\n result = Categorical(result, categories=bins, ordered=True)\\n return result, bins\\n\\n unique_bins = algos.unique(bins)\\n if len(unique_bins) < len(bins) and len(bins) != 2:\\n if duplicates == 'raise':\\n raise ValueError(\\\"Bin edges must be unique: {bins!r}.\\\\nYou \\\"\\n \\\"can drop duplicate edges by setting \\\"\\n \\\"the 'duplicates' kwarg\\\".format(bins=bins))\\n else:\\n bins = unique_bins\\n\\n side = 'left' if right else 'right'\\n ids = ensure_int64(bins.searchsorted(x, side=side))\\n\\n if include_lowest:\\n # Numpy 1.9 support: ensure this mask is a Numpy array\\n ids[np.asarray(x == bins[0])] = 1\\n\\n na_mask = isna(x) | (ids == len(bins)) | (ids == 0)\\n has_nas = na_mask.any()\\n\\n if labels is not False:\\n if labels is None:\\n labels = _format_labels(bins, precision, right=right,\\n include_lowest=include_lowest,\\n dtype=dtype)\\n else:\\n if len(labels) != len(bins) - 1:\\n raise ValueError('Bin labels must be one fewer than '\\n 'the number of bin edges')\\n if not is_categorical_dtype(labels):\\n labels = Categorical(labels, categories=labels, ordered=True)\\n\\n np.putmask(ids, na_mask, 0)\\n result = algos.take_nd(labels, ids - 1)\\n\\n else:\\n result = ids - 1\\n if has\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::_trim_zeros\",\n \"text\": \"pandas/core/reshape/tile.py::_trim_zeros\\ndef _trim_zeros(x):\\n while len(x) > 1 and x[-1] == '0':\\n x = x[:-1]\\n if len(x) > 1 and x[-1] == '.':\\n x = x[:-1]\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::_coerce_to_type\",\n \"text\": \"pandas/core/reshape/tile.py::_coerce_to_type\\ndef _coerce_to_type(x):\\n \\\"\\\"\\\"\\n if the passed data is of datetime/timedelta type,\\n this method converts it to numeric so that cut method can\\n handle it\\n \\\"\\\"\\\"\\n dtype = None\\n\\n if is_datetime64tz_dtype(x):\\n dtype = x.dtype\\n elif is_datetime64_dtype(x):\\n x = to_datetime(x)\\n dtype = np.datetime64\\n elif is_timedelta64_dtype(x):\\n x = to_timedelta(x)\\n dtype = np.timedelta64\\n\\n if dtype is not None:\\n # GH 19768: force NaT to NaN during integer conversion\\n x = np.where(x.notna(), x.view(np.int64), np.nan)\\n\\n return x, dtype\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::_convert_bin_to_numeric_type\",\n \"text\": \"pandas/core/reshape/tile.py::_convert_bin_to_numeric_type\\ndef _convert_bin_to_numeric_type(bins, dtype):\\n \\\"\\\"\\\"\\n if the passed bin is of datetime/timedelta type,\\n this method converts it to integer\\n\\n Parameters\\n ----------\\n bins : list-like of bins\\n dtype : dtype of data\\n\\n Raises\\n ------\\n ValueError if bins are not of a compat dtype to dtype\\n \\\"\\\"\\\"\\n bins_dtype = infer_dtype(bins)\\n if is_timedelta64_dtype(dtype):\\n if bins_dtype in ['timedelta', 'timedelta64']:\\n bins = to_timedelta(bins).view(np.int64)\\n else:\\n raise ValueError(\\\"bins must be of timedelta64 dtype\\\")\\n elif is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype):\\n if bins_dtype in ['datetime', 'datetime64']:\\n bins = to_datetime(bins).view(np.int64)\\n else:\\n raise ValueError(\\\"bins must be of datetime64 dtype\\\")\\n\\n return bins\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::_convert_bin_to_datelike_type\",\n \"text\": \"pandas/core/reshape/tile.py::_convert_bin_to_datelike_type\\ndef _convert_bin_to_datelike_type(bins, dtype):\\n \\\"\\\"\\\"\\n Convert bins to a DatetimeIndex or TimedeltaIndex if the orginal dtype is\\n datelike\\n\\n Parameters\\n ----------\\n bins : list-like of bins\\n dtype : dtype of data\\n\\n Returns\\n -------\\n bins : Array-like of bins, DatetimeIndex or TimedeltaIndex if dtype is\\n datelike\\n \\\"\\\"\\\"\\n if is_datetime64tz_dtype(dtype) or is_datetime_or_timedelta_dtype(dtype):\\n bins = Index(bins.astype(np.int64), dtype=dtype)\\n return bins\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::_format_labels\",\n \"text\": \"pandas/core/reshape/tile.py::_format_labels\\ndef _format_labels(bins, precision, right=True,\\n include_lowest=False, dtype=None):\\n \\\"\\\"\\\" based on the dtype, return our labels \\\"\\\"\\\"\\n\\n closed = 'right' if right else 'left'\\n\\n if is_datetime64tz_dtype(dtype):\\n formatter = partial(Timestamp, tz=dtype.tz)\\n adjust = lambda x: x - Timedelta('1ns')\\n elif is_datetime64_dtype(dtype):\\n formatter = Timestamp\\n adjust = lambda x: x - Timedelta('1ns')\\n elif is_timedelta64_dtype(dtype):\\n formatter = Timedelta\\n adjust = lambda x: x - Timedelta('1ns')\\n else:\\n precision = _infer_precision(precision, bins)\\n formatter = lambda x: _round_frac(x, precision)\\n adjust = lambda x: x - 10 ** (-precision)\\n\\n breaks = [formatter(b) for b in bins]\\n labels = IntervalIndex.from_breaks(breaks, closed=closed)\\n\\n if right and include_lowest:\\n # we will adjust the left hand side by precision to\\n # account that we are all right closed\\n v = adjust(labels[0].left)\\n\\n i = IntervalIndex([Interval(v, labels[0].right, closed='right')])\\n labels = i.append(labels[1:])\\n\\n return labels\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::_preprocess_for_cut\",\n \"text\": \"pandas/core/reshape/tile.py::_preprocess_for_cut\\ndef _preprocess_for_cut(x):\\n \\\"\\\"\\\"\\n handles preprocessing for cut where we convert passed\\n input to array, strip the index information and store it\\n separately\\n \\\"\\\"\\\"\\n x_is_series = isinstance(x, Series)\\n series_index = None\\n name = None\\n\\n if x_is_series:\\n series_index = x.index\\n name = x.name\\n\\n # Check that the passed array is a Pandas or Numpy object\\n # We don't want to strip away a Pandas data-type here (e.g. datetimetz)\\n ndim = getattr(x, 'ndim', None)\\n if ndim is None:\\n x = np.asarray(x)\\n if x.ndim != 1:\\n raise ValueError(\\\"Input array must be 1 dimensional\\\")\\n\\n return x_is_series, series_index, name, x\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::_postprocess_for_cut\",\n \"text\": \"pandas/core/reshape/tile.py::_postprocess_for_cut\\ndef _postprocess_for_cut(fac, bins, retbins, x_is_series,\\n series_index, name, dtype):\\n \\\"\\\"\\\"\\n handles post processing for the cut method where\\n we combine the index information if the originally passed\\n datatype was a series\\n \\\"\\\"\\\"\\n if x_is_series:\\n fac = Series(fac, index=series_index, name=name)\\n\\n if not retbins:\\n return fac\\n\\n bins = _convert_bin_to_datelike_type(bins, dtype)\\n\\n return fac, bins\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::_round_frac\",\n \"text\": \"pandas/core/reshape/tile.py::_round_frac\\ndef _round_frac(x, precision):\\n \\\"\\\"\\\"\\n Round the fractional part of the given number\\n \\\"\\\"\\\"\\n if not np.isfinite(x) or x == 0:\\n return x\\n else:\\n frac, whole = np.modf(x)\\n if whole == 0:\\n digits = -int(np.floor(np.log10(abs(frac)))) - 1 + precision\\n else:\\n digits = precision\\n return np.around(x, digits)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/reshape/tile.py::_infer_precision\",\n \"text\": \"pandas/core/reshape/tile.py::_infer_precision\\ndef _infer_precision(base_precision, bins):\\n \\\"\\\"\\\"Infer an appropriate precision for _round_frac\\n \\\"\\\"\\\"\\n for precision in range(base_precision, 20):\\n levels = [_round_frac(b, precision) for b in bins]\\n if algos.unique(levels).size == bins.size:\\n return precision\\n return base_precision # default\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window.__init__\",\n \"text\": \"pandas/core/window.py::_Window.__init__\\n def __init__(self, obj, window=None, min_periods=None,\\n center=False, win_type=None, axis=0, on=None, closed=None,\\n **kwargs):\\n\\n self.__dict__.update(kwargs)\\n self.blocks = []\\n self.obj = obj\\n self.on = on\\n self.closed = closed\\n self.window = window\\n self.min_periods = min_periods\\n self.center = center\\n self.win_type = win_type\\n self.win_freq = None\\n self.axis = obj._get_axis_number(axis) if axis is not None else None\\n self.validate()\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._constructor\",\n \"text\": \"pandas/core/window.py::_Window._constructor\\n @property\\n def _constructor(self):\\n return Window\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window.is_datetimelike\",\n \"text\": \"pandas/core/window.py::_Window.is_datetimelike\\n @property\\n def is_datetimelike(self):\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._on\",\n \"text\": \"pandas/core/window.py::_Window._on\\n @property\\n def _on(self):\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window.is_freq_type\",\n \"text\": \"pandas/core/window.py::_Window.is_freq_type\\n @property\\n def is_freq_type(self):\\n return self.win_type == 'freq'\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window.validate\",\n \"text\": \"pandas/core/window.py::_Window.validate\\n def validate(self):\\n if self.center is not None and not is_bool(self.center):\\n raise ValueError(\\\"center must be a boolean\\\")\\n if (self.min_periods is not None and\\n not is_integer(self.min_periods)):\\n raise ValueError(\\\"min_periods must be an integer\\\")\\n if (self.closed is not None and\\n self.closed not in ['right', 'both', 'left', 'neither']):\\n raise ValueError(\\\"closed must be 'right', 'left', 'both' or \\\"\\n \\\"'neither'\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._convert_freq\",\n \"text\": \"pandas/core/window.py::_Window._convert_freq\\n def _convert_freq(self):\\n \\\"\\\"\\\" resample according to the how, return a new object \\\"\\\"\\\"\\n\\n obj = self._selected_obj\\n index = None\\n return obj, index\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._create_blocks\",\n \"text\": \"pandas/core/window.py::_Window._create_blocks\\n def _create_blocks(self):\\n \\\"\\\"\\\" split data into blocks & return conformed data \\\"\\\"\\\"\\n\\n obj, index = self._convert_freq()\\n if index is not None:\\n index = self._on\\n\\n # filter out the on from the object\\n if self.on is not None:\\n if obj.ndim == 2:\\n obj = obj.reindex(columns=obj.columns.difference([self.on]),\\n copy=False)\\n blocks = obj._to_dict_of_blocks(copy=False).values()\\n\\n return blocks, obj, index\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._gotitem\",\n \"text\": \"pandas/core/window.py::_Window._gotitem\\n def _gotitem(self, key, ndim, subset=None):\\n \\\"\\\"\\\"\\n sub-classes to define\\n return a sliced object\\n\\n Parameters\\n ----------\\n key : string / list of selections\\n ndim : 1,2\\n requested ndim of result\\n subset : object, default None\\n subset to act on\\n \\\"\\\"\\\"\\n\\n # create a new object to prevent aliasing\\n if subset is None:\\n subset = self.obj\\n self = self._shallow_copy(subset)\\n self._reset_cache()\\n if subset.ndim == 2:\\n if is_scalar(key) and key in subset or is_list_like(key):\\n self._selection = key\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window.__getattr__\",\n \"text\": \"pandas/core/window.py::_Window.__getattr__\\n def __getattr__(self, attr):\\n if attr in self._internal_names_set:\\n return object.__getattribute__(self, attr)\\n if attr in self.obj:\\n return self[attr]\\n\\n raise AttributeError(\\\"%r object has no attribute %r\\\" %\\n (type(self).__name__, attr))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._dir_additions\",\n \"text\": \"pandas/core/window.py::_Window._dir_additions\\n def _dir_additions(self):\\n return self.obj._dir_additions()\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._get_window\",\n \"text\": \"pandas/core/window.py::_Window._get_window\\n def _get_window(self, other=None):\\n return self.window\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._window_type\",\n \"text\": \"pandas/core/window.py::_Window._window_type\\n @property\\n def _window_type(self):\\n return self.__class__.__name__\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window.__unicode__\",\n \"text\": \"pandas/core/window.py::_Window.__unicode__\\n def __unicode__(self):\\n \\\"\\\"\\\" provide a nice str repr of our rolling object \\\"\\\"\\\"\\n\\n attrs = [\\\"{k}={v}\\\".format(k=k, v=getattr(self, k))\\n for k in self._attributes\\n if getattr(self, k, None) is not None]\\n return \\\"{klass} [{attrs}]\\\".format(klass=self._window_type,\\n attrs=','.join(attrs))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window.__iter__\",\n \"text\": \"pandas/core/window.py::_Window.__iter__\\n def __iter__(self):\\n url = 'https://github.com/pandas-dev/pandas/issues/11704'\\n raise NotImplementedError('See issue #11704 {url}'.format(url=url))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._get_index\",\n \"text\": \"pandas/core/window.py::_Window._get_index\\n def _get_index(self, index=None):\\n \\\"\\\"\\\"\\n Return index as ndarrays\\n\\n Returns\\n -------\\n tuple of (index, index_as_ndarray)\\n \\\"\\\"\\\"\\n\\n if self.is_freq_type:\\n if index is None:\\n index = self._on\\n return index, index.asi8\\n return index, index\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._prep_values\",\n \"text\": \"pandas/core/window.py::_Window._prep_values\\n def _prep_values(self, values=None, kill_inf=True):\\n\\n if values is None:\\n values = getattr(self._selected_obj, 'values', self._selected_obj)\\n\\n # GH #12373 : rolling functions error on float32 data\\n # make sure the data is coerced to float64\\n if is_float_dtype(values.dtype):\\n values = ensure_float64(values)\\n elif is_integer_dtype(values.dtype):\\n values = ensure_float64(values)\\n elif needs_i8_conversion(values.dtype):\\n raise NotImplementedError(\\\"ops for {action} for this \\\"\\n \\\"dtype {dtype} are not \\\"\\n \\\"implemented\\\".format(\\n action=self._window_type,\\n dtype=values.dtype))\\n else:\\n try:\\n values = ensure_float64(values)\\n except (ValueError, TypeError):\\n raise TypeError(\\\"cannot handle this type -> {0}\\\"\\n \\\"\\\".format(values.dtype))\\n\\n if kill_inf:\\n values = values.copy()\\n values[np.isinf(values)] = np.NaN\\n\\n return values\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._wrap_result\",\n \"text\": \"pandas/core/window.py::_Window._wrap_result\\n def _wrap_result(self, result, block=None, obj=None):\\n \\\"\\\"\\\" wrap a single result \\\"\\\"\\\"\\n\\n if obj is None:\\n obj = self._selected_obj\\n index = obj.index\\n\\n if isinstance(result, np.ndarray):\\n\\n # coerce if necessary\\n if block is not None:\\n if is_timedelta64_dtype(block.values.dtype):\\n from pandas import to_timedelta\\n result = to_timedelta(\\n result.ravel(), unit='ns').values.reshape(result.shape)\\n\\n if result.ndim == 1:\\n from pandas import Series\\n return Series(result, index, name=obj.name)\\n\\n return type(obj)(result, index=index, columns=block.columns)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._wrap_results\",\n \"text\": \"pandas/core/window.py::_Window._wrap_results\\n def _wrap_results(self, results, blocks, obj):\\n \\\"\\\"\\\"\\n wrap the results\\n\\n Parameters\\n ----------\\n results : list of ndarrays\\n blocks : list of blocks\\n obj : conformed data (may be resampled)\\n \\\"\\\"\\\"\\n\\n from pandas import Series, concat\\n from pandas.core.index import ensure_index\\n\\n final = []\\n for result, block in zip(results, blocks):\\n\\n result = self._wrap_result(result, block=block, obj=obj)\\n if result.ndim == 1:\\n return result\\n final.append(result)\\n\\n # if we have an 'on' column\\n # we want to put it back into the results\\n # in the same location\\n columns = self._selected_obj.columns\\n if self.on is not None and not self._on.equals(obj.index):\\n\\n name = self._on.name\\n final.append(Series(self._on, index=obj.index, name=name))\\n\\n if self._selection is not None:\\n\\n selection = ensure_index(self._selection)\\n\\n # need to reorder to include original location of\\n # the on column (if its not already there)\\n if name not in selection:\\n columns = self.obj.columns\\n indexer = columns.get_indexer(selection.tolist() + [name])\\n columns = columns.take(sorted(indexer))\\n\\n if not len(final):\\n return obj.astype('float64')\\n return concat(final, axis=1).reindex(columns=columns, copy=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window._center_window\",\n \"text\": \"pandas/core/window.py::_Window._center_window\\n def _center_window(self, result, window):\\n \\\"\\\"\\\" center the result in the window \\\"\\\"\\\"\\n if self.axis > result.ndim - 1:\\n raise ValueError(\\\"Requested axis is larger then no. of argument \\\"\\n \\\"dimensions\\\")\\n\\n offset = _offset(window, True)\\n if offset > 0:\\n if isinstance(result, (ABCSeries, ABCDataFrame)):\\n result = result.slice_shift(-offset, axis=self.axis)\\n else:\\n lead_indexer = [slice(None)] * result.ndim\\n lead_indexer[self.axis] = slice(offset, None)\\n result = np.copy(result[tuple(lead_indexer)])\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Window.aggregate\",\n \"text\": \"pandas/core/window.py::_Window.aggregate\\n def aggregate(self, arg, *args, **kwargs):\\n result, how = self._aggregate(arg, *args, **kwargs)\\n if result is None:\\n return self.apply(arg, raw=False, args=args, kwargs=kwargs)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Window.validate\",\n \"text\": \"pandas/core/window.py::Window.validate\\n def validate(self):\\n super(Window, self).validate()\\n\\n window = self.window\\n if isinstance(window, (list, tuple, np.ndarray)):\\n pass\\n elif is_integer(window):\\n if window <= 0:\\n raise ValueError(\\\"window must be > 0 \\\")\\n try:\\n import scipy.signal as sig\\n except ImportError:\\n raise ImportError('Please install scipy to generate window '\\n 'weight')\\n\\n if not isinstance(self.win_type, compat.string_types):\\n raise ValueError('Invalid win_type {0}'.format(self.win_type))\\n if getattr(sig, self.win_type, None) is None:\\n raise ValueError('Invalid win_type {0}'.format(self.win_type))\\n else:\\n raise ValueError('Invalid window {0}'.format(window))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Window._prep_window\",\n \"text\": \"pandas/core/window.py::Window._prep_window\\n def _prep_window(self, **kwargs):\\n \\\"\\\"\\\"\\n provide validation for our window type, return the window\\n we have already been validated\\n \\\"\\\"\\\"\\n\\n window = self._get_window()\\n if isinstance(window, (list, tuple, np.ndarray)):\\n return com.asarray_tuplesafe(window).astype(float)\\n elif is_integer(window):\\n import scipy.signal as sig\\n\\n # the below may pop from kwargs\\n def _validate_win_type(win_type, kwargs):\\n arg_map = {'kaiser': ['beta'],\\n 'gaussian': ['std'],\\n 'general_gaussian': ['power', 'width'],\\n 'slepian': ['width']}\\n if win_type in arg_map:\\n return tuple([win_type] + _pop_args(win_type,\\n arg_map[win_type],\\n kwargs))\\n return win_type\\n\\n def _pop_args(win_type, arg_names, kwargs):\\n msg = '%s window requires %%s' % win_type\\n all_args = []\\n for n in arg_names:\\n if n not in kwargs:\\n raise ValueError(msg % n)\\n all_args.append(kwargs.pop(n))\\n return all_args\\n\\n win_type = _validate_win_type(self.win_type, kwargs)\\n # GH #15662. `False` makes symmetric window, rather than periodic.\\n return sig.get_window(win_type, window, False).astype(float)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Window._apply_window\",\n \"text\": \"pandas/core/window.py::Window._apply_window\\n def _apply_window(self, mean=True, **kwargs):\\n \\\"\\\"\\\"\\n Applies a moving window of type ``window_type`` on the data.\\n\\n Parameters\\n ----------\\n mean : boolean, default True\\n If True computes weighted mean, else weighted sum\\n\\n Returns\\n -------\\n y : same type as input argument\\n\\n \\\"\\\"\\\"\\n window = self._prep_window(**kwargs)\\n center = self.center\\n\\n blocks, obj, index = self._create_blocks()\\n results = []\\n for b in blocks:\\n try:\\n values = self._prep_values(b.values)\\n except TypeError:\\n results.append(b.values.copy())\\n continue\\n\\n if values.size == 0:\\n results.append(values.copy())\\n continue\\n\\n offset = _offset(window, center)\\n additional_nans = np.array([np.NaN] * offset)\\n\\n def f(arg, *args, **kwargs):\\n minp = _use_window(self.min_periods, len(window))\\n return _window.roll_window(np.concatenate((arg,\\n additional_nans))\\n if center else arg, window, minp,\\n avg=mean)\\n\\n result = np.apply_along_axis(f, self.axis, values)\\n\\n if center:\\n result = self._center_window(result, window)\\n results.append(result)\\n\\n return self._wrap_results(results, blocks, obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Window.aggregate\",\n \"text\": \"pandas/core/window.py::Window.aggregate\\n @Appender(_agg_doc)\\n @Appender(_shared_docs['aggregate'] % dict(\\n versionadded='',\\n klass='Series/DataFrame',\\n axis=''))\\n def aggregate(self, arg, *args, **kwargs):\\n result, how = self._aggregate(arg, *args, **kwargs)\\n if result is None:\\n\\n # these must apply directly\\n result = arg(self)\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Window.sum\",\n \"text\": \"pandas/core/window.py::Window.sum\\n @Substitution(name='window')\\n @Appender(_shared_docs['sum'])\\n def sum(self, *args, **kwargs):\\n nv.validate_window_func('sum', args, kwargs)\\n return self._apply_window(mean=False, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Window.mean\",\n \"text\": \"pandas/core/window.py::Window.mean\\n @Substitution(name='window')\\n @Appender(_shared_docs['mean'])\\n def mean(self, *args, **kwargs):\\n nv.validate_window_func('mean', args, kwargs)\\n return self._apply_window(mean=True, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_GroupByMixin.__init__\",\n \"text\": \"pandas/core/window.py::_GroupByMixin.__init__\\n def __init__(self, obj, *args, **kwargs):\\n parent = kwargs.pop('parent', None) # noqa\\n groupby = kwargs.pop('groupby', None)\\n if groupby is None:\\n groupby, obj = obj, obj.obj\\n self._groupby = groupby\\n self._groupby.mutated = True\\n self._groupby.grouper.mutated = True\\n super(GroupByMixin, self).__init__(obj, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_GroupByMixin._apply\",\n \"text\": \"pandas/core/window.py::_GroupByMixin._apply\\n def _apply(self, func, name, window=None, center=None,\\n check_minp=None, **kwargs):\\n \\\"\\\"\\\"\\n dispatch to apply; we are stripping all of the _apply kwargs and\\n performing the original function call on the grouped object\\n \\\"\\\"\\\"\\n\\n def f(x, name=name, *args):\\n x = self._shallow_copy(x)\\n\\n if isinstance(name, compat.string_types):\\n return getattr(x, name)(*args, **kwargs)\\n\\n return x.apply(name, *args, **kwargs)\\n\\n return self._groupby.apply(f)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_GroupByMixin._apply.f\",\n \"text\": \"pandas/core/window.py::_GroupByMixin._apply.f\\n def f(x, name=name, *args):\\n x = self._shallow_copy(x)\\n\\n if isinstance(name, compat.string_types):\\n return getattr(x, name)(*args, **kwargs)\\n\\n return x.apply(name, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling._constructor\",\n \"text\": \"pandas/core/window.py::_Rolling._constructor\\n @property\\n def _constructor(self):\\n return Rolling\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling._apply\",\n \"text\": \"pandas/core/window.py::_Rolling._apply\\n def _apply(self, func, name=None, window=None, center=None,\\n check_minp=None, **kwargs):\\n \\\"\\\"\\\"\\n Rolling statistical measure using supplied function. Designed to be\\n used with passed-in Cython array-based functions.\\n\\n Parameters\\n ----------\\n func : string/callable to apply\\n name : string, optional\\n name of this function\\n window : int/array, default to _get_window()\\n center : boolean, default to self.center\\n check_minp : function, default to _use_window\\n\\n Returns\\n -------\\n y : type of input\\n \\\"\\\"\\\"\\n if center is None:\\n center = self.center\\n if window is None:\\n window = self._get_window()\\n\\n if check_minp is None:\\n check_minp = _use_window\\n\\n blocks, obj, index = self._create_blocks()\\n index, indexi = self._get_index(index=index)\\n results = []\\n for b in blocks:\\n values = self._prep_values(b.values)\\n\\n if values.size == 0:\\n results.append(values.copy())\\n continue\\n\\n # if we have a string function name, wrap it\\n if isinstance(func, compat.string_types):\\n cfunc = getattr(_window, func, None)\\n if cfunc is None:\\n raise ValueError(\\\"we do not support this function \\\"\\n \\\"in _window.{0}\\\".format(func))\\n\\n def func(arg, window, min_periods=None, closed=None):\\n minp = check_minp(min_periods, window)\\n # ensure we are only rolling on floats\\n arg = ensure_float64(arg)\\n return cfunc(arg,\\n window, minp, indexi, closed, **kwargs)\\n\\n # calculation function\\n if center:\\n offset = _offset(window, center)\\n additional_nans = np.array(\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.count\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.count\\n def count(self):\\n\\n blocks, obj, index = self._create_blocks()\\n # Validate the index\\n self._get_index(index=index)\\n\\n window = self._get_window()\\n window = min(window, len(obj)) if not self.center else window\\n\\n results = []\\n for b in blocks:\\n result = b.notna().astype(int)\\n result = self._constructor(result, window=window, min_periods=0,\\n center=self.center,\\n closed=self.closed).sum()\\n results.append(result)\\n\\n return self._wrap_results(results, blocks, obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.apply\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.apply\\n def apply(self, func, raw=None, args=(), kwargs={}):\\n from pandas import Series\\n\\n # TODO: _level is unused?\\n _level = kwargs.pop('_level', None) # noqa\\n window = self._get_window()\\n offset = _offset(window, self.center)\\n index, indexi = self._get_index()\\n\\n # TODO: default is for backward compat\\n # change to False in the future\\n if raw is None:\\n warnings.warn(\\n \\\"Currently, 'apply' passes the values as ndarrays to the \\\"\\n \\\"applied function. In the future, this will change to passing \\\"\\n \\\"it as Series objects. You need to specify 'raw=True' to keep \\\"\\n \\\"the current behaviour, and you can pass 'raw=False' to \\\"\\n \\\"silence this warning\\\", FutureWarning, stacklevel=3)\\n raw = True\\n\\n def f(arg, window, min_periods, closed):\\n minp = _use_window(min_periods, window)\\n if not raw:\\n arg = Series(arg, index=self.obj.index)\\n return _window.roll_generic(\\n arg, window, minp, indexi,\\n closed, offset, func, raw, args, kwargs)\\n\\n return self._apply(f, func, args=args, kwargs=kwargs,\\n center=False, raw=raw)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.apply.f\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.apply.f\\n def f(arg, window, min_periods, closed):\\n minp = _use_window(min_periods, window)\\n if not raw:\\n arg = Series(arg, index=self.obj.index)\\n return _window.roll_generic(\\n arg, window, minp, indexi,\\n closed, offset, func, raw, args, kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.sum\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.sum\\n def sum(self, *args, **kwargs):\\n nv.validate_window_func('sum', args, kwargs)\\n return self._apply('roll_sum', 'sum', **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.max\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.max\\n def max(self, *args, **kwargs):\\n nv.validate_window_func('max', args, kwargs)\\n return self._apply('roll_max', 'max', **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.min\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.min\\n def min(self, *args, **kwargs):\\n nv.validate_window_func('min', args, kwargs)\\n return self._apply('roll_min', 'min', **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.mean\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.mean\\n def mean(self, *args, **kwargs):\\n nv.validate_window_func('mean', args, kwargs)\\n return self._apply('roll_mean', 'mean', **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.median\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.median\\n def median(self, **kwargs):\\n return self._apply('roll_median_c', 'median', **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.std\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.std\\n def std(self, ddof=1, *args, **kwargs):\\n nv.validate_window_func('std', args, kwargs)\\n window = self._get_window()\\n index, indexi = self._get_index()\\n\\n def f(arg, *args, **kwargs):\\n minp = _require_min_periods(1)(self.min_periods, window)\\n return _zsqrt(_window.roll_var(arg, window, minp, indexi,\\n self.closed, ddof))\\n\\n return self._apply(f, 'std', check_minp=_require_min_periods(1),\\n ddof=ddof, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.std.f\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.std.f\\n def f(arg, *args, **kwargs):\\n minp = _require_min_periods(1)(self.min_periods, window)\\n return _zsqrt(_window.roll_var(arg, window, minp, indexi,\\n self.closed, ddof))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.var\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.var\\n def var(self, ddof=1, *args, **kwargs):\\n nv.validate_window_func('var', args, kwargs)\\n return self._apply('roll_var', 'var',\\n check_minp=_require_min_periods(1), ddof=ddof,\\n **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.skew\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.skew\\n def skew(self, **kwargs):\\n return self._apply('roll_skew', 'skew',\\n check_minp=_require_min_periods(3), **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.kurt\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.kurt\\n def kurt(self, **kwargs):\\n return self._apply('roll_kurt', 'kurt',\\n check_minp=_require_min_periods(4), **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.quantile\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.quantile\\n def quantile(self, quantile, interpolation='linear', **kwargs):\\n window = self._get_window()\\n index, indexi = self._get_index()\\n\\n def f(arg, *args, **kwargs):\\n minp = _use_window(self.min_periods, window)\\n if quantile == 1.0:\\n return _window.roll_max(arg, window, minp, indexi,\\n self.closed)\\n elif quantile == 0.0:\\n return _window.roll_min(arg, window, minp, indexi,\\n self.closed)\\n else:\\n return _window.roll_quantile(arg, window, minp, indexi,\\n self.closed, quantile,\\n interpolation)\\n\\n return self._apply(f, 'quantile', quantile=quantile,\\n **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.quantile.f\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.quantile.f\\n def f(arg, *args, **kwargs):\\n minp = _use_window(self.min_periods, window)\\n if quantile == 1.0:\\n return _window.roll_max(arg, window, minp, indexi,\\n self.closed)\\n elif quantile == 0.0:\\n return _window.roll_min(arg, window, minp, indexi,\\n self.closed)\\n else:\\n return _window.roll_quantile(arg, window, minp, indexi,\\n self.closed, quantile,\\n interpolation)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.cov\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.cov\\n def cov(self, other=None, pairwise=None, ddof=1, **kwargs):\\n if other is None:\\n other = self._selected_obj\\n # only default unset\\n pairwise = True if pairwise is None else pairwise\\n other = self._shallow_copy(other)\\n\\n # GH 16058: offset window\\n if self.is_freq_type:\\n window = self.win_freq\\n else:\\n window = self._get_window(other)\\n\\n def _get_cov(X, Y):\\n # GH #12373 : rolling functions error on float32 data\\n # to avoid potential overflow, cast the data to float64\\n X = X.astype('float64')\\n Y = Y.astype('float64')\\n mean = lambda x: x.rolling(window, self.min_periods,\\n center=self.center).mean(**kwargs)\\n count = (X + Y).rolling(window=window,\\n center=self.center).count(**kwargs)\\n bias_adj = count / (count - ddof)\\n return (mean(X * Y) - mean(X) * mean(Y)) * bias_adj\\n\\n return _flex_binary_moment(self._selected_obj, other._selected_obj,\\n _get_cov, pairwise=bool(pairwise))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.cov._get_cov\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.cov._get_cov\\n def _get_cov(X, Y):\\n # GH #12373 : rolling functions error on float32 data\\n # to avoid potential overflow, cast the data to float64\\n X = X.astype('float64')\\n Y = Y.astype('float64')\\n mean = lambda x: x.rolling(window, self.min_periods,\\n center=self.center).mean(**kwargs)\\n count = (X + Y).rolling(window=window,\\n center=self.center).count(**kwargs)\\n bias_adj = count / (count - ddof)\\n return (mean(X * Y) - mean(X) * mean(Y)) * bias_adj\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.corr\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.corr\\n def corr(self, other=None, pairwise=None, **kwargs):\\n if other is None:\\n other = self._selected_obj\\n # only default unset\\n pairwise = True if pairwise is None else pairwise\\n other = self._shallow_copy(other)\\n window = self._get_window(other)\\n\\n def _get_corr(a, b):\\n a = a.rolling(window=window, min_periods=self.min_periods,\\n center=self.center)\\n b = b.rolling(window=window, min_periods=self.min_periods,\\n center=self.center)\\n\\n return a.cov(b, **kwargs) / (a.std(**kwargs) * b.std(**kwargs))\\n\\n return _flex_binary_moment(self._selected_obj, other._selected_obj,\\n _get_corr, pairwise=bool(pairwise))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_Rolling_and_Expanding.corr._get_corr\",\n \"text\": \"pandas/core/window.py::_Rolling_and_Expanding.corr._get_corr\\n def _get_corr(a, b):\\n a = a.rolling(window=window, min_periods=self.min_periods,\\n center=self.center)\\n b = b.rolling(window=window, min_periods=self.min_periods,\\n center=self.center)\\n\\n return a.cov(b, **kwargs) / (a.std(**kwargs) * b.std(**kwargs))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.is_datetimelike\",\n \"text\": \"pandas/core/window.py::Rolling.is_datetimelike\\n @cache_readonly\\n def is_datetimelike(self):\\n return isinstance(self._on,\\n (ABCDatetimeIndex,\\n ABCTimedeltaIndex,\\n ABCPeriodIndex))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling._on\",\n \"text\": \"pandas/core/window.py::Rolling._on\\n @cache_readonly\\n def _on(self):\\n\\n if self.on is None:\\n return self.obj.index\\n elif (isinstance(self.obj, ABCDataFrame) and\\n self.on in self.obj.columns):\\n from pandas import Index\\n return Index(self.obj[self.on])\\n else:\\n raise ValueError(\\\"invalid on specified as {0}, \\\"\\n \\\"must be a column (if DataFrame) \\\"\\n \\\"or None\\\".format(self.on))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.validate\",\n \"text\": \"pandas/core/window.py::Rolling.validate\\n def validate(self):\\n super(Rolling, self).validate()\\n\\n # we allow rolling on a datetimelike index\\n if ((self.obj.empty or self.is_datetimelike) and\\n isinstance(self.window, (compat.string_types, ABCDateOffset,\\n timedelta))):\\n\\n self._validate_monotonic()\\n freq = self._validate_freq()\\n\\n # we don't allow center\\n if self.center:\\n raise NotImplementedError(\\\"center is not implemented \\\"\\n \\\"for datetimelike and offset \\\"\\n \\\"based windows\\\")\\n\\n # this will raise ValueError on non-fixed freqs\\n self.win_freq = self.window\\n self.window = freq.nanos\\n self.win_type = 'freq'\\n\\n # min_periods must be an integer\\n if self.min_periods is None:\\n self.min_periods = 1\\n\\n elif not is_integer(self.window):\\n raise ValueError(\\\"window must be an integer\\\")\\n elif self.window < 0:\\n raise ValueError(\\\"window must be non-negative\\\")\\n\\n if not self.is_datetimelike and self.closed is not None:\\n raise ValueError(\\\"closed only implemented for datetimelike \\\"\\n \\\"and offset based windows\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling._validate_monotonic\",\n \"text\": \"pandas/core/window.py::Rolling._validate_monotonic\\n def _validate_monotonic(self):\\n \\\"\\\"\\\" validate on is monotonic \\\"\\\"\\\"\\n if not self._on.is_monotonic:\\n formatted = self.on or 'index'\\n raise ValueError(\\\"{0} must be \\\"\\n \\\"monotonic\\\".format(formatted))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling._validate_freq\",\n \"text\": \"pandas/core/window.py::Rolling._validate_freq\\n def _validate_freq(self):\\n \\\"\\\"\\\" validate & return window frequency \\\"\\\"\\\"\\n from pandas.tseries.frequencies import to_offset\\n try:\\n return to_offset(self.window)\\n except (TypeError, ValueError):\\n raise ValueError(\\\"passed window {0} is not \\\"\\n \\\"compatible with a datetimelike \\\"\\n \\\"index\\\".format(self.window))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.aggregate\",\n \"text\": \"pandas/core/window.py::Rolling.aggregate\\n @Appender(_agg_doc)\\n @Appender(_shared_docs['aggregate'] % dict(\\n versionadded='',\\n klass='Series/DataFrame',\\n axis=''))\\n def aggregate(self, arg, *args, **kwargs):\\n return super(Rolling, self).aggregate(arg, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.count\",\n \"text\": \"pandas/core/window.py::Rolling.count\\n @Substitution(name='rolling')\\n @Appender(_shared_docs['count'])\\n def count(self):\\n\\n # different impl for freq counting\\n if self.is_freq_type:\\n return self._apply('roll_count', 'count')\\n\\n return super(Rolling, self).count()\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.apply\",\n \"text\": \"pandas/core/window.py::Rolling.apply\\n @Substitution(name='rolling')\\n @Appender(_doc_template)\\n @Appender(_shared_docs['apply'])\\n def apply(self, func, raw=None, args=(), kwargs={}):\\n return super(Rolling, self).apply(\\n func, raw=raw, args=args, kwargs=kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.sum\",\n \"text\": \"pandas/core/window.py::Rolling.sum\\n @Substitution(name='rolling')\\n @Appender(_shared_docs['sum'])\\n def sum(self, *args, **kwargs):\\n nv.validate_rolling_func('sum', args, kwargs)\\n return super(Rolling, self).sum(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.max\",\n \"text\": \"pandas/core/window.py::Rolling.max\\n @Substitution(name='rolling')\\n @Appender(_doc_template)\\n @Appender(_shared_docs['max'])\\n def max(self, *args, **kwargs):\\n nv.validate_rolling_func('max', args, kwargs)\\n return super(Rolling, self).max(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.min\",\n \"text\": \"pandas/core/window.py::Rolling.min\\n @Substitution(name='rolling')\\n @Appender(_shared_docs['min'])\\n def min(self, *args, **kwargs):\\n nv.validate_rolling_func('min', args, kwargs)\\n return super(Rolling, self).min(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.mean\",\n \"text\": \"pandas/core/window.py::Rolling.mean\\n @Substitution(name='rolling')\\n @Appender(_shared_docs['mean'])\\n def mean(self, *args, **kwargs):\\n nv.validate_rolling_func('mean', args, kwargs)\\n return super(Rolling, self).mean(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.median\",\n \"text\": \"pandas/core/window.py::Rolling.median\\n @Substitution(name='rolling')\\n @Appender(_shared_docs['median'])\\n def median(self, **kwargs):\\n return super(Rolling, self).median(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.std\",\n \"text\": \"pandas/core/window.py::Rolling.std\\n @Substitution(name='rolling')\\n @Appender(_shared_docs['std'])\\n def std(self, ddof=1, *args, **kwargs):\\n nv.validate_rolling_func('std', args, kwargs)\\n return super(Rolling, self).std(ddof=ddof, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.var\",\n \"text\": \"pandas/core/window.py::Rolling.var\\n @Substitution(name='rolling')\\n @Appender(_shared_docs['var'])\\n def var(self, ddof=1, *args, **kwargs):\\n nv.validate_rolling_func('var', args, kwargs)\\n return super(Rolling, self).var(ddof=ddof, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.skew\",\n \"text\": \"pandas/core/window.py::Rolling.skew\\n @Substitution(name='rolling')\\n @Appender(_doc_template)\\n @Appender(_shared_docs['skew'])\\n def skew(self, **kwargs):\\n return super(Rolling, self).skew(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.kurt\",\n \"text\": \"pandas/core/window.py::Rolling.kurt\\n @Appender(_agg_doc)\\n @Substitution(name='rolling')\\n @Appender(_shared_docs['kurt'])\\n def kurt(self, **kwargs):\\n return super(Rolling, self).kurt(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.quantile\",\n \"text\": \"pandas/core/window.py::Rolling.quantile\\n @Substitution(name='rolling')\\n @Appender(_shared_docs['quantile'])\\n def quantile(self, quantile, interpolation='linear', **kwargs):\\n return super(Rolling, self).quantile(quantile=quantile,\\n interpolation=interpolation,\\n **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.cov\",\n \"text\": \"pandas/core/window.py::Rolling.cov\\n @Substitution(name='rolling')\\n @Appender(_doc_template)\\n @Appender(_shared_docs['cov'])\\n def cov(self, other=None, pairwise=None, ddof=1, **kwargs):\\n return super(Rolling, self).cov(other=other, pairwise=pairwise,\\n ddof=ddof, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Rolling.corr\",\n \"text\": \"pandas/core/window.py::Rolling.corr\\n @Substitution(name='rolling')\\n @Appender(_shared_docs['corr'])\\n def corr(self, other=None, pairwise=None, **kwargs):\\n return super(Rolling, self).corr(other=other, pairwise=pairwise,\\n **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::RollingGroupby._constructor\",\n \"text\": \"pandas/core/window.py::RollingGroupby._constructor\\n @property\\n def _constructor(self):\\n return Rolling\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::RollingGroupby._gotitem\",\n \"text\": \"pandas/core/window.py::RollingGroupby._gotitem\\n def _gotitem(self, key, ndim, subset=None):\\n\\n # we are setting the index on the actual object\\n # here so our index is carried thru to the selected obj\\n # when we do the splitting for the groupby\\n if self.on is not None:\\n self._groupby.obj = self._groupby.obj.set_index(self._on)\\n self.on = None\\n return super(RollingGroupby, self)._gotitem(key, ndim, subset=subset)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::RollingGroupby._validate_monotonic\",\n \"text\": \"pandas/core/window.py::RollingGroupby._validate_monotonic\\n def _validate_monotonic(self):\\n \\\"\\\"\\\"\\n validate that on is monotonic;\\n we don't care for groupby.rolling\\n because we have already validated at a higher\\n level\\n \\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.__init__\",\n \"text\": \"pandas/core/window.py::Expanding.__init__\\n def __init__(self, obj, min_periods=1, center=False, axis=0,\\n **kwargs):\\n super(Expanding, self).__init__(obj=obj, min_periods=min_periods,\\n center=center, axis=axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding._constructor\",\n \"text\": \"pandas/core/window.py::Expanding._constructor\\n @property\\n def _constructor(self):\\n return Expanding\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding._get_window\",\n \"text\": \"pandas/core/window.py::Expanding._get_window\\n def _get_window(self, other=None):\\n obj = self._selected_obj\\n if other is None:\\n return (max(len(obj), self.min_periods) if self.min_periods\\n else len(obj))\\n return (max((len(obj) + len(obj)), self.min_periods)\\n if self.min_periods else (len(obj) + len(obj)))\",\n \"label\": 1\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.aggregate\",\n \"text\": \"pandas/core/window.py::Expanding.aggregate\\n @Appender(_agg_doc)\\n @Appender(_shared_docs['aggregate'] % dict(\\n versionadded='',\\n klass='Series/DataFrame',\\n axis=''))\\n def aggregate(self, arg, *args, **kwargs):\\n return super(Expanding, self).aggregate(arg, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.count\",\n \"text\": \"pandas/core/window.py::Expanding.count\\n @Substitution(name='expanding')\\n @Appender(_shared_docs['count'])\\n def count(self, **kwargs):\\n return super(Expanding, self).count(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.apply\",\n \"text\": \"pandas/core/window.py::Expanding.apply\\n @Substitution(name='expanding')\\n @Appender(_doc_template)\\n @Appender(_shared_docs['apply'])\\n def apply(self, func, raw=None, args=(), kwargs={}):\\n return super(Expanding, self).apply(\\n func, raw=raw, args=args, kwargs=kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.sum\",\n \"text\": \"pandas/core/window.py::Expanding.sum\\n @Substitution(name='expanding')\\n @Appender(_shared_docs['sum'])\\n def sum(self, *args, **kwargs):\\n nv.validate_expanding_func('sum', args, kwargs)\\n return super(Expanding, self).sum(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.max\",\n \"text\": \"pandas/core/window.py::Expanding.max\\n @Substitution(name='expanding')\\n @Appender(_doc_template)\\n @Appender(_shared_docs['max'])\\n def max(self, *args, **kwargs):\\n nv.validate_expanding_func('max', args, kwargs)\\n return super(Expanding, self).max(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.min\",\n \"text\": \"pandas/core/window.py::Expanding.min\\n @Substitution(name='expanding')\\n @Appender(_shared_docs['min'])\\n def min(self, *args, **kwargs):\\n nv.validate_expanding_func('min', args, kwargs)\\n return super(Expanding, self).min(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.mean\",\n \"text\": \"pandas/core/window.py::Expanding.mean\\n @Substitution(name='expanding')\\n @Appender(_shared_docs['mean'])\\n def mean(self, *args, **kwargs):\\n nv.validate_expanding_func('mean', args, kwargs)\\n return super(Expanding, self).mean(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.median\",\n \"text\": \"pandas/core/window.py::Expanding.median\\n @Substitution(name='expanding')\\n @Appender(_shared_docs['median'])\\n def median(self, **kwargs):\\n return super(Expanding, self).median(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.std\",\n \"text\": \"pandas/core/window.py::Expanding.std\\n @Substitution(name='expanding')\\n @Appender(_shared_docs['std'])\\n def std(self, ddof=1, *args, **kwargs):\\n nv.validate_expanding_func('std', args, kwargs)\\n return super(Expanding, self).std(ddof=ddof, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.var\",\n \"text\": \"pandas/core/window.py::Expanding.var\\n @Substitution(name='expanding')\\n @Appender(_shared_docs['var'])\\n def var(self, ddof=1, *args, **kwargs):\\n nv.validate_expanding_func('var', args, kwargs)\\n return super(Expanding, self).var(ddof=ddof, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.skew\",\n \"text\": \"pandas/core/window.py::Expanding.skew\\n @Substitution(name='expanding')\\n @Appender(_doc_template)\\n @Appender(_shared_docs['skew'])\\n def skew(self, **kwargs):\\n return super(Expanding, self).skew(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.kurt\",\n \"text\": \"pandas/core/window.py::Expanding.kurt\\n @Appender(_agg_doc)\\n @Substitution(name='expanding')\\n @Appender(_shared_docs['kurt'])\\n def kurt(self, **kwargs):\\n return super(Expanding, self).kurt(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.quantile\",\n \"text\": \"pandas/core/window.py::Expanding.quantile\\n @Substitution(name='expanding')\\n @Appender(_shared_docs['quantile'])\\n def quantile(self, quantile, interpolation='linear', **kwargs):\\n return super(Expanding, self).quantile(quantile=quantile,\\n interpolation=interpolation,\\n **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.cov\",\n \"text\": \"pandas/core/window.py::Expanding.cov\\n @Substitution(name='expanding')\\n @Appender(_doc_template)\\n @Appender(_shared_docs['cov'])\\n def cov(self, other=None, pairwise=None, ddof=1, **kwargs):\\n return super(Expanding, self).cov(other=other, pairwise=pairwise,\\n ddof=ddof, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::Expanding.corr\",\n \"text\": \"pandas/core/window.py::Expanding.corr\\n @Substitution(name='expanding')\\n @Appender(_shared_docs['corr'])\\n def corr(self, other=None, pairwise=None, **kwargs):\\n return super(Expanding, self).corr(other=other, pairwise=pairwise,\\n **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::ExpandingGroupby._constructor\",\n \"text\": \"pandas/core/window.py::ExpandingGroupby._constructor\\n @property\\n def _constructor(self):\\n return Expanding\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM.__init__\",\n \"text\": \"pandas/core/window.py::EWM.__init__\\n def __init__(self, obj, com=None, span=None, halflife=None, alpha=None,\\n min_periods=0, adjust=True, ignore_na=False,\\n axis=0):\\n self.obj = obj\\n self.com = _get_center_of_mass(com, span, halflife, alpha)\\n self.min_periods = min_periods\\n self.adjust = adjust\\n self.ignore_na = ignore_na\\n self.axis = axis\\n self.on = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM._constructor\",\n \"text\": \"pandas/core/window.py::EWM._constructor\\n @property\\n def _constructor(self):\\n return EWM\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM.aggregate\",\n \"text\": \"pandas/core/window.py::EWM.aggregate\\n @Appender(_agg_doc)\\n @Appender(_shared_docs['aggregate'] % dict(\\n versionadded='',\\n klass='Series/DataFrame',\\n axis=''))\\n def aggregate(self, arg, *args, **kwargs):\\n return super(EWM, self).aggregate(arg, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM._apply\",\n \"text\": \"pandas/core/window.py::EWM._apply\\n def _apply(self, func, **kwargs):\\n \\\"\\\"\\\"Rolling statistical measure using supplied function. Designed to be\\n used with passed-in Cython array-based functions.\\n\\n Parameters\\n ----------\\n func : string/callable to apply\\n\\n Returns\\n -------\\n y : same type as input argument\\n\\n \\\"\\\"\\\"\\n blocks, obj, index = self._create_blocks()\\n results = []\\n for b in blocks:\\n try:\\n values = self._prep_values(b.values)\\n except TypeError:\\n results.append(b.values.copy())\\n continue\\n\\n if values.size == 0:\\n results.append(values.copy())\\n continue\\n\\n # if we have a string function name, wrap it\\n if isinstance(func, compat.string_types):\\n cfunc = getattr(_window, func, None)\\n if cfunc is None:\\n raise ValueError(\\\"we do not support this function \\\"\\n \\\"in _window.{0}\\\".format(func))\\n\\n def func(arg):\\n return cfunc(arg, self.com, int(self.adjust),\\n int(self.ignore_na), int(self.min_periods))\\n\\n results.append(np.apply_along_axis(func, self.axis, values))\\n\\n return self._wrap_results(results, blocks, obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM.mean\",\n \"text\": \"pandas/core/window.py::EWM.mean\\n @Substitution(name='ewm')\\n @Appender(_doc_template)\\n def mean(self, *args, **kwargs):\\n \\\"\\\"\\\"exponential weighted moving average\\\"\\\"\\\"\\n nv.validate_window_func('mean', args, kwargs)\\n return self._apply('ewma', **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM.std\",\n \"text\": \"pandas/core/window.py::EWM.std\\n @Substitution(name='ewm')\\n @Appender(_doc_template)\\n @Appender(_bias_template)\\n def std(self, bias=False, *args, **kwargs):\\n \\\"\\\"\\\"exponential weighted moving stddev\\\"\\\"\\\"\\n nv.validate_window_func('std', args, kwargs)\\n return _zsqrt(self.var(bias=bias, **kwargs))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM.var\",\n \"text\": \"pandas/core/window.py::EWM.var\\n @Substitution(name='ewm')\\n @Appender(_doc_template)\\n @Appender(_bias_template)\\n def var(self, bias=False, *args, **kwargs):\\n \\\"\\\"\\\"exponential weighted moving variance\\\"\\\"\\\"\\n nv.validate_window_func('var', args, kwargs)\\n\\n def f(arg):\\n return _window.ewmcov(arg, arg, self.com, int(self.adjust),\\n int(self.ignore_na), int(self.min_periods),\\n int(bias))\\n\\n return self._apply(f, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM.var.f\",\n \"text\": \"pandas/core/window.py::EWM.var.f\\n def f(arg):\\n return _window.ewmcov(arg, arg, self.com, int(self.adjust),\\n int(self.ignore_na), int(self.min_periods),\\n int(bias))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM.cov\",\n \"text\": \"pandas/core/window.py::EWM.cov\\n @Substitution(name='ewm')\\n @Appender(_doc_template)\\n @Appender(_pairwise_template)\\n def cov(self, other=None, pairwise=None, bias=False, **kwargs):\\n \\\"\\\"\\\"exponential weighted sample covariance\\\"\\\"\\\"\\n if other is None:\\n other = self._selected_obj\\n # only default unset\\n pairwise = True if pairwise is None else pairwise\\n other = self._shallow_copy(other)\\n\\n def _get_cov(X, Y):\\n X = self._shallow_copy(X)\\n Y = self._shallow_copy(Y)\\n cov = _window.ewmcov(X._prep_values(), Y._prep_values(), self.com,\\n int(self.adjust), int(self.ignore_na),\\n int(self.min_periods), int(bias))\\n return X._wrap_result(cov)\\n\\n return _flex_binary_moment(self._selected_obj, other._selected_obj,\\n _get_cov, pairwise=bool(pairwise))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM.cov._get_cov\",\n \"text\": \"pandas/core/window.py::EWM.cov._get_cov\\n def _get_cov(X, Y):\\n X = self._shallow_copy(X)\\n Y = self._shallow_copy(Y)\\n cov = _window.ewmcov(X._prep_values(), Y._prep_values(), self.com,\\n int(self.adjust), int(self.ignore_na),\\n int(self.min_periods), int(bias))\\n return X._wrap_result(cov)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM.corr\",\n \"text\": \"pandas/core/window.py::EWM.corr\\n @Substitution(name='ewm')\\n @Appender(_doc_template)\\n @Appender(_pairwise_template)\\n def corr(self, other=None, pairwise=None, **kwargs):\\n \\\"\\\"\\\"exponential weighted sample correlation\\\"\\\"\\\"\\n if other is None:\\n other = self._selected_obj\\n # only default unset\\n pairwise = True if pairwise is None else pairwise\\n other = self._shallow_copy(other)\\n\\n def _get_corr(X, Y):\\n X = self._shallow_copy(X)\\n Y = self._shallow_copy(Y)\\n\\n def _cov(x, y):\\n return _window.ewmcov(x, y, self.com, int(self.adjust),\\n int(self.ignore_na),\\n int(self.min_periods),\\n 1)\\n\\n x_values = X._prep_values()\\n y_values = Y._prep_values()\\n with np.errstate(all='ignore'):\\n cov = _cov(x_values, y_values)\\n x_var = _cov(x_values, x_values)\\n y_var = _cov(y_values, y_values)\\n corr = cov / _zsqrt(x_var * y_var)\\n return X._wrap_result(corr)\\n\\n return _flex_binary_moment(self._selected_obj, other._selected_obj,\\n _get_corr, pairwise=bool(pairwise))\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM.corr._get_corr\",\n \"text\": \"pandas/core/window.py::EWM.corr._get_corr\\n def _get_corr(X, Y):\\n X = self._shallow_copy(X)\\n Y = self._shallow_copy(Y)\\n\\n def _cov(x, y):\\n return _window.ewmcov(x, y, self.com, int(self.adjust),\\n int(self.ignore_na),\\n int(self.min_periods),\\n 1)\\n\\n x_values = X._prep_values()\\n y_values = Y._prep_values()\\n with np.errstate(all='ignore'):\\n cov = _cov(x_values, y_values)\\n x_var = _cov(x_values, x_values)\\n y_var = _cov(y_values, y_values)\\n corr = cov / _zsqrt(x_var * y_var)\\n return X._wrap_result(corr)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::EWM.corr._get_corr._cov\",\n \"text\": \"pandas/core/window.py::EWM.corr._get_corr._cov\\n def _cov(x, y):\\n return _window.ewmcov(x, y, self.com, int(self.adjust),\\n int(self.ignore_na),\\n int(self.min_periods),\\n 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_flex_binary_moment\",\n \"text\": \"pandas/core/window.py::_flex_binary_moment\\ndef _flex_binary_moment(arg1, arg2, f, pairwise=False):\\n\\n if not (isinstance(arg1, (np.ndarray, ABCSeries, ABCDataFrame)) and\\n isinstance(arg2, (np.ndarray, ABCSeries, ABCDataFrame))):\\n raise TypeError(\\\"arguments to moment function must be of type \\\"\\n \\\"np.ndarray/Series/DataFrame\\\")\\n\\n if (isinstance(arg1, (np.ndarray, ABCSeries)) and\\n isinstance(arg2, (np.ndarray, ABCSeries))):\\n X, Y = _prep_binary(arg1, arg2)\\n return f(X, Y)\\n\\n elif isinstance(arg1, ABCDataFrame):\\n from pandas import DataFrame\\n\\n def dataframe_from_int_dict(data, frame_template):\\n result = DataFrame(data, index=frame_template.index)\\n if len(result.columns) > 0:\\n result.columns = frame_template.columns[result.columns]\\n return result\\n\\n results = {}\\n if isinstance(arg2, ABCDataFrame):\\n if pairwise is False:\\n if arg1 is arg2:\\n # special case in order to handle duplicate column names\\n for i, col in enumerate(arg1.columns):\\n results[i] = f(arg1.iloc[:, i], arg2.iloc[:, i])\\n return dataframe_from_int_dict(results, arg1)\\n else:\\n if not arg1.columns.is_unique:\\n raise ValueError(\\\"'arg1' columns are not unique\\\")\\n if not arg2.columns.is_unique:\\n raise ValueError(\\\"'arg2' columns are not unique\\\")\\n with warnings.catch_warnings(record=True):\\n warnings.simplefilter(\\\"ignore\\\", RuntimeWarning)\\n X, Y = arg1.align(arg2, join='outer')\\n X = X + 0 * Y\\n Y = Y + 0 * X\\n\\n with warnings.catch_warnings(record=True):\\n warnings.simplefilter(\\\"ignore\\\", RuntimeWarning)\\n res_columns\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_get_center_of_mass\",\n \"text\": \"pandas/core/window.py::_get_center_of_mass\\ndef _get_center_of_mass(comass, span, halflife, alpha):\\n valid_count = com.count_not_none(comass, span, halflife, alpha)\\n if valid_count > 1:\\n raise ValueError(\\\"comass, span, halflife, and alpha \\\"\\n \\\"are mutually exclusive\\\")\\n\\n # Convert to center of mass; domain checks ensure 0 < alpha <= 1\\n if comass is not None:\\n if comass < 0:\\n raise ValueError(\\\"comass must satisfy: comass >= 0\\\")\\n elif span is not None:\\n if span < 1:\\n raise ValueError(\\\"span must satisfy: span >= 1\\\")\\n comass = (span - 1) / 2.\\n elif halflife is not None:\\n if halflife <= 0:\\n raise ValueError(\\\"halflife must satisfy: halflife > 0\\\")\\n decay = 1 - np.exp(np.log(0.5) / halflife)\\n comass = 1 / decay - 1\\n elif alpha is not None:\\n if alpha <= 0 or alpha > 1:\\n raise ValueError(\\\"alpha must satisfy: 0 < alpha <= 1\\\")\\n comass = (1.0 - alpha) / alpha\\n else:\\n raise ValueError(\\\"Must pass one of comass, span, halflife, or alpha\\\")\\n\\n return float(comass)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_offset\",\n \"text\": \"pandas/core/window.py::_offset\\ndef _offset(window, center):\\n if not is_integer(window):\\n window = len(window)\\n offset = (window - 1) / 2. if center else 0\\n try:\\n return int(offset)\\n except TypeError:\\n return offset.astype(int)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_require_min_periods\",\n \"text\": \"pandas/core/window.py::_require_min_periods\\ndef _require_min_periods(p):\\n def _check_func(minp, window):\\n if minp is None:\\n return window\\n else:\\n return max(p, minp)\\n\\n return _check_func\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_require_min_periods._check_func\",\n \"text\": \"pandas/core/window.py::_require_min_periods._check_func\\n def _check_func(minp, window):\\n if minp is None:\\n return window\\n else:\\n return max(p, minp)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_use_window\",\n \"text\": \"pandas/core/window.py::_use_window\\ndef _use_window(minp, window):\\n if minp is None:\\n return window\\n else:\\n return minp\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_zsqrt\",\n \"text\": \"pandas/core/window.py::_zsqrt\\ndef _zsqrt(x):\\n with np.errstate(all='ignore'):\\n result = np.sqrt(x)\\n mask = x < 0\\n\\n if isinstance(x, ABCDataFrame):\\n if mask.values.any():\\n result[mask] = 0\\n else:\\n if mask.any():\\n result[mask] = 0\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::_prep_binary\",\n \"text\": \"pandas/core/window.py::_prep_binary\\ndef _prep_binary(arg1, arg2):\\n if not isinstance(arg2, type(arg1)):\\n raise Exception('Input arrays must be of the same type!')\\n\\n # mask out values, this also makes a common index...\\n X = arg1 + 0 * arg2\\n Y = arg2 + 0 * arg1\\n\\n return X, Y\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::rolling\",\n \"text\": \"pandas/core/window.py::rolling\\ndef rolling(obj, win_type=None, **kwds):\\n if not isinstance(obj, (ABCSeries, ABCDataFrame)):\\n raise TypeError('invalid type: %s' % type(obj))\\n\\n if win_type is not None:\\n return Window(obj, win_type=win_type, **kwds)\\n\\n return Rolling(obj, **kwds)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::expanding\",\n \"text\": \"pandas/core/window.py::expanding\\ndef expanding(obj, **kwds):\\n if not isinstance(obj, (ABCSeries, ABCDataFrame)):\\n raise TypeError('invalid type: %s' % type(obj))\\n\\n return Expanding(obj, **kwds)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/core/window.py::ewm\",\n \"text\": \"pandas/core/window.py::ewm\\ndef ewm(obj, **kwds):\\n if not isinstance(obj, (ABCSeries, ABCDataFrame)):\\n raise TypeError('invalid type: %s' % type(obj))\\n\\n return EWM(obj, **kwds)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":61,"cells":{"instance_id":{"kind":"string","value":"googleapis__google-cloud-python-1172"},"repo":{"kind":"string","value":"googleapis/google-cloud-python"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Normalize module docstring patterns\nFrom https://github.com/GoogleCloudPlatform/gcloud-python/pull/1164/files#r40942738.\n\n```\n$ git grep \"API wrapper\"\ngcloud/bigquery/__init__.py:\"\"\"Google Cloud BigQuery API wrapper.\ngcloud/dns/__init__.py:\"\"\"Google Cloud DNS API wrapper.\ngcloud/pubsub/__init__.py:\"\"\"Google Cloud Pubsub API wrapper.\ngcloud/resource_manager/__init__.py:\"\"\"Google Cloud Resource Manager API wrapper\ngcloud/search/__init__.py:\"\"\"Google Cloud Search API wrapper.\"\"\"\n$ git grep \"interacting with API\"\ngcloud/bigquery/client.py:\"\"\"gcloud bigquery client for interacting with API.\"\"\"\ngcloud/client.py:\"\"\"gcloud client base class for interacting with API.\"\"\"\ngcloud/dns/client.py:\"\"\"gcloud dns client for interacting with API.\"\"\"\ngcloud/pubsub/client.py:\"\"\"gcloud pubsub client for interacting with API.\"\"\"\ngcloud/search/client.py:\"\"\"gcloud dns client for interacting with API.\"\"\"\ngcloud/storage/client.py:\"\"\"gcloud storage client for interacting with API.\"\"\"\n```\n\n"},"gold_files":{"kind":"list like","value":["gcloud/bigquery/client.py","gcloud/client.py","gcloud/dns/client.py","gcloud/pubsub/client.py","gcloud/storage/client.py"],"string":"[\n \"gcloud/bigquery/client.py\",\n \"gcloud/client.py\",\n \"gcloud/dns/client.py\",\n \"gcloud/pubsub/client.py\",\n \"gcloud/storage/client.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["gcloud/bigquery/client.py::<module>","gcloud/client.py::<module>","gcloud/dns/client.py::<module>","gcloud/pubsub/client.py::<module>","gcloud/storage/client.py::<module>"],"string":"[\n \"gcloud/bigquery/client.py::<module>\",\n \"gcloud/client.py::<module>\",\n \"gcloud/dns/client.py::<module>\",\n \"gcloud/pubsub/client.py::<module>\",\n \"gcloud/storage/client.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"gcloud/bigquery/__init__.py","text":"gcloud/bigquery/__init__.py","label":0},{"cid":"gcloud/bigquery/client.py","text":"gcloud/bigquery/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def list_datasets(self, include_all=False, max_results=None, page_token=None) # List datasets for the project associated with this client.\n def dataset(self, name) # Construct a dataset bound to this client.\n def load_table_from_storage(self, name, destination, *source_uris) # Construct a job for loading data into a table from CloudStorage.\n def copy_table(self, name, destination, *sources) # Construct a job for copying one or more tables into another table.\n def extract_table_to_storage(self, name, source, *destination_uris) # Construct a job for extracting a table into Cloud Storage files.\n def run_async_query(self, name, query) # Construct a job for running a SQL query asynchronously.\n def run_sync_query(self, query) # Construct a job for running a SQL query synchronously.","label":1},{"cid":"gcloud/bigquery/job.py","text":"gcloud/bigquery/job.py\nclass _ConfigurationProperty # Base property implementation.\n def __init__(self, name)\n def __get__(self, instance, owner) # Descriptor protocal: accesstor\n def _validate(self, value) # Subclasses override to impose validation policy.\n def __set__(self, instance, value) # Descriptor protocal: mutator\n def __delete__(self, instance) # Descriptor protocal: deleter\nclass _TypedProperty # Property implementation: validates based on value type.\n def __init__(self, name, property_type)\n def _validate(self, value)\nclass _EnumProperty # Psedo-enumeration class.\n def _validate(self, value) # Check that ``value`` is one of the allowed values.\nclass Compression # Pseudo-enum for ``compression`` properties.\nclass CreateDisposition # Pseudo-enum for ``create_disposition`` properties.\nclass DestinationFormat # Pseudo-enum for ``destination_format`` properties.\nclass Encoding # Pseudo-enum for ``encoding`` properties.\nclass QueryPriority # Pseudo-enum for ``RunAsyncQueryJob.priority`` property.\nclass SourceFormat # Pseudo-enum for ``source_format`` properties.\nclass WriteDisposition # Pseudo-enum for ``write_disposition`` properties.\nclass _BaseJob # Base class for jobs.\n def __init__(self, client)\n def project(self) # Project bound to the job.\n def _require_client(self, client) # Check client or verify over-ride.\nclass _AsyncJob # Base class for asynchronous jobs.\n def __init__(self, name, client)\n def path(self) # URL path for the job's APIs.\n def etag(self) # ETag for the job resource.\n def job_id(self) # ID for the job resource.\n def self_link(self) # URL for the job resource.\n def user_email(self) # E-mail address of user who submitted the job.\n def created(self) # Datetime at which the job was created.\n def started(self) # Datetime at which the job was started.\n def ended(self) # Datetime at which the job finished.\n def error_result(self) # Error information about the job as a whole.\n def errors(self) # Information about individual errors generated by the job.\n def state(self) # Status of the job.\n def _scrub_local_properties(self, cleaned) # Helper: handle subclass properties in cleaned.\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\n def begin(self, client=None) # API call: begin the job via a POST request\n def exists(self, client=None) # API call: test for the existence of the job via a GET request\n def reload(self, client=None) # API call: refresh job properties via a GET request\n def cancel(self, client=None) # API call: cancel job via a POST request\nclass _LoadConfiguration # User-settable configuration options for load jobs.\nclass LoadTableFromStorageJob # Asynchronous job for loading data into a table from CloudStorage.\n def __init__(self, name, destination, source_uris, client, schema=())\n def schema(self) # Table's schema.\n de","label":0},{"cid":"gcloud/bigtable/client.py","text":"gcloud/bigtable/client.py\nclass Client # Client for interacting with Google Cloud Bigtable API.\n def __init__(self, project=None, credentials=None, read_only=False, admin=False, user_agent=DEFAULT_USER_AGENT, timeout_seconds=DEFAULT_TIMEOUT_SECONDS)\n def credentials(self) # Getter for client's credentials.","label":0},{"cid":"gcloud/client.py","text":"gcloud/client.py\nclass _ClientFactoryMixin # Mixin to allow factories that create credentials.\n def __init__(self, *args, **kwargs)\n def from_service_account_json(cls, json_credentials_path, *args, **kwargs) # Factory to retrieve JSON credentials while creating client.\n def from_service_account_p12(cls, client_email, private_key_path, *args, **kwargs) # Factory to retrieve P12 credentials while creating client.\nclass Client # Client to bundle configuration needed for API requests.\n def __init__(self, credentials=None, http=None)\nclass _ClientProjectMixin # Mixin to allow setting the project on the client.\n def __init__(self, project=None)\nclass JSONClient # Client to for Google JSON-based API.\n def __init__(self, project=None, credentials=None, http=None)","label":1},{"cid":"gcloud/datastore/client.py","text":"gcloud/datastore/client.py\ndef _get_production_dataset_id() # Gets the production application ID if it can be inferred.\ndef _get_gcd_dataset_id() # Gets the GCD application ID if it can be inferred.\ndef _determine_default_dataset_id(dataset_id=None) # Determine default dataset ID explicitly or implicitly as fall-back.\ndef _extended_lookup(connection, dataset_id, key_pbs, missing=None, deferred=None, eventual=False, transaction_id=None) # Repeat lookup until all keys found (unless stop requested).\nclass Client # Convenience wrapper for invoking APIs/factories w/ a dataset ID.\n def __init__(self, dataset_id=None, namespace=None, credentials=None, http=None)\n def _push_batch(self, batch) # Push a batch/transaction onto our stack.\n def _pop_batch(self) # Pop a batch/transaction from our stack.\n def current_batch(self) # Currently-active batch.\n def current_transaction(self) # Currently-active transaction.\n def get(self, key, missing=None, deferred=None) # Retrieve an entity from a single key (if it exists).\n def get_multi(self, keys, missing=None, deferred=None) # Retrieve entities, along with their attributes.\n def put(self, entity) # Save an entity in the Cloud Datastore.\n def put_multi(self, entities) # Save entities in the Cloud Datastore.\n def delete(self, key) # Delete the key in the Cloud Datastore.\n def delete_multi(self, keys) # Delete keys from the Cloud Datastore.\n def allocate_ids(self, incomplete_key, num_ids) # Allocate a list of IDs from a partial key.\n def key(self, *path_args, **kwargs) # Proxy to :class:`gcloud.datastore.key.Key`.\n def batch(self) # Proxy to :class:`gcloud.datastore.batch.Batch`.\n def transaction(self) # Proxy to :class:`gcloud.datastore.transaction.Transaction`.\n def query(self, **kwargs) # Proxy to :class:`gcloud.datastore.query.Query`.","label":0},{"cid":"gcloud/dns/__init__.py","text":"gcloud/dns/__init__.py","label":0},{"cid":"gcloud/dns/client.py","text":"gcloud/dns/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def quotas(self) # Return DNS quots for the project associated with this client.\n def list_zones(self, max_results=None, page_token=None) # List zones for the project associated with this client.\n def zone(self, name, dns_name) # Construct a zone bound to this client.","label":1},{"cid":"gcloud/dns/connection.py","text":"gcloud/dns/connection.py\nclass Connection # A connection to Google Cloud DNS via the JSON REST API.","label":0},{"cid":"gcloud/pubsub/__init__.py","text":"gcloud/pubsub/__init__.py","label":0},{"cid":"gcloud/pubsub/client.py","text":"gcloud/pubsub/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def list_topics(self, page_size=None, page_token=None) # List topics for the project associated with this client.\n def list_subscriptions(self, page_size=None, page_token=None, topic_name=None) # List subscriptions for the project associated with this client.\n def topic(self, name, timestamp_messages=False) # Creates a topic bound to the current client.","label":1},{"cid":"gcloud/resource_manager/__init__.py","text":"gcloud/resource_manager/__init__.py","label":0},{"cid":"gcloud/resource_manager/client.py","text":"gcloud/resource_manager/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def from_service_account_json(cls, *args, **kwargs) # Factory to retrieve JSON credentials while creating client.\n def from_service_account_p12(cls, *args, **kwargs) # Factory to retrieve P12 credentials while creating client.\n def new_project(self, project_id, name=None, labels=None) # Creates a :class:`.Project` bound to the current client.\n def fetch_project(self, project_id) # Fetch an existing project and it's relevant metadata by ID.\n def list_projects(self, filter_params=None, page_size=None) # List the projects visible to this client.\nclass _ProjectIterator # An iterator over a list of Project resources.\n def __init__(self, client, extra_params=None)\n def get_items_from_response(self, response) # Yield :class:`.Project` items from response.","label":0},{"cid":"gcloud/storage/client.py","text":"gcloud/storage/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def __init__(self, project=None, credentials=None, http=None)\n def connection(self) # Get connection or batch on the client.\n def connection(self, value) # Set connection on the client.\n def _push_batch(self, batch) # Push a batch onto our stack.\n def _pop_batch(self) # Pop a batch from our stack.\n def current_batch(self) # Currently-active batch.\n def bucket(self, bucket_name) # Factory constructor for bucket object.\n def batch(self) # Factory constructor for batch object.\n def get_bucket(self, bucket_name) # Get a bucket by name.\n def lookup_bucket(self, bucket_name) # Get a bucket by name, returning None if not found.\n def create_bucket(self, bucket_name) # Create a new bucket.\n def list_buckets(self, max_results=None, page_token=None, prefix=None, projection='noAcl', fields=None) # Get all buckets in the project associated to the client.\nclass _BucketIterator # An iterator listing all buckets.\n def __init__(self, client, extra_params=None)\n def get_items_from_response(self, response) # Factory method which yields :class:`.Bucket` items from a response.","label":1}],"string":"[\n {\n \"cid\": \"gcloud/bigquery/__init__.py\",\n \"text\": \"gcloud/bigquery/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/client.py\",\n \"text\": \"gcloud/bigquery/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def list_datasets(self, include_all=False, max_results=None, page_token=None) # List datasets for the project associated with this client.\\n def dataset(self, name) # Construct a dataset bound to this client.\\n def load_table_from_storage(self, name, destination, *source_uris) # Construct a job for loading data into a table from CloudStorage.\\n def copy_table(self, name, destination, *sources) # Construct a job for copying one or more tables into another table.\\n def extract_table_to_storage(self, name, source, *destination_uris) # Construct a job for extracting a table into Cloud Storage files.\\n def run_async_query(self, name, query) # Construct a job for running a SQL query asynchronously.\\n def run_sync_query(self, query) # Construct a job for running a SQL query synchronously.\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigquery/job.py\",\n \"text\": \"gcloud/bigquery/job.py\\nclass _ConfigurationProperty # Base property implementation.\\n def __init__(self, name)\\n def __get__(self, instance, owner) # Descriptor protocal: accesstor\\n def _validate(self, value) # Subclasses override to impose validation policy.\\n def __set__(self, instance, value) # Descriptor protocal: mutator\\n def __delete__(self, instance) # Descriptor protocal: deleter\\nclass _TypedProperty # Property implementation: validates based on value type.\\n def __init__(self, name, property_type)\\n def _validate(self, value)\\nclass _EnumProperty # Psedo-enumeration class.\\n def _validate(self, value) # Check that ``value`` is one of the allowed values.\\nclass Compression # Pseudo-enum for ``compression`` properties.\\nclass CreateDisposition # Pseudo-enum for ``create_disposition`` properties.\\nclass DestinationFormat # Pseudo-enum for ``destination_format`` properties.\\nclass Encoding # Pseudo-enum for ``encoding`` properties.\\nclass QueryPriority # Pseudo-enum for ``RunAsyncQueryJob.priority`` property.\\nclass SourceFormat # Pseudo-enum for ``source_format`` properties.\\nclass WriteDisposition # Pseudo-enum for ``write_disposition`` properties.\\nclass _BaseJob # Base class for jobs.\\n def __init__(self, client)\\n def project(self) # Project bound to the job.\\n def _require_client(self, client) # Check client or verify over-ride.\\nclass _AsyncJob # Base class for asynchronous jobs.\\n def __init__(self, name, client)\\n def path(self) # URL path for the job's APIs.\\n def etag(self) # ETag for the job resource.\\n def job_id(self) # ID for the job resource.\\n def self_link(self) # URL for the job resource.\\n def user_email(self) # E-mail address of user who submitted the job.\\n def created(self) # Datetime at which the job was created.\\n def started(self) # Datetime at which the job was started.\\n def ended(self) # Datetime at which the job finished.\\n def error_result(self) # Error information about the job as a whole.\\n def errors(self) # Information about individual errors generated by the job.\\n def state(self) # Status of the job.\\n def _scrub_local_properties(self, cleaned) # Helper: handle subclass properties in cleaned.\\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\\n def begin(self, client=None) # API call: begin the job via a POST request\\n def exists(self, client=None) # API call: test for the existence of the job via a GET request\\n def reload(self, client=None) # API call: refresh job properties via a GET request\\n def cancel(self, client=None) # API call: cancel job via a POST request\\nclass _LoadConfiguration # User-settable configuration options for load jobs.\\nclass LoadTableFromStorageJob # Asynchronous job for loading data into a table from CloudStorage.\\n def __init__(self, name, destination, source_uris, client, schema=())\\n def schema(self) # Table's schema.\\n de\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/client.py\",\n \"text\": \"gcloud/bigtable/client.py\\nclass Client # Client for interacting with Google Cloud Bigtable API.\\n def __init__(self, project=None, credentials=None, read_only=False, admin=False, user_agent=DEFAULT_USER_AGENT, timeout_seconds=DEFAULT_TIMEOUT_SECONDS)\\n def credentials(self) # Getter for client's credentials.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/client.py\",\n \"text\": \"gcloud/client.py\\nclass _ClientFactoryMixin # Mixin to allow factories that create credentials.\\n def __init__(self, *args, **kwargs)\\n def from_service_account_json(cls, json_credentials_path, *args, **kwargs) # Factory to retrieve JSON credentials while creating client.\\n def from_service_account_p12(cls, client_email, private_key_path, *args, **kwargs) # Factory to retrieve P12 credentials while creating client.\\nclass Client # Client to bundle configuration needed for API requests.\\n def __init__(self, credentials=None, http=None)\\nclass _ClientProjectMixin # Mixin to allow setting the project on the client.\\n def __init__(self, project=None)\\nclass JSONClient # Client to for Google JSON-based API.\\n def __init__(self, project=None, credentials=None, http=None)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/client.py\",\n \"text\": \"gcloud/datastore/client.py\\ndef _get_production_dataset_id() # Gets the production application ID if it can be inferred.\\ndef _get_gcd_dataset_id() # Gets the GCD application ID if it can be inferred.\\ndef _determine_default_dataset_id(dataset_id=None) # Determine default dataset ID explicitly or implicitly as fall-back.\\ndef _extended_lookup(connection, dataset_id, key_pbs, missing=None, deferred=None, eventual=False, transaction_id=None) # Repeat lookup until all keys found (unless stop requested).\\nclass Client # Convenience wrapper for invoking APIs/factories w/ a dataset ID.\\n def __init__(self, dataset_id=None, namespace=None, credentials=None, http=None)\\n def _push_batch(self, batch) # Push a batch/transaction onto our stack.\\n def _pop_batch(self) # Pop a batch/transaction from our stack.\\n def current_batch(self) # Currently-active batch.\\n def current_transaction(self) # Currently-active transaction.\\n def get(self, key, missing=None, deferred=None) # Retrieve an entity from a single key (if it exists).\\n def get_multi(self, keys, missing=None, deferred=None) # Retrieve entities, along with their attributes.\\n def put(self, entity) # Save an entity in the Cloud Datastore.\\n def put_multi(self, entities) # Save entities in the Cloud Datastore.\\n def delete(self, key) # Delete the key in the Cloud Datastore.\\n def delete_multi(self, keys) # Delete keys from the Cloud Datastore.\\n def allocate_ids(self, incomplete_key, num_ids) # Allocate a list of IDs from a partial key.\\n def key(self, *path_args, **kwargs) # Proxy to :class:`gcloud.datastore.key.Key`.\\n def batch(self) # Proxy to :class:`gcloud.datastore.batch.Batch`.\\n def transaction(self) # Proxy to :class:`gcloud.datastore.transaction.Transaction`.\\n def query(self, **kwargs) # Proxy to :class:`gcloud.datastore.query.Query`.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/dns/__init__.py\",\n \"text\": \"gcloud/dns/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/dns/client.py\",\n \"text\": \"gcloud/dns/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def quotas(self) # Return DNS quots for the project associated with this client.\\n def list_zones(self, max_results=None, page_token=None) # List zones for the project associated with this client.\\n def zone(self, name, dns_name) # Construct a zone bound to this client.\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/dns/connection.py\",\n \"text\": \"gcloud/dns/connection.py\\nclass Connection # A connection to Google Cloud DNS via the JSON REST API.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/pubsub/__init__.py\",\n \"text\": \"gcloud/pubsub/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/pubsub/client.py\",\n \"text\": \"gcloud/pubsub/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def list_topics(self, page_size=None, page_token=None) # List topics for the project associated with this client.\\n def list_subscriptions(self, page_size=None, page_token=None, topic_name=None) # List subscriptions for the project associated with this client.\\n def topic(self, name, timestamp_messages=False) # Creates a topic bound to the current client.\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/resource_manager/__init__.py\",\n \"text\": \"gcloud/resource_manager/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/resource_manager/client.py\",\n \"text\": \"gcloud/resource_manager/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def from_service_account_json(cls, *args, **kwargs) # Factory to retrieve JSON credentials while creating client.\\n def from_service_account_p12(cls, *args, **kwargs) # Factory to retrieve P12 credentials while creating client.\\n def new_project(self, project_id, name=None, labels=None) # Creates a :class:`.Project` bound to the current client.\\n def fetch_project(self, project_id) # Fetch an existing project and it's relevant metadata by ID.\\n def list_projects(self, filter_params=None, page_size=None) # List the projects visible to this client.\\nclass _ProjectIterator # An iterator over a list of Project resources.\\n def __init__(self, client, extra_params=None)\\n def get_items_from_response(self, response) # Yield :class:`.Project` items from response.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py\",\n \"text\": \"gcloud/storage/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def __init__(self, project=None, credentials=None, http=None)\\n def connection(self) # Get connection or batch on the client.\\n def connection(self, value) # Set connection on the client.\\n def _push_batch(self, batch) # Push a batch onto our stack.\\n def _pop_batch(self) # Pop a batch from our stack.\\n def current_batch(self) # Currently-active batch.\\n def bucket(self, bucket_name) # Factory constructor for bucket object.\\n def batch(self) # Factory constructor for batch object.\\n def get_bucket(self, bucket_name) # Get a bucket by name.\\n def lookup_bucket(self, bucket_name) # Get a bucket by name, returning None if not found.\\n def create_bucket(self, bucket_name) # Create a new bucket.\\n def list_buckets(self, max_results=None, page_token=None, prefix=None, projection='noAcl', fields=None) # Get all buckets in the project associated to the client.\\nclass _BucketIterator # An iterator listing all buckets.\\n def __init__(self, client, extra_params=None)\\n def get_items_from_response(self, response) # Factory method which yields :class:`.Bucket` items from a response.\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"gcloud/bigquery/client.py::Client.list_datasets","text":"gcloud/bigquery/client.py::Client.list_datasets\n def list_datasets(self, include_all=False, max_results=None,\n page_token=None):\n \"\"\"List datasets for the project associated with this client.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/list\n\n :type include_all: boolean\n :param include_all: True if results include hidden datasets.\n\n :type max_results: int\n :param max_results: maximum number of datasets to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of datasets. If\n not passed, the API will return the first page of\n datasets.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`gcloud.bigquery.dataset.Dataset`, plus a\n \"next page token\" string: if the token is not None,\n indicates that more datasets can be retrieved with another\n call (pass that value as ``page_token``).\n \"\"\"\n params = {}\n\n if include_all:\n params['all'] = True\n\n if max_results is not None:\n params['maxResults'] = max_results\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/datasets' % (self.project,)\n resp = self.connection.api_request(method='GET', path=path,\n query_params=params)\n datasets = [Dataset.from_api_repr(resource, self)\n for resource in resp['datasets']]\n return datasets, resp.get('nextPageToken')","label":0},{"cid":"gcloud/bigquery/client.py::Client.dataset","text":"gcloud/bigquery/client.py::Client.dataset\n def dataset(self, name):\n \"\"\"Construct a dataset bound to this client.\n\n :type name: string\n :param name: Name of the dataset.\n\n :rtype: :class:`gcloud.bigquery.dataset.Dataset`\n :returns: a new ``Dataset`` instance\n \"\"\"\n return Dataset(name, client=self)","label":0},{"cid":"gcloud/bigquery/client.py::Client.load_table_from_storage","text":"gcloud/bigquery/client.py::Client.load_table_from_storage\n def load_table_from_storage(self, name, destination, *source_uris):\n \"\"\"Construct a job for loading data into a table from CloudStorage.\n\n :type name: string\n :param name: Name of the job.\n\n :type destination: :class:`gcloud.bigquery.table.Table`\n :param destination: Table into which data is to be loaded.\n\n :type source_uris: sequence of string\n :param source_uris: URIs of data files to be loaded.\n\n :rtype: :class:`gcloud.bigquery.job.LoadTableFromStorageJob`\n :returns: a new ``LoadTableFromStorageJob`` instance\n \"\"\"\n return LoadTableFromStorageJob(name, destination, source_uris,\n client=self)","label":0},{"cid":"gcloud/bigquery/client.py::Client.copy_table","text":"gcloud/bigquery/client.py::Client.copy_table\n def copy_table(self, name, destination, *sources):\n \"\"\"Construct a job for copying one or more tables into another table.\n\n :type name: string\n :param name: Name of the job.\n\n :type destination: :class:`gcloud.bigquery.table.Table`\n :param destination: Table into which data is to be copied.\n\n :type sources: sequence of :class:`gcloud.bigquery.table.Table`\n :param sources: tables to be copied.\n\n :rtype: :class:`gcloud.bigquery.job.CopyJob`\n :returns: a new ``CopyJob`` instance\n \"\"\"\n return CopyJob(name, destination, sources, client=self)","label":0},{"cid":"gcloud/bigquery/client.py::Client.extract_table_to_storage","text":"gcloud/bigquery/client.py::Client.extract_table_to_storage\n def extract_table_to_storage(self, name, source, *destination_uris):\n \"\"\"Construct a job for extracting a table into Cloud Storage files.\n\n :type name: string\n :param name: Name of the job.\n\n :type source: :class:`gcloud.bigquery.table.Table`\n :param source: table to be extracted.\n\n :type destination_uris: sequence of string\n :param destination_uris: URIs of CloudStorage file(s) into which\n table data is to be extracted.\n\n :rtype: :class:`gcloud.bigquery.job.ExtractTableToStorageJob`\n :returns: a new ``ExtractTableToStorageJob`` instance\n \"\"\"\n return ExtractTableToStorageJob(name, source, destination_uris,\n client=self)","label":0},{"cid":"gcloud/bigquery/client.py::Client.run_async_query","text":"gcloud/bigquery/client.py::Client.run_async_query\n def run_async_query(self, name, query):\n \"\"\"Construct a job for running a SQL query asynchronously.\n\n :type name: string\n :param name: Name of the job.\n\n :type query: string\n :param query: SQL query to be executed\n\n :rtype: :class:`gcloud.bigquery.job.RunAsyncQueryJob`\n :returns: a new ``RunAsyncQueryJob`` instance\n \"\"\"\n return RunAsyncQueryJob(name, query, client=self)","label":0},{"cid":"gcloud/bigquery/client.py::Client.run_sync_query","text":"gcloud/bigquery/client.py::Client.run_sync_query\n def run_sync_query(self, query):\n \"\"\"Construct a job for running a SQL query synchronously.\n\n :type query: string\n :param query: SQL query to be executed\n\n :rtype: :class:`gcloud.bigquery.job.RunSyncQueryJob`\n :returns: a new ``RunSyncQueryJob`` instance\n \"\"\"\n return RunSyncQueryJob(query, client=self)","label":0},{"cid":"gcloud/bigquery/job.py::_ConfigurationProperty.__init__","text":"gcloud/bigquery/job.py::_ConfigurationProperty.__init__\n def __init__(self, name):\n self.name = name\n self._backing_name = '_%s' % (self.name,)","label":0},{"cid":"gcloud/bigquery/job.py::_ConfigurationProperty.__get__","text":"gcloud/bigquery/job.py::_ConfigurationProperty.__get__\n def __get__(self, instance, owner):\n \"\"\"Descriptor protocal: accesstor\"\"\"\n if instance is None:\n return self\n return getattr(instance._configuration, self._backing_name)","label":0},{"cid":"gcloud/bigquery/job.py::_ConfigurationProperty._validate","text":"gcloud/bigquery/job.py::_ConfigurationProperty._validate\n def _validate(self, value):\n \"\"\"Subclasses override to impose validation policy.\"\"\"\n pass","label":0},{"cid":"gcloud/bigquery/job.py::_ConfigurationProperty.__set__","text":"gcloud/bigquery/job.py::_ConfigurationProperty.__set__\n def __set__(self, instance, value):\n \"\"\"Descriptor protocal: mutator\"\"\"\n self._validate(value)\n setattr(instance._configuration, self._backing_name, value)","label":0},{"cid":"gcloud/bigquery/job.py::_ConfigurationProperty.__delete__","text":"gcloud/bigquery/job.py::_ConfigurationProperty.__delete__\n def __delete__(self, instance):\n \"\"\"Descriptor protocal: deleter\"\"\"\n delattr(instance._configuration, self._backing_name)","label":0},{"cid":"gcloud/bigquery/job.py::_TypedProperty.__init__","text":"gcloud/bigquery/job.py::_TypedProperty.__init__\n def __init__(self, name, property_type):\n super(_TypedProperty, self).__init__(name)\n self.property_type = property_type","label":0},{"cid":"gcloud/bigquery/job.py::_TypedProperty._validate","text":"gcloud/bigquery/job.py::_TypedProperty._validate\n def _validate(self, value):\n if not isinstance(value, self.property_type):\n raise ValueError('Required type: %s' % (self.property_type,))","label":0},{"cid":"gcloud/bigquery/job.py::_EnumProperty._validate","text":"gcloud/bigquery/job.py::_EnumProperty._validate\n def _validate(self, value):\n \"\"\"Check that ``value`` is one of the allowed values.\n\n :raises: ValueError if value is not allowed.\n \"\"\"\n if value not in self.ALLOWED:\n raise ValueError('Pass one of: %s' ', '.join(self.ALLOWED))","label":0},{"cid":"gcloud/bigquery/job.py::_BaseJob.__init__","text":"gcloud/bigquery/job.py::_BaseJob.__init__\n def __init__(self, client):\n self._client = client\n self._properties = {}","label":0},{"cid":"gcloud/bigquery/job.py::_BaseJob.project","text":"gcloud/bigquery/job.py::_BaseJob.project\n @property\n def project(self):\n \"\"\"Project bound to the job.\n\n :rtype: string\n :returns: the project (derived from the client).\n \"\"\"\n return self._client.project","label":0},{"cid":"gcloud/bigquery/job.py::_BaseJob._require_client","text":"gcloud/bigquery/job.py::_BaseJob._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`gcloud.bigquery.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: :class:`gcloud.bigquery.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._client\n return client","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.__init__","text":"gcloud/bigquery/job.py::_AsyncJob.__init__\n def __init__(self, name, client):\n super(_AsyncJob, self).__init__(client)\n self.name = name","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.path","text":"gcloud/bigquery/job.py::_AsyncJob.path\n @property\n def path(self):\n \"\"\"URL path for the job's APIs.\n\n :rtype: string\n :returns: the path based on project and job name.\n \"\"\"\n return '/projects/%s/jobs/%s' % (self.project, self.name)","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.etag","text":"gcloud/bigquery/job.py::_AsyncJob.etag\n @property\n def etag(self):\n \"\"\"ETag for the job resource.\n\n :rtype: string, or ``NoneType``\n :returns: the ETag (None until set from the server).\n \"\"\"\n return self._properties.get('etag')","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.job_id","text":"gcloud/bigquery/job.py::_AsyncJob.job_id\n @property\n def job_id(self):\n \"\"\"ID for the job resource.\n\n :rtype: string, or ``NoneType``\n :returns: the ID (None until set from the server).\n \"\"\"\n return self._properties.get('id')","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.self_link","text":"gcloud/bigquery/job.py::_AsyncJob.self_link\n @property\n def self_link(self):\n \"\"\"URL for the job resource.\n\n :rtype: string, or ``NoneType``\n :returns: the URL (None until set from the server).\n \"\"\"\n return self._properties.get('selfLink')","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.user_email","text":"gcloud/bigquery/job.py::_AsyncJob.user_email\n @property\n def user_email(self):\n \"\"\"E-mail address of user who submitted the job.\n\n :rtype: string, or ``NoneType``\n :returns: the URL (None until set from the server).\n \"\"\"\n return self._properties.get('user_email')","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.created","text":"gcloud/bigquery/job.py::_AsyncJob.created\n @property\n def created(self):\n \"\"\"Datetime at which the job was created.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the creation time (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n millis = statistics.get('creationTime')\n if millis is not None:\n return _datetime_from_microseconds(millis * 1000.0)","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.started","text":"gcloud/bigquery/job.py::_AsyncJob.started\n @property\n def started(self):\n \"\"\"Datetime at which the job was started.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the start time (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n millis = statistics.get('startTime')\n if millis is not None:\n return _datetime_from_microseconds(millis * 1000.0)","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.ended","text":"gcloud/bigquery/job.py::_AsyncJob.ended\n @property\n def ended(self):\n \"\"\"Datetime at which the job finished.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the end time (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n millis = statistics.get('endTime')\n if millis is not None:\n return _datetime_from_microseconds(millis * 1000.0)","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.error_result","text":"gcloud/bigquery/job.py::_AsyncJob.error_result\n @property\n def error_result(self):\n \"\"\"Error information about the job as a whole.\n\n :rtype: mapping, or ``NoneType``\n :returns: the error information (None until set from the server).\n \"\"\"\n status = self._properties.get('status')\n if status is not None:\n return status.get('errorResult')","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.errors","text":"gcloud/bigquery/job.py::_AsyncJob.errors\n @property\n def errors(self):\n \"\"\"Information about individual errors generated by the job.\n\n :rtype: list of mappings, or ``NoneType``\n :returns: the error information (None until set from the server).\n \"\"\"\n status = self._properties.get('status')\n if status is not None:\n return status.get('errors')","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.state","text":"gcloud/bigquery/job.py::_AsyncJob.state\n @property\n def state(self):\n \"\"\"Status of the job.\n\n :rtype: string, or ``NoneType``\n :returns: the state (None until set from the server).\n \"\"\"\n status = self._properties.get('status')\n if status is not None:\n return status.get('state')","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob._scrub_local_properties","text":"gcloud/bigquery/job.py::_AsyncJob._scrub_local_properties\n def _scrub_local_properties(self, cleaned):\n \"\"\"Helper: handle subclass properties in cleaned.\"\"\"\n pass","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob._set_properties","text":"gcloud/bigquery/job.py::_AsyncJob._set_properties\n def _set_properties(self, api_response):\n \"\"\"Update properties from resource in body of ``api_response``\n\n :type api_response: httplib2.Response\n :param api_response: response returned from an API call\n \"\"\"\n cleaned = api_response.copy()\n self._scrub_local_properties(cleaned)\n\n statistics = cleaned.get('statistics', {})\n if 'creationTime' in statistics:\n statistics['creationTime'] = float(statistics['creationTime'])\n if 'startTime' in statistics:\n statistics['startTime'] = float(statistics['startTime'])\n if 'endTime' in statistics:\n statistics['endTime'] = float(statistics['endTime'])\n\n self._properties.clear()\n self._properties.update(cleaned)","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.begin","text":"gcloud/bigquery/job.py::_AsyncJob.begin\n def begin(self, client=None):\n \"\"\"API call: begin the job via a POST request\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/insert\n\n :type client: :class:`gcloud.bigquery.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n path = '/projects/%s/jobs' % (self.project,)\n api_response = client.connection.api_request(\n method='POST', path=path, data=self._build_resource())\n self._set_properties(api_response)","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.exists","text":"gcloud/bigquery/job.py::_AsyncJob.exists\n def exists(self, client=None):\n \"\"\"API call: test for the existence of the job via a GET request\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/get\n\n :type client: :class:`gcloud.bigquery.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n\n try:\n client.connection.api_request(method='GET', path=self.path,\n query_params={'fields': 'id'})\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.reload","text":"gcloud/bigquery/job.py::_AsyncJob.reload\n def reload(self, client=None):\n \"\"\"API call: refresh job properties via a GET request\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/get\n\n :type client: :class:`gcloud.bigquery.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n\n api_response = client.connection.api_request(\n method='GET', path=self.path)\n self._set_properties(api_response)","label":0},{"cid":"gcloud/bigquery/job.py::_AsyncJob.cancel","text":"gcloud/bigquery/job.py::_AsyncJob.cancel\n def cancel(self, client=None):\n \"\"\"API call: cancel job via a POST request\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/cancel\n\n :type client: :class:`gcloud.bigquery.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n\n api_response = client.connection.api_request(\n method='POST', path='%s/cancel' % (self.path,))\n self._set_properties(api_response)","label":0},{"cid":"gcloud/bigquery/job.py::LoadTableFromStorageJob.__init__","text":"gcloud/bigquery/job.py::LoadTableFromStorageJob.__init__\n def __init__(self, name, destination, source_uris, client, schema=()):\n super(LoadTableFromStorageJob, self).__init__(name, client)\n self.destination = destination\n self.source_uris = source_uris\n self.schema = schema\n self._configuration = _LoadConfiguration()","label":0},{"cid":"gcloud/bigquery/job.py::LoadTableFromStorageJob.schema","text":"gcloud/bigquery/job.py::LoadTableFromStorageJob.schema\n @property\n def schema(self):\n \"\"\"Table's schema.\n\n :rtype: list of :class:`SchemaField`\n :returns: fields describing the schema\n \"\"\"\n return list(self._schema)","label":0},{"cid":"gcloud/bigquery/job.py::LoadTableFromStorageJob.schema","text":"gcloud/bigquery/job.py::LoadTableFromStorageJob.schema\n @schema.setter\n def schema(self, value):\n \"\"\"Update table's schema\n\n :type value: list of :class:`SchemaField`\n :param value: fields describing the schema\n\n :raises: TypeError if 'value' is not a sequence, or ValueError if\n any item in the sequence is not a SchemaField\n \"\"\"\n if not all(isinstance(field, SchemaField) for field in value):\n raise ValueError('Schema items must be fields')\n self._schema = tuple(value)","label":0},{"cid":"gcloud/bigquery/job.py::LoadTableFromStorageJob.input_file_bytes","text":"gcloud/bigquery/job.py::LoadTableFromStorageJob.input_file_bytes\n @property\n def input_file_bytes(self):\n \"\"\"Count of bytes loaded from source files.\n\n :rtype: integer, or ``NoneType``\n :returns: the count (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n return int(statistics['load']['inputFileBytes'])","label":0},{"cid":"gcloud/bigquery/job.py::LoadTableFromStorageJob.input_files","text":"gcloud/bigquery/job.py::LoadTableFromStorageJob.input_files\n @property\n def input_files(self):\n \"\"\"Count of source files.\n\n :rtype: integer, or ``NoneType``\n :returns: the count (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n return int(statistics['load']['inputFiles'])","label":0},{"cid":"gcloud/bigquery/job.py::LoadTableFromStorageJob.output_bytes","text":"gcloud/bigquery/job.py::LoadTableFromStorageJob.output_bytes\n @property\n def output_bytes(self):\n \"\"\"Count of bytes saved to destination table.\n\n :rtype: integer, or ``NoneType``\n :returns: the count (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n return int(statistics['load']['outputBytes'])","label":0},{"cid":"gcloud/bigquery/job.py::LoadTableFromStorageJob.output_rows","text":"gcloud/bigquery/job.py::LoadTableFromStorageJob.output_rows\n @property\n def output_rows(self):\n \"\"\"Count of rows saved to destination table.\n\n :rtype: integer, or ``NoneType``\n :returns: the count (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n return int(statistics['load']['outputRows'])","label":0},{"cid":"gcloud/bigquery/job.py::LoadTableFromStorageJob._populate_config_resource","text":"gcloud/bigquery/job.py::LoadTableFromStorageJob._populate_config_resource\n def _populate_config_resource(self, configuration):\n \"\"\"Helper for _build_resource: copy config properties to resource\"\"\"\n if self.allow_jagged_rows is not None:\n configuration['allowJaggedRows'] = self.allow_jagged_rows\n if self.allow_quoted_newlines is not None:\n configuration['allowQuotedNewlines'] = self.allow_quoted_newlines\n if self.create_disposition is not None:\n configuration['createDisposition'] = self.create_disposition\n if self.encoding is not None:\n configuration['encoding'] = self.encoding\n if self.field_delimiter is not None:\n configuration['fieldDelimiter'] = self.field_delimiter\n if self.ignore_unknown_values is not None:\n configuration['ignoreUnknownValues'] = self.ignore_unknown_values\n if self.max_bad_records is not None:\n configuration['maxBadRecords'] = self.max_bad_records\n if self.quote_character is not None:\n configuration['quote'] = self.quote_character\n if self.skip_leading_rows is not None:\n configuration['skipLeadingRows'] = self.skip_leading_rows\n if self.source_format is not None:\n configuration['sourceFormat'] = self.source_format\n if self.write_disposition is not None:\n configuration['writeDisposition'] = self.write_disposition","label":0},{"cid":"gcloud/bigquery/job.py::LoadTableFromStorageJob._build_resource","text":"gcloud/bigquery/job.py::LoadTableFromStorageJob._build_resource\n def _build_resource(self):\n \"\"\"Generate a resource for :meth:`begin`.\"\"\"\n resource = {\n 'jobReference': {\n 'projectId': self.project,\n 'jobId': self.name,\n },\n 'configuration': {\n 'load': {\n 'sourceUris': self.source_uris,\n 'destinationTable': {\n 'projectId': self.destination.project,\n 'datasetId': self.destination.dataset_name,\n 'tableId': self.destination.name,\n },\n },\n },\n }\n configuration = resource['configuration']['load']\n self._populate_config_resource(configuration)\n\n if len(self.schema) > 0:\n configuration['schema'] = {\n 'fields': _build_schema_resource(self.schema)}\n\n return resource","label":0},{"cid":"gcloud/bigquery/job.py::LoadTableFromStorageJob._scrub_local_properties","text":"gcloud/bigquery/job.py::LoadTableFromStorageJob._scrub_local_properties\n def _scrub_local_properties(self, cleaned):\n \"\"\"Helper: handle subclass properties in cleaned.\"\"\"\n schema = cleaned.pop('schema', {'fields': ()})\n self.schema = _parse_schema_resource(schema)","label":0},{"cid":"gcloud/bigquery/job.py::CopyJob.__init__","text":"gcloud/bigquery/job.py::CopyJob.__init__\n def __init__(self, name, destination, sources, client):\n super(CopyJob, self).__init__(name, client)\n self.destination = destination\n self.sources = sources\n self._configuration = _CopyConfiguration()","label":0},{"cid":"gcloud/bigtable/client.py::Client.__init__","text":"gcloud/bigtable/client.py::Client.__init__\n def __init__(self, project=None, credentials=None,\n read_only=False, admin=False, user_agent=DEFAULT_USER_AGENT,\n timeout_seconds=DEFAULT_TIMEOUT_SECONDS):\n _ClientProjectMixin.__init__(self, project=project)\n if credentials is None:\n credentials = get_credentials()\n\n if read_only and admin:\n raise ValueError('A read-only client cannot also perform'\n 'administrative actions.')\n\n scopes = []\n if read_only:\n scopes.append(READ_ONLY_SCOPE)\n else:\n scopes.append(DATA_SCOPE)\n\n if admin:\n scopes.append(ADMIN_SCOPE)\n\n self._admin = bool(admin)\n self._credentials = credentials.create_scoped(scopes)\n self.user_agent = user_agent\n self.timeout_seconds = timeout_seconds","label":0},{"cid":"gcloud/bigtable/client.py::Client.credentials","text":"gcloud/bigtable/client.py::Client.credentials\n @property\n def credentials(self):\n \"\"\"Getter for client's credentials.\n\n :rtype:\n :class:`OAuth2Credentials <oauth2client.client.OAuth2Credentials>`\n :returns: The credentials stored on the client.\n \"\"\"\n return self._credentials","label":0},{"cid":"gcloud/client.py::_ClientFactoryMixin.__init__","text":"gcloud/client.py::_ClientFactoryMixin.__init__\n def __init__(self, *args, **kwargs):\n raise NotImplementedError('_ClientFactoryMixin is a virtual class')","label":0},{"cid":"gcloud/client.py::_ClientFactoryMixin.from_service_account_json","text":"gcloud/client.py::_ClientFactoryMixin.from_service_account_json\n @classmethod\n def from_service_account_json(cls, json_credentials_path, *args, **kwargs):\n \"\"\"Factory to retrieve JSON credentials while creating client.\n\n :type json_credentials_path: string\n :param json_credentials_path: The path to a private key file (this file\n was given to you when you created the\n service account). This file must contain\n a JSON object with a private key and\n other credentials information (downloaded\n from the Google APIs console).\n\n :type args: tuple\n :param args: Remaining positional arguments to pass to constructor.\n\n :type kwargs: dictionary\n :param kwargs: Remaining keyword arguments to pass to constructor.\n\n :rtype: :class:`gcloud.pubsub.client.Client`\n :returns: The client created with the retrieved JSON credentials.\n :raises: class:`TypeError` if there is a conflict with the kwargs\n and the credentials created by the factory.\n \"\"\"\n if 'credentials' in kwargs:\n raise TypeError('credentials must not be in keyword arguments')\n credentials = get_for_service_account_json(json_credentials_path)\n kwargs['credentials'] = credentials\n return cls(*args, **kwargs)","label":0},{"cid":"gcloud/client.py::_ClientFactoryMixin.from_service_account_p12","text":"gcloud/client.py::_ClientFactoryMixin.from_service_account_p12\n @classmethod\n def from_service_account_p12(cls, client_email, private_key_path,\n *args, **kwargs):\n \"\"\"Factory to retrieve P12 credentials while creating client.\n\n .. note::\n Unless you have an explicit reason to use a PKCS12 key for your\n service account, we recommend using a JSON key.\n\n :type client_email: string\n :param client_email: The e-mail attached to the service account.\n\n :type private_key_path: string\n :param private_key_path: The path to a private key file (this file was\n given to you when you created the service\n account). This file must be in P12 format.\n\n :type args: tuple\n :param args: Remaining positional arguments to pass to constructor.\n\n :type kwargs: dictionary\n :param kwargs: Remaining keyword arguments to pass to constructor.\n\n :rtype: :class:`gcloud.client.Client`\n :returns: The client created with the retrieved P12 credentials.\n :raises: class:`TypeError` if there is a conflict with the kwargs\n and the credentials created by the factory.\n \"\"\"\n if 'credentials' in kwargs:\n raise TypeError('credentials must not be in keyword arguments')\n credentials = get_for_service_account_p12(client_email,\n private_key_path)\n kwargs['credentials'] = credentials\n return cls(*args, **kwargs)","label":0},{"cid":"gcloud/client.py::Client.__init__","text":"gcloud/client.py::Client.__init__\n def __init__(self, credentials=None, http=None):\n if credentials is None and http is None:\n credentials = get_credentials()\n self.connection = self._connection_class(\n credentials=credentials, http=http)","label":0},{"cid":"gcloud/client.py::_ClientProjectMixin.__init__","text":"gcloud/client.py::_ClientProjectMixin.__init__\n def __init__(self, project=None):\n if project is None:\n project = _get_production_project()\n if project is None:\n raise ValueError('Project was not passed and could not be '\n 'determined from the environment.')\n if not isinstance(project, six.string_types):\n raise ValueError('Project must be a string.')\n self.project = project","label":0},{"cid":"gcloud/client.py::JSONClient.__init__","text":"gcloud/client.py::JSONClient.__init__\n def __init__(self, project=None, credentials=None, http=None):\n _ClientProjectMixin.__init__(self, project=project)\n Client.__init__(self, credentials=credentials, http=http)","label":0},{"cid":"gcloud/datastore/client.py::_get_production_dataset_id","text":"gcloud/datastore/client.py::_get_production_dataset_id\ndef _get_production_dataset_id():\n \"\"\"Gets the production application ID if it can be inferred.\"\"\"\n return os.getenv(DATASET)","label":0},{"cid":"gcloud/datastore/client.py::_get_gcd_dataset_id","text":"gcloud/datastore/client.py::_get_gcd_dataset_id\ndef _get_gcd_dataset_id():\n \"\"\"Gets the GCD application ID if it can be inferred.\"\"\"\n return os.getenv(GCD_DATASET)","label":0},{"cid":"gcloud/datastore/client.py::_determine_default_dataset_id","text":"gcloud/datastore/client.py::_determine_default_dataset_id\ndef _determine_default_dataset_id(dataset_id=None):\n \"\"\"Determine default dataset ID explicitly or implicitly as fall-back.\n\n In implicit case, supports four environments. In order of precedence, the\n implicit environments are:\n\n * GCLOUD_DATASET_ID environment variable\n * DATASTORE_DATASET environment variable (for ``gcd`` testing)\n * Google App Engine application ID\n * Google Compute Engine project ID (from metadata server)\n\n :type dataset_id: string\n :param dataset_id: Optional. The dataset ID to use as default.\n\n :rtype: string or ``NoneType``\n :returns: Default dataset ID if it can be determined.\n \"\"\"\n if dataset_id is None:\n dataset_id = _get_production_dataset_id()\n\n if dataset_id is None:\n dataset_id = _get_gcd_dataset_id()\n\n if dataset_id is None:\n dataset_id = _app_engine_id()\n\n if dataset_id is None:\n dataset_id = _compute_engine_id()\n\n return dataset_id","label":0},{"cid":"gcloud/datastore/client.py::_extended_lookup","text":"gcloud/datastore/client.py::_extended_lookup\ndef _extended_lookup(connection, dataset_id, key_pbs,\n missing=None, deferred=None,\n eventual=False, transaction_id=None):\n \"\"\"Repeat lookup until all keys found (unless stop requested).\n\n Helper function for :meth:`Client.get_multi`.\n\n :type connection: :class:`gcloud.datastore.connection.Connection`\n :param connection: The connection used to connect to datastore.\n\n :type dataset_id: string\n :param dataset_id: The ID of the dataset of which to make the request.\n\n :type key_pbs: list of :class:`gcloud.datastore._datastore_v1_pb2.Key`\n :param key_pbs: The keys to retrieve from the datastore.\n\n :type missing: an empty list or None.\n :param missing: If a list is passed, the key-only entity protobufs\n returned by the backend as \"missing\" will be copied\n into it. Use only as a keyword param.\n\n :type deferred: an empty list or None.\n :param deferred: If a list is passed, the key protobufs returned\n by the backend as \"deferred\" will be copied into it.\n Use only as a keyword param.\n\n :type eventual: boolean\n :param eventual: If False (the default), request ``STRONG`` read\n consistency. If True, request ``EVENTUAL`` read\n consistency.\n\n :type transaction_id: string\n :param transaction_id: If passed, make the request in the scope of\n the given transaction. Incompatible with\n ``eventual==True``.\n\n :rtype: list of :class:`gcloud.datastore._datastore_v1_pb2.Entity`\n :returns: The requested entities.\n :raises: :class:`ValueError` if missing / deferred are not null or\n empty list.\n \"\"\"\n if missing is not None and missing != []:\n raise ValueError('missing must be None or an empty list')\n\n if deferred is not None and deferred != []:\n raise","label":0},{"cid":"gcloud/datastore/client.py::Client.__init__","text":"gcloud/datastore/client.py::Client.__init__\n def __init__(self, dataset_id=None, namespace=None,\n credentials=None, http=None):\n dataset_id = _determine_default_dataset_id(dataset_id)\n if dataset_id is None:\n raise EnvironmentError('Dataset ID could not be inferred.')\n self.dataset_id = dataset_id\n self.namespace = namespace\n self._batch_stack = _LocalStack()\n super(Client, self).__init__(credentials, http)","label":0},{"cid":"gcloud/datastore/client.py::Client._push_batch","text":"gcloud/datastore/client.py::Client._push_batch\n def _push_batch(self, batch):\n \"\"\"Push a batch/transaction onto our stack.\n\n \"Protected\", intended for use by batch / transaction context mgrs.\n\n :type batch: :class:`gcloud.datastore.batch.Batch`, or an object\n implementing its API.\n :param batch: newly-active batch/transaction.\n \"\"\"\n self._batch_stack.push(batch)","label":0},{"cid":"gcloud/datastore/client.py::Client._pop_batch","text":"gcloud/datastore/client.py::Client._pop_batch\n def _pop_batch(self):\n \"\"\"Pop a batch/transaction from our stack.\n\n \"Protected\", intended for use by batch / transaction context mgrs.\n\n :raises: IndexError if the stack is empty.\n :rtype: :class:`gcloud.datastore.batch.Batch`, or an object\n implementing its API.\n :returns: the top-most batch/transaction, after removing it.\n \"\"\"\n return self._batch_stack.pop()","label":0},{"cid":"gcloud/datastore/client.py::Client.current_batch","text":"gcloud/datastore/client.py::Client.current_batch\n @property\n def current_batch(self):\n \"\"\"Currently-active batch.\n\n :rtype: :class:`gcloud.datastore.batch.Batch`, or an object\n implementing its API, or ``NoneType`` (if no batch is active).\n :returns: The batch/transaction at the top of the batch stack.\n \"\"\"\n return self._batch_stack.top","label":0},{"cid":"gcloud/datastore/client.py::Client.current_transaction","text":"gcloud/datastore/client.py::Client.current_transaction\n @property\n def current_transaction(self):\n \"\"\"Currently-active transaction.\n\n :rtype: :class:`gcloud.datastore.transaction.Transaction`, or an object\n implementing its API, or ``NoneType`` (if no transaction is\n active).\n :returns: The transaction at the top of the batch stack.\n \"\"\"\n transaction = self.current_batch\n if isinstance(transaction, Transaction):\n return transaction","label":0},{"cid":"gcloud/datastore/client.py::Client.get","text":"gcloud/datastore/client.py::Client.get\n def get(self, key, missing=None, deferred=None):\n \"\"\"Retrieve an entity from a single key (if it exists).\n\n .. note::\n\n This is just a thin wrapper over :meth:`get_multi`.\n The backend API does not make a distinction between a single key or\n multiple keys in a lookup request.\n\n :type key: :class:`gcloud.datastore.key.Key`\n :param key: The key to be retrieved from the datastore.\n\n :type missing: an empty list or None.\n :param missing: If a list is passed, the key-only entities returned\n by the backend as \"missing\" will be copied into it.\n Use only as a keyword param.\n\n :type deferred: an empty list or None.\n :param deferred: If a list is passed, the keys returned\n by the backend as \"deferred\" will be copied into it.\n Use only as a keyword param.\n\n :rtype: :class:`gcloud.datastore.entity.Entity` or ``NoneType``\n :returns: The requested entity if it exists.\n \"\"\"\n entities = self.get_multi(keys=[key], missing=missing,\n deferred=deferred)\n if entities:\n return entities[0]","label":0},{"cid":"gcloud/datastore/client.py::Client.get_multi","text":"gcloud/datastore/client.py::Client.get_multi\n def get_multi(self, keys, missing=None, deferred=None):\n \"\"\"Retrieve entities, along with their attributes.\n\n :type keys: list of :class:`gcloud.datastore.key.Key`\n :param keys: The keys to be retrieved from the datastore.\n\n :type missing: an empty list or None.\n :param missing: If a list is passed, the key-only entities returned\n by the backend as \"missing\" will be copied into it.\n Use only as a keyword param.\n\n :type deferred: an empty list or None.\n :param deferred: If a list is passed, the keys returned\n by the backend as \"deferred\" will be copied into it.\n Use only as a keyword param.\n\n :rtype: list of :class:`gcloud.datastore.entity.Entity`\n :returns: The requested entities.\n :raises: ValueError if one or more of ``keys`` has a dataset ID which\n does not match our dataset ID.\n \"\"\"\n if not keys:\n return []\n\n ids = list(set([key.dataset_id for key in keys]))\n if ids != [self.dataset_id]:\n raise ValueError('Keys do not match dataset ID')\n\n transaction = self.current_transaction\n\n entity_pbs = _extended_lookup(\n connection=self.connection,\n dataset_id=self.dataset_id,\n key_pbs=[k.to_protobuf() for k in keys],\n missing=missing,\n deferred=deferred,\n transaction_id=transaction and transaction.id,\n )\n\n if missing is not None:\n missing[:] = [\n helpers.entity_from_protobuf(missed_pb)\n for missed_pb in missing]\n\n if deferred is not None:\n deferred[:] = [\n helpers.key_from_protobuf(deferred_pb)\n for deferred_pb in deferred]\n\n return [helpers.entity_from_protobuf(entity_pb)\n for entity_pb in entity","label":0},{"cid":"gcloud/datastore/client.py::Client.put","text":"gcloud/datastore/client.py::Client.put\n def put(self, entity):\n \"\"\"Save an entity in the Cloud Datastore.\n\n .. note::\n\n This is just a thin wrapper over :meth:`put_multi`.\n The backend API does not make a distinction between a single\n entity or multiple entities in a commit request.\n\n :type entity: :class:`gcloud.datastore.entity.Entity`\n :param entity: The entity to be saved to the datastore.\n \"\"\"\n self.put_multi(entities=[entity])","label":0},{"cid":"gcloud/datastore/client.py::Client.put_multi","text":"gcloud/datastore/client.py::Client.put_multi\n def put_multi(self, entities):\n \"\"\"Save entities in the Cloud Datastore.\n\n :type entities: list of :class:`gcloud.datastore.entity.Entity`\n :param entities: The entities to be saved to the datastore.\n\n :raises: ValueError if ``entities`` is a single entity.\n \"\"\"\n if isinstance(entities, Entity):\n raise ValueError(\"Pass a sequence of entities\")\n\n if not entities:\n return\n\n current = self.current_batch\n in_batch = current is not None\n\n if not in_batch:\n current = self.batch()\n\n for entity in entities:\n current.put(entity)\n\n if not in_batch:\n current.commit()","label":0},{"cid":"gcloud/datastore/client.py::Client.delete","text":"gcloud/datastore/client.py::Client.delete\n def delete(self, key):\n \"\"\"Delete the key in the Cloud Datastore.\n\n .. note::\n\n This is just a thin wrapper over :meth:`delete_multi`.\n The backend API does not make a distinction between a single key or\n multiple keys in a commit request.\n\n :type key: :class:`gcloud.datastore.key.Key`\n :param key: The key to be deleted from the datastore.\n \"\"\"\n return self.delete_multi(keys=[key])","label":0},{"cid":"gcloud/datastore/client.py::Client.delete_multi","text":"gcloud/datastore/client.py::Client.delete_multi\n def delete_multi(self, keys):\n \"\"\"Delete keys from the Cloud Datastore.\n\n :type keys: list of :class:`gcloud.datastore.key.Key`\n :param keys: The keys to be deleted from the datastore.\n \"\"\"\n if not keys:\n return\n\n # We allow partial keys to attempt a delete, the backend will fail.\n current = self.current_batch\n in_batch = current is not None\n\n if not in_batch:\n current = self.batch()\n\n for key in keys:\n current.delete(key)\n\n if not in_batch:\n current.commit()","label":0},{"cid":"gcloud/datastore/client.py::Client.allocate_ids","text":"gcloud/datastore/client.py::Client.allocate_ids\n def allocate_ids(self, incomplete_key, num_ids):\n \"\"\"Allocate a list of IDs from a partial key.\n\n :type incomplete_key: A :class:`gcloud.datastore.key.Key`\n :param incomplete_key: Partial key to use as base for allocated IDs.\n\n :type num_ids: integer\n :param num_ids: The number of IDs to allocate.\n\n :rtype: list of :class:`gcloud.datastore.key.Key`\n :returns: The (complete) keys allocated with ``incomplete_key`` as\n root.\n :raises: :class:`ValueError` if ``incomplete_key`` is not a\n partial key.\n \"\"\"\n if not incomplete_key.is_partial:\n raise ValueError(('Key is not partial.', incomplete_key))\n\n incomplete_key_pb = incomplete_key.to_protobuf()\n incomplete_key_pbs = [incomplete_key_pb] * num_ids\n\n conn = self.connection\n allocated_key_pbs = conn.allocate_ids(incomplete_key.dataset_id,\n incomplete_key_pbs)\n allocated_ids = [allocated_key_pb.path_element[-1].id\n for allocated_key_pb in allocated_key_pbs]\n return [incomplete_key.completed_key(allocated_id)\n for allocated_id in allocated_ids]","label":0},{"cid":"gcloud/datastore/client.py::Client.key","text":"gcloud/datastore/client.py::Client.key\n def key(self, *path_args, **kwargs):\n \"\"\"Proxy to :class:`gcloud.datastore.key.Key`.\n\n Passes our ``dataset_id``.\n \"\"\"\n if 'dataset_id' in kwargs:\n raise TypeError('Cannot pass dataset_id')\n kwargs['dataset_id'] = self.dataset_id\n if 'namespace' not in kwargs:\n kwargs['namespace'] = self.namespace\n return Key(*path_args, **kwargs)","label":0},{"cid":"gcloud/datastore/client.py::Client.batch","text":"gcloud/datastore/client.py::Client.batch\n def batch(self):\n \"\"\"Proxy to :class:`gcloud.datastore.batch.Batch`.\n\n Passes our ``dataset_id``.\n \"\"\"\n return Batch(self)","label":0},{"cid":"gcloud/datastore/client.py::Client.transaction","text":"gcloud/datastore/client.py::Client.transaction\n def transaction(self):\n \"\"\"Proxy to :class:`gcloud.datastore.transaction.Transaction`.\n\n Passes our ``dataset_id``.\n \"\"\"\n return Transaction(self)","label":0},{"cid":"gcloud/datastore/client.py::Client.query","text":"gcloud/datastore/client.py::Client.query\n def query(self, **kwargs):\n \"\"\"Proxy to :class:`gcloud.datastore.query.Query`.\n\n Passes our ``dataset_id``.\n \"\"\"\n if 'client' in kwargs:\n raise TypeError('Cannot pass client')\n if 'dataset_id' in kwargs:\n raise TypeError('Cannot pass dataset_id')\n kwargs['dataset_id'] = self.dataset_id\n if 'namespace' not in kwargs:\n kwargs['namespace'] = self.namespace\n return Query(self, **kwargs)","label":0},{"cid":"gcloud/dns/client.py::Client.quotas","text":"gcloud/dns/client.py::Client.quotas\n def quotas(self):\n \"\"\"Return DNS quots for the project associated with this client.\n\n See:\n https://cloud.google.com/dns/api/v1/projects/get\n\n :rtype: mapping\n :returns: keys for the mapping correspond to those of the ``quota``\n sub-mapping of the project resource.\n \"\"\"\n path = '/projects/%s' % (self.project,)\n resp = self.connection.api_request(method='GET', path=path)\n return dict([(key, int(value))\n for key, value in resp['quota'].items()])","label":0},{"cid":"gcloud/dns/client.py::Client.list_zones","text":"gcloud/dns/client.py::Client.list_zones\n def list_zones(self, max_results=None, page_token=None):\n \"\"\"List zones for the project associated with this client.\n\n See:\n https://cloud.google.com/dns/api/v1/managedZones/list\n\n :type max_results: int\n :param max_results: maximum number of zones to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of zones. If\n not passed, the API will return the first page of\n zones.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`gcloud.dns.zone.ManagedZone`, plus a\n \"next page token\" string: if the token is not None,\n indicates that more zones can be retrieved with another\n call (pass that value as ``page_token``).\n \"\"\"\n params = {}\n\n if max_results is not None:\n params['maxResults'] = max_results\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/managedZones' % (self.project,)\n resp = self.connection.api_request(method='GET', path=path,\n query_params=params)\n zones = [ManagedZone.from_api_repr(resource, self)\n for resource in resp['managedZones']]\n return zones, resp.get('nextPageToken')","label":0},{"cid":"gcloud/dns/client.py::Client.zone","text":"gcloud/dns/client.py::Client.zone\n def zone(self, name, dns_name):\n \"\"\"Construct a zone bound to this client.\n\n :type name: string\n :param name: Name of the zone.\n\n :type dns_name: string\n :param dns_name: DNS name of the zone.\n\n :rtype: :class:`gcloud.dns.zone.ManagedZone`\n :returns: a new ``ManagedZone`` instance\n \"\"\"\n return ManagedZone(name, dns_name, client=self)","label":0},{"cid":"gcloud/pubsub/client.py::Client.list_topics","text":"gcloud/pubsub/client.py::Client.list_topics\n def list_topics(self, page_size=None, page_token=None):\n \"\"\"List topics for the project associated with this client.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1beta2/projects/topics/list\n\n :type page_size: int\n :param page_size: maximum number of topics to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of topics. If not\n passed, the API will return the first page of\n topics.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`gcloud.pubsub.topic.Topic`, plus a\n \"next page token\" string: if not None, indicates that\n more topics can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n params = {}\n\n if page_size is not None:\n params['pageSize'] = page_size\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/topics' % (self.project,)\n resp = self.connection.api_request(method='GET', path=path,\n query_params=params)\n topics = [Topic.from_api_repr(resource, self)\n for resource in resp['topics']]\n return topics, resp.get('nextPageToken')","label":0},{"cid":"gcloud/pubsub/client.py::Client.list_subscriptions","text":"gcloud/pubsub/client.py::Client.list_subscriptions\n def list_subscriptions(self, page_size=None, page_token=None,\n topic_name=None):\n \"\"\"List subscriptions for the project associated with this client.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1beta2/projects/topics/list\n\n and (where ``topic_name`` is passed):\n https://cloud.google.com/pubsub/reference/rest/v1beta2/projects/topics/subscriptions/list\n\n :type page_size: int\n :param page_size: maximum number of topics to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of topics. If not\n passed, the API will return the first page of\n topics.\n\n :type topic_name: string\n :param topic_name: limit results to subscriptions bound to the given\n topic.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`gcloud.pubsub.subscription.Subscription`,\n plus a \"next page token\" string: if not None, indicates that\n more topics can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n params = {}\n\n if page_size is not None:\n params['pageSize'] = page_size\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n if topic_name is None:\n path = '/projects/%s/subscriptions' % (self.project,)\n else:\n path = '/projects/%s/topics/%s/subscriptions' % (self.project,\n topic_name)\n\n resp = self.connection.api_request(method='GET', path=path,\n query_params=params)\n topics = {}\n subscriptions = [Subscription.from_api_repr(resource, self,\n ","label":0},{"cid":"gcloud/pubsub/client.py::Client.topic","text":"gcloud/pubsub/client.py::Client.topic\n def topic(self, name, timestamp_messages=False):\n \"\"\"Creates a topic bound to the current client.\n\n :type name: string\n :param name: the name of the topic to be constructed.\n\n :type timestamp_messages: boolean\n :param timestamp_messages: To be passed to ``Topic`` constructor.\n\n :rtype: :class:`gcloud.pubsub.topic.Topic`\n :returns: Topic created with the current client.\n \"\"\"\n return Topic(name, client=self, timestamp_messages=timestamp_messages)","label":0},{"cid":"gcloud/resource_manager/client.py::Client.from_service_account_json","text":"gcloud/resource_manager/client.py::Client.from_service_account_json\n @classmethod\n def from_service_account_json(cls, *args, **kwargs):\n \"\"\"Factory to retrieve JSON credentials while creating client.\n\n The behavior from the parent class is disabled here since the Resource\n Manager API can only use credentials from a user account, not a\n service account.\n\n :raises: :class:`NotImplementedError <exceptions.NotImplementedError>`\n always.\n \"\"\"\n raise NotImplementedError('A service account cannot be used with the '\n 'Resource Manager API. Only user '\n 'credentials can be used.')","label":0},{"cid":"gcloud/resource_manager/client.py::Client.from_service_account_p12","text":"gcloud/resource_manager/client.py::Client.from_service_account_p12\n @classmethod\n def from_service_account_p12(cls, *args, **kwargs):\n \"\"\"Factory to retrieve P12 credentials while creating client.\n\n The behavior from the parent class is disabled here since the Resource\n Manager API can only use credentials from a user account, not a\n service account.\n\n :raises: :class:`NotImplementedError <exceptions.NotImplementedError>`\n always.\n \"\"\"\n raise NotImplementedError('A service account cannot be used with the '\n 'Resource Manager API. Only user '\n 'credentials can be used.')","label":0},{"cid":"gcloud/resource_manager/client.py::Client.new_project","text":"gcloud/resource_manager/client.py::Client.new_project\n def new_project(self, project_id, name=None, labels=None):\n \"\"\"Creates a :class:`.Project` bound to the current client.\n\n Use :meth:`Project.reload() \\\n <gcloud.resource_manager.project.Project.reload>` to retrieve\n project metadata after creating a :class:`.Project` instance.\n\n .. note:\n\n This does not make an API call.\n\n :type project_id: str\n :param project_id: The ID for this project.\n\n :type name: string\n :param name: The display name of the project.\n\n :type labels: dict\n :param labels: A list of labels associated with the project.\n\n :rtype: :class:`.Project`\n :returns: A new instance of a :class:`.Project` **without**\n any metadata loaded.\n \"\"\"\n return Project(project_id=project_id,\n client=self, name=name, labels=labels)","label":0},{"cid":"gcloud/resource_manager/client.py::Client.fetch_project","text":"gcloud/resource_manager/client.py::Client.fetch_project\n def fetch_project(self, project_id):\n \"\"\"Fetch an existing project and it's relevant metadata by ID.\n\n .. note::\n\n If the project does not exist, this will raise a\n :class:`NotFound <gcloud.exceptions.NotFound>` error.\n\n :type project_id: str\n :param project_id: The ID for this project.\n\n :rtype: :class:`.Project`\n :returns: A :class:`.Project` with metadata fetched from the API.\n \"\"\"\n project = self.new_project(project_id)\n project.reload()\n return project","label":0},{"cid":"gcloud/resource_manager/client.py::Client.list_projects","text":"gcloud/resource_manager/client.py::Client.list_projects\n def list_projects(self, filter_params=None, page_size=None):\n \"\"\"List the projects visible to this client.\n\n Example::\n\n >>> from gcloud import resource_manager\n >>> client = resource_manager.Client()\n >>> for project in client.list_projects():\n ... print project.project_id\n\n List all projects with label ``'environment'`` set to ``'prod'``\n (filtering by labels)::\n\n >>> from gcloud import resource_manager\n >>> client = resource_manager.Client()\n >>> env_filter = {'labels.environment': 'prod'}\n >>> for project in client.list_projects(env_filter):\n ... print project.project_id\n\n See:\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/list\n\n Complete filtering example::\n\n >>> project_filter = { # Return projects with...\n ... 'name': 'My Project', # name set to 'My Project'.\n ... 'id': 'my-project-id', # id set to 'my-project-id'.\n ... 'labels.stage': 'prod', # the label 'stage' set to 'prod'\n ... 'labels.color': '*' # a label 'color' set to anything.\n ... }\n >>> client.list_projects(project_filter)\n\n :type filter_params: dict\n :param filter_params: (Optional) A dictionary of filter options where\n each key is a property to filter on, and each\n value is the (case-insensitive) value to check\n (or the glob ``*`` to check for existence of the\n property). See the example above for more\n details.\n\n :type page_size: int\n :param page_size: (Optional) Maximum number of projects to return in a\n single page. If not passed, defaults to a value set\n ","label":0},{"cid":"gcloud/resource_manager/client.py::_ProjectIterator.__init__","text":"gcloud/resource_manager/client.py::_ProjectIterator.__init__\n def __init__(self, client, extra_params=None):\n super(_ProjectIterator, self).__init__(client=client, path='/projects',\n extra_params=extra_params)","label":0},{"cid":"gcloud/resource_manager/client.py::_ProjectIterator.get_items_from_response","text":"gcloud/resource_manager/client.py::_ProjectIterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Yield :class:`.Project` items from response.\n\n :type response: dict\n :param response: The JSON API response for a page of projects.\n \"\"\"\n for resource in response.get('projects', []):\n item = Project.from_api_repr(resource, client=self.client)\n yield item","label":0},{"cid":"gcloud/storage/client.py::Client.__init__","text":"gcloud/storage/client.py::Client.__init__\n def __init__(self, project=None, credentials=None, http=None):\n self._connection = None\n super(Client, self).__init__(project=project, credentials=credentials,\n http=http)\n self._batch_stack = _LocalStack()","label":0},{"cid":"gcloud/storage/client.py::Client.connection","text":"gcloud/storage/client.py::Client.connection\n @property\n def connection(self):\n \"\"\"Get connection or batch on the client.\n\n :rtype: :class:`gcloud.storage.connection.Connection`\n :returns: The connection set on the client, or the batch\n if one is set.\n \"\"\"\n if self.current_batch is not None:\n return self.current_batch\n else:\n return self._connection","label":0},{"cid":"gcloud/storage/client.py::Client.connection","text":"gcloud/storage/client.py::Client.connection\n @connection.setter\n def connection(self, value):\n \"\"\"Set connection on the client.\n\n Intended to be used by constructor (since the base class calls)\n self.connection = connection\n Will raise if the connection is set more than once.\n\n :type value: :class:`gcloud.storage.connection.Connection`\n :param value: The connection set on the client.\n\n :raises: :class:`ValueError` if connection has already been set.\n \"\"\"\n if self._connection is not None:\n raise ValueError('Connection already set on client')\n self._connection = value","label":0},{"cid":"gcloud/storage/client.py::Client._push_batch","text":"gcloud/storage/client.py::Client._push_batch\n def _push_batch(self, batch):\n \"\"\"Push a batch onto our stack.\n\n \"Protected\", intended for use by batch context mgrs.\n\n :type batch: :class:`gcloud.storage.batch.Batch`\n :param batch: newly-active batch\n \"\"\"\n self._batch_stack.push(batch)","label":0},{"cid":"gcloud/storage/client.py::Client._pop_batch","text":"gcloud/storage/client.py::Client._pop_batch\n def _pop_batch(self):\n \"\"\"Pop a batch from our stack.\n\n \"Protected\", intended for use by batch context mgrs.\n\n :raises: IndexError if the stack is empty.\n :rtype: :class:`gcloud.storage.batch.Batch`\n :returns: the top-most batch/transaction, after removing it.\n \"\"\"\n return self._batch_stack.pop()","label":0},{"cid":"gcloud/storage/client.py::Client.current_batch","text":"gcloud/storage/client.py::Client.current_batch\n @property\n def current_batch(self):\n \"\"\"Currently-active batch.\n\n :rtype: :class:`gcloud.storage.batch.Batch` or ``NoneType`` (if\n no batch is active).\n :returns: The batch at the top of the batch stack.\n \"\"\"\n return self._batch_stack.top","label":0},{"cid":"gcloud/storage/client.py::Client.bucket","text":"gcloud/storage/client.py::Client.bucket\n def bucket(self, bucket_name):\n \"\"\"Factory constructor for bucket object.\n\n .. note::\n This will not make an HTTP request; it simply instantiates\n a bucket object owned by this client.\n\n :type bucket_name: string\n :param bucket_name: The name of the bucket to be instantiated.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The bucket object created.\n \"\"\"\n return Bucket(client=self, name=bucket_name)","label":0},{"cid":"gcloud/storage/client.py::Client.batch","text":"gcloud/storage/client.py::Client.batch\n def batch(self):\n \"\"\"Factory constructor for batch object.\n\n .. note::\n This will not make an HTTP request; it simply instantiates\n a batch object owned by this client.\n\n :rtype: :class:`gcloud.storage.batch.Batch`\n :returns: The batch object created.\n \"\"\"\n return Batch(client=self)","label":0},{"cid":"gcloud/storage/client.py::Client.get_bucket","text":"gcloud/storage/client.py::Client.get_bucket\n def get_bucket(self, bucket_name):\n \"\"\"Get a bucket by name.\n\n If the bucket isn't found, this will raise a\n :class:`gcloud.storage.exceptions.NotFound`.\n\n For example::\n\n >>> try:\n >>> bucket = client.get_bucket('my-bucket')\n >>> except gcloud.exceptions.NotFound:\n >>> print 'Sorry, that bucket does not exist!'\n\n This implements \"storage.buckets.get\".\n\n :type bucket_name: string\n :param bucket_name: The name of the bucket to get.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The bucket matching the name provided.\n :raises: :class:`gcloud.exceptions.NotFound`\n \"\"\"\n bucket = Bucket(self, name=bucket_name)\n bucket.reload(client=self)\n return bucket","label":0},{"cid":"gcloud/storage/client.py::Client.lookup_bucket","text":"gcloud/storage/client.py::Client.lookup_bucket\n def lookup_bucket(self, bucket_name):\n \"\"\"Get a bucket by name, returning None if not found.\n\n You can use this if you would rather check for a None value\n than catching an exception::\n\n >>> bucket = client.lookup_bucket('doesnt-exist')\n >>> print bucket\n None\n >>> bucket = client.lookup_bucket('my-bucket')\n >>> print bucket\n <Bucket: my-bucket>\n\n :type bucket_name: string\n :param bucket_name: The name of the bucket to get.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The bucket matching the name provided or None if not found.\n \"\"\"\n try:\n return self.get_bucket(bucket_name)\n except NotFound:\n return None","label":0},{"cid":"gcloud/storage/client.py::Client.create_bucket","text":"gcloud/storage/client.py::Client.create_bucket\n def create_bucket(self, bucket_name):\n \"\"\"Create a new bucket.\n\n For example::\n\n >>> bucket = client.create_bucket('my-bucket')\n >>> print bucket\n <Bucket: my-bucket>\n\n This implements \"storage.buckets.insert\".\n\n If the bucket already exists, will raise\n :class:`gcloud.exceptions.Conflict`.\n\n :type bucket_name: string\n :param bucket_name: The bucket name to create.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The newly created bucket.\n \"\"\"\n bucket = Bucket(self, name=bucket_name)\n bucket.create(client=self)\n return bucket","label":0},{"cid":"gcloud/storage/client.py::Client.list_buckets","text":"gcloud/storage/client.py::Client.list_buckets\n def list_buckets(self, max_results=None, page_token=None, prefix=None,\n projection='noAcl', fields=None):\n \"\"\"Get all buckets in the project associated to the client.\n\n This will not populate the list of blobs available in each\n bucket.\n\n >>> for bucket in client.list_buckets():\n >>> print bucket\n\n This implements \"storage.buckets.list\".\n\n :type max_results: integer or ``NoneType``\n :param max_results: Optional. Maximum number of buckets to return.\n\n :type page_token: string or ``NoneType``\n :param page_token: Optional. Opaque marker for the next \"page\" of\n buckets. If not passed, will return the first page\n of buckets.\n\n :type prefix: string or ``NoneType``\n :param prefix: Optional. Filter results to buckets whose names begin\n with this prefix.\n\n :type projection: string or ``NoneType``\n :param projection: If used, must be 'full' or 'noAcl'. Defaults to\n 'noAcl'. Specifies the set of properties to return.\n\n :type fields: string or ``NoneType``\n :param fields: Selector specifying which fields to include in a\n partial response. Must be a list of fields. For example\n to get a partial response with just the next page token\n and the language of each bucket returned:\n 'items/id,nextPageToken'\n\n :rtype: iterable of :class:`gcloud.storage.bucket.Bucket` objects.\n :returns: All buckets belonging to this project.\n \"\"\"\n extra_params = {'project': self.project}\n\n if max_results is not None:\n extra_params['maxResults'] = max_results\n\n if prefix is not None:\n extra_params['prefix'] = prefix\n\n extra_params['projection'] = projection\n\n ","label":0},{"cid":"gcloud/storage/client.py::_BucketIterator.__init__","text":"gcloud/storage/client.py::_BucketIterator.__init__\n def __init__(self, client, extra_params=None):\n super(_BucketIterator, self).__init__(client=client, path='/b',\n extra_params=extra_params)","label":0},{"cid":"gcloud/storage/client.py::_BucketIterator.get_items_from_response","text":"gcloud/storage/client.py::_BucketIterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Factory method which yields :class:`.Bucket` items from a response.\n\n :type response: dict\n :param response: The JSON API response for a page of buckets.\n \"\"\"\n for item in response.get('items', []):\n name = item.get('name')\n bucket = Bucket(self.client, name)\n bucket._set_properties(item)\n yield bucket","label":0}],"string":"[\n {\n \"cid\": \"gcloud/bigquery/client.py::Client.list_datasets\",\n \"text\": \"gcloud/bigquery/client.py::Client.list_datasets\\n def list_datasets(self, include_all=False, max_results=None,\\n page_token=None):\\n \\\"\\\"\\\"List datasets for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/list\\n\\n :type include_all: boolean\\n :param include_all: True if results include hidden datasets.\\n\\n :type max_results: int\\n :param max_results: maximum number of datasets to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of datasets. If\\n not passed, the API will return the first page of\\n datasets.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`gcloud.bigquery.dataset.Dataset`, plus a\\n \\\"next page token\\\" string: if the token is not None,\\n indicates that more datasets can be retrieved with another\\n call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if include_all:\\n params['all'] = True\\n\\n if max_results is not None:\\n params['maxResults'] = max_results\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/datasets' % (self.project,)\\n resp = self.connection.api_request(method='GET', path=path,\\n query_params=params)\\n datasets = [Dataset.from_api_repr(resource, self)\\n for resource in resp['datasets']]\\n return datasets, resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/client.py::Client.dataset\",\n \"text\": \"gcloud/bigquery/client.py::Client.dataset\\n def dataset(self, name):\\n \\\"\\\"\\\"Construct a dataset bound to this client.\\n\\n :type name: string\\n :param name: Name of the dataset.\\n\\n :rtype: :class:`gcloud.bigquery.dataset.Dataset`\\n :returns: a new ``Dataset`` instance\\n \\\"\\\"\\\"\\n return Dataset(name, client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/client.py::Client.load_table_from_storage\",\n \"text\": \"gcloud/bigquery/client.py::Client.load_table_from_storage\\n def load_table_from_storage(self, name, destination, *source_uris):\\n \\\"\\\"\\\"Construct a job for loading data into a table from CloudStorage.\\n\\n :type name: string\\n :param name: Name of the job.\\n\\n :type destination: :class:`gcloud.bigquery.table.Table`\\n :param destination: Table into which data is to be loaded.\\n\\n :type source_uris: sequence of string\\n :param source_uris: URIs of data files to be loaded.\\n\\n :rtype: :class:`gcloud.bigquery.job.LoadTableFromStorageJob`\\n :returns: a new ``LoadTableFromStorageJob`` instance\\n \\\"\\\"\\\"\\n return LoadTableFromStorageJob(name, destination, source_uris,\\n client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/client.py::Client.copy_table\",\n \"text\": \"gcloud/bigquery/client.py::Client.copy_table\\n def copy_table(self, name, destination, *sources):\\n \\\"\\\"\\\"Construct a job for copying one or more tables into another table.\\n\\n :type name: string\\n :param name: Name of the job.\\n\\n :type destination: :class:`gcloud.bigquery.table.Table`\\n :param destination: Table into which data is to be copied.\\n\\n :type sources: sequence of :class:`gcloud.bigquery.table.Table`\\n :param sources: tables to be copied.\\n\\n :rtype: :class:`gcloud.bigquery.job.CopyJob`\\n :returns: a new ``CopyJob`` instance\\n \\\"\\\"\\\"\\n return CopyJob(name, destination, sources, client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/client.py::Client.extract_table_to_storage\",\n \"text\": \"gcloud/bigquery/client.py::Client.extract_table_to_storage\\n def extract_table_to_storage(self, name, source, *destination_uris):\\n \\\"\\\"\\\"Construct a job for extracting a table into Cloud Storage files.\\n\\n :type name: string\\n :param name: Name of the job.\\n\\n :type source: :class:`gcloud.bigquery.table.Table`\\n :param source: table to be extracted.\\n\\n :type destination_uris: sequence of string\\n :param destination_uris: URIs of CloudStorage file(s) into which\\n table data is to be extracted.\\n\\n :rtype: :class:`gcloud.bigquery.job.ExtractTableToStorageJob`\\n :returns: a new ``ExtractTableToStorageJob`` instance\\n \\\"\\\"\\\"\\n return ExtractTableToStorageJob(name, source, destination_uris,\\n client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/client.py::Client.run_async_query\",\n \"text\": \"gcloud/bigquery/client.py::Client.run_async_query\\n def run_async_query(self, name, query):\\n \\\"\\\"\\\"Construct a job for running a SQL query asynchronously.\\n\\n :type name: string\\n :param name: Name of the job.\\n\\n :type query: string\\n :param query: SQL query to be executed\\n\\n :rtype: :class:`gcloud.bigquery.job.RunAsyncQueryJob`\\n :returns: a new ``RunAsyncQueryJob`` instance\\n \\\"\\\"\\\"\\n return RunAsyncQueryJob(name, query, client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/client.py::Client.run_sync_query\",\n \"text\": \"gcloud/bigquery/client.py::Client.run_sync_query\\n def run_sync_query(self, query):\\n \\\"\\\"\\\"Construct a job for running a SQL query synchronously.\\n\\n :type query: string\\n :param query: SQL query to be executed\\n\\n :rtype: :class:`gcloud.bigquery.job.RunSyncQueryJob`\\n :returns: a new ``RunSyncQueryJob`` instance\\n \\\"\\\"\\\"\\n return RunSyncQueryJob(query, client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_ConfigurationProperty.__init__\",\n \"text\": \"gcloud/bigquery/job.py::_ConfigurationProperty.__init__\\n def __init__(self, name):\\n self.name = name\\n self._backing_name = '_%s' % (self.name,)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_ConfigurationProperty.__get__\",\n \"text\": \"gcloud/bigquery/job.py::_ConfigurationProperty.__get__\\n def __get__(self, instance, owner):\\n \\\"\\\"\\\"Descriptor protocal: accesstor\\\"\\\"\\\"\\n if instance is None:\\n return self\\n return getattr(instance._configuration, self._backing_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_ConfigurationProperty._validate\",\n \"text\": \"gcloud/bigquery/job.py::_ConfigurationProperty._validate\\n def _validate(self, value):\\n \\\"\\\"\\\"Subclasses override to impose validation policy.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_ConfigurationProperty.__set__\",\n \"text\": \"gcloud/bigquery/job.py::_ConfigurationProperty.__set__\\n def __set__(self, instance, value):\\n \\\"\\\"\\\"Descriptor protocal: mutator\\\"\\\"\\\"\\n self._validate(value)\\n setattr(instance._configuration, self._backing_name, value)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_ConfigurationProperty.__delete__\",\n \"text\": \"gcloud/bigquery/job.py::_ConfigurationProperty.__delete__\\n def __delete__(self, instance):\\n \\\"\\\"\\\"Descriptor protocal: deleter\\\"\\\"\\\"\\n delattr(instance._configuration, self._backing_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_TypedProperty.__init__\",\n \"text\": \"gcloud/bigquery/job.py::_TypedProperty.__init__\\n def __init__(self, name, property_type):\\n super(_TypedProperty, self).__init__(name)\\n self.property_type = property_type\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_TypedProperty._validate\",\n \"text\": \"gcloud/bigquery/job.py::_TypedProperty._validate\\n def _validate(self, value):\\n if not isinstance(value, self.property_type):\\n raise ValueError('Required type: %s' % (self.property_type,))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_EnumProperty._validate\",\n \"text\": \"gcloud/bigquery/job.py::_EnumProperty._validate\\n def _validate(self, value):\\n \\\"\\\"\\\"Check that ``value`` is one of the allowed values.\\n\\n :raises: ValueError if value is not allowed.\\n \\\"\\\"\\\"\\n if value not in self.ALLOWED:\\n raise ValueError('Pass one of: %s' ', '.join(self.ALLOWED))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_BaseJob.__init__\",\n \"text\": \"gcloud/bigquery/job.py::_BaseJob.__init__\\n def __init__(self, client):\\n self._client = client\\n self._properties = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_BaseJob.project\",\n \"text\": \"gcloud/bigquery/job.py::_BaseJob.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project bound to the job.\\n\\n :rtype: string\\n :returns: the project (derived from the client).\\n \\\"\\\"\\\"\\n return self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_BaseJob._require_client\",\n \"text\": \"gcloud/bigquery/job.py::_BaseJob._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`gcloud.bigquery.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: :class:`gcloud.bigquery.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.__init__\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.__init__\\n def __init__(self, name, client):\\n super(_AsyncJob, self).__init__(client)\\n self.name = name\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.path\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URL path for the job's APIs.\\n\\n :rtype: string\\n :returns: the path based on project and job name.\\n \\\"\\\"\\\"\\n return '/projects/%s/jobs/%s' % (self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.etag\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.etag\\n @property\\n def etag(self):\\n \\\"\\\"\\\"ETag for the job resource.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the ETag (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('etag')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.job_id\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.job_id\\n @property\\n def job_id(self):\\n \\\"\\\"\\\"ID for the job resource.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the ID (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('id')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.self_link\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.self_link\\n @property\\n def self_link(self):\\n \\\"\\\"\\\"URL for the job resource.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the URL (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('selfLink')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.user_email\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.user_email\\n @property\\n def user_email(self):\\n \\\"\\\"\\\"E-mail address of user who submitted the job.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the URL (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('user_email')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.created\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.created\\n @property\\n def created(self):\\n \\\"\\\"\\\"Datetime at which the job was created.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the creation time (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n millis = statistics.get('creationTime')\\n if millis is not None:\\n return _datetime_from_microseconds(millis * 1000.0)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.started\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.started\\n @property\\n def started(self):\\n \\\"\\\"\\\"Datetime at which the job was started.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the start time (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n millis = statistics.get('startTime')\\n if millis is not None:\\n return _datetime_from_microseconds(millis * 1000.0)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.ended\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.ended\\n @property\\n def ended(self):\\n \\\"\\\"\\\"Datetime at which the job finished.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the end time (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n millis = statistics.get('endTime')\\n if millis is not None:\\n return _datetime_from_microseconds(millis * 1000.0)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.error_result\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.error_result\\n @property\\n def error_result(self):\\n \\\"\\\"\\\"Error information about the job as a whole.\\n\\n :rtype: mapping, or ``NoneType``\\n :returns: the error information (None until set from the server).\\n \\\"\\\"\\\"\\n status = self._properties.get('status')\\n if status is not None:\\n return status.get('errorResult')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.errors\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.errors\\n @property\\n def errors(self):\\n \\\"\\\"\\\"Information about individual errors generated by the job.\\n\\n :rtype: list of mappings, or ``NoneType``\\n :returns: the error information (None until set from the server).\\n \\\"\\\"\\\"\\n status = self._properties.get('status')\\n if status is not None:\\n return status.get('errors')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.state\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.state\\n @property\\n def state(self):\\n \\\"\\\"\\\"Status of the job.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the state (None until set from the server).\\n \\\"\\\"\\\"\\n status = self._properties.get('status')\\n if status is not None:\\n return status.get('state')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob._scrub_local_properties\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob._scrub_local_properties\\n def _scrub_local_properties(self, cleaned):\\n \\\"\\\"\\\"Helper: handle subclass properties in cleaned.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob._set_properties\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob._set_properties\\n def _set_properties(self, api_response):\\n \\\"\\\"\\\"Update properties from resource in body of ``api_response``\\n\\n :type api_response: httplib2.Response\\n :param api_response: response returned from an API call\\n \\\"\\\"\\\"\\n cleaned = api_response.copy()\\n self._scrub_local_properties(cleaned)\\n\\n statistics = cleaned.get('statistics', {})\\n if 'creationTime' in statistics:\\n statistics['creationTime'] = float(statistics['creationTime'])\\n if 'startTime' in statistics:\\n statistics['startTime'] = float(statistics['startTime'])\\n if 'endTime' in statistics:\\n statistics['endTime'] = float(statistics['endTime'])\\n\\n self._properties.clear()\\n self._properties.update(cleaned)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.begin\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.begin\\n def begin(self, client=None):\\n \\\"\\\"\\\"API call: begin the job via a POST request\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/insert\\n\\n :type client: :class:`gcloud.bigquery.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n path = '/projects/%s/jobs' % (self.project,)\\n api_response = client.connection.api_request(\\n method='POST', path=path, data=self._build_resource())\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.exists\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"API call: test for the existence of the job via a GET request\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/get\\n\\n :type client: :class:`gcloud.bigquery.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n try:\\n client.connection.api_request(method='GET', path=self.path,\\n query_params={'fields': 'id'})\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.reload\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"API call: refresh job properties via a GET request\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/get\\n\\n :type client: :class:`gcloud.bigquery.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n api_response = client.connection.api_request(\\n method='GET', path=self.path)\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::_AsyncJob.cancel\",\n \"text\": \"gcloud/bigquery/job.py::_AsyncJob.cancel\\n def cancel(self, client=None):\\n \\\"\\\"\\\"API call: cancel job via a POST request\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/cancel\\n\\n :type client: :class:`gcloud.bigquery.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n api_response = client.connection.api_request(\\n method='POST', path='%s/cancel' % (self.path,))\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.__init__\",\n \"text\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.__init__\\n def __init__(self, name, destination, source_uris, client, schema=()):\\n super(LoadTableFromStorageJob, self).__init__(name, client)\\n self.destination = destination\\n self.source_uris = source_uris\\n self.schema = schema\\n self._configuration = _LoadConfiguration()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.schema\",\n \"text\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.schema\\n @property\\n def schema(self):\\n \\\"\\\"\\\"Table's schema.\\n\\n :rtype: list of :class:`SchemaField`\\n :returns: fields describing the schema\\n \\\"\\\"\\\"\\n return list(self._schema)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.schema\",\n \"text\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.schema\\n @schema.setter\\n def schema(self, value):\\n \\\"\\\"\\\"Update table's schema\\n\\n :type value: list of :class:`SchemaField`\\n :param value: fields describing the schema\\n\\n :raises: TypeError if 'value' is not a sequence, or ValueError if\\n any item in the sequence is not a SchemaField\\n \\\"\\\"\\\"\\n if not all(isinstance(field, SchemaField) for field in value):\\n raise ValueError('Schema items must be fields')\\n self._schema = tuple(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.input_file_bytes\",\n \"text\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.input_file_bytes\\n @property\\n def input_file_bytes(self):\\n \\\"\\\"\\\"Count of bytes loaded from source files.\\n\\n :rtype: integer, or ``NoneType``\\n :returns: the count (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n return int(statistics['load']['inputFileBytes'])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.input_files\",\n \"text\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.input_files\\n @property\\n def input_files(self):\\n \\\"\\\"\\\"Count of source files.\\n\\n :rtype: integer, or ``NoneType``\\n :returns: the count (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n return int(statistics['load']['inputFiles'])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.output_bytes\",\n \"text\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.output_bytes\\n @property\\n def output_bytes(self):\\n \\\"\\\"\\\"Count of bytes saved to destination table.\\n\\n :rtype: integer, or ``NoneType``\\n :returns: the count (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n return int(statistics['load']['outputBytes'])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.output_rows\",\n \"text\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob.output_rows\\n @property\\n def output_rows(self):\\n \\\"\\\"\\\"Count of rows saved to destination table.\\n\\n :rtype: integer, or ``NoneType``\\n :returns: the count (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n return int(statistics['load']['outputRows'])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob._populate_config_resource\",\n \"text\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob._populate_config_resource\\n def _populate_config_resource(self, configuration):\\n \\\"\\\"\\\"Helper for _build_resource: copy config properties to resource\\\"\\\"\\\"\\n if self.allow_jagged_rows is not None:\\n configuration['allowJaggedRows'] = self.allow_jagged_rows\\n if self.allow_quoted_newlines is not None:\\n configuration['allowQuotedNewlines'] = self.allow_quoted_newlines\\n if self.create_disposition is not None:\\n configuration['createDisposition'] = self.create_disposition\\n if self.encoding is not None:\\n configuration['encoding'] = self.encoding\\n if self.field_delimiter is not None:\\n configuration['fieldDelimiter'] = self.field_delimiter\\n if self.ignore_unknown_values is not None:\\n configuration['ignoreUnknownValues'] = self.ignore_unknown_values\\n if self.max_bad_records is not None:\\n configuration['maxBadRecords'] = self.max_bad_records\\n if self.quote_character is not None:\\n configuration['quote'] = self.quote_character\\n if self.skip_leading_rows is not None:\\n configuration['skipLeadingRows'] = self.skip_leading_rows\\n if self.source_format is not None:\\n configuration['sourceFormat'] = self.source_format\\n if self.write_disposition is not None:\\n configuration['writeDisposition'] = self.write_disposition\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob._build_resource\",\n \"text\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob._build_resource\\n def _build_resource(self):\\n \\\"\\\"\\\"Generate a resource for :meth:`begin`.\\\"\\\"\\\"\\n resource = {\\n 'jobReference': {\\n 'projectId': self.project,\\n 'jobId': self.name,\\n },\\n 'configuration': {\\n 'load': {\\n 'sourceUris': self.source_uris,\\n 'destinationTable': {\\n 'projectId': self.destination.project,\\n 'datasetId': self.destination.dataset_name,\\n 'tableId': self.destination.name,\\n },\\n },\\n },\\n }\\n configuration = resource['configuration']['load']\\n self._populate_config_resource(configuration)\\n\\n if len(self.schema) > 0:\\n configuration['schema'] = {\\n 'fields': _build_schema_resource(self.schema)}\\n\\n return resource\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob._scrub_local_properties\",\n \"text\": \"gcloud/bigquery/job.py::LoadTableFromStorageJob._scrub_local_properties\\n def _scrub_local_properties(self, cleaned):\\n \\\"\\\"\\\"Helper: handle subclass properties in cleaned.\\\"\\\"\\\"\\n schema = cleaned.pop('schema', {'fields': ()})\\n self.schema = _parse_schema_resource(schema)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigquery/job.py::CopyJob.__init__\",\n \"text\": \"gcloud/bigquery/job.py::CopyJob.__init__\\n def __init__(self, name, destination, sources, client):\\n super(CopyJob, self).__init__(name, client)\\n self.destination = destination\\n self.sources = sources\\n self._configuration = _CopyConfiguration()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/client.py::Client.__init__\",\n \"text\": \"gcloud/bigtable/client.py::Client.__init__\\n def __init__(self, project=None, credentials=None,\\n read_only=False, admin=False, user_agent=DEFAULT_USER_AGENT,\\n timeout_seconds=DEFAULT_TIMEOUT_SECONDS):\\n _ClientProjectMixin.__init__(self, project=project)\\n if credentials is None:\\n credentials = get_credentials()\\n\\n if read_only and admin:\\n raise ValueError('A read-only client cannot also perform'\\n 'administrative actions.')\\n\\n scopes = []\\n if read_only:\\n scopes.append(READ_ONLY_SCOPE)\\n else:\\n scopes.append(DATA_SCOPE)\\n\\n if admin:\\n scopes.append(ADMIN_SCOPE)\\n\\n self._admin = bool(admin)\\n self._credentials = credentials.create_scoped(scopes)\\n self.user_agent = user_agent\\n self.timeout_seconds = timeout_seconds\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/client.py::Client.credentials\",\n \"text\": \"gcloud/bigtable/client.py::Client.credentials\\n @property\\n def credentials(self):\\n \\\"\\\"\\\"Getter for client's credentials.\\n\\n :rtype:\\n :class:`OAuth2Credentials <oauth2client.client.OAuth2Credentials>`\\n :returns: The credentials stored on the client.\\n \\\"\\\"\\\"\\n return self._credentials\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/client.py::_ClientFactoryMixin.__init__\",\n \"text\": \"gcloud/client.py::_ClientFactoryMixin.__init__\\n def __init__(self, *args, **kwargs):\\n raise NotImplementedError('_ClientFactoryMixin is a virtual class')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/client.py::_ClientFactoryMixin.from_service_account_json\",\n \"text\": \"gcloud/client.py::_ClientFactoryMixin.from_service_account_json\\n @classmethod\\n def from_service_account_json(cls, json_credentials_path, *args, **kwargs):\\n \\\"\\\"\\\"Factory to retrieve JSON credentials while creating client.\\n\\n :type json_credentials_path: string\\n :param json_credentials_path: The path to a private key file (this file\\n was given to you when you created the\\n service account). This file must contain\\n a JSON object with a private key and\\n other credentials information (downloaded\\n from the Google APIs console).\\n\\n :type args: tuple\\n :param args: Remaining positional arguments to pass to constructor.\\n\\n :type kwargs: dictionary\\n :param kwargs: Remaining keyword arguments to pass to constructor.\\n\\n :rtype: :class:`gcloud.pubsub.client.Client`\\n :returns: The client created with the retrieved JSON credentials.\\n :raises: class:`TypeError` if there is a conflict with the kwargs\\n and the credentials created by the factory.\\n \\\"\\\"\\\"\\n if 'credentials' in kwargs:\\n raise TypeError('credentials must not be in keyword arguments')\\n credentials = get_for_service_account_json(json_credentials_path)\\n kwargs['credentials'] = credentials\\n return cls(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/client.py::_ClientFactoryMixin.from_service_account_p12\",\n \"text\": \"gcloud/client.py::_ClientFactoryMixin.from_service_account_p12\\n @classmethod\\n def from_service_account_p12(cls, client_email, private_key_path,\\n *args, **kwargs):\\n \\\"\\\"\\\"Factory to retrieve P12 credentials while creating client.\\n\\n .. note::\\n Unless you have an explicit reason to use a PKCS12 key for your\\n service account, we recommend using a JSON key.\\n\\n :type client_email: string\\n :param client_email: The e-mail attached to the service account.\\n\\n :type private_key_path: string\\n :param private_key_path: The path to a private key file (this file was\\n given to you when you created the service\\n account). This file must be in P12 format.\\n\\n :type args: tuple\\n :param args: Remaining positional arguments to pass to constructor.\\n\\n :type kwargs: dictionary\\n :param kwargs: Remaining keyword arguments to pass to constructor.\\n\\n :rtype: :class:`gcloud.client.Client`\\n :returns: The client created with the retrieved P12 credentials.\\n :raises: class:`TypeError` if there is a conflict with the kwargs\\n and the credentials created by the factory.\\n \\\"\\\"\\\"\\n if 'credentials' in kwargs:\\n raise TypeError('credentials must not be in keyword arguments')\\n credentials = get_for_service_account_p12(client_email,\\n private_key_path)\\n kwargs['credentials'] = credentials\\n return cls(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/client.py::Client.__init__\",\n \"text\": \"gcloud/client.py::Client.__init__\\n def __init__(self, credentials=None, http=None):\\n if credentials is None and http is None:\\n credentials = get_credentials()\\n self.connection = self._connection_class(\\n credentials=credentials, http=http)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/client.py::_ClientProjectMixin.__init__\",\n \"text\": \"gcloud/client.py::_ClientProjectMixin.__init__\\n def __init__(self, project=None):\\n if project is None:\\n project = _get_production_project()\\n if project is None:\\n raise ValueError('Project was not passed and could not be '\\n 'determined from the environment.')\\n if not isinstance(project, six.string_types):\\n raise ValueError('Project must be a string.')\\n self.project = project\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/client.py::JSONClient.__init__\",\n \"text\": \"gcloud/client.py::JSONClient.__init__\\n def __init__(self, project=None, credentials=None, http=None):\\n _ClientProjectMixin.__init__(self, project=project)\\n Client.__init__(self, credentials=credentials, http=http)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::_get_production_dataset_id\",\n \"text\": \"gcloud/datastore/client.py::_get_production_dataset_id\\ndef _get_production_dataset_id():\\n \\\"\\\"\\\"Gets the production application ID if it can be inferred.\\\"\\\"\\\"\\n return os.getenv(DATASET)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::_get_gcd_dataset_id\",\n \"text\": \"gcloud/datastore/client.py::_get_gcd_dataset_id\\ndef _get_gcd_dataset_id():\\n \\\"\\\"\\\"Gets the GCD application ID if it can be inferred.\\\"\\\"\\\"\\n return os.getenv(GCD_DATASET)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::_determine_default_dataset_id\",\n \"text\": \"gcloud/datastore/client.py::_determine_default_dataset_id\\ndef _determine_default_dataset_id(dataset_id=None):\\n \\\"\\\"\\\"Determine default dataset ID explicitly or implicitly as fall-back.\\n\\n In implicit case, supports four environments. In order of precedence, the\\n implicit environments are:\\n\\n * GCLOUD_DATASET_ID environment variable\\n * DATASTORE_DATASET environment variable (for ``gcd`` testing)\\n * Google App Engine application ID\\n * Google Compute Engine project ID (from metadata server)\\n\\n :type dataset_id: string\\n :param dataset_id: Optional. The dataset ID to use as default.\\n\\n :rtype: string or ``NoneType``\\n :returns: Default dataset ID if it can be determined.\\n \\\"\\\"\\\"\\n if dataset_id is None:\\n dataset_id = _get_production_dataset_id()\\n\\n if dataset_id is None:\\n dataset_id = _get_gcd_dataset_id()\\n\\n if dataset_id is None:\\n dataset_id = _app_engine_id()\\n\\n if dataset_id is None:\\n dataset_id = _compute_engine_id()\\n\\n return dataset_id\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::_extended_lookup\",\n \"text\": \"gcloud/datastore/client.py::_extended_lookup\\ndef _extended_lookup(connection, dataset_id, key_pbs,\\n missing=None, deferred=None,\\n eventual=False, transaction_id=None):\\n \\\"\\\"\\\"Repeat lookup until all keys found (unless stop requested).\\n\\n Helper function for :meth:`Client.get_multi`.\\n\\n :type connection: :class:`gcloud.datastore.connection.Connection`\\n :param connection: The connection used to connect to datastore.\\n\\n :type dataset_id: string\\n :param dataset_id: The ID of the dataset of which to make the request.\\n\\n :type key_pbs: list of :class:`gcloud.datastore._datastore_v1_pb2.Key`\\n :param key_pbs: The keys to retrieve from the datastore.\\n\\n :type missing: an empty list or None.\\n :param missing: If a list is passed, the key-only entity protobufs\\n returned by the backend as \\\"missing\\\" will be copied\\n into it. Use only as a keyword param.\\n\\n :type deferred: an empty list or None.\\n :param deferred: If a list is passed, the key protobufs returned\\n by the backend as \\\"deferred\\\" will be copied into it.\\n Use only as a keyword param.\\n\\n :type eventual: boolean\\n :param eventual: If False (the default), request ``STRONG`` read\\n consistency. If True, request ``EVENTUAL`` read\\n consistency.\\n\\n :type transaction_id: string\\n :param transaction_id: If passed, make the request in the scope of\\n the given transaction. Incompatible with\\n ``eventual==True``.\\n\\n :rtype: list of :class:`gcloud.datastore._datastore_v1_pb2.Entity`\\n :returns: The requested entities.\\n :raises: :class:`ValueError` if missing / deferred are not null or\\n empty list.\\n \\\"\\\"\\\"\\n if missing is not None and missing != []:\\n raise ValueError('missing must be None or an empty list')\\n\\n if deferred is not None and deferred != []:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.__init__\",\n \"text\": \"gcloud/datastore/client.py::Client.__init__\\n def __init__(self, dataset_id=None, namespace=None,\\n credentials=None, http=None):\\n dataset_id = _determine_default_dataset_id(dataset_id)\\n if dataset_id is None:\\n raise EnvironmentError('Dataset ID could not be inferred.')\\n self.dataset_id = dataset_id\\n self.namespace = namespace\\n self._batch_stack = _LocalStack()\\n super(Client, self).__init__(credentials, http)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client._push_batch\",\n \"text\": \"gcloud/datastore/client.py::Client._push_batch\\n def _push_batch(self, batch):\\n \\\"\\\"\\\"Push a batch/transaction onto our stack.\\n\\n \\\"Protected\\\", intended for use by batch / transaction context mgrs.\\n\\n :type batch: :class:`gcloud.datastore.batch.Batch`, or an object\\n implementing its API.\\n :param batch: newly-active batch/transaction.\\n \\\"\\\"\\\"\\n self._batch_stack.push(batch)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client._pop_batch\",\n \"text\": \"gcloud/datastore/client.py::Client._pop_batch\\n def _pop_batch(self):\\n \\\"\\\"\\\"Pop a batch/transaction from our stack.\\n\\n \\\"Protected\\\", intended for use by batch / transaction context mgrs.\\n\\n :raises: IndexError if the stack is empty.\\n :rtype: :class:`gcloud.datastore.batch.Batch`, or an object\\n implementing its API.\\n :returns: the top-most batch/transaction, after removing it.\\n \\\"\\\"\\\"\\n return self._batch_stack.pop()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.current_batch\",\n \"text\": \"gcloud/datastore/client.py::Client.current_batch\\n @property\\n def current_batch(self):\\n \\\"\\\"\\\"Currently-active batch.\\n\\n :rtype: :class:`gcloud.datastore.batch.Batch`, or an object\\n implementing its API, or ``NoneType`` (if no batch is active).\\n :returns: The batch/transaction at the top of the batch stack.\\n \\\"\\\"\\\"\\n return self._batch_stack.top\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.current_transaction\",\n \"text\": \"gcloud/datastore/client.py::Client.current_transaction\\n @property\\n def current_transaction(self):\\n \\\"\\\"\\\"Currently-active transaction.\\n\\n :rtype: :class:`gcloud.datastore.transaction.Transaction`, or an object\\n implementing its API, or ``NoneType`` (if no transaction is\\n active).\\n :returns: The transaction at the top of the batch stack.\\n \\\"\\\"\\\"\\n transaction = self.current_batch\\n if isinstance(transaction, Transaction):\\n return transaction\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.get\",\n \"text\": \"gcloud/datastore/client.py::Client.get\\n def get(self, key, missing=None, deferred=None):\\n \\\"\\\"\\\"Retrieve an entity from a single key (if it exists).\\n\\n .. note::\\n\\n This is just a thin wrapper over :meth:`get_multi`.\\n The backend API does not make a distinction between a single key or\\n multiple keys in a lookup request.\\n\\n :type key: :class:`gcloud.datastore.key.Key`\\n :param key: The key to be retrieved from the datastore.\\n\\n :type missing: an empty list or None.\\n :param missing: If a list is passed, the key-only entities returned\\n by the backend as \\\"missing\\\" will be copied into it.\\n Use only as a keyword param.\\n\\n :type deferred: an empty list or None.\\n :param deferred: If a list is passed, the keys returned\\n by the backend as \\\"deferred\\\" will be copied into it.\\n Use only as a keyword param.\\n\\n :rtype: :class:`gcloud.datastore.entity.Entity` or ``NoneType``\\n :returns: The requested entity if it exists.\\n \\\"\\\"\\\"\\n entities = self.get_multi(keys=[key], missing=missing,\\n deferred=deferred)\\n if entities:\\n return entities[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.get_multi\",\n \"text\": \"gcloud/datastore/client.py::Client.get_multi\\n def get_multi(self, keys, missing=None, deferred=None):\\n \\\"\\\"\\\"Retrieve entities, along with their attributes.\\n\\n :type keys: list of :class:`gcloud.datastore.key.Key`\\n :param keys: The keys to be retrieved from the datastore.\\n\\n :type missing: an empty list or None.\\n :param missing: If a list is passed, the key-only entities returned\\n by the backend as \\\"missing\\\" will be copied into it.\\n Use only as a keyword param.\\n\\n :type deferred: an empty list or None.\\n :param deferred: If a list is passed, the keys returned\\n by the backend as \\\"deferred\\\" will be copied into it.\\n Use only as a keyword param.\\n\\n :rtype: list of :class:`gcloud.datastore.entity.Entity`\\n :returns: The requested entities.\\n :raises: ValueError if one or more of ``keys`` has a dataset ID which\\n does not match our dataset ID.\\n \\\"\\\"\\\"\\n if not keys:\\n return []\\n\\n ids = list(set([key.dataset_id for key in keys]))\\n if ids != [self.dataset_id]:\\n raise ValueError('Keys do not match dataset ID')\\n\\n transaction = self.current_transaction\\n\\n entity_pbs = _extended_lookup(\\n connection=self.connection,\\n dataset_id=self.dataset_id,\\n key_pbs=[k.to_protobuf() for k in keys],\\n missing=missing,\\n deferred=deferred,\\n transaction_id=transaction and transaction.id,\\n )\\n\\n if missing is not None:\\n missing[:] = [\\n helpers.entity_from_protobuf(missed_pb)\\n for missed_pb in missing]\\n\\n if deferred is not None:\\n deferred[:] = [\\n helpers.key_from_protobuf(deferred_pb)\\n for deferred_pb in deferred]\\n\\n return [helpers.entity_from_protobuf(entity_pb)\\n for entity_pb in entity\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.put\",\n \"text\": \"gcloud/datastore/client.py::Client.put\\n def put(self, entity):\\n \\\"\\\"\\\"Save an entity in the Cloud Datastore.\\n\\n .. note::\\n\\n This is just a thin wrapper over :meth:`put_multi`.\\n The backend API does not make a distinction between a single\\n entity or multiple entities in a commit request.\\n\\n :type entity: :class:`gcloud.datastore.entity.Entity`\\n :param entity: The entity to be saved to the datastore.\\n \\\"\\\"\\\"\\n self.put_multi(entities=[entity])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.put_multi\",\n \"text\": \"gcloud/datastore/client.py::Client.put_multi\\n def put_multi(self, entities):\\n \\\"\\\"\\\"Save entities in the Cloud Datastore.\\n\\n :type entities: list of :class:`gcloud.datastore.entity.Entity`\\n :param entities: The entities to be saved to the datastore.\\n\\n :raises: ValueError if ``entities`` is a single entity.\\n \\\"\\\"\\\"\\n if isinstance(entities, Entity):\\n raise ValueError(\\\"Pass a sequence of entities\\\")\\n\\n if not entities:\\n return\\n\\n current = self.current_batch\\n in_batch = current is not None\\n\\n if not in_batch:\\n current = self.batch()\\n\\n for entity in entities:\\n current.put(entity)\\n\\n if not in_batch:\\n current.commit()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.delete\",\n \"text\": \"gcloud/datastore/client.py::Client.delete\\n def delete(self, key):\\n \\\"\\\"\\\"Delete the key in the Cloud Datastore.\\n\\n .. note::\\n\\n This is just a thin wrapper over :meth:`delete_multi`.\\n The backend API does not make a distinction between a single key or\\n multiple keys in a commit request.\\n\\n :type key: :class:`gcloud.datastore.key.Key`\\n :param key: The key to be deleted from the datastore.\\n \\\"\\\"\\\"\\n return self.delete_multi(keys=[key])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.delete_multi\",\n \"text\": \"gcloud/datastore/client.py::Client.delete_multi\\n def delete_multi(self, keys):\\n \\\"\\\"\\\"Delete keys from the Cloud Datastore.\\n\\n :type keys: list of :class:`gcloud.datastore.key.Key`\\n :param keys: The keys to be deleted from the datastore.\\n \\\"\\\"\\\"\\n if not keys:\\n return\\n\\n # We allow partial keys to attempt a delete, the backend will fail.\\n current = self.current_batch\\n in_batch = current is not None\\n\\n if not in_batch:\\n current = self.batch()\\n\\n for key in keys:\\n current.delete(key)\\n\\n if not in_batch:\\n current.commit()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.allocate_ids\",\n \"text\": \"gcloud/datastore/client.py::Client.allocate_ids\\n def allocate_ids(self, incomplete_key, num_ids):\\n \\\"\\\"\\\"Allocate a list of IDs from a partial key.\\n\\n :type incomplete_key: A :class:`gcloud.datastore.key.Key`\\n :param incomplete_key: Partial key to use as base for allocated IDs.\\n\\n :type num_ids: integer\\n :param num_ids: The number of IDs to allocate.\\n\\n :rtype: list of :class:`gcloud.datastore.key.Key`\\n :returns: The (complete) keys allocated with ``incomplete_key`` as\\n root.\\n :raises: :class:`ValueError` if ``incomplete_key`` is not a\\n partial key.\\n \\\"\\\"\\\"\\n if not incomplete_key.is_partial:\\n raise ValueError(('Key is not partial.', incomplete_key))\\n\\n incomplete_key_pb = incomplete_key.to_protobuf()\\n incomplete_key_pbs = [incomplete_key_pb] * num_ids\\n\\n conn = self.connection\\n allocated_key_pbs = conn.allocate_ids(incomplete_key.dataset_id,\\n incomplete_key_pbs)\\n allocated_ids = [allocated_key_pb.path_element[-1].id\\n for allocated_key_pb in allocated_key_pbs]\\n return [incomplete_key.completed_key(allocated_id)\\n for allocated_id in allocated_ids]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.key\",\n \"text\": \"gcloud/datastore/client.py::Client.key\\n def key(self, *path_args, **kwargs):\\n \\\"\\\"\\\"Proxy to :class:`gcloud.datastore.key.Key`.\\n\\n Passes our ``dataset_id``.\\n \\\"\\\"\\\"\\n if 'dataset_id' in kwargs:\\n raise TypeError('Cannot pass dataset_id')\\n kwargs['dataset_id'] = self.dataset_id\\n if 'namespace' not in kwargs:\\n kwargs['namespace'] = self.namespace\\n return Key(*path_args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.batch\",\n \"text\": \"gcloud/datastore/client.py::Client.batch\\n def batch(self):\\n \\\"\\\"\\\"Proxy to :class:`gcloud.datastore.batch.Batch`.\\n\\n Passes our ``dataset_id``.\\n \\\"\\\"\\\"\\n return Batch(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.transaction\",\n \"text\": \"gcloud/datastore/client.py::Client.transaction\\n def transaction(self):\\n \\\"\\\"\\\"Proxy to :class:`gcloud.datastore.transaction.Transaction`.\\n\\n Passes our ``dataset_id``.\\n \\\"\\\"\\\"\\n return Transaction(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/client.py::Client.query\",\n \"text\": \"gcloud/datastore/client.py::Client.query\\n def query(self, **kwargs):\\n \\\"\\\"\\\"Proxy to :class:`gcloud.datastore.query.Query`.\\n\\n Passes our ``dataset_id``.\\n \\\"\\\"\\\"\\n if 'client' in kwargs:\\n raise TypeError('Cannot pass client')\\n if 'dataset_id' in kwargs:\\n raise TypeError('Cannot pass dataset_id')\\n kwargs['dataset_id'] = self.dataset_id\\n if 'namespace' not in kwargs:\\n kwargs['namespace'] = self.namespace\\n return Query(self, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/dns/client.py::Client.quotas\",\n \"text\": \"gcloud/dns/client.py::Client.quotas\\n def quotas(self):\\n \\\"\\\"\\\"Return DNS quots for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/dns/api/v1/projects/get\\n\\n :rtype: mapping\\n :returns: keys for the mapping correspond to those of the ``quota``\\n sub-mapping of the project resource.\\n \\\"\\\"\\\"\\n path = '/projects/%s' % (self.project,)\\n resp = self.connection.api_request(method='GET', path=path)\\n return dict([(key, int(value))\\n for key, value in resp['quota'].items()])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/dns/client.py::Client.list_zones\",\n \"text\": \"gcloud/dns/client.py::Client.list_zones\\n def list_zones(self, max_results=None, page_token=None):\\n \\\"\\\"\\\"List zones for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/dns/api/v1/managedZones/list\\n\\n :type max_results: int\\n :param max_results: maximum number of zones to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of zones. If\\n not passed, the API will return the first page of\\n zones.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`gcloud.dns.zone.ManagedZone`, plus a\\n \\\"next page token\\\" string: if the token is not None,\\n indicates that more zones can be retrieved with another\\n call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if max_results is not None:\\n params['maxResults'] = max_results\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/managedZones' % (self.project,)\\n resp = self.connection.api_request(method='GET', path=path,\\n query_params=params)\\n zones = [ManagedZone.from_api_repr(resource, self)\\n for resource in resp['managedZones']]\\n return zones, resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/dns/client.py::Client.zone\",\n \"text\": \"gcloud/dns/client.py::Client.zone\\n def zone(self, name, dns_name):\\n \\\"\\\"\\\"Construct a zone bound to this client.\\n\\n :type name: string\\n :param name: Name of the zone.\\n\\n :type dns_name: string\\n :param dns_name: DNS name of the zone.\\n\\n :rtype: :class:`gcloud.dns.zone.ManagedZone`\\n :returns: a new ``ManagedZone`` instance\\n \\\"\\\"\\\"\\n return ManagedZone(name, dns_name, client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/pubsub/client.py::Client.list_topics\",\n \"text\": \"gcloud/pubsub/client.py::Client.list_topics\\n def list_topics(self, page_size=None, page_token=None):\\n \\\"\\\"\\\"List topics for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1beta2/projects/topics/list\\n\\n :type page_size: int\\n :param page_size: maximum number of topics to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of topics. If not\\n passed, the API will return the first page of\\n topics.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`gcloud.pubsub.topic.Topic`, plus a\\n \\\"next page token\\\" string: if not None, indicates that\\n more topics can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if page_size is not None:\\n params['pageSize'] = page_size\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/topics' % (self.project,)\\n resp = self.connection.api_request(method='GET', path=path,\\n query_params=params)\\n topics = [Topic.from_api_repr(resource, self)\\n for resource in resp['topics']]\\n return topics, resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/pubsub/client.py::Client.list_subscriptions\",\n \"text\": \"gcloud/pubsub/client.py::Client.list_subscriptions\\n def list_subscriptions(self, page_size=None, page_token=None,\\n topic_name=None):\\n \\\"\\\"\\\"List subscriptions for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1beta2/projects/topics/list\\n\\n and (where ``topic_name`` is passed):\\n https://cloud.google.com/pubsub/reference/rest/v1beta2/projects/topics/subscriptions/list\\n\\n :type page_size: int\\n :param page_size: maximum number of topics to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of topics. If not\\n passed, the API will return the first page of\\n topics.\\n\\n :type topic_name: string\\n :param topic_name: limit results to subscriptions bound to the given\\n topic.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`gcloud.pubsub.subscription.Subscription`,\\n plus a \\\"next page token\\\" string: if not None, indicates that\\n more topics can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if page_size is not None:\\n params['pageSize'] = page_size\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n if topic_name is None:\\n path = '/projects/%s/subscriptions' % (self.project,)\\n else:\\n path = '/projects/%s/topics/%s/subscriptions' % (self.project,\\n topic_name)\\n\\n resp = self.connection.api_request(method='GET', path=path,\\n query_params=params)\\n topics = {}\\n subscriptions = [Subscription.from_api_repr(resource, self,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/pubsub/client.py::Client.topic\",\n \"text\": \"gcloud/pubsub/client.py::Client.topic\\n def topic(self, name, timestamp_messages=False):\\n \\\"\\\"\\\"Creates a topic bound to the current client.\\n\\n :type name: string\\n :param name: the name of the topic to be constructed.\\n\\n :type timestamp_messages: boolean\\n :param timestamp_messages: To be passed to ``Topic`` constructor.\\n\\n :rtype: :class:`gcloud.pubsub.topic.Topic`\\n :returns: Topic created with the current client.\\n \\\"\\\"\\\"\\n return Topic(name, client=self, timestamp_messages=timestamp_messages)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/resource_manager/client.py::Client.from_service_account_json\",\n \"text\": \"gcloud/resource_manager/client.py::Client.from_service_account_json\\n @classmethod\\n def from_service_account_json(cls, *args, **kwargs):\\n \\\"\\\"\\\"Factory to retrieve JSON credentials while creating client.\\n\\n The behavior from the parent class is disabled here since the Resource\\n Manager API can only use credentials from a user account, not a\\n service account.\\n\\n :raises: :class:`NotImplementedError <exceptions.NotImplementedError>`\\n always.\\n \\\"\\\"\\\"\\n raise NotImplementedError('A service account cannot be used with the '\\n 'Resource Manager API. Only user '\\n 'credentials can be used.')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/resource_manager/client.py::Client.from_service_account_p12\",\n \"text\": \"gcloud/resource_manager/client.py::Client.from_service_account_p12\\n @classmethod\\n def from_service_account_p12(cls, *args, **kwargs):\\n \\\"\\\"\\\"Factory to retrieve P12 credentials while creating client.\\n\\n The behavior from the parent class is disabled here since the Resource\\n Manager API can only use credentials from a user account, not a\\n service account.\\n\\n :raises: :class:`NotImplementedError <exceptions.NotImplementedError>`\\n always.\\n \\\"\\\"\\\"\\n raise NotImplementedError('A service account cannot be used with the '\\n 'Resource Manager API. Only user '\\n 'credentials can be used.')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/resource_manager/client.py::Client.new_project\",\n \"text\": \"gcloud/resource_manager/client.py::Client.new_project\\n def new_project(self, project_id, name=None, labels=None):\\n \\\"\\\"\\\"Creates a :class:`.Project` bound to the current client.\\n\\n Use :meth:`Project.reload() \\\\\\n <gcloud.resource_manager.project.Project.reload>` to retrieve\\n project metadata after creating a :class:`.Project` instance.\\n\\n .. note:\\n\\n This does not make an API call.\\n\\n :type project_id: str\\n :param project_id: The ID for this project.\\n\\n :type name: string\\n :param name: The display name of the project.\\n\\n :type labels: dict\\n :param labels: A list of labels associated with the project.\\n\\n :rtype: :class:`.Project`\\n :returns: A new instance of a :class:`.Project` **without**\\n any metadata loaded.\\n \\\"\\\"\\\"\\n return Project(project_id=project_id,\\n client=self, name=name, labels=labels)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/resource_manager/client.py::Client.fetch_project\",\n \"text\": \"gcloud/resource_manager/client.py::Client.fetch_project\\n def fetch_project(self, project_id):\\n \\\"\\\"\\\"Fetch an existing project and it's relevant metadata by ID.\\n\\n .. note::\\n\\n If the project does not exist, this will raise a\\n :class:`NotFound <gcloud.exceptions.NotFound>` error.\\n\\n :type project_id: str\\n :param project_id: The ID for this project.\\n\\n :rtype: :class:`.Project`\\n :returns: A :class:`.Project` with metadata fetched from the API.\\n \\\"\\\"\\\"\\n project = self.new_project(project_id)\\n project.reload()\\n return project\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/resource_manager/client.py::Client.list_projects\",\n \"text\": \"gcloud/resource_manager/client.py::Client.list_projects\\n def list_projects(self, filter_params=None, page_size=None):\\n \\\"\\\"\\\"List the projects visible to this client.\\n\\n Example::\\n\\n >>> from gcloud import resource_manager\\n >>> client = resource_manager.Client()\\n >>> for project in client.list_projects():\\n ... print project.project_id\\n\\n List all projects with label ``'environment'`` set to ``'prod'``\\n (filtering by labels)::\\n\\n >>> from gcloud import resource_manager\\n >>> client = resource_manager.Client()\\n >>> env_filter = {'labels.environment': 'prod'}\\n >>> for project in client.list_projects(env_filter):\\n ... print project.project_id\\n\\n See:\\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/list\\n\\n Complete filtering example::\\n\\n >>> project_filter = { # Return projects with...\\n ... 'name': 'My Project', # name set to 'My Project'.\\n ... 'id': 'my-project-id', # id set to 'my-project-id'.\\n ... 'labels.stage': 'prod', # the label 'stage' set to 'prod'\\n ... 'labels.color': '*' # a label 'color' set to anything.\\n ... }\\n >>> client.list_projects(project_filter)\\n\\n :type filter_params: dict\\n :param filter_params: (Optional) A dictionary of filter options where\\n each key is a property to filter on, and each\\n value is the (case-insensitive) value to check\\n (or the glob ``*`` to check for existence of the\\n property). See the example above for more\\n details.\\n\\n :type page_size: int\\n :param page_size: (Optional) Maximum number of projects to return in a\\n single page. If not passed, defaults to a value set\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/resource_manager/client.py::_ProjectIterator.__init__\",\n \"text\": \"gcloud/resource_manager/client.py::_ProjectIterator.__init__\\n def __init__(self, client, extra_params=None):\\n super(_ProjectIterator, self).__init__(client=client, path='/projects',\\n extra_params=extra_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/resource_manager/client.py::_ProjectIterator.get_items_from_response\",\n \"text\": \"gcloud/resource_manager/client.py::_ProjectIterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Yield :class:`.Project` items from response.\\n\\n :type response: dict\\n :param response: The JSON API response for a page of projects.\\n \\\"\\\"\\\"\\n for resource in response.get('projects', []):\\n item = Project.from_api_repr(resource, client=self.client)\\n yield item\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client.__init__\",\n \"text\": \"gcloud/storage/client.py::Client.__init__\\n def __init__(self, project=None, credentials=None, http=None):\\n self._connection = None\\n super(Client, self).__init__(project=project, credentials=credentials,\\n http=http)\\n self._batch_stack = _LocalStack()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client.connection\",\n \"text\": \"gcloud/storage/client.py::Client.connection\\n @property\\n def connection(self):\\n \\\"\\\"\\\"Get connection or batch on the client.\\n\\n :rtype: :class:`gcloud.storage.connection.Connection`\\n :returns: The connection set on the client, or the batch\\n if one is set.\\n \\\"\\\"\\\"\\n if self.current_batch is not None:\\n return self.current_batch\\n else:\\n return self._connection\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client.connection\",\n \"text\": \"gcloud/storage/client.py::Client.connection\\n @connection.setter\\n def connection(self, value):\\n \\\"\\\"\\\"Set connection on the client.\\n\\n Intended to be used by constructor (since the base class calls)\\n self.connection = connection\\n Will raise if the connection is set more than once.\\n\\n :type value: :class:`gcloud.storage.connection.Connection`\\n :param value: The connection set on the client.\\n\\n :raises: :class:`ValueError` if connection has already been set.\\n \\\"\\\"\\\"\\n if self._connection is not None:\\n raise ValueError('Connection already set on client')\\n self._connection = value\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client._push_batch\",\n \"text\": \"gcloud/storage/client.py::Client._push_batch\\n def _push_batch(self, batch):\\n \\\"\\\"\\\"Push a batch onto our stack.\\n\\n \\\"Protected\\\", intended for use by batch context mgrs.\\n\\n :type batch: :class:`gcloud.storage.batch.Batch`\\n :param batch: newly-active batch\\n \\\"\\\"\\\"\\n self._batch_stack.push(batch)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client._pop_batch\",\n \"text\": \"gcloud/storage/client.py::Client._pop_batch\\n def _pop_batch(self):\\n \\\"\\\"\\\"Pop a batch from our stack.\\n\\n \\\"Protected\\\", intended for use by batch context mgrs.\\n\\n :raises: IndexError if the stack is empty.\\n :rtype: :class:`gcloud.storage.batch.Batch`\\n :returns: the top-most batch/transaction, after removing it.\\n \\\"\\\"\\\"\\n return self._batch_stack.pop()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client.current_batch\",\n \"text\": \"gcloud/storage/client.py::Client.current_batch\\n @property\\n def current_batch(self):\\n \\\"\\\"\\\"Currently-active batch.\\n\\n :rtype: :class:`gcloud.storage.batch.Batch` or ``NoneType`` (if\\n no batch is active).\\n :returns: The batch at the top of the batch stack.\\n \\\"\\\"\\\"\\n return self._batch_stack.top\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client.bucket\",\n \"text\": \"gcloud/storage/client.py::Client.bucket\\n def bucket(self, bucket_name):\\n \\\"\\\"\\\"Factory constructor for bucket object.\\n\\n .. note::\\n This will not make an HTTP request; it simply instantiates\\n a bucket object owned by this client.\\n\\n :type bucket_name: string\\n :param bucket_name: The name of the bucket to be instantiated.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The bucket object created.\\n \\\"\\\"\\\"\\n return Bucket(client=self, name=bucket_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client.batch\",\n \"text\": \"gcloud/storage/client.py::Client.batch\\n def batch(self):\\n \\\"\\\"\\\"Factory constructor for batch object.\\n\\n .. note::\\n This will not make an HTTP request; it simply instantiates\\n a batch object owned by this client.\\n\\n :rtype: :class:`gcloud.storage.batch.Batch`\\n :returns: The batch object created.\\n \\\"\\\"\\\"\\n return Batch(client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client.get_bucket\",\n \"text\": \"gcloud/storage/client.py::Client.get_bucket\\n def get_bucket(self, bucket_name):\\n \\\"\\\"\\\"Get a bucket by name.\\n\\n If the bucket isn't found, this will raise a\\n :class:`gcloud.storage.exceptions.NotFound`.\\n\\n For example::\\n\\n >>> try:\\n >>> bucket = client.get_bucket('my-bucket')\\n >>> except gcloud.exceptions.NotFound:\\n >>> print 'Sorry, that bucket does not exist!'\\n\\n This implements \\\"storage.buckets.get\\\".\\n\\n :type bucket_name: string\\n :param bucket_name: The name of the bucket to get.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The bucket matching the name provided.\\n :raises: :class:`gcloud.exceptions.NotFound`\\n \\\"\\\"\\\"\\n bucket = Bucket(self, name=bucket_name)\\n bucket.reload(client=self)\\n return bucket\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client.lookup_bucket\",\n \"text\": \"gcloud/storage/client.py::Client.lookup_bucket\\n def lookup_bucket(self, bucket_name):\\n \\\"\\\"\\\"Get a bucket by name, returning None if not found.\\n\\n You can use this if you would rather check for a None value\\n than catching an exception::\\n\\n >>> bucket = client.lookup_bucket('doesnt-exist')\\n >>> print bucket\\n None\\n >>> bucket = client.lookup_bucket('my-bucket')\\n >>> print bucket\\n <Bucket: my-bucket>\\n\\n :type bucket_name: string\\n :param bucket_name: The name of the bucket to get.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The bucket matching the name provided or None if not found.\\n \\\"\\\"\\\"\\n try:\\n return self.get_bucket(bucket_name)\\n except NotFound:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client.create_bucket\",\n \"text\": \"gcloud/storage/client.py::Client.create_bucket\\n def create_bucket(self, bucket_name):\\n \\\"\\\"\\\"Create a new bucket.\\n\\n For example::\\n\\n >>> bucket = client.create_bucket('my-bucket')\\n >>> print bucket\\n <Bucket: my-bucket>\\n\\n This implements \\\"storage.buckets.insert\\\".\\n\\n If the bucket already exists, will raise\\n :class:`gcloud.exceptions.Conflict`.\\n\\n :type bucket_name: string\\n :param bucket_name: The bucket name to create.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The newly created bucket.\\n \\\"\\\"\\\"\\n bucket = Bucket(self, name=bucket_name)\\n bucket.create(client=self)\\n return bucket\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::Client.list_buckets\",\n \"text\": \"gcloud/storage/client.py::Client.list_buckets\\n def list_buckets(self, max_results=None, page_token=None, prefix=None,\\n projection='noAcl', fields=None):\\n \\\"\\\"\\\"Get all buckets in the project associated to the client.\\n\\n This will not populate the list of blobs available in each\\n bucket.\\n\\n >>> for bucket in client.list_buckets():\\n >>> print bucket\\n\\n This implements \\\"storage.buckets.list\\\".\\n\\n :type max_results: integer or ``NoneType``\\n :param max_results: Optional. Maximum number of buckets to return.\\n\\n :type page_token: string or ``NoneType``\\n :param page_token: Optional. Opaque marker for the next \\\"page\\\" of\\n buckets. If not passed, will return the first page\\n of buckets.\\n\\n :type prefix: string or ``NoneType``\\n :param prefix: Optional. Filter results to buckets whose names begin\\n with this prefix.\\n\\n :type projection: string or ``NoneType``\\n :param projection: If used, must be 'full' or 'noAcl'. Defaults to\\n 'noAcl'. Specifies the set of properties to return.\\n\\n :type fields: string or ``NoneType``\\n :param fields: Selector specifying which fields to include in a\\n partial response. Must be a list of fields. For example\\n to get a partial response with just the next page token\\n and the language of each bucket returned:\\n 'items/id,nextPageToken'\\n\\n :rtype: iterable of :class:`gcloud.storage.bucket.Bucket` objects.\\n :returns: All buckets belonging to this project.\\n \\\"\\\"\\\"\\n extra_params = {'project': self.project}\\n\\n if max_results is not None:\\n extra_params['maxResults'] = max_results\\n\\n if prefix is not None:\\n extra_params['prefix'] = prefix\\n\\n extra_params['projection'] = projection\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::_BucketIterator.__init__\",\n \"text\": \"gcloud/storage/client.py::_BucketIterator.__init__\\n def __init__(self, client, extra_params=None):\\n super(_BucketIterator, self).__init__(client=client, path='/b',\\n extra_params=extra_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/client.py::_BucketIterator.get_items_from_response\",\n \"text\": \"gcloud/storage/client.py::_BucketIterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Factory method which yields :class:`.Bucket` items from a response.\\n\\n :type response: dict\\n :param response: The JSON API response for a page of buckets.\\n \\\"\\\"\\\"\\n for item in response.get('items', []):\\n name = item.get('name')\\n bucket = Bucket(self.client, name)\\n bucket._set_properties(item)\\n yield bucket\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":62,"cells":{"instance_id":{"kind":"string","value":"docker__compose-2720"},"repo":{"kind":"string","value":"docker/compose"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"limit fig log\nif i create a container with fig and let it run and produce lots and lots of logs, when i execute:\n\n```\nfig logs\n```\n\nfor the first time it can take minutes to print all the logs (this is because I am running some development application in debug mode). I would like to have a feature like with tail, where I can specify the initial amount of lines say for example:\n\n```\nfig logs -n 1000\n```\n\nwhich would print the last 1000 lines of the logs it could be either the last for each container or the total 1000, as long i can avoid the initial print of thousands of logs... that I am not interested in...\n\n"},"gold_files":{"kind":"list like","value":["compose/cli/log_printer.py","compose/cli/main.py"],"string":"[\n \"compose/cli/log_printer.py\",\n \"compose/cli/main.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["compose/cli/log_printer.py::LogPrinter.__init__","compose/cli/log_printer.py::LogPrinter._make_log_generators","compose/cli/log_printer.py::build_log_generator","compose/cli/log_printer.py::build_no_log_generator","compose/cli/main.py::<module>","compose/cli/main.py::TopLevelCommand.exec_command","compose/cli/main.py::TopLevelCommand.up","compose/cli/main.py::run_one_off_container"],"string":"[\n \"compose/cli/log_printer.py::LogPrinter.__init__\",\n \"compose/cli/log_printer.py::LogPrinter._make_log_generators\",\n \"compose/cli/log_printer.py::build_log_generator\",\n \"compose/cli/log_printer.py::build_no_log_generator\",\n \"compose/cli/main.py::<module>\",\n \"compose/cli/main.py::TopLevelCommand.exec_command\",\n \"compose/cli/main.py::TopLevelCommand.up\",\n \"compose/cli/main.py::run_one_off_container\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"compose/cli/colors.py","text":"compose/cli/colors.py\ndef get_pairs()\ndef ansi(code)\ndef ansi_color(code, s)\ndef make_color_fn(code)\ndef rainbow()","label":0},{"cid":"compose/cli/formatter.py","text":"compose/cli/formatter.py\ndef get_tty_width()\nclass Formatter # Format tabular data for printing.\n def table(self, headers, rows)\nclass ConsoleWarningFormatter # A logging.Formatter which prints WARNING and ERROR messages with\n def get_level_message(self, record)\n def format(self, record)","label":0},{"cid":"compose/cli/log_printer.py","text":"compose/cli/log_printer.py\nclass LogPrinter # Print logs from many containers to a single output stream.\n def __init__(self, containers, output=sys.stdout, monochrome=False, cascade_stop=False)\n def run(self)\n def _make_log_generators(self, monochrome, prefix_width)\n def no_color(text)\ndef build_log_prefix(container, prefix_width)\ndef max_name_width(containers) # Calculate the maximum width of container names so we can make the log\ndef get_log_generator(container)\ndef build_no_log_generator(container, prefix, color_func) # Return a generator that prints a warning about logs and waits for\ndef build_log_generator(container, prefix, color_func)\ndef wait_on_exit(container)","label":1},{"cid":"compose/cli/main.py","text":"compose/cli/main.py\ndef main()\ndef log_api_error(e)\ndef setup_logging()\ndef setup_console_handler(handler, verbose)\ndef parse_doc_section(name, source)\nclass TopLevelCommand # Define and run multi-container applications with Docker.\n def docopt_options(self)\n def perform_command(self, options, handler, command_options)\n def build(self, project, options) # Build or rebuild services.\n def config(self, config_options, options) # Validate and view the compose file.\n def create(self, project, options) # Creates containers for a service.\n def down(self, project, options) # Stop containers and remove containers, networks, volumes, and images\n def events(self, project, options) # Receive real time events from containers.\n def format_event(event)\n def json_format_event(event)\n def exec_command(self, project, options) # Execute a command in a running container\n def help(self, project, options) # Get help on a command.\n def kill(self, project, options) # Force stop service containers.\n def logs(self, project, options) # View output from containers.\n def pause(self, project, options) # Pause services.\n def port(self, project, options) # Print the public port for a port binding.\n def ps(self, project, options) # List containers.\n def pull(self, project, options) # Pulls images for services.\n def rm(self, project, options) # Remove stopped service containers.\n def run(self, project, options) # Run a one-off command on a service.\n def scale(self, project, options) # Set number of containers to run for a service.\n def start(self, project, options) # Start existing containers.\n def stop(self, project, options) # Stop running containers without removing them.\n def restart(self, project, options) # Restart running containers.\n def unpause(self, project, options) # Unpause services.\n def up(self, project, options) # Builds, (re)creates, starts, and attaches to containers for a service.\n def version(self, project, options) # Show version informations\ndef convergence_strategy_from_opts(options)\ndef image_type_from_opt(flag, value)\ndef run_one_off_container(container_options, project, service, options)\n def remove_container(force=False)\ndef build_log_printer(containers, service_names, monochrome, cascade_stop)\ndef up_shutdown_context(project, service_names, timeout, detached)\ndef list_containers(containers)\ndef exit_if(condition, message, exit_code)","label":1},{"cid":"compose/config/validation.py","text":"compose/config/validation.py\ndef format_ports(instance)\ndef format_expose(instance)\ndef format_boolean_in_environment(instance) # Check if there is a boolean in the mapping sections and display a warning.\ndef match_named_volumes(service_dict, project_volumes)\ndef python_type_to_yaml_type(type_)\ndef validate_config_section(filename, config, section) # Validate the structure of a configuration section. This must be done\ndef validate_top_level_object(config_file)\ndef validate_ulimits(service_config)\ndef validate_extends_file_path(service_name, extends_options, filename) # The service to be extended must either be defined in the config key 'file',\ndef validate_network_mode(service_config, service_names)\ndef validate_depends_on(service_config, service_names)\ndef get_unsupported_config_msg(path, error_key)\ndef anglicize_json_type(json_type)\ndef is_service_dict_schema(schema_id)\ndef handle_error_for_schema_with_id(error, path)\ndef handle_generic_error(error, path)\ndef parse_key_from_error_msg(error)\ndef path_string(path)\ndef _parse_valid_types_from_validator(validator) # A validator value can be either an array of valid types or a string of\ndef _parse_oneof_validator(error) # oneOf has multiple schemas, so we need to reason about which schema, sub\ndef process_service_constraint_errors(error, service_name, version)\ndef process_config_schema_errors(error)\ndef validate_against_config_schema(config_file)\ndef validate_service_constraints(config, service_name, version)\n def handler(errors)\ndef get_schema_path()\ndef load_jsonschema(version)\ndef get_resolver_path()\ndef handle_errors(errors, format_error_func, filename) # jsonschema returns an error tree full of information to explain what has","label":0},{"cid":"compose/project.py","text":"compose/project.py\nclass Project # A collection of services.\n def __init__(self, name, services, client, networks=None, volumes=None)\n def labels(self, one_off=False)\n def from_config(cls, name, config_data, client) # Construct a Project from a config.Config object.\n def service_names(self)\n def get_service(self, name) # Retrieve a service by name. Raises NoSuchService\n def validate_service_names(self, service_names) # Validate that the given list of service names only contains valid\n def get_services(self, service_names=None, include_deps=False) # Returns a list of this project's services filtered\n def get_services_without_duplicate(self, service_names=None, include_deps=False)\n def get_links(self, service_dict)\n def get_network_mode(self, service_dict, networks)\n def start(self, service_names=None, **options)\n def stop(self, service_names=None, **options)\n def pause(self, service_names=None, **options)\n def unpause(self, service_names=None, **options)\n def kill(self, service_names=None, **options)\n def remove_stopped(self, service_names=None, **options)\n def down(self, remove_image_type, include_volumes)\n def remove_images(self, remove_image_type)\n def restart(self, service_names=None, **options)\n def build(self, service_names=None, no_cache=False, pull=False, force_rm=False)\n def create(self, service_names=None, strategy=ConvergenceStrategy.changed, do_build=True)\n def events(self)\n def build_container_event(event, container)\n def up(self, service_names=None, start_deps=True, strategy=ConvergenceStrategy.changed, do_build=True, timeout=DEFAULT_TIMEOUT, detached=False)\n def initialize(self)\n def _get_convergence_plans(self, services, strategy)\n def pull(self, service_names=None, ignore_pull_failures=False)\n def containers(self, service_names=None, stopped=False, one_off=False)\n def matches_service_names(container)\n def _inject_deps(self, acc, service)\ndef get_volumes_from(project, service_dict)\n def build_volume_from(spec)\nclass NoSuchService\n def __init__(self, name)\n def __str__(self)","label":0},{"cid":"compose/service.py","text":"compose/service.py\nclass BuildError\n def __init__(self, service, reason)\nclass NeedsBuildError\n def __init__(self, service)\nclass NoSuchImageError\nclass ConvergenceStrategy # Enumeration for all possible convergence strategies. Values refer to\n def allows_recreate(self)\nclass ImageType # Enumeration for the types of images known to compose.\nclass Service\n def __init__(self, name, client=None, project='default', use_networking=False, links=None, volumes_from=None, network_mode=None, networks=None, **options)\n def containers(self, stopped=False, one_off=False, filters={})\n def get_container(self, number=1) # Return a :class:`compose.container.Container` for this service. The\n def start(self, **options)\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT) # Adjusts the number of containers to the specified number and ensures\n def create_and_start(service, number)\n def stop_and_remove(container)\n def create_container(self, one_off=False, do_build=True, previous_container=None, number=None, quiet=False, **override_options) # Create a container for this service. If the image doesn't exist, attempt to pull\n def ensure_image_exists(self, do_build=True)\n def image(self)\n def image_name(self)\n def convergence_plan(self, strategy=ConvergenceStrategy.changed)\n def _containers_have_diverged(self, containers)\n def execute_convergence_plan(self, plan, do_build=True, timeout=DEFAULT_TIMEOUT, detached=False, start=True)\n def recreate_container(self, container, do_build=False, timeout=DEFAULT_TIMEOUT, attach_logs=False, start_new_container=True) # Recreate a container.\n def start_container_if_stopped(self, container, attach_logs=False)\n def start_container(self, container)\n def connect_container_to_networks(self, container)\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT)\n def duplicate_containers(self)\n def config_hash(self)\n def config_dict(self)\n def get_dependency_names(self)\n def get_linked_service_names(self)\n def get_link_names(self)\n def get_volumes_from_names(self)\n def _next_container_number(self, one_off=False)\n def _get_aliases(self, container)\n def _get_links(self, link_to_self)\n def _get_volumes_from(self)\n def _get_container_create_options(self, override_options, number, one_off=False, previous_container=None)\n def _get_container_host_config(self, override_options, one_off=False)\n def build(self, no_cache=False, pull=False, force_rm=False)\n def can_be_built(self)\n def labels(self, one_off=False)\n def custom_container_name(self)\n def get_container_name(self, number, one_off=False)\n def remove_image(self, image_type)\n def specifies_host_port(self)\n def has_host_port(binding)\n def pull(self, ignore_pull_failures=False)\nclass NetworkMode # A `standard` network mode (ex: host, bridge)\n def __init__(self, network_mode)\n def id(self)\nclass ContainerNetworkMode # A network mode that us","label":0},{"cid":"compose/utils.py","text":"compose/utils.py\ndef get_output_stream(stream)\ndef stream_as_text(stream) # Given a stream of bytes or text, if any of the items in the stream\ndef line_splitter(buffer, separator=u'\\n')\ndef split_buffer(stream, splitter=None, decoder=lambda a: a) # Given a generator which yields strings and a splitter function,\ndef json_splitter(buffer) # Attempt to parse a json object from a buffer. If there is at least one\ndef json_stream(stream) # Given a stream of text, return a stream of json objects.\ndef json_hash(obj)\ndef microseconds_from_time_nano(time_nano)\ndef build_string_dict(source_dict)","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py","text":"contrib/migration/migrate-compose-file-v1-to-v2.py\ndef migrate(content)\ndef warn_for_links(name, service)\ndef warn_for_external_links(name, service)\ndef rewrite_net(service, service_names)\ndef rewrite_build(service)\ndef rewrite_logging(service)\ndef rewrite_volumes_from(service, service_names)\ndef create_volumes_section(data)\ndef get_named_volumes(services)\ndef write(stream, new_format, indent, width)\ndef parse_opts(args)\ndef main(args)","label":0},{"cid":"script/travis/render-bintray-config.py","text":"script/travis/render-bintray-config.py","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/colors.py\",\n \"text\": \"compose/cli/colors.py\\ndef get_pairs()\\ndef ansi(code)\\ndef ansi_color(code, s)\\ndef make_color_fn(code)\\ndef rainbow()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/formatter.py\",\n \"text\": \"compose/cli/formatter.py\\ndef get_tty_width()\\nclass Formatter # Format tabular data for printing.\\n def table(self, headers, rows)\\nclass ConsoleWarningFormatter # A logging.Formatter which prints WARNING and ERROR messages with\\n def get_level_message(self, record)\\n def format(self, record)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py\",\n \"text\": \"compose/cli/log_printer.py\\nclass LogPrinter # Print logs from many containers to a single output stream.\\n def __init__(self, containers, output=sys.stdout, monochrome=False, cascade_stop=False)\\n def run(self)\\n def _make_log_generators(self, monochrome, prefix_width)\\n def no_color(text)\\ndef build_log_prefix(container, prefix_width)\\ndef max_name_width(containers) # Calculate the maximum width of container names so we can make the log\\ndef get_log_generator(container)\\ndef build_no_log_generator(container, prefix, color_func) # Return a generator that prints a warning about logs and waits for\\ndef build_log_generator(container, prefix, color_func)\\ndef wait_on_exit(container)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/main.py\",\n \"text\": \"compose/cli/main.py\\ndef main()\\ndef log_api_error(e)\\ndef setup_logging()\\ndef setup_console_handler(handler, verbose)\\ndef parse_doc_section(name, source)\\nclass TopLevelCommand # Define and run multi-container applications with Docker.\\n def docopt_options(self)\\n def perform_command(self, options, handler, command_options)\\n def build(self, project, options) # Build or rebuild services.\\n def config(self, config_options, options) # Validate and view the compose file.\\n def create(self, project, options) # Creates containers for a service.\\n def down(self, project, options) # Stop containers and remove containers, networks, volumes, and images\\n def events(self, project, options) # Receive real time events from containers.\\n def format_event(event)\\n def json_format_event(event)\\n def exec_command(self, project, options) # Execute a command in a running container\\n def help(self, project, options) # Get help on a command.\\n def kill(self, project, options) # Force stop service containers.\\n def logs(self, project, options) # View output from containers.\\n def pause(self, project, options) # Pause services.\\n def port(self, project, options) # Print the public port for a port binding.\\n def ps(self, project, options) # List containers.\\n def pull(self, project, options) # Pulls images for services.\\n def rm(self, project, options) # Remove stopped service containers.\\n def run(self, project, options) # Run a one-off command on a service.\\n def scale(self, project, options) # Set number of containers to run for a service.\\n def start(self, project, options) # Start existing containers.\\n def stop(self, project, options) # Stop running containers without removing them.\\n def restart(self, project, options) # Restart running containers.\\n def unpause(self, project, options) # Unpause services.\\n def up(self, project, options) # Builds, (re)creates, starts, and attaches to containers for a service.\\n def version(self, project, options) # Show version informations\\ndef convergence_strategy_from_opts(options)\\ndef image_type_from_opt(flag, value)\\ndef run_one_off_container(container_options, project, service, options)\\n def remove_container(force=False)\\ndef build_log_printer(containers, service_names, monochrome, cascade_stop)\\ndef up_shutdown_context(project, service_names, timeout, detached)\\ndef list_containers(containers)\\ndef exit_if(condition, message, exit_code)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/validation.py\",\n \"text\": \"compose/config/validation.py\\ndef format_ports(instance)\\ndef format_expose(instance)\\ndef format_boolean_in_environment(instance) # Check if there is a boolean in the mapping sections and display a warning.\\ndef match_named_volumes(service_dict, project_volumes)\\ndef python_type_to_yaml_type(type_)\\ndef validate_config_section(filename, config, section) # Validate the structure of a configuration section. This must be done\\ndef validate_top_level_object(config_file)\\ndef validate_ulimits(service_config)\\ndef validate_extends_file_path(service_name, extends_options, filename) # The service to be extended must either be defined in the config key 'file',\\ndef validate_network_mode(service_config, service_names)\\ndef validate_depends_on(service_config, service_names)\\ndef get_unsupported_config_msg(path, error_key)\\ndef anglicize_json_type(json_type)\\ndef is_service_dict_schema(schema_id)\\ndef handle_error_for_schema_with_id(error, path)\\ndef handle_generic_error(error, path)\\ndef parse_key_from_error_msg(error)\\ndef path_string(path)\\ndef _parse_valid_types_from_validator(validator) # A validator value can be either an array of valid types or a string of\\ndef _parse_oneof_validator(error) # oneOf has multiple schemas, so we need to reason about which schema, sub\\ndef process_service_constraint_errors(error, service_name, version)\\ndef process_config_schema_errors(error)\\ndef validate_against_config_schema(config_file)\\ndef validate_service_constraints(config, service_name, version)\\n def handler(errors)\\ndef get_schema_path()\\ndef load_jsonschema(version)\\ndef get_resolver_path()\\ndef handle_errors(errors, format_error_func, filename) # jsonschema returns an error tree full of information to explain what has\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py\",\n \"text\": \"compose/project.py\\nclass Project # A collection of services.\\n def __init__(self, name, services, client, networks=None, volumes=None)\\n def labels(self, one_off=False)\\n def from_config(cls, name, config_data, client) # Construct a Project from a config.Config object.\\n def service_names(self)\\n def get_service(self, name) # Retrieve a service by name. Raises NoSuchService\\n def validate_service_names(self, service_names) # Validate that the given list of service names only contains valid\\n def get_services(self, service_names=None, include_deps=False) # Returns a list of this project's services filtered\\n def get_services_without_duplicate(self, service_names=None, include_deps=False)\\n def get_links(self, service_dict)\\n def get_network_mode(self, service_dict, networks)\\n def start(self, service_names=None, **options)\\n def stop(self, service_names=None, **options)\\n def pause(self, service_names=None, **options)\\n def unpause(self, service_names=None, **options)\\n def kill(self, service_names=None, **options)\\n def remove_stopped(self, service_names=None, **options)\\n def down(self, remove_image_type, include_volumes)\\n def remove_images(self, remove_image_type)\\n def restart(self, service_names=None, **options)\\n def build(self, service_names=None, no_cache=False, pull=False, force_rm=False)\\n def create(self, service_names=None, strategy=ConvergenceStrategy.changed, do_build=True)\\n def events(self)\\n def build_container_event(event, container)\\n def up(self, service_names=None, start_deps=True, strategy=ConvergenceStrategy.changed, do_build=True, timeout=DEFAULT_TIMEOUT, detached=False)\\n def initialize(self)\\n def _get_convergence_plans(self, services, strategy)\\n def pull(self, service_names=None, ignore_pull_failures=False)\\n def containers(self, service_names=None, stopped=False, one_off=False)\\n def matches_service_names(container)\\n def _inject_deps(self, acc, service)\\ndef get_volumes_from(project, service_dict)\\n def build_volume_from(spec)\\nclass NoSuchService\\n def __init__(self, name)\\n def __str__(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py\",\n \"text\": \"compose/service.py\\nclass BuildError\\n def __init__(self, service, reason)\\nclass NeedsBuildError\\n def __init__(self, service)\\nclass NoSuchImageError\\nclass ConvergenceStrategy # Enumeration for all possible convergence strategies. Values refer to\\n def allows_recreate(self)\\nclass ImageType # Enumeration for the types of images known to compose.\\nclass Service\\n def __init__(self, name, client=None, project='default', use_networking=False, links=None, volumes_from=None, network_mode=None, networks=None, **options)\\n def containers(self, stopped=False, one_off=False, filters={})\\n def get_container(self, number=1) # Return a :class:`compose.container.Container` for this service. The\\n def start(self, **options)\\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT) # Adjusts the number of containers to the specified number and ensures\\n def create_and_start(service, number)\\n def stop_and_remove(container)\\n def create_container(self, one_off=False, do_build=True, previous_container=None, number=None, quiet=False, **override_options) # Create a container for this service. If the image doesn't exist, attempt to pull\\n def ensure_image_exists(self, do_build=True)\\n def image(self)\\n def image_name(self)\\n def convergence_plan(self, strategy=ConvergenceStrategy.changed)\\n def _containers_have_diverged(self, containers)\\n def execute_convergence_plan(self, plan, do_build=True, timeout=DEFAULT_TIMEOUT, detached=False, start=True)\\n def recreate_container(self, container, do_build=False, timeout=DEFAULT_TIMEOUT, attach_logs=False, start_new_container=True) # Recreate a container.\\n def start_container_if_stopped(self, container, attach_logs=False)\\n def start_container(self, container)\\n def connect_container_to_networks(self, container)\\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT)\\n def duplicate_containers(self)\\n def config_hash(self)\\n def config_dict(self)\\n def get_dependency_names(self)\\n def get_linked_service_names(self)\\n def get_link_names(self)\\n def get_volumes_from_names(self)\\n def _next_container_number(self, one_off=False)\\n def _get_aliases(self, container)\\n def _get_links(self, link_to_self)\\n def _get_volumes_from(self)\\n def _get_container_create_options(self, override_options, number, one_off=False, previous_container=None)\\n def _get_container_host_config(self, override_options, one_off=False)\\n def build(self, no_cache=False, pull=False, force_rm=False)\\n def can_be_built(self)\\n def labels(self, one_off=False)\\n def custom_container_name(self)\\n def get_container_name(self, number, one_off=False)\\n def remove_image(self, image_type)\\n def specifies_host_port(self)\\n def has_host_port(binding)\\n def pull(self, ignore_pull_failures=False)\\nclass NetworkMode # A `standard` network mode (ex: host, bridge)\\n def __init__(self, network_mode)\\n def id(self)\\nclass ContainerNetworkMode # A network mode that us\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py\",\n \"text\": \"compose/utils.py\\ndef get_output_stream(stream)\\ndef stream_as_text(stream) # Given a stream of bytes or text, if any of the items in the stream\\ndef line_splitter(buffer, separator=u'\\\\n')\\ndef split_buffer(stream, splitter=None, decoder=lambda a: a) # Given a generator which yields strings and a splitter function,\\ndef json_splitter(buffer) # Attempt to parse a json object from a buffer. If there is at least one\\ndef json_stream(stream) # Given a stream of text, return a stream of json objects.\\ndef json_hash(obj)\\ndef microseconds_from_time_nano(time_nano)\\ndef build_string_dict(source_dict)\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py\\ndef migrate(content)\\ndef warn_for_links(name, service)\\ndef warn_for_external_links(name, service)\\ndef rewrite_net(service, service_names)\\ndef rewrite_build(service)\\ndef rewrite_logging(service)\\ndef rewrite_volumes_from(service, service_names)\\ndef create_volumes_section(data)\\ndef get_named_volumes(services)\\ndef write(stream, new_format, indent, width)\\ndef parse_opts(args)\\ndef main(args)\",\n \"label\": 0\n },\n {\n \"cid\": \"script/travis/render-bintray-config.py\",\n \"text\": \"script/travis/render-bintray-config.py\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"compose/cli/colors.py::get_pairs","text":"compose/cli/colors.py::get_pairs\ndef get_pairs():\n for i, name in enumerate(NAMES):\n yield(name, str(30 + i))\n yield('intense_' + name, str(30 + i) + ';1')","label":0},{"cid":"compose/cli/colors.py::ansi","text":"compose/cli/colors.py::ansi\ndef ansi(code):\n return '\\033[{0}m'.format(code)","label":0},{"cid":"compose/cli/colors.py::ansi_color","text":"compose/cli/colors.py::ansi_color\ndef ansi_color(code, s):\n return '{0}{1}{2}'.format(ansi(code), s, ansi(0))","label":0},{"cid":"compose/cli/colors.py::make_color_fn","text":"compose/cli/colors.py::make_color_fn\ndef make_color_fn(code):\n return lambda s: ansi_color(code, s)","label":0},{"cid":"compose/cli/colors.py::rainbow","text":"compose/cli/colors.py::rainbow\ndef rainbow():\n cs = ['cyan', 'yellow', 'green', 'magenta', 'red', 'blue',\n 'intense_cyan', 'intense_yellow', 'intense_green',\n 'intense_magenta', 'intense_red', 'intense_blue']\n\n for c in cs:\n yield globals()[c]","label":0},{"cid":"compose/cli/formatter.py::get_tty_width","text":"compose/cli/formatter.py::get_tty_width\ndef get_tty_width():\n tty_size = os.popen('stty size', 'r').read().split()\n if len(tty_size) != 2:\n return 0\n _, width = tty_size\n return int(width)","label":0},{"cid":"compose/cli/formatter.py::Formatter.table","text":"compose/cli/formatter.py::Formatter.table\n def table(self, headers, rows):\n table = texttable.Texttable(max_width=get_tty_width())\n table.set_cols_dtype(['t' for h in headers])\n table.add_rows([headers] + rows)\n table.set_deco(table.HEADER)\n table.set_chars(['-', '|', '+', '-'])\n\n return table.draw()","label":0},{"cid":"compose/cli/formatter.py::ConsoleWarningFormatter.get_level_message","text":"compose/cli/formatter.py::ConsoleWarningFormatter.get_level_message\n def get_level_message(self, record):\n separator = ': '\n if record.levelno == logging.WARNING:\n return colors.yellow(record.levelname) + separator\n if record.levelno == logging.ERROR:\n return colors.red(record.levelname) + separator\n\n return ''","label":0},{"cid":"compose/cli/formatter.py::ConsoleWarningFormatter.format","text":"compose/cli/formatter.py::ConsoleWarningFormatter.format\n def format(self, record):\n message = super(ConsoleWarningFormatter, self).format(record)\n return self.get_level_message(record) + message","label":0},{"cid":"compose/cli/log_printer.py::LogPrinter.__init__","text":"compose/cli/log_printer.py::LogPrinter.__init__\n def __init__(self, containers, output=sys.stdout, monochrome=False, cascade_stop=False):\n self.containers = containers\n self.output = utils.get_output_stream(output)\n self.monochrome = monochrome\n self.cascade_stop = cascade_stop","label":1},{"cid":"compose/cli/log_printer.py::LogPrinter.run","text":"compose/cli/log_printer.py::LogPrinter.run\n def run(self):\n if not self.containers:\n return\n\n prefix_width = max_name_width(self.containers)\n generators = list(self._make_log_generators(self.monochrome, prefix_width))\n for line in Multiplexer(generators, cascade_stop=self.cascade_stop).loop():\n self.output.write(line)\n self.output.flush()","label":0},{"cid":"compose/cli/log_printer.py::LogPrinter._make_log_generators","text":"compose/cli/log_printer.py::LogPrinter._make_log_generators\n def _make_log_generators(self, monochrome, prefix_width):\n def no_color(text):\n return text\n\n if monochrome:\n color_funcs = cycle([no_color])\n else:\n color_funcs = cycle(colors.rainbow())\n\n for color_func, container in zip(color_funcs, self.containers):\n generator_func = get_log_generator(container)\n prefix = color_func(build_log_prefix(container, prefix_width))\n yield generator_func(container, prefix, color_func)","label":1},{"cid":"compose/cli/log_printer.py::LogPrinter._make_log_generators.no_color","text":"compose/cli/log_printer.py::LogPrinter._make_log_generators.no_color\n def no_color(text):\n return text","label":0},{"cid":"compose/cli/log_printer.py::build_log_prefix","text":"compose/cli/log_printer.py::build_log_prefix\ndef build_log_prefix(container, prefix_width):\n return container.name_without_project.ljust(prefix_width) + ' | '","label":0},{"cid":"compose/cli/log_printer.py::max_name_width","text":"compose/cli/log_printer.py::max_name_width\ndef max_name_width(containers):\n \"\"\"Calculate the maximum width of container names so we can make the log\n prefixes line up like so:\n\n db_1 | Listening\n web_1 | Listening\n \"\"\"\n return max(len(container.name_without_project) for container in containers)","label":0},{"cid":"compose/cli/log_printer.py::get_log_generator","text":"compose/cli/log_printer.py::get_log_generator\ndef get_log_generator(container):\n if container.has_api_logs:\n return build_log_generator\n return build_no_log_generator","label":0},{"cid":"compose/cli/log_printer.py::build_no_log_generator","text":"compose/cli/log_printer.py::build_no_log_generator\ndef build_no_log_generator(container, prefix, color_func):\n \"\"\"Return a generator that prints a warning about logs and waits for\n container to exit.\n \"\"\"\n yield \"{} WARNING: no logs are available with the '{}' log driver\\n\".format(\n prefix,\n container.log_driver)\n yield color_func(wait_on_exit(container))","label":1},{"cid":"compose/cli/log_printer.py::build_log_generator","text":"compose/cli/log_printer.py::build_log_generator\ndef build_log_generator(container, prefix, color_func):\n # if the container doesn't have a log_stream we need to attach to container\n # before log printer starts running\n if container.log_stream is None:\n stream = container.attach(stdout=True, stderr=True, stream=True, logs=True)\n line_generator = split_buffer(stream)\n else:\n line_generator = split_buffer(container.log_stream)\n\n for line in line_generator:\n yield prefix + line\n yield color_func(wait_on_exit(container))","label":1},{"cid":"compose/cli/log_printer.py::wait_on_exit","text":"compose/cli/log_printer.py::wait_on_exit\ndef wait_on_exit(container):\n exit_code = container.wait()\n return \"%s exited with code %s\\n\" % (container.name, exit_code)","label":0},{"cid":"compose/cli/main.py::main","text":"compose/cli/main.py::main\ndef main():\n setup_logging()\n try:\n command = TopLevelCommand()\n command.sys_dispatch()\n except KeyboardInterrupt:\n log.error(\"Aborting.\")\n sys.exit(1)\n except (UserError, NoSuchService, ConfigurationError) as e:\n log.error(e.msg)\n sys.exit(1)\n except NoSuchCommand as e:\n commands = \"\\n\".join(parse_doc_section(\"commands:\", getdoc(e.supercommand)))\n log.error(\"No such command: %s\\n\\n%s\", e.command, commands)\n sys.exit(1)\n except APIError as e:\n log_api_error(e)\n sys.exit(1)\n except BuildError as e:\n log.error(\"Service '%s' failed to build: %s\" % (e.service.name, e.reason))\n sys.exit(1)\n except StreamOutputError as e:\n log.error(e)\n sys.exit(1)\n except NeedsBuildError as e:\n log.error(\"Service '%s' needs to be built, but --no-build was passed.\" % e.service.name)\n sys.exit(1)\n except ReadTimeout as e:\n log.error(\n \"An HTTP request took too long to complete. Retry with --verbose to \"\n \"obtain debug information.\\n\"\n \"If you encounter this issue regularly because of slow network \"\n \"conditions, consider setting COMPOSE_HTTP_TIMEOUT to a higher \"\n \"value (current value: %s).\" % HTTP_TIMEOUT\n )\n sys.exit(1)","label":0},{"cid":"compose/cli/main.py::log_api_error","text":"compose/cli/main.py::log_api_error\ndef log_api_error(e):\n if 'client is newer than server' in e.explanation:\n # we need JSON formatted errors. In the meantime...\n # TODO: fix this by refactoring project dispatch\n # http://github.com/docker/compose/pull/2832#commitcomment-15923800\n client_version = e.explanation.split('client API version: ')[1].split(',')[0]\n log.error(\n \"The engine version is lesser than the minimum required by \"\n \"compose. Your current project requires a Docker Engine of \"\n \"version {version} or superior.\".format(\n version=API_VERSION_TO_ENGINE_VERSION[client_version]\n ))\n else:\n log.error(e.explanation)","label":0},{"cid":"compose/cli/main.py::setup_logging","text":"compose/cli/main.py::setup_logging\ndef setup_logging():\n root_logger = logging.getLogger()\n root_logger.addHandler(console_handler)\n root_logger.setLevel(logging.DEBUG)\n\n # Disable requests logging\n logging.getLogger(\"requests\").propagate = False","label":0},{"cid":"compose/cli/main.py::setup_console_handler","text":"compose/cli/main.py::setup_console_handler\ndef setup_console_handler(handler, verbose):\n if handler.stream.isatty():\n format_class = ConsoleWarningFormatter\n else:\n format_class = logging.Formatter\n\n if verbose:\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\n handler.setLevel(logging.DEBUG)\n else:\n handler.setFormatter(format_class())\n handler.setLevel(logging.INFO)","label":0},{"cid":"compose/cli/main.py::parse_doc_section","text":"compose/cli/main.py::parse_doc_section\ndef parse_doc_section(name, source):\n pattern = re.compile('^([^\\n]*' + name + '[^\\n]*\\n?(?:[ \\t].*?(?:\\n|$))*)',\n re.IGNORECASE | re.MULTILINE)\n return [s.strip() for s in pattern.findall(source)]","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.docopt_options","text":"compose/cli/main.py::TopLevelCommand.docopt_options\n def docopt_options(self):\n options = super(TopLevelCommand, self).docopt_options()\n options['version'] = get_version_info('compose')\n return options","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.perform_command","text":"compose/cli/main.py::TopLevelCommand.perform_command\n def perform_command(self, options, handler, command_options):\n setup_console_handler(console_handler, options.get('--verbose'))\n\n if options['COMMAND'] in ('help', 'version'):\n # Skip looking up the compose file.\n handler(None, command_options)\n return\n\n if options['COMMAND'] == 'config':\n handler(options, command_options)\n return\n\n project = project_from_options(self.base_dir, options)\n with friendly_error_message():\n handler(project, command_options)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.build","text":"compose/cli/main.py::TopLevelCommand.build\n def build(self, project, options):\n \"\"\"\n Build or rebuild services.\n\n Services are built once and then tagged as `project_service`,\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\n contents of its build directory, you can run `docker-compose build` to rebuild it.\n\n Usage: build [options] [SERVICE...]\n\n Options:\n --force-rm Always remove intermediate containers.\n --no-cache Do not use cache when building the image.\n --pull Always attempt to pull a newer version of the image.\n \"\"\"\n project.build(\n service_names=options['SERVICE'],\n no_cache=bool(options.get('--no-cache', False)),\n pull=bool(options.get('--pull', False)),\n force_rm=bool(options.get('--force-rm', False)))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.config","text":"compose/cli/main.py::TopLevelCommand.config\n def config(self, config_options, options):\n \"\"\"\n Validate and view the compose file.\n\n Usage: config [options]\n\n Options:\n -q, --quiet Only validate the configuration, don't print\n anything.\n --services Print the service names, one per line.\n\n \"\"\"\n config_path = get_config_path_from_options(config_options)\n compose_config = config.load(config.find(self.base_dir, config_path))\n\n if options['--quiet']:\n return\n\n if options['--services']:\n print('\\n'.join(service['name'] for service in compose_config.services))\n return\n\n print(serialize_config(compose_config))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.create","text":"compose/cli/main.py::TopLevelCommand.create\n def create(self, project, options):\n \"\"\"\n Creates containers for a service.\n\n Usage: create [options] [SERVICE...]\n\n Options:\n --force-recreate Recreate containers even if their configuration and\n image haven't changed. Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate them.\n Incompatible with --force-recreate.\n --no-build Don't build an image, even if it's missing\n \"\"\"\n service_names = options['SERVICE']\n\n project.create(\n service_names=service_names,\n strategy=convergence_strategy_from_opts(options),\n do_build=not options['--no-build']\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.down","text":"compose/cli/main.py::TopLevelCommand.down\n def down(self, project, options):\n \"\"\"\n Stop containers and remove containers, networks, volumes, and images\n created by `up`. Only containers and networks are removed by default.\n\n Usage: down [options]\n\n Options:\n --rmi type Remove images, type may be one of: 'all' to remove\n all images, or 'local' to remove only images that\n don't have an custom name set by the `image` field\n -v, --volumes Remove data volumes\n \"\"\"\n image_type = image_type_from_opt('--rmi', options['--rmi'])\n project.down(image_type, options['--volumes'])","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events","text":"compose/cli/main.py::TopLevelCommand.events\n def events(self, project, options):\n \"\"\"\n Receive real time events from containers.\n\n Usage: events [options] [SERVICE...]\n\n Options:\n --json Output events as a stream of json objects\n \"\"\"\n def format_event(event):\n attributes = [\"%s=%s\" % item for item in event['attributes'].items()]\n return (\"{time} {type} {action} {id} ({attrs})\").format(\n attrs=\", \".join(sorted(attributes)),\n **event)\n\n def json_format_event(event):\n event['time'] = event['time'].isoformat()\n return json.dumps(event)\n\n for event in project.events():\n formatter = json_format_event if options['--json'] else format_event\n print(formatter(event))\n sys.stdout.flush()","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events.format_event","text":"compose/cli/main.py::TopLevelCommand.events.format_event\n def format_event(event):\n attributes = [\"%s=%s\" % item for item in event['attributes'].items()]\n return (\"{time} {type} {action} {id} ({attrs})\").format(\n attrs=\", \".join(sorted(attributes)),\n **event)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events.json_format_event","text":"compose/cli/main.py::TopLevelCommand.events.json_format_event\n def json_format_event(event):\n event['time'] = event['time'].isoformat()\n return json.dumps(event)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.exec_command","text":"compose/cli/main.py::TopLevelCommand.exec_command\n def exec_command(self, project, options):\n \"\"\"\n Execute a command in a running container\n\n Usage: exec [options] SERVICE COMMAND [ARGS...]\n\n Options:\n -d Detached mode: Run command in the background.\n --privileged Give extended privileges to the process.\n --user USER Run the command as this user.\n -T Disable pseudo-tty allocation. By default `docker-compose exec`\n allocates a TTY.\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n \"\"\"\n index = int(options.get('--index'))\n service = project.get_service(options['SERVICE'])\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n command = [options['COMMAND']] + options['ARGS']\n tty = not options[\"-T\"]\n\n create_exec_options = {\n \"privileged\": options[\"--privileged\"],\n \"user\": options[\"--user\"],\n \"tty\": tty,\n \"stdin\": tty,\n }\n\n exec_id = container.create_exec(command, **create_exec_options)\n\n if options['-d']:\n container.start_exec(exec_id, tty=tty)\n return\n\n signals.set_signal_handler_to_shutdown()\n try:\n operation = ExecOperation(\n project.client,\n exec_id,\n interactive=tty,\n )\n pty = PseudoTerminal(project.client, operation)\n pty.start()\n except signals.ShutdownException:\n log.info(\"received shutdown exception: closing\")\n exit_code = project.client.exec_inspect(exec_id).get(\"ExitCode\")\n sys.exit(exit_code)","label":1},{"cid":"compose/cli/main.py::TopLevelCommand.help","text":"compose/cli/main.py::TopLevelCommand.help\n def help(self, project, options):\n \"\"\"\n Get help on a command.\n\n Usage: help COMMAND\n \"\"\"\n handler = self.get_handler(options['COMMAND'])\n raise SystemExit(getdoc(handler))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.kill","text":"compose/cli/main.py::TopLevelCommand.kill\n def kill(self, project, options):\n \"\"\"\n Force stop service containers.\n\n Usage: kill [options] [SERVICE...]\n\n Options:\n -s SIGNAL SIGNAL to send to the container.\n Default signal is SIGKILL.\n \"\"\"\n signal = options.get('-s', 'SIGKILL')\n\n project.kill(service_names=options['SERVICE'], signal=signal)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.logs","text":"compose/cli/main.py::TopLevelCommand.logs\n def logs(self, project, options):\n \"\"\"\n View output from containers.\n\n Usage: logs [options] [SERVICE...]\n\n Options:\n --no-color Produce monochrome output.\n \"\"\"\n containers = project.containers(service_names=options['SERVICE'], stopped=True)\n\n monochrome = options['--no-color']\n print(\"Attaching to\", list_containers(containers))\n LogPrinter(containers, monochrome=monochrome).run()","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pause","text":"compose/cli/main.py::TopLevelCommand.pause\n def pause(self, project, options):\n \"\"\"\n Pause services.\n\n Usage: pause [SERVICE...]\n \"\"\"\n containers = project.pause(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to pause', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.port","text":"compose/cli/main.py::TopLevelCommand.port\n def port(self, project, options):\n \"\"\"\n Print the public port for a port binding.\n\n Usage: port [options] SERVICE PRIVATE_PORT\n\n Options:\n --protocol=proto tcp or udp [default: tcp]\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n \"\"\"\n index = int(options.get('--index'))\n service = project.get_service(options['SERVICE'])\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n print(container.get_local_port(\n options['PRIVATE_PORT'],\n protocol=options.get('--protocol') or 'tcp') or '')","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.ps","text":"compose/cli/main.py::TopLevelCommand.ps\n def ps(self, project, options):\n \"\"\"\n List containers.\n\n Usage: ps [options] [SERVICE...]\n\n Options:\n -q Only display IDs\n \"\"\"\n containers = sorted(\n project.containers(service_names=options['SERVICE'], stopped=True) +\n project.containers(service_names=options['SERVICE'], one_off=True),\n key=attrgetter('name'))\n\n if options['-q']:\n for container in containers:\n print(container.id)\n else:\n headers = [\n 'Name',\n 'Command',\n 'State',\n 'Ports',\n ]\n rows = []\n for container in containers:\n command = container.human_readable_command\n if len(command) > 30:\n command = '%s ...' % command[:26]\n rows.append([\n container.name,\n command,\n container.human_readable_state,\n container.human_readable_ports,\n ])\n print(Formatter().table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pull","text":"compose/cli/main.py::TopLevelCommand.pull\n def pull(self, project, options):\n \"\"\"\n Pulls images for services.\n\n Usage: pull [options] [SERVICE...]\n\n Options:\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\n \"\"\"\n project.pull(\n service_names=options['SERVICE'],\n ignore_pull_failures=options.get('--ignore-pull-failures')\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.rm","text":"compose/cli/main.py::TopLevelCommand.rm\n def rm(self, project, options):\n \"\"\"\n Remove stopped service containers.\n\n By default, volumes attached to containers will not be removed. You can see all\n volumes with `docker volume ls`.\n\n Any data which is not in a volume will be lost.\n\n Usage: rm [options] [SERVICE...]\n\n Options:\n -f, --force Don't ask to confirm removal\n -v Remove volumes associated with containers\n \"\"\"\n all_containers = project.containers(service_names=options['SERVICE'], stopped=True)\n stopped_containers = [c for c in all_containers if not c.is_running]\n\n if len(stopped_containers) > 0:\n print(\"Going to remove\", list_containers(stopped_containers))\n if options.get('--force') \\\n or yesno(\"Are you sure? [yN] \", default=False):\n project.remove_stopped(\n service_names=options['SERVICE'],\n v=options.get('-v', False)\n )\n else:\n print(\"No stopped containers\")","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.run","text":"compose/cli/main.py::TopLevelCommand.run\n def run(self, project, options):\n \"\"\"\n Run a one-off command on a service.\n\n For example:\n\n $ docker-compose run web python manage.py shell\n\n By default, linked services will be started, unless they are already\n running. If you do not want to start linked services, use\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\n\n Usage: run [options] [-p PORT...] [-e KEY=VAL...] SERVICE [COMMAND] [ARGS...]\n\n Options:\n -d Detached mode: Run container in the background, print\n new container name.\n --name NAME Assign a name to the container\n --entrypoint CMD Override the entrypoint of the image.\n -e KEY=VAL Set an environment variable (can be used multiple times)\n -u, --user=\"\" Run as specified username or uid\n --no-deps Don't start linked services.\n --rm Remove container after run. Ignored in detached mode.\n -p, --publish=[] Publish a container's port(s) to the host\n --service-ports Run command with the service's ports enabled and mapped\n to the host.\n -T Disable pseudo-tty allocation. By default `docker-compose run`\n allocates a TTY.\n \"\"\"\n service = project.get_service(options['SERVICE'])\n detach = options['-d']\n\n if IS_WINDOWS_PLATFORM and not detach:\n raise UserError(\n \"Interactive mode is not yet supported on Windows.\\n\"\n \"Please pass the -d flag when using `docker-compose run`.\"\n )\n\n if options['COMMAND']:\n command = [options['COMMAND']] + options['ARGS']\n else:\n command = service.options.get('command')\n\n container_opt","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.scale","text":"compose/cli/main.py::TopLevelCommand.scale\n def scale(self, project, options):\n \"\"\"\n Set number of containers to run for a service.\n\n Numbers are specified in the form `service=num` as arguments.\n For example:\n\n $ docker-compose scale web=2 worker=3\n\n Usage: scale [options] [SERVICE=NUM...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\n\n for s in options['SERVICE=NUM']:\n if '=' not in s:\n raise UserError('Arguments to scale should be in the form service=num')\n service_name, num = s.split('=', 1)\n try:\n num = int(num)\n except ValueError:\n raise UserError('Number of containers for service \"%s\" is not a '\n 'number' % service_name)\n project.get_service(service_name).scale(num, timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.start","text":"compose/cli/main.py::TopLevelCommand.start\n def start(self, project, options):\n \"\"\"\n Start existing containers.\n\n Usage: start [SERVICE...]\n \"\"\"\n containers = project.start(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to start', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.stop","text":"compose/cli/main.py::TopLevelCommand.stop\n def stop(self, project, options):\n \"\"\"\n Stop running containers without removing them.\n\n They can be started again with `docker-compose start`.\n\n Usage: stop [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\n project.stop(service_names=options['SERVICE'], timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.restart","text":"compose/cli/main.py::TopLevelCommand.restart\n def restart(self, project, options):\n \"\"\"\n Restart running containers.\n\n Usage: restart [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\n containers = project.restart(service_names=options['SERVICE'], timeout=timeout)\n exit_if(not containers, 'No containers to restart', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.unpause","text":"compose/cli/main.py::TopLevelCommand.unpause\n def unpause(self, project, options):\n \"\"\"\n Unpause services.\n\n Usage: unpause [SERVICE...]\n \"\"\"\n containers = project.unpause(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to unpause', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.up","text":"compose/cli/main.py::TopLevelCommand.up\n def up(self, project, options):\n \"\"\"\n Builds, (re)creates, starts, and attaches to containers for a service.\n\n Unless they are already running, this command also starts any linked services.\n\n The `docker-compose up` command aggregates the output of each container. When\n the command exits, all containers are stopped. Running `docker-compose up -d`\n starts the containers in the background and leaves them running.\n\n If there are existing containers for a service, and the service's configuration\n or image was changed after the container's creation, `docker-compose up` picks\n up the changes by stopping and recreating the containers (preserving mounted\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\n flag.\n\n If you want to force Compose to stop and recreate all containers, use the\n `--force-recreate` flag.\n\n Usage: up [options] [SERVICE...]\n\n Options:\n -d Detached mode: Run containers in the background,\n print new container names.\n Incompatible with --abort-on-container-exit.\n --no-color Produce monochrome output.\n --no-deps Don't start linked services.\n --force-recreate Recreate containers even if their configuration\n and image haven't changed.\n Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate them.\n Incompatible with --force-recreate.\n --no-build Don't build an image, even if it's missing\n --abort-on-container-exit Stops all containers if any container was stopped.\n ","label":1},{"cid":"compose/cli/main.py::TopLevelCommand.version","text":"compose/cli/main.py::TopLevelCommand.version\n def version(self, project, options):\n \"\"\"\n Show version informations\n\n Usage: version [--short]\n\n Options:\n --short Shows only Compose's version number.\n \"\"\"\n if options['--short']:\n print(__version__)\n else:\n print(get_version_info('full'))","label":0},{"cid":"compose/cli/main.py::convergence_strategy_from_opts","text":"compose/cli/main.py::convergence_strategy_from_opts\ndef convergence_strategy_from_opts(options):\n no_recreate = options['--no-recreate']\n force_recreate = options['--force-recreate']\n if force_recreate and no_recreate:\n raise UserError(\"--force-recreate and --no-recreate cannot be combined.\")\n\n if force_recreate:\n return ConvergenceStrategy.always\n\n if no_recreate:\n return ConvergenceStrategy.never\n\n return ConvergenceStrategy.changed","label":0},{"cid":"compose/cli/main.py::image_type_from_opt","text":"compose/cli/main.py::image_type_from_opt\ndef image_type_from_opt(flag, value):\n if not value:\n return ImageType.none\n try:\n return ImageType[value]\n except KeyError:\n raise UserError(\"%s flag must be one of: all, local\" % flag)","label":0},{"cid":"compose/cli/main.py::run_one_off_container","text":"compose/cli/main.py::run_one_off_container\ndef run_one_off_container(container_options, project, service, options):\n if not options['--no-deps']:\n deps = service.get_dependency_names()\n if deps:\n project.up(\n service_names=deps,\n start_deps=True,\n strategy=ConvergenceStrategy.never)\n\n project.initialize()\n\n container = service.create_container(\n quiet=True,\n one_off=True,\n **container_options)\n\n if options['-d']:\n service.start_container(container)\n print(container.name)\n return\n\n def remove_container(force=False):\n if options['--rm']:\n project.client.remove_container(container.id, force=True)\n\n signals.set_signal_handler_to_shutdown()\n try:\n try:\n operation = RunOperation(\n project.client,\n container.id,\n interactive=not options['-T'],\n logs=False,\n )\n pty = PseudoTerminal(project.client, operation)\n sockets = pty.sockets()\n service.start_container(container)\n pty.start(sockets)\n exit_code = container.wait()\n except signals.ShutdownException:\n project.client.stop(container.id)\n exit_code = 1\n except signals.ShutdownException:\n project.client.kill(container.id)\n remove_container(force=True)\n sys.exit(2)\n\n remove_container()\n sys.exit(exit_code)","label":1},{"cid":"compose/cli/main.py::run_one_off_container.remove_container","text":"compose/cli/main.py::run_one_off_container.remove_container\n def remove_container(force=False):\n if options['--rm']:\n project.client.remove_container(container.id, force=True)","label":0},{"cid":"compose/cli/main.py::build_log_printer","text":"compose/cli/main.py::build_log_printer\ndef build_log_printer(containers, service_names, monochrome, cascade_stop):\n if service_names:\n containers = [\n container\n for container in containers if container.service in service_names\n ]\n return LogPrinter(containers, monochrome=monochrome, cascade_stop=cascade_stop)","label":0},{"cid":"compose/cli/main.py::up_shutdown_context","text":"compose/cli/main.py::up_shutdown_context\n@contextlib.contextmanager\ndef up_shutdown_context(project, service_names, timeout, detached):\n if detached:\n yield\n return\n\n signals.set_signal_handler_to_shutdown()\n try:\n try:\n yield\n except signals.ShutdownException:\n print(\"Gracefully stopping... (press Ctrl+C again to force)\")\n project.stop(service_names=service_names, timeout=timeout)\n except signals.ShutdownException:\n project.kill(service_names=service_names)\n sys.exit(2)","label":0},{"cid":"compose/cli/main.py::list_containers","text":"compose/cli/main.py::list_containers\ndef list_containers(containers):\n return \", \".join(c.name for c in containers)","label":0},{"cid":"compose/cli/main.py::exit_if","text":"compose/cli/main.py::exit_if\ndef exit_if(condition, message, exit_code):\n if condition:\n log.error(message)\n raise SystemExit(exit_code)","label":0},{"cid":"compose/config/validation.py::format_ports","text":"compose/config/validation.py::format_ports\n@FormatChecker.cls_checks(format=\"ports\", raises=ValidationError)\ndef format_ports(instance):\n try:\n split_port(instance)\n except ValueError as e:\n raise ValidationError(six.text_type(e))\n return True","label":0},{"cid":"compose/config/validation.py::format_expose","text":"compose/config/validation.py::format_expose\n@FormatChecker.cls_checks(format=\"expose\", raises=ValidationError)\ndef format_expose(instance):\n if isinstance(instance, six.string_types):\n if not re.match(VALID_EXPOSE_FORMAT, instance):\n raise ValidationError(\n \"should be of the format 'PORT[/PROTOCOL]'\")\n\n return True","label":0},{"cid":"compose/config/validation.py::format_boolean_in_environment","text":"compose/config/validation.py::format_boolean_in_environment\n@FormatChecker.cls_checks(format=\"bool-value-in-mapping\")\ndef format_boolean_in_environment(instance):\n \"\"\"Check if there is a boolean in the mapping sections and display a warning.\n Always return True here so the validation won't raise an error.\n \"\"\"\n if isinstance(instance, bool):\n log.warn(\n \"There is a boolean value in the 'environment', 'labels', or \"\n \"'extra_hosts' field of a service.\\n\"\n \"These sections only support string values.\\n\"\n \"Please add quotes to any boolean values to make them strings \"\n \"(eg, 'True', 'false', 'yes', 'N', 'on', 'Off').\\n\"\n \"This warning will become an error in a future release. \\r\\n\"\n )\n return True","label":0},{"cid":"compose/config/validation.py::match_named_volumes","text":"compose/config/validation.py::match_named_volumes\ndef match_named_volumes(service_dict, project_volumes):\n service_volumes = service_dict.get('volumes', [])\n for volume_spec in service_volumes:\n if volume_spec.is_named_volume and volume_spec.external not in project_volumes:\n raise ConfigurationError(\n 'Named volume \"{0}\" is used in service \"{1}\" but no'\n ' declaration was found in the volumes section.'.format(\n volume_spec.repr(), service_dict.get('name')\n )\n )","label":0},{"cid":"compose/config/validation.py::python_type_to_yaml_type","text":"compose/config/validation.py::python_type_to_yaml_type\ndef python_type_to_yaml_type(type_):\n type_name = type(type_).__name__\n return {\n 'dict': 'mapping',\n 'list': 'array',\n 'int': 'number',\n 'float': 'number',\n 'bool': 'boolean',\n 'unicode': 'string',\n 'str': 'string',\n 'bytes': 'string',\n }.get(type_name, type_name)","label":0},{"cid":"compose/config/validation.py::validate_config_section","text":"compose/config/validation.py::validate_config_section\ndef validate_config_section(filename, config, section):\n \"\"\"Validate the structure of a configuration section. This must be done\n before interpolation so it's separate from schema validation.\n \"\"\"\n if not isinstance(config, dict):\n raise ConfigurationError(\n \"In file '{filename}', {section} must be a mapping, not \"\n \"{type}.\".format(\n filename=filename,\n section=section,\n type=anglicize_json_type(python_type_to_yaml_type(config))))\n\n for key, value in config.items():\n if not isinstance(key, six.string_types):\n raise ConfigurationError(\n \"In file '{filename}', the {section} name {name} must be a \"\n \"quoted string, i.e. '{name}'.\".format(\n filename=filename,\n section=section,\n name=key))\n\n if not isinstance(value, (dict, type(None))):\n raise ConfigurationError(\n \"In file '{filename}', {section} '{name}' must be a mapping not \"\n \"{type}.\".format(\n filename=filename,\n section=section,\n name=key,\n type=anglicize_json_type(python_type_to_yaml_type(value))))","label":0},{"cid":"compose/config/validation.py::validate_top_level_object","text":"compose/config/validation.py::validate_top_level_object\ndef validate_top_level_object(config_file):\n if not isinstance(config_file.config, dict):\n raise ConfigurationError(\n \"Top level object in '{}' needs to be an object not '{}'.\".format(\n config_file.filename,\n type(config_file.config)))","label":0},{"cid":"compose/config/validation.py::validate_ulimits","text":"compose/config/validation.py::validate_ulimits\ndef validate_ulimits(service_config):\n ulimit_config = service_config.config.get('ulimits', {})\n for limit_name, soft_hard_values in six.iteritems(ulimit_config):\n if isinstance(soft_hard_values, dict):\n if not soft_hard_values['soft'] <= soft_hard_values['hard']:\n raise ConfigurationError(\n \"Service '{s.name}' has invalid ulimit '{ulimit}'. \"\n \"'soft' value can not be greater than 'hard' value \".format(\n s=service_config,\n ulimit=ulimit_config))","label":0},{"cid":"compose/config/validation.py::validate_extends_file_path","text":"compose/config/validation.py::validate_extends_file_path\ndef validate_extends_file_path(service_name, extends_options, filename):\n \"\"\"\n The service to be extended must either be defined in the config key 'file',\n or within 'filename'.\n \"\"\"\n error_prefix = \"Invalid 'extends' configuration for %s:\" % service_name\n\n if 'file' not in extends_options and filename is None:\n raise ConfigurationError(\n \"%s you need to specify a 'file', e.g. 'file: something.yml'\" % error_prefix\n )","label":0},{"cid":"compose/config/validation.py::validate_network_mode","text":"compose/config/validation.py::validate_network_mode\ndef validate_network_mode(service_config, service_names):\n network_mode = service_config.config.get('network_mode')\n if not network_mode:\n return\n\n if 'networks' in service_config.config:\n raise ConfigurationError(\"'network_mode' and 'networks' cannot be combined\")\n\n dependency = get_service_name_from_network_mode(network_mode)\n if not dependency:\n return\n\n if dependency not in service_names:\n raise ConfigurationError(\n \"Service '{s.name}' uses the network stack of service '{dep}' which \"\n \"is undefined.\".format(s=service_config, dep=dependency))","label":0},{"cid":"compose/config/validation.py::validate_depends_on","text":"compose/config/validation.py::validate_depends_on\ndef validate_depends_on(service_config, service_names):\n for dependency in service_config.config.get('depends_on', []):\n if dependency not in service_names:\n raise ConfigurationError(\n \"Service '{s.name}' depends on service '{dep}' which is \"\n \"undefined.\".format(s=service_config, dep=dependency))","label":0},{"cid":"compose/config/validation.py::get_unsupported_config_msg","text":"compose/config/validation.py::get_unsupported_config_msg\ndef get_unsupported_config_msg(path, error_key):\n msg = \"Unsupported config option for {}: '{}'\".format(path_string(path), error_key)\n if error_key in DOCKER_CONFIG_HINTS:\n msg += \" (did you mean '{}'?)\".format(DOCKER_CONFIG_HINTS[error_key])\n return msg","label":0},{"cid":"compose/config/validation.py::anglicize_json_type","text":"compose/config/validation.py::anglicize_json_type\ndef anglicize_json_type(json_type):\n if json_type.startswith(('a', 'e', 'i', 'o', 'u')):\n return 'an ' + json_type\n return 'a ' + json_type","label":0},{"cid":"compose/config/validation.py::is_service_dict_schema","text":"compose/config/validation.py::is_service_dict_schema\ndef is_service_dict_schema(schema_id):\n return schema_id in ('config_schema_v1.json', '#/properties/services')","label":0},{"cid":"compose/config/validation.py::handle_error_for_schema_with_id","text":"compose/config/validation.py::handle_error_for_schema_with_id\ndef handle_error_for_schema_with_id(error, path):\n schema_id = error.schema['id']\n\n if is_service_dict_schema(schema_id) and error.validator == 'additionalProperties':\n return \"Invalid service name '{}' - only {} characters are allowed\".format(\n # The service_name is the key to the json object\n list(error.instance)[0],\n VALID_NAME_CHARS)\n\n if error.validator == 'additionalProperties':\n if schema_id == '#/definitions/service':\n invalid_config_key = parse_key_from_error_msg(error)\n return get_unsupported_config_msg(path, invalid_config_key)\n\n if not error.path:\n return '{}\\n{}'.format(error.message, VERSION_EXPLANATION)","label":0},{"cid":"compose/config/validation.py::handle_generic_error","text":"compose/config/validation.py::handle_generic_error\ndef handle_generic_error(error, path):\n msg_format = None\n error_msg = error.message\n\n if error.validator == 'oneOf':\n msg_format = \"{path} {msg}\"\n config_key, error_msg = _parse_oneof_validator(error)\n if config_key:\n path.append(config_key)\n\n elif error.validator == 'type':\n msg_format = \"{path} contains an invalid type, it should be {msg}\"\n error_msg = _parse_valid_types_from_validator(error.validator_value)\n\n elif error.validator == 'required':\n error_msg = \", \".join(error.validator_value)\n msg_format = \"{path} is invalid, {msg} is required.\"\n\n elif error.validator == 'dependencies':\n config_key = list(error.validator_value.keys())[0]\n required_keys = \",\".join(error.validator_value[config_key])\n\n msg_format = \"{path} is invalid: {msg}\"\n path.append(config_key)\n error_msg = \"when defining '{}' you must set '{}' as well\".format(\n config_key,\n required_keys)\n\n elif error.cause:\n error_msg = six.text_type(error.cause)\n msg_format = \"{path} is invalid: {msg}\"\n\n elif error.path:\n msg_format = \"{path} value {msg}\"\n\n if msg_format:\n return msg_format.format(path=path_string(path), msg=error_msg)\n\n return error.message","label":0},{"cid":"compose/config/validation.py::parse_key_from_error_msg","text":"compose/config/validation.py::parse_key_from_error_msg\ndef parse_key_from_error_msg(error):\n return error.message.split(\"'\")[1]","label":0},{"cid":"compose/config/validation.py::path_string","text":"compose/config/validation.py::path_string\ndef path_string(path):\n return \".\".join(c for c in path if isinstance(c, six.string_types))","label":0},{"cid":"compose/config/validation.py::_parse_valid_types_from_validator","text":"compose/config/validation.py::_parse_valid_types_from_validator\ndef _parse_valid_types_from_validator(validator):\n \"\"\"A validator value can be either an array of valid types or a string of\n a valid type. Parse the valid types and prefix with the correct article.\n \"\"\"\n if not isinstance(validator, list):\n return anglicize_json_type(validator)\n\n if len(validator) == 1:\n return anglicize_json_type(validator[0])\n\n return \"{}, or {}\".format(\n \", \".join([anglicize_json_type(validator[0])] + validator[1:-1]),\n anglicize_json_type(validator[-1]))","label":0},{"cid":"compose/config/validation.py::_parse_oneof_validator","text":"compose/config/validation.py::_parse_oneof_validator\ndef _parse_oneof_validator(error):\n \"\"\"oneOf has multiple schemas, so we need to reason about which schema, sub\n schema or constraint the validation is failing on.\n Inspecting the context value of a ValidationError gives us information about\n which sub schema failed and which kind of error it is.\n \"\"\"\n types = []\n for context in error.context:\n\n if context.validator == 'oneOf':\n _, error_msg = _parse_oneof_validator(context)\n return path_string(context.path), error_msg\n\n if context.validator == 'required':\n return (None, context.message)\n\n if context.validator == 'additionalProperties':\n invalid_config_key = parse_key_from_error_msg(context)\n return (None, \"contains unsupported option: '{}'\".format(invalid_config_key))\n\n if context.path:\n return (\n path_string(context.path),\n \"contains {}, which is an invalid type, it should be {}\".format(\n json.dumps(context.instance),\n _parse_valid_types_from_validator(context.validator_value)),\n )\n\n if context.validator == 'uniqueItems':\n return (\n None,\n \"contains non unique items, please remove duplicates from {}\".format(\n context.instance),\n )\n\n if context.validator == 'type':\n types.append(context.validator_value)\n\n valid_types = _parse_valid_types_from_validator(types)\n return (None, \"contains an invalid type, it should be {}\".format(valid_types))","label":0},{"cid":"compose/config/validation.py::process_service_constraint_errors","text":"compose/config/validation.py::process_service_constraint_errors\ndef process_service_constraint_errors(error, service_name, version):\n if version == V1:\n if 'image' in error.instance and 'build' in error.instance:\n return (\n \"Service {} has both an image and build path specified. \"\n \"A service can either be built to image or use an existing \"\n \"image, not both.\".format(service_name))\n\n if 'image' in error.instance and 'dockerfile' in error.instance:\n return (\n \"Service {} has both an image and alternate Dockerfile. \"\n \"A service can either be built to image or use an existing \"\n \"image, not both.\".format(service_name))\n\n if 'image' not in error.instance and 'build' not in error.instance:\n return (\n \"Service {} has neither an image nor a build context specified. \"\n \"At least one must be provided.\".format(service_name))","label":0},{"cid":"compose/config/validation.py::process_config_schema_errors","text":"compose/config/validation.py::process_config_schema_errors\ndef process_config_schema_errors(error):\n path = list(error.path)\n\n if 'id' in error.schema:\n error_msg = handle_error_for_schema_with_id(error, path)\n if error_msg:\n return error_msg\n\n return handle_generic_error(error, path)","label":0},{"cid":"compose/config/validation.py::validate_against_config_schema","text":"compose/config/validation.py::validate_against_config_schema\ndef validate_against_config_schema(config_file):\n schema = load_jsonschema(config_file.version)\n format_checker = FormatChecker([\"ports\", \"expose\", \"bool-value-in-mapping\"])\n validator = Draft4Validator(\n schema,\n resolver=RefResolver(get_resolver_path(), schema),\n format_checker=format_checker)\n handle_errors(\n validator.iter_errors(config_file.config),\n process_config_schema_errors,\n config_file.filename)","label":0},{"cid":"compose/config/validation.py::validate_service_constraints","text":"compose/config/validation.py::validate_service_constraints\ndef validate_service_constraints(config, service_name, version):\n def handler(errors):\n return process_service_constraint_errors(errors, service_name, version)\n\n schema = load_jsonschema(version)\n validator = Draft4Validator(schema['definitions']['constraints']['service'])\n handle_errors(validator.iter_errors(config), handler, None)","label":0},{"cid":"compose/config/validation.py::validate_service_constraints.handler","text":"compose/config/validation.py::validate_service_constraints.handler\n def handler(errors):\n return process_service_constraint_errors(errors, service_name, version)","label":0},{"cid":"compose/config/validation.py::get_schema_path","text":"compose/config/validation.py::get_schema_path\ndef get_schema_path():\n return os.path.dirname(os.path.abspath(__file__))","label":0},{"cid":"compose/config/validation.py::load_jsonschema","text":"compose/config/validation.py::load_jsonschema\ndef load_jsonschema(version):\n filename = os.path.join(\n get_schema_path(),\n \"config_schema_v{0}.json\".format(version))\n\n with open(filename, \"r\") as fh:\n return json.load(fh)","label":0},{"cid":"compose/config/validation.py::get_resolver_path","text":"compose/config/validation.py::get_resolver_path\ndef get_resolver_path():\n schema_path = get_schema_path()\n if sys.platform == \"win32\":\n scheme = \"///\"\n # TODO: why is this necessary?\n schema_path = schema_path.replace('\\\\', '/')\n else:\n scheme = \"//\"\n return \"file:{}{}/\".format(scheme, schema_path)","label":0},{"cid":"compose/config/validation.py::handle_errors","text":"compose/config/validation.py::handle_errors\ndef handle_errors(errors, format_error_func, filename):\n \"\"\"jsonschema returns an error tree full of information to explain what has\n gone wrong. Process each error and pull out relevant information and re-write\n helpful error messages that are relevant.\n \"\"\"\n errors = list(sorted(errors, key=str))\n if not errors:\n return\n\n error_msg = '\\n'.join(format_error_func(error) for error in errors)\n raise ConfigurationError(\n \"Validation failed{file_msg}, reason(s):\\n{error_msg}\".format(\n file_msg=\" in file '{}'\".format(filename) if filename else \"\",\n error_msg=error_msg))","label":0},{"cid":"compose/project.py::Project.__init__","text":"compose/project.py::Project.__init__\n def __init__(self, name, services, client, networks=None, volumes=None):\n self.name = name\n self.services = services\n self.client = client\n self.volumes = volumes or ProjectVolumes({})\n self.networks = networks or ProjectNetworks({}, False)","label":0},{"cid":"compose/project.py::Project.labels","text":"compose/project.py::Project.labels\n def labels(self, one_off=False):\n return [\n '{0}={1}'.format(LABEL_PROJECT, self.name),\n '{0}={1}'.format(LABEL_ONE_OFF, \"True\" if one_off else \"False\"),\n ]","label":0},{"cid":"compose/project.py::Project.from_config","text":"compose/project.py::Project.from_config\n @classmethod\n def from_config(cls, name, config_data, client):\n \"\"\"\n Construct a Project from a config.Config object.\n \"\"\"\n use_networking = (config_data.version and config_data.version != V1)\n networks = build_networks(name, config_data, client)\n project_networks = ProjectNetworks.from_services(\n config_data.services,\n networks,\n use_networking)\n volumes = ProjectVolumes.from_config(name, config_data, client)\n project = cls(name, [], client, project_networks, volumes)\n\n for service_dict in config_data.services:\n service_dict = dict(service_dict)\n if use_networking:\n service_networks = get_networks(service_dict, networks)\n else:\n service_networks = {}\n\n service_dict.pop('networks', None)\n links = project.get_links(service_dict)\n network_mode = project.get_network_mode(\n service_dict, list(service_networks.keys())\n )\n volumes_from = get_volumes_from(project, service_dict)\n\n if config_data.version != V1:\n service_dict['volumes'] = [\n volumes.namespace_spec(volume_spec)\n for volume_spec in service_dict.get('volumes', [])\n ]\n\n project.services.append(\n Service(\n service_dict.pop('name'),\n client=client,\n project=name,\n use_networking=use_networking,\n networks=service_networks,\n links=links,\n network_mode=network_mode,\n volumes_from=volumes_from,\n **service_dict)\n )\n\n return project","label":0},{"cid":"compose/project.py::Project.service_names","text":"compose/project.py::Project.service_names\n @property\n def service_names(self):\n return [service.name for service in self.services]","label":0},{"cid":"compose/project.py::Project.get_service","text":"compose/project.py::Project.get_service\n def get_service(self, name):\n \"\"\"\n Retrieve a service by name. Raises NoSuchService\n if the named service does not exist.\n \"\"\"\n for service in self.services:\n if service.name == name:\n return service\n\n raise NoSuchService(name)","label":0},{"cid":"compose/project.py::Project.validate_service_names","text":"compose/project.py::Project.validate_service_names\n def validate_service_names(self, service_names):\n \"\"\"\n Validate that the given list of service names only contains valid\n services. Raises NoSuchService if one of the names is invalid.\n \"\"\"\n valid_names = self.service_names\n for name in service_names:\n if name not in valid_names:\n raise NoSuchService(name)","label":0},{"cid":"compose/project.py::Project.get_services","text":"compose/project.py::Project.get_services\n def get_services(self, service_names=None, include_deps=False):\n \"\"\"\n Returns a list of this project's services filtered\n by the provided list of names, or all services if service_names is None\n or [].\n\n If include_deps is specified, returns a list including the dependencies for\n service_names, in order of dependency.\n\n Preserves the original order of self.services where possible,\n reordering as needed to resolve dependencies.\n\n Raises NoSuchService if any of the named services do not exist.\n \"\"\"\n if service_names is None or len(service_names) == 0:\n service_names = self.service_names\n\n unsorted = [self.get_service(name) for name in service_names]\n services = [s for s in self.services if s in unsorted]\n\n if include_deps:\n services = reduce(self._inject_deps, services, [])\n\n uniques = []\n [uniques.append(s) for s in services if s not in uniques]\n\n return uniques","label":0},{"cid":"compose/project.py::Project.get_services_without_duplicate","text":"compose/project.py::Project.get_services_without_duplicate\n def get_services_without_duplicate(self, service_names=None, include_deps=False):\n services = self.get_services(service_names, include_deps)\n for service in services:\n service.remove_duplicate_containers()\n return services","label":0},{"cid":"compose/project.py::Project.get_links","text":"compose/project.py::Project.get_links\n def get_links(self, service_dict):\n links = []\n if 'links' in service_dict:\n for link in service_dict.get('links', []):\n if ':' in link:\n service_name, link_name = link.split(':', 1)\n else:\n service_name, link_name = link, None\n try:\n links.append((self.get_service(service_name), link_name))\n except NoSuchService:\n raise ConfigurationError(\n 'Service \"%s\" has a link to service \"%s\" which does not '\n 'exist.' % (service_dict['name'], service_name))\n del service_dict['links']\n return links","label":0},{"cid":"compose/project.py::Project.get_network_mode","text":"compose/project.py::Project.get_network_mode\n def get_network_mode(self, service_dict, networks):\n network_mode = service_dict.pop('network_mode', None)\n if not network_mode:\n if self.networks.use_networking:\n return NetworkMode(networks[0]) if networks else NetworkMode('none')\n return NetworkMode(None)\n\n service_name = get_service_name_from_network_mode(network_mode)\n if service_name:\n return ServiceNetworkMode(self.get_service(service_name))\n\n container_name = get_container_name_from_network_mode(network_mode)\n if container_name:\n try:\n return ContainerNetworkMode(Container.from_id(self.client, container_name))\n except APIError:\n raise ConfigurationError(\n \"Service '{name}' uses the network stack of container '{dep}' which \"\n \"does not exist.\".format(name=service_dict['name'], dep=container_name))\n\n return NetworkMode(network_mode)","label":0},{"cid":"compose/project.py::Project.start","text":"compose/project.py::Project.start\n def start(self, service_names=None, **options):\n containers = []\n for service in self.get_services(service_names):\n service_containers = service.start(**options)\n containers.extend(service_containers)\n return containers","label":0},{"cid":"compose/project.py::Project.stop","text":"compose/project.py::Project.stop\n def stop(self, service_names=None, **options):\n parallel.parallel_stop(self.containers(service_names), options)","label":0},{"cid":"compose/project.py::Project.pause","text":"compose/project.py::Project.pause\n def pause(self, service_names=None, **options):\n containers = self.containers(service_names)\n parallel.parallel_pause(reversed(containers), options)\n return containers","label":0},{"cid":"compose/project.py::Project.unpause","text":"compose/project.py::Project.unpause\n def unpause(self, service_names=None, **options):\n containers = self.containers(service_names)\n parallel.parallel_unpause(containers, options)\n return containers","label":0},{"cid":"compose/project.py::Project.kill","text":"compose/project.py::Project.kill\n def kill(self, service_names=None, **options):\n parallel.parallel_kill(self.containers(service_names), options)","label":0},{"cid":"compose/project.py::Project.remove_stopped","text":"compose/project.py::Project.remove_stopped\n def remove_stopped(self, service_names=None, **options):\n parallel.parallel_remove(self.containers(service_names, stopped=True), options)","label":0},{"cid":"compose/project.py::Project.down","text":"compose/project.py::Project.down\n def down(self, remove_image_type, include_volumes):\n self.stop()\n self.remove_stopped(v=include_volumes)\n self.networks.remove()\n\n if include_volumes:\n self.volumes.remove()\n\n self.remove_images(remove_image_type)","label":0},{"cid":"compose/project.py::Project.remove_images","text":"compose/project.py::Project.remove_images\n def remove_images(self, remove_image_type):\n for service in self.get_services():\n service.remove_image(remove_image_type)","label":0},{"cid":"compose/project.py::Project.restart","text":"compose/project.py::Project.restart\n def restart(self, service_names=None, **options):\n containers = self.containers(service_names, stopped=True)\n parallel.parallel_restart(containers, options)\n return containers","label":0},{"cid":"compose/project.py::Project.build","text":"compose/project.py::Project.build\n def build(self, service_names=None, no_cache=False, pull=False, force_rm=False):\n for service in self.get_services(service_names):\n if service.can_be_built():\n service.build(no_cache, pull, force_rm)\n else:\n log.info('%s uses an image, skipping' % service.name)","label":0},{"cid":"compose/project.py::Project.create","text":"compose/project.py::Project.create\n def create(self, service_names=None, strategy=ConvergenceStrategy.changed, do_build=True):\n services = self.get_services_without_duplicate(service_names, include_deps=True)\n\n plans = self._get_convergence_plans(services, strategy)\n\n for service in services:\n service.execute_convergence_plan(\n plans[service.name],\n do_build,\n detached=True,\n start=False)","label":0},{"cid":"compose/project.py::Project.events","text":"compose/project.py::Project.events\n def events(self):\n def build_container_event(event, container):\n time = datetime.datetime.fromtimestamp(event['time'])\n time = time.replace(\n microsecond=microseconds_from_time_nano(event['timeNano']))\n return {\n 'time': time,\n 'type': 'container',\n 'action': event['status'],\n 'id': container.id,\n 'service': container.service,\n 'attributes': {\n 'name': container.name,\n 'image': event['from'],\n }\n }\n\n service_names = set(self.service_names)\n for event in self.client.events(\n filters={'label': self.labels()},\n decode=True\n ):\n if event['status'] in IMAGE_EVENTS:\n # We don't receive any image events because labels aren't applied\n # to images\n continue\n\n # TODO: get labels from the API v1.22 , see github issue 2618\n container = Container.from_id(self.client, event['id'])\n if container.service not in service_names:\n continue\n yield build_container_event(event, container)","label":0},{"cid":"compose/project.py::Project.events.build_container_event","text":"compose/project.py::Project.events.build_container_event\n def build_container_event(event, container):\n time = datetime.datetime.fromtimestamp(event['time'])\n time = time.replace(\n microsecond=microseconds_from_time_nano(event['timeNano']))\n return {\n 'time': time,\n 'type': 'container',\n 'action': event['status'],\n 'id': container.id,\n 'service': container.service,\n 'attributes': {\n 'name': container.name,\n 'image': event['from'],\n }\n }","label":0},{"cid":"compose/project.py::Project.up","text":"compose/project.py::Project.up\n def up(self,\n service_names=None,\n start_deps=True,\n strategy=ConvergenceStrategy.changed,\n do_build=True,\n timeout=DEFAULT_TIMEOUT,\n detached=False):\n\n self.initialize()\n services = self.get_services_without_duplicate(\n service_names,\n include_deps=start_deps)\n\n plans = self._get_convergence_plans(services, strategy)\n return [\n container\n for service in services\n for container in service.execute_convergence_plan(\n plans[service.name],\n do_build=do_build,\n timeout=timeout,\n detached=detached\n )\n ]","label":0},{"cid":"compose/project.py::Project.initialize","text":"compose/project.py::Project.initialize\n def initialize(self):\n self.networks.initialize()\n self.volumes.initialize()","label":0},{"cid":"compose/project.py::Project._get_convergence_plans","text":"compose/project.py::Project._get_convergence_plans\n def _get_convergence_plans(self, services, strategy):\n plans = {}\n\n for service in services:\n updated_dependencies = [\n name\n for name in service.get_dependency_names()\n if name in plans and\n plans[name].action in ('recreate', 'create')\n ]\n\n if updated_dependencies and strategy.allows_recreate:\n log.debug('%s has upstream changes (%s)',\n service.name,\n \", \".join(updated_dependencies))\n plan = service.convergence_plan(ConvergenceStrategy.always)\n else:\n plan = service.convergence_plan(strategy)\n\n plans[service.name] = plan\n\n return plans","label":0},{"cid":"compose/project.py::Project.pull","text":"compose/project.py::Project.pull\n def pull(self, service_names=None, ignore_pull_failures=False):\n for service in self.get_services(service_names, include_deps=False):\n service.pull(ignore_pull_failures)","label":0},{"cid":"compose/project.py::Project.containers","text":"compose/project.py::Project.containers\n def containers(self, service_names=None, stopped=False, one_off=False):\n if service_names:\n self.validate_service_names(service_names)\n else:\n service_names = self.service_names\n\n containers = list(filter(None, [\n Container.from_ps(self.client, container)\n for container in self.client.containers(\n all=stopped,\n filters={'label': self.labels(one_off=one_off)})]))\n\n def matches_service_names(container):\n return container.labels.get(LABEL_SERVICE) in service_names\n\n return [c for c in containers if matches_service_names(c)]","label":0},{"cid":"compose/project.py::Project.containers.matches_service_names","text":"compose/project.py::Project.containers.matches_service_names\n def matches_service_names(container):\n return container.labels.get(LABEL_SERVICE) in service_names","label":0},{"cid":"compose/project.py::Project._inject_deps","text":"compose/project.py::Project._inject_deps\n def _inject_deps(self, acc, service):\n dep_names = service.get_dependency_names()\n\n if len(dep_names) > 0:\n dep_services = self.get_services(\n service_names=list(set(dep_names)),\n include_deps=True\n )\n else:\n dep_services = []\n\n dep_services.append(service)\n return acc + dep_services","label":0},{"cid":"compose/project.py::get_volumes_from","text":"compose/project.py::get_volumes_from\ndef get_volumes_from(project, service_dict):\n volumes_from = service_dict.pop('volumes_from', None)\n if not volumes_from:\n return []\n\n def build_volume_from(spec):\n if spec.type == 'service':\n try:\n return spec._replace(source=project.get_service(spec.source))\n except NoSuchService:\n pass\n\n if spec.type == 'container':\n try:\n container = Container.from_id(project.client, spec.source)\n return spec._replace(source=container)\n except APIError:\n pass\n\n raise ConfigurationError(\n \"Service \\\"{}\\\" mounts volumes from \\\"{}\\\", which is not the name \"\n \"of a service or container.\".format(\n service_dict['name'],\n spec.source))\n\n return [build_volume_from(vf) for vf in volumes_from]","label":0},{"cid":"compose/project.py::get_volumes_from.build_volume_from","text":"compose/project.py::get_volumes_from.build_volume_from\n def build_volume_from(spec):\n if spec.type == 'service':\n try:\n return spec._replace(source=project.get_service(spec.source))\n except NoSuchService:\n pass\n\n if spec.type == 'container':\n try:\n container = Container.from_id(project.client, spec.source)\n return spec._replace(source=container)\n except APIError:\n pass\n\n raise ConfigurationError(\n \"Service \\\"{}\\\" mounts volumes from \\\"{}\\\", which is not the name \"\n \"of a service or container.\".format(\n service_dict['name'],\n spec.source))","label":0},{"cid":"compose/project.py::NoSuchService.__init__","text":"compose/project.py::NoSuchService.__init__\n def __init__(self, name):\n self.name = name\n self.msg = \"No such service: %s\" % self.name","label":0},{"cid":"compose/project.py::NoSuchService.__str__","text":"compose/project.py::NoSuchService.__str__\n def __str__(self):\n return self.msg","label":0},{"cid":"compose/service.py::BuildError.__init__","text":"compose/service.py::BuildError.__init__\n def __init__(self, service, reason):\n self.service = service\n self.reason = reason","label":0},{"cid":"compose/service.py::NeedsBuildError.__init__","text":"compose/service.py::NeedsBuildError.__init__\n def __init__(self, service):\n self.service = service","label":0},{"cid":"compose/service.py::ConvergenceStrategy.allows_recreate","text":"compose/service.py::ConvergenceStrategy.allows_recreate\n @property\n def allows_recreate(self):\n return self is not type(self).never","label":0},{"cid":"compose/service.py::Service.__init__","text":"compose/service.py::Service.__init__\n def __init__(\n self,\n name,\n client=None,\n project='default',\n use_networking=False,\n links=None,\n volumes_from=None,\n network_mode=None,\n networks=None,\n **options\n ):\n self.name = name\n self.client = client\n self.project = project\n self.use_networking = use_networking\n self.links = links or []\n self.volumes_from = volumes_from or []\n self.network_mode = network_mode or NetworkMode(None)\n self.networks = networks or {}\n self.options = options","label":0},{"cid":"compose/service.py::Service.containers","text":"compose/service.py::Service.containers\n def containers(self, stopped=False, one_off=False, filters={}):\n filters.update({'label': self.labels(one_off=one_off)})\n\n return list(filter(None, [\n Container.from_ps(self.client, container)\n for container in self.client.containers(\n all=stopped,\n filters=filters)]))","label":0},{"cid":"compose/service.py::Service.get_container","text":"compose/service.py::Service.get_container\n def get_container(self, number=1):\n \"\"\"Return a :class:`compose.container.Container` for this service. The\n container must be active, and match `number`.\n \"\"\"\n labels = self.labels() + ['{0}={1}'.format(LABEL_CONTAINER_NUMBER, number)]\n for container in self.client.containers(filters={'label': labels}):\n return Container.from_ps(self.client, container)\n\n raise ValueError(\"No container found for %s_%s\" % (self.name, number))","label":0},{"cid":"compose/service.py::Service.start","text":"compose/service.py::Service.start\n def start(self, **options):\n containers = self.containers(stopped=True)\n for c in containers:\n self.start_container_if_stopped(c, **options)\n return containers","label":0},{"cid":"compose/service.py::Service.scale","text":"compose/service.py::Service.scale\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT):\n \"\"\"\n Adjusts the number of containers to the specified number and ensures\n they are running.\n\n - creates containers until there are at least `desired_num`\n - stops containers until there are at most `desired_num` running\n - starts containers until there are at least `desired_num` running\n - removes all stopped containers\n \"\"\"\n if self.custom_container_name and desired_num > 1:\n log.warn('The \"%s\" service is using the custom container name \"%s\". '\n 'Docker requires each container to have a unique name. '\n 'Remove the custom name to scale the service.'\n % (self.name, self.custom_container_name))\n\n if self.specifies_host_port():\n log.warn('The \"%s\" service specifies a port on the host. If multiple containers '\n 'for this service are created on a single host, the port will clash.'\n % self.name)\n\n def create_and_start(service, number):\n container = service.create_container(number=number, quiet=True)\n service.start_container(container)\n return container\n\n def stop_and_remove(container):\n container.stop(timeout=timeout)\n container.remove()\n\n running_containers = self.containers(stopped=False)\n num_running = len(running_containers)\n\n if desired_num == num_running:\n # do nothing as we already have the desired number\n log.info('Desired container number already achieved')\n return\n\n if desired_num > num_running:\n # we need to start/create until we have desired_num\n all_containers = self.containers(stopped=True)\n\n if num_running != len(all_containers):\n # we have some stopped containers, let's start them up again\n ","label":0},{"cid":"compose/service.py::Service.scale.create_and_start","text":"compose/service.py::Service.scale.create_and_start\n def create_and_start(service, number):\n container = service.create_container(number=number, quiet=True)\n service.start_container(container)\n return container","label":0},{"cid":"compose/service.py::Service.scale.stop_and_remove","text":"compose/service.py::Service.scale.stop_and_remove\n def stop_and_remove(container):\n container.stop(timeout=timeout)\n container.remove()","label":0},{"cid":"compose/service.py::Service.create_container","text":"compose/service.py::Service.create_container\n def create_container(self,\n one_off=False,\n do_build=True,\n previous_container=None,\n number=None,\n quiet=False,\n **override_options):\n \"\"\"\n Create a container for this service. If the image doesn't exist, attempt to pull\n it.\n \"\"\"\n self.ensure_image_exists(do_build=do_build)\n\n container_options = self._get_container_create_options(\n override_options,\n number or self._next_container_number(one_off=one_off),\n one_off=one_off,\n previous_container=previous_container,\n )\n\n if 'name' in container_options and not quiet:\n log.info(\"Creating %s\" % container_options['name'])\n\n return Container.create(self.client, **container_options)","label":0},{"cid":"compose/service.py::Service.ensure_image_exists","text":"compose/service.py::Service.ensure_image_exists\n def ensure_image_exists(self, do_build=True):\n try:\n self.image()\n return\n except NoSuchImageError:\n pass\n\n if self.can_be_built():\n if do_build:\n self.build()\n else:\n raise NeedsBuildError(self)\n else:\n self.pull()","label":0},{"cid":"compose/service.py::Service.image","text":"compose/service.py::Service.image\n def image(self):\n try:\n return self.client.inspect_image(self.image_name)\n except APIError as e:\n if e.response.status_code == 404 and e.explanation and 'No such image' in str(e.explanation):\n raise NoSuchImageError(\"Image '{}' not found\".format(self.image_name))\n else:\n raise","label":0},{"cid":"compose/service.py::Service.image_name","text":"compose/service.py::Service.image_name\n @property\n def image_name(self):\n return self.options.get('image', '{s.project}_{s.name}'.format(s=self))","label":0},{"cid":"compose/service.py::Service.convergence_plan","text":"compose/service.py::Service.convergence_plan\n def convergence_plan(self, strategy=ConvergenceStrategy.changed):\n containers = self.containers(stopped=True)\n\n if not containers:\n return ConvergencePlan('create', [])\n\n if strategy is ConvergenceStrategy.never:\n return ConvergencePlan('start', containers)\n\n if (\n strategy is ConvergenceStrategy.always or\n self._containers_have_diverged(containers)\n ):\n return ConvergencePlan('recreate', containers)\n\n stopped = [c for c in containers if not c.is_running]\n\n if stopped:\n return ConvergencePlan('start', stopped)\n\n return ConvergencePlan('noop', containers)","label":0},{"cid":"compose/service.py::Service._containers_have_diverged","text":"compose/service.py::Service._containers_have_diverged\n def _containers_have_diverged(self, containers):\n config_hash = None\n\n try:\n config_hash = self.config_hash\n except NoSuchImageError as e:\n log.debug(\n 'Service %s has diverged: %s',\n self.name, six.text_type(e),\n )\n return True\n\n has_diverged = False\n\n for c in containers:\n container_config_hash = c.labels.get(LABEL_CONFIG_HASH, None)\n if container_config_hash != config_hash:\n log.debug(\n '%s has diverged: %s != %s',\n c.name, container_config_hash, config_hash,\n )\n has_diverged = True\n\n return has_diverged","label":0},{"cid":"compose/service.py::Service.execute_convergence_plan","text":"compose/service.py::Service.execute_convergence_plan\n def execute_convergence_plan(self,\n plan,\n do_build=True,\n timeout=DEFAULT_TIMEOUT,\n detached=False,\n start=True):\n (action, containers) = plan\n should_attach_logs = not detached\n\n if action == 'create':\n container = self.create_container(do_build=do_build)\n\n if should_attach_logs:\n container.attach_log_stream()\n\n if start:\n self.start_container(container)\n\n return [container]\n\n elif action == 'recreate':\n return [\n self.recreate_container(\n container,\n do_build=do_build,\n timeout=timeout,\n attach_logs=should_attach_logs,\n start_new_container=start\n )\n for container in containers\n ]\n\n elif action == 'start':\n if start:\n for container in containers:\n self.start_container_if_stopped(container, attach_logs=should_attach_logs)\n\n return containers\n\n elif action == 'noop':\n for c in containers:\n log.info(\"%s is up-to-date\" % c.name)\n\n return containers\n\n else:\n raise Exception(\"Invalid action: {}\".format(action))","label":0},{"cid":"compose/service.py::Service.recreate_container","text":"compose/service.py::Service.recreate_container\n def recreate_container(\n self,\n container,\n do_build=False,\n timeout=DEFAULT_TIMEOUT,\n attach_logs=False,\n start_new_container=True):\n \"\"\"Recreate a container.\n\n The original container is renamed to a temporary name so that data\n volumes can be copied to the new container, before the original\n container is removed.\n \"\"\"\n log.info(\"Recreating %s\" % container.name)\n\n container.stop(timeout=timeout)\n container.rename_to_tmp_name()\n new_container = self.create_container(\n do_build=do_build,\n previous_container=container,\n number=container.labels.get(LABEL_CONTAINER_NUMBER),\n quiet=True,\n )\n if attach_logs:\n new_container.attach_log_stream()\n if start_new_container:\n self.start_container(new_container)\n container.remove()\n return new_container","label":0},{"cid":"compose/service.py::Service.start_container_if_stopped","text":"compose/service.py::Service.start_container_if_stopped\n def start_container_if_stopped(self, container, attach_logs=False):\n if not container.is_running:\n log.info(\"Starting %s\" % container.name)\n if attach_logs:\n container.attach_log_stream()\n return self.start_container(container)","label":0},{"cid":"compose/service.py::Service.start_container","text":"compose/service.py::Service.start_container\n def start_container(self, container):\n self.connect_container_to_networks(container)\n container.start()\n return container","label":0},{"cid":"compose/service.py::Service.connect_container_to_networks","text":"compose/service.py::Service.connect_container_to_networks\n def connect_container_to_networks(self, container):\n connected_networks = container.get('NetworkSettings.Networks')\n\n for network, aliases in self.networks.items():\n if network in connected_networks:\n self.client.disconnect_container_from_network(\n container.id, network)\n\n self.client.connect_container_to_network(\n container.id, network,\n aliases=list(self._get_aliases(container).union(aliases)),\n links=self._get_links(False),\n )","label":0},{"cid":"compose/service.py::Service.remove_duplicate_containers","text":"compose/service.py::Service.remove_duplicate_containers\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT):\n for c in self.duplicate_containers():\n log.info('Removing %s' % c.name)\n c.stop(timeout=timeout)\n c.remove()","label":0},{"cid":"compose/service.py::Service.duplicate_containers","text":"compose/service.py::Service.duplicate_containers\n def duplicate_containers(self):\n containers = sorted(\n self.containers(stopped=True),\n key=lambda c: c.get('Created'),\n )\n\n numbers = set()\n\n for c in containers:\n if c.number in numbers:\n yield c\n else:\n numbers.add(c.number)","label":0},{"cid":"compose/service.py::Service.config_hash","text":"compose/service.py::Service.config_hash\n @property\n def config_hash(self):\n return json_hash(self.config_dict())","label":0},{"cid":"compose/service.py::Service.config_dict","text":"compose/service.py::Service.config_dict\n def config_dict(self):\n return {\n 'options': self.options,\n 'image_id': self.image()['Id'],\n 'links': self.get_link_names(),\n 'net': self.network_mode.id,\n 'networks': list(self.networks.keys()),\n 'volumes_from': [\n (v.source.name, v.mode)\n for v in self.volumes_from if isinstance(v.source, Service)\n ],\n }","label":0},{"cid":"compose/service.py::Service.get_dependency_names","text":"compose/service.py::Service.get_dependency_names\n def get_dependency_names(self):\n net_name = self.network_mode.service_name\n return (self.get_linked_service_names() +\n self.get_volumes_from_names() +\n ([net_name] if net_name else []) +\n self.options.get('depends_on', []))","label":0},{"cid":"compose/service.py::Service.get_linked_service_names","text":"compose/service.py::Service.get_linked_service_names\n def get_linked_service_names(self):\n return [service.name for (service, _) in self.links]","label":0},{"cid":"compose/service.py::Service.get_link_names","text":"compose/service.py::Service.get_link_names\n def get_link_names(self):\n return [(service.name, alias) for service, alias in self.links]","label":0},{"cid":"compose/service.py::Service.get_volumes_from_names","text":"compose/service.py::Service.get_volumes_from_names\n def get_volumes_from_names(self):\n return [s.source.name for s in self.volumes_from if isinstance(s.source, Service)]","label":0},{"cid":"compose/service.py::Service._next_container_number","text":"compose/service.py::Service._next_container_number\n def _next_container_number(self, one_off=False):\n containers = filter(None, [\n Container.from_ps(self.client, container)\n for container in self.client.containers(\n all=True,\n filters={'label': self.labels(one_off=one_off)})\n ])\n numbers = [c.number for c in containers]\n return 1 if not numbers else max(numbers) + 1","label":0},{"cid":"compose/service.py::Service._get_aliases","text":"compose/service.py::Service._get_aliases\n def _get_aliases(self, container):\n if container.labels.get(LABEL_ONE_OFF) == \"True\":\n return set()\n\n return {self.name, container.short_id}","label":0},{"cid":"compose/service.py::Service._get_links","text":"compose/service.py::Service._get_links\n def _get_links(self, link_to_self):\n links = {}\n\n for service, link_name in self.links:\n for container in service.containers():\n links[link_name or service.name] = container.name\n links[container.name] = container.name\n links[container.name_without_project] = container.name\n\n if link_to_self:\n for container in self.containers():\n links[self.name] = container.name\n links[container.name] = container.name\n links[container.name_without_project] = container.name\n\n for external_link in self.options.get('external_links') or []:\n if ':' not in external_link:\n link_name = external_link\n else:\n external_link, link_name = external_link.split(':')\n links[link_name] = external_link\n\n return [\n (alias, container_name)\n for (container_name, alias) in links.items()\n ]","label":0},{"cid":"compose/service.py::Service._get_volumes_from","text":"compose/service.py::Service._get_volumes_from\n def _get_volumes_from(self):\n return [build_volume_from(spec) for spec in self.volumes_from]","label":0},{"cid":"compose/service.py::Service._get_container_create_options","text":"compose/service.py::Service._get_container_create_options\n def _get_container_create_options(\n self,\n override_options,\n number,\n one_off=False,\n previous_container=None):\n add_config_hash = (not one_off and not override_options)\n\n container_options = dict(\n (k, self.options[k])\n for k in DOCKER_CONFIG_KEYS if k in self.options)\n container_options.update(override_options)\n\n if not container_options.get('name'):\n container_options['name'] = self.get_container_name(number, one_off)\n\n container_options.setdefault('detach', True)\n\n # If a qualified hostname was given, split it into an\n # unqualified hostname and a domainname unless domainname\n # was also given explicitly. This matches the behavior of\n # the official Docker CLI in that scenario.\n if ('hostname' in container_options and\n 'domainname' not in container_options and\n '.' in container_options['hostname']):\n parts = container_options['hostname'].partition('.')\n container_options['hostname'] = parts[0]\n container_options['domainname'] = parts[2]\n\n if 'ports' in container_options or 'expose' in self.options:\n container_options['ports'] = build_container_ports(\n container_options,\n self.options)\n\n container_options['environment'] = merge_environment(\n self.options.get('environment'),\n override_options.get('environment'))\n\n binds, affinity = merge_volume_bindings(\n container_options.get('volumes') or [],\n previous_container)\n override_options['binds'] = binds\n container_options['environment'].update(affinity)\n\n if 'volumes' in container_options:\n container_options['volumes'] = dict(\n (v.internal, {}) for v in container_options['volumes'])\n\n ","label":0},{"cid":"compose/service.py::Service._get_container_host_config","text":"compose/service.py::Service._get_container_host_config\n def _get_container_host_config(self, override_options, one_off=False):\n options = dict(self.options, **override_options)\n\n logging_dict = options.get('logging', None)\n log_config = get_log_config(logging_dict)\n\n return self.client.create_host_config(\n links=self._get_links(link_to_self=one_off),\n port_bindings=build_port_bindings(options.get('ports') or []),\n binds=options.get('binds'),\n volumes_from=self._get_volumes_from(),\n privileged=options.get('privileged', False),\n network_mode=self.network_mode.mode,\n devices=options.get('devices'),\n dns=options.get('dns'),\n dns_search=options.get('dns_search'),\n restart_policy=options.get('restart'),\n cap_add=options.get('cap_add'),\n cap_drop=options.get('cap_drop'),\n mem_limit=options.get('mem_limit'),\n memswap_limit=options.get('memswap_limit'),\n ulimits=build_ulimits(options.get('ulimits')),\n log_config=log_config,\n extra_hosts=options.get('extra_hosts'),\n read_only=options.get('read_only'),\n pid_mode=options.get('pid'),\n security_opt=options.get('security_opt'),\n ipc_mode=options.get('ipc'),\n cgroup_parent=options.get('cgroup_parent'),\n cpu_quota=options.get('cpu_quota'),\n shm_size=options.get('shm_size'),\n )","label":0},{"cid":"compose/service.py::Service.build","text":"compose/service.py::Service.build\n def build(self, no_cache=False, pull=False, force_rm=False):\n log.info('Building %s' % self.name)\n\n build_opts = self.options.get('build', {})\n path = build_opts.get('context')\n # python2 os.path() doesn't support unicode, so we need to encode it to\n # a byte string\n if not six.PY3:\n path = path.encode('utf8')\n\n build_output = self.client.build(\n path=path,\n tag=self.image_name,\n stream=True,\n rm=True,\n forcerm=force_rm,\n pull=pull,\n nocache=no_cache,\n dockerfile=build_opts.get('dockerfile', None),\n buildargs=build_opts.get('args', None),\n )\n\n try:\n all_events = stream_output(build_output, sys.stdout)\n except StreamOutputError as e:\n raise BuildError(self, six.text_type(e))\n\n # Ensure the HTTP connection is not reused for another\n # streaming command, as the Docker daemon can sometimes\n # complain about it\n self.client.close()\n\n image_id = None\n\n for event in all_events:\n if 'stream' in event:\n match = re.search(r'Successfully built ([0-9a-f]+)', event.get('stream', ''))\n if match:\n image_id = match.group(1)\n\n if image_id is None:\n raise BuildError(self, event if all_events else 'Unknown')\n\n return image_id","label":0},{"cid":"compose/service.py::Service.can_be_built","text":"compose/service.py::Service.can_be_built\n def can_be_built(self):\n return 'build' in self.options","label":0},{"cid":"compose/service.py::Service.labels","text":"compose/service.py::Service.labels\n def labels(self, one_off=False):\n return [\n '{0}={1}'.format(LABEL_PROJECT, self.project),\n '{0}={1}'.format(LABEL_SERVICE, self.name),\n '{0}={1}'.format(LABEL_ONE_OFF, \"True\" if one_off else \"False\")\n ]","label":0},{"cid":"compose/service.py::Service.custom_container_name","text":"compose/service.py::Service.custom_container_name\n @property\n def custom_container_name(self):\n return self.options.get('container_name')","label":0},{"cid":"compose/service.py::Service.get_container_name","text":"compose/service.py::Service.get_container_name\n def get_container_name(self, number, one_off=False):\n if self.custom_container_name and not one_off:\n return self.custom_container_name\n\n return build_container_name(self.project, self.name, number, one_off)","label":0},{"cid":"compose/utils.py::get_output_stream","text":"compose/utils.py::get_output_stream\ndef get_output_stream(stream):\n if six.PY3:\n return stream\n return codecs.getwriter('utf-8')(stream)","label":0},{"cid":"compose/utils.py::stream_as_text","text":"compose/utils.py::stream_as_text\ndef stream_as_text(stream):\n \"\"\"Given a stream of bytes or text, if any of the items in the stream\n are bytes convert them to text.\n\n This function can be removed once docker-py returns text streams instead\n of byte streams.\n \"\"\"\n for data in stream:\n if not isinstance(data, six.text_type):\n data = data.decode('utf-8', 'replace')\n yield data","label":0},{"cid":"compose/utils.py::line_splitter","text":"compose/utils.py::line_splitter\ndef line_splitter(buffer, separator=u'\\n'):\n index = buffer.find(six.text_type(separator))\n if index == -1:\n return None\n return buffer[:index + 1], buffer[index + 1:]","label":0},{"cid":"compose/utils.py::split_buffer","text":"compose/utils.py::split_buffer\ndef split_buffer(stream, splitter=None, decoder=lambda a: a):\n \"\"\"Given a generator which yields strings and a splitter function,\n joins all input, splits on the separator and yields each chunk.\n\n Unlike string.split(), each chunk includes the trailing\n separator, except for the last one if none was found on the end\n of the input.\n \"\"\"\n splitter = splitter or line_splitter\n buffered = six.text_type('')\n\n for data in stream_as_text(stream):\n buffered += data\n while True:\n buffer_split = splitter(buffered)\n if buffer_split is None:\n break\n\n item, buffered = buffer_split\n yield item\n\n if buffered:\n yield decoder(buffered)","label":0},{"cid":"compose/utils.py::json_splitter","text":"compose/utils.py::json_splitter\ndef json_splitter(buffer):\n \"\"\"Attempt to parse a json object from a buffer. If there is at least one\n object, return it and the rest of the buffer, otherwise return None.\n \"\"\"\n try:\n obj, index = json_decoder.raw_decode(buffer)\n rest = buffer[json.decoder.WHITESPACE.match(buffer, index).end():]\n return obj, rest\n except ValueError:\n return None","label":0},{"cid":"compose/utils.py::json_stream","text":"compose/utils.py::json_stream\ndef json_stream(stream):\n \"\"\"Given a stream of text, return a stream of json objects.\n This handles streams which are inconsistently buffered (some entries may\n be newline delimited, and others are not).\n \"\"\"\n return split_buffer(stream, json_splitter, json_decoder.decode)","label":0},{"cid":"compose/utils.py::json_hash","text":"compose/utils.py::json_hash\ndef json_hash(obj):\n dump = json.dumps(obj, sort_keys=True, separators=(',', ':'))\n h = hashlib.sha256()\n h.update(dump.encode('utf8'))\n return h.hexdigest()","label":0},{"cid":"compose/utils.py::microseconds_from_time_nano","text":"compose/utils.py::microseconds_from_time_nano\ndef microseconds_from_time_nano(time_nano):\n return int(time_nano % 1000000000 / 1000)","label":0},{"cid":"compose/utils.py::build_string_dict","text":"compose/utils.py::build_string_dict\ndef build_string_dict(source_dict):\n return dict((k, str(v)) for k, v in source_dict.items())","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::migrate","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::migrate\ndef migrate(content):\n data = ruamel.yaml.load(content, ruamel.yaml.RoundTripLoader)\n\n service_names = data.keys()\n\n for name, service in data.items():\n warn_for_links(name, service)\n warn_for_external_links(name, service)\n rewrite_net(service, service_names)\n rewrite_build(service)\n rewrite_logging(service)\n rewrite_volumes_from(service, service_names)\n\n services = {name: data.pop(name) for name in data.keys()}\n\n data['version'] = \"2\"\n data['services'] = services\n create_volumes_section(data)\n\n return data","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_links","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_links\ndef warn_for_links(name, service):\n links = service.get('links')\n if links:\n example_service = links[0].partition(':')[0]\n log.warn(\n \"Service {name} has links, which no longer create environment \"\n \"variables such as {example_service_upper}_PORT. \"\n \"If you are using those in your application code, you should \"\n \"instead connect directly to the hostname, e.g. \"\n \"'{example_service}'.\"\n .format(name=name, example_service=example_service,\n example_service_upper=example_service.upper()))","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_external_links","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_external_links\ndef warn_for_external_links(name, service):\n external_links = service.get('external_links')\n if external_links:\n log.warn(\n \"Service {name} has external_links: {ext}, which now work \"\n \"slightly differently. In particular, two containers must be \"\n \"connected to at least one network in common in order to \"\n \"communicate, even if explicitly linked together.\\n\\n\"\n \"Either connect the external container to your app's default \"\n \"network, or connect both the external container and your \"\n \"service's containers to a pre-existing network. See \"\n \"https://docs.docker.com/compose/networking/ \"\n \"for more on how to do this.\"\n .format(name=name, ext=external_links))","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_net","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_net\ndef rewrite_net(service, service_names):\n if 'net' in service:\n network_mode = service.pop('net')\n\n # \"container:<service name>\" is now \"service:<service name>\"\n if network_mode.startswith('container:'):\n name = network_mode.partition(':')[2]\n if name in service_names:\n network_mode = 'service:{}'.format(name)\n\n service['network_mode'] = network_mode","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_build","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_build\ndef rewrite_build(service):\n if 'dockerfile' in service:\n service['build'] = {\n 'context': service.pop('build'),\n 'dockerfile': service.pop('dockerfile'),\n }","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_logging","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_logging\ndef rewrite_logging(service):\n if 'log_driver' in service:\n service['logging'] = {'driver': service.pop('log_driver')}\n if 'log_opt' in service:\n service['logging']['options'] = service.pop('log_opt')","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_volumes_from","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_volumes_from\ndef rewrite_volumes_from(service, service_names):\n for idx, volume_from in enumerate(service.get('volumes_from', [])):\n if volume_from.split(':', 1)[0] not in service_names:\n service['volumes_from'][idx] = 'container:%s' % volume_from","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::create_volumes_section","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::create_volumes_section\ndef create_volumes_section(data):\n named_volumes = get_named_volumes(data['services'])\n if named_volumes:\n log.warn(\n \"Named volumes ({names}) must be explicitly declared. Creating a \"\n \"'volumes' section with declarations.\\n\\n\"\n \"For backwards-compatibility, they've been declared as external. \"\n \"If you don't mind the volume names being prefixed with the \"\n \"project name, you can remove the 'external' option from each one.\"\n .format(names=', '.join(list(named_volumes))))\n\n data['volumes'] = named_volumes","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::get_named_volumes","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::get_named_volumes\ndef get_named_volumes(services):\n volume_specs = [\n VolumeSpec.parse(volume)\n for service in services.values()\n for volume in service.get('volumes', [])\n ]\n names = {\n spec.external\n for spec in volume_specs\n if spec.is_named_volume\n }\n return {name: {'external': True} for name in names}","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::write","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::write\ndef write(stream, new_format, indent, width):\n ruamel.yaml.dump(\n new_format,\n stream,\n Dumper=ruamel.yaml.RoundTripDumper,\n indent=indent,\n width=width)","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::parse_opts","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::parse_opts\ndef parse_opts(args):\n parser = argparse.ArgumentParser()\n parser.add_argument(\"filename\", help=\"Compose file filename.\")\n parser.add_argument(\"-i\", \"--in-place\", action='store_true')\n parser.add_argument(\n \"--indent\", type=int, default=2,\n help=\"Number of spaces used to indent the output yaml.\")\n parser.add_argument(\n \"--width\", type=int, default=80,\n help=\"Number of spaces used as the output width.\")\n return parser.parse_args()","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::main","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::main\ndef main(args):\n logging.basicConfig(format='\\033[33m%(levelname)s:\\033[37m %(message)s\\033[0m\\n')\n\n opts = parse_opts(args)\n\n with open(opts.filename, 'r') as fh:\n new_format = migrate(fh.read())\n\n if opts.in_place:\n output = open(opts.filename, 'w')\n else:\n output = sys.stdout\n write(output, new_format, opts.indent, opts.width)","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/colors.py::get_pairs\",\n \"text\": \"compose/cli/colors.py::get_pairs\\ndef get_pairs():\\n for i, name in enumerate(NAMES):\\n yield(name, str(30 + i))\\n yield('intense_' + name, str(30 + i) + ';1')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/colors.py::ansi\",\n \"text\": \"compose/cli/colors.py::ansi\\ndef ansi(code):\\n return '\\\\033[{0}m'.format(code)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/colors.py::ansi_color\",\n \"text\": \"compose/cli/colors.py::ansi_color\\ndef ansi_color(code, s):\\n return '{0}{1}{2}'.format(ansi(code), s, ansi(0))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/colors.py::make_color_fn\",\n \"text\": \"compose/cli/colors.py::make_color_fn\\ndef make_color_fn(code):\\n return lambda s: ansi_color(code, s)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/colors.py::rainbow\",\n \"text\": \"compose/cli/colors.py::rainbow\\ndef rainbow():\\n cs = ['cyan', 'yellow', 'green', 'magenta', 'red', 'blue',\\n 'intense_cyan', 'intense_yellow', 'intense_green',\\n 'intense_magenta', 'intense_red', 'intense_blue']\\n\\n for c in cs:\\n yield globals()[c]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/formatter.py::get_tty_width\",\n \"text\": \"compose/cli/formatter.py::get_tty_width\\ndef get_tty_width():\\n tty_size = os.popen('stty size', 'r').read().split()\\n if len(tty_size) != 2:\\n return 0\\n _, width = tty_size\\n return int(width)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/formatter.py::Formatter.table\",\n \"text\": \"compose/cli/formatter.py::Formatter.table\\n def table(self, headers, rows):\\n table = texttable.Texttable(max_width=get_tty_width())\\n table.set_cols_dtype(['t' for h in headers])\\n table.add_rows([headers] + rows)\\n table.set_deco(table.HEADER)\\n table.set_chars(['-', '|', '+', '-'])\\n\\n return table.draw()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/formatter.py::ConsoleWarningFormatter.get_level_message\",\n \"text\": \"compose/cli/formatter.py::ConsoleWarningFormatter.get_level_message\\n def get_level_message(self, record):\\n separator = ': '\\n if record.levelno == logging.WARNING:\\n return colors.yellow(record.levelname) + separator\\n if record.levelno == logging.ERROR:\\n return colors.red(record.levelname) + separator\\n\\n return ''\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/formatter.py::ConsoleWarningFormatter.format\",\n \"text\": \"compose/cli/formatter.py::ConsoleWarningFormatter.format\\n def format(self, record):\\n message = super(ConsoleWarningFormatter, self).format(record)\\n return self.get_level_message(record) + message\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPrinter.__init__\",\n \"text\": \"compose/cli/log_printer.py::LogPrinter.__init__\\n def __init__(self, containers, output=sys.stdout, monochrome=False, cascade_stop=False):\\n self.containers = containers\\n self.output = utils.get_output_stream(output)\\n self.monochrome = monochrome\\n self.cascade_stop = cascade_stop\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPrinter.run\",\n \"text\": \"compose/cli/log_printer.py::LogPrinter.run\\n def run(self):\\n if not self.containers:\\n return\\n\\n prefix_width = max_name_width(self.containers)\\n generators = list(self._make_log_generators(self.monochrome, prefix_width))\\n for line in Multiplexer(generators, cascade_stop=self.cascade_stop).loop():\\n self.output.write(line)\\n self.output.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPrinter._make_log_generators\",\n \"text\": \"compose/cli/log_printer.py::LogPrinter._make_log_generators\\n def _make_log_generators(self, monochrome, prefix_width):\\n def no_color(text):\\n return text\\n\\n if monochrome:\\n color_funcs = cycle([no_color])\\n else:\\n color_funcs = cycle(colors.rainbow())\\n\\n for color_func, container in zip(color_funcs, self.containers):\\n generator_func = get_log_generator(container)\\n prefix = color_func(build_log_prefix(container, prefix_width))\\n yield generator_func(container, prefix, color_func)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPrinter._make_log_generators.no_color\",\n \"text\": \"compose/cli/log_printer.py::LogPrinter._make_log_generators.no_color\\n def no_color(text):\\n return text\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_log_prefix\",\n \"text\": \"compose/cli/log_printer.py::build_log_prefix\\ndef build_log_prefix(container, prefix_width):\\n return container.name_without_project.ljust(prefix_width) + ' | '\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::max_name_width\",\n \"text\": \"compose/cli/log_printer.py::max_name_width\\ndef max_name_width(containers):\\n \\\"\\\"\\\"Calculate the maximum width of container names so we can make the log\\n prefixes line up like so:\\n\\n db_1 | Listening\\n web_1 | Listening\\n \\\"\\\"\\\"\\n return max(len(container.name_without_project) for container in containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::get_log_generator\",\n \"text\": \"compose/cli/log_printer.py::get_log_generator\\ndef get_log_generator(container):\\n if container.has_api_logs:\\n return build_log_generator\\n return build_no_log_generator\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_no_log_generator\",\n \"text\": \"compose/cli/log_printer.py::build_no_log_generator\\ndef build_no_log_generator(container, prefix, color_func):\\n \\\"\\\"\\\"Return a generator that prints a warning about logs and waits for\\n container to exit.\\n \\\"\\\"\\\"\\n yield \\\"{} WARNING: no logs are available with the '{}' log driver\\\\n\\\".format(\\n prefix,\\n container.log_driver)\\n yield color_func(wait_on_exit(container))\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_log_generator\",\n \"text\": \"compose/cli/log_printer.py::build_log_generator\\ndef build_log_generator(container, prefix, color_func):\\n # if the container doesn't have a log_stream we need to attach to container\\n # before log printer starts running\\n if container.log_stream is None:\\n stream = container.attach(stdout=True, stderr=True, stream=True, logs=True)\\n line_generator = split_buffer(stream)\\n else:\\n line_generator = split_buffer(container.log_stream)\\n\\n for line in line_generator:\\n yield prefix + line\\n yield color_func(wait_on_exit(container))\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/log_printer.py::wait_on_exit\",\n \"text\": \"compose/cli/log_printer.py::wait_on_exit\\ndef wait_on_exit(container):\\n exit_code = container.wait()\\n return \\\"%s exited with code %s\\\\n\\\" % (container.name, exit_code)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::main\",\n \"text\": \"compose/cli/main.py::main\\ndef main():\\n setup_logging()\\n try:\\n command = TopLevelCommand()\\n command.sys_dispatch()\\n except KeyboardInterrupt:\\n log.error(\\\"Aborting.\\\")\\n sys.exit(1)\\n except (UserError, NoSuchService, ConfigurationError) as e:\\n log.error(e.msg)\\n sys.exit(1)\\n except NoSuchCommand as e:\\n commands = \\\"\\\\n\\\".join(parse_doc_section(\\\"commands:\\\", getdoc(e.supercommand)))\\n log.error(\\\"No such command: %s\\\\n\\\\n%s\\\", e.command, commands)\\n sys.exit(1)\\n except APIError as e:\\n log_api_error(e)\\n sys.exit(1)\\n except BuildError as e:\\n log.error(\\\"Service '%s' failed to build: %s\\\" % (e.service.name, e.reason))\\n sys.exit(1)\\n except StreamOutputError as e:\\n log.error(e)\\n sys.exit(1)\\n except NeedsBuildError as e:\\n log.error(\\\"Service '%s' needs to be built, but --no-build was passed.\\\" % e.service.name)\\n sys.exit(1)\\n except ReadTimeout as e:\\n log.error(\\n \\\"An HTTP request took too long to complete. Retry with --verbose to \\\"\\n \\\"obtain debug information.\\\\n\\\"\\n \\\"If you encounter this issue regularly because of slow network \\\"\\n \\\"conditions, consider setting COMPOSE_HTTP_TIMEOUT to a higher \\\"\\n \\\"value (current value: %s).\\\" % HTTP_TIMEOUT\\n )\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::log_api_error\",\n \"text\": \"compose/cli/main.py::log_api_error\\ndef log_api_error(e):\\n if 'client is newer than server' in e.explanation:\\n # we need JSON formatted errors. In the meantime...\\n # TODO: fix this by refactoring project dispatch\\n # http://github.com/docker/compose/pull/2832#commitcomment-15923800\\n client_version = e.explanation.split('client API version: ')[1].split(',')[0]\\n log.error(\\n \\\"The engine version is lesser than the minimum required by \\\"\\n \\\"compose. Your current project requires a Docker Engine of \\\"\\n \\\"version {version} or superior.\\\".format(\\n version=API_VERSION_TO_ENGINE_VERSION[client_version]\\n ))\\n else:\\n log.error(e.explanation)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_logging\",\n \"text\": \"compose/cli/main.py::setup_logging\\ndef setup_logging():\\n root_logger = logging.getLogger()\\n root_logger.addHandler(console_handler)\\n root_logger.setLevel(logging.DEBUG)\\n\\n # Disable requests logging\\n logging.getLogger(\\\"requests\\\").propagate = False\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_console_handler\",\n \"text\": \"compose/cli/main.py::setup_console_handler\\ndef setup_console_handler(handler, verbose):\\n if handler.stream.isatty():\\n format_class = ConsoleWarningFormatter\\n else:\\n format_class = logging.Formatter\\n\\n if verbose:\\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\\n handler.setLevel(logging.DEBUG)\\n else:\\n handler.setFormatter(format_class())\\n handler.setLevel(logging.INFO)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::parse_doc_section\",\n \"text\": \"compose/cli/main.py::parse_doc_section\\ndef parse_doc_section(name, source):\\n pattern = re.compile('^([^\\\\n]*' + name + '[^\\\\n]*\\\\n?(?:[ \\\\t].*?(?:\\\\n|$))*)',\\n re.IGNORECASE | re.MULTILINE)\\n return [s.strip() for s in pattern.findall(source)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.docopt_options\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.docopt_options\\n def docopt_options(self):\\n options = super(TopLevelCommand, self).docopt_options()\\n options['version'] = get_version_info('compose')\\n return options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.perform_command\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.perform_command\\n def perform_command(self, options, handler, command_options):\\n setup_console_handler(console_handler, options.get('--verbose'))\\n\\n if options['COMMAND'] in ('help', 'version'):\\n # Skip looking up the compose file.\\n handler(None, command_options)\\n return\\n\\n if options['COMMAND'] == 'config':\\n handler(options, command_options)\\n return\\n\\n project = project_from_options(self.base_dir, options)\\n with friendly_error_message():\\n handler(project, command_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.build\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.build\\n def build(self, project, options):\\n \\\"\\\"\\\"\\n Build or rebuild services.\\n\\n Services are built once and then tagged as `project_service`,\\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\\n contents of its build directory, you can run `docker-compose build` to rebuild it.\\n\\n Usage: build [options] [SERVICE...]\\n\\n Options:\\n --force-rm Always remove intermediate containers.\\n --no-cache Do not use cache when building the image.\\n --pull Always attempt to pull a newer version of the image.\\n \\\"\\\"\\\"\\n project.build(\\n service_names=options['SERVICE'],\\n no_cache=bool(options.get('--no-cache', False)),\\n pull=bool(options.get('--pull', False)),\\n force_rm=bool(options.get('--force-rm', False)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.config\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.config\\n def config(self, config_options, options):\\n \\\"\\\"\\\"\\n Validate and view the compose file.\\n\\n Usage: config [options]\\n\\n Options:\\n -q, --quiet Only validate the configuration, don't print\\n anything.\\n --services Print the service names, one per line.\\n\\n \\\"\\\"\\\"\\n config_path = get_config_path_from_options(config_options)\\n compose_config = config.load(config.find(self.base_dir, config_path))\\n\\n if options['--quiet']:\\n return\\n\\n if options['--services']:\\n print('\\\\n'.join(service['name'] for service in compose_config.services))\\n return\\n\\n print(serialize_config(compose_config))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.create\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.create\\n def create(self, project, options):\\n \\\"\\\"\\\"\\n Creates containers for a service.\\n\\n Usage: create [options] [SERVICE...]\\n\\n Options:\\n --force-recreate Recreate containers even if their configuration and\\n image haven't changed. Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate them.\\n Incompatible with --force-recreate.\\n --no-build Don't build an image, even if it's missing\\n \\\"\\\"\\\"\\n service_names = options['SERVICE']\\n\\n project.create(\\n service_names=service_names,\\n strategy=convergence_strategy_from_opts(options),\\n do_build=not options['--no-build']\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.down\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.down\\n def down(self, project, options):\\n \\\"\\\"\\\"\\n Stop containers and remove containers, networks, volumes, and images\\n created by `up`. Only containers and networks are removed by default.\\n\\n Usage: down [options]\\n\\n Options:\\n --rmi type Remove images, type may be one of: 'all' to remove\\n all images, or 'local' to remove only images that\\n don't have an custom name set by the `image` field\\n -v, --volumes Remove data volumes\\n \\\"\\\"\\\"\\n image_type = image_type_from_opt('--rmi', options['--rmi'])\\n project.down(image_type, options['--volumes'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events\\n def events(self, project, options):\\n \\\"\\\"\\\"\\n Receive real time events from containers.\\n\\n Usage: events [options] [SERVICE...]\\n\\n Options:\\n --json Output events as a stream of json objects\\n \\\"\\\"\\\"\\n def format_event(event):\\n attributes = [\\\"%s=%s\\\" % item for item in event['attributes'].items()]\\n return (\\\"{time} {type} {action} {id} ({attrs})\\\").format(\\n attrs=\\\", \\\".join(sorted(attributes)),\\n **event)\\n\\n def json_format_event(event):\\n event['time'] = event['time'].isoformat()\\n return json.dumps(event)\\n\\n for event in project.events():\\n formatter = json_format_event if options['--json'] else format_event\\n print(formatter(event))\\n sys.stdout.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events.format_event\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events.format_event\\n def format_event(event):\\n attributes = [\\\"%s=%s\\\" % item for item in event['attributes'].items()]\\n return (\\\"{time} {type} {action} {id} ({attrs})\\\").format(\\n attrs=\\\", \\\".join(sorted(attributes)),\\n **event)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events.json_format_event\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events.json_format_event\\n def json_format_event(event):\\n event['time'] = event['time'].isoformat()\\n return json.dumps(event)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.exec_command\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.exec_command\\n def exec_command(self, project, options):\\n \\\"\\\"\\\"\\n Execute a command in a running container\\n\\n Usage: exec [options] SERVICE COMMAND [ARGS...]\\n\\n Options:\\n -d Detached mode: Run command in the background.\\n --privileged Give extended privileges to the process.\\n --user USER Run the command as this user.\\n -T Disable pseudo-tty allocation. By default `docker-compose exec`\\n allocates a TTY.\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n \\\"\\\"\\\"\\n index = int(options.get('--index'))\\n service = project.get_service(options['SERVICE'])\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n command = [options['COMMAND']] + options['ARGS']\\n tty = not options[\\\"-T\\\"]\\n\\n create_exec_options = {\\n \\\"privileged\\\": options[\\\"--privileged\\\"],\\n \\\"user\\\": options[\\\"--user\\\"],\\n \\\"tty\\\": tty,\\n \\\"stdin\\\": tty,\\n }\\n\\n exec_id = container.create_exec(command, **create_exec_options)\\n\\n if options['-d']:\\n container.start_exec(exec_id, tty=tty)\\n return\\n\\n signals.set_signal_handler_to_shutdown()\\n try:\\n operation = ExecOperation(\\n project.client,\\n exec_id,\\n interactive=tty,\\n )\\n pty = PseudoTerminal(project.client, operation)\\n pty.start()\\n except signals.ShutdownException:\\n log.info(\\\"received shutdown exception: closing\\\")\\n exit_code = project.client.exec_inspect(exec_id).get(\\\"ExitCode\\\")\\n sys.exit(exit_code)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.help\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.help\\n def help(self, project, options):\\n \\\"\\\"\\\"\\n Get help on a command.\\n\\n Usage: help COMMAND\\n \\\"\\\"\\\"\\n handler = self.get_handler(options['COMMAND'])\\n raise SystemExit(getdoc(handler))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.kill\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.kill\\n def kill(self, project, options):\\n \\\"\\\"\\\"\\n Force stop service containers.\\n\\n Usage: kill [options] [SERVICE...]\\n\\n Options:\\n -s SIGNAL SIGNAL to send to the container.\\n Default signal is SIGKILL.\\n \\\"\\\"\\\"\\n signal = options.get('-s', 'SIGKILL')\\n\\n project.kill(service_names=options['SERVICE'], signal=signal)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.logs\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.logs\\n def logs(self, project, options):\\n \\\"\\\"\\\"\\n View output from containers.\\n\\n Usage: logs [options] [SERVICE...]\\n\\n Options:\\n --no-color Produce monochrome output.\\n \\\"\\\"\\\"\\n containers = project.containers(service_names=options['SERVICE'], stopped=True)\\n\\n monochrome = options['--no-color']\\n print(\\\"Attaching to\\\", list_containers(containers))\\n LogPrinter(containers, monochrome=monochrome).run()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pause\\n def pause(self, project, options):\\n \\\"\\\"\\\"\\n Pause services.\\n\\n Usage: pause [SERVICE...]\\n \\\"\\\"\\\"\\n containers = project.pause(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to pause', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.port\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.port\\n def port(self, project, options):\\n \\\"\\\"\\\"\\n Print the public port for a port binding.\\n\\n Usage: port [options] SERVICE PRIVATE_PORT\\n\\n Options:\\n --protocol=proto tcp or udp [default: tcp]\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n \\\"\\\"\\\"\\n index = int(options.get('--index'))\\n service = project.get_service(options['SERVICE'])\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n print(container.get_local_port(\\n options['PRIVATE_PORT'],\\n protocol=options.get('--protocol') or 'tcp') or '')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.ps\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.ps\\n def ps(self, project, options):\\n \\\"\\\"\\\"\\n List containers.\\n\\n Usage: ps [options] [SERVICE...]\\n\\n Options:\\n -q Only display IDs\\n \\\"\\\"\\\"\\n containers = sorted(\\n project.containers(service_names=options['SERVICE'], stopped=True) +\\n project.containers(service_names=options['SERVICE'], one_off=True),\\n key=attrgetter('name'))\\n\\n if options['-q']:\\n for container in containers:\\n print(container.id)\\n else:\\n headers = [\\n 'Name',\\n 'Command',\\n 'State',\\n 'Ports',\\n ]\\n rows = []\\n for container in containers:\\n command = container.human_readable_command\\n if len(command) > 30:\\n command = '%s ...' % command[:26]\\n rows.append([\\n container.name,\\n command,\\n container.human_readable_state,\\n container.human_readable_ports,\\n ])\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pull\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pull\\n def pull(self, project, options):\\n \\\"\\\"\\\"\\n Pulls images for services.\\n\\n Usage: pull [options] [SERVICE...]\\n\\n Options:\\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\\n \\\"\\\"\\\"\\n project.pull(\\n service_names=options['SERVICE'],\\n ignore_pull_failures=options.get('--ignore-pull-failures')\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.rm\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.rm\\n def rm(self, project, options):\\n \\\"\\\"\\\"\\n Remove stopped service containers.\\n\\n By default, volumes attached to containers will not be removed. You can see all\\n volumes with `docker volume ls`.\\n\\n Any data which is not in a volume will be lost.\\n\\n Usage: rm [options] [SERVICE...]\\n\\n Options:\\n -f, --force Don't ask to confirm removal\\n -v Remove volumes associated with containers\\n \\\"\\\"\\\"\\n all_containers = project.containers(service_names=options['SERVICE'], stopped=True)\\n stopped_containers = [c for c in all_containers if not c.is_running]\\n\\n if len(stopped_containers) > 0:\\n print(\\\"Going to remove\\\", list_containers(stopped_containers))\\n if options.get('--force') \\\\\\n or yesno(\\\"Are you sure? [yN] \\\", default=False):\\n project.remove_stopped(\\n service_names=options['SERVICE'],\\n v=options.get('-v', False)\\n )\\n else:\\n print(\\\"No stopped containers\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.run\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.run\\n def run(self, project, options):\\n \\\"\\\"\\\"\\n Run a one-off command on a service.\\n\\n For example:\\n\\n $ docker-compose run web python manage.py shell\\n\\n By default, linked services will be started, unless they are already\\n running. If you do not want to start linked services, use\\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\\n\\n Usage: run [options] [-p PORT...] [-e KEY=VAL...] SERVICE [COMMAND] [ARGS...]\\n\\n Options:\\n -d Detached mode: Run container in the background, print\\n new container name.\\n --name NAME Assign a name to the container\\n --entrypoint CMD Override the entrypoint of the image.\\n -e KEY=VAL Set an environment variable (can be used multiple times)\\n -u, --user=\\\"\\\" Run as specified username or uid\\n --no-deps Don't start linked services.\\n --rm Remove container after run. Ignored in detached mode.\\n -p, --publish=[] Publish a container's port(s) to the host\\n --service-ports Run command with the service's ports enabled and mapped\\n to the host.\\n -T Disable pseudo-tty allocation. By default `docker-compose run`\\n allocates a TTY.\\n \\\"\\\"\\\"\\n service = project.get_service(options['SERVICE'])\\n detach = options['-d']\\n\\n if IS_WINDOWS_PLATFORM and not detach:\\n raise UserError(\\n \\\"Interactive mode is not yet supported on Windows.\\\\n\\\"\\n \\\"Please pass the -d flag when using `docker-compose run`.\\\"\\n )\\n\\n if options['COMMAND']:\\n command = [options['COMMAND']] + options['ARGS']\\n else:\\n command = service.options.get('command')\\n\\n container_opt\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.scale\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.scale\\n def scale(self, project, options):\\n \\\"\\\"\\\"\\n Set number of containers to run for a service.\\n\\n Numbers are specified in the form `service=num` as arguments.\\n For example:\\n\\n $ docker-compose scale web=2 worker=3\\n\\n Usage: scale [options] [SERVICE=NUM...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\\n\\n for s in options['SERVICE=NUM']:\\n if '=' not in s:\\n raise UserError('Arguments to scale should be in the form service=num')\\n service_name, num = s.split('=', 1)\\n try:\\n num = int(num)\\n except ValueError:\\n raise UserError('Number of containers for service \\\"%s\\\" is not a '\\n 'number' % service_name)\\n project.get_service(service_name).scale(num, timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.start\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.start\\n def start(self, project, options):\\n \\\"\\\"\\\"\\n Start existing containers.\\n\\n Usage: start [SERVICE...]\\n \\\"\\\"\\\"\\n containers = project.start(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to start', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.stop\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.stop\\n def stop(self, project, options):\\n \\\"\\\"\\\"\\n Stop running containers without removing them.\\n\\n They can be started again with `docker-compose start`.\\n\\n Usage: stop [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\\n project.stop(service_names=options['SERVICE'], timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.restart\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.restart\\n def restart(self, project, options):\\n \\\"\\\"\\\"\\n Restart running containers.\\n\\n Usage: restart [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\\n containers = project.restart(service_names=options['SERVICE'], timeout=timeout)\\n exit_if(not containers, 'No containers to restart', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.unpause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.unpause\\n def unpause(self, project, options):\\n \\\"\\\"\\\"\\n Unpause services.\\n\\n Usage: unpause [SERVICE...]\\n \\\"\\\"\\\"\\n containers = project.unpause(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to unpause', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.up\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.up\\n def up(self, project, options):\\n \\\"\\\"\\\"\\n Builds, (re)creates, starts, and attaches to containers for a service.\\n\\n Unless they are already running, this command also starts any linked services.\\n\\n The `docker-compose up` command aggregates the output of each container. When\\n the command exits, all containers are stopped. Running `docker-compose up -d`\\n starts the containers in the background and leaves them running.\\n\\n If there are existing containers for a service, and the service's configuration\\n or image was changed after the container's creation, `docker-compose up` picks\\n up the changes by stopping and recreating the containers (preserving mounted\\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\\n flag.\\n\\n If you want to force Compose to stop and recreate all containers, use the\\n `--force-recreate` flag.\\n\\n Usage: up [options] [SERVICE...]\\n\\n Options:\\n -d Detached mode: Run containers in the background,\\n print new container names.\\n Incompatible with --abort-on-container-exit.\\n --no-color Produce monochrome output.\\n --no-deps Don't start linked services.\\n --force-recreate Recreate containers even if their configuration\\n and image haven't changed.\\n Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate them.\\n Incompatible with --force-recreate.\\n --no-build Don't build an image, even if it's missing\\n --abort-on-container-exit Stops all containers if any container was stopped.\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.version\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.version\\n def version(self, project, options):\\n \\\"\\\"\\\"\\n Show version informations\\n\\n Usage: version [--short]\\n\\n Options:\\n --short Shows only Compose's version number.\\n \\\"\\\"\\\"\\n if options['--short']:\\n print(__version__)\\n else:\\n print(get_version_info('full'))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::convergence_strategy_from_opts\",\n \"text\": \"compose/cli/main.py::convergence_strategy_from_opts\\ndef convergence_strategy_from_opts(options):\\n no_recreate = options['--no-recreate']\\n force_recreate = options['--force-recreate']\\n if force_recreate and no_recreate:\\n raise UserError(\\\"--force-recreate and --no-recreate cannot be combined.\\\")\\n\\n if force_recreate:\\n return ConvergenceStrategy.always\\n\\n if no_recreate:\\n return ConvergenceStrategy.never\\n\\n return ConvergenceStrategy.changed\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::image_type_from_opt\",\n \"text\": \"compose/cli/main.py::image_type_from_opt\\ndef image_type_from_opt(flag, value):\\n if not value:\\n return ImageType.none\\n try:\\n return ImageType[value]\\n except KeyError:\\n raise UserError(\\\"%s flag must be one of: all, local\\\" % flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::run_one_off_container\",\n \"text\": \"compose/cli/main.py::run_one_off_container\\ndef run_one_off_container(container_options, project, service, options):\\n if not options['--no-deps']:\\n deps = service.get_dependency_names()\\n if deps:\\n project.up(\\n service_names=deps,\\n start_deps=True,\\n strategy=ConvergenceStrategy.never)\\n\\n project.initialize()\\n\\n container = service.create_container(\\n quiet=True,\\n one_off=True,\\n **container_options)\\n\\n if options['-d']:\\n service.start_container(container)\\n print(container.name)\\n return\\n\\n def remove_container(force=False):\\n if options['--rm']:\\n project.client.remove_container(container.id, force=True)\\n\\n signals.set_signal_handler_to_shutdown()\\n try:\\n try:\\n operation = RunOperation(\\n project.client,\\n container.id,\\n interactive=not options['-T'],\\n logs=False,\\n )\\n pty = PseudoTerminal(project.client, operation)\\n sockets = pty.sockets()\\n service.start_container(container)\\n pty.start(sockets)\\n exit_code = container.wait()\\n except signals.ShutdownException:\\n project.client.stop(container.id)\\n exit_code = 1\\n except signals.ShutdownException:\\n project.client.kill(container.id)\\n remove_container(force=True)\\n sys.exit(2)\\n\\n remove_container()\\n sys.exit(exit_code)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/main.py::run_one_off_container.remove_container\",\n \"text\": \"compose/cli/main.py::run_one_off_container.remove_container\\n def remove_container(force=False):\\n if options['--rm']:\\n project.client.remove_container(container.id, force=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::build_log_printer\",\n \"text\": \"compose/cli/main.py::build_log_printer\\ndef build_log_printer(containers, service_names, monochrome, cascade_stop):\\n if service_names:\\n containers = [\\n container\\n for container in containers if container.service in service_names\\n ]\\n return LogPrinter(containers, monochrome=monochrome, cascade_stop=cascade_stop)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::up_shutdown_context\",\n \"text\": \"compose/cli/main.py::up_shutdown_context\\n@contextlib.contextmanager\\ndef up_shutdown_context(project, service_names, timeout, detached):\\n if detached:\\n yield\\n return\\n\\n signals.set_signal_handler_to_shutdown()\\n try:\\n try:\\n yield\\n except signals.ShutdownException:\\n print(\\\"Gracefully stopping... (press Ctrl+C again to force)\\\")\\n project.stop(service_names=service_names, timeout=timeout)\\n except signals.ShutdownException:\\n project.kill(service_names=service_names)\\n sys.exit(2)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::list_containers\",\n \"text\": \"compose/cli/main.py::list_containers\\ndef list_containers(containers):\\n return \\\", \\\".join(c.name for c in containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::exit_if\",\n \"text\": \"compose/cli/main.py::exit_if\\ndef exit_if(condition, message, exit_code):\\n if condition:\\n log.error(message)\\n raise SystemExit(exit_code)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_ports\",\n \"text\": \"compose/config/validation.py::format_ports\\n@FormatChecker.cls_checks(format=\\\"ports\\\", raises=ValidationError)\\ndef format_ports(instance):\\n try:\\n split_port(instance)\\n except ValueError as e:\\n raise ValidationError(six.text_type(e))\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_expose\",\n \"text\": \"compose/config/validation.py::format_expose\\n@FormatChecker.cls_checks(format=\\\"expose\\\", raises=ValidationError)\\ndef format_expose(instance):\\n if isinstance(instance, six.string_types):\\n if not re.match(VALID_EXPOSE_FORMAT, instance):\\n raise ValidationError(\\n \\\"should be of the format 'PORT[/PROTOCOL]'\\\")\\n\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_boolean_in_environment\",\n \"text\": \"compose/config/validation.py::format_boolean_in_environment\\n@FormatChecker.cls_checks(format=\\\"bool-value-in-mapping\\\")\\ndef format_boolean_in_environment(instance):\\n \\\"\\\"\\\"Check if there is a boolean in the mapping sections and display a warning.\\n Always return True here so the validation won't raise an error.\\n \\\"\\\"\\\"\\n if isinstance(instance, bool):\\n log.warn(\\n \\\"There is a boolean value in the 'environment', 'labels', or \\\"\\n \\\"'extra_hosts' field of a service.\\\\n\\\"\\n \\\"These sections only support string values.\\\\n\\\"\\n \\\"Please add quotes to any boolean values to make them strings \\\"\\n \\\"(eg, 'True', 'false', 'yes', 'N', 'on', 'Off').\\\\n\\\"\\n \\\"This warning will become an error in a future release. \\\\r\\\\n\\\"\\n )\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::match_named_volumes\",\n \"text\": \"compose/config/validation.py::match_named_volumes\\ndef match_named_volumes(service_dict, project_volumes):\\n service_volumes = service_dict.get('volumes', [])\\n for volume_spec in service_volumes:\\n if volume_spec.is_named_volume and volume_spec.external not in project_volumes:\\n raise ConfigurationError(\\n 'Named volume \\\"{0}\\\" is used in service \\\"{1}\\\" but no'\\n ' declaration was found in the volumes section.'.format(\\n volume_spec.repr(), service_dict.get('name')\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::python_type_to_yaml_type\",\n \"text\": \"compose/config/validation.py::python_type_to_yaml_type\\ndef python_type_to_yaml_type(type_):\\n type_name = type(type_).__name__\\n return {\\n 'dict': 'mapping',\\n 'list': 'array',\\n 'int': 'number',\\n 'float': 'number',\\n 'bool': 'boolean',\\n 'unicode': 'string',\\n 'str': 'string',\\n 'bytes': 'string',\\n }.get(type_name, type_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_config_section\",\n \"text\": \"compose/config/validation.py::validate_config_section\\ndef validate_config_section(filename, config, section):\\n \\\"\\\"\\\"Validate the structure of a configuration section. This must be done\\n before interpolation so it's separate from schema validation.\\n \\\"\\\"\\\"\\n if not isinstance(config, dict):\\n raise ConfigurationError(\\n \\\"In file '{filename}', {section} must be a mapping, not \\\"\\n \\\"{type}.\\\".format(\\n filename=filename,\\n section=section,\\n type=anglicize_json_type(python_type_to_yaml_type(config))))\\n\\n for key, value in config.items():\\n if not isinstance(key, six.string_types):\\n raise ConfigurationError(\\n \\\"In file '{filename}', the {section} name {name} must be a \\\"\\n \\\"quoted string, i.e. '{name}'.\\\".format(\\n filename=filename,\\n section=section,\\n name=key))\\n\\n if not isinstance(value, (dict, type(None))):\\n raise ConfigurationError(\\n \\\"In file '{filename}', {section} '{name}' must be a mapping not \\\"\\n \\\"{type}.\\\".format(\\n filename=filename,\\n section=section,\\n name=key,\\n type=anglicize_json_type(python_type_to_yaml_type(value))))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_top_level_object\",\n \"text\": \"compose/config/validation.py::validate_top_level_object\\ndef validate_top_level_object(config_file):\\n if not isinstance(config_file.config, dict):\\n raise ConfigurationError(\\n \\\"Top level object in '{}' needs to be an object not '{}'.\\\".format(\\n config_file.filename,\\n type(config_file.config)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_ulimits\",\n \"text\": \"compose/config/validation.py::validate_ulimits\\ndef validate_ulimits(service_config):\\n ulimit_config = service_config.config.get('ulimits', {})\\n for limit_name, soft_hard_values in six.iteritems(ulimit_config):\\n if isinstance(soft_hard_values, dict):\\n if not soft_hard_values['soft'] <= soft_hard_values['hard']:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' has invalid ulimit '{ulimit}'. \\\"\\n \\\"'soft' value can not be greater than 'hard' value \\\".format(\\n s=service_config,\\n ulimit=ulimit_config))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_extends_file_path\",\n \"text\": \"compose/config/validation.py::validate_extends_file_path\\ndef validate_extends_file_path(service_name, extends_options, filename):\\n \\\"\\\"\\\"\\n The service to be extended must either be defined in the config key 'file',\\n or within 'filename'.\\n \\\"\\\"\\\"\\n error_prefix = \\\"Invalid 'extends' configuration for %s:\\\" % service_name\\n\\n if 'file' not in extends_options and filename is None:\\n raise ConfigurationError(\\n \\\"%s you need to specify a 'file', e.g. 'file: something.yml'\\\" % error_prefix\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_network_mode\",\n \"text\": \"compose/config/validation.py::validate_network_mode\\ndef validate_network_mode(service_config, service_names):\\n network_mode = service_config.config.get('network_mode')\\n if not network_mode:\\n return\\n\\n if 'networks' in service_config.config:\\n raise ConfigurationError(\\\"'network_mode' and 'networks' cannot be combined\\\")\\n\\n dependency = get_service_name_from_network_mode(network_mode)\\n if not dependency:\\n return\\n\\n if dependency not in service_names:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' uses the network stack of service '{dep}' which \\\"\\n \\\"is undefined.\\\".format(s=service_config, dep=dependency))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_depends_on\",\n \"text\": \"compose/config/validation.py::validate_depends_on\\ndef validate_depends_on(service_config, service_names):\\n for dependency in service_config.config.get('depends_on', []):\\n if dependency not in service_names:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' depends on service '{dep}' which is \\\"\\n \\\"undefined.\\\".format(s=service_config, dep=dependency))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::get_unsupported_config_msg\",\n \"text\": \"compose/config/validation.py::get_unsupported_config_msg\\ndef get_unsupported_config_msg(path, error_key):\\n msg = \\\"Unsupported config option for {}: '{}'\\\".format(path_string(path), error_key)\\n if error_key in DOCKER_CONFIG_HINTS:\\n msg += \\\" (did you mean '{}'?)\\\".format(DOCKER_CONFIG_HINTS[error_key])\\n return msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::anglicize_json_type\",\n \"text\": \"compose/config/validation.py::anglicize_json_type\\ndef anglicize_json_type(json_type):\\n if json_type.startswith(('a', 'e', 'i', 'o', 'u')):\\n return 'an ' + json_type\\n return 'a ' + json_type\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::is_service_dict_schema\",\n \"text\": \"compose/config/validation.py::is_service_dict_schema\\ndef is_service_dict_schema(schema_id):\\n return schema_id in ('config_schema_v1.json', '#/properties/services')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::handle_error_for_schema_with_id\",\n \"text\": \"compose/config/validation.py::handle_error_for_schema_with_id\\ndef handle_error_for_schema_with_id(error, path):\\n schema_id = error.schema['id']\\n\\n if is_service_dict_schema(schema_id) and error.validator == 'additionalProperties':\\n return \\\"Invalid service name '{}' - only {} characters are allowed\\\".format(\\n # The service_name is the key to the json object\\n list(error.instance)[0],\\n VALID_NAME_CHARS)\\n\\n if error.validator == 'additionalProperties':\\n if schema_id == '#/definitions/service':\\n invalid_config_key = parse_key_from_error_msg(error)\\n return get_unsupported_config_msg(path, invalid_config_key)\\n\\n if not error.path:\\n return '{}\\\\n{}'.format(error.message, VERSION_EXPLANATION)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::handle_generic_error\",\n \"text\": \"compose/config/validation.py::handle_generic_error\\ndef handle_generic_error(error, path):\\n msg_format = None\\n error_msg = error.message\\n\\n if error.validator == 'oneOf':\\n msg_format = \\\"{path} {msg}\\\"\\n config_key, error_msg = _parse_oneof_validator(error)\\n if config_key:\\n path.append(config_key)\\n\\n elif error.validator == 'type':\\n msg_format = \\\"{path} contains an invalid type, it should be {msg}\\\"\\n error_msg = _parse_valid_types_from_validator(error.validator_value)\\n\\n elif error.validator == 'required':\\n error_msg = \\\", \\\".join(error.validator_value)\\n msg_format = \\\"{path} is invalid, {msg} is required.\\\"\\n\\n elif error.validator == 'dependencies':\\n config_key = list(error.validator_value.keys())[0]\\n required_keys = \\\",\\\".join(error.validator_value[config_key])\\n\\n msg_format = \\\"{path} is invalid: {msg}\\\"\\n path.append(config_key)\\n error_msg = \\\"when defining '{}' you must set '{}' as well\\\".format(\\n config_key,\\n required_keys)\\n\\n elif error.cause:\\n error_msg = six.text_type(error.cause)\\n msg_format = \\\"{path} is invalid: {msg}\\\"\\n\\n elif error.path:\\n msg_format = \\\"{path} value {msg}\\\"\\n\\n if msg_format:\\n return msg_format.format(path=path_string(path), msg=error_msg)\\n\\n return error.message\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::parse_key_from_error_msg\",\n \"text\": \"compose/config/validation.py::parse_key_from_error_msg\\ndef parse_key_from_error_msg(error):\\n return error.message.split(\\\"'\\\")[1]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::path_string\",\n \"text\": \"compose/config/validation.py::path_string\\ndef path_string(path):\\n return \\\".\\\".join(c for c in path if isinstance(c, six.string_types))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::_parse_valid_types_from_validator\",\n \"text\": \"compose/config/validation.py::_parse_valid_types_from_validator\\ndef _parse_valid_types_from_validator(validator):\\n \\\"\\\"\\\"A validator value can be either an array of valid types or a string of\\n a valid type. Parse the valid types and prefix with the correct article.\\n \\\"\\\"\\\"\\n if not isinstance(validator, list):\\n return anglicize_json_type(validator)\\n\\n if len(validator) == 1:\\n return anglicize_json_type(validator[0])\\n\\n return \\\"{}, or {}\\\".format(\\n \\\", \\\".join([anglicize_json_type(validator[0])] + validator[1:-1]),\\n anglicize_json_type(validator[-1]))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::_parse_oneof_validator\",\n \"text\": \"compose/config/validation.py::_parse_oneof_validator\\ndef _parse_oneof_validator(error):\\n \\\"\\\"\\\"oneOf has multiple schemas, so we need to reason about which schema, sub\\n schema or constraint the validation is failing on.\\n Inspecting the context value of a ValidationError gives us information about\\n which sub schema failed and which kind of error it is.\\n \\\"\\\"\\\"\\n types = []\\n for context in error.context:\\n\\n if context.validator == 'oneOf':\\n _, error_msg = _parse_oneof_validator(context)\\n return path_string(context.path), error_msg\\n\\n if context.validator == 'required':\\n return (None, context.message)\\n\\n if context.validator == 'additionalProperties':\\n invalid_config_key = parse_key_from_error_msg(context)\\n return (None, \\\"contains unsupported option: '{}'\\\".format(invalid_config_key))\\n\\n if context.path:\\n return (\\n path_string(context.path),\\n \\\"contains {}, which is an invalid type, it should be {}\\\".format(\\n json.dumps(context.instance),\\n _parse_valid_types_from_validator(context.validator_value)),\\n )\\n\\n if context.validator == 'uniqueItems':\\n return (\\n None,\\n \\\"contains non unique items, please remove duplicates from {}\\\".format(\\n context.instance),\\n )\\n\\n if context.validator == 'type':\\n types.append(context.validator_value)\\n\\n valid_types = _parse_valid_types_from_validator(types)\\n return (None, \\\"contains an invalid type, it should be {}\\\".format(valid_types))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_service_constraint_errors\",\n \"text\": \"compose/config/validation.py::process_service_constraint_errors\\ndef process_service_constraint_errors(error, service_name, version):\\n if version == V1:\\n if 'image' in error.instance and 'build' in error.instance:\\n return (\\n \\\"Service {} has both an image and build path specified. \\\"\\n \\\"A service can either be built to image or use an existing \\\"\\n \\\"image, not both.\\\".format(service_name))\\n\\n if 'image' in error.instance and 'dockerfile' in error.instance:\\n return (\\n \\\"Service {} has both an image and alternate Dockerfile. \\\"\\n \\\"A service can either be built to image or use an existing \\\"\\n \\\"image, not both.\\\".format(service_name))\\n\\n if 'image' not in error.instance and 'build' not in error.instance:\\n return (\\n \\\"Service {} has neither an image nor a build context specified. \\\"\\n \\\"At least one must be provided.\\\".format(service_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_config_schema_errors\",\n \"text\": \"compose/config/validation.py::process_config_schema_errors\\ndef process_config_schema_errors(error):\\n path = list(error.path)\\n\\n if 'id' in error.schema:\\n error_msg = handle_error_for_schema_with_id(error, path)\\n if error_msg:\\n return error_msg\\n\\n return handle_generic_error(error, path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_against_config_schema\",\n \"text\": \"compose/config/validation.py::validate_against_config_schema\\ndef validate_against_config_schema(config_file):\\n schema = load_jsonschema(config_file.version)\\n format_checker = FormatChecker([\\\"ports\\\", \\\"expose\\\", \\\"bool-value-in-mapping\\\"])\\n validator = Draft4Validator(\\n schema,\\n resolver=RefResolver(get_resolver_path(), schema),\\n format_checker=format_checker)\\n handle_errors(\\n validator.iter_errors(config_file.config),\\n process_config_schema_errors,\\n config_file.filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_service_constraints\",\n \"text\": \"compose/config/validation.py::validate_service_constraints\\ndef validate_service_constraints(config, service_name, version):\\n def handler(errors):\\n return process_service_constraint_errors(errors, service_name, version)\\n\\n schema = load_jsonschema(version)\\n validator = Draft4Validator(schema['definitions']['constraints']['service'])\\n handle_errors(validator.iter_errors(config), handler, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_service_constraints.handler\",\n \"text\": \"compose/config/validation.py::validate_service_constraints.handler\\n def handler(errors):\\n return process_service_constraint_errors(errors, service_name, version)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::get_schema_path\",\n \"text\": \"compose/config/validation.py::get_schema_path\\ndef get_schema_path():\\n return os.path.dirname(os.path.abspath(__file__))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::load_jsonschema\",\n \"text\": \"compose/config/validation.py::load_jsonschema\\ndef load_jsonschema(version):\\n filename = os.path.join(\\n get_schema_path(),\\n \\\"config_schema_v{0}.json\\\".format(version))\\n\\n with open(filename, \\\"r\\\") as fh:\\n return json.load(fh)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::get_resolver_path\",\n \"text\": \"compose/config/validation.py::get_resolver_path\\ndef get_resolver_path():\\n schema_path = get_schema_path()\\n if sys.platform == \\\"win32\\\":\\n scheme = \\\"///\\\"\\n # TODO: why is this necessary?\\n schema_path = schema_path.replace('\\\\\\\\', '/')\\n else:\\n scheme = \\\"//\\\"\\n return \\\"file:{}{}/\\\".format(scheme, schema_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::handle_errors\",\n \"text\": \"compose/config/validation.py::handle_errors\\ndef handle_errors(errors, format_error_func, filename):\\n \\\"\\\"\\\"jsonschema returns an error tree full of information to explain what has\\n gone wrong. Process each error and pull out relevant information and re-write\\n helpful error messages that are relevant.\\n \\\"\\\"\\\"\\n errors = list(sorted(errors, key=str))\\n if not errors:\\n return\\n\\n error_msg = '\\\\n'.join(format_error_func(error) for error in errors)\\n raise ConfigurationError(\\n \\\"Validation failed{file_msg}, reason(s):\\\\n{error_msg}\\\".format(\\n file_msg=\\\" in file '{}'\\\".format(filename) if filename else \\\"\\\",\\n error_msg=error_msg))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.__init__\",\n \"text\": \"compose/project.py::Project.__init__\\n def __init__(self, name, services, client, networks=None, volumes=None):\\n self.name = name\\n self.services = services\\n self.client = client\\n self.volumes = volumes or ProjectVolumes({})\\n self.networks = networks or ProjectNetworks({}, False)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.labels\",\n \"text\": \"compose/project.py::Project.labels\\n def labels(self, one_off=False):\\n return [\\n '{0}={1}'.format(LABEL_PROJECT, self.name),\\n '{0}={1}'.format(LABEL_ONE_OFF, \\\"True\\\" if one_off else \\\"False\\\"),\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.from_config\",\n \"text\": \"compose/project.py::Project.from_config\\n @classmethod\\n def from_config(cls, name, config_data, client):\\n \\\"\\\"\\\"\\n Construct a Project from a config.Config object.\\n \\\"\\\"\\\"\\n use_networking = (config_data.version and config_data.version != V1)\\n networks = build_networks(name, config_data, client)\\n project_networks = ProjectNetworks.from_services(\\n config_data.services,\\n networks,\\n use_networking)\\n volumes = ProjectVolumes.from_config(name, config_data, client)\\n project = cls(name, [], client, project_networks, volumes)\\n\\n for service_dict in config_data.services:\\n service_dict = dict(service_dict)\\n if use_networking:\\n service_networks = get_networks(service_dict, networks)\\n else:\\n service_networks = {}\\n\\n service_dict.pop('networks', None)\\n links = project.get_links(service_dict)\\n network_mode = project.get_network_mode(\\n service_dict, list(service_networks.keys())\\n )\\n volumes_from = get_volumes_from(project, service_dict)\\n\\n if config_data.version != V1:\\n service_dict['volumes'] = [\\n volumes.namespace_spec(volume_spec)\\n for volume_spec in service_dict.get('volumes', [])\\n ]\\n\\n project.services.append(\\n Service(\\n service_dict.pop('name'),\\n client=client,\\n project=name,\\n use_networking=use_networking,\\n networks=service_networks,\\n links=links,\\n network_mode=network_mode,\\n volumes_from=volumes_from,\\n **service_dict)\\n )\\n\\n return project\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.service_names\",\n \"text\": \"compose/project.py::Project.service_names\\n @property\\n def service_names(self):\\n return [service.name for service in self.services]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.get_service\",\n \"text\": \"compose/project.py::Project.get_service\\n def get_service(self, name):\\n \\\"\\\"\\\"\\n Retrieve a service by name. Raises NoSuchService\\n if the named service does not exist.\\n \\\"\\\"\\\"\\n for service in self.services:\\n if service.name == name:\\n return service\\n\\n raise NoSuchService(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.validate_service_names\",\n \"text\": \"compose/project.py::Project.validate_service_names\\n def validate_service_names(self, service_names):\\n \\\"\\\"\\\"\\n Validate that the given list of service names only contains valid\\n services. Raises NoSuchService if one of the names is invalid.\\n \\\"\\\"\\\"\\n valid_names = self.service_names\\n for name in service_names:\\n if name not in valid_names:\\n raise NoSuchService(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.get_services\",\n \"text\": \"compose/project.py::Project.get_services\\n def get_services(self, service_names=None, include_deps=False):\\n \\\"\\\"\\\"\\n Returns a list of this project's services filtered\\n by the provided list of names, or all services if service_names is None\\n or [].\\n\\n If include_deps is specified, returns a list including the dependencies for\\n service_names, in order of dependency.\\n\\n Preserves the original order of self.services where possible,\\n reordering as needed to resolve dependencies.\\n\\n Raises NoSuchService if any of the named services do not exist.\\n \\\"\\\"\\\"\\n if service_names is None or len(service_names) == 0:\\n service_names = self.service_names\\n\\n unsorted = [self.get_service(name) for name in service_names]\\n services = [s for s in self.services if s in unsorted]\\n\\n if include_deps:\\n services = reduce(self._inject_deps, services, [])\\n\\n uniques = []\\n [uniques.append(s) for s in services if s not in uniques]\\n\\n return uniques\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.get_services_without_duplicate\",\n \"text\": \"compose/project.py::Project.get_services_without_duplicate\\n def get_services_without_duplicate(self, service_names=None, include_deps=False):\\n services = self.get_services(service_names, include_deps)\\n for service in services:\\n service.remove_duplicate_containers()\\n return services\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.get_links\",\n \"text\": \"compose/project.py::Project.get_links\\n def get_links(self, service_dict):\\n links = []\\n if 'links' in service_dict:\\n for link in service_dict.get('links', []):\\n if ':' in link:\\n service_name, link_name = link.split(':', 1)\\n else:\\n service_name, link_name = link, None\\n try:\\n links.append((self.get_service(service_name), link_name))\\n except NoSuchService:\\n raise ConfigurationError(\\n 'Service \\\"%s\\\" has a link to service \\\"%s\\\" which does not '\\n 'exist.' % (service_dict['name'], service_name))\\n del service_dict['links']\\n return links\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.get_network_mode\",\n \"text\": \"compose/project.py::Project.get_network_mode\\n def get_network_mode(self, service_dict, networks):\\n network_mode = service_dict.pop('network_mode', None)\\n if not network_mode:\\n if self.networks.use_networking:\\n return NetworkMode(networks[0]) if networks else NetworkMode('none')\\n return NetworkMode(None)\\n\\n service_name = get_service_name_from_network_mode(network_mode)\\n if service_name:\\n return ServiceNetworkMode(self.get_service(service_name))\\n\\n container_name = get_container_name_from_network_mode(network_mode)\\n if container_name:\\n try:\\n return ContainerNetworkMode(Container.from_id(self.client, container_name))\\n except APIError:\\n raise ConfigurationError(\\n \\\"Service '{name}' uses the network stack of container '{dep}' which \\\"\\n \\\"does not exist.\\\".format(name=service_dict['name'], dep=container_name))\\n\\n return NetworkMode(network_mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.start\",\n \"text\": \"compose/project.py::Project.start\\n def start(self, service_names=None, **options):\\n containers = []\\n for service in self.get_services(service_names):\\n service_containers = service.start(**options)\\n containers.extend(service_containers)\\n return containers\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.stop\",\n \"text\": \"compose/project.py::Project.stop\\n def stop(self, service_names=None, **options):\\n parallel.parallel_stop(self.containers(service_names), options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.pause\",\n \"text\": \"compose/project.py::Project.pause\\n def pause(self, service_names=None, **options):\\n containers = self.containers(service_names)\\n parallel.parallel_pause(reversed(containers), options)\\n return containers\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.unpause\",\n \"text\": \"compose/project.py::Project.unpause\\n def unpause(self, service_names=None, **options):\\n containers = self.containers(service_names)\\n parallel.parallel_unpause(containers, options)\\n return containers\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.kill\",\n \"text\": \"compose/project.py::Project.kill\\n def kill(self, service_names=None, **options):\\n parallel.parallel_kill(self.containers(service_names), options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.remove_stopped\",\n \"text\": \"compose/project.py::Project.remove_stopped\\n def remove_stopped(self, service_names=None, **options):\\n parallel.parallel_remove(self.containers(service_names, stopped=True), options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.down\",\n \"text\": \"compose/project.py::Project.down\\n def down(self, remove_image_type, include_volumes):\\n self.stop()\\n self.remove_stopped(v=include_volumes)\\n self.networks.remove()\\n\\n if include_volumes:\\n self.volumes.remove()\\n\\n self.remove_images(remove_image_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.remove_images\",\n \"text\": \"compose/project.py::Project.remove_images\\n def remove_images(self, remove_image_type):\\n for service in self.get_services():\\n service.remove_image(remove_image_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.restart\",\n \"text\": \"compose/project.py::Project.restart\\n def restart(self, service_names=None, **options):\\n containers = self.containers(service_names, stopped=True)\\n parallel.parallel_restart(containers, options)\\n return containers\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.build\",\n \"text\": \"compose/project.py::Project.build\\n def build(self, service_names=None, no_cache=False, pull=False, force_rm=False):\\n for service in self.get_services(service_names):\\n if service.can_be_built():\\n service.build(no_cache, pull, force_rm)\\n else:\\n log.info('%s uses an image, skipping' % service.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.create\",\n \"text\": \"compose/project.py::Project.create\\n def create(self, service_names=None, strategy=ConvergenceStrategy.changed, do_build=True):\\n services = self.get_services_without_duplicate(service_names, include_deps=True)\\n\\n plans = self._get_convergence_plans(services, strategy)\\n\\n for service in services:\\n service.execute_convergence_plan(\\n plans[service.name],\\n do_build,\\n detached=True,\\n start=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.events\",\n \"text\": \"compose/project.py::Project.events\\n def events(self):\\n def build_container_event(event, container):\\n time = datetime.datetime.fromtimestamp(event['time'])\\n time = time.replace(\\n microsecond=microseconds_from_time_nano(event['timeNano']))\\n return {\\n 'time': time,\\n 'type': 'container',\\n 'action': event['status'],\\n 'id': container.id,\\n 'service': container.service,\\n 'attributes': {\\n 'name': container.name,\\n 'image': event['from'],\\n }\\n }\\n\\n service_names = set(self.service_names)\\n for event in self.client.events(\\n filters={'label': self.labels()},\\n decode=True\\n ):\\n if event['status'] in IMAGE_EVENTS:\\n # We don't receive any image events because labels aren't applied\\n # to images\\n continue\\n\\n # TODO: get labels from the API v1.22 , see github issue 2618\\n container = Container.from_id(self.client, event['id'])\\n if container.service not in service_names:\\n continue\\n yield build_container_event(event, container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.events.build_container_event\",\n \"text\": \"compose/project.py::Project.events.build_container_event\\n def build_container_event(event, container):\\n time = datetime.datetime.fromtimestamp(event['time'])\\n time = time.replace(\\n microsecond=microseconds_from_time_nano(event['timeNano']))\\n return {\\n 'time': time,\\n 'type': 'container',\\n 'action': event['status'],\\n 'id': container.id,\\n 'service': container.service,\\n 'attributes': {\\n 'name': container.name,\\n 'image': event['from'],\\n }\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.up\",\n \"text\": \"compose/project.py::Project.up\\n def up(self,\\n service_names=None,\\n start_deps=True,\\n strategy=ConvergenceStrategy.changed,\\n do_build=True,\\n timeout=DEFAULT_TIMEOUT,\\n detached=False):\\n\\n self.initialize()\\n services = self.get_services_without_duplicate(\\n service_names,\\n include_deps=start_deps)\\n\\n plans = self._get_convergence_plans(services, strategy)\\n return [\\n container\\n for service in services\\n for container in service.execute_convergence_plan(\\n plans[service.name],\\n do_build=do_build,\\n timeout=timeout,\\n detached=detached\\n )\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.initialize\",\n \"text\": \"compose/project.py::Project.initialize\\n def initialize(self):\\n self.networks.initialize()\\n self.volumes.initialize()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project._get_convergence_plans\",\n \"text\": \"compose/project.py::Project._get_convergence_plans\\n def _get_convergence_plans(self, services, strategy):\\n plans = {}\\n\\n for service in services:\\n updated_dependencies = [\\n name\\n for name in service.get_dependency_names()\\n if name in plans and\\n plans[name].action in ('recreate', 'create')\\n ]\\n\\n if updated_dependencies and strategy.allows_recreate:\\n log.debug('%s has upstream changes (%s)',\\n service.name,\\n \\\", \\\".join(updated_dependencies))\\n plan = service.convergence_plan(ConvergenceStrategy.always)\\n else:\\n plan = service.convergence_plan(strategy)\\n\\n plans[service.name] = plan\\n\\n return plans\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.pull\",\n \"text\": \"compose/project.py::Project.pull\\n def pull(self, service_names=None, ignore_pull_failures=False):\\n for service in self.get_services(service_names, include_deps=False):\\n service.pull(ignore_pull_failures)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.containers\",\n \"text\": \"compose/project.py::Project.containers\\n def containers(self, service_names=None, stopped=False, one_off=False):\\n if service_names:\\n self.validate_service_names(service_names)\\n else:\\n service_names = self.service_names\\n\\n containers = list(filter(None, [\\n Container.from_ps(self.client, container)\\n for container in self.client.containers(\\n all=stopped,\\n filters={'label': self.labels(one_off=one_off)})]))\\n\\n def matches_service_names(container):\\n return container.labels.get(LABEL_SERVICE) in service_names\\n\\n return [c for c in containers if matches_service_names(c)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project.containers.matches_service_names\",\n \"text\": \"compose/project.py::Project.containers.matches_service_names\\n def matches_service_names(container):\\n return container.labels.get(LABEL_SERVICE) in service_names\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::Project._inject_deps\",\n \"text\": \"compose/project.py::Project._inject_deps\\n def _inject_deps(self, acc, service):\\n dep_names = service.get_dependency_names()\\n\\n if len(dep_names) > 0:\\n dep_services = self.get_services(\\n service_names=list(set(dep_names)),\\n include_deps=True\\n )\\n else:\\n dep_services = []\\n\\n dep_services.append(service)\\n return acc + dep_services\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::get_volumes_from\",\n \"text\": \"compose/project.py::get_volumes_from\\ndef get_volumes_from(project, service_dict):\\n volumes_from = service_dict.pop('volumes_from', None)\\n if not volumes_from:\\n return []\\n\\n def build_volume_from(spec):\\n if spec.type == 'service':\\n try:\\n return spec._replace(source=project.get_service(spec.source))\\n except NoSuchService:\\n pass\\n\\n if spec.type == 'container':\\n try:\\n container = Container.from_id(project.client, spec.source)\\n return spec._replace(source=container)\\n except APIError:\\n pass\\n\\n raise ConfigurationError(\\n \\\"Service \\\\\\\"{}\\\\\\\" mounts volumes from \\\\\\\"{}\\\\\\\", which is not the name \\\"\\n \\\"of a service or container.\\\".format(\\n service_dict['name'],\\n spec.source))\\n\\n return [build_volume_from(vf) for vf in volumes_from]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::get_volumes_from.build_volume_from\",\n \"text\": \"compose/project.py::get_volumes_from.build_volume_from\\n def build_volume_from(spec):\\n if spec.type == 'service':\\n try:\\n return spec._replace(source=project.get_service(spec.source))\\n except NoSuchService:\\n pass\\n\\n if spec.type == 'container':\\n try:\\n container = Container.from_id(project.client, spec.source)\\n return spec._replace(source=container)\\n except APIError:\\n pass\\n\\n raise ConfigurationError(\\n \\\"Service \\\\\\\"{}\\\\\\\" mounts volumes from \\\\\\\"{}\\\\\\\", which is not the name \\\"\\n \\\"of a service or container.\\\".format(\\n service_dict['name'],\\n spec.source))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::NoSuchService.__init__\",\n \"text\": \"compose/project.py::NoSuchService.__init__\\n def __init__(self, name):\\n self.name = name\\n self.msg = \\\"No such service: %s\\\" % self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/project.py::NoSuchService.__str__\",\n \"text\": \"compose/project.py::NoSuchService.__str__\\n def __str__(self):\\n return self.msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::BuildError.__init__\",\n \"text\": \"compose/service.py::BuildError.__init__\\n def __init__(self, service, reason):\\n self.service = service\\n self.reason = reason\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::NeedsBuildError.__init__\",\n \"text\": \"compose/service.py::NeedsBuildError.__init__\\n def __init__(self, service):\\n self.service = service\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::ConvergenceStrategy.allows_recreate\",\n \"text\": \"compose/service.py::ConvergenceStrategy.allows_recreate\\n @property\\n def allows_recreate(self):\\n return self is not type(self).never\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.__init__\",\n \"text\": \"compose/service.py::Service.__init__\\n def __init__(\\n self,\\n name,\\n client=None,\\n project='default',\\n use_networking=False,\\n links=None,\\n volumes_from=None,\\n network_mode=None,\\n networks=None,\\n **options\\n ):\\n self.name = name\\n self.client = client\\n self.project = project\\n self.use_networking = use_networking\\n self.links = links or []\\n self.volumes_from = volumes_from or []\\n self.network_mode = network_mode or NetworkMode(None)\\n self.networks = networks or {}\\n self.options = options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.containers\",\n \"text\": \"compose/service.py::Service.containers\\n def containers(self, stopped=False, one_off=False, filters={}):\\n filters.update({'label': self.labels(one_off=one_off)})\\n\\n return list(filter(None, [\\n Container.from_ps(self.client, container)\\n for container in self.client.containers(\\n all=stopped,\\n filters=filters)]))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_container\",\n \"text\": \"compose/service.py::Service.get_container\\n def get_container(self, number=1):\\n \\\"\\\"\\\"Return a :class:`compose.container.Container` for this service. The\\n container must be active, and match `number`.\\n \\\"\\\"\\\"\\n labels = self.labels() + ['{0}={1}'.format(LABEL_CONTAINER_NUMBER, number)]\\n for container in self.client.containers(filters={'label': labels}):\\n return Container.from_ps(self.client, container)\\n\\n raise ValueError(\\\"No container found for %s_%s\\\" % (self.name, number))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start\",\n \"text\": \"compose/service.py::Service.start\\n def start(self, **options):\\n containers = self.containers(stopped=True)\\n for c in containers:\\n self.start_container_if_stopped(c, **options)\\n return containers\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale\",\n \"text\": \"compose/service.py::Service.scale\\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT):\\n \\\"\\\"\\\"\\n Adjusts the number of containers to the specified number and ensures\\n they are running.\\n\\n - creates containers until there are at least `desired_num`\\n - stops containers until there are at most `desired_num` running\\n - starts containers until there are at least `desired_num` running\\n - removes all stopped containers\\n \\\"\\\"\\\"\\n if self.custom_container_name and desired_num > 1:\\n log.warn('The \\\"%s\\\" service is using the custom container name \\\"%s\\\". '\\n 'Docker requires each container to have a unique name. '\\n 'Remove the custom name to scale the service.'\\n % (self.name, self.custom_container_name))\\n\\n if self.specifies_host_port():\\n log.warn('The \\\"%s\\\" service specifies a port on the host. If multiple containers '\\n 'for this service are created on a single host, the port will clash.'\\n % self.name)\\n\\n def create_and_start(service, number):\\n container = service.create_container(number=number, quiet=True)\\n service.start_container(container)\\n return container\\n\\n def stop_and_remove(container):\\n container.stop(timeout=timeout)\\n container.remove()\\n\\n running_containers = self.containers(stopped=False)\\n num_running = len(running_containers)\\n\\n if desired_num == num_running:\\n # do nothing as we already have the desired number\\n log.info('Desired container number already achieved')\\n return\\n\\n if desired_num > num_running:\\n # we need to start/create until we have desired_num\\n all_containers = self.containers(stopped=True)\\n\\n if num_running != len(all_containers):\\n # we have some stopped containers, let's start them up again\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale.create_and_start\",\n \"text\": \"compose/service.py::Service.scale.create_and_start\\n def create_and_start(service, number):\\n container = service.create_container(number=number, quiet=True)\\n service.start_container(container)\\n return container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale.stop_and_remove\",\n \"text\": \"compose/service.py::Service.scale.stop_and_remove\\n def stop_and_remove(container):\\n container.stop(timeout=timeout)\\n container.remove()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.create_container\",\n \"text\": \"compose/service.py::Service.create_container\\n def create_container(self,\\n one_off=False,\\n do_build=True,\\n previous_container=None,\\n number=None,\\n quiet=False,\\n **override_options):\\n \\\"\\\"\\\"\\n Create a container for this service. If the image doesn't exist, attempt to pull\\n it.\\n \\\"\\\"\\\"\\n self.ensure_image_exists(do_build=do_build)\\n\\n container_options = self._get_container_create_options(\\n override_options,\\n number or self._next_container_number(one_off=one_off),\\n one_off=one_off,\\n previous_container=previous_container,\\n )\\n\\n if 'name' in container_options and not quiet:\\n log.info(\\\"Creating %s\\\" % container_options['name'])\\n\\n return Container.create(self.client, **container_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.ensure_image_exists\",\n \"text\": \"compose/service.py::Service.ensure_image_exists\\n def ensure_image_exists(self, do_build=True):\\n try:\\n self.image()\\n return\\n except NoSuchImageError:\\n pass\\n\\n if self.can_be_built():\\n if do_build:\\n self.build()\\n else:\\n raise NeedsBuildError(self)\\n else:\\n self.pull()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.image\",\n \"text\": \"compose/service.py::Service.image\\n def image(self):\\n try:\\n return self.client.inspect_image(self.image_name)\\n except APIError as e:\\n if e.response.status_code == 404 and e.explanation and 'No such image' in str(e.explanation):\\n raise NoSuchImageError(\\\"Image '{}' not found\\\".format(self.image_name))\\n else:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.image_name\",\n \"text\": \"compose/service.py::Service.image_name\\n @property\\n def image_name(self):\\n return self.options.get('image', '{s.project}_{s.name}'.format(s=self))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.convergence_plan\",\n \"text\": \"compose/service.py::Service.convergence_plan\\n def convergence_plan(self, strategy=ConvergenceStrategy.changed):\\n containers = self.containers(stopped=True)\\n\\n if not containers:\\n return ConvergencePlan('create', [])\\n\\n if strategy is ConvergenceStrategy.never:\\n return ConvergencePlan('start', containers)\\n\\n if (\\n strategy is ConvergenceStrategy.always or\\n self._containers_have_diverged(containers)\\n ):\\n return ConvergencePlan('recreate', containers)\\n\\n stopped = [c for c in containers if not c.is_running]\\n\\n if stopped:\\n return ConvergencePlan('start', stopped)\\n\\n return ConvergencePlan('noop', containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._containers_have_diverged\",\n \"text\": \"compose/service.py::Service._containers_have_diverged\\n def _containers_have_diverged(self, containers):\\n config_hash = None\\n\\n try:\\n config_hash = self.config_hash\\n except NoSuchImageError as e:\\n log.debug(\\n 'Service %s has diverged: %s',\\n self.name, six.text_type(e),\\n )\\n return True\\n\\n has_diverged = False\\n\\n for c in containers:\\n container_config_hash = c.labels.get(LABEL_CONFIG_HASH, None)\\n if container_config_hash != config_hash:\\n log.debug(\\n '%s has diverged: %s != %s',\\n c.name, container_config_hash, config_hash,\\n )\\n has_diverged = True\\n\\n return has_diverged\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.execute_convergence_plan\",\n \"text\": \"compose/service.py::Service.execute_convergence_plan\\n def execute_convergence_plan(self,\\n plan,\\n do_build=True,\\n timeout=DEFAULT_TIMEOUT,\\n detached=False,\\n start=True):\\n (action, containers) = plan\\n should_attach_logs = not detached\\n\\n if action == 'create':\\n container = self.create_container(do_build=do_build)\\n\\n if should_attach_logs:\\n container.attach_log_stream()\\n\\n if start:\\n self.start_container(container)\\n\\n return [container]\\n\\n elif action == 'recreate':\\n return [\\n self.recreate_container(\\n container,\\n do_build=do_build,\\n timeout=timeout,\\n attach_logs=should_attach_logs,\\n start_new_container=start\\n )\\n for container in containers\\n ]\\n\\n elif action == 'start':\\n if start:\\n for container in containers:\\n self.start_container_if_stopped(container, attach_logs=should_attach_logs)\\n\\n return containers\\n\\n elif action == 'noop':\\n for c in containers:\\n log.info(\\\"%s is up-to-date\\\" % c.name)\\n\\n return containers\\n\\n else:\\n raise Exception(\\\"Invalid action: {}\\\".format(action))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.recreate_container\",\n \"text\": \"compose/service.py::Service.recreate_container\\n def recreate_container(\\n self,\\n container,\\n do_build=False,\\n timeout=DEFAULT_TIMEOUT,\\n attach_logs=False,\\n start_new_container=True):\\n \\\"\\\"\\\"Recreate a container.\\n\\n The original container is renamed to a temporary name so that data\\n volumes can be copied to the new container, before the original\\n container is removed.\\n \\\"\\\"\\\"\\n log.info(\\\"Recreating %s\\\" % container.name)\\n\\n container.stop(timeout=timeout)\\n container.rename_to_tmp_name()\\n new_container = self.create_container(\\n do_build=do_build,\\n previous_container=container,\\n number=container.labels.get(LABEL_CONTAINER_NUMBER),\\n quiet=True,\\n )\\n if attach_logs:\\n new_container.attach_log_stream()\\n if start_new_container:\\n self.start_container(new_container)\\n container.remove()\\n return new_container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start_container_if_stopped\",\n \"text\": \"compose/service.py::Service.start_container_if_stopped\\n def start_container_if_stopped(self, container, attach_logs=False):\\n if not container.is_running:\\n log.info(\\\"Starting %s\\\" % container.name)\\n if attach_logs:\\n container.attach_log_stream()\\n return self.start_container(container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start_container\",\n \"text\": \"compose/service.py::Service.start_container\\n def start_container(self, container):\\n self.connect_container_to_networks(container)\\n container.start()\\n return container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.connect_container_to_networks\",\n \"text\": \"compose/service.py::Service.connect_container_to_networks\\n def connect_container_to_networks(self, container):\\n connected_networks = container.get('NetworkSettings.Networks')\\n\\n for network, aliases in self.networks.items():\\n if network in connected_networks:\\n self.client.disconnect_container_from_network(\\n container.id, network)\\n\\n self.client.connect_container_to_network(\\n container.id, network,\\n aliases=list(self._get_aliases(container).union(aliases)),\\n links=self._get_links(False),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.remove_duplicate_containers\",\n \"text\": \"compose/service.py::Service.remove_duplicate_containers\\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT):\\n for c in self.duplicate_containers():\\n log.info('Removing %s' % c.name)\\n c.stop(timeout=timeout)\\n c.remove()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.duplicate_containers\",\n \"text\": \"compose/service.py::Service.duplicate_containers\\n def duplicate_containers(self):\\n containers = sorted(\\n self.containers(stopped=True),\\n key=lambda c: c.get('Created'),\\n )\\n\\n numbers = set()\\n\\n for c in containers:\\n if c.number in numbers:\\n yield c\\n else:\\n numbers.add(c.number)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.config_hash\",\n \"text\": \"compose/service.py::Service.config_hash\\n @property\\n def config_hash(self):\\n return json_hash(self.config_dict())\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.config_dict\",\n \"text\": \"compose/service.py::Service.config_dict\\n def config_dict(self):\\n return {\\n 'options': self.options,\\n 'image_id': self.image()['Id'],\\n 'links': self.get_link_names(),\\n 'net': self.network_mode.id,\\n 'networks': list(self.networks.keys()),\\n 'volumes_from': [\\n (v.source.name, v.mode)\\n for v in self.volumes_from if isinstance(v.source, Service)\\n ],\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_dependency_names\",\n \"text\": \"compose/service.py::Service.get_dependency_names\\n def get_dependency_names(self):\\n net_name = self.network_mode.service_name\\n return (self.get_linked_service_names() +\\n self.get_volumes_from_names() +\\n ([net_name] if net_name else []) +\\n self.options.get('depends_on', []))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_linked_service_names\",\n \"text\": \"compose/service.py::Service.get_linked_service_names\\n def get_linked_service_names(self):\\n return [service.name for (service, _) in self.links]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_link_names\",\n \"text\": \"compose/service.py::Service.get_link_names\\n def get_link_names(self):\\n return [(service.name, alias) for service, alias in self.links]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_volumes_from_names\",\n \"text\": \"compose/service.py::Service.get_volumes_from_names\\n def get_volumes_from_names(self):\\n return [s.source.name for s in self.volumes_from if isinstance(s.source, Service)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._next_container_number\",\n \"text\": \"compose/service.py::Service._next_container_number\\n def _next_container_number(self, one_off=False):\\n containers = filter(None, [\\n Container.from_ps(self.client, container)\\n for container in self.client.containers(\\n all=True,\\n filters={'label': self.labels(one_off=one_off)})\\n ])\\n numbers = [c.number for c in containers]\\n return 1 if not numbers else max(numbers) + 1\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_aliases\",\n \"text\": \"compose/service.py::Service._get_aliases\\n def _get_aliases(self, container):\\n if container.labels.get(LABEL_ONE_OFF) == \\\"True\\\":\\n return set()\\n\\n return {self.name, container.short_id}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_links\",\n \"text\": \"compose/service.py::Service._get_links\\n def _get_links(self, link_to_self):\\n links = {}\\n\\n for service, link_name in self.links:\\n for container in service.containers():\\n links[link_name or service.name] = container.name\\n links[container.name] = container.name\\n links[container.name_without_project] = container.name\\n\\n if link_to_self:\\n for container in self.containers():\\n links[self.name] = container.name\\n links[container.name] = container.name\\n links[container.name_without_project] = container.name\\n\\n for external_link in self.options.get('external_links') or []:\\n if ':' not in external_link:\\n link_name = external_link\\n else:\\n external_link, link_name = external_link.split(':')\\n links[link_name] = external_link\\n\\n return [\\n (alias, container_name)\\n for (container_name, alias) in links.items()\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_volumes_from\",\n \"text\": \"compose/service.py::Service._get_volumes_from\\n def _get_volumes_from(self):\\n return [build_volume_from(spec) for spec in self.volumes_from]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_container_create_options\",\n \"text\": \"compose/service.py::Service._get_container_create_options\\n def _get_container_create_options(\\n self,\\n override_options,\\n number,\\n one_off=False,\\n previous_container=None):\\n add_config_hash = (not one_off and not override_options)\\n\\n container_options = dict(\\n (k, self.options[k])\\n for k in DOCKER_CONFIG_KEYS if k in self.options)\\n container_options.update(override_options)\\n\\n if not container_options.get('name'):\\n container_options['name'] = self.get_container_name(number, one_off)\\n\\n container_options.setdefault('detach', True)\\n\\n # If a qualified hostname was given, split it into an\\n # unqualified hostname and a domainname unless domainname\\n # was also given explicitly. This matches the behavior of\\n # the official Docker CLI in that scenario.\\n if ('hostname' in container_options and\\n 'domainname' not in container_options and\\n '.' in container_options['hostname']):\\n parts = container_options['hostname'].partition('.')\\n container_options['hostname'] = parts[0]\\n container_options['domainname'] = parts[2]\\n\\n if 'ports' in container_options or 'expose' in self.options:\\n container_options['ports'] = build_container_ports(\\n container_options,\\n self.options)\\n\\n container_options['environment'] = merge_environment(\\n self.options.get('environment'),\\n override_options.get('environment'))\\n\\n binds, affinity = merge_volume_bindings(\\n container_options.get('volumes') or [],\\n previous_container)\\n override_options['binds'] = binds\\n container_options['environment'].update(affinity)\\n\\n if 'volumes' in container_options:\\n container_options['volumes'] = dict(\\n (v.internal, {}) for v in container_options['volumes'])\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_container_host_config\",\n \"text\": \"compose/service.py::Service._get_container_host_config\\n def _get_container_host_config(self, override_options, one_off=False):\\n options = dict(self.options, **override_options)\\n\\n logging_dict = options.get('logging', None)\\n log_config = get_log_config(logging_dict)\\n\\n return self.client.create_host_config(\\n links=self._get_links(link_to_self=one_off),\\n port_bindings=build_port_bindings(options.get('ports') or []),\\n binds=options.get('binds'),\\n volumes_from=self._get_volumes_from(),\\n privileged=options.get('privileged', False),\\n network_mode=self.network_mode.mode,\\n devices=options.get('devices'),\\n dns=options.get('dns'),\\n dns_search=options.get('dns_search'),\\n restart_policy=options.get('restart'),\\n cap_add=options.get('cap_add'),\\n cap_drop=options.get('cap_drop'),\\n mem_limit=options.get('mem_limit'),\\n memswap_limit=options.get('memswap_limit'),\\n ulimits=build_ulimits(options.get('ulimits')),\\n log_config=log_config,\\n extra_hosts=options.get('extra_hosts'),\\n read_only=options.get('read_only'),\\n pid_mode=options.get('pid'),\\n security_opt=options.get('security_opt'),\\n ipc_mode=options.get('ipc'),\\n cgroup_parent=options.get('cgroup_parent'),\\n cpu_quota=options.get('cpu_quota'),\\n shm_size=options.get('shm_size'),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.build\",\n \"text\": \"compose/service.py::Service.build\\n def build(self, no_cache=False, pull=False, force_rm=False):\\n log.info('Building %s' % self.name)\\n\\n build_opts = self.options.get('build', {})\\n path = build_opts.get('context')\\n # python2 os.path() doesn't support unicode, so we need to encode it to\\n # a byte string\\n if not six.PY3:\\n path = path.encode('utf8')\\n\\n build_output = self.client.build(\\n path=path,\\n tag=self.image_name,\\n stream=True,\\n rm=True,\\n forcerm=force_rm,\\n pull=pull,\\n nocache=no_cache,\\n dockerfile=build_opts.get('dockerfile', None),\\n buildargs=build_opts.get('args', None),\\n )\\n\\n try:\\n all_events = stream_output(build_output, sys.stdout)\\n except StreamOutputError as e:\\n raise BuildError(self, six.text_type(e))\\n\\n # Ensure the HTTP connection is not reused for another\\n # streaming command, as the Docker daemon can sometimes\\n # complain about it\\n self.client.close()\\n\\n image_id = None\\n\\n for event in all_events:\\n if 'stream' in event:\\n match = re.search(r'Successfully built ([0-9a-f]+)', event.get('stream', ''))\\n if match:\\n image_id = match.group(1)\\n\\n if image_id is None:\\n raise BuildError(self, event if all_events else 'Unknown')\\n\\n return image_id\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.can_be_built\",\n \"text\": \"compose/service.py::Service.can_be_built\\n def can_be_built(self):\\n return 'build' in self.options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.labels\",\n \"text\": \"compose/service.py::Service.labels\\n def labels(self, one_off=False):\\n return [\\n '{0}={1}'.format(LABEL_PROJECT, self.project),\\n '{0}={1}'.format(LABEL_SERVICE, self.name),\\n '{0}={1}'.format(LABEL_ONE_OFF, \\\"True\\\" if one_off else \\\"False\\\")\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.custom_container_name\",\n \"text\": \"compose/service.py::Service.custom_container_name\\n @property\\n def custom_container_name(self):\\n return self.options.get('container_name')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_container_name\",\n \"text\": \"compose/service.py::Service.get_container_name\\n def get_container_name(self, number, one_off=False):\\n if self.custom_container_name and not one_off:\\n return self.custom_container_name\\n\\n return build_container_name(self.project, self.name, number, one_off)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::get_output_stream\",\n \"text\": \"compose/utils.py::get_output_stream\\ndef get_output_stream(stream):\\n if six.PY3:\\n return stream\\n return codecs.getwriter('utf-8')(stream)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::stream_as_text\",\n \"text\": \"compose/utils.py::stream_as_text\\ndef stream_as_text(stream):\\n \\\"\\\"\\\"Given a stream of bytes or text, if any of the items in the stream\\n are bytes convert them to text.\\n\\n This function can be removed once docker-py returns text streams instead\\n of byte streams.\\n \\\"\\\"\\\"\\n for data in stream:\\n if not isinstance(data, six.text_type):\\n data = data.decode('utf-8', 'replace')\\n yield data\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::line_splitter\",\n \"text\": \"compose/utils.py::line_splitter\\ndef line_splitter(buffer, separator=u'\\\\n'):\\n index = buffer.find(six.text_type(separator))\\n if index == -1:\\n return None\\n return buffer[:index + 1], buffer[index + 1:]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::split_buffer\",\n \"text\": \"compose/utils.py::split_buffer\\ndef split_buffer(stream, splitter=None, decoder=lambda a: a):\\n \\\"\\\"\\\"Given a generator which yields strings and a splitter function,\\n joins all input, splits on the separator and yields each chunk.\\n\\n Unlike string.split(), each chunk includes the trailing\\n separator, except for the last one if none was found on the end\\n of the input.\\n \\\"\\\"\\\"\\n splitter = splitter or line_splitter\\n buffered = six.text_type('')\\n\\n for data in stream_as_text(stream):\\n buffered += data\\n while True:\\n buffer_split = splitter(buffered)\\n if buffer_split is None:\\n break\\n\\n item, buffered = buffer_split\\n yield item\\n\\n if buffered:\\n yield decoder(buffered)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::json_splitter\",\n \"text\": \"compose/utils.py::json_splitter\\ndef json_splitter(buffer):\\n \\\"\\\"\\\"Attempt to parse a json object from a buffer. If there is at least one\\n object, return it and the rest of the buffer, otherwise return None.\\n \\\"\\\"\\\"\\n try:\\n obj, index = json_decoder.raw_decode(buffer)\\n rest = buffer[json.decoder.WHITESPACE.match(buffer, index).end():]\\n return obj, rest\\n except ValueError:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::json_stream\",\n \"text\": \"compose/utils.py::json_stream\\ndef json_stream(stream):\\n \\\"\\\"\\\"Given a stream of text, return a stream of json objects.\\n This handles streams which are inconsistently buffered (some entries may\\n be newline delimited, and others are not).\\n \\\"\\\"\\\"\\n return split_buffer(stream, json_splitter, json_decoder.decode)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::json_hash\",\n \"text\": \"compose/utils.py::json_hash\\ndef json_hash(obj):\\n dump = json.dumps(obj, sort_keys=True, separators=(',', ':'))\\n h = hashlib.sha256()\\n h.update(dump.encode('utf8'))\\n return h.hexdigest()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::microseconds_from_time_nano\",\n \"text\": \"compose/utils.py::microseconds_from_time_nano\\ndef microseconds_from_time_nano(time_nano):\\n return int(time_nano % 1000000000 / 1000)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/utils.py::build_string_dict\",\n \"text\": \"compose/utils.py::build_string_dict\\ndef build_string_dict(source_dict):\\n return dict((k, str(v)) for k, v in source_dict.items())\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::migrate\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::migrate\\ndef migrate(content):\\n data = ruamel.yaml.load(content, ruamel.yaml.RoundTripLoader)\\n\\n service_names = data.keys()\\n\\n for name, service in data.items():\\n warn_for_links(name, service)\\n warn_for_external_links(name, service)\\n rewrite_net(service, service_names)\\n rewrite_build(service)\\n rewrite_logging(service)\\n rewrite_volumes_from(service, service_names)\\n\\n services = {name: data.pop(name) for name in data.keys()}\\n\\n data['version'] = \\\"2\\\"\\n data['services'] = services\\n create_volumes_section(data)\\n\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_links\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_links\\ndef warn_for_links(name, service):\\n links = service.get('links')\\n if links:\\n example_service = links[0].partition(':')[0]\\n log.warn(\\n \\\"Service {name} has links, which no longer create environment \\\"\\n \\\"variables such as {example_service_upper}_PORT. \\\"\\n \\\"If you are using those in your application code, you should \\\"\\n \\\"instead connect directly to the hostname, e.g. \\\"\\n \\\"'{example_service}'.\\\"\\n .format(name=name, example_service=example_service,\\n example_service_upper=example_service.upper()))\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_external_links\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_external_links\\ndef warn_for_external_links(name, service):\\n external_links = service.get('external_links')\\n if external_links:\\n log.warn(\\n \\\"Service {name} has external_links: {ext}, which now work \\\"\\n \\\"slightly differently. In particular, two containers must be \\\"\\n \\\"connected to at least one network in common in order to \\\"\\n \\\"communicate, even if explicitly linked together.\\\\n\\\\n\\\"\\n \\\"Either connect the external container to your app's default \\\"\\n \\\"network, or connect both the external container and your \\\"\\n \\\"service's containers to a pre-existing network. See \\\"\\n \\\"https://docs.docker.com/compose/networking/ \\\"\\n \\\"for more on how to do this.\\\"\\n .format(name=name, ext=external_links))\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_net\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_net\\ndef rewrite_net(service, service_names):\\n if 'net' in service:\\n network_mode = service.pop('net')\\n\\n # \\\"container:<service name>\\\" is now \\\"service:<service name>\\\"\\n if network_mode.startswith('container:'):\\n name = network_mode.partition(':')[2]\\n if name in service_names:\\n network_mode = 'service:{}'.format(name)\\n\\n service['network_mode'] = network_mode\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_build\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_build\\ndef rewrite_build(service):\\n if 'dockerfile' in service:\\n service['build'] = {\\n 'context': service.pop('build'),\\n 'dockerfile': service.pop('dockerfile'),\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_logging\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_logging\\ndef rewrite_logging(service):\\n if 'log_driver' in service:\\n service['logging'] = {'driver': service.pop('log_driver')}\\n if 'log_opt' in service:\\n service['logging']['options'] = service.pop('log_opt')\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_volumes_from\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_volumes_from\\ndef rewrite_volumes_from(service, service_names):\\n for idx, volume_from in enumerate(service.get('volumes_from', [])):\\n if volume_from.split(':', 1)[0] not in service_names:\\n service['volumes_from'][idx] = 'container:%s' % volume_from\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::create_volumes_section\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::create_volumes_section\\ndef create_volumes_section(data):\\n named_volumes = get_named_volumes(data['services'])\\n if named_volumes:\\n log.warn(\\n \\\"Named volumes ({names}) must be explicitly declared. Creating a \\\"\\n \\\"'volumes' section with declarations.\\\\n\\\\n\\\"\\n \\\"For backwards-compatibility, they've been declared as external. \\\"\\n \\\"If you don't mind the volume names being prefixed with the \\\"\\n \\\"project name, you can remove the 'external' option from each one.\\\"\\n .format(names=', '.join(list(named_volumes))))\\n\\n data['volumes'] = named_volumes\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::get_named_volumes\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::get_named_volumes\\ndef get_named_volumes(services):\\n volume_specs = [\\n VolumeSpec.parse(volume)\\n for service in services.values()\\n for volume in service.get('volumes', [])\\n ]\\n names = {\\n spec.external\\n for spec in volume_specs\\n if spec.is_named_volume\\n }\\n return {name: {'external': True} for name in names}\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::write\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::write\\ndef write(stream, new_format, indent, width):\\n ruamel.yaml.dump(\\n new_format,\\n stream,\\n Dumper=ruamel.yaml.RoundTripDumper,\\n indent=indent,\\n width=width)\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::parse_opts\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::parse_opts\\ndef parse_opts(args):\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"filename\\\", help=\\\"Compose file filename.\\\")\\n parser.add_argument(\\\"-i\\\", \\\"--in-place\\\", action='store_true')\\n parser.add_argument(\\n \\\"--indent\\\", type=int, default=2,\\n help=\\\"Number of spaces used to indent the output yaml.\\\")\\n parser.add_argument(\\n \\\"--width\\\", type=int, default=80,\\n help=\\\"Number of spaces used as the output width.\\\")\\n return parser.parse_args()\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::main\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::main\\ndef main(args):\\n logging.basicConfig(format='\\\\033[33m%(levelname)s:\\\\033[37m %(message)s\\\\033[0m\\\\n')\\n\\n opts = parse_opts(args)\\n\\n with open(opts.filename, 'r') as fh:\\n new_format = migrate(fh.read())\\n\\n if opts.in_place:\\n output = open(opts.filename, 'w')\\n else:\\n output = sys.stdout\\n write(output, new_format, opts.indent, opts.width)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":63,"cells":{"instance_id":{"kind":"string","value":"google__jax-2561"},"repo":{"kind":"string","value":"google/jax"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"BatchNorm doesn't work with center or scale turned off\nBatchNorm raises TypeError when using without center or scale or both. Seems pretty straightforward to fix (e.x. as a workaround I've set beta to None `beta = None if not center else beta[ed]` if center is False, the same for gamma but there might be some better solution).\r\n\r\nException:\r\nbeta = beta[ed] # ed=(None, slice(None, None, None))\r\nTypeError: tuple indices must be integers or slices, not tuple\r\n\r\nCode:\r\n```\r\nfrom jax import np\r\nfrom jax import random\r\nfrom jax.experimental import stax\r\n\r\n\r\ninit_fn, apply_fn = stax.BatchNorm(axis=0, center=False, scale=False)\r\n_, params = init_fn(random.PRNGKey(0), (-1, 1))\r\n\r\nx = np.zeros((2, 1))\r\napply_fn(params, x)\r\n```\r\n\r\nDetails:\r\njax==0.1.62\r\njaxlib==0.1.42\n"},"gold_files":{"kind":"list like","value":["jax/experimental/stax.py"],"string":"[\n \"jax/experimental/stax.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["jax/experimental/stax.py::BatchNorm.apply_fun"],"string":"[\n \"jax/experimental/stax.py::BatchNorm.apply_fun\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"jax/experimental/optimizers.py","text":"jax/experimental/optimizers.py\ndef optimizer(opt_maker) # Decorator to make an optimizer defined for arrays generalize to containers.\n def tree_opt_maker(*args, **kwargs)\n def tree_init(x0_tree)\n def tree_update(i, grad_tree, opt_state)\n def tree_get_params(opt_state)\ndef sgd(step_size) # Construct optimizer triple for stochastic gradient descent.\n def init(x0)\n def update(i, g, x)\n def get_params(x)\ndef momentum(step_size, mass) # Construct optimizer triple for SGD with momentum.\n def init(x0)\n def update(i, g, state)\n def get_params(state)\ndef nesterov(step_size, mass) # Construct optimizer triple for SGD with Nesterov momentum.\n def init(x0)\n def update(i, g, state)\n def get_params(state)\ndef adagrad(step_size, momentum=0.9) # Construct optimizer triple for Adagrad.\n def init(x0)\n def update(i, g, state)\n def get_params(state)\ndef rmsprop(step_size, gamma=0.9, eps=1e-08) # Construct optimizer triple for RMSProp.\n def init(x0)\n def update(i, g, state)\n def get_params(state)\ndef rmsprop_momentum(step_size, gamma=0.9, eps=1e-08, momentum=0.9) # Construct optimizer triple for RMSProp with momentum.\n def init(x0)\n def update(i, g, state)\n def get_params(state)\ndef adam(step_size, b1=0.9, b2=0.999, eps=1e-08) # Construct optimizer triple for Adam.\n def init(x0)\n def update(i, g, state)\n def get_params(state)\ndef sm3(step_size, momentum=0.9) # Construct optimizer triple for SM3.\n def splice(seq, i, x)\n def broadcast_into(ndim, x, axis)\n def init(x0)\n def update(i, g, state)\n def get_params(state)\ndef constant(step_size)\n def schedule(i)\ndef exponential_decay(step_size, decay_steps, decay_rate)\n def schedule(i)\ndef inverse_time_decay(step_size, decay_steps, decay_rate, staircase=False)\ndef polynomial_decay(step_size, decay_steps, final_step_size, power=1.0)\n def schedule(step_num)\ndef piecewise_constant(boundaries, values)\n def schedule(i)\ndef make_schedule(scalar_or_schedule)\ndef l2_norm(tree) # Compute the l2 norm of a pytree of arrays. Useful for weight decay.\ndef clip_grads(grad_tree, max_norm) # Clip gradients stored as a pytree of arrays to maximum norm `max_norm`.\nclass JoinPoint # Marks the boundary between two joined (nested) pytrees.\n def __init__(self, subtree)\n def __iter__(self)\ndef unpack_optimizer_state(opt_state) # Converts an OptimizerState to a marked pytree.\ndef pack_optimizer_state(marked_pytree) # Converts a marked pytree to an OptimizerState.","label":0},{"cid":"jax/experimental/optix.py","text":"jax/experimental/optix.py\ndef clip(max_delta) # Clip updates element-wise, to be between -max_delta and +max_delta.\n def init_fn(_)\n def update_fn(updates, state)\ndef global_norm(items)\ndef clip_by_global_norm(max_norm) # Clip updates using their global norm.\n def init_fn(_)\n def update_fn(updates, state)\ndef trace(decay, nesterov) # Compute a trace of past updates.\n def init_fn(params)\n def update_fn(updates, state)\ndef _update_moment(updates, moments, decay, order)\ndef scale_by_rms(decay=0.9, eps=1e-08) # Rescale updates by the root of the exp. moving avg of the square.\n def init_fn(params)\n def update_fn(updates, state)\ndef scale_by_stddev(decay=0.9, eps=1e-08) # Rescale updates by the root of the centered exp. moving average of squares.\n def init_fn(params)\n def update_fn(updates, state)\ndef scale_by_adam(b1=0.9, b2=0.999, eps=1e-08) # Rescale updates according to the Adam algorithm.\n def init_fn(params)\n def update_fn(updates, state)\ndef scale(step_size) # Scale updates by some fixed scalar `step_size`.\n def init_fn(_)\n def update_fn(updates, state)\ndef scale_by_schedule(step_size_fn) # Scale updates using a custom schedule for the `step_size`.\n def init_fn(_)\n def update_fn(updates, state)\ndef add_noise(eta, gamma, seed) # Add gradient noise.\n def init_fn(_)\n def update_fn(updates, state)\ndef apply_every(k=1) # accumulate gradients and apply them every k steps.\n def init_fn(params)\n def update_fn(updates, state)\ndef chain(*args) # Applies a list of chainable update transformations.\n def init_fn(params)\n def update_fn(updates, state)\ndef apply_updates(params, updates) # Applies an update to the corresponding parameters.\ndef sgd(learning_rate, momentum=0.0, nesterov=False)\ndef noisy_sgd(learning_rate, eta=0.01, gamma=0.55, seed=42)\ndef adam(learning_rate, b1=0.9, b2=0.999, eps=1e-08)\ndef rmsprop(learning_rate, decay=0.9, eps=1e-08, centered=False)","label":0},{"cid":"jax/experimental/stax.py","text":"jax/experimental/stax.py\ndef Dense(out_dim, W_init=glorot_normal(), b_init=normal()) # Layer constructor function for a dense (fully-connected) layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef GeneralConv(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=normal(1e-06)) # Layer construction function for a general convolution layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=normal(1e-06)) # Layer construction function for a general transposed-convolution layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-05, center=True, scale=True, beta_init=zeros, gamma_init=ones) # Layer construction function for a batch normalization layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, x, **kwargs)\ndef elementwise(fun, **fun_kwargs) # Layer that applies a scalar function elementwise on its inputs.\ndef _pooling_layer(reducer, init_val, rescaler=None)\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec=None) # Layer construction function for a pooling layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef _normalize_by_window_size(dims, strides, padding)\n def rescale(outputs, inputs, spec)\ndef Flatten() # Layer construction function for flattening all but the leading dim.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef Identity() # Layer construction function for an identity layer.\ndef FanOut(num) # Layer construction function for a fan-out layer.\ndef FanInSum() # Layer construction function for a fan-in sum layer.\ndef FanInConcat(axis=-1) # Layer construction function for a fan-in concatenation layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef Dropout(rate, mode='train') # Layer construction function for a dropout layer with given rate.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef serial(*layers) # Combinator for composing layers in serial.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef parallel(*layers) # Combinator for composing layers in parallel.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef shape_dependent(make_layer) # Combinator to delay layer constructor pair until input shapes are known.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)","label":1},{"cid":"jax/experimental/vectorize.py","text":"jax/experimental/vectorize.py\ndef _parse_gufunc_signature(signature) # Parse string signatures for a generalized universal function.\ndef _update_dim_sizes(dim_sizes, arg, core_dims) # Incrementally check and update core dimension sizes for a single argument.\ndef _parse_input_dimensions(args, input_core_dims) # Parse broadcast and core dimensions for vectorize with a signature.\ndef _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims) # Helper for calculating broadcast shapes with core dimensions.\ndef broadcast_with_core_dims(args, input_core_dims, output_core_dims)\ndef verify_axis_is_supported(input_core_dims, output_core_dims)\ndef reorder_inputs(args, axis, input_core_dims)\ndef reorder_outputs(result, axis, output_core_dims)\ndef vectorize(signature) # Vectorize a function using JAX.\n def decorator(func)\n def wrapper(*args, **kwargs)","label":0},{"cid":"jax/lib/xla_bridge.py","text":"jax/lib/xla_bridge.py\ndef get_compile_options(num_replicas, num_partitions, device_assignment=None) # Returns the compile options to use, as derived from flag values.\ndef register_backend(name, factory)\ndef _get_local_backend(platform=None)\ndef _get_tpu_driver_backend(platform)\ndef get_backend(platform=None)\ndef get_device_backend(device=None) # Returns the Backend associated with `device`, or the default Backend.\ndef device_count(backend=None) # Returns the total number of devices.\ndef local_device_count(backend=None) # Returns the number of devices on this host.\ndef devices(backend=None) # Returns a list of all devices.\ndef local_devices(host_id=None, backend=None) # Returns a list of devices local to a given host (this host by default).\ndef host_id(backend=None) # Returns the integer host ID of this host.\ndef host_ids(backend=None) # Returns a sorted list of all host IDs.\ndef host_count(backend=None) # Returns the number of hosts.\ndef dtype_to_etype(dtype) # Convert from dtype to canonical etype (reading FLAGS.jax_enable_x64).\ndef supported_numpy_dtypes()\ndef normalize_to_xla_dtypes(val) # Normalize dtypes in a value.\nclass _JaxComputationBuilder # Base class implementing all of JaxComputationBuilder.\n def Build(self, *args, **kwargs)\n def NumpyArrayConstant(self, value, canonicalize_types=True)\n def ConstantLike(self, example_value, value, canonicalize_types=True)\n def Constant(self, py_val, canonicalize_types=True) # Translate constant `py_val` to a constant for this ComputationBuilder.\n def CrossReplicaSum(self, operand, replica_groups) # Workaround for CrossReplicaSum not being implemented on some backends.\n def AllToAll(self, operand, split_axis, concat_axis, replica_groups) # Workaround for AllToAll not being implemented on some backends.\ndef make_computation_builder(name)\ndef register_constant_handler(type_, handler_fun)\ndef _ndarray_constant_handler(c, val, canonicalize_types=True) # Constant handler for ndarray literals, handling zero-size strides.\ndef _scalar_constant_handler(c, val, canonicalize_types=True)\ndef _python_scalar_handler(dtype, c, val, canonicalize_dtypes=True)","label":0}],"string":"[\n {\n \"cid\": \"jax/experimental/optimizers.py\",\n \"text\": \"jax/experimental/optimizers.py\\ndef optimizer(opt_maker) # Decorator to make an optimizer defined for arrays generalize to containers.\\n def tree_opt_maker(*args, **kwargs)\\n def tree_init(x0_tree)\\n def tree_update(i, grad_tree, opt_state)\\n def tree_get_params(opt_state)\\ndef sgd(step_size) # Construct optimizer triple for stochastic gradient descent.\\n def init(x0)\\n def update(i, g, x)\\n def get_params(x)\\ndef momentum(step_size, mass) # Construct optimizer triple for SGD with momentum.\\n def init(x0)\\n def update(i, g, state)\\n def get_params(state)\\ndef nesterov(step_size, mass) # Construct optimizer triple for SGD with Nesterov momentum.\\n def init(x0)\\n def update(i, g, state)\\n def get_params(state)\\ndef adagrad(step_size, momentum=0.9) # Construct optimizer triple for Adagrad.\\n def init(x0)\\n def update(i, g, state)\\n def get_params(state)\\ndef rmsprop(step_size, gamma=0.9, eps=1e-08) # Construct optimizer triple for RMSProp.\\n def init(x0)\\n def update(i, g, state)\\n def get_params(state)\\ndef rmsprop_momentum(step_size, gamma=0.9, eps=1e-08, momentum=0.9) # Construct optimizer triple for RMSProp with momentum.\\n def init(x0)\\n def update(i, g, state)\\n def get_params(state)\\ndef adam(step_size, b1=0.9, b2=0.999, eps=1e-08) # Construct optimizer triple for Adam.\\n def init(x0)\\n def update(i, g, state)\\n def get_params(state)\\ndef sm3(step_size, momentum=0.9) # Construct optimizer triple for SM3.\\n def splice(seq, i, x)\\n def broadcast_into(ndim, x, axis)\\n def init(x0)\\n def update(i, g, state)\\n def get_params(state)\\ndef constant(step_size)\\n def schedule(i)\\ndef exponential_decay(step_size, decay_steps, decay_rate)\\n def schedule(i)\\ndef inverse_time_decay(step_size, decay_steps, decay_rate, staircase=False)\\ndef polynomial_decay(step_size, decay_steps, final_step_size, power=1.0)\\n def schedule(step_num)\\ndef piecewise_constant(boundaries, values)\\n def schedule(i)\\ndef make_schedule(scalar_or_schedule)\\ndef l2_norm(tree) # Compute the l2 norm of a pytree of arrays. Useful for weight decay.\\ndef clip_grads(grad_tree, max_norm) # Clip gradients stored as a pytree of arrays to maximum norm `max_norm`.\\nclass JoinPoint # Marks the boundary between two joined (nested) pytrees.\\n def __init__(self, subtree)\\n def __iter__(self)\\ndef unpack_optimizer_state(opt_state) # Converts an OptimizerState to a marked pytree.\\ndef pack_optimizer_state(marked_pytree) # Converts a marked pytree to an OptimizerState.\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py\",\n \"text\": \"jax/experimental/optix.py\\ndef clip(max_delta) # Clip updates element-wise, to be between -max_delta and +max_delta.\\n def init_fn(_)\\n def update_fn(updates, state)\\ndef global_norm(items)\\ndef clip_by_global_norm(max_norm) # Clip updates using their global norm.\\n def init_fn(_)\\n def update_fn(updates, state)\\ndef trace(decay, nesterov) # Compute a trace of past updates.\\n def init_fn(params)\\n def update_fn(updates, state)\\ndef _update_moment(updates, moments, decay, order)\\ndef scale_by_rms(decay=0.9, eps=1e-08) # Rescale updates by the root of the exp. moving avg of the square.\\n def init_fn(params)\\n def update_fn(updates, state)\\ndef scale_by_stddev(decay=0.9, eps=1e-08) # Rescale updates by the root of the centered exp. moving average of squares.\\n def init_fn(params)\\n def update_fn(updates, state)\\ndef scale_by_adam(b1=0.9, b2=0.999, eps=1e-08) # Rescale updates according to the Adam algorithm.\\n def init_fn(params)\\n def update_fn(updates, state)\\ndef scale(step_size) # Scale updates by some fixed scalar `step_size`.\\n def init_fn(_)\\n def update_fn(updates, state)\\ndef scale_by_schedule(step_size_fn) # Scale updates using a custom schedule for the `step_size`.\\n def init_fn(_)\\n def update_fn(updates, state)\\ndef add_noise(eta, gamma, seed) # Add gradient noise.\\n def init_fn(_)\\n def update_fn(updates, state)\\ndef apply_every(k=1) # accumulate gradients and apply them every k steps.\\n def init_fn(params)\\n def update_fn(updates, state)\\ndef chain(*args) # Applies a list of chainable update transformations.\\n def init_fn(params)\\n def update_fn(updates, state)\\ndef apply_updates(params, updates) # Applies an update to the corresponding parameters.\\ndef sgd(learning_rate, momentum=0.0, nesterov=False)\\ndef noisy_sgd(learning_rate, eta=0.01, gamma=0.55, seed=42)\\ndef adam(learning_rate, b1=0.9, b2=0.999, eps=1e-08)\\ndef rmsprop(learning_rate, decay=0.9, eps=1e-08, centered=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py\",\n \"text\": \"jax/experimental/stax.py\\ndef Dense(out_dim, W_init=glorot_normal(), b_init=normal()) # Layer constructor function for a dense (fully-connected) layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef GeneralConv(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=normal(1e-06)) # Layer construction function for a general convolution layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=normal(1e-06)) # Layer construction function for a general transposed-convolution layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-05, center=True, scale=True, beta_init=zeros, gamma_init=ones) # Layer construction function for a batch normalization layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, x, **kwargs)\\ndef elementwise(fun, **fun_kwargs) # Layer that applies a scalar function elementwise on its inputs.\\ndef _pooling_layer(reducer, init_val, rescaler=None)\\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec=None) # Layer construction function for a pooling layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef _normalize_by_window_size(dims, strides, padding)\\n def rescale(outputs, inputs, spec)\\ndef Flatten() # Layer construction function for flattening all but the leading dim.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef Identity() # Layer construction function for an identity layer.\\ndef FanOut(num) # Layer construction function for a fan-out layer.\\ndef FanInSum() # Layer construction function for a fan-in sum layer.\\ndef FanInConcat(axis=-1) # Layer construction function for a fan-in concatenation layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef Dropout(rate, mode='train') # Layer construction function for a dropout layer with given rate.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef serial(*layers) # Combinator for composing layers in serial.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef parallel(*layers) # Combinator for composing layers in parallel.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef shape_dependent(make_layer) # Combinator to delay layer constructor pair until input shapes are known.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/vectorize.py\",\n \"text\": \"jax/experimental/vectorize.py\\ndef _parse_gufunc_signature(signature) # Parse string signatures for a generalized universal function.\\ndef _update_dim_sizes(dim_sizes, arg, core_dims) # Incrementally check and update core dimension sizes for a single argument.\\ndef _parse_input_dimensions(args, input_core_dims) # Parse broadcast and core dimensions for vectorize with a signature.\\ndef _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims) # Helper for calculating broadcast shapes with core dimensions.\\ndef broadcast_with_core_dims(args, input_core_dims, output_core_dims)\\ndef verify_axis_is_supported(input_core_dims, output_core_dims)\\ndef reorder_inputs(args, axis, input_core_dims)\\ndef reorder_outputs(result, axis, output_core_dims)\\ndef vectorize(signature) # Vectorize a function using JAX.\\n def decorator(func)\\n def wrapper(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py\",\n \"text\": \"jax/lib/xla_bridge.py\\ndef get_compile_options(num_replicas, num_partitions, device_assignment=None) # Returns the compile options to use, as derived from flag values.\\ndef register_backend(name, factory)\\ndef _get_local_backend(platform=None)\\ndef _get_tpu_driver_backend(platform)\\ndef get_backend(platform=None)\\ndef get_device_backend(device=None) # Returns the Backend associated with `device`, or the default Backend.\\ndef device_count(backend=None) # Returns the total number of devices.\\ndef local_device_count(backend=None) # Returns the number of devices on this host.\\ndef devices(backend=None) # Returns a list of all devices.\\ndef local_devices(host_id=None, backend=None) # Returns a list of devices local to a given host (this host by default).\\ndef host_id(backend=None) # Returns the integer host ID of this host.\\ndef host_ids(backend=None) # Returns a sorted list of all host IDs.\\ndef host_count(backend=None) # Returns the number of hosts.\\ndef dtype_to_etype(dtype) # Convert from dtype to canonical etype (reading FLAGS.jax_enable_x64).\\ndef supported_numpy_dtypes()\\ndef normalize_to_xla_dtypes(val) # Normalize dtypes in a value.\\nclass _JaxComputationBuilder # Base class implementing all of JaxComputationBuilder.\\n def Build(self, *args, **kwargs)\\n def NumpyArrayConstant(self, value, canonicalize_types=True)\\n def ConstantLike(self, example_value, value, canonicalize_types=True)\\n def Constant(self, py_val, canonicalize_types=True) # Translate constant `py_val` to a constant for this ComputationBuilder.\\n def CrossReplicaSum(self, operand, replica_groups) # Workaround for CrossReplicaSum not being implemented on some backends.\\n def AllToAll(self, operand, split_axis, concat_axis, replica_groups) # Workaround for AllToAll not being implemented on some backends.\\ndef make_computation_builder(name)\\ndef register_constant_handler(type_, handler_fun)\\ndef _ndarray_constant_handler(c, val, canonicalize_types=True) # Constant handler for ndarray literals, handling zero-size strides.\\ndef _scalar_constant_handler(c, val, canonicalize_types=True)\\ndef _python_scalar_handler(dtype, c, val, canonicalize_dtypes=True)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"jax/experimental/optimizers.py::optimizer","text":"jax/experimental/optimizers.py::optimizer\ndef optimizer(opt_maker):\n \"\"\"Decorator to make an optimizer defined for arrays generalize to containers.\n\n With this decorator, you can write init, update, and get_params functions that\n each operate only on single arrays, and convert them to corresponding\n functions that operate on pytrees of parameters. See the optimizers defined in\n optimizers.py for examples.\n\n Args:\n opt_maker: a function that returns an ``(init_fun, update_fun, get_params)``\n triple of functions that might only work with ndarrays, as per\n\n .. code-block:: haskell\n\n init_fun :: ndarray -> OptStatePytree ndarray\n update_fun :: OptStatePytree ndarray -> OptStatePytree ndarray\n get_params :: OptStatePytree ndarray -> ndarray\n\n Returns:\n An ``(init_fun, update_fun, get_params)`` triple of functions that work on\n arbitrary pytrees, as per\n\n .. code-block:: haskell\n\n init_fun :: ParameterPytree ndarray -> OptimizerState\n update_fun :: OptimizerState -> OptimizerState\n get_params :: OptimizerState -> ParameterPytree ndarray\n\n The OptimizerState pytree type used by the returned functions is isomorphic\n to ``ParameterPytree (OptStatePytree ndarray)``, but may store the state\n instead as e.g. a partially-flattened data structure for performance.\n \"\"\"\n\n @functools.wraps(opt_maker)\n def tree_opt_maker(*args, **kwargs):\n init, update, get_params = opt_maker(*args, **kwargs)\n\n @functools.wraps(init)\n def tree_init(x0_tree):\n x0_flat, tree = tree_flatten(x0_tree)\n initial_states = [init(x0) for x0 in x0_flat]\n states_flat, subtrees = unzip2(map(tree_flatten, initial_states))\n packed_state = pack(map(pack, states_flat))\n return OptimizerState(packed_state, tree, subtrees)\n\n @functools.wraps(update)\n def tree_update(i, grad_tree, opt_state):\n packed_state, tree, subtrees = opt_state\n grad_flat, tree2 = tree_flatten(grad_tree)\n","label":0},{"cid":"jax/experimental/optimizers.py::optimizer.tree_opt_maker","text":"jax/experimental/optimizers.py::optimizer.tree_opt_maker\n @functools.wraps(opt_maker)\n def tree_opt_maker(*args, **kwargs):\n init, update, get_params = opt_maker(*args, **kwargs)\n\n @functools.wraps(init)\n def tree_init(x0_tree):\n x0_flat, tree = tree_flatten(x0_tree)\n initial_states = [init(x0) for x0 in x0_flat]\n states_flat, subtrees = unzip2(map(tree_flatten, initial_states))\n packed_state = pack(map(pack, states_flat))\n return OptimizerState(packed_state, tree, subtrees)\n\n @functools.wraps(update)\n def tree_update(i, grad_tree, opt_state):\n packed_state, tree, subtrees = opt_state\n grad_flat, tree2 = tree_flatten(grad_tree)\n if tree2 != tree:\n msg = (\"optimizer update function was passed a gradient tree that did \"\n \"not match the parameter tree structure with which it was \"\n \"initialized: parameter tree {} and grad tree {}.\")\n raise TypeError(msg.format(tree, tree2))\n states = map(tree_unflatten, subtrees, packed_state)\n new_states = map(partial(update, i), grad_flat, states)\n new_states_flat, subtrees2 = unzip2(map(tree_flatten, new_states))\n for subtree, subtree2 in zip(subtrees, subtrees2):\n if subtree2 != subtree:\n msg = (\"optimizer update function produced an output structure that \"\n \"did not match its input structure: input {} and output {}.\")\n raise TypeError(msg.format(subtree, subtree2))\n new_packed_state = pack(map(pack, new_states_flat))\n return OptimizerState(new_packed_state, tree, subtrees)\n\n @functools.wraps(get_params)\n def tree_get_params(opt_state):\n packed_state, tree, subtrees = opt_state\n states = map(tree_unflatten, subtrees, packed_state)\n params = map(get_params, states)\n return tree_unflatten(tree, params)\n\n return tree_init, tree_update, tree_get_params","label":0},{"cid":"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_init","text":"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_init\n @functools.wraps(init)\n def tree_init(x0_tree):\n x0_flat, tree = tree_flatten(x0_tree)\n initial_states = [init(x0) for x0 in x0_flat]\n states_flat, subtrees = unzip2(map(tree_flatten, initial_states))\n packed_state = pack(map(pack, states_flat))\n return OptimizerState(packed_state, tree, subtrees)","label":0},{"cid":"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_update","text":"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_update\n @functools.wraps(update)\n def tree_update(i, grad_tree, opt_state):\n packed_state, tree, subtrees = opt_state\n grad_flat, tree2 = tree_flatten(grad_tree)\n if tree2 != tree:\n msg = (\"optimizer update function was passed a gradient tree that did \"\n \"not match the parameter tree structure with which it was \"\n \"initialized: parameter tree {} and grad tree {}.\")\n raise TypeError(msg.format(tree, tree2))\n states = map(tree_unflatten, subtrees, packed_state)\n new_states = map(partial(update, i), grad_flat, states)\n new_states_flat, subtrees2 = unzip2(map(tree_flatten, new_states))\n for subtree, subtree2 in zip(subtrees, subtrees2):\n if subtree2 != subtree:\n msg = (\"optimizer update function produced an output structure that \"\n \"did not match its input structure: input {} and output {}.\")\n raise TypeError(msg.format(subtree, subtree2))\n new_packed_state = pack(map(pack, new_states_flat))\n return OptimizerState(new_packed_state, tree, subtrees)","label":0},{"cid":"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_get_params","text":"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_get_params\n @functools.wraps(get_params)\n def tree_get_params(opt_state):\n packed_state, tree, subtrees = opt_state\n states = map(tree_unflatten, subtrees, packed_state)\n params = map(get_params, states)\n return tree_unflatten(tree, params)","label":0},{"cid":"jax/experimental/optimizers.py::sgd","text":"jax/experimental/optimizers.py::sgd\n@optimizer\ndef sgd(step_size):\n \"\"\"Construct optimizer triple for stochastic gradient descent.\n\n Args:\n step_size: positive scalar, or a callable representing a step size schedule\n that maps the iteration index to positive scalar.\n\n Returns:\n An (init_fun, update_fun, get_params) triple.\n \"\"\"\n step_size = make_schedule(step_size)\n def init(x0):\n return x0\n def update(i, g, x):\n return x - step_size(i) * g\n def get_params(x):\n return x\n return init, update, get_params","label":0},{"cid":"jax/experimental/optimizers.py::sgd.init","text":"jax/experimental/optimizers.py::sgd.init\n def init(x0):\n return x0","label":0},{"cid":"jax/experimental/optimizers.py::sgd.update","text":"jax/experimental/optimizers.py::sgd.update\n def update(i, g, x):\n return x - step_size(i) * g","label":0},{"cid":"jax/experimental/optimizers.py::sgd.get_params","text":"jax/experimental/optimizers.py::sgd.get_params\n def get_params(x):\n return x","label":0},{"cid":"jax/experimental/optimizers.py::momentum","text":"jax/experimental/optimizers.py::momentum\n@optimizer\ndef momentum(step_size, mass):\n \"\"\"Construct optimizer triple for SGD with momentum.\n\n Args:\n step_size: positive scalar, or a callable representing a step size schedule\n that maps the iteration index to positive scalar.\n mass: positive scalar representing the momentum coefficient.\n\n Returns:\n An (init_fun, update_fun, get_params) triple.\n \"\"\"\n step_size = make_schedule(step_size)\n def init(x0):\n v0 = np.zeros_like(x0)\n return x0, v0\n def update(i, g, state):\n x, velocity = state\n velocity = mass * velocity + g\n x = x - step_size(i) * velocity\n return x, velocity\n def get_params(state):\n x, _ = state\n return x\n return init, update, get_params","label":0},{"cid":"jax/experimental/optimizers.py::momentum.init","text":"jax/experimental/optimizers.py::momentum.init\n def init(x0):\n v0 = np.zeros_like(x0)\n return x0, v0","label":0},{"cid":"jax/experimental/optimizers.py::momentum.update","text":"jax/experimental/optimizers.py::momentum.update\n def update(i, g, state):\n x, velocity = state\n velocity = mass * velocity + g\n x = x - step_size(i) * velocity\n return x, velocity","label":0},{"cid":"jax/experimental/optimizers.py::momentum.get_params","text":"jax/experimental/optimizers.py::momentum.get_params\n def get_params(state):\n x, _ = state\n return x","label":0},{"cid":"jax/experimental/optimizers.py::nesterov","text":"jax/experimental/optimizers.py::nesterov\n@optimizer\ndef nesterov(step_size, mass):\n \"\"\"Construct optimizer triple for SGD with Nesterov momentum.\n\n Args:\n step_size: positive scalar, or a callable representing a step size schedule\n that maps the iteration index to positive scalar.\n mass: positive scalar representing the momentum coefficient.\n\n Returns:\n An (init_fun, update_fun, get_params) triple.\n \"\"\"\n step_size = make_schedule(step_size)\n def init(x0):\n v0 = np.zeros_like(x0)\n return x0, v0\n def update(i, g, state):\n x, velocity = state\n velocity = mass * velocity + g\n x = x - step_size(i) * (mass * velocity + g)\n return x, velocity\n def get_params(state):\n x, _ = state\n return x\n return init, update, get_params","label":0},{"cid":"jax/experimental/optimizers.py::nesterov.init","text":"jax/experimental/optimizers.py::nesterov.init\n def init(x0):\n v0 = np.zeros_like(x0)\n return x0, v0","label":0},{"cid":"jax/experimental/optimizers.py::nesterov.update","text":"jax/experimental/optimizers.py::nesterov.update\n def update(i, g, state):\n x, velocity = state\n velocity = mass * velocity + g\n x = x - step_size(i) * (mass * velocity + g)\n return x, velocity","label":0},{"cid":"jax/experimental/optimizers.py::nesterov.get_params","text":"jax/experimental/optimizers.py::nesterov.get_params\n def get_params(state):\n x, _ = state\n return x","label":0},{"cid":"jax/experimental/optimizers.py::adagrad","text":"jax/experimental/optimizers.py::adagrad\n@optimizer\ndef adagrad(step_size, momentum=0.9):\n \"\"\"Construct optimizer triple for Adagrad.\n\n Adaptive Subgradient Methods for Online Learning and Stochastic Optimization:\n http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n\n Args:\n step_size: positive scalar, or a callable representing a step size schedule\n that maps the iteration index to positive scalar.\n momentum: optional, a positive scalar value for momentum\n\n Returns:\n An (init_fun, update_fun, get_params) triple.\n \"\"\"\n step_size = make_schedule(step_size)\n\n def init(x0):\n g_sq = np.zeros_like(x0)\n m = np.zeros_like(x0)\n return x0, g_sq, m\n\n def update(i, g, state):\n x, g_sq, m = state\n g_sq += g**2\n g_sq_inv_sqrt = np.where(g_sq > 0, 1. / np.sqrt(g_sq), 0.0)\n m = (1. - momentum) * (g * g_sq_inv_sqrt) + momentum * m\n x = x - step_size(i) * m\n return x, g_sq, m\n\n def get_params(state):\n x, _, _ = state\n return x\n\n return init, update, get_params","label":0},{"cid":"jax/experimental/optimizers.py::adagrad.init","text":"jax/experimental/optimizers.py::adagrad.init\n def init(x0):\n g_sq = np.zeros_like(x0)\n m = np.zeros_like(x0)\n return x0, g_sq, m","label":0},{"cid":"jax/experimental/optimizers.py::adagrad.update","text":"jax/experimental/optimizers.py::adagrad.update\n def update(i, g, state):\n x, g_sq, m = state\n g_sq += g**2\n g_sq_inv_sqrt = np.where(g_sq > 0, 1. / np.sqrt(g_sq), 0.0)\n m = (1. - momentum) * (g * g_sq_inv_sqrt) + momentum * m\n x = x - step_size(i) * m\n return x, g_sq, m","label":0},{"cid":"jax/experimental/optimizers.py::adagrad.get_params","text":"jax/experimental/optimizers.py::adagrad.get_params\n def get_params(state):\n x, _, _ = state\n return x","label":0},{"cid":"jax/experimental/optimizers.py::rmsprop","text":"jax/experimental/optimizers.py::rmsprop\n@optimizer\ndef rmsprop(step_size, gamma=0.9, eps=1e-8):\n \"\"\"Construct optimizer triple for RMSProp.\n\n Args:\n step_size: positive scalar, or a callable representing a step size schedule\n that maps the iteration index to positive scalar.\n gamma: Decay parameter.\n eps: Epsilon parameter.\n\n Returns:\n An (init_fun, update_fun, get_params) triple.\n \"\"\"\n step_size = make_schedule(step_size)\n def init(x0):\n avg_sq_grad = np.zeros_like(x0)\n return x0, avg_sq_grad\n def update(i, g, state):\n x, avg_sq_grad = state\n avg_sq_grad = avg_sq_grad * gamma + g**2 * (1. - gamma)\n x = x - step_size(i) * g / np.sqrt(avg_sq_grad + eps)\n return x, avg_sq_grad\n def get_params(state):\n x, _ = state\n return x\n return init, update, get_params","label":0},{"cid":"jax/experimental/optimizers.py::rmsprop.init","text":"jax/experimental/optimizers.py::rmsprop.init\n def init(x0):\n avg_sq_grad = np.zeros_like(x0)\n return x0, avg_sq_grad","label":0},{"cid":"jax/experimental/optimizers.py::rmsprop.update","text":"jax/experimental/optimizers.py::rmsprop.update\n def update(i, g, state):\n x, avg_sq_grad = state\n avg_sq_grad = avg_sq_grad * gamma + g**2 * (1. - gamma)\n x = x - step_size(i) * g / np.sqrt(avg_sq_grad + eps)\n return x, avg_sq_grad","label":0},{"cid":"jax/experimental/optimizers.py::rmsprop.get_params","text":"jax/experimental/optimizers.py::rmsprop.get_params\n def get_params(state):\n x, _ = state\n return x","label":0},{"cid":"jax/experimental/optimizers.py::rmsprop_momentum","text":"jax/experimental/optimizers.py::rmsprop_momentum\n@optimizer\ndef rmsprop_momentum(step_size, gamma=0.9, eps=1e-8, momentum=0.9):\n \"\"\"Construct optimizer triple for RMSProp with momentum.\n\n This optimizer is separate from the rmsprop optimizer because it needs to\n keep track of additional parameters.\n\n Args:\n step_size: positive scalar, or a callable representing a step size schedule\n that maps the iteration index to positive scalar.\n gamma: Decay parameter.\n eps: Epsilon parameter.\n momentum: Momentum parameter.\n\n Returns:\n An (init_fun, update_fun, get_params) triple.\n \"\"\"\n step_size = make_schedule(step_size)\n def init(x0):\n avg_sq_grad = np.zeros_like(x0)\n mom = np.zeros_like(x0)\n return x0, avg_sq_grad, mom\n def update(i, g, state):\n x, avg_sq_grad, mom = state\n avg_sq_grad = avg_sq_grad * gamma + g**2 * (1. - gamma)\n mom = momentum * mom + step_size(i) * g / np.sqrt(avg_sq_grad + eps)\n x = x - mom\n return x, avg_sq_grad, mom\n def get_params(state):\n x, _, _ = state\n return x\n return init, update, get_params","label":0},{"cid":"jax/experimental/optimizers.py::rmsprop_momentum.init","text":"jax/experimental/optimizers.py::rmsprop_momentum.init\n def init(x0):\n avg_sq_grad = np.zeros_like(x0)\n mom = np.zeros_like(x0)\n return x0, avg_sq_grad, mom","label":0},{"cid":"jax/experimental/optimizers.py::rmsprop_momentum.update","text":"jax/experimental/optimizers.py::rmsprop_momentum.update\n def update(i, g, state):\n x, avg_sq_grad, mom = state\n avg_sq_grad = avg_sq_grad * gamma + g**2 * (1. - gamma)\n mom = momentum * mom + step_size(i) * g / np.sqrt(avg_sq_grad + eps)\n x = x - mom\n return x, avg_sq_grad, mom","label":0},{"cid":"jax/experimental/optimizers.py::rmsprop_momentum.get_params","text":"jax/experimental/optimizers.py::rmsprop_momentum.get_params\n def get_params(state):\n x, _, _ = state\n return x","label":0},{"cid":"jax/experimental/optimizers.py::adam","text":"jax/experimental/optimizers.py::adam\n@optimizer\ndef adam(step_size, b1=0.9, b2=0.999, eps=1e-8):\n \"\"\"Construct optimizer triple for Adam.\n\n Args:\n step_size: positive scalar, or a callable representing a step size schedule\n that maps the iteration index to positive scalar.\n b1: optional, a positive scalar value for beta_1, the exponential decay rate\n for the first moment estimates (default 0.9).\n b2: optional, a positive scalar value for beta_2, the exponential decay rate\n for the second moment estimates (default 0.999).\n eps: optional, a positive scalar value for epsilon, a small constant for\n numerical stability (default 1e-8).\n\n Returns:\n An (init_fun, update_fun, get_params) triple.\n \"\"\"\n step_size = make_schedule(step_size)\n def init(x0):\n m0 = np.zeros_like(x0)\n v0 = np.zeros_like(x0)\n return x0, m0, v0\n def update(i, g, state):\n x, m, v = state\n m = (1 - b1) * g + b1 * m # First moment estimate.\n v = (1 - b2) * (g ** 2) + b2 * v # Second moment estimate.\n mhat = m / (1 - b1 ** (i + 1)) # Bias correction.\n vhat = v / (1 - b2 ** (i + 1))\n x = x - step_size(i) * mhat / (np.sqrt(vhat) + eps)\n return x, m, v\n def get_params(state):\n x, m, v = state\n return x\n return init, update, get_params","label":0},{"cid":"jax/experimental/optimizers.py::adam.init","text":"jax/experimental/optimizers.py::adam.init\n def init(x0):\n m0 = np.zeros_like(x0)\n v0 = np.zeros_like(x0)\n return x0, m0, v0","label":0},{"cid":"jax/experimental/optimizers.py::adam.update","text":"jax/experimental/optimizers.py::adam.update\n def update(i, g, state):\n x, m, v = state\n m = (1 - b1) * g + b1 * m # First moment estimate.\n v = (1 - b2) * (g ** 2) + b2 * v # Second moment estimate.\n mhat = m / (1 - b1 ** (i + 1)) # Bias correction.\n vhat = v / (1 - b2 ** (i + 1))\n x = x - step_size(i) * mhat / (np.sqrt(vhat) + eps)\n return x, m, v","label":0},{"cid":"jax/experimental/optimizers.py::adam.get_params","text":"jax/experimental/optimizers.py::adam.get_params\n def get_params(state):\n x, m, v = state\n return x","label":0},{"cid":"jax/experimental/optimizers.py::sm3","text":"jax/experimental/optimizers.py::sm3\n@optimizer\ndef sm3(step_size, momentum=0.9):\n \"\"\"Construct optimizer triple for SM3.\n\n Memory-Efficient Adaptive Optimization for Large-Scale Learning.\n https://arxiv.org/abs/1901.11150\n\n Args:\n step_size: positive scalar, or a callable representing a step size schedule\n that maps the iteration index to positive scalar.\n momentum: optional, a positive scalar value for momentum\n\n Returns:\n An (init_fun, update_fun, get_params) triple.\n \"\"\"\n step_size = make_schedule(step_size)\n\n def splice(seq, i, x):\n lst = list(seq)\n lst[i:i+1] = x\n return lst\n\n def broadcast_into(ndim, x, axis):\n idx = splice([None] * ndim, axis, [slice(None)])\n return x[tuple(idx)]\n\n def init(x0):\n vs = [np.zeros(sz, dtype=x0.dtype) for sz in x0.shape]\n return x0, np.zeros_like(x0), vs\n\n def update(i, g, state):\n x, m, vs = state\n vs = [broadcast_into(g.ndim, v, i) for i, v in enumerate(vs)]\n accum = functools.reduce(np.minimum, vs) + g ** 2\n accum_inv_sqrt = np.where(accum > 0, 1. / np.sqrt(accum), 0)\n m = (1. - momentum) * (g * accum_inv_sqrt) + momentum * m\n x = x - step_size(i) * m\n vs = [accum.max(splice(range(x.ndim), j, [])) for j in range(x.ndim)]\n return x, m, vs\n\n def get_params(state):\n x, _, _ = state\n return x\n\n return init, update, get_params","label":0},{"cid":"jax/experimental/optimizers.py::sm3.splice","text":"jax/experimental/optimizers.py::sm3.splice\n def splice(seq, i, x):\n lst = list(seq)\n lst[i:i+1] = x\n return lst","label":0},{"cid":"jax/experimental/optimizers.py::sm3.broadcast_into","text":"jax/experimental/optimizers.py::sm3.broadcast_into\n def broadcast_into(ndim, x, axis):\n idx = splice([None] * ndim, axis, [slice(None)])\n return x[tuple(idx)]","label":0},{"cid":"jax/experimental/optimizers.py::sm3.init","text":"jax/experimental/optimizers.py::sm3.init\n def init(x0):\n vs = [np.zeros(sz, dtype=x0.dtype) for sz in x0.shape]\n return x0, np.zeros_like(x0), vs","label":0},{"cid":"jax/experimental/optimizers.py::sm3.update","text":"jax/experimental/optimizers.py::sm3.update\n def update(i, g, state):\n x, m, vs = state\n vs = [broadcast_into(g.ndim, v, i) for i, v in enumerate(vs)]\n accum = functools.reduce(np.minimum, vs) + g ** 2\n accum_inv_sqrt = np.where(accum > 0, 1. / np.sqrt(accum), 0)\n m = (1. - momentum) * (g * accum_inv_sqrt) + momentum * m\n x = x - step_size(i) * m\n vs = [accum.max(splice(range(x.ndim), j, [])) for j in range(x.ndim)]\n return x, m, vs","label":0},{"cid":"jax/experimental/optimizers.py::sm3.get_params","text":"jax/experimental/optimizers.py::sm3.get_params\n def get_params(state):\n x, _, _ = state\n return x","label":0},{"cid":"jax/experimental/optimizers.py::constant","text":"jax/experimental/optimizers.py::constant\ndef constant(step_size):\n def schedule(i):\n return step_size\n return schedule","label":0},{"cid":"jax/experimental/optix.py::clip","text":"jax/experimental/optix.py::clip\ndef clip(max_delta):\n \"\"\"Clip updates element-wise, to be between -max_delta and +max_delta.\n\n Args:\n max_delta: the maximum absolute value for each element in the update.\n\n Returns:\n An (init_fn, update_fn) tuple.\n \"\"\"\n\n def init_fn(_):\n return ClipState()\n\n def update_fn(updates, state):\n updates = tree_multimap(\n lambda g: jnp.clip(g, -max_delta, max_delta), updates)\n return updates, state\n\n return InitUpdate(init_fn, update_fn)","label":0},{"cid":"jax/experimental/optix.py::clip.init_fn","text":"jax/experimental/optix.py::clip.init_fn\n def init_fn(_):\n return ClipState()","label":0},{"cid":"jax/experimental/optix.py::clip.update_fn","text":"jax/experimental/optix.py::clip.update_fn\n def update_fn(updates, state):\n updates = tree_multimap(\n lambda g: jnp.clip(g, -max_delta, max_delta), updates)\n return updates, state","label":0},{"cid":"jax/experimental/optix.py::global_norm","text":"jax/experimental/optix.py::global_norm\ndef global_norm(items):\n return jnp.sqrt(jnp.sum([jnp.sum(x**2) for x in tree_leaves(items)]))","label":0},{"cid":"jax/experimental/optix.py::clip_by_global_norm","text":"jax/experimental/optix.py::clip_by_global_norm\ndef clip_by_global_norm(max_norm):\n \"\"\"Clip updates using their global norm.\n\n References:\n [Pascanu et al, 2012](https://arxiv.org/abs/1211.5063)\n\n Args:\n max_norm: the maximum global norm for an update.\n\n Returns:\n An (init_fn, update_fn) tuple.\n \"\"\"\n\n def init_fn(_):\n return ClipByGlobalNormState()\n\n def update_fn(updates, state):\n g_norm = global_norm(updates)\n trigger = g_norm < max_norm\n updates = tree_multimap(\n lambda t: jnp.where(trigger, t, (t / g_norm) * max_norm), updates)\n return updates, state\n\n return InitUpdate(init_fn, update_fn)","label":0},{"cid":"jax/experimental/optix.py::clip_by_global_norm.init_fn","text":"jax/experimental/optix.py::clip_by_global_norm.init_fn\n def init_fn(_):\n return ClipByGlobalNormState()","label":0},{"cid":"jax/experimental/optix.py::clip_by_global_norm.update_fn","text":"jax/experimental/optix.py::clip_by_global_norm.update_fn\n def update_fn(updates, state):\n g_norm = global_norm(updates)\n trigger = g_norm < max_norm\n updates = tree_multimap(\n lambda t: jnp.where(trigger, t, (t / g_norm) * max_norm), updates)\n return updates, state","label":0},{"cid":"jax/experimental/optix.py::trace","text":"jax/experimental/optix.py::trace\ndef trace(decay, nesterov):\n \"\"\"Compute a trace of past updates.\n\n Args:\n decay: the decay rate for the tracing of past updates.\n nesterov: whether to use nesterov momentum.\n\n Returns:\n An (init_fn, update_fn) tuple.\n \"\"\"\n\n def init_fn(params):\n return TraceState(trace=tree_multimap(jnp.zeros_like, params))\n\n def update_fn(updates, state):\n f = lambda g, t: g + decay * t\n update_trace = tree_multimap(f, updates, state.trace)\n updates = (\n tree_multimap(f, updates, update_trace) if nesterov else update_trace)\n return updates, TraceState(trace=update_trace)\n\n return InitUpdate(init_fn, update_fn)","label":0},{"cid":"jax/experimental/optix.py::trace.init_fn","text":"jax/experimental/optix.py::trace.init_fn\n def init_fn(params):\n return TraceState(trace=tree_multimap(jnp.zeros_like, params))","label":0},{"cid":"jax/experimental/optix.py::trace.update_fn","text":"jax/experimental/optix.py::trace.update_fn\n def update_fn(updates, state):\n f = lambda g, t: g + decay * t\n update_trace = tree_multimap(f, updates, state.trace)\n updates = (\n tree_multimap(f, updates, update_trace) if nesterov else update_trace)\n return updates, TraceState(trace=update_trace)","label":0},{"cid":"jax/experimental/optix.py::_update_moment","text":"jax/experimental/optix.py::_update_moment\ndef _update_moment(updates, moments, decay, order):\n return tree_multimap(\n lambda g, t: (1 - decay) * (g ** order) + decay * t, updates, moments)","label":0},{"cid":"jax/experimental/optix.py::scale_by_rms","text":"jax/experimental/optix.py::scale_by_rms\ndef scale_by_rms(decay=0.9, eps=1e-8):\n \"\"\"Rescale updates by the root of the exp. moving avg of the square.\n\n References:\n [Hinton](www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n\n Args:\n decay: decay rate for the exponentially weighted average of squared grads.\n eps: term added to the denominator to improve numerical stability.\n\n Returns:\n An (init_fn, update_fn) tuple.\n \"\"\"\n\n def init_fn(params):\n nu = tree_multimap(jnp.zeros_like, params) # second moment\n return ScaleByRmsState(nu=nu)\n\n def update_fn(updates, state):\n nu = _update_moment(updates, state.nu, decay, 2)\n updates = tree_multimap(lambda g, n: g / (jnp.sqrt(n + eps)), updates, nu)\n return updates, ScaleByRmsState(nu=nu)\n\n return InitUpdate(init_fn, update_fn)","label":0},{"cid":"jax/experimental/optix.py::scale_by_rms.init_fn","text":"jax/experimental/optix.py::scale_by_rms.init_fn\n def init_fn(params):\n nu = tree_multimap(jnp.zeros_like, params) # second moment\n return ScaleByRmsState(nu=nu)","label":0},{"cid":"jax/experimental/optix.py::scale_by_rms.update_fn","text":"jax/experimental/optix.py::scale_by_rms.update_fn\n def update_fn(updates, state):\n nu = _update_moment(updates, state.nu, decay, 2)\n updates = tree_multimap(lambda g, n: g / (jnp.sqrt(n + eps)), updates, nu)\n return updates, ScaleByRmsState(nu=nu)","label":0},{"cid":"jax/experimental/optix.py::scale_by_stddev","text":"jax/experimental/optix.py::scale_by_stddev\ndef scale_by_stddev(decay=0.9, eps=1e-8):\n \"\"\"Rescale updates by the root of the centered exp. moving average of squares.\n\n References:\n [Hinton](www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n\n Args:\n decay: decay rate for the exponentially weighted average of squared grads.\n eps: term added to the denominator to improve numerical stability.\n\n Returns:\n An (init_fn, update_fn) tuple.\n \"\"\"\n\n def init_fn(params):\n mu = tree_multimap(jnp.zeros_like, params) # First moment\n nu = tree_multimap(jnp.zeros_like, params) # Second moment\n return ScaleByRStdDevState(mu=mu, nu=nu)\n\n def update_fn(updates, state):\n mu = _update_moment(updates, state.mu, decay, 1)\n nu = _update_moment(updates, state.nu, decay, 2)\n updates = tree_multimap(\n lambda g, m, n: g / jnp.sqrt(n - m**2 + eps), updates, mu, nu)\n return updates, ScaleByRStdDevState(mu=mu, nu=nu)\n\n return InitUpdate(init_fn, update_fn)","label":0},{"cid":"jax/experimental/optix.py::scale_by_stddev.init_fn","text":"jax/experimental/optix.py::scale_by_stddev.init_fn\n def init_fn(params):\n mu = tree_multimap(jnp.zeros_like, params) # First moment\n nu = tree_multimap(jnp.zeros_like, params) # Second moment\n return ScaleByRStdDevState(mu=mu, nu=nu)","label":0},{"cid":"jax/experimental/optix.py::scale_by_stddev.update_fn","text":"jax/experimental/optix.py::scale_by_stddev.update_fn\n def update_fn(updates, state):\n mu = _update_moment(updates, state.mu, decay, 1)\n nu = _update_moment(updates, state.nu, decay, 2)\n updates = tree_multimap(\n lambda g, m, n: g / jnp.sqrt(n - m**2 + eps), updates, mu, nu)\n return updates, ScaleByRStdDevState(mu=mu, nu=nu)","label":0},{"cid":"jax/experimental/optix.py::scale_by_adam","text":"jax/experimental/optix.py::scale_by_adam\ndef scale_by_adam(b1=0.9, b2=0.999, eps=1e-8):\n \"\"\"Rescale updates according to the Adam algorithm.\n\n References:\n [Kingma et al, 2014](https://arxiv.org/abs/1412.6980)\n\n Args:\n b1: decay rate for the exponentially weighted average of grads.\n b2: decay rate for the exponentially weighted average of squared grads.\n eps: term added to the denominator to improve numerical stability.\n\n Returns:\n An (init_fn, update_fn) tuple.\n \"\"\"\n\n def init_fn(params):\n mu = tree_multimap(jnp.zeros_like, params) # First moment\n nu = tree_multimap(jnp.zeros_like, params) # Second moment\n return ScaleByAdamState(count=jnp.zeros([], jnp.int32), mu=mu, nu=nu)\n\n def update_fn(updates, state):\n mu = _update_moment(updates, state.mu, b1, 1)\n nu = _update_moment(updates, state.nu, b2, 2)\n mu_hat = tree_multimap(lambda t: t / (1 - b1 ** (state.count + 1)), mu)\n nu_hat = tree_multimap(lambda t: t / (1 - b2 ** (state.count + 1)), nu)\n updates = tree_multimap(\n lambda m, v: m / (jnp.sqrt(v) + eps), mu_hat, nu_hat)\n return updates, ScaleByAdamState(count=state.count + 1, mu=mu, nu=nu)\n\n return InitUpdate(init_fn, update_fn)","label":0},{"cid":"jax/experimental/optix.py::scale_by_adam.init_fn","text":"jax/experimental/optix.py::scale_by_adam.init_fn\n def init_fn(params):\n mu = tree_multimap(jnp.zeros_like, params) # First moment\n nu = tree_multimap(jnp.zeros_like, params) # Second moment\n return ScaleByAdamState(count=jnp.zeros([], jnp.int32), mu=mu, nu=nu)","label":0},{"cid":"jax/experimental/optix.py::scale_by_adam.update_fn","text":"jax/experimental/optix.py::scale_by_adam.update_fn\n def update_fn(updates, state):\n mu = _update_moment(updates, state.mu, b1, 1)\n nu = _update_moment(updates, state.nu, b2, 2)\n mu_hat = tree_multimap(lambda t: t / (1 - b1 ** (state.count + 1)), mu)\n nu_hat = tree_multimap(lambda t: t / (1 - b2 ** (state.count + 1)), nu)\n updates = tree_multimap(\n lambda m, v: m / (jnp.sqrt(v) + eps), mu_hat, nu_hat)\n return updates, ScaleByAdamState(count=state.count + 1, mu=mu, nu=nu)","label":0},{"cid":"jax/experimental/optix.py::scale","text":"jax/experimental/optix.py::scale\ndef scale(step_size):\n \"\"\"Scale updates by some fixed scalar `step_size`.\n\n Args:\n step_size: a scalar corresponding to a fixed scaling factor for updates.\n\n Returns:\n An (init_fn, update_fn) tuple.\n \"\"\"\n\n def init_fn(_):\n return ScaleState()\n\n def update_fn(updates, state):\n updates = tree_multimap(lambda g: step_size * g, updates)\n return updates, state\n\n return InitUpdate(init_fn, update_fn)","label":0},{"cid":"jax/experimental/optix.py::scale.init_fn","text":"jax/experimental/optix.py::scale.init_fn\n def init_fn(_):\n return ScaleState()","label":0},{"cid":"jax/experimental/optix.py::scale.update_fn","text":"jax/experimental/optix.py::scale.update_fn\n def update_fn(updates, state):\n updates = tree_multimap(lambda g: step_size * g, updates)\n return updates, state","label":0},{"cid":"jax/experimental/optix.py::scale_by_schedule","text":"jax/experimental/optix.py::scale_by_schedule\ndef scale_by_schedule(step_size_fn):\n \"\"\"Scale updates using a custom schedule for the `step_size`.\n\n Args:\n step_size_fn: a function that takes an update count as input and proposes\n the step_size to multiply the updates by.\n\n Returns:\n An (init_fn, update_fn) tuple.\n \"\"\"\n\n def init_fn(_):\n return ScaleByScheduleState(count=jnp.zeros([], jnp.int32))\n\n def update_fn(updates, state):\n updates = tree_multimap(lambda g: step_size_fn(state.count) * g, updates)\n return updates, ScaleByScheduleState(count=state.count + 1)\n\n return InitUpdate(init_fn, update_fn)","label":0},{"cid":"jax/experimental/optix.py::scale_by_schedule.init_fn","text":"jax/experimental/optix.py::scale_by_schedule.init_fn\n def init_fn(_):\n return ScaleByScheduleState(count=jnp.zeros([], jnp.int32))","label":0},{"cid":"jax/experimental/optix.py::scale_by_schedule.update_fn","text":"jax/experimental/optix.py::scale_by_schedule.update_fn\n def update_fn(updates, state):\n updates = tree_multimap(lambda g: step_size_fn(state.count) * g, updates)\n return updates, ScaleByScheduleState(count=state.count + 1)","label":0},{"cid":"jax/experimental/optix.py::add_noise","text":"jax/experimental/optix.py::add_noise\ndef add_noise(eta, gamma, seed):\n \"\"\"Add gradient noise.\n\n References:\n [Neelakantan et al, 2014](https://arxiv.org/abs/1511.06807)\n\n Args:\n eta: base variance of the gaussian noise added to the gradient.\n gamma: decay exponent for annealing of the variance.\n seed: seed for random number generation.\n\n Returns:\n An (init_fn, update_fn) tuple.\n \"\"\"\n\n def init_fn(_):\n return AddNoiseState(count=jnp.zeros([], jnp.int32),\n rng_key=jrandom.PRNGKey(seed))\n\n def update_fn(updates, state): # pylint: disable=missing-docstring\n num_vars = len(tree_leaves(updates))\n treedef = tree_structure(updates)\n variance = eta / (1 + state.count) ** gamma\n all_keys = jrandom.split(state.rng_key, num=num_vars + 1)\n noise = tree_multimap(\n lambda g, k: jrandom.normal(k, shape=g.shape),\n updates, tree_unflatten(treedef, all_keys[1:]))\n updates = tree_multimap(\n lambda g, n: g + variance * n, updates, noise)\n return updates, AddNoiseState(count=state.count + 1, rng_key=all_keys[0])\n\n return InitUpdate(init_fn, update_fn)","label":0},{"cid":"jax/experimental/optix.py::add_noise.init_fn","text":"jax/experimental/optix.py::add_noise.init_fn\n def init_fn(_):\n return AddNoiseState(count=jnp.zeros([], jnp.int32),\n rng_key=jrandom.PRNGKey(seed))","label":0},{"cid":"jax/experimental/optix.py::add_noise.update_fn","text":"jax/experimental/optix.py::add_noise.update_fn\n def update_fn(updates, state): # pylint: disable=missing-docstring\n num_vars = len(tree_leaves(updates))\n treedef = tree_structure(updates)\n variance = eta / (1 + state.count) ** gamma\n all_keys = jrandom.split(state.rng_key, num=num_vars + 1)\n noise = tree_multimap(\n lambda g, k: jrandom.normal(k, shape=g.shape),\n updates, tree_unflatten(treedef, all_keys[1:]))\n updates = tree_multimap(\n lambda g, n: g + variance * n, updates, noise)\n return updates, AddNoiseState(count=state.count + 1, rng_key=all_keys[0])","label":0},{"cid":"jax/experimental/optix.py::apply_every","text":"jax/experimental/optix.py::apply_every\ndef apply_every(k=1):\n \"\"\"accumulate gradients and apply them every k steps.\n\n Args:\n k: apply the update every k steps otherwise accumulate the gradients.\n\n Returns:\n An (init_fn, update_fn) tuple.\n \"\"\"\n\n def init_fn(params):\n grad_acc = tree_multimap(jnp.zeros_like, params)\n return ApplyEvery(count=jnp.zeros([], jnp.int32), grad_acc=grad_acc)\n\n def update_fn(updates, state):\n\n c = state.count % k\n acc = c != 0\n grad_acc = tree_multimap(\n lambda g, ga: acc * ga + g, updates, state.grad_acc)\n emit = c == (k - 1)\n updates = tree_multimap(lambda ga: emit * ga, grad_acc)\n return updates, ApplyEvery(count=state.count + 1, grad_acc=grad_acc)\n\n return InitUpdate(init_fn, update_fn)","label":0},{"cid":"jax/experimental/optix.py::apply_every.init_fn","text":"jax/experimental/optix.py::apply_every.init_fn\n def init_fn(params):\n grad_acc = tree_multimap(jnp.zeros_like, params)\n return ApplyEvery(count=jnp.zeros([], jnp.int32), grad_acc=grad_acc)","label":0},{"cid":"jax/experimental/optix.py::apply_every.update_fn","text":"jax/experimental/optix.py::apply_every.update_fn\n def update_fn(updates, state):\n\n c = state.count % k\n acc = c != 0\n grad_acc = tree_multimap(\n lambda g, ga: acc * ga + g, updates, state.grad_acc)\n emit = c == (k - 1)\n updates = tree_multimap(lambda ga: emit * ga, grad_acc)\n return updates, ApplyEvery(count=state.count + 1, grad_acc=grad_acc)","label":0},{"cid":"jax/experimental/optix.py::chain","text":"jax/experimental/optix.py::chain\ndef chain(*args):\n \"\"\"Applies a list of chainable update transformations.\n\n Given a sequence of chainable transforms, `chain` returns an `init_fn`\n that constructs a `state` by concatenating the states of the individual\n transforms, and returns an `update_fn` which chains the update transformations\n feeding the appropriate state to each.\n\n Args:\n *args: a sequence of chainable (init_fn, update_fn) tuples.\n\n Returns:\n A single (init_fn, update_fn) tuple.\n \"\"\"\n\n init_fns, update_fns = zip(*args)\n\n def init_fn(params):\n return [fn(params) for fn in init_fns]\n\n def update_fn(updates, state):\n new_state = []\n for s, fn in zip(state, update_fns):\n updates, new_s = fn(updates, s)\n new_state.append(new_s)\n return updates, new_state\n\n return InitUpdate(init_fn, update_fn)","label":0},{"cid":"jax/experimental/optix.py::chain.init_fn","text":"jax/experimental/optix.py::chain.init_fn\n def init_fn(params):\n return [fn(params) for fn in init_fns]","label":0},{"cid":"jax/experimental/optix.py::chain.update_fn","text":"jax/experimental/optix.py::chain.update_fn\n def update_fn(updates, state):\n new_state = []\n for s, fn in zip(state, update_fns):\n updates, new_s = fn(updates, s)\n new_state.append(new_s)\n return updates, new_state","label":0},{"cid":"jax/experimental/optix.py::apply_updates","text":"jax/experimental/optix.py::apply_updates\ndef apply_updates(params, updates):\n \"\"\"Applies an update to the corresponding parameters.\n\n This is an (optional) utility functions that applies an update, and returns\n the updated parameters to the caller. The update itself is typically the\n result of applying any number of `chainable` transformations.\n\n Args:\n params: a tree of parameters.\n updates: a tree of updates, the tree structure and the shape of the leaf\n nodes must match that of `params`.\n\n Returns:\n Updated parameters, with same structure and shape as `params`.\n \"\"\"\n return tree_multimap(lambda p, u: p + u, params, updates)","label":0},{"cid":"jax/experimental/optix.py::sgd","text":"jax/experimental/optix.py::sgd\ndef sgd(learning_rate, momentum=0., nesterov=False):\n return chain(\n trace(decay=momentum, nesterov=nesterov),\n scale(-learning_rate))","label":0},{"cid":"jax/experimental/optix.py::noisy_sgd","text":"jax/experimental/optix.py::noisy_sgd\ndef noisy_sgd(learning_rate, eta=0.01, gamma=0.55, seed=42):\n return chain(\n trace(decay=0., nesterov=False),\n scale(-learning_rate),\n add_noise(eta, gamma, seed))","label":0},{"cid":"jax/experimental/optix.py::adam","text":"jax/experimental/optix.py::adam\ndef adam(learning_rate, b1=0.9, b2=0.999, eps=1e-8):\n return chain(\n scale_by_adam(b1=b1, b2=b2, eps=eps),\n scale(-learning_rate))","label":0},{"cid":"jax/experimental/optix.py::rmsprop","text":"jax/experimental/optix.py::rmsprop\ndef rmsprop(learning_rate, decay=0.9, eps=1e-8, centered=False):\n if not centered:\n return chain(\n scale_by_rms(decay=decay, eps=eps),\n scale(-learning_rate))\n else:\n return chain(\n scale_by_stddev(decay=decay, eps=eps),\n scale(-learning_rate))","label":0},{"cid":"jax/experimental/stax.py::Dense","text":"jax/experimental/stax.py::Dense\ndef Dense(out_dim, W_init=glorot_normal(), b_init=normal()):\n \"\"\"Layer constructor function for a dense (fully-connected) layer.\"\"\"\n def init_fun(rng, input_shape):\n output_shape = input_shape[:-1] + (out_dim,)\n k1, k2 = random.split(rng)\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\n return output_shape, (W, b)\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return np.dot(inputs, W) + b\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Dense.init_fun","text":"jax/experimental/stax.py::Dense.init_fun\n def init_fun(rng, input_shape):\n output_shape = input_shape[:-1] + (out_dim,)\n k1, k2 = random.split(rng)\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::Dense.apply_fun","text":"jax/experimental/stax.py::Dense.apply_fun\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return np.dot(inputs, W) + b","label":0},{"cid":"jax/experimental/stax.py::GeneralConv","text":"jax/experimental/stax.py::GeneralConv\ndef GeneralConv(dimension_numbers, out_chan, filter_shape,\n strides=None, padding='VALID', W_init=None,\n b_init=normal(1e-6)):\n \"\"\"Layer construction function for a general convolution layer.\"\"\"\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n one = (1,) * len(filter_shape)\n strides = strides or one\n W_init = W_init or glorot_normal(rhs_spec.index('I'), rhs_spec.index('O'))\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_general_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\n dimension_numbers=dimension_numbers) + b\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::GeneralConv.init_fun","text":"jax/experimental/stax.py::GeneralConv.init_fun\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_general_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::GeneralConv.apply_fun","text":"jax/experimental/stax.py::GeneralConv.apply_fun\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\n dimension_numbers=dimension_numbers) + b","label":0},{"cid":"jax/experimental/stax.py::GeneralConvTranspose","text":"jax/experimental/stax.py::GeneralConvTranspose\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape,\n strides=None, padding='VALID', W_init=None,\n b_init=normal(1e-6)):\n \"\"\"Layer construction function for a general transposed-convolution layer.\"\"\"\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n one = (1,) * len(filter_shape)\n strides = strides or one\n W_init = W_init or glorot_normal(rhs_spec.index('I'), rhs_spec.index('O'))\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_transpose_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_transpose(inputs, W, strides, padding,\n dimension_numbers=dimension_numbers) + b\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::GeneralConvTranspose.init_fun","text":"jax/experimental/stax.py::GeneralConvTranspose.init_fun\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_transpose_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::GeneralConvTranspose.apply_fun","text":"jax/experimental/stax.py::GeneralConvTranspose.apply_fun\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_transpose(inputs, W, strides, padding,\n dimension_numbers=dimension_numbers) + b","label":0},{"cid":"jax/experimental/stax.py::BatchNorm","text":"jax/experimental/stax.py::BatchNorm\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-5, center=True, scale=True,\n beta_init=zeros, gamma_init=ones):\n \"\"\"Layer construction function for a batch normalization layer.\"\"\"\n _beta_init = lambda rng, shape: beta_init(rng, shape) if center else ()\n _gamma_init = lambda rng, shape: gamma_init(rng, shape) if scale else ()\n axis = (axis,) if np.isscalar(axis) else axis\n def init_fun(rng, input_shape):\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\n k1, k2 = random.split(rng)\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\n return input_shape, (beta, gamma)\n def apply_fun(params, x, **kwargs):\n beta, gamma = params\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\n # (https://github.com/numpy/numpy/issues/12290)\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\n beta = beta[ed]\n gamma = gamma[ed]\n z = normalize(x, axis, epsilon=epsilon)\n if center and scale: return gamma * z + beta\n if center: return z + beta\n if scale: return gamma * z\n return z\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::BatchNorm.init_fun","text":"jax/experimental/stax.py::BatchNorm.init_fun\n def init_fun(rng, input_shape):\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\n k1, k2 = random.split(rng)\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\n return input_shape, (beta, gamma)","label":0},{"cid":"jax/experimental/stax.py::BatchNorm.apply_fun","text":"jax/experimental/stax.py::BatchNorm.apply_fun\n def apply_fun(params, x, **kwargs):\n beta, gamma = params\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\n # (https://github.com/numpy/numpy/issues/12290)\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\n beta = beta[ed]\n gamma = gamma[ed]\n z = normalize(x, axis, epsilon=epsilon)\n if center and scale: return gamma * z + beta\n if center: return z + beta\n if scale: return gamma * z\n return z","label":1},{"cid":"jax/experimental/stax.py::elementwise","text":"jax/experimental/stax.py::elementwise\ndef elementwise(fun, **fun_kwargs):\n \"\"\"Layer that applies a scalar function elementwise on its inputs.\"\"\"\n init_fun = lambda rng, input_shape: (input_shape, ())\n apply_fun = lambda params, inputs, **kwargs: fun(inputs, **fun_kwargs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer","text":"jax/experimental/stax.py::_pooling_layer\ndef _pooling_layer(reducer, init_val, rescaler=None):\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec=None):\n \"\"\"Layer construction function for a pooling layer.\"\"\"\n strides = strides or (1,) * len(window_shape)\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\n\n if spec is None:\n non_spatial_axes = 0, len(window_shape) + 1\n else:\n non_spatial_axes = spec.index('N'), spec.index('C')\n\n for i in sorted(non_spatial_axes):\n window_shape = window_shape[:i] + (1,) + window_shape[i:]\n strides = strides[:i] + (1,) + strides[i:]\n\n def init_fun(rng, input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\n strides, padding)\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\n strides, padding)\n return rescale(out, inputs, spec) if rescale else out\n return init_fun, apply_fun\n return PoolingLayer","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec=None):\n \"\"\"Layer construction function for a pooling layer.\"\"\"\n strides = strides or (1,) * len(window_shape)\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\n\n if spec is None:\n non_spatial_axes = 0, len(window_shape) + 1\n else:\n non_spatial_axes = spec.index('N'), spec.index('C')\n\n for i in sorted(non_spatial_axes):\n window_shape = window_shape[:i] + (1,) + window_shape[i:]\n strides = strides[:i] + (1,) + strides[i:]\n\n def init_fun(rng, input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\n strides, padding)\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\n strides, padding)\n return rescale(out, inputs, spec) if rescale else out\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\n def init_fun(rng, input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\n strides, padding)\n return out_shape, ()","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\n strides, padding)\n return rescale(out, inputs, spec) if rescale else out","label":0},{"cid":"jax/experimental/stax.py::_normalize_by_window_size","text":"jax/experimental/stax.py::_normalize_by_window_size\ndef _normalize_by_window_size(dims, strides, padding):\n def rescale(outputs, inputs, spec):\n if spec is None:\n non_spatial_axes = 0, inputs.ndim - 1\n else:\n non_spatial_axes = spec.index('N'), spec.index('C')\n\n spatial_shape = tuple(inputs.shape[i]\n for i in range(inputs.ndim)\n if i not in non_spatial_axes)\n one = np.ones(spatial_shape, dtype=inputs.dtype)\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\n for i in sorted(non_spatial_axes):\n window_sizes = np.expand_dims(window_sizes, i)\n\n return outputs / window_sizes\n return rescale","label":0},{"cid":"jax/experimental/stax.py::_normalize_by_window_size.rescale","text":"jax/experimental/stax.py::_normalize_by_window_size.rescale\n def rescale(outputs, inputs, spec):\n if spec is None:\n non_spatial_axes = 0, inputs.ndim - 1\n else:\n non_spatial_axes = spec.index('N'), spec.index('C')\n\n spatial_shape = tuple(inputs.shape[i]\n for i in range(inputs.ndim)\n if i not in non_spatial_axes)\n one = np.ones(spatial_shape, dtype=inputs.dtype)\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\n for i in sorted(non_spatial_axes):\n window_sizes = np.expand_dims(window_sizes, i)\n\n return outputs / window_sizes","label":0},{"cid":"jax/experimental/stax.py::Flatten","text":"jax/experimental/stax.py::Flatten\ndef Flatten():\n \"\"\"Layer construction function for flattening all but the leading dim.\"\"\"\n def init_fun(rng, input_shape):\n output_shape = input_shape[0], functools.reduce(op.mul, input_shape[1:], 1)\n return output_shape, ()\n def apply_fun(params, inputs, **kwargs):\n return np.reshape(inputs, (inputs.shape[0], -1))\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Flatten.init_fun","text":"jax/experimental/stax.py::Flatten.init_fun\n def init_fun(rng, input_shape):\n output_shape = input_shape[0], functools.reduce(op.mul, input_shape[1:], 1)\n return output_shape, ()","label":0},{"cid":"jax/experimental/stax.py::Flatten.apply_fun","text":"jax/experimental/stax.py::Flatten.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return np.reshape(inputs, (inputs.shape[0], -1))","label":0},{"cid":"jax/experimental/stax.py::Identity","text":"jax/experimental/stax.py::Identity\ndef Identity():\n \"\"\"Layer construction function for an identity layer.\"\"\"\n init_fun = lambda rng, input_shape: (input_shape, ())\n apply_fun = lambda params, inputs, **kwargs: inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanOut","text":"jax/experimental/stax.py::FanOut\ndef FanOut(num):\n \"\"\"Layer construction function for a fan-out layer.\"\"\"\n init_fun = lambda rng, input_shape: ([input_shape] * num, ())\n apply_fun = lambda params, inputs, **kwargs: [inputs] * num\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInSum","text":"jax/experimental/stax.py::FanInSum\ndef FanInSum():\n \"\"\"Layer construction function for a fan-in sum layer.\"\"\"\n init_fun = lambda rng, input_shape: (input_shape[0], ())\n apply_fun = lambda params, inputs, **kwargs: sum(inputs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInConcat","text":"jax/experimental/stax.py::FanInConcat\ndef FanInConcat(axis=-1):\n \"\"\"Layer construction function for a fan-in concatenation layer.\"\"\"\n def init_fun(rng, input_shape):\n ax = axis % len(input_shape[0])\n concat_size = sum(shape[ax] for shape in input_shape)\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n return np.concatenate(inputs, axis)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInConcat.init_fun","text":"jax/experimental/stax.py::FanInConcat.init_fun\n def init_fun(rng, input_shape):\n ax = axis % len(input_shape[0])\n concat_size = sum(shape[ax] for shape in input_shape)\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\n return out_shape, ()","label":0},{"cid":"jax/experimental/stax.py::FanInConcat.apply_fun","text":"jax/experimental/stax.py::FanInConcat.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return np.concatenate(inputs, axis)","label":0},{"cid":"jax/experimental/stax.py::Dropout","text":"jax/experimental/stax.py::Dropout\ndef Dropout(rate, mode='train'):\n \"\"\"Layer construction function for a dropout layer with given rate.\"\"\"\n def init_fun(rng, input_shape):\n return input_shape, ()\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.get('rng', None)\n if rng is None:\n msg = (\"Dropout layer requires apply_fun to be called with a PRNG key \"\n \"argument. That is, instead of `apply_fun(params, inputs)`, call \"\n \"it like `apply_fun(params, inputs, key)` where `key` is a \"\n \"jax.random.PRNGKey value.\")\n raise ValueError(msg)\n if mode == 'train':\n keep = random.bernoulli(rng, rate, inputs.shape)\n return np.where(keep, inputs / rate, 0)\n else:\n return inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Dropout.init_fun","text":"jax/experimental/stax.py::Dropout.init_fun\n def init_fun(rng, input_shape):\n return input_shape, ()","label":0},{"cid":"jax/experimental/stax.py::Dropout.apply_fun","text":"jax/experimental/stax.py::Dropout.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.get('rng', None)\n if rng is None:\n msg = (\"Dropout layer requires apply_fun to be called with a PRNG key \"\n \"argument. That is, instead of `apply_fun(params, inputs)`, call \"\n \"it like `apply_fun(params, inputs, key)` where `key` is a \"\n \"jax.random.PRNGKey value.\")\n raise ValueError(msg)\n if mode == 'train':\n keep = random.bernoulli(rng, rate, inputs.shape)\n return np.where(keep, inputs / rate, 0)\n else:\n return inputs","label":0},{"cid":"jax/experimental/stax.py::serial","text":"jax/experimental/stax.py::serial\ndef serial(*layers):\n \"\"\"Combinator for composing layers in serial.\n\n Args:\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the serial\n composition of the given sequence of layers.\n \"\"\"\n nlayers = len(layers)\n init_funs, apply_funs = zip(*layers)\n def init_fun(rng, input_shape):\n params = []\n for init_fun in init_funs:\n rng, layer_rng = random.split(rng)\n input_shape, param = init_fun(layer_rng, input_shape)\n params.append(param)\n return input_shape, params\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n for fun, param, rng in zip(apply_funs, params, rngs):\n inputs = fun(param, inputs, rng=rng, **kwargs)\n return inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::serial.init_fun","text":"jax/experimental/stax.py::serial.init_fun\n def init_fun(rng, input_shape):\n params = []\n for init_fun in init_funs:\n rng, layer_rng = random.split(rng)\n input_shape, param = init_fun(layer_rng, input_shape)\n params.append(param)\n return input_shape, params","label":0},{"cid":"jax/experimental/stax.py::serial.apply_fun","text":"jax/experimental/stax.py::serial.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n for fun, param, rng in zip(apply_funs, params, rngs):\n inputs = fun(param, inputs, rng=rng, **kwargs)\n return inputs","label":0},{"cid":"jax/experimental/stax.py::parallel","text":"jax/experimental/stax.py::parallel\ndef parallel(*layers):\n \"\"\"Combinator for composing layers in parallel.\n\n The layer resulting from this combinator is often used with the FanOut and\n FanInSum layers.\n\n Args:\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the\n parallel composition of the given sequence of layers. In particular, the\n returned layer takes a sequence of inputs and returns a sequence of outputs\n with the same length as the argument `layers`.\n \"\"\"\n nlayers = len(layers)\n init_funs, apply_funs = zip(*layers)\n def init_fun(rng, input_shape):\n rngs = random.split(rng, nlayers)\n return zip(*[init(rng, shape) for init, rng, shape\n in zip(init_funs, rngs, input_shape)])\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::parallel.init_fun","text":"jax/experimental/stax.py::parallel.init_fun\n def init_fun(rng, input_shape):\n rngs = random.split(rng, nlayers)\n return zip(*[init(rng, shape) for init, rng, shape\n in zip(init_funs, rngs, input_shape)])","label":0},{"cid":"jax/experimental/stax.py::parallel.apply_fun","text":"jax/experimental/stax.py::parallel.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]","label":0},{"cid":"jax/experimental/stax.py::shape_dependent","text":"jax/experimental/stax.py::shape_dependent\ndef shape_dependent(make_layer):\n \"\"\"Combinator to delay layer constructor pair until input shapes are known.\n\n Args:\n make_layer: a one-argument function that takes an input shape as an argument\n (a tuple of positive integers) and returns an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the same\n layer as returned by `make_layer` but with its construction delayed until\n input shapes are known.\n \"\"\"\n def init_fun(rng, input_shape):\n return make_layer(input_shape)[0](rng, input_shape)\n def apply_fun(params, inputs, **kwargs):\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::shape_dependent.init_fun","text":"jax/experimental/stax.py::shape_dependent.init_fun\n def init_fun(rng, input_shape):\n return make_layer(input_shape)[0](rng, input_shape)","label":0},{"cid":"jax/experimental/stax.py::shape_dependent.apply_fun","text":"jax/experimental/stax.py::shape_dependent.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)","label":0},{"cid":"jax/experimental/vectorize.py::_parse_gufunc_signature","text":"jax/experimental/vectorize.py::_parse_gufunc_signature\ndef _parse_gufunc_signature(signature):\n \"\"\"Parse string signatures for a generalized universal function.\n\n Args:\n signature : string\n\t Generalized universal function signature, e.g., ``(m,n),(n,p)->(m,p)``\n\t for ``np.matmul``.\n\n Returns:\n Tuple of input and output core dimensions parsed from the signature, each\n of the form List[Tuple[str, ...]].\n \"\"\"\n if not re.match(_SIGNATURE, signature):\n raise ValueError(\n 'not a valid gufunc signature: {}'.format(signature))\n return tuple([tuple(re.findall(_DIMENSION_NAME, arg))\n for arg in re.findall(_ARGUMENT, arg_list)]\n for arg_list in signature.split('->'))","label":0},{"cid":"jax/experimental/vectorize.py::_update_dim_sizes","text":"jax/experimental/vectorize.py::_update_dim_sizes\ndef _update_dim_sizes(dim_sizes, arg, core_dims):\n \"\"\"Incrementally check and update core dimension sizes for a single argument.\n\n Args:\n dim_sizes : Dict[str, int]\n\t Sizes of existing core dimensions. Will be updated in-place.\n arg : ndarray\n\t Argument to examine.\n core_dims : Tuple[str, ...]\n\t Core dimensions for this argument.\n \"\"\"\n if not core_dims:\n return\n\n num_core_dims = len(core_dims)\n if arg.ndim < num_core_dims:\n raise ValueError(\n '%d-dimensional argument does not have enough '\n 'dimensions for all core dimensions %r'\n % (arg.ndim, core_dims))\n\n core_shape = arg.shape[-num_core_dims:]\n for dim, size in zip(core_dims, core_shape):\n if dim in dim_sizes:\n if size != dim_sizes[dim]:\n raise ValueError(\n 'inconsistent size for core dimension %r: %r vs %r'\n % (dim, size, dim_sizes[dim]))\n else:\n dim_sizes[dim] = size","label":0},{"cid":"jax/experimental/vectorize.py::_parse_input_dimensions","text":"jax/experimental/vectorize.py::_parse_input_dimensions\ndef _parse_input_dimensions(args, input_core_dims):\n \"\"\"Parse broadcast and core dimensions for vectorize with a signature.\n\n Args:\n args : Tuple[ndarray, ...]\n\t Tuple of input arguments to examine.\n input_core_dims : List[Tuple[str, ...]]\n\t List of core dimensions corresponding to each input.\n\n Returns:\n broadcast_shape : Tuple[int, ...]\n\t Common shape to broadcast all non-core dimensions to.\n dim_sizes : Dict[str, int]\n\t Common sizes for named core dimensions.\n \"\"\"\n broadcast_args = []\n dim_sizes = {}\n for arg, core_dims in zip(args, input_core_dims):\n _update_dim_sizes(dim_sizes, arg, core_dims)\n ndim = arg.ndim - len(core_dims)\n dummy_array = np.lib.stride_tricks.as_strided(0, arg.shape[:ndim])\n broadcast_args.append(dummy_array)\n broadcast_shape = np.lib.stride_tricks._broadcast_shape(*broadcast_args)\n return broadcast_shape, dim_sizes","label":0},{"cid":"jax/experimental/vectorize.py::_calculate_shapes","text":"jax/experimental/vectorize.py::_calculate_shapes\ndef _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims):\n \"\"\"Helper for calculating broadcast shapes with core dimensions.\"\"\"\n return [broadcast_shape + tuple(dim_sizes[dim] for dim in core_dims)\n for core_dims in list_of_core_dims]","label":0},{"cid":"jax/experimental/vectorize.py::broadcast_with_core_dims","text":"jax/experimental/vectorize.py::broadcast_with_core_dims\ndef broadcast_with_core_dims(args, input_core_dims, output_core_dims):\n if len(args) != len(input_core_dims):\n raise TypeError('wrong number of positional arguments: '\n 'expected %r, got %r'\n % (len(input_core_dims), len(args)))\n\n broadcast_shape, dim_sizes = _parse_input_dimensions(\n args, input_core_dims)\n input_shapes = _calculate_shapes(broadcast_shape, dim_sizes,\n input_core_dims)\n args = [jnp.broadcast_to(arg, shape)\n for arg, shape in zip(args, input_shapes)]\n return args","label":0},{"cid":"jax/experimental/vectorize.py::verify_axis_is_supported","text":"jax/experimental/vectorize.py::verify_axis_is_supported\ndef verify_axis_is_supported(input_core_dims, output_core_dims):\n all_core_dims = set()\n for input_or_output_core_dims in [input_core_dims, output_core_dims]:\n for core_dims in input_or_output_core_dims:\n all_core_dims.update(core_dims)\n if len(core_dims) > 1:\n raise ValueError('only one gufuncs with one core dim support axis')","label":0},{"cid":"jax/experimental/vectorize.py::reorder_inputs","text":"jax/experimental/vectorize.py::reorder_inputs\ndef reorder_inputs(args, axis, input_core_dims):\n return tuple(jnp.moveaxis(arg, axis, -1) if core_dims else arg\n for arg, core_dims in zip(args, input_core_dims))","label":0},{"cid":"jax/experimental/vectorize.py::reorder_outputs","text":"jax/experimental/vectorize.py::reorder_outputs\ndef reorder_outputs(result, axis, output_core_dims):\n if not isinstance(result, tuple):\n result = (result,)\n result = tuple(jnp.moveaxis(res, -1, axis) if core_dims else res\n for res, core_dims in zip(result, output_core_dims))\n if len(result) == 1:\n (result,) = result\n return result","label":0},{"cid":"jax/experimental/vectorize.py::vectorize","text":"jax/experimental/vectorize.py::vectorize\ndef vectorize(signature):\n \"\"\"Vectorize a function using JAX.\n\n Turns an arbitrary function into a numpy style \"gufunc\". Once\n you specify the behavior of the core axis, the rest will be \n broadcast naturally.\n\n Args:\n signature: an einsum style signature that defines how the core dimensions are mapped between inputs and outputs.\n\n Returns:\n\tThe vectorized 'gufunc' that will automatically broadcast\n\twhile maintaining the specified core logic, the returned\n\tfunction also has a new ``axis`` parameter for specifying\n\twhich axis should be treated as the core one.\n \"\"\"\n input_core_dims, output_core_dims = _parse_gufunc_signature(signature)\n \n def decorator(func):\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n axis = kwargs.get('axis') # for python2 compat.\n\n if axis is not None:\n verify_axis_is_supported(input_core_dims, output_core_dims)\n args = reorder_inputs(args, axis, input_core_dims)\n\n broadcast_args = broadcast_with_core_dims(\n args, input_core_dims, output_core_dims)\n num_batch_dims = len(broadcast_args[0].shape) - len(input_core_dims[0])\n\n vectorized_func = func\n for _ in range(num_batch_dims):\n vectorized_func = vmap(vectorized_func)\n result = vectorized_func(*broadcast_args)\n\n if axis is not None:\n result = reorder_outputs(result, axis, output_core_dims)\n\n return result\n return wrapper\n return decorator","label":0},{"cid":"jax/experimental/vectorize.py::vectorize.decorator","text":"jax/experimental/vectorize.py::vectorize.decorator\n def decorator(func):\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n axis = kwargs.get('axis') # for python2 compat.\n\n if axis is not None:\n verify_axis_is_supported(input_core_dims, output_core_dims)\n args = reorder_inputs(args, axis, input_core_dims)\n\n broadcast_args = broadcast_with_core_dims(\n args, input_core_dims, output_core_dims)\n num_batch_dims = len(broadcast_args[0].shape) - len(input_core_dims[0])\n\n vectorized_func = func\n for _ in range(num_batch_dims):\n vectorized_func = vmap(vectorized_func)\n result = vectorized_func(*broadcast_args)\n\n if axis is not None:\n result = reorder_outputs(result, axis, output_core_dims)\n\n return result\n return wrapper","label":0},{"cid":"jax/experimental/vectorize.py::vectorize.decorator.wrapper","text":"jax/experimental/vectorize.py::vectorize.decorator.wrapper\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n axis = kwargs.get('axis') # for python2 compat.\n\n if axis is not None:\n verify_axis_is_supported(input_core_dims, output_core_dims)\n args = reorder_inputs(args, axis, input_core_dims)\n\n broadcast_args = broadcast_with_core_dims(\n args, input_core_dims, output_core_dims)\n num_batch_dims = len(broadcast_args[0].shape) - len(input_core_dims[0])\n\n vectorized_func = func\n for _ in range(num_batch_dims):\n vectorized_func = vmap(vectorized_func)\n result = vectorized_func(*broadcast_args)\n\n if axis is not None:\n result = reorder_outputs(result, axis, output_core_dims)\n\n return result","label":0},{"cid":"jax/lib/xla_bridge.py::get_compile_options","text":"jax/lib/xla_bridge.py::get_compile_options\ndef get_compile_options(num_replicas, num_partitions, device_assignment=None):\n \"\"\"Returns the compile options to use, as derived from flag values.\n\n Args:\n num_replicas: int indicating the number of replicas for which to compile.\n num_partitions: int indicating the number of partitions for which to compile.\n device_assignment: Optional tuple of integers indicating the assignment of\n logical replicas to physical devices (default inherited from\n xla_client.CompileOptions). Must be consistent with `num_replicas` and\n `num_partitions`.\n \"\"\"\n compile_options = xla_client.CompileOptions()\n compile_options.num_replicas = num_replicas\n compile_options.num_partitions = num_partitions\n if device_assignment is not None:\n logging.vlog(\n 2,\n 'get_compile_options: num_replicas=%s num_partitions=%s device_assignment=%s',\n num_replicas, num_partitions, device_assignment)\n device_assignment = onp.array(device_assignment)\n\n # Allow 1D device assignment if num_partitions is 1.\n if (device_assignment.ndim == 1) and (num_partitions == 1):\n device_assignment = device_assignment[:, None]\n\n if num_replicas != device_assignment.shape[0]:\n msg = 'device_assignment does not match num_replicas: {} vs {}.'\n raise ValueError(msg.format(device_assignment, num_replicas))\n\n if num_partitions != device_assignment.shape[1]:\n msg = 'device_assignment does not match num_partitions: {} vs {}.'\n raise ValueError(msg.format(device_assignment, num_partitions))\n\n device_assignment = xla_client.DeviceAssignment.create(device_assignment)\n assert device_assignment.replica_count() == num_replicas\n assert device_assignment.computation_count() == num_partitions\n compile_options.device_assignment = device_assignment\n return compile_options","label":0},{"cid":"jax/lib/xla_bridge.py::register_backend","text":"jax/lib/xla_bridge.py::register_backend\ndef register_backend(name, factory):\n _backends[name] = factory","label":0},{"cid":"jax/lib/xla_bridge.py::_get_local_backend","text":"jax/lib/xla_bridge.py::_get_local_backend\ndef _get_local_backend(platform=None):\n if not platform:\n platform = FLAGS.jax_platform_name\n\n # Canonicalize platform names.\n cpu = 'cpu'\n gpu = 'gpu'\n if platform == 'Host':\n platform = cpu\n elif platform == 'CUDA':\n platform = gpu\n elif platform == '':\n platform = None\n\n backend = xla_client.get_local_backend(platform)\n if backend is None:\n raise RuntimeError(\"No local XLA backends found.\")\n\n if backend.platform == cpu and platform != cpu:\n warnings.warn('No GPU/TPU found, falling back to CPU.')\n\n return backend","label":0},{"cid":"jax/lib/xla_bridge.py::_get_tpu_driver_backend","text":"jax/lib/xla_bridge.py::_get_tpu_driver_backend\ndef _get_tpu_driver_backend(platform):\n del platform\n global _tpu_backend\n if _tpu_backend is None:\n backend_target = FLAGS.jax_backend_target\n if backend_target is None:\n raise ValueError('When using TPU Driver as the backend, you must specify '\n '--jax_backend_target=<hostname>:8470.')\n _tpu_backend = tpu_client.TpuBackend.create(worker=backend_target)\n return _tpu_backend","label":0},{"cid":"jax/lib/xla_bridge.py::get_backend","text":"jax/lib/xla_bridge.py::get_backend\n@util.memoize\ndef get_backend(platform=None):\n # TODO(mattjj,skyewm): remove this input polymorphism after we clean up how\n # 'backend' values are handled\n if isinstance(platform, xla_client.Backend):\n return platform\n\n with _backend_lock:\n backend = _backends.get(FLAGS.jax_xla_backend)\n if backend is None:\n msg = 'Unknown jax_xla_backend value \"{}\".'\n raise ValueError(msg.format(FLAGS.jax_xla_backend))\n return backend(platform)","label":0},{"cid":"jax/lib/xla_bridge.py::get_device_backend","text":"jax/lib/xla_bridge.py::get_device_backend\ndef get_device_backend(device=None):\n \"\"\"Returns the Backend associated with `device`, or the default Backend.\"\"\"\n platform = device.platform if device else None\n return get_backend(platform)","label":0},{"cid":"jax/lib/xla_bridge.py::device_count","text":"jax/lib/xla_bridge.py::device_count\ndef device_count(backend=None):\n \"\"\"Returns the total number of devices.\n\n On most platforms, this is the same as ``local_device_count()``. However, on\n multi-host platforms, this will return the total number of devices across all\n hosts.\n\n Args:\n backend: This is an experimental feature and the API is likely to change.\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\n\n Returns:\n Number of devices.\n \"\"\"\n return int(get_backend(backend).device_count())","label":0},{"cid":"jax/lib/xla_bridge.py::local_device_count","text":"jax/lib/xla_bridge.py::local_device_count\ndef local_device_count(backend=None):\n \"\"\"Returns the number of devices on this host.\"\"\"\n return int(get_backend(backend).local_device_count())","label":0},{"cid":"jax/lib/xla_bridge.py::devices","text":"jax/lib/xla_bridge.py::devices\ndef devices(backend=None):\n \"\"\"Returns a list of all devices.\n\n Each device is represented by a subclass of Device (e.g. CpuDevice,\n GpuDevice). The length of the returned list is equal to\n ``device_count()``. Local devices can be identified by comparing\n ``Device.host_id`` to ``host_id()``.\n\n Args:\n backend: This is an experimental feature and the API is likely to change.\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\n\n Returns:\n List of Device subclasses.\n \"\"\"\n return get_backend(backend).devices()","label":0},{"cid":"jax/lib/xla_bridge.py::local_devices","text":"jax/lib/xla_bridge.py::local_devices\ndef local_devices(host_id=None, backend=None):\n \"\"\"Returns a list of devices local to a given host (this host by default).\"\"\"\n if host_id is None:\n host_id = get_backend(backend).host_id()\n return [d for d in devices(backend) if d.host_id == host_id]","label":0},{"cid":"jax/lib/xla_bridge.py::host_id","text":"jax/lib/xla_bridge.py::host_id\ndef host_id(backend=None):\n \"\"\"Returns the integer host ID of this host.\n\n On most platforms, this will always be 0. This will vary on multi-host\n platforms though.\n\n Args:\n backend: This is an experimental feature and the API is likely to change.\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\n\n Returns:\n Integer host ID.\n \"\"\"\n return get_backend(backend).host_id()","label":0},{"cid":"jax/lib/xla_bridge.py::host_ids","text":"jax/lib/xla_bridge.py::host_ids\ndef host_ids(backend=None):\n \"\"\"Returns a sorted list of all host IDs.\"\"\"\n return sorted(list(set(d.host_id for d in devices(backend))))","label":0},{"cid":"jax/lib/xla_bridge.py::host_count","text":"jax/lib/xla_bridge.py::host_count\ndef host_count(backend=None):\n \"\"\"Returns the number of hosts.\"\"\"\n return len(host_ids(backend))","label":0},{"cid":"jax/lib/xla_bridge.py::dtype_to_etype","text":"jax/lib/xla_bridge.py::dtype_to_etype\n@util.memoize\ndef dtype_to_etype(dtype):\n \"\"\"Convert from dtype to canonical etype (reading FLAGS.jax_enable_x64).\"\"\"\n return xla_client.dtype_to_etype(dtypes.canonicalize_dtype(dtype))","label":0},{"cid":"jax/lib/xla_bridge.py::supported_numpy_dtypes","text":"jax/lib/xla_bridge.py::supported_numpy_dtypes\n@util.memoize\ndef supported_numpy_dtypes():\n return {dtypes.canonicalize_dtype(dtype)\n for dtype in xla_client.XLA_ELEMENT_TYPE_TO_DTYPE.values()}","label":0},{"cid":"jax/lib/xla_bridge.py::normalize_to_xla_dtypes","text":"jax/lib/xla_bridge.py::normalize_to_xla_dtypes\ndef normalize_to_xla_dtypes(val):\n \"\"\"Normalize dtypes in a value.\"\"\"\n if hasattr(val, '__array__') or onp.isscalar(val):\n return onp.asarray(val,\n dtype=dtypes.canonicalize_dtype(dtypes.result_type(val)))\n elif isinstance(val, (tuple, list)):\n return tuple(normalize_to_xla_dtypes(x) for x in val)\n raise TypeError('Can\\'t convert to XLA: {}'.format(val))","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.Build","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.Build\n def Build(self, *args, **kwargs):\n return super(_JaxComputationBuilder, self).Build(\n *args, **kwargs)","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.NumpyArrayConstant","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.NumpyArrayConstant\n def NumpyArrayConstant(self, value, canonicalize_types=True):\n if canonicalize_types:\n value = normalize_to_xla_dtypes(value)\n return super(_JaxComputationBuilder, self).Constant(value)","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.ConstantLike","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.ConstantLike\n def ConstantLike(self, example_value, value, canonicalize_types=True):\n example_value = onp.asarray(example_value)\n return self.Constant(onp.array(value, dtype=example_value.dtype))","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.Constant","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.Constant\n def Constant(self, py_val, canonicalize_types=True):\n \"\"\"Translate constant `py_val` to a constant for this ComputationBuilder.\n\n Args:\n py_val: a Python value to be translated to a constant.\n\n Returns:\n A representation of the constant, either a ComputationDataHandle or None\n \"\"\"\n py_type = type(py_val)\n if py_type in _constant_handlers:\n return _constant_handlers[py_type](self, py_val, canonicalize_types)\n else:\n raise TypeError(\"No constant handler for type: {}\".format(py_type))","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.CrossReplicaSum","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.CrossReplicaSum\n def CrossReplicaSum(self, operand, replica_groups):\n \"\"\"Workaround for CrossReplicaSum not being implemented on some backends.\"\"\"\n if len(replica_groups[0]) == 1:\n return operand\n else:\n return super(_JaxComputationBuilder, self).CrossReplicaSum(\n operand, replica_groups)","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.AllToAll","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.AllToAll\n def AllToAll(self, operand, split_axis, concat_axis, replica_groups):\n \"\"\"Workaround for AllToAll not being implemented on some backends.\"\"\"\n if len(replica_groups[0]) == 1:\n return operand\n else:\n return super(_JaxComputationBuilder, self).AllToAll(\n operand, split_axis, concat_axis, replica_groups)","label":0},{"cid":"jax/lib/xla_bridge.py::make_computation_builder","text":"jax/lib/xla_bridge.py::make_computation_builder\ndef make_computation_builder(name):\n return _JaxComputationBuilder(name)","label":0},{"cid":"jax/lib/xla_bridge.py::register_constant_handler","text":"jax/lib/xla_bridge.py::register_constant_handler\ndef register_constant_handler(type_, handler_fun):\n _constant_handlers[type_] = handler_fun","label":0},{"cid":"jax/lib/xla_bridge.py::_ndarray_constant_handler","text":"jax/lib/xla_bridge.py::_ndarray_constant_handler\ndef _ndarray_constant_handler(c, val, canonicalize_types=True):\n \"\"\"Constant handler for ndarray literals, handling zero-size strides.\n\n This function essentially calls c.NumpyArrayConstant(val) except it has\n special handling of arrays with any strides of size zero: for those, it\n generates appropriate calls to NumpyArrayConstant, Broadcast, and Transpose\n to avoid staging in large literals that might arise from np.zeros or np.ones\n or the output of lax.broadcast (which uses onp.broadcast_to which in turn\n uses size-zero strides).\n\n Args:\n c: XLA client ComputationBuilder.\n val: an ndarray.\n\n Returns:\n An XLA ComputationDataHandle / XlaOp representing the constant ndarray\n staged into the XLA Computation.\n \"\"\"\n # TODO(mattjj): revise this to use c.BroadcastInDim rather than Transpose\n if onp.any(onp.equal(0, val.strides)) and val.size > 0:\n zero_stride_axes, = onp.where(onp.equal(0, val.strides))\n other_axes, = onp.where(onp.not_equal(0, val.strides))\n collapsed_val = val[tuple(0 if ax in zero_stride_axes else slice(None)\n for ax in range(val.ndim))]\n xla_val = c.Broadcast(\n c.NumpyArrayConstant(collapsed_val, canonicalize_types),\n onp.take(val.shape, zero_stride_axes))\n permutation = onp.argsort(tuple(zero_stride_axes) + tuple(other_axes))\n return c.Transpose(xla_val, permutation)\n else:\n return c.NumpyArrayConstant(val, canonicalize_types)","label":0},{"cid":"jax/lib/xla_bridge.py::_scalar_constant_handler","text":"jax/lib/xla_bridge.py::_scalar_constant_handler\ndef _scalar_constant_handler(c, val, canonicalize_types=True):\n return c.NumpyArrayConstant(val, canonicalize_types)","label":0},{"cid":"jax/lib/xla_bridge.py::_python_scalar_handler","text":"jax/lib/xla_bridge.py::_python_scalar_handler\ndef _python_scalar_handler(dtype, c, val, canonicalize_dtypes=True):\n return c.NumpyArrayConstant(dtype.type(val))","label":0}],"string":"[\n {\n \"cid\": \"jax/experimental/optimizers.py::optimizer\",\n \"text\": \"jax/experimental/optimizers.py::optimizer\\ndef optimizer(opt_maker):\\n \\\"\\\"\\\"Decorator to make an optimizer defined for arrays generalize to containers.\\n\\n With this decorator, you can write init, update, and get_params functions that\\n each operate only on single arrays, and convert them to corresponding\\n functions that operate on pytrees of parameters. See the optimizers defined in\\n optimizers.py for examples.\\n\\n Args:\\n opt_maker: a function that returns an ``(init_fun, update_fun, get_params)``\\n triple of functions that might only work with ndarrays, as per\\n\\n .. code-block:: haskell\\n\\n init_fun :: ndarray -> OptStatePytree ndarray\\n update_fun :: OptStatePytree ndarray -> OptStatePytree ndarray\\n get_params :: OptStatePytree ndarray -> ndarray\\n\\n Returns:\\n An ``(init_fun, update_fun, get_params)`` triple of functions that work on\\n arbitrary pytrees, as per\\n\\n .. code-block:: haskell\\n\\n init_fun :: ParameterPytree ndarray -> OptimizerState\\n update_fun :: OptimizerState -> OptimizerState\\n get_params :: OptimizerState -> ParameterPytree ndarray\\n\\n The OptimizerState pytree type used by the returned functions is isomorphic\\n to ``ParameterPytree (OptStatePytree ndarray)``, but may store the state\\n instead as e.g. a partially-flattened data structure for performance.\\n \\\"\\\"\\\"\\n\\n @functools.wraps(opt_maker)\\n def tree_opt_maker(*args, **kwargs):\\n init, update, get_params = opt_maker(*args, **kwargs)\\n\\n @functools.wraps(init)\\n def tree_init(x0_tree):\\n x0_flat, tree = tree_flatten(x0_tree)\\n initial_states = [init(x0) for x0 in x0_flat]\\n states_flat, subtrees = unzip2(map(tree_flatten, initial_states))\\n packed_state = pack(map(pack, states_flat))\\n return OptimizerState(packed_state, tree, subtrees)\\n\\n @functools.wraps(update)\\n def tree_update(i, grad_tree, opt_state):\\n packed_state, tree, subtrees = opt_state\\n grad_flat, tree2 = tree_flatten(grad_tree)\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::optimizer.tree_opt_maker\",\n \"text\": \"jax/experimental/optimizers.py::optimizer.tree_opt_maker\\n @functools.wraps(opt_maker)\\n def tree_opt_maker(*args, **kwargs):\\n init, update, get_params = opt_maker(*args, **kwargs)\\n\\n @functools.wraps(init)\\n def tree_init(x0_tree):\\n x0_flat, tree = tree_flatten(x0_tree)\\n initial_states = [init(x0) for x0 in x0_flat]\\n states_flat, subtrees = unzip2(map(tree_flatten, initial_states))\\n packed_state = pack(map(pack, states_flat))\\n return OptimizerState(packed_state, tree, subtrees)\\n\\n @functools.wraps(update)\\n def tree_update(i, grad_tree, opt_state):\\n packed_state, tree, subtrees = opt_state\\n grad_flat, tree2 = tree_flatten(grad_tree)\\n if tree2 != tree:\\n msg = (\\\"optimizer update function was passed a gradient tree that did \\\"\\n \\\"not match the parameter tree structure with which it was \\\"\\n \\\"initialized: parameter tree {} and grad tree {}.\\\")\\n raise TypeError(msg.format(tree, tree2))\\n states = map(tree_unflatten, subtrees, packed_state)\\n new_states = map(partial(update, i), grad_flat, states)\\n new_states_flat, subtrees2 = unzip2(map(tree_flatten, new_states))\\n for subtree, subtree2 in zip(subtrees, subtrees2):\\n if subtree2 != subtree:\\n msg = (\\\"optimizer update function produced an output structure that \\\"\\n \\\"did not match its input structure: input {} and output {}.\\\")\\n raise TypeError(msg.format(subtree, subtree2))\\n new_packed_state = pack(map(pack, new_states_flat))\\n return OptimizerState(new_packed_state, tree, subtrees)\\n\\n @functools.wraps(get_params)\\n def tree_get_params(opt_state):\\n packed_state, tree, subtrees = opt_state\\n states = map(tree_unflatten, subtrees, packed_state)\\n params = map(get_params, states)\\n return tree_unflatten(tree, params)\\n\\n return tree_init, tree_update, tree_get_params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_init\",\n \"text\": \"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_init\\n @functools.wraps(init)\\n def tree_init(x0_tree):\\n x0_flat, tree = tree_flatten(x0_tree)\\n initial_states = [init(x0) for x0 in x0_flat]\\n states_flat, subtrees = unzip2(map(tree_flatten, initial_states))\\n packed_state = pack(map(pack, states_flat))\\n return OptimizerState(packed_state, tree, subtrees)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_update\",\n \"text\": \"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_update\\n @functools.wraps(update)\\n def tree_update(i, grad_tree, opt_state):\\n packed_state, tree, subtrees = opt_state\\n grad_flat, tree2 = tree_flatten(grad_tree)\\n if tree2 != tree:\\n msg = (\\\"optimizer update function was passed a gradient tree that did \\\"\\n \\\"not match the parameter tree structure with which it was \\\"\\n \\\"initialized: parameter tree {} and grad tree {}.\\\")\\n raise TypeError(msg.format(tree, tree2))\\n states = map(tree_unflatten, subtrees, packed_state)\\n new_states = map(partial(update, i), grad_flat, states)\\n new_states_flat, subtrees2 = unzip2(map(tree_flatten, new_states))\\n for subtree, subtree2 in zip(subtrees, subtrees2):\\n if subtree2 != subtree:\\n msg = (\\\"optimizer update function produced an output structure that \\\"\\n \\\"did not match its input structure: input {} and output {}.\\\")\\n raise TypeError(msg.format(subtree, subtree2))\\n new_packed_state = pack(map(pack, new_states_flat))\\n return OptimizerState(new_packed_state, tree, subtrees)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_get_params\",\n \"text\": \"jax/experimental/optimizers.py::optimizer.tree_opt_maker.tree_get_params\\n @functools.wraps(get_params)\\n def tree_get_params(opt_state):\\n packed_state, tree, subtrees = opt_state\\n states = map(tree_unflatten, subtrees, packed_state)\\n params = map(get_params, states)\\n return tree_unflatten(tree, params)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::sgd\",\n \"text\": \"jax/experimental/optimizers.py::sgd\\n@optimizer\\ndef sgd(step_size):\\n \\\"\\\"\\\"Construct optimizer triple for stochastic gradient descent.\\n\\n Args:\\n step_size: positive scalar, or a callable representing a step size schedule\\n that maps the iteration index to positive scalar.\\n\\n Returns:\\n An (init_fun, update_fun, get_params) triple.\\n \\\"\\\"\\\"\\n step_size = make_schedule(step_size)\\n def init(x0):\\n return x0\\n def update(i, g, x):\\n return x - step_size(i) * g\\n def get_params(x):\\n return x\\n return init, update, get_params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::sgd.init\",\n \"text\": \"jax/experimental/optimizers.py::sgd.init\\n def init(x0):\\n return x0\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::sgd.update\",\n \"text\": \"jax/experimental/optimizers.py::sgd.update\\n def update(i, g, x):\\n return x - step_size(i) * g\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::sgd.get_params\",\n \"text\": \"jax/experimental/optimizers.py::sgd.get_params\\n def get_params(x):\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::momentum\",\n \"text\": \"jax/experimental/optimizers.py::momentum\\n@optimizer\\ndef momentum(step_size, mass):\\n \\\"\\\"\\\"Construct optimizer triple for SGD with momentum.\\n\\n Args:\\n step_size: positive scalar, or a callable representing a step size schedule\\n that maps the iteration index to positive scalar.\\n mass: positive scalar representing the momentum coefficient.\\n\\n Returns:\\n An (init_fun, update_fun, get_params) triple.\\n \\\"\\\"\\\"\\n step_size = make_schedule(step_size)\\n def init(x0):\\n v0 = np.zeros_like(x0)\\n return x0, v0\\n def update(i, g, state):\\n x, velocity = state\\n velocity = mass * velocity + g\\n x = x - step_size(i) * velocity\\n return x, velocity\\n def get_params(state):\\n x, _ = state\\n return x\\n return init, update, get_params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::momentum.init\",\n \"text\": \"jax/experimental/optimizers.py::momentum.init\\n def init(x0):\\n v0 = np.zeros_like(x0)\\n return x0, v0\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::momentum.update\",\n \"text\": \"jax/experimental/optimizers.py::momentum.update\\n def update(i, g, state):\\n x, velocity = state\\n velocity = mass * velocity + g\\n x = x - step_size(i) * velocity\\n return x, velocity\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::momentum.get_params\",\n \"text\": \"jax/experimental/optimizers.py::momentum.get_params\\n def get_params(state):\\n x, _ = state\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::nesterov\",\n \"text\": \"jax/experimental/optimizers.py::nesterov\\n@optimizer\\ndef nesterov(step_size, mass):\\n \\\"\\\"\\\"Construct optimizer triple for SGD with Nesterov momentum.\\n\\n Args:\\n step_size: positive scalar, or a callable representing a step size schedule\\n that maps the iteration index to positive scalar.\\n mass: positive scalar representing the momentum coefficient.\\n\\n Returns:\\n An (init_fun, update_fun, get_params) triple.\\n \\\"\\\"\\\"\\n step_size = make_schedule(step_size)\\n def init(x0):\\n v0 = np.zeros_like(x0)\\n return x0, v0\\n def update(i, g, state):\\n x, velocity = state\\n velocity = mass * velocity + g\\n x = x - step_size(i) * (mass * velocity + g)\\n return x, velocity\\n def get_params(state):\\n x, _ = state\\n return x\\n return init, update, get_params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::nesterov.init\",\n \"text\": \"jax/experimental/optimizers.py::nesterov.init\\n def init(x0):\\n v0 = np.zeros_like(x0)\\n return x0, v0\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::nesterov.update\",\n \"text\": \"jax/experimental/optimizers.py::nesterov.update\\n def update(i, g, state):\\n x, velocity = state\\n velocity = mass * velocity + g\\n x = x - step_size(i) * (mass * velocity + g)\\n return x, velocity\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::nesterov.get_params\",\n \"text\": \"jax/experimental/optimizers.py::nesterov.get_params\\n def get_params(state):\\n x, _ = state\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::adagrad\",\n \"text\": \"jax/experimental/optimizers.py::adagrad\\n@optimizer\\ndef adagrad(step_size, momentum=0.9):\\n \\\"\\\"\\\"Construct optimizer triple for Adagrad.\\n\\n Adaptive Subgradient Methods for Online Learning and Stochastic Optimization:\\n http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\\n\\n Args:\\n step_size: positive scalar, or a callable representing a step size schedule\\n that maps the iteration index to positive scalar.\\n momentum: optional, a positive scalar value for momentum\\n\\n Returns:\\n An (init_fun, update_fun, get_params) triple.\\n \\\"\\\"\\\"\\n step_size = make_schedule(step_size)\\n\\n def init(x0):\\n g_sq = np.zeros_like(x0)\\n m = np.zeros_like(x0)\\n return x0, g_sq, m\\n\\n def update(i, g, state):\\n x, g_sq, m = state\\n g_sq += g**2\\n g_sq_inv_sqrt = np.where(g_sq > 0, 1. / np.sqrt(g_sq), 0.0)\\n m = (1. - momentum) * (g * g_sq_inv_sqrt) + momentum * m\\n x = x - step_size(i) * m\\n return x, g_sq, m\\n\\n def get_params(state):\\n x, _, _ = state\\n return x\\n\\n return init, update, get_params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::adagrad.init\",\n \"text\": \"jax/experimental/optimizers.py::adagrad.init\\n def init(x0):\\n g_sq = np.zeros_like(x0)\\n m = np.zeros_like(x0)\\n return x0, g_sq, m\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::adagrad.update\",\n \"text\": \"jax/experimental/optimizers.py::adagrad.update\\n def update(i, g, state):\\n x, g_sq, m = state\\n g_sq += g**2\\n g_sq_inv_sqrt = np.where(g_sq > 0, 1. / np.sqrt(g_sq), 0.0)\\n m = (1. - momentum) * (g * g_sq_inv_sqrt) + momentum * m\\n x = x - step_size(i) * m\\n return x, g_sq, m\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::adagrad.get_params\",\n \"text\": \"jax/experimental/optimizers.py::adagrad.get_params\\n def get_params(state):\\n x, _, _ = state\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::rmsprop\",\n \"text\": \"jax/experimental/optimizers.py::rmsprop\\n@optimizer\\ndef rmsprop(step_size, gamma=0.9, eps=1e-8):\\n \\\"\\\"\\\"Construct optimizer triple for RMSProp.\\n\\n Args:\\n step_size: positive scalar, or a callable representing a step size schedule\\n that maps the iteration index to positive scalar.\\n gamma: Decay parameter.\\n eps: Epsilon parameter.\\n\\n Returns:\\n An (init_fun, update_fun, get_params) triple.\\n \\\"\\\"\\\"\\n step_size = make_schedule(step_size)\\n def init(x0):\\n avg_sq_grad = np.zeros_like(x0)\\n return x0, avg_sq_grad\\n def update(i, g, state):\\n x, avg_sq_grad = state\\n avg_sq_grad = avg_sq_grad * gamma + g**2 * (1. - gamma)\\n x = x - step_size(i) * g / np.sqrt(avg_sq_grad + eps)\\n return x, avg_sq_grad\\n def get_params(state):\\n x, _ = state\\n return x\\n return init, update, get_params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::rmsprop.init\",\n \"text\": \"jax/experimental/optimizers.py::rmsprop.init\\n def init(x0):\\n avg_sq_grad = np.zeros_like(x0)\\n return x0, avg_sq_grad\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::rmsprop.update\",\n \"text\": \"jax/experimental/optimizers.py::rmsprop.update\\n def update(i, g, state):\\n x, avg_sq_grad = state\\n avg_sq_grad = avg_sq_grad * gamma + g**2 * (1. - gamma)\\n x = x - step_size(i) * g / np.sqrt(avg_sq_grad + eps)\\n return x, avg_sq_grad\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::rmsprop.get_params\",\n \"text\": \"jax/experimental/optimizers.py::rmsprop.get_params\\n def get_params(state):\\n x, _ = state\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::rmsprop_momentum\",\n \"text\": \"jax/experimental/optimizers.py::rmsprop_momentum\\n@optimizer\\ndef rmsprop_momentum(step_size, gamma=0.9, eps=1e-8, momentum=0.9):\\n \\\"\\\"\\\"Construct optimizer triple for RMSProp with momentum.\\n\\n This optimizer is separate from the rmsprop optimizer because it needs to\\n keep track of additional parameters.\\n\\n Args:\\n step_size: positive scalar, or a callable representing a step size schedule\\n that maps the iteration index to positive scalar.\\n gamma: Decay parameter.\\n eps: Epsilon parameter.\\n momentum: Momentum parameter.\\n\\n Returns:\\n An (init_fun, update_fun, get_params) triple.\\n \\\"\\\"\\\"\\n step_size = make_schedule(step_size)\\n def init(x0):\\n avg_sq_grad = np.zeros_like(x0)\\n mom = np.zeros_like(x0)\\n return x0, avg_sq_grad, mom\\n def update(i, g, state):\\n x, avg_sq_grad, mom = state\\n avg_sq_grad = avg_sq_grad * gamma + g**2 * (1. - gamma)\\n mom = momentum * mom + step_size(i) * g / np.sqrt(avg_sq_grad + eps)\\n x = x - mom\\n return x, avg_sq_grad, mom\\n def get_params(state):\\n x, _, _ = state\\n return x\\n return init, update, get_params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::rmsprop_momentum.init\",\n \"text\": \"jax/experimental/optimizers.py::rmsprop_momentum.init\\n def init(x0):\\n avg_sq_grad = np.zeros_like(x0)\\n mom = np.zeros_like(x0)\\n return x0, avg_sq_grad, mom\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::rmsprop_momentum.update\",\n \"text\": \"jax/experimental/optimizers.py::rmsprop_momentum.update\\n def update(i, g, state):\\n x, avg_sq_grad, mom = state\\n avg_sq_grad = avg_sq_grad * gamma + g**2 * (1. - gamma)\\n mom = momentum * mom + step_size(i) * g / np.sqrt(avg_sq_grad + eps)\\n x = x - mom\\n return x, avg_sq_grad, mom\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::rmsprop_momentum.get_params\",\n \"text\": \"jax/experimental/optimizers.py::rmsprop_momentum.get_params\\n def get_params(state):\\n x, _, _ = state\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::adam\",\n \"text\": \"jax/experimental/optimizers.py::adam\\n@optimizer\\ndef adam(step_size, b1=0.9, b2=0.999, eps=1e-8):\\n \\\"\\\"\\\"Construct optimizer triple for Adam.\\n\\n Args:\\n step_size: positive scalar, or a callable representing a step size schedule\\n that maps the iteration index to positive scalar.\\n b1: optional, a positive scalar value for beta_1, the exponential decay rate\\n for the first moment estimates (default 0.9).\\n b2: optional, a positive scalar value for beta_2, the exponential decay rate\\n for the second moment estimates (default 0.999).\\n eps: optional, a positive scalar value for epsilon, a small constant for\\n numerical stability (default 1e-8).\\n\\n Returns:\\n An (init_fun, update_fun, get_params) triple.\\n \\\"\\\"\\\"\\n step_size = make_schedule(step_size)\\n def init(x0):\\n m0 = np.zeros_like(x0)\\n v0 = np.zeros_like(x0)\\n return x0, m0, v0\\n def update(i, g, state):\\n x, m, v = state\\n m = (1 - b1) * g + b1 * m # First moment estimate.\\n v = (1 - b2) * (g ** 2) + b2 * v # Second moment estimate.\\n mhat = m / (1 - b1 ** (i + 1)) # Bias correction.\\n vhat = v / (1 - b2 ** (i + 1))\\n x = x - step_size(i) * mhat / (np.sqrt(vhat) + eps)\\n return x, m, v\\n def get_params(state):\\n x, m, v = state\\n return x\\n return init, update, get_params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::adam.init\",\n \"text\": \"jax/experimental/optimizers.py::adam.init\\n def init(x0):\\n m0 = np.zeros_like(x0)\\n v0 = np.zeros_like(x0)\\n return x0, m0, v0\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::adam.update\",\n \"text\": \"jax/experimental/optimizers.py::adam.update\\n def update(i, g, state):\\n x, m, v = state\\n m = (1 - b1) * g + b1 * m # First moment estimate.\\n v = (1 - b2) * (g ** 2) + b2 * v # Second moment estimate.\\n mhat = m / (1 - b1 ** (i + 1)) # Bias correction.\\n vhat = v / (1 - b2 ** (i + 1))\\n x = x - step_size(i) * mhat / (np.sqrt(vhat) + eps)\\n return x, m, v\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::adam.get_params\",\n \"text\": \"jax/experimental/optimizers.py::adam.get_params\\n def get_params(state):\\n x, m, v = state\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::sm3\",\n \"text\": \"jax/experimental/optimizers.py::sm3\\n@optimizer\\ndef sm3(step_size, momentum=0.9):\\n \\\"\\\"\\\"Construct optimizer triple for SM3.\\n\\n Memory-Efficient Adaptive Optimization for Large-Scale Learning.\\n https://arxiv.org/abs/1901.11150\\n\\n Args:\\n step_size: positive scalar, or a callable representing a step size schedule\\n that maps the iteration index to positive scalar.\\n momentum: optional, a positive scalar value for momentum\\n\\n Returns:\\n An (init_fun, update_fun, get_params) triple.\\n \\\"\\\"\\\"\\n step_size = make_schedule(step_size)\\n\\n def splice(seq, i, x):\\n lst = list(seq)\\n lst[i:i+1] = x\\n return lst\\n\\n def broadcast_into(ndim, x, axis):\\n idx = splice([None] * ndim, axis, [slice(None)])\\n return x[tuple(idx)]\\n\\n def init(x0):\\n vs = [np.zeros(sz, dtype=x0.dtype) for sz in x0.shape]\\n return x0, np.zeros_like(x0), vs\\n\\n def update(i, g, state):\\n x, m, vs = state\\n vs = [broadcast_into(g.ndim, v, i) for i, v in enumerate(vs)]\\n accum = functools.reduce(np.minimum, vs) + g ** 2\\n accum_inv_sqrt = np.where(accum > 0, 1. / np.sqrt(accum), 0)\\n m = (1. - momentum) * (g * accum_inv_sqrt) + momentum * m\\n x = x - step_size(i) * m\\n vs = [accum.max(splice(range(x.ndim), j, [])) for j in range(x.ndim)]\\n return x, m, vs\\n\\n def get_params(state):\\n x, _, _ = state\\n return x\\n\\n return init, update, get_params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::sm3.splice\",\n \"text\": \"jax/experimental/optimizers.py::sm3.splice\\n def splice(seq, i, x):\\n lst = list(seq)\\n lst[i:i+1] = x\\n return lst\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::sm3.broadcast_into\",\n \"text\": \"jax/experimental/optimizers.py::sm3.broadcast_into\\n def broadcast_into(ndim, x, axis):\\n idx = splice([None] * ndim, axis, [slice(None)])\\n return x[tuple(idx)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::sm3.init\",\n \"text\": \"jax/experimental/optimizers.py::sm3.init\\n def init(x0):\\n vs = [np.zeros(sz, dtype=x0.dtype) for sz in x0.shape]\\n return x0, np.zeros_like(x0), vs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::sm3.update\",\n \"text\": \"jax/experimental/optimizers.py::sm3.update\\n def update(i, g, state):\\n x, m, vs = state\\n vs = [broadcast_into(g.ndim, v, i) for i, v in enumerate(vs)]\\n accum = functools.reduce(np.minimum, vs) + g ** 2\\n accum_inv_sqrt = np.where(accum > 0, 1. / np.sqrt(accum), 0)\\n m = (1. - momentum) * (g * accum_inv_sqrt) + momentum * m\\n x = x - step_size(i) * m\\n vs = [accum.max(splice(range(x.ndim), j, [])) for j in range(x.ndim)]\\n return x, m, vs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::sm3.get_params\",\n \"text\": \"jax/experimental/optimizers.py::sm3.get_params\\n def get_params(state):\\n x, _, _ = state\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optimizers.py::constant\",\n \"text\": \"jax/experimental/optimizers.py::constant\\ndef constant(step_size):\\n def schedule(i):\\n return step_size\\n return schedule\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::clip\",\n \"text\": \"jax/experimental/optix.py::clip\\ndef clip(max_delta):\\n \\\"\\\"\\\"Clip updates element-wise, to be between -max_delta and +max_delta.\\n\\n Args:\\n max_delta: the maximum absolute value for each element in the update.\\n\\n Returns:\\n An (init_fn, update_fn) tuple.\\n \\\"\\\"\\\"\\n\\n def init_fn(_):\\n return ClipState()\\n\\n def update_fn(updates, state):\\n updates = tree_multimap(\\n lambda g: jnp.clip(g, -max_delta, max_delta), updates)\\n return updates, state\\n\\n return InitUpdate(init_fn, update_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::clip.init_fn\",\n \"text\": \"jax/experimental/optix.py::clip.init_fn\\n def init_fn(_):\\n return ClipState()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::clip.update_fn\",\n \"text\": \"jax/experimental/optix.py::clip.update_fn\\n def update_fn(updates, state):\\n updates = tree_multimap(\\n lambda g: jnp.clip(g, -max_delta, max_delta), updates)\\n return updates, state\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::global_norm\",\n \"text\": \"jax/experimental/optix.py::global_norm\\ndef global_norm(items):\\n return jnp.sqrt(jnp.sum([jnp.sum(x**2) for x in tree_leaves(items)]))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::clip_by_global_norm\",\n \"text\": \"jax/experimental/optix.py::clip_by_global_norm\\ndef clip_by_global_norm(max_norm):\\n \\\"\\\"\\\"Clip updates using their global norm.\\n\\n References:\\n [Pascanu et al, 2012](https://arxiv.org/abs/1211.5063)\\n\\n Args:\\n max_norm: the maximum global norm for an update.\\n\\n Returns:\\n An (init_fn, update_fn) tuple.\\n \\\"\\\"\\\"\\n\\n def init_fn(_):\\n return ClipByGlobalNormState()\\n\\n def update_fn(updates, state):\\n g_norm = global_norm(updates)\\n trigger = g_norm < max_norm\\n updates = tree_multimap(\\n lambda t: jnp.where(trigger, t, (t / g_norm) * max_norm), updates)\\n return updates, state\\n\\n return InitUpdate(init_fn, update_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::clip_by_global_norm.init_fn\",\n \"text\": \"jax/experimental/optix.py::clip_by_global_norm.init_fn\\n def init_fn(_):\\n return ClipByGlobalNormState()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::clip_by_global_norm.update_fn\",\n \"text\": \"jax/experimental/optix.py::clip_by_global_norm.update_fn\\n def update_fn(updates, state):\\n g_norm = global_norm(updates)\\n trigger = g_norm < max_norm\\n updates = tree_multimap(\\n lambda t: jnp.where(trigger, t, (t / g_norm) * max_norm), updates)\\n return updates, state\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::trace\",\n \"text\": \"jax/experimental/optix.py::trace\\ndef trace(decay, nesterov):\\n \\\"\\\"\\\"Compute a trace of past updates.\\n\\n Args:\\n decay: the decay rate for the tracing of past updates.\\n nesterov: whether to use nesterov momentum.\\n\\n Returns:\\n An (init_fn, update_fn) tuple.\\n \\\"\\\"\\\"\\n\\n def init_fn(params):\\n return TraceState(trace=tree_multimap(jnp.zeros_like, params))\\n\\n def update_fn(updates, state):\\n f = lambda g, t: g + decay * t\\n update_trace = tree_multimap(f, updates, state.trace)\\n updates = (\\n tree_multimap(f, updates, update_trace) if nesterov else update_trace)\\n return updates, TraceState(trace=update_trace)\\n\\n return InitUpdate(init_fn, update_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::trace.init_fn\",\n \"text\": \"jax/experimental/optix.py::trace.init_fn\\n def init_fn(params):\\n return TraceState(trace=tree_multimap(jnp.zeros_like, params))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::trace.update_fn\",\n \"text\": \"jax/experimental/optix.py::trace.update_fn\\n def update_fn(updates, state):\\n f = lambda g, t: g + decay * t\\n update_trace = tree_multimap(f, updates, state.trace)\\n updates = (\\n tree_multimap(f, updates, update_trace) if nesterov else update_trace)\\n return updates, TraceState(trace=update_trace)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::_update_moment\",\n \"text\": \"jax/experimental/optix.py::_update_moment\\ndef _update_moment(updates, moments, decay, order):\\n return tree_multimap(\\n lambda g, t: (1 - decay) * (g ** order) + decay * t, updates, moments)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_rms\",\n \"text\": \"jax/experimental/optix.py::scale_by_rms\\ndef scale_by_rms(decay=0.9, eps=1e-8):\\n \\\"\\\"\\\"Rescale updates by the root of the exp. moving avg of the square.\\n\\n References:\\n [Hinton](www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\\n\\n Args:\\n decay: decay rate for the exponentially weighted average of squared grads.\\n eps: term added to the denominator to improve numerical stability.\\n\\n Returns:\\n An (init_fn, update_fn) tuple.\\n \\\"\\\"\\\"\\n\\n def init_fn(params):\\n nu = tree_multimap(jnp.zeros_like, params) # second moment\\n return ScaleByRmsState(nu=nu)\\n\\n def update_fn(updates, state):\\n nu = _update_moment(updates, state.nu, decay, 2)\\n updates = tree_multimap(lambda g, n: g / (jnp.sqrt(n + eps)), updates, nu)\\n return updates, ScaleByRmsState(nu=nu)\\n\\n return InitUpdate(init_fn, update_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_rms.init_fn\",\n \"text\": \"jax/experimental/optix.py::scale_by_rms.init_fn\\n def init_fn(params):\\n nu = tree_multimap(jnp.zeros_like, params) # second moment\\n return ScaleByRmsState(nu=nu)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_rms.update_fn\",\n \"text\": \"jax/experimental/optix.py::scale_by_rms.update_fn\\n def update_fn(updates, state):\\n nu = _update_moment(updates, state.nu, decay, 2)\\n updates = tree_multimap(lambda g, n: g / (jnp.sqrt(n + eps)), updates, nu)\\n return updates, ScaleByRmsState(nu=nu)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_stddev\",\n \"text\": \"jax/experimental/optix.py::scale_by_stddev\\ndef scale_by_stddev(decay=0.9, eps=1e-8):\\n \\\"\\\"\\\"Rescale updates by the root of the centered exp. moving average of squares.\\n\\n References:\\n [Hinton](www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\\n\\n Args:\\n decay: decay rate for the exponentially weighted average of squared grads.\\n eps: term added to the denominator to improve numerical stability.\\n\\n Returns:\\n An (init_fn, update_fn) tuple.\\n \\\"\\\"\\\"\\n\\n def init_fn(params):\\n mu = tree_multimap(jnp.zeros_like, params) # First moment\\n nu = tree_multimap(jnp.zeros_like, params) # Second moment\\n return ScaleByRStdDevState(mu=mu, nu=nu)\\n\\n def update_fn(updates, state):\\n mu = _update_moment(updates, state.mu, decay, 1)\\n nu = _update_moment(updates, state.nu, decay, 2)\\n updates = tree_multimap(\\n lambda g, m, n: g / jnp.sqrt(n - m**2 + eps), updates, mu, nu)\\n return updates, ScaleByRStdDevState(mu=mu, nu=nu)\\n\\n return InitUpdate(init_fn, update_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_stddev.init_fn\",\n \"text\": \"jax/experimental/optix.py::scale_by_stddev.init_fn\\n def init_fn(params):\\n mu = tree_multimap(jnp.zeros_like, params) # First moment\\n nu = tree_multimap(jnp.zeros_like, params) # Second moment\\n return ScaleByRStdDevState(mu=mu, nu=nu)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_stddev.update_fn\",\n \"text\": \"jax/experimental/optix.py::scale_by_stddev.update_fn\\n def update_fn(updates, state):\\n mu = _update_moment(updates, state.mu, decay, 1)\\n nu = _update_moment(updates, state.nu, decay, 2)\\n updates = tree_multimap(\\n lambda g, m, n: g / jnp.sqrt(n - m**2 + eps), updates, mu, nu)\\n return updates, ScaleByRStdDevState(mu=mu, nu=nu)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_adam\",\n \"text\": \"jax/experimental/optix.py::scale_by_adam\\ndef scale_by_adam(b1=0.9, b2=0.999, eps=1e-8):\\n \\\"\\\"\\\"Rescale updates according to the Adam algorithm.\\n\\n References:\\n [Kingma et al, 2014](https://arxiv.org/abs/1412.6980)\\n\\n Args:\\n b1: decay rate for the exponentially weighted average of grads.\\n b2: decay rate for the exponentially weighted average of squared grads.\\n eps: term added to the denominator to improve numerical stability.\\n\\n Returns:\\n An (init_fn, update_fn) tuple.\\n \\\"\\\"\\\"\\n\\n def init_fn(params):\\n mu = tree_multimap(jnp.zeros_like, params) # First moment\\n nu = tree_multimap(jnp.zeros_like, params) # Second moment\\n return ScaleByAdamState(count=jnp.zeros([], jnp.int32), mu=mu, nu=nu)\\n\\n def update_fn(updates, state):\\n mu = _update_moment(updates, state.mu, b1, 1)\\n nu = _update_moment(updates, state.nu, b2, 2)\\n mu_hat = tree_multimap(lambda t: t / (1 - b1 ** (state.count + 1)), mu)\\n nu_hat = tree_multimap(lambda t: t / (1 - b2 ** (state.count + 1)), nu)\\n updates = tree_multimap(\\n lambda m, v: m / (jnp.sqrt(v) + eps), mu_hat, nu_hat)\\n return updates, ScaleByAdamState(count=state.count + 1, mu=mu, nu=nu)\\n\\n return InitUpdate(init_fn, update_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_adam.init_fn\",\n \"text\": \"jax/experimental/optix.py::scale_by_adam.init_fn\\n def init_fn(params):\\n mu = tree_multimap(jnp.zeros_like, params) # First moment\\n nu = tree_multimap(jnp.zeros_like, params) # Second moment\\n return ScaleByAdamState(count=jnp.zeros([], jnp.int32), mu=mu, nu=nu)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_adam.update_fn\",\n \"text\": \"jax/experimental/optix.py::scale_by_adam.update_fn\\n def update_fn(updates, state):\\n mu = _update_moment(updates, state.mu, b1, 1)\\n nu = _update_moment(updates, state.nu, b2, 2)\\n mu_hat = tree_multimap(lambda t: t / (1 - b1 ** (state.count + 1)), mu)\\n nu_hat = tree_multimap(lambda t: t / (1 - b2 ** (state.count + 1)), nu)\\n updates = tree_multimap(\\n lambda m, v: m / (jnp.sqrt(v) + eps), mu_hat, nu_hat)\\n return updates, ScaleByAdamState(count=state.count + 1, mu=mu, nu=nu)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale\",\n \"text\": \"jax/experimental/optix.py::scale\\ndef scale(step_size):\\n \\\"\\\"\\\"Scale updates by some fixed scalar `step_size`.\\n\\n Args:\\n step_size: a scalar corresponding to a fixed scaling factor for updates.\\n\\n Returns:\\n An (init_fn, update_fn) tuple.\\n \\\"\\\"\\\"\\n\\n def init_fn(_):\\n return ScaleState()\\n\\n def update_fn(updates, state):\\n updates = tree_multimap(lambda g: step_size * g, updates)\\n return updates, state\\n\\n return InitUpdate(init_fn, update_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale.init_fn\",\n \"text\": \"jax/experimental/optix.py::scale.init_fn\\n def init_fn(_):\\n return ScaleState()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale.update_fn\",\n \"text\": \"jax/experimental/optix.py::scale.update_fn\\n def update_fn(updates, state):\\n updates = tree_multimap(lambda g: step_size * g, updates)\\n return updates, state\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_schedule\",\n \"text\": \"jax/experimental/optix.py::scale_by_schedule\\ndef scale_by_schedule(step_size_fn):\\n \\\"\\\"\\\"Scale updates using a custom schedule for the `step_size`.\\n\\n Args:\\n step_size_fn: a function that takes an update count as input and proposes\\n the step_size to multiply the updates by.\\n\\n Returns:\\n An (init_fn, update_fn) tuple.\\n \\\"\\\"\\\"\\n\\n def init_fn(_):\\n return ScaleByScheduleState(count=jnp.zeros([], jnp.int32))\\n\\n def update_fn(updates, state):\\n updates = tree_multimap(lambda g: step_size_fn(state.count) * g, updates)\\n return updates, ScaleByScheduleState(count=state.count + 1)\\n\\n return InitUpdate(init_fn, update_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_schedule.init_fn\",\n \"text\": \"jax/experimental/optix.py::scale_by_schedule.init_fn\\n def init_fn(_):\\n return ScaleByScheduleState(count=jnp.zeros([], jnp.int32))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::scale_by_schedule.update_fn\",\n \"text\": \"jax/experimental/optix.py::scale_by_schedule.update_fn\\n def update_fn(updates, state):\\n updates = tree_multimap(lambda g: step_size_fn(state.count) * g, updates)\\n return updates, ScaleByScheduleState(count=state.count + 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::add_noise\",\n \"text\": \"jax/experimental/optix.py::add_noise\\ndef add_noise(eta, gamma, seed):\\n \\\"\\\"\\\"Add gradient noise.\\n\\n References:\\n [Neelakantan et al, 2014](https://arxiv.org/abs/1511.06807)\\n\\n Args:\\n eta: base variance of the gaussian noise added to the gradient.\\n gamma: decay exponent for annealing of the variance.\\n seed: seed for random number generation.\\n\\n Returns:\\n An (init_fn, update_fn) tuple.\\n \\\"\\\"\\\"\\n\\n def init_fn(_):\\n return AddNoiseState(count=jnp.zeros([], jnp.int32),\\n rng_key=jrandom.PRNGKey(seed))\\n\\n def update_fn(updates, state): # pylint: disable=missing-docstring\\n num_vars = len(tree_leaves(updates))\\n treedef = tree_structure(updates)\\n variance = eta / (1 + state.count) ** gamma\\n all_keys = jrandom.split(state.rng_key, num=num_vars + 1)\\n noise = tree_multimap(\\n lambda g, k: jrandom.normal(k, shape=g.shape),\\n updates, tree_unflatten(treedef, all_keys[1:]))\\n updates = tree_multimap(\\n lambda g, n: g + variance * n, updates, noise)\\n return updates, AddNoiseState(count=state.count + 1, rng_key=all_keys[0])\\n\\n return InitUpdate(init_fn, update_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::add_noise.init_fn\",\n \"text\": \"jax/experimental/optix.py::add_noise.init_fn\\n def init_fn(_):\\n return AddNoiseState(count=jnp.zeros([], jnp.int32),\\n rng_key=jrandom.PRNGKey(seed))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::add_noise.update_fn\",\n \"text\": \"jax/experimental/optix.py::add_noise.update_fn\\n def update_fn(updates, state): # pylint: disable=missing-docstring\\n num_vars = len(tree_leaves(updates))\\n treedef = tree_structure(updates)\\n variance = eta / (1 + state.count) ** gamma\\n all_keys = jrandom.split(state.rng_key, num=num_vars + 1)\\n noise = tree_multimap(\\n lambda g, k: jrandom.normal(k, shape=g.shape),\\n updates, tree_unflatten(treedef, all_keys[1:]))\\n updates = tree_multimap(\\n lambda g, n: g + variance * n, updates, noise)\\n return updates, AddNoiseState(count=state.count + 1, rng_key=all_keys[0])\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::apply_every\",\n \"text\": \"jax/experimental/optix.py::apply_every\\ndef apply_every(k=1):\\n \\\"\\\"\\\"accumulate gradients and apply them every k steps.\\n\\n Args:\\n k: apply the update every k steps otherwise accumulate the gradients.\\n\\n Returns:\\n An (init_fn, update_fn) tuple.\\n \\\"\\\"\\\"\\n\\n def init_fn(params):\\n grad_acc = tree_multimap(jnp.zeros_like, params)\\n return ApplyEvery(count=jnp.zeros([], jnp.int32), grad_acc=grad_acc)\\n\\n def update_fn(updates, state):\\n\\n c = state.count % k\\n acc = c != 0\\n grad_acc = tree_multimap(\\n lambda g, ga: acc * ga + g, updates, state.grad_acc)\\n emit = c == (k - 1)\\n updates = tree_multimap(lambda ga: emit * ga, grad_acc)\\n return updates, ApplyEvery(count=state.count + 1, grad_acc=grad_acc)\\n\\n return InitUpdate(init_fn, update_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::apply_every.init_fn\",\n \"text\": \"jax/experimental/optix.py::apply_every.init_fn\\n def init_fn(params):\\n grad_acc = tree_multimap(jnp.zeros_like, params)\\n return ApplyEvery(count=jnp.zeros([], jnp.int32), grad_acc=grad_acc)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::apply_every.update_fn\",\n \"text\": \"jax/experimental/optix.py::apply_every.update_fn\\n def update_fn(updates, state):\\n\\n c = state.count % k\\n acc = c != 0\\n grad_acc = tree_multimap(\\n lambda g, ga: acc * ga + g, updates, state.grad_acc)\\n emit = c == (k - 1)\\n updates = tree_multimap(lambda ga: emit * ga, grad_acc)\\n return updates, ApplyEvery(count=state.count + 1, grad_acc=grad_acc)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::chain\",\n \"text\": \"jax/experimental/optix.py::chain\\ndef chain(*args):\\n \\\"\\\"\\\"Applies a list of chainable update transformations.\\n\\n Given a sequence of chainable transforms, `chain` returns an `init_fn`\\n that constructs a `state` by concatenating the states of the individual\\n transforms, and returns an `update_fn` which chains the update transformations\\n feeding the appropriate state to each.\\n\\n Args:\\n *args: a sequence of chainable (init_fn, update_fn) tuples.\\n\\n Returns:\\n A single (init_fn, update_fn) tuple.\\n \\\"\\\"\\\"\\n\\n init_fns, update_fns = zip(*args)\\n\\n def init_fn(params):\\n return [fn(params) for fn in init_fns]\\n\\n def update_fn(updates, state):\\n new_state = []\\n for s, fn in zip(state, update_fns):\\n updates, new_s = fn(updates, s)\\n new_state.append(new_s)\\n return updates, new_state\\n\\n return InitUpdate(init_fn, update_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::chain.init_fn\",\n \"text\": \"jax/experimental/optix.py::chain.init_fn\\n def init_fn(params):\\n return [fn(params) for fn in init_fns]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::chain.update_fn\",\n \"text\": \"jax/experimental/optix.py::chain.update_fn\\n def update_fn(updates, state):\\n new_state = []\\n for s, fn in zip(state, update_fns):\\n updates, new_s = fn(updates, s)\\n new_state.append(new_s)\\n return updates, new_state\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::apply_updates\",\n \"text\": \"jax/experimental/optix.py::apply_updates\\ndef apply_updates(params, updates):\\n \\\"\\\"\\\"Applies an update to the corresponding parameters.\\n\\n This is an (optional) utility functions that applies an update, and returns\\n the updated parameters to the caller. The update itself is typically the\\n result of applying any number of `chainable` transformations.\\n\\n Args:\\n params: a tree of parameters.\\n updates: a tree of updates, the tree structure and the shape of the leaf\\n nodes must match that of `params`.\\n\\n Returns:\\n Updated parameters, with same structure and shape as `params`.\\n \\\"\\\"\\\"\\n return tree_multimap(lambda p, u: p + u, params, updates)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::sgd\",\n \"text\": \"jax/experimental/optix.py::sgd\\ndef sgd(learning_rate, momentum=0., nesterov=False):\\n return chain(\\n trace(decay=momentum, nesterov=nesterov),\\n scale(-learning_rate))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::noisy_sgd\",\n \"text\": \"jax/experimental/optix.py::noisy_sgd\\ndef noisy_sgd(learning_rate, eta=0.01, gamma=0.55, seed=42):\\n return chain(\\n trace(decay=0., nesterov=False),\\n scale(-learning_rate),\\n add_noise(eta, gamma, seed))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::adam\",\n \"text\": \"jax/experimental/optix.py::adam\\ndef adam(learning_rate, b1=0.9, b2=0.999, eps=1e-8):\\n return chain(\\n scale_by_adam(b1=b1, b2=b2, eps=eps),\\n scale(-learning_rate))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/optix.py::rmsprop\",\n \"text\": \"jax/experimental/optix.py::rmsprop\\ndef rmsprop(learning_rate, decay=0.9, eps=1e-8, centered=False):\\n if not centered:\\n return chain(\\n scale_by_rms(decay=decay, eps=eps),\\n scale(-learning_rate))\\n else:\\n return chain(\\n scale_by_stddev(decay=decay, eps=eps),\\n scale(-learning_rate))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense\",\n \"text\": \"jax/experimental/stax.py::Dense\\ndef Dense(out_dim, W_init=glorot_normal(), b_init=normal()):\\n \\\"\\\"\\\"Layer constructor function for a dense (fully-connected) layer.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[:-1] + (out_dim,)\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return np.dot(inputs, W) + b\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense.init_fun\",\n \"text\": \"jax/experimental/stax.py::Dense.init_fun\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[:-1] + (out_dim,)\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Dense.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return np.dot(inputs, W) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv\",\n \"text\": \"jax/experimental/stax.py::GeneralConv\\ndef GeneralConv(dimension_numbers, out_chan, filter_shape,\\n strides=None, padding='VALID', W_init=None,\\n b_init=normal(1e-6)):\\n \\\"\\\"\\\"Layer construction function for a general convolution layer.\\\"\\\"\\\"\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n one = (1,) * len(filter_shape)\\n strides = strides or one\\n W_init = W_init or glorot_normal(rhs_spec.index('I'), rhs_spec.index('O'))\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_general_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\\n dimension_numbers=dimension_numbers) + b\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv.init_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConv.init_fun\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_general_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv.apply_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConv.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\\n dimension_numbers=dimension_numbers) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConvTranspose\",\n \"text\": \"jax/experimental/stax.py::GeneralConvTranspose\\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape,\\n strides=None, padding='VALID', W_init=None,\\n b_init=normal(1e-6)):\\n \\\"\\\"\\\"Layer construction function for a general transposed-convolution layer.\\\"\\\"\\\"\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n one = (1,) * len(filter_shape)\\n strides = strides or one\\n W_init = W_init or glorot_normal(rhs_spec.index('I'), rhs_spec.index('O'))\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_transpose_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_transpose(inputs, W, strides, padding,\\n dimension_numbers=dimension_numbers) + b\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConvTranspose.init_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConvTranspose.init_fun\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_transpose_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConvTranspose.apply_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConvTranspose.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_transpose(inputs, W, strides, padding,\\n dimension_numbers=dimension_numbers) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm\",\n \"text\": \"jax/experimental/stax.py::BatchNorm\\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-5, center=True, scale=True,\\n beta_init=zeros, gamma_init=ones):\\n \\\"\\\"\\\"Layer construction function for a batch normalization layer.\\\"\\\"\\\"\\n _beta_init = lambda rng, shape: beta_init(rng, shape) if center else ()\\n _gamma_init = lambda rng, shape: gamma_init(rng, shape) if scale else ()\\n axis = (axis,) if np.isscalar(axis) else axis\\n def init_fun(rng, input_shape):\\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\\n k1, k2 = random.split(rng)\\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\\n return input_shape, (beta, gamma)\\n def apply_fun(params, x, **kwargs):\\n beta, gamma = params\\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\\n # (https://github.com/numpy/numpy/issues/12290)\\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\\n beta = beta[ed]\\n gamma = gamma[ed]\\n z = normalize(x, axis, epsilon=epsilon)\\n if center and scale: return gamma * z + beta\\n if center: return z + beta\\n if scale: return gamma * z\\n return z\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm.init_fun\",\n \"text\": \"jax/experimental/stax.py::BatchNorm.init_fun\\n def init_fun(rng, input_shape):\\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\\n k1, k2 = random.split(rng)\\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\\n return input_shape, (beta, gamma)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm.apply_fun\",\n \"text\": \"jax/experimental/stax.py::BatchNorm.apply_fun\\n def apply_fun(params, x, **kwargs):\\n beta, gamma = params\\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\\n # (https://github.com/numpy/numpy/issues/12290)\\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\\n beta = beta[ed]\\n gamma = gamma[ed]\\n z = normalize(x, axis, epsilon=epsilon)\\n if center and scale: return gamma * z + beta\\n if center: return z + beta\\n if scale: return gamma * z\\n return z\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::elementwise\",\n \"text\": \"jax/experimental/stax.py::elementwise\\ndef elementwise(fun, **fun_kwargs):\\n \\\"\\\"\\\"Layer that applies a scalar function elementwise on its inputs.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: (input_shape, ())\\n apply_fun = lambda params, inputs, **kwargs: fun(inputs, **fun_kwargs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer\\ndef _pooling_layer(reducer, init_val, rescaler=None):\\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec=None):\\n \\\"\\\"\\\"Layer construction function for a pooling layer.\\\"\\\"\\\"\\n strides = strides or (1,) * len(window_shape)\\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\\n\\n if spec is None:\\n non_spatial_axes = 0, len(window_shape) + 1\\n else:\\n non_spatial_axes = spec.index('N'), spec.index('C')\\n\\n for i in sorted(non_spatial_axes):\\n window_shape = window_shape[:i] + (1,) + window_shape[i:]\\n strides = strides[:i] + (1,) + strides[i:]\\n\\n def init_fun(rng, input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\\n strides, padding)\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\\n strides, padding)\\n return rescale(out, inputs, spec) if rescale else out\\n return init_fun, apply_fun\\n return PoolingLayer\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer\\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec=None):\\n \\\"\\\"\\\"Layer construction function for a pooling layer.\\\"\\\"\\\"\\n strides = strides or (1,) * len(window_shape)\\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\\n\\n if spec is None:\\n non_spatial_axes = 0, len(window_shape) + 1\\n else:\\n non_spatial_axes = spec.index('N'), spec.index('C')\\n\\n for i in sorted(non_spatial_axes):\\n window_shape = window_shape[:i] + (1,) + window_shape[i:]\\n strides = strides[:i] + (1,) + strides[i:]\\n\\n def init_fun(rng, input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\\n strides, padding)\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\\n strides, padding)\\n return rescale(out, inputs, spec) if rescale else out\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\\n def init_fun(rng, input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\\n strides, padding)\\n return out_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\\n strides, padding)\\n return rescale(out, inputs, spec) if rescale else out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_normalize_by_window_size\",\n \"text\": \"jax/experimental/stax.py::_normalize_by_window_size\\ndef _normalize_by_window_size(dims, strides, padding):\\n def rescale(outputs, inputs, spec):\\n if spec is None:\\n non_spatial_axes = 0, inputs.ndim - 1\\n else:\\n non_spatial_axes = spec.index('N'), spec.index('C')\\n\\n spatial_shape = tuple(inputs.shape[i]\\n for i in range(inputs.ndim)\\n if i not in non_spatial_axes)\\n one = np.ones(spatial_shape, dtype=inputs.dtype)\\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\\n for i in sorted(non_spatial_axes):\\n window_sizes = np.expand_dims(window_sizes, i)\\n\\n return outputs / window_sizes\\n return rescale\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_normalize_by_window_size.rescale\",\n \"text\": \"jax/experimental/stax.py::_normalize_by_window_size.rescale\\n def rescale(outputs, inputs, spec):\\n if spec is None:\\n non_spatial_axes = 0, inputs.ndim - 1\\n else:\\n non_spatial_axes = spec.index('N'), spec.index('C')\\n\\n spatial_shape = tuple(inputs.shape[i]\\n for i in range(inputs.ndim)\\n if i not in non_spatial_axes)\\n one = np.ones(spatial_shape, dtype=inputs.dtype)\\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\\n for i in sorted(non_spatial_axes):\\n window_sizes = np.expand_dims(window_sizes, i)\\n\\n return outputs / window_sizes\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten\",\n \"text\": \"jax/experimental/stax.py::Flatten\\ndef Flatten():\\n \\\"\\\"\\\"Layer construction function for flattening all but the leading dim.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[0], functools.reduce(op.mul, input_shape[1:], 1)\\n return output_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n return np.reshape(inputs, (inputs.shape[0], -1))\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten.init_fun\",\n \"text\": \"jax/experimental/stax.py::Flatten.init_fun\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[0], functools.reduce(op.mul, input_shape[1:], 1)\\n return output_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Flatten.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return np.reshape(inputs, (inputs.shape[0], -1))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Identity\",\n \"text\": \"jax/experimental/stax.py::Identity\\ndef Identity():\\n \\\"\\\"\\\"Layer construction function for an identity layer.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: (input_shape, ())\\n apply_fun = lambda params, inputs, **kwargs: inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanOut\",\n \"text\": \"jax/experimental/stax.py::FanOut\\ndef FanOut(num):\\n \\\"\\\"\\\"Layer construction function for a fan-out layer.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: ([input_shape] * num, ())\\n apply_fun = lambda params, inputs, **kwargs: [inputs] * num\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInSum\",\n \"text\": \"jax/experimental/stax.py::FanInSum\\ndef FanInSum():\\n \\\"\\\"\\\"Layer construction function for a fan-in sum layer.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: (input_shape[0], ())\\n apply_fun = lambda params, inputs, **kwargs: sum(inputs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat\",\n \"text\": \"jax/experimental/stax.py::FanInConcat\\ndef FanInConcat(axis=-1):\\n \\\"\\\"\\\"Layer construction function for a fan-in concatenation layer.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n ax = axis % len(input_shape[0])\\n concat_size = sum(shape[ax] for shape in input_shape)\\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n return np.concatenate(inputs, axis)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat.init_fun\",\n \"text\": \"jax/experimental/stax.py::FanInConcat.init_fun\\n def init_fun(rng, input_shape):\\n ax = axis % len(input_shape[0])\\n concat_size = sum(shape[ax] for shape in input_shape)\\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\\n return out_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat.apply_fun\",\n \"text\": \"jax/experimental/stax.py::FanInConcat.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return np.concatenate(inputs, axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout\",\n \"text\": \"jax/experimental/stax.py::Dropout\\ndef Dropout(rate, mode='train'):\\n \\\"\\\"\\\"Layer construction function for a dropout layer with given rate.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n return input_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.get('rng', None)\\n if rng is None:\\n msg = (\\\"Dropout layer requires apply_fun to be called with a PRNG key \\\"\\n \\\"argument. That is, instead of `apply_fun(params, inputs)`, call \\\"\\n \\\"it like `apply_fun(params, inputs, key)` where `key` is a \\\"\\n \\\"jax.random.PRNGKey value.\\\")\\n raise ValueError(msg)\\n if mode == 'train':\\n keep = random.bernoulli(rng, rate, inputs.shape)\\n return np.where(keep, inputs / rate, 0)\\n else:\\n return inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout.init_fun\",\n \"text\": \"jax/experimental/stax.py::Dropout.init_fun\\n def init_fun(rng, input_shape):\\n return input_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Dropout.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.get('rng', None)\\n if rng is None:\\n msg = (\\\"Dropout layer requires apply_fun to be called with a PRNG key \\\"\\n \\\"argument. That is, instead of `apply_fun(params, inputs)`, call \\\"\\n \\\"it like `apply_fun(params, inputs, key)` where `key` is a \\\"\\n \\\"jax.random.PRNGKey value.\\\")\\n raise ValueError(msg)\\n if mode == 'train':\\n keep = random.bernoulli(rng, rate, inputs.shape)\\n return np.where(keep, inputs / rate, 0)\\n else:\\n return inputs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial\",\n \"text\": \"jax/experimental/stax.py::serial\\ndef serial(*layers):\\n \\\"\\\"\\\"Combinator for composing layers in serial.\\n\\n Args:\\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the serial\\n composition of the given sequence of layers.\\n \\\"\\\"\\\"\\n nlayers = len(layers)\\n init_funs, apply_funs = zip(*layers)\\n def init_fun(rng, input_shape):\\n params = []\\n for init_fun in init_funs:\\n rng, layer_rng = random.split(rng)\\n input_shape, param = init_fun(layer_rng, input_shape)\\n params.append(param)\\n return input_shape, params\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n for fun, param, rng in zip(apply_funs, params, rngs):\\n inputs = fun(param, inputs, rng=rng, **kwargs)\\n return inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial.init_fun\",\n \"text\": \"jax/experimental/stax.py::serial.init_fun\\n def init_fun(rng, input_shape):\\n params = []\\n for init_fun in init_funs:\\n rng, layer_rng = random.split(rng)\\n input_shape, param = init_fun(layer_rng, input_shape)\\n params.append(param)\\n return input_shape, params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial.apply_fun\",\n \"text\": \"jax/experimental/stax.py::serial.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n for fun, param, rng in zip(apply_funs, params, rngs):\\n inputs = fun(param, inputs, rng=rng, **kwargs)\\n return inputs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel\",\n \"text\": \"jax/experimental/stax.py::parallel\\ndef parallel(*layers):\\n \\\"\\\"\\\"Combinator for composing layers in parallel.\\n\\n The layer resulting from this combinator is often used with the FanOut and\\n FanInSum layers.\\n\\n Args:\\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the\\n parallel composition of the given sequence of layers. In particular, the\\n returned layer takes a sequence of inputs and returns a sequence of outputs\\n with the same length as the argument `layers`.\\n \\\"\\\"\\\"\\n nlayers = len(layers)\\n init_funs, apply_funs = zip(*layers)\\n def init_fun(rng, input_shape):\\n rngs = random.split(rng, nlayers)\\n return zip(*[init(rng, shape) for init, rng, shape\\n in zip(init_funs, rngs, input_shape)])\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel.init_fun\",\n \"text\": \"jax/experimental/stax.py::parallel.init_fun\\n def init_fun(rng, input_shape):\\n rngs = random.split(rng, nlayers)\\n return zip(*[init(rng, shape) for init, rng, shape\\n in zip(init_funs, rngs, input_shape)])\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel.apply_fun\",\n \"text\": \"jax/experimental/stax.py::parallel.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent\",\n \"text\": \"jax/experimental/stax.py::shape_dependent\\ndef shape_dependent(make_layer):\\n \\\"\\\"\\\"Combinator to delay layer constructor pair until input shapes are known.\\n\\n Args:\\n make_layer: a one-argument function that takes an input shape as an argument\\n (a tuple of positive integers) and returns an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the same\\n layer as returned by `make_layer` but with its construction delayed until\\n input shapes are known.\\n \\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n return make_layer(input_shape)[0](rng, input_shape)\\n def apply_fun(params, inputs, **kwargs):\\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent.init_fun\",\n \"text\": \"jax/experimental/stax.py::shape_dependent.init_fun\\n def init_fun(rng, input_shape):\\n return make_layer(input_shape)[0](rng, input_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent.apply_fun\",\n \"text\": \"jax/experimental/stax.py::shape_dependent.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/vectorize.py::_parse_gufunc_signature\",\n \"text\": \"jax/experimental/vectorize.py::_parse_gufunc_signature\\ndef _parse_gufunc_signature(signature):\\n \\\"\\\"\\\"Parse string signatures for a generalized universal function.\\n\\n Args:\\n signature : string\\n\\t Generalized universal function signature, e.g., ``(m,n),(n,p)->(m,p)``\\n\\t for ``np.matmul``.\\n\\n Returns:\\n Tuple of input and output core dimensions parsed from the signature, each\\n of the form List[Tuple[str, ...]].\\n \\\"\\\"\\\"\\n if not re.match(_SIGNATURE, signature):\\n raise ValueError(\\n 'not a valid gufunc signature: {}'.format(signature))\\n return tuple([tuple(re.findall(_DIMENSION_NAME, arg))\\n for arg in re.findall(_ARGUMENT, arg_list)]\\n for arg_list in signature.split('->'))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/vectorize.py::_update_dim_sizes\",\n \"text\": \"jax/experimental/vectorize.py::_update_dim_sizes\\ndef _update_dim_sizes(dim_sizes, arg, core_dims):\\n \\\"\\\"\\\"Incrementally check and update core dimension sizes for a single argument.\\n\\n Args:\\n dim_sizes : Dict[str, int]\\n\\t Sizes of existing core dimensions. Will be updated in-place.\\n arg : ndarray\\n\\t Argument to examine.\\n core_dims : Tuple[str, ...]\\n\\t Core dimensions for this argument.\\n \\\"\\\"\\\"\\n if not core_dims:\\n return\\n\\n num_core_dims = len(core_dims)\\n if arg.ndim < num_core_dims:\\n raise ValueError(\\n '%d-dimensional argument does not have enough '\\n 'dimensions for all core dimensions %r'\\n % (arg.ndim, core_dims))\\n\\n core_shape = arg.shape[-num_core_dims:]\\n for dim, size in zip(core_dims, core_shape):\\n if dim in dim_sizes:\\n if size != dim_sizes[dim]:\\n raise ValueError(\\n 'inconsistent size for core dimension %r: %r vs %r'\\n % (dim, size, dim_sizes[dim]))\\n else:\\n dim_sizes[dim] = size\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/vectorize.py::_parse_input_dimensions\",\n \"text\": \"jax/experimental/vectorize.py::_parse_input_dimensions\\ndef _parse_input_dimensions(args, input_core_dims):\\n \\\"\\\"\\\"Parse broadcast and core dimensions for vectorize with a signature.\\n\\n Args:\\n args : Tuple[ndarray, ...]\\n\\t Tuple of input arguments to examine.\\n input_core_dims : List[Tuple[str, ...]]\\n\\t List of core dimensions corresponding to each input.\\n\\n Returns:\\n broadcast_shape : Tuple[int, ...]\\n\\t Common shape to broadcast all non-core dimensions to.\\n dim_sizes : Dict[str, int]\\n\\t Common sizes for named core dimensions.\\n \\\"\\\"\\\"\\n broadcast_args = []\\n dim_sizes = {}\\n for arg, core_dims in zip(args, input_core_dims):\\n _update_dim_sizes(dim_sizes, arg, core_dims)\\n ndim = arg.ndim - len(core_dims)\\n dummy_array = np.lib.stride_tricks.as_strided(0, arg.shape[:ndim])\\n broadcast_args.append(dummy_array)\\n broadcast_shape = np.lib.stride_tricks._broadcast_shape(*broadcast_args)\\n return broadcast_shape, dim_sizes\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/vectorize.py::_calculate_shapes\",\n \"text\": \"jax/experimental/vectorize.py::_calculate_shapes\\ndef _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims):\\n \\\"\\\"\\\"Helper for calculating broadcast shapes with core dimensions.\\\"\\\"\\\"\\n return [broadcast_shape + tuple(dim_sizes[dim] for dim in core_dims)\\n for core_dims in list_of_core_dims]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/vectorize.py::broadcast_with_core_dims\",\n \"text\": \"jax/experimental/vectorize.py::broadcast_with_core_dims\\ndef broadcast_with_core_dims(args, input_core_dims, output_core_dims):\\n if len(args) != len(input_core_dims):\\n raise TypeError('wrong number of positional arguments: '\\n 'expected %r, got %r'\\n % (len(input_core_dims), len(args)))\\n\\n broadcast_shape, dim_sizes = _parse_input_dimensions(\\n args, input_core_dims)\\n input_shapes = _calculate_shapes(broadcast_shape, dim_sizes,\\n input_core_dims)\\n args = [jnp.broadcast_to(arg, shape)\\n for arg, shape in zip(args, input_shapes)]\\n return args\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/vectorize.py::verify_axis_is_supported\",\n \"text\": \"jax/experimental/vectorize.py::verify_axis_is_supported\\ndef verify_axis_is_supported(input_core_dims, output_core_dims):\\n all_core_dims = set()\\n for input_or_output_core_dims in [input_core_dims, output_core_dims]:\\n for core_dims in input_or_output_core_dims:\\n all_core_dims.update(core_dims)\\n if len(core_dims) > 1:\\n raise ValueError('only one gufuncs with one core dim support axis')\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/vectorize.py::reorder_inputs\",\n \"text\": \"jax/experimental/vectorize.py::reorder_inputs\\ndef reorder_inputs(args, axis, input_core_dims):\\n return tuple(jnp.moveaxis(arg, axis, -1) if core_dims else arg\\n for arg, core_dims in zip(args, input_core_dims))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/vectorize.py::reorder_outputs\",\n \"text\": \"jax/experimental/vectorize.py::reorder_outputs\\ndef reorder_outputs(result, axis, output_core_dims):\\n if not isinstance(result, tuple):\\n result = (result,)\\n result = tuple(jnp.moveaxis(res, -1, axis) if core_dims else res\\n for res, core_dims in zip(result, output_core_dims))\\n if len(result) == 1:\\n (result,) = result\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/vectorize.py::vectorize\",\n \"text\": \"jax/experimental/vectorize.py::vectorize\\ndef vectorize(signature):\\n \\\"\\\"\\\"Vectorize a function using JAX.\\n\\n Turns an arbitrary function into a numpy style \\\"gufunc\\\". Once\\n you specify the behavior of the core axis, the rest will be \\n broadcast naturally.\\n\\n Args:\\n signature: an einsum style signature that defines how the core dimensions are mapped between inputs and outputs.\\n\\n Returns:\\n\\tThe vectorized 'gufunc' that will automatically broadcast\\n\\twhile maintaining the specified core logic, the returned\\n\\tfunction also has a new ``axis`` parameter for specifying\\n\\twhich axis should be treated as the core one.\\n \\\"\\\"\\\"\\n input_core_dims, output_core_dims = _parse_gufunc_signature(signature)\\n \\n def decorator(func):\\n @functools.wraps(func)\\n def wrapper(*args, **kwargs):\\n axis = kwargs.get('axis') # for python2 compat.\\n\\n if axis is not None:\\n verify_axis_is_supported(input_core_dims, output_core_dims)\\n args = reorder_inputs(args, axis, input_core_dims)\\n\\n broadcast_args = broadcast_with_core_dims(\\n args, input_core_dims, output_core_dims)\\n num_batch_dims = len(broadcast_args[0].shape) - len(input_core_dims[0])\\n\\n vectorized_func = func\\n for _ in range(num_batch_dims):\\n vectorized_func = vmap(vectorized_func)\\n result = vectorized_func(*broadcast_args)\\n\\n if axis is not None:\\n result = reorder_outputs(result, axis, output_core_dims)\\n\\n return result\\n return wrapper\\n return decorator\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/vectorize.py::vectorize.decorator\",\n \"text\": \"jax/experimental/vectorize.py::vectorize.decorator\\n def decorator(func):\\n @functools.wraps(func)\\n def wrapper(*args, **kwargs):\\n axis = kwargs.get('axis') # for python2 compat.\\n\\n if axis is not None:\\n verify_axis_is_supported(input_core_dims, output_core_dims)\\n args = reorder_inputs(args, axis, input_core_dims)\\n\\n broadcast_args = broadcast_with_core_dims(\\n args, input_core_dims, output_core_dims)\\n num_batch_dims = len(broadcast_args[0].shape) - len(input_core_dims[0])\\n\\n vectorized_func = func\\n for _ in range(num_batch_dims):\\n vectorized_func = vmap(vectorized_func)\\n result = vectorized_func(*broadcast_args)\\n\\n if axis is not None:\\n result = reorder_outputs(result, axis, output_core_dims)\\n\\n return result\\n return wrapper\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/vectorize.py::vectorize.decorator.wrapper\",\n \"text\": \"jax/experimental/vectorize.py::vectorize.decorator.wrapper\\n @functools.wraps(func)\\n def wrapper(*args, **kwargs):\\n axis = kwargs.get('axis') # for python2 compat.\\n\\n if axis is not None:\\n verify_axis_is_supported(input_core_dims, output_core_dims)\\n args = reorder_inputs(args, axis, input_core_dims)\\n\\n broadcast_args = broadcast_with_core_dims(\\n args, input_core_dims, output_core_dims)\\n num_batch_dims = len(broadcast_args[0].shape) - len(input_core_dims[0])\\n\\n vectorized_func = func\\n for _ in range(num_batch_dims):\\n vectorized_func = vmap(vectorized_func)\\n result = vectorized_func(*broadcast_args)\\n\\n if axis is not None:\\n result = reorder_outputs(result, axis, output_core_dims)\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::get_compile_options\",\n \"text\": \"jax/lib/xla_bridge.py::get_compile_options\\ndef get_compile_options(num_replicas, num_partitions, device_assignment=None):\\n \\\"\\\"\\\"Returns the compile options to use, as derived from flag values.\\n\\n Args:\\n num_replicas: int indicating the number of replicas for which to compile.\\n num_partitions: int indicating the number of partitions for which to compile.\\n device_assignment: Optional tuple of integers indicating the assignment of\\n logical replicas to physical devices (default inherited from\\n xla_client.CompileOptions). Must be consistent with `num_replicas` and\\n `num_partitions`.\\n \\\"\\\"\\\"\\n compile_options = xla_client.CompileOptions()\\n compile_options.num_replicas = num_replicas\\n compile_options.num_partitions = num_partitions\\n if device_assignment is not None:\\n logging.vlog(\\n 2,\\n 'get_compile_options: num_replicas=%s num_partitions=%s device_assignment=%s',\\n num_replicas, num_partitions, device_assignment)\\n device_assignment = onp.array(device_assignment)\\n\\n # Allow 1D device assignment if num_partitions is 1.\\n if (device_assignment.ndim == 1) and (num_partitions == 1):\\n device_assignment = device_assignment[:, None]\\n\\n if num_replicas != device_assignment.shape[0]:\\n msg = 'device_assignment does not match num_replicas: {} vs {}.'\\n raise ValueError(msg.format(device_assignment, num_replicas))\\n\\n if num_partitions != device_assignment.shape[1]:\\n msg = 'device_assignment does not match num_partitions: {} vs {}.'\\n raise ValueError(msg.format(device_assignment, num_partitions))\\n\\n device_assignment = xla_client.DeviceAssignment.create(device_assignment)\\n assert device_assignment.replica_count() == num_replicas\\n assert device_assignment.computation_count() == num_partitions\\n compile_options.device_assignment = device_assignment\\n return compile_options\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::register_backend\",\n \"text\": \"jax/lib/xla_bridge.py::register_backend\\ndef register_backend(name, factory):\\n _backends[name] = factory\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_get_local_backend\",\n \"text\": \"jax/lib/xla_bridge.py::_get_local_backend\\ndef _get_local_backend(platform=None):\\n if not platform:\\n platform = FLAGS.jax_platform_name\\n\\n # Canonicalize platform names.\\n cpu = 'cpu'\\n gpu = 'gpu'\\n if platform == 'Host':\\n platform = cpu\\n elif platform == 'CUDA':\\n platform = gpu\\n elif platform == '':\\n platform = None\\n\\n backend = xla_client.get_local_backend(platform)\\n if backend is None:\\n raise RuntimeError(\\\"No local XLA backends found.\\\")\\n\\n if backend.platform == cpu and platform != cpu:\\n warnings.warn('No GPU/TPU found, falling back to CPU.')\\n\\n return backend\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_get_tpu_driver_backend\",\n \"text\": \"jax/lib/xla_bridge.py::_get_tpu_driver_backend\\ndef _get_tpu_driver_backend(platform):\\n del platform\\n global _tpu_backend\\n if _tpu_backend is None:\\n backend_target = FLAGS.jax_backend_target\\n if backend_target is None:\\n raise ValueError('When using TPU Driver as the backend, you must specify '\\n '--jax_backend_target=<hostname>:8470.')\\n _tpu_backend = tpu_client.TpuBackend.create(worker=backend_target)\\n return _tpu_backend\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::get_backend\",\n \"text\": \"jax/lib/xla_bridge.py::get_backend\\n@util.memoize\\ndef get_backend(platform=None):\\n # TODO(mattjj,skyewm): remove this input polymorphism after we clean up how\\n # 'backend' values are handled\\n if isinstance(platform, xla_client.Backend):\\n return platform\\n\\n with _backend_lock:\\n backend = _backends.get(FLAGS.jax_xla_backend)\\n if backend is None:\\n msg = 'Unknown jax_xla_backend value \\\"{}\\\".'\\n raise ValueError(msg.format(FLAGS.jax_xla_backend))\\n return backend(platform)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::get_device_backend\",\n \"text\": \"jax/lib/xla_bridge.py::get_device_backend\\ndef get_device_backend(device=None):\\n \\\"\\\"\\\"Returns the Backend associated with `device`, or the default Backend.\\\"\\\"\\\"\\n platform = device.platform if device else None\\n return get_backend(platform)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::device_count\",\n \"text\": \"jax/lib/xla_bridge.py::device_count\\ndef device_count(backend=None):\\n \\\"\\\"\\\"Returns the total number of devices.\\n\\n On most platforms, this is the same as ``local_device_count()``. However, on\\n multi-host platforms, this will return the total number of devices across all\\n hosts.\\n\\n Args:\\n backend: This is an experimental feature and the API is likely to change.\\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\\n\\n Returns:\\n Number of devices.\\n \\\"\\\"\\\"\\n return int(get_backend(backend).device_count())\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::local_device_count\",\n \"text\": \"jax/lib/xla_bridge.py::local_device_count\\ndef local_device_count(backend=None):\\n \\\"\\\"\\\"Returns the number of devices on this host.\\\"\\\"\\\"\\n return int(get_backend(backend).local_device_count())\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::devices\",\n \"text\": \"jax/lib/xla_bridge.py::devices\\ndef devices(backend=None):\\n \\\"\\\"\\\"Returns a list of all devices.\\n\\n Each device is represented by a subclass of Device (e.g. CpuDevice,\\n GpuDevice). The length of the returned list is equal to\\n ``device_count()``. Local devices can be identified by comparing\\n ``Device.host_id`` to ``host_id()``.\\n\\n Args:\\n backend: This is an experimental feature and the API is likely to change.\\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\\n\\n Returns:\\n List of Device subclasses.\\n \\\"\\\"\\\"\\n return get_backend(backend).devices()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::local_devices\",\n \"text\": \"jax/lib/xla_bridge.py::local_devices\\ndef local_devices(host_id=None, backend=None):\\n \\\"\\\"\\\"Returns a list of devices local to a given host (this host by default).\\\"\\\"\\\"\\n if host_id is None:\\n host_id = get_backend(backend).host_id()\\n return [d for d in devices(backend) if d.host_id == host_id]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::host_id\",\n \"text\": \"jax/lib/xla_bridge.py::host_id\\ndef host_id(backend=None):\\n \\\"\\\"\\\"Returns the integer host ID of this host.\\n\\n On most platforms, this will always be 0. This will vary on multi-host\\n platforms though.\\n\\n Args:\\n backend: This is an experimental feature and the API is likely to change.\\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\\n\\n Returns:\\n Integer host ID.\\n \\\"\\\"\\\"\\n return get_backend(backend).host_id()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::host_ids\",\n \"text\": \"jax/lib/xla_bridge.py::host_ids\\ndef host_ids(backend=None):\\n \\\"\\\"\\\"Returns a sorted list of all host IDs.\\\"\\\"\\\"\\n return sorted(list(set(d.host_id for d in devices(backend))))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::host_count\",\n \"text\": \"jax/lib/xla_bridge.py::host_count\\ndef host_count(backend=None):\\n \\\"\\\"\\\"Returns the number of hosts.\\\"\\\"\\\"\\n return len(host_ids(backend))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::dtype_to_etype\",\n \"text\": \"jax/lib/xla_bridge.py::dtype_to_etype\\n@util.memoize\\ndef dtype_to_etype(dtype):\\n \\\"\\\"\\\"Convert from dtype to canonical etype (reading FLAGS.jax_enable_x64).\\\"\\\"\\\"\\n return xla_client.dtype_to_etype(dtypes.canonicalize_dtype(dtype))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::supported_numpy_dtypes\",\n \"text\": \"jax/lib/xla_bridge.py::supported_numpy_dtypes\\n@util.memoize\\ndef supported_numpy_dtypes():\\n return {dtypes.canonicalize_dtype(dtype)\\n for dtype in xla_client.XLA_ELEMENT_TYPE_TO_DTYPE.values()}\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::normalize_to_xla_dtypes\",\n \"text\": \"jax/lib/xla_bridge.py::normalize_to_xla_dtypes\\ndef normalize_to_xla_dtypes(val):\\n \\\"\\\"\\\"Normalize dtypes in a value.\\\"\\\"\\\"\\n if hasattr(val, '__array__') or onp.isscalar(val):\\n return onp.asarray(val,\\n dtype=dtypes.canonicalize_dtype(dtypes.result_type(val)))\\n elif isinstance(val, (tuple, list)):\\n return tuple(normalize_to_xla_dtypes(x) for x in val)\\n raise TypeError('Can\\\\'t convert to XLA: {}'.format(val))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.Build\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.Build\\n def Build(self, *args, **kwargs):\\n return super(_JaxComputationBuilder, self).Build(\\n *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.NumpyArrayConstant\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.NumpyArrayConstant\\n def NumpyArrayConstant(self, value, canonicalize_types=True):\\n if canonicalize_types:\\n value = normalize_to_xla_dtypes(value)\\n return super(_JaxComputationBuilder, self).Constant(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.ConstantLike\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.ConstantLike\\n def ConstantLike(self, example_value, value, canonicalize_types=True):\\n example_value = onp.asarray(example_value)\\n return self.Constant(onp.array(value, dtype=example_value.dtype))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.Constant\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.Constant\\n def Constant(self, py_val, canonicalize_types=True):\\n \\\"\\\"\\\"Translate constant `py_val` to a constant for this ComputationBuilder.\\n\\n Args:\\n py_val: a Python value to be translated to a constant.\\n\\n Returns:\\n A representation of the constant, either a ComputationDataHandle or None\\n \\\"\\\"\\\"\\n py_type = type(py_val)\\n if py_type in _constant_handlers:\\n return _constant_handlers[py_type](self, py_val, canonicalize_types)\\n else:\\n raise TypeError(\\\"No constant handler for type: {}\\\".format(py_type))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.CrossReplicaSum\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.CrossReplicaSum\\n def CrossReplicaSum(self, operand, replica_groups):\\n \\\"\\\"\\\"Workaround for CrossReplicaSum not being implemented on some backends.\\\"\\\"\\\"\\n if len(replica_groups[0]) == 1:\\n return operand\\n else:\\n return super(_JaxComputationBuilder, self).CrossReplicaSum(\\n operand, replica_groups)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.AllToAll\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.AllToAll\\n def AllToAll(self, operand, split_axis, concat_axis, replica_groups):\\n \\\"\\\"\\\"Workaround for AllToAll not being implemented on some backends.\\\"\\\"\\\"\\n if len(replica_groups[0]) == 1:\\n return operand\\n else:\\n return super(_JaxComputationBuilder, self).AllToAll(\\n operand, split_axis, concat_axis, replica_groups)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::make_computation_builder\",\n \"text\": \"jax/lib/xla_bridge.py::make_computation_builder\\ndef make_computation_builder(name):\\n return _JaxComputationBuilder(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::register_constant_handler\",\n \"text\": \"jax/lib/xla_bridge.py::register_constant_handler\\ndef register_constant_handler(type_, handler_fun):\\n _constant_handlers[type_] = handler_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_ndarray_constant_handler\",\n \"text\": \"jax/lib/xla_bridge.py::_ndarray_constant_handler\\ndef _ndarray_constant_handler(c, val, canonicalize_types=True):\\n \\\"\\\"\\\"Constant handler for ndarray literals, handling zero-size strides.\\n\\n This function essentially calls c.NumpyArrayConstant(val) except it has\\n special handling of arrays with any strides of size zero: for those, it\\n generates appropriate calls to NumpyArrayConstant, Broadcast, and Transpose\\n to avoid staging in large literals that might arise from np.zeros or np.ones\\n or the output of lax.broadcast (which uses onp.broadcast_to which in turn\\n uses size-zero strides).\\n\\n Args:\\n c: XLA client ComputationBuilder.\\n val: an ndarray.\\n\\n Returns:\\n An XLA ComputationDataHandle / XlaOp representing the constant ndarray\\n staged into the XLA Computation.\\n \\\"\\\"\\\"\\n # TODO(mattjj): revise this to use c.BroadcastInDim rather than Transpose\\n if onp.any(onp.equal(0, val.strides)) and val.size > 0:\\n zero_stride_axes, = onp.where(onp.equal(0, val.strides))\\n other_axes, = onp.where(onp.not_equal(0, val.strides))\\n collapsed_val = val[tuple(0 if ax in zero_stride_axes else slice(None)\\n for ax in range(val.ndim))]\\n xla_val = c.Broadcast(\\n c.NumpyArrayConstant(collapsed_val, canonicalize_types),\\n onp.take(val.shape, zero_stride_axes))\\n permutation = onp.argsort(tuple(zero_stride_axes) + tuple(other_axes))\\n return c.Transpose(xla_val, permutation)\\n else:\\n return c.NumpyArrayConstant(val, canonicalize_types)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_scalar_constant_handler\",\n \"text\": \"jax/lib/xla_bridge.py::_scalar_constant_handler\\ndef _scalar_constant_handler(c, val, canonicalize_types=True):\\n return c.NumpyArrayConstant(val, canonicalize_types)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_python_scalar_handler\",\n \"text\": \"jax/lib/xla_bridge.py::_python_scalar_handler\\ndef _python_scalar_handler(dtype, c, val, canonicalize_dtypes=True):\\n return c.NumpyArrayConstant(dtype.type(val))\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":64,"cells":{"instance_id":{"kind":"string","value":"PrefectHQ__prefect-47"},"repo":{"kind":"string","value":"PrefectHQ/prefect"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Bug: groups and tags don't set correctly after changing to _private context variables\nMissed these attributes when changing context to use a private attribute -- need to fix + add tests.\n"},"gold_files":{"kind":"list like","value":["prefect/core/task.py","prefect/utilities/tasks.py"],"string":"[\n \"prefect/core/task.py\",\n \"prefect/utilities/tasks.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["prefect/core/task.py::Task.__init__","prefect/utilities/tasks.py::<module>","prefect/utilities/tasks.py::group","prefect/utilities/tasks.py::tags"],"string":"[\n \"prefect/core/task.py::Task.__init__\",\n \"prefect/utilities/tasks.py::<module>\",\n \"prefect/utilities/tasks.py::group\",\n \"prefect/utilities/tasks.py::tags\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"prefect/__init__.py","text":"prefect/__init__.py","label":0},{"cid":"prefect/client.py","text":"prefect/client.py\nclass AuthorizationError\nclass Client # Client for the Prefect API.\n def __init__(self, api_server=None, graphql_server=None, token=None)\n def _get(self, path, *, _json=True, _server=None, **params) # Convenience function for calling the Prefect API with token auth.\n def _post(self, path, *, _json=True, _server=None, **params) # Convenience function for calling the Prefect API with token auth.\n def _delete(self, path, *, _server=None, _json=True) # Convenience function for calling the Prefect API with token auth.\n def graphql(self, query, **variables) # Convenience function for running queries against the Prefect GraphQL\n def _request(self, method, path, params, server=None)\n def request_fn()\n def login(self, email, password, account_slug=None, account_id=None)\n def refresh_token(self)\nclass ClientModule\n def __init__(self, client, name=None)\n def __repr__(self)\n def _get(self, path, **params)\n def _post(self, path, **data)\n def _delete(self, path, **params)\n def _graphql(self, query, **variables)\nclass Projects\n def create(self, name)\n def list(self, per_page=100, page=1) # Lists all available projects\n def flows(self, project_id) # Returns the Flows for the specified project\nclass Flows\n def load(self, flow_id, safe=True) # Retrieve information about a Flow.\n def set_state(self, flow_id, state) # Update a Flow's state\n def create(self, flow) # Submit a Flow to the server.\nclass FlowRuns\n def get_state(self, flow_run_id) # Retrieve a flow run's state\n def set_state(self, flow_run_id, state, result=None, expected_state=None) # Retrieve a flow run's state\n def create(self, flow_id, parameters=None, parent_taskrun_id=None)\n def run(self, flow_run_id, start_tasks=None, inputs=None) # Queue a flow run to be run\nclass TaskRuns\n def get_state(self, task_run_id) # Retrieve a flow run's state\n def set_state(self, task_run_id, state, result=None, expected_state=None) # Retrieve a task run's state","label":0},{"cid":"prefect/configuration.py","text":"prefect/configuration.py\nclass Config\n def __repr__(self)\n def __iter__(self)\n def get(self, key, default=None)\n def sections(self)\n def to_dict(self)\n def from_dict(cls, dct, recursive=True)\ndef expand(env_var) # Expands (potentially nested) env vars by repeatedly applying\ndef create_user_config(path, source=DEFAULT_CONFIG) # Copies the default configuration to a user-customizable file\n def quote(s)\ndef configure_logging(logger_name)\ndef validate_config(config)\ndef load_config_file(path, existing_config=None)\ndef load_configuration(default_config, user_config, env_var=None)","label":0},{"cid":"prefect/core/flow.py","text":"prefect/core/flow.py\ndef flow_cache_key(flow: 'Flow') # Returns a cache key that can be used to determine if the cache is stale.\nclass Flow\n def __init__(self, name: str=None, version: str=None, schedule: prefect.schedules.Schedule=None, description: str=None, environment: prefect.environments.Environment=None, tasks: Iterable[Task]=None, edges: Iterable[Edge]=None)\n def __eq__(self, other: Any)\n def __repr__(self)\n def __iter__(self)\n def copy(self)\n def __enter__(self)\n def __exit__(self, _type, _value, _tb)\n def root_tasks(self) # Returns the root tasks of the Flow -- tasks that have no upstream\n def terminal_tasks(self) # Returns the terminal tasks of the Flow -- tasks that have no downstream\n def parameters(self, only_required=False) # Returns details about any Parameters of this flow\n def restore_graph_on_error(self, validate: bool=True) # A context manager that saves the Flow's graph (tasks & edges) and\n def add_task(self, task: Task)\n def add_edge(self, upstream_task: Task, downstream_task: Task, key: str=None, validate: bool=True)\n def update(self, flow: 'Flow', validate: bool=True)\n def all_upstream_edges(self)\n def all_downstream_edges(self)\n def edges_to(self, task: Task)\n def edges_from(self, task: Task)\n def upstream_tasks(self, task: Task)\n def downstream_tasks(self, task: Task)\n def validate(self) # Checks the flow for cycles and raises an error if one is found.\n def sorted_tasks(self, root_tasks: Iterable[Task]=None)\n def set_dependencies(self, task: object, upstream_tasks: Iterable[object]=None, downstream_tasks: Iterable[object]=None, keyword_tasks: Mapping[str, object]=None, validate: bool=True) # Convenience function for adding task dependencies on upstream tasks.\n def run(self, parameters=None, executor=None, **kwargs) # Run the flow.\n def serialize(self, seed=None)\n def visualize(self)\n def generate_flow_id(self) # Flows are identified by their name and version.\n def generate_task_ids(self)\ndef get_hash(obj: object) # Returns a deterministic set of bytes for a given input.\ndef xor(hash1: bytes, hash2: bytes) # Computes the bitwise XOR between two byte hashes","label":0},{"cid":"prefect/core/task.py","text":"prefect/core/task.py\ndef get_task_info(task: 'Task')\nclass SignatureValidator\n def __new__(cls, name, parents, methods)\nclass Task\n def __init__(self, name: str=None, slug: str=None, description: str=None, group: str=None, tags: Iterable[str]=None, max_retries: int=0, retry_delay: timedelta=timedelta(minutes=1), timeout: timedelta=None, trigger: Callable[[Dict['Task', 'State']], bool]=None, propagate_skip: bool=False, environment: Environment=None, checkpoint: bool=False)\n def __repr__(self)\n def inputs(self)\n def run(self) # The main entrypoint for tasks.\n def __call__(self, *args: object, upstream_tasks: Iterable[object]=None, **kwargs: object)\n def set_dependencies(self, flow: 'Flow'=None, upstream_tasks: Iterable[object]=None, downstream_tasks: Iterable[object]=None, keyword_tasks: Dict[str, object]=None, validate: bool=True)\n def info(self) # A description of the task.\nclass Parameter # A Parameter is a special task that defines a required flow input.\n def __init__(self, name: str, default: Any=None, required: bool=True) # Args:\n def name(self)\n def name(self, value: str)\n def slug(self) # A Parameter slug is always the same as its name. This information is used by\n def slug(self, value: str)\n def run(self)\n def info(self)","label":1},{"cid":"prefect/engine/executors/base.py","text":"prefect/engine/executors/base.py\nclass Executor\n def __init__(self)\n def start(self) # This method is called\n def submit(self, fn, *args, **kwargs) # Submit a function to the executor for execution. Returns a future.\n def wait(self, futures, timeout=None) # Resolves futures to their values. Blocks until the future is complete.\n def set_state(self, current_state: State, state: State, data: Any=None)\n def run_flow(self, flow: Flow, state: State, task_states: Dict[Task, State], start_tasks: Iterable[Task], return_tasks: Iterable[Task], parameters: Dict, context: Dict)\n def run_task(self, task: Task, state: State, upstream_states: Dict[Task, State], inputs: Dict[str, Any], ignore_trigger=False, context=None)","label":0},{"cid":"prefect/engine/executors/distributed.py","text":"prefect/engine/executors/distributed.py\ndef running_in_cluster() # Returns True if the current thread is running inside a distributed cluster\ndef distributed_client(address=None, separate_thread=False) # Context manager that returns a Distributed client.\nclass DistributedExecutor # An executor that runs functions on a Distributed cluster.\n def __init__(self, address=None, separate_thread=None, client=None)\n def start(self)\n def __getstate__(self)\n def submit(self, fn, *args, **kwargs) # Submit a function to the executor for execution. Returns a future.\n def wait(self, futures, timeout=None) # Resolves futures to their values. Blocks until the future is complete.","label":0},{"cid":"prefect/engine/executors/local.py","text":"prefect/engine/executors/local.py\nclass LocalExecutor # An executor that runs all functions synchronously and in\n def submit(self, fn, *args, **kwargs) # Runs a function locally\n def wait(self, futures, timeout=None) # Returns the provided futures","label":0},{"cid":"prefect/engine/flow_runner.py","text":"prefect/engine/flow_runner.py\nclass FlowRunner\n def __init__(self, flow: Flow, executor: 'prefect.engine.executors.Executor'=None, logger_name: str=None)\n def run(self, state: State=None, task_states: Dict[Task, State]=None, start_tasks: Iterable[Task]=None, return_tasks: Iterable[Task]=None, parameters: Dict[str, Any]=None, context: Dict[str, Any]=None)\n def _run(self, state: State, task_states: Dict[Task, State], start_tasks: Iterable[Task], return_tasks: Iterable[Task], parameters: Dict[str, Any])","label":0},{"cid":"prefect/engine/task_runner.py","text":"prefect/engine/task_runner.py\nclass TaskRunner\n def __init__(self, task: Task, executor: 'prefect.engine.executors.Executor'=None, logger_name: str=None)\n def run(self, state: State=None, upstream_states: Dict[Task, State]=None, inputs: Dict[str, Any]=None, ignore_trigger: bool=False, context: Dict[str, Any]=None)\n def _run(self, state: State, upstream_states: Dict[Task, State], inputs: Dict[str, Any], ignore_trigger: bool)\n def handle_fail(self, state, data=None) # Checks if a task is eligable for retry; otherwise marks it failed.\n def handle_retry(self, state, retry_time=None)","label":0},{"cid":"prefect/signals.py","text":"prefect/signals.py\nclass PrefectError\nclass ContextError\nclass SerializationError\nclass PrefectStateException\n def __init__(self, *args, result=None, **kwargs)\nclass FAIL # Indicates that a task failed.\nclass SUCCESS # Indicates that a task succeeded.\nclass RETRY # Used to indicate that a task should be retried\nclass SKIP # Indicates that a task was skipped. By default, downstream tasks will\nclass SKIP_DOWNSTREAM # Indicates that a task *and all downstream tasks* should be skipped.\nclass DONTRUN # Indicates that a task should not run and its state should not be modified.","label":0},{"cid":"prefect/tasks/core/function_task.py","text":"prefect/tasks/core/function_task.py\nclass FunctionTask\n def __init__(self, fn: Callable, name: str=None, **kwargs)","label":0},{"cid":"prefect/triggers.py","text":"prefect/triggers.py\ndef always_run(upstream_states: Dict['Task', 'State']) # This task will run no matter what the upstream states are.\ndef manual_only(upstream_states: Dict['Task', 'State']) # This task will never run automatically. It will only run if it is\ndef all_successful(upstream_states: Dict['Task', 'State']) # Runs if all upstream tasks were successful. SKIPPED tasks are considered\ndef all_failed(upstream_states: Dict['Task', 'State']) # Runs if all upstream tasks failed. SKIPPED tasks are considered successes.\ndef all_finished(upstream_states: Dict['Task', 'State']) # Runs if all tasks finished (either SUCCESS, FAIL, SKIP)\ndef any_successful(upstream_states: Dict['Task', 'State']) # Runs if any tasks were successful. SKIPPED tasks are considered successes;\ndef any_failed(upstream_states: Dict['Task', 'State']) # No failed tasks -> fail","label":0},{"cid":"prefect/utilities/collections.py","text":"prefect/utilities/collections.py\ndef merge_dicts(d1: dict, d2: dict) # Updates d1 from d2 by replacing each (k, v1) pair in d1 with the\nclass DotDict # A dict that also supports attribute (\"dot\") access\n def __init__(self, *args, **kwargs)\n def __getitem__(self, key)\n def __setitem__(self, key, value)\n def __setattr__(self, attr, value)\n def __iter__(self)\n def __delitem__(self, key)\n def __len__(self)\n def copy(self) # Returns a shallow copy\ndef to_dotdict(obj) # Given a obj formatted as a dictionary, returns an object\nclass CompoundKey\ndef dict_to_flatdict(dct, parent=None) # Converts a (nested) dictionary to a flattened representation.\ndef flatdict_to_dict(dct) # Converts a flattened dictionary back to a nested dictionary.","label":0},{"cid":"prefect/utilities/context.py","text":"prefect/utilities/context.py\nclass PrefectContext # A context store for Prefect data.\n def __repr__(self)\n def __call__(self, *args: MutableMapping, **kwargs: Any) # A context manager for setting / resetting the Prefect context","label":0},{"cid":"prefect/utilities/datetimes.py","text":"prefect/utilities/datetimes.py\ndef retry_delay(interval=None, *, exponential_backoff=False, max_delay=datetime.timedelta(hours=2), **kwargs) # A helper function for generating task retry delays.\n def retry_delay(run_number)\ndef parse_datetime(dt) # Parses a string, bytes, float, or datetime object and returns a","label":0},{"cid":"prefect/utilities/json.py","text":"prefect/utilities/json.py\ndef from_qualified_name(obj_str: str) # Retrives an object from a fully qualified string path. The object must be\ndef to_qualified_name(obj: Any)\ndef register_json_codec(register_type: Type=None) # Decorator that registers a JSON Codec to a corresponding codec_key.\n def _register(codec_class: 'JSONCodec')\ndef get_json_codec(obj: object)\nclass JSONCodec # JSON Codecs define how to serialize objects and deserialize objects to JSON.\n def __init__(self, value: O)\n def serialize(self) # Serializes an object to a JSON-compatible type (str, dict, list, tuple).\n def deserialize(obj: J) # Deserialize an object.\n def __json__(self) # Called by the JSON encoder in order to transform this object into\n def __eq__(self, other: Any)\nclass SetCodec # Serialize/deserialize sets\n def serialize(self)\n def deserialize(obj: list)\nclass BytesCodec # Serialize/deserialize bytes\n def serialize(self)\n def deserialize(obj: str)\nclass UUIDCodec # Serialize/deserialize UUIDs\n def serialize(self)\n def deserialize(obj: str)\nclass DateTimeCodec # Serialize/deserialize DateTimes\n def serialize(self)\n def deserialize(obj: str)\nclass DateCodec # Serialize/deserialize Dates\n def serialize(self)\n def deserialize(obj: str)\nclass TimeDeltaCodec # Serialize/deserialize TimeDeltas\n def serialize(self)\n def deserialize(obj: float)\nclass LoadObjectCodec # Serialize/deserialize objects by referencing their fully qualified name. This do\n def serialize(self)\n def deserialize(obj: str)\ndef serializable(fn: Callable) # Decorator that marks a function as automatically serializable via\nclass EncryptedCodec\n def serialize(self)\n def deserialize(obj: str)\nclass ObjectInitArgsCodec # Serializes an object by storing its class and a dict of initialization args.\n def serialize(self)\n def deserialize(obj: dict)\nclass ObjectAttributesCodec # Serializes an object by storing its class name and a dict of attributes,\n def __init__(self, value: Any, attributes_list: list=None)\n def serialize(self)\n def deserialize(obj: dict)\nclass Serializable # A class that automatically uses a specified JSONCodec to serialize itself.\n def __json__(self)\nclass PrefectJSONEncoder\n def default(self, obj: Any) # Recursive method called when encoding JSON objects\nclass PrefectJSONDecoder\n def __init__(self, *args, **kwargs)\n def object_hook(self, dct)","label":0},{"cid":"prefect/utilities/strings.py","text":"prefect/utilities/strings.py\ndef is_valid_identifier(string: str) # Determines whether a string is a valid Python identifier (meaning it can\ndef name_with_suffix(name, predicate, first_suffix=1, delimiter='_', max_iters=1000, always_add_suffix=False) # Automatically adds a number suffix to a name until it becomes valid.","label":0},{"cid":"prefect/utilities/tasks.py","text":"prefect/utilities/tasks.py\ndef group(name: str) # Context manager for setting a task group.\ndef tags(*tags) # Context manager for setting task tags.\ndef as_task(x: Any) # Wraps a function, collection, or constant with the appropriate Task type.\ndef task(fn: Callable, **task_init_kwargs) # A decorator for creating Tasks from functions.\n def task_generator(*args, **kwargs)","label":1},{"cid":"setup.py","text":"setup.py","label":0}],"string":"[\n {\n \"cid\": \"prefect/__init__.py\",\n \"text\": \"prefect/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py\",\n \"text\": \"prefect/client.py\\nclass AuthorizationError\\nclass Client # Client for the Prefect API.\\n def __init__(self, api_server=None, graphql_server=None, token=None)\\n def _get(self, path, *, _json=True, _server=None, **params) # Convenience function for calling the Prefect API with token auth.\\n def _post(self, path, *, _json=True, _server=None, **params) # Convenience function for calling the Prefect API with token auth.\\n def _delete(self, path, *, _server=None, _json=True) # Convenience function for calling the Prefect API with token auth.\\n def graphql(self, query, **variables) # Convenience function for running queries against the Prefect GraphQL\\n def _request(self, method, path, params, server=None)\\n def request_fn()\\n def login(self, email, password, account_slug=None, account_id=None)\\n def refresh_token(self)\\nclass ClientModule\\n def __init__(self, client, name=None)\\n def __repr__(self)\\n def _get(self, path, **params)\\n def _post(self, path, **data)\\n def _delete(self, path, **params)\\n def _graphql(self, query, **variables)\\nclass Projects\\n def create(self, name)\\n def list(self, per_page=100, page=1) # Lists all available projects\\n def flows(self, project_id) # Returns the Flows for the specified project\\nclass Flows\\n def load(self, flow_id, safe=True) # Retrieve information about a Flow.\\n def set_state(self, flow_id, state) # Update a Flow's state\\n def create(self, flow) # Submit a Flow to the server.\\nclass FlowRuns\\n def get_state(self, flow_run_id) # Retrieve a flow run's state\\n def set_state(self, flow_run_id, state, result=None, expected_state=None) # Retrieve a flow run's state\\n def create(self, flow_id, parameters=None, parent_taskrun_id=None)\\n def run(self, flow_run_id, start_tasks=None, inputs=None) # Queue a flow run to be run\\nclass TaskRuns\\n def get_state(self, task_run_id) # Retrieve a flow run's state\\n def set_state(self, task_run_id, state, result=None, expected_state=None) # Retrieve a task run's state\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py\",\n \"text\": \"prefect/configuration.py\\nclass Config\\n def __repr__(self)\\n def __iter__(self)\\n def get(self, key, default=None)\\n def sections(self)\\n def to_dict(self)\\n def from_dict(cls, dct, recursive=True)\\ndef expand(env_var) # Expands (potentially nested) env vars by repeatedly applying\\ndef create_user_config(path, source=DEFAULT_CONFIG) # Copies the default configuration to a user-customizable file\\n def quote(s)\\ndef configure_logging(logger_name)\\ndef validate_config(config)\\ndef load_config_file(path, existing_config=None)\\ndef load_configuration(default_config, user_config, env_var=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py\",\n \"text\": \"prefect/core/flow.py\\ndef flow_cache_key(flow: 'Flow') # Returns a cache key that can be used to determine if the cache is stale.\\nclass Flow\\n def __init__(self, name: str=None, version: str=None, schedule: prefect.schedules.Schedule=None, description: str=None, environment: prefect.environments.Environment=None, tasks: Iterable[Task]=None, edges: Iterable[Edge]=None)\\n def __eq__(self, other: Any)\\n def __repr__(self)\\n def __iter__(self)\\n def copy(self)\\n def __enter__(self)\\n def __exit__(self, _type, _value, _tb)\\n def root_tasks(self) # Returns the root tasks of the Flow -- tasks that have no upstream\\n def terminal_tasks(self) # Returns the terminal tasks of the Flow -- tasks that have no downstream\\n def parameters(self, only_required=False) # Returns details about any Parameters of this flow\\n def restore_graph_on_error(self, validate: bool=True) # A context manager that saves the Flow's graph (tasks & edges) and\\n def add_task(self, task: Task)\\n def add_edge(self, upstream_task: Task, downstream_task: Task, key: str=None, validate: bool=True)\\n def update(self, flow: 'Flow', validate: bool=True)\\n def all_upstream_edges(self)\\n def all_downstream_edges(self)\\n def edges_to(self, task: Task)\\n def edges_from(self, task: Task)\\n def upstream_tasks(self, task: Task)\\n def downstream_tasks(self, task: Task)\\n def validate(self) # Checks the flow for cycles and raises an error if one is found.\\n def sorted_tasks(self, root_tasks: Iterable[Task]=None)\\n def set_dependencies(self, task: object, upstream_tasks: Iterable[object]=None, downstream_tasks: Iterable[object]=None, keyword_tasks: Mapping[str, object]=None, validate: bool=True) # Convenience function for adding task dependencies on upstream tasks.\\n def run(self, parameters=None, executor=None, **kwargs) # Run the flow.\\n def serialize(self, seed=None)\\n def visualize(self)\\n def generate_flow_id(self) # Flows are identified by their name and version.\\n def generate_task_ids(self)\\ndef get_hash(obj: object) # Returns a deterministic set of bytes for a given input.\\ndef xor(hash1: bytes, hash2: bytes) # Computes the bitwise XOR between two byte hashes\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py\",\n \"text\": \"prefect/core/task.py\\ndef get_task_info(task: 'Task')\\nclass SignatureValidator\\n def __new__(cls, name, parents, methods)\\nclass Task\\n def __init__(self, name: str=None, slug: str=None, description: str=None, group: str=None, tags: Iterable[str]=None, max_retries: int=0, retry_delay: timedelta=timedelta(minutes=1), timeout: timedelta=None, trigger: Callable[[Dict['Task', 'State']], bool]=None, propagate_skip: bool=False, environment: Environment=None, checkpoint: bool=False)\\n def __repr__(self)\\n def inputs(self)\\n def run(self) # The main entrypoint for tasks.\\n def __call__(self, *args: object, upstream_tasks: Iterable[object]=None, **kwargs: object)\\n def set_dependencies(self, flow: 'Flow'=None, upstream_tasks: Iterable[object]=None, downstream_tasks: Iterable[object]=None, keyword_tasks: Dict[str, object]=None, validate: bool=True)\\n def info(self) # A description of the task.\\nclass Parameter # A Parameter is a special task that defines a required flow input.\\n def __init__(self, name: str, default: Any=None, required: bool=True) # Args:\\n def name(self)\\n def name(self, value: str)\\n def slug(self) # A Parameter slug is always the same as its name. This information is used by\\n def slug(self, value: str)\\n def run(self)\\n def info(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"prefect/engine/executors/base.py\",\n \"text\": \"prefect/engine/executors/base.py\\nclass Executor\\n def __init__(self)\\n def start(self) # This method is called\\n def submit(self, fn, *args, **kwargs) # Submit a function to the executor for execution. Returns a future.\\n def wait(self, futures, timeout=None) # Resolves futures to their values. Blocks until the future is complete.\\n def set_state(self, current_state: State, state: State, data: Any=None)\\n def run_flow(self, flow: Flow, state: State, task_states: Dict[Task, State], start_tasks: Iterable[Task], return_tasks: Iterable[Task], parameters: Dict, context: Dict)\\n def run_task(self, task: Task, state: State, upstream_states: Dict[Task, State], inputs: Dict[str, Any], ignore_trigger=False, context=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/distributed.py\",\n \"text\": \"prefect/engine/executors/distributed.py\\ndef running_in_cluster() # Returns True if the current thread is running inside a distributed cluster\\ndef distributed_client(address=None, separate_thread=False) # Context manager that returns a Distributed client.\\nclass DistributedExecutor # An executor that runs functions on a Distributed cluster.\\n def __init__(self, address=None, separate_thread=None, client=None)\\n def start(self)\\n def __getstate__(self)\\n def submit(self, fn, *args, **kwargs) # Submit a function to the executor for execution. Returns a future.\\n def wait(self, futures, timeout=None) # Resolves futures to their values. Blocks until the future is complete.\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/local.py\",\n \"text\": \"prefect/engine/executors/local.py\\nclass LocalExecutor # An executor that runs all functions synchronously and in\\n def submit(self, fn, *args, **kwargs) # Runs a function locally\\n def wait(self, futures, timeout=None) # Returns the provided futures\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/flow_runner.py\",\n \"text\": \"prefect/engine/flow_runner.py\\nclass FlowRunner\\n def __init__(self, flow: Flow, executor: 'prefect.engine.executors.Executor'=None, logger_name: str=None)\\n def run(self, state: State=None, task_states: Dict[Task, State]=None, start_tasks: Iterable[Task]=None, return_tasks: Iterable[Task]=None, parameters: Dict[str, Any]=None, context: Dict[str, Any]=None)\\n def _run(self, state: State, task_states: Dict[Task, State], start_tasks: Iterable[Task], return_tasks: Iterable[Task], parameters: Dict[str, Any])\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/task_runner.py\",\n \"text\": \"prefect/engine/task_runner.py\\nclass TaskRunner\\n def __init__(self, task: Task, executor: 'prefect.engine.executors.Executor'=None, logger_name: str=None)\\n def run(self, state: State=None, upstream_states: Dict[Task, State]=None, inputs: Dict[str, Any]=None, ignore_trigger: bool=False, context: Dict[str, Any]=None)\\n def _run(self, state: State, upstream_states: Dict[Task, State], inputs: Dict[str, Any], ignore_trigger: bool)\\n def handle_fail(self, state, data=None) # Checks if a task is eligable for retry; otherwise marks it failed.\\n def handle_retry(self, state, retry_time=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/signals.py\",\n \"text\": \"prefect/signals.py\\nclass PrefectError\\nclass ContextError\\nclass SerializationError\\nclass PrefectStateException\\n def __init__(self, *args, result=None, **kwargs)\\nclass FAIL # Indicates that a task failed.\\nclass SUCCESS # Indicates that a task succeeded.\\nclass RETRY # Used to indicate that a task should be retried\\nclass SKIP # Indicates that a task was skipped. By default, downstream tasks will\\nclass SKIP_DOWNSTREAM # Indicates that a task *and all downstream tasks* should be skipped.\\nclass DONTRUN # Indicates that a task should not run and its state should not be modified.\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/tasks/core/function_task.py\",\n \"text\": \"prefect/tasks/core/function_task.py\\nclass FunctionTask\\n def __init__(self, fn: Callable, name: str=None, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/triggers.py\",\n \"text\": \"prefect/triggers.py\\ndef always_run(upstream_states: Dict['Task', 'State']) # This task will run no matter what the upstream states are.\\ndef manual_only(upstream_states: Dict['Task', 'State']) # This task will never run automatically. It will only run if it is\\ndef all_successful(upstream_states: Dict['Task', 'State']) # Runs if all upstream tasks were successful. SKIPPED tasks are considered\\ndef all_failed(upstream_states: Dict['Task', 'State']) # Runs if all upstream tasks failed. SKIPPED tasks are considered successes.\\ndef all_finished(upstream_states: Dict['Task', 'State']) # Runs if all tasks finished (either SUCCESS, FAIL, SKIP)\\ndef any_successful(upstream_states: Dict['Task', 'State']) # Runs if any tasks were successful. SKIPPED tasks are considered successes;\\ndef any_failed(upstream_states: Dict['Task', 'State']) # No failed tasks -> fail\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py\",\n \"text\": \"prefect/utilities/collections.py\\ndef merge_dicts(d1: dict, d2: dict) # Updates d1 from d2 by replacing each (k, v1) pair in d1 with the\\nclass DotDict # A dict that also supports attribute (\\\"dot\\\") access\\n def __init__(self, *args, **kwargs)\\n def __getitem__(self, key)\\n def __setitem__(self, key, value)\\n def __setattr__(self, attr, value)\\n def __iter__(self)\\n def __delitem__(self, key)\\n def __len__(self)\\n def copy(self) # Returns a shallow copy\\ndef to_dotdict(obj) # Given a obj formatted as a dictionary, returns an object\\nclass CompoundKey\\ndef dict_to_flatdict(dct, parent=None) # Converts a (nested) dictionary to a flattened representation.\\ndef flatdict_to_dict(dct) # Converts a flattened dictionary back to a nested dictionary.\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/context.py\",\n \"text\": \"prefect/utilities/context.py\\nclass PrefectContext # A context store for Prefect data.\\n def __repr__(self)\\n def __call__(self, *args: MutableMapping, **kwargs: Any) # A context manager for setting / resetting the Prefect context\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/datetimes.py\",\n \"text\": \"prefect/utilities/datetimes.py\\ndef retry_delay(interval=None, *, exponential_backoff=False, max_delay=datetime.timedelta(hours=2), **kwargs) # A helper function for generating task retry delays.\\n def retry_delay(run_number)\\ndef parse_datetime(dt) # Parses a string, bytes, float, or datetime object and returns a\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py\",\n \"text\": \"prefect/utilities/json.py\\ndef from_qualified_name(obj_str: str) # Retrives an object from a fully qualified string path. The object must be\\ndef to_qualified_name(obj: Any)\\ndef register_json_codec(register_type: Type=None) # Decorator that registers a JSON Codec to a corresponding codec_key.\\n def _register(codec_class: 'JSONCodec')\\ndef get_json_codec(obj: object)\\nclass JSONCodec # JSON Codecs define how to serialize objects and deserialize objects to JSON.\\n def __init__(self, value: O)\\n def serialize(self) # Serializes an object to a JSON-compatible type (str, dict, list, tuple).\\n def deserialize(obj: J) # Deserialize an object.\\n def __json__(self) # Called by the JSON encoder in order to transform this object into\\n def __eq__(self, other: Any)\\nclass SetCodec # Serialize/deserialize sets\\n def serialize(self)\\n def deserialize(obj: list)\\nclass BytesCodec # Serialize/deserialize bytes\\n def serialize(self)\\n def deserialize(obj: str)\\nclass UUIDCodec # Serialize/deserialize UUIDs\\n def serialize(self)\\n def deserialize(obj: str)\\nclass DateTimeCodec # Serialize/deserialize DateTimes\\n def serialize(self)\\n def deserialize(obj: str)\\nclass DateCodec # Serialize/deserialize Dates\\n def serialize(self)\\n def deserialize(obj: str)\\nclass TimeDeltaCodec # Serialize/deserialize TimeDeltas\\n def serialize(self)\\n def deserialize(obj: float)\\nclass LoadObjectCodec # Serialize/deserialize objects by referencing their fully qualified name. This do\\n def serialize(self)\\n def deserialize(obj: str)\\ndef serializable(fn: Callable) # Decorator that marks a function as automatically serializable via\\nclass EncryptedCodec\\n def serialize(self)\\n def deserialize(obj: str)\\nclass ObjectInitArgsCodec # Serializes an object by storing its class and a dict of initialization args.\\n def serialize(self)\\n def deserialize(obj: dict)\\nclass ObjectAttributesCodec # Serializes an object by storing its class name and a dict of attributes,\\n def __init__(self, value: Any, attributes_list: list=None)\\n def serialize(self)\\n def deserialize(obj: dict)\\nclass Serializable # A class that automatically uses a specified JSONCodec to serialize itself.\\n def __json__(self)\\nclass PrefectJSONEncoder\\n def default(self, obj: Any) # Recursive method called when encoding JSON objects\\nclass PrefectJSONDecoder\\n def __init__(self, *args, **kwargs)\\n def object_hook(self, dct)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/strings.py\",\n \"text\": \"prefect/utilities/strings.py\\ndef is_valid_identifier(string: str) # Determines whether a string is a valid Python identifier (meaning it can\\ndef name_with_suffix(name, predicate, first_suffix=1, delimiter='_', max_iters=1000, always_add_suffix=False) # Automatically adds a number suffix to a name until it becomes valid.\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/tasks.py\",\n \"text\": \"prefect/utilities/tasks.py\\ndef group(name: str) # Context manager for setting a task group.\\ndef tags(*tags) # Context manager for setting task tags.\\ndef as_task(x: Any) # Wraps a function, collection, or constant with the appropriate Task type.\\ndef task(fn: Callable, **task_init_kwargs) # A decorator for creating Tasks from functions.\\n def task_generator(*args, **kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"prefect/client.py::Client.__init__","text":"prefect/client.py::Client.__init__\n def __init__(self, api_server=None, graphql_server=None, token=None):\n if api_server is None:\n api_server = prefect.config.get(\"prefect\", \"api_server\")\n if not api_server:\n api_server = prefect.context.Context.get(\"api_server\", None)\n if not api_server:\n raise ValueError(\"Could not determine API server.\")\n self._api_server = api_server\n\n if graphql_server is None:\n graphql_server = prefect.config.get(\"prefect\", \"graphql_server\")\n if not graphql_server:\n graphql_server = prefect.context.Context.get(\"graphql_server\", None)\n if not graphql_server:\n graphql_server = api_server\n self._graphql_server = graphql_server\n\n if token is None:\n token = prefect.context.Context.get(\"token\", None)\n self._token = token\n\n self.projects = Projects(client=self)\n self.flows = Flows(client=self)\n self.flow_runs = FlowRuns(client=self)\n self.task_runs = TaskRuns(client=self)","label":0},{"cid":"prefect/client.py::Client._get","text":"prefect/client.py::Client._get\n def _get(self, path, *, _json=True, _server=None, **params):\n \"\"\"\n Convenience function for calling the Prefect API with token auth.\n\n Args:\n path: the path of the API url. For example, to GET\n http://prefect-server/v1/auth/login, path would be 'auth/login'.\n params: GET parameters\n \"\"\"\n response = self._request(method=\"GET\", path=path, params=params, server=_server)\n if _json:\n if response.text:\n response = response.json()\n else:\n response = {}\n return response","label":0},{"cid":"prefect/client.py::Client._post","text":"prefect/client.py::Client._post\n def _post(self, path, *, _json=True, _server=None, **params):\n \"\"\"\n Convenience function for calling the Prefect API with token auth.\n\n Args:\n path: the path of the API url. For example, to POST\n http://prefect-server/v1/auth/login, path would be 'auth/login'.\n params: POST params\n \"\"\"\n response = self._request(\n method=\"POST\", path=path, params=params, server=_server\n )\n if _json:\n if response.text:\n return response.json()\n else:\n return response","label":0},{"cid":"prefect/client.py::Client._delete","text":"prefect/client.py::Client._delete\n def _delete(self, path, *, _server=None, _json=True):\n \"\"\"\n Convenience function for calling the Prefect API with token auth.\n\n Args:\n path: the path of the API url\n \"\"\"\n response = self._request(method=\"delete\", path=path, server=_server)\n if _json:\n if response.text:\n response = response.json()\n else:\n response = {}\n return response","label":0},{"cid":"prefect/client.py::Client.graphql","text":"prefect/client.py::Client.graphql\n def graphql(self, query, **variables):\n \"\"\"\n Convenience function for running queries against the Prefect GraphQL\n API\n \"\"\"\n result = self._post(\n path=\"\",\n query=query,\n variables=json.dumps(variables),\n _server=self._graphql_server,\n )\n if \"errors\" in result:\n raise ValueError(result[\"errors\"])\n else:\n return to_dotdict(result).data","label":0},{"cid":"prefect/client.py::Client._request","text":"prefect/client.py::Client._request\n def _request(self, method, path, params, server=None):\n\n # lazy import for performance\n import requests\n\n if server is None:\n server = self._api_server\n\n if self._token is None:\n raise ValueError(\"Call Client.login() to set the client token.\")\n\n url = os.path.join(server, path.lstrip(\"/\")).rstrip(\"/\")\n\n params = params or {}\n\n # write this as a function to allow reuse in next try/except block\n def request_fn():\n headers = {\"Authorization\": \"Bearer \" + self._token}\n if method == \"GET\":\n response = requests.get(url, headers=headers, params=params)\n elif method == \"POST\":\n response = requests.post(url, headers=headers, json=params)\n elif method == \"DELETE\":\n response = requests.delete(url, headers=headers)\n else:\n raise ValueError(\"Invalid method: {}\".format(method))\n\n response.raise_for_status()\n\n return response\n\n try:\n return request_fn()\n except requests.HTTPError as err:\n if err.response.status_code == 401:\n self.refresh_token()\n return request_fn()\n raise","label":0},{"cid":"prefect/client.py::Client._request.request_fn","text":"prefect/client.py::Client._request.request_fn\n def request_fn():\n headers = {\"Authorization\": \"Bearer \" + self._token}\n if method == \"GET\":\n response = requests.get(url, headers=headers, params=params)\n elif method == \"POST\":\n response = requests.post(url, headers=headers, json=params)\n elif method == \"DELETE\":\n response = requests.delete(url, headers=headers)\n else:\n raise ValueError(\"Invalid method: {}\".format(method))\n\n response.raise_for_status()\n\n return response","label":0},{"cid":"prefect/client.py::Client.login","text":"prefect/client.py::Client.login\n def login(self, email, password, account_slug=None, account_id=None):\n\n # lazy import for performance\n import requests\n\n url = os.path.join(self._api_server, \"auth/login\")\n response = requests.post(\n url,\n auth=(email, password),\n json=dict(account_id=account_id, account_slug=account_slug),\n )\n if not response.ok:\n raise ValueError(\"Could not log in.\")\n self._token = response.json().get(\"token\")\n\n if not (account_id or account_slug):\n print(\"No account provided; returning available accounts.\")\n accounts = self._get(\"auth/accounts\")\n return accounts","label":0},{"cid":"prefect/client.py::Client.refresh_token","text":"prefect/client.py::Client.refresh_token\n def refresh_token(self):\n url = os.path.join(self._api_server, \"auth/refresh\")\n response = requests.post(\n url, headers={\"Authorization\": \"Bearer \" + self._token}\n )\n self._token = response.json().get(\"token\")","label":0},{"cid":"prefect/client.py::ClientModule.__init__","text":"prefect/client.py::ClientModule.__init__\n def __init__(self, client, name=None):\n if name is None:\n name = type(self).__name__\n self._name = name\n self._client = client","label":0},{"cid":"prefect/client.py::ClientModule.__repr__","text":"prefect/client.py::ClientModule.__repr__\n def __repr__(self):\n return \"<Client Module: {name}>\".format(name=self._name)","label":0},{"cid":"prefect/client.py::ClientModule._get","text":"prefect/client.py::ClientModule._get\n def _get(self, path, **params):\n path = path.lstrip(\"/\")\n return self._client._get(os.path.join(self._path, path), **params)","label":0},{"cid":"prefect/client.py::ClientModule._post","text":"prefect/client.py::ClientModule._post\n def _post(self, path, **data):\n path = path.lstrip(\"/\")\n return self._client._post(os.path.join(self._path, path), **data)","label":0},{"cid":"prefect/client.py::ClientModule._delete","text":"prefect/client.py::ClientModule._delete\n def _delete(self, path, **params):\n path = path.lstrip(\"/\")\n return self._client._delete(os.path.join(self._path, path), **params)","label":0},{"cid":"prefect/client.py::ClientModule._graphql","text":"prefect/client.py::ClientModule._graphql\n def _graphql(self, query, **variables):\n return self._client.graphql(query=query, **variables)","label":0},{"cid":"prefect/client.py::Projects.create","text":"prefect/client.py::Projects.create\n def create(self, name):\n return self._post(path=\"/\", name=name)","label":0},{"cid":"prefect/client.py::Projects.list","text":"prefect/client.py::Projects.list\n def list(self, per_page=100, page=1):\n \"\"\"\n Lists all available projects\n \"\"\"\n data = self._graphql(\n \"\"\"\n query ($perPage: Int!, $page: Int!){\n projects(perPage: $perPage, page: $page){\n id\n name\n }\n }\n \"\"\",\n perPage=per_page,\n page=page,\n )\n return data[\"projects\"]","label":0},{"cid":"prefect/client.py::Projects.flows","text":"prefect/client.py::Projects.flows\n def flows(self, project_id):\n \"\"\"\n Returns the Flows for the specified project\n \"\"\"\n\n data = self._graphql(\n \"\"\"\n query ($projectId: String!){\n projects(filter: {id: $projectId}) {\n id\n name\n flows {\n id\n name\n }\n }\n }\n \"\"\",\n projectId=project_id,\n )\n return data[\"projects\"]","label":0},{"cid":"prefect/client.py::Flows.load","text":"prefect/client.py::Flows.load\n def load(self, flow_id, safe=True):\n \"\"\"\n Retrieve information about a Flow.\n\n Args:\n flow_id (str): the Flow's id\n \"\"\"\n if safe:\n data = self._graphql(\n \"\"\"\n query($flowId: String!) {\n flow(id: $flowId) {\n safe_serialized\n }\n }\n \"\"\",\n flowId=flow_id,\n )\n\n return prefect.Flow.safe_deserialize(data[\"flow\"][\"safe_serialized\"])\n else:\n data = self._graphql(\n \"\"\"\n query($flowId: String!) {\n flow(id: $flowId) {\n serialized\n tasks {\n serialized\n }\n edges {\n serialized\n }\n }\n }\n \"\"\",\n flowId=flow_id,\n )\n return prefect.Flow.deserialize(data[\"flow\"])","label":0},{"cid":"prefect/client.py::Flows.set_state","text":"prefect/client.py::Flows.set_state\n def set_state(self, flow_id, state):\n \"\"\"\n Update a Flow's state\n \"\"\"\n return self._post(path=\"/{id}/state\".format(id=flow_id), state=state)","label":0},{"cid":"prefect/client.py::Flows.create","text":"prefect/client.py::Flows.create\n def create(self, flow):\n \"\"\"\n Submit a Flow to the server.\n \"\"\"\n return self._post(path=\"/\", serialized_flow=flow.serialize())","label":0},{"cid":"prefect/client.py::FlowRuns.get_state","text":"prefect/client.py::FlowRuns.get_state\n def get_state(self, flow_run_id):\n \"\"\"\n Retrieve a flow run's state\n \"\"\"\n\n data = self._graphql(\n \"\"\"\n query ($flowRunId: String!){\n flow_runs(filter: {id: $flowRunId}) {\n state {\n state\n result\n }\n }\n }\n \"\"\",\n flowRunId=flow_run_id,\n )\n state = data.flow_runs[0].get(\"state\", {})\n return prefect.engine.state.FlowState(\n state=state.get(\"state\", None), result=state.get(\"result\", None)\n )","label":0},{"cid":"prefect/client.py::FlowRuns.set_state","text":"prefect/client.py::FlowRuns.set_state\n def set_state(self, flow_run_id, state, result=None, expected_state=None):\n \"\"\"\n Retrieve a flow run's state\n \"\"\"\n return self._post(\n path=\"/{id}/state\".format(id=flow_run_id),\n state=state,\n result=dict(result=result),\n expected_state=expected_state,\n )","label":0},{"cid":"prefect/client.py::FlowRuns.create","text":"prefect/client.py::FlowRuns.create\n def create(self, flow_id, parameters=None, parent_taskrun_id=None):\n return self._post(\n path=\"/\",\n flow_id=flow_id,\n parameters=parameters,\n parent_taskrun_id=parent_taskrun_id,\n )","label":0},{"cid":"prefect/client.py::FlowRuns.run","text":"prefect/client.py::FlowRuns.run\n def run(self, flow_run_id, start_tasks=None, inputs=None):\n \"\"\"\n Queue a flow run to be run\n \"\"\"\n if start_tasks is None:\n start_tasks = []\n if inputs is None:\n inputs = {}\n\n return self._post(\n path=\"/{id}/run\".format(id=flow_run_id),\n start_tasks=start_tasks,\n inputs=inputs,\n )","label":0},{"cid":"prefect/client.py::TaskRuns.get_state","text":"prefect/client.py::TaskRuns.get_state\n def get_state(self, task_run_id):\n \"\"\"\n Retrieve a flow run's state\n \"\"\"\n data = self._graphql(\n \"\"\"\n query ($taskRunId: String!){\n task_runs(filter: {id: $taskRunId}) {\n state {\n state\n result\n }\n }\n }\n \"\"\",\n taskRunId=task_run_id,\n )\n state = data.task_runs[0].get(\"state\", {})\n return prefect.engine.state.TaskState(\n state=state.get(\"state\", None), result=state.get(\"result\", None)\n )","label":0},{"cid":"prefect/client.py::TaskRuns.set_state","text":"prefect/client.py::TaskRuns.set_state\n def set_state(self, task_run_id, state, result=None, expected_state=None):\n \"\"\"\n Retrieve a task run's state\n \"\"\"\n return self._post(\n path=\"/{id}/state\".format(id=task_run_id),\n state=state,\n result=dict(result=result),\n expected_state=expected_state,\n )","label":0},{"cid":"prefect/configuration.py::Config.__repr__","text":"prefect/configuration.py::Config.__repr__\n def __repr__(self):\n return \"<Config: {}>\".format(\", \".join(sorted(self.sections())))","label":0},{"cid":"prefect/configuration.py::Config.__iter__","text":"prefect/configuration.py::Config.__iter__\n def __iter__(self):\n return self.sections()","label":0},{"cid":"prefect/configuration.py::Config.get","text":"prefect/configuration.py::Config.get\n def get(self, key, default=None):\n return self.__dict__.get(key, default)","label":0},{"cid":"prefect/configuration.py::Config.sections","text":"prefect/configuration.py::Config.sections\n def sections(self):\n return self.__dict__.keys()","label":0},{"cid":"prefect/configuration.py::Config.to_dict","text":"prefect/configuration.py::Config.to_dict\n def to_dict(self):\n dct = {}\n for k, v in self.__dict__.items():\n if isinstance(v, SimpleNamespace):\n v = v.to_dict()\n dct[k] = v\n return dct","label":0},{"cid":"prefect/configuration.py::Config.from_dict","text":"prefect/configuration.py::Config.from_dict\n @classmethod\n def from_dict(cls, dct, recursive=True):\n if not isinstance(dct, dict):\n return dct\n for key, value in list(dct.items()):\n if isinstance(value, dict):\n dct[key] = cls.from_dict(value, recursive=recursive)\n elif isinstance(value, (list, tuple, set)):\n dct[key] = type(value)(\n [cls.from_dict(v, recursive=recursive) for v in value]\n )\n return cls(**dct)","label":0},{"cid":"prefect/configuration.py::expand","text":"prefect/configuration.py::expand\ndef expand(env_var):\n \"\"\"\n Expands (potentially nested) env vars by repeatedly applying\n `expandvars` and `expanduser` until interpolation stops having\n any effect.\n \"\"\"\n if not env_var or not isinstance(env_var, str):\n return env_var\n while True:\n substituted = os.path.expanduser(os.path.expandvars(str(env_var)))\n if substituted == env_var:\n return substituted\n else:\n env_var = substituted","label":0},{"cid":"prefect/configuration.py::create_user_config","text":"prefect/configuration.py::create_user_config\ndef create_user_config(path, source=DEFAULT_CONFIG):\n \"\"\"\n Copies the default configuration to a user-customizable file\n \"\"\"\n config_file = expand(path)\n if os.path.isfile(config_file):\n raise ValueError(\"File already exists: {}\".format(config_file))\n os.makedirs(os.path.dirname(config_file), exist_ok=True)\n\n def quote(s):\n return '\"' + s + '\"'\n\n with open(config_file, \"w\") as fw:\n with open(source, \"r\") as fr:\n src = fr.read()\n counter = 0\n\n while '\"<<FERNET KEY>>\"' in src:\n key = Fernet.generate_key().decode()\n src = src.replace('\"<<FERNET KEY>>\"', quote(key), 1)\n counter += 1\n if counter > 100:\n raise ValueError(\"Unexpected interpolation error.\")\n\n while '\"<<SECRET>>\"' in src:\n chars = ascii_letters + digits + \"!@#$%^&*()[]<>,.-\"\n secret = \"\".join(random.choice(chars) for i in range(32))\n src = src.replace('\"<<SECRET>>\"', quote(secret), 1)\n counter += 1\n if counter > 100:\n raise ValueError(\"Unexpected interpolation error.\")\n\n while '\"<<UUID>>\"' in src:\n src = src.replace('\"<<UUID>>\"', quote(str(uuid.uuid4())), 1)\n counter += 1\n if counter > 100:\n raise ValueError(\"Unexpected interpolation error.\")\n\n fw.write(src)","label":0},{"cid":"prefect/configuration.py::create_user_config.quote","text":"prefect/configuration.py::create_user_config.quote\n def quote(s):\n return '\"' + s + '\"'","label":0},{"cid":"prefect/configuration.py::configure_logging","text":"prefect/configuration.py::configure_logging\ndef configure_logging(logger_name):\n logger = logging.getLogger(logger_name)\n handler = logging.StreamHandler()\n formatter = logging.Formatter(config.logging.format)\n handler.setFormatter(formatter)\n logger.addHandler(handler)\n logger.setLevel(getattr(logging, config.logging.level))","label":0},{"cid":"prefect/configuration.py::validate_config","text":"prefect/configuration.py::validate_config\ndef validate_config(config):\n pass","label":0},{"cid":"prefect/configuration.py::load_config_file","text":"prefect/configuration.py::load_config_file\ndef load_config_file(path, existing_config=None):\n\n config = toml.load(expand(path))\n flat_config = collections.dict_to_flatdict(config)\n\n # set configuration from env vars\n for env_var in os.environ:\n if not env_var.startswith(ENV_VAR_PREFIX):\n continue\n sections = collections.CompoundKey(env_var.lower().split(\"__\")[1:])\n if sections:\n flat_config[sections] = expand(os.getenv(env_var))\n\n # expand configuration referencing env vars\n for k, v in list(flat_config.items()):\n flat_config[k] = expand(v)\n\n # process interpolation of any variable referencing another with ${}\n # process up to 10 links\n for i in range(10):\n for k, v in list(flat_config.items()):\n if not isinstance(v, str):\n continue\n match = INTERPOLATION_REGEX.search(v)\n if not match:\n continue\n ref_key = collections.CompoundKey(match.group(1).split(\".\"))\n ref_value = flat_config[ref_key]\n if v == match.group(0):\n flat_config[k] = ref_value\n else:\n flat_config[k] = v.replace(match.group(0), str(ref_value), 1)\n\n config = collections.flatdict_to_dict(flat_config)\n\n if existing_config is not None:\n config = collections.merge_dicts(existing_config.to_dict(), config)\n\n return Config.from_dict(config)","label":0},{"cid":"prefect/configuration.py::load_configuration","text":"prefect/configuration.py::load_configuration\ndef load_configuration(default_config, user_config, env_var=None):\n user_config_path = os.getenv(env_var, user_config)\n config = load_config_file(default_config)\n try:\n create_user_config(user_config_path)\n except Exception:\n pass\n config = load_config_file(user_config_path, existing_config=config)\n return config","label":0},{"cid":"prefect/core/flow.py::flow_cache_key","text":"prefect/core/flow.py::flow_cache_key\ndef flow_cache_key(flow: \"Flow\") -> int:\n \"\"\"\n Returns a cache key that can be used to determine if the cache is stale.\n \"\"\"\n\n return hash((frozenset(flow.tasks), frozenset(flow.edges)))","label":0},{"cid":"prefect/core/flow.py::Flow.__init__","text":"prefect/core/flow.py::Flow.__init__\n def __init__(\n self,\n name: str = None,\n version: str = None,\n schedule: prefect.schedules.Schedule = None,\n description: str = None,\n environment: prefect.environments.Environment = None,\n tasks: Iterable[Task] = None,\n edges: Iterable[Edge] = None,\n ) -> None:\n\n self.name = name or type(self).__name__\n self.version = version\n self.description = description\n self.schedule = schedule or prefect.schedules.NoSchedule()\n self.environment = environment\n\n self.tasks = set() # type: Set[Task]\n self.edges = set() # type: Set[Edge]\n\n for t in tasks or []:\n self.add_task(t)\n\n for e in edges or []:\n self.add_edge(\n upstream_task=e.upstream_task,\n downstream_task=e.downstream_task,\n key=e.key,\n )\n\n self._prefect_version = prefect.__version__\n self._cache = {}\n\n super().__init__()","label":0},{"cid":"prefect/core/flow.py::Flow.__eq__","text":"prefect/core/flow.py::Flow.__eq__\n def __eq__(self, other: Any) -> bool:\n if type(self) == type(other):\n s = (self.name, self.version, self.tasks, self.edges)\n o = (other.name, other.version, other.tasks, other.edges)\n return s == o\n return False","label":0},{"cid":"prefect/core/flow.py::Flow.__repr__","text":"prefect/core/flow.py::Flow.__repr__\n def __repr__(self) -> str:\n return \"<{cls}: {self.name}{v}>\".format(\n cls=type(self).__name__,\n self=self,\n v=\" version={}\".format(self.version) if self.version else \"\",\n )","label":0},{"cid":"prefect/core/flow.py::Flow.__iter__","text":"prefect/core/flow.py::Flow.__iter__\n def __iter__(self) -> Iterable[Task]:\n yield from self.sorted_tasks()","label":0},{"cid":"prefect/core/flow.py::Flow.copy","text":"prefect/core/flow.py::Flow.copy\n def copy(self) -> \"Flow\":\n new = copy.copy(self)\n new.tasks = self.tasks.copy()\n new.edges = self.edges.copy()\n return new","label":0},{"cid":"prefect/core/flow.py::Flow.__enter__","text":"prefect/core/flow.py::Flow.__enter__\n def __enter__(self) -> \"Flow\":\n self.__previous_flow = prefect.context.get(\"_flow\")\n prefect.context.update(_flow=self)\n return self","label":0},{"cid":"prefect/core/flow.py::Flow.__exit__","text":"prefect/core/flow.py::Flow.__exit__\n def __exit__(self, _type, _value, _tb) -> None: # type: ignore\n del prefect.context._flow\n if self.__previous_flow is not None:\n prefect.context.update(_flow=self.__previous_flow)\n\n del self.__previous_flow","label":0},{"cid":"prefect/core/flow.py::Flow.root_tasks","text":"prefect/core/flow.py::Flow.root_tasks\n @cache(validation_fn=flow_cache_key)\n def root_tasks(self) -> Set[Task]:\n \"\"\"\n Returns the root tasks of the Flow -- tasks that have no upstream\n dependencies.\n \"\"\"\n return set(t for t in self.tasks if not self.edges_to(t))","label":0},{"cid":"prefect/core/flow.py::Flow.terminal_tasks","text":"prefect/core/flow.py::Flow.terminal_tasks\n @cache(validation_fn=flow_cache_key)\n def terminal_tasks(self) -> Set[Task]:\n \"\"\"\n Returns the terminal tasks of the Flow -- tasks that have no downstream\n dependencies.\n \"\"\"\n return set(t for t in self.tasks if not self.edges_from(t))","label":0},{"cid":"prefect/core/flow.py::Flow.parameters","text":"prefect/core/flow.py::Flow.parameters\n def parameters(self, only_required=False) -> Dict[str, ParameterDetails]:\n \"\"\"\n Returns details about any Parameters of this flow\n \"\"\"\n return {\n t.name: {\"required\": t.required, \"default\": t.default}\n for t in self.tasks\n if isinstance(t, Parameter) and (t.required if only_required else True)\n }","label":0},{"cid":"prefect/core/flow.py::Flow.restore_graph_on_error","text":"prefect/core/flow.py::Flow.restore_graph_on_error\n @contextmanager\n def restore_graph_on_error(self, validate: bool = True) -> Iterator[None]:\n \"\"\"\n A context manager that saves the Flow's graph (tasks & edges) and\n restores it if an error is raised. It can be used to test potentially\n erroneous configurations (for example, ones that might include cycles)\n without modifying the graph.\n\n It will automatically check for cycles when restored.\n\n with flow.restore_graph_on_error():\n # this will raise an error, but the flow graph will not be modified\n add_cycle_to_graph(flow)\n \"\"\"\n tasks, edges = self.tasks.copy(), self.edges.copy()\n try:\n yield\n if validate:\n self.validate()\n except Exception:\n self.tasks, self.edges = tasks, edges\n raise","label":0},{"cid":"prefect/core/flow.py::Flow.add_task","text":"prefect/core/flow.py::Flow.add_task\n def add_task(self, task: Task) -> None:\n if not isinstance(task, Task):\n raise TypeError(\n \"Tasks must be Task instances (received {})\".format(type(task))\n )\n elif task not in self.tasks:\n if task.slug and task.slug in [t.slug for t in self.tasks]:\n raise ValueError(\n 'A task with the slug \"{}\" already exists in this '\n \"flow.\".format(task.slug)\n )\n\n self.tasks.add(task)","label":0},{"cid":"prefect/core/flow.py::Flow.add_edge","text":"prefect/core/flow.py::Flow.add_edge\n def add_edge(\n self,\n upstream_task: Task,\n downstream_task: Task,\n key: str = None,\n validate: bool = True,\n ) -> None:\n if isinstance(downstream_task, Parameter):\n raise ValueError(\n \"Parameters must be root tasks and can not have upstream dependencies.\"\n )\n\n with self.restore_graph_on_error(validate=validate):\n self.add_task(upstream_task)\n self.add_task(downstream_task)\n\n # we can only check the downstream task's edges once it has been added to the\n # flow, so we need to perform this check here and not earlier.\n if key and key in {e.key for e in self.edges_to(downstream_task)}:\n raise ValueError(\n 'Argument \"{a}\" for task {t} has already been assigned in '\n \"this flow. If you are trying to call the task again with \"\n \"new arguments, call Task.copy() before adding the result \"\n \"to this flow.\".format(a=key, t=downstream_task)\n )\n\n edge = Edge(\n upstream_task=upstream_task, downstream_task=downstream_task, key=key\n )\n self.edges.add(edge)\n\n # check that the edges are valid keywords by binding them\n if key is not None:\n edge_keys = {\n e.key: None\n for e in self.edges_to(downstream_task)\n if e.key is not None\n }\n inspect.signature(downstream_task.run).bind_partial(**edge_keys)","label":0},{"cid":"prefect/core/flow.py::Flow.update","text":"prefect/core/flow.py::Flow.update\n def update(self, flow: \"Flow\", validate: bool = True) -> None:\n with self.restore_graph_on_error(validate=validate):\n\n for task in flow.tasks:\n if task not in self.tasks:\n self.add_task(task)\n\n for edge in flow.edges:\n if edge not in self.edges:\n self.add_edge(\n upstream_task=edge.upstream_task,\n downstream_task=edge.downstream_task,\n key=edge.key,\n validate=False,\n )","label":0},{"cid":"prefect/core/flow.py::Flow.all_upstream_edges","text":"prefect/core/flow.py::Flow.all_upstream_edges\n @cache(validation_fn=flow_cache_key)\n def all_upstream_edges(self) -> Dict[Task, Set[Edge]]:\n edges = {t: set() for t in self.tasks} # type: Dict[Task, Set[Edge]]\n for edge in self.edges:\n edges[edge.downstream_task].add(edge)\n return edges","label":0},{"cid":"prefect/core/flow.py::Flow.all_downstream_edges","text":"prefect/core/flow.py::Flow.all_downstream_edges\n @cache(validation_fn=flow_cache_key)\n def all_downstream_edges(self) -> Dict[Task, Set[Edge]]:\n edges = {t: set() for t in self.tasks} # type: Dict[Task, Set[Edge]]\n for edge in self.edges:\n edges[edge.upstream_task].add(edge)\n return edges","label":0},{"cid":"prefect/core/flow.py::Flow.edges_to","text":"prefect/core/flow.py::Flow.edges_to\n def edges_to(self, task: Task) -> Set[Edge]:\n if task not in self.tasks:\n raise ValueError(\n \"Task {t} was not found in Flow {f}\".format(t=task, f=self)\n )\n return self.all_upstream_edges()[task]","label":0},{"cid":"prefect/core/flow.py::Flow.edges_from","text":"prefect/core/flow.py::Flow.edges_from\n def edges_from(self, task: Task) -> Set[Edge]:\n if task not in self.tasks:\n raise ValueError(\n \"Task {t} was not found in Flow {f}\".format(t=task, f=self)\n )\n return self.all_downstream_edges()[task]","label":0},{"cid":"prefect/core/flow.py::Flow.upstream_tasks","text":"prefect/core/flow.py::Flow.upstream_tasks\n def upstream_tasks(self, task: Task) -> Set[Task]:\n return set(e.upstream_task for e in self.edges_to(task))","label":0},{"cid":"prefect/core/flow.py::Flow.downstream_tasks","text":"prefect/core/flow.py::Flow.downstream_tasks\n def downstream_tasks(self, task: Task) -> Set[Task]:\n return set(e.downstream_task for e in self.edges_from(task))","label":0},{"cid":"prefect/core/flow.py::Flow.validate","text":"prefect/core/flow.py::Flow.validate\n def validate(self) -> None:\n \"\"\"\n Checks the flow for cycles and raises an error if one is found.\n \"\"\"\n self.sorted_tasks()","label":0},{"cid":"prefect/core/flow.py::Flow.sorted_tasks","text":"prefect/core/flow.py::Flow.sorted_tasks\n @cache(validation_fn=flow_cache_key)\n def sorted_tasks(self, root_tasks: Iterable[Task] = None) -> Tuple[Task, ...]:\n\n # begin by getting all tasks under consideration (root tasks and all\n # downstream tasks)\n if root_tasks:\n tasks = set(root_tasks)\n seen = set() # type: Set[Task]\n\n # while the set of tasks is different from the seen tasks...\n while tasks.difference(seen):\n # iterate over the new tasks...\n for t in list(tasks.difference(seen)):\n # add its downstream tasks to the task list\n tasks.update(self.downstream_tasks(t))\n # mark it as seen\n seen.add(t)\n else:\n tasks = self.tasks\n\n # build the list of sorted tasks\n remaining_tasks = list(tasks)\n sorted_tasks = []\n while remaining_tasks:\n # mark the flow as cyclic unless we prove otherwise\n cyclic = True\n\n # iterate over each remaining task\n for task in remaining_tasks.copy():\n # check all the upstream tasks of that task\n for upstream_task in self.upstream_tasks(task):\n # if the upstream task is also remaining, it means it\n # hasn't been sorted, so we can't sort this task either\n if upstream_task in remaining_tasks:\n break\n else:\n # but if all upstream tasks have been sorted, we can sort\n # this one too. We note that we found no cycle this time.\n cyclic = False\n remaining_tasks.remove(task)\n sorted_tasks.append(task)\n\n # if we were unable to match any upstream tasks, we have a cycle\n if cyclic:\n raise ValueError(\"Flows must be acyclic!\")\n\n return tu","label":0},{"cid":"prefect/core/flow.py::Flow.set_dependencies","text":"prefect/core/flow.py::Flow.set_dependencies\n def set_dependencies(\n self,\n task: object,\n upstream_tasks: Iterable[object] = None,\n downstream_tasks: Iterable[object] = None,\n keyword_tasks: Mapping[str, object] = None,\n validate: bool = True,\n ) -> None:\n \"\"\"\n Convenience function for adding task dependencies on upstream tasks.\n\n Args:\n task (Object): a Task that will become part of the Flow. If the task is not a\n Task subclass, Prefect will attempt to convert it to one.\n\n upstream_tasks ([object]): Tasks that will run before the task runs. If any task\n is not a Task subclass, Prefect will attempt to convert it to one.\n\n downstream_tasks ([object]): Tasks that will run after the task runs. If any task\n is not a Task subclass, Prefect will attempt to convert it to one.\n\n keyword_tasks ({key: object}): The results of these tasks\n will be provided to the task under the specified keyword\n arguments. If any task is not a Task subclass, Prefect will attempt to\n convert it to one.\n \"\"\"\n\n # restore the original graph if we encounter an error midway through this operation\n with self.restore_graph_on_error(validate=validate):\n\n task = as_task(task)\n assert isinstance(task, Task) # mypy assert\n\n # add the main task (in case it was called with no arguments)\n self.add_task(task)\n\n # add upstream tasks\n for t in upstream_tasks or []:\n t = as_task(t)\n assert isinstance(t, Task) # mypy assert\n self.add_edge(upstream_task=t, downstream_task=task, validate=False)\n\n # add downstream tasks\n for t in downstream_tasks or []:\n t = as_task(t)\n assert isinstance(t, Task) # mypy assert\n self.a","label":0},{"cid":"prefect/core/flow.py::Flow.run","text":"prefect/core/flow.py::Flow.run\n def run(self, parameters=None, executor=None, **kwargs):\n \"\"\"\n Run the flow.\n \"\"\"\n runner = prefect.engine.flow_runner.FlowRunner(flow=self, executor=executor)\n\n parameters = parameters or {}\n for p in self.parameters():\n if p in kwargs:\n parameters[p] = kwargs.pop(p)\n\n return runner.run(parameters=parameters, **kwargs)","label":0},{"cid":"prefect/core/flow.py::Flow.serialize","text":"prefect/core/flow.py::Flow.serialize\n def serialize(self, seed=None) -> dict:\n ref_ids = self.fingerprint(seed=seed)\n\n return dict(\n ref_id=ref_ids[\"flow_id\"],\n name=self.name,\n version=self.version,\n description=self.description,\n parameters=self.parameters(),\n schedule=self.schedule,\n tasks=[\n dict(ref_id=ref_ids[\"task_ids\"][t], **t.serialize()) for t in self.tasks\n ],\n edges=[\n dict(\n upstream_ref_id=ref_ids[\"task_ids\"][e.upstream_task],\n downstream_ref_id=ref_ids[\"task_ids\"][e.downstream_task],\n key=e.key,\n )\n for e in self.edges\n ],\n )","label":0},{"cid":"prefect/core/flow.py::Flow.visualize","text":"prefect/core/flow.py::Flow.visualize\n def visualize(self):\n graph = graphviz.Digraph()\n\n for t in self.tasks:\n graph.node(str(id(t)), t.name)\n\n for e in self.edges:\n graph.edge(str(id(e.upstream_task)), str(id(e.downstream_task)), e.key)\n\n with tempfile.NamedTemporaryFile() as tmp:\n graph.render(tmp.name, view=True)","label":0},{"cid":"prefect/core/flow.py::Flow.generate_flow_id","text":"prefect/core/flow.py::Flow.generate_flow_id\n def generate_flow_id(self) -> str:\n \"\"\"\n Flows are identified by their name and version.\n \"\"\"\n hash_bytes = get_hash(\"{}:{}\".format(self.name, self.version or \"\"))\n return str(uuid.UUID(bytes=hash_bytes))","label":0},{"cid":"prefect/core/flow.py::Flow.generate_task_ids","text":"prefect/core/flow.py::Flow.generate_task_ids\n def generate_task_ids(self) -> Dict[\"Task\", str]:\n final_hashes = {}\n\n # --- initial pass\n # for each task, generate a hash based on that task's attributes\n hashes = {t: get_hash(t) for t in self.tasks}\n counter = Counter(hashes.values())\n\n # --- forward pass #1\n # for each task in order:\n # - if the task hash is unique, put it in final_hashes\n # - if not, hash the task with the hash of all incoming edges\n # the result is a hash that represents each task in terms of all ancestors since the\n # last uniquely defined task.\n for t in self.sorted_tasks():\n if counter[hashes[t]] == 1:\n final_hashes[t] = hashes[t]\n continue\n\n edge_hashes = sorted(\n (e.key, hashes[e.upstream_task]) for e in self.edges_to(t)\n )\n hashes[t] = get_hash((hashes[t], edge_hashes))\n counter[hashes[t]] += 1\n\n # --- backward pass\n # for each task in reverse order:\n # - if the task hash is unique, put it in final_hashes\n # - if not, hash the task with the hash of all outgoing edges\n # the result is a hash that represents each task in terms of both its ancestors (from\n # the foward pass) and also any descendents.\n for t in reversed(self.sorted_tasks()):\n if counter[hashes[t]] == 1:\n final_hashes[t] = hashes[t]\n continue\n\n edge_hashes = sorted(\n (e.key, hashes[e.downstream_task]) for e in self.edges_from(t)\n )\n hashes[t] = get_hash(str((hashes[t], edge_hashes)))\n counter[hashes[t]] += 1\n\n # --- forward pass #2\n # for each task in order:\n # - if the task hash is unique, put it in final_hashes\n # if not, hash the task with the hash of all incoming edges.\n # define each task in terms of the comput","label":0},{"cid":"prefect/core/flow.py::get_hash","text":"prefect/core/flow.py::get_hash\ndef get_hash(obj: object) -> bytes:\n \"\"\"\n Returns a deterministic set of bytes for a given input.\n \"\"\"\n if isinstance(obj, bytes):\n return hashlib.md5(obj).digest()\n elif isinstance(obj, str):\n return hashlib.md5(obj.encode()).digest()\n elif isinstance(obj, Task):\n obj = prefect.core.task.get_task_info(obj)\n elif isinstance(obj, Edge):\n obj = dict(\n upstream=prefect.core.task.get_task_info(obj.upstream_task),\n downstream=prefect.core.task.get_task_info(obj.downstream_task),\n key=obj.key,\n )\n return hashlib.md5(jsonpickle.dumps(obj).encode()).digest()","label":0},{"cid":"prefect/core/flow.py::xor","text":"prefect/core/flow.py::xor\ndef xor(hash1: bytes, hash2: bytes) -> bytes:\n \"\"\"\n Computes the bitwise XOR between two byte hashes\n \"\"\"\n return bytes([x ^ y for x, y in zip(hash1, itertools.cycle(hash2))])","label":0},{"cid":"prefect/core/task.py::get_task_info","text":"prefect/core/task.py::get_task_info\ndef get_task_info(task: \"Task\") -> Dict[str, Any]:\n return dict(\n name=task.name,\n slug=task.slug,\n type=to_qualified_name(type(task)),\n description=task.description,\n max_retries=task.max_retries,\n retry_delay=task.retry_delay,\n timeout=task.timeout,\n trigger=task.trigger,\n propagate_skip=task.propagate_skip,\n environment=task.environment,\n checkpoint=task.checkpoint,\n )","label":0},{"cid":"prefect/core/task.py::SignatureValidator.__new__","text":"prefect/core/task.py::SignatureValidator.__new__\n def __new__(cls, name, parents, methods):\n run = methods.get(\"run\", lambda: None)\n run_sig = inspect.getfullargspec(run)\n if run_sig.varargs:\n raise ValueError(\n \"Tasks with variable positional arguments (*args) are not \"\n \"supported, because all Prefect arguments are stored as \"\n \"keywords. As a workaround, consider modifying the run() \"\n \"method to accept **kwargs and feeding the values \"\n \"to *args.\"\n )\n # necessary to ensure classes that inherit from parent class\n # also get passed through __new__\n return type.__new__(cls, name, parents, methods)","label":0},{"cid":"prefect/core/task.py::Task.__init__","text":"prefect/core/task.py::Task.__init__\n def __init__(\n self,\n name: str = None,\n slug: str = None,\n description: str = None,\n group: str = None,\n tags: Iterable[str] = None,\n max_retries: int = 0,\n retry_delay: timedelta = timedelta(minutes=1),\n timeout: timedelta = None,\n trigger: Callable[[Dict[\"Task\", \"State\"]], bool] = None,\n propagate_skip: bool = False,\n environment: Environment = None,\n checkpoint: bool = False,\n ) -> None:\n\n self.name = name or type(self).__name__\n self.slug = slug\n self.description = description\n\n self.group = str(group or prefect.context.get(\"_group\", \"\"))\n\n if isinstance(tags, str):\n tags = [tags]\n self.tags = set(tags or [])\n self.tags.update(prefect.context.get(\"_tags\", []))\n\n self.environment = environment\n self.checkpoint = checkpoint\n\n self.max_retries = max_retries\n self.retry_delay = retry_delay\n self.timeout = timeout\n\n self.trigger = trigger or prefect.triggers.all_successful\n self.propagate_skip = propagate_skip","label":1},{"cid":"prefect/core/task.py::Task.__repr__","text":"prefect/core/task.py::Task.__repr__\n def __repr__(self) -> str:\n return \"<Task: {self.name}>\".format(self=self)","label":0},{"cid":"prefect/core/task.py::Task.inputs","text":"prefect/core/task.py::Task.inputs\n def inputs(self) -> Tuple[str, ...]:\n return tuple(inspect.signature(self.run).parameters.keys())","label":0},{"cid":"prefect/core/task.py::Task.run","text":"prefect/core/task.py::Task.run\n def run(self): # type: ignore\n \"\"\"\n The main entrypoint for tasks.\n\n In addition to running arbitrary functions, tasks can interact with\n Prefect in a few ways:\n 1. Return an optional result. When this function runs successfully,\n the task is considered successful and the result (if any) is\n made available to downstream edges.\n 2. Raise an error. Errors are interpreted as failure.\n 3. Raise a signal. Signals can include FAIL, SUCCESS, WAIT, etc.\n and indicate that the task should be put in the indicated\n state.\n - FAIL will lead to retries if appropriate\n - WAIT will end execution and skip all downstream tasks with\n state WAITING_FOR_UPSTREAM (unless appropriate triggers\n are set). The task can be run again and should check\n context.is_waiting to see if it was placed in a WAIT.\n \"\"\"\n raise NotImplementedError()","label":0},{"cid":"prefect/core/task.py::Task.__call__","text":"prefect/core/task.py::Task.__call__\n def __call__(\n self, *args: object, upstream_tasks: Iterable[object] = None, **kwargs: object\n ) -> \"Task\":\n # this will raise an error if callargs weren't all provided\n signature = inspect.signature(self.run)\n callargs = dict(signature.bind(*args, **kwargs).arguments) # type: Dict\n\n # bind() compresses all variable keyword arguments under the ** argument name,\n # so we expand them explicitly\n var_kw_arg = next(\n (p for p in signature.parameters.values() if p.kind == VAR_KEYWORD), None\n )\n callargs.update(callargs.pop(var_kw_arg, {}))\n\n flow = prefect.context.get(\"_flow\", None)\n if not flow:\n raise ValueError(\"Could not infer an active Flow context.\")\n\n self.set_dependencies(\n flow=flow, upstream_tasks=upstream_tasks, keyword_tasks=callargs\n )\n\n return self","label":0},{"cid":"prefect/core/task.py::Task.set_dependencies","text":"prefect/core/task.py::Task.set_dependencies\n def set_dependencies(\n self,\n flow: \"Flow\" = None,\n upstream_tasks: Iterable[object] = None,\n downstream_tasks: Iterable[object] = None,\n keyword_tasks: Dict[str, object] = None,\n validate: bool = True,\n ) -> None:\n\n flow = flow or prefect.context.get(\"_flow\", None)\n if not flow:\n raise ValueError(\n \"No Flow was passed, and could not infer an active Flow context.\"\n )\n\n flow.set_dependencies( # type: ignore\n task=self,\n upstream_tasks=upstream_tasks,\n downstream_tasks=downstream_tasks,\n keyword_tasks=keyword_tasks,\n validate=validate,\n )","label":0},{"cid":"prefect/core/task.py::Task.info","text":"prefect/core/task.py::Task.info\n def info(self) -> Dict[str, Any]:\n \"\"\"\n A description of the task.\n \"\"\"\n return get_task_info(self)","label":0},{"cid":"prefect/core/task.py::Parameter.__init__","text":"prefect/core/task.py::Parameter.__init__\n def __init__(self, name: str, default: Any = None, required: bool = True) -> None:\n \"\"\"\n Args:\n name (str): the Parameter name.\n\n required (bool): If True, the Parameter is required and the default\n value is ignored.\n\n default (any): A default value for the parameter. If the default\n is not None, the Parameter will not be required.\n \"\"\"\n if default is not None:\n required = False\n\n self.required = required\n self.default = default\n\n super().__init__(name=name, slug=name)","label":0},{"cid":"prefect/core/task.py::Parameter.name","text":"prefect/core/task.py::Parameter.name\n @property # type: ignore\n def name(self) -> str: # type: ignore\n return self._name","label":0},{"cid":"prefect/core/task.py::Parameter.name","text":"prefect/core/task.py::Parameter.name\n @name.setter\n def name(self, value: str) -> None:\n if hasattr(self, \"_name\"):\n raise AttributeError(\"Parameter name can not be changed\")\n self._name = value # pylint: disable=W0201","label":0},{"cid":"prefect/core/task.py::Parameter.slug","text":"prefect/core/task.py::Parameter.slug\n @property # type: ignore\n def slug(self) -> str: # type: ignore\n \"\"\"\n A Parameter slug is always the same as its name. This information is used by\n Flow objects to enforce parameter name uniqueness.\n \"\"\"\n return self.name","label":0},{"cid":"prefect/core/task.py::Parameter.slug","text":"prefect/core/task.py::Parameter.slug\n @slug.setter\n def slug(self, value: str) -> None:\n # slug is a property, so it's not actually set by this method, but the superclass\n # attempts to set it and we need to allow that without error.\n if value != self.name:\n raise AttributeError(\"Parameter slug must be the same as its name.\")","label":0},{"cid":"prefect/core/task.py::Parameter.run","text":"prefect/core/task.py::Parameter.run\n def run(self) -> Any:\n params = prefect.context.get(\"_parameters\", {})\n if self.required and self.name not in params:\n raise prefect.signals.FAIL(\n 'Parameter \"{}\" was required but not provided.'.format(self.name)\n )\n return params.get(self.name, self.default)","label":0},{"cid":"prefect/core/task.py::Parameter.info","text":"prefect/core/task.py::Parameter.info\n def info(self) -> Dict[str, Any]:\n info = super().info()\n info.update(required=self.required, default=self.default)\n return info","label":0},{"cid":"prefect/engine/executors/base.py::Executor.__init__","text":"prefect/engine/executors/base.py::Executor.__init__\n def __init__(self):\n pass","label":0},{"cid":"prefect/engine/executors/base.py::Executor.start","text":"prefect/engine/executors/base.py::Executor.start\n @contextmanager\n def start(self):\n \"\"\"\n This method is called\n \"\"\"\n yield self","label":0},{"cid":"prefect/engine/executors/base.py::Executor.submit","text":"prefect/engine/executors/base.py::Executor.submit\n def submit(self, fn, *args, **kwargs):\n \"\"\"\n Submit a function to the executor for execution. Returns a future.\n \"\"\"\n raise NotImplementedError()","label":0},{"cid":"prefect/engine/executors/base.py::Executor.wait","text":"prefect/engine/executors/base.py::Executor.wait\n def wait(self, futures, timeout=None):\n \"\"\"\n Resolves futures to their values. Blocks until the future is complete.\n \"\"\"\n raise NotImplementedError()","label":0},{"cid":"prefect/engine/executors/base.py::Executor.set_state","text":"prefect/engine/executors/base.py::Executor.set_state\n def set_state(self, current_state: State, state: State, data: Any = None) -> State:\n return type(current_state)(state, data)","label":0},{"cid":"prefect/engine/executors/base.py::Executor.run_flow","text":"prefect/engine/executors/base.py::Executor.run_flow\n def run_flow(\n self,\n flow: Flow,\n state: State,\n task_states: Dict[Task, State],\n start_tasks: Iterable[Task],\n return_tasks: Iterable[Task],\n parameters: Dict,\n context: Dict,\n ):\n context = context or {}\n context.update(prefect.context)\n flow_runner = FlowRunner(flow=flow, executor=self)\n\n return self.submit(\n flow_runner.run,\n flow=flow,\n state=state,\n task_states=task_states,\n start_tasks=start_tasks,\n return_tasks=return_tasks,\n context=context,\n parameters=parameters,\n )","label":0},{"cid":"prefect/engine/executors/base.py::Executor.run_task","text":"prefect/engine/executors/base.py::Executor.run_task\n def run_task(\n self,\n task: Task,\n state: State,\n upstream_states: Dict[Task, State],\n inputs: Dict[str, Any],\n ignore_trigger=False,\n context=None,\n ):\n context = context or {}\n context.update(prefect.context)\n task_runner = prefect.engine.TaskRunner(task=task, executor=self)\n\n return self.submit(\n task_runner.run,\n state=state,\n upstream_states=upstream_states,\n inputs=inputs,\n ignore_trigger=ignore_trigger,\n context=prefect.context,\n )","label":0},{"cid":"prefect/engine/executors/distributed.py::running_in_cluster","text":"prefect/engine/executors/distributed.py::running_in_cluster\ndef running_in_cluster():\n \"\"\"\n Returns True if the current thread is running inside a distributed cluster\n and False otherwise.\n \"\"\"\n\n # lazy load because distributed is slow\n import distributed\n\n return hasattr(distributed.worker.thread_state, \"execution_state\")","label":0},{"cid":"prefect/engine/executors/distributed.py::distributed_client","text":"prefect/engine/executors/distributed.py::distributed_client\n@contextmanager\ndef distributed_client(address=None, separate_thread=False):\n \"\"\"\n Context manager that returns a Distributed client.\n\n Depending on how this function is called,\n 1. A standard Distributed client. This will be returned if 1) an\n address is provided or 2) this function is called from outside\n a Distributed cluster.\n 2. A Distributed worker_client. This will be returned if the function\n is called from inside a Distributed cluster (unless an address is\n provided).\n 1. If the context is entered from an existing cluster worker,\n a worker_client is returned and closed when the context exits.\n 2. If the context is entered outside the cluster a standard Client is\n returned and closed when the context exits. If the specified\n cluster address is \"local\" or missing, a LocalCluster is started\n and maintained for the life of this Prefect process. Note that\n other Prefect processes will NOT automatically discover the\n LocalCluster.\n\n If the context is entered from inside the cluster, a worker_client is\n yielded; otherwise a standard Client is yielded.\n\n Args:\n address (str): if provided, a Client will be returned that connects\n to that address. If None, the address will be read from the Prefect\n configuration (unless called from inside an existing cluster). If\n 'local' or 'localcluster', a LocalCluster is started and maintained\n for the life of this Prefect process. Other Prefect processes can\n NOT discover the local cluster, but it will be reused within this\n process.\n\n separate_thread (bool): if a worker_client is returned, this determines\n whether it secedes from the threadpool or not. Has no effect\n otherwise.\n \"\"\"\n\n global _LOCAL_CLUSTER\n ","label":0},{"cid":"prefect/engine/executors/distributed.py::DistributedExecutor.__init__","text":"prefect/engine/executors/distributed.py::DistributedExecutor.__init__\n def __init__(self, address=None, separate_thread=None, client=None):\n self.address = address\n self.separate_thread = separate_thread\n self.client = client\n super().__init__()","label":0},{"cid":"prefect/engine/executors/distributed.py::DistributedExecutor.start","text":"prefect/engine/executors/distributed.py::DistributedExecutor.start\n @contextmanager\n def start(self):\n if not self.client:\n old_client = self.client\n with distributed_client(\n address=self.address, separate_thread=self.separate_thread\n ) as client:\n self.client = client\n yield self\n self.client = old_client","label":0},{"cid":"prefect/engine/executors/distributed.py::DistributedExecutor.__getstate__","text":"prefect/engine/executors/distributed.py::DistributedExecutor.__getstate__\n def __getstate__(self):\n state = self.__dict__.copy()\n state[\"client\"] = None\n return state","label":0},{"cid":"prefect/engine/executors/distributed.py::DistributedExecutor.submit","text":"prefect/engine/executors/distributed.py::DistributedExecutor.submit\n def submit(self, fn, *args, **kwargs):\n \"\"\"\n Submit a function to the executor for execution. Returns a future.\n \"\"\"\n if \"pure\" not in kwargs:\n kwargs[\"pure\"] = False\n return self.client.submit(fn, *args, **kwargs)","label":0},{"cid":"prefect/engine/executors/distributed.py::DistributedExecutor.wait","text":"prefect/engine/executors/distributed.py::DistributedExecutor.wait\n def wait(self, futures, timeout=None):\n \"\"\"\n Resolves futures to their values. Blocks until the future is complete.\n \"\"\"\n return self.client.gather(futures)","label":0},{"cid":"prefect/engine/executors/local.py::LocalExecutor.submit","text":"prefect/engine/executors/local.py::LocalExecutor.submit\n def submit(self, fn, *args, **kwargs):\n \"\"\"\n Runs a function locally\n \"\"\"\n return fn(*args, **kwargs)","label":0},{"cid":"prefect/engine/executors/local.py::LocalExecutor.wait","text":"prefect/engine/executors/local.py::LocalExecutor.wait\n def wait(self, futures, timeout=None):\n \"\"\"\n Returns the provided futures\n \"\"\"\n return futures","label":0},{"cid":"prefect/engine/flow_runner.py::FlowRunner.__init__","text":"prefect/engine/flow_runner.py::FlowRunner.__init__\n def __init__(\n self,\n flow: Flow,\n executor: \"prefect.engine.executors.Executor\" = None,\n logger_name: str = None,\n ) -> None:\n self.flow = flow\n self.executor = executor or prefect.engine.executors.LocalExecutor()\n self.logger = logging.getLogger(logger_name)","label":0},{"cid":"prefect/engine/flow_runner.py::FlowRunner.run","text":"prefect/engine/flow_runner.py::FlowRunner.run\n def run(\n self,\n state: State = None,\n task_states: Dict[Task, State] = None,\n start_tasks: Iterable[Task] = None,\n return_tasks: Iterable[Task] = None,\n parameters: Dict[str, Any] = None,\n context: Dict[str, Any] = None,\n ) -> State:\n\n if state is None:\n state = State()\n parameters = parameters or {}\n task_states = task_states or {}\n start_tasks = start_tasks or []\n context = context or {}\n return_tasks = set(return_tasks or [])\n\n context.update(\n _flow_name=self.flow.name,\n _flow_version=self.flow.version,\n _parameters=parameters,\n )\n\n # if the run fails for any reason,\n return_task_states = {t: task_states.get(t, State()) for t in return_tasks}\n\n with prefect.context(context):\n\n with self.executor.start():\n\n try:\n state = self._run(\n state=state,\n task_states=task_states,\n start_tasks=start_tasks,\n return_tasks=return_tasks,\n parameters=parameters,\n )\n except prefect.signals.DONTRUN:\n self.logger.info(\"Flow run DONTRUN\")\n # set state but no need to go through the executor\n state = State(state.state, data=return_task_states)\n except Exception as e:\n self.logger.info(\"Flow run FAIL\")\n # set state through executor\n return_task_states.update(dict(message=e))\n state = self.executor.set_state(\n state, State.FAILED, data=return_task_states\n )\n\n return state","label":0},{"cid":"prefect/engine/flow_runner.py::FlowRunner._run","text":"prefect/engine/flow_runner.py::FlowRunner._run\n def _run(\n self,\n state: State,\n task_states: Dict[Task, State],\n start_tasks: Iterable[Task],\n return_tasks: Iterable[Task],\n parameters: Dict[str, Any],\n ) -> State:\n\n # ---------------------------------------------\n # Check for required parameters\n # ---------------------------------------------\n\n required_params = self.flow.parameters(only_required=True)\n missing = set(required_params).difference(\n prefect.context.get(\"_parameters\", [])\n )\n if missing:\n raise ValueError(\"Required parameters not provided: {}\".format(missing))\n\n # ---------------------------------------------\n # Check if the flow run is ready to run\n # ---------------------------------------------\n\n # this flow run is already finished\n if state.is_finished():\n raise prefect.signals.DONTRUN(\"Flow run has already finished.\")\n\n # this must be pending or running\n elif not (state.is_pending() or state.is_running()):\n raise prefect.signals.DONTRUN(\"Flow is not ready to run.\")\n\n # ---------------------------------------------\n # Start!\n # ---------------------------------------------\n\n # update state\n state = self.executor.set_state(state, state=State.RUNNING)\n\n # -- process each task in order\n for task in self.flow.sorted_tasks(root_tasks=start_tasks):\n\n upstream_states = {}\n upstream_inputs = {}\n\n # -- process each edge to the task\n for edge in self.flow.edges_to(task):\n\n # extract upstream state to pass to the task trigger\n upstream_states[edge.upstream_task] = self.executor.submit(\n lambda s: State(s.state), task_states[edge.upstream_task]\n )\n\n # if the upstream task is supposed to pass data, then ","label":0},{"cid":"prefect/engine/task_runner.py::TaskRunner.__init__","text":"prefect/engine/task_runner.py::TaskRunner.__init__\n def __init__(\n self,\n task: Task,\n executor: \"prefect.engine.executors.Executor\" = None,\n logger_name: str = None,\n ) -> None:\n self.task = task\n if executor is None:\n executor = prefect.engine.executors.LocalExecutor()\n self.executor = executor\n self.logger = logging.getLogger(logger_name)","label":0},{"cid":"prefect/engine/task_runner.py::TaskRunner.run","text":"prefect/engine/task_runner.py::TaskRunner.run\n def run(\n self,\n state: State = None,\n upstream_states: Dict[Task, State] = None,\n inputs: Dict[str, Any] = None,\n ignore_trigger: bool = False,\n context: Dict[str, Any] = None,\n ) -> State:\n if state is None:\n state = State()\n upstream_states = upstream_states or {}\n context = context or {}\n inputs = inputs or {}\n\n # prepare context\n context.update(\n _task_name=self.task.name,\n _task_max_retries=self.task.max_retries,\n _task_run_upstream_states=upstream_states,\n _task_run_inputs=inputs,\n )\n\n # set up context\n with prefect.context(context):\n\n # prepare executor\n with self.executor.start():\n\n try:\n state = self._run(\n state=state,\n upstream_states=upstream_states,\n inputs=inputs,\n ignore_trigger=ignore_trigger,\n )\n\n except signals.DONTRUN:\n pass\n\n except signals.SUCCESS:\n logging.info(\"SUCCESS\")\n state = self.executor.set_state(state, state.SUCCESS)\n\n except signals.FAIL as e:\n state = self.handle_fail(state, data=dict(message=e))\n\n except signals.RETRY:\n state = self.handle_retry(state)\n\n except signals.SKIP:\n logging.info(\"SKIP\")\n state = self.executor.set_state(state, state.SKIPPED)\n\n except Exception as e:\n logging.info(\"Unexpected error while running task.\")\n state = self.handle_fail(state, data=dict(message=e))\n\n return state","label":0},{"cid":"prefect/engine/task_runner.py::TaskRunner._run","text":"prefect/engine/task_runner.py::TaskRunner._run\n def _run(\n self,\n state: State,\n upstream_states: Dict[Task, State],\n inputs: Dict[str, Any],\n ignore_trigger: bool,\n ):\n\n # -------------------------------------------------------------\n # check upstream tasks\n # -------------------------------------------------------------\n\n # make sure all upstream tasks are finished\n if not all(s.is_finished() for s in upstream_states.values()):\n raise signals.DONTRUN(\"Upstream tasks are not finished.\")\n\n # -------------------------------------------------------------\n # check upstream skips and propagate if appropriate\n # -------------------------------------------------------------\n\n if self.task.propagate_skip and any(\n s.is_skipped() for s in upstream_states.values()\n ):\n raise signals.SKIP(\"Upstream tasks skipped.\")\n\n # -------------------------------------------------------------\n # check trigger\n # -------------------------------------------------------------\n\n if not ignore_trigger and not self.task.trigger(upstream_states):\n raise signals.DONTRUN(\"Trigger failed\")\n\n # -------------------------------------------------------------\n # check this task's state\n # -------------------------------------------------------------\n\n # this task is already running\n if state.is_running():\n raise signals.DONTRUN(\"Task is already running.\")\n\n # this task is already finished\n elif state.is_finished():\n raise signals.DONTRUN(\"Task is already finished.\")\n\n # this task is not pending\n elif not state.is_pending():\n raise signals.DONTRUN(\"Task is not ready to run (state {}).\".format(state))\n\n # -------------------------------------------------------------\n # start!\n # --------------------------------","label":0},{"cid":"prefect/engine/task_runner.py::TaskRunner.handle_fail","text":"prefect/engine/task_runner.py::TaskRunner.handle_fail\n def handle_fail(self, state, data=None):\n \"\"\"\n Checks if a task is eligable for retry; otherwise marks it failed.\n \"\"\"\n self.logger.info(\"Task FAILED\")\n run_number = prefect.context.get(\"_task_run_number\", 1)\n if run_number and run_number <= self.task.max_retries:\n return self.handle_retry(state)\n else:\n return self.executor.set_state(state, State.FAILED, data=data)","label":0},{"cid":"prefect/engine/task_runner.py::TaskRunner.handle_retry","text":"prefect/engine/task_runner.py::TaskRunner.handle_retry\n def handle_retry(self, state, retry_time=None):\n # TODO exponential backoff based on run_number\n # run_number = prefect.context.get('_task_run_number', 0)\n\n self.logger.info(\"Task RETRYING\")\n if retry_time is None:\n retry_time = datetime.datetime.utcnow() + self.task.retry_delay\n\n return self.executor.set_state(state, State.RETRYING, data=retry_time)","label":0},{"cid":"prefect/signals.py::PrefectStateException.__init__","text":"prefect/signals.py::PrefectStateException.__init__\n def __init__(self, *args, result=None, **kwargs) -> None: # type: ignore\n self.result = result\n super().__init__(*args, **kwargs)","label":0},{"cid":"prefect/tasks/core/function_task.py::FunctionTask.__init__","text":"prefect/tasks/core/function_task.py::FunctionTask.__init__\n def __init__(self, fn: Callable, name: str = None, **kwargs) -> None:\n if not callable(fn):\n raise TypeError(\"fn must be callable.\")\n\n # set the name from the fn\n if name is None:\n name = getattr(fn, \"__name__\", type(self).__name__)\n\n if inspect.getfullargspec(fn).varargs:\n raise ValueError(\n \"Tasks with variable positional arguments (*args) are not \"\n \"supported, because all Prefect arguments are stored as \"\n \"keywords. As a workaround, consider modifying the function \"\n \"to accept **kwargs and feeding the values \"\n \"to the original function's *args.\"\n )\n\n self.run = fn\n\n super().__init__(name=name, **kwargs)","label":0},{"cid":"prefect/triggers.py::always_run","text":"prefect/triggers.py::always_run\ndef always_run(upstream_states: Dict[\"Task\", \"State\"]) -> bool:\n \"\"\"\n This task will run no matter what the upstream states are.\n \"\"\"\n\n return True","label":0},{"cid":"prefect/triggers.py::manual_only","text":"prefect/triggers.py::manual_only\ndef manual_only(upstream_states: Dict[\"Task\", \"State\"]) -> bool:\n \"\"\"\n This task will never run automatically. It will only run if it is\n specifically instructed, either by ignoring the trigger or adding it\n as a flow run's start task.\n \"\"\"\n\n return False","label":0},{"cid":"prefect/triggers.py::all_successful","text":"prefect/triggers.py::all_successful\ndef all_successful(upstream_states: Dict[\"Task\", \"State\"]) -> bool:\n \"\"\"\n Runs if all upstream tasks were successful. SKIPPED tasks are considered\n successes (SKIP_DOWNSTREAM is not).\n\n If any tasks failed, this task will fail since the trigger can not be\n acheived.\n \"\"\"\n\n if not all(s.is_successful() for s in upstream_states.values()):\n raise signals.FAIL(\"Trigger failed: some preceding tasks failed\")\n return True","label":0},{"cid":"prefect/triggers.py::all_failed","text":"prefect/triggers.py::all_failed\ndef all_failed(upstream_states: Dict[\"Task\", \"State\"]) -> bool:\n \"\"\"\n Runs if all upstream tasks failed. SKIPPED tasks are considered successes.\n \"\"\"\n\n if not all(s.is_failed() for s in upstream_states.values()):\n raise signals.FAIL(\"Trigger failed: some preceding tasks succeeded\")\n return True","label":0},{"cid":"prefect/triggers.py::all_finished","text":"prefect/triggers.py::all_finished\ndef all_finished(upstream_states: Dict[\"Task\", \"State\"]) -> bool:\n \"\"\"\n Runs if all tasks finished (either SUCCESS, FAIL, SKIP)\n \"\"\"\n return all(s.is_finished() for s in upstream_states.values())","label":0},{"cid":"prefect/triggers.py::any_successful","text":"prefect/triggers.py::any_successful\ndef any_successful(upstream_states: Dict[\"Task\", \"State\"]) -> bool:\n \"\"\"\n Runs if any tasks were successful. SKIPPED tasks are considered successes;\n SKIP_DOWNSTREAM is not.\n \"\"\"\n\n if not any(s.is_successful() for s in upstream_states.values()):\n raise signals.FAIL(\"Trigger failed: no preceding tasks succeeded\")\n return True","label":0},{"cid":"prefect/triggers.py::any_failed","text":"prefect/triggers.py::any_failed\ndef any_failed(upstream_states: Dict[\"Task\", \"State\"]) -> bool:\n \"\"\"\n No failed tasks -> fail\n * skipped tasks count as successes\n \"\"\"\n\n if not any(s.is_failed() for s in upstream_states.values()):\n raise signals.FAIL(\"Trigger failed: no preceding tasks failed\")\n return True","label":0},{"cid":"prefect/utilities/collections.py::merge_dicts","text":"prefect/utilities/collections.py::merge_dicts\ndef merge_dicts(d1: dict, d2: dict) -> dict:\n \"\"\"\n Updates d1 from d2 by replacing each (k, v1) pair in d1 with the\n corresponding (k, v2) pair in d2.\n\n If the value of each pair is itself a dict, then the value is updated\n recursively.\n \"\"\"\n\n new_dict = d1.copy()\n\n for k, v in d2.items():\n if isinstance(new_dict.get(k), dict) and isinstance(v, dict):\n new_dict[k] = merge_dicts(new_dict[k], d2[k])\n else:\n new_dict[k] = d2[k]\n return new_dict","label":0},{"cid":"prefect/utilities/collections.py::DotDict.__init__","text":"prefect/utilities/collections.py::DotDict.__init__\n def __init__(self, *args, **kwargs):\n if args:\n arg_dict = args[0]\n self.update(arg_dict)\n self.update(kwargs)","label":0},{"cid":"prefect/utilities/collections.py::DotDict.__getitem__","text":"prefect/utilities/collections.py::DotDict.__getitem__\n def __getitem__(self, key):\n return self.__dict__[key]","label":0},{"cid":"prefect/utilities/collections.py::DotDict.__setitem__","text":"prefect/utilities/collections.py::DotDict.__setitem__\n def __setitem__(self, key, value):\n if hasattr(MutableMapping, key):\n raise ValueError(\"no sir\")\n self.__dict__[key] = value","label":0},{"cid":"prefect/utilities/collections.py::DotDict.__setattr__","text":"prefect/utilities/collections.py::DotDict.__setattr__\n def __setattr__(self, attr, value):\n self[attr] = value","label":0},{"cid":"prefect/utilities/collections.py::DotDict.__iter__","text":"prefect/utilities/collections.py::DotDict.__iter__\n def __iter__(self):\n return iter(self.__dict__.keys())","label":0},{"cid":"prefect/utilities/collections.py::DotDict.__delitem__","text":"prefect/utilities/collections.py::DotDict.__delitem__\n def __delitem__(self, key):\n del self.__dict__[key]","label":0},{"cid":"prefect/utilities/collections.py::DotDict.__len__","text":"prefect/utilities/collections.py::DotDict.__len__\n def __len__(self):\n return len(self.__dict__)","label":0},{"cid":"prefect/utilities/collections.py::DotDict.copy","text":"prefect/utilities/collections.py::DotDict.copy\n def copy(self):\n \"Returns a shallow copy\"\n return type(self)(self.__dict__.copy())","label":0},{"cid":"prefect/utilities/collections.py::to_dotdict","text":"prefect/utilities/collections.py::to_dotdict\ndef to_dotdict(obj):\n \"\"\"\n Given a obj formatted as a dictionary, returns an object\n that also supports \"dot\" access:\n\n obj['data']['child']\n obj.data.child\n \"\"\"\n if isinstance(obj, (list, tuple, set)):\n return type(obj)([to_dotdict(d) for d in obj])\n elif isinstance(obj, dict):\n for key, value in list(obj.items()):\n obj[key] = to_dotdict(value)\n return obj\n return obj","label":0},{"cid":"prefect/utilities/collections.py::dict_to_flatdict","text":"prefect/utilities/collections.py::dict_to_flatdict\ndef dict_to_flatdict(dct, parent=None):\n \"\"\"Converts a (nested) dictionary to a flattened representation.\n\n Each key\n of the flat dict will be a CompoundKey tuple containing the \"chain of keys\"\n for the corresponding value.\n\n Args:\n dct (dict): The dictionary to flatten\n parent (CompoundKey, optional): Defaults to None. The parent key\n (you shouldn't need to set this)\n\n \"\"\"\n\n items = []\n parent = parent or CompoundKey()\n for k, v in dct.items():\n k_parent = CompoundKey(parent + (k,))\n if isinstance(v, dict):\n items.extend(dict_to_flatdict(v, parent=k_parent).items())\n else:\n items.append((k_parent, v))\n return dict(items)","label":0},{"cid":"prefect/utilities/collections.py::flatdict_to_dict","text":"prefect/utilities/collections.py::flatdict_to_dict\ndef flatdict_to_dict(dct):\n \"\"\"Converts a flattened dictionary back to a nested dictionary.\n\n Args:\n dct (dict): The dictionary to be nested. Each key should be a\n CompoundKey, as generated by dict_to_flatdict()\n\n \"\"\"\n\n result = {}\n for k, v in dct.items():\n if isinstance(k, CompoundKey):\n current_dict = result\n for ki in k[:-1]:\n current_dict = current_dict.setdefault(ki, {})\n current_dict[k[-1]] = v\n else:\n result[k] = v\n\n return result","label":0},{"cid":"prefect/utilities/context.py::PrefectContext.__repr__","text":"prefect/utilities/context.py::PrefectContext.__repr__\n def __repr__(self) -> str:\n return \"<Prefect Context>\"","label":0},{"cid":"prefect/utilities/context.py::PrefectContext.__call__","text":"prefect/utilities/context.py::PrefectContext.__call__\n @contextlib.contextmanager\n def __call__(\n self, *args: MutableMapping, **kwargs: Any\n ) -> Iterator[\"PrefectContext\"]:\n \"\"\"\n A context manager for setting / resetting the Prefect context\n\n Example:\n import prefect.context\n with prefect.context(dict(a=1, b=2), c=3):\n print(prefect.context.a) # 1\n \"\"\"\n previous_context = self.copy()\n try:\n self.update(*args, **kwargs)\n yield self\n finally:\n self.clear()\n self.update(previous_context)","label":0},{"cid":"prefect/utilities/datetimes.py::retry_delay","text":"prefect/utilities/datetimes.py::retry_delay\ndef retry_delay(\n interval=None,\n *,\n exponential_backoff=False,\n max_delay=datetime.timedelta(hours=2),\n **kwargs\n):\n \"\"\"\n A helper function for generating task retry delays.\n\n Args:\n interval (timedelta): The amount of time to wait.\n This value is optional; users can also instantiate a new interval\n by passing keyword arguments directly to retry_delay. So:\n retry_delay(interval=timedelta(days=1))\n is equivalent to:\n retry_delay(days=1)\n\n **kwargs: Keyword arguments are passed to timedelta and\n are compatible with the datetime.timedelta API.\n\n exponential_backoff: if True, each retry delay will be exponentially\n longer than the last, starting with the second retry. For example,\n if the retry delay is 1 minute, then:\n - first retry starts after 1 minute\n - second retry also starts after 1 minute (no backoff applied)\n - third retry starts after 2 minutes\n - fourth retry starts after 4 minutes\n - etc.\n\n max_delay (timedelta): If exponential_backoff is supplied,\n delays will be capped by this amount.\n \"\"\"\n if interval is not None and kwargs:\n raise ValueError(\"Provide an interval or interval keywords, but not both.\")\n elif interval is None and not kwargs:\n raise ValueError(\"Provide either an interval or interval keywords.\")\n elif kwargs:\n interval = datetime.timedelta(**kwargs)\n\n def retry_delay(run_number):\n if exponential_backoff:\n scale = 2 ** (max(0, run_number - 2))\n else:\n scale = 1\n if max_delay is not None:\n return min(interval * scale, max_delay)\n else:\n return interval * scale\n\n return retry_delay","label":0},{"cid":"prefect/utilities/datetimes.py::retry_delay.retry_delay","text":"prefect/utilities/datetimes.py::retry_delay.retry_delay\n def retry_delay(run_number):\n if exponential_backoff:\n scale = 2 ** (max(0, run_number - 2))\n else:\n scale = 1\n if max_delay is not None:\n return min(interval * scale, max_delay)\n else:\n return interval * scale","label":0},{"cid":"prefect/utilities/datetimes.py::parse_datetime","text":"prefect/utilities/datetimes.py::parse_datetime\ndef parse_datetime(dt):\n \"\"\"\n Parses a string, bytes, float, or datetime object and returns a\n corresponding datetime\n \"\"\"\n if isinstance(dt, (str, bytes)):\n return dateutil.parser.parse(dt)\n elif isinstance(dt, float):\n return datetime.datetime.fromtimestamp(dt)\n elif isinstance(dt, (datetime.datetime)):\n return dt\n else:\n raise TypeError(\"Unrecognized datetime input: {}\".format(type(dt).__name__))","label":0},{"cid":"prefect/utilities/json.py::from_qualified_name","text":"prefect/utilities/json.py::from_qualified_name\ndef from_qualified_name(obj_str: str) -> object:\n \"\"\"\n Retrives an object from a fully qualified string path. The object must be\n imported in advance.\n \"\"\"\n\n path_components = obj_str.split(\".\")\n for i in range(len(path_components), 0, -1):\n module_path = \".\".join(path_components[:i])\n # import ipdb; ipdb.set_trace()\n if module_path in sys.modules:\n obj = sys.modules[module_path]\n for p in path_components[i:]:\n obj = getattr(obj, p)\n return obj\n raise ValueError(\n \"Couldn't load \\\"{}\\\"; maybe it hasn't been imported yet?\".format(obj_str)\n )","label":0},{"cid":"prefect/utilities/json.py::to_qualified_name","text":"prefect/utilities/json.py::to_qualified_name\ndef to_qualified_name(obj: Any) -> str:\n return obj.__module__ + \".\" + obj.__qualname__","label":0},{"cid":"prefect/utilities/json.py::register_json_codec","text":"prefect/utilities/json.py::register_json_codec\ndef register_json_codec(register_type: Type = None) -> Callable:\n \"\"\"\n Decorator that registers a JSON Codec to a corresponding codec_key.\n\n If a register_type is provided, the codec will automatically be applied\n to that type.\n\n Whenever the codec is used to serialize an object, the result will be a\n key-value pair where the key is the codec_key and the value is the\n serialized object. During deserialization, the codec_key will be used to\n identify the appropriate codec.\n \"\"\"\n\n def _register(codec_class: \"JSONCodec\") -> \"JSONCodec\":\n\n full_codec_key = CODEC_PREFIX + codec_class.codec_key\n\n # register the codec key\n if full_codec_key in JSON_CODECS_KEYS:\n raise ValueError(\n 'A JSON codec is already registered for codec_key \"{}\"'.format(\n codec_class.codec_key\n )\n )\n JSON_CODECS_KEYS[full_codec_key] = codec_class\n\n # register the dispatch type\n if register_type:\n get_json_codec.register(register_type)(lambda obj: codec_class)\n\n return codec_class\n\n return _register","label":0},{"cid":"prefect/utilities/json.py::register_json_codec._register","text":"prefect/utilities/json.py::register_json_codec._register\n def _register(codec_class: \"JSONCodec\") -> \"JSONCodec\":\n\n full_codec_key = CODEC_PREFIX + codec_class.codec_key\n\n # register the codec key\n if full_codec_key in JSON_CODECS_KEYS:\n raise ValueError(\n 'A JSON codec is already registered for codec_key \"{}\"'.format(\n codec_class.codec_key\n )\n )\n JSON_CODECS_KEYS[full_codec_key] = codec_class\n\n # register the dispatch type\n if register_type:\n get_json_codec.register(register_type)(lambda obj: codec_class)\n\n return codec_class","label":0},{"cid":"prefect/utilities/json.py::get_json_codec","text":"prefect/utilities/json.py::get_json_codec\n@singledispatch\ndef get_json_codec(obj: object) -> Optional[\"JSONCodec\"]: # pylint: disable=W0613\n return None","label":0},{"cid":"prefect/utilities/json.py::JSONCodec.__init__","text":"prefect/utilities/json.py::JSONCodec.__init__\n def __init__(self, value: O) -> None:\n if not self.codec_key:\n raise ValueError(\"This JSONCodec class has no codec key set.\")\n self.value = value","label":0},{"cid":"prefect/utilities/json.py::JSONCodec.serialize","text":"prefect/utilities/json.py::JSONCodec.serialize\n def serialize(self) -> J:\n \"\"\"\n Serializes an object to a JSON-compatible type (str, dict, list, tuple).\n \"\"\"\n raise NotImplementedError()","label":0},{"cid":"prefect/utilities/json.py::JSONCodec.deserialize","text":"prefect/utilities/json.py::JSONCodec.deserialize\n @staticmethod\n def deserialize(obj: J) -> O:\n \"\"\"\n Deserialize an object.\n \"\"\"\n raise NotImplementedError()","label":0},{"cid":"prefect/utilities/json.py::JSONCodec.__json__","text":"prefect/utilities/json.py::JSONCodec.__json__\n def __json__(self) -> Dict[str, J]:\n \"\"\"\n Called by the JSON encoder in order to transform this object into\n a serializable dictionary. By default it returns\n {self.codec_key: self.serialize()}\n \"\"\"\n return {CODEC_PREFIX + self.codec_key: self.serialize()}","label":0},{"cid":"prefect/utilities/json.py::JSONCodec.__eq__","text":"prefect/utilities/json.py::JSONCodec.__eq__\n def __eq__(self, other: Any) -> bool:\n return type(self) == type(other) and self.value == other.value","label":0},{"cid":"prefect/utilities/json.py::SetCodec.serialize","text":"prefect/utilities/json.py::SetCodec.serialize\n def serialize(self) -> list:\n return list(self.value)","label":0},{"cid":"prefect/utilities/json.py::SetCodec.deserialize","text":"prefect/utilities/json.py::SetCodec.deserialize\n @staticmethod\n def deserialize(obj: list) -> set:\n return set(obj)","label":0},{"cid":"prefect/utilities/json.py::BytesCodec.serialize","text":"prefect/utilities/json.py::BytesCodec.serialize\n def serialize(self) -> str:\n return self.value.decode()","label":0},{"cid":"prefect/utilities/json.py::BytesCodec.deserialize","text":"prefect/utilities/json.py::BytesCodec.deserialize\n @staticmethod\n def deserialize(obj: str) -> bytes:\n return obj.encode()","label":0},{"cid":"prefect/utilities/json.py::UUIDCodec.serialize","text":"prefect/utilities/json.py::UUIDCodec.serialize\n def serialize(self) -> str:\n return str(self.value)","label":0},{"cid":"prefect/utilities/json.py::UUIDCodec.deserialize","text":"prefect/utilities/json.py::UUIDCodec.deserialize\n @staticmethod\n def deserialize(obj: str) -> uuid.UUID:\n return uuid.UUID(obj)","label":0},{"cid":"prefect/utilities/json.py::DateTimeCodec.serialize","text":"prefect/utilities/json.py::DateTimeCodec.serialize\n def serialize(self) -> str:\n return self.value.isoformat()","label":0},{"cid":"prefect/utilities/json.py::DateTimeCodec.deserialize","text":"prefect/utilities/json.py::DateTimeCodec.deserialize\n @staticmethod\n def deserialize(obj: str) -> datetime.datetime:\n return dateutil.parser.parse(obj)","label":0},{"cid":"prefect/utilities/json.py::DateCodec.serialize","text":"prefect/utilities/json.py::DateCodec.serialize\n def serialize(self) -> str:\n return self.value.isoformat()","label":0},{"cid":"prefect/utilities/json.py::DateCodec.deserialize","text":"prefect/utilities/json.py::DateCodec.deserialize\n @staticmethod\n def deserialize(obj: str) -> datetime.date:\n return dateutil.parser.parse(obj).date()","label":0},{"cid":"prefect/utilities/json.py::TimeDeltaCodec.serialize","text":"prefect/utilities/json.py::TimeDeltaCodec.serialize\n def serialize(self) -> float:\n return self.value.total_seconds()","label":0},{"cid":"prefect/utilities/json.py::TimeDeltaCodec.deserialize","text":"prefect/utilities/json.py::TimeDeltaCodec.deserialize\n @staticmethod\n def deserialize(obj: float) -> datetime.timedelta:\n return datetime.timedelta(seconds=obj)","label":0},{"cid":"prefect/utilities/json.py::LoadObjectCodec.serialize","text":"prefect/utilities/json.py::LoadObjectCodec.serialize\n def serialize(self) -> str:\n return to_qualified_name(self.value)","label":0},{"cid":"prefect/utilities/json.py::LoadObjectCodec.deserialize","text":"prefect/utilities/json.py::LoadObjectCodec.deserialize\n @staticmethod\n def deserialize(obj: str) -> Any:\n return from_qualified_name(obj)","label":0},{"cid":"prefect/utilities/json.py::serializable","text":"prefect/utilities/json.py::serializable\ndef serializable(fn: Callable) -> Callable:\n \"\"\"\n Decorator that marks a function as automatically serializable via\n LoadObjectCodec\n \"\"\"\n if hasattr(fn, \"__json__\"):\n raise ValueError(\"Object already has a __json__() method.\")\n setattr(fn, \"__json__\", lambda: LoadObjectCodec(fn).__json__())\n return fn","label":0},{"cid":"prefect/utilities/json.py::EncryptedCodec.serialize","text":"prefect/utilities/json.py::EncryptedCodec.serialize\n def serialize(self) -> str:\n key = prefect.config.security.encryption_key\n payload = base64.b64encode(json.dumps(self.value).encode())\n return Fernet(key).encrypt(payload).decode()","label":0},{"cid":"prefect/utilities/json.py::EncryptedCodec.deserialize","text":"prefect/utilities/json.py::EncryptedCodec.deserialize\n @staticmethod\n def deserialize(obj: str) -> J:\n key = prefect.config.security.encryption_key\n decrypted = Fernet(key).decrypt(obj.encode())\n decoded = base64.b64decode(decrypted).decode()\n return json.loads(decoded)","label":0},{"cid":"prefect/utilities/json.py::ObjectInitArgsCodec.serialize","text":"prefect/utilities/json.py::ObjectInitArgsCodec.serialize\n def serialize(self) -> dict:\n\n init_args = {}\n\n signature = inspect.signature(self.value.__init__) # type: ignore\n for arg, param in signature.parameters.items():\n\n # see if the init arg was stored in the _init_args dict\n if arg in getattr(self.value, \"_init_args\", {}):\n init_arg = self.value._init_args[arg]\n\n # check for an underscore-prefixed attribute with the same name\n elif hasattr(self.value, \"_\" + arg):\n init_arg = getattr(self.value, \"_\" + arg)\n\n # check for an attribute with the same name\n elif hasattr(self.value, arg):\n init_arg = getattr(self.value, arg)\n\n # default for var_positional\n elif param.kind == param.VAR_POSITIONAL:\n init_arg = ()\n\n # default for var_keyword\n elif param.kind == param.VAR_KEYWORD:\n init_arg = {}\n\n # fail\n else:\n raise ValueError(\n 'Could not find a value for the required init arg \"{a}\" of '\n \"object {o}. The value should be stored in an attribute \"\n \"with the same name as the argument, optionally prefixed \"\n \"with an underscore, or an _init_args dict \"\n \"attribute.\".format(a=arg, o=self.value)\n )\n\n if param.kind == param.VAR_KEYWORD:\n init_args[\"**kwargs\"] = init_arg\n elif param.kind == param.VAR_POSITIONAL:\n init_args[\"*args\"] = init_arg\n else:\n init_args[arg] = init_arg\n\n return dict(type=type(self.value), args=init_args)","label":0},{"cid":"prefect/utilities/json.py::ObjectInitArgsCodec.deserialize","text":"prefect/utilities/json.py::ObjectInitArgsCodec.deserialize\n @staticmethod\n def deserialize(obj: dict) -> object:\n cls = obj[\"type\"]\n kwargs = obj[\"args\"].pop(\"**kwargs\", {})\n args = obj[\"args\"].pop(\"*args\", ())\n kwargs.update(obj[\"args\"])\n return cls(*args, **kwargs)","label":0},{"cid":"prefect/utilities/json.py::ObjectAttributesCodec.__init__","text":"prefect/utilities/json.py::ObjectAttributesCodec.__init__\n def __init__(self, value: Any, attributes_list: list = None) -> None:\n super().__init__(value)\n if attributes_list is None:\n attributes_list = list(value.__dict__.keys())\n self.attrs = attributes_list","label":0},{"cid":"prefect/utilities/json.py::ObjectAttributesCodec.serialize","text":"prefect/utilities/json.py::ObjectAttributesCodec.serialize\n def serialize(self) -> dict:\n return dict(\n type=type(self.value), attrs={a: getattr(self.value, a) for a in self.attrs}\n )","label":0},{"cid":"prefect/utilities/json.py::ObjectAttributesCodec.deserialize","text":"prefect/utilities/json.py::ObjectAttributesCodec.deserialize\n @staticmethod\n def deserialize(obj: dict) -> object:\n instance = object.__new__(obj[\"type\"])\n instance.__dict__.update(obj[\"attrs\"])\n return instance","label":0},{"cid":"prefect/utilities/json.py::Serializable.__json__","text":"prefect/utilities/json.py::Serializable.__json__\n def __json__(self) -> JSONCodec:\n return self._json_codec(value=self)","label":0},{"cid":"prefect/utilities/json.py::PrefectJSONEncoder.default","text":"prefect/utilities/json.py::PrefectJSONEncoder.default\n def default(self, obj: Any):\n \"\"\"\n Recursive method called when encoding JSON objects\n\n - Any class with a __json__() method is stored as the result of\n calling that method.\n - If no __json__ method is found, a JSON codec is applied via\n single dispatch.\n - Otherwise the original json encoding is used.\n \"\"\"\n # call __json__ method if it exists\n if hasattr(obj, \"__json__\") and not isinstance(obj, type):\n return obj.__json__()\n\n # otherwise try to apply a json codec by dispatching on type\n # issue with singledispatch and pylint: https://github.com/PyCQA/pylint/issues/2155\n obj_codec = get_json_codec(obj) # pylint: disable=E1128\n if obj_codec:\n return obj_codec(obj).__json__() # pylint: disable=E1102\n\n else:\n return json.JSONEncoder.default(self, obj)","label":0},{"cid":"prefect/utilities/json.py::PrefectJSONDecoder.__init__","text":"prefect/utilities/json.py::PrefectJSONDecoder.__init__\n def __init__(self, *args, **kwargs):\n super().__init__(*args, object_hook=self.object_hook, **kwargs)","label":0},{"cid":"prefect/utilities/json.py::PrefectJSONDecoder.object_hook","text":"prefect/utilities/json.py::PrefectJSONDecoder.object_hook\n def object_hook(self, dct):\n # iterate over the dictionary looking for a key that matches a codec.\n # it would be extremely unusual to have more than one such key.\n for key, value in dct.items():\n codec = JSON_CODECS_KEYS.get(key, None)\n if codec:\n full_codec_key = CODEC_PREFIX + codec.codec_key\n return codec.deserialize(dct[full_codec_key])\n\n return dct","label":0},{"cid":"prefect/utilities/strings.py::is_valid_identifier","text":"prefect/utilities/strings.py::is_valid_identifier\ndef is_valid_identifier(string: str) -> bool:\n \"\"\"\n Determines whether a string is a valid Python identifier (meaning it can\n be used as a variable name or keyword argument).\n\n Example:\n >>> is_valid_identifier('hi5')\n True\n >>> is_valid_identifier('5hi')\n False\n >>> is_valid_identifier('from')\n False\n >>> is_valid_identifier('hi.5')\n False\n \"\"\"\n return string.isidentifier() and not keyword.iskeyword(string)","label":0},{"cid":"prefect/utilities/strings.py::name_with_suffix","text":"prefect/utilities/strings.py::name_with_suffix\ndef name_with_suffix(\n name,\n predicate,\n first_suffix=1,\n delimiter=\"_\",\n max_iters=1000,\n always_add_suffix=False,\n) -> str:\n \"\"\"\n Automatically adds a number suffix to a name until it becomes valid.\n\n Example:\n >>> name_with_suffix('name', predicate=lambda n: True)\n name_1\n >>> name_with_suffix('name', predicate=lambda n: int(n[-1]) > 2)\n name_3\n\n Args:\n name (string): the desired name\n\n predicate (callable): a function that takes the current name and\n returns whether it is valid. The numerical suffix will be\n incremented as long as the predicate returns False.\n\n first_suffix (int): the first suffix number that will be tried\n\n delimiter (string): a delimiter placed between the name and the suffix\n\n max_iters (int): evaluation will stop after this many iterations. An\n error will be raised.\n\n always_add_suffix (bool): if True, a suffix will be added to the name\n even if the provided name is valid. Defaults to False.\n \"\"\"\n i = 0\n\n if always_add_suffix:\n new_name = \"{}{}{}\".format(name, delimiter, first_suffix + i)\n i += 1\n else:\n new_name = name\n\n while not predicate(new_name) and i <= max_iters:\n new_name = \"{}{}{}\".format(name, delimiter, first_suffix + i)\n i = i + 1\n\n if i > max_iters:\n raise ValueError(\"Maximum iterations reached.\")\n\n return new_name","label":0},{"cid":"prefect/utilities/tasks.py::group","text":"prefect/utilities/tasks.py::group\n@contextmanager\ndef group(name: str) -> None:\n \"\"\"\n Context manager for setting a task group.\n \"\"\"\n current_group = prefect.context.get(\"group\", \"\")\n if current_group:\n name = current_group + \".\" + name\n with prefect.context(group=name):\n yield","label":1},{"cid":"prefect/utilities/tasks.py::tags","text":"prefect/utilities/tasks.py::tags\n@contextmanager\ndef tags(*tags):\n \"\"\"\n Context manager for setting task tags.\n \"\"\"\n tags = set(tags)\n current_tags = prefect.context.get(\"tags\", set())\n if current_tags:\n tags = tags.update(current_tags)\n with prefect.context(tags=tags):\n yield","label":1},{"cid":"prefect/utilities/tasks.py::as_task","text":"prefect/utilities/tasks.py::as_task\ndef as_task(x: Any) -> \"prefect.core.Task\":\n \"\"\"\n Wraps a function, collection, or constant with the appropriate Task type.\n \"\"\"\n # task objects\n if isinstance(x, prefect.core.Task):\n return x\n\n # sequences\n elif isinstance(x, list):\n return prefect.tasks.collections.List(*[as_task(t) for t in x])\n elif isinstance(x, tuple):\n return prefect.tasks.collections.Tuple(*[as_task(t) for t in x])\n elif isinstance(x, set):\n return prefect.tasks.collections.Set(*[as_task(t) for t in x])\n\n # collections\n elif isinstance(x, dict):\n task_dict = {k: as_task(v) for k, v in x.items()}\n return prefect.tasks.collections.Dict(**task_dict)\n\n # functions\n elif callable(x):\n return prefect.tasks.core.function_task.FunctionTask(fn=x)\n\n # constants\n else:\n return prefect.tasks.core.constants.Constant(value=x)","label":0},{"cid":"prefect/utilities/tasks.py::task","text":"prefect/utilities/tasks.py::task\n@curry\ndef task(fn: Callable, **task_init_kwargs):\n \"\"\"\n A decorator for creating Tasks from functions.\n\n Usage:\n\n @task(name='hello', retries=3)\n def hello(name):\n print('hello, {}'.format(name))\n\n with Flow() as flow:\n t1 = hello('foo')\n t2 = hello('bar')\n \"\"\"\n\n def task_generator(*args, **kwargs):\n task = prefect.tasks.core.function_task.FunctionTask(fn=fn, **task_init_kwargs)\n return task(*args, **kwargs)\n\n return task_generator","label":0},{"cid":"prefect/utilities/tasks.py::task.task_generator","text":"prefect/utilities/tasks.py::task.task_generator\n def task_generator(*args, **kwargs):\n task = prefect.tasks.core.function_task.FunctionTask(fn=fn, **task_init_kwargs)\n return task(*args, **kwargs)","label":0}],"string":"[\n {\n \"cid\": \"prefect/client.py::Client.__init__\",\n \"text\": \"prefect/client.py::Client.__init__\\n def __init__(self, api_server=None, graphql_server=None, token=None):\\n if api_server is None:\\n api_server = prefect.config.get(\\\"prefect\\\", \\\"api_server\\\")\\n if not api_server:\\n api_server = prefect.context.Context.get(\\\"api_server\\\", None)\\n if not api_server:\\n raise ValueError(\\\"Could not determine API server.\\\")\\n self._api_server = api_server\\n\\n if graphql_server is None:\\n graphql_server = prefect.config.get(\\\"prefect\\\", \\\"graphql_server\\\")\\n if not graphql_server:\\n graphql_server = prefect.context.Context.get(\\\"graphql_server\\\", None)\\n if not graphql_server:\\n graphql_server = api_server\\n self._graphql_server = graphql_server\\n\\n if token is None:\\n token = prefect.context.Context.get(\\\"token\\\", None)\\n self._token = token\\n\\n self.projects = Projects(client=self)\\n self.flows = Flows(client=self)\\n self.flow_runs = FlowRuns(client=self)\\n self.task_runs = TaskRuns(client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Client._get\",\n \"text\": \"prefect/client.py::Client._get\\n def _get(self, path, *, _json=True, _server=None, **params):\\n \\\"\\\"\\\"\\n Convenience function for calling the Prefect API with token auth.\\n\\n Args:\\n path: the path of the API url. For example, to GET\\n http://prefect-server/v1/auth/login, path would be 'auth/login'.\\n params: GET parameters\\n \\\"\\\"\\\"\\n response = self._request(method=\\\"GET\\\", path=path, params=params, server=_server)\\n if _json:\\n if response.text:\\n response = response.json()\\n else:\\n response = {}\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Client._post\",\n \"text\": \"prefect/client.py::Client._post\\n def _post(self, path, *, _json=True, _server=None, **params):\\n \\\"\\\"\\\"\\n Convenience function for calling the Prefect API with token auth.\\n\\n Args:\\n path: the path of the API url. For example, to POST\\n http://prefect-server/v1/auth/login, path would be 'auth/login'.\\n params: POST params\\n \\\"\\\"\\\"\\n response = self._request(\\n method=\\\"POST\\\", path=path, params=params, server=_server\\n )\\n if _json:\\n if response.text:\\n return response.json()\\n else:\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Client._delete\",\n \"text\": \"prefect/client.py::Client._delete\\n def _delete(self, path, *, _server=None, _json=True):\\n \\\"\\\"\\\"\\n Convenience function for calling the Prefect API with token auth.\\n\\n Args:\\n path: the path of the API url\\n \\\"\\\"\\\"\\n response = self._request(method=\\\"delete\\\", path=path, server=_server)\\n if _json:\\n if response.text:\\n response = response.json()\\n else:\\n response = {}\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Client.graphql\",\n \"text\": \"prefect/client.py::Client.graphql\\n def graphql(self, query, **variables):\\n \\\"\\\"\\\"\\n Convenience function for running queries against the Prefect GraphQL\\n API\\n \\\"\\\"\\\"\\n result = self._post(\\n path=\\\"\\\",\\n query=query,\\n variables=json.dumps(variables),\\n _server=self._graphql_server,\\n )\\n if \\\"errors\\\" in result:\\n raise ValueError(result[\\\"errors\\\"])\\n else:\\n return to_dotdict(result).data\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Client._request\",\n \"text\": \"prefect/client.py::Client._request\\n def _request(self, method, path, params, server=None):\\n\\n # lazy import for performance\\n import requests\\n\\n if server is None:\\n server = self._api_server\\n\\n if self._token is None:\\n raise ValueError(\\\"Call Client.login() to set the client token.\\\")\\n\\n url = os.path.join(server, path.lstrip(\\\"/\\\")).rstrip(\\\"/\\\")\\n\\n params = params or {}\\n\\n # write this as a function to allow reuse in next try/except block\\n def request_fn():\\n headers = {\\\"Authorization\\\": \\\"Bearer \\\" + self._token}\\n if method == \\\"GET\\\":\\n response = requests.get(url, headers=headers, params=params)\\n elif method == \\\"POST\\\":\\n response = requests.post(url, headers=headers, json=params)\\n elif method == \\\"DELETE\\\":\\n response = requests.delete(url, headers=headers)\\n else:\\n raise ValueError(\\\"Invalid method: {}\\\".format(method))\\n\\n response.raise_for_status()\\n\\n return response\\n\\n try:\\n return request_fn()\\n except requests.HTTPError as err:\\n if err.response.status_code == 401:\\n self.refresh_token()\\n return request_fn()\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Client._request.request_fn\",\n \"text\": \"prefect/client.py::Client._request.request_fn\\n def request_fn():\\n headers = {\\\"Authorization\\\": \\\"Bearer \\\" + self._token}\\n if method == \\\"GET\\\":\\n response = requests.get(url, headers=headers, params=params)\\n elif method == \\\"POST\\\":\\n response = requests.post(url, headers=headers, json=params)\\n elif method == \\\"DELETE\\\":\\n response = requests.delete(url, headers=headers)\\n else:\\n raise ValueError(\\\"Invalid method: {}\\\".format(method))\\n\\n response.raise_for_status()\\n\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Client.login\",\n \"text\": \"prefect/client.py::Client.login\\n def login(self, email, password, account_slug=None, account_id=None):\\n\\n # lazy import for performance\\n import requests\\n\\n url = os.path.join(self._api_server, \\\"auth/login\\\")\\n response = requests.post(\\n url,\\n auth=(email, password),\\n json=dict(account_id=account_id, account_slug=account_slug),\\n )\\n if not response.ok:\\n raise ValueError(\\\"Could not log in.\\\")\\n self._token = response.json().get(\\\"token\\\")\\n\\n if not (account_id or account_slug):\\n print(\\\"No account provided; returning available accounts.\\\")\\n accounts = self._get(\\\"auth/accounts\\\")\\n return accounts\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Client.refresh_token\",\n \"text\": \"prefect/client.py::Client.refresh_token\\n def refresh_token(self):\\n url = os.path.join(self._api_server, \\\"auth/refresh\\\")\\n response = requests.post(\\n url, headers={\\\"Authorization\\\": \\\"Bearer \\\" + self._token}\\n )\\n self._token = response.json().get(\\\"token\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::ClientModule.__init__\",\n \"text\": \"prefect/client.py::ClientModule.__init__\\n def __init__(self, client, name=None):\\n if name is None:\\n name = type(self).__name__\\n self._name = name\\n self._client = client\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::ClientModule.__repr__\",\n \"text\": \"prefect/client.py::ClientModule.__repr__\\n def __repr__(self):\\n return \\\"<Client Module: {name}>\\\".format(name=self._name)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::ClientModule._get\",\n \"text\": \"prefect/client.py::ClientModule._get\\n def _get(self, path, **params):\\n path = path.lstrip(\\\"/\\\")\\n return self._client._get(os.path.join(self._path, path), **params)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::ClientModule._post\",\n \"text\": \"prefect/client.py::ClientModule._post\\n def _post(self, path, **data):\\n path = path.lstrip(\\\"/\\\")\\n return self._client._post(os.path.join(self._path, path), **data)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::ClientModule._delete\",\n \"text\": \"prefect/client.py::ClientModule._delete\\n def _delete(self, path, **params):\\n path = path.lstrip(\\\"/\\\")\\n return self._client._delete(os.path.join(self._path, path), **params)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::ClientModule._graphql\",\n \"text\": \"prefect/client.py::ClientModule._graphql\\n def _graphql(self, query, **variables):\\n return self._client.graphql(query=query, **variables)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Projects.create\",\n \"text\": \"prefect/client.py::Projects.create\\n def create(self, name):\\n return self._post(path=\\\"/\\\", name=name)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Projects.list\",\n \"text\": \"prefect/client.py::Projects.list\\n def list(self, per_page=100, page=1):\\n \\\"\\\"\\\"\\n Lists all available projects\\n \\\"\\\"\\\"\\n data = self._graphql(\\n \\\"\\\"\\\"\\n query ($perPage: Int!, $page: Int!){\\n projects(perPage: $perPage, page: $page){\\n id\\n name\\n }\\n }\\n \\\"\\\"\\\",\\n perPage=per_page,\\n page=page,\\n )\\n return data[\\\"projects\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Projects.flows\",\n \"text\": \"prefect/client.py::Projects.flows\\n def flows(self, project_id):\\n \\\"\\\"\\\"\\n Returns the Flows for the specified project\\n \\\"\\\"\\\"\\n\\n data = self._graphql(\\n \\\"\\\"\\\"\\n query ($projectId: String!){\\n projects(filter: {id: $projectId}) {\\n id\\n name\\n flows {\\n id\\n name\\n }\\n }\\n }\\n \\\"\\\"\\\",\\n projectId=project_id,\\n )\\n return data[\\\"projects\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Flows.load\",\n \"text\": \"prefect/client.py::Flows.load\\n def load(self, flow_id, safe=True):\\n \\\"\\\"\\\"\\n Retrieve information about a Flow.\\n\\n Args:\\n flow_id (str): the Flow's id\\n \\\"\\\"\\\"\\n if safe:\\n data = self._graphql(\\n \\\"\\\"\\\"\\n query($flowId: String!) {\\n flow(id: $flowId) {\\n safe_serialized\\n }\\n }\\n \\\"\\\"\\\",\\n flowId=flow_id,\\n )\\n\\n return prefect.Flow.safe_deserialize(data[\\\"flow\\\"][\\\"safe_serialized\\\"])\\n else:\\n data = self._graphql(\\n \\\"\\\"\\\"\\n query($flowId: String!) {\\n flow(id: $flowId) {\\n serialized\\n tasks {\\n serialized\\n }\\n edges {\\n serialized\\n }\\n }\\n }\\n \\\"\\\"\\\",\\n flowId=flow_id,\\n )\\n return prefect.Flow.deserialize(data[\\\"flow\\\"])\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Flows.set_state\",\n \"text\": \"prefect/client.py::Flows.set_state\\n def set_state(self, flow_id, state):\\n \\\"\\\"\\\"\\n Update a Flow's state\\n \\\"\\\"\\\"\\n return self._post(path=\\\"/{id}/state\\\".format(id=flow_id), state=state)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::Flows.create\",\n \"text\": \"prefect/client.py::Flows.create\\n def create(self, flow):\\n \\\"\\\"\\\"\\n Submit a Flow to the server.\\n \\\"\\\"\\\"\\n return self._post(path=\\\"/\\\", serialized_flow=flow.serialize())\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::FlowRuns.get_state\",\n \"text\": \"prefect/client.py::FlowRuns.get_state\\n def get_state(self, flow_run_id):\\n \\\"\\\"\\\"\\n Retrieve a flow run's state\\n \\\"\\\"\\\"\\n\\n data = self._graphql(\\n \\\"\\\"\\\"\\n query ($flowRunId: String!){\\n flow_runs(filter: {id: $flowRunId}) {\\n state {\\n state\\n result\\n }\\n }\\n }\\n \\\"\\\"\\\",\\n flowRunId=flow_run_id,\\n )\\n state = data.flow_runs[0].get(\\\"state\\\", {})\\n return prefect.engine.state.FlowState(\\n state=state.get(\\\"state\\\", None), result=state.get(\\\"result\\\", None)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::FlowRuns.set_state\",\n \"text\": \"prefect/client.py::FlowRuns.set_state\\n def set_state(self, flow_run_id, state, result=None, expected_state=None):\\n \\\"\\\"\\\"\\n Retrieve a flow run's state\\n \\\"\\\"\\\"\\n return self._post(\\n path=\\\"/{id}/state\\\".format(id=flow_run_id),\\n state=state,\\n result=dict(result=result),\\n expected_state=expected_state,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::FlowRuns.create\",\n \"text\": \"prefect/client.py::FlowRuns.create\\n def create(self, flow_id, parameters=None, parent_taskrun_id=None):\\n return self._post(\\n path=\\\"/\\\",\\n flow_id=flow_id,\\n parameters=parameters,\\n parent_taskrun_id=parent_taskrun_id,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::FlowRuns.run\",\n \"text\": \"prefect/client.py::FlowRuns.run\\n def run(self, flow_run_id, start_tasks=None, inputs=None):\\n \\\"\\\"\\\"\\n Queue a flow run to be run\\n \\\"\\\"\\\"\\n if start_tasks is None:\\n start_tasks = []\\n if inputs is None:\\n inputs = {}\\n\\n return self._post(\\n path=\\\"/{id}/run\\\".format(id=flow_run_id),\\n start_tasks=start_tasks,\\n inputs=inputs,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::TaskRuns.get_state\",\n \"text\": \"prefect/client.py::TaskRuns.get_state\\n def get_state(self, task_run_id):\\n \\\"\\\"\\\"\\n Retrieve a flow run's state\\n \\\"\\\"\\\"\\n data = self._graphql(\\n \\\"\\\"\\\"\\n query ($taskRunId: String!){\\n task_runs(filter: {id: $taskRunId}) {\\n state {\\n state\\n result\\n }\\n }\\n }\\n \\\"\\\"\\\",\\n taskRunId=task_run_id,\\n )\\n state = data.task_runs[0].get(\\\"state\\\", {})\\n return prefect.engine.state.TaskState(\\n state=state.get(\\\"state\\\", None), result=state.get(\\\"result\\\", None)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/client.py::TaskRuns.set_state\",\n \"text\": \"prefect/client.py::TaskRuns.set_state\\n def set_state(self, task_run_id, state, result=None, expected_state=None):\\n \\\"\\\"\\\"\\n Retrieve a task run's state\\n \\\"\\\"\\\"\\n return self._post(\\n path=\\\"/{id}/state\\\".format(id=task_run_id),\\n state=state,\\n result=dict(result=result),\\n expected_state=expected_state,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::Config.__repr__\",\n \"text\": \"prefect/configuration.py::Config.__repr__\\n def __repr__(self):\\n return \\\"<Config: {}>\\\".format(\\\", \\\".join(sorted(self.sections())))\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::Config.__iter__\",\n \"text\": \"prefect/configuration.py::Config.__iter__\\n def __iter__(self):\\n return self.sections()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::Config.get\",\n \"text\": \"prefect/configuration.py::Config.get\\n def get(self, key, default=None):\\n return self.__dict__.get(key, default)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::Config.sections\",\n \"text\": \"prefect/configuration.py::Config.sections\\n def sections(self):\\n return self.__dict__.keys()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::Config.to_dict\",\n \"text\": \"prefect/configuration.py::Config.to_dict\\n def to_dict(self):\\n dct = {}\\n for k, v in self.__dict__.items():\\n if isinstance(v, SimpleNamespace):\\n v = v.to_dict()\\n dct[k] = v\\n return dct\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::Config.from_dict\",\n \"text\": \"prefect/configuration.py::Config.from_dict\\n @classmethod\\n def from_dict(cls, dct, recursive=True):\\n if not isinstance(dct, dict):\\n return dct\\n for key, value in list(dct.items()):\\n if isinstance(value, dict):\\n dct[key] = cls.from_dict(value, recursive=recursive)\\n elif isinstance(value, (list, tuple, set)):\\n dct[key] = type(value)(\\n [cls.from_dict(v, recursive=recursive) for v in value]\\n )\\n return cls(**dct)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::expand\",\n \"text\": \"prefect/configuration.py::expand\\ndef expand(env_var):\\n \\\"\\\"\\\"\\n Expands (potentially nested) env vars by repeatedly applying\\n `expandvars` and `expanduser` until interpolation stops having\\n any effect.\\n \\\"\\\"\\\"\\n if not env_var or not isinstance(env_var, str):\\n return env_var\\n while True:\\n substituted = os.path.expanduser(os.path.expandvars(str(env_var)))\\n if substituted == env_var:\\n return substituted\\n else:\\n env_var = substituted\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::create_user_config\",\n \"text\": \"prefect/configuration.py::create_user_config\\ndef create_user_config(path, source=DEFAULT_CONFIG):\\n \\\"\\\"\\\"\\n Copies the default configuration to a user-customizable file\\n \\\"\\\"\\\"\\n config_file = expand(path)\\n if os.path.isfile(config_file):\\n raise ValueError(\\\"File already exists: {}\\\".format(config_file))\\n os.makedirs(os.path.dirname(config_file), exist_ok=True)\\n\\n def quote(s):\\n return '\\\"' + s + '\\\"'\\n\\n with open(config_file, \\\"w\\\") as fw:\\n with open(source, \\\"r\\\") as fr:\\n src = fr.read()\\n counter = 0\\n\\n while '\\\"<<FERNET KEY>>\\\"' in src:\\n key = Fernet.generate_key().decode()\\n src = src.replace('\\\"<<FERNET KEY>>\\\"', quote(key), 1)\\n counter += 1\\n if counter > 100:\\n raise ValueError(\\\"Unexpected interpolation error.\\\")\\n\\n while '\\\"<<SECRET>>\\\"' in src:\\n chars = ascii_letters + digits + \\\"!@#$%^&*()[]<>,.-\\\"\\n secret = \\\"\\\".join(random.choice(chars) for i in range(32))\\n src = src.replace('\\\"<<SECRET>>\\\"', quote(secret), 1)\\n counter += 1\\n if counter > 100:\\n raise ValueError(\\\"Unexpected interpolation error.\\\")\\n\\n while '\\\"<<UUID>>\\\"' in src:\\n src = src.replace('\\\"<<UUID>>\\\"', quote(str(uuid.uuid4())), 1)\\n counter += 1\\n if counter > 100:\\n raise ValueError(\\\"Unexpected interpolation error.\\\")\\n\\n fw.write(src)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::create_user_config.quote\",\n \"text\": \"prefect/configuration.py::create_user_config.quote\\n def quote(s):\\n return '\\\"' + s + '\\\"'\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::configure_logging\",\n \"text\": \"prefect/configuration.py::configure_logging\\ndef configure_logging(logger_name):\\n logger = logging.getLogger(logger_name)\\n handler = logging.StreamHandler()\\n formatter = logging.Formatter(config.logging.format)\\n handler.setFormatter(formatter)\\n logger.addHandler(handler)\\n logger.setLevel(getattr(logging, config.logging.level))\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::validate_config\",\n \"text\": \"prefect/configuration.py::validate_config\\ndef validate_config(config):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::load_config_file\",\n \"text\": \"prefect/configuration.py::load_config_file\\ndef load_config_file(path, existing_config=None):\\n\\n config = toml.load(expand(path))\\n flat_config = collections.dict_to_flatdict(config)\\n\\n # set configuration from env vars\\n for env_var in os.environ:\\n if not env_var.startswith(ENV_VAR_PREFIX):\\n continue\\n sections = collections.CompoundKey(env_var.lower().split(\\\"__\\\")[1:])\\n if sections:\\n flat_config[sections] = expand(os.getenv(env_var))\\n\\n # expand configuration referencing env vars\\n for k, v in list(flat_config.items()):\\n flat_config[k] = expand(v)\\n\\n # process interpolation of any variable referencing another with ${}\\n # process up to 10 links\\n for i in range(10):\\n for k, v in list(flat_config.items()):\\n if not isinstance(v, str):\\n continue\\n match = INTERPOLATION_REGEX.search(v)\\n if not match:\\n continue\\n ref_key = collections.CompoundKey(match.group(1).split(\\\".\\\"))\\n ref_value = flat_config[ref_key]\\n if v == match.group(0):\\n flat_config[k] = ref_value\\n else:\\n flat_config[k] = v.replace(match.group(0), str(ref_value), 1)\\n\\n config = collections.flatdict_to_dict(flat_config)\\n\\n if existing_config is not None:\\n config = collections.merge_dicts(existing_config.to_dict(), config)\\n\\n return Config.from_dict(config)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/configuration.py::load_configuration\",\n \"text\": \"prefect/configuration.py::load_configuration\\ndef load_configuration(default_config, user_config, env_var=None):\\n user_config_path = os.getenv(env_var, user_config)\\n config = load_config_file(default_config)\\n try:\\n create_user_config(user_config_path)\\n except Exception:\\n pass\\n config = load_config_file(user_config_path, existing_config=config)\\n return config\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::flow_cache_key\",\n \"text\": \"prefect/core/flow.py::flow_cache_key\\ndef flow_cache_key(flow: \\\"Flow\\\") -> int:\\n \\\"\\\"\\\"\\n Returns a cache key that can be used to determine if the cache is stale.\\n \\\"\\\"\\\"\\n\\n return hash((frozenset(flow.tasks), frozenset(flow.edges)))\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.__init__\",\n \"text\": \"prefect/core/flow.py::Flow.__init__\\n def __init__(\\n self,\\n name: str = None,\\n version: str = None,\\n schedule: prefect.schedules.Schedule = None,\\n description: str = None,\\n environment: prefect.environments.Environment = None,\\n tasks: Iterable[Task] = None,\\n edges: Iterable[Edge] = None,\\n ) -> None:\\n\\n self.name = name or type(self).__name__\\n self.version = version\\n self.description = description\\n self.schedule = schedule or prefect.schedules.NoSchedule()\\n self.environment = environment\\n\\n self.tasks = set() # type: Set[Task]\\n self.edges = set() # type: Set[Edge]\\n\\n for t in tasks or []:\\n self.add_task(t)\\n\\n for e in edges or []:\\n self.add_edge(\\n upstream_task=e.upstream_task,\\n downstream_task=e.downstream_task,\\n key=e.key,\\n )\\n\\n self._prefect_version = prefect.__version__\\n self._cache = {}\\n\\n super().__init__()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.__eq__\",\n \"text\": \"prefect/core/flow.py::Flow.__eq__\\n def __eq__(self, other: Any) -> bool:\\n if type(self) == type(other):\\n s = (self.name, self.version, self.tasks, self.edges)\\n o = (other.name, other.version, other.tasks, other.edges)\\n return s == o\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.__repr__\",\n \"text\": \"prefect/core/flow.py::Flow.__repr__\\n def __repr__(self) -> str:\\n return \\\"<{cls}: {self.name}{v}>\\\".format(\\n cls=type(self).__name__,\\n self=self,\\n v=\\\" version={}\\\".format(self.version) if self.version else \\\"\\\",\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.__iter__\",\n \"text\": \"prefect/core/flow.py::Flow.__iter__\\n def __iter__(self) -> Iterable[Task]:\\n yield from self.sorted_tasks()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.copy\",\n \"text\": \"prefect/core/flow.py::Flow.copy\\n def copy(self) -> \\\"Flow\\\":\\n new = copy.copy(self)\\n new.tasks = self.tasks.copy()\\n new.edges = self.edges.copy()\\n return new\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.__enter__\",\n \"text\": \"prefect/core/flow.py::Flow.__enter__\\n def __enter__(self) -> \\\"Flow\\\":\\n self.__previous_flow = prefect.context.get(\\\"_flow\\\")\\n prefect.context.update(_flow=self)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.__exit__\",\n \"text\": \"prefect/core/flow.py::Flow.__exit__\\n def __exit__(self, _type, _value, _tb) -> None: # type: ignore\\n del prefect.context._flow\\n if self.__previous_flow is not None:\\n prefect.context.update(_flow=self.__previous_flow)\\n\\n del self.__previous_flow\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.root_tasks\",\n \"text\": \"prefect/core/flow.py::Flow.root_tasks\\n @cache(validation_fn=flow_cache_key)\\n def root_tasks(self) -> Set[Task]:\\n \\\"\\\"\\\"\\n Returns the root tasks of the Flow -- tasks that have no upstream\\n dependencies.\\n \\\"\\\"\\\"\\n return set(t for t in self.tasks if not self.edges_to(t))\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.terminal_tasks\",\n \"text\": \"prefect/core/flow.py::Flow.terminal_tasks\\n @cache(validation_fn=flow_cache_key)\\n def terminal_tasks(self) -> Set[Task]:\\n \\\"\\\"\\\"\\n Returns the terminal tasks of the Flow -- tasks that have no downstream\\n dependencies.\\n \\\"\\\"\\\"\\n return set(t for t in self.tasks if not self.edges_from(t))\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.parameters\",\n \"text\": \"prefect/core/flow.py::Flow.parameters\\n def parameters(self, only_required=False) -> Dict[str, ParameterDetails]:\\n \\\"\\\"\\\"\\n Returns details about any Parameters of this flow\\n \\\"\\\"\\\"\\n return {\\n t.name: {\\\"required\\\": t.required, \\\"default\\\": t.default}\\n for t in self.tasks\\n if isinstance(t, Parameter) and (t.required if only_required else True)\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.restore_graph_on_error\",\n \"text\": \"prefect/core/flow.py::Flow.restore_graph_on_error\\n @contextmanager\\n def restore_graph_on_error(self, validate: bool = True) -> Iterator[None]:\\n \\\"\\\"\\\"\\n A context manager that saves the Flow's graph (tasks & edges) and\\n restores it if an error is raised. It can be used to test potentially\\n erroneous configurations (for example, ones that might include cycles)\\n without modifying the graph.\\n\\n It will automatically check for cycles when restored.\\n\\n with flow.restore_graph_on_error():\\n # this will raise an error, but the flow graph will not be modified\\n add_cycle_to_graph(flow)\\n \\\"\\\"\\\"\\n tasks, edges = self.tasks.copy(), self.edges.copy()\\n try:\\n yield\\n if validate:\\n self.validate()\\n except Exception:\\n self.tasks, self.edges = tasks, edges\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.add_task\",\n \"text\": \"prefect/core/flow.py::Flow.add_task\\n def add_task(self, task: Task) -> None:\\n if not isinstance(task, Task):\\n raise TypeError(\\n \\\"Tasks must be Task instances (received {})\\\".format(type(task))\\n )\\n elif task not in self.tasks:\\n if task.slug and task.slug in [t.slug for t in self.tasks]:\\n raise ValueError(\\n 'A task with the slug \\\"{}\\\" already exists in this '\\n \\\"flow.\\\".format(task.slug)\\n )\\n\\n self.tasks.add(task)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.add_edge\",\n \"text\": \"prefect/core/flow.py::Flow.add_edge\\n def add_edge(\\n self,\\n upstream_task: Task,\\n downstream_task: Task,\\n key: str = None,\\n validate: bool = True,\\n ) -> None:\\n if isinstance(downstream_task, Parameter):\\n raise ValueError(\\n \\\"Parameters must be root tasks and can not have upstream dependencies.\\\"\\n )\\n\\n with self.restore_graph_on_error(validate=validate):\\n self.add_task(upstream_task)\\n self.add_task(downstream_task)\\n\\n # we can only check the downstream task's edges once it has been added to the\\n # flow, so we need to perform this check here and not earlier.\\n if key and key in {e.key for e in self.edges_to(downstream_task)}:\\n raise ValueError(\\n 'Argument \\\"{a}\\\" for task {t} has already been assigned in '\\n \\\"this flow. If you are trying to call the task again with \\\"\\n \\\"new arguments, call Task.copy() before adding the result \\\"\\n \\\"to this flow.\\\".format(a=key, t=downstream_task)\\n )\\n\\n edge = Edge(\\n upstream_task=upstream_task, downstream_task=downstream_task, key=key\\n )\\n self.edges.add(edge)\\n\\n # check that the edges are valid keywords by binding them\\n if key is not None:\\n edge_keys = {\\n e.key: None\\n for e in self.edges_to(downstream_task)\\n if e.key is not None\\n }\\n inspect.signature(downstream_task.run).bind_partial(**edge_keys)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.update\",\n \"text\": \"prefect/core/flow.py::Flow.update\\n def update(self, flow: \\\"Flow\\\", validate: bool = True) -> None:\\n with self.restore_graph_on_error(validate=validate):\\n\\n for task in flow.tasks:\\n if task not in self.tasks:\\n self.add_task(task)\\n\\n for edge in flow.edges:\\n if edge not in self.edges:\\n self.add_edge(\\n upstream_task=edge.upstream_task,\\n downstream_task=edge.downstream_task,\\n key=edge.key,\\n validate=False,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.all_upstream_edges\",\n \"text\": \"prefect/core/flow.py::Flow.all_upstream_edges\\n @cache(validation_fn=flow_cache_key)\\n def all_upstream_edges(self) -> Dict[Task, Set[Edge]]:\\n edges = {t: set() for t in self.tasks} # type: Dict[Task, Set[Edge]]\\n for edge in self.edges:\\n edges[edge.downstream_task].add(edge)\\n return edges\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.all_downstream_edges\",\n \"text\": \"prefect/core/flow.py::Flow.all_downstream_edges\\n @cache(validation_fn=flow_cache_key)\\n def all_downstream_edges(self) -> Dict[Task, Set[Edge]]:\\n edges = {t: set() for t in self.tasks} # type: Dict[Task, Set[Edge]]\\n for edge in self.edges:\\n edges[edge.upstream_task].add(edge)\\n return edges\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.edges_to\",\n \"text\": \"prefect/core/flow.py::Flow.edges_to\\n def edges_to(self, task: Task) -> Set[Edge]:\\n if task not in self.tasks:\\n raise ValueError(\\n \\\"Task {t} was not found in Flow {f}\\\".format(t=task, f=self)\\n )\\n return self.all_upstream_edges()[task]\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.edges_from\",\n \"text\": \"prefect/core/flow.py::Flow.edges_from\\n def edges_from(self, task: Task) -> Set[Edge]:\\n if task not in self.tasks:\\n raise ValueError(\\n \\\"Task {t} was not found in Flow {f}\\\".format(t=task, f=self)\\n )\\n return self.all_downstream_edges()[task]\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.upstream_tasks\",\n \"text\": \"prefect/core/flow.py::Flow.upstream_tasks\\n def upstream_tasks(self, task: Task) -> Set[Task]:\\n return set(e.upstream_task for e in self.edges_to(task))\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.downstream_tasks\",\n \"text\": \"prefect/core/flow.py::Flow.downstream_tasks\\n def downstream_tasks(self, task: Task) -> Set[Task]:\\n return set(e.downstream_task for e in self.edges_from(task))\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.validate\",\n \"text\": \"prefect/core/flow.py::Flow.validate\\n def validate(self) -> None:\\n \\\"\\\"\\\"\\n Checks the flow for cycles and raises an error if one is found.\\n \\\"\\\"\\\"\\n self.sorted_tasks()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.sorted_tasks\",\n \"text\": \"prefect/core/flow.py::Flow.sorted_tasks\\n @cache(validation_fn=flow_cache_key)\\n def sorted_tasks(self, root_tasks: Iterable[Task] = None) -> Tuple[Task, ...]:\\n\\n # begin by getting all tasks under consideration (root tasks and all\\n # downstream tasks)\\n if root_tasks:\\n tasks = set(root_tasks)\\n seen = set() # type: Set[Task]\\n\\n # while the set of tasks is different from the seen tasks...\\n while tasks.difference(seen):\\n # iterate over the new tasks...\\n for t in list(tasks.difference(seen)):\\n # add its downstream tasks to the task list\\n tasks.update(self.downstream_tasks(t))\\n # mark it as seen\\n seen.add(t)\\n else:\\n tasks = self.tasks\\n\\n # build the list of sorted tasks\\n remaining_tasks = list(tasks)\\n sorted_tasks = []\\n while remaining_tasks:\\n # mark the flow as cyclic unless we prove otherwise\\n cyclic = True\\n\\n # iterate over each remaining task\\n for task in remaining_tasks.copy():\\n # check all the upstream tasks of that task\\n for upstream_task in self.upstream_tasks(task):\\n # if the upstream task is also remaining, it means it\\n # hasn't been sorted, so we can't sort this task either\\n if upstream_task in remaining_tasks:\\n break\\n else:\\n # but if all upstream tasks have been sorted, we can sort\\n # this one too. We note that we found no cycle this time.\\n cyclic = False\\n remaining_tasks.remove(task)\\n sorted_tasks.append(task)\\n\\n # if we were unable to match any upstream tasks, we have a cycle\\n if cyclic:\\n raise ValueError(\\\"Flows must be acyclic!\\\")\\n\\n return tu\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.set_dependencies\",\n \"text\": \"prefect/core/flow.py::Flow.set_dependencies\\n def set_dependencies(\\n self,\\n task: object,\\n upstream_tasks: Iterable[object] = None,\\n downstream_tasks: Iterable[object] = None,\\n keyword_tasks: Mapping[str, object] = None,\\n validate: bool = True,\\n ) -> None:\\n \\\"\\\"\\\"\\n Convenience function for adding task dependencies on upstream tasks.\\n\\n Args:\\n task (Object): a Task that will become part of the Flow. If the task is not a\\n Task subclass, Prefect will attempt to convert it to one.\\n\\n upstream_tasks ([object]): Tasks that will run before the task runs. If any task\\n is not a Task subclass, Prefect will attempt to convert it to one.\\n\\n downstream_tasks ([object]): Tasks that will run after the task runs. If any task\\n is not a Task subclass, Prefect will attempt to convert it to one.\\n\\n keyword_tasks ({key: object}): The results of these tasks\\n will be provided to the task under the specified keyword\\n arguments. If any task is not a Task subclass, Prefect will attempt to\\n convert it to one.\\n \\\"\\\"\\\"\\n\\n # restore the original graph if we encounter an error midway through this operation\\n with self.restore_graph_on_error(validate=validate):\\n\\n task = as_task(task)\\n assert isinstance(task, Task) # mypy assert\\n\\n # add the main task (in case it was called with no arguments)\\n self.add_task(task)\\n\\n # add upstream tasks\\n for t in upstream_tasks or []:\\n t = as_task(t)\\n assert isinstance(t, Task) # mypy assert\\n self.add_edge(upstream_task=t, downstream_task=task, validate=False)\\n\\n # add downstream tasks\\n for t in downstream_tasks or []:\\n t = as_task(t)\\n assert isinstance(t, Task) # mypy assert\\n self.a\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.run\",\n \"text\": \"prefect/core/flow.py::Flow.run\\n def run(self, parameters=None, executor=None, **kwargs):\\n \\\"\\\"\\\"\\n Run the flow.\\n \\\"\\\"\\\"\\n runner = prefect.engine.flow_runner.FlowRunner(flow=self, executor=executor)\\n\\n parameters = parameters or {}\\n for p in self.parameters():\\n if p in kwargs:\\n parameters[p] = kwargs.pop(p)\\n\\n return runner.run(parameters=parameters, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.serialize\",\n \"text\": \"prefect/core/flow.py::Flow.serialize\\n def serialize(self, seed=None) -> dict:\\n ref_ids = self.fingerprint(seed=seed)\\n\\n return dict(\\n ref_id=ref_ids[\\\"flow_id\\\"],\\n name=self.name,\\n version=self.version,\\n description=self.description,\\n parameters=self.parameters(),\\n schedule=self.schedule,\\n tasks=[\\n dict(ref_id=ref_ids[\\\"task_ids\\\"][t], **t.serialize()) for t in self.tasks\\n ],\\n edges=[\\n dict(\\n upstream_ref_id=ref_ids[\\\"task_ids\\\"][e.upstream_task],\\n downstream_ref_id=ref_ids[\\\"task_ids\\\"][e.downstream_task],\\n key=e.key,\\n )\\n for e in self.edges\\n ],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.visualize\",\n \"text\": \"prefect/core/flow.py::Flow.visualize\\n def visualize(self):\\n graph = graphviz.Digraph()\\n\\n for t in self.tasks:\\n graph.node(str(id(t)), t.name)\\n\\n for e in self.edges:\\n graph.edge(str(id(e.upstream_task)), str(id(e.downstream_task)), e.key)\\n\\n with tempfile.NamedTemporaryFile() as tmp:\\n graph.render(tmp.name, view=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.generate_flow_id\",\n \"text\": \"prefect/core/flow.py::Flow.generate_flow_id\\n def generate_flow_id(self) -> str:\\n \\\"\\\"\\\"\\n Flows are identified by their name and version.\\n \\\"\\\"\\\"\\n hash_bytes = get_hash(\\\"{}:{}\\\".format(self.name, self.version or \\\"\\\"))\\n return str(uuid.UUID(bytes=hash_bytes))\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::Flow.generate_task_ids\",\n \"text\": \"prefect/core/flow.py::Flow.generate_task_ids\\n def generate_task_ids(self) -> Dict[\\\"Task\\\", str]:\\n final_hashes = {}\\n\\n # --- initial pass\\n # for each task, generate a hash based on that task's attributes\\n hashes = {t: get_hash(t) for t in self.tasks}\\n counter = Counter(hashes.values())\\n\\n # --- forward pass #1\\n # for each task in order:\\n # - if the task hash is unique, put it in final_hashes\\n # - if not, hash the task with the hash of all incoming edges\\n # the result is a hash that represents each task in terms of all ancestors since the\\n # last uniquely defined task.\\n for t in self.sorted_tasks():\\n if counter[hashes[t]] == 1:\\n final_hashes[t] = hashes[t]\\n continue\\n\\n edge_hashes = sorted(\\n (e.key, hashes[e.upstream_task]) for e in self.edges_to(t)\\n )\\n hashes[t] = get_hash((hashes[t], edge_hashes))\\n counter[hashes[t]] += 1\\n\\n # --- backward pass\\n # for each task in reverse order:\\n # - if the task hash is unique, put it in final_hashes\\n # - if not, hash the task with the hash of all outgoing edges\\n # the result is a hash that represents each task in terms of both its ancestors (from\\n # the foward pass) and also any descendents.\\n for t in reversed(self.sorted_tasks()):\\n if counter[hashes[t]] == 1:\\n final_hashes[t] = hashes[t]\\n continue\\n\\n edge_hashes = sorted(\\n (e.key, hashes[e.downstream_task]) for e in self.edges_from(t)\\n )\\n hashes[t] = get_hash(str((hashes[t], edge_hashes)))\\n counter[hashes[t]] += 1\\n\\n # --- forward pass #2\\n # for each task in order:\\n # - if the task hash is unique, put it in final_hashes\\n # if not, hash the task with the hash of all incoming edges.\\n # define each task in terms of the comput\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::get_hash\",\n \"text\": \"prefect/core/flow.py::get_hash\\ndef get_hash(obj: object) -> bytes:\\n \\\"\\\"\\\"\\n Returns a deterministic set of bytes for a given input.\\n \\\"\\\"\\\"\\n if isinstance(obj, bytes):\\n return hashlib.md5(obj).digest()\\n elif isinstance(obj, str):\\n return hashlib.md5(obj.encode()).digest()\\n elif isinstance(obj, Task):\\n obj = prefect.core.task.get_task_info(obj)\\n elif isinstance(obj, Edge):\\n obj = dict(\\n upstream=prefect.core.task.get_task_info(obj.upstream_task),\\n downstream=prefect.core.task.get_task_info(obj.downstream_task),\\n key=obj.key,\\n )\\n return hashlib.md5(jsonpickle.dumps(obj).encode()).digest()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/flow.py::xor\",\n \"text\": \"prefect/core/flow.py::xor\\ndef xor(hash1: bytes, hash2: bytes) -> bytes:\\n \\\"\\\"\\\"\\n Computes the bitwise XOR between two byte hashes\\n \\\"\\\"\\\"\\n return bytes([x ^ y for x, y in zip(hash1, itertools.cycle(hash2))])\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::get_task_info\",\n \"text\": \"prefect/core/task.py::get_task_info\\ndef get_task_info(task: \\\"Task\\\") -> Dict[str, Any]:\\n return dict(\\n name=task.name,\\n slug=task.slug,\\n type=to_qualified_name(type(task)),\\n description=task.description,\\n max_retries=task.max_retries,\\n retry_delay=task.retry_delay,\\n timeout=task.timeout,\\n trigger=task.trigger,\\n propagate_skip=task.propagate_skip,\\n environment=task.environment,\\n checkpoint=task.checkpoint,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::SignatureValidator.__new__\",\n \"text\": \"prefect/core/task.py::SignatureValidator.__new__\\n def __new__(cls, name, parents, methods):\\n run = methods.get(\\\"run\\\", lambda: None)\\n run_sig = inspect.getfullargspec(run)\\n if run_sig.varargs:\\n raise ValueError(\\n \\\"Tasks with variable positional arguments (*args) are not \\\"\\n \\\"supported, because all Prefect arguments are stored as \\\"\\n \\\"keywords. As a workaround, consider modifying the run() \\\"\\n \\\"method to accept **kwargs and feeding the values \\\"\\n \\\"to *args.\\\"\\n )\\n # necessary to ensure classes that inherit from parent class\\n # also get passed through __new__\\n return type.__new__(cls, name, parents, methods)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Task.__init__\",\n \"text\": \"prefect/core/task.py::Task.__init__\\n def __init__(\\n self,\\n name: str = None,\\n slug: str = None,\\n description: str = None,\\n group: str = None,\\n tags: Iterable[str] = None,\\n max_retries: int = 0,\\n retry_delay: timedelta = timedelta(minutes=1),\\n timeout: timedelta = None,\\n trigger: Callable[[Dict[\\\"Task\\\", \\\"State\\\"]], bool] = None,\\n propagate_skip: bool = False,\\n environment: Environment = None,\\n checkpoint: bool = False,\\n ) -> None:\\n\\n self.name = name or type(self).__name__\\n self.slug = slug\\n self.description = description\\n\\n self.group = str(group or prefect.context.get(\\\"_group\\\", \\\"\\\"))\\n\\n if isinstance(tags, str):\\n tags = [tags]\\n self.tags = set(tags or [])\\n self.tags.update(prefect.context.get(\\\"_tags\\\", []))\\n\\n self.environment = environment\\n self.checkpoint = checkpoint\\n\\n self.max_retries = max_retries\\n self.retry_delay = retry_delay\\n self.timeout = timeout\\n\\n self.trigger = trigger or prefect.triggers.all_successful\\n self.propagate_skip = propagate_skip\",\n \"label\": 1\n },\n {\n \"cid\": \"prefect/core/task.py::Task.__repr__\",\n \"text\": \"prefect/core/task.py::Task.__repr__\\n def __repr__(self) -> str:\\n return \\\"<Task: {self.name}>\\\".format(self=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Task.inputs\",\n \"text\": \"prefect/core/task.py::Task.inputs\\n def inputs(self) -> Tuple[str, ...]:\\n return tuple(inspect.signature(self.run).parameters.keys())\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Task.run\",\n \"text\": \"prefect/core/task.py::Task.run\\n def run(self): # type: ignore\\n \\\"\\\"\\\"\\n The main entrypoint for tasks.\\n\\n In addition to running arbitrary functions, tasks can interact with\\n Prefect in a few ways:\\n 1. Return an optional result. When this function runs successfully,\\n the task is considered successful and the result (if any) is\\n made available to downstream edges.\\n 2. Raise an error. Errors are interpreted as failure.\\n 3. Raise a signal. Signals can include FAIL, SUCCESS, WAIT, etc.\\n and indicate that the task should be put in the indicated\\n state.\\n - FAIL will lead to retries if appropriate\\n - WAIT will end execution and skip all downstream tasks with\\n state WAITING_FOR_UPSTREAM (unless appropriate triggers\\n are set). The task can be run again and should check\\n context.is_waiting to see if it was placed in a WAIT.\\n \\\"\\\"\\\"\\n raise NotImplementedError()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Task.__call__\",\n \"text\": \"prefect/core/task.py::Task.__call__\\n def __call__(\\n self, *args: object, upstream_tasks: Iterable[object] = None, **kwargs: object\\n ) -> \\\"Task\\\":\\n # this will raise an error if callargs weren't all provided\\n signature = inspect.signature(self.run)\\n callargs = dict(signature.bind(*args, **kwargs).arguments) # type: Dict\\n\\n # bind() compresses all variable keyword arguments under the ** argument name,\\n # so we expand them explicitly\\n var_kw_arg = next(\\n (p for p in signature.parameters.values() if p.kind == VAR_KEYWORD), None\\n )\\n callargs.update(callargs.pop(var_kw_arg, {}))\\n\\n flow = prefect.context.get(\\\"_flow\\\", None)\\n if not flow:\\n raise ValueError(\\\"Could not infer an active Flow context.\\\")\\n\\n self.set_dependencies(\\n flow=flow, upstream_tasks=upstream_tasks, keyword_tasks=callargs\\n )\\n\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Task.set_dependencies\",\n \"text\": \"prefect/core/task.py::Task.set_dependencies\\n def set_dependencies(\\n self,\\n flow: \\\"Flow\\\" = None,\\n upstream_tasks: Iterable[object] = None,\\n downstream_tasks: Iterable[object] = None,\\n keyword_tasks: Dict[str, object] = None,\\n validate: bool = True,\\n ) -> None:\\n\\n flow = flow or prefect.context.get(\\\"_flow\\\", None)\\n if not flow:\\n raise ValueError(\\n \\\"No Flow was passed, and could not infer an active Flow context.\\\"\\n )\\n\\n flow.set_dependencies( # type: ignore\\n task=self,\\n upstream_tasks=upstream_tasks,\\n downstream_tasks=downstream_tasks,\\n keyword_tasks=keyword_tasks,\\n validate=validate,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Task.info\",\n \"text\": \"prefect/core/task.py::Task.info\\n def info(self) -> Dict[str, Any]:\\n \\\"\\\"\\\"\\n A description of the task.\\n \\\"\\\"\\\"\\n return get_task_info(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Parameter.__init__\",\n \"text\": \"prefect/core/task.py::Parameter.__init__\\n def __init__(self, name: str, default: Any = None, required: bool = True) -> None:\\n \\\"\\\"\\\"\\n Args:\\n name (str): the Parameter name.\\n\\n required (bool): If True, the Parameter is required and the default\\n value is ignored.\\n\\n default (any): A default value for the parameter. If the default\\n is not None, the Parameter will not be required.\\n \\\"\\\"\\\"\\n if default is not None:\\n required = False\\n\\n self.required = required\\n self.default = default\\n\\n super().__init__(name=name, slug=name)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Parameter.name\",\n \"text\": \"prefect/core/task.py::Parameter.name\\n @property # type: ignore\\n def name(self) -> str: # type: ignore\\n return self._name\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Parameter.name\",\n \"text\": \"prefect/core/task.py::Parameter.name\\n @name.setter\\n def name(self, value: str) -> None:\\n if hasattr(self, \\\"_name\\\"):\\n raise AttributeError(\\\"Parameter name can not be changed\\\")\\n self._name = value # pylint: disable=W0201\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Parameter.slug\",\n \"text\": \"prefect/core/task.py::Parameter.slug\\n @property # type: ignore\\n def slug(self) -> str: # type: ignore\\n \\\"\\\"\\\"\\n A Parameter slug is always the same as its name. This information is used by\\n Flow objects to enforce parameter name uniqueness.\\n \\\"\\\"\\\"\\n return self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Parameter.slug\",\n \"text\": \"prefect/core/task.py::Parameter.slug\\n @slug.setter\\n def slug(self, value: str) -> None:\\n # slug is a property, so it's not actually set by this method, but the superclass\\n # attempts to set it and we need to allow that without error.\\n if value != self.name:\\n raise AttributeError(\\\"Parameter slug must be the same as its name.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Parameter.run\",\n \"text\": \"prefect/core/task.py::Parameter.run\\n def run(self) -> Any:\\n params = prefect.context.get(\\\"_parameters\\\", {})\\n if self.required and self.name not in params:\\n raise prefect.signals.FAIL(\\n 'Parameter \\\"{}\\\" was required but not provided.'.format(self.name)\\n )\\n return params.get(self.name, self.default)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/core/task.py::Parameter.info\",\n \"text\": \"prefect/core/task.py::Parameter.info\\n def info(self) -> Dict[str, Any]:\\n info = super().info()\\n info.update(required=self.required, default=self.default)\\n return info\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/base.py::Executor.__init__\",\n \"text\": \"prefect/engine/executors/base.py::Executor.__init__\\n def __init__(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/base.py::Executor.start\",\n \"text\": \"prefect/engine/executors/base.py::Executor.start\\n @contextmanager\\n def start(self):\\n \\\"\\\"\\\"\\n This method is called\\n \\\"\\\"\\\"\\n yield self\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/base.py::Executor.submit\",\n \"text\": \"prefect/engine/executors/base.py::Executor.submit\\n def submit(self, fn, *args, **kwargs):\\n \\\"\\\"\\\"\\n Submit a function to the executor for execution. Returns a future.\\n \\\"\\\"\\\"\\n raise NotImplementedError()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/base.py::Executor.wait\",\n \"text\": \"prefect/engine/executors/base.py::Executor.wait\\n def wait(self, futures, timeout=None):\\n \\\"\\\"\\\"\\n Resolves futures to their values. Blocks until the future is complete.\\n \\\"\\\"\\\"\\n raise NotImplementedError()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/base.py::Executor.set_state\",\n \"text\": \"prefect/engine/executors/base.py::Executor.set_state\\n def set_state(self, current_state: State, state: State, data: Any = None) -> State:\\n return type(current_state)(state, data)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/base.py::Executor.run_flow\",\n \"text\": \"prefect/engine/executors/base.py::Executor.run_flow\\n def run_flow(\\n self,\\n flow: Flow,\\n state: State,\\n task_states: Dict[Task, State],\\n start_tasks: Iterable[Task],\\n return_tasks: Iterable[Task],\\n parameters: Dict,\\n context: Dict,\\n ):\\n context = context or {}\\n context.update(prefect.context)\\n flow_runner = FlowRunner(flow=flow, executor=self)\\n\\n return self.submit(\\n flow_runner.run,\\n flow=flow,\\n state=state,\\n task_states=task_states,\\n start_tasks=start_tasks,\\n return_tasks=return_tasks,\\n context=context,\\n parameters=parameters,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/base.py::Executor.run_task\",\n \"text\": \"prefect/engine/executors/base.py::Executor.run_task\\n def run_task(\\n self,\\n task: Task,\\n state: State,\\n upstream_states: Dict[Task, State],\\n inputs: Dict[str, Any],\\n ignore_trigger=False,\\n context=None,\\n ):\\n context = context or {}\\n context.update(prefect.context)\\n task_runner = prefect.engine.TaskRunner(task=task, executor=self)\\n\\n return self.submit(\\n task_runner.run,\\n state=state,\\n upstream_states=upstream_states,\\n inputs=inputs,\\n ignore_trigger=ignore_trigger,\\n context=prefect.context,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/distributed.py::running_in_cluster\",\n \"text\": \"prefect/engine/executors/distributed.py::running_in_cluster\\ndef running_in_cluster():\\n \\\"\\\"\\\"\\n Returns True if the current thread is running inside a distributed cluster\\n and False otherwise.\\n \\\"\\\"\\\"\\n\\n # lazy load because distributed is slow\\n import distributed\\n\\n return hasattr(distributed.worker.thread_state, \\\"execution_state\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/distributed.py::distributed_client\",\n \"text\": \"prefect/engine/executors/distributed.py::distributed_client\\n@contextmanager\\ndef distributed_client(address=None, separate_thread=False):\\n \\\"\\\"\\\"\\n Context manager that returns a Distributed client.\\n\\n Depending on how this function is called,\\n 1. A standard Distributed client. This will be returned if 1) an\\n address is provided or 2) this function is called from outside\\n a Distributed cluster.\\n 2. A Distributed worker_client. This will be returned if the function\\n is called from inside a Distributed cluster (unless an address is\\n provided).\\n 1. If the context is entered from an existing cluster worker,\\n a worker_client is returned and closed when the context exits.\\n 2. If the context is entered outside the cluster a standard Client is\\n returned and closed when the context exits. If the specified\\n cluster address is \\\"local\\\" or missing, a LocalCluster is started\\n and maintained for the life of this Prefect process. Note that\\n other Prefect processes will NOT automatically discover the\\n LocalCluster.\\n\\n If the context is entered from inside the cluster, a worker_client is\\n yielded; otherwise a standard Client is yielded.\\n\\n Args:\\n address (str): if provided, a Client will be returned that connects\\n to that address. If None, the address will be read from the Prefect\\n configuration (unless called from inside an existing cluster). If\\n 'local' or 'localcluster', a LocalCluster is started and maintained\\n for the life of this Prefect process. Other Prefect processes can\\n NOT discover the local cluster, but it will be reused within this\\n process.\\n\\n separate_thread (bool): if a worker_client is returned, this determines\\n whether it secedes from the threadpool or not. Has no effect\\n otherwise.\\n \\\"\\\"\\\"\\n\\n global _LOCAL_CLUSTER\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/distributed.py::DistributedExecutor.__init__\",\n \"text\": \"prefect/engine/executors/distributed.py::DistributedExecutor.__init__\\n def __init__(self, address=None, separate_thread=None, client=None):\\n self.address = address\\n self.separate_thread = separate_thread\\n self.client = client\\n super().__init__()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/distributed.py::DistributedExecutor.start\",\n \"text\": \"prefect/engine/executors/distributed.py::DistributedExecutor.start\\n @contextmanager\\n def start(self):\\n if not self.client:\\n old_client = self.client\\n with distributed_client(\\n address=self.address, separate_thread=self.separate_thread\\n ) as client:\\n self.client = client\\n yield self\\n self.client = old_client\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/distributed.py::DistributedExecutor.__getstate__\",\n \"text\": \"prefect/engine/executors/distributed.py::DistributedExecutor.__getstate__\\n def __getstate__(self):\\n state = self.__dict__.copy()\\n state[\\\"client\\\"] = None\\n return state\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/distributed.py::DistributedExecutor.submit\",\n \"text\": \"prefect/engine/executors/distributed.py::DistributedExecutor.submit\\n def submit(self, fn, *args, **kwargs):\\n \\\"\\\"\\\"\\n Submit a function to the executor for execution. Returns a future.\\n \\\"\\\"\\\"\\n if \\\"pure\\\" not in kwargs:\\n kwargs[\\\"pure\\\"] = False\\n return self.client.submit(fn, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/distributed.py::DistributedExecutor.wait\",\n \"text\": \"prefect/engine/executors/distributed.py::DistributedExecutor.wait\\n def wait(self, futures, timeout=None):\\n \\\"\\\"\\\"\\n Resolves futures to their values. Blocks until the future is complete.\\n \\\"\\\"\\\"\\n return self.client.gather(futures)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/local.py::LocalExecutor.submit\",\n \"text\": \"prefect/engine/executors/local.py::LocalExecutor.submit\\n def submit(self, fn, *args, **kwargs):\\n \\\"\\\"\\\"\\n Runs a function locally\\n \\\"\\\"\\\"\\n return fn(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/executors/local.py::LocalExecutor.wait\",\n \"text\": \"prefect/engine/executors/local.py::LocalExecutor.wait\\n def wait(self, futures, timeout=None):\\n \\\"\\\"\\\"\\n Returns the provided futures\\n \\\"\\\"\\\"\\n return futures\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/flow_runner.py::FlowRunner.__init__\",\n \"text\": \"prefect/engine/flow_runner.py::FlowRunner.__init__\\n def __init__(\\n self,\\n flow: Flow,\\n executor: \\\"prefect.engine.executors.Executor\\\" = None,\\n logger_name: str = None,\\n ) -> None:\\n self.flow = flow\\n self.executor = executor or prefect.engine.executors.LocalExecutor()\\n self.logger = logging.getLogger(logger_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/flow_runner.py::FlowRunner.run\",\n \"text\": \"prefect/engine/flow_runner.py::FlowRunner.run\\n def run(\\n self,\\n state: State = None,\\n task_states: Dict[Task, State] = None,\\n start_tasks: Iterable[Task] = None,\\n return_tasks: Iterable[Task] = None,\\n parameters: Dict[str, Any] = None,\\n context: Dict[str, Any] = None,\\n ) -> State:\\n\\n if state is None:\\n state = State()\\n parameters = parameters or {}\\n task_states = task_states or {}\\n start_tasks = start_tasks or []\\n context = context or {}\\n return_tasks = set(return_tasks or [])\\n\\n context.update(\\n _flow_name=self.flow.name,\\n _flow_version=self.flow.version,\\n _parameters=parameters,\\n )\\n\\n # if the run fails for any reason,\\n return_task_states = {t: task_states.get(t, State()) for t in return_tasks}\\n\\n with prefect.context(context):\\n\\n with self.executor.start():\\n\\n try:\\n state = self._run(\\n state=state,\\n task_states=task_states,\\n start_tasks=start_tasks,\\n return_tasks=return_tasks,\\n parameters=parameters,\\n )\\n except prefect.signals.DONTRUN:\\n self.logger.info(\\\"Flow run DONTRUN\\\")\\n # set state but no need to go through the executor\\n state = State(state.state, data=return_task_states)\\n except Exception as e:\\n self.logger.info(\\\"Flow run FAIL\\\")\\n # set state through executor\\n return_task_states.update(dict(message=e))\\n state = self.executor.set_state(\\n state, State.FAILED, data=return_task_states\\n )\\n\\n return state\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/flow_runner.py::FlowRunner._run\",\n \"text\": \"prefect/engine/flow_runner.py::FlowRunner._run\\n def _run(\\n self,\\n state: State,\\n task_states: Dict[Task, State],\\n start_tasks: Iterable[Task],\\n return_tasks: Iterable[Task],\\n parameters: Dict[str, Any],\\n ) -> State:\\n\\n # ---------------------------------------------\\n # Check for required parameters\\n # ---------------------------------------------\\n\\n required_params = self.flow.parameters(only_required=True)\\n missing = set(required_params).difference(\\n prefect.context.get(\\\"_parameters\\\", [])\\n )\\n if missing:\\n raise ValueError(\\\"Required parameters not provided: {}\\\".format(missing))\\n\\n # ---------------------------------------------\\n # Check if the flow run is ready to run\\n # ---------------------------------------------\\n\\n # this flow run is already finished\\n if state.is_finished():\\n raise prefect.signals.DONTRUN(\\\"Flow run has already finished.\\\")\\n\\n # this must be pending or running\\n elif not (state.is_pending() or state.is_running()):\\n raise prefect.signals.DONTRUN(\\\"Flow is not ready to run.\\\")\\n\\n # ---------------------------------------------\\n # Start!\\n # ---------------------------------------------\\n\\n # update state\\n state = self.executor.set_state(state, state=State.RUNNING)\\n\\n # -- process each task in order\\n for task in self.flow.sorted_tasks(root_tasks=start_tasks):\\n\\n upstream_states = {}\\n upstream_inputs = {}\\n\\n # -- process each edge to the task\\n for edge in self.flow.edges_to(task):\\n\\n # extract upstream state to pass to the task trigger\\n upstream_states[edge.upstream_task] = self.executor.submit(\\n lambda s: State(s.state), task_states[edge.upstream_task]\\n )\\n\\n # if the upstream task is supposed to pass data, then \",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/task_runner.py::TaskRunner.__init__\",\n \"text\": \"prefect/engine/task_runner.py::TaskRunner.__init__\\n def __init__(\\n self,\\n task: Task,\\n executor: \\\"prefect.engine.executors.Executor\\\" = None,\\n logger_name: str = None,\\n ) -> None:\\n self.task = task\\n if executor is None:\\n executor = prefect.engine.executors.LocalExecutor()\\n self.executor = executor\\n self.logger = logging.getLogger(logger_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/task_runner.py::TaskRunner.run\",\n \"text\": \"prefect/engine/task_runner.py::TaskRunner.run\\n def run(\\n self,\\n state: State = None,\\n upstream_states: Dict[Task, State] = None,\\n inputs: Dict[str, Any] = None,\\n ignore_trigger: bool = False,\\n context: Dict[str, Any] = None,\\n ) -> State:\\n if state is None:\\n state = State()\\n upstream_states = upstream_states or {}\\n context = context or {}\\n inputs = inputs or {}\\n\\n # prepare context\\n context.update(\\n _task_name=self.task.name,\\n _task_max_retries=self.task.max_retries,\\n _task_run_upstream_states=upstream_states,\\n _task_run_inputs=inputs,\\n )\\n\\n # set up context\\n with prefect.context(context):\\n\\n # prepare executor\\n with self.executor.start():\\n\\n try:\\n state = self._run(\\n state=state,\\n upstream_states=upstream_states,\\n inputs=inputs,\\n ignore_trigger=ignore_trigger,\\n )\\n\\n except signals.DONTRUN:\\n pass\\n\\n except signals.SUCCESS:\\n logging.info(\\\"SUCCESS\\\")\\n state = self.executor.set_state(state, state.SUCCESS)\\n\\n except signals.FAIL as e:\\n state = self.handle_fail(state, data=dict(message=e))\\n\\n except signals.RETRY:\\n state = self.handle_retry(state)\\n\\n except signals.SKIP:\\n logging.info(\\\"SKIP\\\")\\n state = self.executor.set_state(state, state.SKIPPED)\\n\\n except Exception as e:\\n logging.info(\\\"Unexpected error while running task.\\\")\\n state = self.handle_fail(state, data=dict(message=e))\\n\\n return state\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/task_runner.py::TaskRunner._run\",\n \"text\": \"prefect/engine/task_runner.py::TaskRunner._run\\n def _run(\\n self,\\n state: State,\\n upstream_states: Dict[Task, State],\\n inputs: Dict[str, Any],\\n ignore_trigger: bool,\\n ):\\n\\n # -------------------------------------------------------------\\n # check upstream tasks\\n # -------------------------------------------------------------\\n\\n # make sure all upstream tasks are finished\\n if not all(s.is_finished() for s in upstream_states.values()):\\n raise signals.DONTRUN(\\\"Upstream tasks are not finished.\\\")\\n\\n # -------------------------------------------------------------\\n # check upstream skips and propagate if appropriate\\n # -------------------------------------------------------------\\n\\n if self.task.propagate_skip and any(\\n s.is_skipped() for s in upstream_states.values()\\n ):\\n raise signals.SKIP(\\\"Upstream tasks skipped.\\\")\\n\\n # -------------------------------------------------------------\\n # check trigger\\n # -------------------------------------------------------------\\n\\n if not ignore_trigger and not self.task.trigger(upstream_states):\\n raise signals.DONTRUN(\\\"Trigger failed\\\")\\n\\n # -------------------------------------------------------------\\n # check this task's state\\n # -------------------------------------------------------------\\n\\n # this task is already running\\n if state.is_running():\\n raise signals.DONTRUN(\\\"Task is already running.\\\")\\n\\n # this task is already finished\\n elif state.is_finished():\\n raise signals.DONTRUN(\\\"Task is already finished.\\\")\\n\\n # this task is not pending\\n elif not state.is_pending():\\n raise signals.DONTRUN(\\\"Task is not ready to run (state {}).\\\".format(state))\\n\\n # -------------------------------------------------------------\\n # start!\\n # --------------------------------\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/task_runner.py::TaskRunner.handle_fail\",\n \"text\": \"prefect/engine/task_runner.py::TaskRunner.handle_fail\\n def handle_fail(self, state, data=None):\\n \\\"\\\"\\\"\\n Checks if a task is eligable for retry; otherwise marks it failed.\\n \\\"\\\"\\\"\\n self.logger.info(\\\"Task FAILED\\\")\\n run_number = prefect.context.get(\\\"_task_run_number\\\", 1)\\n if run_number and run_number <= self.task.max_retries:\\n return self.handle_retry(state)\\n else:\\n return self.executor.set_state(state, State.FAILED, data=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/engine/task_runner.py::TaskRunner.handle_retry\",\n \"text\": \"prefect/engine/task_runner.py::TaskRunner.handle_retry\\n def handle_retry(self, state, retry_time=None):\\n # TODO exponential backoff based on run_number\\n # run_number = prefect.context.get('_task_run_number', 0)\\n\\n self.logger.info(\\\"Task RETRYING\\\")\\n if retry_time is None:\\n retry_time = datetime.datetime.utcnow() + self.task.retry_delay\\n\\n return self.executor.set_state(state, State.RETRYING, data=retry_time)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/signals.py::PrefectStateException.__init__\",\n \"text\": \"prefect/signals.py::PrefectStateException.__init__\\n def __init__(self, *args, result=None, **kwargs) -> None: # type: ignore\\n self.result = result\\n super().__init__(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/tasks/core/function_task.py::FunctionTask.__init__\",\n \"text\": \"prefect/tasks/core/function_task.py::FunctionTask.__init__\\n def __init__(self, fn: Callable, name: str = None, **kwargs) -> None:\\n if not callable(fn):\\n raise TypeError(\\\"fn must be callable.\\\")\\n\\n # set the name from the fn\\n if name is None:\\n name = getattr(fn, \\\"__name__\\\", type(self).__name__)\\n\\n if inspect.getfullargspec(fn).varargs:\\n raise ValueError(\\n \\\"Tasks with variable positional arguments (*args) are not \\\"\\n \\\"supported, because all Prefect arguments are stored as \\\"\\n \\\"keywords. As a workaround, consider modifying the function \\\"\\n \\\"to accept **kwargs and feeding the values \\\"\\n \\\"to the original function's *args.\\\"\\n )\\n\\n self.run = fn\\n\\n super().__init__(name=name, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/triggers.py::always_run\",\n \"text\": \"prefect/triggers.py::always_run\\ndef always_run(upstream_states: Dict[\\\"Task\\\", \\\"State\\\"]) -> bool:\\n \\\"\\\"\\\"\\n This task will run no matter what the upstream states are.\\n \\\"\\\"\\\"\\n\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/triggers.py::manual_only\",\n \"text\": \"prefect/triggers.py::manual_only\\ndef manual_only(upstream_states: Dict[\\\"Task\\\", \\\"State\\\"]) -> bool:\\n \\\"\\\"\\\"\\n This task will never run automatically. It will only run if it is\\n specifically instructed, either by ignoring the trigger or adding it\\n as a flow run's start task.\\n \\\"\\\"\\\"\\n\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/triggers.py::all_successful\",\n \"text\": \"prefect/triggers.py::all_successful\\ndef all_successful(upstream_states: Dict[\\\"Task\\\", \\\"State\\\"]) -> bool:\\n \\\"\\\"\\\"\\n Runs if all upstream tasks were successful. SKIPPED tasks are considered\\n successes (SKIP_DOWNSTREAM is not).\\n\\n If any tasks failed, this task will fail since the trigger can not be\\n acheived.\\n \\\"\\\"\\\"\\n\\n if not all(s.is_successful() for s in upstream_states.values()):\\n raise signals.FAIL(\\\"Trigger failed: some preceding tasks failed\\\")\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/triggers.py::all_failed\",\n \"text\": \"prefect/triggers.py::all_failed\\ndef all_failed(upstream_states: Dict[\\\"Task\\\", \\\"State\\\"]) -> bool:\\n \\\"\\\"\\\"\\n Runs if all upstream tasks failed. SKIPPED tasks are considered successes.\\n \\\"\\\"\\\"\\n\\n if not all(s.is_failed() for s in upstream_states.values()):\\n raise signals.FAIL(\\\"Trigger failed: some preceding tasks succeeded\\\")\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/triggers.py::all_finished\",\n \"text\": \"prefect/triggers.py::all_finished\\ndef all_finished(upstream_states: Dict[\\\"Task\\\", \\\"State\\\"]) -> bool:\\n \\\"\\\"\\\"\\n Runs if all tasks finished (either SUCCESS, FAIL, SKIP)\\n \\\"\\\"\\\"\\n return all(s.is_finished() for s in upstream_states.values())\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/triggers.py::any_successful\",\n \"text\": \"prefect/triggers.py::any_successful\\ndef any_successful(upstream_states: Dict[\\\"Task\\\", \\\"State\\\"]) -> bool:\\n \\\"\\\"\\\"\\n Runs if any tasks were successful. SKIPPED tasks are considered successes;\\n SKIP_DOWNSTREAM is not.\\n \\\"\\\"\\\"\\n\\n if not any(s.is_successful() for s in upstream_states.values()):\\n raise signals.FAIL(\\\"Trigger failed: no preceding tasks succeeded\\\")\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/triggers.py::any_failed\",\n \"text\": \"prefect/triggers.py::any_failed\\ndef any_failed(upstream_states: Dict[\\\"Task\\\", \\\"State\\\"]) -> bool:\\n \\\"\\\"\\\"\\n No failed tasks -> fail\\n * skipped tasks count as successes\\n \\\"\\\"\\\"\\n\\n if not any(s.is_failed() for s in upstream_states.values()):\\n raise signals.FAIL(\\\"Trigger failed: no preceding tasks failed\\\")\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::merge_dicts\",\n \"text\": \"prefect/utilities/collections.py::merge_dicts\\ndef merge_dicts(d1: dict, d2: dict) -> dict:\\n \\\"\\\"\\\"\\n Updates d1 from d2 by replacing each (k, v1) pair in d1 with the\\n corresponding (k, v2) pair in d2.\\n\\n If the value of each pair is itself a dict, then the value is updated\\n recursively.\\n \\\"\\\"\\\"\\n\\n new_dict = d1.copy()\\n\\n for k, v in d2.items():\\n if isinstance(new_dict.get(k), dict) and isinstance(v, dict):\\n new_dict[k] = merge_dicts(new_dict[k], d2[k])\\n else:\\n new_dict[k] = d2[k]\\n return new_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::DotDict.__init__\",\n \"text\": \"prefect/utilities/collections.py::DotDict.__init__\\n def __init__(self, *args, **kwargs):\\n if args:\\n arg_dict = args[0]\\n self.update(arg_dict)\\n self.update(kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::DotDict.__getitem__\",\n \"text\": \"prefect/utilities/collections.py::DotDict.__getitem__\\n def __getitem__(self, key):\\n return self.__dict__[key]\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::DotDict.__setitem__\",\n \"text\": \"prefect/utilities/collections.py::DotDict.__setitem__\\n def __setitem__(self, key, value):\\n if hasattr(MutableMapping, key):\\n raise ValueError(\\\"no sir\\\")\\n self.__dict__[key] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::DotDict.__setattr__\",\n \"text\": \"prefect/utilities/collections.py::DotDict.__setattr__\\n def __setattr__(self, attr, value):\\n self[attr] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::DotDict.__iter__\",\n \"text\": \"prefect/utilities/collections.py::DotDict.__iter__\\n def __iter__(self):\\n return iter(self.__dict__.keys())\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::DotDict.__delitem__\",\n \"text\": \"prefect/utilities/collections.py::DotDict.__delitem__\\n def __delitem__(self, key):\\n del self.__dict__[key]\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::DotDict.__len__\",\n \"text\": \"prefect/utilities/collections.py::DotDict.__len__\\n def __len__(self):\\n return len(self.__dict__)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::DotDict.copy\",\n \"text\": \"prefect/utilities/collections.py::DotDict.copy\\n def copy(self):\\n \\\"Returns a shallow copy\\\"\\n return type(self)(self.__dict__.copy())\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::to_dotdict\",\n \"text\": \"prefect/utilities/collections.py::to_dotdict\\ndef to_dotdict(obj):\\n \\\"\\\"\\\"\\n Given a obj formatted as a dictionary, returns an object\\n that also supports \\\"dot\\\" access:\\n\\n obj['data']['child']\\n obj.data.child\\n \\\"\\\"\\\"\\n if isinstance(obj, (list, tuple, set)):\\n return type(obj)([to_dotdict(d) for d in obj])\\n elif isinstance(obj, dict):\\n for key, value in list(obj.items()):\\n obj[key] = to_dotdict(value)\\n return obj\\n return obj\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::dict_to_flatdict\",\n \"text\": \"prefect/utilities/collections.py::dict_to_flatdict\\ndef dict_to_flatdict(dct, parent=None):\\n \\\"\\\"\\\"Converts a (nested) dictionary to a flattened representation.\\n\\n Each key\\n of the flat dict will be a CompoundKey tuple containing the \\\"chain of keys\\\"\\n for the corresponding value.\\n\\n Args:\\n dct (dict): The dictionary to flatten\\n parent (CompoundKey, optional): Defaults to None. The parent key\\n (you shouldn't need to set this)\\n\\n \\\"\\\"\\\"\\n\\n items = []\\n parent = parent or CompoundKey()\\n for k, v in dct.items():\\n k_parent = CompoundKey(parent + (k,))\\n if isinstance(v, dict):\\n items.extend(dict_to_flatdict(v, parent=k_parent).items())\\n else:\\n items.append((k_parent, v))\\n return dict(items)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/collections.py::flatdict_to_dict\",\n \"text\": \"prefect/utilities/collections.py::flatdict_to_dict\\ndef flatdict_to_dict(dct):\\n \\\"\\\"\\\"Converts a flattened dictionary back to a nested dictionary.\\n\\n Args:\\n dct (dict): The dictionary to be nested. Each key should be a\\n CompoundKey, as generated by dict_to_flatdict()\\n\\n \\\"\\\"\\\"\\n\\n result = {}\\n for k, v in dct.items():\\n if isinstance(k, CompoundKey):\\n current_dict = result\\n for ki in k[:-1]:\\n current_dict = current_dict.setdefault(ki, {})\\n current_dict[k[-1]] = v\\n else:\\n result[k] = v\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/context.py::PrefectContext.__repr__\",\n \"text\": \"prefect/utilities/context.py::PrefectContext.__repr__\\n def __repr__(self) -> str:\\n return \\\"<Prefect Context>\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/context.py::PrefectContext.__call__\",\n \"text\": \"prefect/utilities/context.py::PrefectContext.__call__\\n @contextlib.contextmanager\\n def __call__(\\n self, *args: MutableMapping, **kwargs: Any\\n ) -> Iterator[\\\"PrefectContext\\\"]:\\n \\\"\\\"\\\"\\n A context manager for setting / resetting the Prefect context\\n\\n Example:\\n import prefect.context\\n with prefect.context(dict(a=1, b=2), c=3):\\n print(prefect.context.a) # 1\\n \\\"\\\"\\\"\\n previous_context = self.copy()\\n try:\\n self.update(*args, **kwargs)\\n yield self\\n finally:\\n self.clear()\\n self.update(previous_context)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/datetimes.py::retry_delay\",\n \"text\": \"prefect/utilities/datetimes.py::retry_delay\\ndef retry_delay(\\n interval=None,\\n *,\\n exponential_backoff=False,\\n max_delay=datetime.timedelta(hours=2),\\n **kwargs\\n):\\n \\\"\\\"\\\"\\n A helper function for generating task retry delays.\\n\\n Args:\\n interval (timedelta): The amount of time to wait.\\n This value is optional; users can also instantiate a new interval\\n by passing keyword arguments directly to retry_delay. So:\\n retry_delay(interval=timedelta(days=1))\\n is equivalent to:\\n retry_delay(days=1)\\n\\n **kwargs: Keyword arguments are passed to timedelta and\\n are compatible with the datetime.timedelta API.\\n\\n exponential_backoff: if True, each retry delay will be exponentially\\n longer than the last, starting with the second retry. For example,\\n if the retry delay is 1 minute, then:\\n - first retry starts after 1 minute\\n - second retry also starts after 1 minute (no backoff applied)\\n - third retry starts after 2 minutes\\n - fourth retry starts after 4 minutes\\n - etc.\\n\\n max_delay (timedelta): If exponential_backoff is supplied,\\n delays will be capped by this amount.\\n \\\"\\\"\\\"\\n if interval is not None and kwargs:\\n raise ValueError(\\\"Provide an interval or interval keywords, but not both.\\\")\\n elif interval is None and not kwargs:\\n raise ValueError(\\\"Provide either an interval or interval keywords.\\\")\\n elif kwargs:\\n interval = datetime.timedelta(**kwargs)\\n\\n def retry_delay(run_number):\\n if exponential_backoff:\\n scale = 2 ** (max(0, run_number - 2))\\n else:\\n scale = 1\\n if max_delay is not None:\\n return min(interval * scale, max_delay)\\n else:\\n return interval * scale\\n\\n return retry_delay\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/datetimes.py::retry_delay.retry_delay\",\n \"text\": \"prefect/utilities/datetimes.py::retry_delay.retry_delay\\n def retry_delay(run_number):\\n if exponential_backoff:\\n scale = 2 ** (max(0, run_number - 2))\\n else:\\n scale = 1\\n if max_delay is not None:\\n return min(interval * scale, max_delay)\\n else:\\n return interval * scale\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/datetimes.py::parse_datetime\",\n \"text\": \"prefect/utilities/datetimes.py::parse_datetime\\ndef parse_datetime(dt):\\n \\\"\\\"\\\"\\n Parses a string, bytes, float, or datetime object and returns a\\n corresponding datetime\\n \\\"\\\"\\\"\\n if isinstance(dt, (str, bytes)):\\n return dateutil.parser.parse(dt)\\n elif isinstance(dt, float):\\n return datetime.datetime.fromtimestamp(dt)\\n elif isinstance(dt, (datetime.datetime)):\\n return dt\\n else:\\n raise TypeError(\\\"Unrecognized datetime input: {}\\\".format(type(dt).__name__))\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::from_qualified_name\",\n \"text\": \"prefect/utilities/json.py::from_qualified_name\\ndef from_qualified_name(obj_str: str) -> object:\\n \\\"\\\"\\\"\\n Retrives an object from a fully qualified string path. The object must be\\n imported in advance.\\n \\\"\\\"\\\"\\n\\n path_components = obj_str.split(\\\".\\\")\\n for i in range(len(path_components), 0, -1):\\n module_path = \\\".\\\".join(path_components[:i])\\n # import ipdb; ipdb.set_trace()\\n if module_path in sys.modules:\\n obj = sys.modules[module_path]\\n for p in path_components[i:]:\\n obj = getattr(obj, p)\\n return obj\\n raise ValueError(\\n \\\"Couldn't load \\\\\\\"{}\\\\\\\"; maybe it hasn't been imported yet?\\\".format(obj_str)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::to_qualified_name\",\n \"text\": \"prefect/utilities/json.py::to_qualified_name\\ndef to_qualified_name(obj: Any) -> str:\\n return obj.__module__ + \\\".\\\" + obj.__qualname__\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::register_json_codec\",\n \"text\": \"prefect/utilities/json.py::register_json_codec\\ndef register_json_codec(register_type: Type = None) -> Callable:\\n \\\"\\\"\\\"\\n Decorator that registers a JSON Codec to a corresponding codec_key.\\n\\n If a register_type is provided, the codec will automatically be applied\\n to that type.\\n\\n Whenever the codec is used to serialize an object, the result will be a\\n key-value pair where the key is the codec_key and the value is the\\n serialized object. During deserialization, the codec_key will be used to\\n identify the appropriate codec.\\n \\\"\\\"\\\"\\n\\n def _register(codec_class: \\\"JSONCodec\\\") -> \\\"JSONCodec\\\":\\n\\n full_codec_key = CODEC_PREFIX + codec_class.codec_key\\n\\n # register the codec key\\n if full_codec_key in JSON_CODECS_KEYS:\\n raise ValueError(\\n 'A JSON codec is already registered for codec_key \\\"{}\\\"'.format(\\n codec_class.codec_key\\n )\\n )\\n JSON_CODECS_KEYS[full_codec_key] = codec_class\\n\\n # register the dispatch type\\n if register_type:\\n get_json_codec.register(register_type)(lambda obj: codec_class)\\n\\n return codec_class\\n\\n return _register\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::register_json_codec._register\",\n \"text\": \"prefect/utilities/json.py::register_json_codec._register\\n def _register(codec_class: \\\"JSONCodec\\\") -> \\\"JSONCodec\\\":\\n\\n full_codec_key = CODEC_PREFIX + codec_class.codec_key\\n\\n # register the codec key\\n if full_codec_key in JSON_CODECS_KEYS:\\n raise ValueError(\\n 'A JSON codec is already registered for codec_key \\\"{}\\\"'.format(\\n codec_class.codec_key\\n )\\n )\\n JSON_CODECS_KEYS[full_codec_key] = codec_class\\n\\n # register the dispatch type\\n if register_type:\\n get_json_codec.register(register_type)(lambda obj: codec_class)\\n\\n return codec_class\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::get_json_codec\",\n \"text\": \"prefect/utilities/json.py::get_json_codec\\n@singledispatch\\ndef get_json_codec(obj: object) -> Optional[\\\"JSONCodec\\\"]: # pylint: disable=W0613\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::JSONCodec.__init__\",\n \"text\": \"prefect/utilities/json.py::JSONCodec.__init__\\n def __init__(self, value: O) -> None:\\n if not self.codec_key:\\n raise ValueError(\\\"This JSONCodec class has no codec key set.\\\")\\n self.value = value\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::JSONCodec.serialize\",\n \"text\": \"prefect/utilities/json.py::JSONCodec.serialize\\n def serialize(self) -> J:\\n \\\"\\\"\\\"\\n Serializes an object to a JSON-compatible type (str, dict, list, tuple).\\n \\\"\\\"\\\"\\n raise NotImplementedError()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::JSONCodec.deserialize\",\n \"text\": \"prefect/utilities/json.py::JSONCodec.deserialize\\n @staticmethod\\n def deserialize(obj: J) -> O:\\n \\\"\\\"\\\"\\n Deserialize an object.\\n \\\"\\\"\\\"\\n raise NotImplementedError()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::JSONCodec.__json__\",\n \"text\": \"prefect/utilities/json.py::JSONCodec.__json__\\n def __json__(self) -> Dict[str, J]:\\n \\\"\\\"\\\"\\n Called by the JSON encoder in order to transform this object into\\n a serializable dictionary. By default it returns\\n {self.codec_key: self.serialize()}\\n \\\"\\\"\\\"\\n return {CODEC_PREFIX + self.codec_key: self.serialize()}\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::JSONCodec.__eq__\",\n \"text\": \"prefect/utilities/json.py::JSONCodec.__eq__\\n def __eq__(self, other: Any) -> bool:\\n return type(self) == type(other) and self.value == other.value\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::SetCodec.serialize\",\n \"text\": \"prefect/utilities/json.py::SetCodec.serialize\\n def serialize(self) -> list:\\n return list(self.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::SetCodec.deserialize\",\n \"text\": \"prefect/utilities/json.py::SetCodec.deserialize\\n @staticmethod\\n def deserialize(obj: list) -> set:\\n return set(obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::BytesCodec.serialize\",\n \"text\": \"prefect/utilities/json.py::BytesCodec.serialize\\n def serialize(self) -> str:\\n return self.value.decode()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::BytesCodec.deserialize\",\n \"text\": \"prefect/utilities/json.py::BytesCodec.deserialize\\n @staticmethod\\n def deserialize(obj: str) -> bytes:\\n return obj.encode()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::UUIDCodec.serialize\",\n \"text\": \"prefect/utilities/json.py::UUIDCodec.serialize\\n def serialize(self) -> str:\\n return str(self.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::UUIDCodec.deserialize\",\n \"text\": \"prefect/utilities/json.py::UUIDCodec.deserialize\\n @staticmethod\\n def deserialize(obj: str) -> uuid.UUID:\\n return uuid.UUID(obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::DateTimeCodec.serialize\",\n \"text\": \"prefect/utilities/json.py::DateTimeCodec.serialize\\n def serialize(self) -> str:\\n return self.value.isoformat()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::DateTimeCodec.deserialize\",\n \"text\": \"prefect/utilities/json.py::DateTimeCodec.deserialize\\n @staticmethod\\n def deserialize(obj: str) -> datetime.datetime:\\n return dateutil.parser.parse(obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::DateCodec.serialize\",\n \"text\": \"prefect/utilities/json.py::DateCodec.serialize\\n def serialize(self) -> str:\\n return self.value.isoformat()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::DateCodec.deserialize\",\n \"text\": \"prefect/utilities/json.py::DateCodec.deserialize\\n @staticmethod\\n def deserialize(obj: str) -> datetime.date:\\n return dateutil.parser.parse(obj).date()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::TimeDeltaCodec.serialize\",\n \"text\": \"prefect/utilities/json.py::TimeDeltaCodec.serialize\\n def serialize(self) -> float:\\n return self.value.total_seconds()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::TimeDeltaCodec.deserialize\",\n \"text\": \"prefect/utilities/json.py::TimeDeltaCodec.deserialize\\n @staticmethod\\n def deserialize(obj: float) -> datetime.timedelta:\\n return datetime.timedelta(seconds=obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::LoadObjectCodec.serialize\",\n \"text\": \"prefect/utilities/json.py::LoadObjectCodec.serialize\\n def serialize(self) -> str:\\n return to_qualified_name(self.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::LoadObjectCodec.deserialize\",\n \"text\": \"prefect/utilities/json.py::LoadObjectCodec.deserialize\\n @staticmethod\\n def deserialize(obj: str) -> Any:\\n return from_qualified_name(obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::serializable\",\n \"text\": \"prefect/utilities/json.py::serializable\\ndef serializable(fn: Callable) -> Callable:\\n \\\"\\\"\\\"\\n Decorator that marks a function as automatically serializable via\\n LoadObjectCodec\\n \\\"\\\"\\\"\\n if hasattr(fn, \\\"__json__\\\"):\\n raise ValueError(\\\"Object already has a __json__() method.\\\")\\n setattr(fn, \\\"__json__\\\", lambda: LoadObjectCodec(fn).__json__())\\n return fn\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::EncryptedCodec.serialize\",\n \"text\": \"prefect/utilities/json.py::EncryptedCodec.serialize\\n def serialize(self) -> str:\\n key = prefect.config.security.encryption_key\\n payload = base64.b64encode(json.dumps(self.value).encode())\\n return Fernet(key).encrypt(payload).decode()\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::EncryptedCodec.deserialize\",\n \"text\": \"prefect/utilities/json.py::EncryptedCodec.deserialize\\n @staticmethod\\n def deserialize(obj: str) -> J:\\n key = prefect.config.security.encryption_key\\n decrypted = Fernet(key).decrypt(obj.encode())\\n decoded = base64.b64decode(decrypted).decode()\\n return json.loads(decoded)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::ObjectInitArgsCodec.serialize\",\n \"text\": \"prefect/utilities/json.py::ObjectInitArgsCodec.serialize\\n def serialize(self) -> dict:\\n\\n init_args = {}\\n\\n signature = inspect.signature(self.value.__init__) # type: ignore\\n for arg, param in signature.parameters.items():\\n\\n # see if the init arg was stored in the _init_args dict\\n if arg in getattr(self.value, \\\"_init_args\\\", {}):\\n init_arg = self.value._init_args[arg]\\n\\n # check for an underscore-prefixed attribute with the same name\\n elif hasattr(self.value, \\\"_\\\" + arg):\\n init_arg = getattr(self.value, \\\"_\\\" + arg)\\n\\n # check for an attribute with the same name\\n elif hasattr(self.value, arg):\\n init_arg = getattr(self.value, arg)\\n\\n # default for var_positional\\n elif param.kind == param.VAR_POSITIONAL:\\n init_arg = ()\\n\\n # default for var_keyword\\n elif param.kind == param.VAR_KEYWORD:\\n init_arg = {}\\n\\n # fail\\n else:\\n raise ValueError(\\n 'Could not find a value for the required init arg \\\"{a}\\\" of '\\n \\\"object {o}. The value should be stored in an attribute \\\"\\n \\\"with the same name as the argument, optionally prefixed \\\"\\n \\\"with an underscore, or an _init_args dict \\\"\\n \\\"attribute.\\\".format(a=arg, o=self.value)\\n )\\n\\n if param.kind == param.VAR_KEYWORD:\\n init_args[\\\"**kwargs\\\"] = init_arg\\n elif param.kind == param.VAR_POSITIONAL:\\n init_args[\\\"*args\\\"] = init_arg\\n else:\\n init_args[arg] = init_arg\\n\\n return dict(type=type(self.value), args=init_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::ObjectInitArgsCodec.deserialize\",\n \"text\": \"prefect/utilities/json.py::ObjectInitArgsCodec.deserialize\\n @staticmethod\\n def deserialize(obj: dict) -> object:\\n cls = obj[\\\"type\\\"]\\n kwargs = obj[\\\"args\\\"].pop(\\\"**kwargs\\\", {})\\n args = obj[\\\"args\\\"].pop(\\\"*args\\\", ())\\n kwargs.update(obj[\\\"args\\\"])\\n return cls(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::ObjectAttributesCodec.__init__\",\n \"text\": \"prefect/utilities/json.py::ObjectAttributesCodec.__init__\\n def __init__(self, value: Any, attributes_list: list = None) -> None:\\n super().__init__(value)\\n if attributes_list is None:\\n attributes_list = list(value.__dict__.keys())\\n self.attrs = attributes_list\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::ObjectAttributesCodec.serialize\",\n \"text\": \"prefect/utilities/json.py::ObjectAttributesCodec.serialize\\n def serialize(self) -> dict:\\n return dict(\\n type=type(self.value), attrs={a: getattr(self.value, a) for a in self.attrs}\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::ObjectAttributesCodec.deserialize\",\n \"text\": \"prefect/utilities/json.py::ObjectAttributesCodec.deserialize\\n @staticmethod\\n def deserialize(obj: dict) -> object:\\n instance = object.__new__(obj[\\\"type\\\"])\\n instance.__dict__.update(obj[\\\"attrs\\\"])\\n return instance\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::Serializable.__json__\",\n \"text\": \"prefect/utilities/json.py::Serializable.__json__\\n def __json__(self) -> JSONCodec:\\n return self._json_codec(value=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::PrefectJSONEncoder.default\",\n \"text\": \"prefect/utilities/json.py::PrefectJSONEncoder.default\\n def default(self, obj: Any):\\n \\\"\\\"\\\"\\n Recursive method called when encoding JSON objects\\n\\n - Any class with a __json__() method is stored as the result of\\n calling that method.\\n - If no __json__ method is found, a JSON codec is applied via\\n single dispatch.\\n - Otherwise the original json encoding is used.\\n \\\"\\\"\\\"\\n # call __json__ method if it exists\\n if hasattr(obj, \\\"__json__\\\") and not isinstance(obj, type):\\n return obj.__json__()\\n\\n # otherwise try to apply a json codec by dispatching on type\\n # issue with singledispatch and pylint: https://github.com/PyCQA/pylint/issues/2155\\n obj_codec = get_json_codec(obj) # pylint: disable=E1128\\n if obj_codec:\\n return obj_codec(obj).__json__() # pylint: disable=E1102\\n\\n else:\\n return json.JSONEncoder.default(self, obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::PrefectJSONDecoder.__init__\",\n \"text\": \"prefect/utilities/json.py::PrefectJSONDecoder.__init__\\n def __init__(self, *args, **kwargs):\\n super().__init__(*args, object_hook=self.object_hook, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/json.py::PrefectJSONDecoder.object_hook\",\n \"text\": \"prefect/utilities/json.py::PrefectJSONDecoder.object_hook\\n def object_hook(self, dct):\\n # iterate over the dictionary looking for a key that matches a codec.\\n # it would be extremely unusual to have more than one such key.\\n for key, value in dct.items():\\n codec = JSON_CODECS_KEYS.get(key, None)\\n if codec:\\n full_codec_key = CODEC_PREFIX + codec.codec_key\\n return codec.deserialize(dct[full_codec_key])\\n\\n return dct\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/strings.py::is_valid_identifier\",\n \"text\": \"prefect/utilities/strings.py::is_valid_identifier\\ndef is_valid_identifier(string: str) -> bool:\\n \\\"\\\"\\\"\\n Determines whether a string is a valid Python identifier (meaning it can\\n be used as a variable name or keyword argument).\\n\\n Example:\\n >>> is_valid_identifier('hi5')\\n True\\n >>> is_valid_identifier('5hi')\\n False\\n >>> is_valid_identifier('from')\\n False\\n >>> is_valid_identifier('hi.5')\\n False\\n \\\"\\\"\\\"\\n return string.isidentifier() and not keyword.iskeyword(string)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/strings.py::name_with_suffix\",\n \"text\": \"prefect/utilities/strings.py::name_with_suffix\\ndef name_with_suffix(\\n name,\\n predicate,\\n first_suffix=1,\\n delimiter=\\\"_\\\",\\n max_iters=1000,\\n always_add_suffix=False,\\n) -> str:\\n \\\"\\\"\\\"\\n Automatically adds a number suffix to a name until it becomes valid.\\n\\n Example:\\n >>> name_with_suffix('name', predicate=lambda n: True)\\n name_1\\n >>> name_with_suffix('name', predicate=lambda n: int(n[-1]) > 2)\\n name_3\\n\\n Args:\\n name (string): the desired name\\n\\n predicate (callable): a function that takes the current name and\\n returns whether it is valid. The numerical suffix will be\\n incremented as long as the predicate returns False.\\n\\n first_suffix (int): the first suffix number that will be tried\\n\\n delimiter (string): a delimiter placed between the name and the suffix\\n\\n max_iters (int): evaluation will stop after this many iterations. An\\n error will be raised.\\n\\n always_add_suffix (bool): if True, a suffix will be added to the name\\n even if the provided name is valid. Defaults to False.\\n \\\"\\\"\\\"\\n i = 0\\n\\n if always_add_suffix:\\n new_name = \\\"{}{}{}\\\".format(name, delimiter, first_suffix + i)\\n i += 1\\n else:\\n new_name = name\\n\\n while not predicate(new_name) and i <= max_iters:\\n new_name = \\\"{}{}{}\\\".format(name, delimiter, first_suffix + i)\\n i = i + 1\\n\\n if i > max_iters:\\n raise ValueError(\\\"Maximum iterations reached.\\\")\\n\\n return new_name\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/tasks.py::group\",\n \"text\": \"prefect/utilities/tasks.py::group\\n@contextmanager\\ndef group(name: str) -> None:\\n \\\"\\\"\\\"\\n Context manager for setting a task group.\\n \\\"\\\"\\\"\\n current_group = prefect.context.get(\\\"group\\\", \\\"\\\")\\n if current_group:\\n name = current_group + \\\".\\\" + name\\n with prefect.context(group=name):\\n yield\",\n \"label\": 1\n },\n {\n \"cid\": \"prefect/utilities/tasks.py::tags\",\n \"text\": \"prefect/utilities/tasks.py::tags\\n@contextmanager\\ndef tags(*tags):\\n \\\"\\\"\\\"\\n Context manager for setting task tags.\\n \\\"\\\"\\\"\\n tags = set(tags)\\n current_tags = prefect.context.get(\\\"tags\\\", set())\\n if current_tags:\\n tags = tags.update(current_tags)\\n with prefect.context(tags=tags):\\n yield\",\n \"label\": 1\n },\n {\n \"cid\": \"prefect/utilities/tasks.py::as_task\",\n \"text\": \"prefect/utilities/tasks.py::as_task\\ndef as_task(x: Any) -> \\\"prefect.core.Task\\\":\\n \\\"\\\"\\\"\\n Wraps a function, collection, or constant with the appropriate Task type.\\n \\\"\\\"\\\"\\n # task objects\\n if isinstance(x, prefect.core.Task):\\n return x\\n\\n # sequences\\n elif isinstance(x, list):\\n return prefect.tasks.collections.List(*[as_task(t) for t in x])\\n elif isinstance(x, tuple):\\n return prefect.tasks.collections.Tuple(*[as_task(t) for t in x])\\n elif isinstance(x, set):\\n return prefect.tasks.collections.Set(*[as_task(t) for t in x])\\n\\n # collections\\n elif isinstance(x, dict):\\n task_dict = {k: as_task(v) for k, v in x.items()}\\n return prefect.tasks.collections.Dict(**task_dict)\\n\\n # functions\\n elif callable(x):\\n return prefect.tasks.core.function_task.FunctionTask(fn=x)\\n\\n # constants\\n else:\\n return prefect.tasks.core.constants.Constant(value=x)\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/tasks.py::task\",\n \"text\": \"prefect/utilities/tasks.py::task\\n@curry\\ndef task(fn: Callable, **task_init_kwargs):\\n \\\"\\\"\\\"\\n A decorator for creating Tasks from functions.\\n\\n Usage:\\n\\n @task(name='hello', retries=3)\\n def hello(name):\\n print('hello, {}'.format(name))\\n\\n with Flow() as flow:\\n t1 = hello('foo')\\n t2 = hello('bar')\\n \\\"\\\"\\\"\\n\\n def task_generator(*args, **kwargs):\\n task = prefect.tasks.core.function_task.FunctionTask(fn=fn, **task_init_kwargs)\\n return task(*args, **kwargs)\\n\\n return task_generator\",\n \"label\": 0\n },\n {\n \"cid\": \"prefect/utilities/tasks.py::task.task_generator\",\n \"text\": \"prefect/utilities/tasks.py::task.task_generator\\n def task_generator(*args, **kwargs):\\n task = prefect.tasks.core.function_task.FunctionTask(fn=fn, **task_init_kwargs)\\n return task(*args, **kwargs)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":65,"cells":{"instance_id":{"kind":"string","value":"pyca__cryptography-2606"},"repo":{"kind":"string","value":"pyca/cryptography"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Set a minimum version on setuptools\nApparently it fails in hilarious ways with very very old setuptools (or even distribute). We should set a floor in `setup.py`.\n\n@dstufft do you have opinions on what a reasonable floor would be?\n\n"},"gold_files":{"kind":"list like","value":["setup.py"],"string":"[\n \"setup.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["setup.py::<module>"],"string":"[\n \"setup.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/cryptography-docs.py","text":"docs/cryptography-docs.py\nclass HazmatDirective\n def run(self)\nclass Hazmat\ndef html_visit_hazmat_node(self, node)\ndef latex_visit_hazmat_node(self, node)\ndef depart_hazmat_node(self, node)\ndef setup(app)","label":0},{"cid":"setup.py","text":"setup.py\ndef cc_is_available()\nclass PyTest\n def finalize_options(self)\n def run_tests(self)\ndef keywords_with_side_effects(argv) # Get a dictionary with setup keywords that (can) have side effects.\n def is_short_option(argument) # Check whether a command line argument is a short option.\n def expand_short_options(argument) # Expand combined short options into canonical short options.\n def argument_without_setup_requirements(argv, i) # Check whether a command line argument needs setup requirements.\nclass DummyBuild # This class makes it very obvious when ``keywords_with_side_effects()`` has\n def run(self)\nclass DummyInstall # This class makes it very obvious when ``keywords_with_side_effects()`` has\n def run(self)\nclass DummyPyTest # This class makes it very obvious when ``keywords_with_side_effects()`` has\n def run_tests(self)","label":1},{"cid":"src/_cffi_src/openssl/x509v3.py","text":"src/_cffi_src/openssl/x509v3.py","label":0},{"cid":"src/_cffi_src/utils.py","text":"src/_cffi_src/utils.py\ndef build_ffi_for_binding(module_name, module_prefix, modules, pre_include='', post_include='', libraries=[], extra_compile_args=[], extra_link_args=[]) # Modules listed in ``modules`` should have the following attributes:\ndef build_ffi(module_name, cdef_source, verify_source, libraries=[], extra_compile_args=[], extra_link_args=[])\ndef extra_link_args(platform)","label":0},{"cid":"src/cryptography/hazmat/backends/__init__.py","text":"src/cryptography/hazmat/backends/__init__.py\ndef _available_backends()\ndef default_backend()","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/ciphers.py","text":"src/cryptography/hazmat/backends/openssl/ciphers.py\nclass _CipherContext\n def __init__(self, backend, cipher, mode, operation)\n def update(self, data)\n def finalize(self)\n def authenticate_additional_data(self, data)\nclass _AESCTRCipherContext # This is needed to provide support for AES CTR mode in OpenSSL 0.9.8. It can\n def __init__(self, backend, cipher, mode)\n def update(self, data)\n def finalize(self)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py","text":"src/cryptography/hazmat/backends/openssl/x509.py\ndef _obj2txt(backend, obj)\ndef _decode_x509_name_entry(backend, x509_name_entry)\ndef _decode_x509_name(backend, x509_name)\ndef _decode_general_names(backend, gns)\ndef _decode_general_name(backend, gn)\ndef _decode_ocsp_no_check(backend, ext)\ndef _decode_crl_number(backend, ext)\nclass _X509ExtensionParser\n def __init__(self, ext_count, get_ext, handlers, unsupported_exts=None)\n def parse(self, backend, x509_obj)\nclass _Certificate\n def __init__(self, backend, x509)\n def __repr__(self)\n def __eq__(self, other)\n def __ne__(self, other)\n def __hash__(self)\n def fingerprint(self, algorithm)\n def version(self)\n def serial(self)\n def public_key(self)\n def not_valid_before(self)\n def not_valid_after(self)\n def issuer(self)\n def subject(self)\n def signature_hash_algorithm(self)\n def extensions(self)\n def signature(self)\n def tbs_certificate_bytes(self)\n def public_bytes(self, encoding)\ndef _decode_certificate_policies(backend, cp)\ndef _decode_user_notice(backend, un)\ndef _decode_basic_constraints(backend, bc_st)\ndef _decode_subject_key_identifier(backend, asn1_string)\ndef _decode_authority_key_identifier(backend, akid)\ndef _decode_authority_information_access(backend, aia)\ndef _decode_key_usage(backend, bit_string)\ndef _decode_general_names_extension(backend, gns)\ndef _decode_subject_alt_name(backend, ext)\ndef _decode_issuer_alt_name(backend, ext)\ndef _decode_name_constraints(backend, nc)\ndef _decode_general_subtrees(backend, stack_subtrees)\ndef _decode_extended_key_usage(backend, sk)\ndef _decode_crl_distribution_points(backend, cdps)\ndef _decode_inhibit_any_policy(backend, asn1_int)\ndef _decode_crl_reason(backend, enum)\ndef _decode_invalidity_date(backend, inv_date)\ndef _decode_cert_issuer(backend, ext) # This handler decodes the CertificateIssuer entry extension directly\nclass _RevokedCertificate\n def __init__(self, backend, crl, x509_revoked)\n def serial_number(self)\n def revocation_date(self)\n def extensions(self)\nclass _CertificateRevocationList\n def __init__(self, backend, x509_crl)\n def __eq__(self, other)\n def __ne__(self, other)\n def fingerprint(self, algorithm)\n def signature_hash_algorithm(self)\n def issuer(self)\n def next_update(self)\n def last_update(self)\n def signature(self)\n def tbs_certlist_bytes(self)\n def public_bytes(self, encoding)\n def _revoked_cert(self, idx)\n def __iter__(self)\n def __getitem__(self, idx)\n def __len__(self)\n def extensions(self)\nclass _CertificateSigningRequest\n def __init__(self, backend, x509_req)\n def __eq__(self, other)\n def __ne__(self, other)\n def __hash__(self)\n def public_key(self)\n def subject(self)\n def signature_hash_algorithm(self)\n def extensions(self)\n def public_bytes(self, encoding)\n def tbs_certrequest_bytes(self)\n def signature(self)","label":0},{"cid":"src/cryptography/x509/base.py","text":"src/cryptography/x509/base.py\nclass Version\ndef load_pem_x509_certificate(data, backend)\ndef load_der_x509_certificate(data, backend)\ndef load_pem_x509_csr(data, backend)\ndef load_der_x509_csr(data, backend)\ndef load_pem_x509_crl(data, backend)\ndef load_der_x509_crl(data, backend)\nclass InvalidVersion\n def __init__(self, msg, parsed_version)\nclass Certificate\n def fingerprint(self, algorithm) # Returns bytes using digest passed.\n def serial(self) # Returns certificate serial number\n def version(self) # Returns the certificate version\n def public_key(self) # Returns the public key\n def not_valid_before(self) # Not before time (represented as UTC datetime)\n def not_valid_after(self) # Not after time (represented as UTC datetime)\n def issuer(self) # Returns the issuer name object.\n def subject(self) # Returns the subject name object.\n def signature_hash_algorithm(self) # Returns a HashAlgorithm corresponding to the type of the digest signed\n def extensions(self) # Returns an Extensions object.\n def signature(self) # Returns the signature bytes.\n def tbs_certificate_bytes(self) # Returns the tbsCertificate payload bytes as defined in RFC 5280.\n def __eq__(self, other) # Checks equality.\n def __ne__(self, other) # Checks not equal.\n def __hash__(self) # Computes a hash.\n def public_bytes(self, encoding) # Serializes the certificate to PEM or DER format.\nclass CertificateRevocationList\n def public_bytes(self, encoding) # Serializes the CRL to PEM or DER format.\n def fingerprint(self, algorithm) # Returns bytes using digest passed.\n def signature_hash_algorithm(self) # Returns a HashAlgorithm corresponding to the type of the digest signed\n def issuer(self) # Returns the X509Name with the issuer of this CRL.\n def next_update(self) # Returns the date of next update for this CRL.\n def last_update(self) # Returns the date of last update for this CRL.\n def extensions(self) # Returns an Extensions object containing a list of CRL extensions.\n def signature(self) # Returns the signature bytes.\n def tbs_certlist_bytes(self) # Returns the tbsCertList payload bytes as defined in RFC 5280.\n def __eq__(self, other) # Checks equality.\n def __ne__(self, other) # Checks not equal.\nclass CertificateSigningRequest\n def __eq__(self, other) # Checks equality.\n def __ne__(self, other) # Checks not equal.\n def __hash__(self) # Computes a hash.\n def public_key(self) # Returns the public key\n def subject(self) # Returns the subject name object.\n def signature_hash_algorithm(self) # Returns a HashAlgorithm corresponding to the type of the digest signed\n def extensions(self) # Returns the extensions in the signing request.\n def public_bytes(self, encoding) # Encodes the request to PEM or DER format.\n def signature(self) # Returns the signature bytes.\n def tbs_certrequest_bytes(self) # Returns the PKCS#10 CertificationRequestInfo b","label":0},{"cid":"vectors/setup.py","text":"vectors/setup.py","label":0}],"string":"[\n {\n \"cid\": \"docs/cryptography-docs.py\",\n \"text\": \"docs/cryptography-docs.py\\nclass HazmatDirective\\n def run(self)\\nclass Hazmat\\ndef html_visit_hazmat_node(self, node)\\ndef latex_visit_hazmat_node(self, node)\\ndef depart_hazmat_node(self, node)\\ndef setup(app)\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\\ndef cc_is_available()\\nclass PyTest\\n def finalize_options(self)\\n def run_tests(self)\\ndef keywords_with_side_effects(argv) # Get a dictionary with setup keywords that (can) have side effects.\\n def is_short_option(argument) # Check whether a command line argument is a short option.\\n def expand_short_options(argument) # Expand combined short options into canonical short options.\\n def argument_without_setup_requirements(argv, i) # Check whether a command line argument needs setup requirements.\\nclass DummyBuild # This class makes it very obvious when ``keywords_with_side_effects()`` has\\n def run(self)\\nclass DummyInstall # This class makes it very obvious when ``keywords_with_side_effects()`` has\\n def run(self)\\nclass DummyPyTest # This class makes it very obvious when ``keywords_with_side_effects()`` has\\n def run_tests(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/_cffi_src/openssl/x509v3.py\",\n \"text\": \"src/_cffi_src/openssl/x509v3.py\",\n \"label\": 0\n },\n {\n \"cid\": \"src/_cffi_src/utils.py\",\n \"text\": \"src/_cffi_src/utils.py\\ndef build_ffi_for_binding(module_name, module_prefix, modules, pre_include='', post_include='', libraries=[], extra_compile_args=[], extra_link_args=[]) # Modules listed in ``modules`` should have the following attributes:\\ndef build_ffi(module_name, cdef_source, verify_source, libraries=[], extra_compile_args=[], extra_link_args=[])\\ndef extra_link_args(platform)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/__init__.py\",\n \"text\": \"src/cryptography/hazmat/backends/__init__.py\\ndef _available_backends()\\ndef default_backend()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/ciphers.py\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/ciphers.py\\nclass _CipherContext\\n def __init__(self, backend, cipher, mode, operation)\\n def update(self, data)\\n def finalize(self)\\n def authenticate_additional_data(self, data)\\nclass _AESCTRCipherContext # This is needed to provide support for AES CTR mode in OpenSSL 0.9.8. It can\\n def __init__(self, backend, cipher, mode)\\n def update(self, data)\\n def finalize(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py\\ndef _obj2txt(backend, obj)\\ndef _decode_x509_name_entry(backend, x509_name_entry)\\ndef _decode_x509_name(backend, x509_name)\\ndef _decode_general_names(backend, gns)\\ndef _decode_general_name(backend, gn)\\ndef _decode_ocsp_no_check(backend, ext)\\ndef _decode_crl_number(backend, ext)\\nclass _X509ExtensionParser\\n def __init__(self, ext_count, get_ext, handlers, unsupported_exts=None)\\n def parse(self, backend, x509_obj)\\nclass _Certificate\\n def __init__(self, backend, x509)\\n def __repr__(self)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def __hash__(self)\\n def fingerprint(self, algorithm)\\n def version(self)\\n def serial(self)\\n def public_key(self)\\n def not_valid_before(self)\\n def not_valid_after(self)\\n def issuer(self)\\n def subject(self)\\n def signature_hash_algorithm(self)\\n def extensions(self)\\n def signature(self)\\n def tbs_certificate_bytes(self)\\n def public_bytes(self, encoding)\\ndef _decode_certificate_policies(backend, cp)\\ndef _decode_user_notice(backend, un)\\ndef _decode_basic_constraints(backend, bc_st)\\ndef _decode_subject_key_identifier(backend, asn1_string)\\ndef _decode_authority_key_identifier(backend, akid)\\ndef _decode_authority_information_access(backend, aia)\\ndef _decode_key_usage(backend, bit_string)\\ndef _decode_general_names_extension(backend, gns)\\ndef _decode_subject_alt_name(backend, ext)\\ndef _decode_issuer_alt_name(backend, ext)\\ndef _decode_name_constraints(backend, nc)\\ndef _decode_general_subtrees(backend, stack_subtrees)\\ndef _decode_extended_key_usage(backend, sk)\\ndef _decode_crl_distribution_points(backend, cdps)\\ndef _decode_inhibit_any_policy(backend, asn1_int)\\ndef _decode_crl_reason(backend, enum)\\ndef _decode_invalidity_date(backend, inv_date)\\ndef _decode_cert_issuer(backend, ext) # This handler decodes the CertificateIssuer entry extension directly\\nclass _RevokedCertificate\\n def __init__(self, backend, crl, x509_revoked)\\n def serial_number(self)\\n def revocation_date(self)\\n def extensions(self)\\nclass _CertificateRevocationList\\n def __init__(self, backend, x509_crl)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def fingerprint(self, algorithm)\\n def signature_hash_algorithm(self)\\n def issuer(self)\\n def next_update(self)\\n def last_update(self)\\n def signature(self)\\n def tbs_certlist_bytes(self)\\n def public_bytes(self, encoding)\\n def _revoked_cert(self, idx)\\n def __iter__(self)\\n def __getitem__(self, idx)\\n def __len__(self)\\n def extensions(self)\\nclass _CertificateSigningRequest\\n def __init__(self, backend, x509_req)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def __hash__(self)\\n def public_key(self)\\n def subject(self)\\n def signature_hash_algorithm(self)\\n def extensions(self)\\n def public_bytes(self, encoding)\\n def tbs_certrequest_bytes(self)\\n def signature(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py\",\n \"text\": \"src/cryptography/x509/base.py\\nclass Version\\ndef load_pem_x509_certificate(data, backend)\\ndef load_der_x509_certificate(data, backend)\\ndef load_pem_x509_csr(data, backend)\\ndef load_der_x509_csr(data, backend)\\ndef load_pem_x509_crl(data, backend)\\ndef load_der_x509_crl(data, backend)\\nclass InvalidVersion\\n def __init__(self, msg, parsed_version)\\nclass Certificate\\n def fingerprint(self, algorithm) # Returns bytes using digest passed.\\n def serial(self) # Returns certificate serial number\\n def version(self) # Returns the certificate version\\n def public_key(self) # Returns the public key\\n def not_valid_before(self) # Not before time (represented as UTC datetime)\\n def not_valid_after(self) # Not after time (represented as UTC datetime)\\n def issuer(self) # Returns the issuer name object.\\n def subject(self) # Returns the subject name object.\\n def signature_hash_algorithm(self) # Returns a HashAlgorithm corresponding to the type of the digest signed\\n def extensions(self) # Returns an Extensions object.\\n def signature(self) # Returns the signature bytes.\\n def tbs_certificate_bytes(self) # Returns the tbsCertificate payload bytes as defined in RFC 5280.\\n def __eq__(self, other) # Checks equality.\\n def __ne__(self, other) # Checks not equal.\\n def __hash__(self) # Computes a hash.\\n def public_bytes(self, encoding) # Serializes the certificate to PEM or DER format.\\nclass CertificateRevocationList\\n def public_bytes(self, encoding) # Serializes the CRL to PEM or DER format.\\n def fingerprint(self, algorithm) # Returns bytes using digest passed.\\n def signature_hash_algorithm(self) # Returns a HashAlgorithm corresponding to the type of the digest signed\\n def issuer(self) # Returns the X509Name with the issuer of this CRL.\\n def next_update(self) # Returns the date of next update for this CRL.\\n def last_update(self) # Returns the date of last update for this CRL.\\n def extensions(self) # Returns an Extensions object containing a list of CRL extensions.\\n def signature(self) # Returns the signature bytes.\\n def tbs_certlist_bytes(self) # Returns the tbsCertList payload bytes as defined in RFC 5280.\\n def __eq__(self, other) # Checks equality.\\n def __ne__(self, other) # Checks not equal.\\nclass CertificateSigningRequest\\n def __eq__(self, other) # Checks equality.\\n def __ne__(self, other) # Checks not equal.\\n def __hash__(self) # Computes a hash.\\n def public_key(self) # Returns the public key\\n def subject(self) # Returns the subject name object.\\n def signature_hash_algorithm(self) # Returns a HashAlgorithm corresponding to the type of the digest signed\\n def extensions(self) # Returns the extensions in the signing request.\\n def public_bytes(self, encoding) # Encodes the request to PEM or DER format.\\n def signature(self) # Returns the signature bytes.\\n def tbs_certrequest_bytes(self) # Returns the PKCS#10 CertificationRequestInfo b\",\n \"label\": 0\n },\n {\n \"cid\": \"vectors/setup.py\",\n \"text\": \"vectors/setup.py\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"docs/cryptography-docs.py::HazmatDirective.run","text":"docs/cryptography-docs.py::HazmatDirective.run\n def run(self):\n message = DANGER_MESSAGE\n if self.content:\n message += DANGER_ALTERNATE.format(alternate=self.content[0])\n\n ad = make_admonition(\n Hazmat,\n self.name,\n [],\n self.options,\n nodes.paragraph(\"\", message),\n self.lineno,\n self.content_offset,\n self.block_text,\n self.state,\n self.state_machine\n )\n ad[0].line = self.lineno\n return ad","label":0},{"cid":"docs/cryptography-docs.py::html_visit_hazmat_node","text":"docs/cryptography-docs.py::html_visit_hazmat_node\ndef html_visit_hazmat_node(self, node):\n return self.visit_admonition(node, \"danger\")","label":0},{"cid":"docs/cryptography-docs.py::latex_visit_hazmat_node","text":"docs/cryptography-docs.py::latex_visit_hazmat_node\ndef latex_visit_hazmat_node(self, node):\n return self.visit_admonition(node)","label":0},{"cid":"docs/cryptography-docs.py::depart_hazmat_node","text":"docs/cryptography-docs.py::depart_hazmat_node\ndef depart_hazmat_node(self, node):\n return self.depart_admonition(node)","label":0},{"cid":"docs/cryptography-docs.py::setup","text":"docs/cryptography-docs.py::setup\ndef setup(app):\n app.add_node(\n Hazmat,\n html=(html_visit_hazmat_node, depart_hazmat_node),\n latex=(latex_visit_hazmat_node, depart_hazmat_node),\n )\n app.add_directive(\"hazmat\", HazmatDirective)","label":0},{"cid":"setup.py::cc_is_available","text":"setup.py::cc_is_available\ndef cc_is_available():\n return sys.platform == \"darwin\" and list(map(\n int, platform.mac_ver()[0].split(\".\"))) >= [10, 8, 0]","label":0},{"cid":"setup.py::PyTest.finalize_options","text":"setup.py::PyTest.finalize_options\n def finalize_options(self):\n test.finalize_options(self)\n self.test_args = []\n self.test_suite = True\n\n # This means there's a vectors/ folder with the package in here.\n # cd into it, install the vectors package and then refresh sys.path\n if VECTORS_DEPENDENCY not in test_requirements:\n subprocess.check_call(\n [sys.executable, \"setup.py\", \"install\"], cwd=\"vectors\"\n )\n pkg_resources.get_distribution(\"cryptography_vectors\").activate()","label":0},{"cid":"setup.py::PyTest.run_tests","text":"setup.py::PyTest.run_tests\n def run_tests(self):\n # Import here because in module scope the eggs are not loaded.\n import pytest\n test_args = [os.path.join(base_dir, \"tests\")]\n errno = pytest.main(test_args)\n sys.exit(errno)","label":0},{"cid":"setup.py::keywords_with_side_effects","text":"setup.py::keywords_with_side_effects\ndef keywords_with_side_effects(argv):\n \"\"\"\n Get a dictionary with setup keywords that (can) have side effects.\n\n :param argv: A list of strings with command line arguments.\n :returns: A dictionary with keyword arguments for the ``setup()`` function.\n\n This setup.py script uses the setuptools 'setup_requires' feature because\n this is required by the cffi package to compile extension modules. The\n purpose of ``keywords_with_side_effects()`` is to avoid triggering the cffi\n build process as a result of setup.py invocations that don't need the cffi\n module to be built (setup.py serves the dual purpose of exposing package\n metadata).\n\n All of the options listed by ``python setup.py --help`` that print\n information should be recognized here. The commands ``clean``,\n ``egg_info``, ``register``, ``sdist`` and ``upload`` are also recognized.\n Any combination of these options and commands is also supported.\n\n This function was originally based on the `setup.py script`_ of SciPy (see\n also the discussion in `pip issue #25`_).\n\n .. _pip issue #25: https://github.com/pypa/pip/issues/25\n .. _setup.py script: https://github.com/scipy/scipy/blob/master/setup.py\n \"\"\"\n no_setup_requires_arguments = (\n '-h', '--help',\n '-n', '--dry-run',\n '-q', '--quiet',\n '-v', '--verbose',\n '-V', '--version',\n '--author',\n '--author-email',\n '--classifiers',\n '--contact',\n '--contact-email',\n '--description',\n '--egg-base',\n '--fullname',\n '--help-commands',\n '--keywords',\n '--licence',\n '--license',\n '--long-description',\n '--maintainer',\n '--maintainer-email',\n '--name',\n '--no-user-cfg',\n '--obsoletes',\n '--platforms',\n '--provides',\n '--requires',\n '--url',\n 'clean',\n 'egg_info',\n 'register',\n ","label":0},{"cid":"setup.py::keywords_with_side_effects.is_short_option","text":"setup.py::keywords_with_side_effects.is_short_option\n def is_short_option(argument):\n \"\"\"Check whether a command line argument is a short option.\"\"\"\n return len(argument) >= 2 and argument[0] == '-' and argument[1] != '-'","label":0},{"cid":"setup.py::keywords_with_side_effects.expand_short_options","text":"setup.py::keywords_with_side_effects.expand_short_options\n def expand_short_options(argument):\n \"\"\"Expand combined short options into canonical short options.\"\"\"\n return ('-' + char for char in argument[1:])","label":0},{"cid":"setup.py::keywords_with_side_effects.argument_without_setup_requirements","text":"setup.py::keywords_with_side_effects.argument_without_setup_requirements\n def argument_without_setup_requirements(argv, i):\n \"\"\"Check whether a command line argument needs setup requirements.\"\"\"\n if argv[i] in no_setup_requires_arguments:\n # Simple case: An argument which is either an option or a command\n # which doesn't need setup requirements.\n return True\n elif (is_short_option(argv[i]) and\n all(option in no_setup_requires_arguments\n for option in expand_short_options(argv[i]))):\n # Not so simple case: Combined short options none of which need\n # setup requirements.\n return True\n elif argv[i - 1:i] == ['--egg-base']:\n # Tricky case: --egg-info takes an argument which should not make\n # us use setup_requires (defeating the purpose of this code).\n return True\n else:\n return False","label":0},{"cid":"setup.py::DummyBuild.run","text":"setup.py::DummyBuild.run\n def run(self):\n raise RuntimeError(setup_requires_error)","label":0},{"cid":"setup.py::DummyInstall.run","text":"setup.py::DummyInstall.run\n def run(self):\n raise RuntimeError(setup_requires_error)","label":0},{"cid":"setup.py::DummyPyTest.run_tests","text":"setup.py::DummyPyTest.run_tests\n def run_tests(self):\n raise RuntimeError(setup_requires_error)","label":0},{"cid":"src/_cffi_src/utils.py::build_ffi_for_binding","text":"src/_cffi_src/utils.py::build_ffi_for_binding\ndef build_ffi_for_binding(module_name, module_prefix, modules, pre_include=\"\",\n post_include=\"\", libraries=[], extra_compile_args=[],\n extra_link_args=[]):\n \"\"\"\n Modules listed in ``modules`` should have the following attributes:\n\n * ``INCLUDES``: A string containing C includes.\n * ``TYPES``: A string containing C declarations for types.\n * ``FUNCTIONS``: A string containing C declarations for functions.\n * ``MACROS``: A string containing C declarations for any macros.\n * ``CUSTOMIZATIONS``: A string containing arbitrary top-level C code, this\n can be used to do things like test for a define and provide an\n alternate implementation based on that.\n \"\"\"\n types = []\n includes = []\n functions = []\n macros = []\n customizations = []\n for name in modules:\n __import__(module_prefix + name)\n module = sys.modules[module_prefix + name]\n\n types.append(module.TYPES)\n macros.append(module.MACROS)\n functions.append(module.FUNCTIONS)\n includes.append(module.INCLUDES)\n customizations.append(module.CUSTOMIZATIONS)\n\n # We include functions here so that if we got any of their definitions\n # wrong, the underlying C compiler will explode. In C you are allowed\n # to re-declare a function if it has the same signature. That is:\n # int foo(int);\n # int foo(int);\n # is legal, but the following will fail to compile:\n # int foo(int);\n # int foo(short);\n verify_source = \"\\n\".join(\n [pre_include] +\n includes +\n [post_include] +\n functions +\n customizations\n )\n ffi = build_ffi(\n module_name,\n cdef_source=\"\\n\".join(types + functions + macros),\n verify_source=verify_source,\n libraries=libraries,\n extra_compile_args=extra_compile_args,\n extra_link_args=extra_link_args,\n )\n\n return ","label":0},{"cid":"src/_cffi_src/utils.py::build_ffi","text":"src/_cffi_src/utils.py::build_ffi\ndef build_ffi(module_name, cdef_source, verify_source, libraries=[],\n extra_compile_args=[], extra_link_args=[]):\n ffi = FFI()\n ffi.cdef(cdef_source)\n ffi.set_source(\n module_name,\n verify_source,\n libraries=libraries,\n extra_compile_args=extra_compile_args,\n extra_link_args=extra_link_args,\n )\n return ffi","label":0},{"cid":"src/_cffi_src/utils.py::extra_link_args","text":"src/_cffi_src/utils.py::extra_link_args\ndef extra_link_args(platform):\n if platform != \"win32\":\n return []\n else:\n # Enable NX and ASLR for Windows builds. These are enabled by default\n # on Python 3.3+ but not on 2.x.\n return [\"/NXCOMPAT\", \"/DYNAMICBASE\"]","label":0},{"cid":"src/cryptography/hazmat/backends/__init__.py::_available_backends","text":"src/cryptography/hazmat/backends/__init__.py::_available_backends\ndef _available_backends():\n global _available_backends_list\n\n if _available_backends_list is None:\n _available_backends_list = [\n # setuptools 11.3 deprecated support for the require parameter to\n # load(), and introduced the new resolve() method instead.\n # This can be removed if/when we can assume setuptools>=11.3. At\n # some point we may wish to add a warning, to push people along,\n # but at present this would result in too many warnings.\n ep.resolve() if hasattr(ep, \"resolve\") else ep.load(require=False)\n for ep in pkg_resources.iter_entry_points(\n \"cryptography.backends\"\n )\n ]\n\n return _available_backends_list","label":0},{"cid":"src/cryptography/hazmat/backends/__init__.py::default_backend","text":"src/cryptography/hazmat/backends/__init__.py::default_backend\ndef default_backend():\n global _default_backend\n\n if _default_backend is None:\n _default_backend = MultiBackend(_available_backends())\n\n return _default_backend","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.__init__","text":"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.__init__\n def __init__(self, backend, cipher, mode, operation):\n self._backend = backend\n self._cipher = cipher\n self._mode = mode\n self._operation = operation\n self._tag = None\n\n if isinstance(self._cipher, ciphers.BlockCipherAlgorithm):\n self._block_size = self._cipher.block_size\n else:\n self._block_size = 1\n\n ctx = self._backend._lib.EVP_CIPHER_CTX_new()\n ctx = self._backend._ffi.gc(\n ctx, self._backend._lib.EVP_CIPHER_CTX_free\n )\n\n registry = self._backend._cipher_registry\n try:\n adapter = registry[type(cipher), type(mode)]\n except KeyError:\n raise UnsupportedAlgorithm(\n \"cipher {0} in {1} mode is not supported \"\n \"by this backend.\".format(\n cipher.name, mode.name if mode else mode),\n _Reasons.UNSUPPORTED_CIPHER\n )\n\n evp_cipher = adapter(self._backend, cipher, mode)\n if evp_cipher == self._backend._ffi.NULL:\n raise UnsupportedAlgorithm(\n \"cipher {0} in {1} mode is not supported \"\n \"by this backend.\".format(\n cipher.name, mode.name if mode else mode),\n _Reasons.UNSUPPORTED_CIPHER\n )\n\n if isinstance(mode, modes.ModeWithInitializationVector):\n iv_nonce = mode.initialization_vector\n elif isinstance(mode, modes.ModeWithNonce):\n iv_nonce = mode.nonce\n else:\n iv_nonce = self._backend._ffi.NULL\n # begin init with cipher and operation type\n res = self._backend._lib.EVP_CipherInit_ex(ctx, evp_cipher,\n self._backend._ffi.NULL,\n self._backend._ffi.NULL,\n self._backend._ffi.NU","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.update","text":"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.update\n def update(self, data):\n # OpenSSL 0.9.8e has an assertion in its EVP code that causes it\n # to SIGABRT if you call update with an empty byte string. This can be\n # removed when we drop support for 0.9.8e (CentOS/RHEL 5). This branch\n # should be taken only when length is zero and mode is not GCM because\n # AES GCM can return improper tag values if you don't call update\n # with empty plaintext when authenticating AAD for ...reasons.\n if len(data) == 0 and not isinstance(self._mode, modes.GCM):\n return b\"\"\n\n buf = self._backend._ffi.new(\"unsigned char[]\",\n len(data) + self._block_size - 1)\n outlen = self._backend._ffi.new(\"int *\")\n res = self._backend._lib.EVP_CipherUpdate(self._ctx, buf, outlen, data,\n len(data))\n self._backend.openssl_assert(res != 0)\n return self._backend._ffi.buffer(buf)[:outlen[0]]","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.finalize","text":"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.finalize\n def finalize(self):\n # OpenSSL 1.0.1 on Ubuntu 12.04 (and possibly other distributions)\n # appears to have a bug where you must make at least one call to update\n # even if you are only using authenticate_additional_data or the\n # GCM tag will be wrong. An (empty) call to update resolves this\n # and is harmless for all other versions of OpenSSL.\n if isinstance(self._mode, modes.GCM):\n self.update(b\"\")\n\n buf = self._backend._ffi.new(\"unsigned char[]\", self._block_size)\n outlen = self._backend._ffi.new(\"int *\")\n res = self._backend._lib.EVP_CipherFinal_ex(self._ctx, buf, outlen)\n if res == 0:\n errors = self._backend._consume_errors()\n\n if not errors and isinstance(self._mode, modes.GCM):\n raise InvalidTag\n\n self._backend.openssl_assert(\n errors[0][1:] == (\n self._backend._lib.ERR_LIB_EVP,\n self._backend._lib.EVP_F_EVP_ENCRYPTFINAL_EX,\n self._backend._lib.EVP_R_DATA_NOT_MULTIPLE_OF_BLOCK_LENGTH\n ) or errors[0][1:] == (\n self._backend._lib.ERR_LIB_EVP,\n self._backend._lib.EVP_F_EVP_DECRYPTFINAL_EX,\n self._backend._lib.EVP_R_DATA_NOT_MULTIPLE_OF_BLOCK_LENGTH\n )\n )\n raise ValueError(\n \"The length of the provided data is not a multiple of \"\n \"the block length.\"\n )\n\n if (isinstance(self._mode, modes.GCM) and\n self._operation == self._ENCRYPT):\n block_byte_size = self._block_size // 8\n tag_buf = self._backend._ffi.new(\n \"unsigned char[]\", block_byte_size\n )\n res = self._backend._lib.EVP_CIPHER_CTX_ctrl(\n self._ctx, self._backend._lib.EVP_CTRL_GCM_GET_TAG,\n ","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.authenticate_additional_data","text":"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.authenticate_additional_data\n def authenticate_additional_data(self, data):\n outlen = self._backend._ffi.new(\"int *\")\n res = self._backend._lib.EVP_CipherUpdate(\n self._ctx, self._backend._ffi.NULL, outlen, data, len(data)\n )\n self._backend.openssl_assert(res != 0)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.__init__","text":"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.__init__\n def __init__(self, backend, cipher, mode):\n self._backend = backend\n\n self._key = self._backend._ffi.new(\"AES_KEY *\")\n res = self._backend._lib.AES_set_encrypt_key(\n cipher.key, len(cipher.key) * 8, self._key\n )\n self._backend.openssl_assert(res == 0)\n self._ecount = self._backend._ffi.new(\"char[]\", 16)\n self._nonce = self._backend._ffi.new(\"char[16]\", mode.nonce)\n self._num = self._backend._ffi.new(\"unsigned int *\", 0)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.update","text":"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.update\n def update(self, data):\n buf = self._backend._ffi.new(\"unsigned char[]\", len(data))\n self._backend._lib.AES_ctr128_encrypt(\n data, buf, len(data), self._key, self._nonce,\n self._ecount, self._num\n )\n return self._backend._ffi.buffer(buf)[:]","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.finalize","text":"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.finalize\n def finalize(self):\n self._key = None\n self._ecount = None\n self._nonce = None\n self._num = None\n return b\"\"","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_obj2txt","text":"src/cryptography/hazmat/backends/openssl/x509.py::_obj2txt\ndef _obj2txt(backend, obj):\n # Set to 80 on the recommendation of\n # https://www.openssl.org/docs/crypto/OBJ_nid2ln.html#return_values\n buf_len = 80\n buf = backend._ffi.new(\"char[]\", buf_len)\n res = backend._lib.OBJ_obj2txt(buf, buf_len, obj, 1)\n backend.openssl_assert(res > 0)\n return backend._ffi.buffer(buf, res)[:].decode()","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_x509_name_entry","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_x509_name_entry\ndef _decode_x509_name_entry(backend, x509_name_entry):\n obj = backend._lib.X509_NAME_ENTRY_get_object(x509_name_entry)\n backend.openssl_assert(obj != backend._ffi.NULL)\n data = backend._lib.X509_NAME_ENTRY_get_data(x509_name_entry)\n backend.openssl_assert(data != backend._ffi.NULL)\n value = backend._asn1_string_to_utf8(data)\n oid = _obj2txt(backend, obj)\n\n return x509.NameAttribute(x509.ObjectIdentifier(oid), value)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_x509_name","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_x509_name\ndef _decode_x509_name(backend, x509_name):\n count = backend._lib.X509_NAME_entry_count(x509_name)\n attributes = []\n for x in range(count):\n entry = backend._lib.X509_NAME_get_entry(x509_name, x)\n attributes.append(_decode_x509_name_entry(backend, entry))\n\n return x509.Name(attributes)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_names","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_names\ndef _decode_general_names(backend, gns):\n num = backend._lib.sk_GENERAL_NAME_num(gns)\n names = []\n for i in range(num):\n gn = backend._lib.sk_GENERAL_NAME_value(gns, i)\n backend.openssl_assert(gn != backend._ffi.NULL)\n names.append(_decode_general_name(backend, gn))\n\n return names","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_name","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_name\ndef _decode_general_name(backend, gn):\n if gn.type == backend._lib.GEN_DNS:\n data = backend._asn1_string_to_bytes(gn.d.dNSName)\n if not data:\n decoded = u\"\"\n elif data.startswith(b\"*.\"):\n # This is a wildcard name. We need to remove the leading wildcard,\n # IDNA decode, then re-add the wildcard. Wildcard characters should\n # always be left-most (RFC 2595 section 2.4).\n decoded = u\"*.\" + idna.decode(data[2:])\n else:\n # Not a wildcard, decode away. If the string has a * in it anywhere\n # invalid this will raise an InvalidCodePoint\n decoded = idna.decode(data)\n if data.startswith(b\".\"):\n # idna strips leading periods. Name constraints can have that\n # so we need to re-add it. Sigh.\n decoded = u\".\" + decoded\n\n return x509.DNSName(decoded)\n elif gn.type == backend._lib.GEN_URI:\n data = backend._asn1_string_to_ascii(gn.d.uniformResourceIdentifier)\n parsed = urllib_parse.urlparse(data)\n if parsed.hostname:\n hostname = idna.decode(parsed.hostname)\n else:\n hostname = \"\"\n if parsed.port:\n netloc = hostname + u\":\" + six.text_type(parsed.port)\n else:\n netloc = hostname\n\n # Note that building a URL in this fashion means it should be\n # semantically indistinguishable from the original but is not\n # guaranteed to be exactly the same.\n uri = urllib_parse.urlunparse((\n parsed.scheme,\n netloc,\n parsed.path,\n parsed.params,\n parsed.query,\n parsed.fragment\n ))\n return x509.UniformResourceIdentifier(uri)\n elif gn.type == backend._lib.GEN_RID:\n oid = _obj2txt(backend, gn.d.registeredID)\n return x509.RegisteredID(x509.ObjectIdenti","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_ocsp_no_check","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_ocsp_no_check\ndef _decode_ocsp_no_check(backend, ext):\n return x509.OCSPNoCheck()","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_crl_number","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_crl_number\ndef _decode_crl_number(backend, ext):\n asn1_int = backend._ffi.cast(\"ASN1_INTEGER *\", ext)\n asn1_int = backend._ffi.gc(asn1_int, backend._lib.ASN1_INTEGER_free)\n return x509.CRLNumber(backend._asn1_integer_to_int(asn1_int))","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_X509ExtensionParser.__init__","text":"src/cryptography/hazmat/backends/openssl/x509.py::_X509ExtensionParser.__init__\n def __init__(self, ext_count, get_ext, handlers, unsupported_exts=None):\n self.ext_count = ext_count\n self.get_ext = get_ext\n self.handlers = handlers\n self.unsupported_exts = unsupported_exts","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_X509ExtensionParser.parse","text":"src/cryptography/hazmat/backends/openssl/x509.py::_X509ExtensionParser.parse\n def parse(self, backend, x509_obj):\n extensions = []\n seen_oids = set()\n for i in range(self.ext_count(backend, x509_obj)):\n ext = self.get_ext(backend, x509_obj, i)\n backend.openssl_assert(ext != backend._ffi.NULL)\n crit = backend._lib.X509_EXTENSION_get_critical(ext)\n critical = crit == 1\n oid = x509.ObjectIdentifier(_obj2txt(backend, ext.object))\n if oid in seen_oids:\n raise x509.DuplicateExtension(\n \"Duplicate {0} extension found\".format(oid), oid\n )\n try:\n handler = self.handlers[oid]\n except KeyError:\n if critical:\n raise x509.UnsupportedExtension(\n \"Critical extension {0} is not currently supported\"\n .format(oid), oid\n )\n else:\n # For extensions which are not supported by OpenSSL we pass the\n # extension object directly to the parsing routine so it can\n # be decoded manually.\n if self.unsupported_exts and oid in self.unsupported_exts:\n ext_data = ext\n else:\n ext_data = backend._lib.X509V3_EXT_d2i(ext)\n if ext_data == backend._ffi.NULL:\n backend._consume_errors()\n raise ValueError(\n \"The {0} extension is invalid and can't be \"\n \"parsed\".format(oid)\n )\n\n value = handler(backend, ext_data)\n extensions.append(x509.Extension(oid, critical, value))\n\n seen_oids.add(oid)\n\n return x509.Extensions(extensions)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__init__","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__init__\n def __init__(self, backend, x509):\n self._backend = backend\n self._x509 = x509","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__repr__","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__repr__\n def __repr__(self):\n return \"<Certificate(subject={0}, ...)>\".format(self.subject)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__eq__","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__eq__\n def __eq__(self, other):\n if not isinstance(other, x509.Certificate):\n return NotImplemented\n\n res = self._backend._lib.X509_cmp(self._x509, other._x509)\n return res == 0","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__ne__","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__ne__\n def __ne__(self, other):\n return not self == other","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__hash__","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__hash__\n def __hash__(self):\n return hash(self.public_bytes(serialization.Encoding.DER))","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.fingerprint","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.fingerprint\n def fingerprint(self, algorithm):\n h = hashes.Hash(algorithm, self._backend)\n h.update(self.public_bytes(serialization.Encoding.DER))\n return h.finalize()","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.version","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.version\n @property\n def version(self):\n version = self._backend._lib.X509_get_version(self._x509)\n if version == 0:\n return x509.Version.v1\n elif version == 2:\n return x509.Version.v3\n else:\n raise x509.InvalidVersion(\n \"{0} is not a valid X509 version\".format(version), version\n )","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.serial","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.serial\n @property\n def serial(self):\n asn1_int = self._backend._lib.X509_get_serialNumber(self._x509)\n self._backend.openssl_assert(asn1_int != self._backend._ffi.NULL)\n return self._backend._asn1_integer_to_int(asn1_int)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.public_key","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.public_key\n def public_key(self):\n pkey = self._backend._lib.X509_get_pubkey(self._x509)\n if pkey == self._backend._ffi.NULL:\n # Remove errors from the stack.\n self._backend._consume_errors()\n raise ValueError(\"Certificate public key is of an unknown type\")\n\n pkey = self._backend._ffi.gc(pkey, self._backend._lib.EVP_PKEY_free)\n\n return self._backend._evp_pkey_to_public_key(pkey)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.not_valid_before","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.not_valid_before\n @property\n def not_valid_before(self):\n asn1_time = self._backend._lib.X509_get_notBefore(self._x509)\n return self._backend._parse_asn1_time(asn1_time)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.not_valid_after","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.not_valid_after\n @property\n def not_valid_after(self):\n asn1_time = self._backend._lib.X509_get_notAfter(self._x509)\n return self._backend._parse_asn1_time(asn1_time)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.issuer","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.issuer\n @property\n def issuer(self):\n issuer = self._backend._lib.X509_get_issuer_name(self._x509)\n self._backend.openssl_assert(issuer != self._backend._ffi.NULL)\n return _decode_x509_name(self._backend, issuer)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.subject","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.subject\n @property\n def subject(self):\n subject = self._backend._lib.X509_get_subject_name(self._x509)\n self._backend.openssl_assert(subject != self._backend._ffi.NULL)\n return _decode_x509_name(self._backend, subject)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.signature_hash_algorithm","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.signature_hash_algorithm\n @property\n def signature_hash_algorithm(self):\n oid = _obj2txt(self._backend, self._x509.sig_alg.algorithm)\n try:\n return x509._SIG_OIDS_TO_HASH[oid]\n except KeyError:\n raise UnsupportedAlgorithm(\n \"Signature algorithm OID:{0} not recognized\".format(oid)\n )","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.extensions","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.extensions\n @property\n def extensions(self):\n return _CERTIFICATE_EXTENSION_PARSER.parse(self._backend, self._x509)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.signature","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.signature\n @property\n def signature(self):\n return self._backend._asn1_string_to_bytes(self._x509.signature)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.tbs_certificate_bytes","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.tbs_certificate_bytes\n @property\n def tbs_certificate_bytes(self):\n pp = self._backend._ffi.new(\"unsigned char **\")\n # the X509_CINF struct holds the tbsCertificate data\n res = self._backend._lib.i2d_X509_CINF(self._x509.cert_info, pp)\n self._backend.openssl_assert(res > 0)\n pp = self._backend._ffi.gc(\n pp, lambda pointer: self._backend._lib.OPENSSL_free(pointer[0])\n )\n return self._backend._ffi.buffer(pp[0], res)[:]","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.public_bytes","text":"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.public_bytes\n def public_bytes(self, encoding):\n bio = self._backend._create_mem_bio()\n if encoding is serialization.Encoding.PEM:\n res = self._backend._lib.PEM_write_bio_X509(bio, self._x509)\n elif encoding is serialization.Encoding.DER:\n res = self._backend._lib.i2d_X509_bio(bio, self._x509)\n else:\n raise TypeError(\"encoding must be an item from the Encoding enum\")\n\n self._backend.openssl_assert(res == 1)\n return self._backend._read_mem_bio(bio)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_certificate_policies","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_certificate_policies\ndef _decode_certificate_policies(backend, cp):\n cp = backend._ffi.cast(\"Cryptography_STACK_OF_POLICYINFO *\", cp)\n cp = backend._ffi.gc(cp, backend._lib.sk_POLICYINFO_free)\n num = backend._lib.sk_POLICYINFO_num(cp)\n certificate_policies = []\n for i in range(num):\n qualifiers = None\n pi = backend._lib.sk_POLICYINFO_value(cp, i)\n oid = x509.ObjectIdentifier(_obj2txt(backend, pi.policyid))\n if pi.qualifiers != backend._ffi.NULL:\n qnum = backend._lib.sk_POLICYQUALINFO_num(pi.qualifiers)\n qualifiers = []\n for j in range(qnum):\n pqi = backend._lib.sk_POLICYQUALINFO_value(\n pi.qualifiers, j\n )\n pqualid = x509.ObjectIdentifier(\n _obj2txt(backend, pqi.pqualid)\n )\n if pqualid == CertificatePoliciesOID.CPS_QUALIFIER:\n cpsuri = backend._ffi.buffer(\n pqi.d.cpsuri.data, pqi.d.cpsuri.length\n )[:].decode('ascii')\n qualifiers.append(cpsuri)\n else:\n assert pqualid == CertificatePoliciesOID.CPS_USER_NOTICE\n user_notice = _decode_user_notice(\n backend, pqi.d.usernotice\n )\n qualifiers.append(user_notice)\n\n certificate_policies.append(\n x509.PolicyInformation(oid, qualifiers)\n )\n\n return x509.CertificatePolicies(certificate_policies)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_user_notice","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_user_notice\ndef _decode_user_notice(backend, un):\n explicit_text = None\n notice_reference = None\n\n if un.exptext != backend._ffi.NULL:\n explicit_text = backend._asn1_string_to_utf8(un.exptext)\n\n if un.noticeref != backend._ffi.NULL:\n organization = backend._asn1_string_to_utf8(un.noticeref.organization)\n\n num = backend._lib.sk_ASN1_INTEGER_num(\n un.noticeref.noticenos\n )\n notice_numbers = []\n for i in range(num):\n asn1_int = backend._lib.sk_ASN1_INTEGER_value(\n un.noticeref.noticenos, i\n )\n notice_num = backend._asn1_integer_to_int(asn1_int)\n notice_numbers.append(notice_num)\n\n notice_reference = x509.NoticeReference(\n organization, notice_numbers\n )\n\n return x509.UserNotice(notice_reference, explicit_text)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_basic_constraints","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_basic_constraints\ndef _decode_basic_constraints(backend, bc_st):\n basic_constraints = backend._ffi.cast(\"BASIC_CONSTRAINTS *\", bc_st)\n basic_constraints = backend._ffi.gc(\n basic_constraints, backend._lib.BASIC_CONSTRAINTS_free\n )\n # The byte representation of an ASN.1 boolean true is \\xff. OpenSSL\n # chooses to just map this to its ordinal value, so true is 255 and\n # false is 0.\n ca = basic_constraints.ca == 255\n if basic_constraints.pathlen == backend._ffi.NULL:\n path_length = None\n else:\n path_length = backend._asn1_integer_to_int(basic_constraints.pathlen)\n\n return x509.BasicConstraints(ca, path_length)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_subject_key_identifier","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_subject_key_identifier\ndef _decode_subject_key_identifier(backend, asn1_string):\n asn1_string = backend._ffi.cast(\"ASN1_OCTET_STRING *\", asn1_string)\n asn1_string = backend._ffi.gc(\n asn1_string, backend._lib.ASN1_OCTET_STRING_free\n )\n return x509.SubjectKeyIdentifier(\n backend._ffi.buffer(asn1_string.data, asn1_string.length)[:]\n )","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_authority_key_identifier","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_authority_key_identifier\ndef _decode_authority_key_identifier(backend, akid):\n akid = backend._ffi.cast(\"AUTHORITY_KEYID *\", akid)\n akid = backend._ffi.gc(akid, backend._lib.AUTHORITY_KEYID_free)\n key_identifier = None\n authority_cert_issuer = None\n authority_cert_serial_number = None\n\n if akid.keyid != backend._ffi.NULL:\n key_identifier = backend._ffi.buffer(\n akid.keyid.data, akid.keyid.length\n )[:]\n\n if akid.issuer != backend._ffi.NULL:\n authority_cert_issuer = _decode_general_names(\n backend, akid.issuer\n )\n\n if akid.serial != backend._ffi.NULL:\n authority_cert_serial_number = backend._asn1_integer_to_int(\n akid.serial\n )\n\n return x509.AuthorityKeyIdentifier(\n key_identifier, authority_cert_issuer, authority_cert_serial_number\n )","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_authority_information_access","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_authority_information_access\ndef _decode_authority_information_access(backend, aia):\n aia = backend._ffi.cast(\"Cryptography_STACK_OF_ACCESS_DESCRIPTION *\", aia)\n aia = backend._ffi.gc(aia, backend._lib.sk_ACCESS_DESCRIPTION_free)\n num = backend._lib.sk_ACCESS_DESCRIPTION_num(aia)\n access_descriptions = []\n for i in range(num):\n ad = backend._lib.sk_ACCESS_DESCRIPTION_value(aia, i)\n backend.openssl_assert(ad.method != backend._ffi.NULL)\n oid = x509.ObjectIdentifier(_obj2txt(backend, ad.method))\n backend.openssl_assert(ad.location != backend._ffi.NULL)\n gn = _decode_general_name(backend, ad.location)\n access_descriptions.append(x509.AccessDescription(oid, gn))\n\n return x509.AuthorityInformationAccess(access_descriptions)","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_key_usage","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_key_usage\ndef _decode_key_usage(backend, bit_string):\n bit_string = backend._ffi.cast(\"ASN1_BIT_STRING *\", bit_string)\n bit_string = backend._ffi.gc(bit_string, backend._lib.ASN1_BIT_STRING_free)\n get_bit = backend._lib.ASN1_BIT_STRING_get_bit\n digital_signature = get_bit(bit_string, 0) == 1\n content_commitment = get_bit(bit_string, 1) == 1\n key_encipherment = get_bit(bit_string, 2) == 1\n data_encipherment = get_bit(bit_string, 3) == 1\n key_agreement = get_bit(bit_string, 4) == 1\n key_cert_sign = get_bit(bit_string, 5) == 1\n crl_sign = get_bit(bit_string, 6) == 1\n encipher_only = get_bit(bit_string, 7) == 1\n decipher_only = get_bit(bit_string, 8) == 1\n return x509.KeyUsage(\n digital_signature,\n content_commitment,\n key_encipherment,\n data_encipherment,\n key_agreement,\n key_cert_sign,\n crl_sign,\n encipher_only,\n decipher_only\n )","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_names_extension","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_names_extension\ndef _decode_general_names_extension(backend, gns):\n gns = backend._ffi.cast(\"GENERAL_NAMES *\", gns)\n gns = backend._ffi.gc(gns, backend._lib.GENERAL_NAMES_free)\n general_names = _decode_general_names(backend, gns)\n return general_names","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_subject_alt_name","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_subject_alt_name\ndef _decode_subject_alt_name(backend, ext):\n return x509.SubjectAlternativeName(\n _decode_general_names_extension(backend, ext)\n )","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_issuer_alt_name","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_issuer_alt_name\ndef _decode_issuer_alt_name(backend, ext):\n return x509.IssuerAlternativeName(\n _decode_general_names_extension(backend, ext)\n )","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_name_constraints","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_name_constraints\ndef _decode_name_constraints(backend, nc):\n nc = backend._ffi.cast(\"NAME_CONSTRAINTS *\", nc)\n nc = backend._ffi.gc(nc, backend._lib.NAME_CONSTRAINTS_free)\n permitted = _decode_general_subtrees(backend, nc.permittedSubtrees)\n excluded = _decode_general_subtrees(backend, nc.excludedSubtrees)\n return x509.NameConstraints(\n permitted_subtrees=permitted, excluded_subtrees=excluded\n )","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_subtrees","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_subtrees\ndef _decode_general_subtrees(backend, stack_subtrees):\n if stack_subtrees == backend._ffi.NULL:\n return None\n\n num = backend._lib.sk_GENERAL_SUBTREE_num(stack_subtrees)\n subtrees = []\n\n for i in range(num):\n obj = backend._lib.sk_GENERAL_SUBTREE_value(stack_subtrees, i)\n backend.openssl_assert(obj != backend._ffi.NULL)\n name = _decode_general_name(backend, obj.base)\n subtrees.append(name)\n\n return subtrees","label":0},{"cid":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_extended_key_usage","text":"src/cryptography/hazmat/backends/openssl/x509.py::_decode_extended_key_usage\ndef _decode_extended_key_usage(backend, sk):\n sk = backend._ffi.cast(\"Cryptography_STACK_OF_ASN1_OBJECT *\", sk)\n sk = backend._ffi.gc(sk, backend._lib.sk_ASN1_OBJECT_free)\n num = backend._lib.sk_ASN1_OBJECT_num(sk)\n ekus = []\n\n for i in range(num):\n obj = backend._lib.sk_ASN1_OBJECT_value(sk, i)\n backend.openssl_assert(obj != backend._ffi.NULL)\n oid = x509.ObjectIdentifier(_obj2txt(backend, obj))\n ekus.append(oid)\n\n return x509.ExtendedKeyUsage(ekus)","label":0},{"cid":"src/cryptography/x509/base.py::load_pem_x509_certificate","text":"src/cryptography/x509/base.py::load_pem_x509_certificate\ndef load_pem_x509_certificate(data, backend):\n return backend.load_pem_x509_certificate(data)","label":0},{"cid":"src/cryptography/x509/base.py::load_der_x509_certificate","text":"src/cryptography/x509/base.py::load_der_x509_certificate\ndef load_der_x509_certificate(data, backend):\n return backend.load_der_x509_certificate(data)","label":0},{"cid":"src/cryptography/x509/base.py::load_pem_x509_csr","text":"src/cryptography/x509/base.py::load_pem_x509_csr\ndef load_pem_x509_csr(data, backend):\n return backend.load_pem_x509_csr(data)","label":0},{"cid":"src/cryptography/x509/base.py::load_der_x509_csr","text":"src/cryptography/x509/base.py::load_der_x509_csr\ndef load_der_x509_csr(data, backend):\n return backend.load_der_x509_csr(data)","label":0},{"cid":"src/cryptography/x509/base.py::load_pem_x509_crl","text":"src/cryptography/x509/base.py::load_pem_x509_crl\ndef load_pem_x509_crl(data, backend):\n return backend.load_pem_x509_crl(data)","label":0},{"cid":"src/cryptography/x509/base.py::load_der_x509_crl","text":"src/cryptography/x509/base.py::load_der_x509_crl\ndef load_der_x509_crl(data, backend):\n return backend.load_der_x509_crl(data)","label":0},{"cid":"src/cryptography/x509/base.py::InvalidVersion.__init__","text":"src/cryptography/x509/base.py::InvalidVersion.__init__\n def __init__(self, msg, parsed_version):\n super(InvalidVersion, self).__init__(msg)\n self.parsed_version = parsed_version","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.fingerprint","text":"src/cryptography/x509/base.py::Certificate.fingerprint\n @abc.abstractmethod\n def fingerprint(self, algorithm):\n \"\"\"\n Returns bytes using digest passed.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.serial","text":"src/cryptography/x509/base.py::Certificate.serial\n @abc.abstractproperty\n def serial(self):\n \"\"\"\n Returns certificate serial number\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.version","text":"src/cryptography/x509/base.py::Certificate.version\n @abc.abstractproperty\n def version(self):\n \"\"\"\n Returns the certificate version\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.public_key","text":"src/cryptography/x509/base.py::Certificate.public_key\n @abc.abstractmethod\n def public_key(self):\n \"\"\"\n Returns the public key\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.not_valid_before","text":"src/cryptography/x509/base.py::Certificate.not_valid_before\n @abc.abstractproperty\n def not_valid_before(self):\n \"\"\"\n Not before time (represented as UTC datetime)\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.not_valid_after","text":"src/cryptography/x509/base.py::Certificate.not_valid_after\n @abc.abstractproperty\n def not_valid_after(self):\n \"\"\"\n Not after time (represented as UTC datetime)\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.issuer","text":"src/cryptography/x509/base.py::Certificate.issuer\n @abc.abstractproperty\n def issuer(self):\n \"\"\"\n Returns the issuer name object.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.subject","text":"src/cryptography/x509/base.py::Certificate.subject\n @abc.abstractproperty\n def subject(self):\n \"\"\"\n Returns the subject name object.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.signature_hash_algorithm","text":"src/cryptography/x509/base.py::Certificate.signature_hash_algorithm\n @abc.abstractproperty\n def signature_hash_algorithm(self):\n \"\"\"\n Returns a HashAlgorithm corresponding to the type of the digest signed\n in the certificate.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.extensions","text":"src/cryptography/x509/base.py::Certificate.extensions\n @abc.abstractproperty\n def extensions(self):\n \"\"\"\n Returns an Extensions object.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.signature","text":"src/cryptography/x509/base.py::Certificate.signature\n @abc.abstractproperty\n def signature(self):\n \"\"\"\n Returns the signature bytes.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.tbs_certificate_bytes","text":"src/cryptography/x509/base.py::Certificate.tbs_certificate_bytes\n @abc.abstractproperty\n def tbs_certificate_bytes(self):\n \"\"\"\n Returns the tbsCertificate payload bytes as defined in RFC 5280.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.__eq__","text":"src/cryptography/x509/base.py::Certificate.__eq__\n @abc.abstractmethod\n def __eq__(self, other):\n \"\"\"\n Checks equality.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.__ne__","text":"src/cryptography/x509/base.py::Certificate.__ne__\n @abc.abstractmethod\n def __ne__(self, other):\n \"\"\"\n Checks not equal.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.__hash__","text":"src/cryptography/x509/base.py::Certificate.__hash__\n @abc.abstractmethod\n def __hash__(self):\n \"\"\"\n Computes a hash.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::Certificate.public_bytes","text":"src/cryptography/x509/base.py::Certificate.public_bytes\n @abc.abstractmethod\n def public_bytes(self, encoding):\n \"\"\"\n Serializes the certificate to PEM or DER format.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateRevocationList.public_bytes","text":"src/cryptography/x509/base.py::CertificateRevocationList.public_bytes\n @abc.abstractmethod\n def public_bytes(self, encoding):\n \"\"\"\n Serializes the CRL to PEM or DER format.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateRevocationList.fingerprint","text":"src/cryptography/x509/base.py::CertificateRevocationList.fingerprint\n @abc.abstractmethod\n def fingerprint(self, algorithm):\n \"\"\"\n Returns bytes using digest passed.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateRevocationList.signature_hash_algorithm","text":"src/cryptography/x509/base.py::CertificateRevocationList.signature_hash_algorithm\n @abc.abstractproperty\n def signature_hash_algorithm(self):\n \"\"\"\n Returns a HashAlgorithm corresponding to the type of the digest signed\n in the certificate.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateRevocationList.issuer","text":"src/cryptography/x509/base.py::CertificateRevocationList.issuer\n @abc.abstractproperty\n def issuer(self):\n \"\"\"\n Returns the X509Name with the issuer of this CRL.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateRevocationList.next_update","text":"src/cryptography/x509/base.py::CertificateRevocationList.next_update\n @abc.abstractproperty\n def next_update(self):\n \"\"\"\n Returns the date of next update for this CRL.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateRevocationList.last_update","text":"src/cryptography/x509/base.py::CertificateRevocationList.last_update\n @abc.abstractproperty\n def last_update(self):\n \"\"\"\n Returns the date of last update for this CRL.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateRevocationList.extensions","text":"src/cryptography/x509/base.py::CertificateRevocationList.extensions\n @abc.abstractproperty\n def extensions(self):\n \"\"\"\n Returns an Extensions object containing a list of CRL extensions.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateRevocationList.signature","text":"src/cryptography/x509/base.py::CertificateRevocationList.signature\n @abc.abstractproperty\n def signature(self):\n \"\"\"\n Returns the signature bytes.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateRevocationList.tbs_certlist_bytes","text":"src/cryptography/x509/base.py::CertificateRevocationList.tbs_certlist_bytes\n @abc.abstractproperty\n def tbs_certlist_bytes(self):\n \"\"\"\n Returns the tbsCertList payload bytes as defined in RFC 5280.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateRevocationList.__eq__","text":"src/cryptography/x509/base.py::CertificateRevocationList.__eq__\n @abc.abstractmethod\n def __eq__(self, other):\n \"\"\"\n Checks equality.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateRevocationList.__ne__","text":"src/cryptography/x509/base.py::CertificateRevocationList.__ne__\n @abc.abstractmethod\n def __ne__(self, other):\n \"\"\"\n Checks not equal.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateSigningRequest.__eq__","text":"src/cryptography/x509/base.py::CertificateSigningRequest.__eq__\n @abc.abstractmethod\n def __eq__(self, other):\n \"\"\"\n Checks equality.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateSigningRequest.__ne__","text":"src/cryptography/x509/base.py::CertificateSigningRequest.__ne__\n @abc.abstractmethod\n def __ne__(self, other):\n \"\"\"\n Checks not equal.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateSigningRequest.__hash__","text":"src/cryptography/x509/base.py::CertificateSigningRequest.__hash__\n @abc.abstractmethod\n def __hash__(self):\n \"\"\"\n Computes a hash.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateSigningRequest.public_key","text":"src/cryptography/x509/base.py::CertificateSigningRequest.public_key\n @abc.abstractmethod\n def public_key(self):\n \"\"\"\n Returns the public key\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateSigningRequest.subject","text":"src/cryptography/x509/base.py::CertificateSigningRequest.subject\n @abc.abstractproperty\n def subject(self):\n \"\"\"\n Returns the subject name object.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/base.py::CertificateSigningRequest.signature_hash_algorithm","text":"src/cryptography/x509/base.py::CertificateSigningRequest.signature_hash_algorithm\n @abc.abstractproperty\n def signature_hash_algorithm(self):\n \"\"\"\n Returns a HashAlgorithm corresponding to the type of the digest signed\n in the certificate.\n \"\"\"","label":0}],"string":"[\n {\n \"cid\": \"docs/cryptography-docs.py::HazmatDirective.run\",\n \"text\": \"docs/cryptography-docs.py::HazmatDirective.run\\n def run(self):\\n message = DANGER_MESSAGE\\n if self.content:\\n message += DANGER_ALTERNATE.format(alternate=self.content[0])\\n\\n ad = make_admonition(\\n Hazmat,\\n self.name,\\n [],\\n self.options,\\n nodes.paragraph(\\\"\\\", message),\\n self.lineno,\\n self.content_offset,\\n self.block_text,\\n self.state,\\n self.state_machine\\n )\\n ad[0].line = self.lineno\\n return ad\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/cryptography-docs.py::html_visit_hazmat_node\",\n \"text\": \"docs/cryptography-docs.py::html_visit_hazmat_node\\ndef html_visit_hazmat_node(self, node):\\n return self.visit_admonition(node, \\\"danger\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/cryptography-docs.py::latex_visit_hazmat_node\",\n \"text\": \"docs/cryptography-docs.py::latex_visit_hazmat_node\\ndef latex_visit_hazmat_node(self, node):\\n return self.visit_admonition(node)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/cryptography-docs.py::depart_hazmat_node\",\n \"text\": \"docs/cryptography-docs.py::depart_hazmat_node\\ndef depart_hazmat_node(self, node):\\n return self.depart_admonition(node)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/cryptography-docs.py::setup\",\n \"text\": \"docs/cryptography-docs.py::setup\\ndef setup(app):\\n app.add_node(\\n Hazmat,\\n html=(html_visit_hazmat_node, depart_hazmat_node),\\n latex=(latex_visit_hazmat_node, depart_hazmat_node),\\n )\\n app.add_directive(\\\"hazmat\\\", HazmatDirective)\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::cc_is_available\",\n \"text\": \"setup.py::cc_is_available\\ndef cc_is_available():\\n return sys.platform == \\\"darwin\\\" and list(map(\\n int, platform.mac_ver()[0].split(\\\".\\\"))) >= [10, 8, 0]\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::PyTest.finalize_options\",\n \"text\": \"setup.py::PyTest.finalize_options\\n def finalize_options(self):\\n test.finalize_options(self)\\n self.test_args = []\\n self.test_suite = True\\n\\n # This means there's a vectors/ folder with the package in here.\\n # cd into it, install the vectors package and then refresh sys.path\\n if VECTORS_DEPENDENCY not in test_requirements:\\n subprocess.check_call(\\n [sys.executable, \\\"setup.py\\\", \\\"install\\\"], cwd=\\\"vectors\\\"\\n )\\n pkg_resources.get_distribution(\\\"cryptography_vectors\\\").activate()\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::PyTest.run_tests\",\n \"text\": \"setup.py::PyTest.run_tests\\n def run_tests(self):\\n # Import here because in module scope the eggs are not loaded.\\n import pytest\\n test_args = [os.path.join(base_dir, \\\"tests\\\")]\\n errno = pytest.main(test_args)\\n sys.exit(errno)\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::keywords_with_side_effects\",\n \"text\": \"setup.py::keywords_with_side_effects\\ndef keywords_with_side_effects(argv):\\n \\\"\\\"\\\"\\n Get a dictionary with setup keywords that (can) have side effects.\\n\\n :param argv: A list of strings with command line arguments.\\n :returns: A dictionary with keyword arguments for the ``setup()`` function.\\n\\n This setup.py script uses the setuptools 'setup_requires' feature because\\n this is required by the cffi package to compile extension modules. The\\n purpose of ``keywords_with_side_effects()`` is to avoid triggering the cffi\\n build process as a result of setup.py invocations that don't need the cffi\\n module to be built (setup.py serves the dual purpose of exposing package\\n metadata).\\n\\n All of the options listed by ``python setup.py --help`` that print\\n information should be recognized here. The commands ``clean``,\\n ``egg_info``, ``register``, ``sdist`` and ``upload`` are also recognized.\\n Any combination of these options and commands is also supported.\\n\\n This function was originally based on the `setup.py script`_ of SciPy (see\\n also the discussion in `pip issue #25`_).\\n\\n .. _pip issue #25: https://github.com/pypa/pip/issues/25\\n .. _setup.py script: https://github.com/scipy/scipy/blob/master/setup.py\\n \\\"\\\"\\\"\\n no_setup_requires_arguments = (\\n '-h', '--help',\\n '-n', '--dry-run',\\n '-q', '--quiet',\\n '-v', '--verbose',\\n '-V', '--version',\\n '--author',\\n '--author-email',\\n '--classifiers',\\n '--contact',\\n '--contact-email',\\n '--description',\\n '--egg-base',\\n '--fullname',\\n '--help-commands',\\n '--keywords',\\n '--licence',\\n '--license',\\n '--long-description',\\n '--maintainer',\\n '--maintainer-email',\\n '--name',\\n '--no-user-cfg',\\n '--obsoletes',\\n '--platforms',\\n '--provides',\\n '--requires',\\n '--url',\\n 'clean',\\n 'egg_info',\\n 'register',\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::keywords_with_side_effects.is_short_option\",\n \"text\": \"setup.py::keywords_with_side_effects.is_short_option\\n def is_short_option(argument):\\n \\\"\\\"\\\"Check whether a command line argument is a short option.\\\"\\\"\\\"\\n return len(argument) >= 2 and argument[0] == '-' and argument[1] != '-'\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::keywords_with_side_effects.expand_short_options\",\n \"text\": \"setup.py::keywords_with_side_effects.expand_short_options\\n def expand_short_options(argument):\\n \\\"\\\"\\\"Expand combined short options into canonical short options.\\\"\\\"\\\"\\n return ('-' + char for char in argument[1:])\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::keywords_with_side_effects.argument_without_setup_requirements\",\n \"text\": \"setup.py::keywords_with_side_effects.argument_without_setup_requirements\\n def argument_without_setup_requirements(argv, i):\\n \\\"\\\"\\\"Check whether a command line argument needs setup requirements.\\\"\\\"\\\"\\n if argv[i] in no_setup_requires_arguments:\\n # Simple case: An argument which is either an option or a command\\n # which doesn't need setup requirements.\\n return True\\n elif (is_short_option(argv[i]) and\\n all(option in no_setup_requires_arguments\\n for option in expand_short_options(argv[i]))):\\n # Not so simple case: Combined short options none of which need\\n # setup requirements.\\n return True\\n elif argv[i - 1:i] == ['--egg-base']:\\n # Tricky case: --egg-info takes an argument which should not make\\n # us use setup_requires (defeating the purpose of this code).\\n return True\\n else:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::DummyBuild.run\",\n \"text\": \"setup.py::DummyBuild.run\\n def run(self):\\n raise RuntimeError(setup_requires_error)\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::DummyInstall.run\",\n \"text\": \"setup.py::DummyInstall.run\\n def run(self):\\n raise RuntimeError(setup_requires_error)\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::DummyPyTest.run_tests\",\n \"text\": \"setup.py::DummyPyTest.run_tests\\n def run_tests(self):\\n raise RuntimeError(setup_requires_error)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/_cffi_src/utils.py::build_ffi_for_binding\",\n \"text\": \"src/_cffi_src/utils.py::build_ffi_for_binding\\ndef build_ffi_for_binding(module_name, module_prefix, modules, pre_include=\\\"\\\",\\n post_include=\\\"\\\", libraries=[], extra_compile_args=[],\\n extra_link_args=[]):\\n \\\"\\\"\\\"\\n Modules listed in ``modules`` should have the following attributes:\\n\\n * ``INCLUDES``: A string containing C includes.\\n * ``TYPES``: A string containing C declarations for types.\\n * ``FUNCTIONS``: A string containing C declarations for functions.\\n * ``MACROS``: A string containing C declarations for any macros.\\n * ``CUSTOMIZATIONS``: A string containing arbitrary top-level C code, this\\n can be used to do things like test for a define and provide an\\n alternate implementation based on that.\\n \\\"\\\"\\\"\\n types = []\\n includes = []\\n functions = []\\n macros = []\\n customizations = []\\n for name in modules:\\n __import__(module_prefix + name)\\n module = sys.modules[module_prefix + name]\\n\\n types.append(module.TYPES)\\n macros.append(module.MACROS)\\n functions.append(module.FUNCTIONS)\\n includes.append(module.INCLUDES)\\n customizations.append(module.CUSTOMIZATIONS)\\n\\n # We include functions here so that if we got any of their definitions\\n # wrong, the underlying C compiler will explode. In C you are allowed\\n # to re-declare a function if it has the same signature. That is:\\n # int foo(int);\\n # int foo(int);\\n # is legal, but the following will fail to compile:\\n # int foo(int);\\n # int foo(short);\\n verify_source = \\\"\\\\n\\\".join(\\n [pre_include] +\\n includes +\\n [post_include] +\\n functions +\\n customizations\\n )\\n ffi = build_ffi(\\n module_name,\\n cdef_source=\\\"\\\\n\\\".join(types + functions + macros),\\n verify_source=verify_source,\\n libraries=libraries,\\n extra_compile_args=extra_compile_args,\\n extra_link_args=extra_link_args,\\n )\\n\\n return \",\n \"label\": 0\n },\n {\n \"cid\": \"src/_cffi_src/utils.py::build_ffi\",\n \"text\": \"src/_cffi_src/utils.py::build_ffi\\ndef build_ffi(module_name, cdef_source, verify_source, libraries=[],\\n extra_compile_args=[], extra_link_args=[]):\\n ffi = FFI()\\n ffi.cdef(cdef_source)\\n ffi.set_source(\\n module_name,\\n verify_source,\\n libraries=libraries,\\n extra_compile_args=extra_compile_args,\\n extra_link_args=extra_link_args,\\n )\\n return ffi\",\n \"label\": 0\n },\n {\n \"cid\": \"src/_cffi_src/utils.py::extra_link_args\",\n \"text\": \"src/_cffi_src/utils.py::extra_link_args\\ndef extra_link_args(platform):\\n if platform != \\\"win32\\\":\\n return []\\n else:\\n # Enable NX and ASLR for Windows builds. These are enabled by default\\n # on Python 3.3+ but not on 2.x.\\n return [\\\"/NXCOMPAT\\\", \\\"/DYNAMICBASE\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/__init__.py::_available_backends\",\n \"text\": \"src/cryptography/hazmat/backends/__init__.py::_available_backends\\ndef _available_backends():\\n global _available_backends_list\\n\\n if _available_backends_list is None:\\n _available_backends_list = [\\n # setuptools 11.3 deprecated support for the require parameter to\\n # load(), and introduced the new resolve() method instead.\\n # This can be removed if/when we can assume setuptools>=11.3. At\\n # some point we may wish to add a warning, to push people along,\\n # but at present this would result in too many warnings.\\n ep.resolve() if hasattr(ep, \\\"resolve\\\") else ep.load(require=False)\\n for ep in pkg_resources.iter_entry_points(\\n \\\"cryptography.backends\\\"\\n )\\n ]\\n\\n return _available_backends_list\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/__init__.py::default_backend\",\n \"text\": \"src/cryptography/hazmat/backends/__init__.py::default_backend\\ndef default_backend():\\n global _default_backend\\n\\n if _default_backend is None:\\n _default_backend = MultiBackend(_available_backends())\\n\\n return _default_backend\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.__init__\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.__init__\\n def __init__(self, backend, cipher, mode, operation):\\n self._backend = backend\\n self._cipher = cipher\\n self._mode = mode\\n self._operation = operation\\n self._tag = None\\n\\n if isinstance(self._cipher, ciphers.BlockCipherAlgorithm):\\n self._block_size = self._cipher.block_size\\n else:\\n self._block_size = 1\\n\\n ctx = self._backend._lib.EVP_CIPHER_CTX_new()\\n ctx = self._backend._ffi.gc(\\n ctx, self._backend._lib.EVP_CIPHER_CTX_free\\n )\\n\\n registry = self._backend._cipher_registry\\n try:\\n adapter = registry[type(cipher), type(mode)]\\n except KeyError:\\n raise UnsupportedAlgorithm(\\n \\\"cipher {0} in {1} mode is not supported \\\"\\n \\\"by this backend.\\\".format(\\n cipher.name, mode.name if mode else mode),\\n _Reasons.UNSUPPORTED_CIPHER\\n )\\n\\n evp_cipher = adapter(self._backend, cipher, mode)\\n if evp_cipher == self._backend._ffi.NULL:\\n raise UnsupportedAlgorithm(\\n \\\"cipher {0} in {1} mode is not supported \\\"\\n \\\"by this backend.\\\".format(\\n cipher.name, mode.name if mode else mode),\\n _Reasons.UNSUPPORTED_CIPHER\\n )\\n\\n if isinstance(mode, modes.ModeWithInitializationVector):\\n iv_nonce = mode.initialization_vector\\n elif isinstance(mode, modes.ModeWithNonce):\\n iv_nonce = mode.nonce\\n else:\\n iv_nonce = self._backend._ffi.NULL\\n # begin init with cipher and operation type\\n res = self._backend._lib.EVP_CipherInit_ex(ctx, evp_cipher,\\n self._backend._ffi.NULL,\\n self._backend._ffi.NULL,\\n self._backend._ffi.NU\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.update\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.update\\n def update(self, data):\\n # OpenSSL 0.9.8e has an assertion in its EVP code that causes it\\n # to SIGABRT if you call update with an empty byte string. This can be\\n # removed when we drop support for 0.9.8e (CentOS/RHEL 5). This branch\\n # should be taken only when length is zero and mode is not GCM because\\n # AES GCM can return improper tag values if you don't call update\\n # with empty plaintext when authenticating AAD for ...reasons.\\n if len(data) == 0 and not isinstance(self._mode, modes.GCM):\\n return b\\\"\\\"\\n\\n buf = self._backend._ffi.new(\\\"unsigned char[]\\\",\\n len(data) + self._block_size - 1)\\n outlen = self._backend._ffi.new(\\\"int *\\\")\\n res = self._backend._lib.EVP_CipherUpdate(self._ctx, buf, outlen, data,\\n len(data))\\n self._backend.openssl_assert(res != 0)\\n return self._backend._ffi.buffer(buf)[:outlen[0]]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.finalize\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.finalize\\n def finalize(self):\\n # OpenSSL 1.0.1 on Ubuntu 12.04 (and possibly other distributions)\\n # appears to have a bug where you must make at least one call to update\\n # even if you are only using authenticate_additional_data or the\\n # GCM tag will be wrong. An (empty) call to update resolves this\\n # and is harmless for all other versions of OpenSSL.\\n if isinstance(self._mode, modes.GCM):\\n self.update(b\\\"\\\")\\n\\n buf = self._backend._ffi.new(\\\"unsigned char[]\\\", self._block_size)\\n outlen = self._backend._ffi.new(\\\"int *\\\")\\n res = self._backend._lib.EVP_CipherFinal_ex(self._ctx, buf, outlen)\\n if res == 0:\\n errors = self._backend._consume_errors()\\n\\n if not errors and isinstance(self._mode, modes.GCM):\\n raise InvalidTag\\n\\n self._backend.openssl_assert(\\n errors[0][1:] == (\\n self._backend._lib.ERR_LIB_EVP,\\n self._backend._lib.EVP_F_EVP_ENCRYPTFINAL_EX,\\n self._backend._lib.EVP_R_DATA_NOT_MULTIPLE_OF_BLOCK_LENGTH\\n ) or errors[0][1:] == (\\n self._backend._lib.ERR_LIB_EVP,\\n self._backend._lib.EVP_F_EVP_DECRYPTFINAL_EX,\\n self._backend._lib.EVP_R_DATA_NOT_MULTIPLE_OF_BLOCK_LENGTH\\n )\\n )\\n raise ValueError(\\n \\\"The length of the provided data is not a multiple of \\\"\\n \\\"the block length.\\\"\\n )\\n\\n if (isinstance(self._mode, modes.GCM) and\\n self._operation == self._ENCRYPT):\\n block_byte_size = self._block_size // 8\\n tag_buf = self._backend._ffi.new(\\n \\\"unsigned char[]\\\", block_byte_size\\n )\\n res = self._backend._lib.EVP_CIPHER_CTX_ctrl(\\n self._ctx, self._backend._lib.EVP_CTRL_GCM_GET_TAG,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.authenticate_additional_data\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_CipherContext.authenticate_additional_data\\n def authenticate_additional_data(self, data):\\n outlen = self._backend._ffi.new(\\\"int *\\\")\\n res = self._backend._lib.EVP_CipherUpdate(\\n self._ctx, self._backend._ffi.NULL, outlen, data, len(data)\\n )\\n self._backend.openssl_assert(res != 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.__init__\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.__init__\\n def __init__(self, backend, cipher, mode):\\n self._backend = backend\\n\\n self._key = self._backend._ffi.new(\\\"AES_KEY *\\\")\\n res = self._backend._lib.AES_set_encrypt_key(\\n cipher.key, len(cipher.key) * 8, self._key\\n )\\n self._backend.openssl_assert(res == 0)\\n self._ecount = self._backend._ffi.new(\\\"char[]\\\", 16)\\n self._nonce = self._backend._ffi.new(\\\"char[16]\\\", mode.nonce)\\n self._num = self._backend._ffi.new(\\\"unsigned int *\\\", 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.update\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.update\\n def update(self, data):\\n buf = self._backend._ffi.new(\\\"unsigned char[]\\\", len(data))\\n self._backend._lib.AES_ctr128_encrypt(\\n data, buf, len(data), self._key, self._nonce,\\n self._ecount, self._num\\n )\\n return self._backend._ffi.buffer(buf)[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.finalize\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/ciphers.py::_AESCTRCipherContext.finalize\\n def finalize(self):\\n self._key = None\\n self._ecount = None\\n self._nonce = None\\n self._num = None\\n return b\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_obj2txt\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_obj2txt\\ndef _obj2txt(backend, obj):\\n # Set to 80 on the recommendation of\\n # https://www.openssl.org/docs/crypto/OBJ_nid2ln.html#return_values\\n buf_len = 80\\n buf = backend._ffi.new(\\\"char[]\\\", buf_len)\\n res = backend._lib.OBJ_obj2txt(buf, buf_len, obj, 1)\\n backend.openssl_assert(res > 0)\\n return backend._ffi.buffer(buf, res)[:].decode()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_x509_name_entry\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_x509_name_entry\\ndef _decode_x509_name_entry(backend, x509_name_entry):\\n obj = backend._lib.X509_NAME_ENTRY_get_object(x509_name_entry)\\n backend.openssl_assert(obj != backend._ffi.NULL)\\n data = backend._lib.X509_NAME_ENTRY_get_data(x509_name_entry)\\n backend.openssl_assert(data != backend._ffi.NULL)\\n value = backend._asn1_string_to_utf8(data)\\n oid = _obj2txt(backend, obj)\\n\\n return x509.NameAttribute(x509.ObjectIdentifier(oid), value)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_x509_name\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_x509_name\\ndef _decode_x509_name(backend, x509_name):\\n count = backend._lib.X509_NAME_entry_count(x509_name)\\n attributes = []\\n for x in range(count):\\n entry = backend._lib.X509_NAME_get_entry(x509_name, x)\\n attributes.append(_decode_x509_name_entry(backend, entry))\\n\\n return x509.Name(attributes)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_names\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_names\\ndef _decode_general_names(backend, gns):\\n num = backend._lib.sk_GENERAL_NAME_num(gns)\\n names = []\\n for i in range(num):\\n gn = backend._lib.sk_GENERAL_NAME_value(gns, i)\\n backend.openssl_assert(gn != backend._ffi.NULL)\\n names.append(_decode_general_name(backend, gn))\\n\\n return names\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_name\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_name\\ndef _decode_general_name(backend, gn):\\n if gn.type == backend._lib.GEN_DNS:\\n data = backend._asn1_string_to_bytes(gn.d.dNSName)\\n if not data:\\n decoded = u\\\"\\\"\\n elif data.startswith(b\\\"*.\\\"):\\n # This is a wildcard name. We need to remove the leading wildcard,\\n # IDNA decode, then re-add the wildcard. Wildcard characters should\\n # always be left-most (RFC 2595 section 2.4).\\n decoded = u\\\"*.\\\" + idna.decode(data[2:])\\n else:\\n # Not a wildcard, decode away. If the string has a * in it anywhere\\n # invalid this will raise an InvalidCodePoint\\n decoded = idna.decode(data)\\n if data.startswith(b\\\".\\\"):\\n # idna strips leading periods. Name constraints can have that\\n # so we need to re-add it. Sigh.\\n decoded = u\\\".\\\" + decoded\\n\\n return x509.DNSName(decoded)\\n elif gn.type == backend._lib.GEN_URI:\\n data = backend._asn1_string_to_ascii(gn.d.uniformResourceIdentifier)\\n parsed = urllib_parse.urlparse(data)\\n if parsed.hostname:\\n hostname = idna.decode(parsed.hostname)\\n else:\\n hostname = \\\"\\\"\\n if parsed.port:\\n netloc = hostname + u\\\":\\\" + six.text_type(parsed.port)\\n else:\\n netloc = hostname\\n\\n # Note that building a URL in this fashion means it should be\\n # semantically indistinguishable from the original but is not\\n # guaranteed to be exactly the same.\\n uri = urllib_parse.urlunparse((\\n parsed.scheme,\\n netloc,\\n parsed.path,\\n parsed.params,\\n parsed.query,\\n parsed.fragment\\n ))\\n return x509.UniformResourceIdentifier(uri)\\n elif gn.type == backend._lib.GEN_RID:\\n oid = _obj2txt(backend, gn.d.registeredID)\\n return x509.RegisteredID(x509.ObjectIdenti\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_ocsp_no_check\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_ocsp_no_check\\ndef _decode_ocsp_no_check(backend, ext):\\n return x509.OCSPNoCheck()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_crl_number\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_crl_number\\ndef _decode_crl_number(backend, ext):\\n asn1_int = backend._ffi.cast(\\\"ASN1_INTEGER *\\\", ext)\\n asn1_int = backend._ffi.gc(asn1_int, backend._lib.ASN1_INTEGER_free)\\n return x509.CRLNumber(backend._asn1_integer_to_int(asn1_int))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_X509ExtensionParser.__init__\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_X509ExtensionParser.__init__\\n def __init__(self, ext_count, get_ext, handlers, unsupported_exts=None):\\n self.ext_count = ext_count\\n self.get_ext = get_ext\\n self.handlers = handlers\\n self.unsupported_exts = unsupported_exts\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_X509ExtensionParser.parse\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_X509ExtensionParser.parse\\n def parse(self, backend, x509_obj):\\n extensions = []\\n seen_oids = set()\\n for i in range(self.ext_count(backend, x509_obj)):\\n ext = self.get_ext(backend, x509_obj, i)\\n backend.openssl_assert(ext != backend._ffi.NULL)\\n crit = backend._lib.X509_EXTENSION_get_critical(ext)\\n critical = crit == 1\\n oid = x509.ObjectIdentifier(_obj2txt(backend, ext.object))\\n if oid in seen_oids:\\n raise x509.DuplicateExtension(\\n \\\"Duplicate {0} extension found\\\".format(oid), oid\\n )\\n try:\\n handler = self.handlers[oid]\\n except KeyError:\\n if critical:\\n raise x509.UnsupportedExtension(\\n \\\"Critical extension {0} is not currently supported\\\"\\n .format(oid), oid\\n )\\n else:\\n # For extensions which are not supported by OpenSSL we pass the\\n # extension object directly to the parsing routine so it can\\n # be decoded manually.\\n if self.unsupported_exts and oid in self.unsupported_exts:\\n ext_data = ext\\n else:\\n ext_data = backend._lib.X509V3_EXT_d2i(ext)\\n if ext_data == backend._ffi.NULL:\\n backend._consume_errors()\\n raise ValueError(\\n \\\"The {0} extension is invalid and can't be \\\"\\n \\\"parsed\\\".format(oid)\\n )\\n\\n value = handler(backend, ext_data)\\n extensions.append(x509.Extension(oid, critical, value))\\n\\n seen_oids.add(oid)\\n\\n return x509.Extensions(extensions)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__init__\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__init__\\n def __init__(self, backend, x509):\\n self._backend = backend\\n self._x509 = x509\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__repr__\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__repr__\\n def __repr__(self):\\n return \\\"<Certificate(subject={0}, ...)>\\\".format(self.subject)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__eq__\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, x509.Certificate):\\n return NotImplemented\\n\\n res = self._backend._lib.X509_cmp(self._x509, other._x509)\\n return res == 0\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__ne__\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__ne__\\n def __ne__(self, other):\\n return not self == other\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__hash__\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.__hash__\\n def __hash__(self):\\n return hash(self.public_bytes(serialization.Encoding.DER))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.fingerprint\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.fingerprint\\n def fingerprint(self, algorithm):\\n h = hashes.Hash(algorithm, self._backend)\\n h.update(self.public_bytes(serialization.Encoding.DER))\\n return h.finalize()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.version\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.version\\n @property\\n def version(self):\\n version = self._backend._lib.X509_get_version(self._x509)\\n if version == 0:\\n return x509.Version.v1\\n elif version == 2:\\n return x509.Version.v3\\n else:\\n raise x509.InvalidVersion(\\n \\\"{0} is not a valid X509 version\\\".format(version), version\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.serial\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.serial\\n @property\\n def serial(self):\\n asn1_int = self._backend._lib.X509_get_serialNumber(self._x509)\\n self._backend.openssl_assert(asn1_int != self._backend._ffi.NULL)\\n return self._backend._asn1_integer_to_int(asn1_int)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.public_key\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.public_key\\n def public_key(self):\\n pkey = self._backend._lib.X509_get_pubkey(self._x509)\\n if pkey == self._backend._ffi.NULL:\\n # Remove errors from the stack.\\n self._backend._consume_errors()\\n raise ValueError(\\\"Certificate public key is of an unknown type\\\")\\n\\n pkey = self._backend._ffi.gc(pkey, self._backend._lib.EVP_PKEY_free)\\n\\n return self._backend._evp_pkey_to_public_key(pkey)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.not_valid_before\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.not_valid_before\\n @property\\n def not_valid_before(self):\\n asn1_time = self._backend._lib.X509_get_notBefore(self._x509)\\n return self._backend._parse_asn1_time(asn1_time)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.not_valid_after\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.not_valid_after\\n @property\\n def not_valid_after(self):\\n asn1_time = self._backend._lib.X509_get_notAfter(self._x509)\\n return self._backend._parse_asn1_time(asn1_time)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.issuer\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.issuer\\n @property\\n def issuer(self):\\n issuer = self._backend._lib.X509_get_issuer_name(self._x509)\\n self._backend.openssl_assert(issuer != self._backend._ffi.NULL)\\n return _decode_x509_name(self._backend, issuer)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.subject\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.subject\\n @property\\n def subject(self):\\n subject = self._backend._lib.X509_get_subject_name(self._x509)\\n self._backend.openssl_assert(subject != self._backend._ffi.NULL)\\n return _decode_x509_name(self._backend, subject)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.signature_hash_algorithm\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.signature_hash_algorithm\\n @property\\n def signature_hash_algorithm(self):\\n oid = _obj2txt(self._backend, self._x509.sig_alg.algorithm)\\n try:\\n return x509._SIG_OIDS_TO_HASH[oid]\\n except KeyError:\\n raise UnsupportedAlgorithm(\\n \\\"Signature algorithm OID:{0} not recognized\\\".format(oid)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.extensions\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.extensions\\n @property\\n def extensions(self):\\n return _CERTIFICATE_EXTENSION_PARSER.parse(self._backend, self._x509)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.signature\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.signature\\n @property\\n def signature(self):\\n return self._backend._asn1_string_to_bytes(self._x509.signature)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.tbs_certificate_bytes\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.tbs_certificate_bytes\\n @property\\n def tbs_certificate_bytes(self):\\n pp = self._backend._ffi.new(\\\"unsigned char **\\\")\\n # the X509_CINF struct holds the tbsCertificate data\\n res = self._backend._lib.i2d_X509_CINF(self._x509.cert_info, pp)\\n self._backend.openssl_assert(res > 0)\\n pp = self._backend._ffi.gc(\\n pp, lambda pointer: self._backend._lib.OPENSSL_free(pointer[0])\\n )\\n return self._backend._ffi.buffer(pp[0], res)[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.public_bytes\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_Certificate.public_bytes\\n def public_bytes(self, encoding):\\n bio = self._backend._create_mem_bio()\\n if encoding is serialization.Encoding.PEM:\\n res = self._backend._lib.PEM_write_bio_X509(bio, self._x509)\\n elif encoding is serialization.Encoding.DER:\\n res = self._backend._lib.i2d_X509_bio(bio, self._x509)\\n else:\\n raise TypeError(\\\"encoding must be an item from the Encoding enum\\\")\\n\\n self._backend.openssl_assert(res == 1)\\n return self._backend._read_mem_bio(bio)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_certificate_policies\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_certificate_policies\\ndef _decode_certificate_policies(backend, cp):\\n cp = backend._ffi.cast(\\\"Cryptography_STACK_OF_POLICYINFO *\\\", cp)\\n cp = backend._ffi.gc(cp, backend._lib.sk_POLICYINFO_free)\\n num = backend._lib.sk_POLICYINFO_num(cp)\\n certificate_policies = []\\n for i in range(num):\\n qualifiers = None\\n pi = backend._lib.sk_POLICYINFO_value(cp, i)\\n oid = x509.ObjectIdentifier(_obj2txt(backend, pi.policyid))\\n if pi.qualifiers != backend._ffi.NULL:\\n qnum = backend._lib.sk_POLICYQUALINFO_num(pi.qualifiers)\\n qualifiers = []\\n for j in range(qnum):\\n pqi = backend._lib.sk_POLICYQUALINFO_value(\\n pi.qualifiers, j\\n )\\n pqualid = x509.ObjectIdentifier(\\n _obj2txt(backend, pqi.pqualid)\\n )\\n if pqualid == CertificatePoliciesOID.CPS_QUALIFIER:\\n cpsuri = backend._ffi.buffer(\\n pqi.d.cpsuri.data, pqi.d.cpsuri.length\\n )[:].decode('ascii')\\n qualifiers.append(cpsuri)\\n else:\\n assert pqualid == CertificatePoliciesOID.CPS_USER_NOTICE\\n user_notice = _decode_user_notice(\\n backend, pqi.d.usernotice\\n )\\n qualifiers.append(user_notice)\\n\\n certificate_policies.append(\\n x509.PolicyInformation(oid, qualifiers)\\n )\\n\\n return x509.CertificatePolicies(certificate_policies)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_user_notice\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_user_notice\\ndef _decode_user_notice(backend, un):\\n explicit_text = None\\n notice_reference = None\\n\\n if un.exptext != backend._ffi.NULL:\\n explicit_text = backend._asn1_string_to_utf8(un.exptext)\\n\\n if un.noticeref != backend._ffi.NULL:\\n organization = backend._asn1_string_to_utf8(un.noticeref.organization)\\n\\n num = backend._lib.sk_ASN1_INTEGER_num(\\n un.noticeref.noticenos\\n )\\n notice_numbers = []\\n for i in range(num):\\n asn1_int = backend._lib.sk_ASN1_INTEGER_value(\\n un.noticeref.noticenos, i\\n )\\n notice_num = backend._asn1_integer_to_int(asn1_int)\\n notice_numbers.append(notice_num)\\n\\n notice_reference = x509.NoticeReference(\\n organization, notice_numbers\\n )\\n\\n return x509.UserNotice(notice_reference, explicit_text)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_basic_constraints\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_basic_constraints\\ndef _decode_basic_constraints(backend, bc_st):\\n basic_constraints = backend._ffi.cast(\\\"BASIC_CONSTRAINTS *\\\", bc_st)\\n basic_constraints = backend._ffi.gc(\\n basic_constraints, backend._lib.BASIC_CONSTRAINTS_free\\n )\\n # The byte representation of an ASN.1 boolean true is \\\\xff. OpenSSL\\n # chooses to just map this to its ordinal value, so true is 255 and\\n # false is 0.\\n ca = basic_constraints.ca == 255\\n if basic_constraints.pathlen == backend._ffi.NULL:\\n path_length = None\\n else:\\n path_length = backend._asn1_integer_to_int(basic_constraints.pathlen)\\n\\n return x509.BasicConstraints(ca, path_length)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_subject_key_identifier\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_subject_key_identifier\\ndef _decode_subject_key_identifier(backend, asn1_string):\\n asn1_string = backend._ffi.cast(\\\"ASN1_OCTET_STRING *\\\", asn1_string)\\n asn1_string = backend._ffi.gc(\\n asn1_string, backend._lib.ASN1_OCTET_STRING_free\\n )\\n return x509.SubjectKeyIdentifier(\\n backend._ffi.buffer(asn1_string.data, asn1_string.length)[:]\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_authority_key_identifier\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_authority_key_identifier\\ndef _decode_authority_key_identifier(backend, akid):\\n akid = backend._ffi.cast(\\\"AUTHORITY_KEYID *\\\", akid)\\n akid = backend._ffi.gc(akid, backend._lib.AUTHORITY_KEYID_free)\\n key_identifier = None\\n authority_cert_issuer = None\\n authority_cert_serial_number = None\\n\\n if akid.keyid != backend._ffi.NULL:\\n key_identifier = backend._ffi.buffer(\\n akid.keyid.data, akid.keyid.length\\n )[:]\\n\\n if akid.issuer != backend._ffi.NULL:\\n authority_cert_issuer = _decode_general_names(\\n backend, akid.issuer\\n )\\n\\n if akid.serial != backend._ffi.NULL:\\n authority_cert_serial_number = backend._asn1_integer_to_int(\\n akid.serial\\n )\\n\\n return x509.AuthorityKeyIdentifier(\\n key_identifier, authority_cert_issuer, authority_cert_serial_number\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_authority_information_access\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_authority_information_access\\ndef _decode_authority_information_access(backend, aia):\\n aia = backend._ffi.cast(\\\"Cryptography_STACK_OF_ACCESS_DESCRIPTION *\\\", aia)\\n aia = backend._ffi.gc(aia, backend._lib.sk_ACCESS_DESCRIPTION_free)\\n num = backend._lib.sk_ACCESS_DESCRIPTION_num(aia)\\n access_descriptions = []\\n for i in range(num):\\n ad = backend._lib.sk_ACCESS_DESCRIPTION_value(aia, i)\\n backend.openssl_assert(ad.method != backend._ffi.NULL)\\n oid = x509.ObjectIdentifier(_obj2txt(backend, ad.method))\\n backend.openssl_assert(ad.location != backend._ffi.NULL)\\n gn = _decode_general_name(backend, ad.location)\\n access_descriptions.append(x509.AccessDescription(oid, gn))\\n\\n return x509.AuthorityInformationAccess(access_descriptions)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_key_usage\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_key_usage\\ndef _decode_key_usage(backend, bit_string):\\n bit_string = backend._ffi.cast(\\\"ASN1_BIT_STRING *\\\", bit_string)\\n bit_string = backend._ffi.gc(bit_string, backend._lib.ASN1_BIT_STRING_free)\\n get_bit = backend._lib.ASN1_BIT_STRING_get_bit\\n digital_signature = get_bit(bit_string, 0) == 1\\n content_commitment = get_bit(bit_string, 1) == 1\\n key_encipherment = get_bit(bit_string, 2) == 1\\n data_encipherment = get_bit(bit_string, 3) == 1\\n key_agreement = get_bit(bit_string, 4) == 1\\n key_cert_sign = get_bit(bit_string, 5) == 1\\n crl_sign = get_bit(bit_string, 6) == 1\\n encipher_only = get_bit(bit_string, 7) == 1\\n decipher_only = get_bit(bit_string, 8) == 1\\n return x509.KeyUsage(\\n digital_signature,\\n content_commitment,\\n key_encipherment,\\n data_encipherment,\\n key_agreement,\\n key_cert_sign,\\n crl_sign,\\n encipher_only,\\n decipher_only\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_names_extension\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_names_extension\\ndef _decode_general_names_extension(backend, gns):\\n gns = backend._ffi.cast(\\\"GENERAL_NAMES *\\\", gns)\\n gns = backend._ffi.gc(gns, backend._lib.GENERAL_NAMES_free)\\n general_names = _decode_general_names(backend, gns)\\n return general_names\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_subject_alt_name\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_subject_alt_name\\ndef _decode_subject_alt_name(backend, ext):\\n return x509.SubjectAlternativeName(\\n _decode_general_names_extension(backend, ext)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_issuer_alt_name\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_issuer_alt_name\\ndef _decode_issuer_alt_name(backend, ext):\\n return x509.IssuerAlternativeName(\\n _decode_general_names_extension(backend, ext)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_name_constraints\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_name_constraints\\ndef _decode_name_constraints(backend, nc):\\n nc = backend._ffi.cast(\\\"NAME_CONSTRAINTS *\\\", nc)\\n nc = backend._ffi.gc(nc, backend._lib.NAME_CONSTRAINTS_free)\\n permitted = _decode_general_subtrees(backend, nc.permittedSubtrees)\\n excluded = _decode_general_subtrees(backend, nc.excludedSubtrees)\\n return x509.NameConstraints(\\n permitted_subtrees=permitted, excluded_subtrees=excluded\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_subtrees\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_general_subtrees\\ndef _decode_general_subtrees(backend, stack_subtrees):\\n if stack_subtrees == backend._ffi.NULL:\\n return None\\n\\n num = backend._lib.sk_GENERAL_SUBTREE_num(stack_subtrees)\\n subtrees = []\\n\\n for i in range(num):\\n obj = backend._lib.sk_GENERAL_SUBTREE_value(stack_subtrees, i)\\n backend.openssl_assert(obj != backend._ffi.NULL)\\n name = _decode_general_name(backend, obj.base)\\n subtrees.append(name)\\n\\n return subtrees\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_extended_key_usage\",\n \"text\": \"src/cryptography/hazmat/backends/openssl/x509.py::_decode_extended_key_usage\\ndef _decode_extended_key_usage(backend, sk):\\n sk = backend._ffi.cast(\\\"Cryptography_STACK_OF_ASN1_OBJECT *\\\", sk)\\n sk = backend._ffi.gc(sk, backend._lib.sk_ASN1_OBJECT_free)\\n num = backend._lib.sk_ASN1_OBJECT_num(sk)\\n ekus = []\\n\\n for i in range(num):\\n obj = backend._lib.sk_ASN1_OBJECT_value(sk, i)\\n backend.openssl_assert(obj != backend._ffi.NULL)\\n oid = x509.ObjectIdentifier(_obj2txt(backend, obj))\\n ekus.append(oid)\\n\\n return x509.ExtendedKeyUsage(ekus)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::load_pem_x509_certificate\",\n \"text\": \"src/cryptography/x509/base.py::load_pem_x509_certificate\\ndef load_pem_x509_certificate(data, backend):\\n return backend.load_pem_x509_certificate(data)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::load_der_x509_certificate\",\n \"text\": \"src/cryptography/x509/base.py::load_der_x509_certificate\\ndef load_der_x509_certificate(data, backend):\\n return backend.load_der_x509_certificate(data)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::load_pem_x509_csr\",\n \"text\": \"src/cryptography/x509/base.py::load_pem_x509_csr\\ndef load_pem_x509_csr(data, backend):\\n return backend.load_pem_x509_csr(data)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::load_der_x509_csr\",\n \"text\": \"src/cryptography/x509/base.py::load_der_x509_csr\\ndef load_der_x509_csr(data, backend):\\n return backend.load_der_x509_csr(data)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::load_pem_x509_crl\",\n \"text\": \"src/cryptography/x509/base.py::load_pem_x509_crl\\ndef load_pem_x509_crl(data, backend):\\n return backend.load_pem_x509_crl(data)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::load_der_x509_crl\",\n \"text\": \"src/cryptography/x509/base.py::load_der_x509_crl\\ndef load_der_x509_crl(data, backend):\\n return backend.load_der_x509_crl(data)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::InvalidVersion.__init__\",\n \"text\": \"src/cryptography/x509/base.py::InvalidVersion.__init__\\n def __init__(self, msg, parsed_version):\\n super(InvalidVersion, self).__init__(msg)\\n self.parsed_version = parsed_version\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.fingerprint\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.fingerprint\\n @abc.abstractmethod\\n def fingerprint(self, algorithm):\\n \\\"\\\"\\\"\\n Returns bytes using digest passed.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.serial\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.serial\\n @abc.abstractproperty\\n def serial(self):\\n \\\"\\\"\\\"\\n Returns certificate serial number\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.version\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.version\\n @abc.abstractproperty\\n def version(self):\\n \\\"\\\"\\\"\\n Returns the certificate version\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.public_key\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.public_key\\n @abc.abstractmethod\\n def public_key(self):\\n \\\"\\\"\\\"\\n Returns the public key\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.not_valid_before\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.not_valid_before\\n @abc.abstractproperty\\n def not_valid_before(self):\\n \\\"\\\"\\\"\\n Not before time (represented as UTC datetime)\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.not_valid_after\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.not_valid_after\\n @abc.abstractproperty\\n def not_valid_after(self):\\n \\\"\\\"\\\"\\n Not after time (represented as UTC datetime)\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.issuer\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.issuer\\n @abc.abstractproperty\\n def issuer(self):\\n \\\"\\\"\\\"\\n Returns the issuer name object.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.subject\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.subject\\n @abc.abstractproperty\\n def subject(self):\\n \\\"\\\"\\\"\\n Returns the subject name object.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.signature_hash_algorithm\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.signature_hash_algorithm\\n @abc.abstractproperty\\n def signature_hash_algorithm(self):\\n \\\"\\\"\\\"\\n Returns a HashAlgorithm corresponding to the type of the digest signed\\n in the certificate.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.extensions\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.extensions\\n @abc.abstractproperty\\n def extensions(self):\\n \\\"\\\"\\\"\\n Returns an Extensions object.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.signature\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.signature\\n @abc.abstractproperty\\n def signature(self):\\n \\\"\\\"\\\"\\n Returns the signature bytes.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.tbs_certificate_bytes\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.tbs_certificate_bytes\\n @abc.abstractproperty\\n def tbs_certificate_bytes(self):\\n \\\"\\\"\\\"\\n Returns the tbsCertificate payload bytes as defined in RFC 5280.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.__eq__\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.__eq__\\n @abc.abstractmethod\\n def __eq__(self, other):\\n \\\"\\\"\\\"\\n Checks equality.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.__ne__\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.__ne__\\n @abc.abstractmethod\\n def __ne__(self, other):\\n \\\"\\\"\\\"\\n Checks not equal.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.__hash__\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.__hash__\\n @abc.abstractmethod\\n def __hash__(self):\\n \\\"\\\"\\\"\\n Computes a hash.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::Certificate.public_bytes\",\n \"text\": \"src/cryptography/x509/base.py::Certificate.public_bytes\\n @abc.abstractmethod\\n def public_bytes(self, encoding):\\n \\\"\\\"\\\"\\n Serializes the certificate to PEM or DER format.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateRevocationList.public_bytes\",\n \"text\": \"src/cryptography/x509/base.py::CertificateRevocationList.public_bytes\\n @abc.abstractmethod\\n def public_bytes(self, encoding):\\n \\\"\\\"\\\"\\n Serializes the CRL to PEM or DER format.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateRevocationList.fingerprint\",\n \"text\": \"src/cryptography/x509/base.py::CertificateRevocationList.fingerprint\\n @abc.abstractmethod\\n def fingerprint(self, algorithm):\\n \\\"\\\"\\\"\\n Returns bytes using digest passed.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateRevocationList.signature_hash_algorithm\",\n \"text\": \"src/cryptography/x509/base.py::CertificateRevocationList.signature_hash_algorithm\\n @abc.abstractproperty\\n def signature_hash_algorithm(self):\\n \\\"\\\"\\\"\\n Returns a HashAlgorithm corresponding to the type of the digest signed\\n in the certificate.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateRevocationList.issuer\",\n \"text\": \"src/cryptography/x509/base.py::CertificateRevocationList.issuer\\n @abc.abstractproperty\\n def issuer(self):\\n \\\"\\\"\\\"\\n Returns the X509Name with the issuer of this CRL.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateRevocationList.next_update\",\n \"text\": \"src/cryptography/x509/base.py::CertificateRevocationList.next_update\\n @abc.abstractproperty\\n def next_update(self):\\n \\\"\\\"\\\"\\n Returns the date of next update for this CRL.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateRevocationList.last_update\",\n \"text\": \"src/cryptography/x509/base.py::CertificateRevocationList.last_update\\n @abc.abstractproperty\\n def last_update(self):\\n \\\"\\\"\\\"\\n Returns the date of last update for this CRL.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateRevocationList.extensions\",\n \"text\": \"src/cryptography/x509/base.py::CertificateRevocationList.extensions\\n @abc.abstractproperty\\n def extensions(self):\\n \\\"\\\"\\\"\\n Returns an Extensions object containing a list of CRL extensions.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateRevocationList.signature\",\n \"text\": \"src/cryptography/x509/base.py::CertificateRevocationList.signature\\n @abc.abstractproperty\\n def signature(self):\\n \\\"\\\"\\\"\\n Returns the signature bytes.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateRevocationList.tbs_certlist_bytes\",\n \"text\": \"src/cryptography/x509/base.py::CertificateRevocationList.tbs_certlist_bytes\\n @abc.abstractproperty\\n def tbs_certlist_bytes(self):\\n \\\"\\\"\\\"\\n Returns the tbsCertList payload bytes as defined in RFC 5280.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateRevocationList.__eq__\",\n \"text\": \"src/cryptography/x509/base.py::CertificateRevocationList.__eq__\\n @abc.abstractmethod\\n def __eq__(self, other):\\n \\\"\\\"\\\"\\n Checks equality.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateRevocationList.__ne__\",\n \"text\": \"src/cryptography/x509/base.py::CertificateRevocationList.__ne__\\n @abc.abstractmethod\\n def __ne__(self, other):\\n \\\"\\\"\\\"\\n Checks not equal.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateSigningRequest.__eq__\",\n \"text\": \"src/cryptography/x509/base.py::CertificateSigningRequest.__eq__\\n @abc.abstractmethod\\n def __eq__(self, other):\\n \\\"\\\"\\\"\\n Checks equality.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateSigningRequest.__ne__\",\n \"text\": \"src/cryptography/x509/base.py::CertificateSigningRequest.__ne__\\n @abc.abstractmethod\\n def __ne__(self, other):\\n \\\"\\\"\\\"\\n Checks not equal.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateSigningRequest.__hash__\",\n \"text\": \"src/cryptography/x509/base.py::CertificateSigningRequest.__hash__\\n @abc.abstractmethod\\n def __hash__(self):\\n \\\"\\\"\\\"\\n Computes a hash.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateSigningRequest.public_key\",\n \"text\": \"src/cryptography/x509/base.py::CertificateSigningRequest.public_key\\n @abc.abstractmethod\\n def public_key(self):\\n \\\"\\\"\\\"\\n Returns the public key\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateSigningRequest.subject\",\n \"text\": \"src/cryptography/x509/base.py::CertificateSigningRequest.subject\\n @abc.abstractproperty\\n def subject(self):\\n \\\"\\\"\\\"\\n Returns the subject name object.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/base.py::CertificateSigningRequest.signature_hash_algorithm\",\n \"text\": \"src/cryptography/x509/base.py::CertificateSigningRequest.signature_hash_algorithm\\n @abc.abstractproperty\\n def signature_hash_algorithm(self):\\n \\\"\\\"\\\"\\n Returns a HashAlgorithm corresponding to the type of the digest signed\\n in the certificate.\\n \\\"\\\"\\\"\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":66,"cells":{"instance_id":{"kind":"string","value":"googleapis__google-cloud-python-276"},"repo":{"kind":"string","value":"googleapis/google-cloud-python"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"datastore: default to empty dataset\nWhen working with datasets, the Datastore backend will normalize any empty datasets to the dataset of the calling application. This should be preferred to prepending \"s~\" to dataset ids as that is not always a valid operation.\n\ndatastore: default to empty dataset\nWhen working with datasets, the Datastore backend will normalize any empty datasets to the dataset of the calling application. This should be preferred to prepending \"s~\" to dataset ids as that is not always a valid operation.\n\n"},"gold_files":{"kind":"list like","value":["gcloud/datastore/entity.py","gcloud/datastore/key.py"],"string":"[\n \"gcloud/datastore/entity.py\",\n \"gcloud/datastore/key.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["gcloud/datastore/entity.py::<module>","gcloud/datastore/entity.py::Entity.__init__","gcloud/datastore/entity.py::Entity._must_key","gcloud/datastore/entity.py::Entity.dataset","gcloud/datastore/entity.py::Entity.delete","gcloud/datastore/entity.py::Entity.from_key","gcloud/datastore/entity.py::Entity.from_protobuf","gcloud/datastore/entity.py::Entity.reload","gcloud/datastore/entity.py::Entity.save","gcloud/datastore/key.py::<module>","gcloud/datastore/key.py::Key.__init__","gcloud/datastore/key.py::Key._clone","gcloud/datastore/key.py::Key.dataset","gcloud/datastore/key.py::Key.from_protobuf","gcloud/datastore/key.py::Key.to_protobuf"],"string":"[\n \"gcloud/datastore/entity.py::<module>\",\n \"gcloud/datastore/entity.py::Entity.__init__\",\n \"gcloud/datastore/entity.py::Entity._must_key\",\n \"gcloud/datastore/entity.py::Entity.dataset\",\n \"gcloud/datastore/entity.py::Entity.delete\",\n \"gcloud/datastore/entity.py::Entity.from_key\",\n \"gcloud/datastore/entity.py::Entity.from_protobuf\",\n \"gcloud/datastore/entity.py::Entity.reload\",\n \"gcloud/datastore/entity.py::Entity.save\",\n \"gcloud/datastore/key.py::<module>\",\n \"gcloud/datastore/key.py::Key.__init__\",\n \"gcloud/datastore/key.py::Key._clone\",\n \"gcloud/datastore/key.py::Key.dataset\",\n \"gcloud/datastore/key.py::Key.from_protobuf\",\n \"gcloud/datastore/key.py::Key.to_protobuf\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/conf.py","text":"docs/conf.py","label":0},{"cid":"gcloud/connection.py","text":"gcloud/connection.py\nclass Connection # A generic connection to Google Cloud Platform.\n def __init__(self, credentials=None) # :type credentials: :class:`oauth2client.client.OAuth2Credentials`\n def credentials(self) # :rtype: :class:`oauth2client.client.OAuth2Credentials`, or None\n def http(self) # A getter for the HTTP transport used in talking to the API.","label":0},{"cid":"gcloud/datastore/__init__.py","text":"gcloud/datastore/__init__.py\ndef get_connection(client_email, private_key_path) # Shortcut method to establish a connection to the Cloud Datastore.\ndef get_dataset(dataset_id, client_email, private_key_path) # Establish a connection to a particular dataset in the Cloud Datastore.","label":0},{"cid":"gcloud/datastore/connection.py","text":"gcloud/datastore/connection.py\nclass Connection # A connection to the Google Cloud Datastore via the Protobuf API.\n def __init__(self, credentials=None)\n def _request(self, dataset_id, method, data) # Make a request over the Http transport to the Cloud Datastore API.\n def _rpc(self, dataset_id, method, request_pb, response_pb_cls) # Make a protobuf RPC request.\n def build_api_url(cls, dataset_id, method, base_url=None, api_version=None) # Construct the URL for a particular API call.\n def transaction(self, transaction=connection.Connection._EMPTY) # Getter/setter for the connection's transaction object.\n def mutation(self) # Getter for mutation usable with current connection.\n def dataset(self, *args, **kwargs) # Factory method for Dataset objects.\n def begin_transaction(self, dataset_id, serializable=False) # Begin a transaction.\n def rollback_transaction(self, dataset_id) # Rollback the connection's existing transaction.\n def run_query(self, dataset_id, query_pb, namespace=None) # Run a query on the Cloud Datastore.\n def lookup(self, dataset_id, key_pbs) # Lookup keys from a dataset in the Cloud Datastore.\n def commit(self, dataset_id, mutation_pb) # Commit dataset mutations in context of current transation (if any).\n def save_entity(self, dataset_id, key_pb, properties) # Save an entity to the Cloud Datastore with the provided properties.\n def delete_entities(self, dataset_id, key_pbs) # Delete keys from a dataset in the Cloud Datastore.","label":0},{"cid":"gcloud/datastore/dataset.py","text":"gcloud/datastore/dataset.py\nclass Dataset # A dataset in the Cloud Datastore.\n def __init__(self, id, connection=None)\n def connection(self) # Get the current connection.\n def id(self) # Get the current dataset ID.\n def query(self, *args, **kwargs) # Create a query bound to this dataset.\n def entity(self, kind) # Create an entity bound to this dataset.\n def transaction(self, *args, **kwargs) # Create a transaction bound to this dataset.\n def get_entity(self, key) # Retrieves entity from the dataset, along with its attributes.\n def get_entities(self, keys) # Retrieves entities from the dataset, along with their attributes.","label":0},{"cid":"gcloud/datastore/demo/demo.py","text":"gcloud/datastore/demo/demo.py\n# pragma NO COVER\n# Welcome to the gCloud Datastore Demo! (hit enter)\n\n# We're going to walk through some of the basics...\n# Don't worry though. You don't need to do anything, just keep hitting enter...\n\n# Let's start by importing the demo module and getting a dataset:\nfrom gcloud.datastore import demo\ndataset = demo.get_dataset()\n\n# Let's create a new entity of type \"Thing\" and name it 'Toy':\ntoy = dataset.entity('Thing')\ntoy.update({'name': 'Toy'})\n\n# Now let's save it to our datastore:\ntoy.save()\n\n# If we look it up by its key, we should find it...\nprint dataset.get_entities([toy.key()])\n\n# And we should be able to delete it...\ntoy.delete()\n\n# Since we deleted it, if we do another lookup it shouldn't be there again:\nprint dataset.get_entities([toy.key()])\n\n# Now let's try a more advanced query.\n# We'll start by look at all Thing entities:\nquery = dataset.query().kind('Thing')\n\n# Let's look at the first two.\nprint query.limit(2).fetch()\n\n# Now let's check for Thing entities named 'Computer'\nprint query.filter('name =', 'Computer').fetch()\n\n# If you want to filter by multiple attributes,\n# you can string .filter() calls together.\nprint query.filter('name =', 'Computer').filter('age =', 10).fetch()\n\n# You can also work inside a transaction.\n# (Check the official docs for explanations of what's happening here.)\nwith dataset.transaction():\n print 'Creating and savng an entity...'\n thing = dataset.entity('Thing')\n thing.key(thing.key().name('foo'))\n thing['age'] = 10\n thing.save()\n\n print 'Creating and saving another entity...'\n thing2 = dataset.entity('Thing')\n thing2.key(thing2.key().name('bar'))\n thing2['age'] = 15\n thing2.save()\n\n print 'Committing the transaction...'\n\n# Now that the transaction is commited, let's delete the entities.\nprint thing.delete(), thing2.delete()\n\n# To rollback a transaction, just call .rollback()\nwith dataset.transaction() as t:\n thing = dataset.entity('Thing')\n thing.key(thing.key().name('another'))\n thing.save()\n t.rollback()\n\n# Let's check if the entity was actually created:\ncreated = dataset.get_entities([thing.key()])\nprint 'yes' if created else 'no'\n\n# Remember, a key won't be complete until the transaction is commited.\n# That is, while inside the transaction block, thing.key() will be incomplete.\nwith dataset.transaction():\n thing = dataset.entity('Thing')\n thing.save()\n print thing.key() # This will be partial\n\nprint thing.key() # This will be complete\n\n# Now let's delete the entity.\nthing.delete()\n","label":0},{"cid":"gcloud/datastore/entity.py","text":"gcloud/datastore/entity.py\nclass NoKey # Exception raised by Entity methods which require a key.\nclass Entity # :type dataset: :class:`gcloud.datastore.dataset.Dataset`\n def __init__(self, dataset=None, kind=None)\n def dataset(self) # Get the :class:`.dataset.Dataset` in which this entity belongs.\n def key(self, key=None) # Get or set the :class:`.datastore.key.Key` on the current entity.\n def kind(self) # Get the kind of the current entity.\n def from_key(cls, key) # Create entity based on :class:`.datastore.key.Key`.\n def from_protobuf(cls, pb, dataset=None) # Factory method for creating an entity based on a protobuf.\n def _must_key(self) # Return our key, or raise NoKey if not set.\n def reload(self) # Reloads the contents of this entity from the datastore.\n def save(self) # Save the entity in the Cloud Datastore.\n def delete(self) # Delete the entity in the Cloud Datastore.\n def __repr__(self)","label":1},{"cid":"gcloud/datastore/key.py","text":"gcloud/datastore/key.py\nclass Key # An immutable representation of a datastore Key.\n def __init__(self, dataset=None, namespace=None, path=None) # Constructor / initializer for a key.\n def _clone(self) # Duplicates the Key.\n def from_protobuf(cls, pb, dataset=None) # Factory method for creating a key based on a protobuf.\n def to_protobuf(self) # Return a protobuf corresponding to the key.\n def from_path(cls, *args, **kwargs) # Factory method for creating a key based on a path.\n def is_partial(self) # Boolean test: is the key fully mapped onto a backend entity?\n def dataset(self, dataset=None) # Dataset setter / getter.\n def namespace(self, namespace=None) # Namespace setter / getter.\n def path(self, path=None) # Path setter / getter.\n def kind(self, kind=None) # Kind setter / getter. Based on the last element of path.\n def id(self, id_to_set=None) # ID setter / getter. Based on the last element of path.\n def name(self, name=None) # Name setter / getter. Based on the last element of path.\n def id_or_name(self) # Getter. Based on the last element of path.\n def parent(self) # Getter: return a new key for the next highest element in path.\n def __repr__(self)","label":1},{"cid":"gcloud/datastore/query.py","text":"gcloud/datastore/query.py\nclass Query # A Query against the Cloud Datastore.\n def __init__(self, kind=None, dataset=None, namespace=None)\n def _clone(self) # Create a new Query, copying self.\n def namespace(self) # This query's namespace\n def to_protobuf(self) # Convert :class:`Query` instance to :class:`.datastore_v1_pb2.Query`.\n def filter(self, expression, value) # Filter the query based on an expression and a value.\n def ancestor(self, ancestor) # Filter the query based on an ancestor.\n def kind(self, *kinds) # Get or set the Kind of the Query.\n def limit(self, limit=None) # Get or set the limit of the Query.\n def dataset(self, dataset=None) # Get or set the :class:`.datastore.dataset.Dataset` for this Query.\n def fetch(self, limit=None) # Executes the Query and returns all matching entities.\n def cursor(self) # Returns cursor ID\n def with_cursor(self, start_cursor, end_cursor=None) # Specifies the starting / ending positions in a query's result set.\n def order(self, *properties) # Adds a sort order to the query.","label":0},{"cid":"gcloud/datastore/transaction.py","text":"gcloud/datastore/transaction.py\nclass Transaction # An abstraction representing datastore Transactions.\n def __init__(self, dataset)\n def connection(self) # Getter for current connection over which the transaction will run.\n def dataset(self) # Getter for the current dataset.\n def id(self) # Getter for the transaction ID.\n def mutation(self) # Getter for the current mutation.\n def add_auto_id_entity(self, entity) # Adds an entity to the list of entities to update with IDs.\n def begin(self) # Begins a transaction.\n def rollback(self) # Rolls back the current transaction.\n def commit(self) # Commits the transaction.\n def __enter__(self)\n def __exit__(self, exc_type, exc_val, exc_tb)","label":0},{"cid":"gcloud/storage/connection.py","text":"gcloud/storage/connection.py\ndef _utcnow() # Returns current time as UTC datetime.\nclass Connection # A connection to Google Cloud Storage via the JSON REST API.\n def __init__(self, project, *args, **kwargs) # :type project: string\n def __iter__(self)\n def __contains__(self, bucket_name)\n def build_api_url(self, path, query_params=None, api_base_url=None, api_version=None) # Construct an API url given a few components, some optional.\n def make_request(self, method, url, data=None, content_type=None, headers=None) # A low level method to send a request to the API.\n def api_request(self, method, path, query_params=None, data=None, content_type=None, api_base_url=None, api_version=None, expect_json=True) # Make a request over the HTTP transport to the Cloud Storage API.\n def get_all_buckets(self) # Get all buckets in the project.\n def get_bucket(self, bucket_name) # Get a bucket by name.\n def lookup(self, bucket_name) # Get a bucket by name, returning None if not found.\n def create_bucket(self, bucket) # Create a new bucket.\n def delete_bucket(self, bucket, force=False) # Delete a bucket.\n def new_bucket(self, bucket) # Factory method for creating a new (unsaved) bucket object.\n def generate_signed_url(self, resource, expiration, method='GET', content_md5=None, content_type=None) # Generate signed URL to provide query-string auth'n to a resource.\ndef _get_expiration_seconds(expiration) # Convert 'expiration' to a number of seconds in the future.","label":0},{"cid":"regression/datastore.py","text":"regression/datastore.py\nclass TestDatastore\n def setUp(self)\n def tearDown(self)\n def _get_dataset(self)\n def _get_post(self, name=None, key_id=None, post_content=None)\n def _generic_test_post(self, name=None, key_id=None)\n def test_post_with_name(self)\n def test_post_with_id(self)\n def test_post_with_generated_id(self)\n def test_save_multiple(self)\n def test_empty_kind(self)","label":0}],"string":"[\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/connection.py\",\n \"text\": \"gcloud/connection.py\\nclass Connection # A generic connection to Google Cloud Platform.\\n def __init__(self, credentials=None) # :type credentials: :class:`oauth2client.client.OAuth2Credentials`\\n def credentials(self) # :rtype: :class:`oauth2client.client.OAuth2Credentials`, or None\\n def http(self) # A getter for the HTTP transport used in talking to the API.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/__init__.py\",\n \"text\": \"gcloud/datastore/__init__.py\\ndef get_connection(client_email, private_key_path) # Shortcut method to establish a connection to the Cloud Datastore.\\ndef get_dataset(dataset_id, client_email, private_key_path) # Establish a connection to a particular dataset in the Cloud Datastore.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py\",\n \"text\": \"gcloud/datastore/connection.py\\nclass Connection # A connection to the Google Cloud Datastore via the Protobuf API.\\n def __init__(self, credentials=None)\\n def _request(self, dataset_id, method, data) # Make a request over the Http transport to the Cloud Datastore API.\\n def _rpc(self, dataset_id, method, request_pb, response_pb_cls) # Make a protobuf RPC request.\\n def build_api_url(cls, dataset_id, method, base_url=None, api_version=None) # Construct the URL for a particular API call.\\n def transaction(self, transaction=connection.Connection._EMPTY) # Getter/setter for the connection's transaction object.\\n def mutation(self) # Getter for mutation usable with current connection.\\n def dataset(self, *args, **kwargs) # Factory method for Dataset objects.\\n def begin_transaction(self, dataset_id, serializable=False) # Begin a transaction.\\n def rollback_transaction(self, dataset_id) # Rollback the connection's existing transaction.\\n def run_query(self, dataset_id, query_pb, namespace=None) # Run a query on the Cloud Datastore.\\n def lookup(self, dataset_id, key_pbs) # Lookup keys from a dataset in the Cloud Datastore.\\n def commit(self, dataset_id, mutation_pb) # Commit dataset mutations in context of current transation (if any).\\n def save_entity(self, dataset_id, key_pb, properties) # Save an entity to the Cloud Datastore with the provided properties.\\n def delete_entities(self, dataset_id, key_pbs) # Delete keys from a dataset in the Cloud Datastore.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/dataset.py\",\n \"text\": \"gcloud/datastore/dataset.py\\nclass Dataset # A dataset in the Cloud Datastore.\\n def __init__(self, id, connection=None)\\n def connection(self) # Get the current connection.\\n def id(self) # Get the current dataset ID.\\n def query(self, *args, **kwargs) # Create a query bound to this dataset.\\n def entity(self, kind) # Create an entity bound to this dataset.\\n def transaction(self, *args, **kwargs) # Create a transaction bound to this dataset.\\n def get_entity(self, key) # Retrieves entity from the dataset, along with its attributes.\\n def get_entities(self, keys) # Retrieves entities from the dataset, along with their attributes.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/demo/demo.py\",\n \"text\": \"gcloud/datastore/demo/demo.py\\n# pragma NO COVER\\n# Welcome to the gCloud Datastore Demo! (hit enter)\\n\\n# We're going to walk through some of the basics...\\n# Don't worry though. You don't need to do anything, just keep hitting enter...\\n\\n# Let's start by importing the demo module and getting a dataset:\\nfrom gcloud.datastore import demo\\ndataset = demo.get_dataset()\\n\\n# Let's create a new entity of type \\\"Thing\\\" and name it 'Toy':\\ntoy = dataset.entity('Thing')\\ntoy.update({'name': 'Toy'})\\n\\n# Now let's save it to our datastore:\\ntoy.save()\\n\\n# If we look it up by its key, we should find it...\\nprint dataset.get_entities([toy.key()])\\n\\n# And we should be able to delete it...\\ntoy.delete()\\n\\n# Since we deleted it, if we do another lookup it shouldn't be there again:\\nprint dataset.get_entities([toy.key()])\\n\\n# Now let's try a more advanced query.\\n# We'll start by look at all Thing entities:\\nquery = dataset.query().kind('Thing')\\n\\n# Let's look at the first two.\\nprint query.limit(2).fetch()\\n\\n# Now let's check for Thing entities named 'Computer'\\nprint query.filter('name =', 'Computer').fetch()\\n\\n# If you want to filter by multiple attributes,\\n# you can string .filter() calls together.\\nprint query.filter('name =', 'Computer').filter('age =', 10).fetch()\\n\\n# You can also work inside a transaction.\\n# (Check the official docs for explanations of what's happening here.)\\nwith dataset.transaction():\\n print 'Creating and savng an entity...'\\n thing = dataset.entity('Thing')\\n thing.key(thing.key().name('foo'))\\n thing['age'] = 10\\n thing.save()\\n\\n print 'Creating and saving another entity...'\\n thing2 = dataset.entity('Thing')\\n thing2.key(thing2.key().name('bar'))\\n thing2['age'] = 15\\n thing2.save()\\n\\n print 'Committing the transaction...'\\n\\n# Now that the transaction is commited, let's delete the entities.\\nprint thing.delete(), thing2.delete()\\n\\n# To rollback a transaction, just call .rollback()\\nwith dataset.transaction() as t:\\n thing = dataset.entity('Thing')\\n thing.key(thing.key().name('another'))\\n thing.save()\\n t.rollback()\\n\\n# Let's check if the entity was actually created:\\ncreated = dataset.get_entities([thing.key()])\\nprint 'yes' if created else 'no'\\n\\n# Remember, a key won't be complete until the transaction is commited.\\n# That is, while inside the transaction block, thing.key() will be incomplete.\\nwith dataset.transaction():\\n thing = dataset.entity('Thing')\\n thing.save()\\n print thing.key() # This will be partial\\n\\nprint thing.key() # This will be complete\\n\\n# Now let's delete the entity.\\nthing.delete()\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/entity.py\",\n \"text\": \"gcloud/datastore/entity.py\\nclass NoKey # Exception raised by Entity methods which require a key.\\nclass Entity # :type dataset: :class:`gcloud.datastore.dataset.Dataset`\\n def __init__(self, dataset=None, kind=None)\\n def dataset(self) # Get the :class:`.dataset.Dataset` in which this entity belongs.\\n def key(self, key=None) # Get or set the :class:`.datastore.key.Key` on the current entity.\\n def kind(self) # Get the kind of the current entity.\\n def from_key(cls, key) # Create entity based on :class:`.datastore.key.Key`.\\n def from_protobuf(cls, pb, dataset=None) # Factory method for creating an entity based on a protobuf.\\n def _must_key(self) # Return our key, or raise NoKey if not set.\\n def reload(self) # Reloads the contents of this entity from the datastore.\\n def save(self) # Save the entity in the Cloud Datastore.\\n def delete(self) # Delete the entity in the Cloud Datastore.\\n def __repr__(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/key.py\",\n \"text\": \"gcloud/datastore/key.py\\nclass Key # An immutable representation of a datastore Key.\\n def __init__(self, dataset=None, namespace=None, path=None) # Constructor / initializer for a key.\\n def _clone(self) # Duplicates the Key.\\n def from_protobuf(cls, pb, dataset=None) # Factory method for creating a key based on a protobuf.\\n def to_protobuf(self) # Return a protobuf corresponding to the key.\\n def from_path(cls, *args, **kwargs) # Factory method for creating a key based on a path.\\n def is_partial(self) # Boolean test: is the key fully mapped onto a backend entity?\\n def dataset(self, dataset=None) # Dataset setter / getter.\\n def namespace(self, namespace=None) # Namespace setter / getter.\\n def path(self, path=None) # Path setter / getter.\\n def kind(self, kind=None) # Kind setter / getter. Based on the last element of path.\\n def id(self, id_to_set=None) # ID setter / getter. Based on the last element of path.\\n def name(self, name=None) # Name setter / getter. Based on the last element of path.\\n def id_or_name(self) # Getter. Based on the last element of path.\\n def parent(self) # Getter: return a new key for the next highest element in path.\\n def __repr__(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/query.py\",\n \"text\": \"gcloud/datastore/query.py\\nclass Query # A Query against the Cloud Datastore.\\n def __init__(self, kind=None, dataset=None, namespace=None)\\n def _clone(self) # Create a new Query, copying self.\\n def namespace(self) # This query's namespace\\n def to_protobuf(self) # Convert :class:`Query` instance to :class:`.datastore_v1_pb2.Query`.\\n def filter(self, expression, value) # Filter the query based on an expression and a value.\\n def ancestor(self, ancestor) # Filter the query based on an ancestor.\\n def kind(self, *kinds) # Get or set the Kind of the Query.\\n def limit(self, limit=None) # Get or set the limit of the Query.\\n def dataset(self, dataset=None) # Get or set the :class:`.datastore.dataset.Dataset` for this Query.\\n def fetch(self, limit=None) # Executes the Query and returns all matching entities.\\n def cursor(self) # Returns cursor ID\\n def with_cursor(self, start_cursor, end_cursor=None) # Specifies the starting / ending positions in a query's result set.\\n def order(self, *properties) # Adds a sort order to the query.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py\",\n \"text\": \"gcloud/datastore/transaction.py\\nclass Transaction # An abstraction representing datastore Transactions.\\n def __init__(self, dataset)\\n def connection(self) # Getter for current connection over which the transaction will run.\\n def dataset(self) # Getter for the current dataset.\\n def id(self) # Getter for the transaction ID.\\n def mutation(self) # Getter for the current mutation.\\n def add_auto_id_entity(self, entity) # Adds an entity to the list of entities to update with IDs.\\n def begin(self) # Begins a transaction.\\n def rollback(self) # Rolls back the current transaction.\\n def commit(self) # Commits the transaction.\\n def __enter__(self)\\n def __exit__(self, exc_type, exc_val, exc_tb)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py\",\n \"text\": \"gcloud/storage/connection.py\\ndef _utcnow() # Returns current time as UTC datetime.\\nclass Connection # A connection to Google Cloud Storage via the JSON REST API.\\n def __init__(self, project, *args, **kwargs) # :type project: string\\n def __iter__(self)\\n def __contains__(self, bucket_name)\\n def build_api_url(self, path, query_params=None, api_base_url=None, api_version=None) # Construct an API url given a few components, some optional.\\n def make_request(self, method, url, data=None, content_type=None, headers=None) # A low level method to send a request to the API.\\n def api_request(self, method, path, query_params=None, data=None, content_type=None, api_base_url=None, api_version=None, expect_json=True) # Make a request over the HTTP transport to the Cloud Storage API.\\n def get_all_buckets(self) # Get all buckets in the project.\\n def get_bucket(self, bucket_name) # Get a bucket by name.\\n def lookup(self, bucket_name) # Get a bucket by name, returning None if not found.\\n def create_bucket(self, bucket) # Create a new bucket.\\n def delete_bucket(self, bucket, force=False) # Delete a bucket.\\n def new_bucket(self, bucket) # Factory method for creating a new (unsaved) bucket object.\\n def generate_signed_url(self, resource, expiration, method='GET', content_md5=None, content_type=None) # Generate signed URL to provide query-string auth'n to a resource.\\ndef _get_expiration_seconds(expiration) # Convert 'expiration' to a number of seconds in the future.\",\n \"label\": 0\n },\n {\n \"cid\": \"regression/datastore.py\",\n \"text\": \"regression/datastore.py\\nclass TestDatastore\\n def setUp(self)\\n def tearDown(self)\\n def _get_dataset(self)\\n def _get_post(self, name=None, key_id=None, post_content=None)\\n def _generic_test_post(self, name=None, key_id=None)\\n def test_post_with_name(self)\\n def test_post_with_id(self)\\n def test_post_with_generated_id(self)\\n def test_save_multiple(self)\\n def test_empty_kind(self)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"gcloud/connection.py::Connection.__init__","text":"gcloud/connection.py::Connection.__init__\n def __init__(self, credentials=None):\n \"\"\"\n :type credentials: :class:`oauth2client.client.OAuth2Credentials`\n :param credentials: The OAuth2 Credentials to use for this connection.\n \"\"\"\n self._http = None\n self._credentials = credentials","label":0},{"cid":"gcloud/connection.py::Connection.credentials","text":"gcloud/connection.py::Connection.credentials\n @property\n def credentials(self):\n \"\"\"\n :rtype: :class:`oauth2client.client.OAuth2Credentials`, or None\n :returns: The credentials object associated with this connection.\n \"\"\"\n return self._credentials","label":0},{"cid":"gcloud/connection.py::Connection.http","text":"gcloud/connection.py::Connection.http\n @property\n def http(self):\n \"\"\"A getter for the HTTP transport used in talking to the API.\n\n :rtype: :class:`httplib2.Http`\n :returns: A Http object used to transport data.\n \"\"\"\n if self._http is None:\n self._http = httplib2.Http()\n if self._credentials:\n self._http = self._credentials.authorize(self._http)\n return self._http","label":0},{"cid":"gcloud/datastore/__init__.py::get_connection","text":"gcloud/datastore/__init__.py::get_connection\ndef get_connection(client_email, private_key_path):\n \"\"\"Shortcut method to establish a connection to the Cloud Datastore.\n\n Use this if you are going to access several datasets\n with the same set of credentials (unlikely):\n\n >>> from gcloud import datastore\n >>> connection = datastore.get_connection(email, key_path)\n >>> dataset1 = connection.dataset('dataset1')\n >>> dataset2 = connection.dataset('dataset2')\n\n :type client_email: string\n :param client_email: The e-mail attached to the service account.\n\n :type private_key_path: string\n :param private_key_path: The path to a private key file (this file was\n given to you when you created the service\n account).\n\n :rtype: :class:`gcloud.datastore.connection.Connection`\n :returns: A connection defined with the proper credentials.\n \"\"\"\n from gcloud import credentials\n from gcloud.datastore.connection import Connection\n\n svc_account_credentials = credentials.get_for_service_account(\n client_email, private_key_path, scope=SCOPE)\n return Connection(credentials=svc_account_credentials)","label":0},{"cid":"gcloud/datastore/__init__.py::get_dataset","text":"gcloud/datastore/__init__.py::get_dataset\ndef get_dataset(dataset_id, client_email, private_key_path):\n \"\"\"Establish a connection to a particular dataset in the Cloud Datastore.\n\n This is a shortcut method for creating a connection and using it\n to connect to a dataset.\n\n You'll generally use this as the first call to working with the API:\n\n >>> from gcloud import datastore\n >>> dataset = datastore.get_dataset('dataset-id', email, key_path)\n >>> # Now you can do things with the dataset.\n >>> dataset.query().kind('TestKind').fetch()\n [...]\n\n :type dataset_id: string\n :param dataset_id: The id of the dataset you want to use.\n This is akin to a database name\n and is usually the same as your Cloud Datastore project\n name.\n\n :type client_email: string\n :param client_email: The e-mail attached to the service account.\n\n :type private_key_path: string\n :param private_key_path: The path to a private key file (this file was\n given to you when you created the service\n account).\n\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\n :returns: A dataset with a connection using the provided credentials.\n \"\"\"\n connection = get_connection(client_email, private_key_path)\n return connection.dataset(dataset_id)","label":0},{"cid":"gcloud/datastore/connection.py::Connection.__init__","text":"gcloud/datastore/connection.py::Connection.__init__\n def __init__(self, credentials=None):\n super(Connection, self).__init__(credentials=credentials)\n self._current_transaction = None","label":0},{"cid":"gcloud/datastore/connection.py::Connection._request","text":"gcloud/datastore/connection.py::Connection._request\n def _request(self, dataset_id, method, data):\n \"\"\"Make a request over the Http transport to the Cloud Datastore API.\n\n :type dataset_id: string\n :param dataset_id: The ID of the dataset of which to make the request.\n\n :type method: string\n :param method: The API call method name (ie, ``runQuery``,\n ``lookup``, etc)\n\n :type data: string\n :param data: The data to send with the API call.\n Typically this is a serialized Protobuf string.\n\n :rtype: string\n :returns: The string response content from the API call.\n\n :raises: Exception if the response code is not 200 OK.\n \"\"\"\n headers = {\n 'Content-Type': 'application/x-protobuf',\n 'Content-Length': str(len(data)),\n 'User-Agent': self.USER_AGENT,\n }\n headers, content = self.http.request(\n uri=self.build_api_url(dataset_id=dataset_id, method=method),\n method='POST', headers=headers, body=data)\n\n if headers['status'] != '200':\n raise Exception('Request failed. Error was: %s' % content)\n\n return content","label":0},{"cid":"gcloud/datastore/connection.py::Connection._rpc","text":"gcloud/datastore/connection.py::Connection._rpc\n def _rpc(self, dataset_id, method, request_pb, response_pb_cls):\n \"\"\" Make a protobuf RPC request.\n\n :type dataset_id: string\n :param dataset_id: The ID of the dataset to connect to. This is\n usually your project name in the cloud console.\n\n :type method: string\n :param method: The name of the method to invoke.\n\n :type request_pb: :class:`google.protobuf.message.Message` instance\n :param method: the protobuf instance representing the request.\n\n :type response_pb_cls: a :class:`google.protobuf.message.Message'\n subclass.\n :param method: The class used to unmarshall the response protobuf.\n \"\"\"\n response = self._request(dataset_id=dataset_id, method=method,\n data=request_pb.SerializeToString())\n return response_pb_cls.FromString(response)","label":0},{"cid":"gcloud/datastore/connection.py::Connection.build_api_url","text":"gcloud/datastore/connection.py::Connection.build_api_url\n @classmethod\n def build_api_url(cls, dataset_id, method, base_url=None,\n api_version=None):\n \"\"\"Construct the URL for a particular API call.\n\n This method is used internally\n to come up with the URL\n to use when making RPCs\n to the Cloud Datastore API.\n\n :type dataset_id: string\n :param dataset_id: The ID of the dataset to connect to. This is\n usually your project name in the cloud console.\n\n :type method: string\n :param method: The API method to call (ie, runQuery, lookup, ...).\n\n :type base_url: string\n :param base_url: The base URL where the API lives.\n You shouldn't have to provide this.\n\n :type api_version: string\n :param api_version: The version of the API to connect to.\n You shouldn't have to provide this.\n \"\"\"\n return cls.API_URL_TEMPLATE.format(\n api_base=(base_url or cls.API_BASE_URL),\n api_version=(api_version or cls.API_VERSION),\n dataset_id=dataset_id, method=method)","label":0},{"cid":"gcloud/datastore/connection.py::Connection.transaction","text":"gcloud/datastore/connection.py::Connection.transaction\n def transaction(self, transaction=connection.Connection._EMPTY):\n \"\"\"Getter/setter for the connection's transaction object.\n\n :type transaction: :class:`gcloud.datastore.transaction.Transaction`,\n (setting), or omitted (getting).\n :param transaction: The new transaction (if passed).\n\n :rtype: :class:`gcloud.datastore.transaction.Transaction`, (getting)\n or :class:`gcloud.datastore.connection.Connection` (setting)\n :returns: the current transaction (getting) or self (setting).\n \"\"\"\n if transaction is self._EMPTY:\n return self._current_transaction\n else:\n self._current_transaction = transaction\n return self","label":0},{"cid":"gcloud/datastore/connection.py::Connection.mutation","text":"gcloud/datastore/connection.py::Connection.mutation\n def mutation(self):\n \"\"\"Getter for mutation usable with current connection.\n\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.Mutation`.\n :returns: the mutation instance associated with the current transaction\n (if one exists) or or a new mutation instance.\n \"\"\"\n if self.transaction():\n return self.transaction().mutation()\n else:\n return datastore_pb.Mutation()","label":0},{"cid":"gcloud/datastore/connection.py::Connection.dataset","text":"gcloud/datastore/connection.py::Connection.dataset\n def dataset(self, *args, **kwargs):\n \"\"\"Factory method for Dataset objects.\n\n :param args: All args and kwargs will be passed along to the\n :class:`gcloud.datastore.dataset.Dataset` initializer.\n\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\n :returns: A dataset object that will use this connection as\n its transport.\n \"\"\"\n kwargs['connection'] = self\n return Dataset(*args, **kwargs)","label":0},{"cid":"gcloud/datastore/connection.py::Connection.begin_transaction","text":"gcloud/datastore/connection.py::Connection.begin_transaction\n def begin_transaction(self, dataset_id, serializable=False):\n \"\"\"Begin a transaction.\n\n :type dataset_id: string\n :param dataset_id: The dataset over which to execute the transaction.\n \"\"\"\n\n if self.transaction():\n raise ValueError('Cannot start a transaction with another already '\n 'in progress.')\n\n request = datastore_pb.BeginTransactionRequest()\n\n if serializable:\n request.isolation_level = (\n datastore_pb.BeginTransactionRequest.SERIALIZABLE)\n else:\n request.isolation_level = (\n datastore_pb.BeginTransactionRequest.SNAPSHOT)\n\n response = self._rpc(dataset_id, 'beginTransaction', request,\n datastore_pb.BeginTransactionResponse)\n\n return response.transaction","label":0},{"cid":"gcloud/datastore/connection.py::Connection.rollback_transaction","text":"gcloud/datastore/connection.py::Connection.rollback_transaction\n def rollback_transaction(self, dataset_id):\n \"\"\"Rollback the connection's existing transaction.\n\n Raises a ``ValueError``\n if the connection isn't currently in a transaction.\n\n :type dataset_id: string\n :param dataset_id: The dataset to which the transaction belongs.\n \"\"\"\n if not self.transaction() or not self.transaction().id():\n raise ValueError('No transaction to rollback.')\n\n request = datastore_pb.RollbackRequest()\n request.transaction = self.transaction().id()\n # Nothing to do with this response, so just execute the method.\n self._rpc(dataset_id, 'rollback', request,\n datastore_pb.RollbackResponse)","label":0},{"cid":"gcloud/datastore/connection.py::Connection.run_query","text":"gcloud/datastore/connection.py::Connection.run_query\n def run_query(self, dataset_id, query_pb, namespace=None):\n \"\"\"Run a query on the Cloud Datastore.\n\n Given a Query protobuf,\n sends a ``runQuery`` request to the Cloud Datastore API\n and returns a list of entity protobufs matching the query.\n\n You typically wouldn't use this method directly,\n in favor of the :func:`gcloud.datastore.query.Query.fetch` method.\n\n Under the hood, the :class:`gcloud.datastore.query.Query` class\n uses this method to fetch data:\n\n >>> from gcloud import datastore\n >>> connection = datastore.get_connection(email, key_path)\n >>> dataset = connection.dataset('dataset-id')\n >>> query = dataset.query().kind('MyKind').filter('property =', 'val')\n\n Using the `fetch`` method...\n\n >>> query.fetch()\n [<list of Entity Protobufs>], cursor, more_results, skipped_results\n\n Under the hood this is doing...\n\n >>> connection.run_query('dataset-id', query.to_protobuf())\n [<list of Entity Protobufs>]\n\n :type dataset_id: string\n :param dataset_id: The ID of the dataset over which to run the query.\n\n :type query_pb: :class:`gcloud.datastore.datastore_v1_pb2.Query`\n :param query_pb: The Protobuf representing the query to run.\n\n :type namespace: string\n :param namespace: The namespace over which to run the query.\n \"\"\"\n request = datastore_pb.RunQueryRequest()\n\n if namespace:\n request.partition_id.namespace = namespace\n\n request.query.CopyFrom(query_pb)\n response = self._rpc(dataset_id, 'runQuery', request,\n datastore_pb.RunQueryResponse)\n return (\n [e.entity for e in response.batch.entity_result],\n response.batch.end_cursor,\n response.batch.more_results,\n response.batch.skipped_results,\n )","label":0},{"cid":"gcloud/datastore/connection.py::Connection.lookup","text":"gcloud/datastore/connection.py::Connection.lookup\n def lookup(self, dataset_id, key_pbs):\n \"\"\"Lookup keys from a dataset in the Cloud Datastore.\n\n This method deals only with protobufs\n (:class:`gcloud.datastore.datastore_v1_pb2.Key`\n and\n :class:`gcloud.datastore.datastore_v1_pb2.Entity`)\n and is used under the hood for methods like\n :func:`gcloud.datastore.dataset.Dataset.get_entity`:\n\n >>> from gcloud import datastore\n >>> from gcloud.datastore.key import Key\n >>> connection = datastore.get_connection(email, key_path)\n >>> dataset = connection.dataset('dataset-id')\n >>> key = Key(dataset=dataset).kind('MyKind').id(1234)\n\n Using the :class:`gcloud.datastore.dataset.Dataset` helper:\n\n >>> dataset.get_entity(key)\n <Entity object>\n\n Using the ``connection`` class directly:\n\n >>> connection.lookup('dataset-id', key.to_protobuf())\n <Entity protobuf>\n\n :type dataset_id: string\n :param dataset_id: The dataset to look up the keys.\n\n :type key_pbs: list of :class:`gcloud.datastore.datastore_v1_pb2.Key`\n (or a single Key)\n :param key_pbs: The key (or keys) to retrieve from the datastore.\n\n :rtype: list of :class:`gcloud.datastore.datastore_v1_pb2.Entity`\n (or a single Entity)\n :returns: The entities corresponding to the keys provided.\n If a single key was provided and no results matched,\n this will return None.\n If multiple keys were provided and no results matched,\n this will return an empty list.\n \"\"\"\n lookup_request = datastore_pb.LookupRequest()\n\n single_key = isinstance(key_pbs, datastore_pb.Key)\n\n if single_key:\n key_pbs = [key_pbs]\n\n for key_pb in key_pbs:\n lookup_request.key.add().CopyFrom(key_pb)\n\n lookup_response = self._rpc(datas","label":0},{"cid":"gcloud/datastore/connection.py::Connection.commit","text":"gcloud/datastore/connection.py::Connection.commit\n def commit(self, dataset_id, mutation_pb):\n \"\"\"Commit dataset mutations in context of current transation (if any).\n\n :type dataset_id: string\n :param dataset_id: The dataset in which to perform the changes.\n\n :type mutation_pb: :class:`gcloud.datastore.datastore_v1_pb2.Mutation`.\n :param mutation_pb: The protobuf for the mutations being saved.\n\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.MutationResult`.\n :returns': the result protobuf for the mutation.\n \"\"\"\n request = datastore_pb.CommitRequest()\n\n if self.transaction():\n request.mode = datastore_pb.CommitRequest.TRANSACTIONAL\n request.transaction = self.transaction().id()\n else:\n request.mode = datastore_pb.CommitRequest.NON_TRANSACTIONAL\n\n request.mutation.CopyFrom(mutation_pb)\n response = self._rpc(dataset_id, 'commit', request,\n datastore_pb.CommitResponse)\n return response.mutation_result","label":0},{"cid":"gcloud/datastore/connection.py::Connection.save_entity","text":"gcloud/datastore/connection.py::Connection.save_entity\n def save_entity(self, dataset_id, key_pb, properties):\n \"\"\"Save an entity to the Cloud Datastore with the provided properties.\n\n .. note::\n Any existing properties for the entity identified by 'key_pb'\n will be replaced by those passed in 'properties'; properties\n not passed in 'properties' no longer be set for the entity.\n\n :type dataset_id: string\n :param dataset_id: The dataset in which to save the entity.\n\n :type key_pb: :class:`gcloud.datastore.datastore_v1_pb2.Key`\n :param key_pb: The complete or partial key for the entity.\n\n :type properties: dict\n :param properties: The properties to store on the entity.\n \"\"\"\n mutation = self.mutation()\n\n # If the Key is complete, we should upsert\n # instead of using insert_auto_id.\n path = key_pb.path_element[-1]\n auto_id = not (path.HasField('id') or path.HasField('name'))\n\n if auto_id:\n insert = mutation.insert_auto_id.add()\n else:\n insert = mutation.upsert.add()\n\n insert.key.CopyFrom(key_pb)\n\n for name, value in properties.iteritems():\n prop = insert.property.add()\n # Set the name of the property.\n prop.name = name\n\n # Set the appropriate value.\n _helpers._set_protobuf_value(prop.value, value)\n\n # If this is in a transaction, we should just return True. The\n # transaction will handle assigning any keys as necessary.\n if self.transaction():\n return True\n\n result = self.commit(dataset_id, mutation)\n # If this was an auto-assigned ID, return the new Key.\n if auto_id:\n return result.insert_auto_id_key[0]\n\n return True","label":0},{"cid":"gcloud/datastore/connection.py::Connection.delete_entities","text":"gcloud/datastore/connection.py::Connection.delete_entities\n def delete_entities(self, dataset_id, key_pbs):\n \"\"\"Delete keys from a dataset in the Cloud Datastore.\n\n This method deals only with\n :class:`gcloud.datastore.datastore_v1_pb2.Key` protobufs\n and not with any of the other abstractions.\n For example, it's used under the hood in the\n :func:`gcloud.datastore.entity.Entity.delete` method.\n\n :type dataset_id: string\n :param dataset_id: The dataset from which to delete the keys.\n\n :type key_pbs: list of :class:`gcloud.datastore.datastore_v1_pb2.Key`\n :param key_pbs: The keys to delete from the datastore.\n\n :rtype: boolean (if in a transaction) or else\n :class:`gcloud.datastore.datastore_v1_pb2.MutationResult`.\n :returns: True\n \"\"\"\n mutation = self.mutation()\n\n for key_pb in key_pbs:\n delete = mutation.delete.add()\n delete.CopyFrom(key_pb)\n\n if not self.transaction():\n self.commit(dataset_id, mutation)\n\n return True","label":0},{"cid":"gcloud/datastore/dataset.py::Dataset.__init__","text":"gcloud/datastore/dataset.py::Dataset.__init__\n def __init__(self, id, connection=None):\n self._connection = connection\n self._id = id","label":0},{"cid":"gcloud/datastore/dataset.py::Dataset.connection","text":"gcloud/datastore/dataset.py::Dataset.connection\n def connection(self):\n \"\"\"Get the current connection.\n\n >>> dataset = Dataset('dataset-id', connection=conn)\n >>> dataset.connection()\n <Connection object>\n\n :rtype: :class:`gcloud.datastore.connection.Connection`\n :returns: Returns the current connection.\n \"\"\"\n\n return self._connection","label":0},{"cid":"gcloud/datastore/dataset.py::Dataset.id","text":"gcloud/datastore/dataset.py::Dataset.id\n def id(self):\n \"\"\"Get the current dataset ID.\n\n >>> dataset = Dataset('dataset-id', connection=conn)\n >>> dataset.id()\n 'dataset-id'\n\n :rtype: string\n :returns: The current dataset ID.\n \"\"\"\n\n return self._id","label":0},{"cid":"gcloud/datastore/dataset.py::Dataset.query","text":"gcloud/datastore/dataset.py::Dataset.query\n def query(self, *args, **kwargs):\n \"\"\"Create a query bound to this dataset.\n\n :param args: positional arguments, passed through to the Query\n\n :param kw: keyword arguments, passed through to the Query\n\n :rtype: :class:`gcloud.datastore.query.Query`\n :returns: a new Query instance, bound to this dataset.\n \"\"\"\n # This import is here to avoid circular references.\n from gcloud.datastore.query import Query\n kwargs['dataset'] = self\n return Query(*args, **kwargs)","label":0},{"cid":"gcloud/datastore/dataset.py::Dataset.entity","text":"gcloud/datastore/dataset.py::Dataset.entity\n def entity(self, kind):\n \"\"\"Create an entity bound to this dataset.\n\n :type kind: string\n :param kind: the \"kind\" of the new entity.\n\n :rtype: :class:`gcloud.datastore.entity.Entity`\n :returns: a new Entity instance, bound to this dataset.\n \"\"\"\n # This import is here to avoid circular references.\n from gcloud.datastore.entity import Entity\n return Entity(dataset=self, kind=kind)","label":0},{"cid":"gcloud/datastore/dataset.py::Dataset.transaction","text":"gcloud/datastore/dataset.py::Dataset.transaction\n def transaction(self, *args, **kwargs):\n \"\"\"Create a transaction bound to this dataset.\n\n :param args: positional arguments, passed through to the Transaction\n\n :param kw: keyword arguments, passed through to the Transaction\n\n :rtype: :class:`gcloud.datastore.transaction.Transaction`\n :returns: a new Transaction instance, bound to this dataset.\n \"\"\"\n # This import is here to avoid circular references.\n from gcloud.datastore.transaction import Transaction\n kwargs['dataset'] = self\n return Transaction(*args, **kwargs)","label":0},{"cid":"gcloud/datastore/dataset.py::Dataset.get_entity","text":"gcloud/datastore/dataset.py::Dataset.get_entity\n def get_entity(self, key):\n \"\"\"Retrieves entity from the dataset, along with its attributes.\n\n :type key: :class:`gcloud.datastore.key.Key`\n :param item_name: The name of the item to retrieve.\n\n :rtype: :class:`gcloud.datastore.entity.Entity` or ``None``\n :return: The requested entity, or ``None`` if there was no match found.\n \"\"\"\n entities = self.get_entities([key])\n if entities:\n return entities[0]","label":0},{"cid":"gcloud/datastore/dataset.py::Dataset.get_entities","text":"gcloud/datastore/dataset.py::Dataset.get_entities\n def get_entities(self, keys):\n \"\"\"Retrieves entities from the dataset, along with their attributes.\n\n :type key: list of :class:`gcloud.datastore.key.Key`\n :param item_name: The name of the item to retrieve.\n\n :rtype: list of :class:`gcloud.datastore.entity.Entity`\n :return: The requested entities.\n \"\"\"\n # This import is here to avoid circular references.\n from gcloud.datastore.entity import Entity\n\n entity_pbs = self.connection().lookup(\n dataset_id=self.id(),\n key_pbs=[k.to_protobuf() for k in keys]\n )\n\n entities = []\n for entity_pb in entity_pbs:\n entities.append(Entity.from_protobuf(entity_pb, dataset=self))\n return entities","label":0},{"cid":"gcloud/datastore/entity.py::Entity.__init__","text":"gcloud/datastore/entity.py::Entity.__init__\n def __init__(self, dataset=None, kind=None):\n super(Entity, self).__init__()\n if dataset and kind:\n self._key = Key(dataset=dataset).kind(kind)\n else:\n self._key = None","label":1},{"cid":"gcloud/datastore/entity.py::Entity.dataset","text":"gcloud/datastore/entity.py::Entity.dataset\n def dataset(self):\n \"\"\"Get the :class:`.dataset.Dataset` in which this entity belongs.\n\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\n :returns: The Dataset containing the entity if there is a key,\n else None.\n\n .. note::\n This is based on the :class:`gcloud.datastore.key.Key` set on the\n entity. That means that if you have no key set, the dataset might\n be `None`. It also means that if you change the key on the entity,\n this will refer to that key's dataset.\n \"\"\"\n if self._key:\n return self._key.dataset()","label":1},{"cid":"gcloud/datastore/entity.py::Entity.key","text":"gcloud/datastore/entity.py::Entity.key\n def key(self, key=None):\n \"\"\"Get or set the :class:`.datastore.key.Key` on the current entity.\n\n :type key: :class:`glcouddatastore.key.Key`\n :param key: The key you want to set on the entity.\n\n :rtype: :class:`gcloud.datastore.key.Key` or :class:`Entity`.\n :returns: Either the current key (on get) or the current\n object (on set).\n\n >>> entity.key(my_other_key) # This returns the original entity.\n <Entity[{'kind': 'OtherKeyKind', 'id': 1234}] {'property': 'value'}>\n >>> entity.key() # This returns the key.\n <Key[{'kind': 'OtherKeyKind', 'id': 1234}]>\n \"\"\"\n\n if key is not None:\n self._key = key\n return self\n else:\n return self._key","label":0},{"cid":"gcloud/datastore/entity.py::Entity.kind","text":"gcloud/datastore/entity.py::Entity.kind\n def kind(self):\n \"\"\"Get the kind of the current entity.\n\n .. note::\n This relies entirely on\n the :class:`gcloud.datastore.key.Key`\n set on the entity.\n That means that we're not storing the kind of the entity at all,\n just the properties and a pointer to a Key\n which knows its Kind.\n \"\"\"\n\n if self._key:\n return self._key.kind()","label":0},{"cid":"gcloud/datastore/entity.py::Entity.from_key","text":"gcloud/datastore/entity.py::Entity.from_key\n @classmethod\n def from_key(cls, key):\n \"\"\"Create entity based on :class:`.datastore.key.Key`.\n\n .. note::\n This is a factory method.\n\n :type key: :class:`gcloud.datastore.key.Key`\n :param key: The key for the entity.\n\n :returns: The :class:`Entity` derived from the\n :class:`gcloud.datastore.key.Key`.\n \"\"\"\n\n return cls().key(key)","label":1},{"cid":"gcloud/datastore/entity.py::Entity.from_protobuf","text":"gcloud/datastore/entity.py::Entity.from_protobuf\n @classmethod\n def from_protobuf(cls, pb, dataset=None):\n \"\"\"Factory method for creating an entity based on a protobuf.\n\n The protobuf should be one returned from the Cloud Datastore\n Protobuf API.\n\n :type pb: :class:`gcloud.datastore.datastore_v1_pb2.Entity`\n :param pb: The Protobuf representing the entity.\n\n :returns: The :class:`Entity` derived from the\n :class:`gcloud.datastore.datastore_v1_pb2.Entity`.\n \"\"\"\n\n # This is here to avoid circular imports.\n from gcloud.datastore import _helpers\n\n key = Key.from_protobuf(pb.key, dataset=dataset)\n entity = cls.from_key(key)\n\n for property_pb in pb.property:\n value = _helpers._get_value_from_property_pb(property_pb)\n entity[property_pb.name] = value\n\n return entity","label":1},{"cid":"gcloud/datastore/entity.py::Entity._must_key","text":"gcloud/datastore/entity.py::Entity._must_key\n @property\n def _must_key(self):\n \"\"\"Return our key, or raise NoKey if not set.\n\n :rtype: :class:`gcloud.datastore.key.Key`.\n :returns: our key\n :raises: NoKey if key is None\n \"\"\"\n if self._key is None:\n raise NoKey('no key')\n return self._key","label":1},{"cid":"gcloud/datastore/entity.py::Entity.reload","text":"gcloud/datastore/entity.py::Entity.reload\n def reload(self):\n \"\"\"Reloads the contents of this entity from the datastore.\n\n This method takes the :class:`gcloud.datastore.key.Key`, loads all\n properties from the Cloud Datastore, and sets the updated properties on\n the current object.\n\n .. warning::\n This will override any existing properties if a different value\n exists remotely, however it will *not* override any properties that\n exist only locally.\n \"\"\"\n key = self._must_key\n entity = key.dataset().get_entity(key.to_protobuf())\n\n if entity:\n self.update(entity)\n return self","label":1},{"cid":"gcloud/datastore/entity.py::Entity.save","text":"gcloud/datastore/entity.py::Entity.save\n def save(self):\n \"\"\"Save the entity in the Cloud Datastore.\n\n .. note::\n Any existing properties for the entity will be replaced by those\n currently set on this instance. Already-stored properties which do\n not correspond to keys set on this instance will be removed from\n the datastore.\n\n .. note::\n Property values which are \"text\" ('unicode' in Python2, 'str' in\n Python3) map to 'string_value' in the datastore; values which are\n \"bytes\" ('str' in Python2, 'bytes' in Python3) map to 'blob_value'.\n\n :rtype: :class:`gcloud.datastore.entity.Entity`\n :returns: The entity with a possibly updated Key.\n \"\"\"\n key = self._must_key\n dataset = key.dataset()\n connection = dataset.connection()\n key_pb = connection.save_entity(\n dataset_id=dataset.id(),\n key_pb=key.to_protobuf(),\n properties=dict(self))\n\n # If we are in a transaction and the current entity needs an\n # automatically assigned ID, tell the transaction where to put that.\n transaction = connection.transaction()\n if transaction and key.is_partial():\n transaction.add_auto_id_entity(self)\n\n if isinstance(key_pb, datastore_pb.Key):\n updated_key = Key.from_protobuf(key_pb)\n # Update the path (which may have been altered).\n self._key = key.path(updated_key.path())\n\n return self","label":1},{"cid":"gcloud/datastore/entity.py::Entity.delete","text":"gcloud/datastore/entity.py::Entity.delete\n def delete(self):\n \"\"\"Delete the entity in the Cloud Datastore.\n\n .. note::\n This is based entirely off of the :class:`gcloud.datastore.key.Key`\n set on the entity. Whatever is stored remotely using the key on the\n entity will be deleted.\n \"\"\"\n key = self._must_key\n dataset = key.dataset()\n dataset.connection().delete_entities(\n dataset_id=dataset.id(),\n key_pbs=[key.to_protobuf()],\n )","label":1},{"cid":"gcloud/datastore/entity.py::Entity.__repr__","text":"gcloud/datastore/entity.py::Entity.__repr__\n def __repr__(self):\n if self._key:\n return '<Entity%s %s>' % (self._key.path(),\n super(Entity, self).__repr__())\n else:\n return '<Entity %s>' % (super(Entity, self).__repr__())","label":0},{"cid":"gcloud/datastore/key.py::Key.__init__","text":"gcloud/datastore/key.py::Key.__init__\n def __init__(self, dataset=None, namespace=None, path=None):\n \"\"\"Constructor / initializer for a key.\n\n :type dataset: :class:`gcloud.datastore.dataset.Dataset`\n :param dataset: A dataset instance for the key.\n\n :type namespace: :class:`str`\n :param namespace: A namespace identifier for the key.\n\n :type path: sequence of dicts\n :param path: Each dict must have keys 'kind' (a string) and optionally\n 'name' (a string) or 'id' (an integer).\n \"\"\"\n self._dataset = dataset\n self._namespace = namespace\n self._path = path or [{'kind': ''}]","label":1},{"cid":"gcloud/datastore/key.py::Key._clone","text":"gcloud/datastore/key.py::Key._clone\n def _clone(self):\n \"\"\"Duplicates the Key.\n\n We make a shallow copy of the :class:`gcloud.datastore.dataset.Dataset`\n because it holds a reference an authenticated connection,\n which we don't want to lose.\n\n :rtype: :class:`gcloud.datastore.key.Key`\n :returns: a new `Key` instance\n \"\"\"\n clone = copy.deepcopy(self)\n clone._dataset = self._dataset # Make a shallow copy of the Dataset.\n return clone","label":1},{"cid":"gcloud/datastore/key.py::Key.from_protobuf","text":"gcloud/datastore/key.py::Key.from_protobuf\n @classmethod\n def from_protobuf(cls, pb, dataset=None):\n \"\"\"Factory method for creating a key based on a protobuf.\n\n The protobuf should be one returned from the Cloud Datastore\n Protobuf API.\n\n :type pb: :class:`gcloud.datastore.datastore_v1_pb2.Key`\n :param pb: The Protobuf representing the key.\n\n :type dataset: :class:`gcloud.datastore.dataset.Dataset`\n :param dataset: A dataset instance. If not passed, defaults to an\n instance whose ID is derived from pb.\n\n :rtype: :class:`gcloud.datastore.key.Key`\n :returns: a new `Key` instance\n \"\"\"\n path = []\n for element in pb.path_element:\n element_dict = {'kind': element.kind}\n\n if element.HasField('id'):\n element_dict['id'] = element.id\n\n # This is safe: we expect proto objects returned will only have\n # one of `name` or `id` set.\n if element.HasField('name'):\n element_dict['name'] = element.name\n\n path.append(element_dict)\n\n if not dataset:\n dataset = Dataset(id=pb.partition_id.dataset_id)\n namespace = pb.partition_id.namespace\n else:\n namespace = None\n\n return cls(dataset, namespace, path)","label":1},{"cid":"gcloud/datastore/key.py::Key.to_protobuf","text":"gcloud/datastore/key.py::Key.to_protobuf\n def to_protobuf(self):\n \"\"\"Return a protobuf corresponding to the key.\n\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.Key`\n :returns: The Protobuf representing the key.\n \"\"\"\n key = datastore_pb.Key()\n\n # Technically a dataset is required to do anything with the key,\n # but we shouldn't throw a cryptic error if one isn't provided\n # in the initializer.\n if self.dataset():\n # Apparently 's~' is a prefix for High-Replication and is necessary\n # here. Another valid preflix is 'e~' indicating EU datacenters.\n dataset_id = self.dataset().id()\n if dataset_id:\n if dataset_id[:2] not in ['s~', 'e~']:\n dataset_id = 's~' + dataset_id\n\n key.partition_id.dataset_id = dataset_id\n\n if self._namespace:\n key.partition_id.namespace = self._namespace\n\n for item in self.path():\n element = key.path_element.add()\n if 'kind' in item:\n element.kind = item['kind']\n if 'id' in item:\n element.id = item['id']\n if 'name' in item:\n element.name = item['name']\n\n return key","label":1},{"cid":"gcloud/datastore/key.py::Key.from_path","text":"gcloud/datastore/key.py::Key.from_path\n @classmethod\n def from_path(cls, *args, **kwargs):\n \"\"\"Factory method for creating a key based on a path.\n\n :type args: :class:`tuple`\n :param args: sequence of even length, where the first of each pair is a\n string representing the 'kind' of the path element, and\n the second of the pair is either a string (for the path\n element's name) or an integer (for its id).\n\n :type kwargs: :class:`dict`\n :param kwargs: Other named parameters which can be passed to\n :func:`Key.__init__`.\n\n :rtype: :class:`gcloud.datastore.key.Key`\n :returns: a new :class:`Key` instance\n \"\"\"\n if len(args) % 2:\n raise ValueError('Must pass an even number of args.')\n\n path = []\n items = iter(args)\n\n for kind, id_or_name in izip(items, items):\n entry = {'kind': kind}\n if isinstance(id_or_name, basestring):\n entry['name'] = id_or_name\n else:\n entry['id'] = id_or_name\n path.append(entry)\n\n kwargs['path'] = path\n return cls(**kwargs)","label":0},{"cid":"gcloud/datastore/key.py::Key.is_partial","text":"gcloud/datastore/key.py::Key.is_partial\n def is_partial(self):\n \"\"\"Boolean test: is the key fully mapped onto a backend entity?\n\n :rtype: :class:`bool`\n :returns: True if the last element of the key's path does not have\n an 'id' or a 'name'.\n \"\"\"\n return self.id_or_name() is None","label":0},{"cid":"gcloud/datastore/key.py::Key.dataset","text":"gcloud/datastore/key.py::Key.dataset\n def dataset(self, dataset=None):\n \"\"\"Dataset setter / getter.\n\n :type dataset: :class:`gcloud.datastore.dataset.Dataset`\n :param dataset: A dataset instance for the key.\n\n :rtype: :class:`Key` (for setter); or\n :class:`gcloud.datastore.dataset.Dataset` (for getter)\n :returns: a new key, cloned from self., with the given dataset\n (setter); or self's dataset (getter).\n \"\"\"\n if dataset:\n clone = self._clone()\n clone._dataset = dataset\n return clone\n else:\n return self._dataset","label":1},{"cid":"gcloud/datastore/key.py::Key.namespace","text":"gcloud/datastore/key.py::Key.namespace\n def namespace(self, namespace=None):\n \"\"\"Namespace setter / getter.\n\n :type namespace: :class:`str`\n :param namespace: A namespace identifier for the key.\n\n :rtype: :class:`Key` (for setter); or :class:`str` (for getter)\n :returns: a new key, cloned from self., with the given namespace\n (setter); or self's namespace (getter).\n \"\"\"\n if namespace:\n clone = self._clone()\n clone._namespace = namespace\n return clone\n else:\n return self._namespace","label":0},{"cid":"gcloud/datastore/key.py::Key.path","text":"gcloud/datastore/key.py::Key.path\n def path(self, path=None):\n \"\"\"Path setter / getter.\n\n :type path: sequence of dicts\n :param path: Each dict must have keys 'kind' (a string) and optionally\n 'name' (a string) or 'id' (an integer).\n\n :rtype: :class:`Key` (for setter); or :class:`str` (for getter)\n :returns: a new key, cloned from self., with the given path (setter);\n or self's path (getter).\n \"\"\"\n if path:\n clone = self._clone()\n clone._path = path\n return clone\n else:\n return self._path","label":0},{"cid":"gcloud/datastore/key.py::Key.kind","text":"gcloud/datastore/key.py::Key.kind\n def kind(self, kind=None):\n \"\"\"Kind setter / getter. Based on the last element of path.\n\n :type kind: :class:`str`\n :param kind: The new kind for the key.\n\n :rtype: :class:`Key` (for setter); or :class:`str` (for getter)\n :returns: a new key, cloned from self., with the given kind (setter);\n or self's kind (getter).\n \"\"\"\n if kind:\n clone = self._clone()\n clone._path[-1]['kind'] = kind\n return clone\n elif self.path():\n return self._path[-1]['kind']","label":0},{"cid":"gcloud/datastore/key.py::Key.id","text":"gcloud/datastore/key.py::Key.id\n def id(self, id_to_set=None):\n \"\"\"ID setter / getter. Based on the last element of path.\n\n :type id_to_set: :class:`int`\n :param id_to_set: The new ID for the key.\n\n :rtype: :class:`Key` (for setter); or :class:`int` (for getter)\n :returns: a new key, cloned from self., with the given id (setter);\n or self's id (getter).\n \"\"\"\n if id_to_set:\n clone = self._clone()\n clone._path[-1]['id'] = id_to_set\n return clone\n elif self.path():\n return self._path[-1].get('id')","label":0},{"cid":"gcloud/datastore/key.py::Key.name","text":"gcloud/datastore/key.py::Key.name\n def name(self, name=None):\n \"\"\"Name setter / getter. Based on the last element of path.\n\n :type kind: :class:`str`\n :param kind: The new name for the key.\n\n :rtype: :class:`Key` (for setter); or :class:`str` (for getter)\n :returns: a new key, cloned from self., with the given name (setter);\n or self's name (getter).\n \"\"\"\n if name:\n clone = self._clone()\n clone._path[-1]['name'] = name\n return clone\n elif self.path():\n return self._path[-1].get('name')","label":0},{"cid":"gcloud/datastore/key.py::Key.id_or_name","text":"gcloud/datastore/key.py::Key.id_or_name\n def id_or_name(self):\n \"\"\"Getter. Based on the last element of path.\n\n :rtype: :class:`int` (if 'id' is set); or :class:`str` (the 'name')\n :returns: True if the last element of the key's path has either an 'id'\n or a 'name'.\n \"\"\"\n return self.id() or self.name()","label":0},{"cid":"gcloud/datastore/key.py::Key.parent","text":"gcloud/datastore/key.py::Key.parent\n def parent(self):\n \"\"\"Getter: return a new key for the next highest element in path.\n\n :rtype: :class:`gcloud.datastore.key.Key`\n :returns: a new `Key` instance, whose path consists of all but the last\n element of self's path. If self has only one path element,\n return None.\n \"\"\"\n if len(self._path) <= 1:\n return None\n return self.path(self.path()[:-1])","label":0},{"cid":"gcloud/datastore/key.py::Key.__repr__","text":"gcloud/datastore/key.py::Key.__repr__\n def __repr__(self):\n return '<Key%s>' % self.path()","label":0},{"cid":"gcloud/datastore/query.py::Query.__init__","text":"gcloud/datastore/query.py::Query.__init__\n def __init__(self, kind=None, dataset=None, namespace=None):\n self._dataset = dataset\n self._namespace = namespace\n self._pb = datastore_pb.Query()\n self._cursor = None\n\n if kind:\n self._pb.kind.add().name = kind","label":0},{"cid":"gcloud/datastore/query.py::Query._clone","text":"gcloud/datastore/query.py::Query._clone\n def _clone(self):\n \"\"\"Create a new Query, copying self.\n\n :rtype: :class:`gcloud.datastore.query.Query`\n :returns: a copy of 'self'.\n \"\"\"\n clone = self.__class__(dataset=self._dataset,\n namespace=self._namespace)\n clone._pb.CopyFrom(self._pb)\n clone._cursor = self._cursor\n return clone","label":0},{"cid":"gcloud/datastore/query.py::Query.namespace","text":"gcloud/datastore/query.py::Query.namespace\n def namespace(self):\n \"\"\"This query's namespace\n\n :rtype: string or None\n :returns: the namespace assigned to this query\n \"\"\"\n return self._namespace","label":0},{"cid":"gcloud/datastore/query.py::Query.to_protobuf","text":"gcloud/datastore/query.py::Query.to_protobuf\n def to_protobuf(self):\n \"\"\"Convert :class:`Query` instance to :class:`.datastore_v1_pb2.Query`.\n\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.Query`\n :returns: A Query protobuf that can be sent to the protobuf API.\n \"\"\"\n return self._pb","label":0},{"cid":"gcloud/datastore/query.py::Query.filter","text":"gcloud/datastore/query.py::Query.filter\n def filter(self, expression, value):\n \"\"\"Filter the query based on an expression and a value.\n\n This will return a clone of the current :class:`Query`\n filtered by the expression and value provided.\n\n Expressions take the form of::\n\n .filter('<property> <operator>', <value>)\n\n where property is a property stored on the entity in the datastore\n and operator is one of ``OPERATORS``\n (ie, ``=``, ``<``, ``<=``, ``>``, ``>=``)::\n\n >>> query = Query('Person')\n >>> filtered_query = query.filter('name =', 'James')\n >>> filtered_query = query.filter('age >', 50)\n\n Because each call to ``.filter()`` returns a cloned ``Query`` object\n we are able to string these together::\n\n >>> query = Query('Person').filter(\n ... 'name =', 'James').filter('age >', 50)\n\n :type expression: string\n :param expression: An expression of a property and an\n operator (ie, ``=``).\n\n :type value: integer, string, boolean, float, None, datetime\n :param value: The value to filter on.\n\n :rtype: :class:`Query`\n :returns: A Query filtered by the expression and value provided.\n \"\"\"\n clone = self._clone()\n\n # Take an expression like 'property >=', and parse it into\n # useful pieces.\n property_name, operator = None, None\n expression = expression.strip()\n\n for operator_string in self.OPERATORS:\n if expression.endswith(operator_string):\n operator = self.OPERATORS[operator_string]\n property_name = expression[0:-len(operator_string)].strip()\n\n if not operator or not property_name:\n raise ValueError('Invalid expression: \"%s\"' % expression)\n\n # Build a composite filter AND'd together.\n composite_filter = clone._pb.filter.composite_filter\n composite_filter.opera","label":0},{"cid":"gcloud/datastore/query.py::Query.ancestor","text":"gcloud/datastore/query.py::Query.ancestor\n def ancestor(self, ancestor):\n \"\"\"Filter the query based on an ancestor.\n\n This will return a clone of the current :class:`Query`\n filtered by the ancestor provided.\n\n For example::\n\n >>> parent_key = Key.from_path('Person', '1')\n >>> query = dataset.query('Person')\n >>> filtered_query = query.ancestor(parent_key)\n\n If you don't have a :class:`gcloud.datastore.key.Key` but just\n know the path, you can provide that as well::\n\n >>> query = dataset.query('Person')\n >>> filtered_query = query.ancestor(['Person', '1'])\n\n Each call to ``.ancestor()`` returns a cloned :class:`Query`,\n however a query may only have one ancestor at a time.\n\n :type ancestor: :class:`gcloud.datastore.key.Key` or list\n :param ancestor: Either a Key or a path of the form\n ``['Kind', 'id or name', 'Kind', 'id or name', ...]``.\n\n :rtype: :class:`Query`\n :returns: A Query filtered by the ancestor provided.\n \"\"\"\n\n clone = self._clone()\n\n # If an ancestor filter already exists, remove it.\n for i, filter in enumerate(clone._pb.filter.composite_filter.filter):\n property_filter = filter.property_filter\n if (property_filter.operator ==\n datastore_pb.PropertyFilter.HAS_ANCESTOR):\n del clone._pb.filter.composite_filter.filter[i]\n\n # If we just deleted the last item, make sure to clear out the\n # filter property all together.\n if not clone._pb.filter.composite_filter.filter:\n clone._pb.ClearField('filter')\n\n # If the ancestor is None, just return (we already removed the filter).\n if not ancestor:\n return clone\n\n # If a list was provided, turn it into a Key.\n if isinstance(ancestor, list):\n ancestor = Key.from_path(*an","label":0},{"cid":"gcloud/datastore/query.py::Query.kind","text":"gcloud/datastore/query.py::Query.kind\n def kind(self, *kinds):\n \"\"\"Get or set the Kind of the Query.\n\n .. note::\n This is an **additive** operation.\n That is, if the Query is set for kinds A and B,\n and you call ``.kind('C')``,\n it will query for kinds A, B, *and*, C.\n\n :type kinds: string\n :param kinds: The entity kinds for which to query.\n\n :rtype: string or :class:`Query`\n :returns: If no arguments, returns the kind.\n If a kind is provided, returns a clone of the :class:`Query`\n with those kinds set.\n \"\"\"\n if kinds:\n clone = self._clone()\n for kind in kinds:\n clone._pb.kind.add().name = kind\n return clone\n else:\n return self._pb.kind","label":0},{"cid":"gcloud/datastore/query.py::Query.limit","text":"gcloud/datastore/query.py::Query.limit\n def limit(self, limit=None):\n \"\"\"Get or set the limit of the Query.\n\n This is the maximum number of rows (Entities) to return for this Query.\n\n This is a hybrid getter / setter, used as::\n\n >>> query = Query('Person')\n >>> query = query.limit(100) # Set the limit to 100 rows.\n >>> query.limit() # Get the limit for this query.\n 100\n\n :rtype: integer, None, or :class:`Query`\n :returns: If no arguments, returns the current limit.\n If a limit is provided, returns a clone of the :class:`Query`\n with that limit set.\n \"\"\"\n if limit:\n clone = self._clone()\n clone._pb.limit = limit\n return clone\n else:\n return self._pb.limit","label":0},{"cid":"gcloud/datastore/query.py::Query.dataset","text":"gcloud/datastore/query.py::Query.dataset\n def dataset(self, dataset=None):\n \"\"\"Get or set the :class:`.datastore.dataset.Dataset` for this Query.\n\n This is the dataset against which the Query will be run.\n\n This is a hybrid getter / setter, used as::\n\n >>> query = Query('Person')\n >>> query = query.dataset(my_dataset) # Set the dataset.\n >>> query.dataset() # Get the current dataset.\n <Dataset object>\n\n :rtype: :class:`gcloud.datastore.dataset.Dataset`, None,\n or :class:`Query`\n :returns: If no arguments, returns the current dataset.\n If a dataset is provided, returns a clone of the\n :class:`Query` with that dataset set.\n \"\"\"\n if dataset:\n clone = self._clone()\n clone._dataset = dataset\n return clone\n else:\n return self._dataset","label":0},{"cid":"gcloud/datastore/query.py::Query.fetch","text":"gcloud/datastore/query.py::Query.fetch\n def fetch(self, limit=None):\n \"\"\"Executes the Query and returns all matching entities.\n\n This makes an API call to the Cloud Datastore, sends the Query as a\n protobuf, parses the responses to Entity protobufs, and then converts\n them to :class:`gcloud.datastore.entity.Entity` objects.\n\n For example::\n\n >>> from gcloud import datastore\n >>> dataset = datastore.get_dataset('dataset-id', email, key_path)\n >>> query = dataset.query('Person').filter('name =', 'Sally')\n >>> query.fetch()\n [<Entity object>, <Entity object>, ...]\n >>> query.fetch(1)\n [<Entity object>]\n >>> query.limit()\n None\n\n :type limit: integer\n :param limit: An optional limit to apply temporarily to this query.\n That is, the Query itself won't be altered,\n but the limit will be applied to the query\n before it is executed.\n\n :rtype: list of :class:`gcloud.datastore.entity.Entity`'s\n :returns: The list of entities matching this query's criteria.\n \"\"\"\n clone = self\n\n if limit:\n clone = self.limit(limit)\n\n query_results = self.dataset().connection().run_query(\n query_pb=clone.to_protobuf(),\n dataset_id=self.dataset().id(),\n namespace=self._namespace,\n )\n entity_pbs, end_cursor = query_results[:2]\n\n self._cursor = end_cursor\n return [Entity.from_protobuf(entity, dataset=self.dataset())\n for entity in entity_pbs]","label":0},{"cid":"gcloud/datastore/query.py::Query.cursor","text":"gcloud/datastore/query.py::Query.cursor\n def cursor(self):\n \"\"\"Returns cursor ID\n\n .. Caution:: Invoking this method on a query that has not yet been\n executed will raise a RuntimeError.\n\n :rtype: string\n :returns: base64-encoded cursor ID string denoting the last position\n consumed in the query's result set.\n \"\"\"\n if not self._cursor:\n raise RuntimeError('No cursor')\n return base64.b64encode(self._cursor)","label":0},{"cid":"gcloud/datastore/query.py::Query.with_cursor","text":"gcloud/datastore/query.py::Query.with_cursor\n def with_cursor(self, start_cursor, end_cursor=None):\n \"\"\"Specifies the starting / ending positions in a query's result set.\n\n :type start_cursor: bytes\n :param start_cursor: Base64-encoded cursor string specifying where to\n start reading query results.\n\n :type end_cursor: bytes\n :param end_cursor: Base64-encoded cursor string specifying where to\n stop reading query results.\n\n :rtype: :class:`Query`\n :returns: If neither cursor is passed, returns self; else, returns a\n clone of the :class:`Query`, with cursors updated.\n \"\"\"\n clone = self\n if start_cursor or end_cursor:\n clone = self._clone()\n if start_cursor:\n clone._pb.start_cursor = base64.b64decode(start_cursor)\n if end_cursor:\n clone._pb.end_cursor = base64.b64decode(end_cursor)\n return clone","label":0},{"cid":"gcloud/datastore/query.py::Query.order","text":"gcloud/datastore/query.py::Query.order\n def order(self, *properties):\n \"\"\"Adds a sort order to the query.\n\n Sort fields will be applied in the order specified.\n\n :type properties: sequence of strings\n :param properties: Each value is a string giving the name of the\n property on which to sort, optionally preceded by a\n hyphen (-) to specify descending order.\n Omitting the hyphen implies ascending order.\n\n :rtype: :class:`Query`\n :returns: A new Query instance, ordered as specified.\n \"\"\"\n clone = self._clone()\n\n for prop in properties:\n property_order = clone._pb.order.add()\n\n if prop.startswith('-'):\n property_order.property.name = prop[1:]\n property_order.direction = property_order.DESCENDING\n else:\n property_order.property.name = prop\n property_order.direction = property_order.ASCENDING\n\n return clone","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.__init__","text":"gcloud/datastore/transaction.py::Transaction.__init__\n def __init__(self, dataset):\n self._dataset = dataset\n self._id = None\n self._mutation = datastore_pb.Mutation()\n self._auto_id_entities = []","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.connection","text":"gcloud/datastore/transaction.py::Transaction.connection\n def connection(self):\n \"\"\"Getter for current connection over which the transaction will run.\n\n :rtype: :class:`gcloud.datastore.connection.Connection`\n :returns: The connection over which the transaction will run.\n \"\"\"\n\n return self.dataset().connection()","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.dataset","text":"gcloud/datastore/transaction.py::Transaction.dataset\n def dataset(self):\n \"\"\"Getter for the current dataset.\n\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\n :returns: The dataset to which the transaction belongs.\n \"\"\"\n\n return self._dataset","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.id","text":"gcloud/datastore/transaction.py::Transaction.id\n def id(self):\n \"\"\"Getter for the transaction ID.\n\n :rtype: string\n :returns: The ID of the current transaction.\n \"\"\"\n\n return self._id","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.mutation","text":"gcloud/datastore/transaction.py::Transaction.mutation\n def mutation(self):\n \"\"\"Getter for the current mutation.\n\n Every transaction is committed\n with a single Mutation\n representing the 'work' to be done as part of the transaction.\n Inside a transaction,\n calling ``save()`` on an entity\n builds up the mutation.\n This getter returns the Mutation protobuf\n that has been built-up so far.\n\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.Mutation`\n :returns: The Mutation protobuf to be sent in the commit request.\n \"\"\"\n return self._mutation","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.add_auto_id_entity","text":"gcloud/datastore/transaction.py::Transaction.add_auto_id_entity\n def add_auto_id_entity(self, entity):\n \"\"\"Adds an entity to the list of entities to update with IDs.\n\n When an entity has a partial key,\n calling ``save()`` adds an insert_auto_id entry in the mutation.\n In order to make sure we update the Entity\n once the transaction is committed,\n we need to keep track of which entities to update\n (and the order is important).\n\n When you call ``save()`` on an entity inside a transaction,\n if the entity has a partial key,\n it adds itself to the list of entities to be updated\n once the transaction is committed\n by calling this method.\n \"\"\"\n self._auto_id_entities.append(entity)","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.begin","text":"gcloud/datastore/transaction.py::Transaction.begin\n def begin(self):\n \"\"\"Begins a transaction.\n\n This method is called automatically when entering a with statement,\n however it can be called explicitly\n if you don't want to use a context manager.\n \"\"\"\n self._id = self.connection().begin_transaction(self.dataset().id())\n self.connection().transaction(self)","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.rollback","text":"gcloud/datastore/transaction.py::Transaction.rollback\n def rollback(self):\n \"\"\"Rolls back the current transaction.\n\n This method has necessary side-effects:\n\n - Sets the current connection's transaction reference to None.\n - Sets the current transaction's ID to None.\n \"\"\"\n self.connection().rollback_transaction(self.dataset().id())\n self.connection().transaction(None)\n self._id = None","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.commit","text":"gcloud/datastore/transaction.py::Transaction.commit\n def commit(self):\n \"\"\"Commits the transaction.\n\n This is called automatically upon exiting a with statement,\n however it can be called explicitly\n if you don't want to use a context manager.\n\n This method has necessary side-effects:\n\n - Sets the current connection's transaction reference to None.\n - Sets the current transaction's ID to None.\n - Updates paths for any keys that needed an automatically generated ID.\n \"\"\"\n # It's possible that they called commit() already, in which case\n # we shouldn't do any committing of our own.\n if self.connection().transaction():\n result = self.connection().commit(self.dataset().id(),\n self.mutation())\n\n # For any of the auto-id entities, make sure we update their keys.\n for i, entity in enumerate(self._auto_id_entities):\n key_pb = result.insert_auto_id_key[i]\n key = Key.from_protobuf(key_pb)\n entity.key(entity.key().path(key.path()))\n\n # Tell the connection that the transaction is over.\n self.connection().transaction(None)\n\n # Clear our own ID in case this gets accidentally reused.\n self._id = None","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.__enter__","text":"gcloud/datastore/transaction.py::Transaction.__enter__\n def __enter__(self):\n self.begin()\n return self","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.__exit__","text":"gcloud/datastore/transaction.py::Transaction.__exit__\n def __exit__(self, exc_type, exc_val, exc_tb):\n if exc_type is None:\n self.commit()\n else:\n self.rollback()","label":0},{"cid":"gcloud/storage/connection.py::_utcnow","text":"gcloud/storage/connection.py::_utcnow\ndef _utcnow(): # pragma: NO COVER testing replaces\n \"\"\"Returns current time as UTC datetime.\n\n NOTE: on the module namespace so tests can replace it.\n \"\"\"\n return datetime.datetime.utcnow()","label":0},{"cid":"gcloud/storage/connection.py::Connection.__init__","text":"gcloud/storage/connection.py::Connection.__init__\n def __init__(self, project, *args, **kwargs):\n \"\"\":type project: string\n :param project: The project name to connect to.\n\n \"\"\"\n\n super(Connection, self).__init__(*args, **kwargs)\n\n self.project = project","label":0},{"cid":"gcloud/storage/connection.py::Connection.__iter__","text":"gcloud/storage/connection.py::Connection.__iter__\n def __iter__(self):\n return iter(BucketIterator(connection=self))","label":0},{"cid":"gcloud/storage/connection.py::Connection.__contains__","text":"gcloud/storage/connection.py::Connection.__contains__\n def __contains__(self, bucket_name):\n return self.lookup(bucket_name) is not None","label":0},{"cid":"gcloud/storage/connection.py::Connection.build_api_url","text":"gcloud/storage/connection.py::Connection.build_api_url\n def build_api_url(self, path, query_params=None, api_base_url=None,\n api_version=None):\n \"\"\"Construct an API url given a few components, some optional.\n\n Typically, you shouldn't need to use this method.\n\n :type path: string\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\n\n :type query_params: dict\n :param query_params: A dictionary of keys and values to insert into\n the query string of the URL.\n\n :type api_base_url: string\n :param api_base_url: The base URL for the API endpoint.\n Typically you won't have to provide this.\n\n :type api_version: string\n :param api_version: The version of the API to call.\n Typically you shouldn't provide this and instead\n use the default for the library.\n\n :rtype: string\n :returns: The URL assembled from the pieces provided.\n \"\"\"\n\n url = self.API_URL_TEMPLATE.format(\n api_base_url=(api_base_url or self.API_BASE_URL),\n api_version=(api_version or self.API_VERSION),\n path=path)\n\n query_params = query_params or {}\n query_params.update({'project': self.project})\n url += '?' + urllib.urlencode(query_params)\n\n return url","label":0},{"cid":"gcloud/storage/connection.py::Connection.make_request","text":"gcloud/storage/connection.py::Connection.make_request\n def make_request(self, method, url, data=None, content_type=None,\n headers=None):\n \"\"\"A low level method to send a request to the API.\n\n Typically, you shouldn't need to use this method.\n\n :type method: string\n :param method: The HTTP method to use in the request.\n\n :type url: string\n :param url: The URL to send the request to.\n\n :type data: string\n :param data: The data to send as the body of the request.\n\n :type content_type: string\n :param content_type: The proper MIME type of the data provided.\n\n :type headers: dict\n :param headers: A dictionary of HTTP headers to send with the request.\n\n :rtype: tuple of ``response`` (a dictionary of sorts)\n and ``content`` (a string).\n :returns: The HTTP response object and the content of the response.\n \"\"\"\n\n headers = headers or {}\n headers['Accept-Encoding'] = 'gzip'\n\n if data:\n content_length = len(str(data))\n else:\n content_length = 0\n\n headers['Content-Length'] = content_length\n\n if content_type:\n headers['Content-Type'] = content_type\n\n headers['User-Agent'] = self.USER_AGENT\n\n return self.http.request(uri=url, method=method, headers=headers,\n body=data)","label":0},{"cid":"gcloud/storage/connection.py::Connection.api_request","text":"gcloud/storage/connection.py::Connection.api_request\n def api_request(self, method, path, query_params=None,\n data=None, content_type=None,\n api_base_url=None, api_version=None,\n expect_json=True):\n \"\"\"Make a request over the HTTP transport to the Cloud Storage API.\n\n You shouldn't need to use this method,\n but if you plan to interact with the API using these primitives,\n this is the correct one to use...\n\n :type method: string\n :param method: The HTTP method name (ie, ``GET``, ``POST``, etc).\n Required.\n\n :type path: string\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\n Required.\n\n :type query_params: dict\n :param query_params: A dictionary of keys and values to insert into\n the query string of the URL.\n Default is empty dict.\n\n :type data: string\n :param data: The data to send as the body of the request. Default is\n the empty string.\n\n :type content_type: string\n :param content_type: The proper MIME type of the data provided. Default\n is None.\n\n :type api_base_url: string\n :param api_base_url: The base URL for the API endpoint.\n Typically you won't have to provide this.\n Default is the standard API base URL.\n\n :type api_version: string\n :param api_version: The version of the API to call.\n Typically you shouldn't provide this and instead\n use the default for the library.\n Default is the latest API version supported by\n gcloud-python.\n\n :type expect_json: bool\n :param expect_json: If True, this method will try to parse the response\n ","label":0},{"cid":"gcloud/storage/connection.py::Connection.get_all_buckets","text":"gcloud/storage/connection.py::Connection.get_all_buckets\n def get_all_buckets(self):\n \"\"\"Get all buckets in the project.\n\n This will not populate the list of keys available\n in each bucket.\n\n You can also iterate over the connection object,\n so these two operations are identical::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> for bucket in connection.get_all_buckets():\n >>> print bucket\n >>> # ... is the same as ...\n >>> for bucket in connection:\n >>> print bucket\n\n :rtype: list of :class:`gcloud.storage.bucket.Bucket` objects.\n :returns: All buckets belonging to this project.\n \"\"\"\n\n return list(self)","label":0},{"cid":"gcloud/storage/connection.py::Connection.get_bucket","text":"gcloud/storage/connection.py::Connection.get_bucket\n def get_bucket(self, bucket_name):\n \"\"\"Get a bucket by name.\n\n If the bucket isn't found,\n this will raise a :class:`gcloud.storage.exceptions.NotFoundError`.\n If you would rather get a bucket by name,\n and return ``None`` if the bucket isn't found\n (like ``{}.get('...')``)\n then use :func:`Connection.lookup`.\n\n For example::\n\n >>> from gcloud import storage\n >>> from gcloud.storage import exceptions\n >>> connection = storage.get_connection(project, email, key_path)\n >>> try:\n >>> bucket = connection.get_bucket('my-bucket')\n >>> except exceptions.NotFoundError:\n >>> print 'Sorry, that bucket does not exist!'\n\n :type bucket_name: string\n :param bucket_name: The name of the bucket to get.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The bucket matching the name provided.\n :raises: :class:`gcloud.storage.exceptions.NotFoundError`\n \"\"\"\n bucket = self.new_bucket(bucket_name)\n response = self.api_request(method='GET', path=bucket.path)\n return Bucket.from_dict(response, connection=self)","label":0},{"cid":"gcloud/storage/connection.py::Connection.lookup","text":"gcloud/storage/connection.py::Connection.lookup\n def lookup(self, bucket_name):\n \"\"\"Get a bucket by name, returning None if not found.\n\n You can use this if you would rather\n checking for a None value\n than catching an exception::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> bucket = connection.get_bucket('doesnt-exist')\n >>> print bucket\n None\n >>> bucket = connection.get_bucket('my-bucket')\n >>> print bucket\n <Bucket: my-bucket>\n\n :type bucket_name: string\n :param bucket_name: The name of the bucket to get.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The bucket matching the name provided or None if not found.\n \"\"\"\n\n try:\n return self.get_bucket(bucket_name)\n except exceptions.NotFoundError:\n return None","label":0},{"cid":"gcloud/storage/connection.py::Connection.create_bucket","text":"gcloud/storage/connection.py::Connection.create_bucket\n def create_bucket(self, bucket):\n \"\"\"Create a new bucket.\n\n For example::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, client, key_path)\n >>> bucket = connection.create_bucket('my-bucket')\n >>> print bucket\n <Bucket: my-bucket>\n\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\n :param bucket: The bucket name (or bucket object) to create.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The newly created bucket.\n \"\"\"\n\n bucket = self.new_bucket(bucket)\n response = self.api_request(method='POST', path='/b',\n data={'name': bucket.name})\n return Bucket.from_dict(response, connection=self)","label":0},{"cid":"gcloud/storage/connection.py::Connection.delete_bucket","text":"gcloud/storage/connection.py::Connection.delete_bucket\n def delete_bucket(self, bucket, force=False):\n \"\"\"Delete a bucket.\n\n You can use this method to delete a bucket by name,\n or to delete a bucket object::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> connection.delete_bucket('my-bucket')\n True\n\n You can also delete pass in the bucket object::\n\n >>> bucket = connection.get_bucket('other-bucket')\n >>> connection.delete_bucket(bucket)\n True\n\n If the bucket doesn't exist,\n this will raise a :class:`gcloud.storage.exceptions.NotFoundError`::\n\n >>> from gcloud.storage import exceptions\n >>> try:\n >>> connection.delete_bucket('my-bucket')\n >>> except exceptions.NotFoundError:\n >>> print 'That bucket does not exist!'\n\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\n :param bucket: The bucket name (or bucket object) to create.\n\n :type force: bool\n :param full: If True, empties the bucket's objects then deletes it.\n\n :rtype: bool\n :returns: True if the bucket was deleted.\n :raises: :class:`gcloud.storage.exceptions.NotFoundError`\n \"\"\"\n\n bucket = self.new_bucket(bucket)\n\n # This force delete operation is slow.\n if force:\n for key in bucket:\n key.delete()\n\n self.api_request(method='DELETE', path=bucket.path)\n return True","label":0},{"cid":"gcloud/storage/connection.py::Connection.new_bucket","text":"gcloud/storage/connection.py::Connection.new_bucket\n def new_bucket(self, bucket):\n \"\"\"Factory method for creating a new (unsaved) bucket object.\n\n This method is really useful when you're not sure whether\n you have an actual :class:`gcloud.storage.bucket.Bucket` object\n or just a name of a bucket.\n It always returns the object::\n\n >>> bucket = connection.new_bucket('bucket')\n >>> print bucket\n <Bucket: bucket>\n >>> bucket = connection.new_bucket(bucket)\n >>> print bucket\n <Bucket: bucket>\n\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\n :param bucket: A name of a bucket or an existing Bucket object.\n \"\"\"\n\n if isinstance(bucket, Bucket):\n return bucket\n\n # Support Python 2 and 3.\n try:\n string_type = basestring\n except NameError: # pragma: NO COVER PY3k\n string_type = str\n\n if isinstance(bucket, string_type):\n return Bucket(connection=self, name=bucket)\n\n raise TypeError('Invalid bucket: %s' % bucket)","label":0},{"cid":"gcloud/storage/connection.py::Connection.generate_signed_url","text":"gcloud/storage/connection.py::Connection.generate_signed_url\n def generate_signed_url(self, resource, expiration,\n method='GET', content_md5=None,\n content_type=None):\n \"\"\"Generate signed URL to provide query-string auth'n to a resource.\n\n :type resource: string\n :param resource: A pointer to a specific resource\n (typically, ``/bucket-name/path/to/key.txt``).\n\n :type expiration: int, long, datetime.datetime, datetime.timedelta\n :param expiration: When the signed URL should expire.\n\n :type method: string\n :param method: The HTTP verb that will be used when requesting the URL.\n\n :type content_md5: string\n :param content_md5: The MD5 hash of the object referenced by\n ``resource``.\n\n :type content_type: string\n :param content_type: The content type of the object referenced by\n ``resource``.\n\n :rtype: string\n :returns: A signed URL you can use to access the resource\n until expiration.\n \"\"\"\n\n expiration = _get_expiration_seconds(expiration)\n\n # Generate the string to sign.\n signature_string = '\\n'.join([\n method,\n content_md5 or '',\n content_type or '',\n str(expiration),\n resource])\n\n # Take our PKCS12 (.p12) key and make it into a RSA key we can use...\n pkcs12 = crypto.load_pkcs12(\n base64.b64decode(self.credentials.private_key),\n 'notasecret')\n pem = crypto.dump_privatekey(\n crypto.FILETYPE_PEM, pkcs12.get_privatekey())\n pem_key = RSA.importKey(pem)\n\n # Sign the string with the RSA key.\n signer = PKCS1_v1_5.new(pem_key)\n signature_hash = SHA256.new(signature_string)\n signature_bytes = signer.sign(signature_hash)\n signature = base64.b64encode(signature_byt","label":0},{"cid":"gcloud/storage/connection.py::_get_expiration_seconds","text":"gcloud/storage/connection.py::_get_expiration_seconds\ndef _get_expiration_seconds(expiration):\n \"\"\"Convert 'expiration' to a number of seconds in the future.\n\n :type expiration: int, long, datetime.datetime, datetime.timedelta\n :param expiration: When the signed URL should expire.\n\n :rtype: int\n :returns: a timestamp as an absolute number of seconds.\n \"\"\"\n\n # If it's a timedelta, add it to `now` in UTC.\n if isinstance(expiration, datetime.timedelta):\n now = _utcnow().replace(tzinfo=pytz.utc)\n expiration = now + expiration\n\n # If it's a datetime, convert to a timestamp.\n if isinstance(expiration, datetime.datetime):\n # Make sure the timezone on the value is UTC\n # (either by converting or replacing the value).\n if expiration.tzinfo:\n expiration = expiration.astimezone(pytz.utc)\n else:\n expiration = expiration.replace(tzinfo=pytz.utc)\n\n # Turn the datetime into a timestamp (seconds, not microseconds).\n expiration = int(calendar.timegm(expiration.timetuple()))\n\n if not isinstance(expiration, (int, long)):\n raise TypeError('Expected an integer timestamp, datetime, or '\n 'timedelta. Got %s' % type(expiration))\n return expiration","label":0},{"cid":"regression/datastore.py::TestDatastore.setUp","text":"regression/datastore.py::TestDatastore.setUp\n def setUp(self):\n environ = regression_utils.get_environ()\n self._dataset_id = environ['dataset_id']\n self._client_email = environ['client_email']\n self._key_filename = environ['key_filename']\n self._datasets = {}\n\n self.entities_to_delete = []","label":0},{"cid":"regression/datastore.py::TestDatastore.tearDown","text":"regression/datastore.py::TestDatastore.tearDown\n def tearDown(self):\n for entity in self.entities_to_delete:\n entity.delete()","label":0},{"cid":"regression/datastore.py::TestDatastore._get_dataset","text":"regression/datastore.py::TestDatastore._get_dataset\n def _get_dataset(self):\n if self._dataset_id not in self._datasets:\n self._datasets[self._dataset_id] = datastore.get_dataset(\n self._dataset_id, self._client_email, self._key_filename)\n return self._datasets[self._dataset_id]","label":0},{"cid":"regression/datastore.py::TestDatastore._get_post","text":"regression/datastore.py::TestDatastore._get_post\n def _get_post(self, name=None, key_id=None, post_content=None):\n post_content = post_content or {\n 'title': 'How to make the perfect pizza in your grill',\n 'tags': ['pizza', 'grill'],\n # NOTE: We don't support datetime.date, but should.\n # NOTE: Without a tz, assertEqual fails with\n # \"can't compare offset-naive and offset-aware datetimes\"\n 'publishedAt': datetime.datetime(2001, 1, 1, tzinfo=pytz.utc),\n 'author': 'Silvano',\n 'isDraft': False,\n 'wordCount': 400,\n 'rating': 5.0,\n }\n # Create an entity with the given content in our dataset.\n dataset = self._get_dataset()\n entity = dataset.entity(kind='Post')\n entity.update(post_content)\n\n # Update the entity key.\n key = None\n if name is not None:\n key = entity.key().name(name)\n if key_id is not None:\n key = entity.key().id(key_id)\n if key is not None:\n entity.key(key)\n\n return entity","label":0},{"cid":"regression/datastore.py::TestDatastore._generic_test_post","text":"regression/datastore.py::TestDatastore._generic_test_post\n def _generic_test_post(self, name=None, key_id=None):\n entity = self._get_post(name=name, key_id=key_id)\n entity.save()\n\n # Register entity to be deleted.\n self.entities_to_delete.append(entity)\n\n if name is not None:\n self.assertEqual(entity.key().name(), name)\n if key_id is not None:\n self.assertEqual(entity.key().id(), key_id)\n retrieved_entity = self._get_dataset().get_entity(entity.key())\n # Check the keys are the same.\n self.assertEqual(retrieved_entity.key().path(),\n entity.key().path())\n # Check the data is the same.\n retrieved_dict = dict(retrieved_entity.items())\n entity_dict = dict(entity.items())\n self.assertEqual(retrieved_dict, entity_dict)","label":0},{"cid":"regression/datastore.py::TestDatastore.test_post_with_name","text":"regression/datastore.py::TestDatastore.test_post_with_name\n def test_post_with_name(self):\n self._generic_test_post(name='post1')","label":0},{"cid":"regression/datastore.py::TestDatastore.test_post_with_id","text":"regression/datastore.py::TestDatastore.test_post_with_id\n def test_post_with_id(self):\n self._generic_test_post(key_id=123456789)","label":0},{"cid":"regression/datastore.py::TestDatastore.test_post_with_generated_id","text":"regression/datastore.py::TestDatastore.test_post_with_generated_id\n def test_post_with_generated_id(self):\n self._generic_test_post()","label":0},{"cid":"regression/datastore.py::TestDatastore.test_save_multiple","text":"regression/datastore.py::TestDatastore.test_save_multiple\n def test_save_multiple(self):\n dataset = self._get_dataset()\n with dataset.transaction():\n entity1 = self._get_post()\n entity1.save()\n # Register entity to be deleted.\n self.entities_to_delete.append(entity1)\n\n second_post_content = {\n 'title': 'How to make the perfect homemade pasta',\n 'tags': ['pasta', 'homemade'],\n 'publishedAt': datetime.datetime(2001, 1, 1),\n 'author': 'Silvano',\n 'isDraft': False,\n 'wordCount': 450,\n 'rating': 4.5,\n }\n entity2 = self._get_post(post_content=second_post_content)\n entity2.save()\n # Register entity to be deleted.\n self.entities_to_delete.append(entity2)\n\n keys = [entity1.key(), entity2.key()]\n matches = dataset.get_entities(keys)\n self.assertEqual(len(matches), 2)","label":0},{"cid":"regression/datastore.py::TestDatastore.test_empty_kind","text":"regression/datastore.py::TestDatastore.test_empty_kind\n def test_empty_kind(self):\n posts = self._get_dataset().query().kind('Post').limit(2).fetch()\n self.assertEqual(posts, [])","label":0}],"string":"[\n {\n \"cid\": \"gcloud/connection.py::Connection.__init__\",\n \"text\": \"gcloud/connection.py::Connection.__init__\\n def __init__(self, credentials=None):\\n \\\"\\\"\\\"\\n :type credentials: :class:`oauth2client.client.OAuth2Credentials`\\n :param credentials: The OAuth2 Credentials to use for this connection.\\n \\\"\\\"\\\"\\n self._http = None\\n self._credentials = credentials\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/connection.py::Connection.credentials\",\n \"text\": \"gcloud/connection.py::Connection.credentials\\n @property\\n def credentials(self):\\n \\\"\\\"\\\"\\n :rtype: :class:`oauth2client.client.OAuth2Credentials`, or None\\n :returns: The credentials object associated with this connection.\\n \\\"\\\"\\\"\\n return self._credentials\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/connection.py::Connection.http\",\n \"text\": \"gcloud/connection.py::Connection.http\\n @property\\n def http(self):\\n \\\"\\\"\\\"A getter for the HTTP transport used in talking to the API.\\n\\n :rtype: :class:`httplib2.Http`\\n :returns: A Http object used to transport data.\\n \\\"\\\"\\\"\\n if self._http is None:\\n self._http = httplib2.Http()\\n if self._credentials:\\n self._http = self._credentials.authorize(self._http)\\n return self._http\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/__init__.py::get_connection\",\n \"text\": \"gcloud/datastore/__init__.py::get_connection\\ndef get_connection(client_email, private_key_path):\\n \\\"\\\"\\\"Shortcut method to establish a connection to the Cloud Datastore.\\n\\n Use this if you are going to access several datasets\\n with the same set of credentials (unlikely):\\n\\n >>> from gcloud import datastore\\n >>> connection = datastore.get_connection(email, key_path)\\n >>> dataset1 = connection.dataset('dataset1')\\n >>> dataset2 = connection.dataset('dataset2')\\n\\n :type client_email: string\\n :param client_email: The e-mail attached to the service account.\\n\\n :type private_key_path: string\\n :param private_key_path: The path to a private key file (this file was\\n given to you when you created the service\\n account).\\n\\n :rtype: :class:`gcloud.datastore.connection.Connection`\\n :returns: A connection defined with the proper credentials.\\n \\\"\\\"\\\"\\n from gcloud import credentials\\n from gcloud.datastore.connection import Connection\\n\\n svc_account_credentials = credentials.get_for_service_account(\\n client_email, private_key_path, scope=SCOPE)\\n return Connection(credentials=svc_account_credentials)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/__init__.py::get_dataset\",\n \"text\": \"gcloud/datastore/__init__.py::get_dataset\\ndef get_dataset(dataset_id, client_email, private_key_path):\\n \\\"\\\"\\\"Establish a connection to a particular dataset in the Cloud Datastore.\\n\\n This is a shortcut method for creating a connection and using it\\n to connect to a dataset.\\n\\n You'll generally use this as the first call to working with the API:\\n\\n >>> from gcloud import datastore\\n >>> dataset = datastore.get_dataset('dataset-id', email, key_path)\\n >>> # Now you can do things with the dataset.\\n >>> dataset.query().kind('TestKind').fetch()\\n [...]\\n\\n :type dataset_id: string\\n :param dataset_id: The id of the dataset you want to use.\\n This is akin to a database name\\n and is usually the same as your Cloud Datastore project\\n name.\\n\\n :type client_email: string\\n :param client_email: The e-mail attached to the service account.\\n\\n :type private_key_path: string\\n :param private_key_path: The path to a private key file (this file was\\n given to you when you created the service\\n account).\\n\\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\\n :returns: A dataset with a connection using the provided credentials.\\n \\\"\\\"\\\"\\n connection = get_connection(client_email, private_key_path)\\n return connection.dataset(dataset_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.__init__\",\n \"text\": \"gcloud/datastore/connection.py::Connection.__init__\\n def __init__(self, credentials=None):\\n super(Connection, self).__init__(credentials=credentials)\\n self._current_transaction = None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection._request\",\n \"text\": \"gcloud/datastore/connection.py::Connection._request\\n def _request(self, dataset_id, method, data):\\n \\\"\\\"\\\"Make a request over the Http transport to the Cloud Datastore API.\\n\\n :type dataset_id: string\\n :param dataset_id: The ID of the dataset of which to make the request.\\n\\n :type method: string\\n :param method: The API call method name (ie, ``runQuery``,\\n ``lookup``, etc)\\n\\n :type data: string\\n :param data: The data to send with the API call.\\n Typically this is a serialized Protobuf string.\\n\\n :rtype: string\\n :returns: The string response content from the API call.\\n\\n :raises: Exception if the response code is not 200 OK.\\n \\\"\\\"\\\"\\n headers = {\\n 'Content-Type': 'application/x-protobuf',\\n 'Content-Length': str(len(data)),\\n 'User-Agent': self.USER_AGENT,\\n }\\n headers, content = self.http.request(\\n uri=self.build_api_url(dataset_id=dataset_id, method=method),\\n method='POST', headers=headers, body=data)\\n\\n if headers['status'] != '200':\\n raise Exception('Request failed. Error was: %s' % content)\\n\\n return content\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection._rpc\",\n \"text\": \"gcloud/datastore/connection.py::Connection._rpc\\n def _rpc(self, dataset_id, method, request_pb, response_pb_cls):\\n \\\"\\\"\\\" Make a protobuf RPC request.\\n\\n :type dataset_id: string\\n :param dataset_id: The ID of the dataset to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type method: string\\n :param method: The name of the method to invoke.\\n\\n :type request_pb: :class:`google.protobuf.message.Message` instance\\n :param method: the protobuf instance representing the request.\\n\\n :type response_pb_cls: a :class:`google.protobuf.message.Message'\\n subclass.\\n :param method: The class used to unmarshall the response protobuf.\\n \\\"\\\"\\\"\\n response = self._request(dataset_id=dataset_id, method=method,\\n data=request_pb.SerializeToString())\\n return response_pb_cls.FromString(response)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.build_api_url\",\n \"text\": \"gcloud/datastore/connection.py::Connection.build_api_url\\n @classmethod\\n def build_api_url(cls, dataset_id, method, base_url=None,\\n api_version=None):\\n \\\"\\\"\\\"Construct the URL for a particular API call.\\n\\n This method is used internally\\n to come up with the URL\\n to use when making RPCs\\n to the Cloud Datastore API.\\n\\n :type dataset_id: string\\n :param dataset_id: The ID of the dataset to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type method: string\\n :param method: The API method to call (ie, runQuery, lookup, ...).\\n\\n :type base_url: string\\n :param base_url: The base URL where the API lives.\\n You shouldn't have to provide this.\\n\\n :type api_version: string\\n :param api_version: The version of the API to connect to.\\n You shouldn't have to provide this.\\n \\\"\\\"\\\"\\n return cls.API_URL_TEMPLATE.format(\\n api_base=(base_url or cls.API_BASE_URL),\\n api_version=(api_version or cls.API_VERSION),\\n dataset_id=dataset_id, method=method)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.transaction\",\n \"text\": \"gcloud/datastore/connection.py::Connection.transaction\\n def transaction(self, transaction=connection.Connection._EMPTY):\\n \\\"\\\"\\\"Getter/setter for the connection's transaction object.\\n\\n :type transaction: :class:`gcloud.datastore.transaction.Transaction`,\\n (setting), or omitted (getting).\\n :param transaction: The new transaction (if passed).\\n\\n :rtype: :class:`gcloud.datastore.transaction.Transaction`, (getting)\\n or :class:`gcloud.datastore.connection.Connection` (setting)\\n :returns: the current transaction (getting) or self (setting).\\n \\\"\\\"\\\"\\n if transaction is self._EMPTY:\\n return self._current_transaction\\n else:\\n self._current_transaction = transaction\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.mutation\",\n \"text\": \"gcloud/datastore/connection.py::Connection.mutation\\n def mutation(self):\\n \\\"\\\"\\\"Getter for mutation usable with current connection.\\n\\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.Mutation`.\\n :returns: the mutation instance associated with the current transaction\\n (if one exists) or or a new mutation instance.\\n \\\"\\\"\\\"\\n if self.transaction():\\n return self.transaction().mutation()\\n else:\\n return datastore_pb.Mutation()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.dataset\",\n \"text\": \"gcloud/datastore/connection.py::Connection.dataset\\n def dataset(self, *args, **kwargs):\\n \\\"\\\"\\\"Factory method for Dataset objects.\\n\\n :param args: All args and kwargs will be passed along to the\\n :class:`gcloud.datastore.dataset.Dataset` initializer.\\n\\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\\n :returns: A dataset object that will use this connection as\\n its transport.\\n \\\"\\\"\\\"\\n kwargs['connection'] = self\\n return Dataset(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.begin_transaction\",\n \"text\": \"gcloud/datastore/connection.py::Connection.begin_transaction\\n def begin_transaction(self, dataset_id, serializable=False):\\n \\\"\\\"\\\"Begin a transaction.\\n\\n :type dataset_id: string\\n :param dataset_id: The dataset over which to execute the transaction.\\n \\\"\\\"\\\"\\n\\n if self.transaction():\\n raise ValueError('Cannot start a transaction with another already '\\n 'in progress.')\\n\\n request = datastore_pb.BeginTransactionRequest()\\n\\n if serializable:\\n request.isolation_level = (\\n datastore_pb.BeginTransactionRequest.SERIALIZABLE)\\n else:\\n request.isolation_level = (\\n datastore_pb.BeginTransactionRequest.SNAPSHOT)\\n\\n response = self._rpc(dataset_id, 'beginTransaction', request,\\n datastore_pb.BeginTransactionResponse)\\n\\n return response.transaction\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.rollback_transaction\",\n \"text\": \"gcloud/datastore/connection.py::Connection.rollback_transaction\\n def rollback_transaction(self, dataset_id):\\n \\\"\\\"\\\"Rollback the connection's existing transaction.\\n\\n Raises a ``ValueError``\\n if the connection isn't currently in a transaction.\\n\\n :type dataset_id: string\\n :param dataset_id: The dataset to which the transaction belongs.\\n \\\"\\\"\\\"\\n if not self.transaction() or not self.transaction().id():\\n raise ValueError('No transaction to rollback.')\\n\\n request = datastore_pb.RollbackRequest()\\n request.transaction = self.transaction().id()\\n # Nothing to do with this response, so just execute the method.\\n self._rpc(dataset_id, 'rollback', request,\\n datastore_pb.RollbackResponse)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.run_query\",\n \"text\": \"gcloud/datastore/connection.py::Connection.run_query\\n def run_query(self, dataset_id, query_pb, namespace=None):\\n \\\"\\\"\\\"Run a query on the Cloud Datastore.\\n\\n Given a Query protobuf,\\n sends a ``runQuery`` request to the Cloud Datastore API\\n and returns a list of entity protobufs matching the query.\\n\\n You typically wouldn't use this method directly,\\n in favor of the :func:`gcloud.datastore.query.Query.fetch` method.\\n\\n Under the hood, the :class:`gcloud.datastore.query.Query` class\\n uses this method to fetch data:\\n\\n >>> from gcloud import datastore\\n >>> connection = datastore.get_connection(email, key_path)\\n >>> dataset = connection.dataset('dataset-id')\\n >>> query = dataset.query().kind('MyKind').filter('property =', 'val')\\n\\n Using the `fetch`` method...\\n\\n >>> query.fetch()\\n [<list of Entity Protobufs>], cursor, more_results, skipped_results\\n\\n Under the hood this is doing...\\n\\n >>> connection.run_query('dataset-id', query.to_protobuf())\\n [<list of Entity Protobufs>]\\n\\n :type dataset_id: string\\n :param dataset_id: The ID of the dataset over which to run the query.\\n\\n :type query_pb: :class:`gcloud.datastore.datastore_v1_pb2.Query`\\n :param query_pb: The Protobuf representing the query to run.\\n\\n :type namespace: string\\n :param namespace: The namespace over which to run the query.\\n \\\"\\\"\\\"\\n request = datastore_pb.RunQueryRequest()\\n\\n if namespace:\\n request.partition_id.namespace = namespace\\n\\n request.query.CopyFrom(query_pb)\\n response = self._rpc(dataset_id, 'runQuery', request,\\n datastore_pb.RunQueryResponse)\\n return (\\n [e.entity for e in response.batch.entity_result],\\n response.batch.end_cursor,\\n response.batch.more_results,\\n response.batch.skipped_results,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.lookup\",\n \"text\": \"gcloud/datastore/connection.py::Connection.lookup\\n def lookup(self, dataset_id, key_pbs):\\n \\\"\\\"\\\"Lookup keys from a dataset in the Cloud Datastore.\\n\\n This method deals only with protobufs\\n (:class:`gcloud.datastore.datastore_v1_pb2.Key`\\n and\\n :class:`gcloud.datastore.datastore_v1_pb2.Entity`)\\n and is used under the hood for methods like\\n :func:`gcloud.datastore.dataset.Dataset.get_entity`:\\n\\n >>> from gcloud import datastore\\n >>> from gcloud.datastore.key import Key\\n >>> connection = datastore.get_connection(email, key_path)\\n >>> dataset = connection.dataset('dataset-id')\\n >>> key = Key(dataset=dataset).kind('MyKind').id(1234)\\n\\n Using the :class:`gcloud.datastore.dataset.Dataset` helper:\\n\\n >>> dataset.get_entity(key)\\n <Entity object>\\n\\n Using the ``connection`` class directly:\\n\\n >>> connection.lookup('dataset-id', key.to_protobuf())\\n <Entity protobuf>\\n\\n :type dataset_id: string\\n :param dataset_id: The dataset to look up the keys.\\n\\n :type key_pbs: list of :class:`gcloud.datastore.datastore_v1_pb2.Key`\\n (or a single Key)\\n :param key_pbs: The key (or keys) to retrieve from the datastore.\\n\\n :rtype: list of :class:`gcloud.datastore.datastore_v1_pb2.Entity`\\n (or a single Entity)\\n :returns: The entities corresponding to the keys provided.\\n If a single key was provided and no results matched,\\n this will return None.\\n If multiple keys were provided and no results matched,\\n this will return an empty list.\\n \\\"\\\"\\\"\\n lookup_request = datastore_pb.LookupRequest()\\n\\n single_key = isinstance(key_pbs, datastore_pb.Key)\\n\\n if single_key:\\n key_pbs = [key_pbs]\\n\\n for key_pb in key_pbs:\\n lookup_request.key.add().CopyFrom(key_pb)\\n\\n lookup_response = self._rpc(datas\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.commit\",\n \"text\": \"gcloud/datastore/connection.py::Connection.commit\\n def commit(self, dataset_id, mutation_pb):\\n \\\"\\\"\\\"Commit dataset mutations in context of current transation (if any).\\n\\n :type dataset_id: string\\n :param dataset_id: The dataset in which to perform the changes.\\n\\n :type mutation_pb: :class:`gcloud.datastore.datastore_v1_pb2.Mutation`.\\n :param mutation_pb: The protobuf for the mutations being saved.\\n\\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.MutationResult`.\\n :returns': the result protobuf for the mutation.\\n \\\"\\\"\\\"\\n request = datastore_pb.CommitRequest()\\n\\n if self.transaction():\\n request.mode = datastore_pb.CommitRequest.TRANSACTIONAL\\n request.transaction = self.transaction().id()\\n else:\\n request.mode = datastore_pb.CommitRequest.NON_TRANSACTIONAL\\n\\n request.mutation.CopyFrom(mutation_pb)\\n response = self._rpc(dataset_id, 'commit', request,\\n datastore_pb.CommitResponse)\\n return response.mutation_result\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.save_entity\",\n \"text\": \"gcloud/datastore/connection.py::Connection.save_entity\\n def save_entity(self, dataset_id, key_pb, properties):\\n \\\"\\\"\\\"Save an entity to the Cloud Datastore with the provided properties.\\n\\n .. note::\\n Any existing properties for the entity identified by 'key_pb'\\n will be replaced by those passed in 'properties'; properties\\n not passed in 'properties' no longer be set for the entity.\\n\\n :type dataset_id: string\\n :param dataset_id: The dataset in which to save the entity.\\n\\n :type key_pb: :class:`gcloud.datastore.datastore_v1_pb2.Key`\\n :param key_pb: The complete or partial key for the entity.\\n\\n :type properties: dict\\n :param properties: The properties to store on the entity.\\n \\\"\\\"\\\"\\n mutation = self.mutation()\\n\\n # If the Key is complete, we should upsert\\n # instead of using insert_auto_id.\\n path = key_pb.path_element[-1]\\n auto_id = not (path.HasField('id') or path.HasField('name'))\\n\\n if auto_id:\\n insert = mutation.insert_auto_id.add()\\n else:\\n insert = mutation.upsert.add()\\n\\n insert.key.CopyFrom(key_pb)\\n\\n for name, value in properties.iteritems():\\n prop = insert.property.add()\\n # Set the name of the property.\\n prop.name = name\\n\\n # Set the appropriate value.\\n _helpers._set_protobuf_value(prop.value, value)\\n\\n # If this is in a transaction, we should just return True. The\\n # transaction will handle assigning any keys as necessary.\\n if self.transaction():\\n return True\\n\\n result = self.commit(dataset_id, mutation)\\n # If this was an auto-assigned ID, return the new Key.\\n if auto_id:\\n return result.insert_auto_id_key[0]\\n\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/connection.py::Connection.delete_entities\",\n \"text\": \"gcloud/datastore/connection.py::Connection.delete_entities\\n def delete_entities(self, dataset_id, key_pbs):\\n \\\"\\\"\\\"Delete keys from a dataset in the Cloud Datastore.\\n\\n This method deals only with\\n :class:`gcloud.datastore.datastore_v1_pb2.Key` protobufs\\n and not with any of the other abstractions.\\n For example, it's used under the hood in the\\n :func:`gcloud.datastore.entity.Entity.delete` method.\\n\\n :type dataset_id: string\\n :param dataset_id: The dataset from which to delete the keys.\\n\\n :type key_pbs: list of :class:`gcloud.datastore.datastore_v1_pb2.Key`\\n :param key_pbs: The keys to delete from the datastore.\\n\\n :rtype: boolean (if in a transaction) or else\\n :class:`gcloud.datastore.datastore_v1_pb2.MutationResult`.\\n :returns: True\\n \\\"\\\"\\\"\\n mutation = self.mutation()\\n\\n for key_pb in key_pbs:\\n delete = mutation.delete.add()\\n delete.CopyFrom(key_pb)\\n\\n if not self.transaction():\\n self.commit(dataset_id, mutation)\\n\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/dataset.py::Dataset.__init__\",\n \"text\": \"gcloud/datastore/dataset.py::Dataset.__init__\\n def __init__(self, id, connection=None):\\n self._connection = connection\\n self._id = id\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/dataset.py::Dataset.connection\",\n \"text\": \"gcloud/datastore/dataset.py::Dataset.connection\\n def connection(self):\\n \\\"\\\"\\\"Get the current connection.\\n\\n >>> dataset = Dataset('dataset-id', connection=conn)\\n >>> dataset.connection()\\n <Connection object>\\n\\n :rtype: :class:`gcloud.datastore.connection.Connection`\\n :returns: Returns the current connection.\\n \\\"\\\"\\\"\\n\\n return self._connection\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/dataset.py::Dataset.id\",\n \"text\": \"gcloud/datastore/dataset.py::Dataset.id\\n def id(self):\\n \\\"\\\"\\\"Get the current dataset ID.\\n\\n >>> dataset = Dataset('dataset-id', connection=conn)\\n >>> dataset.id()\\n 'dataset-id'\\n\\n :rtype: string\\n :returns: The current dataset ID.\\n \\\"\\\"\\\"\\n\\n return self._id\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/dataset.py::Dataset.query\",\n \"text\": \"gcloud/datastore/dataset.py::Dataset.query\\n def query(self, *args, **kwargs):\\n \\\"\\\"\\\"Create a query bound to this dataset.\\n\\n :param args: positional arguments, passed through to the Query\\n\\n :param kw: keyword arguments, passed through to the Query\\n\\n :rtype: :class:`gcloud.datastore.query.Query`\\n :returns: a new Query instance, bound to this dataset.\\n \\\"\\\"\\\"\\n # This import is here to avoid circular references.\\n from gcloud.datastore.query import Query\\n kwargs['dataset'] = self\\n return Query(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/dataset.py::Dataset.entity\",\n \"text\": \"gcloud/datastore/dataset.py::Dataset.entity\\n def entity(self, kind):\\n \\\"\\\"\\\"Create an entity bound to this dataset.\\n\\n :type kind: string\\n :param kind: the \\\"kind\\\" of the new entity.\\n\\n :rtype: :class:`gcloud.datastore.entity.Entity`\\n :returns: a new Entity instance, bound to this dataset.\\n \\\"\\\"\\\"\\n # This import is here to avoid circular references.\\n from gcloud.datastore.entity import Entity\\n return Entity(dataset=self, kind=kind)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/dataset.py::Dataset.transaction\",\n \"text\": \"gcloud/datastore/dataset.py::Dataset.transaction\\n def transaction(self, *args, **kwargs):\\n \\\"\\\"\\\"Create a transaction bound to this dataset.\\n\\n :param args: positional arguments, passed through to the Transaction\\n\\n :param kw: keyword arguments, passed through to the Transaction\\n\\n :rtype: :class:`gcloud.datastore.transaction.Transaction`\\n :returns: a new Transaction instance, bound to this dataset.\\n \\\"\\\"\\\"\\n # This import is here to avoid circular references.\\n from gcloud.datastore.transaction import Transaction\\n kwargs['dataset'] = self\\n return Transaction(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/dataset.py::Dataset.get_entity\",\n \"text\": \"gcloud/datastore/dataset.py::Dataset.get_entity\\n def get_entity(self, key):\\n \\\"\\\"\\\"Retrieves entity from the dataset, along with its attributes.\\n\\n :type key: :class:`gcloud.datastore.key.Key`\\n :param item_name: The name of the item to retrieve.\\n\\n :rtype: :class:`gcloud.datastore.entity.Entity` or ``None``\\n :return: The requested entity, or ``None`` if there was no match found.\\n \\\"\\\"\\\"\\n entities = self.get_entities([key])\\n if entities:\\n return entities[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/dataset.py::Dataset.get_entities\",\n \"text\": \"gcloud/datastore/dataset.py::Dataset.get_entities\\n def get_entities(self, keys):\\n \\\"\\\"\\\"Retrieves entities from the dataset, along with their attributes.\\n\\n :type key: list of :class:`gcloud.datastore.key.Key`\\n :param item_name: The name of the item to retrieve.\\n\\n :rtype: list of :class:`gcloud.datastore.entity.Entity`\\n :return: The requested entities.\\n \\\"\\\"\\\"\\n # This import is here to avoid circular references.\\n from gcloud.datastore.entity import Entity\\n\\n entity_pbs = self.connection().lookup(\\n dataset_id=self.id(),\\n key_pbs=[k.to_protobuf() for k in keys]\\n )\\n\\n entities = []\\n for entity_pb in entity_pbs:\\n entities.append(Entity.from_protobuf(entity_pb, dataset=self))\\n return entities\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.__init__\",\n \"text\": \"gcloud/datastore/entity.py::Entity.__init__\\n def __init__(self, dataset=None, kind=None):\\n super(Entity, self).__init__()\\n if dataset and kind:\\n self._key = Key(dataset=dataset).kind(kind)\\n else:\\n self._key = None\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.dataset\",\n \"text\": \"gcloud/datastore/entity.py::Entity.dataset\\n def dataset(self):\\n \\\"\\\"\\\"Get the :class:`.dataset.Dataset` in which this entity belongs.\\n\\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\\n :returns: The Dataset containing the entity if there is a key,\\n else None.\\n\\n .. note::\\n This is based on the :class:`gcloud.datastore.key.Key` set on the\\n entity. That means that if you have no key set, the dataset might\\n be `None`. It also means that if you change the key on the entity,\\n this will refer to that key's dataset.\\n \\\"\\\"\\\"\\n if self._key:\\n return self._key.dataset()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.key\",\n \"text\": \"gcloud/datastore/entity.py::Entity.key\\n def key(self, key=None):\\n \\\"\\\"\\\"Get or set the :class:`.datastore.key.Key` on the current entity.\\n\\n :type key: :class:`glcouddatastore.key.Key`\\n :param key: The key you want to set on the entity.\\n\\n :rtype: :class:`gcloud.datastore.key.Key` or :class:`Entity`.\\n :returns: Either the current key (on get) or the current\\n object (on set).\\n\\n >>> entity.key(my_other_key) # This returns the original entity.\\n <Entity[{'kind': 'OtherKeyKind', 'id': 1234}] {'property': 'value'}>\\n >>> entity.key() # This returns the key.\\n <Key[{'kind': 'OtherKeyKind', 'id': 1234}]>\\n \\\"\\\"\\\"\\n\\n if key is not None:\\n self._key = key\\n return self\\n else:\\n return self._key\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.kind\",\n \"text\": \"gcloud/datastore/entity.py::Entity.kind\\n def kind(self):\\n \\\"\\\"\\\"Get the kind of the current entity.\\n\\n .. note::\\n This relies entirely on\\n the :class:`gcloud.datastore.key.Key`\\n set on the entity.\\n That means that we're not storing the kind of the entity at all,\\n just the properties and a pointer to a Key\\n which knows its Kind.\\n \\\"\\\"\\\"\\n\\n if self._key:\\n return self._key.kind()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.from_key\",\n \"text\": \"gcloud/datastore/entity.py::Entity.from_key\\n @classmethod\\n def from_key(cls, key):\\n \\\"\\\"\\\"Create entity based on :class:`.datastore.key.Key`.\\n\\n .. note::\\n This is a factory method.\\n\\n :type key: :class:`gcloud.datastore.key.Key`\\n :param key: The key for the entity.\\n\\n :returns: The :class:`Entity` derived from the\\n :class:`gcloud.datastore.key.Key`.\\n \\\"\\\"\\\"\\n\\n return cls().key(key)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.from_protobuf\",\n \"text\": \"gcloud/datastore/entity.py::Entity.from_protobuf\\n @classmethod\\n def from_protobuf(cls, pb, dataset=None):\\n \\\"\\\"\\\"Factory method for creating an entity based on a protobuf.\\n\\n The protobuf should be one returned from the Cloud Datastore\\n Protobuf API.\\n\\n :type pb: :class:`gcloud.datastore.datastore_v1_pb2.Entity`\\n :param pb: The Protobuf representing the entity.\\n\\n :returns: The :class:`Entity` derived from the\\n :class:`gcloud.datastore.datastore_v1_pb2.Entity`.\\n \\\"\\\"\\\"\\n\\n # This is here to avoid circular imports.\\n from gcloud.datastore import _helpers\\n\\n key = Key.from_protobuf(pb.key, dataset=dataset)\\n entity = cls.from_key(key)\\n\\n for property_pb in pb.property:\\n value = _helpers._get_value_from_property_pb(property_pb)\\n entity[property_pb.name] = value\\n\\n return entity\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity._must_key\",\n \"text\": \"gcloud/datastore/entity.py::Entity._must_key\\n @property\\n def _must_key(self):\\n \\\"\\\"\\\"Return our key, or raise NoKey if not set.\\n\\n :rtype: :class:`gcloud.datastore.key.Key`.\\n :returns: our key\\n :raises: NoKey if key is None\\n \\\"\\\"\\\"\\n if self._key is None:\\n raise NoKey('no key')\\n return self._key\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.reload\",\n \"text\": \"gcloud/datastore/entity.py::Entity.reload\\n def reload(self):\\n \\\"\\\"\\\"Reloads the contents of this entity from the datastore.\\n\\n This method takes the :class:`gcloud.datastore.key.Key`, loads all\\n properties from the Cloud Datastore, and sets the updated properties on\\n the current object.\\n\\n .. warning::\\n This will override any existing properties if a different value\\n exists remotely, however it will *not* override any properties that\\n exist only locally.\\n \\\"\\\"\\\"\\n key = self._must_key\\n entity = key.dataset().get_entity(key.to_protobuf())\\n\\n if entity:\\n self.update(entity)\\n return self\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.save\",\n \"text\": \"gcloud/datastore/entity.py::Entity.save\\n def save(self):\\n \\\"\\\"\\\"Save the entity in the Cloud Datastore.\\n\\n .. note::\\n Any existing properties for the entity will be replaced by those\\n currently set on this instance. Already-stored properties which do\\n not correspond to keys set on this instance will be removed from\\n the datastore.\\n\\n .. note::\\n Property values which are \\\"text\\\" ('unicode' in Python2, 'str' in\\n Python3) map to 'string_value' in the datastore; values which are\\n \\\"bytes\\\" ('str' in Python2, 'bytes' in Python3) map to 'blob_value'.\\n\\n :rtype: :class:`gcloud.datastore.entity.Entity`\\n :returns: The entity with a possibly updated Key.\\n \\\"\\\"\\\"\\n key = self._must_key\\n dataset = key.dataset()\\n connection = dataset.connection()\\n key_pb = connection.save_entity(\\n dataset_id=dataset.id(),\\n key_pb=key.to_protobuf(),\\n properties=dict(self))\\n\\n # If we are in a transaction and the current entity needs an\\n # automatically assigned ID, tell the transaction where to put that.\\n transaction = connection.transaction()\\n if transaction and key.is_partial():\\n transaction.add_auto_id_entity(self)\\n\\n if isinstance(key_pb, datastore_pb.Key):\\n updated_key = Key.from_protobuf(key_pb)\\n # Update the path (which may have been altered).\\n self._key = key.path(updated_key.path())\\n\\n return self\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.delete\",\n \"text\": \"gcloud/datastore/entity.py::Entity.delete\\n def delete(self):\\n \\\"\\\"\\\"Delete the entity in the Cloud Datastore.\\n\\n .. note::\\n This is based entirely off of the :class:`gcloud.datastore.key.Key`\\n set on the entity. Whatever is stored remotely using the key on the\\n entity will be deleted.\\n \\\"\\\"\\\"\\n key = self._must_key\\n dataset = key.dataset()\\n dataset.connection().delete_entities(\\n dataset_id=dataset.id(),\\n key_pbs=[key.to_protobuf()],\\n )\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.__repr__\",\n \"text\": \"gcloud/datastore/entity.py::Entity.__repr__\\n def __repr__(self):\\n if self._key:\\n return '<Entity%s %s>' % (self._key.path(),\\n super(Entity, self).__repr__())\\n else:\\n return '<Entity %s>' % (super(Entity, self).__repr__())\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.__init__\",\n \"text\": \"gcloud/datastore/key.py::Key.__init__\\n def __init__(self, dataset=None, namespace=None, path=None):\\n \\\"\\\"\\\"Constructor / initializer for a key.\\n\\n :type dataset: :class:`gcloud.datastore.dataset.Dataset`\\n :param dataset: A dataset instance for the key.\\n\\n :type namespace: :class:`str`\\n :param namespace: A namespace identifier for the key.\\n\\n :type path: sequence of dicts\\n :param path: Each dict must have keys 'kind' (a string) and optionally\\n 'name' (a string) or 'id' (an integer).\\n \\\"\\\"\\\"\\n self._dataset = dataset\\n self._namespace = namespace\\n self._path = path or [{'kind': ''}]\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key._clone\",\n \"text\": \"gcloud/datastore/key.py::Key._clone\\n def _clone(self):\\n \\\"\\\"\\\"Duplicates the Key.\\n\\n We make a shallow copy of the :class:`gcloud.datastore.dataset.Dataset`\\n because it holds a reference an authenticated connection,\\n which we don't want to lose.\\n\\n :rtype: :class:`gcloud.datastore.key.Key`\\n :returns: a new `Key` instance\\n \\\"\\\"\\\"\\n clone = copy.deepcopy(self)\\n clone._dataset = self._dataset # Make a shallow copy of the Dataset.\\n return clone\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.from_protobuf\",\n \"text\": \"gcloud/datastore/key.py::Key.from_protobuf\\n @classmethod\\n def from_protobuf(cls, pb, dataset=None):\\n \\\"\\\"\\\"Factory method for creating a key based on a protobuf.\\n\\n The protobuf should be one returned from the Cloud Datastore\\n Protobuf API.\\n\\n :type pb: :class:`gcloud.datastore.datastore_v1_pb2.Key`\\n :param pb: The Protobuf representing the key.\\n\\n :type dataset: :class:`gcloud.datastore.dataset.Dataset`\\n :param dataset: A dataset instance. If not passed, defaults to an\\n instance whose ID is derived from pb.\\n\\n :rtype: :class:`gcloud.datastore.key.Key`\\n :returns: a new `Key` instance\\n \\\"\\\"\\\"\\n path = []\\n for element in pb.path_element:\\n element_dict = {'kind': element.kind}\\n\\n if element.HasField('id'):\\n element_dict['id'] = element.id\\n\\n # This is safe: we expect proto objects returned will only have\\n # one of `name` or `id` set.\\n if element.HasField('name'):\\n element_dict['name'] = element.name\\n\\n path.append(element_dict)\\n\\n if not dataset:\\n dataset = Dataset(id=pb.partition_id.dataset_id)\\n namespace = pb.partition_id.namespace\\n else:\\n namespace = None\\n\\n return cls(dataset, namespace, path)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.to_protobuf\",\n \"text\": \"gcloud/datastore/key.py::Key.to_protobuf\\n def to_protobuf(self):\\n \\\"\\\"\\\"Return a protobuf corresponding to the key.\\n\\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.Key`\\n :returns: The Protobuf representing the key.\\n \\\"\\\"\\\"\\n key = datastore_pb.Key()\\n\\n # Technically a dataset is required to do anything with the key,\\n # but we shouldn't throw a cryptic error if one isn't provided\\n # in the initializer.\\n if self.dataset():\\n # Apparently 's~' is a prefix for High-Replication and is necessary\\n # here. Another valid preflix is 'e~' indicating EU datacenters.\\n dataset_id = self.dataset().id()\\n if dataset_id:\\n if dataset_id[:2] not in ['s~', 'e~']:\\n dataset_id = 's~' + dataset_id\\n\\n key.partition_id.dataset_id = dataset_id\\n\\n if self._namespace:\\n key.partition_id.namespace = self._namespace\\n\\n for item in self.path():\\n element = key.path_element.add()\\n if 'kind' in item:\\n element.kind = item['kind']\\n if 'id' in item:\\n element.id = item['id']\\n if 'name' in item:\\n element.name = item['name']\\n\\n return key\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.from_path\",\n \"text\": \"gcloud/datastore/key.py::Key.from_path\\n @classmethod\\n def from_path(cls, *args, **kwargs):\\n \\\"\\\"\\\"Factory method for creating a key based on a path.\\n\\n :type args: :class:`tuple`\\n :param args: sequence of even length, where the first of each pair is a\\n string representing the 'kind' of the path element, and\\n the second of the pair is either a string (for the path\\n element's name) or an integer (for its id).\\n\\n :type kwargs: :class:`dict`\\n :param kwargs: Other named parameters which can be passed to\\n :func:`Key.__init__`.\\n\\n :rtype: :class:`gcloud.datastore.key.Key`\\n :returns: a new :class:`Key` instance\\n \\\"\\\"\\\"\\n if len(args) % 2:\\n raise ValueError('Must pass an even number of args.')\\n\\n path = []\\n items = iter(args)\\n\\n for kind, id_or_name in izip(items, items):\\n entry = {'kind': kind}\\n if isinstance(id_or_name, basestring):\\n entry['name'] = id_or_name\\n else:\\n entry['id'] = id_or_name\\n path.append(entry)\\n\\n kwargs['path'] = path\\n return cls(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.is_partial\",\n \"text\": \"gcloud/datastore/key.py::Key.is_partial\\n def is_partial(self):\\n \\\"\\\"\\\"Boolean test: is the key fully mapped onto a backend entity?\\n\\n :rtype: :class:`bool`\\n :returns: True if the last element of the key's path does not have\\n an 'id' or a 'name'.\\n \\\"\\\"\\\"\\n return self.id_or_name() is None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.dataset\",\n \"text\": \"gcloud/datastore/key.py::Key.dataset\\n def dataset(self, dataset=None):\\n \\\"\\\"\\\"Dataset setter / getter.\\n\\n :type dataset: :class:`gcloud.datastore.dataset.Dataset`\\n :param dataset: A dataset instance for the key.\\n\\n :rtype: :class:`Key` (for setter); or\\n :class:`gcloud.datastore.dataset.Dataset` (for getter)\\n :returns: a new key, cloned from self., with the given dataset\\n (setter); or self's dataset (getter).\\n \\\"\\\"\\\"\\n if dataset:\\n clone = self._clone()\\n clone._dataset = dataset\\n return clone\\n else:\\n return self._dataset\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.namespace\",\n \"text\": \"gcloud/datastore/key.py::Key.namespace\\n def namespace(self, namespace=None):\\n \\\"\\\"\\\"Namespace setter / getter.\\n\\n :type namespace: :class:`str`\\n :param namespace: A namespace identifier for the key.\\n\\n :rtype: :class:`Key` (for setter); or :class:`str` (for getter)\\n :returns: a new key, cloned from self., with the given namespace\\n (setter); or self's namespace (getter).\\n \\\"\\\"\\\"\\n if namespace:\\n clone = self._clone()\\n clone._namespace = namespace\\n return clone\\n else:\\n return self._namespace\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.path\",\n \"text\": \"gcloud/datastore/key.py::Key.path\\n def path(self, path=None):\\n \\\"\\\"\\\"Path setter / getter.\\n\\n :type path: sequence of dicts\\n :param path: Each dict must have keys 'kind' (a string) and optionally\\n 'name' (a string) or 'id' (an integer).\\n\\n :rtype: :class:`Key` (for setter); or :class:`str` (for getter)\\n :returns: a new key, cloned from self., with the given path (setter);\\n or self's path (getter).\\n \\\"\\\"\\\"\\n if path:\\n clone = self._clone()\\n clone._path = path\\n return clone\\n else:\\n return self._path\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.kind\",\n \"text\": \"gcloud/datastore/key.py::Key.kind\\n def kind(self, kind=None):\\n \\\"\\\"\\\"Kind setter / getter. Based on the last element of path.\\n\\n :type kind: :class:`str`\\n :param kind: The new kind for the key.\\n\\n :rtype: :class:`Key` (for setter); or :class:`str` (for getter)\\n :returns: a new key, cloned from self., with the given kind (setter);\\n or self's kind (getter).\\n \\\"\\\"\\\"\\n if kind:\\n clone = self._clone()\\n clone._path[-1]['kind'] = kind\\n return clone\\n elif self.path():\\n return self._path[-1]['kind']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.id\",\n \"text\": \"gcloud/datastore/key.py::Key.id\\n def id(self, id_to_set=None):\\n \\\"\\\"\\\"ID setter / getter. Based on the last element of path.\\n\\n :type id_to_set: :class:`int`\\n :param id_to_set: The new ID for the key.\\n\\n :rtype: :class:`Key` (for setter); or :class:`int` (for getter)\\n :returns: a new key, cloned from self., with the given id (setter);\\n or self's id (getter).\\n \\\"\\\"\\\"\\n if id_to_set:\\n clone = self._clone()\\n clone._path[-1]['id'] = id_to_set\\n return clone\\n elif self.path():\\n return self._path[-1].get('id')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.name\",\n \"text\": \"gcloud/datastore/key.py::Key.name\\n def name(self, name=None):\\n \\\"\\\"\\\"Name setter / getter. Based on the last element of path.\\n\\n :type kind: :class:`str`\\n :param kind: The new name for the key.\\n\\n :rtype: :class:`Key` (for setter); or :class:`str` (for getter)\\n :returns: a new key, cloned from self., with the given name (setter);\\n or self's name (getter).\\n \\\"\\\"\\\"\\n if name:\\n clone = self._clone()\\n clone._path[-1]['name'] = name\\n return clone\\n elif self.path():\\n return self._path[-1].get('name')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.id_or_name\",\n \"text\": \"gcloud/datastore/key.py::Key.id_or_name\\n def id_or_name(self):\\n \\\"\\\"\\\"Getter. Based on the last element of path.\\n\\n :rtype: :class:`int` (if 'id' is set); or :class:`str` (the 'name')\\n :returns: True if the last element of the key's path has either an 'id'\\n or a 'name'.\\n \\\"\\\"\\\"\\n return self.id() or self.name()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.parent\",\n \"text\": \"gcloud/datastore/key.py::Key.parent\\n def parent(self):\\n \\\"\\\"\\\"Getter: return a new key for the next highest element in path.\\n\\n :rtype: :class:`gcloud.datastore.key.Key`\\n :returns: a new `Key` instance, whose path consists of all but the last\\n element of self's path. If self has only one path element,\\n return None.\\n \\\"\\\"\\\"\\n if len(self._path) <= 1:\\n return None\\n return self.path(self.path()[:-1])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/key.py::Key.__repr__\",\n \"text\": \"gcloud/datastore/key.py::Key.__repr__\\n def __repr__(self):\\n return '<Key%s>' % self.path()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.__init__\",\n \"text\": \"gcloud/datastore/query.py::Query.__init__\\n def __init__(self, kind=None, dataset=None, namespace=None):\\n self._dataset = dataset\\n self._namespace = namespace\\n self._pb = datastore_pb.Query()\\n self._cursor = None\\n\\n if kind:\\n self._pb.kind.add().name = kind\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query._clone\",\n \"text\": \"gcloud/datastore/query.py::Query._clone\\n def _clone(self):\\n \\\"\\\"\\\"Create a new Query, copying self.\\n\\n :rtype: :class:`gcloud.datastore.query.Query`\\n :returns: a copy of 'self'.\\n \\\"\\\"\\\"\\n clone = self.__class__(dataset=self._dataset,\\n namespace=self._namespace)\\n clone._pb.CopyFrom(self._pb)\\n clone._cursor = self._cursor\\n return clone\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.namespace\",\n \"text\": \"gcloud/datastore/query.py::Query.namespace\\n def namespace(self):\\n \\\"\\\"\\\"This query's namespace\\n\\n :rtype: string or None\\n :returns: the namespace assigned to this query\\n \\\"\\\"\\\"\\n return self._namespace\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.to_protobuf\",\n \"text\": \"gcloud/datastore/query.py::Query.to_protobuf\\n def to_protobuf(self):\\n \\\"\\\"\\\"Convert :class:`Query` instance to :class:`.datastore_v1_pb2.Query`.\\n\\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.Query`\\n :returns: A Query protobuf that can be sent to the protobuf API.\\n \\\"\\\"\\\"\\n return self._pb\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.filter\",\n \"text\": \"gcloud/datastore/query.py::Query.filter\\n def filter(self, expression, value):\\n \\\"\\\"\\\"Filter the query based on an expression and a value.\\n\\n This will return a clone of the current :class:`Query`\\n filtered by the expression and value provided.\\n\\n Expressions take the form of::\\n\\n .filter('<property> <operator>', <value>)\\n\\n where property is a property stored on the entity in the datastore\\n and operator is one of ``OPERATORS``\\n (ie, ``=``, ``<``, ``<=``, ``>``, ``>=``)::\\n\\n >>> query = Query('Person')\\n >>> filtered_query = query.filter('name =', 'James')\\n >>> filtered_query = query.filter('age >', 50)\\n\\n Because each call to ``.filter()`` returns a cloned ``Query`` object\\n we are able to string these together::\\n\\n >>> query = Query('Person').filter(\\n ... 'name =', 'James').filter('age >', 50)\\n\\n :type expression: string\\n :param expression: An expression of a property and an\\n operator (ie, ``=``).\\n\\n :type value: integer, string, boolean, float, None, datetime\\n :param value: The value to filter on.\\n\\n :rtype: :class:`Query`\\n :returns: A Query filtered by the expression and value provided.\\n \\\"\\\"\\\"\\n clone = self._clone()\\n\\n # Take an expression like 'property >=', and parse it into\\n # useful pieces.\\n property_name, operator = None, None\\n expression = expression.strip()\\n\\n for operator_string in self.OPERATORS:\\n if expression.endswith(operator_string):\\n operator = self.OPERATORS[operator_string]\\n property_name = expression[0:-len(operator_string)].strip()\\n\\n if not operator or not property_name:\\n raise ValueError('Invalid expression: \\\"%s\\\"' % expression)\\n\\n # Build a composite filter AND'd together.\\n composite_filter = clone._pb.filter.composite_filter\\n composite_filter.opera\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.ancestor\",\n \"text\": \"gcloud/datastore/query.py::Query.ancestor\\n def ancestor(self, ancestor):\\n \\\"\\\"\\\"Filter the query based on an ancestor.\\n\\n This will return a clone of the current :class:`Query`\\n filtered by the ancestor provided.\\n\\n For example::\\n\\n >>> parent_key = Key.from_path('Person', '1')\\n >>> query = dataset.query('Person')\\n >>> filtered_query = query.ancestor(parent_key)\\n\\n If you don't have a :class:`gcloud.datastore.key.Key` but just\\n know the path, you can provide that as well::\\n\\n >>> query = dataset.query('Person')\\n >>> filtered_query = query.ancestor(['Person', '1'])\\n\\n Each call to ``.ancestor()`` returns a cloned :class:`Query`,\\n however a query may only have one ancestor at a time.\\n\\n :type ancestor: :class:`gcloud.datastore.key.Key` or list\\n :param ancestor: Either a Key or a path of the form\\n ``['Kind', 'id or name', 'Kind', 'id or name', ...]``.\\n\\n :rtype: :class:`Query`\\n :returns: A Query filtered by the ancestor provided.\\n \\\"\\\"\\\"\\n\\n clone = self._clone()\\n\\n # If an ancestor filter already exists, remove it.\\n for i, filter in enumerate(clone._pb.filter.composite_filter.filter):\\n property_filter = filter.property_filter\\n if (property_filter.operator ==\\n datastore_pb.PropertyFilter.HAS_ANCESTOR):\\n del clone._pb.filter.composite_filter.filter[i]\\n\\n # If we just deleted the last item, make sure to clear out the\\n # filter property all together.\\n if not clone._pb.filter.composite_filter.filter:\\n clone._pb.ClearField('filter')\\n\\n # If the ancestor is None, just return (we already removed the filter).\\n if not ancestor:\\n return clone\\n\\n # If a list was provided, turn it into a Key.\\n if isinstance(ancestor, list):\\n ancestor = Key.from_path(*an\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.kind\",\n \"text\": \"gcloud/datastore/query.py::Query.kind\\n def kind(self, *kinds):\\n \\\"\\\"\\\"Get or set the Kind of the Query.\\n\\n .. note::\\n This is an **additive** operation.\\n That is, if the Query is set for kinds A and B,\\n and you call ``.kind('C')``,\\n it will query for kinds A, B, *and*, C.\\n\\n :type kinds: string\\n :param kinds: The entity kinds for which to query.\\n\\n :rtype: string or :class:`Query`\\n :returns: If no arguments, returns the kind.\\n If a kind is provided, returns a clone of the :class:`Query`\\n with those kinds set.\\n \\\"\\\"\\\"\\n if kinds:\\n clone = self._clone()\\n for kind in kinds:\\n clone._pb.kind.add().name = kind\\n return clone\\n else:\\n return self._pb.kind\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.limit\",\n \"text\": \"gcloud/datastore/query.py::Query.limit\\n def limit(self, limit=None):\\n \\\"\\\"\\\"Get or set the limit of the Query.\\n\\n This is the maximum number of rows (Entities) to return for this Query.\\n\\n This is a hybrid getter / setter, used as::\\n\\n >>> query = Query('Person')\\n >>> query = query.limit(100) # Set the limit to 100 rows.\\n >>> query.limit() # Get the limit for this query.\\n 100\\n\\n :rtype: integer, None, or :class:`Query`\\n :returns: If no arguments, returns the current limit.\\n If a limit is provided, returns a clone of the :class:`Query`\\n with that limit set.\\n \\\"\\\"\\\"\\n if limit:\\n clone = self._clone()\\n clone._pb.limit = limit\\n return clone\\n else:\\n return self._pb.limit\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.dataset\",\n \"text\": \"gcloud/datastore/query.py::Query.dataset\\n def dataset(self, dataset=None):\\n \\\"\\\"\\\"Get or set the :class:`.datastore.dataset.Dataset` for this Query.\\n\\n This is the dataset against which the Query will be run.\\n\\n This is a hybrid getter / setter, used as::\\n\\n >>> query = Query('Person')\\n >>> query = query.dataset(my_dataset) # Set the dataset.\\n >>> query.dataset() # Get the current dataset.\\n <Dataset object>\\n\\n :rtype: :class:`gcloud.datastore.dataset.Dataset`, None,\\n or :class:`Query`\\n :returns: If no arguments, returns the current dataset.\\n If a dataset is provided, returns a clone of the\\n :class:`Query` with that dataset set.\\n \\\"\\\"\\\"\\n if dataset:\\n clone = self._clone()\\n clone._dataset = dataset\\n return clone\\n else:\\n return self._dataset\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.fetch\",\n \"text\": \"gcloud/datastore/query.py::Query.fetch\\n def fetch(self, limit=None):\\n \\\"\\\"\\\"Executes the Query and returns all matching entities.\\n\\n This makes an API call to the Cloud Datastore, sends the Query as a\\n protobuf, parses the responses to Entity protobufs, and then converts\\n them to :class:`gcloud.datastore.entity.Entity` objects.\\n\\n For example::\\n\\n >>> from gcloud import datastore\\n >>> dataset = datastore.get_dataset('dataset-id', email, key_path)\\n >>> query = dataset.query('Person').filter('name =', 'Sally')\\n >>> query.fetch()\\n [<Entity object>, <Entity object>, ...]\\n >>> query.fetch(1)\\n [<Entity object>]\\n >>> query.limit()\\n None\\n\\n :type limit: integer\\n :param limit: An optional limit to apply temporarily to this query.\\n That is, the Query itself won't be altered,\\n but the limit will be applied to the query\\n before it is executed.\\n\\n :rtype: list of :class:`gcloud.datastore.entity.Entity`'s\\n :returns: The list of entities matching this query's criteria.\\n \\\"\\\"\\\"\\n clone = self\\n\\n if limit:\\n clone = self.limit(limit)\\n\\n query_results = self.dataset().connection().run_query(\\n query_pb=clone.to_protobuf(),\\n dataset_id=self.dataset().id(),\\n namespace=self._namespace,\\n )\\n entity_pbs, end_cursor = query_results[:2]\\n\\n self._cursor = end_cursor\\n return [Entity.from_protobuf(entity, dataset=self.dataset())\\n for entity in entity_pbs]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.cursor\",\n \"text\": \"gcloud/datastore/query.py::Query.cursor\\n def cursor(self):\\n \\\"\\\"\\\"Returns cursor ID\\n\\n .. Caution:: Invoking this method on a query that has not yet been\\n executed will raise a RuntimeError.\\n\\n :rtype: string\\n :returns: base64-encoded cursor ID string denoting the last position\\n consumed in the query's result set.\\n \\\"\\\"\\\"\\n if not self._cursor:\\n raise RuntimeError('No cursor')\\n return base64.b64encode(self._cursor)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.with_cursor\",\n \"text\": \"gcloud/datastore/query.py::Query.with_cursor\\n def with_cursor(self, start_cursor, end_cursor=None):\\n \\\"\\\"\\\"Specifies the starting / ending positions in a query's result set.\\n\\n :type start_cursor: bytes\\n :param start_cursor: Base64-encoded cursor string specifying where to\\n start reading query results.\\n\\n :type end_cursor: bytes\\n :param end_cursor: Base64-encoded cursor string specifying where to\\n stop reading query results.\\n\\n :rtype: :class:`Query`\\n :returns: If neither cursor is passed, returns self; else, returns a\\n clone of the :class:`Query`, with cursors updated.\\n \\\"\\\"\\\"\\n clone = self\\n if start_cursor or end_cursor:\\n clone = self._clone()\\n if start_cursor:\\n clone._pb.start_cursor = base64.b64decode(start_cursor)\\n if end_cursor:\\n clone._pb.end_cursor = base64.b64decode(end_cursor)\\n return clone\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.order\",\n \"text\": \"gcloud/datastore/query.py::Query.order\\n def order(self, *properties):\\n \\\"\\\"\\\"Adds a sort order to the query.\\n\\n Sort fields will be applied in the order specified.\\n\\n :type properties: sequence of strings\\n :param properties: Each value is a string giving the name of the\\n property on which to sort, optionally preceded by a\\n hyphen (-) to specify descending order.\\n Omitting the hyphen implies ascending order.\\n\\n :rtype: :class:`Query`\\n :returns: A new Query instance, ordered as specified.\\n \\\"\\\"\\\"\\n clone = self._clone()\\n\\n for prop in properties:\\n property_order = clone._pb.order.add()\\n\\n if prop.startswith('-'):\\n property_order.property.name = prop[1:]\\n property_order.direction = property_order.DESCENDING\\n else:\\n property_order.property.name = prop\\n property_order.direction = property_order.ASCENDING\\n\\n return clone\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.__init__\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.__init__\\n def __init__(self, dataset):\\n self._dataset = dataset\\n self._id = None\\n self._mutation = datastore_pb.Mutation()\\n self._auto_id_entities = []\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.connection\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.connection\\n def connection(self):\\n \\\"\\\"\\\"Getter for current connection over which the transaction will run.\\n\\n :rtype: :class:`gcloud.datastore.connection.Connection`\\n :returns: The connection over which the transaction will run.\\n \\\"\\\"\\\"\\n\\n return self.dataset().connection()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.dataset\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.dataset\\n def dataset(self):\\n \\\"\\\"\\\"Getter for the current dataset.\\n\\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\\n :returns: The dataset to which the transaction belongs.\\n \\\"\\\"\\\"\\n\\n return self._dataset\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.id\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.id\\n def id(self):\\n \\\"\\\"\\\"Getter for the transaction ID.\\n\\n :rtype: string\\n :returns: The ID of the current transaction.\\n \\\"\\\"\\\"\\n\\n return self._id\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.mutation\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.mutation\\n def mutation(self):\\n \\\"\\\"\\\"Getter for the current mutation.\\n\\n Every transaction is committed\\n with a single Mutation\\n representing the 'work' to be done as part of the transaction.\\n Inside a transaction,\\n calling ``save()`` on an entity\\n builds up the mutation.\\n This getter returns the Mutation protobuf\\n that has been built-up so far.\\n\\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.Mutation`\\n :returns: The Mutation protobuf to be sent in the commit request.\\n \\\"\\\"\\\"\\n return self._mutation\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.add_auto_id_entity\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.add_auto_id_entity\\n def add_auto_id_entity(self, entity):\\n \\\"\\\"\\\"Adds an entity to the list of entities to update with IDs.\\n\\n When an entity has a partial key,\\n calling ``save()`` adds an insert_auto_id entry in the mutation.\\n In order to make sure we update the Entity\\n once the transaction is committed,\\n we need to keep track of which entities to update\\n (and the order is important).\\n\\n When you call ``save()`` on an entity inside a transaction,\\n if the entity has a partial key,\\n it adds itself to the list of entities to be updated\\n once the transaction is committed\\n by calling this method.\\n \\\"\\\"\\\"\\n self._auto_id_entities.append(entity)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.begin\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.begin\\n def begin(self):\\n \\\"\\\"\\\"Begins a transaction.\\n\\n This method is called automatically when entering a with statement,\\n however it can be called explicitly\\n if you don't want to use a context manager.\\n \\\"\\\"\\\"\\n self._id = self.connection().begin_transaction(self.dataset().id())\\n self.connection().transaction(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.rollback\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.rollback\\n def rollback(self):\\n \\\"\\\"\\\"Rolls back the current transaction.\\n\\n This method has necessary side-effects:\\n\\n - Sets the current connection's transaction reference to None.\\n - Sets the current transaction's ID to None.\\n \\\"\\\"\\\"\\n self.connection().rollback_transaction(self.dataset().id())\\n self.connection().transaction(None)\\n self._id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.commit\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.commit\\n def commit(self):\\n \\\"\\\"\\\"Commits the transaction.\\n\\n This is called automatically upon exiting a with statement,\\n however it can be called explicitly\\n if you don't want to use a context manager.\\n\\n This method has necessary side-effects:\\n\\n - Sets the current connection's transaction reference to None.\\n - Sets the current transaction's ID to None.\\n - Updates paths for any keys that needed an automatically generated ID.\\n \\\"\\\"\\\"\\n # It's possible that they called commit() already, in which case\\n # we shouldn't do any committing of our own.\\n if self.connection().transaction():\\n result = self.connection().commit(self.dataset().id(),\\n self.mutation())\\n\\n # For any of the auto-id entities, make sure we update their keys.\\n for i, entity in enumerate(self._auto_id_entities):\\n key_pb = result.insert_auto_id_key[i]\\n key = Key.from_protobuf(key_pb)\\n entity.key(entity.key().path(key.path()))\\n\\n # Tell the connection that the transaction is over.\\n self.connection().transaction(None)\\n\\n # Clear our own ID in case this gets accidentally reused.\\n self._id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.__enter__\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.__enter__\\n def __enter__(self):\\n self.begin()\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.__exit__\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.__exit__\\n def __exit__(self, exc_type, exc_val, exc_tb):\\n if exc_type is None:\\n self.commit()\\n else:\\n self.rollback()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::_utcnow\",\n \"text\": \"gcloud/storage/connection.py::_utcnow\\ndef _utcnow(): # pragma: NO COVER testing replaces\\n \\\"\\\"\\\"Returns current time as UTC datetime.\\n\\n NOTE: on the module namespace so tests can replace it.\\n \\\"\\\"\\\"\\n return datetime.datetime.utcnow()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.__init__\",\n \"text\": \"gcloud/storage/connection.py::Connection.__init__\\n def __init__(self, project, *args, **kwargs):\\n \\\"\\\"\\\":type project: string\\n :param project: The project name to connect to.\\n\\n \\\"\\\"\\\"\\n\\n super(Connection, self).__init__(*args, **kwargs)\\n\\n self.project = project\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.__iter__\",\n \"text\": \"gcloud/storage/connection.py::Connection.__iter__\\n def __iter__(self):\\n return iter(BucketIterator(connection=self))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.__contains__\",\n \"text\": \"gcloud/storage/connection.py::Connection.__contains__\\n def __contains__(self, bucket_name):\\n return self.lookup(bucket_name) is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.build_api_url\",\n \"text\": \"gcloud/storage/connection.py::Connection.build_api_url\\n def build_api_url(self, path, query_params=None, api_base_url=None,\\n api_version=None):\\n \\\"\\\"\\\"Construct an API url given a few components, some optional.\\n\\n Typically, you shouldn't need to use this method.\\n\\n :type path: string\\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\\n\\n :type query_params: dict\\n :param query_params: A dictionary of keys and values to insert into\\n the query string of the URL.\\n\\n :type api_base_url: string\\n :param api_base_url: The base URL for the API endpoint.\\n Typically you won't have to provide this.\\n\\n :type api_version: string\\n :param api_version: The version of the API to call.\\n Typically you shouldn't provide this and instead\\n use the default for the library.\\n\\n :rtype: string\\n :returns: The URL assembled from the pieces provided.\\n \\\"\\\"\\\"\\n\\n url = self.API_URL_TEMPLATE.format(\\n api_base_url=(api_base_url or self.API_BASE_URL),\\n api_version=(api_version or self.API_VERSION),\\n path=path)\\n\\n query_params = query_params or {}\\n query_params.update({'project': self.project})\\n url += '?' + urllib.urlencode(query_params)\\n\\n return url\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.make_request\",\n \"text\": \"gcloud/storage/connection.py::Connection.make_request\\n def make_request(self, method, url, data=None, content_type=None,\\n headers=None):\\n \\\"\\\"\\\"A low level method to send a request to the API.\\n\\n Typically, you shouldn't need to use this method.\\n\\n :type method: string\\n :param method: The HTTP method to use in the request.\\n\\n :type url: string\\n :param url: The URL to send the request to.\\n\\n :type data: string\\n :param data: The data to send as the body of the request.\\n\\n :type content_type: string\\n :param content_type: The proper MIME type of the data provided.\\n\\n :type headers: dict\\n :param headers: A dictionary of HTTP headers to send with the request.\\n\\n :rtype: tuple of ``response`` (a dictionary of sorts)\\n and ``content`` (a string).\\n :returns: The HTTP response object and the content of the response.\\n \\\"\\\"\\\"\\n\\n headers = headers or {}\\n headers['Accept-Encoding'] = 'gzip'\\n\\n if data:\\n content_length = len(str(data))\\n else:\\n content_length = 0\\n\\n headers['Content-Length'] = content_length\\n\\n if content_type:\\n headers['Content-Type'] = content_type\\n\\n headers['User-Agent'] = self.USER_AGENT\\n\\n return self.http.request(uri=url, method=method, headers=headers,\\n body=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.api_request\",\n \"text\": \"gcloud/storage/connection.py::Connection.api_request\\n def api_request(self, method, path, query_params=None,\\n data=None, content_type=None,\\n api_base_url=None, api_version=None,\\n expect_json=True):\\n \\\"\\\"\\\"Make a request over the HTTP transport to the Cloud Storage API.\\n\\n You shouldn't need to use this method,\\n but if you plan to interact with the API using these primitives,\\n this is the correct one to use...\\n\\n :type method: string\\n :param method: The HTTP method name (ie, ``GET``, ``POST``, etc).\\n Required.\\n\\n :type path: string\\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\\n Required.\\n\\n :type query_params: dict\\n :param query_params: A dictionary of keys and values to insert into\\n the query string of the URL.\\n Default is empty dict.\\n\\n :type data: string\\n :param data: The data to send as the body of the request. Default is\\n the empty string.\\n\\n :type content_type: string\\n :param content_type: The proper MIME type of the data provided. Default\\n is None.\\n\\n :type api_base_url: string\\n :param api_base_url: The base URL for the API endpoint.\\n Typically you won't have to provide this.\\n Default is the standard API base URL.\\n\\n :type api_version: string\\n :param api_version: The version of the API to call.\\n Typically you shouldn't provide this and instead\\n use the default for the library.\\n Default is the latest API version supported by\\n gcloud-python.\\n\\n :type expect_json: bool\\n :param expect_json: If True, this method will try to parse the response\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.get_all_buckets\",\n \"text\": \"gcloud/storage/connection.py::Connection.get_all_buckets\\n def get_all_buckets(self):\\n \\\"\\\"\\\"Get all buckets in the project.\\n\\n This will not populate the list of keys available\\n in each bucket.\\n\\n You can also iterate over the connection object,\\n so these two operations are identical::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> for bucket in connection.get_all_buckets():\\n >>> print bucket\\n >>> # ... is the same as ...\\n >>> for bucket in connection:\\n >>> print bucket\\n\\n :rtype: list of :class:`gcloud.storage.bucket.Bucket` objects.\\n :returns: All buckets belonging to this project.\\n \\\"\\\"\\\"\\n\\n return list(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.get_bucket\",\n \"text\": \"gcloud/storage/connection.py::Connection.get_bucket\\n def get_bucket(self, bucket_name):\\n \\\"\\\"\\\"Get a bucket by name.\\n\\n If the bucket isn't found,\\n this will raise a :class:`gcloud.storage.exceptions.NotFoundError`.\\n If you would rather get a bucket by name,\\n and return ``None`` if the bucket isn't found\\n (like ``{}.get('...')``)\\n then use :func:`Connection.lookup`.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> from gcloud.storage import exceptions\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> try:\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> except exceptions.NotFoundError:\\n >>> print 'Sorry, that bucket does not exist!'\\n\\n :type bucket_name: string\\n :param bucket_name: The name of the bucket to get.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The bucket matching the name provided.\\n :raises: :class:`gcloud.storage.exceptions.NotFoundError`\\n \\\"\\\"\\\"\\n bucket = self.new_bucket(bucket_name)\\n response = self.api_request(method='GET', path=bucket.path)\\n return Bucket.from_dict(response, connection=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.lookup\",\n \"text\": \"gcloud/storage/connection.py::Connection.lookup\\n def lookup(self, bucket_name):\\n \\\"\\\"\\\"Get a bucket by name, returning None if not found.\\n\\n You can use this if you would rather\\n checking for a None value\\n than catching an exception::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> bucket = connection.get_bucket('doesnt-exist')\\n >>> print bucket\\n None\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> print bucket\\n <Bucket: my-bucket>\\n\\n :type bucket_name: string\\n :param bucket_name: The name of the bucket to get.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The bucket matching the name provided or None if not found.\\n \\\"\\\"\\\"\\n\\n try:\\n return self.get_bucket(bucket_name)\\n except exceptions.NotFoundError:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.create_bucket\",\n \"text\": \"gcloud/storage/connection.py::Connection.create_bucket\\n def create_bucket(self, bucket):\\n \\\"\\\"\\\"Create a new bucket.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, client, key_path)\\n >>> bucket = connection.create_bucket('my-bucket')\\n >>> print bucket\\n <Bucket: my-bucket>\\n\\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\\n :param bucket: The bucket name (or bucket object) to create.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The newly created bucket.\\n \\\"\\\"\\\"\\n\\n bucket = self.new_bucket(bucket)\\n response = self.api_request(method='POST', path='/b',\\n data={'name': bucket.name})\\n return Bucket.from_dict(response, connection=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.delete_bucket\",\n \"text\": \"gcloud/storage/connection.py::Connection.delete_bucket\\n def delete_bucket(self, bucket, force=False):\\n \\\"\\\"\\\"Delete a bucket.\\n\\n You can use this method to delete a bucket by name,\\n or to delete a bucket object::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> connection.delete_bucket('my-bucket')\\n True\\n\\n You can also delete pass in the bucket object::\\n\\n >>> bucket = connection.get_bucket('other-bucket')\\n >>> connection.delete_bucket(bucket)\\n True\\n\\n If the bucket doesn't exist,\\n this will raise a :class:`gcloud.storage.exceptions.NotFoundError`::\\n\\n >>> from gcloud.storage import exceptions\\n >>> try:\\n >>> connection.delete_bucket('my-bucket')\\n >>> except exceptions.NotFoundError:\\n >>> print 'That bucket does not exist!'\\n\\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\\n :param bucket: The bucket name (or bucket object) to create.\\n\\n :type force: bool\\n :param full: If True, empties the bucket's objects then deletes it.\\n\\n :rtype: bool\\n :returns: True if the bucket was deleted.\\n :raises: :class:`gcloud.storage.exceptions.NotFoundError`\\n \\\"\\\"\\\"\\n\\n bucket = self.new_bucket(bucket)\\n\\n # This force delete operation is slow.\\n if force:\\n for key in bucket:\\n key.delete()\\n\\n self.api_request(method='DELETE', path=bucket.path)\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.new_bucket\",\n \"text\": \"gcloud/storage/connection.py::Connection.new_bucket\\n def new_bucket(self, bucket):\\n \\\"\\\"\\\"Factory method for creating a new (unsaved) bucket object.\\n\\n This method is really useful when you're not sure whether\\n you have an actual :class:`gcloud.storage.bucket.Bucket` object\\n or just a name of a bucket.\\n It always returns the object::\\n\\n >>> bucket = connection.new_bucket('bucket')\\n >>> print bucket\\n <Bucket: bucket>\\n >>> bucket = connection.new_bucket(bucket)\\n >>> print bucket\\n <Bucket: bucket>\\n\\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\\n :param bucket: A name of a bucket or an existing Bucket object.\\n \\\"\\\"\\\"\\n\\n if isinstance(bucket, Bucket):\\n return bucket\\n\\n # Support Python 2 and 3.\\n try:\\n string_type = basestring\\n except NameError: # pragma: NO COVER PY3k\\n string_type = str\\n\\n if isinstance(bucket, string_type):\\n return Bucket(connection=self, name=bucket)\\n\\n raise TypeError('Invalid bucket: %s' % bucket)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.generate_signed_url\",\n \"text\": \"gcloud/storage/connection.py::Connection.generate_signed_url\\n def generate_signed_url(self, resource, expiration,\\n method='GET', content_md5=None,\\n content_type=None):\\n \\\"\\\"\\\"Generate signed URL to provide query-string auth'n to a resource.\\n\\n :type resource: string\\n :param resource: A pointer to a specific resource\\n (typically, ``/bucket-name/path/to/key.txt``).\\n\\n :type expiration: int, long, datetime.datetime, datetime.timedelta\\n :param expiration: When the signed URL should expire.\\n\\n :type method: string\\n :param method: The HTTP verb that will be used when requesting the URL.\\n\\n :type content_md5: string\\n :param content_md5: The MD5 hash of the object referenced by\\n ``resource``.\\n\\n :type content_type: string\\n :param content_type: The content type of the object referenced by\\n ``resource``.\\n\\n :rtype: string\\n :returns: A signed URL you can use to access the resource\\n until expiration.\\n \\\"\\\"\\\"\\n\\n expiration = _get_expiration_seconds(expiration)\\n\\n # Generate the string to sign.\\n signature_string = '\\\\n'.join([\\n method,\\n content_md5 or '',\\n content_type or '',\\n str(expiration),\\n resource])\\n\\n # Take our PKCS12 (.p12) key and make it into a RSA key we can use...\\n pkcs12 = crypto.load_pkcs12(\\n base64.b64decode(self.credentials.private_key),\\n 'notasecret')\\n pem = crypto.dump_privatekey(\\n crypto.FILETYPE_PEM, pkcs12.get_privatekey())\\n pem_key = RSA.importKey(pem)\\n\\n # Sign the string with the RSA key.\\n signer = PKCS1_v1_5.new(pem_key)\\n signature_hash = SHA256.new(signature_string)\\n signature_bytes = signer.sign(signature_hash)\\n signature = base64.b64encode(signature_byt\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::_get_expiration_seconds\",\n \"text\": \"gcloud/storage/connection.py::_get_expiration_seconds\\ndef _get_expiration_seconds(expiration):\\n \\\"\\\"\\\"Convert 'expiration' to a number of seconds in the future.\\n\\n :type expiration: int, long, datetime.datetime, datetime.timedelta\\n :param expiration: When the signed URL should expire.\\n\\n :rtype: int\\n :returns: a timestamp as an absolute number of seconds.\\n \\\"\\\"\\\"\\n\\n # If it's a timedelta, add it to `now` in UTC.\\n if isinstance(expiration, datetime.timedelta):\\n now = _utcnow().replace(tzinfo=pytz.utc)\\n expiration = now + expiration\\n\\n # If it's a datetime, convert to a timestamp.\\n if isinstance(expiration, datetime.datetime):\\n # Make sure the timezone on the value is UTC\\n # (either by converting or replacing the value).\\n if expiration.tzinfo:\\n expiration = expiration.astimezone(pytz.utc)\\n else:\\n expiration = expiration.replace(tzinfo=pytz.utc)\\n\\n # Turn the datetime into a timestamp (seconds, not microseconds).\\n expiration = int(calendar.timegm(expiration.timetuple()))\\n\\n if not isinstance(expiration, (int, long)):\\n raise TypeError('Expected an integer timestamp, datetime, or '\\n 'timedelta. Got %s' % type(expiration))\\n return expiration\",\n \"label\": 0\n },\n {\n \"cid\": \"regression/datastore.py::TestDatastore.setUp\",\n \"text\": \"regression/datastore.py::TestDatastore.setUp\\n def setUp(self):\\n environ = regression_utils.get_environ()\\n self._dataset_id = environ['dataset_id']\\n self._client_email = environ['client_email']\\n self._key_filename = environ['key_filename']\\n self._datasets = {}\\n\\n self.entities_to_delete = []\",\n \"label\": 0\n },\n {\n \"cid\": \"regression/datastore.py::TestDatastore.tearDown\",\n \"text\": \"regression/datastore.py::TestDatastore.tearDown\\n def tearDown(self):\\n for entity in self.entities_to_delete:\\n entity.delete()\",\n \"label\": 0\n },\n {\n \"cid\": \"regression/datastore.py::TestDatastore._get_dataset\",\n \"text\": \"regression/datastore.py::TestDatastore._get_dataset\\n def _get_dataset(self):\\n if self._dataset_id not in self._datasets:\\n self._datasets[self._dataset_id] = datastore.get_dataset(\\n self._dataset_id, self._client_email, self._key_filename)\\n return self._datasets[self._dataset_id]\",\n \"label\": 0\n },\n {\n \"cid\": \"regression/datastore.py::TestDatastore._get_post\",\n \"text\": \"regression/datastore.py::TestDatastore._get_post\\n def _get_post(self, name=None, key_id=None, post_content=None):\\n post_content = post_content or {\\n 'title': 'How to make the perfect pizza in your grill',\\n 'tags': ['pizza', 'grill'],\\n # NOTE: We don't support datetime.date, but should.\\n # NOTE: Without a tz, assertEqual fails with\\n # \\\"can't compare offset-naive and offset-aware datetimes\\\"\\n 'publishedAt': datetime.datetime(2001, 1, 1, tzinfo=pytz.utc),\\n 'author': 'Silvano',\\n 'isDraft': False,\\n 'wordCount': 400,\\n 'rating': 5.0,\\n }\\n # Create an entity with the given content in our dataset.\\n dataset = self._get_dataset()\\n entity = dataset.entity(kind='Post')\\n entity.update(post_content)\\n\\n # Update the entity key.\\n key = None\\n if name is not None:\\n key = entity.key().name(name)\\n if key_id is not None:\\n key = entity.key().id(key_id)\\n if key is not None:\\n entity.key(key)\\n\\n return entity\",\n \"label\": 0\n },\n {\n \"cid\": \"regression/datastore.py::TestDatastore._generic_test_post\",\n \"text\": \"regression/datastore.py::TestDatastore._generic_test_post\\n def _generic_test_post(self, name=None, key_id=None):\\n entity = self._get_post(name=name, key_id=key_id)\\n entity.save()\\n\\n # Register entity to be deleted.\\n self.entities_to_delete.append(entity)\\n\\n if name is not None:\\n self.assertEqual(entity.key().name(), name)\\n if key_id is not None:\\n self.assertEqual(entity.key().id(), key_id)\\n retrieved_entity = self._get_dataset().get_entity(entity.key())\\n # Check the keys are the same.\\n self.assertEqual(retrieved_entity.key().path(),\\n entity.key().path())\\n # Check the data is the same.\\n retrieved_dict = dict(retrieved_entity.items())\\n entity_dict = dict(entity.items())\\n self.assertEqual(retrieved_dict, entity_dict)\",\n \"label\": 0\n },\n {\n \"cid\": \"regression/datastore.py::TestDatastore.test_post_with_name\",\n \"text\": \"regression/datastore.py::TestDatastore.test_post_with_name\\n def test_post_with_name(self):\\n self._generic_test_post(name='post1')\",\n \"label\": 0\n },\n {\n \"cid\": \"regression/datastore.py::TestDatastore.test_post_with_id\",\n \"text\": \"regression/datastore.py::TestDatastore.test_post_with_id\\n def test_post_with_id(self):\\n self._generic_test_post(key_id=123456789)\",\n \"label\": 0\n },\n {\n \"cid\": \"regression/datastore.py::TestDatastore.test_post_with_generated_id\",\n \"text\": \"regression/datastore.py::TestDatastore.test_post_with_generated_id\\n def test_post_with_generated_id(self):\\n self._generic_test_post()\",\n \"label\": 0\n },\n {\n \"cid\": \"regression/datastore.py::TestDatastore.test_save_multiple\",\n \"text\": \"regression/datastore.py::TestDatastore.test_save_multiple\\n def test_save_multiple(self):\\n dataset = self._get_dataset()\\n with dataset.transaction():\\n entity1 = self._get_post()\\n entity1.save()\\n # Register entity to be deleted.\\n self.entities_to_delete.append(entity1)\\n\\n second_post_content = {\\n 'title': 'How to make the perfect homemade pasta',\\n 'tags': ['pasta', 'homemade'],\\n 'publishedAt': datetime.datetime(2001, 1, 1),\\n 'author': 'Silvano',\\n 'isDraft': False,\\n 'wordCount': 450,\\n 'rating': 4.5,\\n }\\n entity2 = self._get_post(post_content=second_post_content)\\n entity2.save()\\n # Register entity to be deleted.\\n self.entities_to_delete.append(entity2)\\n\\n keys = [entity1.key(), entity2.key()]\\n matches = dataset.get_entities(keys)\\n self.assertEqual(len(matches), 2)\",\n \"label\": 0\n },\n {\n \"cid\": \"regression/datastore.py::TestDatastore.test_empty_kind\",\n \"text\": \"regression/datastore.py::TestDatastore.test_empty_kind\\n def test_empty_kind(self):\\n posts = self._get_dataset().query().kind('Post').limit(2).fetch()\\n self.assertEqual(posts, [])\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":67,"cells":{"instance_id":{"kind":"string","value":"pantsbuild__pants-5386"},"repo":{"kind":"string","value":"pantsbuild/pants"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Using pantsd in multiple workspaces results in watchman crashes\nIf you use `pantsd` in multiple workspaces, the startup of the daemon in one workspace will cause watchman in the other workspace to crash with:\r\n```\r\nW1218 11:12:33.162800 41529 pants_daemon.py:50] Traceback (most recent call last):\r\nW1218 11:12:33.163083 41529 pants_daemon.py:50] File \"/opt/ee/python/2.7/lib/python2.7/threading.py\", line 810, in __bootstrap_inner\r\nW1218 11:12:33.163443 41529 pants_daemon.py:50] self.run()\r\nW1218 11:12:33.163623 41529 pants_daemon.py:50] File \"/opt/ee/python/2.7/lib/python2.7/threading.py\", line 763, in run\r\nW1218 11:12:33.163882 41529 pants_daemon.py:50] self.__target(*self.__args, **self.__kwargs)\r\nW1218 11:12:33.164051 41529 pants_daemon.py:50] File \"/Users/stuhood/.pex/install/pantsbuild.pants-1.4.0.dev16+811811700-cp27-none-macosx_10_8_x86_64.whl.f06e58a5054ed2f4674a13da4dee0c9606177989/pantsbuild.pants-1.4.0.dev16+811811700-cp27-none-macosx_10_8_x86_64.whl/pants/pantsd/service/fs_event_service.py\", line 116, in run\r\nW1218 11:12:33.164232 41529 pants_daemon.py:50] for handler_name, event_data in self._watchman.subscribed(self._build_root, subscriptions):\r\nW1218 11:12:33.164388 41529 pants_daemon.py:50] File \"/Users/stuhood/.pex/install/pantsbuild.pants-1.4.0.dev16+811811700-cp27-none-macosx_10_8_x86_64.whl.f06e58a5054ed2f4674a13da4dee0c9606177989/pantsbuild.pants-1.4.0.dev16+811811700-cp27-none-macosx_10_8_x86_64.whl/pants/pantsd/watchman.py\", line 188, in subscribed\r\nW1218 11:12:33.164566 41529 pants_daemon.py:50] raise self.WatchmanCrash(e)\r\nW1218 11:12:33.164716 41529 pants_daemon.py:50] WatchmanCrash: empty watchman response\r\n```\r\n\r\nProbably some watchman path is global, but shouldn't be.\n"},"gold_files":{"kind":"list like","value":["src/python/pants/option/global_options.py","src/python/pants/pantsd/watchman.py"],"string":"[\n \"src/python/pants/option/global_options.py\",\n \"src/python/pants/pantsd/watchman.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options","src/python/pants/pantsd/watchman.py::Watchman.__init__","src/python/pants/pantsd/watchman.py::Watchman._construct_cmd","src/python/pants/pantsd/watchman.py::Watchman.launch"],"string":"[\n \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\",\n \"src/python/pants/pantsd/watchman.py::Watchman.__init__\",\n \"src/python/pants/pantsd/watchman.py::Watchman._construct_cmd\",\n \"src/python/pants/pantsd/watchman.py::Watchman.launch\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"src/python/pants/backend/project_info/tasks/idea_gen.py","text":"src/python/pants/backend/project_info/tasks/idea_gen.py\nclass IdeaGen # Create an IntelliJ IDEA project from the given targets.\n def register_options(cls, register)\n def __init__(self, *args, **kwargs)\n def _maven_targets_excludes(repo_root)\n def annotation_processing_template(self)\n def generate_project(self, project)\n def create_content_root(source_set)\n def _generate_to_tempfile(self, generator) # Applies the specified generator to a temp file and returns the path to that file\n def _get_resource_extensions(self, project)\n def _parse_xml_component_elements(self, path) # Returns a list of pairs (component_name, xml_fragment) where xml_fragment is the\n def _get_components_to_merge(self, mergable_components, path) # Returns a list of the <component> fragments in mergable_components that are not","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py","text":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py\nclass IdeaPluginGen # Invoke IntelliJ Pants plugin (installation required) to create a project.\n def register_options(cls, register)\n def __init__(self, *args, **kwargs)\n def get_project_name(cls, target_specs)\n def generate_project(self)\n def _generate_to_tempfile(self, generator) # Applies the specified generator to a temp file and returns the path to that file\n def execute(self)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py","text":"src/python/pants/backend/python/tasks/pytest_run.py\nclass _Workdirs\n def for_partition(cls, work_dir, partition)\n def target_set_id(self, *targets)\n def junitxml_path(self, *targets)\n def coverage_path(self)\n def files(self)\n def files_iter()\nclass PytestResult\n def _map_exit_code(cls, value)\nclass PytestRun\n def implementation_version(cls)\n def register_options(cls, register)\n def supports_passthru_args(cls)\n def prepare(cls, options, round_manager)\n def _test_target_filter(self)\n def target_filter(target)\n def _validate_target(self, target)\n def _execute(self, all_targets)\n class InvalidShardSpecification # Indicates an invalid `--test-shard` option.\n def _format_string_list(values)\n def _debug(self)\n def _generate_coverage_config(self, source_mappings)\n def add_realpath(path)\n def _cov_setup(self, workdirs, source_mappings, coverage_sources=None)\n def _maybe_emit_coverage_data(self, workdirs, targets, pex)\n def ensure_trailing_sep(path)\n def _get_shard_conftest_content(self)\n def _get_conftest_content(self, sources_map)\n def _conftest(self, sources_map) # Creates a conftest.py to customize our pytest run.\n def _test_runner(self, workdirs, targets, sources_map)\n def _do_run_tests_with_args(self, pex, args)\n def _map_relsrc_to_targets(self, targets)\n def _get_failed_targets_from_junitxml(self, junitxml, targets)\n def _get_target_from_test(self, test_info, targets)\n def _iter_partitions(self, targets)\n def _run_tests(self, targets)\n def _vts_for_partition(invalidation_check)\n def check_artifact_cache_for(self, invalidation_check)\n class NeverCacheFingerprintStrategy\n def compute_fingerprint(self, target)\n def _fingerprint_strategy(self)\n def _do_run_tests(self, partition)\n def _expose_results(self, invalid_tgts, workdirs)\n def _run_pytest_checked(self, workdirs, targets)\n def _run_pytest(self, workdirs, targets)\n def _source_chroot_path(self)\n def _pex_run(self, pex, workunit_name, args, env)\n def _run_in_chroot(self)\n def _maybe_run_in_chroot(self)\n def _spawn(self, pex, workunit, args, setsid=False, env=None)","label":0},{"cid":"src/python/pants/option/global_options.py","text":"src/python/pants/option/global_options.py\nclass GlobalOptionsRegistrar\n def register_bootstrap_options(cls, register) # Register bootstrap options.\n def register_options(cls, register) # Register options not tied to any particular task or subsystem.","label":1},{"cid":"src/python/pants/pantsd/pants_daemon.py","text":"src/python/pants/pantsd/pants_daemon.py\nclass _LoggerStream # A sys.{stdout,stderr} replacement that pipes output to a logger.\n def __init__(self, logger, log_level, handler) # :param logging.Logger logger: The logger instance to emit writes to.\n def write(self, msg)\n def flush(self)\n def isatty(self)\n def fileno(self)\nclass PantsDaemon # A daemon that manages PantsService instances.\n class StartupFailure # Represents a failure to start pantsd.\n class RuntimeFailure # Represents a pantsd failure at runtime, usually from an underlying service failu\n class Factory\n def create(cls, bootstrap_options=None) # :param Options bootstrap_options: The bootstrap options, if available.\n def _parse_bootstrap_options()\n def _setup_legacy_graph_helper(native, bootstrap_options) # Initializes a `LegacyGraphHelper` instance.\n def _setup_services(build_root, bootstrap_options, legacy_graph_helper, watchman) # Initialize pantsd services.\n def __init__(self, native, build_root, work_dir, log_level, services, socket_map, metadata_base_dir, bootstrap_options=None) # :param Native native: A `Native` instance.\n def watchman_launcher(self)\n def is_killed(self)\n def options_fingerprint(self)\n def shutdown(self, service_thread_map) # Gracefully terminate all services and kill the main PantsDaemon loop.\n def _close_stdio() # Close stdio streams to avoid output in the tty that launched pantsd.\n def _pantsd_logging(self) # A context manager that runs with pantsd logging.\n def _setup_services(self, services)\n def _make_thread(target)\n def _run_services(self, services) # Service runner main loop.\n def _write_named_sockets(self, socket_map) # Write multiple named sockets using a socket mapping.\n def run_sync(self) # Synchronously run pantsd.\n def post_fork_child(self) # Post-fork() child callback for ProcessManager.daemon_spawn().\n def maybe_launch(self) # Launches pantsd (if not already running) in a subprocess.\n def terminate(self, include_watchman=True) # Terminates pantsd and watchman.\ndef launch() # An external entrypoint that spawns a new pantsd instance.","label":0},{"cid":"src/python/pants/pantsd/service/fs_event_service.py","text":"src/python/pants/pantsd/service/fs_event_service.py\nclass FSEventService # Filesystem Event Service.\n def __init__(self, watchman, build_root, worker_count) # :param Watchman watchman: The Watchman instance as provided by the WatchmanLaunc\n def setup(self, lifecycle_lock, fork_lock, executor=None)\n def terminate(self) # An extension of PantsService.terminate() that shuts down the executor if so conf\n def register_all_files_handler(self, callback, name='all_files') # Registers a subscription for all files under a given watch path.\n def register_handler(self, name, metadata, callback) # Register subscriptions and their event handlers.\n def fire_callback(self, handler_name, event_data) # Fire an event callback for a given handler.\n def run(self) # Main service entrypoint. Called via Thread.start() via PantsDaemon.run().","label":0},{"cid":"src/python/pants/pantsd/watchman.py","text":"src/python/pants/pantsd/watchman.py\nclass Watchman # Watchman process manager and helper class.\n class WatchmanCrash # Raised when Watchman crashes.\n def __init__(self, watchman_path, metadata_base_dir, log_level='1', startup_timeout=STARTUP_TIMEOUT_SECONDS, timeout=SOCKET_TIMEOUT_SECONDS, socket_path_override=None) # :param str watchman_path: The path to the watchman binary.\n def client(self)\n def _make_client(self) # Create a new watchman client using the BSER protocol over a UNIX socket.\n def _is_valid_executable(self, binary_path)\n def _normalize_watchman_path(self, watchman_path)\n def _maybe_init_metadata(self)\n def _construct_cmd(self, cmd_parts, state_file, sock_file, log_file, log_level)\n def _parse_pid_from_output(self, output)\n def launch(self) # Launch and synchronously write metadata.\n def _attempt_set_timeout(self, timeout) # Sets a timeout on the inner watchman client's socket.\n def watch_project(self, path) # Issues the watch-project command to watchman to begin watching the buildroot.\n def subscribed(self, build_root, handlers) # Bulk subscribe generator for StreamableWatchmanClient.","label":1}],"string":"[\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_gen.py\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_gen.py\\nclass IdeaGen # Create an IntelliJ IDEA project from the given targets.\\n def register_options(cls, register)\\n def __init__(self, *args, **kwargs)\\n def _maven_targets_excludes(repo_root)\\n def annotation_processing_template(self)\\n def generate_project(self, project)\\n def create_content_root(source_set)\\n def _generate_to_tempfile(self, generator) # Applies the specified generator to a temp file and returns the path to that file\\n def _get_resource_extensions(self, project)\\n def _parse_xml_component_elements(self, path) # Returns a list of pairs (component_name, xml_fragment) where xml_fragment is the\\n def _get_components_to_merge(self, mergable_components, path) # Returns a list of the <component> fragments in mergable_components that are not\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py\\nclass IdeaPluginGen # Invoke IntelliJ Pants plugin (installation required) to create a project.\\n def register_options(cls, register)\\n def __init__(self, *args, **kwargs)\\n def get_project_name(cls, target_specs)\\n def generate_project(self)\\n def _generate_to_tempfile(self, generator) # Applies the specified generator to a temp file and returns the path to that file\\n def execute(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py\\nclass _Workdirs\\n def for_partition(cls, work_dir, partition)\\n def target_set_id(self, *targets)\\n def junitxml_path(self, *targets)\\n def coverage_path(self)\\n def files(self)\\n def files_iter()\\nclass PytestResult\\n def _map_exit_code(cls, value)\\nclass PytestRun\\n def implementation_version(cls)\\n def register_options(cls, register)\\n def supports_passthru_args(cls)\\n def prepare(cls, options, round_manager)\\n def _test_target_filter(self)\\n def target_filter(target)\\n def _validate_target(self, target)\\n def _execute(self, all_targets)\\n class InvalidShardSpecification # Indicates an invalid `--test-shard` option.\\n def _format_string_list(values)\\n def _debug(self)\\n def _generate_coverage_config(self, source_mappings)\\n def add_realpath(path)\\n def _cov_setup(self, workdirs, source_mappings, coverage_sources=None)\\n def _maybe_emit_coverage_data(self, workdirs, targets, pex)\\n def ensure_trailing_sep(path)\\n def _get_shard_conftest_content(self)\\n def _get_conftest_content(self, sources_map)\\n def _conftest(self, sources_map) # Creates a conftest.py to customize our pytest run.\\n def _test_runner(self, workdirs, targets, sources_map)\\n def _do_run_tests_with_args(self, pex, args)\\n def _map_relsrc_to_targets(self, targets)\\n def _get_failed_targets_from_junitxml(self, junitxml, targets)\\n def _get_target_from_test(self, test_info, targets)\\n def _iter_partitions(self, targets)\\n def _run_tests(self, targets)\\n def _vts_for_partition(invalidation_check)\\n def check_artifact_cache_for(self, invalidation_check)\\n class NeverCacheFingerprintStrategy\\n def compute_fingerprint(self, target)\\n def _fingerprint_strategy(self)\\n def _do_run_tests(self, partition)\\n def _expose_results(self, invalid_tgts, workdirs)\\n def _run_pytest_checked(self, workdirs, targets)\\n def _run_pytest(self, workdirs, targets)\\n def _source_chroot_path(self)\\n def _pex_run(self, pex, workunit_name, args, env)\\n def _run_in_chroot(self)\\n def _maybe_run_in_chroot(self)\\n def _spawn(self, pex, workunit, args, setsid=False, env=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py\",\n \"text\": \"src/python/pants/option/global_options.py\\nclass GlobalOptionsRegistrar\\n def register_bootstrap_options(cls, register) # Register bootstrap options.\\n def register_options(cls, register) # Register options not tied to any particular task or subsystem.\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py\\nclass _LoggerStream # A sys.{stdout,stderr} replacement that pipes output to a logger.\\n def __init__(self, logger, log_level, handler) # :param logging.Logger logger: The logger instance to emit writes to.\\n def write(self, msg)\\n def flush(self)\\n def isatty(self)\\n def fileno(self)\\nclass PantsDaemon # A daemon that manages PantsService instances.\\n class StartupFailure # Represents a failure to start pantsd.\\n class RuntimeFailure # Represents a pantsd failure at runtime, usually from an underlying service failu\\n class Factory\\n def create(cls, bootstrap_options=None) # :param Options bootstrap_options: The bootstrap options, if available.\\n def _parse_bootstrap_options()\\n def _setup_legacy_graph_helper(native, bootstrap_options) # Initializes a `LegacyGraphHelper` instance.\\n def _setup_services(build_root, bootstrap_options, legacy_graph_helper, watchman) # Initialize pantsd services.\\n def __init__(self, native, build_root, work_dir, log_level, services, socket_map, metadata_base_dir, bootstrap_options=None) # :param Native native: A `Native` instance.\\n def watchman_launcher(self)\\n def is_killed(self)\\n def options_fingerprint(self)\\n def shutdown(self, service_thread_map) # Gracefully terminate all services and kill the main PantsDaemon loop.\\n def _close_stdio() # Close stdio streams to avoid output in the tty that launched pantsd.\\n def _pantsd_logging(self) # A context manager that runs with pantsd logging.\\n def _setup_services(self, services)\\n def _make_thread(target)\\n def _run_services(self, services) # Service runner main loop.\\n def _write_named_sockets(self, socket_map) # Write multiple named sockets using a socket mapping.\\n def run_sync(self) # Synchronously run pantsd.\\n def post_fork_child(self) # Post-fork() child callback for ProcessManager.daemon_spawn().\\n def maybe_launch(self) # Launches pantsd (if not already running) in a subprocess.\\n def terminate(self, include_watchman=True) # Terminates pantsd and watchman.\\ndef launch() # An external entrypoint that spawns a new pantsd instance.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/service/fs_event_service.py\",\n \"text\": \"src/python/pants/pantsd/service/fs_event_service.py\\nclass FSEventService # Filesystem Event Service.\\n def __init__(self, watchman, build_root, worker_count) # :param Watchman watchman: The Watchman instance as provided by the WatchmanLaunc\\n def setup(self, lifecycle_lock, fork_lock, executor=None)\\n def terminate(self) # An extension of PantsService.terminate() that shuts down the executor if so conf\\n def register_all_files_handler(self, callback, name='all_files') # Registers a subscription for all files under a given watch path.\\n def register_handler(self, name, metadata, callback) # Register subscriptions and their event handlers.\\n def fire_callback(self, handler_name, event_data) # Fire an event callback for a given handler.\\n def run(self) # Main service entrypoint. Called via Thread.start() via PantsDaemon.run().\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py\",\n \"text\": \"src/python/pants/pantsd/watchman.py\\nclass Watchman # Watchman process manager and helper class.\\n class WatchmanCrash # Raised when Watchman crashes.\\n def __init__(self, watchman_path, metadata_base_dir, log_level='1', startup_timeout=STARTUP_TIMEOUT_SECONDS, timeout=SOCKET_TIMEOUT_SECONDS, socket_path_override=None) # :param str watchman_path: The path to the watchman binary.\\n def client(self)\\n def _make_client(self) # Create a new watchman client using the BSER protocol over a UNIX socket.\\n def _is_valid_executable(self, binary_path)\\n def _normalize_watchman_path(self, watchman_path)\\n def _maybe_init_metadata(self)\\n def _construct_cmd(self, cmd_parts, state_file, sock_file, log_file, log_level)\\n def _parse_pid_from_output(self, output)\\n def launch(self) # Launch and synchronously write metadata.\\n def _attempt_set_timeout(self, timeout) # Sets a timeout on the inner watchman client's socket.\\n def watch_project(self, path) # Issues the watch-project command to watchman to begin watching the buildroot.\\n def subscribed(self, build_root, handlers) # Bulk subscribe generator for StreamableWatchmanClient.\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.register_options","text":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.register_options\n @classmethod\n def register_options(cls, register):\n super(IdeaGen, cls).register_options(register)\n register('--version', choices=sorted(list(_VERSIONS.keys())), default='11',\n help='The IntelliJ IDEA version the project config should be generated for.')\n register('--merge', type=bool, default=True,\n help='Merge any manual customizations in existing '\n 'Intellij IDEA configuration. If False, manual customizations '\n 'will be over-written.')\n register('--open', type=bool, default=True,\n help='Attempts to open the generated project in IDEA.')\n register('--bash', type=bool,\n help='Adds a bash facet to the generated project configuration.')\n register('--scala-language-level',\n choices=_SCALA_VERSIONS.keys(), default=_SCALA_VERSION_DEFAULT,\n help='Set the scala language level used for IDEA linting.')\n register('--scala-maximum-heap-size-mb', type=int, default=512,\n help='Sets the maximum heap size (in megabytes) for scalac.')\n register('--fsc', type=bool,\n help='If the project contains any scala targets this specifies the '\n 'fsc compiler should be enabled.')\n register('--java-encoding', default='UTF-8',\n help='Sets the file encoding for java files in this project.')\n register('--java-maximum-heap-size-mb', type=int, default=512,\n help='Sets the maximum heap size (in megabytes) for javac.')\n register('--exclude-maven-target', type=bool,\n help=\"Exclude 'target' directories for directories containing \"\n \"pom.xml files. These directories contain generated code and\"\n \"copies of files staged for deployment.\")\n register('--exclude_folders', type=list,\n default=[\n '.pants.d/compile',\n '.pants.d/ivy',\n ","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.__init__","text":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.__init__\n def __init__(self, *args, **kwargs):\n super(IdeaGen, self).__init__(*args, **kwargs)\n\n self.intellij_output_dir = os.path.join(self.gen_project_workdir, 'out')\n self.nomerge = not self.get_options().merge\n self.open = self.get_options().open\n self.bash = self.get_options().bash\n\n self.scala_language_level = _SCALA_VERSIONS.get(\n self.get_options().scala_language_level, None)\n self.scala_maximum_heap_size = self.get_options().scala_maximum_heap_size_mb\n\n self.fsc = self.get_options().fsc\n\n self.java_encoding = self.get_options().java_encoding\n self.java_maximum_heap_size = self.get_options().java_maximum_heap_size_mb\n\n idea_version = _VERSIONS[self.get_options().version]\n self.project_template = os.path.join(_TEMPLATE_BASEDIR,\n 'project-{}.mustache'.format(idea_version))\n self.module_template = os.path.join(_TEMPLATE_BASEDIR,\n 'module-{}.mustache'.format(idea_version))\n\n self.project_filename = os.path.join(self.cwd,\n '{}.ipr'.format(self.project_name))\n self.module_filename = os.path.join(self.gen_project_workdir,\n '{}.iml'.format(self.project_name))","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._maven_targets_excludes","text":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._maven_targets_excludes\n @staticmethod\n def _maven_targets_excludes(repo_root):\n excludes = []\n for (dirpath, dirnames, filenames) in safe_walk(repo_root):\n if \"pom.xml\" in filenames:\n excludes.append(os.path.join(os.path.relpath(dirpath, start=repo_root), \"target\"))\n return excludes","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.annotation_processing_template","text":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.annotation_processing_template\n @property\n def annotation_processing_template(self):\n return TemplateData(\n enabled=self.get_options().annotation_processing_enabled,\n rel_source_output_dir=os.path.join('..','..','..',\n self.get_options().annotation_generated_sources_dir),\n source_output_dir=\n os.path.join(self.gen_project_workdir,\n self.get_options().annotation_generated_sources_dir),\n rel_test_source_output_dir=os.path.join('..','..','..',\n self.get_options().annotation_generated_test_sources_dir),\n test_source_output_dir=\n os.path.join(self.gen_project_workdir,\n self.get_options().annotation_generated_test_sources_dir),\n processors=[{'class_name' : processor}\n for processor in self.get_options().annotation_processor],\n )","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.generate_project","text":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.generate_project\n def generate_project(self, project):\n def create_content_root(source_set):\n root_relative_path = os.path.join(source_set.source_base, source_set.path) \\\n if source_set.path else source_set.source_base\n if source_set.resources_only:\n if source_set.is_test:\n content_type = 'java-test-resource'\n else:\n content_type = 'java-resource'\n else:\n content_type = ''\n\n sources = TemplateData(\n path=root_relative_path,\n package_prefix=source_set.path.replace('/', '.') if source_set.path else None,\n is_test=source_set.is_test,\n content_type=content_type\n )\n\n return TemplateData(\n path=root_relative_path,\n sources=[sources],\n exclude_paths=[os.path.join(source_set.source_base, x) for x in source_set.excludes],\n )\n\n content_roots = [create_content_root(source_set) for source_set in project.sources]\n if project.has_python:\n content_roots.extend(create_content_root(source_set) for source_set in project.py_sources)\n\n scala = None\n if project.has_scala:\n scala = TemplateData(\n language_level=self.scala_language_level,\n maximum_heap_size=self.scala_maximum_heap_size,\n fsc=self.fsc,\n compiler_classpath=project.scala_compiler_classpath\n )\n\n exclude_folders = []\n if self.get_options().exclude_maven_target:\n exclude_folders += IdeaGen._maven_targets_excludes(get_buildroot())\n\n exclude_folders += self.get_options().exclude_folders\n\n java_language_level = None\n for target in project.targets:\n if isinstance(target, JvmTarget):\n if java_language_level is None or java_language_level < target.platform.source_level:\n java_language_level = target.platform.source_level\n if java_language_level is not None:\n java_language_level = 'JDK_{0}_{1}'.format(*java_language_le","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.generate_project.create_content_root","text":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.generate_project.create_content_root\n def create_content_root(source_set):\n root_relative_path = os.path.join(source_set.source_base, source_set.path) \\\n if source_set.path else source_set.source_base\n if source_set.resources_only:\n if source_set.is_test:\n content_type = 'java-test-resource'\n else:\n content_type = 'java-resource'\n else:\n content_type = ''\n\n sources = TemplateData(\n path=root_relative_path,\n package_prefix=source_set.path.replace('/', '.') if source_set.path else None,\n is_test=source_set.is_test,\n content_type=content_type\n )\n\n return TemplateData(\n path=root_relative_path,\n sources=[sources],\n exclude_paths=[os.path.join(source_set.source_base, x) for x in source_set.excludes],\n )","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._generate_to_tempfile","text":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._generate_to_tempfile\n def _generate_to_tempfile(self, generator):\n \"\"\"Applies the specified generator to a temp file and returns the path to that file.\n We generate into a temp file so that we don't lose any manual customizations on error.\"\"\"\n (output_fd, output_path) = tempfile.mkstemp()\n with os.fdopen(output_fd, 'w') as output:\n generator.write(output)\n return output_path","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._get_resource_extensions","text":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._get_resource_extensions\n def _get_resource_extensions(self, project):\n resource_extensions = set()\n resource_extensions.update(project.resource_extensions)\n\n # TODO(John Sirois): make test resources 1st class in ant build and punch this through to pants\n # model\n for _, _, files in safe_walk(os.path.join(get_buildroot(), 'tests', 'resources')):\n resource_extensions.update(Project.extract_resource_extensions(files))\n\n return resource_extensions","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._parse_xml_component_elements","text":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._parse_xml_component_elements\n def _parse_xml_component_elements(self, path):\n \"\"\"Returns a list of pairs (component_name, xml_fragment) where xml_fragment is the xml text of\n that <component> in the specified xml file.\"\"\"\n if not os.path.exists(path):\n return [] # No existing components.\n dom = minidom.parse(path)\n # .ipr and .iml files both consist of <component> elements directly under a root element.\n return [(x.getAttribute('name'), x.toxml()) for x in dom.getElementsByTagName('component')]","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._get_components_to_merge","text":"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._get_components_to_merge\n def _get_components_to_merge(self, mergable_components, path):\n \"\"\"Returns a list of the <component> fragments in mergable_components that are not\n superceded by a <component> in the specified xml file.\n mergable_components is a list of (name, xml_fragment) pairs.\"\"\"\n\n # As a convenience, we use _parse_xml_component_elements to get the\n # superceding component names, ignoring the generated xml fragments.\n # This is fine, since performance is not an issue.\n generated_component_names = set(\n [name for (name, _) in self._parse_xml_component_elements(path)])\n return [x[1] for x in mergable_components if x[0] not in generated_component_names]","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.register_options","text":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.register_options\n @classmethod\n def register_options(cls, register):\n super(IdeaPluginGen, cls).register_options(register)\n # TODO: https://github.com/pantsbuild/pants/issues/3198\n # scala/java-language level should use what Pants already knows.\n register('--open', type=bool, default=True,\n help='Attempts to open the generated project in IDEA.')\n register('--java-encoding', default='UTF-8',\n help='Sets the file encoding for java files in this project.')\n register('--open-with', type=str, default=None, recursive=True,\n help='Program used to open the generated IntelliJ project.')\n register('--debug_port', type=int, default=5005,\n help='Port to use for launching tasks under the debugger.')\n register('--java-jdk-name', default=None,\n help='Sets the jdk used to compile the project\\'s java sources. If unset the default '\n 'jdk name for the --java-language-level is used')\n register('--java-language-level', type=int, default=8,\n help='Sets the java language and jdk used to compile the project\\'s java sources.')","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.__init__","text":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.__init__\n def __init__(self, *args, **kwargs):\n super(IdeaPluginGen, self).__init__(*args, **kwargs)\n\n self.open = self.get_options().open\n\n self.java_encoding = self.get_options().java_encoding\n self.project_template = os.path.join(_TEMPLATE_BASEDIR,\n 'project-12.mustache')\n self.workspace_template = os.path.join(_TEMPLATE_BASEDIR,\n 'workspace-12.mustache')\n self.java_language_level = self.get_options().java_language_level\n\n if self.get_options().java_jdk_name:\n self.java_jdk = self.get_options().java_jdk_name\n else:\n self.java_jdk = '1.{}'.format(self.java_language_level)\n\n output_dir = os.path.join(get_buildroot(), \".idea\", self.__class__.__name__)\n safe_mkdir(output_dir)\n\n with temporary_dir(root_dir=output_dir, cleanup=False) as output_project_dir:\n project_name = self.get_project_name(self.context.options.target_specs)\n\n self.gen_project_workdir = output_project_dir\n self.project_filename = os.path.join(self.gen_project_workdir,\n '{}.ipr'.format(project_name))\n self.workspace_filename = os.path.join(self.gen_project_workdir,\n '{}.iws'.format(project_name))\n self.intellij_output_dir = os.path.join(self.gen_project_workdir, 'out')","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.get_project_name","text":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.get_project_name\n @classmethod\n def get_project_name(cls, target_specs):\n # take up to PROJECT_NAME_LIMIT chars as project file name due to filesystem constraint.\n return re.sub('[^0-9a-zA-Z:_]+', '.', '__'.join(target_specs))[:cls.PROJECT_NAME_LIMIT]","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.generate_project","text":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.generate_project\n def generate_project(self):\n outdir = os.path.abspath(self.intellij_output_dir)\n if not os.path.exists(outdir):\n os.makedirs(outdir)\n\n scm = get_scm()\n configured_project = TemplateData(\n root_dir=get_buildroot(),\n outdir=outdir,\n git_root=scm.worktree,\n java=TemplateData(\n encoding=self.java_encoding,\n jdk=self.java_jdk,\n language_level='JDK_1_{}'.format(self.java_language_level)\n ),\n debug_port=self.get_options().debug_port,\n )\n\n if not self.context.options.target_specs:\n raise TaskError(\"No targets specified.\")\n\n abs_target_specs = [os.path.join(get_buildroot(), spec) for spec in self.context.options.target_specs]\n configured_workspace = TemplateData(\n targets=json.dumps(abs_target_specs),\n project_path=os.path.join(get_buildroot(), abs_target_specs[0].split(':')[0]),\n idea_plugin_version=IDEA_PLUGIN_VERSION\n )\n\n # Generate (without merging in any extra components).\n safe_mkdir(os.path.abspath(self.intellij_output_dir))\n\n ipr = self._generate_to_tempfile(\n Generator(pkgutil.get_data(__name__, self.project_template), project=configured_project))\n iws = self._generate_to_tempfile(\n Generator(pkgutil.get_data(__name__, self.workspace_template), workspace=configured_workspace))\n\n self._outstream.write(self.gen_project_workdir)\n\n shutil.move(ipr, self.project_filename)\n shutil.move(iws, self.workspace_filename)\n return self.project_filename","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen._generate_to_tempfile","text":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen._generate_to_tempfile\n def _generate_to_tempfile(self, generator):\n \"\"\"Applies the specified generator to a temp file and returns the path to that file.\n We generate into a temp file so that we don't lose any manual customizations on error.\"\"\"\n with temporary_file(cleanup=False) as output:\n generator.write(output)\n return output.name","label":0},{"cid":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.execute","text":"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.execute\n def execute(self):\n # Heuristics to guess whether user tries to load a python project,\n # in which case intellij project sdk has to be set up manually.\n jvm_target_num = len(filter(lambda x: isinstance(x, JvmTarget), self.context.target_roots))\n python_target_num = len(filter(lambda x: isinstance(x, PythonTarget), self.context.target_roots))\n if python_target_num > jvm_target_num:\n logging.warn('This is likely a python project. Please make sure to '\n 'select the proper python interpreter as Project SDK in IntelliJ.')\n\n ide_file = self.generate_project()\n if ide_file and self.get_options().open:\n open_with = self.get_options().open_with\n if open_with:\n null = open(os.devnull, 'w')\n subprocess.Popen([open_with, ide_file], stdout=null, stderr=null)\n else:\n try:\n desktop.ui_open(ide_file)\n except desktop.OpenError as e:\n raise TaskError(e)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.for_partition","text":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.for_partition\n @classmethod\n def for_partition(cls, work_dir, partition):\n root_dir = os.path.join(work_dir, Target.maybe_readable_identify(partition))\n safe_mkdir(root_dir, clean=False)\n return cls(root_dir=root_dir, partition=partition)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.target_set_id","text":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.target_set_id\n @memoized_method\n def target_set_id(self, *targets):\n return Target.maybe_readable_identify(targets or self.partition)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.junitxml_path","text":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.junitxml_path\n @memoized_method\n def junitxml_path(self, *targets):\n xml_path = os.path.join(self.root_dir, 'junitxml',\n 'TEST-{}.xml'.format(self.target_set_id(*targets)))\n safe_mkdir_for(xml_path)\n return xml_path","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.coverage_path","text":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.coverage_path\n @memoized_property\n def coverage_path(self):\n coverage_workdir = os.path.join(self.root_dir, 'coverage')\n safe_mkdir(coverage_workdir)\n return coverage_workdir","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.files","text":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.files\n def files(self):\n def files_iter():\n for dir_path, _, file_names in os.walk(self.root_dir):\n for filename in file_names:\n yield os.path.join(dir_path, filename)\n return list(files_iter())","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.files.files_iter","text":"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.files.files_iter\n def files_iter():\n for dir_path, _, file_names in os.walk(self.root_dir):\n for filename in file_names:\n yield os.path.join(dir_path, filename)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestResult._map_exit_code","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestResult._map_exit_code\n @classmethod\n def _map_exit_code(cls, value):\n return 0 if value in cls._SUCCESS_EXIT_CODES else value","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.implementation_version","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.implementation_version\n @classmethod\n def implementation_version(cls):\n return super(PytestRun, cls).implementation_version() + [('PytestRun', 2)]","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.register_options","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.register_options\n @classmethod\n def register_options(cls, register):\n super(PytestRun, cls).register_options(register)\n register('--fast', type=bool, default=True, fingerprint=True,\n help='Run all tests in a single pytest invocation. If turned off, each test target '\n 'will run in its own pytest invocation, which will be slower, but isolates '\n 'tests from process-wide state created by tests in other targets.')\n\n register('--chroot', advanced=True, fingerprint=True, type=bool, default=False,\n help='Run tests in a chroot. Any loose files tests depend on via `{}` dependencies '\n 'will be copied to the chroot.'\n .format(Files.alias()))\n\n # NB: We always produce junit xml privately, and if this option is specified, we then copy\n # it to the user-specified directory, post any interaction with the cache to retrieve the\n # privately generated and cached xml files. As such, this option is not part of the\n # fingerprint.\n register('--junit-xml-dir', metavar='<DIR>',\n help='Specifying a directory causes junit xml results files to be emitted under '\n 'that dir for each test run.')\n\n register('--profile', metavar='<FILE>', fingerprint=True,\n help=\"Specifying a file path causes tests to be profiled with the profiling data \"\n \"emitted to that file (prefix). Note that tests may run in a different cwd, so \"\n \"it's best to use an absolute path to make it easy to find the subprocess \"\n \"profiles later.\")\n\n register('--options', type=list, fingerprint=True, help='Pass these options to pytest.')\n\n register('--coverage', fingerprint=True,\n help='Emit coverage information for specified packages or directories (absolute or '\n 'relative to the build root). The special value \"auto\" indicat","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.supports_passthru_args","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.supports_passthru_args\n @classmethod\n def supports_passthru_args(cls):\n return True","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.prepare","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.prepare\n @classmethod\n def prepare(cls, options, round_manager):\n super(PytestRun, cls).prepare(options, round_manager)\n round_manager.require_data(PytestPrep.PYTEST_BINARY)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_target_filter","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_target_filter\n def _test_target_filter(self):\n def target_filter(target):\n return isinstance(target, PythonTests)\n\n return target_filter","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_target_filter.target_filter","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_target_filter.target_filter\n def target_filter(target):\n return isinstance(target, PythonTests)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._validate_target","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._validate_target\n def _validate_target(self, target):\n pass","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._execute","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._execute\n def _execute(self, all_targets):\n test_targets = self._get_test_targets()\n if test_targets:\n self.context.release_lock()\n self._run_tests(test_targets)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._format_string_list","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._format_string_list\n @staticmethod\n def _format_string_list(values):\n # The coverage rc ini files accept \"Multi-valued strings\" - ie: lists of strings - denoted by\n # indenting values on multiple lines like so:\n # [section]\n # name =\n # value1\n # value2\n #\n # See http://nedbatchelder.com/code/coverage/config.html for details.\n return '\\n\\t{values}'.format(values='\\n\\t'.join(values))","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._debug","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._debug\n @property\n def _debug(self):\n return self.get_options().level == 'debug'","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._generate_coverage_config","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._generate_coverage_config\n def _generate_coverage_config(self, source_mappings):\n # For the benefit of macos testing, add the 'real' path the directory as an equivalent.\n def add_realpath(path):\n realpath = os.path.realpath(path)\n if realpath != canonical and realpath not in alternates:\n realpaths.add(realpath)\n\n cp = configparser.SafeConfigParser()\n cp.readfp(StringIO(self.DEFAULT_COVERAGE_CONFIG))\n\n # We use the source_mappings to setup the `combine` coverage command to transform paths in\n # coverage data files into canonical form.\n # See the \"[paths]\" entry here: http://nedbatchelder.com/code/coverage/config.html for details.\n cp.add_section('paths')\n for canonical, alternates in source_mappings.items():\n key = canonical.replace(os.sep, '.')\n realpaths = set()\n add_realpath(canonical)\n for path in alternates:\n add_realpath(path)\n cp.set('paths',\n key,\n self._format_string_list([canonical] + list(alternates) + list(realpaths)))\n\n # See the debug options here: http://nedbatchelder.com/code/coverage/cmd.html#cmd-run-debug\n if self._debug:\n debug_options = self._format_string_list([\n # Dumps the coverage config realized values.\n 'config',\n # Logs which files are skipped or traced and why.\n 'trace'])\n cp.set('run', 'debug', debug_options)\n\n return cp","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._generate_coverage_config.add_realpath","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._generate_coverage_config.add_realpath\n def add_realpath(path):\n realpath = os.path.realpath(path)\n if realpath != canonical and realpath not in alternates:\n realpaths.add(realpath)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._cov_setup","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._cov_setup\n @contextmanager\n def _cov_setup(self, workdirs, source_mappings, coverage_sources=None):\n cp = self._generate_coverage_config(source_mappings=source_mappings)\n # Note that it's important to put the tmpfile under the workdir, because pytest\n # uses all arguments that look like paths to compute its rootdir, and we want\n # it to pick the buildroot.\n with temporary_file(root_dir=workdirs.root_dir) as fp:\n cp.write(fp)\n fp.close()\n coverage_rc = fp.name\n # Note that --cov-report= with no value turns off terminal reporting, which\n # we handle separately.\n args = ['--cov-report=', '--cov-config', coverage_rc]\n for module in coverage_sources:\n args.extend(['--cov', module])\n yield args, coverage_rc","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._maybe_emit_coverage_data","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._maybe_emit_coverage_data\n @contextmanager\n def _maybe_emit_coverage_data(self, workdirs, targets, pex):\n coverage = self.get_options().coverage\n if coverage is None:\n yield []\n return\n\n pex_src_root = os.path.relpath(self._source_chroot_path, get_buildroot())\n\n source_mappings = {}\n for target in targets:\n libs = (tgt for tgt in target.closure()\n if tgt.has_sources('.py') and not isinstance(tgt, PythonTests))\n for lib in libs:\n source_mappings[lib.target_base] = [pex_src_root]\n\n def ensure_trailing_sep(path):\n return path if path.endswith(os.path.sep) else path + os.path.sep\n\n if coverage == 'auto':\n def compute_coverage_sources(tgt):\n if tgt.coverage:\n return tgt.coverage\n else:\n # This makes the assumption that tests/python/<tgt> will be testing src/python/<tgt>.\n # Note in particular that this doesn't work for pants' own tests, as those are under\n # the top level package 'pants_tests', rather than just 'pants'.\n # TODO(John Sirois): consider failing fast if there is no explicit coverage scheme;\n # but also consider supporting configuration of a global scheme whether that be parallel\n # dirs/packages or some arbitrary function that can be registered that takes a test target\n # and hands back the source packages or paths under test.\n return set(os.path.dirname(s).replace(os.sep, '.')\n for s in tgt.sources_relative_to_source_root())\n coverage_sources = set(itertools.chain(*[compute_coverage_sources(t) for t in targets]))\n else:\n coverage_sources = []\n for source in coverage.split(','):\n if os.path.isdir(source):\n # The source is a dir, so correct its prefix for the chroot.\n # E.g. if source is /path/to/src/python/foo/bar or src/python/foo/bar then\n # rel_source is sr","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._maybe_emit_coverage_data.ensure_trailing_sep","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._maybe_emit_coverage_data.ensure_trailing_sep\n def ensure_trailing_sep(path):\n return path if path.endswith(os.path.sep) else path + os.path.sep","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_shard_conftest_content","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_shard_conftest_content\n def _get_shard_conftest_content(self):\n shard_spec = self.get_options().test_shard\n if shard_spec is None:\n return ''\n\n try:\n sharder = Sharder(shard_spec)\n if sharder.nshards < 2:\n return ''\n return dedent(\"\"\"\n\n ### GENERATED BY PANTS ###\n\n def pytest_report_header(config):\n return 'shard: {shard} of {nshards} (0-based shard numbering)'\n\n def pytest_collection_modifyitems(session, config, items):\n total_count = len(items)\n removed = 0\n def is_conftest(itm):\n return itm.fspath and itm.fspath.basename == 'conftest.py'\n for i, item in enumerate(list(x for x in items if not is_conftest(x))):\n if i % {nshards} != {shard}:\n del items[i - removed]\n removed += 1\n reporter = config.pluginmanager.getplugin('terminalreporter')\n reporter.write_line('Only executing {{}} of {{}} total tests in shard {shard} of '\n '{nshards}'.format(total_count - removed, total_count),\n bold=True, invert=True, yellow=True)\n \"\"\".format(shard=sharder.shard, nshards=sharder.nshards))\n except Sharder.InvalidShardSpec as e:\n raise self.InvalidShardSpecification(e)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_conftest_content","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_conftest_content\n def _get_conftest_content(self, sources_map):\n # A conftest hook to modify the console output, replacing the chroot-based\n # source paths with the source-tree based ones, which are more readable to the end user.\n # Note that python stringifies a dict to its source representation, so we can use sources_map\n # as a format argument directly.\n #\n # We'd prefer to hook into pytest_runtest_logstart(), which actually prints the line we\n # want to fix, but we can't because we won't have access to any of its state, so\n # we can't actually change what it prints.\n #\n # Alternatively, we could hook into pytest_collect_file() and just set a custom nodeid\n # for the entire pytest run. However this interferes with pytest internals, including\n # fixture registration, leading to fixtures not running when they should.\n # It also requires the generated conftest to be in the root of the source tree, which\n # complicates matters when there's already a user conftest.py there.\n console_output_conftest_content = dedent(\"\"\"\n\n ### GENERATED BY PANTS ###\n\n import pytest\n\n # Map from source path relative to chroot -> source path relative to buildroot.\n _SOURCES_MAP = {}\n\n @pytest.hookimpl(hookwrapper=True)\n def pytest_runtest_protocol(item, nextitem):\n # Temporarily change the nodeid, which pytest uses for display here.\n real_nodeid = item.nodeid\n real_path = real_nodeid.split('::', 1)[0]\n fixed_path = _SOURCES_MAP.get(real_path, real_path)\n fixed_nodeid = fixed_path + real_nodeid[len(real_path):]\n try:\n item._nodeid = fixed_nodeid\n yield\n finally:\n item._nodeid = real_nodeid\n \"\"\".format(sources_map))\n # Add in the sharding conftest, if any.\n shard_conftest_content = self._get_shard_conftest_content()\n return (console_output_conftest_content + sh","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._conftest","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._conftest\n @contextmanager\n def _conftest(self, sources_map):\n \"\"\"Creates a conftest.py to customize our pytest run.\"\"\"\n conftest_content = self._get_conftest_content(sources_map)\n # Note that it's important to put the tmpdir under the workdir, because pytest\n # uses all arguments that look like paths to compute its rootdir, and we want\n # it to pick the buildroot.\n with temporary_dir(root_dir=self.workdir) as conftest_dir:\n conftest = os.path.join(conftest_dir, 'conftest.py')\n with open(conftest, 'w') as fp:\n fp.write(conftest_content)\n yield conftest","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_runner","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_runner\n @contextmanager\n def _test_runner(self, workdirs, targets, sources_map):\n pex = self.context.products.get_data(PytestPrep.PYTEST_BINARY)\n with self._conftest(sources_map) as conftest:\n with self._maybe_emit_coverage_data(workdirs, targets, pex) as coverage_args:\n yield pex, [conftest] + coverage_args","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._do_run_tests_with_args","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._do_run_tests_with_args\n def _do_run_tests_with_args(self, pex, args):\n try:\n # The pytest runner we use accepts a --pdb argument that will launch an interactive pdb\n # session on any test failure. In order to support use of this pass-through flag we must\n # turn off stdin buffering that otherwise occurs. Setting the PYTHONUNBUFFERED env var to\n # any value achieves this in python2.7. We'll need a different solution when we support\n # running pants under CPython 3 which does not unbuffer stdin using this trick.\n env = dict(os.environ)\n env['PYTHONUNBUFFERED'] = '1'\n\n # pytest uses py.io.terminalwriter for output. That class detects the terminal\n # width and attempts to use all of it. However we capture and indent the console\n # output, leading to weird-looking line wraps. So we trick the detection code\n # into thinking the terminal window is narrower than it is.\n env['COLUMNS'] = str(int(os.environ.get('COLUMNS', 80)) - 30)\n\n profile = self.get_options().profile\n if profile:\n env['PEX_PROFILE_FILENAME'] = '{0}.subprocess.{1:.6f}'.format(profile, time.time())\n\n with self.context.new_workunit(name='run',\n cmd=pex.cmdline(args),\n labels=[WorkUnitLabel.TOOL, WorkUnitLabel.TEST]) as workunit:\n rc = self._spawn_and_wait(pex, workunit=workunit, args=args, setsid=True, env=env)\n return PytestResult.rc(rc)\n except ErrorWhileTesting:\n # _spawn_and_wait wraps the test runner in a timeout, so it could\n # fail with a ErrorWhileTesting. We can't just set PythonTestResult\n # to a failure because the resultslog doesn't have all the failures\n # when tests are killed with a timeout. Therefore we need to re-raise\n # here.\n raise\n except Exception:\n self.context.log.error('Failed to run test!')\n self.contex","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._map_relsrc_to_targets","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._map_relsrc_to_targets\n def _map_relsrc_to_targets(self, targets):\n pex_src_root = os.path.relpath(self._source_chroot_path, get_buildroot())\n # First map chrooted sources back to their targets.\n relsrc_to_target = {os.path.join(pex_src_root, src): target for target in targets\n for src in target.sources_relative_to_source_root()}\n # Also map the source tree-rooted sources, because in some cases (e.g., a failure to even\n # eval the test file during test collection), that's the path pytest will use in the junit xml.\n relsrc_to_target.update({src: target for target in targets\n for src in target.sources_relative_to_buildroot()})\n\n return relsrc_to_target","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_failed_targets_from_junitxml","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_failed_targets_from_junitxml\n def _get_failed_targets_from_junitxml(self, junitxml, targets):\n relsrc_to_target = self._map_relsrc_to_targets(targets)\n\n # Now find the sources that contained failing tests.\n failed_targets = set()\n\n try:\n xml = XmlParser.from_file(junitxml)\n failures = int(xml.get_attribute('testsuite', 'failures'))\n errors = int(xml.get_attribute('testsuite', 'errors'))\n if failures or errors:\n for testcase in xml.parsed.getElementsByTagName('testcase'):\n test_failed = testcase.getElementsByTagName('failure')\n test_errored = testcase.getElementsByTagName('error')\n if test_failed or test_errored:\n # The 'file' attribute is a relsrc, because that's what we passed in to pytest.\n failed_targets.add(relsrc_to_target.get(testcase.getAttribute('file')))\n except (XmlParser.XmlError, ValueError) as e:\n raise TaskError('Error parsing xml file at {}: {}'.format(junitxml, e))\n\n return failed_targets","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_target_from_test","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_target_from_test\n def _get_target_from_test(self, test_info, targets):\n relsrc_to_target = self._map_relsrc_to_targets(targets)\n file_info = test_info['file']\n return relsrc_to_target.get(file_info)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._iter_partitions","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._iter_partitions\n def _iter_partitions(self, targets):\n # TODO(John Sirois): Consume `py.test` pexes matched to the partitioning in effect after\n # https://github.com/pantsbuild/pants/pull/4638 lands.\n if self.get_options().fast:\n yield tuple(targets)\n else:\n for target in targets:\n yield (target,)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_tests","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_tests\n def _run_tests(self, targets):\n results = {}\n failure = False\n for partition in self._iter_partitions(targets):\n try:\n rv = self._do_run_tests(partition)\n except ErrorWhileTesting as e:\n rv = PytestResult.from_error(e)\n results[partition] = rv\n if not rv.success:\n failure = True\n if self.get_options().fail_fast:\n break\n\n for partition in sorted(results):\n rv = results[partition]\n if len(partition) == 1 or rv.success:\n log = self.context.log.info if rv.success else self.context.log.error\n for target in partition:\n log('{0:80}.....{1:>10}'.format(target.address.reference(), rv))\n else:\n # There is not much useful we can display in summary for a multi-target partition with\n # failures without parsing those failures to link them to individual targets; ie: targets\n # 2 and 8 failed in this partition of 10 targets.\n # TODO(John Sirois): Punting here works since we have in practice just 2 partitionings:\n # 1. All targets in singleton partitions\n # 2. All targets in 1 partition\n # If we get to the point where we have multiple partitions with multiple targets, some sort\n # of summary for the multi-target partitions will probably be needed.\n pass\n\n failed_targets = [target\n for _rv in results.values() if not _rv.success\n for target in _rv.failed_targets]\n if failed_targets:\n raise ErrorWhileTesting(failed_targets=failed_targets)\n elif failure:\n # A low-level test execution failure occurred before tests were run.\n raise TaskError()","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._vts_for_partition","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._vts_for_partition\n @staticmethod\n def _vts_for_partition(invalidation_check):\n return VersionedTargetSet.from_versioned_targets(invalidation_check.all_vts)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.check_artifact_cache_for","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.check_artifact_cache_for\n def check_artifact_cache_for(self, invalidation_check):\n # We generate artifacts, namely junit.xml and coverage reports, that cover the full target set\n # whether that is all targets in the context (`--fast`) or each target\n # individually (`--no-fast`).\n return [self._vts_for_partition(invalidation_check)]","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.NeverCacheFingerprintStrategy.compute_fingerprint","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.NeverCacheFingerprintStrategy.compute_fingerprint\n def compute_fingerprint(self, target):\n return uuid.uuid4()","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._fingerprint_strategy","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._fingerprint_strategy\n def _fingerprint_strategy(self):\n if self.get_options().profile:\n # A profile is machine-specific and we assume anyone wanting a profile wants to run it here\n # and now and not accept some old result, even if on the same inputs.\n return self.NeverCacheFingerprintStrategy()\n else:\n return None # Accept the default fingerprint strategy.","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._do_run_tests","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._do_run_tests\n def _do_run_tests(self, partition):\n with self.invalidated(partition,\n fingerprint_strategy=self._fingerprint_strategy(),\n # Re-run tests when the code they test (and depend on) changes.\n invalidate_dependents=True) as invalidation_check:\n\n invalid_tgts = [invalid_tgt\n for vts in invalidation_check.invalid_vts\n for invalid_tgt in vts.targets]\n\n # Processing proceeds through:\n # 1.) output -> workdir\n # 2.) [iff all == invalid] workdir -> cache: We do this manually for now.\n # 3.) [iff invalid == 0 and all > 0] cache -> workdir: Done transparently by `invalidated`.\n\n # 1.) Write all results that will be potentially cached to workdir.\n workdirs = _Workdirs.for_partition(self.workdir, partition)\n result = self._run_pytest_checked(workdirs, invalid_tgts)\n\n cache_vts = self._vts_for_partition(invalidation_check)\n if invalidation_check.all_vts == invalidation_check.invalid_vts:\n # 2.) The full partition was invalid, cache successful test results.\n if result.success and self.artifact_cache_writes_enabled():\n self.update_artifact_cache([(cache_vts, workdirs.files())])\n elif not invalidation_check.invalid_vts:\n # 3.) The full partition was valid, our results will have been staged for/by caching if not\n # already local.\n pass\n else:\n # The partition was partially invalid.\n\n # We don't cache results; so others will need to re-run this partition.\n # NB: We will presumably commit this change now though and so others will get this\n # partition in a state that executes successfully; so when the 1st of the others\n # executes against this partition; they will hit `all_vts == invalid_vts` and\n # cache the results. That 1st of others is hopefully","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._expose_results","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._expose_results\n def _expose_results(self, invalid_tgts, workdirs):\n external_junit_xml_dir = self.get_options().junit_xml_dir\n if external_junit_xml_dir:\n # Either we just ran pytest for a set of invalid targets and generated a junit xml file\n # specific to that (sub)set or else we hit the cache for the whole partition and skipped\n # running pytest, simply retrieving the partition's full junit xml file.\n junitxml_path = workdirs.junitxml_path(*invalid_tgts)\n\n safe_mkdir(external_junit_xml_dir)\n shutil.copy2(junitxml_path, external_junit_xml_dir)\n\n if self.get_options().coverage:\n coverage_output_dir = self.get_options().coverage_output_dir\n if coverage_output_dir:\n target_dir = coverage_output_dir\n else:\n pants_distdir = self.context.options.for_global_scope().pants_distdir\n relpath = workdirs.target_set_id()\n target_dir = os.path.join(pants_distdir, 'coverage', relpath)\n mergetree(workdirs.coverage_path, target_dir)","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_pytest_checked","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_pytest_checked\n def _run_pytest_checked(self, workdirs, targets):\n result = self._run_pytest(workdirs, targets)\n\n # Unconditionally pluck any results that an end user might need to interact with from the\n # workdir to the locations they expect.\n self._expose_results(targets, workdirs)\n\n return result.checked()","label":0},{"cid":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_pytest","text":"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_pytest\n def _run_pytest(self, workdirs, targets):\n if not targets:\n return PytestResult.rc(0)\n\n if self._run_in_chroot:\n path_func = lambda rel_src: rel_src\n else:\n source_chroot = os.path.relpath(self._source_chroot_path, get_buildroot())\n path_func = lambda rel_src: os.path.join(source_chroot, rel_src)\n\n sources_map = {} # Path from chroot -> Path from buildroot.\n for t in targets:\n for p in t.sources_relative_to_source_root():\n sources_map[path_func(p)] = os.path.join(t.target_base, p)\n\n if not sources_map:\n return PytestResult.rc(0)\n\n with self._test_runner(workdirs, targets, sources_map) as (pex, test_args):\n # Validate that the user didn't provide any passthru args that conflict\n # with those we must set ourselves.\n for arg in self.get_passthru_args():\n if arg.startswith('--junitxml') or arg.startswith('--confcutdir'):\n raise TaskError('Cannot pass this arg through to pytest: {}'.format(arg))\n\n junitxml_path = workdirs.junitxml_path(*targets)\n\n # N.B. the `--confcutdir` here instructs pytest to stop scanning for conftest.py files at the\n # top of the buildroot. This prevents conftest.py files from outside (e.g. in users home dirs)\n # from leaking into pants test runs. See: https://github.com/pantsbuild/pants/issues/2726\n args = ['--junitxml', junitxml_path, '--confcutdir', get_buildroot(),\n '--continue-on-collection-errors']\n if self.get_options().fail_fast:\n args.extend(['-x'])\n if self._debug:\n args.extend(['-s'])\n if self.get_options().colors:\n args.extend(['--color', 'yes'])\n for options in self.get_options().options + self.get_passthru_args():\n args.extend(safe_shlex_split(options))\n args.extend(test_args)\n args.extend(sources_map.keys())\n\n # We want to ensure our reporting based off junit xml is fro","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options","text":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\n @classmethod\n def register_bootstrap_options(cls, register):\n \"\"\"Register bootstrap options.\n\n \"Bootstrap options\" are a small set of options whose values are useful when registering other\n options. Therefore we must bootstrap them early, before other options are registered, let\n alone parsed.\n\n Bootstrap option values can be interpolated into the config file, and can be referenced\n programatically in registration code, e.g., as register.bootstrap.pants_workdir.\n\n Note that regular code can also access these options as normal global-scope options. Their\n status as \"bootstrap options\" is only pertinent during option registration.\n \"\"\"\n buildroot = get_buildroot()\n default_distdir_name = 'dist'\n default_distdir = os.path.join(buildroot, default_distdir_name)\n default_rel_distdir = '/{}/'.format(default_distdir_name)\n\n # Although logging supports the WARN level, its not documented and could conceivably be yanked.\n # Since pants has supported 'warn' since inception, leave the 'warn' choice as-is but explicitly\n # setup a 'WARN' logging level name that maps to 'WARNING'.\n logging.addLevelName(logging.WARNING, 'WARN')\n register('-l', '--level', choices=['debug', 'info', 'warn'], default='info', recursive=True,\n help='Set the logging level.')\n register('-q', '--quiet', type=bool, recursive=True, daemon=False,\n help='Squelches most console output. NOTE: Some tasks default to behaving quietly: '\n 'inverting this option supports making them noisier than they would be otherwise.')\n # Not really needed in bootstrap options, but putting it here means it displays right\n # after -l and -q in help output, which is conveniently contextual.\n register('--colors', type=bool, default=sys.stdout.isatty(), recursive=True, daemon=False,\n help='Set whether log messages are displa","label":1},{"cid":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options","text":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\n @classmethod\n def register_options(cls, register):\n \"\"\"Register options not tied to any particular task or subsystem.\"\"\"\n # The bootstrap options need to be registered on the post-bootstrap Options instance, so it\n # won't choke on them on the command line, and also so we can access their values as regular\n # global-scope options, for convenience.\n cls.register_bootstrap_options(register)\n\n register('-x', '--time', type=bool,\n help='Output a timing report at the end of the run.')\n register('-e', '--explain', type=bool,\n help='Explain the execution of goals.')\n register('--tag', type=list, metavar='[+-]tag1,tag2,...',\n help=\"Include only targets with these tags (optional '+' prefix) or without these \"\n \"tags ('-' prefix). Useful with ::, to find subsets of targets \"\n \"(e.g., integration tests.)\")\n\n register('-t', '--timeout', advanced=True, type=int, metavar='<seconds>',\n help='Number of seconds to wait for http connections.')\n # TODO: After moving to the new options system these abstraction leaks can go away.\n register('-k', '--kill-nailguns', advanced=True, type=bool,\n help='Kill nailguns before exiting')\n register('--fail-fast', advanced=True, type=bool, recursive=True,\n help='Exit as quickly as possible on error, rather than attempting to continue '\n 'to process the non-erroneous subset of the input.')\n register('--cache-key-gen-version', advanced=True, default='200', recursive=True,\n help='The cache key generation. Bump this to invalidate every artifact for a scope.')\n register('--workdir-max-build-entries', advanced=True, type=int, default=None,\n help='Maximum number of previous builds to keep per task target pair in workdir. '\n 'If set, minimum 2 will always be kept to support ","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.__init__","text":"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.__init__\n def __init__(self, logger, log_level, handler):\n \"\"\"\n :param logging.Logger logger: The logger instance to emit writes to.\n :param int log_level: The log level to use for the given logger.\n :param Handler handler: The underlying log handler, for determining the fileno\n to support faulthandler logging.\n \"\"\"\n self._logger = logger\n self._log_level = log_level\n self._handler = handler","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.write","text":"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.write\n def write(self, msg):\n for line in msg.rstrip().splitlines():\n self._logger.log(self._log_level, line.rstrip())","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.flush","text":"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.flush\n def flush(self):\n return","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.isatty","text":"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.isatty\n def isatty(self):\n return False","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.fileno","text":"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.fileno\n def fileno(self):\n return self._handler.stream.fileno()","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory.create","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory.create\n @classmethod\n def create(cls, bootstrap_options=None):\n \"\"\"\n :param Options bootstrap_options: The bootstrap options, if available.\n \"\"\"\n bootstrap_options = bootstrap_options or cls._parse_bootstrap_options()\n bootstrap_options_values = bootstrap_options.for_global_scope()\n\n build_root = get_buildroot()\n native = Native.create(bootstrap_options_values)\n # TODO: https://github.com/pantsbuild/pants/issues/3479\n watchman = WatchmanLauncher.create(bootstrap_options_values).watchman\n legacy_graph_helper = cls._setup_legacy_graph_helper(native, bootstrap_options_values)\n services, port_map = cls._setup_services(\n build_root,\n bootstrap_options_values,\n legacy_graph_helper,\n watchman\n )\n\n return PantsDaemon(\n native,\n build_root,\n bootstrap_options_values.pants_workdir,\n bootstrap_options_values.level.upper(),\n services,\n port_map,\n bootstrap_options_values.pants_subprocessdir,\n bootstrap_options\n )","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._parse_bootstrap_options","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._parse_bootstrap_options\n @staticmethod\n def _parse_bootstrap_options():\n return OptionsBootstrapper().get_bootstrap_options()","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._setup_legacy_graph_helper","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._setup_legacy_graph_helper\n @staticmethod\n def _setup_legacy_graph_helper(native, bootstrap_options):\n \"\"\"Initializes a `LegacyGraphHelper` instance.\"\"\"\n return EngineInitializer.setup_legacy_graph(\n bootstrap_options.pants_ignore,\n bootstrap_options.pants_workdir,\n bootstrap_options.build_file_imports,\n native=native,\n build_ignore_patterns=bootstrap_options.build_ignore,\n exclude_target_regexps=bootstrap_options.exclude_target_regexp,\n subproject_roots=bootstrap_options.subproject_roots,\n )","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._setup_services","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._setup_services\n @staticmethod\n def _setup_services(build_root, bootstrap_options, legacy_graph_helper, watchman):\n \"\"\"Initialize pantsd services.\n\n :returns: A tuple of (`tuple` service_instances, `dict` port_map).\n \"\"\"\n fs_event_service = FSEventService(watchman, build_root, bootstrap_options.pantsd_fs_event_workers)\n scheduler_service = SchedulerService(fs_event_service, legacy_graph_helper)\n pailgun_service = PailgunService(\n bind_addr=(bootstrap_options.pantsd_pailgun_host, bootstrap_options.pantsd_pailgun_port),\n exiter_class=DaemonExiter,\n runner_class=DaemonPantsRunner,\n target_roots_class=TargetRoots,\n scheduler_service=scheduler_service\n )\n store_gc_service = StoreGCService(legacy_graph_helper.scheduler)\n\n return (\n # Services.\n (fs_event_service, scheduler_service, pailgun_service, store_gc_service),\n # Port map.\n dict(pailgun=pailgun_service.pailgun_port)\n )","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.__init__","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.__init__\n def __init__(self, native, build_root, work_dir, log_level, services, socket_map,\n metadata_base_dir, bootstrap_options=None):\n \"\"\"\n :param Native native: A `Native` instance.\n :param string build_root: The pants build root.\n :param string work_dir: The pants work directory.\n :param string log_level: The log level to use for daemon logging.\n :param string metadata_base_dir: The ProcessManager metadata base dir.\n :param Options bootstrap_options: The bootstrap options, if available.\n \"\"\"\n super(PantsDaemon, self).__init__(name='pantsd', metadata_base_dir=metadata_base_dir)\n self._native = native\n self._build_root = build_root\n self._work_dir = work_dir\n self._log_level = log_level\n self._services = services\n self._socket_map = socket_map\n self._bootstrap_options = bootstrap_options\n\n self._log_dir = os.path.join(work_dir, self.name)\n self._logger = logging.getLogger(__name__)\n # A lock to guard the service thread lifecycles. This can be used by individual services\n # to safeguard daemon-synchronous sections that should be protected from abrupt teardown.\n self._lifecycle_lock = threading.RLock()\n # A lock to guard pantsd->runner forks. This can be used by services to safeguard resources\n # held by threads at fork time, so that we can fork without deadlocking.\n self._fork_lock = threading.RLock()\n # N.B. This Event is used as nothing more than a convenient atomic flag - nothing waits on it.\n self._kill_switch = threading.Event()\n self._exiter = Exiter()","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.watchman_launcher","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.watchman_launcher\n @memoized_property\n def watchman_launcher(self):\n return WatchmanLauncher.create(self._bootstrap_options.for_global_scope())","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.is_killed","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.is_killed\n @property\n def is_killed(self):\n return self._kill_switch.is_set()","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.options_fingerprint","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.options_fingerprint\n @property\n def options_fingerprint(self):\n return OptionsFingerprinter.combined_options_fingerprint_for_scope(\n GLOBAL_SCOPE,\n self._bootstrap_options,\n fingerprint_key='daemon',\n invert=True\n )","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.shutdown","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.shutdown\n def shutdown(self, service_thread_map):\n \"\"\"Gracefully terminate all services and kill the main PantsDaemon loop.\"\"\"\n with self._lifecycle_lock:\n for service, service_thread in service_thread_map.items():\n self._logger.info('terminating pantsd service: {}'.format(service))\n service.terminate()\n service_thread.join(self.JOIN_TIMEOUT_SECONDS)\n self._logger.info('terminating pantsd')\n self._kill_switch.set()","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._close_stdio","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._close_stdio\n @staticmethod\n def _close_stdio():\n \"\"\"Close stdio streams to avoid output in the tty that launched pantsd.\"\"\"\n for fd in (sys.stdin, sys.stdout, sys.stderr):\n file_no = fd.fileno()\n fd.flush()\n fd.close()\n os.close(file_no)","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._pantsd_logging","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._pantsd_logging\n @contextmanager\n def _pantsd_logging(self):\n \"\"\"A context manager that runs with pantsd logging.\n\n Asserts that stdio (represented by file handles 0, 1, 2) is closed to ensure that\n we can safely reuse those fd numbers.\n \"\"\"\n\n # Ensure that stdio is closed so that we can safely reuse those file descriptors.\n for fd in (0, 1, 2):\n try:\n os.fdopen(fd)\n raise AssertionError(\n 'pantsd logging cannot initialize while stdio is open: {}'.format(fd))\n except OSError:\n pass\n\n # Redirect stdio to /dev/null for the rest of the run, to reserve those file descriptors\n # for further forks.\n with stdio_as(stdin_fd=-1, stdout_fd=-1, stderr_fd=-1):\n # Reinitialize logging for the daemon context.\n result = setup_logging(self._log_level, log_dir=self._log_dir, log_name=self.LOG_NAME)\n\n # Do a python-level redirect of stdout/stderr, which will not disturb `0,1,2`.\n # TODO: Consider giving these pipes/actual fds, in order to make them \"deep\" replacements\n # for `1,2`, and allow them to be used via `stdio_as`.\n sys.stdout = _LoggerStream(logging.getLogger(), logging.INFO, result.log_handler)\n sys.stderr = _LoggerStream(logging.getLogger(), logging.WARN, result.log_handler)\n\n self._logger.debug('logging initialized')\n yield result.log_handler.stream","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._setup_services","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._setup_services\n def _setup_services(self, services):\n assert self._lifecycle_lock is not None, 'PantsDaemon lock has not been set!'\n assert self._fork_lock is not None, 'PantsDaemon fork lock has not been set!'\n for service in services:\n self._logger.info('setting up service {}'.format(service))\n service.setup(self._lifecycle_lock, self._fork_lock)","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._make_thread","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._make_thread\n @staticmethod\n def _make_thread(target):\n t = threading.Thread(target=target)\n t.daemon = True\n return t","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._run_services","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._run_services\n def _run_services(self, services):\n \"\"\"Service runner main loop.\"\"\"\n if not services:\n self._logger.critical('no services to run, bailing!')\n return\n\n service_thread_map = {service: self._make_thread(service.run) for service in services}\n\n # Start services.\n for service, service_thread in service_thread_map.items():\n self._logger.info('starting service {}'.format(service))\n try:\n service_thread.start()\n except (RuntimeError, service.ServiceError):\n self.shutdown(service_thread_map)\n raise self.StartupFailure('service {} failed to start, shutting down!'.format(service))\n\n # Once all services are started, write our pid.\n self.write_pid()\n self.write_metadata_by_name('pantsd', self.FINGERPRINT_KEY, self.options_fingerprint)\n\n # Monitor services.\n while not self.is_killed:\n for service, service_thread in service_thread_map.items():\n if not service_thread.is_alive():\n self.shutdown(service_thread_map)\n raise self.RuntimeFailure('service failure for {}, shutting down!'.format(service))\n else:\n # Avoid excessive CPU utilization.\n service_thread.join(self.JOIN_TIMEOUT_SECONDS)","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._write_named_sockets","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._write_named_sockets\n def _write_named_sockets(self, socket_map):\n \"\"\"Write multiple named sockets using a socket mapping.\"\"\"\n for socket_name, socket_info in socket_map.items():\n self.write_named_socket(socket_name, socket_info)","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.run_sync","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.run_sync\n def run_sync(self):\n \"\"\"Synchronously run pantsd.\"\"\"\n # Switch log output to the daemon's log stream from here forward.\n self._close_stdio()\n with self._pantsd_logging() as log_stream:\n self._exiter.set_except_hook(log_stream)\n self._logger.info('pantsd starting, log level is {}'.format(self._log_level))\n\n self._native.set_panic_handler()\n\n # Set the process name in ps output to 'pantsd' vs './pants compile src/etc:: -ldebug'.\n set_process_title('pantsd [{}]'.format(self._build_root))\n\n # Write service socket information to .pids.\n self._write_named_sockets(self._socket_map)\n\n # Enter the main service runner loop.\n self._setup_services(self._services)\n self._run_services(self._services)","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.post_fork_child","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.post_fork_child\n def post_fork_child(self):\n \"\"\"Post-fork() child callback for ProcessManager.daemon_spawn().\"\"\"\n entry_point = '{}:launch'.format(__name__)\n exec_env = combined_dict(os.environ, dict(PANTS_ENTRYPOINT=entry_point))\n # Pass all of sys.argv so that we can proxy arg flags e.g. `-ldebug`.\n cmd = [sys.executable] + sys.argv\n self._logger.debug('cmd is: PANTS_ENTRYPOINT={} {}'.format(entry_point, ' '.join(cmd)))\n # TODO: Improve error handling on launch failures.\n os.spawnve(os.P_NOWAIT, sys.executable, cmd, env=exec_env)","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.maybe_launch","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.maybe_launch\n def maybe_launch(self):\n \"\"\"Launches pantsd (if not already running) in a subprocess.\n\n :returns: The port that pantsd is listening on.\n :rtype: int\n \"\"\"\n self.watchman_launcher.maybe_launch()\n self._logger.debug('acquiring lock: {}'.format(self.process_lock))\n with self.process_lock:\n new_fingerprint = self.options_fingerprint\n self._logger.debug('pantsd: is_alive={} new_fingerprint={} current_fingerprint={}'\n .format(self.is_alive(), new_fingerprint, self.fingerprint))\n if self.needs_restart(new_fingerprint):\n self.terminate(include_watchman=False)\n self._logger.debug('launching pantsd')\n self.daemon_spawn()\n # Wait up to 60 seconds for pantsd to write its pidfile.\n self.await_pid(60)\n listening_port = self.read_named_socket('pailgun', int)\n pantsd_pid = self.pid\n self._logger.debug('released lock: {}'.format(self.process_lock))\n self._logger.debug('pantsd is running at pid {}, pailgun port is {}'\n .format(pantsd_pid, listening_port))\n return listening_port","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.terminate","text":"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.terminate\n def terminate(self, include_watchman=True):\n \"\"\"Terminates pantsd and watchman.\"\"\"\n super(PantsDaemon, self).terminate()\n if include_watchman:\n self.watchman_launcher.terminate()","label":0},{"cid":"src/python/pants/pantsd/pants_daemon.py::launch","text":"src/python/pants/pantsd/pants_daemon.py::launch\ndef launch():\n \"\"\"An external entrypoint that spawns a new pantsd instance.\"\"\"\n PantsDaemon.Factory.create().run_sync()","label":0},{"cid":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.__init__","text":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.__init__\n def __init__(self, watchman, build_root, worker_count):\n \"\"\"\n :param Watchman watchman: The Watchman instance as provided by the WatchmanLauncher subsystem.\n :param str build_root: The current build root.\n :param int worker_count: The total number of workers to use for the internally managed\n ThreadPoolExecutor.\n \"\"\"\n super(FSEventService, self).__init__()\n self._logger = logging.getLogger(__name__)\n self._watchman = watchman\n self._build_root = os.path.realpath(build_root)\n self._worker_count = worker_count\n self._executor = None\n self._handlers = {}","label":0},{"cid":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.setup","text":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.setup\n def setup(self, lifecycle_lock, fork_lock, executor=None):\n super(FSEventService, self).setup(lifecycle_lock, fork_lock)\n self._executor = executor or ThreadPoolExecutor(max_workers=self._worker_count)","label":0},{"cid":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.terminate","text":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.terminate\n def terminate(self):\n \"\"\"An extension of PantsService.terminate() that shuts down the executor if so configured.\"\"\"\n if self._executor:\n self._logger.info('shutting down threadpool')\n self._executor.shutdown()\n super(FSEventService, self).terminate()","label":0},{"cid":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.register_all_files_handler","text":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.register_all_files_handler\n def register_all_files_handler(self, callback, name='all_files'):\n \"\"\"Registers a subscription for all files under a given watch path.\n\n :param func callback: the callback to execute on each filesystem event\n :param str name: the subscription name as used by watchman\n \"\"\"\n self.register_handler(\n name,\n dict(\n fields=['name'],\n # Request events for all file types.\n # NB: Touching a file invalidates its parent directory due to:\n # https://github.com/facebook/watchman/issues/305\n # ...but if we were to skip watching directories, we'd still have to invalidate\n # the parents of any changed files, and we wouldn't see creation/deletion of\n # empty directories.\n expression=[\n 'allof', # All of the below rules must be true to match.\n ['not', ['dirname', 'dist', self.ZERO_DEPTH]], # Exclude the ./dist dir.\n # N.B. 'wholename' ensures we match against the absolute ('x/y/z') vs base path ('z').\n ['not', ['pcre', r'^\\..*', 'wholename']], # Exclude files in hidden dirs (.pants.d etc).\n ['not', ['match', '*.pyc']] # Exclude .pyc files.\n # TODO(kwlzn): Make exclusions here optionable.\n # Related: https://github.com/pantsbuild/pants/issues/2956\n ]\n ),\n callback\n )","label":0},{"cid":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.register_handler","text":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.register_handler\n def register_handler(self, name, metadata, callback):\n \"\"\"Register subscriptions and their event handlers.\n\n :param str name: the subscription name as used by watchman\n :param dict metadata: a dictionary of metadata to be serialized and passed to the watchman\n subscribe command. this should include the match expression as well\n as any required callback fields.\n :param func callback: the callback to execute on each matching filesystem event\n \"\"\"\n assert name not in self._handlers, 'duplicate handler name: {}'.format(name)\n assert (\n isinstance(metadata, dict) and 'fields' in metadata and 'expression' in metadata\n ), 'invalid handler metadata!'\n self._handlers[name] = Watchman.EventHandler(name=name, metadata=metadata, callback=callback)","label":0},{"cid":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.fire_callback","text":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.fire_callback\n def fire_callback(self, handler_name, event_data):\n \"\"\"Fire an event callback for a given handler.\"\"\"\n return self._handlers[handler_name].callback(event_data)","label":0},{"cid":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.run","text":"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.run\n def run(self):\n \"\"\"Main service entrypoint. Called via Thread.start() via PantsDaemon.run().\"\"\"\n\n if not (self._watchman and self._watchman.is_alive()):\n raise self.ServiceError('watchman is not running, bailing!')\n\n # Enable watchman for the build root.\n self._watchman.watch_project(self._build_root)\n\n futures = {}\n id_counter = 0\n subscriptions = self._handlers.values()\n\n # Setup subscriptions and begin the main event firing loop.\n for handler_name, event_data in self._watchman.subscribed(self._build_root, subscriptions):\n # On death, break from the loop and contextmgr to terminate callback threads.\n if self.is_killed: break\n\n if event_data:\n # As we receive events from watchman, submit them asynchronously to the executor.\n future = self._executor.submit(self.fire_callback, handler_name, event_data)\n futures[future] = handler_name\n\n # Process and log results for completed futures.\n for completed_future in [_future for _future in futures if _future.done()]:\n handler_name = futures.pop(completed_future)\n id_counter += 1\n\n try:\n result = completed_future.result()\n except Exception:\n result = traceback.format_exc()\n\n if result is not None:\n # Truthy results or those that raise exceptions are treated as failures.\n self._logger.warning('callback ID {} for {} failed: {}'\n .format(id_counter, handler_name, result))\n else:\n self._logger.debug('callback ID {} for {} succeeded'.format(id_counter, handler_name))","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman.__init__","text":"src/python/pants/pantsd/watchman.py::Watchman.__init__\n def __init__(self, watchman_path, metadata_base_dir, log_level='1', startup_timeout=STARTUP_TIMEOUT_SECONDS,\n timeout=SOCKET_TIMEOUT_SECONDS, socket_path_override=None):\n \"\"\"\n :param str watchman_path: The path to the watchman binary.\n :param str metadata_base_dir: The metadata base dir for `ProcessMetadataManager`.\n :param float startup_timeout: The timeout for the initial `watch-project` query (in seconds).\n :param float timeout: The watchman socket timeout for all subsequent queries (in seconds).\n :param str log_level: The watchman log level. Watchman has 3 log levels: '0' for no logging,\n '1' for standard logging and '2' for verbose logging.\n :param str socket_path_override: The overridden target path of the watchman socket, if any.\n \"\"\"\n super(Watchman, self).__init__(name='watchman',\n process_name='watchman',\n socket_type=str,\n metadata_base_dir=metadata_base_dir)\n self._watchman_path = self._normalize_watchman_path(watchman_path)\n self._watchman_work_dir = os.path.join(metadata_base_dir, self.name)\n self._log_level = log_level\n self._startup_timeout = startup_timeout\n self._timeout = timeout\n\n self._state_file = os.path.join(self._watchman_work_dir, '{}.state'.format(self.name))\n self._log_file = os.path.join(self._watchman_work_dir, '{}.log'.format(self.name))\n self._sock_file = socket_path_override or os.path.join(self._watchman_work_dir,\n '{}.sock'.format(self.name))\n\n self._logger = logging.getLogger(__name__)\n self._watchman_client = None","label":1},{"cid":"src/python/pants/pantsd/watchman.py::Watchman.client","text":"src/python/pants/pantsd/watchman.py::Watchman.client\n @property\n def client(self):\n if not self._watchman_client:\n self._watchman_client = self._make_client()\n return self._watchman_client","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._make_client","text":"src/python/pants/pantsd/watchman.py::Watchman._make_client\n def _make_client(self):\n \"\"\"Create a new watchman client using the BSER protocol over a UNIX socket.\"\"\"\n self._logger.debug('setting initial watchman timeout to %s', self._startup_timeout)\n return StreamableWatchmanClient(sockpath=self.socket,\n transport='local',\n timeout=self._startup_timeout)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._is_valid_executable","text":"src/python/pants/pantsd/watchman.py::Watchman._is_valid_executable\n def _is_valid_executable(self, binary_path):\n return os.path.isfile(binary_path) and os.access(binary_path, os.X_OK)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._normalize_watchman_path","text":"src/python/pants/pantsd/watchman.py::Watchman._normalize_watchman_path\n def _normalize_watchman_path(self, watchman_path):\n if not self._is_valid_executable(watchman_path):\n raise self.ExecutionError('invalid watchman binary at {}!'.format(watchman_path))\n return os.path.abspath(watchman_path)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._maybe_init_metadata","text":"src/python/pants/pantsd/watchman.py::Watchman._maybe_init_metadata\n def _maybe_init_metadata(self):\n safe_mkdir(self._watchman_work_dir)\n # Initialize watchman with an empty, but valid statefile so it doesn't complain on startup.\n safe_file_dump(self._state_file, '{}')","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._construct_cmd","text":"src/python/pants/pantsd/watchman.py::Watchman._construct_cmd\n def _construct_cmd(self, cmd_parts, state_file, sock_file, log_file, log_level):\n return [part for part in cmd_parts] + ['--no-save-state',\n '--statefile={}'.format(state_file),\n '--sockname={}'.format(sock_file),\n '--logfile={}'.format(log_file),\n '--log-level', log_level]","label":1},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._parse_pid_from_output","text":"src/python/pants/pantsd/watchman.py::Watchman._parse_pid_from_output\n def _parse_pid_from_output(self, output):\n try:\n # Parse the watchman pidfile from the cli output (in JSON).\n return json.loads(output)['pid']\n except ValueError:\n # JSON parse failure.\n self._logger.critical('invalid output from watchman!\\n{output!s}'.format(output=output))\n raise self.InvalidCommandOutput(output)\n except KeyError:\n # Key access error on 'pid' - bad output from watchman.\n self._logger.critical('no pid from watchman!')\n raise self.InvalidCommandOutput(output)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman.launch","text":"src/python/pants/pantsd/watchman.py::Watchman.launch\n def launch(self):\n \"\"\"Launch and synchronously write metadata.\n\n This is possible due to watchman's built-in async server startup - no double-forking required.\n \"\"\"\n cmd = self._construct_cmd((self._watchman_path, 'get-pid'),\n state_file=self._state_file,\n sock_file=self._sock_file,\n log_file=self._log_file,\n log_level=str(self._log_level))\n self._logger.debug('watchman cmd is: {}'.format(' '.join(cmd)))\n self._maybe_init_metadata()\n\n # Watchman is launched via its cli. By running the 'get-pid' command on the client we implicitly\n # launch the Watchman daemon. This approach is somewhat error-prone - in some cases the client\n # can successfully trigger the launch of the Watchman daemon, but fail to return successfully\n # for the 'get-pid' result due to server <-> daemon socket issues - these can look like:\n #\n # 2016-04-01T17:31:23,820: [cli] unable to talk to your watchman\n # on .../watchman.sock! (Permission denied)\n #\n # This results in a subprocess execution failure and leaves us with no pid information to write\n # to the metadata directory - while in reality a Watchman daemon is actually running but now\n # untracked. To safeguard against this, we retry the (idempotent) 'get-pid' command a few times\n # to give the server-side socket setup a few chances to quiesce before potentially orphaning it.\n\n get_output = functools.partial(self.get_subprocess_output, cmd)\n output = retry_on_exception(get_output, 3, (self.ExecutionError,), lambda n: n * .5)\n\n # Parse the watchman PID from the cli output.\n pid = self._parse_pid_from_output(output)\n\n # Write the process metadata to disk.\n self.write_pid(pid)\n self.write_socket(self._sock_file)","label":1},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._attempt_set_timeout","text":"src/python/pants/pantsd/watchman.py::Watchman._attempt_set_timeout\n def _attempt_set_timeout(self, timeout):\n \"\"\"Sets a timeout on the inner watchman client's socket.\"\"\"\n try:\n self.client.setTimeout(timeout)\n except Exception:\n self._logger.debug('failed to set post-startup watchman timeout to %s', self._timeout)\n else:\n self._logger.debug('set post-startup watchman timeout to %s', self._timeout)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman.watch_project","text":"src/python/pants/pantsd/watchman.py::Watchman.watch_project\n def watch_project(self, path):\n \"\"\"Issues the watch-project command to watchman to begin watching the buildroot.\n\n :param string path: the path to the watchman project root/pants build root.\n \"\"\"\n # TODO(kwlzn): Add a client.query(timeout=X) param to the upstream pywatchman project.\n try:\n return self.client.query('watch-project', os.path.realpath(path))\n finally:\n self._attempt_set_timeout(self._timeout)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman.subscribed","text":"src/python/pants/pantsd/watchman.py::Watchman.subscribed\n def subscribed(self, build_root, handlers):\n \"\"\"Bulk subscribe generator for StreamableWatchmanClient.\n\n :param str build_root: the build_root for all subscriptions.\n :param iterable handlers: a sequence of Watchman.EventHandler namedtuple objects.\n :yields: a stream of tuples in the form (subscription_name: str, subscription_event: dict).\n \"\"\"\n command_list = [['subscribe',\n build_root,\n handler.name,\n handler.metadata] for handler in handlers]\n\n self._logger.debug('watchman command_list is: {}'.format(command_list))\n\n try:\n for event in self.client.stream_query(command_list):\n if event is None:\n yield None, None\n elif 'subscribe' in event:\n self._logger.info('confirmed watchman subscription: {}'.format(event))\n yield None, None\n elif 'subscription' in event:\n yield event.get('subscription'), event\n else:\n self._logger.warning('encountered non-subscription event: {}'.format(event))\n except self.client.WatchmanError as e:\n raise self.WatchmanCrash(e)","label":0}],"string":"[\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.register_options\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.register_options\\n @classmethod\\n def register_options(cls, register):\\n super(IdeaGen, cls).register_options(register)\\n register('--version', choices=sorted(list(_VERSIONS.keys())), default='11',\\n help='The IntelliJ IDEA version the project config should be generated for.')\\n register('--merge', type=bool, default=True,\\n help='Merge any manual customizations in existing '\\n 'Intellij IDEA configuration. If False, manual customizations '\\n 'will be over-written.')\\n register('--open', type=bool, default=True,\\n help='Attempts to open the generated project in IDEA.')\\n register('--bash', type=bool,\\n help='Adds a bash facet to the generated project configuration.')\\n register('--scala-language-level',\\n choices=_SCALA_VERSIONS.keys(), default=_SCALA_VERSION_DEFAULT,\\n help='Set the scala language level used for IDEA linting.')\\n register('--scala-maximum-heap-size-mb', type=int, default=512,\\n help='Sets the maximum heap size (in megabytes) for scalac.')\\n register('--fsc', type=bool,\\n help='If the project contains any scala targets this specifies the '\\n 'fsc compiler should be enabled.')\\n register('--java-encoding', default='UTF-8',\\n help='Sets the file encoding for java files in this project.')\\n register('--java-maximum-heap-size-mb', type=int, default=512,\\n help='Sets the maximum heap size (in megabytes) for javac.')\\n register('--exclude-maven-target', type=bool,\\n help=\\\"Exclude 'target' directories for directories containing \\\"\\n \\\"pom.xml files. These directories contain generated code and\\\"\\n \\\"copies of files staged for deployment.\\\")\\n register('--exclude_folders', type=list,\\n default=[\\n '.pants.d/compile',\\n '.pants.d/ivy',\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.__init__\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.__init__\\n def __init__(self, *args, **kwargs):\\n super(IdeaGen, self).__init__(*args, **kwargs)\\n\\n self.intellij_output_dir = os.path.join(self.gen_project_workdir, 'out')\\n self.nomerge = not self.get_options().merge\\n self.open = self.get_options().open\\n self.bash = self.get_options().bash\\n\\n self.scala_language_level = _SCALA_VERSIONS.get(\\n self.get_options().scala_language_level, None)\\n self.scala_maximum_heap_size = self.get_options().scala_maximum_heap_size_mb\\n\\n self.fsc = self.get_options().fsc\\n\\n self.java_encoding = self.get_options().java_encoding\\n self.java_maximum_heap_size = self.get_options().java_maximum_heap_size_mb\\n\\n idea_version = _VERSIONS[self.get_options().version]\\n self.project_template = os.path.join(_TEMPLATE_BASEDIR,\\n 'project-{}.mustache'.format(idea_version))\\n self.module_template = os.path.join(_TEMPLATE_BASEDIR,\\n 'module-{}.mustache'.format(idea_version))\\n\\n self.project_filename = os.path.join(self.cwd,\\n '{}.ipr'.format(self.project_name))\\n self.module_filename = os.path.join(self.gen_project_workdir,\\n '{}.iml'.format(self.project_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._maven_targets_excludes\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._maven_targets_excludes\\n @staticmethod\\n def _maven_targets_excludes(repo_root):\\n excludes = []\\n for (dirpath, dirnames, filenames) in safe_walk(repo_root):\\n if \\\"pom.xml\\\" in filenames:\\n excludes.append(os.path.join(os.path.relpath(dirpath, start=repo_root), \\\"target\\\"))\\n return excludes\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.annotation_processing_template\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.annotation_processing_template\\n @property\\n def annotation_processing_template(self):\\n return TemplateData(\\n enabled=self.get_options().annotation_processing_enabled,\\n rel_source_output_dir=os.path.join('..','..','..',\\n self.get_options().annotation_generated_sources_dir),\\n source_output_dir=\\n os.path.join(self.gen_project_workdir,\\n self.get_options().annotation_generated_sources_dir),\\n rel_test_source_output_dir=os.path.join('..','..','..',\\n self.get_options().annotation_generated_test_sources_dir),\\n test_source_output_dir=\\n os.path.join(self.gen_project_workdir,\\n self.get_options().annotation_generated_test_sources_dir),\\n processors=[{'class_name' : processor}\\n for processor in self.get_options().annotation_processor],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.generate_project\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.generate_project\\n def generate_project(self, project):\\n def create_content_root(source_set):\\n root_relative_path = os.path.join(source_set.source_base, source_set.path) \\\\\\n if source_set.path else source_set.source_base\\n if source_set.resources_only:\\n if source_set.is_test:\\n content_type = 'java-test-resource'\\n else:\\n content_type = 'java-resource'\\n else:\\n content_type = ''\\n\\n sources = TemplateData(\\n path=root_relative_path,\\n package_prefix=source_set.path.replace('/', '.') if source_set.path else None,\\n is_test=source_set.is_test,\\n content_type=content_type\\n )\\n\\n return TemplateData(\\n path=root_relative_path,\\n sources=[sources],\\n exclude_paths=[os.path.join(source_set.source_base, x) for x in source_set.excludes],\\n )\\n\\n content_roots = [create_content_root(source_set) for source_set in project.sources]\\n if project.has_python:\\n content_roots.extend(create_content_root(source_set) for source_set in project.py_sources)\\n\\n scala = None\\n if project.has_scala:\\n scala = TemplateData(\\n language_level=self.scala_language_level,\\n maximum_heap_size=self.scala_maximum_heap_size,\\n fsc=self.fsc,\\n compiler_classpath=project.scala_compiler_classpath\\n )\\n\\n exclude_folders = []\\n if self.get_options().exclude_maven_target:\\n exclude_folders += IdeaGen._maven_targets_excludes(get_buildroot())\\n\\n exclude_folders += self.get_options().exclude_folders\\n\\n java_language_level = None\\n for target in project.targets:\\n if isinstance(target, JvmTarget):\\n if java_language_level is None or java_language_level < target.platform.source_level:\\n java_language_level = target.platform.source_level\\n if java_language_level is not None:\\n java_language_level = 'JDK_{0}_{1}'.format(*java_language_le\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.generate_project.create_content_root\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen.generate_project.create_content_root\\n def create_content_root(source_set):\\n root_relative_path = os.path.join(source_set.source_base, source_set.path) \\\\\\n if source_set.path else source_set.source_base\\n if source_set.resources_only:\\n if source_set.is_test:\\n content_type = 'java-test-resource'\\n else:\\n content_type = 'java-resource'\\n else:\\n content_type = ''\\n\\n sources = TemplateData(\\n path=root_relative_path,\\n package_prefix=source_set.path.replace('/', '.') if source_set.path else None,\\n is_test=source_set.is_test,\\n content_type=content_type\\n )\\n\\n return TemplateData(\\n path=root_relative_path,\\n sources=[sources],\\n exclude_paths=[os.path.join(source_set.source_base, x) for x in source_set.excludes],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._generate_to_tempfile\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._generate_to_tempfile\\n def _generate_to_tempfile(self, generator):\\n \\\"\\\"\\\"Applies the specified generator to a temp file and returns the path to that file.\\n We generate into a temp file so that we don't lose any manual customizations on error.\\\"\\\"\\\"\\n (output_fd, output_path) = tempfile.mkstemp()\\n with os.fdopen(output_fd, 'w') as output:\\n generator.write(output)\\n return output_path\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._get_resource_extensions\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._get_resource_extensions\\n def _get_resource_extensions(self, project):\\n resource_extensions = set()\\n resource_extensions.update(project.resource_extensions)\\n\\n # TODO(John Sirois): make test resources 1st class in ant build and punch this through to pants\\n # model\\n for _, _, files in safe_walk(os.path.join(get_buildroot(), 'tests', 'resources')):\\n resource_extensions.update(Project.extract_resource_extensions(files))\\n\\n return resource_extensions\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._parse_xml_component_elements\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._parse_xml_component_elements\\n def _parse_xml_component_elements(self, path):\\n \\\"\\\"\\\"Returns a list of pairs (component_name, xml_fragment) where xml_fragment is the xml text of\\n that <component> in the specified xml file.\\\"\\\"\\\"\\n if not os.path.exists(path):\\n return [] # No existing components.\\n dom = minidom.parse(path)\\n # .ipr and .iml files both consist of <component> elements directly under a root element.\\n return [(x.getAttribute('name'), x.toxml()) for x in dom.getElementsByTagName('component')]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._get_components_to_merge\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_gen.py::IdeaGen._get_components_to_merge\\n def _get_components_to_merge(self, mergable_components, path):\\n \\\"\\\"\\\"Returns a list of the <component> fragments in mergable_components that are not\\n superceded by a <component> in the specified xml file.\\n mergable_components is a list of (name, xml_fragment) pairs.\\\"\\\"\\\"\\n\\n # As a convenience, we use _parse_xml_component_elements to get the\\n # superceding component names, ignoring the generated xml fragments.\\n # This is fine, since performance is not an issue.\\n generated_component_names = set(\\n [name for (name, _) in self._parse_xml_component_elements(path)])\\n return [x[1] for x in mergable_components if x[0] not in generated_component_names]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.register_options\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.register_options\\n @classmethod\\n def register_options(cls, register):\\n super(IdeaPluginGen, cls).register_options(register)\\n # TODO: https://github.com/pantsbuild/pants/issues/3198\\n # scala/java-language level should use what Pants already knows.\\n register('--open', type=bool, default=True,\\n help='Attempts to open the generated project in IDEA.')\\n register('--java-encoding', default='UTF-8',\\n help='Sets the file encoding for java files in this project.')\\n register('--open-with', type=str, default=None, recursive=True,\\n help='Program used to open the generated IntelliJ project.')\\n register('--debug_port', type=int, default=5005,\\n help='Port to use for launching tasks under the debugger.')\\n register('--java-jdk-name', default=None,\\n help='Sets the jdk used to compile the project\\\\'s java sources. If unset the default '\\n 'jdk name for the --java-language-level is used')\\n register('--java-language-level', type=int, default=8,\\n help='Sets the java language and jdk used to compile the project\\\\'s java sources.')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.__init__\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.__init__\\n def __init__(self, *args, **kwargs):\\n super(IdeaPluginGen, self).__init__(*args, **kwargs)\\n\\n self.open = self.get_options().open\\n\\n self.java_encoding = self.get_options().java_encoding\\n self.project_template = os.path.join(_TEMPLATE_BASEDIR,\\n 'project-12.mustache')\\n self.workspace_template = os.path.join(_TEMPLATE_BASEDIR,\\n 'workspace-12.mustache')\\n self.java_language_level = self.get_options().java_language_level\\n\\n if self.get_options().java_jdk_name:\\n self.java_jdk = self.get_options().java_jdk_name\\n else:\\n self.java_jdk = '1.{}'.format(self.java_language_level)\\n\\n output_dir = os.path.join(get_buildroot(), \\\".idea\\\", self.__class__.__name__)\\n safe_mkdir(output_dir)\\n\\n with temporary_dir(root_dir=output_dir, cleanup=False) as output_project_dir:\\n project_name = self.get_project_name(self.context.options.target_specs)\\n\\n self.gen_project_workdir = output_project_dir\\n self.project_filename = os.path.join(self.gen_project_workdir,\\n '{}.ipr'.format(project_name))\\n self.workspace_filename = os.path.join(self.gen_project_workdir,\\n '{}.iws'.format(project_name))\\n self.intellij_output_dir = os.path.join(self.gen_project_workdir, 'out')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.get_project_name\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.get_project_name\\n @classmethod\\n def get_project_name(cls, target_specs):\\n # take up to PROJECT_NAME_LIMIT chars as project file name due to filesystem constraint.\\n return re.sub('[^0-9a-zA-Z:_]+', '.', '__'.join(target_specs))[:cls.PROJECT_NAME_LIMIT]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.generate_project\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.generate_project\\n def generate_project(self):\\n outdir = os.path.abspath(self.intellij_output_dir)\\n if not os.path.exists(outdir):\\n os.makedirs(outdir)\\n\\n scm = get_scm()\\n configured_project = TemplateData(\\n root_dir=get_buildroot(),\\n outdir=outdir,\\n git_root=scm.worktree,\\n java=TemplateData(\\n encoding=self.java_encoding,\\n jdk=self.java_jdk,\\n language_level='JDK_1_{}'.format(self.java_language_level)\\n ),\\n debug_port=self.get_options().debug_port,\\n )\\n\\n if not self.context.options.target_specs:\\n raise TaskError(\\\"No targets specified.\\\")\\n\\n abs_target_specs = [os.path.join(get_buildroot(), spec) for spec in self.context.options.target_specs]\\n configured_workspace = TemplateData(\\n targets=json.dumps(abs_target_specs),\\n project_path=os.path.join(get_buildroot(), abs_target_specs[0].split(':')[0]),\\n idea_plugin_version=IDEA_PLUGIN_VERSION\\n )\\n\\n # Generate (without merging in any extra components).\\n safe_mkdir(os.path.abspath(self.intellij_output_dir))\\n\\n ipr = self._generate_to_tempfile(\\n Generator(pkgutil.get_data(__name__, self.project_template), project=configured_project))\\n iws = self._generate_to_tempfile(\\n Generator(pkgutil.get_data(__name__, self.workspace_template), workspace=configured_workspace))\\n\\n self._outstream.write(self.gen_project_workdir)\\n\\n shutil.move(ipr, self.project_filename)\\n shutil.move(iws, self.workspace_filename)\\n return self.project_filename\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen._generate_to_tempfile\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen._generate_to_tempfile\\n def _generate_to_tempfile(self, generator):\\n \\\"\\\"\\\"Applies the specified generator to a temp file and returns the path to that file.\\n We generate into a temp file so that we don't lose any manual customizations on error.\\\"\\\"\\\"\\n with temporary_file(cleanup=False) as output:\\n generator.write(output)\\n return output.name\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.execute\",\n \"text\": \"src/python/pants/backend/project_info/tasks/idea_plugin_gen.py::IdeaPluginGen.execute\\n def execute(self):\\n # Heuristics to guess whether user tries to load a python project,\\n # in which case intellij project sdk has to be set up manually.\\n jvm_target_num = len(filter(lambda x: isinstance(x, JvmTarget), self.context.target_roots))\\n python_target_num = len(filter(lambda x: isinstance(x, PythonTarget), self.context.target_roots))\\n if python_target_num > jvm_target_num:\\n logging.warn('This is likely a python project. Please make sure to '\\n 'select the proper python interpreter as Project SDK in IntelliJ.')\\n\\n ide_file = self.generate_project()\\n if ide_file and self.get_options().open:\\n open_with = self.get_options().open_with\\n if open_with:\\n null = open(os.devnull, 'w')\\n subprocess.Popen([open_with, ide_file], stdout=null, stderr=null)\\n else:\\n try:\\n desktop.ui_open(ide_file)\\n except desktop.OpenError as e:\\n raise TaskError(e)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.for_partition\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.for_partition\\n @classmethod\\n def for_partition(cls, work_dir, partition):\\n root_dir = os.path.join(work_dir, Target.maybe_readable_identify(partition))\\n safe_mkdir(root_dir, clean=False)\\n return cls(root_dir=root_dir, partition=partition)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.target_set_id\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.target_set_id\\n @memoized_method\\n def target_set_id(self, *targets):\\n return Target.maybe_readable_identify(targets or self.partition)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.junitxml_path\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.junitxml_path\\n @memoized_method\\n def junitxml_path(self, *targets):\\n xml_path = os.path.join(self.root_dir, 'junitxml',\\n 'TEST-{}.xml'.format(self.target_set_id(*targets)))\\n safe_mkdir_for(xml_path)\\n return xml_path\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.coverage_path\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.coverage_path\\n @memoized_property\\n def coverage_path(self):\\n coverage_workdir = os.path.join(self.root_dir, 'coverage')\\n safe_mkdir(coverage_workdir)\\n return coverage_workdir\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.files\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.files\\n def files(self):\\n def files_iter():\\n for dir_path, _, file_names in os.walk(self.root_dir):\\n for filename in file_names:\\n yield os.path.join(dir_path, filename)\\n return list(files_iter())\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.files.files_iter\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::_Workdirs.files.files_iter\\n def files_iter():\\n for dir_path, _, file_names in os.walk(self.root_dir):\\n for filename in file_names:\\n yield os.path.join(dir_path, filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestResult._map_exit_code\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestResult._map_exit_code\\n @classmethod\\n def _map_exit_code(cls, value):\\n return 0 if value in cls._SUCCESS_EXIT_CODES else value\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.implementation_version\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.implementation_version\\n @classmethod\\n def implementation_version(cls):\\n return super(PytestRun, cls).implementation_version() + [('PytestRun', 2)]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.register_options\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.register_options\\n @classmethod\\n def register_options(cls, register):\\n super(PytestRun, cls).register_options(register)\\n register('--fast', type=bool, default=True, fingerprint=True,\\n help='Run all tests in a single pytest invocation. If turned off, each test target '\\n 'will run in its own pytest invocation, which will be slower, but isolates '\\n 'tests from process-wide state created by tests in other targets.')\\n\\n register('--chroot', advanced=True, fingerprint=True, type=bool, default=False,\\n help='Run tests in a chroot. Any loose files tests depend on via `{}` dependencies '\\n 'will be copied to the chroot.'\\n .format(Files.alias()))\\n\\n # NB: We always produce junit xml privately, and if this option is specified, we then copy\\n # it to the user-specified directory, post any interaction with the cache to retrieve the\\n # privately generated and cached xml files. As such, this option is not part of the\\n # fingerprint.\\n register('--junit-xml-dir', metavar='<DIR>',\\n help='Specifying a directory causes junit xml results files to be emitted under '\\n 'that dir for each test run.')\\n\\n register('--profile', metavar='<FILE>', fingerprint=True,\\n help=\\\"Specifying a file path causes tests to be profiled with the profiling data \\\"\\n \\\"emitted to that file (prefix). Note that tests may run in a different cwd, so \\\"\\n \\\"it's best to use an absolute path to make it easy to find the subprocess \\\"\\n \\\"profiles later.\\\")\\n\\n register('--options', type=list, fingerprint=True, help='Pass these options to pytest.')\\n\\n register('--coverage', fingerprint=True,\\n help='Emit coverage information for specified packages or directories (absolute or '\\n 'relative to the build root). The special value \\\"auto\\\" indicat\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.supports_passthru_args\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.supports_passthru_args\\n @classmethod\\n def supports_passthru_args(cls):\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.prepare\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.prepare\\n @classmethod\\n def prepare(cls, options, round_manager):\\n super(PytestRun, cls).prepare(options, round_manager)\\n round_manager.require_data(PytestPrep.PYTEST_BINARY)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_target_filter\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_target_filter\\n def _test_target_filter(self):\\n def target_filter(target):\\n return isinstance(target, PythonTests)\\n\\n return target_filter\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_target_filter.target_filter\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_target_filter.target_filter\\n def target_filter(target):\\n return isinstance(target, PythonTests)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._validate_target\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._validate_target\\n def _validate_target(self, target):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._execute\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._execute\\n def _execute(self, all_targets):\\n test_targets = self._get_test_targets()\\n if test_targets:\\n self.context.release_lock()\\n self._run_tests(test_targets)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._format_string_list\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._format_string_list\\n @staticmethod\\n def _format_string_list(values):\\n # The coverage rc ini files accept \\\"Multi-valued strings\\\" - ie: lists of strings - denoted by\\n # indenting values on multiple lines like so:\\n # [section]\\n # name =\\n # value1\\n # value2\\n #\\n # See http://nedbatchelder.com/code/coverage/config.html for details.\\n return '\\\\n\\\\t{values}'.format(values='\\\\n\\\\t'.join(values))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._debug\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._debug\\n @property\\n def _debug(self):\\n return self.get_options().level == 'debug'\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._generate_coverage_config\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._generate_coverage_config\\n def _generate_coverage_config(self, source_mappings):\\n # For the benefit of macos testing, add the 'real' path the directory as an equivalent.\\n def add_realpath(path):\\n realpath = os.path.realpath(path)\\n if realpath != canonical and realpath not in alternates:\\n realpaths.add(realpath)\\n\\n cp = configparser.SafeConfigParser()\\n cp.readfp(StringIO(self.DEFAULT_COVERAGE_CONFIG))\\n\\n # We use the source_mappings to setup the `combine` coverage command to transform paths in\\n # coverage data files into canonical form.\\n # See the \\\"[paths]\\\" entry here: http://nedbatchelder.com/code/coverage/config.html for details.\\n cp.add_section('paths')\\n for canonical, alternates in source_mappings.items():\\n key = canonical.replace(os.sep, '.')\\n realpaths = set()\\n add_realpath(canonical)\\n for path in alternates:\\n add_realpath(path)\\n cp.set('paths',\\n key,\\n self._format_string_list([canonical] + list(alternates) + list(realpaths)))\\n\\n # See the debug options here: http://nedbatchelder.com/code/coverage/cmd.html#cmd-run-debug\\n if self._debug:\\n debug_options = self._format_string_list([\\n # Dumps the coverage config realized values.\\n 'config',\\n # Logs which files are skipped or traced and why.\\n 'trace'])\\n cp.set('run', 'debug', debug_options)\\n\\n return cp\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._generate_coverage_config.add_realpath\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._generate_coverage_config.add_realpath\\n def add_realpath(path):\\n realpath = os.path.realpath(path)\\n if realpath != canonical and realpath not in alternates:\\n realpaths.add(realpath)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._cov_setup\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._cov_setup\\n @contextmanager\\n def _cov_setup(self, workdirs, source_mappings, coverage_sources=None):\\n cp = self._generate_coverage_config(source_mappings=source_mappings)\\n # Note that it's important to put the tmpfile under the workdir, because pytest\\n # uses all arguments that look like paths to compute its rootdir, and we want\\n # it to pick the buildroot.\\n with temporary_file(root_dir=workdirs.root_dir) as fp:\\n cp.write(fp)\\n fp.close()\\n coverage_rc = fp.name\\n # Note that --cov-report= with no value turns off terminal reporting, which\\n # we handle separately.\\n args = ['--cov-report=', '--cov-config', coverage_rc]\\n for module in coverage_sources:\\n args.extend(['--cov', module])\\n yield args, coverage_rc\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._maybe_emit_coverage_data\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._maybe_emit_coverage_data\\n @contextmanager\\n def _maybe_emit_coverage_data(self, workdirs, targets, pex):\\n coverage = self.get_options().coverage\\n if coverage is None:\\n yield []\\n return\\n\\n pex_src_root = os.path.relpath(self._source_chroot_path, get_buildroot())\\n\\n source_mappings = {}\\n for target in targets:\\n libs = (tgt for tgt in target.closure()\\n if tgt.has_sources('.py') and not isinstance(tgt, PythonTests))\\n for lib in libs:\\n source_mappings[lib.target_base] = [pex_src_root]\\n\\n def ensure_trailing_sep(path):\\n return path if path.endswith(os.path.sep) else path + os.path.sep\\n\\n if coverage == 'auto':\\n def compute_coverage_sources(tgt):\\n if tgt.coverage:\\n return tgt.coverage\\n else:\\n # This makes the assumption that tests/python/<tgt> will be testing src/python/<tgt>.\\n # Note in particular that this doesn't work for pants' own tests, as those are under\\n # the top level package 'pants_tests', rather than just 'pants'.\\n # TODO(John Sirois): consider failing fast if there is no explicit coverage scheme;\\n # but also consider supporting configuration of a global scheme whether that be parallel\\n # dirs/packages or some arbitrary function that can be registered that takes a test target\\n # and hands back the source packages or paths under test.\\n return set(os.path.dirname(s).replace(os.sep, '.')\\n for s in tgt.sources_relative_to_source_root())\\n coverage_sources = set(itertools.chain(*[compute_coverage_sources(t) for t in targets]))\\n else:\\n coverage_sources = []\\n for source in coverage.split(','):\\n if os.path.isdir(source):\\n # The source is a dir, so correct its prefix for the chroot.\\n # E.g. if source is /path/to/src/python/foo/bar or src/python/foo/bar then\\n # rel_source is sr\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._maybe_emit_coverage_data.ensure_trailing_sep\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._maybe_emit_coverage_data.ensure_trailing_sep\\n def ensure_trailing_sep(path):\\n return path if path.endswith(os.path.sep) else path + os.path.sep\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_shard_conftest_content\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_shard_conftest_content\\n def _get_shard_conftest_content(self):\\n shard_spec = self.get_options().test_shard\\n if shard_spec is None:\\n return ''\\n\\n try:\\n sharder = Sharder(shard_spec)\\n if sharder.nshards < 2:\\n return ''\\n return dedent(\\\"\\\"\\\"\\n\\n ### GENERATED BY PANTS ###\\n\\n def pytest_report_header(config):\\n return 'shard: {shard} of {nshards} (0-based shard numbering)'\\n\\n def pytest_collection_modifyitems(session, config, items):\\n total_count = len(items)\\n removed = 0\\n def is_conftest(itm):\\n return itm.fspath and itm.fspath.basename == 'conftest.py'\\n for i, item in enumerate(list(x for x in items if not is_conftest(x))):\\n if i % {nshards} != {shard}:\\n del items[i - removed]\\n removed += 1\\n reporter = config.pluginmanager.getplugin('terminalreporter')\\n reporter.write_line('Only executing {{}} of {{}} total tests in shard {shard} of '\\n '{nshards}'.format(total_count - removed, total_count),\\n bold=True, invert=True, yellow=True)\\n \\\"\\\"\\\".format(shard=sharder.shard, nshards=sharder.nshards))\\n except Sharder.InvalidShardSpec as e:\\n raise self.InvalidShardSpecification(e)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_conftest_content\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_conftest_content\\n def _get_conftest_content(self, sources_map):\\n # A conftest hook to modify the console output, replacing the chroot-based\\n # source paths with the source-tree based ones, which are more readable to the end user.\\n # Note that python stringifies a dict to its source representation, so we can use sources_map\\n # as a format argument directly.\\n #\\n # We'd prefer to hook into pytest_runtest_logstart(), which actually prints the line we\\n # want to fix, but we can't because we won't have access to any of its state, so\\n # we can't actually change what it prints.\\n #\\n # Alternatively, we could hook into pytest_collect_file() and just set a custom nodeid\\n # for the entire pytest run. However this interferes with pytest internals, including\\n # fixture registration, leading to fixtures not running when they should.\\n # It also requires the generated conftest to be in the root of the source tree, which\\n # complicates matters when there's already a user conftest.py there.\\n console_output_conftest_content = dedent(\\\"\\\"\\\"\\n\\n ### GENERATED BY PANTS ###\\n\\n import pytest\\n\\n # Map from source path relative to chroot -> source path relative to buildroot.\\n _SOURCES_MAP = {}\\n\\n @pytest.hookimpl(hookwrapper=True)\\n def pytest_runtest_protocol(item, nextitem):\\n # Temporarily change the nodeid, which pytest uses for display here.\\n real_nodeid = item.nodeid\\n real_path = real_nodeid.split('::', 1)[0]\\n fixed_path = _SOURCES_MAP.get(real_path, real_path)\\n fixed_nodeid = fixed_path + real_nodeid[len(real_path):]\\n try:\\n item._nodeid = fixed_nodeid\\n yield\\n finally:\\n item._nodeid = real_nodeid\\n \\\"\\\"\\\".format(sources_map))\\n # Add in the sharding conftest, if any.\\n shard_conftest_content = self._get_shard_conftest_content()\\n return (console_output_conftest_content + sh\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._conftest\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._conftest\\n @contextmanager\\n def _conftest(self, sources_map):\\n \\\"\\\"\\\"Creates a conftest.py to customize our pytest run.\\\"\\\"\\\"\\n conftest_content = self._get_conftest_content(sources_map)\\n # Note that it's important to put the tmpdir under the workdir, because pytest\\n # uses all arguments that look like paths to compute its rootdir, and we want\\n # it to pick the buildroot.\\n with temporary_dir(root_dir=self.workdir) as conftest_dir:\\n conftest = os.path.join(conftest_dir, 'conftest.py')\\n with open(conftest, 'w') as fp:\\n fp.write(conftest_content)\\n yield conftest\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_runner\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._test_runner\\n @contextmanager\\n def _test_runner(self, workdirs, targets, sources_map):\\n pex = self.context.products.get_data(PytestPrep.PYTEST_BINARY)\\n with self._conftest(sources_map) as conftest:\\n with self._maybe_emit_coverage_data(workdirs, targets, pex) as coverage_args:\\n yield pex, [conftest] + coverage_args\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._do_run_tests_with_args\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._do_run_tests_with_args\\n def _do_run_tests_with_args(self, pex, args):\\n try:\\n # The pytest runner we use accepts a --pdb argument that will launch an interactive pdb\\n # session on any test failure. In order to support use of this pass-through flag we must\\n # turn off stdin buffering that otherwise occurs. Setting the PYTHONUNBUFFERED env var to\\n # any value achieves this in python2.7. We'll need a different solution when we support\\n # running pants under CPython 3 which does not unbuffer stdin using this trick.\\n env = dict(os.environ)\\n env['PYTHONUNBUFFERED'] = '1'\\n\\n # pytest uses py.io.terminalwriter for output. That class detects the terminal\\n # width and attempts to use all of it. However we capture and indent the console\\n # output, leading to weird-looking line wraps. So we trick the detection code\\n # into thinking the terminal window is narrower than it is.\\n env['COLUMNS'] = str(int(os.environ.get('COLUMNS', 80)) - 30)\\n\\n profile = self.get_options().profile\\n if profile:\\n env['PEX_PROFILE_FILENAME'] = '{0}.subprocess.{1:.6f}'.format(profile, time.time())\\n\\n with self.context.new_workunit(name='run',\\n cmd=pex.cmdline(args),\\n labels=[WorkUnitLabel.TOOL, WorkUnitLabel.TEST]) as workunit:\\n rc = self._spawn_and_wait(pex, workunit=workunit, args=args, setsid=True, env=env)\\n return PytestResult.rc(rc)\\n except ErrorWhileTesting:\\n # _spawn_and_wait wraps the test runner in a timeout, so it could\\n # fail with a ErrorWhileTesting. We can't just set PythonTestResult\\n # to a failure because the resultslog doesn't have all the failures\\n # when tests are killed with a timeout. Therefore we need to re-raise\\n # here.\\n raise\\n except Exception:\\n self.context.log.error('Failed to run test!')\\n self.contex\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._map_relsrc_to_targets\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._map_relsrc_to_targets\\n def _map_relsrc_to_targets(self, targets):\\n pex_src_root = os.path.relpath(self._source_chroot_path, get_buildroot())\\n # First map chrooted sources back to their targets.\\n relsrc_to_target = {os.path.join(pex_src_root, src): target for target in targets\\n for src in target.sources_relative_to_source_root()}\\n # Also map the source tree-rooted sources, because in some cases (e.g., a failure to even\\n # eval the test file during test collection), that's the path pytest will use in the junit xml.\\n relsrc_to_target.update({src: target for target in targets\\n for src in target.sources_relative_to_buildroot()})\\n\\n return relsrc_to_target\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_failed_targets_from_junitxml\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_failed_targets_from_junitxml\\n def _get_failed_targets_from_junitxml(self, junitxml, targets):\\n relsrc_to_target = self._map_relsrc_to_targets(targets)\\n\\n # Now find the sources that contained failing tests.\\n failed_targets = set()\\n\\n try:\\n xml = XmlParser.from_file(junitxml)\\n failures = int(xml.get_attribute('testsuite', 'failures'))\\n errors = int(xml.get_attribute('testsuite', 'errors'))\\n if failures or errors:\\n for testcase in xml.parsed.getElementsByTagName('testcase'):\\n test_failed = testcase.getElementsByTagName('failure')\\n test_errored = testcase.getElementsByTagName('error')\\n if test_failed or test_errored:\\n # The 'file' attribute is a relsrc, because that's what we passed in to pytest.\\n failed_targets.add(relsrc_to_target.get(testcase.getAttribute('file')))\\n except (XmlParser.XmlError, ValueError) as e:\\n raise TaskError('Error parsing xml file at {}: {}'.format(junitxml, e))\\n\\n return failed_targets\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_target_from_test\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._get_target_from_test\\n def _get_target_from_test(self, test_info, targets):\\n relsrc_to_target = self._map_relsrc_to_targets(targets)\\n file_info = test_info['file']\\n return relsrc_to_target.get(file_info)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._iter_partitions\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._iter_partitions\\n def _iter_partitions(self, targets):\\n # TODO(John Sirois): Consume `py.test` pexes matched to the partitioning in effect after\\n # https://github.com/pantsbuild/pants/pull/4638 lands.\\n if self.get_options().fast:\\n yield tuple(targets)\\n else:\\n for target in targets:\\n yield (target,)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_tests\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_tests\\n def _run_tests(self, targets):\\n results = {}\\n failure = False\\n for partition in self._iter_partitions(targets):\\n try:\\n rv = self._do_run_tests(partition)\\n except ErrorWhileTesting as e:\\n rv = PytestResult.from_error(e)\\n results[partition] = rv\\n if not rv.success:\\n failure = True\\n if self.get_options().fail_fast:\\n break\\n\\n for partition in sorted(results):\\n rv = results[partition]\\n if len(partition) == 1 or rv.success:\\n log = self.context.log.info if rv.success else self.context.log.error\\n for target in partition:\\n log('{0:80}.....{1:>10}'.format(target.address.reference(), rv))\\n else:\\n # There is not much useful we can display in summary for a multi-target partition with\\n # failures without parsing those failures to link them to individual targets; ie: targets\\n # 2 and 8 failed in this partition of 10 targets.\\n # TODO(John Sirois): Punting here works since we have in practice just 2 partitionings:\\n # 1. All targets in singleton partitions\\n # 2. All targets in 1 partition\\n # If we get to the point where we have multiple partitions with multiple targets, some sort\\n # of summary for the multi-target partitions will probably be needed.\\n pass\\n\\n failed_targets = [target\\n for _rv in results.values() if not _rv.success\\n for target in _rv.failed_targets]\\n if failed_targets:\\n raise ErrorWhileTesting(failed_targets=failed_targets)\\n elif failure:\\n # A low-level test execution failure occurred before tests were run.\\n raise TaskError()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._vts_for_partition\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._vts_for_partition\\n @staticmethod\\n def _vts_for_partition(invalidation_check):\\n return VersionedTargetSet.from_versioned_targets(invalidation_check.all_vts)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.check_artifact_cache_for\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.check_artifact_cache_for\\n def check_artifact_cache_for(self, invalidation_check):\\n # We generate artifacts, namely junit.xml and coverage reports, that cover the full target set\\n # whether that is all targets in the context (`--fast`) or each target\\n # individually (`--no-fast`).\\n return [self._vts_for_partition(invalidation_check)]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.NeverCacheFingerprintStrategy.compute_fingerprint\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun.NeverCacheFingerprintStrategy.compute_fingerprint\\n def compute_fingerprint(self, target):\\n return uuid.uuid4()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._fingerprint_strategy\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._fingerprint_strategy\\n def _fingerprint_strategy(self):\\n if self.get_options().profile:\\n # A profile is machine-specific and we assume anyone wanting a profile wants to run it here\\n # and now and not accept some old result, even if on the same inputs.\\n return self.NeverCacheFingerprintStrategy()\\n else:\\n return None # Accept the default fingerprint strategy.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._do_run_tests\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._do_run_tests\\n def _do_run_tests(self, partition):\\n with self.invalidated(partition,\\n fingerprint_strategy=self._fingerprint_strategy(),\\n # Re-run tests when the code they test (and depend on) changes.\\n invalidate_dependents=True) as invalidation_check:\\n\\n invalid_tgts = [invalid_tgt\\n for vts in invalidation_check.invalid_vts\\n for invalid_tgt in vts.targets]\\n\\n # Processing proceeds through:\\n # 1.) output -> workdir\\n # 2.) [iff all == invalid] workdir -> cache: We do this manually for now.\\n # 3.) [iff invalid == 0 and all > 0] cache -> workdir: Done transparently by `invalidated`.\\n\\n # 1.) Write all results that will be potentially cached to workdir.\\n workdirs = _Workdirs.for_partition(self.workdir, partition)\\n result = self._run_pytest_checked(workdirs, invalid_tgts)\\n\\n cache_vts = self._vts_for_partition(invalidation_check)\\n if invalidation_check.all_vts == invalidation_check.invalid_vts:\\n # 2.) The full partition was invalid, cache successful test results.\\n if result.success and self.artifact_cache_writes_enabled():\\n self.update_artifact_cache([(cache_vts, workdirs.files())])\\n elif not invalidation_check.invalid_vts:\\n # 3.) The full partition was valid, our results will have been staged for/by caching if not\\n # already local.\\n pass\\n else:\\n # The partition was partially invalid.\\n\\n # We don't cache results; so others will need to re-run this partition.\\n # NB: We will presumably commit this change now though and so others will get this\\n # partition in a state that executes successfully; so when the 1st of the others\\n # executes against this partition; they will hit `all_vts == invalid_vts` and\\n # cache the results. That 1st of others is hopefully\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._expose_results\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._expose_results\\n def _expose_results(self, invalid_tgts, workdirs):\\n external_junit_xml_dir = self.get_options().junit_xml_dir\\n if external_junit_xml_dir:\\n # Either we just ran pytest for a set of invalid targets and generated a junit xml file\\n # specific to that (sub)set or else we hit the cache for the whole partition and skipped\\n # running pytest, simply retrieving the partition's full junit xml file.\\n junitxml_path = workdirs.junitxml_path(*invalid_tgts)\\n\\n safe_mkdir(external_junit_xml_dir)\\n shutil.copy2(junitxml_path, external_junit_xml_dir)\\n\\n if self.get_options().coverage:\\n coverage_output_dir = self.get_options().coverage_output_dir\\n if coverage_output_dir:\\n target_dir = coverage_output_dir\\n else:\\n pants_distdir = self.context.options.for_global_scope().pants_distdir\\n relpath = workdirs.target_set_id()\\n target_dir = os.path.join(pants_distdir, 'coverage', relpath)\\n mergetree(workdirs.coverage_path, target_dir)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_pytest_checked\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_pytest_checked\\n def _run_pytest_checked(self, workdirs, targets):\\n result = self._run_pytest(workdirs, targets)\\n\\n # Unconditionally pluck any results that an end user might need to interact with from the\\n # workdir to the locations they expect.\\n self._expose_results(targets, workdirs)\\n\\n return result.checked()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_pytest\",\n \"text\": \"src/python/pants/backend/python/tasks/pytest_run.py::PytestRun._run_pytest\\n def _run_pytest(self, workdirs, targets):\\n if not targets:\\n return PytestResult.rc(0)\\n\\n if self._run_in_chroot:\\n path_func = lambda rel_src: rel_src\\n else:\\n source_chroot = os.path.relpath(self._source_chroot_path, get_buildroot())\\n path_func = lambda rel_src: os.path.join(source_chroot, rel_src)\\n\\n sources_map = {} # Path from chroot -> Path from buildroot.\\n for t in targets:\\n for p in t.sources_relative_to_source_root():\\n sources_map[path_func(p)] = os.path.join(t.target_base, p)\\n\\n if not sources_map:\\n return PytestResult.rc(0)\\n\\n with self._test_runner(workdirs, targets, sources_map) as (pex, test_args):\\n # Validate that the user didn't provide any passthru args that conflict\\n # with those we must set ourselves.\\n for arg in self.get_passthru_args():\\n if arg.startswith('--junitxml') or arg.startswith('--confcutdir'):\\n raise TaskError('Cannot pass this arg through to pytest: {}'.format(arg))\\n\\n junitxml_path = workdirs.junitxml_path(*targets)\\n\\n # N.B. the `--confcutdir` here instructs pytest to stop scanning for conftest.py files at the\\n # top of the buildroot. This prevents conftest.py files from outside (e.g. in users home dirs)\\n # from leaking into pants test runs. See: https://github.com/pantsbuild/pants/issues/2726\\n args = ['--junitxml', junitxml_path, '--confcutdir', get_buildroot(),\\n '--continue-on-collection-errors']\\n if self.get_options().fail_fast:\\n args.extend(['-x'])\\n if self._debug:\\n args.extend(['-s'])\\n if self.get_options().colors:\\n args.extend(['--color', 'yes'])\\n for options in self.get_options().options + self.get_passthru_args():\\n args.extend(safe_shlex_split(options))\\n args.extend(test_args)\\n args.extend(sources_map.keys())\\n\\n # We want to ensure our reporting based off junit xml is fro\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\\n @classmethod\\n def register_bootstrap_options(cls, register):\\n \\\"\\\"\\\"Register bootstrap options.\\n\\n \\\"Bootstrap options\\\" are a small set of options whose values are useful when registering other\\n options. Therefore we must bootstrap them early, before other options are registered, let\\n alone parsed.\\n\\n Bootstrap option values can be interpolated into the config file, and can be referenced\\n programatically in registration code, e.g., as register.bootstrap.pants_workdir.\\n\\n Note that regular code can also access these options as normal global-scope options. Their\\n status as \\\"bootstrap options\\\" is only pertinent during option registration.\\n \\\"\\\"\\\"\\n buildroot = get_buildroot()\\n default_distdir_name = 'dist'\\n default_distdir = os.path.join(buildroot, default_distdir_name)\\n default_rel_distdir = '/{}/'.format(default_distdir_name)\\n\\n # Although logging supports the WARN level, its not documented and could conceivably be yanked.\\n # Since pants has supported 'warn' since inception, leave the 'warn' choice as-is but explicitly\\n # setup a 'WARN' logging level name that maps to 'WARNING'.\\n logging.addLevelName(logging.WARNING, 'WARN')\\n register('-l', '--level', choices=['debug', 'info', 'warn'], default='info', recursive=True,\\n help='Set the logging level.')\\n register('-q', '--quiet', type=bool, recursive=True, daemon=False,\\n help='Squelches most console output. NOTE: Some tasks default to behaving quietly: '\\n 'inverting this option supports making them noisier than they would be otherwise.')\\n # Not really needed in bootstrap options, but putting it here means it displays right\\n # after -l and -q in help output, which is conveniently contextual.\\n register('--colors', type=bool, default=sys.stdout.isatty(), recursive=True, daemon=False,\\n help='Set whether log messages are displa\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\\n @classmethod\\n def register_options(cls, register):\\n \\\"\\\"\\\"Register options not tied to any particular task or subsystem.\\\"\\\"\\\"\\n # The bootstrap options need to be registered on the post-bootstrap Options instance, so it\\n # won't choke on them on the command line, and also so we can access their values as regular\\n # global-scope options, for convenience.\\n cls.register_bootstrap_options(register)\\n\\n register('-x', '--time', type=bool,\\n help='Output a timing report at the end of the run.')\\n register('-e', '--explain', type=bool,\\n help='Explain the execution of goals.')\\n register('--tag', type=list, metavar='[+-]tag1,tag2,...',\\n help=\\\"Include only targets with these tags (optional '+' prefix) or without these \\\"\\n \\\"tags ('-' prefix). Useful with ::, to find subsets of targets \\\"\\n \\\"(e.g., integration tests.)\\\")\\n\\n register('-t', '--timeout', advanced=True, type=int, metavar='<seconds>',\\n help='Number of seconds to wait for http connections.')\\n # TODO: After moving to the new options system these abstraction leaks can go away.\\n register('-k', '--kill-nailguns', advanced=True, type=bool,\\n help='Kill nailguns before exiting')\\n register('--fail-fast', advanced=True, type=bool, recursive=True,\\n help='Exit as quickly as possible on error, rather than attempting to continue '\\n 'to process the non-erroneous subset of the input.')\\n register('--cache-key-gen-version', advanced=True, default='200', recursive=True,\\n help='The cache key generation. Bump this to invalidate every artifact for a scope.')\\n register('--workdir-max-build-entries', advanced=True, type=int, default=None,\\n help='Maximum number of previous builds to keep per task target pair in workdir. '\\n 'If set, minimum 2 will always be kept to support \",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.__init__\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.__init__\\n def __init__(self, logger, log_level, handler):\\n \\\"\\\"\\\"\\n :param logging.Logger logger: The logger instance to emit writes to.\\n :param int log_level: The log level to use for the given logger.\\n :param Handler handler: The underlying log handler, for determining the fileno\\n to support faulthandler logging.\\n \\\"\\\"\\\"\\n self._logger = logger\\n self._log_level = log_level\\n self._handler = handler\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.write\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.write\\n def write(self, msg):\\n for line in msg.rstrip().splitlines():\\n self._logger.log(self._log_level, line.rstrip())\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.flush\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.flush\\n def flush(self):\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.isatty\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.isatty\\n def isatty(self):\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.fileno\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::_LoggerStream.fileno\\n def fileno(self):\\n return self._handler.stream.fileno()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory.create\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory.create\\n @classmethod\\n def create(cls, bootstrap_options=None):\\n \\\"\\\"\\\"\\n :param Options bootstrap_options: The bootstrap options, if available.\\n \\\"\\\"\\\"\\n bootstrap_options = bootstrap_options or cls._parse_bootstrap_options()\\n bootstrap_options_values = bootstrap_options.for_global_scope()\\n\\n build_root = get_buildroot()\\n native = Native.create(bootstrap_options_values)\\n # TODO: https://github.com/pantsbuild/pants/issues/3479\\n watchman = WatchmanLauncher.create(bootstrap_options_values).watchman\\n legacy_graph_helper = cls._setup_legacy_graph_helper(native, bootstrap_options_values)\\n services, port_map = cls._setup_services(\\n build_root,\\n bootstrap_options_values,\\n legacy_graph_helper,\\n watchman\\n )\\n\\n return PantsDaemon(\\n native,\\n build_root,\\n bootstrap_options_values.pants_workdir,\\n bootstrap_options_values.level.upper(),\\n services,\\n port_map,\\n bootstrap_options_values.pants_subprocessdir,\\n bootstrap_options\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._parse_bootstrap_options\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._parse_bootstrap_options\\n @staticmethod\\n def _parse_bootstrap_options():\\n return OptionsBootstrapper().get_bootstrap_options()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._setup_legacy_graph_helper\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._setup_legacy_graph_helper\\n @staticmethod\\n def _setup_legacy_graph_helper(native, bootstrap_options):\\n \\\"\\\"\\\"Initializes a `LegacyGraphHelper` instance.\\\"\\\"\\\"\\n return EngineInitializer.setup_legacy_graph(\\n bootstrap_options.pants_ignore,\\n bootstrap_options.pants_workdir,\\n bootstrap_options.build_file_imports,\\n native=native,\\n build_ignore_patterns=bootstrap_options.build_ignore,\\n exclude_target_regexps=bootstrap_options.exclude_target_regexp,\\n subproject_roots=bootstrap_options.subproject_roots,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._setup_services\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.Factory._setup_services\\n @staticmethod\\n def _setup_services(build_root, bootstrap_options, legacy_graph_helper, watchman):\\n \\\"\\\"\\\"Initialize pantsd services.\\n\\n :returns: A tuple of (`tuple` service_instances, `dict` port_map).\\n \\\"\\\"\\\"\\n fs_event_service = FSEventService(watchman, build_root, bootstrap_options.pantsd_fs_event_workers)\\n scheduler_service = SchedulerService(fs_event_service, legacy_graph_helper)\\n pailgun_service = PailgunService(\\n bind_addr=(bootstrap_options.pantsd_pailgun_host, bootstrap_options.pantsd_pailgun_port),\\n exiter_class=DaemonExiter,\\n runner_class=DaemonPantsRunner,\\n target_roots_class=TargetRoots,\\n scheduler_service=scheduler_service\\n )\\n store_gc_service = StoreGCService(legacy_graph_helper.scheduler)\\n\\n return (\\n # Services.\\n (fs_event_service, scheduler_service, pailgun_service, store_gc_service),\\n # Port map.\\n dict(pailgun=pailgun_service.pailgun_port)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.__init__\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.__init__\\n def __init__(self, native, build_root, work_dir, log_level, services, socket_map,\\n metadata_base_dir, bootstrap_options=None):\\n \\\"\\\"\\\"\\n :param Native native: A `Native` instance.\\n :param string build_root: The pants build root.\\n :param string work_dir: The pants work directory.\\n :param string log_level: The log level to use for daemon logging.\\n :param string metadata_base_dir: The ProcessManager metadata base dir.\\n :param Options bootstrap_options: The bootstrap options, if available.\\n \\\"\\\"\\\"\\n super(PantsDaemon, self).__init__(name='pantsd', metadata_base_dir=metadata_base_dir)\\n self._native = native\\n self._build_root = build_root\\n self._work_dir = work_dir\\n self._log_level = log_level\\n self._services = services\\n self._socket_map = socket_map\\n self._bootstrap_options = bootstrap_options\\n\\n self._log_dir = os.path.join(work_dir, self.name)\\n self._logger = logging.getLogger(__name__)\\n # A lock to guard the service thread lifecycles. This can be used by individual services\\n # to safeguard daemon-synchronous sections that should be protected from abrupt teardown.\\n self._lifecycle_lock = threading.RLock()\\n # A lock to guard pantsd->runner forks. This can be used by services to safeguard resources\\n # held by threads at fork time, so that we can fork without deadlocking.\\n self._fork_lock = threading.RLock()\\n # N.B. This Event is used as nothing more than a convenient atomic flag - nothing waits on it.\\n self._kill_switch = threading.Event()\\n self._exiter = Exiter()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.watchman_launcher\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.watchman_launcher\\n @memoized_property\\n def watchman_launcher(self):\\n return WatchmanLauncher.create(self._bootstrap_options.for_global_scope())\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.is_killed\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.is_killed\\n @property\\n def is_killed(self):\\n return self._kill_switch.is_set()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.options_fingerprint\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.options_fingerprint\\n @property\\n def options_fingerprint(self):\\n return OptionsFingerprinter.combined_options_fingerprint_for_scope(\\n GLOBAL_SCOPE,\\n self._bootstrap_options,\\n fingerprint_key='daemon',\\n invert=True\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.shutdown\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.shutdown\\n def shutdown(self, service_thread_map):\\n \\\"\\\"\\\"Gracefully terminate all services and kill the main PantsDaemon loop.\\\"\\\"\\\"\\n with self._lifecycle_lock:\\n for service, service_thread in service_thread_map.items():\\n self._logger.info('terminating pantsd service: {}'.format(service))\\n service.terminate()\\n service_thread.join(self.JOIN_TIMEOUT_SECONDS)\\n self._logger.info('terminating pantsd')\\n self._kill_switch.set()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._close_stdio\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._close_stdio\\n @staticmethod\\n def _close_stdio():\\n \\\"\\\"\\\"Close stdio streams to avoid output in the tty that launched pantsd.\\\"\\\"\\\"\\n for fd in (sys.stdin, sys.stdout, sys.stderr):\\n file_no = fd.fileno()\\n fd.flush()\\n fd.close()\\n os.close(file_no)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._pantsd_logging\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._pantsd_logging\\n @contextmanager\\n def _pantsd_logging(self):\\n \\\"\\\"\\\"A context manager that runs with pantsd logging.\\n\\n Asserts that stdio (represented by file handles 0, 1, 2) is closed to ensure that\\n we can safely reuse those fd numbers.\\n \\\"\\\"\\\"\\n\\n # Ensure that stdio is closed so that we can safely reuse those file descriptors.\\n for fd in (0, 1, 2):\\n try:\\n os.fdopen(fd)\\n raise AssertionError(\\n 'pantsd logging cannot initialize while stdio is open: {}'.format(fd))\\n except OSError:\\n pass\\n\\n # Redirect stdio to /dev/null for the rest of the run, to reserve those file descriptors\\n # for further forks.\\n with stdio_as(stdin_fd=-1, stdout_fd=-1, stderr_fd=-1):\\n # Reinitialize logging for the daemon context.\\n result = setup_logging(self._log_level, log_dir=self._log_dir, log_name=self.LOG_NAME)\\n\\n # Do a python-level redirect of stdout/stderr, which will not disturb `0,1,2`.\\n # TODO: Consider giving these pipes/actual fds, in order to make them \\\"deep\\\" replacements\\n # for `1,2`, and allow them to be used via `stdio_as`.\\n sys.stdout = _LoggerStream(logging.getLogger(), logging.INFO, result.log_handler)\\n sys.stderr = _LoggerStream(logging.getLogger(), logging.WARN, result.log_handler)\\n\\n self._logger.debug('logging initialized')\\n yield result.log_handler.stream\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._setup_services\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._setup_services\\n def _setup_services(self, services):\\n assert self._lifecycle_lock is not None, 'PantsDaemon lock has not been set!'\\n assert self._fork_lock is not None, 'PantsDaemon fork lock has not been set!'\\n for service in services:\\n self._logger.info('setting up service {}'.format(service))\\n service.setup(self._lifecycle_lock, self._fork_lock)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._make_thread\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._make_thread\\n @staticmethod\\n def _make_thread(target):\\n t = threading.Thread(target=target)\\n t.daemon = True\\n return t\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._run_services\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._run_services\\n def _run_services(self, services):\\n \\\"\\\"\\\"Service runner main loop.\\\"\\\"\\\"\\n if not services:\\n self._logger.critical('no services to run, bailing!')\\n return\\n\\n service_thread_map = {service: self._make_thread(service.run) for service in services}\\n\\n # Start services.\\n for service, service_thread in service_thread_map.items():\\n self._logger.info('starting service {}'.format(service))\\n try:\\n service_thread.start()\\n except (RuntimeError, service.ServiceError):\\n self.shutdown(service_thread_map)\\n raise self.StartupFailure('service {} failed to start, shutting down!'.format(service))\\n\\n # Once all services are started, write our pid.\\n self.write_pid()\\n self.write_metadata_by_name('pantsd', self.FINGERPRINT_KEY, self.options_fingerprint)\\n\\n # Monitor services.\\n while not self.is_killed:\\n for service, service_thread in service_thread_map.items():\\n if not service_thread.is_alive():\\n self.shutdown(service_thread_map)\\n raise self.RuntimeFailure('service failure for {}, shutting down!'.format(service))\\n else:\\n # Avoid excessive CPU utilization.\\n service_thread.join(self.JOIN_TIMEOUT_SECONDS)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._write_named_sockets\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon._write_named_sockets\\n def _write_named_sockets(self, socket_map):\\n \\\"\\\"\\\"Write multiple named sockets using a socket mapping.\\\"\\\"\\\"\\n for socket_name, socket_info in socket_map.items():\\n self.write_named_socket(socket_name, socket_info)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.run_sync\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.run_sync\\n def run_sync(self):\\n \\\"\\\"\\\"Synchronously run pantsd.\\\"\\\"\\\"\\n # Switch log output to the daemon's log stream from here forward.\\n self._close_stdio()\\n with self._pantsd_logging() as log_stream:\\n self._exiter.set_except_hook(log_stream)\\n self._logger.info('pantsd starting, log level is {}'.format(self._log_level))\\n\\n self._native.set_panic_handler()\\n\\n # Set the process name in ps output to 'pantsd' vs './pants compile src/etc:: -ldebug'.\\n set_process_title('pantsd [{}]'.format(self._build_root))\\n\\n # Write service socket information to .pids.\\n self._write_named_sockets(self._socket_map)\\n\\n # Enter the main service runner loop.\\n self._setup_services(self._services)\\n self._run_services(self._services)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.post_fork_child\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.post_fork_child\\n def post_fork_child(self):\\n \\\"\\\"\\\"Post-fork() child callback for ProcessManager.daemon_spawn().\\\"\\\"\\\"\\n entry_point = '{}:launch'.format(__name__)\\n exec_env = combined_dict(os.environ, dict(PANTS_ENTRYPOINT=entry_point))\\n # Pass all of sys.argv so that we can proxy arg flags e.g. `-ldebug`.\\n cmd = [sys.executable] + sys.argv\\n self._logger.debug('cmd is: PANTS_ENTRYPOINT={} {}'.format(entry_point, ' '.join(cmd)))\\n # TODO: Improve error handling on launch failures.\\n os.spawnve(os.P_NOWAIT, sys.executable, cmd, env=exec_env)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.maybe_launch\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.maybe_launch\\n def maybe_launch(self):\\n \\\"\\\"\\\"Launches pantsd (if not already running) in a subprocess.\\n\\n :returns: The port that pantsd is listening on.\\n :rtype: int\\n \\\"\\\"\\\"\\n self.watchman_launcher.maybe_launch()\\n self._logger.debug('acquiring lock: {}'.format(self.process_lock))\\n with self.process_lock:\\n new_fingerprint = self.options_fingerprint\\n self._logger.debug('pantsd: is_alive={} new_fingerprint={} current_fingerprint={}'\\n .format(self.is_alive(), new_fingerprint, self.fingerprint))\\n if self.needs_restart(new_fingerprint):\\n self.terminate(include_watchman=False)\\n self._logger.debug('launching pantsd')\\n self.daemon_spawn()\\n # Wait up to 60 seconds for pantsd to write its pidfile.\\n self.await_pid(60)\\n listening_port = self.read_named_socket('pailgun', int)\\n pantsd_pid = self.pid\\n self._logger.debug('released lock: {}'.format(self.process_lock))\\n self._logger.debug('pantsd is running at pid {}, pailgun port is {}'\\n .format(pantsd_pid, listening_port))\\n return listening_port\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.terminate\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::PantsDaemon.terminate\\n def terminate(self, include_watchman=True):\\n \\\"\\\"\\\"Terminates pantsd and watchman.\\\"\\\"\\\"\\n super(PantsDaemon, self).terminate()\\n if include_watchman:\\n self.watchman_launcher.terminate()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/pants_daemon.py::launch\",\n \"text\": \"src/python/pants/pantsd/pants_daemon.py::launch\\ndef launch():\\n \\\"\\\"\\\"An external entrypoint that spawns a new pantsd instance.\\\"\\\"\\\"\\n PantsDaemon.Factory.create().run_sync()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.__init__\",\n \"text\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.__init__\\n def __init__(self, watchman, build_root, worker_count):\\n \\\"\\\"\\\"\\n :param Watchman watchman: The Watchman instance as provided by the WatchmanLauncher subsystem.\\n :param str build_root: The current build root.\\n :param int worker_count: The total number of workers to use for the internally managed\\n ThreadPoolExecutor.\\n \\\"\\\"\\\"\\n super(FSEventService, self).__init__()\\n self._logger = logging.getLogger(__name__)\\n self._watchman = watchman\\n self._build_root = os.path.realpath(build_root)\\n self._worker_count = worker_count\\n self._executor = None\\n self._handlers = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.setup\",\n \"text\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.setup\\n def setup(self, lifecycle_lock, fork_lock, executor=None):\\n super(FSEventService, self).setup(lifecycle_lock, fork_lock)\\n self._executor = executor or ThreadPoolExecutor(max_workers=self._worker_count)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.terminate\",\n \"text\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.terminate\\n def terminate(self):\\n \\\"\\\"\\\"An extension of PantsService.terminate() that shuts down the executor if so configured.\\\"\\\"\\\"\\n if self._executor:\\n self._logger.info('shutting down threadpool')\\n self._executor.shutdown()\\n super(FSEventService, self).terminate()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.register_all_files_handler\",\n \"text\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.register_all_files_handler\\n def register_all_files_handler(self, callback, name='all_files'):\\n \\\"\\\"\\\"Registers a subscription for all files under a given watch path.\\n\\n :param func callback: the callback to execute on each filesystem event\\n :param str name: the subscription name as used by watchman\\n \\\"\\\"\\\"\\n self.register_handler(\\n name,\\n dict(\\n fields=['name'],\\n # Request events for all file types.\\n # NB: Touching a file invalidates its parent directory due to:\\n # https://github.com/facebook/watchman/issues/305\\n # ...but if we were to skip watching directories, we'd still have to invalidate\\n # the parents of any changed files, and we wouldn't see creation/deletion of\\n # empty directories.\\n expression=[\\n 'allof', # All of the below rules must be true to match.\\n ['not', ['dirname', 'dist', self.ZERO_DEPTH]], # Exclude the ./dist dir.\\n # N.B. 'wholename' ensures we match against the absolute ('x/y/z') vs base path ('z').\\n ['not', ['pcre', r'^\\\\..*', 'wholename']], # Exclude files in hidden dirs (.pants.d etc).\\n ['not', ['match', '*.pyc']] # Exclude .pyc files.\\n # TODO(kwlzn): Make exclusions here optionable.\\n # Related: https://github.com/pantsbuild/pants/issues/2956\\n ]\\n ),\\n callback\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.register_handler\",\n \"text\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.register_handler\\n def register_handler(self, name, metadata, callback):\\n \\\"\\\"\\\"Register subscriptions and their event handlers.\\n\\n :param str name: the subscription name as used by watchman\\n :param dict metadata: a dictionary of metadata to be serialized and passed to the watchman\\n subscribe command. this should include the match expression as well\\n as any required callback fields.\\n :param func callback: the callback to execute on each matching filesystem event\\n \\\"\\\"\\\"\\n assert name not in self._handlers, 'duplicate handler name: {}'.format(name)\\n assert (\\n isinstance(metadata, dict) and 'fields' in metadata and 'expression' in metadata\\n ), 'invalid handler metadata!'\\n self._handlers[name] = Watchman.EventHandler(name=name, metadata=metadata, callback=callback)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.fire_callback\",\n \"text\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.fire_callback\\n def fire_callback(self, handler_name, event_data):\\n \\\"\\\"\\\"Fire an event callback for a given handler.\\\"\\\"\\\"\\n return self._handlers[handler_name].callback(event_data)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.run\",\n \"text\": \"src/python/pants/pantsd/service/fs_event_service.py::FSEventService.run\\n def run(self):\\n \\\"\\\"\\\"Main service entrypoint. Called via Thread.start() via PantsDaemon.run().\\\"\\\"\\\"\\n\\n if not (self._watchman and self._watchman.is_alive()):\\n raise self.ServiceError('watchman is not running, bailing!')\\n\\n # Enable watchman for the build root.\\n self._watchman.watch_project(self._build_root)\\n\\n futures = {}\\n id_counter = 0\\n subscriptions = self._handlers.values()\\n\\n # Setup subscriptions and begin the main event firing loop.\\n for handler_name, event_data in self._watchman.subscribed(self._build_root, subscriptions):\\n # On death, break from the loop and contextmgr to terminate callback threads.\\n if self.is_killed: break\\n\\n if event_data:\\n # As we receive events from watchman, submit them asynchronously to the executor.\\n future = self._executor.submit(self.fire_callback, handler_name, event_data)\\n futures[future] = handler_name\\n\\n # Process and log results for completed futures.\\n for completed_future in [_future for _future in futures if _future.done()]:\\n handler_name = futures.pop(completed_future)\\n id_counter += 1\\n\\n try:\\n result = completed_future.result()\\n except Exception:\\n result = traceback.format_exc()\\n\\n if result is not None:\\n # Truthy results or those that raise exceptions are treated as failures.\\n self._logger.warning('callback ID {} for {} failed: {}'\\n .format(id_counter, handler_name, result))\\n else:\\n self._logger.debug('callback ID {} for {} succeeded'.format(id_counter, handler_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman.__init__\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman.__init__\\n def __init__(self, watchman_path, metadata_base_dir, log_level='1', startup_timeout=STARTUP_TIMEOUT_SECONDS,\\n timeout=SOCKET_TIMEOUT_SECONDS, socket_path_override=None):\\n \\\"\\\"\\\"\\n :param str watchman_path: The path to the watchman binary.\\n :param str metadata_base_dir: The metadata base dir for `ProcessMetadataManager`.\\n :param float startup_timeout: The timeout for the initial `watch-project` query (in seconds).\\n :param float timeout: The watchman socket timeout for all subsequent queries (in seconds).\\n :param str log_level: The watchman log level. Watchman has 3 log levels: '0' for no logging,\\n '1' for standard logging and '2' for verbose logging.\\n :param str socket_path_override: The overridden target path of the watchman socket, if any.\\n \\\"\\\"\\\"\\n super(Watchman, self).__init__(name='watchman',\\n process_name='watchman',\\n socket_type=str,\\n metadata_base_dir=metadata_base_dir)\\n self._watchman_path = self._normalize_watchman_path(watchman_path)\\n self._watchman_work_dir = os.path.join(metadata_base_dir, self.name)\\n self._log_level = log_level\\n self._startup_timeout = startup_timeout\\n self._timeout = timeout\\n\\n self._state_file = os.path.join(self._watchman_work_dir, '{}.state'.format(self.name))\\n self._log_file = os.path.join(self._watchman_work_dir, '{}.log'.format(self.name))\\n self._sock_file = socket_path_override or os.path.join(self._watchman_work_dir,\\n '{}.sock'.format(self.name))\\n\\n self._logger = logging.getLogger(__name__)\\n self._watchman_client = None\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman.client\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman.client\\n @property\\n def client(self):\\n if not self._watchman_client:\\n self._watchman_client = self._make_client()\\n return self._watchman_client\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._make_client\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._make_client\\n def _make_client(self):\\n \\\"\\\"\\\"Create a new watchman client using the BSER protocol over a UNIX socket.\\\"\\\"\\\"\\n self._logger.debug('setting initial watchman timeout to %s', self._startup_timeout)\\n return StreamableWatchmanClient(sockpath=self.socket,\\n transport='local',\\n timeout=self._startup_timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._is_valid_executable\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._is_valid_executable\\n def _is_valid_executable(self, binary_path):\\n return os.path.isfile(binary_path) and os.access(binary_path, os.X_OK)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._normalize_watchman_path\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._normalize_watchman_path\\n def _normalize_watchman_path(self, watchman_path):\\n if not self._is_valid_executable(watchman_path):\\n raise self.ExecutionError('invalid watchman binary at {}!'.format(watchman_path))\\n return os.path.abspath(watchman_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._maybe_init_metadata\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._maybe_init_metadata\\n def _maybe_init_metadata(self):\\n safe_mkdir(self._watchman_work_dir)\\n # Initialize watchman with an empty, but valid statefile so it doesn't complain on startup.\\n safe_file_dump(self._state_file, '{}')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._construct_cmd\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._construct_cmd\\n def _construct_cmd(self, cmd_parts, state_file, sock_file, log_file, log_level):\\n return [part for part in cmd_parts] + ['--no-save-state',\\n '--statefile={}'.format(state_file),\\n '--sockname={}'.format(sock_file),\\n '--logfile={}'.format(log_file),\\n '--log-level', log_level]\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._parse_pid_from_output\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._parse_pid_from_output\\n def _parse_pid_from_output(self, output):\\n try:\\n # Parse the watchman pidfile from the cli output (in JSON).\\n return json.loads(output)['pid']\\n except ValueError:\\n # JSON parse failure.\\n self._logger.critical('invalid output from watchman!\\\\n{output!s}'.format(output=output))\\n raise self.InvalidCommandOutput(output)\\n except KeyError:\\n # Key access error on 'pid' - bad output from watchman.\\n self._logger.critical('no pid from watchman!')\\n raise self.InvalidCommandOutput(output)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman.launch\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman.launch\\n def launch(self):\\n \\\"\\\"\\\"Launch and synchronously write metadata.\\n\\n This is possible due to watchman's built-in async server startup - no double-forking required.\\n \\\"\\\"\\\"\\n cmd = self._construct_cmd((self._watchman_path, 'get-pid'),\\n state_file=self._state_file,\\n sock_file=self._sock_file,\\n log_file=self._log_file,\\n log_level=str(self._log_level))\\n self._logger.debug('watchman cmd is: {}'.format(' '.join(cmd)))\\n self._maybe_init_metadata()\\n\\n # Watchman is launched via its cli. By running the 'get-pid' command on the client we implicitly\\n # launch the Watchman daemon. This approach is somewhat error-prone - in some cases the client\\n # can successfully trigger the launch of the Watchman daemon, but fail to return successfully\\n # for the 'get-pid' result due to server <-> daemon socket issues - these can look like:\\n #\\n # 2016-04-01T17:31:23,820: [cli] unable to talk to your watchman\\n # on .../watchman.sock! (Permission denied)\\n #\\n # This results in a subprocess execution failure and leaves us with no pid information to write\\n # to the metadata directory - while in reality a Watchman daemon is actually running but now\\n # untracked. To safeguard against this, we retry the (idempotent) 'get-pid' command a few times\\n # to give the server-side socket setup a few chances to quiesce before potentially orphaning it.\\n\\n get_output = functools.partial(self.get_subprocess_output, cmd)\\n output = retry_on_exception(get_output, 3, (self.ExecutionError,), lambda n: n * .5)\\n\\n # Parse the watchman PID from the cli output.\\n pid = self._parse_pid_from_output(output)\\n\\n # Write the process metadata to disk.\\n self.write_pid(pid)\\n self.write_socket(self._sock_file)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._attempt_set_timeout\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._attempt_set_timeout\\n def _attempt_set_timeout(self, timeout):\\n \\\"\\\"\\\"Sets a timeout on the inner watchman client's socket.\\\"\\\"\\\"\\n try:\\n self.client.setTimeout(timeout)\\n except Exception:\\n self._logger.debug('failed to set post-startup watchman timeout to %s', self._timeout)\\n else:\\n self._logger.debug('set post-startup watchman timeout to %s', self._timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman.watch_project\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman.watch_project\\n def watch_project(self, path):\\n \\\"\\\"\\\"Issues the watch-project command to watchman to begin watching the buildroot.\\n\\n :param string path: the path to the watchman project root/pants build root.\\n \\\"\\\"\\\"\\n # TODO(kwlzn): Add a client.query(timeout=X) param to the upstream pywatchman project.\\n try:\\n return self.client.query('watch-project', os.path.realpath(path))\\n finally:\\n self._attempt_set_timeout(self._timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman.subscribed\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman.subscribed\\n def subscribed(self, build_root, handlers):\\n \\\"\\\"\\\"Bulk subscribe generator for StreamableWatchmanClient.\\n\\n :param str build_root: the build_root for all subscriptions.\\n :param iterable handlers: a sequence of Watchman.EventHandler namedtuple objects.\\n :yields: a stream of tuples in the form (subscription_name: str, subscription_event: dict).\\n \\\"\\\"\\\"\\n command_list = [['subscribe',\\n build_root,\\n handler.name,\\n handler.metadata] for handler in handlers]\\n\\n self._logger.debug('watchman command_list is: {}'.format(command_list))\\n\\n try:\\n for event in self.client.stream_query(command_list):\\n if event is None:\\n yield None, None\\n elif 'subscribe' in event:\\n self._logger.info('confirmed watchman subscription: {}'.format(event))\\n yield None, None\\n elif 'subscription' in event:\\n yield event.get('subscription'), event\\n else:\\n self._logger.warning('encountered non-subscription event: {}'.format(event))\\n except self.client.WatchmanError as e:\\n raise self.WatchmanCrash(e)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":68,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-2185"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Logging dominating the training time\n<!-- \r\n### Common bugs:\r\n1. Tensorboard not showing in Jupyter-notebook see [issue 79](https://github.com/PyTorchLightning/pytorch-lightning/issues/79). \r\n2. PyTorch 1.1.0 vs 1.2.0 support [see FAQ](https://github.com/PyTorchLightning/pytorch-lightning#faq) \r\n-->\r\n\r\n## 🐛 Bug\r\n\r\nAfter migrating a simple 1-layer LSTM sequence classifiactino model to lightning, we found that it was ~7 times slower than the original non-lightning version. we are using tensorboard logger\r\n- after examination, we found that the training time was dominated by calls to log_metrics (in training_loop.py)\r\n- setting the options `row_log_interval=1000` and `log_save_interval=1000` increased the speed by ~6 times\r\n\r\nIt looks like under certain circumstances, the default logging frequency is too high, causing drastic increases in training times. It was not intuitive to realize that this was the cause of the slowdown: took several hours of investigation.\r\n\r\n### To Reproduce\r\n\r\nSteps to reproduce the behavior:\r\n\r\n1. create a 1-layer LSTM classifier model. 100 neurons for input and hidden dimensions\r\n - use a tensorboard logger\r\n2. train using batches of 32 sequences of ~50 tokens or so\r\n - note the speed\r\n4. add the options to trainer `row_log_interval=1000` and `log_save_interval=1000`\r\n5. notice that the speed increases by 6 times\r\n\r\n#### Code sample\r\n\r\n### Expected behavior\r\n\r\ndefault logging frequency for training should be low enough to cover common scenarios such as 1-layer lstm, without causing unexpected slowdowns, which are hard to diagnose\r\n- those people who realize they wish to increase logging frequency will know what they want to do, and find the information fairly easily\r\n- those who just want something up and running fairly quickly wont need to run profiling to figure out why their script is very slow\r\n\r\n### Environment\r\n\r\nNote that in the instructions it says `python collect_env.py`, but the actual command is `python collect_env_details.py`\r\n\r\n```\r\n* CUDA:\r\n - GPU:\r\n - Tesla T4\r\n - available: True\r\n - version: 10.1\r\n* Packages:\r\n - numpy: 1.18.1\r\n - pyTorch_debug: False\r\n - pyTorch_version: 1.4.0\r\n - pytorch-lightning: 0.7.6\r\n - tensorboard: 2.2.1\r\n - tqdm: 4.45.0\r\n* System:\r\n - OS: Linux\r\n - architecture:\r\n - 64bit\r\n -\r\n - processor: x86_64\r\n - python: 3.7.4\r\n - version: #18~18.04.1-Ubuntu SMP Wed Apr 8 15:12:16 UTC 2020\r\n```\r\n\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/trainer/__init__.py","pytorch_lightning/trainer/trainer.py"],"string":"[\n \"pytorch_lightning/trainer/__init__.py\",\n \"pytorch_lightning/trainer/trainer.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/trainer/__init__.py::<module>","pytorch_lightning/trainer/trainer.py::Trainer.__init__"],"string":"[\n \"pytorch_lightning/trainer/__init__.py::<module>\",\n \"pytorch_lightning/trainer/trainer.py::Trainer.__init__\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"pytorch_lightning/trainer/__init__.py","text":"pytorch_lightning/trainer/__init__.py","label":1},{"cid":"pytorch_lightning/trainer/trainer.py","text":"pytorch_lightning/trainer/trainer.py\nclass Trainer\n def __init__(self, logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool]=True, checkpoint_callback: Union[ModelCheckpoint, bool]=True, early_stop_callback: Optional[Union[EarlyStopping, bool]]=False, callbacks: Optional[List[Callback]]=None, default_root_dir: Optional[str]=None, gradient_clip_val: float=0, process_position: int=0, num_nodes: int=1, num_processes: int=1, gpus: Optional[Union[List[int], str, int]]=None, auto_select_gpus: bool=False, tpu_cores: Optional[Union[List[int], int]]=None, log_gpu_memory: Optional[str]=None, progress_bar_refresh_rate: int=1, overfit_pct: float=0.0, track_grad_norm: Union[int, float, str]=-1, check_val_every_n_epoch: int=1, fast_dev_run: bool=False, accumulate_grad_batches: Union[int, Dict[int, int], List[list]]=1, max_epochs: int=1000, min_epochs: int=1, max_steps: Optional[int]=None, min_steps: Optional[int]=None, train_percent_check: float=1.0, val_percent_check: float=1.0, test_percent_check: float=1.0, val_check_interval: float=1.0, log_save_interval: int=100, row_log_interval: int=10, distributed_backend: Optional[str]=None, precision: int=32, print_nan_grads: bool=False, weights_summary: Optional[str]='top', weights_save_path: Optional[str]=None, num_sanity_val_steps: int=2, truncated_bptt_steps: Optional[int]=None, resume_from_checkpoint: Optional[str]=None, profiler: Optional[Union[BaseProfiler, bool]]=None, benchmark: bool=False, deterministic: bool=False, reload_dataloaders_every_epoch: bool=False, auto_lr_find: Union[bool, str]=False, replace_sampler_ddp: bool=True, terminate_on_nan: bool=False, auto_scale_batch_size: Union[str, bool]=False, prepare_data_per_node: bool=True, amp_level: str='O1', num_tpu_cores: Optional[int]=None, use_amp=None, show_progress_bar=None) # Customize every aspect of training via flags\n def is_global_zero(self)\n def slurm_job_id(self)\n def default_attributes(cls)\n def get_init_arguments_and_types(cls) # Scans the Trainer signature and returns argument names, types and default values\n def get_deprecated_arg_names(cls) # Returns a list with deprecated Trainer arguments.\n def add_argparse_args(cls, parent_parser: ArgumentParser) # Extends existing argparse by default `Trainer` attributes.\n def _allowed_type(x)\n def _arg_default(x)\n def parse_argparser(arg_parser: Union[ArgumentParser, Namespace]) # Parse CLI arguments, required for custom bool types.\n def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs) # Create an instance from CLI arguments.\n def num_gpus(self)\n def data_parallel(self)\n def progress_bar_callback(self)\n def progress_bar_dict(self) # Read-only for progress bar metrics.\n def fit(self, model: LightningModule, train_dataloader: Optional[DataLoader]=None, val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]]=None) # Runs the full optimization routine.\n def can_prepare_data(self)\n def __attach_dat","label":1},{"cid":"pytorch_lightning/trainer/training_io.py","text":"pytorch_lightning/trainer/training_io.py\nclass TrainerIOMixin\n def get_model(self)\n def restore_weights(self, model: LightningModule) # We attempt to restore weights in this order:\n def register_slurm_signal_handlers(self)\n def sig_handler(self, signum, frame)\n def term_handler(self, signum, frame)\n def _atomic_save(self, checkpoint, filepath: str) # Saves a checkpoint atomically, avoiding the creation of incomplete checkpoints.\n def save_checkpoint(self, filepath, weights_only: bool=False)\n def restore(self, checkpoint_path: str, on_gpu: bool) # Restore training state from checkpoint.\n def dump_checkpoint(self, weights_only: bool=False) # Creating model checkpoint.\n def restore_hpc_weights_if_needed(self, model: LightningModule) # If there is a set of hpc weights, use as signal to restore model.\n def restore_training_state(self, checkpoint) # Restore trainer state.\n def hpc_save(self, folderpath: str, logger)\n def hpc_load(self, folderpath, on_gpu)\n def max_ckpt_in_folder(self, path, name_key='ckpt_')","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py","text":"pytorch_lightning/trainer/training_loop.py\nclass TrainerTrainLoopMixin\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\n def is_function_implemented(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def run_evaluation(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def clip_gradients(self) # Warning: this is just empty shell for code implemented in other class.\n def detect_nan_tensors(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def add_progress_bar_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def process_output(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_train_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_val_dataloader(self, model) # Warning: this is just empty shell for code implemented in other class.\n def has_arg(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def train(self)\n def run_training_epoch(self)\n def run_training_batch(self, batch, batch_idx)\n def _get_optimizers_iterable(self)\n def run_training_teardown(self)\n def training_forward(self, batch, batch_idx, opt_idx, hiddens) # Handle forward for each training case (distributed, single gpu, etc...)\n def update_learning_rates(self, interval: str) # Update learning rates.\n def call_checkpoint_callback(self)\ndef _with_is_last(iterable) # Pass through values from the given iterable with an added boolean indicating if ","label":0}],"string":"[\n {\n \"cid\": \"pytorch_lightning/trainer/__init__.py\",\n \"text\": \"pytorch_lightning/trainer/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py\",\n \"text\": \"pytorch_lightning/trainer/trainer.py\\nclass Trainer\\n def __init__(self, logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool]=True, checkpoint_callback: Union[ModelCheckpoint, bool]=True, early_stop_callback: Optional[Union[EarlyStopping, bool]]=False, callbacks: Optional[List[Callback]]=None, default_root_dir: Optional[str]=None, gradient_clip_val: float=0, process_position: int=0, num_nodes: int=1, num_processes: int=1, gpus: Optional[Union[List[int], str, int]]=None, auto_select_gpus: bool=False, tpu_cores: Optional[Union[List[int], int]]=None, log_gpu_memory: Optional[str]=None, progress_bar_refresh_rate: int=1, overfit_pct: float=0.0, track_grad_norm: Union[int, float, str]=-1, check_val_every_n_epoch: int=1, fast_dev_run: bool=False, accumulate_grad_batches: Union[int, Dict[int, int], List[list]]=1, max_epochs: int=1000, min_epochs: int=1, max_steps: Optional[int]=None, min_steps: Optional[int]=None, train_percent_check: float=1.0, val_percent_check: float=1.0, test_percent_check: float=1.0, val_check_interval: float=1.0, log_save_interval: int=100, row_log_interval: int=10, distributed_backend: Optional[str]=None, precision: int=32, print_nan_grads: bool=False, weights_summary: Optional[str]='top', weights_save_path: Optional[str]=None, num_sanity_val_steps: int=2, truncated_bptt_steps: Optional[int]=None, resume_from_checkpoint: Optional[str]=None, profiler: Optional[Union[BaseProfiler, bool]]=None, benchmark: bool=False, deterministic: bool=False, reload_dataloaders_every_epoch: bool=False, auto_lr_find: Union[bool, str]=False, replace_sampler_ddp: bool=True, terminate_on_nan: bool=False, auto_scale_batch_size: Union[str, bool]=False, prepare_data_per_node: bool=True, amp_level: str='O1', num_tpu_cores: Optional[int]=None, use_amp=None, show_progress_bar=None) # Customize every aspect of training via flags\\n def is_global_zero(self)\\n def slurm_job_id(self)\\n def default_attributes(cls)\\n def get_init_arguments_and_types(cls) # Scans the Trainer signature and returns argument names, types and default values\\n def get_deprecated_arg_names(cls) # Returns a list with deprecated Trainer arguments.\\n def add_argparse_args(cls, parent_parser: ArgumentParser) # Extends existing argparse by default `Trainer` attributes.\\n def _allowed_type(x)\\n def _arg_default(x)\\n def parse_argparser(arg_parser: Union[ArgumentParser, Namespace]) # Parse CLI arguments, required for custom bool types.\\n def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs) # Create an instance from CLI arguments.\\n def num_gpus(self)\\n def data_parallel(self)\\n def progress_bar_callback(self)\\n def progress_bar_dict(self) # Read-only for progress bar metrics.\\n def fit(self, model: LightningModule, train_dataloader: Optional[DataLoader]=None, val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]]=None) # Runs the full optimization routine.\\n def can_prepare_data(self)\\n def __attach_dat\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py\",\n \"text\": \"pytorch_lightning/trainer/training_io.py\\nclass TrainerIOMixin\\n def get_model(self)\\n def restore_weights(self, model: LightningModule) # We attempt to restore weights in this order:\\n def register_slurm_signal_handlers(self)\\n def sig_handler(self, signum, frame)\\n def term_handler(self, signum, frame)\\n def _atomic_save(self, checkpoint, filepath: str) # Saves a checkpoint atomically, avoiding the creation of incomplete checkpoints.\\n def save_checkpoint(self, filepath, weights_only: bool=False)\\n def restore(self, checkpoint_path: str, on_gpu: bool) # Restore training state from checkpoint.\\n def dump_checkpoint(self, weights_only: bool=False) # Creating model checkpoint.\\n def restore_hpc_weights_if_needed(self, model: LightningModule) # If there is a set of hpc weights, use as signal to restore model.\\n def restore_training_state(self, checkpoint) # Restore trainer state.\\n def hpc_save(self, folderpath: str, logger)\\n def hpc_load(self, folderpath, on_gpu)\\n def max_ckpt_in_folder(self, path, name_key='ckpt_')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py\\nclass TrainerTrainLoopMixin\\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\\n def is_function_implemented(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def run_evaluation(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def clip_gradients(self) # Warning: this is just empty shell for code implemented in other class.\\n def detect_nan_tensors(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def add_progress_bar_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def process_output(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_train_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_val_dataloader(self, model) # Warning: this is just empty shell for code implemented in other class.\\n def has_arg(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def train(self)\\n def run_training_epoch(self)\\n def run_training_batch(self, batch, batch_idx)\\n def _get_optimizers_iterable(self)\\n def run_training_teardown(self)\\n def training_forward(self, batch, batch_idx, opt_idx, hiddens) # Handle forward for each training case (distributed, single gpu, etc...)\\n def update_learning_rates(self, interval: str) # Update learning rates.\\n def call_checkpoint_callback(self)\\ndef _with_is_last(iterable) # Pass through values from the given iterable with an added boolean indicating if \",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__init__","text":"pytorch_lightning/trainer/trainer.py::Trainer.__init__\n def __init__(\n self,\n logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool] = True,\n checkpoint_callback: Union[ModelCheckpoint, bool] = True,\n early_stop_callback: Optional[Union[EarlyStopping, bool]] = False,\n callbacks: Optional[List[Callback]] = None,\n default_root_dir: Optional[str] = None,\n gradient_clip_val: float = 0,\n process_position: int = 0,\n num_nodes: int = 1,\n num_processes: int = 1,\n gpus: Optional[Union[List[int], str, int]] = None,\n auto_select_gpus: bool = False,\n tpu_cores: Optional[Union[List[int], int]] = None,\n log_gpu_memory: Optional[str] = None,\n progress_bar_refresh_rate: int = 1,\n overfit_pct: float = 0.0,\n track_grad_norm: Union[int, float, str] = -1,\n check_val_every_n_epoch: int = 1,\n fast_dev_run: bool = False,\n accumulate_grad_batches: Union[int, Dict[int, int], List[list]] = 1,\n max_epochs: int = 1000,\n min_epochs: int = 1,\n max_steps: Optional[int] = None,\n min_steps: Optional[int] = None,\n train_percent_check: float = 1.0,\n val_percent_check: float = 1.0,\n test_percent_check: float = 1.0,\n val_check_interval: float = 1.0,\n log_save_interval: int = 100,\n row_log_interval: int = 10,\n distributed_backend: Optional[str] = None,\n precision: int = 32,\n print_nan_grads: bool = False, # backward compatible, todo: remove in v0.9.0\n weights_summary: Optional[str] = 'top',\n weights_save_path: Optional[str] = None,\n num_sanity_val_steps: int = 2,\n truncated_bptt_steps: Optional[int] = None,\n resume_from_checkpoint: Optional[str] = None,\n profiler: Optional[Union[BaseProfiler, bool]] = None,\n benchmark: bool = False,\n deterministic: bool = False,\n reload_dataloaders_every_epoch:","label":1},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.is_global_zero","text":"pytorch_lightning/trainer/trainer.py::Trainer.is_global_zero\n @property\n def is_global_zero(self):\n return self.global_rank == 0","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id","text":"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\n @property\n def slurm_job_id(self) -> Optional[int]:\n try:\n job_id = os.environ['SLURM_JOB_ID']\n job_id = int(job_id)\n\n # in interactive mode, don't make logs use the same job id\n in_slurm_interactive_mode = os.environ['SLURM_JOB_NAME'] == 'bash'\n if in_slurm_interactive_mode:\n job_id = None\n\n except Exception:\n job_id = None\n return job_id","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.default_attributes","text":"pytorch_lightning/trainer/trainer.py::Trainer.default_attributes\n @classmethod\n def default_attributes(cls):\n init_signature = inspect.signature(Trainer)\n\n args = {}\n for param_name in init_signature.parameters:\n value = init_signature.parameters[param_name].default\n args[param_name] = value\n\n return args","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.get_init_arguments_and_types","text":"pytorch_lightning/trainer/trainer.py::Trainer.get_init_arguments_and_types\n @classmethod\n def get_init_arguments_and_types(cls) -> List[Tuple[str, Tuple, Any]]:\n r\"\"\"Scans the Trainer signature and returns argument names, types and default values.\n\n Returns:\n List with tuples of 3 values:\n (argument name, set with argument types, argument default value).\n\n Examples:\n >>> args = Trainer.get_init_arguments_and_types()\n >>> import pprint\n >>> pprint.pprint(sorted(args)) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE\n [('accumulate_grad_batches',\n (<class 'int'>, typing.Dict[int, int], typing.List[list]),\n 1),\n ...\n ('callbacks',\n (typing.List[pytorch_lightning.callbacks.base.Callback],\n <class 'NoneType'>),\n None),\n ('check_val_every_n_epoch', (<class 'int'>,), 1),\n ...\n ('max_epochs', (<class 'int'>,), 1000),\n ...\n ('precision', (<class 'int'>,), 32),\n ('prepare_data_per_node', (<class 'bool'>,), True),\n ('print_nan_grads', (<class 'bool'>,), False),\n ('process_position', (<class 'int'>,), 0),\n ('profiler',\n (<class 'pytorch_lightning.profiler.profilers.BaseProfiler'>,\n <class 'bool'>,\n <class 'NoneType'>),\n None),\n ...\n \"\"\"\n trainer_default_params = inspect.signature(cls).parameters\n name_type_default = []\n for arg in trainer_default_params:\n arg_type = trainer_default_params[arg].annotation\n arg_default = trainer_default_params[arg].default\n try:\n arg_types = tuple(arg_type.__args__)\n except AttributeError:\n arg_types = (arg_type,)\n\n name_type_default.append((arg, arg_types, arg_default))\n\n return na","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.get_deprecated_arg_names","text":"pytorch_lightning/trainer/trainer.py::Trainer.get_deprecated_arg_names\n @classmethod\n def get_deprecated_arg_names(cls) -> List:\n \"\"\"Returns a list with deprecated Trainer arguments.\"\"\"\n depr_arg_names = []\n for name, val in cls.__dict__.items():\n if name.startswith('DEPRECATED') and isinstance(val, (tuple, list)):\n depr_arg_names.extend(val)\n return depr_arg_names","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.add_argparse_args","text":"pytorch_lightning/trainer/trainer.py::Trainer.add_argparse_args\n @classmethod\n def add_argparse_args(cls, parent_parser: ArgumentParser) -> ArgumentParser:\n r\"\"\"Extends existing argparse by default `Trainer` attributes.\n\n Args:\n parent_parser:\n The custom cli arguments parser, which will be extended by\n the Trainer default arguments.\n\n Only arguments of the allowed types (str, float, int, bool) will\n extend the `parent_parser`.\n\n Examples:\n >>> import argparse\n >>> import pprint\n >>> parser = argparse.ArgumentParser()\n >>> parser = Trainer.add_argparse_args(parser)\n >>> args = parser.parse_args([])\n >>> pprint.pprint(vars(args)) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE\n {...\n 'check_val_every_n_epoch': 1,\n 'checkpoint_callback': True,\n 'default_root_dir': None,\n 'deterministic': False,\n 'distributed_backend': None,\n 'early_stop_callback': False,\n ...\n 'logger': True,\n 'max_epochs': 1000,\n 'max_steps': None,\n 'min_epochs': 1,\n 'min_steps': None,\n ...\n 'profiler': None,\n 'progress_bar_refresh_rate': 1,\n ...}\n\n \"\"\"\n parser = ArgumentParser(parents=[parent_parser], add_help=False, )\n\n blacklist = ['kwargs']\n depr_arg_names = cls.get_deprecated_arg_names() + blacklist\n\n allowed_types = (str, float, int, bool)\n\n # TODO: get \"help\" from docstring :)\n for arg, arg_types, arg_default in (at for at in cls.get_init_arguments_and_types()\n if at[0] not in depr_arg_names):\n arg_types = [at for at in allowed_types if at in arg_types]\n if not arg_types:\n # skip argument with not supported type\n ","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer._allowed_type","text":"pytorch_lightning/trainer/trainer.py::Trainer._allowed_type\n def _allowed_type(x) -> Union[int, str]:\n if ',' in x:\n return str(x)\n else:\n return int(x)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer._arg_default","text":"pytorch_lightning/trainer/trainer.py::Trainer._arg_default\n def _arg_default(x) -> Union[int, str]:\n if ',' in x:\n return str(x)\n else:\n return int(x)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.parse_argparser","text":"pytorch_lightning/trainer/trainer.py::Trainer.parse_argparser\n @staticmethod\n def parse_argparser(arg_parser: Union[ArgumentParser, Namespace]) -> Namespace:\n \"\"\"Parse CLI arguments, required for custom bool types.\"\"\"\n args = arg_parser.parse_args() if isinstance(arg_parser, ArgumentParser) else arg_parser\n args = {k: True if v is None else v for k, v in vars(args).items()}\n return Namespace(**args)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.from_argparse_args","text":"pytorch_lightning/trainer/trainer.py::Trainer.from_argparse_args\n @classmethod\n def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs) -> 'Trainer':\n \"\"\"\n Create an instance from CLI arguments.\n\n Args:\n args: The parser or namespace to take arguments from. Only known arguments will be\n parsed and passed to the :class:`Trainer`.\n **kwargs: Additional keyword arguments that may override ones in the parser or namespace.\n These must be valid Trainer arguments.\n\n Example:\n >>> parser = ArgumentParser(add_help=False)\n >>> parser = Trainer.add_argparse_args(parser)\n >>> parser.add_argument('--my_custom_arg', default='something') # doctest: +SKIP\n >>> args = Trainer.parse_argparser(parser.parse_args(\"\"))\n >>> trainer = Trainer.from_argparse_args(args, logger=False)\n \"\"\"\n if isinstance(args, ArgumentParser):\n args = cls.parse_argparser(args)\n params = vars(args)\n\n # we only want to pass in valid Trainer args, the rest may be user specific\n valid_kwargs = inspect.signature(cls.__init__).parameters\n trainer_kwargs = dict((name, params[name]) for name in valid_kwargs if name in params)\n trainer_kwargs.update(**kwargs)\n\n return cls(**trainer_kwargs)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus","text":"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\n @property\n def num_gpus(self) -> int:\n gpus = self.data_parallel_device_ids\n if gpus is None:\n return 0\n return len(gpus)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel","text":"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\n @property\n def data_parallel(self) -> bool:\n return self.use_dp or self.use_ddp or self.use_ddp2","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_callback","text":"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_callback\n @property\n def progress_bar_callback(self):\n return self._progress_bar_callback","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_dict","text":"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_dict\n @property\n def progress_bar_dict(self) -> dict:\n \"\"\" Read-only for progress bar metrics. \"\"\"\n ref_model = self.model if not self.data_parallel else self.model.module\n return dict(**ref_model.get_progress_bar_dict(), **self.progress_bar_metrics)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.fit","text":"pytorch_lightning/trainer/trainer.py::Trainer.fit\n def fit(\n self,\n model: LightningModule,\n train_dataloader: Optional[DataLoader] = None,\n val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None\n ):\n r\"\"\"\n Runs the full optimization routine.\n\n Args:\n model: Model to fit.\n\n train_dataloader: A Pytorch\n DataLoader with training samples. If the model has\n a predefined train_dataloader method this will be skipped.\n\n val_dataloaders: Either a single\n Pytorch Dataloader or a list of them, specifying validation samples.\n If the model has a predefined val_dataloaders method this will be skipped\n\n Example::\n\n # Option 1,\n # Define the train_dataloader() and val_dataloader() fxs\n # in the lightningModule\n # RECOMMENDED FOR MOST RESEARCH AND APPLICATIONS TO MAINTAIN READABILITY\n trainer = Trainer()\n model = LightningModule()\n trainer.fit(model)\n\n # Option 2\n # in production cases we might want to pass different datasets to the same model\n # Recommended for PRODUCTION SYSTEMS\n train, val = DataLoader(...), DataLoader(...)\n trainer = Trainer()\n model = LightningModule()\n trainer.fit(model, train_dataloader=train, val_dataloaders=val)\n\n # Option 1 & 2 can be mixed, for example the training set can be\n # defined as part of the model, and validation can then be feed to .fit()\n\n \"\"\"\n # bind logger and other properties\n model.logger = self.logger\n self.copy_trainer_model_properties(model)\n\n # clean hparams\n if hasattr(model, 'hparams'):\n parsing.clean_namespace(model.hparams)\n\n # set up the passed in dataloaders (if needed)\n self.__attach_dataloaders(model, trai","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.can_prepare_data","text":"pytorch_lightning/trainer/trainer.py::Trainer.can_prepare_data\n def can_prepare_data(self):\n if self.prepare_data_per_node:\n return self.local_rank == 0\n\n else:\n return self.node_rank == 0 and self.local_rank == 0","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__attach_dataloaders","text":"pytorch_lightning/trainer/trainer.py::Trainer.__attach_dataloaders\n def __attach_dataloaders(self, model, train_dataloader=None, val_dataloaders=None, test_dataloaders=None):\n # when dataloader is passed via fit, patch the train_dataloader\n # functions to overwrite with these implementations\n if train_dataloader is not None:\n model.train_dataloader = _PatchDataLoader(train_dataloader)\n\n if val_dataloaders is not None:\n model.val_dataloader = _PatchDataLoader(val_dataloaders)\n\n if test_dataloaders is not None:\n model.test_dataloader = _PatchDataLoader(test_dataloaders)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine","text":"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine\n def run_pretrain_routine(self, model: LightningModule):\n \"\"\"Sanity check a few things before starting actual training.\n\n Args:\n model: The model to run sanity test on.\n \"\"\"\n ref_model = model\n if self.data_parallel:\n ref_model = model.module\n\n # give model convenience properties\n ref_model.trainer = self\n\n # set local properties on the model\n self.copy_trainer_model_properties(ref_model)\n\n # init amp. Must be done here instead of __init__ to allow ddp to work\n if self.use_native_amp and self.precision == 16:\n self.scaler = torch.cuda.amp.GradScaler()\n\n # log hyper-parameters\n if self.logger is not None:\n # save exp to get started\n self.logger.log_hyperparams(ref_model.hparams)\n\n self.logger.save()\n\n if self.use_ddp or self.use_ddp2:\n torch_distrib.barrier()\n\n # wait for all models to restore weights\n if self.on_tpu and XLA_AVAILABLE:\n # wait for all processes to catch up\n torch_xla.core.xla_model.rendezvous(\"pl.Trainer.run_pretrain_routine\")\n\n elif self.use_horovod:\n # wait for all processes to catch up\n hvd.join()\n\n # register auto-resubmit when on SLURM\n self.register_slurm_signal_handlers()\n\n # print model summary\n # TODO: remove self.testing condition because model.summarize() is wiping out the weights\n if self.is_global_zero and self.weights_summary is not None and not self.testing:\n if self.weights_summary in ['full', 'top']:\n ref_model.summarize(mode=self.weights_summary)\n else:\n raise MisconfigurationException(\"weights_summary can be None, 'full' or 'top'\")\n\n # track model now.\n # if cluster resets state, the model will update with the saved weights\n ","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.test","text":"pytorch_lightning/trainer/trainer.py::Trainer.test\n def test(\n self,\n model: Optional[LightningModule] = None,\n test_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None\n ):\n r\"\"\"\n\n Separates from fit to make sure you never run on your test set until you want to.\n\n Args:\n model: The model to test.\n\n test_dataloaders: Either a single\n Pytorch Dataloader or a list of them, specifying validation samples.\n\n Example::\n\n # Option 1\n # run test after fitting\n test = DataLoader(...)\n trainer = Trainer()\n model = LightningModule()\n\n trainer.fit(model)\n trainer.test(test_dataloaders=test)\n\n # Option 2\n # run test from a loaded model\n test = DataLoader(...)\n model = LightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')\n trainer = Trainer()\n trainer.test(model, test_dataloaders=test)\n \"\"\"\n\n self.testing = True\n\n if test_dataloaders is not None:\n if model:\n self.__attach_dataloaders(model, test_dataloaders=test_dataloaders)\n else:\n self.__attach_dataloaders(self.model, test_dataloaders=test_dataloaders)\n\n if model is not None:\n self.model = model\n self.fit(model)\n\n # on tpu, .spawn means we don't have a trained model\n # TODO: remove TPU spawn\n elif self.use_tpu: # pragma: no-cover\n # attempt to load weights from a spawn\n path = os.path.join(self.default_root_dir, '__temp_weight_ddp_end.ckpt')\n test_model = self.model\n if os.path.exists(path):\n test_model = self.load_spawn_weights(self.model)\n\n self.fit(test_model)\n else:\n self.run_evaluation(test_mode=True)\n\n self.testing = False","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.check_model_configuration","text":"pytorch_lightning/trainer/trainer.py::Trainer.check_model_configuration\n def check_model_configuration(self, model: LightningModule):\n r\"\"\"\n Checks that the model is configured correctly before training or testing is started.\n\n Args:\n model: The model to check the configuration.\n\n \"\"\"\n # Check training_step, train_dataloader, configure_optimizer methods\n if not self.testing:\n if not self.is_overridden('training_step', model):\n raise MisconfigurationException(\n 'No `training_step()` method defined. Lightning `Trainer` expects as minimum a'\n ' `training_step()`, `training_dataloader()` and `configure_optimizers()` to be defined.')\n\n if not self.is_overridden('train_dataloader', model):\n raise MisconfigurationException(\n 'No `train_dataloader()` method defined. Lightning `Trainer` expects as minimum a'\n ' `training_step()`, `training_dataloader()` and `configure_optimizers()` to be defined.')\n\n if not self.is_overridden('configure_optimizers', model):\n raise MisconfigurationException(\n 'No `configure_optimizers()` method defined. Lightning `Trainer` expects as minimum a'\n ' `training_step()`, `training_dataloader()` and `configure_optimizers()` to be defined.')\n\n # Check val_dataloader, validation_step and validation_epoch_end\n if self.is_overridden('val_dataloader', model):\n if not self.is_overridden('validation_step', model):\n raise MisconfigurationException('You have passed in a `val_dataloader()`'\n ' but have not defined `validation_step()`.')\n else:\n if not self.is_overridden('validation_epoch_end', model):\n rank_zero_warn(\n 'You hav","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__init__","text":"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__init__\n def __init__(self, dataloader: Union[List[DataLoader], DataLoader]):\n self.dataloader = dataloader\n\n # cannot pickle __code__ so cannot verify if PatchDataloader\n # exists which shows dataloader methods have been overwritten.\n # so, we hack it by using the string representation\n self.patch_loader_code = str(self.__call__.__code__)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__call__","text":"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__call__\n def __call__(self) -> Union[List[DataLoader], DataLoader]:\n return self.dataloader","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.get_model","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.get_model\n def get_model(self):\n is_dp_module = isinstance(self.model, (LightningDistributedDataParallel,\n LightningDataParallel))\n model = self.model.module if is_dp_module else self.model\n return model","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_weights","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_weights\n def restore_weights(self, model: LightningModule):\n \"\"\"\n We attempt to restore weights in this order:\n 1. HPC weights.\n 2. if no HPC weights restore checkpoint_path weights\n 3. otherwise don't restore weights\n \"\"\"\n # clear cache before restore\n if self.on_gpu:\n torch.cuda.empty_cache()\n\n # if script called from hpc resubmit, load weights\n did_restore_hpc_weights = self.restore_hpc_weights_if_needed(model)\n\n # clear cache after restore\n if self.on_gpu:\n torch.cuda.empty_cache()\n\n if not did_restore_hpc_weights:\n if self.resume_from_checkpoint is not None:\n self.restore(self.resume_from_checkpoint, on_gpu=self.on_gpu)\n\n # wait for all models to restore weights\n if self.use_ddp or self.use_ddp2:\n # wait for all processes to catch up\n torch_distrib.barrier()\n\n # wait for all models to restore weights\n if self.on_tpu and XLA_AVAILABLE:\n # wait for all processes to catch up\n torch_xla.core.xla_model.rendezvous(\"pl.TrainerIOMixin.restore_weights\")\n\n elif self.use_horovod:\n # wait for all processes to catch up\n hvd.join()\n\n # clear cache after restore\n if self.on_gpu:\n torch.cuda.empty_cache()","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.register_slurm_signal_handlers","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.register_slurm_signal_handlers\n def register_slurm_signal_handlers(self):\n # see if we're using slurm (not interactive)\n on_slurm = False\n try:\n job_name = os.environ['SLURM_JOB_NAME']\n if job_name != 'bash':\n on_slurm = True\n except Exception:\n pass\n\n if on_slurm:\n log.info('Set SLURM handle signals.')\n signal.signal(signal.SIGUSR1, self.sig_handler)\n signal.signal(signal.SIGTERM, self.term_handler)","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.sig_handler","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.sig_handler\n def sig_handler(self, signum, frame): # pragma: no-cover\n if self.is_global_zero:\n # save weights\n log.info('handling SIGUSR1')\n self.hpc_save(self.weights_save_path, self.logger)\n\n # find job id\n job_id = os.environ['SLURM_JOB_ID']\n cmd = 'scontrol requeue {}'.format(job_id)\n\n # requeue job\n log.info(f'requeing job {job_id}...')\n result = call(cmd, shell=True)\n\n # print result text\n if result == 0:\n log.info(f'requeued exp {job_id}')\n else:\n log.warning('requeue failed...')\n\n # close experiment to avoid issues\n self.logger.close()","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.term_handler","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.term_handler\n def term_handler(self, signum, frame):\n # save\n log.info(\"bypassing sigterm\")","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin._atomic_save","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin._atomic_save\n def _atomic_save(self, checkpoint, filepath: str):\n \"\"\"Saves a checkpoint atomically, avoiding the creation of incomplete checkpoints.\n\n This will create a temporary checkpoint with a suffix of ``.part``, then copy it to the final location once\n saving is finished.\n\n Args:\n checkpoint: The object to save.\n Built to be used with the ``dump_checkpoint`` method, but can deal with anything which ``torch.save``\n accepts.\n filepath: The path to which the checkpoint will be saved.\n This points to the file that the checkpoint will be stored in.\n \"\"\"\n tmp_path = str(filepath) + \".part\"\n torch.save(checkpoint, tmp_path)\n os.replace(tmp_path, filepath)","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.save_checkpoint","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.save_checkpoint\n def save_checkpoint(self, filepath, weights_only: bool = False):\n checkpoint = self.dump_checkpoint(weights_only)\n\n if self.is_global_zero:\n # do the actual save\n try:\n self._atomic_save(checkpoint, filepath)\n except AttributeError as err:\n if LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:\n del checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]\n rank_zero_warn('Warning, `module_arguments` dropped from checkpoint.'\n f' An attribute is not picklable {err}')\n self._atomic_save(checkpoint, filepath)","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore\n def restore(self, checkpoint_path: str, on_gpu: bool):\n \"\"\"\n Restore training state from checkpoint.\n Also restores all training state like:\n - epoch\n - callbacks\n - schedulers\n - optimizer\n \"\"\"\n\n # if on_gpu:\n # checkpoint = torch.load(checkpoint_path)\n # else:\n # load on CPU first\n checkpoint = pl_load(checkpoint_path, map_location=lambda storage, loc: storage)\n\n # load model state\n model = self.get_model()\n\n # load the state_dict on the model automatically\n model.load_state_dict(checkpoint['state_dict'])\n\n # give model a chance to load something\n model.on_load_checkpoint(checkpoint)\n\n if on_gpu:\n model.cuda(self.root_gpu)\n\n # restore amp scaling\n if self.use_amp and self.use_native_amp and 'native_amp_scaling_state' in checkpoint:\n self.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])\n\n # load training state (affects trainer only)\n self.restore_training_state(checkpoint)","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.dump_checkpoint","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.dump_checkpoint\n def dump_checkpoint(self, weights_only: bool = False) -> dict:\n \"\"\"Creating model checkpoint.\n\n Args:\n weights_only: saving model weights only\n\n Return:\n structured dictionary\n \"\"\"\n checkpoint = {\n 'epoch': self.current_epoch + 1,\n 'global_step': self.global_step + 1,\n 'pytorch-ligthning_version': pytorch_lightning.__version__,\n }\n\n if not weights_only:\n if self.checkpoint_callback:\n checkpoint['checkpoint_callback_best_model_score'] = self.checkpoint_callback.best_model_score\n checkpoint['checkpoint_callback_best_model_path'] = self.checkpoint_callback.best_model_path\n\n if self.early_stop_callback:\n checkpoint['early_stop_callback_wait'] = self.early_stop_callback.wait\n checkpoint['early_stop_callback_patience'] = self.early_stop_callback.patience\n\n # save optimizers\n optimizer_states = []\n for i, optimizer in enumerate(self.optimizers):\n optimizer_states.append(optimizer.state_dict())\n\n checkpoint['optimizer_states'] = optimizer_states\n\n # save lr schedulers\n lr_schedulers = []\n for scheduler in self.lr_schedulers:\n lr_schedulers.append(scheduler['scheduler'].state_dict())\n\n checkpoint['lr_schedulers'] = lr_schedulers\n\n # save native amp scaling\n if self.use_amp and self.use_native_amp:\n checkpoint['native_amp_scaling_state'] = self.scaler.state_dict()\n\n # add the module_arguments and state_dict from the model\n model = self.get_model()\n\n checkpoint['state_dict'] = model.state_dict()\n\n if model.hparams:\n if hasattr(model, '_hparams_name'):\n checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_NAME] = mo","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_hpc_weights_if_needed","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_hpc_weights_if_needed\n def restore_hpc_weights_if_needed(self, model: LightningModule):\n \"\"\"If there is a set of hpc weights, use as signal to restore model.\"\"\"\n did_restore = False\n\n # look for hpc weights\n folderpath = self.weights_save_path\n if os.path.exists(folderpath):\n files = os.listdir(folderpath)\n hpc_weight_paths = [x for x in files if 'hpc_ckpt' in x]\n\n # if hpc weights exist restore model\n if len(hpc_weight_paths) > 0:\n self.hpc_load(folderpath, self.on_gpu)\n did_restore = True\n return did_restore","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_training_state","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_training_state\n def restore_training_state(self, checkpoint):\n \"\"\"\n Restore trainer state.\n Model will get its change to update\n :param checkpoint:\n :return:\n \"\"\"\n if 'optimizer_states' not in checkpoint or 'lr_schedulers' not in checkpoint:\n raise KeyError(\n 'Trying to restore training state but checkpoint contains only the model.'\n ' This is probably due to `ModelCheckpoint.save_weights_only` being set to `True`.'\n )\n\n if self.checkpoint_callback:\n if 'checkpoint_callback_best_model_score' in checkpoint:\n self.checkpoint_callback.best_model_score = checkpoint['checkpoint_callback_best_model_score']\n else:\n # Old naming until version 0.7.6\n rank_zero_warn(\n 'Loading a checkpoint created with an old version of Lightning; '\n 'this will not be supported in the future.'\n )\n self.checkpoint_callback.best_model_score = checkpoint['checkpoint_callback_best']\n self.checkpoint_callback.best_model_path = checkpoint['checkpoint_callback_best_model_path']\n\n if self.early_stop_callback:\n self.early_stop_callback.wait = checkpoint['early_stop_callback_wait']\n self.early_stop_callback.patience = checkpoint['early_stop_callback_patience']\n\n self.global_step = checkpoint['global_step']\n self.current_epoch = checkpoint['epoch']\n\n # Division deals with global step stepping once per accumulated batch\n # Inequality deals with different global step for odd vs even num_training_batches\n n_accum = 1 if self.accumulate_grad_batches is None else self.accumulate_grad_batches\n expected_steps = self.num_training_batches / n_accum\n if self.num_training_batches != 0 and self.global_step % expected_steps","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.hpc_save","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.hpc_save\n def hpc_save(self, folderpath: str, logger):\n # make sure the checkpoint folder exists\n os.makedirs(folderpath, exist_ok=True)\n\n # save logger to make sure we get all the metrics\n logger.save()\n\n ckpt_number = self.max_ckpt_in_folder(folderpath) + 1\n\n if not os.path.exists(folderpath):\n os.makedirs(folderpath, exist_ok=True)\n filepath = os.path.join(folderpath, f'hpc_ckpt_{ckpt_number}.ckpt')\n\n # give model a chance to do something on hpc_save\n model = self.get_model()\n checkpoint = self.dump_checkpoint()\n\n model.on_hpc_save(checkpoint)\n\n # do the actual save\n # TODO: fix for anything with multiprocess DP, DDP, DDP2\n try:\n self._atomic_save(checkpoint, filepath)\n except AttributeError as err:\n if LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:\n del checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]\n rank_zero_warn('warning, `module_arguments` dropped from checkpoint.'\n f' An attribute is not picklable {err}')\n self._atomic_save(checkpoint, filepath)\n\n return filepath","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.hpc_load","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.hpc_load\n def hpc_load(self, folderpath, on_gpu):\n filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, self.max_ckpt_in_folder(folderpath))\n\n # load on CPU first\n checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)\n\n # load model state\n model = self.get_model()\n\n # load the state_dict on the model automatically\n model.load_state_dict(checkpoint['state_dict'])\n\n # restore amp scaling\n if self.use_amp and self.use_native_amp and 'native_amp_scaling_state' in checkpoint:\n self.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])\n\n if self.root_gpu is not None:\n model.cuda(self.root_gpu)\n\n # load training state (affects trainer only)\n self.restore_training_state(checkpoint)\n\n # call model hook\n model.on_hpc_load(checkpoint)\n\n log.info(f'restored hpc model from: {filepath}')","label":0},{"cid":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.max_ckpt_in_folder","text":"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.max_ckpt_in_folder\n def max_ckpt_in_folder(self, path, name_key='ckpt_'):\n files = os.listdir(path)\n files = [x for x in files if name_key in x]\n if len(files) == 0:\n return 0\n\n ckpt_vs = []\n for name in files:\n name = name.split(name_key)[-1]\n name = re.sub('[^0-9]', '', name)\n ckpt_vs.append(int(name))\n\n return max(ckpt_vs)","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model\n @abstractmethod\n def get_model(self) -> LightningModule:\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented\n @abstractmethod\n def is_function_implemented(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation\n @abstractmethod\n def run_evaluation(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu\n @abstractmethod\n def transfer_batch_to_gpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu\n @abstractmethod\n def transfer_batch_to_tpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients\n @abstractmethod\n def clip_gradients(self):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.detect_nan_tensors","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.detect_nan_tensors\n @abstractmethod\n def detect_nan_tensors(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overridden","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overridden\n @abstractmethod\n def is_overridden(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_progress_bar_metrics","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_progress_bar_metrics\n @abstractmethod\n def add_progress_bar_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics\n @abstractmethod\n def log_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output\n @abstractmethod\n def process_output(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader\n @abstractmethod\n def reset_train_dataloader(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader\n @abstractmethod\n def reset_val_dataloader(self, model):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg\n @abstractmethod\n def has_arg(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train\n def train(self):\n # add signal handlers for process kills\n # def _signal_kill_handler(*args):\n # return TrainerTrainLoopMixin.run_training_teardown(self)\n #\n # orig_signal_handlers = {}\n # for sig_name in SIGNAL_TERMINATE:\n # orig_signal_handlers[sig_name] = signal.signal(getattr(signal, sig_name),\n # _signal_kill_handler)\n\n # get model\n model = self.get_model()\n\n # load data\n # if reload_dataloaders_every_epoch, this is moved to the epoch loop\n if not self.reload_dataloaders_every_epoch:\n self.reset_train_dataloader(model)\n self.reset_val_dataloader(model)\n\n # Train start events\n with self.profiler.profile('on_train_start'):\n # callbacks\n self.on_train_start()\n # initialize early stop callback\n if self.early_stop_callback is not None:\n self.early_stop_callback.on_train_start(self, self.get_model())\n # model hooks\n model.on_train_start()\n\n try:\n # run all epochs\n for epoch in range(self.current_epoch, self.max_epochs):\n # reset train dataloader\n if self.reload_dataloaders_every_epoch:\n self.reset_train_dataloader(model)\n # set seed for distributed sampler (enables shuffling for each epoch)\n if (self.use_ddp or self.use_horovod) \\\n and hasattr(self.train_dataloader, 'sampler') \\\n and hasattr(self.train_dataloader.sampler, 'set_epoch'):\n self.train_dataloader.sampler.set_epoch(epoch)\n\n # update training progress in trainer and model\n model.current_epoch = epoch\n self.current_epoch = epoch\n\n # changing gradi","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch\n def run_training_epoch(self):\n\n # get model\n model = self.get_model()\n\n # Epoch start events\n with self.profiler.profile('on_epoch_start'):\n # callbacks\n self.on_epoch_start()\n\n # model hooks\n if self.is_function_implemented('on_epoch_start'):\n model.on_epoch_start()\n\n # track local dataloader so TPU can wrap each epoch\n train_dataloader = self.train_dataloader\n\n # on TPU we have to wrap it under the ParallelLoader\n if self.use_tpu:\n device = xm.xla_device(self.tpu_id)\n train_dataloader = xla_pl.ParallelLoader(train_dataloader, [device])\n train_dataloader = train_dataloader.per_device_loader(device)\n\n # bookkeeping\n outputs = []\n\n # run epoch\n for batch_idx, (batch, is_last_batch) in self.profiler.profile_iterable(\n enumerate(_with_is_last(train_dataloader)), \"get_train_batch\"\n ):\n # stop epoch if we limited the number of training batches\n if batch_idx >= self.num_training_batches:\n break\n\n self.batch_idx = batch_idx\n\n model.global_step = self.global_step\n\n # ---------------\n # RUN TRAIN STEP\n # ---------------\n _outputs = self.run_training_batch(batch, batch_idx)\n batch_result, grad_norm_dic, batch_step_metrics, batch_output = _outputs\n\n # only track outputs when user implements training_epoch_end\n # otherwise we will build up unnecessary memory\n if self.is_overridden('training_epoch_end', model=self.get_model()):\n outputs.append(batch_output)\n\n # when returning -1 from train_step, we end epoch early\n early_stop_epoch = batch_result == -1\n\n # TODO: consolidate all actions that need to take pl","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch\n def run_training_batch(self, batch, batch_idx):\n # track grad norms\n grad_norm_dic = {}\n\n # track all metrics for callbacks\n all_callback_metrics = []\n\n # track metrics to log\n all_log_metrics = []\n\n if batch is None:\n return 0, grad_norm_dic, {}, {}\n\n # Batch start events\n with self.profiler.profile('on_batch_start'):\n # callbacks\n self.on_batch_start()\n # hooks\n if self.is_function_implemented('on_batch_start'):\n response = self.get_model().on_batch_start(batch)\n if response == -1:\n return -1, grad_norm_dic, {}, {}\n\n splits = [batch]\n if self.truncated_bptt_steps is not None:\n model_ref = self.get_model()\n with self.profiler.profile('tbptt_split_batch'):\n splits = model_ref.tbptt_split_batch(batch, self.truncated_bptt_steps)\n\n self.hiddens = None\n for split_idx, split_batch in enumerate(splits):\n self.split_idx = split_idx\n\n for opt_idx, optimizer in self._get_optimizers_iterable():\n # make sure only the gradients of the current optimizer's parameters are calculated\n # in the training step to prevent dangling gradients in multiple-optimizer setup.\n if len(self.optimizers) > 1:\n for param in self.get_model().parameters():\n param.requires_grad = False\n for group in optimizer.param_groups:\n for param in group['params']:\n param.requires_grad = True\n\n # wrap the forward step in a closure so second order methods work\n def optimizer_closure():\n # forward pass\n with self.profiler.profile('model_forward'):\n ","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin._get_optimizers_iterable","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin._get_optimizers_iterable\n def _get_optimizers_iterable(self):\n if not self.optimizer_frequencies:\n # call training_step once per optimizer\n return list(enumerate(self.optimizers))\n\n optimizer_freq_cumsum = np.cumsum(self.optimizer_frequencies)\n optimizers_loop_length = optimizer_freq_cumsum[-1]\n current_place_in_loop = self.total_batch_idx % optimizers_loop_length\n\n # find optimzier index by looking for the first {item > current_place} in the cumsum list\n opt_idx = np.argmax(optimizer_freq_cumsum > current_place_in_loop)\n return [(opt_idx, self.optimizers[opt_idx])]","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown\n def run_training_teardown(self):\n if hasattr(self, '_teardown_already_run') and self._teardown_already_run:\n return\n # Train end events\n with self.profiler.profile('on_train_end'):\n # callbacks\n self.on_train_end()\n # model hooks\n if self.is_function_implemented('on_train_end'):\n self.get_model().on_train_end()\n\n if self.logger is not None:\n self.logger.finalize(\"success\")\n\n # summarize profile results\n self.profiler.describe()\n\n self._teardown_already_run = True","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward\n def training_forward(self, batch, batch_idx, opt_idx, hiddens):\n \"\"\"\n Handle forward for each training case (distributed, single gpu, etc...)\n :param batch:\n :param batch_idx:\n :return:\n \"\"\"\n # ---------------\n # FORWARD\n # ---------------\n # enable not needing to add opt_idx to training_step\n args = [batch, batch_idx]\n\n if len(self.optimizers) > 1:\n if self.has_arg('training_step', 'optimizer_idx'):\n args.append(opt_idx)\n else:\n num_opts = len(self.optimizers)\n raise ValueError(\n f'Your LightningModule defines {num_opts} optimizers but '\n f'training_step is missing the \"optimizer_idx\" argument.'\n )\n\n # pass hiddens if using tbptt\n if self.truncated_bptt_steps is not None:\n args.append(hiddens)\n\n # distributed forward\n if self.use_ddp or self.use_ddp2 or self.use_dp:\n output = self.model(*args)\n\n # Horovod\n elif self.use_horovod and self.on_gpu:\n batch = self.transfer_batch_to_gpu(batch, hvd.local_rank())\n args[0] = batch\n output = self.model.training_step(*args)\n\n # single GPU forward\n elif self.single_gpu:\n gpu_id = 0\n if isinstance(self.data_parallel_device_ids, list):\n gpu_id = self.data_parallel_device_ids[0]\n\n # Don't copy the batch since there is a single gpu that the batch could\n # be referenced from and if there are multiple optimizers the batch will\n # wind up copying it to the same device repeatedly.\n batch = self.transfer_batch_to_gpu(batch, gpu_id)\n args[0] = batch\n output = self.model.training_step(*args)\n\n # TPU support\n elif self.use_tpu:\n ","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates\n def update_learning_rates(self, interval: str):\n \"\"\"Update learning rates.\n\n Args:\n interval: either 'epoch' or 'step'.\n \"\"\"\n if not self.lr_schedulers:\n return\n\n for lr_scheduler in self.lr_schedulers:\n current_idx = self.batch_idx if interval == 'step' else self.current_epoch\n current_idx += 1 # account for both batch and epoch starts from 0\n # Take step if call to update_learning_rates matches the interval key and\n # the current step modulo the schedulers frequency is zero\n if lr_scheduler['interval'] == interval and current_idx % lr_scheduler['frequency'] == 0:\n # If instance of ReduceLROnPlateau, we need to pass validation loss\n if lr_scheduler['reduce_on_plateau']:\n monitor_key = lr_scheduler['monitor']\n monitor_val = self.callback_metrics.get(monitor_key)\n if monitor_val is None:\n avail_metrics = ','.join(list(self.callback_metrics.keys()))\n raise MisconfigurationException(\n f'ReduceLROnPlateau conditioned on metric {monitor_key}'\n f' which is not available. Available metrics are: {avail_metrics}.'\n ' Condition can be set using `monitor` key in lr scheduler dict'\n )\n lr_scheduler['scheduler'].step(monitor_val)\n else:\n lr_scheduler['scheduler'].step()","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_checkpoint_callback","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_checkpoint_callback\n def call_checkpoint_callback(self):\n if self.checkpoint_callback is not None:\n self.checkpoint_callback.on_validation_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::_with_is_last","text":"pytorch_lightning/trainer/training_loop.py::_with_is_last\ndef _with_is_last(iterable):\n \"\"\"Pass through values from the given iterable with an added boolean indicating if this is the last item.\n See `https://stackoverflow.com/a/1630350 <https://stackoverflow.com/a/1630350>`_\"\"\"\n it = iter(iterable)\n last = next(it)\n for val in it:\n # yield last and has next\n yield last, False\n last = val\n # yield last, no longer has next\n yield last, True","label":0}],"string":"[\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init__\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init__\\n def __init__(\\n self,\\n logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool] = True,\\n checkpoint_callback: Union[ModelCheckpoint, bool] = True,\\n early_stop_callback: Optional[Union[EarlyStopping, bool]] = False,\\n callbacks: Optional[List[Callback]] = None,\\n default_root_dir: Optional[str] = None,\\n gradient_clip_val: float = 0,\\n process_position: int = 0,\\n num_nodes: int = 1,\\n num_processes: int = 1,\\n gpus: Optional[Union[List[int], str, int]] = None,\\n auto_select_gpus: bool = False,\\n tpu_cores: Optional[Union[List[int], int]] = None,\\n log_gpu_memory: Optional[str] = None,\\n progress_bar_refresh_rate: int = 1,\\n overfit_pct: float = 0.0,\\n track_grad_norm: Union[int, float, str] = -1,\\n check_val_every_n_epoch: int = 1,\\n fast_dev_run: bool = False,\\n accumulate_grad_batches: Union[int, Dict[int, int], List[list]] = 1,\\n max_epochs: int = 1000,\\n min_epochs: int = 1,\\n max_steps: Optional[int] = None,\\n min_steps: Optional[int] = None,\\n train_percent_check: float = 1.0,\\n val_percent_check: float = 1.0,\\n test_percent_check: float = 1.0,\\n val_check_interval: float = 1.0,\\n log_save_interval: int = 100,\\n row_log_interval: int = 10,\\n distributed_backend: Optional[str] = None,\\n precision: int = 32,\\n print_nan_grads: bool = False, # backward compatible, todo: remove in v0.9.0\\n weights_summary: Optional[str] = 'top',\\n weights_save_path: Optional[str] = None,\\n num_sanity_val_steps: int = 2,\\n truncated_bptt_steps: Optional[int] = None,\\n resume_from_checkpoint: Optional[str] = None,\\n profiler: Optional[Union[BaseProfiler, bool]] = None,\\n benchmark: bool = False,\\n deterministic: bool = False,\\n reload_dataloaders_every_epoch:\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.is_global_zero\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.is_global_zero\\n @property\\n def is_global_zero(self):\\n return self.global_rank == 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\\n @property\\n def slurm_job_id(self) -> Optional[int]:\\n try:\\n job_id = os.environ['SLURM_JOB_ID']\\n job_id = int(job_id)\\n\\n # in interactive mode, don't make logs use the same job id\\n in_slurm_interactive_mode = os.environ['SLURM_JOB_NAME'] == 'bash'\\n if in_slurm_interactive_mode:\\n job_id = None\\n\\n except Exception:\\n job_id = None\\n return job_id\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.default_attributes\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.default_attributes\\n @classmethod\\n def default_attributes(cls):\\n init_signature = inspect.signature(Trainer)\\n\\n args = {}\\n for param_name in init_signature.parameters:\\n value = init_signature.parameters[param_name].default\\n args[param_name] = value\\n\\n return args\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.get_init_arguments_and_types\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.get_init_arguments_and_types\\n @classmethod\\n def get_init_arguments_and_types(cls) -> List[Tuple[str, Tuple, Any]]:\\n r\\\"\\\"\\\"Scans the Trainer signature and returns argument names, types and default values.\\n\\n Returns:\\n List with tuples of 3 values:\\n (argument name, set with argument types, argument default value).\\n\\n Examples:\\n >>> args = Trainer.get_init_arguments_and_types()\\n >>> import pprint\\n >>> pprint.pprint(sorted(args)) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE\\n [('accumulate_grad_batches',\\n (<class 'int'>, typing.Dict[int, int], typing.List[list]),\\n 1),\\n ...\\n ('callbacks',\\n (typing.List[pytorch_lightning.callbacks.base.Callback],\\n <class 'NoneType'>),\\n None),\\n ('check_val_every_n_epoch', (<class 'int'>,), 1),\\n ...\\n ('max_epochs', (<class 'int'>,), 1000),\\n ...\\n ('precision', (<class 'int'>,), 32),\\n ('prepare_data_per_node', (<class 'bool'>,), True),\\n ('print_nan_grads', (<class 'bool'>,), False),\\n ('process_position', (<class 'int'>,), 0),\\n ('profiler',\\n (<class 'pytorch_lightning.profiler.profilers.BaseProfiler'>,\\n <class 'bool'>,\\n <class 'NoneType'>),\\n None),\\n ...\\n \\\"\\\"\\\"\\n trainer_default_params = inspect.signature(cls).parameters\\n name_type_default = []\\n for arg in trainer_default_params:\\n arg_type = trainer_default_params[arg].annotation\\n arg_default = trainer_default_params[arg].default\\n try:\\n arg_types = tuple(arg_type.__args__)\\n except AttributeError:\\n arg_types = (arg_type,)\\n\\n name_type_default.append((arg, arg_types, arg_default))\\n\\n return na\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.get_deprecated_arg_names\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.get_deprecated_arg_names\\n @classmethod\\n def get_deprecated_arg_names(cls) -> List:\\n \\\"\\\"\\\"Returns a list with deprecated Trainer arguments.\\\"\\\"\\\"\\n depr_arg_names = []\\n for name, val in cls.__dict__.items():\\n if name.startswith('DEPRECATED') and isinstance(val, (tuple, list)):\\n depr_arg_names.extend(val)\\n return depr_arg_names\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.add_argparse_args\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.add_argparse_args\\n @classmethod\\n def add_argparse_args(cls, parent_parser: ArgumentParser) -> ArgumentParser:\\n r\\\"\\\"\\\"Extends existing argparse by default `Trainer` attributes.\\n\\n Args:\\n parent_parser:\\n The custom cli arguments parser, which will be extended by\\n the Trainer default arguments.\\n\\n Only arguments of the allowed types (str, float, int, bool) will\\n extend the `parent_parser`.\\n\\n Examples:\\n >>> import argparse\\n >>> import pprint\\n >>> parser = argparse.ArgumentParser()\\n >>> parser = Trainer.add_argparse_args(parser)\\n >>> args = parser.parse_args([])\\n >>> pprint.pprint(vars(args)) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE\\n {...\\n 'check_val_every_n_epoch': 1,\\n 'checkpoint_callback': True,\\n 'default_root_dir': None,\\n 'deterministic': False,\\n 'distributed_backend': None,\\n 'early_stop_callback': False,\\n ...\\n 'logger': True,\\n 'max_epochs': 1000,\\n 'max_steps': None,\\n 'min_epochs': 1,\\n 'min_steps': None,\\n ...\\n 'profiler': None,\\n 'progress_bar_refresh_rate': 1,\\n ...}\\n\\n \\\"\\\"\\\"\\n parser = ArgumentParser(parents=[parent_parser], add_help=False, )\\n\\n blacklist = ['kwargs']\\n depr_arg_names = cls.get_deprecated_arg_names() + blacklist\\n\\n allowed_types = (str, float, int, bool)\\n\\n # TODO: get \\\"help\\\" from docstring :)\\n for arg, arg_types, arg_default in (at for at in cls.get_init_arguments_and_types()\\n if at[0] not in depr_arg_names):\\n arg_types = [at for at in allowed_types if at in arg_types]\\n if not arg_types:\\n # skip argument with not supported type\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer._allowed_type\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer._allowed_type\\n def _allowed_type(x) -> Union[int, str]:\\n if ',' in x:\\n return str(x)\\n else:\\n return int(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer._arg_default\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer._arg_default\\n def _arg_default(x) -> Union[int, str]:\\n if ',' in x:\\n return str(x)\\n else:\\n return int(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.parse_argparser\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.parse_argparser\\n @staticmethod\\n def parse_argparser(arg_parser: Union[ArgumentParser, Namespace]) -> Namespace:\\n \\\"\\\"\\\"Parse CLI arguments, required for custom bool types.\\\"\\\"\\\"\\n args = arg_parser.parse_args() if isinstance(arg_parser, ArgumentParser) else arg_parser\\n args = {k: True if v is None else v for k, v in vars(args).items()}\\n return Namespace(**args)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.from_argparse_args\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.from_argparse_args\\n @classmethod\\n def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs) -> 'Trainer':\\n \\\"\\\"\\\"\\n Create an instance from CLI arguments.\\n\\n Args:\\n args: The parser or namespace to take arguments from. Only known arguments will be\\n parsed and passed to the :class:`Trainer`.\\n **kwargs: Additional keyword arguments that may override ones in the parser or namespace.\\n These must be valid Trainer arguments.\\n\\n Example:\\n >>> parser = ArgumentParser(add_help=False)\\n >>> parser = Trainer.add_argparse_args(parser)\\n >>> parser.add_argument('--my_custom_arg', default='something') # doctest: +SKIP\\n >>> args = Trainer.parse_argparser(parser.parse_args(\\\"\\\"))\\n >>> trainer = Trainer.from_argparse_args(args, logger=False)\\n \\\"\\\"\\\"\\n if isinstance(args, ArgumentParser):\\n args = cls.parse_argparser(args)\\n params = vars(args)\\n\\n # we only want to pass in valid Trainer args, the rest may be user specific\\n valid_kwargs = inspect.signature(cls.__init__).parameters\\n trainer_kwargs = dict((name, params[name]) for name in valid_kwargs if name in params)\\n trainer_kwargs.update(**kwargs)\\n\\n return cls(**trainer_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\\n @property\\n def num_gpus(self) -> int:\\n gpus = self.data_parallel_device_ids\\n if gpus is None:\\n return 0\\n return len(gpus)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\\n @property\\n def data_parallel(self) -> bool:\\n return self.use_dp or self.use_ddp or self.use_ddp2\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_callback\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_callback\\n @property\\n def progress_bar_callback(self):\\n return self._progress_bar_callback\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_dict\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_dict\\n @property\\n def progress_bar_dict(self) -> dict:\\n \\\"\\\"\\\" Read-only for progress bar metrics. \\\"\\\"\\\"\\n ref_model = self.model if not self.data_parallel else self.model.module\\n return dict(**ref_model.get_progress_bar_dict(), **self.progress_bar_metrics)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.fit\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.fit\\n def fit(\\n self,\\n model: LightningModule,\\n train_dataloader: Optional[DataLoader] = None,\\n val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None\\n ):\\n r\\\"\\\"\\\"\\n Runs the full optimization routine.\\n\\n Args:\\n model: Model to fit.\\n\\n train_dataloader: A Pytorch\\n DataLoader with training samples. If the model has\\n a predefined train_dataloader method this will be skipped.\\n\\n val_dataloaders: Either a single\\n Pytorch Dataloader or a list of them, specifying validation samples.\\n If the model has a predefined val_dataloaders method this will be skipped\\n\\n Example::\\n\\n # Option 1,\\n # Define the train_dataloader() and val_dataloader() fxs\\n # in the lightningModule\\n # RECOMMENDED FOR MOST RESEARCH AND APPLICATIONS TO MAINTAIN READABILITY\\n trainer = Trainer()\\n model = LightningModule()\\n trainer.fit(model)\\n\\n # Option 2\\n # in production cases we might want to pass different datasets to the same model\\n # Recommended for PRODUCTION SYSTEMS\\n train, val = DataLoader(...), DataLoader(...)\\n trainer = Trainer()\\n model = LightningModule()\\n trainer.fit(model, train_dataloader=train, val_dataloaders=val)\\n\\n # Option 1 & 2 can be mixed, for example the training set can be\\n # defined as part of the model, and validation can then be feed to .fit()\\n\\n \\\"\\\"\\\"\\n # bind logger and other properties\\n model.logger = self.logger\\n self.copy_trainer_model_properties(model)\\n\\n # clean hparams\\n if hasattr(model, 'hparams'):\\n parsing.clean_namespace(model.hparams)\\n\\n # set up the passed in dataloaders (if needed)\\n self.__attach_dataloaders(model, trai\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.can_prepare_data\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.can_prepare_data\\n def can_prepare_data(self):\\n if self.prepare_data_per_node:\\n return self.local_rank == 0\\n\\n else:\\n return self.node_rank == 0 and self.local_rank == 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__attach_dataloaders\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__attach_dataloaders\\n def __attach_dataloaders(self, model, train_dataloader=None, val_dataloaders=None, test_dataloaders=None):\\n # when dataloader is passed via fit, patch the train_dataloader\\n # functions to overwrite with these implementations\\n if train_dataloader is not None:\\n model.train_dataloader = _PatchDataLoader(train_dataloader)\\n\\n if val_dataloaders is not None:\\n model.val_dataloader = _PatchDataLoader(val_dataloaders)\\n\\n if test_dataloaders is not None:\\n model.test_dataloader = _PatchDataLoader(test_dataloaders)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine\\n def run_pretrain_routine(self, model: LightningModule):\\n \\\"\\\"\\\"Sanity check a few things before starting actual training.\\n\\n Args:\\n model: The model to run sanity test on.\\n \\\"\\\"\\\"\\n ref_model = model\\n if self.data_parallel:\\n ref_model = model.module\\n\\n # give model convenience properties\\n ref_model.trainer = self\\n\\n # set local properties on the model\\n self.copy_trainer_model_properties(ref_model)\\n\\n # init amp. Must be done here instead of __init__ to allow ddp to work\\n if self.use_native_amp and self.precision == 16:\\n self.scaler = torch.cuda.amp.GradScaler()\\n\\n # log hyper-parameters\\n if self.logger is not None:\\n # save exp to get started\\n self.logger.log_hyperparams(ref_model.hparams)\\n\\n self.logger.save()\\n\\n if self.use_ddp or self.use_ddp2:\\n torch_distrib.barrier()\\n\\n # wait for all models to restore weights\\n if self.on_tpu and XLA_AVAILABLE:\\n # wait for all processes to catch up\\n torch_xla.core.xla_model.rendezvous(\\\"pl.Trainer.run_pretrain_routine\\\")\\n\\n elif self.use_horovod:\\n # wait for all processes to catch up\\n hvd.join()\\n\\n # register auto-resubmit when on SLURM\\n self.register_slurm_signal_handlers()\\n\\n # print model summary\\n # TODO: remove self.testing condition because model.summarize() is wiping out the weights\\n if self.is_global_zero and self.weights_summary is not None and not self.testing:\\n if self.weights_summary in ['full', 'top']:\\n ref_model.summarize(mode=self.weights_summary)\\n else:\\n raise MisconfigurationException(\\\"weights_summary can be None, 'full' or 'top'\\\")\\n\\n # track model now.\\n # if cluster resets state, the model will update with the saved weights\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.test\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.test\\n def test(\\n self,\\n model: Optional[LightningModule] = None,\\n test_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None\\n ):\\n r\\\"\\\"\\\"\\n\\n Separates from fit to make sure you never run on your test set until you want to.\\n\\n Args:\\n model: The model to test.\\n\\n test_dataloaders: Either a single\\n Pytorch Dataloader or a list of them, specifying validation samples.\\n\\n Example::\\n\\n # Option 1\\n # run test after fitting\\n test = DataLoader(...)\\n trainer = Trainer()\\n model = LightningModule()\\n\\n trainer.fit(model)\\n trainer.test(test_dataloaders=test)\\n\\n # Option 2\\n # run test from a loaded model\\n test = DataLoader(...)\\n model = LightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')\\n trainer = Trainer()\\n trainer.test(model, test_dataloaders=test)\\n \\\"\\\"\\\"\\n\\n self.testing = True\\n\\n if test_dataloaders is not None:\\n if model:\\n self.__attach_dataloaders(model, test_dataloaders=test_dataloaders)\\n else:\\n self.__attach_dataloaders(self.model, test_dataloaders=test_dataloaders)\\n\\n if model is not None:\\n self.model = model\\n self.fit(model)\\n\\n # on tpu, .spawn means we don't have a trained model\\n # TODO: remove TPU spawn\\n elif self.use_tpu: # pragma: no-cover\\n # attempt to load weights from a spawn\\n path = os.path.join(self.default_root_dir, '__temp_weight_ddp_end.ckpt')\\n test_model = self.model\\n if os.path.exists(path):\\n test_model = self.load_spawn_weights(self.model)\\n\\n self.fit(test_model)\\n else:\\n self.run_evaluation(test_mode=True)\\n\\n self.testing = False\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.check_model_configuration\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.check_model_configuration\\n def check_model_configuration(self, model: LightningModule):\\n r\\\"\\\"\\\"\\n Checks that the model is configured correctly before training or testing is started.\\n\\n Args:\\n model: The model to check the configuration.\\n\\n \\\"\\\"\\\"\\n # Check training_step, train_dataloader, configure_optimizer methods\\n if not self.testing:\\n if not self.is_overridden('training_step', model):\\n raise MisconfigurationException(\\n 'No `training_step()` method defined. Lightning `Trainer` expects as minimum a'\\n ' `training_step()`, `training_dataloader()` and `configure_optimizers()` to be defined.')\\n\\n if not self.is_overridden('train_dataloader', model):\\n raise MisconfigurationException(\\n 'No `train_dataloader()` method defined. Lightning `Trainer` expects as minimum a'\\n ' `training_step()`, `training_dataloader()` and `configure_optimizers()` to be defined.')\\n\\n if not self.is_overridden('configure_optimizers', model):\\n raise MisconfigurationException(\\n 'No `configure_optimizers()` method defined. Lightning `Trainer` expects as minimum a'\\n ' `training_step()`, `training_dataloader()` and `configure_optimizers()` to be defined.')\\n\\n # Check val_dataloader, validation_step and validation_epoch_end\\n if self.is_overridden('val_dataloader', model):\\n if not self.is_overridden('validation_step', model):\\n raise MisconfigurationException('You have passed in a `val_dataloader()`'\\n ' but have not defined `validation_step()`.')\\n else:\\n if not self.is_overridden('validation_epoch_end', model):\\n rank_zero_warn(\\n 'You hav\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__init__\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__init__\\n def __init__(self, dataloader: Union[List[DataLoader], DataLoader]):\\n self.dataloader = dataloader\\n\\n # cannot pickle __code__ so cannot verify if PatchDataloader\\n # exists which shows dataloader methods have been overwritten.\\n # so, we hack it by using the string representation\\n self.patch_loader_code = str(self.__call__.__code__)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__call__\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__call__\\n def __call__(self) -> Union[List[DataLoader], DataLoader]:\\n return self.dataloader\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.get_model\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.get_model\\n def get_model(self):\\n is_dp_module = isinstance(self.model, (LightningDistributedDataParallel,\\n LightningDataParallel))\\n model = self.model.module if is_dp_module else self.model\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_weights\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_weights\\n def restore_weights(self, model: LightningModule):\\n \\\"\\\"\\\"\\n We attempt to restore weights in this order:\\n 1. HPC weights.\\n 2. if no HPC weights restore checkpoint_path weights\\n 3. otherwise don't restore weights\\n \\\"\\\"\\\"\\n # clear cache before restore\\n if self.on_gpu:\\n torch.cuda.empty_cache()\\n\\n # if script called from hpc resubmit, load weights\\n did_restore_hpc_weights = self.restore_hpc_weights_if_needed(model)\\n\\n # clear cache after restore\\n if self.on_gpu:\\n torch.cuda.empty_cache()\\n\\n if not did_restore_hpc_weights:\\n if self.resume_from_checkpoint is not None:\\n self.restore(self.resume_from_checkpoint, on_gpu=self.on_gpu)\\n\\n # wait for all models to restore weights\\n if self.use_ddp or self.use_ddp2:\\n # wait for all processes to catch up\\n torch_distrib.barrier()\\n\\n # wait for all models to restore weights\\n if self.on_tpu and XLA_AVAILABLE:\\n # wait for all processes to catch up\\n torch_xla.core.xla_model.rendezvous(\\\"pl.TrainerIOMixin.restore_weights\\\")\\n\\n elif self.use_horovod:\\n # wait for all processes to catch up\\n hvd.join()\\n\\n # clear cache after restore\\n if self.on_gpu:\\n torch.cuda.empty_cache()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.register_slurm_signal_handlers\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.register_slurm_signal_handlers\\n def register_slurm_signal_handlers(self):\\n # see if we're using slurm (not interactive)\\n on_slurm = False\\n try:\\n job_name = os.environ['SLURM_JOB_NAME']\\n if job_name != 'bash':\\n on_slurm = True\\n except Exception:\\n pass\\n\\n if on_slurm:\\n log.info('Set SLURM handle signals.')\\n signal.signal(signal.SIGUSR1, self.sig_handler)\\n signal.signal(signal.SIGTERM, self.term_handler)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.sig_handler\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.sig_handler\\n def sig_handler(self, signum, frame): # pragma: no-cover\\n if self.is_global_zero:\\n # save weights\\n log.info('handling SIGUSR1')\\n self.hpc_save(self.weights_save_path, self.logger)\\n\\n # find job id\\n job_id = os.environ['SLURM_JOB_ID']\\n cmd = 'scontrol requeue {}'.format(job_id)\\n\\n # requeue job\\n log.info(f'requeing job {job_id}...')\\n result = call(cmd, shell=True)\\n\\n # print result text\\n if result == 0:\\n log.info(f'requeued exp {job_id}')\\n else:\\n log.warning('requeue failed...')\\n\\n # close experiment to avoid issues\\n self.logger.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.term_handler\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.term_handler\\n def term_handler(self, signum, frame):\\n # save\\n log.info(\\\"bypassing sigterm\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin._atomic_save\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin._atomic_save\\n def _atomic_save(self, checkpoint, filepath: str):\\n \\\"\\\"\\\"Saves a checkpoint atomically, avoiding the creation of incomplete checkpoints.\\n\\n This will create a temporary checkpoint with a suffix of ``.part``, then copy it to the final location once\\n saving is finished.\\n\\n Args:\\n checkpoint: The object to save.\\n Built to be used with the ``dump_checkpoint`` method, but can deal with anything which ``torch.save``\\n accepts.\\n filepath: The path to which the checkpoint will be saved.\\n This points to the file that the checkpoint will be stored in.\\n \\\"\\\"\\\"\\n tmp_path = str(filepath) + \\\".part\\\"\\n torch.save(checkpoint, tmp_path)\\n os.replace(tmp_path, filepath)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.save_checkpoint\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.save_checkpoint\\n def save_checkpoint(self, filepath, weights_only: bool = False):\\n checkpoint = self.dump_checkpoint(weights_only)\\n\\n if self.is_global_zero:\\n # do the actual save\\n try:\\n self._atomic_save(checkpoint, filepath)\\n except AttributeError as err:\\n if LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:\\n del checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]\\n rank_zero_warn('Warning, `module_arguments` dropped from checkpoint.'\\n f' An attribute is not picklable {err}')\\n self._atomic_save(checkpoint, filepath)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore\\n def restore(self, checkpoint_path: str, on_gpu: bool):\\n \\\"\\\"\\\"\\n Restore training state from checkpoint.\\n Also restores all training state like:\\n - epoch\\n - callbacks\\n - schedulers\\n - optimizer\\n \\\"\\\"\\\"\\n\\n # if on_gpu:\\n # checkpoint = torch.load(checkpoint_path)\\n # else:\\n # load on CPU first\\n checkpoint = pl_load(checkpoint_path, map_location=lambda storage, loc: storage)\\n\\n # load model state\\n model = self.get_model()\\n\\n # load the state_dict on the model automatically\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n # give model a chance to load something\\n model.on_load_checkpoint(checkpoint)\\n\\n if on_gpu:\\n model.cuda(self.root_gpu)\\n\\n # restore amp scaling\\n if self.use_amp and self.use_native_amp and 'native_amp_scaling_state' in checkpoint:\\n self.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])\\n\\n # load training state (affects trainer only)\\n self.restore_training_state(checkpoint)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.dump_checkpoint\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.dump_checkpoint\\n def dump_checkpoint(self, weights_only: bool = False) -> dict:\\n \\\"\\\"\\\"Creating model checkpoint.\\n\\n Args:\\n weights_only: saving model weights only\\n\\n Return:\\n structured dictionary\\n \\\"\\\"\\\"\\n checkpoint = {\\n 'epoch': self.current_epoch + 1,\\n 'global_step': self.global_step + 1,\\n 'pytorch-ligthning_version': pytorch_lightning.__version__,\\n }\\n\\n if not weights_only:\\n if self.checkpoint_callback:\\n checkpoint['checkpoint_callback_best_model_score'] = self.checkpoint_callback.best_model_score\\n checkpoint['checkpoint_callback_best_model_path'] = self.checkpoint_callback.best_model_path\\n\\n if self.early_stop_callback:\\n checkpoint['early_stop_callback_wait'] = self.early_stop_callback.wait\\n checkpoint['early_stop_callback_patience'] = self.early_stop_callback.patience\\n\\n # save optimizers\\n optimizer_states = []\\n for i, optimizer in enumerate(self.optimizers):\\n optimizer_states.append(optimizer.state_dict())\\n\\n checkpoint['optimizer_states'] = optimizer_states\\n\\n # save lr schedulers\\n lr_schedulers = []\\n for scheduler in self.lr_schedulers:\\n lr_schedulers.append(scheduler['scheduler'].state_dict())\\n\\n checkpoint['lr_schedulers'] = lr_schedulers\\n\\n # save native amp scaling\\n if self.use_amp and self.use_native_amp:\\n checkpoint['native_amp_scaling_state'] = self.scaler.state_dict()\\n\\n # add the module_arguments and state_dict from the model\\n model = self.get_model()\\n\\n checkpoint['state_dict'] = model.state_dict()\\n\\n if model.hparams:\\n if hasattr(model, '_hparams_name'):\\n checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_NAME] = mo\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_hpc_weights_if_needed\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_hpc_weights_if_needed\\n def restore_hpc_weights_if_needed(self, model: LightningModule):\\n \\\"\\\"\\\"If there is a set of hpc weights, use as signal to restore model.\\\"\\\"\\\"\\n did_restore = False\\n\\n # look for hpc weights\\n folderpath = self.weights_save_path\\n if os.path.exists(folderpath):\\n files = os.listdir(folderpath)\\n hpc_weight_paths = [x for x in files if 'hpc_ckpt' in x]\\n\\n # if hpc weights exist restore model\\n if len(hpc_weight_paths) > 0:\\n self.hpc_load(folderpath, self.on_gpu)\\n did_restore = True\\n return did_restore\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_training_state\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.restore_training_state\\n def restore_training_state(self, checkpoint):\\n \\\"\\\"\\\"\\n Restore trainer state.\\n Model will get its change to update\\n :param checkpoint:\\n :return:\\n \\\"\\\"\\\"\\n if 'optimizer_states' not in checkpoint or 'lr_schedulers' not in checkpoint:\\n raise KeyError(\\n 'Trying to restore training state but checkpoint contains only the model.'\\n ' This is probably due to `ModelCheckpoint.save_weights_only` being set to `True`.'\\n )\\n\\n if self.checkpoint_callback:\\n if 'checkpoint_callback_best_model_score' in checkpoint:\\n self.checkpoint_callback.best_model_score = checkpoint['checkpoint_callback_best_model_score']\\n else:\\n # Old naming until version 0.7.6\\n rank_zero_warn(\\n 'Loading a checkpoint created with an old version of Lightning; '\\n 'this will not be supported in the future.'\\n )\\n self.checkpoint_callback.best_model_score = checkpoint['checkpoint_callback_best']\\n self.checkpoint_callback.best_model_path = checkpoint['checkpoint_callback_best_model_path']\\n\\n if self.early_stop_callback:\\n self.early_stop_callback.wait = checkpoint['early_stop_callback_wait']\\n self.early_stop_callback.patience = checkpoint['early_stop_callback_patience']\\n\\n self.global_step = checkpoint['global_step']\\n self.current_epoch = checkpoint['epoch']\\n\\n # Division deals with global step stepping once per accumulated batch\\n # Inequality deals with different global step for odd vs even num_training_batches\\n n_accum = 1 if self.accumulate_grad_batches is None else self.accumulate_grad_batches\\n expected_steps = self.num_training_batches / n_accum\\n if self.num_training_batches != 0 and self.global_step % expected_steps\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.hpc_save\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.hpc_save\\n def hpc_save(self, folderpath: str, logger):\\n # make sure the checkpoint folder exists\\n os.makedirs(folderpath, exist_ok=True)\\n\\n # save logger to make sure we get all the metrics\\n logger.save()\\n\\n ckpt_number = self.max_ckpt_in_folder(folderpath) + 1\\n\\n if not os.path.exists(folderpath):\\n os.makedirs(folderpath, exist_ok=True)\\n filepath = os.path.join(folderpath, f'hpc_ckpt_{ckpt_number}.ckpt')\\n\\n # give model a chance to do something on hpc_save\\n model = self.get_model()\\n checkpoint = self.dump_checkpoint()\\n\\n model.on_hpc_save(checkpoint)\\n\\n # do the actual save\\n # TODO: fix for anything with multiprocess DP, DDP, DDP2\\n try:\\n self._atomic_save(checkpoint, filepath)\\n except AttributeError as err:\\n if LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in checkpoint:\\n del checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]\\n rank_zero_warn('warning, `module_arguments` dropped from checkpoint.'\\n f' An attribute is not picklable {err}')\\n self._atomic_save(checkpoint, filepath)\\n\\n return filepath\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.hpc_load\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.hpc_load\\n def hpc_load(self, folderpath, on_gpu):\\n filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, self.max_ckpt_in_folder(folderpath))\\n\\n # load on CPU first\\n checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)\\n\\n # load model state\\n model = self.get_model()\\n\\n # load the state_dict on the model automatically\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n # restore amp scaling\\n if self.use_amp and self.use_native_amp and 'native_amp_scaling_state' in checkpoint:\\n self.scaler.load_state_dict(checkpoint['native_amp_scaling_state'])\\n\\n if self.root_gpu is not None:\\n model.cuda(self.root_gpu)\\n\\n # load training state (affects trainer only)\\n self.restore_training_state(checkpoint)\\n\\n # call model hook\\n model.on_hpc_load(checkpoint)\\n\\n log.info(f'restored hpc model from: {filepath}')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.max_ckpt_in_folder\",\n \"text\": \"pytorch_lightning/trainer/training_io.py::TrainerIOMixin.max_ckpt_in_folder\\n def max_ckpt_in_folder(self, path, name_key='ckpt_'):\\n files = os.listdir(path)\\n files = [x for x in files if name_key in x]\\n if len(files) == 0:\\n return 0\\n\\n ckpt_vs = []\\n for name in files:\\n name = name.split(name_key)[-1]\\n name = re.sub('[^0-9]', '', name)\\n ckpt_vs.append(int(name))\\n\\n return max(ckpt_vs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model\\n @abstractmethod\\n def get_model(self) -> LightningModule:\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented\\n @abstractmethod\\n def is_function_implemented(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation\\n @abstractmethod\\n def run_evaluation(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu\\n @abstractmethod\\n def transfer_batch_to_gpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu\\n @abstractmethod\\n def transfer_batch_to_tpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients\\n @abstractmethod\\n def clip_gradients(self):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.detect_nan_tensors\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.detect_nan_tensors\\n @abstractmethod\\n def detect_nan_tensors(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overridden\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overridden\\n @abstractmethod\\n def is_overridden(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_progress_bar_metrics\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_progress_bar_metrics\\n @abstractmethod\\n def add_progress_bar_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics\\n @abstractmethod\\n def log_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output\\n @abstractmethod\\n def process_output(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader\\n @abstractmethod\\n def reset_train_dataloader(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader\\n @abstractmethod\\n def reset_val_dataloader(self, model):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg\\n @abstractmethod\\n def has_arg(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train\\n def train(self):\\n # add signal handlers for process kills\\n # def _signal_kill_handler(*args):\\n # return TrainerTrainLoopMixin.run_training_teardown(self)\\n #\\n # orig_signal_handlers = {}\\n # for sig_name in SIGNAL_TERMINATE:\\n # orig_signal_handlers[sig_name] = signal.signal(getattr(signal, sig_name),\\n # _signal_kill_handler)\\n\\n # get model\\n model = self.get_model()\\n\\n # load data\\n # if reload_dataloaders_every_epoch, this is moved to the epoch loop\\n if not self.reload_dataloaders_every_epoch:\\n self.reset_train_dataloader(model)\\n self.reset_val_dataloader(model)\\n\\n # Train start events\\n with self.profiler.profile('on_train_start'):\\n # callbacks\\n self.on_train_start()\\n # initialize early stop callback\\n if self.early_stop_callback is not None:\\n self.early_stop_callback.on_train_start(self, self.get_model())\\n # model hooks\\n model.on_train_start()\\n\\n try:\\n # run all epochs\\n for epoch in range(self.current_epoch, self.max_epochs):\\n # reset train dataloader\\n if self.reload_dataloaders_every_epoch:\\n self.reset_train_dataloader(model)\\n # set seed for distributed sampler (enables shuffling for each epoch)\\n if (self.use_ddp or self.use_horovod) \\\\\\n and hasattr(self.train_dataloader, 'sampler') \\\\\\n and hasattr(self.train_dataloader.sampler, 'set_epoch'):\\n self.train_dataloader.sampler.set_epoch(epoch)\\n\\n # update training progress in trainer and model\\n model.current_epoch = epoch\\n self.current_epoch = epoch\\n\\n # changing gradi\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch\\n def run_training_epoch(self):\\n\\n # get model\\n model = self.get_model()\\n\\n # Epoch start events\\n with self.profiler.profile('on_epoch_start'):\\n # callbacks\\n self.on_epoch_start()\\n\\n # model hooks\\n if self.is_function_implemented('on_epoch_start'):\\n model.on_epoch_start()\\n\\n # track local dataloader so TPU can wrap each epoch\\n train_dataloader = self.train_dataloader\\n\\n # on TPU we have to wrap it under the ParallelLoader\\n if self.use_tpu:\\n device = xm.xla_device(self.tpu_id)\\n train_dataloader = xla_pl.ParallelLoader(train_dataloader, [device])\\n train_dataloader = train_dataloader.per_device_loader(device)\\n\\n # bookkeeping\\n outputs = []\\n\\n # run epoch\\n for batch_idx, (batch, is_last_batch) in self.profiler.profile_iterable(\\n enumerate(_with_is_last(train_dataloader)), \\\"get_train_batch\\\"\\n ):\\n # stop epoch if we limited the number of training batches\\n if batch_idx >= self.num_training_batches:\\n break\\n\\n self.batch_idx = batch_idx\\n\\n model.global_step = self.global_step\\n\\n # ---------------\\n # RUN TRAIN STEP\\n # ---------------\\n _outputs = self.run_training_batch(batch, batch_idx)\\n batch_result, grad_norm_dic, batch_step_metrics, batch_output = _outputs\\n\\n # only track outputs when user implements training_epoch_end\\n # otherwise we will build up unnecessary memory\\n if self.is_overridden('training_epoch_end', model=self.get_model()):\\n outputs.append(batch_output)\\n\\n # when returning -1 from train_step, we end epoch early\\n early_stop_epoch = batch_result == -1\\n\\n # TODO: consolidate all actions that need to take pl\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch\\n def run_training_batch(self, batch, batch_idx):\\n # track grad norms\\n grad_norm_dic = {}\\n\\n # track all metrics for callbacks\\n all_callback_metrics = []\\n\\n # track metrics to log\\n all_log_metrics = []\\n\\n if batch is None:\\n return 0, grad_norm_dic, {}, {}\\n\\n # Batch start events\\n with self.profiler.profile('on_batch_start'):\\n # callbacks\\n self.on_batch_start()\\n # hooks\\n if self.is_function_implemented('on_batch_start'):\\n response = self.get_model().on_batch_start(batch)\\n if response == -1:\\n return -1, grad_norm_dic, {}, {}\\n\\n splits = [batch]\\n if self.truncated_bptt_steps is not None:\\n model_ref = self.get_model()\\n with self.profiler.profile('tbptt_split_batch'):\\n splits = model_ref.tbptt_split_batch(batch, self.truncated_bptt_steps)\\n\\n self.hiddens = None\\n for split_idx, split_batch in enumerate(splits):\\n self.split_idx = split_idx\\n\\n for opt_idx, optimizer in self._get_optimizers_iterable():\\n # make sure only the gradients of the current optimizer's parameters are calculated\\n # in the training step to prevent dangling gradients in multiple-optimizer setup.\\n if len(self.optimizers) > 1:\\n for param in self.get_model().parameters():\\n param.requires_grad = False\\n for group in optimizer.param_groups:\\n for param in group['params']:\\n param.requires_grad = True\\n\\n # wrap the forward step in a closure so second order methods work\\n def optimizer_closure():\\n # forward pass\\n with self.profiler.profile('model_forward'):\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin._get_optimizers_iterable\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin._get_optimizers_iterable\\n def _get_optimizers_iterable(self):\\n if not self.optimizer_frequencies:\\n # call training_step once per optimizer\\n return list(enumerate(self.optimizers))\\n\\n optimizer_freq_cumsum = np.cumsum(self.optimizer_frequencies)\\n optimizers_loop_length = optimizer_freq_cumsum[-1]\\n current_place_in_loop = self.total_batch_idx % optimizers_loop_length\\n\\n # find optimzier index by looking for the first {item > current_place} in the cumsum list\\n opt_idx = np.argmax(optimizer_freq_cumsum > current_place_in_loop)\\n return [(opt_idx, self.optimizers[opt_idx])]\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown\\n def run_training_teardown(self):\\n if hasattr(self, '_teardown_already_run') and self._teardown_already_run:\\n return\\n # Train end events\\n with self.profiler.profile('on_train_end'):\\n # callbacks\\n self.on_train_end()\\n # model hooks\\n if self.is_function_implemented('on_train_end'):\\n self.get_model().on_train_end()\\n\\n if self.logger is not None:\\n self.logger.finalize(\\\"success\\\")\\n\\n # summarize profile results\\n self.profiler.describe()\\n\\n self._teardown_already_run = True\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward\\n def training_forward(self, batch, batch_idx, opt_idx, hiddens):\\n \\\"\\\"\\\"\\n Handle forward for each training case (distributed, single gpu, etc...)\\n :param batch:\\n :param batch_idx:\\n :return:\\n \\\"\\\"\\\"\\n # ---------------\\n # FORWARD\\n # ---------------\\n # enable not needing to add opt_idx to training_step\\n args = [batch, batch_idx]\\n\\n if len(self.optimizers) > 1:\\n if self.has_arg('training_step', 'optimizer_idx'):\\n args.append(opt_idx)\\n else:\\n num_opts = len(self.optimizers)\\n raise ValueError(\\n f'Your LightningModule defines {num_opts} optimizers but '\\n f'training_step is missing the \\\"optimizer_idx\\\" argument.'\\n )\\n\\n # pass hiddens if using tbptt\\n if self.truncated_bptt_steps is not None:\\n args.append(hiddens)\\n\\n # distributed forward\\n if self.use_ddp or self.use_ddp2 or self.use_dp:\\n output = self.model(*args)\\n\\n # Horovod\\n elif self.use_horovod and self.on_gpu:\\n batch = self.transfer_batch_to_gpu(batch, hvd.local_rank())\\n args[0] = batch\\n output = self.model.training_step(*args)\\n\\n # single GPU forward\\n elif self.single_gpu:\\n gpu_id = 0\\n if isinstance(self.data_parallel_device_ids, list):\\n gpu_id = self.data_parallel_device_ids[0]\\n\\n # Don't copy the batch since there is a single gpu that the batch could\\n # be referenced from and if there are multiple optimizers the batch will\\n # wind up copying it to the same device repeatedly.\\n batch = self.transfer_batch_to_gpu(batch, gpu_id)\\n args[0] = batch\\n output = self.model.training_step(*args)\\n\\n # TPU support\\n elif self.use_tpu:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates\\n def update_learning_rates(self, interval: str):\\n \\\"\\\"\\\"Update learning rates.\\n\\n Args:\\n interval: either 'epoch' or 'step'.\\n \\\"\\\"\\\"\\n if not self.lr_schedulers:\\n return\\n\\n for lr_scheduler in self.lr_schedulers:\\n current_idx = self.batch_idx if interval == 'step' else self.current_epoch\\n current_idx += 1 # account for both batch and epoch starts from 0\\n # Take step if call to update_learning_rates matches the interval key and\\n # the current step modulo the schedulers frequency is zero\\n if lr_scheduler['interval'] == interval and current_idx % lr_scheduler['frequency'] == 0:\\n # If instance of ReduceLROnPlateau, we need to pass validation loss\\n if lr_scheduler['reduce_on_plateau']:\\n monitor_key = lr_scheduler['monitor']\\n monitor_val = self.callback_metrics.get(monitor_key)\\n if monitor_val is None:\\n avail_metrics = ','.join(list(self.callback_metrics.keys()))\\n raise MisconfigurationException(\\n f'ReduceLROnPlateau conditioned on metric {monitor_key}'\\n f' which is not available. Available metrics are: {avail_metrics}.'\\n ' Condition can be set using `monitor` key in lr scheduler dict'\\n )\\n lr_scheduler['scheduler'].step(monitor_val)\\n else:\\n lr_scheduler['scheduler'].step()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_checkpoint_callback\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_checkpoint_callback\\n def call_checkpoint_callback(self):\\n if self.checkpoint_callback is not None:\\n self.checkpoint_callback.on_validation_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::_with_is_last\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::_with_is_last\\ndef _with_is_last(iterable):\\n \\\"\\\"\\\"Pass through values from the given iterable with an added boolean indicating if this is the last item.\\n See `https://stackoverflow.com/a/1630350 <https://stackoverflow.com/a/1630350>`_\\\"\\\"\\\"\\n it = iter(iterable)\\n last = next(it)\\n for val in it:\\n # yield last and has next\\n yield last, False\\n last = val\\n # yield last, no longer has next\\n yield last, True\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":69,"cells":{"instance_id":{"kind":"string","value":"ipython__ipython-1687"},"repo":{"kind":"string","value":"ipython/ipython"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Parallel controller failing with Pymongo 2.2\n[Test failure](https://jenkins.shiningpanda.com/ipython/job/ipython-mac/94/testReport/junit/nose.failure/Failure/runTest/)\n\nThe mongodb backend for the controller tries to import from `pymongo.binary`, but this no longer exists in 2.2; it should import from `bson.binary` instead.\n\n"},"gold_files":{"kind":"list like","value":["IPython/parallel/controller/mongodb.py"],"string":"[\n \"IPython/parallel/controller/mongodb.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["IPython/parallel/controller/mongodb.py::<module>"],"string":"[\n \"IPython/parallel/controller/mongodb.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"IPython/parallel/apps/ipcontrollerapp.py","text":"IPython/parallel/apps/ipcontrollerapp.py\n#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\nThe IPython controller application.\n\nAuthors:\n\n* Brian Granger\n* MinRK\n\n\"\"\"\n\n#-----------------------------------------------------------------------------\n# Copyright (C) 2008-2011 The IPython Development Team\n#\n# Distributed under the terms of the BSD License. The full license is in\n# the file COPYING, distributed as part of this software.\n#-----------------------------------------------------------------------------\n\n#-----------------------------------------------------------------------------\n# Imports\n#-----------------------------------------------------------------------------\n\nfrom __future__ import with_statement\n\nimport json\nimport os\nimport socket\nimport stat\nimport sys\n\nfrom multiprocessing import Process\nfrom signal import signal, SIGINT, SIGABRT, SIGTERM\n\nimport zmq\nfrom zmq.devices import ProcessMonitoredQueue\nfrom zmq.log.handlers import PUBHandler\n\nfrom IPython.core.profiledir import ProfileDir\n\nfrom IPython.parallel.apps.baseapp import (\n BaseParallelApplication,\n base_aliases,\n base_flags,\n catch_config_error,\n)\nfrom IPython.utils.importstring import import_item\nfrom IPython.utils.traitlets import Instance, Unicode, Bool, List, Dict, TraitError\n\nfrom IPython.zmq.session import (\n Session, session_aliases, session_flags, default_secure\n)\n\nfrom IPython.parallel.controller.heartmonitor import HeartMonitor\nfrom IPython.parallel.controller.hub import HubFactory\nfrom IPython.parallel.controller.scheduler import TaskScheduler,launch_scheduler\nfrom IPython.parallel.controller.sqlitedb import SQLiteDB\n\nfrom IPython.parallel.util import split_url, disambiguate_url\n\n# conditional import of MongoDB backend class\n\ntry:\n from IPython.parallel.controller.mongodb import MongoDB\nexcept ImportError:\n maybe_mongo = []\nelse:\n maybe_mongo = [MongoDB]\n\n\n#-----------------------------------------------------------------------------\n# Module level variables\n#-----------------------------------------------------------------------------\n\n\n#: The default config file name for this application\ndefault_config_file_name = u'ipcontroller_config.py'\n\n\n_description = \"\"\"Start the IPython controller for parallel computing.\n\nThe IPython controller provides a gateway between the IPython engines and\nclients. The controller needs to be started before the engines and can be\nconfigured using command line options or using a cluster directory. Cluster\ndirectories contain config, log and security files and are usually located in\nyour ipython directory and named as \"profile_name\". See the `profile`\nand `profile-dir` options for details.\n\"\"\"\n\n_examples = \"\"\"\nipcontroller --ip=192.168.0.1 --port=1000 # listen on ip, port for engines\nipcontroller --scheme=pure # use the pure zeromq scheduler\n\"\"\"\n\n\n#-----------------------------------------------------------------------------\n# The main application\n#--------------------------------------------------------------","label":0},{"cid":"IPython/parallel/client/client.py","text":"IPython/parallel/client/client.py\n\"\"\"A semi-synchronous Client for the ZMQ cluster\n\nAuthors:\n\n* MinRK\n\"\"\"\n#-----------------------------------------------------------------------------\n# Copyright (C) 2010-2011 The IPython Development Team\n#\n# Distributed under the terms of the BSD License. The full license is in\n# the file COPYING, distributed as part of this software.\n#-----------------------------------------------------------------------------\n\n#-----------------------------------------------------------------------------\n# Imports\n#-----------------------------------------------------------------------------\n\nimport os\nimport json\nimport sys\nfrom threading import Thread, Event\nimport time\nimport warnings\nfrom datetime import datetime\nfrom getpass import getpass\nfrom pprint import pprint\n\npjoin = os.path.join\n\nimport zmq\n# from zmq.eventloop import ioloop, zmqstream\n\nfrom IPython.config.configurable import MultipleInstanceError\nfrom IPython.core.application import BaseIPythonApplication\n\nfrom IPython.utils.jsonutil import rekey\nfrom IPython.utils.localinterfaces import LOCAL_IPS\nfrom IPython.utils.path import get_ipython_dir\nfrom IPython.utils.traitlets import (HasTraits, Integer, Instance, Unicode,\n Dict, List, Bool, Set, Any)\nfrom IPython.external.decorator import decorator\nfrom IPython.external.ssh import tunnel\n\nfrom IPython.parallel import Reference\nfrom IPython.parallel import error\nfrom IPython.parallel import util\n\nfrom IPython.zmq.session import Session, Message\n\nfrom .asyncresult import AsyncResult, AsyncHubResult\nfrom IPython.core.profiledir import ProfileDir, ProfileDirError\nfrom .view import DirectView, LoadBalancedView\n\nif sys.version_info[0] >= 3:\n # xrange is used in a couple 'isinstance' tests in py2\n # should be just 'range' in 3k\n xrange = range\n\n#--------------------------------------------------------------------------\n# Decorators for Client methods\n#--------------------------------------------------------------------------\n\n@decorator\ndef spin_first(f, self, *args, **kwargs):\n \"\"\"Call spin() to sync state prior to calling the method.\"\"\"\n self.spin()\n return f(self, *args, **kwargs)\n\n\n#--------------------------------------------------------------------------\n# Classes\n#--------------------------------------------------------------------------\n\nclass Metadata(dict):\n \"\"\"Subclass of dict for initializing metadata values.\n\n Attribute access works on keys.\n\n These objects have a strict set of keys - errors will raise if you try\n to add new keys.\n \"\"\"\n def __init__(self, *args, **kwargs):\n dict.__init__(self)\n md = {'msg_id' : None,\n 'submitted' : None,\n 'started' : None,\n 'completed' : None,\n 'received' : None,\n 'engine_uuid' : None,\n 'engine_id' : None,\n 'follow' : None,\n 'after' : None,\n 'status' : None,\n\n ","label":0},{"cid":"IPython/parallel/controller/dependency.py","text":"IPython/parallel/controller/dependency.py\n\"\"\"Dependency utilities\n\nAuthors:\n\n* Min RK\n\"\"\"\n#-----------------------------------------------------------------------------\n# Copyright (C) 2010-2011 The IPython Development Team\n#\n# Distributed under the terms of the BSD License. The full license is in\n# the file COPYING, distributed as part of this software.\n#-----------------------------------------------------------------------------\n\nfrom types import ModuleType\n\nfrom IPython.parallel.client.asyncresult import AsyncResult\nfrom IPython.parallel.error import UnmetDependency\nfrom IPython.parallel.util import interactive\nfrom IPython.utils import py3compat\n\nclass depend(object):\n \"\"\"Dependency decorator, for use with tasks.\n \n `@depend` lets you define a function for engine dependencies\n just like you use `apply` for tasks.\n \n \n Examples\n --------\n ::\n \n @depend(df, a,b, c=5)\n def f(m,n,p)\n \n view.apply(f, 1,2,3)\n \n will call df(a,b,c=5) on the engine, and if it returns False or\n raises an UnmetDependency error, then the task will not be run\n and another engine will be tried.\n \"\"\"\n def __init__(self, f, *args, **kwargs):\n self.f = f\n self.args = args\n self.kwargs = kwargs\n \n def __call__(self, f):\n return dependent(f, self.f, *self.args, **self.kwargs)\n\nclass dependent(object):\n \"\"\"A function that depends on another function.\n This is an object to prevent the closure used\n in traditional decorators, which are not picklable.\n \"\"\"\n\n def __init__(self, f, df, *dargs, **dkwargs):\n self.f = f\n self.func_name = getattr(f, '__name__', 'f')\n self.df = df\n self.dargs = dargs\n self.dkwargs = dkwargs\n\n def __call__(self, *args, **kwargs):\n # if hasattr(self.f, 'func_globals') and hasattr(self.df, 'func_globals'):\n # self.df.func_globals = self.f.func_globals\n if self.df(*self.dargs, **self.dkwargs) is False:\n raise UnmetDependency()\n return self.f(*args, **kwargs)\n \n if not py3compat.PY3:\n @property\n def __name__(self):\n return self.func_name\n\n@interactive\ndef _require(*names):\n \"\"\"Helper for @require decorator.\"\"\"\n from IPython.parallel.error import UnmetDependency\n user_ns = globals()\n for name in names:\n if name in user_ns:\n continue\n try:\n exec 'import %s'%name in user_ns\n except ImportError:\n raise UnmetDependency(name)\n return True\n\ndef require(*mods):\n \"\"\"Simple decorator for requiring names to be importable.\n \n Examples\n --------\n \n In [1]: @require('numpy')\n ...: def norm(a):\n ...: import numpy\n ...: return numpy.linalg.norm(a,2)\n \"\"\"\n names = []\n for mod in mods:\n if isinstance(mod, ModuleType):\n mod = mod.__name__\n \n if isinstance(mod, basestring):\n na","label":0},{"cid":"IPython/parallel/controller/dictdb.py","text":"IPython/parallel/controller/dictdb.py\nclass CompositeFilter # Composite filter for matching multiple properties.\n def __init__(self, dikt)\n def __call__(self, value)\nclass BaseDB # Empty Parent class so traitlets work on DB.\nclass DictDB # Basic in-memory dict-based object for saving Task Records.\n def _match_one(self, rec, tests) # Check if a specific record matches tests.\n def _match(self, check) # Find all the matches for a check dict.\n def _extract_subdict(self, rec, keys) # extract subdict of keys\n def add_record(self, msg_id, rec) # Add a new Task Record, by msg_id.\n def get_record(self, msg_id) # Get a specific Task Record, by msg_id.\n def update_record(self, msg_id, rec) # Update the data in an existing record.\n def drop_matching_records(self, check) # Remove a record from the DB.\n def drop_record(self, msg_id) # Remove a record from the DB.\n def find_records(self, check, keys=None) # Find records matching a query dict, optionally extracting subset of keys.\n def get_history(self) # get all msg_ids, ordered by time submitted.\nclass NoDB # A blackhole db backend that actually stores no information.\n def add_record(self, msg_id, record)\n def get_record(self, msg_id)\n def update_record(self, msg_id, record)\n def drop_matching_records(self, check)\n def drop_record(self, msg_id)\n def find_records(self, check, keys=None)\n def get_history(self)","label":0},{"cid":"IPython/parallel/controller/mongodb.py","text":"IPython/parallel/controller/mongodb.py\nclass MongoDB # MongoDB TaskRecord backend.\n def __init__(self, **kwargs)\n def _binary_buffers(self, rec)\n def add_record(self, msg_id, rec) # Add a new Task Record, by msg_id.\n def get_record(self, msg_id) # Get a specific Task Record, by msg_id.\n def update_record(self, msg_id, rec) # Update the data in an existing record.\n def drop_matching_records(self, check) # Remove a record from the DB.\n def drop_record(self, msg_id) # Remove a record from the DB.\n def find_records(self, check, keys=None) # Find records matching a query dict, optionally extracting subset of keys.\n def get_history(self) # get all msg_ids, ordered by time submitted.","label":1},{"cid":"IPython/parallel/controller/sqlitedb.py","text":"IPython/parallel/controller/sqlitedb.py\ndef _adapt_dict(d)\ndef _convert_dict(ds)\ndef _adapt_bufs(bufs)\ndef _convert_bufs(bs)\nclass SQLiteDB # SQLite3 TaskRecord backend.\n def __init__(self, **kwargs)\n def _defaults(self, keys=None) # create an empty record\n def _check_table(self) # Ensure that an incorrect table doesn't exist\n def _init_db(self) # Connect to the database and get new session number.\n def _dict_to_list(self, d) # turn a mongodb-style record dict into a list.\n def _list_to_dict(self, line, keys=None) # Inverse of dict_to_list\n def _render_expression(self, check) # Turn a mongodb-style search dict into an SQL query.\n def add_record(self, msg_id, rec) # Add a new Task Record, by msg_id.\n def get_record(self, msg_id) # Get a specific Task Record, by msg_id.\n def update_record(self, msg_id, rec) # Update the data in an existing record.\n def drop_record(self, msg_id) # Remove a record from the DB.\n def drop_matching_records(self, check) # Remove a record from the DB.\n def find_records(self, check, keys=None) # Find records matching a query dict, optionally extracting subset of keys.\n def get_history(self) # get all msg_ids, ordered by time submitted.","label":0}],"string":"[\n {\n \"cid\": \"IPython/parallel/apps/ipcontrollerapp.py\",\n \"text\": \"IPython/parallel/apps/ipcontrollerapp.py\\n#!/usr/bin/env python\\n# encoding: utf-8\\n\\\"\\\"\\\"\\nThe IPython controller application.\\n\\nAuthors:\\n\\n* Brian Granger\\n* MinRK\\n\\n\\\"\\\"\\\"\\n\\n#-----------------------------------------------------------------------------\\n# Copyright (C) 2008-2011 The IPython Development Team\\n#\\n# Distributed under the terms of the BSD License. The full license is in\\n# the file COPYING, distributed as part of this software.\\n#-----------------------------------------------------------------------------\\n\\n#-----------------------------------------------------------------------------\\n# Imports\\n#-----------------------------------------------------------------------------\\n\\nfrom __future__ import with_statement\\n\\nimport json\\nimport os\\nimport socket\\nimport stat\\nimport sys\\n\\nfrom multiprocessing import Process\\nfrom signal import signal, SIGINT, SIGABRT, SIGTERM\\n\\nimport zmq\\nfrom zmq.devices import ProcessMonitoredQueue\\nfrom zmq.log.handlers import PUBHandler\\n\\nfrom IPython.core.profiledir import ProfileDir\\n\\nfrom IPython.parallel.apps.baseapp import (\\n BaseParallelApplication,\\n base_aliases,\\n base_flags,\\n catch_config_error,\\n)\\nfrom IPython.utils.importstring import import_item\\nfrom IPython.utils.traitlets import Instance, Unicode, Bool, List, Dict, TraitError\\n\\nfrom IPython.zmq.session import (\\n Session, session_aliases, session_flags, default_secure\\n)\\n\\nfrom IPython.parallel.controller.heartmonitor import HeartMonitor\\nfrom IPython.parallel.controller.hub import HubFactory\\nfrom IPython.parallel.controller.scheduler import TaskScheduler,launch_scheduler\\nfrom IPython.parallel.controller.sqlitedb import SQLiteDB\\n\\nfrom IPython.parallel.util import split_url, disambiguate_url\\n\\n# conditional import of MongoDB backend class\\n\\ntry:\\n from IPython.parallel.controller.mongodb import MongoDB\\nexcept ImportError:\\n maybe_mongo = []\\nelse:\\n maybe_mongo = [MongoDB]\\n\\n\\n#-----------------------------------------------------------------------------\\n# Module level variables\\n#-----------------------------------------------------------------------------\\n\\n\\n#: The default config file name for this application\\ndefault_config_file_name = u'ipcontroller_config.py'\\n\\n\\n_description = \\\"\\\"\\\"Start the IPython controller for parallel computing.\\n\\nThe IPython controller provides a gateway between the IPython engines and\\nclients. The controller needs to be started before the engines and can be\\nconfigured using command line options or using a cluster directory. Cluster\\ndirectories contain config, log and security files and are usually located in\\nyour ipython directory and named as \\\"profile_name\\\". See the `profile`\\nand `profile-dir` options for details.\\n\\\"\\\"\\\"\\n\\n_examples = \\\"\\\"\\\"\\nipcontroller --ip=192.168.0.1 --port=1000 # listen on ip, port for engines\\nipcontroller --scheme=pure # use the pure zeromq scheduler\\n\\\"\\\"\\\"\\n\\n\\n#-----------------------------------------------------------------------------\\n# The main application\\n#--------------------------------------------------------------\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/client/client.py\",\n \"text\": \"IPython/parallel/client/client.py\\n\\\"\\\"\\\"A semi-synchronous Client for the ZMQ cluster\\n\\nAuthors:\\n\\n* MinRK\\n\\\"\\\"\\\"\\n#-----------------------------------------------------------------------------\\n# Copyright (C) 2010-2011 The IPython Development Team\\n#\\n# Distributed under the terms of the BSD License. The full license is in\\n# the file COPYING, distributed as part of this software.\\n#-----------------------------------------------------------------------------\\n\\n#-----------------------------------------------------------------------------\\n# Imports\\n#-----------------------------------------------------------------------------\\n\\nimport os\\nimport json\\nimport sys\\nfrom threading import Thread, Event\\nimport time\\nimport warnings\\nfrom datetime import datetime\\nfrom getpass import getpass\\nfrom pprint import pprint\\n\\npjoin = os.path.join\\n\\nimport zmq\\n# from zmq.eventloop import ioloop, zmqstream\\n\\nfrom IPython.config.configurable import MultipleInstanceError\\nfrom IPython.core.application import BaseIPythonApplication\\n\\nfrom IPython.utils.jsonutil import rekey\\nfrom IPython.utils.localinterfaces import LOCAL_IPS\\nfrom IPython.utils.path import get_ipython_dir\\nfrom IPython.utils.traitlets import (HasTraits, Integer, Instance, Unicode,\\n Dict, List, Bool, Set, Any)\\nfrom IPython.external.decorator import decorator\\nfrom IPython.external.ssh import tunnel\\n\\nfrom IPython.parallel import Reference\\nfrom IPython.parallel import error\\nfrom IPython.parallel import util\\n\\nfrom IPython.zmq.session import Session, Message\\n\\nfrom .asyncresult import AsyncResult, AsyncHubResult\\nfrom IPython.core.profiledir import ProfileDir, ProfileDirError\\nfrom .view import DirectView, LoadBalancedView\\n\\nif sys.version_info[0] >= 3:\\n # xrange is used in a couple 'isinstance' tests in py2\\n # should be just 'range' in 3k\\n xrange = range\\n\\n#--------------------------------------------------------------------------\\n# Decorators for Client methods\\n#--------------------------------------------------------------------------\\n\\n@decorator\\ndef spin_first(f, self, *args, **kwargs):\\n \\\"\\\"\\\"Call spin() to sync state prior to calling the method.\\\"\\\"\\\"\\n self.spin()\\n return f(self, *args, **kwargs)\\n\\n\\n#--------------------------------------------------------------------------\\n# Classes\\n#--------------------------------------------------------------------------\\n\\nclass Metadata(dict):\\n \\\"\\\"\\\"Subclass of dict for initializing metadata values.\\n\\n Attribute access works on keys.\\n\\n These objects have a strict set of keys - errors will raise if you try\\n to add new keys.\\n \\\"\\\"\\\"\\n def __init__(self, *args, **kwargs):\\n dict.__init__(self)\\n md = {'msg_id' : None,\\n 'submitted' : None,\\n 'started' : None,\\n 'completed' : None,\\n 'received' : None,\\n 'engine_uuid' : None,\\n 'engine_id' : None,\\n 'follow' : None,\\n 'after' : None,\\n 'status' : None,\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dependency.py\",\n \"text\": \"IPython/parallel/controller/dependency.py\\n\\\"\\\"\\\"Dependency utilities\\n\\nAuthors:\\n\\n* Min RK\\n\\\"\\\"\\\"\\n#-----------------------------------------------------------------------------\\n# Copyright (C) 2010-2011 The IPython Development Team\\n#\\n# Distributed under the terms of the BSD License. The full license is in\\n# the file COPYING, distributed as part of this software.\\n#-----------------------------------------------------------------------------\\n\\nfrom types import ModuleType\\n\\nfrom IPython.parallel.client.asyncresult import AsyncResult\\nfrom IPython.parallel.error import UnmetDependency\\nfrom IPython.parallel.util import interactive\\nfrom IPython.utils import py3compat\\n\\nclass depend(object):\\n \\\"\\\"\\\"Dependency decorator, for use with tasks.\\n \\n `@depend` lets you define a function for engine dependencies\\n just like you use `apply` for tasks.\\n \\n \\n Examples\\n --------\\n ::\\n \\n @depend(df, a,b, c=5)\\n def f(m,n,p)\\n \\n view.apply(f, 1,2,3)\\n \\n will call df(a,b,c=5) on the engine, and if it returns False or\\n raises an UnmetDependency error, then the task will not be run\\n and another engine will be tried.\\n \\\"\\\"\\\"\\n def __init__(self, f, *args, **kwargs):\\n self.f = f\\n self.args = args\\n self.kwargs = kwargs\\n \\n def __call__(self, f):\\n return dependent(f, self.f, *self.args, **self.kwargs)\\n\\nclass dependent(object):\\n \\\"\\\"\\\"A function that depends on another function.\\n This is an object to prevent the closure used\\n in traditional decorators, which are not picklable.\\n \\\"\\\"\\\"\\n\\n def __init__(self, f, df, *dargs, **dkwargs):\\n self.f = f\\n self.func_name = getattr(f, '__name__', 'f')\\n self.df = df\\n self.dargs = dargs\\n self.dkwargs = dkwargs\\n\\n def __call__(self, *args, **kwargs):\\n # if hasattr(self.f, 'func_globals') and hasattr(self.df, 'func_globals'):\\n # self.df.func_globals = self.f.func_globals\\n if self.df(*self.dargs, **self.dkwargs) is False:\\n raise UnmetDependency()\\n return self.f(*args, **kwargs)\\n \\n if not py3compat.PY3:\\n @property\\n def __name__(self):\\n return self.func_name\\n\\n@interactive\\ndef _require(*names):\\n \\\"\\\"\\\"Helper for @require decorator.\\\"\\\"\\\"\\n from IPython.parallel.error import UnmetDependency\\n user_ns = globals()\\n for name in names:\\n if name in user_ns:\\n continue\\n try:\\n exec 'import %s'%name in user_ns\\n except ImportError:\\n raise UnmetDependency(name)\\n return True\\n\\ndef require(*mods):\\n \\\"\\\"\\\"Simple decorator for requiring names to be importable.\\n \\n Examples\\n --------\\n \\n In [1]: @require('numpy')\\n ...: def norm(a):\\n ...: import numpy\\n ...: return numpy.linalg.norm(a,2)\\n \\\"\\\"\\\"\\n names = []\\n for mod in mods:\\n if isinstance(mod, ModuleType):\\n mod = mod.__name__\\n \\n if isinstance(mod, basestring):\\n na\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py\",\n \"text\": \"IPython/parallel/controller/dictdb.py\\nclass CompositeFilter # Composite filter for matching multiple properties.\\n def __init__(self, dikt)\\n def __call__(self, value)\\nclass BaseDB # Empty Parent class so traitlets work on DB.\\nclass DictDB # Basic in-memory dict-based object for saving Task Records.\\n def _match_one(self, rec, tests) # Check if a specific record matches tests.\\n def _match(self, check) # Find all the matches for a check dict.\\n def _extract_subdict(self, rec, keys) # extract subdict of keys\\n def add_record(self, msg_id, rec) # Add a new Task Record, by msg_id.\\n def get_record(self, msg_id) # Get a specific Task Record, by msg_id.\\n def update_record(self, msg_id, rec) # Update the data in an existing record.\\n def drop_matching_records(self, check) # Remove a record from the DB.\\n def drop_record(self, msg_id) # Remove a record from the DB.\\n def find_records(self, check, keys=None) # Find records matching a query dict, optionally extracting subset of keys.\\n def get_history(self) # get all msg_ids, ordered by time submitted.\\nclass NoDB # A blackhole db backend that actually stores no information.\\n def add_record(self, msg_id, record)\\n def get_record(self, msg_id)\\n def update_record(self, msg_id, record)\\n def drop_matching_records(self, check)\\n def drop_record(self, msg_id)\\n def find_records(self, check, keys=None)\\n def get_history(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/mongodb.py\",\n \"text\": \"IPython/parallel/controller/mongodb.py\\nclass MongoDB # MongoDB TaskRecord backend.\\n def __init__(self, **kwargs)\\n def _binary_buffers(self, rec)\\n def add_record(self, msg_id, rec) # Add a new Task Record, by msg_id.\\n def get_record(self, msg_id) # Get a specific Task Record, by msg_id.\\n def update_record(self, msg_id, rec) # Update the data in an existing record.\\n def drop_matching_records(self, check) # Remove a record from the DB.\\n def drop_record(self, msg_id) # Remove a record from the DB.\\n def find_records(self, check, keys=None) # Find records matching a query dict, optionally extracting subset of keys.\\n def get_history(self) # get all msg_ids, ordered by time submitted.\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py\\ndef _adapt_dict(d)\\ndef _convert_dict(ds)\\ndef _adapt_bufs(bufs)\\ndef _convert_bufs(bs)\\nclass SQLiteDB # SQLite3 TaskRecord backend.\\n def __init__(self, **kwargs)\\n def _defaults(self, keys=None) # create an empty record\\n def _check_table(self) # Ensure that an incorrect table doesn't exist\\n def _init_db(self) # Connect to the database and get new session number.\\n def _dict_to_list(self, d) # turn a mongodb-style record dict into a list.\\n def _list_to_dict(self, line, keys=None) # Inverse of dict_to_list\\n def _render_expression(self, check) # Turn a mongodb-style search dict into an SQL query.\\n def add_record(self, msg_id, rec) # Add a new Task Record, by msg_id.\\n def get_record(self, msg_id) # Get a specific Task Record, by msg_id.\\n def update_record(self, msg_id, rec) # Update the data in an existing record.\\n def drop_record(self, msg_id) # Remove a record from the DB.\\n def drop_matching_records(self, check) # Remove a record from the DB.\\n def find_records(self, check, keys=None) # Find records matching a query dict, optionally extracting subset of keys.\\n def get_history(self) # get all msg_ids, ordered by time submitted.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"IPython/parallel/controller/dictdb.py::CompositeFilter.__init__","text":"IPython/parallel/controller/dictdb.py::CompositeFilter.__init__\n def __init__(self, dikt):\n self.tests = []\n self.values = []\n for key, value in dikt.iteritems():\n self.tests.append(filters[key])\n self.values.append(value)","label":0},{"cid":"IPython/parallel/controller/dictdb.py::CompositeFilter.__call__","text":"IPython/parallel/controller/dictdb.py::CompositeFilter.__call__\n def __call__(self, value):\n for test,check in zip(self.tests, self.values):\n if not test(value, check):\n return False\n return True","label":0},{"cid":"IPython/parallel/controller/dictdb.py::DictDB._match_one","text":"IPython/parallel/controller/dictdb.py::DictDB._match_one\n def _match_one(self, rec, tests):\n \"\"\"Check if a specific record matches tests.\"\"\"\n for key,test in tests.iteritems():\n if not test(rec.get(key, None)):\n return False\n return True","label":0},{"cid":"IPython/parallel/controller/dictdb.py::DictDB._match","text":"IPython/parallel/controller/dictdb.py::DictDB._match\n def _match(self, check):\n \"\"\"Find all the matches for a check dict.\"\"\"\n matches = []\n tests = {}\n for k,v in check.iteritems():\n if isinstance(v, dict):\n tests[k] = CompositeFilter(v)\n else:\n tests[k] = lambda o: o==v\n\n for rec in self._records.itervalues():\n if self._match_one(rec, tests):\n matches.append(copy(rec))\n return matches","label":0},{"cid":"IPython/parallel/controller/dictdb.py::DictDB._extract_subdict","text":"IPython/parallel/controller/dictdb.py::DictDB._extract_subdict\n def _extract_subdict(self, rec, keys):\n \"\"\"extract subdict of keys\"\"\"\n d = {}\n d['msg_id'] = rec['msg_id']\n for key in keys:\n d[key] = rec[key]\n return d","label":0},{"cid":"IPython/parallel/controller/dictdb.py::DictDB.add_record","text":"IPython/parallel/controller/dictdb.py::DictDB.add_record\n def add_record(self, msg_id, rec):\n \"\"\"Add a new Task Record, by msg_id.\"\"\"\n if self._records.has_key(msg_id):\n raise KeyError(\"Already have msg_id %r\"%(msg_id))\n self._records[msg_id] = rec","label":0},{"cid":"IPython/parallel/controller/dictdb.py::DictDB.get_record","text":"IPython/parallel/controller/dictdb.py::DictDB.get_record\n def get_record(self, msg_id):\n \"\"\"Get a specific Task Record, by msg_id.\"\"\"\n if not msg_id in self._records:\n raise KeyError(\"No such msg_id %r\"%(msg_id))\n return copy(self._records[msg_id])","label":0},{"cid":"IPython/parallel/controller/dictdb.py::DictDB.update_record","text":"IPython/parallel/controller/dictdb.py::DictDB.update_record\n def update_record(self, msg_id, rec):\n \"\"\"Update the data in an existing record.\"\"\"\n self._records[msg_id].update(rec)","label":0},{"cid":"IPython/parallel/controller/dictdb.py::DictDB.drop_matching_records","text":"IPython/parallel/controller/dictdb.py::DictDB.drop_matching_records\n def drop_matching_records(self, check):\n \"\"\"Remove a record from the DB.\"\"\"\n matches = self._match(check)\n for m in matches:\n del self._records[m['msg_id']]","label":0},{"cid":"IPython/parallel/controller/dictdb.py::DictDB.drop_record","text":"IPython/parallel/controller/dictdb.py::DictDB.drop_record\n def drop_record(self, msg_id):\n \"\"\"Remove a record from the DB.\"\"\"\n del self._records[msg_id]","label":0},{"cid":"IPython/parallel/controller/dictdb.py::DictDB.find_records","text":"IPython/parallel/controller/dictdb.py::DictDB.find_records\n def find_records(self, check, keys=None):\n \"\"\"Find records matching a query dict, optionally extracting subset of keys.\n\n Returns dict keyed by msg_id of matching records.\n\n Parameters\n ----------\n\n check: dict\n mongodb-style query argument\n keys: list of strs [optional]\n if specified, the subset of keys to extract. msg_id will *always* be\n included.\n \"\"\"\n matches = self._match(check)\n if keys:\n return [ self._extract_subdict(rec, keys) for rec in matches ]\n else:\n return matches","label":0},{"cid":"IPython/parallel/controller/dictdb.py::DictDB.get_history","text":"IPython/parallel/controller/dictdb.py::DictDB.get_history\n def get_history(self):\n \"\"\"get all msg_ids, ordered by time submitted.\"\"\"\n msg_ids = self._records.keys()\n return sorted(msg_ids, key=lambda m: self._records[m]['submitted'])","label":0},{"cid":"IPython/parallel/controller/dictdb.py::NoDB.add_record","text":"IPython/parallel/controller/dictdb.py::NoDB.add_record\n def add_record(self, msg_id, record):\n pass","label":0},{"cid":"IPython/parallel/controller/dictdb.py::NoDB.get_record","text":"IPython/parallel/controller/dictdb.py::NoDB.get_record\n def get_record(self, msg_id):\n raise KeyError(\"NoDB does not support record access\")","label":0},{"cid":"IPython/parallel/controller/dictdb.py::NoDB.update_record","text":"IPython/parallel/controller/dictdb.py::NoDB.update_record\n def update_record(self, msg_id, record):\n pass","label":0},{"cid":"IPython/parallel/controller/dictdb.py::NoDB.drop_matching_records","text":"IPython/parallel/controller/dictdb.py::NoDB.drop_matching_records\n def drop_matching_records(self, check):\n pass","label":0},{"cid":"IPython/parallel/controller/dictdb.py::NoDB.drop_record","text":"IPython/parallel/controller/dictdb.py::NoDB.drop_record\n def drop_record(self, msg_id):\n pass","label":0},{"cid":"IPython/parallel/controller/dictdb.py::NoDB.find_records","text":"IPython/parallel/controller/dictdb.py::NoDB.find_records\n def find_records(self, check, keys=None):\n raise KeyError(\"NoDB does not store information\")","label":0},{"cid":"IPython/parallel/controller/dictdb.py::NoDB.get_history","text":"IPython/parallel/controller/dictdb.py::NoDB.get_history\n def get_history(self):\n raise KeyError(\"NoDB does not store information\")","label":0},{"cid":"IPython/parallel/controller/mongodb.py::MongoDB.__init__","text":"IPython/parallel/controller/mongodb.py::MongoDB.__init__\n def __init__(self, **kwargs):\n super(MongoDB, self).__init__(**kwargs)\n if self._connection is None:\n self._connection = Connection(*self.connection_args, **self.connection_kwargs)\n if not self.database:\n self.database = self.session\n self._db = self._connection[self.database]\n self._records = self._db['task_records']\n self._records.ensure_index('msg_id', unique=True)\n self._records.ensure_index('submitted') # for sorting history","label":0},{"cid":"IPython/parallel/controller/mongodb.py::MongoDB._binary_buffers","text":"IPython/parallel/controller/mongodb.py::MongoDB._binary_buffers\n def _binary_buffers(self, rec):\n for key in ('buffers', 'result_buffers'):\n if rec.get(key, None):\n rec[key] = map(Binary, rec[key])\n return rec","label":0},{"cid":"IPython/parallel/controller/mongodb.py::MongoDB.add_record","text":"IPython/parallel/controller/mongodb.py::MongoDB.add_record\n def add_record(self, msg_id, rec):\n \"\"\"Add a new Task Record, by msg_id.\"\"\"\n # print rec\n rec = self._binary_buffers(rec)\n self._records.insert(rec)","label":0},{"cid":"IPython/parallel/controller/mongodb.py::MongoDB.get_record","text":"IPython/parallel/controller/mongodb.py::MongoDB.get_record\n def get_record(self, msg_id):\n \"\"\"Get a specific Task Record, by msg_id.\"\"\"\n r = self._records.find_one({'msg_id': msg_id})\n if not r:\n # r will be '' if nothing is found\n raise KeyError(msg_id)\n return r","label":0},{"cid":"IPython/parallel/controller/mongodb.py::MongoDB.update_record","text":"IPython/parallel/controller/mongodb.py::MongoDB.update_record\n def update_record(self, msg_id, rec):\n \"\"\"Update the data in an existing record.\"\"\"\n rec = self._binary_buffers(rec)\n\n self._records.update({'msg_id':msg_id}, {'$set': rec})","label":0},{"cid":"IPython/parallel/controller/mongodb.py::MongoDB.drop_matching_records","text":"IPython/parallel/controller/mongodb.py::MongoDB.drop_matching_records\n def drop_matching_records(self, check):\n \"\"\"Remove a record from the DB.\"\"\"\n self._records.remove(check)","label":0},{"cid":"IPython/parallel/controller/mongodb.py::MongoDB.drop_record","text":"IPython/parallel/controller/mongodb.py::MongoDB.drop_record\n def drop_record(self, msg_id):\n \"\"\"Remove a record from the DB.\"\"\"\n self._records.remove({'msg_id':msg_id})","label":0},{"cid":"IPython/parallel/controller/mongodb.py::MongoDB.find_records","text":"IPython/parallel/controller/mongodb.py::MongoDB.find_records\n def find_records(self, check, keys=None):\n \"\"\"Find records matching a query dict, optionally extracting subset of keys.\n \n Returns list of matching records.\n \n Parameters\n ----------\n \n check: dict\n mongodb-style query argument\n keys: list of strs [optional]\n if specified, the subset of keys to extract. msg_id will *always* be\n included.\n \"\"\"\n if keys and 'msg_id' not in keys:\n keys.append('msg_id')\n matches = list(self._records.find(check,keys))\n for rec in matches:\n rec.pop('_id')\n return matches","label":0},{"cid":"IPython/parallel/controller/mongodb.py::MongoDB.get_history","text":"IPython/parallel/controller/mongodb.py::MongoDB.get_history\n def get_history(self):\n \"\"\"get all msg_ids, ordered by time submitted.\"\"\"\n cursor = self._records.find({},{'msg_id':1}).sort('submitted')\n return [ rec['msg_id'] for rec in cursor ]","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::_adapt_dict","text":"IPython/parallel/controller/sqlitedb.py::_adapt_dict\ndef _adapt_dict(d):\n return json.dumps(d, default=date_default)","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::_convert_dict","text":"IPython/parallel/controller/sqlitedb.py::_convert_dict\ndef _convert_dict(ds):\n if ds is None:\n return ds\n else:\n if isinstance(ds, bytes):\n # If I understand the sqlite doc correctly, this will always be utf8\n ds = ds.decode('utf8')\n return extract_dates(json.loads(ds))","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::_adapt_bufs","text":"IPython/parallel/controller/sqlitedb.py::_adapt_bufs\ndef _adapt_bufs(bufs):\n # this is *horrible*\n # copy buffers into single list and pickle it:\n if bufs and isinstance(bufs[0], (bytes, buffer)):\n return sqlite3.Binary(pickle.dumps(map(bytes, bufs),-1))\n elif bufs:\n return bufs\n else:\n return None","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::_convert_bufs","text":"IPython/parallel/controller/sqlitedb.py::_convert_bufs\ndef _convert_bufs(bs):\n if bs is None:\n return []\n else:\n return pickle.loads(bytes(bs))","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.__init__","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.__init__\n def __init__(self, **kwargs):\n super(SQLiteDB, self).__init__(**kwargs)\n if sqlite3 is None:\n raise ImportError(\"SQLiteDB requires sqlite3\")\n if not self.table:\n # use session, and prefix _, since starting with # is illegal\n self.table = '_'+self.session.replace('-','_')\n if not self.location:\n # get current profile\n from IPython.core.application import BaseIPythonApplication\n if BaseIPythonApplication.initialized():\n app = BaseIPythonApplication.instance()\n if app.profile_dir is not None:\n self.location = app.profile_dir.location\n else:\n self.location = u'.'\n else:\n self.location = u'.'\n self._init_db()\n\n # register db commit as 2s periodic callback\n # to prevent clogging pipes\n # assumes we are being run in a zmq ioloop app\n loop = ioloop.IOLoop.instance()\n pc = ioloop.PeriodicCallback(self._db.commit, 2000, loop)\n pc.start()","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._defaults","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._defaults\n def _defaults(self, keys=None):\n \"\"\"create an empty record\"\"\"\n d = {}\n keys = self._keys if keys is None else keys\n for key in keys:\n d[key] = None\n return d","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._check_table","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._check_table\n def _check_table(self):\n \"\"\"Ensure that an incorrect table doesn't exist\n\n If a bad (old) table does exist, return False\n \"\"\"\n cursor = self._db.execute(\"PRAGMA table_info(%s)\"%self.table)\n lines = cursor.fetchall()\n if not lines:\n # table does not exist\n return True\n types = {}\n keys = []\n for line in lines:\n keys.append(line[1])\n types[line[1]] = line[2]\n if self._keys != keys:\n # key mismatch\n self.log.warn('keys mismatch')\n return False\n for key in self._keys:\n if types[key] != self._types[key]:\n self.log.warn(\n 'type mismatch: %s: %s != %s'%(key,types[key],self._types[key])\n )\n return False\n return True","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._init_db","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._init_db\n def _init_db(self):\n \"\"\"Connect to the database and get new session number.\"\"\"\n # register adapters\n sqlite3.register_adapter(dict, _adapt_dict)\n sqlite3.register_converter('dict', _convert_dict)\n sqlite3.register_adapter(list, _adapt_bufs)\n sqlite3.register_converter('bufs', _convert_bufs)\n # connect to the db\n dbfile = os.path.join(self.location, self.filename)\n self._db = sqlite3.connect(dbfile, detect_types=sqlite3.PARSE_DECLTYPES,\n # isolation_level = None)#,\n cached_statements=64)\n # print dir(self._db)\n first_table = self.table\n i=0\n while not self._check_table():\n i+=1\n self.table = first_table+'_%i'%i\n self.log.warn(\n \"Table %s exists and doesn't match db format, trying %s\"%\n (first_table,self.table)\n )\n\n self._db.execute(\"\"\"CREATE TABLE IF NOT EXISTS %s\n (msg_id text PRIMARY KEY,\n header dict text,\n content dict text,\n buffers bufs blob,\n submitted timestamp,\n client_uuid text,\n engine_uuid text,\n started timestamp,\n completed timestamp,\n resubmitted timestamp,\n received timestamp,\n result_header dict text,\n result_content dict text,\n result_buffers bufs blob,\n queue text,\n pyin text,\n pyout text,\n pyerr text,\n stdout text,\n stderr text)\n \"\"\"%self.table)\n self._db.commit()","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._dict_to_list","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._dict_to_list\n def _dict_to_list(self, d):\n \"\"\"turn a mongodb-style record dict into a list.\"\"\"\n\n return [ d[key] for key in self._keys ]","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._list_to_dict","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._list_to_dict\n def _list_to_dict(self, line, keys=None):\n \"\"\"Inverse of dict_to_list\"\"\"\n keys = self._keys if keys is None else keys\n d = self._defaults(keys)\n for key,value in zip(keys, line):\n d[key] = value\n\n return d","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._render_expression","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB._render_expression\n def _render_expression(self, check):\n \"\"\"Turn a mongodb-style search dict into an SQL query.\"\"\"\n expressions = []\n args = []\n\n skeys = set(check.keys())\n skeys.difference_update(set(self._keys))\n skeys.difference_update(set(['buffers', 'result_buffers']))\n if skeys:\n raise KeyError(\"Illegal testing key(s): %s\"%skeys)\n\n for name,sub_check in check.iteritems():\n if isinstance(sub_check, dict):\n for test,value in sub_check.iteritems():\n try:\n op = operators[test]\n except KeyError:\n raise KeyError(\"Unsupported operator: %r\"%test)\n if isinstance(op, tuple):\n op, join = op\n\n if value is None and op in null_operators:\n expr = \"%s %s\" % (name, null_operators[op])\n else:\n expr = \"%s %s ?\"%(name, op)\n if isinstance(value, (tuple,list)):\n if op in null_operators and any([v is None for v in value]):\n # equality tests don't work with NULL\n raise ValueError(\"Cannot use %r test with NULL values on SQLite backend\"%test)\n expr = '( %s )'%( join.join([expr]*len(value)) )\n args.extend(value)\n else:\n args.append(value)\n expressions.append(expr)\n else:\n # it's an equality check\n if sub_check is None:\n expressions.append(\"%s IS NULL\" % name)\n else:\n expressions.append(\"%s = ?\"%name)\n args.append(sub_check)\n\n expr = \" AND \".join(expressions)\n return expr, args","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.add_record","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.add_record\n def add_record(self, msg_id, rec):\n \"\"\"Add a new Task Record, by msg_id.\"\"\"\n d = self._defaults()\n d.update(rec)\n d['msg_id'] = msg_id\n line = self._dict_to_list(d)\n tups = '(%s)'%(','.join(['?']*len(line)))\n self._db.execute(\"INSERT INTO %s VALUES %s\"%(self.table, tups), line)","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.get_record","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.get_record\n def get_record(self, msg_id):\n \"\"\"Get a specific Task Record, by msg_id.\"\"\"\n cursor = self._db.execute(\"\"\"SELECT * FROM %s WHERE msg_id==?\"\"\"%self.table, (msg_id,))\n line = cursor.fetchone()\n if line is None:\n raise KeyError(\"No such msg: %r\"%msg_id)\n return self._list_to_dict(line)","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.update_record","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.update_record\n def update_record(self, msg_id, rec):\n \"\"\"Update the data in an existing record.\"\"\"\n query = \"UPDATE %s SET \"%self.table\n sets = []\n keys = sorted(rec.keys())\n values = []\n for key in keys:\n sets.append('%s = ?'%key)\n values.append(rec[key])\n query += ', '.join(sets)\n query += ' WHERE msg_id == ?'\n values.append(msg_id)\n self._db.execute(query, values)","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.drop_record","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.drop_record\n def drop_record(self, msg_id):\n \"\"\"Remove a record from the DB.\"\"\"\n self._db.execute(\"\"\"DELETE FROM %s WHERE msg_id==?\"\"\"%self.table, (msg_id,))","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.drop_matching_records","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.drop_matching_records\n def drop_matching_records(self, check):\n \"\"\"Remove a record from the DB.\"\"\"\n expr,args = self._render_expression(check)\n query = \"DELETE FROM %s WHERE %s\"%(self.table, expr)\n self._db.execute(query,args)","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.find_records","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.find_records\n def find_records(self, check, keys=None):\n \"\"\"Find records matching a query dict, optionally extracting subset of keys.\n\n Returns list of matching records.\n\n Parameters\n ----------\n\n check: dict\n mongodb-style query argument\n keys: list of strs [optional]\n if specified, the subset of keys to extract. msg_id will *always* be\n included.\n \"\"\"\n if keys:\n bad_keys = [ key for key in keys if key not in self._keys ]\n if bad_keys:\n raise KeyError(\"Bad record key(s): %s\"%bad_keys)\n\n if keys:\n # ensure msg_id is present and first:\n if 'msg_id' in keys:\n keys.remove('msg_id')\n keys.insert(0, 'msg_id')\n req = ', '.join(keys)\n else:\n req = '*'\n expr,args = self._render_expression(check)\n query = \"\"\"SELECT %s FROM %s WHERE %s\"\"\"%(req, self.table, expr)\n cursor = self._db.execute(query, args)\n matches = cursor.fetchall()\n records = []\n for line in matches:\n rec = self._list_to_dict(line, keys)\n records.append(rec)\n return records","label":0},{"cid":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.get_history","text":"IPython/parallel/controller/sqlitedb.py::SQLiteDB.get_history\n def get_history(self):\n \"\"\"get all msg_ids, ordered by time submitted.\"\"\"\n query = \"\"\"SELECT msg_id FROM %s ORDER by submitted ASC\"\"\"%self.table\n cursor = self._db.execute(query)\n # will be a list of length 1 tuples\n return [ tup[0] for tup in cursor.fetchall()]","label":0}],"string":"[\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::CompositeFilter.__init__\",\n \"text\": \"IPython/parallel/controller/dictdb.py::CompositeFilter.__init__\\n def __init__(self, dikt):\\n self.tests = []\\n self.values = []\\n for key, value in dikt.iteritems():\\n self.tests.append(filters[key])\\n self.values.append(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::CompositeFilter.__call__\",\n \"text\": \"IPython/parallel/controller/dictdb.py::CompositeFilter.__call__\\n def __call__(self, value):\\n for test,check in zip(self.tests, self.values):\\n if not test(value, check):\\n return False\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::DictDB._match_one\",\n \"text\": \"IPython/parallel/controller/dictdb.py::DictDB._match_one\\n def _match_one(self, rec, tests):\\n \\\"\\\"\\\"Check if a specific record matches tests.\\\"\\\"\\\"\\n for key,test in tests.iteritems():\\n if not test(rec.get(key, None)):\\n return False\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::DictDB._match\",\n \"text\": \"IPython/parallel/controller/dictdb.py::DictDB._match\\n def _match(self, check):\\n \\\"\\\"\\\"Find all the matches for a check dict.\\\"\\\"\\\"\\n matches = []\\n tests = {}\\n for k,v in check.iteritems():\\n if isinstance(v, dict):\\n tests[k] = CompositeFilter(v)\\n else:\\n tests[k] = lambda o: o==v\\n\\n for rec in self._records.itervalues():\\n if self._match_one(rec, tests):\\n matches.append(copy(rec))\\n return matches\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::DictDB._extract_subdict\",\n \"text\": \"IPython/parallel/controller/dictdb.py::DictDB._extract_subdict\\n def _extract_subdict(self, rec, keys):\\n \\\"\\\"\\\"extract subdict of keys\\\"\\\"\\\"\\n d = {}\\n d['msg_id'] = rec['msg_id']\\n for key in keys:\\n d[key] = rec[key]\\n return d\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::DictDB.add_record\",\n \"text\": \"IPython/parallel/controller/dictdb.py::DictDB.add_record\\n def add_record(self, msg_id, rec):\\n \\\"\\\"\\\"Add a new Task Record, by msg_id.\\\"\\\"\\\"\\n if self._records.has_key(msg_id):\\n raise KeyError(\\\"Already have msg_id %r\\\"%(msg_id))\\n self._records[msg_id] = rec\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::DictDB.get_record\",\n \"text\": \"IPython/parallel/controller/dictdb.py::DictDB.get_record\\n def get_record(self, msg_id):\\n \\\"\\\"\\\"Get a specific Task Record, by msg_id.\\\"\\\"\\\"\\n if not msg_id in self._records:\\n raise KeyError(\\\"No such msg_id %r\\\"%(msg_id))\\n return copy(self._records[msg_id])\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::DictDB.update_record\",\n \"text\": \"IPython/parallel/controller/dictdb.py::DictDB.update_record\\n def update_record(self, msg_id, rec):\\n \\\"\\\"\\\"Update the data in an existing record.\\\"\\\"\\\"\\n self._records[msg_id].update(rec)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::DictDB.drop_matching_records\",\n \"text\": \"IPython/parallel/controller/dictdb.py::DictDB.drop_matching_records\\n def drop_matching_records(self, check):\\n \\\"\\\"\\\"Remove a record from the DB.\\\"\\\"\\\"\\n matches = self._match(check)\\n for m in matches:\\n del self._records[m['msg_id']]\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::DictDB.drop_record\",\n \"text\": \"IPython/parallel/controller/dictdb.py::DictDB.drop_record\\n def drop_record(self, msg_id):\\n \\\"\\\"\\\"Remove a record from the DB.\\\"\\\"\\\"\\n del self._records[msg_id]\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::DictDB.find_records\",\n \"text\": \"IPython/parallel/controller/dictdb.py::DictDB.find_records\\n def find_records(self, check, keys=None):\\n \\\"\\\"\\\"Find records matching a query dict, optionally extracting subset of keys.\\n\\n Returns dict keyed by msg_id of matching records.\\n\\n Parameters\\n ----------\\n\\n check: dict\\n mongodb-style query argument\\n keys: list of strs [optional]\\n if specified, the subset of keys to extract. msg_id will *always* be\\n included.\\n \\\"\\\"\\\"\\n matches = self._match(check)\\n if keys:\\n return [ self._extract_subdict(rec, keys) for rec in matches ]\\n else:\\n return matches\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::DictDB.get_history\",\n \"text\": \"IPython/parallel/controller/dictdb.py::DictDB.get_history\\n def get_history(self):\\n \\\"\\\"\\\"get all msg_ids, ordered by time submitted.\\\"\\\"\\\"\\n msg_ids = self._records.keys()\\n return sorted(msg_ids, key=lambda m: self._records[m]['submitted'])\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::NoDB.add_record\",\n \"text\": \"IPython/parallel/controller/dictdb.py::NoDB.add_record\\n def add_record(self, msg_id, record):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::NoDB.get_record\",\n \"text\": \"IPython/parallel/controller/dictdb.py::NoDB.get_record\\n def get_record(self, msg_id):\\n raise KeyError(\\\"NoDB does not support record access\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::NoDB.update_record\",\n \"text\": \"IPython/parallel/controller/dictdb.py::NoDB.update_record\\n def update_record(self, msg_id, record):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::NoDB.drop_matching_records\",\n \"text\": \"IPython/parallel/controller/dictdb.py::NoDB.drop_matching_records\\n def drop_matching_records(self, check):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::NoDB.drop_record\",\n \"text\": \"IPython/parallel/controller/dictdb.py::NoDB.drop_record\\n def drop_record(self, msg_id):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::NoDB.find_records\",\n \"text\": \"IPython/parallel/controller/dictdb.py::NoDB.find_records\\n def find_records(self, check, keys=None):\\n raise KeyError(\\\"NoDB does not store information\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/dictdb.py::NoDB.get_history\",\n \"text\": \"IPython/parallel/controller/dictdb.py::NoDB.get_history\\n def get_history(self):\\n raise KeyError(\\\"NoDB does not store information\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/mongodb.py::MongoDB.__init__\",\n \"text\": \"IPython/parallel/controller/mongodb.py::MongoDB.__init__\\n def __init__(self, **kwargs):\\n super(MongoDB, self).__init__(**kwargs)\\n if self._connection is None:\\n self._connection = Connection(*self.connection_args, **self.connection_kwargs)\\n if not self.database:\\n self.database = self.session\\n self._db = self._connection[self.database]\\n self._records = self._db['task_records']\\n self._records.ensure_index('msg_id', unique=True)\\n self._records.ensure_index('submitted') # for sorting history\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/mongodb.py::MongoDB._binary_buffers\",\n \"text\": \"IPython/parallel/controller/mongodb.py::MongoDB._binary_buffers\\n def _binary_buffers(self, rec):\\n for key in ('buffers', 'result_buffers'):\\n if rec.get(key, None):\\n rec[key] = map(Binary, rec[key])\\n return rec\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/mongodb.py::MongoDB.add_record\",\n \"text\": \"IPython/parallel/controller/mongodb.py::MongoDB.add_record\\n def add_record(self, msg_id, rec):\\n \\\"\\\"\\\"Add a new Task Record, by msg_id.\\\"\\\"\\\"\\n # print rec\\n rec = self._binary_buffers(rec)\\n self._records.insert(rec)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/mongodb.py::MongoDB.get_record\",\n \"text\": \"IPython/parallel/controller/mongodb.py::MongoDB.get_record\\n def get_record(self, msg_id):\\n \\\"\\\"\\\"Get a specific Task Record, by msg_id.\\\"\\\"\\\"\\n r = self._records.find_one({'msg_id': msg_id})\\n if not r:\\n # r will be '' if nothing is found\\n raise KeyError(msg_id)\\n return r\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/mongodb.py::MongoDB.update_record\",\n \"text\": \"IPython/parallel/controller/mongodb.py::MongoDB.update_record\\n def update_record(self, msg_id, rec):\\n \\\"\\\"\\\"Update the data in an existing record.\\\"\\\"\\\"\\n rec = self._binary_buffers(rec)\\n\\n self._records.update({'msg_id':msg_id}, {'$set': rec})\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/mongodb.py::MongoDB.drop_matching_records\",\n \"text\": \"IPython/parallel/controller/mongodb.py::MongoDB.drop_matching_records\\n def drop_matching_records(self, check):\\n \\\"\\\"\\\"Remove a record from the DB.\\\"\\\"\\\"\\n self._records.remove(check)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/mongodb.py::MongoDB.drop_record\",\n \"text\": \"IPython/parallel/controller/mongodb.py::MongoDB.drop_record\\n def drop_record(self, msg_id):\\n \\\"\\\"\\\"Remove a record from the DB.\\\"\\\"\\\"\\n self._records.remove({'msg_id':msg_id})\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/mongodb.py::MongoDB.find_records\",\n \"text\": \"IPython/parallel/controller/mongodb.py::MongoDB.find_records\\n def find_records(self, check, keys=None):\\n \\\"\\\"\\\"Find records matching a query dict, optionally extracting subset of keys.\\n \\n Returns list of matching records.\\n \\n Parameters\\n ----------\\n \\n check: dict\\n mongodb-style query argument\\n keys: list of strs [optional]\\n if specified, the subset of keys to extract. msg_id will *always* be\\n included.\\n \\\"\\\"\\\"\\n if keys and 'msg_id' not in keys:\\n keys.append('msg_id')\\n matches = list(self._records.find(check,keys))\\n for rec in matches:\\n rec.pop('_id')\\n return matches\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/mongodb.py::MongoDB.get_history\",\n \"text\": \"IPython/parallel/controller/mongodb.py::MongoDB.get_history\\n def get_history(self):\\n \\\"\\\"\\\"get all msg_ids, ordered by time submitted.\\\"\\\"\\\"\\n cursor = self._records.find({},{'msg_id':1}).sort('submitted')\\n return [ rec['msg_id'] for rec in cursor ]\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::_adapt_dict\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::_adapt_dict\\ndef _adapt_dict(d):\\n return json.dumps(d, default=date_default)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::_convert_dict\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::_convert_dict\\ndef _convert_dict(ds):\\n if ds is None:\\n return ds\\n else:\\n if isinstance(ds, bytes):\\n # If I understand the sqlite doc correctly, this will always be utf8\\n ds = ds.decode('utf8')\\n return extract_dates(json.loads(ds))\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::_adapt_bufs\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::_adapt_bufs\\ndef _adapt_bufs(bufs):\\n # this is *horrible*\\n # copy buffers into single list and pickle it:\\n if bufs and isinstance(bufs[0], (bytes, buffer)):\\n return sqlite3.Binary(pickle.dumps(map(bytes, bufs),-1))\\n elif bufs:\\n return bufs\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::_convert_bufs\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::_convert_bufs\\ndef _convert_bufs(bs):\\n if bs is None:\\n return []\\n else:\\n return pickle.loads(bytes(bs))\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.__init__\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.__init__\\n def __init__(self, **kwargs):\\n super(SQLiteDB, self).__init__(**kwargs)\\n if sqlite3 is None:\\n raise ImportError(\\\"SQLiteDB requires sqlite3\\\")\\n if not self.table:\\n # use session, and prefix _, since starting with # is illegal\\n self.table = '_'+self.session.replace('-','_')\\n if not self.location:\\n # get current profile\\n from IPython.core.application import BaseIPythonApplication\\n if BaseIPythonApplication.initialized():\\n app = BaseIPythonApplication.instance()\\n if app.profile_dir is not None:\\n self.location = app.profile_dir.location\\n else:\\n self.location = u'.'\\n else:\\n self.location = u'.'\\n self._init_db()\\n\\n # register db commit as 2s periodic callback\\n # to prevent clogging pipes\\n # assumes we are being run in a zmq ioloop app\\n loop = ioloop.IOLoop.instance()\\n pc = ioloop.PeriodicCallback(self._db.commit, 2000, loop)\\n pc.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._defaults\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._defaults\\n def _defaults(self, keys=None):\\n \\\"\\\"\\\"create an empty record\\\"\\\"\\\"\\n d = {}\\n keys = self._keys if keys is None else keys\\n for key in keys:\\n d[key] = None\\n return d\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._check_table\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._check_table\\n def _check_table(self):\\n \\\"\\\"\\\"Ensure that an incorrect table doesn't exist\\n\\n If a bad (old) table does exist, return False\\n \\\"\\\"\\\"\\n cursor = self._db.execute(\\\"PRAGMA table_info(%s)\\\"%self.table)\\n lines = cursor.fetchall()\\n if not lines:\\n # table does not exist\\n return True\\n types = {}\\n keys = []\\n for line in lines:\\n keys.append(line[1])\\n types[line[1]] = line[2]\\n if self._keys != keys:\\n # key mismatch\\n self.log.warn('keys mismatch')\\n return False\\n for key in self._keys:\\n if types[key] != self._types[key]:\\n self.log.warn(\\n 'type mismatch: %s: %s != %s'%(key,types[key],self._types[key])\\n )\\n return False\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._init_db\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._init_db\\n def _init_db(self):\\n \\\"\\\"\\\"Connect to the database and get new session number.\\\"\\\"\\\"\\n # register adapters\\n sqlite3.register_adapter(dict, _adapt_dict)\\n sqlite3.register_converter('dict', _convert_dict)\\n sqlite3.register_adapter(list, _adapt_bufs)\\n sqlite3.register_converter('bufs', _convert_bufs)\\n # connect to the db\\n dbfile = os.path.join(self.location, self.filename)\\n self._db = sqlite3.connect(dbfile, detect_types=sqlite3.PARSE_DECLTYPES,\\n # isolation_level = None)#,\\n cached_statements=64)\\n # print dir(self._db)\\n first_table = self.table\\n i=0\\n while not self._check_table():\\n i+=1\\n self.table = first_table+'_%i'%i\\n self.log.warn(\\n \\\"Table %s exists and doesn't match db format, trying %s\\\"%\\n (first_table,self.table)\\n )\\n\\n self._db.execute(\\\"\\\"\\\"CREATE TABLE IF NOT EXISTS %s\\n (msg_id text PRIMARY KEY,\\n header dict text,\\n content dict text,\\n buffers bufs blob,\\n submitted timestamp,\\n client_uuid text,\\n engine_uuid text,\\n started timestamp,\\n completed timestamp,\\n resubmitted timestamp,\\n received timestamp,\\n result_header dict text,\\n result_content dict text,\\n result_buffers bufs blob,\\n queue text,\\n pyin text,\\n pyout text,\\n pyerr text,\\n stdout text,\\n stderr text)\\n \\\"\\\"\\\"%self.table)\\n self._db.commit()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._dict_to_list\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._dict_to_list\\n def _dict_to_list(self, d):\\n \\\"\\\"\\\"turn a mongodb-style record dict into a list.\\\"\\\"\\\"\\n\\n return [ d[key] for key in self._keys ]\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._list_to_dict\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._list_to_dict\\n def _list_to_dict(self, line, keys=None):\\n \\\"\\\"\\\"Inverse of dict_to_list\\\"\\\"\\\"\\n keys = self._keys if keys is None else keys\\n d = self._defaults(keys)\\n for key,value in zip(keys, line):\\n d[key] = value\\n\\n return d\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._render_expression\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB._render_expression\\n def _render_expression(self, check):\\n \\\"\\\"\\\"Turn a mongodb-style search dict into an SQL query.\\\"\\\"\\\"\\n expressions = []\\n args = []\\n\\n skeys = set(check.keys())\\n skeys.difference_update(set(self._keys))\\n skeys.difference_update(set(['buffers', 'result_buffers']))\\n if skeys:\\n raise KeyError(\\\"Illegal testing key(s): %s\\\"%skeys)\\n\\n for name,sub_check in check.iteritems():\\n if isinstance(sub_check, dict):\\n for test,value in sub_check.iteritems():\\n try:\\n op = operators[test]\\n except KeyError:\\n raise KeyError(\\\"Unsupported operator: %r\\\"%test)\\n if isinstance(op, tuple):\\n op, join = op\\n\\n if value is None and op in null_operators:\\n expr = \\\"%s %s\\\" % (name, null_operators[op])\\n else:\\n expr = \\\"%s %s ?\\\"%(name, op)\\n if isinstance(value, (tuple,list)):\\n if op in null_operators and any([v is None for v in value]):\\n # equality tests don't work with NULL\\n raise ValueError(\\\"Cannot use %r test with NULL values on SQLite backend\\\"%test)\\n expr = '( %s )'%( join.join([expr]*len(value)) )\\n args.extend(value)\\n else:\\n args.append(value)\\n expressions.append(expr)\\n else:\\n # it's an equality check\\n if sub_check is None:\\n expressions.append(\\\"%s IS NULL\\\" % name)\\n else:\\n expressions.append(\\\"%s = ?\\\"%name)\\n args.append(sub_check)\\n\\n expr = \\\" AND \\\".join(expressions)\\n return expr, args\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.add_record\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.add_record\\n def add_record(self, msg_id, rec):\\n \\\"\\\"\\\"Add a new Task Record, by msg_id.\\\"\\\"\\\"\\n d = self._defaults()\\n d.update(rec)\\n d['msg_id'] = msg_id\\n line = self._dict_to_list(d)\\n tups = '(%s)'%(','.join(['?']*len(line)))\\n self._db.execute(\\\"INSERT INTO %s VALUES %s\\\"%(self.table, tups), line)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.get_record\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.get_record\\n def get_record(self, msg_id):\\n \\\"\\\"\\\"Get a specific Task Record, by msg_id.\\\"\\\"\\\"\\n cursor = self._db.execute(\\\"\\\"\\\"SELECT * FROM %s WHERE msg_id==?\\\"\\\"\\\"%self.table, (msg_id,))\\n line = cursor.fetchone()\\n if line is None:\\n raise KeyError(\\\"No such msg: %r\\\"%msg_id)\\n return self._list_to_dict(line)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.update_record\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.update_record\\n def update_record(self, msg_id, rec):\\n \\\"\\\"\\\"Update the data in an existing record.\\\"\\\"\\\"\\n query = \\\"UPDATE %s SET \\\"%self.table\\n sets = []\\n keys = sorted(rec.keys())\\n values = []\\n for key in keys:\\n sets.append('%s = ?'%key)\\n values.append(rec[key])\\n query += ', '.join(sets)\\n query += ' WHERE msg_id == ?'\\n values.append(msg_id)\\n self._db.execute(query, values)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.drop_record\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.drop_record\\n def drop_record(self, msg_id):\\n \\\"\\\"\\\"Remove a record from the DB.\\\"\\\"\\\"\\n self._db.execute(\\\"\\\"\\\"DELETE FROM %s WHERE msg_id==?\\\"\\\"\\\"%self.table, (msg_id,))\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.drop_matching_records\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.drop_matching_records\\n def drop_matching_records(self, check):\\n \\\"\\\"\\\"Remove a record from the DB.\\\"\\\"\\\"\\n expr,args = self._render_expression(check)\\n query = \\\"DELETE FROM %s WHERE %s\\\"%(self.table, expr)\\n self._db.execute(query,args)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.find_records\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.find_records\\n def find_records(self, check, keys=None):\\n \\\"\\\"\\\"Find records matching a query dict, optionally extracting subset of keys.\\n\\n Returns list of matching records.\\n\\n Parameters\\n ----------\\n\\n check: dict\\n mongodb-style query argument\\n keys: list of strs [optional]\\n if specified, the subset of keys to extract. msg_id will *always* be\\n included.\\n \\\"\\\"\\\"\\n if keys:\\n bad_keys = [ key for key in keys if key not in self._keys ]\\n if bad_keys:\\n raise KeyError(\\\"Bad record key(s): %s\\\"%bad_keys)\\n\\n if keys:\\n # ensure msg_id is present and first:\\n if 'msg_id' in keys:\\n keys.remove('msg_id')\\n keys.insert(0, 'msg_id')\\n req = ', '.join(keys)\\n else:\\n req = '*'\\n expr,args = self._render_expression(check)\\n query = \\\"\\\"\\\"SELECT %s FROM %s WHERE %s\\\"\\\"\\\"%(req, self.table, expr)\\n cursor = self._db.execute(query, args)\\n matches = cursor.fetchall()\\n records = []\\n for line in matches:\\n rec = self._list_to_dict(line, keys)\\n records.append(rec)\\n return records\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.get_history\",\n \"text\": \"IPython/parallel/controller/sqlitedb.py::SQLiteDB.get_history\\n def get_history(self):\\n \\\"\\\"\\\"get all msg_ids, ordered by time submitted.\\\"\\\"\\\"\\n query = \\\"\\\"\\\"SELECT msg_id FROM %s ORDER by submitted ASC\\\"\\\"\\\"%self.table\\n cursor = self._db.execute(query)\\n # will be a list of length 1 tuples\\n return [ tup[0] for tup in cursor.fetchall()]\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":70,"cells":{"instance_id":{"kind":"string","value":"docker__compose-4333"},"repo":{"kind":"string","value":"docker/compose"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Add option to resolve tags to hashes for config command\n**Description**\r\n\r\nDocker 1.13 adds the option to deploy stacks using Docker Compose version 3 files in addition to Distributed Application Bundle (DAB) files. When `docker-compose bundle` is used to produce a DAB, it fetches image digests and replaces image tags with digests in the compose project. This produces a DAB whose images are pinned to a specific digest, rather than a tag that can be changed to different digests over time.\r\n\r\n`docker-compose config` can be used to “compile” or combine multiple compose files together, resolve `.env` variables, and handle all of Docker Compose's options to produce a single compose file suitable for use with `docker stack deploy --compose-file`. It would be useful to add an option to the `config` command that replaces image tags with digests—like the `bundle` command—to produce a compose file destined for stack deployments that acts like DAB files with respect to image pinning.\r\n\r\n**Steps to reproduce the issue:**\r\n1. Given Docker Compose file(s) forming a project, call `docker-compose config`.\r\n\r\n**Describe the results you received:**\r\nA combined/resolved Docker Compose configuration is outputted. Any image tags remain as tags.\r\n\r\n**Describe the results you expected:**\r\nAn option on `docker-compose config` that causes Docker Compose to output image digests instead of tags.\r\n\r\n**Additional information you deem important (e.g. issue happens only occasionally):**\r\nThe brief description of the `config` command says that is is to “Validate and view the compose file.” But, it is the only way to produce a Docker Compose file that uses features not supported by `docker stack deploy --compose-file`. I believe this feature request points to a broader need for `docker-compose config` to be treated more like `docker-compose bundle`, and be used for generating a single/compiled/resolved Compose file for a Docker Compose project. This includes adopting bundle's options (`--push-images` and `--output`).\r\n\n"},"gold_files":{"kind":"list like","value":["compose/cli/main.py","compose/config/serialize.py"],"string":"[\n \"compose/cli/main.py\",\n \"compose/config/serialize.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["compose/cli/main.py::<module>","compose/cli/main.py::TopLevelCommand.bundle","compose/cli/main.py::TopLevelCommand.config","compose/config/serialize.py::denormalize_config","compose/config/serialize.py::denormalize_service_dict","compose/config/serialize.py::serialize_config"],"string":"[\n \"compose/cli/main.py::<module>\",\n \"compose/cli/main.py::TopLevelCommand.bundle\",\n \"compose/cli/main.py::TopLevelCommand.config\",\n \"compose/config/serialize.py::denormalize_config\",\n \"compose/config/serialize.py::denormalize_service_dict\",\n \"compose/config/serialize.py::serialize_config\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"compose/bundle.py","text":"compose/bundle.py\nclass NeedsPush\n def __init__(self, image_name)\nclass NeedsPull\n def __init__(self, image_name, service_name)\nclass MissingDigests\n def __init__(self, needs_push, needs_pull)\ndef serialize_bundle(config, image_digests)\ndef get_image_digests(project, allow_push=False)\ndef get_image_digest(service, allow_push=False)\ndef push_image(service)\ndef to_bundle(config, image_digests)\ndef convert_service_to_bundle(name, service_dict, image_digest)\ndef set_command_and_args(config, entrypoint, command)\ndef make_service_networks(name, service_dict)\ndef make_port_specs(service_dict)\ndef make_port_spec(value)","label":0},{"cid":"compose/cli/main.py","text":"compose/cli/main.py\ndef main()\ndef dispatch()\ndef perform_command(options, handler, command_options)\ndef setup_logging()\ndef setup_console_handler(handler, verbose)\ndef parse_doc_section(name, source)\nclass TopLevelCommand # Define and run multi-container applications with Docker.\n def __init__(self, project, project_dir='.')\n def build(self, options) # Build or rebuild services.\n def bundle(self, config_options, options) # Generate a Distributed Application Bundle (DAB) from the Compose file.\n def config(self, config_options, options) # Validate and view the compose file.\n def create(self, options) # Creates containers for a service.\n def down(self, options) # Stops containers and removes containers, networks, volumes, and images\n def events(self, options) # Receive real time events from containers.\n def format_event(event)\n def json_format_event(event)\n def exec_command(self, options) # Execute a command in a running container\n def help(cls, options) # Get help on a command.\n def images(self, options) # List images used by the created containers.\n def kill(self, options) # Force stop service containers.\n def logs(self, options) # View output from containers.\n def pause(self, options) # Pause services.\n def port(self, options) # Print the public port for a port binding.\n def ps(self, options) # List containers.\n def pull(self, options) # Pulls images for services.\n def push(self, options) # Pushes images for services.\n def rm(self, options) # Removes stopped service containers.\n def run(self, options) # Run a one-off command on a service.\n def scale(self, options) # Set number of containers to run for a service.\n def start(self, options) # Start existing containers.\n def stop(self, options) # Stop running containers without removing them.\n def restart(self, options) # Restart running containers.\n def top(self, options) # Display the running processes\n def unpause(self, options) # Unpause services.\n def up(self, options) # Builds, (re)creates, starts, and attaches to containers for a service.\n def version(cls, options) # Show version informations\ndef convergence_strategy_from_opts(options)\ndef timeout_from_opts(options)\ndef exitval_from_opts(options, project)\ndef image_type_from_opt(flag, value)\ndef build_action_from_opts(options)\ndef build_container_options(options, detach, command)\ndef run_one_off_container(container_options, project, service, options)\n def remove_container(force=False)\ndef log_printer_from_project(project, containers, monochrome, log_args, cascade_stop=False, event_stream=None)\ndef filter_containers_to_service_names(containers, service_names)\ndef up_shutdown_context(project, service_names, timeout, detached)\ndef list_containers(containers)\ndef exit_if(condition, message, exit_code)\ndef call_docker(args)","label":1},{"cid":"compose/config/environment.py","text":"compose/config/environment.py\ndef split_env(env)\ndef env_vars_from_file(filename) # Read in a line delimited file of environment variables.\nclass Environment\n def __init__(self, *args, **kwargs)\n def from_env_file(cls, base_dir)\n def _initialize()\n def from_command_line(cls, parsed_env_opts)\n def __getitem__(self, key)\n def __contains__(self, key)\n def get(self, key, *args, **kwargs)\n def get_boolean(self, key)","label":0},{"cid":"compose/config/serialize.py","text":"compose/config/serialize.py\ndef serialize_config_type(dumper, data)\ndef serialize_dict_type(dumper, data)\ndef denormalize_config(config)\ndef serialize_config(config)\ndef serialize_ns_time_value(value)\ndef denormalize_service_dict(service_dict, version)","label":1},{"cid":"compose/container.py","text":"compose/container.py\nclass Container # Represents a Docker container, constructed from the output of\n def __init__(self, client, dictionary, has_been_inspected=False)\n def from_ps(cls, client, dictionary, **kwargs) # Construct a container object from the output of GET /containers/json.\n def from_id(cls, client, id)\n def create(cls, client, **options)\n def id(self)\n def image(self)\n def image_config(self)\n def short_id(self)\n def name(self)\n def service(self)\n def name_without_project(self)\n def number(self)\n def ports(self)\n def human_readable_ports(self)\n def format_port(private, public)\n def labels(self)\n def stop_signal(self)\n def log_config(self)\n def human_readable_state(self)\n def human_readable_command(self)\n def environment(self)\n def parse_env(var)\n def exit_code(self)\n def is_running(self)\n def is_restarting(self)\n def is_paused(self)\n def log_driver(self)\n def has_api_logs(self)\n def attach_log_stream(self) # A log stream can only be attached if the container uses a json-file\n def get(self, key) # Return a value from the container or None if the value is not set.\n def get_value(dictionary, key)\n def get_local_port(self, port, protocol='tcp')\n def get_mount(self, mount_dest)\n def start(self, **options)\n def stop(self, **options)\n def pause(self, **options)\n def unpause(self, **options)\n def kill(self, **options)\n def restart(self, **options)\n def remove(self, **options)\n def create_exec(self, command, **options)\n def start_exec(self, exec_id, **options)\n def rename_to_tmp_name(self) # Rename the container to a hopefully unique temporary container name\n def inspect_if_not_inspected(self)\n def wait(self)\n def logs(self, *args, **kwargs)\n def inspect(self)\n def attach(self, *args, **kwargs)\n def __repr__(self)\n def __eq__(self, other)\n def __hash__(self)\ndef get_container_name(container)","label":0},{"cid":"compose/service.py","text":"compose/service.py\nclass BuildError\n def __init__(self, service, reason)\nclass NeedsBuildError\n def __init__(self, service)\nclass NoSuchImageError\nclass ConvergenceStrategy # Enumeration for all possible convergence strategies. Values refer to\n def allows_recreate(self)\nclass ImageType # Enumeration for the types of images known to compose.\nclass BuildAction # Enumeration for the possible build actions.\nclass Service\n def __init__(self, name, client=None, project='default', use_networking=False, links=None, volumes_from=None, network_mode=None, networks=None, secrets=None, **options)\n def __repr__(self)\n def containers(self, stopped=False, one_off=False, filters={})\n def get_container(self, number=1) # Return a :class:`compose.container.Container` for this service. The\n def start(self, **options)\n def scale(self, desired_num, timeout=None) # Adjusts the number of containers to the specified number and ensures\n def create_and_start(service, number)\n def stop_and_remove(container)\n def create_container(self, one_off=False, previous_container=None, number=None, quiet=False, **override_options) # Create a container for this service. If the image doesn't exist, attempt to pull\n def ensure_image_exists(self, do_build=BuildAction.none)\n def image(self)\n def image_name(self)\n def convergence_plan(self, strategy=ConvergenceStrategy.changed)\n def _containers_have_diverged(self, containers)\n def execute_convergence_plan(self, plan, timeout=None, detached=False, start=True)\n def recreate_container(self, container, timeout=None, attach_logs=False, start_new_container=True) # Recreate a container.\n def stop_timeout(self, timeout)\n def start_container_if_stopped(self, container, attach_logs=False, quiet=False)\n def start_container(self, container)\n def connect_container_to_networks(self, container)\n def remove_duplicate_containers(self, timeout=None)\n def duplicate_containers(self)\n def config_hash(self)\n def config_dict(self)\n def get_dependency_names(self)\n def get_dependency_configs(self)\n def get_linked_service_names(self)\n def get_link_names(self)\n def get_volumes_from_names(self)\n def _next_container_number(self, one_off=False)\n def _get_aliases(self, network, container=None)\n def build_default_networking_config(self)\n def _get_links(self, link_to_self)\n def _get_volumes_from(self)\n def _get_container_create_options(self, override_options, number, one_off=False, previous_container=None)\n def _get_container_host_config(self, override_options, one_off=False)\n def get_secret_volumes(self)\n def build_spec(secret)\n def build(self, no_cache=False, pull=False, force_rm=False, build_args_override=None)\n def can_be_built(self)\n def labels(self, one_off=False)\n def custom_container_name(self)\n def get_container_name(self, number, one_off=False)\n def remove_image(self, image_type)\n def specifies_host_port(","label":0}],"string":"[\n {\n \"cid\": \"compose/bundle.py\",\n \"text\": \"compose/bundle.py\\nclass NeedsPush\\n def __init__(self, image_name)\\nclass NeedsPull\\n def __init__(self, image_name, service_name)\\nclass MissingDigests\\n def __init__(self, needs_push, needs_pull)\\ndef serialize_bundle(config, image_digests)\\ndef get_image_digests(project, allow_push=False)\\ndef get_image_digest(service, allow_push=False)\\ndef push_image(service)\\ndef to_bundle(config, image_digests)\\ndef convert_service_to_bundle(name, service_dict, image_digest)\\ndef set_command_and_args(config, entrypoint, command)\\ndef make_service_networks(name, service_dict)\\ndef make_port_specs(service_dict)\\ndef make_port_spec(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py\",\n \"text\": \"compose/cli/main.py\\ndef main()\\ndef dispatch()\\ndef perform_command(options, handler, command_options)\\ndef setup_logging()\\ndef setup_console_handler(handler, verbose)\\ndef parse_doc_section(name, source)\\nclass TopLevelCommand # Define and run multi-container applications with Docker.\\n def __init__(self, project, project_dir='.')\\n def build(self, options) # Build or rebuild services.\\n def bundle(self, config_options, options) # Generate a Distributed Application Bundle (DAB) from the Compose file.\\n def config(self, config_options, options) # Validate and view the compose file.\\n def create(self, options) # Creates containers for a service.\\n def down(self, options) # Stops containers and removes containers, networks, volumes, and images\\n def events(self, options) # Receive real time events from containers.\\n def format_event(event)\\n def json_format_event(event)\\n def exec_command(self, options) # Execute a command in a running container\\n def help(cls, options) # Get help on a command.\\n def images(self, options) # List images used by the created containers.\\n def kill(self, options) # Force stop service containers.\\n def logs(self, options) # View output from containers.\\n def pause(self, options) # Pause services.\\n def port(self, options) # Print the public port for a port binding.\\n def ps(self, options) # List containers.\\n def pull(self, options) # Pulls images for services.\\n def push(self, options) # Pushes images for services.\\n def rm(self, options) # Removes stopped service containers.\\n def run(self, options) # Run a one-off command on a service.\\n def scale(self, options) # Set number of containers to run for a service.\\n def start(self, options) # Start existing containers.\\n def stop(self, options) # Stop running containers without removing them.\\n def restart(self, options) # Restart running containers.\\n def top(self, options) # Display the running processes\\n def unpause(self, options) # Unpause services.\\n def up(self, options) # Builds, (re)creates, starts, and attaches to containers for a service.\\n def version(cls, options) # Show version informations\\ndef convergence_strategy_from_opts(options)\\ndef timeout_from_opts(options)\\ndef exitval_from_opts(options, project)\\ndef image_type_from_opt(flag, value)\\ndef build_action_from_opts(options)\\ndef build_container_options(options, detach, command)\\ndef run_one_off_container(container_options, project, service, options)\\n def remove_container(force=False)\\ndef log_printer_from_project(project, containers, monochrome, log_args, cascade_stop=False, event_stream=None)\\ndef filter_containers_to_service_names(containers, service_names)\\ndef up_shutdown_context(project, service_names, timeout, detached)\\ndef list_containers(containers)\\ndef exit_if(condition, message, exit_code)\\ndef call_docker(args)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/environment.py\",\n \"text\": \"compose/config/environment.py\\ndef split_env(env)\\ndef env_vars_from_file(filename) # Read in a line delimited file of environment variables.\\nclass Environment\\n def __init__(self, *args, **kwargs)\\n def from_env_file(cls, base_dir)\\n def _initialize()\\n def from_command_line(cls, parsed_env_opts)\\n def __getitem__(self, key)\\n def __contains__(self, key)\\n def get(self, key, *args, **kwargs)\\n def get_boolean(self, key)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/serialize.py\",\n \"text\": \"compose/config/serialize.py\\ndef serialize_config_type(dumper, data)\\ndef serialize_dict_type(dumper, data)\\ndef denormalize_config(config)\\ndef serialize_config(config)\\ndef serialize_ns_time_value(value)\\ndef denormalize_service_dict(service_dict, version)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/container.py\",\n \"text\": \"compose/container.py\\nclass Container # Represents a Docker container, constructed from the output of\\n def __init__(self, client, dictionary, has_been_inspected=False)\\n def from_ps(cls, client, dictionary, **kwargs) # Construct a container object from the output of GET /containers/json.\\n def from_id(cls, client, id)\\n def create(cls, client, **options)\\n def id(self)\\n def image(self)\\n def image_config(self)\\n def short_id(self)\\n def name(self)\\n def service(self)\\n def name_without_project(self)\\n def number(self)\\n def ports(self)\\n def human_readable_ports(self)\\n def format_port(private, public)\\n def labels(self)\\n def stop_signal(self)\\n def log_config(self)\\n def human_readable_state(self)\\n def human_readable_command(self)\\n def environment(self)\\n def parse_env(var)\\n def exit_code(self)\\n def is_running(self)\\n def is_restarting(self)\\n def is_paused(self)\\n def log_driver(self)\\n def has_api_logs(self)\\n def attach_log_stream(self) # A log stream can only be attached if the container uses a json-file\\n def get(self, key) # Return a value from the container or None if the value is not set.\\n def get_value(dictionary, key)\\n def get_local_port(self, port, protocol='tcp')\\n def get_mount(self, mount_dest)\\n def start(self, **options)\\n def stop(self, **options)\\n def pause(self, **options)\\n def unpause(self, **options)\\n def kill(self, **options)\\n def restart(self, **options)\\n def remove(self, **options)\\n def create_exec(self, command, **options)\\n def start_exec(self, exec_id, **options)\\n def rename_to_tmp_name(self) # Rename the container to a hopefully unique temporary container name\\n def inspect_if_not_inspected(self)\\n def wait(self)\\n def logs(self, *args, **kwargs)\\n def inspect(self)\\n def attach(self, *args, **kwargs)\\n def __repr__(self)\\n def __eq__(self, other)\\n def __hash__(self)\\ndef get_container_name(container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py\",\n \"text\": \"compose/service.py\\nclass BuildError\\n def __init__(self, service, reason)\\nclass NeedsBuildError\\n def __init__(self, service)\\nclass NoSuchImageError\\nclass ConvergenceStrategy # Enumeration for all possible convergence strategies. Values refer to\\n def allows_recreate(self)\\nclass ImageType # Enumeration for the types of images known to compose.\\nclass BuildAction # Enumeration for the possible build actions.\\nclass Service\\n def __init__(self, name, client=None, project='default', use_networking=False, links=None, volumes_from=None, network_mode=None, networks=None, secrets=None, **options)\\n def __repr__(self)\\n def containers(self, stopped=False, one_off=False, filters={})\\n def get_container(self, number=1) # Return a :class:`compose.container.Container` for this service. The\\n def start(self, **options)\\n def scale(self, desired_num, timeout=None) # Adjusts the number of containers to the specified number and ensures\\n def create_and_start(service, number)\\n def stop_and_remove(container)\\n def create_container(self, one_off=False, previous_container=None, number=None, quiet=False, **override_options) # Create a container for this service. If the image doesn't exist, attempt to pull\\n def ensure_image_exists(self, do_build=BuildAction.none)\\n def image(self)\\n def image_name(self)\\n def convergence_plan(self, strategy=ConvergenceStrategy.changed)\\n def _containers_have_diverged(self, containers)\\n def execute_convergence_plan(self, plan, timeout=None, detached=False, start=True)\\n def recreate_container(self, container, timeout=None, attach_logs=False, start_new_container=True) # Recreate a container.\\n def stop_timeout(self, timeout)\\n def start_container_if_stopped(self, container, attach_logs=False, quiet=False)\\n def start_container(self, container)\\n def connect_container_to_networks(self, container)\\n def remove_duplicate_containers(self, timeout=None)\\n def duplicate_containers(self)\\n def config_hash(self)\\n def config_dict(self)\\n def get_dependency_names(self)\\n def get_dependency_configs(self)\\n def get_linked_service_names(self)\\n def get_link_names(self)\\n def get_volumes_from_names(self)\\n def _next_container_number(self, one_off=False)\\n def _get_aliases(self, network, container=None)\\n def build_default_networking_config(self)\\n def _get_links(self, link_to_self)\\n def _get_volumes_from(self)\\n def _get_container_create_options(self, override_options, number, one_off=False, previous_container=None)\\n def _get_container_host_config(self, override_options, one_off=False)\\n def get_secret_volumes(self)\\n def build_spec(secret)\\n def build(self, no_cache=False, pull=False, force_rm=False, build_args_override=None)\\n def can_be_built(self)\\n def labels(self, one_off=False)\\n def custom_container_name(self)\\n def get_container_name(self, number, one_off=False)\\n def remove_image(self, image_type)\\n def specifies_host_port(\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"compose/bundle.py::NeedsPush.__init__","text":"compose/bundle.py::NeedsPush.__init__\n def __init__(self, image_name):\n self.image_name = image_name","label":0},{"cid":"compose/bundle.py::NeedsPull.__init__","text":"compose/bundle.py::NeedsPull.__init__\n def __init__(self, image_name, service_name):\n self.image_name = image_name\n self.service_name = service_name","label":0},{"cid":"compose/bundle.py::MissingDigests.__init__","text":"compose/bundle.py::MissingDigests.__init__\n def __init__(self, needs_push, needs_pull):\n self.needs_push = needs_push\n self.needs_pull = needs_pull","label":0},{"cid":"compose/bundle.py::serialize_bundle","text":"compose/bundle.py::serialize_bundle\ndef serialize_bundle(config, image_digests):\n return json.dumps(to_bundle(config, image_digests), indent=2, sort_keys=True)","label":0},{"cid":"compose/bundle.py::get_image_digests","text":"compose/bundle.py::get_image_digests\ndef get_image_digests(project, allow_push=False):\n digests = {}\n needs_push = set()\n needs_pull = set()\n\n for service in project.services:\n try:\n digests[service.name] = get_image_digest(\n service,\n allow_push=allow_push,\n )\n except NeedsPush as e:\n needs_push.add(e.image_name)\n except NeedsPull as e:\n needs_pull.add(e.service_name)\n\n if needs_push or needs_pull:\n raise MissingDigests(needs_push, needs_pull)\n\n return digests","label":0},{"cid":"compose/bundle.py::get_image_digest","text":"compose/bundle.py::get_image_digest\ndef get_image_digest(service, allow_push=False):\n if 'image' not in service.options:\n raise UserError(\n \"Service '{s.name}' doesn't define an image tag. An image name is \"\n \"required to generate a proper image digest for the bundle. Specify \"\n \"an image repo and tag with the 'image' option.\".format(s=service))\n\n _, _, separator = parse_repository_tag(service.options['image'])\n # Compose file already uses a digest, no lookup required\n if separator == '@':\n return service.options['image']\n\n try:\n image = service.image()\n except NoSuchImageError:\n action = 'build' if 'build' in service.options else 'pull'\n raise UserError(\n \"Image not found for service '{service}'. \"\n \"You might need to run `docker-compose {action} {service}`.\"\n .format(service=service.name, action=action))\n\n if image['RepoDigests']:\n # TODO: pick a digest based on the image tag if there are multiple\n # digests\n return image['RepoDigests'][0]\n\n if 'build' not in service.options:\n raise NeedsPull(service.image_name, service.name)\n\n if not allow_push:\n raise NeedsPush(service.image_name)\n\n return push_image(service)","label":0},{"cid":"compose/bundle.py::push_image","text":"compose/bundle.py::push_image\ndef push_image(service):\n try:\n digest = service.push()\n except:\n log.error(\n \"Failed to push image for service '{s.name}'. Please use an \"\n \"image tag that can be pushed to a Docker \"\n \"registry.\".format(s=service))\n raise\n\n if not digest:\n raise ValueError(\"Failed to get digest for %s\" % service.name)\n\n repo, _, _ = parse_repository_tag(service.options['image'])\n identifier = '{repo}@{digest}'.format(repo=repo, digest=digest)\n\n # only do this if RepoDigests isn't already populated\n image = service.image()\n if not image['RepoDigests']:\n # Pull by digest so that image['RepoDigests'] is populated for next time\n # and we don't have to pull/push again\n service.client.pull(identifier)\n log.info(\"Stored digest for {}\".format(service.image_name))\n\n return identifier","label":0},{"cid":"compose/bundle.py::to_bundle","text":"compose/bundle.py::to_bundle\ndef to_bundle(config, image_digests):\n if config.networks:\n log.warn(\"Unsupported top level key 'networks' - ignoring\")\n\n if config.volumes:\n log.warn(\"Unsupported top level key 'volumes' - ignoring\")\n\n config = denormalize_config(config)\n\n return {\n 'Version': VERSION,\n 'Services': {\n name: convert_service_to_bundle(\n name,\n service_dict,\n image_digests[name],\n )\n for name, service_dict in config['services'].items()\n },\n }","label":0},{"cid":"compose/bundle.py::convert_service_to_bundle","text":"compose/bundle.py::convert_service_to_bundle\ndef convert_service_to_bundle(name, service_dict, image_digest):\n container_config = {'Image': image_digest}\n\n for key, value in service_dict.items():\n if key in IGNORED_KEYS:\n continue\n\n if key not in SUPPORTED_KEYS:\n log.warn(\"Unsupported key '{}' in services.{} - ignoring\".format(key, name))\n continue\n\n if key == 'environment':\n container_config['Env'] = format_environment({\n envkey: envvalue for envkey, envvalue in value.items()\n if envvalue\n })\n continue\n\n if key in SERVICE_KEYS:\n container_config[SERVICE_KEYS[key]] = value\n continue\n\n set_command_and_args(\n container_config,\n service_dict.get('entrypoint', []),\n service_dict.get('command', []))\n container_config['Networks'] = make_service_networks(name, service_dict)\n\n ports = make_port_specs(service_dict)\n if ports:\n container_config['Ports'] = ports\n\n return container_config","label":0},{"cid":"compose/bundle.py::set_command_and_args","text":"compose/bundle.py::set_command_and_args\ndef set_command_and_args(config, entrypoint, command):\n if isinstance(entrypoint, six.string_types):\n entrypoint = split_command(entrypoint)\n if isinstance(command, six.string_types):\n command = split_command(command)\n\n if entrypoint:\n config['Command'] = entrypoint + command\n return\n\n if command:\n config['Args'] = command","label":0},{"cid":"compose/bundle.py::make_service_networks","text":"compose/bundle.py::make_service_networks\ndef make_service_networks(name, service_dict):\n networks = []\n\n for network_name, network_def in get_network_defs_for_service(service_dict).items():\n for key in network_def.keys():\n log.warn(\n \"Unsupported key '{}' in services.{}.networks.{} - ignoring\"\n .format(key, name, network_name))\n\n networks.append(network_name)\n\n return networks","label":0},{"cid":"compose/bundle.py::make_port_specs","text":"compose/bundle.py::make_port_specs\ndef make_port_specs(service_dict):\n ports = []\n\n internal_ports = [\n internal_port\n for port_def in service_dict.get('ports', [])\n for internal_port in split_port(port_def)[0]\n ]\n\n internal_ports += service_dict.get('expose', [])\n\n for internal_port in internal_ports:\n spec = make_port_spec(internal_port)\n if spec not in ports:\n ports.append(spec)\n\n return ports","label":0},{"cid":"compose/bundle.py::make_port_spec","text":"compose/bundle.py::make_port_spec\ndef make_port_spec(value):\n components = six.text_type(value).partition('/')\n return {\n 'Protocol': components[2] or 'tcp',\n 'Port': int(components[0]),\n }","label":0},{"cid":"compose/cli/main.py::main","text":"compose/cli/main.py::main\ndef main():\n signals.ignore_sigpipe()\n try:\n command = dispatch()\n command()\n except (KeyboardInterrupt, signals.ShutdownException):\n log.error(\"Aborting.\")\n sys.exit(1)\n except (UserError, NoSuchService, ConfigurationError,\n ProjectError, OperationFailedError) as e:\n log.error(e.msg)\n sys.exit(1)\n except BuildError as e:\n log.error(\"Service '%s' failed to build: %s\" % (e.service.name, e.reason))\n sys.exit(1)\n except StreamOutputError as e:\n log.error(e)\n sys.exit(1)\n except NeedsBuildError as e:\n log.error(\"Service '%s' needs to be built, but --no-build was passed.\" % e.service.name)\n sys.exit(1)\n except NoSuchCommand as e:\n commands = \"\\n\".join(parse_doc_section(\"commands:\", getdoc(e.supercommand)))\n log.error(\"No such command: %s\\n\\n%s\", e.command, commands)\n sys.exit(1)\n except (errors.ConnectionError, StreamParseError):\n sys.exit(1)","label":0},{"cid":"compose/cli/main.py::dispatch","text":"compose/cli/main.py::dispatch\ndef dispatch():\n setup_logging()\n dispatcher = DocoptDispatcher(\n TopLevelCommand,\n {'options_first': True, 'version': get_version_info('compose')})\n\n options, handler, command_options = dispatcher.parse(sys.argv[1:])\n setup_console_handler(console_handler, options.get('--verbose'))\n return functools.partial(perform_command, options, handler, command_options)","label":0},{"cid":"compose/cli/main.py::perform_command","text":"compose/cli/main.py::perform_command\ndef perform_command(options, handler, command_options):\n if options['COMMAND'] in ('help', 'version'):\n # Skip looking up the compose file.\n handler(command_options)\n return\n\n if options['COMMAND'] in ('config', 'bundle'):\n command = TopLevelCommand(None)\n handler(command, options, command_options)\n return\n\n project = project_from_options('.', options)\n command = TopLevelCommand(project)\n with errors.handle_connection_errors(project.client):\n handler(command, command_options)","label":0},{"cid":"compose/cli/main.py::setup_logging","text":"compose/cli/main.py::setup_logging\ndef setup_logging():\n root_logger = logging.getLogger()\n root_logger.addHandler(console_handler)\n root_logger.setLevel(logging.DEBUG)\n\n # Disable requests logging\n logging.getLogger(\"requests\").propagate = False","label":0},{"cid":"compose/cli/main.py::setup_console_handler","text":"compose/cli/main.py::setup_console_handler\ndef setup_console_handler(handler, verbose):\n if handler.stream.isatty():\n format_class = ConsoleWarningFormatter\n else:\n format_class = logging.Formatter\n\n if verbose:\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\n handler.setLevel(logging.DEBUG)\n else:\n handler.setFormatter(format_class())\n handler.setLevel(logging.INFO)","label":0},{"cid":"compose/cli/main.py::parse_doc_section","text":"compose/cli/main.py::parse_doc_section\ndef parse_doc_section(name, source):\n pattern = re.compile('^([^\\n]*' + name + '[^\\n]*\\n?(?:[ \\t].*?(?:\\n|$))*)',\n re.IGNORECASE | re.MULTILINE)\n return [s.strip() for s in pattern.findall(source)]","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.__init__","text":"compose/cli/main.py::TopLevelCommand.__init__\n def __init__(self, project, project_dir='.'):\n self.project = project\n self.project_dir = '.'","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.build","text":"compose/cli/main.py::TopLevelCommand.build\n def build(self, options):\n \"\"\"\n Build or rebuild services.\n\n Services are built once and then tagged as `project_service`,\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\n contents of its build directory, you can run `docker-compose build` to rebuild it.\n\n Usage: build [options] [--build-arg key=val...] [SERVICE...]\n\n Options:\n --force-rm Always remove intermediate containers.\n --no-cache Do not use cache when building the image.\n --pull Always attempt to pull a newer version of the image.\n --build-arg key=val Set build-time variables for one service.\n \"\"\"\n service_names = options['SERVICE']\n build_args = options.get('--build-arg', None)\n if build_args:\n environment = Environment.from_env_file(self.project_dir)\n build_args = resolve_build_args(build_args, environment)\n\n if not service_names and build_args:\n raise UserError(\"Need service name for --build-arg option\")\n\n self.project.build(\n service_names=service_names,\n no_cache=bool(options.get('--no-cache', False)),\n pull=bool(options.get('--pull', False)),\n force_rm=bool(options.get('--force-rm', False)),\n build_args=build_args)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.bundle","text":"compose/cli/main.py::TopLevelCommand.bundle\n def bundle(self, config_options, options):\n \"\"\"\n Generate a Distributed Application Bundle (DAB) from the Compose file.\n\n Images must have digests stored, which requires interaction with a\n Docker registry. If digests aren't stored for all images, you can fetch\n them with `docker-compose pull` or `docker-compose push`. To push images\n automatically when bundling, pass `--push-images`. Only services with\n a `build` option specified will have their images pushed.\n\n Usage: bundle [options]\n\n Options:\n --push-images Automatically push images for any services\n which have a `build` option specified.\n\n -o, --output PATH Path to write the bundle file to.\n Defaults to \"<project name>.dab\".\n \"\"\"\n self.project = project_from_options('.', config_options)\n compose_config = get_config_from_options(self.project_dir, config_options)\n\n output = options[\"--output\"]\n if not output:\n output = \"{}.dab\".format(self.project.name)\n\n with errors.handle_connection_errors(self.project.client):\n try:\n image_digests = get_image_digests(\n self.project,\n allow_push=options['--push-images'],\n )\n except MissingDigests as e:\n def list_images(images):\n return \"\\n\".join(\" {}\".format(name) for name in sorted(images))\n\n paras = [\"Some images are missing digests.\"]\n\n if e.needs_push:\n command_hint = (\n \"Use `docker-compose push {}` to push them. \"\n \"You can do this automatically with `docker-compose bundle --push-images`.\"\n .format(\" \".join(sorted(e.needs_push)))\n )\n","label":1},{"cid":"compose/cli/main.py::TopLevelCommand.config","text":"compose/cli/main.py::TopLevelCommand.config\n def config(self, config_options, options):\n \"\"\"\n Validate and view the compose file.\n\n Usage: config [options]\n\n Options:\n -q, --quiet Only validate the configuration, don't print\n anything.\n --services Print the service names, one per line.\n --volumes Print the volume names, one per line.\n\n \"\"\"\n compose_config = get_config_from_options(self.project_dir, config_options)\n\n if options['--quiet']:\n return\n\n if options['--services']:\n print('\\n'.join(service['name'] for service in compose_config.services))\n return\n\n if options['--volumes']:\n print('\\n'.join(volume for volume in compose_config.volumes))\n return\n\n print(serialize_config(compose_config))","label":1},{"cid":"compose/cli/main.py::TopLevelCommand.create","text":"compose/cli/main.py::TopLevelCommand.create\n def create(self, options):\n \"\"\"\n Creates containers for a service.\n\n Usage: create [options] [SERVICE...]\n\n Options:\n --force-recreate Recreate containers even if their configuration and\n image haven't changed. Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate them.\n Incompatible with --force-recreate.\n --no-build Don't build an image, even if it's missing.\n --build Build images before creating containers.\n \"\"\"\n service_names = options['SERVICE']\n\n self.project.create(\n service_names=service_names,\n strategy=convergence_strategy_from_opts(options),\n do_build=build_action_from_opts(options),\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.down","text":"compose/cli/main.py::TopLevelCommand.down\n def down(self, options):\n \"\"\"\n Stops containers and removes containers, networks, volumes, and images\n created by `up`.\n\n By default, the only things removed are:\n\n - Containers for services defined in the Compose file\n - Networks defined in the `networks` section of the Compose file\n - The default network, if one is used\n\n Networks and volumes defined as `external` are never removed.\n\n Usage: down [options]\n\n Options:\n --rmi type Remove images. Type must be one of:\n 'all': Remove all images used by any service.\n 'local': Remove only images that don't have a custom tag\n set by the `image` field.\n -v, --volumes Remove named volumes declared in the `volumes` section\n of the Compose file and anonymous volumes\n attached to containers.\n --remove-orphans Remove containers for services not defined in the\n Compose file\n \"\"\"\n image_type = image_type_from_opt('--rmi', options['--rmi'])\n self.project.down(image_type, options['--volumes'], options['--remove-orphans'])","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events","text":"compose/cli/main.py::TopLevelCommand.events\n def events(self, options):\n \"\"\"\n Receive real time events from containers.\n\n Usage: events [options] [SERVICE...]\n\n Options:\n --json Output events as a stream of json objects\n \"\"\"\n def format_event(event):\n attributes = [\"%s=%s\" % item for item in event['attributes'].items()]\n return (\"{time} {type} {action} {id} ({attrs})\").format(\n attrs=\", \".join(sorted(attributes)),\n **event)\n\n def json_format_event(event):\n event['time'] = event['time'].isoformat()\n event.pop('container')\n return json.dumps(event)\n\n for event in self.project.events():\n formatter = json_format_event if options['--json'] else format_event\n print(formatter(event))\n sys.stdout.flush()","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events.format_event","text":"compose/cli/main.py::TopLevelCommand.events.format_event\n def format_event(event):\n attributes = [\"%s=%s\" % item for item in event['attributes'].items()]\n return (\"{time} {type} {action} {id} ({attrs})\").format(\n attrs=\", \".join(sorted(attributes)),\n **event)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events.json_format_event","text":"compose/cli/main.py::TopLevelCommand.events.json_format_event\n def json_format_event(event):\n event['time'] = event['time'].isoformat()\n event.pop('container')\n return json.dumps(event)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.exec_command","text":"compose/cli/main.py::TopLevelCommand.exec_command\n def exec_command(self, options):\n \"\"\"\n Execute a command in a running container\n\n Usage: exec [options] SERVICE COMMAND [ARGS...]\n\n Options:\n -d Detached mode: Run command in the background.\n --privileged Give extended privileges to the process.\n --user USER Run the command as this user.\n -T Disable pseudo-tty allocation. By default `docker-compose exec`\n allocates a TTY.\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n \"\"\"\n index = int(options.get('--index'))\n service = self.project.get_service(options['SERVICE'])\n detach = options['-d']\n\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n command = [options['COMMAND']] + options['ARGS']\n tty = not options[\"-T\"]\n\n if IS_WINDOWS_PLATFORM and not detach:\n args = [\"exec\"]\n\n if options[\"-d\"]:\n args += [\"--detach\"]\n else:\n args += [\"--interactive\"]\n\n if not options[\"-T\"]:\n args += [\"--tty\"]\n\n if options[\"--privileged\"]:\n args += [\"--privileged\"]\n\n if options[\"--user\"]:\n args += [\"--user\", options[\"--user\"]]\n\n args += [container.id]\n args += command\n\n sys.exit(call_docker(args))\n\n create_exec_options = {\n \"privileged\": options[\"--privileged\"],\n \"user\": options[\"--user\"],\n \"tty\": tty,\n \"stdin\": tty,\n }\n\n exec_id = container.create_exec(command, **create_exec_options)\n\n if detach:\n container.start_exec(exec_id, tty=tty)\n return\n\n sig","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.help","text":"compose/cli/main.py::TopLevelCommand.help\n @classmethod\n def help(cls, options):\n \"\"\"\n Get help on a command.\n\n Usage: help [COMMAND]\n \"\"\"\n if options['COMMAND']:\n subject = get_handler(cls, options['COMMAND'])\n else:\n subject = cls\n\n print(getdoc(subject))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.images","text":"compose/cli/main.py::TopLevelCommand.images\n def images(self, options):\n \"\"\"\n List images used by the created containers.\n Usage: images [options] [SERVICE...]\n\n Options:\n -q Only display IDs\n \"\"\"\n containers = sorted(\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\n key=attrgetter('name'))\n\n if options['-q']:\n for image in set(c.image for c in containers):\n print(image.split(':')[1])\n else:\n headers = [\n 'Container',\n 'Repository',\n 'Tag',\n 'Image Id',\n 'Size'\n ]\n rows = []\n for container in containers:\n image_config = container.image_config\n repo_tags = image_config['RepoTags'][0].split(':')\n image_id = image_config['Id'].split(':')[1][:12]\n size = human_readable_file_size(image_config['Size'])\n rows.append([\n container.name,\n repo_tags[0],\n repo_tags[1],\n image_id,\n size\n ])\n print(Formatter().table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.kill","text":"compose/cli/main.py::TopLevelCommand.kill\n def kill(self, options):\n \"\"\"\n Force stop service containers.\n\n Usage: kill [options] [SERVICE...]\n\n Options:\n -s SIGNAL SIGNAL to send to the container.\n Default signal is SIGKILL.\n \"\"\"\n signal = options.get('-s', 'SIGKILL')\n\n self.project.kill(service_names=options['SERVICE'], signal=signal)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.logs","text":"compose/cli/main.py::TopLevelCommand.logs\n def logs(self, options):\n \"\"\"\n View output from containers.\n\n Usage: logs [options] [SERVICE...]\n\n Options:\n --no-color Produce monochrome output.\n -f, --follow Follow log output.\n -t, --timestamps Show timestamps.\n --tail=\"all\" Number of lines to show from the end of the logs\n for each container.\n \"\"\"\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\n\n tail = options['--tail']\n if tail is not None:\n if tail.isdigit():\n tail = int(tail)\n elif tail != 'all':\n raise UserError(\"tail flag must be all or a number\")\n log_args = {\n 'follow': options['--follow'],\n 'tail': tail,\n 'timestamps': options['--timestamps']\n }\n print(\"Attaching to\", list_containers(containers))\n log_printer_from_project(\n self.project,\n containers,\n options['--no-color'],\n log_args,\n event_stream=self.project.events(service_names=options['SERVICE'])).run()","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pause","text":"compose/cli/main.py::TopLevelCommand.pause\n def pause(self, options):\n \"\"\"\n Pause services.\n\n Usage: pause [SERVICE...]\n \"\"\"\n containers = self.project.pause(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to pause', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.port","text":"compose/cli/main.py::TopLevelCommand.port\n def port(self, options):\n \"\"\"\n Print the public port for a port binding.\n\n Usage: port [options] SERVICE PRIVATE_PORT\n\n Options:\n --protocol=proto tcp or udp [default: tcp]\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n \"\"\"\n index = int(options.get('--index'))\n service = self.project.get_service(options['SERVICE'])\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n print(container.get_local_port(\n options['PRIVATE_PORT'],\n protocol=options.get('--protocol') or 'tcp') or '')","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.ps","text":"compose/cli/main.py::TopLevelCommand.ps\n def ps(self, options):\n \"\"\"\n List containers.\n\n Usage: ps [options] [SERVICE...]\n\n Options:\n -q Only display IDs\n \"\"\"\n containers = sorted(\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\n key=attrgetter('name'))\n\n if options['-q']:\n for container in containers:\n print(container.id)\n else:\n headers = [\n 'Name',\n 'Command',\n 'State',\n 'Ports',\n ]\n rows = []\n for container in containers:\n command = container.human_readable_command\n if len(command) > 30:\n command = '%s ...' % command[:26]\n rows.append([\n container.name,\n command,\n container.human_readable_state,\n container.human_readable_ports,\n ])\n print(Formatter().table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pull","text":"compose/cli/main.py::TopLevelCommand.pull\n def pull(self, options):\n \"\"\"\n Pulls images for services.\n\n Usage: pull [options] [SERVICE...]\n\n Options:\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\n --parallel Pull multiple images in parallel.\n \"\"\"\n self.project.pull(\n service_names=options['SERVICE'],\n ignore_pull_failures=options.get('--ignore-pull-failures'),\n parallel_pull=options.get('--parallel')\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.push","text":"compose/cli/main.py::TopLevelCommand.push\n def push(self, options):\n \"\"\"\n Pushes images for services.\n\n Usage: push [options] [SERVICE...]\n\n Options:\n --ignore-push-failures Push what it can and ignores images with push failures.\n \"\"\"\n self.project.push(\n service_names=options['SERVICE'],\n ignore_push_failures=options.get('--ignore-push-failures')\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.rm","text":"compose/cli/main.py::TopLevelCommand.rm\n def rm(self, options):\n \"\"\"\n Removes stopped service containers.\n\n By default, anonymous volumes attached to containers will not be removed. You\n can override this with `-v`. To list all volumes, use `docker volume ls`.\n\n Any data which is not in a volume will be lost.\n\n Usage: rm [options] [SERVICE...]\n\n Options:\n -f, --force Don't ask to confirm removal\n -s, --stop Stop the containers, if required, before removing\n -v Remove any anonymous volumes attached to containers\n -a, --all Deprecated - no effect.\n \"\"\"\n if options.get('--all'):\n log.warn(\n '--all flag is obsolete. This is now the default behavior '\n 'of `docker-compose rm`'\n )\n one_off = OneOffFilter.include\n\n if options.get('--stop'):\n running_containers = self.project.containers(\n service_names=options['SERVICE'], stopped=False, one_off=one_off\n )\n self.project.stop(\n service_names=running_containers,\n one_off=one_off\n )\n\n all_containers = self.project.containers(\n service_names=options['SERVICE'], stopped=True, one_off=one_off\n )\n stopped_containers = [c for c in all_containers if not c.is_running]\n\n if len(stopped_containers) > 0:\n print(\"Going to remove\", list_containers(stopped_containers))\n if options.get('--force') \\\n or yesno(\"Are you sure? [yN] \", default=False):\n self.project.remove_stopped(\n service_names=options['SERVICE'],\n v=options.get('-v', False),\n one_off=one_off\n )\n else:\n print(\"No stopped containers\")","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.run","text":"compose/cli/main.py::TopLevelCommand.run\n def run(self, options):\n \"\"\"\n Run a one-off command on a service.\n\n For example:\n\n $ docker-compose run web python manage.py shell\n\n By default, linked services will be started, unless they are already\n running. If you do not want to start linked services, use\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\n\n Usage: run [options] [-v VOLUME...] [-p PORT...] [-e KEY=VAL...] SERVICE [COMMAND] [ARGS...]\n\n Options:\n -d Detached mode: Run container in the background, print\n new container name.\n --name NAME Assign a name to the container\n --entrypoint CMD Override the entrypoint of the image.\n -e KEY=VAL Set an environment variable (can be used multiple times)\n -u, --user=\"\" Run as specified username or uid\n --no-deps Don't start linked services.\n --rm Remove container after run. Ignored in detached mode.\n -p, --publish=[] Publish a container's port(s) to the host\n --service-ports Run command with the service's ports enabled and mapped\n to the host.\n -v, --volume=[] Bind mount a volume (default [])\n -T Disable pseudo-tty allocation. By default `docker-compose run`\n allocates a TTY.\n -w, --workdir=\"\" Working directory inside the container\n \"\"\"\n service = self.project.get_service(options['SERVICE'])\n detach = options['-d']\n\n if options['--publish'] and options['--service-ports']:\n raise UserError(\n 'Service port mapping and manual port mapping '\n 'can not be used together'\n )\n\n if options['COMMAND'] is not None:\n ","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.scale","text":"compose/cli/main.py::TopLevelCommand.scale\n def scale(self, options):\n \"\"\"\n Set number of containers to run for a service.\n\n Numbers are specified in the form `service=num` as arguments.\n For example:\n\n $ docker-compose scale web=2 worker=3\n\n Usage: scale [options] [SERVICE=NUM...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n\n for s in options['SERVICE=NUM']:\n if '=' not in s:\n raise UserError('Arguments to scale should be in the form service=num')\n service_name, num = s.split('=', 1)\n try:\n num = int(num)\n except ValueError:\n raise UserError('Number of containers for service \"%s\" is not a '\n 'number' % service_name)\n self.project.get_service(service_name).scale(num, timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.start","text":"compose/cli/main.py::TopLevelCommand.start\n def start(self, options):\n \"\"\"\n Start existing containers.\n\n Usage: start [SERVICE...]\n \"\"\"\n containers = self.project.start(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to start', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.stop","text":"compose/cli/main.py::TopLevelCommand.stop\n def stop(self, options):\n \"\"\"\n Stop running containers without removing them.\n\n They can be started again with `docker-compose start`.\n\n Usage: stop [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n self.project.stop(service_names=options['SERVICE'], timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.restart","text":"compose/cli/main.py::TopLevelCommand.restart\n def restart(self, options):\n \"\"\"\n Restart running containers.\n\n Usage: restart [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n containers = self.project.restart(service_names=options['SERVICE'], timeout=timeout)\n exit_if(not containers, 'No containers to restart', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.top","text":"compose/cli/main.py::TopLevelCommand.top\n def top(self, options):\n \"\"\"\n Display the running processes\n\n Usage: top [SERVICE...]\n\n \"\"\"\n containers = sorted(\n self.project.containers(service_names=options['SERVICE'], stopped=False) +\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\n key=attrgetter('name')\n )\n\n for idx, container in enumerate(containers):\n if idx > 0:\n print()\n\n top_data = self.project.client.top(container.name)\n headers = top_data.get(\"Titles\")\n rows = []\n\n for process in top_data.get(\"Processes\", []):\n rows.append(process)\n\n print(container.name)\n print(Formatter().table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.unpause","text":"compose/cli/main.py::TopLevelCommand.unpause\n def unpause(self, options):\n \"\"\"\n Unpause services.\n\n Usage: unpause [SERVICE...]\n \"\"\"\n containers = self.project.unpause(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to unpause', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.up","text":"compose/cli/main.py::TopLevelCommand.up\n def up(self, options):\n \"\"\"\n Builds, (re)creates, starts, and attaches to containers for a service.\n\n Unless they are already running, this command also starts any linked services.\n\n The `docker-compose up` command aggregates the output of each container. When\n the command exits, all containers are stopped. Running `docker-compose up -d`\n starts the containers in the background and leaves them running.\n\n If there are existing containers for a service, and the service's configuration\n or image was changed after the container's creation, `docker-compose up` picks\n up the changes by stopping and recreating the containers (preserving mounted\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\n flag.\n\n If you want to force Compose to stop and recreate all containers, use the\n `--force-recreate` flag.\n\n Usage: up [options] [SERVICE...]\n\n Options:\n -d Detached mode: Run containers in the background,\n print new container names.\n Incompatible with --abort-on-container-exit.\n --no-color Produce monochrome output.\n --no-deps Don't start linked services.\n --force-recreate Recreate containers even if their configuration\n and image haven't changed.\n Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate them.\n Incompatible with --force-recreate.\n --no-build Don't build an image, even if it's missing.\n --build Build images before starting containers.\n --abort-on-container-exit Stops al","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.version","text":"compose/cli/main.py::TopLevelCommand.version\n @classmethod\n def version(cls, options):\n \"\"\"\n Show version informations\n\n Usage: version [--short]\n\n Options:\n --short Shows only Compose's version number.\n \"\"\"\n if options['--short']:\n print(__version__)\n else:\n print(get_version_info('full'))","label":0},{"cid":"compose/cli/main.py::convergence_strategy_from_opts","text":"compose/cli/main.py::convergence_strategy_from_opts\ndef convergence_strategy_from_opts(options):\n no_recreate = options['--no-recreate']\n force_recreate = options['--force-recreate']\n if force_recreate and no_recreate:\n raise UserError(\"--force-recreate and --no-recreate cannot be combined.\")\n\n if force_recreate:\n return ConvergenceStrategy.always\n\n if no_recreate:\n return ConvergenceStrategy.never\n\n return ConvergenceStrategy.changed","label":0},{"cid":"compose/cli/main.py::timeout_from_opts","text":"compose/cli/main.py::timeout_from_opts\ndef timeout_from_opts(options):\n timeout = options.get('--timeout')\n return None if timeout is None else int(timeout)","label":0},{"cid":"compose/cli/main.py::exitval_from_opts","text":"compose/cli/main.py::exitval_from_opts\ndef exitval_from_opts(options, project):\n exit_value_from = options.get('--exit-code-from')\n if exit_value_from:\n if not options.get('--abort-on-container-exit'):\n log.warn('using --exit-code-from implies --abort-on-container-exit')\n options['--abort-on-container-exit'] = True\n if exit_value_from not in [s.name for s in project.get_services()]:\n log.error('No service named \"%s\" was found in your compose file.',\n exit_value_from)\n sys.exit(2)\n return exit_value_from","label":0},{"cid":"compose/cli/main.py::image_type_from_opt","text":"compose/cli/main.py::image_type_from_opt\ndef image_type_from_opt(flag, value):\n if not value:\n return ImageType.none\n try:\n return ImageType[value]\n except KeyError:\n raise UserError(\"%s flag must be one of: all, local\" % flag)","label":0},{"cid":"compose/cli/main.py::build_action_from_opts","text":"compose/cli/main.py::build_action_from_opts\ndef build_action_from_opts(options):\n if options['--build'] and options['--no-build']:\n raise UserError(\"--build and --no-build can not be combined.\")\n\n if options['--build']:\n return BuildAction.force\n\n if options['--no-build']:\n return BuildAction.skip\n\n return BuildAction.none","label":0},{"cid":"compose/cli/main.py::build_container_options","text":"compose/cli/main.py::build_container_options\ndef build_container_options(options, detach, command):\n container_options = {\n 'command': command,\n 'tty': not (detach or options['-T'] or not sys.stdin.isatty()),\n 'stdin_open': not detach,\n 'detach': detach,\n }\n\n if options['-e']:\n container_options['environment'] = Environment.from_command_line(\n parse_environment(options['-e'])\n )\n\n if options['--entrypoint']:\n container_options['entrypoint'] = options.get('--entrypoint')\n\n if options['--rm']:\n container_options['restart'] = None\n\n if options['--user']:\n container_options['user'] = options.get('--user')\n\n if not options['--service-ports']:\n container_options['ports'] = []\n\n if options['--publish']:\n container_options['ports'] = options.get('--publish')\n\n if options['--name']:\n container_options['name'] = options['--name']\n\n if options['--workdir']:\n container_options['working_dir'] = options['--workdir']\n\n if options['--volume']:\n volumes = [VolumeSpec.parse(i) for i in options['--volume']]\n container_options['volumes'] = volumes\n\n return container_options","label":0},{"cid":"compose/cli/main.py::run_one_off_container","text":"compose/cli/main.py::run_one_off_container\ndef run_one_off_container(container_options, project, service, options):\n if not options['--no-deps']:\n deps = service.get_dependency_names()\n if deps:\n project.up(\n service_names=deps,\n start_deps=True,\n strategy=ConvergenceStrategy.never)\n\n project.initialize()\n\n container = service.create_container(\n quiet=True,\n one_off=True,\n **container_options)\n\n if options['-d']:\n service.start_container(container)\n print(container.name)\n return\n\n def remove_container(force=False):\n if options['--rm']:\n project.client.remove_container(container.id, force=True, v=True)\n\n signals.set_signal_handler_to_shutdown()\n try:\n try:\n if IS_WINDOWS_PLATFORM:\n service.connect_container_to_networks(container)\n exit_code = call_docker([\"start\", \"--attach\", \"--interactive\", container.id])\n else:\n operation = RunOperation(\n project.client,\n container.id,\n interactive=not options['-T'],\n logs=False,\n )\n pty = PseudoTerminal(project.client, operation)\n sockets = pty.sockets()\n service.start_container(container)\n pty.start(sockets)\n exit_code = container.wait()\n except signals.ShutdownException:\n project.client.stop(container.id)\n exit_code = 1\n except signals.ShutdownException:\n project.client.kill(container.id)\n remove_container(force=True)\n sys.exit(2)\n\n remove_container()\n sys.exit(exit_code)","label":0},{"cid":"compose/cli/main.py::run_one_off_container.remove_container","text":"compose/cli/main.py::run_one_off_container.remove_container\n def remove_container(force=False):\n if options['--rm']:\n project.client.remove_container(container.id, force=True, v=True)","label":0},{"cid":"compose/cli/main.py::log_printer_from_project","text":"compose/cli/main.py::log_printer_from_project\ndef log_printer_from_project(\n project,\n containers,\n monochrome,\n log_args,\n cascade_stop=False,\n event_stream=None,\n):\n return LogPrinter(\n containers,\n build_log_presenters(project.service_names, monochrome),\n event_stream or project.events(),\n cascade_stop=cascade_stop,\n log_args=log_args)","label":0},{"cid":"compose/cli/main.py::filter_containers_to_service_names","text":"compose/cli/main.py::filter_containers_to_service_names\ndef filter_containers_to_service_names(containers, service_names):\n if not service_names:\n return containers\n\n return [\n container\n for container in containers if container.service in service_names\n ]","label":0},{"cid":"compose/cli/main.py::up_shutdown_context","text":"compose/cli/main.py::up_shutdown_context\n@contextlib.contextmanager\ndef up_shutdown_context(project, service_names, timeout, detached):\n if detached:\n yield\n return\n\n signals.set_signal_handler_to_shutdown()\n try:\n try:\n yield\n except signals.ShutdownException:\n print(\"Gracefully stopping... (press Ctrl+C again to force)\")\n project.stop(service_names=service_names, timeout=timeout)\n except signals.ShutdownException:\n project.kill(service_names=service_names)\n sys.exit(2)","label":0},{"cid":"compose/cli/main.py::list_containers","text":"compose/cli/main.py::list_containers\ndef list_containers(containers):\n return \", \".join(c.name for c in containers)","label":0},{"cid":"compose/cli/main.py::exit_if","text":"compose/cli/main.py::exit_if\ndef exit_if(condition, message, exit_code):\n if condition:\n log.error(message)\n raise SystemExit(exit_code)","label":0},{"cid":"compose/cli/main.py::call_docker","text":"compose/cli/main.py::call_docker\ndef call_docker(args):\n executable_path = find_executable('docker')\n if not executable_path:\n raise UserError(errors.docker_not_found_msg(\"Couldn't find `docker` binary.\"))\n\n args = [executable_path] + args\n log.debug(\" \".join(map(pipes.quote, args)))\n\n return subprocess.call(args)","label":0},{"cid":"compose/config/environment.py::split_env","text":"compose/config/environment.py::split_env\ndef split_env(env):\n if isinstance(env, six.binary_type):\n env = env.decode('utf-8', 'replace')\n if '=' in env:\n return env.split('=', 1)\n else:\n return env, None","label":0},{"cid":"compose/config/environment.py::env_vars_from_file","text":"compose/config/environment.py::env_vars_from_file\ndef env_vars_from_file(filename):\n \"\"\"\n Read in a line delimited file of environment variables.\n \"\"\"\n if not os.path.exists(filename):\n raise ConfigurationError(\"Couldn't find env file: %s\" % filename)\n elif not os.path.isfile(filename):\n raise ConfigurationError(\"%s is not a file.\" % (filename))\n env = {}\n with contextlib.closing(codecs.open(filename, 'r', 'utf-8')) as fileobj:\n for line in fileobj:\n line = line.strip()\n if line and not line.startswith('#'):\n k, v = split_env(line)\n env[k] = v\n return env","label":0},{"cid":"compose/config/environment.py::Environment.__init__","text":"compose/config/environment.py::Environment.__init__\n def __init__(self, *args, **kwargs):\n super(Environment, self).__init__(*args, **kwargs)\n self.missing_keys = []","label":0},{"cid":"compose/config/environment.py::Environment.from_env_file","text":"compose/config/environment.py::Environment.from_env_file\n @classmethod\n def from_env_file(cls, base_dir):\n def _initialize():\n result = cls()\n if base_dir is None:\n return result\n env_file_path = os.path.join(base_dir, '.env')\n try:\n return cls(env_vars_from_file(env_file_path))\n except ConfigurationError:\n pass\n return result\n instance = _initialize()\n instance.update(os.environ)\n return instance","label":0},{"cid":"compose/config/environment.py::Environment.from_env_file._initialize","text":"compose/config/environment.py::Environment.from_env_file._initialize\n def _initialize():\n result = cls()\n if base_dir is None:\n return result\n env_file_path = os.path.join(base_dir, '.env')\n try:\n return cls(env_vars_from_file(env_file_path))\n except ConfigurationError:\n pass\n return result","label":0},{"cid":"compose/config/environment.py::Environment.from_command_line","text":"compose/config/environment.py::Environment.from_command_line\n @classmethod\n def from_command_line(cls, parsed_env_opts):\n result = cls()\n for k, v in parsed_env_opts.items():\n # Values from the command line take priority, unless they're unset\n # in which case they take the value from the system's environment\n if v is None and k in os.environ:\n result[k] = os.environ[k]\n else:\n result[k] = v\n return result","label":0},{"cid":"compose/config/environment.py::Environment.__getitem__","text":"compose/config/environment.py::Environment.__getitem__\n def __getitem__(self, key):\n try:\n return super(Environment, self).__getitem__(key)\n except KeyError:\n if IS_WINDOWS_PLATFORM:\n try:\n return super(Environment, self).__getitem__(key.upper())\n except KeyError:\n pass\n if key not in self.missing_keys:\n log.warn(\n \"The {} variable is not set. Defaulting to a blank string.\"\n .format(key)\n )\n self.missing_keys.append(key)\n\n return \"\"","label":0},{"cid":"compose/config/environment.py::Environment.__contains__","text":"compose/config/environment.py::Environment.__contains__\n def __contains__(self, key):\n result = super(Environment, self).__contains__(key)\n if IS_WINDOWS_PLATFORM:\n return (\n result or super(Environment, self).__contains__(key.upper())\n )\n return result","label":0},{"cid":"compose/config/environment.py::Environment.get","text":"compose/config/environment.py::Environment.get\n def get(self, key, *args, **kwargs):\n if IS_WINDOWS_PLATFORM:\n return super(Environment, self).get(\n key,\n super(Environment, self).get(key.upper(), *args, **kwargs)\n )\n return super(Environment, self).get(key, *args, **kwargs)","label":0},{"cid":"compose/config/environment.py::Environment.get_boolean","text":"compose/config/environment.py::Environment.get_boolean\n def get_boolean(self, key):\n # Convert a value to a boolean using \"common sense\" rules.\n # Unset, empty, \"0\" and \"false\" (i-case) yield False.\n # All other values yield True.\n value = self.get(key)\n if not value:\n return False\n if value.lower() in ['0', 'false']:\n return False\n return True","label":0},{"cid":"compose/config/serialize.py::serialize_config_type","text":"compose/config/serialize.py::serialize_config_type\ndef serialize_config_type(dumper, data):\n representer = dumper.represent_str if six.PY3 else dumper.represent_unicode\n return representer(data.repr())","label":0},{"cid":"compose/config/serialize.py::serialize_dict_type","text":"compose/config/serialize.py::serialize_dict_type\ndef serialize_dict_type(dumper, data):\n return dumper.represent_dict(data.repr())","label":0},{"cid":"compose/config/serialize.py::denormalize_config","text":"compose/config/serialize.py::denormalize_config\ndef denormalize_config(config):\n result = {'version': V2_1 if config.version == V1 else config.version}\n denormalized_services = [\n denormalize_service_dict(service_dict, config.version)\n for service_dict in config.services\n ]\n result['services'] = {\n service_dict.pop('name'): service_dict\n for service_dict in denormalized_services\n }\n result['networks'] = config.networks.copy()\n for net_name, net_conf in result['networks'].items():\n if 'external_name' in net_conf:\n del net_conf['external_name']\n\n result['volumes'] = config.volumes.copy()\n for vol_name, vol_conf in result['volumes'].items():\n if 'external_name' in vol_conf:\n del vol_conf['external_name']\n\n if config.version in (V3_1, V3_2):\n result['secrets'] = config.secrets\n return result","label":1},{"cid":"compose/config/serialize.py::serialize_config","text":"compose/config/serialize.py::serialize_config\ndef serialize_config(config):\n return yaml.safe_dump(\n denormalize_config(config),\n default_flow_style=False,\n indent=2,\n width=80)","label":1},{"cid":"compose/config/serialize.py::serialize_ns_time_value","text":"compose/config/serialize.py::serialize_ns_time_value\ndef serialize_ns_time_value(value):\n result = (value, 'ns')\n table = [\n (1000., 'us'),\n (1000., 'ms'),\n (1000., 's'),\n (60., 'm'),\n (60., 'h')\n ]\n for stage in table:\n tmp = value / stage[0]\n if tmp == int(value / stage[0]):\n value = tmp\n result = (int(value), stage[1])\n else:\n break\n return '{0}{1}'.format(*result)","label":0},{"cid":"compose/config/serialize.py::denormalize_service_dict","text":"compose/config/serialize.py::denormalize_service_dict\ndef denormalize_service_dict(service_dict, version):\n service_dict = service_dict.copy()\n\n if 'restart' in service_dict:\n service_dict['restart'] = types.serialize_restart_spec(\n service_dict['restart']\n )\n\n if version == V1 and 'network_mode' not in service_dict:\n service_dict['network_mode'] = 'bridge'\n\n if 'depends_on' in service_dict and version != V2_1:\n service_dict['depends_on'] = sorted([\n svc for svc in service_dict['depends_on'].keys()\n ])\n\n if 'healthcheck' in service_dict:\n if 'interval' in service_dict['healthcheck']:\n service_dict['healthcheck']['interval'] = serialize_ns_time_value(\n service_dict['healthcheck']['interval']\n )\n if 'timeout' in service_dict['healthcheck']:\n service_dict['healthcheck']['timeout'] = serialize_ns_time_value(\n service_dict['healthcheck']['timeout']\n )\n\n if 'ports' in service_dict and version not in (V3_2,):\n service_dict['ports'] = map(\n lambda p: p.legacy_repr() if isinstance(p, types.ServicePort) else p,\n service_dict['ports']\n )\n\n return service_dict","label":1},{"cid":"compose/container.py::Container.__init__","text":"compose/container.py::Container.__init__\n def __init__(self, client, dictionary, has_been_inspected=False):\n self.client = client\n self.dictionary = dictionary\n self.has_been_inspected = has_been_inspected\n self.log_stream = None","label":0},{"cid":"compose/container.py::Container.from_ps","text":"compose/container.py::Container.from_ps\n @classmethod\n def from_ps(cls, client, dictionary, **kwargs):\n \"\"\"\n Construct a container object from the output of GET /containers/json.\n \"\"\"\n name = get_container_name(dictionary)\n if name is None:\n return None\n\n new_dictionary = {\n 'Id': dictionary['Id'],\n 'Image': dictionary['Image'],\n 'Name': '/' + name,\n }\n return cls(client, new_dictionary, **kwargs)","label":0},{"cid":"compose/container.py::Container.from_id","text":"compose/container.py::Container.from_id\n @classmethod\n def from_id(cls, client, id):\n return cls(client, client.inspect_container(id), has_been_inspected=True)","label":0},{"cid":"compose/container.py::Container.create","text":"compose/container.py::Container.create\n @classmethod\n def create(cls, client, **options):\n response = client.create_container(**options)\n return cls.from_id(client, response['Id'])","label":0},{"cid":"compose/container.py::Container.id","text":"compose/container.py::Container.id\n @property\n def id(self):\n return self.dictionary['Id']","label":0},{"cid":"compose/container.py::Container.image","text":"compose/container.py::Container.image\n @property\n def image(self):\n return self.dictionary['Image']","label":0},{"cid":"compose/container.py::Container.image_config","text":"compose/container.py::Container.image_config\n @property\n def image_config(self):\n return self.client.inspect_image(self.image)","label":0},{"cid":"compose/container.py::Container.short_id","text":"compose/container.py::Container.short_id\n @property\n def short_id(self):\n return self.id[:12]","label":0},{"cid":"compose/container.py::Container.name","text":"compose/container.py::Container.name\n @property\n def name(self):\n return self.dictionary['Name'][1:]","label":0},{"cid":"compose/container.py::Container.service","text":"compose/container.py::Container.service\n @property\n def service(self):\n return self.labels.get(LABEL_SERVICE)","label":0},{"cid":"compose/container.py::Container.name_without_project","text":"compose/container.py::Container.name_without_project\n @property\n def name_without_project(self):\n project = self.labels.get(LABEL_PROJECT)\n\n if self.name.startswith('{0}_{1}'.format(project, self.service)):\n return '{0}_{1}'.format(self.service, self.number)\n else:\n return self.name","label":0},{"cid":"compose/container.py::Container.number","text":"compose/container.py::Container.number\n @property\n def number(self):\n number = self.labels.get(LABEL_CONTAINER_NUMBER)\n if not number:\n raise ValueError(\"Container {0} does not have a {1} label\".format(\n self.short_id, LABEL_CONTAINER_NUMBER))\n return int(number)","label":0},{"cid":"compose/container.py::Container.ports","text":"compose/container.py::Container.ports\n @property\n def ports(self):\n self.inspect_if_not_inspected()\n return self.get('NetworkSettings.Ports') or {}","label":0},{"cid":"compose/container.py::Container.human_readable_ports","text":"compose/container.py::Container.human_readable_ports\n @property\n def human_readable_ports(self):\n def format_port(private, public):\n if not public:\n return private\n return '{HostIp}:{HostPort}->{private}'.format(\n private=private, **public[0])\n\n return ', '.join(format_port(*item)\n for item in sorted(six.iteritems(self.ports)))","label":0},{"cid":"compose/container.py::Container.human_readable_ports.format_port","text":"compose/container.py::Container.human_readable_ports.format_port\n def format_port(private, public):\n if not public:\n return private\n return '{HostIp}:{HostPort}->{private}'.format(\n private=private, **public[0])","label":0},{"cid":"compose/container.py::Container.labels","text":"compose/container.py::Container.labels\n @property\n def labels(self):\n return self.get('Config.Labels') or {}","label":0},{"cid":"compose/container.py::Container.stop_signal","text":"compose/container.py::Container.stop_signal\n @property\n def stop_signal(self):\n return self.get('Config.StopSignal')","label":0},{"cid":"compose/container.py::Container.log_config","text":"compose/container.py::Container.log_config\n @property\n def log_config(self):\n return self.get('HostConfig.LogConfig') or None","label":0},{"cid":"compose/container.py::Container.human_readable_state","text":"compose/container.py::Container.human_readable_state\n @property\n def human_readable_state(self):\n if self.is_paused:\n return 'Paused'\n if self.is_restarting:\n return 'Restarting'\n if self.is_running:\n return 'Ghost' if self.get('State.Ghost') else 'Up'\n else:\n return 'Exit %s' % self.get('State.ExitCode')","label":0},{"cid":"compose/container.py::Container.human_readable_command","text":"compose/container.py::Container.human_readable_command\n @property\n def human_readable_command(self):\n entrypoint = self.get('Config.Entrypoint') or []\n cmd = self.get('Config.Cmd') or []\n return ' '.join(entrypoint + cmd)","label":0},{"cid":"compose/container.py::Container.environment","text":"compose/container.py::Container.environment\n @property\n def environment(self):\n def parse_env(var):\n if '=' in var:\n return var.split(\"=\", 1)\n return var, None\n return dict(parse_env(var) for var in self.get('Config.Env') or [])","label":0},{"cid":"compose/container.py::Container.environment.parse_env","text":"compose/container.py::Container.environment.parse_env\n def parse_env(var):\n if '=' in var:\n return var.split(\"=\", 1)\n return var, None","label":0},{"cid":"compose/container.py::Container.exit_code","text":"compose/container.py::Container.exit_code\n @property\n def exit_code(self):\n return self.get('State.ExitCode')","label":0},{"cid":"compose/container.py::Container.is_running","text":"compose/container.py::Container.is_running\n @property\n def is_running(self):\n return self.get('State.Running')","label":0},{"cid":"compose/container.py::Container.is_restarting","text":"compose/container.py::Container.is_restarting\n @property\n def is_restarting(self):\n return self.get('State.Restarting')","label":0},{"cid":"compose/container.py::Container.is_paused","text":"compose/container.py::Container.is_paused\n @property\n def is_paused(self):\n return self.get('State.Paused')","label":0},{"cid":"compose/container.py::Container.log_driver","text":"compose/container.py::Container.log_driver\n @property\n def log_driver(self):\n return self.get('HostConfig.LogConfig.Type')","label":0},{"cid":"compose/container.py::Container.has_api_logs","text":"compose/container.py::Container.has_api_logs\n @property\n def has_api_logs(self):\n log_type = self.log_driver\n return not log_type or log_type in ('json-file', 'journald')","label":0},{"cid":"compose/container.py::Container.attach_log_stream","text":"compose/container.py::Container.attach_log_stream\n def attach_log_stream(self):\n \"\"\"A log stream can only be attached if the container uses a json-file\n log driver.\n \"\"\"\n if self.has_api_logs:\n self.log_stream = self.attach(stdout=True, stderr=True, stream=True)","label":0},{"cid":"compose/container.py::Container.get","text":"compose/container.py::Container.get\n def get(self, key):\n \"\"\"Return a value from the container or None if the value is not set.\n\n :param key: a string using dotted notation for nested dictionary\n lookups\n \"\"\"\n self.inspect_if_not_inspected()\n\n def get_value(dictionary, key):\n return (dictionary or {}).get(key)\n\n return reduce(get_value, key.split('.'), self.dictionary)","label":0},{"cid":"compose/container.py::Container.get.get_value","text":"compose/container.py::Container.get.get_value\n def get_value(dictionary, key):\n return (dictionary or {}).get(key)","label":0},{"cid":"compose/container.py::Container.get_local_port","text":"compose/container.py::Container.get_local_port\n def get_local_port(self, port, protocol='tcp'):\n port = self.ports.get(\"%s/%s\" % (port, protocol))\n return \"{HostIp}:{HostPort}\".format(**port[0]) if port else None","label":0},{"cid":"compose/container.py::Container.get_mount","text":"compose/container.py::Container.get_mount\n def get_mount(self, mount_dest):\n for mount in self.get('Mounts'):\n if mount['Destination'] == mount_dest:\n return mount\n return None","label":0},{"cid":"compose/container.py::Container.start","text":"compose/container.py::Container.start\n def start(self, **options):\n return self.client.start(self.id, **options)","label":0},{"cid":"compose/container.py::Container.stop","text":"compose/container.py::Container.stop\n def stop(self, **options):\n return self.client.stop(self.id, **options)","label":0},{"cid":"compose/container.py::Container.pause","text":"compose/container.py::Container.pause\n def pause(self, **options):\n return self.client.pause(self.id, **options)","label":0},{"cid":"compose/container.py::Container.unpause","text":"compose/container.py::Container.unpause\n def unpause(self, **options):\n return self.client.unpause(self.id, **options)","label":0},{"cid":"compose/container.py::Container.kill","text":"compose/container.py::Container.kill\n def kill(self, **options):\n return self.client.kill(self.id, **options)","label":0},{"cid":"compose/container.py::Container.restart","text":"compose/container.py::Container.restart\n def restart(self, **options):\n return self.client.restart(self.id, **options)","label":0},{"cid":"compose/container.py::Container.remove","text":"compose/container.py::Container.remove\n def remove(self, **options):\n return self.client.remove_container(self.id, **options)","label":0},{"cid":"compose/service.py::BuildError.__init__","text":"compose/service.py::BuildError.__init__\n def __init__(self, service, reason):\n self.service = service\n self.reason = reason","label":0},{"cid":"compose/service.py::NeedsBuildError.__init__","text":"compose/service.py::NeedsBuildError.__init__\n def __init__(self, service):\n self.service = service","label":0},{"cid":"compose/service.py::ConvergenceStrategy.allows_recreate","text":"compose/service.py::ConvergenceStrategy.allows_recreate\n @property\n def allows_recreate(self):\n return self is not type(self).never","label":0},{"cid":"compose/service.py::Service.__init__","text":"compose/service.py::Service.__init__\n def __init__(\n self,\n name,\n client=None,\n project='default',\n use_networking=False,\n links=None,\n volumes_from=None,\n network_mode=None,\n networks=None,\n secrets=None,\n **options\n ):\n self.name = name\n self.client = client\n self.project = project\n self.use_networking = use_networking\n self.links = links or []\n self.volumes_from = volumes_from or []\n self.network_mode = network_mode or NetworkMode(None)\n self.networks = networks or {}\n self.secrets = secrets or []\n self.options = options","label":0},{"cid":"compose/service.py::Service.__repr__","text":"compose/service.py::Service.__repr__\n def __repr__(self):\n return '<Service: {}>'.format(self.name)","label":0},{"cid":"compose/service.py::Service.containers","text":"compose/service.py::Service.containers\n def containers(self, stopped=False, one_off=False, filters={}):\n filters.update({'label': self.labels(one_off=one_off)})\n\n return list(filter(None, [\n Container.from_ps(self.client, container)\n for container in self.client.containers(\n all=stopped,\n filters=filters)]))","label":0},{"cid":"compose/service.py::Service.get_container","text":"compose/service.py::Service.get_container\n def get_container(self, number=1):\n \"\"\"Return a :class:`compose.container.Container` for this service. The\n container must be active, and match `number`.\n \"\"\"\n labels = self.labels() + ['{0}={1}'.format(LABEL_CONTAINER_NUMBER, number)]\n for container in self.client.containers(filters={'label': labels}):\n return Container.from_ps(self.client, container)\n\n raise ValueError(\"No container found for %s_%s\" % (self.name, number))","label":0},{"cid":"compose/service.py::Service.start","text":"compose/service.py::Service.start\n def start(self, **options):\n containers = self.containers(stopped=True)\n for c in containers:\n self.start_container_if_stopped(c, **options)\n return containers","label":0},{"cid":"compose/service.py::Service.scale","text":"compose/service.py::Service.scale\n def scale(self, desired_num, timeout=None):\n \"\"\"\n Adjusts the number of containers to the specified number and ensures\n they are running.\n\n - creates containers until there are at least `desired_num`\n - stops containers until there are at most `desired_num` running\n - starts containers until there are at least `desired_num` running\n - removes all stopped containers\n \"\"\"\n if self.custom_container_name and desired_num > 1:\n log.warn('The \"%s\" service is using the custom container name \"%s\". '\n 'Docker requires each container to have a unique name. '\n 'Remove the custom name to scale the service.'\n % (self.name, self.custom_container_name))\n\n if self.specifies_host_port() and desired_num > 1:\n log.warn('The \"%s\" service specifies a port on the host. If multiple containers '\n 'for this service are created on a single host, the port will clash.'\n % self.name)\n\n def create_and_start(service, number):\n container = service.create_container(number=number, quiet=True)\n service.start_container(container)\n return container\n\n def stop_and_remove(container):\n container.stop(timeout=self.stop_timeout(timeout))\n container.remove()\n\n running_containers = self.containers(stopped=False)\n num_running = len(running_containers)\n\n if desired_num == num_running:\n # do nothing as we already have the desired number\n log.info('Desired container number already achieved')\n return\n\n if desired_num > num_running:\n # we need to start/create until we have desired_num\n all_containers = self.containers(stopped=True)\n\n if num_running != len(all_containers):\n # we have some stopped containers, let's st","label":0},{"cid":"compose/service.py::Service.scale.create_and_start","text":"compose/service.py::Service.scale.create_and_start\n def create_and_start(service, number):\n container = service.create_container(number=number, quiet=True)\n service.start_container(container)\n return container","label":0},{"cid":"compose/service.py::Service.scale.stop_and_remove","text":"compose/service.py::Service.scale.stop_and_remove\n def stop_and_remove(container):\n container.stop(timeout=self.stop_timeout(timeout))\n container.remove()","label":0},{"cid":"compose/service.py::Service.create_container","text":"compose/service.py::Service.create_container\n def create_container(self,\n one_off=False,\n previous_container=None,\n number=None,\n quiet=False,\n **override_options):\n \"\"\"\n Create a container for this service. If the image doesn't exist, attempt to pull\n it.\n \"\"\"\n # This is only necessary for `scale` and `volumes_from`\n # auto-creating containers to satisfy the dependency.\n self.ensure_image_exists()\n\n container_options = self._get_container_create_options(\n override_options,\n number or self._next_container_number(one_off=one_off),\n one_off=one_off,\n previous_container=previous_container,\n )\n\n if 'name' in container_options and not quiet:\n log.info(\"Creating %s\" % container_options['name'])\n\n try:\n return Container.create(self.client, **container_options)\n except APIError as ex:\n raise OperationFailedError(\"Cannot create container for service %s: %s\" %\n (self.name, ex.explanation))","label":0},{"cid":"compose/service.py::Service.ensure_image_exists","text":"compose/service.py::Service.ensure_image_exists\n def ensure_image_exists(self, do_build=BuildAction.none):\n if self.can_be_built() and do_build == BuildAction.force:\n self.build()\n return\n\n try:\n self.image()\n return\n except NoSuchImageError:\n pass\n\n if not self.can_be_built():\n self.pull()\n return\n\n if do_build == BuildAction.skip:\n raise NeedsBuildError(self)\n\n self.build()\n log.warn(\n \"Image for service {} was built because it did not already exist. To \"\n \"rebuild this image you must use `docker-compose build` or \"\n \"`docker-compose up --build`.\".format(self.name))","label":0},{"cid":"compose/service.py::Service.image","text":"compose/service.py::Service.image\n def image(self):\n try:\n return self.client.inspect_image(self.image_name)\n except ImageNotFound:\n raise NoSuchImageError(\"Image '{}' not found\".format(self.image_name))","label":0},{"cid":"compose/service.py::Service.image_name","text":"compose/service.py::Service.image_name\n @property\n def image_name(self):\n return self.options.get('image', '{s.project}_{s.name}'.format(s=self))","label":0},{"cid":"compose/service.py::Service.convergence_plan","text":"compose/service.py::Service.convergence_plan\n def convergence_plan(self, strategy=ConvergenceStrategy.changed):\n containers = self.containers(stopped=True)\n\n if not containers:\n return ConvergencePlan('create', [])\n\n if strategy is ConvergenceStrategy.never:\n return ConvergencePlan('start', containers)\n\n if (\n strategy is ConvergenceStrategy.always or\n self._containers_have_diverged(containers)\n ):\n return ConvergencePlan('recreate', containers)\n\n stopped = [c for c in containers if not c.is_running]\n\n if stopped:\n return ConvergencePlan('start', stopped)\n\n return ConvergencePlan('noop', containers)","label":0},{"cid":"compose/service.py::Service._containers_have_diverged","text":"compose/service.py::Service._containers_have_diverged\n def _containers_have_diverged(self, containers):\n config_hash = None\n\n try:\n config_hash = self.config_hash\n except NoSuchImageError as e:\n log.debug(\n 'Service %s has diverged: %s',\n self.name, six.text_type(e),\n )\n return True\n\n has_diverged = False\n\n for c in containers:\n container_config_hash = c.labels.get(LABEL_CONFIG_HASH, None)\n if container_config_hash != config_hash:\n log.debug(\n '%s has diverged: %s != %s',\n c.name, container_config_hash, config_hash,\n )\n has_diverged = True\n\n return has_diverged","label":0},{"cid":"compose/service.py::Service.execute_convergence_plan","text":"compose/service.py::Service.execute_convergence_plan\n def execute_convergence_plan(self,\n plan,\n timeout=None,\n detached=False,\n start=True):\n (action, containers) = plan\n should_attach_logs = not detached\n\n if action == 'create':\n container = self.create_container()\n\n if should_attach_logs:\n container.attach_log_stream()\n\n if start:\n self.start_container(container)\n\n return [container]\n\n elif action == 'recreate':\n return [\n self.recreate_container(\n container,\n timeout=timeout,\n attach_logs=should_attach_logs,\n start_new_container=start\n )\n for container in containers\n ]\n\n elif action == 'start':\n if start:\n for container in containers:\n self.start_container_if_stopped(container, attach_logs=should_attach_logs)\n\n return containers\n\n elif action == 'noop':\n for c in containers:\n log.info(\"%s is up-to-date\" % c.name)\n\n return containers\n\n else:\n raise Exception(\"Invalid action: {}\".format(action))","label":0},{"cid":"compose/service.py::Service.recreate_container","text":"compose/service.py::Service.recreate_container\n def recreate_container(\n self,\n container,\n timeout=None,\n attach_logs=False,\n start_new_container=True):\n \"\"\"Recreate a container.\n\n The original container is renamed to a temporary name so that data\n volumes can be copied to the new container, before the original\n container is removed.\n \"\"\"\n log.info(\"Recreating %s\" % container.name)\n\n container.stop(timeout=self.stop_timeout(timeout))\n container.rename_to_tmp_name()\n new_container = self.create_container(\n previous_container=container,\n number=container.labels.get(LABEL_CONTAINER_NUMBER),\n quiet=True,\n )\n if attach_logs:\n new_container.attach_log_stream()\n if start_new_container:\n self.start_container(new_container)\n container.remove()\n return new_container","label":0},{"cid":"compose/service.py::Service.stop_timeout","text":"compose/service.py::Service.stop_timeout\n def stop_timeout(self, timeout):\n if timeout is not None:\n return timeout\n timeout = parse_seconds_float(self.options.get('stop_grace_period'))\n if timeout is not None:\n return timeout\n return DEFAULT_TIMEOUT","label":0},{"cid":"compose/service.py::Service.start_container_if_stopped","text":"compose/service.py::Service.start_container_if_stopped\n def start_container_if_stopped(self, container, attach_logs=False, quiet=False):\n if not container.is_running:\n if not quiet:\n log.info(\"Starting %s\" % container.name)\n if attach_logs:\n container.attach_log_stream()\n return self.start_container(container)","label":0},{"cid":"compose/service.py::Service.start_container","text":"compose/service.py::Service.start_container\n def start_container(self, container):\n self.connect_container_to_networks(container)\n try:\n container.start()\n except APIError as ex:\n raise OperationFailedError(\"Cannot start service %s: %s\" % (self.name, ex.explanation))\n return container","label":0},{"cid":"compose/service.py::Service.connect_container_to_networks","text":"compose/service.py::Service.connect_container_to_networks\n def connect_container_to_networks(self, container):\n connected_networks = container.get('NetworkSettings.Networks')\n\n for network, netdefs in self.networks.items():\n if network in connected_networks:\n if short_id_alias_exists(container, network):\n continue\n\n self.client.disconnect_container_from_network(\n container.id,\n network)\n\n self.client.connect_container_to_network(\n container.id, network,\n aliases=self._get_aliases(netdefs, container),\n ipv4_address=netdefs.get('ipv4_address', None),\n ipv6_address=netdefs.get('ipv6_address', None),\n links=self._get_links(False),\n link_local_ips=netdefs.get('link_local_ips', None),\n )","label":0},{"cid":"compose/service.py::Service.remove_duplicate_containers","text":"compose/service.py::Service.remove_duplicate_containers\n def remove_duplicate_containers(self, timeout=None):\n for c in self.duplicate_containers():\n log.info('Removing %s' % c.name)\n c.stop(timeout=self.stop_timeout(timeout))\n c.remove()","label":0},{"cid":"compose/service.py::Service.duplicate_containers","text":"compose/service.py::Service.duplicate_containers\n def duplicate_containers(self):\n containers = sorted(\n self.containers(stopped=True),\n key=lambda c: c.get('Created'),\n )\n\n numbers = set()\n\n for c in containers:\n if c.number in numbers:\n yield c\n else:\n numbers.add(c.number)","label":0},{"cid":"compose/service.py::Service.config_hash","text":"compose/service.py::Service.config_hash\n @property\n def config_hash(self):\n return json_hash(self.config_dict())","label":0},{"cid":"compose/service.py::Service.config_dict","text":"compose/service.py::Service.config_dict\n def config_dict(self):\n return {\n 'options': self.options,\n 'image_id': self.image()['Id'],\n 'links': self.get_link_names(),\n 'net': self.network_mode.id,\n 'networks': self.networks,\n 'volumes_from': [\n (v.source.name, v.mode)\n for v in self.volumes_from if isinstance(v.source, Service)\n ],\n }","label":0},{"cid":"compose/service.py::Service.get_dependency_names","text":"compose/service.py::Service.get_dependency_names\n def get_dependency_names(self):\n net_name = self.network_mode.service_name\n return (\n self.get_linked_service_names() +\n self.get_volumes_from_names() +\n ([net_name] if net_name else []) +\n list(self.options.get('depends_on', {}).keys())\n )","label":0},{"cid":"compose/service.py::Service.get_dependency_configs","text":"compose/service.py::Service.get_dependency_configs\n def get_dependency_configs(self):\n net_name = self.network_mode.service_name\n configs = dict(\n [(name, None) for name in self.get_linked_service_names()]\n )\n configs.update(dict(\n [(name, None) for name in self.get_volumes_from_names()]\n ))\n configs.update({net_name: None} if net_name else {})\n configs.update(self.options.get('depends_on', {}))\n for svc, config in self.options.get('depends_on', {}).items():\n if config['condition'] == CONDITION_STARTED:\n configs[svc] = lambda s: True\n elif config['condition'] == CONDITION_HEALTHY:\n configs[svc] = lambda s: s.is_healthy()\n else:\n # The config schema already prevents this, but it might be\n # bypassed if Compose is called programmatically.\n raise ValueError(\n 'depends_on condition \"{}\" is invalid.'.format(\n config['condition']\n )\n )\n\n return configs","label":0},{"cid":"compose/service.py::Service.get_linked_service_names","text":"compose/service.py::Service.get_linked_service_names\n def get_linked_service_names(self):\n return [service.name for (service, _) in self.links]","label":0},{"cid":"compose/service.py::Service.get_link_names","text":"compose/service.py::Service.get_link_names\n def get_link_names(self):\n return [(service.name, alias) for service, alias in self.links]","label":0},{"cid":"compose/service.py::Service.get_volumes_from_names","text":"compose/service.py::Service.get_volumes_from_names\n def get_volumes_from_names(self):\n return [s.source.name for s in self.volumes_from if isinstance(s.source, Service)]","label":0},{"cid":"compose/service.py::Service._next_container_number","text":"compose/service.py::Service._next_container_number\n def _next_container_number(self, one_off=False):\n containers = filter(None, [\n Container.from_ps(self.client, container)\n for container in self.client.containers(\n all=True,\n filters={'label': self.labels(one_off=one_off)})\n ])\n numbers = [c.number for c in containers]\n return 1 if not numbers else max(numbers) + 1","label":0},{"cid":"compose/service.py::Service._get_aliases","text":"compose/service.py::Service._get_aliases\n def _get_aliases(self, network, container=None):\n if container and container.labels.get(LABEL_ONE_OFF) == \"True\":\n return []\n\n return list(\n {self.name} |\n ({container.short_id} if container else set()) |\n set(network.get('aliases', ()))\n )","label":0},{"cid":"compose/service.py::Service.build_default_networking_config","text":"compose/service.py::Service.build_default_networking_config\n def build_default_networking_config(self):\n if not self.networks:\n return {}\n\n network = self.networks[self.network_mode.id]\n endpoint = {\n 'Aliases': self._get_aliases(network),\n 'IPAMConfig': {},\n }\n\n if network.get('ipv4_address'):\n endpoint['IPAMConfig']['IPv4Address'] = network.get('ipv4_address')\n if network.get('ipv6_address'):\n endpoint['IPAMConfig']['IPv6Address'] = network.get('ipv6_address')\n\n return {\"EndpointsConfig\": {self.network_mode.id: endpoint}}","label":0},{"cid":"compose/service.py::Service._get_links","text":"compose/service.py::Service._get_links\n def _get_links(self, link_to_self):\n links = {}\n\n for service, link_name in self.links:\n for container in service.containers():\n links[link_name or service.name] = container.name\n links[container.name] = container.name\n links[container.name_without_project] = container.name\n\n if link_to_self:\n for container in self.containers():\n links[self.name] = container.name\n links[container.name] = container.name\n links[container.name_without_project] = container.name\n\n for external_link in self.options.get('external_links') or []:\n if ':' not in external_link:\n link_name = external_link\n else:\n external_link, link_name = external_link.split(':')\n links[link_name] = external_link\n\n return [\n (alias, container_name)\n for (container_name, alias) in links.items()\n ]","label":0},{"cid":"compose/service.py::Service._get_volumes_from","text":"compose/service.py::Service._get_volumes_from\n def _get_volumes_from(self):\n return [build_volume_from(spec) for spec in self.volumes_from]","label":0},{"cid":"compose/service.py::Service._get_container_create_options","text":"compose/service.py::Service._get_container_create_options\n def _get_container_create_options(\n self,\n override_options,\n number,\n one_off=False,\n previous_container=None):\n add_config_hash = (not one_off and not override_options)\n\n container_options = dict(\n (k, self.options[k])\n for k in DOCKER_CONFIG_KEYS if k in self.options)\n container_options.update(override_options)\n\n if not container_options.get('name'):\n container_options['name'] = self.get_container_name(number, one_off)\n\n container_options.setdefault('detach', True)\n\n # If a qualified hostname was given, split it into an\n # unqualified hostname and a domainname unless domainname\n # was also given explicitly. This matches the behavior of\n # the official Docker CLI in that scenario.\n if ('hostname' in container_options and\n 'domainname' not in container_options and\n '.' in container_options['hostname']):\n parts = container_options['hostname'].partition('.')\n container_options['hostname'] = parts[0]\n container_options['domainname'] = parts[2]\n\n if 'ports' in container_options or 'expose' in self.options:\n container_options['ports'] = build_container_ports(\n formatted_ports(container_options.get('ports', [])),\n self.options)\n\n container_options['environment'] = merge_environment(\n self.options.get('environment'),\n override_options.get('environment'))\n\n binds, affinity = merge_volume_bindings(\n container_options.get('volumes') or [],\n previous_container)\n override_options['binds'] = binds\n container_options['environment'].update(affinity)\n\n container_options['volumes'] = dict(\n (v.internal, {}) for v in container_options.get('volumes') or {})\n\n secr","label":0},{"cid":"compose/service.py::Service._get_container_host_config","text":"compose/service.py::Service._get_container_host_config\n def _get_container_host_config(self, override_options, one_off=False):\n options = dict(self.options, **override_options)\n\n logging_dict = options.get('logging', None)\n log_config = get_log_config(logging_dict)\n\n host_config = self.client.create_host_config(\n links=self._get_links(link_to_self=one_off),\n port_bindings=build_port_bindings(\n formatted_ports(options.get('ports', []))\n ),\n binds=options.get('binds'),\n volumes_from=self._get_volumes_from(),\n privileged=options.get('privileged', False),\n network_mode=self.network_mode.mode,\n devices=options.get('devices'),\n dns=options.get('dns'),\n dns_opt=options.get('dns_opt'),\n dns_search=options.get('dns_search'),\n restart_policy=options.get('restart'),\n cap_add=options.get('cap_add'),\n cap_drop=options.get('cap_drop'),\n mem_limit=options.get('mem_limit'),\n mem_reservation=options.get('mem_reservation'),\n memswap_limit=options.get('memswap_limit'),\n ulimits=build_ulimits(options.get('ulimits')),\n log_config=log_config,\n extra_hosts=options.get('extra_hosts'),\n read_only=options.get('read_only'),\n pid_mode=options.get('pid'),\n security_opt=options.get('security_opt'),\n ipc_mode=options.get('ipc'),\n cgroup_parent=options.get('cgroup_parent'),\n cpu_quota=options.get('cpu_quota'),\n shm_size=options.get('shm_size'),\n sysctls=options.get('sysctls'),\n pids_limit=options.get('pids_limit'),\n tmpfs=options.get('tmpfs'),\n oom_score_adj=options.get('oom_score_adj'),\n mem_swappiness=options.get('mem_swappiness'),\n group_add=options.get('group_add'),\n userns_mode=op","label":0},{"cid":"compose/service.py::Service.get_secret_volumes","text":"compose/service.py::Service.get_secret_volumes\n def get_secret_volumes(self):\n def build_spec(secret):\n target = '{}/{}'.format(\n const.SECRETS_PATH,\n secret['secret'].target or secret['secret'].source)\n return VolumeSpec(secret['file'], target, 'ro')\n\n return [build_spec(secret) for secret in self.secrets]","label":0}],"string":"[\n {\n \"cid\": \"compose/bundle.py::NeedsPush.__init__\",\n \"text\": \"compose/bundle.py::NeedsPush.__init__\\n def __init__(self, image_name):\\n self.image_name = image_name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::NeedsPull.__init__\",\n \"text\": \"compose/bundle.py::NeedsPull.__init__\\n def __init__(self, image_name, service_name):\\n self.image_name = image_name\\n self.service_name = service_name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::MissingDigests.__init__\",\n \"text\": \"compose/bundle.py::MissingDigests.__init__\\n def __init__(self, needs_push, needs_pull):\\n self.needs_push = needs_push\\n self.needs_pull = needs_pull\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::serialize_bundle\",\n \"text\": \"compose/bundle.py::serialize_bundle\\ndef serialize_bundle(config, image_digests):\\n return json.dumps(to_bundle(config, image_digests), indent=2, sort_keys=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::get_image_digests\",\n \"text\": \"compose/bundle.py::get_image_digests\\ndef get_image_digests(project, allow_push=False):\\n digests = {}\\n needs_push = set()\\n needs_pull = set()\\n\\n for service in project.services:\\n try:\\n digests[service.name] = get_image_digest(\\n service,\\n allow_push=allow_push,\\n )\\n except NeedsPush as e:\\n needs_push.add(e.image_name)\\n except NeedsPull as e:\\n needs_pull.add(e.service_name)\\n\\n if needs_push or needs_pull:\\n raise MissingDigests(needs_push, needs_pull)\\n\\n return digests\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::get_image_digest\",\n \"text\": \"compose/bundle.py::get_image_digest\\ndef get_image_digest(service, allow_push=False):\\n if 'image' not in service.options:\\n raise UserError(\\n \\\"Service '{s.name}' doesn't define an image tag. An image name is \\\"\\n \\\"required to generate a proper image digest for the bundle. Specify \\\"\\n \\\"an image repo and tag with the 'image' option.\\\".format(s=service))\\n\\n _, _, separator = parse_repository_tag(service.options['image'])\\n # Compose file already uses a digest, no lookup required\\n if separator == '@':\\n return service.options['image']\\n\\n try:\\n image = service.image()\\n except NoSuchImageError:\\n action = 'build' if 'build' in service.options else 'pull'\\n raise UserError(\\n \\\"Image not found for service '{service}'. \\\"\\n \\\"You might need to run `docker-compose {action} {service}`.\\\"\\n .format(service=service.name, action=action))\\n\\n if image['RepoDigests']:\\n # TODO: pick a digest based on the image tag if there are multiple\\n # digests\\n return image['RepoDigests'][0]\\n\\n if 'build' not in service.options:\\n raise NeedsPull(service.image_name, service.name)\\n\\n if not allow_push:\\n raise NeedsPush(service.image_name)\\n\\n return push_image(service)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::push_image\",\n \"text\": \"compose/bundle.py::push_image\\ndef push_image(service):\\n try:\\n digest = service.push()\\n except:\\n log.error(\\n \\\"Failed to push image for service '{s.name}'. Please use an \\\"\\n \\\"image tag that can be pushed to a Docker \\\"\\n \\\"registry.\\\".format(s=service))\\n raise\\n\\n if not digest:\\n raise ValueError(\\\"Failed to get digest for %s\\\" % service.name)\\n\\n repo, _, _ = parse_repository_tag(service.options['image'])\\n identifier = '{repo}@{digest}'.format(repo=repo, digest=digest)\\n\\n # only do this if RepoDigests isn't already populated\\n image = service.image()\\n if not image['RepoDigests']:\\n # Pull by digest so that image['RepoDigests'] is populated for next time\\n # and we don't have to pull/push again\\n service.client.pull(identifier)\\n log.info(\\\"Stored digest for {}\\\".format(service.image_name))\\n\\n return identifier\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::to_bundle\",\n \"text\": \"compose/bundle.py::to_bundle\\ndef to_bundle(config, image_digests):\\n if config.networks:\\n log.warn(\\\"Unsupported top level key 'networks' - ignoring\\\")\\n\\n if config.volumes:\\n log.warn(\\\"Unsupported top level key 'volumes' - ignoring\\\")\\n\\n config = denormalize_config(config)\\n\\n return {\\n 'Version': VERSION,\\n 'Services': {\\n name: convert_service_to_bundle(\\n name,\\n service_dict,\\n image_digests[name],\\n )\\n for name, service_dict in config['services'].items()\\n },\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::convert_service_to_bundle\",\n \"text\": \"compose/bundle.py::convert_service_to_bundle\\ndef convert_service_to_bundle(name, service_dict, image_digest):\\n container_config = {'Image': image_digest}\\n\\n for key, value in service_dict.items():\\n if key in IGNORED_KEYS:\\n continue\\n\\n if key not in SUPPORTED_KEYS:\\n log.warn(\\\"Unsupported key '{}' in services.{} - ignoring\\\".format(key, name))\\n continue\\n\\n if key == 'environment':\\n container_config['Env'] = format_environment({\\n envkey: envvalue for envkey, envvalue in value.items()\\n if envvalue\\n })\\n continue\\n\\n if key in SERVICE_KEYS:\\n container_config[SERVICE_KEYS[key]] = value\\n continue\\n\\n set_command_and_args(\\n container_config,\\n service_dict.get('entrypoint', []),\\n service_dict.get('command', []))\\n container_config['Networks'] = make_service_networks(name, service_dict)\\n\\n ports = make_port_specs(service_dict)\\n if ports:\\n container_config['Ports'] = ports\\n\\n return container_config\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::set_command_and_args\",\n \"text\": \"compose/bundle.py::set_command_and_args\\ndef set_command_and_args(config, entrypoint, command):\\n if isinstance(entrypoint, six.string_types):\\n entrypoint = split_command(entrypoint)\\n if isinstance(command, six.string_types):\\n command = split_command(command)\\n\\n if entrypoint:\\n config['Command'] = entrypoint + command\\n return\\n\\n if command:\\n config['Args'] = command\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::make_service_networks\",\n \"text\": \"compose/bundle.py::make_service_networks\\ndef make_service_networks(name, service_dict):\\n networks = []\\n\\n for network_name, network_def in get_network_defs_for_service(service_dict).items():\\n for key in network_def.keys():\\n log.warn(\\n \\\"Unsupported key '{}' in services.{}.networks.{} - ignoring\\\"\\n .format(key, name, network_name))\\n\\n networks.append(network_name)\\n\\n return networks\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::make_port_specs\",\n \"text\": \"compose/bundle.py::make_port_specs\\ndef make_port_specs(service_dict):\\n ports = []\\n\\n internal_ports = [\\n internal_port\\n for port_def in service_dict.get('ports', [])\\n for internal_port in split_port(port_def)[0]\\n ]\\n\\n internal_ports += service_dict.get('expose', [])\\n\\n for internal_port in internal_ports:\\n spec = make_port_spec(internal_port)\\n if spec not in ports:\\n ports.append(spec)\\n\\n return ports\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/bundle.py::make_port_spec\",\n \"text\": \"compose/bundle.py::make_port_spec\\ndef make_port_spec(value):\\n components = six.text_type(value).partition('/')\\n return {\\n 'Protocol': components[2] or 'tcp',\\n 'Port': int(components[0]),\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::main\",\n \"text\": \"compose/cli/main.py::main\\ndef main():\\n signals.ignore_sigpipe()\\n try:\\n command = dispatch()\\n command()\\n except (KeyboardInterrupt, signals.ShutdownException):\\n log.error(\\\"Aborting.\\\")\\n sys.exit(1)\\n except (UserError, NoSuchService, ConfigurationError,\\n ProjectError, OperationFailedError) as e:\\n log.error(e.msg)\\n sys.exit(1)\\n except BuildError as e:\\n log.error(\\\"Service '%s' failed to build: %s\\\" % (e.service.name, e.reason))\\n sys.exit(1)\\n except StreamOutputError as e:\\n log.error(e)\\n sys.exit(1)\\n except NeedsBuildError as e:\\n log.error(\\\"Service '%s' needs to be built, but --no-build was passed.\\\" % e.service.name)\\n sys.exit(1)\\n except NoSuchCommand as e:\\n commands = \\\"\\\\n\\\".join(parse_doc_section(\\\"commands:\\\", getdoc(e.supercommand)))\\n log.error(\\\"No such command: %s\\\\n\\\\n%s\\\", e.command, commands)\\n sys.exit(1)\\n except (errors.ConnectionError, StreamParseError):\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::dispatch\",\n \"text\": \"compose/cli/main.py::dispatch\\ndef dispatch():\\n setup_logging()\\n dispatcher = DocoptDispatcher(\\n TopLevelCommand,\\n {'options_first': True, 'version': get_version_info('compose')})\\n\\n options, handler, command_options = dispatcher.parse(sys.argv[1:])\\n setup_console_handler(console_handler, options.get('--verbose'))\\n return functools.partial(perform_command, options, handler, command_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::perform_command\",\n \"text\": \"compose/cli/main.py::perform_command\\ndef perform_command(options, handler, command_options):\\n if options['COMMAND'] in ('help', 'version'):\\n # Skip looking up the compose file.\\n handler(command_options)\\n return\\n\\n if options['COMMAND'] in ('config', 'bundle'):\\n command = TopLevelCommand(None)\\n handler(command, options, command_options)\\n return\\n\\n project = project_from_options('.', options)\\n command = TopLevelCommand(project)\\n with errors.handle_connection_errors(project.client):\\n handler(command, command_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_logging\",\n \"text\": \"compose/cli/main.py::setup_logging\\ndef setup_logging():\\n root_logger = logging.getLogger()\\n root_logger.addHandler(console_handler)\\n root_logger.setLevel(logging.DEBUG)\\n\\n # Disable requests logging\\n logging.getLogger(\\\"requests\\\").propagate = False\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_console_handler\",\n \"text\": \"compose/cli/main.py::setup_console_handler\\ndef setup_console_handler(handler, verbose):\\n if handler.stream.isatty():\\n format_class = ConsoleWarningFormatter\\n else:\\n format_class = logging.Formatter\\n\\n if verbose:\\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\\n handler.setLevel(logging.DEBUG)\\n else:\\n handler.setFormatter(format_class())\\n handler.setLevel(logging.INFO)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::parse_doc_section\",\n \"text\": \"compose/cli/main.py::parse_doc_section\\ndef parse_doc_section(name, source):\\n pattern = re.compile('^([^\\\\n]*' + name + '[^\\\\n]*\\\\n?(?:[ \\\\t].*?(?:\\\\n|$))*)',\\n re.IGNORECASE | re.MULTILINE)\\n return [s.strip() for s in pattern.findall(source)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.__init__\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.__init__\\n def __init__(self, project, project_dir='.'):\\n self.project = project\\n self.project_dir = '.'\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.build\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.build\\n def build(self, options):\\n \\\"\\\"\\\"\\n Build or rebuild services.\\n\\n Services are built once and then tagged as `project_service`,\\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\\n contents of its build directory, you can run `docker-compose build` to rebuild it.\\n\\n Usage: build [options] [--build-arg key=val...] [SERVICE...]\\n\\n Options:\\n --force-rm Always remove intermediate containers.\\n --no-cache Do not use cache when building the image.\\n --pull Always attempt to pull a newer version of the image.\\n --build-arg key=val Set build-time variables for one service.\\n \\\"\\\"\\\"\\n service_names = options['SERVICE']\\n build_args = options.get('--build-arg', None)\\n if build_args:\\n environment = Environment.from_env_file(self.project_dir)\\n build_args = resolve_build_args(build_args, environment)\\n\\n if not service_names and build_args:\\n raise UserError(\\\"Need service name for --build-arg option\\\")\\n\\n self.project.build(\\n service_names=service_names,\\n no_cache=bool(options.get('--no-cache', False)),\\n pull=bool(options.get('--pull', False)),\\n force_rm=bool(options.get('--force-rm', False)),\\n build_args=build_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.bundle\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.bundle\\n def bundle(self, config_options, options):\\n \\\"\\\"\\\"\\n Generate a Distributed Application Bundle (DAB) from the Compose file.\\n\\n Images must have digests stored, which requires interaction with a\\n Docker registry. If digests aren't stored for all images, you can fetch\\n them with `docker-compose pull` or `docker-compose push`. To push images\\n automatically when bundling, pass `--push-images`. Only services with\\n a `build` option specified will have their images pushed.\\n\\n Usage: bundle [options]\\n\\n Options:\\n --push-images Automatically push images for any services\\n which have a `build` option specified.\\n\\n -o, --output PATH Path to write the bundle file to.\\n Defaults to \\\"<project name>.dab\\\".\\n \\\"\\\"\\\"\\n self.project = project_from_options('.', config_options)\\n compose_config = get_config_from_options(self.project_dir, config_options)\\n\\n output = options[\\\"--output\\\"]\\n if not output:\\n output = \\\"{}.dab\\\".format(self.project.name)\\n\\n with errors.handle_connection_errors(self.project.client):\\n try:\\n image_digests = get_image_digests(\\n self.project,\\n allow_push=options['--push-images'],\\n )\\n except MissingDigests as e:\\n def list_images(images):\\n return \\\"\\\\n\\\".join(\\\" {}\\\".format(name) for name in sorted(images))\\n\\n paras = [\\\"Some images are missing digests.\\\"]\\n\\n if e.needs_push:\\n command_hint = (\\n \\\"Use `docker-compose push {}` to push them. \\\"\\n \\\"You can do this automatically with `docker-compose bundle --push-images`.\\\"\\n .format(\\\" \\\".join(sorted(e.needs_push)))\\n )\\n\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.config\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.config\\n def config(self, config_options, options):\\n \\\"\\\"\\\"\\n Validate and view the compose file.\\n\\n Usage: config [options]\\n\\n Options:\\n -q, --quiet Only validate the configuration, don't print\\n anything.\\n --services Print the service names, one per line.\\n --volumes Print the volume names, one per line.\\n\\n \\\"\\\"\\\"\\n compose_config = get_config_from_options(self.project_dir, config_options)\\n\\n if options['--quiet']:\\n return\\n\\n if options['--services']:\\n print('\\\\n'.join(service['name'] for service in compose_config.services))\\n return\\n\\n if options['--volumes']:\\n print('\\\\n'.join(volume for volume in compose_config.volumes))\\n return\\n\\n print(serialize_config(compose_config))\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.create\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.create\\n def create(self, options):\\n \\\"\\\"\\\"\\n Creates containers for a service.\\n\\n Usage: create [options] [SERVICE...]\\n\\n Options:\\n --force-recreate Recreate containers even if their configuration and\\n image haven't changed. Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate them.\\n Incompatible with --force-recreate.\\n --no-build Don't build an image, even if it's missing.\\n --build Build images before creating containers.\\n \\\"\\\"\\\"\\n service_names = options['SERVICE']\\n\\n self.project.create(\\n service_names=service_names,\\n strategy=convergence_strategy_from_opts(options),\\n do_build=build_action_from_opts(options),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.down\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.down\\n def down(self, options):\\n \\\"\\\"\\\"\\n Stops containers and removes containers, networks, volumes, and images\\n created by `up`.\\n\\n By default, the only things removed are:\\n\\n - Containers for services defined in the Compose file\\n - Networks defined in the `networks` section of the Compose file\\n - The default network, if one is used\\n\\n Networks and volumes defined as `external` are never removed.\\n\\n Usage: down [options]\\n\\n Options:\\n --rmi type Remove images. Type must be one of:\\n 'all': Remove all images used by any service.\\n 'local': Remove only images that don't have a custom tag\\n set by the `image` field.\\n -v, --volumes Remove named volumes declared in the `volumes` section\\n of the Compose file and anonymous volumes\\n attached to containers.\\n --remove-orphans Remove containers for services not defined in the\\n Compose file\\n \\\"\\\"\\\"\\n image_type = image_type_from_opt('--rmi', options['--rmi'])\\n self.project.down(image_type, options['--volumes'], options['--remove-orphans'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events\\n def events(self, options):\\n \\\"\\\"\\\"\\n Receive real time events from containers.\\n\\n Usage: events [options] [SERVICE...]\\n\\n Options:\\n --json Output events as a stream of json objects\\n \\\"\\\"\\\"\\n def format_event(event):\\n attributes = [\\\"%s=%s\\\" % item for item in event['attributes'].items()]\\n return (\\\"{time} {type} {action} {id} ({attrs})\\\").format(\\n attrs=\\\", \\\".join(sorted(attributes)),\\n **event)\\n\\n def json_format_event(event):\\n event['time'] = event['time'].isoformat()\\n event.pop('container')\\n return json.dumps(event)\\n\\n for event in self.project.events():\\n formatter = json_format_event if options['--json'] else format_event\\n print(formatter(event))\\n sys.stdout.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events.format_event\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events.format_event\\n def format_event(event):\\n attributes = [\\\"%s=%s\\\" % item for item in event['attributes'].items()]\\n return (\\\"{time} {type} {action} {id} ({attrs})\\\").format(\\n attrs=\\\", \\\".join(sorted(attributes)),\\n **event)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events.json_format_event\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events.json_format_event\\n def json_format_event(event):\\n event['time'] = event['time'].isoformat()\\n event.pop('container')\\n return json.dumps(event)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.exec_command\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.exec_command\\n def exec_command(self, options):\\n \\\"\\\"\\\"\\n Execute a command in a running container\\n\\n Usage: exec [options] SERVICE COMMAND [ARGS...]\\n\\n Options:\\n -d Detached mode: Run command in the background.\\n --privileged Give extended privileges to the process.\\n --user USER Run the command as this user.\\n -T Disable pseudo-tty allocation. By default `docker-compose exec`\\n allocates a TTY.\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n \\\"\\\"\\\"\\n index = int(options.get('--index'))\\n service = self.project.get_service(options['SERVICE'])\\n detach = options['-d']\\n\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n command = [options['COMMAND']] + options['ARGS']\\n tty = not options[\\\"-T\\\"]\\n\\n if IS_WINDOWS_PLATFORM and not detach:\\n args = [\\\"exec\\\"]\\n\\n if options[\\\"-d\\\"]:\\n args += [\\\"--detach\\\"]\\n else:\\n args += [\\\"--interactive\\\"]\\n\\n if not options[\\\"-T\\\"]:\\n args += [\\\"--tty\\\"]\\n\\n if options[\\\"--privileged\\\"]:\\n args += [\\\"--privileged\\\"]\\n\\n if options[\\\"--user\\\"]:\\n args += [\\\"--user\\\", options[\\\"--user\\\"]]\\n\\n args += [container.id]\\n args += command\\n\\n sys.exit(call_docker(args))\\n\\n create_exec_options = {\\n \\\"privileged\\\": options[\\\"--privileged\\\"],\\n \\\"user\\\": options[\\\"--user\\\"],\\n \\\"tty\\\": tty,\\n \\\"stdin\\\": tty,\\n }\\n\\n exec_id = container.create_exec(command, **create_exec_options)\\n\\n if detach:\\n container.start_exec(exec_id, tty=tty)\\n return\\n\\n sig\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.help\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.help\\n @classmethod\\n def help(cls, options):\\n \\\"\\\"\\\"\\n Get help on a command.\\n\\n Usage: help [COMMAND]\\n \\\"\\\"\\\"\\n if options['COMMAND']:\\n subject = get_handler(cls, options['COMMAND'])\\n else:\\n subject = cls\\n\\n print(getdoc(subject))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.images\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.images\\n def images(self, options):\\n \\\"\\\"\\\"\\n List images used by the created containers.\\n Usage: images [options] [SERVICE...]\\n\\n Options:\\n -q Only display IDs\\n \\\"\\\"\\\"\\n containers = sorted(\\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\\n key=attrgetter('name'))\\n\\n if options['-q']:\\n for image in set(c.image for c in containers):\\n print(image.split(':')[1])\\n else:\\n headers = [\\n 'Container',\\n 'Repository',\\n 'Tag',\\n 'Image Id',\\n 'Size'\\n ]\\n rows = []\\n for container in containers:\\n image_config = container.image_config\\n repo_tags = image_config['RepoTags'][0].split(':')\\n image_id = image_config['Id'].split(':')[1][:12]\\n size = human_readable_file_size(image_config['Size'])\\n rows.append([\\n container.name,\\n repo_tags[0],\\n repo_tags[1],\\n image_id,\\n size\\n ])\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.kill\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.kill\\n def kill(self, options):\\n \\\"\\\"\\\"\\n Force stop service containers.\\n\\n Usage: kill [options] [SERVICE...]\\n\\n Options:\\n -s SIGNAL SIGNAL to send to the container.\\n Default signal is SIGKILL.\\n \\\"\\\"\\\"\\n signal = options.get('-s', 'SIGKILL')\\n\\n self.project.kill(service_names=options['SERVICE'], signal=signal)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.logs\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.logs\\n def logs(self, options):\\n \\\"\\\"\\\"\\n View output from containers.\\n\\n Usage: logs [options] [SERVICE...]\\n\\n Options:\\n --no-color Produce monochrome output.\\n -f, --follow Follow log output.\\n -t, --timestamps Show timestamps.\\n --tail=\\\"all\\\" Number of lines to show from the end of the logs\\n for each container.\\n \\\"\\\"\\\"\\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\\n\\n tail = options['--tail']\\n if tail is not None:\\n if tail.isdigit():\\n tail = int(tail)\\n elif tail != 'all':\\n raise UserError(\\\"tail flag must be all or a number\\\")\\n log_args = {\\n 'follow': options['--follow'],\\n 'tail': tail,\\n 'timestamps': options['--timestamps']\\n }\\n print(\\\"Attaching to\\\", list_containers(containers))\\n log_printer_from_project(\\n self.project,\\n containers,\\n options['--no-color'],\\n log_args,\\n event_stream=self.project.events(service_names=options['SERVICE'])).run()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pause\\n def pause(self, options):\\n \\\"\\\"\\\"\\n Pause services.\\n\\n Usage: pause [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.pause(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to pause', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.port\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.port\\n def port(self, options):\\n \\\"\\\"\\\"\\n Print the public port for a port binding.\\n\\n Usage: port [options] SERVICE PRIVATE_PORT\\n\\n Options:\\n --protocol=proto tcp or udp [default: tcp]\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n \\\"\\\"\\\"\\n index = int(options.get('--index'))\\n service = self.project.get_service(options['SERVICE'])\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n print(container.get_local_port(\\n options['PRIVATE_PORT'],\\n protocol=options.get('--protocol') or 'tcp') or '')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.ps\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.ps\\n def ps(self, options):\\n \\\"\\\"\\\"\\n List containers.\\n\\n Usage: ps [options] [SERVICE...]\\n\\n Options:\\n -q Only display IDs\\n \\\"\\\"\\\"\\n containers = sorted(\\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\\n key=attrgetter('name'))\\n\\n if options['-q']:\\n for container in containers:\\n print(container.id)\\n else:\\n headers = [\\n 'Name',\\n 'Command',\\n 'State',\\n 'Ports',\\n ]\\n rows = []\\n for container in containers:\\n command = container.human_readable_command\\n if len(command) > 30:\\n command = '%s ...' % command[:26]\\n rows.append([\\n container.name,\\n command,\\n container.human_readable_state,\\n container.human_readable_ports,\\n ])\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pull\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pull\\n def pull(self, options):\\n \\\"\\\"\\\"\\n Pulls images for services.\\n\\n Usage: pull [options] [SERVICE...]\\n\\n Options:\\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\\n --parallel Pull multiple images in parallel.\\n \\\"\\\"\\\"\\n self.project.pull(\\n service_names=options['SERVICE'],\\n ignore_pull_failures=options.get('--ignore-pull-failures'),\\n parallel_pull=options.get('--parallel')\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.push\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.push\\n def push(self, options):\\n \\\"\\\"\\\"\\n Pushes images for services.\\n\\n Usage: push [options] [SERVICE...]\\n\\n Options:\\n --ignore-push-failures Push what it can and ignores images with push failures.\\n \\\"\\\"\\\"\\n self.project.push(\\n service_names=options['SERVICE'],\\n ignore_push_failures=options.get('--ignore-push-failures')\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.rm\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.rm\\n def rm(self, options):\\n \\\"\\\"\\\"\\n Removes stopped service containers.\\n\\n By default, anonymous volumes attached to containers will not be removed. You\\n can override this with `-v`. To list all volumes, use `docker volume ls`.\\n\\n Any data which is not in a volume will be lost.\\n\\n Usage: rm [options] [SERVICE...]\\n\\n Options:\\n -f, --force Don't ask to confirm removal\\n -s, --stop Stop the containers, if required, before removing\\n -v Remove any anonymous volumes attached to containers\\n -a, --all Deprecated - no effect.\\n \\\"\\\"\\\"\\n if options.get('--all'):\\n log.warn(\\n '--all flag is obsolete. This is now the default behavior '\\n 'of `docker-compose rm`'\\n )\\n one_off = OneOffFilter.include\\n\\n if options.get('--stop'):\\n running_containers = self.project.containers(\\n service_names=options['SERVICE'], stopped=False, one_off=one_off\\n )\\n self.project.stop(\\n service_names=running_containers,\\n one_off=one_off\\n )\\n\\n all_containers = self.project.containers(\\n service_names=options['SERVICE'], stopped=True, one_off=one_off\\n )\\n stopped_containers = [c for c in all_containers if not c.is_running]\\n\\n if len(stopped_containers) > 0:\\n print(\\\"Going to remove\\\", list_containers(stopped_containers))\\n if options.get('--force') \\\\\\n or yesno(\\\"Are you sure? [yN] \\\", default=False):\\n self.project.remove_stopped(\\n service_names=options['SERVICE'],\\n v=options.get('-v', False),\\n one_off=one_off\\n )\\n else:\\n print(\\\"No stopped containers\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.run\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.run\\n def run(self, options):\\n \\\"\\\"\\\"\\n Run a one-off command on a service.\\n\\n For example:\\n\\n $ docker-compose run web python manage.py shell\\n\\n By default, linked services will be started, unless they are already\\n running. If you do not want to start linked services, use\\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\\n\\n Usage: run [options] [-v VOLUME...] [-p PORT...] [-e KEY=VAL...] SERVICE [COMMAND] [ARGS...]\\n\\n Options:\\n -d Detached mode: Run container in the background, print\\n new container name.\\n --name NAME Assign a name to the container\\n --entrypoint CMD Override the entrypoint of the image.\\n -e KEY=VAL Set an environment variable (can be used multiple times)\\n -u, --user=\\\"\\\" Run as specified username or uid\\n --no-deps Don't start linked services.\\n --rm Remove container after run. Ignored in detached mode.\\n -p, --publish=[] Publish a container's port(s) to the host\\n --service-ports Run command with the service's ports enabled and mapped\\n to the host.\\n -v, --volume=[] Bind mount a volume (default [])\\n -T Disable pseudo-tty allocation. By default `docker-compose run`\\n allocates a TTY.\\n -w, --workdir=\\\"\\\" Working directory inside the container\\n \\\"\\\"\\\"\\n service = self.project.get_service(options['SERVICE'])\\n detach = options['-d']\\n\\n if options['--publish'] and options['--service-ports']:\\n raise UserError(\\n 'Service port mapping and manual port mapping '\\n 'can not be used together'\\n )\\n\\n if options['COMMAND'] is not None:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.scale\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.scale\\n def scale(self, options):\\n \\\"\\\"\\\"\\n Set number of containers to run for a service.\\n\\n Numbers are specified in the form `service=num` as arguments.\\n For example:\\n\\n $ docker-compose scale web=2 worker=3\\n\\n Usage: scale [options] [SERVICE=NUM...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n\\n for s in options['SERVICE=NUM']:\\n if '=' not in s:\\n raise UserError('Arguments to scale should be in the form service=num')\\n service_name, num = s.split('=', 1)\\n try:\\n num = int(num)\\n except ValueError:\\n raise UserError('Number of containers for service \\\"%s\\\" is not a '\\n 'number' % service_name)\\n self.project.get_service(service_name).scale(num, timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.start\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.start\\n def start(self, options):\\n \\\"\\\"\\\"\\n Start existing containers.\\n\\n Usage: start [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.start(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to start', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.stop\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.stop\\n def stop(self, options):\\n \\\"\\\"\\\"\\n Stop running containers without removing them.\\n\\n They can be started again with `docker-compose start`.\\n\\n Usage: stop [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n self.project.stop(service_names=options['SERVICE'], timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.restart\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.restart\\n def restart(self, options):\\n \\\"\\\"\\\"\\n Restart running containers.\\n\\n Usage: restart [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n containers = self.project.restart(service_names=options['SERVICE'], timeout=timeout)\\n exit_if(not containers, 'No containers to restart', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.top\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.top\\n def top(self, options):\\n \\\"\\\"\\\"\\n Display the running processes\\n\\n Usage: top [SERVICE...]\\n\\n \\\"\\\"\\\"\\n containers = sorted(\\n self.project.containers(service_names=options['SERVICE'], stopped=False) +\\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\\n key=attrgetter('name')\\n )\\n\\n for idx, container in enumerate(containers):\\n if idx > 0:\\n print()\\n\\n top_data = self.project.client.top(container.name)\\n headers = top_data.get(\\\"Titles\\\")\\n rows = []\\n\\n for process in top_data.get(\\\"Processes\\\", []):\\n rows.append(process)\\n\\n print(container.name)\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.unpause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.unpause\\n def unpause(self, options):\\n \\\"\\\"\\\"\\n Unpause services.\\n\\n Usage: unpause [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.unpause(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to unpause', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.up\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.up\\n def up(self, options):\\n \\\"\\\"\\\"\\n Builds, (re)creates, starts, and attaches to containers for a service.\\n\\n Unless they are already running, this command also starts any linked services.\\n\\n The `docker-compose up` command aggregates the output of each container. When\\n the command exits, all containers are stopped. Running `docker-compose up -d`\\n starts the containers in the background and leaves them running.\\n\\n If there are existing containers for a service, and the service's configuration\\n or image was changed after the container's creation, `docker-compose up` picks\\n up the changes by stopping and recreating the containers (preserving mounted\\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\\n flag.\\n\\n If you want to force Compose to stop and recreate all containers, use the\\n `--force-recreate` flag.\\n\\n Usage: up [options] [SERVICE...]\\n\\n Options:\\n -d Detached mode: Run containers in the background,\\n print new container names.\\n Incompatible with --abort-on-container-exit.\\n --no-color Produce monochrome output.\\n --no-deps Don't start linked services.\\n --force-recreate Recreate containers even if their configuration\\n and image haven't changed.\\n Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate them.\\n Incompatible with --force-recreate.\\n --no-build Don't build an image, even if it's missing.\\n --build Build images before starting containers.\\n --abort-on-container-exit Stops al\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.version\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.version\\n @classmethod\\n def version(cls, options):\\n \\\"\\\"\\\"\\n Show version informations\\n\\n Usage: version [--short]\\n\\n Options:\\n --short Shows only Compose's version number.\\n \\\"\\\"\\\"\\n if options['--short']:\\n print(__version__)\\n else:\\n print(get_version_info('full'))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::convergence_strategy_from_opts\",\n \"text\": \"compose/cli/main.py::convergence_strategy_from_opts\\ndef convergence_strategy_from_opts(options):\\n no_recreate = options['--no-recreate']\\n force_recreate = options['--force-recreate']\\n if force_recreate and no_recreate:\\n raise UserError(\\\"--force-recreate and --no-recreate cannot be combined.\\\")\\n\\n if force_recreate:\\n return ConvergenceStrategy.always\\n\\n if no_recreate:\\n return ConvergenceStrategy.never\\n\\n return ConvergenceStrategy.changed\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::timeout_from_opts\",\n \"text\": \"compose/cli/main.py::timeout_from_opts\\ndef timeout_from_opts(options):\\n timeout = options.get('--timeout')\\n return None if timeout is None else int(timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::exitval_from_opts\",\n \"text\": \"compose/cli/main.py::exitval_from_opts\\ndef exitval_from_opts(options, project):\\n exit_value_from = options.get('--exit-code-from')\\n if exit_value_from:\\n if not options.get('--abort-on-container-exit'):\\n log.warn('using --exit-code-from implies --abort-on-container-exit')\\n options['--abort-on-container-exit'] = True\\n if exit_value_from not in [s.name for s in project.get_services()]:\\n log.error('No service named \\\"%s\\\" was found in your compose file.',\\n exit_value_from)\\n sys.exit(2)\\n return exit_value_from\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::image_type_from_opt\",\n \"text\": \"compose/cli/main.py::image_type_from_opt\\ndef image_type_from_opt(flag, value):\\n if not value:\\n return ImageType.none\\n try:\\n return ImageType[value]\\n except KeyError:\\n raise UserError(\\\"%s flag must be one of: all, local\\\" % flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::build_action_from_opts\",\n \"text\": \"compose/cli/main.py::build_action_from_opts\\ndef build_action_from_opts(options):\\n if options['--build'] and options['--no-build']:\\n raise UserError(\\\"--build and --no-build can not be combined.\\\")\\n\\n if options['--build']:\\n return BuildAction.force\\n\\n if options['--no-build']:\\n return BuildAction.skip\\n\\n return BuildAction.none\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::build_container_options\",\n \"text\": \"compose/cli/main.py::build_container_options\\ndef build_container_options(options, detach, command):\\n container_options = {\\n 'command': command,\\n 'tty': not (detach or options['-T'] or not sys.stdin.isatty()),\\n 'stdin_open': not detach,\\n 'detach': detach,\\n }\\n\\n if options['-e']:\\n container_options['environment'] = Environment.from_command_line(\\n parse_environment(options['-e'])\\n )\\n\\n if options['--entrypoint']:\\n container_options['entrypoint'] = options.get('--entrypoint')\\n\\n if options['--rm']:\\n container_options['restart'] = None\\n\\n if options['--user']:\\n container_options['user'] = options.get('--user')\\n\\n if not options['--service-ports']:\\n container_options['ports'] = []\\n\\n if options['--publish']:\\n container_options['ports'] = options.get('--publish')\\n\\n if options['--name']:\\n container_options['name'] = options['--name']\\n\\n if options['--workdir']:\\n container_options['working_dir'] = options['--workdir']\\n\\n if options['--volume']:\\n volumes = [VolumeSpec.parse(i) for i in options['--volume']]\\n container_options['volumes'] = volumes\\n\\n return container_options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::run_one_off_container\",\n \"text\": \"compose/cli/main.py::run_one_off_container\\ndef run_one_off_container(container_options, project, service, options):\\n if not options['--no-deps']:\\n deps = service.get_dependency_names()\\n if deps:\\n project.up(\\n service_names=deps,\\n start_deps=True,\\n strategy=ConvergenceStrategy.never)\\n\\n project.initialize()\\n\\n container = service.create_container(\\n quiet=True,\\n one_off=True,\\n **container_options)\\n\\n if options['-d']:\\n service.start_container(container)\\n print(container.name)\\n return\\n\\n def remove_container(force=False):\\n if options['--rm']:\\n project.client.remove_container(container.id, force=True, v=True)\\n\\n signals.set_signal_handler_to_shutdown()\\n try:\\n try:\\n if IS_WINDOWS_PLATFORM:\\n service.connect_container_to_networks(container)\\n exit_code = call_docker([\\\"start\\\", \\\"--attach\\\", \\\"--interactive\\\", container.id])\\n else:\\n operation = RunOperation(\\n project.client,\\n container.id,\\n interactive=not options['-T'],\\n logs=False,\\n )\\n pty = PseudoTerminal(project.client, operation)\\n sockets = pty.sockets()\\n service.start_container(container)\\n pty.start(sockets)\\n exit_code = container.wait()\\n except signals.ShutdownException:\\n project.client.stop(container.id)\\n exit_code = 1\\n except signals.ShutdownException:\\n project.client.kill(container.id)\\n remove_container(force=True)\\n sys.exit(2)\\n\\n remove_container()\\n sys.exit(exit_code)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::run_one_off_container.remove_container\",\n \"text\": \"compose/cli/main.py::run_one_off_container.remove_container\\n def remove_container(force=False):\\n if options['--rm']:\\n project.client.remove_container(container.id, force=True, v=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::log_printer_from_project\",\n \"text\": \"compose/cli/main.py::log_printer_from_project\\ndef log_printer_from_project(\\n project,\\n containers,\\n monochrome,\\n log_args,\\n cascade_stop=False,\\n event_stream=None,\\n):\\n return LogPrinter(\\n containers,\\n build_log_presenters(project.service_names, monochrome),\\n event_stream or project.events(),\\n cascade_stop=cascade_stop,\\n log_args=log_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::filter_containers_to_service_names\",\n \"text\": \"compose/cli/main.py::filter_containers_to_service_names\\ndef filter_containers_to_service_names(containers, service_names):\\n if not service_names:\\n return containers\\n\\n return [\\n container\\n for container in containers if container.service in service_names\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::up_shutdown_context\",\n \"text\": \"compose/cli/main.py::up_shutdown_context\\n@contextlib.contextmanager\\ndef up_shutdown_context(project, service_names, timeout, detached):\\n if detached:\\n yield\\n return\\n\\n signals.set_signal_handler_to_shutdown()\\n try:\\n try:\\n yield\\n except signals.ShutdownException:\\n print(\\\"Gracefully stopping... (press Ctrl+C again to force)\\\")\\n project.stop(service_names=service_names, timeout=timeout)\\n except signals.ShutdownException:\\n project.kill(service_names=service_names)\\n sys.exit(2)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::list_containers\",\n \"text\": \"compose/cli/main.py::list_containers\\ndef list_containers(containers):\\n return \\\", \\\".join(c.name for c in containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::exit_if\",\n \"text\": \"compose/cli/main.py::exit_if\\ndef exit_if(condition, message, exit_code):\\n if condition:\\n log.error(message)\\n raise SystemExit(exit_code)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::call_docker\",\n \"text\": \"compose/cli/main.py::call_docker\\ndef call_docker(args):\\n executable_path = find_executable('docker')\\n if not executable_path:\\n raise UserError(errors.docker_not_found_msg(\\\"Couldn't find `docker` binary.\\\"))\\n\\n args = [executable_path] + args\\n log.debug(\\\" \\\".join(map(pipes.quote, args)))\\n\\n return subprocess.call(args)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::split_env\",\n \"text\": \"compose/config/environment.py::split_env\\ndef split_env(env):\\n if isinstance(env, six.binary_type):\\n env = env.decode('utf-8', 'replace')\\n if '=' in env:\\n return env.split('=', 1)\\n else:\\n return env, None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::env_vars_from_file\",\n \"text\": \"compose/config/environment.py::env_vars_from_file\\ndef env_vars_from_file(filename):\\n \\\"\\\"\\\"\\n Read in a line delimited file of environment variables.\\n \\\"\\\"\\\"\\n if not os.path.exists(filename):\\n raise ConfigurationError(\\\"Couldn't find env file: %s\\\" % filename)\\n elif not os.path.isfile(filename):\\n raise ConfigurationError(\\\"%s is not a file.\\\" % (filename))\\n env = {}\\n with contextlib.closing(codecs.open(filename, 'r', 'utf-8')) as fileobj:\\n for line in fileobj:\\n line = line.strip()\\n if line and not line.startswith('#'):\\n k, v = split_env(line)\\n env[k] = v\\n return env\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.__init__\",\n \"text\": \"compose/config/environment.py::Environment.__init__\\n def __init__(self, *args, **kwargs):\\n super(Environment, self).__init__(*args, **kwargs)\\n self.missing_keys = []\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.from_env_file\",\n \"text\": \"compose/config/environment.py::Environment.from_env_file\\n @classmethod\\n def from_env_file(cls, base_dir):\\n def _initialize():\\n result = cls()\\n if base_dir is None:\\n return result\\n env_file_path = os.path.join(base_dir, '.env')\\n try:\\n return cls(env_vars_from_file(env_file_path))\\n except ConfigurationError:\\n pass\\n return result\\n instance = _initialize()\\n instance.update(os.environ)\\n return instance\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.from_env_file._initialize\",\n \"text\": \"compose/config/environment.py::Environment.from_env_file._initialize\\n def _initialize():\\n result = cls()\\n if base_dir is None:\\n return result\\n env_file_path = os.path.join(base_dir, '.env')\\n try:\\n return cls(env_vars_from_file(env_file_path))\\n except ConfigurationError:\\n pass\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.from_command_line\",\n \"text\": \"compose/config/environment.py::Environment.from_command_line\\n @classmethod\\n def from_command_line(cls, parsed_env_opts):\\n result = cls()\\n for k, v in parsed_env_opts.items():\\n # Values from the command line take priority, unless they're unset\\n # in which case they take the value from the system's environment\\n if v is None and k in os.environ:\\n result[k] = os.environ[k]\\n else:\\n result[k] = v\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.__getitem__\",\n \"text\": \"compose/config/environment.py::Environment.__getitem__\\n def __getitem__(self, key):\\n try:\\n return super(Environment, self).__getitem__(key)\\n except KeyError:\\n if IS_WINDOWS_PLATFORM:\\n try:\\n return super(Environment, self).__getitem__(key.upper())\\n except KeyError:\\n pass\\n if key not in self.missing_keys:\\n log.warn(\\n \\\"The {} variable is not set. Defaulting to a blank string.\\\"\\n .format(key)\\n )\\n self.missing_keys.append(key)\\n\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.__contains__\",\n \"text\": \"compose/config/environment.py::Environment.__contains__\\n def __contains__(self, key):\\n result = super(Environment, self).__contains__(key)\\n if IS_WINDOWS_PLATFORM:\\n return (\\n result or super(Environment, self).__contains__(key.upper())\\n )\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.get\",\n \"text\": \"compose/config/environment.py::Environment.get\\n def get(self, key, *args, **kwargs):\\n if IS_WINDOWS_PLATFORM:\\n return super(Environment, self).get(\\n key,\\n super(Environment, self).get(key.upper(), *args, **kwargs)\\n )\\n return super(Environment, self).get(key, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.get_boolean\",\n \"text\": \"compose/config/environment.py::Environment.get_boolean\\n def get_boolean(self, key):\\n # Convert a value to a boolean using \\\"common sense\\\" rules.\\n # Unset, empty, \\\"0\\\" and \\\"false\\\" (i-case) yield False.\\n # All other values yield True.\\n value = self.get(key)\\n if not value:\\n return False\\n if value.lower() in ['0', 'false']:\\n return False\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/serialize.py::serialize_config_type\",\n \"text\": \"compose/config/serialize.py::serialize_config_type\\ndef serialize_config_type(dumper, data):\\n representer = dumper.represent_str if six.PY3 else dumper.represent_unicode\\n return representer(data.repr())\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/serialize.py::serialize_dict_type\",\n \"text\": \"compose/config/serialize.py::serialize_dict_type\\ndef serialize_dict_type(dumper, data):\\n return dumper.represent_dict(data.repr())\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/serialize.py::denormalize_config\",\n \"text\": \"compose/config/serialize.py::denormalize_config\\ndef denormalize_config(config):\\n result = {'version': V2_1 if config.version == V1 else config.version}\\n denormalized_services = [\\n denormalize_service_dict(service_dict, config.version)\\n for service_dict in config.services\\n ]\\n result['services'] = {\\n service_dict.pop('name'): service_dict\\n for service_dict in denormalized_services\\n }\\n result['networks'] = config.networks.copy()\\n for net_name, net_conf in result['networks'].items():\\n if 'external_name' in net_conf:\\n del net_conf['external_name']\\n\\n result['volumes'] = config.volumes.copy()\\n for vol_name, vol_conf in result['volumes'].items():\\n if 'external_name' in vol_conf:\\n del vol_conf['external_name']\\n\\n if config.version in (V3_1, V3_2):\\n result['secrets'] = config.secrets\\n return result\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/serialize.py::serialize_config\",\n \"text\": \"compose/config/serialize.py::serialize_config\\ndef serialize_config(config):\\n return yaml.safe_dump(\\n denormalize_config(config),\\n default_flow_style=False,\\n indent=2,\\n width=80)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/serialize.py::serialize_ns_time_value\",\n \"text\": \"compose/config/serialize.py::serialize_ns_time_value\\ndef serialize_ns_time_value(value):\\n result = (value, 'ns')\\n table = [\\n (1000., 'us'),\\n (1000., 'ms'),\\n (1000., 's'),\\n (60., 'm'),\\n (60., 'h')\\n ]\\n for stage in table:\\n tmp = value / stage[0]\\n if tmp == int(value / stage[0]):\\n value = tmp\\n result = (int(value), stage[1])\\n else:\\n break\\n return '{0}{1}'.format(*result)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/serialize.py::denormalize_service_dict\",\n \"text\": \"compose/config/serialize.py::denormalize_service_dict\\ndef denormalize_service_dict(service_dict, version):\\n service_dict = service_dict.copy()\\n\\n if 'restart' in service_dict:\\n service_dict['restart'] = types.serialize_restart_spec(\\n service_dict['restart']\\n )\\n\\n if version == V1 and 'network_mode' not in service_dict:\\n service_dict['network_mode'] = 'bridge'\\n\\n if 'depends_on' in service_dict and version != V2_1:\\n service_dict['depends_on'] = sorted([\\n svc for svc in service_dict['depends_on'].keys()\\n ])\\n\\n if 'healthcheck' in service_dict:\\n if 'interval' in service_dict['healthcheck']:\\n service_dict['healthcheck']['interval'] = serialize_ns_time_value(\\n service_dict['healthcheck']['interval']\\n )\\n if 'timeout' in service_dict['healthcheck']:\\n service_dict['healthcheck']['timeout'] = serialize_ns_time_value(\\n service_dict['healthcheck']['timeout']\\n )\\n\\n if 'ports' in service_dict and version not in (V3_2,):\\n service_dict['ports'] = map(\\n lambda p: p.legacy_repr() if isinstance(p, types.ServicePort) else p,\\n service_dict['ports']\\n )\\n\\n return service_dict\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/container.py::Container.__init__\",\n \"text\": \"compose/container.py::Container.__init__\\n def __init__(self, client, dictionary, has_been_inspected=False):\\n self.client = client\\n self.dictionary = dictionary\\n self.has_been_inspected = has_been_inspected\\n self.log_stream = None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.from_ps\",\n \"text\": \"compose/container.py::Container.from_ps\\n @classmethod\\n def from_ps(cls, client, dictionary, **kwargs):\\n \\\"\\\"\\\"\\n Construct a container object from the output of GET /containers/json.\\n \\\"\\\"\\\"\\n name = get_container_name(dictionary)\\n if name is None:\\n return None\\n\\n new_dictionary = {\\n 'Id': dictionary['Id'],\\n 'Image': dictionary['Image'],\\n 'Name': '/' + name,\\n }\\n return cls(client, new_dictionary, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.from_id\",\n \"text\": \"compose/container.py::Container.from_id\\n @classmethod\\n def from_id(cls, client, id):\\n return cls(client, client.inspect_container(id), has_been_inspected=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.create\",\n \"text\": \"compose/container.py::Container.create\\n @classmethod\\n def create(cls, client, **options):\\n response = client.create_container(**options)\\n return cls.from_id(client, response['Id'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.id\",\n \"text\": \"compose/container.py::Container.id\\n @property\\n def id(self):\\n return self.dictionary['Id']\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.image\",\n \"text\": \"compose/container.py::Container.image\\n @property\\n def image(self):\\n return self.dictionary['Image']\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.image_config\",\n \"text\": \"compose/container.py::Container.image_config\\n @property\\n def image_config(self):\\n return self.client.inspect_image(self.image)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.short_id\",\n \"text\": \"compose/container.py::Container.short_id\\n @property\\n def short_id(self):\\n return self.id[:12]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.name\",\n \"text\": \"compose/container.py::Container.name\\n @property\\n def name(self):\\n return self.dictionary['Name'][1:]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.service\",\n \"text\": \"compose/container.py::Container.service\\n @property\\n def service(self):\\n return self.labels.get(LABEL_SERVICE)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.name_without_project\",\n \"text\": \"compose/container.py::Container.name_without_project\\n @property\\n def name_without_project(self):\\n project = self.labels.get(LABEL_PROJECT)\\n\\n if self.name.startswith('{0}_{1}'.format(project, self.service)):\\n return '{0}_{1}'.format(self.service, self.number)\\n else:\\n return self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.number\",\n \"text\": \"compose/container.py::Container.number\\n @property\\n def number(self):\\n number = self.labels.get(LABEL_CONTAINER_NUMBER)\\n if not number:\\n raise ValueError(\\\"Container {0} does not have a {1} label\\\".format(\\n self.short_id, LABEL_CONTAINER_NUMBER))\\n return int(number)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.ports\",\n \"text\": \"compose/container.py::Container.ports\\n @property\\n def ports(self):\\n self.inspect_if_not_inspected()\\n return self.get('NetworkSettings.Ports') or {}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.human_readable_ports\",\n \"text\": \"compose/container.py::Container.human_readable_ports\\n @property\\n def human_readable_ports(self):\\n def format_port(private, public):\\n if not public:\\n return private\\n return '{HostIp}:{HostPort}->{private}'.format(\\n private=private, **public[0])\\n\\n return ', '.join(format_port(*item)\\n for item in sorted(six.iteritems(self.ports)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.human_readable_ports.format_port\",\n \"text\": \"compose/container.py::Container.human_readable_ports.format_port\\n def format_port(private, public):\\n if not public:\\n return private\\n return '{HostIp}:{HostPort}->{private}'.format(\\n private=private, **public[0])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.labels\",\n \"text\": \"compose/container.py::Container.labels\\n @property\\n def labels(self):\\n return self.get('Config.Labels') or {}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.stop_signal\",\n \"text\": \"compose/container.py::Container.stop_signal\\n @property\\n def stop_signal(self):\\n return self.get('Config.StopSignal')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.log_config\",\n \"text\": \"compose/container.py::Container.log_config\\n @property\\n def log_config(self):\\n return self.get('HostConfig.LogConfig') or None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.human_readable_state\",\n \"text\": \"compose/container.py::Container.human_readable_state\\n @property\\n def human_readable_state(self):\\n if self.is_paused:\\n return 'Paused'\\n if self.is_restarting:\\n return 'Restarting'\\n if self.is_running:\\n return 'Ghost' if self.get('State.Ghost') else 'Up'\\n else:\\n return 'Exit %s' % self.get('State.ExitCode')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.human_readable_command\",\n \"text\": \"compose/container.py::Container.human_readable_command\\n @property\\n def human_readable_command(self):\\n entrypoint = self.get('Config.Entrypoint') or []\\n cmd = self.get('Config.Cmd') or []\\n return ' '.join(entrypoint + cmd)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.environment\",\n \"text\": \"compose/container.py::Container.environment\\n @property\\n def environment(self):\\n def parse_env(var):\\n if '=' in var:\\n return var.split(\\\"=\\\", 1)\\n return var, None\\n return dict(parse_env(var) for var in self.get('Config.Env') or [])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.environment.parse_env\",\n \"text\": \"compose/container.py::Container.environment.parse_env\\n def parse_env(var):\\n if '=' in var:\\n return var.split(\\\"=\\\", 1)\\n return var, None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.exit_code\",\n \"text\": \"compose/container.py::Container.exit_code\\n @property\\n def exit_code(self):\\n return self.get('State.ExitCode')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.is_running\",\n \"text\": \"compose/container.py::Container.is_running\\n @property\\n def is_running(self):\\n return self.get('State.Running')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.is_restarting\",\n \"text\": \"compose/container.py::Container.is_restarting\\n @property\\n def is_restarting(self):\\n return self.get('State.Restarting')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.is_paused\",\n \"text\": \"compose/container.py::Container.is_paused\\n @property\\n def is_paused(self):\\n return self.get('State.Paused')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.log_driver\",\n \"text\": \"compose/container.py::Container.log_driver\\n @property\\n def log_driver(self):\\n return self.get('HostConfig.LogConfig.Type')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.has_api_logs\",\n \"text\": \"compose/container.py::Container.has_api_logs\\n @property\\n def has_api_logs(self):\\n log_type = self.log_driver\\n return not log_type or log_type in ('json-file', 'journald')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.attach_log_stream\",\n \"text\": \"compose/container.py::Container.attach_log_stream\\n def attach_log_stream(self):\\n \\\"\\\"\\\"A log stream can only be attached if the container uses a json-file\\n log driver.\\n \\\"\\\"\\\"\\n if self.has_api_logs:\\n self.log_stream = self.attach(stdout=True, stderr=True, stream=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.get\",\n \"text\": \"compose/container.py::Container.get\\n def get(self, key):\\n \\\"\\\"\\\"Return a value from the container or None if the value is not set.\\n\\n :param key: a string using dotted notation for nested dictionary\\n lookups\\n \\\"\\\"\\\"\\n self.inspect_if_not_inspected()\\n\\n def get_value(dictionary, key):\\n return (dictionary or {}).get(key)\\n\\n return reduce(get_value, key.split('.'), self.dictionary)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.get.get_value\",\n \"text\": \"compose/container.py::Container.get.get_value\\n def get_value(dictionary, key):\\n return (dictionary or {}).get(key)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.get_local_port\",\n \"text\": \"compose/container.py::Container.get_local_port\\n def get_local_port(self, port, protocol='tcp'):\\n port = self.ports.get(\\\"%s/%s\\\" % (port, protocol))\\n return \\\"{HostIp}:{HostPort}\\\".format(**port[0]) if port else None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.get_mount\",\n \"text\": \"compose/container.py::Container.get_mount\\n def get_mount(self, mount_dest):\\n for mount in self.get('Mounts'):\\n if mount['Destination'] == mount_dest:\\n return mount\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.start\",\n \"text\": \"compose/container.py::Container.start\\n def start(self, **options):\\n return self.client.start(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.stop\",\n \"text\": \"compose/container.py::Container.stop\\n def stop(self, **options):\\n return self.client.stop(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.pause\",\n \"text\": \"compose/container.py::Container.pause\\n def pause(self, **options):\\n return self.client.pause(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.unpause\",\n \"text\": \"compose/container.py::Container.unpause\\n def unpause(self, **options):\\n return self.client.unpause(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.kill\",\n \"text\": \"compose/container.py::Container.kill\\n def kill(self, **options):\\n return self.client.kill(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.restart\",\n \"text\": \"compose/container.py::Container.restart\\n def restart(self, **options):\\n return self.client.restart(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.remove\",\n \"text\": \"compose/container.py::Container.remove\\n def remove(self, **options):\\n return self.client.remove_container(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::BuildError.__init__\",\n \"text\": \"compose/service.py::BuildError.__init__\\n def __init__(self, service, reason):\\n self.service = service\\n self.reason = reason\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::NeedsBuildError.__init__\",\n \"text\": \"compose/service.py::NeedsBuildError.__init__\\n def __init__(self, service):\\n self.service = service\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::ConvergenceStrategy.allows_recreate\",\n \"text\": \"compose/service.py::ConvergenceStrategy.allows_recreate\\n @property\\n def allows_recreate(self):\\n return self is not type(self).never\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.__init__\",\n \"text\": \"compose/service.py::Service.__init__\\n def __init__(\\n self,\\n name,\\n client=None,\\n project='default',\\n use_networking=False,\\n links=None,\\n volumes_from=None,\\n network_mode=None,\\n networks=None,\\n secrets=None,\\n **options\\n ):\\n self.name = name\\n self.client = client\\n self.project = project\\n self.use_networking = use_networking\\n self.links = links or []\\n self.volumes_from = volumes_from or []\\n self.network_mode = network_mode or NetworkMode(None)\\n self.networks = networks or {}\\n self.secrets = secrets or []\\n self.options = options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.__repr__\",\n \"text\": \"compose/service.py::Service.__repr__\\n def __repr__(self):\\n return '<Service: {}>'.format(self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.containers\",\n \"text\": \"compose/service.py::Service.containers\\n def containers(self, stopped=False, one_off=False, filters={}):\\n filters.update({'label': self.labels(one_off=one_off)})\\n\\n return list(filter(None, [\\n Container.from_ps(self.client, container)\\n for container in self.client.containers(\\n all=stopped,\\n filters=filters)]))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_container\",\n \"text\": \"compose/service.py::Service.get_container\\n def get_container(self, number=1):\\n \\\"\\\"\\\"Return a :class:`compose.container.Container` for this service. The\\n container must be active, and match `number`.\\n \\\"\\\"\\\"\\n labels = self.labels() + ['{0}={1}'.format(LABEL_CONTAINER_NUMBER, number)]\\n for container in self.client.containers(filters={'label': labels}):\\n return Container.from_ps(self.client, container)\\n\\n raise ValueError(\\\"No container found for %s_%s\\\" % (self.name, number))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start\",\n \"text\": \"compose/service.py::Service.start\\n def start(self, **options):\\n containers = self.containers(stopped=True)\\n for c in containers:\\n self.start_container_if_stopped(c, **options)\\n return containers\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale\",\n \"text\": \"compose/service.py::Service.scale\\n def scale(self, desired_num, timeout=None):\\n \\\"\\\"\\\"\\n Adjusts the number of containers to the specified number and ensures\\n they are running.\\n\\n - creates containers until there are at least `desired_num`\\n - stops containers until there are at most `desired_num` running\\n - starts containers until there are at least `desired_num` running\\n - removes all stopped containers\\n \\\"\\\"\\\"\\n if self.custom_container_name and desired_num > 1:\\n log.warn('The \\\"%s\\\" service is using the custom container name \\\"%s\\\". '\\n 'Docker requires each container to have a unique name. '\\n 'Remove the custom name to scale the service.'\\n % (self.name, self.custom_container_name))\\n\\n if self.specifies_host_port() and desired_num > 1:\\n log.warn('The \\\"%s\\\" service specifies a port on the host. If multiple containers '\\n 'for this service are created on a single host, the port will clash.'\\n % self.name)\\n\\n def create_and_start(service, number):\\n container = service.create_container(number=number, quiet=True)\\n service.start_container(container)\\n return container\\n\\n def stop_and_remove(container):\\n container.stop(timeout=self.stop_timeout(timeout))\\n container.remove()\\n\\n running_containers = self.containers(stopped=False)\\n num_running = len(running_containers)\\n\\n if desired_num == num_running:\\n # do nothing as we already have the desired number\\n log.info('Desired container number already achieved')\\n return\\n\\n if desired_num > num_running:\\n # we need to start/create until we have desired_num\\n all_containers = self.containers(stopped=True)\\n\\n if num_running != len(all_containers):\\n # we have some stopped containers, let's st\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale.create_and_start\",\n \"text\": \"compose/service.py::Service.scale.create_and_start\\n def create_and_start(service, number):\\n container = service.create_container(number=number, quiet=True)\\n service.start_container(container)\\n return container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale.stop_and_remove\",\n \"text\": \"compose/service.py::Service.scale.stop_and_remove\\n def stop_and_remove(container):\\n container.stop(timeout=self.stop_timeout(timeout))\\n container.remove()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.create_container\",\n \"text\": \"compose/service.py::Service.create_container\\n def create_container(self,\\n one_off=False,\\n previous_container=None,\\n number=None,\\n quiet=False,\\n **override_options):\\n \\\"\\\"\\\"\\n Create a container for this service. If the image doesn't exist, attempt to pull\\n it.\\n \\\"\\\"\\\"\\n # This is only necessary for `scale` and `volumes_from`\\n # auto-creating containers to satisfy the dependency.\\n self.ensure_image_exists()\\n\\n container_options = self._get_container_create_options(\\n override_options,\\n number or self._next_container_number(one_off=one_off),\\n one_off=one_off,\\n previous_container=previous_container,\\n )\\n\\n if 'name' in container_options and not quiet:\\n log.info(\\\"Creating %s\\\" % container_options['name'])\\n\\n try:\\n return Container.create(self.client, **container_options)\\n except APIError as ex:\\n raise OperationFailedError(\\\"Cannot create container for service %s: %s\\\" %\\n (self.name, ex.explanation))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.ensure_image_exists\",\n \"text\": \"compose/service.py::Service.ensure_image_exists\\n def ensure_image_exists(self, do_build=BuildAction.none):\\n if self.can_be_built() and do_build == BuildAction.force:\\n self.build()\\n return\\n\\n try:\\n self.image()\\n return\\n except NoSuchImageError:\\n pass\\n\\n if not self.can_be_built():\\n self.pull()\\n return\\n\\n if do_build == BuildAction.skip:\\n raise NeedsBuildError(self)\\n\\n self.build()\\n log.warn(\\n \\\"Image for service {} was built because it did not already exist. To \\\"\\n \\\"rebuild this image you must use `docker-compose build` or \\\"\\n \\\"`docker-compose up --build`.\\\".format(self.name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.image\",\n \"text\": \"compose/service.py::Service.image\\n def image(self):\\n try:\\n return self.client.inspect_image(self.image_name)\\n except ImageNotFound:\\n raise NoSuchImageError(\\\"Image '{}' not found\\\".format(self.image_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.image_name\",\n \"text\": \"compose/service.py::Service.image_name\\n @property\\n def image_name(self):\\n return self.options.get('image', '{s.project}_{s.name}'.format(s=self))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.convergence_plan\",\n \"text\": \"compose/service.py::Service.convergence_plan\\n def convergence_plan(self, strategy=ConvergenceStrategy.changed):\\n containers = self.containers(stopped=True)\\n\\n if not containers:\\n return ConvergencePlan('create', [])\\n\\n if strategy is ConvergenceStrategy.never:\\n return ConvergencePlan('start', containers)\\n\\n if (\\n strategy is ConvergenceStrategy.always or\\n self._containers_have_diverged(containers)\\n ):\\n return ConvergencePlan('recreate', containers)\\n\\n stopped = [c for c in containers if not c.is_running]\\n\\n if stopped:\\n return ConvergencePlan('start', stopped)\\n\\n return ConvergencePlan('noop', containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._containers_have_diverged\",\n \"text\": \"compose/service.py::Service._containers_have_diverged\\n def _containers_have_diverged(self, containers):\\n config_hash = None\\n\\n try:\\n config_hash = self.config_hash\\n except NoSuchImageError as e:\\n log.debug(\\n 'Service %s has diverged: %s',\\n self.name, six.text_type(e),\\n )\\n return True\\n\\n has_diverged = False\\n\\n for c in containers:\\n container_config_hash = c.labels.get(LABEL_CONFIG_HASH, None)\\n if container_config_hash != config_hash:\\n log.debug(\\n '%s has diverged: %s != %s',\\n c.name, container_config_hash, config_hash,\\n )\\n has_diverged = True\\n\\n return has_diverged\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.execute_convergence_plan\",\n \"text\": \"compose/service.py::Service.execute_convergence_plan\\n def execute_convergence_plan(self,\\n plan,\\n timeout=None,\\n detached=False,\\n start=True):\\n (action, containers) = plan\\n should_attach_logs = not detached\\n\\n if action == 'create':\\n container = self.create_container()\\n\\n if should_attach_logs:\\n container.attach_log_stream()\\n\\n if start:\\n self.start_container(container)\\n\\n return [container]\\n\\n elif action == 'recreate':\\n return [\\n self.recreate_container(\\n container,\\n timeout=timeout,\\n attach_logs=should_attach_logs,\\n start_new_container=start\\n )\\n for container in containers\\n ]\\n\\n elif action == 'start':\\n if start:\\n for container in containers:\\n self.start_container_if_stopped(container, attach_logs=should_attach_logs)\\n\\n return containers\\n\\n elif action == 'noop':\\n for c in containers:\\n log.info(\\\"%s is up-to-date\\\" % c.name)\\n\\n return containers\\n\\n else:\\n raise Exception(\\\"Invalid action: {}\\\".format(action))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.recreate_container\",\n \"text\": \"compose/service.py::Service.recreate_container\\n def recreate_container(\\n self,\\n container,\\n timeout=None,\\n attach_logs=False,\\n start_new_container=True):\\n \\\"\\\"\\\"Recreate a container.\\n\\n The original container is renamed to a temporary name so that data\\n volumes can be copied to the new container, before the original\\n container is removed.\\n \\\"\\\"\\\"\\n log.info(\\\"Recreating %s\\\" % container.name)\\n\\n container.stop(timeout=self.stop_timeout(timeout))\\n container.rename_to_tmp_name()\\n new_container = self.create_container(\\n previous_container=container,\\n number=container.labels.get(LABEL_CONTAINER_NUMBER),\\n quiet=True,\\n )\\n if attach_logs:\\n new_container.attach_log_stream()\\n if start_new_container:\\n self.start_container(new_container)\\n container.remove()\\n return new_container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.stop_timeout\",\n \"text\": \"compose/service.py::Service.stop_timeout\\n def stop_timeout(self, timeout):\\n if timeout is not None:\\n return timeout\\n timeout = parse_seconds_float(self.options.get('stop_grace_period'))\\n if timeout is not None:\\n return timeout\\n return DEFAULT_TIMEOUT\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start_container_if_stopped\",\n \"text\": \"compose/service.py::Service.start_container_if_stopped\\n def start_container_if_stopped(self, container, attach_logs=False, quiet=False):\\n if not container.is_running:\\n if not quiet:\\n log.info(\\\"Starting %s\\\" % container.name)\\n if attach_logs:\\n container.attach_log_stream()\\n return self.start_container(container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start_container\",\n \"text\": \"compose/service.py::Service.start_container\\n def start_container(self, container):\\n self.connect_container_to_networks(container)\\n try:\\n container.start()\\n except APIError as ex:\\n raise OperationFailedError(\\\"Cannot start service %s: %s\\\" % (self.name, ex.explanation))\\n return container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.connect_container_to_networks\",\n \"text\": \"compose/service.py::Service.connect_container_to_networks\\n def connect_container_to_networks(self, container):\\n connected_networks = container.get('NetworkSettings.Networks')\\n\\n for network, netdefs in self.networks.items():\\n if network in connected_networks:\\n if short_id_alias_exists(container, network):\\n continue\\n\\n self.client.disconnect_container_from_network(\\n container.id,\\n network)\\n\\n self.client.connect_container_to_network(\\n container.id, network,\\n aliases=self._get_aliases(netdefs, container),\\n ipv4_address=netdefs.get('ipv4_address', None),\\n ipv6_address=netdefs.get('ipv6_address', None),\\n links=self._get_links(False),\\n link_local_ips=netdefs.get('link_local_ips', None),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.remove_duplicate_containers\",\n \"text\": \"compose/service.py::Service.remove_duplicate_containers\\n def remove_duplicate_containers(self, timeout=None):\\n for c in self.duplicate_containers():\\n log.info('Removing %s' % c.name)\\n c.stop(timeout=self.stop_timeout(timeout))\\n c.remove()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.duplicate_containers\",\n \"text\": \"compose/service.py::Service.duplicate_containers\\n def duplicate_containers(self):\\n containers = sorted(\\n self.containers(stopped=True),\\n key=lambda c: c.get('Created'),\\n )\\n\\n numbers = set()\\n\\n for c in containers:\\n if c.number in numbers:\\n yield c\\n else:\\n numbers.add(c.number)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.config_hash\",\n \"text\": \"compose/service.py::Service.config_hash\\n @property\\n def config_hash(self):\\n return json_hash(self.config_dict())\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.config_dict\",\n \"text\": \"compose/service.py::Service.config_dict\\n def config_dict(self):\\n return {\\n 'options': self.options,\\n 'image_id': self.image()['Id'],\\n 'links': self.get_link_names(),\\n 'net': self.network_mode.id,\\n 'networks': self.networks,\\n 'volumes_from': [\\n (v.source.name, v.mode)\\n for v in self.volumes_from if isinstance(v.source, Service)\\n ],\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_dependency_names\",\n \"text\": \"compose/service.py::Service.get_dependency_names\\n def get_dependency_names(self):\\n net_name = self.network_mode.service_name\\n return (\\n self.get_linked_service_names() +\\n self.get_volumes_from_names() +\\n ([net_name] if net_name else []) +\\n list(self.options.get('depends_on', {}).keys())\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_dependency_configs\",\n \"text\": \"compose/service.py::Service.get_dependency_configs\\n def get_dependency_configs(self):\\n net_name = self.network_mode.service_name\\n configs = dict(\\n [(name, None) for name in self.get_linked_service_names()]\\n )\\n configs.update(dict(\\n [(name, None) for name in self.get_volumes_from_names()]\\n ))\\n configs.update({net_name: None} if net_name else {})\\n configs.update(self.options.get('depends_on', {}))\\n for svc, config in self.options.get('depends_on', {}).items():\\n if config['condition'] == CONDITION_STARTED:\\n configs[svc] = lambda s: True\\n elif config['condition'] == CONDITION_HEALTHY:\\n configs[svc] = lambda s: s.is_healthy()\\n else:\\n # The config schema already prevents this, but it might be\\n # bypassed if Compose is called programmatically.\\n raise ValueError(\\n 'depends_on condition \\\"{}\\\" is invalid.'.format(\\n config['condition']\\n )\\n )\\n\\n return configs\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_linked_service_names\",\n \"text\": \"compose/service.py::Service.get_linked_service_names\\n def get_linked_service_names(self):\\n return [service.name for (service, _) in self.links]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_link_names\",\n \"text\": \"compose/service.py::Service.get_link_names\\n def get_link_names(self):\\n return [(service.name, alias) for service, alias in self.links]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_volumes_from_names\",\n \"text\": \"compose/service.py::Service.get_volumes_from_names\\n def get_volumes_from_names(self):\\n return [s.source.name for s in self.volumes_from if isinstance(s.source, Service)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._next_container_number\",\n \"text\": \"compose/service.py::Service._next_container_number\\n def _next_container_number(self, one_off=False):\\n containers = filter(None, [\\n Container.from_ps(self.client, container)\\n for container in self.client.containers(\\n all=True,\\n filters={'label': self.labels(one_off=one_off)})\\n ])\\n numbers = [c.number for c in containers]\\n return 1 if not numbers else max(numbers) + 1\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_aliases\",\n \"text\": \"compose/service.py::Service._get_aliases\\n def _get_aliases(self, network, container=None):\\n if container and container.labels.get(LABEL_ONE_OFF) == \\\"True\\\":\\n return []\\n\\n return list(\\n {self.name} |\\n ({container.short_id} if container else set()) |\\n set(network.get('aliases', ()))\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.build_default_networking_config\",\n \"text\": \"compose/service.py::Service.build_default_networking_config\\n def build_default_networking_config(self):\\n if not self.networks:\\n return {}\\n\\n network = self.networks[self.network_mode.id]\\n endpoint = {\\n 'Aliases': self._get_aliases(network),\\n 'IPAMConfig': {},\\n }\\n\\n if network.get('ipv4_address'):\\n endpoint['IPAMConfig']['IPv4Address'] = network.get('ipv4_address')\\n if network.get('ipv6_address'):\\n endpoint['IPAMConfig']['IPv6Address'] = network.get('ipv6_address')\\n\\n return {\\\"EndpointsConfig\\\": {self.network_mode.id: endpoint}}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_links\",\n \"text\": \"compose/service.py::Service._get_links\\n def _get_links(self, link_to_self):\\n links = {}\\n\\n for service, link_name in self.links:\\n for container in service.containers():\\n links[link_name or service.name] = container.name\\n links[container.name] = container.name\\n links[container.name_without_project] = container.name\\n\\n if link_to_self:\\n for container in self.containers():\\n links[self.name] = container.name\\n links[container.name] = container.name\\n links[container.name_without_project] = container.name\\n\\n for external_link in self.options.get('external_links') or []:\\n if ':' not in external_link:\\n link_name = external_link\\n else:\\n external_link, link_name = external_link.split(':')\\n links[link_name] = external_link\\n\\n return [\\n (alias, container_name)\\n for (container_name, alias) in links.items()\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_volumes_from\",\n \"text\": \"compose/service.py::Service._get_volumes_from\\n def _get_volumes_from(self):\\n return [build_volume_from(spec) for spec in self.volumes_from]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_container_create_options\",\n \"text\": \"compose/service.py::Service._get_container_create_options\\n def _get_container_create_options(\\n self,\\n override_options,\\n number,\\n one_off=False,\\n previous_container=None):\\n add_config_hash = (not one_off and not override_options)\\n\\n container_options = dict(\\n (k, self.options[k])\\n for k in DOCKER_CONFIG_KEYS if k in self.options)\\n container_options.update(override_options)\\n\\n if not container_options.get('name'):\\n container_options['name'] = self.get_container_name(number, one_off)\\n\\n container_options.setdefault('detach', True)\\n\\n # If a qualified hostname was given, split it into an\\n # unqualified hostname and a domainname unless domainname\\n # was also given explicitly. This matches the behavior of\\n # the official Docker CLI in that scenario.\\n if ('hostname' in container_options and\\n 'domainname' not in container_options and\\n '.' in container_options['hostname']):\\n parts = container_options['hostname'].partition('.')\\n container_options['hostname'] = parts[0]\\n container_options['domainname'] = parts[2]\\n\\n if 'ports' in container_options or 'expose' in self.options:\\n container_options['ports'] = build_container_ports(\\n formatted_ports(container_options.get('ports', [])),\\n self.options)\\n\\n container_options['environment'] = merge_environment(\\n self.options.get('environment'),\\n override_options.get('environment'))\\n\\n binds, affinity = merge_volume_bindings(\\n container_options.get('volumes') or [],\\n previous_container)\\n override_options['binds'] = binds\\n container_options['environment'].update(affinity)\\n\\n container_options['volumes'] = dict(\\n (v.internal, {}) for v in container_options.get('volumes') or {})\\n\\n secr\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_container_host_config\",\n \"text\": \"compose/service.py::Service._get_container_host_config\\n def _get_container_host_config(self, override_options, one_off=False):\\n options = dict(self.options, **override_options)\\n\\n logging_dict = options.get('logging', None)\\n log_config = get_log_config(logging_dict)\\n\\n host_config = self.client.create_host_config(\\n links=self._get_links(link_to_self=one_off),\\n port_bindings=build_port_bindings(\\n formatted_ports(options.get('ports', []))\\n ),\\n binds=options.get('binds'),\\n volumes_from=self._get_volumes_from(),\\n privileged=options.get('privileged', False),\\n network_mode=self.network_mode.mode,\\n devices=options.get('devices'),\\n dns=options.get('dns'),\\n dns_opt=options.get('dns_opt'),\\n dns_search=options.get('dns_search'),\\n restart_policy=options.get('restart'),\\n cap_add=options.get('cap_add'),\\n cap_drop=options.get('cap_drop'),\\n mem_limit=options.get('mem_limit'),\\n mem_reservation=options.get('mem_reservation'),\\n memswap_limit=options.get('memswap_limit'),\\n ulimits=build_ulimits(options.get('ulimits')),\\n log_config=log_config,\\n extra_hosts=options.get('extra_hosts'),\\n read_only=options.get('read_only'),\\n pid_mode=options.get('pid'),\\n security_opt=options.get('security_opt'),\\n ipc_mode=options.get('ipc'),\\n cgroup_parent=options.get('cgroup_parent'),\\n cpu_quota=options.get('cpu_quota'),\\n shm_size=options.get('shm_size'),\\n sysctls=options.get('sysctls'),\\n pids_limit=options.get('pids_limit'),\\n tmpfs=options.get('tmpfs'),\\n oom_score_adj=options.get('oom_score_adj'),\\n mem_swappiness=options.get('mem_swappiness'),\\n group_add=options.get('group_add'),\\n userns_mode=op\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_secret_volumes\",\n \"text\": \"compose/service.py::Service.get_secret_volumes\\n def get_secret_volumes(self):\\n def build_spec(secret):\\n target = '{}/{}'.format(\\n const.SECRETS_PATH,\\n secret['secret'].target or secret['secret'].source)\\n return VolumeSpec(secret['file'], target, 'ro')\\n\\n return [build_spec(secret) for secret in self.secrets]\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":71,"cells":{"instance_id":{"kind":"string","value":"conan-io__conan-9360"},"repo":{"kind":"string","value":"conan-io/conan"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"[question] Is new cmake_layout compatible with cpp_info.components ?\nHi, I've been using CMakeToolchain for a while with the CMake build helper that comes with it.\r\nBut after updating today I saw that `CMake` build helper does not accept a `build_folder` parameter anymore.\r\n\r\nLooking at why it happened I arrived at https://github.com/conan-io/conan/pull/8554/files#diff-d72013a45b00a0adf06f4536d6a8c8844461e51b72911937d63e6dda9a3d440aR64 this means that the new way of providing the folder topology is via `def layout(self)` right?\r\n\r\nMy first move was to employ the new `cmake_layout` utility, but I was unable to create a package if this one used `self.cpp_info.components`\r\n\r\n```\r\nERROR: \r\n\tConanException: say/0.1 package_info(): self.cpp_info.components cannot be used with self.cpp_info global values at the same time\r\n```\r\n\r\nConanfile example:\r\n```\r\nfrom conans import ConanFile, CMake\r\nfrom conan.tools.layout import cmake_layout, LayoutPackager\r\n\r\nclass Pkg(ConanFile):\r\n name = \"say\"\r\n version = \"0.1\"\r\n settings = \"os\", \"compiler\", \"arch\", \"build_type\"\r\n generators = \"cmake\"\r\n exports_sources = \"src/*\"\r\n\r\n def layout(self):\r\n cmake_layout(self)\r\n\r\n def build(self):\r\n cmake = CMake(self)\r\n cmake.configure()\r\n cmake.build()\r\n\r\n def package(self):\r\n LayoutPackager(self).package()\r\n\r\n def package_info(self):\r\n self.cpp_info.components[\"say\"].libs = [\"say\"]\r\n```\r\n\r\nSo, I am doing something wrong? Is this intended or just a bug/missing feature?\r\n\r\nThanks!\r\n\r\n- [x] I've read the [CONTRIBUTING guide](https://github.com/conan-io/conan/blob/develop/.github/CONTRIBUTING.md).\n"},"gold_files":{"kind":"list like","value":["conans/client/installer.py"],"string":"[\n \"conans/client/installer.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["conans/client/installer.py::BinaryInstaller._call_package_info"],"string":"[\n \"conans/client/installer.py::BinaryInstaller._call_package_info\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"conans/client/build/cmake.py","text":"conans/client/build/cmake.py\nclass CMake\n def __init__(self, conanfile, generator=None, cmake_system_name=True, parallel=True, build_type=None, toolset=None, make_program=None, set_cmake_flags=False, msbuild_verbosity='minimal', cmake_program=None, generator_platform=None, append_vcvars=False) # :param conanfile: Conanfile instance\n def generator(self)\n def generator(self, value)\n def generator_platform(self)\n def generator_platform(self, value)\n def build_folder(self)\n def build_folder(self, value)\n def build_type(self)\n def build_type(self, build_type)\n def in_local_cache(self)\n def runtime(self)\n def flags(self)\n def is_multi_configuration(self)\n def command_line(self)\n def build_config(self) # cmake --build tool have a --config option for Multi-configuration IDEs\n def _get_dirs(self, source_folder, build_folder, source_dir, build_dir, cache_build_folder)\n def get_dir(folder, origin)\n def _run(self, command)\n def configure(self, args=None, defs=None, source_dir=None, build_dir=None, source_folder=None, build_folder=None, cache_build_folder=None, pkg_config_paths=None)\n def build(self, args=None, build_dir=None, target=None)\n def _build(self, args=None, build_dir=None, target=None)\n def install(self, args=None, build_dir=None)\n def test(self, args=None, build_dir=None, target=None, output_on_failure=False)\n def verbose(self)\n def verbose(self, value)\n def patch_config_paths(self) # changes references to the absolute path of the installed package and its depende\n def get_version()","label":0},{"cid":"conans/client/cmd/new.py","text":"conans/client/cmd/new.py\ndef _render_template(text, name, version, package_name, defines)\ndef _get_files_from_template_dir(template_dir, name, version, package_name, defines)\ndef cmd_new(ref, header=False, pure_c=False, test=False, exports_sources=False, bare=False, visual_versions=None, linux_gcc_versions=None, linux_clang_versions=None, osx_clang_versions=None, shared=None, upload_url=None, gitignore=None, gitlab_gcc_versions=None, gitlab_clang_versions=None, circleci_gcc_versions=None, circleci_clang_versions=None, circleci_osx_versions=None, template=None, cache=None, defines=None)","label":0},{"cid":"conans/client/installer.py","text":"conans/client/installer.py\ndef build_id(conan_file)\ndef add_env_conaninfo(conan_file, subtree_libnames)\nclass _PackageBuilder\n def __init__(self, cache, output, hook_manager, remote_manager, generators)\n def _get_build_folder(self, conanfile, package_layout, pref, keep_build, recorder)\n def _prepare_sources(self, conanfile, pref, package_layout, remotes)\n def _copy_sources(conanfile, source_folder, build_folder)\n def _build(self, conanfile, pref)\n def _package(self, conanfile, pref, package_layout, conanfile_path)\n def build_package(self, node, keep_build, recorder, remotes)\ndef _remove_folder_raising(folder)\ndef _handle_system_requirements(conan_file, pref, cache, out) # check first the system_reqs/system_requirements.txt existence, if not existing\ndef call_system_requirements(conanfile, output)\nclass BinaryInstaller # main responsible of retrieving binary packages or building them from source\n def __init__(self, app, recorder)\n def install(self, deps_graph, remotes, build_mode, update, profile_host, profile_build, graph_lock, keep_build=False)\n def _classify(nodes_by_level)\n def _raise_missing(self, missing)\n def _download(self, downloads, processed_package_refs) # executes the download of packages (both download and update), only once for a gi\n def _download(n)\n def _download_pkg(self, layout, node)\n def _build(self, nodes_by_level, keep_build, root_node, profile_host, profile_build, graph_lock, remotes, build_mode, update)\n def _handle_node_editable(self, node, profile_host, profile_build, graph_lock)\n def _handle_node_cache(self, node, keep_build, processed_package_references, remotes)\n def _build_package(self, node, output, keep_build, remotes)\n def _propagate_info(self, node, using_build_profile)\n def _call_package_info(self, conanfile, package_folder, ref, is_editable)","label":1},{"cid":"conans/client/migrations.py","text":"conans/client/migrations.py\nclass ClientMigrator\n def __init__(self, cache_folder, current_version, out)\n def _update_settings_yml(self, cache, old_version)\n def save_new()\n def _make_migrations(self, old_version)\ndef _get_refs(cache)\ndef _get_prefs(layout)\ndef migrate_tgz_location(cache, out) # In Conan 1.31, the temporary .tgz are no longer stored in the content folders. I\ndef migrate_localdb_refresh_token(cache, out)\ndef _migrate_full_metadata(cache, out)\ndef _migrate_create_metadata(cache, out)\ndef _migrate_lock_files(cache, out)\ndef migrate_config_install(cache)\ndef migrate_to_default_profile(conf_path, default_profile_path)\ndef migrate_plugins_to_hooks(cache, output=None)\ndef migrate_editables_use_conanfile_name(cache) # In Conan v1.26 we store full path to the conanfile in the editable_package.json ","label":0},{"cid":"conans/model/build_info.py","text":"conans/model/build_info.py\nclass DefaultOrderedDict\n def __init__(self, factory)\n def __getitem__(self, key)\n def __copy__(self)\nclass BuildModulesDict # A dictionary with append and extend for cmake build modules to keep it backwards\n def __getitem__(self, key)\n def _append(self, item)\n def append(self, item)\n def extend(self, items)\n def from_list(cls, build_modules)\ndef dict_to_abs_paths(the_dict, rootpath)\ndef merge_lists(seq1, seq2)\ndef merge_dicts(d1, d2)\n def merge_lists(seq1, seq2)\nclass _CppInfo # Object that stores all the necessary information to build in C/C++.\n def __init__(self)\n def _filter_paths(self, paths)\n def build_modules_paths(self)\n def include_paths(self)\n def lib_paths(self)\n def src_paths(self)\n def bin_paths(self)\n def build_paths(self)\n def res_paths(self)\n def framework_paths(self)\n def name(self)\n def name(self, value)\n def get_name(self, generator, default_name=True)\n def get_filename(self, generator, default_name=True)\n def get_build_modules(self)\n def set_property(self, property_name, value, generator=None)\n def get_property(self, property_name, generator=None)\n def get_cppflags(self)\n def set_cppflags(self, value)\nclass Component\n def __init__(self, rootpath, version, default_values)\nclass CppInfoDefaultValues\n def __init__(self, includedir=None, libdir=None, bindir=None, resdir=None, builddir=None, frameworkdir=None)\nclass CppInfo # Build Information declared to be used by the CONSUMERS of a\n def __init__(self, ref_name, root_folder, default_values=None)\n def __str__(self)\n def get_name(self, generator, default_name=True)\n def configs(self)\n def __getattr__(self, config)\n def _get_cpp_info()\n def _raise_incorrect_components_definition(self, package_name, package_requires)\n def _check_components_requires_instersection(comp_requires)\nclass _BaseDepsCppInfo\n def __init__(self)\n def update(self, dep_cpp_info)\n def merge_lists(seq1, seq2)\n def build_modules_paths(self)\n def include_paths(self)\n def lib_paths(self)\n def src_paths(self)\n def bin_paths(self)\n def build_paths(self)\n def res_paths(self)\n def framework_paths(self)\nclass DepCppInfo\n def __init__(self, cpp_info)\n def __str__(self)\n def __getattr__(self, item)\n def _aggregated_dict_values(self, item)\n def _aggregated_list_values(self, item)\n def _filter_component_requires(requires)\n def _check_component_requires(self)\n def _get_sorted_components(self) # Sort Components from most dependent one first to the less dependent one last\n def build_modules_paths(self)\n def include_paths(self)\n def lib_paths(self)\n def src_paths(self)\n def bin_paths(self)\n def build_paths(self)\n def res_paths(self)\n def framework_paths(self)\n def libs(self)\n def system_libs(self)\n def frameworks(self)\n def defines(self)\n def cxxflags(self)\n def cflags(self)\n ","label":0},{"cid":"conans/model/conan_file.py","text":"conans/model/conan_file.py\ndef create_options(conanfile)\ndef create_requirements(conanfile)\ndef create_settings(conanfile, settings)\ndef _env_and_python(conanfile)\ndef get_env_context_manager(conanfile, without_python=False)\nclass ConanFile # The base class for all package recipes\n def __init__(self, output, runner, display_name='', user=None, channel=None)\n def context(self)\n def dependencies(self)\n def ref(self)\n def pref(self)\n def buildenv(self)\n def initialize(self, settings, env, buildenv=None)\n def new_cpp_info(self)\n def source_folder(self)\n def source_folder(self, folder)\n def build_folder(self)\n def build_folder(self, folder)\n def package_folder(self)\n def package_folder(self, folder)\n def install_folder(self)\n def install_folder(self, folder)\n def generators_folder(self)\n def imports_folder(self)\n def imports_folder(self, folder)\n def env(self) # Apply the self.deps_env_info into a copy of self._conan_env_values (will priorit\n def channel(self)\n def user(self)\n def collect_libs(self, folder=None)\n def build_policy_missing(self)\n def build_policy_always(self)\n def source(self)\n def system_requirements(self) # this method can be overwritten to implement logic for system package\n def config_options(self) # modify options, probably conditioned to some settings. This call is executed\n def configure(self) # modify settings, probably conditioned to some options. This call is executed\n def build(self) # build your project calling the desired build tools as done in the command line.\n def package(self) # package the needed files from source and build folders.\n def package_info(self) # define cpp_build_info, flags, etc\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True, ignore_errors=False, run_environment=False, with_login=True, env=None)\n def _run(cmd, _env)\n def package_id(self) # modify the binary info, typically to narrow values\n def test(self) # test the generated executable.\n def __repr__(self)","label":0}],"string":"[\n {\n \"cid\": \"conans/client/build/cmake.py\",\n \"text\": \"conans/client/build/cmake.py\\nclass CMake\\n def __init__(self, conanfile, generator=None, cmake_system_name=True, parallel=True, build_type=None, toolset=None, make_program=None, set_cmake_flags=False, msbuild_verbosity='minimal', cmake_program=None, generator_platform=None, append_vcvars=False) # :param conanfile: Conanfile instance\\n def generator(self)\\n def generator(self, value)\\n def generator_platform(self)\\n def generator_platform(self, value)\\n def build_folder(self)\\n def build_folder(self, value)\\n def build_type(self)\\n def build_type(self, build_type)\\n def in_local_cache(self)\\n def runtime(self)\\n def flags(self)\\n def is_multi_configuration(self)\\n def command_line(self)\\n def build_config(self) # cmake --build tool have a --config option for Multi-configuration IDEs\\n def _get_dirs(self, source_folder, build_folder, source_dir, build_dir, cache_build_folder)\\n def get_dir(folder, origin)\\n def _run(self, command)\\n def configure(self, args=None, defs=None, source_dir=None, build_dir=None, source_folder=None, build_folder=None, cache_build_folder=None, pkg_config_paths=None)\\n def build(self, args=None, build_dir=None, target=None)\\n def _build(self, args=None, build_dir=None, target=None)\\n def install(self, args=None, build_dir=None)\\n def test(self, args=None, build_dir=None, target=None, output_on_failure=False)\\n def verbose(self)\\n def verbose(self, value)\\n def patch_config_paths(self) # changes references to the absolute path of the installed package and its depende\\n def get_version()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/new.py\",\n \"text\": \"conans/client/cmd/new.py\\ndef _render_template(text, name, version, package_name, defines)\\ndef _get_files_from_template_dir(template_dir, name, version, package_name, defines)\\ndef cmd_new(ref, header=False, pure_c=False, test=False, exports_sources=False, bare=False, visual_versions=None, linux_gcc_versions=None, linux_clang_versions=None, osx_clang_versions=None, shared=None, upload_url=None, gitignore=None, gitlab_gcc_versions=None, gitlab_clang_versions=None, circleci_gcc_versions=None, circleci_clang_versions=None, circleci_osx_versions=None, template=None, cache=None, defines=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py\",\n \"text\": \"conans/client/installer.py\\ndef build_id(conan_file)\\ndef add_env_conaninfo(conan_file, subtree_libnames)\\nclass _PackageBuilder\\n def __init__(self, cache, output, hook_manager, remote_manager, generators)\\n def _get_build_folder(self, conanfile, package_layout, pref, keep_build, recorder)\\n def _prepare_sources(self, conanfile, pref, package_layout, remotes)\\n def _copy_sources(conanfile, source_folder, build_folder)\\n def _build(self, conanfile, pref)\\n def _package(self, conanfile, pref, package_layout, conanfile_path)\\n def build_package(self, node, keep_build, recorder, remotes)\\ndef _remove_folder_raising(folder)\\ndef _handle_system_requirements(conan_file, pref, cache, out) # check first the system_reqs/system_requirements.txt existence, if not existing\\ndef call_system_requirements(conanfile, output)\\nclass BinaryInstaller # main responsible of retrieving binary packages or building them from source\\n def __init__(self, app, recorder)\\n def install(self, deps_graph, remotes, build_mode, update, profile_host, profile_build, graph_lock, keep_build=False)\\n def _classify(nodes_by_level)\\n def _raise_missing(self, missing)\\n def _download(self, downloads, processed_package_refs) # executes the download of packages (both download and update), only once for a gi\\n def _download(n)\\n def _download_pkg(self, layout, node)\\n def _build(self, nodes_by_level, keep_build, root_node, profile_host, profile_build, graph_lock, remotes, build_mode, update)\\n def _handle_node_editable(self, node, profile_host, profile_build, graph_lock)\\n def _handle_node_cache(self, node, keep_build, processed_package_references, remotes)\\n def _build_package(self, node, output, keep_build, remotes)\\n def _propagate_info(self, node, using_build_profile)\\n def _call_package_info(self, conanfile, package_folder, ref, is_editable)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/migrations.py\",\n \"text\": \"conans/client/migrations.py\\nclass ClientMigrator\\n def __init__(self, cache_folder, current_version, out)\\n def _update_settings_yml(self, cache, old_version)\\n def save_new()\\n def _make_migrations(self, old_version)\\ndef _get_refs(cache)\\ndef _get_prefs(layout)\\ndef migrate_tgz_location(cache, out) # In Conan 1.31, the temporary .tgz are no longer stored in the content folders. I\\ndef migrate_localdb_refresh_token(cache, out)\\ndef _migrate_full_metadata(cache, out)\\ndef _migrate_create_metadata(cache, out)\\ndef _migrate_lock_files(cache, out)\\ndef migrate_config_install(cache)\\ndef migrate_to_default_profile(conf_path, default_profile_path)\\ndef migrate_plugins_to_hooks(cache, output=None)\\ndef migrate_editables_use_conanfile_name(cache) # In Conan v1.26 we store full path to the conanfile in the editable_package.json \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py\",\n \"text\": \"conans/model/build_info.py\\nclass DefaultOrderedDict\\n def __init__(self, factory)\\n def __getitem__(self, key)\\n def __copy__(self)\\nclass BuildModulesDict # A dictionary with append and extend for cmake build modules to keep it backwards\\n def __getitem__(self, key)\\n def _append(self, item)\\n def append(self, item)\\n def extend(self, items)\\n def from_list(cls, build_modules)\\ndef dict_to_abs_paths(the_dict, rootpath)\\ndef merge_lists(seq1, seq2)\\ndef merge_dicts(d1, d2)\\n def merge_lists(seq1, seq2)\\nclass _CppInfo # Object that stores all the necessary information to build in C/C++.\\n def __init__(self)\\n def _filter_paths(self, paths)\\n def build_modules_paths(self)\\n def include_paths(self)\\n def lib_paths(self)\\n def src_paths(self)\\n def bin_paths(self)\\n def build_paths(self)\\n def res_paths(self)\\n def framework_paths(self)\\n def name(self)\\n def name(self, value)\\n def get_name(self, generator, default_name=True)\\n def get_filename(self, generator, default_name=True)\\n def get_build_modules(self)\\n def set_property(self, property_name, value, generator=None)\\n def get_property(self, property_name, generator=None)\\n def get_cppflags(self)\\n def set_cppflags(self, value)\\nclass Component\\n def __init__(self, rootpath, version, default_values)\\nclass CppInfoDefaultValues\\n def __init__(self, includedir=None, libdir=None, bindir=None, resdir=None, builddir=None, frameworkdir=None)\\nclass CppInfo # Build Information declared to be used by the CONSUMERS of a\\n def __init__(self, ref_name, root_folder, default_values=None)\\n def __str__(self)\\n def get_name(self, generator, default_name=True)\\n def configs(self)\\n def __getattr__(self, config)\\n def _get_cpp_info()\\n def _raise_incorrect_components_definition(self, package_name, package_requires)\\n def _check_components_requires_instersection(comp_requires)\\nclass _BaseDepsCppInfo\\n def __init__(self)\\n def update(self, dep_cpp_info)\\n def merge_lists(seq1, seq2)\\n def build_modules_paths(self)\\n def include_paths(self)\\n def lib_paths(self)\\n def src_paths(self)\\n def bin_paths(self)\\n def build_paths(self)\\n def res_paths(self)\\n def framework_paths(self)\\nclass DepCppInfo\\n def __init__(self, cpp_info)\\n def __str__(self)\\n def __getattr__(self, item)\\n def _aggregated_dict_values(self, item)\\n def _aggregated_list_values(self, item)\\n def _filter_component_requires(requires)\\n def _check_component_requires(self)\\n def _get_sorted_components(self) # Sort Components from most dependent one first to the less dependent one last\\n def build_modules_paths(self)\\n def include_paths(self)\\n def lib_paths(self)\\n def src_paths(self)\\n def bin_paths(self)\\n def build_paths(self)\\n def res_paths(self)\\n def framework_paths(self)\\n def libs(self)\\n def system_libs(self)\\n def frameworks(self)\\n def defines(self)\\n def cxxflags(self)\\n def cflags(self)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py\",\n \"text\": \"conans/model/conan_file.py\\ndef create_options(conanfile)\\ndef create_requirements(conanfile)\\ndef create_settings(conanfile, settings)\\ndef _env_and_python(conanfile)\\ndef get_env_context_manager(conanfile, without_python=False)\\nclass ConanFile # The base class for all package recipes\\n def __init__(self, output, runner, display_name='', user=None, channel=None)\\n def context(self)\\n def dependencies(self)\\n def ref(self)\\n def pref(self)\\n def buildenv(self)\\n def initialize(self, settings, env, buildenv=None)\\n def new_cpp_info(self)\\n def source_folder(self)\\n def source_folder(self, folder)\\n def build_folder(self)\\n def build_folder(self, folder)\\n def package_folder(self)\\n def package_folder(self, folder)\\n def install_folder(self)\\n def install_folder(self, folder)\\n def generators_folder(self)\\n def imports_folder(self)\\n def imports_folder(self, folder)\\n def env(self) # Apply the self.deps_env_info into a copy of self._conan_env_values (will priorit\\n def channel(self)\\n def user(self)\\n def collect_libs(self, folder=None)\\n def build_policy_missing(self)\\n def build_policy_always(self)\\n def source(self)\\n def system_requirements(self) # this method can be overwritten to implement logic for system package\\n def config_options(self) # modify options, probably conditioned to some settings. This call is executed\\n def configure(self) # modify settings, probably conditioned to some options. This call is executed\\n def build(self) # build your project calling the desired build tools as done in the command line.\\n def package(self) # package the needed files from source and build folders.\\n def package_info(self) # define cpp_build_info, flags, etc\\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True, ignore_errors=False, run_environment=False, with_login=True, env=None)\\n def _run(cmd, _env)\\n def package_id(self) # modify the binary info, typically to narrow values\\n def test(self) # test the generated executable.\\n def __repr__(self)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"conans/client/build/cmake.py::CMake.__init__","text":"conans/client/build/cmake.py::CMake.__init__\n def __init__(self, conanfile, generator=None, cmake_system_name=True,\n parallel=True, build_type=None, toolset=None, make_program=None,\n set_cmake_flags=False, msbuild_verbosity=\"minimal\", cmake_program=None,\n generator_platform=None, append_vcvars=False):\n \"\"\"\n :param conanfile: Conanfile instance\n :param generator: Generator name to use or none to autodetect\n :param cmake_system_name: False to not use CMAKE_SYSTEM_NAME variable,\n True for auto-detect or directly a string with the system name\n :param parallel: Try to build with multiple cores if available\n :param build_type: Overrides default build type coming from settings\n :param toolset: Toolset name to use (such as llvm-vs2014) or none for default one,\n applies only to certain generators (e.g. Visual Studio)\n :param set_cmake_flags: whether or not to set CMake flags like CMAKE_CXX_FLAGS,\n CMAKE_C_FLAGS, etc. it's vital to set for certain projects\n (e.g. using CMAKE_SIZEOF_VOID_P or CMAKE_LIBRARY_ARCHITECTURE)\n :param msbuild_verbosity: verbosity level for MSBuild (in case of Visual Studio generator)\n :param cmake_program: Path to the custom cmake executable\n :param generator_platform: Generator platform name or none to autodetect (-A cmake option)\n \"\"\"\n self._append_vcvars = append_vcvars\n self._conanfile = conanfile\n self._settings = conanfile.settings\n self._build_type = build_type or conanfile.settings.get_safe(\"build_type\")\n self._cmake_program = os.getenv(\"CONAN_CMAKE_PROGRAM\") or cmake_program or \"cmake\"\n\n self.generator_platform = generator_platform\n self.generator = generator or get_generator(conanfile)\n\n if not self.generator:\n self._conanfile.output.warn(\"CMake generator could not be deduced from set","label":0},{"cid":"conans/client/build/cmake.py::CMake.generator","text":"conans/client/build/cmake.py::CMake.generator\n @property\n def generator(self):\n return self._generator","label":0},{"cid":"conans/client/build/cmake.py::CMake.generator","text":"conans/client/build/cmake.py::CMake.generator\n @generator.setter\n def generator(self, value):\n self._generator = value\n if not self._generator_platform_is_assigned:\n self._generator_platform = get_generator_platform(self._settings, self._generator)","label":0},{"cid":"conans/client/build/cmake.py::CMake.generator_platform","text":"conans/client/build/cmake.py::CMake.generator_platform\n @property\n def generator_platform(self):\n return self._generator_platform","label":0},{"cid":"conans/client/build/cmake.py::CMake.generator_platform","text":"conans/client/build/cmake.py::CMake.generator_platform\n @generator_platform.setter\n def generator_platform(self, value):\n self._generator_platform = value\n self._generator_platform_is_assigned = bool(value is not None)","label":0},{"cid":"conans/client/build/cmake.py::CMake.build_folder","text":"conans/client/build/cmake.py::CMake.build_folder\n @property\n def build_folder(self):\n return self.build_dir","label":0},{"cid":"conans/client/build/cmake.py::CMake.build_folder","text":"conans/client/build/cmake.py::CMake.build_folder\n @build_folder.setter\n def build_folder(self, value):\n self.build_dir = value","label":0},{"cid":"conans/client/build/cmake.py::CMake.build_type","text":"conans/client/build/cmake.py::CMake.build_type\n @property\n def build_type(self):\n return self._build_type","label":0},{"cid":"conans/client/build/cmake.py::CMake.build_type","text":"conans/client/build/cmake.py::CMake.build_type\n @build_type.setter\n def build_type(self, build_type):\n settings_build_type = self._settings.get_safe(\"build_type\")\n self.definitions.pop(\"CMAKE_BUILD_TYPE\", None)\n self.definitions.update(build_type_definition(build_type, settings_build_type,\n self.generator, self._conanfile.output))\n self._build_type = build_type","label":0},{"cid":"conans/client/build/cmake.py::CMake.in_local_cache","text":"conans/client/build/cmake.py::CMake.in_local_cache\n @property\n def in_local_cache(self):\n try:\n in_local_cache = self.definitions[cmake_in_local_cache_var_name]\n return get_bool_from_text(str(in_local_cache))\n except KeyError:\n return False","label":0},{"cid":"conans/client/build/cmake.py::CMake.runtime","text":"conans/client/build/cmake.py::CMake.runtime\n @property\n def runtime(self):\n return defs_to_string(self.definitions.get(runtime_definition_var_name))","label":0},{"cid":"conans/client/build/cmake.py::CMake.flags","text":"conans/client/build/cmake.py::CMake.flags\n @property\n def flags(self):\n return defs_to_string(self.definitions)","label":0},{"cid":"conans/client/build/cmake.py::CMake.is_multi_configuration","text":"conans/client/build/cmake.py::CMake.is_multi_configuration\n @property\n def is_multi_configuration(self):\n return is_multi_configuration(self.generator)","label":0},{"cid":"conans/client/build/cmake.py::CMake.command_line","text":"conans/client/build/cmake.py::CMake.command_line\n @property\n def command_line(self):\n if self.generator_platform and not is_generator_platform_supported(self.generator):\n raise ConanException('CMake does not support generator platform with generator '\n '\"%s:. Please check your conan profile to either remove the '\n 'generator platform, or change the CMake generator.'\n % self.generator)\n\n if self.toolset and not is_toolset_supported(self.generator):\n raise ConanException('CMake does not support toolsets with generator \"%s:.'\n 'Please check your conan profile to either remove the toolset,'\n ' or change the CMake generator.' % self.generator)\n\n generator = self.generator\n generator_platform = self.generator_platform\n\n if self.generator_platform and 'Visual Studio' in generator:\n # FIXME: Conan 2.0 We are adding the platform to the generator instead of using\n # the -A argument to keep previous implementation, but any modern CMake will support\n # (and recommend) passing the platform in its own argument.\n # Get the version from the generator, as it could have been defined by user argument\n compiler_version = re.search(\"Visual Studio ([0-9]*)\", generator).group(1)\n if Version(compiler_version) < \"16\" and self._settings.get_safe(\"os\") != \"WindowsCE\":\n if self.generator_platform == \"x64\":\n generator += \" Win64\" if not generator.endswith(\" Win64\") else \"\"\n generator_platform = None\n elif self.generator_platform == \"ARM\":\n generator += \" ARM\" if not generator.endswith(\" ARM\") else \"\"\n generator_platform = None\n elif self.generator_platform == \"Win32\":\n ge","label":0},{"cid":"conans/client/build/cmake.py::CMake.build_config","text":"conans/client/build/cmake.py::CMake.build_config\n @property\n def build_config(self):\n \"\"\" cmake --build tool have a --config option for Multi-configuration IDEs\n \"\"\"\n if self._build_type and self.is_multi_configuration:\n return \"--config %s\" % self._build_type\n return \"\"","label":0},{"cid":"conans/client/build/cmake.py::CMake._get_dirs","text":"conans/client/build/cmake.py::CMake._get_dirs\n def _get_dirs(self, source_folder, build_folder, source_dir, build_dir, cache_build_folder):\n if (source_folder or build_folder) and (source_dir or build_dir):\n raise ConanException(\"Use 'build_folder'/'source_folder' arguments\")\n\n def get_dir(folder, origin):\n if folder:\n if os.path.isabs(folder):\n return folder\n return os.path.join(origin, folder)\n return origin\n\n if source_dir or build_dir: # OLD MODE\n build_ret = build_dir or self.build_dir or self._conanfile.build_folder\n source_ret = source_dir or self._conanfile.source_folder\n else:\n build_ret = get_dir(build_folder, self._conanfile.build_folder)\n source_ret = get_dir(source_folder, self._conanfile.source_folder)\n\n if self._conanfile.in_local_cache and cache_build_folder:\n build_ret = get_dir(cache_build_folder, self._conanfile.build_folder)\n\n return source_ret, build_ret","label":0},{"cid":"conans/client/build/cmake.py::CMake._get_dirs.get_dir","text":"conans/client/build/cmake.py::CMake._get_dirs.get_dir\n def get_dir(folder, origin):\n if folder:\n if os.path.isabs(folder):\n return folder\n return os.path.join(origin, folder)\n return origin","label":0},{"cid":"conans/client/build/cmake.py::CMake._run","text":"conans/client/build/cmake.py::CMake._run\n def _run(self, command):\n compiler = self._settings.get_safe(\"compiler\")\n conan_v2_error(\"compiler setting should be defined.\", not compiler)\n the_os = self._settings.get_safe(\"os\")\n is_clangcl = the_os == \"Windows\" and compiler == \"clang\"\n is_msvc = compiler == \"Visual Studio\"\n is_intel = compiler == \"intel\"\n context = tools.no_op()\n\n if (is_msvc or is_clangcl) and platform.system() == \"Windows\":\n if self.generator in [\"Ninja\", \"Ninja Multi-Config\",\n \"NMake Makefiles\", \"NMake Makefiles JOM\"]:\n vcvars_dict = tools.vcvars_dict(self._settings, force=True, filter_known_paths=False,\n output=self._conanfile.output)\n context = _environment_add(vcvars_dict, post=self._append_vcvars)\n elif is_intel:\n if self.generator in [\"Ninja\", \"Ninja Multi-Config\",\n \"NMake Makefiles\", \"NMake Makefiles JOM\", \"Unix Makefiles\"]:\n intel_compilervars_dict = tools.intel_compilervars_dict(self._conanfile, force=True)\n context = _environment_add(intel_compilervars_dict, post=self._append_vcvars)\n with context:\n self._conanfile.run(command)","label":0},{"cid":"conans/client/build/cmake.py::CMake.configure","text":"conans/client/build/cmake.py::CMake.configure\n def configure(self, args=None, defs=None, source_dir=None, build_dir=None,\n source_folder=None, build_folder=None, cache_build_folder=None,\n pkg_config_paths=None):\n\n # TODO: Deprecate source_dir and build_dir in favor of xxx_folder\n if not self._conanfile.should_configure:\n return\n args = args or []\n defs = defs or {}\n source_dir, self.build_dir = self._get_dirs(source_folder, build_folder,\n source_dir, build_dir,\n cache_build_folder)\n mkdir(self.build_dir)\n arg_list = join_arguments([\n self.command_line,\n args_to_string(args),\n defs_to_string(defs),\n args_to_string([source_dir])\n ])\n\n if pkg_config_paths:\n pkg_env = {\"PKG_CONFIG_PATH\":\n os.pathsep.join(get_abs_path(f, self._conanfile.install_folder)\n for f in pkg_config_paths)}\n else:\n # If we are using pkg_config generator automate the pcs location, otherwise it could\n # read wrong files\n set_env = \"pkg_config\" in self._conanfile.generators \\\n and \"PKG_CONFIG_PATH\" not in os.environ\n pkg_env = {\"PKG_CONFIG_PATH\": self._conanfile.install_folder} if set_env else None\n\n with environment_append(pkg_env):\n command = \"cd %s && %s %s\" % (args_to_string([self.build_dir]), self._cmake_program,\n arg_list)\n if platform.system() == \"Windows\" and self.generator == \"MinGW Makefiles\":\n with tools.remove_from_path(\"sh\"):\n self._run(command)\n else:\n self._run(command)","label":0},{"cid":"conans/client/build/cmake.py::CMake.build","text":"conans/client/build/cmake.py::CMake.build\n def build(self, args=None, build_dir=None, target=None):\n if not self._conanfile.should_build:\n return\n conan_v2_error(\"build_type setting should be defined.\", not self._build_type)\n self._build(args, build_dir, target)","label":0},{"cid":"conans/client/build/cmake.py::CMake._build","text":"conans/client/build/cmake.py::CMake._build\n def _build(self, args=None, build_dir=None, target=None):\n args = args or []\n build_dir = build_dir or self.build_dir or self._conanfile.build_folder\n if target is not None:\n args = [\"--target\", target] + args\n\n if self.generator and self.parallel:\n if (\"Makefiles\" in self.generator or \"Ninja\" in self.generator) and \\\n \"NMake\" not in self.generator:\n if \"--\" not in args:\n args.append(\"--\")\n args.append(\"-j%i\" % cpu_count(self._conanfile.output))\n elif \"Visual Studio\" in self.generator:\n compiler_version = re.search(\"Visual Studio ([0-9]*)\", self.generator).group(1)\n if Version(compiler_version) >= \"10\":\n if \"--\" not in args:\n args.append(\"--\")\n # Parallel for building projects in the solution\n args.append(\"/m:%i\" % cpu_count(output=self._conanfile.output))\n\n if self.generator and self.msbuild_verbosity:\n if \"Visual Studio\" in self.generator:\n compiler_version = re.search(\"Visual Studio ([0-9]*)\", self.generator).group(1)\n if Version(compiler_version) >= \"10\":\n if \"--\" not in args:\n args.append(\"--\")\n args.append(\"/verbosity:%s\" % self.msbuild_verbosity)\n\n arg_list = join_arguments([\n args_to_string([build_dir]),\n self.build_config,\n args_to_string(args)\n ])\n command = \"%s --build %s\" % (self._cmake_program, arg_list)\n self._run(command)","label":0},{"cid":"conans/client/build/cmake.py::CMake.install","text":"conans/client/build/cmake.py::CMake.install\n def install(self, args=None, build_dir=None):\n if not self._conanfile.should_install:\n return\n mkdir(self._conanfile.package_folder)\n if not self.definitions.get(cmake_install_prefix_var_name):\n raise ConanException(\"%s not defined for 'cmake.install()'\\n\"\n \"Make sure 'package_folder' is \"\n \"defined\" % cmake_install_prefix_var_name)\n self._build(args=args, build_dir=build_dir, target=\"install\")","label":0},{"cid":"conans/client/build/cmake.py::CMake.test","text":"conans/client/build/cmake.py::CMake.test\n def test(self, args=None, build_dir=None, target=None, output_on_failure=False):\n if not self._conanfile.should_test or not get_env(\"CONAN_RUN_TESTS\", True) or \\\n self._conanfile.conf[\"tools.build:skip_test\"]:\n return\n if not target:\n target = \"RUN_TESTS\" if self.is_multi_configuration else \"test\"\n\n test_env = {'CTEST_OUTPUT_ON_FAILURE': '1' if output_on_failure else '0'}\n if self.parallel:\n test_env['CTEST_PARALLEL_LEVEL'] = str(cpu_count(self._conanfile.output))\n with environment_append(test_env):\n self._build(args=args, build_dir=build_dir, target=target)","label":0},{"cid":"conans/client/build/cmake.py::CMake.verbose","text":"conans/client/build/cmake.py::CMake.verbose\n @property\n def verbose(self):\n try:\n verbose = self.definitions[verbose_definition_name]\n return get_bool_from_text(str(verbose))\n except KeyError:\n return False","label":0},{"cid":"conans/client/build/cmake.py::CMake.verbose","text":"conans/client/build/cmake.py::CMake.verbose\n @verbose.setter\n def verbose(self, value):\n self.definitions.update(verbose_definition(value))","label":0},{"cid":"conans/client/build/cmake.py::CMake.patch_config_paths","text":"conans/client/build/cmake.py::CMake.patch_config_paths\n def patch_config_paths(self):\n \"\"\"\n changes references to the absolute path of the installed package and its dependencies in\n exported cmake config files to the appropriate conan variable. This makes\n most (sensible) cmake config files portable.\n\n For example, if a package foo installs a file called \"fooConfig.cmake\" to\n be used by cmake's find_package method, normally this file will contain\n absolute paths to the installed package folder, for example it will contain\n a line such as:\n\n SET(Foo_INSTALL_DIR /home/developer/.conan/data/Foo/1.0.0/...)\n\n This will cause cmake find_package() method to fail when someone else\n installs the package via conan.\n\n This function will replace such mentions to\n\n SET(Foo_INSTALL_DIR ${CONAN_FOO_ROOT})\n\n which is a variable that is set by conanbuildinfo.cmake, so that find_package()\n now correctly works on this conan package.\n\n For dependent packages, if a package foo installs a file called \"fooConfig.cmake\" to\n be used by cmake's find_package method and if it depends to a package bar,\n normally this file will contain absolute paths to the bar package folder,\n for example it will contain a line such as:\n\n SET_TARGET_PROPERTIES(foo PROPERTIES\n INTERFACE_INCLUDE_DIRECTORIES\n \"/home/developer/.conan/data/Bar/1.0.0/user/channel/id/include\")\n\n This function will replace such mentions to\n\n SET_TARGET_PROPERTIES(foo PROPERTIES\n INTERFACE_INCLUDE_DIRECTORIES\n \"${CONAN_BAR_ROOT}/include\")\n\n If the install() method of the CMake object in the conan file is used, this\n function should be called _after_ that invocation. For example:\n\n def build(self):\n cmake = CMake(self)\n cmake.configure()\n ","label":0},{"cid":"conans/client/build/cmake.py::CMake.get_version","text":"conans/client/build/cmake.py::CMake.get_version\n @staticmethod\n def get_version():\n try:\n out = version_runner([\"cmake\", \"--version\"])\n version_line = decode_text(out).split('\\n', 1)[0]\n version_str = version_line.rsplit(' ', 1)[-1]\n return Version(version_str)\n except Exception as e:\n raise ConanException(\"Error retrieving CMake version: '{}'\".format(e))","label":0},{"cid":"conans/client/cmd/new.py::_render_template","text":"conans/client/cmd/new.py::_render_template\ndef _render_template(text, name, version, package_name, defines):\n context = {'name': name,\n 'version': version,\n 'package_name': package_name,\n 'conan_version': client_version}\n context.update(defines)\n t = Template(text, keep_trailing_newline=True)\n return t.render(**context)","label":0},{"cid":"conans/client/cmd/new.py::_get_files_from_template_dir","text":"conans/client/cmd/new.py::_get_files_from_template_dir\ndef _get_files_from_template_dir(template_dir, name, version, package_name, defines):\n files = []\n for d, _, fs in os.walk(template_dir):\n for f in fs:\n rel_d = os.path.relpath(d, template_dir)\n rel_f = os.path.join(rel_d, f)\n files.append(rel_f)\n\n out_files = dict()\n for f in files:\n f_path = os.path.join(template_dir, f)\n rendered_path = _render_template(f, name=name, version=version, package_name=package_name,\n defines=defines)\n rendered_file = _render_template(load(f_path), name=name, version=version,\n package_name=package_name, defines=defines)\n out_files[rendered_path] = rendered_file\n\n return out_files","label":0},{"cid":"conans/client/cmd/new.py::cmd_new","text":"conans/client/cmd/new.py::cmd_new\ndef cmd_new(ref, header=False, pure_c=False, test=False, exports_sources=False, bare=False,\n visual_versions=None, linux_gcc_versions=None, linux_clang_versions=None,\n osx_clang_versions=None, shared=None, upload_url=None, gitignore=None,\n gitlab_gcc_versions=None, gitlab_clang_versions=None,\n circleci_gcc_versions=None, circleci_clang_versions=None, circleci_osx_versions=None,\n template=None, cache=None, defines=None):\n try:\n name, version, user, channel, revision = get_reference_fields(ref, user_channel_input=False)\n # convert \"package_name\" -> \"PackageName\"\n package_name = re.sub(r\"(?:^|[\\W_])(\\w)\", lambda x: x.group(1).upper(), name)\n except ValueError:\n raise ConanException(\"Bad parameter, please use full package name,\"\n \"e.g.: MyLib/1.2.3@user/testing\")\n\n # Validate it is a valid reference\n ConanFileReference(name, version, user, channel)\n\n if header and exports_sources:\n raise ConanException(\"'header' and 'sources' are incompatible options\")\n if pure_c and header:\n raise ConanException(\"'pure_c' is incompatible with 'header'\")\n if pure_c and not exports_sources:\n raise ConanException(\"'pure_c' requires the use of --source\")\n if bare and (header or exports_sources):\n raise ConanException(\"'bare' is incompatible with 'header' and 'sources'\")\n if template and (header or exports_sources or bare or pure_c):\n raise ConanException(\"'template' is incompatible with 'header', \"\n \"'sources', 'pure-c' and 'bare'\")\n\n defines = defines or dict()\n\n if header:\n files = {\"conanfile.py\": conanfile_header.format(name=name, version=version,\n package_name=package_name)}\n elif exports_sources:\n if not pure_c:\n files = {\"conanfile.py\": conanfile_sources.format(na","label":0},{"cid":"conans/client/installer.py::build_id","text":"conans/client/installer.py::build_id\ndef build_id(conan_file):\n if hasattr(conan_file, \"build_id\"):\n # construct new ConanInfo\n build_id_info = conan_file.info.copy()\n conan_file.info_build = build_id_info\n # effectively call the user function to change the package values\n with conanfile_exception_formatter(str(conan_file), \"build_id\"):\n conan_file.build_id()\n # compute modified ID\n return build_id_info.package_id()\n return None","label":0},{"cid":"conans/client/installer.py::add_env_conaninfo","text":"conans/client/installer.py::add_env_conaninfo\ndef add_env_conaninfo(conan_file, subtree_libnames):\n for package_name, env_vars in conan_file._conan_env_values.data.items():\n for name, value in env_vars.items():\n if not package_name or package_name in subtree_libnames or \\\n package_name == conan_file.name:\n conan_file.info.env_values.add(name, value, package_name)","label":0},{"cid":"conans/client/installer.py::_PackageBuilder.__init__","text":"conans/client/installer.py::_PackageBuilder.__init__\n def __init__(self, cache, output, hook_manager, remote_manager, generators):\n self._cache = cache\n self._output = output\n self._hook_manager = hook_manager\n self._remote_manager = remote_manager\n self._generator_manager = generators","label":0},{"cid":"conans/client/installer.py::_PackageBuilder._get_build_folder","text":"conans/client/installer.py::_PackageBuilder._get_build_folder\n def _get_build_folder(self, conanfile, package_layout, pref, keep_build, recorder):\n # Build folder can use a different package_ID if build_id() is defined.\n # This function decides if the build folder should be re-used (not build again)\n # and returns the build folder\n new_id = build_id(conanfile)\n build_pref = PackageReference(pref.ref, new_id) if new_id else pref\n build_folder = package_layout.build(build_pref)\n\n if is_dirty(build_folder):\n self._output.warn(\"Build folder is dirty, removing it: %s\" % build_folder)\n rmdir(build_folder)\n clean_dirty(build_folder)\n\n # Decide if the build folder should be kept\n skip_build = conanfile.develop and keep_build\n if skip_build:\n self._output.info(\"Won't be built as specified by --keep-build\")\n if not os.path.exists(build_folder):\n msg = \"--keep-build specified, but build folder not found\"\n recorder.package_install_error(pref, INSTALL_ERROR_MISSING_BUILD_FOLDER,\n msg, remote_name=None)\n raise ConanException(msg)\n elif build_pref != pref and os.path.exists(build_folder) and hasattr(conanfile, \"build_id\"):\n self._output.info(\"Won't be built, using previous build folder as defined in build_id()\")\n skip_build = True\n\n return build_folder, skip_build","label":0},{"cid":"conans/client/installer.py::_PackageBuilder._prepare_sources","text":"conans/client/installer.py::_PackageBuilder._prepare_sources\n def _prepare_sources(self, conanfile, pref, package_layout, remotes):\n export_folder = package_layout.export()\n export_source_folder = package_layout.export_sources()\n scm_sources_folder = package_layout.scm_sources()\n conanfile_path = package_layout.conanfile()\n source_folder = package_layout.source()\n\n retrieve_exports_sources(self._remote_manager, self._cache, conanfile, pref.ref, remotes)\n\n conanfile.folders.set_base_source(source_folder)\n conanfile.folders.set_base_build(None)\n conanfile.folders.set_base_package(None)\n\n config_source(export_folder, export_source_folder, scm_sources_folder,\n conanfile, self._output, conanfile_path, pref.ref,\n self._hook_manager, self._cache)","label":0},{"cid":"conans/client/installer.py::_PackageBuilder._copy_sources","text":"conans/client/installer.py::_PackageBuilder._copy_sources\n @staticmethod\n def _copy_sources(conanfile, source_folder, build_folder):\n # Copies the sources to the build-folder, unless no_copy_source is defined\n _remove_folder_raising(build_folder)\n if not getattr(conanfile, 'no_copy_source', False):\n conanfile.output.info('Copying sources to build folder')\n try:\n shutil.copytree(source_folder, build_folder, symlinks=True)\n except Exception as e:\n msg = str(e)\n if \"206\" in msg: # System error shutil.Error 206: Filename or extension too long\n msg += \"\\nUse short_paths=True if paths too long\"\n raise ConanException(\"%s\\nError copying sources to build folder\" % msg)\n logger.debug(\"BUILD: Copied to %s\", build_folder)\n logger.debug(\"BUILD: Files copied %s\", \",\".join(os.listdir(build_folder)))","label":0},{"cid":"conans/client/installer.py::_PackageBuilder._build","text":"conans/client/installer.py::_PackageBuilder._build\n def _build(self, conanfile, pref):\n # Read generators from conanfile and generate the needed files\n logger.info(\"GENERATORS: Writing generators\")\n self._generator_manager.write_generators(conanfile, conanfile.build_folder,\n conanfile.generators_folder, self._output)\n\n logger.info(\"TOOLCHAIN: Writing toolchain\")\n write_toolchain(conanfile, conanfile.generators_folder, self._output)\n\n # Build step might need DLLs, binaries as protoc to generate source files\n # So execute imports() before build, storing the list of copied_files\n\n copied_files = run_imports(conanfile)\n\n try:\n mkdir(conanfile.build_folder)\n with tools.chdir(conanfile.build_folder):\n run_build_method(conanfile, self._hook_manager, reference=pref.ref, package_id=pref.id)\n self._output.success(\"Package '%s' built\" % pref.id)\n self._output.info(\"Build folder %s\" % conanfile.build_folder)\n except Exception as exc:\n self._output.writeln(\"\")\n self._output.error(\"Package '%s' build failed\" % pref.id)\n self._output.warn(\"Build folder %s\" % conanfile.build_folder)\n if isinstance(exc, ConanExceptionInUserConanfileMethod):\n raise exc\n raise ConanException(exc)\n finally:\n # Now remove all files that were imported with imports()\n remove_imports(conanfile, copied_files, self._output)","label":0},{"cid":"conans/client/installer.py::_PackageBuilder._package","text":"conans/client/installer.py::_PackageBuilder._package\n def _package(self, conanfile, pref, package_layout, conanfile_path):\n # FIXME: Is weak to assign here the recipe_hash\n manifest = package_layout.recipe_manifest()\n conanfile.info.recipe_hash = manifest.summary_hash\n\n # Creating ***info.txt files\n save(os.path.join(conanfile.folders.base_build, CONANINFO), conanfile.info.dumps())\n self._output.info(\"Generated %s\" % CONANINFO)\n save(os.path.join(conanfile.folders.base_build, BUILD_INFO),\n TXTGenerator(conanfile).content)\n self._output.info(\"Generated %s\" % BUILD_INFO)\n\n package_id = pref.id\n # Do the actual copy, call the conanfile.package() method\n # While installing, the infos goes to build folder\n conanfile.folders.set_base_install(conanfile.folders.base_build)\n\n prev = run_package_method(conanfile, package_id, self._hook_manager, conanfile_path,\n pref.ref)\n\n update_package_metadata(prev, package_layout, package_id, pref.ref.revision)\n\n if get_env(\"CONAN_READ_ONLY_CACHE\", False):\n make_read_only(conanfile.folders.base_package)\n # FIXME: Conan 2.0 Clear the registry entry (package ref)\n return prev","label":0},{"cid":"conans/client/installer.py::_PackageBuilder.build_package","text":"conans/client/installer.py::_PackageBuilder.build_package\n def build_package(self, node, keep_build, recorder, remotes):\n t1 = time.time()\n\n conanfile = node.conanfile\n pref = node.pref\n\n package_layout = self._cache.package_layout(pref.ref, conanfile.short_paths)\n base_source = package_layout.source()\n conanfile_path = package_layout.conanfile()\n base_package = package_layout.package(pref)\n\n base_build, skip_build = self._get_build_folder(conanfile, package_layout,\n pref, keep_build, recorder)\n # PREPARE SOURCES\n if not skip_build:\n with package_layout.conanfile_write_lock(self._output):\n set_dirty(base_build)\n self._prepare_sources(conanfile, pref, package_layout, remotes)\n self._copy_sources(conanfile, base_source, base_build)\n\n # BUILD & PACKAGE\n with package_layout.conanfile_read_lock(self._output):\n self._output.info('Building your package in %s' % base_build)\n try:\n if getattr(conanfile, 'no_copy_source', False):\n conanfile.folders.set_base_source(base_source)\n else:\n conanfile.folders.set_base_source(base_build)\n\n conanfile.folders.set_base_build(base_build)\n conanfile.folders.set_base_imports(base_build)\n conanfile.folders.set_base_package(base_package)\n\n if not skip_build:\n # In local cache, generators folder always in build_folder\n conanfile.folders.set_base_generators(base_build)\n # In local cache, install folder always is build_folder\n conanfile.folders.set_base_install(base_build)\n self._build(conanfile, pref)\n clean_dirty(base_build)\n\n prev = self._package(conanfile, pref, pac","label":0},{"cid":"conans/client/installer.py::_remove_folder_raising","text":"conans/client/installer.py::_remove_folder_raising\ndef _remove_folder_raising(folder):\n try:\n rmdir(folder)\n except OSError as e:\n raise ConanException(\"%s\\n\\nCouldn't remove folder, might be busy or open\\n\"\n \"Close any app using it, and retry\" % str(e))","label":0},{"cid":"conans/client/installer.py::_handle_system_requirements","text":"conans/client/installer.py::_handle_system_requirements\ndef _handle_system_requirements(conan_file, pref, cache, out):\n \"\"\" check first the system_reqs/system_requirements.txt existence, if not existing\n check package/sha1/\n\n Used after remote package retrieving and before package building\n \"\"\"\n # TODO: Check if this idiom should be generalize to all methods defined in base ConanFile\n # Instead of calling empty methods\n if type(conan_file).system_requirements == ConanFile.system_requirements:\n return\n\n package_layout = cache.package_layout(pref.ref)\n system_reqs_path = package_layout.system_reqs()\n system_reqs_package_path = package_layout.system_reqs_package(pref)\n if os.path.exists(system_reqs_path) or os.path.exists(system_reqs_package_path):\n return\n\n ret = call_system_requirements(conan_file, out)\n\n try:\n ret = str(ret or \"\")\n except Exception:\n out.warn(\"System requirements didn't return a string\")\n ret = \"\"\n if getattr(conan_file, \"global_system_requirements\", None):\n save(system_reqs_path, ret)\n else:\n save(system_reqs_package_path, ret)","label":0},{"cid":"conans/client/installer.py::call_system_requirements","text":"conans/client/installer.py::call_system_requirements\ndef call_system_requirements(conanfile, output):\n try:\n return conanfile.system_requirements()\n except Exception as e:\n output.error(\"while executing system_requirements(): %s\" % str(e))\n raise ConanException(\"Error in system requirements\")","label":0},{"cid":"conans/client/installer.py::BinaryInstaller.__init__","text":"conans/client/installer.py::BinaryInstaller.__init__\n def __init__(self, app, recorder):\n self._cache = app.cache\n self._out = app.out\n self._remote_manager = app.remote_manager\n self._recorder = recorder\n self._binaries_analyzer = app.binaries_analyzer\n self._hook_manager = app.hook_manager\n self._generator_manager = app.generator_manager\n # Load custom generators from the cache, generators are part of the binary\n # build and install. Generators loaded here from the cache will have precedence\n # and overwrite possible generators loaded from packages (requires)\n for generator_path in app.cache.generators:\n app.loader.load_generators(generator_path)","label":0},{"cid":"conans/client/installer.py::BinaryInstaller.install","text":"conans/client/installer.py::BinaryInstaller.install\n def install(self, deps_graph, remotes, build_mode, update, profile_host, profile_build,\n graph_lock, keep_build=False):\n # order by levels and separate the root node (ref=None) from the rest\n nodes_by_level = deps_graph.by_levels()\n root_level = nodes_by_level.pop()\n root_node = root_level[0]\n # Get the nodes in order and if we have to build them\n self._out.info(\"Installing (downloading, building) binaries...\")\n self._build(nodes_by_level, keep_build, root_node, profile_host, profile_build,\n graph_lock, remotes, build_mode, update)","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._classify","text":"conans/client/installer.py::BinaryInstaller._classify\n @staticmethod\n def _classify(nodes_by_level):\n missing, invalid, downloads = [], [], []\n for level in nodes_by_level:\n for node in level:\n if node.binary == BINARY_MISSING:\n missing.append(node)\n elif node.binary == BINARY_INVALID:\n invalid.append(node)\n elif node.binary in (BINARY_UPDATE, BINARY_DOWNLOAD):\n downloads.append(node)\n return missing, invalid, downloads","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._raise_missing","text":"conans/client/installer.py::BinaryInstaller._raise_missing\n def _raise_missing(self, missing):\n if not missing:\n return\n\n missing_prefs = set(n.pref for n in missing) # avoid duplicated\n missing_prefs = list(sorted(missing_prefs))\n for pref in missing_prefs:\n self._out.error(\"Missing binary: %s\" % str(pref))\n self._out.writeln(\"\")\n\n # Report details just the first one\n node = missing[0]\n package_id = node.package_id\n ref, conanfile = node.ref, node.conanfile\n dependencies = [str(dep.dst) for dep in node.dependencies]\n\n settings_text = \", \".join(conanfile.info.full_settings.dumps().splitlines())\n options_text = \", \".join(conanfile.info.full_options.dumps().splitlines())\n dependencies_text = ', '.join(dependencies)\n requires_text = \", \".join(conanfile.info.requires.dumps().splitlines())\n\n msg = textwrap.dedent('''\\\n Can't find a '%s' package for the specified settings, options and dependencies:\n - Settings: %s\n - Options: %s\n - Dependencies: %s\n - Requirements: %s\n - Package ID: %s\n ''' % (ref, settings_text, options_text, dependencies_text, requires_text, package_id))\n conanfile.output.warn(msg)\n self._recorder.package_install_error(PackageReference(ref, package_id),\n INSTALL_ERROR_MISSING, msg)\n missing_pkgs = \"', '\".join([str(pref.ref) for pref in missing_prefs])\n if len(missing_prefs) >= 5:\n build_str = \"--build=missing\"\n else:\n build_str = \" \".join([\"--build=%s\" % pref.ref.name for pref in missing_prefs])\n\n raise ConanException(textwrap.dedent('''\\\n Missing prebuilt package for '%s'\n Try to build from sources with '%s'\n Use 'conan search <reference> --table table.html'\n Or read 'http://docs.conan.io/en/latest/","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._download","text":"conans/client/installer.py::BinaryInstaller._download\n def _download(self, downloads, processed_package_refs):\n \"\"\" executes the download of packages (both download and update), only once for a given\n PREF, even if node duplicated\n :param downloads: all nodes to be downloaded or updated, included repetitions\n \"\"\"\n if not downloads:\n return\n\n download_nodes = []\n for node in downloads:\n pref = node.pref\n bare_pref = PackageReference(pref.ref, pref.id)\n if bare_pref in processed_package_refs:\n continue\n processed_package_refs[bare_pref] = pref.revision\n assert node.prev, \"PREV for %s is None\" % str(node.pref)\n download_nodes.append(node)\n\n def _download(n):\n npref = n.pref\n layout = self._cache.package_layout(npref.ref, n.conanfile.short_paths)\n # We cannot embed the package_lock inside the remote.get_package()\n # because the handle_node_cache has its own lock\n with layout.package_lock(pref):\n self._download_pkg(layout, n)\n\n parallel = self._cache.config.parallel_download\n if parallel is not None:\n self._out.info(\"Downloading binary packages in %s parallel threads\" % parallel)\n thread_pool = ThreadPool(parallel)\n thread_pool.map(_download, [n for n in download_nodes])\n thread_pool.close()\n thread_pool.join()\n else:\n for node in download_nodes:\n _download(node)","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._download._download","text":"conans/client/installer.py::BinaryInstaller._download._download\n def _download(n):\n npref = n.pref\n layout = self._cache.package_layout(npref.ref, n.conanfile.short_paths)\n # We cannot embed the package_lock inside the remote.get_package()\n # because the handle_node_cache has its own lock\n with layout.package_lock(pref):\n self._download_pkg(layout, n)","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._download_pkg","text":"conans/client/installer.py::BinaryInstaller._download_pkg\n def _download_pkg(self, layout, node):\n self._remote_manager.get_package(node.conanfile, node.pref, layout, node.binary_remote,\n node.conanfile.output, self._recorder)","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._build","text":"conans/client/installer.py::BinaryInstaller._build\n def _build(self, nodes_by_level, keep_build, root_node, profile_host, profile_build, graph_lock,\n remotes, build_mode, update):\n using_build_profile = bool(profile_build)\n missing, invalid, downloads = self._classify(nodes_by_level)\n if invalid:\n msg = [\"There are invalid packages (packages that cannot exist for this configuration):\"]\n for node in invalid:\n msg.append(\"{}: Invalid ID: {}\".format(node.conanfile, node.conanfile.info.invalid))\n raise ConanInvalidConfiguration(\"\\n\".join(msg))\n self._raise_missing(missing)\n processed_package_refs = {}\n self._download(downloads, processed_package_refs)\n\n for level in nodes_by_level:\n for node in level:\n ref, conan_file = node.ref, node.conanfile\n output = conan_file.output\n\n self._propagate_info(node, using_build_profile)\n if node.binary == BINARY_EDITABLE:\n self._handle_node_editable(node, profile_host, profile_build, graph_lock)\n # Need a temporary package revision for package_revision_mode\n # Cannot be PREV_UNKNOWN otherwise the consumers can't compute their packageID\n node.prev = \"editable\"\n else:\n if node.binary == BINARY_SKIP: # Privates not necessary\n continue\n assert ref.revision is not None, \"Installer should receive RREV always\"\n if node.binary == BINARY_UNKNOWN:\n self._binaries_analyzer.reevaluate_node(node, remotes, build_mode, update)\n if node.binary == BINARY_MISSING:\n self._raise_missing([node])\n _handle_system_requirements(conan_file, node.pref, self._cache, output)\n self._handle_node_cache(node,","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._handle_node_editable","text":"conans/client/installer.py::BinaryInstaller._handle_node_editable\n def _handle_node_editable(self, node, profile_host, profile_build, graph_lock):\n # Get source of information\n conanfile = node.conanfile\n ref = node.ref\n package_layout = self._cache.package_layout(ref)\n base_path = package_layout.base_folder()\n self._call_package_info(conanfile, package_folder=base_path, ref=ref, is_editable=True)\n\n # New editables mechanism based on Folders\n if hasattr(conanfile, \"layout\"):\n conanfile.folders.set_base_package(base_path)\n conanfile.folders.set_base_source(base_path)\n conanfile.folders.set_base_build(base_path)\n conanfile.folders.set_base_install(base_path)\n conanfile.folders.set_base_imports(base_path)\n\n output = conanfile.output\n output.info(\"Rewriting files of editable package \"\n \"'{}' at '{}'\".format(conanfile.name, conanfile.generators_folder))\n self._generator_manager.write_generators(conanfile, conanfile.install_folder,\n conanfile.generators_folder, output)\n write_toolchain(conanfile, conanfile.generators_folder, output)\n output.info(\"Generated toolchain\")\n graph_info_node = GraphInfo(profile_host, root_ref=node.ref)\n graph_info_node.options = node.conanfile.options.values\n graph_info_node.graph_lock = graph_lock\n graph_info_node.save(base_path)\n output.info(\"Generated conan.lock\")\n copied_files = run_imports(conanfile)\n report_copied_files(copied_files, output)\n return\n\n node.conanfile.cpp_info.filter_empty = False\n # OLD EDITABLE LAYOUTS:\n # Try with package-provided file\n editable_cpp_info = package_layout.editable_cpp_info()\n if editable_cpp_info:\n editable_cpp_info.apply_to(ref,\n ","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._handle_node_cache","text":"conans/client/installer.py::BinaryInstaller._handle_node_cache\n def _handle_node_cache(self, node, keep_build, processed_package_references, remotes):\n pref = node.pref\n assert pref.id, \"Package-ID without value\"\n assert pref.id != PACKAGE_ID_UNKNOWN, \"Package-ID error: %s\" % str(pref)\n conanfile = node.conanfile\n output = conanfile.output\n\n layout = self._cache.package_layout(pref.ref, conanfile.short_paths)\n\n with layout.package_lock(pref):\n bare_pref = PackageReference(pref.ref, pref.id)\n processed_prev = processed_package_references.get(bare_pref)\n if processed_prev is None: # This package-id has not been processed before\n if node.binary == BINARY_BUILD:\n assert node.prev is None, \"PREV for %s to be built should be None\" % str(pref)\n layout.package_remove(pref)\n with layout.set_dirty_context_manager(pref):\n pref = self._build_package(node, output, keep_build, remotes)\n assert node.prev, \"Node PREV shouldn't be empty\"\n assert node.pref.revision, \"Node PREF revision shouldn't be empty\"\n assert pref.revision is not None, \"PREV for %s to be built is None\" % str(pref)\n elif node.binary in (BINARY_UPDATE, BINARY_DOWNLOAD):\n # this can happen after a re-evaluation of packageID with Package_ID_unknown\n self._download_pkg(layout, node)\n elif node.binary == BINARY_CACHE:\n assert node.prev, \"PREV for %s is None\" % str(pref)\n output.success('Already installed!')\n log_package_got_from_local_cache(pref)\n self._recorder.package_fetched_from_cache(pref)\n processed_package_references[bare_pref] = node.prev\n else:\n # We need to update the PREV of this node, as its ","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._build_package","text":"conans/client/installer.py::BinaryInstaller._build_package\n def _build_package(self, node, output, keep_build, remotes):\n conanfile = node.conanfile\n # It is necessary to complete the sources of python requires, which might be used\n # Only the legacy python_requires allow this\n python_requires = getattr(conanfile, \"python_requires\", None)\n if python_requires and isinstance(python_requires, dict): # Old legacy python_requires\n for python_require in python_requires.values():\n assert python_require.ref.revision is not None, \\\n \"Installer should receive python_require.ref always\"\n retrieve_exports_sources(self._remote_manager, self._cache,\n python_require.conanfile, python_require.ref, remotes)\n\n builder = _PackageBuilder(self._cache, output, self._hook_manager, self._remote_manager,\n self._generator_manager)\n pref = builder.build_package(node, keep_build, self._recorder, remotes)\n if node.graph_lock_node:\n node.graph_lock_node.prev = pref.revision\n return pref","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._propagate_info","text":"conans/client/installer.py::BinaryInstaller._propagate_info\n def _propagate_info(self, node, using_build_profile):\n # it is necessary to recompute\n # the node transitive information necessary to compute the package_id\n # as it will be used by reevaluate_node() when package_revision_mode is used and\n # PACKAGE_ID_UNKNOWN happens due to unknown revisions\n self._binaries_analyzer.package_id_transitive_reqs(node)\n # Get deps_cpp_info from upstream nodes\n node_order = [n for n in node.public_closure if n.binary != BINARY_SKIP]\n # List sort is stable, will keep the original order of the closure, but prioritize levels\n conan_file = node.conanfile\n # FIXME: Not the best place to assign the _conan_using_build_profile\n conan_file._conan_using_build_profile = using_build_profile\n transitive = [it for it in node.transitive_closure.values()]\n\n br_host = []\n for it in node.dependencies:\n if it.require.build_require_context == CONTEXT_HOST:\n br_host.extend(it.dst.transitive_closure.values())\n\n # Initialize some members if we are using different contexts\n if using_build_profile:\n conan_file.user_info_build = DepsUserInfo()\n\n for n in node_order:\n if n not in transitive:\n conan_file.output.info(\"Applying build-requirement: %s\" % str(n.ref))\n\n dep_cpp_info = n.conanfile._conan_dep_cpp_info\n\n if not using_build_profile: # Do not touch anything\n conan_file.deps_user_info[n.ref.name] = n.conanfile.user_info\n conan_file.deps_cpp_info.add(n.ref.name, dep_cpp_info)\n conan_file.deps_env_info.update(n.conanfile.env_info, n.ref.name)\n else:\n if n in transitive or n in br_host:\n conan_file.deps_user_info[n.ref.name] = n.conanfile.user_info\n conan_file.deps_cpp_info.add(n.ref.name, d","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._call_package_info","text":"conans/client/installer.py::BinaryInstaller._call_package_info\n def _call_package_info(self, conanfile, package_folder, ref, is_editable):\n conanfile.cpp_info = CppInfo(conanfile.name, package_folder)\n conanfile.cpp_info.version = conanfile.version\n conanfile.cpp_info.description = conanfile.description\n\n conanfile.folders.set_base_package(package_folder)\n conanfile.folders.set_base_source(None)\n conanfile.folders.set_base_build(None)\n conanfile.folders.set_base_install(None)\n\n conanfile.env_info = EnvInfo()\n conanfile.user_info = UserInfo()\n\n # Get deps_cpp_info from upstream nodes\n public_deps = [name for name, req in conanfile.requires.items() if not req.private\n and not req.override]\n conanfile.cpp_info.public_deps = public_deps\n # Once the node is build, execute package info, so it has access to the\n # package folder and artifacts\n # Minimal pythonpath, not the whole context, make it 50% slower\n # FIXME Conan 2.0, Remove old ways of reusing python code\n with pythonpath(conanfile):\n with tools.chdir(package_folder):\n with conanfile_exception_formatter(str(conanfile), \"package_info\"):\n self._hook_manager.execute(\"pre_package_info\", conanfile=conanfile,\n reference=ref)\n if hasattr(conanfile, \"layout\"):\n # Old cpp info without defaults (the defaults are in the new one)\n conanfile.cpp_info = CppInfo(conanfile.name, package_folder,\n default_values=CppInfoDefaultValues())\n if not is_editable:\n package_cppinfo = conanfile.cpp.package.copy()\n package_cppinfo.set_relative_base_folder(conanfile.folders.package)\n # Copy the i","label":1},{"cid":"conans/client/migrations.py::ClientMigrator.__init__","text":"conans/client/migrations.py::ClientMigrator.__init__\n def __init__(self, cache_folder, current_version, out):\n self.cache_folder = cache_folder\n super(ClientMigrator, self).__init__(cache_folder, current_version, out)","label":0},{"cid":"conans/client/migrations.py::ClientMigrator._update_settings_yml","text":"conans/client/migrations.py::ClientMigrator._update_settings_yml\n def _update_settings_yml(self, cache, old_version):\n\n from conans.client.conf import get_default_settings_yml\n settings_path = cache.settings_path\n if not os.path.exists(settings_path):\n self.out.warn(\"Migration: This conan installation doesn't have settings yet\")\n self.out.warn(\"Nothing to migrate here, settings will be generated automatically\")\n return\n\n var_name = \"settings_{}\".format(old_version.replace(\".\", \"_\"))\n\n def save_new():\n new_path = cache.settings_path + \".new\"\n save(new_path, get_default_settings_yml())\n self.out.warn(\"*\" * 40)\n self.out.warn(\"settings.yml is locally modified, can't be updated\")\n self.out.warn(\"The new settings.yml has been stored in: %s\" % new_path)\n self.out.warn(\"*\" * 40)\n\n self.out.warn(\"Migration: Updating settings.yml\")\n if hasattr(migrations_settings, var_name):\n version_default_contents = getattr(migrations_settings, var_name)\n if version_default_contents != get_default_settings_yml():\n current_settings = load(cache.settings_path)\n if current_settings != version_default_contents:\n save_new()\n else:\n save(cache.settings_path, get_default_settings_yml())\n else:\n self.out.info(\"Migration: Settings already up to date\")\n else:\n # We don't have the value for that version, so don't override\n save_new()","label":0},{"cid":"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new","text":"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new\n def save_new():\n new_path = cache.settings_path + \".new\"\n save(new_path, get_default_settings_yml())\n self.out.warn(\"*\" * 40)\n self.out.warn(\"settings.yml is locally modified, can't be updated\")\n self.out.warn(\"The new settings.yml has been stored in: %s\" % new_path)\n self.out.warn(\"*\" * 40)","label":0},{"cid":"conans/client/migrations.py::ClientMigrator._make_migrations","text":"conans/client/migrations.py::ClientMigrator._make_migrations\n def _make_migrations(self, old_version):\n # ############### FILL THIS METHOD WITH THE REQUIRED ACTIONS ##############\n # VERSION 0.1\n if old_version is None:\n return\n\n # Migrate the settings if they were the default for that version\n cache = ClientCache(self.cache_folder, self.out)\n self._update_settings_yml(cache, old_version)\n\n if old_version < Version(\"1.0\"):\n _migrate_lock_files(cache, self.out)\n\n if old_version < Version(\"1.12.0\"):\n migrate_plugins_to_hooks(cache)\n\n if old_version < Version(\"1.13.0\"):\n # MIGRATE LOCAL CACHE TO GENERATE MISSING METADATA.json\n _migrate_create_metadata(cache, self.out)\n\n if old_version < Version(\"1.14.0\"):\n migrate_config_install(cache)\n\n if old_version < Version(\"1.14.2\"):\n _migrate_full_metadata(cache, self.out)\n\n if old_version < Version(\"1.15.0\"):\n migrate_registry_file(cache, self.out)\n\n if old_version < Version(\"1.19.0\"):\n migrate_localdb_refresh_token(cache, self.out)\n\n if old_version < Version(\"1.26.0\"):\n migrate_editables_use_conanfile_name(cache)\n\n if old_version < Version(\"1.31.0\"):\n migrate_tgz_location(cache, self.out)","label":0},{"cid":"conans/client/migrations.py::_get_refs","text":"conans/client/migrations.py::_get_refs\ndef _get_refs(cache):\n folders = list_folder_subdirs(cache.store, 4)\n return [ConanFileReference(*s.split(\"/\")) for s in folders]","label":0},{"cid":"conans/client/migrations.py::_get_prefs","text":"conans/client/migrations.py::_get_prefs\ndef _get_prefs(layout):\n packages_folder = layout.packages()\n folders = list_folder_subdirs(packages_folder, 1)\n return [PackageReference(layout.ref, s) for s in folders]","label":0},{"cid":"conans/client/migrations.py::migrate_tgz_location","text":"conans/client/migrations.py::migrate_tgz_location\ndef migrate_tgz_location(cache, out):\n \"\"\" In Conan 1.31, the temporary .tgz are no longer stored in the content folders. In case\n they are found there, they can be removed, and the next time they are needed (upload), they\n will be compressed again\n \"\"\"\n out.info(\"Removing temporary .tgz files, they are stored in a different location now\")\n refs = _get_refs(cache)\n for ref in refs:\n try:\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\n for d, _, fs in os.walk(base_folder):\n for f in fs:\n if f in (EXPORT_SOURCES_TGZ_NAME, EXPORT_TGZ_NAME, PACKAGE_TGZ_NAME):\n tgz_file = os.path.join(d, f)\n os.remove(tgz_file)\n except Exception as e:\n raise ConanException(\"Something went wrong while removing temporary .tgz files \"\n \"in the cache, please try to fix the issue or wipe the cache: {}\"\n \":{}\".format(ref, e))","label":0},{"cid":"conans/client/migrations.py::migrate_localdb_refresh_token","text":"conans/client/migrations.py::migrate_localdb_refresh_token\ndef migrate_localdb_refresh_token(cache, out):\n from sqlite3 import OperationalError\n\n with cache.localdb._connect() as connection:\n try:\n statement = connection.cursor()\n statement.execute(\"ALTER TABLE users_remotes ADD refresh_token TEXT;\")\n except OperationalError:\n # This likely means the column is already there (fresh created table)\n # In the worst scenario the user will be requested to remove the file by hand\n pass","label":0},{"cid":"conans/client/migrations.py::_migrate_full_metadata","text":"conans/client/migrations.py::_migrate_full_metadata\ndef _migrate_full_metadata(cache, out):\n # Fix for https://github.com/conan-io/conan/issues/4898\n out.warn(\"Running a full revision metadata migration\")\n refs = _get_refs(cache)\n for ref in refs:\n try:\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=None,\n no_lock=True)\n with layout.update_metadata() as metadata:\n # Updating the RREV\n if metadata.recipe.revision is None:\n out.warn(\"Package %s metadata had recipe revision None, migrating\" % str(ref))\n folder = layout.export()\n try:\n manifest = FileTreeManifest.load(folder)\n rrev = manifest.summary_hash\n except Exception:\n rrev = DEFAULT_REVISION_V1\n metadata.recipe.revision = rrev\n\n prefs = _get_prefs(layout)\n existing_ids = [pref.id for pref in prefs]\n for pkg_id in list(metadata.packages.keys()):\n if pkg_id not in existing_ids:\n out.warn(\"Package %s metadata had stalled package information %s, removing\"\n % (str(ref), pkg_id))\n del metadata.packages[pkg_id]\n # UPDATING PREVS\n for pref in prefs:\n try:\n pmanifest = FileTreeManifest.load(layout.package(pref))\n prev = pmanifest.summary_hash\n except Exception:\n prev = DEFAULT_REVISION_V1\n metadata.packages[pref.id].revision = prev\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\n\n except Exception as ","label":0},{"cid":"conans/client/migrations.py::_migrate_create_metadata","text":"conans/client/migrations.py::_migrate_create_metadata\ndef _migrate_create_metadata(cache, out):\n out.warn(\"Migration: Generating missing metadata files\")\n refs = _get_refs(cache)\n\n for ref in refs:\n try:\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\n # Force using a package cache layout for everything, we want to alter the cache,\n # not the editables\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=False,\n no_lock=True)\n folder = layout.export()\n try:\n manifest = FileTreeManifest.load(folder)\n rrev = manifest.summary_hash\n except Exception:\n rrev = DEFAULT_REVISION_V1\n metadata_path = layout.package_metadata()\n if not os.path.exists(metadata_path):\n out.info(\"Creating {} for {}\".format(PACKAGE_METADATA, ref))\n prefs = _get_prefs(layout)\n metadata = PackageMetadata()\n metadata.recipe.revision = rrev\n for pref in prefs:\n try:\n pmanifest = FileTreeManifest.load(layout.package(pref))\n prev = pmanifest.summary_hash\n except Exception:\n prev = DEFAULT_REVISION_V1\n metadata.packages[pref.id].revision = prev\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\n save(metadata_path, metadata.dumps())\n except Exception as e:\n raise ConanException(\"Something went wrong while generating the metadata.json files \"\n \"in the cache, please try to fix the issue or wipe the cache: {}\"\n \":{}\".format(ref, e))\n out.success(\"Migration: Generating missing metadata files finished OK!\\n\")","label":0},{"cid":"conans/client/migrations.py::_migrate_lock_files","text":"conans/client/migrations.py::_migrate_lock_files\ndef _migrate_lock_files(cache, out):\n out.warn(\"Migration: Removing old lock files\")\n base_dir = cache.store\n pkgs = list_folder_subdirs(base_dir, 4)\n for pkg in pkgs:\n out.info(\"Removing locks for %s\" % pkg)\n try:\n count = os.path.join(base_dir, pkg, \"rw.count\")\n if os.path.exists(count):\n os.remove(count)\n count = os.path.join(base_dir, pkg, \"rw.count.lock\")\n if os.path.exists(count):\n os.remove(count)\n locks = os.path.join(base_dir, pkg, \"locks\")\n if os.path.exists(locks):\n shutil.rmtree(locks)\n except Exception as e:\n raise ConanException(\"Something went wrong while removing %s locks\\n\"\n \"Error: %s\\n\"\n \"Please clean your local conan cache manually\"\n % (pkg, str(e)))\n out.warn(\"Migration: Removing old lock files finished\\n\")","label":0},{"cid":"conans/client/migrations.py::migrate_config_install","text":"conans/client/migrations.py::migrate_config_install\ndef migrate_config_install(cache):\n try:\n item = cache.config.get_item(\"general.config_install\")\n items = [r.strip() for r in item.split(\",\")]\n if len(items) == 4:\n config_type, uri, verify_ssl, args = items\n elif len(items) == 1:\n uri = items[0]\n verify_ssl = \"True\"\n args = \"None\"\n config_type = None\n else:\n raise Exception(\"I don't know how to migrate this config install: %s\" % items)\n verify_ssl = \"true\" in verify_ssl.lower()\n args = None if \"none\" in args.lower() else args\n config = _ConfigOrigin.from_item(uri, config_type, verify_ssl, args, None, None)\n _save_configs(cache.config_install_file, [config])\n cache.config.rm_item(\"general.config_install\")\n except ConanException:\n pass","label":0},{"cid":"conans/client/migrations.py::migrate_to_default_profile","text":"conans/client/migrations.py::migrate_to_default_profile\ndef migrate_to_default_profile(conf_path, default_profile_path):\n tag = \"[settings_defaults]\"\n old_conf = load(conf_path)\n if tag not in old_conf:\n return\n tmp = old_conf.find(tag)\n new_conf = old_conf[0:tmp]\n rest = old_conf[tmp + len(tag):]\n if tmp:\n if \"]\" in rest: # More sections after the settings_defaults\n new_conf += rest[rest.find(\"[\"):]\n save(conf_path, new_conf)\n settings = rest[:rest.find(\"[\")].strip()\n else:\n save(conf_path, new_conf)\n settings = rest.strip()\n # Now generate the default profile from the read settings_defaults\n new_profile = \"[settings]\\n%s\" % settings\n save(default_profile_path, new_profile)","label":0},{"cid":"conans/client/migrations.py::migrate_plugins_to_hooks","text":"conans/client/migrations.py::migrate_plugins_to_hooks\ndef migrate_plugins_to_hooks(cache, output=None):\n plugins_path = os.path.join(cache.cache_folder, \"plugins\")\n if os.path.exists(plugins_path) and not os.path.exists(cache.hooks_path):\n os.rename(plugins_path, cache.hooks_path)\n conf_path = cache.conan_conf_path\n replace_in_file(conf_path, \"[plugins]\", \"[hooks]\", strict=False, output=output)","label":0},{"cid":"conans/client/migrations.py::migrate_editables_use_conanfile_name","text":"conans/client/migrations.py::migrate_editables_use_conanfile_name\ndef migrate_editables_use_conanfile_name(cache):\n \"\"\"\n In Conan v1.26 we store full path to the conanfile in the editable_package.json file, before\n it Conan was storing just the directory and assume that the 'conanfile' was a file\n named 'conanfile.py' inside that folder\n \"\"\"\n for ref, data in cache.editable_packages.edited_refs.items():\n path = data[\"path\"]\n if os.path.isdir(path):\n path = os.path.join(path, CONANFILE)\n cache.editable_packages.add(ref, path, layout=data[\"layout\"])","label":0},{"cid":"conans/model/build_info.py::DefaultOrderedDict.__init__","text":"conans/model/build_info.py::DefaultOrderedDict.__init__\n def __init__(self, factory):\n self.factory = factory\n super(DefaultOrderedDict, self).__init__()","label":0},{"cid":"conans/model/build_info.py::DefaultOrderedDict.__getitem__","text":"conans/model/build_info.py::DefaultOrderedDict.__getitem__\n def __getitem__(self, key):\n if key not in self.keys():\n super(DefaultOrderedDict, self).__setitem__(key, self.factory())\n super(DefaultOrderedDict, self).__getitem__(key).name = key\n return super(DefaultOrderedDict, self).__getitem__(key)","label":0},{"cid":"conans/model/build_info.py::DefaultOrderedDict.__copy__","text":"conans/model/build_info.py::DefaultOrderedDict.__copy__\n def __copy__(self):\n the_copy = DefaultOrderedDict(self.factory)\n for key, value in super(DefaultOrderedDict, self).items():\n the_copy[key] = value\n return the_copy","label":0},{"cid":"conans/model/build_info.py::BuildModulesDict.__getitem__","text":"conans/model/build_info.py::BuildModulesDict.__getitem__\n def __getitem__(self, key):\n if key not in self.keys():\n super(BuildModulesDict, self).__setitem__(key, list())\n return super(BuildModulesDict, self).__getitem__(key)","label":0},{"cid":"conans/model/build_info.py::BuildModulesDict._append","text":"conans/model/build_info.py::BuildModulesDict._append\n def _append(self, item):\n if item.endswith(\".cmake\"):\n self[\"cmake\"].append(item)\n self[\"cmake_multi\"].append(item)\n self[\"cmake_find_package\"].append(item)\n self[\"cmake_find_package_multi\"].append(item)","label":0},{"cid":"conans/model/build_info.py::BuildModulesDict.append","text":"conans/model/build_info.py::BuildModulesDict.append\n def append(self, item):\n conan_v2_error(\"Use 'self.cpp_info.build_modules[\\\"<generator>\\\"].append(\\\"{item}\\\")' \"\n 'instead'.format(item=item))\n self._append(item)","label":0},{"cid":"conans/model/build_info.py::BuildModulesDict.extend","text":"conans/model/build_info.py::BuildModulesDict.extend\n def extend(self, items):\n conan_v2_error(\"Use 'self.cpp_info.build_modules[\\\"<generator>\\\"].extend({items})' \"\n \"instead\".format(items=items))\n for item in items:\n self._append(item)","label":0},{"cid":"conans/model/build_info.py::BuildModulesDict.from_list","text":"conans/model/build_info.py::BuildModulesDict.from_list\n @classmethod\n def from_list(cls, build_modules):\n the_dict = BuildModulesDict()\n the_dict.extend(build_modules)\n return the_dict","label":0},{"cid":"conans/model/build_info.py::dict_to_abs_paths","text":"conans/model/build_info.py::dict_to_abs_paths\ndef dict_to_abs_paths(the_dict, rootpath):\n new_dict = {}\n for generator, values in the_dict.items():\n new_dict[generator] = [os.path.join(rootpath, p) if not os.path.isabs(p) else p\n for p in values]\n return new_dict","label":0},{"cid":"conans/model/build_info.py::merge_lists","text":"conans/model/build_info.py::merge_lists\ndef merge_lists(seq1, seq2):\n return seq1 + [s for s in seq2 if s not in seq1]","label":0},{"cid":"conans/model/build_info.py::merge_dicts","text":"conans/model/build_info.py::merge_dicts\ndef merge_dicts(d1, d2):\n def merge_lists(seq1, seq2):\n return [s for s in seq1 if s not in seq2] + seq2\n\n result = d1.copy()\n for k, v in d2.items():\n if k not in d1.keys():\n result[k] = v\n else:\n result[k] = merge_lists(d1[k], d2[k])\n return result","label":0},{"cid":"conans/model/build_info.py::merge_dicts.merge_lists","text":"conans/model/build_info.py::merge_dicts.merge_lists\n def merge_lists(seq1, seq2):\n return [s for s in seq1 if s not in seq2] + seq2","label":0},{"cid":"conans/model/build_info.py::_CppInfo.__init__","text":"conans/model/build_info.py::_CppInfo.__init__\n def __init__(self):\n self._name = None\n self._generator_properties = {}\n self.names = {}\n self.system_libs = [] # Ordered list of system libraries\n self.includedirs = [] # Ordered list of include paths\n self.srcdirs = [] # Ordered list of source paths\n self.libdirs = [] # Directories to find libraries\n self.resdirs = [] # Directories to find resources, data, etc\n self.bindirs = [] # Directories to find executables and shared libs\n self.builddirs = []\n self.frameworks = [] # Macos .framework\n self.frameworkdirs = []\n self.rootpaths = []\n self.libs = [] # The libs to link against\n self.defines = [] # preprocessor definitions\n self.cflags = [] # pure C flags\n self.cxxflags = [] # C++ compilation flags\n self.sharedlinkflags = [] # linker flags\n self.exelinkflags = [] # linker flags\n self.build_modules = BuildModulesDict() # FIXME: This should be just a plain dict\n self.filenames = {} # name of filename to create for various generators\n self.rootpath = \"\"\n self.sysroot = \"\"\n self.requires = []\n self._build_modules_paths = None\n self._build_modules = None\n self._include_paths = None\n self._lib_paths = None\n self._bin_paths = None\n self._build_paths = None\n self._res_paths = None\n self._src_paths = None\n self._framework_paths = None\n self.version = None # Version of the conan package\n self.description = None # Description of the conan package\n # When package is editable, filter_empty=False, so empty dirs are maintained\n self.filter_empty = True","label":0},{"cid":"conans/model/build_info.py::_CppInfo._filter_paths","text":"conans/model/build_info.py::_CppInfo._filter_paths\n def _filter_paths(self, paths):\n abs_paths = [os.path.join(self.rootpath, p)\n if not os.path.isabs(p) else p for p in paths]\n if self.filter_empty:\n return [p for p in abs_paths if os.path.isdir(p)]\n else:\n return abs_paths","label":0},{"cid":"conans/model/build_info.py::_CppInfo.build_modules_paths","text":"conans/model/build_info.py::_CppInfo.build_modules_paths\n @property\n def build_modules_paths(self):\n if self._build_modules_paths is None:\n if isinstance(self.build_modules, list): # FIXME: This should be just a plain dict\n conan_v2_error(\"Use 'self.cpp_info.build_modules[\\\"<generator>\\\"] = \"\n \"{the_list}' instead\".format(the_list=self.build_modules))\n self.build_modules = BuildModulesDict.from_list(self.build_modules)\n # Invalidate necessary, get_build_modules used raise_incorrect_components_definition\n self._build_modules = None\n tmp = dict_to_abs_paths(BuildModulesDict(self.get_build_modules()), self.rootpath)\n self._build_modules_paths = tmp\n return self._build_modules_paths","label":0},{"cid":"conans/model/build_info.py::_CppInfo.include_paths","text":"conans/model/build_info.py::_CppInfo.include_paths\n @property\n def include_paths(self):\n if self._include_paths is None:\n self._include_paths = self._filter_paths(self.includedirs)\n return self._include_paths","label":0},{"cid":"conans/model/build_info.py::_CppInfo.lib_paths","text":"conans/model/build_info.py::_CppInfo.lib_paths\n @property\n def lib_paths(self):\n if self._lib_paths is None:\n self._lib_paths = self._filter_paths(self.libdirs)\n return self._lib_paths","label":0},{"cid":"conans/model/build_info.py::_CppInfo.src_paths","text":"conans/model/build_info.py::_CppInfo.src_paths\n @property\n def src_paths(self):\n if self._src_paths is None:\n self._src_paths = self._filter_paths(self.srcdirs)\n return self._src_paths","label":0},{"cid":"conans/model/build_info.py::_CppInfo.bin_paths","text":"conans/model/build_info.py::_CppInfo.bin_paths\n @property\n def bin_paths(self):\n if self._bin_paths is None:\n self._bin_paths = self._filter_paths(self.bindirs)\n return self._bin_paths","label":0},{"cid":"conans/model/build_info.py::_CppInfo.build_paths","text":"conans/model/build_info.py::_CppInfo.build_paths\n @property\n def build_paths(self):\n if self._build_paths is None:\n self._build_paths = self._filter_paths(self.builddirs)\n return self._build_paths","label":0},{"cid":"conans/model/build_info.py::_CppInfo.res_paths","text":"conans/model/build_info.py::_CppInfo.res_paths\n @property\n def res_paths(self):\n if self._res_paths is None:\n self._res_paths = self._filter_paths(self.resdirs)\n return self._res_paths","label":0},{"cid":"conans/model/build_info.py::_CppInfo.framework_paths","text":"conans/model/build_info.py::_CppInfo.framework_paths\n @property\n def framework_paths(self):\n if self._framework_paths is None:\n self._framework_paths = self._filter_paths(self.frameworkdirs)\n return self._framework_paths","label":0},{"cid":"conans/model/build_info.py::_CppInfo.name","text":"conans/model/build_info.py::_CppInfo.name\n @property\n def name(self):\n conan_v2_error(\"Use 'get_name(generator)' instead\")\n return self._name","label":0},{"cid":"conans/model/build_info.py::_CppInfo.name","text":"conans/model/build_info.py::_CppInfo.name\n @name.setter\n def name(self, value):\n self._name = value","label":0},{"cid":"conans/model/build_info.py::_CppInfo.get_name","text":"conans/model/build_info.py::_CppInfo.get_name\n def get_name(self, generator, default_name=True):\n property_name = None\n if \"cmake\" in generator:\n property_name = \"cmake_target_name\"\n elif \"pkg_config\" in generator:\n property_name = \"pkg_config_name\"\n return self.get_property(property_name, generator) \\\n or self.names.get(generator, self._name if default_name else None)","label":0},{"cid":"conans/model/build_info.py::_CppInfo.get_filename","text":"conans/model/build_info.py::_CppInfo.get_filename\n def get_filename(self, generator, default_name=True):\n result = self.get_property(\"cmake_file_name\", generator) or self.filenames.get(generator)\n if result:\n return result\n return self.get_name(generator, default_name=default_name)","label":0},{"cid":"conans/model/build_info.py::_CppInfo.get_build_modules","text":"conans/model/build_info.py::_CppInfo.get_build_modules\n def get_build_modules(self):\n if self._build_modules is None: # Not cached yet\n try:\n default_build_modules_value = self._generator_properties[None][\"cmake_build_modules\"]\n except KeyError:\n ret_dict = {}\n else:\n ret_dict = {\"cmake_find_package\": default_build_modules_value,\n \"cmake_find_package_multi\": default_build_modules_value,\n \"cmake\": default_build_modules_value,\n \"cmake_multi\": default_build_modules_value}\n\n for generator, values in self._generator_properties.items():\n if generator:\n v = values.get(\"cmake_build_modules\")\n if v:\n ret_dict[generator] = v\n self._build_modules = ret_dict if ret_dict else self.build_modules\n return self._build_modules","label":0},{"cid":"conans/model/build_info.py::_CppInfo.set_property","text":"conans/model/build_info.py::_CppInfo.set_property\n def set_property(self, property_name, value, generator=None):\n self._generator_properties.setdefault(generator, {})[property_name] = value","label":0},{"cid":"conans/model/build_info.py::_CppInfo.get_property","text":"conans/model/build_info.py::_CppInfo.get_property\n def get_property(self, property_name, generator=None):\n if generator:\n try:\n return self._generator_properties[generator][property_name]\n except KeyError:\n pass\n try:\n return self._generator_properties[None][property_name]\n except KeyError:\n pass","label":0},{"cid":"conans/model/build_info.py::_CppInfo.get_cppflags","text":"conans/model/build_info.py::_CppInfo.get_cppflags\n def get_cppflags(self):\n conan_v2_error(\"'cpp_info.cppflags' is deprecated, use 'cxxflags' instead\")\n return self.cxxflags","label":0},{"cid":"conans/model/build_info.py::_CppInfo.set_cppflags","text":"conans/model/build_info.py::_CppInfo.set_cppflags\n def set_cppflags(self, value):\n conan_v2_error(\"'cpp_info.cppflags' is deprecated, use 'cxxflags' instead\")\n self.cxxflags = value","label":0},{"cid":"conans/model/build_info.py::Component.__init__","text":"conans/model/build_info.py::Component.__init__\n def __init__(self, rootpath, version, default_values):\n super(Component, self).__init__()\n self.rootpath = rootpath\n if default_values.includedir is not None:\n self.includedirs.append(default_values.includedir)\n if default_values.libdir is not None:\n self.libdirs.append(default_values.libdir)\n if default_values.bindir is not None:\n self.bindirs.append(default_values.bindir)\n if default_values.resdir is not None:\n self.resdirs.append(default_values.resdir)\n if default_values.builddir is not None:\n self.builddirs.append(default_values.builddir)\n if default_values.frameworkdir is not None:\n self.frameworkdirs.append(default_values.frameworkdir)\n self.requires = []\n self.version = version","label":0},{"cid":"conans/model/build_info.py::CppInfoDefaultValues.__init__","text":"conans/model/build_info.py::CppInfoDefaultValues.__init__\n def __init__(self, includedir=None, libdir=None, bindir=None,\n resdir=None, builddir=None, frameworkdir=None):\n self.includedir = includedir\n self.libdir = libdir\n self.bindir = bindir\n self.resdir = resdir\n self.builddir = builddir\n self.frameworkdir = frameworkdir","label":0},{"cid":"conans/model/build_info.py::CppInfo.__init__","text":"conans/model/build_info.py::CppInfo.__init__\n def __init__(self, ref_name, root_folder, default_values=None):\n super(CppInfo, self).__init__()\n self._ref_name = ref_name\n self._name = ref_name\n self.rootpath = root_folder # the full path of the package in which the conans is found\n self._default_values = default_values or CppInfoDefaultValues(DEFAULT_INCLUDE, DEFAULT_LIB,\n DEFAULT_BIN, DEFAULT_RES,\n DEFAULT_BUILD,\n DEFAULT_FRAMEWORK)\n if self._default_values.includedir is not None:\n self.includedirs.append(self._default_values.includedir)\n if self._default_values.libdir is not None:\n self.libdirs.append(self._default_values.libdir)\n if self._default_values.bindir is not None:\n self.bindirs.append(self._default_values.bindir)\n if self._default_values.resdir is not None:\n self.resdirs.append(self._default_values.resdir)\n if self._default_values.builddir is not None:\n self.builddirs.append(self._default_values.builddir)\n if self._default_values.frameworkdir is not None:\n self.frameworkdirs.append(self._default_values.frameworkdir)\n self.components = DefaultOrderedDict(lambda: Component(self.rootpath,\n self.version, self._default_values))\n # public_deps is needed to accumulate list of deps for cmake targets\n self.public_deps = []\n self._configs = {}","label":0},{"cid":"conans/model/build_info.py::CppInfo.__str__","text":"conans/model/build_info.py::CppInfo.__str__\n def __str__(self):\n return self._ref_name","label":0},{"cid":"conans/model/build_info.py::CppInfo.get_name","text":"conans/model/build_info.py::CppInfo.get_name\n def get_name(self, generator, default_name=True):\n name = super(CppInfo, self).get_name(generator, default_name=default_name)\n\n # Legacy logic for pkg_config generator\n from conans.client.generators.pkg_config import PkgConfigGenerator\n if generator == PkgConfigGenerator.name:\n fallback = self._name.lower() if self._name != self._ref_name else self._ref_name\n if PkgConfigGenerator.name not in self.names and self._name != self._name.lower():\n conan_v2_error(\"Generated file and name for {gen} generator will change in\"\n \" Conan v2 to '{name}'. Use 'self.cpp_info.names[\\\"{gen}\\\"]\"\n \" = \\\"{fallback}\\\"' in your recipe to continue using current name.\"\n .format(gen=PkgConfigGenerator.name, name=name, fallback=fallback))\n name = self.names.get(generator, fallback)\n return name","label":0},{"cid":"conans/model/build_info.py::CppInfo.configs","text":"conans/model/build_info.py::CppInfo.configs\n @property\n def configs(self):\n return self._configs","label":0},{"cid":"conans/model/build_info.py::CppInfo.__getattr__","text":"conans/model/build_info.py::CppInfo.__getattr__\n def __getattr__(self, config):\n def _get_cpp_info():\n result = _CppInfo()\n result.filter_empty = self.filter_empty\n result.rootpath = self.rootpath\n result.sysroot = self.sysroot\n result.includedirs.append(self._default_values.includedir)\n result.libdirs.append(self._default_values.libdir)\n result.bindirs.append(self._default_values.bindir)\n result.resdirs.append(self._default_values.resdir)\n result.builddirs.append(self._default_values.builddir)\n result.frameworkdirs.append(self._default_values.frameworkdir)\n return result\n\n return self._configs.setdefault(config, _get_cpp_info())","label":0},{"cid":"conans/model/build_info.py::CppInfo.__getattr__._get_cpp_info","text":"conans/model/build_info.py::CppInfo.__getattr__._get_cpp_info\n def _get_cpp_info():\n result = _CppInfo()\n result.filter_empty = self.filter_empty\n result.rootpath = self.rootpath\n result.sysroot = self.sysroot\n result.includedirs.append(self._default_values.includedir)\n result.libdirs.append(self._default_values.libdir)\n result.bindirs.append(self._default_values.bindir)\n result.resdirs.append(self._default_values.resdir)\n result.builddirs.append(self._default_values.builddir)\n result.frameworkdirs.append(self._default_values.frameworkdir)\n return result","label":0},{"cid":"conans/model/build_info.py::CppInfo._raise_incorrect_components_definition","text":"conans/model/build_info.py::CppInfo._raise_incorrect_components_definition\n def _raise_incorrect_components_definition(self, package_name, package_requires):\n if not self.components and not self.requires:\n return\n\n # Raise if mixing components\n if self.components and \\\n (self.includedirs != ([self._default_values.includedir]\n if self._default_values.includedir is not None else []) or\n self.libdirs != ([self._default_values.libdir]\n if self._default_values.libdir is not None else []) or\n self.bindirs != ([self._default_values.bindir]\n if self._default_values.bindir is not None else []) or\n self.resdirs != ([self._default_values.resdir]\n if self._default_values.resdir is not None else []) or\n self.builddirs != ([self._default_values.builddir]\n if self._default_values.builddir is not None else []) or\n self.frameworkdirs != ([self._default_values.frameworkdir]\n if self._default_values.frameworkdir is not None else []) or\n self.libs or\n self.system_libs or\n self.frameworks or\n self.defines or\n self.cflags or\n self.cxxflags or\n self.sharedlinkflags or\n self.exelinkflags or\n self.get_build_modules() or\n self.requires):\n raise ConanException(\"self.cpp_info.components cannot be used with self.cpp_info \"\n \"global values at the same time\")\n if self._configs:\n raise ConanException(\"self.cpp_info.components cannot be used with self.cpp_info configs\"\n \" (release/debug/...) at the same time\")\n\n pkg_requires = [require.ref.name for require in package_requires.values()]\n\n def _check_components_requires_instersection(comp_requires):\n reqs = [it.split(COMPON","label":0},{"cid":"conans/model/conan_file.py::create_options","text":"conans/model/conan_file.py::create_options\ndef create_options(conanfile):\n try:\n package_options = PackageOptions(getattr(conanfile, \"options\", None))\n options = Options(package_options)\n\n default_options = getattr(conanfile, \"default_options\", None)\n if default_options:\n if isinstance(default_options, dict):\n default_values = OptionsValues(default_options)\n elif isinstance(default_options, (list, tuple)):\n conan_v2_error(\"Declare 'default_options' as a dictionary\")\n default_values = OptionsValues(default_options)\n elif isinstance(default_options, six.string_types):\n conan_v2_error(\"Declare 'default_options' as a dictionary\")\n default_values = OptionsValues.loads(default_options)\n else:\n raise ConanException(\"Please define your default_options as list, \"\n \"multiline string or dictionary\")\n options.values = default_values\n return options\n except Exception as e:\n raise ConanException(\"Error while initializing options. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::create_requirements","text":"conans/model/conan_file.py::create_requirements\ndef create_requirements(conanfile):\n try:\n # Actual requirements of this package\n if not hasattr(conanfile, \"requires\"):\n return Requirements()\n else:\n if not conanfile.requires:\n return Requirements()\n if isinstance(conanfile.requires, (tuple, list)):\n return Requirements(*conanfile.requires)\n else:\n return Requirements(conanfile.requires, )\n except Exception as e:\n raise ConanException(\"Error while initializing requirements. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::create_settings","text":"conans/model/conan_file.py::create_settings\ndef create_settings(conanfile, settings):\n try:\n defined_settings = getattr(conanfile, \"settings\", None)\n if isinstance(defined_settings, str):\n defined_settings = [defined_settings]\n current = defined_settings or {}\n settings.constraint(current)\n return settings\n except Exception as e:\n raise ConanInvalidConfiguration(\"The recipe %s is constraining settings. %s\" % (\n conanfile.display_name, str(e)))","label":0},{"cid":"conans/model/conan_file.py::_env_and_python","text":"conans/model/conan_file.py::_env_and_python\n@contextmanager\ndef _env_and_python(conanfile):\n with environment_append(conanfile.env):\n # FIXME Conan 2.0, Remove old ways of reusing python code\n with pythonpath(conanfile):\n yield","label":0},{"cid":"conans/model/conan_file.py::get_env_context_manager","text":"conans/model/conan_file.py::get_env_context_manager\ndef get_env_context_manager(conanfile, without_python=False):\n if not conanfile.apply_env:\n return no_op()\n if without_python:\n return environment_append(conanfile.env)\n return _env_and_python(conanfile)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.__init__","text":"conans/model/conan_file.py::ConanFile.__init__\n def __init__(self, output, runner, display_name=\"\", user=None, channel=None):\n # an output stream (writeln, info, warn error)\n self.output = ScopedOutput(display_name, output)\n self.display_name = display_name\n # something that can run commands, as os.sytem\n self._conan_runner = runner\n self._conan_user = user\n self._conan_channel = channel\n\n self.compatible_packages = []\n self._conan_using_build_profile = False\n self._conan_requester = None\n\n self.buildenv_info = Environment(self)\n self.runenv_info = Environment(self)\n # At the moment only for build_requires, others will be ignored\n self.conf_info = Conf()\n self._conan_buildenv = None # The profile buildenv, will be assigned initialize()\n self._conan_node = None # access to container Node object, to access info, context, deps...\n self._conan_new_cpp_info = None # Will be calculated lazy in the getter\n self._conan_dependencies = None\n\n self.environment_scripts = [] # Accumulate the env scripts generated in order\n\n # layout() method related variables:\n self.folders = Folders()\n self.patterns = Patterns()\n self.cpp = Infos()\n\n self.patterns.source.include = [\"*.h\", \"*.hpp\", \"*.hxx\"]\n self.patterns.source.lib = []\n self.patterns.source.bin = []\n\n self.patterns.build.include = [\"*.h\", \"*.hpp\", \"*.hxx\"]\n self.patterns.build.lib = [\"*.so\", \"*.so.*\", \"*.a\", \"*.lib\", \"*.dylib\"]\n self.patterns.build.bin = [\"*.exe\", \"*.dll\"]\n\n self.cpp.package.includedirs = [\"include\"]\n self.cpp.package.libdirs = [\"lib\"]\n self.cpp.package.bindirs = [\"bin\"]\n self.cpp.package.resdirs = [\"res\"]\n self.cpp.package.builddirs = [\"\"]\n self.cpp.package.frameworkdirs = [\"Frameworks\"]","label":0},{"cid":"conans/model/conan_file.py::ConanFile.context","text":"conans/model/conan_file.py::ConanFile.context\n @property\n def context(self):\n return self._conan_node.context","label":0},{"cid":"conans/model/conan_file.py::ConanFile.dependencies","text":"conans/model/conan_file.py::ConanFile.dependencies\n @property\n def dependencies(self):\n # Caching it, this object is requested many times\n if self._conan_dependencies is None:\n self._conan_dependencies = ConanFileDependencies.from_node(self._conan_node)\n return self._conan_dependencies","label":0},{"cid":"conans/model/conan_file.py::ConanFile.ref","text":"conans/model/conan_file.py::ConanFile.ref\n @property\n def ref(self):\n return self._conan_node.ref","label":0},{"cid":"conans/model/conan_file.py::ConanFile.pref","text":"conans/model/conan_file.py::ConanFile.pref\n @property\n def pref(self):\n return self._conan_node.pref","label":0},{"cid":"conans/model/conan_file.py::ConanFile.buildenv","text":"conans/model/conan_file.py::ConanFile.buildenv\n @property\n def buildenv(self):\n # Lazy computation of the package buildenv based on the profileone\n if not isinstance(self._conan_buildenv, Environment):\n # TODO: missing user/channel\n ref_str = \"{}/{}\".format(self.name, self.version)\n self._conan_buildenv = self._conan_buildenv.get_env(self, ref_str)\n return self._conan_buildenv","label":0},{"cid":"conans/model/conan_file.py::ConanFile.initialize","text":"conans/model/conan_file.py::ConanFile.initialize\n def initialize(self, settings, env, buildenv=None):\n self._conan_buildenv = buildenv\n if isinstance(self.generators, str):\n self.generators = [self.generators]\n # User defined options\n self.options = create_options(self)\n self.requires = create_requirements(self)\n self.settings = create_settings(self, settings)\n\n conan_v2_error(\"Setting 'cppstd' is deprecated in favor of 'compiler.cppstd',\"\n \" please update your recipe.\", 'cppstd' in self.settings.fields)\n\n # needed variables to pack the project\n self.cpp_info = None # Will be initialized at processing time\n self._conan_dep_cpp_info = None # Will be initialized at processing time\n self.deps_cpp_info = DepsCppInfo()\n\n # environment variables declared in the package_info\n self.env_info = None # Will be initialized at processing time\n self.deps_env_info = DepsEnvInfo()\n\n # user declared variables\n self.user_info = None\n # Keys are the package names (only 'host' if different contexts)\n self.deps_user_info = DepsUserInfo()\n\n # user specified env variables\n self._conan_env_values = env.copy() # user specified -e\n\n if self.description is not None and not isinstance(self.description, six.string_types):\n raise ConanException(\"Recipe 'description' must be a string.\")\n\n if not hasattr(self, \"virtualenv\"): # Allow the user to override it with True or False\n self.virtualenv = True","label":0},{"cid":"conans/model/conan_file.py::ConanFile.new_cpp_info","text":"conans/model/conan_file.py::ConanFile.new_cpp_info\n @property\n def new_cpp_info(self):\n if not self._conan_new_cpp_info:\n self._conan_new_cpp_info = from_old_cppinfo(self.cpp_info)\n return self._conan_new_cpp_info","label":0},{"cid":"conans/model/conan_file.py::ConanFile.source_folder","text":"conans/model/conan_file.py::ConanFile.source_folder\n @property\n def source_folder(self):\n return self.folders.source_folder","label":0},{"cid":"conans/model/conan_file.py::ConanFile.source_folder","text":"conans/model/conan_file.py::ConanFile.source_folder\n @source_folder.setter\n def source_folder(self, folder):\n self.folders.set_base_source(folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_folder","text":"conans/model/conan_file.py::ConanFile.build_folder\n @property\n def build_folder(self):\n return self.folders.build_folder","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_folder","text":"conans/model/conan_file.py::ConanFile.build_folder\n @build_folder.setter\n def build_folder(self, folder):\n self.folders.set_base_build(folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_folder","text":"conans/model/conan_file.py::ConanFile.package_folder\n @property\n def package_folder(self):\n return self.folders.package_folder","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_folder","text":"conans/model/conan_file.py::ConanFile.package_folder\n @package_folder.setter\n def package_folder(self, folder):\n self.folders.set_base_package(folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.install_folder","text":"conans/model/conan_file.py::ConanFile.install_folder\n @property\n def install_folder(self):\n # FIXME: Remove in 2.0, no self.install_folder\n return self.folders.base_install","label":0},{"cid":"conans/model/conan_file.py::ConanFile.install_folder","text":"conans/model/conan_file.py::ConanFile.install_folder\n @install_folder.setter\n def install_folder(self, folder):\n # FIXME: Remove in 2.0, no self.install_folder\n self.folders.set_base_install(folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.generators_folder","text":"conans/model/conan_file.py::ConanFile.generators_folder\n @property\n def generators_folder(self):\n # FIXME: Remove in 2.0, no self.install_folder\n return self.folders.generators_folder if self.folders.generators else self.install_folder","label":0},{"cid":"conans/model/conan_file.py::ConanFile.imports_folder","text":"conans/model/conan_file.py::ConanFile.imports_folder\n @property\n def imports_folder(self):\n return self.folders.imports_folder","label":0},{"cid":"conans/model/conan_file.py::ConanFile.imports_folder","text":"conans/model/conan_file.py::ConanFile.imports_folder\n @imports_folder.setter\n def imports_folder(self, folder):\n self.folders.set_base_imports(folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.env","text":"conans/model/conan_file.py::ConanFile.env\n @property\n def env(self):\n \"\"\"Apply the self.deps_env_info into a copy of self._conan_env_values (will prioritize the\n self._conan_env_values, user specified from profiles or -e first, then inherited)\"\"\"\n # Cannot be lazy cached, because it's called in configure node, and we still don't have\n # the deps_env_info objects available\n tmp_env_values = self._conan_env_values.copy()\n tmp_env_values.update(self.deps_env_info)\n ret, multiple = tmp_env_values.env_dicts(self.name, self.version, self._conan_user,\n self._conan_channel)\n ret.update(multiple)\n return ret","label":0},{"cid":"conans/model/conan_file.py::ConanFile.channel","text":"conans/model/conan_file.py::ConanFile.channel\n @property\n def channel(self):\n if not self._conan_channel:\n _env_channel = os.getenv(\"CONAN_CHANNEL\")\n conan_v2_error(\"Environment variable 'CONAN_CHANNEL' is deprecated\", _env_channel)\n self._conan_channel = _env_channel or self.default_channel\n if not self._conan_channel:\n raise ConanException(\"channel not defined, but self.channel is used in conanfile\")\n return self._conan_channel","label":0},{"cid":"conans/model/conan_file.py::ConanFile.user","text":"conans/model/conan_file.py::ConanFile.user\n @property\n def user(self):\n if not self._conan_user:\n _env_username = os.getenv(\"CONAN_USERNAME\")\n conan_v2_error(\"Environment variable 'CONAN_USERNAME' is deprecated\", _env_username)\n self._conan_user = _env_username or self.default_user\n if not self._conan_user:\n raise ConanException(\"user not defined, but self.user is used in conanfile\")\n return self._conan_user","label":0},{"cid":"conans/model/conan_file.py::ConanFile.collect_libs","text":"conans/model/conan_file.py::ConanFile.collect_libs\n def collect_libs(self, folder=None):\n conan_v2_error(\"'self.collect_libs' is deprecated, use 'tools.collect_libs(self)' instead\")\n return tools.collect_libs(self, folder=folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_policy_missing","text":"conans/model/conan_file.py::ConanFile.build_policy_missing\n @property\n def build_policy_missing(self):\n return self.build_policy == \"missing\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_policy_always","text":"conans/model/conan_file.py::ConanFile.build_policy_always\n @property\n def build_policy_always(self):\n return self.build_policy == \"always\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.source","text":"conans/model/conan_file.py::ConanFile.source\n def source(self):\n pass","label":0},{"cid":"conans/model/conan_file.py::ConanFile.system_requirements","text":"conans/model/conan_file.py::ConanFile.system_requirements\n def system_requirements(self):\n \"\"\" this method can be overwritten to implement logic for system package\n managers, as apt-get\n\n You can define self.global_system_requirements = True, if you want the installation\n to be for all packages (not depending on settings/options/requirements)\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.config_options","text":"conans/model/conan_file.py::ConanFile.config_options\n def config_options(self):\n \"\"\" modify options, probably conditioned to some settings. This call is executed\n before config_settings. E.g.\n if self.settings.os == \"Windows\":\n del self.options.shared # shared/static not supported in win\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.configure","text":"conans/model/conan_file.py::ConanFile.configure\n def configure(self):\n \"\"\" modify settings, probably conditioned to some options. This call is executed\n after config_options. E.g.\n if self.options.header_only:\n self.settings.clear()\n This is also the place for conditional requirements\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build","text":"conans/model/conan_file.py::ConanFile.build\n def build(self):\n \"\"\" build your project calling the desired build tools as done in the command line.\n E.g. self.run(\"cmake --build .\") Or use the provided build helpers. E.g. cmake.build()\n \"\"\"\n self.output.warn(\"This conanfile has no build step\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package","text":"conans/model/conan_file.py::ConanFile.package\n def package(self):\n \"\"\" package the needed files from source and build folders.\n E.g. self.copy(\"*.h\", src=\"src/includes\", dst=\"includes\")\n \"\"\"\n self.output.warn(\"This conanfile has no package step\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_info","text":"conans/model/conan_file.py::ConanFile.package_info\n def package_info(self):\n \"\"\" define cpp_build_info, flags, etc\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.run","text":"conans/model/conan_file.py::ConanFile.run\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True,\n ignore_errors=False, run_environment=False, with_login=True, env=None):\n # NOTE: \"self.win_bash\" is the new parameter \"win_bash\" for Conan 2.0\n\n def _run(cmd, _env):\n # FIXME: run in windows bash is not using output\n if platform.system() == \"Windows\":\n if win_bash:\n return tools.run_in_windows_bash(self, bashcmd=cmd, cwd=cwd, subsystem=subsystem,\n msys_mingw=msys_mingw, with_login=with_login)\n elif self.win_bash: # New, Conan 2.0\n from conan.tools.microsoft.subsystems import run_in_windows_bash\n return run_in_windows_bash(self, command=cmd, cwd=cwd, env=_env)\n _env = _env or \"conanenv\"\n wrapped_cmd = environment_wrap_command(self, _env, cmd, cwd=self.generators_folder)\n return self._conan_runner(wrapped_cmd, output, os.path.abspath(RUN_LOG_NAME), cwd)\n\n if run_environment:\n # When using_build_profile the required environment is already applied through\n # 'conanfile.env' in the contextmanager 'get_env_context_manager'\n with tools.run_environment(self) if not self._conan_using_build_profile else no_op():\n if OSInfo().is_macos and isinstance(command, string_types):\n # Security policy on macOS clears this variable when executing /bin/sh. To\n # keep its value, set it again inside the shell when running the command.\n command = 'DYLD_LIBRARY_PATH=\"%s\" DYLD_FRAMEWORK_PATH=\"%s\" %s' % \\\n (os.environ.get('DYLD_LIBRARY_PATH', ''),\n os.environ.get(\"DYLD_FRAMEWORK_PATH\", ''),\n command)\n retcode = _run(command, en","label":0},{"cid":"conans/model/conan_file.py::ConanFile.run._run","text":"conans/model/conan_file.py::ConanFile.run._run\n def _run(cmd, _env):\n # FIXME: run in windows bash is not using output\n if platform.system() == \"Windows\":\n if win_bash:\n return tools.run_in_windows_bash(self, bashcmd=cmd, cwd=cwd, subsystem=subsystem,\n msys_mingw=msys_mingw, with_login=with_login)\n elif self.win_bash: # New, Conan 2.0\n from conan.tools.microsoft.subsystems import run_in_windows_bash\n return run_in_windows_bash(self, command=cmd, cwd=cwd, env=_env)\n _env = _env or \"conanenv\"\n wrapped_cmd = environment_wrap_command(self, _env, cmd, cwd=self.generators_folder)\n return self._conan_runner(wrapped_cmd, output, os.path.abspath(RUN_LOG_NAME), cwd)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_id","text":"conans/model/conan_file.py::ConanFile.package_id\n def package_id(self):\n \"\"\" modify the binary info, typically to narrow values\n e.g.: self.info.settings.compiler = \"Any\" => All compilers will generate same ID\n \"\"\"","label":0}],"string":"[\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.__init__\",\n \"text\": \"conans/client/build/cmake.py::CMake.__init__\\n def __init__(self, conanfile, generator=None, cmake_system_name=True,\\n parallel=True, build_type=None, toolset=None, make_program=None,\\n set_cmake_flags=False, msbuild_verbosity=\\\"minimal\\\", cmake_program=None,\\n generator_platform=None, append_vcvars=False):\\n \\\"\\\"\\\"\\n :param conanfile: Conanfile instance\\n :param generator: Generator name to use or none to autodetect\\n :param cmake_system_name: False to not use CMAKE_SYSTEM_NAME variable,\\n True for auto-detect or directly a string with the system name\\n :param parallel: Try to build with multiple cores if available\\n :param build_type: Overrides default build type coming from settings\\n :param toolset: Toolset name to use (such as llvm-vs2014) or none for default one,\\n applies only to certain generators (e.g. Visual Studio)\\n :param set_cmake_flags: whether or not to set CMake flags like CMAKE_CXX_FLAGS,\\n CMAKE_C_FLAGS, etc. it's vital to set for certain projects\\n (e.g. using CMAKE_SIZEOF_VOID_P or CMAKE_LIBRARY_ARCHITECTURE)\\n :param msbuild_verbosity: verbosity level for MSBuild (in case of Visual Studio generator)\\n :param cmake_program: Path to the custom cmake executable\\n :param generator_platform: Generator platform name or none to autodetect (-A cmake option)\\n \\\"\\\"\\\"\\n self._append_vcvars = append_vcvars\\n self._conanfile = conanfile\\n self._settings = conanfile.settings\\n self._build_type = build_type or conanfile.settings.get_safe(\\\"build_type\\\")\\n self._cmake_program = os.getenv(\\\"CONAN_CMAKE_PROGRAM\\\") or cmake_program or \\\"cmake\\\"\\n\\n self.generator_platform = generator_platform\\n self.generator = generator or get_generator(conanfile)\\n\\n if not self.generator:\\n self._conanfile.output.warn(\\\"CMake generator could not be deduced from set\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.generator\",\n \"text\": \"conans/client/build/cmake.py::CMake.generator\\n @property\\n def generator(self):\\n return self._generator\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.generator\",\n \"text\": \"conans/client/build/cmake.py::CMake.generator\\n @generator.setter\\n def generator(self, value):\\n self._generator = value\\n if not self._generator_platform_is_assigned:\\n self._generator_platform = get_generator_platform(self._settings, self._generator)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.generator_platform\",\n \"text\": \"conans/client/build/cmake.py::CMake.generator_platform\\n @property\\n def generator_platform(self):\\n return self._generator_platform\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.generator_platform\",\n \"text\": \"conans/client/build/cmake.py::CMake.generator_platform\\n @generator_platform.setter\\n def generator_platform(self, value):\\n self._generator_platform = value\\n self._generator_platform_is_assigned = bool(value is not None)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_folder\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_folder\\n @property\\n def build_folder(self):\\n return self.build_dir\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_folder\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_folder\\n @build_folder.setter\\n def build_folder(self, value):\\n self.build_dir = value\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_type\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_type\\n @property\\n def build_type(self):\\n return self._build_type\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_type\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_type\\n @build_type.setter\\n def build_type(self, build_type):\\n settings_build_type = self._settings.get_safe(\\\"build_type\\\")\\n self.definitions.pop(\\\"CMAKE_BUILD_TYPE\\\", None)\\n self.definitions.update(build_type_definition(build_type, settings_build_type,\\n self.generator, self._conanfile.output))\\n self._build_type = build_type\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.in_local_cache\",\n \"text\": \"conans/client/build/cmake.py::CMake.in_local_cache\\n @property\\n def in_local_cache(self):\\n try:\\n in_local_cache = self.definitions[cmake_in_local_cache_var_name]\\n return get_bool_from_text(str(in_local_cache))\\n except KeyError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.runtime\",\n \"text\": \"conans/client/build/cmake.py::CMake.runtime\\n @property\\n def runtime(self):\\n return defs_to_string(self.definitions.get(runtime_definition_var_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.flags\",\n \"text\": \"conans/client/build/cmake.py::CMake.flags\\n @property\\n def flags(self):\\n return defs_to_string(self.definitions)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.is_multi_configuration\",\n \"text\": \"conans/client/build/cmake.py::CMake.is_multi_configuration\\n @property\\n def is_multi_configuration(self):\\n return is_multi_configuration(self.generator)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.command_line\",\n \"text\": \"conans/client/build/cmake.py::CMake.command_line\\n @property\\n def command_line(self):\\n if self.generator_platform and not is_generator_platform_supported(self.generator):\\n raise ConanException('CMake does not support generator platform with generator '\\n '\\\"%s:. Please check your conan profile to either remove the '\\n 'generator platform, or change the CMake generator.'\\n % self.generator)\\n\\n if self.toolset and not is_toolset_supported(self.generator):\\n raise ConanException('CMake does not support toolsets with generator \\\"%s:.'\\n 'Please check your conan profile to either remove the toolset,'\\n ' or change the CMake generator.' % self.generator)\\n\\n generator = self.generator\\n generator_platform = self.generator_platform\\n\\n if self.generator_platform and 'Visual Studio' in generator:\\n # FIXME: Conan 2.0 We are adding the platform to the generator instead of using\\n # the -A argument to keep previous implementation, but any modern CMake will support\\n # (and recommend) passing the platform in its own argument.\\n # Get the version from the generator, as it could have been defined by user argument\\n compiler_version = re.search(\\\"Visual Studio ([0-9]*)\\\", generator).group(1)\\n if Version(compiler_version) < \\\"16\\\" and self._settings.get_safe(\\\"os\\\") != \\\"WindowsCE\\\":\\n if self.generator_platform == \\\"x64\\\":\\n generator += \\\" Win64\\\" if not generator.endswith(\\\" Win64\\\") else \\\"\\\"\\n generator_platform = None\\n elif self.generator_platform == \\\"ARM\\\":\\n generator += \\\" ARM\\\" if not generator.endswith(\\\" ARM\\\") else \\\"\\\"\\n generator_platform = None\\n elif self.generator_platform == \\\"Win32\\\":\\n ge\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_config\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_config\\n @property\\n def build_config(self):\\n \\\"\\\"\\\" cmake --build tool have a --config option for Multi-configuration IDEs\\n \\\"\\\"\\\"\\n if self._build_type and self.is_multi_configuration:\\n return \\\"--config %s\\\" % self._build_type\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._get_dirs\",\n \"text\": \"conans/client/build/cmake.py::CMake._get_dirs\\n def _get_dirs(self, source_folder, build_folder, source_dir, build_dir, cache_build_folder):\\n if (source_folder or build_folder) and (source_dir or build_dir):\\n raise ConanException(\\\"Use 'build_folder'/'source_folder' arguments\\\")\\n\\n def get_dir(folder, origin):\\n if folder:\\n if os.path.isabs(folder):\\n return folder\\n return os.path.join(origin, folder)\\n return origin\\n\\n if source_dir or build_dir: # OLD MODE\\n build_ret = build_dir or self.build_dir or self._conanfile.build_folder\\n source_ret = source_dir or self._conanfile.source_folder\\n else:\\n build_ret = get_dir(build_folder, self._conanfile.build_folder)\\n source_ret = get_dir(source_folder, self._conanfile.source_folder)\\n\\n if self._conanfile.in_local_cache and cache_build_folder:\\n build_ret = get_dir(cache_build_folder, self._conanfile.build_folder)\\n\\n return source_ret, build_ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._get_dirs.get_dir\",\n \"text\": \"conans/client/build/cmake.py::CMake._get_dirs.get_dir\\n def get_dir(folder, origin):\\n if folder:\\n if os.path.isabs(folder):\\n return folder\\n return os.path.join(origin, folder)\\n return origin\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._run\",\n \"text\": \"conans/client/build/cmake.py::CMake._run\\n def _run(self, command):\\n compiler = self._settings.get_safe(\\\"compiler\\\")\\n conan_v2_error(\\\"compiler setting should be defined.\\\", not compiler)\\n the_os = self._settings.get_safe(\\\"os\\\")\\n is_clangcl = the_os == \\\"Windows\\\" and compiler == \\\"clang\\\"\\n is_msvc = compiler == \\\"Visual Studio\\\"\\n is_intel = compiler == \\\"intel\\\"\\n context = tools.no_op()\\n\\n if (is_msvc or is_clangcl) and platform.system() == \\\"Windows\\\":\\n if self.generator in [\\\"Ninja\\\", \\\"Ninja Multi-Config\\\",\\n \\\"NMake Makefiles\\\", \\\"NMake Makefiles JOM\\\"]:\\n vcvars_dict = tools.vcvars_dict(self._settings, force=True, filter_known_paths=False,\\n output=self._conanfile.output)\\n context = _environment_add(vcvars_dict, post=self._append_vcvars)\\n elif is_intel:\\n if self.generator in [\\\"Ninja\\\", \\\"Ninja Multi-Config\\\",\\n \\\"NMake Makefiles\\\", \\\"NMake Makefiles JOM\\\", \\\"Unix Makefiles\\\"]:\\n intel_compilervars_dict = tools.intel_compilervars_dict(self._conanfile, force=True)\\n context = _environment_add(intel_compilervars_dict, post=self._append_vcvars)\\n with context:\\n self._conanfile.run(command)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.configure\",\n \"text\": \"conans/client/build/cmake.py::CMake.configure\\n def configure(self, args=None, defs=None, source_dir=None, build_dir=None,\\n source_folder=None, build_folder=None, cache_build_folder=None,\\n pkg_config_paths=None):\\n\\n # TODO: Deprecate source_dir and build_dir in favor of xxx_folder\\n if not self._conanfile.should_configure:\\n return\\n args = args or []\\n defs = defs or {}\\n source_dir, self.build_dir = self._get_dirs(source_folder, build_folder,\\n source_dir, build_dir,\\n cache_build_folder)\\n mkdir(self.build_dir)\\n arg_list = join_arguments([\\n self.command_line,\\n args_to_string(args),\\n defs_to_string(defs),\\n args_to_string([source_dir])\\n ])\\n\\n if pkg_config_paths:\\n pkg_env = {\\\"PKG_CONFIG_PATH\\\":\\n os.pathsep.join(get_abs_path(f, self._conanfile.install_folder)\\n for f in pkg_config_paths)}\\n else:\\n # If we are using pkg_config generator automate the pcs location, otherwise it could\\n # read wrong files\\n set_env = \\\"pkg_config\\\" in self._conanfile.generators \\\\\\n and \\\"PKG_CONFIG_PATH\\\" not in os.environ\\n pkg_env = {\\\"PKG_CONFIG_PATH\\\": self._conanfile.install_folder} if set_env else None\\n\\n with environment_append(pkg_env):\\n command = \\\"cd %s && %s %s\\\" % (args_to_string([self.build_dir]), self._cmake_program,\\n arg_list)\\n if platform.system() == \\\"Windows\\\" and self.generator == \\\"MinGW Makefiles\\\":\\n with tools.remove_from_path(\\\"sh\\\"):\\n self._run(command)\\n else:\\n self._run(command)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build\",\n \"text\": \"conans/client/build/cmake.py::CMake.build\\n def build(self, args=None, build_dir=None, target=None):\\n if not self._conanfile.should_build:\\n return\\n conan_v2_error(\\\"build_type setting should be defined.\\\", not self._build_type)\\n self._build(args, build_dir, target)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._build\",\n \"text\": \"conans/client/build/cmake.py::CMake._build\\n def _build(self, args=None, build_dir=None, target=None):\\n args = args or []\\n build_dir = build_dir or self.build_dir or self._conanfile.build_folder\\n if target is not None:\\n args = [\\\"--target\\\", target] + args\\n\\n if self.generator and self.parallel:\\n if (\\\"Makefiles\\\" in self.generator or \\\"Ninja\\\" in self.generator) and \\\\\\n \\\"NMake\\\" not in self.generator:\\n if \\\"--\\\" not in args:\\n args.append(\\\"--\\\")\\n args.append(\\\"-j%i\\\" % cpu_count(self._conanfile.output))\\n elif \\\"Visual Studio\\\" in self.generator:\\n compiler_version = re.search(\\\"Visual Studio ([0-9]*)\\\", self.generator).group(1)\\n if Version(compiler_version) >= \\\"10\\\":\\n if \\\"--\\\" not in args:\\n args.append(\\\"--\\\")\\n # Parallel for building projects in the solution\\n args.append(\\\"/m:%i\\\" % cpu_count(output=self._conanfile.output))\\n\\n if self.generator and self.msbuild_verbosity:\\n if \\\"Visual Studio\\\" in self.generator:\\n compiler_version = re.search(\\\"Visual Studio ([0-9]*)\\\", self.generator).group(1)\\n if Version(compiler_version) >= \\\"10\\\":\\n if \\\"--\\\" not in args:\\n args.append(\\\"--\\\")\\n args.append(\\\"/verbosity:%s\\\" % self.msbuild_verbosity)\\n\\n arg_list = join_arguments([\\n args_to_string([build_dir]),\\n self.build_config,\\n args_to_string(args)\\n ])\\n command = \\\"%s --build %s\\\" % (self._cmake_program, arg_list)\\n self._run(command)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.install\",\n \"text\": \"conans/client/build/cmake.py::CMake.install\\n def install(self, args=None, build_dir=None):\\n if not self._conanfile.should_install:\\n return\\n mkdir(self._conanfile.package_folder)\\n if not self.definitions.get(cmake_install_prefix_var_name):\\n raise ConanException(\\\"%s not defined for 'cmake.install()'\\\\n\\\"\\n \\\"Make sure 'package_folder' is \\\"\\n \\\"defined\\\" % cmake_install_prefix_var_name)\\n self._build(args=args, build_dir=build_dir, target=\\\"install\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.test\",\n \"text\": \"conans/client/build/cmake.py::CMake.test\\n def test(self, args=None, build_dir=None, target=None, output_on_failure=False):\\n if not self._conanfile.should_test or not get_env(\\\"CONAN_RUN_TESTS\\\", True) or \\\\\\n self._conanfile.conf[\\\"tools.build:skip_test\\\"]:\\n return\\n if not target:\\n target = \\\"RUN_TESTS\\\" if self.is_multi_configuration else \\\"test\\\"\\n\\n test_env = {'CTEST_OUTPUT_ON_FAILURE': '1' if output_on_failure else '0'}\\n if self.parallel:\\n test_env['CTEST_PARALLEL_LEVEL'] = str(cpu_count(self._conanfile.output))\\n with environment_append(test_env):\\n self._build(args=args, build_dir=build_dir, target=target)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.verbose\",\n \"text\": \"conans/client/build/cmake.py::CMake.verbose\\n @property\\n def verbose(self):\\n try:\\n verbose = self.definitions[verbose_definition_name]\\n return get_bool_from_text(str(verbose))\\n except KeyError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.verbose\",\n \"text\": \"conans/client/build/cmake.py::CMake.verbose\\n @verbose.setter\\n def verbose(self, value):\\n self.definitions.update(verbose_definition(value))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.patch_config_paths\",\n \"text\": \"conans/client/build/cmake.py::CMake.patch_config_paths\\n def patch_config_paths(self):\\n \\\"\\\"\\\"\\n changes references to the absolute path of the installed package and its dependencies in\\n exported cmake config files to the appropriate conan variable. This makes\\n most (sensible) cmake config files portable.\\n\\n For example, if a package foo installs a file called \\\"fooConfig.cmake\\\" to\\n be used by cmake's find_package method, normally this file will contain\\n absolute paths to the installed package folder, for example it will contain\\n a line such as:\\n\\n SET(Foo_INSTALL_DIR /home/developer/.conan/data/Foo/1.0.0/...)\\n\\n This will cause cmake find_package() method to fail when someone else\\n installs the package via conan.\\n\\n This function will replace such mentions to\\n\\n SET(Foo_INSTALL_DIR ${CONAN_FOO_ROOT})\\n\\n which is a variable that is set by conanbuildinfo.cmake, so that find_package()\\n now correctly works on this conan package.\\n\\n For dependent packages, if a package foo installs a file called \\\"fooConfig.cmake\\\" to\\n be used by cmake's find_package method and if it depends to a package bar,\\n normally this file will contain absolute paths to the bar package folder,\\n for example it will contain a line such as:\\n\\n SET_TARGET_PROPERTIES(foo PROPERTIES\\n INTERFACE_INCLUDE_DIRECTORIES\\n \\\"/home/developer/.conan/data/Bar/1.0.0/user/channel/id/include\\\")\\n\\n This function will replace such mentions to\\n\\n SET_TARGET_PROPERTIES(foo PROPERTIES\\n INTERFACE_INCLUDE_DIRECTORIES\\n \\\"${CONAN_BAR_ROOT}/include\\\")\\n\\n If the install() method of the CMake object in the conan file is used, this\\n function should be called _after_ that invocation. For example:\\n\\n def build(self):\\n cmake = CMake(self)\\n cmake.configure()\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.get_version\",\n \"text\": \"conans/client/build/cmake.py::CMake.get_version\\n @staticmethod\\n def get_version():\\n try:\\n out = version_runner([\\\"cmake\\\", \\\"--version\\\"])\\n version_line = decode_text(out).split('\\\\n', 1)[0]\\n version_str = version_line.rsplit(' ', 1)[-1]\\n return Version(version_str)\\n except Exception as e:\\n raise ConanException(\\\"Error retrieving CMake version: '{}'\\\".format(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/new.py::_render_template\",\n \"text\": \"conans/client/cmd/new.py::_render_template\\ndef _render_template(text, name, version, package_name, defines):\\n context = {'name': name,\\n 'version': version,\\n 'package_name': package_name,\\n 'conan_version': client_version}\\n context.update(defines)\\n t = Template(text, keep_trailing_newline=True)\\n return t.render(**context)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/new.py::_get_files_from_template_dir\",\n \"text\": \"conans/client/cmd/new.py::_get_files_from_template_dir\\ndef _get_files_from_template_dir(template_dir, name, version, package_name, defines):\\n files = []\\n for d, _, fs in os.walk(template_dir):\\n for f in fs:\\n rel_d = os.path.relpath(d, template_dir)\\n rel_f = os.path.join(rel_d, f)\\n files.append(rel_f)\\n\\n out_files = dict()\\n for f in files:\\n f_path = os.path.join(template_dir, f)\\n rendered_path = _render_template(f, name=name, version=version, package_name=package_name,\\n defines=defines)\\n rendered_file = _render_template(load(f_path), name=name, version=version,\\n package_name=package_name, defines=defines)\\n out_files[rendered_path] = rendered_file\\n\\n return out_files\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/new.py::cmd_new\",\n \"text\": \"conans/client/cmd/new.py::cmd_new\\ndef cmd_new(ref, header=False, pure_c=False, test=False, exports_sources=False, bare=False,\\n visual_versions=None, linux_gcc_versions=None, linux_clang_versions=None,\\n osx_clang_versions=None, shared=None, upload_url=None, gitignore=None,\\n gitlab_gcc_versions=None, gitlab_clang_versions=None,\\n circleci_gcc_versions=None, circleci_clang_versions=None, circleci_osx_versions=None,\\n template=None, cache=None, defines=None):\\n try:\\n name, version, user, channel, revision = get_reference_fields(ref, user_channel_input=False)\\n # convert \\\"package_name\\\" -> \\\"PackageName\\\"\\n package_name = re.sub(r\\\"(?:^|[\\\\W_])(\\\\w)\\\", lambda x: x.group(1).upper(), name)\\n except ValueError:\\n raise ConanException(\\\"Bad parameter, please use full package name,\\\"\\n \\\"e.g.: MyLib/1.2.3@user/testing\\\")\\n\\n # Validate it is a valid reference\\n ConanFileReference(name, version, user, channel)\\n\\n if header and exports_sources:\\n raise ConanException(\\\"'header' and 'sources' are incompatible options\\\")\\n if pure_c and header:\\n raise ConanException(\\\"'pure_c' is incompatible with 'header'\\\")\\n if pure_c and not exports_sources:\\n raise ConanException(\\\"'pure_c' requires the use of --source\\\")\\n if bare and (header or exports_sources):\\n raise ConanException(\\\"'bare' is incompatible with 'header' and 'sources'\\\")\\n if template and (header or exports_sources or bare or pure_c):\\n raise ConanException(\\\"'template' is incompatible with 'header', \\\"\\n \\\"'sources', 'pure-c' and 'bare'\\\")\\n\\n defines = defines or dict()\\n\\n if header:\\n files = {\\\"conanfile.py\\\": conanfile_header.format(name=name, version=version,\\n package_name=package_name)}\\n elif exports_sources:\\n if not pure_c:\\n files = {\\\"conanfile.py\\\": conanfile_sources.format(na\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::build_id\",\n \"text\": \"conans/client/installer.py::build_id\\ndef build_id(conan_file):\\n if hasattr(conan_file, \\\"build_id\\\"):\\n # construct new ConanInfo\\n build_id_info = conan_file.info.copy()\\n conan_file.info_build = build_id_info\\n # effectively call the user function to change the package values\\n with conanfile_exception_formatter(str(conan_file), \\\"build_id\\\"):\\n conan_file.build_id()\\n # compute modified ID\\n return build_id_info.package_id()\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::add_env_conaninfo\",\n \"text\": \"conans/client/installer.py::add_env_conaninfo\\ndef add_env_conaninfo(conan_file, subtree_libnames):\\n for package_name, env_vars in conan_file._conan_env_values.data.items():\\n for name, value in env_vars.items():\\n if not package_name or package_name in subtree_libnames or \\\\\\n package_name == conan_file.name:\\n conan_file.info.env_values.add(name, value, package_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder.__init__\",\n \"text\": \"conans/client/installer.py::_PackageBuilder.__init__\\n def __init__(self, cache, output, hook_manager, remote_manager, generators):\\n self._cache = cache\\n self._output = output\\n self._hook_manager = hook_manager\\n self._remote_manager = remote_manager\\n self._generator_manager = generators\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder._get_build_folder\",\n \"text\": \"conans/client/installer.py::_PackageBuilder._get_build_folder\\n def _get_build_folder(self, conanfile, package_layout, pref, keep_build, recorder):\\n # Build folder can use a different package_ID if build_id() is defined.\\n # This function decides if the build folder should be re-used (not build again)\\n # and returns the build folder\\n new_id = build_id(conanfile)\\n build_pref = PackageReference(pref.ref, new_id) if new_id else pref\\n build_folder = package_layout.build(build_pref)\\n\\n if is_dirty(build_folder):\\n self._output.warn(\\\"Build folder is dirty, removing it: %s\\\" % build_folder)\\n rmdir(build_folder)\\n clean_dirty(build_folder)\\n\\n # Decide if the build folder should be kept\\n skip_build = conanfile.develop and keep_build\\n if skip_build:\\n self._output.info(\\\"Won't be built as specified by --keep-build\\\")\\n if not os.path.exists(build_folder):\\n msg = \\\"--keep-build specified, but build folder not found\\\"\\n recorder.package_install_error(pref, INSTALL_ERROR_MISSING_BUILD_FOLDER,\\n msg, remote_name=None)\\n raise ConanException(msg)\\n elif build_pref != pref and os.path.exists(build_folder) and hasattr(conanfile, \\\"build_id\\\"):\\n self._output.info(\\\"Won't be built, using previous build folder as defined in build_id()\\\")\\n skip_build = True\\n\\n return build_folder, skip_build\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder._prepare_sources\",\n \"text\": \"conans/client/installer.py::_PackageBuilder._prepare_sources\\n def _prepare_sources(self, conanfile, pref, package_layout, remotes):\\n export_folder = package_layout.export()\\n export_source_folder = package_layout.export_sources()\\n scm_sources_folder = package_layout.scm_sources()\\n conanfile_path = package_layout.conanfile()\\n source_folder = package_layout.source()\\n\\n retrieve_exports_sources(self._remote_manager, self._cache, conanfile, pref.ref, remotes)\\n\\n conanfile.folders.set_base_source(source_folder)\\n conanfile.folders.set_base_build(None)\\n conanfile.folders.set_base_package(None)\\n\\n config_source(export_folder, export_source_folder, scm_sources_folder,\\n conanfile, self._output, conanfile_path, pref.ref,\\n self._hook_manager, self._cache)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder._copy_sources\",\n \"text\": \"conans/client/installer.py::_PackageBuilder._copy_sources\\n @staticmethod\\n def _copy_sources(conanfile, source_folder, build_folder):\\n # Copies the sources to the build-folder, unless no_copy_source is defined\\n _remove_folder_raising(build_folder)\\n if not getattr(conanfile, 'no_copy_source', False):\\n conanfile.output.info('Copying sources to build folder')\\n try:\\n shutil.copytree(source_folder, build_folder, symlinks=True)\\n except Exception as e:\\n msg = str(e)\\n if \\\"206\\\" in msg: # System error shutil.Error 206: Filename or extension too long\\n msg += \\\"\\\\nUse short_paths=True if paths too long\\\"\\n raise ConanException(\\\"%s\\\\nError copying sources to build folder\\\" % msg)\\n logger.debug(\\\"BUILD: Copied to %s\\\", build_folder)\\n logger.debug(\\\"BUILD: Files copied %s\\\", \\\",\\\".join(os.listdir(build_folder)))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder._build\",\n \"text\": \"conans/client/installer.py::_PackageBuilder._build\\n def _build(self, conanfile, pref):\\n # Read generators from conanfile and generate the needed files\\n logger.info(\\\"GENERATORS: Writing generators\\\")\\n self._generator_manager.write_generators(conanfile, conanfile.build_folder,\\n conanfile.generators_folder, self._output)\\n\\n logger.info(\\\"TOOLCHAIN: Writing toolchain\\\")\\n write_toolchain(conanfile, conanfile.generators_folder, self._output)\\n\\n # Build step might need DLLs, binaries as protoc to generate source files\\n # So execute imports() before build, storing the list of copied_files\\n\\n copied_files = run_imports(conanfile)\\n\\n try:\\n mkdir(conanfile.build_folder)\\n with tools.chdir(conanfile.build_folder):\\n run_build_method(conanfile, self._hook_manager, reference=pref.ref, package_id=pref.id)\\n self._output.success(\\\"Package '%s' built\\\" % pref.id)\\n self._output.info(\\\"Build folder %s\\\" % conanfile.build_folder)\\n except Exception as exc:\\n self._output.writeln(\\\"\\\")\\n self._output.error(\\\"Package '%s' build failed\\\" % pref.id)\\n self._output.warn(\\\"Build folder %s\\\" % conanfile.build_folder)\\n if isinstance(exc, ConanExceptionInUserConanfileMethod):\\n raise exc\\n raise ConanException(exc)\\n finally:\\n # Now remove all files that were imported with imports()\\n remove_imports(conanfile, copied_files, self._output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder._package\",\n \"text\": \"conans/client/installer.py::_PackageBuilder._package\\n def _package(self, conanfile, pref, package_layout, conanfile_path):\\n # FIXME: Is weak to assign here the recipe_hash\\n manifest = package_layout.recipe_manifest()\\n conanfile.info.recipe_hash = manifest.summary_hash\\n\\n # Creating ***info.txt files\\n save(os.path.join(conanfile.folders.base_build, CONANINFO), conanfile.info.dumps())\\n self._output.info(\\\"Generated %s\\\" % CONANINFO)\\n save(os.path.join(conanfile.folders.base_build, BUILD_INFO),\\n TXTGenerator(conanfile).content)\\n self._output.info(\\\"Generated %s\\\" % BUILD_INFO)\\n\\n package_id = pref.id\\n # Do the actual copy, call the conanfile.package() method\\n # While installing, the infos goes to build folder\\n conanfile.folders.set_base_install(conanfile.folders.base_build)\\n\\n prev = run_package_method(conanfile, package_id, self._hook_manager, conanfile_path,\\n pref.ref)\\n\\n update_package_metadata(prev, package_layout, package_id, pref.ref.revision)\\n\\n if get_env(\\\"CONAN_READ_ONLY_CACHE\\\", False):\\n make_read_only(conanfile.folders.base_package)\\n # FIXME: Conan 2.0 Clear the registry entry (package ref)\\n return prev\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder.build_package\",\n \"text\": \"conans/client/installer.py::_PackageBuilder.build_package\\n def build_package(self, node, keep_build, recorder, remotes):\\n t1 = time.time()\\n\\n conanfile = node.conanfile\\n pref = node.pref\\n\\n package_layout = self._cache.package_layout(pref.ref, conanfile.short_paths)\\n base_source = package_layout.source()\\n conanfile_path = package_layout.conanfile()\\n base_package = package_layout.package(pref)\\n\\n base_build, skip_build = self._get_build_folder(conanfile, package_layout,\\n pref, keep_build, recorder)\\n # PREPARE SOURCES\\n if not skip_build:\\n with package_layout.conanfile_write_lock(self._output):\\n set_dirty(base_build)\\n self._prepare_sources(conanfile, pref, package_layout, remotes)\\n self._copy_sources(conanfile, base_source, base_build)\\n\\n # BUILD & PACKAGE\\n with package_layout.conanfile_read_lock(self._output):\\n self._output.info('Building your package in %s' % base_build)\\n try:\\n if getattr(conanfile, 'no_copy_source', False):\\n conanfile.folders.set_base_source(base_source)\\n else:\\n conanfile.folders.set_base_source(base_build)\\n\\n conanfile.folders.set_base_build(base_build)\\n conanfile.folders.set_base_imports(base_build)\\n conanfile.folders.set_base_package(base_package)\\n\\n if not skip_build:\\n # In local cache, generators folder always in build_folder\\n conanfile.folders.set_base_generators(base_build)\\n # In local cache, install folder always is build_folder\\n conanfile.folders.set_base_install(base_build)\\n self._build(conanfile, pref)\\n clean_dirty(base_build)\\n\\n prev = self._package(conanfile, pref, pac\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_remove_folder_raising\",\n \"text\": \"conans/client/installer.py::_remove_folder_raising\\ndef _remove_folder_raising(folder):\\n try:\\n rmdir(folder)\\n except OSError as e:\\n raise ConanException(\\\"%s\\\\n\\\\nCouldn't remove folder, might be busy or open\\\\n\\\"\\n \\\"Close any app using it, and retry\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_handle_system_requirements\",\n \"text\": \"conans/client/installer.py::_handle_system_requirements\\ndef _handle_system_requirements(conan_file, pref, cache, out):\\n \\\"\\\"\\\" check first the system_reqs/system_requirements.txt existence, if not existing\\n check package/sha1/\\n\\n Used after remote package retrieving and before package building\\n \\\"\\\"\\\"\\n # TODO: Check if this idiom should be generalize to all methods defined in base ConanFile\\n # Instead of calling empty methods\\n if type(conan_file).system_requirements == ConanFile.system_requirements:\\n return\\n\\n package_layout = cache.package_layout(pref.ref)\\n system_reqs_path = package_layout.system_reqs()\\n system_reqs_package_path = package_layout.system_reqs_package(pref)\\n if os.path.exists(system_reqs_path) or os.path.exists(system_reqs_package_path):\\n return\\n\\n ret = call_system_requirements(conan_file, out)\\n\\n try:\\n ret = str(ret or \\\"\\\")\\n except Exception:\\n out.warn(\\\"System requirements didn't return a string\\\")\\n ret = \\\"\\\"\\n if getattr(conan_file, \\\"global_system_requirements\\\", None):\\n save(system_reqs_path, ret)\\n else:\\n save(system_reqs_package_path, ret)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::call_system_requirements\",\n \"text\": \"conans/client/installer.py::call_system_requirements\\ndef call_system_requirements(conanfile, output):\\n try:\\n return conanfile.system_requirements()\\n except Exception as e:\\n output.error(\\\"while executing system_requirements(): %s\\\" % str(e))\\n raise ConanException(\\\"Error in system requirements\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller.__init__\",\n \"text\": \"conans/client/installer.py::BinaryInstaller.__init__\\n def __init__(self, app, recorder):\\n self._cache = app.cache\\n self._out = app.out\\n self._remote_manager = app.remote_manager\\n self._recorder = recorder\\n self._binaries_analyzer = app.binaries_analyzer\\n self._hook_manager = app.hook_manager\\n self._generator_manager = app.generator_manager\\n # Load custom generators from the cache, generators are part of the binary\\n # build and install. Generators loaded here from the cache will have precedence\\n # and overwrite possible generators loaded from packages (requires)\\n for generator_path in app.cache.generators:\\n app.loader.load_generators(generator_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller.install\",\n \"text\": \"conans/client/installer.py::BinaryInstaller.install\\n def install(self, deps_graph, remotes, build_mode, update, profile_host, profile_build,\\n graph_lock, keep_build=False):\\n # order by levels and separate the root node (ref=None) from the rest\\n nodes_by_level = deps_graph.by_levels()\\n root_level = nodes_by_level.pop()\\n root_node = root_level[0]\\n # Get the nodes in order and if we have to build them\\n self._out.info(\\\"Installing (downloading, building) binaries...\\\")\\n self._build(nodes_by_level, keep_build, root_node, profile_host, profile_build,\\n graph_lock, remotes, build_mode, update)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._classify\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._classify\\n @staticmethod\\n def _classify(nodes_by_level):\\n missing, invalid, downloads = [], [], []\\n for level in nodes_by_level:\\n for node in level:\\n if node.binary == BINARY_MISSING:\\n missing.append(node)\\n elif node.binary == BINARY_INVALID:\\n invalid.append(node)\\n elif node.binary in (BINARY_UPDATE, BINARY_DOWNLOAD):\\n downloads.append(node)\\n return missing, invalid, downloads\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._raise_missing\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._raise_missing\\n def _raise_missing(self, missing):\\n if not missing:\\n return\\n\\n missing_prefs = set(n.pref for n in missing) # avoid duplicated\\n missing_prefs = list(sorted(missing_prefs))\\n for pref in missing_prefs:\\n self._out.error(\\\"Missing binary: %s\\\" % str(pref))\\n self._out.writeln(\\\"\\\")\\n\\n # Report details just the first one\\n node = missing[0]\\n package_id = node.package_id\\n ref, conanfile = node.ref, node.conanfile\\n dependencies = [str(dep.dst) for dep in node.dependencies]\\n\\n settings_text = \\\", \\\".join(conanfile.info.full_settings.dumps().splitlines())\\n options_text = \\\", \\\".join(conanfile.info.full_options.dumps().splitlines())\\n dependencies_text = ', '.join(dependencies)\\n requires_text = \\\", \\\".join(conanfile.info.requires.dumps().splitlines())\\n\\n msg = textwrap.dedent('''\\\\\\n Can't find a '%s' package for the specified settings, options and dependencies:\\n - Settings: %s\\n - Options: %s\\n - Dependencies: %s\\n - Requirements: %s\\n - Package ID: %s\\n ''' % (ref, settings_text, options_text, dependencies_text, requires_text, package_id))\\n conanfile.output.warn(msg)\\n self._recorder.package_install_error(PackageReference(ref, package_id),\\n INSTALL_ERROR_MISSING, msg)\\n missing_pkgs = \\\"', '\\\".join([str(pref.ref) for pref in missing_prefs])\\n if len(missing_prefs) >= 5:\\n build_str = \\\"--build=missing\\\"\\n else:\\n build_str = \\\" \\\".join([\\\"--build=%s\\\" % pref.ref.name for pref in missing_prefs])\\n\\n raise ConanException(textwrap.dedent('''\\\\\\n Missing prebuilt package for '%s'\\n Try to build from sources with '%s'\\n Use 'conan search <reference> --table table.html'\\n Or read 'http://docs.conan.io/en/latest/\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._download\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._download\\n def _download(self, downloads, processed_package_refs):\\n \\\"\\\"\\\" executes the download of packages (both download and update), only once for a given\\n PREF, even if node duplicated\\n :param downloads: all nodes to be downloaded or updated, included repetitions\\n \\\"\\\"\\\"\\n if not downloads:\\n return\\n\\n download_nodes = []\\n for node in downloads:\\n pref = node.pref\\n bare_pref = PackageReference(pref.ref, pref.id)\\n if bare_pref in processed_package_refs:\\n continue\\n processed_package_refs[bare_pref] = pref.revision\\n assert node.prev, \\\"PREV for %s is None\\\" % str(node.pref)\\n download_nodes.append(node)\\n\\n def _download(n):\\n npref = n.pref\\n layout = self._cache.package_layout(npref.ref, n.conanfile.short_paths)\\n # We cannot embed the package_lock inside the remote.get_package()\\n # because the handle_node_cache has its own lock\\n with layout.package_lock(pref):\\n self._download_pkg(layout, n)\\n\\n parallel = self._cache.config.parallel_download\\n if parallel is not None:\\n self._out.info(\\\"Downloading binary packages in %s parallel threads\\\" % parallel)\\n thread_pool = ThreadPool(parallel)\\n thread_pool.map(_download, [n for n in download_nodes])\\n thread_pool.close()\\n thread_pool.join()\\n else:\\n for node in download_nodes:\\n _download(node)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._download._download\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._download._download\\n def _download(n):\\n npref = n.pref\\n layout = self._cache.package_layout(npref.ref, n.conanfile.short_paths)\\n # We cannot embed the package_lock inside the remote.get_package()\\n # because the handle_node_cache has its own lock\\n with layout.package_lock(pref):\\n self._download_pkg(layout, n)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._download_pkg\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._download_pkg\\n def _download_pkg(self, layout, node):\\n self._remote_manager.get_package(node.conanfile, node.pref, layout, node.binary_remote,\\n node.conanfile.output, self._recorder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._build\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._build\\n def _build(self, nodes_by_level, keep_build, root_node, profile_host, profile_build, graph_lock,\\n remotes, build_mode, update):\\n using_build_profile = bool(profile_build)\\n missing, invalid, downloads = self._classify(nodes_by_level)\\n if invalid:\\n msg = [\\\"There are invalid packages (packages that cannot exist for this configuration):\\\"]\\n for node in invalid:\\n msg.append(\\\"{}: Invalid ID: {}\\\".format(node.conanfile, node.conanfile.info.invalid))\\n raise ConanInvalidConfiguration(\\\"\\\\n\\\".join(msg))\\n self._raise_missing(missing)\\n processed_package_refs = {}\\n self._download(downloads, processed_package_refs)\\n\\n for level in nodes_by_level:\\n for node in level:\\n ref, conan_file = node.ref, node.conanfile\\n output = conan_file.output\\n\\n self._propagate_info(node, using_build_profile)\\n if node.binary == BINARY_EDITABLE:\\n self._handle_node_editable(node, profile_host, profile_build, graph_lock)\\n # Need a temporary package revision for package_revision_mode\\n # Cannot be PREV_UNKNOWN otherwise the consumers can't compute their packageID\\n node.prev = \\\"editable\\\"\\n else:\\n if node.binary == BINARY_SKIP: # Privates not necessary\\n continue\\n assert ref.revision is not None, \\\"Installer should receive RREV always\\\"\\n if node.binary == BINARY_UNKNOWN:\\n self._binaries_analyzer.reevaluate_node(node, remotes, build_mode, update)\\n if node.binary == BINARY_MISSING:\\n self._raise_missing([node])\\n _handle_system_requirements(conan_file, node.pref, self._cache, output)\\n self._handle_node_cache(node,\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._handle_node_editable\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._handle_node_editable\\n def _handle_node_editable(self, node, profile_host, profile_build, graph_lock):\\n # Get source of information\\n conanfile = node.conanfile\\n ref = node.ref\\n package_layout = self._cache.package_layout(ref)\\n base_path = package_layout.base_folder()\\n self._call_package_info(conanfile, package_folder=base_path, ref=ref, is_editable=True)\\n\\n # New editables mechanism based on Folders\\n if hasattr(conanfile, \\\"layout\\\"):\\n conanfile.folders.set_base_package(base_path)\\n conanfile.folders.set_base_source(base_path)\\n conanfile.folders.set_base_build(base_path)\\n conanfile.folders.set_base_install(base_path)\\n conanfile.folders.set_base_imports(base_path)\\n\\n output = conanfile.output\\n output.info(\\\"Rewriting files of editable package \\\"\\n \\\"'{}' at '{}'\\\".format(conanfile.name, conanfile.generators_folder))\\n self._generator_manager.write_generators(conanfile, conanfile.install_folder,\\n conanfile.generators_folder, output)\\n write_toolchain(conanfile, conanfile.generators_folder, output)\\n output.info(\\\"Generated toolchain\\\")\\n graph_info_node = GraphInfo(profile_host, root_ref=node.ref)\\n graph_info_node.options = node.conanfile.options.values\\n graph_info_node.graph_lock = graph_lock\\n graph_info_node.save(base_path)\\n output.info(\\\"Generated conan.lock\\\")\\n copied_files = run_imports(conanfile)\\n report_copied_files(copied_files, output)\\n return\\n\\n node.conanfile.cpp_info.filter_empty = False\\n # OLD EDITABLE LAYOUTS:\\n # Try with package-provided file\\n editable_cpp_info = package_layout.editable_cpp_info()\\n if editable_cpp_info:\\n editable_cpp_info.apply_to(ref,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._handle_node_cache\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._handle_node_cache\\n def _handle_node_cache(self, node, keep_build, processed_package_references, remotes):\\n pref = node.pref\\n assert pref.id, \\\"Package-ID without value\\\"\\n assert pref.id != PACKAGE_ID_UNKNOWN, \\\"Package-ID error: %s\\\" % str(pref)\\n conanfile = node.conanfile\\n output = conanfile.output\\n\\n layout = self._cache.package_layout(pref.ref, conanfile.short_paths)\\n\\n with layout.package_lock(pref):\\n bare_pref = PackageReference(pref.ref, pref.id)\\n processed_prev = processed_package_references.get(bare_pref)\\n if processed_prev is None: # This package-id has not been processed before\\n if node.binary == BINARY_BUILD:\\n assert node.prev is None, \\\"PREV for %s to be built should be None\\\" % str(pref)\\n layout.package_remove(pref)\\n with layout.set_dirty_context_manager(pref):\\n pref = self._build_package(node, output, keep_build, remotes)\\n assert node.prev, \\\"Node PREV shouldn't be empty\\\"\\n assert node.pref.revision, \\\"Node PREF revision shouldn't be empty\\\"\\n assert pref.revision is not None, \\\"PREV for %s to be built is None\\\" % str(pref)\\n elif node.binary in (BINARY_UPDATE, BINARY_DOWNLOAD):\\n # this can happen after a re-evaluation of packageID with Package_ID_unknown\\n self._download_pkg(layout, node)\\n elif node.binary == BINARY_CACHE:\\n assert node.prev, \\\"PREV for %s is None\\\" % str(pref)\\n output.success('Already installed!')\\n log_package_got_from_local_cache(pref)\\n self._recorder.package_fetched_from_cache(pref)\\n processed_package_references[bare_pref] = node.prev\\n else:\\n # We need to update the PREV of this node, as its \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._build_package\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._build_package\\n def _build_package(self, node, output, keep_build, remotes):\\n conanfile = node.conanfile\\n # It is necessary to complete the sources of python requires, which might be used\\n # Only the legacy python_requires allow this\\n python_requires = getattr(conanfile, \\\"python_requires\\\", None)\\n if python_requires and isinstance(python_requires, dict): # Old legacy python_requires\\n for python_require in python_requires.values():\\n assert python_require.ref.revision is not None, \\\\\\n \\\"Installer should receive python_require.ref always\\\"\\n retrieve_exports_sources(self._remote_manager, self._cache,\\n python_require.conanfile, python_require.ref, remotes)\\n\\n builder = _PackageBuilder(self._cache, output, self._hook_manager, self._remote_manager,\\n self._generator_manager)\\n pref = builder.build_package(node, keep_build, self._recorder, remotes)\\n if node.graph_lock_node:\\n node.graph_lock_node.prev = pref.revision\\n return pref\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._propagate_info\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._propagate_info\\n def _propagate_info(self, node, using_build_profile):\\n # it is necessary to recompute\\n # the node transitive information necessary to compute the package_id\\n # as it will be used by reevaluate_node() when package_revision_mode is used and\\n # PACKAGE_ID_UNKNOWN happens due to unknown revisions\\n self._binaries_analyzer.package_id_transitive_reqs(node)\\n # Get deps_cpp_info from upstream nodes\\n node_order = [n for n in node.public_closure if n.binary != BINARY_SKIP]\\n # List sort is stable, will keep the original order of the closure, but prioritize levels\\n conan_file = node.conanfile\\n # FIXME: Not the best place to assign the _conan_using_build_profile\\n conan_file._conan_using_build_profile = using_build_profile\\n transitive = [it for it in node.transitive_closure.values()]\\n\\n br_host = []\\n for it in node.dependencies:\\n if it.require.build_require_context == CONTEXT_HOST:\\n br_host.extend(it.dst.transitive_closure.values())\\n\\n # Initialize some members if we are using different contexts\\n if using_build_profile:\\n conan_file.user_info_build = DepsUserInfo()\\n\\n for n in node_order:\\n if n not in transitive:\\n conan_file.output.info(\\\"Applying build-requirement: %s\\\" % str(n.ref))\\n\\n dep_cpp_info = n.conanfile._conan_dep_cpp_info\\n\\n if not using_build_profile: # Do not touch anything\\n conan_file.deps_user_info[n.ref.name] = n.conanfile.user_info\\n conan_file.deps_cpp_info.add(n.ref.name, dep_cpp_info)\\n conan_file.deps_env_info.update(n.conanfile.env_info, n.ref.name)\\n else:\\n if n in transitive or n in br_host:\\n conan_file.deps_user_info[n.ref.name] = n.conanfile.user_info\\n conan_file.deps_cpp_info.add(n.ref.name, d\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._call_package_info\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._call_package_info\\n def _call_package_info(self, conanfile, package_folder, ref, is_editable):\\n conanfile.cpp_info = CppInfo(conanfile.name, package_folder)\\n conanfile.cpp_info.version = conanfile.version\\n conanfile.cpp_info.description = conanfile.description\\n\\n conanfile.folders.set_base_package(package_folder)\\n conanfile.folders.set_base_source(None)\\n conanfile.folders.set_base_build(None)\\n conanfile.folders.set_base_install(None)\\n\\n conanfile.env_info = EnvInfo()\\n conanfile.user_info = UserInfo()\\n\\n # Get deps_cpp_info from upstream nodes\\n public_deps = [name for name, req in conanfile.requires.items() if not req.private\\n and not req.override]\\n conanfile.cpp_info.public_deps = public_deps\\n # Once the node is build, execute package info, so it has access to the\\n # package folder and artifacts\\n # Minimal pythonpath, not the whole context, make it 50% slower\\n # FIXME Conan 2.0, Remove old ways of reusing python code\\n with pythonpath(conanfile):\\n with tools.chdir(package_folder):\\n with conanfile_exception_formatter(str(conanfile), \\\"package_info\\\"):\\n self._hook_manager.execute(\\\"pre_package_info\\\", conanfile=conanfile,\\n reference=ref)\\n if hasattr(conanfile, \\\"layout\\\"):\\n # Old cpp info without defaults (the defaults are in the new one)\\n conanfile.cpp_info = CppInfo(conanfile.name, package_folder,\\n default_values=CppInfoDefaultValues())\\n if not is_editable:\\n package_cppinfo = conanfile.cpp.package.copy()\\n package_cppinfo.set_relative_base_folder(conanfile.folders.package)\\n # Copy the i\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator.__init__\",\n \"text\": \"conans/client/migrations.py::ClientMigrator.__init__\\n def __init__(self, cache_folder, current_version, out):\\n self.cache_folder = cache_folder\\n super(ClientMigrator, self).__init__(cache_folder, current_version, out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml\",\n \"text\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml\\n def _update_settings_yml(self, cache, old_version):\\n\\n from conans.client.conf import get_default_settings_yml\\n settings_path = cache.settings_path\\n if not os.path.exists(settings_path):\\n self.out.warn(\\\"Migration: This conan installation doesn't have settings yet\\\")\\n self.out.warn(\\\"Nothing to migrate here, settings will be generated automatically\\\")\\n return\\n\\n var_name = \\\"settings_{}\\\".format(old_version.replace(\\\".\\\", \\\"_\\\"))\\n\\n def save_new():\\n new_path = cache.settings_path + \\\".new\\\"\\n save(new_path, get_default_settings_yml())\\n self.out.warn(\\\"*\\\" * 40)\\n self.out.warn(\\\"settings.yml is locally modified, can't be updated\\\")\\n self.out.warn(\\\"The new settings.yml has been stored in: %s\\\" % new_path)\\n self.out.warn(\\\"*\\\" * 40)\\n\\n self.out.warn(\\\"Migration: Updating settings.yml\\\")\\n if hasattr(migrations_settings, var_name):\\n version_default_contents = getattr(migrations_settings, var_name)\\n if version_default_contents != get_default_settings_yml():\\n current_settings = load(cache.settings_path)\\n if current_settings != version_default_contents:\\n save_new()\\n else:\\n save(cache.settings_path, get_default_settings_yml())\\n else:\\n self.out.info(\\\"Migration: Settings already up to date\\\")\\n else:\\n # We don't have the value for that version, so don't override\\n save_new()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new\",\n \"text\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new\\n def save_new():\\n new_path = cache.settings_path + \\\".new\\\"\\n save(new_path, get_default_settings_yml())\\n self.out.warn(\\\"*\\\" * 40)\\n self.out.warn(\\\"settings.yml is locally modified, can't be updated\\\")\\n self.out.warn(\\\"The new settings.yml has been stored in: %s\\\" % new_path)\\n self.out.warn(\\\"*\\\" * 40)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator._make_migrations\",\n \"text\": \"conans/client/migrations.py::ClientMigrator._make_migrations\\n def _make_migrations(self, old_version):\\n # ############### FILL THIS METHOD WITH THE REQUIRED ACTIONS ##############\\n # VERSION 0.1\\n if old_version is None:\\n return\\n\\n # Migrate the settings if they were the default for that version\\n cache = ClientCache(self.cache_folder, self.out)\\n self._update_settings_yml(cache, old_version)\\n\\n if old_version < Version(\\\"1.0\\\"):\\n _migrate_lock_files(cache, self.out)\\n\\n if old_version < Version(\\\"1.12.0\\\"):\\n migrate_plugins_to_hooks(cache)\\n\\n if old_version < Version(\\\"1.13.0\\\"):\\n # MIGRATE LOCAL CACHE TO GENERATE MISSING METADATA.json\\n _migrate_create_metadata(cache, self.out)\\n\\n if old_version < Version(\\\"1.14.0\\\"):\\n migrate_config_install(cache)\\n\\n if old_version < Version(\\\"1.14.2\\\"):\\n _migrate_full_metadata(cache, self.out)\\n\\n if old_version < Version(\\\"1.15.0\\\"):\\n migrate_registry_file(cache, self.out)\\n\\n if old_version < Version(\\\"1.19.0\\\"):\\n migrate_localdb_refresh_token(cache, self.out)\\n\\n if old_version < Version(\\\"1.26.0\\\"):\\n migrate_editables_use_conanfile_name(cache)\\n\\n if old_version < Version(\\\"1.31.0\\\"):\\n migrate_tgz_location(cache, self.out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_get_refs\",\n \"text\": \"conans/client/migrations.py::_get_refs\\ndef _get_refs(cache):\\n folders = list_folder_subdirs(cache.store, 4)\\n return [ConanFileReference(*s.split(\\\"/\\\")) for s in folders]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_get_prefs\",\n \"text\": \"conans/client/migrations.py::_get_prefs\\ndef _get_prefs(layout):\\n packages_folder = layout.packages()\\n folders = list_folder_subdirs(packages_folder, 1)\\n return [PackageReference(layout.ref, s) for s in folders]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_tgz_location\",\n \"text\": \"conans/client/migrations.py::migrate_tgz_location\\ndef migrate_tgz_location(cache, out):\\n \\\"\\\"\\\" In Conan 1.31, the temporary .tgz are no longer stored in the content folders. In case\\n they are found there, they can be removed, and the next time they are needed (upload), they\\n will be compressed again\\n \\\"\\\"\\\"\\n out.info(\\\"Removing temporary .tgz files, they are stored in a different location now\\\")\\n refs = _get_refs(cache)\\n for ref in refs:\\n try:\\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\\n for d, _, fs in os.walk(base_folder):\\n for f in fs:\\n if f in (EXPORT_SOURCES_TGZ_NAME, EXPORT_TGZ_NAME, PACKAGE_TGZ_NAME):\\n tgz_file = os.path.join(d, f)\\n os.remove(tgz_file)\\n except Exception as e:\\n raise ConanException(\\\"Something went wrong while removing temporary .tgz files \\\"\\n \\\"in the cache, please try to fix the issue or wipe the cache: {}\\\"\\n \\\":{}\\\".format(ref, e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_localdb_refresh_token\",\n \"text\": \"conans/client/migrations.py::migrate_localdb_refresh_token\\ndef migrate_localdb_refresh_token(cache, out):\\n from sqlite3 import OperationalError\\n\\n with cache.localdb._connect() as connection:\\n try:\\n statement = connection.cursor()\\n statement.execute(\\\"ALTER TABLE users_remotes ADD refresh_token TEXT;\\\")\\n except OperationalError:\\n # This likely means the column is already there (fresh created table)\\n # In the worst scenario the user will be requested to remove the file by hand\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_migrate_full_metadata\",\n \"text\": \"conans/client/migrations.py::_migrate_full_metadata\\ndef _migrate_full_metadata(cache, out):\\n # Fix for https://github.com/conan-io/conan/issues/4898\\n out.warn(\\\"Running a full revision metadata migration\\\")\\n refs = _get_refs(cache)\\n for ref in refs:\\n try:\\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=None,\\n no_lock=True)\\n with layout.update_metadata() as metadata:\\n # Updating the RREV\\n if metadata.recipe.revision is None:\\n out.warn(\\\"Package %s metadata had recipe revision None, migrating\\\" % str(ref))\\n folder = layout.export()\\n try:\\n manifest = FileTreeManifest.load(folder)\\n rrev = manifest.summary_hash\\n except Exception:\\n rrev = DEFAULT_REVISION_V1\\n metadata.recipe.revision = rrev\\n\\n prefs = _get_prefs(layout)\\n existing_ids = [pref.id for pref in prefs]\\n for pkg_id in list(metadata.packages.keys()):\\n if pkg_id not in existing_ids:\\n out.warn(\\\"Package %s metadata had stalled package information %s, removing\\\"\\n % (str(ref), pkg_id))\\n del metadata.packages[pkg_id]\\n # UPDATING PREVS\\n for pref in prefs:\\n try:\\n pmanifest = FileTreeManifest.load(layout.package(pref))\\n prev = pmanifest.summary_hash\\n except Exception:\\n prev = DEFAULT_REVISION_V1\\n metadata.packages[pref.id].revision = prev\\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\\n\\n except Exception as \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_migrate_create_metadata\",\n \"text\": \"conans/client/migrations.py::_migrate_create_metadata\\ndef _migrate_create_metadata(cache, out):\\n out.warn(\\\"Migration: Generating missing metadata files\\\")\\n refs = _get_refs(cache)\\n\\n for ref in refs:\\n try:\\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\\n # Force using a package cache layout for everything, we want to alter the cache,\\n # not the editables\\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=False,\\n no_lock=True)\\n folder = layout.export()\\n try:\\n manifest = FileTreeManifest.load(folder)\\n rrev = manifest.summary_hash\\n except Exception:\\n rrev = DEFAULT_REVISION_V1\\n metadata_path = layout.package_metadata()\\n if not os.path.exists(metadata_path):\\n out.info(\\\"Creating {} for {}\\\".format(PACKAGE_METADATA, ref))\\n prefs = _get_prefs(layout)\\n metadata = PackageMetadata()\\n metadata.recipe.revision = rrev\\n for pref in prefs:\\n try:\\n pmanifest = FileTreeManifest.load(layout.package(pref))\\n prev = pmanifest.summary_hash\\n except Exception:\\n prev = DEFAULT_REVISION_V1\\n metadata.packages[pref.id].revision = prev\\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\\n save(metadata_path, metadata.dumps())\\n except Exception as e:\\n raise ConanException(\\\"Something went wrong while generating the metadata.json files \\\"\\n \\\"in the cache, please try to fix the issue or wipe the cache: {}\\\"\\n \\\":{}\\\".format(ref, e))\\n out.success(\\\"Migration: Generating missing metadata files finished OK!\\\\n\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_migrate_lock_files\",\n \"text\": \"conans/client/migrations.py::_migrate_lock_files\\ndef _migrate_lock_files(cache, out):\\n out.warn(\\\"Migration: Removing old lock files\\\")\\n base_dir = cache.store\\n pkgs = list_folder_subdirs(base_dir, 4)\\n for pkg in pkgs:\\n out.info(\\\"Removing locks for %s\\\" % pkg)\\n try:\\n count = os.path.join(base_dir, pkg, \\\"rw.count\\\")\\n if os.path.exists(count):\\n os.remove(count)\\n count = os.path.join(base_dir, pkg, \\\"rw.count.lock\\\")\\n if os.path.exists(count):\\n os.remove(count)\\n locks = os.path.join(base_dir, pkg, \\\"locks\\\")\\n if os.path.exists(locks):\\n shutil.rmtree(locks)\\n except Exception as e:\\n raise ConanException(\\\"Something went wrong while removing %s locks\\\\n\\\"\\n \\\"Error: %s\\\\n\\\"\\n \\\"Please clean your local conan cache manually\\\"\\n % (pkg, str(e)))\\n out.warn(\\\"Migration: Removing old lock files finished\\\\n\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_config_install\",\n \"text\": \"conans/client/migrations.py::migrate_config_install\\ndef migrate_config_install(cache):\\n try:\\n item = cache.config.get_item(\\\"general.config_install\\\")\\n items = [r.strip() for r in item.split(\\\",\\\")]\\n if len(items) == 4:\\n config_type, uri, verify_ssl, args = items\\n elif len(items) == 1:\\n uri = items[0]\\n verify_ssl = \\\"True\\\"\\n args = \\\"None\\\"\\n config_type = None\\n else:\\n raise Exception(\\\"I don't know how to migrate this config install: %s\\\" % items)\\n verify_ssl = \\\"true\\\" in verify_ssl.lower()\\n args = None if \\\"none\\\" in args.lower() else args\\n config = _ConfigOrigin.from_item(uri, config_type, verify_ssl, args, None, None)\\n _save_configs(cache.config_install_file, [config])\\n cache.config.rm_item(\\\"general.config_install\\\")\\n except ConanException:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_to_default_profile\",\n \"text\": \"conans/client/migrations.py::migrate_to_default_profile\\ndef migrate_to_default_profile(conf_path, default_profile_path):\\n tag = \\\"[settings_defaults]\\\"\\n old_conf = load(conf_path)\\n if tag not in old_conf:\\n return\\n tmp = old_conf.find(tag)\\n new_conf = old_conf[0:tmp]\\n rest = old_conf[tmp + len(tag):]\\n if tmp:\\n if \\\"]\\\" in rest: # More sections after the settings_defaults\\n new_conf += rest[rest.find(\\\"[\\\"):]\\n save(conf_path, new_conf)\\n settings = rest[:rest.find(\\\"[\\\")].strip()\\n else:\\n save(conf_path, new_conf)\\n settings = rest.strip()\\n # Now generate the default profile from the read settings_defaults\\n new_profile = \\\"[settings]\\\\n%s\\\" % settings\\n save(default_profile_path, new_profile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_plugins_to_hooks\",\n \"text\": \"conans/client/migrations.py::migrate_plugins_to_hooks\\ndef migrate_plugins_to_hooks(cache, output=None):\\n plugins_path = os.path.join(cache.cache_folder, \\\"plugins\\\")\\n if os.path.exists(plugins_path) and not os.path.exists(cache.hooks_path):\\n os.rename(plugins_path, cache.hooks_path)\\n conf_path = cache.conan_conf_path\\n replace_in_file(conf_path, \\\"[plugins]\\\", \\\"[hooks]\\\", strict=False, output=output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_editables_use_conanfile_name\",\n \"text\": \"conans/client/migrations.py::migrate_editables_use_conanfile_name\\ndef migrate_editables_use_conanfile_name(cache):\\n \\\"\\\"\\\"\\n In Conan v1.26 we store full path to the conanfile in the editable_package.json file, before\\n it Conan was storing just the directory and assume that the 'conanfile' was a file\\n named 'conanfile.py' inside that folder\\n \\\"\\\"\\\"\\n for ref, data in cache.editable_packages.edited_refs.items():\\n path = data[\\\"path\\\"]\\n if os.path.isdir(path):\\n path = os.path.join(path, CONANFILE)\\n cache.editable_packages.add(ref, path, layout=data[\\\"layout\\\"])\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::DefaultOrderedDict.__init__\",\n \"text\": \"conans/model/build_info.py::DefaultOrderedDict.__init__\\n def __init__(self, factory):\\n self.factory = factory\\n super(DefaultOrderedDict, self).__init__()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::DefaultOrderedDict.__getitem__\",\n \"text\": \"conans/model/build_info.py::DefaultOrderedDict.__getitem__\\n def __getitem__(self, key):\\n if key not in self.keys():\\n super(DefaultOrderedDict, self).__setitem__(key, self.factory())\\n super(DefaultOrderedDict, self).__getitem__(key).name = key\\n return super(DefaultOrderedDict, self).__getitem__(key)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::DefaultOrderedDict.__copy__\",\n \"text\": \"conans/model/build_info.py::DefaultOrderedDict.__copy__\\n def __copy__(self):\\n the_copy = DefaultOrderedDict(self.factory)\\n for key, value in super(DefaultOrderedDict, self).items():\\n the_copy[key] = value\\n return the_copy\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::BuildModulesDict.__getitem__\",\n \"text\": \"conans/model/build_info.py::BuildModulesDict.__getitem__\\n def __getitem__(self, key):\\n if key not in self.keys():\\n super(BuildModulesDict, self).__setitem__(key, list())\\n return super(BuildModulesDict, self).__getitem__(key)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::BuildModulesDict._append\",\n \"text\": \"conans/model/build_info.py::BuildModulesDict._append\\n def _append(self, item):\\n if item.endswith(\\\".cmake\\\"):\\n self[\\\"cmake\\\"].append(item)\\n self[\\\"cmake_multi\\\"].append(item)\\n self[\\\"cmake_find_package\\\"].append(item)\\n self[\\\"cmake_find_package_multi\\\"].append(item)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::BuildModulesDict.append\",\n \"text\": \"conans/model/build_info.py::BuildModulesDict.append\\n def append(self, item):\\n conan_v2_error(\\\"Use 'self.cpp_info.build_modules[\\\\\\\"<generator>\\\\\\\"].append(\\\\\\\"{item}\\\\\\\")' \\\"\\n 'instead'.format(item=item))\\n self._append(item)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::BuildModulesDict.extend\",\n \"text\": \"conans/model/build_info.py::BuildModulesDict.extend\\n def extend(self, items):\\n conan_v2_error(\\\"Use 'self.cpp_info.build_modules[\\\\\\\"<generator>\\\\\\\"].extend({items})' \\\"\\n \\\"instead\\\".format(items=items))\\n for item in items:\\n self._append(item)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::BuildModulesDict.from_list\",\n \"text\": \"conans/model/build_info.py::BuildModulesDict.from_list\\n @classmethod\\n def from_list(cls, build_modules):\\n the_dict = BuildModulesDict()\\n the_dict.extend(build_modules)\\n return the_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::dict_to_abs_paths\",\n \"text\": \"conans/model/build_info.py::dict_to_abs_paths\\ndef dict_to_abs_paths(the_dict, rootpath):\\n new_dict = {}\\n for generator, values in the_dict.items():\\n new_dict[generator] = [os.path.join(rootpath, p) if not os.path.isabs(p) else p\\n for p in values]\\n return new_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::merge_lists\",\n \"text\": \"conans/model/build_info.py::merge_lists\\ndef merge_lists(seq1, seq2):\\n return seq1 + [s for s in seq2 if s not in seq1]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::merge_dicts\",\n \"text\": \"conans/model/build_info.py::merge_dicts\\ndef merge_dicts(d1, d2):\\n def merge_lists(seq1, seq2):\\n return [s for s in seq1 if s not in seq2] + seq2\\n\\n result = d1.copy()\\n for k, v in d2.items():\\n if k not in d1.keys():\\n result[k] = v\\n else:\\n result[k] = merge_lists(d1[k], d2[k])\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::merge_dicts.merge_lists\",\n \"text\": \"conans/model/build_info.py::merge_dicts.merge_lists\\n def merge_lists(seq1, seq2):\\n return [s for s in seq1 if s not in seq2] + seq2\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.__init__\",\n \"text\": \"conans/model/build_info.py::_CppInfo.__init__\\n def __init__(self):\\n self._name = None\\n self._generator_properties = {}\\n self.names = {}\\n self.system_libs = [] # Ordered list of system libraries\\n self.includedirs = [] # Ordered list of include paths\\n self.srcdirs = [] # Ordered list of source paths\\n self.libdirs = [] # Directories to find libraries\\n self.resdirs = [] # Directories to find resources, data, etc\\n self.bindirs = [] # Directories to find executables and shared libs\\n self.builddirs = []\\n self.frameworks = [] # Macos .framework\\n self.frameworkdirs = []\\n self.rootpaths = []\\n self.libs = [] # The libs to link against\\n self.defines = [] # preprocessor definitions\\n self.cflags = [] # pure C flags\\n self.cxxflags = [] # C++ compilation flags\\n self.sharedlinkflags = [] # linker flags\\n self.exelinkflags = [] # linker flags\\n self.build_modules = BuildModulesDict() # FIXME: This should be just a plain dict\\n self.filenames = {} # name of filename to create for various generators\\n self.rootpath = \\\"\\\"\\n self.sysroot = \\\"\\\"\\n self.requires = []\\n self._build_modules_paths = None\\n self._build_modules = None\\n self._include_paths = None\\n self._lib_paths = None\\n self._bin_paths = None\\n self._build_paths = None\\n self._res_paths = None\\n self._src_paths = None\\n self._framework_paths = None\\n self.version = None # Version of the conan package\\n self.description = None # Description of the conan package\\n # When package is editable, filter_empty=False, so empty dirs are maintained\\n self.filter_empty = True\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo._filter_paths\",\n \"text\": \"conans/model/build_info.py::_CppInfo._filter_paths\\n def _filter_paths(self, paths):\\n abs_paths = [os.path.join(self.rootpath, p)\\n if not os.path.isabs(p) else p for p in paths]\\n if self.filter_empty:\\n return [p for p in abs_paths if os.path.isdir(p)]\\n else:\\n return abs_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.build_modules_paths\",\n \"text\": \"conans/model/build_info.py::_CppInfo.build_modules_paths\\n @property\\n def build_modules_paths(self):\\n if self._build_modules_paths is None:\\n if isinstance(self.build_modules, list): # FIXME: This should be just a plain dict\\n conan_v2_error(\\\"Use 'self.cpp_info.build_modules[\\\\\\\"<generator>\\\\\\\"] = \\\"\\n \\\"{the_list}' instead\\\".format(the_list=self.build_modules))\\n self.build_modules = BuildModulesDict.from_list(self.build_modules)\\n # Invalidate necessary, get_build_modules used raise_incorrect_components_definition\\n self._build_modules = None\\n tmp = dict_to_abs_paths(BuildModulesDict(self.get_build_modules()), self.rootpath)\\n self._build_modules_paths = tmp\\n return self._build_modules_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.include_paths\",\n \"text\": \"conans/model/build_info.py::_CppInfo.include_paths\\n @property\\n def include_paths(self):\\n if self._include_paths is None:\\n self._include_paths = self._filter_paths(self.includedirs)\\n return self._include_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.lib_paths\",\n \"text\": \"conans/model/build_info.py::_CppInfo.lib_paths\\n @property\\n def lib_paths(self):\\n if self._lib_paths is None:\\n self._lib_paths = self._filter_paths(self.libdirs)\\n return self._lib_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.src_paths\",\n \"text\": \"conans/model/build_info.py::_CppInfo.src_paths\\n @property\\n def src_paths(self):\\n if self._src_paths is None:\\n self._src_paths = self._filter_paths(self.srcdirs)\\n return self._src_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.bin_paths\",\n \"text\": \"conans/model/build_info.py::_CppInfo.bin_paths\\n @property\\n def bin_paths(self):\\n if self._bin_paths is None:\\n self._bin_paths = self._filter_paths(self.bindirs)\\n return self._bin_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.build_paths\",\n \"text\": \"conans/model/build_info.py::_CppInfo.build_paths\\n @property\\n def build_paths(self):\\n if self._build_paths is None:\\n self._build_paths = self._filter_paths(self.builddirs)\\n return self._build_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.res_paths\",\n \"text\": \"conans/model/build_info.py::_CppInfo.res_paths\\n @property\\n def res_paths(self):\\n if self._res_paths is None:\\n self._res_paths = self._filter_paths(self.resdirs)\\n return self._res_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.framework_paths\",\n \"text\": \"conans/model/build_info.py::_CppInfo.framework_paths\\n @property\\n def framework_paths(self):\\n if self._framework_paths is None:\\n self._framework_paths = self._filter_paths(self.frameworkdirs)\\n return self._framework_paths\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.name\",\n \"text\": \"conans/model/build_info.py::_CppInfo.name\\n @property\\n def name(self):\\n conan_v2_error(\\\"Use 'get_name(generator)' instead\\\")\\n return self._name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.name\",\n \"text\": \"conans/model/build_info.py::_CppInfo.name\\n @name.setter\\n def name(self, value):\\n self._name = value\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.get_name\",\n \"text\": \"conans/model/build_info.py::_CppInfo.get_name\\n def get_name(self, generator, default_name=True):\\n property_name = None\\n if \\\"cmake\\\" in generator:\\n property_name = \\\"cmake_target_name\\\"\\n elif \\\"pkg_config\\\" in generator:\\n property_name = \\\"pkg_config_name\\\"\\n return self.get_property(property_name, generator) \\\\\\n or self.names.get(generator, self._name if default_name else None)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.get_filename\",\n \"text\": \"conans/model/build_info.py::_CppInfo.get_filename\\n def get_filename(self, generator, default_name=True):\\n result = self.get_property(\\\"cmake_file_name\\\", generator) or self.filenames.get(generator)\\n if result:\\n return result\\n return self.get_name(generator, default_name=default_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.get_build_modules\",\n \"text\": \"conans/model/build_info.py::_CppInfo.get_build_modules\\n def get_build_modules(self):\\n if self._build_modules is None: # Not cached yet\\n try:\\n default_build_modules_value = self._generator_properties[None][\\\"cmake_build_modules\\\"]\\n except KeyError:\\n ret_dict = {}\\n else:\\n ret_dict = {\\\"cmake_find_package\\\": default_build_modules_value,\\n \\\"cmake_find_package_multi\\\": default_build_modules_value,\\n \\\"cmake\\\": default_build_modules_value,\\n \\\"cmake_multi\\\": default_build_modules_value}\\n\\n for generator, values in self._generator_properties.items():\\n if generator:\\n v = values.get(\\\"cmake_build_modules\\\")\\n if v:\\n ret_dict[generator] = v\\n self._build_modules = ret_dict if ret_dict else self.build_modules\\n return self._build_modules\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.set_property\",\n \"text\": \"conans/model/build_info.py::_CppInfo.set_property\\n def set_property(self, property_name, value, generator=None):\\n self._generator_properties.setdefault(generator, {})[property_name] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.get_property\",\n \"text\": \"conans/model/build_info.py::_CppInfo.get_property\\n def get_property(self, property_name, generator=None):\\n if generator:\\n try:\\n return self._generator_properties[generator][property_name]\\n except KeyError:\\n pass\\n try:\\n return self._generator_properties[None][property_name]\\n except KeyError:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.get_cppflags\",\n \"text\": \"conans/model/build_info.py::_CppInfo.get_cppflags\\n def get_cppflags(self):\\n conan_v2_error(\\\"'cpp_info.cppflags' is deprecated, use 'cxxflags' instead\\\")\\n return self.cxxflags\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::_CppInfo.set_cppflags\",\n \"text\": \"conans/model/build_info.py::_CppInfo.set_cppflags\\n def set_cppflags(self, value):\\n conan_v2_error(\\\"'cpp_info.cppflags' is deprecated, use 'cxxflags' instead\\\")\\n self.cxxflags = value\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::Component.__init__\",\n \"text\": \"conans/model/build_info.py::Component.__init__\\n def __init__(self, rootpath, version, default_values):\\n super(Component, self).__init__()\\n self.rootpath = rootpath\\n if default_values.includedir is not None:\\n self.includedirs.append(default_values.includedir)\\n if default_values.libdir is not None:\\n self.libdirs.append(default_values.libdir)\\n if default_values.bindir is not None:\\n self.bindirs.append(default_values.bindir)\\n if default_values.resdir is not None:\\n self.resdirs.append(default_values.resdir)\\n if default_values.builddir is not None:\\n self.builddirs.append(default_values.builddir)\\n if default_values.frameworkdir is not None:\\n self.frameworkdirs.append(default_values.frameworkdir)\\n self.requires = []\\n self.version = version\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::CppInfoDefaultValues.__init__\",\n \"text\": \"conans/model/build_info.py::CppInfoDefaultValues.__init__\\n def __init__(self, includedir=None, libdir=None, bindir=None,\\n resdir=None, builddir=None, frameworkdir=None):\\n self.includedir = includedir\\n self.libdir = libdir\\n self.bindir = bindir\\n self.resdir = resdir\\n self.builddir = builddir\\n self.frameworkdir = frameworkdir\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::CppInfo.__init__\",\n \"text\": \"conans/model/build_info.py::CppInfo.__init__\\n def __init__(self, ref_name, root_folder, default_values=None):\\n super(CppInfo, self).__init__()\\n self._ref_name = ref_name\\n self._name = ref_name\\n self.rootpath = root_folder # the full path of the package in which the conans is found\\n self._default_values = default_values or CppInfoDefaultValues(DEFAULT_INCLUDE, DEFAULT_LIB,\\n DEFAULT_BIN, DEFAULT_RES,\\n DEFAULT_BUILD,\\n DEFAULT_FRAMEWORK)\\n if self._default_values.includedir is not None:\\n self.includedirs.append(self._default_values.includedir)\\n if self._default_values.libdir is not None:\\n self.libdirs.append(self._default_values.libdir)\\n if self._default_values.bindir is not None:\\n self.bindirs.append(self._default_values.bindir)\\n if self._default_values.resdir is not None:\\n self.resdirs.append(self._default_values.resdir)\\n if self._default_values.builddir is not None:\\n self.builddirs.append(self._default_values.builddir)\\n if self._default_values.frameworkdir is not None:\\n self.frameworkdirs.append(self._default_values.frameworkdir)\\n self.components = DefaultOrderedDict(lambda: Component(self.rootpath,\\n self.version, self._default_values))\\n # public_deps is needed to accumulate list of deps for cmake targets\\n self.public_deps = []\\n self._configs = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::CppInfo.__str__\",\n \"text\": \"conans/model/build_info.py::CppInfo.__str__\\n def __str__(self):\\n return self._ref_name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::CppInfo.get_name\",\n \"text\": \"conans/model/build_info.py::CppInfo.get_name\\n def get_name(self, generator, default_name=True):\\n name = super(CppInfo, self).get_name(generator, default_name=default_name)\\n\\n # Legacy logic for pkg_config generator\\n from conans.client.generators.pkg_config import PkgConfigGenerator\\n if generator == PkgConfigGenerator.name:\\n fallback = self._name.lower() if self._name != self._ref_name else self._ref_name\\n if PkgConfigGenerator.name not in self.names and self._name != self._name.lower():\\n conan_v2_error(\\\"Generated file and name for {gen} generator will change in\\\"\\n \\\" Conan v2 to '{name}'. Use 'self.cpp_info.names[\\\\\\\"{gen}\\\\\\\"]\\\"\\n \\\" = \\\\\\\"{fallback}\\\\\\\"' in your recipe to continue using current name.\\\"\\n .format(gen=PkgConfigGenerator.name, name=name, fallback=fallback))\\n name = self.names.get(generator, fallback)\\n return name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::CppInfo.configs\",\n \"text\": \"conans/model/build_info.py::CppInfo.configs\\n @property\\n def configs(self):\\n return self._configs\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::CppInfo.__getattr__\",\n \"text\": \"conans/model/build_info.py::CppInfo.__getattr__\\n def __getattr__(self, config):\\n def _get_cpp_info():\\n result = _CppInfo()\\n result.filter_empty = self.filter_empty\\n result.rootpath = self.rootpath\\n result.sysroot = self.sysroot\\n result.includedirs.append(self._default_values.includedir)\\n result.libdirs.append(self._default_values.libdir)\\n result.bindirs.append(self._default_values.bindir)\\n result.resdirs.append(self._default_values.resdir)\\n result.builddirs.append(self._default_values.builddir)\\n result.frameworkdirs.append(self._default_values.frameworkdir)\\n return result\\n\\n return self._configs.setdefault(config, _get_cpp_info())\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::CppInfo.__getattr__._get_cpp_info\",\n \"text\": \"conans/model/build_info.py::CppInfo.__getattr__._get_cpp_info\\n def _get_cpp_info():\\n result = _CppInfo()\\n result.filter_empty = self.filter_empty\\n result.rootpath = self.rootpath\\n result.sysroot = self.sysroot\\n result.includedirs.append(self._default_values.includedir)\\n result.libdirs.append(self._default_values.libdir)\\n result.bindirs.append(self._default_values.bindir)\\n result.resdirs.append(self._default_values.resdir)\\n result.builddirs.append(self._default_values.builddir)\\n result.frameworkdirs.append(self._default_values.frameworkdir)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/build_info.py::CppInfo._raise_incorrect_components_definition\",\n \"text\": \"conans/model/build_info.py::CppInfo._raise_incorrect_components_definition\\n def _raise_incorrect_components_definition(self, package_name, package_requires):\\n if not self.components and not self.requires:\\n return\\n\\n # Raise if mixing components\\n if self.components and \\\\\\n (self.includedirs != ([self._default_values.includedir]\\n if self._default_values.includedir is not None else []) or\\n self.libdirs != ([self._default_values.libdir]\\n if self._default_values.libdir is not None else []) or\\n self.bindirs != ([self._default_values.bindir]\\n if self._default_values.bindir is not None else []) or\\n self.resdirs != ([self._default_values.resdir]\\n if self._default_values.resdir is not None else []) or\\n self.builddirs != ([self._default_values.builddir]\\n if self._default_values.builddir is not None else []) or\\n self.frameworkdirs != ([self._default_values.frameworkdir]\\n if self._default_values.frameworkdir is not None else []) or\\n self.libs or\\n self.system_libs or\\n self.frameworks or\\n self.defines or\\n self.cflags or\\n self.cxxflags or\\n self.sharedlinkflags or\\n self.exelinkflags or\\n self.get_build_modules() or\\n self.requires):\\n raise ConanException(\\\"self.cpp_info.components cannot be used with self.cpp_info \\\"\\n \\\"global values at the same time\\\")\\n if self._configs:\\n raise ConanException(\\\"self.cpp_info.components cannot be used with self.cpp_info configs\\\"\\n \\\" (release/debug/...) at the same time\\\")\\n\\n pkg_requires = [require.ref.name for require in package_requires.values()]\\n\\n def _check_components_requires_instersection(comp_requires):\\n reqs = [it.split(COMPON\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_options\",\n \"text\": \"conans/model/conan_file.py::create_options\\ndef create_options(conanfile):\\n try:\\n package_options = PackageOptions(getattr(conanfile, \\\"options\\\", None))\\n options = Options(package_options)\\n\\n default_options = getattr(conanfile, \\\"default_options\\\", None)\\n if default_options:\\n if isinstance(default_options, dict):\\n default_values = OptionsValues(default_options)\\n elif isinstance(default_options, (list, tuple)):\\n conan_v2_error(\\\"Declare 'default_options' as a dictionary\\\")\\n default_values = OptionsValues(default_options)\\n elif isinstance(default_options, six.string_types):\\n conan_v2_error(\\\"Declare 'default_options' as a dictionary\\\")\\n default_values = OptionsValues.loads(default_options)\\n else:\\n raise ConanException(\\\"Please define your default_options as list, \\\"\\n \\\"multiline string or dictionary\\\")\\n options.values = default_values\\n return options\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing options. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_requirements\",\n \"text\": \"conans/model/conan_file.py::create_requirements\\ndef create_requirements(conanfile):\\n try:\\n # Actual requirements of this package\\n if not hasattr(conanfile, \\\"requires\\\"):\\n return Requirements()\\n else:\\n if not conanfile.requires:\\n return Requirements()\\n if isinstance(conanfile.requires, (tuple, list)):\\n return Requirements(*conanfile.requires)\\n else:\\n return Requirements(conanfile.requires, )\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing requirements. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_settings\",\n \"text\": \"conans/model/conan_file.py::create_settings\\ndef create_settings(conanfile, settings):\\n try:\\n defined_settings = getattr(conanfile, \\\"settings\\\", None)\\n if isinstance(defined_settings, str):\\n defined_settings = [defined_settings]\\n current = defined_settings or {}\\n settings.constraint(current)\\n return settings\\n except Exception as e:\\n raise ConanInvalidConfiguration(\\\"The recipe %s is constraining settings. %s\\\" % (\\n conanfile.display_name, str(e)))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::_env_and_python\",\n \"text\": \"conans/model/conan_file.py::_env_and_python\\n@contextmanager\\ndef _env_and_python(conanfile):\\n with environment_append(conanfile.env):\\n # FIXME Conan 2.0, Remove old ways of reusing python code\\n with pythonpath(conanfile):\\n yield\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::get_env_context_manager\",\n \"text\": \"conans/model/conan_file.py::get_env_context_manager\\ndef get_env_context_manager(conanfile, without_python=False):\\n if not conanfile.apply_env:\\n return no_op()\\n if without_python:\\n return environment_append(conanfile.env)\\n return _env_and_python(conanfile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.__init__\",\n \"text\": \"conans/model/conan_file.py::ConanFile.__init__\\n def __init__(self, output, runner, display_name=\\\"\\\", user=None, channel=None):\\n # an output stream (writeln, info, warn error)\\n self.output = ScopedOutput(display_name, output)\\n self.display_name = display_name\\n # something that can run commands, as os.sytem\\n self._conan_runner = runner\\n self._conan_user = user\\n self._conan_channel = channel\\n\\n self.compatible_packages = []\\n self._conan_using_build_profile = False\\n self._conan_requester = None\\n\\n self.buildenv_info = Environment(self)\\n self.runenv_info = Environment(self)\\n # At the moment only for build_requires, others will be ignored\\n self.conf_info = Conf()\\n self._conan_buildenv = None # The profile buildenv, will be assigned initialize()\\n self._conan_node = None # access to container Node object, to access info, context, deps...\\n self._conan_new_cpp_info = None # Will be calculated lazy in the getter\\n self._conan_dependencies = None\\n\\n self.environment_scripts = [] # Accumulate the env scripts generated in order\\n\\n # layout() method related variables:\\n self.folders = Folders()\\n self.patterns = Patterns()\\n self.cpp = Infos()\\n\\n self.patterns.source.include = [\\\"*.h\\\", \\\"*.hpp\\\", \\\"*.hxx\\\"]\\n self.patterns.source.lib = []\\n self.patterns.source.bin = []\\n\\n self.patterns.build.include = [\\\"*.h\\\", \\\"*.hpp\\\", \\\"*.hxx\\\"]\\n self.patterns.build.lib = [\\\"*.so\\\", \\\"*.so.*\\\", \\\"*.a\\\", \\\"*.lib\\\", \\\"*.dylib\\\"]\\n self.patterns.build.bin = [\\\"*.exe\\\", \\\"*.dll\\\"]\\n\\n self.cpp.package.includedirs = [\\\"include\\\"]\\n self.cpp.package.libdirs = [\\\"lib\\\"]\\n self.cpp.package.bindirs = [\\\"bin\\\"]\\n self.cpp.package.resdirs = [\\\"res\\\"]\\n self.cpp.package.builddirs = [\\\"\\\"]\\n self.cpp.package.frameworkdirs = [\\\"Frameworks\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.context\",\n \"text\": \"conans/model/conan_file.py::ConanFile.context\\n @property\\n def context(self):\\n return self._conan_node.context\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.dependencies\",\n \"text\": \"conans/model/conan_file.py::ConanFile.dependencies\\n @property\\n def dependencies(self):\\n # Caching it, this object is requested many times\\n if self._conan_dependencies is None:\\n self._conan_dependencies = ConanFileDependencies.from_node(self._conan_node)\\n return self._conan_dependencies\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.ref\",\n \"text\": \"conans/model/conan_file.py::ConanFile.ref\\n @property\\n def ref(self):\\n return self._conan_node.ref\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.pref\",\n \"text\": \"conans/model/conan_file.py::ConanFile.pref\\n @property\\n def pref(self):\\n return self._conan_node.pref\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.buildenv\",\n \"text\": \"conans/model/conan_file.py::ConanFile.buildenv\\n @property\\n def buildenv(self):\\n # Lazy computation of the package buildenv based on the profileone\\n if not isinstance(self._conan_buildenv, Environment):\\n # TODO: missing user/channel\\n ref_str = \\\"{}/{}\\\".format(self.name, self.version)\\n self._conan_buildenv = self._conan_buildenv.get_env(self, ref_str)\\n return self._conan_buildenv\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.initialize\",\n \"text\": \"conans/model/conan_file.py::ConanFile.initialize\\n def initialize(self, settings, env, buildenv=None):\\n self._conan_buildenv = buildenv\\n if isinstance(self.generators, str):\\n self.generators = [self.generators]\\n # User defined options\\n self.options = create_options(self)\\n self.requires = create_requirements(self)\\n self.settings = create_settings(self, settings)\\n\\n conan_v2_error(\\\"Setting 'cppstd' is deprecated in favor of 'compiler.cppstd',\\\"\\n \\\" please update your recipe.\\\", 'cppstd' in self.settings.fields)\\n\\n # needed variables to pack the project\\n self.cpp_info = None # Will be initialized at processing time\\n self._conan_dep_cpp_info = None # Will be initialized at processing time\\n self.deps_cpp_info = DepsCppInfo()\\n\\n # environment variables declared in the package_info\\n self.env_info = None # Will be initialized at processing time\\n self.deps_env_info = DepsEnvInfo()\\n\\n # user declared variables\\n self.user_info = None\\n # Keys are the package names (only 'host' if different contexts)\\n self.deps_user_info = DepsUserInfo()\\n\\n # user specified env variables\\n self._conan_env_values = env.copy() # user specified -e\\n\\n if self.description is not None and not isinstance(self.description, six.string_types):\\n raise ConanException(\\\"Recipe 'description' must be a string.\\\")\\n\\n if not hasattr(self, \\\"virtualenv\\\"): # Allow the user to override it with True or False\\n self.virtualenv = True\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.new_cpp_info\",\n \"text\": \"conans/model/conan_file.py::ConanFile.new_cpp_info\\n @property\\n def new_cpp_info(self):\\n if not self._conan_new_cpp_info:\\n self._conan_new_cpp_info = from_old_cppinfo(self.cpp_info)\\n return self._conan_new_cpp_info\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.source_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.source_folder\\n @property\\n def source_folder(self):\\n return self.folders.source_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.source_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.source_folder\\n @source_folder.setter\\n def source_folder(self, folder):\\n self.folders.set_base_source(folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_folder\\n @property\\n def build_folder(self):\\n return self.folders.build_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_folder\\n @build_folder.setter\\n def build_folder(self, folder):\\n self.folders.set_base_build(folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_folder\\n @property\\n def package_folder(self):\\n return self.folders.package_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_folder\\n @package_folder.setter\\n def package_folder(self, folder):\\n self.folders.set_base_package(folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.install_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.install_folder\\n @property\\n def install_folder(self):\\n # FIXME: Remove in 2.0, no self.install_folder\\n return self.folders.base_install\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.install_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.install_folder\\n @install_folder.setter\\n def install_folder(self, folder):\\n # FIXME: Remove in 2.0, no self.install_folder\\n self.folders.set_base_install(folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.generators_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.generators_folder\\n @property\\n def generators_folder(self):\\n # FIXME: Remove in 2.0, no self.install_folder\\n return self.folders.generators_folder if self.folders.generators else self.install_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.imports_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.imports_folder\\n @property\\n def imports_folder(self):\\n return self.folders.imports_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.imports_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.imports_folder\\n @imports_folder.setter\\n def imports_folder(self, folder):\\n self.folders.set_base_imports(folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.env\",\n \"text\": \"conans/model/conan_file.py::ConanFile.env\\n @property\\n def env(self):\\n \\\"\\\"\\\"Apply the self.deps_env_info into a copy of self._conan_env_values (will prioritize the\\n self._conan_env_values, user specified from profiles or -e first, then inherited)\\\"\\\"\\\"\\n # Cannot be lazy cached, because it's called in configure node, and we still don't have\\n # the deps_env_info objects available\\n tmp_env_values = self._conan_env_values.copy()\\n tmp_env_values.update(self.deps_env_info)\\n ret, multiple = tmp_env_values.env_dicts(self.name, self.version, self._conan_user,\\n self._conan_channel)\\n ret.update(multiple)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.channel\",\n \"text\": \"conans/model/conan_file.py::ConanFile.channel\\n @property\\n def channel(self):\\n if not self._conan_channel:\\n _env_channel = os.getenv(\\\"CONAN_CHANNEL\\\")\\n conan_v2_error(\\\"Environment variable 'CONAN_CHANNEL' is deprecated\\\", _env_channel)\\n self._conan_channel = _env_channel or self.default_channel\\n if not self._conan_channel:\\n raise ConanException(\\\"channel not defined, but self.channel is used in conanfile\\\")\\n return self._conan_channel\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.user\",\n \"text\": \"conans/model/conan_file.py::ConanFile.user\\n @property\\n def user(self):\\n if not self._conan_user:\\n _env_username = os.getenv(\\\"CONAN_USERNAME\\\")\\n conan_v2_error(\\\"Environment variable 'CONAN_USERNAME' is deprecated\\\", _env_username)\\n self._conan_user = _env_username or self.default_user\\n if not self._conan_user:\\n raise ConanException(\\\"user not defined, but self.user is used in conanfile\\\")\\n return self._conan_user\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.collect_libs\",\n \"text\": \"conans/model/conan_file.py::ConanFile.collect_libs\\n def collect_libs(self, folder=None):\\n conan_v2_error(\\\"'self.collect_libs' is deprecated, use 'tools.collect_libs(self)' instead\\\")\\n return tools.collect_libs(self, folder=folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_policy_missing\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_policy_missing\\n @property\\n def build_policy_missing(self):\\n return self.build_policy == \\\"missing\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_policy_always\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_policy_always\\n @property\\n def build_policy_always(self):\\n return self.build_policy == \\\"always\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.source\",\n \"text\": \"conans/model/conan_file.py::ConanFile.source\\n def source(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.system_requirements\",\n \"text\": \"conans/model/conan_file.py::ConanFile.system_requirements\\n def system_requirements(self):\\n \\\"\\\"\\\" this method can be overwritten to implement logic for system package\\n managers, as apt-get\\n\\n You can define self.global_system_requirements = True, if you want the installation\\n to be for all packages (not depending on settings/options/requirements)\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.config_options\",\n \"text\": \"conans/model/conan_file.py::ConanFile.config_options\\n def config_options(self):\\n \\\"\\\"\\\" modify options, probably conditioned to some settings. This call is executed\\n before config_settings. E.g.\\n if self.settings.os == \\\"Windows\\\":\\n del self.options.shared # shared/static not supported in win\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.configure\",\n \"text\": \"conans/model/conan_file.py::ConanFile.configure\\n def configure(self):\\n \\\"\\\"\\\" modify settings, probably conditioned to some options. This call is executed\\n after config_options. E.g.\\n if self.options.header_only:\\n self.settings.clear()\\n This is also the place for conditional requirements\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build\\n def build(self):\\n \\\"\\\"\\\" build your project calling the desired build tools as done in the command line.\\n E.g. self.run(\\\"cmake --build .\\\") Or use the provided build helpers. E.g. cmake.build()\\n \\\"\\\"\\\"\\n self.output.warn(\\\"This conanfile has no build step\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package\\n def package(self):\\n \\\"\\\"\\\" package the needed files from source and build folders.\\n E.g. self.copy(\\\"*.h\\\", src=\\\"src/includes\\\", dst=\\\"includes\\\")\\n \\\"\\\"\\\"\\n self.output.warn(\\\"This conanfile has no package step\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_info\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_info\\n def package_info(self):\\n \\\"\\\"\\\" define cpp_build_info, flags, etc\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.run\",\n \"text\": \"conans/model/conan_file.py::ConanFile.run\\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True,\\n ignore_errors=False, run_environment=False, with_login=True, env=None):\\n # NOTE: \\\"self.win_bash\\\" is the new parameter \\\"win_bash\\\" for Conan 2.0\\n\\n def _run(cmd, _env):\\n # FIXME: run in windows bash is not using output\\n if platform.system() == \\\"Windows\\\":\\n if win_bash:\\n return tools.run_in_windows_bash(self, bashcmd=cmd, cwd=cwd, subsystem=subsystem,\\n msys_mingw=msys_mingw, with_login=with_login)\\n elif self.win_bash: # New, Conan 2.0\\n from conan.tools.microsoft.subsystems import run_in_windows_bash\\n return run_in_windows_bash(self, command=cmd, cwd=cwd, env=_env)\\n _env = _env or \\\"conanenv\\\"\\n wrapped_cmd = environment_wrap_command(self, _env, cmd, cwd=self.generators_folder)\\n return self._conan_runner(wrapped_cmd, output, os.path.abspath(RUN_LOG_NAME), cwd)\\n\\n if run_environment:\\n # When using_build_profile the required environment is already applied through\\n # 'conanfile.env' in the contextmanager 'get_env_context_manager'\\n with tools.run_environment(self) if not self._conan_using_build_profile else no_op():\\n if OSInfo().is_macos and isinstance(command, string_types):\\n # Security policy on macOS clears this variable when executing /bin/sh. To\\n # keep its value, set it again inside the shell when running the command.\\n command = 'DYLD_LIBRARY_PATH=\\\"%s\\\" DYLD_FRAMEWORK_PATH=\\\"%s\\\" %s' % \\\\\\n (os.environ.get('DYLD_LIBRARY_PATH', ''),\\n os.environ.get(\\\"DYLD_FRAMEWORK_PATH\\\", ''),\\n command)\\n retcode = _run(command, en\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.run._run\",\n \"text\": \"conans/model/conan_file.py::ConanFile.run._run\\n def _run(cmd, _env):\\n # FIXME: run in windows bash is not using output\\n if platform.system() == \\\"Windows\\\":\\n if win_bash:\\n return tools.run_in_windows_bash(self, bashcmd=cmd, cwd=cwd, subsystem=subsystem,\\n msys_mingw=msys_mingw, with_login=with_login)\\n elif self.win_bash: # New, Conan 2.0\\n from conan.tools.microsoft.subsystems import run_in_windows_bash\\n return run_in_windows_bash(self, command=cmd, cwd=cwd, env=_env)\\n _env = _env or \\\"conanenv\\\"\\n wrapped_cmd = environment_wrap_command(self, _env, cmd, cwd=self.generators_folder)\\n return self._conan_runner(wrapped_cmd, output, os.path.abspath(RUN_LOG_NAME), cwd)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_id\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_id\\n def package_id(self):\\n \\\"\\\"\\\" modify the binary info, typically to narrow values\\n e.g.: self.info.settings.compiler = \\\"Any\\\" => All compilers will generate same ID\\n \\\"\\\"\\\"\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":72,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-2842"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"More granular callbacks\n## 🚀 Make callback system more granular\r\n\r\n### Motivation\r\n\r\nI am currently implementing #765 (make progress bar into a callback) and I need additional callback methods to do this.\r\n\r\n### Pitch\r\n\r\nintroduce these new callback methods:\r\n\r\n- `on_train_batch_start` (currently named `on_batch_start`)\r\n- `on_train_batch_end` (currently named `on_batch_end`)\r\n- `on_val_batch_start`\r\n- `on_val_batch_end`\r\n- `on_test_batch_start`\r\n- `on_test_batch_end`\r\n\r\nand make `on_batch_start` run on any of the above `*_start` (same for `on_batch_end`)\r\n\r\nFurther suggestions:\r\n- introduce `on_train_epoch_start`, `on_val_epoch_start`, `on_test_epoch_start` and corresponding `*_end` methods.\r\n\r\n\r\n### Alternatives\r\n\r\nKeep as is, but I don't know how to implement the progress bar callback otherwise for validation/test updates.\r\n\r\n\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/callbacks/base.py","pytorch_lightning/callbacks/lr_logger.py","pytorch_lightning/callbacks/progress.py","pytorch_lightning/core/hooks.py","pytorch_lightning/core/lightning.py","pytorch_lightning/trainer/callback_hook.py","pytorch_lightning/trainer/lr_finder.py","pytorch_lightning/trainer/training_loop.py"],"string":"[\n \"pytorch_lightning/callbacks/base.py\",\n \"pytorch_lightning/callbacks/lr_logger.py\",\n \"pytorch_lightning/callbacks/progress.py\",\n \"pytorch_lightning/core/hooks.py\",\n \"pytorch_lightning/core/lightning.py\",\n \"pytorch_lightning/trainer/callback_hook.py\",\n \"pytorch_lightning/trainer/lr_finder.py\",\n \"pytorch_lightning/trainer/training_loop.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/callbacks/base.py::<module>","pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_batch_start","pytorch_lightning/callbacks/progress.py::<module>","pytorch_lightning/callbacks/progress.py::ProgressBar.on_batch_end","pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_batch_end","pytorch_lightning/core/hooks.py::<module>","pytorch_lightning/core/hooks.py::ModelHooks.on_batch_end","pytorch_lightning/core/hooks.py::ModelHooks.on_batch_start","pytorch_lightning/core/lightning.py::LightningModule.to_onnx","pytorch_lightning/trainer/callback_hook.py::<module>","pytorch_lightning/trainer/lr_finder.py::_LRCallback.on_batch_end","pytorch_lightning/trainer/training_loop.py::<module>","pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch"],"string":"[\n \"pytorch_lightning/callbacks/base.py::<module>\",\n \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_batch_start\",\n \"pytorch_lightning/callbacks/progress.py::<module>\",\n \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_batch_end\",\n \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_batch_end\",\n \"pytorch_lightning/core/hooks.py::<module>\",\n \"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_end\",\n \"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_start\",\n \"pytorch_lightning/core/lightning.py::LightningModule.to_onnx\",\n \"pytorch_lightning/trainer/callback_hook.py::<module>\",\n \"pytorch_lightning/trainer/lr_finder.py::_LRCallback.on_batch_end\",\n \"pytorch_lightning/trainer/training_loop.py::<module>\",\n \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"pytorch_lightning/callbacks/base.py","text":"pytorch_lightning/callbacks/base.py\nclass Callback # Abstract base class used to build new callbacks.\n def setup(self, trainer, stage: str) # Called when fit or test begins\n def teardown(self, trainer, stage: str) # Called when fit or test ends\n def on_init_start(self, trainer) # Called when the trainer initialization begins, model has not yet been set.\n def on_init_end(self, trainer) # Called when the trainer initialization ends, model has not yet been set.\n def on_fit_start(self, trainer) # Called when fit begins\n def on_fit_end(self, trainer) # Called when fit ends\n def on_sanity_check_start(self, trainer, pl_module) # Called when the validation sanity check starts.\n def on_sanity_check_end(self, trainer, pl_module) # Called when the validation sanity check ends.\n def on_train_epoch_start(self, trainer, pl_module) # Called when the train epoch begins.\n def on_train_epoch_end(self, trainer, pl_module) # Called when the train epoch ends.\n def on_validation_epoch_start(self, trainer, pl_module) # Called when the val epoch begins.\n def on_validation_epoch_end(self, trainer, pl_module) # Called when the val epoch ends.\n def on_test_epoch_start(self, trainer, pl_module) # Called when the test epoch begins.\n def on_test_epoch_end(self, trainer, pl_module) # Called when the test epoch ends.\n def on_epoch_start(self, trainer, pl_module) # Called when the epoch begins.\n def on_epoch_end(self, trainer, pl_module) # Called when the epoch ends.\n def on_batch_start(self, trainer, pl_module) # Called when the training batch begins.\n def on_validation_batch_start(self, trainer, pl_module) # Called when the validation batch begins.\n def on_validation_batch_end(self, trainer, pl_module) # Called when the validation batch ends.\n def on_test_batch_start(self, trainer, pl_module) # Called when the test batch begins.\n def on_test_batch_end(self, trainer, pl_module) # Called when the test batch ends.\n def on_batch_end(self, trainer, pl_module) # Called when the training batch ends.\n def on_train_start(self, trainer, pl_module) # Called when the train begins.\n def on_train_end(self, trainer, pl_module) # Called when the train ends.\n def on_validation_start(self, trainer, pl_module) # Called when the validation loop begins.\n def on_validation_end(self, trainer, pl_module) # Called when the validation loop ends.\n def on_test_start(self, trainer, pl_module) # Called when the test begins.\n def on_test_end(self, trainer, pl_module) # Called when the test ends.\n def on_keyboard_interrupt(self, trainer, pl_module) # Called when the training is interrupted by KeyboardInterrupt.","label":1},{"cid":"pytorch_lightning/callbacks/lr_logger.py","text":"pytorch_lightning/callbacks/lr_logger.py\nclass LearningRateLogger # Automatically logs learning rate for learning rate schedulers during training.\n def __init__(self)\n def on_train_start(self, trainer, pl_module) # Called before training, determines unique names for all lr\n def on_batch_start(self, trainer, pl_module)\n def on_epoch_start(self, trainer, pl_module)\n def _extract_lr(self, trainer, interval) # Extracts learning rates for lr schedulers and saves information\n def _find_names(self, lr_schedulers)","label":1},{"cid":"pytorch_lightning/callbacks/progress.py","text":"pytorch_lightning/callbacks/progress.py\nclass ProgressBarBase # The base class for progress bars in Lightning. It is a :class:`~pytorch_lightnin\n def __init__(self)\n def trainer(self)\n def train_batch_idx(self) # The current batch index being processed during training.\n def val_batch_idx(self) # The current batch index being processed during validation.\n def test_batch_idx(self) # The current batch index being processed during testing.\n def total_train_batches(self) # The total number of training batches during training, which may change from epoc\n def total_val_batches(self) # The total number of training batches during validation, which may change from ep\n def total_test_batches(self) # The total number of training batches during testing, which may change from epoch\n def disable(self) # You should provide a way to disable the progress bar.\n def enable(self) # You should provide a way to enable the progress bar.\n def on_init_end(self, trainer)\n def on_train_start(self, trainer, pl_module)\n def on_epoch_start(self, trainer, pl_module)\n def on_batch_end(self, trainer, pl_module)\n def on_validation_start(self, trainer, pl_module)\n def on_validation_batch_end(self, trainer, pl_module)\n def on_test_start(self, trainer, pl_module)\n def on_test_batch_end(self, trainer, pl_module)\nclass ProgressBar # This is the default progress bar used by Lightning. It prints to `stdout` using \n def __init__(self, refresh_rate: int=1, process_position: int=0)\n def __getstate__(self)\n def refresh_rate(self)\n def process_position(self)\n def is_enabled(self)\n def is_disabled(self)\n def disable(self)\n def enable(self)\n def init_sanity_tqdm(self) # Override this to customize the tqdm bar for the validation sanity run.\n def init_train_tqdm(self) # Override this to customize the tqdm bar for training.\n def init_validation_tqdm(self) # Override this to customize the tqdm bar for validation.\n def init_test_tqdm(self) # Override this to customize the tqdm bar for testing.\n def on_sanity_check_start(self, trainer, pl_module)\n def on_sanity_check_end(self, trainer, pl_module)\n def on_train_start(self, trainer, pl_module)\n def on_epoch_start(self, trainer, pl_module)\n def on_batch_end(self, trainer, pl_module)\n def on_validation_start(self, trainer, pl_module)\n def on_validation_batch_end(self, trainer, pl_module)\n def on_validation_end(self, trainer, pl_module)\n def on_train_end(self, trainer, pl_module)\n def on_test_start(self, trainer, pl_module)\n def on_test_batch_end(self, trainer, pl_module)\n def on_test_end(self, trainer, pl_module)\ndef convert_inf(x) # The tqdm doesn't support inf values. We have to convert it to None.","label":1},{"cid":"pytorch_lightning/core/hooks.py","text":"pytorch_lightning/core/hooks.py\nclass ModelHooks\n def setup(self, stage: str) # Called at the beginning of fit and test.\n def teardown(self, stage: str) # Called at the end of fit and test.\n def on_fit_start(self) # Called at the very beginning of fit.\n def on_fit_end(self) # Called at the very end of fit.\n def on_train_start(self) # Called at the beginning of training before sanity check.\n def on_train_end(self) # Called at the end of training before logger experiment is closed.\n def on_batch_start(self, batch: Any) # Called in the training loop before anything happens for that batch.\n def on_batch_end(self) # Called in the training loop after the batch.\n def on_epoch_start(self) # Called in the training loop at the very beginning of the epoch.\n def on_epoch_end(self) # Called in the training loop at the very end of the epoch.\n def on_train_epoch_start(self) # Called in the training loop at the very beginning of the epoch.\n def on_train_epoch_end(self) # Called in the training loop at the very end of the epoch.\n def on_validation_epoch_start(self) # Called in the validation loop at the very beginning of the epoch.\n def on_validation_epoch_end(self) # Called in the validation loop at the very end of the epoch.\n def on_test_epoch_start(self) # Called in the test loop at the very beginning of the epoch.\n def on_test_epoch_end(self) # Called in the test loop at the very end of the epoch.\n def on_pre_performance_check(self) # Called at the very beginning of the validation loop.\n def on_post_performance_check(self) # Called at the very end of the validation loop.\n def on_before_zero_grad(self, optimizer: Optimizer) # Called after optimizer.step() and before optimizer.zero_grad().\n def on_after_backward(self) # Called in the training loop after loss.backward() and before optimizers do anyth\n def backward(self, trainer, loss: Tensor, optimizer: Optimizer, optimizer_idx: int) # Override backward with your own implementation if you need to.\n def amp_scale_loss(self, unscaled_loss, optimizer, optimizer_idx)\n def transfer_batch_to_device(self, batch: Any, device: torch.device) # Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors","label":1},{"cid":"pytorch_lightning/core/lightning.py","text":"pytorch_lightning/core/lightning.py\nclass LightningModule\n def __init__(self, *args, **kwargs)\n def example_input_array(self)\n def example_input_array(self, example: Any)\n def datamodule(self)\n def datamodule(self, datamodule: Any)\n def on_gpu(self) # True if your model is currently running on GPUs.\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once.\n def forward(self, *args, **kwargs) # Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to\n def training_step(self, *args, **kwargs) # Here you compute and return the training loss and some additional metrics for e.\n def training_end(self, *args, **kwargs) # Warnings:\n def training_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the training epoch with the outputs of all training steps.\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because :meth:`training_step`\n def validation_step(self, *args, **kwargs) # Operates on a single batch of data from the validation set.\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because :meth:`validation_step`\n def validation_end(self, outputs) # Warnings:\n def validation_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the validation epoch with the outputs of all validation ste\n def test_step(self, *args, **kwargs) # Operates on a single batch of data from the test set.\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because :meth:`test_step` will operate\n def test_end(self, outputs) # Warnings:\n def test_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of a test epoch with the output of all test steps.\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) # Override to init DDP in your own way or with your own wrapper.\n def _init_slurm_connection(self) # Sets up environment variables necessary for pytorch distributed communications\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool=True) # Override to define your custom way of setting up a distributed environment.\n def configure_sync_batchnorm(self, model: 'LightningModule') # Add global batchnorm for a model spread across multiple GPUs and nodes.\n def configure_apex(self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str) # Override to init AMP your own way.\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\n def optimizer_step(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int, second_order_closure: Optional[Callable]=None, on_tpu: bool=False, u","label":1},{"cid":"pytorch_lightning/trainer/callback_config.py","text":"pytorch_lightning/trainer/callback_config.py\nclass TrainerCallbackConfigMixin\n def slurm_job_id(self) # Warning: this is just empty shell for code implemented in other class.\n def save_checkpoint(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def configure_checkpoint_callback(self, checkpoint_callback)\n def configure_early_stopping(self, early_stop_callback)\n def configure_progress_bar(self, refresh_rate=1, process_position=0)","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py","text":"pytorch_lightning/trainer/callback_hook.py\nclass TrainerCallbackHookMixin\n def setup(self, stage: str) # Called in the beginning of fit and test\n def teardown(self, stage: str) # Called at the end of fit and test\n def on_init_start(self) # Called when the trainer initialization begins, model has not yet been set.\n def on_init_end(self) # Called when the trainer initialization ends, model has not yet been set.\n def on_fit_start(self) # Called when the trainer initialization begins, model has not yet been set.\n def on_fit_end(self) # Called when the trainer initialization begins, model has not yet been set.\n def on_sanity_check_start(self) # Called when the validation sanity check starts.\n def on_sanity_check_end(self) # Called when the validation sanity check ends.\n def on_train_epoch_start(self) # Called when the epoch begins.\n def on_train_epoch_end(self) # Called when the epoch ends.\n def on_validation_epoch_start(self) # Called when the epoch begins.\n def on_validation_epoch_end(self) # Called when the epoch ends.\n def on_test_epoch_start(self) # Called when the epoch begins.\n def on_test_epoch_end(self) # Called when the epoch ends.\n def on_epoch_start(self) # Called when the epoch begins.\n def on_epoch_end(self) # Called when the epoch ends.\n def on_train_start(self) # Called when the train begins.\n def on_train_end(self) # Called when the train ends.\n def on_batch_start(self) # Called when the training batch begins.\n def on_batch_end(self) # Called when the training batch ends.\n def on_validation_batch_start(self) # Called when the validation batch begins.\n def on_validation_batch_end(self) # Called when the validation batch ends.\n def on_test_batch_start(self) # Called when the test batch begins.\n def on_test_batch_end(self) # Called when the test batch ends.\n def on_validation_start(self) # Called when the validation loop begins.\n def on_validation_end(self) # Called when the validation loop ends.\n def on_test_start(self) # Called when the test begins.\n def on_test_end(self) # Called when the test ends.\n def on_keyboard_interrupt(self) # Called when the training is interrupted by KeyboardInterrupt.","label":1},{"cid":"pytorch_lightning/trainer/lr_finder.py","text":"pytorch_lightning/trainer/lr_finder.py\nclass TrainerLRFinderMixin\n def save_checkpoint(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def restore(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def init_optimizers(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def fit(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def _run_lr_finder_internally(self, model: LightningModule) # Call lr finder internally during Trainer.fit()\n def lr_find(self, model: LightningModule, train_dataloader: Optional[DataLoader]=None, val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]]=None, min_lr: float=1e-08, max_lr: float=1, num_training: int=100, mode: str='exponential', early_stop_threshold: float=4.0) # lr_find enables the user to do a range test of good initial learning rates,\n def __lr_finder_dump_params(self, model)\n def __lr_finder_restore_params(self, model)\nclass _LRFinder # LR finder object. This object stores the results of Trainer.lr_find().\n def __init__(self, mode: str, lr_min: float, lr_max: float, num_training: int)\n def _get_new_optimizer(self, optimizer: torch.optim.Optimizer) # Construct a new `configure_optimizers()` method, that has a optimizer\n def configure_optimizers()\n def plot(self, suggest: bool=False, show: bool=False) # Plot results from lr_find run\n def suggestion(self, skip_begin: int=10, skip_end: int=1) # This will propose a suggestion for choice of initial learning rate\nclass _LRCallback # Special callback used by the learning rate finder. This callbacks log\n def __init__(self, num_training: int, early_stop_threshold: float=4.0, progress_bar_refresh_rate: int=0, beta: float=0.98)\n def on_batch_start(self, trainer, pl_module) # Called before each training batch, logs the lr that will be used\n def on_batch_end(self, trainer, pl_module) # Called when the training batch ends, logs the calculated loss\nclass _LinearLR # Linearly increases the learning rate between two boundaries\n def __init__(self, optimizer: torch.optim.Optimizer, end_lr: float, num_iter: int, last_epoch: int=-1)\n def get_lr(self)\n def lr(self)\nclass _ExponentialLR # Exponentially increases the learning rate between two boundaries\n def __init__(self, optimizer: torch.optim.Optimizer, end_lr: float, num_iter: int, last_epoch: int=-1)\n def get_lr(self)\n def lr(self)\ndef _nested_hasattr(obj, path)\ndef _nested_setattr(obj, path, val)","label":1},{"cid":"pytorch_lightning/trainer/training_loop.py","text":"pytorch_lightning/trainer/training_loop.py\nclass TrainerTrainLoopMixin\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\n def is_function_implemented(self, *args, **kwargs) # Warning: this is just empty shell for code implemented in other class.\n def run_evaluation(self, *args, **kwargs) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def clip_gradients(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def detect_nan_tensors(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def add_progress_bar_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def process_output(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_train_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_val_dataloader(self, model) # Warning: this is just empty shell for code implemented in other class.\n def has_arg(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def train(self)\n def prepare_train_loop_dataloader(self, train_dataloader)\n def run_on_epoch_start_hook(self, model)\n def run_training_epoch(self)\n def check_checkpoint_callback(self, should_check_val)\n def update_train_loop_lr_schedulers(self)\n def run_on_epoch_end_hook(self, model)\n def run_training_epoch_end(self, epoch_output, checkpoint_accumulator, early_stopping_accumulator)\n def sync_horovod(self)\n def increment_accumulated_grad_global_step(self)\n def save_train_loop_metrics_to_loggers(self, batch_idx, batch_output)\n def save_loggers_in_training_loop(self, batch_idx)\n def should_check_val(self, batch_idx, is_last_batch)\n def run_training_batch(self, batch, batch_idx)\n def run_batch_backward_pass(self, split_batch, batch_idx, opt_idx, optimizer)\n def call_optimizer_step(self, optimizer, opt_idx, batch_idx, split_batch)\n def optimizer_closure(self, split_batch, batch_idx, opt_idx, optimizer, hiddens) # wrap the forward step in a closure so second order methods work\n def _get_optimizers_iterable(self)\n def run_training_teardown(self)\n def training_forward(self, batch, batch_idx, opt_idx, hiddens) # Handle forward for each training case (distributed, single gpu, etc...)\n def update_learning_rates(self, interval: str) # Update learning rates.\ndef _wit","label":1}],"string":"[\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py\",\n \"text\": \"pytorch_lightning/callbacks/base.py\\nclass Callback # Abstract base class used to build new callbacks.\\n def setup(self, trainer, stage: str) # Called when fit or test begins\\n def teardown(self, trainer, stage: str) # Called when fit or test ends\\n def on_init_start(self, trainer) # Called when the trainer initialization begins, model has not yet been set.\\n def on_init_end(self, trainer) # Called when the trainer initialization ends, model has not yet been set.\\n def on_fit_start(self, trainer) # Called when fit begins\\n def on_fit_end(self, trainer) # Called when fit ends\\n def on_sanity_check_start(self, trainer, pl_module) # Called when the validation sanity check starts.\\n def on_sanity_check_end(self, trainer, pl_module) # Called when the validation sanity check ends.\\n def on_train_epoch_start(self, trainer, pl_module) # Called when the train epoch begins.\\n def on_train_epoch_end(self, trainer, pl_module) # Called when the train epoch ends.\\n def on_validation_epoch_start(self, trainer, pl_module) # Called when the val epoch begins.\\n def on_validation_epoch_end(self, trainer, pl_module) # Called when the val epoch ends.\\n def on_test_epoch_start(self, trainer, pl_module) # Called when the test epoch begins.\\n def on_test_epoch_end(self, trainer, pl_module) # Called when the test epoch ends.\\n def on_epoch_start(self, trainer, pl_module) # Called when the epoch begins.\\n def on_epoch_end(self, trainer, pl_module) # Called when the epoch ends.\\n def on_batch_start(self, trainer, pl_module) # Called when the training batch begins.\\n def on_validation_batch_start(self, trainer, pl_module) # Called when the validation batch begins.\\n def on_validation_batch_end(self, trainer, pl_module) # Called when the validation batch ends.\\n def on_test_batch_start(self, trainer, pl_module) # Called when the test batch begins.\\n def on_test_batch_end(self, trainer, pl_module) # Called when the test batch ends.\\n def on_batch_end(self, trainer, pl_module) # Called when the training batch ends.\\n def on_train_start(self, trainer, pl_module) # Called when the train begins.\\n def on_train_end(self, trainer, pl_module) # Called when the train ends.\\n def on_validation_start(self, trainer, pl_module) # Called when the validation loop begins.\\n def on_validation_end(self, trainer, pl_module) # Called when the validation loop ends.\\n def on_test_start(self, trainer, pl_module) # Called when the test begins.\\n def on_test_end(self, trainer, pl_module) # Called when the test ends.\\n def on_keyboard_interrupt(self, trainer, pl_module) # Called when the training is interrupted by KeyboardInterrupt.\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/lr_logger.py\",\n \"text\": \"pytorch_lightning/callbacks/lr_logger.py\\nclass LearningRateLogger # Automatically logs learning rate for learning rate schedulers during training.\\n def __init__(self)\\n def on_train_start(self, trainer, pl_module) # Called before training, determines unique names for all lr\\n def on_batch_start(self, trainer, pl_module)\\n def on_epoch_start(self, trainer, pl_module)\\n def _extract_lr(self, trainer, interval) # Extracts learning rates for lr schedulers and saves information\\n def _find_names(self, lr_schedulers)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py\",\n \"text\": \"pytorch_lightning/callbacks/progress.py\\nclass ProgressBarBase # The base class for progress bars in Lightning. It is a :class:`~pytorch_lightnin\\n def __init__(self)\\n def trainer(self)\\n def train_batch_idx(self) # The current batch index being processed during training.\\n def val_batch_idx(self) # The current batch index being processed during validation.\\n def test_batch_idx(self) # The current batch index being processed during testing.\\n def total_train_batches(self) # The total number of training batches during training, which may change from epoc\\n def total_val_batches(self) # The total number of training batches during validation, which may change from ep\\n def total_test_batches(self) # The total number of training batches during testing, which may change from epoch\\n def disable(self) # You should provide a way to disable the progress bar.\\n def enable(self) # You should provide a way to enable the progress bar.\\n def on_init_end(self, trainer)\\n def on_train_start(self, trainer, pl_module)\\n def on_epoch_start(self, trainer, pl_module)\\n def on_batch_end(self, trainer, pl_module)\\n def on_validation_start(self, trainer, pl_module)\\n def on_validation_batch_end(self, trainer, pl_module)\\n def on_test_start(self, trainer, pl_module)\\n def on_test_batch_end(self, trainer, pl_module)\\nclass ProgressBar # This is the default progress bar used by Lightning. It prints to `stdout` using \\n def __init__(self, refresh_rate: int=1, process_position: int=0)\\n def __getstate__(self)\\n def refresh_rate(self)\\n def process_position(self)\\n def is_enabled(self)\\n def is_disabled(self)\\n def disable(self)\\n def enable(self)\\n def init_sanity_tqdm(self) # Override this to customize the tqdm bar for the validation sanity run.\\n def init_train_tqdm(self) # Override this to customize the tqdm bar for training.\\n def init_validation_tqdm(self) # Override this to customize the tqdm bar for validation.\\n def init_test_tqdm(self) # Override this to customize the tqdm bar for testing.\\n def on_sanity_check_start(self, trainer, pl_module)\\n def on_sanity_check_end(self, trainer, pl_module)\\n def on_train_start(self, trainer, pl_module)\\n def on_epoch_start(self, trainer, pl_module)\\n def on_batch_end(self, trainer, pl_module)\\n def on_validation_start(self, trainer, pl_module)\\n def on_validation_batch_end(self, trainer, pl_module)\\n def on_validation_end(self, trainer, pl_module)\\n def on_train_end(self, trainer, pl_module)\\n def on_test_start(self, trainer, pl_module)\\n def on_test_batch_end(self, trainer, pl_module)\\n def on_test_end(self, trainer, pl_module)\\ndef convert_inf(x) # The tqdm doesn't support inf values. We have to convert it to None.\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py\",\n \"text\": \"pytorch_lightning/core/hooks.py\\nclass ModelHooks\\n def setup(self, stage: str) # Called at the beginning of fit and test.\\n def teardown(self, stage: str) # Called at the end of fit and test.\\n def on_fit_start(self) # Called at the very beginning of fit.\\n def on_fit_end(self) # Called at the very end of fit.\\n def on_train_start(self) # Called at the beginning of training before sanity check.\\n def on_train_end(self) # Called at the end of training before logger experiment is closed.\\n def on_batch_start(self, batch: Any) # Called in the training loop before anything happens for that batch.\\n def on_batch_end(self) # Called in the training loop after the batch.\\n def on_epoch_start(self) # Called in the training loop at the very beginning of the epoch.\\n def on_epoch_end(self) # Called in the training loop at the very end of the epoch.\\n def on_train_epoch_start(self) # Called in the training loop at the very beginning of the epoch.\\n def on_train_epoch_end(self) # Called in the training loop at the very end of the epoch.\\n def on_validation_epoch_start(self) # Called in the validation loop at the very beginning of the epoch.\\n def on_validation_epoch_end(self) # Called in the validation loop at the very end of the epoch.\\n def on_test_epoch_start(self) # Called in the test loop at the very beginning of the epoch.\\n def on_test_epoch_end(self) # Called in the test loop at the very end of the epoch.\\n def on_pre_performance_check(self) # Called at the very beginning of the validation loop.\\n def on_post_performance_check(self) # Called at the very end of the validation loop.\\n def on_before_zero_grad(self, optimizer: Optimizer) # Called after optimizer.step() and before optimizer.zero_grad().\\n def on_after_backward(self) # Called in the training loop after loss.backward() and before optimizers do anyth\\n def backward(self, trainer, loss: Tensor, optimizer: Optimizer, optimizer_idx: int) # Override backward with your own implementation if you need to.\\n def amp_scale_loss(self, unscaled_loss, optimizer, optimizer_idx)\\n def transfer_batch_to_device(self, batch: Any, device: torch.device) # Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py\",\n \"text\": \"pytorch_lightning/core/lightning.py\\nclass LightningModule\\n def __init__(self, *args, **kwargs)\\n def example_input_array(self)\\n def example_input_array(self, example: Any)\\n def datamodule(self)\\n def datamodule(self, datamodule: Any)\\n def on_gpu(self) # True if your model is currently running on GPUs.\\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once.\\n def forward(self, *args, **kwargs) # Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to\\n def training_step(self, *args, **kwargs) # Here you compute and return the training loss and some additional metrics for e.\\n def training_end(self, *args, **kwargs) # Warnings:\\n def training_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the training epoch with the outputs of all training steps.\\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because :meth:`training_step`\\n def validation_step(self, *args, **kwargs) # Operates on a single batch of data from the validation set.\\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because :meth:`validation_step`\\n def validation_end(self, outputs) # Warnings:\\n def validation_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the validation epoch with the outputs of all validation ste\\n def test_step(self, *args, **kwargs) # Operates on a single batch of data from the test set.\\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because :meth:`test_step` will operate\\n def test_end(self, outputs) # Warnings:\\n def test_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of a test epoch with the output of all test steps.\\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) # Override to init DDP in your own way or with your own wrapper.\\n def _init_slurm_connection(self) # Sets up environment variables necessary for pytorch distributed communications\\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool=True) # Override to define your custom way of setting up a distributed environment.\\n def configure_sync_batchnorm(self, model: 'LightningModule') # Add global batchnorm for a model spread across multiple GPUs and nodes.\\n def configure_apex(self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str) # Override to init AMP your own way.\\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\\n def optimizer_step(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int, second_order_closure: Optional[Callable]=None, on_tpu: bool=False, u\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_config.py\",\n \"text\": \"pytorch_lightning/trainer/callback_config.py\\nclass TrainerCallbackConfigMixin\\n def slurm_job_id(self) # Warning: this is just empty shell for code implemented in other class.\\n def save_checkpoint(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def configure_checkpoint_callback(self, checkpoint_callback)\\n def configure_early_stopping(self, early_stop_callback)\\n def configure_progress_bar(self, refresh_rate=1, process_position=0)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py\\nclass TrainerCallbackHookMixin\\n def setup(self, stage: str) # Called in the beginning of fit and test\\n def teardown(self, stage: str) # Called at the end of fit and test\\n def on_init_start(self) # Called when the trainer initialization begins, model has not yet been set.\\n def on_init_end(self) # Called when the trainer initialization ends, model has not yet been set.\\n def on_fit_start(self) # Called when the trainer initialization begins, model has not yet been set.\\n def on_fit_end(self) # Called when the trainer initialization begins, model has not yet been set.\\n def on_sanity_check_start(self) # Called when the validation sanity check starts.\\n def on_sanity_check_end(self) # Called when the validation sanity check ends.\\n def on_train_epoch_start(self) # Called when the epoch begins.\\n def on_train_epoch_end(self) # Called when the epoch ends.\\n def on_validation_epoch_start(self) # Called when the epoch begins.\\n def on_validation_epoch_end(self) # Called when the epoch ends.\\n def on_test_epoch_start(self) # Called when the epoch begins.\\n def on_test_epoch_end(self) # Called when the epoch ends.\\n def on_epoch_start(self) # Called when the epoch begins.\\n def on_epoch_end(self) # Called when the epoch ends.\\n def on_train_start(self) # Called when the train begins.\\n def on_train_end(self) # Called when the train ends.\\n def on_batch_start(self) # Called when the training batch begins.\\n def on_batch_end(self) # Called when the training batch ends.\\n def on_validation_batch_start(self) # Called when the validation batch begins.\\n def on_validation_batch_end(self) # Called when the validation batch ends.\\n def on_test_batch_start(self) # Called when the test batch begins.\\n def on_test_batch_end(self) # Called when the test batch ends.\\n def on_validation_start(self) # Called when the validation loop begins.\\n def on_validation_end(self) # Called when the validation loop ends.\\n def on_test_start(self) # Called when the test begins.\\n def on_test_end(self) # Called when the test ends.\\n def on_keyboard_interrupt(self) # Called when the training is interrupted by KeyboardInterrupt.\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py\\nclass TrainerLRFinderMixin\\n def save_checkpoint(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def restore(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def init_optimizers(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def fit(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def _run_lr_finder_internally(self, model: LightningModule) # Call lr finder internally during Trainer.fit()\\n def lr_find(self, model: LightningModule, train_dataloader: Optional[DataLoader]=None, val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]]=None, min_lr: float=1e-08, max_lr: float=1, num_training: int=100, mode: str='exponential', early_stop_threshold: float=4.0) # lr_find enables the user to do a range test of good initial learning rates,\\n def __lr_finder_dump_params(self, model)\\n def __lr_finder_restore_params(self, model)\\nclass _LRFinder # LR finder object. This object stores the results of Trainer.lr_find().\\n def __init__(self, mode: str, lr_min: float, lr_max: float, num_training: int)\\n def _get_new_optimizer(self, optimizer: torch.optim.Optimizer) # Construct a new `configure_optimizers()` method, that has a optimizer\\n def configure_optimizers()\\n def plot(self, suggest: bool=False, show: bool=False) # Plot results from lr_find run\\n def suggestion(self, skip_begin: int=10, skip_end: int=1) # This will propose a suggestion for choice of initial learning rate\\nclass _LRCallback # Special callback used by the learning rate finder. This callbacks log\\n def __init__(self, num_training: int, early_stop_threshold: float=4.0, progress_bar_refresh_rate: int=0, beta: float=0.98)\\n def on_batch_start(self, trainer, pl_module) # Called before each training batch, logs the lr that will be used\\n def on_batch_end(self, trainer, pl_module) # Called when the training batch ends, logs the calculated loss\\nclass _LinearLR # Linearly increases the learning rate between two boundaries\\n def __init__(self, optimizer: torch.optim.Optimizer, end_lr: float, num_iter: int, last_epoch: int=-1)\\n def get_lr(self)\\n def lr(self)\\nclass _ExponentialLR # Exponentially increases the learning rate between two boundaries\\n def __init__(self, optimizer: torch.optim.Optimizer, end_lr: float, num_iter: int, last_epoch: int=-1)\\n def get_lr(self)\\n def lr(self)\\ndef _nested_hasattr(obj, path)\\ndef _nested_setattr(obj, path, val)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py\\nclass TrainerTrainLoopMixin\\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\\n def is_function_implemented(self, *args, **kwargs) # Warning: this is just empty shell for code implemented in other class.\\n def run_evaluation(self, *args, **kwargs) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def clip_gradients(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def detect_nan_tensors(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def add_progress_bar_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def process_output(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_train_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_val_dataloader(self, model) # Warning: this is just empty shell for code implemented in other class.\\n def has_arg(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def train(self)\\n def prepare_train_loop_dataloader(self, train_dataloader)\\n def run_on_epoch_start_hook(self, model)\\n def run_training_epoch(self)\\n def check_checkpoint_callback(self, should_check_val)\\n def update_train_loop_lr_schedulers(self)\\n def run_on_epoch_end_hook(self, model)\\n def run_training_epoch_end(self, epoch_output, checkpoint_accumulator, early_stopping_accumulator)\\n def sync_horovod(self)\\n def increment_accumulated_grad_global_step(self)\\n def save_train_loop_metrics_to_loggers(self, batch_idx, batch_output)\\n def save_loggers_in_training_loop(self, batch_idx)\\n def should_check_val(self, batch_idx, is_last_batch)\\n def run_training_batch(self, batch, batch_idx)\\n def run_batch_backward_pass(self, split_batch, batch_idx, opt_idx, optimizer)\\n def call_optimizer_step(self, optimizer, opt_idx, batch_idx, split_batch)\\n def optimizer_closure(self, split_batch, batch_idx, opt_idx, optimizer, hiddens) # wrap the forward step in a closure so second order methods work\\n def _get_optimizers_iterable(self)\\n def run_training_teardown(self)\\n def training_forward(self, batch, batch_idx, opt_idx, hiddens) # Handle forward for each training case (distributed, single gpu, etc...)\\n def update_learning_rates(self, interval: str) # Update learning rates.\\ndef _wit\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"pytorch_lightning/callbacks/base.py::Callback.setup","text":"pytorch_lightning/callbacks/base.py::Callback.setup\n def setup(self, trainer, stage: str):\n \"\"\"Called when fit or test begins\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.teardown","text":"pytorch_lightning/callbacks/base.py::Callback.teardown\n def teardown(self, trainer, stage: str):\n \"\"\"Called when fit or test ends\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_init_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_init_start\n def on_init_start(self, trainer):\n \"\"\"Called when the trainer initialization begins, model has not yet been set.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_init_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_init_end\n def on_init_end(self, trainer):\n \"\"\"Called when the trainer initialization ends, model has not yet been set.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_fit_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_fit_start\n def on_fit_start(self, trainer):\n \"\"\"Called when fit begins\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_fit_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_fit_end\n def on_fit_end(self, trainer):\n \"\"\"Called when fit ends\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_sanity_check_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_sanity_check_start\n def on_sanity_check_start(self, trainer, pl_module):\n \"\"\"Called when the validation sanity check starts.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_sanity_check_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_sanity_check_end\n def on_sanity_check_end(self, trainer, pl_module):\n \"\"\"Called when the validation sanity check ends.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_train_epoch_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_train_epoch_start\n def on_train_epoch_start(self, trainer, pl_module):\n \"\"\"Called when the train epoch begins.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_train_epoch_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_train_epoch_end\n def on_train_epoch_end(self, trainer, pl_module):\n \"\"\"Called when the train epoch ends.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_validation_epoch_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_validation_epoch_start\n def on_validation_epoch_start(self, trainer, pl_module):\n \"\"\"Called when the val epoch begins.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_validation_epoch_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_validation_epoch_end\n def on_validation_epoch_end(self, trainer, pl_module):\n \"\"\"Called when the val epoch ends.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_test_epoch_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_test_epoch_start\n def on_test_epoch_start(self, trainer, pl_module):\n \"\"\"Called when the test epoch begins.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_test_epoch_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_test_epoch_end\n def on_test_epoch_end(self, trainer, pl_module):\n \"\"\"Called when the test epoch ends.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_epoch_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_epoch_start\n def on_epoch_start(self, trainer, pl_module):\n \"\"\"Called when the epoch begins.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_epoch_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_epoch_end\n def on_epoch_end(self, trainer, pl_module):\n \"\"\"Called when the epoch ends.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_batch_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_batch_start\n def on_batch_start(self, trainer, pl_module):\n \"\"\"Called when the training batch begins.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_validation_batch_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_validation_batch_start\n def on_validation_batch_start(self, trainer, pl_module):\n \"\"\"Called when the validation batch begins.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_validation_batch_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_validation_batch_end\n def on_validation_batch_end(self, trainer, pl_module):\n \"\"\"Called when the validation batch ends.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_test_batch_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_test_batch_start\n def on_test_batch_start(self, trainer, pl_module):\n \"\"\"Called when the test batch begins.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_test_batch_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_test_batch_end\n def on_test_batch_end(self, trainer, pl_module):\n \"\"\"Called when the test batch ends.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_batch_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_batch_end\n def on_batch_end(self, trainer, pl_module):\n \"\"\"Called when the training batch ends.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_train_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_train_start\n def on_train_start(self, trainer, pl_module):\n \"\"\"Called when the train begins.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_train_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_train_end\n def on_train_end(self, trainer, pl_module):\n \"\"\"Called when the train ends.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_validation_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_validation_start\n def on_validation_start(self, trainer, pl_module):\n \"\"\"Called when the validation loop begins.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_validation_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_validation_end\n def on_validation_end(self, trainer, pl_module):\n \"\"\"Called when the validation loop ends.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_test_start","text":"pytorch_lightning/callbacks/base.py::Callback.on_test_start\n def on_test_start(self, trainer, pl_module):\n \"\"\"Called when the test begins.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_test_end","text":"pytorch_lightning/callbacks/base.py::Callback.on_test_end\n def on_test_end(self, trainer, pl_module):\n \"\"\"Called when the test ends.\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/callbacks/base.py::Callback.on_keyboard_interrupt","text":"pytorch_lightning/callbacks/base.py::Callback.on_keyboard_interrupt\n def on_keyboard_interrupt(self, trainer, pl_module):\n \"\"\"Called when the training is interrupted by KeyboardInterrupt.\"\"\"","label":0},{"cid":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.__init__","text":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.__init__\n def __init__(self):\n self.lrs = None\n self.lr_sch_names = []","label":0},{"cid":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_train_start","text":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_train_start\n def on_train_start(self, trainer, pl_module):\n \"\"\" Called before training, determines unique names for all lr\n schedulers in the case of multiple of the same type or in\n the case of multiple parameter groups\n \"\"\"\n if not trainer.logger:\n raise MisconfigurationException(\n 'Cannot use LearningRateLogger callback with Trainer that has no logger.')\n\n if not trainer.lr_schedulers:\n rank_zero_warn(\n 'You are using LearningRateLogger callback with models that'\n ' have no learning rate schedulers. Please see documentation'\n ' for `configure_optimizers` method.', RuntimeWarning\n )\n\n # Find names for schedulers\n names = self._find_names(trainer.lr_schedulers)\n\n # Initialize for storing values\n self.lrs = {name: [] for name in names}","label":0},{"cid":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_batch_start","text":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_batch_start\n def on_batch_start(self, trainer, pl_module):\n latest_stat = self._extract_lr(trainer, 'step')\n if trainer.logger and latest_stat:\n trainer.logger.log_metrics(latest_stat, step=trainer.global_step)","label":1},{"cid":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_epoch_start","text":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_epoch_start\n def on_epoch_start(self, trainer, pl_module):\n latest_stat = self._extract_lr(trainer, 'epoch')\n if trainer.logger and latest_stat:\n trainer.logger.log_metrics(latest_stat, step=trainer.global_step)","label":0},{"cid":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger._extract_lr","text":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger._extract_lr\n def _extract_lr(self, trainer, interval):\n \"\"\" Extracts learning rates for lr schedulers and saves information\n into dict structure. \"\"\"\n latest_stat = {}\n for name, scheduler in zip(self.lr_sch_names, trainer.lr_schedulers):\n if scheduler['interval'] == interval:\n param_groups = scheduler['scheduler'].optimizer.param_groups\n if len(param_groups) != 1:\n for i, pg in enumerate(param_groups):\n lr, key = pg['lr'], f'{name}/pg{i + 1}'\n self.lrs[key].append(lr)\n latest_stat[key] = lr\n else:\n self.lrs[name].append(param_groups[0]['lr'])\n latest_stat[name] = param_groups[0]['lr']\n return latest_stat","label":0},{"cid":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger._find_names","text":"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger._find_names\n def _find_names(self, lr_schedulers):\n # Create uniqe names in the case we have multiple of the same learning\n # rate schduler + multiple parameter groups\n names = []\n for scheduler in lr_schedulers:\n sch = scheduler['scheduler']\n if 'name' in scheduler:\n name = scheduler['name']\n else:\n opt_name = 'lr-' + sch.optimizer.__class__.__name__\n i, name = 1, opt_name\n # Multiple schduler of the same type\n while True:\n if name not in names:\n break\n i, name = i + 1, f'{opt_name}-{i}'\n\n # Multiple param groups for the same schduler\n param_groups = sch.optimizer.param_groups\n if len(param_groups) != 1:\n for i, pg in enumerate(param_groups):\n temp = f'{name}/pg{i + 1}'\n names.append(temp)\n else:\n names.append(name)\n\n self.lr_sch_names.append(name)\n return names","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.__init__","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.__init__\n def __init__(self):\n\n self._trainer = None\n self._train_batch_idx = 0\n self._val_batch_idx = 0\n self._test_batch_idx = 0","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.trainer","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.trainer\n @property\n def trainer(self):\n return self._trainer","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.train_batch_idx","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.train_batch_idx\n @property\n def train_batch_idx(self) -> int:\n \"\"\"\n The current batch index being processed during training.\n Use this to update your progress bar.\n \"\"\"\n return self._train_batch_idx","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.val_batch_idx","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.val_batch_idx\n @property\n def val_batch_idx(self) -> int:\n \"\"\"\n The current batch index being processed during validation.\n Use this to update your progress bar.\n \"\"\"\n return self._val_batch_idx","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.test_batch_idx","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.test_batch_idx\n @property\n def test_batch_idx(self) -> int:\n \"\"\"\n The current batch index being processed during testing.\n Use this to update your progress bar.\n \"\"\"\n return self._test_batch_idx","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_train_batches","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_train_batches\n @property\n def total_train_batches(self) -> int:\n \"\"\"\n The total number of training batches during training, which may change from epoch to epoch.\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\n training dataloader is of infinite size.\n \"\"\"\n return self.trainer.num_training_batches","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_val_batches","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_val_batches\n @property\n def total_val_batches(self) -> int:\n \"\"\"\n The total number of training batches during validation, which may change from epoch to epoch.\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\n validation dataloader is of infinite size.\n \"\"\"\n total_val_batches = 0\n if not self.trainer.disable_validation:\n is_val_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0\n total_val_batches = sum(self.trainer.num_val_batches) if is_val_epoch else 0\n return total_val_batches","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_test_batches","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_test_batches\n @property\n def total_test_batches(self) -> int:\n \"\"\"\n The total number of training batches during testing, which may change from epoch to epoch.\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\n test dataloader is of infinite size.\n \"\"\"\n return sum(self.trainer.num_test_batches)","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.disable","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.disable\n def disable(self):\n \"\"\"\n You should provide a way to disable the progress bar.\n The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this to disable the\n output on processes that have a rank different from 0, e.g., in multi-node training.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.enable","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.enable\n def enable(self):\n \"\"\"\n You should provide a way to enable the progress bar.\n The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this in e.g. pre-training\n routines like the `learning rate finder <lr_finder.rst>`_ to temporarily enable and\n disable the main progress bar.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_init_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_init_end\n def on_init_end(self, trainer):\n self._trainer = trainer","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_train_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_train_start\n def on_train_start(self, trainer, pl_module):\n self._train_batch_idx = trainer.batch_idx","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_epoch_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_epoch_start\n def on_epoch_start(self, trainer, pl_module):\n self._train_batch_idx = 0","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_batch_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_batch_end\n def on_batch_end(self, trainer, pl_module):\n self._train_batch_idx += 1","label":1},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_start\n def on_validation_start(self, trainer, pl_module):\n self._val_batch_idx = 0","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_batch_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_batch_end\n def on_validation_batch_end(self, trainer, pl_module):\n self._val_batch_idx += 1","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_start\n def on_test_start(self, trainer, pl_module):\n self._test_batch_idx = 0","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_batch_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_batch_end\n def on_test_batch_end(self, trainer, pl_module):\n self._test_batch_idx += 1","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.__init__","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.__init__\n def __init__(self, refresh_rate: int = 1, process_position: int = 0):\n super().__init__()\n self._refresh_rate = refresh_rate\n self._process_position = process_position\n self._enabled = True\n self.main_progress_bar = None\n self.val_progress_bar = None\n self.test_progress_bar = None","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.__getstate__","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.__getstate__\n def __getstate__(self):\n # can't pickle the tqdm objects\n state = self.__dict__.copy()\n state['main_progress_bar'] = None\n state['val_progress_bar'] = None\n state['test_progress_bar'] = None\n return state","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.refresh_rate","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.refresh_rate\n @property\n def refresh_rate(self) -> int:\n return self._refresh_rate","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.process_position","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.process_position\n @property\n def process_position(self) -> int:\n return self._process_position","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.is_enabled","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.is_enabled\n @property\n def is_enabled(self) -> bool:\n return self._enabled and self.refresh_rate > 0","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.is_disabled","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.is_disabled\n @property\n def is_disabled(self) -> bool:\n return not self.is_enabled","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.disable","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.disable\n def disable(self) -> None:\n self._enabled = False","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.enable","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.enable\n def enable(self) -> None:\n self._enabled = True","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_sanity_tqdm","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_sanity_tqdm\n def init_sanity_tqdm(self) -> tqdm:\n \"\"\" Override this to customize the tqdm bar for the validation sanity run. \"\"\"\n bar = tqdm(\n desc='Validation sanity check',\n position=(2 * self.process_position),\n disable=self.is_disabled,\n leave=False,\n dynamic_ncols=True,\n file=sys.stdout,\n )\n return bar","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_train_tqdm","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_train_tqdm\n def init_train_tqdm(self) -> tqdm:\n \"\"\" Override this to customize the tqdm bar for training. \"\"\"\n bar = tqdm(\n desc='Training',\n initial=self.train_batch_idx,\n position=(2 * self.process_position),\n disable=self.is_disabled,\n leave=True,\n dynamic_ncols=True,\n file=sys.stdout,\n smoothing=0,\n )\n return bar","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_validation_tqdm","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_validation_tqdm\n def init_validation_tqdm(self) -> tqdm:\n \"\"\" Override this to customize the tqdm bar for validation. \"\"\"\n bar = tqdm(\n desc='Validating',\n position=(2 * self.process_position + 1),\n disable=self.is_disabled,\n leave=False,\n dynamic_ncols=True,\n file=sys.stdout\n )\n return bar","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_test_tqdm","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_test_tqdm\n def init_test_tqdm(self) -> tqdm:\n \"\"\" Override this to customize the tqdm bar for testing. \"\"\"\n bar = tqdm(\n desc='Testing',\n position=(2 * self.process_position),\n disable=self.is_disabled,\n leave=True,\n dynamic_ncols=True,\n file=sys.stdout\n )\n return bar","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_start\n def on_sanity_check_start(self, trainer, pl_module):\n super().on_sanity_check_start(trainer, pl_module)\n self.val_progress_bar = self.init_sanity_tqdm()\n self.val_progress_bar.total = convert_inf(trainer.num_sanity_val_steps * len(trainer.val_dataloaders))\n self.main_progress_bar = tqdm(disable=True) # dummy progress bar","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_end\n def on_sanity_check_end(self, trainer, pl_module):\n super().on_sanity_check_end(trainer, pl_module)\n self.main_progress_bar.close()\n self.val_progress_bar.close()","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_start\n def on_train_start(self, trainer, pl_module):\n super().on_train_start(trainer, pl_module)\n self.main_progress_bar = self.init_train_tqdm()","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_epoch_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_epoch_start\n def on_epoch_start(self, trainer, pl_module):\n super().on_epoch_start(trainer, pl_module)\n total_train_batches = self.total_train_batches\n total_val_batches = self.total_val_batches\n if total_train_batches != float('inf') and not trainer.fast_dev_run:\n # val can be checked multiple times per epoch\n val_checks_per_epoch = total_train_batches // trainer.val_check_batch\n total_val_batches = total_val_batches * val_checks_per_epoch\n total_batches = total_train_batches + total_val_batches\n if not self.main_progress_bar.disable:\n self.main_progress_bar.reset(convert_inf(total_batches))\n self.main_progress_bar.set_description(f'Epoch {trainer.current_epoch + 1}')","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_batch_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_batch_end\n def on_batch_end(self, trainer, pl_module):\n super().on_batch_end(trainer, pl_module)\n if self.is_enabled and self.train_batch_idx % self.refresh_rate == 0:\n self.main_progress_bar.update(self.refresh_rate)\n self.main_progress_bar.set_postfix(trainer.progress_bar_dict)","label":1},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_start\n def on_validation_start(self, trainer, pl_module):\n super().on_validation_start(trainer, pl_module)\n self.val_progress_bar = self.init_validation_tqdm()\n self.val_progress_bar.total = convert_inf(self.total_val_batches)","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_batch_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_batch_end\n def on_validation_batch_end(self, trainer, pl_module):\n super().on_validation_batch_end(trainer, pl_module)\n if self.is_enabled and self.val_batch_idx % self.refresh_rate == 0:\n self.val_progress_bar.update(self.refresh_rate)\n self.main_progress_bar.update(self.refresh_rate)","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_end\n def on_validation_end(self, trainer, pl_module):\n super().on_validation_end(trainer, pl_module)\n self.main_progress_bar.set_postfix(trainer.progress_bar_dict)\n self.val_progress_bar.close()","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_end\n def on_train_end(self, trainer, pl_module):\n super().on_train_end(trainer, pl_module)\n self.main_progress_bar.close()","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_start\n def on_test_start(self, trainer, pl_module):\n super().on_test_start(trainer, pl_module)\n self.test_progress_bar = self.init_test_tqdm()\n self.test_progress_bar.total = convert_inf(self.total_test_batches)","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_batch_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_batch_end\n def on_test_batch_end(self, trainer, pl_module):\n super().on_test_batch_end(trainer, pl_module)\n if self.is_enabled and self.test_batch_idx % self.refresh_rate == 0:\n self.test_progress_bar.update(self.refresh_rate)","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_end\n def on_test_end(self, trainer, pl_module):\n super().on_test_end(trainer, pl_module)\n self.test_progress_bar.close()","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::convert_inf","text":"pytorch_lightning/callbacks/progress.py::convert_inf\ndef convert_inf(x):\n \"\"\" The tqdm doesn't support inf values. We have to convert it to None. \"\"\"\n if x == float('inf'):\n return None\n return x","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.setup","text":"pytorch_lightning/core/hooks.py::ModelHooks.setup\n def setup(self, stage: str):\n \"\"\"\n Called at the beginning of fit and test.\n This is a good hook when you need to build models dynamically or adjust something about them.\n This hook is called on every process when using DDP.\n\n Args:\n stage: either 'fit' or 'test'\n\n Example::\n\n class LitModel(...):\n def __init__(self):\n self.l1 = None\n\n def prepare_data(self):\n download_data()\n tokenize()\n\n # don't do this\n self.something = else\n\n def setup(stage):\n data = Load_data(...)\n self.l1 = nn.Linear(28, data.num_classes)\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.teardown","text":"pytorch_lightning/core/hooks.py::ModelHooks.teardown\n def teardown(self, stage: str):\n \"\"\"\n Called at the end of fit and test.\n\n Args:\n stage: either 'fit' or 'test'\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_start\n def on_fit_start(self):\n \"\"\"\n Called at the very beginning of fit.\n If on DDP it is called on every process\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_end\n def on_fit_end(self):\n \"\"\"\n Called at the very end of fit.\n If on DDP it is called on every process\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_start\n def on_train_start(self) -> None:\n \"\"\"\n Called at the beginning of training before sanity check.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_end\n def on_train_end(self) -> None:\n \"\"\"\n Called at the end of training before logger experiment is closed.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_start\n def on_batch_start(self, batch: Any) -> None:\n \"\"\"\n Called in the training loop before anything happens for that batch.\n\n If you return -1 here, you will skip training for the rest of the current epoch.\n\n Args:\n batch: The batched data as it is returned by the training DataLoader.\n \"\"\"","label":1},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_end\n def on_batch_end(self) -> None:\n \"\"\"\n Called in the training loop after the batch.\n \"\"\"","label":1},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_start\n def on_epoch_start(self) -> None:\n \"\"\"\n Called in the training loop at the very beginning of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_end\n def on_epoch_end(self) -> None:\n \"\"\"\n Called in the training loop at the very end of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_start\n def on_train_epoch_start(self) -> None:\n \"\"\"\n Called in the training loop at the very beginning of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_end\n def on_train_epoch_end(self) -> None:\n \"\"\"\n Called in the training loop at the very end of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_start\n def on_validation_epoch_start(self) -> None:\n \"\"\"\n Called in the validation loop at the very beginning of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_end\n def on_validation_epoch_end(self) -> None:\n \"\"\"\n Called in the validation loop at the very end of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_start\n def on_test_epoch_start(self) -> None:\n \"\"\"\n Called in the test loop at the very beginning of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_end\n def on_test_epoch_end(self) -> None:\n \"\"\"\n Called in the test loop at the very end of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_pre_performance_check","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_pre_performance_check\n def on_pre_performance_check(self) -> None:\n \"\"\"\n Called at the very beginning of the validation loop.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_post_performance_check","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_post_performance_check\n def on_post_performance_check(self) -> None:\n \"\"\"\n Called at the very end of the validation loop.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_before_zero_grad","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_before_zero_grad\n def on_before_zero_grad(self, optimizer: Optimizer) -> None:\n \"\"\"\n Called after optimizer.step() and before optimizer.zero_grad().\n\n Called in the training loop after taking an optimizer step and before zeroing grads.\n Good place to inspect weight information with weights updated.\n\n This is where it is called::\n\n for optimizer in optimizers:\n optimizer.step()\n model.on_before_zero_grad(optimizer) # < ---- called here\n optimizer.zero_grad\n\n Args:\n optimizer: The optimizer for which grads should be zeroed.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_after_backward","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_after_backward\n def on_after_backward(self) -> None:\n \"\"\"\n Called in the training loop after loss.backward() and before optimizers do anything.\n This is the ideal place to inspect or log gradient information.\n\n Example::\n\n def on_after_backward(self):\n # example to inspect gradient information in tensorboard\n if self.trainer.global_step % 25 == 0: # don't make the tf file huge\n params = self.state_dict()\n for k, v in params.items():\n grads = v\n name = k\n self.logger.experiment.add_histogram(tag=name, values=grads,\n global_step=self.trainer.global_step)\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.backward","text":"pytorch_lightning/core/hooks.py::ModelHooks.backward\n def backward(self, trainer, loss: Tensor, optimizer: Optimizer, optimizer_idx: int) -> None:\n \"\"\"\n Override backward with your own implementation if you need to.\n\n Args:\n trainer: Pointer to the trainer\n loss: Loss is already scaled by accumulated grads\n optimizer: Current optimizer being used\n optimizer_idx: Index of the current optimizer being used\n\n Called to perform backward step.\n Feel free to override as needed.\n\n The loss passed in has already been scaled for accumulated gradients if requested.\n\n Example::\n\n def backward(self, trainer, loss, optimizer, optimizer_idx):\n loss.backward()\n\n \"\"\"\n loss.backward()","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.amp_scale_loss","text":"pytorch_lightning/core/hooks.py::ModelHooks.amp_scale_loss\n def amp_scale_loss(self, unscaled_loss, optimizer, optimizer_idx):\n if NATIVE_AMP_AVALAIBLE:\n scaled_loss = self.trainer.scaler.scale(unscaled_loss)\n else:\n scaled_loss = amp.scale_loss(unscaled_loss, optimizer)\n\n return scaled_loss","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.transfer_batch_to_device","text":"pytorch_lightning/core/hooks.py::ModelHooks.transfer_batch_to_device\n def transfer_batch_to_device(self, batch: Any, device: torch.device) -> Any:\n \"\"\"\n Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors\n wrapped in a custom data structure.\n\n The data types listed below (and any arbitrary nesting of them) are supported out of the box:\n\n - :class:`torch.Tensor` or anything that implements `.to(...)`\n - :class:`list`\n - :class:`dict`\n - :class:`tuple`\n - :class:`torchtext.data.batch.Batch`\n\n For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, ...).\n\n Example::\n\n def transfer_batch_to_device(self, batch, device)\n if isinstance(batch, CustomBatch):\n # move all tensors in your custom data structure to the device\n batch.samples = batch.samples.to(device)\n batch.targets = batch.targets.to(device)\n else:\n batch = super().transfer_batch_to_device(data, device)\n return batch\n\n Args:\n batch: A batch of data that needs to be transferred to a new device.\n device: The target device as defined in PyTorch.\n\n Returns:\n A reference to the data on the new device.\n\n Note:\n This hook should only transfer the data and not modify it, nor should it move the data to\n any other device than the one passed in as argument (unless you know what you are doing).\n The :class:`~pytorch_lightning.trainer.trainer.Trainer` already takes care of splitting the\n batch and determines the target devices.\n\n See Also:\n - :func:`~pytorch_lightning.utilities.apply_func.move_data_to_device`\n - :func:`~pytorch_lightning.utilities.apply_func.apply_to_collection`\n \"\"\"\n return move_data_to_device","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.__init__","text":"pytorch_lightning/core/lightning.py::LightningModule.__init__\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self.exp_save_path = None\n\n #: The current epoch\n self.current_epoch = 0\n\n #: Total training batches seen across all epochs\n self.global_step = 0\n\n self.loaded_optimizer_states_dict = {}\n\n #: Pointer to the trainer object\n self.trainer = None\n\n #: Pointer to the logger object\n self.logger = None\n\n #: True if using dp\n self.use_dp = False\n\n #: True if using ddp\n self.use_ddp = False\n\n #: True if using ddp2\n self.use_ddp2 = False\n\n # True if on tpu\n self.use_tpu = False\n\n #: True if using amp\n self.use_amp = False\n\n # optionally can be set by user\n self._example_input_array = None\n self._datamodule = None","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array","text":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\n @property\n def example_input_array(self) -> Any:\n return self._example_input_array","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array","text":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\n @example_input_array.setter\n def example_input_array(self, example: Any) -> None:\n self._example_input_array = example","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.datamodule","text":"pytorch_lightning/core/lightning.py::LightningModule.datamodule\n @property\n def datamodule(self) -> Any:\n return self._datamodule","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.datamodule","text":"pytorch_lightning/core/lightning.py::LightningModule.datamodule\n @datamodule.setter\n def datamodule(self, datamodule: Any) -> None:\n self._datamodule = datamodule","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_gpu","text":"pytorch_lightning/core/lightning.py::LightningModule.on_gpu\n @property\n def on_gpu(self):\n \"\"\"\n True if your model is currently running on GPUs.\n Useful to set flags around the LightningModule for different CPU vs GPU behavior.\n \"\"\"\n return self.device.type == 'cuda'","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.print","text":"pytorch_lightning/core/lightning.py::LightningModule.print\n def print(self, *args, **kwargs) -> None:\n r\"\"\"\n Prints only from process 0. Use this in any distributed mode to log only once.\n\n Args:\n *args: The thing to print. Will be passed to Python's built-in print function.\n **kwargs: Will be passed to Python's built-in print function.\n\n Example:\n\n .. code-block:: python\n\n def forward(self, x):\n self.print(x, 'in forward')\n\n \"\"\"\n if self.trainer.is_global_zero:\n print(*args, **kwargs)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.forward","text":"pytorch_lightning/core/lightning.py::LightningModule.forward\n @abstractmethod\n def forward(self, *args, **kwargs):\n r\"\"\"\n Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define\n the operations you want to use for prediction (i.e.: on a server or as a feature extractor).\n\n Normally you'd call ``self()`` from your :meth:`training_step` method.\n This makes it easy to write a complex system for training with the outputs\n you'd want in a prediction setting.\n\n You may also find the :func:`~pytorch_lightning.core.decorators.auto_move_data` decorator useful\n when using the module outside Lightning in a production setting.\n\n Args:\n *args: Whatever you decide to pass into the forward method.\n **kwargs: Keyword arguments are also possible.\n\n Return:\n Predicted output\n\n Examples:\n .. code-block:: python\n\n # example if we were using this model as a feature extractor\n def forward(self, x):\n feature_maps = self.convnet(x)\n return feature_maps\n\n def training_step(self, batch, batch_idx):\n x, y = batch\n feature_maps = self(x)\n logits = self.classifier(feature_maps)\n\n # ...\n return loss\n\n # splitting it this way allows model to be used a feature extractor\n model = MyModelAbove()\n\n inputs = server.get_request()\n results = model(inputs)\n server.write_results(results)\n\n # -------------\n # This is in stark contrast to torch.nn.Module where normally you would have this:\n def forward(self, batch):\n x, y = batch\n feature_maps = self.convnet(x)\n logits = self.classifier(feature_maps)\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step\n def training_step(self, *args, **kwargs) -> Union[int, Dict[str, Union[Tensor, Dict[str, Tensor]]]]:\n r\"\"\"\n Here you compute and return the training loss and some additional metrics for e.g.\n the progress bar or logger.\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): Integer displaying index of this batch\n optimizer_idx (int): When using multiple optimizers, this argument will also be present.\n hiddens(:class:`~torch.Tensor`): Passed in if\n :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.\n\n Return:\n Dict with loss key and optional log or progress bar keys.\n When implementing :meth:`training_step`, return whatever you need in that step:\n\n - loss -> tensor scalar **REQUIRED**\n - progress_bar -> Dict for progress bar display. Must have only tensors\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n In this step you'd normally do the forward pass and calculate the loss for a batch.\n You can also do fancier things like multiple forward passes or something model specific.\n\n Examples:\n .. code-block:: python\n\n def training_step(self, batch, batch_idx):\n x, y, z = batch\n\n # implement your own\n out = self(x)\n loss = self.loss(out, x)\n\n logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)\n\n # if using TestTubeLogger or TensorBoardLogger you can nest scalars\n logger_logs = {'losses': logger_logs} # optional (MUST ALL BE TENSORS)\n\n","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_end\n def training_end(self, *args, **kwargs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`training_step_end` instead.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\n def training_epoch_end(\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"Called at the end of the training epoch with the outputs of all training steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n train_outs = []\n for train_batch in train_data:\n out = training_step(train_batch)\n train_outs.append(out)\n training_epoch_end(train_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`training_step`, or if there are\n multiple dataloaders, a list containing a list of outputs for each dataloader.\n\n Return:\n Dict or OrderedDict.\n May contain the following optional keys:\n\n - log (metrics to be added to the logger; only tensors)\n - progress_bar (dict for progress bar display)\n - any metric used in a callback (e.g. early stopping).\n\n Note:\n If this method is not overridden, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def training_epoch_end(self, outputs):\n train_acc_mean = 0\n for output in outputs:\n train_acc_mean += output['train_acc']\n\n train_acc_mean /= len(outputs)\n\n # log training accuracy at the end of an epoch\n results = {\n 'log': {'train_acc': train_acc_mean.item()},\n 'progress_bar': {'train_acc': train_acc_m","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\n def training_step_end(self, *args, **kwargs) -> Dict[str, Union[Tensor, Dict[str, Tensor]]]:\n \"\"\"\n Use this when training with dp or ddp2 because :meth:`training_step`\n will operate on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]\n training_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in `training_step` for each batch part.\n\n Return:\n Dict with loss key and optional log or progress bar keys.\n\n - loss -> tensor scalar **REQUIRED**\n - progress_bar -> Dict for progress bar display. Must have only tensors\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n Examples:\n .. code-block:: python\n\n # WITHOUT training_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with training_step_end to do softmax over the full batch\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step\n def validation_step(self, *args, **kwargs) -> Dict[str, Tensor]:\n r\"\"\"\n Operates on a single batch of data from the validation set.\n In this step you'd might generate examples or calculate anything of interest like accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n val_outs = []\n for val_batch in val_data:\n out = validation_step(train_batch)\n val_outs.append(out)\n validation_epoch_end(val_outs)\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): The index of this batch\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple val datasets used)\n\n Return:\n Dict or OrderedDict - passed to :meth:`validation_epoch_end`.\n If you defined :meth:`validation_step_end` it will go to that first.\n\n .. code-block:: python\n\n # pseudocode of order\n out = validation_step()\n if defined('validation_step_end'):\n out = validation_step_end(out)\n out = validation_epoch_end(out)\n\n\n .. code-block:: python\n\n # if you have one val dataloader:\n def validation_step(self, batch, batch_idx)\n\n # if you have multiple val dataloaders:\n def validation_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single validation dataset\n def validation_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self(x)\n loss = self.los","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\n def validation_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\n \"\"\"\n Use this when validating with dp or ddp2 because :meth:`validation_step`\n will operate on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code.\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]\n validation_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in :meth:`validation_step`\n for each batch part.\n\n Return:\n Dict or OrderedDict - passed to the :meth:`validation_epoch_end` method.\n\n Examples:\n .. code-block:: python\n\n # WITHOUT validation_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with validation_step_end to do softmax over the full batch\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n return {'out': out}\n\n def validation_epoch_end(self, outputs):\n # this out is now the full size of the batch\n out = outputs['out']\n\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_end\n def validation_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead.\n Will be removed in 1.0.0.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\n def validation_epoch_end(\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"\n Called at the end of the validation epoch with the outputs of all validation steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n val_outs = []\n for val_batch in val_data:\n out = validation_step(val_batch)\n val_outs.append(out)\n validation_epoch_end(val_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`validation_step`, or if there\n are multiple dataloaders, a list containing a list of outputs for each dataloader.\n\n Return:\n Dict or OrderedDict.\n May have the following optional keys:\n\n - progress_bar (dict for progress bar display; only tensors)\n - log (dict of metrics to add to logger; only tensors).\n\n Note:\n If you didn't define a :meth:`validation_step`, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def validation_epoch_end(self, outputs):\n val_acc_mean = 0\n for output in outputs:\n val_acc_mean += output['val_acc']\n\n val_acc_mean /= len(outputs)\n tqdm_dict = {'val_acc': val_acc_mean.item()}\n\n # show val_acc in progress bar but only log val_loss\n results = {\n 'progress_bar': tqdm_dict,\n 'log': {'val_acc': val_ac","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step\n def test_step(self, *args, **kwargs) -> Dict[str, Tensor]:\n r\"\"\"\n Operates on a single batch of data from the test set.\n In this step you'd normally generate examples or calculate anything of interest\n such as accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n test_outs = []\n for test_batch in test_data:\n out = test_step(test_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): The index of this batch.\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple test datasets used).\n\n Return:\n Dict or OrderedDict - passed to the :meth:`test_epoch_end` method.\n If you defined :meth:`test_step_end` it will go to that first.\n\n .. code-block:: python\n\n # if you have one test dataloader:\n def test_step(self, batch, batch_idx)\n\n # if you have multiple test dataloaders:\n def test_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single test dataset\n def test_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self(x)\n loss = self.loss(out, y)\n\n # log 6 example images\n # or generated text... or whatever\n sample_imgs = x[:6]\n grid = torchvision.utils.make_grid(sample_imgs)\n self.logger.experiment.add_image('example","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\n def test_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\n \"\"\"\n Use this when testing with dp or ddp2 because :meth:`test_step` will operate\n on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code.\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]\n test_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in :meth:`test_step` for each batch part.\n\n Return:\n Dict or OrderedDict - passed to the :meth:`test_epoch_end`.\n\n Examples:\n .. code-block:: python\n\n # WITHOUT test_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with test_step_end to do softmax over the full batch\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n return {'out': out}\n\n def test_step_end(self, outputs):\n # this out is now the full size of the batch\n out = outputs['out']\n\n # this softmax now uses the full batch size\n loss = nce_loss(loss)\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_end\n def test_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead.\n Will be removed in 1.0.0.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\n def test_epoch_end(\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"\n Called at the end of a test epoch with the output of all test steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n test_outs = []\n for test_batch in test_data:\n out = test_step(test_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`test_step_end`, or if there\n are multiple dataloaders, a list containing a list of outputs for each dataloader\n\n Return:\n Dict or OrderedDict: Dict has the following optional keys:\n\n - progress_bar -> Dict for progress bar display. Must have only tensors.\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc).\n\n Note:\n If you didn't define a :meth:`test_step`, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def test_epoch_end(self, outputs):\n test_acc_mean = 0\n for output in outputs:\n test_acc_mean += output['test_acc']\n\n test_acc_mean /= len(outputs)\n tqdm_dict = {'test_acc': test_acc_mean.item()}\n\n # show test_loss and test_acc in progress bar but only log test_loss\n results = {\n 'progress_bar': tqdm_dict,\n 'log': {'test_acc': test","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) -> DistributedDataParallel:\n r\"\"\"\n Override to init DDP in your own way or with your own wrapper.\n The only requirements are that:\n\n 1. On a validation batch, the call goes to ``model.validation_step``.\n 2. On a training batch, the call goes to ``model.training_step``.\n 3. On a testing batch, the call goes to ``model.test_step``.\n\n Args:\n model: the :class:`LightningModule` currently being optimized.\n device_ids: the list of GPU ids.\n\n Return:\n DDP wrapped model\n\n Examples:\n .. code-block:: python\n\n # default implementation used in Trainer\n def configure_ddp(self, model, device_ids):\n # Lightning DDP simply routes to test_step, val_step, etc...\n model = LightningDistributedDataParallel(\n model,\n device_ids=device_ids,\n find_unused_parameters=True\n )\n return model\n\n \"\"\"\n model = LightningDistributedDataParallel(model, device_ids=device_ids, find_unused_parameters=True)\n return model","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection","text":"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\n def _init_slurm_connection(self) -> None:\n \"\"\"\n Sets up environment variables necessary for pytorch distributed communications\n based on slurm environment.\n \"\"\"\n # use slurm job id for the port number\n # guarantees unique ports across jobs from same grid search\n try:\n # use the last 4 numbers in the job id as the id\n default_port = os.environ['SLURM_JOB_ID']\n default_port = default_port[-4:]\n\n # all ports should be in the 10k+ range\n default_port = int(default_port) + 15000\n\n except Exception:\n default_port = 12910\n\n # if user gave a port number, use that one instead\n try:\n default_port = os.environ['MASTER_PORT']\n except Exception:\n os.environ['MASTER_PORT'] = str(default_port)\n\n # figure out the root node addr\n try:\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\n except Exception:\n root_node = '127.0.0.1'\n\n root_node = self.trainer.resolve_root_node_address(root_node)\n os.environ['MASTER_ADDR'] = root_node","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection","text":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True) -> None:\n \"\"\"\n Override to define your custom way of setting up a distributed environment.\n\n Lightning's implementation uses env:// init by default and sets the first node as root\n for SLURM managed cluster.\n\n Args:\n global_rank: The global process idx.\n world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).\n is_slurm_managing_tasks: is cluster managed by SLURM.\n\n \"\"\"\n if is_slurm_managing_tasks:\n self._init_slurm_connection()\n\n if 'MASTER_ADDR' not in os.environ:\n rank_zero_warn(\"MASTER_ADDR environment variable is not defined. Set as localhost\")\n os.environ['MASTER_ADDR'] = '127.0.0.1'\n log.debug(f\"MASTER_ADDR: {os.environ['MASTER_ADDR']}\")\n\n if 'MASTER_PORT' not in os.environ:\n rank_zero_warn(\"MASTER_PORT environment variable is not defined. Set as 12910\")\n os.environ['MASTER_PORT'] = '12910'\n log.debug(f\"MASTER_PORT: {os.environ['MASTER_PORT']}\")\n\n if 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE']) != world_size:\n rank_zero_warn(\n f\"WORLD_SIZE environment variable ({os.environ['WORLD_SIZE']}) \"\n f\"is not equal to the computed world size ({world_size}). Ignored.\"\n )\n\n torch_backend = \"nccl\" if self.trainer.on_gpu else \"gloo\"\n log.info(f\"initializing ddp: GLOBAL_RANK: {global_rank}, MEMBER: {global_rank+1}/{world_size}\")\n torch_distrib.init_process_group(torch_backend, rank=global_rank, world_size=world_size)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_sync_batchnorm","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_sync_batchnorm\n def configure_sync_batchnorm(self, model: 'LightningModule') -> 'LightningModule':\n \"\"\"\n Add global batchnorm for a model spread across multiple GPUs and nodes.\n\n Override to synchronize batchnorm between specific process groups instead\n of the whole world or use a different sync_bn like `apex`'s version.\n\n Args:\n model: pointer to current :class:`LightningModule`.\n\n Return:\n LightningModule with batchnorm layers synchronized between process groups\n \"\"\"\n model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model, process_group=None)\n\n return model","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\n def configure_apex(\n self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str\n ) -> Tuple['LightningModule', List[Optimizer]]:\n r\"\"\"\n Override to init AMP your own way.\n Must return a model and list of optimizers.\n\n Args:\n amp: pointer to amp library object.\n model: pointer to current :class:`LightningModule`.\n optimizers: list of optimizers passed in :meth:`configure_optimizers`.\n amp_level: AMP mode chosen ('O1', 'O2', etc...)\n\n Return:\n Apex wrapped model and optimizers\n\n Examples:\n .. code-block:: python\n\n # Default implementation used by Trainer.\n def configure_apex(self, amp, model, optimizers, amp_level):\n model, optimizers = amp.initialize(\n model, optimizers, opt_level=amp_level,\n )\n\n return model, optimizers\n \"\"\"\n model, optimizers = amp.initialize(model, optimizers, opt_level=amp_level)\n\n return model, optimizers","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\n def configure_optimizers(\n self,\n ) -> Optional[Union[Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List]]]:\n r\"\"\"\n Choose what optimizers and learning-rate schedulers to use in your optimization.\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\n\n Return:\n Any of these 6 options.\n\n - Single optimizer.\n - List or Tuple - List of optimizers.\n - Two lists - The first list has multiple optimizers, the second a list of LR schedulers (or lr_dict).\n - Dictionary, with an 'optimizer' key, and (optionally) a 'lr_scheduler' key which value is a single LR scheduler or lr_dict.\n - Tuple of dictionaries as described, with an optional 'frequency' key.\n - None - Fit will run without any optimizer.\n\n Note:\n The 'frequency' value is an int corresponding to the number of sequential batches\n optimized with the specific optimizer. It should be given to none or to all of the optimizers.\n There is a difference between passing multiple optimizers in a list,\n and passing multiple optimizers in dictionaries with a frequency of 1:\n In the former case, all optimizers will operate on the given batch in each optimization step.\n In the latter, only one optimizer will operate on the given batch at every step.\n\n The lr_dict is a dictionary which contains scheduler and its associated configuration.\n It has five keys. The default configuration is shown below.\n\n .. code-block:: python\n\n {\n 'scheduler': lr_scheduler, # The LR schduler\n 'interval': 'epoch', # The unit of the scheduler's step size\n 'frequency': 1, # The frequency of the scheduler\n 'reduce_on_platea","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step","text":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\n def optimizer_step(\n self,\n epoch: int,\n batch_idx: int,\n optimizer: Optimizer,\n optimizer_idx: int,\n second_order_closure: Optional[Callable] = None,\n on_tpu: bool = False,\n using_native_amp: bool = False,\n using_lbfgs: bool = False,\n ) -> None:\n r\"\"\"\n Override this method to adjust the default way the\n :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.\n By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example\n once per optimizer.\n\n Args:\n epoch: Current epoch\n batch_idx: Index of current batch\n optimizer: A PyTorch optimizer\n optimizer_idx: If you used multiple optimizers this indexes into that list.\n second_order_closure: closure for second order methods\n on_tpu: true if TPU backward is required\n using_native_amp: True if using native amp\n using_lbfgs: True if the matching optimizer is lbfgs\n\n Examples:\n .. code-block:: python\n\n # DEFAULT\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\n optimizer.step()\n\n # Alternating schedule for optimizer steps (i.e.: GANs)\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\n # update generator opt every 2 steps\n if optimizer_idx == 0:\n if batch_idx % 2 == 0 :\n optimizer.step()\n optimizer.zero_grad()\n\n # update discriminator opt every 4 steps\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad","text":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad\n def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int):\n optimizer.zero_grad()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch","text":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\n def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:\n r\"\"\"\n When using truncated backpropagation through time, each batch must be split along the\n time dimension. Lightning handles this by default, but for custom behavior override\n this function.\n\n Args:\n batch: Current batch\n split_size: The size of the split\n\n Return:\n List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated\n back propagation through time. The default implementation splits root level Tensors and\n Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.\n\n Examples:\n .. code-block:: python\n\n def tbptt_split_batch(self, batch, split_size):\n splits = []\n for t in range(0, time_dims[0], split_size):\n batch_split = []\n for i, x in enumerate(batch):\n if isinstance(x, torch.Tensor):\n split_x = x[:, t:t + split_size]\n elif isinstance(x, collections.Sequence):\n split_x = [None] * len(x)\n for batch_idx in range(len(x)):\n split_x[batch_idx] = x[batch_idx][t:t + split_size]\n\n batch_split.append(split_x)\n\n splits.append(batch_split)\n\n return splits\n\n Note:\n Called in the training loop after\n :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`\n if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.\n Each returned batch split is passed separately to :meth:`training_step`.\n\n \"\"\"\n time_dims = [len(x[0]) for x in batch if isinstance(x","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data","text":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\n def prepare_data(self) -> None:\n \"\"\"\n Use this to download and prepare data.\n\n .. warning:: DO NOT set state to the model (use `setup` instead)\n since this is NOT called on every GPU in DDP/TPU\n\n Example::\n\n def prepare_data(self):\n # good\n download_data()\n tokenize()\n etc()\n\n # bad\n self.split = data_split\n self.some_state = some_other_state()\n\n In DDP prepare_data can be called in two ways (using Trainer(prepare_data_per_node)):\n\n 1. Once per node. This is the default and is only called on LOCAL_RANK=0.\n 2. Once in total. Only called on GLOBAL_RANK=0.\n\n Example::\n\n # DEFAULT\n # called once per node on LOCAL_RANK=0 of that node\n Trainer(prepare_data_per_node=True)\n\n # call on GLOBAL_RANK=0 (great for shared file systems)\n Trainer(prepare_data_per_node=False)\n\n This is called before requesting the dataloaders:\n\n .. code-block:: python\n\n model.prepare_data()\n if ddp/tpu: init()\n model.setup(stage)\n model.train_dataloader()\n model.val_dataloader()\n model.test_dataloader()\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\n def train_dataloader(self) -> DataLoader:\n \"\"\"\n Implement a PyTorch DataLoader for training.\n\n Return:\n Single PyTorch :class:`~torch.utils.data.DataLoader`.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n For data processing use the following pattern:\n\n - download in :meth:`prepare_data`\n - process and split in :meth:`setup`\n\n However, the above are only necessary for distributed processing.\n\n .. warning:: do not assign state in prepare_data\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`setup`\n - :meth:`train_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware.\n There is no need to set it yourself.\n\n Example:\n .. code-block:: python\n\n def train_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.batch_size,\n shuffle=True\n )\n return loader\n\n \"\"\"\n rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\n def tng_dataloader(self): # todo: remove in v1.0.0\n \"\"\"\n Warnings:\n Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.\n \"\"\"\n output = self.train_dataloader()\n rank_zero_warn(\n \"`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0.\"\n \" and this method will be removed in v1.0.0\",\n DeprecationWarning,\n )\n return output","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\n def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\n r\"\"\"\n Implement one or multiple PyTorch DataLoaders for testing.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n For data processing use the following pattern:\n\n - download in :meth:`prepare_data`\n - process and split in :meth:`setup`\n\n However, the above are only necessary for distributed processing.\n\n .. warning:: do not assign state in prepare_data\n\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`setup`\n - :meth:`train_dataloader`\n - :meth:`val_dataloader`\n - :meth:`test_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware.\n There is no need to set it yourself.\n\n Return:\n Single or multiple PyTorch DataLoaders.\n\n Example:\n .. code-block:: python\n\n def test_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.batch_size,\n shuffle=False\n )\n\n return loader\n\n # can also return multiple dataloaders\n def test_dataloader(self):\n return [loader_a, loader_b, ..., loader_n]\n\n Note:\n If you don't need a te","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\n def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\n r\"\"\"\n Implement one or multiple PyTorch DataLoaders for validation.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`train_dataloader`\n - :meth:`val_dataloader`\n - :meth:`test_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware\n There is no need to set it yourself.\n\n Return:\n Single or multiple PyTorch DataLoaders.\n\n Examples:\n .. code-block:: python\n\n def val_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False,\n transform=transform, download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.batch_size,\n shuffle=False\n )\n\n return loader\n\n # can also return multiple dataloaders\n def val_dataloader(self):\n return [loader_a, loader_b, ..., loader_n]\n\n Note:\n If you don't need a validation dataset and a :meth:`validation_step`, you don't need to\n implement this method.\n\n Note:\n In the case where you return multiple validation dataloaders, the :meth:`validation_st","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.summarize","text":"pytorch_lightning/core/lightning.py::LightningModule.summarize\n def summarize(self, mode: str = ModelSummary.MODE_DEFAULT) -> ModelSummary:\n model_summary = ModelSummary(self, mode=mode)\n log.info('\\n' + str(model_summary))\n return model_summary","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.freeze","text":"pytorch_lightning/core/lightning.py::LightningModule.freeze\n def freeze(self) -> None:\n r\"\"\"\n Freeze all params for inference.\n\n Example:\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.freeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = False\n\n self.eval()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze","text":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\n def unfreeze(self) -> None:\n \"\"\"\n Unfreeze all parameters for training.\n\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.unfreeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = True\n\n self.train()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n r\"\"\"\n Called by Lightning to restore your model.\n If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.\n\n Args:\n checkpoint: Loaded checkpoint\n\n\n Example:\n .. code-block:: python\n\n def on_load_checkpoint(self, checkpoint):\n # 99% of the time you don't need to implement this method\n self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']\n\n Note:\n Lightning auto-restores global step, epoch, and train state including amp scaling.\n There is no need for you to restore anything regarding training.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n r\"\"\"\n Called by Lightning when saving a checkpoint to give you a chance to store anything\n else you might want to save.\n\n Args:\n checkpoint: Checkpoint to be saved\n\n Example:\n .. code-block:: python\n\n def on_save_checkpoint(self, checkpoint):\n # 99% of use cases you don't need to implement this method\n checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object\n\n Note:\n Lightning saves all aspects of training (epoch, global step, etc...)\n including amp scaling.\n There is no need for you to store anything about training.\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict","text":"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\n def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:\n r\"\"\"\n Implement this to override the default items displayed in the progress bar.\n By default it includes the average loss value, split index of BPTT (if used)\n and the version of the experiment when using a logger.\n\n .. code-block::\n\n Epoch 1: 4%|▎ | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10]\n\n Here is an example how to override the defaults:\n\n .. code-block:: python\n\n def get_progress_bar_dict(self):\n # don't show the version number\n items = super().get_progress_bar_dict()\n items.pop(\"v_num\", None)\n return items\n\n Return:\n Dictionary with the items to be displayed in the progress bar.\n \"\"\"\n # call .item() only once but store elements without graphs\n running_train_loss = self.trainer.running_loss.mean()\n avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')\n tqdm_dict = {'loss': '{:.3f}'.format(avg_training_loss)}\n\n if self.trainer.truncated_bptt_steps is not None:\n tqdm_dict['split_idx'] = self.trainer.split_idx\n\n if self.trainer.logger is not None and self.trainer.logger.version is not None:\n version = self.trainer.logger.version\n # show last 4 places of long version strings\n version = version[-4:] if isinstance(version, str) else version\n tqdm_dict['v_num'] = version\n\n return tqdm_dict","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict","text":"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\n def get_tqdm_dict(self) -> Dict[str, Union[int, str]]:\n \"\"\"\n Additional items to be displayed in the progress bar.\n\n Return:\n Dictionary with the items to be displayed in the progress bar.\n\n Warning:\n Deprecated since v0.7.3.\n Use :meth:`get_progress_bar_dict` instead.\n \"\"\"\n rank_zero_warn(\n \"`get_tqdm_dict` was renamed to `get_progress_bar_dict` in v0.7.3\"\n \" and this method will be removed in v1.0.0\",\n DeprecationWarning,\n )\n return self.get_progress_bar_dict()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._auto_collect_arguments","text":"pytorch_lightning/core/lightning.py::LightningModule._auto_collect_arguments\n @classmethod\n def _auto_collect_arguments(cls, frame=None) -> Tuple[Dict, Dict]:\n \"\"\"\n Collect all module arguments in the current constructor and all child constructors.\n The child constructors are all the ``__init__`` methods that reach the current class through\n (chained) ``super().__init__()`` calls.\n\n Args:\n frame: instance frame\n\n Returns:\n self_arguments: arguments dictionary of the first instance\n parents_arguments: arguments dictionary of the parent's instances\n \"\"\"\n if not frame:\n frame = inspect.currentframe()\n\n frame_args = collect_init_args(frame.f_back, [])\n self_arguments = frame_args[-1]\n\n # set module_arguments in child\n self_arguments = self_arguments\n parents_arguments = {}\n\n # add all arguments from parents\n for args in frame_args[:-1]:\n parents_arguments.update(args)\n return self_arguments, parents_arguments","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.save_hyperparameters","text":"pytorch_lightning/core/lightning.py::LightningModule.save_hyperparameters\n def save_hyperparameters(self, *args, frame=None) -> None:\n \"\"\"Save all model arguments.\n\n Args:\n args: single object of `dict`, `NameSpace` or `OmegaConf`\n or string names or argumenst from class `__init__`\n\n >>> from collections import OrderedDict\n >>> class ManuallyArgsModel(LightningModule):\n ... def __init__(self, arg1, arg2, arg3):\n ... super().__init__()\n ... # manually assine arguments\n ... self.save_hyperparameters('arg1', 'arg3')\n ... def forward(self, *args, **kwargs):\n ... ...\n >>> model = ManuallyArgsModel(1, 'abc', 3.14)\n >>> model.hparams\n \"arg1\": 1\n \"arg3\": 3.14\n\n >>> class AutomaticArgsModel(LightningModule):\n ... def __init__(self, arg1, arg2, arg3):\n ... super().__init__()\n ... # equivalent automatic\n ... self.save_hyperparameters()\n ... def forward(self, *args, **kwargs):\n ... ...\n >>> model = AutomaticArgsModel(1, 'abc', 3.14)\n >>> model.hparams\n \"arg1\": 1\n \"arg2\": abc\n \"arg3\": 3.14\n\n >>> class SingleArgModel(LightningModule):\n ... def __init__(self, params):\n ... super().__init__()\n ... # manually assign single argument\n ... self.save_hyperparameters(params)\n ... def forward(self, *args, **kwargs):\n ... ...\n >>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14))\n >>> model.hparams\n \"p1\": 1\n \"p2\": abc\n \"p3\": 3.14\n \"\"\"\n if not frame:\n frame = inspect.currentframe().f_back\n init_args = get_init_args(frame)\n assert init_args, 'failed to inspect the self init'\n if not args:\n hp = init_args\n self._","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._set_hparams","text":"pytorch_lightning/core/lightning.py::LightningModule._set_hparams\n def _set_hparams(self, hp: Union[dict, Namespace, str]) -> None:\n if isinstance(hp, Namespace):\n hp = vars(hp)\n if isinstance(hp, dict):\n hp = AttributeDict(hp)\n elif isinstance(hp, PRIMITIVE_TYPES):\n raise ValueError(f'Primitives {PRIMITIVE_TYPES} are not allowed.')\n elif not isinstance(hp, ALLOWED_CONFIG_TYPES):\n raise ValueError(f'Unsupported config type of {type(hp)}.')\n\n if isinstance(hp, dict) and isinstance(self.hparams, dict):\n self.hparams.update(hp)\n else:\n self._hparams = hp","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.to_onnx","text":"pytorch_lightning/core/lightning.py::LightningModule.to_onnx\n def to_onnx(self, file_path: str, input_sample: Optional[Tensor] = None, **kwargs):\n \"\"\"Saves the model in ONNX format\n\n Args:\n file_path: The path of the file the model should be saved to.\n input_sample: A sample of an input tensor for tracing.\n **kwargs: Will be passed to torch.onnx.export function.\n\n Example:\n >>> class SimpleModel(LightningModule):\n ... def __init__(self):\n ... super().__init__()\n ... self.l1 = torch.nn.Linear(in_features=64, out_features=4)\n ...\n ... def forward(self, x):\n ... return torch.relu(self.l1(x.view(x.size(0), -1)))\n\n >>> with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:\n ... model = SimpleModel()\n ... input_sample = torch.randn((1, 64))\n ... model.to_onnx(tmpfile.name, input_sample, export_params=True)\n ... os.path.isfile(tmpfile.name)\n True\n \"\"\"\n\n if isinstance(input_sample, Tensor):\n input_data = input_sample\n elif self.example_input_array is not None:\n input_data = self.example_input_array\n else:\n raise ValueError(f'input_sample and example_input_array tensors are both missing.')\n\n if 'example_outputs' not in kwargs:\n self.eval()\n kwargs['example_outputs'] = self(input_data)\n\n torch.onnx.export(self, input_data, file_path, **kwargs)","label":1},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.hparams","text":"pytorch_lightning/core/lightning.py::LightningModule.hparams\n @property\n def hparams(self) -> Union[AttributeDict, str]:\n if not hasattr(self, '_hparams'):\n self._hparams = AttributeDict()\n return self._hparams","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.hparams","text":"pytorch_lightning/core/lightning.py::LightningModule.hparams\n @hparams.setter\n def hparams(self, hp: Union[dict, Namespace, Any]):\n hparams_assignment_name = self.__get_hparams_assignment_variable()\n self._hparams_name = hparams_assignment_name\n self._set_hparams(hp)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.__get_hparams_assignment_variable","text":"pytorch_lightning/core/lightning.py::LightningModule.__get_hparams_assignment_variable\n def __get_hparams_assignment_variable(self):\n \"\"\"\n looks at the code of the class to figure out what the user named self.hparams\n this only happens when the user explicitly sets self.hparams\n \"\"\"\n try:\n class_code = inspect.getsource(self.__class__)\n lines = class_code.split('\\n')\n for line in lines:\n line = re.sub(r\"\\s+\", \"\", line, flags=re.UNICODE)\n if '.hparams=' in line:\n return line.split('=')[1]\n except Exception as e:\n return 'hparams'\n\n return None","label":0},{"cid":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.slurm_job_id","text":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.slurm_job_id\n @property\n @abstractmethod\n def slurm_job_id(self) -> int:\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.save_checkpoint","text":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.save_checkpoint\n @abstractmethod\n def save_checkpoint(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.is_overridden","text":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.is_overridden\n @abstractmethod\n def is_overridden(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_checkpoint_callback","text":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_checkpoint_callback\n def configure_checkpoint_callback(self, checkpoint_callback):\n if checkpoint_callback is True:\n # when no val step is defined, use 'loss' otherwise 'val_loss'\n train_step_only = not self.is_overridden('validation_step')\n monitor_key = 'loss' if train_step_only else 'val_loss'\n checkpoint_callback = ModelCheckpoint(\n filepath=None,\n monitor=monitor_key\n )\n elif checkpoint_callback is False:\n checkpoint_callback = None\n\n if checkpoint_callback:\n checkpoint_callback.save_function = self.save_checkpoint\n\n return checkpoint_callback","label":0},{"cid":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_early_stopping","text":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_early_stopping\n def configure_early_stopping(self, early_stop_callback):\n if early_stop_callback is True or None:\n early_stop_callback = EarlyStopping(\n monitor='val_loss',\n patience=3,\n strict=True,\n verbose=True,\n mode='min'\n )\n elif not early_stop_callback:\n early_stop_callback = None\n else:\n early_stop_callback = early_stop_callback\n return early_stop_callback","label":0},{"cid":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_progress_bar","text":"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_progress_bar\n def configure_progress_bar(self, refresh_rate=1, process_position=0):\n progress_bars = [c for c in self.callbacks if isinstance(c, ProgressBarBase)]\n if len(progress_bars) > 1:\n raise MisconfigurationException(\n 'You added multiple progress bar callbacks to the Trainer, but currently only one'\n ' progress bar is supported.'\n )\n elif len(progress_bars) == 1:\n progress_bar_callback = progress_bars[0]\n elif refresh_rate > 0:\n progress_bar_callback = ProgressBar(\n refresh_rate=refresh_rate,\n process_position=process_position,\n )\n self.callbacks.append(progress_bar_callback)\n else:\n progress_bar_callback = None\n\n return progress_bar_callback","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.setup","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.setup\n def setup(self, stage: str):\n \"\"\"Called in the beginning of fit and test\"\"\"\n for callback in self.callbacks:\n callback.setup(self, stage)","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.teardown","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.teardown\n def teardown(self, stage: str):\n \"\"\"Called at the end of fit and test\"\"\"\n for callback in self.callbacks:\n callback.teardown(self, stage)","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_init_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_init_start\n def on_init_start(self):\n \"\"\"Called when the trainer initialization begins, model has not yet been set.\"\"\"\n for callback in self.callbacks:\n callback.on_init_start(self)","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_init_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_init_end\n def on_init_end(self):\n \"\"\"Called when the trainer initialization ends, model has not yet been set.\"\"\"\n for callback in self.callbacks:\n callback.on_init_end(self)","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_fit_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_fit_start\n def on_fit_start(self):\n \"\"\"Called when the trainer initialization begins, model has not yet been set.\"\"\"\n for callback in self.callbacks:\n callback.on_fit_start(self)","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_fit_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_fit_end\n def on_fit_end(self):\n \"\"\"Called when the trainer initialization begins, model has not yet been set.\"\"\"\n for callback in self.callbacks:\n callback.on_fit_end(self)","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_sanity_check_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_sanity_check_start\n def on_sanity_check_start(self):\n \"\"\"Called when the validation sanity check starts.\"\"\"\n for callback in self.callbacks:\n callback.on_sanity_check_start(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_sanity_check_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_sanity_check_end\n def on_sanity_check_end(self):\n \"\"\"Called when the validation sanity check ends.\"\"\"\n for callback in self.callbacks:\n callback.on_sanity_check_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_epoch_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_epoch_start\n def on_train_epoch_start(self):\n \"\"\"Called when the epoch begins.\"\"\"\n for callback in self.callbacks:\n callback.on_train_epoch_start(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_epoch_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_epoch_end\n def on_train_epoch_end(self):\n \"\"\"Called when the epoch ends.\"\"\"\n for callback in self.callbacks:\n callback.on_train_epoch_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_epoch_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_epoch_start\n def on_validation_epoch_start(self):\n \"\"\"Called when the epoch begins.\"\"\"\n for callback in self.callbacks:\n callback.on_validation_epoch_start(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_epoch_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_epoch_end\n def on_validation_epoch_end(self):\n \"\"\"Called when the epoch ends.\"\"\"\n for callback in self.callbacks:\n callback.on_validation_epoch_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_epoch_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_epoch_start\n def on_test_epoch_start(self):\n \"\"\"Called when the epoch begins.\"\"\"\n for callback in self.callbacks:\n callback.on_test_epoch_start(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_epoch_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_epoch_end\n def on_test_epoch_end(self):\n \"\"\"Called when the epoch ends.\"\"\"\n for callback in self.callbacks:\n callback.on_test_epoch_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_epoch_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_epoch_start\n def on_epoch_start(self):\n \"\"\"Called when the epoch begins.\"\"\"\n for callback in self.callbacks:\n callback.on_epoch_start(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_epoch_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_epoch_end\n def on_epoch_end(self):\n \"\"\"Called when the epoch ends.\"\"\"\n for callback in self.callbacks:\n callback.on_epoch_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_start\n def on_train_start(self):\n \"\"\"Called when the train begins.\"\"\"\n for callback in self.callbacks:\n callback.on_train_start(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_end\n def on_train_end(self):\n \"\"\"Called when the train ends.\"\"\"\n for callback in self.callbacks:\n callback.on_train_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_batch_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_batch_start\n def on_batch_start(self):\n \"\"\"Called when the training batch begins.\"\"\"\n for callback in self.callbacks:\n callback.on_batch_start(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_batch_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_batch_end\n def on_batch_end(self):\n \"\"\"Called when the training batch ends.\"\"\"\n for callback in self.callbacks:\n callback.on_batch_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_batch_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_batch_start\n def on_validation_batch_start(self):\n \"\"\"Called when the validation batch begins.\"\"\"\n for callback in self.callbacks:\n callback.on_validation_batch_start(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_batch_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_batch_end\n def on_validation_batch_end(self):\n \"\"\"Called when the validation batch ends.\"\"\"\n for callback in self.callbacks:\n callback.on_validation_batch_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_batch_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_batch_start\n def on_test_batch_start(self):\n \"\"\"Called when the test batch begins.\"\"\"\n for callback in self.callbacks:\n callback.on_test_batch_start(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_batch_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_batch_end\n def on_test_batch_end(self):\n \"\"\"Called when the test batch ends.\"\"\"\n for callback in self.callbacks:\n callback.on_test_batch_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_start\n def on_validation_start(self):\n \"\"\"Called when the validation loop begins.\"\"\"\n for callback in self.callbacks:\n callback.on_validation_start(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_end\n def on_validation_end(self):\n \"\"\"Called when the validation loop ends.\"\"\"\n for callback in self.callbacks:\n callback.on_validation_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_start","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_start\n def on_test_start(self):\n \"\"\"Called when the test begins.\"\"\"\n for callback in self.callbacks:\n callback.on_test_start(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_end","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_end\n def on_test_end(self):\n \"\"\"Called when the test ends.\"\"\"\n for callback in self.callbacks:\n callback.on_test_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_keyboard_interrupt","text":"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_keyboard_interrupt\n def on_keyboard_interrupt(self):\n \"\"\"Called when the training is interrupted by KeyboardInterrupt.\"\"\"\n for callback in self.callbacks:\n callback.on_keyboard_interrupt(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.save_checkpoint","text":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.save_checkpoint\n @abstractmethod\n def save_checkpoint(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.restore","text":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.restore\n @abstractmethod\n def restore(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.init_optimizers","text":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.init_optimizers\n @abstractmethod\n def init_optimizers(self, *args) -> Tuple[List, List, List]:\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.fit","text":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.fit\n @abstractmethod\n def fit(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin._run_lr_finder_internally","text":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin._run_lr_finder_internally\n def _run_lr_finder_internally(self, model: LightningModule):\n \"\"\" Call lr finder internally during Trainer.fit() \"\"\"\n lr_finder = self.lr_find(model)\n lr = lr_finder.suggestion()\n # TODO: log lr.results to self.logger\n if isinstance(self.auto_lr_find, str):\n # Try to find requested field, may be nested\n if _nested_hasattr(model, self.auto_lr_find):\n _nested_setattr(model, self.auto_lr_find, lr)\n else:\n raise MisconfigurationException(\n f'`auto_lr_find` was set to {self.auto_lr_find}, however'\n ' could not find this as a field in `model.hparams`.')\n else:\n if hasattr(model, 'lr'):\n model.lr = lr\n elif hasattr(model, 'learning_rate'):\n model.learning_rate = lr\n else:\n raise MisconfigurationException(\n 'When auto_lr_find is set to True, expects that hparams'\n ' either has field `lr` or `learning_rate` that can overridden')\n log.info(f'Learning rate set to {lr}')","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.lr_find","text":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.lr_find\n def lr_find(\n self,\n model: LightningModule,\n train_dataloader: Optional[DataLoader] = None,\n val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,\n min_lr: float = 1e-8,\n max_lr: float = 1,\n num_training: int = 100,\n mode: str = 'exponential',\n early_stop_threshold: float = 4.0,\n ):\n r\"\"\"\n lr_find enables the user to do a range test of good initial learning rates,\n to reduce the amount of guesswork in picking a good starting learning rate.\n\n Args:\n model: Model to do range testing for\n\n train_dataloader: A PyTorch\n DataLoader with training samples. If the model has\n a predefined train_dataloader method this will be skipped.\n\n min_lr: minimum learning rate to investigate\n\n max_lr: maximum learning rate to investigate\n\n num_training: number of learning rates to test\n\n mode: search strategy, either 'linear' or 'exponential'. If set to\n 'linear' the learning rate will be searched by linearly increasing\n after each batch. If set to 'exponential', will increase learning\n rate exponentially.\n\n early_stop_threshold: threshold for stopping the search. If the\n loss at any point is larger than early_stop_threshold*best_loss\n then the search is stopped. To disable, set to None.\n\n Example::\n\n # Setup model and trainer\n model = MyModelClass(hparams)\n trainer = pl.Trainer()\n\n # Run lr finder\n lr_finder = trainer.lr_find(model, ...)\n\n # Inspect results\n fig = lr_finder.plot(); fig.show()\n suggested_lr = lr_finder.suggestion()\n\n # Overwrite lr and create new model\n hparams","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.__lr_finder_dump_params","text":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.__lr_finder_dump_params\n def __lr_finder_dump_params(self, model):\n # Prevent going into infinite loop\n self.__dumped_params = {\n 'auto_lr_find': self.auto_lr_find,\n 'callbacks': self.callbacks,\n 'logger': self.logger,\n 'max_steps': self.max_steps,\n 'checkpoint_callback': self.checkpoint_callback,\n 'early_stop_callback': self.early_stop_callback,\n 'configure_optimizers': model.configure_optimizers,\n }","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.__lr_finder_restore_params","text":"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.__lr_finder_restore_params\n def __lr_finder_restore_params(self, model):\n self.auto_lr_find = self.__dumped_params['auto_lr_find']\n self.logger = self.__dumped_params['logger']\n self.callbacks = self.__dumped_params['callbacks']\n self.max_steps = self.__dumped_params['max_steps']\n self.checkpoint_callback = self.__dumped_params['checkpoint_callback']\n self.early_stop_callback = self.__dumped_params['early_stop_callback']\n model.configure_optimizers = self.__dumped_params['configure_optimizers']\n del self.__dumped_params","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_LRFinder.__init__","text":"pytorch_lightning/trainer/lr_finder.py::_LRFinder.__init__\n def __init__(self, mode: str, lr_min: float, lr_max: float, num_training: int):\n assert mode in ('linear', 'exponential'), \\\n 'mode should be either `linear` or `exponential`'\n\n self.mode = mode\n self.lr_min = lr_min\n self.lr_max = lr_max\n self.num_training = num_training\n\n self.results = {}\n self._total_batch_idx = 0 # for debug purpose","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_LRFinder._get_new_optimizer","text":"pytorch_lightning/trainer/lr_finder.py::_LRFinder._get_new_optimizer\n def _get_new_optimizer(self, optimizer: torch.optim.Optimizer):\n \"\"\" Construct a new `configure_optimizers()` method, that has a optimizer\n with initial lr set to lr_min and a scheduler that will either\n linearly or exponentially increase the lr to lr_max in num_training steps.\n\n Args:\n optimizer: instance of `torch.optim.Optimizer`\n\n \"\"\"\n new_lrs = [self.lr_min] * len(optimizer.param_groups)\n for param_group, new_lr in zip(optimizer.param_groups, new_lrs):\n param_group[\"lr\"] = new_lr\n param_group[\"initial_lr\"] = new_lr\n\n args = (optimizer, self.lr_max, self.num_training)\n scheduler = _LinearLR(*args) if self.mode == 'linear' else _ExponentialLR(*args)\n\n def configure_optimizers():\n return [optimizer], [{'scheduler': scheduler,\n 'interval': 'step'}]\n\n return configure_optimizers","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_LRFinder._get_new_optimizer.configure_optimizers","text":"pytorch_lightning/trainer/lr_finder.py::_LRFinder._get_new_optimizer.configure_optimizers\n def configure_optimizers():\n return [optimizer], [{'scheduler': scheduler,\n 'interval': 'step'}]","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_LRFinder.plot","text":"pytorch_lightning/trainer/lr_finder.py::_LRFinder.plot\n def plot(self, suggest: bool = False, show: bool = False):\n \"\"\" Plot results from lr_find run\n Args:\n suggest: if True, will mark suggested lr to use with a red point\n\n show: if True, will show figure\n \"\"\"\n import matplotlib.pyplot as plt\n\n lrs = self.results[\"lr\"]\n losses = self.results[\"loss\"]\n\n fig, ax = plt.subplots()\n\n # Plot loss as a function of the learning rate\n ax.plot(lrs, losses)\n if self.mode == 'exponential':\n ax.set_xscale(\"log\")\n ax.set_xlabel(\"Learning rate\")\n ax.set_ylabel(\"Loss\")\n\n if suggest:\n _ = self.suggestion()\n if self._optimal_idx:\n ax.plot(lrs[self._optimal_idx], losses[self._optimal_idx],\n markersize=10, marker='o', color='red')\n\n if show:\n plt.show()\n\n return fig","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_LRFinder.suggestion","text":"pytorch_lightning/trainer/lr_finder.py::_LRFinder.suggestion\n def suggestion(self, skip_begin: int = 10, skip_end: int = 1):\n \"\"\" This will propose a suggestion for choice of initial learning rate\n as the point with the steepest negative gradient.\n\n Returns:\n lr: suggested initial learning rate to use\n skip_begin: how many samples to skip in the beginning. Prevent too naive estimates\n skip_end: how many samples to skip in the end. Prevent too optimistic estimates\n\n \"\"\"\n try:\n loss = np.array(self.results[\"loss\"][skip_begin:-skip_end])\n loss = loss[np.isfinite(loss)]\n min_grad = np.gradient(loss).argmin()\n self._optimal_idx = min_grad + skip_begin\n return self.results[\"lr\"][self._optimal_idx]\n except Exception:\n log.exception('Failed to compute suggesting for `lr`. There might not be enough points.')\n self._optimal_idx = None","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_LRCallback.__init__","text":"pytorch_lightning/trainer/lr_finder.py::_LRCallback.__init__\n def __init__(self, num_training: int,\n early_stop_threshold: float = 4.0,\n progress_bar_refresh_rate: int = 0,\n beta: float = 0.98):\n self.num_training = num_training\n self.early_stop_threshold = early_stop_threshold\n self.beta = beta\n self.losses = []\n self.lrs = []\n self.avg_loss = 0.0\n self.best_loss = 0.0\n self.progress_bar_refresh_rate = progress_bar_refresh_rate\n self.progress_bar = None","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_LRCallback.on_batch_start","text":"pytorch_lightning/trainer/lr_finder.py::_LRCallback.on_batch_start\n def on_batch_start(self, trainer, pl_module):\n \"\"\" Called before each training batch, logs the lr that will be used \"\"\"\n if (trainer.batch_idx + 1) % trainer.accumulate_grad_batches != 0:\n return\n\n if self.progress_bar_refresh_rate and self.progress_bar is None:\n self.progress_bar = tqdm(desc='Finding best initial lr', total=self.num_training)\n\n self.lrs.append(trainer.lr_schedulers[0]['scheduler'].lr[0])","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_LRCallback.on_batch_end","text":"pytorch_lightning/trainer/lr_finder.py::_LRCallback.on_batch_end\n def on_batch_end(self, trainer, pl_module):\n \"\"\" Called when the training batch ends, logs the calculated loss \"\"\"\n if (trainer.batch_idx + 1) % trainer.accumulate_grad_batches != 0:\n return\n\n if self.progress_bar:\n self.progress_bar.update()\n\n current_loss = trainer.running_loss.last().item()\n current_step = trainer.global_step + 1 # remove the +1 in 1.0\n\n # Avg loss (loss with momentum) + smoothing\n self.avg_loss = self.beta * self.avg_loss + (1 - self.beta) * current_loss\n smoothed_loss = self.avg_loss / (1 - self.beta**current_step)\n\n # Check if we diverging\n if self.early_stop_threshold is not None:\n if current_step > 1 and smoothed_loss > self.early_stop_threshold * self.best_loss:\n trainer.max_steps = current_step # stop signal\n if self.progress_bar:\n self.progress_bar.close()\n\n # Save best loss for diverging checking\n if smoothed_loss < self.best_loss or current_step == 1:\n self.best_loss = smoothed_loss\n\n self.losses.append(smoothed_loss)","label":1},{"cid":"pytorch_lightning/trainer/lr_finder.py::_LinearLR.__init__","text":"pytorch_lightning/trainer/lr_finder.py::_LinearLR.__init__\n def __init__(self,\n optimizer: torch.optim.Optimizer,\n end_lr: float,\n num_iter: int,\n last_epoch: int = -1):\n self.end_lr = end_lr\n self.num_iter = num_iter\n super(_LinearLR, self).__init__(optimizer, last_epoch)","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_LinearLR.get_lr","text":"pytorch_lightning/trainer/lr_finder.py::_LinearLR.get_lr\n def get_lr(self):\n curr_iter = self.last_epoch + 1\n r = curr_iter / self.num_iter\n\n if self.last_epoch > 0:\n val = [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]\n else:\n val = [base_lr for base_lr in self.base_lrs]\n self._lr = val\n return val","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_LinearLR.lr","text":"pytorch_lightning/trainer/lr_finder.py::_LinearLR.lr\n @property\n def lr(self):\n return self._lr","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.__init__","text":"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.__init__\n def __init__(self,\n optimizer: torch.optim.Optimizer,\n end_lr: float,\n num_iter: int,\n last_epoch: int = -1):\n self.end_lr = end_lr\n self.num_iter = num_iter\n super(_ExponentialLR, self).__init__(optimizer, last_epoch)","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.get_lr","text":"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.get_lr\n def get_lr(self):\n curr_iter = self.last_epoch + 1\n r = curr_iter / self.num_iter\n\n if self.last_epoch > 0:\n val = [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]\n else:\n val = [base_lr for base_lr in self.base_lrs]\n self._lr = val\n return val","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.lr","text":"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.lr\n @property\n def lr(self):\n return self._lr","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_nested_hasattr","text":"pytorch_lightning/trainer/lr_finder.py::_nested_hasattr\ndef _nested_hasattr(obj, path):\n parts = path.split(\".\")\n for part in parts:\n if hasattr(obj, part):\n obj = getattr(obj, part)\n else:\n return False\n else:\n return True","label":0},{"cid":"pytorch_lightning/trainer/lr_finder.py::_nested_setattr","text":"pytorch_lightning/trainer/lr_finder.py::_nested_setattr\ndef _nested_setattr(obj, path, val):\n parts = path.split(\".\")\n for part in parts[:-1]:\n if hasattr(obj, part):\n obj = getattr(obj, part)\n setattr(obj, parts[-1], val)","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model\n @abstractmethod\n def get_model(self) -> LightningModule:\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented\n @abstractmethod\n def is_function_implemented(self, *args, **kwargs):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation\n @abstractmethod\n def run_evaluation(self, *args, **kwargs):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu\n @abstractmethod\n def transfer_batch_to_gpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu\n @abstractmethod\n def transfer_batch_to_tpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients\n @abstractmethod\n def clip_gradients(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.detect_nan_tensors","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.detect_nan_tensors\n @abstractmethod\n def detect_nan_tensors(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overridden","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overridden\n @abstractmethod\n def is_overridden(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_progress_bar_metrics","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_progress_bar_metrics\n @abstractmethod\n def add_progress_bar_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics\n @abstractmethod\n def log_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output\n @abstractmethod\n def process_output(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader\n @abstractmethod\n def reset_train_dataloader(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader\n @abstractmethod\n def reset_val_dataloader(self, model):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg\n @abstractmethod\n def has_arg(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train\n def train(self):\n # add signal handlers for process kills\n # def _signal_kill_handler(*args):\n # return TrainerTrainLoopMixin.run_training_teardown(self)\n #\n # orig_signal_handlers = {}\n # for sig_name in SIGNAL_TERMINATE:\n # orig_signal_handlers[sig_name] = signal.signal(getattr(signal, sig_name),\n # _signal_kill_handler)\n\n # get model\n model = self.get_model()\n\n # enable train mode\n model.train()\n\n # enable gradients\n torch.set_grad_enabled(True)\n\n # load data\n # if reload_dataloaders_every_epoch, this is moved to the epoch loop\n if not self.reload_dataloaders_every_epoch:\n self.reset_train_dataloader(model)\n\n if model.val_dataloader is not None:\n self.reset_val_dataloader(model)\n\n # Train start events\n with self.profiler.profile('on_train_start'):\n # callbacks\n self.on_train_start()\n # model hooks\n model.on_train_start()\n\n try:\n # run all epochs\n for epoch in range(self.current_epoch, self.max_epochs):\n # reset train dataloader\n if self.reload_dataloaders_every_epoch:\n self.reset_train_dataloader(model)\n # set seed for distributed sampler (enables shuffling for each epoch)\n if (self.use_ddp or self.use_horovod) \\\n and hasattr(self.train_dataloader, 'sampler') \\\n and hasattr(self.train_dataloader.sampler, 'set_epoch'):\n self.train_dataloader.sampler.set_epoch(epoch)\n\n # update training progress in trainer and model\n model.current_epoch = epoch\n self.current_epoch = epoch\n\n # changing gradient accordin","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.prepare_train_loop_dataloader","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.prepare_train_loop_dataloader\n def prepare_train_loop_dataloader(self, train_dataloader):\n # on TPU we have to wrap it under the ParallelLoader\n if self.use_tpu:\n device = xm.xla_device(self.tpu_id)\n train_dataloader = xla_pl.ParallelLoader(train_dataloader, [device])\n train_dataloader = train_dataloader.per_device_loader(device)\n\n return train_dataloader","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_on_epoch_start_hook","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_on_epoch_start_hook\n def run_on_epoch_start_hook(self, model):\n # Epoch start events\n with self.profiler.profile('on_epoch_start'):\n # callbacks\n self.on_epoch_start()\n\n # model hooks\n if self.is_function_implemented('on_epoch_start'):\n model.on_epoch_start()\n\n # Epoch start events\n with self.profiler.profile('on_train_epoch_start'):\n # callbacks\n self.on_train_epoch_start()\n\n # model hooks\n if self.is_function_implemented('on_train_epoch_start'):\n model.on_train_epoch_start()","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch\n def run_training_epoch(self):\n\n # get model\n model = self.get_model()\n\n # Epoch start events\n self.run_on_epoch_start_hook(model)\n\n # modify dataloader if needed (ddp, etc...)\n train_dataloader = self.prepare_train_loop_dataloader(self.train_dataloader)\n\n # bookkeeping\n epoch_output = []\n should_check_val = False\n\n # structured result accumulators for callbacks\n early_stopping_accumulator = Accumulator()\n checkpoint_accumulator = Accumulator()\n\n # run epoch\n for batch_idx, (batch, is_last_batch) in self.profiler.profile_iterable(\n enumerate(_with_is_last(train_dataloader)), \"get_train_batch\"\n ):\n # stop epoch if we limited the number of training batches\n if batch_idx >= self.num_training_batches:\n break\n\n self.batch_idx = batch_idx\n model.global_step = self.global_step\n\n # ------------------------------------\n # TRAINING_STEP + TRAINING_STEP_END\n # ------------------------------------\n batch_output = self.run_training_batch(batch, batch_idx)\n\n # only track outputs when user implements training_epoch_end\n # otherwise we will build up unnecessary memory\n step_out = batch_output.training_step_output_for_epoch_end\n should_auto_reduce_train_result = isinstance(step_out, Result) and step_out.should_reduce_on_epoch_end\n if isinstance(step_out, dict) and 'early_stop_on' in step_out:\n early_stopping_accumulator.accumulate(step_out['early_stop_on'])\n\n if isinstance(step_out, dict) and 'checkpoint_on' in step_out:\n checkpoint_accumulator.accumulate(step_out['checkpoint_on'])\n\n if self.is_overridden('training_epoch_end', model=self.get_model()) or should_auto_reduce_","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.check_checkpoint_callback","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.check_checkpoint_callback\n def check_checkpoint_callback(self, should_check_val):\n # when no val loop is present or fast-dev-run still need to call checkpoints\n # TODO bake this logic into the checkpoint callback\n should_activate = not self.is_overridden('validation_step') and not should_check_val\n if should_activate:\n checkpoint_callbacks = [c for c in self.callbacks if isinstance(c, ModelCheckpoint)]\n [c.on_validation_end(self, self.get_model()) for c in checkpoint_callbacks]","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_train_loop_lr_schedulers","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_train_loop_lr_schedulers\n def update_train_loop_lr_schedulers(self):\n if (self.batch_idx + 1) % self.accumulate_grad_batches == 0:\n # update lr\n self.update_learning_rates(interval='step')","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_on_epoch_end_hook","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_on_epoch_end_hook\n def run_on_epoch_end_hook(self, model):\n with self.profiler.profile('on_epoch_end'):\n # callbacks\n self.on_epoch_end()\n # model hooks\n if self.is_function_implemented('on_epoch_end'):\n model.on_epoch_end()\n\n with self.profiler.profile('on_train_epoch_end'):\n # callbacks\n self.on_train_epoch_end()\n\n # model hooks\n if self.is_function_implemented('on_train_epoch_end'):\n model.on_train_epoch_end()","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch_end","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch_end\n def run_training_epoch_end(self, epoch_output, checkpoint_accumulator, early_stopping_accumulator):\n model = self.get_model()\n is_result_obj = len(epoch_output) > 0 and isinstance(epoch_output[0], Result)\n\n epoch_log_metrics = {}\n epoch_callback_metrics = {}\n epoch_progress_bar_metrics = {}\n\n # -----------------------\n # Calculate epoch callback values if given\n # -----------------------\n if checkpoint_accumulator.num_values > 0:\n epoch_callback_metrics['checkpoint_on'] = checkpoint_accumulator.mean()\n\n if early_stopping_accumulator.num_values > 0:\n epoch_callback_metrics['early_stop_on'] = early_stopping_accumulator.mean()\n\n # --------------------------\n # EPOCH END STEP IF DEFINED\n # --------------------------\n if self.is_overridden('training_epoch_end', model=model):\n self.global_step += 1\n\n # remove the protected keys so the user doesn't have to deal with them\n if is_result_obj:\n epoch_output = epoch_output[0].__class__.gather(epoch_output)\n\n # run training_epoch_end\n epoch_output = model.training_epoch_end(epoch_output)\n\n if isinstance(epoch_output, Result):\n epoch_log_metrics = epoch_output.epoch_log_metrics\n epoch_progress_bar_metrics = epoch_output.epoch_pbar_metrics\n else:\n _processed_outputs = self.process_output(epoch_output)\n epoch_progress_bar_metrics = _processed_outputs[1]\n epoch_log_metrics = _processed_outputs[2]\n epoch_callback_metrics = _processed_outputs[3]\n\n # --------------------------\n # Structured Result (auto epoch end)\n # --------------------------\n elif is_result_obj:\n epoch_output = epoch_output[0].__class__.redu","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.sync_horovod","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.sync_horovod\n def sync_horovod(self):\n if self.use_horovod:\n hvd.join(hvd.local_rank() if self.on_gpu else -1)","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.increment_accumulated_grad_global_step","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.increment_accumulated_grad_global_step\n def increment_accumulated_grad_global_step(self):\n # progress global step according to grads progress\n if (self.batch_idx + 1) % self.accumulate_grad_batches == 0:\n self.global_step += 1\n self.total_batch_idx += 1","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.save_train_loop_metrics_to_loggers","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.save_train_loop_metrics_to_loggers\n def save_train_loop_metrics_to_loggers(self, batch_idx, batch_output):\n # when metrics should be logged\n should_log_metrics = (batch_idx + 1) % self.row_log_interval == 0 or self.should_stop\n if should_log_metrics or self.fast_dev_run:\n # logs user requested information to logger\n metrics = batch_output.batch_log_metrics\n grad_norm_dic = batch_output.grad_norm_dic\n if len(metrics) > 0 or len(grad_norm_dic) > 0:\n self.log_metrics(metrics, grad_norm_dic)","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.save_loggers_in_training_loop","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.save_loggers_in_training_loop\n def save_loggers_in_training_loop(self, batch_idx):\n # when loggers should save to disk\n should_save_log = (batch_idx + 1) % self.log_save_interval == 0 or self.should_stop\n if should_save_log or self.fast_dev_run:\n if self.is_global_zero and self.logger is not None:\n self.logger.save()","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.should_check_val","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.should_check_val\n def should_check_val(self, batch_idx, is_last_batch):\n # decide if we should run validation\n is_val_check_batch = (batch_idx + 1) % self.val_check_batch == 0\n can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0\n can_check_val = self.enable_validation and can_check_epoch\n should_check_val = is_val_check_batch or self.should_stop\n is_last_batch_for_infinite_dataset = (is_last_batch and self.val_check_batch == float('inf'))\n should_check_val = can_check_val and (should_check_val or is_last_batch_for_infinite_dataset)\n\n return should_check_val","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch\n def run_training_batch(self, batch, batch_idx):\n # track grad norms\n grad_norm_dic = {}\n\n # track all metrics for callbacks\n batch_callback_metrics = []\n\n # track metrics to log\n batch_log_metrics = []\n\n using_results_obj = False\n\n if batch is None:\n return AttributeDict(signal=0, grad_norm_dic=grad_norm_dic)\n\n # Batch start events\n with self.profiler.profile('on_batch_start'):\n # callbacks\n self.on_batch_start()\n # hooks\n if self.is_function_implemented('on_batch_start'):\n response = self.get_model().on_batch_start(batch)\n if response == -1:\n return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic)\n\n splits = [batch]\n if self.truncated_bptt_steps is not None:\n model_ref = self.get_model()\n with self.profiler.profile('tbptt_split_batch'):\n splits = model_ref.tbptt_split_batch(batch, self.truncated_bptt_steps)\n\n self.hiddens = None\n for split_idx, split_batch in enumerate(splits):\n self.split_idx = split_idx\n\n for opt_idx, optimizer in self._get_optimizers_iterable():\n # make sure only the gradients of the current optimizer's parameters are calculated\n # in the training step to prevent dangling gradients in multiple-optimizer setup.\n if len(self.optimizers) > 1:\n for param in self.get_model().parameters():\n param.requires_grad = False\n for group in optimizer.param_groups:\n for param in group['params']:\n param.requires_grad = True\n\n # -------------------\n # calculate loss (train step + train step end)\n # -------------------","label":1},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_batch_backward_pass","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_batch_backward_pass\n def run_batch_backward_pass(self, split_batch, batch_idx, opt_idx, optimizer):\n # ------------------\n # GRAD NORMS\n # ------------------\n # track gradient norms when requested\n grad_norm_dic = {}\n if batch_idx % self.row_log_interval == 0:\n if float(self.track_grad_norm) > 0:\n model = self.get_model()\n grad_norm_dic = model.grad_norm(\n self.track_grad_norm)\n\n # ------------------\n # CLIP GRADS\n # ------------------\n if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:\n self.scaler.unscale_(optimizer)\n self.clip_gradients(optimizer)\n\n # ------------------\n # .STEP + ZERO_GRAD\n # ------------------\n self.call_optimizer_step(optimizer, opt_idx, batch_idx, split_batch)\n\n return grad_norm_dic","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_optimizer_step","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_optimizer_step\n def call_optimizer_step(self, optimizer, opt_idx, batch_idx, split_batch):\n # calls .step(), .zero_grad()\n # override function to modify this behavior\n model = self.get_model()\n\n with self.profiler.profile('optimizer_step'):\n lambda_closure = lambda: self.optimizer_closure(\n split_batch,\n batch_idx,\n opt_idx,\n optimizer,\n self.hiddens\n ).loss\n\n # apply TPU optimizer\n if self.use_tpu and XLA_AVAILABLE:\n model.optimizer_step(self.current_epoch, batch_idx,\n optimizer, opt_idx, lambda_closure, on_tpu=True)\n\n # for LBFGS do something a bit different\n elif isinstance(optimizer, torch.optim.LBFGS):\n\n # native amp + lbfgs is a no go right now\n if self.use_amp and NATIVE_AMP_AVALAIBLE:\n raise MisconfigurationException(\n 'native PyTorch amp and lbfgs are not compatible.'\n ' To request, please file a Github issue in PyTorch and tag @mcarilli')\n model.optimizer_step(self.current_epoch, batch_idx, optimizer, opt_idx, lambda_closure,\n using_lbfgs=True)\n\n # when using 16-bit\n else:\n native_amp = self.use_amp and NATIVE_AMP_AVALAIBLE\n model.optimizer_step(self.current_epoch, batch_idx, optimizer, opt_idx, lambda_closure,\n using_native_amp=native_amp)\n\n # in native 16-bit we need to update scaler after optimizer step\n if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:\n self.scaler.update()\n\n # model hook\n model.on_before_zero_grad(optimizer)\n\n # clear gradients\n ","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.optimizer_closure","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.optimizer_closure\n def optimizer_closure(self, split_batch, batch_idx, opt_idx, optimizer, hiddens):\n \"\"\"\n wrap the forward step in a closure so second order methods work\n \"\"\"\n # ---------------------------\n # FORWARD (TRAINING STEP + TRAIN STEP END)\n # ---------------------------\n with self.profiler.profile('model_forward'):\n if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:\n with torch.cuda.amp.autocast():\n training_step_output = self.training_forward(split_batch, batch_idx,\n opt_idx, hiddens)\n else:\n training_step_output = self.training_forward(split_batch, batch_idx, opt_idx,\n hiddens)\n\n # ----------------------------\n # PROCESS THE RESULT\n # ----------------------------\n # format and reduce outputs accordingly\n training_step_output_for_epoch_end = training_step_output\n is_result_obj = isinstance(training_step_output, Result)\n\n # don't allow EvalResult in the training_step\n if isinstance(training_step_output, EvalResult):\n raise MisconfigurationException('training_step cannot return EvalResult, '\n 'use a dict or TrainResult instead')\n\n # handle regular dicts\n if not is_result_obj:\n training_step_output = self.process_output(training_step_output, train=True)\n\n training_step_output = AttributeDict(\n batch_loss=training_step_output[0],\n pbar_on_batch_end=training_step_output[1],\n log_metrics=training_step_output[2],\n callback_metrics=training_step_output[3],\n ","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin._get_optimizers_iterable","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin._get_optimizers_iterable\n def _get_optimizers_iterable(self):\n if not self.optimizer_frequencies:\n # call training_step once per optimizer\n return list(enumerate(self.optimizers))\n\n optimizer_freq_cumsum = np.cumsum(self.optimizer_frequencies)\n optimizers_loop_length = optimizer_freq_cumsum[-1]\n current_place_in_loop = self.total_batch_idx % optimizers_loop_length\n\n # find optimzier index by looking for the first {item > current_place} in the cumsum list\n opt_idx = np.argmax(optimizer_freq_cumsum > current_place_in_loop)\n return [(opt_idx, self.optimizers[opt_idx])]","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown\n def run_training_teardown(self):\n if hasattr(self, '_teardown_already_run') and self._teardown_already_run:\n return\n\n self._teardown_already_run = True\n\n # Train end events\n with self.profiler.profile('on_train_end'):\n # callbacks\n self.on_train_end()\n # model hooks\n if self.is_function_implemented('on_train_end'):\n self.get_model().on_train_end()\n\n if self.logger is not None:\n self.logger.finalize(\"success\")\n\n # summarize profile results\n if self.global_rank == 0:\n self.profiler.describe()\n\n if self.global_rank == 0:\n for proc in self.interactive_ddp_procs:\n subprocess.Popen.kill(proc)\n\n # clean up dist group\n if self.use_ddp or self.use_ddp2:\n torch_distrib.destroy_process_group()\n\n # clear mem\n if self.on_gpu:\n model = self.get_model()\n model.cpu()\n torch.cuda.empty_cache()","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward\n def training_forward(self, batch, batch_idx, opt_idx, hiddens):\n \"\"\"\n Handle forward for each training case (distributed, single gpu, etc...)\n :param batch:\n :param batch_idx:\n :return:\n \"\"\"\n # ---------------\n # FORWARD\n # ---------------\n # enable not needing to add opt_idx to training_step\n args = [batch, batch_idx]\n\n if len(self.optimizers) > 1:\n if self.has_arg('training_step', 'optimizer_idx'):\n args.append(opt_idx)\n else:\n num_opts = len(self.optimizers)\n raise ValueError(\n f'Your LightningModule defines {num_opts} optimizers but '\n f'training_step is missing the \"optimizer_idx\" argument.'\n )\n\n # pass hiddens if using tbptt\n if self.truncated_bptt_steps is not None:\n args.append(hiddens)\n\n # distributed forward\n if self.use_ddp or self.use_ddp2 or self.use_dp:\n output = self.model(*args)\n\n # Horovod\n elif self.use_horovod and self.on_gpu:\n batch = self.transfer_batch_to_gpu(batch, hvd.local_rank())\n args[0] = batch\n output = self.model.training_step(*args)\n\n # single GPU forward\n elif self.use_single_gpu:\n gpu_id = 0\n if isinstance(self.data_parallel_device_ids, list):\n gpu_id = self.data_parallel_device_ids[0]\n\n # Don't copy the batch since there is a single gpu that the batch could\n # be referenced from and if there are multiple optimizers the batch will\n # wind up copying it to the same device repeatedly.\n batch = self.transfer_batch_to_gpu(batch, gpu_id)\n args[0] = batch\n output = self.model.training_step(*args)\n\n # TPU support\n elif self.use_tpu","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates\n def update_learning_rates(self, interval: str):\n \"\"\"Update learning rates.\n\n Args:\n interval: either 'epoch' or 'step'.\n \"\"\"\n if not self.lr_schedulers:\n return\n\n for lr_scheduler in self.lr_schedulers:\n current_idx = self.batch_idx if interval == 'step' else self.current_epoch\n current_idx += 1 # account for both batch and epoch starts from 0\n # Take step if call to update_learning_rates matches the interval key and\n # the current step modulo the schedulers frequency is zero\n if lr_scheduler['interval'] == interval and current_idx % lr_scheduler['frequency'] == 0:\n # If instance of ReduceLROnPlateau, we need to pass validation loss\n if lr_scheduler['reduce_on_plateau']:\n monitor_key = lr_scheduler['monitor']\n monitor_val = self.callback_metrics.get(monitor_key)\n if monitor_val is None:\n avail_metrics = ','.join(list(self.callback_metrics.keys()))\n raise MisconfigurationException(\n f'ReduceLROnPlateau conditioned on metric {monitor_key}'\n f' which is not available. Available metrics are: {avail_metrics}.'\n ' Condition can be set using `monitor` key in lr scheduler dict'\n )\n lr_scheduler['scheduler'].step(monitor_val)\n else:\n lr_scheduler['scheduler'].step()","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::_with_is_last","text":"pytorch_lightning/trainer/training_loop.py::_with_is_last\ndef _with_is_last(iterable):\n \"\"\"Pass through values from the given iterable with an added boolean indicating if this is the last item.\n See `https://stackoverflow.com/a/1630350 <https://stackoverflow.com/a/1630350>`_\"\"\"\n it = iter(iterable)\n last = next(it)\n for val in it:\n # yield last and has next\n yield last, False\n last = val\n # yield last, no longer has next\n yield last, True","label":0}],"string":"[\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.setup\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.setup\\n def setup(self, trainer, stage: str):\\n \\\"\\\"\\\"Called when fit or test begins\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.teardown\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.teardown\\n def teardown(self, trainer, stage: str):\\n \\\"\\\"\\\"Called when fit or test ends\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_init_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_init_start\\n def on_init_start(self, trainer):\\n \\\"\\\"\\\"Called when the trainer initialization begins, model has not yet been set.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_init_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_init_end\\n def on_init_end(self, trainer):\\n \\\"\\\"\\\"Called when the trainer initialization ends, model has not yet been set.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_fit_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_fit_start\\n def on_fit_start(self, trainer):\\n \\\"\\\"\\\"Called when fit begins\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_fit_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_fit_end\\n def on_fit_end(self, trainer):\\n \\\"\\\"\\\"Called when fit ends\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_sanity_check_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_sanity_check_start\\n def on_sanity_check_start(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the validation sanity check starts.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_sanity_check_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_sanity_check_end\\n def on_sanity_check_end(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the validation sanity check ends.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_train_epoch_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_train_epoch_start\\n def on_train_epoch_start(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the train epoch begins.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_train_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_train_epoch_end\\n def on_train_epoch_end(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the train epoch ends.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_epoch_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_epoch_start\\n def on_validation_epoch_start(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the val epoch begins.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_epoch_end\\n def on_validation_epoch_end(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the val epoch ends.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_epoch_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_epoch_start\\n def on_test_epoch_start(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the test epoch begins.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_epoch_end\\n def on_test_epoch_end(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the test epoch ends.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_epoch_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_epoch_start\\n def on_epoch_start(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the epoch begins.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_epoch_end\\n def on_epoch_end(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the epoch ends.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_batch_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_batch_start\\n def on_batch_start(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the training batch begins.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_batch_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_batch_start\\n def on_validation_batch_start(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the validation batch begins.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_batch_end\\n def on_validation_batch_end(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the validation batch ends.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_batch_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_batch_start\\n def on_test_batch_start(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the test batch begins.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_batch_end\\n def on_test_batch_end(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the test batch ends.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_batch_end\\n def on_batch_end(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the training batch ends.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_train_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_train_start\\n def on_train_start(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the train begins.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_train_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_train_end\\n def on_train_end(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the train ends.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_start\\n def on_validation_start(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the validation loop begins.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_validation_end\\n def on_validation_end(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the validation loop ends.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_start\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_start\\n def on_test_start(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the test begins.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_end\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_test_end\\n def on_test_end(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the test ends.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/base.py::Callback.on_keyboard_interrupt\",\n \"text\": \"pytorch_lightning/callbacks/base.py::Callback.on_keyboard_interrupt\\n def on_keyboard_interrupt(self, trainer, pl_module):\\n \\\"\\\"\\\"Called when the training is interrupted by KeyboardInterrupt.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.__init__\",\n \"text\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.__init__\\n def __init__(self):\\n self.lrs = None\\n self.lr_sch_names = []\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_train_start\",\n \"text\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_train_start\\n def on_train_start(self, trainer, pl_module):\\n \\\"\\\"\\\" Called before training, determines unique names for all lr\\n schedulers in the case of multiple of the same type or in\\n the case of multiple parameter groups\\n \\\"\\\"\\\"\\n if not trainer.logger:\\n raise MisconfigurationException(\\n 'Cannot use LearningRateLogger callback with Trainer that has no logger.')\\n\\n if not trainer.lr_schedulers:\\n rank_zero_warn(\\n 'You are using LearningRateLogger callback with models that'\\n ' have no learning rate schedulers. Please see documentation'\\n ' for `configure_optimizers` method.', RuntimeWarning\\n )\\n\\n # Find names for schedulers\\n names = self._find_names(trainer.lr_schedulers)\\n\\n # Initialize for storing values\\n self.lrs = {name: [] for name in names}\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_batch_start\",\n \"text\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_batch_start\\n def on_batch_start(self, trainer, pl_module):\\n latest_stat = self._extract_lr(trainer, 'step')\\n if trainer.logger and latest_stat:\\n trainer.logger.log_metrics(latest_stat, step=trainer.global_step)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_epoch_start\",\n \"text\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger.on_epoch_start\\n def on_epoch_start(self, trainer, pl_module):\\n latest_stat = self._extract_lr(trainer, 'epoch')\\n if trainer.logger and latest_stat:\\n trainer.logger.log_metrics(latest_stat, step=trainer.global_step)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger._extract_lr\",\n \"text\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger._extract_lr\\n def _extract_lr(self, trainer, interval):\\n \\\"\\\"\\\" Extracts learning rates for lr schedulers and saves information\\n into dict structure. \\\"\\\"\\\"\\n latest_stat = {}\\n for name, scheduler in zip(self.lr_sch_names, trainer.lr_schedulers):\\n if scheduler['interval'] == interval:\\n param_groups = scheduler['scheduler'].optimizer.param_groups\\n if len(param_groups) != 1:\\n for i, pg in enumerate(param_groups):\\n lr, key = pg['lr'], f'{name}/pg{i + 1}'\\n self.lrs[key].append(lr)\\n latest_stat[key] = lr\\n else:\\n self.lrs[name].append(param_groups[0]['lr'])\\n latest_stat[name] = param_groups[0]['lr']\\n return latest_stat\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger._find_names\",\n \"text\": \"pytorch_lightning/callbacks/lr_logger.py::LearningRateLogger._find_names\\n def _find_names(self, lr_schedulers):\\n # Create uniqe names in the case we have multiple of the same learning\\n # rate schduler + multiple parameter groups\\n names = []\\n for scheduler in lr_schedulers:\\n sch = scheduler['scheduler']\\n if 'name' in scheduler:\\n name = scheduler['name']\\n else:\\n opt_name = 'lr-' + sch.optimizer.__class__.__name__\\n i, name = 1, opt_name\\n # Multiple schduler of the same type\\n while True:\\n if name not in names:\\n break\\n i, name = i + 1, f'{opt_name}-{i}'\\n\\n # Multiple param groups for the same schduler\\n param_groups = sch.optimizer.param_groups\\n if len(param_groups) != 1:\\n for i, pg in enumerate(param_groups):\\n temp = f'{name}/pg{i + 1}'\\n names.append(temp)\\n else:\\n names.append(name)\\n\\n self.lr_sch_names.append(name)\\n return names\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.__init__\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.__init__\\n def __init__(self):\\n\\n self._trainer = None\\n self._train_batch_idx = 0\\n self._val_batch_idx = 0\\n self._test_batch_idx = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.trainer\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.trainer\\n @property\\n def trainer(self):\\n return self._trainer\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.train_batch_idx\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.train_batch_idx\\n @property\\n def train_batch_idx(self) -> int:\\n \\\"\\\"\\\"\\n The current batch index being processed during training.\\n Use this to update your progress bar.\\n \\\"\\\"\\\"\\n return self._train_batch_idx\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.val_batch_idx\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.val_batch_idx\\n @property\\n def val_batch_idx(self) -> int:\\n \\\"\\\"\\\"\\n The current batch index being processed during validation.\\n Use this to update your progress bar.\\n \\\"\\\"\\\"\\n return self._val_batch_idx\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.test_batch_idx\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.test_batch_idx\\n @property\\n def test_batch_idx(self) -> int:\\n \\\"\\\"\\\"\\n The current batch index being processed during testing.\\n Use this to update your progress bar.\\n \\\"\\\"\\\"\\n return self._test_batch_idx\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_train_batches\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_train_batches\\n @property\\n def total_train_batches(self) -> int:\\n \\\"\\\"\\\"\\n The total number of training batches during training, which may change from epoch to epoch.\\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\\n training dataloader is of infinite size.\\n \\\"\\\"\\\"\\n return self.trainer.num_training_batches\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_val_batches\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_val_batches\\n @property\\n def total_val_batches(self) -> int:\\n \\\"\\\"\\\"\\n The total number of training batches during validation, which may change from epoch to epoch.\\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\\n validation dataloader is of infinite size.\\n \\\"\\\"\\\"\\n total_val_batches = 0\\n if not self.trainer.disable_validation:\\n is_val_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0\\n total_val_batches = sum(self.trainer.num_val_batches) if is_val_epoch else 0\\n return total_val_batches\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_test_batches\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_test_batches\\n @property\\n def total_test_batches(self) -> int:\\n \\\"\\\"\\\"\\n The total number of training batches during testing, which may change from epoch to epoch.\\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\\n test dataloader is of infinite size.\\n \\\"\\\"\\\"\\n return sum(self.trainer.num_test_batches)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.disable\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.disable\\n def disable(self):\\n \\\"\\\"\\\"\\n You should provide a way to disable the progress bar.\\n The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this to disable the\\n output on processes that have a rank different from 0, e.g., in multi-node training.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.enable\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.enable\\n def enable(self):\\n \\\"\\\"\\\"\\n You should provide a way to enable the progress bar.\\n The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this in e.g. pre-training\\n routines like the `learning rate finder <lr_finder.rst>`_ to temporarily enable and\\n disable the main progress bar.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_init_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_init_end\\n def on_init_end(self, trainer):\\n self._trainer = trainer\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_train_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_train_start\\n def on_train_start(self, trainer, pl_module):\\n self._train_batch_idx = trainer.batch_idx\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_epoch_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_epoch_start\\n def on_epoch_start(self, trainer, pl_module):\\n self._train_batch_idx = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_batch_end\\n def on_batch_end(self, trainer, pl_module):\\n self._train_batch_idx += 1\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_start\\n def on_validation_start(self, trainer, pl_module):\\n self._val_batch_idx = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_batch_end\\n def on_validation_batch_end(self, trainer, pl_module):\\n self._val_batch_idx += 1\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_start\\n def on_test_start(self, trainer, pl_module):\\n self._test_batch_idx = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_batch_end\\n def on_test_batch_end(self, trainer, pl_module):\\n self._test_batch_idx += 1\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.__init__\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.__init__\\n def __init__(self, refresh_rate: int = 1, process_position: int = 0):\\n super().__init__()\\n self._refresh_rate = refresh_rate\\n self._process_position = process_position\\n self._enabled = True\\n self.main_progress_bar = None\\n self.val_progress_bar = None\\n self.test_progress_bar = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.__getstate__\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.__getstate__\\n def __getstate__(self):\\n # can't pickle the tqdm objects\\n state = self.__dict__.copy()\\n state['main_progress_bar'] = None\\n state['val_progress_bar'] = None\\n state['test_progress_bar'] = None\\n return state\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.refresh_rate\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.refresh_rate\\n @property\\n def refresh_rate(self) -> int:\\n return self._refresh_rate\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.process_position\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.process_position\\n @property\\n def process_position(self) -> int:\\n return self._process_position\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.is_enabled\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.is_enabled\\n @property\\n def is_enabled(self) -> bool:\\n return self._enabled and self.refresh_rate > 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.is_disabled\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.is_disabled\\n @property\\n def is_disabled(self) -> bool:\\n return not self.is_enabled\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.disable\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.disable\\n def disable(self) -> None:\\n self._enabled = False\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.enable\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.enable\\n def enable(self) -> None:\\n self._enabled = True\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.init_sanity_tqdm\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.init_sanity_tqdm\\n def init_sanity_tqdm(self) -> tqdm:\\n \\\"\\\"\\\" Override this to customize the tqdm bar for the validation sanity run. \\\"\\\"\\\"\\n bar = tqdm(\\n desc='Validation sanity check',\\n position=(2 * self.process_position),\\n disable=self.is_disabled,\\n leave=False,\\n dynamic_ncols=True,\\n file=sys.stdout,\\n )\\n return bar\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.init_train_tqdm\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.init_train_tqdm\\n def init_train_tqdm(self) -> tqdm:\\n \\\"\\\"\\\" Override this to customize the tqdm bar for training. \\\"\\\"\\\"\\n bar = tqdm(\\n desc='Training',\\n initial=self.train_batch_idx,\\n position=(2 * self.process_position),\\n disable=self.is_disabled,\\n leave=True,\\n dynamic_ncols=True,\\n file=sys.stdout,\\n smoothing=0,\\n )\\n return bar\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.init_validation_tqdm\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.init_validation_tqdm\\n def init_validation_tqdm(self) -> tqdm:\\n \\\"\\\"\\\" Override this to customize the tqdm bar for validation. \\\"\\\"\\\"\\n bar = tqdm(\\n desc='Validating',\\n position=(2 * self.process_position + 1),\\n disable=self.is_disabled,\\n leave=False,\\n dynamic_ncols=True,\\n file=sys.stdout\\n )\\n return bar\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.init_test_tqdm\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.init_test_tqdm\\n def init_test_tqdm(self) -> tqdm:\\n \\\"\\\"\\\" Override this to customize the tqdm bar for testing. \\\"\\\"\\\"\\n bar = tqdm(\\n desc='Testing',\\n position=(2 * self.process_position),\\n disable=self.is_disabled,\\n leave=True,\\n dynamic_ncols=True,\\n file=sys.stdout\\n )\\n return bar\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_start\\n def on_sanity_check_start(self, trainer, pl_module):\\n super().on_sanity_check_start(trainer, pl_module)\\n self.val_progress_bar = self.init_sanity_tqdm()\\n self.val_progress_bar.total = convert_inf(trainer.num_sanity_val_steps * len(trainer.val_dataloaders))\\n self.main_progress_bar = tqdm(disable=True) # dummy progress bar\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_end\\n def on_sanity_check_end(self, trainer, pl_module):\\n super().on_sanity_check_end(trainer, pl_module)\\n self.main_progress_bar.close()\\n self.val_progress_bar.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_start\\n def on_train_start(self, trainer, pl_module):\\n super().on_train_start(trainer, pl_module)\\n self.main_progress_bar = self.init_train_tqdm()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_epoch_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_epoch_start\\n def on_epoch_start(self, trainer, pl_module):\\n super().on_epoch_start(trainer, pl_module)\\n total_train_batches = self.total_train_batches\\n total_val_batches = self.total_val_batches\\n if total_train_batches != float('inf') and not trainer.fast_dev_run:\\n # val can be checked multiple times per epoch\\n val_checks_per_epoch = total_train_batches // trainer.val_check_batch\\n total_val_batches = total_val_batches * val_checks_per_epoch\\n total_batches = total_train_batches + total_val_batches\\n if not self.main_progress_bar.disable:\\n self.main_progress_bar.reset(convert_inf(total_batches))\\n self.main_progress_bar.set_description(f'Epoch {trainer.current_epoch + 1}')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_batch_end\\n def on_batch_end(self, trainer, pl_module):\\n super().on_batch_end(trainer, pl_module)\\n if self.is_enabled and self.train_batch_idx % self.refresh_rate == 0:\\n self.main_progress_bar.update(self.refresh_rate)\\n self.main_progress_bar.set_postfix(trainer.progress_bar_dict)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_start\\n def on_validation_start(self, trainer, pl_module):\\n super().on_validation_start(trainer, pl_module)\\n self.val_progress_bar = self.init_validation_tqdm()\\n self.val_progress_bar.total = convert_inf(self.total_val_batches)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_batch_end\\n def on_validation_batch_end(self, trainer, pl_module):\\n super().on_validation_batch_end(trainer, pl_module)\\n if self.is_enabled and self.val_batch_idx % self.refresh_rate == 0:\\n self.val_progress_bar.update(self.refresh_rate)\\n self.main_progress_bar.update(self.refresh_rate)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_end\\n def on_validation_end(self, trainer, pl_module):\\n super().on_validation_end(trainer, pl_module)\\n self.main_progress_bar.set_postfix(trainer.progress_bar_dict)\\n self.val_progress_bar.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_end\\n def on_train_end(self, trainer, pl_module):\\n super().on_train_end(trainer, pl_module)\\n self.main_progress_bar.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_start\\n def on_test_start(self, trainer, pl_module):\\n super().on_test_start(trainer, pl_module)\\n self.test_progress_bar = self.init_test_tqdm()\\n self.test_progress_bar.total = convert_inf(self.total_test_batches)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_batch_end\\n def on_test_batch_end(self, trainer, pl_module):\\n super().on_test_batch_end(trainer, pl_module)\\n if self.is_enabled and self.test_batch_idx % self.refresh_rate == 0:\\n self.test_progress_bar.update(self.refresh_rate)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_end\\n def on_test_end(self, trainer, pl_module):\\n super().on_test_end(trainer, pl_module)\\n self.test_progress_bar.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::convert_inf\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::convert_inf\\ndef convert_inf(x):\\n \\\"\\\"\\\" The tqdm doesn't support inf values. We have to convert it to None. \\\"\\\"\\\"\\n if x == float('inf'):\\n return None\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.setup\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.setup\\n def setup(self, stage: str):\\n \\\"\\\"\\\"\\n Called at the beginning of fit and test.\\n This is a good hook when you need to build models dynamically or adjust something about them.\\n This hook is called on every process when using DDP.\\n\\n Args:\\n stage: either 'fit' or 'test'\\n\\n Example::\\n\\n class LitModel(...):\\n def __init__(self):\\n self.l1 = None\\n\\n def prepare_data(self):\\n download_data()\\n tokenize()\\n\\n # don't do this\\n self.something = else\\n\\n def setup(stage):\\n data = Load_data(...)\\n self.l1 = nn.Linear(28, data.num_classes)\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.teardown\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.teardown\\n def teardown(self, stage: str):\\n \\\"\\\"\\\"\\n Called at the end of fit and test.\\n\\n Args:\\n stage: either 'fit' or 'test'\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_start\\n def on_fit_start(self):\\n \\\"\\\"\\\"\\n Called at the very beginning of fit.\\n If on DDP it is called on every process\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_end\\n def on_fit_end(self):\\n \\\"\\\"\\\"\\n Called at the very end of fit.\\n If on DDP it is called on every process\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_start\\n def on_train_start(self) -> None:\\n \\\"\\\"\\\"\\n Called at the beginning of training before sanity check.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_end\\n def on_train_end(self) -> None:\\n \\\"\\\"\\\"\\n Called at the end of training before logger experiment is closed.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_start\\n def on_batch_start(self, batch: Any) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop before anything happens for that batch.\\n\\n If you return -1 here, you will skip training for the rest of the current epoch.\\n\\n Args:\\n batch: The batched data as it is returned by the training DataLoader.\\n \\\"\\\"\\\"\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_end\\n def on_batch_end(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop after the batch.\\n \\\"\\\"\\\"\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_start\\n def on_epoch_start(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop at the very beginning of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_end\\n def on_epoch_end(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop at the very end of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_start\\n def on_train_epoch_start(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop at the very beginning of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_end\\n def on_train_epoch_end(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop at the very end of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_start\\n def on_validation_epoch_start(self) -> None:\\n \\\"\\\"\\\"\\n Called in the validation loop at the very beginning of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_end\\n def on_validation_epoch_end(self) -> None:\\n \\\"\\\"\\\"\\n Called in the validation loop at the very end of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_start\\n def on_test_epoch_start(self) -> None:\\n \\\"\\\"\\\"\\n Called in the test loop at the very beginning of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_end\\n def on_test_epoch_end(self) -> None:\\n \\\"\\\"\\\"\\n Called in the test loop at the very end of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_pre_performance_check\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_pre_performance_check\\n def on_pre_performance_check(self) -> None:\\n \\\"\\\"\\\"\\n Called at the very beginning of the validation loop.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_post_performance_check\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_post_performance_check\\n def on_post_performance_check(self) -> None:\\n \\\"\\\"\\\"\\n Called at the very end of the validation loop.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_before_zero_grad\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_before_zero_grad\\n def on_before_zero_grad(self, optimizer: Optimizer) -> None:\\n \\\"\\\"\\\"\\n Called after optimizer.step() and before optimizer.zero_grad().\\n\\n Called in the training loop after taking an optimizer step and before zeroing grads.\\n Good place to inspect weight information with weights updated.\\n\\n This is where it is called::\\n\\n for optimizer in optimizers:\\n optimizer.step()\\n model.on_before_zero_grad(optimizer) # < ---- called here\\n optimizer.zero_grad\\n\\n Args:\\n optimizer: The optimizer for which grads should be zeroed.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_after_backward\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_after_backward\\n def on_after_backward(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop after loss.backward() and before optimizers do anything.\\n This is the ideal place to inspect or log gradient information.\\n\\n Example::\\n\\n def on_after_backward(self):\\n # example to inspect gradient information in tensorboard\\n if self.trainer.global_step % 25 == 0: # don't make the tf file huge\\n params = self.state_dict()\\n for k, v in params.items():\\n grads = v\\n name = k\\n self.logger.experiment.add_histogram(tag=name, values=grads,\\n global_step=self.trainer.global_step)\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.backward\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.backward\\n def backward(self, trainer, loss: Tensor, optimizer: Optimizer, optimizer_idx: int) -> None:\\n \\\"\\\"\\\"\\n Override backward with your own implementation if you need to.\\n\\n Args:\\n trainer: Pointer to the trainer\\n loss: Loss is already scaled by accumulated grads\\n optimizer: Current optimizer being used\\n optimizer_idx: Index of the current optimizer being used\\n\\n Called to perform backward step.\\n Feel free to override as needed.\\n\\n The loss passed in has already been scaled for accumulated gradients if requested.\\n\\n Example::\\n\\n def backward(self, trainer, loss, optimizer, optimizer_idx):\\n loss.backward()\\n\\n \\\"\\\"\\\"\\n loss.backward()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.amp_scale_loss\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.amp_scale_loss\\n def amp_scale_loss(self, unscaled_loss, optimizer, optimizer_idx):\\n if NATIVE_AMP_AVALAIBLE:\\n scaled_loss = self.trainer.scaler.scale(unscaled_loss)\\n else:\\n scaled_loss = amp.scale_loss(unscaled_loss, optimizer)\\n\\n return scaled_loss\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.transfer_batch_to_device\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.transfer_batch_to_device\\n def transfer_batch_to_device(self, batch: Any, device: torch.device) -> Any:\\n \\\"\\\"\\\"\\n Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors\\n wrapped in a custom data structure.\\n\\n The data types listed below (and any arbitrary nesting of them) are supported out of the box:\\n\\n - :class:`torch.Tensor` or anything that implements `.to(...)`\\n - :class:`list`\\n - :class:`dict`\\n - :class:`tuple`\\n - :class:`torchtext.data.batch.Batch`\\n\\n For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, ...).\\n\\n Example::\\n\\n def transfer_batch_to_device(self, batch, device)\\n if isinstance(batch, CustomBatch):\\n # move all tensors in your custom data structure to the device\\n batch.samples = batch.samples.to(device)\\n batch.targets = batch.targets.to(device)\\n else:\\n batch = super().transfer_batch_to_device(data, device)\\n return batch\\n\\n Args:\\n batch: A batch of data that needs to be transferred to a new device.\\n device: The target device as defined in PyTorch.\\n\\n Returns:\\n A reference to the data on the new device.\\n\\n Note:\\n This hook should only transfer the data and not modify it, nor should it move the data to\\n any other device than the one passed in as argument (unless you know what you are doing).\\n The :class:`~pytorch_lightning.trainer.trainer.Trainer` already takes care of splitting the\\n batch and determines the target devices.\\n\\n See Also:\\n - :func:`~pytorch_lightning.utilities.apply_func.move_data_to_device`\\n - :func:`~pytorch_lightning.utilities.apply_func.apply_to_collection`\\n \\\"\\\"\\\"\\n return move_data_to_device\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.__init__\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.__init__\\n def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n\\n self.exp_save_path = None\\n\\n #: The current epoch\\n self.current_epoch = 0\\n\\n #: Total training batches seen across all epochs\\n self.global_step = 0\\n\\n self.loaded_optimizer_states_dict = {}\\n\\n #: Pointer to the trainer object\\n self.trainer = None\\n\\n #: Pointer to the logger object\\n self.logger = None\\n\\n #: True if using dp\\n self.use_dp = False\\n\\n #: True if using ddp\\n self.use_ddp = False\\n\\n #: True if using ddp2\\n self.use_ddp2 = False\\n\\n # True if on tpu\\n self.use_tpu = False\\n\\n #: True if using amp\\n self.use_amp = False\\n\\n # optionally can be set by user\\n self._example_input_array = None\\n self._datamodule = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\\n @property\\n def example_input_array(self) -> Any:\\n return self._example_input_array\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\\n @example_input_array.setter\\n def example_input_array(self, example: Any) -> None:\\n self._example_input_array = example\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\\n @property\\n def datamodule(self) -> Any:\\n return self._datamodule\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\\n @datamodule.setter\\n def datamodule(self, datamodule: Any) -> None:\\n self._datamodule = datamodule\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_gpu\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_gpu\\n @property\\n def on_gpu(self):\\n \\\"\\\"\\\"\\n True if your model is currently running on GPUs.\\n Useful to set flags around the LightningModule for different CPU vs GPU behavior.\\n \\\"\\\"\\\"\\n return self.device.type == 'cuda'\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.print\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.print\\n def print(self, *args, **kwargs) -> None:\\n r\\\"\\\"\\\"\\n Prints only from process 0. Use this in any distributed mode to log only once.\\n\\n Args:\\n *args: The thing to print. Will be passed to Python's built-in print function.\\n **kwargs: Will be passed to Python's built-in print function.\\n\\n Example:\\n\\n .. code-block:: python\\n\\n def forward(self, x):\\n self.print(x, 'in forward')\\n\\n \\\"\\\"\\\"\\n if self.trainer.is_global_zero:\\n print(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.forward\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.forward\\n @abstractmethod\\n def forward(self, *args, **kwargs):\\n r\\\"\\\"\\\"\\n Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define\\n the operations you want to use for prediction (i.e.: on a server or as a feature extractor).\\n\\n Normally you'd call ``self()`` from your :meth:`training_step` method.\\n This makes it easy to write a complex system for training with the outputs\\n you'd want in a prediction setting.\\n\\n You may also find the :func:`~pytorch_lightning.core.decorators.auto_move_data` decorator useful\\n when using the module outside Lightning in a production setting.\\n\\n Args:\\n *args: Whatever you decide to pass into the forward method.\\n **kwargs: Keyword arguments are also possible.\\n\\n Return:\\n Predicted output\\n\\n Examples:\\n .. code-block:: python\\n\\n # example if we were using this model as a feature extractor\\n def forward(self, x):\\n feature_maps = self.convnet(x)\\n return feature_maps\\n\\n def training_step(self, batch, batch_idx):\\n x, y = batch\\n feature_maps = self(x)\\n logits = self.classifier(feature_maps)\\n\\n # ...\\n return loss\\n\\n # splitting it this way allows model to be used a feature extractor\\n model = MyModelAbove()\\n\\n inputs = server.get_request()\\n results = model(inputs)\\n server.write_results(results)\\n\\n # -------------\\n # This is in stark contrast to torch.nn.Module where normally you would have this:\\n def forward(self, batch):\\n x, y = batch\\n feature_maps = self.convnet(x)\\n logits = self.classifier(feature_maps)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step\\n def training_step(self, *args, **kwargs) -> Union[int, Dict[str, Union[Tensor, Dict[str, Tensor]]]]:\\n r\\\"\\\"\\\"\\n Here you compute and return the training loss and some additional metrics for e.g.\\n the progress bar or logger.\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): Integer displaying index of this batch\\n optimizer_idx (int): When using multiple optimizers, this argument will also be present.\\n hiddens(:class:`~torch.Tensor`): Passed in if\\n :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.\\n\\n Return:\\n Dict with loss key and optional log or progress bar keys.\\n When implementing :meth:`training_step`, return whatever you need in that step:\\n\\n - loss -> tensor scalar **REQUIRED**\\n - progress_bar -> Dict for progress bar display. Must have only tensors\\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\\n\\n In this step you'd normally do the forward pass and calculate the loss for a batch.\\n You can also do fancier things like multiple forward passes or something model specific.\\n\\n Examples:\\n .. code-block:: python\\n\\n def training_step(self, batch, batch_idx):\\n x, y, z = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.loss(out, x)\\n\\n logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)\\n\\n # if using TestTubeLogger or TensorBoardLogger you can nest scalars\\n logger_logs = {'losses': logger_logs} # optional (MUST ALL BE TENSORS)\\n\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_end\\n def training_end(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`training_step_end` instead.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\\n def training_epoch_end(\\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\\n ) -> Dict[str, Dict[str, Tensor]]:\\n \\\"\\\"\\\"Called at the end of the training epoch with the outputs of all training steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n train_outs = []\\n for train_batch in train_data:\\n out = training_step(train_batch)\\n train_outs.append(out)\\n training_epoch_end(train_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`training_step`, or if there are\\n multiple dataloaders, a list containing a list of outputs for each dataloader.\\n\\n Return:\\n Dict or OrderedDict.\\n May contain the following optional keys:\\n\\n - log (metrics to be added to the logger; only tensors)\\n - progress_bar (dict for progress bar display)\\n - any metric used in a callback (e.g. early stopping).\\n\\n Note:\\n If this method is not overridden, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\\n\\n Examples:\\n With a single dataloader:\\n\\n .. code-block:: python\\n\\n def training_epoch_end(self, outputs):\\n train_acc_mean = 0\\n for output in outputs:\\n train_acc_mean += output['train_acc']\\n\\n train_acc_mean /= len(outputs)\\n\\n # log training accuracy at the end of an epoch\\n results = {\\n 'log': {'train_acc': train_acc_mean.item()},\\n 'progress_bar': {'train_acc': train_acc_m\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\\n def training_step_end(self, *args, **kwargs) -> Dict[str, Union[Tensor, Dict[str, Tensor]]]:\\n \\\"\\\"\\\"\\n Use this when training with dp or ddp2 because :meth:`training_step`\\n will operate on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]\\n training_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in `training_step` for each batch part.\\n\\n Return:\\n Dict with loss key and optional log or progress bar keys.\\n\\n - loss -> tensor scalar **REQUIRED**\\n - progress_bar -> Dict for progress bar display. Must have only tensors\\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT training_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def training_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with training_step_end to do softmax over the full batch\\n def training_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step\\n def validation_step(self, *args, **kwargs) -> Dict[str, Tensor]:\\n r\\\"\\\"\\\"\\n Operates on a single batch of data from the validation set.\\n In this step you'd might generate examples or calculate anything of interest like accuracy.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n val_outs = []\\n for val_batch in val_data:\\n out = validation_step(train_batch)\\n val_outs.append(out)\\n validation_epoch_end(val_outs)\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): The index of this batch\\n dataloader_idx (int): The index of the dataloader that produced this batch\\n (only if multiple val datasets used)\\n\\n Return:\\n Dict or OrderedDict - passed to :meth:`validation_epoch_end`.\\n If you defined :meth:`validation_step_end` it will go to that first.\\n\\n .. code-block:: python\\n\\n # pseudocode of order\\n out = validation_step()\\n if defined('validation_step_end'):\\n out = validation_step_end(out)\\n out = validation_epoch_end(out)\\n\\n\\n .. code-block:: python\\n\\n # if you have one val dataloader:\\n def validation_step(self, batch, batch_idx)\\n\\n # if you have multiple val dataloaders:\\n def validation_step(self, batch, batch_idx, dataloader_idx)\\n\\n Examples:\\n .. code-block:: python\\n\\n # CASE 1: A single validation dataset\\n def validation_step(self, batch, batch_idx):\\n x, y = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.los\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\\n def validation_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\\n \\\"\\\"\\\"\\n Use this when validating with dp or ddp2 because :meth:`validation_step`\\n will operate on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code.\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]\\n validation_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in :meth:`validation_step`\\n for each batch part.\\n\\n Return:\\n Dict or OrderedDict - passed to the :meth:`validation_epoch_end` method.\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT validation_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def validation_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with validation_step_end to do softmax over the full batch\\n def validation_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n return {'out': out}\\n\\n def validation_epoch_end(self, outputs):\\n # this out is now the full size of the batch\\n out = outputs['out']\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_end\\n def validation_end(self, outputs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead.\\n Will be removed in 1.0.0.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\\n def validation_epoch_end(\\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\\n ) -> Dict[str, Dict[str, Tensor]]:\\n \\\"\\\"\\\"\\n Called at the end of the validation epoch with the outputs of all validation steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n val_outs = []\\n for val_batch in val_data:\\n out = validation_step(val_batch)\\n val_outs.append(out)\\n validation_epoch_end(val_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`validation_step`, or if there\\n are multiple dataloaders, a list containing a list of outputs for each dataloader.\\n\\n Return:\\n Dict or OrderedDict.\\n May have the following optional keys:\\n\\n - progress_bar (dict for progress bar display; only tensors)\\n - log (dict of metrics to add to logger; only tensors).\\n\\n Note:\\n If you didn't define a :meth:`validation_step`, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\\n\\n Examples:\\n With a single dataloader:\\n\\n .. code-block:: python\\n\\n def validation_epoch_end(self, outputs):\\n val_acc_mean = 0\\n for output in outputs:\\n val_acc_mean += output['val_acc']\\n\\n val_acc_mean /= len(outputs)\\n tqdm_dict = {'val_acc': val_acc_mean.item()}\\n\\n # show val_acc in progress bar but only log val_loss\\n results = {\\n 'progress_bar': tqdm_dict,\\n 'log': {'val_acc': val_ac\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step\\n def test_step(self, *args, **kwargs) -> Dict[str, Tensor]:\\n r\\\"\\\"\\\"\\n Operates on a single batch of data from the test set.\\n In this step you'd normally generate examples or calculate anything of interest\\n such as accuracy.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n test_outs = []\\n for test_batch in test_data:\\n out = test_step(test_batch)\\n test_outs.append(out)\\n test_epoch_end(test_outs)\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): The index of this batch.\\n dataloader_idx (int): The index of the dataloader that produced this batch\\n (only if multiple test datasets used).\\n\\n Return:\\n Dict or OrderedDict - passed to the :meth:`test_epoch_end` method.\\n If you defined :meth:`test_step_end` it will go to that first.\\n\\n .. code-block:: python\\n\\n # if you have one test dataloader:\\n def test_step(self, batch, batch_idx)\\n\\n # if you have multiple test dataloaders:\\n def test_step(self, batch, batch_idx, dataloader_idx)\\n\\n Examples:\\n .. code-block:: python\\n\\n # CASE 1: A single test dataset\\n def test_step(self, batch, batch_idx):\\n x, y = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.loss(out, y)\\n\\n # log 6 example images\\n # or generated text... or whatever\\n sample_imgs = x[:6]\\n grid = torchvision.utils.make_grid(sample_imgs)\\n self.logger.experiment.add_image('example\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\\n def test_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\\n \\\"\\\"\\\"\\n Use this when testing with dp or ddp2 because :meth:`test_step` will operate\\n on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code.\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]\\n test_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in :meth:`test_step` for each batch part.\\n\\n Return:\\n Dict or OrderedDict - passed to the :meth:`test_epoch_end`.\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT test_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def test_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with test_step_end to do softmax over the full batch\\n def test_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n return {'out': out}\\n\\n def test_step_end(self, outputs):\\n # this out is now the full size of the batch\\n out = outputs['out']\\n\\n # this softmax now uses the full batch size\\n loss = nce_loss(loss)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_end\\n def test_end(self, outputs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead.\\n Will be removed in 1.0.0.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\\n def test_epoch_end(\\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\\n ) -> Dict[str, Dict[str, Tensor]]:\\n \\\"\\\"\\\"\\n Called at the end of a test epoch with the output of all test steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n test_outs = []\\n for test_batch in test_data:\\n out = test_step(test_batch)\\n test_outs.append(out)\\n test_epoch_end(test_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`test_step_end`, or if there\\n are multiple dataloaders, a list containing a list of outputs for each dataloader\\n\\n Return:\\n Dict or OrderedDict: Dict has the following optional keys:\\n\\n - progress_bar -> Dict for progress bar display. Must have only tensors.\\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc).\\n\\n Note:\\n If you didn't define a :meth:`test_step`, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict\\n\\n Examples:\\n With a single dataloader:\\n\\n .. code-block:: python\\n\\n def test_epoch_end(self, outputs):\\n test_acc_mean = 0\\n for output in outputs:\\n test_acc_mean += output['test_acc']\\n\\n test_acc_mean /= len(outputs)\\n tqdm_dict = {'test_acc': test_acc_mean.item()}\\n\\n # show test_loss and test_acc in progress bar but only log test_loss\\n results = {\\n 'progress_bar': tqdm_dict,\\n 'log': {'test_acc': test\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) -> DistributedDataParallel:\\n r\\\"\\\"\\\"\\n Override to init DDP in your own way or with your own wrapper.\\n The only requirements are that:\\n\\n 1. On a validation batch, the call goes to ``model.validation_step``.\\n 2. On a training batch, the call goes to ``model.training_step``.\\n 3. On a testing batch, the call goes to ``model.test_step``.\\n\\n Args:\\n model: the :class:`LightningModule` currently being optimized.\\n device_ids: the list of GPU ids.\\n\\n Return:\\n DDP wrapped model\\n\\n Examples:\\n .. code-block:: python\\n\\n # default implementation used in Trainer\\n def configure_ddp(self, model, device_ids):\\n # Lightning DDP simply routes to test_step, val_step, etc...\\n model = LightningDistributedDataParallel(\\n model,\\n device_ids=device_ids,\\n find_unused_parameters=True\\n )\\n return model\\n\\n \\\"\\\"\\\"\\n model = LightningDistributedDataParallel(model, device_ids=device_ids, find_unused_parameters=True)\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\\n def _init_slurm_connection(self) -> None:\\n \\\"\\\"\\\"\\n Sets up environment variables necessary for pytorch distributed communications\\n based on slurm environment.\\n \\\"\\\"\\\"\\n # use slurm job id for the port number\\n # guarantees unique ports across jobs from same grid search\\n try:\\n # use the last 4 numbers in the job id as the id\\n default_port = os.environ['SLURM_JOB_ID']\\n default_port = default_port[-4:]\\n\\n # all ports should be in the 10k+ range\\n default_port = int(default_port) + 15000\\n\\n except Exception:\\n default_port = 12910\\n\\n # if user gave a port number, use that one instead\\n try:\\n default_port = os.environ['MASTER_PORT']\\n except Exception:\\n os.environ['MASTER_PORT'] = str(default_port)\\n\\n # figure out the root node addr\\n try:\\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\\n except Exception:\\n root_node = '127.0.0.1'\\n\\n root_node = self.trainer.resolve_root_node_address(root_node)\\n os.environ['MASTER_ADDR'] = root_node\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True) -> None:\\n \\\"\\\"\\\"\\n Override to define your custom way of setting up a distributed environment.\\n\\n Lightning's implementation uses env:// init by default and sets the first node as root\\n for SLURM managed cluster.\\n\\n Args:\\n global_rank: The global process idx.\\n world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).\\n is_slurm_managing_tasks: is cluster managed by SLURM.\\n\\n \\\"\\\"\\\"\\n if is_slurm_managing_tasks:\\n self._init_slurm_connection()\\n\\n if 'MASTER_ADDR' not in os.environ:\\n rank_zero_warn(\\\"MASTER_ADDR environment variable is not defined. Set as localhost\\\")\\n os.environ['MASTER_ADDR'] = '127.0.0.1'\\n log.debug(f\\\"MASTER_ADDR: {os.environ['MASTER_ADDR']}\\\")\\n\\n if 'MASTER_PORT' not in os.environ:\\n rank_zero_warn(\\\"MASTER_PORT environment variable is not defined. Set as 12910\\\")\\n os.environ['MASTER_PORT'] = '12910'\\n log.debug(f\\\"MASTER_PORT: {os.environ['MASTER_PORT']}\\\")\\n\\n if 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE']) != world_size:\\n rank_zero_warn(\\n f\\\"WORLD_SIZE environment variable ({os.environ['WORLD_SIZE']}) \\\"\\n f\\\"is not equal to the computed world size ({world_size}). Ignored.\\\"\\n )\\n\\n torch_backend = \\\"nccl\\\" if self.trainer.on_gpu else \\\"gloo\\\"\\n log.info(f\\\"initializing ddp: GLOBAL_RANK: {global_rank}, MEMBER: {global_rank+1}/{world_size}\\\")\\n torch_distrib.init_process_group(torch_backend, rank=global_rank, world_size=world_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_sync_batchnorm\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_sync_batchnorm\\n def configure_sync_batchnorm(self, model: 'LightningModule') -> 'LightningModule':\\n \\\"\\\"\\\"\\n Add global batchnorm for a model spread across multiple GPUs and nodes.\\n\\n Override to synchronize batchnorm between specific process groups instead\\n of the whole world or use a different sync_bn like `apex`'s version.\\n\\n Args:\\n model: pointer to current :class:`LightningModule`.\\n\\n Return:\\n LightningModule with batchnorm layers synchronized between process groups\\n \\\"\\\"\\\"\\n model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model, process_group=None)\\n\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\\n def configure_apex(\\n self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str\\n ) -> Tuple['LightningModule', List[Optimizer]]:\\n r\\\"\\\"\\\"\\n Override to init AMP your own way.\\n Must return a model and list of optimizers.\\n\\n Args:\\n amp: pointer to amp library object.\\n model: pointer to current :class:`LightningModule`.\\n optimizers: list of optimizers passed in :meth:`configure_optimizers`.\\n amp_level: AMP mode chosen ('O1', 'O2', etc...)\\n\\n Return:\\n Apex wrapped model and optimizers\\n\\n Examples:\\n .. code-block:: python\\n\\n # Default implementation used by Trainer.\\n def configure_apex(self, amp, model, optimizers, amp_level):\\n model, optimizers = amp.initialize(\\n model, optimizers, opt_level=amp_level,\\n )\\n\\n return model, optimizers\\n \\\"\\\"\\\"\\n model, optimizers = amp.initialize(model, optimizers, opt_level=amp_level)\\n\\n return model, optimizers\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\\n def configure_optimizers(\\n self,\\n ) -> Optional[Union[Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List]]]:\\n r\\\"\\\"\\\"\\n Choose what optimizers and learning-rate schedulers to use in your optimization.\\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\\n\\n Return:\\n Any of these 6 options.\\n\\n - Single optimizer.\\n - List or Tuple - List of optimizers.\\n - Two lists - The first list has multiple optimizers, the second a list of LR schedulers (or lr_dict).\\n - Dictionary, with an 'optimizer' key, and (optionally) a 'lr_scheduler' key which value is a single LR scheduler or lr_dict.\\n - Tuple of dictionaries as described, with an optional 'frequency' key.\\n - None - Fit will run without any optimizer.\\n\\n Note:\\n The 'frequency' value is an int corresponding to the number of sequential batches\\n optimized with the specific optimizer. It should be given to none or to all of the optimizers.\\n There is a difference between passing multiple optimizers in a list,\\n and passing multiple optimizers in dictionaries with a frequency of 1:\\n In the former case, all optimizers will operate on the given batch in each optimization step.\\n In the latter, only one optimizer will operate on the given batch at every step.\\n\\n The lr_dict is a dictionary which contains scheduler and its associated configuration.\\n It has five keys. The default configuration is shown below.\\n\\n .. code-block:: python\\n\\n {\\n 'scheduler': lr_scheduler, # The LR schduler\\n 'interval': 'epoch', # The unit of the scheduler's step size\\n 'frequency': 1, # The frequency of the scheduler\\n 'reduce_on_platea\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\\n def optimizer_step(\\n self,\\n epoch: int,\\n batch_idx: int,\\n optimizer: Optimizer,\\n optimizer_idx: int,\\n second_order_closure: Optional[Callable] = None,\\n on_tpu: bool = False,\\n using_native_amp: bool = False,\\n using_lbfgs: bool = False,\\n ) -> None:\\n r\\\"\\\"\\\"\\n Override this method to adjust the default way the\\n :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.\\n By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example\\n once per optimizer.\\n\\n Args:\\n epoch: Current epoch\\n batch_idx: Index of current batch\\n optimizer: A PyTorch optimizer\\n optimizer_idx: If you used multiple optimizers this indexes into that list.\\n second_order_closure: closure for second order methods\\n on_tpu: true if TPU backward is required\\n using_native_amp: True if using native amp\\n using_lbfgs: True if the matching optimizer is lbfgs\\n\\n Examples:\\n .. code-block:: python\\n\\n # DEFAULT\\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\\n optimizer.step()\\n\\n # Alternating schedule for optimizer steps (i.e.: GANs)\\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\\n # update generator opt every 2 steps\\n if optimizer_idx == 0:\\n if batch_idx % 2 == 0 :\\n optimizer.step()\\n optimizer.zero_grad()\\n\\n # update discriminator opt every 4 steps\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad\\n def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int):\\n optimizer.zero_grad()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\\n def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:\\n r\\\"\\\"\\\"\\n When using truncated backpropagation through time, each batch must be split along the\\n time dimension. Lightning handles this by default, but for custom behavior override\\n this function.\\n\\n Args:\\n batch: Current batch\\n split_size: The size of the split\\n\\n Return:\\n List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated\\n back propagation through time. The default implementation splits root level Tensors and\\n Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.\\n\\n Examples:\\n .. code-block:: python\\n\\n def tbptt_split_batch(self, batch, split_size):\\n splits = []\\n for t in range(0, time_dims[0], split_size):\\n batch_split = []\\n for i, x in enumerate(batch):\\n if isinstance(x, torch.Tensor):\\n split_x = x[:, t:t + split_size]\\n elif isinstance(x, collections.Sequence):\\n split_x = [None] * len(x)\\n for batch_idx in range(len(x)):\\n split_x[batch_idx] = x[batch_idx][t:t + split_size]\\n\\n batch_split.append(split_x)\\n\\n splits.append(batch_split)\\n\\n return splits\\n\\n Note:\\n Called in the training loop after\\n :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`\\n if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.\\n Each returned batch split is passed separately to :meth:`training_step`.\\n\\n \\\"\\\"\\\"\\n time_dims = [len(x[0]) for x in batch if isinstance(x\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\\n def prepare_data(self) -> None:\\n \\\"\\\"\\\"\\n Use this to download and prepare data.\\n\\n .. warning:: DO NOT set state to the model (use `setup` instead)\\n since this is NOT called on every GPU in DDP/TPU\\n\\n Example::\\n\\n def prepare_data(self):\\n # good\\n download_data()\\n tokenize()\\n etc()\\n\\n # bad\\n self.split = data_split\\n self.some_state = some_other_state()\\n\\n In DDP prepare_data can be called in two ways (using Trainer(prepare_data_per_node)):\\n\\n 1. Once per node. This is the default and is only called on LOCAL_RANK=0.\\n 2. Once in total. Only called on GLOBAL_RANK=0.\\n\\n Example::\\n\\n # DEFAULT\\n # called once per node on LOCAL_RANK=0 of that node\\n Trainer(prepare_data_per_node=True)\\n\\n # call on GLOBAL_RANK=0 (great for shared file systems)\\n Trainer(prepare_data_per_node=False)\\n\\n This is called before requesting the dataloaders:\\n\\n .. code-block:: python\\n\\n model.prepare_data()\\n if ddp/tpu: init()\\n model.setup(stage)\\n model.train_dataloader()\\n model.val_dataloader()\\n model.test_dataloader()\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\\n def train_dataloader(self) -> DataLoader:\\n \\\"\\\"\\\"\\n Implement a PyTorch DataLoader for training.\\n\\n Return:\\n Single PyTorch :class:`~torch.utils.data.DataLoader`.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n For data processing use the following pattern:\\n\\n - download in :meth:`prepare_data`\\n - process and split in :meth:`setup`\\n\\n However, the above are only necessary for distributed processing.\\n\\n .. warning:: do not assign state in prepare_data\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`setup`\\n - :meth:`train_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware.\\n There is no need to set it yourself.\\n\\n Example:\\n .. code-block:: python\\n\\n def train_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,\\n download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.batch_size,\\n shuffle=True\\n )\\n return loader\\n\\n \\\"\\\"\\\"\\n rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\\n def tng_dataloader(self): # todo: remove in v1.0.0\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.\\n \\\"\\\"\\\"\\n output = self.train_dataloader()\\n rank_zero_warn(\\n \\\"`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0.\\\"\\n \\\" and this method will be removed in v1.0.0\\\",\\n DeprecationWarning,\\n )\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\\n def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\\n r\\\"\\\"\\\"\\n Implement one or multiple PyTorch DataLoaders for testing.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n For data processing use the following pattern:\\n\\n - download in :meth:`prepare_data`\\n - process and split in :meth:`setup`\\n\\n However, the above are only necessary for distributed processing.\\n\\n .. warning:: do not assign state in prepare_data\\n\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`setup`\\n - :meth:`train_dataloader`\\n - :meth:`val_dataloader`\\n - :meth:`test_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware.\\n There is no need to set it yourself.\\n\\n Return:\\n Single or multiple PyTorch DataLoaders.\\n\\n Example:\\n .. code-block:: python\\n\\n def test_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,\\n download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.batch_size,\\n shuffle=False\\n )\\n\\n return loader\\n\\n # can also return multiple dataloaders\\n def test_dataloader(self):\\n return [loader_a, loader_b, ..., loader_n]\\n\\n Note:\\n If you don't need a te\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\\n def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\\n r\\\"\\\"\\\"\\n Implement one or multiple PyTorch DataLoaders for validation.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`train_dataloader`\\n - :meth:`val_dataloader`\\n - :meth:`test_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware\\n There is no need to set it yourself.\\n\\n Return:\\n Single or multiple PyTorch DataLoaders.\\n\\n Examples:\\n .. code-block:: python\\n\\n def val_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=False,\\n transform=transform, download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.batch_size,\\n shuffle=False\\n )\\n\\n return loader\\n\\n # can also return multiple dataloaders\\n def val_dataloader(self):\\n return [loader_a, loader_b, ..., loader_n]\\n\\n Note:\\n If you don't need a validation dataset and a :meth:`validation_step`, you don't need to\\n implement this method.\\n\\n Note:\\n In the case where you return multiple validation dataloaders, the :meth:`validation_st\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.summarize\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.summarize\\n def summarize(self, mode: str = ModelSummary.MODE_DEFAULT) -> ModelSummary:\\n model_summary = ModelSummary(self, mode=mode)\\n log.info('\\\\n' + str(model_summary))\\n return model_summary\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.freeze\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.freeze\\n def freeze(self) -> None:\\n r\\\"\\\"\\\"\\n Freeze all params for inference.\\n\\n Example:\\n .. code-block:: python\\n\\n model = MyLightningModule(...)\\n model.freeze()\\n\\n \\\"\\\"\\\"\\n for param in self.parameters():\\n param.requires_grad = False\\n\\n self.eval()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\\n def unfreeze(self) -> None:\\n \\\"\\\"\\\"\\n Unfreeze all parameters for training.\\n\\n .. code-block:: python\\n\\n model = MyLightningModule(...)\\n model.unfreeze()\\n\\n \\\"\\\"\\\"\\n for param in self.parameters():\\n param.requires_grad = True\\n\\n self.train()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\\n r\\\"\\\"\\\"\\n Called by Lightning to restore your model.\\n If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.\\n\\n Args:\\n checkpoint: Loaded checkpoint\\n\\n\\n Example:\\n .. code-block:: python\\n\\n def on_load_checkpoint(self, checkpoint):\\n # 99% of the time you don't need to implement this method\\n self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']\\n\\n Note:\\n Lightning auto-restores global step, epoch, and train state including amp scaling.\\n There is no need for you to restore anything regarding training.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\\n r\\\"\\\"\\\"\\n Called by Lightning when saving a checkpoint to give you a chance to store anything\\n else you might want to save.\\n\\n Args:\\n checkpoint: Checkpoint to be saved\\n\\n Example:\\n .. code-block:: python\\n\\n def on_save_checkpoint(self, checkpoint):\\n # 99% of use cases you don't need to implement this method\\n checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object\\n\\n Note:\\n Lightning saves all aspects of training (epoch, global step, etc...)\\n including amp scaling.\\n There is no need for you to store anything about training.\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\\n def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:\\n r\\\"\\\"\\\"\\n Implement this to override the default items displayed in the progress bar.\\n By default it includes the average loss value, split index of BPTT (if used)\\n and the version of the experiment when using a logger.\\n\\n .. code-block::\\n\\n Epoch 1: 4%|▎ | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10]\\n\\n Here is an example how to override the defaults:\\n\\n .. code-block:: python\\n\\n def get_progress_bar_dict(self):\\n # don't show the version number\\n items = super().get_progress_bar_dict()\\n items.pop(\\\"v_num\\\", None)\\n return items\\n\\n Return:\\n Dictionary with the items to be displayed in the progress bar.\\n \\\"\\\"\\\"\\n # call .item() only once but store elements without graphs\\n running_train_loss = self.trainer.running_loss.mean()\\n avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')\\n tqdm_dict = {'loss': '{:.3f}'.format(avg_training_loss)}\\n\\n if self.trainer.truncated_bptt_steps is not None:\\n tqdm_dict['split_idx'] = self.trainer.split_idx\\n\\n if self.trainer.logger is not None and self.trainer.logger.version is not None:\\n version = self.trainer.logger.version\\n # show last 4 places of long version strings\\n version = version[-4:] if isinstance(version, str) else version\\n tqdm_dict['v_num'] = version\\n\\n return tqdm_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\\n def get_tqdm_dict(self) -> Dict[str, Union[int, str]]:\\n \\\"\\\"\\\"\\n Additional items to be displayed in the progress bar.\\n\\n Return:\\n Dictionary with the items to be displayed in the progress bar.\\n\\n Warning:\\n Deprecated since v0.7.3.\\n Use :meth:`get_progress_bar_dict` instead.\\n \\\"\\\"\\\"\\n rank_zero_warn(\\n \\\"`get_tqdm_dict` was renamed to `get_progress_bar_dict` in v0.7.3\\\"\\n \\\" and this method will be removed in v1.0.0\\\",\\n DeprecationWarning,\\n )\\n return self.get_progress_bar_dict()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule._auto_collect_arguments\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule._auto_collect_arguments\\n @classmethod\\n def _auto_collect_arguments(cls, frame=None) -> Tuple[Dict, Dict]:\\n \\\"\\\"\\\"\\n Collect all module arguments in the current constructor and all child constructors.\\n The child constructors are all the ``__init__`` methods that reach the current class through\\n (chained) ``super().__init__()`` calls.\\n\\n Args:\\n frame: instance frame\\n\\n Returns:\\n self_arguments: arguments dictionary of the first instance\\n parents_arguments: arguments dictionary of the parent's instances\\n \\\"\\\"\\\"\\n if not frame:\\n frame = inspect.currentframe()\\n\\n frame_args = collect_init_args(frame.f_back, [])\\n self_arguments = frame_args[-1]\\n\\n # set module_arguments in child\\n self_arguments = self_arguments\\n parents_arguments = {}\\n\\n # add all arguments from parents\\n for args in frame_args[:-1]:\\n parents_arguments.update(args)\\n return self_arguments, parents_arguments\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.save_hyperparameters\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.save_hyperparameters\\n def save_hyperparameters(self, *args, frame=None) -> None:\\n \\\"\\\"\\\"Save all model arguments.\\n\\n Args:\\n args: single object of `dict`, `NameSpace` or `OmegaConf`\\n or string names or argumenst from class `__init__`\\n\\n >>> from collections import OrderedDict\\n >>> class ManuallyArgsModel(LightningModule):\\n ... def __init__(self, arg1, arg2, arg3):\\n ... super().__init__()\\n ... # manually assine arguments\\n ... self.save_hyperparameters('arg1', 'arg3')\\n ... def forward(self, *args, **kwargs):\\n ... ...\\n >>> model = ManuallyArgsModel(1, 'abc', 3.14)\\n >>> model.hparams\\n \\\"arg1\\\": 1\\n \\\"arg3\\\": 3.14\\n\\n >>> class AutomaticArgsModel(LightningModule):\\n ... def __init__(self, arg1, arg2, arg3):\\n ... super().__init__()\\n ... # equivalent automatic\\n ... self.save_hyperparameters()\\n ... def forward(self, *args, **kwargs):\\n ... ...\\n >>> model = AutomaticArgsModel(1, 'abc', 3.14)\\n >>> model.hparams\\n \\\"arg1\\\": 1\\n \\\"arg2\\\": abc\\n \\\"arg3\\\": 3.14\\n\\n >>> class SingleArgModel(LightningModule):\\n ... def __init__(self, params):\\n ... super().__init__()\\n ... # manually assign single argument\\n ... self.save_hyperparameters(params)\\n ... def forward(self, *args, **kwargs):\\n ... ...\\n >>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14))\\n >>> model.hparams\\n \\\"p1\\\": 1\\n \\\"p2\\\": abc\\n \\\"p3\\\": 3.14\\n \\\"\\\"\\\"\\n if not frame:\\n frame = inspect.currentframe().f_back\\n init_args = get_init_args(frame)\\n assert init_args, 'failed to inspect the self init'\\n if not args:\\n hp = init_args\\n self._\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule._set_hparams\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule._set_hparams\\n def _set_hparams(self, hp: Union[dict, Namespace, str]) -> None:\\n if isinstance(hp, Namespace):\\n hp = vars(hp)\\n if isinstance(hp, dict):\\n hp = AttributeDict(hp)\\n elif isinstance(hp, PRIMITIVE_TYPES):\\n raise ValueError(f'Primitives {PRIMITIVE_TYPES} are not allowed.')\\n elif not isinstance(hp, ALLOWED_CONFIG_TYPES):\\n raise ValueError(f'Unsupported config type of {type(hp)}.')\\n\\n if isinstance(hp, dict) and isinstance(self.hparams, dict):\\n self.hparams.update(hp)\\n else:\\n self._hparams = hp\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.to_onnx\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.to_onnx\\n def to_onnx(self, file_path: str, input_sample: Optional[Tensor] = None, **kwargs):\\n \\\"\\\"\\\"Saves the model in ONNX format\\n\\n Args:\\n file_path: The path of the file the model should be saved to.\\n input_sample: A sample of an input tensor for tracing.\\n **kwargs: Will be passed to torch.onnx.export function.\\n\\n Example:\\n >>> class SimpleModel(LightningModule):\\n ... def __init__(self):\\n ... super().__init__()\\n ... self.l1 = torch.nn.Linear(in_features=64, out_features=4)\\n ...\\n ... def forward(self, x):\\n ... return torch.relu(self.l1(x.view(x.size(0), -1)))\\n\\n >>> with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:\\n ... model = SimpleModel()\\n ... input_sample = torch.randn((1, 64))\\n ... model.to_onnx(tmpfile.name, input_sample, export_params=True)\\n ... os.path.isfile(tmpfile.name)\\n True\\n \\\"\\\"\\\"\\n\\n if isinstance(input_sample, Tensor):\\n input_data = input_sample\\n elif self.example_input_array is not None:\\n input_data = self.example_input_array\\n else:\\n raise ValueError(f'input_sample and example_input_array tensors are both missing.')\\n\\n if 'example_outputs' not in kwargs:\\n self.eval()\\n kwargs['example_outputs'] = self(input_data)\\n\\n torch.onnx.export(self, input_data, file_path, **kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.hparams\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.hparams\\n @property\\n def hparams(self) -> Union[AttributeDict, str]:\\n if not hasattr(self, '_hparams'):\\n self._hparams = AttributeDict()\\n return self._hparams\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.hparams\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.hparams\\n @hparams.setter\\n def hparams(self, hp: Union[dict, Namespace, Any]):\\n hparams_assignment_name = self.__get_hparams_assignment_variable()\\n self._hparams_name = hparams_assignment_name\\n self._set_hparams(hp)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.__get_hparams_assignment_variable\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.__get_hparams_assignment_variable\\n def __get_hparams_assignment_variable(self):\\n \\\"\\\"\\\"\\n looks at the code of the class to figure out what the user named self.hparams\\n this only happens when the user explicitly sets self.hparams\\n \\\"\\\"\\\"\\n try:\\n class_code = inspect.getsource(self.__class__)\\n lines = class_code.split('\\\\n')\\n for line in lines:\\n line = re.sub(r\\\"\\\\s+\\\", \\\"\\\", line, flags=re.UNICODE)\\n if '.hparams=' in line:\\n return line.split('=')[1]\\n except Exception as e:\\n return 'hparams'\\n\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.slurm_job_id\",\n \"text\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.slurm_job_id\\n @property\\n @abstractmethod\\n def slurm_job_id(self) -> int:\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.save_checkpoint\",\n \"text\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.save_checkpoint\\n @abstractmethod\\n def save_checkpoint(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.is_overridden\",\n \"text\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.is_overridden\\n @abstractmethod\\n def is_overridden(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_checkpoint_callback\",\n \"text\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_checkpoint_callback\\n def configure_checkpoint_callback(self, checkpoint_callback):\\n if checkpoint_callback is True:\\n # when no val step is defined, use 'loss' otherwise 'val_loss'\\n train_step_only = not self.is_overridden('validation_step')\\n monitor_key = 'loss' if train_step_only else 'val_loss'\\n checkpoint_callback = ModelCheckpoint(\\n filepath=None,\\n monitor=monitor_key\\n )\\n elif checkpoint_callback is False:\\n checkpoint_callback = None\\n\\n if checkpoint_callback:\\n checkpoint_callback.save_function = self.save_checkpoint\\n\\n return checkpoint_callback\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_early_stopping\",\n \"text\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_early_stopping\\n def configure_early_stopping(self, early_stop_callback):\\n if early_stop_callback is True or None:\\n early_stop_callback = EarlyStopping(\\n monitor='val_loss',\\n patience=3,\\n strict=True,\\n verbose=True,\\n mode='min'\\n )\\n elif not early_stop_callback:\\n early_stop_callback = None\\n else:\\n early_stop_callback = early_stop_callback\\n return early_stop_callback\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_progress_bar\",\n \"text\": \"pytorch_lightning/trainer/callback_config.py::TrainerCallbackConfigMixin.configure_progress_bar\\n def configure_progress_bar(self, refresh_rate=1, process_position=0):\\n progress_bars = [c for c in self.callbacks if isinstance(c, ProgressBarBase)]\\n if len(progress_bars) > 1:\\n raise MisconfigurationException(\\n 'You added multiple progress bar callbacks to the Trainer, but currently only one'\\n ' progress bar is supported.'\\n )\\n elif len(progress_bars) == 1:\\n progress_bar_callback = progress_bars[0]\\n elif refresh_rate > 0:\\n progress_bar_callback = ProgressBar(\\n refresh_rate=refresh_rate,\\n process_position=process_position,\\n )\\n self.callbacks.append(progress_bar_callback)\\n else:\\n progress_bar_callback = None\\n\\n return progress_bar_callback\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.setup\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.setup\\n def setup(self, stage: str):\\n \\\"\\\"\\\"Called in the beginning of fit and test\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.setup(self, stage)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.teardown\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.teardown\\n def teardown(self, stage: str):\\n \\\"\\\"\\\"Called at the end of fit and test\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.teardown(self, stage)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_init_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_init_start\\n def on_init_start(self):\\n \\\"\\\"\\\"Called when the trainer initialization begins, model has not yet been set.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_init_start(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_init_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_init_end\\n def on_init_end(self):\\n \\\"\\\"\\\"Called when the trainer initialization ends, model has not yet been set.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_init_end(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_fit_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_fit_start\\n def on_fit_start(self):\\n \\\"\\\"\\\"Called when the trainer initialization begins, model has not yet been set.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_fit_start(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_fit_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_fit_end\\n def on_fit_end(self):\\n \\\"\\\"\\\"Called when the trainer initialization begins, model has not yet been set.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_fit_end(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_sanity_check_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_sanity_check_start\\n def on_sanity_check_start(self):\\n \\\"\\\"\\\"Called when the validation sanity check starts.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_sanity_check_start(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_sanity_check_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_sanity_check_end\\n def on_sanity_check_end(self):\\n \\\"\\\"\\\"Called when the validation sanity check ends.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_sanity_check_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_epoch_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_epoch_start\\n def on_train_epoch_start(self):\\n \\\"\\\"\\\"Called when the epoch begins.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_train_epoch_start(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_epoch_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_epoch_end\\n def on_train_epoch_end(self):\\n \\\"\\\"\\\"Called when the epoch ends.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_train_epoch_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_epoch_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_epoch_start\\n def on_validation_epoch_start(self):\\n \\\"\\\"\\\"Called when the epoch begins.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_validation_epoch_start(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_epoch_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_epoch_end\\n def on_validation_epoch_end(self):\\n \\\"\\\"\\\"Called when the epoch ends.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_validation_epoch_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_epoch_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_epoch_start\\n def on_test_epoch_start(self):\\n \\\"\\\"\\\"Called when the epoch begins.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_test_epoch_start(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_epoch_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_epoch_end\\n def on_test_epoch_end(self):\\n \\\"\\\"\\\"Called when the epoch ends.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_test_epoch_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_epoch_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_epoch_start\\n def on_epoch_start(self):\\n \\\"\\\"\\\"Called when the epoch begins.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_epoch_start(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_epoch_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_epoch_end\\n def on_epoch_end(self):\\n \\\"\\\"\\\"Called when the epoch ends.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_epoch_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_start\\n def on_train_start(self):\\n \\\"\\\"\\\"Called when the train begins.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_train_start(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_train_end\\n def on_train_end(self):\\n \\\"\\\"\\\"Called when the train ends.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_train_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_batch_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_batch_start\\n def on_batch_start(self):\\n \\\"\\\"\\\"Called when the training batch begins.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_batch_start(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_batch_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_batch_end\\n def on_batch_end(self):\\n \\\"\\\"\\\"Called when the training batch ends.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_batch_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_batch_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_batch_start\\n def on_validation_batch_start(self):\\n \\\"\\\"\\\"Called when the validation batch begins.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_validation_batch_start(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_batch_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_batch_end\\n def on_validation_batch_end(self):\\n \\\"\\\"\\\"Called when the validation batch ends.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_validation_batch_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_batch_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_batch_start\\n def on_test_batch_start(self):\\n \\\"\\\"\\\"Called when the test batch begins.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_test_batch_start(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_batch_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_batch_end\\n def on_test_batch_end(self):\\n \\\"\\\"\\\"Called when the test batch ends.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_test_batch_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_start\\n def on_validation_start(self):\\n \\\"\\\"\\\"Called when the validation loop begins.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_validation_start(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_validation_end\\n def on_validation_end(self):\\n \\\"\\\"\\\"Called when the validation loop ends.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_validation_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_start\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_start\\n def on_test_start(self):\\n \\\"\\\"\\\"Called when the test begins.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_test_start(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_end\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_test_end\\n def on_test_end(self):\\n \\\"\\\"\\\"Called when the test ends.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_test_end(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_keyboard_interrupt\",\n \"text\": \"pytorch_lightning/trainer/callback_hook.py::TrainerCallbackHookMixin.on_keyboard_interrupt\\n def on_keyboard_interrupt(self):\\n \\\"\\\"\\\"Called when the training is interrupted by KeyboardInterrupt.\\\"\\\"\\\"\\n for callback in self.callbacks:\\n callback.on_keyboard_interrupt(self, self.get_model())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.save_checkpoint\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.save_checkpoint\\n @abstractmethod\\n def save_checkpoint(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.restore\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.restore\\n @abstractmethod\\n def restore(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.init_optimizers\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.init_optimizers\\n @abstractmethod\\n def init_optimizers(self, *args) -> Tuple[List, List, List]:\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.fit\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.fit\\n @abstractmethod\\n def fit(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin._run_lr_finder_internally\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin._run_lr_finder_internally\\n def _run_lr_finder_internally(self, model: LightningModule):\\n \\\"\\\"\\\" Call lr finder internally during Trainer.fit() \\\"\\\"\\\"\\n lr_finder = self.lr_find(model)\\n lr = lr_finder.suggestion()\\n # TODO: log lr.results to self.logger\\n if isinstance(self.auto_lr_find, str):\\n # Try to find requested field, may be nested\\n if _nested_hasattr(model, self.auto_lr_find):\\n _nested_setattr(model, self.auto_lr_find, lr)\\n else:\\n raise MisconfigurationException(\\n f'`auto_lr_find` was set to {self.auto_lr_find}, however'\\n ' could not find this as a field in `model.hparams`.')\\n else:\\n if hasattr(model, 'lr'):\\n model.lr = lr\\n elif hasattr(model, 'learning_rate'):\\n model.learning_rate = lr\\n else:\\n raise MisconfigurationException(\\n 'When auto_lr_find is set to True, expects that hparams'\\n ' either has field `lr` or `learning_rate` that can overridden')\\n log.info(f'Learning rate set to {lr}')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.lr_find\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.lr_find\\n def lr_find(\\n self,\\n model: LightningModule,\\n train_dataloader: Optional[DataLoader] = None,\\n val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,\\n min_lr: float = 1e-8,\\n max_lr: float = 1,\\n num_training: int = 100,\\n mode: str = 'exponential',\\n early_stop_threshold: float = 4.0,\\n ):\\n r\\\"\\\"\\\"\\n lr_find enables the user to do a range test of good initial learning rates,\\n to reduce the amount of guesswork in picking a good starting learning rate.\\n\\n Args:\\n model: Model to do range testing for\\n\\n train_dataloader: A PyTorch\\n DataLoader with training samples. If the model has\\n a predefined train_dataloader method this will be skipped.\\n\\n min_lr: minimum learning rate to investigate\\n\\n max_lr: maximum learning rate to investigate\\n\\n num_training: number of learning rates to test\\n\\n mode: search strategy, either 'linear' or 'exponential'. If set to\\n 'linear' the learning rate will be searched by linearly increasing\\n after each batch. If set to 'exponential', will increase learning\\n rate exponentially.\\n\\n early_stop_threshold: threshold for stopping the search. If the\\n loss at any point is larger than early_stop_threshold*best_loss\\n then the search is stopped. To disable, set to None.\\n\\n Example::\\n\\n # Setup model and trainer\\n model = MyModelClass(hparams)\\n trainer = pl.Trainer()\\n\\n # Run lr finder\\n lr_finder = trainer.lr_find(model, ...)\\n\\n # Inspect results\\n fig = lr_finder.plot(); fig.show()\\n suggested_lr = lr_finder.suggestion()\\n\\n # Overwrite lr and create new model\\n hparams\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.__lr_finder_dump_params\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.__lr_finder_dump_params\\n def __lr_finder_dump_params(self, model):\\n # Prevent going into infinite loop\\n self.__dumped_params = {\\n 'auto_lr_find': self.auto_lr_find,\\n 'callbacks': self.callbacks,\\n 'logger': self.logger,\\n 'max_steps': self.max_steps,\\n 'checkpoint_callback': self.checkpoint_callback,\\n 'early_stop_callback': self.early_stop_callback,\\n 'configure_optimizers': model.configure_optimizers,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.__lr_finder_restore_params\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::TrainerLRFinderMixin.__lr_finder_restore_params\\n def __lr_finder_restore_params(self, model):\\n self.auto_lr_find = self.__dumped_params['auto_lr_find']\\n self.logger = self.__dumped_params['logger']\\n self.callbacks = self.__dumped_params['callbacks']\\n self.max_steps = self.__dumped_params['max_steps']\\n self.checkpoint_callback = self.__dumped_params['checkpoint_callback']\\n self.early_stop_callback = self.__dumped_params['early_stop_callback']\\n model.configure_optimizers = self.__dumped_params['configure_optimizers']\\n del self.__dumped_params\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_LRFinder.__init__\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_LRFinder.__init__\\n def __init__(self, mode: str, lr_min: float, lr_max: float, num_training: int):\\n assert mode in ('linear', 'exponential'), \\\\\\n 'mode should be either `linear` or `exponential`'\\n\\n self.mode = mode\\n self.lr_min = lr_min\\n self.lr_max = lr_max\\n self.num_training = num_training\\n\\n self.results = {}\\n self._total_batch_idx = 0 # for debug purpose\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_LRFinder._get_new_optimizer\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_LRFinder._get_new_optimizer\\n def _get_new_optimizer(self, optimizer: torch.optim.Optimizer):\\n \\\"\\\"\\\" Construct a new `configure_optimizers()` method, that has a optimizer\\n with initial lr set to lr_min and a scheduler that will either\\n linearly or exponentially increase the lr to lr_max in num_training steps.\\n\\n Args:\\n optimizer: instance of `torch.optim.Optimizer`\\n\\n \\\"\\\"\\\"\\n new_lrs = [self.lr_min] * len(optimizer.param_groups)\\n for param_group, new_lr in zip(optimizer.param_groups, new_lrs):\\n param_group[\\\"lr\\\"] = new_lr\\n param_group[\\\"initial_lr\\\"] = new_lr\\n\\n args = (optimizer, self.lr_max, self.num_training)\\n scheduler = _LinearLR(*args) if self.mode == 'linear' else _ExponentialLR(*args)\\n\\n def configure_optimizers():\\n return [optimizer], [{'scheduler': scheduler,\\n 'interval': 'step'}]\\n\\n return configure_optimizers\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_LRFinder._get_new_optimizer.configure_optimizers\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_LRFinder._get_new_optimizer.configure_optimizers\\n def configure_optimizers():\\n return [optimizer], [{'scheduler': scheduler,\\n 'interval': 'step'}]\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_LRFinder.plot\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_LRFinder.plot\\n def plot(self, suggest: bool = False, show: bool = False):\\n \\\"\\\"\\\" Plot results from lr_find run\\n Args:\\n suggest: if True, will mark suggested lr to use with a red point\\n\\n show: if True, will show figure\\n \\\"\\\"\\\"\\n import matplotlib.pyplot as plt\\n\\n lrs = self.results[\\\"lr\\\"]\\n losses = self.results[\\\"loss\\\"]\\n\\n fig, ax = plt.subplots()\\n\\n # Plot loss as a function of the learning rate\\n ax.plot(lrs, losses)\\n if self.mode == 'exponential':\\n ax.set_xscale(\\\"log\\\")\\n ax.set_xlabel(\\\"Learning rate\\\")\\n ax.set_ylabel(\\\"Loss\\\")\\n\\n if suggest:\\n _ = self.suggestion()\\n if self._optimal_idx:\\n ax.plot(lrs[self._optimal_idx], losses[self._optimal_idx],\\n markersize=10, marker='o', color='red')\\n\\n if show:\\n plt.show()\\n\\n return fig\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_LRFinder.suggestion\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_LRFinder.suggestion\\n def suggestion(self, skip_begin: int = 10, skip_end: int = 1):\\n \\\"\\\"\\\" This will propose a suggestion for choice of initial learning rate\\n as the point with the steepest negative gradient.\\n\\n Returns:\\n lr: suggested initial learning rate to use\\n skip_begin: how many samples to skip in the beginning. Prevent too naive estimates\\n skip_end: how many samples to skip in the end. Prevent too optimistic estimates\\n\\n \\\"\\\"\\\"\\n try:\\n loss = np.array(self.results[\\\"loss\\\"][skip_begin:-skip_end])\\n loss = loss[np.isfinite(loss)]\\n min_grad = np.gradient(loss).argmin()\\n self._optimal_idx = min_grad + skip_begin\\n return self.results[\\\"lr\\\"][self._optimal_idx]\\n except Exception:\\n log.exception('Failed to compute suggesting for `lr`. There might not be enough points.')\\n self._optimal_idx = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_LRCallback.__init__\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_LRCallback.__init__\\n def __init__(self, num_training: int,\\n early_stop_threshold: float = 4.0,\\n progress_bar_refresh_rate: int = 0,\\n beta: float = 0.98):\\n self.num_training = num_training\\n self.early_stop_threshold = early_stop_threshold\\n self.beta = beta\\n self.losses = []\\n self.lrs = []\\n self.avg_loss = 0.0\\n self.best_loss = 0.0\\n self.progress_bar_refresh_rate = progress_bar_refresh_rate\\n self.progress_bar = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_LRCallback.on_batch_start\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_LRCallback.on_batch_start\\n def on_batch_start(self, trainer, pl_module):\\n \\\"\\\"\\\" Called before each training batch, logs the lr that will be used \\\"\\\"\\\"\\n if (trainer.batch_idx + 1) % trainer.accumulate_grad_batches != 0:\\n return\\n\\n if self.progress_bar_refresh_rate and self.progress_bar is None:\\n self.progress_bar = tqdm(desc='Finding best initial lr', total=self.num_training)\\n\\n self.lrs.append(trainer.lr_schedulers[0]['scheduler'].lr[0])\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_LRCallback.on_batch_end\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_LRCallback.on_batch_end\\n def on_batch_end(self, trainer, pl_module):\\n \\\"\\\"\\\" Called when the training batch ends, logs the calculated loss \\\"\\\"\\\"\\n if (trainer.batch_idx + 1) % trainer.accumulate_grad_batches != 0:\\n return\\n\\n if self.progress_bar:\\n self.progress_bar.update()\\n\\n current_loss = trainer.running_loss.last().item()\\n current_step = trainer.global_step + 1 # remove the +1 in 1.0\\n\\n # Avg loss (loss with momentum) + smoothing\\n self.avg_loss = self.beta * self.avg_loss + (1 - self.beta) * current_loss\\n smoothed_loss = self.avg_loss / (1 - self.beta**current_step)\\n\\n # Check if we diverging\\n if self.early_stop_threshold is not None:\\n if current_step > 1 and smoothed_loss > self.early_stop_threshold * self.best_loss:\\n trainer.max_steps = current_step # stop signal\\n if self.progress_bar:\\n self.progress_bar.close()\\n\\n # Save best loss for diverging checking\\n if smoothed_loss < self.best_loss or current_step == 1:\\n self.best_loss = smoothed_loss\\n\\n self.losses.append(smoothed_loss)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_LinearLR.__init__\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_LinearLR.__init__\\n def __init__(self,\\n optimizer: torch.optim.Optimizer,\\n end_lr: float,\\n num_iter: int,\\n last_epoch: int = -1):\\n self.end_lr = end_lr\\n self.num_iter = num_iter\\n super(_LinearLR, self).__init__(optimizer, last_epoch)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_LinearLR.get_lr\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_LinearLR.get_lr\\n def get_lr(self):\\n curr_iter = self.last_epoch + 1\\n r = curr_iter / self.num_iter\\n\\n if self.last_epoch > 0:\\n val = [base_lr + r * (self.end_lr - base_lr) for base_lr in self.base_lrs]\\n else:\\n val = [base_lr for base_lr in self.base_lrs]\\n self._lr = val\\n return val\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_LinearLR.lr\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_LinearLR.lr\\n @property\\n def lr(self):\\n return self._lr\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.__init__\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.__init__\\n def __init__(self,\\n optimizer: torch.optim.Optimizer,\\n end_lr: float,\\n num_iter: int,\\n last_epoch: int = -1):\\n self.end_lr = end_lr\\n self.num_iter = num_iter\\n super(_ExponentialLR, self).__init__(optimizer, last_epoch)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.get_lr\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.get_lr\\n def get_lr(self):\\n curr_iter = self.last_epoch + 1\\n r = curr_iter / self.num_iter\\n\\n if self.last_epoch > 0:\\n val = [base_lr * (self.end_lr / base_lr) ** r for base_lr in self.base_lrs]\\n else:\\n val = [base_lr for base_lr in self.base_lrs]\\n self._lr = val\\n return val\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.lr\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_ExponentialLR.lr\\n @property\\n def lr(self):\\n return self._lr\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_nested_hasattr\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_nested_hasattr\\ndef _nested_hasattr(obj, path):\\n parts = path.split(\\\".\\\")\\n for part in parts:\\n if hasattr(obj, part):\\n obj = getattr(obj, part)\\n else:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/lr_finder.py::_nested_setattr\",\n \"text\": \"pytorch_lightning/trainer/lr_finder.py::_nested_setattr\\ndef _nested_setattr(obj, path, val):\\n parts = path.split(\\\".\\\")\\n for part in parts[:-1]:\\n if hasattr(obj, part):\\n obj = getattr(obj, part)\\n setattr(obj, parts[-1], val)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model\\n @abstractmethod\\n def get_model(self) -> LightningModule:\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented\\n @abstractmethod\\n def is_function_implemented(self, *args, **kwargs):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation\\n @abstractmethod\\n def run_evaluation(self, *args, **kwargs):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu\\n @abstractmethod\\n def transfer_batch_to_gpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu\\n @abstractmethod\\n def transfer_batch_to_tpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients\\n @abstractmethod\\n def clip_gradients(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.detect_nan_tensors\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.detect_nan_tensors\\n @abstractmethod\\n def detect_nan_tensors(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overridden\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overridden\\n @abstractmethod\\n def is_overridden(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_progress_bar_metrics\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_progress_bar_metrics\\n @abstractmethod\\n def add_progress_bar_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics\\n @abstractmethod\\n def log_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output\\n @abstractmethod\\n def process_output(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader\\n @abstractmethod\\n def reset_train_dataloader(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader\\n @abstractmethod\\n def reset_val_dataloader(self, model):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg\\n @abstractmethod\\n def has_arg(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train\\n def train(self):\\n # add signal handlers for process kills\\n # def _signal_kill_handler(*args):\\n # return TrainerTrainLoopMixin.run_training_teardown(self)\\n #\\n # orig_signal_handlers = {}\\n # for sig_name in SIGNAL_TERMINATE:\\n # orig_signal_handlers[sig_name] = signal.signal(getattr(signal, sig_name),\\n # _signal_kill_handler)\\n\\n # get model\\n model = self.get_model()\\n\\n # enable train mode\\n model.train()\\n\\n # enable gradients\\n torch.set_grad_enabled(True)\\n\\n # load data\\n # if reload_dataloaders_every_epoch, this is moved to the epoch loop\\n if not self.reload_dataloaders_every_epoch:\\n self.reset_train_dataloader(model)\\n\\n if model.val_dataloader is not None:\\n self.reset_val_dataloader(model)\\n\\n # Train start events\\n with self.profiler.profile('on_train_start'):\\n # callbacks\\n self.on_train_start()\\n # model hooks\\n model.on_train_start()\\n\\n try:\\n # run all epochs\\n for epoch in range(self.current_epoch, self.max_epochs):\\n # reset train dataloader\\n if self.reload_dataloaders_every_epoch:\\n self.reset_train_dataloader(model)\\n # set seed for distributed sampler (enables shuffling for each epoch)\\n if (self.use_ddp or self.use_horovod) \\\\\\n and hasattr(self.train_dataloader, 'sampler') \\\\\\n and hasattr(self.train_dataloader.sampler, 'set_epoch'):\\n self.train_dataloader.sampler.set_epoch(epoch)\\n\\n # update training progress in trainer and model\\n model.current_epoch = epoch\\n self.current_epoch = epoch\\n\\n # changing gradient accordin\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.prepare_train_loop_dataloader\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.prepare_train_loop_dataloader\\n def prepare_train_loop_dataloader(self, train_dataloader):\\n # on TPU we have to wrap it under the ParallelLoader\\n if self.use_tpu:\\n device = xm.xla_device(self.tpu_id)\\n train_dataloader = xla_pl.ParallelLoader(train_dataloader, [device])\\n train_dataloader = train_dataloader.per_device_loader(device)\\n\\n return train_dataloader\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_on_epoch_start_hook\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_on_epoch_start_hook\\n def run_on_epoch_start_hook(self, model):\\n # Epoch start events\\n with self.profiler.profile('on_epoch_start'):\\n # callbacks\\n self.on_epoch_start()\\n\\n # model hooks\\n if self.is_function_implemented('on_epoch_start'):\\n model.on_epoch_start()\\n\\n # Epoch start events\\n with self.profiler.profile('on_train_epoch_start'):\\n # callbacks\\n self.on_train_epoch_start()\\n\\n # model hooks\\n if self.is_function_implemented('on_train_epoch_start'):\\n model.on_train_epoch_start()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch\\n def run_training_epoch(self):\\n\\n # get model\\n model = self.get_model()\\n\\n # Epoch start events\\n self.run_on_epoch_start_hook(model)\\n\\n # modify dataloader if needed (ddp, etc...)\\n train_dataloader = self.prepare_train_loop_dataloader(self.train_dataloader)\\n\\n # bookkeeping\\n epoch_output = []\\n should_check_val = False\\n\\n # structured result accumulators for callbacks\\n early_stopping_accumulator = Accumulator()\\n checkpoint_accumulator = Accumulator()\\n\\n # run epoch\\n for batch_idx, (batch, is_last_batch) in self.profiler.profile_iterable(\\n enumerate(_with_is_last(train_dataloader)), \\\"get_train_batch\\\"\\n ):\\n # stop epoch if we limited the number of training batches\\n if batch_idx >= self.num_training_batches:\\n break\\n\\n self.batch_idx = batch_idx\\n model.global_step = self.global_step\\n\\n # ------------------------------------\\n # TRAINING_STEP + TRAINING_STEP_END\\n # ------------------------------------\\n batch_output = self.run_training_batch(batch, batch_idx)\\n\\n # only track outputs when user implements training_epoch_end\\n # otherwise we will build up unnecessary memory\\n step_out = batch_output.training_step_output_for_epoch_end\\n should_auto_reduce_train_result = isinstance(step_out, Result) and step_out.should_reduce_on_epoch_end\\n if isinstance(step_out, dict) and 'early_stop_on' in step_out:\\n early_stopping_accumulator.accumulate(step_out['early_stop_on'])\\n\\n if isinstance(step_out, dict) and 'checkpoint_on' in step_out:\\n checkpoint_accumulator.accumulate(step_out['checkpoint_on'])\\n\\n if self.is_overridden('training_epoch_end', model=self.get_model()) or should_auto_reduce_\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.check_checkpoint_callback\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.check_checkpoint_callback\\n def check_checkpoint_callback(self, should_check_val):\\n # when no val loop is present or fast-dev-run still need to call checkpoints\\n # TODO bake this logic into the checkpoint callback\\n should_activate = not self.is_overridden('validation_step') and not should_check_val\\n if should_activate:\\n checkpoint_callbacks = [c for c in self.callbacks if isinstance(c, ModelCheckpoint)]\\n [c.on_validation_end(self, self.get_model()) for c in checkpoint_callbacks]\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_train_loop_lr_schedulers\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_train_loop_lr_schedulers\\n def update_train_loop_lr_schedulers(self):\\n if (self.batch_idx + 1) % self.accumulate_grad_batches == 0:\\n # update lr\\n self.update_learning_rates(interval='step')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_on_epoch_end_hook\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_on_epoch_end_hook\\n def run_on_epoch_end_hook(self, model):\\n with self.profiler.profile('on_epoch_end'):\\n # callbacks\\n self.on_epoch_end()\\n # model hooks\\n if self.is_function_implemented('on_epoch_end'):\\n model.on_epoch_end()\\n\\n with self.profiler.profile('on_train_epoch_end'):\\n # callbacks\\n self.on_train_epoch_end()\\n\\n # model hooks\\n if self.is_function_implemented('on_train_epoch_end'):\\n model.on_train_epoch_end()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch_end\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch_end\\n def run_training_epoch_end(self, epoch_output, checkpoint_accumulator, early_stopping_accumulator):\\n model = self.get_model()\\n is_result_obj = len(epoch_output) > 0 and isinstance(epoch_output[0], Result)\\n\\n epoch_log_metrics = {}\\n epoch_callback_metrics = {}\\n epoch_progress_bar_metrics = {}\\n\\n # -----------------------\\n # Calculate epoch callback values if given\\n # -----------------------\\n if checkpoint_accumulator.num_values > 0:\\n epoch_callback_metrics['checkpoint_on'] = checkpoint_accumulator.mean()\\n\\n if early_stopping_accumulator.num_values > 0:\\n epoch_callback_metrics['early_stop_on'] = early_stopping_accumulator.mean()\\n\\n # --------------------------\\n # EPOCH END STEP IF DEFINED\\n # --------------------------\\n if self.is_overridden('training_epoch_end', model=model):\\n self.global_step += 1\\n\\n # remove the protected keys so the user doesn't have to deal with them\\n if is_result_obj:\\n epoch_output = epoch_output[0].__class__.gather(epoch_output)\\n\\n # run training_epoch_end\\n epoch_output = model.training_epoch_end(epoch_output)\\n\\n if isinstance(epoch_output, Result):\\n epoch_log_metrics = epoch_output.epoch_log_metrics\\n epoch_progress_bar_metrics = epoch_output.epoch_pbar_metrics\\n else:\\n _processed_outputs = self.process_output(epoch_output)\\n epoch_progress_bar_metrics = _processed_outputs[1]\\n epoch_log_metrics = _processed_outputs[2]\\n epoch_callback_metrics = _processed_outputs[3]\\n\\n # --------------------------\\n # Structured Result (auto epoch end)\\n # --------------------------\\n elif is_result_obj:\\n epoch_output = epoch_output[0].__class__.redu\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.sync_horovod\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.sync_horovod\\n def sync_horovod(self):\\n if self.use_horovod:\\n hvd.join(hvd.local_rank() if self.on_gpu else -1)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.increment_accumulated_grad_global_step\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.increment_accumulated_grad_global_step\\n def increment_accumulated_grad_global_step(self):\\n # progress global step according to grads progress\\n if (self.batch_idx + 1) % self.accumulate_grad_batches == 0:\\n self.global_step += 1\\n self.total_batch_idx += 1\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.save_train_loop_metrics_to_loggers\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.save_train_loop_metrics_to_loggers\\n def save_train_loop_metrics_to_loggers(self, batch_idx, batch_output):\\n # when metrics should be logged\\n should_log_metrics = (batch_idx + 1) % self.row_log_interval == 0 or self.should_stop\\n if should_log_metrics or self.fast_dev_run:\\n # logs user requested information to logger\\n metrics = batch_output.batch_log_metrics\\n grad_norm_dic = batch_output.grad_norm_dic\\n if len(metrics) > 0 or len(grad_norm_dic) > 0:\\n self.log_metrics(metrics, grad_norm_dic)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.save_loggers_in_training_loop\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.save_loggers_in_training_loop\\n def save_loggers_in_training_loop(self, batch_idx):\\n # when loggers should save to disk\\n should_save_log = (batch_idx + 1) % self.log_save_interval == 0 or self.should_stop\\n if should_save_log or self.fast_dev_run:\\n if self.is_global_zero and self.logger is not None:\\n self.logger.save()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.should_check_val\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.should_check_val\\n def should_check_val(self, batch_idx, is_last_batch):\\n # decide if we should run validation\\n is_val_check_batch = (batch_idx + 1) % self.val_check_batch == 0\\n can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0\\n can_check_val = self.enable_validation and can_check_epoch\\n should_check_val = is_val_check_batch or self.should_stop\\n is_last_batch_for_infinite_dataset = (is_last_batch and self.val_check_batch == float('inf'))\\n should_check_val = can_check_val and (should_check_val or is_last_batch_for_infinite_dataset)\\n\\n return should_check_val\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch\\n def run_training_batch(self, batch, batch_idx):\\n # track grad norms\\n grad_norm_dic = {}\\n\\n # track all metrics for callbacks\\n batch_callback_metrics = []\\n\\n # track metrics to log\\n batch_log_metrics = []\\n\\n using_results_obj = False\\n\\n if batch is None:\\n return AttributeDict(signal=0, grad_norm_dic=grad_norm_dic)\\n\\n # Batch start events\\n with self.profiler.profile('on_batch_start'):\\n # callbacks\\n self.on_batch_start()\\n # hooks\\n if self.is_function_implemented('on_batch_start'):\\n response = self.get_model().on_batch_start(batch)\\n if response == -1:\\n return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic)\\n\\n splits = [batch]\\n if self.truncated_bptt_steps is not None:\\n model_ref = self.get_model()\\n with self.profiler.profile('tbptt_split_batch'):\\n splits = model_ref.tbptt_split_batch(batch, self.truncated_bptt_steps)\\n\\n self.hiddens = None\\n for split_idx, split_batch in enumerate(splits):\\n self.split_idx = split_idx\\n\\n for opt_idx, optimizer in self._get_optimizers_iterable():\\n # make sure only the gradients of the current optimizer's parameters are calculated\\n # in the training step to prevent dangling gradients in multiple-optimizer setup.\\n if len(self.optimizers) > 1:\\n for param in self.get_model().parameters():\\n param.requires_grad = False\\n for group in optimizer.param_groups:\\n for param in group['params']:\\n param.requires_grad = True\\n\\n # -------------------\\n # calculate loss (train step + train step end)\\n # -------------------\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_batch_backward_pass\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_batch_backward_pass\\n def run_batch_backward_pass(self, split_batch, batch_idx, opt_idx, optimizer):\\n # ------------------\\n # GRAD NORMS\\n # ------------------\\n # track gradient norms when requested\\n grad_norm_dic = {}\\n if batch_idx % self.row_log_interval == 0:\\n if float(self.track_grad_norm) > 0:\\n model = self.get_model()\\n grad_norm_dic = model.grad_norm(\\n self.track_grad_norm)\\n\\n # ------------------\\n # CLIP GRADS\\n # ------------------\\n if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:\\n self.scaler.unscale_(optimizer)\\n self.clip_gradients(optimizer)\\n\\n # ------------------\\n # .STEP + ZERO_GRAD\\n # ------------------\\n self.call_optimizer_step(optimizer, opt_idx, batch_idx, split_batch)\\n\\n return grad_norm_dic\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_optimizer_step\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_optimizer_step\\n def call_optimizer_step(self, optimizer, opt_idx, batch_idx, split_batch):\\n # calls .step(), .zero_grad()\\n # override function to modify this behavior\\n model = self.get_model()\\n\\n with self.profiler.profile('optimizer_step'):\\n lambda_closure = lambda: self.optimizer_closure(\\n split_batch,\\n batch_idx,\\n opt_idx,\\n optimizer,\\n self.hiddens\\n ).loss\\n\\n # apply TPU optimizer\\n if self.use_tpu and XLA_AVAILABLE:\\n model.optimizer_step(self.current_epoch, batch_idx,\\n optimizer, opt_idx, lambda_closure, on_tpu=True)\\n\\n # for LBFGS do something a bit different\\n elif isinstance(optimizer, torch.optim.LBFGS):\\n\\n # native amp + lbfgs is a no go right now\\n if self.use_amp and NATIVE_AMP_AVALAIBLE:\\n raise MisconfigurationException(\\n 'native PyTorch amp and lbfgs are not compatible.'\\n ' To request, please file a Github issue in PyTorch and tag @mcarilli')\\n model.optimizer_step(self.current_epoch, batch_idx, optimizer, opt_idx, lambda_closure,\\n using_lbfgs=True)\\n\\n # when using 16-bit\\n else:\\n native_amp = self.use_amp and NATIVE_AMP_AVALAIBLE\\n model.optimizer_step(self.current_epoch, batch_idx, optimizer, opt_idx, lambda_closure,\\n using_native_amp=native_amp)\\n\\n # in native 16-bit we need to update scaler after optimizer step\\n if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:\\n self.scaler.update()\\n\\n # model hook\\n model.on_before_zero_grad(optimizer)\\n\\n # clear gradients\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.optimizer_closure\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.optimizer_closure\\n def optimizer_closure(self, split_batch, batch_idx, opt_idx, optimizer, hiddens):\\n \\\"\\\"\\\"\\n wrap the forward step in a closure so second order methods work\\n \\\"\\\"\\\"\\n # ---------------------------\\n # FORWARD (TRAINING STEP + TRAIN STEP END)\\n # ---------------------------\\n with self.profiler.profile('model_forward'):\\n if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:\\n with torch.cuda.amp.autocast():\\n training_step_output = self.training_forward(split_batch, batch_idx,\\n opt_idx, hiddens)\\n else:\\n training_step_output = self.training_forward(split_batch, batch_idx, opt_idx,\\n hiddens)\\n\\n # ----------------------------\\n # PROCESS THE RESULT\\n # ----------------------------\\n # format and reduce outputs accordingly\\n training_step_output_for_epoch_end = training_step_output\\n is_result_obj = isinstance(training_step_output, Result)\\n\\n # don't allow EvalResult in the training_step\\n if isinstance(training_step_output, EvalResult):\\n raise MisconfigurationException('training_step cannot return EvalResult, '\\n 'use a dict or TrainResult instead')\\n\\n # handle regular dicts\\n if not is_result_obj:\\n training_step_output = self.process_output(training_step_output, train=True)\\n\\n training_step_output = AttributeDict(\\n batch_loss=training_step_output[0],\\n pbar_on_batch_end=training_step_output[1],\\n log_metrics=training_step_output[2],\\n callback_metrics=training_step_output[3],\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin._get_optimizers_iterable\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin._get_optimizers_iterable\\n def _get_optimizers_iterable(self):\\n if not self.optimizer_frequencies:\\n # call training_step once per optimizer\\n return list(enumerate(self.optimizers))\\n\\n optimizer_freq_cumsum = np.cumsum(self.optimizer_frequencies)\\n optimizers_loop_length = optimizer_freq_cumsum[-1]\\n current_place_in_loop = self.total_batch_idx % optimizers_loop_length\\n\\n # find optimzier index by looking for the first {item > current_place} in the cumsum list\\n opt_idx = np.argmax(optimizer_freq_cumsum > current_place_in_loop)\\n return [(opt_idx, self.optimizers[opt_idx])]\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown\\n def run_training_teardown(self):\\n if hasattr(self, '_teardown_already_run') and self._teardown_already_run:\\n return\\n\\n self._teardown_already_run = True\\n\\n # Train end events\\n with self.profiler.profile('on_train_end'):\\n # callbacks\\n self.on_train_end()\\n # model hooks\\n if self.is_function_implemented('on_train_end'):\\n self.get_model().on_train_end()\\n\\n if self.logger is not None:\\n self.logger.finalize(\\\"success\\\")\\n\\n # summarize profile results\\n if self.global_rank == 0:\\n self.profiler.describe()\\n\\n if self.global_rank == 0:\\n for proc in self.interactive_ddp_procs:\\n subprocess.Popen.kill(proc)\\n\\n # clean up dist group\\n if self.use_ddp or self.use_ddp2:\\n torch_distrib.destroy_process_group()\\n\\n # clear mem\\n if self.on_gpu:\\n model = self.get_model()\\n model.cpu()\\n torch.cuda.empty_cache()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward\\n def training_forward(self, batch, batch_idx, opt_idx, hiddens):\\n \\\"\\\"\\\"\\n Handle forward for each training case (distributed, single gpu, etc...)\\n :param batch:\\n :param batch_idx:\\n :return:\\n \\\"\\\"\\\"\\n # ---------------\\n # FORWARD\\n # ---------------\\n # enable not needing to add opt_idx to training_step\\n args = [batch, batch_idx]\\n\\n if len(self.optimizers) > 1:\\n if self.has_arg('training_step', 'optimizer_idx'):\\n args.append(opt_idx)\\n else:\\n num_opts = len(self.optimizers)\\n raise ValueError(\\n f'Your LightningModule defines {num_opts} optimizers but '\\n f'training_step is missing the \\\"optimizer_idx\\\" argument.'\\n )\\n\\n # pass hiddens if using tbptt\\n if self.truncated_bptt_steps is not None:\\n args.append(hiddens)\\n\\n # distributed forward\\n if self.use_ddp or self.use_ddp2 or self.use_dp:\\n output = self.model(*args)\\n\\n # Horovod\\n elif self.use_horovod and self.on_gpu:\\n batch = self.transfer_batch_to_gpu(batch, hvd.local_rank())\\n args[0] = batch\\n output = self.model.training_step(*args)\\n\\n # single GPU forward\\n elif self.use_single_gpu:\\n gpu_id = 0\\n if isinstance(self.data_parallel_device_ids, list):\\n gpu_id = self.data_parallel_device_ids[0]\\n\\n # Don't copy the batch since there is a single gpu that the batch could\\n # be referenced from and if there are multiple optimizers the batch will\\n # wind up copying it to the same device repeatedly.\\n batch = self.transfer_batch_to_gpu(batch, gpu_id)\\n args[0] = batch\\n output = self.model.training_step(*args)\\n\\n # TPU support\\n elif self.use_tpu\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates\\n def update_learning_rates(self, interval: str):\\n \\\"\\\"\\\"Update learning rates.\\n\\n Args:\\n interval: either 'epoch' or 'step'.\\n \\\"\\\"\\\"\\n if not self.lr_schedulers:\\n return\\n\\n for lr_scheduler in self.lr_schedulers:\\n current_idx = self.batch_idx if interval == 'step' else self.current_epoch\\n current_idx += 1 # account for both batch and epoch starts from 0\\n # Take step if call to update_learning_rates matches the interval key and\\n # the current step modulo the schedulers frequency is zero\\n if lr_scheduler['interval'] == interval and current_idx % lr_scheduler['frequency'] == 0:\\n # If instance of ReduceLROnPlateau, we need to pass validation loss\\n if lr_scheduler['reduce_on_plateau']:\\n monitor_key = lr_scheduler['monitor']\\n monitor_val = self.callback_metrics.get(monitor_key)\\n if monitor_val is None:\\n avail_metrics = ','.join(list(self.callback_metrics.keys()))\\n raise MisconfigurationException(\\n f'ReduceLROnPlateau conditioned on metric {monitor_key}'\\n f' which is not available. Available metrics are: {avail_metrics}.'\\n ' Condition can be set using `monitor` key in lr scheduler dict'\\n )\\n lr_scheduler['scheduler'].step(monitor_val)\\n else:\\n lr_scheduler['scheduler'].step()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::_with_is_last\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::_with_is_last\\ndef _with_is_last(iterable):\\n \\\"\\\"\\\"Pass through values from the given iterable with an added boolean indicating if this is the last item.\\n See `https://stackoverflow.com/a/1630350 <https://stackoverflow.com/a/1630350>`_\\\"\\\"\\\"\\n it = iter(iterable)\\n last = next(it)\\n for val in it:\\n # yield last and has next\\n yield last, False\\n last = val\\n # yield last, no longer has next\\n yield last, True\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":73,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-1104"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Support IterableDatasets for validation and test, not just train set [blocked by #953]\n## 🚀 Feature\r\nCurrently Lightning supports `IterableDatasets` only in the training set (see [code](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/trainer/data_loading.py#L177)). This makes them second-class citizens compared to the map-style datasets, and supporting them seems a low hanging fruit.\r\n\r\n### Motivation\r\nThis enables having larger test sets that may not fit into a machine's memory (they could be very large in production settings, or of modest size running in a student's cheap laptop). Moreover, \r\n datasets are usually generated together (eg train, val, test can come from the same process). It is very likely that the same process has the same signature, so you may end up having IterableDatasets even when their size may not deem it strictly necessary.\r\n\r\n### Pitch\r\n<!-- A clear and concise description of what you want to happen. -->\r\nChanging a few lines of code by bringing in the checks we are doing for training should be enough unless I'm missing something.\r\n\r\n### Additional context\r\n<!-- Add any other context or screenshots about the feature request here. -->\r\nAre there any gotchas that make this harder than it looks?\r\n\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/trainer/data_loading.py","pytorch_lightning/trainer/evaluation_loop.py","pytorch_lightning/trainer/training_loop.py"],"string":"[\n \"pytorch_lightning/trainer/data_loading.py\",\n \"pytorch_lightning/trainer/evaluation_loop.py\",\n \"pytorch_lightning/trainer/training_loop.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/trainer/data_loading.py::<module>","pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._percent_range_check","pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler","pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount","pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_infinite_dataloader","pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_data_loader","pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader","pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader","pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader","pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation","pytorch_lightning/trainer/training_loop.py::<module>","pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_infinite_dataloader","pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train"],"string":"[\n \"pytorch_lightning/trainer/data_loading.py::<module>\",\n \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._percent_range_check\",\n \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler\",\n \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount\",\n \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_infinite_dataloader\",\n \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_data_loader\",\n \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader\",\n \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader\",\n \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader\",\n \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation\",\n \"pytorch_lightning/trainer/training_loop.py::<module>\",\n \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_infinite_dataloader\",\n \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"pytorch_lightning/core/lightning.py","text":"pytorch_lightning/core/lightning.py\nclass LightningModule\n def __init__(self, *args, **kwargs)\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once\n def forward(self, *args, **kwargs) # Same as torch.nn.Module.forward(), however in Lightning you want this to define\n def training_step(self, *args, **kwargs) # return loss, dict with metrics for tqdm\n def training_end(self, *args, **kwargs) # Warnings:\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because training_step will operate\n def validation_step(self, *args, **kwargs) # Operate on a single batch of data from the validation set\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because validation_step will operate\n def validation_end(self, outputs) # Warnings:\n def validation_epoch_end(self, outputs: list) # Called at end of validation epoch with the output of all validation_steps\n def test_step(self, *args, **kwargs) # Operate on a single batch of data from the test set\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because test_step will operate\n def test_end(self, outputs) # Warnings:\n def test_epoch_end(self, outputs) # Called at end of test epoch with the output of all test_steps.\n def configure_ddp(self, model, device_ids) # Override to init DDP in your own way or with your own wrapper.\n def init_ddp_connection(self, proc_rank, world_size) # Override to define your custom way of setting up a distributed environment.\n def configure_apex(self, amp, model, optimizers, amp_level) # Override to init AMP your own way\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\n def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None) # Override this method to adjust the default way the Trainer calls each optimizer.\n def tbptt_split_batch(self, batch, split_size) # When using truncated backpropagation through time, each batch must be split alon\n def prepare_data(self) # Use this to download and prepare data.\n def train_dataloader(self) # Implement a PyTorch DataLoader\n def tng_dataloader(self) # Implement a PyTorch DataLoader.\n def test_dataloader(self) # Return a dataloader. It will not be called every epoch unless you set\n def val_dataloader(self) # Return a dataloader. It will not be called every epoch unless you set\n def load_from_metrics(cls, weights_path, tags_csv, map_location=None) # Warning:\n def load_from_checkpoint(cls, checkpoint_path: str, map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]]=None, tags_csv: Optional[str]=None) # Primary way of loading model from a checkpoint. When Lightning saves a checkpoin\n def _load_model_state(cls, checkpoint)\n def summarize(self, mode)\n d","label":0},{"cid":"pytorch_lightning/profiler/__init__.py","text":"pytorch_lightning/profiler/__init__.py","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py","text":"pytorch_lightning/trainer/data_loading.py\nclass TrainerDataLoadingMixin\n def is_overriden(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def _percent_range_check(self, name)\n def auto_add_sampler(self, dataloader, train)\n def reset_train_dataloader(self, model) # Dataloaders are provided by the model\n def is_infinite_dataloader(self, dataloader)\n def reset_val_dataloader(self, model) # Dataloaders are provided by the model\n def reset_test_dataloader(self, model) # Dataloaders are provided by the model.\n def request_data_loader(self, data_loader_fx) # Handles downloading data in the GPU or TPU case.\n def determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct) # Use less data for debugging purposes","label":1},{"cid":"pytorch_lightning/trainer/distrib_parts.py","text":"pytorch_lightning/trainer/distrib_parts.py\nclass TrainerDPMixin\n def run_pretrain_routine(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def init_optimizers(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def copy_trainer_model_properties(self, model)\n def transfer_batch_to_tpu(self, batch)\n def transfer_batch_to_gpu(self, batch, gpu_id)\n def __transfer_data_to_device(self, batch, device, gpu_id=None)\n def single_gpu_train(self, model)\n def tpu_train(self, tpu_core_idx, model)\n def dp_train(self, model)\ndef normalize_parse_gpu_string_input(s)\ndef get_all_available_gpus() # :return: a list of all available gpus\ndef check_gpus_data_type(gpus) # :param gpus: gpus parameter as passed to the Trainer\ndef normalize_parse_gpu_input_to_list(gpus)\ndef sanitize_gpu_ids(gpus) # :param gpus: list of ints corresponding to GPU indices\ndef parse_gpu_ids(gpus) # :param gpus: Int, string or list\ndef determine_root_gpu_device(gpus) # :param gpus: non empty list of ints representing which gpus to use","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py","text":"pytorch_lightning/trainer/evaluation_loop.py\nclass TrainerEvaluationLoopMixin\n def copy_trainer_model_properties(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\n def is_overriden(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def add_tqdm_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_test_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_val_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def evaluate(self, model, dataloaders, max_batches, test_mode: bool=False) # Run evaluation code.\n def run_evaluation(self, test_mode: bool=False)\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool=False)","label":1},{"cid":"pytorch_lightning/trainer/training_loop.py","text":"pytorch_lightning/trainer/training_loop.py\nclass TrainerTrainLoopMixin\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\n def is_function_implemented(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def is_infinite_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def run_evaluation(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def clip_gradients(self) # Warning: this is just empty shell for code implemented in other class.\n def print_nan_gradients(self) # Warning: this is just empty shell for code implemented in other class.\n def is_overriden(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def add_tqdm_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def process_output(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_train_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_val_dataloader(self, model) # Warning: this is just empty shell for code implemented in other class.\n def has_arg(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def train(self)\n def run_training_epoch(self)\n def run_training_batch(self, batch, batch_idx)\n def run_training_teardown(self)\n def training_forward(self, batch, batch_idx, opt_idx, hiddens) # Handle forward for each training case (distributed, single gpu, etc...)\n def update_learning_rates(self, interval) # Update learning rates\n def call_checkpoint_callback(self)","label":1}],"string":"[\n {\n \"cid\": \"pytorch_lightning/core/lightning.py\",\n \"text\": \"pytorch_lightning/core/lightning.py\\nclass LightningModule\\n def __init__(self, *args, **kwargs)\\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once\\n def forward(self, *args, **kwargs) # Same as torch.nn.Module.forward(), however in Lightning you want this to define\\n def training_step(self, *args, **kwargs) # return loss, dict with metrics for tqdm\\n def training_end(self, *args, **kwargs) # Warnings:\\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because training_step will operate\\n def validation_step(self, *args, **kwargs) # Operate on a single batch of data from the validation set\\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because validation_step will operate\\n def validation_end(self, outputs) # Warnings:\\n def validation_epoch_end(self, outputs: list) # Called at end of validation epoch with the output of all validation_steps\\n def test_step(self, *args, **kwargs) # Operate on a single batch of data from the test set\\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because test_step will operate\\n def test_end(self, outputs) # Warnings:\\n def test_epoch_end(self, outputs) # Called at end of test epoch with the output of all test_steps.\\n def configure_ddp(self, model, device_ids) # Override to init DDP in your own way or with your own wrapper.\\n def init_ddp_connection(self, proc_rank, world_size) # Override to define your custom way of setting up a distributed environment.\\n def configure_apex(self, amp, model, optimizers, amp_level) # Override to init AMP your own way\\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\\n def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None) # Override this method to adjust the default way the Trainer calls each optimizer.\\n def tbptt_split_batch(self, batch, split_size) # When using truncated backpropagation through time, each batch must be split alon\\n def prepare_data(self) # Use this to download and prepare data.\\n def train_dataloader(self) # Implement a PyTorch DataLoader\\n def tng_dataloader(self) # Implement a PyTorch DataLoader.\\n def test_dataloader(self) # Return a dataloader. It will not be called every epoch unless you set\\n def val_dataloader(self) # Return a dataloader. It will not be called every epoch unless you set\\n def load_from_metrics(cls, weights_path, tags_csv, map_location=None) # Warning:\\n def load_from_checkpoint(cls, checkpoint_path: str, map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]]=None, tags_csv: Optional[str]=None) # Primary way of loading model from a checkpoint. When Lightning saves a checkpoin\\n def _load_model_state(cls, checkpoint)\\n def summarize(self, mode)\\n d\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/profiler/__init__.py\",\n \"text\": \"pytorch_lightning/profiler/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py\\nclass TrainerDataLoadingMixin\\n def is_overriden(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def _percent_range_check(self, name)\\n def auto_add_sampler(self, dataloader, train)\\n def reset_train_dataloader(self, model) # Dataloaders are provided by the model\\n def is_infinite_dataloader(self, dataloader)\\n def reset_val_dataloader(self, model) # Dataloaders are provided by the model\\n def reset_test_dataloader(self, model) # Dataloaders are provided by the model.\\n def request_data_loader(self, data_loader_fx) # Handles downloading data in the GPU or TPU case.\\n def determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct) # Use less data for debugging purposes\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py\\nclass TrainerDPMixin\\n def run_pretrain_routine(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def init_optimizers(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def copy_trainer_model_properties(self, model)\\n def transfer_batch_to_tpu(self, batch)\\n def transfer_batch_to_gpu(self, batch, gpu_id)\\n def __transfer_data_to_device(self, batch, device, gpu_id=None)\\n def single_gpu_train(self, model)\\n def tpu_train(self, tpu_core_idx, model)\\n def dp_train(self, model)\\ndef normalize_parse_gpu_string_input(s)\\ndef get_all_available_gpus() # :return: a list of all available gpus\\ndef check_gpus_data_type(gpus) # :param gpus: gpus parameter as passed to the Trainer\\ndef normalize_parse_gpu_input_to_list(gpus)\\ndef sanitize_gpu_ids(gpus) # :param gpus: list of ints corresponding to GPU indices\\ndef parse_gpu_ids(gpus) # :param gpus: Int, string or list\\ndef determine_root_gpu_device(gpus) # :param gpus: non empty list of ints representing which gpus to use\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py\\nclass TrainerEvaluationLoopMixin\\n def copy_trainer_model_properties(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\\n def is_overriden(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def add_tqdm_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_test_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_val_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def evaluate(self, model, dataloaders, max_batches, test_mode: bool=False) # Run evaluation code.\\n def run_evaluation(self, test_mode: bool=False)\\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool=False)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py\\nclass TrainerTrainLoopMixin\\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\\n def is_function_implemented(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def is_infinite_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def run_evaluation(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def clip_gradients(self) # Warning: this is just empty shell for code implemented in other class.\\n def print_nan_gradients(self) # Warning: this is just empty shell for code implemented in other class.\\n def is_overriden(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def add_tqdm_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def process_output(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_train_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_val_dataloader(self, model) # Warning: this is just empty shell for code implemented in other class.\\n def has_arg(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def train(self)\\n def run_training_epoch(self)\\n def run_training_batch(self, batch, batch_idx)\\n def run_training_teardown(self)\\n def training_forward(self, batch, batch_idx, opt_idx, hiddens) # Handle forward for each training case (distributed, single gpu, etc...)\\n def update_learning_rates(self, interval) # Update learning rates\\n def call_checkpoint_callback(self)\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"pytorch_lightning/core/lightning.py::LightningModule.__init__","text":"pytorch_lightning/core/lightning.py::LightningModule.__init__\n def __init__(self, *args, **kwargs):\n super(LightningModule, self).__init__(*args, **kwargs)\n\n #: Current dtype\n self.dtype = torch.FloatTensor\n\n self.exp_save_path = None\n\n #: The current epoch\n self.current_epoch = 0\n\n #: Total training batches seen across all epochs\n self.global_step = 0\n\n self.loaded_optimizer_states_dict = {}\n\n #: Pointer to the trainer object\n self.trainer = None\n\n #: Pointer to the logger object\n self.logger = None\n self.example_input_array = None\n\n #: True if your model is currently running on GPUs.\n #: Useful to set flags around the LightningModule for different CPU vs GPU behavior.\n self.on_gpu = False\n\n #: True if using dp\n self.use_dp = False\n\n #: True if using ddp\n self.use_ddp = False\n\n #: True if using ddp2\n self.use_ddp2 = False\n\n #: True if using amp\n self.use_amp = False\n\n self.hparams = None","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.print","text":"pytorch_lightning/core/lightning.py::LightningModule.print\n def print(self, *args, **kwargs):\n r\"\"\"\n Prints only from process 0. Use this in any distributed mode to log only once\n\n Args:\n x (object): The thing to print\n\n Examples:\n .. code-block:: python\n\n # example if we were using this model as a feature extractor\n def forward(self, x):\n self.print(x, 'in loader')\n\n \"\"\"\n if self.trainer.proc_rank == 0:\n print(*args, **kwargs)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.forward","text":"pytorch_lightning/core/lightning.py::LightningModule.forward\n @abstractmethod\n def forward(self, *args, **kwargs):\n r\"\"\"\n Same as torch.nn.Module.forward(), however in Lightning you want this to define\n the operations you want to use for prediction (ie: on a server or as a feature extractor).\n\n Normally you'd call self.forward() from your training_step() method.\n This makes it easy to write a complex system for training with the outputs\n you'd want in a prediction setting.\n\n Args:\n x (tensor): Whatever you decide to define in the forward method\n\n Return:\n Predicted output\n\n Examples:\n .. code-block:: python\n\n # example if we were using this model as a feature extractor\n def forward(self, x):\n feature_maps = self.convnet(x)\n return feature_maps\n\n def training_step(self, batch, batch_idx):\n x, y = batch\n feature_maps = self.forward(x)\n logits = self.classifier(feature_maps)\n\n # ...\n return loss\n\n # splitting it this way allows model to be used a feature extractor\n model = MyModelAbove()\n\n inputs = server.get_request()\n results = model(inputs)\n server.write_results(results)\n\n # -------------\n # This is in stark contrast to torch.nn.Module where normally you would have this:\n def forward(self, batch):\n x, y = batch\n feature_maps = self.convnet(x)\n logits = self.classifier(feature_maps)\n return logits\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step\n def training_step(self, *args, **kwargs):\n r\"\"\"return loss, dict with metrics for tqdm\n\n Args:\n batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your\n dataloader. A tensor, tuple or list\n batch_idx (int): Integer displaying index of this batch\n optimizer_idx (int): If using multiple optimizers, this argument will also be present.\n hiddens(:`Tensor <https://pytorch.org/docs/stable/tensors.html>`_):\n Passed in if truncated_bptt_steps > 0.\n\n Return:\n dict with loss key and optional log, progress keys\n if implementing training_step, return whatever you need in that step:\n\n - loss -> tensor scalar [REQUIRED]\n - progress_bar -> Dict for progress bar display. Must have only tensors\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n In this step you'd normally do the forward pass and calculate the loss for a batch.\n You can also do fancier things like multiple forward passes or something model specific.\n\n Examples:\n .. code-block:: python\n\n def training_step(self, batch, batch_idx):\n x, y, z = batch\n\n # implement your own\n out = self.forward(x)\n loss = self.loss(out, x)\n\n logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)\n\n # if using TestTubeLogger or TensorBoardLogger you can nest scalars\n logger_logs = {'losses': logger_logs} # optional (MUST ALL BE TENSORS)\n\n output = {\n 'loss': loss, # required\n 'progress_bar': {'training_loss': loss}, # optional (MUST ALL BE TENSORS)\n 'log': logger_logs\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_end\n def training_end(self, *args, **kwargs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. use training_step_end instead\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\n def training_step_end(self, *args, **kwargs):\n \"\"\"\n Use this when training with dp or ddp2 because training_step will operate\n on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n .. note:: If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]\n training_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in `training_step` for each batch part.\n\n Return:\n dictionary with loss key and optional log, progress keys:\n - loss -> tensor scalar [REQUIRED]\n - progress_bar -> Dict for progress bar display. Must have only tensors\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n In this case you should define training_step_end to perform those calculations.\n\n Examples:\n .. code-block:: python\n\n # WITHOUT training_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self.forward(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with training_step_end to do softmax over the full batch\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step\n def validation_step(self, *args, **kwargs):\n r\"\"\"\n Operate on a single batch of data from the validation set\n In this step you'd might generate examples or calculate anything of interest like accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n val_outs = []\n for val_batch in val_data:\n out = validation_step(train_batch)\n val_outs.append(out\n validation_epoch_end(val_outs)\n\n Args:\n batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your\n dataloader. A tensor, tuple or list\n batch_idx (int): The index of this batch\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple val datasets used)\n\n Return:\n Dict or OrderedDict - passed to validation_epoch_end.\n If you defined validation_step_end it will go to that first.\n\n .. code-block:: python\n\n # pseudocode of order\n out = validation_step()\n if defined('validation_step_end'):\n out = validation_step_end(out)\n out = validation_epoch_end(out)\n\n\n .. code-block:: python\n\n # if you have one val dataloader:\n def validation_step(self, batch, batch_idx)\n\n # if you have multiple val dataloaders:\n def validation_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single validation dataset\n def validation_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self.forward(x)\n loss = self.loss(out, y)\n\n # log 6 example images\n # or generated text","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\n def validation_step_end(self, *args, **kwargs):\n \"\"\"\n Use this when validating with dp or ddp2 because validation_step will operate\n on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n .. note:: If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]\n validation_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in `validation_step` for each batch part.\n\n Return:\n Dict or OrderedDict - passed to the validation_epoch_end\n\n In this case you should define validation_step_end to perform those calculations.\n\n Examples:\n .. code-block:: python\n\n # WITHOUT validation_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self.forward(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with validation_step_end to do softmax over the full batch\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self.forward(x)\n return {'out': out}\n\n def validation_epoch_end(self, outputs):\n # this out is now the full size of the batch\n o","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_end\n def validation_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. use validation_epoch_end instead. Will be removed 1.0.0\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\n def validation_epoch_end(self, outputs: list):\n \"\"\"\n Called at end of validation epoch with the output of all validation_steps\n\n .. code-block:: python\n\n # the pseudocode for these calls\n\n val_outs = []\n for val_batch in val_data:\n out = validation_step(train_batch)\n train_outs.append(out)\n validation_epoch_end(val_outs)\n\n Args:\n outputs: List of outputs you defined in validation_step, or if there are multiple\n dataloaders, a list containing a list of outputs for each dataloader\n\n Return:\n Dict or OrderedDict (dict): Dict has the following optional keys:\n progress_bar -> Dict for progress bar display. Must have only tensors\n log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n .. note:: If you didn't define a validation_step, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, you can specify the 'step' key in the 'log' Dict\n\n Examples:\n With a single dataloader\n\n .. code-block:: python\n\n def validation_epoch_end(self, outputs):\n val_acc_mean = 0\n for output in outputs:\n val_acc_mean += output['val_acc']\n\n val_acc_mean /= len(outputs)\n tqdm_dict = {'val_acc': val_acc_mean.item()}\n\n # show val_loss and val_acc in progress bar but only log val_loss\n results = {\n 'progress_bar': tqdm_dict,\n 'log': {'val_acc': val_acc_mean.item()}\n }\n return results\n\n With multiple","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step\n def test_step(self, *args, **kwargs):\n r\"\"\"\n Operate on a single batch of data from the test set\n In this step you'd normally generate examples or calculate anything of interest\n such as accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n\n test_outs = []\n for test_batch in test_data:\n out = test_step(train_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your\n dataloader. A tensor, tuple or list\n batch_idx (int): The index of this batch\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple test datasets used)\n\n Return:\n Dict or OrderedDict - passed to the test_step_end\n\n .. code-block:: python\n\n # if you have one test dataloader:\n def test_step(self, batch, batch_idx)\n\n # if you have multiple test dataloaders:\n def test_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single test dataset\n def test_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self.forward(x)\n loss = self.loss(out, y)\n\n # log 6 example images\n # or generated text... or whatever\n sample_imgs = x[:6]\n grid = torchvision.utils.make_grid(sample_imgs)\n self.logger.experiment.add_image('example_images', grid, 0)\n\n # calculate acc\n labels_hat = torch.argmax(out, dim=1)\n val_acc = torch.sum(y == labels_ha","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\n def test_step_end(self, *args, **kwargs):\n \"\"\"\n Use this when testing with dp or ddp2 because test_step will operate\n on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n .. note:: If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]\n test_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in `training_step` for each batch part.\n\n Return:\n Dict or OrderedDict - passed to the test_epoch_end\n\n In this case you should define test_step_end to perform those calculations.\n\n Examples:\n .. code-block:: python\n\n # WITHOUT test_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self.forward(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with test_step_end to do softmax over the full batch\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self.forward(x)\n return {'out': out}\n\n def test_step_end(self, outputs):\n # this out is now the full size of the batch\n out = outputs['out']\n\n # this softmax now uses the full ba","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_end\n def test_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. use test_epoch_end instead. Will be removed 1.0.0\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\n def test_epoch_end(self, outputs):\n \"\"\"\n Called at end of test epoch with the output of all test_steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n\n test_outs = []\n for test_batch in test_data:\n out = test_step(test_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n outputs (list): List of outputs you defined in test_step, or if there are multiple\n dataloaders, a list containing a list of outputs for each dataloader\n\n Return:\n Dict or OrderedDict (dict): Dict has the following optional keys:\n progress_bar -> Dict for progress bar display. Must have only tensors\n log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n .. note:: If you didn't define a test_step, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict\n\n Examples:\n With a single dataloader\n\n .. code-block:: python\n\n def test_epoch_end(self, outputs):\n test_acc_mean = 0\n for output in outputs:\n test_acc_mean += output['test_acc']\n\n test_acc_mean /= len(outputs)\n tqdm_dict = {'test_acc': test_acc_mean.item()}\n\n # show test_loss and test_acc in progress bar but only log test_loss\n results = {\n 'progress_bar': tqdm_dict,\n 'log': {'test_acc': test_acc_mean.item()}\n }\n return results\n\n With multiple dataloaders, `outputs` will be a list ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\n def configure_ddp(self, model, device_ids):\n r\"\"\"\n Override to init DDP in your own way or with your own wrapper.\n The only requirements are that:\n\n 1. On a validation batch the call goes to model.validation_step.\n 2. On a training batch the call goes to model.training_step.\n 3. On a testing batch, the call goes to model.test_step\n\n Args:\n model (:class:`.LightningModule`): the LightningModule currently being optimized\n device_ids (list): the list of GPU ids\n\n Return:\n DDP wrapped model\n\n Examples:\n .. code-block:: python\n\n # default implementation used in Trainer\n def configure_ddp(self, model, device_ids):\n # Lightning DDP simply routes to test_step, val_step, etc...\n model = LightningDistributedDataParallel(\n model,\n device_ids=device_ids,\n find_unused_parameters=True\n )\n return model\n\n \"\"\"\n model = LightningDistributedDataParallel(\n model,\n device_ids=device_ids,\n find_unused_parameters=True\n )\n return model","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection","text":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\n def init_ddp_connection(self, proc_rank, world_size):\n r\"\"\"\n\n Override to define your custom way of setting up a distributed environment.\n\n Lightning's implementation uses env:// init by default and sets the first node as root.\n\n Args:\n proc_rank (int): The current process rank within the node.\n world_size (int): Number of GPUs being use across all nodes. (num_nodes*nb_gpu_nodes).\n\n Examples:\n .. code-block:: python\n\n def init_ddp_connection(self):\n # use slurm job id for the port number\n # guarantees unique ports across jobs from same grid search\n try:\n # use the last 4 numbers in the job id as the id\n default_port = os.environ['SLURM_JOB_ID']\n default_port = default_port[-4:]\n\n # all ports should be in the 10k+ range\n default_port = int(default_port) + 15000\n\n except Exception as e:\n default_port = 12910\n\n # if user gave a port number, use that one instead\n try:\n default_port = os.environ['MASTER_PORT']\n except Exception:\n os.environ['MASTER_PORT'] = str(default_port)\n\n # figure out the root node addr\n try:\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\n except Exception:\n root_node = '127.0.0.2'\n\n root_node = self.trainer.resolve_root_node_address(root_node)\n os.environ['MASTER_ADDR'] = root_node\n dist.init_process_group(\n 'nccl',\n rank=self.proc_rank,\n world_si","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\n def configure_apex(self, amp, model, optimizers, amp_level):\n r\"\"\"\n Override to init AMP your own way\n Must return a model and list of optimizers\n\n Args:\n amp (object): pointer to amp library object\n model (:class:`.LightningModule`): pointer to current lightningModule\n optimizers (list): list of optimizers passed in configure_optimizers()\n amp_level (str): AMP mode chosen ('O1', 'O2', etc...)\n\n Return:\n Apex wrapped model and optimizers\n\n Examples:\n .. code-block:: python\n\n # Default implementation used by Trainer.\n def configure_apex(self, amp, model, optimizers, amp_level):\n model, optimizers = amp.initialize(\n model, optimizers, opt_level=amp_level,\n )\n\n return model, optimizers\n \"\"\"\n model, optimizers = amp.initialize(\n model, optimizers, opt_level=amp_level,\n )\n\n return model, optimizers","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\n def configure_optimizers(self):\n r\"\"\"\n Choose what optimizers and learning-rate schedulers to use in your optimization.\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\n\n If you don't define this method Lightning will automatically use Adam(lr=1e-3)\n\n Return: any of these 3 options:\n - Single optimizer\n - List or Tuple - List of optimizers\n - Two lists - The first list has multiple optimizers, the second a list of LR schedulers\n\n Examples:\n .. code-block:: python\n\n # most cases (default if not defined)\n def configure_optimizers(self):\n opt = Adam(self.parameters(), lr=1e-3)\n return opt\n\n # multiple optimizer case (eg: GAN)\n def configure_optimizers(self):\n generator_opt = Adam(self.model_gen.parameters(), lr=0.01)\n disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)\n return generator_opt, disriminator_opt\n\n # example with learning_rate schedulers\n def configure_optimizers(self):\n generator_opt = Adam(self.model_gen.parameters(), lr=0.01)\n disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)\n discriminator_sched = CosineAnnealing(discriminator_opt, T_max=10)\n return [generator_opt, disriminator_opt], [discriminator_sched]\n\n # example with step-based learning_rate schedulers\n def configure_optimizers(self):\n gen_opt = Adam(self.model_gen.parameters(), lr=0.01)\n dis_opt = Adam(self.model_disc.parameters(), lr=0.02)\n gen_sched = {'scheduler': ExponentialLR(gen_opt, 0.99),\n 'inter","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step","text":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\n def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):\n r\"\"\"\n\n Override this method to adjust the default way the Trainer calls each optimizer.\n By default, Lightning calls .step() and zero_grad() as shown in the example\n once per optimizer.\n\n Args:\n epoch (int): Current epoch\n batch_idx (int): Index of current batch\n optimizer (torch.nn.Optimizer): A PyTorch optimizer\n optimizer_idx (int): If you used multiple optimizers this indexes into that list\n second_order_closure (int): closure for second order methods\n\n Examples:\n .. code-block:: python\n\n # DEFAULT\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure=None):\n optimizer.step()\n optimizer.zero_grad()\n\n # Alternating schedule for optimizer steps (ie: GANs)\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure=None):\n # update generator opt every 2 steps\n if optimizer_idx == 0:\n if batch_idx % 2 == 0 :\n optimizer.step()\n optimizer.zero_grad()\n\n # update discriminator opt every 4 steps\n if optimizer_idx == 1:\n if batch_idx % 4 == 0 :\n optimizer.step()\n optimizer.zero_grad()\n\n # ...\n # add as many optimizers as you want\n\n\n Here's another example showing how to use this for more advanced things such as\n learning-rate warm-up:\n\n .. code-block:: pyth","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch","text":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\n def tbptt_split_batch(self, batch, split_size):\n r\"\"\"\n\n When using truncated backpropagation through time, each batch must be split along the\n time dimension. Lightning handles this by default, but for custom behavior override\n this function.\n\n Args:\n batch (torch.nn.Tensor): Current batch\n split_size (int): How big the split is\n\n Return:\n list of batch splits. Each split will be passed to forward_step to enable truncated\n back propagation through time. The default implementation splits root level Tensors and\n Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.\n\n Examples:\n .. code-block:: python\n\n def tbptt_split_batch(self, batch, split_size):\n splits = []\n for t in range(0, time_dims[0], split_size):\n batch_split = []\n for i, x in enumerate(batch):\n if isinstance(x, torch.Tensor):\n split_x = x[:, t:t + split_size]\n elif isinstance(x, collections.Sequence):\n split_x = [None] * len(x)\n for batch_idx in range(len(x)):\n split_x[batch_idx] = x[batch_idx][t:t + split_size]\n\n batch_split.append(split_x)\n\n splits.append(batch_split)\n\n return splits\n\n .. note:: Called in the training loop after on_batch_start if ``truncated_bptt_steps > 0``.\n Each returned batch split is passed separately to ``training_step(...)``.\n\n \"\"\"\n time_dims = [len(x[0]) for x in batch if isinstance(x, (torch.Tensor, collections.Sequence))]\n assert len(time_dims) >= 1, \"Unable to determine batch time dimension\"\n a","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data","text":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\n def prepare_data(self):\n \"\"\"Use this to download and prepare data.\n In distributed (GPU, TPU), this will only be called once\n\n Return:\n PyTorch DataLoader\n\n This is called before requesting the dataloaders\n\n .. code-block:: python\n\n model.prepare_data()\n model.train_dataloader()\n model.val_dataloader()\n model.test_dataloader()\n\n Examples:\n .. code-block:: python\n\n def prepare_data(self):\n download_imagenet()\n clean_imagenet()\n cache_imagenet()\n \"\"\"\n return None","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\n def train_dataloader(self):\n \"\"\"Implement a PyTorch DataLoader\n\n Return:\n PyTorch DataLoader\n\n Return a dataloader. It will not be called every epoch unless you set\n ```Trainer(reload_dataloaders_every_epoch=True)```.\n\n It's recommended that all data downloads and preparation happen in prepare_data().\n\n .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware.\n No need to set yourself.\n\n - .fit()\n - ...\n - prepare_data()\n - train_dataloader\n\n Example:\n .. code-block:: python\n\n def train_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.hparams.batch_size,\n shuffle=True\n )\n return loader\n\n \"\"\"\n return None","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\n def tng_dataloader(self): # todo: remove in v1.0.0\n \"\"\"Implement a PyTorch DataLoader.\n\n Warnings:\n Deprecated in v0.5.0. use train_dataloader instead. Will be removed 1.0.0\n \"\"\"\n output = self.train_dataloader()\n warnings.warn(\"`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0.\"\n \" and this method will be removed in v1.0.0\", DeprecationWarning)\n return output","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\n def test_dataloader(self):\n r\"\"\"\n\n Return a dataloader. It will not be called every epoch unless you set\n ```Trainer(reload_dataloaders_every_epoch=True)```.\n\n It's recommended that all data downloads and preparation happen in prepare_data().\n\n - .fit()\n - ...\n - prepare_data()\n - train_dataloader\n - val_dataloader\n - test_dataloader\n\n .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware.\n No need to set yourself.\n\n Return:\n PyTorch DataLoader\n\n Example:\n .. code-block:: python\n\n def test_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.hparams.batch_size,\n shuffle=True\n )\n\n return loader\n\n .. note:: If you don't need a test dataset and a test_step, you don't need to implement\n this method.\n\n \"\"\"\n return None","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\n def val_dataloader(self):\n r\"\"\"\n\n Return a dataloader. It will not be called every epoch unless you set\n ```Trainer(reload_dataloaders_every_epoch=True)```.\n\n It's recommended that all data downloads and preparation happen in prepare_data().\n\n - .fit()\n - ...\n - prepare_data()\n - train_dataloader\n - val_dataloader\n\n .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware\n No need to set yourself.\n\n Return:\n PyTorch DataLoader\n\n Examples:\n .. code-block:: python\n\n def val_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False,\n transform=transform, download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.hparams.batch_size,\n shuffle=True\n )\n\n return loader\n\n # can also return multiple dataloaders\n def val_dataloader(self):\n return [loader_a, loader_b, ..., loader_n]\n\n .. code-block:: python\n\n def val_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False,\n transform=transform, download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.hparams.batch_size,\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.load_from_metrics","text":"pytorch_lightning/core/lightning.py::LightningModule.load_from_metrics\n @classmethod\n def load_from_metrics(cls, weights_path, tags_csv, map_location=None):\n r\"\"\"\n Warning:\n Deprecated in version 0.7.0. You should use `load_from_checkpoint` instead.\n Will be removed in v0.9.0.\n \"\"\"\n warnings.warn(\n \"`load_from_metrics` method has been unified with `load_from_checkpoint` in v0.7.0.\"\n \" The deprecated method will be removed in v0.9.0.\", DeprecationWarning\n )\n return cls.load_from_checkpoint(weights_path, tags_csv=tags_csv, map_location=map_location)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint\n @classmethod\n def load_from_checkpoint(\n cls,\n checkpoint_path: str,\n map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,\n tags_csv: Optional[str] = None,\n ) -> 'LightningModule':\n r\"\"\"\n\n Primary way of loading model from a checkpoint. When Lightning saves a checkpoint\n it stores the hyperparameters in the checkpoint if you initialized your LightningModule\n with an argument called `hparams` which is a Namespace (output of using argparse\n to parse command line arguments).\n\n Example:\n .. code-block:: python\n\n from argparse import Namespace\n hparams = Namespace(**{'learning_rate': 0.1})\n\n model = MyModel(hparams)\n\n class MyModel(LightningModule):\n def __init__(self, hparams):\n self.learning_rate = hparams.learning_rate\n\n Args:\n checkpoint_path: Path to checkpoint.\n map_location:\n If your checkpoint saved a GPU model and you now load on CPUs\n or a different number of GPUs, use this to map to the new setup.\n The behaviour is the same as in\n `torch.load <https://pytorch.org/docs/stable/torch.html#torch.load>`_.\n tags_csv: Optional path to a .csv file with two columns (key, value)\n as in this example::\n\n key,value\n drop_prob,0.2\n batch_size,32\n\n You most likely won't need this since Lightning will always save the hyperparameters\n to the checkpoint.\n However, if your checkpoint weights don't have the hyperparameters saved,\n use this method to pass in a .csv file with the hparams you'd like to use.\n These will be conver","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._load_model_state","text":"pytorch_lightning/core/lightning.py::LightningModule._load_model_state\n @classmethod\n def _load_model_state(cls, checkpoint):\n cls_takes_hparams = 'hparams' in inspect.signature(cls.__init__).parameters\n ckpt_hparams = checkpoint.get('hparams')\n\n if cls_takes_hparams:\n if ckpt_hparams is not None:\n is_namespace = checkpoint.get('hparams_type') == 'namespace'\n hparams = Namespace(**ckpt_hparams) if is_namespace else ckpt_hparams\n else:\n warnings.warn(\n f\"Checkpoint does not contain hyperparameters but {cls.__name__}'s __init__ \"\n f\"contains argument 'hparams'. Will pass in an empty Namespace instead.\"\n \" Did you forget to store your model hyperparameters in self.hparams?\"\n )\n hparams = Namespace()\n else: # The user's LightningModule does not define a hparams argument\n if ckpt_hparams is None:\n hparams = None\n else:\n raise MisconfigurationException(\n f\"Checkpoint contains hyperparameters but {cls.__name__}'s __init__ \"\n f\"is missing the argument 'hparams'. Are you loading the correct checkpoint?\"\n )\n\n # load the state_dict on the model automatically\n model_args = [hparams] if hparams else []\n model = cls(*model_args)\n model.load_state_dict(checkpoint['state_dict'])\n\n # give model a chance to load something\n model.on_load_checkpoint(checkpoint)\n\n return model","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.summarize","text":"pytorch_lightning/core/lightning.py::LightningModule.summarize\n def summarize(self, mode):\n model_summary = ModelSummary(self, mode=mode)\n log.info('\\n' + model_summary.__str__())","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.freeze","text":"pytorch_lightning/core/lightning.py::LightningModule.freeze\n def freeze(self):\n r\"\"\"\n Freeze all params for inference\n\n Example:\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.freeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = False\n\n self.eval()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze","text":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\n def unfreeze(self):\n \"\"\"Unfreeze all params for training.\n\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.unfreeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = True\n\n self.train()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\n def on_load_checkpoint(self, checkpoint):\n r\"\"\"\n Called by lightning to restore your model.\n If you saved something with **on_save_checkpoint** this is your chance to restore this.\n\n Args:\n checkpoint (dict): Loaded checkpoint\n\n\n Example:\n .. code-block:: python\n\n def on_load_checkpoint(self, checkpoint):\n # 99% of the time you don't need to implement this method\n self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']\n\n .. note:: Lighting auto-restores global step, epoch, and train state including amp scaling.\n No need for you to restore anything regarding training.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\n def on_save_checkpoint(self, checkpoint):\n r\"\"\"\n\n Called by lightning when saving a checkpoint to give you a chance to store anything\n else you might want to save\n\n Args:\n checkpoint (dic): Checkpoint to be saved\n\n Example:\n .. code-block:: python\n\n def on_save_checkpoint(self, checkpoint):\n # 99% of use cases you don't need to implement this method\n checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object\n\n .. note:: Lighting saves all aspects of training (epoch, global step, etc...)\n including amp scaling. No need\n for you to store anything about training.\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict","text":"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\n def get_tqdm_dict(self):\n r\"\"\"\n Additional items to be displayed in the progress bar.\n\n Return:\n Dictionary with the items to be displayed in the progress bar.\n \"\"\"\n tqdm_dict = {\n 'loss': '{:.3f}'.format(self.trainer.avg_loss)\n }\n\n if self.trainer.truncated_bptt_steps is not None:\n tqdm_dict['split_idx'] = self.trainer.split_idx\n\n if self.trainer.logger is not None and self.trainer.logger.version is not None:\n tqdm_dict['v_num'] = self.trainer.logger.version\n\n return tqdm_dict","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overriden","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overriden\n @abstractmethod\n def is_overriden(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._percent_range_check","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._percent_range_check\n def _percent_range_check(self, name):\n value = getattr(self, name)\n msg = f\"`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}.\"\n if name == \"val_check_interval\":\n msg += \" If you want to disable validation set `val_percent_check` to 0.0 instead.\"\n\n if not 0. <= value <= 1.:\n raise ValueError(msg)","label":1},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler\n def auto_add_sampler(self, dataloader, train):\n if self.use_ddp or self.use_ddp2 or self.use_tpu:\n dl_args = {\n 'dataset': dataloader.dataset,\n 'batch_size': dataloader.batch_size,\n 'shuffle': False,\n 'num_workers': dataloader.num_workers,\n 'collate_fn': dataloader.collate_fn,\n 'pin_memory': dataloader.pin_memory,\n 'drop_last': dataloader.drop_last,\n 'timeout': dataloader.timeout,\n 'worker_init_fn': dataloader.worker_init_fn\n }\n\n if self.use_tpu:\n sampler = DistributedSampler(\n dataloader.dataset,\n num_replicas=xm.xrt_world_size(),\n rank=xm.get_ordinal()\n )\n dl_args['shuffle'] = False\n else:\n if train:\n sampler = DistributedSampler(dataloader.dataset)\n dl_args['shuffle'] = False\n else:\n sampler = SequentialSampler(dataloader.dataset)\n\n dl_args['sampler'] = sampler\n\n dataloader = DataLoader(**dl_args)\n return dataloader","label":1},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader\n def reset_train_dataloader(self, model):\n \"\"\"\n Dataloaders are provided by the model\n :param model:\n :return:\n \"\"\"\n\n self.train_dataloader = self.request_data_loader(model.train_dataloader)\n self.num_training_batches = 0\n\n # automatically add samplers\n self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)\n\n self._percent_range_check('train_percent_check')\n\n if self.is_infinite_dataloader(self.train_dataloader):\n self.num_training_batches = float('inf')\n else:\n # try getting the length\n self.num_training_batches = len(self.train_dataloader)\n self.num_training_batches = int(self.num_training_batches * self.train_percent_check)\n\n # determine when to check validation\n # if int passed in, val checks that often\n # otherwise, it checks in [0, 1.0] % range of a training epoch\n if isinstance(self.val_check_interval, int):\n self.val_check_batch = self.val_check_interval\n if self.val_check_batch > self.num_training_batches:\n raise ValueError(\n f\"`val_check_interval` ({self.val_check_interval}) must be less than or equal \"\n f\"to the number of the training batches ({self.num_training_batches}). \"\n f\"If you want to disable validation set `val_percent_check` to 0.0 instead.\")\n else:\n if self.is_infinite_dataloader(self.train_dataloader):\n m = '''\n When using an infinite DataLoader (e.g. with an IterableDataset or when DataLoader\n does not implement `__len__`) for `train_dataloader`, `Trainer(val_check_interval)`\n must be an int. An int k specifies checking validation every k training batches.\n '''\n raise Miscon","label":1},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_infinite_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_infinite_dataloader\n def is_infinite_dataloader(self, dataloader):\n try:\n # try getting the length\n _ = len(dataloader)\n return False\n except TypeError as e:\n return True","label":1},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader\n def reset_val_dataloader(self, model):\n \"\"\"\n Dataloaders are provided by the model\n :param model:\n :return:\n \"\"\"\n if not self.is_overriden('validation_step'):\n return\n\n self.val_dataloaders = self.request_data_loader(model.val_dataloader)\n if not isinstance(self.val_dataloaders, list):\n self.val_dataloaders = [self.val_dataloaders]\n self.num_val_batches = 0\n\n # add samplers\n self.val_dataloaders = [self.auto_add_sampler(dl, train=False)\n for dl in self.val_dataloaders if dl]\n\n # determine number of validation batches\n # val datasets could be none, 1 or 2+\n if self.val_dataloaders is not None:\n self._percent_range_check('val_percent_check')\n\n self.num_val_batches = sum(len(dataloader) for dataloader in self.val_dataloaders)\n self.num_val_batches = int(self.num_val_batches * self.val_percent_check)","label":1},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader\n def reset_test_dataloader(self, model):\n \"\"\"Dataloaders are provided by the model.\n\n :param model:\n \"\"\"\n if not self.is_overriden('test_step'):\n return\n\n # get actual loader\n self.test_dataloaders = self.request_data_loader(model.test_dataloader)\n if not isinstance(self.test_dataloaders, list):\n self.test_dataloaders = [self.test_dataloaders]\n self.num_test_batches = 0\n\n # add samplers\n self.test_dataloaders = [self.auto_add_sampler(dl, train=False)\n for dl in self.test_dataloaders if dl]\n\n # determine number of test batches\n if self.test_dataloaders is not None:\n self._percent_range_check('test_percent_check')\n\n len_sum = sum(len(dataloader) for dataloader in self.test_dataloaders)\n self.num_test_batches = len_sum\n self.num_test_batches = int(self.num_test_batches * self.test_percent_check)","label":1},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_data_loader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_data_loader\n def request_data_loader(self, data_loader_fx):\n \"\"\"\n Handles downloading data in the GPU or TPU case.\n\n :param data_loader_fx:\n :return:\n \"\"\"\n # get the function we'll use to get data\n if self.use_ddp or self.use_ddp2:\n data_loader = data_loader_fx()\n\n # all processes wait until data download has happened\n dist.barrier()\n\n # data download/load on TPU\n elif self.use_tpu and XLA_AVAILABLE:\n data_loader = data_loader_fx()\n\n # all processes wait until data download has happened\n torch_xla.core.xla_model.rendezvous(\"pl.TrainerDataLoadingMixin.get_dataloaders\")\n\n # regular start\n else:\n data_loader = data_loader_fx()\n\n return data_loader","label":1},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount\n def determine_data_use_amount(self, train_percent_check, val_percent_check,\n test_percent_check, overfit_pct):\n \"\"\"\n Use less data for debugging purposes\n \"\"\"\n self.train_percent_check = train_percent_check\n self.val_percent_check = val_percent_check\n self.test_percent_check = test_percent_check\n if overfit_pct > 0:\n if overfit_pct > 1:\n raise ValueError(f\"`overfit_pct` must be not greater than 1.0, but got \"\n f\"{overfit_pct:.3f}.\")\n\n self.train_percent_check = overfit_pct\n self.val_percent_check = overfit_pct\n self.test_percent_check = overfit_pct","label":1},{"cid":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.run_pretrain_routine","text":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.run_pretrain_routine\n @abstractmethod\n def run_pretrain_routine(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.init_optimizers","text":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.init_optimizers\n @abstractmethod\n def init_optimizers(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.copy_trainer_model_properties","text":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.copy_trainer_model_properties\n def copy_trainer_model_properties(self, model):\n if isinstance(model, LightningDataParallel):\n ref_model = model.module\n elif isinstance(model, LightningDistributedDataParallel):\n ref_model = model.module\n else:\n ref_model = model\n\n for m in [model, ref_model]:\n m.trainer = self\n m.on_gpu = self.on_gpu\n m.use_dp = self.use_dp\n m.use_ddp2 = self.use_ddp2\n m.use_ddp = self.use_ddp\n m.use_amp = self.use_amp\n m.testing = self.testing\n m.single_gpu = self.single_gpu\n m.use_tpu = self.use_tpu\n m.tpu_local_core_rank = self.tpu_local_core_rank\n m.tpu_global_core_rank = self.tpu_global_core_rank","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.transfer_batch_to_tpu","text":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.transfer_batch_to_tpu\n def transfer_batch_to_tpu(self, batch):\n return self.__transfer_data_to_device(batch, device='tpu')","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.transfer_batch_to_gpu","text":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.transfer_batch_to_gpu\n def transfer_batch_to_gpu(self, batch, gpu_id):\n return self.__transfer_data_to_device(batch, device='gpu', gpu_id=gpu_id)","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.__transfer_data_to_device","text":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.__transfer_data_to_device\n def __transfer_data_to_device(self, batch, device, gpu_id=None):\n if device == 'tpu' and XLA_AVAILABLE:\n # base case: object can be directly moved using `to`\n if callable(getattr(batch, 'to', None)):\n return batch.to(xm.xla_device())\n\n if device == 'gpu':\n # base case: object can be directly moved using `cuda` or `to`\n if callable(getattr(batch, 'cuda', None)):\n return batch.cuda(gpu_id)\n\n if callable(getattr(batch, 'to', None)):\n return batch.to(torch.device('cuda', gpu_id))\n\n # when list\n if isinstance(batch, list):\n for i, x in enumerate(batch):\n batch[i] = self.__transfer_data_to_device(x, device, gpu_id)\n return batch\n\n # when tuple\n if isinstance(batch, tuple):\n batch = list(batch)\n for i, x in enumerate(batch):\n batch[i] = self.__transfer_data_to_device(x, device, gpu_id)\n return tuple(batch)\n\n # when dict\n if isinstance(batch, dict):\n for k, v in batch.items():\n batch[k] = self.__transfer_data_to_device(v, device, gpu_id)\n\n return batch\n\n # nothing matches, return the value as is without transform\n return batch","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.single_gpu_train","text":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.single_gpu_train\n def single_gpu_train(self, model):\n model.cuda(self.root_gpu)\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n if self.use_amp:\n # An example\n model, optimizers = model.configure_apex(amp, model, self.optimizers, self.amp_level)\n self.optimizers = optimizers\n\n self.run_pretrain_routine(model)","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.tpu_train","text":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.tpu_train\n def tpu_train(self, tpu_core_idx, model):\n # put model on tpu\n model.to(xm.xla_device())\n\n # get the appropriate tpu ranks\n self.tpu_local_core_rank = xm.get_local_ordinal()\n self.tpu_global_core_rank = xm.get_ordinal()\n\n # avoid duplicating progress bar\n self.show_progress_bar = self.show_progress_bar and self.tpu_global_core_rank == 0\n\n # track current tpu\n self.current_tpu_idx = tpu_core_idx\n self.proc_rank = self.tpu_local_core_rank\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n # init 16 bit for TPU\n if self.precision == 16:\n os.environ['XLA_USE_BF16'] = str(1)\n\n m = f'INIT TPU local core: {self.tpu_local_core_rank}, ' \\\n f'global rank: {self.tpu_global_core_rank}'\n log.info(m)\n\n # continue training routine\n self.run_pretrain_routine(model)\n\n self.save_spawn_weights(model)","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.dp_train","text":"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.dp_train\n def dp_train(self, model):\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n model.cuda(self.root_gpu)\n\n # check for this bug (amp + dp + !01 doesn't work)\n # https://github.com/NVIDIA/apex/issues/227\n if self.use_dp and self.use_amp:\n if self.amp_level == 'O2': # pragma: no cover\n m = f\"\"\"\n Amp level {self.amp_level} with DataParallel is not supported.\n See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.\n We recommend you switch to ddp if you want to use amp\n \"\"\"\n raise MisconfigurationException(m)\n else:\n model, optimizers = model.configure_apex(amp, model, self.optimizers, self.amp_level)\n\n # create list of device ids\n device_ids = self.data_parallel_device_ids\n if isinstance(device_ids, int):\n device_ids = list(range(device_ids))\n\n model = LightningDataParallel(model, device_ids=device_ids)\n\n self.run_pretrain_routine(model)","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::normalize_parse_gpu_string_input","text":"pytorch_lightning/trainer/distrib_parts.py::normalize_parse_gpu_string_input\ndef normalize_parse_gpu_string_input(s):\n if isinstance(s, str):\n if s == '-1':\n return -1\n else:\n return [int(x.strip()) for x in s.split(',')]\n else:\n return s","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::get_all_available_gpus","text":"pytorch_lightning/trainer/distrib_parts.py::get_all_available_gpus\ndef get_all_available_gpus():\n \"\"\"\n :return: a list of all available gpus\n \"\"\"\n return list(range(torch.cuda.device_count()))","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::check_gpus_data_type","text":"pytorch_lightning/trainer/distrib_parts.py::check_gpus_data_type\ndef check_gpus_data_type(gpus):\n \"\"\"\n :param gpus: gpus parameter as passed to the Trainer\n Function checks that it is one of: None, Int, String or List\n Throws otherwise\n :return: return unmodified gpus variable\n \"\"\"\n\n if gpus is not None and type(gpus) not in (int, str, list):\n raise MisconfigurationException(\"GPUs must be int, string or list of ints or None.\")","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::normalize_parse_gpu_input_to_list","text":"pytorch_lightning/trainer/distrib_parts.py::normalize_parse_gpu_input_to_list\ndef normalize_parse_gpu_input_to_list(gpus):\n assert gpus is not None\n if isinstance(gpus, list):\n return gpus\n\n # must be an int\n if not gpus: # gpus==0\n return None\n if gpus == -1:\n return get_all_available_gpus()\n\n return list(range(gpus))","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::sanitize_gpu_ids","text":"pytorch_lightning/trainer/distrib_parts.py::sanitize_gpu_ids\ndef sanitize_gpu_ids(gpus):\n \"\"\"\n :param gpus: list of ints corresponding to GPU indices\n Checks that each of the GPUs in the list is actually available.\n Throws if any of the GPUs is not available.\n :return: unmodified gpus variable\n \"\"\"\n all_available_gpus = get_all_available_gpus()\n for gpu in gpus:\n if gpu not in all_available_gpus:\n message = f\"\"\"\n You requested GPUs: {gpus}\n But your machine only has: {all_available_gpus}\n \"\"\"\n raise MisconfigurationException(message)\n return gpus","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::parse_gpu_ids","text":"pytorch_lightning/trainer/distrib_parts.py::parse_gpu_ids\ndef parse_gpu_ids(gpus):\n \"\"\"\n :param gpus: Int, string or list\n An int -1 or string '-1' indicate that all available GPUs should be used.\n A list of ints or a string containing list of comma separated integers\n indicates specific GPUs to use\n An int 0 means that no GPUs should be used\n Any int N > 0 indicates that GPUs [0..N) should be used.\n :return: List of gpus to be used\n\n If no GPUs are available but the value of gpus variable indicates request for GPUs\n then a misconfiguration exception is raised.\n \"\"\"\n\n # Check that gpus param is None, Int, String or List\n check_gpus_data_type(gpus)\n\n # Handle the case when no gpus are requested\n if gpus is None or isinstance(gpus, int) and gpus == 0:\n return None\n\n # We know user requested GPUs therefore if some of the\n # requested GPUs are not available an exception is thrown.\n\n gpus = normalize_parse_gpu_string_input(gpus)\n gpus = normalize_parse_gpu_input_to_list(gpus)\n gpus = sanitize_gpu_ids(gpus)\n\n if not gpus:\n raise MisconfigurationException(\"GPUs requested but none are available.\")\n return gpus","label":0},{"cid":"pytorch_lightning/trainer/distrib_parts.py::determine_root_gpu_device","text":"pytorch_lightning/trainer/distrib_parts.py::determine_root_gpu_device\ndef determine_root_gpu_device(gpus):\n \"\"\"\n :param gpus: non empty list of ints representing which gpus to use\n :return: designated root GPU device\n \"\"\"\n if gpus is None:\n return None\n\n assert isinstance(gpus, list), \"gpus should be a list\"\n assert len(gpus) > 0, \"gpus should be a non empty list\"\n\n # set root gpu\n root_gpu = gpus[0]\n\n return root_gpu","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties\n @abstractmethod\n def copy_trainer_model_properties(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model\n @abstractmethod\n def get_model(self):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overriden","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overriden\n @abstractmethod\n def is_overriden(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu\n @abstractmethod\n def transfer_batch_to_tpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu\n @abstractmethod\n def transfer_batch_to_gpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_tqdm_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_tqdm_metrics\n @abstractmethod\n def add_tqdm_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics\n @abstractmethod\n def log_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader\n @abstractmethod\n def reset_test_dataloader(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader\n @abstractmethod\n def reset_val_dataloader(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluate","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluate\n def evaluate(self, model, dataloaders, max_batches, test_mode: bool = False):\n \"\"\"Run evaluation code.\n\n :param model: PT model\n :param dataloaders: list of PT dataloaders\n :param max_batches: Scalar\n :param test_mode\n :return:\n \"\"\"\n # enable eval mode\n model.zero_grad()\n model.eval()\n\n # copy properties for forward overrides\n self.copy_trainer_model_properties(model)\n\n # disable gradients to save memory\n torch.set_grad_enabled(False)\n\n # bookkeeping\n outputs = []\n\n # run validation\n for dataloader_idx, dataloader in enumerate(dataloaders):\n dl_outputs = []\n\n # on TPU we have to wrap it under the ParallelLoader\n if self.use_tpu:\n device = xm.xla_device()\n dataloader = xla_pl.ParallelLoader(dataloader, [device])\n dataloader = dataloader.per_device_loader(device)\n\n for batch_idx, batch in enumerate(dataloader):\n if batch is None: # pragma: no cover\n continue\n\n # stop short when on fast_dev_run (sets max_batch=1)\n if batch_idx >= max_batches:\n break\n\n # -----------------\n # RUN EVALUATION STEP\n # -----------------\n output = self.evaluation_forward(model, batch, batch_idx, dataloader_idx, test_mode)\n\n # on dp / ddp2 might still want to do something with the batch parts\n if test_mode:\n if self.is_overriden('test_step_end'):\n model_ref = self.get_model()\n with self.profiler.profile('test_step_end'):\n output = model_ref.test_step_end(output)\n else:\n if self.is_overriden('validation_s","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation\n def run_evaluation(self, test_mode: bool = False):\n # when testing make sure user defined a test step\n if test_mode and not self.is_overriden('test_step'):\n m = \"You called `.test()` without defining model's `.test_step()`.\" \\\n \" Please define and try again\"\n raise MisconfigurationException(m)\n\n # Validation/Test begin callbacks\n if test_mode:\n self.on_test_start()\n else:\n self.on_validation_start()\n\n # hook\n model = self.get_model()\n model.on_pre_performance_check()\n\n # select dataloaders\n if test_mode:\n if self.reload_dataloaders_every_epoch or self.test_dataloaders is None:\n self.reset_test_dataloader(model)\n\n dataloaders = self.test_dataloaders\n max_batches = self.num_test_batches\n else:\n # val\n if self.reload_dataloaders_every_epoch or self.val_dataloaders is None:\n self.reset_val_dataloader(model)\n\n dataloaders = self.val_dataloaders\n max_batches = self.num_val_batches\n\n # cap max batches to 1 when using fast_dev_run\n if self.fast_dev_run:\n max_batches = 1\n\n # init validation or test progress bar\n # main progress bar will already be closed when testing so initial position is free\n position = 2 * self.process_position + (not test_mode)\n desc = 'Testing' if test_mode else 'Validating'\n pbar = tqdm(desc=desc, total=max_batches, leave=test_mode, position=position,\n disable=not self.show_progress_bar, dynamic_ncols=True,\n file=sys.stdout)\n setattr(self, f'{\"test\" if test_mode else \"val\"}_progress_bar', pbar)\n\n # run evaluation\n eval_results = self.evaluate(self.model, dataloaders, max_batches, test_mode)\n _, prog","label":1},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool = False):\n # make dataloader_idx arg in validation_step optional\n args = [batch, batch_idx]\n\n if test_mode and len(self.test_dataloaders) > 1:\n args.append(dataloader_idx)\n\n elif not test_mode and len(self.val_dataloaders) > 1:\n args.append(dataloader_idx)\n\n # handle DP, DDP forward\n if self.use_ddp or self.use_dp or self.use_ddp2:\n output = model(*args)\n return output\n\n # single GPU data transfer\n if self.single_gpu:\n # for single GPU put inputs on gpu manually\n root_gpu = 0\n if isinstance(self.data_parallel_device_ids, list):\n root_gpu = self.data_parallel_device_ids[0]\n batch = self.transfer_batch_to_gpu(batch, root_gpu)\n args[0] = batch\n\n # TPU data transfer\n if self.use_tpu:\n batch = self.transfer_batch_to_tpu(batch)\n args[0] = batch\n\n # CPU, TPU or gpu step\n if test_mode:\n output = model.test_step(*args)\n else:\n output = model.validation_step(*args)\n\n return output","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model\n @abstractmethod\n def get_model(self):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented\n @abstractmethod\n def is_function_implemented(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_infinite_dataloader","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_infinite_dataloader\n @abstractmethod\n def is_infinite_dataloader(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":1},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation\n @abstractmethod\n def run_evaluation(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu\n @abstractmethod\n def transfer_batch_to_gpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu\n @abstractmethod\n def transfer_batch_to_tpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients\n @abstractmethod\n def clip_gradients(self):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.print_nan_gradients","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.print_nan_gradients\n @abstractmethod\n def print_nan_gradients(self):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overriden","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overriden\n @abstractmethod\n def is_overriden(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_tqdm_metrics","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_tqdm_metrics\n @abstractmethod\n def add_tqdm_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics\n @abstractmethod\n def log_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output\n @abstractmethod\n def process_output(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader\n @abstractmethod\n def reset_train_dataloader(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader\n @abstractmethod\n def reset_val_dataloader(self, model):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg\n @abstractmethod\n def has_arg(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train\n def train(self):\n warnings.warn('Displayed epoch numbers in the progress bar start from \"1\" until v0.6.x,'\n ' but will start from \"0\" in v0.8.0.', DeprecationWarning)\n\n # get model\n model = self.get_model()\n\n # load data\n self.reset_train_dataloader(model)\n self.reset_val_dataloader(model)\n\n # Train start events\n with self.profiler.profile('on_train_start'):\n # callbacks\n self.on_train_start()\n # initialize early stop callback\n if self.early_stop_callback is not None:\n self.early_stop_callback.on_train_start(self, self.get_model())\n # model hooks\n model.on_train_start()\n\n try:\n # run all epochs\n for epoch in range(self.current_epoch, self.max_epochs):\n # set seed for distributed sampler (enables shuffling for each epoch)\n if self.use_ddp \\\n and hasattr(self.train_dataloader.sampler, 'set_epoch'):\n self.train_dataloader.sampler.set_epoch(epoch)\n\n # update training progress in trainer and model\n model.current_epoch = epoch\n self.current_epoch = epoch\n\n total_val_batches = 0\n is_val_epoch = False\n if not self.disable_validation:\n # val can be checked multiple times in epoch\n is_val_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0\n val_checks_per_epoch = self.num_training_batches // self.val_check_batch\n val_checks_per_epoch = val_checks_per_epoch if is_val_epoch else 0\n total_val_batches = self.num_val_batches * val_checks_per_epoch\n\n # total batches includes multiple val checks\n self.total_batches = self.num_","label":1},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch\n def run_training_epoch(self):\n\n # Epoch start events\n with self.profiler.profile('on_epoch_start'):\n # callbacks\n self.on_epoch_start()\n # changing gradient according accumulation_scheduler\n self.accumulation_scheduler.on_epoch_start(self, self.get_model())\n # model hooks\n if self.is_function_implemented('on_epoch_start'):\n self.get_model().on_epoch_start()\n\n # reset train dataloader\n if self.reload_dataloaders_every_epoch:\n self.reset_train_dataloader(self.get_model())\n\n # track local dataloader so TPU can wrap each epoch\n train_dataloader = self.train_dataloader\n\n # on TPU we have to wrap it under the ParallelLoader\n if self.use_tpu:\n device = xm.xla_device()\n train_dataloader = xla_pl.ParallelLoader(train_dataloader, [device])\n train_dataloader = train_dataloader.per_device_loader(device)\n\n # run epoch\n for batch_idx, batch in self.profiler.profile_iterable(\n enumerate(train_dataloader), \"get_train_batch\"\n ):\n # stop epoch if we limited the number of training batches\n if batch_idx >= self.num_training_batches:\n break\n\n self.batch_idx = batch_idx\n\n model = self.get_model()\n model.global_step = self.global_step\n\n # ---------------\n # RUN TRAIN STEP\n # ---------------\n output = self.run_training_batch(batch, batch_idx)\n batch_result, grad_norm_dic, batch_step_metrics = output\n\n # when returning -1 from train_step, we end epoch early\n early_stop_epoch = batch_result == -1\n\n # update lr\n self.update_learning_rates(interval='step')\n\n # ---------------\n # RUN VAL STEP\n # ----","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch\n def run_training_batch(self, batch, batch_idx):\n # track grad norms\n grad_norm_dic = {}\n\n # track all metrics for callbacks\n all_callback_metrics = []\n\n # track metrics to log\n all_log_metrics = []\n\n if batch is None:\n return 0, grad_norm_dic, {}\n\n # Batch start events\n with self.profiler.profile('on_batch_start'):\n # callbacks\n self.on_batch_start()\n # hooks\n if self.is_function_implemented('on_batch_start'):\n response = self.get_model().on_batch_start(batch)\n if response == -1:\n return -1, grad_norm_dic, {}\n\n splits = [batch]\n if self.truncated_bptt_steps is not None:\n model_ref = self.get_model()\n with self.profiler.profile('tbptt_split_batch'):\n splits = model_ref.tbptt_split_batch(batch, self.truncated_bptt_steps)\n\n self.hiddens = None\n for split_idx, split_batch in enumerate(splits):\n self.split_idx = split_idx\n\n # call training_step once per optimizer\n for opt_idx, optimizer in enumerate(self.optimizers):\n # make sure only the gradients of the current optimizer's paramaters are calculated\n # in the training step to prevent dangling gradients in multiple-optimizer setup.\n if len(self.optimizers) > 1:\n for param in self.get_model().parameters():\n param.requires_grad = False\n for group in optimizer.param_groups:\n for param in group['params']:\n param.requires_grad = True\n\n # wrap the forward step in a closure so second order methods work\n def optimizer_closure():\n # forward pass\n with self.profil","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown\n def run_training_teardown(self):\n self.main_progress_bar.close()\n\n # Train end events\n with self.profiler.profile('on_train_end'):\n # callbacks\n self.on_train_end()\n # model hooks\n if self.is_function_implemented('on_train_end'):\n self.get_model().on_train_end()\n\n if self.logger is not None:\n self.logger.finalize(\"success\")\n\n # summarize profile results\n self.profiler.describe()","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward\n def training_forward(self, batch, batch_idx, opt_idx, hiddens):\n \"\"\"\n Handle forward for each training case (distributed, single gpu, etc...)\n :param batch:\n :param batch_idx:\n :return:\n \"\"\"\n # ---------------\n # FORWARD\n # ---------------\n # enable not needing to add opt_idx to training_step\n args = [batch, batch_idx]\n\n if len(self.optimizers) > 1:\n if self.has_arg('training_step', 'optimizer_idx'):\n args.append(opt_idx)\n else:\n num_opts = len(self.optimizers)\n raise ValueError(\n f'Your LightningModule defines {num_opts} optimizers but '\n f'training_step is missing the \"optimizer_idx\" argument.'\n )\n\n # pass hiddens if using tbptt\n if self.truncated_bptt_steps is not None:\n args.append(hiddens)\n\n # distributed forward\n if self.use_ddp or self.use_ddp2 or self.use_dp:\n output = self.model(*args)\n\n # single GPU forward\n elif self.single_gpu:\n gpu_id = 0\n if isinstance(self.data_parallel_device_ids, list):\n gpu_id = self.data_parallel_device_ids[0]\n batch = self.transfer_batch_to_gpu(copy.copy(batch), gpu_id)\n args[0] = batch\n output = self.model.training_step(*args)\n\n # TPU support\n elif self.use_tpu:\n batch = self.transfer_batch_to_tpu(copy.copy(batch))\n args[0] = batch\n output = self.model.training_step(*args)\n\n # CPU forward\n else:\n output = self.model.training_step(*args)\n\n # allow any mode to define training_step_end\n # do something will all the dp outputs (like softmax)\n if self.is_overriden('training_step_end'):\n model_ref = self.get_model()\n","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates\n def update_learning_rates(self, interval):\n ''' Update learning rates\n Args:\n interval (str): either 'epoch' or 'step'.\n '''\n if not self.lr_schedulers:\n return\n\n for lr_scheduler in self.lr_schedulers:\n current_idx = self.batch_idx if interval == 'step' else self.current_epoch\n current_idx += 1 # account for both batch and epoch starts from 0\n # Take step if call to update_learning_rates matches the interval key and\n # the current step modulo the schedulers frequency is zero\n if lr_scheduler['interval'] == interval and current_idx % lr_scheduler['frequency'] == 0:\n # If instance of ReduceLROnPlateau, we need to pass validation loss\n if lr_scheduler['reduce_on_plateau']:\n monitor_key = lr_scheduler['monitor']\n monitor_val = self.callback_metrics.get(monitor_key)\n if monitor_val is None:\n avail_metrics = ','.join(list(self.callback_metrics.keys()))\n m = f'ReduceLROnPlateau conditioned on metric {monitor_key} ' \\\n f'which is not available. Available metrics are: {avail_metrics}. ' \\\n 'Condition can be set using `monitor` key in lr scheduler dict'\n raise MisconfigurationException(m)\n lr_scheduler['scheduler'].step(monitor_val)\n else:\n lr_scheduler['scheduler'].step()","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_checkpoint_callback","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_checkpoint_callback\n def call_checkpoint_callback(self):\n if self.checkpoint_callback is not None:\n self.checkpoint_callback.on_validation_end(self, self.get_model())\n self.on_validation_end()","label":0}],"string":"[\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.__init__\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.__init__\\n def __init__(self, *args, **kwargs):\\n super(LightningModule, self).__init__(*args, **kwargs)\\n\\n #: Current dtype\\n self.dtype = torch.FloatTensor\\n\\n self.exp_save_path = None\\n\\n #: The current epoch\\n self.current_epoch = 0\\n\\n #: Total training batches seen across all epochs\\n self.global_step = 0\\n\\n self.loaded_optimizer_states_dict = {}\\n\\n #: Pointer to the trainer object\\n self.trainer = None\\n\\n #: Pointer to the logger object\\n self.logger = None\\n self.example_input_array = None\\n\\n #: True if your model is currently running on GPUs.\\n #: Useful to set flags around the LightningModule for different CPU vs GPU behavior.\\n self.on_gpu = False\\n\\n #: True if using dp\\n self.use_dp = False\\n\\n #: True if using ddp\\n self.use_ddp = False\\n\\n #: True if using ddp2\\n self.use_ddp2 = False\\n\\n #: True if using amp\\n self.use_amp = False\\n\\n self.hparams = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.print\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.print\\n def print(self, *args, **kwargs):\\n r\\\"\\\"\\\"\\n Prints only from process 0. Use this in any distributed mode to log only once\\n\\n Args:\\n x (object): The thing to print\\n\\n Examples:\\n .. code-block:: python\\n\\n # example if we were using this model as a feature extractor\\n def forward(self, x):\\n self.print(x, 'in loader')\\n\\n \\\"\\\"\\\"\\n if self.trainer.proc_rank == 0:\\n print(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.forward\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.forward\\n @abstractmethod\\n def forward(self, *args, **kwargs):\\n r\\\"\\\"\\\"\\n Same as torch.nn.Module.forward(), however in Lightning you want this to define\\n the operations you want to use for prediction (ie: on a server or as a feature extractor).\\n\\n Normally you'd call self.forward() from your training_step() method.\\n This makes it easy to write a complex system for training with the outputs\\n you'd want in a prediction setting.\\n\\n Args:\\n x (tensor): Whatever you decide to define in the forward method\\n\\n Return:\\n Predicted output\\n\\n Examples:\\n .. code-block:: python\\n\\n # example if we were using this model as a feature extractor\\n def forward(self, x):\\n feature_maps = self.convnet(x)\\n return feature_maps\\n\\n def training_step(self, batch, batch_idx):\\n x, y = batch\\n feature_maps = self.forward(x)\\n logits = self.classifier(feature_maps)\\n\\n # ...\\n return loss\\n\\n # splitting it this way allows model to be used a feature extractor\\n model = MyModelAbove()\\n\\n inputs = server.get_request()\\n results = model(inputs)\\n server.write_results(results)\\n\\n # -------------\\n # This is in stark contrast to torch.nn.Module where normally you would have this:\\n def forward(self, batch):\\n x, y = batch\\n feature_maps = self.convnet(x)\\n logits = self.classifier(feature_maps)\\n return logits\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step\\n def training_step(self, *args, **kwargs):\\n r\\\"\\\"\\\"return loss, dict with metrics for tqdm\\n\\n Args:\\n batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your\\n dataloader. A tensor, tuple or list\\n batch_idx (int): Integer displaying index of this batch\\n optimizer_idx (int): If using multiple optimizers, this argument will also be present.\\n hiddens(:`Tensor <https://pytorch.org/docs/stable/tensors.html>`_):\\n Passed in if truncated_bptt_steps > 0.\\n\\n Return:\\n dict with loss key and optional log, progress keys\\n if implementing training_step, return whatever you need in that step:\\n\\n - loss -> tensor scalar [REQUIRED]\\n - progress_bar -> Dict for progress bar display. Must have only tensors\\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\\n\\n In this step you'd normally do the forward pass and calculate the loss for a batch.\\n You can also do fancier things like multiple forward passes or something model specific.\\n\\n Examples:\\n .. code-block:: python\\n\\n def training_step(self, batch, batch_idx):\\n x, y, z = batch\\n\\n # implement your own\\n out = self.forward(x)\\n loss = self.loss(out, x)\\n\\n logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)\\n\\n # if using TestTubeLogger or TensorBoardLogger you can nest scalars\\n logger_logs = {'losses': logger_logs} # optional (MUST ALL BE TENSORS)\\n\\n output = {\\n 'loss': loss, # required\\n 'progress_bar': {'training_loss': loss}, # optional (MUST ALL BE TENSORS)\\n 'log': logger_logs\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_end\\n def training_end(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. use training_step_end instead\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\\n def training_step_end(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Use this when training with dp or ddp2 because training_step will operate\\n on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n .. note:: If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]\\n training_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in `training_step` for each batch part.\\n\\n Return:\\n dictionary with loss key and optional log, progress keys:\\n - loss -> tensor scalar [REQUIRED]\\n - progress_bar -> Dict for progress bar display. Must have only tensors\\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\\n\\n In this case you should define training_step_end to perform those calculations.\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT training_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def training_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self.forward(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with training_step_end to do softmax over the full batch\\n def training_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step\\n def validation_step(self, *args, **kwargs):\\n r\\\"\\\"\\\"\\n Operate on a single batch of data from the validation set\\n In this step you'd might generate examples or calculate anything of interest like accuracy.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n val_outs = []\\n for val_batch in val_data:\\n out = validation_step(train_batch)\\n val_outs.append(out\\n validation_epoch_end(val_outs)\\n\\n Args:\\n batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your\\n dataloader. A tensor, tuple or list\\n batch_idx (int): The index of this batch\\n dataloader_idx (int): The index of the dataloader that produced this batch\\n (only if multiple val datasets used)\\n\\n Return:\\n Dict or OrderedDict - passed to validation_epoch_end.\\n If you defined validation_step_end it will go to that first.\\n\\n .. code-block:: python\\n\\n # pseudocode of order\\n out = validation_step()\\n if defined('validation_step_end'):\\n out = validation_step_end(out)\\n out = validation_epoch_end(out)\\n\\n\\n .. code-block:: python\\n\\n # if you have one val dataloader:\\n def validation_step(self, batch, batch_idx)\\n\\n # if you have multiple val dataloaders:\\n def validation_step(self, batch, batch_idx, dataloader_idx)\\n\\n Examples:\\n .. code-block:: python\\n\\n # CASE 1: A single validation dataset\\n def validation_step(self, batch, batch_idx):\\n x, y = batch\\n\\n # implement your own\\n out = self.forward(x)\\n loss = self.loss(out, y)\\n\\n # log 6 example images\\n # or generated text\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\\n def validation_step_end(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Use this when validating with dp or ddp2 because validation_step will operate\\n on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n .. note:: If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]\\n validation_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in `validation_step` for each batch part.\\n\\n Return:\\n Dict or OrderedDict - passed to the validation_epoch_end\\n\\n In this case you should define validation_step_end to perform those calculations.\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT validation_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def validation_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self.forward(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with validation_step_end to do softmax over the full batch\\n def validation_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self.forward(x)\\n return {'out': out}\\n\\n def validation_epoch_end(self, outputs):\\n # this out is now the full size of the batch\\n o\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_end\\n def validation_end(self, outputs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. use validation_epoch_end instead. Will be removed 1.0.0\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\\n def validation_epoch_end(self, outputs: list):\\n \\\"\\\"\\\"\\n Called at end of validation epoch with the output of all validation_steps\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n\\n val_outs = []\\n for val_batch in val_data:\\n out = validation_step(train_batch)\\n train_outs.append(out)\\n validation_epoch_end(val_outs)\\n\\n Args:\\n outputs: List of outputs you defined in validation_step, or if there are multiple\\n dataloaders, a list containing a list of outputs for each dataloader\\n\\n Return:\\n Dict or OrderedDict (dict): Dict has the following optional keys:\\n progress_bar -> Dict for progress bar display. Must have only tensors\\n log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\\n\\n .. note:: If you didn't define a validation_step, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n - If you want to manually set current step, you can specify the 'step' key in the 'log' Dict\\n\\n Examples:\\n With a single dataloader\\n\\n .. code-block:: python\\n\\n def validation_epoch_end(self, outputs):\\n val_acc_mean = 0\\n for output in outputs:\\n val_acc_mean += output['val_acc']\\n\\n val_acc_mean /= len(outputs)\\n tqdm_dict = {'val_acc': val_acc_mean.item()}\\n\\n # show val_loss and val_acc in progress bar but only log val_loss\\n results = {\\n 'progress_bar': tqdm_dict,\\n 'log': {'val_acc': val_acc_mean.item()}\\n }\\n return results\\n\\n With multiple\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step\\n def test_step(self, *args, **kwargs):\\n r\\\"\\\"\\\"\\n Operate on a single batch of data from the test set\\n In this step you'd normally generate examples or calculate anything of interest\\n such as accuracy.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n\\n test_outs = []\\n for test_batch in test_data:\\n out = test_step(train_batch)\\n test_outs.append(out)\\n test_epoch_end(test_outs)\\n\\n Args:\\n batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your\\n dataloader. A tensor, tuple or list\\n batch_idx (int): The index of this batch\\n dataloader_idx (int): The index of the dataloader that produced this batch\\n (only if multiple test datasets used)\\n\\n Return:\\n Dict or OrderedDict - passed to the test_step_end\\n\\n .. code-block:: python\\n\\n # if you have one test dataloader:\\n def test_step(self, batch, batch_idx)\\n\\n # if you have multiple test dataloaders:\\n def test_step(self, batch, batch_idx, dataloader_idx)\\n\\n Examples:\\n .. code-block:: python\\n\\n # CASE 1: A single test dataset\\n def test_step(self, batch, batch_idx):\\n x, y = batch\\n\\n # implement your own\\n out = self.forward(x)\\n loss = self.loss(out, y)\\n\\n # log 6 example images\\n # or generated text... or whatever\\n sample_imgs = x[:6]\\n grid = torchvision.utils.make_grid(sample_imgs)\\n self.logger.experiment.add_image('example_images', grid, 0)\\n\\n # calculate acc\\n labels_hat = torch.argmax(out, dim=1)\\n val_acc = torch.sum(y == labels_ha\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\\n def test_step_end(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Use this when testing with dp or ddp2 because test_step will operate\\n on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n .. note:: If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]\\n test_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in `training_step` for each batch part.\\n\\n Return:\\n Dict or OrderedDict - passed to the test_epoch_end\\n\\n In this case you should define test_step_end to perform those calculations.\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT test_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def test_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self.forward(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with test_step_end to do softmax over the full batch\\n def test_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self.forward(x)\\n return {'out': out}\\n\\n def test_step_end(self, outputs):\\n # this out is now the full size of the batch\\n out = outputs['out']\\n\\n # this softmax now uses the full ba\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_end\\n def test_end(self, outputs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. use test_epoch_end instead. Will be removed 1.0.0\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\\n def test_epoch_end(self, outputs):\\n \\\"\\\"\\\"\\n Called at end of test epoch with the output of all test_steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n\\n test_outs = []\\n for test_batch in test_data:\\n out = test_step(test_batch)\\n test_outs.append(out)\\n test_epoch_end(test_outs)\\n\\n Args:\\n outputs (list): List of outputs you defined in test_step, or if there are multiple\\n dataloaders, a list containing a list of outputs for each dataloader\\n\\n Return:\\n Dict or OrderedDict (dict): Dict has the following optional keys:\\n progress_bar -> Dict for progress bar display. Must have only tensors\\n log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\\n\\n .. note:: If you didn't define a test_step, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict\\n\\n Examples:\\n With a single dataloader\\n\\n .. code-block:: python\\n\\n def test_epoch_end(self, outputs):\\n test_acc_mean = 0\\n for output in outputs:\\n test_acc_mean += output['test_acc']\\n\\n test_acc_mean /= len(outputs)\\n tqdm_dict = {'test_acc': test_acc_mean.item()}\\n\\n # show test_loss and test_acc in progress bar but only log test_loss\\n results = {\\n 'progress_bar': tqdm_dict,\\n 'log': {'test_acc': test_acc_mean.item()}\\n }\\n return results\\n\\n With multiple dataloaders, `outputs` will be a list \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\\n def configure_ddp(self, model, device_ids):\\n r\\\"\\\"\\\"\\n Override to init DDP in your own way or with your own wrapper.\\n The only requirements are that:\\n\\n 1. On a validation batch the call goes to model.validation_step.\\n 2. On a training batch the call goes to model.training_step.\\n 3. On a testing batch, the call goes to model.test_step\\n\\n Args:\\n model (:class:`.LightningModule`): the LightningModule currently being optimized\\n device_ids (list): the list of GPU ids\\n\\n Return:\\n DDP wrapped model\\n\\n Examples:\\n .. code-block:: python\\n\\n # default implementation used in Trainer\\n def configure_ddp(self, model, device_ids):\\n # Lightning DDP simply routes to test_step, val_step, etc...\\n model = LightningDistributedDataParallel(\\n model,\\n device_ids=device_ids,\\n find_unused_parameters=True\\n )\\n return model\\n\\n \\\"\\\"\\\"\\n model = LightningDistributedDataParallel(\\n model,\\n device_ids=device_ids,\\n find_unused_parameters=True\\n )\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\\n def init_ddp_connection(self, proc_rank, world_size):\\n r\\\"\\\"\\\"\\n\\n Override to define your custom way of setting up a distributed environment.\\n\\n Lightning's implementation uses env:// init by default and sets the first node as root.\\n\\n Args:\\n proc_rank (int): The current process rank within the node.\\n world_size (int): Number of GPUs being use across all nodes. (num_nodes*nb_gpu_nodes).\\n\\n Examples:\\n .. code-block:: python\\n\\n def init_ddp_connection(self):\\n # use slurm job id for the port number\\n # guarantees unique ports across jobs from same grid search\\n try:\\n # use the last 4 numbers in the job id as the id\\n default_port = os.environ['SLURM_JOB_ID']\\n default_port = default_port[-4:]\\n\\n # all ports should be in the 10k+ range\\n default_port = int(default_port) + 15000\\n\\n except Exception as e:\\n default_port = 12910\\n\\n # if user gave a port number, use that one instead\\n try:\\n default_port = os.environ['MASTER_PORT']\\n except Exception:\\n os.environ['MASTER_PORT'] = str(default_port)\\n\\n # figure out the root node addr\\n try:\\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\\n except Exception:\\n root_node = '127.0.0.2'\\n\\n root_node = self.trainer.resolve_root_node_address(root_node)\\n os.environ['MASTER_ADDR'] = root_node\\n dist.init_process_group(\\n 'nccl',\\n rank=self.proc_rank,\\n world_si\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\\n def configure_apex(self, amp, model, optimizers, amp_level):\\n r\\\"\\\"\\\"\\n Override to init AMP your own way\\n Must return a model and list of optimizers\\n\\n Args:\\n amp (object): pointer to amp library object\\n model (:class:`.LightningModule`): pointer to current lightningModule\\n optimizers (list): list of optimizers passed in configure_optimizers()\\n amp_level (str): AMP mode chosen ('O1', 'O2', etc...)\\n\\n Return:\\n Apex wrapped model and optimizers\\n\\n Examples:\\n .. code-block:: python\\n\\n # Default implementation used by Trainer.\\n def configure_apex(self, amp, model, optimizers, amp_level):\\n model, optimizers = amp.initialize(\\n model, optimizers, opt_level=amp_level,\\n )\\n\\n return model, optimizers\\n \\\"\\\"\\\"\\n model, optimizers = amp.initialize(\\n model, optimizers, opt_level=amp_level,\\n )\\n\\n return model, optimizers\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\\n def configure_optimizers(self):\\n r\\\"\\\"\\\"\\n Choose what optimizers and learning-rate schedulers to use in your optimization.\\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\\n\\n If you don't define this method Lightning will automatically use Adam(lr=1e-3)\\n\\n Return: any of these 3 options:\\n - Single optimizer\\n - List or Tuple - List of optimizers\\n - Two lists - The first list has multiple optimizers, the second a list of LR schedulers\\n\\n Examples:\\n .. code-block:: python\\n\\n # most cases (default if not defined)\\n def configure_optimizers(self):\\n opt = Adam(self.parameters(), lr=1e-3)\\n return opt\\n\\n # multiple optimizer case (eg: GAN)\\n def configure_optimizers(self):\\n generator_opt = Adam(self.model_gen.parameters(), lr=0.01)\\n disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)\\n return generator_opt, disriminator_opt\\n\\n # example with learning_rate schedulers\\n def configure_optimizers(self):\\n generator_opt = Adam(self.model_gen.parameters(), lr=0.01)\\n disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)\\n discriminator_sched = CosineAnnealing(discriminator_opt, T_max=10)\\n return [generator_opt, disriminator_opt], [discriminator_sched]\\n\\n # example with step-based learning_rate schedulers\\n def configure_optimizers(self):\\n gen_opt = Adam(self.model_gen.parameters(), lr=0.01)\\n dis_opt = Adam(self.model_disc.parameters(), lr=0.02)\\n gen_sched = {'scheduler': ExponentialLR(gen_opt, 0.99),\\n 'inter\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\\n def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, second_order_closure=None):\\n r\\\"\\\"\\\"\\n\\n Override this method to adjust the default way the Trainer calls each optimizer.\\n By default, Lightning calls .step() and zero_grad() as shown in the example\\n once per optimizer.\\n\\n Args:\\n epoch (int): Current epoch\\n batch_idx (int): Index of current batch\\n optimizer (torch.nn.Optimizer): A PyTorch optimizer\\n optimizer_idx (int): If you used multiple optimizers this indexes into that list\\n second_order_closure (int): closure for second order methods\\n\\n Examples:\\n .. code-block:: python\\n\\n # DEFAULT\\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\\n second_order_closure=None):\\n optimizer.step()\\n optimizer.zero_grad()\\n\\n # Alternating schedule for optimizer steps (ie: GANs)\\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\\n second_order_closure=None):\\n # update generator opt every 2 steps\\n if optimizer_idx == 0:\\n if batch_idx % 2 == 0 :\\n optimizer.step()\\n optimizer.zero_grad()\\n\\n # update discriminator opt every 4 steps\\n if optimizer_idx == 1:\\n if batch_idx % 4 == 0 :\\n optimizer.step()\\n optimizer.zero_grad()\\n\\n # ...\\n # add as many optimizers as you want\\n\\n\\n Here's another example showing how to use this for more advanced things such as\\n learning-rate warm-up:\\n\\n .. code-block:: pyth\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\\n def tbptt_split_batch(self, batch, split_size):\\n r\\\"\\\"\\\"\\n\\n When using truncated backpropagation through time, each batch must be split along the\\n time dimension. Lightning handles this by default, but for custom behavior override\\n this function.\\n\\n Args:\\n batch (torch.nn.Tensor): Current batch\\n split_size (int): How big the split is\\n\\n Return:\\n list of batch splits. Each split will be passed to forward_step to enable truncated\\n back propagation through time. The default implementation splits root level Tensors and\\n Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.\\n\\n Examples:\\n .. code-block:: python\\n\\n def tbptt_split_batch(self, batch, split_size):\\n splits = []\\n for t in range(0, time_dims[0], split_size):\\n batch_split = []\\n for i, x in enumerate(batch):\\n if isinstance(x, torch.Tensor):\\n split_x = x[:, t:t + split_size]\\n elif isinstance(x, collections.Sequence):\\n split_x = [None] * len(x)\\n for batch_idx in range(len(x)):\\n split_x[batch_idx] = x[batch_idx][t:t + split_size]\\n\\n batch_split.append(split_x)\\n\\n splits.append(batch_split)\\n\\n return splits\\n\\n .. note:: Called in the training loop after on_batch_start if ``truncated_bptt_steps > 0``.\\n Each returned batch split is passed separately to ``training_step(...)``.\\n\\n \\\"\\\"\\\"\\n time_dims = [len(x[0]) for x in batch if isinstance(x, (torch.Tensor, collections.Sequence))]\\n assert len(time_dims) >= 1, \\\"Unable to determine batch time dimension\\\"\\n a\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\\n def prepare_data(self):\\n \\\"\\\"\\\"Use this to download and prepare data.\\n In distributed (GPU, TPU), this will only be called once\\n\\n Return:\\n PyTorch DataLoader\\n\\n This is called before requesting the dataloaders\\n\\n .. code-block:: python\\n\\n model.prepare_data()\\n model.train_dataloader()\\n model.val_dataloader()\\n model.test_dataloader()\\n\\n Examples:\\n .. code-block:: python\\n\\n def prepare_data(self):\\n download_imagenet()\\n clean_imagenet()\\n cache_imagenet()\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\\n def train_dataloader(self):\\n \\\"\\\"\\\"Implement a PyTorch DataLoader\\n\\n Return:\\n PyTorch DataLoader\\n\\n Return a dataloader. It will not be called every epoch unless you set\\n ```Trainer(reload_dataloaders_every_epoch=True)```.\\n\\n It's recommended that all data downloads and preparation happen in prepare_data().\\n\\n .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware.\\n No need to set yourself.\\n\\n - .fit()\\n - ...\\n - prepare_data()\\n - train_dataloader\\n\\n Example:\\n .. code-block:: python\\n\\n def train_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,\\n download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.hparams.batch_size,\\n shuffle=True\\n )\\n return loader\\n\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\\n def tng_dataloader(self): # todo: remove in v1.0.0\\n \\\"\\\"\\\"Implement a PyTorch DataLoader.\\n\\n Warnings:\\n Deprecated in v0.5.0. use train_dataloader instead. Will be removed 1.0.0\\n \\\"\\\"\\\"\\n output = self.train_dataloader()\\n warnings.warn(\\\"`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0.\\\"\\n \\\" and this method will be removed in v1.0.0\\\", DeprecationWarning)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\\n def test_dataloader(self):\\n r\\\"\\\"\\\"\\n\\n Return a dataloader. It will not be called every epoch unless you set\\n ```Trainer(reload_dataloaders_every_epoch=True)```.\\n\\n It's recommended that all data downloads and preparation happen in prepare_data().\\n\\n - .fit()\\n - ...\\n - prepare_data()\\n - train_dataloader\\n - val_dataloader\\n - test_dataloader\\n\\n .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware.\\n No need to set yourself.\\n\\n Return:\\n PyTorch DataLoader\\n\\n Example:\\n .. code-block:: python\\n\\n def test_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,\\n download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.hparams.batch_size,\\n shuffle=True\\n )\\n\\n return loader\\n\\n .. note:: If you don't need a test dataset and a test_step, you don't need to implement\\n this method.\\n\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\\n def val_dataloader(self):\\n r\\\"\\\"\\\"\\n\\n Return a dataloader. It will not be called every epoch unless you set\\n ```Trainer(reload_dataloaders_every_epoch=True)```.\\n\\n It's recommended that all data downloads and preparation happen in prepare_data().\\n\\n - .fit()\\n - ...\\n - prepare_data()\\n - train_dataloader\\n - val_dataloader\\n\\n .. note:: Lightning adds the correct sampler for distributed and arbitrary hardware\\n No need to set yourself.\\n\\n Return:\\n PyTorch DataLoader\\n\\n Examples:\\n .. code-block:: python\\n\\n def val_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=False,\\n transform=transform, download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.hparams.batch_size,\\n shuffle=True\\n )\\n\\n return loader\\n\\n # can also return multiple dataloaders\\n def val_dataloader(self):\\n return [loader_a, loader_b, ..., loader_n]\\n\\n .. code-block:: python\\n\\n def val_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=False,\\n transform=transform, download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.hparams.batch_size,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.load_from_metrics\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.load_from_metrics\\n @classmethod\\n def load_from_metrics(cls, weights_path, tags_csv, map_location=None):\\n r\\\"\\\"\\\"\\n Warning:\\n Deprecated in version 0.7.0. You should use `load_from_checkpoint` instead.\\n Will be removed in v0.9.0.\\n \\\"\\\"\\\"\\n warnings.warn(\\n \\\"`load_from_metrics` method has been unified with `load_from_checkpoint` in v0.7.0.\\\"\\n \\\" The deprecated method will be removed in v0.9.0.\\\", DeprecationWarning\\n )\\n return cls.load_from_checkpoint(weights_path, tags_csv=tags_csv, map_location=map_location)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint\\n @classmethod\\n def load_from_checkpoint(\\n cls,\\n checkpoint_path: str,\\n map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,\\n tags_csv: Optional[str] = None,\\n ) -> 'LightningModule':\\n r\\\"\\\"\\\"\\n\\n Primary way of loading model from a checkpoint. When Lightning saves a checkpoint\\n it stores the hyperparameters in the checkpoint if you initialized your LightningModule\\n with an argument called `hparams` which is a Namespace (output of using argparse\\n to parse command line arguments).\\n\\n Example:\\n .. code-block:: python\\n\\n from argparse import Namespace\\n hparams = Namespace(**{'learning_rate': 0.1})\\n\\n model = MyModel(hparams)\\n\\n class MyModel(LightningModule):\\n def __init__(self, hparams):\\n self.learning_rate = hparams.learning_rate\\n\\n Args:\\n checkpoint_path: Path to checkpoint.\\n map_location:\\n If your checkpoint saved a GPU model and you now load on CPUs\\n or a different number of GPUs, use this to map to the new setup.\\n The behaviour is the same as in\\n `torch.load <https://pytorch.org/docs/stable/torch.html#torch.load>`_.\\n tags_csv: Optional path to a .csv file with two columns (key, value)\\n as in this example::\\n\\n key,value\\n drop_prob,0.2\\n batch_size,32\\n\\n You most likely won't need this since Lightning will always save the hyperparameters\\n to the checkpoint.\\n However, if your checkpoint weights don't have the hyperparameters saved,\\n use this method to pass in a .csv file with the hparams you'd like to use.\\n These will be conver\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule._load_model_state\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule._load_model_state\\n @classmethod\\n def _load_model_state(cls, checkpoint):\\n cls_takes_hparams = 'hparams' in inspect.signature(cls.__init__).parameters\\n ckpt_hparams = checkpoint.get('hparams')\\n\\n if cls_takes_hparams:\\n if ckpt_hparams is not None:\\n is_namespace = checkpoint.get('hparams_type') == 'namespace'\\n hparams = Namespace(**ckpt_hparams) if is_namespace else ckpt_hparams\\n else:\\n warnings.warn(\\n f\\\"Checkpoint does not contain hyperparameters but {cls.__name__}'s __init__ \\\"\\n f\\\"contains argument 'hparams'. Will pass in an empty Namespace instead.\\\"\\n \\\" Did you forget to store your model hyperparameters in self.hparams?\\\"\\n )\\n hparams = Namespace()\\n else: # The user's LightningModule does not define a hparams argument\\n if ckpt_hparams is None:\\n hparams = None\\n else:\\n raise MisconfigurationException(\\n f\\\"Checkpoint contains hyperparameters but {cls.__name__}'s __init__ \\\"\\n f\\\"is missing the argument 'hparams'. Are you loading the correct checkpoint?\\\"\\n )\\n\\n # load the state_dict on the model automatically\\n model_args = [hparams] if hparams else []\\n model = cls(*model_args)\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n # give model a chance to load something\\n model.on_load_checkpoint(checkpoint)\\n\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.summarize\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.summarize\\n def summarize(self, mode):\\n model_summary = ModelSummary(self, mode=mode)\\n log.info('\\\\n' + model_summary.__str__())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.freeze\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.freeze\\n def freeze(self):\\n r\\\"\\\"\\\"\\n Freeze all params for inference\\n\\n Example:\\n .. code-block:: python\\n\\n model = MyLightningModule(...)\\n model.freeze()\\n\\n \\\"\\\"\\\"\\n for param in self.parameters():\\n param.requires_grad = False\\n\\n self.eval()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\\n def unfreeze(self):\\n \\\"\\\"\\\"Unfreeze all params for training.\\n\\n .. code-block:: python\\n\\n model = MyLightningModule(...)\\n model.unfreeze()\\n\\n \\\"\\\"\\\"\\n for param in self.parameters():\\n param.requires_grad = True\\n\\n self.train()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\\n def on_load_checkpoint(self, checkpoint):\\n r\\\"\\\"\\\"\\n Called by lightning to restore your model.\\n If you saved something with **on_save_checkpoint** this is your chance to restore this.\\n\\n Args:\\n checkpoint (dict): Loaded checkpoint\\n\\n\\n Example:\\n .. code-block:: python\\n\\n def on_load_checkpoint(self, checkpoint):\\n # 99% of the time you don't need to implement this method\\n self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']\\n\\n .. note:: Lighting auto-restores global step, epoch, and train state including amp scaling.\\n No need for you to restore anything regarding training.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\\n def on_save_checkpoint(self, checkpoint):\\n r\\\"\\\"\\\"\\n\\n Called by lightning when saving a checkpoint to give you a chance to store anything\\n else you might want to save\\n\\n Args:\\n checkpoint (dic): Checkpoint to be saved\\n\\n Example:\\n .. code-block:: python\\n\\n def on_save_checkpoint(self, checkpoint):\\n # 99% of use cases you don't need to implement this method\\n checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object\\n\\n .. note:: Lighting saves all aspects of training (epoch, global step, etc...)\\n including amp scaling. No need\\n for you to store anything about training.\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\\n def get_tqdm_dict(self):\\n r\\\"\\\"\\\"\\n Additional items to be displayed in the progress bar.\\n\\n Return:\\n Dictionary with the items to be displayed in the progress bar.\\n \\\"\\\"\\\"\\n tqdm_dict = {\\n 'loss': '{:.3f}'.format(self.trainer.avg_loss)\\n }\\n\\n if self.trainer.truncated_bptt_steps is not None:\\n tqdm_dict['split_idx'] = self.trainer.split_idx\\n\\n if self.trainer.logger is not None and self.trainer.logger.version is not None:\\n tqdm_dict['v_num'] = self.trainer.logger.version\\n\\n return tqdm_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overriden\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overriden\\n @abstractmethod\\n def is_overriden(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._percent_range_check\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._percent_range_check\\n def _percent_range_check(self, name):\\n value = getattr(self, name)\\n msg = f\\\"`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}.\\\"\\n if name == \\\"val_check_interval\\\":\\n msg += \\\" If you want to disable validation set `val_percent_check` to 0.0 instead.\\\"\\n\\n if not 0. <= value <= 1.:\\n raise ValueError(msg)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler\\n def auto_add_sampler(self, dataloader, train):\\n if self.use_ddp or self.use_ddp2 or self.use_tpu:\\n dl_args = {\\n 'dataset': dataloader.dataset,\\n 'batch_size': dataloader.batch_size,\\n 'shuffle': False,\\n 'num_workers': dataloader.num_workers,\\n 'collate_fn': dataloader.collate_fn,\\n 'pin_memory': dataloader.pin_memory,\\n 'drop_last': dataloader.drop_last,\\n 'timeout': dataloader.timeout,\\n 'worker_init_fn': dataloader.worker_init_fn\\n }\\n\\n if self.use_tpu:\\n sampler = DistributedSampler(\\n dataloader.dataset,\\n num_replicas=xm.xrt_world_size(),\\n rank=xm.get_ordinal()\\n )\\n dl_args['shuffle'] = False\\n else:\\n if train:\\n sampler = DistributedSampler(dataloader.dataset)\\n dl_args['shuffle'] = False\\n else:\\n sampler = SequentialSampler(dataloader.dataset)\\n\\n dl_args['sampler'] = sampler\\n\\n dataloader = DataLoader(**dl_args)\\n return dataloader\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader\\n def reset_train_dataloader(self, model):\\n \\\"\\\"\\\"\\n Dataloaders are provided by the model\\n :param model:\\n :return:\\n \\\"\\\"\\\"\\n\\n self.train_dataloader = self.request_data_loader(model.train_dataloader)\\n self.num_training_batches = 0\\n\\n # automatically add samplers\\n self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)\\n\\n self._percent_range_check('train_percent_check')\\n\\n if self.is_infinite_dataloader(self.train_dataloader):\\n self.num_training_batches = float('inf')\\n else:\\n # try getting the length\\n self.num_training_batches = len(self.train_dataloader)\\n self.num_training_batches = int(self.num_training_batches * self.train_percent_check)\\n\\n # determine when to check validation\\n # if int passed in, val checks that often\\n # otherwise, it checks in [0, 1.0] % range of a training epoch\\n if isinstance(self.val_check_interval, int):\\n self.val_check_batch = self.val_check_interval\\n if self.val_check_batch > self.num_training_batches:\\n raise ValueError(\\n f\\\"`val_check_interval` ({self.val_check_interval}) must be less than or equal \\\"\\n f\\\"to the number of the training batches ({self.num_training_batches}). \\\"\\n f\\\"If you want to disable validation set `val_percent_check` to 0.0 instead.\\\")\\n else:\\n if self.is_infinite_dataloader(self.train_dataloader):\\n m = '''\\n When using an infinite DataLoader (e.g. with an IterableDataset or when DataLoader\\n does not implement `__len__`) for `train_dataloader`, `Trainer(val_check_interval)`\\n must be an int. An int k specifies checking validation every k training batches.\\n '''\\n raise Miscon\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_infinite_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_infinite_dataloader\\n def is_infinite_dataloader(self, dataloader):\\n try:\\n # try getting the length\\n _ = len(dataloader)\\n return False\\n except TypeError as e:\\n return True\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader\\n def reset_val_dataloader(self, model):\\n \\\"\\\"\\\"\\n Dataloaders are provided by the model\\n :param model:\\n :return:\\n \\\"\\\"\\\"\\n if not self.is_overriden('validation_step'):\\n return\\n\\n self.val_dataloaders = self.request_data_loader(model.val_dataloader)\\n if not isinstance(self.val_dataloaders, list):\\n self.val_dataloaders = [self.val_dataloaders]\\n self.num_val_batches = 0\\n\\n # add samplers\\n self.val_dataloaders = [self.auto_add_sampler(dl, train=False)\\n for dl in self.val_dataloaders if dl]\\n\\n # determine number of validation batches\\n # val datasets could be none, 1 or 2+\\n if self.val_dataloaders is not None:\\n self._percent_range_check('val_percent_check')\\n\\n self.num_val_batches = sum(len(dataloader) for dataloader in self.val_dataloaders)\\n self.num_val_batches = int(self.num_val_batches * self.val_percent_check)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader\\n def reset_test_dataloader(self, model):\\n \\\"\\\"\\\"Dataloaders are provided by the model.\\n\\n :param model:\\n \\\"\\\"\\\"\\n if not self.is_overriden('test_step'):\\n return\\n\\n # get actual loader\\n self.test_dataloaders = self.request_data_loader(model.test_dataloader)\\n if not isinstance(self.test_dataloaders, list):\\n self.test_dataloaders = [self.test_dataloaders]\\n self.num_test_batches = 0\\n\\n # add samplers\\n self.test_dataloaders = [self.auto_add_sampler(dl, train=False)\\n for dl in self.test_dataloaders if dl]\\n\\n # determine number of test batches\\n if self.test_dataloaders is not None:\\n self._percent_range_check('test_percent_check')\\n\\n len_sum = sum(len(dataloader) for dataloader in self.test_dataloaders)\\n self.num_test_batches = len_sum\\n self.num_test_batches = int(self.num_test_batches * self.test_percent_check)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_data_loader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_data_loader\\n def request_data_loader(self, data_loader_fx):\\n \\\"\\\"\\\"\\n Handles downloading data in the GPU or TPU case.\\n\\n :param data_loader_fx:\\n :return:\\n \\\"\\\"\\\"\\n # get the function we'll use to get data\\n if self.use_ddp or self.use_ddp2:\\n data_loader = data_loader_fx()\\n\\n # all processes wait until data download has happened\\n dist.barrier()\\n\\n # data download/load on TPU\\n elif self.use_tpu and XLA_AVAILABLE:\\n data_loader = data_loader_fx()\\n\\n # all processes wait until data download has happened\\n torch_xla.core.xla_model.rendezvous(\\\"pl.TrainerDataLoadingMixin.get_dataloaders\\\")\\n\\n # regular start\\n else:\\n data_loader = data_loader_fx()\\n\\n return data_loader\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount\\n def determine_data_use_amount(self, train_percent_check, val_percent_check,\\n test_percent_check, overfit_pct):\\n \\\"\\\"\\\"\\n Use less data for debugging purposes\\n \\\"\\\"\\\"\\n self.train_percent_check = train_percent_check\\n self.val_percent_check = val_percent_check\\n self.test_percent_check = test_percent_check\\n if overfit_pct > 0:\\n if overfit_pct > 1:\\n raise ValueError(f\\\"`overfit_pct` must be not greater than 1.0, but got \\\"\\n f\\\"{overfit_pct:.3f}.\\\")\\n\\n self.train_percent_check = overfit_pct\\n self.val_percent_check = overfit_pct\\n self.test_percent_check = overfit_pct\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.run_pretrain_routine\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.run_pretrain_routine\\n @abstractmethod\\n def run_pretrain_routine(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.init_optimizers\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.init_optimizers\\n @abstractmethod\\n def init_optimizers(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.copy_trainer_model_properties\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.copy_trainer_model_properties\\n def copy_trainer_model_properties(self, model):\\n if isinstance(model, LightningDataParallel):\\n ref_model = model.module\\n elif isinstance(model, LightningDistributedDataParallel):\\n ref_model = model.module\\n else:\\n ref_model = model\\n\\n for m in [model, ref_model]:\\n m.trainer = self\\n m.on_gpu = self.on_gpu\\n m.use_dp = self.use_dp\\n m.use_ddp2 = self.use_ddp2\\n m.use_ddp = self.use_ddp\\n m.use_amp = self.use_amp\\n m.testing = self.testing\\n m.single_gpu = self.single_gpu\\n m.use_tpu = self.use_tpu\\n m.tpu_local_core_rank = self.tpu_local_core_rank\\n m.tpu_global_core_rank = self.tpu_global_core_rank\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.transfer_batch_to_tpu\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.transfer_batch_to_tpu\\n def transfer_batch_to_tpu(self, batch):\\n return self.__transfer_data_to_device(batch, device='tpu')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.transfer_batch_to_gpu\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.transfer_batch_to_gpu\\n def transfer_batch_to_gpu(self, batch, gpu_id):\\n return self.__transfer_data_to_device(batch, device='gpu', gpu_id=gpu_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.__transfer_data_to_device\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.__transfer_data_to_device\\n def __transfer_data_to_device(self, batch, device, gpu_id=None):\\n if device == 'tpu' and XLA_AVAILABLE:\\n # base case: object can be directly moved using `to`\\n if callable(getattr(batch, 'to', None)):\\n return batch.to(xm.xla_device())\\n\\n if device == 'gpu':\\n # base case: object can be directly moved using `cuda` or `to`\\n if callable(getattr(batch, 'cuda', None)):\\n return batch.cuda(gpu_id)\\n\\n if callable(getattr(batch, 'to', None)):\\n return batch.to(torch.device('cuda', gpu_id))\\n\\n # when list\\n if isinstance(batch, list):\\n for i, x in enumerate(batch):\\n batch[i] = self.__transfer_data_to_device(x, device, gpu_id)\\n return batch\\n\\n # when tuple\\n if isinstance(batch, tuple):\\n batch = list(batch)\\n for i, x in enumerate(batch):\\n batch[i] = self.__transfer_data_to_device(x, device, gpu_id)\\n return tuple(batch)\\n\\n # when dict\\n if isinstance(batch, dict):\\n for k, v in batch.items():\\n batch[k] = self.__transfer_data_to_device(v, device, gpu_id)\\n\\n return batch\\n\\n # nothing matches, return the value as is without transform\\n return batch\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.single_gpu_train\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.single_gpu_train\\n def single_gpu_train(self, model):\\n model.cuda(self.root_gpu)\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n if self.use_amp:\\n # An example\\n model, optimizers = model.configure_apex(amp, model, self.optimizers, self.amp_level)\\n self.optimizers = optimizers\\n\\n self.run_pretrain_routine(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.tpu_train\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.tpu_train\\n def tpu_train(self, tpu_core_idx, model):\\n # put model on tpu\\n model.to(xm.xla_device())\\n\\n # get the appropriate tpu ranks\\n self.tpu_local_core_rank = xm.get_local_ordinal()\\n self.tpu_global_core_rank = xm.get_ordinal()\\n\\n # avoid duplicating progress bar\\n self.show_progress_bar = self.show_progress_bar and self.tpu_global_core_rank == 0\\n\\n # track current tpu\\n self.current_tpu_idx = tpu_core_idx\\n self.proc_rank = self.tpu_local_core_rank\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n # init 16 bit for TPU\\n if self.precision == 16:\\n os.environ['XLA_USE_BF16'] = str(1)\\n\\n m = f'INIT TPU local core: {self.tpu_local_core_rank}, ' \\\\\\n f'global rank: {self.tpu_global_core_rank}'\\n log.info(m)\\n\\n # continue training routine\\n self.run_pretrain_routine(model)\\n\\n self.save_spawn_weights(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.dp_train\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::TrainerDPMixin.dp_train\\n def dp_train(self, model):\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n model.cuda(self.root_gpu)\\n\\n # check for this bug (amp + dp + !01 doesn't work)\\n # https://github.com/NVIDIA/apex/issues/227\\n if self.use_dp and self.use_amp:\\n if self.amp_level == 'O2': # pragma: no cover\\n m = f\\\"\\\"\\\"\\n Amp level {self.amp_level} with DataParallel is not supported.\\n See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.\\n We recommend you switch to ddp if you want to use amp\\n \\\"\\\"\\\"\\n raise MisconfigurationException(m)\\n else:\\n model, optimizers = model.configure_apex(amp, model, self.optimizers, self.amp_level)\\n\\n # create list of device ids\\n device_ids = self.data_parallel_device_ids\\n if isinstance(device_ids, int):\\n device_ids = list(range(device_ids))\\n\\n model = LightningDataParallel(model, device_ids=device_ids)\\n\\n self.run_pretrain_routine(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::normalize_parse_gpu_string_input\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::normalize_parse_gpu_string_input\\ndef normalize_parse_gpu_string_input(s):\\n if isinstance(s, str):\\n if s == '-1':\\n return -1\\n else:\\n return [int(x.strip()) for x in s.split(',')]\\n else:\\n return s\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::get_all_available_gpus\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::get_all_available_gpus\\ndef get_all_available_gpus():\\n \\\"\\\"\\\"\\n :return: a list of all available gpus\\n \\\"\\\"\\\"\\n return list(range(torch.cuda.device_count()))\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::check_gpus_data_type\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::check_gpus_data_type\\ndef check_gpus_data_type(gpus):\\n \\\"\\\"\\\"\\n :param gpus: gpus parameter as passed to the Trainer\\n Function checks that it is one of: None, Int, String or List\\n Throws otherwise\\n :return: return unmodified gpus variable\\n \\\"\\\"\\\"\\n\\n if gpus is not None and type(gpus) not in (int, str, list):\\n raise MisconfigurationException(\\\"GPUs must be int, string or list of ints or None.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::normalize_parse_gpu_input_to_list\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::normalize_parse_gpu_input_to_list\\ndef normalize_parse_gpu_input_to_list(gpus):\\n assert gpus is not None\\n if isinstance(gpus, list):\\n return gpus\\n\\n # must be an int\\n if not gpus: # gpus==0\\n return None\\n if gpus == -1:\\n return get_all_available_gpus()\\n\\n return list(range(gpus))\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::sanitize_gpu_ids\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::sanitize_gpu_ids\\ndef sanitize_gpu_ids(gpus):\\n \\\"\\\"\\\"\\n :param gpus: list of ints corresponding to GPU indices\\n Checks that each of the GPUs in the list is actually available.\\n Throws if any of the GPUs is not available.\\n :return: unmodified gpus variable\\n \\\"\\\"\\\"\\n all_available_gpus = get_all_available_gpus()\\n for gpu in gpus:\\n if gpu not in all_available_gpus:\\n message = f\\\"\\\"\\\"\\n You requested GPUs: {gpus}\\n But your machine only has: {all_available_gpus}\\n \\\"\\\"\\\"\\n raise MisconfigurationException(message)\\n return gpus\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::parse_gpu_ids\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::parse_gpu_ids\\ndef parse_gpu_ids(gpus):\\n \\\"\\\"\\\"\\n :param gpus: Int, string or list\\n An int -1 or string '-1' indicate that all available GPUs should be used.\\n A list of ints or a string containing list of comma separated integers\\n indicates specific GPUs to use\\n An int 0 means that no GPUs should be used\\n Any int N > 0 indicates that GPUs [0..N) should be used.\\n :return: List of gpus to be used\\n\\n If no GPUs are available but the value of gpus variable indicates request for GPUs\\n then a misconfiguration exception is raised.\\n \\\"\\\"\\\"\\n\\n # Check that gpus param is None, Int, String or List\\n check_gpus_data_type(gpus)\\n\\n # Handle the case when no gpus are requested\\n if gpus is None or isinstance(gpus, int) and gpus == 0:\\n return None\\n\\n # We know user requested GPUs therefore if some of the\\n # requested GPUs are not available an exception is thrown.\\n\\n gpus = normalize_parse_gpu_string_input(gpus)\\n gpus = normalize_parse_gpu_input_to_list(gpus)\\n gpus = sanitize_gpu_ids(gpus)\\n\\n if not gpus:\\n raise MisconfigurationException(\\\"GPUs requested but none are available.\\\")\\n return gpus\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/distrib_parts.py::determine_root_gpu_device\",\n \"text\": \"pytorch_lightning/trainer/distrib_parts.py::determine_root_gpu_device\\ndef determine_root_gpu_device(gpus):\\n \\\"\\\"\\\"\\n :param gpus: non empty list of ints representing which gpus to use\\n :return: designated root GPU device\\n \\\"\\\"\\\"\\n if gpus is None:\\n return None\\n\\n assert isinstance(gpus, list), \\\"gpus should be a list\\\"\\n assert len(gpus) > 0, \\\"gpus should be a non empty list\\\"\\n\\n # set root gpu\\n root_gpu = gpus[0]\\n\\n return root_gpu\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties\\n @abstractmethod\\n def copy_trainer_model_properties(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model\\n @abstractmethod\\n def get_model(self):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overriden\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overriden\\n @abstractmethod\\n def is_overriden(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu\\n @abstractmethod\\n def transfer_batch_to_tpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu\\n @abstractmethod\\n def transfer_batch_to_gpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_tqdm_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_tqdm_metrics\\n @abstractmethod\\n def add_tqdm_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics\\n @abstractmethod\\n def log_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader\\n @abstractmethod\\n def reset_test_dataloader(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader\\n @abstractmethod\\n def reset_val_dataloader(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluate\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluate\\n def evaluate(self, model, dataloaders, max_batches, test_mode: bool = False):\\n \\\"\\\"\\\"Run evaluation code.\\n\\n :param model: PT model\\n :param dataloaders: list of PT dataloaders\\n :param max_batches: Scalar\\n :param test_mode\\n :return:\\n \\\"\\\"\\\"\\n # enable eval mode\\n model.zero_grad()\\n model.eval()\\n\\n # copy properties for forward overrides\\n self.copy_trainer_model_properties(model)\\n\\n # disable gradients to save memory\\n torch.set_grad_enabled(False)\\n\\n # bookkeeping\\n outputs = []\\n\\n # run validation\\n for dataloader_idx, dataloader in enumerate(dataloaders):\\n dl_outputs = []\\n\\n # on TPU we have to wrap it under the ParallelLoader\\n if self.use_tpu:\\n device = xm.xla_device()\\n dataloader = xla_pl.ParallelLoader(dataloader, [device])\\n dataloader = dataloader.per_device_loader(device)\\n\\n for batch_idx, batch in enumerate(dataloader):\\n if batch is None: # pragma: no cover\\n continue\\n\\n # stop short when on fast_dev_run (sets max_batch=1)\\n if batch_idx >= max_batches:\\n break\\n\\n # -----------------\\n # RUN EVALUATION STEP\\n # -----------------\\n output = self.evaluation_forward(model, batch, batch_idx, dataloader_idx, test_mode)\\n\\n # on dp / ddp2 might still want to do something with the batch parts\\n if test_mode:\\n if self.is_overriden('test_step_end'):\\n model_ref = self.get_model()\\n with self.profiler.profile('test_step_end'):\\n output = model_ref.test_step_end(output)\\n else:\\n if self.is_overriden('validation_s\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation\\n def run_evaluation(self, test_mode: bool = False):\\n # when testing make sure user defined a test step\\n if test_mode and not self.is_overriden('test_step'):\\n m = \\\"You called `.test()` without defining model's `.test_step()`.\\\" \\\\\\n \\\" Please define and try again\\\"\\n raise MisconfigurationException(m)\\n\\n # Validation/Test begin callbacks\\n if test_mode:\\n self.on_test_start()\\n else:\\n self.on_validation_start()\\n\\n # hook\\n model = self.get_model()\\n model.on_pre_performance_check()\\n\\n # select dataloaders\\n if test_mode:\\n if self.reload_dataloaders_every_epoch or self.test_dataloaders is None:\\n self.reset_test_dataloader(model)\\n\\n dataloaders = self.test_dataloaders\\n max_batches = self.num_test_batches\\n else:\\n # val\\n if self.reload_dataloaders_every_epoch or self.val_dataloaders is None:\\n self.reset_val_dataloader(model)\\n\\n dataloaders = self.val_dataloaders\\n max_batches = self.num_val_batches\\n\\n # cap max batches to 1 when using fast_dev_run\\n if self.fast_dev_run:\\n max_batches = 1\\n\\n # init validation or test progress bar\\n # main progress bar will already be closed when testing so initial position is free\\n position = 2 * self.process_position + (not test_mode)\\n desc = 'Testing' if test_mode else 'Validating'\\n pbar = tqdm(desc=desc, total=max_batches, leave=test_mode, position=position,\\n disable=not self.show_progress_bar, dynamic_ncols=True,\\n file=sys.stdout)\\n setattr(self, f'{\\\"test\\\" if test_mode else \\\"val\\\"}_progress_bar', pbar)\\n\\n # run evaluation\\n eval_results = self.evaluate(self.model, dataloaders, max_batches, test_mode)\\n _, prog\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward\\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool = False):\\n # make dataloader_idx arg in validation_step optional\\n args = [batch, batch_idx]\\n\\n if test_mode and len(self.test_dataloaders) > 1:\\n args.append(dataloader_idx)\\n\\n elif not test_mode and len(self.val_dataloaders) > 1:\\n args.append(dataloader_idx)\\n\\n # handle DP, DDP forward\\n if self.use_ddp or self.use_dp or self.use_ddp2:\\n output = model(*args)\\n return output\\n\\n # single GPU data transfer\\n if self.single_gpu:\\n # for single GPU put inputs on gpu manually\\n root_gpu = 0\\n if isinstance(self.data_parallel_device_ids, list):\\n root_gpu = self.data_parallel_device_ids[0]\\n batch = self.transfer_batch_to_gpu(batch, root_gpu)\\n args[0] = batch\\n\\n # TPU data transfer\\n if self.use_tpu:\\n batch = self.transfer_batch_to_tpu(batch)\\n args[0] = batch\\n\\n # CPU, TPU or gpu step\\n if test_mode:\\n output = model.test_step(*args)\\n else:\\n output = model.validation_step(*args)\\n\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model\\n @abstractmethod\\n def get_model(self):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented\\n @abstractmethod\\n def is_function_implemented(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_infinite_dataloader\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_infinite_dataloader\\n @abstractmethod\\n def is_infinite_dataloader(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation\\n @abstractmethod\\n def run_evaluation(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu\\n @abstractmethod\\n def transfer_batch_to_gpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu\\n @abstractmethod\\n def transfer_batch_to_tpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients\\n @abstractmethod\\n def clip_gradients(self):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.print_nan_gradients\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.print_nan_gradients\\n @abstractmethod\\n def print_nan_gradients(self):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overriden\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overriden\\n @abstractmethod\\n def is_overriden(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_tqdm_metrics\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_tqdm_metrics\\n @abstractmethod\\n def add_tqdm_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics\\n @abstractmethod\\n def log_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output\\n @abstractmethod\\n def process_output(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader\\n @abstractmethod\\n def reset_train_dataloader(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader\\n @abstractmethod\\n def reset_val_dataloader(self, model):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg\\n @abstractmethod\\n def has_arg(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train\\n def train(self):\\n warnings.warn('Displayed epoch numbers in the progress bar start from \\\"1\\\" until v0.6.x,'\\n ' but will start from \\\"0\\\" in v0.8.0.', DeprecationWarning)\\n\\n # get model\\n model = self.get_model()\\n\\n # load data\\n self.reset_train_dataloader(model)\\n self.reset_val_dataloader(model)\\n\\n # Train start events\\n with self.profiler.profile('on_train_start'):\\n # callbacks\\n self.on_train_start()\\n # initialize early stop callback\\n if self.early_stop_callback is not None:\\n self.early_stop_callback.on_train_start(self, self.get_model())\\n # model hooks\\n model.on_train_start()\\n\\n try:\\n # run all epochs\\n for epoch in range(self.current_epoch, self.max_epochs):\\n # set seed for distributed sampler (enables shuffling for each epoch)\\n if self.use_ddp \\\\\\n and hasattr(self.train_dataloader.sampler, 'set_epoch'):\\n self.train_dataloader.sampler.set_epoch(epoch)\\n\\n # update training progress in trainer and model\\n model.current_epoch = epoch\\n self.current_epoch = epoch\\n\\n total_val_batches = 0\\n is_val_epoch = False\\n if not self.disable_validation:\\n # val can be checked multiple times in epoch\\n is_val_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0\\n val_checks_per_epoch = self.num_training_batches // self.val_check_batch\\n val_checks_per_epoch = val_checks_per_epoch if is_val_epoch else 0\\n total_val_batches = self.num_val_batches * val_checks_per_epoch\\n\\n # total batches includes multiple val checks\\n self.total_batches = self.num_\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch\\n def run_training_epoch(self):\\n\\n # Epoch start events\\n with self.profiler.profile('on_epoch_start'):\\n # callbacks\\n self.on_epoch_start()\\n # changing gradient according accumulation_scheduler\\n self.accumulation_scheduler.on_epoch_start(self, self.get_model())\\n # model hooks\\n if self.is_function_implemented('on_epoch_start'):\\n self.get_model().on_epoch_start()\\n\\n # reset train dataloader\\n if self.reload_dataloaders_every_epoch:\\n self.reset_train_dataloader(self.get_model())\\n\\n # track local dataloader so TPU can wrap each epoch\\n train_dataloader = self.train_dataloader\\n\\n # on TPU we have to wrap it under the ParallelLoader\\n if self.use_tpu:\\n device = xm.xla_device()\\n train_dataloader = xla_pl.ParallelLoader(train_dataloader, [device])\\n train_dataloader = train_dataloader.per_device_loader(device)\\n\\n # run epoch\\n for batch_idx, batch in self.profiler.profile_iterable(\\n enumerate(train_dataloader), \\\"get_train_batch\\\"\\n ):\\n # stop epoch if we limited the number of training batches\\n if batch_idx >= self.num_training_batches:\\n break\\n\\n self.batch_idx = batch_idx\\n\\n model = self.get_model()\\n model.global_step = self.global_step\\n\\n # ---------------\\n # RUN TRAIN STEP\\n # ---------------\\n output = self.run_training_batch(batch, batch_idx)\\n batch_result, grad_norm_dic, batch_step_metrics = output\\n\\n # when returning -1 from train_step, we end epoch early\\n early_stop_epoch = batch_result == -1\\n\\n # update lr\\n self.update_learning_rates(interval='step')\\n\\n # ---------------\\n # RUN VAL STEP\\n # ----\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch\\n def run_training_batch(self, batch, batch_idx):\\n # track grad norms\\n grad_norm_dic = {}\\n\\n # track all metrics for callbacks\\n all_callback_metrics = []\\n\\n # track metrics to log\\n all_log_metrics = []\\n\\n if batch is None:\\n return 0, grad_norm_dic, {}\\n\\n # Batch start events\\n with self.profiler.profile('on_batch_start'):\\n # callbacks\\n self.on_batch_start()\\n # hooks\\n if self.is_function_implemented('on_batch_start'):\\n response = self.get_model().on_batch_start(batch)\\n if response == -1:\\n return -1, grad_norm_dic, {}\\n\\n splits = [batch]\\n if self.truncated_bptt_steps is not None:\\n model_ref = self.get_model()\\n with self.profiler.profile('tbptt_split_batch'):\\n splits = model_ref.tbptt_split_batch(batch, self.truncated_bptt_steps)\\n\\n self.hiddens = None\\n for split_idx, split_batch in enumerate(splits):\\n self.split_idx = split_idx\\n\\n # call training_step once per optimizer\\n for opt_idx, optimizer in enumerate(self.optimizers):\\n # make sure only the gradients of the current optimizer's paramaters are calculated\\n # in the training step to prevent dangling gradients in multiple-optimizer setup.\\n if len(self.optimizers) > 1:\\n for param in self.get_model().parameters():\\n param.requires_grad = False\\n for group in optimizer.param_groups:\\n for param in group['params']:\\n param.requires_grad = True\\n\\n # wrap the forward step in a closure so second order methods work\\n def optimizer_closure():\\n # forward pass\\n with self.profil\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown\\n def run_training_teardown(self):\\n self.main_progress_bar.close()\\n\\n # Train end events\\n with self.profiler.profile('on_train_end'):\\n # callbacks\\n self.on_train_end()\\n # model hooks\\n if self.is_function_implemented('on_train_end'):\\n self.get_model().on_train_end()\\n\\n if self.logger is not None:\\n self.logger.finalize(\\\"success\\\")\\n\\n # summarize profile results\\n self.profiler.describe()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward\\n def training_forward(self, batch, batch_idx, opt_idx, hiddens):\\n \\\"\\\"\\\"\\n Handle forward for each training case (distributed, single gpu, etc...)\\n :param batch:\\n :param batch_idx:\\n :return:\\n \\\"\\\"\\\"\\n # ---------------\\n # FORWARD\\n # ---------------\\n # enable not needing to add opt_idx to training_step\\n args = [batch, batch_idx]\\n\\n if len(self.optimizers) > 1:\\n if self.has_arg('training_step', 'optimizer_idx'):\\n args.append(opt_idx)\\n else:\\n num_opts = len(self.optimizers)\\n raise ValueError(\\n f'Your LightningModule defines {num_opts} optimizers but '\\n f'training_step is missing the \\\"optimizer_idx\\\" argument.'\\n )\\n\\n # pass hiddens if using tbptt\\n if self.truncated_bptt_steps is not None:\\n args.append(hiddens)\\n\\n # distributed forward\\n if self.use_ddp or self.use_ddp2 or self.use_dp:\\n output = self.model(*args)\\n\\n # single GPU forward\\n elif self.single_gpu:\\n gpu_id = 0\\n if isinstance(self.data_parallel_device_ids, list):\\n gpu_id = self.data_parallel_device_ids[0]\\n batch = self.transfer_batch_to_gpu(copy.copy(batch), gpu_id)\\n args[0] = batch\\n output = self.model.training_step(*args)\\n\\n # TPU support\\n elif self.use_tpu:\\n batch = self.transfer_batch_to_tpu(copy.copy(batch))\\n args[0] = batch\\n output = self.model.training_step(*args)\\n\\n # CPU forward\\n else:\\n output = self.model.training_step(*args)\\n\\n # allow any mode to define training_step_end\\n # do something will all the dp outputs (like softmax)\\n if self.is_overriden('training_step_end'):\\n model_ref = self.get_model()\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates\\n def update_learning_rates(self, interval):\\n ''' Update learning rates\\n Args:\\n interval (str): either 'epoch' or 'step'.\\n '''\\n if not self.lr_schedulers:\\n return\\n\\n for lr_scheduler in self.lr_schedulers:\\n current_idx = self.batch_idx if interval == 'step' else self.current_epoch\\n current_idx += 1 # account for both batch and epoch starts from 0\\n # Take step if call to update_learning_rates matches the interval key and\\n # the current step modulo the schedulers frequency is zero\\n if lr_scheduler['interval'] == interval and current_idx % lr_scheduler['frequency'] == 0:\\n # If instance of ReduceLROnPlateau, we need to pass validation loss\\n if lr_scheduler['reduce_on_plateau']:\\n monitor_key = lr_scheduler['monitor']\\n monitor_val = self.callback_metrics.get(monitor_key)\\n if monitor_val is None:\\n avail_metrics = ','.join(list(self.callback_metrics.keys()))\\n m = f'ReduceLROnPlateau conditioned on metric {monitor_key} ' \\\\\\n f'which is not available. Available metrics are: {avail_metrics}. ' \\\\\\n 'Condition can be set using `monitor` key in lr scheduler dict'\\n raise MisconfigurationException(m)\\n lr_scheduler['scheduler'].step(monitor_val)\\n else:\\n lr_scheduler['scheduler'].step()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_checkpoint_callback\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_checkpoint_callback\\n def call_checkpoint_callback(self):\\n if self.checkpoint_callback is not None:\\n self.checkpoint_callback.on_validation_end(self, self.get_model())\\n self.on_validation_end()\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":74,"cells":{"instance_id":{"kind":"string","value":"googleapis__google-cloud-python-371"},"repo":{"kind":"string","value":"googleapis/google-cloud-python"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Key name w/ slash quoted twice during upload\nThe fix for #354 in #364 mistakenly quotes the `Key.name` when adding it the the `query_params` for the upload API call, but those params are already going to be quoted inside `Connection.make_api_request` (by way of `urllib.urlencode`).\n\n"},"gold_files":{"kind":"list like","value":["gcloud/storage/key.py"],"string":"[\n \"gcloud/storage/key.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["gcloud/storage/key.py::Key.upload_from_file"],"string":"[\n \"gcloud/storage/key.py::Key.upload_from_file\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/conf.py","text":"docs/conf.py","label":0},{"cid":"gcloud/datastore/__init__.py","text":"gcloud/datastore/__init__.py\ndef get_connection(client_email, private_key_path) # Shortcut method to establish a connection to the Cloud Datastore.\ndef get_dataset(dataset_id, client_email, private_key_path) # Establish a connection to a particular dataset in the Cloud Datastore.","label":0},{"cid":"gcloud/datastore/transaction.py","text":"gcloud/datastore/transaction.py\nclass Transaction # An abstraction representing datastore Transactions.\n def __init__(self, dataset)\n def connection(self) # Getter for current connection over which the transaction will run.\n def dataset(self) # Getter for the current dataset.\n def id(self) # Getter for the transaction ID.\n def mutation(self) # Getter for the current mutation.\n def add_auto_id_entity(self, entity) # Adds an entity to the list of entities to update with IDs.\n def begin(self) # Begins a transaction.\n def rollback(self) # Rolls back the current transaction.\n def commit(self) # Commits the transaction.\n def __enter__(self)\n def __exit__(self, exc_type, exc_val, exc_tb)","label":0},{"cid":"gcloud/storage/__init__.py","text":"gcloud/storage/__init__.py\ndef get_connection(project, client_email, private_key_path) # Shortcut method to establish a connection to Cloud Storage.\ndef get_bucket(bucket_name, project, client_email, private_key_path) # Shortcut method to establish a connection to a particular bucket.","label":0},{"cid":"gcloud/storage/_helpers.py","text":"gcloud/storage/_helpers.py\nclass _PropertyMixin # Abstract mixin for cloud storage classes with associated propertties.\n def connection(self) # Abstract getter for the connection to use.\n def path(self) # Abstract getter for the object path.\n def __init__(self, name=None, properties=None) # _PropertyMixin constructor.\n def properties(self) # Ensure properties are loaded, and return a copy.\n def batch(self) # Return a context manager which defers/batches updates.\n def _reload_properties(self) # Reload properties from Cloud Storage.\n def _patch_properties(self, properties) # Update particular fields of this object's properties.\n def _get_property(self, field, default=None) # Return the value of a field from the server-side representation.\n def get_acl(self) # Get ACL as an object.\nclass _PropertyBatch # Context manager: Batch updates to object's ``_patch_properties``\n def __init__(self, wrapped)\n def __enter__(self) # Intercept / defer property updates.\n def __exit__(self, type, value, traceback) # Patch deferred property updates if no error.\ndef _scalar_property(fieldname) # Create a property descriptor around the :class:`_PropertyMixin` helpers.\n def _getter(self) # Scalar property getter.\n def _setter(self, value) # Scalar property setter.","label":0},{"cid":"gcloud/storage/bucket.py","text":"gcloud/storage/bucket.py\nclass _KeyIterator # An iterator listing keys in a bucket\n def __init__(self, bucket, extra_params=None)\n def get_items_from_response(self, response) # Yield :class:`.storage.key.Key` items from response.\nclass Bucket # A class representing a Bucket on Cloud Storage.\n def __init__(self, connection=None, name=None, properties=None)\n def from_dict(cls, bucket_dict, connection=None) # Construct a new bucket from a dictionary of data from Cloud Storage.\n def __repr__(self)\n def __iter__(self)\n def __contains__(self, key)\n def acl(self) # Create our ACL on demand.\n def default_object_acl(self) # Create our defaultObjectACL on demand.\n def connection(self) # Getter property for the connection to use with this Bucket.\n def path(self) # The URL path to this bucket.\n def get_key(self, key) # Get a key object by name.\n def get_all_keys(self) # List all the keys in this bucket.\n def iterator(self, prefix=None, delimiter=None, max_results=None, versions=None) # Return an iterator used to find keys in the bucket.\n def new_key(self, key) # Given path name (or Key), return a :class:`.storage.key.Key` object.\n def delete(self, force=False) # Delete this bucket.\n def delete_key(self, key) # Deletes a key from the current bucket.\n def delete_keys(self, keys, on_error=None) # Deletes a list of keys from the current bucket.\n def copy_key(self, key, destination_bucket, new_name=None) # Copy the given key to the given bucket, optionally with a new name.\n def upload_file(self, filename, key=None) # Shortcut method to upload a file into this bucket.\n def upload_file_object(self, file_obj, key=None) # Shortcut method to upload a file object into this bucket.\n def get_cors(self) # Retrieve CORS policies configured for this bucket.\n def update_cors(self, entries) # Update CORS policies configured for this bucket.\n def get_default_object_acl(self) # Get the current Default Object ACL rules.\n def etag(self) # Retrieve the ETag for the bucket.\n def id(self) # Retrieve the ID for the bucket.\n def get_lifecycle(self) # Retrieve lifecycle rules configured for this bucket.\n def update_lifecycle(self, rules) # Update CORS policies configured for this bucket.\n def get_logging(self) # Return info about access logging for this bucket.\n def enable_logging(self, bucket_name, object_prefix='') # Enable access logging for this bucket.\n def disable_logging(self) # Disable access logging for this bucket.\n def metageneration(self) # Retrieve the metageneration for the bucket.\n def owner(self) # Retrieve info about the owner of the bucket.\n def project_number(self) # Retrieve the number of the project to which the bucket is assigned.\n def self_link(self) # Retrieve the URI for the bucket.\n def storage_class(self) # Retrieve the storage class for the bucket.\n def time_created(self) # Retrieve the timestamp at which the bucket","label":0},{"cid":"gcloud/storage/connection.py","text":"gcloud/storage/connection.py\ndef _utcnow() # Returns current time as UTC datetime.\nclass Connection # A connection to Google Cloud Storage via the JSON REST API.\n def __init__(self, project, *args, **kwargs) # :type project: string\n def __iter__(self)\n def __contains__(self, bucket_name)\n def build_api_url(self, path, query_params=None, api_base_url=None, api_version=None) # Construct an API url given a few components, some optional.\n def make_request(self, method, url, data=None, content_type=None, headers=None) # A low level method to send a request to the API.\n def api_request(self, method, path, query_params=None, data=None, content_type=None, api_base_url=None, api_version=None, expect_json=True) # Make a request over the HTTP transport to the Cloud Storage API.\n def get_all_buckets(self) # Get all buckets in the project.\n def get_bucket(self, bucket_name) # Get a bucket by name.\n def lookup(self, bucket_name) # Get a bucket by name, returning None if not found.\n def create_bucket(self, bucket) # Create a new bucket.\n def delete_bucket(self, bucket, force=False) # Delete a bucket.\n def new_bucket(self, bucket) # Factory method for creating a new (unsaved) bucket object.\n def generate_signed_url(self, resource, expiration, method='GET', content_md5=None, content_type=None) # Generate signed URL to provide query-string auth'n to a resource.\nclass _BucketIterator # An iterator listing all buckets.\n def __init__(self, connection)\n def get_items_from_response(self, response) # Factory method which yields :class:`.Bucket` items from a response.\ndef _get_expiration_seconds(expiration) # Convert 'expiration' to a number of seconds in the future.","label":0},{"cid":"gcloud/storage/iterator.py","text":"gcloud/storage/iterator.py\nclass Iterator # A generic class for iterating through Cloud Storage list responses.\n def __init__(self, connection, path, extra_params=None)\n def __iter__(self) # Iterate through the list of items.\n def has_next_page(self) # Determines whether or not this iterator has more pages.\n def get_query_params(self) # Getter for query parameters for the next request.\n def get_next_page_response(self) # Requests the next page from the path provided.\n def reset(self) # Resets the iterator to the beginning.\n def get_items_from_response(self, response) # Factory method called while iterating. This should be overriden.","label":0},{"cid":"gcloud/storage/key.py","text":"gcloud/storage/key.py\nclass Key # A wrapper around Cloud Storage's concept of an ``Object``.\n def __init__(self, bucket=None, name=None, properties=None) # Key constructor.\n def acl(self) # Create our ACL on demand.\n def from_dict(cls, key_dict, bucket=None) # Instantiate a :class:`Key` from data returned by the JSON API.\n def __repr__(self)\n def connection(self) # Getter property for the connection to use with this Key.\n def path(self) # Getter property for the URL path to this Key.\n def public_url(self) # The public URL for this key's object.\n def generate_signed_url(self, expiration, method='GET') # Generates a signed URL for this key.\n def exists(self) # Determines whether or not this key exists.\n def rename(self, new_name) # Renames this key using copy and delete operations.\n def delete(self) # Deletes a key from Cloud Storage.\n def download_to_file(self, file_obj) # Download the contents of this key into a file-like object.\n def download_to_filename(self, filename) # Download the contents of this key into a named file.\n def download_as_string(self) # Download the contents of this key as a string.\n def upload_from_file(self, file_obj, rewind=False, size=None, content_type=None) # Upload the contents of this key from a file-like object.\n def upload_from_filename(self, filename) # Upload this key's contents from the content of f named file.\n def upload_from_string(self, data, content_type='text/plain') # Upload contents of this key from the provided string.\n def make_public(self) # Make this key public giving all users read access.\n def component_count(self) # Number of underlying components that make up this object.\n def etag(self) # Retrieve the ETag for the object.\n def generation(self) # Retrieve the generation for the object.\n def id(self) # Retrieve the ID for the object.\n def media_link(self) # Retrieve the media download URI for the object.\n def metadata(self) # Retrieve arbitrary/application specific metadata for the object.\n def metadata(self, value) # Update arbitrary/application specific metadata for the object.\n def metageneration(self) # Retrieve the metageneration for the object.\n def owner(self) # Retrieve info about the owner of the object.\n def self_link(self) # Retrieve the URI for the object.\n def size(self) # Size of the object, in bytes.\n def storage_class(self) # Retrieve the storage class for the object.\n def time_deleted(self) # Retrieve the timestamp at which the object was deleted.\n def updated(self) # Retrieve the timestamp at which the object was updated.\nclass _KeyDataIterator # An iterator listing data stored in a key.\n def __init__(self, key)\n def __iter__(self)\n def reset(self) # Resets the iterator to the beginning.\n def has_more_data(self) # Determines whether or not this iterator has more data to read.\n def get_headers(self) # Gets range header(s) for next chunk ","label":1}],"string":"[\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/__init__.py\",\n \"text\": \"gcloud/datastore/__init__.py\\ndef get_connection(client_email, private_key_path) # Shortcut method to establish a connection to the Cloud Datastore.\\ndef get_dataset(dataset_id, client_email, private_key_path) # Establish a connection to a particular dataset in the Cloud Datastore.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py\",\n \"text\": \"gcloud/datastore/transaction.py\\nclass Transaction # An abstraction representing datastore Transactions.\\n def __init__(self, dataset)\\n def connection(self) # Getter for current connection over which the transaction will run.\\n def dataset(self) # Getter for the current dataset.\\n def id(self) # Getter for the transaction ID.\\n def mutation(self) # Getter for the current mutation.\\n def add_auto_id_entity(self, entity) # Adds an entity to the list of entities to update with IDs.\\n def begin(self) # Begins a transaction.\\n def rollback(self) # Rolls back the current transaction.\\n def commit(self) # Commits the transaction.\\n def __enter__(self)\\n def __exit__(self, exc_type, exc_val, exc_tb)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/__init__.py\",\n \"text\": \"gcloud/storage/__init__.py\\ndef get_connection(project, client_email, private_key_path) # Shortcut method to establish a connection to Cloud Storage.\\ndef get_bucket(bucket_name, project, client_email, private_key_path) # Shortcut method to establish a connection to a particular bucket.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py\",\n \"text\": \"gcloud/storage/_helpers.py\\nclass _PropertyMixin # Abstract mixin for cloud storage classes with associated propertties.\\n def connection(self) # Abstract getter for the connection to use.\\n def path(self) # Abstract getter for the object path.\\n def __init__(self, name=None, properties=None) # _PropertyMixin constructor.\\n def properties(self) # Ensure properties are loaded, and return a copy.\\n def batch(self) # Return a context manager which defers/batches updates.\\n def _reload_properties(self) # Reload properties from Cloud Storage.\\n def _patch_properties(self, properties) # Update particular fields of this object's properties.\\n def _get_property(self, field, default=None) # Return the value of a field from the server-side representation.\\n def get_acl(self) # Get ACL as an object.\\nclass _PropertyBatch # Context manager: Batch updates to object's ``_patch_properties``\\n def __init__(self, wrapped)\\n def __enter__(self) # Intercept / defer property updates.\\n def __exit__(self, type, value, traceback) # Patch deferred property updates if no error.\\ndef _scalar_property(fieldname) # Create a property descriptor around the :class:`_PropertyMixin` helpers.\\n def _getter(self) # Scalar property getter.\\n def _setter(self, value) # Scalar property setter.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py\",\n \"text\": \"gcloud/storage/bucket.py\\nclass _KeyIterator # An iterator listing keys in a bucket\\n def __init__(self, bucket, extra_params=None)\\n def get_items_from_response(self, response) # Yield :class:`.storage.key.Key` items from response.\\nclass Bucket # A class representing a Bucket on Cloud Storage.\\n def __init__(self, connection=None, name=None, properties=None)\\n def from_dict(cls, bucket_dict, connection=None) # Construct a new bucket from a dictionary of data from Cloud Storage.\\n def __repr__(self)\\n def __iter__(self)\\n def __contains__(self, key)\\n def acl(self) # Create our ACL on demand.\\n def default_object_acl(self) # Create our defaultObjectACL on demand.\\n def connection(self) # Getter property for the connection to use with this Bucket.\\n def path(self) # The URL path to this bucket.\\n def get_key(self, key) # Get a key object by name.\\n def get_all_keys(self) # List all the keys in this bucket.\\n def iterator(self, prefix=None, delimiter=None, max_results=None, versions=None) # Return an iterator used to find keys in the bucket.\\n def new_key(self, key) # Given path name (or Key), return a :class:`.storage.key.Key` object.\\n def delete(self, force=False) # Delete this bucket.\\n def delete_key(self, key) # Deletes a key from the current bucket.\\n def delete_keys(self, keys, on_error=None) # Deletes a list of keys from the current bucket.\\n def copy_key(self, key, destination_bucket, new_name=None) # Copy the given key to the given bucket, optionally with a new name.\\n def upload_file(self, filename, key=None) # Shortcut method to upload a file into this bucket.\\n def upload_file_object(self, file_obj, key=None) # Shortcut method to upload a file object into this bucket.\\n def get_cors(self) # Retrieve CORS policies configured for this bucket.\\n def update_cors(self, entries) # Update CORS policies configured for this bucket.\\n def get_default_object_acl(self) # Get the current Default Object ACL rules.\\n def etag(self) # Retrieve the ETag for the bucket.\\n def id(self) # Retrieve the ID for the bucket.\\n def get_lifecycle(self) # Retrieve lifecycle rules configured for this bucket.\\n def update_lifecycle(self, rules) # Update CORS policies configured for this bucket.\\n def get_logging(self) # Return info about access logging for this bucket.\\n def enable_logging(self, bucket_name, object_prefix='') # Enable access logging for this bucket.\\n def disable_logging(self) # Disable access logging for this bucket.\\n def metageneration(self) # Retrieve the metageneration for the bucket.\\n def owner(self) # Retrieve info about the owner of the bucket.\\n def project_number(self) # Retrieve the number of the project to which the bucket is assigned.\\n def self_link(self) # Retrieve the URI for the bucket.\\n def storage_class(self) # Retrieve the storage class for the bucket.\\n def time_created(self) # Retrieve the timestamp at which the bucket\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py\",\n \"text\": \"gcloud/storage/connection.py\\ndef _utcnow() # Returns current time as UTC datetime.\\nclass Connection # A connection to Google Cloud Storage via the JSON REST API.\\n def __init__(self, project, *args, **kwargs) # :type project: string\\n def __iter__(self)\\n def __contains__(self, bucket_name)\\n def build_api_url(self, path, query_params=None, api_base_url=None, api_version=None) # Construct an API url given a few components, some optional.\\n def make_request(self, method, url, data=None, content_type=None, headers=None) # A low level method to send a request to the API.\\n def api_request(self, method, path, query_params=None, data=None, content_type=None, api_base_url=None, api_version=None, expect_json=True) # Make a request over the HTTP transport to the Cloud Storage API.\\n def get_all_buckets(self) # Get all buckets in the project.\\n def get_bucket(self, bucket_name) # Get a bucket by name.\\n def lookup(self, bucket_name) # Get a bucket by name, returning None if not found.\\n def create_bucket(self, bucket) # Create a new bucket.\\n def delete_bucket(self, bucket, force=False) # Delete a bucket.\\n def new_bucket(self, bucket) # Factory method for creating a new (unsaved) bucket object.\\n def generate_signed_url(self, resource, expiration, method='GET', content_md5=None, content_type=None) # Generate signed URL to provide query-string auth'n to a resource.\\nclass _BucketIterator # An iterator listing all buckets.\\n def __init__(self, connection)\\n def get_items_from_response(self, response) # Factory method which yields :class:`.Bucket` items from a response.\\ndef _get_expiration_seconds(expiration) # Convert 'expiration' to a number of seconds in the future.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/iterator.py\",\n \"text\": \"gcloud/storage/iterator.py\\nclass Iterator # A generic class for iterating through Cloud Storage list responses.\\n def __init__(self, connection, path, extra_params=None)\\n def __iter__(self) # Iterate through the list of items.\\n def has_next_page(self) # Determines whether or not this iterator has more pages.\\n def get_query_params(self) # Getter for query parameters for the next request.\\n def get_next_page_response(self) # Requests the next page from the path provided.\\n def reset(self) # Resets the iterator to the beginning.\\n def get_items_from_response(self, response) # Factory method called while iterating. This should be overriden.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py\",\n \"text\": \"gcloud/storage/key.py\\nclass Key # A wrapper around Cloud Storage's concept of an ``Object``.\\n def __init__(self, bucket=None, name=None, properties=None) # Key constructor.\\n def acl(self) # Create our ACL on demand.\\n def from_dict(cls, key_dict, bucket=None) # Instantiate a :class:`Key` from data returned by the JSON API.\\n def __repr__(self)\\n def connection(self) # Getter property for the connection to use with this Key.\\n def path(self) # Getter property for the URL path to this Key.\\n def public_url(self) # The public URL for this key's object.\\n def generate_signed_url(self, expiration, method='GET') # Generates a signed URL for this key.\\n def exists(self) # Determines whether or not this key exists.\\n def rename(self, new_name) # Renames this key using copy and delete operations.\\n def delete(self) # Deletes a key from Cloud Storage.\\n def download_to_file(self, file_obj) # Download the contents of this key into a file-like object.\\n def download_to_filename(self, filename) # Download the contents of this key into a named file.\\n def download_as_string(self) # Download the contents of this key as a string.\\n def upload_from_file(self, file_obj, rewind=False, size=None, content_type=None) # Upload the contents of this key from a file-like object.\\n def upload_from_filename(self, filename) # Upload this key's contents from the content of f named file.\\n def upload_from_string(self, data, content_type='text/plain') # Upload contents of this key from the provided string.\\n def make_public(self) # Make this key public giving all users read access.\\n def component_count(self) # Number of underlying components that make up this object.\\n def etag(self) # Retrieve the ETag for the object.\\n def generation(self) # Retrieve the generation for the object.\\n def id(self) # Retrieve the ID for the object.\\n def media_link(self) # Retrieve the media download URI for the object.\\n def metadata(self) # Retrieve arbitrary/application specific metadata for the object.\\n def metadata(self, value) # Update arbitrary/application specific metadata for the object.\\n def metageneration(self) # Retrieve the metageneration for the object.\\n def owner(self) # Retrieve info about the owner of the object.\\n def self_link(self) # Retrieve the URI for the object.\\n def size(self) # Size of the object, in bytes.\\n def storage_class(self) # Retrieve the storage class for the object.\\n def time_deleted(self) # Retrieve the timestamp at which the object was deleted.\\n def updated(self) # Retrieve the timestamp at which the object was updated.\\nclass _KeyDataIterator # An iterator listing data stored in a key.\\n def __init__(self, key)\\n def __iter__(self)\\n def reset(self) # Resets the iterator to the beginning.\\n def has_more_data(self) # Determines whether or not this iterator has more data to read.\\n def get_headers(self) # Gets range header(s) for next chunk \",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"gcloud/datastore/__init__.py::get_connection","text":"gcloud/datastore/__init__.py::get_connection\ndef get_connection(client_email, private_key_path):\n \"\"\"Shortcut method to establish a connection to the Cloud Datastore.\n\n Use this if you are going to access several datasets\n with the same set of credentials (unlikely):\n\n >>> from gcloud import datastore\n >>> connection = datastore.get_connection(email, key_path)\n >>> dataset1 = connection.dataset('dataset1')\n >>> dataset2 = connection.dataset('dataset2')\n\n :type client_email: string\n :param client_email: The e-mail attached to the service account.\n\n :type private_key_path: string\n :param private_key_path: The path to a private key file (this file was\n given to you when you created the service\n account).\n\n :rtype: :class:`gcloud.datastore.connection.Connection`\n :returns: A connection defined with the proper credentials.\n \"\"\"\n from gcloud import credentials\n from gcloud.datastore.connection import Connection\n\n svc_account_credentials = credentials.get_for_service_account(\n client_email, private_key_path, scope=SCOPE)\n return Connection(credentials=svc_account_credentials)","label":0},{"cid":"gcloud/datastore/__init__.py::get_dataset","text":"gcloud/datastore/__init__.py::get_dataset\ndef get_dataset(dataset_id, client_email, private_key_path):\n \"\"\"Establish a connection to a particular dataset in the Cloud Datastore.\n\n This is a shortcut method for creating a connection and using it\n to connect to a dataset.\n\n You'll generally use this as the first call to working with the API:\n\n >>> from gcloud import datastore\n >>> dataset = datastore.get_dataset('dataset-id', email, key_path)\n >>> # Now you can do things with the dataset.\n >>> dataset.query().kind('TestKind').fetch()\n [...]\n\n :type dataset_id: string\n :param dataset_id: The id of the dataset you want to use.\n This is akin to a database name\n and is usually the same as your Cloud Datastore project\n name.\n\n :type client_email: string\n :param client_email: The e-mail attached to the service account.\n\n :type private_key_path: string\n :param private_key_path: The path to a private key file (this file was\n given to you when you created the service\n account).\n\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\n :returns: A dataset with a connection using the provided credentials.\n \"\"\"\n connection = get_connection(client_email, private_key_path)\n return connection.dataset(dataset_id)","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.__init__","text":"gcloud/datastore/transaction.py::Transaction.__init__\n def __init__(self, dataset):\n self._dataset = dataset\n self._id = None\n self._mutation = datastore_pb.Mutation()\n self._auto_id_entities = []","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.connection","text":"gcloud/datastore/transaction.py::Transaction.connection\n def connection(self):\n \"\"\"Getter for current connection over which the transaction will run.\n\n :rtype: :class:`gcloud.datastore.connection.Connection`\n :returns: The connection over which the transaction will run.\n \"\"\"\n\n return self.dataset().connection()","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.dataset","text":"gcloud/datastore/transaction.py::Transaction.dataset\n def dataset(self):\n \"\"\"Getter for the current dataset.\n\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\n :returns: The dataset to which the transaction belongs.\n \"\"\"\n\n return self._dataset","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.id","text":"gcloud/datastore/transaction.py::Transaction.id\n def id(self):\n \"\"\"Getter for the transaction ID.\n\n :rtype: string\n :returns: The ID of the current transaction.\n \"\"\"\n\n return self._id","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.mutation","text":"gcloud/datastore/transaction.py::Transaction.mutation\n def mutation(self):\n \"\"\"Getter for the current mutation.\n\n Every transaction is committed\n with a single Mutation\n representing the 'work' to be done as part of the transaction.\n Inside a transaction,\n calling ``save()`` on an entity\n builds up the mutation.\n This getter returns the Mutation protobuf\n that has been built-up so far.\n\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.Mutation`\n :returns: The Mutation protobuf to be sent in the commit request.\n \"\"\"\n return self._mutation","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.add_auto_id_entity","text":"gcloud/datastore/transaction.py::Transaction.add_auto_id_entity\n def add_auto_id_entity(self, entity):\n \"\"\"Adds an entity to the list of entities to update with IDs.\n\n When an entity has a partial key,\n calling ``save()`` adds an insert_auto_id entry in the mutation.\n In order to make sure we update the Entity\n once the transaction is committed,\n we need to keep track of which entities to update\n (and the order is important).\n\n When you call ``save()`` on an entity inside a transaction,\n if the entity has a partial key,\n it adds itself to the list of entities to be updated\n once the transaction is committed\n by calling this method.\n \"\"\"\n self._auto_id_entities.append(entity)","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.begin","text":"gcloud/datastore/transaction.py::Transaction.begin\n def begin(self):\n \"\"\"Begins a transaction.\n\n This method is called automatically when entering a with statement,\n however it can be called explicitly\n if you don't want to use a context manager.\n \"\"\"\n self._id = self.connection().begin_transaction(self.dataset().id())\n self.connection().transaction(self)","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.rollback","text":"gcloud/datastore/transaction.py::Transaction.rollback\n def rollback(self):\n \"\"\"Rolls back the current transaction.\n\n This method has necessary side-effects:\n\n - Sets the current connection's transaction reference to None.\n - Sets the current transaction's ID to None.\n \"\"\"\n self.connection().rollback(self.dataset().id())\n self.connection().transaction(None)\n self._id = None","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.commit","text":"gcloud/datastore/transaction.py::Transaction.commit\n def commit(self):\n \"\"\"Commits the transaction.\n\n This is called automatically upon exiting a with statement,\n however it can be called explicitly\n if you don't want to use a context manager.\n\n This method has necessary side-effects:\n\n - Sets the current connection's transaction reference to None.\n - Sets the current transaction's ID to None.\n - Updates paths for any keys that needed an automatically generated ID.\n \"\"\"\n # It's possible that they called commit() already, in which case\n # we shouldn't do any committing of our own.\n if self.connection().transaction():\n result = self.connection().commit(self.dataset().id(),\n self.mutation())\n\n # For any of the auto-id entities, make sure we update their keys.\n for i, entity in enumerate(self._auto_id_entities):\n key_pb = result.insert_auto_id_key[i]\n key = helpers.key_from_protobuf(key_pb)\n entity.key(entity.key().path(key.path()))\n\n # Tell the connection that the transaction is over.\n self.connection().transaction(None)\n\n # Clear our own ID in case this gets accidentally reused.\n self._id = None","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.__enter__","text":"gcloud/datastore/transaction.py::Transaction.__enter__\n def __enter__(self):\n self.begin()\n return self","label":0},{"cid":"gcloud/datastore/transaction.py::Transaction.__exit__","text":"gcloud/datastore/transaction.py::Transaction.__exit__\n def __exit__(self, exc_type, exc_val, exc_tb):\n if exc_type is None:\n self.commit()\n else:\n self.rollback()","label":0},{"cid":"gcloud/storage/__init__.py::get_connection","text":"gcloud/storage/__init__.py::get_connection\ndef get_connection(project, client_email, private_key_path):\n \"\"\"Shortcut method to establish a connection to Cloud Storage.\n\n Use this if you are going to access several buckets with the same\n set of credentials:\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> bucket1 = connection.get_bucket('bucket1')\n >>> bucket2 = connection.get_bucket('bucket2')\n\n :type project: string\n :param project: The name of the project to connect to.\n\n :type client_email: string\n :param client_email: The e-mail attached to the service account.\n\n :type private_key_path: string\n :param private_key_path: The path to a private key file (this file was\n given to you when you created the service\n account).\n\n :rtype: :class:`gcloud.storage.connection.Connection`\n :returns: A connection defined with the proper credentials.\n \"\"\"\n from gcloud import credentials\n from gcloud.storage.connection import Connection\n\n svc_account_credentials = credentials.get_for_service_account(\n client_email, private_key_path, scope=SCOPE)\n return Connection(project=project, credentials=svc_account_credentials)","label":0},{"cid":"gcloud/storage/__init__.py::get_bucket","text":"gcloud/storage/__init__.py::get_bucket\ndef get_bucket(bucket_name, project, client_email, private_key_path):\n \"\"\"Shortcut method to establish a connection to a particular bucket.\n\n You'll generally use this as the first call to working with the API:\n\n >>> from gcloud import storage\n >>> bucket = storage.get_bucket(project, bucket_name, email, key_path)\n >>> # Now you can do things with the bucket.\n >>> bucket.exists('/path/to/file.txt')\n False\n\n :type bucket_name: string\n :param bucket_name: The id of the bucket you want to use.\n This is akin to a disk name on a file system.\n\n :type project: string\n :param project: The name of the project to connect to.\n\n :type client_email: string\n :param client_email: The e-mail attached to the service account.\n\n :type private_key_path: string\n :param private_key_path: The path to a private key file (this file was\n given to you when you created the service\n account).\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: A bucket with a connection using the provided credentials.\n \"\"\"\n connection = get_connection(project, client_email, private_key_path)\n return connection.get_bucket(bucket_name)","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.connection","text":"gcloud/storage/_helpers.py::_PropertyMixin.connection\n @property\n def connection(self):\n \"\"\"Abstract getter for the connection to use.\"\"\"\n raise NotImplementedError","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.path","text":"gcloud/storage/_helpers.py::_PropertyMixin.path\n @property\n def path(self):\n \"\"\"Abstract getter for the object path.\"\"\"\n raise NotImplementedError","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.__init__","text":"gcloud/storage/_helpers.py::_PropertyMixin.__init__\n def __init__(self, name=None, properties=None):\n \"\"\"_PropertyMixin constructor.\n\n :type name: string\n :param name: The name of the object.\n\n :type properties: dict\n :param properties: All the other data provided by Cloud Storage.\n \"\"\"\n self.name = name\n self._properties = {}\n if properties is not None:\n self._properties.update(properties)","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.properties","text":"gcloud/storage/_helpers.py::_PropertyMixin.properties\n @property\n def properties(self):\n \"\"\"Ensure properties are loaded, and return a copy.\n\n :rtype: dict\n \"\"\"\n if not self._properties:\n self._reload_properties()\n return self._properties.copy()","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.batch","text":"gcloud/storage/_helpers.py::_PropertyMixin.batch\n @property\n def batch(self):\n \"\"\"Return a context manager which defers/batches updates.\n\n E.g., to batch multiple updates to a bucket::\n\n >>> with bucket.batch:\n ... bucket.enable_versioning()\n ... bucket.disable_website()\n\n or for a key::\n\n >>> with key.batch:\n ... key.content_type = 'image/jpeg'\n ... key.content_encoding = 'gzip'\n\n Updates will be aggregated and sent as a single call to\n :meth:`_patch_properties` IFF the ``with`` block exits without\n an exception.\n\n :rtype: :class:`_PropertyBatch`\n \"\"\"\n return _PropertyBatch(self)","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin._reload_properties","text":"gcloud/storage/_helpers.py::_PropertyMixin._reload_properties\n def _reload_properties(self):\n \"\"\"Reload properties from Cloud Storage.\n\n :rtype: :class:`_PropertyMixin`\n :returns: The object you just reloaded data for.\n \"\"\"\n # Pass only '?projection=noAcl' here because 'acl' and related\n # are handled via custom endpoints..\n query_params = {'projection': 'noAcl'}\n self._properties = self.connection.api_request(\n method='GET', path=self.path, query_params=query_params)\n return self","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin._patch_properties","text":"gcloud/storage/_helpers.py::_PropertyMixin._patch_properties\n def _patch_properties(self, properties):\n \"\"\"Update particular fields of this object's properties.\n\n This method will only update the fields provided and will not\n touch the other fields.\n\n It will also reload the properties locally based on the server's\n response.\n\n :type properties: dict\n :param properties: The dictionary of values to update.\n\n :rtype: :class:`_PropertyMixin`\n :returns: The current object.\n \"\"\"\n # Pass '?projection=full' here because 'PATCH' documented not\n # to work properly w/ 'noAcl'.\n self._properties = self.connection.api_request(\n method='PATCH', path=self.path, data=properties,\n query_params={'projection': 'full'})\n return self","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin._get_property","text":"gcloud/storage/_helpers.py::_PropertyMixin._get_property\n def _get_property(self, field, default=None):\n \"\"\"Return the value of a field from the server-side representation.\n\n If you request a field that isn't available, and that field can\n be retrieved by refreshing data from Cloud Storage, this method\n will reload the data using :func:`_PropertyMixin._reload_properties`.\n\n :type field: string\n :param field: A particular field to retrieve from properties.\n\n :type default: anything\n :param default: The value to return if the field provided wasn't found.\n\n :rtype: anything\n :returns: value of the specific field, or the default if not found.\n \"\"\"\n # Raise for fields which have custom accessors.\n custom = self.CUSTOM_PROPERTY_ACCESSORS.get(field)\n if custom is not None:\n message = \"Use '%s' or related methods instead.\" % custom\n raise KeyError((field, message))\n\n return self.properties.get(field, default)","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.get_acl","text":"gcloud/storage/_helpers.py::_PropertyMixin.get_acl\n def get_acl(self):\n \"\"\"Get ACL as an object.\n\n :returns: An ACL object for the current object.\n \"\"\"\n if not self.acl.loaded:\n self.acl.reload()\n return self.acl","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyBatch.__init__","text":"gcloud/storage/_helpers.py::_PropertyBatch.__init__\n def __init__(self, wrapped):\n self._wrapped = wrapped\n self._deferred = {}","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyBatch.__enter__","text":"gcloud/storage/_helpers.py::_PropertyBatch.__enter__\n def __enter__(self):\n \"\"\"Intercept / defer property updates.\"\"\"\n self._wrapped._patch_properties = self._deferred.update","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyBatch.__exit__","text":"gcloud/storage/_helpers.py::_PropertyBatch.__exit__\n def __exit__(self, type, value, traceback):\n \"\"\"Patch deferred property updates if no error.\"\"\"\n del self._wrapped._patch_properties\n if type is None:\n if self._deferred:\n self._wrapped._patch_properties(self._deferred)","label":0},{"cid":"gcloud/storage/_helpers.py::_scalar_property","text":"gcloud/storage/_helpers.py::_scalar_property\ndef _scalar_property(fieldname):\n \"\"\"Create a property descriptor around the :class:`_PropertyMixin` helpers.\n \"\"\"\n def _getter(self):\n \"\"\"Scalar property getter.\"\"\"\n return self.properties[fieldname]\n\n def _setter(self, value):\n \"\"\"Scalar property setter.\"\"\"\n self._patch_properties({fieldname: value})\n\n return property(_getter, _setter)","label":0},{"cid":"gcloud/storage/_helpers.py::_scalar_property._getter","text":"gcloud/storage/_helpers.py::_scalar_property._getter\n def _getter(self):\n \"\"\"Scalar property getter.\"\"\"\n return self.properties[fieldname]","label":0},{"cid":"gcloud/storage/_helpers.py::_scalar_property._setter","text":"gcloud/storage/_helpers.py::_scalar_property._setter\n def _setter(self, value):\n \"\"\"Scalar property setter.\"\"\"\n self._patch_properties({fieldname: value})","label":0},{"cid":"gcloud/storage/bucket.py::_KeyIterator.__init__","text":"gcloud/storage/bucket.py::_KeyIterator.__init__\n def __init__(self, bucket, extra_params=None):\n self.bucket = bucket\n self.prefixes = ()\n super(_KeyIterator, self).__init__(\n connection=bucket.connection, path=bucket.path + '/o',\n extra_params=extra_params)","label":0},{"cid":"gcloud/storage/bucket.py::_KeyIterator.get_items_from_response","text":"gcloud/storage/bucket.py::_KeyIterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Yield :class:`.storage.key.Key` items from response.\n\n :type response: dict\n :param response: The JSON API response for a page of keys.\n \"\"\"\n self.prefixes = tuple(response.get('prefixes', ()))\n for item in response.get('items', []):\n yield Key.from_dict(item, bucket=self.bucket)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.__init__","text":"gcloud/storage/bucket.py::Bucket.__init__\n def __init__(self, connection=None, name=None, properties=None):\n super(Bucket, self).__init__(name=name, properties=properties)\n self._connection = connection","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.from_dict","text":"gcloud/storage/bucket.py::Bucket.from_dict\n @classmethod\n def from_dict(cls, bucket_dict, connection=None):\n \"\"\"Construct a new bucket from a dictionary of data from Cloud Storage.\n\n :type bucket_dict: dict\n :param bucket_dict: The dictionary of data to construct a bucket from.\n\n :rtype: :class:`Bucket`\n :returns: A bucket constructed from the data provided.\n \"\"\"\n return cls(connection=connection, name=bucket_dict['name'],\n properties=bucket_dict)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.__repr__","text":"gcloud/storage/bucket.py::Bucket.__repr__\n def __repr__(self):\n return '<Bucket: %s>' % self.name","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.__iter__","text":"gcloud/storage/bucket.py::Bucket.__iter__\n def __iter__(self):\n return iter(self._iterator_class(bucket=self))","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.__contains__","text":"gcloud/storage/bucket.py::Bucket.__contains__\n def __contains__(self, key):\n return self.get_key(key) is not None","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.acl","text":"gcloud/storage/bucket.py::Bucket.acl\n @property\n def acl(self):\n \"\"\"Create our ACL on demand.\"\"\"\n if self._acl is None:\n self._acl = BucketACL(self)\n return self._acl","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.default_object_acl","text":"gcloud/storage/bucket.py::Bucket.default_object_acl\n @property\n def default_object_acl(self):\n \"\"\"Create our defaultObjectACL on demand.\"\"\"\n if self._default_object_acl is None:\n self._default_object_acl = DefaultObjectACL(self)\n return self._default_object_acl","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.connection","text":"gcloud/storage/bucket.py::Bucket.connection\n @property\n def connection(self):\n \"\"\"Getter property for the connection to use with this Bucket.\n\n :rtype: :class:`gcloud.storage.connection.Connection`\n :returns: The connection to use.\n \"\"\"\n return self._connection","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.path","text":"gcloud/storage/bucket.py::Bucket.path\n @property\n def path(self):\n \"\"\"The URL path to this bucket.\"\"\"\n if not self.name:\n raise ValueError('Cannot determine path without bucket name.')\n\n return '/b/' + self.name","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_key","text":"gcloud/storage/bucket.py::Bucket.get_key\n def get_key(self, key):\n \"\"\"Get a key object by name.\n\n This will return None if the key doesn't exist::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> print bucket.get_key('/path/to/key.txt')\n <Key: my-bucket, /path/to/key.txt>\n >>> print bucket.get_key('/does-not-exist.txt')\n None\n\n :type key: string or :class:`gcloud.storage.key.Key`\n :param key: The name of the key to retrieve.\n\n :rtype: :class:`gcloud.storage.key.Key` or None\n :returns: The key object if it exists, otherwise None.\n \"\"\"\n # Coerce this to a key object (either from a Key or a string).\n key = self.new_key(key)\n\n try:\n response = self.connection.api_request(method='GET', path=key.path)\n return Key.from_dict(response, bucket=self)\n except exceptions.NotFound:\n return None","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_all_keys","text":"gcloud/storage/bucket.py::Bucket.get_all_keys\n def get_all_keys(self):\n \"\"\"List all the keys in this bucket.\n\n This will **not** retrieve all the data for all the keys, it\n will only retrieve the keys.\n\n This is equivalent to::\n\n keys = [key for key in bucket]\n\n :rtype: list of :class:`gcloud.storage.key.Key`\n :returns: A list of all the Key objects in this bucket.\n \"\"\"\n return list(self)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.iterator","text":"gcloud/storage/bucket.py::Bucket.iterator\n def iterator(self, prefix=None, delimiter=None, max_results=None,\n versions=None):\n \"\"\"Return an iterator used to find keys in the bucket.\n\n :type prefix: string or None\n :param prefix: optional prefix used to filter keys.\n\n :type delimiter: string or None\n :param delimiter: optional delimter, used with ``prefix`` to\n emulate hierarchy.\n\n :type max_results: integer or None\n :param max_results: maximum number of keys to return.\n\n :type versions: boolean or None\n :param versions: whether object versions should be returned as\n separate keys.\n\n :rtype: :class:`_KeyIterator`\n \"\"\"\n extra_params = {}\n\n if prefix is not None:\n extra_params['prefix'] = prefix\n\n if delimiter is not None:\n extra_params['delimiter'] = delimiter\n\n if max_results is not None:\n extra_params['maxResults'] = max_results\n\n if versions is not None:\n extra_params['versions'] = versions\n\n return self._iterator_class(self, extra_params=extra_params)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.new_key","text":"gcloud/storage/bucket.py::Bucket.new_key\n def new_key(self, key):\n \"\"\"Given path name (or Key), return a :class:`.storage.key.Key` object.\n\n This is really useful when you're not sure if you have a Key\n object or a string path name. Given either of those types, this\n returns the corresponding Key object.\n\n :type key: string or :class:`gcloud.storage.key.Key`\n :param key: A path name or actual key object.\n\n :rtype: :class:`gcloud.storage.key.Key`\n :returns: A Key object with the path provided.\n \"\"\"\n if isinstance(key, Key):\n return key\n\n # Support Python 2 and 3.\n try:\n string_type = basestring\n except NameError: # pragma: NO COVER PY3k\n string_type = str\n\n if isinstance(key, string_type):\n return Key(bucket=self, name=key)\n\n raise TypeError('Invalid key: %s' % key)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.delete","text":"gcloud/storage/bucket.py::Bucket.delete\n def delete(self, force=False):\n \"\"\"Delete this bucket.\n\n The bucket **must** be empty in order to delete it. If the\n bucket doesn't exist, this will raise a\n :class:`gcloud.storage.exceptions.NotFound`. If the bucket\n is not empty, this will raise an Exception.\n\n If you want to delete a non-empty bucket you can pass in a force\n parameter set to true. This will iterate through the bucket's\n keys and delete the related objects, before deleting the bucket.\n\n :type force: bool\n :param full: If True, empties the bucket's objects then deletes it.\n\n :raises: :class:`gcloud.storage.exceptions.NotFound` if the\n bucket does not exist, or\n :class:`gcloud.storage.exceptions.Conflict` if the\n bucket has keys and `force` is not passed.\n \"\"\"\n return self.connection.delete_bucket(self.name, force=force)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.delete_key","text":"gcloud/storage/bucket.py::Bucket.delete_key\n def delete_key(self, key):\n \"\"\"Deletes a key from the current bucket.\n\n If the key isn't found,\n this will throw a :class:`gcloud.storage.exceptions.NotFound`.\n\n For example::\n\n >>> from gcloud import storage\n >>> from gcloud.storage import exceptions\n >>> connection = storage.get_connection(project, email, key_path)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> print bucket.get_all_keys()\n [<Key: my-bucket, my-file.txt>]\n >>> bucket.delete_key('my-file.txt')\n >>> try:\n ... bucket.delete_key('doesnt-exist')\n ... except exceptions.NotFound:\n ... pass\n\n\n :type key: string or :class:`gcloud.storage.key.Key`\n :param key: A key name or Key object to delete.\n\n :rtype: :class:`gcloud.storage.key.Key`\n :returns: The key that was just deleted.\n :raises: :class:`gcloud.storage.exceptions.NotFound` (to suppress\n the exception, call ``delete_keys``, passing a no-op\n ``on_error`` callback, e.g.::\n\n >>> bucket.delete_keys([key], on_error=lambda key: pass)\n \"\"\"\n key = self.new_key(key)\n self.connection.api_request(method='DELETE', path=key.path)\n return key","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.delete_keys","text":"gcloud/storage/bucket.py::Bucket.delete_keys\n def delete_keys(self, keys, on_error=None):\n \"\"\"Deletes a list of keys from the current bucket.\n\n Uses :func:`Bucket.delete_key` to delete each individual key.\n\n :type keys: list of string or :class:`gcloud.storage.key.Key`\n :param keys: A list of key names or Key objects to delete.\n\n :type on_error: a callable taking (key)\n :param on_error: If not ``None``, called once for each key raising\n :class:`gcloud.storage.exceptions.NotFound`;\n otherwise, the exception is propagated.\n\n :raises: :class:`gcloud.storage.exceptions.NotFound` (if\n `on_error` is not passed).\n \"\"\"\n for key in keys:\n try:\n self.delete_key(key)\n except exceptions.NotFound:\n if on_error is not None:\n on_error(key)\n else:\n raise","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.copy_key","text":"gcloud/storage/bucket.py::Bucket.copy_key\n def copy_key(self, key, destination_bucket, new_name=None):\n \"\"\"Copy the given key to the given bucket, optionally with a new name.\n\n :type key: string or :class:`gcloud.storage.key.Key`\n :param key: The key to be copied.\n\n :type destination_bucket: :class:`gcloud.storage.bucket.Bucket`\n :param destination_bucket: The bucket into which the key should be\n copied.\n\n :type new_name: string\n :param new_name: (optional) the new name for the copied file.\n\n :rtype: :class:`gcloud.storage.key.Key`\n :returns: The new Key.\n \"\"\"\n if new_name is None:\n new_name = key.name\n new_key = destination_bucket.new_key(new_name)\n api_path = key.path + '/copyTo' + new_key.path\n self.connection.api_request(method='POST', path=api_path)\n return new_key","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.upload_file","text":"gcloud/storage/bucket.py::Bucket.upload_file\n def upload_file(self, filename, key=None):\n \"\"\"Shortcut method to upload a file into this bucket.\n\n Use this method to quickly put a local file in Cloud Storage.\n\n For example::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> bucket.upload_file('~/my-file.txt', 'remote-text-file.txt')\n >>> print bucket.get_all_keys()\n [<Key: my-bucket, remote-text-file.txt>]\n\n If you don't provide a key value, we will try to upload the file\n using the local filename as the key (**not** the complete\n path)::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> bucket.upload_file('~/my-file.txt')\n >>> print bucket.get_all_keys()\n [<Key: my-bucket, my-file.txt>]\n\n :type filename: string\n :param filename: Local path to the file you want to upload.\n\n :type key: string or :class:`gcloud.storage.key.Key`\n :param key: The key (either an object or a remote path) of where\n to put the file. If this is blank, we will try to\n upload the file to the root of the bucket with the\n same name as on your local file system.\n \"\"\"\n if key is None:\n key = os.path.basename(filename)\n key = self.new_key(key)\n key.upload_from_filename(filename)\n return key","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.upload_file_object","text":"gcloud/storage/bucket.py::Bucket.upload_file_object\n def upload_file_object(self, file_obj, key=None):\n \"\"\"Shortcut method to upload a file object into this bucket.\n\n Use this method to quickly put a local file in Cloud Storage.\n\n For example::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> bucket.upload_file(open('~/my-file.txt'), 'remote-text-file.txt')\n >>> print bucket.get_all_keys()\n [<Key: my-bucket, remote-text-file.txt>]\n\n If you don't provide a key value, we will try to upload the file\n using the local filename as the key (**not** the complete\n path)::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> bucket = connection.get_bucket('my-bucket')\n >>> bucket.upload_file(open('~/my-file.txt'))\n >>> print bucket.get_all_keys()\n [<Key: my-bucket, my-file.txt>]\n\n :type file_obj: file\n :param file_obj: A file handle open for reading.\n\n :type key: string or :class:`gcloud.storage.key.Key`\n :param key: The key (either an object or a remote path) of where\n to put the file. If this is blank, we will try to\n upload the file to the root of the bucket with the\n same name as on your local file system.\n \"\"\"\n if key:\n key = self.new_key(key)\n else:\n key = self.new_key(os.path.basename(file_obj.name))\n return key.upload_from_file(file_obj)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_cors","text":"gcloud/storage/bucket.py::Bucket.get_cors\n def get_cors(self):\n \"\"\"Retrieve CORS policies configured for this bucket.\n\n See: http://www.w3.org/TR/cors/ and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: list(dict)\n :returns: A sequence of mappings describing each CORS policy.\n \"\"\"\n return [policy.copy() for policy in self.properties.get('cors', ())]","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.update_cors","text":"gcloud/storage/bucket.py::Bucket.update_cors\n def update_cors(self, entries):\n \"\"\"Update CORS policies configured for this bucket.\n\n See: http://www.w3.org/TR/cors/ and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :type entries: list(dict)\n :param entries: A sequence of mappings describing each CORS policy.\n \"\"\"\n self._patch_properties({'cors': entries})","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_default_object_acl","text":"gcloud/storage/bucket.py::Bucket.get_default_object_acl\n def get_default_object_acl(self):\n \"\"\"Get the current Default Object ACL rules.\n\n If the acl isn't available locally, this method will reload it from\n Cloud Storage.\n\n :rtype: :class:`gcloud.storage.acl.DefaultObjectACL`\n :returns: A DefaultObjectACL object for this bucket.\n \"\"\"\n if not self.default_object_acl.loaded:\n self.default_object_acl.reload()\n return self.default_object_acl","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.etag","text":"gcloud/storage/bucket.py::Bucket.etag\n @property\n def etag(self):\n \"\"\"Retrieve the ETag for the bucket.\n\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n \"\"\"\n return self.properties['etag']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.id","text":"gcloud/storage/bucket.py::Bucket.id\n @property\n def id(self):\n \"\"\"Retrieve the ID for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n \"\"\"\n return self.properties['id']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_lifecycle","text":"gcloud/storage/bucket.py::Bucket.get_lifecycle\n def get_lifecycle(self):\n \"\"\"Retrieve lifecycle rules configured for this bucket.\n\n See: https://cloud.google.com/storage/docs/lifecycle and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: list(dict)\n :returns: A sequence of mappings describing each lifecycle rule.\n \"\"\"\n info = self.properties.get('lifecycle', {})\n return [rule.copy() for rule in info.get('rule', ())]","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.update_lifecycle","text":"gcloud/storage/bucket.py::Bucket.update_lifecycle\n def update_lifecycle(self, rules):\n \"\"\"Update CORS policies configured for this bucket.\n\n See: https://cloud.google.com/storage/docs/lifecycle and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :type rules: list(dict)\n :param rules: A sequence of mappings describing each lifecycle rule.\n \"\"\"\n self._patch_properties({'lifecycle': {'rule': rules}})","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_logging","text":"gcloud/storage/bucket.py::Bucket.get_logging\n def get_logging(self):\n \"\"\"Return info about access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#status\n\n :rtype: dict or None\n :returns: a dict w/ keys, ``logBucket`` and ``logObjectPrefix``\n (if logging is enabled), or None (if not).\n \"\"\"\n info = self.properties.get('logging')\n if info is not None:\n return info.copy()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.enable_logging","text":"gcloud/storage/bucket.py::Bucket.enable_logging\n def enable_logging(self, bucket_name, object_prefix=''):\n \"\"\"Enable access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#delivery\n\n :type bucket_name: string\n :param bucket_name: name of bucket in which to store access logs\n\n :type object_prefix: string\n :param object_prefix: prefix for access log filenames\n \"\"\"\n info = {'logBucket': bucket_name, 'logObjectPrefix': object_prefix}\n self._patch_properties({'logging': info})","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.disable_logging","text":"gcloud/storage/bucket.py::Bucket.disable_logging\n def disable_logging(self):\n \"\"\"Disable access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#disabling\n \"\"\"\n self._patch_properties({'logging': None})","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.metageneration","text":"gcloud/storage/bucket.py::Bucket.metageneration\n @property\n def metageneration(self):\n \"\"\"Retrieve the metageneration for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: integer\n \"\"\"\n return self.properties['metageneration']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.owner","text":"gcloud/storage/bucket.py::Bucket.owner\n @property\n def owner(self):\n \"\"\"Retrieve info about the owner of the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: dict\n :returns: mapping of owner's role/ID.\n \"\"\"\n return self.properties['owner'].copy()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.project_number","text":"gcloud/storage/bucket.py::Bucket.project_number\n @property\n def project_number(self):\n \"\"\"Retrieve the number of the project to which the bucket is assigned.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: integer\n \"\"\"\n return self.properties['projectNumber']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.self_link","text":"gcloud/storage/bucket.py::Bucket.self_link\n @property\n def self_link(self):\n \"\"\"Retrieve the URI for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n \"\"\"\n return self.properties['selfLink']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.storage_class","text":"gcloud/storage/bucket.py::Bucket.storage_class\n @property\n def storage_class(self):\n \"\"\"Retrieve the storage class for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets and\n https://cloud.google.com/storage/docs/durable-reduced-availability\n\n :rtype: string\n :returns: Currently one of \"STANDARD\", \"DURABLE_REDUCED_AVAILABILITY\"\n \"\"\"\n return self.properties['storageClass']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.time_created","text":"gcloud/storage/bucket.py::Bucket.time_created\n @property\n def time_created(self):\n \"\"\"Retrieve the timestamp at which the bucket was created.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n :returns: timestamp in RFC 3339 format.\n \"\"\"\n return self.properties['timeCreated']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.versioning_enabled","text":"gcloud/storage/bucket.py::Bucket.versioning_enabled\n @property\n def versioning_enabled(self):\n \"\"\"Is versioning enabled for this bucket?\n\n See: https://cloud.google.com/storage/docs/object-versioning for\n details.\n\n :rtype: boolean\n :returns: True if enabled, else False.\n \"\"\"\n versioning = self.properties.get('versioning', {})\n return versioning.get('enabled', False)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.versioning_enabled","text":"gcloud/storage/bucket.py::Bucket.versioning_enabled\n @versioning_enabled.setter\n def versioning_enabled(self, value):\n \"\"\"Enable versioning for this bucket.\n\n See: https://cloud.google.com/storage/docs/object-versioning for\n details.\n\n :type value: convertible to bool\n :param value: should versioning be anabled for the bucket?\n \"\"\"\n self._patch_properties({'versioning': {'enabled': bool(value)}})","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.configure_website","text":"gcloud/storage/bucket.py::Bucket.configure_website\n def configure_website(self, main_page_suffix=None, not_found_page=None):\n \"\"\"Configure website-related properties.\n\n See: https://developers.google.com/storage/docs/website-configuration\n\n .. note::\n This (apparently) only works\n if your bucket name is a domain name\n (and to do that, you need to get approved somehow...).\n\n If you want this bucket to host a website, just provide the name\n of an index page and a page to use when a key isn't found::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email,\n private_key_path)\n >>> bucket = connection.get_bucket(bucket_name)\n >>> bucket.configure_website('index.html', '404.html')\n\n You probably should also make the whole bucket public::\n\n >>> bucket.make_public(recursive=True, future=True)\n\n This says: \"Make the bucket public, and all the stuff already in\n the bucket, and anything else I add to the bucket. Just make it\n all public.\"\n\n :type main_page_suffix: string\n :param main_page_suffix: The page to use as the main page\n of a directory.\n Typically something like index.html.\n\n :type not_found_page: string\n :param not_found_page: The file to use when a page isn't found.\n \"\"\"\n data = {\n 'website': {\n 'mainPageSuffix': main_page_suffix,\n 'notFoundPage': not_found_page,\n },\n }\n return self._patch_properties(data)","label":0},{"cid":"gcloud/storage/connection.py::_utcnow","text":"gcloud/storage/connection.py::_utcnow\ndef _utcnow(): # pragma: NO COVER testing replaces\n \"\"\"Returns current time as UTC datetime.\n\n NOTE: on the module namespace so tests can replace it.\n \"\"\"\n return datetime.datetime.utcnow()","label":0},{"cid":"gcloud/storage/connection.py::Connection.__init__","text":"gcloud/storage/connection.py::Connection.__init__\n def __init__(self, project, *args, **kwargs):\n \"\"\":type project: string\n\n :param project: The project name to connect to.\n \"\"\"\n super(Connection, self).__init__(*args, **kwargs)\n self.project = project","label":0},{"cid":"gcloud/storage/connection.py::Connection.__iter__","text":"gcloud/storage/connection.py::Connection.__iter__\n def __iter__(self):\n return iter(_BucketIterator(connection=self))","label":0},{"cid":"gcloud/storage/connection.py::Connection.__contains__","text":"gcloud/storage/connection.py::Connection.__contains__\n def __contains__(self, bucket_name):\n return self.lookup(bucket_name) is not None","label":0},{"cid":"gcloud/storage/connection.py::Connection.build_api_url","text":"gcloud/storage/connection.py::Connection.build_api_url\n def build_api_url(self, path, query_params=None, api_base_url=None,\n api_version=None):\n \"\"\"Construct an API url given a few components, some optional.\n\n Typically, you shouldn't need to use this method.\n\n :type path: string\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\n\n :type query_params: dict\n :param query_params: A dictionary of keys and values to insert into\n the query string of the URL.\n\n :type api_base_url: string\n :param api_base_url: The base URL for the API endpoint.\n Typically you won't have to provide this.\n\n :type api_version: string\n :param api_version: The version of the API to call.\n Typically you shouldn't provide this and instead\n use the default for the library.\n\n :rtype: string\n :returns: The URL assembled from the pieces provided.\n \"\"\"\n url = self.API_URL_TEMPLATE.format(\n api_base_url=(api_base_url or self.API_BASE_URL),\n api_version=(api_version or self.API_VERSION),\n path=path)\n\n query_params = query_params or {}\n query_params.update({'project': self.project})\n url += '?' + urllib.urlencode(query_params)\n\n return url","label":0},{"cid":"gcloud/storage/connection.py::Connection.make_request","text":"gcloud/storage/connection.py::Connection.make_request\n def make_request(self, method, url, data=None, content_type=None,\n headers=None):\n \"\"\"A low level method to send a request to the API.\n\n Typically, you shouldn't need to use this method.\n\n :type method: string\n :param method: The HTTP method to use in the request.\n\n :type url: string\n :param url: The URL to send the request to.\n\n :type data: string\n :param data: The data to send as the body of the request.\n\n :type content_type: string\n :param content_type: The proper MIME type of the data provided.\n\n :type headers: dict\n :param headers: A dictionary of HTTP headers to send with the request.\n\n :rtype: tuple of ``response`` (a dictionary of sorts)\n and ``content`` (a string).\n :returns: The HTTP response object and the content of the response.\n \"\"\"\n headers = headers or {}\n headers['Accept-Encoding'] = 'gzip'\n\n if data:\n content_length = len(str(data))\n else:\n content_length = 0\n\n headers['Content-Length'] = content_length\n\n if content_type:\n headers['Content-Type'] = content_type\n\n headers['User-Agent'] = self.USER_AGENT\n\n return self.http.request(uri=url, method=method, headers=headers,\n body=data)","label":0},{"cid":"gcloud/storage/connection.py::Connection.api_request","text":"gcloud/storage/connection.py::Connection.api_request\n def api_request(self, method, path, query_params=None,\n data=None, content_type=None,\n api_base_url=None, api_version=None,\n expect_json=True):\n \"\"\"Make a request over the HTTP transport to the Cloud Storage API.\n\n You shouldn't need to use this method, but if you plan to\n interact with the API using these primitives, this is the\n correct one to use...\n\n :type method: string\n :param method: The HTTP method name (ie, ``GET``, ``POST``, etc).\n Required.\n\n :type path: string\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\n Required.\n\n :type query_params: dict\n :param query_params: A dictionary of keys and values to insert into\n the query string of the URL. Default is\n empty dict.\n\n :type data: string\n :param data: The data to send as the body of the request. Default is\n the empty string.\n\n :type content_type: string\n :param content_type: The proper MIME type of the data provided. Default\n is None.\n\n :type api_base_url: string\n :param api_base_url: The base URL for the API endpoint.\n Typically you won't have to provide this.\n Default is the standard API base URL.\n\n :type api_version: string\n :param api_version: The version of the API to call. Typically\n you shouldn't provide this and instead use\n the default for the library. Default is the\n latest API version supported by\n gcloud-python.\n\n :type expect_json: bool\n :param expect_json: If True, this method will try to parse the\n ","label":0},{"cid":"gcloud/storage/connection.py::Connection.get_all_buckets","text":"gcloud/storage/connection.py::Connection.get_all_buckets\n def get_all_buckets(self):\n \"\"\"Get all buckets in the project.\n\n This will not populate the list of keys available in each\n bucket.\n\n You can also iterate over the connection object, so these two\n operations are identical::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> for bucket in connection.get_all_buckets():\n >>> print bucket\n >>> # ... is the same as ...\n >>> for bucket in connection:\n >>> print bucket\n\n :rtype: list of :class:`gcloud.storage.bucket.Bucket` objects.\n :returns: All buckets belonging to this project.\n \"\"\"\n return list(self)","label":0},{"cid":"gcloud/storage/connection.py::Connection.get_bucket","text":"gcloud/storage/connection.py::Connection.get_bucket\n def get_bucket(self, bucket_name):\n \"\"\"Get a bucket by name.\n\n If the bucket isn't found, this will raise a\n :class:`gcloud.storage.exceptions.NotFound`. If you would\n rather get a bucket by name, and return ``None`` if the bucket\n isn't found (like ``{}.get('...')``) then use\n :func:`Connection.lookup`.\n\n For example::\n\n >>> from gcloud import storage\n >>> from gcloud.storage import exceptions\n >>> connection = storage.get_connection(project, email, key_path)\n >>> try:\n >>> bucket = connection.get_bucket('my-bucket')\n >>> except exceptions.NotFound:\n >>> print 'Sorry, that bucket does not exist!'\n\n :type bucket_name: string\n :param bucket_name: The name of the bucket to get.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The bucket matching the name provided.\n :raises: :class:`gcloud.storage.exceptions.NotFound`\n \"\"\"\n bucket = self.new_bucket(bucket_name)\n response = self.api_request(method='GET', path=bucket.path)\n return Bucket.from_dict(response, connection=self)","label":0},{"cid":"gcloud/storage/connection.py::Connection.lookup","text":"gcloud/storage/connection.py::Connection.lookup\n def lookup(self, bucket_name):\n \"\"\"Get a bucket by name, returning None if not found.\n\n You can use this if you would rather checking for a None value\n than catching an exception::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> bucket = connection.get_bucket('doesnt-exist')\n >>> print bucket\n None\n >>> bucket = connection.get_bucket('my-bucket')\n >>> print bucket\n <Bucket: my-bucket>\n\n :type bucket_name: string\n :param bucket_name: The name of the bucket to get.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The bucket matching the name provided or None if not found.\n \"\"\"\n try:\n return self.get_bucket(bucket_name)\n except exceptions.NotFound:\n return None","label":0},{"cid":"gcloud/storage/connection.py::Connection.create_bucket","text":"gcloud/storage/connection.py::Connection.create_bucket\n def create_bucket(self, bucket):\n \"\"\"Create a new bucket.\n\n For example::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, client, key_path)\n >>> bucket = connection.create_bucket('my-bucket')\n >>> print bucket\n <Bucket: my-bucket>\n\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\n :param bucket: The bucket name (or bucket object) to create.\n\n :rtype: :class:`gcloud.storage.bucket.Bucket`\n :returns: The newly created bucket.\n :raises: :class:`gcloud.storage.exceptions.Conflict` if\n there is a confict (bucket already exists, invalid name, etc.)\n \"\"\"\n bucket = self.new_bucket(bucket)\n response = self.api_request(method='POST', path='/b',\n data={'name': bucket.name})\n return Bucket.from_dict(response, connection=self)","label":0},{"cid":"gcloud/storage/connection.py::Connection.delete_bucket","text":"gcloud/storage/connection.py::Connection.delete_bucket\n def delete_bucket(self, bucket, force=False):\n \"\"\"Delete a bucket.\n\n You can use this method to delete a bucket by name, or to delete\n a bucket object::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection(project, email, key_path)\n >>> connection.delete_bucket('my-bucket')\n True\n\n You can also delete pass in the bucket object::\n\n >>> bucket = connection.get_bucket('other-bucket')\n >>> connection.delete_bucket(bucket)\n True\n\n If the bucket doesn't exist, this will raise a\n :class:`gcloud.storage.exceptions.NotFound`::\n\n >>> from gcloud.storage import exceptions\n >>> try:\n >>> connection.delete_bucket('my-bucket')\n >>> except exceptions.NotFound:\n >>> print 'That bucket does not exist!'\n\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\n :param bucket: The bucket name (or bucket object) to create.\n\n :type force: bool\n :param full: If True, empties the bucket's objects then deletes it.\n\n :rtype: bool\n :returns: True if the bucket was deleted.\n :raises: :class:`gcloud.storage.exceptions.NotFound` if the\n bucket doesn't exist, or\n :class:`gcloud.storage.exceptions.Conflict` if the\n bucket has keys and `force` is not passed.\n \"\"\"\n bucket = self.new_bucket(bucket)\n\n # This force delete operation is slow.\n if force:\n for key in bucket:\n key.delete()\n\n self.api_request(method='DELETE', path=bucket.path)\n return True","label":0},{"cid":"gcloud/storage/connection.py::Connection.new_bucket","text":"gcloud/storage/connection.py::Connection.new_bucket\n def new_bucket(self, bucket):\n \"\"\"Factory method for creating a new (unsaved) bucket object.\n\n This method is really useful when you're not sure whether you\n have an actual :class:`gcloud.storage.bucket.Bucket` object or\n just a name of a bucket. It always returns the object::\n\n >>> bucket = connection.new_bucket('bucket')\n >>> print bucket\n <Bucket: bucket>\n >>> bucket = connection.new_bucket(bucket)\n >>> print bucket\n <Bucket: bucket>\n\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\n :param bucket: A name of a bucket or an existing Bucket object.\n \"\"\"\n if isinstance(bucket, Bucket):\n return bucket\n\n # Support Python 2 and 3.\n try:\n string_type = basestring\n except NameError: # pragma: NO COVER PY3k\n string_type = str\n\n if isinstance(bucket, string_type):\n return Bucket(connection=self, name=bucket)\n\n raise TypeError('Invalid bucket: %s' % bucket)","label":0},{"cid":"gcloud/storage/connection.py::Connection.generate_signed_url","text":"gcloud/storage/connection.py::Connection.generate_signed_url\n def generate_signed_url(self, resource, expiration,\n method='GET', content_md5=None,\n content_type=None):\n \"\"\"Generate signed URL to provide query-string auth'n to a resource.\n\n :type resource: string\n :param resource: A pointer to a specific resource\n (typically, ``/bucket-name/path/to/key.txt``).\n\n :type expiration: int, long, datetime.datetime, datetime.timedelta\n :param expiration: When the signed URL should expire.\n\n :type method: string\n :param method: The HTTP verb that will be used when requesting the URL.\n\n :type content_md5: string\n :param content_md5: The MD5 hash of the object referenced by\n ``resource``.\n\n :type content_type: string\n :param content_type: The content type of the object referenced by\n ``resource``.\n\n :rtype: string\n :returns: A signed URL you can use to access the resource\n until expiration.\n \"\"\"\n expiration = _get_expiration_seconds(expiration)\n\n # Generate the string to sign.\n signature_string = '\\n'.join([\n method,\n content_md5 or '',\n content_type or '',\n str(expiration),\n resource])\n\n # Take our PKCS12 (.p12) key and make it into a RSA key we can use...\n pkcs12 = crypto.load_pkcs12(\n base64.b64decode(self.credentials.private_key),\n 'notasecret')\n pem = crypto.dump_privatekey(\n crypto.FILETYPE_PEM, pkcs12.get_privatekey())\n pem_key = RSA.importKey(pem)\n\n # Sign the string with the RSA key.\n signer = PKCS1_v1_5.new(pem_key)\n signature_hash = SHA256.new(signature_string)\n signature_bytes = signer.sign(signature_hash)\n signature = base64.b64encode(signature_byte","label":0},{"cid":"gcloud/storage/connection.py::_BucketIterator.__init__","text":"gcloud/storage/connection.py::_BucketIterator.__init__\n def __init__(self, connection):\n super(_BucketIterator, self).__init__(connection=connection, path='/b')","label":0},{"cid":"gcloud/storage/connection.py::_BucketIterator.get_items_from_response","text":"gcloud/storage/connection.py::_BucketIterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Factory method which yields :class:`.Bucket` items from a response.\n\n :type response: dict\n :param response: The JSON API response for a page of buckets.\n \"\"\"\n for item in response.get('items', []):\n yield Bucket.from_dict(item, connection=self.connection)","label":0},{"cid":"gcloud/storage/connection.py::_get_expiration_seconds","text":"gcloud/storage/connection.py::_get_expiration_seconds\ndef _get_expiration_seconds(expiration):\n \"\"\"Convert 'expiration' to a number of seconds in the future.\n\n :type expiration: int, long, datetime.datetime, datetime.timedelta\n :param expiration: When the signed URL should expire.\n\n :rtype: int\n :returns: a timestamp as an absolute number of seconds.\n \"\"\"\n # If it's a timedelta, add it to `now` in UTC.\n if isinstance(expiration, datetime.timedelta):\n now = _utcnow().replace(tzinfo=pytz.utc)\n expiration = now + expiration\n\n # If it's a datetime, convert to a timestamp.\n if isinstance(expiration, datetime.datetime):\n # Make sure the timezone on the value is UTC\n # (either by converting or replacing the value).\n if expiration.tzinfo:\n expiration = expiration.astimezone(pytz.utc)\n else:\n expiration = expiration.replace(tzinfo=pytz.utc)\n\n # Turn the datetime into a timestamp (seconds, not microseconds).\n expiration = int(calendar.timegm(expiration.timetuple()))\n\n if not isinstance(expiration, (int, long)):\n raise TypeError('Expected an integer timestamp, datetime, or '\n 'timedelta. Got %s' % type(expiration))\n return expiration","label":0},{"cid":"gcloud/storage/iterator.py::Iterator.__init__","text":"gcloud/storage/iterator.py::Iterator.__init__\n def __init__(self, connection, path, extra_params=None):\n self.connection = connection\n self.path = path\n self.page_number = 0\n self.next_page_token = None\n self.extra_params = extra_params or {}\n reserved_in_use = self.RESERVED_PARAMS.intersection(\n self.extra_params.keys())\n if reserved_in_use:\n raise ValueError(('Using a reserved parameter',\n reserved_in_use))","label":0},{"cid":"gcloud/storage/iterator.py::Iterator.__iter__","text":"gcloud/storage/iterator.py::Iterator.__iter__\n def __iter__(self):\n \"\"\"Iterate through the list of items.\"\"\"\n while self.has_next_page():\n response = self.get_next_page_response()\n for item in self.get_items_from_response(response):\n yield item","label":0},{"cid":"gcloud/storage/iterator.py::Iterator.has_next_page","text":"gcloud/storage/iterator.py::Iterator.has_next_page\n def has_next_page(self):\n \"\"\"Determines whether or not this iterator has more pages.\n\n :rtype: bool\n :returns: Whether the iterator has more pages or not.\n \"\"\"\n if self.page_number == 0:\n return True\n\n return self.next_page_token is not None","label":0},{"cid":"gcloud/storage/iterator.py::Iterator.get_query_params","text":"gcloud/storage/iterator.py::Iterator.get_query_params\n def get_query_params(self):\n \"\"\"Getter for query parameters for the next request.\n\n :rtype: dict\n :returns: A dictionary of query parameters.\n \"\"\"\n result = ({self.PAGE_TOKEN: self.next_page_token}\n if self.next_page_token else {})\n result.update(self.extra_params)\n return result","label":0},{"cid":"gcloud/storage/iterator.py::Iterator.get_next_page_response","text":"gcloud/storage/iterator.py::Iterator.get_next_page_response\n def get_next_page_response(self):\n \"\"\"Requests the next page from the path provided.\n\n :rtype: dict\n :returns: The parsed JSON response of the next page's contents.\n \"\"\"\n if not self.has_next_page():\n raise RuntimeError('No more pages. Try resetting the iterator.')\n\n response = self.connection.api_request(\n method='GET', path=self.path, query_params=self.get_query_params())\n\n self.page_number += 1\n self.next_page_token = response.get('nextPageToken')\n\n return response","label":0},{"cid":"gcloud/storage/iterator.py::Iterator.reset","text":"gcloud/storage/iterator.py::Iterator.reset\n def reset(self):\n \"\"\"Resets the iterator to the beginning.\"\"\"\n self.page_number = 0\n self.next_page_token = None","label":0},{"cid":"gcloud/storage/iterator.py::Iterator.get_items_from_response","text":"gcloud/storage/iterator.py::Iterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Factory method called while iterating. This should be overriden.\n\n This method should be overridden by a subclass. It should\n accept the API response of a request for the next page of items,\n and return a list (or other iterable) of items.\n\n Typically this method will construct a Bucket or a Key from the\n page of results in the response.\n\n :type response: dict\n :param response: The response of asking for the next page of items.\n\n :rtype: iterable\n :returns: Items that the iterator should yield.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"gcloud/storage/key.py::Key.__init__","text":"gcloud/storage/key.py::Key.__init__\n def __init__(self, bucket=None, name=None, properties=None):\n \"\"\"Key constructor.\n\n :type bucket: :class:`gcloud.storage.bucket.Bucket`\n :param bucket: The bucket to which this key belongs.\n\n :type name: string\n :param name: The name of the key. This corresponds to the\n unique path of the object in the bucket.\n\n :type properties: dict\n :param properties: All the other data provided by Cloud Storage.\n \"\"\"\n super(Key, self).__init__(name=name, properties=properties)\n self.bucket = bucket","label":0},{"cid":"gcloud/storage/key.py::Key.acl","text":"gcloud/storage/key.py::Key.acl\n @property\n def acl(self):\n \"\"\"Create our ACL on demand.\"\"\"\n if self._acl is None:\n self._acl = ObjectACL(self)\n return self._acl","label":0},{"cid":"gcloud/storage/key.py::Key.from_dict","text":"gcloud/storage/key.py::Key.from_dict\n @classmethod\n def from_dict(cls, key_dict, bucket=None):\n \"\"\"Instantiate a :class:`Key` from data returned by the JSON API.\n\n :type key_dict: dict\n :param key_dict: A dictionary of data returned from getting an\n Cloud Storage object.\n\n :type bucket: :class:`gcloud.storage.bucket.Bucket`\n :param bucket: The bucket to which this key belongs (and by\n proxy, which connection to use).\n\n :rtype: :class:`Key`\n :returns: A key based on the data provided.\n \"\"\"\n\n return cls(bucket=bucket, name=key_dict['name'], properties=key_dict)","label":0},{"cid":"gcloud/storage/key.py::Key.__repr__","text":"gcloud/storage/key.py::Key.__repr__\n def __repr__(self):\n if self.bucket:\n bucket_name = self.bucket.name\n else:\n bucket_name = None\n\n return '<Key: %s, %s>' % (bucket_name, self.name)","label":0},{"cid":"gcloud/storage/key.py::Key.connection","text":"gcloud/storage/key.py::Key.connection\n @property\n def connection(self):\n \"\"\"Getter property for the connection to use with this Key.\n\n :rtype: :class:`gcloud.storage.connection.Connection` or None\n :returns: The connection to use, or None if no connection is set.\n \"\"\"\n if self.bucket and self.bucket.connection:\n return self.bucket.connection","label":0},{"cid":"gcloud/storage/key.py::Key.path","text":"gcloud/storage/key.py::Key.path\n @property\n def path(self):\n \"\"\"Getter property for the URL path to this Key.\n\n :rtype: string\n :returns: The URL path to this Key.\n \"\"\"\n if not self.bucket:\n raise ValueError('Cannot determine path without a bucket defined.')\n elif not self.name:\n raise ValueError('Cannot determine path without a key name.')\n\n return self.bucket.path + '/o/' + urllib.quote(self.name, safe='')","label":0},{"cid":"gcloud/storage/key.py::Key.public_url","text":"gcloud/storage/key.py::Key.public_url\n @property\n def public_url(self):\n \"\"\"The public URL for this key's object.\n\n :rtype: `string`\n :returns: The public URL for this key.\n \"\"\"\n return '{storage_base_url}/{bucket_name}/{quoted_name}'.format(\n storage_base_url='http://commondatastorage.googleapis.com',\n bucket_name=self.bucket.name,\n quoted_name=urllib.quote(self.name, safe=''))","label":0},{"cid":"gcloud/storage/key.py::Key.generate_signed_url","text":"gcloud/storage/key.py::Key.generate_signed_url\n def generate_signed_url(self, expiration, method='GET'):\n \"\"\"Generates a signed URL for this key.\n\n If you have a key that you want to allow access to for a set\n amount of time, you can use this method to generate a URL that\n is only valid within a certain time period.\n\n This is particularly useful if you don't want publicly\n accessible keys, but don't want to require users to explicitly\n log in.\n\n :type expiration: int, long, datetime.datetime, datetime.timedelta\n :param expiration: When the signed URL should expire.\n\n :type method: string\n :param method: The HTTP verb that will be used when requesting the URL.\n\n :rtype: string\n :returns: A signed URL you can use to access the resource\n until expiration.\n \"\"\"\n resource = '/{bucket_name}/{quoted_name}'.format(\n bucket_name=self.bucket.name,\n quoted_name=urllib.quote(self.name, safe=''))\n return self.connection.generate_signed_url(resource=resource,\n expiration=expiration,\n method=method)","label":0},{"cid":"gcloud/storage/key.py::Key.exists","text":"gcloud/storage/key.py::Key.exists\n def exists(self):\n \"\"\"Determines whether or not this key exists.\n\n :rtype: bool\n :returns: True if the key exists in Cloud Storage.\n \"\"\"\n return self.bucket.get_key(self.name) is not None","label":0},{"cid":"gcloud/storage/key.py::Key.rename","text":"gcloud/storage/key.py::Key.rename\n def rename(self, new_name):\n \"\"\"Renames this key using copy and delete operations.\n\n Effectively, copies key to the same bucket with a new name, then\n deletes the key.\n\n .. warning::\n This method will first duplicate the data and then delete the\n old key. This means that with very large objects renaming\n could be a very (temporarily) costly or a very slow operation.\n\n :type new_name: string\n :param new_name: The new name for this key.\n\n :rtype: :class:`Key`\n :returns: The newly-copied key.\n \"\"\"\n new_key = self.bucket.copy_key(self, self.bucket, new_name)\n self.bucket.delete_key(self)\n return new_key","label":0},{"cid":"gcloud/storage/key.py::Key.delete","text":"gcloud/storage/key.py::Key.delete\n def delete(self):\n \"\"\"Deletes a key from Cloud Storage.\n\n :rtype: :class:`Key`\n :returns: The key that was just deleted.\n :raises: :class:`gcloud.storage.exceptions.NotFound`\n (propagated from\n :meth:`gcloud.storage.bucket.Bucket.delete_key`).\n \"\"\"\n return self.bucket.delete_key(self)","label":0},{"cid":"gcloud/storage/key.py::Key.download_to_file","text":"gcloud/storage/key.py::Key.download_to_file\n def download_to_file(self, file_obj):\n \"\"\"Download the contents of this key into a file-like object.\n\n :type file_obj: file\n :param file_obj: A file handle to which to write the key's data.\n\n :raises: :class:`gcloud.storage.exceptions.NotFound`\n \"\"\"\n for chunk in _KeyDataIterator(self):\n file_obj.write(chunk)","label":0},{"cid":"gcloud/storage/key.py::Key.download_to_filename","text":"gcloud/storage/key.py::Key.download_to_filename\n def download_to_filename(self, filename):\n \"\"\"Download the contents of this key into a named file.\n\n :type filename: string\n :param filename: A filename to be passed to ``open``.\n\n :raises: :class:`gcloud.storage.exceptions.NotFound`\n \"\"\"\n with open(filename, 'wb') as file_obj:\n self.download_to_file(file_obj)","label":0},{"cid":"gcloud/storage/key.py::Key.download_as_string","text":"gcloud/storage/key.py::Key.download_as_string\n def download_as_string(self):\n \"\"\"Download the contents of this key as a string.\n\n :rtype: string\n :returns: The data stored in this key.\n :raises: :class:`gcloud.storage.exceptions.NotFound`\n \"\"\"\n string_buffer = StringIO()\n self.download_to_file(string_buffer)\n return string_buffer.getvalue()","label":0},{"cid":"gcloud/storage/key.py::Key.upload_from_file","text":"gcloud/storage/key.py::Key.upload_from_file\n def upload_from_file(self, file_obj, rewind=False, size=None,\n content_type=None):\n \"\"\"Upload the contents of this key from a file-like object.\n\n .. note::\n\n The effect of uploading to an existing key depends on the\n \"versioning\" and \"lifecycle\" policies defined on the key's\n bucket. In the absence of those policies, upload will\n overwrite any existing contents.\n\n See the `object versioning\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\n API documents for details.\n\n :type file_obj: file\n :param file_obj: A file handle open for reading.\n\n :type rewind: bool\n :param rewind: If True, seek to the beginning of the file handle before\n writing the file to Cloud Storage.\n\n :type size: int\n :param size: The number of bytes to read from the file handle.\n If not provided, we'll try to guess the size using\n :func:`os.fstat`\n \"\"\"\n # Rewind the file if desired.\n if rewind:\n file_obj.seek(0, os.SEEK_SET)\n\n # Get the basic stats about the file.\n total_bytes = size or os.fstat(file_obj.fileno()).st_size\n bytes_uploaded = 0\n\n # Set up a resumable upload session.\n headers = {\n 'X-Upload-Content-Type': content_type or 'application/unknown',\n 'X-Upload-Content-Length': total_bytes,\n }\n\n query_params = {\n 'uploadType': 'resumable',\n 'name': urllib.quote_plus(self.name),\n }\n\n upload_url = self.connection.build_api_url(\n path=self.bucket.path + '/o',\n query_params=query_params,\n api_base_url=self.connection.API_BASE_URL + '/upload')\n\n response, _ = self.connection.mak","label":1},{"cid":"gcloud/storage/key.py::Key.upload_from_filename","text":"gcloud/storage/key.py::Key.upload_from_filename\n def upload_from_filename(self, filename):\n \"\"\"Upload this key's contents from the content of f named file.\n\n .. note::\n\n The effect of uploading to an existing key depends on the\n \"versioning\" and \"lifecycle\" policies defined on the key's\n bucket. In the absence of those policies, upload will\n overwrite any existing contents.\n\n See the `object versioning\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\n API documents for details.\n\n :type filename: string\n :param filename: The path to the file.\n \"\"\"\n content_type, _ = mimetypes.guess_type(filename)\n\n with open(filename, 'rb') as file_obj:\n self.upload_from_file(file_obj, content_type=content_type)","label":0},{"cid":"gcloud/storage/key.py::Key.upload_from_string","text":"gcloud/storage/key.py::Key.upload_from_string\n def upload_from_string(self, data, content_type='text/plain'):\n \"\"\"Upload contents of this key from the provided string.\n\n .. note::\n\n The effect of uploading to an existing key depends on the\n \"versioning\" and \"lifecycle\" policies defined on the key's\n bucket. In the absence of those policies, upload will\n overwrite any existing contents.\n\n See the `object versioning\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\n API documents for details.\n\n :type data: string\n :param data: The data to store in this key.\n\n :rtype: :class:`Key`\n :returns: The updated Key object.\n \"\"\"\n string_buffer = StringIO()\n string_buffer.write(data)\n self.upload_from_file(file_obj=string_buffer, rewind=True,\n size=string_buffer.len,\n content_type=content_type)\n return self","label":0},{"cid":"gcloud/storage/key.py::Key.make_public","text":"gcloud/storage/key.py::Key.make_public\n def make_public(self):\n \"\"\"Make this key public giving all users read access.\n\n :returns: The current object.\n \"\"\"\n self.get_acl().all().grant_read()\n self.acl.save()\n return self","label":0},{"cid":"gcloud/storage/key.py::Key.component_count","text":"gcloud/storage/key.py::Key.component_count\n @property\n def component_count(self):\n \"\"\"Number of underlying components that make up this object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer\n \"\"\"\n return self.properties['componentCount']","label":0},{"cid":"gcloud/storage/key.py::Key.etag","text":"gcloud/storage/key.py::Key.etag\n @property\n def etag(self):\n \"\"\"Retrieve the ETag for the object.\n\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\n https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string\n \"\"\"\n return self.properties['etag']","label":0},{"cid":"gcloud/storage/key.py::Key.generation","text":"gcloud/storage/key.py::Key.generation\n @property\n def generation(self):\n \"\"\"Retrieve the generation for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer\n \"\"\"\n return self.properties['generation']","label":0},{"cid":"gcloud/storage/key.py::Key.id","text":"gcloud/storage/key.py::Key.id\n @property\n def id(self):\n \"\"\"Retrieve the ID for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string\n \"\"\"\n return self.properties['id']","label":0},{"cid":"gcloud/storage/key.py::Key.media_link","text":"gcloud/storage/key.py::Key.media_link\n @property\n def media_link(self):\n \"\"\"Retrieve the media download URI for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string\n \"\"\"\n return self.properties['mediaLink']","label":0},{"cid":"gcloud/storage/key.py::Key.metadata","text":"gcloud/storage/key.py::Key.metadata\n @property\n def metadata(self):\n \"\"\"Retrieve arbitrary/application specific metadata for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: dict\n \"\"\"\n return copy.deepcopy(self.properties['metadata'])","label":0},{"cid":"gcloud/storage/key.py::Key.metadata","text":"gcloud/storage/key.py::Key.metadata\n @metadata.setter\n def metadata(self, value):\n \"\"\"Update arbitrary/application specific metadata for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :type value: dict\n \"\"\"\n self._patch_properties({'metadata': value})","label":0},{"cid":"gcloud/storage/key.py::Key.metageneration","text":"gcloud/storage/key.py::Key.metageneration\n @property\n def metageneration(self):\n \"\"\"Retrieve the metageneration for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer\n \"\"\"\n return self.properties['metageneration']","label":0},{"cid":"gcloud/storage/key.py::Key.owner","text":"gcloud/storage/key.py::Key.owner\n @property\n def owner(self):\n \"\"\"Retrieve info about the owner of the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: dict\n :returns: mapping of owner's role/ID.\n \"\"\"\n return self.properties['owner'].copy()","label":0},{"cid":"gcloud/storage/key.py::Key.self_link","text":"gcloud/storage/key.py::Key.self_link\n @property\n def self_link(self):\n \"\"\"Retrieve the URI for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string\n \"\"\"\n return self.properties['selfLink']","label":0},{"cid":"gcloud/storage/key.py::Key.size","text":"gcloud/storage/key.py::Key.size\n @property\n def size(self):\n \"\"\"Size of the object, in bytes.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer\n \"\"\"\n return self.properties['size']","label":0},{"cid":"gcloud/storage/key.py::Key.storage_class","text":"gcloud/storage/key.py::Key.storage_class\n @property\n def storage_class(self):\n \"\"\"Retrieve the storage class for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects and\n https://cloud.google.com/storage/docs/durable-reduced-availability#_DRA_Bucket\n\n :rtype: string\n :returns: Currently one of \"STANDARD\", \"DURABLE_REDUCED_AVAILABILITY\"\n \"\"\"\n return self.properties['storageClass']","label":0},{"cid":"gcloud/storage/key.py::Key.time_deleted","text":"gcloud/storage/key.py::Key.time_deleted\n @property\n def time_deleted(self):\n \"\"\"Retrieve the timestamp at which the object was deleted.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string or None\n :returns: timestamp in RFC 3339 format, or None if the object\n has a \"live\" version.\n \"\"\"\n return self.properties.get('timeDeleted')","label":0},{"cid":"gcloud/storage/key.py::Key.updated","text":"gcloud/storage/key.py::Key.updated\n @property\n def updated(self):\n \"\"\"Retrieve the timestamp at which the object was updated.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string\n :returns: timestamp in RFC 3339 format.\n \"\"\"\n return self.properties['updated']","label":0},{"cid":"gcloud/storage/key.py::_KeyDataIterator.__init__","text":"gcloud/storage/key.py::_KeyDataIterator.__init__\n def __init__(self, key):\n self.key = key\n # NOTE: These variables will be initialized by reset().\n self._bytes_written = None\n self._total_bytes = None\n self.reset()","label":0},{"cid":"gcloud/storage/key.py::_KeyDataIterator.__iter__","text":"gcloud/storage/key.py::_KeyDataIterator.__iter__\n def __iter__(self):\n while self.has_more_data():\n yield self.get_next_chunk()","label":0},{"cid":"gcloud/storage/key.py::_KeyDataIterator.reset","text":"gcloud/storage/key.py::_KeyDataIterator.reset\n def reset(self):\n \"\"\"Resets the iterator to the beginning.\"\"\"\n self._bytes_written = 0\n self._total_bytes = None","label":0},{"cid":"gcloud/storage/key.py::_KeyDataIterator.has_more_data","text":"gcloud/storage/key.py::_KeyDataIterator.has_more_data\n def has_more_data(self):\n \"\"\"Determines whether or not this iterator has more data to read.\n\n :rtype: bool\n :returns: Whether the iterator has more data or not.\n \"\"\"\n if self._bytes_written == 0:\n return True\n elif not self._total_bytes:\n # self._total_bytes **should** be set by this point.\n # If it isn't, something is wrong.\n raise ValueError('Size of object is unknown.')\n else:\n return self._bytes_written < self._total_bytes","label":0},{"cid":"gcloud/storage/key.py::_KeyDataIterator.get_headers","text":"gcloud/storage/key.py::_KeyDataIterator.get_headers\n def get_headers(self):\n \"\"\"Gets range header(s) for next chunk of data.\n\n :rtype: dict\n :returns: A dictionary of query parameters.\n \"\"\"\n start = self._bytes_written\n end = self._bytes_written + self.key.CHUNK_SIZE - 1\n\n if self._total_bytes and end > self._total_bytes:\n end = ''\n\n return {'Range': 'bytes=%s-%s' % (start, end)}","label":0},{"cid":"gcloud/storage/key.py::_KeyDataIterator.get_url","text":"gcloud/storage/key.py::_KeyDataIterator.get_url\n def get_url(self):\n \"\"\"Gets URL to read next chunk of data.\n\n :rtype: string\n :returns: A URL.\n \"\"\"\n return self.key.connection.build_api_url(\n path=self.key.path, query_params={'alt': 'media'})","label":0},{"cid":"gcloud/storage/key.py::_KeyDataIterator.get_next_chunk","text":"gcloud/storage/key.py::_KeyDataIterator.get_next_chunk\n def get_next_chunk(self):\n \"\"\"Gets the next chunk of data.\n\n Uses CHUNK_SIZE to determine how much data to get.\n\n :rtype: string\n :returns: The chunk of data read from the key.\n :raises: :class:`RuntimeError` if no more data or\n :class:`gcloud.storage.exceptions.StorageError` in the\n case of an unexpected response status code.\n \"\"\"\n if not self.has_more_data():\n raise RuntimeError('No more data in this iterator. Try resetting.')\n\n response, content = self.key.connection.make_request(\n method='GET', url=self.get_url(), headers=self.get_headers())\n\n if response.status in (200, 206):\n self._bytes_written += len(content)\n\n if 'content-range' in response:\n content_range = response['content-range']\n self._total_bytes = int(content_range.rsplit('/', 1)[1])\n\n return content\n\n # Expected a 200 or a 206. Got something else, which is unknown.\n raise StorageError(response)","label":0}],"string":"[\n {\n \"cid\": \"gcloud/datastore/__init__.py::get_connection\",\n \"text\": \"gcloud/datastore/__init__.py::get_connection\\ndef get_connection(client_email, private_key_path):\\n \\\"\\\"\\\"Shortcut method to establish a connection to the Cloud Datastore.\\n\\n Use this if you are going to access several datasets\\n with the same set of credentials (unlikely):\\n\\n >>> from gcloud import datastore\\n >>> connection = datastore.get_connection(email, key_path)\\n >>> dataset1 = connection.dataset('dataset1')\\n >>> dataset2 = connection.dataset('dataset2')\\n\\n :type client_email: string\\n :param client_email: The e-mail attached to the service account.\\n\\n :type private_key_path: string\\n :param private_key_path: The path to a private key file (this file was\\n given to you when you created the service\\n account).\\n\\n :rtype: :class:`gcloud.datastore.connection.Connection`\\n :returns: A connection defined with the proper credentials.\\n \\\"\\\"\\\"\\n from gcloud import credentials\\n from gcloud.datastore.connection import Connection\\n\\n svc_account_credentials = credentials.get_for_service_account(\\n client_email, private_key_path, scope=SCOPE)\\n return Connection(credentials=svc_account_credentials)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/__init__.py::get_dataset\",\n \"text\": \"gcloud/datastore/__init__.py::get_dataset\\ndef get_dataset(dataset_id, client_email, private_key_path):\\n \\\"\\\"\\\"Establish a connection to a particular dataset in the Cloud Datastore.\\n\\n This is a shortcut method for creating a connection and using it\\n to connect to a dataset.\\n\\n You'll generally use this as the first call to working with the API:\\n\\n >>> from gcloud import datastore\\n >>> dataset = datastore.get_dataset('dataset-id', email, key_path)\\n >>> # Now you can do things with the dataset.\\n >>> dataset.query().kind('TestKind').fetch()\\n [...]\\n\\n :type dataset_id: string\\n :param dataset_id: The id of the dataset you want to use.\\n This is akin to a database name\\n and is usually the same as your Cloud Datastore project\\n name.\\n\\n :type client_email: string\\n :param client_email: The e-mail attached to the service account.\\n\\n :type private_key_path: string\\n :param private_key_path: The path to a private key file (this file was\\n given to you when you created the service\\n account).\\n\\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\\n :returns: A dataset with a connection using the provided credentials.\\n \\\"\\\"\\\"\\n connection = get_connection(client_email, private_key_path)\\n return connection.dataset(dataset_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.__init__\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.__init__\\n def __init__(self, dataset):\\n self._dataset = dataset\\n self._id = None\\n self._mutation = datastore_pb.Mutation()\\n self._auto_id_entities = []\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.connection\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.connection\\n def connection(self):\\n \\\"\\\"\\\"Getter for current connection over which the transaction will run.\\n\\n :rtype: :class:`gcloud.datastore.connection.Connection`\\n :returns: The connection over which the transaction will run.\\n \\\"\\\"\\\"\\n\\n return self.dataset().connection()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.dataset\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.dataset\\n def dataset(self):\\n \\\"\\\"\\\"Getter for the current dataset.\\n\\n :rtype: :class:`gcloud.datastore.dataset.Dataset`\\n :returns: The dataset to which the transaction belongs.\\n \\\"\\\"\\\"\\n\\n return self._dataset\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.id\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.id\\n def id(self):\\n \\\"\\\"\\\"Getter for the transaction ID.\\n\\n :rtype: string\\n :returns: The ID of the current transaction.\\n \\\"\\\"\\\"\\n\\n return self._id\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.mutation\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.mutation\\n def mutation(self):\\n \\\"\\\"\\\"Getter for the current mutation.\\n\\n Every transaction is committed\\n with a single Mutation\\n representing the 'work' to be done as part of the transaction.\\n Inside a transaction,\\n calling ``save()`` on an entity\\n builds up the mutation.\\n This getter returns the Mutation protobuf\\n that has been built-up so far.\\n\\n :rtype: :class:`gcloud.datastore.datastore_v1_pb2.Mutation`\\n :returns: The Mutation protobuf to be sent in the commit request.\\n \\\"\\\"\\\"\\n return self._mutation\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.add_auto_id_entity\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.add_auto_id_entity\\n def add_auto_id_entity(self, entity):\\n \\\"\\\"\\\"Adds an entity to the list of entities to update with IDs.\\n\\n When an entity has a partial key,\\n calling ``save()`` adds an insert_auto_id entry in the mutation.\\n In order to make sure we update the Entity\\n once the transaction is committed,\\n we need to keep track of which entities to update\\n (and the order is important).\\n\\n When you call ``save()`` on an entity inside a transaction,\\n if the entity has a partial key,\\n it adds itself to the list of entities to be updated\\n once the transaction is committed\\n by calling this method.\\n \\\"\\\"\\\"\\n self._auto_id_entities.append(entity)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.begin\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.begin\\n def begin(self):\\n \\\"\\\"\\\"Begins a transaction.\\n\\n This method is called automatically when entering a with statement,\\n however it can be called explicitly\\n if you don't want to use a context manager.\\n \\\"\\\"\\\"\\n self._id = self.connection().begin_transaction(self.dataset().id())\\n self.connection().transaction(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.rollback\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.rollback\\n def rollback(self):\\n \\\"\\\"\\\"Rolls back the current transaction.\\n\\n This method has necessary side-effects:\\n\\n - Sets the current connection's transaction reference to None.\\n - Sets the current transaction's ID to None.\\n \\\"\\\"\\\"\\n self.connection().rollback(self.dataset().id())\\n self.connection().transaction(None)\\n self._id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.commit\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.commit\\n def commit(self):\\n \\\"\\\"\\\"Commits the transaction.\\n\\n This is called automatically upon exiting a with statement,\\n however it can be called explicitly\\n if you don't want to use a context manager.\\n\\n This method has necessary side-effects:\\n\\n - Sets the current connection's transaction reference to None.\\n - Sets the current transaction's ID to None.\\n - Updates paths for any keys that needed an automatically generated ID.\\n \\\"\\\"\\\"\\n # It's possible that they called commit() already, in which case\\n # we shouldn't do any committing of our own.\\n if self.connection().transaction():\\n result = self.connection().commit(self.dataset().id(),\\n self.mutation())\\n\\n # For any of the auto-id entities, make sure we update their keys.\\n for i, entity in enumerate(self._auto_id_entities):\\n key_pb = result.insert_auto_id_key[i]\\n key = helpers.key_from_protobuf(key_pb)\\n entity.key(entity.key().path(key.path()))\\n\\n # Tell the connection that the transaction is over.\\n self.connection().transaction(None)\\n\\n # Clear our own ID in case this gets accidentally reused.\\n self._id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.__enter__\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.__enter__\\n def __enter__(self):\\n self.begin()\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/transaction.py::Transaction.__exit__\",\n \"text\": \"gcloud/datastore/transaction.py::Transaction.__exit__\\n def __exit__(self, exc_type, exc_val, exc_tb):\\n if exc_type is None:\\n self.commit()\\n else:\\n self.rollback()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/__init__.py::get_connection\",\n \"text\": \"gcloud/storage/__init__.py::get_connection\\ndef get_connection(project, client_email, private_key_path):\\n \\\"\\\"\\\"Shortcut method to establish a connection to Cloud Storage.\\n\\n Use this if you are going to access several buckets with the same\\n set of credentials:\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> bucket1 = connection.get_bucket('bucket1')\\n >>> bucket2 = connection.get_bucket('bucket2')\\n\\n :type project: string\\n :param project: The name of the project to connect to.\\n\\n :type client_email: string\\n :param client_email: The e-mail attached to the service account.\\n\\n :type private_key_path: string\\n :param private_key_path: The path to a private key file (this file was\\n given to you when you created the service\\n account).\\n\\n :rtype: :class:`gcloud.storage.connection.Connection`\\n :returns: A connection defined with the proper credentials.\\n \\\"\\\"\\\"\\n from gcloud import credentials\\n from gcloud.storage.connection import Connection\\n\\n svc_account_credentials = credentials.get_for_service_account(\\n client_email, private_key_path, scope=SCOPE)\\n return Connection(project=project, credentials=svc_account_credentials)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/__init__.py::get_bucket\",\n \"text\": \"gcloud/storage/__init__.py::get_bucket\\ndef get_bucket(bucket_name, project, client_email, private_key_path):\\n \\\"\\\"\\\"Shortcut method to establish a connection to a particular bucket.\\n\\n You'll generally use this as the first call to working with the API:\\n\\n >>> from gcloud import storage\\n >>> bucket = storage.get_bucket(project, bucket_name, email, key_path)\\n >>> # Now you can do things with the bucket.\\n >>> bucket.exists('/path/to/file.txt')\\n False\\n\\n :type bucket_name: string\\n :param bucket_name: The id of the bucket you want to use.\\n This is akin to a disk name on a file system.\\n\\n :type project: string\\n :param project: The name of the project to connect to.\\n\\n :type client_email: string\\n :param client_email: The e-mail attached to the service account.\\n\\n :type private_key_path: string\\n :param private_key_path: The path to a private key file (this file was\\n given to you when you created the service\\n account).\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: A bucket with a connection using the provided credentials.\\n \\\"\\\"\\\"\\n connection = get_connection(project, client_email, private_key_path)\\n return connection.get_bucket(bucket_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.connection\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.connection\\n @property\\n def connection(self):\\n \\\"\\\"\\\"Abstract getter for the connection to use.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.path\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"Abstract getter for the object path.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.__init__\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.__init__\\n def __init__(self, name=None, properties=None):\\n \\\"\\\"\\\"_PropertyMixin constructor.\\n\\n :type name: string\\n :param name: The name of the object.\\n\\n :type properties: dict\\n :param properties: All the other data provided by Cloud Storage.\\n \\\"\\\"\\\"\\n self.name = name\\n self._properties = {}\\n if properties is not None:\\n self._properties.update(properties)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.properties\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.properties\\n @property\\n def properties(self):\\n \\\"\\\"\\\"Ensure properties are loaded, and return a copy.\\n\\n :rtype: dict\\n \\\"\\\"\\\"\\n if not self._properties:\\n self._reload_properties()\\n return self._properties.copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.batch\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.batch\\n @property\\n def batch(self):\\n \\\"\\\"\\\"Return a context manager which defers/batches updates.\\n\\n E.g., to batch multiple updates to a bucket::\\n\\n >>> with bucket.batch:\\n ... bucket.enable_versioning()\\n ... bucket.disable_website()\\n\\n or for a key::\\n\\n >>> with key.batch:\\n ... key.content_type = 'image/jpeg'\\n ... key.content_encoding = 'gzip'\\n\\n Updates will be aggregated and sent as a single call to\\n :meth:`_patch_properties` IFF the ``with`` block exits without\\n an exception.\\n\\n :rtype: :class:`_PropertyBatch`\\n \\\"\\\"\\\"\\n return _PropertyBatch(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin._reload_properties\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin._reload_properties\\n def _reload_properties(self):\\n \\\"\\\"\\\"Reload properties from Cloud Storage.\\n\\n :rtype: :class:`_PropertyMixin`\\n :returns: The object you just reloaded data for.\\n \\\"\\\"\\\"\\n # Pass only '?projection=noAcl' here because 'acl' and related\\n # are handled via custom endpoints..\\n query_params = {'projection': 'noAcl'}\\n self._properties = self.connection.api_request(\\n method='GET', path=self.path, query_params=query_params)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin._patch_properties\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin._patch_properties\\n def _patch_properties(self, properties):\\n \\\"\\\"\\\"Update particular fields of this object's properties.\\n\\n This method will only update the fields provided and will not\\n touch the other fields.\\n\\n It will also reload the properties locally based on the server's\\n response.\\n\\n :type properties: dict\\n :param properties: The dictionary of values to update.\\n\\n :rtype: :class:`_PropertyMixin`\\n :returns: The current object.\\n \\\"\\\"\\\"\\n # Pass '?projection=full' here because 'PATCH' documented not\\n # to work properly w/ 'noAcl'.\\n self._properties = self.connection.api_request(\\n method='PATCH', path=self.path, data=properties,\\n query_params={'projection': 'full'})\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin._get_property\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin._get_property\\n def _get_property(self, field, default=None):\\n \\\"\\\"\\\"Return the value of a field from the server-side representation.\\n\\n If you request a field that isn't available, and that field can\\n be retrieved by refreshing data from Cloud Storage, this method\\n will reload the data using :func:`_PropertyMixin._reload_properties`.\\n\\n :type field: string\\n :param field: A particular field to retrieve from properties.\\n\\n :type default: anything\\n :param default: The value to return if the field provided wasn't found.\\n\\n :rtype: anything\\n :returns: value of the specific field, or the default if not found.\\n \\\"\\\"\\\"\\n # Raise for fields which have custom accessors.\\n custom = self.CUSTOM_PROPERTY_ACCESSORS.get(field)\\n if custom is not None:\\n message = \\\"Use '%s' or related methods instead.\\\" % custom\\n raise KeyError((field, message))\\n\\n return self.properties.get(field, default)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.get_acl\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.get_acl\\n def get_acl(self):\\n \\\"\\\"\\\"Get ACL as an object.\\n\\n :returns: An ACL object for the current object.\\n \\\"\\\"\\\"\\n if not self.acl.loaded:\\n self.acl.reload()\\n return self.acl\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyBatch.__init__\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyBatch.__init__\\n def __init__(self, wrapped):\\n self._wrapped = wrapped\\n self._deferred = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyBatch.__enter__\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyBatch.__enter__\\n def __enter__(self):\\n \\\"\\\"\\\"Intercept / defer property updates.\\\"\\\"\\\"\\n self._wrapped._patch_properties = self._deferred.update\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyBatch.__exit__\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyBatch.__exit__\\n def __exit__(self, type, value, traceback):\\n \\\"\\\"\\\"Patch deferred property updates if no error.\\\"\\\"\\\"\\n del self._wrapped._patch_properties\\n if type is None:\\n if self._deferred:\\n self._wrapped._patch_properties(self._deferred)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_scalar_property\",\n \"text\": \"gcloud/storage/_helpers.py::_scalar_property\\ndef _scalar_property(fieldname):\\n \\\"\\\"\\\"Create a property descriptor around the :class:`_PropertyMixin` helpers.\\n \\\"\\\"\\\"\\n def _getter(self):\\n \\\"\\\"\\\"Scalar property getter.\\\"\\\"\\\"\\n return self.properties[fieldname]\\n\\n def _setter(self, value):\\n \\\"\\\"\\\"Scalar property setter.\\\"\\\"\\\"\\n self._patch_properties({fieldname: value})\\n\\n return property(_getter, _setter)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_scalar_property._getter\",\n \"text\": \"gcloud/storage/_helpers.py::_scalar_property._getter\\n def _getter(self):\\n \\\"\\\"\\\"Scalar property getter.\\\"\\\"\\\"\\n return self.properties[fieldname]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_scalar_property._setter\",\n \"text\": \"gcloud/storage/_helpers.py::_scalar_property._setter\\n def _setter(self, value):\\n \\\"\\\"\\\"Scalar property setter.\\\"\\\"\\\"\\n self._patch_properties({fieldname: value})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::_KeyIterator.__init__\",\n \"text\": \"gcloud/storage/bucket.py::_KeyIterator.__init__\\n def __init__(self, bucket, extra_params=None):\\n self.bucket = bucket\\n self.prefixes = ()\\n super(_KeyIterator, self).__init__(\\n connection=bucket.connection, path=bucket.path + '/o',\\n extra_params=extra_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::_KeyIterator.get_items_from_response\",\n \"text\": \"gcloud/storage/bucket.py::_KeyIterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Yield :class:`.storage.key.Key` items from response.\\n\\n :type response: dict\\n :param response: The JSON API response for a page of keys.\\n \\\"\\\"\\\"\\n self.prefixes = tuple(response.get('prefixes', ()))\\n for item in response.get('items', []):\\n yield Key.from_dict(item, bucket=self.bucket)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__init__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__init__\\n def __init__(self, connection=None, name=None, properties=None):\\n super(Bucket, self).__init__(name=name, properties=properties)\\n self._connection = connection\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.from_dict\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.from_dict\\n @classmethod\\n def from_dict(cls, bucket_dict, connection=None):\\n \\\"\\\"\\\"Construct a new bucket from a dictionary of data from Cloud Storage.\\n\\n :type bucket_dict: dict\\n :param bucket_dict: The dictionary of data to construct a bucket from.\\n\\n :rtype: :class:`Bucket`\\n :returns: A bucket constructed from the data provided.\\n \\\"\\\"\\\"\\n return cls(connection=connection, name=bucket_dict['name'],\\n properties=bucket_dict)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__repr__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__repr__\\n def __repr__(self):\\n return '<Bucket: %s>' % self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__iter__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__iter__\\n def __iter__(self):\\n return iter(self._iterator_class(bucket=self))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__contains__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__contains__\\n def __contains__(self, key):\\n return self.get_key(key) is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.acl\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.acl\\n @property\\n def acl(self):\\n \\\"\\\"\\\"Create our ACL on demand.\\\"\\\"\\\"\\n if self._acl is None:\\n self._acl = BucketACL(self)\\n return self._acl\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.default_object_acl\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.default_object_acl\\n @property\\n def default_object_acl(self):\\n \\\"\\\"\\\"Create our defaultObjectACL on demand.\\\"\\\"\\\"\\n if self._default_object_acl is None:\\n self._default_object_acl = DefaultObjectACL(self)\\n return self._default_object_acl\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.connection\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.connection\\n @property\\n def connection(self):\\n \\\"\\\"\\\"Getter property for the connection to use with this Bucket.\\n\\n :rtype: :class:`gcloud.storage.connection.Connection`\\n :returns: The connection to use.\\n \\\"\\\"\\\"\\n return self._connection\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.path\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"The URL path to this bucket.\\\"\\\"\\\"\\n if not self.name:\\n raise ValueError('Cannot determine path without bucket name.')\\n\\n return '/b/' + self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_key\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_key\\n def get_key(self, key):\\n \\\"\\\"\\\"Get a key object by name.\\n\\n This will return None if the key doesn't exist::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> print bucket.get_key('/path/to/key.txt')\\n <Key: my-bucket, /path/to/key.txt>\\n >>> print bucket.get_key('/does-not-exist.txt')\\n None\\n\\n :type key: string or :class:`gcloud.storage.key.Key`\\n :param key: The name of the key to retrieve.\\n\\n :rtype: :class:`gcloud.storage.key.Key` or None\\n :returns: The key object if it exists, otherwise None.\\n \\\"\\\"\\\"\\n # Coerce this to a key object (either from a Key or a string).\\n key = self.new_key(key)\\n\\n try:\\n response = self.connection.api_request(method='GET', path=key.path)\\n return Key.from_dict(response, bucket=self)\\n except exceptions.NotFound:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_all_keys\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_all_keys\\n def get_all_keys(self):\\n \\\"\\\"\\\"List all the keys in this bucket.\\n\\n This will **not** retrieve all the data for all the keys, it\\n will only retrieve the keys.\\n\\n This is equivalent to::\\n\\n keys = [key for key in bucket]\\n\\n :rtype: list of :class:`gcloud.storage.key.Key`\\n :returns: A list of all the Key objects in this bucket.\\n \\\"\\\"\\\"\\n return list(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.iterator\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.iterator\\n def iterator(self, prefix=None, delimiter=None, max_results=None,\\n versions=None):\\n \\\"\\\"\\\"Return an iterator used to find keys in the bucket.\\n\\n :type prefix: string or None\\n :param prefix: optional prefix used to filter keys.\\n\\n :type delimiter: string or None\\n :param delimiter: optional delimter, used with ``prefix`` to\\n emulate hierarchy.\\n\\n :type max_results: integer or None\\n :param max_results: maximum number of keys to return.\\n\\n :type versions: boolean or None\\n :param versions: whether object versions should be returned as\\n separate keys.\\n\\n :rtype: :class:`_KeyIterator`\\n \\\"\\\"\\\"\\n extra_params = {}\\n\\n if prefix is not None:\\n extra_params['prefix'] = prefix\\n\\n if delimiter is not None:\\n extra_params['delimiter'] = delimiter\\n\\n if max_results is not None:\\n extra_params['maxResults'] = max_results\\n\\n if versions is not None:\\n extra_params['versions'] = versions\\n\\n return self._iterator_class(self, extra_params=extra_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.new_key\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.new_key\\n def new_key(self, key):\\n \\\"\\\"\\\"Given path name (or Key), return a :class:`.storage.key.Key` object.\\n\\n This is really useful when you're not sure if you have a Key\\n object or a string path name. Given either of those types, this\\n returns the corresponding Key object.\\n\\n :type key: string or :class:`gcloud.storage.key.Key`\\n :param key: A path name or actual key object.\\n\\n :rtype: :class:`gcloud.storage.key.Key`\\n :returns: A Key object with the path provided.\\n \\\"\\\"\\\"\\n if isinstance(key, Key):\\n return key\\n\\n # Support Python 2 and 3.\\n try:\\n string_type = basestring\\n except NameError: # pragma: NO COVER PY3k\\n string_type = str\\n\\n if isinstance(key, string_type):\\n return Key(bucket=self, name=key)\\n\\n raise TypeError('Invalid key: %s' % key)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.delete\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.delete\\n def delete(self, force=False):\\n \\\"\\\"\\\"Delete this bucket.\\n\\n The bucket **must** be empty in order to delete it. If the\\n bucket doesn't exist, this will raise a\\n :class:`gcloud.storage.exceptions.NotFound`. If the bucket\\n is not empty, this will raise an Exception.\\n\\n If you want to delete a non-empty bucket you can pass in a force\\n parameter set to true. This will iterate through the bucket's\\n keys and delete the related objects, before deleting the bucket.\\n\\n :type force: bool\\n :param full: If True, empties the bucket's objects then deletes it.\\n\\n :raises: :class:`gcloud.storage.exceptions.NotFound` if the\\n bucket does not exist, or\\n :class:`gcloud.storage.exceptions.Conflict` if the\\n bucket has keys and `force` is not passed.\\n \\\"\\\"\\\"\\n return self.connection.delete_bucket(self.name, force=force)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.delete_key\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.delete_key\\n def delete_key(self, key):\\n \\\"\\\"\\\"Deletes a key from the current bucket.\\n\\n If the key isn't found,\\n this will throw a :class:`gcloud.storage.exceptions.NotFound`.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> from gcloud.storage import exceptions\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> print bucket.get_all_keys()\\n [<Key: my-bucket, my-file.txt>]\\n >>> bucket.delete_key('my-file.txt')\\n >>> try:\\n ... bucket.delete_key('doesnt-exist')\\n ... except exceptions.NotFound:\\n ... pass\\n\\n\\n :type key: string or :class:`gcloud.storage.key.Key`\\n :param key: A key name or Key object to delete.\\n\\n :rtype: :class:`gcloud.storage.key.Key`\\n :returns: The key that was just deleted.\\n :raises: :class:`gcloud.storage.exceptions.NotFound` (to suppress\\n the exception, call ``delete_keys``, passing a no-op\\n ``on_error`` callback, e.g.::\\n\\n >>> bucket.delete_keys([key], on_error=lambda key: pass)\\n \\\"\\\"\\\"\\n key = self.new_key(key)\\n self.connection.api_request(method='DELETE', path=key.path)\\n return key\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.delete_keys\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.delete_keys\\n def delete_keys(self, keys, on_error=None):\\n \\\"\\\"\\\"Deletes a list of keys from the current bucket.\\n\\n Uses :func:`Bucket.delete_key` to delete each individual key.\\n\\n :type keys: list of string or :class:`gcloud.storage.key.Key`\\n :param keys: A list of key names or Key objects to delete.\\n\\n :type on_error: a callable taking (key)\\n :param on_error: If not ``None``, called once for each key raising\\n :class:`gcloud.storage.exceptions.NotFound`;\\n otherwise, the exception is propagated.\\n\\n :raises: :class:`gcloud.storage.exceptions.NotFound` (if\\n `on_error` is not passed).\\n \\\"\\\"\\\"\\n for key in keys:\\n try:\\n self.delete_key(key)\\n except exceptions.NotFound:\\n if on_error is not None:\\n on_error(key)\\n else:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.copy_key\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.copy_key\\n def copy_key(self, key, destination_bucket, new_name=None):\\n \\\"\\\"\\\"Copy the given key to the given bucket, optionally with a new name.\\n\\n :type key: string or :class:`gcloud.storage.key.Key`\\n :param key: The key to be copied.\\n\\n :type destination_bucket: :class:`gcloud.storage.bucket.Bucket`\\n :param destination_bucket: The bucket into which the key should be\\n copied.\\n\\n :type new_name: string\\n :param new_name: (optional) the new name for the copied file.\\n\\n :rtype: :class:`gcloud.storage.key.Key`\\n :returns: The new Key.\\n \\\"\\\"\\\"\\n if new_name is None:\\n new_name = key.name\\n new_key = destination_bucket.new_key(new_name)\\n api_path = key.path + '/copyTo' + new_key.path\\n self.connection.api_request(method='POST', path=api_path)\\n return new_key\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.upload_file\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.upload_file\\n def upload_file(self, filename, key=None):\\n \\\"\\\"\\\"Shortcut method to upload a file into this bucket.\\n\\n Use this method to quickly put a local file in Cloud Storage.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> bucket.upload_file('~/my-file.txt', 'remote-text-file.txt')\\n >>> print bucket.get_all_keys()\\n [<Key: my-bucket, remote-text-file.txt>]\\n\\n If you don't provide a key value, we will try to upload the file\\n using the local filename as the key (**not** the complete\\n path)::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> bucket.upload_file('~/my-file.txt')\\n >>> print bucket.get_all_keys()\\n [<Key: my-bucket, my-file.txt>]\\n\\n :type filename: string\\n :param filename: Local path to the file you want to upload.\\n\\n :type key: string or :class:`gcloud.storage.key.Key`\\n :param key: The key (either an object or a remote path) of where\\n to put the file. If this is blank, we will try to\\n upload the file to the root of the bucket with the\\n same name as on your local file system.\\n \\\"\\\"\\\"\\n if key is None:\\n key = os.path.basename(filename)\\n key = self.new_key(key)\\n key.upload_from_filename(filename)\\n return key\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.upload_file_object\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.upload_file_object\\n def upload_file_object(self, file_obj, key=None):\\n \\\"\\\"\\\"Shortcut method to upload a file object into this bucket.\\n\\n Use this method to quickly put a local file in Cloud Storage.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> bucket.upload_file(open('~/my-file.txt'), 'remote-text-file.txt')\\n >>> print bucket.get_all_keys()\\n [<Key: my-bucket, remote-text-file.txt>]\\n\\n If you don't provide a key value, we will try to upload the file\\n using the local filename as the key (**not** the complete\\n path)::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> bucket.upload_file(open('~/my-file.txt'))\\n >>> print bucket.get_all_keys()\\n [<Key: my-bucket, my-file.txt>]\\n\\n :type file_obj: file\\n :param file_obj: A file handle open for reading.\\n\\n :type key: string or :class:`gcloud.storage.key.Key`\\n :param key: The key (either an object or a remote path) of where\\n to put the file. If this is blank, we will try to\\n upload the file to the root of the bucket with the\\n same name as on your local file system.\\n \\\"\\\"\\\"\\n if key:\\n key = self.new_key(key)\\n else:\\n key = self.new_key(os.path.basename(file_obj.name))\\n return key.upload_from_file(file_obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_cors\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_cors\\n def get_cors(self):\\n \\\"\\\"\\\"Retrieve CORS policies configured for this bucket.\\n\\n See: http://www.w3.org/TR/cors/ and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: list(dict)\\n :returns: A sequence of mappings describing each CORS policy.\\n \\\"\\\"\\\"\\n return [policy.copy() for policy in self.properties.get('cors', ())]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.update_cors\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.update_cors\\n def update_cors(self, entries):\\n \\\"\\\"\\\"Update CORS policies configured for this bucket.\\n\\n See: http://www.w3.org/TR/cors/ and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :type entries: list(dict)\\n :param entries: A sequence of mappings describing each CORS policy.\\n \\\"\\\"\\\"\\n self._patch_properties({'cors': entries})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_default_object_acl\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_default_object_acl\\n def get_default_object_acl(self):\\n \\\"\\\"\\\"Get the current Default Object ACL rules.\\n\\n If the acl isn't available locally, this method will reload it from\\n Cloud Storage.\\n\\n :rtype: :class:`gcloud.storage.acl.DefaultObjectACL`\\n :returns: A DefaultObjectACL object for this bucket.\\n \\\"\\\"\\\"\\n if not self.default_object_acl.loaded:\\n self.default_object_acl.reload()\\n return self.default_object_acl\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.etag\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.etag\\n @property\\n def etag(self):\\n \\\"\\\"\\\"Retrieve the ETag for the bucket.\\n\\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['etag']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.id\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.id\\n @property\\n def id(self):\\n \\\"\\\"\\\"Retrieve the ID for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['id']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_lifecycle\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_lifecycle\\n def get_lifecycle(self):\\n \\\"\\\"\\\"Retrieve lifecycle rules configured for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/lifecycle and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: list(dict)\\n :returns: A sequence of mappings describing each lifecycle rule.\\n \\\"\\\"\\\"\\n info = self.properties.get('lifecycle', {})\\n return [rule.copy() for rule in info.get('rule', ())]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.update_lifecycle\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.update_lifecycle\\n def update_lifecycle(self, rules):\\n \\\"\\\"\\\"Update CORS policies configured for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/lifecycle and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :type rules: list(dict)\\n :param rules: A sequence of mappings describing each lifecycle rule.\\n \\\"\\\"\\\"\\n self._patch_properties({'lifecycle': {'rule': rules}})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_logging\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_logging\\n def get_logging(self):\\n \\\"\\\"\\\"Return info about access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#status\\n\\n :rtype: dict or None\\n :returns: a dict w/ keys, ``logBucket`` and ``logObjectPrefix``\\n (if logging is enabled), or None (if not).\\n \\\"\\\"\\\"\\n info = self.properties.get('logging')\\n if info is not None:\\n return info.copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.enable_logging\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.enable_logging\\n def enable_logging(self, bucket_name, object_prefix=''):\\n \\\"\\\"\\\"Enable access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#delivery\\n\\n :type bucket_name: string\\n :param bucket_name: name of bucket in which to store access logs\\n\\n :type object_prefix: string\\n :param object_prefix: prefix for access log filenames\\n \\\"\\\"\\\"\\n info = {'logBucket': bucket_name, 'logObjectPrefix': object_prefix}\\n self._patch_properties({'logging': info})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.disable_logging\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.disable_logging\\n def disable_logging(self):\\n \\\"\\\"\\\"Disable access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#disabling\\n \\\"\\\"\\\"\\n self._patch_properties({'logging': None})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.metageneration\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.metageneration\\n @property\\n def metageneration(self):\\n \\\"\\\"\\\"Retrieve the metageneration for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['metageneration']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.owner\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.owner\\n @property\\n def owner(self):\\n \\\"\\\"\\\"Retrieve info about the owner of the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: dict\\n :returns: mapping of owner's role/ID.\\n \\\"\\\"\\\"\\n return self.properties['owner'].copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.project_number\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.project_number\\n @property\\n def project_number(self):\\n \\\"\\\"\\\"Retrieve the number of the project to which the bucket is assigned.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['projectNumber']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.self_link\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.self_link\\n @property\\n def self_link(self):\\n \\\"\\\"\\\"Retrieve the URI for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['selfLink']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.storage_class\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.storage_class\\n @property\\n def storage_class(self):\\n \\\"\\\"\\\"Retrieve the storage class for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets and\\n https://cloud.google.com/storage/docs/durable-reduced-availability\\n\\n :rtype: string\\n :returns: Currently one of \\\"STANDARD\\\", \\\"DURABLE_REDUCED_AVAILABILITY\\\"\\n \\\"\\\"\\\"\\n return self.properties['storageClass']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.time_created\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.time_created\\n @property\\n def time_created(self):\\n \\\"\\\"\\\"Retrieve the timestamp at which the bucket was created.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n :returns: timestamp in RFC 3339 format.\\n \\\"\\\"\\\"\\n return self.properties['timeCreated']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\\n @property\\n def versioning_enabled(self):\\n \\\"\\\"\\\"Is versioning enabled for this bucket?\\n\\n See: https://cloud.google.com/storage/docs/object-versioning for\\n details.\\n\\n :rtype: boolean\\n :returns: True if enabled, else False.\\n \\\"\\\"\\\"\\n versioning = self.properties.get('versioning', {})\\n return versioning.get('enabled', False)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\\n @versioning_enabled.setter\\n def versioning_enabled(self, value):\\n \\\"\\\"\\\"Enable versioning for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/object-versioning for\\n details.\\n\\n :type value: convertible to bool\\n :param value: should versioning be anabled for the bucket?\\n \\\"\\\"\\\"\\n self._patch_properties({'versioning': {'enabled': bool(value)}})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.configure_website\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.configure_website\\n def configure_website(self, main_page_suffix=None, not_found_page=None):\\n \\\"\\\"\\\"Configure website-related properties.\\n\\n See: https://developers.google.com/storage/docs/website-configuration\\n\\n .. note::\\n This (apparently) only works\\n if your bucket name is a domain name\\n (and to do that, you need to get approved somehow...).\\n\\n If you want this bucket to host a website, just provide the name\\n of an index page and a page to use when a key isn't found::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email,\\n private_key_path)\\n >>> bucket = connection.get_bucket(bucket_name)\\n >>> bucket.configure_website('index.html', '404.html')\\n\\n You probably should also make the whole bucket public::\\n\\n >>> bucket.make_public(recursive=True, future=True)\\n\\n This says: \\\"Make the bucket public, and all the stuff already in\\n the bucket, and anything else I add to the bucket. Just make it\\n all public.\\\"\\n\\n :type main_page_suffix: string\\n :param main_page_suffix: The page to use as the main page\\n of a directory.\\n Typically something like index.html.\\n\\n :type not_found_page: string\\n :param not_found_page: The file to use when a page isn't found.\\n \\\"\\\"\\\"\\n data = {\\n 'website': {\\n 'mainPageSuffix': main_page_suffix,\\n 'notFoundPage': not_found_page,\\n },\\n }\\n return self._patch_properties(data)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::_utcnow\",\n \"text\": \"gcloud/storage/connection.py::_utcnow\\ndef _utcnow(): # pragma: NO COVER testing replaces\\n \\\"\\\"\\\"Returns current time as UTC datetime.\\n\\n NOTE: on the module namespace so tests can replace it.\\n \\\"\\\"\\\"\\n return datetime.datetime.utcnow()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.__init__\",\n \"text\": \"gcloud/storage/connection.py::Connection.__init__\\n def __init__(self, project, *args, **kwargs):\\n \\\"\\\"\\\":type project: string\\n\\n :param project: The project name to connect to.\\n \\\"\\\"\\\"\\n super(Connection, self).__init__(*args, **kwargs)\\n self.project = project\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.__iter__\",\n \"text\": \"gcloud/storage/connection.py::Connection.__iter__\\n def __iter__(self):\\n return iter(_BucketIterator(connection=self))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.__contains__\",\n \"text\": \"gcloud/storage/connection.py::Connection.__contains__\\n def __contains__(self, bucket_name):\\n return self.lookup(bucket_name) is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.build_api_url\",\n \"text\": \"gcloud/storage/connection.py::Connection.build_api_url\\n def build_api_url(self, path, query_params=None, api_base_url=None,\\n api_version=None):\\n \\\"\\\"\\\"Construct an API url given a few components, some optional.\\n\\n Typically, you shouldn't need to use this method.\\n\\n :type path: string\\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\\n\\n :type query_params: dict\\n :param query_params: A dictionary of keys and values to insert into\\n the query string of the URL.\\n\\n :type api_base_url: string\\n :param api_base_url: The base URL for the API endpoint.\\n Typically you won't have to provide this.\\n\\n :type api_version: string\\n :param api_version: The version of the API to call.\\n Typically you shouldn't provide this and instead\\n use the default for the library.\\n\\n :rtype: string\\n :returns: The URL assembled from the pieces provided.\\n \\\"\\\"\\\"\\n url = self.API_URL_TEMPLATE.format(\\n api_base_url=(api_base_url or self.API_BASE_URL),\\n api_version=(api_version or self.API_VERSION),\\n path=path)\\n\\n query_params = query_params or {}\\n query_params.update({'project': self.project})\\n url += '?' + urllib.urlencode(query_params)\\n\\n return url\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.make_request\",\n \"text\": \"gcloud/storage/connection.py::Connection.make_request\\n def make_request(self, method, url, data=None, content_type=None,\\n headers=None):\\n \\\"\\\"\\\"A low level method to send a request to the API.\\n\\n Typically, you shouldn't need to use this method.\\n\\n :type method: string\\n :param method: The HTTP method to use in the request.\\n\\n :type url: string\\n :param url: The URL to send the request to.\\n\\n :type data: string\\n :param data: The data to send as the body of the request.\\n\\n :type content_type: string\\n :param content_type: The proper MIME type of the data provided.\\n\\n :type headers: dict\\n :param headers: A dictionary of HTTP headers to send with the request.\\n\\n :rtype: tuple of ``response`` (a dictionary of sorts)\\n and ``content`` (a string).\\n :returns: The HTTP response object and the content of the response.\\n \\\"\\\"\\\"\\n headers = headers or {}\\n headers['Accept-Encoding'] = 'gzip'\\n\\n if data:\\n content_length = len(str(data))\\n else:\\n content_length = 0\\n\\n headers['Content-Length'] = content_length\\n\\n if content_type:\\n headers['Content-Type'] = content_type\\n\\n headers['User-Agent'] = self.USER_AGENT\\n\\n return self.http.request(uri=url, method=method, headers=headers,\\n body=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.api_request\",\n \"text\": \"gcloud/storage/connection.py::Connection.api_request\\n def api_request(self, method, path, query_params=None,\\n data=None, content_type=None,\\n api_base_url=None, api_version=None,\\n expect_json=True):\\n \\\"\\\"\\\"Make a request over the HTTP transport to the Cloud Storage API.\\n\\n You shouldn't need to use this method, but if you plan to\\n interact with the API using these primitives, this is the\\n correct one to use...\\n\\n :type method: string\\n :param method: The HTTP method name (ie, ``GET``, ``POST``, etc).\\n Required.\\n\\n :type path: string\\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\\n Required.\\n\\n :type query_params: dict\\n :param query_params: A dictionary of keys and values to insert into\\n the query string of the URL. Default is\\n empty dict.\\n\\n :type data: string\\n :param data: The data to send as the body of the request. Default is\\n the empty string.\\n\\n :type content_type: string\\n :param content_type: The proper MIME type of the data provided. Default\\n is None.\\n\\n :type api_base_url: string\\n :param api_base_url: The base URL for the API endpoint.\\n Typically you won't have to provide this.\\n Default is the standard API base URL.\\n\\n :type api_version: string\\n :param api_version: The version of the API to call. Typically\\n you shouldn't provide this and instead use\\n the default for the library. Default is the\\n latest API version supported by\\n gcloud-python.\\n\\n :type expect_json: bool\\n :param expect_json: If True, this method will try to parse the\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.get_all_buckets\",\n \"text\": \"gcloud/storage/connection.py::Connection.get_all_buckets\\n def get_all_buckets(self):\\n \\\"\\\"\\\"Get all buckets in the project.\\n\\n This will not populate the list of keys available in each\\n bucket.\\n\\n You can also iterate over the connection object, so these two\\n operations are identical::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> for bucket in connection.get_all_buckets():\\n >>> print bucket\\n >>> # ... is the same as ...\\n >>> for bucket in connection:\\n >>> print bucket\\n\\n :rtype: list of :class:`gcloud.storage.bucket.Bucket` objects.\\n :returns: All buckets belonging to this project.\\n \\\"\\\"\\\"\\n return list(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.get_bucket\",\n \"text\": \"gcloud/storage/connection.py::Connection.get_bucket\\n def get_bucket(self, bucket_name):\\n \\\"\\\"\\\"Get a bucket by name.\\n\\n If the bucket isn't found, this will raise a\\n :class:`gcloud.storage.exceptions.NotFound`. If you would\\n rather get a bucket by name, and return ``None`` if the bucket\\n isn't found (like ``{}.get('...')``) then use\\n :func:`Connection.lookup`.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> from gcloud.storage import exceptions\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> try:\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> except exceptions.NotFound:\\n >>> print 'Sorry, that bucket does not exist!'\\n\\n :type bucket_name: string\\n :param bucket_name: The name of the bucket to get.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The bucket matching the name provided.\\n :raises: :class:`gcloud.storage.exceptions.NotFound`\\n \\\"\\\"\\\"\\n bucket = self.new_bucket(bucket_name)\\n response = self.api_request(method='GET', path=bucket.path)\\n return Bucket.from_dict(response, connection=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.lookup\",\n \"text\": \"gcloud/storage/connection.py::Connection.lookup\\n def lookup(self, bucket_name):\\n \\\"\\\"\\\"Get a bucket by name, returning None if not found.\\n\\n You can use this if you would rather checking for a None value\\n than catching an exception::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> bucket = connection.get_bucket('doesnt-exist')\\n >>> print bucket\\n None\\n >>> bucket = connection.get_bucket('my-bucket')\\n >>> print bucket\\n <Bucket: my-bucket>\\n\\n :type bucket_name: string\\n :param bucket_name: The name of the bucket to get.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The bucket matching the name provided or None if not found.\\n \\\"\\\"\\\"\\n try:\\n return self.get_bucket(bucket_name)\\n except exceptions.NotFound:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.create_bucket\",\n \"text\": \"gcloud/storage/connection.py::Connection.create_bucket\\n def create_bucket(self, bucket):\\n \\\"\\\"\\\"Create a new bucket.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, client, key_path)\\n >>> bucket = connection.create_bucket('my-bucket')\\n >>> print bucket\\n <Bucket: my-bucket>\\n\\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\\n :param bucket: The bucket name (or bucket object) to create.\\n\\n :rtype: :class:`gcloud.storage.bucket.Bucket`\\n :returns: The newly created bucket.\\n :raises: :class:`gcloud.storage.exceptions.Conflict` if\\n there is a confict (bucket already exists, invalid name, etc.)\\n \\\"\\\"\\\"\\n bucket = self.new_bucket(bucket)\\n response = self.api_request(method='POST', path='/b',\\n data={'name': bucket.name})\\n return Bucket.from_dict(response, connection=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.delete_bucket\",\n \"text\": \"gcloud/storage/connection.py::Connection.delete_bucket\\n def delete_bucket(self, bucket, force=False):\\n \\\"\\\"\\\"Delete a bucket.\\n\\n You can use this method to delete a bucket by name, or to delete\\n a bucket object::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection(project, email, key_path)\\n >>> connection.delete_bucket('my-bucket')\\n True\\n\\n You can also delete pass in the bucket object::\\n\\n >>> bucket = connection.get_bucket('other-bucket')\\n >>> connection.delete_bucket(bucket)\\n True\\n\\n If the bucket doesn't exist, this will raise a\\n :class:`gcloud.storage.exceptions.NotFound`::\\n\\n >>> from gcloud.storage import exceptions\\n >>> try:\\n >>> connection.delete_bucket('my-bucket')\\n >>> except exceptions.NotFound:\\n >>> print 'That bucket does not exist!'\\n\\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\\n :param bucket: The bucket name (or bucket object) to create.\\n\\n :type force: bool\\n :param full: If True, empties the bucket's objects then deletes it.\\n\\n :rtype: bool\\n :returns: True if the bucket was deleted.\\n :raises: :class:`gcloud.storage.exceptions.NotFound` if the\\n bucket doesn't exist, or\\n :class:`gcloud.storage.exceptions.Conflict` if the\\n bucket has keys and `force` is not passed.\\n \\\"\\\"\\\"\\n bucket = self.new_bucket(bucket)\\n\\n # This force delete operation is slow.\\n if force:\\n for key in bucket:\\n key.delete()\\n\\n self.api_request(method='DELETE', path=bucket.path)\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.new_bucket\",\n \"text\": \"gcloud/storage/connection.py::Connection.new_bucket\\n def new_bucket(self, bucket):\\n \\\"\\\"\\\"Factory method for creating a new (unsaved) bucket object.\\n\\n This method is really useful when you're not sure whether you\\n have an actual :class:`gcloud.storage.bucket.Bucket` object or\\n just a name of a bucket. It always returns the object::\\n\\n >>> bucket = connection.new_bucket('bucket')\\n >>> print bucket\\n <Bucket: bucket>\\n >>> bucket = connection.new_bucket(bucket)\\n >>> print bucket\\n <Bucket: bucket>\\n\\n :type bucket: string or :class:`gcloud.storage.bucket.Bucket`\\n :param bucket: A name of a bucket or an existing Bucket object.\\n \\\"\\\"\\\"\\n if isinstance(bucket, Bucket):\\n return bucket\\n\\n # Support Python 2 and 3.\\n try:\\n string_type = basestring\\n except NameError: # pragma: NO COVER PY3k\\n string_type = str\\n\\n if isinstance(bucket, string_type):\\n return Bucket(connection=self, name=bucket)\\n\\n raise TypeError('Invalid bucket: %s' % bucket)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::Connection.generate_signed_url\",\n \"text\": \"gcloud/storage/connection.py::Connection.generate_signed_url\\n def generate_signed_url(self, resource, expiration,\\n method='GET', content_md5=None,\\n content_type=None):\\n \\\"\\\"\\\"Generate signed URL to provide query-string auth'n to a resource.\\n\\n :type resource: string\\n :param resource: A pointer to a specific resource\\n (typically, ``/bucket-name/path/to/key.txt``).\\n\\n :type expiration: int, long, datetime.datetime, datetime.timedelta\\n :param expiration: When the signed URL should expire.\\n\\n :type method: string\\n :param method: The HTTP verb that will be used when requesting the URL.\\n\\n :type content_md5: string\\n :param content_md5: The MD5 hash of the object referenced by\\n ``resource``.\\n\\n :type content_type: string\\n :param content_type: The content type of the object referenced by\\n ``resource``.\\n\\n :rtype: string\\n :returns: A signed URL you can use to access the resource\\n until expiration.\\n \\\"\\\"\\\"\\n expiration = _get_expiration_seconds(expiration)\\n\\n # Generate the string to sign.\\n signature_string = '\\\\n'.join([\\n method,\\n content_md5 or '',\\n content_type or '',\\n str(expiration),\\n resource])\\n\\n # Take our PKCS12 (.p12) key and make it into a RSA key we can use...\\n pkcs12 = crypto.load_pkcs12(\\n base64.b64decode(self.credentials.private_key),\\n 'notasecret')\\n pem = crypto.dump_privatekey(\\n crypto.FILETYPE_PEM, pkcs12.get_privatekey())\\n pem_key = RSA.importKey(pem)\\n\\n # Sign the string with the RSA key.\\n signer = PKCS1_v1_5.new(pem_key)\\n signature_hash = SHA256.new(signature_string)\\n signature_bytes = signer.sign(signature_hash)\\n signature = base64.b64encode(signature_byte\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::_BucketIterator.__init__\",\n \"text\": \"gcloud/storage/connection.py::_BucketIterator.__init__\\n def __init__(self, connection):\\n super(_BucketIterator, self).__init__(connection=connection, path='/b')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::_BucketIterator.get_items_from_response\",\n \"text\": \"gcloud/storage/connection.py::_BucketIterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Factory method which yields :class:`.Bucket` items from a response.\\n\\n :type response: dict\\n :param response: The JSON API response for a page of buckets.\\n \\\"\\\"\\\"\\n for item in response.get('items', []):\\n yield Bucket.from_dict(item, connection=self.connection)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/connection.py::_get_expiration_seconds\",\n \"text\": \"gcloud/storage/connection.py::_get_expiration_seconds\\ndef _get_expiration_seconds(expiration):\\n \\\"\\\"\\\"Convert 'expiration' to a number of seconds in the future.\\n\\n :type expiration: int, long, datetime.datetime, datetime.timedelta\\n :param expiration: When the signed URL should expire.\\n\\n :rtype: int\\n :returns: a timestamp as an absolute number of seconds.\\n \\\"\\\"\\\"\\n # If it's a timedelta, add it to `now` in UTC.\\n if isinstance(expiration, datetime.timedelta):\\n now = _utcnow().replace(tzinfo=pytz.utc)\\n expiration = now + expiration\\n\\n # If it's a datetime, convert to a timestamp.\\n if isinstance(expiration, datetime.datetime):\\n # Make sure the timezone on the value is UTC\\n # (either by converting or replacing the value).\\n if expiration.tzinfo:\\n expiration = expiration.astimezone(pytz.utc)\\n else:\\n expiration = expiration.replace(tzinfo=pytz.utc)\\n\\n # Turn the datetime into a timestamp (seconds, not microseconds).\\n expiration = int(calendar.timegm(expiration.timetuple()))\\n\\n if not isinstance(expiration, (int, long)):\\n raise TypeError('Expected an integer timestamp, datetime, or '\\n 'timedelta. Got %s' % type(expiration))\\n return expiration\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/iterator.py::Iterator.__init__\",\n \"text\": \"gcloud/storage/iterator.py::Iterator.__init__\\n def __init__(self, connection, path, extra_params=None):\\n self.connection = connection\\n self.path = path\\n self.page_number = 0\\n self.next_page_token = None\\n self.extra_params = extra_params or {}\\n reserved_in_use = self.RESERVED_PARAMS.intersection(\\n self.extra_params.keys())\\n if reserved_in_use:\\n raise ValueError(('Using a reserved parameter',\\n reserved_in_use))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/iterator.py::Iterator.__iter__\",\n \"text\": \"gcloud/storage/iterator.py::Iterator.__iter__\\n def __iter__(self):\\n \\\"\\\"\\\"Iterate through the list of items.\\\"\\\"\\\"\\n while self.has_next_page():\\n response = self.get_next_page_response()\\n for item in self.get_items_from_response(response):\\n yield item\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/iterator.py::Iterator.has_next_page\",\n \"text\": \"gcloud/storage/iterator.py::Iterator.has_next_page\\n def has_next_page(self):\\n \\\"\\\"\\\"Determines whether or not this iterator has more pages.\\n\\n :rtype: bool\\n :returns: Whether the iterator has more pages or not.\\n \\\"\\\"\\\"\\n if self.page_number == 0:\\n return True\\n\\n return self.next_page_token is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/iterator.py::Iterator.get_query_params\",\n \"text\": \"gcloud/storage/iterator.py::Iterator.get_query_params\\n def get_query_params(self):\\n \\\"\\\"\\\"Getter for query parameters for the next request.\\n\\n :rtype: dict\\n :returns: A dictionary of query parameters.\\n \\\"\\\"\\\"\\n result = ({self.PAGE_TOKEN: self.next_page_token}\\n if self.next_page_token else {})\\n result.update(self.extra_params)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/iterator.py::Iterator.get_next_page_response\",\n \"text\": \"gcloud/storage/iterator.py::Iterator.get_next_page_response\\n def get_next_page_response(self):\\n \\\"\\\"\\\"Requests the next page from the path provided.\\n\\n :rtype: dict\\n :returns: The parsed JSON response of the next page's contents.\\n \\\"\\\"\\\"\\n if not self.has_next_page():\\n raise RuntimeError('No more pages. Try resetting the iterator.')\\n\\n response = self.connection.api_request(\\n method='GET', path=self.path, query_params=self.get_query_params())\\n\\n self.page_number += 1\\n self.next_page_token = response.get('nextPageToken')\\n\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/iterator.py::Iterator.reset\",\n \"text\": \"gcloud/storage/iterator.py::Iterator.reset\\n def reset(self):\\n \\\"\\\"\\\"Resets the iterator to the beginning.\\\"\\\"\\\"\\n self.page_number = 0\\n self.next_page_token = None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/iterator.py::Iterator.get_items_from_response\",\n \"text\": \"gcloud/storage/iterator.py::Iterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Factory method called while iterating. This should be overriden.\\n\\n This method should be overridden by a subclass. It should\\n accept the API response of a request for the next page of items,\\n and return a list (or other iterable) of items.\\n\\n Typically this method will construct a Bucket or a Key from the\\n page of results in the response.\\n\\n :type response: dict\\n :param response: The response of asking for the next page of items.\\n\\n :rtype: iterable\\n :returns: Items that the iterator should yield.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.__init__\",\n \"text\": \"gcloud/storage/key.py::Key.__init__\\n def __init__(self, bucket=None, name=None, properties=None):\\n \\\"\\\"\\\"Key constructor.\\n\\n :type bucket: :class:`gcloud.storage.bucket.Bucket`\\n :param bucket: The bucket to which this key belongs.\\n\\n :type name: string\\n :param name: The name of the key. This corresponds to the\\n unique path of the object in the bucket.\\n\\n :type properties: dict\\n :param properties: All the other data provided by Cloud Storage.\\n \\\"\\\"\\\"\\n super(Key, self).__init__(name=name, properties=properties)\\n self.bucket = bucket\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.acl\",\n \"text\": \"gcloud/storage/key.py::Key.acl\\n @property\\n def acl(self):\\n \\\"\\\"\\\"Create our ACL on demand.\\\"\\\"\\\"\\n if self._acl is None:\\n self._acl = ObjectACL(self)\\n return self._acl\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.from_dict\",\n \"text\": \"gcloud/storage/key.py::Key.from_dict\\n @classmethod\\n def from_dict(cls, key_dict, bucket=None):\\n \\\"\\\"\\\"Instantiate a :class:`Key` from data returned by the JSON API.\\n\\n :type key_dict: dict\\n :param key_dict: A dictionary of data returned from getting an\\n Cloud Storage object.\\n\\n :type bucket: :class:`gcloud.storage.bucket.Bucket`\\n :param bucket: The bucket to which this key belongs (and by\\n proxy, which connection to use).\\n\\n :rtype: :class:`Key`\\n :returns: A key based on the data provided.\\n \\\"\\\"\\\"\\n\\n return cls(bucket=bucket, name=key_dict['name'], properties=key_dict)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.__repr__\",\n \"text\": \"gcloud/storage/key.py::Key.__repr__\\n def __repr__(self):\\n if self.bucket:\\n bucket_name = self.bucket.name\\n else:\\n bucket_name = None\\n\\n return '<Key: %s, %s>' % (bucket_name, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.connection\",\n \"text\": \"gcloud/storage/key.py::Key.connection\\n @property\\n def connection(self):\\n \\\"\\\"\\\"Getter property for the connection to use with this Key.\\n\\n :rtype: :class:`gcloud.storage.connection.Connection` or None\\n :returns: The connection to use, or None if no connection is set.\\n \\\"\\\"\\\"\\n if self.bucket and self.bucket.connection:\\n return self.bucket.connection\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.path\",\n \"text\": \"gcloud/storage/key.py::Key.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"Getter property for the URL path to this Key.\\n\\n :rtype: string\\n :returns: The URL path to this Key.\\n \\\"\\\"\\\"\\n if not self.bucket:\\n raise ValueError('Cannot determine path without a bucket defined.')\\n elif not self.name:\\n raise ValueError('Cannot determine path without a key name.')\\n\\n return self.bucket.path + '/o/' + urllib.quote(self.name, safe='')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.public_url\",\n \"text\": \"gcloud/storage/key.py::Key.public_url\\n @property\\n def public_url(self):\\n \\\"\\\"\\\"The public URL for this key's object.\\n\\n :rtype: `string`\\n :returns: The public URL for this key.\\n \\\"\\\"\\\"\\n return '{storage_base_url}/{bucket_name}/{quoted_name}'.format(\\n storage_base_url='http://commondatastorage.googleapis.com',\\n bucket_name=self.bucket.name,\\n quoted_name=urllib.quote(self.name, safe=''))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.generate_signed_url\",\n \"text\": \"gcloud/storage/key.py::Key.generate_signed_url\\n def generate_signed_url(self, expiration, method='GET'):\\n \\\"\\\"\\\"Generates a signed URL for this key.\\n\\n If you have a key that you want to allow access to for a set\\n amount of time, you can use this method to generate a URL that\\n is only valid within a certain time period.\\n\\n This is particularly useful if you don't want publicly\\n accessible keys, but don't want to require users to explicitly\\n log in.\\n\\n :type expiration: int, long, datetime.datetime, datetime.timedelta\\n :param expiration: When the signed URL should expire.\\n\\n :type method: string\\n :param method: The HTTP verb that will be used when requesting the URL.\\n\\n :rtype: string\\n :returns: A signed URL you can use to access the resource\\n until expiration.\\n \\\"\\\"\\\"\\n resource = '/{bucket_name}/{quoted_name}'.format(\\n bucket_name=self.bucket.name,\\n quoted_name=urllib.quote(self.name, safe=''))\\n return self.connection.generate_signed_url(resource=resource,\\n expiration=expiration,\\n method=method)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.exists\",\n \"text\": \"gcloud/storage/key.py::Key.exists\\n def exists(self):\\n \\\"\\\"\\\"Determines whether or not this key exists.\\n\\n :rtype: bool\\n :returns: True if the key exists in Cloud Storage.\\n \\\"\\\"\\\"\\n return self.bucket.get_key(self.name) is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.rename\",\n \"text\": \"gcloud/storage/key.py::Key.rename\\n def rename(self, new_name):\\n \\\"\\\"\\\"Renames this key using copy and delete operations.\\n\\n Effectively, copies key to the same bucket with a new name, then\\n deletes the key.\\n\\n .. warning::\\n This method will first duplicate the data and then delete the\\n old key. This means that with very large objects renaming\\n could be a very (temporarily) costly or a very slow operation.\\n\\n :type new_name: string\\n :param new_name: The new name for this key.\\n\\n :rtype: :class:`Key`\\n :returns: The newly-copied key.\\n \\\"\\\"\\\"\\n new_key = self.bucket.copy_key(self, self.bucket, new_name)\\n self.bucket.delete_key(self)\\n return new_key\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.delete\",\n \"text\": \"gcloud/storage/key.py::Key.delete\\n def delete(self):\\n \\\"\\\"\\\"Deletes a key from Cloud Storage.\\n\\n :rtype: :class:`Key`\\n :returns: The key that was just deleted.\\n :raises: :class:`gcloud.storage.exceptions.NotFound`\\n (propagated from\\n :meth:`gcloud.storage.bucket.Bucket.delete_key`).\\n \\\"\\\"\\\"\\n return self.bucket.delete_key(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.download_to_file\",\n \"text\": \"gcloud/storage/key.py::Key.download_to_file\\n def download_to_file(self, file_obj):\\n \\\"\\\"\\\"Download the contents of this key into a file-like object.\\n\\n :type file_obj: file\\n :param file_obj: A file handle to which to write the key's data.\\n\\n :raises: :class:`gcloud.storage.exceptions.NotFound`\\n \\\"\\\"\\\"\\n for chunk in _KeyDataIterator(self):\\n file_obj.write(chunk)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.download_to_filename\",\n \"text\": \"gcloud/storage/key.py::Key.download_to_filename\\n def download_to_filename(self, filename):\\n \\\"\\\"\\\"Download the contents of this key into a named file.\\n\\n :type filename: string\\n :param filename: A filename to be passed to ``open``.\\n\\n :raises: :class:`gcloud.storage.exceptions.NotFound`\\n \\\"\\\"\\\"\\n with open(filename, 'wb') as file_obj:\\n self.download_to_file(file_obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.download_as_string\",\n \"text\": \"gcloud/storage/key.py::Key.download_as_string\\n def download_as_string(self):\\n \\\"\\\"\\\"Download the contents of this key as a string.\\n\\n :rtype: string\\n :returns: The data stored in this key.\\n :raises: :class:`gcloud.storage.exceptions.NotFound`\\n \\\"\\\"\\\"\\n string_buffer = StringIO()\\n self.download_to_file(string_buffer)\\n return string_buffer.getvalue()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.upload_from_file\",\n \"text\": \"gcloud/storage/key.py::Key.upload_from_file\\n def upload_from_file(self, file_obj, rewind=False, size=None,\\n content_type=None):\\n \\\"\\\"\\\"Upload the contents of this key from a file-like object.\\n\\n .. note::\\n\\n The effect of uploading to an existing key depends on the\\n \\\"versioning\\\" and \\\"lifecycle\\\" policies defined on the key's\\n bucket. In the absence of those policies, upload will\\n overwrite any existing contents.\\n\\n See the `object versioning\\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\\n API documents for details.\\n\\n :type file_obj: file\\n :param file_obj: A file handle open for reading.\\n\\n :type rewind: bool\\n :param rewind: If True, seek to the beginning of the file handle before\\n writing the file to Cloud Storage.\\n\\n :type size: int\\n :param size: The number of bytes to read from the file handle.\\n If not provided, we'll try to guess the size using\\n :func:`os.fstat`\\n \\\"\\\"\\\"\\n # Rewind the file if desired.\\n if rewind:\\n file_obj.seek(0, os.SEEK_SET)\\n\\n # Get the basic stats about the file.\\n total_bytes = size or os.fstat(file_obj.fileno()).st_size\\n bytes_uploaded = 0\\n\\n # Set up a resumable upload session.\\n headers = {\\n 'X-Upload-Content-Type': content_type or 'application/unknown',\\n 'X-Upload-Content-Length': total_bytes,\\n }\\n\\n query_params = {\\n 'uploadType': 'resumable',\\n 'name': urllib.quote_plus(self.name),\\n }\\n\\n upload_url = self.connection.build_api_url(\\n path=self.bucket.path + '/o',\\n query_params=query_params,\\n api_base_url=self.connection.API_BASE_URL + '/upload')\\n\\n response, _ = self.connection.mak\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.upload_from_filename\",\n \"text\": \"gcloud/storage/key.py::Key.upload_from_filename\\n def upload_from_filename(self, filename):\\n \\\"\\\"\\\"Upload this key's contents from the content of f named file.\\n\\n .. note::\\n\\n The effect of uploading to an existing key depends on the\\n \\\"versioning\\\" and \\\"lifecycle\\\" policies defined on the key's\\n bucket. In the absence of those policies, upload will\\n overwrite any existing contents.\\n\\n See the `object versioning\\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\\n API documents for details.\\n\\n :type filename: string\\n :param filename: The path to the file.\\n \\\"\\\"\\\"\\n content_type, _ = mimetypes.guess_type(filename)\\n\\n with open(filename, 'rb') as file_obj:\\n self.upload_from_file(file_obj, content_type=content_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.upload_from_string\",\n \"text\": \"gcloud/storage/key.py::Key.upload_from_string\\n def upload_from_string(self, data, content_type='text/plain'):\\n \\\"\\\"\\\"Upload contents of this key from the provided string.\\n\\n .. note::\\n\\n The effect of uploading to an existing key depends on the\\n \\\"versioning\\\" and \\\"lifecycle\\\" policies defined on the key's\\n bucket. In the absence of those policies, upload will\\n overwrite any existing contents.\\n\\n See the `object versioning\\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\\n API documents for details.\\n\\n :type data: string\\n :param data: The data to store in this key.\\n\\n :rtype: :class:`Key`\\n :returns: The updated Key object.\\n \\\"\\\"\\\"\\n string_buffer = StringIO()\\n string_buffer.write(data)\\n self.upload_from_file(file_obj=string_buffer, rewind=True,\\n size=string_buffer.len,\\n content_type=content_type)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.make_public\",\n \"text\": \"gcloud/storage/key.py::Key.make_public\\n def make_public(self):\\n \\\"\\\"\\\"Make this key public giving all users read access.\\n\\n :returns: The current object.\\n \\\"\\\"\\\"\\n self.get_acl().all().grant_read()\\n self.acl.save()\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.component_count\",\n \"text\": \"gcloud/storage/key.py::Key.component_count\\n @property\\n def component_count(self):\\n \\\"\\\"\\\"Number of underlying components that make up this object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['componentCount']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.etag\",\n \"text\": \"gcloud/storage/key.py::Key.etag\\n @property\\n def etag(self):\\n \\\"\\\"\\\"Retrieve the ETag for the object.\\n\\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\\n https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['etag']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.generation\",\n \"text\": \"gcloud/storage/key.py::Key.generation\\n @property\\n def generation(self):\\n \\\"\\\"\\\"Retrieve the generation for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['generation']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.id\",\n \"text\": \"gcloud/storage/key.py::Key.id\\n @property\\n def id(self):\\n \\\"\\\"\\\"Retrieve the ID for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['id']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.media_link\",\n \"text\": \"gcloud/storage/key.py::Key.media_link\\n @property\\n def media_link(self):\\n \\\"\\\"\\\"Retrieve the media download URI for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['mediaLink']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.metadata\",\n \"text\": \"gcloud/storage/key.py::Key.metadata\\n @property\\n def metadata(self):\\n \\\"\\\"\\\"Retrieve arbitrary/application specific metadata for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: dict\\n \\\"\\\"\\\"\\n return copy.deepcopy(self.properties['metadata'])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.metadata\",\n \"text\": \"gcloud/storage/key.py::Key.metadata\\n @metadata.setter\\n def metadata(self, value):\\n \\\"\\\"\\\"Update arbitrary/application specific metadata for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :type value: dict\\n \\\"\\\"\\\"\\n self._patch_properties({'metadata': value})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.metageneration\",\n \"text\": \"gcloud/storage/key.py::Key.metageneration\\n @property\\n def metageneration(self):\\n \\\"\\\"\\\"Retrieve the metageneration for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['metageneration']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.owner\",\n \"text\": \"gcloud/storage/key.py::Key.owner\\n @property\\n def owner(self):\\n \\\"\\\"\\\"Retrieve info about the owner of the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: dict\\n :returns: mapping of owner's role/ID.\\n \\\"\\\"\\\"\\n return self.properties['owner'].copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.self_link\",\n \"text\": \"gcloud/storage/key.py::Key.self_link\\n @property\\n def self_link(self):\\n \\\"\\\"\\\"Retrieve the URI for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['selfLink']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.size\",\n \"text\": \"gcloud/storage/key.py::Key.size\\n @property\\n def size(self):\\n \\\"\\\"\\\"Size of the object, in bytes.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['size']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.storage_class\",\n \"text\": \"gcloud/storage/key.py::Key.storage_class\\n @property\\n def storage_class(self):\\n \\\"\\\"\\\"Retrieve the storage class for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects and\\n https://cloud.google.com/storage/docs/durable-reduced-availability#_DRA_Bucket\\n\\n :rtype: string\\n :returns: Currently one of \\\"STANDARD\\\", \\\"DURABLE_REDUCED_AVAILABILITY\\\"\\n \\\"\\\"\\\"\\n return self.properties['storageClass']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.time_deleted\",\n \"text\": \"gcloud/storage/key.py::Key.time_deleted\\n @property\\n def time_deleted(self):\\n \\\"\\\"\\\"Retrieve the timestamp at which the object was deleted.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string or None\\n :returns: timestamp in RFC 3339 format, or None if the object\\n has a \\\"live\\\" version.\\n \\\"\\\"\\\"\\n return self.properties.get('timeDeleted')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::Key.updated\",\n \"text\": \"gcloud/storage/key.py::Key.updated\\n @property\\n def updated(self):\\n \\\"\\\"\\\"Retrieve the timestamp at which the object was updated.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string\\n :returns: timestamp in RFC 3339 format.\\n \\\"\\\"\\\"\\n return self.properties['updated']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::_KeyDataIterator.__init__\",\n \"text\": \"gcloud/storage/key.py::_KeyDataIterator.__init__\\n def __init__(self, key):\\n self.key = key\\n # NOTE: These variables will be initialized by reset().\\n self._bytes_written = None\\n self._total_bytes = None\\n self.reset()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::_KeyDataIterator.__iter__\",\n \"text\": \"gcloud/storage/key.py::_KeyDataIterator.__iter__\\n def __iter__(self):\\n while self.has_more_data():\\n yield self.get_next_chunk()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::_KeyDataIterator.reset\",\n \"text\": \"gcloud/storage/key.py::_KeyDataIterator.reset\\n def reset(self):\\n \\\"\\\"\\\"Resets the iterator to the beginning.\\\"\\\"\\\"\\n self._bytes_written = 0\\n self._total_bytes = None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::_KeyDataIterator.has_more_data\",\n \"text\": \"gcloud/storage/key.py::_KeyDataIterator.has_more_data\\n def has_more_data(self):\\n \\\"\\\"\\\"Determines whether or not this iterator has more data to read.\\n\\n :rtype: bool\\n :returns: Whether the iterator has more data or not.\\n \\\"\\\"\\\"\\n if self._bytes_written == 0:\\n return True\\n elif not self._total_bytes:\\n # self._total_bytes **should** be set by this point.\\n # If it isn't, something is wrong.\\n raise ValueError('Size of object is unknown.')\\n else:\\n return self._bytes_written < self._total_bytes\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::_KeyDataIterator.get_headers\",\n \"text\": \"gcloud/storage/key.py::_KeyDataIterator.get_headers\\n def get_headers(self):\\n \\\"\\\"\\\"Gets range header(s) for next chunk of data.\\n\\n :rtype: dict\\n :returns: A dictionary of query parameters.\\n \\\"\\\"\\\"\\n start = self._bytes_written\\n end = self._bytes_written + self.key.CHUNK_SIZE - 1\\n\\n if self._total_bytes and end > self._total_bytes:\\n end = ''\\n\\n return {'Range': 'bytes=%s-%s' % (start, end)}\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::_KeyDataIterator.get_url\",\n \"text\": \"gcloud/storage/key.py::_KeyDataIterator.get_url\\n def get_url(self):\\n \\\"\\\"\\\"Gets URL to read next chunk of data.\\n\\n :rtype: string\\n :returns: A URL.\\n \\\"\\\"\\\"\\n return self.key.connection.build_api_url(\\n path=self.key.path, query_params={'alt': 'media'})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/key.py::_KeyDataIterator.get_next_chunk\",\n \"text\": \"gcloud/storage/key.py::_KeyDataIterator.get_next_chunk\\n def get_next_chunk(self):\\n \\\"\\\"\\\"Gets the next chunk of data.\\n\\n Uses CHUNK_SIZE to determine how much data to get.\\n\\n :rtype: string\\n :returns: The chunk of data read from the key.\\n :raises: :class:`RuntimeError` if no more data or\\n :class:`gcloud.storage.exceptions.StorageError` in the\\n case of an unexpected response status code.\\n \\\"\\\"\\\"\\n if not self.has_more_data():\\n raise RuntimeError('No more data in this iterator. Try resetting.')\\n\\n response, content = self.key.connection.make_request(\\n method='GET', url=self.get_url(), headers=self.get_headers())\\n\\n if response.status in (200, 206):\\n self._bytes_written += len(content)\\n\\n if 'content-range' in response:\\n content_range = response['content-range']\\n self._total_bytes = int(content_range.rsplit('/', 1)[1])\\n\\n return content\\n\\n # Expected a 200 or a 206. Got something else, which is unknown.\\n raise StorageError(response)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":75,"cells":{"instance_id":{"kind":"string","value":"conan-io__conan-9596"},"repo":{"kind":"string","value":"conan-io/conan"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Default ``cmake_layout()`` source folder\nThis layout is assuming that the main library ``CMakeLists.txt`` is inside a ``src`` subfolder in the repo.\r\n\r\nThis is probably not the most common user layout, in which they will have a CMakeLists.txt in the root of the folder:\r\n\r\n- How to model this?\r\n- Should we change the ``cmake_layout()`` default to ``folders.source = \".\"``?\r\n- Check implications of defining the root as source folder.\n"},"gold_files":{"kind":"list like","value":["conan/tools/layout/__init__.py","conans/assets/templates/new_v2_cmake.py"],"string":"[\n \"conan/tools/layout/__init__.py\",\n \"conans/assets/templates/new_v2_cmake.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["conan/tools/layout/__init__.py::cmake_layout","conans/assets/templates/new_v2_cmake.py::<module>","conans/assets/templates/new_v2_cmake.py::get_cmake_exe_files","conans/assets/templates/new_v2_cmake.py::get_cmake_lib_files"],"string":"[\n \"conan/tools/layout/__init__.py::cmake_layout\",\n \"conans/assets/templates/new_v2_cmake.py::<module>\",\n \"conans/assets/templates/new_v2_cmake.py::get_cmake_exe_files\",\n \"conans/assets/templates/new_v2_cmake.py::get_cmake_lib_files\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"conan/tools/layout/__init__.py","text":"conan/tools/layout/__init__.py\ndef cmake_layout(conanfile, generator=None)\ndef clion_layout(conanfile)\ndef vs_layout(conanfile)\nclass LayoutPackager\n def __init__(self, conanfile)\n def package(self)\n def _package_cppinfo(self, origin_name, origin_cppinfo, dest_cppinfo) # @param origin_name: one from [\"source\", \"build\"]","label":1},{"cid":"conans/assets/templates/new_v2_cmake.py","text":"conans/assets/templates/new_v2_cmake.py\ndef get_cmake_lib_files(name, version, package_name='Pkg')\ndef get_cmake_exe_files(name, version, package_name='Pkg')","label":1},{"cid":"conans/client/cmd/export.py","text":"conans/client/cmd/export.py\ndef export_alias(package_layout, target_ref, output, revisions_enabled)\ndef check_casing_conflict(cache, ref)\ndef cmd_export(app, conanfile_path, name, version, user, channel, keep_source, export=True, graph_lock=None, ignore_dirty=False) # Export the recipe\ndef _check_settings_for_warnings(conanfile, output)\ndef _capture_scm_auto_fields(conanfile, conanfile_dir, package_layout, output, ignore_dirty, scm_to_conandata) # Deduce the values for the scm auto fields or functions assigned to 'url' or 'rev\ndef _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata)\ndef _replace_scm_data_in_conanfile(conanfile_path, scm_data)\ndef _detect_scm_revision(path)\ndef _update_revision_in_metadata(package_layout, revisions_enabled, output, path, manifest, revision_mode)\ndef _classify_patterns(patterns)\ndef _export_scm(scm_data, origin_folder, scm_sources_folder, output) # Copy the local folder to the scm_sources folder in the cache, this enables to wo\ndef export_source(conanfile, origin_folder, destination_source_folder)\ndef export_recipe(conanfile, origin_folder, destination_folder)\ndef _run_method(conanfile, method, origin_folder, destination_folder, output)","label":0},{"cid":"conans/client/file_copier.py","text":"conans/client/file_copier.py\ndef report_copied_files(copied, output, message_suffix='Copied')\nclass FileCopier # main responsible of copying files from place to place:\n def __init__(self, source_folders, root_destination_folder) # Takes the base folders to copy resources src -> dst. These folders names\n def report(self, output)\n def __call__(self, pattern, dst='', src='', keep_path=True, links=False, symlinks=None, excludes=None, ignore_case=True) # param pattern: an fnmatch file pattern of the files that should be copied. Eg. *\n def _copy(self, base_src, pattern, src, dst, symlinks, ignore_case, excludes, keep_path, excluded_folders)\n def _filter_files(src, pattern, links, excludes, ignore_case, excluded_folders) # return a list of the files matching the patterns\n def link_folders(src, dst, linked_folders)\n def _copy_files(files, src, dst, keep_path, symlinks) # executes a multiple file copy from [(src_file, dst_file), (..)]","label":0},{"cid":"conans/client/installer.py","text":"conans/client/installer.py\ndef build_id(conan_file)\ndef add_env_conaninfo(conan_file, subtree_libnames)\nclass _PackageBuilder\n def __init__(self, cache, output, hook_manager, remote_manager, generators)\n def _get_build_folder(self, conanfile, package_layout, pref, keep_build, recorder)\n def _prepare_sources(self, conanfile, pref, package_layout, remotes)\n def _copy_sources(conanfile, source_folder, build_folder)\n def _build(self, conanfile, pref)\n def _package(self, conanfile, pref, package_layout, conanfile_path)\n def build_package(self, node, keep_build, recorder, remotes)\ndef _remove_folder_raising(folder)\ndef _handle_system_requirements(conan_file, pref, cache, out) # check first the system_reqs/system_requirements.txt existence, if not existing\ndef call_system_requirements(conanfile, output)\nclass BinaryInstaller # main responsible of retrieving binary packages or building them from source\n def __init__(self, app, recorder)\n def install(self, deps_graph, remotes, build_mode, update, profile_host, profile_build, graph_lock, keep_build=False)\n def _classify(nodes_by_level)\n def _raise_missing(self, missing)\n def _download(self, downloads, processed_package_refs) # executes the download of packages (both download and update), only once for a gi\n def _download(n)\n def _download_pkg(self, layout, node)\n def _build(self, nodes_by_level, keep_build, root_node, profile_host, profile_build, graph_lock, remotes, build_mode, update)\n def _handle_node_editable(self, node, profile_host, profile_build, graph_lock)\n def _handle_node_cache(self, node, keep_build, processed_package_references, remotes)\n def _build_package(self, node, output, keep_build, remotes)\n def _propagate_info(self, node, using_build_profile)\n def _call_package_info(self, conanfile, package_folder, ref, is_editable)","label":0},{"cid":"conans/model/conan_file.py","text":"conans/model/conan_file.py\ndef create_options(conanfile)\ndef create_requirements(conanfile)\ndef create_settings(conanfile, settings)\ndef _env_and_python(conanfile)\ndef get_env_context_manager(conanfile, without_python=False)\nclass ConanFile # The base class for all package recipes\n def __init__(self, output, runner, display_name='', user=None, channel=None)\n def context(self)\n def dependencies(self)\n def ref(self)\n def pref(self)\n def buildenv(self)\n def initialize(self, settings, env, buildenv=None)\n def new_cpp_info(self)\n def source_folder(self)\n def source_folder(self, folder)\n def build_folder(self)\n def build_folder(self, folder)\n def package_folder(self)\n def package_folder(self, folder)\n def install_folder(self)\n def install_folder(self, folder)\n def generators_folder(self)\n def imports_folder(self)\n def imports_folder(self, folder)\n def env(self) # Apply the self.deps_env_info into a copy of self._conan_env_values (will priorit\n def channel(self)\n def user(self)\n def collect_libs(self, folder=None)\n def build_policy_missing(self)\n def build_policy_always(self)\n def source(self)\n def system_requirements(self) # this method can be overwritten to implement logic for system package\n def config_options(self) # modify options, probably conditioned to some settings. This call is executed\n def configure(self) # modify settings, probably conditioned to some options. This call is executed\n def build(self) # build your project calling the desired build tools as done in the command line.\n def package(self) # package the needed files from source and build folders.\n def package_info(self) # define cpp_build_info, flags, etc\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True, ignore_errors=False, run_environment=False, with_login=True, env=None)\n def _run(cmd, _env)\n def package_id(self) # modify the binary info, typically to narrow values\n def test(self) # test the generated executable.\n def __repr__(self)","label":0}],"string":"[\n {\n \"cid\": \"conan/tools/layout/__init__.py\",\n \"text\": \"conan/tools/layout/__init__.py\\ndef cmake_layout(conanfile, generator=None)\\ndef clion_layout(conanfile)\\ndef vs_layout(conanfile)\\nclass LayoutPackager\\n def __init__(self, conanfile)\\n def package(self)\\n def _package_cppinfo(self, origin_name, origin_cppinfo, dest_cppinfo) # @param origin_name: one from [\\\"source\\\", \\\"build\\\"]\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/assets/templates/new_v2_cmake.py\",\n \"text\": \"conans/assets/templates/new_v2_cmake.py\\ndef get_cmake_lib_files(name, version, package_name='Pkg')\\ndef get_cmake_exe_files(name, version, package_name='Pkg')\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/cmd/export.py\",\n \"text\": \"conans/client/cmd/export.py\\ndef export_alias(package_layout, target_ref, output, revisions_enabled)\\ndef check_casing_conflict(cache, ref)\\ndef cmd_export(app, conanfile_path, name, version, user, channel, keep_source, export=True, graph_lock=None, ignore_dirty=False) # Export the recipe\\ndef _check_settings_for_warnings(conanfile, output)\\ndef _capture_scm_auto_fields(conanfile, conanfile_dir, package_layout, output, ignore_dirty, scm_to_conandata) # Deduce the values for the scm auto fields or functions assigned to 'url' or 'rev\\ndef _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata)\\ndef _replace_scm_data_in_conanfile(conanfile_path, scm_data)\\ndef _detect_scm_revision(path)\\ndef _update_revision_in_metadata(package_layout, revisions_enabled, output, path, manifest, revision_mode)\\ndef _classify_patterns(patterns)\\ndef _export_scm(scm_data, origin_folder, scm_sources_folder, output) # Copy the local folder to the scm_sources folder in the cache, this enables to wo\\ndef export_source(conanfile, origin_folder, destination_source_folder)\\ndef export_recipe(conanfile, origin_folder, destination_folder)\\ndef _run_method(conanfile, method, origin_folder, destination_folder, output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/file_copier.py\",\n \"text\": \"conans/client/file_copier.py\\ndef report_copied_files(copied, output, message_suffix='Copied')\\nclass FileCopier # main responsible of copying files from place to place:\\n def __init__(self, source_folders, root_destination_folder) # Takes the base folders to copy resources src -> dst. These folders names\\n def report(self, output)\\n def __call__(self, pattern, dst='', src='', keep_path=True, links=False, symlinks=None, excludes=None, ignore_case=True) # param pattern: an fnmatch file pattern of the files that should be copied. Eg. *\\n def _copy(self, base_src, pattern, src, dst, symlinks, ignore_case, excludes, keep_path, excluded_folders)\\n def _filter_files(src, pattern, links, excludes, ignore_case, excluded_folders) # return a list of the files matching the patterns\\n def link_folders(src, dst, linked_folders)\\n def _copy_files(files, src, dst, keep_path, symlinks) # executes a multiple file copy from [(src_file, dst_file), (..)]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py\",\n \"text\": \"conans/client/installer.py\\ndef build_id(conan_file)\\ndef add_env_conaninfo(conan_file, subtree_libnames)\\nclass _PackageBuilder\\n def __init__(self, cache, output, hook_manager, remote_manager, generators)\\n def _get_build_folder(self, conanfile, package_layout, pref, keep_build, recorder)\\n def _prepare_sources(self, conanfile, pref, package_layout, remotes)\\n def _copy_sources(conanfile, source_folder, build_folder)\\n def _build(self, conanfile, pref)\\n def _package(self, conanfile, pref, package_layout, conanfile_path)\\n def build_package(self, node, keep_build, recorder, remotes)\\ndef _remove_folder_raising(folder)\\ndef _handle_system_requirements(conan_file, pref, cache, out) # check first the system_reqs/system_requirements.txt existence, if not existing\\ndef call_system_requirements(conanfile, output)\\nclass BinaryInstaller # main responsible of retrieving binary packages or building them from source\\n def __init__(self, app, recorder)\\n def install(self, deps_graph, remotes, build_mode, update, profile_host, profile_build, graph_lock, keep_build=False)\\n def _classify(nodes_by_level)\\n def _raise_missing(self, missing)\\n def _download(self, downloads, processed_package_refs) # executes the download of packages (both download and update), only once for a gi\\n def _download(n)\\n def _download_pkg(self, layout, node)\\n def _build(self, nodes_by_level, keep_build, root_node, profile_host, profile_build, graph_lock, remotes, build_mode, update)\\n def _handle_node_editable(self, node, profile_host, profile_build, graph_lock)\\n def _handle_node_cache(self, node, keep_build, processed_package_references, remotes)\\n def _build_package(self, node, output, keep_build, remotes)\\n def _propagate_info(self, node, using_build_profile)\\n def _call_package_info(self, conanfile, package_folder, ref, is_editable)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py\",\n \"text\": \"conans/model/conan_file.py\\ndef create_options(conanfile)\\ndef create_requirements(conanfile)\\ndef create_settings(conanfile, settings)\\ndef _env_and_python(conanfile)\\ndef get_env_context_manager(conanfile, without_python=False)\\nclass ConanFile # The base class for all package recipes\\n def __init__(self, output, runner, display_name='', user=None, channel=None)\\n def context(self)\\n def dependencies(self)\\n def ref(self)\\n def pref(self)\\n def buildenv(self)\\n def initialize(self, settings, env, buildenv=None)\\n def new_cpp_info(self)\\n def source_folder(self)\\n def source_folder(self, folder)\\n def build_folder(self)\\n def build_folder(self, folder)\\n def package_folder(self)\\n def package_folder(self, folder)\\n def install_folder(self)\\n def install_folder(self, folder)\\n def generators_folder(self)\\n def imports_folder(self)\\n def imports_folder(self, folder)\\n def env(self) # Apply the self.deps_env_info into a copy of self._conan_env_values (will priorit\\n def channel(self)\\n def user(self)\\n def collect_libs(self, folder=None)\\n def build_policy_missing(self)\\n def build_policy_always(self)\\n def source(self)\\n def system_requirements(self) # this method can be overwritten to implement logic for system package\\n def config_options(self) # modify options, probably conditioned to some settings. This call is executed\\n def configure(self) # modify settings, probably conditioned to some options. This call is executed\\n def build(self) # build your project calling the desired build tools as done in the command line.\\n def package(self) # package the needed files from source and build folders.\\n def package_info(self) # define cpp_build_info, flags, etc\\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True, ignore_errors=False, run_environment=False, with_login=True, env=None)\\n def _run(cmd, _env)\\n def package_id(self) # modify the binary info, typically to narrow values\\n def test(self) # test the generated executable.\\n def __repr__(self)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"conan/tools/layout/__init__.py::cmake_layout","text":"conan/tools/layout/__init__.py::cmake_layout\ndef cmake_layout(conanfile, generator=None):\n gen = conanfile.conf[\"tools.cmake.cmaketoolchain:generator\"] or generator\n if gen:\n multi = \"Visual\" in gen or \"Xcode\" in gen or \"Multi-Config\" in gen\n elif conanfile.settings.compiler == \"Visual Studio\" or conanfile.settings.compiler == \"msvc\":\n multi = True\n else:\n multi = False\n\n conanfile.folders.source = \"src\"\n if multi:\n conanfile.folders.build = \"build\"\n conanfile.folders.generators = \"build/conan\"\n else:\n build_type = str(conanfile.settings.build_type).lower()\n conanfile.folders.build = \"cmake-build-{}\".format(build_type)\n conanfile.folders.generators = os.path.join(conanfile.folders.build, \"conan\")\n\n conanfile.cpp.source.includedirs = [\".\"]\n if multi:\n conanfile.cpp.build.libdirs = [\"{}\".format(conanfile.settings.build_type)]\n conanfile.cpp.build.bindirs = [\"{}\".format(conanfile.settings.build_type)]\n else:\n conanfile.cpp.build.libdirs = [\".\"]\n conanfile.cpp.build.bindirs = [\".\"]","label":1},{"cid":"conan/tools/layout/__init__.py::clion_layout","text":"conan/tools/layout/__init__.py::clion_layout\ndef clion_layout(conanfile):\n if not conanfile.settings.get_safe(\"build_type\"):\n raise ConanException(\"The 'clion_layout' requires the 'build_type' setting\")\n base = \"cmake-build-{}\".format(str(conanfile.settings.build_type).lower())\n conanfile.folders.build = base\n conanfile.cpp.build.libdirs = [\".\"]\n conanfile.folders.generators = os.path.join(base, \"generators\")\n conanfile.folders.source = \".\"\n conanfile.cpp.source.includedirs = [\".\"]","label":0},{"cid":"conan/tools/layout/__init__.py::vs_layout","text":"conan/tools/layout/__init__.py::vs_layout\ndef vs_layout(conanfile):\n if not conanfile.settings.get_safe(\"build_type\"):\n raise ConanException(\"The 'vs_layout' requires the 'build_type' setting\")\n if not conanfile.settings.get_safe(\"arch\"):\n raise ConanException(\"The 'vs_layout' requires the 'arch' setting\")\n\n if conanfile.settings.arch != \"x86\":\n arch = msbuild_arch(conanfile.settings.arch)\n if not arch:\n raise ConanException(\"The 'vs_layout' doesn't \"\n \"work with the arch '{}'\".format(conanfile.settings.arch))\n base = \"{}/{}\".format(arch, str(conanfile.settings.build_type))\n else:\n base = str(conanfile.settings.build_type)\n\n conanfile.folders.build = base\n conanfile.cpp.build.libdirs = [\".\"]\n conanfile.folders.generators = os.path.join(base, \"generators\")\n conanfile.folders.source = \".\"\n conanfile.cpp.source.includedirs = [\".\"]","label":0},{"cid":"conan/tools/layout/__init__.py::LayoutPackager.__init__","text":"conan/tools/layout/__init__.py::LayoutPackager.__init__\n def __init__(self, conanfile):\n self._conanfile = conanfile","label":0},{"cid":"conan/tools/layout/__init__.py::LayoutPackager.package","text":"conan/tools/layout/__init__.py::LayoutPackager.package\n def package(self):\n cf = self._conanfile\n\n # Check that the components declared in source/build are in package\n cnames = set(cf.cpp.source.component_names)\n cnames = cnames.union(set(cf.cpp.build.component_names))\n if cnames.difference(set(cf.cpp.package.component_names)):\n # TODO: Raise? Warning? Ignore?\n raise ConanException(\"There are components declared in source_cpp_info.components\"\n \" or in build_cpp_info.components that are not declared in\"\n \" package_cpp_info.components\")\n\n if cnames:\n for cname in cnames:\n if cname in cf.cpp.source.components:\n self._package_cppinfo(\"source\", cf.cpp.source.components[cname],\n cf.cpp.package.components[cname])\n if cname in cf.cpp.build.components:\n self._package_cppinfo(\"build\", cf.cpp.build.components[cname],\n cf.cpp.package.components[cname])\n else: # No components declared\n self._package_cppinfo(\"source\", cf.cpp.source, cf.cpp.package)\n self._package_cppinfo(\"build\", cf.cpp.build, cf.cpp.package)","label":0},{"cid":"conan/tools/layout/__init__.py::LayoutPackager._package_cppinfo","text":"conan/tools/layout/__init__.py::LayoutPackager._package_cppinfo\n def _package_cppinfo(self, origin_name, origin_cppinfo, dest_cppinfo):\n \"\"\"\n @param origin_name: one from [\"source\", \"build\"]\n @param origin_cppinfo: cpp_info object of an origin (can be a component cppinfo too)\n @param dest_cppinfo: cpp_info object of the package or a component from package\n \"\"\"\n patterns_var = getattr(self._conanfile.patterns, origin_name)\n base_folder = getattr(self._conanfile, \"{}_folder\".format(origin_name))\n for var in [\"include\", \"lib\", \"bin\", \"framework\", \"src\", \"build\", \"res\"]:\n dirs_var_name = \"{}dirs\".format(var)\n origin_paths = getattr(origin_cppinfo, dirs_var_name)\n if not origin_paths:\n continue\n patterns = getattr(patterns_var, var)\n destinations = getattr(dest_cppinfo, dirs_var_name)\n if not destinations: # For example: Not declared \"includedirs\" in package.cpp_info\n continue\n if len(destinations) > 1:\n # Check if there is only one possible destination at package, otherwise the\n # copy would need to be done manually\n err_msg = \"The package has more than 1 cpp_info.{}, cannot package automatically\"\n raise ConanException(err_msg.format(dirs_var_name))\n\n for d in origin_paths:\n copier = FileCopier([os.path.join(base_folder, d)],\n self._conanfile.folders.base_package)\n for pattern in patterns:\n copier(pattern, dst=destinations[0])","label":0},{"cid":"conans/assets/templates/new_v2_cmake.py::get_cmake_lib_files","text":"conans/assets/templates/new_v2_cmake.py::get_cmake_lib_files\ndef get_cmake_lib_files(name, version, package_name=\"Pkg\"):\n files = {\"conanfile.py\": conanfile_sources_v2.format(name=name, version=version,\n package_name=package_name),\n \"src/{}.cpp\".format(name): source_cpp.format(name=name, version=version),\n \"src/{}.h\".format(name): source_h.format(name=name, version=version),\n \"src/CMakeLists.txt\": cmake_v2.format(name=name, version=version),\n \"test_package/conanfile.py\": test_conanfile_v2.format(name=name,\n version=version,\n package_name=package_name),\n \"test_package/src/example.cpp\": test_main.format(name=name),\n \"test_package/src/CMakeLists.txt\": test_cmake_v2.format(name=name)}\n return files","label":1},{"cid":"conans/assets/templates/new_v2_cmake.py::get_cmake_exe_files","text":"conans/assets/templates/new_v2_cmake.py::get_cmake_exe_files\ndef get_cmake_exe_files(name, version, package_name=\"Pkg\"):\n files = {\"conanfile.py\": conanfile_exe.format(name=name, version=version,\n package_name=package_name),\n \"src/{}.cpp\".format(name): source_cpp.format(name=name, version=version),\n \"src/{}.h\".format(name): source_h.format(name=name, version=version),\n \"src/main.cpp\": test_main.format(name=name),\n \"src/CMakeLists.txt\": cmake_exe_v2.format(name=name, version=version),\n \"test_package/conanfile.py\": test_conanfile_exe_v2.format(name=name,\n version=version,\n package_name=package_name)\n }\n return files","label":1},{"cid":"conans/client/cmd/export.py::export_alias","text":"conans/client/cmd/export.py::export_alias\ndef export_alias(package_layout, target_ref, output, revisions_enabled):\n revision_mode = \"hash\"\n conanfile = \"\"\"\nfrom conans import ConanFile\n\nclass AliasConanfile(ConanFile):\n alias = \"%s\"\n revision_mode = \"%s\"\n\"\"\" % (target_ref.full_str(), revision_mode)\n\n save(package_layout.conanfile(), conanfile)\n manifest = FileTreeManifest.create(package_layout.export())\n manifest.save(folder=package_layout.export())\n\n # Create the metadata for the alias\n _update_revision_in_metadata(package_layout=package_layout, revisions_enabled=revisions_enabled,\n output=output, path=None, manifest=manifest,\n revision_mode=revision_mode)","label":0},{"cid":"conans/client/cmd/export.py::check_casing_conflict","text":"conans/client/cmd/export.py::check_casing_conflict\ndef check_casing_conflict(cache, ref):\n # Check for casing conflict\n # Maybe a platform check could be added, but depends on disk partition\n refs = search_recipes(cache, ref, ignorecase=True)\n refs2 = [ConanFileReference(r.name, r.version, r.user if ref.user else None,\n r.channel if ref.channel else None, validate=False) for r in refs]\n\n if refs and ref not in refs2:\n raise ConanException(\"Cannot export package with same name but different case\\n\"\n \"You exported '%s' but already existing '%s'\"\n % (str(ref), \" \".join(str(s) for s in refs)))","label":0},{"cid":"conans/client/cmd/export.py::cmd_export","text":"conans/client/cmd/export.py::cmd_export\ndef cmd_export(app, conanfile_path, name, version, user, channel, keep_source,\n export=True, graph_lock=None, ignore_dirty=False):\n \"\"\" Export the recipe\n param conanfile_path: the original source directory of the user containing a\n conanfile.py\n \"\"\"\n loader, cache, hook_manager, output = app.loader, app.cache, app.hook_manager, app.out\n revisions_enabled = app.config.revisions_enabled\n scm_to_conandata = app.config.scm_to_conandata\n conanfile = loader.load_export(conanfile_path, name, version, user, channel)\n\n # FIXME: Conan 2.0, deprecate CONAN_USER AND CONAN_CHANNEL and remove this try excepts\n # Take the default from the env vars if they exist to not break behavior\n try:\n user = conanfile.user\n except ConanV2Exception:\n raise\n except ConanException:\n user = None\n\n try:\n channel = conanfile.channel\n except ConanV2Exception:\n raise\n except ConanException:\n channel = None\n\n ref = ConanFileReference(conanfile.name, conanfile.version, user, channel)\n conanfile.display_name = str(ref)\n conanfile.output.scope = conanfile.display_name\n\n # If we receive lock information, python_requires could have been locked\n if graph_lock:\n node_id = graph_lock.get_consumer(ref)\n python_requires = graph_lock.python_requires(node_id)\n # TODO: check that the locked python_requires are different from the loaded ones\n app.range_resolver.clear_output() # invalidate previous version range output\n conanfile = loader.load_export(conanfile_path, conanfile.name, conanfile.version,\n ref.user, ref.channel, python_requires)\n\n check_casing_conflict(cache=cache, ref=ref)\n package_layout = cache.package_layout(ref, short_paths=conanfile.short_paths)\n if not export:\n metadata = package_layout.load_metadata()\n recipe_revision = metad","label":0},{"cid":"conans/client/cmd/export.py::_check_settings_for_warnings","text":"conans/client/cmd/export.py::_check_settings_for_warnings\ndef _check_settings_for_warnings(conanfile, output):\n if not conanfile.settings:\n return\n try:\n if 'os_build' not in conanfile.settings:\n return\n if 'os' not in conanfile.settings:\n return\n\n output.writeln(\"*\" * 60, front=Color.BRIGHT_RED)\n output.writeln(\" This package defines both 'os' and 'os_build' \",\n front=Color.BRIGHT_RED)\n output.writeln(\" Please use 'os' for libraries and 'os_build'\",\n front=Color.BRIGHT_RED)\n output.writeln(\" only for build-requires used for cross-building\",\n front=Color.BRIGHT_RED)\n output.writeln(\"*\" * 60, front=Color.BRIGHT_RED)\n except ConanException:\n pass","label":0},{"cid":"conans/client/cmd/export.py::_capture_scm_auto_fields","text":"conans/client/cmd/export.py::_capture_scm_auto_fields\ndef _capture_scm_auto_fields(conanfile, conanfile_dir, package_layout, output, ignore_dirty,\n scm_to_conandata):\n \"\"\"Deduce the values for the scm auto fields or functions assigned to 'url' or 'revision'\n and replace the conanfile.py contents.\n Returns a tuple with (scm_data, path_to_scm_local_directory)\"\"\"\n scm_data = get_scm_data(conanfile)\n if not scm_data:\n return None, None\n\n # Resolve SCMData in the user workspace (someone may want to access CVS or import some py)\n scm = SCM(scm_data, conanfile_dir, output)\n captured = scm_data.capture_origin or scm_data.capture_revision\n\n if not captured:\n # We replace not only \"auto\" values, also evaluated functions (e.g from a python_require)\n _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata)\n return scm_data, None\n\n if not scm.is_pristine() and not ignore_dirty:\n output.warn(\"There are uncommitted changes, skipping the replacement of 'scm.url' and \"\n \"'scm.revision' auto fields. Use --ignore-dirty to force it. The 'conan \"\n \"upload' command will prevent uploading recipes with 'auto' values in these \"\n \"fields.\")\n origin = scm.get_qualified_remote_url(remove_credentials=True)\n local_src_path = scm.get_local_path_to_url(origin)\n return scm_data, local_src_path\n\n if scm_data.url == \"auto\":\n origin = scm.get_qualified_remote_url(remove_credentials=True)\n if not origin:\n output.warn(\"Repo origin cannot be deduced, 'auto' fields won't be replaced.\"\n \" 'conan upload' command will prevent uploading recipes with 'auto'\"\n \" values in these fields.\")\n local_src_path = scm.get_local_path_to_url(origin)\n return scm_data, local_src_path\n if scm.is_local_repository():\n output.w","label":0},{"cid":"conans/client/cmd/export.py::_replace_scm_data_in_recipe","text":"conans/client/cmd/export.py::_replace_scm_data_in_recipe\ndef _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata):\n if scm_to_conandata:\n conandata_path = os.path.join(package_layout.export(), DATA_YML)\n conandata_yml = {}\n if os.path.exists(conandata_path):\n conandata_yml = yaml.safe_load(load(conandata_path))\n conandata_yml = conandata_yml or {} # In case the conandata is a blank file\n if '.conan' in conandata_yml:\n raise ConanException(\"Field '.conan' inside '{}' file is reserved to \"\n \"Conan usage.\".format(DATA_YML))\n scm_data_copied = scm_data.as_dict()\n scm_data_copied.pop('username', None)\n scm_data_copied.pop('password', None)\n conandata_yml['.conan'] = {'scm': scm_data_copied}\n\n save(conandata_path, yaml.safe_dump(conandata_yml, default_flow_style=False))\n else:\n conan_v2_error(\"general.scm_to_conandata should be set to 1\")\n _replace_scm_data_in_conanfile(package_layout.conanfile(), scm_data)","label":0},{"cid":"conans/client/cmd/export.py::_replace_scm_data_in_conanfile","text":"conans/client/cmd/export.py::_replace_scm_data_in_conanfile\ndef _replace_scm_data_in_conanfile(conanfile_path, scm_data):\n # FIXME: Remove in Conan 2.0, it will use conandata.yml as the only way\n # Parsing and replacing the SCM field\n content = load(conanfile_path)\n headers = []\n\n if six.PY2:\n # Workaround for https://bugs.python.org/issue22221\n lines_without_headers = []\n lines = content.splitlines(True)\n for line in lines:\n if not lines_without_headers and line.startswith(\"#\"):\n headers.append(line)\n else:\n lines_without_headers.append(line)\n content = ''.join(lines_without_headers)\n\n lines = content.splitlines(True)\n tree = ast.parse(content)\n to_replace = []\n comments = []\n class_line = None\n tab_size = 4\n for i_body, item in enumerate(tree.body):\n if isinstance(item, ast.ClassDef):\n statements = item.body\n class_line = item.lineno\n for i, stmt in enumerate(item.body):\n if isinstance(stmt, ast.Assign) and len(stmt.targets) == 1:\n line = lines[stmt.lineno - 1]\n tab_size = len(line) - len(line.lstrip())\n if isinstance(stmt.targets[0], ast.Name) and stmt.targets[0].id == \"scm\":\n try:\n if i + 1 == len(statements): # Last statement in my ClassDef\n if i_body + 1 == len(tree.body): # Last statement over all\n next_line = len(lines)\n else:\n next_line = tree.body[i_body + 1].lineno - 1\n else:\n # Next statement can be a comment or anything else\n next_statement = statements[i + 1]\n if isPY38 and isinstance(next_statement, ast.Expr):\n ","label":0},{"cid":"conans/client/cmd/export.py::_detect_scm_revision","text":"conans/client/cmd/export.py::_detect_scm_revision\ndef _detect_scm_revision(path):\n if not path:\n raise ConanException(\"Not path supplied\")\n\n repo_type = SCM.detect_scm(path)\n if not repo_type:\n raise ConanException(\"'{}' repository not detected\".format(repo_type))\n\n repo_obj = SCM.availables.get(repo_type)(path)\n return repo_obj.get_revision(), repo_type, repo_obj.is_pristine()","label":0},{"cid":"conans/client/cmd/export.py::_update_revision_in_metadata","text":"conans/client/cmd/export.py::_update_revision_in_metadata\ndef _update_revision_in_metadata(package_layout, revisions_enabled, output, path, manifest,\n revision_mode):\n if revision_mode not in [\"scm\", \"hash\"]:\n raise ConanException(\"Revision mode should be one of 'hash' (default) or 'scm'\")\n\n # Use the proper approach depending on 'revision_mode'\n if revision_mode == \"hash\":\n revision = manifest.summary_hash\n if revisions_enabled:\n output.info(\"Using the exported files summary hash as the recipe\"\n \" revision: {} \".format(revision))\n else:\n try:\n rev_detected, repo_type, is_pristine = _detect_scm_revision(path)\n except Exception as exc:\n error_msg = \"Cannot detect revision using '{}' mode from repository at \" \\\n \"'{}'\".format(revision_mode, path)\n raise ConanException(\"{}: {}\".format(error_msg, exc))\n\n revision = rev_detected\n\n if revisions_enabled:\n output.info(\"Using %s commit as the recipe revision: %s\" % (repo_type, revision))\n if not is_pristine:\n output.warn(\"Repo status is not pristine: there might be modified files\")\n\n with package_layout.update_metadata() as metadata:\n metadata.recipe.revision = revision\n\n return revision","label":0},{"cid":"conans/client/cmd/export.py::_classify_patterns","text":"conans/client/cmd/export.py::_classify_patterns\ndef _classify_patterns(patterns):\n patterns = patterns or []\n included, excluded = [], []\n for p in patterns:\n if p.startswith(\"!\"):\n excluded.append(p[1:])\n else:\n included.append(p)\n return included, excluded","label":0},{"cid":"conans/client/cmd/export.py::_export_scm","text":"conans/client/cmd/export.py::_export_scm\ndef _export_scm(scm_data, origin_folder, scm_sources_folder, output):\n \"\"\" Copy the local folder to the scm_sources folder in the cache, this enables to work\n with local sources without committing and pushing changes to the scm remote.\n https://github.com/conan-io/conan/issues/5195\"\"\"\n excluded = SCM(scm_data, origin_folder, output).excluded_files\n excluded.append(\"conanfile.py\")\n output.info(\"SCM: Getting sources from folder: %s\" % origin_folder)\n merge_directories(origin_folder, scm_sources_folder, excluded=excluded)","label":0},{"cid":"conans/client/cmd/export.py::export_source","text":"conans/client/cmd/export.py::export_source\ndef export_source(conanfile, origin_folder, destination_source_folder):\n if callable(conanfile.exports_sources):\n raise ConanException(\"conanfile 'exports_sources' shouldn't be a method, \"\n \"use 'export_sources()' instead\")\n\n if isinstance(conanfile.exports_sources, str):\n conanfile.exports_sources = (conanfile.exports_sources,)\n\n included_sources, excluded_sources = _classify_patterns(conanfile.exports_sources)\n copier = FileCopier([origin_folder], destination_source_folder)\n for pattern in included_sources:\n copier(pattern, links=True, excludes=excluded_sources)\n output = conanfile.output\n package_output = ScopedOutput(\"%s exports_sources\" % output.scope, output)\n copier.report(package_output)\n\n _run_method(conanfile, \"export_sources\", origin_folder, destination_source_folder, output)","label":0},{"cid":"conans/client/cmd/export.py::export_recipe","text":"conans/client/cmd/export.py::export_recipe\ndef export_recipe(conanfile, origin_folder, destination_folder):\n if callable(conanfile.exports):\n raise ConanException(\"conanfile 'exports' shouldn't be a method, use 'export()' instead\")\n if isinstance(conanfile.exports, str):\n conanfile.exports = (conanfile.exports,)\n\n output = conanfile.output\n package_output = ScopedOutput(\"%s exports\" % output.scope, output)\n\n if os.path.exists(os.path.join(origin_folder, DATA_YML)):\n package_output.info(\"File '{}' found. Exporting it...\".format(DATA_YML))\n tmp = [DATA_YML]\n if conanfile.exports:\n tmp.extend(conanfile.exports) # conanfile.exports could be a tuple (immutable)\n conanfile.exports = tmp\n\n included_exports, excluded_exports = _classify_patterns(conanfile.exports)\n\n try:\n os.unlink(os.path.join(origin_folder, CONANFILE + 'c'))\n except OSError:\n pass\n\n copier = FileCopier([origin_folder], destination_folder)\n for pattern in included_exports:\n copier(pattern, links=True, excludes=excluded_exports)\n copier.report(package_output)\n\n _run_method(conanfile, \"export\", origin_folder, destination_folder, output)","label":0},{"cid":"conans/client/cmd/export.py::_run_method","text":"conans/client/cmd/export.py::_run_method\ndef _run_method(conanfile, method, origin_folder, destination_folder, output):\n export_method = getattr(conanfile, method, None)\n if export_method:\n if not callable(export_method):\n raise ConanException(\"conanfile '%s' must be a method\" % method)\n output.highlight(\"Calling %s()\" % method)\n copier = FileCopier([origin_folder], destination_folder)\n conanfile.copy = copier\n folder_name = \"%s_folder\" % method\n setattr(conanfile, folder_name, destination_folder)\n default_options = conanfile.default_options\n try:\n # TODO: Poor man attribute control access. Convert to nice decorator\n conanfile.default_options = None\n with chdir(origin_folder):\n with conanfile_exception_formatter(str(conanfile), method):\n export_method()\n finally:\n conanfile.default_options = default_options\n delattr(conanfile, folder_name)\n export_method_output = ScopedOutput(\"%s %s() method\" % (output.scope, method), output)\n copier.report(export_method_output)","label":0},{"cid":"conans/client/file_copier.py::report_copied_files","text":"conans/client/file_copier.py::report_copied_files\ndef report_copied_files(copied, output, message_suffix=\"Copied\"):\n ext_files = defaultdict(list)\n for f in copied:\n _, ext = os.path.splitext(f)\n ext_files[ext].append(os.path.basename(f))\n\n if not ext_files:\n return False\n\n for ext, files in ext_files.items():\n files_str = (\": \" + \", \".join(files)) if len(files) < 5 else \"\"\n file_or_files = \"file\" if len(files) == 1 else \"files\"\n if not ext:\n output.info(\"%s %d %s%s\" % (message_suffix, len(files), file_or_files, files_str))\n else:\n output.info(\"%s %d '%s' %s%s\"\n % (message_suffix, len(files), ext, file_or_files, files_str))\n return True","label":0},{"cid":"conans/client/file_copier.py::FileCopier.__init__","text":"conans/client/file_copier.py::FileCopier.__init__\n def __init__(self, source_folders, root_destination_folder):\n \"\"\"\n Takes the base folders to copy resources src -> dst. These folders names\n will not be used in the relative names while copying\n param source_folders: list of folders to copy things from, typically the\n store build folder\n param root_destination_folder: The base folder to copy things to, typically the\n store package folder\n \"\"\"\n assert isinstance(source_folders, list), \"source folders must be a list\"\n self._src_folders = source_folders\n self._dst_folder = root_destination_folder\n self._copied = []","label":0},{"cid":"conans/client/file_copier.py::FileCopier.report","text":"conans/client/file_copier.py::FileCopier.report\n def report(self, output):\n return report_copied_files(self._copied, output)","label":0},{"cid":"conans/client/file_copier.py::FileCopier.__call__","text":"conans/client/file_copier.py::FileCopier.__call__\n def __call__(self, pattern, dst=\"\", src=\"\", keep_path=True, links=False, symlinks=None,\n excludes=None, ignore_case=True):\n \"\"\"\n param pattern: an fnmatch file pattern of the files that should be copied. Eg. *.dll\n param dst: the destination local folder, wrt to current conanfile dir, to which\n the files will be copied. Eg: \"bin\"\n param src: the source folder in which those files will be searched. This folder\n will be stripped from the dst name. Eg.: lib/Debug/x86\n param keep_path: False if you want the relative paths to be maintained from\n src to dst folders, or just drop. False is useful if you want\n to collect e.g. many *.libs among many dirs into a single\n lib dir\n param links: True to activate symlink copying\n param excludes: Single pattern or a tuple of patterns to be excluded from the copy\n param ignore_case: will do a case-insensitive pattern matching when True\n return: list of copied files\n \"\"\"\n # TODO: Remove the old \"links\" arg for Conan 2.0\n if symlinks is not None:\n links = symlinks\n\n if os.path.isabs(src):\n # Avoid repeatedly copying absolute paths\n return self._copy(os.curdir, pattern, src, dst, links,\n ignore_case, excludes, keep_path,\n excluded_folders=[self._dst_folder])\n\n files = []\n for src_folder in self._src_folders:\n excluded = [self._dst_folder]\n excluded.extend([d for d in self._src_folders if d is not src_folder])\n fs = self._copy(src_folder, pattern, src, dst, links, ignore_case, excludes,\n keep_path, excluded_folders=excluded)\n files.extend(fs)\n\n return files","label":0},{"cid":"conans/client/file_copier.py::FileCopier._copy","text":"conans/client/file_copier.py::FileCopier._copy\n def _copy(self, base_src, pattern, src, dst, symlinks, ignore_case, excludes, keep_path,\n excluded_folders):\n # Check for ../ patterns and allow them\n if pattern.startswith(\"..\"):\n rel_dir = os.path.abspath(os.path.join(base_src, pattern))\n base_src = os.path.dirname(rel_dir)\n pattern = os.path.basename(rel_dir)\n\n src = os.path.join(base_src, src)\n dst = os.path.join(self._dst_folder, dst)\n\n files_to_copy, link_folders = self._filter_files(src, pattern, symlinks, excludes,\n ignore_case, excluded_folders)\n copied_files = self._copy_files(files_to_copy, src, dst, keep_path, symlinks)\n self.link_folders(src, dst, link_folders)\n self._copied.extend(files_to_copy)\n return copied_files","label":0},{"cid":"conans/client/file_copier.py::FileCopier._filter_files","text":"conans/client/file_copier.py::FileCopier._filter_files\n @staticmethod\n def _filter_files(src, pattern, links, excludes, ignore_case, excluded_folders):\n\n \"\"\" return a list of the files matching the patterns\n The list will be relative path names wrt to the root src folder\n \"\"\"\n filenames = []\n linked_folders = []\n\n if excludes:\n if not isinstance(excludes, (tuple, list)):\n excludes = (excludes, )\n if ignore_case:\n excludes = [e.lower() for e in excludes]\n else:\n excludes = []\n\n for root, subfolders, files in walk(src, followlinks=True):\n if root in excluded_folders:\n subfolders[:] = []\n continue\n\n if links and os.path.islink(root):\n linked_folders.append(os.path.relpath(root, src))\n subfolders[:] = []\n continue\n basename = os.path.basename(root)\n # Skip git or svn subfolders\n if basename in [\".git\", \".svn\"]:\n subfolders[:] = []\n continue\n if basename == \"test_package\": # DO NOT export test_package/build folder\n try:\n subfolders.remove(\"build\")\n except ValueError:\n pass\n\n relative_path = os.path.relpath(root, src)\n compare_relative_path = relative_path.lower() if ignore_case else relative_path\n for exclude in excludes:\n if fnmatch.fnmatch(compare_relative_path, exclude):\n subfolders[:] = []\n files = []\n break\n for f in files:\n relative_name = os.path.normpath(os.path.join(relative_path, f))\n filenames.append(relative_name)\n\n if ignore_case:\n pattern = pattern.lower()\n files_to_copy = [n for n in filenames if fnmatch.fnmatch(os.path.normpa","label":0},{"cid":"conans/client/file_copier.py::FileCopier.link_folders","text":"conans/client/file_copier.py::FileCopier.link_folders\n @staticmethod\n def link_folders(src, dst, linked_folders):\n created_links = []\n for linked_folder in linked_folders:\n src_link = os.path.join(src, linked_folder)\n # Discard symlinks that go out of the src folder\n abs_path = os.path.realpath(src_link)\n relpath = os.path.relpath(abs_path, os.path.realpath(src))\n if relpath.startswith(\".\"):\n continue\n\n link = os.readlink(src_link)\n # Absoluted path symlinks are a problem, convert it to relative\n if os.path.isabs(link):\n try:\n link = os.path.relpath(link, os.path.dirname(src_link))\n except ValueError as e:\n # https://github.com/conan-io/conan/issues/6197 fails if Windows and other Drive\n raise ConanException(\"Symlink '%s' pointing to '%s' couldn't be made relative:\"\n \" %s\" % (src_link, link, str(e)))\n\n dst_link = os.path.join(dst, linked_folder)\n try:\n # Remove the previous symlink\n os.remove(dst_link)\n except OSError:\n pass\n # link is a string relative to linked_folder\n # e.g.: os.symlink(\"test/bar\", \"./foo/test_link\") will create a link\n # to foo/test/bar in ./foo/test_link\n mkdir(os.path.dirname(dst_link))\n os.symlink(link, dst_link)\n created_links.append(dst_link)\n # Remove empty links\n for dst_link in created_links:\n abs_path = os.path.realpath(dst_link)\n if not os.path.exists(abs_path):\n base_path = os.path.dirname(dst_link)\n os.remove(dst_link)\n while base_path.startswith(dst):\n try: # Take advantage that os.rmdir does not delete non-empty dirs\n os.rm","label":0},{"cid":"conans/client/file_copier.py::FileCopier._copy_files","text":"conans/client/file_copier.py::FileCopier._copy_files\n @staticmethod\n def _copy_files(files, src, dst, keep_path, symlinks):\n \"\"\" executes a multiple file copy from [(src_file, dst_file), (..)]\n managing symlinks if necessary\n \"\"\"\n copied_files = []\n for filename in files:\n abs_src_name = os.path.join(src, filename)\n filename = filename if keep_path else os.path.basename(filename)\n abs_dst_name = os.path.normpath(os.path.join(dst, filename))\n try:\n os.makedirs(os.path.dirname(abs_dst_name))\n except Exception:\n pass\n if symlinks and os.path.islink(abs_src_name):\n linkto = os.readlink(abs_src_name) # @UndefinedVariable\n try:\n os.remove(abs_dst_name)\n except OSError:\n pass\n os.symlink(linkto, abs_dst_name) # @UndefinedVariable\n else:\n shutil.copy2(abs_src_name, abs_dst_name)\n copied_files.append(abs_dst_name)\n return copied_files","label":0},{"cid":"conans/client/installer.py::build_id","text":"conans/client/installer.py::build_id\ndef build_id(conan_file):\n if hasattr(conan_file, \"build_id\"):\n # construct new ConanInfo\n build_id_info = conan_file.info.copy()\n conan_file.info_build = build_id_info\n # effectively call the user function to change the package values\n with conanfile_exception_formatter(str(conan_file), \"build_id\"):\n conan_file.build_id()\n # compute modified ID\n return build_id_info.package_id()\n return None","label":0},{"cid":"conans/client/installer.py::add_env_conaninfo","text":"conans/client/installer.py::add_env_conaninfo\ndef add_env_conaninfo(conan_file, subtree_libnames):\n for package_name, env_vars in conan_file._conan_env_values.data.items():\n for name, value in env_vars.items():\n if not package_name or package_name in subtree_libnames or \\\n package_name == conan_file.name:\n conan_file.info.env_values.add(name, value, package_name)","label":0},{"cid":"conans/client/installer.py::_PackageBuilder.__init__","text":"conans/client/installer.py::_PackageBuilder.__init__\n def __init__(self, cache, output, hook_manager, remote_manager, generators):\n self._cache = cache\n self._output = output\n self._hook_manager = hook_manager\n self._remote_manager = remote_manager\n self._generator_manager = generators","label":0},{"cid":"conans/client/installer.py::_PackageBuilder._get_build_folder","text":"conans/client/installer.py::_PackageBuilder._get_build_folder\n def _get_build_folder(self, conanfile, package_layout, pref, keep_build, recorder):\n # Build folder can use a different package_ID if build_id() is defined.\n # This function decides if the build folder should be re-used (not build again)\n # and returns the build folder\n new_id = build_id(conanfile)\n build_pref = PackageReference(pref.ref, new_id) if new_id else pref\n build_folder = package_layout.build(build_pref)\n\n if is_dirty(build_folder):\n self._output.warn(\"Build folder is dirty, removing it: %s\" % build_folder)\n rmdir(build_folder)\n clean_dirty(build_folder)\n\n # Decide if the build folder should be kept\n skip_build = conanfile.develop and keep_build\n if skip_build:\n self._output.info(\"Won't be built as specified by --keep-build\")\n if not os.path.exists(build_folder):\n msg = \"--keep-build specified, but build folder not found\"\n recorder.package_install_error(pref, INSTALL_ERROR_MISSING_BUILD_FOLDER,\n msg, remote_name=None)\n raise ConanException(msg)\n elif build_pref != pref and os.path.exists(build_folder) and hasattr(conanfile, \"build_id\"):\n self._output.info(\"Won't be built, using previous build folder as defined in build_id()\")\n skip_build = True\n\n return build_folder, skip_build","label":0},{"cid":"conans/client/installer.py::_PackageBuilder._prepare_sources","text":"conans/client/installer.py::_PackageBuilder._prepare_sources\n def _prepare_sources(self, conanfile, pref, package_layout, remotes):\n export_folder = package_layout.export()\n export_source_folder = package_layout.export_sources()\n scm_sources_folder = package_layout.scm_sources()\n conanfile_path = package_layout.conanfile()\n source_folder = package_layout.source()\n\n retrieve_exports_sources(self._remote_manager, self._cache, conanfile, pref.ref, remotes)\n\n conanfile.folders.set_base_source(source_folder)\n conanfile.folders.set_base_build(None)\n conanfile.folders.set_base_package(None)\n\n config_source(export_folder, export_source_folder, scm_sources_folder,\n conanfile, self._output, conanfile_path, pref.ref,\n self._hook_manager, self._cache)","label":0},{"cid":"conans/client/installer.py::_PackageBuilder._copy_sources","text":"conans/client/installer.py::_PackageBuilder._copy_sources\n @staticmethod\n def _copy_sources(conanfile, source_folder, build_folder):\n # Copies the sources to the build-folder, unless no_copy_source is defined\n _remove_folder_raising(build_folder)\n if not getattr(conanfile, 'no_copy_source', False):\n conanfile.output.info('Copying sources to build folder')\n try:\n shutil.copytree(source_folder, build_folder, symlinks=True)\n except Exception as e:\n msg = str(e)\n if \"206\" in msg: # System error shutil.Error 206: Filename or extension too long\n msg += \"\\nUse short_paths=True if paths too long\"\n raise ConanException(\"%s\\nError copying sources to build folder\" % msg)\n logger.debug(\"BUILD: Copied to %s\", build_folder)\n logger.debug(\"BUILD: Files copied %s\", \",\".join(os.listdir(build_folder)))","label":0},{"cid":"conans/client/installer.py::_PackageBuilder._build","text":"conans/client/installer.py::_PackageBuilder._build\n def _build(self, conanfile, pref):\n # Read generators from conanfile and generate the needed files\n logger.info(\"GENERATORS: Writing generators\")\n self._generator_manager.write_generators(conanfile, conanfile.build_folder,\n conanfile.generators_folder, self._output)\n\n logger.info(\"TOOLCHAIN: Writing toolchain\")\n write_toolchain(conanfile, conanfile.generators_folder, self._output)\n\n # Build step might need DLLs, binaries as protoc to generate source files\n # So execute imports() before build, storing the list of copied_files\n\n copied_files = run_imports(conanfile)\n\n try:\n mkdir(conanfile.build_folder)\n with tools.chdir(conanfile.build_folder):\n run_build_method(conanfile, self._hook_manager, reference=pref.ref, package_id=pref.id)\n self._output.success(\"Package '%s' built\" % pref.id)\n self._output.info(\"Build folder %s\" % conanfile.build_folder)\n except Exception as exc:\n self._output.writeln(\"\")\n self._output.error(\"Package '%s' build failed\" % pref.id)\n self._output.warn(\"Build folder %s\" % conanfile.build_folder)\n if isinstance(exc, ConanExceptionInUserConanfileMethod):\n raise exc\n raise ConanException(exc)\n finally:\n # Now remove all files that were imported with imports()\n remove_imports(conanfile, copied_files, self._output)","label":0},{"cid":"conans/client/installer.py::_PackageBuilder._package","text":"conans/client/installer.py::_PackageBuilder._package\n def _package(self, conanfile, pref, package_layout, conanfile_path):\n # FIXME: Is weak to assign here the recipe_hash\n manifest = package_layout.recipe_manifest()\n conanfile.info.recipe_hash = manifest.summary_hash\n\n # Creating ***info.txt files\n save(os.path.join(conanfile.folders.base_build, CONANINFO), conanfile.info.dumps())\n self._output.info(\"Generated %s\" % CONANINFO)\n save(os.path.join(conanfile.folders.base_build, BUILD_INFO),\n TXTGenerator(conanfile).content)\n self._output.info(\"Generated %s\" % BUILD_INFO)\n\n package_id = pref.id\n # Do the actual copy, call the conanfile.package() method\n # While installing, the infos goes to build folder\n conanfile.folders.set_base_install(conanfile.folders.base_build)\n\n prev = run_package_method(conanfile, package_id, self._hook_manager, conanfile_path,\n pref.ref)\n\n update_package_metadata(prev, package_layout, package_id, pref.ref.revision)\n\n if get_env(\"CONAN_READ_ONLY_CACHE\", False):\n make_read_only(conanfile.folders.base_package)\n # FIXME: Conan 2.0 Clear the registry entry (package ref)\n return prev","label":0},{"cid":"conans/client/installer.py::_PackageBuilder.build_package","text":"conans/client/installer.py::_PackageBuilder.build_package\n def build_package(self, node, keep_build, recorder, remotes):\n t1 = time.time()\n\n conanfile = node.conanfile\n pref = node.pref\n\n package_layout = self._cache.package_layout(pref.ref, conanfile.short_paths)\n base_source = package_layout.source()\n conanfile_path = package_layout.conanfile()\n base_package = package_layout.package(pref)\n\n base_build, skip_build = self._get_build_folder(conanfile, package_layout,\n pref, keep_build, recorder)\n # PREPARE SOURCES\n if not skip_build:\n with package_layout.conanfile_write_lock(self._output):\n set_dirty(base_build)\n self._prepare_sources(conanfile, pref, package_layout, remotes)\n self._copy_sources(conanfile, base_source, base_build)\n\n # BUILD & PACKAGE\n with package_layout.conanfile_read_lock(self._output):\n self._output.info('Building your package in %s' % base_build)\n try:\n if getattr(conanfile, 'no_copy_source', False):\n conanfile.folders.set_base_source(base_source)\n else:\n conanfile.folders.set_base_source(base_build)\n\n conanfile.folders.set_base_build(base_build)\n conanfile.folders.set_base_imports(base_build)\n conanfile.folders.set_base_package(base_package)\n\n if not skip_build:\n # In local cache, generators folder always in build_folder\n conanfile.folders.set_base_generators(base_build)\n # In local cache, install folder always is build_folder\n conanfile.folders.set_base_install(base_build)\n self._build(conanfile, pref)\n clean_dirty(base_build)\n\n prev = self._package(conanfile, pref, pac","label":0},{"cid":"conans/client/installer.py::_remove_folder_raising","text":"conans/client/installer.py::_remove_folder_raising\ndef _remove_folder_raising(folder):\n try:\n rmdir(folder)\n except OSError as e:\n raise ConanException(\"%s\\n\\nCouldn't remove folder, might be busy or open\\n\"\n \"Close any app using it, and retry\" % str(e))","label":0},{"cid":"conans/client/installer.py::_handle_system_requirements","text":"conans/client/installer.py::_handle_system_requirements\ndef _handle_system_requirements(conan_file, pref, cache, out):\n \"\"\" check first the system_reqs/system_requirements.txt existence, if not existing\n check package/sha1/\n\n Used after remote package retrieving and before package building\n \"\"\"\n # TODO: Check if this idiom should be generalize to all methods defined in base ConanFile\n # Instead of calling empty methods\n if type(conan_file).system_requirements == ConanFile.system_requirements:\n return\n\n package_layout = cache.package_layout(pref.ref)\n system_reqs_path = package_layout.system_reqs()\n system_reqs_package_path = package_layout.system_reqs_package(pref)\n if os.path.exists(system_reqs_path) or os.path.exists(system_reqs_package_path):\n return\n\n ret = call_system_requirements(conan_file, out)\n\n try:\n ret = str(ret or \"\")\n except Exception:\n out.warn(\"System requirements didn't return a string\")\n ret = \"\"\n if getattr(conan_file, \"global_system_requirements\", None):\n save(system_reqs_path, ret)\n else:\n save(system_reqs_package_path, ret)","label":0},{"cid":"conans/client/installer.py::call_system_requirements","text":"conans/client/installer.py::call_system_requirements\ndef call_system_requirements(conanfile, output):\n try:\n return conanfile.system_requirements()\n except Exception as e:\n output.error(\"while executing system_requirements(): %s\" % str(e))\n raise ConanException(\"Error in system requirements\")","label":0},{"cid":"conans/client/installer.py::BinaryInstaller.__init__","text":"conans/client/installer.py::BinaryInstaller.__init__\n def __init__(self, app, recorder):\n self._cache = app.cache\n self._out = app.out\n self._remote_manager = app.remote_manager\n self._recorder = recorder\n self._binaries_analyzer = app.binaries_analyzer\n self._hook_manager = app.hook_manager\n self._generator_manager = app.generator_manager\n # Load custom generators from the cache, generators are part of the binary\n # build and install. Generators loaded here from the cache will have precedence\n # and overwrite possible generators loaded from packages (requires)\n for generator_path in app.cache.generators:\n app.loader.load_generators(generator_path)","label":0},{"cid":"conans/client/installer.py::BinaryInstaller.install","text":"conans/client/installer.py::BinaryInstaller.install\n def install(self, deps_graph, remotes, build_mode, update, profile_host, profile_build,\n graph_lock, keep_build=False):\n # order by levels and separate the root node (ref=None) from the rest\n nodes_by_level = deps_graph.by_levels()\n root_level = nodes_by_level.pop()\n root_node = root_level[0]\n # Get the nodes in order and if we have to build them\n self._out.info(\"Installing (downloading, building) binaries...\")\n self._build(nodes_by_level, keep_build, root_node, profile_host, profile_build,\n graph_lock, remotes, build_mode, update)","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._classify","text":"conans/client/installer.py::BinaryInstaller._classify\n @staticmethod\n def _classify(nodes_by_level):\n missing, invalid, downloads = [], [], []\n for level in nodes_by_level:\n for node in level:\n if node.binary == BINARY_MISSING:\n missing.append(node)\n elif node.binary == BINARY_INVALID:\n invalid.append(node)\n elif node.binary in (BINARY_UPDATE, BINARY_DOWNLOAD):\n downloads.append(node)\n return missing, invalid, downloads","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._raise_missing","text":"conans/client/installer.py::BinaryInstaller._raise_missing\n def _raise_missing(self, missing):\n if not missing:\n return\n\n missing_prefs = set(n.pref for n in missing) # avoid duplicated\n missing_prefs = list(sorted(missing_prefs))\n for pref in missing_prefs:\n self._out.error(\"Missing binary: %s\" % str(pref))\n self._out.writeln(\"\")\n\n # Report details just the first one\n node = missing[0]\n package_id = node.package_id\n ref, conanfile = node.ref, node.conanfile\n dependencies = [str(dep.dst) for dep in node.dependencies]\n\n settings_text = \", \".join(conanfile.info.full_settings.dumps().splitlines())\n options_text = \", \".join(conanfile.info.full_options.dumps().splitlines())\n dependencies_text = ', '.join(dependencies)\n requires_text = \", \".join(conanfile.info.requires.dumps().splitlines())\n\n msg = textwrap.dedent('''\\\n Can't find a '%s' package for the specified settings, options and dependencies:\n - Settings: %s\n - Options: %s\n - Dependencies: %s\n - Requirements: %s\n - Package ID: %s\n ''' % (ref, settings_text, options_text, dependencies_text, requires_text, package_id))\n conanfile.output.warn(msg)\n self._recorder.package_install_error(PackageReference(ref, package_id),\n INSTALL_ERROR_MISSING, msg)\n missing_pkgs = \"', '\".join([str(pref.ref) for pref in missing_prefs])\n if len(missing_prefs) >= 5:\n build_str = \"--build=missing\"\n else:\n build_str = \" \".join([\"--build=%s\" % pref.ref.name for pref in missing_prefs])\n\n raise ConanException(textwrap.dedent('''\\\n Missing prebuilt package for '%s'\n Try to build from sources with '%s'\n Use 'conan search <reference> --table table.html'\n Or read 'http://docs.conan.io/en/latest/","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._download","text":"conans/client/installer.py::BinaryInstaller._download\n def _download(self, downloads, processed_package_refs):\n \"\"\" executes the download of packages (both download and update), only once for a given\n PREF, even if node duplicated\n :param downloads: all nodes to be downloaded or updated, included repetitions\n \"\"\"\n if not downloads:\n return\n\n download_nodes = []\n for node in downloads:\n pref = node.pref\n bare_pref = PackageReference(pref.ref, pref.id)\n if bare_pref in processed_package_refs:\n continue\n processed_package_refs[bare_pref] = pref.revision\n assert node.prev, \"PREV for %s is None\" % str(node.pref)\n download_nodes.append(node)\n\n def _download(n):\n npref = n.pref\n layout = self._cache.package_layout(npref.ref, n.conanfile.short_paths)\n # We cannot embed the package_lock inside the remote.get_package()\n # because the handle_node_cache has its own lock\n with layout.package_lock(pref):\n self._download_pkg(layout, n)\n\n parallel = self._cache.config.parallel_download\n if parallel is not None:\n self._out.info(\"Downloading binary packages in %s parallel threads\" % parallel)\n thread_pool = ThreadPool(parallel)\n thread_pool.map(_download, [n for n in download_nodes])\n thread_pool.close()\n thread_pool.join()\n else:\n for node in download_nodes:\n _download(node)","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._download._download","text":"conans/client/installer.py::BinaryInstaller._download._download\n def _download(n):\n npref = n.pref\n layout = self._cache.package_layout(npref.ref, n.conanfile.short_paths)\n # We cannot embed the package_lock inside the remote.get_package()\n # because the handle_node_cache has its own lock\n with layout.package_lock(pref):\n self._download_pkg(layout, n)","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._download_pkg","text":"conans/client/installer.py::BinaryInstaller._download_pkg\n def _download_pkg(self, layout, node):\n self._remote_manager.get_package(node.conanfile, node.pref, layout, node.binary_remote,\n node.conanfile.output, self._recorder)","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._build","text":"conans/client/installer.py::BinaryInstaller._build\n def _build(self, nodes_by_level, keep_build, root_node, profile_host, profile_build, graph_lock,\n remotes, build_mode, update):\n using_build_profile = bool(profile_build)\n missing, invalid, downloads = self._classify(nodes_by_level)\n if invalid:\n msg = [\"There are invalid packages (packages that cannot exist for this configuration):\"]\n for node in invalid:\n msg.append(\"{}: Invalid ID: {}\".format(node.conanfile, node.conanfile.info.invalid))\n raise ConanInvalidConfiguration(\"\\n\".join(msg))\n self._raise_missing(missing)\n processed_package_refs = {}\n self._download(downloads, processed_package_refs)\n\n for level in nodes_by_level:\n for node in level:\n ref, conan_file = node.ref, node.conanfile\n output = conan_file.output\n\n self._propagate_info(node, using_build_profile)\n if node.binary == BINARY_EDITABLE:\n self._handle_node_editable(node, profile_host, profile_build, graph_lock)\n # Need a temporary package revision for package_revision_mode\n # Cannot be PREV_UNKNOWN otherwise the consumers can't compute their packageID\n node.prev = \"editable\"\n else:\n if node.binary == BINARY_SKIP: # Privates not necessary\n continue\n assert ref.revision is not None, \"Installer should receive RREV always\"\n if node.binary == BINARY_UNKNOWN:\n self._binaries_analyzer.reevaluate_node(node, remotes, build_mode, update)\n if node.binary == BINARY_MISSING:\n self._raise_missing([node])\n _handle_system_requirements(conan_file, node.pref, self._cache, output)\n self._handle_node_cache(node,","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._handle_node_editable","text":"conans/client/installer.py::BinaryInstaller._handle_node_editable\n def _handle_node_editable(self, node, profile_host, profile_build, graph_lock):\n # Get source of information\n conanfile = node.conanfile\n ref = node.ref\n package_layout = self._cache.package_layout(ref)\n base_path = package_layout.base_folder()\n self._call_package_info(conanfile, package_folder=base_path, ref=ref, is_editable=True)\n\n # New editables mechanism based on Folders\n if hasattr(conanfile, \"layout\"):\n conanfile.folders.set_base_package(base_path)\n conanfile.folders.set_base_source(base_path)\n conanfile.folders.set_base_build(base_path)\n conanfile.folders.set_base_install(base_path)\n conanfile.folders.set_base_imports(base_path)\n\n output = conanfile.output\n output.info(\"Rewriting files of editable package \"\n \"'{}' at '{}'\".format(conanfile.name, conanfile.generators_folder))\n self._generator_manager.write_generators(conanfile, conanfile.install_folder,\n conanfile.generators_folder, output)\n write_toolchain(conanfile, conanfile.generators_folder, output)\n output.info(\"Generated toolchain\")\n graph_info_node = GraphInfo(profile_host, root_ref=node.ref)\n graph_info_node.options = node.conanfile.options.values\n graph_info_node.graph_lock = graph_lock\n graph_info_node.save(base_path)\n output.info(\"Generated conan.lock\")\n copied_files = run_imports(conanfile)\n report_copied_files(copied_files, output)\n return\n\n node.conanfile.cpp_info.filter_empty = False\n # OLD EDITABLE LAYOUTS:\n # Try with package-provided file\n editable_cpp_info = package_layout.editable_cpp_info()\n if editable_cpp_info:\n editable_cpp_info.apply_to(ref,\n ","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._handle_node_cache","text":"conans/client/installer.py::BinaryInstaller._handle_node_cache\n def _handle_node_cache(self, node, keep_build, processed_package_references, remotes):\n pref = node.pref\n assert pref.id, \"Package-ID without value\"\n assert pref.id != PACKAGE_ID_UNKNOWN, \"Package-ID error: %s\" % str(pref)\n conanfile = node.conanfile\n output = conanfile.output\n\n layout = self._cache.package_layout(pref.ref, conanfile.short_paths)\n\n with layout.package_lock(pref):\n bare_pref = PackageReference(pref.ref, pref.id)\n processed_prev = processed_package_references.get(bare_pref)\n if processed_prev is None: # This package-id has not been processed before\n if node.binary == BINARY_BUILD:\n assert node.prev is None, \"PREV for %s to be built should be None\" % str(pref)\n layout.package_remove(pref)\n with layout.set_dirty_context_manager(pref):\n pref = self._build_package(node, output, keep_build, remotes)\n assert node.prev, \"Node PREV shouldn't be empty\"\n assert node.pref.revision, \"Node PREF revision shouldn't be empty\"\n assert pref.revision is not None, \"PREV for %s to be built is None\" % str(pref)\n elif node.binary in (BINARY_UPDATE, BINARY_DOWNLOAD):\n # this can happen after a re-evaluation of packageID with Package_ID_unknown\n self._download_pkg(layout, node)\n elif node.binary == BINARY_CACHE:\n assert node.prev, \"PREV for %s is None\" % str(pref)\n output.success('Already installed!')\n log_package_got_from_local_cache(pref)\n self._recorder.package_fetched_from_cache(pref)\n processed_package_references[bare_pref] = node.prev\n else:\n # We need to update the PREV of this node, as its ","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._build_package","text":"conans/client/installer.py::BinaryInstaller._build_package\n def _build_package(self, node, output, keep_build, remotes):\n conanfile = node.conanfile\n # It is necessary to complete the sources of python requires, which might be used\n # Only the legacy python_requires allow this\n python_requires = getattr(conanfile, \"python_requires\", None)\n if python_requires and isinstance(python_requires, dict): # Old legacy python_requires\n for python_require in python_requires.values():\n assert python_require.ref.revision is not None, \\\n \"Installer should receive python_require.ref always\"\n retrieve_exports_sources(self._remote_manager, self._cache,\n python_require.conanfile, python_require.ref, remotes)\n\n builder = _PackageBuilder(self._cache, output, self._hook_manager, self._remote_manager,\n self._generator_manager)\n pref = builder.build_package(node, keep_build, self._recorder, remotes)\n if node.graph_lock_node:\n node.graph_lock_node.prev = pref.revision\n return pref","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._propagate_info","text":"conans/client/installer.py::BinaryInstaller._propagate_info\n def _propagate_info(self, node, using_build_profile):\n # it is necessary to recompute\n # the node transitive information necessary to compute the package_id\n # as it will be used by reevaluate_node() when package_revision_mode is used and\n # PACKAGE_ID_UNKNOWN happens due to unknown revisions\n self._binaries_analyzer.package_id_transitive_reqs(node)\n # Get deps_cpp_info from upstream nodes\n node_order = [n for n in node.public_closure if n.binary != BINARY_SKIP]\n # List sort is stable, will keep the original order of the closure, but prioritize levels\n conan_file = node.conanfile\n # FIXME: Not the best place to assign the _conan_using_build_profile\n conan_file._conan_using_build_profile = using_build_profile\n transitive = [it for it in node.transitive_closure.values()]\n\n br_host = []\n for it in node.dependencies:\n if it.require.build_require_context == CONTEXT_HOST:\n br_host.extend(it.dst.transitive_closure.values())\n\n # Initialize some members if we are using different contexts\n if using_build_profile:\n conan_file.user_info_build = DepsUserInfo()\n\n for n in node_order:\n if n not in transitive:\n conan_file.output.info(\"Applying build-requirement: %s\" % str(n.ref))\n\n dep_cpp_info = n.conanfile._conan_dep_cpp_info\n\n if not using_build_profile: # Do not touch anything\n conan_file.deps_user_info[n.ref.name] = n.conanfile.user_info\n conan_file.deps_cpp_info.add(n.ref.name, dep_cpp_info)\n conan_file.deps_env_info.update(n.conanfile.env_info, n.ref.name)\n else:\n if n in transitive or n in br_host:\n conan_file.deps_user_info[n.ref.name] = n.conanfile.user_info\n conan_file.deps_cpp_info.add(n.ref.name, d","label":0},{"cid":"conans/client/installer.py::BinaryInstaller._call_package_info","text":"conans/client/installer.py::BinaryInstaller._call_package_info\n def _call_package_info(self, conanfile, package_folder, ref, is_editable):\n conanfile.cpp_info = CppInfo(conanfile.name, package_folder)\n conanfile.cpp_info.version = conanfile.version\n conanfile.cpp_info.description = conanfile.description\n\n conanfile.folders.set_base_package(package_folder)\n conanfile.folders.set_base_source(None)\n conanfile.folders.set_base_build(None)\n conanfile.folders.set_base_install(None)\n\n conanfile.env_info = EnvInfo()\n conanfile.user_info = UserInfo()\n\n # Get deps_cpp_info from upstream nodes\n public_deps = [name for name, req in conanfile.requires.items() if not req.private\n and not req.override]\n conanfile.cpp_info.public_deps = public_deps\n # Once the node is build, execute package info, so it has access to the\n # package folder and artifacts\n # Minimal pythonpath, not the whole context, make it 50% slower\n # FIXME Conan 2.0, Remove old ways of reusing python code\n with pythonpath(conanfile):\n with tools.chdir(package_folder):\n with conanfile_exception_formatter(str(conanfile), \"package_info\"):\n self._hook_manager.execute(\"pre_package_info\", conanfile=conanfile,\n reference=ref)\n if hasattr(conanfile, \"layout\"):\n # Old cpp info without defaults (the defaults are in the new one)\n conanfile.cpp_info = CppInfo(conanfile.name, package_folder,\n default_values=CppInfoDefaultValues())\n if not is_editable:\n package_cppinfo = conanfile.cpp.package.copy()\n package_cppinfo.set_relative_base_folder(conanfile.folders.package)\n # Copy the i","label":0},{"cid":"conans/model/conan_file.py::create_options","text":"conans/model/conan_file.py::create_options\ndef create_options(conanfile):\n try:\n package_options = PackageOptions(getattr(conanfile, \"options\", None))\n options = Options(package_options)\n\n default_options = getattr(conanfile, \"default_options\", None)\n if default_options:\n if isinstance(default_options, dict):\n default_values = OptionsValues(default_options)\n elif isinstance(default_options, (list, tuple)):\n conan_v2_error(\"Declare 'default_options' as a dictionary\")\n default_values = OptionsValues(default_options)\n elif isinstance(default_options, six.string_types):\n conan_v2_error(\"Declare 'default_options' as a dictionary\")\n default_values = OptionsValues.loads(default_options)\n else:\n raise ConanException(\"Please define your default_options as list, \"\n \"multiline string or dictionary\")\n options.values = default_values\n return options\n except Exception as e:\n raise ConanException(\"Error while initializing options. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::create_requirements","text":"conans/model/conan_file.py::create_requirements\ndef create_requirements(conanfile):\n try:\n # Actual requirements of this package\n if not hasattr(conanfile, \"requires\"):\n return Requirements()\n else:\n if not conanfile.requires:\n return Requirements()\n if isinstance(conanfile.requires, (tuple, list)):\n return Requirements(*conanfile.requires)\n else:\n return Requirements(conanfile.requires, )\n except Exception as e:\n raise ConanException(\"Error while initializing requirements. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::create_settings","text":"conans/model/conan_file.py::create_settings\ndef create_settings(conanfile, settings):\n try:\n defined_settings = getattr(conanfile, \"settings\", None)\n if isinstance(defined_settings, str):\n defined_settings = [defined_settings]\n current = defined_settings or {}\n settings.constraint(current)\n return settings\n except Exception as e:\n raise ConanInvalidConfiguration(\"The recipe %s is constraining settings. %s\" % (\n conanfile.display_name, str(e)))","label":0},{"cid":"conans/model/conan_file.py::_env_and_python","text":"conans/model/conan_file.py::_env_and_python\n@contextmanager\ndef _env_and_python(conanfile):\n with environment_append(conanfile.env):\n # FIXME Conan 2.0, Remove old ways of reusing python code\n with pythonpath(conanfile):\n yield","label":0},{"cid":"conans/model/conan_file.py::get_env_context_manager","text":"conans/model/conan_file.py::get_env_context_manager\ndef get_env_context_manager(conanfile, without_python=False):\n if not conanfile.apply_env:\n return no_op()\n if without_python:\n return environment_append(conanfile.env)\n return _env_and_python(conanfile)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.__init__","text":"conans/model/conan_file.py::ConanFile.__init__\n def __init__(self, output, runner, display_name=\"\", user=None, channel=None):\n # an output stream (writeln, info, warn error)\n self.output = ScopedOutput(display_name, output)\n self.display_name = display_name\n # something that can run commands, as os.sytem\n self._conan_runner = runner\n self._conan_user = user\n self._conan_channel = channel\n\n self.compatible_packages = []\n self._conan_using_build_profile = False\n self._conan_requester = None\n\n self.buildenv_info = Environment(self)\n self.runenv_info = Environment(self)\n # At the moment only for build_requires, others will be ignored\n self.conf_info = Conf()\n self._conan_buildenv = None # The profile buildenv, will be assigned initialize()\n self._conan_node = None # access to container Node object, to access info, context, deps...\n self._conan_new_cpp_info = None # Will be calculated lazy in the getter\n self._conan_dependencies = None\n\n self.environment_scripts = [] # Accumulate the env scripts generated in order\n\n # layout() method related variables:\n self.folders = Folders()\n self.patterns = Patterns()\n self.cpp = Infos()\n\n self.patterns.source.include = [\"*.h\", \"*.hpp\", \"*.hxx\"]\n self.patterns.source.lib = []\n self.patterns.source.bin = []\n\n self.patterns.build.include = [\"*.h\", \"*.hpp\", \"*.hxx\"]\n self.patterns.build.lib = [\"*.so\", \"*.so.*\", \"*.a\", \"*.lib\", \"*.dylib\"]\n self.patterns.build.bin = [\"*.exe\", \"*.dll\"]\n\n self.cpp.package.includedirs = [\"include\"]\n self.cpp.package.libdirs = [\"lib\"]\n self.cpp.package.bindirs = [\"bin\"]\n self.cpp.package.resdirs = [\"res\"]\n self.cpp.package.builddirs = [\"\"]\n self.cpp.package.frameworkdirs = [\"Frameworks\"]","label":0},{"cid":"conans/model/conan_file.py::ConanFile.context","text":"conans/model/conan_file.py::ConanFile.context\n @property\n def context(self):\n return self._conan_node.context","label":0},{"cid":"conans/model/conan_file.py::ConanFile.dependencies","text":"conans/model/conan_file.py::ConanFile.dependencies\n @property\n def dependencies(self):\n # Caching it, this object is requested many times\n if self._conan_dependencies is None:\n self._conan_dependencies = ConanFileDependencies.from_node(self._conan_node)\n return self._conan_dependencies","label":0},{"cid":"conans/model/conan_file.py::ConanFile.ref","text":"conans/model/conan_file.py::ConanFile.ref\n @property\n def ref(self):\n return self._conan_node.ref","label":0},{"cid":"conans/model/conan_file.py::ConanFile.pref","text":"conans/model/conan_file.py::ConanFile.pref\n @property\n def pref(self):\n return self._conan_node.pref","label":0},{"cid":"conans/model/conan_file.py::ConanFile.buildenv","text":"conans/model/conan_file.py::ConanFile.buildenv\n @property\n def buildenv(self):\n # Lazy computation of the package buildenv based on the profileone\n if not isinstance(self._conan_buildenv, Environment):\n # TODO: missing user/channel\n ref_str = \"{}/{}\".format(self.name, self.version)\n self._conan_buildenv = self._conan_buildenv.get_env(self, ref_str)\n return self._conan_buildenv","label":0},{"cid":"conans/model/conan_file.py::ConanFile.initialize","text":"conans/model/conan_file.py::ConanFile.initialize\n def initialize(self, settings, env, buildenv=None):\n self._conan_buildenv = buildenv\n if isinstance(self.generators, str):\n self.generators = [self.generators]\n # User defined options\n self.options = create_options(self)\n self.requires = create_requirements(self)\n self.settings = create_settings(self, settings)\n\n conan_v2_error(\"Setting 'cppstd' is deprecated in favor of 'compiler.cppstd',\"\n \" please update your recipe.\", 'cppstd' in self.settings.fields)\n\n # needed variables to pack the project\n self.cpp_info = None # Will be initialized at processing time\n self._conan_dep_cpp_info = None # Will be initialized at processing time\n self.deps_cpp_info = DepsCppInfo()\n\n # environment variables declared in the package_info\n self.env_info = None # Will be initialized at processing time\n self.deps_env_info = DepsEnvInfo()\n\n # user declared variables\n self.user_info = None\n # Keys are the package names (only 'host' if different contexts)\n self.deps_user_info = DepsUserInfo()\n\n # user specified env variables\n self._conan_env_values = env.copy() # user specified -e\n\n if self.description is not None and not isinstance(self.description, six.string_types):\n raise ConanException(\"Recipe 'description' must be a string.\")\n\n if not hasattr(self, \"virtualenv\"): # Allow the user to override it with True or False\n self.virtualenv = True","label":0},{"cid":"conans/model/conan_file.py::ConanFile.new_cpp_info","text":"conans/model/conan_file.py::ConanFile.new_cpp_info\n @property\n def new_cpp_info(self):\n if not self._conan_new_cpp_info:\n self._conan_new_cpp_info = from_old_cppinfo(self.cpp_info)\n return self._conan_new_cpp_info","label":0},{"cid":"conans/model/conan_file.py::ConanFile.source_folder","text":"conans/model/conan_file.py::ConanFile.source_folder\n @property\n def source_folder(self):\n return self.folders.source_folder","label":0},{"cid":"conans/model/conan_file.py::ConanFile.source_folder","text":"conans/model/conan_file.py::ConanFile.source_folder\n @source_folder.setter\n def source_folder(self, folder):\n self.folders.set_base_source(folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_folder","text":"conans/model/conan_file.py::ConanFile.build_folder\n @property\n def build_folder(self):\n return self.folders.build_folder","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_folder","text":"conans/model/conan_file.py::ConanFile.build_folder\n @build_folder.setter\n def build_folder(self, folder):\n self.folders.set_base_build(folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_folder","text":"conans/model/conan_file.py::ConanFile.package_folder\n @property\n def package_folder(self):\n return self.folders.package_folder","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_folder","text":"conans/model/conan_file.py::ConanFile.package_folder\n @package_folder.setter\n def package_folder(self, folder):\n self.folders.set_base_package(folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.install_folder","text":"conans/model/conan_file.py::ConanFile.install_folder\n @property\n def install_folder(self):\n # FIXME: Remove in 2.0, no self.install_folder\n return self.folders.base_install","label":0},{"cid":"conans/model/conan_file.py::ConanFile.install_folder","text":"conans/model/conan_file.py::ConanFile.install_folder\n @install_folder.setter\n def install_folder(self, folder):\n # FIXME: Remove in 2.0, no self.install_folder\n self.folders.set_base_install(folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.generators_folder","text":"conans/model/conan_file.py::ConanFile.generators_folder\n @property\n def generators_folder(self):\n # FIXME: Remove in 2.0, no self.install_folder\n return self.folders.generators_folder if self.folders.generators else self.install_folder","label":0},{"cid":"conans/model/conan_file.py::ConanFile.imports_folder","text":"conans/model/conan_file.py::ConanFile.imports_folder\n @property\n def imports_folder(self):\n return self.folders.imports_folder","label":0},{"cid":"conans/model/conan_file.py::ConanFile.imports_folder","text":"conans/model/conan_file.py::ConanFile.imports_folder\n @imports_folder.setter\n def imports_folder(self, folder):\n self.folders.set_base_imports(folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.env","text":"conans/model/conan_file.py::ConanFile.env\n @property\n def env(self):\n \"\"\"Apply the self.deps_env_info into a copy of self._conan_env_values (will prioritize the\n self._conan_env_values, user specified from profiles or -e first, then inherited)\"\"\"\n # Cannot be lazy cached, because it's called in configure node, and we still don't have\n # the deps_env_info objects available\n tmp_env_values = self._conan_env_values.copy()\n tmp_env_values.update(self.deps_env_info)\n ret, multiple = tmp_env_values.env_dicts(self.name, self.version, self._conan_user,\n self._conan_channel)\n ret.update(multiple)\n return ret","label":0},{"cid":"conans/model/conan_file.py::ConanFile.channel","text":"conans/model/conan_file.py::ConanFile.channel\n @property\n def channel(self):\n if not self._conan_channel:\n _env_channel = os.getenv(\"CONAN_CHANNEL\")\n conan_v2_error(\"Environment variable 'CONAN_CHANNEL' is deprecated\", _env_channel)\n self._conan_channel = _env_channel or self.default_channel\n if not self._conan_channel:\n raise ConanException(\"channel not defined, but self.channel is used in conanfile\")\n return self._conan_channel","label":0},{"cid":"conans/model/conan_file.py::ConanFile.user","text":"conans/model/conan_file.py::ConanFile.user\n @property\n def user(self):\n if not self._conan_user:\n _env_username = os.getenv(\"CONAN_USERNAME\")\n conan_v2_error(\"Environment variable 'CONAN_USERNAME' is deprecated\", _env_username)\n self._conan_user = _env_username or self.default_user\n if not self._conan_user:\n raise ConanException(\"user not defined, but self.user is used in conanfile\")\n return self._conan_user","label":0},{"cid":"conans/model/conan_file.py::ConanFile.collect_libs","text":"conans/model/conan_file.py::ConanFile.collect_libs\n def collect_libs(self, folder=None):\n conan_v2_error(\"'self.collect_libs' is deprecated, use 'tools.collect_libs(self)' instead\")\n return tools.collect_libs(self, folder=folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_policy_missing","text":"conans/model/conan_file.py::ConanFile.build_policy_missing\n @property\n def build_policy_missing(self):\n return self.build_policy == \"missing\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_policy_always","text":"conans/model/conan_file.py::ConanFile.build_policy_always\n @property\n def build_policy_always(self):\n return self.build_policy == \"always\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.source","text":"conans/model/conan_file.py::ConanFile.source\n def source(self):\n pass","label":0},{"cid":"conans/model/conan_file.py::ConanFile.system_requirements","text":"conans/model/conan_file.py::ConanFile.system_requirements\n def system_requirements(self):\n \"\"\" this method can be overwritten to implement logic for system package\n managers, as apt-get\n\n You can define self.global_system_requirements = True, if you want the installation\n to be for all packages (not depending on settings/options/requirements)\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.config_options","text":"conans/model/conan_file.py::ConanFile.config_options\n def config_options(self):\n \"\"\" modify options, probably conditioned to some settings. This call is executed\n before config_settings. E.g.\n if self.settings.os == \"Windows\":\n del self.options.shared # shared/static not supported in win\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.configure","text":"conans/model/conan_file.py::ConanFile.configure\n def configure(self):\n \"\"\" modify settings, probably conditioned to some options. This call is executed\n after config_options. E.g.\n if self.options.header_only:\n self.settings.clear()\n This is also the place for conditional requirements\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build","text":"conans/model/conan_file.py::ConanFile.build\n def build(self):\n \"\"\" build your project calling the desired build tools as done in the command line.\n E.g. self.run(\"cmake --build .\") Or use the provided build helpers. E.g. cmake.build()\n \"\"\"\n self.output.warn(\"This conanfile has no build step\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package","text":"conans/model/conan_file.py::ConanFile.package\n def package(self):\n \"\"\" package the needed files from source and build folders.\n E.g. self.copy(\"*.h\", src=\"src/includes\", dst=\"includes\")\n \"\"\"\n self.output.warn(\"This conanfile has no package step\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_info","text":"conans/model/conan_file.py::ConanFile.package_info\n def package_info(self):\n \"\"\" define cpp_build_info, flags, etc\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.run","text":"conans/model/conan_file.py::ConanFile.run\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True,\n ignore_errors=False, run_environment=False, with_login=True, env=None):\n # NOTE: \"self.win_bash\" is the new parameter \"win_bash\" for Conan 2.0\n\n def _run(cmd, _env):\n # FIXME: run in windows bash is not using output\n if platform.system() == \"Windows\":\n if win_bash:\n return tools.run_in_windows_bash(self, bashcmd=cmd, cwd=cwd, subsystem=subsystem,\n msys_mingw=msys_mingw, with_login=with_login)\n elif self.win_bash: # New, Conan 2.0\n from conan.tools.microsoft.subsystems import run_in_windows_bash\n return run_in_windows_bash(self, command=cmd, cwd=cwd, env=_env)\n _env = _env or \"conanenv\"\n wrapped_cmd = environment_wrap_command(self, _env, cmd, cwd=self.generators_folder)\n return self._conan_runner(wrapped_cmd, output, os.path.abspath(RUN_LOG_NAME), cwd)\n\n if run_environment:\n # When using_build_profile the required environment is already applied through\n # 'conanfile.env' in the contextmanager 'get_env_context_manager'\n with tools.run_environment(self) if not self._conan_using_build_profile else no_op():\n if OSInfo().is_macos and isinstance(command, string_types):\n # Security policy on macOS clears this variable when executing /bin/sh. To\n # keep its value, set it again inside the shell when running the command.\n command = 'DYLD_LIBRARY_PATH=\"%s\" DYLD_FRAMEWORK_PATH=\"%s\" %s' % \\\n (os.environ.get('DYLD_LIBRARY_PATH', ''),\n os.environ.get(\"DYLD_FRAMEWORK_PATH\", ''),\n command)\n retcode = _run(command, en","label":0},{"cid":"conans/model/conan_file.py::ConanFile.run._run","text":"conans/model/conan_file.py::ConanFile.run._run\n def _run(cmd, _env):\n # FIXME: run in windows bash is not using output\n if platform.system() == \"Windows\":\n if win_bash:\n return tools.run_in_windows_bash(self, bashcmd=cmd, cwd=cwd, subsystem=subsystem,\n msys_mingw=msys_mingw, with_login=with_login)\n elif self.win_bash: # New, Conan 2.0\n from conan.tools.microsoft.subsystems import run_in_windows_bash\n return run_in_windows_bash(self, command=cmd, cwd=cwd, env=_env)\n _env = _env or \"conanenv\"\n wrapped_cmd = environment_wrap_command(self, _env, cmd, cwd=self.generators_folder)\n return self._conan_runner(wrapped_cmd, output, os.path.abspath(RUN_LOG_NAME), cwd)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_id","text":"conans/model/conan_file.py::ConanFile.package_id\n def package_id(self):\n \"\"\" modify the binary info, typically to narrow values\n e.g.: self.info.settings.compiler = \"Any\" => All compilers will generate same ID\n \"\"\"","label":0}],"string":"[\n {\n \"cid\": \"conan/tools/layout/__init__.py::cmake_layout\",\n \"text\": \"conan/tools/layout/__init__.py::cmake_layout\\ndef cmake_layout(conanfile, generator=None):\\n gen = conanfile.conf[\\\"tools.cmake.cmaketoolchain:generator\\\"] or generator\\n if gen:\\n multi = \\\"Visual\\\" in gen or \\\"Xcode\\\" in gen or \\\"Multi-Config\\\" in gen\\n elif conanfile.settings.compiler == \\\"Visual Studio\\\" or conanfile.settings.compiler == \\\"msvc\\\":\\n multi = True\\n else:\\n multi = False\\n\\n conanfile.folders.source = \\\"src\\\"\\n if multi:\\n conanfile.folders.build = \\\"build\\\"\\n conanfile.folders.generators = \\\"build/conan\\\"\\n else:\\n build_type = str(conanfile.settings.build_type).lower()\\n conanfile.folders.build = \\\"cmake-build-{}\\\".format(build_type)\\n conanfile.folders.generators = os.path.join(conanfile.folders.build, \\\"conan\\\")\\n\\n conanfile.cpp.source.includedirs = [\\\".\\\"]\\n if multi:\\n conanfile.cpp.build.libdirs = [\\\"{}\\\".format(conanfile.settings.build_type)]\\n conanfile.cpp.build.bindirs = [\\\"{}\\\".format(conanfile.settings.build_type)]\\n else:\\n conanfile.cpp.build.libdirs = [\\\".\\\"]\\n conanfile.cpp.build.bindirs = [\\\".\\\"]\",\n \"label\": 1\n },\n {\n \"cid\": \"conan/tools/layout/__init__.py::clion_layout\",\n \"text\": \"conan/tools/layout/__init__.py::clion_layout\\ndef clion_layout(conanfile):\\n if not conanfile.settings.get_safe(\\\"build_type\\\"):\\n raise ConanException(\\\"The 'clion_layout' requires the 'build_type' setting\\\")\\n base = \\\"cmake-build-{}\\\".format(str(conanfile.settings.build_type).lower())\\n conanfile.folders.build = base\\n conanfile.cpp.build.libdirs = [\\\".\\\"]\\n conanfile.folders.generators = os.path.join(base, \\\"generators\\\")\\n conanfile.folders.source = \\\".\\\"\\n conanfile.cpp.source.includedirs = [\\\".\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"conan/tools/layout/__init__.py::vs_layout\",\n \"text\": \"conan/tools/layout/__init__.py::vs_layout\\ndef vs_layout(conanfile):\\n if not conanfile.settings.get_safe(\\\"build_type\\\"):\\n raise ConanException(\\\"The 'vs_layout' requires the 'build_type' setting\\\")\\n if not conanfile.settings.get_safe(\\\"arch\\\"):\\n raise ConanException(\\\"The 'vs_layout' requires the 'arch' setting\\\")\\n\\n if conanfile.settings.arch != \\\"x86\\\":\\n arch = msbuild_arch(conanfile.settings.arch)\\n if not arch:\\n raise ConanException(\\\"The 'vs_layout' doesn't \\\"\\n \\\"work with the arch '{}'\\\".format(conanfile.settings.arch))\\n base = \\\"{}/{}\\\".format(arch, str(conanfile.settings.build_type))\\n else:\\n base = str(conanfile.settings.build_type)\\n\\n conanfile.folders.build = base\\n conanfile.cpp.build.libdirs = [\\\".\\\"]\\n conanfile.folders.generators = os.path.join(base, \\\"generators\\\")\\n conanfile.folders.source = \\\".\\\"\\n conanfile.cpp.source.includedirs = [\\\".\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"conan/tools/layout/__init__.py::LayoutPackager.__init__\",\n \"text\": \"conan/tools/layout/__init__.py::LayoutPackager.__init__\\n def __init__(self, conanfile):\\n self._conanfile = conanfile\",\n \"label\": 0\n },\n {\n \"cid\": \"conan/tools/layout/__init__.py::LayoutPackager.package\",\n \"text\": \"conan/tools/layout/__init__.py::LayoutPackager.package\\n def package(self):\\n cf = self._conanfile\\n\\n # Check that the components declared in source/build are in package\\n cnames = set(cf.cpp.source.component_names)\\n cnames = cnames.union(set(cf.cpp.build.component_names))\\n if cnames.difference(set(cf.cpp.package.component_names)):\\n # TODO: Raise? Warning? Ignore?\\n raise ConanException(\\\"There are components declared in source_cpp_info.components\\\"\\n \\\" or in build_cpp_info.components that are not declared in\\\"\\n \\\" package_cpp_info.components\\\")\\n\\n if cnames:\\n for cname in cnames:\\n if cname in cf.cpp.source.components:\\n self._package_cppinfo(\\\"source\\\", cf.cpp.source.components[cname],\\n cf.cpp.package.components[cname])\\n if cname in cf.cpp.build.components:\\n self._package_cppinfo(\\\"build\\\", cf.cpp.build.components[cname],\\n cf.cpp.package.components[cname])\\n else: # No components declared\\n self._package_cppinfo(\\\"source\\\", cf.cpp.source, cf.cpp.package)\\n self._package_cppinfo(\\\"build\\\", cf.cpp.build, cf.cpp.package)\",\n \"label\": 0\n },\n {\n \"cid\": \"conan/tools/layout/__init__.py::LayoutPackager._package_cppinfo\",\n \"text\": \"conan/tools/layout/__init__.py::LayoutPackager._package_cppinfo\\n def _package_cppinfo(self, origin_name, origin_cppinfo, dest_cppinfo):\\n \\\"\\\"\\\"\\n @param origin_name: one from [\\\"source\\\", \\\"build\\\"]\\n @param origin_cppinfo: cpp_info object of an origin (can be a component cppinfo too)\\n @param dest_cppinfo: cpp_info object of the package or a component from package\\n \\\"\\\"\\\"\\n patterns_var = getattr(self._conanfile.patterns, origin_name)\\n base_folder = getattr(self._conanfile, \\\"{}_folder\\\".format(origin_name))\\n for var in [\\\"include\\\", \\\"lib\\\", \\\"bin\\\", \\\"framework\\\", \\\"src\\\", \\\"build\\\", \\\"res\\\"]:\\n dirs_var_name = \\\"{}dirs\\\".format(var)\\n origin_paths = getattr(origin_cppinfo, dirs_var_name)\\n if not origin_paths:\\n continue\\n patterns = getattr(patterns_var, var)\\n destinations = getattr(dest_cppinfo, dirs_var_name)\\n if not destinations: # For example: Not declared \\\"includedirs\\\" in package.cpp_info\\n continue\\n if len(destinations) > 1:\\n # Check if there is only one possible destination at package, otherwise the\\n # copy would need to be done manually\\n err_msg = \\\"The package has more than 1 cpp_info.{}, cannot package automatically\\\"\\n raise ConanException(err_msg.format(dirs_var_name))\\n\\n for d in origin_paths:\\n copier = FileCopier([os.path.join(base_folder, d)],\\n self._conanfile.folders.base_package)\\n for pattern in patterns:\\n copier(pattern, dst=destinations[0])\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/assets/templates/new_v2_cmake.py::get_cmake_lib_files\",\n \"text\": \"conans/assets/templates/new_v2_cmake.py::get_cmake_lib_files\\ndef get_cmake_lib_files(name, version, package_name=\\\"Pkg\\\"):\\n files = {\\\"conanfile.py\\\": conanfile_sources_v2.format(name=name, version=version,\\n package_name=package_name),\\n \\\"src/{}.cpp\\\".format(name): source_cpp.format(name=name, version=version),\\n \\\"src/{}.h\\\".format(name): source_h.format(name=name, version=version),\\n \\\"src/CMakeLists.txt\\\": cmake_v2.format(name=name, version=version),\\n \\\"test_package/conanfile.py\\\": test_conanfile_v2.format(name=name,\\n version=version,\\n package_name=package_name),\\n \\\"test_package/src/example.cpp\\\": test_main.format(name=name),\\n \\\"test_package/src/CMakeLists.txt\\\": test_cmake_v2.format(name=name)}\\n return files\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/assets/templates/new_v2_cmake.py::get_cmake_exe_files\",\n \"text\": \"conans/assets/templates/new_v2_cmake.py::get_cmake_exe_files\\ndef get_cmake_exe_files(name, version, package_name=\\\"Pkg\\\"):\\n files = {\\\"conanfile.py\\\": conanfile_exe.format(name=name, version=version,\\n package_name=package_name),\\n \\\"src/{}.cpp\\\".format(name): source_cpp.format(name=name, version=version),\\n \\\"src/{}.h\\\".format(name): source_h.format(name=name, version=version),\\n \\\"src/main.cpp\\\": test_main.format(name=name),\\n \\\"src/CMakeLists.txt\\\": cmake_exe_v2.format(name=name, version=version),\\n \\\"test_package/conanfile.py\\\": test_conanfile_exe_v2.format(name=name,\\n version=version,\\n package_name=package_name)\\n }\\n return files\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/cmd/export.py::export_alias\",\n \"text\": \"conans/client/cmd/export.py::export_alias\\ndef export_alias(package_layout, target_ref, output, revisions_enabled):\\n revision_mode = \\\"hash\\\"\\n conanfile = \\\"\\\"\\\"\\nfrom conans import ConanFile\\n\\nclass AliasConanfile(ConanFile):\\n alias = \\\"%s\\\"\\n revision_mode = \\\"%s\\\"\\n\\\"\\\"\\\" % (target_ref.full_str(), revision_mode)\\n\\n save(package_layout.conanfile(), conanfile)\\n manifest = FileTreeManifest.create(package_layout.export())\\n manifest.save(folder=package_layout.export())\\n\\n # Create the metadata for the alias\\n _update_revision_in_metadata(package_layout=package_layout, revisions_enabled=revisions_enabled,\\n output=output, path=None, manifest=manifest,\\n revision_mode=revision_mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::check_casing_conflict\",\n \"text\": \"conans/client/cmd/export.py::check_casing_conflict\\ndef check_casing_conflict(cache, ref):\\n # Check for casing conflict\\n # Maybe a platform check could be added, but depends on disk partition\\n refs = search_recipes(cache, ref, ignorecase=True)\\n refs2 = [ConanFileReference(r.name, r.version, r.user if ref.user else None,\\n r.channel if ref.channel else None, validate=False) for r in refs]\\n\\n if refs and ref not in refs2:\\n raise ConanException(\\\"Cannot export package with same name but different case\\\\n\\\"\\n \\\"You exported '%s' but already existing '%s'\\\"\\n % (str(ref), \\\" \\\".join(str(s) for s in refs)))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::cmd_export\",\n \"text\": \"conans/client/cmd/export.py::cmd_export\\ndef cmd_export(app, conanfile_path, name, version, user, channel, keep_source,\\n export=True, graph_lock=None, ignore_dirty=False):\\n \\\"\\\"\\\" Export the recipe\\n param conanfile_path: the original source directory of the user containing a\\n conanfile.py\\n \\\"\\\"\\\"\\n loader, cache, hook_manager, output = app.loader, app.cache, app.hook_manager, app.out\\n revisions_enabled = app.config.revisions_enabled\\n scm_to_conandata = app.config.scm_to_conandata\\n conanfile = loader.load_export(conanfile_path, name, version, user, channel)\\n\\n # FIXME: Conan 2.0, deprecate CONAN_USER AND CONAN_CHANNEL and remove this try excepts\\n # Take the default from the env vars if they exist to not break behavior\\n try:\\n user = conanfile.user\\n except ConanV2Exception:\\n raise\\n except ConanException:\\n user = None\\n\\n try:\\n channel = conanfile.channel\\n except ConanV2Exception:\\n raise\\n except ConanException:\\n channel = None\\n\\n ref = ConanFileReference(conanfile.name, conanfile.version, user, channel)\\n conanfile.display_name = str(ref)\\n conanfile.output.scope = conanfile.display_name\\n\\n # If we receive lock information, python_requires could have been locked\\n if graph_lock:\\n node_id = graph_lock.get_consumer(ref)\\n python_requires = graph_lock.python_requires(node_id)\\n # TODO: check that the locked python_requires are different from the loaded ones\\n app.range_resolver.clear_output() # invalidate previous version range output\\n conanfile = loader.load_export(conanfile_path, conanfile.name, conanfile.version,\\n ref.user, ref.channel, python_requires)\\n\\n check_casing_conflict(cache=cache, ref=ref)\\n package_layout = cache.package_layout(ref, short_paths=conanfile.short_paths)\\n if not export:\\n metadata = package_layout.load_metadata()\\n recipe_revision = metad\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_check_settings_for_warnings\",\n \"text\": \"conans/client/cmd/export.py::_check_settings_for_warnings\\ndef _check_settings_for_warnings(conanfile, output):\\n if not conanfile.settings:\\n return\\n try:\\n if 'os_build' not in conanfile.settings:\\n return\\n if 'os' not in conanfile.settings:\\n return\\n\\n output.writeln(\\\"*\\\" * 60, front=Color.BRIGHT_RED)\\n output.writeln(\\\" This package defines both 'os' and 'os_build' \\\",\\n front=Color.BRIGHT_RED)\\n output.writeln(\\\" Please use 'os' for libraries and 'os_build'\\\",\\n front=Color.BRIGHT_RED)\\n output.writeln(\\\" only for build-requires used for cross-building\\\",\\n front=Color.BRIGHT_RED)\\n output.writeln(\\\"*\\\" * 60, front=Color.BRIGHT_RED)\\n except ConanException:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_capture_scm_auto_fields\",\n \"text\": \"conans/client/cmd/export.py::_capture_scm_auto_fields\\ndef _capture_scm_auto_fields(conanfile, conanfile_dir, package_layout, output, ignore_dirty,\\n scm_to_conandata):\\n \\\"\\\"\\\"Deduce the values for the scm auto fields or functions assigned to 'url' or 'revision'\\n and replace the conanfile.py contents.\\n Returns a tuple with (scm_data, path_to_scm_local_directory)\\\"\\\"\\\"\\n scm_data = get_scm_data(conanfile)\\n if not scm_data:\\n return None, None\\n\\n # Resolve SCMData in the user workspace (someone may want to access CVS or import some py)\\n scm = SCM(scm_data, conanfile_dir, output)\\n captured = scm_data.capture_origin or scm_data.capture_revision\\n\\n if not captured:\\n # We replace not only \\\"auto\\\" values, also evaluated functions (e.g from a python_require)\\n _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata)\\n return scm_data, None\\n\\n if not scm.is_pristine() and not ignore_dirty:\\n output.warn(\\\"There are uncommitted changes, skipping the replacement of 'scm.url' and \\\"\\n \\\"'scm.revision' auto fields. Use --ignore-dirty to force it. The 'conan \\\"\\n \\\"upload' command will prevent uploading recipes with 'auto' values in these \\\"\\n \\\"fields.\\\")\\n origin = scm.get_qualified_remote_url(remove_credentials=True)\\n local_src_path = scm.get_local_path_to_url(origin)\\n return scm_data, local_src_path\\n\\n if scm_data.url == \\\"auto\\\":\\n origin = scm.get_qualified_remote_url(remove_credentials=True)\\n if not origin:\\n output.warn(\\\"Repo origin cannot be deduced, 'auto' fields won't be replaced.\\\"\\n \\\" 'conan upload' command will prevent uploading recipes with 'auto'\\\"\\n \\\" values in these fields.\\\")\\n local_src_path = scm.get_local_path_to_url(origin)\\n return scm_data, local_src_path\\n if scm.is_local_repository():\\n output.w\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_replace_scm_data_in_recipe\",\n \"text\": \"conans/client/cmd/export.py::_replace_scm_data_in_recipe\\ndef _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata):\\n if scm_to_conandata:\\n conandata_path = os.path.join(package_layout.export(), DATA_YML)\\n conandata_yml = {}\\n if os.path.exists(conandata_path):\\n conandata_yml = yaml.safe_load(load(conandata_path))\\n conandata_yml = conandata_yml or {} # In case the conandata is a blank file\\n if '.conan' in conandata_yml:\\n raise ConanException(\\\"Field '.conan' inside '{}' file is reserved to \\\"\\n \\\"Conan usage.\\\".format(DATA_YML))\\n scm_data_copied = scm_data.as_dict()\\n scm_data_copied.pop('username', None)\\n scm_data_copied.pop('password', None)\\n conandata_yml['.conan'] = {'scm': scm_data_copied}\\n\\n save(conandata_path, yaml.safe_dump(conandata_yml, default_flow_style=False))\\n else:\\n conan_v2_error(\\\"general.scm_to_conandata should be set to 1\\\")\\n _replace_scm_data_in_conanfile(package_layout.conanfile(), scm_data)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_replace_scm_data_in_conanfile\",\n \"text\": \"conans/client/cmd/export.py::_replace_scm_data_in_conanfile\\ndef _replace_scm_data_in_conanfile(conanfile_path, scm_data):\\n # FIXME: Remove in Conan 2.0, it will use conandata.yml as the only way\\n # Parsing and replacing the SCM field\\n content = load(conanfile_path)\\n headers = []\\n\\n if six.PY2:\\n # Workaround for https://bugs.python.org/issue22221\\n lines_without_headers = []\\n lines = content.splitlines(True)\\n for line in lines:\\n if not lines_without_headers and line.startswith(\\\"#\\\"):\\n headers.append(line)\\n else:\\n lines_without_headers.append(line)\\n content = ''.join(lines_without_headers)\\n\\n lines = content.splitlines(True)\\n tree = ast.parse(content)\\n to_replace = []\\n comments = []\\n class_line = None\\n tab_size = 4\\n for i_body, item in enumerate(tree.body):\\n if isinstance(item, ast.ClassDef):\\n statements = item.body\\n class_line = item.lineno\\n for i, stmt in enumerate(item.body):\\n if isinstance(stmt, ast.Assign) and len(stmt.targets) == 1:\\n line = lines[stmt.lineno - 1]\\n tab_size = len(line) - len(line.lstrip())\\n if isinstance(stmt.targets[0], ast.Name) and stmt.targets[0].id == \\\"scm\\\":\\n try:\\n if i + 1 == len(statements): # Last statement in my ClassDef\\n if i_body + 1 == len(tree.body): # Last statement over all\\n next_line = len(lines)\\n else:\\n next_line = tree.body[i_body + 1].lineno - 1\\n else:\\n # Next statement can be a comment or anything else\\n next_statement = statements[i + 1]\\n if isPY38 and isinstance(next_statement, ast.Expr):\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_detect_scm_revision\",\n \"text\": \"conans/client/cmd/export.py::_detect_scm_revision\\ndef _detect_scm_revision(path):\\n if not path:\\n raise ConanException(\\\"Not path supplied\\\")\\n\\n repo_type = SCM.detect_scm(path)\\n if not repo_type:\\n raise ConanException(\\\"'{}' repository not detected\\\".format(repo_type))\\n\\n repo_obj = SCM.availables.get(repo_type)(path)\\n return repo_obj.get_revision(), repo_type, repo_obj.is_pristine()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_update_revision_in_metadata\",\n \"text\": \"conans/client/cmd/export.py::_update_revision_in_metadata\\ndef _update_revision_in_metadata(package_layout, revisions_enabled, output, path, manifest,\\n revision_mode):\\n if revision_mode not in [\\\"scm\\\", \\\"hash\\\"]:\\n raise ConanException(\\\"Revision mode should be one of 'hash' (default) or 'scm'\\\")\\n\\n # Use the proper approach depending on 'revision_mode'\\n if revision_mode == \\\"hash\\\":\\n revision = manifest.summary_hash\\n if revisions_enabled:\\n output.info(\\\"Using the exported files summary hash as the recipe\\\"\\n \\\" revision: {} \\\".format(revision))\\n else:\\n try:\\n rev_detected, repo_type, is_pristine = _detect_scm_revision(path)\\n except Exception as exc:\\n error_msg = \\\"Cannot detect revision using '{}' mode from repository at \\\" \\\\\\n \\\"'{}'\\\".format(revision_mode, path)\\n raise ConanException(\\\"{}: {}\\\".format(error_msg, exc))\\n\\n revision = rev_detected\\n\\n if revisions_enabled:\\n output.info(\\\"Using %s commit as the recipe revision: %s\\\" % (repo_type, revision))\\n if not is_pristine:\\n output.warn(\\\"Repo status is not pristine: there might be modified files\\\")\\n\\n with package_layout.update_metadata() as metadata:\\n metadata.recipe.revision = revision\\n\\n return revision\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_classify_patterns\",\n \"text\": \"conans/client/cmd/export.py::_classify_patterns\\ndef _classify_patterns(patterns):\\n patterns = patterns or []\\n included, excluded = [], []\\n for p in patterns:\\n if p.startswith(\\\"!\\\"):\\n excluded.append(p[1:])\\n else:\\n included.append(p)\\n return included, excluded\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_export_scm\",\n \"text\": \"conans/client/cmd/export.py::_export_scm\\ndef _export_scm(scm_data, origin_folder, scm_sources_folder, output):\\n \\\"\\\"\\\" Copy the local folder to the scm_sources folder in the cache, this enables to work\\n with local sources without committing and pushing changes to the scm remote.\\n https://github.com/conan-io/conan/issues/5195\\\"\\\"\\\"\\n excluded = SCM(scm_data, origin_folder, output).excluded_files\\n excluded.append(\\\"conanfile.py\\\")\\n output.info(\\\"SCM: Getting sources from folder: %s\\\" % origin_folder)\\n merge_directories(origin_folder, scm_sources_folder, excluded=excluded)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::export_source\",\n \"text\": \"conans/client/cmd/export.py::export_source\\ndef export_source(conanfile, origin_folder, destination_source_folder):\\n if callable(conanfile.exports_sources):\\n raise ConanException(\\\"conanfile 'exports_sources' shouldn't be a method, \\\"\\n \\\"use 'export_sources()' instead\\\")\\n\\n if isinstance(conanfile.exports_sources, str):\\n conanfile.exports_sources = (conanfile.exports_sources,)\\n\\n included_sources, excluded_sources = _classify_patterns(conanfile.exports_sources)\\n copier = FileCopier([origin_folder], destination_source_folder)\\n for pattern in included_sources:\\n copier(pattern, links=True, excludes=excluded_sources)\\n output = conanfile.output\\n package_output = ScopedOutput(\\\"%s exports_sources\\\" % output.scope, output)\\n copier.report(package_output)\\n\\n _run_method(conanfile, \\\"export_sources\\\", origin_folder, destination_source_folder, output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::export_recipe\",\n \"text\": \"conans/client/cmd/export.py::export_recipe\\ndef export_recipe(conanfile, origin_folder, destination_folder):\\n if callable(conanfile.exports):\\n raise ConanException(\\\"conanfile 'exports' shouldn't be a method, use 'export()' instead\\\")\\n if isinstance(conanfile.exports, str):\\n conanfile.exports = (conanfile.exports,)\\n\\n output = conanfile.output\\n package_output = ScopedOutput(\\\"%s exports\\\" % output.scope, output)\\n\\n if os.path.exists(os.path.join(origin_folder, DATA_YML)):\\n package_output.info(\\\"File '{}' found. Exporting it...\\\".format(DATA_YML))\\n tmp = [DATA_YML]\\n if conanfile.exports:\\n tmp.extend(conanfile.exports) # conanfile.exports could be a tuple (immutable)\\n conanfile.exports = tmp\\n\\n included_exports, excluded_exports = _classify_patterns(conanfile.exports)\\n\\n try:\\n os.unlink(os.path.join(origin_folder, CONANFILE + 'c'))\\n except OSError:\\n pass\\n\\n copier = FileCopier([origin_folder], destination_folder)\\n for pattern in included_exports:\\n copier(pattern, links=True, excludes=excluded_exports)\\n copier.report(package_output)\\n\\n _run_method(conanfile, \\\"export\\\", origin_folder, destination_folder, output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_run_method\",\n \"text\": \"conans/client/cmd/export.py::_run_method\\ndef _run_method(conanfile, method, origin_folder, destination_folder, output):\\n export_method = getattr(conanfile, method, None)\\n if export_method:\\n if not callable(export_method):\\n raise ConanException(\\\"conanfile '%s' must be a method\\\" % method)\\n output.highlight(\\\"Calling %s()\\\" % method)\\n copier = FileCopier([origin_folder], destination_folder)\\n conanfile.copy = copier\\n folder_name = \\\"%s_folder\\\" % method\\n setattr(conanfile, folder_name, destination_folder)\\n default_options = conanfile.default_options\\n try:\\n # TODO: Poor man attribute control access. Convert to nice decorator\\n conanfile.default_options = None\\n with chdir(origin_folder):\\n with conanfile_exception_formatter(str(conanfile), method):\\n export_method()\\n finally:\\n conanfile.default_options = default_options\\n delattr(conanfile, folder_name)\\n export_method_output = ScopedOutput(\\\"%s %s() method\\\" % (output.scope, method), output)\\n copier.report(export_method_output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/file_copier.py::report_copied_files\",\n \"text\": \"conans/client/file_copier.py::report_copied_files\\ndef report_copied_files(copied, output, message_suffix=\\\"Copied\\\"):\\n ext_files = defaultdict(list)\\n for f in copied:\\n _, ext = os.path.splitext(f)\\n ext_files[ext].append(os.path.basename(f))\\n\\n if not ext_files:\\n return False\\n\\n for ext, files in ext_files.items():\\n files_str = (\\\": \\\" + \\\", \\\".join(files)) if len(files) < 5 else \\\"\\\"\\n file_or_files = \\\"file\\\" if len(files) == 1 else \\\"files\\\"\\n if not ext:\\n output.info(\\\"%s %d %s%s\\\" % (message_suffix, len(files), file_or_files, files_str))\\n else:\\n output.info(\\\"%s %d '%s' %s%s\\\"\\n % (message_suffix, len(files), ext, file_or_files, files_str))\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/file_copier.py::FileCopier.__init__\",\n \"text\": \"conans/client/file_copier.py::FileCopier.__init__\\n def __init__(self, source_folders, root_destination_folder):\\n \\\"\\\"\\\"\\n Takes the base folders to copy resources src -> dst. These folders names\\n will not be used in the relative names while copying\\n param source_folders: list of folders to copy things from, typically the\\n store build folder\\n param root_destination_folder: The base folder to copy things to, typically the\\n store package folder\\n \\\"\\\"\\\"\\n assert isinstance(source_folders, list), \\\"source folders must be a list\\\"\\n self._src_folders = source_folders\\n self._dst_folder = root_destination_folder\\n self._copied = []\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/file_copier.py::FileCopier.report\",\n \"text\": \"conans/client/file_copier.py::FileCopier.report\\n def report(self, output):\\n return report_copied_files(self._copied, output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/file_copier.py::FileCopier.__call__\",\n \"text\": \"conans/client/file_copier.py::FileCopier.__call__\\n def __call__(self, pattern, dst=\\\"\\\", src=\\\"\\\", keep_path=True, links=False, symlinks=None,\\n excludes=None, ignore_case=True):\\n \\\"\\\"\\\"\\n param pattern: an fnmatch file pattern of the files that should be copied. Eg. *.dll\\n param dst: the destination local folder, wrt to current conanfile dir, to which\\n the files will be copied. Eg: \\\"bin\\\"\\n param src: the source folder in which those files will be searched. This folder\\n will be stripped from the dst name. Eg.: lib/Debug/x86\\n param keep_path: False if you want the relative paths to be maintained from\\n src to dst folders, or just drop. False is useful if you want\\n to collect e.g. many *.libs among many dirs into a single\\n lib dir\\n param links: True to activate symlink copying\\n param excludes: Single pattern or a tuple of patterns to be excluded from the copy\\n param ignore_case: will do a case-insensitive pattern matching when True\\n return: list of copied files\\n \\\"\\\"\\\"\\n # TODO: Remove the old \\\"links\\\" arg for Conan 2.0\\n if symlinks is not None:\\n links = symlinks\\n\\n if os.path.isabs(src):\\n # Avoid repeatedly copying absolute paths\\n return self._copy(os.curdir, pattern, src, dst, links,\\n ignore_case, excludes, keep_path,\\n excluded_folders=[self._dst_folder])\\n\\n files = []\\n for src_folder in self._src_folders:\\n excluded = [self._dst_folder]\\n excluded.extend([d for d in self._src_folders if d is not src_folder])\\n fs = self._copy(src_folder, pattern, src, dst, links, ignore_case, excludes,\\n keep_path, excluded_folders=excluded)\\n files.extend(fs)\\n\\n return files\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/file_copier.py::FileCopier._copy\",\n \"text\": \"conans/client/file_copier.py::FileCopier._copy\\n def _copy(self, base_src, pattern, src, dst, symlinks, ignore_case, excludes, keep_path,\\n excluded_folders):\\n # Check for ../ patterns and allow them\\n if pattern.startswith(\\\"..\\\"):\\n rel_dir = os.path.abspath(os.path.join(base_src, pattern))\\n base_src = os.path.dirname(rel_dir)\\n pattern = os.path.basename(rel_dir)\\n\\n src = os.path.join(base_src, src)\\n dst = os.path.join(self._dst_folder, dst)\\n\\n files_to_copy, link_folders = self._filter_files(src, pattern, symlinks, excludes,\\n ignore_case, excluded_folders)\\n copied_files = self._copy_files(files_to_copy, src, dst, keep_path, symlinks)\\n self.link_folders(src, dst, link_folders)\\n self._copied.extend(files_to_copy)\\n return copied_files\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/file_copier.py::FileCopier._filter_files\",\n \"text\": \"conans/client/file_copier.py::FileCopier._filter_files\\n @staticmethod\\n def _filter_files(src, pattern, links, excludes, ignore_case, excluded_folders):\\n\\n \\\"\\\"\\\" return a list of the files matching the patterns\\n The list will be relative path names wrt to the root src folder\\n \\\"\\\"\\\"\\n filenames = []\\n linked_folders = []\\n\\n if excludes:\\n if not isinstance(excludes, (tuple, list)):\\n excludes = (excludes, )\\n if ignore_case:\\n excludes = [e.lower() for e in excludes]\\n else:\\n excludes = []\\n\\n for root, subfolders, files in walk(src, followlinks=True):\\n if root in excluded_folders:\\n subfolders[:] = []\\n continue\\n\\n if links and os.path.islink(root):\\n linked_folders.append(os.path.relpath(root, src))\\n subfolders[:] = []\\n continue\\n basename = os.path.basename(root)\\n # Skip git or svn subfolders\\n if basename in [\\\".git\\\", \\\".svn\\\"]:\\n subfolders[:] = []\\n continue\\n if basename == \\\"test_package\\\": # DO NOT export test_package/build folder\\n try:\\n subfolders.remove(\\\"build\\\")\\n except ValueError:\\n pass\\n\\n relative_path = os.path.relpath(root, src)\\n compare_relative_path = relative_path.lower() if ignore_case else relative_path\\n for exclude in excludes:\\n if fnmatch.fnmatch(compare_relative_path, exclude):\\n subfolders[:] = []\\n files = []\\n break\\n for f in files:\\n relative_name = os.path.normpath(os.path.join(relative_path, f))\\n filenames.append(relative_name)\\n\\n if ignore_case:\\n pattern = pattern.lower()\\n files_to_copy = [n for n in filenames if fnmatch.fnmatch(os.path.normpa\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/file_copier.py::FileCopier.link_folders\",\n \"text\": \"conans/client/file_copier.py::FileCopier.link_folders\\n @staticmethod\\n def link_folders(src, dst, linked_folders):\\n created_links = []\\n for linked_folder in linked_folders:\\n src_link = os.path.join(src, linked_folder)\\n # Discard symlinks that go out of the src folder\\n abs_path = os.path.realpath(src_link)\\n relpath = os.path.relpath(abs_path, os.path.realpath(src))\\n if relpath.startswith(\\\".\\\"):\\n continue\\n\\n link = os.readlink(src_link)\\n # Absoluted path symlinks are a problem, convert it to relative\\n if os.path.isabs(link):\\n try:\\n link = os.path.relpath(link, os.path.dirname(src_link))\\n except ValueError as e:\\n # https://github.com/conan-io/conan/issues/6197 fails if Windows and other Drive\\n raise ConanException(\\\"Symlink '%s' pointing to '%s' couldn't be made relative:\\\"\\n \\\" %s\\\" % (src_link, link, str(e)))\\n\\n dst_link = os.path.join(dst, linked_folder)\\n try:\\n # Remove the previous symlink\\n os.remove(dst_link)\\n except OSError:\\n pass\\n # link is a string relative to linked_folder\\n # e.g.: os.symlink(\\\"test/bar\\\", \\\"./foo/test_link\\\") will create a link\\n # to foo/test/bar in ./foo/test_link\\n mkdir(os.path.dirname(dst_link))\\n os.symlink(link, dst_link)\\n created_links.append(dst_link)\\n # Remove empty links\\n for dst_link in created_links:\\n abs_path = os.path.realpath(dst_link)\\n if not os.path.exists(abs_path):\\n base_path = os.path.dirname(dst_link)\\n os.remove(dst_link)\\n while base_path.startswith(dst):\\n try: # Take advantage that os.rmdir does not delete non-empty dirs\\n os.rm\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/file_copier.py::FileCopier._copy_files\",\n \"text\": \"conans/client/file_copier.py::FileCopier._copy_files\\n @staticmethod\\n def _copy_files(files, src, dst, keep_path, symlinks):\\n \\\"\\\"\\\" executes a multiple file copy from [(src_file, dst_file), (..)]\\n managing symlinks if necessary\\n \\\"\\\"\\\"\\n copied_files = []\\n for filename in files:\\n abs_src_name = os.path.join(src, filename)\\n filename = filename if keep_path else os.path.basename(filename)\\n abs_dst_name = os.path.normpath(os.path.join(dst, filename))\\n try:\\n os.makedirs(os.path.dirname(abs_dst_name))\\n except Exception:\\n pass\\n if symlinks and os.path.islink(abs_src_name):\\n linkto = os.readlink(abs_src_name) # @UndefinedVariable\\n try:\\n os.remove(abs_dst_name)\\n except OSError:\\n pass\\n os.symlink(linkto, abs_dst_name) # @UndefinedVariable\\n else:\\n shutil.copy2(abs_src_name, abs_dst_name)\\n copied_files.append(abs_dst_name)\\n return copied_files\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::build_id\",\n \"text\": \"conans/client/installer.py::build_id\\ndef build_id(conan_file):\\n if hasattr(conan_file, \\\"build_id\\\"):\\n # construct new ConanInfo\\n build_id_info = conan_file.info.copy()\\n conan_file.info_build = build_id_info\\n # effectively call the user function to change the package values\\n with conanfile_exception_formatter(str(conan_file), \\\"build_id\\\"):\\n conan_file.build_id()\\n # compute modified ID\\n return build_id_info.package_id()\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::add_env_conaninfo\",\n \"text\": \"conans/client/installer.py::add_env_conaninfo\\ndef add_env_conaninfo(conan_file, subtree_libnames):\\n for package_name, env_vars in conan_file._conan_env_values.data.items():\\n for name, value in env_vars.items():\\n if not package_name or package_name in subtree_libnames or \\\\\\n package_name == conan_file.name:\\n conan_file.info.env_values.add(name, value, package_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder.__init__\",\n \"text\": \"conans/client/installer.py::_PackageBuilder.__init__\\n def __init__(self, cache, output, hook_manager, remote_manager, generators):\\n self._cache = cache\\n self._output = output\\n self._hook_manager = hook_manager\\n self._remote_manager = remote_manager\\n self._generator_manager = generators\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder._get_build_folder\",\n \"text\": \"conans/client/installer.py::_PackageBuilder._get_build_folder\\n def _get_build_folder(self, conanfile, package_layout, pref, keep_build, recorder):\\n # Build folder can use a different package_ID if build_id() is defined.\\n # This function decides if the build folder should be re-used (not build again)\\n # and returns the build folder\\n new_id = build_id(conanfile)\\n build_pref = PackageReference(pref.ref, new_id) if new_id else pref\\n build_folder = package_layout.build(build_pref)\\n\\n if is_dirty(build_folder):\\n self._output.warn(\\\"Build folder is dirty, removing it: %s\\\" % build_folder)\\n rmdir(build_folder)\\n clean_dirty(build_folder)\\n\\n # Decide if the build folder should be kept\\n skip_build = conanfile.develop and keep_build\\n if skip_build:\\n self._output.info(\\\"Won't be built as specified by --keep-build\\\")\\n if not os.path.exists(build_folder):\\n msg = \\\"--keep-build specified, but build folder not found\\\"\\n recorder.package_install_error(pref, INSTALL_ERROR_MISSING_BUILD_FOLDER,\\n msg, remote_name=None)\\n raise ConanException(msg)\\n elif build_pref != pref and os.path.exists(build_folder) and hasattr(conanfile, \\\"build_id\\\"):\\n self._output.info(\\\"Won't be built, using previous build folder as defined in build_id()\\\")\\n skip_build = True\\n\\n return build_folder, skip_build\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder._prepare_sources\",\n \"text\": \"conans/client/installer.py::_PackageBuilder._prepare_sources\\n def _prepare_sources(self, conanfile, pref, package_layout, remotes):\\n export_folder = package_layout.export()\\n export_source_folder = package_layout.export_sources()\\n scm_sources_folder = package_layout.scm_sources()\\n conanfile_path = package_layout.conanfile()\\n source_folder = package_layout.source()\\n\\n retrieve_exports_sources(self._remote_manager, self._cache, conanfile, pref.ref, remotes)\\n\\n conanfile.folders.set_base_source(source_folder)\\n conanfile.folders.set_base_build(None)\\n conanfile.folders.set_base_package(None)\\n\\n config_source(export_folder, export_source_folder, scm_sources_folder,\\n conanfile, self._output, conanfile_path, pref.ref,\\n self._hook_manager, self._cache)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder._copy_sources\",\n \"text\": \"conans/client/installer.py::_PackageBuilder._copy_sources\\n @staticmethod\\n def _copy_sources(conanfile, source_folder, build_folder):\\n # Copies the sources to the build-folder, unless no_copy_source is defined\\n _remove_folder_raising(build_folder)\\n if not getattr(conanfile, 'no_copy_source', False):\\n conanfile.output.info('Copying sources to build folder')\\n try:\\n shutil.copytree(source_folder, build_folder, symlinks=True)\\n except Exception as e:\\n msg = str(e)\\n if \\\"206\\\" in msg: # System error shutil.Error 206: Filename or extension too long\\n msg += \\\"\\\\nUse short_paths=True if paths too long\\\"\\n raise ConanException(\\\"%s\\\\nError copying sources to build folder\\\" % msg)\\n logger.debug(\\\"BUILD: Copied to %s\\\", build_folder)\\n logger.debug(\\\"BUILD: Files copied %s\\\", \\\",\\\".join(os.listdir(build_folder)))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder._build\",\n \"text\": \"conans/client/installer.py::_PackageBuilder._build\\n def _build(self, conanfile, pref):\\n # Read generators from conanfile and generate the needed files\\n logger.info(\\\"GENERATORS: Writing generators\\\")\\n self._generator_manager.write_generators(conanfile, conanfile.build_folder,\\n conanfile.generators_folder, self._output)\\n\\n logger.info(\\\"TOOLCHAIN: Writing toolchain\\\")\\n write_toolchain(conanfile, conanfile.generators_folder, self._output)\\n\\n # Build step might need DLLs, binaries as protoc to generate source files\\n # So execute imports() before build, storing the list of copied_files\\n\\n copied_files = run_imports(conanfile)\\n\\n try:\\n mkdir(conanfile.build_folder)\\n with tools.chdir(conanfile.build_folder):\\n run_build_method(conanfile, self._hook_manager, reference=pref.ref, package_id=pref.id)\\n self._output.success(\\\"Package '%s' built\\\" % pref.id)\\n self._output.info(\\\"Build folder %s\\\" % conanfile.build_folder)\\n except Exception as exc:\\n self._output.writeln(\\\"\\\")\\n self._output.error(\\\"Package '%s' build failed\\\" % pref.id)\\n self._output.warn(\\\"Build folder %s\\\" % conanfile.build_folder)\\n if isinstance(exc, ConanExceptionInUserConanfileMethod):\\n raise exc\\n raise ConanException(exc)\\n finally:\\n # Now remove all files that were imported with imports()\\n remove_imports(conanfile, copied_files, self._output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder._package\",\n \"text\": \"conans/client/installer.py::_PackageBuilder._package\\n def _package(self, conanfile, pref, package_layout, conanfile_path):\\n # FIXME: Is weak to assign here the recipe_hash\\n manifest = package_layout.recipe_manifest()\\n conanfile.info.recipe_hash = manifest.summary_hash\\n\\n # Creating ***info.txt files\\n save(os.path.join(conanfile.folders.base_build, CONANINFO), conanfile.info.dumps())\\n self._output.info(\\\"Generated %s\\\" % CONANINFO)\\n save(os.path.join(conanfile.folders.base_build, BUILD_INFO),\\n TXTGenerator(conanfile).content)\\n self._output.info(\\\"Generated %s\\\" % BUILD_INFO)\\n\\n package_id = pref.id\\n # Do the actual copy, call the conanfile.package() method\\n # While installing, the infos goes to build folder\\n conanfile.folders.set_base_install(conanfile.folders.base_build)\\n\\n prev = run_package_method(conanfile, package_id, self._hook_manager, conanfile_path,\\n pref.ref)\\n\\n update_package_metadata(prev, package_layout, package_id, pref.ref.revision)\\n\\n if get_env(\\\"CONAN_READ_ONLY_CACHE\\\", False):\\n make_read_only(conanfile.folders.base_package)\\n # FIXME: Conan 2.0 Clear the registry entry (package ref)\\n return prev\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_PackageBuilder.build_package\",\n \"text\": \"conans/client/installer.py::_PackageBuilder.build_package\\n def build_package(self, node, keep_build, recorder, remotes):\\n t1 = time.time()\\n\\n conanfile = node.conanfile\\n pref = node.pref\\n\\n package_layout = self._cache.package_layout(pref.ref, conanfile.short_paths)\\n base_source = package_layout.source()\\n conanfile_path = package_layout.conanfile()\\n base_package = package_layout.package(pref)\\n\\n base_build, skip_build = self._get_build_folder(conanfile, package_layout,\\n pref, keep_build, recorder)\\n # PREPARE SOURCES\\n if not skip_build:\\n with package_layout.conanfile_write_lock(self._output):\\n set_dirty(base_build)\\n self._prepare_sources(conanfile, pref, package_layout, remotes)\\n self._copy_sources(conanfile, base_source, base_build)\\n\\n # BUILD & PACKAGE\\n with package_layout.conanfile_read_lock(self._output):\\n self._output.info('Building your package in %s' % base_build)\\n try:\\n if getattr(conanfile, 'no_copy_source', False):\\n conanfile.folders.set_base_source(base_source)\\n else:\\n conanfile.folders.set_base_source(base_build)\\n\\n conanfile.folders.set_base_build(base_build)\\n conanfile.folders.set_base_imports(base_build)\\n conanfile.folders.set_base_package(base_package)\\n\\n if not skip_build:\\n # In local cache, generators folder always in build_folder\\n conanfile.folders.set_base_generators(base_build)\\n # In local cache, install folder always is build_folder\\n conanfile.folders.set_base_install(base_build)\\n self._build(conanfile, pref)\\n clean_dirty(base_build)\\n\\n prev = self._package(conanfile, pref, pac\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_remove_folder_raising\",\n \"text\": \"conans/client/installer.py::_remove_folder_raising\\ndef _remove_folder_raising(folder):\\n try:\\n rmdir(folder)\\n except OSError as e:\\n raise ConanException(\\\"%s\\\\n\\\\nCouldn't remove folder, might be busy or open\\\\n\\\"\\n \\\"Close any app using it, and retry\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::_handle_system_requirements\",\n \"text\": \"conans/client/installer.py::_handle_system_requirements\\ndef _handle_system_requirements(conan_file, pref, cache, out):\\n \\\"\\\"\\\" check first the system_reqs/system_requirements.txt existence, if not existing\\n check package/sha1/\\n\\n Used after remote package retrieving and before package building\\n \\\"\\\"\\\"\\n # TODO: Check if this idiom should be generalize to all methods defined in base ConanFile\\n # Instead of calling empty methods\\n if type(conan_file).system_requirements == ConanFile.system_requirements:\\n return\\n\\n package_layout = cache.package_layout(pref.ref)\\n system_reqs_path = package_layout.system_reqs()\\n system_reqs_package_path = package_layout.system_reqs_package(pref)\\n if os.path.exists(system_reqs_path) or os.path.exists(system_reqs_package_path):\\n return\\n\\n ret = call_system_requirements(conan_file, out)\\n\\n try:\\n ret = str(ret or \\\"\\\")\\n except Exception:\\n out.warn(\\\"System requirements didn't return a string\\\")\\n ret = \\\"\\\"\\n if getattr(conan_file, \\\"global_system_requirements\\\", None):\\n save(system_reqs_path, ret)\\n else:\\n save(system_reqs_package_path, ret)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::call_system_requirements\",\n \"text\": \"conans/client/installer.py::call_system_requirements\\ndef call_system_requirements(conanfile, output):\\n try:\\n return conanfile.system_requirements()\\n except Exception as e:\\n output.error(\\\"while executing system_requirements(): %s\\\" % str(e))\\n raise ConanException(\\\"Error in system requirements\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller.__init__\",\n \"text\": \"conans/client/installer.py::BinaryInstaller.__init__\\n def __init__(self, app, recorder):\\n self._cache = app.cache\\n self._out = app.out\\n self._remote_manager = app.remote_manager\\n self._recorder = recorder\\n self._binaries_analyzer = app.binaries_analyzer\\n self._hook_manager = app.hook_manager\\n self._generator_manager = app.generator_manager\\n # Load custom generators from the cache, generators are part of the binary\\n # build and install. Generators loaded here from the cache will have precedence\\n # and overwrite possible generators loaded from packages (requires)\\n for generator_path in app.cache.generators:\\n app.loader.load_generators(generator_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller.install\",\n \"text\": \"conans/client/installer.py::BinaryInstaller.install\\n def install(self, deps_graph, remotes, build_mode, update, profile_host, profile_build,\\n graph_lock, keep_build=False):\\n # order by levels and separate the root node (ref=None) from the rest\\n nodes_by_level = deps_graph.by_levels()\\n root_level = nodes_by_level.pop()\\n root_node = root_level[0]\\n # Get the nodes in order and if we have to build them\\n self._out.info(\\\"Installing (downloading, building) binaries...\\\")\\n self._build(nodes_by_level, keep_build, root_node, profile_host, profile_build,\\n graph_lock, remotes, build_mode, update)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._classify\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._classify\\n @staticmethod\\n def _classify(nodes_by_level):\\n missing, invalid, downloads = [], [], []\\n for level in nodes_by_level:\\n for node in level:\\n if node.binary == BINARY_MISSING:\\n missing.append(node)\\n elif node.binary == BINARY_INVALID:\\n invalid.append(node)\\n elif node.binary in (BINARY_UPDATE, BINARY_DOWNLOAD):\\n downloads.append(node)\\n return missing, invalid, downloads\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._raise_missing\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._raise_missing\\n def _raise_missing(self, missing):\\n if not missing:\\n return\\n\\n missing_prefs = set(n.pref for n in missing) # avoid duplicated\\n missing_prefs = list(sorted(missing_prefs))\\n for pref in missing_prefs:\\n self._out.error(\\\"Missing binary: %s\\\" % str(pref))\\n self._out.writeln(\\\"\\\")\\n\\n # Report details just the first one\\n node = missing[0]\\n package_id = node.package_id\\n ref, conanfile = node.ref, node.conanfile\\n dependencies = [str(dep.dst) for dep in node.dependencies]\\n\\n settings_text = \\\", \\\".join(conanfile.info.full_settings.dumps().splitlines())\\n options_text = \\\", \\\".join(conanfile.info.full_options.dumps().splitlines())\\n dependencies_text = ', '.join(dependencies)\\n requires_text = \\\", \\\".join(conanfile.info.requires.dumps().splitlines())\\n\\n msg = textwrap.dedent('''\\\\\\n Can't find a '%s' package for the specified settings, options and dependencies:\\n - Settings: %s\\n - Options: %s\\n - Dependencies: %s\\n - Requirements: %s\\n - Package ID: %s\\n ''' % (ref, settings_text, options_text, dependencies_text, requires_text, package_id))\\n conanfile.output.warn(msg)\\n self._recorder.package_install_error(PackageReference(ref, package_id),\\n INSTALL_ERROR_MISSING, msg)\\n missing_pkgs = \\\"', '\\\".join([str(pref.ref) for pref in missing_prefs])\\n if len(missing_prefs) >= 5:\\n build_str = \\\"--build=missing\\\"\\n else:\\n build_str = \\\" \\\".join([\\\"--build=%s\\\" % pref.ref.name for pref in missing_prefs])\\n\\n raise ConanException(textwrap.dedent('''\\\\\\n Missing prebuilt package for '%s'\\n Try to build from sources with '%s'\\n Use 'conan search <reference> --table table.html'\\n Or read 'http://docs.conan.io/en/latest/\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._download\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._download\\n def _download(self, downloads, processed_package_refs):\\n \\\"\\\"\\\" executes the download of packages (both download and update), only once for a given\\n PREF, even if node duplicated\\n :param downloads: all nodes to be downloaded or updated, included repetitions\\n \\\"\\\"\\\"\\n if not downloads:\\n return\\n\\n download_nodes = []\\n for node in downloads:\\n pref = node.pref\\n bare_pref = PackageReference(pref.ref, pref.id)\\n if bare_pref in processed_package_refs:\\n continue\\n processed_package_refs[bare_pref] = pref.revision\\n assert node.prev, \\\"PREV for %s is None\\\" % str(node.pref)\\n download_nodes.append(node)\\n\\n def _download(n):\\n npref = n.pref\\n layout = self._cache.package_layout(npref.ref, n.conanfile.short_paths)\\n # We cannot embed the package_lock inside the remote.get_package()\\n # because the handle_node_cache has its own lock\\n with layout.package_lock(pref):\\n self._download_pkg(layout, n)\\n\\n parallel = self._cache.config.parallel_download\\n if parallel is not None:\\n self._out.info(\\\"Downloading binary packages in %s parallel threads\\\" % parallel)\\n thread_pool = ThreadPool(parallel)\\n thread_pool.map(_download, [n for n in download_nodes])\\n thread_pool.close()\\n thread_pool.join()\\n else:\\n for node in download_nodes:\\n _download(node)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._download._download\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._download._download\\n def _download(n):\\n npref = n.pref\\n layout = self._cache.package_layout(npref.ref, n.conanfile.short_paths)\\n # We cannot embed the package_lock inside the remote.get_package()\\n # because the handle_node_cache has its own lock\\n with layout.package_lock(pref):\\n self._download_pkg(layout, n)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._download_pkg\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._download_pkg\\n def _download_pkg(self, layout, node):\\n self._remote_manager.get_package(node.conanfile, node.pref, layout, node.binary_remote,\\n node.conanfile.output, self._recorder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._build\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._build\\n def _build(self, nodes_by_level, keep_build, root_node, profile_host, profile_build, graph_lock,\\n remotes, build_mode, update):\\n using_build_profile = bool(profile_build)\\n missing, invalid, downloads = self._classify(nodes_by_level)\\n if invalid:\\n msg = [\\\"There are invalid packages (packages that cannot exist for this configuration):\\\"]\\n for node in invalid:\\n msg.append(\\\"{}: Invalid ID: {}\\\".format(node.conanfile, node.conanfile.info.invalid))\\n raise ConanInvalidConfiguration(\\\"\\\\n\\\".join(msg))\\n self._raise_missing(missing)\\n processed_package_refs = {}\\n self._download(downloads, processed_package_refs)\\n\\n for level in nodes_by_level:\\n for node in level:\\n ref, conan_file = node.ref, node.conanfile\\n output = conan_file.output\\n\\n self._propagate_info(node, using_build_profile)\\n if node.binary == BINARY_EDITABLE:\\n self._handle_node_editable(node, profile_host, profile_build, graph_lock)\\n # Need a temporary package revision for package_revision_mode\\n # Cannot be PREV_UNKNOWN otherwise the consumers can't compute their packageID\\n node.prev = \\\"editable\\\"\\n else:\\n if node.binary == BINARY_SKIP: # Privates not necessary\\n continue\\n assert ref.revision is not None, \\\"Installer should receive RREV always\\\"\\n if node.binary == BINARY_UNKNOWN:\\n self._binaries_analyzer.reevaluate_node(node, remotes, build_mode, update)\\n if node.binary == BINARY_MISSING:\\n self._raise_missing([node])\\n _handle_system_requirements(conan_file, node.pref, self._cache, output)\\n self._handle_node_cache(node,\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._handle_node_editable\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._handle_node_editable\\n def _handle_node_editable(self, node, profile_host, profile_build, graph_lock):\\n # Get source of information\\n conanfile = node.conanfile\\n ref = node.ref\\n package_layout = self._cache.package_layout(ref)\\n base_path = package_layout.base_folder()\\n self._call_package_info(conanfile, package_folder=base_path, ref=ref, is_editable=True)\\n\\n # New editables mechanism based on Folders\\n if hasattr(conanfile, \\\"layout\\\"):\\n conanfile.folders.set_base_package(base_path)\\n conanfile.folders.set_base_source(base_path)\\n conanfile.folders.set_base_build(base_path)\\n conanfile.folders.set_base_install(base_path)\\n conanfile.folders.set_base_imports(base_path)\\n\\n output = conanfile.output\\n output.info(\\\"Rewriting files of editable package \\\"\\n \\\"'{}' at '{}'\\\".format(conanfile.name, conanfile.generators_folder))\\n self._generator_manager.write_generators(conanfile, conanfile.install_folder,\\n conanfile.generators_folder, output)\\n write_toolchain(conanfile, conanfile.generators_folder, output)\\n output.info(\\\"Generated toolchain\\\")\\n graph_info_node = GraphInfo(profile_host, root_ref=node.ref)\\n graph_info_node.options = node.conanfile.options.values\\n graph_info_node.graph_lock = graph_lock\\n graph_info_node.save(base_path)\\n output.info(\\\"Generated conan.lock\\\")\\n copied_files = run_imports(conanfile)\\n report_copied_files(copied_files, output)\\n return\\n\\n node.conanfile.cpp_info.filter_empty = False\\n # OLD EDITABLE LAYOUTS:\\n # Try with package-provided file\\n editable_cpp_info = package_layout.editable_cpp_info()\\n if editable_cpp_info:\\n editable_cpp_info.apply_to(ref,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._handle_node_cache\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._handle_node_cache\\n def _handle_node_cache(self, node, keep_build, processed_package_references, remotes):\\n pref = node.pref\\n assert pref.id, \\\"Package-ID without value\\\"\\n assert pref.id != PACKAGE_ID_UNKNOWN, \\\"Package-ID error: %s\\\" % str(pref)\\n conanfile = node.conanfile\\n output = conanfile.output\\n\\n layout = self._cache.package_layout(pref.ref, conanfile.short_paths)\\n\\n with layout.package_lock(pref):\\n bare_pref = PackageReference(pref.ref, pref.id)\\n processed_prev = processed_package_references.get(bare_pref)\\n if processed_prev is None: # This package-id has not been processed before\\n if node.binary == BINARY_BUILD:\\n assert node.prev is None, \\\"PREV for %s to be built should be None\\\" % str(pref)\\n layout.package_remove(pref)\\n with layout.set_dirty_context_manager(pref):\\n pref = self._build_package(node, output, keep_build, remotes)\\n assert node.prev, \\\"Node PREV shouldn't be empty\\\"\\n assert node.pref.revision, \\\"Node PREF revision shouldn't be empty\\\"\\n assert pref.revision is not None, \\\"PREV for %s to be built is None\\\" % str(pref)\\n elif node.binary in (BINARY_UPDATE, BINARY_DOWNLOAD):\\n # this can happen after a re-evaluation of packageID with Package_ID_unknown\\n self._download_pkg(layout, node)\\n elif node.binary == BINARY_CACHE:\\n assert node.prev, \\\"PREV for %s is None\\\" % str(pref)\\n output.success('Already installed!')\\n log_package_got_from_local_cache(pref)\\n self._recorder.package_fetched_from_cache(pref)\\n processed_package_references[bare_pref] = node.prev\\n else:\\n # We need to update the PREV of this node, as its \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._build_package\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._build_package\\n def _build_package(self, node, output, keep_build, remotes):\\n conanfile = node.conanfile\\n # It is necessary to complete the sources of python requires, which might be used\\n # Only the legacy python_requires allow this\\n python_requires = getattr(conanfile, \\\"python_requires\\\", None)\\n if python_requires and isinstance(python_requires, dict): # Old legacy python_requires\\n for python_require in python_requires.values():\\n assert python_require.ref.revision is not None, \\\\\\n \\\"Installer should receive python_require.ref always\\\"\\n retrieve_exports_sources(self._remote_manager, self._cache,\\n python_require.conanfile, python_require.ref, remotes)\\n\\n builder = _PackageBuilder(self._cache, output, self._hook_manager, self._remote_manager,\\n self._generator_manager)\\n pref = builder.build_package(node, keep_build, self._recorder, remotes)\\n if node.graph_lock_node:\\n node.graph_lock_node.prev = pref.revision\\n return pref\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._propagate_info\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._propagate_info\\n def _propagate_info(self, node, using_build_profile):\\n # it is necessary to recompute\\n # the node transitive information necessary to compute the package_id\\n # as it will be used by reevaluate_node() when package_revision_mode is used and\\n # PACKAGE_ID_UNKNOWN happens due to unknown revisions\\n self._binaries_analyzer.package_id_transitive_reqs(node)\\n # Get deps_cpp_info from upstream nodes\\n node_order = [n for n in node.public_closure if n.binary != BINARY_SKIP]\\n # List sort is stable, will keep the original order of the closure, but prioritize levels\\n conan_file = node.conanfile\\n # FIXME: Not the best place to assign the _conan_using_build_profile\\n conan_file._conan_using_build_profile = using_build_profile\\n transitive = [it for it in node.transitive_closure.values()]\\n\\n br_host = []\\n for it in node.dependencies:\\n if it.require.build_require_context == CONTEXT_HOST:\\n br_host.extend(it.dst.transitive_closure.values())\\n\\n # Initialize some members if we are using different contexts\\n if using_build_profile:\\n conan_file.user_info_build = DepsUserInfo()\\n\\n for n in node_order:\\n if n not in transitive:\\n conan_file.output.info(\\\"Applying build-requirement: %s\\\" % str(n.ref))\\n\\n dep_cpp_info = n.conanfile._conan_dep_cpp_info\\n\\n if not using_build_profile: # Do not touch anything\\n conan_file.deps_user_info[n.ref.name] = n.conanfile.user_info\\n conan_file.deps_cpp_info.add(n.ref.name, dep_cpp_info)\\n conan_file.deps_env_info.update(n.conanfile.env_info, n.ref.name)\\n else:\\n if n in transitive or n in br_host:\\n conan_file.deps_user_info[n.ref.name] = n.conanfile.user_info\\n conan_file.deps_cpp_info.add(n.ref.name, d\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/installer.py::BinaryInstaller._call_package_info\",\n \"text\": \"conans/client/installer.py::BinaryInstaller._call_package_info\\n def _call_package_info(self, conanfile, package_folder, ref, is_editable):\\n conanfile.cpp_info = CppInfo(conanfile.name, package_folder)\\n conanfile.cpp_info.version = conanfile.version\\n conanfile.cpp_info.description = conanfile.description\\n\\n conanfile.folders.set_base_package(package_folder)\\n conanfile.folders.set_base_source(None)\\n conanfile.folders.set_base_build(None)\\n conanfile.folders.set_base_install(None)\\n\\n conanfile.env_info = EnvInfo()\\n conanfile.user_info = UserInfo()\\n\\n # Get deps_cpp_info from upstream nodes\\n public_deps = [name for name, req in conanfile.requires.items() if not req.private\\n and not req.override]\\n conanfile.cpp_info.public_deps = public_deps\\n # Once the node is build, execute package info, so it has access to the\\n # package folder and artifacts\\n # Minimal pythonpath, not the whole context, make it 50% slower\\n # FIXME Conan 2.0, Remove old ways of reusing python code\\n with pythonpath(conanfile):\\n with tools.chdir(package_folder):\\n with conanfile_exception_formatter(str(conanfile), \\\"package_info\\\"):\\n self._hook_manager.execute(\\\"pre_package_info\\\", conanfile=conanfile,\\n reference=ref)\\n if hasattr(conanfile, \\\"layout\\\"):\\n # Old cpp info without defaults (the defaults are in the new one)\\n conanfile.cpp_info = CppInfo(conanfile.name, package_folder,\\n default_values=CppInfoDefaultValues())\\n if not is_editable:\\n package_cppinfo = conanfile.cpp.package.copy()\\n package_cppinfo.set_relative_base_folder(conanfile.folders.package)\\n # Copy the i\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_options\",\n \"text\": \"conans/model/conan_file.py::create_options\\ndef create_options(conanfile):\\n try:\\n package_options = PackageOptions(getattr(conanfile, \\\"options\\\", None))\\n options = Options(package_options)\\n\\n default_options = getattr(conanfile, \\\"default_options\\\", None)\\n if default_options:\\n if isinstance(default_options, dict):\\n default_values = OptionsValues(default_options)\\n elif isinstance(default_options, (list, tuple)):\\n conan_v2_error(\\\"Declare 'default_options' as a dictionary\\\")\\n default_values = OptionsValues(default_options)\\n elif isinstance(default_options, six.string_types):\\n conan_v2_error(\\\"Declare 'default_options' as a dictionary\\\")\\n default_values = OptionsValues.loads(default_options)\\n else:\\n raise ConanException(\\\"Please define your default_options as list, \\\"\\n \\\"multiline string or dictionary\\\")\\n options.values = default_values\\n return options\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing options. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_requirements\",\n \"text\": \"conans/model/conan_file.py::create_requirements\\ndef create_requirements(conanfile):\\n try:\\n # Actual requirements of this package\\n if not hasattr(conanfile, \\\"requires\\\"):\\n return Requirements()\\n else:\\n if not conanfile.requires:\\n return Requirements()\\n if isinstance(conanfile.requires, (tuple, list)):\\n return Requirements(*conanfile.requires)\\n else:\\n return Requirements(conanfile.requires, )\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing requirements. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_settings\",\n \"text\": \"conans/model/conan_file.py::create_settings\\ndef create_settings(conanfile, settings):\\n try:\\n defined_settings = getattr(conanfile, \\\"settings\\\", None)\\n if isinstance(defined_settings, str):\\n defined_settings = [defined_settings]\\n current = defined_settings or {}\\n settings.constraint(current)\\n return settings\\n except Exception as e:\\n raise ConanInvalidConfiguration(\\\"The recipe %s is constraining settings. %s\\\" % (\\n conanfile.display_name, str(e)))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::_env_and_python\",\n \"text\": \"conans/model/conan_file.py::_env_and_python\\n@contextmanager\\ndef _env_and_python(conanfile):\\n with environment_append(conanfile.env):\\n # FIXME Conan 2.0, Remove old ways of reusing python code\\n with pythonpath(conanfile):\\n yield\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::get_env_context_manager\",\n \"text\": \"conans/model/conan_file.py::get_env_context_manager\\ndef get_env_context_manager(conanfile, without_python=False):\\n if not conanfile.apply_env:\\n return no_op()\\n if without_python:\\n return environment_append(conanfile.env)\\n return _env_and_python(conanfile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.__init__\",\n \"text\": \"conans/model/conan_file.py::ConanFile.__init__\\n def __init__(self, output, runner, display_name=\\\"\\\", user=None, channel=None):\\n # an output stream (writeln, info, warn error)\\n self.output = ScopedOutput(display_name, output)\\n self.display_name = display_name\\n # something that can run commands, as os.sytem\\n self._conan_runner = runner\\n self._conan_user = user\\n self._conan_channel = channel\\n\\n self.compatible_packages = []\\n self._conan_using_build_profile = False\\n self._conan_requester = None\\n\\n self.buildenv_info = Environment(self)\\n self.runenv_info = Environment(self)\\n # At the moment only for build_requires, others will be ignored\\n self.conf_info = Conf()\\n self._conan_buildenv = None # The profile buildenv, will be assigned initialize()\\n self._conan_node = None # access to container Node object, to access info, context, deps...\\n self._conan_new_cpp_info = None # Will be calculated lazy in the getter\\n self._conan_dependencies = None\\n\\n self.environment_scripts = [] # Accumulate the env scripts generated in order\\n\\n # layout() method related variables:\\n self.folders = Folders()\\n self.patterns = Patterns()\\n self.cpp = Infos()\\n\\n self.patterns.source.include = [\\\"*.h\\\", \\\"*.hpp\\\", \\\"*.hxx\\\"]\\n self.patterns.source.lib = []\\n self.patterns.source.bin = []\\n\\n self.patterns.build.include = [\\\"*.h\\\", \\\"*.hpp\\\", \\\"*.hxx\\\"]\\n self.patterns.build.lib = [\\\"*.so\\\", \\\"*.so.*\\\", \\\"*.a\\\", \\\"*.lib\\\", \\\"*.dylib\\\"]\\n self.patterns.build.bin = [\\\"*.exe\\\", \\\"*.dll\\\"]\\n\\n self.cpp.package.includedirs = [\\\"include\\\"]\\n self.cpp.package.libdirs = [\\\"lib\\\"]\\n self.cpp.package.bindirs = [\\\"bin\\\"]\\n self.cpp.package.resdirs = [\\\"res\\\"]\\n self.cpp.package.builddirs = [\\\"\\\"]\\n self.cpp.package.frameworkdirs = [\\\"Frameworks\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.context\",\n \"text\": \"conans/model/conan_file.py::ConanFile.context\\n @property\\n def context(self):\\n return self._conan_node.context\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.dependencies\",\n \"text\": \"conans/model/conan_file.py::ConanFile.dependencies\\n @property\\n def dependencies(self):\\n # Caching it, this object is requested many times\\n if self._conan_dependencies is None:\\n self._conan_dependencies = ConanFileDependencies.from_node(self._conan_node)\\n return self._conan_dependencies\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.ref\",\n \"text\": \"conans/model/conan_file.py::ConanFile.ref\\n @property\\n def ref(self):\\n return self._conan_node.ref\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.pref\",\n \"text\": \"conans/model/conan_file.py::ConanFile.pref\\n @property\\n def pref(self):\\n return self._conan_node.pref\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.buildenv\",\n \"text\": \"conans/model/conan_file.py::ConanFile.buildenv\\n @property\\n def buildenv(self):\\n # Lazy computation of the package buildenv based on the profileone\\n if not isinstance(self._conan_buildenv, Environment):\\n # TODO: missing user/channel\\n ref_str = \\\"{}/{}\\\".format(self.name, self.version)\\n self._conan_buildenv = self._conan_buildenv.get_env(self, ref_str)\\n return self._conan_buildenv\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.initialize\",\n \"text\": \"conans/model/conan_file.py::ConanFile.initialize\\n def initialize(self, settings, env, buildenv=None):\\n self._conan_buildenv = buildenv\\n if isinstance(self.generators, str):\\n self.generators = [self.generators]\\n # User defined options\\n self.options = create_options(self)\\n self.requires = create_requirements(self)\\n self.settings = create_settings(self, settings)\\n\\n conan_v2_error(\\\"Setting 'cppstd' is deprecated in favor of 'compiler.cppstd',\\\"\\n \\\" please update your recipe.\\\", 'cppstd' in self.settings.fields)\\n\\n # needed variables to pack the project\\n self.cpp_info = None # Will be initialized at processing time\\n self._conan_dep_cpp_info = None # Will be initialized at processing time\\n self.deps_cpp_info = DepsCppInfo()\\n\\n # environment variables declared in the package_info\\n self.env_info = None # Will be initialized at processing time\\n self.deps_env_info = DepsEnvInfo()\\n\\n # user declared variables\\n self.user_info = None\\n # Keys are the package names (only 'host' if different contexts)\\n self.deps_user_info = DepsUserInfo()\\n\\n # user specified env variables\\n self._conan_env_values = env.copy() # user specified -e\\n\\n if self.description is not None and not isinstance(self.description, six.string_types):\\n raise ConanException(\\\"Recipe 'description' must be a string.\\\")\\n\\n if not hasattr(self, \\\"virtualenv\\\"): # Allow the user to override it with True or False\\n self.virtualenv = True\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.new_cpp_info\",\n \"text\": \"conans/model/conan_file.py::ConanFile.new_cpp_info\\n @property\\n def new_cpp_info(self):\\n if not self._conan_new_cpp_info:\\n self._conan_new_cpp_info = from_old_cppinfo(self.cpp_info)\\n return self._conan_new_cpp_info\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.source_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.source_folder\\n @property\\n def source_folder(self):\\n return self.folders.source_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.source_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.source_folder\\n @source_folder.setter\\n def source_folder(self, folder):\\n self.folders.set_base_source(folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_folder\\n @property\\n def build_folder(self):\\n return self.folders.build_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_folder\\n @build_folder.setter\\n def build_folder(self, folder):\\n self.folders.set_base_build(folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_folder\\n @property\\n def package_folder(self):\\n return self.folders.package_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_folder\\n @package_folder.setter\\n def package_folder(self, folder):\\n self.folders.set_base_package(folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.install_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.install_folder\\n @property\\n def install_folder(self):\\n # FIXME: Remove in 2.0, no self.install_folder\\n return self.folders.base_install\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.install_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.install_folder\\n @install_folder.setter\\n def install_folder(self, folder):\\n # FIXME: Remove in 2.0, no self.install_folder\\n self.folders.set_base_install(folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.generators_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.generators_folder\\n @property\\n def generators_folder(self):\\n # FIXME: Remove in 2.0, no self.install_folder\\n return self.folders.generators_folder if self.folders.generators else self.install_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.imports_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.imports_folder\\n @property\\n def imports_folder(self):\\n return self.folders.imports_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.imports_folder\",\n \"text\": \"conans/model/conan_file.py::ConanFile.imports_folder\\n @imports_folder.setter\\n def imports_folder(self, folder):\\n self.folders.set_base_imports(folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.env\",\n \"text\": \"conans/model/conan_file.py::ConanFile.env\\n @property\\n def env(self):\\n \\\"\\\"\\\"Apply the self.deps_env_info into a copy of self._conan_env_values (will prioritize the\\n self._conan_env_values, user specified from profiles or -e first, then inherited)\\\"\\\"\\\"\\n # Cannot be lazy cached, because it's called in configure node, and we still don't have\\n # the deps_env_info objects available\\n tmp_env_values = self._conan_env_values.copy()\\n tmp_env_values.update(self.deps_env_info)\\n ret, multiple = tmp_env_values.env_dicts(self.name, self.version, self._conan_user,\\n self._conan_channel)\\n ret.update(multiple)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.channel\",\n \"text\": \"conans/model/conan_file.py::ConanFile.channel\\n @property\\n def channel(self):\\n if not self._conan_channel:\\n _env_channel = os.getenv(\\\"CONAN_CHANNEL\\\")\\n conan_v2_error(\\\"Environment variable 'CONAN_CHANNEL' is deprecated\\\", _env_channel)\\n self._conan_channel = _env_channel or self.default_channel\\n if not self._conan_channel:\\n raise ConanException(\\\"channel not defined, but self.channel is used in conanfile\\\")\\n return self._conan_channel\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.user\",\n \"text\": \"conans/model/conan_file.py::ConanFile.user\\n @property\\n def user(self):\\n if not self._conan_user:\\n _env_username = os.getenv(\\\"CONAN_USERNAME\\\")\\n conan_v2_error(\\\"Environment variable 'CONAN_USERNAME' is deprecated\\\", _env_username)\\n self._conan_user = _env_username or self.default_user\\n if not self._conan_user:\\n raise ConanException(\\\"user not defined, but self.user is used in conanfile\\\")\\n return self._conan_user\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.collect_libs\",\n \"text\": \"conans/model/conan_file.py::ConanFile.collect_libs\\n def collect_libs(self, folder=None):\\n conan_v2_error(\\\"'self.collect_libs' is deprecated, use 'tools.collect_libs(self)' instead\\\")\\n return tools.collect_libs(self, folder=folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_policy_missing\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_policy_missing\\n @property\\n def build_policy_missing(self):\\n return self.build_policy == \\\"missing\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_policy_always\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_policy_always\\n @property\\n def build_policy_always(self):\\n return self.build_policy == \\\"always\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.source\",\n \"text\": \"conans/model/conan_file.py::ConanFile.source\\n def source(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.system_requirements\",\n \"text\": \"conans/model/conan_file.py::ConanFile.system_requirements\\n def system_requirements(self):\\n \\\"\\\"\\\" this method can be overwritten to implement logic for system package\\n managers, as apt-get\\n\\n You can define self.global_system_requirements = True, if you want the installation\\n to be for all packages (not depending on settings/options/requirements)\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.config_options\",\n \"text\": \"conans/model/conan_file.py::ConanFile.config_options\\n def config_options(self):\\n \\\"\\\"\\\" modify options, probably conditioned to some settings. This call is executed\\n before config_settings. E.g.\\n if self.settings.os == \\\"Windows\\\":\\n del self.options.shared # shared/static not supported in win\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.configure\",\n \"text\": \"conans/model/conan_file.py::ConanFile.configure\\n def configure(self):\\n \\\"\\\"\\\" modify settings, probably conditioned to some options. This call is executed\\n after config_options. E.g.\\n if self.options.header_only:\\n self.settings.clear()\\n This is also the place for conditional requirements\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build\\n def build(self):\\n \\\"\\\"\\\" build your project calling the desired build tools as done in the command line.\\n E.g. self.run(\\\"cmake --build .\\\") Or use the provided build helpers. E.g. cmake.build()\\n \\\"\\\"\\\"\\n self.output.warn(\\\"This conanfile has no build step\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package\\n def package(self):\\n \\\"\\\"\\\" package the needed files from source and build folders.\\n E.g. self.copy(\\\"*.h\\\", src=\\\"src/includes\\\", dst=\\\"includes\\\")\\n \\\"\\\"\\\"\\n self.output.warn(\\\"This conanfile has no package step\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_info\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_info\\n def package_info(self):\\n \\\"\\\"\\\" define cpp_build_info, flags, etc\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.run\",\n \"text\": \"conans/model/conan_file.py::ConanFile.run\\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True,\\n ignore_errors=False, run_environment=False, with_login=True, env=None):\\n # NOTE: \\\"self.win_bash\\\" is the new parameter \\\"win_bash\\\" for Conan 2.0\\n\\n def _run(cmd, _env):\\n # FIXME: run in windows bash is not using output\\n if platform.system() == \\\"Windows\\\":\\n if win_bash:\\n return tools.run_in_windows_bash(self, bashcmd=cmd, cwd=cwd, subsystem=subsystem,\\n msys_mingw=msys_mingw, with_login=with_login)\\n elif self.win_bash: # New, Conan 2.0\\n from conan.tools.microsoft.subsystems import run_in_windows_bash\\n return run_in_windows_bash(self, command=cmd, cwd=cwd, env=_env)\\n _env = _env or \\\"conanenv\\\"\\n wrapped_cmd = environment_wrap_command(self, _env, cmd, cwd=self.generators_folder)\\n return self._conan_runner(wrapped_cmd, output, os.path.abspath(RUN_LOG_NAME), cwd)\\n\\n if run_environment:\\n # When using_build_profile the required environment is already applied through\\n # 'conanfile.env' in the contextmanager 'get_env_context_manager'\\n with tools.run_environment(self) if not self._conan_using_build_profile else no_op():\\n if OSInfo().is_macos and isinstance(command, string_types):\\n # Security policy on macOS clears this variable when executing /bin/sh. To\\n # keep its value, set it again inside the shell when running the command.\\n command = 'DYLD_LIBRARY_PATH=\\\"%s\\\" DYLD_FRAMEWORK_PATH=\\\"%s\\\" %s' % \\\\\\n (os.environ.get('DYLD_LIBRARY_PATH', ''),\\n os.environ.get(\\\"DYLD_FRAMEWORK_PATH\\\", ''),\\n command)\\n retcode = _run(command, en\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.run._run\",\n \"text\": \"conans/model/conan_file.py::ConanFile.run._run\\n def _run(cmd, _env):\\n # FIXME: run in windows bash is not using output\\n if platform.system() == \\\"Windows\\\":\\n if win_bash:\\n return tools.run_in_windows_bash(self, bashcmd=cmd, cwd=cwd, subsystem=subsystem,\\n msys_mingw=msys_mingw, with_login=with_login)\\n elif self.win_bash: # New, Conan 2.0\\n from conan.tools.microsoft.subsystems import run_in_windows_bash\\n return run_in_windows_bash(self, command=cmd, cwd=cwd, env=_env)\\n _env = _env or \\\"conanenv\\\"\\n wrapped_cmd = environment_wrap_command(self, _env, cmd, cwd=self.generators_folder)\\n return self._conan_runner(wrapped_cmd, output, os.path.abspath(RUN_LOG_NAME), cwd)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_id\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_id\\n def package_id(self):\\n \\\"\\\"\\\" modify the binary info, typically to narrow values\\n e.g.: self.info.settings.compiler = \\\"Any\\\" => All compilers will generate same ID\\n \\\"\\\"\\\"\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":76,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-1797"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Customizing hparams after loading checkpoint\n## ❓ Questions and Help\r\n\r\n### Before asking: \r\n1. search the issues. \r\n2. search the docs. \r\n\r\n#### What is your question?\r\nI'm wondering what the best practice for loading a model with different hparams than what is stored in the checkpoint?\r\n\r\nI realize I could just load the model and set them afterwards e.g.: \r\n```\r\nmodel = model.load_from_checkpoint(args.checkpoint_file) # Load model\r\n\r\n# Set hparams etc..\r\nmodel.hparams.arg1 = 0.0\r\nmodel.hparams.arg2 = 1.0\r\n \r\n```\r\nBut the problem is that my model __init__ function depends on the hparams arg1 and arg2 so they're set too late.\r\n\r\nI could also do \r\n```\r\ncheckpoint = torch.load(args.checkpoint_file)\r\ncheckpoint['hparams']['arg1'] = 0.0\r\ncheckpoint['hparams']['arg2'] = 1.0\r\nmodel = model._load_state_dict(checkpoint)\r\n```\r\n\r\nThe problem here is that i'm using the protected function _load_state_dict. Is there another way of solving this that i've missed? Or could we consider making _load_state_dict public?\r\n\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/core/lightning.py","pytorch_lightning/core/saving.py"],"string":"[\n \"pytorch_lightning/core/lightning.py\",\n \"pytorch_lightning/core/saving.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/core/lightning.py::<module>","pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint","pytorch_lightning/core/saving.py::<module>"],"string":"[\n \"pytorch_lightning/core/lightning.py::<module>\",\n \"pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint\",\n \"pytorch_lightning/core/saving.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"pytorch_lightning/core/lightning.py","text":"pytorch_lightning/core/lightning.py\nclass LightningModule\n def __init__(self, *args, **kwargs)\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once.\n def forward(self, *args, **kwargs) # Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to\n def training_step(self, *args, **kwargs) # Here you compute and return the training loss and some additional metrics for e.\n def training_end(self, *args, **kwargs) # Warnings:\n def training_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the training epoch with the outputs of all training steps.\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because :meth:`training_step`\n def validation_step(self, *args, **kwargs) # Operates on a single batch of data from the validation set.\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because :meth:`validation_step`\n def validation_end(self, outputs) # Warnings:\n def validation_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the validation epoch with the outputs of all validation ste\n def test_step(self, *args, **kwargs) # Operates on a single batch of data from the test set.\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because :meth:`test_step` will operate\n def test_end(self, outputs) # Warnings:\n def test_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of a test epoch with the output of all test steps.\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) # Override to init DDP in your own way or with your own wrapper.\n def _init_slurm_connection(self) # Sets up environment variables necessary for pytorch distributed communications\n def init_ddp_connection(self, proc_rank: int, world_size: int, is_slurm_managing_tasks: bool=True) # Override to define your custom way of setting up a distributed environment.\n def configure_apex(self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str) # Override to init AMP your own way.\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\n def optimizer_step(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int, second_order_closure: Optional[Callable]=None) # Override this method to adjust the default way the\n def tbptt_split_batch(self, batch: Tensor, split_size: int) # When using truncated backpropagation through time, each batch must be split alon\n def prepare_data(self) # Use this to download and prepare data.\n def train_dataloader(self) # Implement a PyTorch DataLoader for training.\n def tng_dataloader(self) # War","label":1},{"cid":"pytorch_lightning/core/saving.py","text":"pytorch_lightning/core/saving.py\nclass ModelIO\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) # Do something with the checkpoint.\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) # Give the model a chance to add something to the checkpoint.\n def on_hpc_save(self, checkpoint: Dict[str, Any]) # Hook to do whatever you need right before Slurm manager saves the model.\n def on_hpc_load(self, checkpoint: Dict[str, Any]) # Hook to do whatever you need right before Slurm manager loads the model.\ndef load_hparams_from_tags_csv(tags_csv: str)\ndef convert(val: str)","label":1},{"cid":"pytorch_lightning/trainer/data_loading.py","text":"pytorch_lightning/trainer/data_loading.py\ndef _has_len(dataloader: DataLoader) # Checks if a given Dataloader has __len__ method implemented i.e. if\nclass TrainerDataLoadingMixin\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def _percent_range_check(self, name: str)\n def _worker_check(self, dataloader: DataLoader, name: str)\n def auto_add_sampler(self, dataloader: DataLoader, train: bool)\n def reset_train_dataloader(self, model: LightningModule) # Resets the train dataloader and initialises required variables\n def _reset_eval_dataloader(self, model: LightningModule, mode: str) # Generic method to reset a dataloader for evaluation.\n def reset_val_dataloader(self, model: LightningModule) # Resets the validation dataloader and determines the number of batches.\n def reset_test_dataloader(self, model) # Resets the validation dataloader and determines the number of batches.\n def request_dataloader(self, dataloader_fx: Callable) # Handles downloading data in the GPU or TPU case.\n def determine_data_use_amount(self, train_percent_check: float, val_percent_check: float, test_percent_check: float, overfit_pct: float) # Use less data for debugging purposes","label":0}],"string":"[\n {\n \"cid\": \"pytorch_lightning/core/lightning.py\",\n \"text\": \"pytorch_lightning/core/lightning.py\\nclass LightningModule\\n def __init__(self, *args, **kwargs)\\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once.\\n def forward(self, *args, **kwargs) # Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to\\n def training_step(self, *args, **kwargs) # Here you compute and return the training loss and some additional metrics for e.\\n def training_end(self, *args, **kwargs) # Warnings:\\n def training_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the training epoch with the outputs of all training steps.\\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because :meth:`training_step`\\n def validation_step(self, *args, **kwargs) # Operates on a single batch of data from the validation set.\\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because :meth:`validation_step`\\n def validation_end(self, outputs) # Warnings:\\n def validation_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the validation epoch with the outputs of all validation ste\\n def test_step(self, *args, **kwargs) # Operates on a single batch of data from the test set.\\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because :meth:`test_step` will operate\\n def test_end(self, outputs) # Warnings:\\n def test_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of a test epoch with the output of all test steps.\\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) # Override to init DDP in your own way or with your own wrapper.\\n def _init_slurm_connection(self) # Sets up environment variables necessary for pytorch distributed communications\\n def init_ddp_connection(self, proc_rank: int, world_size: int, is_slurm_managing_tasks: bool=True) # Override to define your custom way of setting up a distributed environment.\\n def configure_apex(self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str) # Override to init AMP your own way.\\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\\n def optimizer_step(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int, second_order_closure: Optional[Callable]=None) # Override this method to adjust the default way the\\n def tbptt_split_batch(self, batch: Tensor, split_size: int) # When using truncated backpropagation through time, each batch must be split alon\\n def prepare_data(self) # Use this to download and prepare data.\\n def train_dataloader(self) # Implement a PyTorch DataLoader for training.\\n def tng_dataloader(self) # War\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/core/saving.py\",\n \"text\": \"pytorch_lightning/core/saving.py\\nclass ModelIO\\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) # Do something with the checkpoint.\\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) # Give the model a chance to add something to the checkpoint.\\n def on_hpc_save(self, checkpoint: Dict[str, Any]) # Hook to do whatever you need right before Slurm manager saves the model.\\n def on_hpc_load(self, checkpoint: Dict[str, Any]) # Hook to do whatever you need right before Slurm manager loads the model.\\ndef load_hparams_from_tags_csv(tags_csv: str)\\ndef convert(val: str)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py\\ndef _has_len(dataloader: DataLoader) # Checks if a given Dataloader has __len__ method implemented i.e. if\\nclass TrainerDataLoadingMixin\\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def _percent_range_check(self, name: str)\\n def _worker_check(self, dataloader: DataLoader, name: str)\\n def auto_add_sampler(self, dataloader: DataLoader, train: bool)\\n def reset_train_dataloader(self, model: LightningModule) # Resets the train dataloader and initialises required variables\\n def _reset_eval_dataloader(self, model: LightningModule, mode: str) # Generic method to reset a dataloader for evaluation.\\n def reset_val_dataloader(self, model: LightningModule) # Resets the validation dataloader and determines the number of batches.\\n def reset_test_dataloader(self, model) # Resets the validation dataloader and determines the number of batches.\\n def request_dataloader(self, dataloader_fx: Callable) # Handles downloading data in the GPU or TPU case.\\n def determine_data_use_amount(self, train_percent_check: float, val_percent_check: float, test_percent_check: float, overfit_pct: float) # Use less data for debugging purposes\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"pytorch_lightning/core/lightning.py::LightningModule.__init__","text":"pytorch_lightning/core/lightning.py::LightningModule.__init__\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self.exp_save_path = None\n\n #: The current epoch\n self.current_epoch = 0\n\n #: Total training batches seen across all epochs\n self.global_step = 0\n\n self.loaded_optimizer_states_dict = {}\n\n #: Pointer to the trainer object\n self.trainer = None\n\n #: Pointer to the logger object\n self.logger = None\n self.example_input_array = None\n\n #: True if your model is currently running on GPUs.\n #: Useful to set flags around the LightningModule for different CPU vs GPU behavior.\n self.on_gpu = False\n\n #: True if using dp\n self.use_dp = False\n\n #: True if using ddp\n self.use_ddp = False\n\n #: True if using ddp2\n self.use_ddp2 = False\n\n #: True if using amp\n self.use_amp = False\n\n self.hparams = None\n\n #: Current dtype\n self._dtype = torch.FloatTensor\n #: device reference\n self._device = torch.device('cpu')","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.print","text":"pytorch_lightning/core/lightning.py::LightningModule.print\n def print(self, *args, **kwargs) -> None:\n r\"\"\"\n Prints only from process 0. Use this in any distributed mode to log only once.\n\n Args:\n *args: The thing to print. Will be passed to Python's built-in print function.\n **kwargs: Will be passed to Python's built-in print function.\n\n Example:\n\n .. code-block:: python\n\n def forward(self, x):\n self.print(x, 'in forward')\n\n \"\"\"\n if self.trainer.proc_rank == 0:\n print(*args, **kwargs)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.forward","text":"pytorch_lightning/core/lightning.py::LightningModule.forward\n @abstractmethod\n def forward(self, *args, **kwargs):\n r\"\"\"\n Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define\n the operations you want to use for prediction (i.e.: on a server or as a feature extractor).\n\n Normally you'd call ``self()`` from your :meth:`training_step` method.\n This makes it easy to write a complex system for training with the outputs\n you'd want in a prediction setting.\n\n Args:\n *args: Whatever you decide to pass into the forward method.\n **kwargs: Keyword arguments are also possible.\n\n Return:\n Predicted output\n\n Examples:\n .. code-block:: python\n\n # example if we were using this model as a feature extractor\n def forward(self, x):\n feature_maps = self.convnet(x)\n return feature_maps\n\n def training_step(self, batch, batch_idx):\n x, y = batch\n feature_maps = self(x)\n logits = self.classifier(feature_maps)\n\n # ...\n return loss\n\n # splitting it this way allows model to be used a feature extractor\n model = MyModelAbove()\n\n inputs = server.get_request()\n results = model(inputs)\n server.write_results(results)\n\n # -------------\n # This is in stark contrast to torch.nn.Module where normally you would have this:\n def forward(self, batch):\n x, y = batch\n feature_maps = self.convnet(x)\n logits = self.classifier(feature_maps)\n return logits\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step\n def training_step(self, *args, **kwargs) -> Union[\n int, Dict[str, Union[Tensor, Dict[str, Tensor]]]\n ]:\n r\"\"\"\n Here you compute and return the training loss and some additional metrics for e.g.\n the progress bar or logger.\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): Integer displaying index of this batch\n optimizer_idx (int): When using multiple optimizers, this argument will also be present.\n hiddens(:class:`~torch.Tensor`): Passed in if\n :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.\n\n Return:\n Dict with loss key and optional log or progress bar keys.\n When implementing :meth:`training_step`, return whatever you need in that step:\n\n - loss -> tensor scalar **REQUIRED**\n - progress_bar -> Dict for progress bar display. Must have only tensors\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n In this step you'd normally do the forward pass and calculate the loss for a batch.\n You can also do fancier things like multiple forward passes or something model specific.\n\n Examples:\n .. code-block:: python\n\n def training_step(self, batch, batch_idx):\n x, y, z = batch\n\n # implement your own\n out = self(x)\n loss = self.loss(out, x)\n\n logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)\n\n # if using TestTubeLogger or TensorBoardLogger you can nest scalars\n logger_logs = {'losses': logger_logs} # optional (MUST ALL","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_end\n def training_end(self, *args, **kwargs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`training_step_end` instead.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\n def training_epoch_end(\n self,\n outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"Called at the end of the training epoch with the outputs of all training steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n train_outs = []\n for train_batch in train_data:\n out = training_step(train_batch)\n train_outs.append(out)\n training_epoch_end(train_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`training_step`, or if there are\n multiple dataloaders, a list containing a list of outputs for each dataloader.\n\n Return:\n Dict or OrderedDict.\n May contain the following optional keys:\n\n - log (metrics to be added to the logger; only tensors)\n - progress_bar (dict for progress bar display)\n - any metric used in a callback (e.g. early stopping).\n\n Note:\n If this method is not overridden, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def training_epoch_end(self, outputs):\n train_acc_mean = 0\n for output in outputs:\n train_acc_mean += output['train_acc']\n\n train_acc_mean /= len(outputs)\n\n # log training accuracy at the end of an epoch\n results = {\n 'log': {'train_acc': train_acc_mean.item()},\n 'progress_bar': {'train_a","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\n def training_step_end(self, *args, **kwargs) -> Dict[\n str, Union[Tensor, Dict[str, Tensor]]\n ]:\n \"\"\"\n Use this when training with dp or ddp2 because :meth:`training_step`\n will operate on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]\n training_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in `training_step` for each batch part.\n\n Return:\n Dict with loss key and optional log or progress bar keys.\n\n - loss -> tensor scalar **REQUIRED**\n - progress_bar -> Dict for progress bar display. Must have only tensors\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n Examples:\n .. code-block:: python\n\n # WITHOUT training_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with training_step_end to do softmax over the full batch\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step\n def validation_step(self, *args, **kwargs) -> Dict[str, Tensor]:\n r\"\"\"\n Operates on a single batch of data from the validation set.\n In this step you'd might generate examples or calculate anything of interest like accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n val_outs = []\n for val_batch in val_data:\n out = validation_step(train_batch)\n val_outs.append(out)\n validation_epoch_end(val_outs)\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): The index of this batch\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple val datasets used)\n\n Return:\n Dict or OrderedDict - passed to :meth:`validation_epoch_end`.\n If you defined :meth:`validation_step_end` it will go to that first.\n\n .. code-block:: python\n\n # pseudocode of order\n out = validation_step()\n if defined('validation_step_end'):\n out = validation_step_end(out)\n out = validation_epoch_end(out)\n\n\n .. code-block:: python\n\n # if you have one val dataloader:\n def validation_step(self, batch, batch_idx)\n\n # if you have multiple val dataloaders:\n def validation_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single validation dataset\n def validation_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self(x)\n loss = self.los","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\n def validation_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\n \"\"\"\n Use this when validating with dp or ddp2 because :meth:`validation_step`\n will operate on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code.\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]\n validation_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in :meth:`validation_step`\n for each batch part.\n\n Return:\n Dict or OrderedDict - passed to the :meth:`validation_epoch_end` method.\n\n Examples:\n .. code-block:: python\n\n # WITHOUT validation_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with validation_step_end to do softmax over the full batch\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n return {'out': out}\n\n def validation_epoch_end(self, outputs):\n # this out is now the full size of the batch\n out = outputs['out']\n\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_end\n def validation_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead.\n Will be removed in 1.0.0.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\n def validation_epoch_end(\n self,\n outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"\n Called at the end of the validation epoch with the outputs of all validation steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n val_outs = []\n for val_batch in val_data:\n out = validation_step(val_batch)\n val_outs.append(out)\n validation_epoch_end(val_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`validation_step`, or if there\n are multiple dataloaders, a list containing a list of outputs for each dataloader.\n\n Return:\n Dict or OrderedDict.\n May have the following optional keys:\n\n - progress_bar (dict for progress bar display; only tensors)\n - log (dict of metrics to add to logger; only tensors).\n\n Note:\n If you didn't define a :meth:`validation_step`, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def validation_epoch_end(self, outputs):\n val_acc_mean = 0\n for output in outputs:\n val_acc_mean += output['val_acc']\n\n val_acc_mean /= len(outputs)\n tqdm_dict = {'val_acc': val_acc_mean.item()}\n\n # show val_acc in progress bar but only log val_loss\n results = {\n 'progress_bar': tqdm_dict,\n 'log': {'","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step\n def test_step(self, *args, **kwargs) -> Dict[str, Tensor]:\n r\"\"\"\n Operates on a single batch of data from the test set.\n In this step you'd normally generate examples or calculate anything of interest\n such as accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n test_outs = []\n for test_batch in test_data:\n out = test_step(test_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): The index of this batch.\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple test datasets used).\n\n Return:\n Dict or OrderedDict - passed to the :meth:`test_epoch_end` method.\n If you defined :meth:`test_step_end` it will go to that first.\n\n .. code-block:: python\n\n # if you have one test dataloader:\n def test_step(self, batch, batch_idx)\n\n # if you have multiple test dataloaders:\n def test_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single test dataset\n def test_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self(x)\n loss = self.loss(out, y)\n\n # log 6 example images\n # or generated text... or whatever\n sample_imgs = x[:6]\n grid = torchvision.utils.make_grid(sample_imgs)\n self.logger.experiment.add_image('example","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\n def test_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\n \"\"\"\n Use this when testing with dp or ddp2 because :meth:`test_step` will operate\n on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code.\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]\n test_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in :meth:`test_step` for each batch part.\n\n Return:\n Dict or OrderedDict - passed to the :meth:`test_epoch_end`.\n\n Examples:\n .. code-block:: python\n\n # WITHOUT test_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with test_step_end to do softmax over the full batch\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n return {'out': out}\n\n def test_step_end(self, outputs):\n # this out is now the full size of the batch\n out = outputs['out']\n\n # this softmax now uses the full batch size\n loss = nce_loss(loss)\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_end\n def test_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead.\n Will be removed in 1.0.0.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\n def test_epoch_end(\n self,\n outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"\n Called at the end of a test epoch with the output of all test steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n test_outs = []\n for test_batch in test_data:\n out = test_step(test_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`test_step_end`, or if there\n are multiple dataloaders, a list containing a list of outputs for each dataloader\n\n Return:\n Dict or OrderedDict: Dict has the following optional keys:\n\n - progress_bar -> Dict for progress bar display. Must have only tensors.\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc).\n\n Note:\n If you didn't define a :meth:`test_step`, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def test_epoch_end(self, outputs):\n test_acc_mean = 0\n for output in outputs:\n test_acc_mean += output['test_acc']\n\n test_acc_mean /= len(outputs)\n tqdm_dict = {'test_acc': test_acc_mean.item()}\n\n # show test_loss and test_acc in progress bar but only log test_loss\n results = {\n 'progress_bar': tqdm_dict,\n 'log': {","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\n def configure_ddp(\n self,\n model: 'LightningModule',\n device_ids: List[int]\n ) -> DistributedDataParallel:\n r\"\"\"\n Override to init DDP in your own way or with your own wrapper.\n The only requirements are that:\n\n 1. On a validation batch the call goes to ``model.validation_step``.\n 2. On a training batch the call goes to ``model.training_step``.\n 3. On a testing batch, the call goes to ``model.test_step``.+\n\n Args:\n model: the :class:`LightningModule` currently being optimized.\n device_ids: the list of GPU ids.\n\n Return:\n DDP wrapped model\n\n Examples:\n .. code-block:: python\n\n # default implementation used in Trainer\n def configure_ddp(self, model, device_ids):\n # Lightning DDP simply routes to test_step, val_step, etc...\n model = LightningDistributedDataParallel(\n model,\n device_ids=device_ids,\n find_unused_parameters=True\n )\n return model\n\n \"\"\"\n model = LightningDistributedDataParallel(\n model,\n device_ids=device_ids,\n find_unused_parameters=True\n )\n return model","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection","text":"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\n def _init_slurm_connection(self) -> None:\n \"\"\"\n Sets up environment variables necessary for pytorch distributed communications\n based on slurm environment.\n \"\"\"\n # use slurm job id for the port number\n # guarantees unique ports across jobs from same grid search\n try:\n # use the last 4 numbers in the job id as the id\n default_port = os.environ['SLURM_JOB_ID']\n default_port = default_port[-4:]\n\n # all ports should be in the 10k+ range\n default_port = int(default_port) + 15000\n\n except Exception:\n default_port = 12910\n\n # if user gave a port number, use that one instead\n try:\n default_port = os.environ['MASTER_PORT']\n except Exception:\n os.environ['MASTER_PORT'] = str(default_port)\n\n # figure out the root node addr\n try:\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\n except Exception:\n root_node = '127.0.0.1'\n\n root_node = self.trainer.resolve_root_node_address(root_node)\n os.environ['MASTER_ADDR'] = root_node","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection","text":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\n def init_ddp_connection(\n self,\n proc_rank: int,\n world_size: int,\n is_slurm_managing_tasks: bool = True\n ) -> None:\n \"\"\"\n Override to define your custom way of setting up a distributed environment.\n\n Lightning's implementation uses env:// init by default and sets the first node as root\n for SLURM managed cluster.\n\n Args:\n proc_rank: The current process rank within the node.\n world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).\n is_slurm_managing_tasks: is cluster managed by SLURM.\n\n \"\"\"\n if is_slurm_managing_tasks:\n self._init_slurm_connection()\n\n if 'MASTER_ADDR' not in os.environ:\n log.warning(\"MASTER_ADDR environment variable is not defined. Set as localhost\")\n os.environ['MASTER_ADDR'] = '127.0.0.1'\n log.debug(f\"MASTER_ADDR: {os.environ['MASTER_ADDR']}\")\n\n if 'MASTER_PORT' not in os.environ:\n log.warning(\"MASTER_PORT environment variable is not defined. Set as 12910\")\n os.environ['MASTER_PORT'] = '12910'\n log.debug(f\"MASTER_PORT: {os.environ['MASTER_PORT']}\")\n\n if 'WORLD_SIZE' in os.environ and os.environ['WORLD_SIZE'] != world_size:\n log.warning(\"WORLD_SIZE environment variable is not equal to the computed \"\n \"world size. Ignored.\")\n\n torch_backend = \"nccl\" if self.trainer.on_gpu else \"gloo\"\n torch_distrib.init_process_group(torch_backend, rank=proc_rank, world_size=world_size)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\n def configure_apex(\n self,\n amp: object,\n model: 'LightningModule',\n optimizers: List[Optimizer],\n amp_level: str\n ) -> Tuple['LightningModule', List[Optimizer]]:\n r\"\"\"\n Override to init AMP your own way.\n Must return a model and list of optimizers.\n\n Args:\n amp: pointer to amp library object.\n model: pointer to current :class:`LightningModule`.\n optimizers: list of optimizers passed in :meth:`configure_optimizers`.\n amp_level: AMP mode chosen ('O1', 'O2', etc...)\n\n Return:\n Apex wrapped model and optimizers\n\n Examples:\n .. code-block:: python\n\n # Default implementation used by Trainer.\n def configure_apex(self, amp, model, optimizers, amp_level):\n model, optimizers = amp.initialize(\n model, optimizers, opt_level=amp_level,\n )\n\n return model, optimizers\n \"\"\"\n model, optimizers = amp.initialize(\n model, optimizers, opt_level=amp_level,\n )\n\n return model, optimizers","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\n def configure_optimizers(self) -> Optional[Union[\n Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List]\n ]]:\n r\"\"\"\n Choose what optimizers and learning-rate schedulers to use in your optimization.\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\n\n Return:\n Any of these 6 options.\n\n - Single optimizer.\n - List or Tuple - List of optimizers.\n - Two lists - The first list has multiple optimizers, the second a list of LR schedulers.\n - Dictionary, with an 'optimizer' key and (optionally) a 'lr_scheduler' key.\n - Tuple of dictionaries as described, with an optional 'frequency' key.\n - None - Fit will run without any optimizer.\n\n Note:\n The 'frequency' value is an int corresponding to the number of sequential batches\n optimized with the specific optimizer. It should be given to none or to all of the optimizers.\n There is a difference between passing multiple optimizers in a list,\n and passing multiple optimizers in dictionaries with a frequency of 1:\n In the former case, all optimizers will operate on the given batch in each optimization step.\n In the latter, only one optimizer will operate on the given batch at every step.\n\n Examples:\n .. code-block:: python\n\n # most cases\n def configure_optimizers(self):\n opt = Adam(self.parameters(), lr=1e-3)\n return opt\n\n # multiple optimizer case (e.g.: GAN)\n def configure_optimizers(self):\n generator_opt = Adam(self.model_gen.parameters(), lr=0.01)\n disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)\n return generator_opt, disriminator_","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step","text":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\n def optimizer_step(\n self,\n epoch: int,\n batch_idx: int,\n optimizer: Optimizer,\n optimizer_idx: int,\n second_order_closure: Optional[Callable] = None,\n ) -> None:\n r\"\"\"\n Override this method to adjust the default way the\n :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.\n By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example\n once per optimizer.\n\n Args:\n epoch: Current epoch\n batch_idx: Index of current batch\n optimizer: A PyTorch optimizer\n optimizer_idx: If you used multiple optimizers this indexes into that list.\n second_order_closure: closure for second order methods\n\n Examples:\n .. code-block:: python\n\n # DEFAULT\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure=None):\n optimizer.step()\n optimizer.zero_grad()\n\n # Alternating schedule for optimizer steps (i.e.: GANs)\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure=None):\n # update generator opt every 2 steps\n if optimizer_idx == 0:\n if batch_idx % 2 == 0 :\n optimizer.step()\n optimizer.zero_grad()\n\n # update discriminator opt every 4 steps\n if optimizer_idx == 1:\n if batch_idx % 4 == 0 :\n optimizer.step()\n optimizer.zero_grad()\n\n # ...\n # add as many optimizers as you want\n\n\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch","text":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\n def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:\n r\"\"\"\n When using truncated backpropagation through time, each batch must be split along the\n time dimension. Lightning handles this by default, but for custom behavior override\n this function.\n\n Args:\n batch: Current batch\n split_size: The size of the split\n\n Return:\n List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated\n back propagation through time. The default implementation splits root level Tensors and\n Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.\n\n Examples:\n .. code-block:: python\n\n def tbptt_split_batch(self, batch, split_size):\n splits = []\n for t in range(0, time_dims[0], split_size):\n batch_split = []\n for i, x in enumerate(batch):\n if isinstance(x, torch.Tensor):\n split_x = x[:, t:t + split_size]\n elif isinstance(x, collections.Sequence):\n split_x = [None] * len(x)\n for batch_idx in range(len(x)):\n split_x[batch_idx] = x[batch_idx][t:t + split_size]\n\n batch_split.append(split_x)\n\n splits.append(batch_split)\n\n return splits\n\n Note:\n Called in the training loop after\n :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`\n if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.\n Each returned batch split is passed separately to :meth:`training_step`.\n\n \"\"\"\n time_dims = [len(x[0]) for x in batch if isinstance(x","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data","text":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\n def prepare_data(self) -> None:\n \"\"\"\n Use this to download and prepare data.\n In distributed (GPU, TPU), this will only be called once.\n This is called before requesting the dataloaders:\n\n .. code-block:: python\n\n model.prepare_data()\n model.train_dataloader()\n model.val_dataloader()\n model.test_dataloader()\n\n Examples:\n .. code-block:: python\n\n def prepare_data(self):\n download_imagenet()\n clean_imagenet()\n cache_imagenet()\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\n def train_dataloader(self) -> DataLoader:\n \"\"\"\n Implement a PyTorch DataLoader for training.\n\n Return:\n Single PyTorch :class:`~torch.utils.data.DataLoader`.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`train_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware.\n There is no need to set it yourself.\n\n Example:\n .. code-block:: python\n\n def train_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.hparams.batch_size,\n shuffle=True\n )\n return loader\n\n \"\"\"\n rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\n def tng_dataloader(self): # todo: remove in v1.0.0\n \"\"\"\n Warnings:\n Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.\n \"\"\"\n output = self.train_dataloader()\n rank_zero_warn(\"`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0.\"\n \" and this method will be removed in v1.0.0\", DeprecationWarning)\n return output","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\n def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\n r\"\"\"\n Implement one or multiple PyTorch DataLoaders for testing.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`train_dataloader`\n - :meth:`val_dataloader`\n - :meth:`test_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware.\n There is no need to set it yourself.\n\n Return:\n Single or multiple PyTorch DataLoaders.\n\n Example:\n .. code-block:: python\n\n def test_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.hparams.batch_size,\n shuffle=False\n )\n\n return loader\n\n Note:\n If you don't need a test dataset and a :meth:`test_step`, you don't need to implement\n this method.\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\n def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\n r\"\"\"\n Implement one or multiple PyTorch DataLoaders for validation.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`train_dataloader`\n - :meth:`val_dataloader`\n - :meth:`test_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware\n There is no need to set it yourself.\n\n Return:\n Single or multiple PyTorch DataLoaders.\n\n Examples:\n .. code-block:: python\n\n def val_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False,\n transform=transform, download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.hparams.batch_size,\n shuffle=False\n )\n\n return loader\n\n # can also return multiple dataloaders\n def val_dataloader(self):\n return [loader_a, loader_b, ..., loader_n]\n\n Note:\n If you don't need a validation dataset and a :meth:`validation_step`, you don't need to\n implement this method.\n\n Note:\n In the case where you return multiple validation dataloaders, the :meth:`valid","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.load_from_metrics","text":"pytorch_lightning/core/lightning.py::LightningModule.load_from_metrics\n @classmethod\n def load_from_metrics(cls, weights_path, tags_csv, map_location=None):\n r\"\"\"\n Warning:\n Deprecated in version 0.7.0. You should use :meth:`load_from_checkpoint` instead.\n Will be removed in v0.9.0.\n \"\"\"\n rank_zero_warn(\n \"`load_from_metrics` method has been unified with `load_from_checkpoint` in v0.7.0.\"\n \" The deprecated method will be removed in v0.9.0.\", DeprecationWarning\n )\n return cls.load_from_checkpoint(weights_path, tags_csv=tags_csv, map_location=map_location)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint\n @classmethod\n def load_from_checkpoint(\n cls,\n checkpoint_path: str,\n map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,\n tags_csv: Optional[str] = None,\n *args, **kwargs\n ) -> 'LightningModule':\n r\"\"\"\n Primary way of loading a model from a checkpoint. When Lightning saves a checkpoint\n it stores the hyperparameters in the checkpoint if you initialized your :class:`LightningModule`\n with an argument called ``hparams`` which is a :class:`~argparse.Namespace`\n (output of :meth:`~argparse.ArgumentParser.parse_args` when parsing command line arguments).\n Any other arguments specified through \\*args and \\*\\*kwargs will be passed to the model.\n\n Example:\n .. code-block:: python\n\n from argparse import Namespace\n hparams = Namespace(**{'learning_rate': 0.1})\n\n model = MyModel(hparams)\n\n class MyModel(LightningModule):\n def __init__(self, hparams):\n self.learning_rate = hparams.learning_rate\n\n Args:\n checkpoint_path: Path to checkpoint.\n model_args: Any keyword args needed to init the model.\n map_location:\n If your checkpoint saved a GPU model and you now load on CPUs\n or a different number of GPUs, use this to map to the new setup.\n The behaviour is the same as in :func:`torch.load`.\n tags_csv: Optional path to a .csv file with two columns (key, value)\n as in this example::\n\n key,value\n drop_prob,0.2\n batch_size,32\n\n You most likely won't need this since Lightning will always save the hyperparameters\n to the checkpoint.\n However, if ","label":1},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._load_model_state","text":"pytorch_lightning/core/lightning.py::LightningModule._load_model_state\n @classmethod\n def _load_model_state(cls, checkpoint: Dict[str, Any], *args, **kwargs) -> 'LightningModule':\n cls_takes_hparams = 'hparams' in inspect.signature(cls.__init__).parameters\n ckpt_hparams = checkpoint.get('hparams')\n\n if cls_takes_hparams:\n if ckpt_hparams is not None:\n is_namespace = checkpoint.get('hparams_type', 'namespace') == 'namespace'\n hparams = Namespace(**ckpt_hparams) if is_namespace else ckpt_hparams\n else:\n rank_zero_warn(\n f\"Checkpoint does not contain hyperparameters but {cls.__name__}'s __init__\"\n \" contains argument 'hparams'. Will pass in an empty Namespace instead.\"\n \" Did you forget to store your model hyperparameters in self.hparams?\"\n )\n hparams = Namespace()\n else: # The user's LightningModule does not define a hparams argument\n if ckpt_hparams is None:\n hparams = None\n else:\n raise MisconfigurationException(\n f\"Checkpoint contains hyperparameters but {cls.__name__}'s __init__ \"\n f\"is missing the argument 'hparams'. Are you loading the correct checkpoint?\"\n )\n\n # load the state_dict on the model automatically\n if hparams:\n kwargs.update(hparams=hparams)\n model = cls(*args, **kwargs)\n model.load_state_dict(checkpoint['state_dict'])\n\n # give model a chance to load something\n model.on_load_checkpoint(checkpoint)\n\n return model","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.summarize","text":"pytorch_lightning/core/lightning.py::LightningModule.summarize\n def summarize(self, mode: str) -> None:\n model_summary = ModelSummary(self, mode=mode)\n log.info('\\n' + model_summary.__str__())","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.freeze","text":"pytorch_lightning/core/lightning.py::LightningModule.freeze\n def freeze(self) -> None:\n r\"\"\"\n Freeze all params for inference.\n\n Example:\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.freeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = False\n\n self.eval()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze","text":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\n def unfreeze(self) -> None:\n \"\"\"\n Unfreeze all parameters for training.\n\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.unfreeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = True\n\n self.train()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n r\"\"\"\n Called by Lightning to restore your model.\n If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.\n\n Args:\n checkpoint: Loaded checkpoint\n\n\n Example:\n .. code-block:: python\n\n def on_load_checkpoint(self, checkpoint):\n # 99% of the time you don't need to implement this method\n self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']\n\n Note:\n Lightning auto-restores global step, epoch, and train state including amp scaling.\n There is no need for you to restore anything regarding training.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n r\"\"\"\n Called by Lightning when saving a checkpoint to give you a chance to store anything\n else you might want to save.\n\n Args:\n checkpoint: Checkpoint to be saved\n\n Example:\n .. code-block:: python\n\n\n def on_save_checkpoint(self, checkpoint):\n # 99% of use cases you don't need to implement this method\n checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object\n\n Note:\n Lightning saves all aspects of training (epoch, global step, etc...)\n including amp scaling.\n There is no need for you to store anything about training.\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict","text":"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\n def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:\n r\"\"\"\n Additional items to be displayed in the progress bar.\n\n Return:\n Dictionary with the items to be displayed in the progress bar.\n \"\"\"\n # call .item() only once but store elements without graphs\n running_train_loss = self.trainer.running_loss.mean()\n avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')\n tqdm_dict = {\n 'loss': '{:.3f}'.format(avg_training_loss)\n }\n\n if self.trainer.truncated_bptt_steps is not None:\n tqdm_dict['split_idx'] = self.trainer.split_idx\n\n if self.trainer.logger is not None and self.trainer.logger.version is not None:\n tqdm_dict['v_num'] = self.trainer.logger.version\n\n return tqdm_dict","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict","text":"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\n def get_tqdm_dict(self) -> Dict[str, Union[int, str]]:\n \"\"\"\n Additional items to be displayed in the progress bar.\n\n Return:\n Dictionary with the items to be displayed in the progress bar.\n\n Warning:\n Deprecated since v0.7.3.\n Use :meth:`get_progress_bar_dict` instead.\n \"\"\"\n rank_zero_warn(\"`get_tqdm_dict` was renamed to `get_progress_bar_dict` in v0.7.3\"\n \" and this method will be removed in v1.0.0\", DeprecationWarning)\n return self.get_progress_bar_dict()","label":0},{"cid":"pytorch_lightning/core/saving.py::ModelIO.on_load_checkpoint","text":"pytorch_lightning/core/saving.py::ModelIO.on_load_checkpoint\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n \"\"\"\n Do something with the checkpoint.\n Gives model a chance to load something before ``state_dict`` is restored.\n\n Args:\n checkpoint: A dictionary with variables from the checkpoint.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/saving.py::ModelIO.on_save_checkpoint","text":"pytorch_lightning/core/saving.py::ModelIO.on_save_checkpoint\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n \"\"\"\n Give the model a chance to add something to the checkpoint.\n ``state_dict`` is already there.\n\n Args:\n checkpoint: A dictionary in which you can save variables to save in a checkpoint.\n Contents need to be pickleable.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/saving.py::ModelIO.on_hpc_save","text":"pytorch_lightning/core/saving.py::ModelIO.on_hpc_save\n def on_hpc_save(self, checkpoint: Dict[str, Any]) -> None:\n \"\"\"\n Hook to do whatever you need right before Slurm manager saves the model.\n\n Args:\n checkpoint: A dictionary in which you can save variables to save in a checkpoint.\n Contents need to be pickleable.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/saving.py::ModelIO.on_hpc_load","text":"pytorch_lightning/core/saving.py::ModelIO.on_hpc_load\n def on_hpc_load(self, checkpoint: Dict[str, Any]) -> None:\n \"\"\"\n Hook to do whatever you need right before Slurm manager loads the model.\n\n Args:\n checkpoint: A dictionary with variables from the checkpoint.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/saving.py::load_hparams_from_tags_csv","text":"pytorch_lightning/core/saving.py::load_hparams_from_tags_csv\ndef load_hparams_from_tags_csv(tags_csv: str) -> Namespace:\n if not os.path.isfile(tags_csv):\n log.warning(f'Missing Tags: {tags_csv}.')\n return Namespace()\n\n with open(tags_csv) as f:\n csv_reader = csv.reader(f, delimiter=',')\n tags = {row[0]: convert(row[1]) for row in list(csv_reader)[1:]}\n ns = Namespace(**tags)\n return ns","label":0},{"cid":"pytorch_lightning/core/saving.py::convert","text":"pytorch_lightning/core/saving.py::convert\ndef convert(val: str) -> Union[int, float, bool, str]:\n constructors = [int, float, str]\n\n if isinstance(val, str):\n if val.lower() == 'true':\n return True\n if val.lower() == 'false':\n return False\n\n for c in constructors:\n try:\n return c(val)\n except ValueError:\n pass\n return val","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::_has_len","text":"pytorch_lightning/trainer/data_loading.py::_has_len\ndef _has_len(dataloader: DataLoader) -> bool:\n \"\"\" Checks if a given Dataloader has __len__ method implemented i.e. if\n it is a finite dataloader or infinite dataloader \"\"\"\n try:\n # try getting the length\n if len(dataloader) == 0:\n raise ValueError('`Dataloader` returned 0 length.'\n ' Please make sure that your Dataloader at least returns 1 batch')\n return True\n except TypeError:\n return False","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overridden","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overridden\n @abstractmethod\n def is_overridden(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._percent_range_check","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._percent_range_check\n def _percent_range_check(self, name: str) -> None:\n value = getattr(self, name)\n msg = f'`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}.'\n if name == 'val_check_interval':\n msg += ' If you want to disable validation set `val_percent_check` to 0.0 instead.'\n\n if not 0. <= value <= 1.:\n raise ValueError(msg)","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._worker_check","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._worker_check\n def _worker_check(self, dataloader: DataLoader, name: str) -> None:\n on_windows = platform.system() == 'Windows'\n\n if isinstance(dataloader, DataLoader) and dataloader.num_workers <= 2 and not on_windows:\n rank_zero_warn(f'The dataloader, {name}, does not have many workers which may be a bottleneck.'\n ' Consider increasing the value of the `num_workers` argument`'\n ' in the `DataLoader` init to improve performance.')","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler\n def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:\n\n # don't do anything if it's not a dataloader\n # don't manipulate iterable datasets\n is_dataloader = isinstance(dataloader, DataLoader)\n\n is_iterable_ds = False\n if ITERABLE_DATASET_EXISTS and hasattr(dataloader, 'dataset'):\n is_iterable_ds = isinstance(dataloader.dataset, IterableDataset)\n\n if not is_dataloader or is_iterable_ds:\n return dataloader\n need_dist_sampler = (self.use_ddp or self.use_ddp2 or self.use_horovod or self.use_tpu)\n if self.replace_sampler_ddp and need_dist_sampler:\n\n skip_keys = ['sampler', 'batch_sampler', 'dataset_kind']\n\n dl_args = {\n k: v for k, v in dataloader.__dict__.items() if not k.startswith('_') and k not in skip_keys\n }\n\n if self.use_tpu:\n sampler = DistributedSampler(\n dataloader.dataset,\n num_replicas=xm.xrt_world_size(),\n rank=xm.get_ordinal(),\n )\n elif self.use_horovod:\n sampler = DistributedSampler(dataloader.dataset,\n num_replicas=hvd.size(),\n rank=hvd.rank())\n else:\n world_size = {\n 'ddp': self.num_nodes * self.num_processes,\n 'ddp2': self.num_nodes,\n 'ddp_cpu': self.num_processes * self.num_nodes\n }\n sampler = DistributedSampler(\n dataloader.dataset,\n num_replicas=world_size.get(self.distributed_backend, 0),\n rank=self.proc_rank,\n )\n\n dl_args['sampler'] = sampler\n dataloader = type(dataloader)(**dl_args)\n\n return data","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader\n def reset_train_dataloader(self, model: LightningModule) -> None:\n \"\"\"Resets the train dataloader and initialises required variables\n (number of batches, when to validate, etc.).\n\n Args:\n model: The current `LightningModule`\n \"\"\"\n self.train_dataloader = self.request_dataloader(model.train_dataloader)\n\n self.num_training_batches = 0\n\n # automatically add samplers\n self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)\n\n self._worker_check(self.train_dataloader, 'train dataloader')\n self._percent_range_check('train_percent_check')\n\n if not _has_len(self.train_dataloader):\n self.num_training_batches = float('inf')\n else:\n # try getting the length\n self.num_training_batches = len(self.train_dataloader)\n self.num_training_batches = int(self.num_training_batches * self.train_percent_check)\n\n # determine when to check validation\n # if int passed in, val checks that often\n # otherwise, it checks in [0, 1.0] % range of a training epoch\n if isinstance(self.val_check_interval, int):\n self.val_check_batch = self.val_check_interval\n if self.val_check_batch > self.num_training_batches:\n raise ValueError(\n f'`val_check_interval` ({self.val_check_interval}) must be less than or equal '\n f'to the number of the training batches ({self.num_training_batches}). '\n 'If you want to disable validation set `val_percent_check` to 0.0 instead.')\n else:\n if not _has_len(self.train_dataloader):\n if self.val_check_interval == 1.0:\n self.val_check_batch = float('inf')\n else:\n raise MisconfigurationException(\n 'When","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._reset_eval_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._reset_eval_dataloader\n def _reset_eval_dataloader(self, model: LightningModule, mode: str) -> Tuple[int, List[DataLoader]]:\n \"\"\"Generic method to reset a dataloader for evaluation.\n\n Args:\n model: The current `LightningModule`\n mode: Either `'val'` or `'test'`\n\n Returns:\n Tuple (num_batches, dataloaders)\n \"\"\"\n dataloaders = self.request_dataloader(getattr(model, f'{mode}_dataloader'))\n\n if not isinstance(dataloaders, list):\n dataloaders = [dataloaders]\n\n # shuffling in val and test set is bad practice\n for loader in dataloaders:\n if mode in ('val', 'test') and hasattr(loader, 'sampler') and isinstance(loader.sampler, RandomSampler):\n raise MisconfigurationException(\n f'Your {mode}_dataloader has shuffle=True, it is best practice to turn'\n ' this off for validation and test dataloaders.')\n\n if any([dl is None for dl in dataloaders]):\n rank_zero_warn(\"One of given dataloaders is None and it will be skipped.\")\n\n # add samplers\n dataloaders = [self.auto_add_sampler(dl, train=False) for dl in dataloaders if dl is not None]\n\n num_batches = 0\n\n # determine number of batches\n # datasets could be none, 1 or 2+\n if len(dataloaders) != 0:\n for i, dataloader in enumerate(dataloaders):\n self._worker_check(dataloader, f'{mode} dataloader {i}')\n if not _has_len(dataloader):\n num_batches = float('inf')\n\n percent_check = getattr(self, f'{mode}_percent_check')\n\n if num_batches != float('inf'):\n self._percent_range_check(f'{mode}_percent_check')\n\n num_batches = sum(len(dataloader) for dataloader in dataloaders)\n num_batches = int(num_batches * percent_check)\n ","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader\n def reset_val_dataloader(self, model: LightningModule) -> None:\n \"\"\"Resets the validation dataloader and determines the number of batches.\n\n Args:\n model: The current `LightningModule`\n \"\"\"\n if self.is_overridden('validation_step'):\n self.num_val_batches, self.val_dataloaders = \\\n self._reset_eval_dataloader(model, 'val')","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader\n def reset_test_dataloader(self, model) -> None:\n \"\"\"Resets the validation dataloader and determines the number of batches.\n\n Args:\n model: The current `LightningModule`\n \"\"\"\n if self.is_overridden('test_step'):\n self.num_test_batches, self.test_dataloaders =\\\n self._reset_eval_dataloader(model, 'test')","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_dataloader\n def request_dataloader(self, dataloader_fx: Callable) -> DataLoader:\n \"\"\"Handles downloading data in the GPU or TPU case.\n\n Args:\n dataloader_fx: The bound dataloader getter\n\n Returns:\n The dataloader\n \"\"\"\n dataloader = dataloader_fx()\n\n # get the function we'll use to get data\n if self.use_ddp or self.use_ddp2:\n # all processes wait until data download has happened\n torch_distrib.barrier()\n\n # data download/load on TPU\n elif self.use_tpu and XLA_AVAILABLE:\n # all processes wait until data download has happened\n torch_xla.core.xla_model.rendezvous('pl.TrainerDataLoadingMixin.get_dataloaders')\n\n elif self.use_horovod:\n # all processes wait until data download has happened\n hvd.join()\n\n return dataloader","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount\n def determine_data_use_amount(self, train_percent_check: float, val_percent_check: float,\n test_percent_check: float, overfit_pct: float) -> None:\n \"\"\"Use less data for debugging purposes\n \"\"\"\n self.train_percent_check = train_percent_check\n self.val_percent_check = val_percent_check\n self.test_percent_check = test_percent_check\n if overfit_pct > 0:\n if overfit_pct > 1:\n raise ValueError(\n f'`overfit_pct` must be not greater than 1.0, but got {overfit_pct:.3f}.')\n\n self.train_percent_check = overfit_pct\n self.val_percent_check = overfit_pct\n self.test_percent_check = overfit_pct","label":0}],"string":"[\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.__init__\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.__init__\\n def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n\\n self.exp_save_path = None\\n\\n #: The current epoch\\n self.current_epoch = 0\\n\\n #: Total training batches seen across all epochs\\n self.global_step = 0\\n\\n self.loaded_optimizer_states_dict = {}\\n\\n #: Pointer to the trainer object\\n self.trainer = None\\n\\n #: Pointer to the logger object\\n self.logger = None\\n self.example_input_array = None\\n\\n #: True if your model is currently running on GPUs.\\n #: Useful to set flags around the LightningModule for different CPU vs GPU behavior.\\n self.on_gpu = False\\n\\n #: True if using dp\\n self.use_dp = False\\n\\n #: True if using ddp\\n self.use_ddp = False\\n\\n #: True if using ddp2\\n self.use_ddp2 = False\\n\\n #: True if using amp\\n self.use_amp = False\\n\\n self.hparams = None\\n\\n #: Current dtype\\n self._dtype = torch.FloatTensor\\n #: device reference\\n self._device = torch.device('cpu')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.print\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.print\\n def print(self, *args, **kwargs) -> None:\\n r\\\"\\\"\\\"\\n Prints only from process 0. Use this in any distributed mode to log only once.\\n\\n Args:\\n *args: The thing to print. Will be passed to Python's built-in print function.\\n **kwargs: Will be passed to Python's built-in print function.\\n\\n Example:\\n\\n .. code-block:: python\\n\\n def forward(self, x):\\n self.print(x, 'in forward')\\n\\n \\\"\\\"\\\"\\n if self.trainer.proc_rank == 0:\\n print(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.forward\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.forward\\n @abstractmethod\\n def forward(self, *args, **kwargs):\\n r\\\"\\\"\\\"\\n Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define\\n the operations you want to use for prediction (i.e.: on a server or as a feature extractor).\\n\\n Normally you'd call ``self()`` from your :meth:`training_step` method.\\n This makes it easy to write a complex system for training with the outputs\\n you'd want in a prediction setting.\\n\\n Args:\\n *args: Whatever you decide to pass into the forward method.\\n **kwargs: Keyword arguments are also possible.\\n\\n Return:\\n Predicted output\\n\\n Examples:\\n .. code-block:: python\\n\\n # example if we were using this model as a feature extractor\\n def forward(self, x):\\n feature_maps = self.convnet(x)\\n return feature_maps\\n\\n def training_step(self, batch, batch_idx):\\n x, y = batch\\n feature_maps = self(x)\\n logits = self.classifier(feature_maps)\\n\\n # ...\\n return loss\\n\\n # splitting it this way allows model to be used a feature extractor\\n model = MyModelAbove()\\n\\n inputs = server.get_request()\\n results = model(inputs)\\n server.write_results(results)\\n\\n # -------------\\n # This is in stark contrast to torch.nn.Module where normally you would have this:\\n def forward(self, batch):\\n x, y = batch\\n feature_maps = self.convnet(x)\\n logits = self.classifier(feature_maps)\\n return logits\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step\\n def training_step(self, *args, **kwargs) -> Union[\\n int, Dict[str, Union[Tensor, Dict[str, Tensor]]]\\n ]:\\n r\\\"\\\"\\\"\\n Here you compute and return the training loss and some additional metrics for e.g.\\n the progress bar or logger.\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): Integer displaying index of this batch\\n optimizer_idx (int): When using multiple optimizers, this argument will also be present.\\n hiddens(:class:`~torch.Tensor`): Passed in if\\n :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.\\n\\n Return:\\n Dict with loss key and optional log or progress bar keys.\\n When implementing :meth:`training_step`, return whatever you need in that step:\\n\\n - loss -> tensor scalar **REQUIRED**\\n - progress_bar -> Dict for progress bar display. Must have only tensors\\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\\n\\n In this step you'd normally do the forward pass and calculate the loss for a batch.\\n You can also do fancier things like multiple forward passes or something model specific.\\n\\n Examples:\\n .. code-block:: python\\n\\n def training_step(self, batch, batch_idx):\\n x, y, z = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.loss(out, x)\\n\\n logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)\\n\\n # if using TestTubeLogger or TensorBoardLogger you can nest scalars\\n logger_logs = {'losses': logger_logs} # optional (MUST ALL\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_end\\n def training_end(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`training_step_end` instead.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\\n def training_epoch_end(\\n self,\\n outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\\n ) -> Dict[str, Dict[str, Tensor]]:\\n \\\"\\\"\\\"Called at the end of the training epoch with the outputs of all training steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n train_outs = []\\n for train_batch in train_data:\\n out = training_step(train_batch)\\n train_outs.append(out)\\n training_epoch_end(train_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`training_step`, or if there are\\n multiple dataloaders, a list containing a list of outputs for each dataloader.\\n\\n Return:\\n Dict or OrderedDict.\\n May contain the following optional keys:\\n\\n - log (metrics to be added to the logger; only tensors)\\n - progress_bar (dict for progress bar display)\\n - any metric used in a callback (e.g. early stopping).\\n\\n Note:\\n If this method is not overridden, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\\n\\n Examples:\\n With a single dataloader:\\n\\n .. code-block:: python\\n\\n def training_epoch_end(self, outputs):\\n train_acc_mean = 0\\n for output in outputs:\\n train_acc_mean += output['train_acc']\\n\\n train_acc_mean /= len(outputs)\\n\\n # log training accuracy at the end of an epoch\\n results = {\\n 'log': {'train_acc': train_acc_mean.item()},\\n 'progress_bar': {'train_a\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\\n def training_step_end(self, *args, **kwargs) -> Dict[\\n str, Union[Tensor, Dict[str, Tensor]]\\n ]:\\n \\\"\\\"\\\"\\n Use this when training with dp or ddp2 because :meth:`training_step`\\n will operate on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]\\n training_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in `training_step` for each batch part.\\n\\n Return:\\n Dict with loss key and optional log or progress bar keys.\\n\\n - loss -> tensor scalar **REQUIRED**\\n - progress_bar -> Dict for progress bar display. Must have only tensors\\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT training_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def training_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with training_step_end to do softmax over the full batch\\n def training_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step\\n def validation_step(self, *args, **kwargs) -> Dict[str, Tensor]:\\n r\\\"\\\"\\\"\\n Operates on a single batch of data from the validation set.\\n In this step you'd might generate examples or calculate anything of interest like accuracy.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n val_outs = []\\n for val_batch in val_data:\\n out = validation_step(train_batch)\\n val_outs.append(out)\\n validation_epoch_end(val_outs)\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): The index of this batch\\n dataloader_idx (int): The index of the dataloader that produced this batch\\n (only if multiple val datasets used)\\n\\n Return:\\n Dict or OrderedDict - passed to :meth:`validation_epoch_end`.\\n If you defined :meth:`validation_step_end` it will go to that first.\\n\\n .. code-block:: python\\n\\n # pseudocode of order\\n out = validation_step()\\n if defined('validation_step_end'):\\n out = validation_step_end(out)\\n out = validation_epoch_end(out)\\n\\n\\n .. code-block:: python\\n\\n # if you have one val dataloader:\\n def validation_step(self, batch, batch_idx)\\n\\n # if you have multiple val dataloaders:\\n def validation_step(self, batch, batch_idx, dataloader_idx)\\n\\n Examples:\\n .. code-block:: python\\n\\n # CASE 1: A single validation dataset\\n def validation_step(self, batch, batch_idx):\\n x, y = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.los\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\\n def validation_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\\n \\\"\\\"\\\"\\n Use this when validating with dp or ddp2 because :meth:`validation_step`\\n will operate on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code.\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]\\n validation_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in :meth:`validation_step`\\n for each batch part.\\n\\n Return:\\n Dict or OrderedDict - passed to the :meth:`validation_epoch_end` method.\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT validation_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def validation_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with validation_step_end to do softmax over the full batch\\n def validation_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n return {'out': out}\\n\\n def validation_epoch_end(self, outputs):\\n # this out is now the full size of the batch\\n out = outputs['out']\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_end\\n def validation_end(self, outputs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead.\\n Will be removed in 1.0.0.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\\n def validation_epoch_end(\\n self,\\n outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\\n ) -> Dict[str, Dict[str, Tensor]]:\\n \\\"\\\"\\\"\\n Called at the end of the validation epoch with the outputs of all validation steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n val_outs = []\\n for val_batch in val_data:\\n out = validation_step(val_batch)\\n val_outs.append(out)\\n validation_epoch_end(val_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`validation_step`, or if there\\n are multiple dataloaders, a list containing a list of outputs for each dataloader.\\n\\n Return:\\n Dict or OrderedDict.\\n May have the following optional keys:\\n\\n - progress_bar (dict for progress bar display; only tensors)\\n - log (dict of metrics to add to logger; only tensors).\\n\\n Note:\\n If you didn't define a :meth:`validation_step`, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\\n\\n Examples:\\n With a single dataloader:\\n\\n .. code-block:: python\\n\\n def validation_epoch_end(self, outputs):\\n val_acc_mean = 0\\n for output in outputs:\\n val_acc_mean += output['val_acc']\\n\\n val_acc_mean /= len(outputs)\\n tqdm_dict = {'val_acc': val_acc_mean.item()}\\n\\n # show val_acc in progress bar but only log val_loss\\n results = {\\n 'progress_bar': tqdm_dict,\\n 'log': {'\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step\\n def test_step(self, *args, **kwargs) -> Dict[str, Tensor]:\\n r\\\"\\\"\\\"\\n Operates on a single batch of data from the test set.\\n In this step you'd normally generate examples or calculate anything of interest\\n such as accuracy.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n test_outs = []\\n for test_batch in test_data:\\n out = test_step(test_batch)\\n test_outs.append(out)\\n test_epoch_end(test_outs)\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): The index of this batch.\\n dataloader_idx (int): The index of the dataloader that produced this batch\\n (only if multiple test datasets used).\\n\\n Return:\\n Dict or OrderedDict - passed to the :meth:`test_epoch_end` method.\\n If you defined :meth:`test_step_end` it will go to that first.\\n\\n .. code-block:: python\\n\\n # if you have one test dataloader:\\n def test_step(self, batch, batch_idx)\\n\\n # if you have multiple test dataloaders:\\n def test_step(self, batch, batch_idx, dataloader_idx)\\n\\n Examples:\\n .. code-block:: python\\n\\n # CASE 1: A single test dataset\\n def test_step(self, batch, batch_idx):\\n x, y = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.loss(out, y)\\n\\n # log 6 example images\\n # or generated text... or whatever\\n sample_imgs = x[:6]\\n grid = torchvision.utils.make_grid(sample_imgs)\\n self.logger.experiment.add_image('example\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\\n def test_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\\n \\\"\\\"\\\"\\n Use this when testing with dp or ddp2 because :meth:`test_step` will operate\\n on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code.\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]\\n test_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in :meth:`test_step` for each batch part.\\n\\n Return:\\n Dict or OrderedDict - passed to the :meth:`test_epoch_end`.\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT test_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def test_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with test_step_end to do softmax over the full batch\\n def test_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n return {'out': out}\\n\\n def test_step_end(self, outputs):\\n # this out is now the full size of the batch\\n out = outputs['out']\\n\\n # this softmax now uses the full batch size\\n loss = nce_loss(loss)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_end\\n def test_end(self, outputs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead.\\n Will be removed in 1.0.0.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\\n def test_epoch_end(\\n self,\\n outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\\n ) -> Dict[str, Dict[str, Tensor]]:\\n \\\"\\\"\\\"\\n Called at the end of a test epoch with the output of all test steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n test_outs = []\\n for test_batch in test_data:\\n out = test_step(test_batch)\\n test_outs.append(out)\\n test_epoch_end(test_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`test_step_end`, or if there\\n are multiple dataloaders, a list containing a list of outputs for each dataloader\\n\\n Return:\\n Dict or OrderedDict: Dict has the following optional keys:\\n\\n - progress_bar -> Dict for progress bar display. Must have only tensors.\\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc).\\n\\n Note:\\n If you didn't define a :meth:`test_step`, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict\\n\\n Examples:\\n With a single dataloader:\\n\\n .. code-block:: python\\n\\n def test_epoch_end(self, outputs):\\n test_acc_mean = 0\\n for output in outputs:\\n test_acc_mean += output['test_acc']\\n\\n test_acc_mean /= len(outputs)\\n tqdm_dict = {'test_acc': test_acc_mean.item()}\\n\\n # show test_loss and test_acc in progress bar but only log test_loss\\n results = {\\n 'progress_bar': tqdm_dict,\\n 'log': {\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\\n def configure_ddp(\\n self,\\n model: 'LightningModule',\\n device_ids: List[int]\\n ) -> DistributedDataParallel:\\n r\\\"\\\"\\\"\\n Override to init DDP in your own way or with your own wrapper.\\n The only requirements are that:\\n\\n 1. On a validation batch the call goes to ``model.validation_step``.\\n 2. On a training batch the call goes to ``model.training_step``.\\n 3. On a testing batch, the call goes to ``model.test_step``.+\\n\\n Args:\\n model: the :class:`LightningModule` currently being optimized.\\n device_ids: the list of GPU ids.\\n\\n Return:\\n DDP wrapped model\\n\\n Examples:\\n .. code-block:: python\\n\\n # default implementation used in Trainer\\n def configure_ddp(self, model, device_ids):\\n # Lightning DDP simply routes to test_step, val_step, etc...\\n model = LightningDistributedDataParallel(\\n model,\\n device_ids=device_ids,\\n find_unused_parameters=True\\n )\\n return model\\n\\n \\\"\\\"\\\"\\n model = LightningDistributedDataParallel(\\n model,\\n device_ids=device_ids,\\n find_unused_parameters=True\\n )\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\\n def _init_slurm_connection(self) -> None:\\n \\\"\\\"\\\"\\n Sets up environment variables necessary for pytorch distributed communications\\n based on slurm environment.\\n \\\"\\\"\\\"\\n # use slurm job id for the port number\\n # guarantees unique ports across jobs from same grid search\\n try:\\n # use the last 4 numbers in the job id as the id\\n default_port = os.environ['SLURM_JOB_ID']\\n default_port = default_port[-4:]\\n\\n # all ports should be in the 10k+ range\\n default_port = int(default_port) + 15000\\n\\n except Exception:\\n default_port = 12910\\n\\n # if user gave a port number, use that one instead\\n try:\\n default_port = os.environ['MASTER_PORT']\\n except Exception:\\n os.environ['MASTER_PORT'] = str(default_port)\\n\\n # figure out the root node addr\\n try:\\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\\n except Exception:\\n root_node = '127.0.0.1'\\n\\n root_node = self.trainer.resolve_root_node_address(root_node)\\n os.environ['MASTER_ADDR'] = root_node\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\\n def init_ddp_connection(\\n self,\\n proc_rank: int,\\n world_size: int,\\n is_slurm_managing_tasks: bool = True\\n ) -> None:\\n \\\"\\\"\\\"\\n Override to define your custom way of setting up a distributed environment.\\n\\n Lightning's implementation uses env:// init by default and sets the first node as root\\n for SLURM managed cluster.\\n\\n Args:\\n proc_rank: The current process rank within the node.\\n world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).\\n is_slurm_managing_tasks: is cluster managed by SLURM.\\n\\n \\\"\\\"\\\"\\n if is_slurm_managing_tasks:\\n self._init_slurm_connection()\\n\\n if 'MASTER_ADDR' not in os.environ:\\n log.warning(\\\"MASTER_ADDR environment variable is not defined. Set as localhost\\\")\\n os.environ['MASTER_ADDR'] = '127.0.0.1'\\n log.debug(f\\\"MASTER_ADDR: {os.environ['MASTER_ADDR']}\\\")\\n\\n if 'MASTER_PORT' not in os.environ:\\n log.warning(\\\"MASTER_PORT environment variable is not defined. Set as 12910\\\")\\n os.environ['MASTER_PORT'] = '12910'\\n log.debug(f\\\"MASTER_PORT: {os.environ['MASTER_PORT']}\\\")\\n\\n if 'WORLD_SIZE' in os.environ and os.environ['WORLD_SIZE'] != world_size:\\n log.warning(\\\"WORLD_SIZE environment variable is not equal to the computed \\\"\\n \\\"world size. Ignored.\\\")\\n\\n torch_backend = \\\"nccl\\\" if self.trainer.on_gpu else \\\"gloo\\\"\\n torch_distrib.init_process_group(torch_backend, rank=proc_rank, world_size=world_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\\n def configure_apex(\\n self,\\n amp: object,\\n model: 'LightningModule',\\n optimizers: List[Optimizer],\\n amp_level: str\\n ) -> Tuple['LightningModule', List[Optimizer]]:\\n r\\\"\\\"\\\"\\n Override to init AMP your own way.\\n Must return a model and list of optimizers.\\n\\n Args:\\n amp: pointer to amp library object.\\n model: pointer to current :class:`LightningModule`.\\n optimizers: list of optimizers passed in :meth:`configure_optimizers`.\\n amp_level: AMP mode chosen ('O1', 'O2', etc...)\\n\\n Return:\\n Apex wrapped model and optimizers\\n\\n Examples:\\n .. code-block:: python\\n\\n # Default implementation used by Trainer.\\n def configure_apex(self, amp, model, optimizers, amp_level):\\n model, optimizers = amp.initialize(\\n model, optimizers, opt_level=amp_level,\\n )\\n\\n return model, optimizers\\n \\\"\\\"\\\"\\n model, optimizers = amp.initialize(\\n model, optimizers, opt_level=amp_level,\\n )\\n\\n return model, optimizers\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\\n def configure_optimizers(self) -> Optional[Union[\\n Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List]\\n ]]:\\n r\\\"\\\"\\\"\\n Choose what optimizers and learning-rate schedulers to use in your optimization.\\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\\n\\n Return:\\n Any of these 6 options.\\n\\n - Single optimizer.\\n - List or Tuple - List of optimizers.\\n - Two lists - The first list has multiple optimizers, the second a list of LR schedulers.\\n - Dictionary, with an 'optimizer' key and (optionally) a 'lr_scheduler' key.\\n - Tuple of dictionaries as described, with an optional 'frequency' key.\\n - None - Fit will run without any optimizer.\\n\\n Note:\\n The 'frequency' value is an int corresponding to the number of sequential batches\\n optimized with the specific optimizer. It should be given to none or to all of the optimizers.\\n There is a difference between passing multiple optimizers in a list,\\n and passing multiple optimizers in dictionaries with a frequency of 1:\\n In the former case, all optimizers will operate on the given batch in each optimization step.\\n In the latter, only one optimizer will operate on the given batch at every step.\\n\\n Examples:\\n .. code-block:: python\\n\\n # most cases\\n def configure_optimizers(self):\\n opt = Adam(self.parameters(), lr=1e-3)\\n return opt\\n\\n # multiple optimizer case (e.g.: GAN)\\n def configure_optimizers(self):\\n generator_opt = Adam(self.model_gen.parameters(), lr=0.01)\\n disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)\\n return generator_opt, disriminator_\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\\n def optimizer_step(\\n self,\\n epoch: int,\\n batch_idx: int,\\n optimizer: Optimizer,\\n optimizer_idx: int,\\n second_order_closure: Optional[Callable] = None,\\n ) -> None:\\n r\\\"\\\"\\\"\\n Override this method to adjust the default way the\\n :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.\\n By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example\\n once per optimizer.\\n\\n Args:\\n epoch: Current epoch\\n batch_idx: Index of current batch\\n optimizer: A PyTorch optimizer\\n optimizer_idx: If you used multiple optimizers this indexes into that list.\\n second_order_closure: closure for second order methods\\n\\n Examples:\\n .. code-block:: python\\n\\n # DEFAULT\\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\\n second_order_closure=None):\\n optimizer.step()\\n optimizer.zero_grad()\\n\\n # Alternating schedule for optimizer steps (i.e.: GANs)\\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\\n second_order_closure=None):\\n # update generator opt every 2 steps\\n if optimizer_idx == 0:\\n if batch_idx % 2 == 0 :\\n optimizer.step()\\n optimizer.zero_grad()\\n\\n # update discriminator opt every 4 steps\\n if optimizer_idx == 1:\\n if batch_idx % 4 == 0 :\\n optimizer.step()\\n optimizer.zero_grad()\\n\\n # ...\\n # add as many optimizers as you want\\n\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\\n def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:\\n r\\\"\\\"\\\"\\n When using truncated backpropagation through time, each batch must be split along the\\n time dimension. Lightning handles this by default, but for custom behavior override\\n this function.\\n\\n Args:\\n batch: Current batch\\n split_size: The size of the split\\n\\n Return:\\n List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated\\n back propagation through time. The default implementation splits root level Tensors and\\n Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.\\n\\n Examples:\\n .. code-block:: python\\n\\n def tbptt_split_batch(self, batch, split_size):\\n splits = []\\n for t in range(0, time_dims[0], split_size):\\n batch_split = []\\n for i, x in enumerate(batch):\\n if isinstance(x, torch.Tensor):\\n split_x = x[:, t:t + split_size]\\n elif isinstance(x, collections.Sequence):\\n split_x = [None] * len(x)\\n for batch_idx in range(len(x)):\\n split_x[batch_idx] = x[batch_idx][t:t + split_size]\\n\\n batch_split.append(split_x)\\n\\n splits.append(batch_split)\\n\\n return splits\\n\\n Note:\\n Called in the training loop after\\n :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`\\n if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.\\n Each returned batch split is passed separately to :meth:`training_step`.\\n\\n \\\"\\\"\\\"\\n time_dims = [len(x[0]) for x in batch if isinstance(x\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\\n def prepare_data(self) -> None:\\n \\\"\\\"\\\"\\n Use this to download and prepare data.\\n In distributed (GPU, TPU), this will only be called once.\\n This is called before requesting the dataloaders:\\n\\n .. code-block:: python\\n\\n model.prepare_data()\\n model.train_dataloader()\\n model.val_dataloader()\\n model.test_dataloader()\\n\\n Examples:\\n .. code-block:: python\\n\\n def prepare_data(self):\\n download_imagenet()\\n clean_imagenet()\\n cache_imagenet()\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\\n def train_dataloader(self) -> DataLoader:\\n \\\"\\\"\\\"\\n Implement a PyTorch DataLoader for training.\\n\\n Return:\\n Single PyTorch :class:`~torch.utils.data.DataLoader`.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`train_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware.\\n There is no need to set it yourself.\\n\\n Example:\\n .. code-block:: python\\n\\n def train_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,\\n download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.hparams.batch_size,\\n shuffle=True\\n )\\n return loader\\n\\n \\\"\\\"\\\"\\n rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\\n def tng_dataloader(self): # todo: remove in v1.0.0\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.\\n \\\"\\\"\\\"\\n output = self.train_dataloader()\\n rank_zero_warn(\\\"`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0.\\\"\\n \\\" and this method will be removed in v1.0.0\\\", DeprecationWarning)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\\n def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\\n r\\\"\\\"\\\"\\n Implement one or multiple PyTorch DataLoaders for testing.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`train_dataloader`\\n - :meth:`val_dataloader`\\n - :meth:`test_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware.\\n There is no need to set it yourself.\\n\\n Return:\\n Single or multiple PyTorch DataLoaders.\\n\\n Example:\\n .. code-block:: python\\n\\n def test_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,\\n download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.hparams.batch_size,\\n shuffle=False\\n )\\n\\n return loader\\n\\n Note:\\n If you don't need a test dataset and a :meth:`test_step`, you don't need to implement\\n this method.\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\\n def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\\n r\\\"\\\"\\\"\\n Implement one or multiple PyTorch DataLoaders for validation.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`train_dataloader`\\n - :meth:`val_dataloader`\\n - :meth:`test_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware\\n There is no need to set it yourself.\\n\\n Return:\\n Single or multiple PyTorch DataLoaders.\\n\\n Examples:\\n .. code-block:: python\\n\\n def val_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=False,\\n transform=transform, download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.hparams.batch_size,\\n shuffle=False\\n )\\n\\n return loader\\n\\n # can also return multiple dataloaders\\n def val_dataloader(self):\\n return [loader_a, loader_b, ..., loader_n]\\n\\n Note:\\n If you don't need a validation dataset and a :meth:`validation_step`, you don't need to\\n implement this method.\\n\\n Note:\\n In the case where you return multiple validation dataloaders, the :meth:`valid\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.load_from_metrics\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.load_from_metrics\\n @classmethod\\n def load_from_metrics(cls, weights_path, tags_csv, map_location=None):\\n r\\\"\\\"\\\"\\n Warning:\\n Deprecated in version 0.7.0. You should use :meth:`load_from_checkpoint` instead.\\n Will be removed in v0.9.0.\\n \\\"\\\"\\\"\\n rank_zero_warn(\\n \\\"`load_from_metrics` method has been unified with `load_from_checkpoint` in v0.7.0.\\\"\\n \\\" The deprecated method will be removed in v0.9.0.\\\", DeprecationWarning\\n )\\n return cls.load_from_checkpoint(weights_path, tags_csv=tags_csv, map_location=map_location)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint\\n @classmethod\\n def load_from_checkpoint(\\n cls,\\n checkpoint_path: str,\\n map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,\\n tags_csv: Optional[str] = None,\\n *args, **kwargs\\n ) -> 'LightningModule':\\n r\\\"\\\"\\\"\\n Primary way of loading a model from a checkpoint. When Lightning saves a checkpoint\\n it stores the hyperparameters in the checkpoint if you initialized your :class:`LightningModule`\\n with an argument called ``hparams`` which is a :class:`~argparse.Namespace`\\n (output of :meth:`~argparse.ArgumentParser.parse_args` when parsing command line arguments).\\n Any other arguments specified through \\\\*args and \\\\*\\\\*kwargs will be passed to the model.\\n\\n Example:\\n .. code-block:: python\\n\\n from argparse import Namespace\\n hparams = Namespace(**{'learning_rate': 0.1})\\n\\n model = MyModel(hparams)\\n\\n class MyModel(LightningModule):\\n def __init__(self, hparams):\\n self.learning_rate = hparams.learning_rate\\n\\n Args:\\n checkpoint_path: Path to checkpoint.\\n model_args: Any keyword args needed to init the model.\\n map_location:\\n If your checkpoint saved a GPU model and you now load on CPUs\\n or a different number of GPUs, use this to map to the new setup.\\n The behaviour is the same as in :func:`torch.load`.\\n tags_csv: Optional path to a .csv file with two columns (key, value)\\n as in this example::\\n\\n key,value\\n drop_prob,0.2\\n batch_size,32\\n\\n You most likely won't need this since Lightning will always save the hyperparameters\\n to the checkpoint.\\n However, if \",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule._load_model_state\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule._load_model_state\\n @classmethod\\n def _load_model_state(cls, checkpoint: Dict[str, Any], *args, **kwargs) -> 'LightningModule':\\n cls_takes_hparams = 'hparams' in inspect.signature(cls.__init__).parameters\\n ckpt_hparams = checkpoint.get('hparams')\\n\\n if cls_takes_hparams:\\n if ckpt_hparams is not None:\\n is_namespace = checkpoint.get('hparams_type', 'namespace') == 'namespace'\\n hparams = Namespace(**ckpt_hparams) if is_namespace else ckpt_hparams\\n else:\\n rank_zero_warn(\\n f\\\"Checkpoint does not contain hyperparameters but {cls.__name__}'s __init__\\\"\\n \\\" contains argument 'hparams'. Will pass in an empty Namespace instead.\\\"\\n \\\" Did you forget to store your model hyperparameters in self.hparams?\\\"\\n )\\n hparams = Namespace()\\n else: # The user's LightningModule does not define a hparams argument\\n if ckpt_hparams is None:\\n hparams = None\\n else:\\n raise MisconfigurationException(\\n f\\\"Checkpoint contains hyperparameters but {cls.__name__}'s __init__ \\\"\\n f\\\"is missing the argument 'hparams'. Are you loading the correct checkpoint?\\\"\\n )\\n\\n # load the state_dict on the model automatically\\n if hparams:\\n kwargs.update(hparams=hparams)\\n model = cls(*args, **kwargs)\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n # give model a chance to load something\\n model.on_load_checkpoint(checkpoint)\\n\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.summarize\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.summarize\\n def summarize(self, mode: str) -> None:\\n model_summary = ModelSummary(self, mode=mode)\\n log.info('\\\\n' + model_summary.__str__())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.freeze\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.freeze\\n def freeze(self) -> None:\\n r\\\"\\\"\\\"\\n Freeze all params for inference.\\n\\n Example:\\n .. code-block:: python\\n\\n model = MyLightningModule(...)\\n model.freeze()\\n\\n \\\"\\\"\\\"\\n for param in self.parameters():\\n param.requires_grad = False\\n\\n self.eval()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\\n def unfreeze(self) -> None:\\n \\\"\\\"\\\"\\n Unfreeze all parameters for training.\\n\\n .. code-block:: python\\n\\n model = MyLightningModule(...)\\n model.unfreeze()\\n\\n \\\"\\\"\\\"\\n for param in self.parameters():\\n param.requires_grad = True\\n\\n self.train()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\\n r\\\"\\\"\\\"\\n Called by Lightning to restore your model.\\n If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.\\n\\n Args:\\n checkpoint: Loaded checkpoint\\n\\n\\n Example:\\n .. code-block:: python\\n\\n def on_load_checkpoint(self, checkpoint):\\n # 99% of the time you don't need to implement this method\\n self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']\\n\\n Note:\\n Lightning auto-restores global step, epoch, and train state including amp scaling.\\n There is no need for you to restore anything regarding training.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\\n r\\\"\\\"\\\"\\n Called by Lightning when saving a checkpoint to give you a chance to store anything\\n else you might want to save.\\n\\n Args:\\n checkpoint: Checkpoint to be saved\\n\\n Example:\\n .. code-block:: python\\n\\n\\n def on_save_checkpoint(self, checkpoint):\\n # 99% of use cases you don't need to implement this method\\n checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object\\n\\n Note:\\n Lightning saves all aspects of training (epoch, global step, etc...)\\n including amp scaling.\\n There is no need for you to store anything about training.\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\\n def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:\\n r\\\"\\\"\\\"\\n Additional items to be displayed in the progress bar.\\n\\n Return:\\n Dictionary with the items to be displayed in the progress bar.\\n \\\"\\\"\\\"\\n # call .item() only once but store elements without graphs\\n running_train_loss = self.trainer.running_loss.mean()\\n avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')\\n tqdm_dict = {\\n 'loss': '{:.3f}'.format(avg_training_loss)\\n }\\n\\n if self.trainer.truncated_bptt_steps is not None:\\n tqdm_dict['split_idx'] = self.trainer.split_idx\\n\\n if self.trainer.logger is not None and self.trainer.logger.version is not None:\\n tqdm_dict['v_num'] = self.trainer.logger.version\\n\\n return tqdm_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\\n def get_tqdm_dict(self) -> Dict[str, Union[int, str]]:\\n \\\"\\\"\\\"\\n Additional items to be displayed in the progress bar.\\n\\n Return:\\n Dictionary with the items to be displayed in the progress bar.\\n\\n Warning:\\n Deprecated since v0.7.3.\\n Use :meth:`get_progress_bar_dict` instead.\\n \\\"\\\"\\\"\\n rank_zero_warn(\\\"`get_tqdm_dict` was renamed to `get_progress_bar_dict` in v0.7.3\\\"\\n \\\" and this method will be removed in v1.0.0\\\", DeprecationWarning)\\n return self.get_progress_bar_dict()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/saving.py::ModelIO.on_load_checkpoint\",\n \"text\": \"pytorch_lightning/core/saving.py::ModelIO.on_load_checkpoint\\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\\n \\\"\\\"\\\"\\n Do something with the checkpoint.\\n Gives model a chance to load something before ``state_dict`` is restored.\\n\\n Args:\\n checkpoint: A dictionary with variables from the checkpoint.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/saving.py::ModelIO.on_save_checkpoint\",\n \"text\": \"pytorch_lightning/core/saving.py::ModelIO.on_save_checkpoint\\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\\n \\\"\\\"\\\"\\n Give the model a chance to add something to the checkpoint.\\n ``state_dict`` is already there.\\n\\n Args:\\n checkpoint: A dictionary in which you can save variables to save in a checkpoint.\\n Contents need to be pickleable.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/saving.py::ModelIO.on_hpc_save\",\n \"text\": \"pytorch_lightning/core/saving.py::ModelIO.on_hpc_save\\n def on_hpc_save(self, checkpoint: Dict[str, Any]) -> None:\\n \\\"\\\"\\\"\\n Hook to do whatever you need right before Slurm manager saves the model.\\n\\n Args:\\n checkpoint: A dictionary in which you can save variables to save in a checkpoint.\\n Contents need to be pickleable.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/saving.py::ModelIO.on_hpc_load\",\n \"text\": \"pytorch_lightning/core/saving.py::ModelIO.on_hpc_load\\n def on_hpc_load(self, checkpoint: Dict[str, Any]) -> None:\\n \\\"\\\"\\\"\\n Hook to do whatever you need right before Slurm manager loads the model.\\n\\n Args:\\n checkpoint: A dictionary with variables from the checkpoint.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/saving.py::load_hparams_from_tags_csv\",\n \"text\": \"pytorch_lightning/core/saving.py::load_hparams_from_tags_csv\\ndef load_hparams_from_tags_csv(tags_csv: str) -> Namespace:\\n if not os.path.isfile(tags_csv):\\n log.warning(f'Missing Tags: {tags_csv}.')\\n return Namespace()\\n\\n with open(tags_csv) as f:\\n csv_reader = csv.reader(f, delimiter=',')\\n tags = {row[0]: convert(row[1]) for row in list(csv_reader)[1:]}\\n ns = Namespace(**tags)\\n return ns\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/saving.py::convert\",\n \"text\": \"pytorch_lightning/core/saving.py::convert\\ndef convert(val: str) -> Union[int, float, bool, str]:\\n constructors = [int, float, str]\\n\\n if isinstance(val, str):\\n if val.lower() == 'true':\\n return True\\n if val.lower() == 'false':\\n return False\\n\\n for c in constructors:\\n try:\\n return c(val)\\n except ValueError:\\n pass\\n return val\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::_has_len\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::_has_len\\ndef _has_len(dataloader: DataLoader) -> bool:\\n \\\"\\\"\\\" Checks if a given Dataloader has __len__ method implemented i.e. if\\n it is a finite dataloader or infinite dataloader \\\"\\\"\\\"\\n try:\\n # try getting the length\\n if len(dataloader) == 0:\\n raise ValueError('`Dataloader` returned 0 length.'\\n ' Please make sure that your Dataloader at least returns 1 batch')\\n return True\\n except TypeError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overridden\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overridden\\n @abstractmethod\\n def is_overridden(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._percent_range_check\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._percent_range_check\\n def _percent_range_check(self, name: str) -> None:\\n value = getattr(self, name)\\n msg = f'`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}.'\\n if name == 'val_check_interval':\\n msg += ' If you want to disable validation set `val_percent_check` to 0.0 instead.'\\n\\n if not 0. <= value <= 1.:\\n raise ValueError(msg)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._worker_check\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._worker_check\\n def _worker_check(self, dataloader: DataLoader, name: str) -> None:\\n on_windows = platform.system() == 'Windows'\\n\\n if isinstance(dataloader, DataLoader) and dataloader.num_workers <= 2 and not on_windows:\\n rank_zero_warn(f'The dataloader, {name}, does not have many workers which may be a bottleneck.'\\n ' Consider increasing the value of the `num_workers` argument`'\\n ' in the `DataLoader` init to improve performance.')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler\\n def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:\\n\\n # don't do anything if it's not a dataloader\\n # don't manipulate iterable datasets\\n is_dataloader = isinstance(dataloader, DataLoader)\\n\\n is_iterable_ds = False\\n if ITERABLE_DATASET_EXISTS and hasattr(dataloader, 'dataset'):\\n is_iterable_ds = isinstance(dataloader.dataset, IterableDataset)\\n\\n if not is_dataloader or is_iterable_ds:\\n return dataloader\\n need_dist_sampler = (self.use_ddp or self.use_ddp2 or self.use_horovod or self.use_tpu)\\n if self.replace_sampler_ddp and need_dist_sampler:\\n\\n skip_keys = ['sampler', 'batch_sampler', 'dataset_kind']\\n\\n dl_args = {\\n k: v for k, v in dataloader.__dict__.items() if not k.startswith('_') and k not in skip_keys\\n }\\n\\n if self.use_tpu:\\n sampler = DistributedSampler(\\n dataloader.dataset,\\n num_replicas=xm.xrt_world_size(),\\n rank=xm.get_ordinal(),\\n )\\n elif self.use_horovod:\\n sampler = DistributedSampler(dataloader.dataset,\\n num_replicas=hvd.size(),\\n rank=hvd.rank())\\n else:\\n world_size = {\\n 'ddp': self.num_nodes * self.num_processes,\\n 'ddp2': self.num_nodes,\\n 'ddp_cpu': self.num_processes * self.num_nodes\\n }\\n sampler = DistributedSampler(\\n dataloader.dataset,\\n num_replicas=world_size.get(self.distributed_backend, 0),\\n rank=self.proc_rank,\\n )\\n\\n dl_args['sampler'] = sampler\\n dataloader = type(dataloader)(**dl_args)\\n\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader\\n def reset_train_dataloader(self, model: LightningModule) -> None:\\n \\\"\\\"\\\"Resets the train dataloader and initialises required variables\\n (number of batches, when to validate, etc.).\\n\\n Args:\\n model: The current `LightningModule`\\n \\\"\\\"\\\"\\n self.train_dataloader = self.request_dataloader(model.train_dataloader)\\n\\n self.num_training_batches = 0\\n\\n # automatically add samplers\\n self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)\\n\\n self._worker_check(self.train_dataloader, 'train dataloader')\\n self._percent_range_check('train_percent_check')\\n\\n if not _has_len(self.train_dataloader):\\n self.num_training_batches = float('inf')\\n else:\\n # try getting the length\\n self.num_training_batches = len(self.train_dataloader)\\n self.num_training_batches = int(self.num_training_batches * self.train_percent_check)\\n\\n # determine when to check validation\\n # if int passed in, val checks that often\\n # otherwise, it checks in [0, 1.0] % range of a training epoch\\n if isinstance(self.val_check_interval, int):\\n self.val_check_batch = self.val_check_interval\\n if self.val_check_batch > self.num_training_batches:\\n raise ValueError(\\n f'`val_check_interval` ({self.val_check_interval}) must be less than or equal '\\n f'to the number of the training batches ({self.num_training_batches}). '\\n 'If you want to disable validation set `val_percent_check` to 0.0 instead.')\\n else:\\n if not _has_len(self.train_dataloader):\\n if self.val_check_interval == 1.0:\\n self.val_check_batch = float('inf')\\n else:\\n raise MisconfigurationException(\\n 'When\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._reset_eval_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._reset_eval_dataloader\\n def _reset_eval_dataloader(self, model: LightningModule, mode: str) -> Tuple[int, List[DataLoader]]:\\n \\\"\\\"\\\"Generic method to reset a dataloader for evaluation.\\n\\n Args:\\n model: The current `LightningModule`\\n mode: Either `'val'` or `'test'`\\n\\n Returns:\\n Tuple (num_batches, dataloaders)\\n \\\"\\\"\\\"\\n dataloaders = self.request_dataloader(getattr(model, f'{mode}_dataloader'))\\n\\n if not isinstance(dataloaders, list):\\n dataloaders = [dataloaders]\\n\\n # shuffling in val and test set is bad practice\\n for loader in dataloaders:\\n if mode in ('val', 'test') and hasattr(loader, 'sampler') and isinstance(loader.sampler, RandomSampler):\\n raise MisconfigurationException(\\n f'Your {mode}_dataloader has shuffle=True, it is best practice to turn'\\n ' this off for validation and test dataloaders.')\\n\\n if any([dl is None for dl in dataloaders]):\\n rank_zero_warn(\\\"One of given dataloaders is None and it will be skipped.\\\")\\n\\n # add samplers\\n dataloaders = [self.auto_add_sampler(dl, train=False) for dl in dataloaders if dl is not None]\\n\\n num_batches = 0\\n\\n # determine number of batches\\n # datasets could be none, 1 or 2+\\n if len(dataloaders) != 0:\\n for i, dataloader in enumerate(dataloaders):\\n self._worker_check(dataloader, f'{mode} dataloader {i}')\\n if not _has_len(dataloader):\\n num_batches = float('inf')\\n\\n percent_check = getattr(self, f'{mode}_percent_check')\\n\\n if num_batches != float('inf'):\\n self._percent_range_check(f'{mode}_percent_check')\\n\\n num_batches = sum(len(dataloader) for dataloader in dataloaders)\\n num_batches = int(num_batches * percent_check)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader\\n def reset_val_dataloader(self, model: LightningModule) -> None:\\n \\\"\\\"\\\"Resets the validation dataloader and determines the number of batches.\\n\\n Args:\\n model: The current `LightningModule`\\n \\\"\\\"\\\"\\n if self.is_overridden('validation_step'):\\n self.num_val_batches, self.val_dataloaders = \\\\\\n self._reset_eval_dataloader(model, 'val')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader\\n def reset_test_dataloader(self, model) -> None:\\n \\\"\\\"\\\"Resets the validation dataloader and determines the number of batches.\\n\\n Args:\\n model: The current `LightningModule`\\n \\\"\\\"\\\"\\n if self.is_overridden('test_step'):\\n self.num_test_batches, self.test_dataloaders =\\\\\\n self._reset_eval_dataloader(model, 'test')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_dataloader\\n def request_dataloader(self, dataloader_fx: Callable) -> DataLoader:\\n \\\"\\\"\\\"Handles downloading data in the GPU or TPU case.\\n\\n Args:\\n dataloader_fx: The bound dataloader getter\\n\\n Returns:\\n The dataloader\\n \\\"\\\"\\\"\\n dataloader = dataloader_fx()\\n\\n # get the function we'll use to get data\\n if self.use_ddp or self.use_ddp2:\\n # all processes wait until data download has happened\\n torch_distrib.barrier()\\n\\n # data download/load on TPU\\n elif self.use_tpu and XLA_AVAILABLE:\\n # all processes wait until data download has happened\\n torch_xla.core.xla_model.rendezvous('pl.TrainerDataLoadingMixin.get_dataloaders')\\n\\n elif self.use_horovod:\\n # all processes wait until data download has happened\\n hvd.join()\\n\\n return dataloader\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount\\n def determine_data_use_amount(self, train_percent_check: float, val_percent_check: float,\\n test_percent_check: float, overfit_pct: float) -> None:\\n \\\"\\\"\\\"Use less data for debugging purposes\\n \\\"\\\"\\\"\\n self.train_percent_check = train_percent_check\\n self.val_percent_check = val_percent_check\\n self.test_percent_check = test_percent_check\\n if overfit_pct > 0:\\n if overfit_pct > 1:\\n raise ValueError(\\n f'`overfit_pct` must be not greater than 1.0, but got {overfit_pct:.3f}.')\\n\\n self.train_percent_check = overfit_pct\\n self.val_percent_check = overfit_pct\\n self.test_percent_check = overfit_pct\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":77,"cells":{"instance_id":{"kind":"string","value":"wagtail__wagtail-9393"},"repo":{"kind":"string","value":"wagtail/wagtail"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Feature request: page editor minimap\n### Is your proposal related to a problem?\r\n\r\nLong page editing UIs are tedious to navigate.\r\n\r\n### Describe the solution you'd like\r\n\r\nThe minimap is a proposed feature from the [page editor 2022](https://github.com/wagtail/wagtail/discussions/7739) project, to help with navigation of those pages. We’re currently [looking for sponsors or contributors](https://wagtail.org/blog/build-update-on-new-page-editor/) to help deliver the minimap.\r\n\r\nThis new component serves different use cases:\r\n\r\n- For mouse users, the minimap presents all of the page’s sections (panels, potentially blocks, potentially fields) as a table of contents that can be reached at any point\r\n- For keyboard users, the minimap functions as a set of skip links – to jump straight to any part of the page\r\n- For sighted users, the minimap always displays a visual indicator of the user’s current position through the page structure\r\n- When opened, it also has a button to collapse all of the page’s sections at once.\r\n\r\nHere is what it looks like in practice:\r\n\r\n\r\n\r\n[Figma design link](https://www.figma.com/file/h67EsVXdWsfu38WGGxWfpi/Wagtail-Design-System?node-id=4859%3A44694)\r\n\r\n### Additional context\r\n\r\n- Expecting this to be built based on our upcoming refactor of field panels, to use a more semantic document outline, as well as having the ability to link to individual section headings\r\n- The collapsing is a feature of individual sections – \"collapse all\" just triggers the action for all of them at once.\r\n- The currently-active (visible?) sections of the page are shown in the minimap, using the IntersectionObserver API\n"},"gold_files":{"kind":"list like","value":["wagtail/admin/views/home.py"],"string":"[\n \"wagtail/admin/views/home.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["wagtail/admin/views/home.py::icons"],"string":"[\n \"wagtail/admin/views/home.py::icons\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/conf.py","text":"docs/conf.py\ndef setup(app)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py","text":"wagtail/admin/templatetags/wagtailadmin_tags.py\ndef breadcrumbs(context, page, url_name, url_root_name=None, include_self=True, is_expanded=False, page_perms=None, querystring_value=None, trailing_breadcrumb_title=None, classname=None)\ndef search_other(context, current=None)\ndef ellipsistrim(value, max_length)\ndef fieldtype(bound_field)\ndef widgettype(bound_field)\ndef _get_user_page_permissions(context)\ndef page_permissions(context, page) # Usage: {% page_permissions page as page_perms %}\ndef classnames(*classes) # Usage <div class=\"{% classnames \"w-base\" classname active|yesno:\"w-base--active,\ndef test_collection_is_public(context, collection) # Usage: {% test_collection_is_public collection as is_public %}\ndef test_page_is_public(context, page) # Usage: {% test_page_is_public page as is_public %}\ndef hook_output(hook_name) # Example: {% hook_output 'insert_editor_css' %}\ndef base_url_setting(default=None)\ndef allow_unicode_slugs()\nclass EscapeScriptNode\n def __init__(self, nodelist)\n def render(self, context)\n def handle(cls, parser, token)\ndef render_with_errors(bound_field) # Usage: {{ field|render_with_errors }} as opposed to {{ field }}.\ndef has_unrendered_errors(bound_field) # Return true if this field has errors that were not accounted for by render_with_\ndef cautious_slugify(value)\ndef querystring(context, **kwargs) # Print out the current querystring. Any keyword arguments to this template\ndef page_table_header_label(context, label=None, parent_page_title=None, **kwargs) # Wraps table_header_label to add a title attribute based on the parent page title\ndef table_header_label(context, label=None, sortable=True, ordering=None, sort_context_var='ordering', sort_param='ordering', sort_field=None, ascending_title_text=None, descending_title_text=None) # A label to go in a table header cell, optionally with a 'sort' link that alterna\ndef pagination_querystring(context, page_number, page_key='p') # Print out a querystring with an updated page number:\ndef paginate(context, page, base_url='', page_key='p', classnames='') # Print pagination previous/next links, and the page count. Take the\ndef page_listing_buttons(context, page, page_perms)\ndef page_header_buttons(context, page, page_perms)\ndef bulk_action_choices(context, app_label, model_name)\ndef message_level_tag(message) # Return the tag for this message's level as defined in\ndef message_tags(message)\ndef _abs(val)\ndef admin_urlquote(value)\ndef avatar_url(user, size=50, gravatar_only=False) # A template tag that receives a user and size and return\ndef js_translation_strings()\ndef notification_static(path) # Variant of the {% static %}` tag for use in notification emails - tries to form\ndef versioned_static(path) # Wrapper for Django's static file finder to append a cache-busting query paramete\ndef icon(name=None, class_name='icon', title=None, wrapped=False) # Abstracts away the actual icon implementation.\ndef timesince_simple(d) # Returns a simplified timesince:\nd","label":0},{"cid":"wagtail/admin/views/home.py","text":"wagtail/admin/views/home.py\nclass UpgradeNotificationPanel\n def get_upgrade_check_setting(self)\n def upgrade_check_lts_only(self)\n def get_context_data(self, parent_context: Mapping[str, Any])\n def render_html(self, parent_context: Mapping[str, Any]=None)\nclass WhatsNewInWagtailVersionPanel\n def get_whats_new_banner_setting(self)\n def get_dismissible_id(self)\n def get_context_data(self, parent_context: Mapping[str, Any])\n def is_shown(self, parent_context: Mapping[str, Any]=None)\n def render_html(self, parent_context: Mapping[str, Any]=None)\nclass PagesForModerationPanel\n def get_context_data(self, parent_context)\nclass UserPagesInWorkflowModerationPanel\n def get_context_data(self, parent_context)\nclass WorkflowPagesToModeratePanel\n def get_context_data(self, parent_context)\nclass LockedPagesPanel\n def get_context_data(self, parent_context)\nclass RecentEditsPanel\n def get_context_data(self, parent_context)\nclass HomeView\n def get_context_data(self, **kwargs)\n def get_media(self, panels=[])\n def get_panels(self)\n def get_site_details(self)\ndef error_test(request)\ndef default(request) # Called whenever a request comes in with the correct prefix (eg /admin/) but\ndef icons()\ndef sprite(request)","label":1},{"cid":"wagtail/blocks/base.py","text":"wagtail/blocks/base.py\nclass BaseBlock\n def __new__(mcs, name, bases, attrs)\nclass Block\n class Meta\n def __new__(cls, *args, **kwargs)\n def __init__(self, **kwargs)\n def set_name(self, name)\n def set_meta_options(self, opts) # Update this block's meta options (out of the ones designated as mutable) from th\n def value_from_datadict(self, data, files, prefix)\n def value_omitted_from_data(self, data, files, name) # Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only Stre\n def bind(self, value, prefix=None, errors=None) # Return a BoundBlock which represents the association of this block definition wi\n def get_default(self) # Return this block's default value (conventionally found in self.meta.default),\n def clean(self, value) # Validate value and return a cleaned version of it, or throw a ValidationError if\n def to_python(self, value) # Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) \n def bulk_to_python(self, values) # Apply the to_python conversion to a list of values. The default implementation s\n def get_prep_value(self, value) # The reverse of to_python; convert the python value into JSON-serialisable form.\n def get_form_state(self, value) # Convert a python value for this block into a JSON-serialisable representation co\n def get_context(self, value, parent_context=None) # Return a dict of context variables (derived from the block value and combined wi\n def get_template(self, context=None) # Return the template to use for rendering the block if specified on meta class.\n def render(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. By defaul\n def get_api_representation(self, value, context=None) # Can be used to customise the API response and defaults to the value returned by \n def render_basic(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. render() \n def get_searchable_content(self, value) # Returns a list of strings containing text content within this block to be used i\n def extract_references(self, value)\n def check(self, **kwargs) # Hook for the Django system checks framework -\n def _check_name(self, **kwargs) # Helper method called by container blocks as part of the system checks framework,\n def id_for_label(self, prefix) # Return the ID to be used as the 'for' attribute of <label> elements that refer t\n def required(self) # Flag used to determine whether labels for this block should display a 'required'\n def deconstruct(self)\n def __eq__(self, other) # Implement equality on block objects so that two blocks with matching definitions\nclass BoundBlock\n def __init__(self, block, value, prefix=None, errors=None)\n def render(self, context=None)\n def render_as_block(self, context=None) # Alias for render; the include_block tag will specifically check for the presence\n def id_","label":0},{"cid":"wagtail/query.py","text":"wagtail/query.py\nclass TreeQuerySet # Extends Treebeard's MP_NodeQuerySet with additional useful tree-related operatio\n def delete(self) # Redefine the delete method unbound, so we can set the queryset_only parameter.\n def descendant_of_q(self, other, inclusive=False)\n def descendant_of(self, other, inclusive=False) # This filters the QuerySet to only contain pages that descend from the specified \n def not_descendant_of(self, other, inclusive=False) # This filters the QuerySet to not contain any pages that descend from the specifi\n def child_of_q(self, other)\n def child_of(self, other) # This filters the QuerySet to only contain pages that are direct children of the \n def not_child_of(self, other) # This filters the QuerySet to not contain any pages that are direct children of t\n def ancestor_of_q(self, other, inclusive=False)\n def ancestor_of(self, other, inclusive=False) # This filters the QuerySet to only contain pages that are ancestors of the specif\n def not_ancestor_of(self, other, inclusive=False) # This filters the QuerySet to not contain any pages that are ancestors of the spe\n def parent_of_q(self, other)\n def parent_of(self, other) # This filters the QuerySet to only contain the parent of the specified page.\n def not_parent_of(self, other) # This filters the QuerySet to exclude the parent of the specified page.\n def sibling_of_q(self, other, inclusive=True)\n def sibling_of(self, other, inclusive=True) # This filters the QuerySet to only contain pages that are siblings of the specifi\n def not_sibling_of(self, other, inclusive=True) # This filters the QuerySet to not contain any pages that are siblings of the spec\nclass PageQuerySet\n def __init__(self, *args, **kwargs) # Set custom instance attributes\n def _clone(self) # Ensure clones inherit custom attribute values.\n def live_q(self)\n def live(self) # This filters the QuerySet to only contain published pages.\n def not_live(self) # This filters the QuerySet to only contain unpublished pages.\n def in_menu_q(self)\n def in_menu(self) # This filters the QuerySet to only contain pages that are in the menus.\n def not_in_menu(self) # This filters the QuerySet to only contain pages that are not in the menus.\n def page_q(self, other)\n def page(self, other) # This filters the QuerySet so it only contains the specified page.\n def not_page(self, other) # This filters the QuerySet so it doesn't contain the specified page.\n def type_q(self, *types)\n def type(self, *types) # This filters the QuerySet to only contain pages that are an instance\n def not_type(self, *types) # This filters the QuerySet to exclude any pages which are an instance of the spec\n def exact_type_q(self, *types)\n def exact_type(self, *types) # This filters the QuerySet to only contain pages that are an instance of the spec\n def not_exact_type(self, *types) # This filters the QuerySet to exclude any pages which ","label":0}],"string":"[\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\\ndef setup(app)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py\\ndef breadcrumbs(context, page, url_name, url_root_name=None, include_self=True, is_expanded=False, page_perms=None, querystring_value=None, trailing_breadcrumb_title=None, classname=None)\\ndef search_other(context, current=None)\\ndef ellipsistrim(value, max_length)\\ndef fieldtype(bound_field)\\ndef widgettype(bound_field)\\ndef _get_user_page_permissions(context)\\ndef page_permissions(context, page) # Usage: {% page_permissions page as page_perms %}\\ndef classnames(*classes) # Usage <div class=\\\"{% classnames \\\"w-base\\\" classname active|yesno:\\\"w-base--active,\\ndef test_collection_is_public(context, collection) # Usage: {% test_collection_is_public collection as is_public %}\\ndef test_page_is_public(context, page) # Usage: {% test_page_is_public page as is_public %}\\ndef hook_output(hook_name) # Example: {% hook_output 'insert_editor_css' %}\\ndef base_url_setting(default=None)\\ndef allow_unicode_slugs()\\nclass EscapeScriptNode\\n def __init__(self, nodelist)\\n def render(self, context)\\n def handle(cls, parser, token)\\ndef render_with_errors(bound_field) # Usage: {{ field|render_with_errors }} as opposed to {{ field }}.\\ndef has_unrendered_errors(bound_field) # Return true if this field has errors that were not accounted for by render_with_\\ndef cautious_slugify(value)\\ndef querystring(context, **kwargs) # Print out the current querystring. Any keyword arguments to this template\\ndef page_table_header_label(context, label=None, parent_page_title=None, **kwargs) # Wraps table_header_label to add a title attribute based on the parent page title\\ndef table_header_label(context, label=None, sortable=True, ordering=None, sort_context_var='ordering', sort_param='ordering', sort_field=None, ascending_title_text=None, descending_title_text=None) # A label to go in a table header cell, optionally with a 'sort' link that alterna\\ndef pagination_querystring(context, page_number, page_key='p') # Print out a querystring with an updated page number:\\ndef paginate(context, page, base_url='', page_key='p', classnames='') # Print pagination previous/next links, and the page count. Take the\\ndef page_listing_buttons(context, page, page_perms)\\ndef page_header_buttons(context, page, page_perms)\\ndef bulk_action_choices(context, app_label, model_name)\\ndef message_level_tag(message) # Return the tag for this message's level as defined in\\ndef message_tags(message)\\ndef _abs(val)\\ndef admin_urlquote(value)\\ndef avatar_url(user, size=50, gravatar_only=False) # A template tag that receives a user and size and return\\ndef js_translation_strings()\\ndef notification_static(path) # Variant of the {% static %}` tag for use in notification emails - tries to form\\ndef versioned_static(path) # Wrapper for Django's static file finder to append a cache-busting query paramete\\ndef icon(name=None, class_name='icon', title=None, wrapped=False) # Abstracts away the actual icon implementation.\\ndef timesince_simple(d) # Returns a simplified timesince:\\nd\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py\",\n \"text\": \"wagtail/admin/views/home.py\\nclass UpgradeNotificationPanel\\n def get_upgrade_check_setting(self)\\n def upgrade_check_lts_only(self)\\n def get_context_data(self, parent_context: Mapping[str, Any])\\n def render_html(self, parent_context: Mapping[str, Any]=None)\\nclass WhatsNewInWagtailVersionPanel\\n def get_whats_new_banner_setting(self)\\n def get_dismissible_id(self)\\n def get_context_data(self, parent_context: Mapping[str, Any])\\n def is_shown(self, parent_context: Mapping[str, Any]=None)\\n def render_html(self, parent_context: Mapping[str, Any]=None)\\nclass PagesForModerationPanel\\n def get_context_data(self, parent_context)\\nclass UserPagesInWorkflowModerationPanel\\n def get_context_data(self, parent_context)\\nclass WorkflowPagesToModeratePanel\\n def get_context_data(self, parent_context)\\nclass LockedPagesPanel\\n def get_context_data(self, parent_context)\\nclass RecentEditsPanel\\n def get_context_data(self, parent_context)\\nclass HomeView\\n def get_context_data(self, **kwargs)\\n def get_media(self, panels=[])\\n def get_panels(self)\\n def get_site_details(self)\\ndef error_test(request)\\ndef default(request) # Called whenever a request comes in with the correct prefix (eg /admin/) but\\ndef icons()\\ndef sprite(request)\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/blocks/base.py\",\n \"text\": \"wagtail/blocks/base.py\\nclass BaseBlock\\n def __new__(mcs, name, bases, attrs)\\nclass Block\\n class Meta\\n def __new__(cls, *args, **kwargs)\\n def __init__(self, **kwargs)\\n def set_name(self, name)\\n def set_meta_options(self, opts) # Update this block's meta options (out of the ones designated as mutable) from th\\n def value_from_datadict(self, data, files, prefix)\\n def value_omitted_from_data(self, data, files, name) # Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only Stre\\n def bind(self, value, prefix=None, errors=None) # Return a BoundBlock which represents the association of this block definition wi\\n def get_default(self) # Return this block's default value (conventionally found in self.meta.default),\\n def clean(self, value) # Validate value and return a cleaned version of it, or throw a ValidationError if\\n def to_python(self, value) # Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) \\n def bulk_to_python(self, values) # Apply the to_python conversion to a list of values. The default implementation s\\n def get_prep_value(self, value) # The reverse of to_python; convert the python value into JSON-serialisable form.\\n def get_form_state(self, value) # Convert a python value for this block into a JSON-serialisable representation co\\n def get_context(self, value, parent_context=None) # Return a dict of context variables (derived from the block value and combined wi\\n def get_template(self, context=None) # Return the template to use for rendering the block if specified on meta class.\\n def render(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. By defaul\\n def get_api_representation(self, value, context=None) # Can be used to customise the API response and defaults to the value returned by \\n def render_basic(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. render() \\n def get_searchable_content(self, value) # Returns a list of strings containing text content within this block to be used i\\n def extract_references(self, value)\\n def check(self, **kwargs) # Hook for the Django system checks framework -\\n def _check_name(self, **kwargs) # Helper method called by container blocks as part of the system checks framework,\\n def id_for_label(self, prefix) # Return the ID to be used as the 'for' attribute of <label> elements that refer t\\n def required(self) # Flag used to determine whether labels for this block should display a 'required'\\n def deconstruct(self)\\n def __eq__(self, other) # Implement equality on block objects so that two blocks with matching definitions\\nclass BoundBlock\\n def __init__(self, block, value, prefix=None, errors=None)\\n def render(self, context=None)\\n def render_as_block(self, context=None) # Alias for render; the include_block tag will specifically check for the presence\\n def id_\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py\",\n \"text\": \"wagtail/query.py\\nclass TreeQuerySet # Extends Treebeard's MP_NodeQuerySet with additional useful tree-related operatio\\n def delete(self) # Redefine the delete method unbound, so we can set the queryset_only parameter.\\n def descendant_of_q(self, other, inclusive=False)\\n def descendant_of(self, other, inclusive=False) # This filters the QuerySet to only contain pages that descend from the specified \\n def not_descendant_of(self, other, inclusive=False) # This filters the QuerySet to not contain any pages that descend from the specifi\\n def child_of_q(self, other)\\n def child_of(self, other) # This filters the QuerySet to only contain pages that are direct children of the \\n def not_child_of(self, other) # This filters the QuerySet to not contain any pages that are direct children of t\\n def ancestor_of_q(self, other, inclusive=False)\\n def ancestor_of(self, other, inclusive=False) # This filters the QuerySet to only contain pages that are ancestors of the specif\\n def not_ancestor_of(self, other, inclusive=False) # This filters the QuerySet to not contain any pages that are ancestors of the spe\\n def parent_of_q(self, other)\\n def parent_of(self, other) # This filters the QuerySet to only contain the parent of the specified page.\\n def not_parent_of(self, other) # This filters the QuerySet to exclude the parent of the specified page.\\n def sibling_of_q(self, other, inclusive=True)\\n def sibling_of(self, other, inclusive=True) # This filters the QuerySet to only contain pages that are siblings of the specifi\\n def not_sibling_of(self, other, inclusive=True) # This filters the QuerySet to not contain any pages that are siblings of the spec\\nclass PageQuerySet\\n def __init__(self, *args, **kwargs) # Set custom instance attributes\\n def _clone(self) # Ensure clones inherit custom attribute values.\\n def live_q(self)\\n def live(self) # This filters the QuerySet to only contain published pages.\\n def not_live(self) # This filters the QuerySet to only contain unpublished pages.\\n def in_menu_q(self)\\n def in_menu(self) # This filters the QuerySet to only contain pages that are in the menus.\\n def not_in_menu(self) # This filters the QuerySet to only contain pages that are not in the menus.\\n def page_q(self, other)\\n def page(self, other) # This filters the QuerySet so it only contains the specified page.\\n def not_page(self, other) # This filters the QuerySet so it doesn't contain the specified page.\\n def type_q(self, *types)\\n def type(self, *types) # This filters the QuerySet to only contain pages that are an instance\\n def not_type(self, *types) # This filters the QuerySet to exclude any pages which are an instance of the spec\\n def exact_type_q(self, *types)\\n def exact_type(self, *types) # This filters the QuerySet to only contain pages that are an instance of the spec\\n def not_exact_type(self, *types) # This filters the QuerySet to exclude any pages which \",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"docs/conf.py::setup","text":"docs/conf.py::setup\ndef setup(app):\n app.add_js_file(\"js/banner.js\")","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::breadcrumbs","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::breadcrumbs\n@register.inclusion_tag(\"wagtailadmin/shared/breadcrumbs.html\", takes_context=True)\ndef breadcrumbs(\n context,\n page,\n url_name,\n url_root_name=None,\n include_self=True,\n is_expanded=False,\n page_perms=None,\n querystring_value=None,\n trailing_breadcrumb_title=None,\n classname=None,\n):\n user = context[\"request\"].user\n\n # find the closest common ancestor of the pages that this user has direct explore permission\n # (i.e. add/edit/publish/lock) over; this will be the root of the breadcrumb\n cca = get_explorable_root_page(user)\n if not cca:\n return {\"pages\": Page.objects.none()}\n\n return {\n \"pages\": page.get_ancestors(inclusive=include_self)\n .descendant_of(cca, inclusive=True)\n .specific(),\n \"current_page\": page,\n \"is_expanded\": is_expanded,\n \"page_perms\": page_perms,\n \"querystring_value\": querystring_value or \"\",\n \"trailing_breadcrumb_title\": trailing_breadcrumb_title, # Only used in collapsible breadcrumb templates\n \"url_name\": url_name,\n \"url_root_name\": url_root_name,\n \"classname\": classname,\n }","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::search_other","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::search_other\n@register.inclusion_tag(\"wagtailadmin/shared/search_other.html\", takes_context=True)\ndef search_other(context, current=None):\n request = context[\"request\"]\n\n return {\n \"options_html\": admin_search_areas.render_html(request, current),\n \"request\": request,\n }","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::ellipsistrim","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::ellipsistrim\n@register.filter(\"ellipsistrim\")\ndef ellipsistrim(value, max_length):\n if len(value) > max_length:\n truncd_val = value[:max_length]\n if not len(value) == (max_length + 1) and value[max_length + 1] != \" \":\n truncd_val = truncd_val[: truncd_val.rfind(\" \")]\n return truncd_val + \"…\"\n return value","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::fieldtype","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::fieldtype\n@register.filter\ndef fieldtype(bound_field):\n try:\n return camelcase_to_underscore(bound_field.field.__class__.__name__)\n except AttributeError:\n try:\n return camelcase_to_underscore(bound_field.__class__.__name__)\n except AttributeError:\n return \"\"","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::widgettype","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::widgettype\n@register.filter\ndef widgettype(bound_field):\n try:\n return camelcase_to_underscore(bound_field.field.widget.__class__.__name__)\n except AttributeError:\n try:\n return camelcase_to_underscore(bound_field.widget.__class__.__name__)\n except AttributeError:\n return \"\"","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::_get_user_page_permissions","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::_get_user_page_permissions\ndef _get_user_page_permissions(context):\n # Create a UserPagePermissionsProxy object to represent the user's global permissions, and\n # cache it in the context for the duration of the page request, if one does not exist already\n if \"user_page_permissions\" not in context:\n context[\"user_page_permissions\"] = UserPagePermissionsProxy(\n context[\"request\"].user\n )\n\n return context[\"user_page_permissions\"]","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_permissions","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_permissions\n@register.simple_tag(takes_context=True)\ndef page_permissions(context, page):\n \"\"\"\n Usage: {% page_permissions page as page_perms %}\n Sets the variable 'page_perms' to a PagePermissionTester object that can be queried to find out\n what actions the current logged-in user can perform on the given page.\n \"\"\"\n return _get_user_page_permissions(context).for_page(page)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::classnames","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::classnames\n@register.simple_tag\ndef classnames(*classes):\n \"\"\"\n Usage <div class=\"{% classnames \"w-base\" classname active|yesno:\"w-base--active,\" any_other_var %}\"></div>\n Returns any args as a space-separated joined string for using in HTML class names.\n \"\"\"\n return \" \".join([classname.strip() for classname in classes if classname])","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::test_collection_is_public","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::test_collection_is_public\n@register.simple_tag(takes_context=True)\ndef test_collection_is_public(context, collection):\n \"\"\"\n Usage: {% test_collection_is_public collection as is_public %}\n Sets 'is_public' to True iff there are no collection view restrictions in place\n on this collection.\n Caches the list of collection view restrictions in the context, to avoid repeated\n DB queries on repeated calls.\n \"\"\"\n if \"all_collection_view_restrictions\" not in context:\n context[\n \"all_collection_view_restrictions\"\n ] = CollectionViewRestriction.objects.select_related(\"collection\").values_list(\n \"collection__name\", flat=True\n )\n\n is_private = collection.name in context[\"all_collection_view_restrictions\"]\n\n return not is_private","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::test_page_is_public","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::test_page_is_public\n@register.simple_tag(takes_context=True)\ndef test_page_is_public(context, page):\n \"\"\"\n Usage: {% test_page_is_public page as is_public %}\n Sets 'is_public' to True iff there are no page view restrictions in place on\n this page.\n Caches the list of page view restrictions on the request, to avoid repeated\n DB queries on repeated calls.\n \"\"\"\n if not hasattr(context[\"request\"], \"all_page_view_restriction_paths\"):\n context[\n \"request\"\n ].all_page_view_restriction_paths = PageViewRestriction.objects.select_related(\n \"page\"\n ).values_list(\n \"page__path\", flat=True\n )\n\n is_private = any(\n [\n page.path.startswith(restricted_path)\n for restricted_path in context[\"request\"].all_page_view_restriction_paths\n ]\n )\n\n return not is_private","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::hook_output","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::hook_output\n@register.simple_tag\ndef hook_output(hook_name):\n \"\"\"\n Example: {% hook_output 'insert_editor_css' %}\n Whenever we have a hook whose functions take no parameters and return a string, this tag can be used\n to output the concatenation of all of those return values onto the page.\n Note that the output is not escaped - it is the hook function's responsibility to escape unsafe content.\n \"\"\"\n snippets = [fn() for fn in hooks.get_hooks(hook_name)]\n return mark_safe(\"\".join(snippets))","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::base_url_setting","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::base_url_setting\n@register.simple_tag\ndef base_url_setting(default=None):\n return get_admin_base_url() or default","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::allow_unicode_slugs","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::allow_unicode_slugs\n@register.simple_tag\ndef allow_unicode_slugs():\n return getattr(settings, \"WAGTAIL_ALLOW_UNICODE_SLUGS\", True)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.__init__","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.__init__\n def __init__(self, nodelist):\n super().__init__()\n self.nodelist = nodelist","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.render","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.render\n def render(self, context):\n out = self.nodelist.render(context)\n return escape_script(out)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.handle","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.handle\n @classmethod\n def handle(cls, parser, token):\n nodelist = parser.parse((\"end\" + EscapeScriptNode.TAG_NAME,))\n parser.delete_first_token()\n return cls(nodelist)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::render_with_errors","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::render_with_errors\n@register.filter\ndef render_with_errors(bound_field):\n \"\"\"\n Usage: {{ field|render_with_errors }} as opposed to {{ field }}.\n If the field (a BoundField instance) has errors on it, and the associated widget implements\n a render_with_errors method, call that; otherwise, call the regular widget rendering mechanism.\n \"\"\"\n widget = bound_field.field.widget\n if bound_field.errors and hasattr(widget, \"render_with_errors\"):\n return widget.render_with_errors(\n bound_field.html_name,\n bound_field.value(),\n attrs={\"id\": bound_field.auto_id},\n errors=bound_field.errors,\n )\n else:\n return bound_field.as_widget()","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::has_unrendered_errors","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::has_unrendered_errors\n@register.filter\ndef has_unrendered_errors(bound_field):\n \"\"\"\n Return true if this field has errors that were not accounted for by render_with_errors, because\n the widget does not support the render_with_errors method\n \"\"\"\n return bound_field.errors and not hasattr(\n bound_field.field.widget, \"render_with_errors\"\n )","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::cautious_slugify","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::cautious_slugify\n@register.filter(is_safe=True)\n@stringfilter\ndef cautious_slugify(value):\n return _cautious_slugify(value)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::querystring","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::querystring\n@register.simple_tag(takes_context=True)\ndef querystring(context, **kwargs):\n \"\"\"\n Print out the current querystring. Any keyword arguments to this template\n tag will be added to the querystring before it is printed out.\n\n <a href=\"/page/{% querystring key='value' %}\">\n\n Will result in something like:\n\n <a href=\"/page/?foo=bar&key=value\">\n \"\"\"\n request = context[\"request\"]\n querydict = request.GET.copy()\n # Can't do querydict.update(kwargs), because QueryDict.update() appends to\n # the list of values, instead of replacing the values.\n for key, value in kwargs.items():\n if value is None:\n # Remove the key if the value is None\n querydict.pop(key, None)\n else:\n # Set the key otherwise\n querydict[key] = str(value)\n\n return \"?\" + querydict.urlencode()","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_table_header_label","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_table_header_label\n@register.simple_tag(takes_context=True)\ndef page_table_header_label(context, label=None, parent_page_title=None, **kwargs):\n \"\"\"\n Wraps table_header_label to add a title attribute based on the parent page title and the column label\n \"\"\"\n if label:\n translation_context = {\"parent\": parent_page_title, \"label\": label}\n ascending_title_text = (\n _(\n \"Sort the order of child pages within '%(parent)s' by '%(label)s' in ascending order.\"\n )\n % translation_context\n )\n descending_title_text = (\n _(\n \"Sort the order of child pages within '%(parent)s' by '%(label)s' in descending order.\"\n )\n % translation_context\n )\n else:\n ascending_title_text = None\n descending_title_text = None\n\n return table_header_label(\n context,\n label=label,\n ascending_title_text=ascending_title_text,\n descending_title_text=descending_title_text,\n **kwargs,\n )","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::table_header_label","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::table_header_label\n@register.simple_tag(takes_context=True)\ndef table_header_label(\n context,\n label=None,\n sortable=True,\n ordering=None,\n sort_context_var=\"ordering\",\n sort_param=\"ordering\",\n sort_field=None,\n ascending_title_text=None,\n descending_title_text=None,\n):\n \"\"\"\n A label to go in a table header cell, optionally with a 'sort' link that alternates between\n forward and reverse sorting\n\n label = label text\n ordering = current active ordering. If not specified, we will fetch it from the template context variable\n given by sort_context_var. (We don't fetch it from the URL because that wouldn't give the view method\n the opportunity to set a default)\n sort_param = URL parameter that indicates the current active ordering\n sort_field = the value for sort_param that indicates that sorting is currently on this column.\n For example, if sort_param='ordering' and sort_field='title', then a URL parameter of\n ordering=title indicates that the listing is ordered forwards on this column, and a URL parameter\n of ordering=-title indicated that the listing is ordered in reverse on this column\n ascending_title_text = title attribute to use on the link when the link action will sort in ascending order\n descending_title_text = title attribute to use on the link when the link action will sort in descending order\n\n To disable sorting on this column, set sortable=False or leave sort_field unspecified.\n \"\"\"\n if not sortable or not sort_field:\n # render label without a sort link\n return label\n\n if ordering is None:\n ordering = context.get(sort_context_var)\n reverse_sort_field = \"-%s\" % sort_field\n\n if ordering == sort_field:\n # currently ordering forwards on this column; link should change to reverse ordering\n attrs = {\n \"href\": querystring(context, **{sort_param: reverse_sort_field})","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::pagination_querystring","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::pagination_querystring\n@register.simple_tag(takes_context=True)\ndef pagination_querystring(context, page_number, page_key=\"p\"):\n \"\"\"\n Print out a querystring with an updated page number:\n\n {% if page.has_next_page %}\n <a href=\"{% pagination_link page.next_page_number %}\">Next page</a>\n {% endif %}\n \"\"\"\n return querystring(context, **{page_key: page_number})","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::paginate","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::paginate\n@register.inclusion_tag(\n \"wagtailadmin/pages/listing/_pagination.html\", takes_context=True\n)\ndef paginate(context, page, base_url=\"\", page_key=\"p\", classnames=\"\"):\n \"\"\"\n Print pagination previous/next links, and the page count. Take the\n following arguments:\n\n page\n The current page of results. This should be a Django pagination `Page`\n instance\n\n base_url\n The base URL of the next/previous page, with no querystring.\n This is optional, and defaults to the current page by just printing the\n querystring for the next/previous page.\n\n page_key\n The name of the page variable in the query string. Defaults to 'p'.\n\n classnames\n Extra classes to add to the next/previous links.\n \"\"\"\n request = context[\"request\"]\n return {\n \"base_url\": base_url,\n \"classnames\": classnames,\n \"request\": request,\n \"page\": page,\n \"page_key\": page_key,\n \"paginator\": page.paginator,\n }","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_listing_buttons","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_listing_buttons\n@register.inclusion_tag(\"wagtailadmin/pages/listing/_buttons.html\", takes_context=True)\ndef page_listing_buttons(context, page, page_perms):\n next_url = context.request.path\n button_hooks = hooks.get_hooks(\"register_page_listing_buttons\")\n\n buttons = []\n for hook in button_hooks:\n buttons.extend(hook(page, page_perms, next_url))\n\n buttons.sort()\n\n for hook in hooks.get_hooks(\"construct_page_listing_buttons\"):\n hook(buttons, page, page_perms, context)\n\n return {\"page\": page, \"buttons\": buttons}","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_header_buttons","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::page_header_buttons\n@register.inclusion_tag(\n \"wagtailadmin/pages/listing/_modern_dropdown.html\", takes_context=True\n)\ndef page_header_buttons(context, page, page_perms):\n next_url = context.request.path\n button_hooks = hooks.get_hooks(\"register_page_header_buttons\")\n\n buttons = []\n for hook in button_hooks:\n buttons.extend(hook(page, page_perms, next_url))\n\n buttons.sort()\n return {\n \"page\": page,\n \"buttons\": buttons,\n \"title\": _(\"Actions\"),\n \"icon_name\": \"dots-horizontal\",\n \"classes\": [\n \"w-flex\",\n \"w-justify-center\",\n \"w-items-center\",\n \"w-h-slim-header\",\n ],\n \"button_classes\": [\n \"w-p-0\",\n \"w-w-12\",\n \"w-h-full\",\n \"w-text-primary\",\n \"w-bg-transparent\",\n \"hover:w-scale-110\",\n \"w-transition\",\n \"w-outline-offset-inside\",\n \"w-relative\",\n \"w-z-30\",\n ],\n \"hide_title\": True,\n }","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::bulk_action_choices","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::bulk_action_choices\n@register.inclusion_tag(\"wagtailadmin/pages/listing/_buttons.html\", takes_context=True)\ndef bulk_action_choices(context, app_label, model_name):\n bulk_actions_list = list(\n bulk_action_registry.get_bulk_actions_for_model(app_label, model_name)\n )\n bulk_actions_list.sort(key=lambda x: x.action_priority)\n\n bulk_action_more_list = []\n if len(bulk_actions_list) > 4:\n bulk_action_more_list = bulk_actions_list[4:]\n bulk_actions_list = bulk_actions_list[:4]\n\n next_url = get_valid_next_url_from_request(context[\"request\"])\n if not next_url:\n next_url = context[\"request\"].path\n\n bulk_action_buttons = [\n PageListingButton(\n action.display_name,\n reverse(\n \"wagtail_bulk_action\", args=[app_label, model_name, action.action_type]\n )\n + \"?\"\n + urlencode({\"next\": next_url}),\n attrs={\"aria-label\": action.aria_label},\n priority=action.action_priority,\n classes=action.classes | {\"bulk-action-btn\"},\n )\n for action in bulk_actions_list\n ]\n\n if bulk_action_more_list:\n more_button = ButtonWithDropdown(\n label=_(\"More\"),\n attrs={\"title\": _(\"View more bulk actions\")},\n classes={\"bulk-actions-more\", \"dropup\"},\n button_classes={\"button\", \"button-small\"},\n buttons_data=[\n {\n \"label\": action.display_name,\n \"url\": reverse(\n \"wagtail_bulk_action\",\n args=[app_label, model_name, action.action_type],\n )\n + \"?\"\n + urlencode({\"next\": next_url}),\n \"attrs\": {\"aria-label\": action.aria_label},\n \"priority\": action.action_priority,\n \"classes\": {\"bulk-action-btn\"},\n }\n ","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::message_level_tag","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::message_level_tag\n@register.simple_tag\ndef message_level_tag(message):\n \"\"\"\n Return the tag for this message's level as defined in\n django.contrib.messages.constants.DEFAULT_TAGS, ignoring the project-level\n MESSAGE_TAGS setting (which end-users might customise).\n \"\"\"\n return MESSAGE_TAGS.get(message.level)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::message_tags","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::message_tags\n@register.simple_tag\ndef message_tags(message):\n level_tag = message_level_tag(message)\n if message.extra_tags and level_tag:\n return message.extra_tags + \" \" + level_tag\n elif message.extra_tags:\n return message.extra_tags\n elif level_tag:\n return level_tag\n else:\n return \"\"","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::_abs","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::_abs\n@register.filter(\"abs\")\ndef _abs(val):\n return abs(val)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::admin_urlquote","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::admin_urlquote\n@register.filter\ndef admin_urlquote(value):\n return quote(value)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::avatar_url","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::avatar_url\n@register.simple_tag\ndef avatar_url(user, size=50, gravatar_only=False):\n \"\"\"\n A template tag that receives a user and size and return\n the appropriate avatar url for that user.\n Example usage: {% avatar_url request.user 50 %}\n \"\"\"\n\n if (\n not gravatar_only\n and hasattr(user, \"wagtail_userprofile\")\n and user.wagtail_userprofile.avatar\n ):\n return user.wagtail_userprofile.avatar.url\n\n if hasattr(user, \"email\"):\n gravatar_url = get_gravatar_url(user.email, size=size)\n if gravatar_url is not None:\n return gravatar_url\n\n return versioned_static_func(\"wagtailadmin/images/default-user-avatar.png\")","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::js_translation_strings","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::js_translation_strings\n@register.simple_tag\ndef js_translation_strings():\n return mark_safe(json.dumps(get_js_translation_strings()))","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::notification_static","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::notification_static\n@register.simple_tag\ndef notification_static(path):\n \"\"\"\n Variant of the {% static %}` tag for use in notification emails - tries to form\n a full URL using WAGTAILADMIN_BASE_URL if the static URL isn't already a full URL.\n \"\"\"\n return urljoin(base_url_setting(), static(path))","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::versioned_static","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::versioned_static\n@register.simple_tag\ndef versioned_static(path):\n \"\"\"\n Wrapper for Django's static file finder to append a cache-busting query parameter\n that updates on each Wagtail version\n \"\"\"\n return versioned_static_func(path)","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::icon","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::icon\n@register.inclusion_tag(\"wagtailadmin/shared/icon.html\", takes_context=False)\ndef icon(name=None, class_name=\"icon\", title=None, wrapped=False):\n \"\"\"\n Abstracts away the actual icon implementation.\n\n Usage:\n {% load wagtailadmin_tags %}\n ...\n {% icon name=\"cogs\" class_name=\"icon--red\" title=\"Settings\" %}\n\n :param name: the icon name/id, required (string)\n :param class_name: default 'icon' (string)\n :param title: accessible label intended for screen readers (string)\n :return: Rendered template snippet (string)\n \"\"\"\n if not name:\n raise ValueError(\"You must supply an icon name\")\n\n return {\"name\": name, \"class_name\": class_name, \"title\": title, \"wrapped\": wrapped}","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_simple","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_simple\n@register.filter()\ndef timesince_simple(d):\n \"\"\"\n Returns a simplified timesince:\n 19 hours, 48 minutes ago -> 19 hours ago\n 1 week, 1 day ago -> 1 week ago\n 0 minutes ago -> just now\n \"\"\"\n # Note: Duplicate code in timesince_last_update()\n time_period = timesince(d).split(\",\")[0]\n if time_period == avoid_wrapping(_(\"0 minutes\")):\n return _(\"just now\")\n return _(\"%(time_period)s ago\") % {\"time_period\": time_period}","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_last_update","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_last_update\n@register.simple_tag\ndef timesince_last_update(\n last_update, show_time_prefix=False, user_display_name=\"\", use_shorthand=True\n):\n \"\"\"\n Returns:\n - the time of update if last_update is today, if show_time_prefix=True, the output will be prefixed with \"at \"\n - time since last update otherwise. Defaults to the simplified timesince,\n but can return the full string if needed\n \"\"\"\n # translation usage below is intentionally verbose to be easier to work with translations\n\n if last_update.date() == datetime.today().date():\n if timezone.is_aware(last_update):\n time_str = timezone.localtime(last_update).strftime(\"%H:%M\")\n else:\n time_str = last_update.strftime(\"%H:%M\")\n\n if show_time_prefix:\n if user_display_name:\n return _(\"at %(time)s by %(user_display_name)s\") % {\n \"time\": time_str,\n \"user_display_name\": user_display_name,\n }\n else:\n return _(\"at %(time)s\") % {\"time\": time_str}\n else:\n if user_display_name:\n return _(\"%(time)s by %(user_display_name)s\") % {\n \"time\": time_str,\n \"user_display_name\": user_display_name,\n }\n else:\n return _(\"%(time)s\") % {\"time\": time_str}\n else:\n if use_shorthand:\n # Note: Duplicate code in timesince_simple()\n time_period = timesince(last_update).split(\",\")[0]\n if time_period == avoid_wrapping(_(\"0 minutes\")):\n if user_display_name:\n return _(\"just now by %(user_display_name)s\") % {\n \"user_display_name\": user_display_name\n }\n else:\n return _(\"just now\")\n else:\n time_period = timesince(last_update)\n\n ","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::user_display_name","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::user_display_name\n@register.filter\ndef user_display_name(user):\n \"\"\"\n Returns the preferred display name for the given user object: the result of\n user.get_full_name() if implemented and non-empty, or user.get_username() otherwise.\n \"\"\"\n try:\n full_name = user.get_full_name().strip()\n if full_name:\n return full_name\n except AttributeError:\n pass\n\n try:\n return user.get_username()\n except AttributeError:\n # we were passed None or something else that isn't a valid user object; return\n # empty string to replicate the behaviour of {{ user.get_full_name|default:user.get_username }}\n return \"\"","label":0},{"cid":"wagtail/admin/templatetags/wagtailadmin_tags.py::format_content_type","text":"wagtail/admin/templatetags/wagtailadmin_tags.py::format_content_type\n@register.filter\ndef format_content_type(content_type):\n return get_content_type_label(content_type)","label":0},{"cid":"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_upgrade_check_setting","text":"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_upgrade_check_setting\n def get_upgrade_check_setting(self) -> Union[bool, str]:\n return getattr(settings, \"WAGTAIL_ENABLE_UPDATE_CHECK\", True)","label":0},{"cid":"wagtail/admin/views/home.py::UpgradeNotificationPanel.upgrade_check_lts_only","text":"wagtail/admin/views/home.py::UpgradeNotificationPanel.upgrade_check_lts_only\n def upgrade_check_lts_only(self) -> bool:\n upgrade_check = self.get_upgrade_check_setting()\n if isinstance(upgrade_check, str) and upgrade_check.lower() == \"lts\":\n return True\n return False","label":0},{"cid":"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_context_data","text":"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_context_data\n def get_context_data(self, parent_context: Mapping[str, Any]) -> Mapping[str, Any]:\n return {\"lts_only\": self.upgrade_check_lts_only()}","label":0},{"cid":"wagtail/admin/views/home.py::UpgradeNotificationPanel.render_html","text":"wagtail/admin/views/home.py::UpgradeNotificationPanel.render_html\n def render_html(self, parent_context: Mapping[str, Any] = None) -> str:\n if (\n parent_context[\"request\"].user.is_superuser\n and self.get_upgrade_check_setting()\n ):\n return super().render_html(parent_context)\n else:\n return \"\"","label":0},{"cid":"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_whats_new_banner_setting","text":"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_whats_new_banner_setting\n def get_whats_new_banner_setting(self) -> Union[bool, str]:\n return getattr(settings, \"WAGTAIL_ENABLE_WHATS_NEW_BANNER\", True)","label":0},{"cid":"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_dismissible_id","text":"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_dismissible_id\n def get_dismissible_id(self) -> str:\n return f\"{self.name}_{self._version}\"","label":0},{"cid":"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_context_data","text":"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_context_data\n def get_context_data(self, parent_context: Mapping[str, Any]) -> Mapping[str, Any]:\n return {\"dismissible_id\": self.get_dismissible_id(), \"version\": self._version}","label":0},{"cid":"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.is_shown","text":"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.is_shown\n def is_shown(self, parent_context: Mapping[str, Any] = None) -> bool:\n if not self.get_whats_new_banner_setting():\n return False\n\n profile = getattr(parent_context[\"request\"].user, \"wagtail_userprofile\", None)\n if profile and profile.dismissibles.get(self.get_dismissible_id()):\n return False\n\n return True","label":0},{"cid":"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.render_html","text":"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.render_html\n def render_html(self, parent_context: Mapping[str, Any] = None) -> str:\n if not self.is_shown(parent_context):\n return \"\"\n return super().render_html(parent_context)","label":0},{"cid":"wagtail/admin/views/home.py::PagesForModerationPanel.get_context_data","text":"wagtail/admin/views/home.py::PagesForModerationPanel.get_context_data\n def get_context_data(self, parent_context):\n request = parent_context[\"request\"]\n context = super().get_context_data(parent_context)\n user_perms = UserPagePermissionsProxy(request.user)\n context[\"page_revisions_for_moderation\"] = (\n user_perms.revisions_for_moderation()\n .select_related(\"user\")\n .order_by(\"-created_at\")\n )\n context[\"request\"] = request\n context[\"csrf_token\"] = parent_context[\"csrf_token\"]\n return context","label":0},{"cid":"wagtail/admin/views/home.py::UserPagesInWorkflowModerationPanel.get_context_data","text":"wagtail/admin/views/home.py::UserPagesInWorkflowModerationPanel.get_context_data\n def get_context_data(self, parent_context):\n request = parent_context[\"request\"]\n context = super().get_context_data(parent_context)\n if getattr(settings, \"WAGTAIL_WORKFLOW_ENABLED\", True):\n # Find in progress workflow states which are either requested by the user or on pages owned by the user\n context[\"workflow_states\"] = (\n WorkflowState.objects.active()\n .filter(Q(page__owner=request.user) | Q(requested_by=request.user))\n .select_related(\n \"page\",\n \"current_task_state\",\n \"current_task_state__task\",\n \"current_task_state__page_revision\",\n )\n .order_by(\"-current_task_state__started_at\")\n )\n else:\n context[\"workflow_states\"] = WorkflowState.objects.none()\n context[\"request\"] = request\n return context","label":0},{"cid":"wagtail/admin/views/home.py::WorkflowPagesToModeratePanel.get_context_data","text":"wagtail/admin/views/home.py::WorkflowPagesToModeratePanel.get_context_data\n def get_context_data(self, parent_context):\n request = parent_context[\"request\"]\n context = super().get_context_data(parent_context)\n if getattr(settings, \"WAGTAIL_WORKFLOW_ENABLED\", True):\n states = (\n TaskState.objects.reviewable_by(request.user)\n .select_related(\n \"page_revision\",\n \"task\",\n \"page_revision__user\",\n )\n .order_by(\"-started_at\")\n )\n context[\"states\"] = [\n (\n state,\n state.task.specific.get_actions(\n page=state.page_revision.content_object, user=request.user\n ),\n state.workflow_state.all_tasks_with_status(),\n )\n for state in states\n ]\n else:\n context[\"states\"] = []\n context[\"request\"] = request\n context[\"csrf_token\"] = parent_context[\"csrf_token\"]\n return context","label":0},{"cid":"wagtail/admin/views/home.py::LockedPagesPanel.get_context_data","text":"wagtail/admin/views/home.py::LockedPagesPanel.get_context_data\n def get_context_data(self, parent_context):\n request = parent_context[\"request\"]\n context = super().get_context_data(parent_context)\n context.update(\n {\n \"locked_pages\": Page.objects.filter(\n locked=True,\n locked_by=request.user,\n ),\n \"can_remove_locks\": UserPagePermissionsProxy(\n request.user\n ).can_remove_locks(),\n \"request\": request,\n \"csrf_token\": parent_context[\"csrf_token\"],\n }\n )\n return context","label":0},{"cid":"wagtail/admin/views/home.py::RecentEditsPanel.get_context_data","text":"wagtail/admin/views/home.py::RecentEditsPanel.get_context_data\n def get_context_data(self, parent_context):\n request = parent_context[\"request\"]\n context = super().get_context_data(parent_context)\n\n # Last n edited pages\n edit_count = getattr(settings, \"WAGTAILADMIN_RECENT_EDITS_LIMIT\", 5)\n if connection.vendor == \"mysql\":\n # MySQL can't handle the subselect created by the ORM version -\n # it fails with \"This version of MySQL doesn't yet support 'LIMIT & IN/ALL/ANY/SOME subquery'\"\n last_edits = Revision.objects.raw(\n \"\"\"\n SELECT wr.* FROM\n wagtailcore_revision wr JOIN (\n SELECT max(created_at) AS max_created_at, object_id FROM\n wagtailcore_revision WHERE user_id = %s AND base_content_type_id = %s GROUP BY object_id ORDER BY max_created_at DESC LIMIT %s\n ) AS max_rev ON max_rev.max_created_at = wr.created_at ORDER BY wr.created_at DESC\n \"\"\",\n [\n User._meta.pk.get_db_prep_value(request.user.pk, connection),\n get_default_page_content_type().id,\n edit_count,\n ],\n )\n else:\n last_edits_dates = (\n Revision.page_revisions.filter(user=request.user)\n .values(\"object_id\")\n .annotate(latest_date=Max(\"created_at\"))\n .order_by(\"-latest_date\")\n .values(\"latest_date\")[:edit_count]\n )\n last_edits = Revision.page_revisions.filter(\n created_at__in=last_edits_dates\n ).order_by(\"-created_at\")\n\n # The revision's object_id is a string, so cast it to int first.\n page_keys = [int(pr.object_id) for pr in last_edits]\n pages = Page.objects.specific().in_bulk(page_keys)\n context[\"last_edits\"] = []\n for revision in last_edits","label":0},{"cid":"wagtail/admin/views/home.py::HomeView.get_context_data","text":"wagtail/admin/views/home.py::HomeView.get_context_data\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n panels = self.get_panels()\n site_details = self.get_site_details()\n\n context[\"media\"] = self.get_media(panels)\n context[\"panels\"] = sorted(panels, key=lambda p: p.order)\n context[\"user\"] = self.request.user\n\n return {**context, **site_details}","label":0},{"cid":"wagtail/admin/views/home.py::HomeView.get_media","text":"wagtail/admin/views/home.py::HomeView.get_media\n def get_media(self, panels=[]):\n media = Media()\n\n for panel in panels:\n media += panel.media\n\n return media","label":0},{"cid":"wagtail/admin/views/home.py::HomeView.get_panels","text":"wagtail/admin/views/home.py::HomeView.get_panels\n def get_panels(self):\n request = self.request\n panels = [\n SiteSummaryPanel(request),\n WhatsNewInWagtailVersionPanel(),\n UpgradeNotificationPanel(),\n WorkflowPagesToModeratePanel(),\n PagesForModerationPanel(),\n UserPagesInWorkflowModerationPanel(),\n RecentEditsPanel(),\n LockedPagesPanel(),\n ]\n\n for fn in hooks.get_hooks(\"construct_homepage_panels\"):\n fn(request, panels)\n\n return panels","label":0},{"cid":"wagtail/admin/views/home.py::HomeView.get_site_details","text":"wagtail/admin/views/home.py::HomeView.get_site_details\n def get_site_details(self):\n request = self.request\n site = get_site_for_user(request.user)\n\n return {\n \"root_page\": site[\"root_page\"],\n \"root_site\": site[\"root_site\"],\n \"site_name\": site[\"site_name\"],\n }","label":0},{"cid":"wagtail/admin/views/home.py::error_test","text":"wagtail/admin/views/home.py::error_test\ndef error_test(request):\n raise Exception(\"This is a test of the emergency broadcast system.\")","label":0},{"cid":"wagtail/admin/views/home.py::default","text":"wagtail/admin/views/home.py::default\n@permission_required(\"wagtailadmin.access_admin\", login_url=\"wagtailadmin_login\")\ndef default(request):\n \"\"\"\n Called whenever a request comes in with the correct prefix (eg /admin/) but\n doesn't actually correspond to a Wagtail view.\n\n For authenticated users, it'll raise a 404 error. Anonymous users will be\n redirected to the login page.\n \"\"\"\n raise Http404","label":0},{"cid":"wagtail/admin/views/home.py::icons","text":"wagtail/admin/views/home.py::icons\ndef icons():\n global _icons_html\n if _icons_html is None:\n icon_hooks = hooks.get_hooks(\"register_icons\")\n all_icons = sorted(\n itertools.chain.from_iterable(hook([]) for hook in icon_hooks)\n )\n combined_icon_markup = \"\"\n for icon in all_icons:\n combined_icon_markup += render_to_string(icon).replace(\"svg\", \"symbol\")\n\n _full_sprite_html = render_to_string(\n \"wagtailadmin/shared/icons.html\", {\"icons\": combined_icon_markup}\n )\n return _full_sprite_html","label":1},{"cid":"wagtail/admin/views/home.py::sprite","text":"wagtail/admin/views/home.py::sprite\ndef sprite(request):\n return HttpResponse(icons())","label":0},{"cid":"wagtail/blocks/base.py::BaseBlock.__new__","text":"wagtail/blocks/base.py::BaseBlock.__new__\n def __new__(mcs, name, bases, attrs):\n meta_class = attrs.pop(\"Meta\", None)\n\n cls = super(BaseBlock, mcs).__new__(mcs, name, bases, attrs)\n\n # Get all the Meta classes from all the bases\n meta_class_bases = [meta_class] + [\n getattr(base, \"_meta_class\", None) for base in bases\n ]\n meta_class_bases = tuple(filter(bool, meta_class_bases))\n cls._meta_class = type(str(name + \"Meta\"), meta_class_bases, {})\n\n return cls","label":0},{"cid":"wagtail/blocks/base.py::Block.__new__","text":"wagtail/blocks/base.py::Block.__new__\n def __new__(cls, *args, **kwargs):\n # adapted from django.utils.deconstruct.deconstructible; capture the arguments\n # so that we can return them in the 'deconstruct' method\n obj = super(Block, cls).__new__(cls)\n obj._constructor_args = (args, kwargs)\n return obj","label":0},{"cid":"wagtail/blocks/base.py::Block.__init__","text":"wagtail/blocks/base.py::Block.__init__\n def __init__(self, **kwargs):\n if \"classname\" in self._constructor_args[1]:\n # Adding this so that migrations are not triggered\n # when form_classname is used instead of classname\n # in the initialisation of the FieldBlock\n classname = self._constructor_args[1].pop(\"classname\")\n self._constructor_args[1].setdefault(\"form_classname\", classname)\n\n self.meta = self._meta_class()\n\n for attr, value in kwargs.items():\n setattr(self.meta, attr, value)\n\n # Increase the creation counter, and save our local copy.\n self.creation_counter = Block.creation_counter\n Block.creation_counter += 1\n self.definition_prefix = \"blockdef-%d\" % self.creation_counter\n\n self.label = self.meta.label or \"\"","label":0},{"cid":"wagtail/blocks/base.py::Block.set_name","text":"wagtail/blocks/base.py::Block.set_name\n def set_name(self, name):\n self.name = name\n if not self.meta.label:\n self.label = capfirst(force_str(name).replace(\"_\", \" \"))","label":0},{"cid":"wagtail/blocks/base.py::Block.set_meta_options","text":"wagtail/blocks/base.py::Block.set_meta_options\n def set_meta_options(self, opts):\n \"\"\"\n Update this block's meta options (out of the ones designated as mutable) from the given dict.\n Used by the StreamField constructor to pass on kwargs that are to be handled by the block,\n since the block object has already been created by that point, e.g.:\n body = StreamField(SomeStreamBlock(), max_num=5)\n \"\"\"\n for attr, value in opts.items():\n if attr in self.MUTABLE_META_ATTRIBUTES:\n setattr(self.meta, attr, value)\n else:\n raise TypeError(\n \"set_meta_options received unexpected option: %r\" % attr\n )","label":0},{"cid":"wagtail/blocks/base.py::Block.value_from_datadict","text":"wagtail/blocks/base.py::Block.value_from_datadict\n def value_from_datadict(self, data, files, prefix):\n raise NotImplementedError(\"%s.value_from_datadict\" % self.__class__)","label":0},{"cid":"wagtail/blocks/base.py::Block.value_omitted_from_data","text":"wagtail/blocks/base.py::Block.value_omitted_from_data\n def value_omitted_from_data(self, data, files, name):\n \"\"\"\n Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only StreamBlock)\n to inform ModelForm logic on Django >=1.10.2 whether the field is absent from the form\n submission (and should therefore revert to the field default).\n \"\"\"\n return name not in data","label":0},{"cid":"wagtail/blocks/base.py::Block.bind","text":"wagtail/blocks/base.py::Block.bind\n def bind(self, value, prefix=None, errors=None):\n \"\"\"\n Return a BoundBlock which represents the association of this block definition with a value\n and a prefix (and optionally, a ValidationError to be rendered).\n BoundBlock primarily exists as a convenience to allow rendering within templates:\n bound_block.render() rather than blockdef.render(value, prefix) which can't be called from\n within a template.\n \"\"\"\n return BoundBlock(self, value, prefix=prefix, errors=errors)","label":0},{"cid":"wagtail/blocks/base.py::Block.get_default","text":"wagtail/blocks/base.py::Block.get_default\n def get_default(self):\n \"\"\"\n Return this block's default value (conventionally found in self.meta.default),\n converted to the value type expected by this block. This caters for the case\n where that value type is not something that can be expressed statically at\n model definition type (e.g. something like StructValue which incorporates a\n pointer back to the block definion object).\n \"\"\"\n return self.meta.default","label":0},{"cid":"wagtail/blocks/base.py::Block.clean","text":"wagtail/blocks/base.py::Block.clean\n def clean(self, value):\n \"\"\"\n Validate value and return a cleaned version of it, or throw a ValidationError if validation fails.\n The thrown ValidationError instance will subsequently be passed to render() to display the\n error message; the ValidationError must therefore include all detail necessary to perform that\n rendering, such as identifying the specific child block(s) with errors, in the case of nested\n blocks. (It is suggested that you use the 'params' attribute for this; using error_list /\n error_dict is unreliable because Django tends to hack around with these when nested.)\n \"\"\"\n return value","label":0},{"cid":"wagtail/blocks/base.py::Block.to_python","text":"wagtail/blocks/base.py::Block.to_python\n def to_python(self, value):\n \"\"\"\n Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) Python value to be\n used in the rest of the block API and within front-end templates . In simple cases this might be\n the value itself; alternatively, it might be a 'smart' version of the value which behaves mostly\n like the original value but provides a native HTML rendering when inserted into a template; or it\n might be something totally different (e.g. an image chooser will use the image ID as the clean\n value, and turn this back into an actual image object here).\n \"\"\"\n return value","label":0},{"cid":"wagtail/blocks/base.py::Block.bulk_to_python","text":"wagtail/blocks/base.py::Block.bulk_to_python\n def bulk_to_python(self, values):\n \"\"\"\n Apply the to_python conversion to a list of values. The default implementation simply\n iterates over the list; subclasses may optimise this, e.g. by combining database lookups\n into a single query.\n \"\"\"\n return [self.to_python(value) for value in values]","label":0},{"cid":"wagtail/blocks/base.py::Block.get_prep_value","text":"wagtail/blocks/base.py::Block.get_prep_value\n def get_prep_value(self, value):\n \"\"\"\n The reverse of to_python; convert the python value into JSON-serialisable form.\n \"\"\"\n return value","label":0},{"cid":"wagtail/blocks/base.py::Block.get_form_state","text":"wagtail/blocks/base.py::Block.get_form_state\n def get_form_state(self, value):\n \"\"\"\n Convert a python value for this block into a JSON-serialisable representation containing\n all the data needed to present the value in a form field, to be received by the block's\n client-side component. Examples of where this conversion is not trivial include rich text\n (where it needs to be supplied in a format that the editor can process, e.g. ContentState\n for Draftail) and page / image / document choosers (where it needs to include all displayed\n data for the selected item, such as title or thumbnail).\n \"\"\"\n return value","label":0},{"cid":"wagtail/blocks/base.py::Block.get_context","text":"wagtail/blocks/base.py::Block.get_context\n def get_context(self, value, parent_context=None):\n \"\"\"\n Return a dict of context variables (derived from the block value and combined with the parent_context)\n to be used as the template context when rendering this value through a template.\n \"\"\"\n\n context = parent_context or {}\n context.update(\n {\n \"self\": value,\n self.TEMPLATE_VAR: value,\n }\n )\n return context","label":0},{"cid":"wagtail/blocks/base.py::Block.get_template","text":"wagtail/blocks/base.py::Block.get_template\n def get_template(self, context=None):\n \"\"\"\n Return the template to use for rendering the block if specified on meta class.\n This extraction was added to make dynamic templates possible if you override this method\n \"\"\"\n return getattr(self.meta, \"template\", None)","label":0},{"cid":"wagtail/blocks/base.py::Block.render","text":"wagtail/blocks/base.py::Block.render\n def render(self, value, context=None):\n \"\"\"\n Return a text rendering of 'value', suitable for display on templates. By default, this will\n use a template (with the passed context, supplemented by the result of get_context) if a\n 'template' property is specified on the block, and fall back on render_basic otherwise.\n \"\"\"\n template = self.get_template(context=context)\n if not template:\n return self.render_basic(value, context=context)\n\n if context is None:\n new_context = self.get_context(value)\n else:\n new_context = self.get_context(value, parent_context=dict(context))\n\n return mark_safe(render_to_string(template, new_context))","label":0},{"cid":"wagtail/blocks/base.py::Block.get_api_representation","text":"wagtail/blocks/base.py::Block.get_api_representation\n def get_api_representation(self, value, context=None):\n \"\"\"\n Can be used to customise the API response and defaults to the value returned by get_prep_value.\n \"\"\"\n return self.get_prep_value(value)","label":0},{"cid":"wagtail/blocks/base.py::Block.render_basic","text":"wagtail/blocks/base.py::Block.render_basic\n def render_basic(self, value, context=None):\n \"\"\"\n Return a text rendering of 'value', suitable for display on templates. render() will fall back on\n this if the block does not define a 'template' property.\n \"\"\"\n return force_str(value)","label":0},{"cid":"wagtail/blocks/base.py::Block.get_searchable_content","text":"wagtail/blocks/base.py::Block.get_searchable_content\n def get_searchable_content(self, value):\n \"\"\"\n Returns a list of strings containing text content within this block to be used in a search engine.\n \"\"\"\n return []","label":0},{"cid":"wagtail/blocks/base.py::Block.extract_references","text":"wagtail/blocks/base.py::Block.extract_references\n def extract_references(self, value):\n return []","label":0},{"cid":"wagtail/blocks/base.py::Block.check","text":"wagtail/blocks/base.py::Block.check\n def check(self, **kwargs):\n \"\"\"\n Hook for the Django system checks framework -\n returns a list of django.core.checks.Error objects indicating validity errors in the block\n \"\"\"\n return []","label":0},{"cid":"wagtail/blocks/base.py::Block._check_name","text":"wagtail/blocks/base.py::Block._check_name\n def _check_name(self, **kwargs):\n \"\"\"\n Helper method called by container blocks as part of the system checks framework,\n to validate that this block's name is a valid identifier.\n (Not called universally, because not all blocks need names)\n \"\"\"\n errors = []\n if not self.name:\n errors.append(\n checks.Error(\n \"Block name %r is invalid\" % self.name,\n hint=\"Block name cannot be empty\",\n obj=kwargs.get(\"field\", self),\n id=\"wagtailcore.E001\",\n )\n )\n\n if \" \" in self.name:\n errors.append(\n checks.Error(\n \"Block name %r is invalid\" % self.name,\n hint=\"Block names cannot contain spaces\",\n obj=kwargs.get(\"field\", self),\n id=\"wagtailcore.E001\",\n )\n )\n\n if \"-\" in self.name:\n errors.append(\n checks.Error(\n \"Block name %r is invalid\" % self.name,\n \"Block names cannot contain dashes\",\n obj=kwargs.get(\"field\", self),\n id=\"wagtailcore.E001\",\n )\n )\n\n if self.name and self.name[0].isdigit():\n errors.append(\n checks.Error(\n \"Block name %r is invalid\" % self.name,\n \"Block names cannot begin with a digit\",\n obj=kwargs.get(\"field\", self),\n id=\"wagtailcore.E001\",\n )\n )\n\n if not errors and not re.match(r\"^[_a-zA-Z][_a-zA-Z0-9]*$\", self.name):\n errors.append(\n checks.Error(\n \"Block name %r is invalid\" % self.name,\n \"Block names should follow standard Python conventions for \"\n \"variable na","label":0},{"cid":"wagtail/blocks/base.py::Block.id_for_label","text":"wagtail/blocks/base.py::Block.id_for_label\n def id_for_label(self, prefix):\n \"\"\"\n Return the ID to be used as the 'for' attribute of <label> elements that refer to this block,\n when the given field prefix is in use. Return None if no 'for' attribute should be used.\n \"\"\"\n return None","label":0},{"cid":"wagtail/blocks/base.py::Block.required","text":"wagtail/blocks/base.py::Block.required\n @property\n def required(self):\n \"\"\"\n Flag used to determine whether labels for this block should display a 'required' asterisk.\n False by default, since Block does not provide any validation of its own - it's up to subclasses\n to define what required-ness means.\n \"\"\"\n return False","label":0},{"cid":"wagtail/blocks/base.py::Block.deconstruct","text":"wagtail/blocks/base.py::Block.deconstruct\n def deconstruct(self):\n # adapted from django.utils.deconstruct.deconstructible\n module_name = self.__module__\n name = self.__class__.__name__\n\n # Make sure it's actually there and not an inner class\n module = import_module(module_name)\n if not hasattr(module, name):\n raise ValueError(\n \"Could not find object %s in %s.\\n\"\n \"Please note that you cannot serialize things like inner \"\n \"classes. Please move the object into the main module \"\n \"body to use migrations.\\n\" % (name, module_name)\n )\n\n # if the module defines a DECONSTRUCT_ALIASES dictionary, see if the class has an entry in there;\n # if so, use that instead of the real path\n try:\n path = module.DECONSTRUCT_ALIASES[self.__class__]\n except (AttributeError, KeyError):\n path = \"%s.%s\" % (module_name, name)\n\n return (\n path,\n self._constructor_args[0],\n self._constructor_args[1],\n )","label":0},{"cid":"wagtail/blocks/base.py::Block.__eq__","text":"wagtail/blocks/base.py::Block.__eq__\n def __eq__(self, other):\n \"\"\"\n Implement equality on block objects so that two blocks with matching definitions are considered\n equal. Block objects are intended to be immutable with the exception of set_name() and any meta\n attributes identified in MUTABLE_META_ATTRIBUTES, so checking these along with the result of\n deconstruct (which captures the constructor arguments) is sufficient to identify (valid) differences.\n\n This was originally necessary as a workaround for https://code.djangoproject.com/ticket/24340\n in Django <1.9; the deep_deconstruct function used to detect changes for migrations did not\n recurse into the block lists, and left them as Block instances. This __eq__ method therefore\n came into play when identifying changes within migrations.\n\n As of Django >=1.9, this *probably* isn't required any more. However, it may be useful in\n future as a way of identifying blocks that can be re-used within StreamField definitions\n (https://github.com/wagtail/wagtail/issues/4298#issuecomment-367656028).\n \"\"\"\n\n if not isinstance(other, Block):\n # if the other object isn't a block at all, it clearly isn't equal.\n return False\n\n # Note that we do not require the two blocks to be of the exact same class. This is because\n # we may wish the following blocks to be considered equal:\n #\n # class FooBlock(StructBlock):\n # first_name = CharBlock()\n # surname = CharBlock()\n #\n # class BarBlock(StructBlock):\n # first_name = CharBlock()\n # surname = CharBlock()\n #\n # FooBlock() == BarBlock() == StructBlock([('first_name', CharBlock()), ('surname': CharBlock())])\n #\n # For this to work, StructBlock will need to ensure that 'deconstruct' returns the same s","label":0},{"cid":"wagtail/blocks/base.py::BoundBlock.__init__","text":"wagtail/blocks/base.py::BoundBlock.__init__\n def __init__(self, block, value, prefix=None, errors=None):\n self.block = block\n self.value = value\n self.prefix = prefix\n self.errors = errors","label":0},{"cid":"wagtail/blocks/base.py::BoundBlock.render","text":"wagtail/blocks/base.py::BoundBlock.render\n def render(self, context=None):\n return self.block.render(self.value, context=context)","label":0},{"cid":"wagtail/blocks/base.py::BoundBlock.render_as_block","text":"wagtail/blocks/base.py::BoundBlock.render_as_block\n def render_as_block(self, context=None):\n \"\"\"\n Alias for render; the include_block tag will specifically check for the presence of a method\n with this name. (This is because {% include_block %} is just as likely to be invoked on a bare\n value as a BoundBlock. If we looked for a `render` method instead, we'd run the risk of finding\n an unrelated method that just happened to have that name - for example, when called on a\n PageChooserBlock it could end up calling page.render.\n \"\"\"\n return self.block.render(self.value, context=context)","label":0},{"cid":"wagtail/blocks/base.py::BoundBlock.id_for_label","text":"wagtail/blocks/base.py::BoundBlock.id_for_label\n def id_for_label(self):\n return self.block.id_for_label(self.prefix)","label":0},{"cid":"wagtail/blocks/base.py::BoundBlock.__str__","text":"wagtail/blocks/base.py::BoundBlock.__str__\n def __str__(self):\n \"\"\"Render the value according to the block's native rendering\"\"\"\n return self.block.render(self.value)","label":0},{"cid":"wagtail/blocks/base.py::BoundBlock.__repr__","text":"wagtail/blocks/base.py::BoundBlock.__repr__\n def __repr__(self):\n return \"<block %s: %r>\" % (\n self.block.name or type(self.block).__name__,\n self.value,\n )","label":0},{"cid":"wagtail/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__","text":"wagtail/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__\n def __new__(mcs, name, bases, attrs):\n # Collect sub-blocks declared on the current class.\n # These are available on the class as `declared_blocks`\n current_blocks = []\n for key, value in list(attrs.items()):\n if isinstance(value, Block):\n current_blocks.append((key, value))\n value.set_name(key)\n attrs.pop(key)\n current_blocks.sort(key=lambda x: x[1].creation_counter)\n attrs[\"declared_blocks\"] = collections.OrderedDict(current_blocks)\n\n new_class = super(DeclarativeSubBlocksMetaclass, mcs).__new__(\n mcs, name, bases, attrs\n )\n\n # Walk through the MRO, collecting all inherited sub-blocks, to make\n # the combined `base_blocks`.\n base_blocks = collections.OrderedDict()\n for base in reversed(new_class.__mro__):\n # Collect sub-blocks from base class.\n if hasattr(base, \"declared_blocks\"):\n base_blocks.update(base.declared_blocks)\n\n # Field shadowing.\n for attr, value in base.__dict__.items():\n if value is None and attr in base_blocks:\n base_blocks.pop(attr)\n new_class.base_blocks = base_blocks\n\n return new_class","label":0},{"cid":"wagtail/blocks/base.py::BlockWidget.__init__","text":"wagtail/blocks/base.py::BlockWidget.__init__\n def __init__(self, block_def, attrs=None):\n super().__init__(attrs=attrs)\n self.block_def = block_def\n self._js_context = None","label":0},{"cid":"wagtail/blocks/base.py::BlockWidget._build_block_json","text":"wagtail/blocks/base.py::BlockWidget._build_block_json\n def _build_block_json(self):\n self._js_context = JSContext()\n self._block_json = json.dumps(self._js_context.pack(self.block_def))","label":0},{"cid":"wagtail/blocks/base.py::BlockWidget.js_context","text":"wagtail/blocks/base.py::BlockWidget.js_context\n @property\n def js_context(self):\n if self._js_context is None:\n self._build_block_json()\n\n return self._js_context","label":0},{"cid":"wagtail/blocks/base.py::BlockWidget.block_json","text":"wagtail/blocks/base.py::BlockWidget.block_json\n @property\n def block_json(self):\n if self._js_context is None:\n self._build_block_json()\n\n return self._block_json","label":0},{"cid":"wagtail/blocks/base.py::BlockWidget.id_for_label","text":"wagtail/blocks/base.py::BlockWidget.id_for_label\n def id_for_label(self, prefix):\n # Delegate the job of choosing a label ID to the top-level block.\n # (In practice, the top-level block will typically be a StreamBlock, which returns None.)\n return self.block_def.id_for_label(prefix)","label":0},{"cid":"wagtail/blocks/base.py::BlockWidget.render_with_errors","text":"wagtail/blocks/base.py::BlockWidget.render_with_errors\n def render_with_errors(self, name, value, attrs=None, errors=None, renderer=None):\n value_json = json.dumps(self.block_def.get_form_state(value))\n\n if errors:\n errors_json = json.dumps(self.js_context.pack(errors.as_data()))\n else:\n errors_json = \"[]\"\n\n return format_html(\n \"\"\"\n <div id=\"{id}\" data-block=\"{block_json}\" data-value=\"{value_json}\" data-errors=\"{errors_json}\"></div>\n <script>\n initBlockWidget('{id}');\n </script>\n \"\"\",\n id=name,\n block_json=self.block_json,\n value_json=value_json,\n errors_json=errors_json,\n )","label":0},{"cid":"wagtail/query.py::TreeQuerySet.delete","text":"wagtail/query.py::TreeQuerySet.delete\n def delete(self):\n \"\"\"Redefine the delete method unbound, so we can set the queryset_only parameter.\"\"\"\n super().delete()","label":0},{"cid":"wagtail/query.py::TreeQuerySet.descendant_of_q","text":"wagtail/query.py::TreeQuerySet.descendant_of_q\n def descendant_of_q(self, other, inclusive=False):\n q = Q(path__startswith=other.path) & Q(depth__gte=other.depth)\n\n if not inclusive:\n q &= ~Q(pk=other.pk)\n\n return q","label":0},{"cid":"wagtail/query.py::TreeQuerySet.descendant_of","text":"wagtail/query.py::TreeQuerySet.descendant_of\n def descendant_of(self, other, inclusive=False):\n \"\"\"\n This filters the QuerySet to only contain pages that descend from the specified page.\n\n If inclusive is set to True, it will also contain the page itself (instead of just its descendants).\n \"\"\"\n return self.filter(self.descendant_of_q(other, inclusive))","label":0},{"cid":"wagtail/query.py::TreeQuerySet.not_descendant_of","text":"wagtail/query.py::TreeQuerySet.not_descendant_of\n def not_descendant_of(self, other, inclusive=False):\n \"\"\"\n This filters the QuerySet to not contain any pages that descend from the specified page.\n\n If inclusive is set to True, it will also exclude the specified page.\n \"\"\"\n return self.exclude(self.descendant_of_q(other, inclusive))","label":0},{"cid":"wagtail/query.py::TreeQuerySet.child_of_q","text":"wagtail/query.py::TreeQuerySet.child_of_q\n def child_of_q(self, other):\n return self.descendant_of_q(other) & Q(depth=other.depth + 1)","label":0},{"cid":"wagtail/query.py::TreeQuerySet.child_of","text":"wagtail/query.py::TreeQuerySet.child_of\n def child_of(self, other):\n \"\"\"\n This filters the QuerySet to only contain pages that are direct children of the specified page.\n \"\"\"\n return self.filter(self.child_of_q(other))","label":0},{"cid":"wagtail/query.py::TreeQuerySet.not_child_of","text":"wagtail/query.py::TreeQuerySet.not_child_of\n def not_child_of(self, other):\n \"\"\"\n This filters the QuerySet to not contain any pages that are direct children of the specified page.\n \"\"\"\n return self.exclude(self.child_of_q(other))","label":0},{"cid":"wagtail/query.py::TreeQuerySet.ancestor_of_q","text":"wagtail/query.py::TreeQuerySet.ancestor_of_q\n def ancestor_of_q(self, other, inclusive=False):\n paths = [\n other.path[0:pos]\n for pos in range(0, len(other.path) + 1, other.steplen)[1:]\n ]\n q = Q(path__in=paths)\n\n if not inclusive:\n q &= ~Q(pk=other.pk)\n\n return q","label":0},{"cid":"wagtail/query.py::TreeQuerySet.ancestor_of","text":"wagtail/query.py::TreeQuerySet.ancestor_of\n def ancestor_of(self, other, inclusive=False):\n \"\"\"\n This filters the QuerySet to only contain pages that are ancestors of the specified page.\n\n If inclusive is set to True, it will also include the specified page.\n \"\"\"\n return self.filter(self.ancestor_of_q(other, inclusive))","label":0},{"cid":"wagtail/query.py::TreeQuerySet.not_ancestor_of","text":"wagtail/query.py::TreeQuerySet.not_ancestor_of\n def not_ancestor_of(self, other, inclusive=False):\n \"\"\"\n This filters the QuerySet to not contain any pages that are ancestors of the specified page.\n\n If inclusive is set to True, it will also exclude the specified page.\n \"\"\"\n return self.exclude(self.ancestor_of_q(other, inclusive))","label":0},{"cid":"wagtail/query.py::TreeQuerySet.parent_of_q","text":"wagtail/query.py::TreeQuerySet.parent_of_q\n def parent_of_q(self, other):\n return Q(path=self.model._get_parent_path_from_path(other.path))","label":0},{"cid":"wagtail/query.py::TreeQuerySet.parent_of","text":"wagtail/query.py::TreeQuerySet.parent_of\n def parent_of(self, other):\n \"\"\"\n This filters the QuerySet to only contain the parent of the specified page.\n \"\"\"\n return self.filter(self.parent_of_q(other))","label":0},{"cid":"wagtail/query.py::TreeQuerySet.not_parent_of","text":"wagtail/query.py::TreeQuerySet.not_parent_of\n def not_parent_of(self, other):\n \"\"\"\n This filters the QuerySet to exclude the parent of the specified page.\n \"\"\"\n return self.exclude(self.parent_of_q(other))","label":0},{"cid":"wagtail/query.py::TreeQuerySet.sibling_of_q","text":"wagtail/query.py::TreeQuerySet.sibling_of_q\n def sibling_of_q(self, other, inclusive=True):\n q = Q(path__startswith=self.model._get_parent_path_from_path(other.path)) & Q(\n depth=other.depth\n )\n\n if not inclusive:\n q &= ~Q(pk=other.pk)\n\n return q","label":0},{"cid":"wagtail/query.py::TreeQuerySet.sibling_of","text":"wagtail/query.py::TreeQuerySet.sibling_of\n def sibling_of(self, other, inclusive=True):\n \"\"\"\n This filters the QuerySet to only contain pages that are siblings of the specified page.\n\n By default, inclusive is set to True so it will include the specified page in the results.\n\n If inclusive is set to False, the page will be excluded from the results.\n \"\"\"\n return self.filter(self.sibling_of_q(other, inclusive))","label":0},{"cid":"wagtail/query.py::TreeQuerySet.not_sibling_of","text":"wagtail/query.py::TreeQuerySet.not_sibling_of\n def not_sibling_of(self, other, inclusive=True):\n \"\"\"\n This filters the QuerySet to not contain any pages that are siblings of the specified page.\n\n By default, inclusive is set to True so it will exclude the specified page from the results.\n\n If inclusive is set to False, the page will be included in the results.\n \"\"\"\n return self.exclude(self.sibling_of_q(other, inclusive))","label":0},{"cid":"wagtail/query.py::PageQuerySet.__init__","text":"wagtail/query.py::PageQuerySet.__init__\n def __init__(self, *args, **kwargs):\n \"\"\"Set custom instance attributes\"\"\"\n super().__init__(*args, **kwargs)\n # set by defer_streamfields()\n self._defer_streamfields = False","label":0},{"cid":"wagtail/query.py::PageQuerySet._clone","text":"wagtail/query.py::PageQuerySet._clone\n def _clone(self):\n \"\"\"Ensure clones inherit custom attribute values.\"\"\"\n clone = super()._clone()\n clone._defer_streamfields = self._defer_streamfields\n return clone","label":0},{"cid":"wagtail/query.py::PageQuerySet.live_q","text":"wagtail/query.py::PageQuerySet.live_q\n def live_q(self):\n return Q(live=True)","label":0},{"cid":"wagtail/query.py::PageQuerySet.live","text":"wagtail/query.py::PageQuerySet.live\n def live(self):\n \"\"\"\n This filters the QuerySet to only contain published pages.\n \"\"\"\n return self.filter(self.live_q())","label":0},{"cid":"wagtail/query.py::PageQuerySet.not_live","text":"wagtail/query.py::PageQuerySet.not_live\n def not_live(self):\n \"\"\"\n This filters the QuerySet to only contain unpublished pages.\n \"\"\"\n return self.exclude(self.live_q())","label":0},{"cid":"wagtail/query.py::PageQuerySet.in_menu_q","text":"wagtail/query.py::PageQuerySet.in_menu_q\n def in_menu_q(self):\n return Q(show_in_menus=True)","label":0},{"cid":"wagtail/query.py::PageQuerySet.in_menu","text":"wagtail/query.py::PageQuerySet.in_menu\n def in_menu(self):\n \"\"\"\n This filters the QuerySet to only contain pages that are in the menus.\n \"\"\"\n return self.filter(self.in_menu_q())","label":0},{"cid":"wagtail/query.py::PageQuerySet.not_in_menu","text":"wagtail/query.py::PageQuerySet.not_in_menu\n def not_in_menu(self):\n \"\"\"\n This filters the QuerySet to only contain pages that are not in the menus.\n \"\"\"\n return self.exclude(self.in_menu_q())","label":0},{"cid":"wagtail/query.py::PageQuerySet.page_q","text":"wagtail/query.py::PageQuerySet.page_q\n def page_q(self, other):\n return Q(id=other.id)","label":0},{"cid":"wagtail/query.py::PageQuerySet.page","text":"wagtail/query.py::PageQuerySet.page\n def page(self, other):\n \"\"\"\n This filters the QuerySet so it only contains the specified page.\n \"\"\"\n return self.filter(self.page_q(other))","label":0},{"cid":"wagtail/query.py::PageQuerySet.not_page","text":"wagtail/query.py::PageQuerySet.not_page\n def not_page(self, other):\n \"\"\"\n This filters the QuerySet so it doesn't contain the specified page.\n \"\"\"\n return self.exclude(self.page_q(other))","label":0},{"cid":"wagtail/query.py::PageQuerySet.type_q","text":"wagtail/query.py::PageQuerySet.type_q\n def type_q(self, *types):\n all_subclasses = {\n model for model in apps.get_models() if issubclass(model, types)\n }\n content_types = ContentType.objects.get_for_models(*all_subclasses)\n return Q(content_type__in=list(content_types.values()))","label":0},{"cid":"wagtail/query.py::PageQuerySet.type","text":"wagtail/query.py::PageQuerySet.type\n def type(self, *types):\n \"\"\"\n This filters the QuerySet to only contain pages that are an instance\n of the specified model(s) (including subclasses).\n \"\"\"\n return self.filter(self.type_q(*types))","label":0},{"cid":"wagtail/query.py::PageQuerySet.not_type","text":"wagtail/query.py::PageQuerySet.not_type\n def not_type(self, *types):\n \"\"\"\n This filters the QuerySet to exclude any pages which are an instance of the specified model(s).\n \"\"\"\n return self.exclude(self.type_q(*types))","label":0},{"cid":"wagtail/query.py::PageQuerySet.exact_type_q","text":"wagtail/query.py::PageQuerySet.exact_type_q\n def exact_type_q(self, *types):\n content_types = ContentType.objects.get_for_models(*types)\n return Q(content_type__in=list(content_types.values()))","label":0},{"cid":"wagtail/query.py::PageQuerySet.exact_type","text":"wagtail/query.py::PageQuerySet.exact_type\n def exact_type(self, *types):\n \"\"\"\n This filters the QuerySet to only contain pages that are an instance of the specified model(s)\n (matching the model exactly, not subclasses).\n \"\"\"\n return self.filter(self.exact_type_q(*types))","label":0},{"cid":"wagtail/query.py::PageQuerySet.not_exact_type","text":"wagtail/query.py::PageQuerySet.not_exact_type\n def not_exact_type(self, *types):\n \"\"\"\n This filters the QuerySet to exclude any pages which are an instance of the specified model(s)\n (matching the model exactly, not subclasses).\n \"\"\"\n return self.exclude(self.exact_type_q(*types))","label":0},{"cid":"wagtail/query.py::PageQuerySet.private_q","text":"wagtail/query.py::PageQuerySet.private_q\n def private_q(self):\n from wagtail.models import PageViewRestriction\n\n q = Q()\n for restriction in PageViewRestriction.objects.select_related(\"page\").all():\n q |= self.descendant_of_q(restriction.page, inclusive=True)\n\n # do not match any page if no private section exists.\n return q if q else Q(pk__in=[])","label":0},{"cid":"wagtail/query.py::PageQuerySet.public","text":"wagtail/query.py::PageQuerySet.public\n def public(self):\n \"\"\"\n Filters the QuerySet to only contain pages that are not in a private\n section and their descendants.\n \"\"\"\n return self.exclude(self.private_q())","label":0},{"cid":"wagtail/query.py::PageQuerySet.not_public","text":"wagtail/query.py::PageQuerySet.not_public\n def not_public(self):\n \"\"\"\n Filters the QuerySet to only contain pages that are in a private\n section and their descendants.\n \"\"\"\n return self.filter(self.private_q())","label":0},{"cid":"wagtail/query.py::PageQuerySet.private","text":"wagtail/query.py::PageQuerySet.private\n def private(self):\n \"\"\"\n Filters the QuerySet to only contain pages that are in a private\n section and their descendants.\n \"\"\"\n return self.filter(self.private_q())","label":0},{"cid":"wagtail/query.py::PageQuerySet.first_common_ancestor","text":"wagtail/query.py::PageQuerySet.first_common_ancestor\n def first_common_ancestor(self, include_self=False, strict=False):\n \"\"\"\n Find the first ancestor that all pages in this queryset have in common.\n For example, consider a page hierarchy like::\n\n - Home/\n - Foo Event Index/\n - Foo Event Page 1/\n - Foo Event Page 2/\n - Bar Event Index/\n - Bar Event Page 1/\n - Bar Event Page 2/\n\n The common ancestors for some queries would be:\n\n .. code-block:: python\n\n >>> Page.objects\\\\\n ... .type(EventPage)\\\\\n ... .first_common_ancestor()\n <Page: Home>\n >>> Page.objects\\\\\n ... .type(EventPage)\\\\\n ... .filter(title__contains='Foo')\\\\\n ... .first_common_ancestor()\n <Page: Foo Event Index>\n\n This method tries to be efficient, but if you have millions of pages\n scattered across your page tree, it will be slow.\n\n If `include_self` is True, the ancestor can be one of the pages in the\n queryset:\n\n .. code-block:: python\n\n >>> Page.objects\\\\\n ... .filter(title__contains='Foo')\\\\\n ... .first_common_ancestor()\n <Page: Foo Event Index>\n >>> Page.objects\\\\\n ... .filter(title__exact='Bar Event Index')\\\\\n ... .first_common_ancestor()\n <Page: Bar Event Index>\n\n A few invalid cases exist: when the queryset is empty, when the root\n Page is in the queryset and ``include_self`` is False, and when there\n are multiple page trees with no common root (a case Wagtail does not\n support). If ``strict`` is False (the default), then the first root\n node is returned in these cases. If ``strict`` is True, then a\n ``ObjectDoesNotExist`` is raised.\n \"\"\"\n ","label":0},{"cid":"wagtail/query.py::PageQuerySet.unpublish","text":"wagtail/query.py::PageQuerySet.unpublish\n def unpublish(self):\n \"\"\"\n This unpublishes all live pages in the QuerySet.\n \"\"\"\n for page in self.live():\n page.unpublish()","label":0},{"cid":"wagtail/query.py::PageQuerySet.defer_streamfields","text":"wagtail/query.py::PageQuerySet.defer_streamfields\n def defer_streamfields(self):\n \"\"\"\n Apply to a queryset to prevent fetching/decoding of StreamField values on\n evaluation. Useful when working with potentially large numbers of results,\n where StreamField values are unlikely to be needed. For example, when\n generating a sitemap or a long list of page links.\n \"\"\"\n clone = self._clone()\n clone._defer_streamfields = True # used by specific_iterator()\n streamfield_names = self.model.get_streamfield_names()\n if not streamfield_names:\n return clone\n return clone.defer(*streamfield_names)","label":0}],"string":"[\n {\n \"cid\": \"docs/conf.py::setup\",\n \"text\": \"docs/conf.py::setup\\ndef setup(app):\\n app.add_js_file(\\\"js/banner.js\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::breadcrumbs\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::breadcrumbs\\n@register.inclusion_tag(\\\"wagtailadmin/shared/breadcrumbs.html\\\", takes_context=True)\\ndef breadcrumbs(\\n context,\\n page,\\n url_name,\\n url_root_name=None,\\n include_self=True,\\n is_expanded=False,\\n page_perms=None,\\n querystring_value=None,\\n trailing_breadcrumb_title=None,\\n classname=None,\\n):\\n user = context[\\\"request\\\"].user\\n\\n # find the closest common ancestor of the pages that this user has direct explore permission\\n # (i.e. add/edit/publish/lock) over; this will be the root of the breadcrumb\\n cca = get_explorable_root_page(user)\\n if not cca:\\n return {\\\"pages\\\": Page.objects.none()}\\n\\n return {\\n \\\"pages\\\": page.get_ancestors(inclusive=include_self)\\n .descendant_of(cca, inclusive=True)\\n .specific(),\\n \\\"current_page\\\": page,\\n \\\"is_expanded\\\": is_expanded,\\n \\\"page_perms\\\": page_perms,\\n \\\"querystring_value\\\": querystring_value or \\\"\\\",\\n \\\"trailing_breadcrumb_title\\\": trailing_breadcrumb_title, # Only used in collapsible breadcrumb templates\\n \\\"url_name\\\": url_name,\\n \\\"url_root_name\\\": url_root_name,\\n \\\"classname\\\": classname,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::search_other\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::search_other\\n@register.inclusion_tag(\\\"wagtailadmin/shared/search_other.html\\\", takes_context=True)\\ndef search_other(context, current=None):\\n request = context[\\\"request\\\"]\\n\\n return {\\n \\\"options_html\\\": admin_search_areas.render_html(request, current),\\n \\\"request\\\": request,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::ellipsistrim\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::ellipsistrim\\n@register.filter(\\\"ellipsistrim\\\")\\ndef ellipsistrim(value, max_length):\\n if len(value) > max_length:\\n truncd_val = value[:max_length]\\n if not len(value) == (max_length + 1) and value[max_length + 1] != \\\" \\\":\\n truncd_val = truncd_val[: truncd_val.rfind(\\\" \\\")]\\n return truncd_val + \\\"…\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::fieldtype\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::fieldtype\\n@register.filter\\ndef fieldtype(bound_field):\\n try:\\n return camelcase_to_underscore(bound_field.field.__class__.__name__)\\n except AttributeError:\\n try:\\n return camelcase_to_underscore(bound_field.__class__.__name__)\\n except AttributeError:\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::widgettype\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::widgettype\\n@register.filter\\ndef widgettype(bound_field):\\n try:\\n return camelcase_to_underscore(bound_field.field.widget.__class__.__name__)\\n except AttributeError:\\n try:\\n return camelcase_to_underscore(bound_field.widget.__class__.__name__)\\n except AttributeError:\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::_get_user_page_permissions\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::_get_user_page_permissions\\ndef _get_user_page_permissions(context):\\n # Create a UserPagePermissionsProxy object to represent the user's global permissions, and\\n # cache it in the context for the duration of the page request, if one does not exist already\\n if \\\"user_page_permissions\\\" not in context:\\n context[\\\"user_page_permissions\\\"] = UserPagePermissionsProxy(\\n context[\\\"request\\\"].user\\n )\\n\\n return context[\\\"user_page_permissions\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_permissions\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_permissions\\n@register.simple_tag(takes_context=True)\\ndef page_permissions(context, page):\\n \\\"\\\"\\\"\\n Usage: {% page_permissions page as page_perms %}\\n Sets the variable 'page_perms' to a PagePermissionTester object that can be queried to find out\\n what actions the current logged-in user can perform on the given page.\\n \\\"\\\"\\\"\\n return _get_user_page_permissions(context).for_page(page)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::classnames\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::classnames\\n@register.simple_tag\\ndef classnames(*classes):\\n \\\"\\\"\\\"\\n Usage <div class=\\\"{% classnames \\\"w-base\\\" classname active|yesno:\\\"w-base--active,\\\" any_other_var %}\\\"></div>\\n Returns any args as a space-separated joined string for using in HTML class names.\\n \\\"\\\"\\\"\\n return \\\" \\\".join([classname.strip() for classname in classes if classname])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::test_collection_is_public\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::test_collection_is_public\\n@register.simple_tag(takes_context=True)\\ndef test_collection_is_public(context, collection):\\n \\\"\\\"\\\"\\n Usage: {% test_collection_is_public collection as is_public %}\\n Sets 'is_public' to True iff there are no collection view restrictions in place\\n on this collection.\\n Caches the list of collection view restrictions in the context, to avoid repeated\\n DB queries on repeated calls.\\n \\\"\\\"\\\"\\n if \\\"all_collection_view_restrictions\\\" not in context:\\n context[\\n \\\"all_collection_view_restrictions\\\"\\n ] = CollectionViewRestriction.objects.select_related(\\\"collection\\\").values_list(\\n \\\"collection__name\\\", flat=True\\n )\\n\\n is_private = collection.name in context[\\\"all_collection_view_restrictions\\\"]\\n\\n return not is_private\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::test_page_is_public\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::test_page_is_public\\n@register.simple_tag(takes_context=True)\\ndef test_page_is_public(context, page):\\n \\\"\\\"\\\"\\n Usage: {% test_page_is_public page as is_public %}\\n Sets 'is_public' to True iff there are no page view restrictions in place on\\n this page.\\n Caches the list of page view restrictions on the request, to avoid repeated\\n DB queries on repeated calls.\\n \\\"\\\"\\\"\\n if not hasattr(context[\\\"request\\\"], \\\"all_page_view_restriction_paths\\\"):\\n context[\\n \\\"request\\\"\\n ].all_page_view_restriction_paths = PageViewRestriction.objects.select_related(\\n \\\"page\\\"\\n ).values_list(\\n \\\"page__path\\\", flat=True\\n )\\n\\n is_private = any(\\n [\\n page.path.startswith(restricted_path)\\n for restricted_path in context[\\\"request\\\"].all_page_view_restriction_paths\\n ]\\n )\\n\\n return not is_private\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::hook_output\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::hook_output\\n@register.simple_tag\\ndef hook_output(hook_name):\\n \\\"\\\"\\\"\\n Example: {% hook_output 'insert_editor_css' %}\\n Whenever we have a hook whose functions take no parameters and return a string, this tag can be used\\n to output the concatenation of all of those return values onto the page.\\n Note that the output is not escaped - it is the hook function's responsibility to escape unsafe content.\\n \\\"\\\"\\\"\\n snippets = [fn() for fn in hooks.get_hooks(hook_name)]\\n return mark_safe(\\\"\\\".join(snippets))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::base_url_setting\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::base_url_setting\\n@register.simple_tag\\ndef base_url_setting(default=None):\\n return get_admin_base_url() or default\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::allow_unicode_slugs\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::allow_unicode_slugs\\n@register.simple_tag\\ndef allow_unicode_slugs():\\n return getattr(settings, \\\"WAGTAIL_ALLOW_UNICODE_SLUGS\\\", True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.__init__\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.__init__\\n def __init__(self, nodelist):\\n super().__init__()\\n self.nodelist = nodelist\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.render\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.render\\n def render(self, context):\\n out = self.nodelist.render(context)\\n return escape_script(out)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.handle\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::EscapeScriptNode.handle\\n @classmethod\\n def handle(cls, parser, token):\\n nodelist = parser.parse((\\\"end\\\" + EscapeScriptNode.TAG_NAME,))\\n parser.delete_first_token()\\n return cls(nodelist)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::render_with_errors\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::render_with_errors\\n@register.filter\\ndef render_with_errors(bound_field):\\n \\\"\\\"\\\"\\n Usage: {{ field|render_with_errors }} as opposed to {{ field }}.\\n If the field (a BoundField instance) has errors on it, and the associated widget implements\\n a render_with_errors method, call that; otherwise, call the regular widget rendering mechanism.\\n \\\"\\\"\\\"\\n widget = bound_field.field.widget\\n if bound_field.errors and hasattr(widget, \\\"render_with_errors\\\"):\\n return widget.render_with_errors(\\n bound_field.html_name,\\n bound_field.value(),\\n attrs={\\\"id\\\": bound_field.auto_id},\\n errors=bound_field.errors,\\n )\\n else:\\n return bound_field.as_widget()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::has_unrendered_errors\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::has_unrendered_errors\\n@register.filter\\ndef has_unrendered_errors(bound_field):\\n \\\"\\\"\\\"\\n Return true if this field has errors that were not accounted for by render_with_errors, because\\n the widget does not support the render_with_errors method\\n \\\"\\\"\\\"\\n return bound_field.errors and not hasattr(\\n bound_field.field.widget, \\\"render_with_errors\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::cautious_slugify\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::cautious_slugify\\n@register.filter(is_safe=True)\\n@stringfilter\\ndef cautious_slugify(value):\\n return _cautious_slugify(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::querystring\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::querystring\\n@register.simple_tag(takes_context=True)\\ndef querystring(context, **kwargs):\\n \\\"\\\"\\\"\\n Print out the current querystring. Any keyword arguments to this template\\n tag will be added to the querystring before it is printed out.\\n\\n <a href=\\\"/page/{% querystring key='value' %}\\\">\\n\\n Will result in something like:\\n\\n <a href=\\\"/page/?foo=bar&key=value\\\">\\n \\\"\\\"\\\"\\n request = context[\\\"request\\\"]\\n querydict = request.GET.copy()\\n # Can't do querydict.update(kwargs), because QueryDict.update() appends to\\n # the list of values, instead of replacing the values.\\n for key, value in kwargs.items():\\n if value is None:\\n # Remove the key if the value is None\\n querydict.pop(key, None)\\n else:\\n # Set the key otherwise\\n querydict[key] = str(value)\\n\\n return \\\"?\\\" + querydict.urlencode()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_table_header_label\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_table_header_label\\n@register.simple_tag(takes_context=True)\\ndef page_table_header_label(context, label=None, parent_page_title=None, **kwargs):\\n \\\"\\\"\\\"\\n Wraps table_header_label to add a title attribute based on the parent page title and the column label\\n \\\"\\\"\\\"\\n if label:\\n translation_context = {\\\"parent\\\": parent_page_title, \\\"label\\\": label}\\n ascending_title_text = (\\n _(\\n \\\"Sort the order of child pages within '%(parent)s' by '%(label)s' in ascending order.\\\"\\n )\\n % translation_context\\n )\\n descending_title_text = (\\n _(\\n \\\"Sort the order of child pages within '%(parent)s' by '%(label)s' in descending order.\\\"\\n )\\n % translation_context\\n )\\n else:\\n ascending_title_text = None\\n descending_title_text = None\\n\\n return table_header_label(\\n context,\\n label=label,\\n ascending_title_text=ascending_title_text,\\n descending_title_text=descending_title_text,\\n **kwargs,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::table_header_label\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::table_header_label\\n@register.simple_tag(takes_context=True)\\ndef table_header_label(\\n context,\\n label=None,\\n sortable=True,\\n ordering=None,\\n sort_context_var=\\\"ordering\\\",\\n sort_param=\\\"ordering\\\",\\n sort_field=None,\\n ascending_title_text=None,\\n descending_title_text=None,\\n):\\n \\\"\\\"\\\"\\n A label to go in a table header cell, optionally with a 'sort' link that alternates between\\n forward and reverse sorting\\n\\n label = label text\\n ordering = current active ordering. If not specified, we will fetch it from the template context variable\\n given by sort_context_var. (We don't fetch it from the URL because that wouldn't give the view method\\n the opportunity to set a default)\\n sort_param = URL parameter that indicates the current active ordering\\n sort_field = the value for sort_param that indicates that sorting is currently on this column.\\n For example, if sort_param='ordering' and sort_field='title', then a URL parameter of\\n ordering=title indicates that the listing is ordered forwards on this column, and a URL parameter\\n of ordering=-title indicated that the listing is ordered in reverse on this column\\n ascending_title_text = title attribute to use on the link when the link action will sort in ascending order\\n descending_title_text = title attribute to use on the link when the link action will sort in descending order\\n\\n To disable sorting on this column, set sortable=False or leave sort_field unspecified.\\n \\\"\\\"\\\"\\n if not sortable or not sort_field:\\n # render label without a sort link\\n return label\\n\\n if ordering is None:\\n ordering = context.get(sort_context_var)\\n reverse_sort_field = \\\"-%s\\\" % sort_field\\n\\n if ordering == sort_field:\\n # currently ordering forwards on this column; link should change to reverse ordering\\n attrs = {\\n \\\"href\\\": querystring(context, **{sort_param: reverse_sort_field})\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::pagination_querystring\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::pagination_querystring\\n@register.simple_tag(takes_context=True)\\ndef pagination_querystring(context, page_number, page_key=\\\"p\\\"):\\n \\\"\\\"\\\"\\n Print out a querystring with an updated page number:\\n\\n {% if page.has_next_page %}\\n <a href=\\\"{% pagination_link page.next_page_number %}\\\">Next page</a>\\n {% endif %}\\n \\\"\\\"\\\"\\n return querystring(context, **{page_key: page_number})\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::paginate\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::paginate\\n@register.inclusion_tag(\\n \\\"wagtailadmin/pages/listing/_pagination.html\\\", takes_context=True\\n)\\ndef paginate(context, page, base_url=\\\"\\\", page_key=\\\"p\\\", classnames=\\\"\\\"):\\n \\\"\\\"\\\"\\n Print pagination previous/next links, and the page count. Take the\\n following arguments:\\n\\n page\\n The current page of results. This should be a Django pagination `Page`\\n instance\\n\\n base_url\\n The base URL of the next/previous page, with no querystring.\\n This is optional, and defaults to the current page by just printing the\\n querystring for the next/previous page.\\n\\n page_key\\n The name of the page variable in the query string. Defaults to 'p'.\\n\\n classnames\\n Extra classes to add to the next/previous links.\\n \\\"\\\"\\\"\\n request = context[\\\"request\\\"]\\n return {\\n \\\"base_url\\\": base_url,\\n \\\"classnames\\\": classnames,\\n \\\"request\\\": request,\\n \\\"page\\\": page,\\n \\\"page_key\\\": page_key,\\n \\\"paginator\\\": page.paginator,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_listing_buttons\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_listing_buttons\\n@register.inclusion_tag(\\\"wagtailadmin/pages/listing/_buttons.html\\\", takes_context=True)\\ndef page_listing_buttons(context, page, page_perms):\\n next_url = context.request.path\\n button_hooks = hooks.get_hooks(\\\"register_page_listing_buttons\\\")\\n\\n buttons = []\\n for hook in button_hooks:\\n buttons.extend(hook(page, page_perms, next_url))\\n\\n buttons.sort()\\n\\n for hook in hooks.get_hooks(\\\"construct_page_listing_buttons\\\"):\\n hook(buttons, page, page_perms, context)\\n\\n return {\\\"page\\\": page, \\\"buttons\\\": buttons}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_header_buttons\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::page_header_buttons\\n@register.inclusion_tag(\\n \\\"wagtailadmin/pages/listing/_modern_dropdown.html\\\", takes_context=True\\n)\\ndef page_header_buttons(context, page, page_perms):\\n next_url = context.request.path\\n button_hooks = hooks.get_hooks(\\\"register_page_header_buttons\\\")\\n\\n buttons = []\\n for hook in button_hooks:\\n buttons.extend(hook(page, page_perms, next_url))\\n\\n buttons.sort()\\n return {\\n \\\"page\\\": page,\\n \\\"buttons\\\": buttons,\\n \\\"title\\\": _(\\\"Actions\\\"),\\n \\\"icon_name\\\": \\\"dots-horizontal\\\",\\n \\\"classes\\\": [\\n \\\"w-flex\\\",\\n \\\"w-justify-center\\\",\\n \\\"w-items-center\\\",\\n \\\"w-h-slim-header\\\",\\n ],\\n \\\"button_classes\\\": [\\n \\\"w-p-0\\\",\\n \\\"w-w-12\\\",\\n \\\"w-h-full\\\",\\n \\\"w-text-primary\\\",\\n \\\"w-bg-transparent\\\",\\n \\\"hover:w-scale-110\\\",\\n \\\"w-transition\\\",\\n \\\"w-outline-offset-inside\\\",\\n \\\"w-relative\\\",\\n \\\"w-z-30\\\",\\n ],\\n \\\"hide_title\\\": True,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::bulk_action_choices\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::bulk_action_choices\\n@register.inclusion_tag(\\\"wagtailadmin/pages/listing/_buttons.html\\\", takes_context=True)\\ndef bulk_action_choices(context, app_label, model_name):\\n bulk_actions_list = list(\\n bulk_action_registry.get_bulk_actions_for_model(app_label, model_name)\\n )\\n bulk_actions_list.sort(key=lambda x: x.action_priority)\\n\\n bulk_action_more_list = []\\n if len(bulk_actions_list) > 4:\\n bulk_action_more_list = bulk_actions_list[4:]\\n bulk_actions_list = bulk_actions_list[:4]\\n\\n next_url = get_valid_next_url_from_request(context[\\\"request\\\"])\\n if not next_url:\\n next_url = context[\\\"request\\\"].path\\n\\n bulk_action_buttons = [\\n PageListingButton(\\n action.display_name,\\n reverse(\\n \\\"wagtail_bulk_action\\\", args=[app_label, model_name, action.action_type]\\n )\\n + \\\"?\\\"\\n + urlencode({\\\"next\\\": next_url}),\\n attrs={\\\"aria-label\\\": action.aria_label},\\n priority=action.action_priority,\\n classes=action.classes | {\\\"bulk-action-btn\\\"},\\n )\\n for action in bulk_actions_list\\n ]\\n\\n if bulk_action_more_list:\\n more_button = ButtonWithDropdown(\\n label=_(\\\"More\\\"),\\n attrs={\\\"title\\\": _(\\\"View more bulk actions\\\")},\\n classes={\\\"bulk-actions-more\\\", \\\"dropup\\\"},\\n button_classes={\\\"button\\\", \\\"button-small\\\"},\\n buttons_data=[\\n {\\n \\\"label\\\": action.display_name,\\n \\\"url\\\": reverse(\\n \\\"wagtail_bulk_action\\\",\\n args=[app_label, model_name, action.action_type],\\n )\\n + \\\"?\\\"\\n + urlencode({\\\"next\\\": next_url}),\\n \\\"attrs\\\": {\\\"aria-label\\\": action.aria_label},\\n \\\"priority\\\": action.action_priority,\\n \\\"classes\\\": {\\\"bulk-action-btn\\\"},\\n }\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::message_level_tag\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::message_level_tag\\n@register.simple_tag\\ndef message_level_tag(message):\\n \\\"\\\"\\\"\\n Return the tag for this message's level as defined in\\n django.contrib.messages.constants.DEFAULT_TAGS, ignoring the project-level\\n MESSAGE_TAGS setting (which end-users might customise).\\n \\\"\\\"\\\"\\n return MESSAGE_TAGS.get(message.level)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::message_tags\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::message_tags\\n@register.simple_tag\\ndef message_tags(message):\\n level_tag = message_level_tag(message)\\n if message.extra_tags and level_tag:\\n return message.extra_tags + \\\" \\\" + level_tag\\n elif message.extra_tags:\\n return message.extra_tags\\n elif level_tag:\\n return level_tag\\n else:\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::_abs\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::_abs\\n@register.filter(\\\"abs\\\")\\ndef _abs(val):\\n return abs(val)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::admin_urlquote\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::admin_urlquote\\n@register.filter\\ndef admin_urlquote(value):\\n return quote(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::avatar_url\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::avatar_url\\n@register.simple_tag\\ndef avatar_url(user, size=50, gravatar_only=False):\\n \\\"\\\"\\\"\\n A template tag that receives a user and size and return\\n the appropriate avatar url for that user.\\n Example usage: {% avatar_url request.user 50 %}\\n \\\"\\\"\\\"\\n\\n if (\\n not gravatar_only\\n and hasattr(user, \\\"wagtail_userprofile\\\")\\n and user.wagtail_userprofile.avatar\\n ):\\n return user.wagtail_userprofile.avatar.url\\n\\n if hasattr(user, \\\"email\\\"):\\n gravatar_url = get_gravatar_url(user.email, size=size)\\n if gravatar_url is not None:\\n return gravatar_url\\n\\n return versioned_static_func(\\\"wagtailadmin/images/default-user-avatar.png\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::js_translation_strings\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::js_translation_strings\\n@register.simple_tag\\ndef js_translation_strings():\\n return mark_safe(json.dumps(get_js_translation_strings()))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::notification_static\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::notification_static\\n@register.simple_tag\\ndef notification_static(path):\\n \\\"\\\"\\\"\\n Variant of the {% static %}` tag for use in notification emails - tries to form\\n a full URL using WAGTAILADMIN_BASE_URL if the static URL isn't already a full URL.\\n \\\"\\\"\\\"\\n return urljoin(base_url_setting(), static(path))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::versioned_static\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::versioned_static\\n@register.simple_tag\\ndef versioned_static(path):\\n \\\"\\\"\\\"\\n Wrapper for Django's static file finder to append a cache-busting query parameter\\n that updates on each Wagtail version\\n \\\"\\\"\\\"\\n return versioned_static_func(path)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::icon\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::icon\\n@register.inclusion_tag(\\\"wagtailadmin/shared/icon.html\\\", takes_context=False)\\ndef icon(name=None, class_name=\\\"icon\\\", title=None, wrapped=False):\\n \\\"\\\"\\\"\\n Abstracts away the actual icon implementation.\\n\\n Usage:\\n {% load wagtailadmin_tags %}\\n ...\\n {% icon name=\\\"cogs\\\" class_name=\\\"icon--red\\\" title=\\\"Settings\\\" %}\\n\\n :param name: the icon name/id, required (string)\\n :param class_name: default 'icon' (string)\\n :param title: accessible label intended for screen readers (string)\\n :return: Rendered template snippet (string)\\n \\\"\\\"\\\"\\n if not name:\\n raise ValueError(\\\"You must supply an icon name\\\")\\n\\n return {\\\"name\\\": name, \\\"class_name\\\": class_name, \\\"title\\\": title, \\\"wrapped\\\": wrapped}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_simple\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_simple\\n@register.filter()\\ndef timesince_simple(d):\\n \\\"\\\"\\\"\\n Returns a simplified timesince:\\n 19 hours, 48 minutes ago -> 19 hours ago\\n 1 week, 1 day ago -> 1 week ago\\n 0 minutes ago -> just now\\n \\\"\\\"\\\"\\n # Note: Duplicate code in timesince_last_update()\\n time_period = timesince(d).split(\\\",\\\")[0]\\n if time_period == avoid_wrapping(_(\\\"0 minutes\\\")):\\n return _(\\\"just now\\\")\\n return _(\\\"%(time_period)s ago\\\") % {\\\"time_period\\\": time_period}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_last_update\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::timesince_last_update\\n@register.simple_tag\\ndef timesince_last_update(\\n last_update, show_time_prefix=False, user_display_name=\\\"\\\", use_shorthand=True\\n):\\n \\\"\\\"\\\"\\n Returns:\\n - the time of update if last_update is today, if show_time_prefix=True, the output will be prefixed with \\\"at \\\"\\n - time since last update otherwise. Defaults to the simplified timesince,\\n but can return the full string if needed\\n \\\"\\\"\\\"\\n # translation usage below is intentionally verbose to be easier to work with translations\\n\\n if last_update.date() == datetime.today().date():\\n if timezone.is_aware(last_update):\\n time_str = timezone.localtime(last_update).strftime(\\\"%H:%M\\\")\\n else:\\n time_str = last_update.strftime(\\\"%H:%M\\\")\\n\\n if show_time_prefix:\\n if user_display_name:\\n return _(\\\"at %(time)s by %(user_display_name)s\\\") % {\\n \\\"time\\\": time_str,\\n \\\"user_display_name\\\": user_display_name,\\n }\\n else:\\n return _(\\\"at %(time)s\\\") % {\\\"time\\\": time_str}\\n else:\\n if user_display_name:\\n return _(\\\"%(time)s by %(user_display_name)s\\\") % {\\n \\\"time\\\": time_str,\\n \\\"user_display_name\\\": user_display_name,\\n }\\n else:\\n return _(\\\"%(time)s\\\") % {\\\"time\\\": time_str}\\n else:\\n if use_shorthand:\\n # Note: Duplicate code in timesince_simple()\\n time_period = timesince(last_update).split(\\\",\\\")[0]\\n if time_period == avoid_wrapping(_(\\\"0 minutes\\\")):\\n if user_display_name:\\n return _(\\\"just now by %(user_display_name)s\\\") % {\\n \\\"user_display_name\\\": user_display_name\\n }\\n else:\\n return _(\\\"just now\\\")\\n else:\\n time_period = timesince(last_update)\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::user_display_name\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::user_display_name\\n@register.filter\\ndef user_display_name(user):\\n \\\"\\\"\\\"\\n Returns the preferred display name for the given user object: the result of\\n user.get_full_name() if implemented and non-empty, or user.get_username() otherwise.\\n \\\"\\\"\\\"\\n try:\\n full_name = user.get_full_name().strip()\\n if full_name:\\n return full_name\\n except AttributeError:\\n pass\\n\\n try:\\n return user.get_username()\\n except AttributeError:\\n # we were passed None or something else that isn't a valid user object; return\\n # empty string to replicate the behaviour of {{ user.get_full_name|default:user.get_username }}\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::format_content_type\",\n \"text\": \"wagtail/admin/templatetags/wagtailadmin_tags.py::format_content_type\\n@register.filter\\ndef format_content_type(content_type):\\n return get_content_type_label(content_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_upgrade_check_setting\",\n \"text\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_upgrade_check_setting\\n def get_upgrade_check_setting(self) -> Union[bool, str]:\\n return getattr(settings, \\\"WAGTAIL_ENABLE_UPDATE_CHECK\\\", True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.upgrade_check_lts_only\",\n \"text\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.upgrade_check_lts_only\\n def upgrade_check_lts_only(self) -> bool:\\n upgrade_check = self.get_upgrade_check_setting()\\n if isinstance(upgrade_check, str) and upgrade_check.lower() == \\\"lts\\\":\\n return True\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_context_data\\n def get_context_data(self, parent_context: Mapping[str, Any]) -> Mapping[str, Any]:\\n return {\\\"lts_only\\\": self.upgrade_check_lts_only()}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.render_html\",\n \"text\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.render_html\\n def render_html(self, parent_context: Mapping[str, Any] = None) -> str:\\n if (\\n parent_context[\\\"request\\\"].user.is_superuser\\n and self.get_upgrade_check_setting()\\n ):\\n return super().render_html(parent_context)\\n else:\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_whats_new_banner_setting\",\n \"text\": \"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_whats_new_banner_setting\\n def get_whats_new_banner_setting(self) -> Union[bool, str]:\\n return getattr(settings, \\\"WAGTAIL_ENABLE_WHATS_NEW_BANNER\\\", True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_dismissible_id\",\n \"text\": \"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_dismissible_id\\n def get_dismissible_id(self) -> str:\\n return f\\\"{self.name}_{self._version}\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.get_context_data\\n def get_context_data(self, parent_context: Mapping[str, Any]) -> Mapping[str, Any]:\\n return {\\\"dismissible_id\\\": self.get_dismissible_id(), \\\"version\\\": self._version}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.is_shown\",\n \"text\": \"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.is_shown\\n def is_shown(self, parent_context: Mapping[str, Any] = None) -> bool:\\n if not self.get_whats_new_banner_setting():\\n return False\\n\\n profile = getattr(parent_context[\\\"request\\\"].user, \\\"wagtail_userprofile\\\", None)\\n if profile and profile.dismissibles.get(self.get_dismissible_id()):\\n return False\\n\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.render_html\",\n \"text\": \"wagtail/admin/views/home.py::WhatsNewInWagtailVersionPanel.render_html\\n def render_html(self, parent_context: Mapping[str, Any] = None) -> str:\\n if not self.is_shown(parent_context):\\n return \\\"\\\"\\n return super().render_html(parent_context)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::PagesForModerationPanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::PagesForModerationPanel.get_context_data\\n def get_context_data(self, parent_context):\\n request = parent_context[\\\"request\\\"]\\n context = super().get_context_data(parent_context)\\n user_perms = UserPagePermissionsProxy(request.user)\\n context[\\\"page_revisions_for_moderation\\\"] = (\\n user_perms.revisions_for_moderation()\\n .select_related(\\\"user\\\")\\n .order_by(\\\"-created_at\\\")\\n )\\n context[\\\"request\\\"] = request\\n context[\\\"csrf_token\\\"] = parent_context[\\\"csrf_token\\\"]\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::UserPagesInWorkflowModerationPanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::UserPagesInWorkflowModerationPanel.get_context_data\\n def get_context_data(self, parent_context):\\n request = parent_context[\\\"request\\\"]\\n context = super().get_context_data(parent_context)\\n if getattr(settings, \\\"WAGTAIL_WORKFLOW_ENABLED\\\", True):\\n # Find in progress workflow states which are either requested by the user or on pages owned by the user\\n context[\\\"workflow_states\\\"] = (\\n WorkflowState.objects.active()\\n .filter(Q(page__owner=request.user) | Q(requested_by=request.user))\\n .select_related(\\n \\\"page\\\",\\n \\\"current_task_state\\\",\\n \\\"current_task_state__task\\\",\\n \\\"current_task_state__page_revision\\\",\\n )\\n .order_by(\\\"-current_task_state__started_at\\\")\\n )\\n else:\\n context[\\\"workflow_states\\\"] = WorkflowState.objects.none()\\n context[\\\"request\\\"] = request\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::WorkflowPagesToModeratePanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::WorkflowPagesToModeratePanel.get_context_data\\n def get_context_data(self, parent_context):\\n request = parent_context[\\\"request\\\"]\\n context = super().get_context_data(parent_context)\\n if getattr(settings, \\\"WAGTAIL_WORKFLOW_ENABLED\\\", True):\\n states = (\\n TaskState.objects.reviewable_by(request.user)\\n .select_related(\\n \\\"page_revision\\\",\\n \\\"task\\\",\\n \\\"page_revision__user\\\",\\n )\\n .order_by(\\\"-started_at\\\")\\n )\\n context[\\\"states\\\"] = [\\n (\\n state,\\n state.task.specific.get_actions(\\n page=state.page_revision.content_object, user=request.user\\n ),\\n state.workflow_state.all_tasks_with_status(),\\n )\\n for state in states\\n ]\\n else:\\n context[\\\"states\\\"] = []\\n context[\\\"request\\\"] = request\\n context[\\\"csrf_token\\\"] = parent_context[\\\"csrf_token\\\"]\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::LockedPagesPanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::LockedPagesPanel.get_context_data\\n def get_context_data(self, parent_context):\\n request = parent_context[\\\"request\\\"]\\n context = super().get_context_data(parent_context)\\n context.update(\\n {\\n \\\"locked_pages\\\": Page.objects.filter(\\n locked=True,\\n locked_by=request.user,\\n ),\\n \\\"can_remove_locks\\\": UserPagePermissionsProxy(\\n request.user\\n ).can_remove_locks(),\\n \\\"request\\\": request,\\n \\\"csrf_token\\\": parent_context[\\\"csrf_token\\\"],\\n }\\n )\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::RecentEditsPanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::RecentEditsPanel.get_context_data\\n def get_context_data(self, parent_context):\\n request = parent_context[\\\"request\\\"]\\n context = super().get_context_data(parent_context)\\n\\n # Last n edited pages\\n edit_count = getattr(settings, \\\"WAGTAILADMIN_RECENT_EDITS_LIMIT\\\", 5)\\n if connection.vendor == \\\"mysql\\\":\\n # MySQL can't handle the subselect created by the ORM version -\\n # it fails with \\\"This version of MySQL doesn't yet support 'LIMIT & IN/ALL/ANY/SOME subquery'\\\"\\n last_edits = Revision.objects.raw(\\n \\\"\\\"\\\"\\n SELECT wr.* FROM\\n wagtailcore_revision wr JOIN (\\n SELECT max(created_at) AS max_created_at, object_id FROM\\n wagtailcore_revision WHERE user_id = %s AND base_content_type_id = %s GROUP BY object_id ORDER BY max_created_at DESC LIMIT %s\\n ) AS max_rev ON max_rev.max_created_at = wr.created_at ORDER BY wr.created_at DESC\\n \\\"\\\"\\\",\\n [\\n User._meta.pk.get_db_prep_value(request.user.pk, connection),\\n get_default_page_content_type().id,\\n edit_count,\\n ],\\n )\\n else:\\n last_edits_dates = (\\n Revision.page_revisions.filter(user=request.user)\\n .values(\\\"object_id\\\")\\n .annotate(latest_date=Max(\\\"created_at\\\"))\\n .order_by(\\\"-latest_date\\\")\\n .values(\\\"latest_date\\\")[:edit_count]\\n )\\n last_edits = Revision.page_revisions.filter(\\n created_at__in=last_edits_dates\\n ).order_by(\\\"-created_at\\\")\\n\\n # The revision's object_id is a string, so cast it to int first.\\n page_keys = [int(pr.object_id) for pr in last_edits]\\n pages = Page.objects.specific().in_bulk(page_keys)\\n context[\\\"last_edits\\\"] = []\\n for revision in last_edits\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::HomeView.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::HomeView.get_context_data\\n def get_context_data(self, **kwargs):\\n context = super().get_context_data(**kwargs)\\n panels = self.get_panels()\\n site_details = self.get_site_details()\\n\\n context[\\\"media\\\"] = self.get_media(panels)\\n context[\\\"panels\\\"] = sorted(panels, key=lambda p: p.order)\\n context[\\\"user\\\"] = self.request.user\\n\\n return {**context, **site_details}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::HomeView.get_media\",\n \"text\": \"wagtail/admin/views/home.py::HomeView.get_media\\n def get_media(self, panels=[]):\\n media = Media()\\n\\n for panel in panels:\\n media += panel.media\\n\\n return media\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::HomeView.get_panels\",\n \"text\": \"wagtail/admin/views/home.py::HomeView.get_panels\\n def get_panels(self):\\n request = self.request\\n panels = [\\n SiteSummaryPanel(request),\\n WhatsNewInWagtailVersionPanel(),\\n UpgradeNotificationPanel(),\\n WorkflowPagesToModeratePanel(),\\n PagesForModerationPanel(),\\n UserPagesInWorkflowModerationPanel(),\\n RecentEditsPanel(),\\n LockedPagesPanel(),\\n ]\\n\\n for fn in hooks.get_hooks(\\\"construct_homepage_panels\\\"):\\n fn(request, panels)\\n\\n return panels\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::HomeView.get_site_details\",\n \"text\": \"wagtail/admin/views/home.py::HomeView.get_site_details\\n def get_site_details(self):\\n request = self.request\\n site = get_site_for_user(request.user)\\n\\n return {\\n \\\"root_page\\\": site[\\\"root_page\\\"],\\n \\\"root_site\\\": site[\\\"root_site\\\"],\\n \\\"site_name\\\": site[\\\"site_name\\\"],\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::error_test\",\n \"text\": \"wagtail/admin/views/home.py::error_test\\ndef error_test(request):\\n raise Exception(\\\"This is a test of the emergency broadcast system.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::default\",\n \"text\": \"wagtail/admin/views/home.py::default\\n@permission_required(\\\"wagtailadmin.access_admin\\\", login_url=\\\"wagtailadmin_login\\\")\\ndef default(request):\\n \\\"\\\"\\\"\\n Called whenever a request comes in with the correct prefix (eg /admin/) but\\n doesn't actually correspond to a Wagtail view.\\n\\n For authenticated users, it'll raise a 404 error. Anonymous users will be\\n redirected to the login page.\\n \\\"\\\"\\\"\\n raise Http404\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::icons\",\n \"text\": \"wagtail/admin/views/home.py::icons\\ndef icons():\\n global _icons_html\\n if _icons_html is None:\\n icon_hooks = hooks.get_hooks(\\\"register_icons\\\")\\n all_icons = sorted(\\n itertools.chain.from_iterable(hook([]) for hook in icon_hooks)\\n )\\n combined_icon_markup = \\\"\\\"\\n for icon in all_icons:\\n combined_icon_markup += render_to_string(icon).replace(\\\"svg\\\", \\\"symbol\\\")\\n\\n _full_sprite_html = render_to_string(\\n \\\"wagtailadmin/shared/icons.html\\\", {\\\"icons\\\": combined_icon_markup}\\n )\\n return _full_sprite_html\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::sprite\",\n \"text\": \"wagtail/admin/views/home.py::sprite\\ndef sprite(request):\\n return HttpResponse(icons())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BaseBlock.__new__\",\n \"text\": \"wagtail/blocks/base.py::BaseBlock.__new__\\n def __new__(mcs, name, bases, attrs):\\n meta_class = attrs.pop(\\\"Meta\\\", None)\\n\\n cls = super(BaseBlock, mcs).__new__(mcs, name, bases, attrs)\\n\\n # Get all the Meta classes from all the bases\\n meta_class_bases = [meta_class] + [\\n getattr(base, \\\"_meta_class\\\", None) for base in bases\\n ]\\n meta_class_bases = tuple(filter(bool, meta_class_bases))\\n cls._meta_class = type(str(name + \\\"Meta\\\"), meta_class_bases, {})\\n\\n return cls\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.__new__\",\n \"text\": \"wagtail/blocks/base.py::Block.__new__\\n def __new__(cls, *args, **kwargs):\\n # adapted from django.utils.deconstruct.deconstructible; capture the arguments\\n # so that we can return them in the 'deconstruct' method\\n obj = super(Block, cls).__new__(cls)\\n obj._constructor_args = (args, kwargs)\\n return obj\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.__init__\",\n \"text\": \"wagtail/blocks/base.py::Block.__init__\\n def __init__(self, **kwargs):\\n if \\\"classname\\\" in self._constructor_args[1]:\\n # Adding this so that migrations are not triggered\\n # when form_classname is used instead of classname\\n # in the initialisation of the FieldBlock\\n classname = self._constructor_args[1].pop(\\\"classname\\\")\\n self._constructor_args[1].setdefault(\\\"form_classname\\\", classname)\\n\\n self.meta = self._meta_class()\\n\\n for attr, value in kwargs.items():\\n setattr(self.meta, attr, value)\\n\\n # Increase the creation counter, and save our local copy.\\n self.creation_counter = Block.creation_counter\\n Block.creation_counter += 1\\n self.definition_prefix = \\\"blockdef-%d\\\" % self.creation_counter\\n\\n self.label = self.meta.label or \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.set_name\",\n \"text\": \"wagtail/blocks/base.py::Block.set_name\\n def set_name(self, name):\\n self.name = name\\n if not self.meta.label:\\n self.label = capfirst(force_str(name).replace(\\\"_\\\", \\\" \\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.set_meta_options\",\n \"text\": \"wagtail/blocks/base.py::Block.set_meta_options\\n def set_meta_options(self, opts):\\n \\\"\\\"\\\"\\n Update this block's meta options (out of the ones designated as mutable) from the given dict.\\n Used by the StreamField constructor to pass on kwargs that are to be handled by the block,\\n since the block object has already been created by that point, e.g.:\\n body = StreamField(SomeStreamBlock(), max_num=5)\\n \\\"\\\"\\\"\\n for attr, value in opts.items():\\n if attr in self.MUTABLE_META_ATTRIBUTES:\\n setattr(self.meta, attr, value)\\n else:\\n raise TypeError(\\n \\\"set_meta_options received unexpected option: %r\\\" % attr\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.value_from_datadict\",\n \"text\": \"wagtail/blocks/base.py::Block.value_from_datadict\\n def value_from_datadict(self, data, files, prefix):\\n raise NotImplementedError(\\\"%s.value_from_datadict\\\" % self.__class__)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.value_omitted_from_data\",\n \"text\": \"wagtail/blocks/base.py::Block.value_omitted_from_data\\n def value_omitted_from_data(self, data, files, name):\\n \\\"\\\"\\\"\\n Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only StreamBlock)\\n to inform ModelForm logic on Django >=1.10.2 whether the field is absent from the form\\n submission (and should therefore revert to the field default).\\n \\\"\\\"\\\"\\n return name not in data\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.bind\",\n \"text\": \"wagtail/blocks/base.py::Block.bind\\n def bind(self, value, prefix=None, errors=None):\\n \\\"\\\"\\\"\\n Return a BoundBlock which represents the association of this block definition with a value\\n and a prefix (and optionally, a ValidationError to be rendered).\\n BoundBlock primarily exists as a convenience to allow rendering within templates:\\n bound_block.render() rather than blockdef.render(value, prefix) which can't be called from\\n within a template.\\n \\\"\\\"\\\"\\n return BoundBlock(self, value, prefix=prefix, errors=errors)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.get_default\",\n \"text\": \"wagtail/blocks/base.py::Block.get_default\\n def get_default(self):\\n \\\"\\\"\\\"\\n Return this block's default value (conventionally found in self.meta.default),\\n converted to the value type expected by this block. This caters for the case\\n where that value type is not something that can be expressed statically at\\n model definition type (e.g. something like StructValue which incorporates a\\n pointer back to the block definion object).\\n \\\"\\\"\\\"\\n return self.meta.default\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.clean\",\n \"text\": \"wagtail/blocks/base.py::Block.clean\\n def clean(self, value):\\n \\\"\\\"\\\"\\n Validate value and return a cleaned version of it, or throw a ValidationError if validation fails.\\n The thrown ValidationError instance will subsequently be passed to render() to display the\\n error message; the ValidationError must therefore include all detail necessary to perform that\\n rendering, such as identifying the specific child block(s) with errors, in the case of nested\\n blocks. (It is suggested that you use the 'params' attribute for this; using error_list /\\n error_dict is unreliable because Django tends to hack around with these when nested.)\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.to_python\",\n \"text\": \"wagtail/blocks/base.py::Block.to_python\\n def to_python(self, value):\\n \\\"\\\"\\\"\\n Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) Python value to be\\n used in the rest of the block API and within front-end templates . In simple cases this might be\\n the value itself; alternatively, it might be a 'smart' version of the value which behaves mostly\\n like the original value but provides a native HTML rendering when inserted into a template; or it\\n might be something totally different (e.g. an image chooser will use the image ID as the clean\\n value, and turn this back into an actual image object here).\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.bulk_to_python\",\n \"text\": \"wagtail/blocks/base.py::Block.bulk_to_python\\n def bulk_to_python(self, values):\\n \\\"\\\"\\\"\\n Apply the to_python conversion to a list of values. The default implementation simply\\n iterates over the list; subclasses may optimise this, e.g. by combining database lookups\\n into a single query.\\n \\\"\\\"\\\"\\n return [self.to_python(value) for value in values]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.get_prep_value\",\n \"text\": \"wagtail/blocks/base.py::Block.get_prep_value\\n def get_prep_value(self, value):\\n \\\"\\\"\\\"\\n The reverse of to_python; convert the python value into JSON-serialisable form.\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.get_form_state\",\n \"text\": \"wagtail/blocks/base.py::Block.get_form_state\\n def get_form_state(self, value):\\n \\\"\\\"\\\"\\n Convert a python value for this block into a JSON-serialisable representation containing\\n all the data needed to present the value in a form field, to be received by the block's\\n client-side component. Examples of where this conversion is not trivial include rich text\\n (where it needs to be supplied in a format that the editor can process, e.g. ContentState\\n for Draftail) and page / image / document choosers (where it needs to include all displayed\\n data for the selected item, such as title or thumbnail).\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.get_context\",\n \"text\": \"wagtail/blocks/base.py::Block.get_context\\n def get_context(self, value, parent_context=None):\\n \\\"\\\"\\\"\\n Return a dict of context variables (derived from the block value and combined with the parent_context)\\n to be used as the template context when rendering this value through a template.\\n \\\"\\\"\\\"\\n\\n context = parent_context or {}\\n context.update(\\n {\\n \\\"self\\\": value,\\n self.TEMPLATE_VAR: value,\\n }\\n )\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.get_template\",\n \"text\": \"wagtail/blocks/base.py::Block.get_template\\n def get_template(self, context=None):\\n \\\"\\\"\\\"\\n Return the template to use for rendering the block if specified on meta class.\\n This extraction was added to make dynamic templates possible if you override this method\\n \\\"\\\"\\\"\\n return getattr(self.meta, \\\"template\\\", None)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.render\",\n \"text\": \"wagtail/blocks/base.py::Block.render\\n def render(self, value, context=None):\\n \\\"\\\"\\\"\\n Return a text rendering of 'value', suitable for display on templates. By default, this will\\n use a template (with the passed context, supplemented by the result of get_context) if a\\n 'template' property is specified on the block, and fall back on render_basic otherwise.\\n \\\"\\\"\\\"\\n template = self.get_template(context=context)\\n if not template:\\n return self.render_basic(value, context=context)\\n\\n if context is None:\\n new_context = self.get_context(value)\\n else:\\n new_context = self.get_context(value, parent_context=dict(context))\\n\\n return mark_safe(render_to_string(template, new_context))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.get_api_representation\",\n \"text\": \"wagtail/blocks/base.py::Block.get_api_representation\\n def get_api_representation(self, value, context=None):\\n \\\"\\\"\\\"\\n Can be used to customise the API response and defaults to the value returned by get_prep_value.\\n \\\"\\\"\\\"\\n return self.get_prep_value(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.render_basic\",\n \"text\": \"wagtail/blocks/base.py::Block.render_basic\\n def render_basic(self, value, context=None):\\n \\\"\\\"\\\"\\n Return a text rendering of 'value', suitable for display on templates. render() will fall back on\\n this if the block does not define a 'template' property.\\n \\\"\\\"\\\"\\n return force_str(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.get_searchable_content\",\n \"text\": \"wagtail/blocks/base.py::Block.get_searchable_content\\n def get_searchable_content(self, value):\\n \\\"\\\"\\\"\\n Returns a list of strings containing text content within this block to be used in a search engine.\\n \\\"\\\"\\\"\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.extract_references\",\n \"text\": \"wagtail/blocks/base.py::Block.extract_references\\n def extract_references(self, value):\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.check\",\n \"text\": \"wagtail/blocks/base.py::Block.check\\n def check(self, **kwargs):\\n \\\"\\\"\\\"\\n Hook for the Django system checks framework -\\n returns a list of django.core.checks.Error objects indicating validity errors in the block\\n \\\"\\\"\\\"\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block._check_name\",\n \"text\": \"wagtail/blocks/base.py::Block._check_name\\n def _check_name(self, **kwargs):\\n \\\"\\\"\\\"\\n Helper method called by container blocks as part of the system checks framework,\\n to validate that this block's name is a valid identifier.\\n (Not called universally, because not all blocks need names)\\n \\\"\\\"\\\"\\n errors = []\\n if not self.name:\\n errors.append(\\n checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n hint=\\\"Block name cannot be empty\\\",\\n obj=kwargs.get(\\\"field\\\", self),\\n id=\\\"wagtailcore.E001\\\",\\n )\\n )\\n\\n if \\\" \\\" in self.name:\\n errors.append(\\n checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n hint=\\\"Block names cannot contain spaces\\\",\\n obj=kwargs.get(\\\"field\\\", self),\\n id=\\\"wagtailcore.E001\\\",\\n )\\n )\\n\\n if \\\"-\\\" in self.name:\\n errors.append(\\n checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n \\\"Block names cannot contain dashes\\\",\\n obj=kwargs.get(\\\"field\\\", self),\\n id=\\\"wagtailcore.E001\\\",\\n )\\n )\\n\\n if self.name and self.name[0].isdigit():\\n errors.append(\\n checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n \\\"Block names cannot begin with a digit\\\",\\n obj=kwargs.get(\\\"field\\\", self),\\n id=\\\"wagtailcore.E001\\\",\\n )\\n )\\n\\n if not errors and not re.match(r\\\"^[_a-zA-Z][_a-zA-Z0-9]*$\\\", self.name):\\n errors.append(\\n checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n \\\"Block names should follow standard Python conventions for \\\"\\n \\\"variable na\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.id_for_label\",\n \"text\": \"wagtail/blocks/base.py::Block.id_for_label\\n def id_for_label(self, prefix):\\n \\\"\\\"\\\"\\n Return the ID to be used as the 'for' attribute of <label> elements that refer to this block,\\n when the given field prefix is in use. Return None if no 'for' attribute should be used.\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.required\",\n \"text\": \"wagtail/blocks/base.py::Block.required\\n @property\\n def required(self):\\n \\\"\\\"\\\"\\n Flag used to determine whether labels for this block should display a 'required' asterisk.\\n False by default, since Block does not provide any validation of its own - it's up to subclasses\\n to define what required-ness means.\\n \\\"\\\"\\\"\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.deconstruct\",\n \"text\": \"wagtail/blocks/base.py::Block.deconstruct\\n def deconstruct(self):\\n # adapted from django.utils.deconstruct.deconstructible\\n module_name = self.__module__\\n name = self.__class__.__name__\\n\\n # Make sure it's actually there and not an inner class\\n module = import_module(module_name)\\n if not hasattr(module, name):\\n raise ValueError(\\n \\\"Could not find object %s in %s.\\\\n\\\"\\n \\\"Please note that you cannot serialize things like inner \\\"\\n \\\"classes. Please move the object into the main module \\\"\\n \\\"body to use migrations.\\\\n\\\" % (name, module_name)\\n )\\n\\n # if the module defines a DECONSTRUCT_ALIASES dictionary, see if the class has an entry in there;\\n # if so, use that instead of the real path\\n try:\\n path = module.DECONSTRUCT_ALIASES[self.__class__]\\n except (AttributeError, KeyError):\\n path = \\\"%s.%s\\\" % (module_name, name)\\n\\n return (\\n path,\\n self._constructor_args[0],\\n self._constructor_args[1],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::Block.__eq__\",\n \"text\": \"wagtail/blocks/base.py::Block.__eq__\\n def __eq__(self, other):\\n \\\"\\\"\\\"\\n Implement equality on block objects so that two blocks with matching definitions are considered\\n equal. Block objects are intended to be immutable with the exception of set_name() and any meta\\n attributes identified in MUTABLE_META_ATTRIBUTES, so checking these along with the result of\\n deconstruct (which captures the constructor arguments) is sufficient to identify (valid) differences.\\n\\n This was originally necessary as a workaround for https://code.djangoproject.com/ticket/24340\\n in Django <1.9; the deep_deconstruct function used to detect changes for migrations did not\\n recurse into the block lists, and left them as Block instances. This __eq__ method therefore\\n came into play when identifying changes within migrations.\\n\\n As of Django >=1.9, this *probably* isn't required any more. However, it may be useful in\\n future as a way of identifying blocks that can be re-used within StreamField definitions\\n (https://github.com/wagtail/wagtail/issues/4298#issuecomment-367656028).\\n \\\"\\\"\\\"\\n\\n if not isinstance(other, Block):\\n # if the other object isn't a block at all, it clearly isn't equal.\\n return False\\n\\n # Note that we do not require the two blocks to be of the exact same class. This is because\\n # we may wish the following blocks to be considered equal:\\n #\\n # class FooBlock(StructBlock):\\n # first_name = CharBlock()\\n # surname = CharBlock()\\n #\\n # class BarBlock(StructBlock):\\n # first_name = CharBlock()\\n # surname = CharBlock()\\n #\\n # FooBlock() == BarBlock() == StructBlock([('first_name', CharBlock()), ('surname': CharBlock())])\\n #\\n # For this to work, StructBlock will need to ensure that 'deconstruct' returns the same s\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BoundBlock.__init__\",\n \"text\": \"wagtail/blocks/base.py::BoundBlock.__init__\\n def __init__(self, block, value, prefix=None, errors=None):\\n self.block = block\\n self.value = value\\n self.prefix = prefix\\n self.errors = errors\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BoundBlock.render\",\n \"text\": \"wagtail/blocks/base.py::BoundBlock.render\\n def render(self, context=None):\\n return self.block.render(self.value, context=context)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BoundBlock.render_as_block\",\n \"text\": \"wagtail/blocks/base.py::BoundBlock.render_as_block\\n def render_as_block(self, context=None):\\n \\\"\\\"\\\"\\n Alias for render; the include_block tag will specifically check for the presence of a method\\n with this name. (This is because {% include_block %} is just as likely to be invoked on a bare\\n value as a BoundBlock. If we looked for a `render` method instead, we'd run the risk of finding\\n an unrelated method that just happened to have that name - for example, when called on a\\n PageChooserBlock it could end up calling page.render.\\n \\\"\\\"\\\"\\n return self.block.render(self.value, context=context)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BoundBlock.id_for_label\",\n \"text\": \"wagtail/blocks/base.py::BoundBlock.id_for_label\\n def id_for_label(self):\\n return self.block.id_for_label(self.prefix)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BoundBlock.__str__\",\n \"text\": \"wagtail/blocks/base.py::BoundBlock.__str__\\n def __str__(self):\\n \\\"\\\"\\\"Render the value according to the block's native rendering\\\"\\\"\\\"\\n return self.block.render(self.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BoundBlock.__repr__\",\n \"text\": \"wagtail/blocks/base.py::BoundBlock.__repr__\\n def __repr__(self):\\n return \\\"<block %s: %r>\\\" % (\\n self.block.name or type(self.block).__name__,\\n self.value,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__\",\n \"text\": \"wagtail/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__\\n def __new__(mcs, name, bases, attrs):\\n # Collect sub-blocks declared on the current class.\\n # These are available on the class as `declared_blocks`\\n current_blocks = []\\n for key, value in list(attrs.items()):\\n if isinstance(value, Block):\\n current_blocks.append((key, value))\\n value.set_name(key)\\n attrs.pop(key)\\n current_blocks.sort(key=lambda x: x[1].creation_counter)\\n attrs[\\\"declared_blocks\\\"] = collections.OrderedDict(current_blocks)\\n\\n new_class = super(DeclarativeSubBlocksMetaclass, mcs).__new__(\\n mcs, name, bases, attrs\\n )\\n\\n # Walk through the MRO, collecting all inherited sub-blocks, to make\\n # the combined `base_blocks`.\\n base_blocks = collections.OrderedDict()\\n for base in reversed(new_class.__mro__):\\n # Collect sub-blocks from base class.\\n if hasattr(base, \\\"declared_blocks\\\"):\\n base_blocks.update(base.declared_blocks)\\n\\n # Field shadowing.\\n for attr, value in base.__dict__.items():\\n if value is None and attr in base_blocks:\\n base_blocks.pop(attr)\\n new_class.base_blocks = base_blocks\\n\\n return new_class\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BlockWidget.__init__\",\n \"text\": \"wagtail/blocks/base.py::BlockWidget.__init__\\n def __init__(self, block_def, attrs=None):\\n super().__init__(attrs=attrs)\\n self.block_def = block_def\\n self._js_context = None\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BlockWidget._build_block_json\",\n \"text\": \"wagtail/blocks/base.py::BlockWidget._build_block_json\\n def _build_block_json(self):\\n self._js_context = JSContext()\\n self._block_json = json.dumps(self._js_context.pack(self.block_def))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BlockWidget.js_context\",\n \"text\": \"wagtail/blocks/base.py::BlockWidget.js_context\\n @property\\n def js_context(self):\\n if self._js_context is None:\\n self._build_block_json()\\n\\n return self._js_context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BlockWidget.block_json\",\n \"text\": \"wagtail/blocks/base.py::BlockWidget.block_json\\n @property\\n def block_json(self):\\n if self._js_context is None:\\n self._build_block_json()\\n\\n return self._block_json\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BlockWidget.id_for_label\",\n \"text\": \"wagtail/blocks/base.py::BlockWidget.id_for_label\\n def id_for_label(self, prefix):\\n # Delegate the job of choosing a label ID to the top-level block.\\n # (In practice, the top-level block will typically be a StreamBlock, which returns None.)\\n return self.block_def.id_for_label(prefix)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/blocks/base.py::BlockWidget.render_with_errors\",\n \"text\": \"wagtail/blocks/base.py::BlockWidget.render_with_errors\\n def render_with_errors(self, name, value, attrs=None, errors=None, renderer=None):\\n value_json = json.dumps(self.block_def.get_form_state(value))\\n\\n if errors:\\n errors_json = json.dumps(self.js_context.pack(errors.as_data()))\\n else:\\n errors_json = \\\"[]\\\"\\n\\n return format_html(\\n \\\"\\\"\\\"\\n <div id=\\\"{id}\\\" data-block=\\\"{block_json}\\\" data-value=\\\"{value_json}\\\" data-errors=\\\"{errors_json}\\\"></div>\\n <script>\\n initBlockWidget('{id}');\\n </script>\\n \\\"\\\"\\\",\\n id=name,\\n block_json=self.block_json,\\n value_json=value_json,\\n errors_json=errors_json,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.delete\",\n \"text\": \"wagtail/query.py::TreeQuerySet.delete\\n def delete(self):\\n \\\"\\\"\\\"Redefine the delete method unbound, so we can set the queryset_only parameter.\\\"\\\"\\\"\\n super().delete()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.descendant_of_q\",\n \"text\": \"wagtail/query.py::TreeQuerySet.descendant_of_q\\n def descendant_of_q(self, other, inclusive=False):\\n q = Q(path__startswith=other.path) & Q(depth__gte=other.depth)\\n\\n if not inclusive:\\n q &= ~Q(pk=other.pk)\\n\\n return q\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.descendant_of\",\n \"text\": \"wagtail/query.py::TreeQuerySet.descendant_of\\n def descendant_of(self, other, inclusive=False):\\n \\\"\\\"\\\"\\n This filters the QuerySet to only contain pages that descend from the specified page.\\n\\n If inclusive is set to True, it will also contain the page itself (instead of just its descendants).\\n \\\"\\\"\\\"\\n return self.filter(self.descendant_of_q(other, inclusive))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.not_descendant_of\",\n \"text\": \"wagtail/query.py::TreeQuerySet.not_descendant_of\\n def not_descendant_of(self, other, inclusive=False):\\n \\\"\\\"\\\"\\n This filters the QuerySet to not contain any pages that descend from the specified page.\\n\\n If inclusive is set to True, it will also exclude the specified page.\\n \\\"\\\"\\\"\\n return self.exclude(self.descendant_of_q(other, inclusive))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.child_of_q\",\n \"text\": \"wagtail/query.py::TreeQuerySet.child_of_q\\n def child_of_q(self, other):\\n return self.descendant_of_q(other) & Q(depth=other.depth + 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.child_of\",\n \"text\": \"wagtail/query.py::TreeQuerySet.child_of\\n def child_of(self, other):\\n \\\"\\\"\\\"\\n This filters the QuerySet to only contain pages that are direct children of the specified page.\\n \\\"\\\"\\\"\\n return self.filter(self.child_of_q(other))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.not_child_of\",\n \"text\": \"wagtail/query.py::TreeQuerySet.not_child_of\\n def not_child_of(self, other):\\n \\\"\\\"\\\"\\n This filters the QuerySet to not contain any pages that are direct children of the specified page.\\n \\\"\\\"\\\"\\n return self.exclude(self.child_of_q(other))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.ancestor_of_q\",\n \"text\": \"wagtail/query.py::TreeQuerySet.ancestor_of_q\\n def ancestor_of_q(self, other, inclusive=False):\\n paths = [\\n other.path[0:pos]\\n for pos in range(0, len(other.path) + 1, other.steplen)[1:]\\n ]\\n q = Q(path__in=paths)\\n\\n if not inclusive:\\n q &= ~Q(pk=other.pk)\\n\\n return q\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.ancestor_of\",\n \"text\": \"wagtail/query.py::TreeQuerySet.ancestor_of\\n def ancestor_of(self, other, inclusive=False):\\n \\\"\\\"\\\"\\n This filters the QuerySet to only contain pages that are ancestors of the specified page.\\n\\n If inclusive is set to True, it will also include the specified page.\\n \\\"\\\"\\\"\\n return self.filter(self.ancestor_of_q(other, inclusive))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.not_ancestor_of\",\n \"text\": \"wagtail/query.py::TreeQuerySet.not_ancestor_of\\n def not_ancestor_of(self, other, inclusive=False):\\n \\\"\\\"\\\"\\n This filters the QuerySet to not contain any pages that are ancestors of the specified page.\\n\\n If inclusive is set to True, it will also exclude the specified page.\\n \\\"\\\"\\\"\\n return self.exclude(self.ancestor_of_q(other, inclusive))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.parent_of_q\",\n \"text\": \"wagtail/query.py::TreeQuerySet.parent_of_q\\n def parent_of_q(self, other):\\n return Q(path=self.model._get_parent_path_from_path(other.path))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.parent_of\",\n \"text\": \"wagtail/query.py::TreeQuerySet.parent_of\\n def parent_of(self, other):\\n \\\"\\\"\\\"\\n This filters the QuerySet to only contain the parent of the specified page.\\n \\\"\\\"\\\"\\n return self.filter(self.parent_of_q(other))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.not_parent_of\",\n \"text\": \"wagtail/query.py::TreeQuerySet.not_parent_of\\n def not_parent_of(self, other):\\n \\\"\\\"\\\"\\n This filters the QuerySet to exclude the parent of the specified page.\\n \\\"\\\"\\\"\\n return self.exclude(self.parent_of_q(other))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.sibling_of_q\",\n \"text\": \"wagtail/query.py::TreeQuerySet.sibling_of_q\\n def sibling_of_q(self, other, inclusive=True):\\n q = Q(path__startswith=self.model._get_parent_path_from_path(other.path)) & Q(\\n depth=other.depth\\n )\\n\\n if not inclusive:\\n q &= ~Q(pk=other.pk)\\n\\n return q\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.sibling_of\",\n \"text\": \"wagtail/query.py::TreeQuerySet.sibling_of\\n def sibling_of(self, other, inclusive=True):\\n \\\"\\\"\\\"\\n This filters the QuerySet to only contain pages that are siblings of the specified page.\\n\\n By default, inclusive is set to True so it will include the specified page in the results.\\n\\n If inclusive is set to False, the page will be excluded from the results.\\n \\\"\\\"\\\"\\n return self.filter(self.sibling_of_q(other, inclusive))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::TreeQuerySet.not_sibling_of\",\n \"text\": \"wagtail/query.py::TreeQuerySet.not_sibling_of\\n def not_sibling_of(self, other, inclusive=True):\\n \\\"\\\"\\\"\\n This filters the QuerySet to not contain any pages that are siblings of the specified page.\\n\\n By default, inclusive is set to True so it will exclude the specified page from the results.\\n\\n If inclusive is set to False, the page will be included in the results.\\n \\\"\\\"\\\"\\n return self.exclude(self.sibling_of_q(other, inclusive))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.__init__\",\n \"text\": \"wagtail/query.py::PageQuerySet.__init__\\n def __init__(self, *args, **kwargs):\\n \\\"\\\"\\\"Set custom instance attributes\\\"\\\"\\\"\\n super().__init__(*args, **kwargs)\\n # set by defer_streamfields()\\n self._defer_streamfields = False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet._clone\",\n \"text\": \"wagtail/query.py::PageQuerySet._clone\\n def _clone(self):\\n \\\"\\\"\\\"Ensure clones inherit custom attribute values.\\\"\\\"\\\"\\n clone = super()._clone()\\n clone._defer_streamfields = self._defer_streamfields\\n return clone\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.live_q\",\n \"text\": \"wagtail/query.py::PageQuerySet.live_q\\n def live_q(self):\\n return Q(live=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.live\",\n \"text\": \"wagtail/query.py::PageQuerySet.live\\n def live(self):\\n \\\"\\\"\\\"\\n This filters the QuerySet to only contain published pages.\\n \\\"\\\"\\\"\\n return self.filter(self.live_q())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.not_live\",\n \"text\": \"wagtail/query.py::PageQuerySet.not_live\\n def not_live(self):\\n \\\"\\\"\\\"\\n This filters the QuerySet to only contain unpublished pages.\\n \\\"\\\"\\\"\\n return self.exclude(self.live_q())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.in_menu_q\",\n \"text\": \"wagtail/query.py::PageQuerySet.in_menu_q\\n def in_menu_q(self):\\n return Q(show_in_menus=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.in_menu\",\n \"text\": \"wagtail/query.py::PageQuerySet.in_menu\\n def in_menu(self):\\n \\\"\\\"\\\"\\n This filters the QuerySet to only contain pages that are in the menus.\\n \\\"\\\"\\\"\\n return self.filter(self.in_menu_q())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.not_in_menu\",\n \"text\": \"wagtail/query.py::PageQuerySet.not_in_menu\\n def not_in_menu(self):\\n \\\"\\\"\\\"\\n This filters the QuerySet to only contain pages that are not in the menus.\\n \\\"\\\"\\\"\\n return self.exclude(self.in_menu_q())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.page_q\",\n \"text\": \"wagtail/query.py::PageQuerySet.page_q\\n def page_q(self, other):\\n return Q(id=other.id)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.page\",\n \"text\": \"wagtail/query.py::PageQuerySet.page\\n def page(self, other):\\n \\\"\\\"\\\"\\n This filters the QuerySet so it only contains the specified page.\\n \\\"\\\"\\\"\\n return self.filter(self.page_q(other))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.not_page\",\n \"text\": \"wagtail/query.py::PageQuerySet.not_page\\n def not_page(self, other):\\n \\\"\\\"\\\"\\n This filters the QuerySet so it doesn't contain the specified page.\\n \\\"\\\"\\\"\\n return self.exclude(self.page_q(other))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.type_q\",\n \"text\": \"wagtail/query.py::PageQuerySet.type_q\\n def type_q(self, *types):\\n all_subclasses = {\\n model for model in apps.get_models() if issubclass(model, types)\\n }\\n content_types = ContentType.objects.get_for_models(*all_subclasses)\\n return Q(content_type__in=list(content_types.values()))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.type\",\n \"text\": \"wagtail/query.py::PageQuerySet.type\\n def type(self, *types):\\n \\\"\\\"\\\"\\n This filters the QuerySet to only contain pages that are an instance\\n of the specified model(s) (including subclasses).\\n \\\"\\\"\\\"\\n return self.filter(self.type_q(*types))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.not_type\",\n \"text\": \"wagtail/query.py::PageQuerySet.not_type\\n def not_type(self, *types):\\n \\\"\\\"\\\"\\n This filters the QuerySet to exclude any pages which are an instance of the specified model(s).\\n \\\"\\\"\\\"\\n return self.exclude(self.type_q(*types))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.exact_type_q\",\n \"text\": \"wagtail/query.py::PageQuerySet.exact_type_q\\n def exact_type_q(self, *types):\\n content_types = ContentType.objects.get_for_models(*types)\\n return Q(content_type__in=list(content_types.values()))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.exact_type\",\n \"text\": \"wagtail/query.py::PageQuerySet.exact_type\\n def exact_type(self, *types):\\n \\\"\\\"\\\"\\n This filters the QuerySet to only contain pages that are an instance of the specified model(s)\\n (matching the model exactly, not subclasses).\\n \\\"\\\"\\\"\\n return self.filter(self.exact_type_q(*types))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.not_exact_type\",\n \"text\": \"wagtail/query.py::PageQuerySet.not_exact_type\\n def not_exact_type(self, *types):\\n \\\"\\\"\\\"\\n This filters the QuerySet to exclude any pages which are an instance of the specified model(s)\\n (matching the model exactly, not subclasses).\\n \\\"\\\"\\\"\\n return self.exclude(self.exact_type_q(*types))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.private_q\",\n \"text\": \"wagtail/query.py::PageQuerySet.private_q\\n def private_q(self):\\n from wagtail.models import PageViewRestriction\\n\\n q = Q()\\n for restriction in PageViewRestriction.objects.select_related(\\\"page\\\").all():\\n q |= self.descendant_of_q(restriction.page, inclusive=True)\\n\\n # do not match any page if no private section exists.\\n return q if q else Q(pk__in=[])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.public\",\n \"text\": \"wagtail/query.py::PageQuerySet.public\\n def public(self):\\n \\\"\\\"\\\"\\n Filters the QuerySet to only contain pages that are not in a private\\n section and their descendants.\\n \\\"\\\"\\\"\\n return self.exclude(self.private_q())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.not_public\",\n \"text\": \"wagtail/query.py::PageQuerySet.not_public\\n def not_public(self):\\n \\\"\\\"\\\"\\n Filters the QuerySet to only contain pages that are in a private\\n section and their descendants.\\n \\\"\\\"\\\"\\n return self.filter(self.private_q())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.private\",\n \"text\": \"wagtail/query.py::PageQuerySet.private\\n def private(self):\\n \\\"\\\"\\\"\\n Filters the QuerySet to only contain pages that are in a private\\n section and their descendants.\\n \\\"\\\"\\\"\\n return self.filter(self.private_q())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.first_common_ancestor\",\n \"text\": \"wagtail/query.py::PageQuerySet.first_common_ancestor\\n def first_common_ancestor(self, include_self=False, strict=False):\\n \\\"\\\"\\\"\\n Find the first ancestor that all pages in this queryset have in common.\\n For example, consider a page hierarchy like::\\n\\n - Home/\\n - Foo Event Index/\\n - Foo Event Page 1/\\n - Foo Event Page 2/\\n - Bar Event Index/\\n - Bar Event Page 1/\\n - Bar Event Page 2/\\n\\n The common ancestors for some queries would be:\\n\\n .. code-block:: python\\n\\n >>> Page.objects\\\\\\\\\\n ... .type(EventPage)\\\\\\\\\\n ... .first_common_ancestor()\\n <Page: Home>\\n >>> Page.objects\\\\\\\\\\n ... .type(EventPage)\\\\\\\\\\n ... .filter(title__contains='Foo')\\\\\\\\\\n ... .first_common_ancestor()\\n <Page: Foo Event Index>\\n\\n This method tries to be efficient, but if you have millions of pages\\n scattered across your page tree, it will be slow.\\n\\n If `include_self` is True, the ancestor can be one of the pages in the\\n queryset:\\n\\n .. code-block:: python\\n\\n >>> Page.objects\\\\\\\\\\n ... .filter(title__contains='Foo')\\\\\\\\\\n ... .first_common_ancestor()\\n <Page: Foo Event Index>\\n >>> Page.objects\\\\\\\\\\n ... .filter(title__exact='Bar Event Index')\\\\\\\\\\n ... .first_common_ancestor()\\n <Page: Bar Event Index>\\n\\n A few invalid cases exist: when the queryset is empty, when the root\\n Page is in the queryset and ``include_self`` is False, and when there\\n are multiple page trees with no common root (a case Wagtail does not\\n support). If ``strict`` is False (the default), then the first root\\n node is returned in these cases. If ``strict`` is True, then a\\n ``ObjectDoesNotExist`` is raised.\\n \\\"\\\"\\\"\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.unpublish\",\n \"text\": \"wagtail/query.py::PageQuerySet.unpublish\\n def unpublish(self):\\n \\\"\\\"\\\"\\n This unpublishes all live pages in the QuerySet.\\n \\\"\\\"\\\"\\n for page in self.live():\\n page.unpublish()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/query.py::PageQuerySet.defer_streamfields\",\n \"text\": \"wagtail/query.py::PageQuerySet.defer_streamfields\\n def defer_streamfields(self):\\n \\\"\\\"\\\"\\n Apply to a queryset to prevent fetching/decoding of StreamField values on\\n evaluation. Useful when working with potentially large numbers of results,\\n where StreamField values are unlikely to be needed. For example, when\\n generating a sitemap or a long list of page links.\\n \\\"\\\"\\\"\\n clone = self._clone()\\n clone._defer_streamfields = True # used by specific_iterator()\\n streamfield_names = self.model.get_streamfield_names()\\n if not streamfield_names:\\n return clone\\n return clone.defer(*streamfield_names)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":78,"cells":{"instance_id":{"kind":"string","value":"google__jax-1972"},"repo":{"kind":"string","value":"google/jax"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Nit: lax.conv_general_dilated does not support 0D-convolution\nAFAIK 0-dimensional convolution should reduce to a fully-connected layer, but fails with the following example and error. While this is not a practically useful scenario, degenerate cases like these could be useful for testing.\r\n```\r\nfrom jax import lax\r\nimport jax.numpy as np\r\n\r\nlax.conv_general_dilated(lhs=np.ones((10, 5)), \r\n rhs=np.ones((5, 7)), \r\n strides=(), \r\n paddign='SAME', \r\n dimension_numbers=('NC', 'IO', 'NC'))\r\n```\r\n```\r\n---------------------------------------------------------------------------\r\nValueError Traceback (most recent call last)\r\n<ipython-input-17-573e7695a148> in <module>()\r\n----> 1 lax.conv_general_dilated(np.ones((10, 5)), np.ones((5, 7)), (), 'SAME', dimension_numbers=('NC', 'IO', 'NC'))\r\n\r\n6 frames\r\ngoogle3/third_party/py/jax/lax/lax.py in conv_general_dilated(lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation, dimension_numbers, feature_group_count, precision)\r\n 516 feature_group_count=feature_group_count,\r\n 517 lhs_shape=lhs.shape, rhs_shape=rhs.shape,\r\n--> 518 precision=_canonicalize_precision(precision))\r\n 519 \r\n 520 def dot(lhs, rhs, precision=None):\r\n\r\ngoogle3/third_party/py/jax/core.py in bind(self, *args, **kwargs)\r\n 148 top_trace = find_top_trace(args)\r\n 149 if top_trace is None:\r\n--> 150 return self.impl(*args, **kwargs)\r\n 151 \r\n 152 tracers = map(top_trace.full_raise, args)\r\n\r\ngoogle3/third_party/py/jax/interpreters/xla.py in apply_primitive(prim, *args, **params)\r\n 150 def apply_primitive(prim, *args, **params):\r\n 151 \"\"\"Impl rule that compiles and runs a single primitive 'prim' using XLA.\"\"\"\r\n--> 152 compiled_fun = xla_primitive_callable(prim, *map(arg_spec, args), **params)\r\n 153 return compiled_fun(*args)\r\n 154 \r\n\r\ngoogle3/third_party/py/jax/interpreters/xla.py in xla_primitive_callable(prim, *arg_specs, **params)\r\n 174 device = device and next(d for d in all_devices if (type(d), d.id) == device)\r\n 175 backend = xb.get_device_backend(device)\r\n--> 176 aval_out = prim.abstract_eval(*avals, **params)\r\n 177 if prim.multiple_results:\r\n 178 handlers = tuple(map(aval_to_result_handler, aval_out))\r\n\r\ngoogle3/third_party/py/jax/lax/lax.py in standard_abstract_eval(prim, shape_rule, dtype_rule, *args, **kwargs)\r\n 1500 return ConcreteArray(prim.impl(*[x.val for x in args], **kwargs))\r\n 1501 elif least_specialized is ShapedArray:\r\n-> 1502 return ShapedArray(shape_rule(*args, **kwargs), dtype_rule(*args, **kwargs))\r\n 1503 elif least_specialized is UnshapedArray:\r\n 1504 return UnshapedArray(dtype_rule(*args, **kwargs))\r\n\r\ngoogle3/third_party/py/jax/lax/lax.py in _conv_general_dilated_shape_rule(lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation, dimension_numbers, feature_group_count, **unused_kwargs)\r\n 1943 lhs_trans = _dilate_shape(onp.take(lhs.shape, lhs_perm), lhs_dilation)\r\n 1944 rhs_trans = _dilate_shape(onp.take(rhs.shape, rhs_perm), rhs_dilation)\r\n-> 1945 out_trans = conv_shape_tuple(lhs_trans, rhs_trans, window_strides, padding)\r\n 1946 return tuple(onp.take(out_trans, onp.argsort(out_perm)))\r\n 1947 \r\n\r\ngoogle3/third_party/py/jax/lax/lax.py in conv_shape_tuple(lhs_shape, rhs_shape, strides, pads)\r\n 4334 raise TypeError(msg.format(len(lhs_shape) - 2, len(pads)))\r\n 4335 \r\n-> 4336 lhs_padded = onp.add(lhs_shape[2:], onp.add(*zip(*pads)))\r\n 4337 out_space = onp.floor_divide(\r\n 4338 onp.subtract(lhs_padded, rhs_shape[2:]), strides) + 1\r\n\r\nValueError: invalid number of arguments\r\n```\n"},"gold_files":{"kind":"list like","value":["jax/lax/lax.py"],"string":"[\n \"jax/lax/lax.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["jax/lax/lax.py::conv_shape_tuple"],"string":"[\n \"jax/lax/lax.py::conv_shape_tuple\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"jax/experimental/stax.py","text":"jax/experimental/stax.py\ndef Dense(out_dim, W_init=glorot_normal(), b_init=normal()) # Layer constructor function for a dense (fully-connected) layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef GeneralConv(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=normal(1e-06)) # Layer construction function for a general convolution layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=normal(1e-06)) # Layer construction function for a general transposed-convolution layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-05, center=True, scale=True, beta_init=zeros, gamma_init=ones) # Layer construction function for a batch normalization layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, x, **kwargs)\ndef elementwise(fun, **fun_kwargs) # Layer that applies a scalar function elementwise on its inputs.\ndef _pooling_layer(reducer, init_val, rescaler=None)\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec='NHWC') # Layer construction function for a pooling layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef _normalize_by_window_size(dims, strides, padding)\n def rescale(outputs, inputs, spec)\ndef Flatten() # Layer construction function for flattening all but the leading dim.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef Identity() # Layer construction function for an identity layer.\ndef FanOut(num) # Layer construction function for a fan-out layer.\ndef FanInSum() # Layer construction function for a fan-in sum layer.\ndef FanInConcat(axis=-1) # Layer construction function for a fan-in concatenation layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef Dropout(rate, mode='train') # Layer construction function for a dropout layer with given rate.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef serial(*layers) # Combinator for composing layers in serial.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef parallel(*layers) # Combinator for composing layers in parallel.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef shape_dependent(make_layer) # Combinator to delay layer constructor pair until input shapes are known.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)","label":0},{"cid":"jax/lax/lax.py","text":"jax/lax/lax.py\ndef broadcast_shapes(*shapes) # Returns the shape that results from NumPy broadcasting of `shapes`.\ndef _canonicalize_shape(shape) # Canonicalizes and checks for errors in a user-provided shape value.\ndef _identity(x)\ndef neg(x) # Elementwise negation: :math:`-x`.\ndef sign(x) # Elementwise sign.\ndef nextafter(x1, x2) # Returns the next representable value after `x1` in the direction of `x2`.\ndef floor(x) # Elementwise floor: :math:`\\left\\lfloor x \\right\\rfloor`.\ndef ceil(x) # Elementwise ceiling: :math:`\\left\\lceil x \\right\\rceil`.\ndef round(x) # Elementwise round.\ndef is_finite(x) # Elementwise :math:`\\mathrm{isfinite}`.\ndef exp(x) # Elementwise exponential: :math:`e^x`.\ndef expm1(x) # Elementwise :math:`e^{x - 1}`.\ndef log(x) # Elementwise natural logarithm: :math:`\\mathrm{log}(x)`.\ndef log1p(x) # Elementwise :math:`\\mathrm{log}(1 + x)`.\ndef tanh(x) # Elementwise hyperbolic tangent: :math:`\\mathrm{tanh}(x)`.\ndef sin(x) # Elementwise sine: :math:`\\mathrm{sin}(x)`.\ndef cos(x) # Elementwise cosine: :math:`\\mathrm{cos}(x)`.\ndef atan2(x, y) # Elementwise arc tangent of two variables:\ndef lgamma(x) # Elementwise log gamma: :math:`\\mathrm{log}(\\Gamma(x))`.\ndef digamma(x) # Elementwise digamma: :math:`\\psi(x)`.\ndef bessel_i0e(x) # Exponentially scaled modified Bessel function of order 0:\ndef bessel_i1e(x) # Exponentially scaled modified Bessel function of order 1:\ndef erf(x) # Elementwise error function: :math:`\\mathrm{erf}(x)`.\ndef erfc(x) # Elementwise complementary error function:\ndef erf_inv(x) # Elementwise inverse error function: :math:`\\mathrm{erf}^{-1}(x)`.\ndef real(x) # Elementwise extract real part: :math:`\\mathrm{Re}(x)`.\ndef imag(x) # Elementwise extract imaginary part: :math:`\\mathrm{Im}(x)`.\ndef complex(x, y) # Elementwise make complex number: :math:`x + jy`.\ndef conj(x) # Elementwise complex conjugate function: :math:`\\overline{x}`.\ndef abs(x) # Elementwise absolute value: :math:`|x|`.\ndef pow(x, y) # Elementwise power: :math:`x^y`.\ndef sqrt(x) # Elementwise square root: :math:`\\sqrt{x}`.\ndef rsqrt(x) # Elementwise reciprocal square root: :math:`1 \\over \\sqrt{x}.\ndef bitwise_not(x) # Elementwise NOT: :math:`\\neg x`.\ndef bitwise_and(x, y) # Elementwise AND: :math:`x \\wedge y`.\ndef bitwise_or(x, y) # Elementwise OR: :math:`x \\vee y`.\ndef bitwise_xor(x, y) # Elementwise exclusive OR: :math:`x \\oplus y`.\ndef add(x, y) # Elementwise addition: :math:`x + y`.\ndef sub(x, y) # Elementwise subtraction: :math:`x - y`.\ndef mul(x, y) # Elementwise multiplication: :math:`x \\times y`.\ndef div(x, y) # Elementwise division: :math:`x \\over y`.\ndef rem(x, y) # Elementwise remainder: :math:`x \\bmod y`.\ndef max(x, y) # Elementwise maximum: :math:`\\mathrm{max}(x, y)`\ndef min(x, y) # Elementwise minimum: :math:`\\mathrm{min}(x, y)`\ndef shift_left(x, y) # Elementwise left shift: :math:`x \\ll y`.\ndef shift_right_arithmetic(x, y) # Elementwise arithmetic right shift: :math:`x \\gg y`.\ndef shift_right_log","label":1},{"cid":"jax/lax/lax_parallel.py","text":"jax/lax/lax_parallel.py\ndef psum(x, axis_name) # Compute an all-reduce sum on ``x`` over the pmapped axis ``axis_name``.\ndef pmax(x, axis_name) # Compute an all-reduce max on ``x`` over the pmapped axis ``axis_name``.\ndef pmin(x, axis_name) # Compute an all-reduce min on ``x`` over the pmapped axis ``axis_name``.\ndef ppermute(x, axis_name, perm) # Perform a collective permutation according to the permutation ``perm``.\ndef pswapaxes(x, axis_name, axis) # Swap the pmapped axis ``axis_name`` with the unmapped axis ``axis``.\ndef all_to_all(x, axis_name, split_axis, concat_axis) # Materialize the mapped axis and map a different axis.\ndef standard_pmap_primitive(name)\ndef _allreduce_split_axis_rule(prim, reducer, vals, which_mapped, axis_name)\ndef _allreduce_translation_rule(prim, c, val, replica_groups)\ndef _psum_translation_rule(c, val, replica_groups)\ndef _ppermute_translation_rule(c, x, replica_groups, perm)\ndef _ppermute_transpose_rule(t, perm, axis_name)\ndef _all_to_all_translation_rule(c, x, split_axis, concat_axis, replica_groups)\ndef _all_to_all_split_axis_rule(vals, which_mapped, split_axis, concat_axis, axis_name)\ndef _moveaxis(src, dst, x)\ndef _drop(x, dim, axis_name)\ndef _allgather(x, dim, size, axis_name)\ndef _broadcasting_papply(prim, name, size, vals, axes, **params)\ndef _defbroadcasting(prim)\ndef _vectorized_papply(prim, name, size, vals, axes, **params)\ndef _defvectorized(prim)\ndef _reducer_papply(prim, cprim, name, size, vals, papply_axes, axes, **kwargs)\ndef _defreducer(prim, collective_prim)\ndef _identity_papply(prim, argnum, name, size, vals, axes, **params)\ndef _defidentity(prim, argnum=0)\ndef _dot_general_papply_rule(name, size, vals, dims, dimension_numbers, precision)\n def adjust_dims(dims, thresh)\n def sub_dims(xdim, ydim, xcontract, ycontract, xbatch, ybatch)\n def cases(x, y, xdim, ydim, xc, yc, xb, yb)\ndef _reshape_papply_rule(name, size, vals, axes, new_sizes, dimensions, old_sizes)\n def filter_ones(xs)\n def find_new_axis(old_axis, old_sizes, new_sizes)\ndef _transpose_papply_rule(name, size, vals, dims, permutation)\ndef _select_papply_rule(name, size, vals, dims)\n def drop(x, d)\ndef _add_jaxvals_papply_rule(name, size, vals, dims)\ndef _convert_element_type_papply_rule(name, size, vals, dims, new_dtype, **params)\ndef _conv_general_dilated_papply_rule(name, size, vals, dims, window_strides, padding, lhs_dilation, rhs_dilation, dimension_numbers, feature_group_count, precision, **unused_kwargs)\ndef _broadcast_in_dim_papply_rule(name, size, vals, dims, shape, broadcast_dimensions)\ndef _pad_papply_rule(name, size, vals, dims, padding_config)\ndef _slice_papply_rule(name, size, vals, dims, start_indices, limit_indices, strides, **kwargs)\ndef _gather_papply_rule(name, size, vals, dims, dimension_numbers, slice_sizes, operand_shape)","label":0},{"cid":"jax/lax_reference.py","text":"jax/lax_reference.py\ndef conj(x)\ndef complex(x, y)\ndef div(lhs, rhs)\ndef rem(lhs, rhs)\ndef convert_element_type(operand, dtype)\ndef bitcast_convert_type(operand, dtype)\ndef clamp(min, operand, max)\ndef concatenate(operands, dimension)\ndef conv(lhs, rhs, window_strides, padding)\ndef conv_with_general_padding(lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation)\ndef conv_general_dilated(lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation, dimension_numbers)\ndef dot_general(lhs, rhs, dimension_numbers)\ndef broadcast(operand, sizes)\ndef broadcast_in_dim(operand, shape, broadcast_dimensions)\ndef reshape(operand, new_sizes, dimensions=None)\ndef pad(operand, padding_value, padding_config)\ndef rev(operand, dimensions)\ndef slice(operand, start_indices, limit_indices, strides=None)\ndef dynamic_slice(operand, start_indices, slice_sizes)\ndef dynamic_update_slice(operand, update, start_indices)\ndef reduce(operand, init_value, computation, dimensions)\ndef reduce_window(operand, init_value, computation, window_dimensions, window_strides, padding)\ndef sort_key_val(keys, values, dimension=-1)\ndef _conv(lhs, rhs, window_strides, pads)\ndef padtype_to_pads(in_shape, filter_shape, window_strides, padding)\ndef _conv_view(lhs, rhs_shape, window_strides, pads, pad_value) # Compute the view (and its axes) of a convolution or window reduction.\ndef _pad(arr, pads, pad_value)\ndef _dilate(operand, factors)\ndef _conv_general_permutations(dimension_numbers)\ndef _make_reducer(py_binop, init_val) # Make a reducer function given a Python binop and an initial value.\ndef _get_max_identity(dt)\ndef _get_min_identity(dt)\ndef _identity_getter(op)\ndef _reducer_from_pyfunc(py_binop, init_val)\n def reducer(operand, axis=0)","label":0},{"cid":"jax/lib/xla_bridge.py","text":"jax/lib/xla_bridge.py\ndef get_compile_options(num_replicas=None, device_assignment=None) # Returns the compile options to use, as derived from flag values.\ndef register_backend(name, factory)\ndef _get_local_backend(platform=None)\ndef _get_tpu_driver_backend(platform)\ndef get_backend(platform=None)\ndef get_device_backend(device=None) # Returns the Backend associated with `device`, or the default Backend.\ndef device_count(backend=None) # Returns the total number of devices.\ndef local_device_count(backend=None) # Returns the number of devices on this host.\ndef devices(backend=None) # Returns a list of all devices.\ndef local_devices(host_id=None, backend=None) # Returns a list of devices local to a given host (this host by default).\ndef host_id(backend=None) # Returns the integer host ID of this host.\ndef host_ids(backend=None) # Returns a list of all host IDs.\ndef host_count(backend=None) # Returns the number of hosts.\ndef dtype_to_etype(dtype) # Convert from dtype to canonical etype (reading FLAGS.jax_enable_x64).\ndef supported_numpy_dtypes()\ndef normalize_to_xla_dtypes(val) # Normalize dtypes in a value.\nclass _JaxComputationBuilder # Base class implementing all of JaxComputationBuilder.\n def Build(self, *args, **kwargs)\n def NumpyArrayConstant(self, value, canonicalize_types=True)\n def ConstantLike(self, example_value, value, canonicalize_types=True)\n def Constant(self, py_val, canonicalize_types=True) # Translate constant `py_val` to a constant for this ComputationBuilder.\n def CrossReplicaSum(self, operand, replica_groups) # Workaround for CrossReplicaSum not being implemented on some backends.\n def AllToAll(self, operand, split_axis, concat_axis, replica_groups) # Workaround for AllToAll not being implemented on some backends.\ndef make_computation_builder(name)\ndef register_constant_handler(type_, handler_fun)\ndef _ndarray_constant_handler(c, val, canonicalize_types=True) # Constant handler for ndarray literals, handling zero-size strides.\ndef _scalar_constant_handler(c, val, canonicalize_types=True)\ndef _python_scalar_handler(dtype, c, val, canonicalize_dtypes=True)","label":0}],"string":"[\n {\n \"cid\": \"jax/experimental/stax.py\",\n \"text\": \"jax/experimental/stax.py\\ndef Dense(out_dim, W_init=glorot_normal(), b_init=normal()) # Layer constructor function for a dense (fully-connected) layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef GeneralConv(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=normal(1e-06)) # Layer construction function for a general convolution layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=normal(1e-06)) # Layer construction function for a general transposed-convolution layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-05, center=True, scale=True, beta_init=zeros, gamma_init=ones) # Layer construction function for a batch normalization layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, x, **kwargs)\\ndef elementwise(fun, **fun_kwargs) # Layer that applies a scalar function elementwise on its inputs.\\ndef _pooling_layer(reducer, init_val, rescaler=None)\\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec='NHWC') # Layer construction function for a pooling layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef _normalize_by_window_size(dims, strides, padding)\\n def rescale(outputs, inputs, spec)\\ndef Flatten() # Layer construction function for flattening all but the leading dim.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef Identity() # Layer construction function for an identity layer.\\ndef FanOut(num) # Layer construction function for a fan-out layer.\\ndef FanInSum() # Layer construction function for a fan-in sum layer.\\ndef FanInConcat(axis=-1) # Layer construction function for a fan-in concatenation layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef Dropout(rate, mode='train') # Layer construction function for a dropout layer with given rate.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef serial(*layers) # Combinator for composing layers in serial.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef parallel(*layers) # Combinator for composing layers in parallel.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef shape_dependent(make_layer) # Combinator to delay layer constructor pair until input shapes are known.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py\",\n \"text\": \"jax/lax/lax.py\\ndef broadcast_shapes(*shapes) # Returns the shape that results from NumPy broadcasting of `shapes`.\\ndef _canonicalize_shape(shape) # Canonicalizes and checks for errors in a user-provided shape value.\\ndef _identity(x)\\ndef neg(x) # Elementwise negation: :math:`-x`.\\ndef sign(x) # Elementwise sign.\\ndef nextafter(x1, x2) # Returns the next representable value after `x1` in the direction of `x2`.\\ndef floor(x) # Elementwise floor: :math:`\\\\left\\\\lfloor x \\\\right\\\\rfloor`.\\ndef ceil(x) # Elementwise ceiling: :math:`\\\\left\\\\lceil x \\\\right\\\\rceil`.\\ndef round(x) # Elementwise round.\\ndef is_finite(x) # Elementwise :math:`\\\\mathrm{isfinite}`.\\ndef exp(x) # Elementwise exponential: :math:`e^x`.\\ndef expm1(x) # Elementwise :math:`e^{x - 1}`.\\ndef log(x) # Elementwise natural logarithm: :math:`\\\\mathrm{log}(x)`.\\ndef log1p(x) # Elementwise :math:`\\\\mathrm{log}(1 + x)`.\\ndef tanh(x) # Elementwise hyperbolic tangent: :math:`\\\\mathrm{tanh}(x)`.\\ndef sin(x) # Elementwise sine: :math:`\\\\mathrm{sin}(x)`.\\ndef cos(x) # Elementwise cosine: :math:`\\\\mathrm{cos}(x)`.\\ndef atan2(x, y) # Elementwise arc tangent of two variables:\\ndef lgamma(x) # Elementwise log gamma: :math:`\\\\mathrm{log}(\\\\Gamma(x))`.\\ndef digamma(x) # Elementwise digamma: :math:`\\\\psi(x)`.\\ndef bessel_i0e(x) # Exponentially scaled modified Bessel function of order 0:\\ndef bessel_i1e(x) # Exponentially scaled modified Bessel function of order 1:\\ndef erf(x) # Elementwise error function: :math:`\\\\mathrm{erf}(x)`.\\ndef erfc(x) # Elementwise complementary error function:\\ndef erf_inv(x) # Elementwise inverse error function: :math:`\\\\mathrm{erf}^{-1}(x)`.\\ndef real(x) # Elementwise extract real part: :math:`\\\\mathrm{Re}(x)`.\\ndef imag(x) # Elementwise extract imaginary part: :math:`\\\\mathrm{Im}(x)`.\\ndef complex(x, y) # Elementwise make complex number: :math:`x + jy`.\\ndef conj(x) # Elementwise complex conjugate function: :math:`\\\\overline{x}`.\\ndef abs(x) # Elementwise absolute value: :math:`|x|`.\\ndef pow(x, y) # Elementwise power: :math:`x^y`.\\ndef sqrt(x) # Elementwise square root: :math:`\\\\sqrt{x}`.\\ndef rsqrt(x) # Elementwise reciprocal square root: :math:`1 \\\\over \\\\sqrt{x}.\\ndef bitwise_not(x) # Elementwise NOT: :math:`\\\\neg x`.\\ndef bitwise_and(x, y) # Elementwise AND: :math:`x \\\\wedge y`.\\ndef bitwise_or(x, y) # Elementwise OR: :math:`x \\\\vee y`.\\ndef bitwise_xor(x, y) # Elementwise exclusive OR: :math:`x \\\\oplus y`.\\ndef add(x, y) # Elementwise addition: :math:`x + y`.\\ndef sub(x, y) # Elementwise subtraction: :math:`x - y`.\\ndef mul(x, y) # Elementwise multiplication: :math:`x \\\\times y`.\\ndef div(x, y) # Elementwise division: :math:`x \\\\over y`.\\ndef rem(x, y) # Elementwise remainder: :math:`x \\\\bmod y`.\\ndef max(x, y) # Elementwise maximum: :math:`\\\\mathrm{max}(x, y)`\\ndef min(x, y) # Elementwise minimum: :math:`\\\\mathrm{min}(x, y)`\\ndef shift_left(x, y) # Elementwise left shift: :math:`x \\\\ll y`.\\ndef shift_right_arithmetic(x, y) # Elementwise arithmetic right shift: :math:`x \\\\gg y`.\\ndef shift_right_log\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py\",\n \"text\": \"jax/lax/lax_parallel.py\\ndef psum(x, axis_name) # Compute an all-reduce sum on ``x`` over the pmapped axis ``axis_name``.\\ndef pmax(x, axis_name) # Compute an all-reduce max on ``x`` over the pmapped axis ``axis_name``.\\ndef pmin(x, axis_name) # Compute an all-reduce min on ``x`` over the pmapped axis ``axis_name``.\\ndef ppermute(x, axis_name, perm) # Perform a collective permutation according to the permutation ``perm``.\\ndef pswapaxes(x, axis_name, axis) # Swap the pmapped axis ``axis_name`` with the unmapped axis ``axis``.\\ndef all_to_all(x, axis_name, split_axis, concat_axis) # Materialize the mapped axis and map a different axis.\\ndef standard_pmap_primitive(name)\\ndef _allreduce_split_axis_rule(prim, reducer, vals, which_mapped, axis_name)\\ndef _allreduce_translation_rule(prim, c, val, replica_groups)\\ndef _psum_translation_rule(c, val, replica_groups)\\ndef _ppermute_translation_rule(c, x, replica_groups, perm)\\ndef _ppermute_transpose_rule(t, perm, axis_name)\\ndef _all_to_all_translation_rule(c, x, split_axis, concat_axis, replica_groups)\\ndef _all_to_all_split_axis_rule(vals, which_mapped, split_axis, concat_axis, axis_name)\\ndef _moveaxis(src, dst, x)\\ndef _drop(x, dim, axis_name)\\ndef _allgather(x, dim, size, axis_name)\\ndef _broadcasting_papply(prim, name, size, vals, axes, **params)\\ndef _defbroadcasting(prim)\\ndef _vectorized_papply(prim, name, size, vals, axes, **params)\\ndef _defvectorized(prim)\\ndef _reducer_papply(prim, cprim, name, size, vals, papply_axes, axes, **kwargs)\\ndef _defreducer(prim, collective_prim)\\ndef _identity_papply(prim, argnum, name, size, vals, axes, **params)\\ndef _defidentity(prim, argnum=0)\\ndef _dot_general_papply_rule(name, size, vals, dims, dimension_numbers, precision)\\n def adjust_dims(dims, thresh)\\n def sub_dims(xdim, ydim, xcontract, ycontract, xbatch, ybatch)\\n def cases(x, y, xdim, ydim, xc, yc, xb, yb)\\ndef _reshape_papply_rule(name, size, vals, axes, new_sizes, dimensions, old_sizes)\\n def filter_ones(xs)\\n def find_new_axis(old_axis, old_sizes, new_sizes)\\ndef _transpose_papply_rule(name, size, vals, dims, permutation)\\ndef _select_papply_rule(name, size, vals, dims)\\n def drop(x, d)\\ndef _add_jaxvals_papply_rule(name, size, vals, dims)\\ndef _convert_element_type_papply_rule(name, size, vals, dims, new_dtype, **params)\\ndef _conv_general_dilated_papply_rule(name, size, vals, dims, window_strides, padding, lhs_dilation, rhs_dilation, dimension_numbers, feature_group_count, precision, **unused_kwargs)\\ndef _broadcast_in_dim_papply_rule(name, size, vals, dims, shape, broadcast_dimensions)\\ndef _pad_papply_rule(name, size, vals, dims, padding_config)\\ndef _slice_papply_rule(name, size, vals, dims, start_indices, limit_indices, strides, **kwargs)\\ndef _gather_papply_rule(name, size, vals, dims, dimension_numbers, slice_sizes, operand_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py\",\n \"text\": \"jax/lax_reference.py\\ndef conj(x)\\ndef complex(x, y)\\ndef div(lhs, rhs)\\ndef rem(lhs, rhs)\\ndef convert_element_type(operand, dtype)\\ndef bitcast_convert_type(operand, dtype)\\ndef clamp(min, operand, max)\\ndef concatenate(operands, dimension)\\ndef conv(lhs, rhs, window_strides, padding)\\ndef conv_with_general_padding(lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation)\\ndef conv_general_dilated(lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation, dimension_numbers)\\ndef dot_general(lhs, rhs, dimension_numbers)\\ndef broadcast(operand, sizes)\\ndef broadcast_in_dim(operand, shape, broadcast_dimensions)\\ndef reshape(operand, new_sizes, dimensions=None)\\ndef pad(operand, padding_value, padding_config)\\ndef rev(operand, dimensions)\\ndef slice(operand, start_indices, limit_indices, strides=None)\\ndef dynamic_slice(operand, start_indices, slice_sizes)\\ndef dynamic_update_slice(operand, update, start_indices)\\ndef reduce(operand, init_value, computation, dimensions)\\ndef reduce_window(operand, init_value, computation, window_dimensions, window_strides, padding)\\ndef sort_key_val(keys, values, dimension=-1)\\ndef _conv(lhs, rhs, window_strides, pads)\\ndef padtype_to_pads(in_shape, filter_shape, window_strides, padding)\\ndef _conv_view(lhs, rhs_shape, window_strides, pads, pad_value) # Compute the view (and its axes) of a convolution or window reduction.\\ndef _pad(arr, pads, pad_value)\\ndef _dilate(operand, factors)\\ndef _conv_general_permutations(dimension_numbers)\\ndef _make_reducer(py_binop, init_val) # Make a reducer function given a Python binop and an initial value.\\ndef _get_max_identity(dt)\\ndef _get_min_identity(dt)\\ndef _identity_getter(op)\\ndef _reducer_from_pyfunc(py_binop, init_val)\\n def reducer(operand, axis=0)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py\",\n \"text\": \"jax/lib/xla_bridge.py\\ndef get_compile_options(num_replicas=None, device_assignment=None) # Returns the compile options to use, as derived from flag values.\\ndef register_backend(name, factory)\\ndef _get_local_backend(platform=None)\\ndef _get_tpu_driver_backend(platform)\\ndef get_backend(platform=None)\\ndef get_device_backend(device=None) # Returns the Backend associated with `device`, or the default Backend.\\ndef device_count(backend=None) # Returns the total number of devices.\\ndef local_device_count(backend=None) # Returns the number of devices on this host.\\ndef devices(backend=None) # Returns a list of all devices.\\ndef local_devices(host_id=None, backend=None) # Returns a list of devices local to a given host (this host by default).\\ndef host_id(backend=None) # Returns the integer host ID of this host.\\ndef host_ids(backend=None) # Returns a list of all host IDs.\\ndef host_count(backend=None) # Returns the number of hosts.\\ndef dtype_to_etype(dtype) # Convert from dtype to canonical etype (reading FLAGS.jax_enable_x64).\\ndef supported_numpy_dtypes()\\ndef normalize_to_xla_dtypes(val) # Normalize dtypes in a value.\\nclass _JaxComputationBuilder # Base class implementing all of JaxComputationBuilder.\\n def Build(self, *args, **kwargs)\\n def NumpyArrayConstant(self, value, canonicalize_types=True)\\n def ConstantLike(self, example_value, value, canonicalize_types=True)\\n def Constant(self, py_val, canonicalize_types=True) # Translate constant `py_val` to a constant for this ComputationBuilder.\\n def CrossReplicaSum(self, operand, replica_groups) # Workaround for CrossReplicaSum not being implemented on some backends.\\n def AllToAll(self, operand, split_axis, concat_axis, replica_groups) # Workaround for AllToAll not being implemented on some backends.\\ndef make_computation_builder(name)\\ndef register_constant_handler(type_, handler_fun)\\ndef _ndarray_constant_handler(c, val, canonicalize_types=True) # Constant handler for ndarray literals, handling zero-size strides.\\ndef _scalar_constant_handler(c, val, canonicalize_types=True)\\ndef _python_scalar_handler(dtype, c, val, canonicalize_dtypes=True)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"jax/experimental/stax.py::Dense","text":"jax/experimental/stax.py::Dense\ndef Dense(out_dim, W_init=glorot_normal(), b_init=normal()):\n \"\"\"Layer constructor function for a dense (fully-connected) layer.\"\"\"\n def init_fun(rng, input_shape):\n output_shape = input_shape[:-1] + (out_dim,)\n k1, k2 = random.split(rng)\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\n return output_shape, (W, b)\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return np.dot(inputs, W) + b\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Dense.init_fun","text":"jax/experimental/stax.py::Dense.init_fun\n def init_fun(rng, input_shape):\n output_shape = input_shape[:-1] + (out_dim,)\n k1, k2 = random.split(rng)\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::Dense.apply_fun","text":"jax/experimental/stax.py::Dense.apply_fun\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return np.dot(inputs, W) + b","label":0},{"cid":"jax/experimental/stax.py::GeneralConv","text":"jax/experimental/stax.py::GeneralConv\ndef GeneralConv(dimension_numbers, out_chan, filter_shape,\n strides=None, padding='VALID', W_init=None,\n b_init=normal(1e-6)):\n \"\"\"Layer construction function for a general convolution layer.\"\"\"\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n one = (1,) * len(filter_shape)\n strides = strides or one\n W_init = W_init or glorot_normal(rhs_spec.index('I'), rhs_spec.index('O'))\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_general_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\n dimension_numbers=dimension_numbers) + b\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::GeneralConv.init_fun","text":"jax/experimental/stax.py::GeneralConv.init_fun\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_general_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::GeneralConv.apply_fun","text":"jax/experimental/stax.py::GeneralConv.apply_fun\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\n dimension_numbers=dimension_numbers) + b","label":0},{"cid":"jax/experimental/stax.py::GeneralConvTranspose","text":"jax/experimental/stax.py::GeneralConvTranspose\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape,\n strides=None, padding='VALID', W_init=None,\n b_init=normal(1e-6)):\n \"\"\"Layer construction function for a general transposed-convolution layer.\"\"\"\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n one = (1,) * len(filter_shape)\n strides = strides or one\n W_init = W_init or glorot_normal(rhs_spec.index('I'), rhs_spec.index('O'))\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_transpose_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_transpose(inputs, W, strides, padding,\n dimension_numbers=dimension_numbers) + b\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::GeneralConvTranspose.init_fun","text":"jax/experimental/stax.py::GeneralConvTranspose.init_fun\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_transpose_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::GeneralConvTranspose.apply_fun","text":"jax/experimental/stax.py::GeneralConvTranspose.apply_fun\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_transpose(inputs, W, strides, padding,\n dimension_numbers=dimension_numbers) + b","label":0},{"cid":"jax/experimental/stax.py::BatchNorm","text":"jax/experimental/stax.py::BatchNorm\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-5, center=True, scale=True,\n beta_init=zeros, gamma_init=ones):\n \"\"\"Layer construction function for a batch normalization layer.\"\"\"\n _beta_init = lambda rng, shape: beta_init(rng, shape) if center else ()\n _gamma_init = lambda rng, shape: gamma_init(rng, shape) if scale else ()\n axis = (axis,) if np.isscalar(axis) else axis\n def init_fun(rng, input_shape):\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\n k1, k2 = random.split(rng)\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\n return input_shape, (beta, gamma)\n def apply_fun(params, x, **kwargs):\n beta, gamma = params\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\n # (https://github.com/numpy/numpy/issues/12290)\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\n beta = beta[ed]\n gamma = gamma[ed]\n z = normalize(x, axis, epsilon=epsilon)\n if center and scale: return gamma * z + beta\n if center: return z + beta\n if scale: return gamma * z\n return z\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::BatchNorm.init_fun","text":"jax/experimental/stax.py::BatchNorm.init_fun\n def init_fun(rng, input_shape):\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\n k1, k2 = random.split(rng)\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\n return input_shape, (beta, gamma)","label":0},{"cid":"jax/experimental/stax.py::BatchNorm.apply_fun","text":"jax/experimental/stax.py::BatchNorm.apply_fun\n def apply_fun(params, x, **kwargs):\n beta, gamma = params\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\n # (https://github.com/numpy/numpy/issues/12290)\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\n beta = beta[ed]\n gamma = gamma[ed]\n z = normalize(x, axis, epsilon=epsilon)\n if center and scale: return gamma * z + beta\n if center: return z + beta\n if scale: return gamma * z\n return z","label":0},{"cid":"jax/experimental/stax.py::elementwise","text":"jax/experimental/stax.py::elementwise\ndef elementwise(fun, **fun_kwargs):\n \"\"\"Layer that applies a scalar function elementwise on its inputs.\"\"\"\n init_fun = lambda rng, input_shape: (input_shape, ())\n apply_fun = lambda params, inputs, **kwargs: fun(inputs, **fun_kwargs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer","text":"jax/experimental/stax.py::_pooling_layer\ndef _pooling_layer(reducer, init_val, rescaler=None):\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec='NHWC'):\n \"\"\"Layer construction function for a pooling layer.\"\"\"\n strides = strides or (1,) * len(window_shape)\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\n\n non_spatial_axes = spec.index('N'), spec.index('C')\n for i in sorted(non_spatial_axes):\n window_shape = window_shape[:i] + (1,) + window_shape[i:]\n strides = strides[:i] + (1,) + strides[i:]\n\n def init_fun(rng, input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\n strides, padding)\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\n strides, padding)\n return rescale(out, inputs, spec) if rescale else out\n return init_fun, apply_fun\n return PoolingLayer","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec='NHWC'):\n \"\"\"Layer construction function for a pooling layer.\"\"\"\n strides = strides or (1,) * len(window_shape)\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\n\n non_spatial_axes = spec.index('N'), spec.index('C')\n for i in sorted(non_spatial_axes):\n window_shape = window_shape[:i] + (1,) + window_shape[i:]\n strides = strides[:i] + (1,) + strides[i:]\n\n def init_fun(rng, input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\n strides, padding)\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\n strides, padding)\n return rescale(out, inputs, spec) if rescale else out\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\n def init_fun(rng, input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\n strides, padding)\n return out_shape, ()","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\n strides, padding)\n return rescale(out, inputs, spec) if rescale else out","label":0},{"cid":"jax/experimental/stax.py::_normalize_by_window_size","text":"jax/experimental/stax.py::_normalize_by_window_size\ndef _normalize_by_window_size(dims, strides, padding):\n def rescale(outputs, inputs, spec):\n non_spatial_axes = spec.index('N'), spec.index('C')\n spatial_shape = tuple(inputs.shape[i]\n for i in range(inputs.ndim)\n if i not in non_spatial_axes)\n one = np.ones(spatial_shape, dtype=inputs.dtype)\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\n for i in sorted(non_spatial_axes):\n window_sizes = np.expand_dims(window_sizes, i)\n\n return outputs / window_sizes\n return rescale","label":0},{"cid":"jax/experimental/stax.py::_normalize_by_window_size.rescale","text":"jax/experimental/stax.py::_normalize_by_window_size.rescale\n def rescale(outputs, inputs, spec):\n non_spatial_axes = spec.index('N'), spec.index('C')\n spatial_shape = tuple(inputs.shape[i]\n for i in range(inputs.ndim)\n if i not in non_spatial_axes)\n one = np.ones(spatial_shape, dtype=inputs.dtype)\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\n for i in sorted(non_spatial_axes):\n window_sizes = np.expand_dims(window_sizes, i)\n\n return outputs / window_sizes","label":0},{"cid":"jax/experimental/stax.py::Flatten","text":"jax/experimental/stax.py::Flatten\ndef Flatten():\n \"\"\"Layer construction function for flattening all but the leading dim.\"\"\"\n def init_fun(rng, input_shape):\n output_shape = input_shape[0], functools.reduce(op.mul, input_shape[1:], 1)\n return output_shape, ()\n def apply_fun(params, inputs, **kwargs):\n return np.reshape(inputs, (inputs.shape[0], -1))\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Flatten.init_fun","text":"jax/experimental/stax.py::Flatten.init_fun\n def init_fun(rng, input_shape):\n output_shape = input_shape[0], functools.reduce(op.mul, input_shape[1:], 1)\n return output_shape, ()","label":0},{"cid":"jax/experimental/stax.py::Flatten.apply_fun","text":"jax/experimental/stax.py::Flatten.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return np.reshape(inputs, (inputs.shape[0], -1))","label":0},{"cid":"jax/experimental/stax.py::Identity","text":"jax/experimental/stax.py::Identity\ndef Identity():\n \"\"\"Layer construction function for an identity layer.\"\"\"\n init_fun = lambda rng, input_shape: (input_shape, ())\n apply_fun = lambda params, inputs, **kwargs: inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanOut","text":"jax/experimental/stax.py::FanOut\ndef FanOut(num):\n \"\"\"Layer construction function for a fan-out layer.\"\"\"\n init_fun = lambda rng, input_shape: ([input_shape] * num, ())\n apply_fun = lambda params, inputs, **kwargs: [inputs] * num\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInSum","text":"jax/experimental/stax.py::FanInSum\ndef FanInSum():\n \"\"\"Layer construction function for a fan-in sum layer.\"\"\"\n init_fun = lambda rng, input_shape: (input_shape[0], ())\n apply_fun = lambda params, inputs, **kwargs: sum(inputs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInConcat","text":"jax/experimental/stax.py::FanInConcat\ndef FanInConcat(axis=-1):\n \"\"\"Layer construction function for a fan-in concatenation layer.\"\"\"\n def init_fun(rng, input_shape):\n ax = axis % len(input_shape[0])\n concat_size = sum(shape[ax] for shape in input_shape)\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n return np.concatenate(inputs, axis)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInConcat.init_fun","text":"jax/experimental/stax.py::FanInConcat.init_fun\n def init_fun(rng, input_shape):\n ax = axis % len(input_shape[0])\n concat_size = sum(shape[ax] for shape in input_shape)\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\n return out_shape, ()","label":0},{"cid":"jax/experimental/stax.py::FanInConcat.apply_fun","text":"jax/experimental/stax.py::FanInConcat.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return np.concatenate(inputs, axis)","label":0},{"cid":"jax/experimental/stax.py::Dropout","text":"jax/experimental/stax.py::Dropout\ndef Dropout(rate, mode='train'):\n \"\"\"Layer construction function for a dropout layer with given rate.\"\"\"\n def init_fun(rng, input_shape):\n return input_shape, ()\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.get('rng', None)\n if rng is None:\n msg = (\"Dropout layer requires apply_fun to be called with a PRNG key \"\n \"argument. That is, instead of `apply_fun(params, inputs)`, call \"\n \"it like `apply_fun(params, inputs, key)` where `key` is a \"\n \"jax.random.PRNGKey value.\")\n raise ValueError(msg)\n if mode == 'train':\n keep = random.bernoulli(rng, rate, inputs.shape)\n return np.where(keep, inputs / rate, 0)\n else:\n return inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Dropout.init_fun","text":"jax/experimental/stax.py::Dropout.init_fun\n def init_fun(rng, input_shape):\n return input_shape, ()","label":0},{"cid":"jax/experimental/stax.py::Dropout.apply_fun","text":"jax/experimental/stax.py::Dropout.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.get('rng', None)\n if rng is None:\n msg = (\"Dropout layer requires apply_fun to be called with a PRNG key \"\n \"argument. That is, instead of `apply_fun(params, inputs)`, call \"\n \"it like `apply_fun(params, inputs, key)` where `key` is a \"\n \"jax.random.PRNGKey value.\")\n raise ValueError(msg)\n if mode == 'train':\n keep = random.bernoulli(rng, rate, inputs.shape)\n return np.where(keep, inputs / rate, 0)\n else:\n return inputs","label":0},{"cid":"jax/experimental/stax.py::serial","text":"jax/experimental/stax.py::serial\ndef serial(*layers):\n \"\"\"Combinator for composing layers in serial.\n\n Args:\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the serial\n composition of the given sequence of layers.\n \"\"\"\n nlayers = len(layers)\n init_funs, apply_funs = zip(*layers)\n def init_fun(rng, input_shape):\n params = []\n for init_fun in init_funs:\n rng, layer_rng = random.split(rng)\n input_shape, param = init_fun(layer_rng, input_shape)\n params.append(param)\n return input_shape, params\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n for fun, param, rng in zip(apply_funs, params, rngs):\n inputs = fun(param, inputs, rng=rng, **kwargs)\n return inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::serial.init_fun","text":"jax/experimental/stax.py::serial.init_fun\n def init_fun(rng, input_shape):\n params = []\n for init_fun in init_funs:\n rng, layer_rng = random.split(rng)\n input_shape, param = init_fun(layer_rng, input_shape)\n params.append(param)\n return input_shape, params","label":0},{"cid":"jax/experimental/stax.py::serial.apply_fun","text":"jax/experimental/stax.py::serial.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n for fun, param, rng in zip(apply_funs, params, rngs):\n inputs = fun(param, inputs, rng=rng, **kwargs)\n return inputs","label":0},{"cid":"jax/experimental/stax.py::parallel","text":"jax/experimental/stax.py::parallel\ndef parallel(*layers):\n \"\"\"Combinator for composing layers in parallel.\n\n The layer resulting from this combinator is often used with the FanOut and\n FanInSum layers.\n\n Args:\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the\n parallel composition of the given sequence of layers. In particular, the\n returned layer takes a sequence of inputs and returns a sequence of outputs\n with the same length as the argument `layers`.\n \"\"\"\n nlayers = len(layers)\n init_funs, apply_funs = zip(*layers)\n def init_fun(rng, input_shape):\n rngs = random.split(rng, nlayers)\n return zip(*[init(rng, shape) for init, rng, shape\n in zip(init_funs, rngs, input_shape)])\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::parallel.init_fun","text":"jax/experimental/stax.py::parallel.init_fun\n def init_fun(rng, input_shape):\n rngs = random.split(rng, nlayers)\n return zip(*[init(rng, shape) for init, rng, shape\n in zip(init_funs, rngs, input_shape)])","label":0},{"cid":"jax/experimental/stax.py::parallel.apply_fun","text":"jax/experimental/stax.py::parallel.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]","label":0},{"cid":"jax/experimental/stax.py::shape_dependent","text":"jax/experimental/stax.py::shape_dependent\ndef shape_dependent(make_layer):\n \"\"\"Combinator to delay layer constructor pair until input shapes are known.\n\n Args:\n make_layer: a one-argument function that takes an input shape as an argument\n (a tuple of positive integers) and returns an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the same\n layer as returned by `make_layer` but with its construction delayed until\n input shapes are known.\n \"\"\"\n def init_fun(rng, input_shape):\n return make_layer(input_shape)[0](rng, input_shape)\n def apply_fun(params, inputs, **kwargs):\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::shape_dependent.init_fun","text":"jax/experimental/stax.py::shape_dependent.init_fun\n def init_fun(rng, input_shape):\n return make_layer(input_shape)[0](rng, input_shape)","label":0},{"cid":"jax/experimental/stax.py::shape_dependent.apply_fun","text":"jax/experimental/stax.py::shape_dependent.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)","label":0},{"cid":"jax/lax/lax.py::broadcast_shapes","text":"jax/lax/lax.py::broadcast_shapes\n@cache()\ndef broadcast_shapes(*shapes):\n \"\"\"Returns the shape that results from NumPy broadcasting of `shapes`.\"\"\"\n if len(shapes) == 1:\n return shapes[0]\n ndim = _max(len(shape) for shape in shapes)\n shapes = onp.array([(1,) * (ndim - len(shape)) + shape for shape in shapes])\n min_shape = onp.min(shapes, axis=0)\n max_shape = onp.max(shapes, axis=0)\n result_shape = onp.where(min_shape == 0, 0, max_shape)\n if not onp.all((shapes == result_shape) | (shapes == 1)):\n raise ValueError(\"Incompatible shapes for broadcasting: {}\"\n .format(tuple(map(tuple, shapes))))\n return tuple(result_shape)","label":0},{"cid":"jax/lax/lax.py::_canonicalize_shape","text":"jax/lax/lax.py::_canonicalize_shape\ndef _canonicalize_shape(shape):\n \"\"\"Canonicalizes and checks for errors in a user-provided shape value.\n\n Args:\n shape: a Python value that represents a shape.\n\n Returns:\n A tuple of integers.\n \"\"\"\n # TODO(mattjj): this next check is a temporary workaround for masking\n if (type(shape) is ShapeExpr\n or type(shape) is tuple and any(type(d) is masking.Poly for d in shape)):\n return shape\n try:\n return tuple(map(operator.index, shape))\n except TypeError:\n pass\n msg = (\"Shapes must be 1D sequences of concrete values of integer type, \"\n \"got {}.\")\n if any(isinstance(x, core.Tracer) and isinstance(core.get_aval(x), ShapedArray)\n and not isinstance(core.get_aval(x), ConcreteArray) for x in shape):\n msg += (\"\\nIf using `jit`, try using `static_argnums` or applying `jit` to \"\n \"smaller subfunctions.\")\n raise TypeError(msg.format(shape))","label":0},{"cid":"jax/lax/lax.py::_identity","text":"jax/lax/lax.py::_identity\ndef _identity(x): return x","label":0},{"cid":"jax/lax/lax.py::neg","text":"jax/lax/lax.py::neg\ndef neg(x):\n r\"\"\"Elementwise negation: :math:`-x`.\"\"\"\n return neg_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::sign","text":"jax/lax/lax.py::sign\ndef sign(x):\n r\"\"\"Elementwise sign.\n\n For floating-point inputs, returns\n :math:`\\mathrm{sign}(x) = \\begin{cases}\n -1 & x < 0\\\\\n -0 & x = -0\\\\\n \\mathit{NaN} & x = \\mathit{NaN}\\\\\n +0 & x = +0\\\\\n 1 & x > 0\n \\end{cases}`\n\n For signed integer inputs, returns\n :math:`\\mathrm{sign}(x) = \\begin{cases}\n -1 & x < 0\\\\\n 0 & x = 0\\\\\n 1 & x > 0\n \\end{cases}`\n\n For complex inputs, returns the complex phase, i.e.\n :math:`\\mathrm{sign}(x) = \\frac{x}{|x|}`.\n \"\"\"\n return sign_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::nextafter","text":"jax/lax/lax.py::nextafter\ndef nextafter(x1, x2):\n r\"\"\"Returns the next representable value after `x1` in the direction of `x2`.\"\"\"\n return nextafter_p.bind(_brcast(x1, x2), _brcast(x2, x1))","label":0},{"cid":"jax/lax/lax.py::floor","text":"jax/lax/lax.py::floor\ndef floor(x):\n r\"\"\"Elementwise floor: :math:`\\left\\lfloor x \\right\\rfloor`.\"\"\"\n return floor_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::ceil","text":"jax/lax/lax.py::ceil\ndef ceil(x):\n r\"\"\"Elementwise ceiling: :math:`\\left\\lceil x \\right\\rceil`.\"\"\"\n return ceil_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::round","text":"jax/lax/lax.py::round\ndef round(x):\n r\"\"\"Elementwise round.\n\n Rounds values to the nearest integer. Halfway values (e.g., `0.5`) are rounded\n away from zero.\"\"\"\n return round_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::is_finite","text":"jax/lax/lax.py::is_finite\ndef is_finite(x):\n r\"\"\"Elementwise :math:`\\mathrm{isfinite}`.\n\n For each element x returns `True` if and only if x is not :math:`\\pm\\infty` or\n :math:`\\mathit{NaN}`.\n \"\"\"\n return is_finite_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::exp","text":"jax/lax/lax.py::exp\ndef exp(x):\n r\"\"\"Elementwise exponential: :math:`e^x`.\"\"\"\n return exp_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::expm1","text":"jax/lax/lax.py::expm1\ndef expm1(x):\n r\"\"\"Elementwise :math:`e^{x - 1}`.\"\"\"\n return expm1_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::log","text":"jax/lax/lax.py::log\ndef log(x):\n r\"\"\"Elementwise natural logarithm: :math:`\\mathrm{log}(x)`.\"\"\"\n return log_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::log1p","text":"jax/lax/lax.py::log1p\ndef log1p(x):\n r\"\"\"Elementwise :math:`\\mathrm{log}(1 + x)`.\"\"\"\n return log1p_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::tanh","text":"jax/lax/lax.py::tanh\ndef tanh(x):\n r\"\"\"Elementwise hyperbolic tangent: :math:`\\mathrm{tanh}(x)`.\"\"\"\n return tanh_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::sin","text":"jax/lax/lax.py::sin\ndef sin(x):\n r\"\"\"Elementwise sine: :math:`\\mathrm{sin}(x)`.\"\"\"\n return sin_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::cos","text":"jax/lax/lax.py::cos\ndef cos(x):\n r\"\"\"Elementwise cosine: :math:`\\mathrm{cos}(x)`.\"\"\"\n return cos_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::atan2","text":"jax/lax/lax.py::atan2\ndef atan2(x, y):\n r\"\"\"Elementwise arc tangent of two variables:\n :math:`\\mathrm{atan}({x \\over y})`.\"\"\"\n return atan2_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::lgamma","text":"jax/lax/lax.py::lgamma\ndef lgamma(x):\n r\"\"\"Elementwise log gamma: :math:`\\mathrm{log}(\\Gamma(x))`.\"\"\"\n return lgamma_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::digamma","text":"jax/lax/lax.py::digamma\ndef digamma(x):\n r\"\"\"Elementwise digamma: :math:`\\psi(x)`.\"\"\"\n return digamma_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::bessel_i0e","text":"jax/lax/lax.py::bessel_i0e\ndef bessel_i0e(x):\n r\"\"\"Exponentially scaled modified Bessel function of order 0:\n :math:`\\mathrm{i0e}(x) = e^{-|x|} \\mathrm{i0}(x)`\n \"\"\"\n return bessel_i0e_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::bessel_i1e","text":"jax/lax/lax.py::bessel_i1e\ndef bessel_i1e(x):\n r\"\"\"Exponentially scaled modified Bessel function of order 1:\n :math:`\\mathrm{i1e}(x) = e^{-|x|} \\mathrm{i1}(x)`\n \"\"\"\n return bessel_i1e_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::erf","text":"jax/lax/lax.py::erf\ndef erf(x):\n r\"\"\"Elementwise error function: :math:`\\mathrm{erf}(x)`.\"\"\"\n return erf_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::erfc","text":"jax/lax/lax.py::erfc\ndef erfc(x):\n r\"\"\"Elementwise complementary error function:\n :math:`\\mathrm{erfc}(x) = 1 - \\mathrm{erf}(x)`.\"\"\"\n return erfc_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::erf_inv","text":"jax/lax/lax.py::erf_inv\ndef erf_inv(x):\n r\"\"\"Elementwise inverse error function: :math:`\\mathrm{erf}^{-1}(x)`.\"\"\"\n return erf_inv_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::real","text":"jax/lax/lax.py::real\ndef real(x):\n r\"\"\"Elementwise extract real part: :math:`\\mathrm{Re}(x)`.\n\n Returns the real part of a complex number.\n \"\"\"\n return real_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::imag","text":"jax/lax/lax.py::imag\ndef imag(x):\n r\"\"\"Elementwise extract imaginary part: :math:`\\mathrm{Im}(x)`.\n\n Returns the imaginary part of a complex number.\n \"\"\"\n return imag_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::complex","text":"jax/lax/lax.py::complex\ndef complex(x, y):\n r\"\"\"Elementwise make complex number: :math:`x + jy`.\n\n Builds a complex number from real and imaginary parts.\n \"\"\"\n return complex_p.bind(_brcast(x, y), _brcast(y, x))","label":0},{"cid":"jax/lax/lax.py::conj","text":"jax/lax/lax.py::conj\ndef conj(x):\n r\"\"\"Elementwise complex conjugate function: :math:`\\overline{x}`.\"\"\"\n return conj_p.bind(x, input_dtype=_dtype(x))","label":0},{"cid":"jax/lax/lax.py::abs","text":"jax/lax/lax.py::abs\ndef abs(x):\n r\"\"\"Elementwise absolute value: :math:`|x|`.\"\"\"\n return abs_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::pow","text":"jax/lax/lax.py::pow\ndef pow(x, y):\n r\"\"\"Elementwise power: :math:`x^y`.\"\"\"\n return pow_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::sqrt","text":"jax/lax/lax.py::sqrt\ndef sqrt(x):\n r\"\"\"Elementwise square root: :math:`\\sqrt{x}`.\"\"\"\n return sqrt_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::rsqrt","text":"jax/lax/lax.py::rsqrt\ndef rsqrt(x):\n r\"\"\"Elementwise reciprocal square root: :math:`1 \\over \\sqrt{x}.\"\"\"\n return rsqrt_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::bitwise_not","text":"jax/lax/lax.py::bitwise_not\ndef bitwise_not(x):\n r\"\"\"Elementwise NOT: :math:`\\neg x`.\"\"\"\n return not_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::bitwise_and","text":"jax/lax/lax.py::bitwise_and\ndef bitwise_and(x, y):\n r\"\"\"Elementwise AND: :math:`x \\wedge y`.\"\"\"\n return and_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::bitwise_or","text":"jax/lax/lax.py::bitwise_or\ndef bitwise_or(x, y):\n r\"\"\"Elementwise OR: :math:`x \\vee y`.\"\"\"\n return or_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::bitwise_xor","text":"jax/lax/lax.py::bitwise_xor\ndef bitwise_xor(x, y):\n r\"\"\"Elementwise exclusive OR: :math:`x \\oplus y`.\"\"\"\n return xor_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::add","text":"jax/lax/lax.py::add\ndef add(x, y):\n r\"\"\"Elementwise addition: :math:`x + y`.\"\"\"\n return add_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::sub","text":"jax/lax/lax.py::sub\ndef sub(x, y):\n r\"\"\"Elementwise subtraction: :math:`x - y`.\"\"\"\n return sub_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::mul","text":"jax/lax/lax.py::mul\ndef mul(x, y):\n r\"\"\"Elementwise multiplication: :math:`x \\times y`.\"\"\"\n return mul_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::div","text":"jax/lax/lax.py::div\ndef div(x, y):\n r\"\"\"Elementwise division: :math:`x \\over y`.\"\"\"\n return div_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::rem","text":"jax/lax/lax.py::rem\ndef rem(x, y):\n r\"\"\"Elementwise remainder: :math:`x \\bmod y`.\"\"\"\n return rem_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::max","text":"jax/lax/lax.py::max\ndef max(x, y):\n r\"\"\"Elementwise maximum: :math:`\\mathrm{max}(x, y)`\n\n For complex numbers, uses a lexicographic comparison on the\n `(real, imaginary)` pairs.\"\"\"\n return max_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::min","text":"jax/lax/lax.py::min\ndef min(x, y):\n r\"\"\"Elementwise minimum: :math:`\\mathrm{min}(x, y)`\n\n For complex numbers, uses a lexicographic comparison on the\n `(real, imaginary)` pairs.\"\"\"\n return min_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::shift_left","text":"jax/lax/lax.py::shift_left\ndef shift_left(x, y):\n r\"\"\"Elementwise left shift: :math:`x \\ll y`.\"\"\"\n return shift_left_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::shift_right_arithmetic","text":"jax/lax/lax.py::shift_right_arithmetic\ndef shift_right_arithmetic(x, y):\n r\"\"\"Elementwise arithmetic right shift: :math:`x \\gg y`.\"\"\"\n return shift_right_arithmetic_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::shift_right_logical","text":"jax/lax/lax.py::shift_right_logical\ndef shift_right_logical(x, y):\n r\"\"\"Elementwise logical right shift: :math:`x \\gg y`.\"\"\"\n return shift_right_logical_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::eq","text":"jax/lax/lax.py::eq\ndef eq(x, y):\n r\"\"\"Elementwise equals: :math:`x = y`.\"\"\"\n return eq_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::ne","text":"jax/lax/lax.py::ne\ndef ne(x, y):\n r\"\"\"Elementwise not-equals: :math:`x \\neq y`.\"\"\"\n return ne_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::ge","text":"jax/lax/lax.py::ge\ndef ge(x, y):\n r\"\"\"Elementwise greater-than-or-equals: :math:`x \\geq y`.\"\"\"\n return ge_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::gt","text":"jax/lax/lax.py::gt\ndef gt(x, y):\n r\"\"\"Elementwise greater-than: :math:`x > y`.\"\"\"\n return gt_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::le","text":"jax/lax/lax.py::le\ndef le(x, y):\n r\"\"\"Elementwise less-than-or-equals: :math:`x \\leq y`.\"\"\"\n return le_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::lt","text":"jax/lax/lax.py::lt\ndef lt(x, y):\n r\"\"\"Elementwise less-than: :math:`x < y`.\"\"\"\n return lt_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::convert_element_type","text":"jax/lax/lax.py::convert_element_type\ndef convert_element_type(operand, new_dtype):\n \"\"\"Elementwise cast.\n\n Wraps XLA's `ConvertElementType\n <https://www.tensorflow.org/xla/operation_semantics#convertelementtype>`_\n operator, which performs an elementwise conversion from one type to another.\n Similar to a C++ `static_cast`.\n\n Args:\n operand: an array or scalar value to be cast\n new_dtype: the new type. Should be a NumPy type.\n\n Returns:\n An array with the same shape as `operand`, cast elementwise to `new_dtype`.\n \"\"\"\n new_dtype = dtypes.canonicalize_dtype(new_dtype)\n # Avoids dropping precision by casting Python scalars to the default Jax\n # type. If we passed a Python scalar directly to the bind call below, it is\n # cast to the default type as part of the calling convention.\n if type(operand) in dtypes.python_scalar_dtypes:\n operand = onp.asarray(operand, new_dtype)\n old_dtype = dtypes.canonicalize_dtype(_dtype(operand))\n if old_dtype == new_dtype:\n return operand\n if (dtypes.issubdtype(old_dtype, onp.complexfloating) and\n not dtypes.issubdtype(new_dtype, onp.complexfloating)):\n msg = \"Casting complex values to real discards the imaginary part\"\n warnings.warn(msg, onp.ComplexWarning, stacklevel=2)\n operand = real(operand)\n old_dtype = _dtype(operand)\n # TODO(b/143311238, b/142974574): work around bfloat16 conversion bugs by\n # introducing an intermediate cast via float32.\n if ((old_dtype == dtypes.bfloat16 and new_dtype != onp.float32) or\n (new_dtype == dtypes.bfloat16 and old_dtype != onp.float32)):\n operand = convert_element_type_p.bind(\n operand, new_dtype=onp.float32, old_dtype=old_dtype)\n old_dtype = onp.float32\n return convert_element_type_p.bind(\n operand, new_dtype=new_dtype, old_dtype=old_dtype)","label":0},{"cid":"jax/lax/lax.py::bitcast_convert_type","text":"jax/lax/lax.py::bitcast_convert_type\ndef bitcast_convert_type(operand, new_dtype):\n \"\"\"Elementwise bitcast.\n\n Wraps XLA's `BitcastConvertType\n <https://www.tensorflow.org/xla/operation_semantics#bitcastconverttype>`_\n operator, which performs a bit cast from one type to another. The bitwidth\n of the source and destination types must match.\n\n Args:\n operand: an array or scalar value to be cast\n new_dtype: the new type. Should be a NumPy type.\n\n Returns:\n An array with the same shape as `operand`, bitcast elementwise to\n `new_dtype`.\n \"\"\"\n new_dtype = dtypes.canonicalize_dtype(new_dtype)\n old_dtype = _dtype(operand)\n if old_dtype != new_dtype:\n return bitcast_convert_type_p.bind(operand, new_dtype=new_dtype)\n else:\n return operand","label":0},{"cid":"jax/lax/lax.py::clamp","text":"jax/lax/lax.py::clamp\ndef clamp(min, x, max):\n r\"\"\"Elementwise clamp.\n\n Returns :math:`\\mathrm{clamp}(x) = \\begin{cases}\n \\mathit{min} & \\text{if } x < \\mathit{min},\\\\\n \\mathit{max} & \\text{if } x > \\mathit{max},\\\\\n x & \\text{otherwise}\n \\end{cases}`.\n \"\"\"\n return clamp_p.bind(min, x, max)","label":0},{"cid":"jax/lax/lax.py::concatenate","text":"jax/lax/lax.py::concatenate\ndef concatenate(operands, dimension):\n \"\"\"Concatenates a sequence of arrays along `dimension`.\n\n Wraps XLA's `Concatenate\n <https://www.tensorflow.org/xla/operation_semantics#concatenate>`_\n operator.\n\n Args:\n operands: a sequence of arrays to concatenate. The arrays must have equal\n shapes, except in the `dimension` axis.\n dimension: the dimension along which to concatenate the arrays.\n\n Returns:\n An array containing the concatenation.\n \"\"\"\n return concatenate_p.bind(*operands, dimension=dimension,\n operand_shapes=tuple(o.shape for o in operands))","label":0},{"cid":"jax/lax/lax.py::conv_general_dilated","text":"jax/lax/lax.py::conv_general_dilated\ndef conv_general_dilated(lhs, rhs, window_strides, padding, lhs_dilation=None,\n rhs_dilation=None, dimension_numbers=None,\n feature_group_count=1, precision=None):\n \"\"\"General n-dimensional convolution operator, with optional dilation.\n\n Wraps XLA's `Conv\n <https://www.tensorflow.org/xla/operation_semantics#conv_convolution>`_\n operator.\n\n Args:\n lhs: a rank `n+2` dimensional input array.\n rhs: a rank `n+2` dimensional array of kernel weights.\n window_strides: a sequence of `n` integers, representing the inter-window\n strides.\n padding: either the string `'SAME'`, the string `'VALID'`, or a sequence of\n `n` `(low, high)` integer pairs that give the padding to apply before and\n after each spatial dimension.\n lhs_dilation: `None`, or a sequence of `n` integers, giving the\n dilation factor to apply in each spatial dimension of `lhs`. LHS dilation\n is also known as transposed convolution.\n rhs_dilation: `None`, or a sequence of `n` integers, giving the\n dilation factor to apply in each spatial dimension of `rhs`. RHS dilation\n is also known as atrous convolution.\n dimension_numbers: either `None`, a `ConvDimensionNumbers` object, or\n a 3-tuple `(lhs_spec, rhs_spec, out_spec)`, where each element is a string\n of length `n+2`.\n feature_group_count: integer, default 1. See XLA HLO docs.\n precision: Optional. Either `None`, which means the default precision for\n the backend, or a `Precision` enum value.\n\n Returns:\n An array containing the convolution result.\n\n In the string case of `dimension_numbers`, each character identifies by\n position:\n\n - the batch dimensions in `lhs`, `rhs`, and the output with the character\n 'N',\n - the feature dimensions in `lhs` and the output with the character 'C',\n - the input and output feature dimensions in rhs with the characters 'I'\n and 'O' respectively, and\n ","label":0},{"cid":"jax/lax/lax.py::dot","text":"jax/lax/lax.py::dot\ndef dot(lhs, rhs, precision=None):\n \"\"\"Vector/vector, matrix/vector, and matrix/matrix multiplication.\n\n Wraps XLA's `Dot\n <https://www.tensorflow.org/xla/operation_semantics#dot>`_\n operator.\n\n For more general contraction, see the `dot_general` operator.\n\n Args:\n lhs: an array of rank 1 or 2.\n rhs: an array of rank 1 or 2.\n precision: Optional. Either `None`, which means the default precision for\n the backend, or a `Precision` enum value.\n\n Returns:\n An array containing the product.\n \"\"\"\n if 1 <= lhs.ndim <= 2 and 1 <= rhs.ndim <= 2 and lhs.shape[-1] == rhs.shape[0]:\n return dot_general(lhs, rhs, (((lhs.ndim - 1,), (0,)), ((), ())),\n precision=precision)\n else:\n raise TypeError(\"Incompatible shapes for dot: got {} and {}.\".format(\n lhs.shape, rhs.shape))","label":0},{"cid":"jax/lax/lax.py::dot_general","text":"jax/lax/lax.py::dot_general\ndef dot_general(lhs, rhs, dimension_numbers, precision=None):\n \"\"\"More general contraction operator.\n\n Wraps XLA's `DotGeneral\n <https://www.tensorflow.org/xla/operation_semantics#dotgeneral>`_\n operator.\n\n Args:\n lhs: an array\n rhs: an array\n dimension_numbers: a tuple of tuples of the form\n `((lhs_contracting_dims, rhs_contracting_dims),\n (lhs_batch_dims, rhs_batch_dims))`\n precision: Optional. Either `None`, which means the default precision for\n the backend, or a `Precision` enum value.\n\n Returns:\n An array containing the result.\n \"\"\"\n contract_dims, batch_dims = dimension_numbers\n contract_dims = tuple(map(tuple, contract_dims))\n batch_dims = tuple(map(tuple, batch_dims))\n if not dtypes.issubdtype(lhs.dtype, onp.inexact):\n # TODO(b/134526360): XLA doesn't support bool or integer dots, so we emit a\n # sum of products instead.\n lhs_contract_dims, rhs_contract_dims = contract_dims\n lhs_batch_dims, rhs_batch_dims = batch_dims\n lhs_noncontract_dims = tuple(sorted(\n set(range(onp.ndim(lhs))) - set(lhs_batch_dims) - set(lhs_contract_dims)))\n rhs_noncontract_dims = tuple(sorted(\n set(range(onp.ndim(rhs))) - set(rhs_batch_dims) - set(rhs_contract_dims)))\n lhs = transpose(lhs,\n lhs_batch_dims + lhs_noncontract_dims + lhs_contract_dims)\n rhs = transpose(rhs,\n rhs_batch_dims + rhs_noncontract_dims + rhs_contract_dims)\n new_lhs_shape = onp.insert(onp.array(onp.shape(lhs), dtype=onp.int64),\n len(lhs_batch_dims) + len(lhs_noncontract_dims),\n (1,) * len(rhs_noncontract_dims))\n new_rhs_shape = onp.insert(onp.array(onp.shape(rhs), dtype=onp.int64),\n len(lhs_batch_dims),\n (1,) * len(lhs_noncontract_dims))\n lhs = reshape(lhs, new_lhs_shape)\n rhs = reshape(rhs, new_rhs_shape)\n out_ndim = (len(lhs_batch_dims) + ","label":0},{"cid":"jax/lax/lax.py::broadcast","text":"jax/lax/lax.py::broadcast\ndef broadcast(operand, sizes):\n \"\"\"Broadcasts an array, adding new major dimensions.\n\n Wraps XLA's `Broadcast\n <https://www.tensorflow.org/xla/operation_semantics#broadcast>`_\n operator.\n\n Args:\n operand: an array\n sizes: a sequence of integers, giving the sizes of new major dimensions\n to add.\n\n Returns:\n An array containing the result.\n \"\"\"\n dims = tuple(range(len(sizes), len(sizes) + onp.ndim(operand)))\n return broadcast_in_dim(operand, tuple(sizes) + onp.shape(operand), dims)","label":0},{"cid":"jax/lax/lax.py::broadcast_in_dim","text":"jax/lax/lax.py::broadcast_in_dim\ndef broadcast_in_dim(operand, shape, broadcast_dimensions):\n if onp.ndim(operand) == len(shape) and not len(broadcast_dimensions):\n return operand\n return broadcast_in_dim_p.bind(\n operand, shape=tuple(shape),\n broadcast_dimensions=tuple(broadcast_dimensions))","label":0},{"cid":"jax/lax/lax.py::reshape","text":"jax/lax/lax.py::reshape\ndef reshape(operand, new_sizes, dimensions=None):\n \"\"\"Wraps XLA's `Reshape\n <https://www.tensorflow.org/xla/operation_semantics#reshape>`_\n operator.\n \"\"\"\n new_sizes = _canonicalize_shape(new_sizes) # TODO\n new_sizes = tuple(new_sizes)\n same_shape = onp.shape(operand) == new_sizes\n same_dims = dimensions is None or tuple(dimensions) == tuple(range(onp.ndim(operand)))\n if onp.shape(operand) and same_shape and same_dims:\n return operand\n else:\n return reshape_p.bind(\n operand, new_sizes=new_sizes,\n dimensions=None if same_dims else tuple(dimensions),\n old_sizes=onp.shape(operand))","label":0},{"cid":"jax/lax/lax.py::pad","text":"jax/lax/lax.py::pad\ndef pad(operand, padding_value, padding_config):\n \"\"\"Wraps XLA's `Pad\n <https://www.tensorflow.org/xla/operation_semantics#pad>`_\n operator.\n \"\"\"\n return pad_p.bind(operand, padding_value, padding_config=tuple(padding_config))","label":0},{"cid":"jax/lax/lax.py::rev","text":"jax/lax/lax.py::rev\ndef rev(operand, dimensions):\n \"\"\"Wraps XLA's `Rev\n <https://www.tensorflow.org/xla/operation_semantics#rev_reverse>`_\n operator.\n \"\"\"\n return rev_p.bind(operand, dimensions=tuple(dimensions))","label":0},{"cid":"jax/lax/lax.py::select","text":"jax/lax/lax.py::select\ndef select(pred, on_true, on_false):\n \"\"\"Wraps XLA's `Select\n <https://www.tensorflow.org/xla/operation_semantics#select>`_\n operator.\n \"\"\"\n return select_p.bind(pred, on_true, on_false)","label":0},{"cid":"jax/lax/lax.py::slice","text":"jax/lax/lax.py::slice\ndef slice(operand, start_indices, limit_indices, strides=None):\n \"\"\"Wraps XLA's `Slice\n <https://www.tensorflow.org/xla/operation_semantics#slice>`_\n operator.\n \"\"\"\n if (onp.all(onp.equal(start_indices, 0))\n and onp.all(onp.equal(limit_indices, operand.shape))\n and strides is None):\n return operand\n else:\n return slice_p.bind(operand, start_indices=tuple(start_indices),\n limit_indices=tuple(limit_indices),\n strides=None if strides is None else tuple(strides),\n operand_shape=operand.shape)","label":0},{"cid":"jax/lax/lax.py::dynamic_slice","text":"jax/lax/lax.py::dynamic_slice\ndef dynamic_slice(operand, start_indices, slice_sizes):\n \"\"\"Wraps XLA's `DynamicSlice\n <https://www.tensorflow.org/xla/operation_semantics#dynamicslice>`_\n operator.\n\n Args:\n operand: an array to slice.\n start_indices: a list of scalar indices, one per dimension.\n slice_sizes: the size of the slice. Must be a sequence of non-negative\n integers with length equal to `ndim(operand)`.\n\n Returns:\n An array containing the slice.\n \"\"\"\n start_indices = _dynamic_slice_indices(operand, start_indices)\n return dynamic_slice_p.bind(\n operand, *start_indices, slice_sizes=tuple(slice_sizes),\n operand_shape=operand.shape)","label":0},{"cid":"jax/lax/lax.py::dynamic_update_slice","text":"jax/lax/lax.py::dynamic_update_slice\ndef dynamic_update_slice(operand, update, start_indices):\n \"\"\"Wraps XLA's `DynamicUpdateSlice\n <https://www.tensorflow.org/xla/operation_semantics#dynamicupdateslice>`_\n operator.\n\n Args:\n operand: an array to slice.\n update: an array containing the new values to write onto `operand`.\n start_indices: a list of scalar indices, one per dimension.\n\n Returns:\n An array containing the slice.\n \"\"\"\n start_indices = _dynamic_slice_indices(operand, start_indices)\n return dynamic_update_slice_p.bind(operand, update, *start_indices,\n update_shape=update.shape)","label":0},{"cid":"jax/lax/lax.py::gather","text":"jax/lax/lax.py::gather\ndef gather(operand, start_indices, dimension_numbers, slice_sizes):\n \"\"\"Gather operator.\n\n Wraps `XLA's Gather operator\n <https://www.tensorflow.org/xla/operation_semantics#gather>`_.\n\n The semantics of gather are complicated, and its API might change in the\n future. For most use cases, you should prefer `Numpy-style indexing\n <https://docs.scipy.org/doc/numpy-1.16.0/reference/arrays.indexing.html>`_\n (e.g., `x[:, (1,4,7), ...]`), rather than using `gather` directly.\n\n Args:\n operand: an array from which slices should be taken\n start_indices: the indices at which slices should be taken\n dimension_numbers: a `lax.GatherDimensionNumbers` object that describes\n how dimensions of `operand`, `start_indices` and the output relate.\n slice_sizes: the size of each slice. Must be a sequence of non-negative\n integers with length equal to `ndim(operand)`.\n\n Returns:\n An array containing the gather output.\n \"\"\"\n return gather_p.bind(\n operand, start_indices, dimension_numbers=dimension_numbers,\n slice_sizes=_canonicalize_shape(slice_sizes), operand_shape=operand.shape)","label":0},{"cid":"jax/lax/lax.py::scatter_add","text":"jax/lax/lax.py::scatter_add\ndef scatter_add(operand, scatter_indices, updates, dimension_numbers):\n \"\"\"Scatter-add operator.\n\n Wraps `XLA's Scatter operator\n <https://www.tensorflow.org/xla/operation_semantics#scatter>`_, where\n addition is used to combine updates and values from `operand`.\n\n The semantics of scatter are complicated and its API is subject to change.\n\n Args:\n operand: an array to which the scatter should be applied\n scatter_indices: an array that gives the indices in `operand` to which each\n update in `updates` should be applied.\n updates: the updates that should be scattered onto `operand`.\n dimension_numbers: a `lax.ScatterDimensionNumbers` object that describes\n how dimensions of `operand`, `start_indices`, `updates` and the output\n relate.\n\n Returns:\n An array containing the sum of `operand` and the scattered updates.\n \"\"\"\n jaxpr, consts = _reduction_jaxpr(add, _abstractify(_const(operand, 0)))\n return scatter_add_p.bind(\n operand, scatter_indices, updates, update_jaxpr=jaxpr,\n update_consts=consts, dimension_numbers=dimension_numbers,\n updates_shape=updates.shape)","label":0},{"cid":"jax/lax/lax.py::scatter_min","text":"jax/lax/lax.py::scatter_min\ndef scatter_min(operand, scatter_indices, updates, dimension_numbers):\n \"\"\"Scatter-min operator.\n\n Wraps `XLA's Scatter operator\n <https://www.tensorflow.org/xla/operation_semantics#scatter>`_, where\n the `min` function is used to combine updates and values from `operand`.\n\n The semantics of scatter are complicated and its API is subject to change.\n\n Args:\n operand: an array to which the scatter should be applied\n scatter_indices: an array that gives the indices in `operand` to which each\n update in `updates` should be applied.\n updates: the updates that should be scattered onto `operand`.\n dimension_numbers: a `lax.ScatterDimensionNumbers` object that describes\n how dimensions of `operand`, `start_indices`, `updates` and the output\n relate.\n\n Returns:\n An array containing the sum of `operand` and the scattered updates.\n \"\"\"\n jaxpr, consts = _reduction_jaxpr(min, _abstractify(_const(operand, 0)))\n return scatter_min_p.bind(\n operand, scatter_indices, updates, update_jaxpr=jaxpr,\n update_consts=consts, dimension_numbers=dimension_numbers,\n updates_shape=updates.shape)","label":0},{"cid":"jax/lax/lax.py::scatter_max","text":"jax/lax/lax.py::scatter_max\ndef scatter_max(operand, scatter_indices, updates, dimension_numbers):\n \"\"\"Scatter-max operator.\n\n Wraps `XLA's Scatter operator\n <https://www.tensorflow.org/xla/operation_semantics#scatter>`_, where\n the `max` function is used to combine updates and values from `operand`.\n\n The semantics of scatter are complicated and its API is subject to change.\n\n Args:\n operand: an array to which the scatter should be applied\n scatter_indices: an array that gives the indices in `operand` to which each\n update in `updates` should be applied.\n updates: the updates that should be scattered onto `operand`.\n dimension_numbers: a `lax.ScatterDimensionNumbers` object that describes\n how dimensions of `operand`, `start_indices`, `updates` and the output\n relate.\n\n Returns:\n An array containing the sum of `operand` and the scattered updates.\n \"\"\"\n jaxpr, consts = _reduction_jaxpr(max, _abstractify(_const(operand, 0)))\n return scatter_max_p.bind(\n operand, scatter_indices, updates, update_jaxpr=jaxpr,\n update_consts=consts, dimension_numbers=dimension_numbers,\n updates_shape=updates.shape)","label":0},{"cid":"jax/lax/lax.py::scatter","text":"jax/lax/lax.py::scatter\ndef scatter(operand, scatter_indices, updates, dimension_numbers):\n \"\"\"Scatter-update operator.\n\n Wraps `XLA's Scatter operator\n <https://www.tensorflow.org/xla/operation_semantics#scatter>`_, where updates\n replace values from `operand`.\n\n If multiple updates are performed to the same index of operand, they may be\n applied in any order.\n\n The semantics of scatter are complicated and its API is subject to change.\n\n Args:\n operand: an array to which the scatter should be applied\n scatter_indices: an array that gives the indices in `operand` to which each\n update in `updates` should be applied.\n updates: the updates that should be scattered onto `operand`.\n dimension_numbers: a `lax.ScatterDimensionNumbers` object that describes\n how dimensions of `operand`, `start_indices`, `updates` and the output\n relate.\n\n Returns:\n An array containing the sum of `operand` and the scattered updates.\n \"\"\"\n jaxpr, consts = _reduction_jaxpr(_scatter_reduction_computation,\n _abstractify(_const(operand, 0)))\n return scatter_p.bind(\n operand, scatter_indices, updates, update_jaxpr=jaxpr,\n update_consts=consts, dimension_numbers=dimension_numbers,\n updates_shape=updates.shape)","label":0},{"cid":"jax/lax/lax.py::index_take","text":"jax/lax/lax.py::index_take\ndef index_take(src, idxs, axes):\n indices = concatenate([reshape(i, [i.shape[0], 1]) for i in idxs], 1)\n indices = indices % onp.array([src.shape[ax] for ax in axes])\n slice_sizes = list(src.shape)\n for ax in axes:\n slice_sizes[ax] = 1\n slice_sizes = tuple(slice_sizes)\n offset_dims = tuple(range(1, src.ndim - indices.shape[1] + 1))\n dnums = GatherDimensionNumbers(\n offset_dims=offset_dims,\n collapsed_slice_dims=axes,\n start_index_map=axes)\n return gather(src, indices, dimension_numbers=dnums, slice_sizes=slice_sizes)","label":0},{"cid":"jax/lax/lax.py::transpose","text":"jax/lax/lax.py::transpose\ndef transpose(operand, permutation):\n \"\"\"Wraps XLA's `Transpose\n <https://www.tensorflow.org/xla/operation_semantics#transpose>`_\n operator.\n \"\"\"\n permutation = tuple(permutation)\n if permutation == tuple(range(len(permutation))):\n return operand\n else:\n return transpose_p.bind(operand, permutation=permutation)","label":0},{"cid":"jax/lax/lax.py::reduce","text":"jax/lax/lax.py::reduce\ndef reduce(operand, init_value, computation, dimensions):\n \"\"\"Wraps XLA's `Reduce\n <https://www.tensorflow.org/xla/operation_semantics#reduce>`_\n operator.\n \"\"\"\n monoid_reducer = _get_monoid_reducer(computation, init_value)\n if monoid_reducer:\n return monoid_reducer(operand, dimensions)\n else:\n jaxpr, consts = _reduction_jaxpr(computation, _abstractify(init_value))\n return reduce_p.bind(operand, init_value, computation=computation,\n jaxpr=jaxpr, consts=consts, dimensions=tuple(dimensions))","label":0},{"cid":"jax/lax/lax.py::_reduction_jaxpr","text":"jax/lax/lax.py::_reduction_jaxpr\n@cache()\ndef _reduction_jaxpr(computation, aval):\n pval = pe.PartialVal((aval, core.unit))\n comp = lu.wrap_init(lambda x, y: (computation(x, y),))\n jaxpr, _, consts = pe.trace_to_jaxpr(comp, (pval, pval), instantiate=False)\n return jaxpr, consts","label":0},{"cid":"jax/lax/lax.py::_get_monoid_reducer","text":"jax/lax/lax.py::_get_monoid_reducer\ndef _get_monoid_reducer(monoid_op, x):\n aval = core.get_aval(x)\n dtype = _dtype(x)\n if (type(aval) is ConcreteArray) and aval.shape == ():\n if monoid_op is add:\n return aval.val == 0 and _reduce_sum\n if monoid_op is mul:\n return aval.val == 1 and _reduce_prod\n elif monoid_op is bitwise_or and dtype == onp.bool_:\n return aval.val == _get_max_identity(dtype) and _reduce_or\n elif monoid_op is bitwise_and and dtype == onp.bool_:\n return aval.val == _get_min_identity(dtype) and _reduce_and\n elif monoid_op is max:\n return aval.val == _get_max_identity(dtype) and _reduce_max\n elif monoid_op is min:\n return aval.val == _get_min_identity(dtype) and _reduce_min","label":0},{"cid":"jax/lax/lax.py::_get_max_identity","text":"jax/lax/lax.py::_get_max_identity\ndef _get_max_identity(dtype):\n if dtypes.issubdtype(dtype, onp.inexact):\n return onp.array(-onp.inf, dtype)\n elif dtypes.issubdtype(dtype, onp.integer):\n return onp.array(dtypes.iinfo(dtype).min, dtype)\n elif dtypes.issubdtype(dtype, onp.bool_):\n return onp.array(False, onp.bool_)","label":0},{"cid":"jax/lax/lax.py::_get_min_identity","text":"jax/lax/lax.py::_get_min_identity\ndef _get_min_identity(dtype):\n if dtypes.issubdtype(dtype, onp.inexact):\n return onp.array(onp.inf, dtype)\n elif dtypes.issubdtype(dtype, onp.integer):\n return onp.array(dtypes.iinfo(dtype).max, dtype)\n elif dtypes.issubdtype(dtype, onp.bool_):\n return onp.array(True, onp.bool_)","label":0},{"cid":"jax/lax/lax.py::_reduce_sum","text":"jax/lax/lax.py::_reduce_sum\ndef _reduce_sum(operand, axes):\n return reduce_sum_p.bind(operand, axes=tuple(axes),\n input_shape=onp.shape(operand))","label":0},{"cid":"jax/lax/lax.py::_reduce_prod","text":"jax/lax/lax.py::_reduce_prod\ndef _reduce_prod(operand, axes):\n return reduce_prod_p.bind(operand, axes=tuple(axes))","label":0},{"cid":"jax/lax/lax.py::_reduce_max","text":"jax/lax/lax.py::_reduce_max\ndef _reduce_max(operand, axes):\n return reduce_max_p.bind(operand, axes=tuple(axes))","label":0},{"cid":"jax/lax/lax.py::_reduce_min","text":"jax/lax/lax.py::_reduce_min\ndef _reduce_min(operand, axes):\n return reduce_min_p.bind(operand, axes=tuple(axes))","label":0},{"cid":"jax/lax/lax.py::_reduce_or","text":"jax/lax/lax.py::_reduce_or\ndef _reduce_or(operand, axes):\n return reduce_or_p.bind(operand, axes=tuple(axes))","label":0},{"cid":"jax/lax/lax.py::_reduce_and","text":"jax/lax/lax.py::_reduce_and\ndef _reduce_and(operand, axes):\n return reduce_and_p.bind(operand, axes=tuple(axes))","label":0},{"cid":"jax/lax/lax.py::reduce_window","text":"jax/lax/lax.py::reduce_window\ndef reduce_window(operand, init_value, computation, window_dimensions,\n window_strides, padding):\n \"\"\"Wraps XLA's `ReduceWindow\n <https://www.tensorflow.org/xla/operation_semantics#reducewindow>`_\n operator.\n \"\"\"\n monoid_reducer = _get_monoid_window_reducer(computation, init_value)\n if monoid_reducer:\n return monoid_reducer(operand, window_dimensions, window_strides, padding)\n else:\n jaxpr, consts = _reduction_jaxpr(computation, _abstractify(init_value))\n return reduce_window_p.bind(\n operand, init_value, jaxpr=jaxpr, consts=consts,\n window_dimensions=tuple(window_dimensions),\n window_strides=tuple(window_strides), padding=padding)","label":0},{"cid":"jax/lax/lax.py::_get_monoid_window_reducer","text":"jax/lax/lax.py::_get_monoid_window_reducer\ndef _get_monoid_window_reducer(monoid_op, x):\n aval = core.get_aval(x)\n if (type(aval) is ConcreteArray) and aval.shape == ():\n if monoid_op is add:\n return aval.val == 0 and _reduce_window_sum\n elif monoid_op is max:\n return aval.val == _get_max_identity(aval.dtype) and _reduce_window_max\n elif monoid_op is min:\n return aval.val == _get_min_identity(aval.dtype) and _reduce_window_min","label":0},{"cid":"jax/lax/lax.py::_reduce_window_sum","text":"jax/lax/lax.py::_reduce_window_sum\ndef _reduce_window_sum(operand, window_dimensions, window_strides, padding):\n return reduce_window_sum_p.bind(\n operand, window_dimensions=tuple(window_dimensions),\n window_strides=tuple(window_strides), padding=padding,\n input_shape=operand.shape)","label":0},{"cid":"jax/lax/lax.py::_reduce_window_prod","text":"jax/lax/lax.py::_reduce_window_prod\ndef _reduce_window_prod(operand, window_dimensions, window_strides, padding):\n init_value = _const(operand, 1)\n jaxpr, consts = _reduction_jaxpr(mul, _abstractify(init_value))\n return reduce_window_p.bind(\n operand, init_value, jaxpr=jaxpr, consts=consts,\n window_dimensions=tuple(window_dimensions),\n window_strides=tuple(window_strides), padding=padding)","label":0},{"cid":"jax/lax/lax.py::_reduce_window_max","text":"jax/lax/lax.py::_reduce_window_max\ndef _reduce_window_max(operand, window_dimensions, window_strides, padding):\n return reduce_window_max_p.bind(\n operand, window_dimensions=tuple(window_dimensions),\n window_strides=tuple(window_strides), padding=padding)","label":0},{"cid":"jax/lax/lax.py::_reduce_window_min","text":"jax/lax/lax.py::_reduce_window_min\ndef _reduce_window_min(operand, window_dimensions, window_strides, padding):\n return reduce_window_min_p.bind(\n operand, window_dimensions=tuple(window_dimensions),\n window_strides=tuple(window_strides), padding=padding)","label":0},{"cid":"jax/lax/lax.py::_select_and_scatter","text":"jax/lax/lax.py::_select_and_scatter\ndef _select_and_scatter(operand, select, window_dimensions, window_strides,\n padding, source, init_value, scatter):\n select_jaxpr, select_consts = _reduction_jaxpr(select, _abstractify(init_value))\n scatter_jaxpr, scatter_consts = _reduction_jaxpr(scatter, _abstractify(init_value))\n return select_and_scatter_p.bind(\n operand, source, init_value, select_jaxpr=select_jaxpr,\n select_consts=select_consts, scatter_jaxpr=scatter_jaxpr,\n scatter_consts=scatter_consts, window_dimensions=tuple(window_dimensions),\n window_strides=tuple(window_strides), padding=padding)","label":0},{"cid":"jax/lax/lax.py::_select_and_scatter_add","text":"jax/lax/lax.py::_select_and_scatter_add\ndef _select_and_scatter_add(source, operand, select_prim, window_dimensions,\n window_strides, padding):\n return select_and_scatter_add_p.bind(\n source, operand, select_prim=select_prim,\n window_dimensions=tuple(window_dimensions),\n window_strides=tuple(window_strides), padding=padding)","label":0},{"cid":"jax/lax/lax.py::_select_and_gather_add","text":"jax/lax/lax.py::_select_and_gather_add\ndef _select_and_gather_add(tangents, operand, select_prim, window_dimensions,\n window_strides, padding):\n return select_and_gather_add_p.bind(\n tangents, operand, select_prim=select_prim,\n window_dimensions=tuple(window_dimensions),\n window_strides=tuple(window_strides), padding=padding)","label":0},{"cid":"jax/lax/lax.py::sort","text":"jax/lax/lax.py::sort\ndef sort(operand, dimension=-1):\n \"\"\"Wraps XLA's `Sort\n <https://www.tensorflow.org/xla/operation_semantics#sort>`_\n operator.\n \"\"\"\n return sort_p.bind(operand, dimension=dimension)","label":0},{"cid":"jax/lax/lax.py::sort_key_val","text":"jax/lax/lax.py::sort_key_val\ndef sort_key_val(keys, values, dimension=-1):\n # TODO(mattjj): new sort_key_val is variadic\n result = sort_key_val_p.bind(keys, values, dimension=dimension)\n sorted_keys, sorted_values = result\n return sorted_keys, sorted_values","label":0},{"cid":"jax/lax/lax.py::tie_in","text":"jax/lax/lax.py::tie_in\ndef tie_in(x, y):\n return tie_in_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::full","text":"jax/lax/lax.py::full\ndef full(shape, fill_value, dtype=None):\n \"\"\"Returns an array of `shape` filled with `fill_value`.\n\n Arguments:\n shape: sequence of integers, describing the shape of the output array.\n fill_value: the value to fill the new array with.\n dtype: the type of the output array, or `None`. If not `None`, `fill_value`\n will be cast to `dtype`.\n \"\"\"\n shape = _canonicalize_shape(shape)\n if onp.shape(fill_value):\n msg = \"full must be called with scalar fill_value, got fill_value.shape {}.\"\n raise TypeError(msg.format(onp.shape(fill_value)))\n dtype = dtypes.canonicalize_dtype(dtype or _dtype(fill_value))\n # TODO(mattjj): remove device_put when dtype conversion produces DeviceArray\n fill_value = xla.device_put_p.bind(convert_element_type(fill_value, dtype))\n return broadcast(fill_value, shape)","label":0},{"cid":"jax/lax/lax.py::iota","text":"jax/lax/lax.py::iota\ndef iota(dtype, size):\n \"\"\"Wraps XLA's `Iota\n <https://www.tensorflow.org/xla/operation_semantics#iota>`_\n operator.\n \"\"\"\n size = int(size)\n dtype = dtypes.canonicalize_dtype(dtype)\n lazy_expr = lazy.iota(dtype, size)\n aval = ShapedArray((size,), dtype)\n return xla.DeviceArray(aval, None, lazy_expr, xla.DeviceConstant())","label":0},{"cid":"jax/lax/lax.py::broadcasted_iota","text":"jax/lax/lax.py::broadcasted_iota\ndef broadcasted_iota(dtype, shape, dimension):\n \"\"\"Convenience wrapper around ``iota``.\"\"\"\n dtype = dtypes.canonicalize_dtype(dtype)\n shape = _canonicalize_shape(shape)\n dimension = int(dimension)\n return broadcast_in_dim(iota(dtype, shape[dimension]), shape, [dimension])","label":0},{"cid":"jax/lax/lax.py::_eye","text":"jax/lax/lax.py::_eye\ndef _eye(dtype, shape, offset):\n \"\"\"Like numpy.eye, create a 2D array with ones on a diagonal.\n\n This function exists for creating lazy identity matrices; that is,\n materialization of the array is delayed and it may be fused into consumers to\n avoid materialization at all.\"\"\"\n N, M = tuple(map(int, shape))\n offset = int(offset)\n dtype = dtypes.canonicalize_dtype(dtype)\n lazy_expr = lazy.eye(dtype, (N, M), offset)\n aval = ShapedArray((N, M), dtype)\n return xla.DeviceArray(aval, None, lazy_expr, xla.DeviceConstant())","label":0},{"cid":"jax/lax/lax.py::_delta","text":"jax/lax/lax.py::_delta\ndef _delta(dtype, shape, axes):\n \"\"\"This function exists for creating lazy Kronecker delta arrays, particularly\n for use in jax.numpy.einsum to express traces. It differs from ``eye`` in that\n it can create arrays of any rank, but doesn't allow offsets.\"\"\"\n shape = tuple(map(int, shape))\n axes = tuple(map(int, axes))\n dtype = dtypes.canonicalize_dtype(dtype)\n base_shape = tuple(onp.take(shape, axes))\n lazy_expr = lazy.broadcast(lazy.delta(dtype, base_shape), shape, axes)\n aval = ShapedArray(shape, dtype)\n return xla.DeviceArray(aval, None, lazy_expr, xla.DeviceConstant())","label":0},{"cid":"jax/lax/lax.py::_tri","text":"jax/lax/lax.py::_tri\ndef _tri(dtype, shape, offset):\n \"\"\"Like numpy.tri, create a 2D array with ones below a diagonal.\n This function exists for creating lazy triangular matrices, particularly for\n use in jax.numpy.tri.\"\"\"\n N, M = tuple(map(int, shape))\n offset = int(offset)\n dtype = dtypes.canonicalize_dtype(dtype)\n lazy_expr = lazy.tri(dtype, (N, M), offset)\n aval = ShapedArray((N, M), dtype)\n return xla.DeviceArray(aval, None, lazy_expr, xla.DeviceConstant())","label":0},{"cid":"jax/lax/lax.py::stop_gradient","text":"jax/lax/lax.py::stop_gradient\ndef stop_gradient(x):\n \"\"\"Stops gradient computation.\n\n Operationally `stop_gradient` is the identity function, that is, it returns\n argument `x` unchanged. However, `stop_gradient` prevents the flow of\n gradients during forward or reverse-mode automatic differentiation. If there\n are multiple nested gradient computations, `stop_gradient` stops gradients\n for all of them.\n\n For example:\n\n >>> jax.grad(lambda x: x**2)(3.)\n array(6., dtype=float32)\n >>> jax.grad(lambda x: jax.lax.stop_gradient(x)**2)(3.)\n array(0., dtype=float32)\n >>> jax.grad(jax.grad(lambda x: x**2))(3.)\n array(2., dtype=float32)\n >>> jax.grad(jax.grad(lambda x: jax.lax.stop_gradient(x)**2))(3.)\n array(0., dtype=float32)\n \"\"\"\n return tree_map(stop_gradient_p.bind, x)","label":0},{"cid":"jax/lax/lax.py::_safe_mul","text":"jax/lax/lax.py::_safe_mul\ndef _safe_mul(x, y): return safe_mul_p.bind(x, y)","label":0},{"cid":"jax/lax/lax.py::conv","text":"jax/lax/lax.py::conv\ndef conv(lhs, rhs, window_strides, padding, precision=None):\n \"\"\"Convenience wrapper around `conv_general_dilated`.\n\n Args:\n lhs: a rank `n+2` dimensional input array.\n rhs: a rank `n+2` dimensional array of kernel weights.\n window_strides: a sequence of `n` integers, representing the inter-window\n strides.\n padding: either the string `'SAME'`, the string `'VALID'`.\n precision: Optional. Either `None`, which means the default precision for\n the backend, or a `Precision` enum value.\n\n Returns:\n An array containing the convolution result.\n \"\"\"\n pads = padtype_to_pads(lhs.shape[2:], rhs.shape[2:], window_strides, padding)\n return conv_general_dilated(lhs, rhs, window_strides, padding,\n precision=precision)","label":0},{"cid":"jax/lax/lax.py::conv_with_general_padding","text":"jax/lax/lax.py::conv_with_general_padding\ndef conv_with_general_padding(lhs, rhs, window_strides, padding,\n lhs_dilation, rhs_dilation, precision=None):\n \"\"\"Convenience wrapper around `conv_general_dilated`.\n\n Args:\n lhs: a rank `n+2` dimensional input array.\n rhs: a rank `n+2` dimensional array of kernel weights.\n window_strides: a sequence of `n` integers, representing the inter-window\n strides.\n padding: either the string `'SAME'`, the string `'VALID'`, or a sequence of\n `n` `(low, high)` integer pairs that give the padding to apply before and\n after each spatial dimension.\n lhs_dilation: `None`, or a sequence of `n` integers, giving the\n dilation factor to apply in each spatial dimension of `lhs`. LHS dilation\n is also known as transposed convolution.\n rhs_dilation: `None`, or a sequence of `n` integers, giving the\n dilation factor to apply in each spatial dimension of `rhs`. RHS dilation\n is also known as atrous convolution.\n precision: Optional. Either `None`, which means the default precision for\n the backend, or a `Precision` enum value.\n\n Returns:\n An array containing the convolution result.\n \"\"\"\n return conv_general_dilated(\n lhs, rhs, window_strides, padding, lhs_dilation=lhs_dilation,\n rhs_dilation=rhs_dilation, precision=precision)","label":0},{"cid":"jax/lax/lax.py::_conv_transpose_padding","text":"jax/lax/lax.py::_conv_transpose_padding\ndef _conv_transpose_padding(k, s, padding):\n \"\"\"Calculate before and after padding for a dim of transposed convolution.\n\n Args:\n k: int: kernel dimension.\n s: int: dimension stride value.\n padding: 'same' or 'valid' padding mode for original forward conv.\n\n Returns:\n 2-tuple: ints: before and after padding for transposed convolution.\n \"\"\"\n if padding == 'SAME':\n pad_len = k + s - 2\n if s > k - 1:\n pad_a = k - 1\n else:\n pad_a = int(onp.ceil(pad_len / 2))\n elif padding == 'VALID':\n pad_len = k + s - 2 + _max(k - s, 0)\n pad_a = k - 1\n else:\n raise ValueError('Padding mode must be `SAME` or `VALID`.')\n pad_b = pad_len - pad_a\n return pad_a, pad_b","label":0},{"cid":"jax/lax/lax.py::_flip_axes","text":"jax/lax/lax.py::_flip_axes\ndef _flip_axes(x, axes):\n \"\"\"Flip ndarray 'x' along each axis specified in axes tuple.\"\"\"\n for axis in axes:\n x = onp.flip(x, axis)\n return x","label":0},{"cid":"jax/lax/lax.py::conv_transpose","text":"jax/lax/lax.py::conv_transpose\ndef conv_transpose(lhs, rhs, strides, padding, rhs_dilation=None,\n dimension_numbers=None, transpose_kernel=False, precision=None):\n \"\"\"Convenience wrapper for calculating the N-d convolution \"transpose\".\n\n This function directly calculates a fractionally strided conv rather than\n indirectly calculating the gradient (transpose) of a forward convolution.\n\n Args:\n lhs: a rank `n+2` dimensional input array.\n rhs: a rank `n+2` dimensional array of kernel weights.\n strides: sequence of `n` integers, sets fractional stride.\n padding: 'SAME', 'VALID' will set as transpose of corresponding forward\n conv, or a sequence of `n` integer 2-tuples describing before-and-after\n padding for each `n` spatial dimension.\n rhs_dilation: `None`, or a sequence of `n` integers, giving the\n dilation factor to apply in each spatial dimension of `rhs`. RHS dilation\n is also known as atrous convolution.\n dimension_numbers: tuple of dimension descriptors as in\n lax.conv_general_dilated. Defaults to tensorflow convention.\n transpose_kernel: if True flips spatial axes and swaps the input/output\n channel axes of the kernel. This makes the output of this function identical\n to the gradient-derived functions like keras.layers.Conv2DTranspose\n applied to the same kernel. For typical use in neural nets this is completely\n pointless and just makes input/output channel specification confusing.\n precision: Optional. Either `None`, which means the default precision for\n the backend, or a `Precision` enum value.\n\n Returns:\n Transposed N-d convolution, with output padding following the conventions of\n keras.layers.Conv2DTranspose.\n \"\"\"\n assert len(lhs.shape) == len(rhs.shape) and len(lhs.shape) > 2\n ndims = len(lhs.shape)\n one = (1,) * (ndims - 2)\n # Set dimensional layout defaults if not specified.\n if dimension_numbers is None:\n if ndims == 3:\n dimension_numbe","label":0},{"cid":"jax/lax/lax.py::full_like","text":"jax/lax/lax.py::full_like\ndef full_like(x, fill_value, dtype=None, shape=None):\n \"\"\"Create a full array like np.full based on the example array `x`.\n\n Args:\n x: example array-like, used for shape and dtype information.\n fill_value: a scalar value to fill the entries of the output array.\n dtype: optional, a dtype parameter for the output ndarray.\n shape: optional, a shape parameter for the output ndarray.\n\n Returns:\n An ndarray with the same shape as `x` with its entries set equal to\n `fill_value`, similar to the output of np.full.\n \"\"\"\n shape = onp.shape(x) if shape is None else _canonicalize_shape(shape)\n fill_value = tie_in(x, fill_value)\n return full(shape, fill_value, dtype or _dtype(x))","label":0},{"cid":"jax/lax/lax.py::collapse","text":"jax/lax/lax.py::collapse\ndef collapse(operand, start_dimension, stop_dimension):\n lo, hi = start_dimension, stop_dimension\n size = prod(operand.shape[lo:hi])\n new_shape = operand.shape[:lo] + (size,) + operand.shape[hi:]\n return reshape(operand, new_shape)","label":0},{"cid":"jax/lax/lax.py::slice_in_dim","text":"jax/lax/lax.py::slice_in_dim\ndef slice_in_dim(operand, start_index, limit_index, stride=1, axis=0):\n \"\"\"Convenience wrapper around slice applying to only one dimension.\"\"\"\n start_indices = [0] * operand.ndim\n limit_indices = list(operand.shape)\n strides = [1] * operand.ndim\n\n axis = int(axis)\n start_indices[axis] = int(start_index)\n limit_indices[axis] = int(limit_index)\n strides[axis] = int(stride)\n\n return slice(operand, start_indices, limit_indices, strides)","label":0},{"cid":"jax/lax/lax.py::index_in_dim","text":"jax/lax/lax.py::index_in_dim\ndef index_in_dim(operand, index, axis=0, keepdims=True):\n \"\"\"Convenience wrapper around slice to perform int indexing.\"\"\"\n index, axis = int(index), int(axis)\n axis_size = operand.shape[axis]\n wrapped_index = index + axis_size if index < 0 else index\n if not 0 <= wrapped_index < axis_size:\n msg = 'index {} is out of bounds for axis {} with size {}'\n raise IndexError(msg.format(index, axis, axis_size))\n result = slice_in_dim(operand, wrapped_index, wrapped_index + 1, 1, axis)\n if keepdims:\n return result\n else:\n return reshape(result, onp.delete(operand.shape, axis))","label":0},{"cid":"jax/lax/lax.py::dynamic_slice_in_dim","text":"jax/lax/lax.py::dynamic_slice_in_dim\ndef dynamic_slice_in_dim(operand, start_index, slice_size, axis=0):\n \"\"\"Convenience wrapper around dynamic_slice applying to one dimension.\"\"\"\n start_indices = [_zero(start_index)] * operand.ndim\n slice_sizes = list(operand.shape)\n\n axis = int(axis)\n start_indices[axis] = start_index\n slice_sizes[axis] = int(slice_size)\n return dynamic_slice(operand, start_indices, slice_sizes)","label":0},{"cid":"jax/lax/lax.py::dynamic_index_in_dim","text":"jax/lax/lax.py::dynamic_index_in_dim\ndef dynamic_index_in_dim(operand, index, axis=0, keepdims=True):\n \"\"\"Convenience wrapper around dynamic_slice to perform int indexing.\"\"\"\n result = dynamic_slice_in_dim(operand, index, 1, axis)\n if keepdims:\n return result\n else:\n return reshape(result, onp.delete(operand.shape, axis))","label":0},{"cid":"jax/lax/lax.py::dynamic_update_slice_in_dim","text":"jax/lax/lax.py::dynamic_update_slice_in_dim\ndef dynamic_update_slice_in_dim(operand, update, start_index, axis):\n axis = int(axis)\n start_indices = [_zero(start_index)] * _ndim(operand)\n start_indices[axis] = start_index\n return dynamic_update_slice(operand, update, start_indices)","label":0},{"cid":"jax/lax/lax.py::dynamic_update_index_in_dim","text":"jax/lax/lax.py::dynamic_update_index_in_dim\ndef dynamic_update_index_in_dim(operand, update, index, axis):\n axis = int(axis)\n if _ndim(update) != _ndim(operand):\n assert _ndim(update) + 1 == _ndim(operand)\n ax = axis % _ndim(operand)\n update = reshape(update, operand.shape[:ax] + (1,) + operand.shape[ax+1:])\n return dynamic_update_slice_in_dim(operand, update, index, axis)","label":0},{"cid":"jax/lax/lax.py::batch_matmul","text":"jax/lax/lax.py::batch_matmul\ndef batch_matmul(lhs, rhs, precision=None):\n \"\"\"Batch matrix multiplication.\"\"\"\n if _min(lhs.ndim, rhs.ndim) < 2:\n raise ValueError('Arguments to batch_matmul must be at least 2D, got {}, {}'\n .format(lhs.ndim, rhs.ndim))\n if lhs.ndim != rhs.ndim:\n raise ValueError('Arguments to batch_matmul must have same ndim, got {}, {}'\n .format(lhs.ndim, rhs.ndim))\n lhs_contract = (lhs.ndim - 1,)\n rhs_contract = (rhs.ndim - 2,)\n batch = tuple(range(lhs.ndim - 2))\n return dot_general(lhs, rhs, [(lhs_contract, rhs_contract), (batch, batch)],\n precision=precision)","label":0},{"cid":"jax/lax/lax.py::square","text":"jax/lax/lax.py::square\ndef square(x):\n r\"\"\"Elementwise square: :math:`x^2`.\"\"\"\n return mul(x, x)","label":0},{"cid":"jax/lax/lax.py::reciprocal","text":"jax/lax/lax.py::reciprocal\ndef reciprocal(x):\n r\"\"\"Elementwise reciprocal: :math:`1 \\over x`.\"\"\"\n return div(_const(x, 1), x)","label":0},{"cid":"jax/lax/lax.py::_upcast_fp16_for_computation","text":"jax/lax/lax.py::_upcast_fp16_for_computation\ndef _upcast_fp16_for_computation(f):\n @functools.wraps(f)\n def f_wrapped(x):\n dtype = _dtype(x)\n if dtype == onp.float16 or dtype == dtypes.bfloat16:\n return convert_element_type(\n f(convert_element_type(x, onp.float32)), dtype)\n return f(x)\n\n return f_wrapped","label":0},{"cid":"jax/lax/lax.py::_upcast_fp16_for_computation.f_wrapped","text":"jax/lax/lax.py::_upcast_fp16_for_computation.f_wrapped\n @functools.wraps(f)\n def f_wrapped(x):\n dtype = _dtype(x)\n if dtype == onp.float16 or dtype == dtypes.bfloat16:\n return convert_element_type(\n f(convert_element_type(x, onp.float32)), dtype)\n return f(x)","label":0},{"cid":"jax/lax/lax.py::tan","text":"jax/lax/lax.py::tan\n@api.jit\n@_upcast_fp16_for_computation\ndef tan(x):\n r\"\"\"Elementwise tangent: :math:`\\mathrm{tan}(x)`.\"\"\"\n return div(sin(x), cos(x))","label":0},{"cid":"jax/lax/lax.py::asin","text":"jax/lax/lax.py::asin\n@api.jit\ndef asin(x):\n r\"\"\"Elementwise arc sine: :math:`\\mathrm{asin}(x)`.\"\"\"\n return mul(_const(x, 2),\n atan2(x, add(_const(x, 1), sqrt(sub(_const(x, 1), square(x))))))","label":0},{"cid":"jax/lax/lax.py::acos","text":"jax/lax/lax.py::acos\n@api.jit\ndef acos(x):\n r\"\"\"Elementwise arc cosine: :math:`\\mathrm{acos}(x)`.\"\"\"\n return select(\n ne(x, _const(x, -1.0)),\n mul(_const(x, 2),\n atan2(sqrt(sub(_const(x, 1), square(x))), add(_const(x, 1), x))),\n full_like(x, onp.pi))","label":0},{"cid":"jax/lax/lax.py::atan","text":"jax/lax/lax.py::atan\ndef atan(x):\n r\"\"\"Elementwise arc tangent: :math:`\\mathrm{atan}(x)`.\"\"\"\n return atan2(x, _const(x, 1))","label":0},{"cid":"jax/lax/lax.py::sinh","text":"jax/lax/lax.py::sinh\n@api.jit\n@_upcast_fp16_for_computation\ndef sinh(x):\n r\"\"\"Elementwise hyperbolic sine: :math:`\\mathrm{sinh}(x)`.\"\"\"\n log_half = _const(x, onp.log(0.5))\n # This formulation avoids overflow when e^x is inf but e^x/2 is not inf.\n return sub(exp(add(log_half, x)), exp(sub(log_half, x)))","label":0},{"cid":"jax/lax/lax.py::cosh","text":"jax/lax/lax.py::cosh\n@api.jit\n@_upcast_fp16_for_computation\ndef cosh(x):\n r\"\"\"Elementwise hyperbolic cosine: :math:`\\mathrm{cosh}(x)`.\"\"\"\n log_half = _const(x, onp.log(0.5))\n # This formulation avoids overflow when e^x is inf but e^x/2 is not inf.\n return add(exp(add(log_half, x)), exp(sub(log_half, x)))","label":0},{"cid":"jax/lax/lax.py::_iter","text":"jax/lax/lax.py::_iter\ndef _iter(tracer):\n if tracer.ndim == 0:\n raise TypeError(\"iteration over a 0-d array\") # same as numpy error\n else:\n n = tracer.shape[0]\n # return (index_in_dim(tracer, i, keepdims=False) for i in range(n))\n return iter([index_in_dim(tracer, i, keepdims=False) for i in range(n)])","label":0},{"cid":"jax/lax/lax.py::zeros_like_array","text":"jax/lax/lax.py::zeros_like_array\ndef zeros_like_array(x):\n return full_like(x, 0)","label":0},{"cid":"jax/lax/lax.py::standard_primitive","text":"jax/lax/lax.py::standard_primitive\ndef standard_primitive(shape_rule, dtype_rule, name, translation_rule=None):\n prim = Primitive(name)\n prim.def_impl(partial(xla.apply_primitive, prim))\n prim.def_abstract_eval(partial(standard_abstract_eval, prim, shape_rule, dtype_rule))\n xla.translations[prim] = translation_rule or partial(standard_translate, name)\n return prim","label":0},{"cid":"jax/lax/lax.py::standard_abstract_eval","text":"jax/lax/lax.py::standard_abstract_eval\ndef standard_abstract_eval(prim, shape_rule, dtype_rule, *args, **kwargs):\n assert all(isinstance(arg, UnshapedArray) for arg in args), args\n least_specialized = _max(\n map(type, args), key=operator.attrgetter('array_abstraction_level'))\n if least_specialized is ConcreteArray:\n msg = (\"If you see this error, please let us know by opening an issue at\\n\"\n \"https://github.com/google/jax/issues \\n\"\n \"since we thought this was unreachable!\")\n assert pe._thread_local_state.remat, msg\n return ConcreteArray(prim.impl(*[x.val for x in args], **kwargs))\n elif least_specialized is ShapedArray:\n return ShapedArray(shape_rule(*args, **kwargs), dtype_rule(*args, **kwargs))\n elif least_specialized is UnshapedArray:\n return UnshapedArray(dtype_rule(*args, **kwargs))\n else:\n raise TypeError(args, least_specialized)","label":0},{"cid":"jax/lax/lax.py::standard_translate","text":"jax/lax/lax.py::standard_translate\ndef standard_translate(name, c, *args, **kwargs):\n xla_opname = ''.join(term.capitalize() for term in name.split('_'))\n return getattr(c, xla_opname)(*args, **kwargs)","label":0},{"cid":"jax/lax/lax.py::unop_dtype_rule","text":"jax/lax/lax.py::unop_dtype_rule\ndef unop_dtype_rule(result_dtype, accepted_dtypes, name, aval, **kwargs):\n if not any(dtypes.issubdtype(aval.dtype, t) for t in accepted_dtypes):\n msg = '{} does not accept dtype {}. Accepted dtypes are subtypes of {}.'\n typename = str(onp.dtype(aval.dtype).name)\n accepted_typenames = (t.__name__ for t in accepted_dtypes)\n raise TypeError(msg.format(name, typename, ', '.join(accepted_typenames)))\n return result_dtype(aval.dtype)","label":0},{"cid":"jax/lax/lax.py::unop","text":"jax/lax/lax.py::unop\ndef unop(result_dtype, accepted_dtypes, name, translation_rule=None):\n dtype_rule = partial(unop_dtype_rule, result_dtype, accepted_dtypes, name)\n prim = standard_primitive(_attrgetter('shape'), dtype_rule, name,\n translation_rule=translation_rule)\n batching.defvectorized(prim)\n masking.defvectorized(prim)\n return prim","label":0},{"cid":"jax/lax/lax.py::binop_dtype_rule","text":"jax/lax/lax.py::binop_dtype_rule\ndef binop_dtype_rule(result_dtype, accepted_dtypes, name, *avals, **kwargs):\n aval_dtypes = [aval.dtype for aval in avals]\n for i, (aval_dtype, types) in enumerate(zip(aval_dtypes, accepted_dtypes)):\n if not any(dtypes.issubdtype(aval_dtype, t) for t in types):\n msg = ('{} does not accept dtype {} at position {}. '\n 'Accepted dtypes at position {} are subtypes of {}.')\n typename = str(onp.dtype(aval_dtype).name)\n typenames = ', '.join(t.__name__ for t in types)\n raise TypeError(msg.format(name, typename, i, i, typenames))\n _check_same_dtypes(name, False, *aval_dtypes)\n return result_dtype(*avals)","label":0},{"cid":"jax/lax/lax.py::_broadcasting_shape_rule","text":"jax/lax/lax.py::_broadcasting_shape_rule\ndef _broadcasting_shape_rule(name, *avals):\n shapes = onp.array([aval.shape for aval in avals if aval.shape])\n if not shapes.size:\n return ()\n if len({len(shape) for shape in shapes}) != 1:\n msg = '{} got arrays of different rank: {}.'\n raise TypeError(msg.format(name, ', '.join(map(str, map(tuple, shapes)))))\n min_shape = onp.min(shapes, axis=0)\n max_shape = onp.max(shapes, axis=0)\n result_shape = onp.where(min_shape == 0, 0, max_shape)\n if not onp.all((shapes == result_shape) | (shapes == 1)):\n msg = '{} got incompatible shapes for broadcasting: {}.'\n raise TypeError(msg.format(name, ', '.join(map(str, map(tuple, shapes)))))\n return tuple(result_shape)","label":0},{"cid":"jax/lax/lax.py::binop","text":"jax/lax/lax.py::binop\ndef binop(result_dtype, accepted_dtypes, name, translation_rule=None):\n dtype_rule = partial(binop_dtype_rule, result_dtype, accepted_dtypes, name)\n shape_rule = partial(_broadcasting_shape_rule, name)\n prim = standard_primitive(shape_rule, dtype_rule, name,\n translation_rule=translation_rule)\n batching.defbroadcasting(prim)\n masking.defbinop(prim)\n return prim","label":0},{"cid":"jax/lax/lax.py::_brcast","text":"jax/lax/lax.py::_brcast\ndef _brcast(x, *others):\n # Used in jvprules to make binop broadcasting explicit for transposability.\n # Requires shape info during jvp tracing, which isn't strictly necessary.\n # We don't need full numpy broadcasting, but otherwise the logic is the same\n # so we reuse the broadcast_shapes function after filtering out scalars.\n shapes = tuple(filter(None, map(onp.shape, (x,) + others)))\n shape = shapes and broadcast_shapes(*shapes)\n if onp.shape(x) != shape:\n return _brcast_to(x, shape)\n else:\n return x","label":0},{"cid":"jax/lax/lax.py::_brcast_to","text":"jax/lax/lax.py::_brcast_to\ndef _brcast_to(x, shape):\n x_shape = onp.shape(x)\n assert x_shape != shape\n if x_shape:\n assert len(x_shape) == len(shape)\n broadcast_dimensions, = onp.where(onp.equal(x_shape, shape))\n squeezed_dimensions, = onp.where(onp.not_equal(x_shape, shape))\n inshape = onp.delete(x_shape, squeezed_dimensions)\n return broadcast_in_dim(reshape(x, inshape), shape, broadcast_dimensions)\n else:\n return broadcast(x, shape)","label":0},{"cid":"jax/lax/lax.py::_sign_translation_rule","text":"jax/lax/lax.py::_sign_translation_rule\ndef _sign_translation_rule(c, x):\n shape = c.GetShape(x)\n dtype = shape.numpy_dtype()\n if dtypes.issubdtype(dtype, onp.unsignedinteger):\n zero = c.Constant(onp.array(0, dtype=dtype))\n dims = c.GetShape(x).dimensions()\n return c.Select(c.Eq(x, zero), c.Broadcast(zero, dims),\n c.Broadcast(c.Constant(onp.array(1, dtype=dtype)), dims))\n return c.Sign(x)","label":0},{"cid":"jax/lax/lax.py::_bessel_i1e_jvp","text":"jax/lax/lax.py::_bessel_i1e_jvp\ndef _bessel_i1e_jvp(g, y, x):\n eps = dtypes.finfo(_dtype(x)).eps\n x_is_not_tiny = abs(x) > eps\n safe_x = select(x_is_not_tiny, x, full_like(x, eps))\n dy_dx = bessel_i0e(safe_x) - y * (sign(safe_x) + reciprocal(safe_x))\n dy_dx = select(x_is_not_tiny, dy_dx, full_like(x, 0.5))\n return g * dy_dx","label":0},{"cid":"jax/lax/lax.py::_conj_transpose_rule","text":"jax/lax/lax.py::_conj_transpose_rule\ndef _conj_transpose_rule(t, x, input_dtype):\n assert x is ad.undefined_primal\n if dtypes.issubdtype(input_dtype, onp.complexfloating):\n return [conj(t)]\n else:\n return [real(t)]","label":0},{"cid":"jax/lax/lax.py::_abs_jvp_rule","text":"jax/lax/lax.py::_abs_jvp_rule\ndef _abs_jvp_rule(g, ans, x):\n if _iscomplex(x):\n return _maybe_real(mul(g, div(_maybe_conj(x),\n _replace_zero(convert_element_type(ans, _dtype(x))))))\n else:\n return select(ge(x, _zero(x)), g, neg(g))","label":0},{"cid":"jax/lax/lax.py::_pow_jvp_lhs","text":"jax/lax/lax.py::_pow_jvp_lhs\ndef _pow_jvp_lhs(g, ans, x, y):\n # we call _safe_mul here so that we get the behavior 0*inf = 0, since when a\n # coefficient in `g` is zero we want to keep it at zero, not produce a nan.\n # see https://github.com/google/jax/pull/383\n jac = mul(y, pow(x, select(eq(y, _zeros(y)), _ones(y), sub(y, _ones(y)))))\n return _safe_mul(_brcast(g, y), jac)","label":0},{"cid":"jax/lax/lax.py::_pow_jvp_rhs","text":"jax/lax/lax.py::_pow_jvp_rhs\ndef _pow_jvp_rhs(g, ans, x, y):\n return mul(_brcast(g, x), mul(log(_replace_zero(x)), ans))","label":0},{"cid":"jax/lax/lax.py::_add_transpose","text":"jax/lax/lax.py::_add_transpose\ndef _add_transpose(t, x, y):\n # assert x is ad.undefined_primal and y is ad.undefined_primal # not affine\n return [t, t]","label":0},{"cid":"jax/lax/lax.py::_sub_transpose","text":"jax/lax/lax.py::_sub_transpose\ndef _sub_transpose(t, x, y):\n assert x is ad.undefined_primal and y is ad.undefined_primal # not affine\n return [t, neg(t) if t is not ad_util.zero else ad_util.zero]","label":0},{"cid":"jax/lax/lax.py::_safe_mul_translation_rule","text":"jax/lax/lax.py::_safe_mul_translation_rule\ndef _safe_mul_translation_rule(c, x, y):\n dtype = c.GetShape(x).numpy_dtype()\n zero = c.Constant(onp.array(0, dtype=dtype))\n out_shape = broadcast_shapes(c.GetShape(x).dimensions(),\n c.GetShape(y).dimensions())\n return c.Select(c.Or(c.Eq(x, zero), c.Eq(y, zero)),\n c.Broadcast(zero, out_shape),\n c.Mul(x, y))","label":0},{"cid":"jax/lax/lax.py::_div_transpose_rule","text":"jax/lax/lax.py::_div_transpose_rule\ndef _div_transpose_rule(cotangent, x, y):\n assert x is ad.undefined_primal and y is not ad.undefined_primal\n res = ad_util.zero if cotangent is ad_util.zero else div(cotangent, y)\n return res, None","label":0},{"cid":"jax/lax/lax.py::_broadcasting_select","text":"jax/lax/lax.py::_broadcasting_select\ndef _broadcasting_select(c, which, x, y):\n \"\"\"Wrapper around XLA `Select` that broadcasts its arguments.\"\"\"\n which_shape, x_shape, y_shape = (\n c.GetShape(t).dimensions() for t in (which, x, y))\n out_shape = broadcast_shapes(which_shape, x_shape, y_shape)\n bcast_dims = lambda shape: tuple(range(len(out_shape) - len(shape),\n len(out_shape)))\n which = c.BroadcastInDim(which, out_shape, bcast_dims(which_shape))\n x = c.BroadcastInDim(x, out_shape, bcast_dims(x_shape))\n y = c.BroadcastInDim(y, out_shape, bcast_dims(y_shape))\n return c.Select(which, x, y)","label":0},{"cid":"jax/lax/lax.py::_minmax_translation_rule","text":"jax/lax/lax.py::_minmax_translation_rule\ndef _minmax_translation_rule(c, x, y, minmax=None, cmp=None):\n dtype = c.GetShape(x).numpy_dtype()\n if dtypes.issubdtype(dtype, onp.complexfloating):\n comparator = cmp(c)\n rx = c.Real(x)\n ry = c.Real(y)\n return _broadcasting_select(\n c, c.Select(c.Eq(rx, ry), comparator(c.Imag(x), c.Imag(y)),\n comparator(rx, ry)),\n x, y)\n return minmax(c)(x, y)","label":0},{"cid":"jax/lax/lax.py::_convert_element_type_shape_rule","text":"jax/lax/lax.py::_convert_element_type_shape_rule\ndef _convert_element_type_shape_rule(operand, new_dtype, old_dtype):\n return operand.shape","label":0},{"cid":"jax/lax/lax.py::_convert_element_type_dtype_rule","text":"jax/lax/lax.py::_convert_element_type_dtype_rule\ndef _convert_element_type_dtype_rule(operand, new_dtype, old_dtype):\n return new_dtype","label":0},{"cid":"jax/lax/lax.py::_convert_element_type_translation_rule","text":"jax/lax/lax.py::_convert_element_type_translation_rule\ndef _convert_element_type_translation_rule(c, operand, new_dtype, old_dtype):\n new_etype = xla_client.dtype_to_etype(new_dtype)\n return c.ConvertElementType(operand, new_element_type=new_etype)","label":0},{"cid":"jax/lax/lax.py::_bitcast_convert_type_shape_rule","text":"jax/lax/lax.py::_bitcast_convert_type_shape_rule\ndef _bitcast_convert_type_shape_rule(operand, new_dtype):\n return operand.shape","label":0},{"cid":"jax/lax/lax.py::_bitcast_convert_type_dtype_rule","text":"jax/lax/lax.py::_bitcast_convert_type_dtype_rule\ndef _bitcast_convert_type_dtype_rule(operand, new_dtype):\n return new_dtype","label":0},{"cid":"jax/lax/lax.py::_bitcast_convert_type_translation_rule","text":"jax/lax/lax.py::_bitcast_convert_type_translation_rule\ndef _bitcast_convert_type_translation_rule(c, operand, new_dtype):\n new_etype = xla_bridge.dtype_to_etype(new_dtype)\n return c.BitcastConvertType(operand, new_element_type=new_etype)","label":0},{"cid":"jax/lax/lax.py::_conv_general_dilated_shape_rule","text":"jax/lax/lax.py::_conv_general_dilated_shape_rule\ndef _conv_general_dilated_shape_rule(\n lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\n dimension_numbers, feature_group_count, **unused_kwargs):\n assert type(dimension_numbers) is ConvDimensionNumbers\n if not feature_group_count > 0:\n msg = (\"conv_general_dilated feature_group_count \"\n \"must be a positive integer, got {}.\")\n raise ValueError(msg.format(feature_group_count))\n lhs_feature_count = lhs.shape[dimension_numbers.lhs_spec[1]]\n quot, rem = divmod(lhs_feature_count, feature_group_count)\n if rem:\n msg = (\"conv_general_dilated feature_group_count must divide lhs feature \"\n \"dimension size, but {} does not divide {}.\")\n raise ValueError(msg.format(feature_group_count, lhs_feature_count))\n if quot != rhs.shape[dimension_numbers.rhs_spec[1]]:\n msg = (\"conv_general_dilated lhs feature dimension size divided by \"\n \"feature_group_count must equal the rhs input feature dimension \"\n \"size, but {} // {} != {}.\")\n raise ValueError(msg.format(lhs_feature_count, feature_group_count,\n rhs.shape[dimension_numbers.rhs_spec[1]]))\n if rhs.shape[dimension_numbers.rhs_spec[0]] % feature_group_count:\n msg = (\"conv_general_dilated rhs output feature dimension size must be a \"\n \"multiple of feature_group_count, but {} is not a multiple of {}.\")\n raise ValueError(msg.format(rhs.shape[dimension_numbers.rhs_spec[0]],\n feature_group_count))\n lhs_perm, rhs_perm, out_perm = dimension_numbers\n lhs_trans = _dilate_shape(onp.take(lhs.shape, lhs_perm), lhs_dilation)\n rhs_trans = _dilate_shape(onp.take(rhs.shape, rhs_perm), rhs_dilation)\n out_trans = conv_shape_tuple(lhs_trans, rhs_trans, window_strides, padding)\n return tuple(onp.take(out_trans, onp.argsort(out_perm)))","label":0},{"cid":"jax/lax/lax.py::_conv_general_dilated_dtype_rule","text":"jax/lax/lax.py::_conv_general_dilated_dtype_rule\ndef _conv_general_dilated_dtype_rule(\n lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\n dimension_numbers, **unused_kwargs):\n return binop_dtype_rule(_input_dtype, [_float, _float],\n 'conv_general_dilated', lhs, rhs)","label":0},{"cid":"jax/lax/lax.py::_conv_general_dilated_transpose_lhs","text":"jax/lax/lax.py::_conv_general_dilated_transpose_lhs\ndef _conv_general_dilated_transpose_lhs(\n g, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\n dimension_numbers, feature_group_count,\n lhs_shape, rhs_shape, precision):\n assert type(dimension_numbers) is ConvDimensionNumbers\n lhs_sdims, rhs_sdims, out_sdims = map(_conv_sdims, dimension_numbers)\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n t_rhs_spec = _conv_spec_transpose(rhs_spec)\n if feature_group_count > 1:\n # in addition to switching the dims in the spec, need to move the feature\n # group axis into the transposed rhs's output feature dim\n rhs = _reshape_axis_out_of(rhs_spec[0], feature_group_count, rhs)\n rhs = _reshape_axis_into(rhs_spec[0], rhs_spec[1], rhs)\n trans_dimension_numbers = ConvDimensionNumbers(out_spec, t_rhs_spec, lhs_spec)\n padding = _conv_general_vjp_lhs_padding(\n onp.take(lhs_shape, lhs_sdims), onp.take(rhs_shape, rhs_sdims),\n window_strides, onp.take(g.shape, out_sdims), padding, lhs_dilation,\n rhs_dilation)\n revd_weights = rev(rhs, rhs_sdims)\n return conv_general_dilated(\n g, revd_weights, window_strides=lhs_dilation, padding=padding,\n lhs_dilation=window_strides, rhs_dilation=rhs_dilation,\n dimension_numbers=trans_dimension_numbers,\n feature_group_count=feature_group_count, precision=precision)","label":0},{"cid":"jax/lax/lax.py::_conv_general_dilated_transpose_rhs","text":"jax/lax/lax.py::_conv_general_dilated_transpose_rhs\ndef _conv_general_dilated_transpose_rhs(\n g, lhs, window_strides, padding, lhs_dilation, rhs_dilation,\n dimension_numbers, feature_group_count,\n lhs_shape, rhs_shape, precision):\n assert type(dimension_numbers) is ConvDimensionNumbers\n if onp.size(g) == 0:\n # Avoids forming degenerate convolutions where the RHS has spatial size 0.\n return ad_util.zero\n lhs_sdims, rhs_sdims, out_sdims = map(_conv_sdims, dimension_numbers)\n lhs_trans, rhs_trans, out_trans = map(_conv_spec_transpose, dimension_numbers)\n if feature_group_count > 1:\n lhs = _reshape_axis_out_of(lhs_trans[0], feature_group_count, lhs)\n lhs = _reshape_axis_into(lhs_trans[0], lhs_trans[1], lhs)\n trans_dimension_numbers = ConvDimensionNumbers(lhs_trans, out_trans, rhs_trans)\n padding = _conv_general_vjp_rhs_padding(\n onp.take(lhs_shape, lhs_sdims), onp.take(rhs_shape, rhs_sdims),\n window_strides, onp.take(g.shape, out_sdims), padding, lhs_dilation,\n rhs_dilation)\n return conv_general_dilated(\n lhs, g, window_strides=rhs_dilation, padding=padding,\n lhs_dilation=lhs_dilation, rhs_dilation=window_strides,\n dimension_numbers=trans_dimension_numbers,\n feature_group_count=feature_group_count, precision=precision)","label":0},{"cid":"jax/lax/lax.py::_conv_general_dilated_translation_rule","text":"jax/lax/lax.py::_conv_general_dilated_translation_rule\ndef _conv_general_dilated_translation_rule(\n c, lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\n dimension_numbers, feature_group_count, precision, **unused_kwargs):\n assert type(dimension_numbers) is ConvDimensionNumbers\n dimension_numbers = _conv_general_proto(dimension_numbers)\n return c.ConvGeneralDilated(lhs, rhs, window_strides, padding, lhs_dilation,\n rhs_dilation, dimension_numbers,\n feature_group_count,\n precision_config=_precision_config(precision))","label":0},{"cid":"jax/lax/lax.py::_conv_general_dilated_batch_rule","text":"jax/lax/lax.py::_conv_general_dilated_batch_rule\ndef _conv_general_dilated_batch_rule(\n batched_args, batch_dims, window_strides, padding,\n lhs_dilation, rhs_dilation, dimension_numbers,\n feature_group_count, precision, **unused_kwargs):\n lhs, rhs = batched_args\n lhs_bdim, rhs_bdim = batch_dims\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n\n if lhs_bdim is not None and rhs_bdim is not None:\n assert lhs.shape[lhs_bdim] == rhs.shape[rhs_bdim]\n new_lhs = _reshape_axis_into(lhs_bdim, lhs_spec[1], lhs)\n new_rhs = _reshape_axis_into(rhs_bdim, rhs_spec[0], rhs)\n out = conv_general_dilated(\n new_lhs, new_rhs, window_strides, padding, lhs_dilation, rhs_dilation,\n dimension_numbers,\n feature_group_count=lhs.shape[lhs_bdim] * feature_group_count,\n precision=precision)\n out = _reshape_axis_out_of(out_spec[1], lhs.shape[lhs_bdim], out)\n return out, out_spec[1]\n\n elif lhs_bdim is not None:\n new_lhs = _reshape_axis_into(lhs_bdim, lhs_spec[0], lhs)\n out = conv_general_dilated(new_lhs, rhs, window_strides, padding,\n lhs_dilation, rhs_dilation, dimension_numbers,\n feature_group_count, precision=precision)\n out = _reshape_axis_out_of(out_spec[0], lhs.shape[lhs_bdim], out)\n return out, out_spec[0]\n\n elif rhs_bdim is not None:\n if feature_group_count == 1:\n new_rhs = _reshape_axis_into(rhs_bdim, rhs_spec[0], rhs)\n out = conv_general_dilated(lhs, new_rhs, window_strides, padding,\n lhs_dilation, rhs_dilation, dimension_numbers,\n feature_group_count, precision=precision)\n out = _reshape_axis_out_of(out_spec[1], rhs.shape[rhs_bdim], out)\n return out, out_spec[1]\n else:\n # feature_group needs to be outermost, so we need to factor it out of the\n # rhs output feature dim, then factor the batch dim into the remaining rhs\n # output feature dim, then put feature_group ","label":0},{"cid":"jax/lax/lax.py::_reshape_axis_into","text":"jax/lax/lax.py::_reshape_axis_into\ndef _reshape_axis_into(src, dst, x):\n perm = [i for i in range(x.ndim) if i != src]\n perm.insert(dst, src)\n new_shape = list(onp.delete(x.shape, src))\n new_shape[dst] *= x.shape[src]\n return reshape(x, new_shape, perm)","label":0},{"cid":"jax/lax/lax.py::_reshape_axis_out_of","text":"jax/lax/lax.py::_reshape_axis_out_of\ndef _reshape_axis_out_of(src, size1, x):\n shape = list(x.shape)\n size2, ragged = divmod(shape[src], size1)\n assert not ragged\n shape[src:src+1] = [size1, size2]\n return reshape(x, shape)","label":0},{"cid":"jax/lax/lax.py::_precision_config","text":"jax/lax/lax.py::_precision_config\ndef _precision_config(precision):\n if precision is not None:\n config = xla_client.PrecisionConfig()\n config.operand_precision.extend((precision, precision))\n return config\n return None","label":0},{"cid":"jax/lax/lax.py::_dot_general_shape_rule","text":"jax/lax/lax.py::_dot_general_shape_rule\ndef _dot_general_shape_rule(lhs, rhs, dimension_numbers, precision):\n (lhs_contracting, rhs_contracting), (lhs_batch, rhs_batch) = dimension_numbers\n if len(lhs_batch) != len(rhs_batch):\n msg = (\"dot_general requires equal numbers of lhs_batch and rhs_batch \"\n \"dimensions, got lhs_batch {} and rhs_batch {}.\")\n raise TypeError(msg.format(lhs_batch, rhs_batch))\n if not onp.all(onp.equal(lhs_batch, rhs_batch)):\n msg = (\"dot_general requires same lhs and rhs batch dimension numbers, \"\n \"got {} and {}.\")\n raise TypeError(msg.format(lhs_batch, rhs_batch))\n lhs_batch_shape = onp.take(lhs.shape, lhs_batch)\n rhs_batch_shape = onp.take(rhs.shape, rhs_batch)\n if not onp.all(onp.equal(lhs_batch_shape, rhs_batch_shape)):\n msg = (\"dot_general requires lhs batch dimensions and rhs batch dimensions \"\n \"to have the same shape, got {} and {}.\")\n raise TypeError(msg.format(lhs_batch_shape, rhs_batch_shape))\n if tuple(sorted(lhs_batch)) != tuple(range(len(lhs_batch))):\n msg = (\"dot_general requires lhs batch dimensions to precede contracting \"\n \"and non-contracting dimensions, got lhs_batch {}.\")\n raise TypeError(msg.format(lhs_batch))\n if tuple(sorted(rhs_batch)) != tuple(range(len(rhs_batch))):\n msg = (\"dot_general requires rhs batch dimensions to precede contracting \"\n \"and non-contracting dimensions, got rhs_batch {}.\")\n raise TypeError(msg.format(rhs_batch))\n lhs_contracting_shape = onp.take(lhs.shape, lhs_contracting)\n rhs_contracting_shape = onp.take(rhs.shape, rhs_contracting)\n if not onp.all(onp.equal(lhs_contracting_shape, rhs_contracting_shape)):\n msg = (\"dot_general requires contracting dimensions to have the same \"\n \"shape, got {} and {}.\")\n raise TypeError(msg.format(lhs_contracting_shape, rhs_contracting_shape))\n\n batch_shape = tuple(onp.take(lhs.shape, lhs_batch))\n lhs_contract_or_batch = tuple(lhs_contracting) + tuple(lhs_","label":0},{"cid":"jax/lax/lax.py::_dot_general_dtype_rule","text":"jax/lax/lax.py::_dot_general_dtype_rule\ndef _dot_general_dtype_rule(lhs, rhs, dimension_numbers, precision):\n return binop_dtype_rule(_input_dtype, [_num, _num], 'dot_general', lhs, rhs)","label":0},{"cid":"jax/lax/lax.py::_dot_general_transpose_lhs","text":"jax/lax/lax.py::_dot_general_transpose_lhs\ndef _dot_general_transpose_lhs(g, y, dimension_numbers, precision,\n swap_ans=False):\n (x_contract, y_contract), (x_batch, y_batch) = dimension_numbers\n x_ndim = g.ndim - y.ndim + len(x_batch) + 2 * len(x_contract)\n x_kept = remaining(range(x_ndim), x_contract, x_batch)\n y_kept = remaining(range(y.ndim), y_contract, y_batch)\n if swap_ans:\n ans_batch, ans_y, _ = ranges_like(x_batch, y_kept, x_kept)\n else:\n ans_batch, _, ans_y = ranges_like(x_batch, x_kept, y_kept)\n dims = ((ans_y, y_kept), (ans_batch, y_batch))\n x_contract_sorted_by_y = list(onp.take(x_contract, onp.argsort(y_contract)))\n out_axes = onp.argsort(list(x_batch) + x_kept + x_contract_sorted_by_y)\n return transpose(dot_general(g, y, dims, precision=precision),\n tuple(out_axes))","label":0},{"cid":"jax/lax/lax.py::_dot_general_transpose_rhs","text":"jax/lax/lax.py::_dot_general_transpose_rhs\ndef _dot_general_transpose_rhs(g, x, dimension_numbers, precision):\n (x_contract, y_contract), (x_batch, y_batch) = dimension_numbers\n swapped_dimension_numbers = ((y_contract, x_contract), (y_batch, x_batch))\n return _dot_general_transpose_lhs(g, x, swapped_dimension_numbers,\n precision, swap_ans=True)","label":0},{"cid":"jax/lax/lax.py::_dot_general_batch_rule","text":"jax/lax/lax.py::_dot_general_batch_rule\ndef _dot_general_batch_rule(batched_args, batch_dims, dimension_numbers,\n precision):\n # there are three kinds of dimensions in a dot_general:\n # - contraction dimensions appear in lhs and rhs but not the result\n # - batch dimensions appear in lhs, rhs, and result\n # - tensor product dimensions appear in the result and one of lhs or rhs\n (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers\n lhs, rhs = batched_args\n lbd, rbd = batch_dims\n assert lbd is not None or rbd is not None\n if lbd is not None and rbd is not None:\n # adding a batch dimension\n if lbd != 0:\n lhs = batching.moveaxis(lhs, lbd, 0)\n if rbd != 0:\n rhs = batching.moveaxis(rhs, rbd, 0)\n lhs_batch = (0,) + tuple(onp.add(1, lhs_batch))\n rhs_batch = (0,) + tuple(onp.add(1, rhs_batch))\n lhs_contract = tuple(onp.add(1, lhs_contract))\n rhs_contract = tuple(onp.add(1, rhs_contract))\n result_batch_dim = 0\n else:\n # adding a tensor product dimension\n if lbd is not None:\n if lhs_batch == () or lbd > onp.max(lhs_batch):\n # can avoid transposes\n bump_lhs_contract = onp.greater_equal(lhs_contract, lbd)\n lhs_contract = tuple(onp.add(lhs_contract, bump_lhs_contract))\n result_batch_dim = lbd - len(lhs_contract) + sum(bump_lhs_contract)\n else:\n # move the new dimension to the end of lhs to avoid changing batch dims\n lhs = batching.moveaxis(lhs, lbd, lhs.ndim - 1)\n # lhs tensor product dims in result come after batch dims\n result_batch_dim = lhs.ndim - len(lhs_contract) - 1\n else:\n if rhs_batch == () or rbd > onp.max(rhs_batch):\n # can avoid transposes\n bump_rhs_contract = onp.greater_equal(rhs_contract, rbd)\n rhs_contract = tuple(onp.add(rhs_contract, bump_rhs_contract))\n result_batch_dim = (rbd + (lhs.ndim - len(lhs_contract) - len(lhs_batch))\n - (len(rh","label":0},{"cid":"jax/lax/lax.py::_dot_general_translation_rule","text":"jax/lax/lax.py::_dot_general_translation_rule\ndef _dot_general_translation_rule(c, lhs, rhs, dimension_numbers, precision):\n return c.DotGeneral(lhs, rhs, dimension_numbers,\n precision_config=_precision_config(precision))","label":0},{"cid":"jax/lax/lax.py::_dot_general_polymorphic_shape_rule","text":"jax/lax/lax.py::_dot_general_polymorphic_shape_rule\ndef _dot_general_polymorphic_shape_rule(shape_exprs, dimension_numbers,\n precision):\n del precision # Unused.\n lhs_shape, rhs_shape = shape_exprs\n lhs_ndim, rhs_ndim = len(lhs_shape), len(rhs_shape)\n (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers\n\n lhs_batch_shape = [lhs_shape[d] for d in lhs_batch]\n rhs_batch_shape = [rhs_shape[d] for d in rhs_batch]\n if lhs_batch_shape != rhs_batch_shape: raise ShapeError\n\n lhs_contract_shape = [lhs_shape[d] for d in lhs_contract]\n rhs_contract_shape = [rhs_shape[d] for d in rhs_contract]\n if lhs_contract_shape != rhs_contract_shape: raise ShapeError\n\n lhs_tensorprod_shape = [lhs_shape[d] for d in range(lhs_ndim)\n if d not in lhs_batch and d not in lhs_contract]\n rhs_tensorprod_shape = [rhs_shape[d] for d in range(rhs_ndim)\n if d not in rhs_batch and d not in rhs_contract]\n return ShapeExpr(\n lhs_batch_shape + lhs_tensorprod_shape + rhs_tensorprod_shape)","label":0},{"cid":"jax/lax/lax.py::_dot_general_masking_rule","text":"jax/lax/lax.py::_dot_general_masking_rule\ndef _dot_general_masking_rule(padded_vals, logical_shapes, dimension_numbers,\n precision):\n lhs, rhs = padded_vals\n lhs_shape, rhs_shape = logical_shapes\n lhs_ndim, rhs_ndim = len(lhs_shape), len(rhs_shape)\n (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers\n\n # we need only mask the lhs contraction dimensions\n if len(lhs_contract) == 0:\n return dot_general(lhs, rhs, dimension_numbers, precision=precision)\n else:\n masks = [broadcasted_iota(onp.int32, lhs.shape, d) < lhs_shape[d]\n for d in lhs_contract]\n mask_intersection = masks[0]\n for mask in masks[1:]:\n mask_intersection &= mask\n masked_lhs = select(mask_intersection, lhs, zeros_like_array(lhs))\n return dot_general(masked_lhs, rhs, dimension_numbers, precision=precision)","label":0},{"cid":"jax/lax/lax.py::_broadcast_shape_rule","text":"jax/lax/lax.py::_broadcast_shape_rule\ndef _broadcast_shape_rule(operand, sizes):\n _check_shapelike('broadcast', 'sizes', sizes)\n return tuple(sizes) + operand.shape","label":0},{"cid":"jax/lax/lax.py::_broadcast_batch_rule","text":"jax/lax/lax.py::_broadcast_batch_rule\ndef _broadcast_batch_rule(batched_args, batch_dims, sizes):\n operand, = batched_args\n bdim, = batch_dims\n new_bdim = None if bdim is None else bdim + len(sizes)\n return broadcast(operand, sizes), new_bdim","label":0},{"cid":"jax/lax/lax.py::_broadcast_in_dim_impl","text":"jax/lax/lax.py::_broadcast_in_dim_impl\ndef _broadcast_in_dim_impl(operand, shape, broadcast_dimensions):\n if type(operand) is xla.DeviceArray:\n aval = ShapedArray(shape, _dtype(operand))\n lazy_expr = lazy.broadcast(operand._lazy_expr, shape, broadcast_dimensions)\n return xla.DeviceArray(aval, None, lazy_expr, operand.device_buffer)\n else:\n return xla.apply_primitive(broadcast_in_dim_p, operand, shape=shape,\n broadcast_dimensions=broadcast_dimensions)","label":0},{"cid":"jax/lax/lax.py::_broadcast_in_dim_shape_rule","text":"jax/lax/lax.py::_broadcast_in_dim_shape_rule\ndef _broadcast_in_dim_shape_rule(operand, shape, broadcast_dimensions):\n _check_shapelike('broadcast_in_dim', 'shape', shape)\n _check_shapelike('broadcast_in_dim', 'broadcast_dimensions',\n broadcast_dimensions)\n if any(x >= len(shape) for x in broadcast_dimensions):\n msg = (\"broadcast_in_dim broadcast dimensions must be less than \"\n \"ndim(shape), got {} for shape {}.\")\n raise ValueError(msg.format(broadcast_dimensions, shape))\n if operand.ndim != len(broadcast_dimensions):\n msg = ('broadcast_in_dim broadcast_dimensions must have length equal to '\n 'operand ndim, got broadcast_dimensions {} for operand ndim {}.')\n raise TypeError(msg.format(broadcast_dimensions, operand.ndim))\n if not set(broadcast_dimensions).issubset(set(range(len(shape)))):\n msg = ('broadcast_in_dim broadcast_dimensions must be a subset of output '\n 'dimensions, got {} for operand ndim {} and shape {}.')\n raise TypeError(msg.format(broadcast_dimensions, operand.ndim, shape))\n return shape","label":0},{"cid":"jax/lax/lax.py::_broadcast_in_dim_transpose_rule","text":"jax/lax/lax.py::_broadcast_in_dim_transpose_rule\ndef _broadcast_in_dim_transpose_rule(t, shape, broadcast_dimensions):\n axes = tuple(onp.delete(range(len(shape)), broadcast_dimensions))\n return [_reduce_sum(t, axes)]","label":0},{"cid":"jax/lax/lax.py::_broadcast_in_dim_batch_rule","text":"jax/lax/lax.py::_broadcast_in_dim_batch_rule\ndef _broadcast_in_dim_batch_rule(batched_args, batch_dims, shape,\n broadcast_dimensions):\n operand, = batched_args\n bdim, = batch_dims\n new_operand = batching.moveaxis(operand, bdim, 0)\n new_shape = (operand.shape[bdim],) + shape\n new_broadcast_dimensions = (0,) + tuple(onp.add(1, broadcast_dimensions))\n return broadcast_in_dim(new_operand, new_shape, new_broadcast_dimensions), 0","label":0},{"cid":"jax/lax/lax.py::_clamp_shape_rule","text":"jax/lax/lax.py::_clamp_shape_rule\ndef _clamp_shape_rule(min, operand, max):\n if min.shape and min.shape != operand.shape:\n m = \"clamp requires min.shape == operand.shape or min.shape == (), got {}.\"\n raise TypeError(m.format(min.shape))\n if max.shape and max.shape != operand.shape:\n m = \"clamp requires max.shape == operand.shape or max.shape == (), got {}.\"\n raise TypeError(m.format(max.shape))\n return operand.shape","label":0},{"cid":"jax/lax/lax.py::_concatenate_shape_rule","text":"jax/lax/lax.py::_concatenate_shape_rule\ndef _concatenate_shape_rule(*operands, **kwargs):\n dimension = kwargs.pop('dimension')\n if not operands:\n msg = \"concatenate expects at least one operand, got 0.\"\n raise TypeError(msg)\n if not all(isinstance(operand, UnshapedArray) for operand in operands):\n msg = \"All objects to concatenate must be arrays, got {}.\"\n op = next(op for op in operands if not isinstance(op, UnshapedArray))\n raise TypeError(msg.format(type(op)))\n if len(set(operand.ndim for operand in operands)) != 1:\n msg = \"Cannot concatenate arrays with different ranks, got {}.\"\n raise TypeError(msg.format(\", \".join(str(o.ndim) for o in operands)))\n shapes = onp.array([operand.shape for operand in operands])\n if not 0 <= dimension < shapes.shape[1]:\n msg = \"concatenate dimension out of bounds: dimension {} for shapes {}.\"\n raise TypeError(msg.format(dimension, \", \".join(map(str, shapes))))\n if not onp.all(onp.delete(shapes[0] == shapes, dimension, axis=1)):\n msg = (\"Cannot concatenate arrays with shapes that differ in dimensions \"\n \"other than the one being concatenated: dimension {} for shapes {}.\")\n raise TypeError(msg.format(dimension, \", \".join(map(str, shapes))))\n\n concat_size = sum(o.shape[dimension] for o in operands)\n ex_shape = operands[0].shape\n return ex_shape[:dimension] + (concat_size,) + ex_shape[dimension+1:]","label":0},{"cid":"jax/lax/lax.py::_concatenate_dtype_rule","text":"jax/lax/lax.py::_concatenate_dtype_rule\ndef _concatenate_dtype_rule(*operands, **kwargs):\n _check_same_dtypes('concatenate', False, *(o.dtype for o in operands))\n return operands[0].dtype","label":0},{"cid":"jax/lax/lax.py::_concatenate_translation_rule","text":"jax/lax/lax.py::_concatenate_translation_rule\ndef _concatenate_translation_rule(c, *operands, **kwargs):\n dimension = kwargs.pop('dimension')\n return c.Concatenate(operands, dimension=dimension)","label":0},{"cid":"jax/lax/lax.py::_concatenate_transpose_rule","text":"jax/lax/lax.py::_concatenate_transpose_rule\ndef _concatenate_transpose_rule(t, *operands, **kwargs):\n dimension = kwargs.pop('dimension')\n operand_shapes = kwargs.pop('operand_shapes')\n\n if t is ad_util.zero:\n return [ad_util.zero if o is ad.undefined_primal else None for o in operands]\n else:\n limit_points = onp.cumsum([shape[dimension] for shape in operand_shapes])\n starts = onp.zeros((len(operands), t.ndim), dtype=int)\n starts[1:, dimension] = limit_points[:-1]\n limits = onp.tile(t.shape, (len(operands), 1))\n limits[:, dimension] = limit_points\n\n return [slice(t, start, limit) if o is ad.undefined_primal else None\n for o, start, limit in zip(operands, starts, limits)]","label":0},{"cid":"jax/lax/lax.py::_concatenate_batch_rule","text":"jax/lax/lax.py::_concatenate_batch_rule\ndef _concatenate_batch_rule(batched_args, batch_dims, dimension, operand_shapes):\n size = next(op.shape[bdim] for op, bdim in zip(batched_args, batch_dims)\n if bdim is not None)\n operands = [batching.moveaxis(op, bdim, 0) if bdim is not None\n else broadcast(op, (size,))\n for op, bdim in zip(batched_args, batch_dims)]\n return concatenate(operands, dimension + 1), 0","label":0},{"cid":"jax/lax/lax.py::_concat_polymorphic_shape_rule","text":"jax/lax/lax.py::_concat_polymorphic_shape_rule\ndef _concat_polymorphic_shape_rule(shape_exprs, dimension, operand_shapes):\n out_shape = list(shape_exprs[0])\n out_shape[dimension] = _reduce(operator.add, [e[dimension] for e in shape_exprs])\n return ShapeExpr(out_shape)","label":0},{"cid":"jax/lax/lax.py::_pad_shape_rule","text":"jax/lax/lax.py::_pad_shape_rule\ndef _pad_shape_rule(operand, padding_value, padding_config):\n if operand.dtype != padding_value.dtype:\n msg = \"pad operand and padding_value must be same dtype: got {} and {}.\"\n raise TypeError(msg.format(operand.dtype, padding_value.dtype))\n\n lo, hi, interior = zip(*padding_config)\n out_shape = onp.add(onp.add(onp.add(lo, hi), operand.shape),\n onp.multiply(interior, onp.subtract(operand.shape, 1)))\n return tuple(out_shape)","label":0},{"cid":"jax/lax/lax.py::_pad_transpose","text":"jax/lax/lax.py::_pad_transpose\ndef _pad_transpose(t, operand, padding_value, padding_config):\n if t is ad_util.zero:\n return [ad_util.zero if operand is ad.undefined_primal else None,\n ad_util.zero if padding_value is ad.undefined_primal else None]\n\n lo, hi, interior = zip(*padding_config)\n total = lambda x: _reduce_sum(x, list(range(t.ndim)))\n\n def t_op():\n unpad_config = zip(onp.negative(lo), onp.negative(hi), onp.zeros_like(interior))\n unpadded = pad(t, onp.array(0., t.dtype), unpad_config)\n return slice(unpadded, onp.zeros_like(lo), unpadded.shape, onp.add(interior, 1))\n\n t_operand = t_op() if operand is ad.undefined_primal else None\n t_padv = sub(total(t), total(t_operand)) if padding_value is ad.undefined_primal else None\n\n return [t_operand, t_padv]","label":0},{"cid":"jax/lax/lax.py::_pad_transpose.t_op","text":"jax/lax/lax.py::_pad_transpose.t_op\n def t_op():\n unpad_config = zip(onp.negative(lo), onp.negative(hi), onp.zeros_like(interior))\n unpadded = pad(t, onp.array(0., t.dtype), unpad_config)\n return slice(unpadded, onp.zeros_like(lo), unpadded.shape, onp.add(interior, 1))","label":0},{"cid":"jax/lax/lax.py::_pad_batch_rule","text":"jax/lax/lax.py::_pad_batch_rule\ndef _pad_batch_rule(batched_args, batch_dims, padding_config):\n operand, padding_value = batched_args\n operand_bdim, padding_value_bdim = batch_dims\n if padding_value_bdim is None:\n assert operand_bdim is not None\n padding_config = list(padding_config)\n padding_config.insert(operand_bdim, (0, 0, 0))\n return pad(operand, padding_value, padding_config), operand_bdim\n else:\n raise NotImplementedError # loop and stack","label":0},{"cid":"jax/lax/lax.py::_reshape_impl","text":"jax/lax/lax.py::_reshape_impl\ndef _reshape_impl(operand, new_sizes, dimensions, old_sizes):\n if type(operand) is xla.DeviceArray and dimensions is None:\n bcast_dims = _is_singleton_reshape(old_sizes, new_sizes)\n if bcast_dims is not None:\n aval = ShapedArray(new_sizes, operand.dtype)\n lazy_expr = lazy.broadcast(operand._lazy_expr, new_sizes, bcast_dims)\n return xla.DeviceArray(aval, None, lazy_expr, operand.device_buffer)\n if (type(operand) is pxla.ShardedDeviceArray and dimensions is None\n and _is_axis_merge(old_sizes, new_sizes)):\n aval = ShapedArray(new_sizes, operand.dtype)\n return pxla.ChunkedDeviceArray(old_sizes[0], aval, operand.device_buffers)\n elif (type(operand) is pxla.ChunkedDeviceArray and dimensions is None\n and _is_axis_split(old_sizes, new_sizes)\n and operand.axis_size == new_sizes[0]):\n aval = ShapedArray(new_sizes, operand.dtype)\n return pxla.ShardedDeviceArray(aval, operand.device_buffers)\n else:\n return xla.apply_primitive(reshape_p, operand, new_sizes=new_sizes,\n dimensions=dimensions, old_sizes=old_sizes)","label":0},{"cid":"jax/lax/lax.py::_is_singleton_reshape","text":"jax/lax/lax.py::_is_singleton_reshape\ndef _is_singleton_reshape(old, new):\n # A singleton reshape is one where only singleton dimensions are added. We\n # want to detect them because they can be expressed as (lazy) broadcasts.\n old, new = iter(old), iter(new)\n d1, d2 = next(old, None), next(new, None)\n bcast_dims = []\n i = 0\n while True:\n if d1 is d2 is None:\n return bcast_dims\n elif d1 == d2:\n bcast_dims.append(i)\n i += 1\n d1, d2 = next(old, None), next(new, None)\n elif d2 == 1:\n i += 1\n d2 = next(new, None)\n else:\n return None","label":0},{"cid":"jax/lax/lax.py::_is_axis_merge","text":"jax/lax/lax.py::_is_axis_merge\ndef _is_axis_merge(s1, s2):\n return s1[2:] == s2[1:] and s1[0] * s1[1] == s2[0]","label":0},{"cid":"jax/lax/lax.py::_is_axis_split","text":"jax/lax/lax.py::_is_axis_split\ndef _is_axis_split(s1, s2):\n return _is_axis_merge(s2, s1)","label":0},{"cid":"jax/lax/lax.py::_reshape_shape_rule","text":"jax/lax/lax.py::_reshape_shape_rule\ndef _reshape_shape_rule(operand, new_sizes, dimensions, **unused_kwargs):\n if not onp.all(onp.greater_equal(new_sizes, 0)):\n msg = 'reshape new_sizes must all be positive, got {}.'\n raise TypeError(msg.format(new_sizes))\n if prod(onp.shape(operand)) != prod(new_sizes):\n msg = 'reshape total size must be unchanged, got new_sizes {} for shape {}.'\n raise TypeError(msg.format(new_sizes, onp.shape(operand)))\n if dimensions is not None:\n if set(dimensions) != set(range(onp.ndim(operand))):\n msg = ('reshape dimensions must be a permutation of operand dimensions, '\n 'got dimensions {} for shape {}.')\n raise TypeError(msg.format(dimensions, onp.shape(operand)))\n return tuple(new_sizes)","label":0},{"cid":"jax/lax/lax.py::_reshape_dtype_rule","text":"jax/lax/lax.py::_reshape_dtype_rule\ndef _reshape_dtype_rule(operand, new_sizes, dimensions, **unused_kwargs):\n return operand.dtype","label":0},{"cid":"jax/lax/lax.py::_reshape_translation_rule","text":"jax/lax/lax.py::_reshape_translation_rule\ndef _reshape_translation_rule(c, operand, new_sizes, dimensions, old_sizes):\n del old_sizes # Unused.\n return c.Reshape(operand, new_sizes=new_sizes, dimensions=dimensions)","label":0},{"cid":"jax/lax/lax.py::_reshape_transpose_rule","text":"jax/lax/lax.py::_reshape_transpose_rule\ndef _reshape_transpose_rule(t, new_sizes, dimensions, old_sizes):\n if dimensions is None:\n return [reshape(t, old_sizes)]\n else:\n return [transpose(reshape(t, onp.take(old_sizes, dimensions)),\n onp.argsort(dimensions))]","label":0},{"cid":"jax/lax/lax.py::_reshape_batch_rule","text":"jax/lax/lax.py::_reshape_batch_rule\ndef _reshape_batch_rule(batched_args, batch_dims, new_sizes, dimensions, **unused):\n operand, = batched_args\n bdim, = batch_dims\n operand = batching.moveaxis(operand, bdim, 0)\n if dimensions is not None:\n dimensions = (0,) + tuple(onp.add(1, dimensions))\n return reshape(operand, operand.shape[:1] + new_sizes, dimensions), 0","label":0},{"cid":"jax/lax/lax.py::_reshape_polymorphic_shape_rule","text":"jax/lax/lax.py::_reshape_polymorphic_shape_rule\ndef _reshape_polymorphic_shape_rule(shape_exprs, new_sizes, dimensions, old_sizes):\n if dimensions is not None: raise NotImplementedError\n shape_expr, = shape_exprs\n if masking.prod(shape_expr) != masking.prod(new_sizes): raise ShapeError\n return new_sizes","label":0},{"cid":"jax/lax/lax.py::_rev_shape_rule","text":"jax/lax/lax.py::_rev_shape_rule\ndef _rev_shape_rule(operand, dimensions):\n _check_shapelike('rev', 'dimensions', dimensions)\n if len(set(dimensions)) != len(dimensions):\n msg = 'rev dimensions must be unique, got {}.'\n raise TypeError(msg.format(dimensions))\n if not _max(dimensions) < operand.ndim:\n msg = ('rev dimensions must all be less than operand ndim, got dimensions '\n '{} for operand ndim {}.')\n raise TypeError(msg.format(dimensions, operand.ndim))\n return operand.shape","label":0},{"cid":"jax/lax/lax.py::_rev_batch_rule","text":"jax/lax/lax.py::_rev_batch_rule\ndef _rev_batch_rule(batched_args, batch_dims, dimensions):\n operand, = batched_args\n bdim, = batch_dims\n new_dimensions = [i + 1 if i >= bdim else i for i in dimensions]\n return rev(operand, new_dimensions), bdim","label":0},{"cid":"jax/lax/lax.py::_transpose_impl","text":"jax/lax/lax.py::_transpose_impl\ndef _transpose_impl(operand, permutation):\n if type(operand) is xla.DeviceArray:\n lazy_expr = lazy.transpose(operand._lazy_expr, permutation)\n aval = ShapedArray(lazy_expr.shape, operand.dtype)\n return xla.DeviceArray(aval, None, lazy_expr, operand.device_buffer)\n else:\n return xla.apply_primitive(transpose_p, operand, permutation=permutation)","label":0},{"cid":"jax/lax/lax.py::_transpose_shape_rule","text":"jax/lax/lax.py::_transpose_shape_rule\ndef _transpose_shape_rule(operand, permutation):\n if not isinstance(permutation, (tuple, list, onp.ndarray)):\n msg = \"transpose permutation must be a tuple/list/ndarray, got {}.\"\n raise TypeError(msg.format(type(permutation)))\n if tuple(sorted(permutation)) != tuple(range(operand.ndim)):\n msg = (\"transpose permutation isn't a permutation of operand dimensions, \"\n \"got permutation {} for operand shape {}.\")\n raise TypeError(msg.format(permutation, operand.shape))\n return tuple(onp.take(operand.shape, permutation))","label":0},{"cid":"jax/lax/lax.py::_transpose_batch_rule","text":"jax/lax/lax.py::_transpose_batch_rule\ndef _transpose_batch_rule(batched_args, batch_dims, permutation):\n operand, = batched_args\n bdim, = batch_dims\n perm = (bdim,) + tuple(i if i < bdim else i+1 for i in permutation)\n return transpose(operand, perm), 0","label":0},{"cid":"jax/lax/lax.py::_select_shape_rule","text":"jax/lax/lax.py::_select_shape_rule\ndef _select_shape_rule(pred, on_true, on_false):\n if on_true.shape != on_false.shape:\n msg = \"select on_true and on_false must have the same shape, got {} and {}.\"\n raise TypeError(msg.format(on_true.shape, on_false.shape))\n if pred.shape and pred.shape != on_true.shape:\n msg = (\"select pred must be scalar or have the same shape as on_true and \"\n \"on_false, got pred shape {} for on_true and on_false of shape {}.\")\n raise TypeError(msg.format(pred.shape, on_true.shape))\n return on_true.shape","label":0},{"cid":"jax/lax/lax.py::_select_dtype_rule","text":"jax/lax/lax.py::_select_dtype_rule\ndef _select_dtype_rule(pred, on_true, on_false):\n _check_same_dtypes(\"select\", False, on_true.dtype, on_false.dtype)\n if not dtypes.issubdtype(pred.dtype, onp.bool_):\n msg = \"select pred must be boolean type, got {}.\"\n raise TypeError(msg.format(pred.dtype))\n return on_true.dtype","label":0},{"cid":"jax/lax/lax.py::_select_transpose_rule","text":"jax/lax/lax.py::_select_transpose_rule\ndef _select_transpose_rule(t, pred, on_true, on_false):\n assert pred is not ad.undefined_primal\n if t is ad_util.zero:\n return [None,\n ad_util.zero if on_true is ad.undefined_primal else None,\n ad_util.zero if on_false is ad.undefined_primal else None]\n else:\n zeros = full_like(t, 0)\n return [None,\n select(pred, t, zeros) if on_true is ad.undefined_primal else None,\n select(pred, zeros, t) if on_false is ad.undefined_primal else None]","label":0},{"cid":"jax/lax/lax.py::_select_batch_rule","text":"jax/lax/lax.py::_select_batch_rule\ndef _select_batch_rule(batched_args, batch_dims, **unused_kwargs):\n pred, on_true, on_false, = batched_args\n pred_bdim, ot_bdim, of_bdim = batch_dims\n size = next(x.shape[i] for x, i in zip(batched_args, batch_dims)\n if i is not None)\n\n # avoid transposes and some broadcasts in special cases\n if pred_bdim == ot_bdim == of_bdim:\n if onp.shape(pred) == onp.shape(on_true):\n return select(pred, on_true, on_false), pred_bdim\n else:\n # vmapped function had a scalar pred with nonscalar args\n assert onp.ndim(pred) == 1\n pred = broadcast_in_dim(pred, on_true.shape, [pred_bdim])\n return select(pred, on_true, on_false), pred_bdim\n elif onp.ndim(pred) == 0 and ot_bdim is not None and of_bdim is not None:\n if ot_bdim == of_bdim:\n return select(pred, on_true, on_false), ot_bdim\n elif onp.shape(on_true) == onp.shape(on_false):\n on_false = batching.moveaxis(on_false, of_bdim, ot_bdim)\n return select(pred, on_true, on_false), ot_bdim\n\n pred = batching.bdim_at_front(pred, pred_bdim, size) if onp.shape(pred) else pred\n if not onp.shape(on_true) == onp.shape(on_false) == ():\n on_true = batching.bdim_at_front(on_true, ot_bdim, size)\n on_false = batching.bdim_at_front(on_false, of_bdim, size)\n assert onp.shape(on_true) == onp.shape(on_false)\n if 0 < onp.ndim(pred) < onp.ndim(on_true):\n # vmapped function had a scalar pred with nonscalar args\n assert onp.ndim(pred) == 1\n pred = broadcast_in_dim(pred, on_true.shape, [0])\n if onp.ndim(pred) > onp.ndim(on_true):\n assert onp.ndim(on_true) == 0\n on_true = broadcast(on_true, pred.shape)\n on_false = broadcast(on_false, pred.shape)\n return select(pred, on_true, on_false), 0","label":0},{"cid":"jax/lax/lax.py::_slice_shape_rule","text":"jax/lax/lax.py::_slice_shape_rule\ndef _slice_shape_rule(operand, start_indices, limit_indices, strides,\n operand_shape):\n _check_shapelike(\"slice\", \"start_indices\", start_indices)\n _check_shapelike(\"slice\", \"limit_indices\", limit_indices)\n if operand.ndim != len(start_indices):\n msg = (\"slice start_indices must have length equal to the number of \"\n \"dimensions of the operand, got indices {} for operand shape {}.\")\n raise TypeError(msg.format(start_indices, operand.shape))\n if len(start_indices) != len(limit_indices):\n msg = (\"slice limit_indices must have the same length as start_indices, \"\n \"got start_inidices {} and limit_indices {}.\")\n raise TypeError(msg.format(start_indices, limit_indices))\n if not onp.all(onp.less_equal(limit_indices, operand.shape)):\n msg = (\"slice limit_indices must be less than or equal to operand shape, \"\n \"got limit_indices {} for operand shape {}.\")\n raise TypeError(msg.format(limit_indices, operand.shape))\n if not onp.all(onp.greater_equal(start_indices, 0)):\n msg = (\"slice start_indices must be greater than or equal to zero, \"\n \"got start_indices of {}.\")\n raise TypeError(msg.format(start_indices))\n if not onp.all(onp.greater_equal(limit_indices, start_indices)):\n msg = (\"slice limit_indices must be greater than or equal to start_indices,\"\n \" got start_indices {} and limit_indices {}.\")\n raise TypeError(msg.format(start_indices, limit_indices))\n if strides is None:\n strides = onp.ones(operand.ndim, onp.int32)\n else:\n _check_shapelike(\"slice\", \"strides\", strides)\n if len(strides) != operand.ndim:\n msg = (\"slice strides must have length equal to the number of dimensions \"\n \"of the operand, got strides {} for operand shape {}.\")\n raise TypeError(msg.format(strides, operand.shape))\n if not onp.all(onp.greater(strides, 0)):\n msg = \"slice strides must be positive, got {}\"\n raise TypeError(m","label":0},{"cid":"jax/lax/lax.py::_slice_translation_rule","text":"jax/lax/lax.py::_slice_translation_rule\ndef _slice_translation_rule(c, operand, start_indices, limit_indices, strides,\n operand_shape):\n return c.Slice(operand, start_indices, limit_indices, strides)","label":0},{"cid":"jax/lax/lax.py::_slice_transpose_rule","text":"jax/lax/lax.py::_slice_transpose_rule\ndef _slice_transpose_rule(t, start_indices, limit_indices, strides,\n operand_shape):\n if strides is None or onp.all(onp.equal(strides, 1)):\n pads = zip(start_indices, onp.subtract(operand_shape, limit_indices),\n (0,) * len(start_indices))\n else:\n real_limits = onp.add(onp.add(start_indices, 1),\n onp.multiply(onp.subtract(t.shape, 1), strides))\n pads = zip(start_indices, onp.subtract(operand_shape, real_limits),\n onp.subtract(strides, 1))\n result = pad(t, _const(t, 0), pads)\n assert result.shape == operand_shape\n return [result]","label":0},{"cid":"jax/lax/lax.py::_slice_batching_rule","text":"jax/lax/lax.py::_slice_batching_rule\ndef _slice_batching_rule(batched_args, batch_dims, start_indices, limit_indices,\n strides, **unused_kwargs):\n operand, = batched_args\n bdim, = batch_dims\n\n new_start_indices = list(start_indices)\n new_start_indices.insert(bdim, 0)\n\n new_limit_indices = list(limit_indices)\n new_limit_indices.insert(bdim, operand.shape[bdim])\n\n if strides is None:\n new_strides = None\n else:\n new_strides = list(strides)\n new_strides.insert(bdim, 1)\n\n out = slice(operand, new_start_indices, new_limit_indices, new_strides)\n return out, bdim","label":0},{"cid":"jax/lax/lax.py::_dynamic_slice_shape_rule","text":"jax/lax/lax.py::_dynamic_slice_shape_rule\ndef _dynamic_slice_shape_rule(operand, *start_indices, **kwargs):\n slice_sizes = kwargs[\"slice_sizes\"]\n if operand.ndim != len(start_indices):\n msg = (\"dynamic_slice start_indices must have length equal to the number \"\n \"of dimensions of the operand, got indices {} for operand shape {}.\")\n raise TypeError(msg.format(start_indices, operand.shape))\n if len(start_indices) != len(slice_sizes):\n msg = (\"dynamic_slice slice_sizes must have the same length as \"\n \"start_indices, got start_inidices length {} and slice_sizes {}.\")\n raise TypeError(msg.format(len(start_indices), slice_sizes))\n if not onp.all(onp.less_equal(slice_sizes, operand.shape)):\n msg = (\"slice slice_sizes must be less than or equal to operand shape, \"\n \"got slice_sizes {} for operand shape {}.\")\n raise TypeError(msg.format(slice_sizes, operand.shape))\n if not onp.all(onp.greater_equal(slice_sizes, 0)):\n msg = (\"slice slice_sizes must be greater than or equal to zero, \"\n \"got slice_sizes of {}.\")\n raise TypeError(msg.format(slice_sizes))\n return tuple(slice_sizes)","label":0},{"cid":"jax/lax/lax.py::_dynamic_slice_dtype_rule","text":"jax/lax/lax.py::_dynamic_slice_dtype_rule\ndef _dynamic_slice_dtype_rule(operand, *start_indices, **kw):\n if any(i.dtype != start_indices[0].dtype or\n not dtypes.issubdtype(i.dtype, onp.integer) for i in start_indices):\n msg = (\"index arguments to dynamic_slice must be integers of the same \"\n \"type, got: {}\")\n raise TypeError(msg.format(\", \".join(i.dtype.name for i in start_indices)))\n return operand.dtype","label":0},{"cid":"jax/lax/lax.py::_dynamic_slice_translation_rule","text":"jax/lax/lax.py::_dynamic_slice_translation_rule\ndef _dynamic_slice_translation_rule(c, operand, *start_indices, **kwargs):\n slice_sizes = kwargs[\"slice_sizes\"]\n return c.DynamicSlice(operand, start_indices, slice_sizes)","label":0},{"cid":"jax/lax/lax.py::_dynamic_slice_jvp","text":"jax/lax/lax.py::_dynamic_slice_jvp\ndef _dynamic_slice_jvp(primals, tangents, slice_sizes, operand_shape):\n tangent_out = ad_util.zero\n if tangents[0] is not ad_util.zero:\n tangent_out = dynamic_slice(tangents[0], primals[1:], slice_sizes)\n return dynamic_slice(primals[0], primals[1:], slice_sizes), tangent_out","label":0},{"cid":"jax/lax/lax.py::_dynamic_slice_transpose_rule","text":"jax/lax/lax.py::_dynamic_slice_transpose_rule\ndef _dynamic_slice_transpose_rule(t, operand, *start_indices, **kwargs):\n operand_shape = kwargs[\"operand_shape\"]\n assert operand is ad.undefined_primal\n assert all(s is not ad.undefined_primal for s in start_indices)\n zeros = full(operand_shape, tie_in(t, _zero(t)))\n return ([dynamic_update_slice(zeros, t, start_indices)] +\n [None] * len(start_indices))","label":0},{"cid":"jax/lax/lax.py::_batch_dynamic_slice_indices","text":"jax/lax/lax.py::_batch_dynamic_slice_indices\ndef _batch_dynamic_slice_indices(indices, bdims):\n size = next((x.shape[i] for x, i in zip(indices, bdims) if i is not None), -1)\n if size < 0:\n return concatenate([reshape(i, [1]) for i in indices], 0), None\n indices = concatenate(\n [broadcast_in_dim(x, (size, 1),\n broadcast_dimensions=((0,) if i is not None else ()))\n for x, i in zip(indices, bdims)],\n dimension=1)\n return indices, 0","label":0},{"cid":"jax/lax/lax.py::_dynamic_slice_batching_rule","text":"jax/lax/lax.py::_dynamic_slice_batching_rule\ndef _dynamic_slice_batching_rule(batched_args, batch_dims, slice_sizes,\n operand_shape):\n # A dynamic slice is a special case of gather; we can delegate to the gather\n # batching rule.\n # TODO(phawkins): consider removing dynamic_slice entirely and using gather\n # always.\n dims = tuple(range(len(operand_shape)))\n dnums = GatherDimensionNumbers(offset_dims=dims, collapsed_slice_dims=(),\n start_index_map=dims)\n index, index_bdim = _batch_dynamic_slice_indices(batched_args[1:],\n batch_dims[1:])\n return _gather_batching_rule(\n [batched_args[0], index], [batch_dims[0], index_bdim], dnums, slice_sizes,\n operand_shape)","label":0},{"cid":"jax/lax/lax.py::_dynamic_update_slice_shape_rule","text":"jax/lax/lax.py::_dynamic_update_slice_shape_rule\ndef _dynamic_update_slice_shape_rule(operand, update, *start_indices, **kwargs):\n if operand.ndim != update.ndim:\n msg = (\"dynamic_update_slice update must have the same rank as operand, \"\n \"got update shape {} for operand shape {}.\")\n raise TypeError(msg.format(update.shape, operand.shape))\n if operand.ndim != len(start_indices):\n msg = (\"dynamic_update_slice start_indices must have length equal to the \"\n \"rank of operand, got indices {} for operand shape {}.\")\n raise TypeError(msg.format(start_indices, operand.shape))\n if not onp.all(onp.less_equal(update.shape, operand.shape)):\n msg = (\"dynamic_update_slice update shape must be smaller than operand \"\n \"shape, got update shape {} for operand shape {}.\")\n raise TypeError(msg.format(update.shape, operand.shape))\n return operand.shape","label":0},{"cid":"jax/lax/lax.py::_dynamic_update_slice_dtype_rule","text":"jax/lax/lax.py::_dynamic_update_slice_dtype_rule\ndef _dynamic_update_slice_dtype_rule(operand, update, *start_indices, **kwargs):\n _check_same_dtypes(\"dynamic_update_slice\", False, operand.dtype, update.dtype)\n if any(i.dtype != start_indices[0].dtype or\n not dtypes.issubdtype(i.dtype, onp.integer) for i in start_indices):\n msg = (\"index arguments to dynamic_update_slice must be integers of the \"\n \"same type, got {}\")\n raise TypeError(msg.format(\", \".join(i.dtype.name for i in start_indices)))\n return operand.dtype","label":0},{"cid":"jax/lax/lax.py::_dynamic_update_slice_jvp","text":"jax/lax/lax.py::_dynamic_update_slice_jvp\ndef _dynamic_update_slice_jvp(primals, tangents, update_shape):\n operand, update = primals[:2]\n start_indices = primals[2:]\n g_operand, g_update = tangents[:2]\n val_out = dynamic_update_slice(operand, update, start_indices)\n if g_operand is ad_util.zero and g_update is ad_util.zero:\n tangent_out = ad_util.zero\n else:\n g_operand = ad.instantiate_zeros(operand, g_operand)\n g_update = ad.instantiate_zeros(update, g_update)\n tangent_out = dynamic_update_slice(g_operand, g_update, start_indices)\n return val_out, tangent_out","label":0},{"cid":"jax/lax/lax.py::_dynamic_update_slice_transpose_rule","text":"jax/lax/lax.py::_dynamic_update_slice_transpose_rule\ndef _dynamic_update_slice_transpose_rule(t, operand, update, *start_indices,\n **kwargs):\n update_shape = kwargs[\"update_shape\"]\n assert all(x is not ad.undefined_primal for x in start_indices)\n dus = dynamic_update_slice\n ds = dynamic_slice\n zeros = _zeros(t, shape=update_shape)\n operand_t = dus(t, zeros, start_indices) if operand is ad.undefined_primal else None\n update_t = ds(t, start_indices, update_shape) if update is ad.undefined_primal else None\n return [operand_t, update_t] + [None] * len(start_indices)","label":0},{"cid":"jax/lax/lax.py::_dynamic_update_slice_translation_rule","text":"jax/lax/lax.py::_dynamic_update_slice_translation_rule\ndef _dynamic_update_slice_translation_rule(c, operand, update, *start_indices,\n **kwargs):\n return c.DynamicUpdateSlice(operand, update, start_indices)","label":0},{"cid":"jax/lax/lax.py::_dynamic_update_slice_batching_rule","text":"jax/lax/lax.py::_dynamic_update_slice_batching_rule\ndef _dynamic_update_slice_batching_rule(batched_args, batch_dims, update_shape):\n # A dynamic update slice is a special case of scatter; we can delegate to the\n # scatter batching rule.\n # TODO(phawkins): consider removing dynamic_update_slice entirely and using\n # scatter always.\n operand, update = batched_args[:2]\n operand_bdims, update_bdims = batch_dims[:2]\n dims = tuple(range(len(update_shape)))\n dnums = ScatterDimensionNumbers(update_window_dims=dims,\n inserted_window_dims=(),\n scatter_dims_to_operand_dims=dims)\n index, index_bdim = _batch_dynamic_slice_indices(batched_args[2:],\n batch_dims[2:])\n return _scatter_batching_rule(\n scatter,\n (operand, index, update), (operand_bdims, index_bdim, update_bdims),\n None, None, dnums, update_shape)","label":0},{"cid":"jax/lax/lax.py::_gather_dimensions_proto","text":"jax/lax/lax.py::_gather_dimensions_proto\ndef _gather_dimensions_proto(indices_shape, dimension_numbers):\n assert type(dimension_numbers) is GatherDimensionNumbers\n proto = xla_client.GatherDimensionNumbers()\n proto.offset_dims.extend(dimension_numbers.offset_dims)\n proto.collapsed_slice_dims.extend(dimension_numbers.collapsed_slice_dims)\n proto.start_index_map.extend(dimension_numbers.start_index_map)\n assert indices_shape.rank() > 0\n proto.index_vector_dim = indices_shape.rank() - 1\n return proto","label":0},{"cid":"jax/lax/lax.py::_gather_dtype_rule","text":"jax/lax/lax.py::_gather_dtype_rule\ndef _gather_dtype_rule(operand, start_indices, **kwargs):\n if not dtypes.issubdtype(start_indices.dtype, onp.integer):\n raise ValueError(\"start_indices must have an integer type\")\n return dtypes.canonicalize_dtype(operand.dtype)","label":0},{"cid":"jax/lax/lax.py::_gather_shape_rule","text":"jax/lax/lax.py::_gather_shape_rule\ndef _gather_shape_rule(operand, start_indices, dimension_numbers, slice_sizes,\n operand_shape):\n assert operand.shape == operand_shape\n if len(operand_shape) != len(slice_sizes):\n msg = (\"slice_sizes must have rank equal to the gather operand; \"\n \"operand.shape={}, slice_sizes={}\".format(operand_shape, slice_sizes))\n raise ValueError(msg)\n result_rank = len(dimension_numbers.offset_dims) + start_indices.ndim - 1\n start_indices_shape = iter(start_indices.shape[:-1])\n slice_sizes = iter(onp.delete(slice_sizes, dimension_numbers.collapsed_slice_dims))\n return tuple(next(slice_sizes) if i in dimension_numbers.offset_dims\n else next(start_indices_shape) for i in range(result_rank))","label":0},{"cid":"jax/lax/lax.py::_gather_translation_rule","text":"jax/lax/lax.py::_gather_translation_rule\ndef _gather_translation_rule(c, operand, start_indices, dimension_numbers,\n slice_sizes, operand_shape):\n indices_shape = c.GetShape(start_indices)\n return c.Gather(\n operand, start_indices,\n _gather_dimensions_proto(indices_shape, dimension_numbers), slice_sizes)","label":0},{"cid":"jax/lax/lax.py::_gather_jvp_rule","text":"jax/lax/lax.py::_gather_jvp_rule\ndef _gather_jvp_rule(g, operand, start_indices, dimension_numbers, slice_sizes,\n operand_shape):\n return gather(g, start_indices, dimension_numbers, slice_sizes)","label":0},{"cid":"jax/lax/lax.py::_gather_transpose_rule","text":"jax/lax/lax.py::_gather_transpose_rule\ndef _gather_transpose_rule(t, operand, start_indices, dimension_numbers,\n slice_sizes, operand_shape):\n assert operand is ad.undefined_primal\n if t is ad_util.zero:\n return [ad_util.zero, ad_util.zero]\n zeros = full(operand_shape, tie_in(t, _zero(t)))\n scatter_dnums = ScatterDimensionNumbers(\n update_window_dims=dimension_numbers.offset_dims,\n inserted_window_dims=dimension_numbers.collapsed_slice_dims,\n scatter_dims_to_operand_dims=dimension_numbers.start_index_map)\n return [scatter_add(zeros, start_indices, t, scatter_dnums), ad_util.zero]","label":0},{"cid":"jax/lax/lax.py::_gather_batching_rule","text":"jax/lax/lax.py::_gather_batching_rule\ndef _gather_batching_rule(batched_args, batch_dims, dimension_numbers,\n slice_sizes, operand_shape):\n operand, start_indices = batched_args\n operand_bdim, start_indices_bdim = batch_dims\n\n if operand_bdim is not None and start_indices_bdim is None:\n operand = batching.moveaxis(operand, operand_bdim, 0)\n slice_sizes = (operand.shape[0],) + slice_sizes\n offset_dims = (0,) + tuple(onp.add(1, dimension_numbers.offset_dims))\n collapsed_slice_dims = tuple(onp.add(1, dimension_numbers.collapsed_slice_dims))\n start_index_map = tuple(onp.add(1, dimension_numbers.start_index_map))\n dnums = GatherDimensionNumbers(\n offset_dims=offset_dims,\n collapsed_slice_dims=collapsed_slice_dims,\n start_index_map=start_index_map)\n return gather(operand, start_indices, dimension_numbers=dnums,\n slice_sizes=slice_sizes), 0\n\n elif operand_bdim is None and start_indices_bdim is not None:\n start_indices = batching.moveaxis(start_indices, start_indices_bdim, 0)\n offset_dims = tuple(onp.add(1, dimension_numbers.offset_dims))\n dnums = GatherDimensionNumbers(\n offset_dims=offset_dims,\n collapsed_slice_dims=dimension_numbers.collapsed_slice_dims,\n start_index_map=dimension_numbers.start_index_map)\n return gather(operand, start_indices, dimension_numbers=dnums,\n slice_sizes=slice_sizes), 0\n\n else:\n # move our batch dimensions to the front to preserve sanity\n operand = batching.moveaxis(operand, operand_bdim, 0)\n start_indices = batching.moveaxis(start_indices, start_indices_bdim, 0)\n\n # Example: user code had start_indices shape (3, 4, 5), and we have to deal\n # with start_indices shape (7, 3, 4, 5). We transform that to a\n # start_indices of shape (7, 3, 4, 6) where we concatenated an iota that\n # counts along our batch dimension to the front of the ndindex.\n count_shape = list(start_indices.shape)\n ","label":0},{"cid":"jax/lax/lax.py::_scatter_dimensions_proto","text":"jax/lax/lax.py::_scatter_dimensions_proto\ndef _scatter_dimensions_proto(indices_shape, dimension_numbers):\n assert type(dimension_numbers) is ScatterDimensionNumbers\n proto = xla_client.ScatterDimensionNumbers()\n proto.update_window_dims.extend(dimension_numbers.update_window_dims)\n proto.inserted_window_dims.extend(dimension_numbers.inserted_window_dims)\n proto.scatter_dims_to_operand_dims.extend(\n dimension_numbers.scatter_dims_to_operand_dims)\n assert indices_shape.rank() > 0\n proto.index_vector_dim = indices_shape.rank() - 1\n return proto","label":0},{"cid":"jax/lax/lax.py::_scatter_dtype_rule","text":"jax/lax/lax.py::_scatter_dtype_rule\ndef _scatter_dtype_rule(operand, scatter_indices, updates, **kwargs):\n if not dtypes.issubdtype(scatter_indices.dtype, onp.integer):\n raise ValueError(\"scatter_indices must have an integer type\")\n _check_same_dtypes(\"scatter\", False, operand.dtype, updates.dtype)\n return dtypes.canonicalize_dtype(operand.dtype)","label":0},{"cid":"jax/lax/lax.py::_scatter_shape_rule","text":"jax/lax/lax.py::_scatter_shape_rule\ndef _scatter_shape_rule(operand, scatter_indices, updates, **kwargs):\n return operand.shape","label":0},{"cid":"jax/lax/lax.py::_scatter_translation_rule","text":"jax/lax/lax.py::_scatter_translation_rule\ndef _scatter_translation_rule(c, operand, scatter_indices, updates,\n update_jaxpr, update_consts, dimension_numbers,\n updates_shape):\n dtype = c.GetShape(operand).numpy_dtype()\n init_value = c.Constant(onp.array(0, dtype))\n update_computation = _reduction_computation(\n c, update_jaxpr, update_consts, init_value)\n indices_shape = c.GetShape(scatter_indices)\n return c.Scatter(operand, scatter_indices, updates, update_computation,\n _scatter_dimensions_proto(indices_shape, dimension_numbers))","label":0},{"cid":"jax/lax/lax.py::_scatter_add_jvp","text":"jax/lax/lax.py::_scatter_add_jvp\ndef _scatter_add_jvp(primals, tangents, update_jaxpr, update_consts,\n dimension_numbers, updates_shape):\n operand, scatter_indices, updates = primals\n g_operand, g_scatter_indices, g_updates = tangents\n val_out = scatter_add_p.bind(\n operand, scatter_indices, updates, update_jaxpr=update_jaxpr,\n update_consts=update_consts, dimension_numbers=dimension_numbers,\n updates_shape=updates_shape)\n if g_operand is ad_util.zero and g_updates is ad_util.zero:\n tangent_out = ad_util.zero\n else:\n g_operand = ad.instantiate_zeros(operand, g_operand)\n g_updates = ad.instantiate_zeros(updates, g_updates)\n tangent_out = scatter_add_p.bind(\n g_operand, scatter_indices, g_updates, update_jaxpr=update_jaxpr,\n update_consts=update_consts, dimension_numbers=dimension_numbers,\n updates_shape=updates_shape)\n return val_out, tangent_out","label":0},{"cid":"jax/lax/lax.py::_scatter_add_transpose_rule","text":"jax/lax/lax.py::_scatter_add_transpose_rule\ndef _scatter_add_transpose_rule(t, operand, scatter_indices, updates,\n update_jaxpr, update_consts, dimension_numbers,\n updates_shape):\n assert scatter_indices is not ad.undefined_primal\n if t is ad_util.zero:\n return [ad_util.zero, None, ad_util.zero]\n\n operand_t = update_t = None\n if operand is ad.undefined_primal:\n operand_t = t\n\n if updates is ad.undefined_primal:\n gather_dnums = GatherDimensionNumbers(\n offset_dims=dimension_numbers.update_window_dims,\n collapsed_slice_dims=dimension_numbers.inserted_window_dims,\n start_index_map=dimension_numbers.scatter_dims_to_operand_dims)\n slice_sizes = []\n pos = 0\n for i in range(len(t.shape)):\n if i in dimension_numbers.inserted_window_dims:\n slice_sizes.append(1)\n else:\n slice_sizes.append(updates_shape[dimension_numbers.update_window_dims[pos]])\n pos += 1\n update_t = gather(t, scatter_indices, dimension_numbers=gather_dnums,\n slice_sizes=slice_sizes)\n return [operand_t, None, update_t]","label":0},{"cid":"jax/lax/lax.py::_scatter_batching_rule","text":"jax/lax/lax.py::_scatter_batching_rule\ndef _scatter_batching_rule(\n scatter_op, batched_args, batch_dims, update_jaxpr, update_consts,\n dimension_numbers, updates_shape):\n operand, scatter_indices, updates = batched_args\n operand_bdim, scatter_indices_bdim, updates_bdim = batch_dims\n del update_jaxpr, update_consts, updates_shape # Unused.\n\n # move the operand batch dim to the front if it is not None, otherwise create\n # it at the front (so that we can scatter into it)\n size = next(x.shape[ax] for x, ax in zip(batched_args, batch_dims)\n if ax is not None)\n operand = batching.bdim_at_front(operand, operand_bdim, size)\n operand_bdim = 0\n\n updates = batching.bdim_at_front(updates, updates_bdim, size)\n\n if scatter_indices_bdim is None:\n inserted_window_dims = tuple(onp.add(1, dimension_numbers.inserted_window_dims))\n update_window_dims = (0,) + tuple(onp.add(1, dimension_numbers.update_window_dims))\n scatter_dims_to_operand_dims = tuple(onp.add(1, dimension_numbers.scatter_dims_to_operand_dims))\n dnums = ScatterDimensionNumbers(\n update_window_dims=update_window_dims,\n inserted_window_dims=inserted_window_dims,\n scatter_dims_to_operand_dims=scatter_dims_to_operand_dims)\n return scatter_op(operand, scatter_indices, updates, dnums), 0\n\n\n # see the third case in _gather_batching_rule for comparison and comments\n scatter_indices = batching.bdim_at_front(\n scatter_indices, scatter_indices_bdim, size)\n\n count_shape = list(scatter_indices.shape)\n count_shape[-1] = 1\n counts = broadcasted_iota(scatter_indices.dtype, tuple(count_shape), 0)\n scatter_indices = concatenate([counts, scatter_indices],\n len(count_shape) - 1)\n\n update_window_dims = tuple(onp.add(1, dimension_numbers.update_window_dims))\n inserted_window_dims = (0,) + tuple(onp.add(1, dimension_numbers.inserted_window_dims))\n scatter_dims_to_operand_dims = (0,) + tuple(onp.add(1, dimension_numbers.scatter_dims_to_ope","label":0},{"cid":"jax/lax/lax.py::_scatter_jvp","text":"jax/lax/lax.py::_scatter_jvp\ndef _scatter_jvp(primals, tangents, update_jaxpr, update_consts,\n dimension_numbers, updates_shape):\n operand, scatter_indices, updates = primals\n g_operand, g_scatter_indices, g_updates = tangents\n dnums = dimension_numbers\n\n if g_operand is ad_util.zero and g_updates is ad_util.zero:\n val_out = scatter_p.bind(\n operand, scatter_indices, updates, update_jaxpr=update_jaxpr,\n update_consts=update_consts, dimension_numbers=dnums,\n updates_shape=updates_shape)\n tangent_out = ad_util.zero\n return val_out, tangent_out\n\n g_operand = ad.instantiate_zeros(operand, g_operand)\n g_updates = ad.instantiate_zeros(updates, g_updates)\n\n # If there are overlapping indices in the scatter, it is unspecified which\n # update \"wins\". So we use the following perhaps surprising scheme:\n # a) attach a positive ID to each update in updates, forming (value, id) pairs\n # (using a new array dimension because scatter doesn't actually support\n # pairs).\n # b) perform the scatter, yielding (value, id) updates, which we split apart.\n # c) perform the inverse gather on the ids (similar to\n # _scatter_add_transpose), and use it to build a mask for the tangent of\n # `updates`.\n # d) perform a scatter-add on the masked JVP values. A benefit of using\n # scatter-add here is that we don't need a `scatter` transpose rule.\n\n # a) add unique positive IDs (iotas) to the updates, and zeros to the operand.\n operand_shape = operand.shape\n updates_shape = updates.shape\n updates_dtype = _dtype(updates)\n\n new_operand = reshape(operand, (1,) + operand_shape)\n new_operand = pad(new_operand, _zero(operand),\n ((0, 1, 0),) + tuple((0, 0, 0) for _ in operand_shape))\n\n ids_shape = onp.array(updates_shape)\n ids_shape[dnums.update_window_dims,] = 1\n num_ids = onp.prod(ids_shape)\n update_ids = add(reshape(iota(updates_dtype, num_ids), ids_shape),\n _ones(updates))\n\n # TODO(phaw","label":0},{"cid":"jax/lax/lax.py::_reduce_shape_rule","text":"jax/lax/lax.py::_reduce_shape_rule\ndef _reduce_shape_rule(operand, init_value, computation, jaxpr, consts, dimensions):\n return tuple(onp.delete(operand.shape, dimensions))","label":0},{"cid":"jax/lax/lax.py::_reduce_translation_rule","text":"jax/lax/lax.py::_reduce_translation_rule\ndef _reduce_translation_rule(c, operand, init_value, computation, jaxpr, consts, dimensions):\n xla_computation = _reduction_computation(c, jaxpr, consts, init_value)\n return c.Reduce(operand, init_value, xla_computation, dimensions)","label":0},{"cid":"jax/lax/lax.py::_reduce_batch_rule","text":"jax/lax/lax.py::_reduce_batch_rule\ndef _reduce_batch_rule(batched_args, batch_dims, computation, jaxpr, consts, dimensions):\n operand, init_value = batched_args\n operand_bdim, init_value_bdim = batch_dims\n if init_value_bdim is None:\n assert operand_bdim is not None\n new_dimensions = [d + bool(d >= operand_bdim) for d in dimensions]\n new_operand_bdim = operand_bdim - int(onp.sum(onp.less(dimensions, operand_bdim)))\n return reduce(operand, init_value, computation, new_dimensions), new_operand_bdim\n else:\n raise NotImplementedError # loop and stack","label":0},{"cid":"jax/lax/lax.py::_reduction_computation","text":"jax/lax/lax.py::_reduction_computation\ndef _reduction_computation(c, jaxpr, consts, init_value):\n shape = c.GetShape(init_value)\n axis_env = xla.AxisEnv() # no parallel primitives inside reductions\n subc = xla_bridge.make_computation_builder(\"reduction_computation\")\n consts = [subc.ParameterWithShape(const) for const in consts]\n args = [subc.ParameterWithShape(shape), subc.ParameterWithShape(shape)]\n out, = xla.jaxpr_subcomp(subc, jaxpr, None, axis_env, consts, (), *args)\n return subc.Build(out)","label":0},{"cid":"jax/lax/lax.py::_masking_defreducer","text":"jax/lax/lax.py::_masking_defreducer\ndef _masking_defreducer(prim, identity):\n masking.shape_rules[prim] = _reducer_polymorphic_shape_rule\n masking.masking_rules[prim] = partial(_reducer_masking_rule, prim, identity)","label":0},{"cid":"jax/lax/lax.py::_reducer_polymorphic_shape_rule","text":"jax/lax/lax.py::_reducer_polymorphic_shape_rule\ndef _reducer_polymorphic_shape_rule(shape_exprs, axes, **unused_params):\n shape_expr, = shape_exprs\n return ShapeExpr([d for i, d in enumerate(shape_expr) if i not in axes])","label":0},{"cid":"jax/lax/lax.py::_reducer_masking_rule","text":"jax/lax/lax.py::_reducer_masking_rule\ndef _reducer_masking_rule(prim, identity, padded_vals, logical_shapes,\n axes, input_shape):\n del input_shape # Unused.\n (padded_val,), (logical_shape,) = padded_vals, logical_shapes\n padded_shape = masking.padded_shape_as_value(padded_val.shape)\n masks = [broadcasted_iota(onp.int32, padded_shape, i) < d\n for i, d in enumerate(logical_shape) if i in axes]\n mask = _reduce(operator.and_, masks)\n masked_val = select(mask, padded_val, identity(padded_shape, padded_val.dtype))\n return prim.bind(masked_val, axes=axes, input_shape=padded_shape)","label":0},{"cid":"jax/lax/lax.py::_reduce_number_dtype_rule","text":"jax/lax/lax.py::_reduce_number_dtype_rule\ndef _reduce_number_dtype_rule(name, operand, *args, **kw):\n if not dtypes.issubdtype(operand.dtype, onp.number):\n raise TypeError(\"{} does not accept dtype {}. Accepted dtypes are subtypes \"\n \"of number.\".format(name, onp.dtype(operand.dtype).name))\n return dtypes.canonicalize_dtype(operand.dtype)","label":0},{"cid":"jax/lax/lax.py::_reduce_sum_shape_rule","text":"jax/lax/lax.py::_reduce_sum_shape_rule\ndef _reduce_sum_shape_rule(operand, axes, input_shape):\n assert operand.shape == input_shape, ('{} != {}'\n .format(operand.shape, input_shape))\n return tuple(onp.delete(operand.shape, axes))","label":0},{"cid":"jax/lax/lax.py::_reduce_sum_translation_rule","text":"jax/lax/lax.py::_reduce_sum_translation_rule\ndef _reduce_sum_translation_rule(c, operand, axes, input_shape):\n dtype = c.GetShape(operand).numpy_dtype()\n scalar = ShapedArray((), dtype)\n return c.Reduce(operand, c.Constant(onp.array(0, dtype)),\n xla.primitive_subcomputation(add_p, scalar, scalar),\n axes)","label":0},{"cid":"jax/lax/lax.py::_reduce_sum_transpose_rule","text":"jax/lax/lax.py::_reduce_sum_transpose_rule\ndef _reduce_sum_transpose_rule(cotangent, input_shape, axes):\n broadcast_dimensions = tuple(onp.delete(onp.arange(len(input_shape)), axes))\n result = broadcast_in_dim(cotangent, input_shape, broadcast_dimensions)\n assert result.shape == input_shape\n return [result]","label":0},{"cid":"jax/lax/lax.py::_reduce_prod_shape_rule","text":"jax/lax/lax.py::_reduce_prod_shape_rule\ndef _reduce_prod_shape_rule(operand, axes):\n return tuple(onp.delete(operand.shape, axes))","label":0},{"cid":"jax/lax/lax.py::_reduce_prod_translation_rule","text":"jax/lax/lax.py::_reduce_prod_translation_rule\ndef _reduce_prod_translation_rule(c, operand, axes):\n dtype = c.GetShape(operand).numpy_dtype()\n scalar = ShapedArray((), dtype)\n return c.Reduce(operand, c.Constant(onp.array(1, dtype)),\n xla.primitive_subcomputation(mul_p, scalar, scalar), axes)","label":0},{"cid":"jax/lax/lax.py::_reduce_prod_jvp_rule","text":"jax/lax/lax.py::_reduce_prod_jvp_rule\ndef _reduce_prod_jvp_rule(tangent, operand, axes):\n input_shape = onp.array(operand.shape)\n\n n = onp.prod(input_shape[list(axes)])\n non_axes = onp.delete(onp.arange(len(input_shape)), axes)\n\n # Move the reduced axes to the front, and flatten them to 1D.\n permutation = axes + tuple(non_axes)\n new_shape = (n,) + tuple(input_shape[non_axes])\n operand = reshape(operand, new_shape, permutation)\n tangent = reshape(tangent, new_shape, permutation)\n\n one = _const(operand, 1)\n window_dims = [n] + [1] * len(non_axes)\n window_strides = [1] * (len(non_axes) + 1)\n\n # Form the partial products of all elements to the left and right of each\n # element.\n left_padding = [(n, -1, 0)] + [(0, 0, 0)] * len(non_axes)\n right_padding = [(-1, n, 0)] + [(0, 0, 0)] * len(non_axes)\n left_products = _reduce_window_prod(pad(operand, one, left_padding),\n window_dims, window_strides,\n xla_client.PaddingType.VALID)\n right_products = _reduce_window_prod(pad(operand, one, right_padding),\n window_dims, window_strides,\n xla_client.PaddingType.VALID)\n\n # Multiply partial products with the tangents and sum.\n return _reduce_sum(mul(tangent, mul(left_products, right_products)), (0,))","label":0},{"cid":"jax/lax/lax.py::_reduce_chooser_shape_rule","text":"jax/lax/lax.py::_reduce_chooser_shape_rule\ndef _reduce_chooser_shape_rule(operand, axes):\n return tuple(onp.delete(operand.shape, axes))","label":0},{"cid":"jax/lax/lax.py::_reduce_chooser_translation_rule","text":"jax/lax/lax.py::_reduce_chooser_translation_rule\ndef _reduce_chooser_translation_rule(prim, identity, c, operand, axes):\n dtype = c.GetShape(operand).numpy_dtype()\n scalar = ShapedArray((), dtype)\n return c.Reduce(operand, c.Constant(identity(dtype)),\n xla.primitive_subcomputation(prim, scalar, scalar), axes)","label":0},{"cid":"jax/lax/lax.py::_reduce_chooser_jvp_rule","text":"jax/lax/lax.py::_reduce_chooser_jvp_rule\ndef _reduce_chooser_jvp_rule(g, ans, operand, axes):\n # TODO(mattjj): an alternative is to use variadic reduce to compute the chosen\n # locations in a single pass (rather than comparing equality) and use a\n # gather, and/or even push along the chosen elements of g (b/112040122)\n shape = [1 if i in axes else d for i, d in enumerate(operand.shape)]\n location_indicators = convert_element_type(\n _eq_meet(operand, reshape(ans, shape)), g.dtype)\n counts = _reduce_sum(location_indicators, axes)\n return div(_reduce_sum(mul(g, location_indicators), axes), counts)","label":0},{"cid":"jax/lax/lax.py::_reduce_logical_shape_rule","text":"jax/lax/lax.py::_reduce_logical_shape_rule\ndef _reduce_logical_shape_rule(operand, axes):\n if operand.dtype != onp.bool_:\n msg = \"logical reduction requires operand dtype bool, got {}.\"\n raise TypeError(msg.format(operand.dtype))\n return tuple(onp.delete(operand.shape, axes))","label":0},{"cid":"jax/lax/lax.py::_reduce_logical_translation_rule","text":"jax/lax/lax.py::_reduce_logical_translation_rule\ndef _reduce_logical_translation_rule(prim, identity, c, operand, axes):\n scalar = ShapedArray((), onp.bool_)\n return c.Reduce(operand, c.Constant(identity(onp.bool_)),\n xla.primitive_subcomputation(prim, scalar, scalar), axes)","label":0},{"cid":"jax/lax/lax.py::_reduce_window_shape_rule","text":"jax/lax/lax.py::_reduce_window_shape_rule\ndef _reduce_window_shape_rule(operand, init_value, jaxpr, consts,\n window_dimensions, window_strides, padding):\n if operand.dtype != init_value.dtype:\n msg = (\"reduce_window got inconsistent dtypes for operand and init_value: \"\n \" got operand dtype {} and init_value dtype {}.\")\n raise TypeError(msg.format(operand.dtype, init_value.dtype))\n return _common_reduce_window_shape_rule(operand, window_dimensions,\n window_strides, padding)","label":0},{"cid":"jax/lax/lax.py::_reduce_window_translation_rule","text":"jax/lax/lax.py::_reduce_window_translation_rule\ndef _reduce_window_translation_rule(c, operand, init_value, jaxpr, consts,\n window_dimensions, window_strides, padding):\n xla_computation = _reduction_computation(c, jaxpr, consts, init_value)\n return c.ReduceWindow(operand, init_value, xla_computation, window_dimensions,\n window_strides, padding)","label":0},{"cid":"jax/lax/lax.py::_generic_reduce_window_batch_rule","text":"jax/lax/lax.py::_generic_reduce_window_batch_rule\ndef _generic_reduce_window_batch_rule(\n batched_args, batch_dims, jaxpr, consts, window_dimensions, window_strides,\n padding):\n operand, init = batched_args\n bdim, init_bdim = batch_dims\n if init_bdim is not None:\n raise NotImplementedError(\"reduce_window batching is not implemented for \"\n \"initial values\")\n\n def reduce_window(x, window_dimensions, window_strides, padding):\n return reduce_window_p.bind(\n x, init, jaxpr=jaxpr, consts=consts, window_dimensions=window_dimensions,\n window_strides=window_strides, padding=padding)\n return _reduce_window_batch_rule(reduce_window, (operand,), (bdim,),\n window_dimensions, window_strides, padding)","label":0},{"cid":"jax/lax/lax.py::_generic_reduce_window_batch_rule.reduce_window","text":"jax/lax/lax.py::_generic_reduce_window_batch_rule.reduce_window\n def reduce_window(x, window_dimensions, window_strides, padding):\n return reduce_window_p.bind(\n x, init, jaxpr=jaxpr, consts=consts, window_dimensions=window_dimensions,\n window_strides=window_strides, padding=padding)","label":0},{"cid":"jax/lax/lax.py::_reduce_window_sum_shape_rule","text":"jax/lax/lax.py::_reduce_window_sum_shape_rule\ndef _reduce_window_sum_shape_rule(operand, window_dimensions, window_strides,\n padding, input_shape):\n return _common_reduce_window_shape_rule(operand, window_dimensions,\n window_strides, padding)","label":0},{"cid":"jax/lax/lax.py::_reduce_window_sum_translation_rule","text":"jax/lax/lax.py::_reduce_window_sum_translation_rule\ndef _reduce_window_sum_translation_rule(c, operand, window_dimensions,\n window_strides, padding, input_shape):\n dtype = c.GetShape(operand).numpy_dtype()\n scalar = ShapedArray((), dtype)\n return c.ReduceWindow(operand, c.Constant(onp.array(0, dtype)),\n xla.primitive_subcomputation(add_p, scalar, scalar),\n window_dimensions, window_strides, padding)","label":0},{"cid":"jax/lax/lax.py::_reduce_window_sum_transpose_rule","text":"jax/lax/lax.py::_reduce_window_sum_transpose_rule\ndef _reduce_window_sum_transpose_rule(cotangent, window_dimensions,\n window_strides, padding, input_shape):\n in_pads = padtype_to_pads(input_shape, window_dimensions, window_strides,\n padding)\n ones = [1] * len(input_shape)\n pads = _conv_general_vjp_lhs_padding(\n input_shape, window_dimensions, window_strides, cotangent.shape, in_pads,\n ones, ones)\n padding_config = [(lo, hi, stride - 1)\n for (lo, hi), stride in zip(pads, window_strides)]\n pad_cotangent = pad(cotangent, _zero(cotangent), padding_config)\n result = _reduce_window_sum(pad_cotangent, window_dimensions, ones,\n xla_client.PaddingType.VALID)\n assert result.shape == input_shape\n return [result]","label":0},{"cid":"jax/lax/lax.py::_reduce_window_batch_rule","text":"jax/lax/lax.py::_reduce_window_batch_rule\ndef _reduce_window_batch_rule(\n reduce_window, batched_args, bdims, window_dimensions, window_strides,\n padding, input_shape=None):\n operand, = batched_args\n bdim, = bdims\n\n if bdim is not None:\n window_dimensions = \\\n window_dimensions[:bdim] + (1,) + window_dimensions[bdim:]\n window_strides = window_strides[:bdim] + (1,) + window_strides[bdim:]\n\n operand = reduce_window(\n operand, window_dimensions, window_strides, padding)\n\n return operand, bdim","label":0},{"cid":"jax/lax/lax.py::_reduce_window_chooser_translation_rule","text":"jax/lax/lax.py::_reduce_window_chooser_translation_rule\ndef _reduce_window_chooser_translation_rule(\n prim, identity, c, operand, window_dimensions, window_strides, padding):\n dtype = c.GetShape(operand).numpy_dtype()\n scalar = ShapedArray((), dtype)\n return c.ReduceWindow(operand, c.Constant(identity(dtype)),\n xla.primitive_subcomputation(prim, scalar, scalar),\n window_dimensions, window_strides, padding)","label":0},{"cid":"jax/lax/lax.py::_reduce_window_chooser_jvp_rule","text":"jax/lax/lax.py::_reduce_window_chooser_jvp_rule\ndef _reduce_window_chooser_jvp_rule(prim, g, operand, window_dimensions,\n window_strides, padding):\n assert prim is max_p or prim is min_p\n select_prim = ge_p if prim is max_p else le_p\n return _select_and_gather_add(g, operand, select_prim, window_dimensions,\n window_strides, padding)","label":0},{"cid":"jax/lax/lax.py::_common_reduce_window_shape_rule","text":"jax/lax/lax.py::_common_reduce_window_shape_rule\ndef _common_reduce_window_shape_rule(operand, window_dimensions, window_strides,\n padding):\n _check_shapelike(\"reduce_window\", \"window_dimensions\", window_dimensions)\n _check_shapelike(\"reduce_window\", \"window_strides\", window_strides)\n if operand.ndim != len(window_dimensions):\n msg = (\"reduce_window got the wrong number of window_dimensions for \"\n \"operand: got operand shape {} with window_dimensions {}.\")\n raise TypeError(msg.format(operand.shape, window_dimensions))\n if len(window_strides) != len(window_dimensions):\n msg = (\"reduce_window got inconsistent window_strides and \"\n \"window_dimensions: got window_strides {} and window_dimensions {}.\")\n raise TypeError(msg.format(window_strides, window_dimensions))\n\n return reduce_window_shape_tuple(operand.shape, window_dimensions,\n window_strides, padding)","label":0},{"cid":"jax/lax/lax.py::reduce_window_shape_tuple","text":"jax/lax/lax.py::reduce_window_shape_tuple\ndef reduce_window_shape_tuple(operand_shape, window_dimensions, window_strides,\n padding):\n pads = padtype_to_pads(operand_shape, window_dimensions, window_strides, padding)\n operand_padded = onp.add(operand_shape, onp.add(*zip(*pads)))\n t = onp.floor_divide(\n onp.subtract(operand_padded, window_dimensions), window_strides) + 1\n return tuple(t)","label":0},{"cid":"jax/lax/lax.py::_select_and_scatter_shape_rule","text":"jax/lax/lax.py::_select_and_scatter_shape_rule\ndef _select_and_scatter_shape_rule(\n operand, source, init_value, select_jaxpr, select_consts, scatter_jaxpr,\n scatter_consts, window_dimensions, window_strides, padding):\n _check_shapelike(\"select_and_scatter\", \"window_dimensions\", window_dimensions)\n _check_shapelike(\"select_and_scatter\", \"window_strides\", window_strides)\n if len(window_dimensions) != len(window_strides):\n msg = (\"select_and_scatter got inconsistent window_strides and \"\n \"window_dimensions: got window_strides {} and window_dimensions {}.\")\n raise TypeError(msg.format(window_strides, window_dimensions))\n return operand.shape","label":0},{"cid":"jax/lax/lax.py::_select_and_scatter_translation","text":"jax/lax/lax.py::_select_and_scatter_translation\ndef _select_and_scatter_translation(\n c, operand, source, init_value, select_jaxpr, select_consts, scatter_jaxpr,\n scatter_consts, window_dimensions, window_strides, padding):\n select = _reduction_computation(c, select_jaxpr, select_consts, init_value)\n scatter = _reduction_computation(c, scatter_jaxpr, scatter_consts, init_value)\n return c.SelectAndScatter(operand, select, window_dimensions, window_strides,\n padding, source, init_value, scatter)","label":0},{"cid":"jax/lax/lax.py::_select_and_scatter_add_shape_rule","text":"jax/lax/lax.py::_select_and_scatter_add_shape_rule\ndef _select_and_scatter_add_shape_rule(\n source, operand, select_prim, window_dimensions, window_strides, padding):\n return operand.shape","label":0},{"cid":"jax/lax/lax.py::_select_and_scatter_add_translation","text":"jax/lax/lax.py::_select_and_scatter_add_translation\ndef _select_and_scatter_add_translation(\n c, source, operand, select_prim, window_dimensions, window_strides,\n padding):\n dtype = c.GetShape(operand).numpy_dtype()\n scalar = ShapedArray((), dtype)\n select = xla.primitive_subcomputation(select_prim, scalar, scalar)\n scatter = xla.primitive_subcomputation(add_p, scalar, scalar)\n zero = c.Constant(onp.array(0, dtype))\n return c.SelectAndScatter(operand, select, window_dimensions, window_strides,\n padding, source, zero, scatter)","label":0},{"cid":"jax/lax/lax.py::_select_and_scatter_add_jvp","text":"jax/lax/lax.py::_select_and_scatter_add_jvp\ndef _select_and_scatter_add_jvp(\n primals, tangents, select_prim, window_dimensions, window_strides,\n padding):\n source, operand = primals\n g_source, g_operand = tangents\n val_out = _select_and_scatter_add(\n source, operand, select_prim, window_dimensions, window_strides,\n padding)\n del g_operand\n if g_source is ad_util.zero:\n tangent_out = ad_util.zero\n else:\n tangent_out = _select_and_scatter_add(\n g_source, operand, select_prim, window_dimensions,\n window_strides, padding)\n return val_out, tangent_out","label":0},{"cid":"jax/lax/lax.py::_select_and_scatter_add_transpose","text":"jax/lax/lax.py::_select_and_scatter_add_transpose\ndef _select_and_scatter_add_transpose(\n t, source, operand, select_prim, window_dimensions, window_strides,\n padding):\n assert source is ad.undefined_primal and operand is not ad.undefined_primal\n source_t = _select_and_gather_add(t, operand, select_prim, window_dimensions,\n window_strides, padding)\n return [source_t, None]","label":0},{"cid":"jax/lax/lax.py::_select_and_scatter_add_batch_rule","text":"jax/lax/lax.py::_select_and_scatter_add_batch_rule\ndef _select_and_scatter_add_batch_rule(batched_args, batch_dims, **kwargs):\n source, operand = batched_args\n s_bdims, o_bdims = batch_dims\n\n if s_bdims is not None and o_bdims is not None:\n #TODO(#212): use a map construct instead of unrolling.\n source = batching.moveaxis(source, s_bdims, 0)\n operand = batching.moveaxis(operand, o_bdims, 0)\n outputs = [\n _select_and_scatter_add(s, o, **kwargs) for s, o in zip(source, operand)]\n outputs = [reshape(out, (1,) + out.shape) for out in outputs]\n outputs = concatenate(outputs, 0)\n return outputs, 0\n elif s_bdims is not None:\n #TODO(#212): use a map construct instead of unrolling.\n source = batching.moveaxis(source, s_bdims, 0)\n outputs = [\n _select_and_scatter_add(s, operand, **kwargs) for s in source]\n outputs = [reshape(out, (1,) + out.shape) for out in outputs]\n outputs = concatenate(outputs, 0)\n return outputs, 0\n elif o_bdims is not None:\n #TODO(#212): use a map construct instead of unrolling.\n operand = batching.moveaxis(operand, o_bdims, 0)\n outputs = [\n _select_and_scatter_add(source, o, **kwargs) for o in operand]\n outputs = [reshape(out, (1,) + out.shape) for out in outputs]\n outputs = concatenate(outputs, 0)\n return outputs, 0","label":0},{"cid":"jax/lax/lax.py::_select_and_gather_add_shape_rule","text":"jax/lax/lax.py::_select_and_gather_add_shape_rule\ndef _select_and_gather_add_shape_rule(\n tangents, operand, select_prim, window_dimensions, window_strides, padding):\n if tangents.shape != operand.shape:\n msg = (\"select_and_gather_add tangents and operand shapes must match, \"\n \"got {} and {}.\")\n raise TypeError(msg.format(tangents.shape, operand.shape))\n return _common_reduce_window_shape_rule(operand, window_dimensions,\n window_strides, padding)","label":0},{"cid":"jax/lax/lax.py::_select_and_gather_add_translation","text":"jax/lax/lax.py::_select_and_gather_add_translation\ndef _select_and_gather_add_translation(\n c, tangents, operand, select_prim, window_dimensions, window_strides,\n padding, max_bits=64):\n shape = c.GetShape(operand)\n dtype = shape.numpy_dtype()\n etype = shape.xla_element_type()\n nbits = dtypes.finfo(dtype).bits\n\n assert nbits <= max_bits\n double_word_reduction = nbits * 2 <= max_bits\n\n const = lambda c, dtype, x: c.Constant(onp.array(x, dtype=dtype),\n canonicalize_types=False)\n\n if double_word_reduction:\n # TODO(b/73062247): XLA doesn't yet implement ReduceWindow on tuples, so\n # we implement a pair-wise ReduceWindow by packing two k-bit values into\n # 2k-bit unsigned integer using bit tricks.\n word_dtype = _UINT_DTYPES[nbits]\n double_word_dtype = _UINT_DTYPES[nbits * 2]\n word_type = xla_client.dtype_to_etype(word_dtype)\n double_word_type = xla_client.dtype_to_etype(double_word_dtype)\n\n # Packs two values into a tuple.\n def pack(a, b):\n a = c.BitcastConvertType(a, word_type)\n b = c.BitcastConvertType(b, word_type)\n a = c.ConvertElementType(a, double_word_type)\n b = c.ConvertElementType(b, double_word_type)\n a = c.ShiftLeft(a, const(c, double_word_dtype, nbits))\n return c.Or(a, b)\n\n # Unpacks the first element of a tuple.\n def fst(c, t):\n st = c.ShiftRightLogical(t, const(c, double_word_dtype, nbits))\n return c.BitcastConvertType(c.ConvertElementType(st, word_type), etype)\n\n # Unpacks the second element of a tuple.\n def snd(t):\n return c.BitcastConvertType(c.ConvertElementType(t, word_type), etype)\n\n else:\n # The double-word trick above only works if we have a sufficiently large\n # type. As an alternative, we can pack two half words into a single word,\n # at the cost of precision.\n # TODO(b/73062247): add support for tuple reductions and remove this case.\n warnings.warn(\"Using reduced precision for gradient of reduce","label":0},{"cid":"jax/lax/lax.py::_select_and_gather_add_translation.reducer","text":"jax/lax/lax.py::_select_and_gather_add_translation.reducer\n def reducer():\n c = xla_bridge.make_computation_builder(\"select_and_gather_pair_reducer\")\n x = c.ParameterWithShape(\n xla_client.Shape.array_shape(onp.dtype(double_word_dtype), ()))\n y = c.ParameterWithShape(\n xla_client.Shape.array_shape(onp.dtype(double_word_dtype), ()))\n assert select_prim is ge_p or select_prim is le_p\n which = c.Ge if select_prim is ge_p else c.Le\n c.Select(which(fst(c, x), fst(c, y)), x, y)\n return c.Build()","label":0},{"cid":"jax/lax/lax.py::_select_and_gather_add_jvp","text":"jax/lax/lax.py::_select_and_gather_add_jvp\ndef _select_and_gather_add_jvp(\n primals, tangents, select_prim, window_dimensions, window_strides,\n padding):\n source, operand = primals\n g_source, g_operand = tangents\n val_out = _select_and_gather_add(\n source, operand, select_prim, window_dimensions, window_strides,\n padding)\n del g_operand\n if g_source is ad_util.zero:\n tangent_out = ad_util.zero\n else:\n tangent_out = _select_and_gather_add(\n g_source, operand, select_prim, window_dimensions,\n window_strides, padding)\n return val_out, tangent_out","label":0},{"cid":"jax/lax/lax.py::_select_and_gather_add_transpose","text":"jax/lax/lax.py::_select_and_gather_add_transpose\ndef _select_and_gather_add_transpose(\n t, tangents, operand, select_prim, window_dimensions, window_strides,\n padding):\n assert tangents is ad.undefined_primal and operand is not ad.undefined_primal\n result = _select_and_scatter_add(t, operand, select_prim, window_dimensions,\n window_strides, padding)\n return [result, None]","label":0},{"cid":"jax/lax/lax.py::_select_and_gather_add_batching_rule","text":"jax/lax/lax.py::_select_and_gather_add_batching_rule\ndef _select_and_gather_add_batching_rule(\n batched_args, batch_dims, select_prim, window_dimensions, window_strides,\n padding):\n t, x = batched_args\n t_bdim, x_bdim = batch_dims\n size = next(a.shape[bdim] for a, bdim in zip(batched_args, batch_dims)\n if bdim is not None)\n t = batching.bdim_at_front(t, t_bdim, size)\n x = batching.bdim_at_front(x, x_bdim, size)\n window_dimensions = (1,) + window_dimensions\n window_strides = (1,) + window_strides\n out = _select_and_gather_add(t, x, select_prim, window_dimensions,\n window_strides, padding)\n return (out, 0)","label":0},{"cid":"jax/lax/lax.py::_sort_jvp_rule","text":"jax/lax/lax.py::_sort_jvp_rule\ndef _sort_jvp_rule(g, operand, dimension):\n _, g_out = sort_key_val(operand, g, dimension)\n return g_out","label":0},{"cid":"jax/lax/lax.py::_sort_batch_rule","text":"jax/lax/lax.py::_sort_batch_rule\ndef _sort_batch_rule(batched_args, batch_dims, dimension):\n operand, = batched_args\n bdim, = batch_dims\n dimension = dimension % (operand.ndim - 1)\n new_dimension = dimension + (bdim <= dimension)\n return sort(operand, dimension=new_dimension), bdim","label":0},{"cid":"jax/lax/lax.py::_sort_key_val_abstract_eval","text":"jax/lax/lax.py::_sort_key_val_abstract_eval\ndef _sort_key_val_abstract_eval(keys, values, dimension):\n return raise_to_shaped(keys), raise_to_shaped(values)","label":0},{"cid":"jax/lax/lax.py::_sort_key_val_jvp","text":"jax/lax/lax.py::_sort_key_val_jvp\ndef _sort_key_val_jvp(primals, tangents, dimension):\n # NOTE(mattjj): this re-sorts three times, but if we had a variadic\n # sort_key_val, or if we could apply a fixed permutation efficiently, we could\n # implement this jvp rule with a single sort. The apply_permutation primitive\n # would make the jvp (and corresponding transpose rule) faster and easier.\n # This would also be cleaner if we didn't get the sorted keys out.\n # TODO(mattjj): make sort_key_val variadic, no sorted keys out by default\n keys, values = primals\n keys_tangents, values_tangents = tangents\n\n val_out = sort_key_val(keys, values, dimension)\n\n if keys_tangents is ad_util.zero:\n keys_tangents_out = ad_util.zero\n else:\n keys_tangents_out = _sort_jvp_rule(keys_tangents, keys, dimension)\n\n if values_tangents is ad_util.zero:\n values_tangents_out = ad_util.zero\n else:\n values_tangents_out = _sort_jvp_rule(values_tangents, keys, dimension)\n\n tangents_out = keys_tangents_out, values_tangents_out\n return val_out, tangents_out","label":0},{"cid":"jax/lax/lax.py::_sort_key_val_transpose_rule","text":"jax/lax/lax.py::_sort_key_val_transpose_rule\ndef _sort_key_val_transpose_rule(t, keys, values, dimension):\n t_keys, t_values = t\n assert t_keys is ad_util.zero\n iota = broadcasted_iota(onp.int32, keys.shape, dimension % keys.ndim)\n _, perm = sort_key_val(keys, iota)\n keys_result = ad_util.zero if keys is ad.undefined_primal else None\n values_result = sort_key_val(perm, t_values)[1] if values is ad.undefined_primal else None\n return [keys_result, values_result]","label":0},{"cid":"jax/lax/lax.py::_sort_key_val_batch_rule","text":"jax/lax/lax.py::_sort_key_val_batch_rule\ndef _sort_key_val_batch_rule(batched_args, batch_dims, dimension):\n keys, values = batched_args\n keys_bdim, values_bdim = batch_dims\n assert keys_bdim is not None or values_bdim is not None\n if keys_bdim == values_bdim:\n new_dimension = dimension + (keys_bdim <= dimension)\n return sort_key_val(keys, values, new_dimension), (keys_bdim, keys_bdim)\n elif keys_bdim is not None and values_bdim is not None:\n keys_trans = batching.moveaxis(keys, keys_bdim, values_bdim)\n new_dimension = dimension + (values_bdim <= dimension)\n return sort_key_val(keys_trans, values, new_dimension), (values_bdim, values_bdim)\n elif keys_bdim is None:\n broadcast_dimensions = onp.delete(onp.arange(values.ndim), values_bdim)\n new_keys = broadcast_in_dim(keys, values.shape, broadcast_dimensions)\n new_dimension = dimension + (values_bdim <= dimension)\n return sort_key_val(new_keys, values, new_dimension), (values_bdim, values_bdim)\n elif values_bdim is None:\n broadcast_dimensions = onp.delete(onp.arange(keys.ndim), keys_bdim)\n new_values = broadcast_in_dim(values, keys.shape, broadcast_dimensions)\n new_dimension = dimension + (keys_bdim <= dimension)\n return sort_key_val(keys, new_values, new_dimension), (keys_bdim, keys_bdim)\n else:\n assert False # unreachable","label":0},{"cid":"jax/lax/lax.py::_tie_in_transpose_rule","text":"jax/lax/lax.py::_tie_in_transpose_rule\ndef _tie_in_transpose_rule(t):\n return [ad_util.zero, t]","label":0},{"cid":"jax/lax/lax.py::_tie_in_batch_rule","text":"jax/lax/lax.py::_tie_in_batch_rule\ndef _tie_in_batch_rule(batched_args, batch_dims):\n y = tie_in(*batched_args)\n _, bdim_y = batch_dims\n return y, bdim_y","label":0},{"cid":"jax/lax/lax.py::_stop_gradient_jvp_rule","text":"jax/lax/lax.py::_stop_gradient_jvp_rule\ndef _stop_gradient_jvp_rule(primals, tangents):\n # if we don't call stop_gradient here, we'd only peel off one autodiff tracer\n x, = primals\n return stop_gradient(x), ad_util.zero","label":0},{"cid":"jax/lax/lax.py::_stop_gradient_batch_rule","text":"jax/lax/lax.py::_stop_gradient_batch_rule\ndef _stop_gradient_batch_rule(batched_args, batch_dims):\n x, = batched_args\n dim, = batch_dims\n return stop_gradient(x), dim","label":0},{"cid":"jax/lax/lax.py::create_token","text":"jax/lax/lax.py::create_token\ndef create_token(x):\n \"\"\"Creates an XLA token value with no preconditions for sequencing effects.\n\n Experimental.\n\n Args:\n x: a dummy argument used to tie the CreateToken operator into a trace. The\n value of `x` is ignored.\n \"\"\"\n # x is a dummy argument used to tie the operator into a trace.\n return create_token_p.bind(x)","label":0},{"cid":"jax/lax/lax.py::after_all","text":"jax/lax/lax.py::after_all\ndef after_all(*operands):\n \"\"\"Merges one or more XLA token values. Experimental.\n\n Wraps the XLA AfterAll operator.\"\"\"\n return after_all_p.bind(*operands)","label":0},{"cid":"jax/lax/lax.py::_after_all_abstract_eval","text":"jax/lax/lax.py::_after_all_abstract_eval\ndef _after_all_abstract_eval(*operands):\n if any(x is not abstract_token for x in operands):\n raise TypeError(\"Arguments to after_all must be tokens\")\n return abstract_token","label":0},{"cid":"jax/lax/lax.py::_after_all_translation_rule","text":"jax/lax/lax.py::_after_all_translation_rule\ndef _after_all_translation_rule(c, *operands):\n return c.AfterAll(operands)","label":0},{"cid":"jax/lax/lax.py::infeed","text":"jax/lax/lax.py::infeed\ndef infeed(token, shape=None):\n \"\"\"Consumes an infeed value of `shape` from the host. Experimental.\n\n `token` is used to sequence infeed and outfeed effects.\n \"\"\"\n flat_shapes, treedef = pytree.flatten(shape)\n for shape in flat_shapes:\n if not isinstance(shape, ShapedArray):\n raise TypeError(\"shapes argument to infeed must be a pytree of \"\n \"ShapedArray values, got {}\".format(shapes))\n xs_and_token = infeed_p.bind(token, shapes=tuple(flat_shapes))\n return (treedef.unflatten(xs_and_token[:-1]), xs_and_token[-1])","label":0},{"cid":"jax/lax/lax.py::_infeed_abstract_eval","text":"jax/lax/lax.py::_infeed_abstract_eval\ndef _infeed_abstract_eval(token, shapes=None):\n if token is not abstract_token:\n raise TypeError(\"First argument to infeed must be a token\")\n return shapes + (abstract_token,)","label":0},{"cid":"jax/lax/lax.py::_infeed_translation_rule","text":"jax/lax/lax.py::_infeed_translation_rule\ndef _infeed_translation_rule(c, token, shapes=None):\n shape = tuple(map(xla.aval_to_xla_shape, shapes))\n xs_and_token = c.Infeed(xla_client.Shape.tuple_shape(shape), token)\n xs = c.GetTupleElement(xs_and_token, 0)\n token = c.GetTupleElement(xs_and_token, 1)\n outs = [c.GetTupleElement(xs, i) for i in range(len(shapes))] + [token]\n return c.Tuple(*outs)","label":0},{"cid":"jax/lax/lax.py::outfeed","text":"jax/lax/lax.py::outfeed\ndef outfeed(token, xs):\n \"\"\"Outfeeds value `xs` to the host. Experimental.\n\n `token` is used to sequence infeed and outfeed effects.\n \"\"\"\n flat_xs, _ = pytree.flatten(xs)\n return outfeed_p.bind(token, *flat_xs)","label":0},{"cid":"jax/lax/lax.py::_outfeed_abstract_eval","text":"jax/lax/lax.py::_outfeed_abstract_eval\ndef _outfeed_abstract_eval(token, *xs):\n if token is not abstract_token:\n raise TypeError(\"First argument to outfeed must be a token\")\n return abstract_token","label":0},{"cid":"jax/lax/lax.py::_outfeed_translation_rule","text":"jax/lax/lax.py::_outfeed_translation_rule\ndef _outfeed_translation_rule(c, token, *xs):\n return c.Outfeed(c.Tuple(*xs), token)","label":0},{"cid":"jax/lax/lax.py::_dilate_shape","text":"jax/lax/lax.py::_dilate_shape\ndef _dilate_shape(shape, dilation):\n \"\"\"Utility function for computing the shape resulting from a dilation.\"\"\"\n if not onp.all(onp.greater(dilation, 0)):\n msg = \"All dilations must be positive, got {}.\"\n raise TypeError(msg.format(dilation))\n dilation = (1,) * (len(shape) - len(dilation)) + tuple(dilation)\n return onp.where(shape == 0, 0,\n onp.multiply(dilation, onp.subtract(shape, 1)) + 1)","label":0},{"cid":"jax/lax/lax.py::padtype_to_pads","text":"jax/lax/lax.py::padtype_to_pads\ndef padtype_to_pads(in_shape, window_shape, window_strides, padding):\n \"\"\"Convert padding string to list of pairs of pad values.\"\"\"\n PaddingType = xla_client.PaddingType\n\n if isinstance(padding, str):\n mapping = {'VALID': PaddingType.VALID, 'SAME': PaddingType.SAME}\n try:\n padding = mapping[padding.upper()]\n except KeyError:\n msg = \"Unrecognized padding type: expected 'VALID' or 'SAME', got {}.\"\n raise RuntimeError(msg.format(padding))\n\n if padding == PaddingType.SAME:\n out_shape = onp.ceil(onp.true_divide(in_shape, window_strides)).astype(int)\n pad_sizes = [_max((out_size - 1) * stride + window_shape - in_size, 0)\n for out_size, stride, window_shape, in_size\n in zip(out_shape, window_strides, window_shape, in_shape)]\n return [(pad_size // 2, pad_size - pad_size // 2) for pad_size in pad_sizes]\n elif padding == PaddingType.VALID:\n return [(0, 0)] * len(in_shape)\n else:\n msg = \"Unknown padding type: {}.\"\n raise TypeError(msg.format(padding))","label":0},{"cid":"jax/lax/lax.py::_check_same_dtypes","text":"jax/lax/lax.py::_check_same_dtypes\ndef _check_same_dtypes(name, ignore_fp_precision, *ttypes):\n \"\"\"Check that dtypes agree, possibly ignoring float precision.\"\"\"\n # the `ignore_fp_precision` flag exists because the XLA shape inference logic\n # allows mixed floating point precision, but the HLO verifier often rejects it\n types = list(map(onp.dtype, ttypes)) # canonicalize\n if ignore_fp_precision:\n types = [\n onp.floating if dtypes.issubdtype(dtype, onp.floating)\n else onp.complexfloating if dtypes.issubdtype(dtype, onp.complexfloating)\n else dtype for dtype in types]\n if len({dtypes.canonicalize_dtype(t) for t in types}) != 1:\n if ignore_fp_precision:\n msg = (\"{} requires arguments to have same dtypes up to floating point \"\n \"precision, got {}.\")\n else:\n msg = \"{} requires arguments to have the same dtypes, got {}.\"\n raise TypeError(msg.format(name, \", \".join(map(str, types))))","label":0},{"cid":"jax/lax/lax.py::_check_conv_shapes","text":"jax/lax/lax.py::_check_conv_shapes\ndef _check_conv_shapes(name, lhs_shape, rhs_shape, window_strides):\n \"\"\"Check that conv shapes are valid and are consistent with window_strides.\"\"\"\n if len(lhs_shape) != len(rhs_shape):\n msg = \"Arguments to {} must have same rank, got {} and {}.\"\n raise TypeError(msg.format(name, len(lhs_shape), len(rhs_shape)))\n if len(lhs_shape) < 2:\n msg = \"Arguments to {} must have rank at least 2, got {} and {}.\"\n raise TypeError(msg.format(name, len(lhs_shape), len(rhs_shape)))\n if lhs_shape[1] != rhs_shape[1]:\n msg = \"Arguments to {} must agree on input feature size, got {} and {}.\"\n raise TypeError(msg.format(name, lhs_shape[1], rhs_shape[1]))\n _check_shapelike(name, \"window_strides\", window_strides)\n if not onp.all(onp.greater(window_strides, 0)):\n msg = \"All elements of window_strides must be positive, got {}.\"\n raise TypeError(msg.format(window_strides))\n if len(window_strides) != len(lhs_shape) - 2:\n msg = \"{} window_strides has wrong length: expected {}, got {}.\"\n expected_length = len(lhs_shape) - 2\n raise TypeError(msg.format(name, expected_length, len(window_strides)))","label":0},{"cid":"jax/lax/lax.py::conv_shape_tuple","text":"jax/lax/lax.py::conv_shape_tuple\ndef conv_shape_tuple(lhs_shape, rhs_shape, strides, pads):\n \"\"\"Compute the shape tuple of a conv given input shapes in canonical order.\"\"\"\n if isinstance(pads, str):\n pads = padtype_to_pads(lhs_shape[2:], rhs_shape[2:], strides, pads)\n if len(pads) != len(lhs_shape) - 2:\n msg = \"Wrong number of explicit pads for convolution: expected {}, got {}.\"\n raise TypeError(msg.format(len(lhs_shape) - 2, len(pads)))\n\n lhs_padded = onp.add(lhs_shape[2:], onp.add(*zip(*pads)))\n out_space = onp.floor_divide(\n onp.subtract(lhs_padded, rhs_shape[2:]), strides) + 1\n out_space = onp.maximum(0, out_space)\n out_shape = (lhs_shape[0], rhs_shape[0]) + tuple(out_space)\n return tuple(out_shape)","label":1},{"cid":"jax/lax/lax.py::conv_general_shape_tuple","text":"jax/lax/lax.py::conv_general_shape_tuple\ndef conv_general_shape_tuple(lhs_shape, rhs_shape, window_strides, padding,\n dimension_numbers):\n lhs_perm, rhs_perm, out_perm = conv_general_permutations(dimension_numbers)\n lhs_trans = onp.take(lhs_shape, lhs_perm)\n rhs_trans = onp.take(rhs_shape, rhs_perm)\n out_trans = conv_shape_tuple(lhs_trans, rhs_trans, window_strides, padding)\n return tuple(onp.take(out_trans, onp.argsort(out_perm)))","label":0},{"cid":"jax/lax/lax.py::conv_transpose_shape_tuple","text":"jax/lax/lax.py::conv_transpose_shape_tuple\ndef conv_transpose_shape_tuple(lhs_shape, rhs_shape, window_strides, padding,\n dimension_numbers):\n lhs_perm, rhs_perm, out_perm = conv_general_permutations(dimension_numbers)\n lhs_trans = onp.take(lhs_shape, lhs_perm)\n rhs_trans = onp.take(rhs_shape, rhs_perm)\n if isinstance(padding, str):\n padding = [_conv_transpose_padding(k, s, padding)\n for k,s in zip(rhs_trans[2:], window_strides)]\n padding = list(map(onp.sum, padding))\n unpad_out_space = [(i-1) * s - k + 2\n for i, k, s in zip(lhs_trans[2:],\n rhs_trans[2:],\n window_strides)]\n out_space = onp.sum([unpad_out_space, padding], axis=0).tolist()\n out_trans = tuple((lhs_trans[0], rhs_trans[0]) + tuple(out_space))\n return tuple(onp.take(out_trans, onp.argsort(out_perm)))","label":0},{"cid":"jax/lax/lax.py::_check_shapelike","text":"jax/lax/lax.py::_check_shapelike\ndef _check_shapelike(fun_name, arg_name, obj):\n \"\"\"Check that `obj` is a shape-like value (e.g. tuple of nonnegative ints).\"\"\"\n if (type(obj) is masking.ShapeExpr\n or type(obj) is tuple and any(type(d) is masking.Poly for d in obj)):\n return obj\n if not isinstance(obj, (tuple, list, onp.ndarray)):\n msg = \"{} {} must be of type tuple/list/ndarray, got {}.\"\n raise TypeError(msg.format(fun_name, arg_name, type(obj)))\n # bool(obj) for an ndarray raises an error, so we check len\n if not len(obj): # pylint: disable=g-explicit-length-test\n return\n obj_arr = onp.array(obj)\n if obj_arr.ndim != 1:\n msg = \"{} {} must be rank 1, got {}.\"\n raise TypeError(msg.format(obj_arr.ndim))\n if not dtypes.issubdtype(obj_arr.dtype, onp.integer):\n msg = \"{} {} must have every element be an integer type, got {}.\"\n raise TypeError(msg.format(fun_name, arg_name, tuple(map(type, obj))))\n if not (obj_arr >= 0).all():\n msg = \"{} {} must have every element be nonnegative, got {}.\"\n raise TypeError(msg.format(fun_name, arg_name, obj))","label":0},{"cid":"jax/lax/lax.py::_dynamic_slice_indices","text":"jax/lax/lax.py::_dynamic_slice_indices\ndef _dynamic_slice_indices(operand, start_indices):\n if not isinstance(start_indices, (tuple, list)):\n if start_indices.ndim != 1:\n raise ValueError(\"Slice indices must be a 1D sequence, got {}\"\n .format(start_indices.shape))\n start_indices = [reshape(slice(start_indices, [i], [i+1]), ())\n for i in range(operand.ndim)]\n else:\n start_indices = [onp.asarray(i, dtype=dtypes.int_) if isinstance(i, int)\n else i for i in start_indices]\n if len(start_indices) != operand.ndim:\n msg = (\"Length of slice indices must match number of operand dimensions ({} \"\n \"vs {})\")\n raise ValueError(msg.format(len(start_indices, operand.shape)))\n # map int over operand.shape to raise any dynamic-shape errors\n return [select(lt(i, _const(i, 0)), add(i, _const(i, int(d))), i)\n for i, d in zip(start_indices, operand.shape)]","label":0},{"cid":"jax/lax/lax.py::_const","text":"jax/lax/lax.py::_const\ndef _const(example, val):\n if dtypes.is_python_scalar(example):\n return dtypes.scalar_type_of(example)(val)\n return onp.array(val, _dtype(example))","label":0},{"cid":"jax/lax/lax.py::ranges_like","text":"jax/lax/lax.py::ranges_like\ndef ranges_like(*xs):\n start = 0\n for x in xs:\n x_len = len(x)\n yield range(start, start + x_len)\n start += x_len","label":0},{"cid":"jax/lax/lax.py::remaining","text":"jax/lax/lax.py::remaining\ndef remaining(original, *removed_lists):\n blacklist = set(itertools.chain(*removed_lists))\n return [i for i in original if i not in blacklist]","label":0},{"cid":"jax/lax/lax.py::_canonicalize_precision","text":"jax/lax/lax.py::_canonicalize_precision\ndef _canonicalize_precision(precision):\n if precision is None:\n return None\n if isinstance(precision, Precision):\n return precision\n else:\n msg = \"Precision argument must be None or a lax.Precision value; got {}\"\n raise ValueError(msg.format(precision))","label":0},{"cid":"jax/lax/lax.py::conv_dimension_numbers","text":"jax/lax/lax.py::conv_dimension_numbers\ndef conv_dimension_numbers(lhs_shape, rhs_shape, dimension_numbers):\n \"\"\"Converts convolution `dimension_numbers` to a `ConvDimensionNumbers`.\n\n Args:\n lhs_shape: tuple of nonnegative integers, shape of the convolution input.\n rhs_shape: tuple of nonnegative integers, shape of the convolution kernel.\n dimension_numbers: None or a tuple/list of strings, following the\n convolution dimension number specification format in xla_client.py.\n\n Returns:\n A `ConvDimensionNumbers` object that represents `dimension_numbers` in the\n canonical form used by lax functions.\n \"\"\"\n if len(lhs_shape) != len(rhs_shape):\n msg = \"convolution requires lhs and rhs ndim to be equal, got {} and {}.\"\n raise TypeError(msg.format(len(lhs_shape), len(rhs_shape)))\n\n if dimension_numbers is None:\n iota = tuple(range(len(lhs_shape)))\n return ConvDimensionNumbers(iota, iota, iota)\n elif isinstance(dimension_numbers, (list, tuple)):\n if len(dimension_numbers) != 3:\n msg = \"convolution dimension_numbers list/tuple must be length 3, got {}.\"\n raise TypeError(msg.format(len(dimension_numbers)))\n if not all(isinstance(elt, str) for elt in dimension_numbers):\n msg = \"convolution dimension_numbers elements must be strings, got {}.\"\n raise TypeError(msg.format(tuple(map(type, dimension_numbers))))\n msg = (\"convolution dimension_numbers[{}] must have len equal to the ndim \"\n \"of lhs and rhs, got {} for lhs and rhs shapes {} and {}.\")\n for i, elt in enumerate(dimension_numbers):\n if len(elt) != len(lhs_shape):\n raise TypeError(msg.format(i, len(elt), lhs_shape, rhs_shape))\n\n lhs_spec, rhs_spec, out_spec = conv_general_permutations(dimension_numbers)\n return ConvDimensionNumbers(lhs_spec, rhs_spec, out_spec)\n else:\n msg = \"convolution dimension_numbers must be tuple/list or None, got {}.\"\n raise TypeError(msg.format(type(dimension_numbers)))","label":0},{"cid":"jax/lax/lax.py::conv_general_permutations","text":"jax/lax/lax.py::conv_general_permutations\ndef conv_general_permutations(dimension_numbers):\n \"\"\"Utility for convolution dimension permutations relative to Conv HLO.\"\"\"\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n lhs_char, rhs_char, out_char = charpairs = (\"N\", \"C\"), (\"O\", \"I\"), (\"N\", \"C\")\n for i, (a, b) in enumerate(charpairs):\n if not dimension_numbers[i].count(a) == dimension_numbers[i].count(b) == 1:\n msg = (\"convolution dimension_numbers[{}] must contain the characters \"\n \"'{}' and '{}' exatly once, got {}.\")\n raise TypeError(msg.format(i, a, b, dimension_numbers[i]))\n if len(dimension_numbers[i]) != len(set(dimension_numbers[i])):\n msg = (\"convolution dimension_numbers[{}] cannot have duplicate \"\n \"characters, got {}.\")\n raise TypeError(msg.format(i, dimension_numbers[i]))\n if not (set(lhs_spec) - set(lhs_char) == set(rhs_spec) - set(rhs_char) ==\n set(out_spec) - set(out_char)):\n msg = (\"convolution dimension_numbers elements must each have the same \"\n \"set of spatial characters, got {}.\")\n raise TypeError(msg.format(dimension_numbers))\n\n def getperm(spec, charpair):\n spatial = (i for i, c in enumerate(spec) if c not in charpair)\n if spec is not rhs_spec:\n spatial = sorted(spatial, key=lambda i: rhs_spec.index(spec[i]))\n return (spec.index(charpair[0]), spec.index(charpair[1])) + tuple(spatial)\n\n lhs_perm, rhs_perm, out_perm = map(getperm, dimension_numbers, charpairs)\n return lhs_perm, rhs_perm, out_perm","label":0},{"cid":"jax/lax/lax.py::conv_general_permutations.getperm","text":"jax/lax/lax.py::conv_general_permutations.getperm\n def getperm(spec, charpair):\n spatial = (i for i, c in enumerate(spec) if c not in charpair)\n if spec is not rhs_spec:\n spatial = sorted(spatial, key=lambda i: rhs_spec.index(spec[i]))\n return (spec.index(charpair[0]), spec.index(charpair[1])) + tuple(spatial)","label":0},{"cid":"jax/lax/lax.py::_conv_general_proto","text":"jax/lax/lax.py::_conv_general_proto\ndef _conv_general_proto(dimension_numbers):\n assert type(dimension_numbers) is ConvDimensionNumbers\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n proto = xla_client.ConvolutionDimensionNumbers()\n proto.input_batch_dimension = lhs_spec[0]\n proto.input_feature_dimension = lhs_spec[1]\n proto.output_batch_dimension = out_spec[0]\n proto.output_feature_dimension = out_spec[1]\n proto.kernel_output_feature_dimension = rhs_spec[0]\n proto.kernel_input_feature_dimension = rhs_spec[1]\n proto.input_spatial_dimensions.extend(lhs_spec[2:])\n proto.kernel_spatial_dimensions.extend(rhs_spec[2:])\n proto.output_spatial_dimensions.extend(out_spec[2:])\n return proto","label":0},{"cid":"jax/lax/lax.py::_conv_general_vjp_lhs_padding","text":"jax/lax/lax.py::_conv_general_vjp_lhs_padding\ndef _conv_general_vjp_lhs_padding(\n in_shape, window_dimensions, window_strides, out_shape, padding,\n lhs_dilation, rhs_dilation):\n lhs_dilated_shape = _dilate_shape(in_shape, lhs_dilation)\n rhs_dilated_shape = _dilate_shape(window_dimensions, rhs_dilation)\n out_dilated_shape = _dilate_shape(out_shape, window_strides)\n pad_before = onp.subtract(rhs_dilated_shape, [lo for lo, _ in padding]) - 1\n pad_after = (onp.add(lhs_dilated_shape, rhs_dilated_shape) - 1\n - out_dilated_shape - pad_before)\n return zip(pad_before, pad_after)","label":0},{"cid":"jax/lax/lax.py::_conv_general_vjp_rhs_padding","text":"jax/lax/lax.py::_conv_general_vjp_rhs_padding\ndef _conv_general_vjp_rhs_padding(\n in_shape, window_dimensions, window_strides, out_shape, padding,\n lhs_dilation, rhs_dilation):\n lhs_dilated_shape = _dilate_shape(in_shape, lhs_dilation)\n rhs_dilated_shape = _dilate_shape(window_dimensions, rhs_dilation)\n out_dilated_shape = _dilate_shape(out_shape, window_strides)\n total_in_pad = out_dilated_shape + rhs_dilated_shape - lhs_dilated_shape - 1\n return [(pad[0], tot - pad[0]) for pad, tot in zip(padding, total_in_pad)]","label":0},{"cid":"jax/lax/lax.py::_balanced_eq","text":"jax/lax/lax.py::_balanced_eq\ndef _balanced_eq(x, z, y):\n return div(select(_eq_meet(x, z), _ones(z), _zeros(z)),\n select(_eq_meet(y, z), _twos(z), _ones(z)))","label":0},{"cid":"jax/lax/lax.py::_eq_meet","text":"jax/lax/lax.py::_eq_meet\ndef _eq_meet(a, b):\n a_dtype, b_dtype = _dtype(a), _dtype(b)\n if a_dtype != b_dtype:\n higher_dtype = dtypes.promote_types(a_dtype, b_dtype)\n if higher_dtype == a_dtype:\n a = convert_element_type(a, b_dtype)\n else:\n b = convert_element_type(b, a_dtype)\n return eq(a, b)","label":0},{"cid":"jax/lax/lax.py::_abstractify","text":"jax/lax/lax.py::_abstractify\ndef _abstractify(x):\n return raise_to_shaped(core.get_aval(x))","label":0},{"cid":"jax/lax/lax.py::_check_user_dtype_supported","text":"jax/lax/lax.py::_check_user_dtype_supported\ndef _check_user_dtype_supported(dtype, fun_name=None):\n if dtype is not None and onp.dtype(dtype) != dtypes.canonicalize_dtype(dtype):\n msg = (\"Explicitly requested dtype {} {} is not available, \"\n \"and will be truncated to dtype {}. To enable more dtypes, set the \"\n \"jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell \"\n \"environment variable. \"\n \"See https://github.com/google/jax#current-gotchas for more.\")\n fun_name = \"requested in {}\".format(fun_name) if fun_name else \"\"\n truncated_dtype = dtypes.canonicalize_dtype(dtype).name\n warnings.warn(msg.format(dtype, fun_name , truncated_dtype))","label":0},{"cid":"jax/lax/lax_parallel.py::psum","text":"jax/lax/lax_parallel.py::psum\ndef psum(x, axis_name):\n \"\"\"Compute an all-reduce sum on ``x`` over the pmapped axis ``axis_name``.\n\n Args:\n x: array with a mapped axis named ``axis_name``.\n axis_name: hashable Python object used to name a pmapped axis (see the\n ``pmap`` docstring for more details).\n\n Returns:\n An array with the same shape as ``x`` representing the result of an\n all-reduce sum along the axis ``axis_name``.\n\n For example, with 4 XLA devices available:\n\n >>> x = np.arange(4)\n >>> y = jax.pmap(lambda x: jax.lax.psum(x, 'i'), axis_name='i')(x)\n >>> print(y)\n [6 6 6 6]\n >>> y = jax.pmap(lambda x: x / jax.lax.psum(x, 'i'), axis_name='i')(x)\n >>> print(y)\n [ 0. 0.16666667 0.33333334 0.5 ]\n \"\"\"\n return psum_p.bind(x, axis_name=axis_name)","label":0},{"cid":"jax/lax/lax_parallel.py::pmax","text":"jax/lax/lax_parallel.py::pmax\ndef pmax(x, axis_name):\n \"\"\"Compute an all-reduce max on ``x`` over the pmapped axis ``axis_name``.\n\n Args:\n x: array with a mapped axis named ``axis_name``.\n axis_name: hashable Python object used to name a pmapped axis (see the\n ``pmap`` docstring for more details).\n\n Returns:\n An array with the same shape as ``x`` representing the result of an\n all-reduce max along the axis ``axis_name``.\n \"\"\"\n return pmax_p.bind(x, axis_name=axis_name)","label":0},{"cid":"jax/lax/lax_parallel.py::pmin","text":"jax/lax/lax_parallel.py::pmin\ndef pmin(x, axis_name):\n \"\"\"Compute an all-reduce min on ``x`` over the pmapped axis ``axis_name``.\n\n Args:\n x: array with a mapped axis named ``axis_name``.\n axis_name: hashable Python object used to name a pmapped axis (see the\n ``pmap`` docstring for more details).\n\n Returns:\n An array with the same shape as ``x`` representing the result of an\n all-reduce min along the axis ``axis_name``.\n \"\"\"\n return pmin_p.bind(x, axis_name=axis_name)","label":0},{"cid":"jax/lax/lax_parallel.py::ppermute","text":"jax/lax/lax_parallel.py::ppermute\ndef ppermute(x, axis_name, perm):\n \"\"\"Perform a collective permutation according to the permutation ``perm``.\n\n This function is an analog of the CollectivePermute XLA HLO.\n\n Args:\n x: array with a mapped axis named ``axis_name``.\n axis_name: hashable Python object used to name a pmapped axis (see the\n ``pmap`` docstring for more details).\n perm: list of pairs of ints, representing (source_index, destination_index)\n pairs that encode how the mapped axis named ``axis_name`` should be\n shuffled. The integer values are treated as indices into the mapped axis\n ``axis_name``. Any two pairs should not have the same source index or the\n same destination index. For each index of the axis ``axis_name`` that does\n not correspond to a destination index in ``perm``, the corresponding\n values in the result are filled with zeros of the appropriate type.\n\n Returns:\n An array with the same shape as ``x`` with slices along the axis\n ``axis_name`` gathered from ``x`` according to the permutation ``perm``.\n \"\"\"\n return ppermute_p.bind(x, axis_name=axis_name, perm=tuple(perm))","label":0},{"cid":"jax/lax/lax_parallel.py::pswapaxes","text":"jax/lax/lax_parallel.py::pswapaxes\ndef pswapaxes(x, axis_name, axis):\n \"\"\"Swap the pmapped axis ``axis_name`` with the unmapped axis ``axis``.\n\n The mapped axis size must be equal to the size of the unmapped axis; that is,\n we must have ``lax.psum(1, axis_name) == x.shape[axis]``.\n\n This function is a special case of ``all_to_all`` where the pmapped axis of\n the input is placed at the position ``axis`` in the output. That is, it is\n equivalent to ``all_to_all(x, axis_name, axis, axis)``.\n\n Args:\n x: array with a mapped axis named ``axis_name``.\n axis_name: hashable Python object used to name a pmapped axis (see the\n ``pmap`` docstring for more details).\n axis: int indicating the unmapped axis of ``x`` to map with the name\n ``axis_name``.\n\n Returns:\n An array with shape ``np.insert(np.delete(x.shape, axis), axis, axis_size)``\n where ``axis_size`` is the size of the mapped axis named ``axis_name`` in\n the input ``x``.\n \"\"\"\n return all_to_all(x, axis_name, axis, axis)","label":0},{"cid":"jax/lax/lax_parallel.py::all_to_all","text":"jax/lax/lax_parallel.py::all_to_all\ndef all_to_all(x, axis_name, split_axis, concat_axis):\n \"\"\"Materialize the mapped axis and map a different axis.\n\n In the output, the input mapped axis ``axis_name`` is materialized at the\n logical axis position ``concat_axis``, and the input unmapped axis at position\n ``split_axis`` is mapped with the name ``axis_name``.\n\n The input mapped axis size must be equal to the size of the axis to be mapped;\n that is, we must have ``lax.psum(1, axis_name) == x.shape[split_axis]``.\n\n Args:\n x: array with a mapped axis named ``axis_name``.\n axis_name: hashable Python object used to name a pmapped axis (see the\n ``pmap`` docstring for more details).\n split_axis: int indicating the unmapped axis of ``x`` to map with the name\n ``axis_name``.\n concat_axis: int indicating the position in the output to materialize the\n mapped axis of the input with the name ``axis_name``.\n\n Returns:\n An array with shape given by the expression::\n np.insert(np.delete(x.shape, split_axis), concat_axis, axis_size)\n\n where ``axis_size`` is the size of the mapped axis named ``axis_name`` in\n the input ``x``, i.e. ``axis_size = lax.psum(1, axis_name)``.\n \"\"\"\n if psum(1, axis_name) != x.shape[split_axis]:\n msg = (\"all_to_all requires the size of the mapped axis axis_name to equal \"\n \"x.shape[split_axis], but they are {} and {} respectively.\")\n raise ValueError(msg.format(psum(1, axis_name), x.shape[split_axis]))\n return all_to_all_p.bind(x, split_axis=split_axis, concat_axis=concat_axis,\n axis_name=axis_name)","label":0},{"cid":"jax/lax/lax_parallel.py::standard_pmap_primitive","text":"jax/lax/lax_parallel.py::standard_pmap_primitive\ndef standard_pmap_primitive(name):\n prim = core.Primitive(name)\n prim.def_impl(partial(pxla.apply_parallel_primitive, prim))\n prim.def_abstract_eval(lambda x, *args, **params: x)\n return prim","label":0},{"cid":"jax/lax/lax_parallel.py::_allreduce_split_axis_rule","text":"jax/lax/lax_parallel.py::_allreduce_split_axis_rule\ndef _allreduce_split_axis_rule(prim, reducer, vals, which_mapped, axis_name):\n assert tuple(which_mapped) == (True,)\n x, = vals\n return prim.bind(reducer(x, [0]), axis_name=axis_name), False","label":0},{"cid":"jax/lax/lax_parallel.py::_allreduce_translation_rule","text":"jax/lax/lax_parallel.py::_allreduce_translation_rule\ndef _allreduce_translation_rule(prim, c, val, replica_groups):\n dtype = c.GetShape(val).numpy_dtype()\n scalar = ShapedArray((), dtype)\n computation = xla.primitive_subcomputation(prim, scalar, scalar)\n return c.AllReduce(val, computation, replica_groups=replica_groups)","label":0},{"cid":"jax/lax/lax_parallel.py::_psum_translation_rule","text":"jax/lax/lax_parallel.py::_psum_translation_rule\ndef _psum_translation_rule(c, val, replica_groups):\n psum = partial(_allreduce_translation_rule, lax.add_p, c,\n replica_groups=replica_groups)\n dtype = c.GetShape(val).numpy_dtype()\n if dtypes.issubdtype(dtype, onp.complexfloating):\n return c.Complex(psum(c.Real(val)), psum(c.Imag(val)))\n else:\n return psum(val)","label":0},{"cid":"jax/lax/lax_parallel.py::_ppermute_translation_rule","text":"jax/lax/lax_parallel.py::_ppermute_translation_rule\ndef _ppermute_translation_rule(c, x, replica_groups, perm):\n group_size = len(replica_groups[0])\n srcs, dsts = unzip2((src % group_size, dst % group_size) for src, dst in perm)\n if not (len(srcs) == len(set(srcs)) and len(dsts) == len(set(dsts))):\n msg = \"ppermute sources and destinations must be unique, got {}.\"\n raise ValueError(msg.format(perm))\n\n full_perm = []\n for grp in replica_groups:\n grp = list(sorted(grp))\n full_perm.extend((grp[src], grp[dst]) for src, dst in perm)\n return c.CollectivePermute(x, full_perm)","label":0},{"cid":"jax/lax/lax_parallel.py::_ppermute_transpose_rule","text":"jax/lax/lax_parallel.py::_ppermute_transpose_rule\ndef _ppermute_transpose_rule(t, perm, axis_name):\n srcs, dsts = unzip2(perm)\n inverse_perm = list(zip(dsts, srcs))\n return [ppermute(t, axis_name=axis_name, perm=inverse_perm)]","label":0},{"cid":"jax/lax/lax_parallel.py::_all_to_all_translation_rule","text":"jax/lax/lax_parallel.py::_all_to_all_translation_rule\ndef _all_to_all_translation_rule(c, x, split_axis, concat_axis, replica_groups):\n return c.AllToAll(x, split_axis, concat_axis, replica_groups)","label":0},{"cid":"jax/lax/lax_parallel.py::_all_to_all_split_axis_rule","text":"jax/lax/lax_parallel.py::_all_to_all_split_axis_rule\ndef _all_to_all_split_axis_rule(vals, which_mapped, split_axis, concat_axis,\n axis_name):\n assert tuple(which_mapped) == (True,)\n x, = vals\n # perform the communication to swap the hardware-mapped axes\n stacked = all_to_all_p.bind(x, split_axis=split_axis + 1, concat_axis=0,\n axis_name=axis_name)\n # transpose the newly mapped axis to the front, newly unmapped to concat_axis\n out = _moveaxis(split_axis + 1, 0, stacked)\n out = _moveaxis(1, concat_axis + 1, out)\n return out, True","label":0},{"cid":"jax/lax/lax_parallel.py::_moveaxis","text":"jax/lax/lax_parallel.py::_moveaxis\ndef _moveaxis(src, dst, x):\n perm = [i for i in range(x.ndim) if i != src]\n perm.insert(dst, src)\n return lax.transpose(x, perm)","label":0},{"cid":"jax/lax/lax_parallel.py::_drop","text":"jax/lax/lax_parallel.py::_drop\ndef _drop(x, dim, axis_name):\n return lax.dynamic_index_in_dim(x, axis_index(axis_name), dim, False)","label":0},{"cid":"jax/lax/lax_parallel.py::_allgather","text":"jax/lax/lax_parallel.py::_allgather\ndef _allgather(x, dim, size, axis_name):\n shape = list(x.shape)\n shape.insert(dim, size)\n out = lax.full(shape, lax._const(x, 0))\n out = lax.dynamic_update_index_in_dim(out, x, axis_index(axis_name), dim)\n return psum(out, axis_name)","label":0},{"cid":"jax/lax/lax_parallel.py::_broadcasting_papply","text":"jax/lax/lax_parallel.py::_broadcasting_papply\ndef _broadcasting_papply(prim, name, size, vals, axes, **params):\n x, y = vals\n xdim, ydim = axes\n\n if xdim is None:\n if x.shape:\n if x.shape[ydim] == 1:\n x = x.reshape(onp.delete(x.shape, ydim))\n else:\n x = _drop(x, ydim, name)\n return prim.bind(x, y, **params), ydim\n elif ydim is None:\n if y.shape:\n if y.shape[xdim] == 1:\n y = y.reshape(onp.delete(y.shape, xdim))\n else:\n y = _drop(y, xdim, name)\n return prim.bind(x, y, **params), xdim\n elif xdim == ydim:\n return prim.bind(x, y, **params), xdim\n else:\n x_tosplit = ydim - int(xdim <= ydim)\n y_tosplit = xdim - int(ydim <= xdim)\n if y.shape[y_tosplit] == 1:\n y = _allgather(y, ydim, size, name)\n y = y.reshape(onp.delete(y.shape, xdim))\n return prim.bind(x, y, **params), ydim\n elif x.shape[x_tosplit] == 1:\n x = _allgather(x, xdim, size, name)\n x = x.reshape(onp.delete(x.shape, ydim))\n return prim.bind(x, y, **params), ydim\n else:\n x = all_to_all(x, name, x_tosplit, xdim)\n return prim.bind(x, y, **params), ydim","label":0},{"cid":"jax/lax/lax_parallel.py::_defbroadcasting","text":"jax/lax/lax_parallel.py::_defbroadcasting\ndef _defbroadcasting(prim):\n parallel.papply_primitive_rules[prim] = partial(_broadcasting_papply, prim)","label":0},{"cid":"jax/lax/lax_parallel.py::_vectorized_papply","text":"jax/lax/lax_parallel.py::_vectorized_papply\ndef _vectorized_papply(prim, name, size, vals, axes, **params):\n assert all(axes[0] == a for a in axes[1:])\n return prim.bind(*vals, **params), axes[0]","label":0},{"cid":"jax/lax/lax_parallel.py::_defvectorized","text":"jax/lax/lax_parallel.py::_defvectorized\ndef _defvectorized(prim):\n parallel.papply_primitive_rules[prim] = partial(_vectorized_papply, prim)","label":0},{"cid":"jax/lax/lax_parallel.py::_reducer_papply","text":"jax/lax/lax_parallel.py::_reducer_papply\ndef _reducer_papply(prim, cprim, name, size, vals, papply_axes, axes, **kwargs):\n operand, = vals\n papply_axis, = papply_axes\n\n other_axes = [i for i in axes if i != papply_axis]\n other_axes = [i - 1 if i > papply_axis else i for i in other_axes]\n\n if other_axes:\n if 'input_shape' in kwargs: # special to the reduce-sum family\n s = kwargs['input_shape']\n kwargs['input_shape'] = s[:papply_axis] + s[papply_axis + 1:]\n result = prim.bind(operand, axes=tuple(other_axes), **kwargs)\n else:\n result = operand\n\n if not axes or papply_axis in axes:\n return cprim.bind(result, axis_name=name), None\n else:\n new_papply_axis = papply_axis - onp.sum(onp.less(other_axes, papply_axis))\n return result, new_papply_axis","label":0},{"cid":"jax/lax/lax_parallel.py::_defreducer","text":"jax/lax/lax_parallel.py::_defreducer\ndef _defreducer(prim, collective_prim):\n parallel.papply_primitive_rules[prim] = partial(_reducer_papply, prim, collective_prim)","label":0},{"cid":"jax/lax/lax_parallel.py::_identity_papply","text":"jax/lax/lax_parallel.py::_identity_papply\ndef _identity_papply(prim, argnum, name, size, vals, axes, **params):\n return prim.bind(*vals, **params), axes[argnum]","label":0},{"cid":"jax/lax/lax_parallel.py::_defidentity","text":"jax/lax/lax_parallel.py::_defidentity\ndef _defidentity(prim, argnum=0):\n parallel.papply_primitive_rules[prim] = partial(_identity_papply, prim, argnum)","label":0},{"cid":"jax/lax/lax_parallel.py::_dot_general_papply_rule","text":"jax/lax/lax_parallel.py::_dot_general_papply_rule\ndef _dot_general_papply_rule(name, size, vals, dims, dimension_numbers,\n precision):\n x, y = vals\n xdim, ydim = dims\n\n (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers\n\n if lhs_batch or rhs_batch:\n raise NotImplementedError(\n ('papply of dot_general with batch dimensions: '\n 'xdim={}, ydim={}, dimension_numbers={}').format(\n xdim, ydim, dimension_numbers))\n\n def adjust_dims(dims, thresh):\n return tuple(i - 1 if i > thresh else i for i in dims if i != thresh)\n\n def sub_dims(xdim, ydim, xcontract, ycontract, xbatch, ybatch):\n if xdim is not None:\n xbatch = adjust_dims(xbatch, xdim)\n xcontract = adjust_dims(xcontract, xdim)\n if ydim is not None:\n ybatch = adjust_dims(ybatch, ydim)\n ycontract = adjust_dims(ycontract, ydim)\n return ((xcontract, ycontract), (xbatch, ybatch))\n\n def cases(x, y, xdim, ydim, xc, yc, xb, yb):\n # Consider three states in which an operand may be\n # 1: split, contracting\n # 2: split, not contracting\n # 3: not split\n #\n # We will handle the following cases, marked by corresponding letter\n # symbols:\n #\n # |1 2 3|y\n # -+-----+-\n # 1|a b c\n # 2|d e f\n # 3|g h i\n # -+\n # x|\n #\n # Case i is already covered and we can assume that it is excluded at the\n # outset, since a papply rule is not invoked when no operands are split.\n\n if xdim in xc:\n # cases a, b, c\n if ydim in yc:\n # case a: both operands are split and contracting\n # TODO(frostig): Might the following work?\n # z = lax.dot_general(\n # x, y, sub_dims(xdim, ydim, xc, yc, xb, yb), precision)\n # return True, (psum(z, name), None)\n return False, 'both operands split and contracting'\n elif ydim is not None:\n # case b: x split and contracting, y split but not contracting\n # TODO(frostig): Might th","label":0},{"cid":"jax/lax/lax_parallel.py::_dot_general_papply_rule.adjust_dims","text":"jax/lax/lax_parallel.py::_dot_general_papply_rule.adjust_dims\n def adjust_dims(dims, thresh):\n return tuple(i - 1 if i > thresh else i for i in dims if i != thresh)","label":0},{"cid":"jax/lax/lax_parallel.py::_dot_general_papply_rule.sub_dims","text":"jax/lax/lax_parallel.py::_dot_general_papply_rule.sub_dims\n def sub_dims(xdim, ydim, xcontract, ycontract, xbatch, ybatch):\n if xdim is not None:\n xbatch = adjust_dims(xbatch, xdim)\n xcontract = adjust_dims(xcontract, xdim)\n if ydim is not None:\n ybatch = adjust_dims(ybatch, ydim)\n ycontract = adjust_dims(ycontract, ydim)\n return ((xcontract, ycontract), (xbatch, ybatch))","label":0},{"cid":"jax/lax/lax_parallel.py::_dot_general_papply_rule.cases","text":"jax/lax/lax_parallel.py::_dot_general_papply_rule.cases\n def cases(x, y, xdim, ydim, xc, yc, xb, yb):\n # Consider three states in which an operand may be\n # 1: split, contracting\n # 2: split, not contracting\n # 3: not split\n #\n # We will handle the following cases, marked by corresponding letter\n # symbols:\n #\n # |1 2 3|y\n # -+-----+-\n # 1|a b c\n # 2|d e f\n # 3|g h i\n # -+\n # x|\n #\n # Case i is already covered and we can assume that it is excluded at the\n # outset, since a papply rule is not invoked when no operands are split.\n\n if xdim in xc:\n # cases a, b, c\n if ydim in yc:\n # case a: both operands are split and contracting\n # TODO(frostig): Might the following work?\n # z = lax.dot_general(\n # x, y, sub_dims(xdim, ydim, xc, yc, xb, yb), precision)\n # return True, (psum(z, name), None)\n return False, 'both operands split and contracting'\n elif ydim is not None:\n # case b: x split and contracting, y split but not contracting\n # TODO(frostig): Might the following work?\n # new_ydim = yc[xc.index(xdim)]\n # y = all_to_all(y, name, new_ydim, ydim)\n # z = lax.dot_general(\n # x, y, sub_dims(xdim, new_ydim, xc, yc, xb, yb), precision)\n # return True, (psum(z, name), None)\n return False, 'rhs split but not contracting, lhs split and contracting'\n else:\n # case c: x split and contracting, y not split\n assert ydim is None\n return False, 'one operand split and contracting, other is not split'\n elif xdim is not None:\n # cases d, e, f\n if ydim in yc:\n # case d: x split but not contracting, y split and contracting\n # TODO(frostig): Might the following work?\n # new_xdim = xc[yc.index(ydim)]\n # x = all_to_all(x, name, new_xdim, xdim)\n # z = lax.dot_general(\n # x, y, sub_dims(new_xdim, ydim, xc, yc, xb, yb), precision","label":0},{"cid":"jax/lax/lax_parallel.py::_reshape_papply_rule","text":"jax/lax/lax_parallel.py::_reshape_papply_rule\ndef _reshape_papply_rule(name, size, vals, axes, new_sizes, dimensions,\n old_sizes):\n operand, = vals\n axis, = axes\n\n def filter_ones(xs):\n return filter(lambda x: x != 1, xs)\n\n def find_new_axis(old_axis, old_sizes, new_sizes):\n left = onp.prod(old_sizes[:old_axis])\n size = old_sizes[old_axis]\n prod = 1\n for i, cur_sz in enumerate(new_sizes):\n if prod == left and cur_sz == size:\n return i\n prod = prod * cur_sz\n return None\n\n if dimensions is None:\n new_axis = find_new_axis(axis, old_sizes, new_sizes)\n if new_axis is not None:\n new_sizes_ = new_sizes[:new_axis] + new_sizes[new_axis + 1:]\n return lax.reshape(operand, new_sizes_, dimensions=dimensions), new_axis\n else:\n raise NotImplementedError(\n 'papply of reshape that would change hidden dimension size')\n else:\n raise NotImplementedError('papply of reshape with `dimensions`')","label":0},{"cid":"jax/lax/lax_parallel.py::_reshape_papply_rule.filter_ones","text":"jax/lax/lax_parallel.py::_reshape_papply_rule.filter_ones\n def filter_ones(xs):\n return filter(lambda x: x != 1, xs)","label":0},{"cid":"jax/lax/lax_parallel.py::_reshape_papply_rule.find_new_axis","text":"jax/lax/lax_parallel.py::_reshape_papply_rule.find_new_axis\n def find_new_axis(old_axis, old_sizes, new_sizes):\n left = onp.prod(old_sizes[:old_axis])\n size = old_sizes[old_axis]\n prod = 1\n for i, cur_sz in enumerate(new_sizes):\n if prod == left and cur_sz == size:\n return i\n prod = prod * cur_sz\n return None","label":0},{"cid":"jax/lax/lax_parallel.py::_transpose_papply_rule","text":"jax/lax/lax_parallel.py::_transpose_papply_rule\ndef _transpose_papply_rule(name, size, vals, dims, permutation):\n x, = vals\n xdim, = dims\n local_perm = [i if i < xdim else i - 1 for i in permutation if i != xdim]\n return lax.transpose(x, local_perm), permutation.index(xdim)","label":0},{"cid":"jax/lax/lax_parallel.py::_select_papply_rule","text":"jax/lax/lax_parallel.py::_select_papply_rule\ndef _select_papply_rule(name, size, vals, dims):\n dimset = {d for d in dims if d is not None}\n if len(dimset) != 1:\n raise NotImplementedError(\n 'papply of select with operands split along different dimensions')\n dim, = dimset\n def drop(x, d):\n return _drop(x, dim, name) if d is None else x\n return lax.select_p.bind(*map(drop, vals, dims)), dim","label":0},{"cid":"jax/lax/lax_parallel.py::_select_papply_rule.drop","text":"jax/lax/lax_parallel.py::_select_papply_rule.drop\n def drop(x, d):\n return _drop(x, dim, name) if d is None else x","label":0},{"cid":"jax/lax/lax_parallel.py::_add_jaxvals_papply_rule","text":"jax/lax/lax_parallel.py::_add_jaxvals_papply_rule\ndef _add_jaxvals_papply_rule(name, size, vals, dims):\n x, y = vals\n xdim, ydim = dims\n if xdim == ydim:\n out_dim = xdim\n elif ydim is None:\n y = lax.psplit_like(y, x, name)\n out_dim = xdim\n else:\n x = lax.psplit_like(x, y, name)\n out_dim = ydim\n return ad_util.add_jaxvals_p.bind(x, y), out_dim","label":0},{"cid":"jax/lax/lax_parallel.py::_convert_element_type_papply_rule","text":"jax/lax/lax_parallel.py::_convert_element_type_papply_rule\ndef _convert_element_type_papply_rule(\n name, size, vals, dims, new_dtype, **params):\n operand, = vals\n dim, = dims\n return lax.convert_element_type(operand, new_dtype), dim","label":0},{"cid":"jax/lax/lax_parallel.py::_conv_general_dilated_papply_rule","text":"jax/lax/lax_parallel.py::_conv_general_dilated_papply_rule\ndef _conv_general_dilated_papply_rule(\n name, size, vals, dims, window_strides, padding, lhs_dilation, rhs_dilation,\n dimension_numbers, feature_group_count, precision, **unused_kwargs):\n lhs, rhs = vals\n lhs_dim, rhs_dim = dims\n lhs_spec_batch_dim = dimension_numbers.lhs_spec[0]\n if rhs_dim is None and lhs_dim == lhs_spec_batch_dim:\n lhs = lax.reshape(lhs, tuple(onp.insert(lhs.shape, lhs_dim, 1)))\n out = lax.conv_general_dilated(\n lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\n dimension_numbers, feature_group_count, precision)\n return out, lhs_dim\n else:\n raise NotImplementedError(\n \"splitting a convolution along anything but input batch dimension\")","label":0},{"cid":"jax/lax/lax_parallel.py::_broadcast_in_dim_papply_rule","text":"jax/lax/lax_parallel.py::_broadcast_in_dim_papply_rule\ndef _broadcast_in_dim_papply_rule(name, size, vals, dims, shape,\n broadcast_dimensions):\n operand, = vals\n dim, = dims\n out_dim = broadcast_dimensions[dim]\n if shape[out_dim] != shape[dim]:\n raise ValueError(\n \"broadcast_in_dim changes hidden dimension size: {} to {}\".format(\n shape[dim], shape[out_dim]))\n sub_bdims = tuple(onp.delete(broadcast_dimensions, dim))\n sub_shape = tuple(onp.delete(shape, out_dim))\n return lax.broadcast_in_dim(operand, sub_shape, sub_bdims), out_dim","label":0},{"cid":"jax/lax/lax_parallel.py::_pad_papply_rule","text":"jax/lax/lax_parallel.py::_pad_papply_rule\ndef _pad_papply_rule(name, size, vals, dims, padding_config):\n operand, padding_value = vals\n operand_dim, padding_value_dim = dims\n assert padding_value_dim is None\n padding_config = list(padding_config)\n if padding_config[operand_dim] == (0, 0, 0):\n padded = lax.pad(\n operand,\n padding_value,\n padding_config[:operand_dim] + padding_config[operand_dim + 1:])\n return padded, operand_dim\n else:\n raise NotImplementedError(\n 'pad changes size of hidden dimension {} with config {}'.format(\n operand_dim, padding_config))","label":0},{"cid":"jax/lax_reference.py::conj","text":"jax/lax_reference.py::conj\ndef conj(x):\n return onp.conj(x) + onp.complex64(0)","label":0},{"cid":"jax/lax_reference.py::complex","text":"jax/lax_reference.py::complex\ndef complex(x, y):\n return x + onp.complex64(1j) * y","label":0},{"cid":"jax/lax_reference.py::div","text":"jax/lax_reference.py::div\ndef div(lhs, rhs):\n if dtypes.issubdtype(dtypes.result_type(lhs), onp.integer):\n quotient = onp.floor_divide(lhs, rhs)\n select = onp.logical_and(onp.sign(lhs) != onp.sign(rhs),\n onp.remainder(lhs, rhs) != 0)\n return onp.where(select, quotient + 1, quotient)\n else:\n return onp.divide(lhs, rhs)","label":0},{"cid":"jax/lax_reference.py::rem","text":"jax/lax_reference.py::rem\ndef rem(lhs, rhs):\n return onp.sign(lhs) * onp.remainder(onp.abs(lhs), onp.abs(rhs))","label":0},{"cid":"jax/lax_reference.py::convert_element_type","text":"jax/lax_reference.py::convert_element_type\ndef convert_element_type(operand, dtype):\n return onp.asarray(operand, dtype=dtype)","label":0},{"cid":"jax/lax_reference.py::bitcast_convert_type","text":"jax/lax_reference.py::bitcast_convert_type\ndef bitcast_convert_type(operand, dtype):\n return onp.asarray(operand).view(dtype)","label":0},{"cid":"jax/lax_reference.py::clamp","text":"jax/lax_reference.py::clamp\ndef clamp(min, operand, max):\n return onp.clip(operand, onp.clip(min, None, max), max)","label":0},{"cid":"jax/lax_reference.py::concatenate","text":"jax/lax_reference.py::concatenate\ndef concatenate(operands, dimension):\n return onp.concatenate(operands, axis=dimension)","label":0},{"cid":"jax/lax_reference.py::conv","text":"jax/lax_reference.py::conv\ndef conv(lhs, rhs, window_strides, padding):\n pads = padtype_to_pads(lhs.shape[2:], rhs.shape[2:], window_strides, padding)\n return _conv(lhs, rhs, window_strides, pads)","label":0},{"cid":"jax/lax_reference.py::conv_with_general_padding","text":"jax/lax_reference.py::conv_with_general_padding\ndef conv_with_general_padding(\n lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation):\n return _conv(_dilate(lhs, lhs_dilation), _dilate(rhs, rhs_dilation),\n window_strides, padding)","label":0},{"cid":"jax/lax_reference.py::conv_general_dilated","text":"jax/lax_reference.py::conv_general_dilated\ndef conv_general_dilated(lhs, rhs, window_strides, padding, lhs_dilation,\n rhs_dilation, dimension_numbers):\n lhs_perm, rhs_perm, out_perm = _conv_general_permutations(dimension_numbers)\n if isinstance(padding, str):\n padding = padtype_to_pads(onp.take(lhs.shape, lhs_perm)[2:],\n onp.take(rhs.shape, rhs_perm)[2:],\n window_strides, padding)\n trans_lhs = transpose(lhs, lhs_perm)\n trans_rhs = transpose(rhs, rhs_perm)\n out = conv_with_general_padding(trans_lhs, trans_rhs, window_strides, padding,\n lhs_dilation, rhs_dilation)\n return transpose(out, onp.argsort(out_perm))","label":0},{"cid":"jax/lax_reference.py::dot_general","text":"jax/lax_reference.py::dot_general\ndef dot_general(lhs, rhs, dimension_numbers):\n (lhs_contracting, rhs_contracting), (lhs_batch, rhs_batch) = dimension_numbers\n new_id = itertools.count()\n lhs_axis_ids = [next(new_id) for _ in lhs.shape]\n rhs_axis_ids = [next(new_id) for _ in rhs.shape]\n lhs_out_axis_ids = lhs_axis_ids[:]\n rhs_out_axis_ids = rhs_axis_ids[:]\n\n for lhs_axis, rhs_axis in zip(lhs_contracting, rhs_contracting):\n shared_id = next(new_id)\n lhs_axis_ids[lhs_axis] = shared_id\n rhs_axis_ids[rhs_axis] = shared_id\n lhs_out_axis_ids[lhs_axis] = None\n rhs_out_axis_ids[rhs_axis] = None\n\n batch_ids = []\n for lhs_axis, rhs_axis in zip(lhs_batch, rhs_batch):\n shared_id = next(new_id)\n lhs_axis_ids[lhs_axis] = shared_id\n rhs_axis_ids[rhs_axis] = shared_id\n lhs_out_axis_ids[lhs_axis] = None\n rhs_out_axis_ids[rhs_axis] = None\n batch_ids.append(shared_id)\n\n not_none = lambda x: x is not None\n out_axis_ids = filter(not_none,\n batch_ids + lhs_out_axis_ids + rhs_out_axis_ids)\n assert lhs.dtype == rhs.dtype\n dtype = onp.float32 if lhs.dtype == dtypes.bfloat16 else None\n out = onp.einsum(lhs, lhs_axis_ids, rhs, rhs_axis_ids, out_axis_ids,\n dtype=dtype)\n return out.astype(dtypes.bfloat16) if lhs.dtype == dtypes.bfloat16 else out","label":0},{"cid":"jax/lax_reference.py::broadcast","text":"jax/lax_reference.py::broadcast\ndef broadcast(operand, sizes):\n return onp.broadcast_to(operand, sizes + onp.shape(operand))","label":0},{"cid":"jax/lax_reference.py::broadcast_in_dim","text":"jax/lax_reference.py::broadcast_in_dim\ndef broadcast_in_dim(operand, shape, broadcast_dimensions):\n inshape = tuple(1 if i not in broadcast_dimensions else d\n for i, d in enumerate(shape))\n return onp.broadcast_to(onp.reshape(operand, inshape), shape)","label":0},{"cid":"jax/lax_reference.py::reshape","text":"jax/lax_reference.py::reshape\ndef reshape(operand, new_sizes, dimensions=None):\n if dimensions is None:\n dimensions = range(len(onp.shape(operand)))\n return onp.reshape(onp.transpose(operand, dimensions), new_sizes)","label":0},{"cid":"jax/lax_reference.py::pad","text":"jax/lax_reference.py::pad\ndef pad(operand, padding_value, padding_config):\n lo, hi, interior = zip(*padding_config)\n outshape = onp.add(onp.add(onp.add(lo, hi), operand.shape),\n onp.multiply(interior, onp.subtract(operand.shape, 1)))\n out = onp.full(outshape, padding_value, operand.dtype)\n lhs_slices = tuple(_slice(l if l > 0 else 0, -h if h > 0 else None, step)\n for l, h, step in zip(lo, hi, onp.add(1, interior)))\n rhs_slices = tuple(_slice(l if l < 0 else 0, -h if h < 0 else None)\n for l, h in zip(lo, hi))\n out[lhs_slices] = operand[rhs_slices]\n return out","label":0},{"cid":"jax/lax_reference.py::rev","text":"jax/lax_reference.py::rev\ndef rev(operand, dimensions):\n dimensions = frozenset(dimensions)\n indexer = (_slice(None, None, -1) if d in dimensions else _slice(None)\n for d in range(onp.ndim(operand)))\n return operand[tuple(indexer)]","label":0},{"cid":"jax/lax_reference.py::slice","text":"jax/lax_reference.py::slice\ndef slice(operand, start_indices, limit_indices, strides=None): # pylint: disable=redefined-builtin\n if strides is None:\n strides = onp.ones(len(start_indices)).astype(int)\n slices = tuple(_map(_slice, start_indices, limit_indices, strides))\n return operand[slices]","label":0},{"cid":"jax/lax_reference.py::dynamic_slice","text":"jax/lax_reference.py::dynamic_slice\ndef dynamic_slice(operand, start_indices, slice_sizes):\n out = onp.zeros(slice_sizes, dtype=operand.dtype)\n idx = tuple(_slice(start, start+size)\n for start, size in zip(start_indices, slice_sizes))\n section = operand[idx]\n out[tuple(_slice(None, stop) for stop in section.shape)] = section\n return out","label":0},{"cid":"jax/lax_reference.py::dynamic_update_slice","text":"jax/lax_reference.py::dynamic_update_slice\ndef dynamic_update_slice(operand, update, start_indices):\n slices = tuple(_map(_slice, start_indices, onp.add(start_indices, update.shape)))\n updated_operand = onp.copy(operand)\n updated_operand[slices] = update\n return updated_operand","label":0},{"cid":"jax/lax_reference.py::reduce","text":"jax/lax_reference.py::reduce\ndef reduce(operand, init_value, computation, dimensions): # pylint: disable=redefined-builtin\n reducer = _make_reducer(computation, init_value)\n return reducer(operand, tuple(dimensions)).astype(onp.asarray(operand).dtype)","label":0},{"cid":"jax/lax_reference.py::reduce_window","text":"jax/lax_reference.py::reduce_window\ndef reduce_window(operand, init_value, computation, window_dimensions,\n window_strides, padding):\n op, dims, strides = operand, window_dimensions, window_strides\n pads = padtype_to_pads(op.shape, dims, strides, padding)\n view = _conv_view(op.reshape((1, 1) + op.shape), (1, 1) + dims, strides, pads,\n pad_value=init_value)[0]\n view = view.reshape(view.shape[1:1+len(dims)] + (-1,))\n reducer = _make_reducer(computation, init_value)\n return reducer(view, axis=-1)","label":0},{"cid":"jax/lax_reference.py::sort_key_val","text":"jax/lax_reference.py::sort_key_val\ndef sort_key_val(keys, values, dimension=-1):\n idxs = list(onp.ix_(*[onp.arange(d) for d in keys.shape]))\n idxs[dimension] = onp.argsort(keys, axis=dimension)\n return keys[idxs], values[idxs]","label":0},{"cid":"jax/lax_reference.py::_conv","text":"jax/lax_reference.py::_conv\ndef _conv(lhs, rhs, window_strides, pads):\n view, view_axes, rhs_axes, out_axes = _conv_view(\n lhs, rhs.shape, window_strides, pads, 0.)\n return opt_einsum.contract(\n view, view_axes, rhs, rhs_axes, out_axes, use_blas=True)","label":0},{"cid":"jax/lax_reference.py::padtype_to_pads","text":"jax/lax_reference.py::padtype_to_pads\ndef padtype_to_pads(in_shape, filter_shape, window_strides, padding):\n if padding.upper() == 'SAME':\n out_shape = onp.ceil(onp.true_divide(in_shape, window_strides)).astype(int)\n pad_sizes = [_max((out_size - 1) * stride + filter_size - in_size, 0)\n for out_size, stride, filter_size, in_size\n in zip(out_shape, window_strides, filter_shape, in_shape)]\n return [(pad_size // 2, pad_size - pad_size // 2) for pad_size in pad_sizes]\n else:\n return [(0, 0)] * len(in_shape)","label":0},{"cid":"jax/lax_reference.py::_conv_view","text":"jax/lax_reference.py::_conv_view\ndef _conv_view(lhs, rhs_shape, window_strides, pads, pad_value):\n \"\"\"Compute the view (and its axes) of a convolution or window reduction.\"\"\"\n if (_min(lhs.ndim, len(rhs_shape)) < 2 or lhs.ndim != len(rhs_shape)\n or lhs.shape[1] != rhs_shape[1]):\n raise ValueError('Dimension mismatch')\n if len(window_strides) != len(rhs_shape) - 2:\n raise ValueError('Wrong number of strides for spatial dimensions')\n if len(pads) != len(rhs_shape) - 2:\n raise ValueError('Wrong number of pads for spatial dimensions')\n\n lhs = _pad(lhs, [(0, 0)] * 2 + list(pads), pad_value)\n in_shape = lhs.shape[2:]\n filter_shape = rhs_shape[2:]\n dim = len(filter_shape) # number of 'spatial' dimensions in convolution\n\n out_strides = onp.multiply(window_strides, lhs.strides[2:])\n view_strides = lhs.strides[:1] + tuple(out_strides) + lhs.strides[1:]\n\n out_shape = onp.floor_divide(\n onp.subtract(in_shape, filter_shape), window_strides) + 1\n view_shape = lhs.shape[:1] + tuple(out_shape) + rhs_shape[1:]\n\n view = onp.lib.stride_tricks.as_strided(lhs, view_shape, view_strides)\n\n view_axes = list(range(view.ndim))\n sum_axes = view_axes[-dim-1:]\n rhs_axes = [view.ndim] + sum_axes\n out_axes = [0, view.ndim] + list(range(1, dim+1))\n\n return view, view_axes, rhs_axes, out_axes","label":0},{"cid":"jax/lax_reference.py::_pad","text":"jax/lax_reference.py::_pad\ndef _pad(arr, pads, pad_value):\n out = onp.pad(arr, onp.maximum(0, pads), mode='constant',\n constant_values=pad_value).astype(arr.dtype)\n slices = tuple(_slice(abs(lo) if lo < 0 else 0, hi % dim if hi < 0 else None)\n for (lo, hi), dim in zip(pads, onp.shape(arr)))\n return out[slices]","label":0},{"cid":"jax/lax_reference.py::_dilate","text":"jax/lax_reference.py::_dilate\ndef _dilate(operand, factors):\n # this logic is like lax.pad, but with two leading dimensions, no edge\n # padding, and factors are at least 1 (interior padding is at least 0)\n outspace = onp.add(operand.shape[2:],\n onp.multiply(onp.subtract(factors, 1),\n onp.subtract(operand.shape[2:], 1)))\n out = onp.zeros(operand.shape[:2] + tuple(outspace), operand.dtype)\n lhs_slices = tuple(_slice(None, None, step) for step in factors)\n out[(_slice(None),) * 2 + lhs_slices] = operand\n return out","label":0},{"cid":"jax/lax_reference.py::_conv_general_permutations","text":"jax/lax_reference.py::_conv_general_permutations\ndef _conv_general_permutations(dimension_numbers):\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n rhs_perm = ((rhs_spec.index('O'), rhs_spec.index('I'))\n + tuple(i for i, c in enumerate(rhs_spec) if c not in {'O', 'I'}))\n lhs_perm = ((lhs_spec.index('N'), lhs_spec.index('C'))\n + tuple(sorted((i for i, c in enumerate(lhs_spec)\n if c not in {'N', 'C'}),\n key=lambda i: rhs_spec.index(lhs_spec[i]))))\n out_perm = ((out_spec.index('N'), out_spec.index('C'))\n + tuple(sorted((i for i, c in enumerate(out_spec)\n if c not in {'N', 'C'}),\n key=lambda i: rhs_spec.index(out_spec[i]))))\n return lhs_perm, rhs_perm, out_perm","label":0},{"cid":"jax/lax_reference.py::_make_reducer","text":"jax/lax_reference.py::_make_reducer\ndef _make_reducer(py_binop, init_val):\n \"\"\"Make a reducer function given a Python binop and an initial value.\"\"\"\n # It's tempting to use onp.ufunc.reduce (even with a ufunc generated by\n # onp.frompyfunc(py_binop)), but this may not agree with custom init_val.\n # We make an attempt to uncover an underlying numpy ufunc (which might be\n # wrapped by autograd or lax) and check its identity against init_val.\n monoid_record = _monoids.get(getattr(py_binop, '__name__'))\n if monoid_record:\n reducer, monoid_identity = monoid_record\n if init_val == monoid_identity(dtypes.result_type(init_val)):\n return reducer\n return _reducer_from_pyfunc(py_binop, init_val)","label":0},{"cid":"jax/lax_reference.py::_get_max_identity","text":"jax/lax_reference.py::_get_max_identity\ndef _get_max_identity(dt):\n return -onp.inf if dtypes.issubdtype(dt, onp.floating) else onp.iinfo(dt).min","label":0},{"cid":"jax/lax_reference.py::_get_min_identity","text":"jax/lax_reference.py::_get_min_identity\ndef _get_min_identity(dt):\n return onp.inf if dtypes.issubdtype(dt, onp.floating) else onp.iinfo(dt).max","label":0},{"cid":"jax/lax_reference.py::_identity_getter","text":"jax/lax_reference.py::_identity_getter\ndef _identity_getter(op):\n return lambda dtype: onp.asarray(op.identity, dtype=dtype)","label":0},{"cid":"jax/lax_reference.py::_reducer_from_pyfunc","text":"jax/lax_reference.py::_reducer_from_pyfunc\ndef _reducer_from_pyfunc(py_binop, init_val):\n def reducer(operand, axis=0):\n axis = range(onp.ndim(operand)) if axis is None else axis\n result = onp.full(onp.delete(onp.shape(operand), axis), init_val,\n dtype=onp.asarray(operand).dtype)\n for idx, _ in onp.ndenumerate(operand):\n out_idx = tuple(onp.delete(idx, axis))\n result[out_idx] = py_binop(result[out_idx], operand[idx])\n return result\n return reducer","label":0},{"cid":"jax/lax_reference.py::_reducer_from_pyfunc.reducer","text":"jax/lax_reference.py::_reducer_from_pyfunc.reducer\n def reducer(operand, axis=0):\n axis = range(onp.ndim(operand)) if axis is None else axis\n result = onp.full(onp.delete(onp.shape(operand), axis), init_val,\n dtype=onp.asarray(operand).dtype)\n for idx, _ in onp.ndenumerate(operand):\n out_idx = tuple(onp.delete(idx, axis))\n result[out_idx] = py_binop(result[out_idx], operand[idx])\n return result","label":0},{"cid":"jax/lib/xla_bridge.py::get_compile_options","text":"jax/lib/xla_bridge.py::get_compile_options\ndef get_compile_options(num_replicas=None, device_assignment=None):\n \"\"\"Returns the compile options to use, as derived from flag values.\n\n Args:\n num_replicas: Optional int indicating the number of replicas for which to\n compile (default inherited from xla_client.CompileOptions).\n device_assignment: Optional tuple of integers indicating the assignment of\n logical replicas to physical devices (default inherited from\n xla_client.CompileOptions). Must be consistent with `num_replicas`.\n \"\"\"\n compile_options = None\n if num_replicas is not None:\n compile_options = compile_options or xla_client.CompileOptions()\n compile_options.num_replicas = num_replicas\n if device_assignment is not None:\n logging.vlog(2, \"get_compile_options: num_replicas=%s device_assignment=%s\",\n num_replicas, device_assignment)\n # NOTE(mattjj): xla_client.DeviceAssignment.create expects a 2D ndarray\n # indexed by replica number and computation per replica, respectively, while\n # here we currently assume only one computation per replica, hence the\n # second axis is always trivial.\n if num_replicas is not None and num_replicas != len(device_assignment):\n msg = \"device_assignment does not match num_replicas: {} vs {}.\"\n raise ValueError(msg.format(device_assignment, num_replicas))\n compile_options = compile_options or xla_client.CompileOptions()\n device_assignment = onp.array(device_assignment)\n if device_assignment.ndim == 1:\n device_assignment = device_assignment[:, None]\n device_assignment = xla_client.DeviceAssignment.create(device_assignment)\n assert num_replicas is None or device_assignment.replica_count() == num_replicas\n compile_options.device_assignment = device_assignment\n return compile_options","label":0},{"cid":"jax/lib/xla_bridge.py::register_backend","text":"jax/lib/xla_bridge.py::register_backend\ndef register_backend(name, factory):\n _backends[name] = factory","label":0},{"cid":"jax/lib/xla_bridge.py::_get_local_backend","text":"jax/lib/xla_bridge.py::_get_local_backend\ndef _get_local_backend(platform=None):\n if not platform:\n platform = FLAGS.jax_platform_name\n\n # Canonicalize platform names.\n cpu = 'cpu'\n gpu = 'gpu'\n if platform == 'Host':\n platform = cpu\n elif platform == 'CUDA':\n platform = gpu\n elif platform == '':\n platform = None\n\n backend = xla_client.get_local_backend(platform)\n if backend is None:\n raise RuntimeError(\"No local XLA backends found.\")\n\n if backend.platform == cpu and platform != cpu:\n warnings.warn('No GPU/TPU found, falling back to CPU.')\n\n return backend","label":0},{"cid":"jax/lib/xla_bridge.py::_get_tpu_driver_backend","text":"jax/lib/xla_bridge.py::_get_tpu_driver_backend\ndef _get_tpu_driver_backend(platform):\n del platform\n global _tpu_backend\n if _tpu_backend is None:\n backend_target = FLAGS.jax_backend_target\n if backend_target is None:\n raise ValueError('When using TPU Driver as the backend, you must specify '\n '--jax_backend_target=<hostname>:8470.')\n _tpu_backend = tpu_client.TpuBackend.create(worker=backend_target)\n return _tpu_backend","label":0},{"cid":"jax/lib/xla_bridge.py::get_backend","text":"jax/lib/xla_bridge.py::get_backend\n@util.memoize\ndef get_backend(platform=None):\n # TODO(mattjj,skyewm): remove this input polymorphism after we clean up how\n # 'backend' values are handled\n if isinstance(platform, xla_client.Backend):\n return platform\n\n with _backend_lock:\n backend = _backends.get(FLAGS.jax_xla_backend)\n if backend is None:\n msg = 'Unknown jax_xla_backend value \"{}\".'\n raise ValueError(msg.format(FLAGS.jax_xla_backend))\n return backend(platform)","label":0},{"cid":"jax/lib/xla_bridge.py::get_device_backend","text":"jax/lib/xla_bridge.py::get_device_backend\ndef get_device_backend(device=None):\n \"\"\"Returns the Backend associated with `device`, or the default Backend.\"\"\"\n platform = device.platform if device else None\n return get_backend(platform)","label":0},{"cid":"jax/lib/xla_bridge.py::device_count","text":"jax/lib/xla_bridge.py::device_count\ndef device_count(backend=None):\n \"\"\"Returns the total number of devices.\n\n On most platforms, this is the same as ``local_device_count()``. However, on\n multi-host platforms, this will return the total number of devices across all\n hosts.\n\n Args:\n backend: This is an experimental feature and the API is likely to change.\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\n\n Returns:\n Number of devices.\n \"\"\"\n return int(get_backend(backend).device_count())","label":0},{"cid":"jax/lib/xla_bridge.py::local_device_count","text":"jax/lib/xla_bridge.py::local_device_count\ndef local_device_count(backend=None):\n \"\"\"Returns the number of devices on this host.\"\"\"\n return int(get_backend(backend).local_device_count())","label":0},{"cid":"jax/lib/xla_bridge.py::devices","text":"jax/lib/xla_bridge.py::devices\ndef devices(backend=None):\n \"\"\"Returns a list of all devices.\n\n Each device is represented by a subclass of Device (e.g. CpuDevice,\n GpuDevice). The length of the returned list is equal to\n ``device_count()``. Local devices can be identified by comparing\n ``Device.host_id`` to ``host_id()``.\n\n Args:\n backend: This is an experimental feature and the API is likely to change.\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\n\n Returns:\n List of Device subclasses.\n \"\"\"\n return get_backend(backend).devices()","label":0},{"cid":"jax/lib/xla_bridge.py::local_devices","text":"jax/lib/xla_bridge.py::local_devices\ndef local_devices(host_id=None, backend=None):\n \"\"\"Returns a list of devices local to a given host (this host by default).\"\"\"\n if host_id is None:\n host_id = get_backend(backend).host_id()\n return [d for d in devices(backend) if d.host_id == host_id]","label":0},{"cid":"jax/lib/xla_bridge.py::host_id","text":"jax/lib/xla_bridge.py::host_id\ndef host_id(backend=None):\n \"\"\"Returns the integer host ID of this host.\n\n On most platforms, this will always be 0. This will vary on multi-host\n platforms though.\n\n Args:\n backend: This is an experimental feature and the API is likely to change.\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\n\n Returns:\n Integer host ID.\n \"\"\"\n return get_backend(backend).host_id()","label":0},{"cid":"jax/lib/xla_bridge.py::host_ids","text":"jax/lib/xla_bridge.py::host_ids\ndef host_ids(backend=None):\n \"\"\"Returns a list of all host IDs.\"\"\"\n return list(set(d.host_id for d in devices(backend)))","label":0},{"cid":"jax/lib/xla_bridge.py::host_count","text":"jax/lib/xla_bridge.py::host_count\ndef host_count(backend=None):\n \"\"\"Returns the number of hosts.\"\"\"\n return len(host_ids(backend))","label":0},{"cid":"jax/lib/xla_bridge.py::dtype_to_etype","text":"jax/lib/xla_bridge.py::dtype_to_etype\n@util.memoize\ndef dtype_to_etype(dtype):\n \"\"\"Convert from dtype to canonical etype (reading FLAGS.jax_enable_x64).\"\"\"\n return xla_client.dtype_to_etype(dtypes.canonicalize_dtype(dtype))","label":0},{"cid":"jax/lib/xla_bridge.py::supported_numpy_dtypes","text":"jax/lib/xla_bridge.py::supported_numpy_dtypes\n@util.memoize\ndef supported_numpy_dtypes():\n return {dtypes.canonicalize_dtype(dtype)\n for dtype in xla_client.XLA_ELEMENT_TYPE_TO_DTYPE.values()}","label":0},{"cid":"jax/lib/xla_bridge.py::normalize_to_xla_dtypes","text":"jax/lib/xla_bridge.py::normalize_to_xla_dtypes\ndef normalize_to_xla_dtypes(val):\n \"\"\"Normalize dtypes in a value.\"\"\"\n if hasattr(val, '__array__') or onp.isscalar(val):\n return onp.asarray(val,\n dtype=dtypes.canonicalize_dtype(dtypes.result_type(val)))\n elif isinstance(val, (tuple, list)):\n return tuple(normalize_to_xla_dtypes(x) for x in val)\n raise TypeError('Can\\'t convert to XLA: {}'.format(val))","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.Build","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.Build\n def Build(self, *args, **kwargs):\n return super(_JaxComputationBuilder, self).Build(\n *args, **kwargs)","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.NumpyArrayConstant","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.NumpyArrayConstant\n def NumpyArrayConstant(self, value, canonicalize_types=True):\n if canonicalize_types:\n value = normalize_to_xla_dtypes(value)\n return super(_JaxComputationBuilder, self).Constant(value)","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.ConstantLike","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.ConstantLike\n def ConstantLike(self, example_value, value, canonicalize_types=True):\n example_value = onp.asarray(example_value)\n return self.Constant(onp.array(value, dtype=example_value.dtype))","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.Constant","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.Constant\n def Constant(self, py_val, canonicalize_types=True):\n \"\"\"Translate constant `py_val` to a constant for this ComputationBuilder.\n\n Args:\n py_val: a Python value to be translated to a constant.\n\n Returns:\n A representation of the constant, either a ComputationDataHandle or None\n \"\"\"\n py_type = type(py_val)\n if py_type in _constant_handlers:\n return _constant_handlers[py_type](self, py_val, canonicalize_types)\n else:\n raise TypeError(\"No constant handler for type: {}\".format(py_type))","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.CrossReplicaSum","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.CrossReplicaSum\n def CrossReplicaSum(self, operand, replica_groups):\n \"\"\"Workaround for CrossReplicaSum not being implemented on some backends.\"\"\"\n if len(replica_groups[0]) == 1:\n return operand\n else:\n return super(_JaxComputationBuilder, self).CrossReplicaSum(\n operand, replica_groups)","label":0},{"cid":"jax/lib/xla_bridge.py::_JaxComputationBuilder.AllToAll","text":"jax/lib/xla_bridge.py::_JaxComputationBuilder.AllToAll\n def AllToAll(self, operand, split_axis, concat_axis, replica_groups):\n \"\"\"Workaround for AllToAll not being implemented on some backends.\"\"\"\n if len(replica_groups[0]) == 1:\n return operand\n else:\n return super(_JaxComputationBuilder, self).AllToAll(\n operand, split_axis, concat_axis, replica_groups)","label":0},{"cid":"jax/lib/xla_bridge.py::make_computation_builder","text":"jax/lib/xla_bridge.py::make_computation_builder\ndef make_computation_builder(name):\n return _JaxComputationBuilder(name)","label":0},{"cid":"jax/lib/xla_bridge.py::register_constant_handler","text":"jax/lib/xla_bridge.py::register_constant_handler\ndef register_constant_handler(type_, handler_fun):\n _constant_handlers[type_] = handler_fun","label":0},{"cid":"jax/lib/xla_bridge.py::_ndarray_constant_handler","text":"jax/lib/xla_bridge.py::_ndarray_constant_handler\ndef _ndarray_constant_handler(c, val, canonicalize_types=True):\n \"\"\"Constant handler for ndarray literals, handling zero-size strides.\n\n This function essentially calls c.NumpyArrayConstant(val) except it has\n special handling of arrays with any strides of size zero: for those, it\n generates appropriate calls to NumpyArrayConstant, Broadcast, and Transpose\n to avoid staging in large literals that might arise from np.zeros or np.ones\n or the output of lax.broadcast (which uses onp.broadcast_to which in turn\n uses size-zero strides).\n\n Args:\n c: XLA client ComputationBuilder.\n val: an ndarray.\n\n Returns:\n An XLA ComputationDataHandle / XlaOp representing the constant ndarray\n staged into the XLA Computation.\n \"\"\"\n # TODO(mattjj): revise this to use c.BroadcastInDim rather than Transpose\n if onp.any(onp.equal(0, val.strides)) and val.size > 0:\n zero_stride_axes, = onp.where(onp.equal(0, val.strides))\n other_axes, = onp.where(onp.not_equal(0, val.strides))\n collapsed_val = val[tuple(0 if ax in zero_stride_axes else slice(None)\n for ax in range(val.ndim))]\n xla_val = c.Broadcast(\n c.NumpyArrayConstant(collapsed_val, canonicalize_types),\n onp.take(val.shape, zero_stride_axes))\n permutation = onp.argsort(tuple(zero_stride_axes) + tuple(other_axes))\n return c.Transpose(xla_val, permutation)\n else:\n return c.NumpyArrayConstant(val, canonicalize_types)","label":0},{"cid":"jax/lib/xla_bridge.py::_scalar_constant_handler","text":"jax/lib/xla_bridge.py::_scalar_constant_handler\ndef _scalar_constant_handler(c, val, canonicalize_types=True):\n return c.NumpyArrayConstant(val, canonicalize_types)","label":0},{"cid":"jax/lib/xla_bridge.py::_python_scalar_handler","text":"jax/lib/xla_bridge.py::_python_scalar_handler\ndef _python_scalar_handler(dtype, c, val, canonicalize_dtypes=True):\n return c.NumpyArrayConstant(dtype.type(val))","label":0}],"string":"[\n {\n \"cid\": \"jax/experimental/stax.py::Dense\",\n \"text\": \"jax/experimental/stax.py::Dense\\ndef Dense(out_dim, W_init=glorot_normal(), b_init=normal()):\\n \\\"\\\"\\\"Layer constructor function for a dense (fully-connected) layer.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[:-1] + (out_dim,)\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return np.dot(inputs, W) + b\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense.init_fun\",\n \"text\": \"jax/experimental/stax.py::Dense.init_fun\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[:-1] + (out_dim,)\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Dense.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return np.dot(inputs, W) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv\",\n \"text\": \"jax/experimental/stax.py::GeneralConv\\ndef GeneralConv(dimension_numbers, out_chan, filter_shape,\\n strides=None, padding='VALID', W_init=None,\\n b_init=normal(1e-6)):\\n \\\"\\\"\\\"Layer construction function for a general convolution layer.\\\"\\\"\\\"\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n one = (1,) * len(filter_shape)\\n strides = strides or one\\n W_init = W_init or glorot_normal(rhs_spec.index('I'), rhs_spec.index('O'))\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_general_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\\n dimension_numbers=dimension_numbers) + b\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv.init_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConv.init_fun\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_general_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv.apply_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConv.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\\n dimension_numbers=dimension_numbers) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConvTranspose\",\n \"text\": \"jax/experimental/stax.py::GeneralConvTranspose\\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape,\\n strides=None, padding='VALID', W_init=None,\\n b_init=normal(1e-6)):\\n \\\"\\\"\\\"Layer construction function for a general transposed-convolution layer.\\\"\\\"\\\"\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n one = (1,) * len(filter_shape)\\n strides = strides or one\\n W_init = W_init or glorot_normal(rhs_spec.index('I'), rhs_spec.index('O'))\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_transpose_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_transpose(inputs, W, strides, padding,\\n dimension_numbers=dimension_numbers) + b\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConvTranspose.init_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConvTranspose.init_fun\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_transpose_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConvTranspose.apply_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConvTranspose.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_transpose(inputs, W, strides, padding,\\n dimension_numbers=dimension_numbers) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm\",\n \"text\": \"jax/experimental/stax.py::BatchNorm\\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-5, center=True, scale=True,\\n beta_init=zeros, gamma_init=ones):\\n \\\"\\\"\\\"Layer construction function for a batch normalization layer.\\\"\\\"\\\"\\n _beta_init = lambda rng, shape: beta_init(rng, shape) if center else ()\\n _gamma_init = lambda rng, shape: gamma_init(rng, shape) if scale else ()\\n axis = (axis,) if np.isscalar(axis) else axis\\n def init_fun(rng, input_shape):\\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\\n k1, k2 = random.split(rng)\\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\\n return input_shape, (beta, gamma)\\n def apply_fun(params, x, **kwargs):\\n beta, gamma = params\\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\\n # (https://github.com/numpy/numpy/issues/12290)\\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\\n beta = beta[ed]\\n gamma = gamma[ed]\\n z = normalize(x, axis, epsilon=epsilon)\\n if center and scale: return gamma * z + beta\\n if center: return z + beta\\n if scale: return gamma * z\\n return z\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm.init_fun\",\n \"text\": \"jax/experimental/stax.py::BatchNorm.init_fun\\n def init_fun(rng, input_shape):\\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\\n k1, k2 = random.split(rng)\\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\\n return input_shape, (beta, gamma)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm.apply_fun\",\n \"text\": \"jax/experimental/stax.py::BatchNorm.apply_fun\\n def apply_fun(params, x, **kwargs):\\n beta, gamma = params\\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\\n # (https://github.com/numpy/numpy/issues/12290)\\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\\n beta = beta[ed]\\n gamma = gamma[ed]\\n z = normalize(x, axis, epsilon=epsilon)\\n if center and scale: return gamma * z + beta\\n if center: return z + beta\\n if scale: return gamma * z\\n return z\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::elementwise\",\n \"text\": \"jax/experimental/stax.py::elementwise\\ndef elementwise(fun, **fun_kwargs):\\n \\\"\\\"\\\"Layer that applies a scalar function elementwise on its inputs.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: (input_shape, ())\\n apply_fun = lambda params, inputs, **kwargs: fun(inputs, **fun_kwargs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer\\ndef _pooling_layer(reducer, init_val, rescaler=None):\\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec='NHWC'):\\n \\\"\\\"\\\"Layer construction function for a pooling layer.\\\"\\\"\\\"\\n strides = strides or (1,) * len(window_shape)\\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\\n\\n non_spatial_axes = spec.index('N'), spec.index('C')\\n for i in sorted(non_spatial_axes):\\n window_shape = window_shape[:i] + (1,) + window_shape[i:]\\n strides = strides[:i] + (1,) + strides[i:]\\n\\n def init_fun(rng, input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\\n strides, padding)\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\\n strides, padding)\\n return rescale(out, inputs, spec) if rescale else out\\n return init_fun, apply_fun\\n return PoolingLayer\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer\\n def PoolingLayer(window_shape, strides=None, padding='VALID', spec='NHWC'):\\n \\\"\\\"\\\"Layer construction function for a pooling layer.\\\"\\\"\\\"\\n strides = strides or (1,) * len(window_shape)\\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\\n\\n non_spatial_axes = spec.index('N'), spec.index('C')\\n for i in sorted(non_spatial_axes):\\n window_shape = window_shape[:i] + (1,) + window_shape[i:]\\n strides = strides[:i] + (1,) + strides[i:]\\n\\n def init_fun(rng, input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\\n strides, padding)\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\\n strides, padding)\\n return rescale(out, inputs, spec) if rescale else out\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\\n def init_fun(rng, input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape,\\n strides, padding)\\n return out_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, window_shape,\\n strides, padding)\\n return rescale(out, inputs, spec) if rescale else out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_normalize_by_window_size\",\n \"text\": \"jax/experimental/stax.py::_normalize_by_window_size\\ndef _normalize_by_window_size(dims, strides, padding):\\n def rescale(outputs, inputs, spec):\\n non_spatial_axes = spec.index('N'), spec.index('C')\\n spatial_shape = tuple(inputs.shape[i]\\n for i in range(inputs.ndim)\\n if i not in non_spatial_axes)\\n one = np.ones(spatial_shape, dtype=inputs.dtype)\\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\\n for i in sorted(non_spatial_axes):\\n window_sizes = np.expand_dims(window_sizes, i)\\n\\n return outputs / window_sizes\\n return rescale\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_normalize_by_window_size.rescale\",\n \"text\": \"jax/experimental/stax.py::_normalize_by_window_size.rescale\\n def rescale(outputs, inputs, spec):\\n non_spatial_axes = spec.index('N'), spec.index('C')\\n spatial_shape = tuple(inputs.shape[i]\\n for i in range(inputs.ndim)\\n if i not in non_spatial_axes)\\n one = np.ones(spatial_shape, dtype=inputs.dtype)\\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\\n for i in sorted(non_spatial_axes):\\n window_sizes = np.expand_dims(window_sizes, i)\\n\\n return outputs / window_sizes\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten\",\n \"text\": \"jax/experimental/stax.py::Flatten\\ndef Flatten():\\n \\\"\\\"\\\"Layer construction function for flattening all but the leading dim.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[0], functools.reduce(op.mul, input_shape[1:], 1)\\n return output_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n return np.reshape(inputs, (inputs.shape[0], -1))\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten.init_fun\",\n \"text\": \"jax/experimental/stax.py::Flatten.init_fun\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[0], functools.reduce(op.mul, input_shape[1:], 1)\\n return output_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Flatten.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return np.reshape(inputs, (inputs.shape[0], -1))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Identity\",\n \"text\": \"jax/experimental/stax.py::Identity\\ndef Identity():\\n \\\"\\\"\\\"Layer construction function for an identity layer.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: (input_shape, ())\\n apply_fun = lambda params, inputs, **kwargs: inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanOut\",\n \"text\": \"jax/experimental/stax.py::FanOut\\ndef FanOut(num):\\n \\\"\\\"\\\"Layer construction function for a fan-out layer.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: ([input_shape] * num, ())\\n apply_fun = lambda params, inputs, **kwargs: [inputs] * num\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInSum\",\n \"text\": \"jax/experimental/stax.py::FanInSum\\ndef FanInSum():\\n \\\"\\\"\\\"Layer construction function for a fan-in sum layer.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: (input_shape[0], ())\\n apply_fun = lambda params, inputs, **kwargs: sum(inputs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat\",\n \"text\": \"jax/experimental/stax.py::FanInConcat\\ndef FanInConcat(axis=-1):\\n \\\"\\\"\\\"Layer construction function for a fan-in concatenation layer.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n ax = axis % len(input_shape[0])\\n concat_size = sum(shape[ax] for shape in input_shape)\\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n return np.concatenate(inputs, axis)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat.init_fun\",\n \"text\": \"jax/experimental/stax.py::FanInConcat.init_fun\\n def init_fun(rng, input_shape):\\n ax = axis % len(input_shape[0])\\n concat_size = sum(shape[ax] for shape in input_shape)\\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\\n return out_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat.apply_fun\",\n \"text\": \"jax/experimental/stax.py::FanInConcat.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return np.concatenate(inputs, axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout\",\n \"text\": \"jax/experimental/stax.py::Dropout\\ndef Dropout(rate, mode='train'):\\n \\\"\\\"\\\"Layer construction function for a dropout layer with given rate.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n return input_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.get('rng', None)\\n if rng is None:\\n msg = (\\\"Dropout layer requires apply_fun to be called with a PRNG key \\\"\\n \\\"argument. That is, instead of `apply_fun(params, inputs)`, call \\\"\\n \\\"it like `apply_fun(params, inputs, key)` where `key` is a \\\"\\n \\\"jax.random.PRNGKey value.\\\")\\n raise ValueError(msg)\\n if mode == 'train':\\n keep = random.bernoulli(rng, rate, inputs.shape)\\n return np.where(keep, inputs / rate, 0)\\n else:\\n return inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout.init_fun\",\n \"text\": \"jax/experimental/stax.py::Dropout.init_fun\\n def init_fun(rng, input_shape):\\n return input_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Dropout.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.get('rng', None)\\n if rng is None:\\n msg = (\\\"Dropout layer requires apply_fun to be called with a PRNG key \\\"\\n \\\"argument. That is, instead of `apply_fun(params, inputs)`, call \\\"\\n \\\"it like `apply_fun(params, inputs, key)` where `key` is a \\\"\\n \\\"jax.random.PRNGKey value.\\\")\\n raise ValueError(msg)\\n if mode == 'train':\\n keep = random.bernoulli(rng, rate, inputs.shape)\\n return np.where(keep, inputs / rate, 0)\\n else:\\n return inputs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial\",\n \"text\": \"jax/experimental/stax.py::serial\\ndef serial(*layers):\\n \\\"\\\"\\\"Combinator for composing layers in serial.\\n\\n Args:\\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the serial\\n composition of the given sequence of layers.\\n \\\"\\\"\\\"\\n nlayers = len(layers)\\n init_funs, apply_funs = zip(*layers)\\n def init_fun(rng, input_shape):\\n params = []\\n for init_fun in init_funs:\\n rng, layer_rng = random.split(rng)\\n input_shape, param = init_fun(layer_rng, input_shape)\\n params.append(param)\\n return input_shape, params\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n for fun, param, rng in zip(apply_funs, params, rngs):\\n inputs = fun(param, inputs, rng=rng, **kwargs)\\n return inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial.init_fun\",\n \"text\": \"jax/experimental/stax.py::serial.init_fun\\n def init_fun(rng, input_shape):\\n params = []\\n for init_fun in init_funs:\\n rng, layer_rng = random.split(rng)\\n input_shape, param = init_fun(layer_rng, input_shape)\\n params.append(param)\\n return input_shape, params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial.apply_fun\",\n \"text\": \"jax/experimental/stax.py::serial.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n for fun, param, rng in zip(apply_funs, params, rngs):\\n inputs = fun(param, inputs, rng=rng, **kwargs)\\n return inputs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel\",\n \"text\": \"jax/experimental/stax.py::parallel\\ndef parallel(*layers):\\n \\\"\\\"\\\"Combinator for composing layers in parallel.\\n\\n The layer resulting from this combinator is often used with the FanOut and\\n FanInSum layers.\\n\\n Args:\\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the\\n parallel composition of the given sequence of layers. In particular, the\\n returned layer takes a sequence of inputs and returns a sequence of outputs\\n with the same length as the argument `layers`.\\n \\\"\\\"\\\"\\n nlayers = len(layers)\\n init_funs, apply_funs = zip(*layers)\\n def init_fun(rng, input_shape):\\n rngs = random.split(rng, nlayers)\\n return zip(*[init(rng, shape) for init, rng, shape\\n in zip(init_funs, rngs, input_shape)])\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel.init_fun\",\n \"text\": \"jax/experimental/stax.py::parallel.init_fun\\n def init_fun(rng, input_shape):\\n rngs = random.split(rng, nlayers)\\n return zip(*[init(rng, shape) for init, rng, shape\\n in zip(init_funs, rngs, input_shape)])\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel.apply_fun\",\n \"text\": \"jax/experimental/stax.py::parallel.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent\",\n \"text\": \"jax/experimental/stax.py::shape_dependent\\ndef shape_dependent(make_layer):\\n \\\"\\\"\\\"Combinator to delay layer constructor pair until input shapes are known.\\n\\n Args:\\n make_layer: a one-argument function that takes an input shape as an argument\\n (a tuple of positive integers) and returns an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the same\\n layer as returned by `make_layer` but with its construction delayed until\\n input shapes are known.\\n \\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n return make_layer(input_shape)[0](rng, input_shape)\\n def apply_fun(params, inputs, **kwargs):\\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent.init_fun\",\n \"text\": \"jax/experimental/stax.py::shape_dependent.init_fun\\n def init_fun(rng, input_shape):\\n return make_layer(input_shape)[0](rng, input_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent.apply_fun\",\n \"text\": \"jax/experimental/stax.py::shape_dependent.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::broadcast_shapes\",\n \"text\": \"jax/lax/lax.py::broadcast_shapes\\n@cache()\\ndef broadcast_shapes(*shapes):\\n \\\"\\\"\\\"Returns the shape that results from NumPy broadcasting of `shapes`.\\\"\\\"\\\"\\n if len(shapes) == 1:\\n return shapes[0]\\n ndim = _max(len(shape) for shape in shapes)\\n shapes = onp.array([(1,) * (ndim - len(shape)) + shape for shape in shapes])\\n min_shape = onp.min(shapes, axis=0)\\n max_shape = onp.max(shapes, axis=0)\\n result_shape = onp.where(min_shape == 0, 0, max_shape)\\n if not onp.all((shapes == result_shape) | (shapes == 1)):\\n raise ValueError(\\\"Incompatible shapes for broadcasting: {}\\\"\\n .format(tuple(map(tuple, shapes))))\\n return tuple(result_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_canonicalize_shape\",\n \"text\": \"jax/lax/lax.py::_canonicalize_shape\\ndef _canonicalize_shape(shape):\\n \\\"\\\"\\\"Canonicalizes and checks for errors in a user-provided shape value.\\n\\n Args:\\n shape: a Python value that represents a shape.\\n\\n Returns:\\n A tuple of integers.\\n \\\"\\\"\\\"\\n # TODO(mattjj): this next check is a temporary workaround for masking\\n if (type(shape) is ShapeExpr\\n or type(shape) is tuple and any(type(d) is masking.Poly for d in shape)):\\n return shape\\n try:\\n return tuple(map(operator.index, shape))\\n except TypeError:\\n pass\\n msg = (\\\"Shapes must be 1D sequences of concrete values of integer type, \\\"\\n \\\"got {}.\\\")\\n if any(isinstance(x, core.Tracer) and isinstance(core.get_aval(x), ShapedArray)\\n and not isinstance(core.get_aval(x), ConcreteArray) for x in shape):\\n msg += (\\\"\\\\nIf using `jit`, try using `static_argnums` or applying `jit` to \\\"\\n \\\"smaller subfunctions.\\\")\\n raise TypeError(msg.format(shape))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_identity\",\n \"text\": \"jax/lax/lax.py::_identity\\ndef _identity(x): return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::neg\",\n \"text\": \"jax/lax/lax.py::neg\\ndef neg(x):\\n r\\\"\\\"\\\"Elementwise negation: :math:`-x`.\\\"\\\"\\\"\\n return neg_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::sign\",\n \"text\": \"jax/lax/lax.py::sign\\ndef sign(x):\\n r\\\"\\\"\\\"Elementwise sign.\\n\\n For floating-point inputs, returns\\n :math:`\\\\mathrm{sign}(x) = \\\\begin{cases}\\n -1 & x < 0\\\\\\\\\\n -0 & x = -0\\\\\\\\\\n \\\\mathit{NaN} & x = \\\\mathit{NaN}\\\\\\\\\\n +0 & x = +0\\\\\\\\\\n 1 & x > 0\\n \\\\end{cases}`\\n\\n For signed integer inputs, returns\\n :math:`\\\\mathrm{sign}(x) = \\\\begin{cases}\\n -1 & x < 0\\\\\\\\\\n 0 & x = 0\\\\\\\\\\n 1 & x > 0\\n \\\\end{cases}`\\n\\n For complex inputs, returns the complex phase, i.e.\\n :math:`\\\\mathrm{sign}(x) = \\\\frac{x}{|x|}`.\\n \\\"\\\"\\\"\\n return sign_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::nextafter\",\n \"text\": \"jax/lax/lax.py::nextafter\\ndef nextafter(x1, x2):\\n r\\\"\\\"\\\"Returns the next representable value after `x1` in the direction of `x2`.\\\"\\\"\\\"\\n return nextafter_p.bind(_brcast(x1, x2), _brcast(x2, x1))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::floor\",\n \"text\": \"jax/lax/lax.py::floor\\ndef floor(x):\\n r\\\"\\\"\\\"Elementwise floor: :math:`\\\\left\\\\lfloor x \\\\right\\\\rfloor`.\\\"\\\"\\\"\\n return floor_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::ceil\",\n \"text\": \"jax/lax/lax.py::ceil\\ndef ceil(x):\\n r\\\"\\\"\\\"Elementwise ceiling: :math:`\\\\left\\\\lceil x \\\\right\\\\rceil`.\\\"\\\"\\\"\\n return ceil_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::round\",\n \"text\": \"jax/lax/lax.py::round\\ndef round(x):\\n r\\\"\\\"\\\"Elementwise round.\\n\\n Rounds values to the nearest integer. Halfway values (e.g., `0.5`) are rounded\\n away from zero.\\\"\\\"\\\"\\n return round_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::is_finite\",\n \"text\": \"jax/lax/lax.py::is_finite\\ndef is_finite(x):\\n r\\\"\\\"\\\"Elementwise :math:`\\\\mathrm{isfinite}`.\\n\\n For each element x returns `True` if and only if x is not :math:`\\\\pm\\\\infty` or\\n :math:`\\\\mathit{NaN}`.\\n \\\"\\\"\\\"\\n return is_finite_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::exp\",\n \"text\": \"jax/lax/lax.py::exp\\ndef exp(x):\\n r\\\"\\\"\\\"Elementwise exponential: :math:`e^x`.\\\"\\\"\\\"\\n return exp_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::expm1\",\n \"text\": \"jax/lax/lax.py::expm1\\ndef expm1(x):\\n r\\\"\\\"\\\"Elementwise :math:`e^{x - 1}`.\\\"\\\"\\\"\\n return expm1_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::log\",\n \"text\": \"jax/lax/lax.py::log\\ndef log(x):\\n r\\\"\\\"\\\"Elementwise natural logarithm: :math:`\\\\mathrm{log}(x)`.\\\"\\\"\\\"\\n return log_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::log1p\",\n \"text\": \"jax/lax/lax.py::log1p\\ndef log1p(x):\\n r\\\"\\\"\\\"Elementwise :math:`\\\\mathrm{log}(1 + x)`.\\\"\\\"\\\"\\n return log1p_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::tanh\",\n \"text\": \"jax/lax/lax.py::tanh\\ndef tanh(x):\\n r\\\"\\\"\\\"Elementwise hyperbolic tangent: :math:`\\\\mathrm{tanh}(x)`.\\\"\\\"\\\"\\n return tanh_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::sin\",\n \"text\": \"jax/lax/lax.py::sin\\ndef sin(x):\\n r\\\"\\\"\\\"Elementwise sine: :math:`\\\\mathrm{sin}(x)`.\\\"\\\"\\\"\\n return sin_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::cos\",\n \"text\": \"jax/lax/lax.py::cos\\ndef cos(x):\\n r\\\"\\\"\\\"Elementwise cosine: :math:`\\\\mathrm{cos}(x)`.\\\"\\\"\\\"\\n return cos_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::atan2\",\n \"text\": \"jax/lax/lax.py::atan2\\ndef atan2(x, y):\\n r\\\"\\\"\\\"Elementwise arc tangent of two variables:\\n :math:`\\\\mathrm{atan}({x \\\\over y})`.\\\"\\\"\\\"\\n return atan2_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::lgamma\",\n \"text\": \"jax/lax/lax.py::lgamma\\ndef lgamma(x):\\n r\\\"\\\"\\\"Elementwise log gamma: :math:`\\\\mathrm{log}(\\\\Gamma(x))`.\\\"\\\"\\\"\\n return lgamma_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::digamma\",\n \"text\": \"jax/lax/lax.py::digamma\\ndef digamma(x):\\n r\\\"\\\"\\\"Elementwise digamma: :math:`\\\\psi(x)`.\\\"\\\"\\\"\\n return digamma_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::bessel_i0e\",\n \"text\": \"jax/lax/lax.py::bessel_i0e\\ndef bessel_i0e(x):\\n r\\\"\\\"\\\"Exponentially scaled modified Bessel function of order 0:\\n :math:`\\\\mathrm{i0e}(x) = e^{-|x|} \\\\mathrm{i0}(x)`\\n \\\"\\\"\\\"\\n return bessel_i0e_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::bessel_i1e\",\n \"text\": \"jax/lax/lax.py::bessel_i1e\\ndef bessel_i1e(x):\\n r\\\"\\\"\\\"Exponentially scaled modified Bessel function of order 1:\\n :math:`\\\\mathrm{i1e}(x) = e^{-|x|} \\\\mathrm{i1}(x)`\\n \\\"\\\"\\\"\\n return bessel_i1e_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::erf\",\n \"text\": \"jax/lax/lax.py::erf\\ndef erf(x):\\n r\\\"\\\"\\\"Elementwise error function: :math:`\\\\mathrm{erf}(x)`.\\\"\\\"\\\"\\n return erf_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::erfc\",\n \"text\": \"jax/lax/lax.py::erfc\\ndef erfc(x):\\n r\\\"\\\"\\\"Elementwise complementary error function:\\n :math:`\\\\mathrm{erfc}(x) = 1 - \\\\mathrm{erf}(x)`.\\\"\\\"\\\"\\n return erfc_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::erf_inv\",\n \"text\": \"jax/lax/lax.py::erf_inv\\ndef erf_inv(x):\\n r\\\"\\\"\\\"Elementwise inverse error function: :math:`\\\\mathrm{erf}^{-1}(x)`.\\\"\\\"\\\"\\n return erf_inv_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::real\",\n \"text\": \"jax/lax/lax.py::real\\ndef real(x):\\n r\\\"\\\"\\\"Elementwise extract real part: :math:`\\\\mathrm{Re}(x)`.\\n\\n Returns the real part of a complex number.\\n \\\"\\\"\\\"\\n return real_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::imag\",\n \"text\": \"jax/lax/lax.py::imag\\ndef imag(x):\\n r\\\"\\\"\\\"Elementwise extract imaginary part: :math:`\\\\mathrm{Im}(x)`.\\n\\n Returns the imaginary part of a complex number.\\n \\\"\\\"\\\"\\n return imag_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::complex\",\n \"text\": \"jax/lax/lax.py::complex\\ndef complex(x, y):\\n r\\\"\\\"\\\"Elementwise make complex number: :math:`x + jy`.\\n\\n Builds a complex number from real and imaginary parts.\\n \\\"\\\"\\\"\\n return complex_p.bind(_brcast(x, y), _brcast(y, x))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::conj\",\n \"text\": \"jax/lax/lax.py::conj\\ndef conj(x):\\n r\\\"\\\"\\\"Elementwise complex conjugate function: :math:`\\\\overline{x}`.\\\"\\\"\\\"\\n return conj_p.bind(x, input_dtype=_dtype(x))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::abs\",\n \"text\": \"jax/lax/lax.py::abs\\ndef abs(x):\\n r\\\"\\\"\\\"Elementwise absolute value: :math:`|x|`.\\\"\\\"\\\"\\n return abs_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::pow\",\n \"text\": \"jax/lax/lax.py::pow\\ndef pow(x, y):\\n r\\\"\\\"\\\"Elementwise power: :math:`x^y`.\\\"\\\"\\\"\\n return pow_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::sqrt\",\n \"text\": \"jax/lax/lax.py::sqrt\\ndef sqrt(x):\\n r\\\"\\\"\\\"Elementwise square root: :math:`\\\\sqrt{x}`.\\\"\\\"\\\"\\n return sqrt_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::rsqrt\",\n \"text\": \"jax/lax/lax.py::rsqrt\\ndef rsqrt(x):\\n r\\\"\\\"\\\"Elementwise reciprocal square root: :math:`1 \\\\over \\\\sqrt{x}.\\\"\\\"\\\"\\n return rsqrt_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::bitwise_not\",\n \"text\": \"jax/lax/lax.py::bitwise_not\\ndef bitwise_not(x):\\n r\\\"\\\"\\\"Elementwise NOT: :math:`\\\\neg x`.\\\"\\\"\\\"\\n return not_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::bitwise_and\",\n \"text\": \"jax/lax/lax.py::bitwise_and\\ndef bitwise_and(x, y):\\n r\\\"\\\"\\\"Elementwise AND: :math:`x \\\\wedge y`.\\\"\\\"\\\"\\n return and_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::bitwise_or\",\n \"text\": \"jax/lax/lax.py::bitwise_or\\ndef bitwise_or(x, y):\\n r\\\"\\\"\\\"Elementwise OR: :math:`x \\\\vee y`.\\\"\\\"\\\"\\n return or_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::bitwise_xor\",\n \"text\": \"jax/lax/lax.py::bitwise_xor\\ndef bitwise_xor(x, y):\\n r\\\"\\\"\\\"Elementwise exclusive OR: :math:`x \\\\oplus y`.\\\"\\\"\\\"\\n return xor_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::add\",\n \"text\": \"jax/lax/lax.py::add\\ndef add(x, y):\\n r\\\"\\\"\\\"Elementwise addition: :math:`x + y`.\\\"\\\"\\\"\\n return add_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::sub\",\n \"text\": \"jax/lax/lax.py::sub\\ndef sub(x, y):\\n r\\\"\\\"\\\"Elementwise subtraction: :math:`x - y`.\\\"\\\"\\\"\\n return sub_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::mul\",\n \"text\": \"jax/lax/lax.py::mul\\ndef mul(x, y):\\n r\\\"\\\"\\\"Elementwise multiplication: :math:`x \\\\times y`.\\\"\\\"\\\"\\n return mul_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::div\",\n \"text\": \"jax/lax/lax.py::div\\ndef div(x, y):\\n r\\\"\\\"\\\"Elementwise division: :math:`x \\\\over y`.\\\"\\\"\\\"\\n return div_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::rem\",\n \"text\": \"jax/lax/lax.py::rem\\ndef rem(x, y):\\n r\\\"\\\"\\\"Elementwise remainder: :math:`x \\\\bmod y`.\\\"\\\"\\\"\\n return rem_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::max\",\n \"text\": \"jax/lax/lax.py::max\\ndef max(x, y):\\n r\\\"\\\"\\\"Elementwise maximum: :math:`\\\\mathrm{max}(x, y)`\\n\\n For complex numbers, uses a lexicographic comparison on the\\n `(real, imaginary)` pairs.\\\"\\\"\\\"\\n return max_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::min\",\n \"text\": \"jax/lax/lax.py::min\\ndef min(x, y):\\n r\\\"\\\"\\\"Elementwise minimum: :math:`\\\\mathrm{min}(x, y)`\\n\\n For complex numbers, uses a lexicographic comparison on the\\n `(real, imaginary)` pairs.\\\"\\\"\\\"\\n return min_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::shift_left\",\n \"text\": \"jax/lax/lax.py::shift_left\\ndef shift_left(x, y):\\n r\\\"\\\"\\\"Elementwise left shift: :math:`x \\\\ll y`.\\\"\\\"\\\"\\n return shift_left_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::shift_right_arithmetic\",\n \"text\": \"jax/lax/lax.py::shift_right_arithmetic\\ndef shift_right_arithmetic(x, y):\\n r\\\"\\\"\\\"Elementwise arithmetic right shift: :math:`x \\\\gg y`.\\\"\\\"\\\"\\n return shift_right_arithmetic_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::shift_right_logical\",\n \"text\": \"jax/lax/lax.py::shift_right_logical\\ndef shift_right_logical(x, y):\\n r\\\"\\\"\\\"Elementwise logical right shift: :math:`x \\\\gg y`.\\\"\\\"\\\"\\n return shift_right_logical_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::eq\",\n \"text\": \"jax/lax/lax.py::eq\\ndef eq(x, y):\\n r\\\"\\\"\\\"Elementwise equals: :math:`x = y`.\\\"\\\"\\\"\\n return eq_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::ne\",\n \"text\": \"jax/lax/lax.py::ne\\ndef ne(x, y):\\n r\\\"\\\"\\\"Elementwise not-equals: :math:`x \\\\neq y`.\\\"\\\"\\\"\\n return ne_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::ge\",\n \"text\": \"jax/lax/lax.py::ge\\ndef ge(x, y):\\n r\\\"\\\"\\\"Elementwise greater-than-or-equals: :math:`x \\\\geq y`.\\\"\\\"\\\"\\n return ge_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::gt\",\n \"text\": \"jax/lax/lax.py::gt\\ndef gt(x, y):\\n r\\\"\\\"\\\"Elementwise greater-than: :math:`x > y`.\\\"\\\"\\\"\\n return gt_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::le\",\n \"text\": \"jax/lax/lax.py::le\\ndef le(x, y):\\n r\\\"\\\"\\\"Elementwise less-than-or-equals: :math:`x \\\\leq y`.\\\"\\\"\\\"\\n return le_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::lt\",\n \"text\": \"jax/lax/lax.py::lt\\ndef lt(x, y):\\n r\\\"\\\"\\\"Elementwise less-than: :math:`x < y`.\\\"\\\"\\\"\\n return lt_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::convert_element_type\",\n \"text\": \"jax/lax/lax.py::convert_element_type\\ndef convert_element_type(operand, new_dtype):\\n \\\"\\\"\\\"Elementwise cast.\\n\\n Wraps XLA's `ConvertElementType\\n <https://www.tensorflow.org/xla/operation_semantics#convertelementtype>`_\\n operator, which performs an elementwise conversion from one type to another.\\n Similar to a C++ `static_cast`.\\n\\n Args:\\n operand: an array or scalar value to be cast\\n new_dtype: the new type. Should be a NumPy type.\\n\\n Returns:\\n An array with the same shape as `operand`, cast elementwise to `new_dtype`.\\n \\\"\\\"\\\"\\n new_dtype = dtypes.canonicalize_dtype(new_dtype)\\n # Avoids dropping precision by casting Python scalars to the default Jax\\n # type. If we passed a Python scalar directly to the bind call below, it is\\n # cast to the default type as part of the calling convention.\\n if type(operand) in dtypes.python_scalar_dtypes:\\n operand = onp.asarray(operand, new_dtype)\\n old_dtype = dtypes.canonicalize_dtype(_dtype(operand))\\n if old_dtype == new_dtype:\\n return operand\\n if (dtypes.issubdtype(old_dtype, onp.complexfloating) and\\n not dtypes.issubdtype(new_dtype, onp.complexfloating)):\\n msg = \\\"Casting complex values to real discards the imaginary part\\\"\\n warnings.warn(msg, onp.ComplexWarning, stacklevel=2)\\n operand = real(operand)\\n old_dtype = _dtype(operand)\\n # TODO(b/143311238, b/142974574): work around bfloat16 conversion bugs by\\n # introducing an intermediate cast via float32.\\n if ((old_dtype == dtypes.bfloat16 and new_dtype != onp.float32) or\\n (new_dtype == dtypes.bfloat16 and old_dtype != onp.float32)):\\n operand = convert_element_type_p.bind(\\n operand, new_dtype=onp.float32, old_dtype=old_dtype)\\n old_dtype = onp.float32\\n return convert_element_type_p.bind(\\n operand, new_dtype=new_dtype, old_dtype=old_dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::bitcast_convert_type\",\n \"text\": \"jax/lax/lax.py::bitcast_convert_type\\ndef bitcast_convert_type(operand, new_dtype):\\n \\\"\\\"\\\"Elementwise bitcast.\\n\\n Wraps XLA's `BitcastConvertType\\n <https://www.tensorflow.org/xla/operation_semantics#bitcastconverttype>`_\\n operator, which performs a bit cast from one type to another. The bitwidth\\n of the source and destination types must match.\\n\\n Args:\\n operand: an array or scalar value to be cast\\n new_dtype: the new type. Should be a NumPy type.\\n\\n Returns:\\n An array with the same shape as `operand`, bitcast elementwise to\\n `new_dtype`.\\n \\\"\\\"\\\"\\n new_dtype = dtypes.canonicalize_dtype(new_dtype)\\n old_dtype = _dtype(operand)\\n if old_dtype != new_dtype:\\n return bitcast_convert_type_p.bind(operand, new_dtype=new_dtype)\\n else:\\n return operand\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::clamp\",\n \"text\": \"jax/lax/lax.py::clamp\\ndef clamp(min, x, max):\\n r\\\"\\\"\\\"Elementwise clamp.\\n\\n Returns :math:`\\\\mathrm{clamp}(x) = \\\\begin{cases}\\n \\\\mathit{min} & \\\\text{if } x < \\\\mathit{min},\\\\\\\\\\n \\\\mathit{max} & \\\\text{if } x > \\\\mathit{max},\\\\\\\\\\n x & \\\\text{otherwise}\\n \\\\end{cases}`.\\n \\\"\\\"\\\"\\n return clamp_p.bind(min, x, max)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::concatenate\",\n \"text\": \"jax/lax/lax.py::concatenate\\ndef concatenate(operands, dimension):\\n \\\"\\\"\\\"Concatenates a sequence of arrays along `dimension`.\\n\\n Wraps XLA's `Concatenate\\n <https://www.tensorflow.org/xla/operation_semantics#concatenate>`_\\n operator.\\n\\n Args:\\n operands: a sequence of arrays to concatenate. The arrays must have equal\\n shapes, except in the `dimension` axis.\\n dimension: the dimension along which to concatenate the arrays.\\n\\n Returns:\\n An array containing the concatenation.\\n \\\"\\\"\\\"\\n return concatenate_p.bind(*operands, dimension=dimension,\\n operand_shapes=tuple(o.shape for o in operands))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::conv_general_dilated\",\n \"text\": \"jax/lax/lax.py::conv_general_dilated\\ndef conv_general_dilated(lhs, rhs, window_strides, padding, lhs_dilation=None,\\n rhs_dilation=None, dimension_numbers=None,\\n feature_group_count=1, precision=None):\\n \\\"\\\"\\\"General n-dimensional convolution operator, with optional dilation.\\n\\n Wraps XLA's `Conv\\n <https://www.tensorflow.org/xla/operation_semantics#conv_convolution>`_\\n operator.\\n\\n Args:\\n lhs: a rank `n+2` dimensional input array.\\n rhs: a rank `n+2` dimensional array of kernel weights.\\n window_strides: a sequence of `n` integers, representing the inter-window\\n strides.\\n padding: either the string `'SAME'`, the string `'VALID'`, or a sequence of\\n `n` `(low, high)` integer pairs that give the padding to apply before and\\n after each spatial dimension.\\n lhs_dilation: `None`, or a sequence of `n` integers, giving the\\n dilation factor to apply in each spatial dimension of `lhs`. LHS dilation\\n is also known as transposed convolution.\\n rhs_dilation: `None`, or a sequence of `n` integers, giving the\\n dilation factor to apply in each spatial dimension of `rhs`. RHS dilation\\n is also known as atrous convolution.\\n dimension_numbers: either `None`, a `ConvDimensionNumbers` object, or\\n a 3-tuple `(lhs_spec, rhs_spec, out_spec)`, where each element is a string\\n of length `n+2`.\\n feature_group_count: integer, default 1. See XLA HLO docs.\\n precision: Optional. Either `None`, which means the default precision for\\n the backend, or a `Precision` enum value.\\n\\n Returns:\\n An array containing the convolution result.\\n\\n In the string case of `dimension_numbers`, each character identifies by\\n position:\\n\\n - the batch dimensions in `lhs`, `rhs`, and the output with the character\\n 'N',\\n - the feature dimensions in `lhs` and the output with the character 'C',\\n - the input and output feature dimensions in rhs with the characters 'I'\\n and 'O' respectively, and\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::dot\",\n \"text\": \"jax/lax/lax.py::dot\\ndef dot(lhs, rhs, precision=None):\\n \\\"\\\"\\\"Vector/vector, matrix/vector, and matrix/matrix multiplication.\\n\\n Wraps XLA's `Dot\\n <https://www.tensorflow.org/xla/operation_semantics#dot>`_\\n operator.\\n\\n For more general contraction, see the `dot_general` operator.\\n\\n Args:\\n lhs: an array of rank 1 or 2.\\n rhs: an array of rank 1 or 2.\\n precision: Optional. Either `None`, which means the default precision for\\n the backend, or a `Precision` enum value.\\n\\n Returns:\\n An array containing the product.\\n \\\"\\\"\\\"\\n if 1 <= lhs.ndim <= 2 and 1 <= rhs.ndim <= 2 and lhs.shape[-1] == rhs.shape[0]:\\n return dot_general(lhs, rhs, (((lhs.ndim - 1,), (0,)), ((), ())),\\n precision=precision)\\n else:\\n raise TypeError(\\\"Incompatible shapes for dot: got {} and {}.\\\".format(\\n lhs.shape, rhs.shape))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::dot_general\",\n \"text\": \"jax/lax/lax.py::dot_general\\ndef dot_general(lhs, rhs, dimension_numbers, precision=None):\\n \\\"\\\"\\\"More general contraction operator.\\n\\n Wraps XLA's `DotGeneral\\n <https://www.tensorflow.org/xla/operation_semantics#dotgeneral>`_\\n operator.\\n\\n Args:\\n lhs: an array\\n rhs: an array\\n dimension_numbers: a tuple of tuples of the form\\n `((lhs_contracting_dims, rhs_contracting_dims),\\n (lhs_batch_dims, rhs_batch_dims))`\\n precision: Optional. Either `None`, which means the default precision for\\n the backend, or a `Precision` enum value.\\n\\n Returns:\\n An array containing the result.\\n \\\"\\\"\\\"\\n contract_dims, batch_dims = dimension_numbers\\n contract_dims = tuple(map(tuple, contract_dims))\\n batch_dims = tuple(map(tuple, batch_dims))\\n if not dtypes.issubdtype(lhs.dtype, onp.inexact):\\n # TODO(b/134526360): XLA doesn't support bool or integer dots, so we emit a\\n # sum of products instead.\\n lhs_contract_dims, rhs_contract_dims = contract_dims\\n lhs_batch_dims, rhs_batch_dims = batch_dims\\n lhs_noncontract_dims = tuple(sorted(\\n set(range(onp.ndim(lhs))) - set(lhs_batch_dims) - set(lhs_contract_dims)))\\n rhs_noncontract_dims = tuple(sorted(\\n set(range(onp.ndim(rhs))) - set(rhs_batch_dims) - set(rhs_contract_dims)))\\n lhs = transpose(lhs,\\n lhs_batch_dims + lhs_noncontract_dims + lhs_contract_dims)\\n rhs = transpose(rhs,\\n rhs_batch_dims + rhs_noncontract_dims + rhs_contract_dims)\\n new_lhs_shape = onp.insert(onp.array(onp.shape(lhs), dtype=onp.int64),\\n len(lhs_batch_dims) + len(lhs_noncontract_dims),\\n (1,) * len(rhs_noncontract_dims))\\n new_rhs_shape = onp.insert(onp.array(onp.shape(rhs), dtype=onp.int64),\\n len(lhs_batch_dims),\\n (1,) * len(lhs_noncontract_dims))\\n lhs = reshape(lhs, new_lhs_shape)\\n rhs = reshape(rhs, new_rhs_shape)\\n out_ndim = (len(lhs_batch_dims) + \",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::broadcast\",\n \"text\": \"jax/lax/lax.py::broadcast\\ndef broadcast(operand, sizes):\\n \\\"\\\"\\\"Broadcasts an array, adding new major dimensions.\\n\\n Wraps XLA's `Broadcast\\n <https://www.tensorflow.org/xla/operation_semantics#broadcast>`_\\n operator.\\n\\n Args:\\n operand: an array\\n sizes: a sequence of integers, giving the sizes of new major dimensions\\n to add.\\n\\n Returns:\\n An array containing the result.\\n \\\"\\\"\\\"\\n dims = tuple(range(len(sizes), len(sizes) + onp.ndim(operand)))\\n return broadcast_in_dim(operand, tuple(sizes) + onp.shape(operand), dims)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::broadcast_in_dim\",\n \"text\": \"jax/lax/lax.py::broadcast_in_dim\\ndef broadcast_in_dim(operand, shape, broadcast_dimensions):\\n if onp.ndim(operand) == len(shape) and not len(broadcast_dimensions):\\n return operand\\n return broadcast_in_dim_p.bind(\\n operand, shape=tuple(shape),\\n broadcast_dimensions=tuple(broadcast_dimensions))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::reshape\",\n \"text\": \"jax/lax/lax.py::reshape\\ndef reshape(operand, new_sizes, dimensions=None):\\n \\\"\\\"\\\"Wraps XLA's `Reshape\\n <https://www.tensorflow.org/xla/operation_semantics#reshape>`_\\n operator.\\n \\\"\\\"\\\"\\n new_sizes = _canonicalize_shape(new_sizes) # TODO\\n new_sizes = tuple(new_sizes)\\n same_shape = onp.shape(operand) == new_sizes\\n same_dims = dimensions is None or tuple(dimensions) == tuple(range(onp.ndim(operand)))\\n if onp.shape(operand) and same_shape and same_dims:\\n return operand\\n else:\\n return reshape_p.bind(\\n operand, new_sizes=new_sizes,\\n dimensions=None if same_dims else tuple(dimensions),\\n old_sizes=onp.shape(operand))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::pad\",\n \"text\": \"jax/lax/lax.py::pad\\ndef pad(operand, padding_value, padding_config):\\n \\\"\\\"\\\"Wraps XLA's `Pad\\n <https://www.tensorflow.org/xla/operation_semantics#pad>`_\\n operator.\\n \\\"\\\"\\\"\\n return pad_p.bind(operand, padding_value, padding_config=tuple(padding_config))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::rev\",\n \"text\": \"jax/lax/lax.py::rev\\ndef rev(operand, dimensions):\\n \\\"\\\"\\\"Wraps XLA's `Rev\\n <https://www.tensorflow.org/xla/operation_semantics#rev_reverse>`_\\n operator.\\n \\\"\\\"\\\"\\n return rev_p.bind(operand, dimensions=tuple(dimensions))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::select\",\n \"text\": \"jax/lax/lax.py::select\\ndef select(pred, on_true, on_false):\\n \\\"\\\"\\\"Wraps XLA's `Select\\n <https://www.tensorflow.org/xla/operation_semantics#select>`_\\n operator.\\n \\\"\\\"\\\"\\n return select_p.bind(pred, on_true, on_false)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::slice\",\n \"text\": \"jax/lax/lax.py::slice\\ndef slice(operand, start_indices, limit_indices, strides=None):\\n \\\"\\\"\\\"Wraps XLA's `Slice\\n <https://www.tensorflow.org/xla/operation_semantics#slice>`_\\n operator.\\n \\\"\\\"\\\"\\n if (onp.all(onp.equal(start_indices, 0))\\n and onp.all(onp.equal(limit_indices, operand.shape))\\n and strides is None):\\n return operand\\n else:\\n return slice_p.bind(operand, start_indices=tuple(start_indices),\\n limit_indices=tuple(limit_indices),\\n strides=None if strides is None else tuple(strides),\\n operand_shape=operand.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::dynamic_slice\",\n \"text\": \"jax/lax/lax.py::dynamic_slice\\ndef dynamic_slice(operand, start_indices, slice_sizes):\\n \\\"\\\"\\\"Wraps XLA's `DynamicSlice\\n <https://www.tensorflow.org/xla/operation_semantics#dynamicslice>`_\\n operator.\\n\\n Args:\\n operand: an array to slice.\\n start_indices: a list of scalar indices, one per dimension.\\n slice_sizes: the size of the slice. Must be a sequence of non-negative\\n integers with length equal to `ndim(operand)`.\\n\\n Returns:\\n An array containing the slice.\\n \\\"\\\"\\\"\\n start_indices = _dynamic_slice_indices(operand, start_indices)\\n return dynamic_slice_p.bind(\\n operand, *start_indices, slice_sizes=tuple(slice_sizes),\\n operand_shape=operand.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::dynamic_update_slice\",\n \"text\": \"jax/lax/lax.py::dynamic_update_slice\\ndef dynamic_update_slice(operand, update, start_indices):\\n \\\"\\\"\\\"Wraps XLA's `DynamicUpdateSlice\\n <https://www.tensorflow.org/xla/operation_semantics#dynamicupdateslice>`_\\n operator.\\n\\n Args:\\n operand: an array to slice.\\n update: an array containing the new values to write onto `operand`.\\n start_indices: a list of scalar indices, one per dimension.\\n\\n Returns:\\n An array containing the slice.\\n \\\"\\\"\\\"\\n start_indices = _dynamic_slice_indices(operand, start_indices)\\n return dynamic_update_slice_p.bind(operand, update, *start_indices,\\n update_shape=update.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::gather\",\n \"text\": \"jax/lax/lax.py::gather\\ndef gather(operand, start_indices, dimension_numbers, slice_sizes):\\n \\\"\\\"\\\"Gather operator.\\n\\n Wraps `XLA's Gather operator\\n <https://www.tensorflow.org/xla/operation_semantics#gather>`_.\\n\\n The semantics of gather are complicated, and its API might change in the\\n future. For most use cases, you should prefer `Numpy-style indexing\\n <https://docs.scipy.org/doc/numpy-1.16.0/reference/arrays.indexing.html>`_\\n (e.g., `x[:, (1,4,7), ...]`), rather than using `gather` directly.\\n\\n Args:\\n operand: an array from which slices should be taken\\n start_indices: the indices at which slices should be taken\\n dimension_numbers: a `lax.GatherDimensionNumbers` object that describes\\n how dimensions of `operand`, `start_indices` and the output relate.\\n slice_sizes: the size of each slice. Must be a sequence of non-negative\\n integers with length equal to `ndim(operand)`.\\n\\n Returns:\\n An array containing the gather output.\\n \\\"\\\"\\\"\\n return gather_p.bind(\\n operand, start_indices, dimension_numbers=dimension_numbers,\\n slice_sizes=_canonicalize_shape(slice_sizes), operand_shape=operand.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::scatter_add\",\n \"text\": \"jax/lax/lax.py::scatter_add\\ndef scatter_add(operand, scatter_indices, updates, dimension_numbers):\\n \\\"\\\"\\\"Scatter-add operator.\\n\\n Wraps `XLA's Scatter operator\\n <https://www.tensorflow.org/xla/operation_semantics#scatter>`_, where\\n addition is used to combine updates and values from `operand`.\\n\\n The semantics of scatter are complicated and its API is subject to change.\\n\\n Args:\\n operand: an array to which the scatter should be applied\\n scatter_indices: an array that gives the indices in `operand` to which each\\n update in `updates` should be applied.\\n updates: the updates that should be scattered onto `operand`.\\n dimension_numbers: a `lax.ScatterDimensionNumbers` object that describes\\n how dimensions of `operand`, `start_indices`, `updates` and the output\\n relate.\\n\\n Returns:\\n An array containing the sum of `operand` and the scattered updates.\\n \\\"\\\"\\\"\\n jaxpr, consts = _reduction_jaxpr(add, _abstractify(_const(operand, 0)))\\n return scatter_add_p.bind(\\n operand, scatter_indices, updates, update_jaxpr=jaxpr,\\n update_consts=consts, dimension_numbers=dimension_numbers,\\n updates_shape=updates.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::scatter_min\",\n \"text\": \"jax/lax/lax.py::scatter_min\\ndef scatter_min(operand, scatter_indices, updates, dimension_numbers):\\n \\\"\\\"\\\"Scatter-min operator.\\n\\n Wraps `XLA's Scatter operator\\n <https://www.tensorflow.org/xla/operation_semantics#scatter>`_, where\\n the `min` function is used to combine updates and values from `operand`.\\n\\n The semantics of scatter are complicated and its API is subject to change.\\n\\n Args:\\n operand: an array to which the scatter should be applied\\n scatter_indices: an array that gives the indices in `operand` to which each\\n update in `updates` should be applied.\\n updates: the updates that should be scattered onto `operand`.\\n dimension_numbers: a `lax.ScatterDimensionNumbers` object that describes\\n how dimensions of `operand`, `start_indices`, `updates` and the output\\n relate.\\n\\n Returns:\\n An array containing the sum of `operand` and the scattered updates.\\n \\\"\\\"\\\"\\n jaxpr, consts = _reduction_jaxpr(min, _abstractify(_const(operand, 0)))\\n return scatter_min_p.bind(\\n operand, scatter_indices, updates, update_jaxpr=jaxpr,\\n update_consts=consts, dimension_numbers=dimension_numbers,\\n updates_shape=updates.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::scatter_max\",\n \"text\": \"jax/lax/lax.py::scatter_max\\ndef scatter_max(operand, scatter_indices, updates, dimension_numbers):\\n \\\"\\\"\\\"Scatter-max operator.\\n\\n Wraps `XLA's Scatter operator\\n <https://www.tensorflow.org/xla/operation_semantics#scatter>`_, where\\n the `max` function is used to combine updates and values from `operand`.\\n\\n The semantics of scatter are complicated and its API is subject to change.\\n\\n Args:\\n operand: an array to which the scatter should be applied\\n scatter_indices: an array that gives the indices in `operand` to which each\\n update in `updates` should be applied.\\n updates: the updates that should be scattered onto `operand`.\\n dimension_numbers: a `lax.ScatterDimensionNumbers` object that describes\\n how dimensions of `operand`, `start_indices`, `updates` and the output\\n relate.\\n\\n Returns:\\n An array containing the sum of `operand` and the scattered updates.\\n \\\"\\\"\\\"\\n jaxpr, consts = _reduction_jaxpr(max, _abstractify(_const(operand, 0)))\\n return scatter_max_p.bind(\\n operand, scatter_indices, updates, update_jaxpr=jaxpr,\\n update_consts=consts, dimension_numbers=dimension_numbers,\\n updates_shape=updates.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::scatter\",\n \"text\": \"jax/lax/lax.py::scatter\\ndef scatter(operand, scatter_indices, updates, dimension_numbers):\\n \\\"\\\"\\\"Scatter-update operator.\\n\\n Wraps `XLA's Scatter operator\\n <https://www.tensorflow.org/xla/operation_semantics#scatter>`_, where updates\\n replace values from `operand`.\\n\\n If multiple updates are performed to the same index of operand, they may be\\n applied in any order.\\n\\n The semantics of scatter are complicated and its API is subject to change.\\n\\n Args:\\n operand: an array to which the scatter should be applied\\n scatter_indices: an array that gives the indices in `operand` to which each\\n update in `updates` should be applied.\\n updates: the updates that should be scattered onto `operand`.\\n dimension_numbers: a `lax.ScatterDimensionNumbers` object that describes\\n how dimensions of `operand`, `start_indices`, `updates` and the output\\n relate.\\n\\n Returns:\\n An array containing the sum of `operand` and the scattered updates.\\n \\\"\\\"\\\"\\n jaxpr, consts = _reduction_jaxpr(_scatter_reduction_computation,\\n _abstractify(_const(operand, 0)))\\n return scatter_p.bind(\\n operand, scatter_indices, updates, update_jaxpr=jaxpr,\\n update_consts=consts, dimension_numbers=dimension_numbers,\\n updates_shape=updates.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::index_take\",\n \"text\": \"jax/lax/lax.py::index_take\\ndef index_take(src, idxs, axes):\\n indices = concatenate([reshape(i, [i.shape[0], 1]) for i in idxs], 1)\\n indices = indices % onp.array([src.shape[ax] for ax in axes])\\n slice_sizes = list(src.shape)\\n for ax in axes:\\n slice_sizes[ax] = 1\\n slice_sizes = tuple(slice_sizes)\\n offset_dims = tuple(range(1, src.ndim - indices.shape[1] + 1))\\n dnums = GatherDimensionNumbers(\\n offset_dims=offset_dims,\\n collapsed_slice_dims=axes,\\n start_index_map=axes)\\n return gather(src, indices, dimension_numbers=dnums, slice_sizes=slice_sizes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::transpose\",\n \"text\": \"jax/lax/lax.py::transpose\\ndef transpose(operand, permutation):\\n \\\"\\\"\\\"Wraps XLA's `Transpose\\n <https://www.tensorflow.org/xla/operation_semantics#transpose>`_\\n operator.\\n \\\"\\\"\\\"\\n permutation = tuple(permutation)\\n if permutation == tuple(range(len(permutation))):\\n return operand\\n else:\\n return transpose_p.bind(operand, permutation=permutation)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::reduce\",\n \"text\": \"jax/lax/lax.py::reduce\\ndef reduce(operand, init_value, computation, dimensions):\\n \\\"\\\"\\\"Wraps XLA's `Reduce\\n <https://www.tensorflow.org/xla/operation_semantics#reduce>`_\\n operator.\\n \\\"\\\"\\\"\\n monoid_reducer = _get_monoid_reducer(computation, init_value)\\n if monoid_reducer:\\n return monoid_reducer(operand, dimensions)\\n else:\\n jaxpr, consts = _reduction_jaxpr(computation, _abstractify(init_value))\\n return reduce_p.bind(operand, init_value, computation=computation,\\n jaxpr=jaxpr, consts=consts, dimensions=tuple(dimensions))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduction_jaxpr\",\n \"text\": \"jax/lax/lax.py::_reduction_jaxpr\\n@cache()\\ndef _reduction_jaxpr(computation, aval):\\n pval = pe.PartialVal((aval, core.unit))\\n comp = lu.wrap_init(lambda x, y: (computation(x, y),))\\n jaxpr, _, consts = pe.trace_to_jaxpr(comp, (pval, pval), instantiate=False)\\n return jaxpr, consts\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_get_monoid_reducer\",\n \"text\": \"jax/lax/lax.py::_get_monoid_reducer\\ndef _get_monoid_reducer(monoid_op, x):\\n aval = core.get_aval(x)\\n dtype = _dtype(x)\\n if (type(aval) is ConcreteArray) and aval.shape == ():\\n if monoid_op is add:\\n return aval.val == 0 and _reduce_sum\\n if monoid_op is mul:\\n return aval.val == 1 and _reduce_prod\\n elif monoid_op is bitwise_or and dtype == onp.bool_:\\n return aval.val == _get_max_identity(dtype) and _reduce_or\\n elif monoid_op is bitwise_and and dtype == onp.bool_:\\n return aval.val == _get_min_identity(dtype) and _reduce_and\\n elif monoid_op is max:\\n return aval.val == _get_max_identity(dtype) and _reduce_max\\n elif monoid_op is min:\\n return aval.val == _get_min_identity(dtype) and _reduce_min\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_get_max_identity\",\n \"text\": \"jax/lax/lax.py::_get_max_identity\\ndef _get_max_identity(dtype):\\n if dtypes.issubdtype(dtype, onp.inexact):\\n return onp.array(-onp.inf, dtype)\\n elif dtypes.issubdtype(dtype, onp.integer):\\n return onp.array(dtypes.iinfo(dtype).min, dtype)\\n elif dtypes.issubdtype(dtype, onp.bool_):\\n return onp.array(False, onp.bool_)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_get_min_identity\",\n \"text\": \"jax/lax/lax.py::_get_min_identity\\ndef _get_min_identity(dtype):\\n if dtypes.issubdtype(dtype, onp.inexact):\\n return onp.array(onp.inf, dtype)\\n elif dtypes.issubdtype(dtype, onp.integer):\\n return onp.array(dtypes.iinfo(dtype).max, dtype)\\n elif dtypes.issubdtype(dtype, onp.bool_):\\n return onp.array(True, onp.bool_)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_sum\",\n \"text\": \"jax/lax/lax.py::_reduce_sum\\ndef _reduce_sum(operand, axes):\\n return reduce_sum_p.bind(operand, axes=tuple(axes),\\n input_shape=onp.shape(operand))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_prod\",\n \"text\": \"jax/lax/lax.py::_reduce_prod\\ndef _reduce_prod(operand, axes):\\n return reduce_prod_p.bind(operand, axes=tuple(axes))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_max\",\n \"text\": \"jax/lax/lax.py::_reduce_max\\ndef _reduce_max(operand, axes):\\n return reduce_max_p.bind(operand, axes=tuple(axes))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_min\",\n \"text\": \"jax/lax/lax.py::_reduce_min\\ndef _reduce_min(operand, axes):\\n return reduce_min_p.bind(operand, axes=tuple(axes))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_or\",\n \"text\": \"jax/lax/lax.py::_reduce_or\\ndef _reduce_or(operand, axes):\\n return reduce_or_p.bind(operand, axes=tuple(axes))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_and\",\n \"text\": \"jax/lax/lax.py::_reduce_and\\ndef _reduce_and(operand, axes):\\n return reduce_and_p.bind(operand, axes=tuple(axes))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::reduce_window\",\n \"text\": \"jax/lax/lax.py::reduce_window\\ndef reduce_window(operand, init_value, computation, window_dimensions,\\n window_strides, padding):\\n \\\"\\\"\\\"Wraps XLA's `ReduceWindow\\n <https://www.tensorflow.org/xla/operation_semantics#reducewindow>`_\\n operator.\\n \\\"\\\"\\\"\\n monoid_reducer = _get_monoid_window_reducer(computation, init_value)\\n if monoid_reducer:\\n return monoid_reducer(operand, window_dimensions, window_strides, padding)\\n else:\\n jaxpr, consts = _reduction_jaxpr(computation, _abstractify(init_value))\\n return reduce_window_p.bind(\\n operand, init_value, jaxpr=jaxpr, consts=consts,\\n window_dimensions=tuple(window_dimensions),\\n window_strides=tuple(window_strides), padding=padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_get_monoid_window_reducer\",\n \"text\": \"jax/lax/lax.py::_get_monoid_window_reducer\\ndef _get_monoid_window_reducer(monoid_op, x):\\n aval = core.get_aval(x)\\n if (type(aval) is ConcreteArray) and aval.shape == ():\\n if monoid_op is add:\\n return aval.val == 0 and _reduce_window_sum\\n elif monoid_op is max:\\n return aval.val == _get_max_identity(aval.dtype) and _reduce_window_max\\n elif monoid_op is min:\\n return aval.val == _get_min_identity(aval.dtype) and _reduce_window_min\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_sum\",\n \"text\": \"jax/lax/lax.py::_reduce_window_sum\\ndef _reduce_window_sum(operand, window_dimensions, window_strides, padding):\\n return reduce_window_sum_p.bind(\\n operand, window_dimensions=tuple(window_dimensions),\\n window_strides=tuple(window_strides), padding=padding,\\n input_shape=operand.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_prod\",\n \"text\": \"jax/lax/lax.py::_reduce_window_prod\\ndef _reduce_window_prod(operand, window_dimensions, window_strides, padding):\\n init_value = _const(operand, 1)\\n jaxpr, consts = _reduction_jaxpr(mul, _abstractify(init_value))\\n return reduce_window_p.bind(\\n operand, init_value, jaxpr=jaxpr, consts=consts,\\n window_dimensions=tuple(window_dimensions),\\n window_strides=tuple(window_strides), padding=padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_max\",\n \"text\": \"jax/lax/lax.py::_reduce_window_max\\ndef _reduce_window_max(operand, window_dimensions, window_strides, padding):\\n return reduce_window_max_p.bind(\\n operand, window_dimensions=tuple(window_dimensions),\\n window_strides=tuple(window_strides), padding=padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_min\",\n \"text\": \"jax/lax/lax.py::_reduce_window_min\\ndef _reduce_window_min(operand, window_dimensions, window_strides, padding):\\n return reduce_window_min_p.bind(\\n operand, window_dimensions=tuple(window_dimensions),\\n window_strides=tuple(window_strides), padding=padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_scatter\",\n \"text\": \"jax/lax/lax.py::_select_and_scatter\\ndef _select_and_scatter(operand, select, window_dimensions, window_strides,\\n padding, source, init_value, scatter):\\n select_jaxpr, select_consts = _reduction_jaxpr(select, _abstractify(init_value))\\n scatter_jaxpr, scatter_consts = _reduction_jaxpr(scatter, _abstractify(init_value))\\n return select_and_scatter_p.bind(\\n operand, source, init_value, select_jaxpr=select_jaxpr,\\n select_consts=select_consts, scatter_jaxpr=scatter_jaxpr,\\n scatter_consts=scatter_consts, window_dimensions=tuple(window_dimensions),\\n window_strides=tuple(window_strides), padding=padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_scatter_add\",\n \"text\": \"jax/lax/lax.py::_select_and_scatter_add\\ndef _select_and_scatter_add(source, operand, select_prim, window_dimensions,\\n window_strides, padding):\\n return select_and_scatter_add_p.bind(\\n source, operand, select_prim=select_prim,\\n window_dimensions=tuple(window_dimensions),\\n window_strides=tuple(window_strides), padding=padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_gather_add\",\n \"text\": \"jax/lax/lax.py::_select_and_gather_add\\ndef _select_and_gather_add(tangents, operand, select_prim, window_dimensions,\\n window_strides, padding):\\n return select_and_gather_add_p.bind(\\n tangents, operand, select_prim=select_prim,\\n window_dimensions=tuple(window_dimensions),\\n window_strides=tuple(window_strides), padding=padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::sort\",\n \"text\": \"jax/lax/lax.py::sort\\ndef sort(operand, dimension=-1):\\n \\\"\\\"\\\"Wraps XLA's `Sort\\n <https://www.tensorflow.org/xla/operation_semantics#sort>`_\\n operator.\\n \\\"\\\"\\\"\\n return sort_p.bind(operand, dimension=dimension)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::sort_key_val\",\n \"text\": \"jax/lax/lax.py::sort_key_val\\ndef sort_key_val(keys, values, dimension=-1):\\n # TODO(mattjj): new sort_key_val is variadic\\n result = sort_key_val_p.bind(keys, values, dimension=dimension)\\n sorted_keys, sorted_values = result\\n return sorted_keys, sorted_values\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::tie_in\",\n \"text\": \"jax/lax/lax.py::tie_in\\ndef tie_in(x, y):\\n return tie_in_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::full\",\n \"text\": \"jax/lax/lax.py::full\\ndef full(shape, fill_value, dtype=None):\\n \\\"\\\"\\\"Returns an array of `shape` filled with `fill_value`.\\n\\n Arguments:\\n shape: sequence of integers, describing the shape of the output array.\\n fill_value: the value to fill the new array with.\\n dtype: the type of the output array, or `None`. If not `None`, `fill_value`\\n will be cast to `dtype`.\\n \\\"\\\"\\\"\\n shape = _canonicalize_shape(shape)\\n if onp.shape(fill_value):\\n msg = \\\"full must be called with scalar fill_value, got fill_value.shape {}.\\\"\\n raise TypeError(msg.format(onp.shape(fill_value)))\\n dtype = dtypes.canonicalize_dtype(dtype or _dtype(fill_value))\\n # TODO(mattjj): remove device_put when dtype conversion produces DeviceArray\\n fill_value = xla.device_put_p.bind(convert_element_type(fill_value, dtype))\\n return broadcast(fill_value, shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::iota\",\n \"text\": \"jax/lax/lax.py::iota\\ndef iota(dtype, size):\\n \\\"\\\"\\\"Wraps XLA's `Iota\\n <https://www.tensorflow.org/xla/operation_semantics#iota>`_\\n operator.\\n \\\"\\\"\\\"\\n size = int(size)\\n dtype = dtypes.canonicalize_dtype(dtype)\\n lazy_expr = lazy.iota(dtype, size)\\n aval = ShapedArray((size,), dtype)\\n return xla.DeviceArray(aval, None, lazy_expr, xla.DeviceConstant())\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::broadcasted_iota\",\n \"text\": \"jax/lax/lax.py::broadcasted_iota\\ndef broadcasted_iota(dtype, shape, dimension):\\n \\\"\\\"\\\"Convenience wrapper around ``iota``.\\\"\\\"\\\"\\n dtype = dtypes.canonicalize_dtype(dtype)\\n shape = _canonicalize_shape(shape)\\n dimension = int(dimension)\\n return broadcast_in_dim(iota(dtype, shape[dimension]), shape, [dimension])\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_eye\",\n \"text\": \"jax/lax/lax.py::_eye\\ndef _eye(dtype, shape, offset):\\n \\\"\\\"\\\"Like numpy.eye, create a 2D array with ones on a diagonal.\\n\\n This function exists for creating lazy identity matrices; that is,\\n materialization of the array is delayed and it may be fused into consumers to\\n avoid materialization at all.\\\"\\\"\\\"\\n N, M = tuple(map(int, shape))\\n offset = int(offset)\\n dtype = dtypes.canonicalize_dtype(dtype)\\n lazy_expr = lazy.eye(dtype, (N, M), offset)\\n aval = ShapedArray((N, M), dtype)\\n return xla.DeviceArray(aval, None, lazy_expr, xla.DeviceConstant())\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_delta\",\n \"text\": \"jax/lax/lax.py::_delta\\ndef _delta(dtype, shape, axes):\\n \\\"\\\"\\\"This function exists for creating lazy Kronecker delta arrays, particularly\\n for use in jax.numpy.einsum to express traces. It differs from ``eye`` in that\\n it can create arrays of any rank, but doesn't allow offsets.\\\"\\\"\\\"\\n shape = tuple(map(int, shape))\\n axes = tuple(map(int, axes))\\n dtype = dtypes.canonicalize_dtype(dtype)\\n base_shape = tuple(onp.take(shape, axes))\\n lazy_expr = lazy.broadcast(lazy.delta(dtype, base_shape), shape, axes)\\n aval = ShapedArray(shape, dtype)\\n return xla.DeviceArray(aval, None, lazy_expr, xla.DeviceConstant())\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_tri\",\n \"text\": \"jax/lax/lax.py::_tri\\ndef _tri(dtype, shape, offset):\\n \\\"\\\"\\\"Like numpy.tri, create a 2D array with ones below a diagonal.\\n This function exists for creating lazy triangular matrices, particularly for\\n use in jax.numpy.tri.\\\"\\\"\\\"\\n N, M = tuple(map(int, shape))\\n offset = int(offset)\\n dtype = dtypes.canonicalize_dtype(dtype)\\n lazy_expr = lazy.tri(dtype, (N, M), offset)\\n aval = ShapedArray((N, M), dtype)\\n return xla.DeviceArray(aval, None, lazy_expr, xla.DeviceConstant())\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::stop_gradient\",\n \"text\": \"jax/lax/lax.py::stop_gradient\\ndef stop_gradient(x):\\n \\\"\\\"\\\"Stops gradient computation.\\n\\n Operationally `stop_gradient` is the identity function, that is, it returns\\n argument `x` unchanged. However, `stop_gradient` prevents the flow of\\n gradients during forward or reverse-mode automatic differentiation. If there\\n are multiple nested gradient computations, `stop_gradient` stops gradients\\n for all of them.\\n\\n For example:\\n\\n >>> jax.grad(lambda x: x**2)(3.)\\n array(6., dtype=float32)\\n >>> jax.grad(lambda x: jax.lax.stop_gradient(x)**2)(3.)\\n array(0., dtype=float32)\\n >>> jax.grad(jax.grad(lambda x: x**2))(3.)\\n array(2., dtype=float32)\\n >>> jax.grad(jax.grad(lambda x: jax.lax.stop_gradient(x)**2))(3.)\\n array(0., dtype=float32)\\n \\\"\\\"\\\"\\n return tree_map(stop_gradient_p.bind, x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_safe_mul\",\n \"text\": \"jax/lax/lax.py::_safe_mul\\ndef _safe_mul(x, y): return safe_mul_p.bind(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::conv\",\n \"text\": \"jax/lax/lax.py::conv\\ndef conv(lhs, rhs, window_strides, padding, precision=None):\\n \\\"\\\"\\\"Convenience wrapper around `conv_general_dilated`.\\n\\n Args:\\n lhs: a rank `n+2` dimensional input array.\\n rhs: a rank `n+2` dimensional array of kernel weights.\\n window_strides: a sequence of `n` integers, representing the inter-window\\n strides.\\n padding: either the string `'SAME'`, the string `'VALID'`.\\n precision: Optional. Either `None`, which means the default precision for\\n the backend, or a `Precision` enum value.\\n\\n Returns:\\n An array containing the convolution result.\\n \\\"\\\"\\\"\\n pads = padtype_to_pads(lhs.shape[2:], rhs.shape[2:], window_strides, padding)\\n return conv_general_dilated(lhs, rhs, window_strides, padding,\\n precision=precision)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::conv_with_general_padding\",\n \"text\": \"jax/lax/lax.py::conv_with_general_padding\\ndef conv_with_general_padding(lhs, rhs, window_strides, padding,\\n lhs_dilation, rhs_dilation, precision=None):\\n \\\"\\\"\\\"Convenience wrapper around `conv_general_dilated`.\\n\\n Args:\\n lhs: a rank `n+2` dimensional input array.\\n rhs: a rank `n+2` dimensional array of kernel weights.\\n window_strides: a sequence of `n` integers, representing the inter-window\\n strides.\\n padding: either the string `'SAME'`, the string `'VALID'`, or a sequence of\\n `n` `(low, high)` integer pairs that give the padding to apply before and\\n after each spatial dimension.\\n lhs_dilation: `None`, or a sequence of `n` integers, giving the\\n dilation factor to apply in each spatial dimension of `lhs`. LHS dilation\\n is also known as transposed convolution.\\n rhs_dilation: `None`, or a sequence of `n` integers, giving the\\n dilation factor to apply in each spatial dimension of `rhs`. RHS dilation\\n is also known as atrous convolution.\\n precision: Optional. Either `None`, which means the default precision for\\n the backend, or a `Precision` enum value.\\n\\n Returns:\\n An array containing the convolution result.\\n \\\"\\\"\\\"\\n return conv_general_dilated(\\n lhs, rhs, window_strides, padding, lhs_dilation=lhs_dilation,\\n rhs_dilation=rhs_dilation, precision=precision)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_conv_transpose_padding\",\n \"text\": \"jax/lax/lax.py::_conv_transpose_padding\\ndef _conv_transpose_padding(k, s, padding):\\n \\\"\\\"\\\"Calculate before and after padding for a dim of transposed convolution.\\n\\n Args:\\n k: int: kernel dimension.\\n s: int: dimension stride value.\\n padding: 'same' or 'valid' padding mode for original forward conv.\\n\\n Returns:\\n 2-tuple: ints: before and after padding for transposed convolution.\\n \\\"\\\"\\\"\\n if padding == 'SAME':\\n pad_len = k + s - 2\\n if s > k - 1:\\n pad_a = k - 1\\n else:\\n pad_a = int(onp.ceil(pad_len / 2))\\n elif padding == 'VALID':\\n pad_len = k + s - 2 + _max(k - s, 0)\\n pad_a = k - 1\\n else:\\n raise ValueError('Padding mode must be `SAME` or `VALID`.')\\n pad_b = pad_len - pad_a\\n return pad_a, pad_b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_flip_axes\",\n \"text\": \"jax/lax/lax.py::_flip_axes\\ndef _flip_axes(x, axes):\\n \\\"\\\"\\\"Flip ndarray 'x' along each axis specified in axes tuple.\\\"\\\"\\\"\\n for axis in axes:\\n x = onp.flip(x, axis)\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::conv_transpose\",\n \"text\": \"jax/lax/lax.py::conv_transpose\\ndef conv_transpose(lhs, rhs, strides, padding, rhs_dilation=None,\\n dimension_numbers=None, transpose_kernel=False, precision=None):\\n \\\"\\\"\\\"Convenience wrapper for calculating the N-d convolution \\\"transpose\\\".\\n\\n This function directly calculates a fractionally strided conv rather than\\n indirectly calculating the gradient (transpose) of a forward convolution.\\n\\n Args:\\n lhs: a rank `n+2` dimensional input array.\\n rhs: a rank `n+2` dimensional array of kernel weights.\\n strides: sequence of `n` integers, sets fractional stride.\\n padding: 'SAME', 'VALID' will set as transpose of corresponding forward\\n conv, or a sequence of `n` integer 2-tuples describing before-and-after\\n padding for each `n` spatial dimension.\\n rhs_dilation: `None`, or a sequence of `n` integers, giving the\\n dilation factor to apply in each spatial dimension of `rhs`. RHS dilation\\n is also known as atrous convolution.\\n dimension_numbers: tuple of dimension descriptors as in\\n lax.conv_general_dilated. Defaults to tensorflow convention.\\n transpose_kernel: if True flips spatial axes and swaps the input/output\\n channel axes of the kernel. This makes the output of this function identical\\n to the gradient-derived functions like keras.layers.Conv2DTranspose\\n applied to the same kernel. For typical use in neural nets this is completely\\n pointless and just makes input/output channel specification confusing.\\n precision: Optional. Either `None`, which means the default precision for\\n the backend, or a `Precision` enum value.\\n\\n Returns:\\n Transposed N-d convolution, with output padding following the conventions of\\n keras.layers.Conv2DTranspose.\\n \\\"\\\"\\\"\\n assert len(lhs.shape) == len(rhs.shape) and len(lhs.shape) > 2\\n ndims = len(lhs.shape)\\n one = (1,) * (ndims - 2)\\n # Set dimensional layout defaults if not specified.\\n if dimension_numbers is None:\\n if ndims == 3:\\n dimension_numbe\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::full_like\",\n \"text\": \"jax/lax/lax.py::full_like\\ndef full_like(x, fill_value, dtype=None, shape=None):\\n \\\"\\\"\\\"Create a full array like np.full based on the example array `x`.\\n\\n Args:\\n x: example array-like, used for shape and dtype information.\\n fill_value: a scalar value to fill the entries of the output array.\\n dtype: optional, a dtype parameter for the output ndarray.\\n shape: optional, a shape parameter for the output ndarray.\\n\\n Returns:\\n An ndarray with the same shape as `x` with its entries set equal to\\n `fill_value`, similar to the output of np.full.\\n \\\"\\\"\\\"\\n shape = onp.shape(x) if shape is None else _canonicalize_shape(shape)\\n fill_value = tie_in(x, fill_value)\\n return full(shape, fill_value, dtype or _dtype(x))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::collapse\",\n \"text\": \"jax/lax/lax.py::collapse\\ndef collapse(operand, start_dimension, stop_dimension):\\n lo, hi = start_dimension, stop_dimension\\n size = prod(operand.shape[lo:hi])\\n new_shape = operand.shape[:lo] + (size,) + operand.shape[hi:]\\n return reshape(operand, new_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::slice_in_dim\",\n \"text\": \"jax/lax/lax.py::slice_in_dim\\ndef slice_in_dim(operand, start_index, limit_index, stride=1, axis=0):\\n \\\"\\\"\\\"Convenience wrapper around slice applying to only one dimension.\\\"\\\"\\\"\\n start_indices = [0] * operand.ndim\\n limit_indices = list(operand.shape)\\n strides = [1] * operand.ndim\\n\\n axis = int(axis)\\n start_indices[axis] = int(start_index)\\n limit_indices[axis] = int(limit_index)\\n strides[axis] = int(stride)\\n\\n return slice(operand, start_indices, limit_indices, strides)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::index_in_dim\",\n \"text\": \"jax/lax/lax.py::index_in_dim\\ndef index_in_dim(operand, index, axis=0, keepdims=True):\\n \\\"\\\"\\\"Convenience wrapper around slice to perform int indexing.\\\"\\\"\\\"\\n index, axis = int(index), int(axis)\\n axis_size = operand.shape[axis]\\n wrapped_index = index + axis_size if index < 0 else index\\n if not 0 <= wrapped_index < axis_size:\\n msg = 'index {} is out of bounds for axis {} with size {}'\\n raise IndexError(msg.format(index, axis, axis_size))\\n result = slice_in_dim(operand, wrapped_index, wrapped_index + 1, 1, axis)\\n if keepdims:\\n return result\\n else:\\n return reshape(result, onp.delete(operand.shape, axis))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::dynamic_slice_in_dim\",\n \"text\": \"jax/lax/lax.py::dynamic_slice_in_dim\\ndef dynamic_slice_in_dim(operand, start_index, slice_size, axis=0):\\n \\\"\\\"\\\"Convenience wrapper around dynamic_slice applying to one dimension.\\\"\\\"\\\"\\n start_indices = [_zero(start_index)] * operand.ndim\\n slice_sizes = list(operand.shape)\\n\\n axis = int(axis)\\n start_indices[axis] = start_index\\n slice_sizes[axis] = int(slice_size)\\n return dynamic_slice(operand, start_indices, slice_sizes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::dynamic_index_in_dim\",\n \"text\": \"jax/lax/lax.py::dynamic_index_in_dim\\ndef dynamic_index_in_dim(operand, index, axis=0, keepdims=True):\\n \\\"\\\"\\\"Convenience wrapper around dynamic_slice to perform int indexing.\\\"\\\"\\\"\\n result = dynamic_slice_in_dim(operand, index, 1, axis)\\n if keepdims:\\n return result\\n else:\\n return reshape(result, onp.delete(operand.shape, axis))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::dynamic_update_slice_in_dim\",\n \"text\": \"jax/lax/lax.py::dynamic_update_slice_in_dim\\ndef dynamic_update_slice_in_dim(operand, update, start_index, axis):\\n axis = int(axis)\\n start_indices = [_zero(start_index)] * _ndim(operand)\\n start_indices[axis] = start_index\\n return dynamic_update_slice(operand, update, start_indices)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::dynamic_update_index_in_dim\",\n \"text\": \"jax/lax/lax.py::dynamic_update_index_in_dim\\ndef dynamic_update_index_in_dim(operand, update, index, axis):\\n axis = int(axis)\\n if _ndim(update) != _ndim(operand):\\n assert _ndim(update) + 1 == _ndim(operand)\\n ax = axis % _ndim(operand)\\n update = reshape(update, operand.shape[:ax] + (1,) + operand.shape[ax+1:])\\n return dynamic_update_slice_in_dim(operand, update, index, axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::batch_matmul\",\n \"text\": \"jax/lax/lax.py::batch_matmul\\ndef batch_matmul(lhs, rhs, precision=None):\\n \\\"\\\"\\\"Batch matrix multiplication.\\\"\\\"\\\"\\n if _min(lhs.ndim, rhs.ndim) < 2:\\n raise ValueError('Arguments to batch_matmul must be at least 2D, got {}, {}'\\n .format(lhs.ndim, rhs.ndim))\\n if lhs.ndim != rhs.ndim:\\n raise ValueError('Arguments to batch_matmul must have same ndim, got {}, {}'\\n .format(lhs.ndim, rhs.ndim))\\n lhs_contract = (lhs.ndim - 1,)\\n rhs_contract = (rhs.ndim - 2,)\\n batch = tuple(range(lhs.ndim - 2))\\n return dot_general(lhs, rhs, [(lhs_contract, rhs_contract), (batch, batch)],\\n precision=precision)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::square\",\n \"text\": \"jax/lax/lax.py::square\\ndef square(x):\\n r\\\"\\\"\\\"Elementwise square: :math:`x^2`.\\\"\\\"\\\"\\n return mul(x, x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::reciprocal\",\n \"text\": \"jax/lax/lax.py::reciprocal\\ndef reciprocal(x):\\n r\\\"\\\"\\\"Elementwise reciprocal: :math:`1 \\\\over x`.\\\"\\\"\\\"\\n return div(_const(x, 1), x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_upcast_fp16_for_computation\",\n \"text\": \"jax/lax/lax.py::_upcast_fp16_for_computation\\ndef _upcast_fp16_for_computation(f):\\n @functools.wraps(f)\\n def f_wrapped(x):\\n dtype = _dtype(x)\\n if dtype == onp.float16 or dtype == dtypes.bfloat16:\\n return convert_element_type(\\n f(convert_element_type(x, onp.float32)), dtype)\\n return f(x)\\n\\n return f_wrapped\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_upcast_fp16_for_computation.f_wrapped\",\n \"text\": \"jax/lax/lax.py::_upcast_fp16_for_computation.f_wrapped\\n @functools.wraps(f)\\n def f_wrapped(x):\\n dtype = _dtype(x)\\n if dtype == onp.float16 or dtype == dtypes.bfloat16:\\n return convert_element_type(\\n f(convert_element_type(x, onp.float32)), dtype)\\n return f(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::tan\",\n \"text\": \"jax/lax/lax.py::tan\\n@api.jit\\n@_upcast_fp16_for_computation\\ndef tan(x):\\n r\\\"\\\"\\\"Elementwise tangent: :math:`\\\\mathrm{tan}(x)`.\\\"\\\"\\\"\\n return div(sin(x), cos(x))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::asin\",\n \"text\": \"jax/lax/lax.py::asin\\n@api.jit\\ndef asin(x):\\n r\\\"\\\"\\\"Elementwise arc sine: :math:`\\\\mathrm{asin}(x)`.\\\"\\\"\\\"\\n return mul(_const(x, 2),\\n atan2(x, add(_const(x, 1), sqrt(sub(_const(x, 1), square(x))))))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::acos\",\n \"text\": \"jax/lax/lax.py::acos\\n@api.jit\\ndef acos(x):\\n r\\\"\\\"\\\"Elementwise arc cosine: :math:`\\\\mathrm{acos}(x)`.\\\"\\\"\\\"\\n return select(\\n ne(x, _const(x, -1.0)),\\n mul(_const(x, 2),\\n atan2(sqrt(sub(_const(x, 1), square(x))), add(_const(x, 1), x))),\\n full_like(x, onp.pi))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::atan\",\n \"text\": \"jax/lax/lax.py::atan\\ndef atan(x):\\n r\\\"\\\"\\\"Elementwise arc tangent: :math:`\\\\mathrm{atan}(x)`.\\\"\\\"\\\"\\n return atan2(x, _const(x, 1))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::sinh\",\n \"text\": \"jax/lax/lax.py::sinh\\n@api.jit\\n@_upcast_fp16_for_computation\\ndef sinh(x):\\n r\\\"\\\"\\\"Elementwise hyperbolic sine: :math:`\\\\mathrm{sinh}(x)`.\\\"\\\"\\\"\\n log_half = _const(x, onp.log(0.5))\\n # This formulation avoids overflow when e^x is inf but e^x/2 is not inf.\\n return sub(exp(add(log_half, x)), exp(sub(log_half, x)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::cosh\",\n \"text\": \"jax/lax/lax.py::cosh\\n@api.jit\\n@_upcast_fp16_for_computation\\ndef cosh(x):\\n r\\\"\\\"\\\"Elementwise hyperbolic cosine: :math:`\\\\mathrm{cosh}(x)`.\\\"\\\"\\\"\\n log_half = _const(x, onp.log(0.5))\\n # This formulation avoids overflow when e^x is inf but e^x/2 is not inf.\\n return add(exp(add(log_half, x)), exp(sub(log_half, x)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_iter\",\n \"text\": \"jax/lax/lax.py::_iter\\ndef _iter(tracer):\\n if tracer.ndim == 0:\\n raise TypeError(\\\"iteration over a 0-d array\\\") # same as numpy error\\n else:\\n n = tracer.shape[0]\\n # return (index_in_dim(tracer, i, keepdims=False) for i in range(n))\\n return iter([index_in_dim(tracer, i, keepdims=False) for i in range(n)])\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::zeros_like_array\",\n \"text\": \"jax/lax/lax.py::zeros_like_array\\ndef zeros_like_array(x):\\n return full_like(x, 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::standard_primitive\",\n \"text\": \"jax/lax/lax.py::standard_primitive\\ndef standard_primitive(shape_rule, dtype_rule, name, translation_rule=None):\\n prim = Primitive(name)\\n prim.def_impl(partial(xla.apply_primitive, prim))\\n prim.def_abstract_eval(partial(standard_abstract_eval, prim, shape_rule, dtype_rule))\\n xla.translations[prim] = translation_rule or partial(standard_translate, name)\\n return prim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::standard_abstract_eval\",\n \"text\": \"jax/lax/lax.py::standard_abstract_eval\\ndef standard_abstract_eval(prim, shape_rule, dtype_rule, *args, **kwargs):\\n assert all(isinstance(arg, UnshapedArray) for arg in args), args\\n least_specialized = _max(\\n map(type, args), key=operator.attrgetter('array_abstraction_level'))\\n if least_specialized is ConcreteArray:\\n msg = (\\\"If you see this error, please let us know by opening an issue at\\\\n\\\"\\n \\\"https://github.com/google/jax/issues \\\\n\\\"\\n \\\"since we thought this was unreachable!\\\")\\n assert pe._thread_local_state.remat, msg\\n return ConcreteArray(prim.impl(*[x.val for x in args], **kwargs))\\n elif least_specialized is ShapedArray:\\n return ShapedArray(shape_rule(*args, **kwargs), dtype_rule(*args, **kwargs))\\n elif least_specialized is UnshapedArray:\\n return UnshapedArray(dtype_rule(*args, **kwargs))\\n else:\\n raise TypeError(args, least_specialized)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::standard_translate\",\n \"text\": \"jax/lax/lax.py::standard_translate\\ndef standard_translate(name, c, *args, **kwargs):\\n xla_opname = ''.join(term.capitalize() for term in name.split('_'))\\n return getattr(c, xla_opname)(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::unop_dtype_rule\",\n \"text\": \"jax/lax/lax.py::unop_dtype_rule\\ndef unop_dtype_rule(result_dtype, accepted_dtypes, name, aval, **kwargs):\\n if not any(dtypes.issubdtype(aval.dtype, t) for t in accepted_dtypes):\\n msg = '{} does not accept dtype {}. Accepted dtypes are subtypes of {}.'\\n typename = str(onp.dtype(aval.dtype).name)\\n accepted_typenames = (t.__name__ for t in accepted_dtypes)\\n raise TypeError(msg.format(name, typename, ', '.join(accepted_typenames)))\\n return result_dtype(aval.dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::unop\",\n \"text\": \"jax/lax/lax.py::unop\\ndef unop(result_dtype, accepted_dtypes, name, translation_rule=None):\\n dtype_rule = partial(unop_dtype_rule, result_dtype, accepted_dtypes, name)\\n prim = standard_primitive(_attrgetter('shape'), dtype_rule, name,\\n translation_rule=translation_rule)\\n batching.defvectorized(prim)\\n masking.defvectorized(prim)\\n return prim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::binop_dtype_rule\",\n \"text\": \"jax/lax/lax.py::binop_dtype_rule\\ndef binop_dtype_rule(result_dtype, accepted_dtypes, name, *avals, **kwargs):\\n aval_dtypes = [aval.dtype for aval in avals]\\n for i, (aval_dtype, types) in enumerate(zip(aval_dtypes, accepted_dtypes)):\\n if not any(dtypes.issubdtype(aval_dtype, t) for t in types):\\n msg = ('{} does not accept dtype {} at position {}. '\\n 'Accepted dtypes at position {} are subtypes of {}.')\\n typename = str(onp.dtype(aval_dtype).name)\\n typenames = ', '.join(t.__name__ for t in types)\\n raise TypeError(msg.format(name, typename, i, i, typenames))\\n _check_same_dtypes(name, False, *aval_dtypes)\\n return result_dtype(*avals)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_broadcasting_shape_rule\",\n \"text\": \"jax/lax/lax.py::_broadcasting_shape_rule\\ndef _broadcasting_shape_rule(name, *avals):\\n shapes = onp.array([aval.shape for aval in avals if aval.shape])\\n if not shapes.size:\\n return ()\\n if len({len(shape) for shape in shapes}) != 1:\\n msg = '{} got arrays of different rank: {}.'\\n raise TypeError(msg.format(name, ', '.join(map(str, map(tuple, shapes)))))\\n min_shape = onp.min(shapes, axis=0)\\n max_shape = onp.max(shapes, axis=0)\\n result_shape = onp.where(min_shape == 0, 0, max_shape)\\n if not onp.all((shapes == result_shape) | (shapes == 1)):\\n msg = '{} got incompatible shapes for broadcasting: {}.'\\n raise TypeError(msg.format(name, ', '.join(map(str, map(tuple, shapes)))))\\n return tuple(result_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::binop\",\n \"text\": \"jax/lax/lax.py::binop\\ndef binop(result_dtype, accepted_dtypes, name, translation_rule=None):\\n dtype_rule = partial(binop_dtype_rule, result_dtype, accepted_dtypes, name)\\n shape_rule = partial(_broadcasting_shape_rule, name)\\n prim = standard_primitive(shape_rule, dtype_rule, name,\\n translation_rule=translation_rule)\\n batching.defbroadcasting(prim)\\n masking.defbinop(prim)\\n return prim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_brcast\",\n \"text\": \"jax/lax/lax.py::_brcast\\ndef _brcast(x, *others):\\n # Used in jvprules to make binop broadcasting explicit for transposability.\\n # Requires shape info during jvp tracing, which isn't strictly necessary.\\n # We don't need full numpy broadcasting, but otherwise the logic is the same\\n # so we reuse the broadcast_shapes function after filtering out scalars.\\n shapes = tuple(filter(None, map(onp.shape, (x,) + others)))\\n shape = shapes and broadcast_shapes(*shapes)\\n if onp.shape(x) != shape:\\n return _brcast_to(x, shape)\\n else:\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_brcast_to\",\n \"text\": \"jax/lax/lax.py::_brcast_to\\ndef _brcast_to(x, shape):\\n x_shape = onp.shape(x)\\n assert x_shape != shape\\n if x_shape:\\n assert len(x_shape) == len(shape)\\n broadcast_dimensions, = onp.where(onp.equal(x_shape, shape))\\n squeezed_dimensions, = onp.where(onp.not_equal(x_shape, shape))\\n inshape = onp.delete(x_shape, squeezed_dimensions)\\n return broadcast_in_dim(reshape(x, inshape), shape, broadcast_dimensions)\\n else:\\n return broadcast(x, shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_sign_translation_rule\",\n \"text\": \"jax/lax/lax.py::_sign_translation_rule\\ndef _sign_translation_rule(c, x):\\n shape = c.GetShape(x)\\n dtype = shape.numpy_dtype()\\n if dtypes.issubdtype(dtype, onp.unsignedinteger):\\n zero = c.Constant(onp.array(0, dtype=dtype))\\n dims = c.GetShape(x).dimensions()\\n return c.Select(c.Eq(x, zero), c.Broadcast(zero, dims),\\n c.Broadcast(c.Constant(onp.array(1, dtype=dtype)), dims))\\n return c.Sign(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_bessel_i1e_jvp\",\n \"text\": \"jax/lax/lax.py::_bessel_i1e_jvp\\ndef _bessel_i1e_jvp(g, y, x):\\n eps = dtypes.finfo(_dtype(x)).eps\\n x_is_not_tiny = abs(x) > eps\\n safe_x = select(x_is_not_tiny, x, full_like(x, eps))\\n dy_dx = bessel_i0e(safe_x) - y * (sign(safe_x) + reciprocal(safe_x))\\n dy_dx = select(x_is_not_tiny, dy_dx, full_like(x, 0.5))\\n return g * dy_dx\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_conj_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_conj_transpose_rule\\ndef _conj_transpose_rule(t, x, input_dtype):\\n assert x is ad.undefined_primal\\n if dtypes.issubdtype(input_dtype, onp.complexfloating):\\n return [conj(t)]\\n else:\\n return [real(t)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_abs_jvp_rule\",\n \"text\": \"jax/lax/lax.py::_abs_jvp_rule\\ndef _abs_jvp_rule(g, ans, x):\\n if _iscomplex(x):\\n return _maybe_real(mul(g, div(_maybe_conj(x),\\n _replace_zero(convert_element_type(ans, _dtype(x))))))\\n else:\\n return select(ge(x, _zero(x)), g, neg(g))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_pow_jvp_lhs\",\n \"text\": \"jax/lax/lax.py::_pow_jvp_lhs\\ndef _pow_jvp_lhs(g, ans, x, y):\\n # we call _safe_mul here so that we get the behavior 0*inf = 0, since when a\\n # coefficient in `g` is zero we want to keep it at zero, not produce a nan.\\n # see https://github.com/google/jax/pull/383\\n jac = mul(y, pow(x, select(eq(y, _zeros(y)), _ones(y), sub(y, _ones(y)))))\\n return _safe_mul(_brcast(g, y), jac)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_pow_jvp_rhs\",\n \"text\": \"jax/lax/lax.py::_pow_jvp_rhs\\ndef _pow_jvp_rhs(g, ans, x, y):\\n return mul(_brcast(g, x), mul(log(_replace_zero(x)), ans))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_add_transpose\",\n \"text\": \"jax/lax/lax.py::_add_transpose\\ndef _add_transpose(t, x, y):\\n # assert x is ad.undefined_primal and y is ad.undefined_primal # not affine\\n return [t, t]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_sub_transpose\",\n \"text\": \"jax/lax/lax.py::_sub_transpose\\ndef _sub_transpose(t, x, y):\\n assert x is ad.undefined_primal and y is ad.undefined_primal # not affine\\n return [t, neg(t) if t is not ad_util.zero else ad_util.zero]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_safe_mul_translation_rule\",\n \"text\": \"jax/lax/lax.py::_safe_mul_translation_rule\\ndef _safe_mul_translation_rule(c, x, y):\\n dtype = c.GetShape(x).numpy_dtype()\\n zero = c.Constant(onp.array(0, dtype=dtype))\\n out_shape = broadcast_shapes(c.GetShape(x).dimensions(),\\n c.GetShape(y).dimensions())\\n return c.Select(c.Or(c.Eq(x, zero), c.Eq(y, zero)),\\n c.Broadcast(zero, out_shape),\\n c.Mul(x, y))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_div_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_div_transpose_rule\\ndef _div_transpose_rule(cotangent, x, y):\\n assert x is ad.undefined_primal and y is not ad.undefined_primal\\n res = ad_util.zero if cotangent is ad_util.zero else div(cotangent, y)\\n return res, None\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_broadcasting_select\",\n \"text\": \"jax/lax/lax.py::_broadcasting_select\\ndef _broadcasting_select(c, which, x, y):\\n \\\"\\\"\\\"Wrapper around XLA `Select` that broadcasts its arguments.\\\"\\\"\\\"\\n which_shape, x_shape, y_shape = (\\n c.GetShape(t).dimensions() for t in (which, x, y))\\n out_shape = broadcast_shapes(which_shape, x_shape, y_shape)\\n bcast_dims = lambda shape: tuple(range(len(out_shape) - len(shape),\\n len(out_shape)))\\n which = c.BroadcastInDim(which, out_shape, bcast_dims(which_shape))\\n x = c.BroadcastInDim(x, out_shape, bcast_dims(x_shape))\\n y = c.BroadcastInDim(y, out_shape, bcast_dims(y_shape))\\n return c.Select(which, x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_minmax_translation_rule\",\n \"text\": \"jax/lax/lax.py::_minmax_translation_rule\\ndef _minmax_translation_rule(c, x, y, minmax=None, cmp=None):\\n dtype = c.GetShape(x).numpy_dtype()\\n if dtypes.issubdtype(dtype, onp.complexfloating):\\n comparator = cmp(c)\\n rx = c.Real(x)\\n ry = c.Real(y)\\n return _broadcasting_select(\\n c, c.Select(c.Eq(rx, ry), comparator(c.Imag(x), c.Imag(y)),\\n comparator(rx, ry)),\\n x, y)\\n return minmax(c)(x, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_convert_element_type_shape_rule\",\n \"text\": \"jax/lax/lax.py::_convert_element_type_shape_rule\\ndef _convert_element_type_shape_rule(operand, new_dtype, old_dtype):\\n return operand.shape\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_convert_element_type_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_convert_element_type_dtype_rule\\ndef _convert_element_type_dtype_rule(operand, new_dtype, old_dtype):\\n return new_dtype\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_convert_element_type_translation_rule\",\n \"text\": \"jax/lax/lax.py::_convert_element_type_translation_rule\\ndef _convert_element_type_translation_rule(c, operand, new_dtype, old_dtype):\\n new_etype = xla_client.dtype_to_etype(new_dtype)\\n return c.ConvertElementType(operand, new_element_type=new_etype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_bitcast_convert_type_shape_rule\",\n \"text\": \"jax/lax/lax.py::_bitcast_convert_type_shape_rule\\ndef _bitcast_convert_type_shape_rule(operand, new_dtype):\\n return operand.shape\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_bitcast_convert_type_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_bitcast_convert_type_dtype_rule\\ndef _bitcast_convert_type_dtype_rule(operand, new_dtype):\\n return new_dtype\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_bitcast_convert_type_translation_rule\",\n \"text\": \"jax/lax/lax.py::_bitcast_convert_type_translation_rule\\ndef _bitcast_convert_type_translation_rule(c, operand, new_dtype):\\n new_etype = xla_bridge.dtype_to_etype(new_dtype)\\n return c.BitcastConvertType(operand, new_element_type=new_etype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_conv_general_dilated_shape_rule\",\n \"text\": \"jax/lax/lax.py::_conv_general_dilated_shape_rule\\ndef _conv_general_dilated_shape_rule(\\n lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\\n dimension_numbers, feature_group_count, **unused_kwargs):\\n assert type(dimension_numbers) is ConvDimensionNumbers\\n if not feature_group_count > 0:\\n msg = (\\\"conv_general_dilated feature_group_count \\\"\\n \\\"must be a positive integer, got {}.\\\")\\n raise ValueError(msg.format(feature_group_count))\\n lhs_feature_count = lhs.shape[dimension_numbers.lhs_spec[1]]\\n quot, rem = divmod(lhs_feature_count, feature_group_count)\\n if rem:\\n msg = (\\\"conv_general_dilated feature_group_count must divide lhs feature \\\"\\n \\\"dimension size, but {} does not divide {}.\\\")\\n raise ValueError(msg.format(feature_group_count, lhs_feature_count))\\n if quot != rhs.shape[dimension_numbers.rhs_spec[1]]:\\n msg = (\\\"conv_general_dilated lhs feature dimension size divided by \\\"\\n \\\"feature_group_count must equal the rhs input feature dimension \\\"\\n \\\"size, but {} // {} != {}.\\\")\\n raise ValueError(msg.format(lhs_feature_count, feature_group_count,\\n rhs.shape[dimension_numbers.rhs_spec[1]]))\\n if rhs.shape[dimension_numbers.rhs_spec[0]] % feature_group_count:\\n msg = (\\\"conv_general_dilated rhs output feature dimension size must be a \\\"\\n \\\"multiple of feature_group_count, but {} is not a multiple of {}.\\\")\\n raise ValueError(msg.format(rhs.shape[dimension_numbers.rhs_spec[0]],\\n feature_group_count))\\n lhs_perm, rhs_perm, out_perm = dimension_numbers\\n lhs_trans = _dilate_shape(onp.take(lhs.shape, lhs_perm), lhs_dilation)\\n rhs_trans = _dilate_shape(onp.take(rhs.shape, rhs_perm), rhs_dilation)\\n out_trans = conv_shape_tuple(lhs_trans, rhs_trans, window_strides, padding)\\n return tuple(onp.take(out_trans, onp.argsort(out_perm)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_conv_general_dilated_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_conv_general_dilated_dtype_rule\\ndef _conv_general_dilated_dtype_rule(\\n lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\\n dimension_numbers, **unused_kwargs):\\n return binop_dtype_rule(_input_dtype, [_float, _float],\\n 'conv_general_dilated', lhs, rhs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_conv_general_dilated_transpose_lhs\",\n \"text\": \"jax/lax/lax.py::_conv_general_dilated_transpose_lhs\\ndef _conv_general_dilated_transpose_lhs(\\n g, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\\n dimension_numbers, feature_group_count,\\n lhs_shape, rhs_shape, precision):\\n assert type(dimension_numbers) is ConvDimensionNumbers\\n lhs_sdims, rhs_sdims, out_sdims = map(_conv_sdims, dimension_numbers)\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n t_rhs_spec = _conv_spec_transpose(rhs_spec)\\n if feature_group_count > 1:\\n # in addition to switching the dims in the spec, need to move the feature\\n # group axis into the transposed rhs's output feature dim\\n rhs = _reshape_axis_out_of(rhs_spec[0], feature_group_count, rhs)\\n rhs = _reshape_axis_into(rhs_spec[0], rhs_spec[1], rhs)\\n trans_dimension_numbers = ConvDimensionNumbers(out_spec, t_rhs_spec, lhs_spec)\\n padding = _conv_general_vjp_lhs_padding(\\n onp.take(lhs_shape, lhs_sdims), onp.take(rhs_shape, rhs_sdims),\\n window_strides, onp.take(g.shape, out_sdims), padding, lhs_dilation,\\n rhs_dilation)\\n revd_weights = rev(rhs, rhs_sdims)\\n return conv_general_dilated(\\n g, revd_weights, window_strides=lhs_dilation, padding=padding,\\n lhs_dilation=window_strides, rhs_dilation=rhs_dilation,\\n dimension_numbers=trans_dimension_numbers,\\n feature_group_count=feature_group_count, precision=precision)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_conv_general_dilated_transpose_rhs\",\n \"text\": \"jax/lax/lax.py::_conv_general_dilated_transpose_rhs\\ndef _conv_general_dilated_transpose_rhs(\\n g, lhs, window_strides, padding, lhs_dilation, rhs_dilation,\\n dimension_numbers, feature_group_count,\\n lhs_shape, rhs_shape, precision):\\n assert type(dimension_numbers) is ConvDimensionNumbers\\n if onp.size(g) == 0:\\n # Avoids forming degenerate convolutions where the RHS has spatial size 0.\\n return ad_util.zero\\n lhs_sdims, rhs_sdims, out_sdims = map(_conv_sdims, dimension_numbers)\\n lhs_trans, rhs_trans, out_trans = map(_conv_spec_transpose, dimension_numbers)\\n if feature_group_count > 1:\\n lhs = _reshape_axis_out_of(lhs_trans[0], feature_group_count, lhs)\\n lhs = _reshape_axis_into(lhs_trans[0], lhs_trans[1], lhs)\\n trans_dimension_numbers = ConvDimensionNumbers(lhs_trans, out_trans, rhs_trans)\\n padding = _conv_general_vjp_rhs_padding(\\n onp.take(lhs_shape, lhs_sdims), onp.take(rhs_shape, rhs_sdims),\\n window_strides, onp.take(g.shape, out_sdims), padding, lhs_dilation,\\n rhs_dilation)\\n return conv_general_dilated(\\n lhs, g, window_strides=rhs_dilation, padding=padding,\\n lhs_dilation=lhs_dilation, rhs_dilation=window_strides,\\n dimension_numbers=trans_dimension_numbers,\\n feature_group_count=feature_group_count, precision=precision)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_conv_general_dilated_translation_rule\",\n \"text\": \"jax/lax/lax.py::_conv_general_dilated_translation_rule\\ndef _conv_general_dilated_translation_rule(\\n c, lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\\n dimension_numbers, feature_group_count, precision, **unused_kwargs):\\n assert type(dimension_numbers) is ConvDimensionNumbers\\n dimension_numbers = _conv_general_proto(dimension_numbers)\\n return c.ConvGeneralDilated(lhs, rhs, window_strides, padding, lhs_dilation,\\n rhs_dilation, dimension_numbers,\\n feature_group_count,\\n precision_config=_precision_config(precision))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_conv_general_dilated_batch_rule\",\n \"text\": \"jax/lax/lax.py::_conv_general_dilated_batch_rule\\ndef _conv_general_dilated_batch_rule(\\n batched_args, batch_dims, window_strides, padding,\\n lhs_dilation, rhs_dilation, dimension_numbers,\\n feature_group_count, precision, **unused_kwargs):\\n lhs, rhs = batched_args\\n lhs_bdim, rhs_bdim = batch_dims\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n\\n if lhs_bdim is not None and rhs_bdim is not None:\\n assert lhs.shape[lhs_bdim] == rhs.shape[rhs_bdim]\\n new_lhs = _reshape_axis_into(lhs_bdim, lhs_spec[1], lhs)\\n new_rhs = _reshape_axis_into(rhs_bdim, rhs_spec[0], rhs)\\n out = conv_general_dilated(\\n new_lhs, new_rhs, window_strides, padding, lhs_dilation, rhs_dilation,\\n dimension_numbers,\\n feature_group_count=lhs.shape[lhs_bdim] * feature_group_count,\\n precision=precision)\\n out = _reshape_axis_out_of(out_spec[1], lhs.shape[lhs_bdim], out)\\n return out, out_spec[1]\\n\\n elif lhs_bdim is not None:\\n new_lhs = _reshape_axis_into(lhs_bdim, lhs_spec[0], lhs)\\n out = conv_general_dilated(new_lhs, rhs, window_strides, padding,\\n lhs_dilation, rhs_dilation, dimension_numbers,\\n feature_group_count, precision=precision)\\n out = _reshape_axis_out_of(out_spec[0], lhs.shape[lhs_bdim], out)\\n return out, out_spec[0]\\n\\n elif rhs_bdim is not None:\\n if feature_group_count == 1:\\n new_rhs = _reshape_axis_into(rhs_bdim, rhs_spec[0], rhs)\\n out = conv_general_dilated(lhs, new_rhs, window_strides, padding,\\n lhs_dilation, rhs_dilation, dimension_numbers,\\n feature_group_count, precision=precision)\\n out = _reshape_axis_out_of(out_spec[1], rhs.shape[rhs_bdim], out)\\n return out, out_spec[1]\\n else:\\n # feature_group needs to be outermost, so we need to factor it out of the\\n # rhs output feature dim, then factor the batch dim into the remaining rhs\\n # output feature dim, then put feature_group \",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reshape_axis_into\",\n \"text\": \"jax/lax/lax.py::_reshape_axis_into\\ndef _reshape_axis_into(src, dst, x):\\n perm = [i for i in range(x.ndim) if i != src]\\n perm.insert(dst, src)\\n new_shape = list(onp.delete(x.shape, src))\\n new_shape[dst] *= x.shape[src]\\n return reshape(x, new_shape, perm)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reshape_axis_out_of\",\n \"text\": \"jax/lax/lax.py::_reshape_axis_out_of\\ndef _reshape_axis_out_of(src, size1, x):\\n shape = list(x.shape)\\n size2, ragged = divmod(shape[src], size1)\\n assert not ragged\\n shape[src:src+1] = [size1, size2]\\n return reshape(x, shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_precision_config\",\n \"text\": \"jax/lax/lax.py::_precision_config\\ndef _precision_config(precision):\\n if precision is not None:\\n config = xla_client.PrecisionConfig()\\n config.operand_precision.extend((precision, precision))\\n return config\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dot_general_shape_rule\",\n \"text\": \"jax/lax/lax.py::_dot_general_shape_rule\\ndef _dot_general_shape_rule(lhs, rhs, dimension_numbers, precision):\\n (lhs_contracting, rhs_contracting), (lhs_batch, rhs_batch) = dimension_numbers\\n if len(lhs_batch) != len(rhs_batch):\\n msg = (\\\"dot_general requires equal numbers of lhs_batch and rhs_batch \\\"\\n \\\"dimensions, got lhs_batch {} and rhs_batch {}.\\\")\\n raise TypeError(msg.format(lhs_batch, rhs_batch))\\n if not onp.all(onp.equal(lhs_batch, rhs_batch)):\\n msg = (\\\"dot_general requires same lhs and rhs batch dimension numbers, \\\"\\n \\\"got {} and {}.\\\")\\n raise TypeError(msg.format(lhs_batch, rhs_batch))\\n lhs_batch_shape = onp.take(lhs.shape, lhs_batch)\\n rhs_batch_shape = onp.take(rhs.shape, rhs_batch)\\n if not onp.all(onp.equal(lhs_batch_shape, rhs_batch_shape)):\\n msg = (\\\"dot_general requires lhs batch dimensions and rhs batch dimensions \\\"\\n \\\"to have the same shape, got {} and {}.\\\")\\n raise TypeError(msg.format(lhs_batch_shape, rhs_batch_shape))\\n if tuple(sorted(lhs_batch)) != tuple(range(len(lhs_batch))):\\n msg = (\\\"dot_general requires lhs batch dimensions to precede contracting \\\"\\n \\\"and non-contracting dimensions, got lhs_batch {}.\\\")\\n raise TypeError(msg.format(lhs_batch))\\n if tuple(sorted(rhs_batch)) != tuple(range(len(rhs_batch))):\\n msg = (\\\"dot_general requires rhs batch dimensions to precede contracting \\\"\\n \\\"and non-contracting dimensions, got rhs_batch {}.\\\")\\n raise TypeError(msg.format(rhs_batch))\\n lhs_contracting_shape = onp.take(lhs.shape, lhs_contracting)\\n rhs_contracting_shape = onp.take(rhs.shape, rhs_contracting)\\n if not onp.all(onp.equal(lhs_contracting_shape, rhs_contracting_shape)):\\n msg = (\\\"dot_general requires contracting dimensions to have the same \\\"\\n \\\"shape, got {} and {}.\\\")\\n raise TypeError(msg.format(lhs_contracting_shape, rhs_contracting_shape))\\n\\n batch_shape = tuple(onp.take(lhs.shape, lhs_batch))\\n lhs_contract_or_batch = tuple(lhs_contracting) + tuple(lhs_\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dot_general_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_dot_general_dtype_rule\\ndef _dot_general_dtype_rule(lhs, rhs, dimension_numbers, precision):\\n return binop_dtype_rule(_input_dtype, [_num, _num], 'dot_general', lhs, rhs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dot_general_transpose_lhs\",\n \"text\": \"jax/lax/lax.py::_dot_general_transpose_lhs\\ndef _dot_general_transpose_lhs(g, y, dimension_numbers, precision,\\n swap_ans=False):\\n (x_contract, y_contract), (x_batch, y_batch) = dimension_numbers\\n x_ndim = g.ndim - y.ndim + len(x_batch) + 2 * len(x_contract)\\n x_kept = remaining(range(x_ndim), x_contract, x_batch)\\n y_kept = remaining(range(y.ndim), y_contract, y_batch)\\n if swap_ans:\\n ans_batch, ans_y, _ = ranges_like(x_batch, y_kept, x_kept)\\n else:\\n ans_batch, _, ans_y = ranges_like(x_batch, x_kept, y_kept)\\n dims = ((ans_y, y_kept), (ans_batch, y_batch))\\n x_contract_sorted_by_y = list(onp.take(x_contract, onp.argsort(y_contract)))\\n out_axes = onp.argsort(list(x_batch) + x_kept + x_contract_sorted_by_y)\\n return transpose(dot_general(g, y, dims, precision=precision),\\n tuple(out_axes))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dot_general_transpose_rhs\",\n \"text\": \"jax/lax/lax.py::_dot_general_transpose_rhs\\ndef _dot_general_transpose_rhs(g, x, dimension_numbers, precision):\\n (x_contract, y_contract), (x_batch, y_batch) = dimension_numbers\\n swapped_dimension_numbers = ((y_contract, x_contract), (y_batch, x_batch))\\n return _dot_general_transpose_lhs(g, x, swapped_dimension_numbers,\\n precision, swap_ans=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dot_general_batch_rule\",\n \"text\": \"jax/lax/lax.py::_dot_general_batch_rule\\ndef _dot_general_batch_rule(batched_args, batch_dims, dimension_numbers,\\n precision):\\n # there are three kinds of dimensions in a dot_general:\\n # - contraction dimensions appear in lhs and rhs but not the result\\n # - batch dimensions appear in lhs, rhs, and result\\n # - tensor product dimensions appear in the result and one of lhs or rhs\\n (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers\\n lhs, rhs = batched_args\\n lbd, rbd = batch_dims\\n assert lbd is not None or rbd is not None\\n if lbd is not None and rbd is not None:\\n # adding a batch dimension\\n if lbd != 0:\\n lhs = batching.moveaxis(lhs, lbd, 0)\\n if rbd != 0:\\n rhs = batching.moveaxis(rhs, rbd, 0)\\n lhs_batch = (0,) + tuple(onp.add(1, lhs_batch))\\n rhs_batch = (0,) + tuple(onp.add(1, rhs_batch))\\n lhs_contract = tuple(onp.add(1, lhs_contract))\\n rhs_contract = tuple(onp.add(1, rhs_contract))\\n result_batch_dim = 0\\n else:\\n # adding a tensor product dimension\\n if lbd is not None:\\n if lhs_batch == () or lbd > onp.max(lhs_batch):\\n # can avoid transposes\\n bump_lhs_contract = onp.greater_equal(lhs_contract, lbd)\\n lhs_contract = tuple(onp.add(lhs_contract, bump_lhs_contract))\\n result_batch_dim = lbd - len(lhs_contract) + sum(bump_lhs_contract)\\n else:\\n # move the new dimension to the end of lhs to avoid changing batch dims\\n lhs = batching.moveaxis(lhs, lbd, lhs.ndim - 1)\\n # lhs tensor product dims in result come after batch dims\\n result_batch_dim = lhs.ndim - len(lhs_contract) - 1\\n else:\\n if rhs_batch == () or rbd > onp.max(rhs_batch):\\n # can avoid transposes\\n bump_rhs_contract = onp.greater_equal(rhs_contract, rbd)\\n rhs_contract = tuple(onp.add(rhs_contract, bump_rhs_contract))\\n result_batch_dim = (rbd + (lhs.ndim - len(lhs_contract) - len(lhs_batch))\\n - (len(rh\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dot_general_translation_rule\",\n \"text\": \"jax/lax/lax.py::_dot_general_translation_rule\\ndef _dot_general_translation_rule(c, lhs, rhs, dimension_numbers, precision):\\n return c.DotGeneral(lhs, rhs, dimension_numbers,\\n precision_config=_precision_config(precision))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dot_general_polymorphic_shape_rule\",\n \"text\": \"jax/lax/lax.py::_dot_general_polymorphic_shape_rule\\ndef _dot_general_polymorphic_shape_rule(shape_exprs, dimension_numbers,\\n precision):\\n del precision # Unused.\\n lhs_shape, rhs_shape = shape_exprs\\n lhs_ndim, rhs_ndim = len(lhs_shape), len(rhs_shape)\\n (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers\\n\\n lhs_batch_shape = [lhs_shape[d] for d in lhs_batch]\\n rhs_batch_shape = [rhs_shape[d] for d in rhs_batch]\\n if lhs_batch_shape != rhs_batch_shape: raise ShapeError\\n\\n lhs_contract_shape = [lhs_shape[d] for d in lhs_contract]\\n rhs_contract_shape = [rhs_shape[d] for d in rhs_contract]\\n if lhs_contract_shape != rhs_contract_shape: raise ShapeError\\n\\n lhs_tensorprod_shape = [lhs_shape[d] for d in range(lhs_ndim)\\n if d not in lhs_batch and d not in lhs_contract]\\n rhs_tensorprod_shape = [rhs_shape[d] for d in range(rhs_ndim)\\n if d not in rhs_batch and d not in rhs_contract]\\n return ShapeExpr(\\n lhs_batch_shape + lhs_tensorprod_shape + rhs_tensorprod_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dot_general_masking_rule\",\n \"text\": \"jax/lax/lax.py::_dot_general_masking_rule\\ndef _dot_general_masking_rule(padded_vals, logical_shapes, dimension_numbers,\\n precision):\\n lhs, rhs = padded_vals\\n lhs_shape, rhs_shape = logical_shapes\\n lhs_ndim, rhs_ndim = len(lhs_shape), len(rhs_shape)\\n (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers\\n\\n # we need only mask the lhs contraction dimensions\\n if len(lhs_contract) == 0:\\n return dot_general(lhs, rhs, dimension_numbers, precision=precision)\\n else:\\n masks = [broadcasted_iota(onp.int32, lhs.shape, d) < lhs_shape[d]\\n for d in lhs_contract]\\n mask_intersection = masks[0]\\n for mask in masks[1:]:\\n mask_intersection &= mask\\n masked_lhs = select(mask_intersection, lhs, zeros_like_array(lhs))\\n return dot_general(masked_lhs, rhs, dimension_numbers, precision=precision)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_broadcast_shape_rule\",\n \"text\": \"jax/lax/lax.py::_broadcast_shape_rule\\ndef _broadcast_shape_rule(operand, sizes):\\n _check_shapelike('broadcast', 'sizes', sizes)\\n return tuple(sizes) + operand.shape\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_broadcast_batch_rule\",\n \"text\": \"jax/lax/lax.py::_broadcast_batch_rule\\ndef _broadcast_batch_rule(batched_args, batch_dims, sizes):\\n operand, = batched_args\\n bdim, = batch_dims\\n new_bdim = None if bdim is None else bdim + len(sizes)\\n return broadcast(operand, sizes), new_bdim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_broadcast_in_dim_impl\",\n \"text\": \"jax/lax/lax.py::_broadcast_in_dim_impl\\ndef _broadcast_in_dim_impl(operand, shape, broadcast_dimensions):\\n if type(operand) is xla.DeviceArray:\\n aval = ShapedArray(shape, _dtype(operand))\\n lazy_expr = lazy.broadcast(operand._lazy_expr, shape, broadcast_dimensions)\\n return xla.DeviceArray(aval, None, lazy_expr, operand.device_buffer)\\n else:\\n return xla.apply_primitive(broadcast_in_dim_p, operand, shape=shape,\\n broadcast_dimensions=broadcast_dimensions)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_broadcast_in_dim_shape_rule\",\n \"text\": \"jax/lax/lax.py::_broadcast_in_dim_shape_rule\\ndef _broadcast_in_dim_shape_rule(operand, shape, broadcast_dimensions):\\n _check_shapelike('broadcast_in_dim', 'shape', shape)\\n _check_shapelike('broadcast_in_dim', 'broadcast_dimensions',\\n broadcast_dimensions)\\n if any(x >= len(shape) for x in broadcast_dimensions):\\n msg = (\\\"broadcast_in_dim broadcast dimensions must be less than \\\"\\n \\\"ndim(shape), got {} for shape {}.\\\")\\n raise ValueError(msg.format(broadcast_dimensions, shape))\\n if operand.ndim != len(broadcast_dimensions):\\n msg = ('broadcast_in_dim broadcast_dimensions must have length equal to '\\n 'operand ndim, got broadcast_dimensions {} for operand ndim {}.')\\n raise TypeError(msg.format(broadcast_dimensions, operand.ndim))\\n if not set(broadcast_dimensions).issubset(set(range(len(shape)))):\\n msg = ('broadcast_in_dim broadcast_dimensions must be a subset of output '\\n 'dimensions, got {} for operand ndim {} and shape {}.')\\n raise TypeError(msg.format(broadcast_dimensions, operand.ndim, shape))\\n return shape\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_broadcast_in_dim_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_broadcast_in_dim_transpose_rule\\ndef _broadcast_in_dim_transpose_rule(t, shape, broadcast_dimensions):\\n axes = tuple(onp.delete(range(len(shape)), broadcast_dimensions))\\n return [_reduce_sum(t, axes)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_broadcast_in_dim_batch_rule\",\n \"text\": \"jax/lax/lax.py::_broadcast_in_dim_batch_rule\\ndef _broadcast_in_dim_batch_rule(batched_args, batch_dims, shape,\\n broadcast_dimensions):\\n operand, = batched_args\\n bdim, = batch_dims\\n new_operand = batching.moveaxis(operand, bdim, 0)\\n new_shape = (operand.shape[bdim],) + shape\\n new_broadcast_dimensions = (0,) + tuple(onp.add(1, broadcast_dimensions))\\n return broadcast_in_dim(new_operand, new_shape, new_broadcast_dimensions), 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_clamp_shape_rule\",\n \"text\": \"jax/lax/lax.py::_clamp_shape_rule\\ndef _clamp_shape_rule(min, operand, max):\\n if min.shape and min.shape != operand.shape:\\n m = \\\"clamp requires min.shape == operand.shape or min.shape == (), got {}.\\\"\\n raise TypeError(m.format(min.shape))\\n if max.shape and max.shape != operand.shape:\\n m = \\\"clamp requires max.shape == operand.shape or max.shape == (), got {}.\\\"\\n raise TypeError(m.format(max.shape))\\n return operand.shape\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_concatenate_shape_rule\",\n \"text\": \"jax/lax/lax.py::_concatenate_shape_rule\\ndef _concatenate_shape_rule(*operands, **kwargs):\\n dimension = kwargs.pop('dimension')\\n if not operands:\\n msg = \\\"concatenate expects at least one operand, got 0.\\\"\\n raise TypeError(msg)\\n if not all(isinstance(operand, UnshapedArray) for operand in operands):\\n msg = \\\"All objects to concatenate must be arrays, got {}.\\\"\\n op = next(op for op in operands if not isinstance(op, UnshapedArray))\\n raise TypeError(msg.format(type(op)))\\n if len(set(operand.ndim for operand in operands)) != 1:\\n msg = \\\"Cannot concatenate arrays with different ranks, got {}.\\\"\\n raise TypeError(msg.format(\\\", \\\".join(str(o.ndim) for o in operands)))\\n shapes = onp.array([operand.shape for operand in operands])\\n if not 0 <= dimension < shapes.shape[1]:\\n msg = \\\"concatenate dimension out of bounds: dimension {} for shapes {}.\\\"\\n raise TypeError(msg.format(dimension, \\\", \\\".join(map(str, shapes))))\\n if not onp.all(onp.delete(shapes[0] == shapes, dimension, axis=1)):\\n msg = (\\\"Cannot concatenate arrays with shapes that differ in dimensions \\\"\\n \\\"other than the one being concatenated: dimension {} for shapes {}.\\\")\\n raise TypeError(msg.format(dimension, \\\", \\\".join(map(str, shapes))))\\n\\n concat_size = sum(o.shape[dimension] for o in operands)\\n ex_shape = operands[0].shape\\n return ex_shape[:dimension] + (concat_size,) + ex_shape[dimension+1:]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_concatenate_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_concatenate_dtype_rule\\ndef _concatenate_dtype_rule(*operands, **kwargs):\\n _check_same_dtypes('concatenate', False, *(o.dtype for o in operands))\\n return operands[0].dtype\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_concatenate_translation_rule\",\n \"text\": \"jax/lax/lax.py::_concatenate_translation_rule\\ndef _concatenate_translation_rule(c, *operands, **kwargs):\\n dimension = kwargs.pop('dimension')\\n return c.Concatenate(operands, dimension=dimension)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_concatenate_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_concatenate_transpose_rule\\ndef _concatenate_transpose_rule(t, *operands, **kwargs):\\n dimension = kwargs.pop('dimension')\\n operand_shapes = kwargs.pop('operand_shapes')\\n\\n if t is ad_util.zero:\\n return [ad_util.zero if o is ad.undefined_primal else None for o in operands]\\n else:\\n limit_points = onp.cumsum([shape[dimension] for shape in operand_shapes])\\n starts = onp.zeros((len(operands), t.ndim), dtype=int)\\n starts[1:, dimension] = limit_points[:-1]\\n limits = onp.tile(t.shape, (len(operands), 1))\\n limits[:, dimension] = limit_points\\n\\n return [slice(t, start, limit) if o is ad.undefined_primal else None\\n for o, start, limit in zip(operands, starts, limits)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_concatenate_batch_rule\",\n \"text\": \"jax/lax/lax.py::_concatenate_batch_rule\\ndef _concatenate_batch_rule(batched_args, batch_dims, dimension, operand_shapes):\\n size = next(op.shape[bdim] for op, bdim in zip(batched_args, batch_dims)\\n if bdim is not None)\\n operands = [batching.moveaxis(op, bdim, 0) if bdim is not None\\n else broadcast(op, (size,))\\n for op, bdim in zip(batched_args, batch_dims)]\\n return concatenate(operands, dimension + 1), 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_concat_polymorphic_shape_rule\",\n \"text\": \"jax/lax/lax.py::_concat_polymorphic_shape_rule\\ndef _concat_polymorphic_shape_rule(shape_exprs, dimension, operand_shapes):\\n out_shape = list(shape_exprs[0])\\n out_shape[dimension] = _reduce(operator.add, [e[dimension] for e in shape_exprs])\\n return ShapeExpr(out_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_pad_shape_rule\",\n \"text\": \"jax/lax/lax.py::_pad_shape_rule\\ndef _pad_shape_rule(operand, padding_value, padding_config):\\n if operand.dtype != padding_value.dtype:\\n msg = \\\"pad operand and padding_value must be same dtype: got {} and {}.\\\"\\n raise TypeError(msg.format(operand.dtype, padding_value.dtype))\\n\\n lo, hi, interior = zip(*padding_config)\\n out_shape = onp.add(onp.add(onp.add(lo, hi), operand.shape),\\n onp.multiply(interior, onp.subtract(operand.shape, 1)))\\n return tuple(out_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_pad_transpose\",\n \"text\": \"jax/lax/lax.py::_pad_transpose\\ndef _pad_transpose(t, operand, padding_value, padding_config):\\n if t is ad_util.zero:\\n return [ad_util.zero if operand is ad.undefined_primal else None,\\n ad_util.zero if padding_value is ad.undefined_primal else None]\\n\\n lo, hi, interior = zip(*padding_config)\\n total = lambda x: _reduce_sum(x, list(range(t.ndim)))\\n\\n def t_op():\\n unpad_config = zip(onp.negative(lo), onp.negative(hi), onp.zeros_like(interior))\\n unpadded = pad(t, onp.array(0., t.dtype), unpad_config)\\n return slice(unpadded, onp.zeros_like(lo), unpadded.shape, onp.add(interior, 1))\\n\\n t_operand = t_op() if operand is ad.undefined_primal else None\\n t_padv = sub(total(t), total(t_operand)) if padding_value is ad.undefined_primal else None\\n\\n return [t_operand, t_padv]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_pad_transpose.t_op\",\n \"text\": \"jax/lax/lax.py::_pad_transpose.t_op\\n def t_op():\\n unpad_config = zip(onp.negative(lo), onp.negative(hi), onp.zeros_like(interior))\\n unpadded = pad(t, onp.array(0., t.dtype), unpad_config)\\n return slice(unpadded, onp.zeros_like(lo), unpadded.shape, onp.add(interior, 1))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_pad_batch_rule\",\n \"text\": \"jax/lax/lax.py::_pad_batch_rule\\ndef _pad_batch_rule(batched_args, batch_dims, padding_config):\\n operand, padding_value = batched_args\\n operand_bdim, padding_value_bdim = batch_dims\\n if padding_value_bdim is None:\\n assert operand_bdim is not None\\n padding_config = list(padding_config)\\n padding_config.insert(operand_bdim, (0, 0, 0))\\n return pad(operand, padding_value, padding_config), operand_bdim\\n else:\\n raise NotImplementedError # loop and stack\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reshape_impl\",\n \"text\": \"jax/lax/lax.py::_reshape_impl\\ndef _reshape_impl(operand, new_sizes, dimensions, old_sizes):\\n if type(operand) is xla.DeviceArray and dimensions is None:\\n bcast_dims = _is_singleton_reshape(old_sizes, new_sizes)\\n if bcast_dims is not None:\\n aval = ShapedArray(new_sizes, operand.dtype)\\n lazy_expr = lazy.broadcast(operand._lazy_expr, new_sizes, bcast_dims)\\n return xla.DeviceArray(aval, None, lazy_expr, operand.device_buffer)\\n if (type(operand) is pxla.ShardedDeviceArray and dimensions is None\\n and _is_axis_merge(old_sizes, new_sizes)):\\n aval = ShapedArray(new_sizes, operand.dtype)\\n return pxla.ChunkedDeviceArray(old_sizes[0], aval, operand.device_buffers)\\n elif (type(operand) is pxla.ChunkedDeviceArray and dimensions is None\\n and _is_axis_split(old_sizes, new_sizes)\\n and operand.axis_size == new_sizes[0]):\\n aval = ShapedArray(new_sizes, operand.dtype)\\n return pxla.ShardedDeviceArray(aval, operand.device_buffers)\\n else:\\n return xla.apply_primitive(reshape_p, operand, new_sizes=new_sizes,\\n dimensions=dimensions, old_sizes=old_sizes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_is_singleton_reshape\",\n \"text\": \"jax/lax/lax.py::_is_singleton_reshape\\ndef _is_singleton_reshape(old, new):\\n # A singleton reshape is one where only singleton dimensions are added. We\\n # want to detect them because they can be expressed as (lazy) broadcasts.\\n old, new = iter(old), iter(new)\\n d1, d2 = next(old, None), next(new, None)\\n bcast_dims = []\\n i = 0\\n while True:\\n if d1 is d2 is None:\\n return bcast_dims\\n elif d1 == d2:\\n bcast_dims.append(i)\\n i += 1\\n d1, d2 = next(old, None), next(new, None)\\n elif d2 == 1:\\n i += 1\\n d2 = next(new, None)\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_is_axis_merge\",\n \"text\": \"jax/lax/lax.py::_is_axis_merge\\ndef _is_axis_merge(s1, s2):\\n return s1[2:] == s2[1:] and s1[0] * s1[1] == s2[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_is_axis_split\",\n \"text\": \"jax/lax/lax.py::_is_axis_split\\ndef _is_axis_split(s1, s2):\\n return _is_axis_merge(s2, s1)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reshape_shape_rule\",\n \"text\": \"jax/lax/lax.py::_reshape_shape_rule\\ndef _reshape_shape_rule(operand, new_sizes, dimensions, **unused_kwargs):\\n if not onp.all(onp.greater_equal(new_sizes, 0)):\\n msg = 'reshape new_sizes must all be positive, got {}.'\\n raise TypeError(msg.format(new_sizes))\\n if prod(onp.shape(operand)) != prod(new_sizes):\\n msg = 'reshape total size must be unchanged, got new_sizes {} for shape {}.'\\n raise TypeError(msg.format(new_sizes, onp.shape(operand)))\\n if dimensions is not None:\\n if set(dimensions) != set(range(onp.ndim(operand))):\\n msg = ('reshape dimensions must be a permutation of operand dimensions, '\\n 'got dimensions {} for shape {}.')\\n raise TypeError(msg.format(dimensions, onp.shape(operand)))\\n return tuple(new_sizes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reshape_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_reshape_dtype_rule\\ndef _reshape_dtype_rule(operand, new_sizes, dimensions, **unused_kwargs):\\n return operand.dtype\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reshape_translation_rule\",\n \"text\": \"jax/lax/lax.py::_reshape_translation_rule\\ndef _reshape_translation_rule(c, operand, new_sizes, dimensions, old_sizes):\\n del old_sizes # Unused.\\n return c.Reshape(operand, new_sizes=new_sizes, dimensions=dimensions)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reshape_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_reshape_transpose_rule\\ndef _reshape_transpose_rule(t, new_sizes, dimensions, old_sizes):\\n if dimensions is None:\\n return [reshape(t, old_sizes)]\\n else:\\n return [transpose(reshape(t, onp.take(old_sizes, dimensions)),\\n onp.argsort(dimensions))]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reshape_batch_rule\",\n \"text\": \"jax/lax/lax.py::_reshape_batch_rule\\ndef _reshape_batch_rule(batched_args, batch_dims, new_sizes, dimensions, **unused):\\n operand, = batched_args\\n bdim, = batch_dims\\n operand = batching.moveaxis(operand, bdim, 0)\\n if dimensions is not None:\\n dimensions = (0,) + tuple(onp.add(1, dimensions))\\n return reshape(operand, operand.shape[:1] + new_sizes, dimensions), 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reshape_polymorphic_shape_rule\",\n \"text\": \"jax/lax/lax.py::_reshape_polymorphic_shape_rule\\ndef _reshape_polymorphic_shape_rule(shape_exprs, new_sizes, dimensions, old_sizes):\\n if dimensions is not None: raise NotImplementedError\\n shape_expr, = shape_exprs\\n if masking.prod(shape_expr) != masking.prod(new_sizes): raise ShapeError\\n return new_sizes\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_rev_shape_rule\",\n \"text\": \"jax/lax/lax.py::_rev_shape_rule\\ndef _rev_shape_rule(operand, dimensions):\\n _check_shapelike('rev', 'dimensions', dimensions)\\n if len(set(dimensions)) != len(dimensions):\\n msg = 'rev dimensions must be unique, got {}.'\\n raise TypeError(msg.format(dimensions))\\n if not _max(dimensions) < operand.ndim:\\n msg = ('rev dimensions must all be less than operand ndim, got dimensions '\\n '{} for operand ndim {}.')\\n raise TypeError(msg.format(dimensions, operand.ndim))\\n return operand.shape\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_rev_batch_rule\",\n \"text\": \"jax/lax/lax.py::_rev_batch_rule\\ndef _rev_batch_rule(batched_args, batch_dims, dimensions):\\n operand, = batched_args\\n bdim, = batch_dims\\n new_dimensions = [i + 1 if i >= bdim else i for i in dimensions]\\n return rev(operand, new_dimensions), bdim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_transpose_impl\",\n \"text\": \"jax/lax/lax.py::_transpose_impl\\ndef _transpose_impl(operand, permutation):\\n if type(operand) is xla.DeviceArray:\\n lazy_expr = lazy.transpose(operand._lazy_expr, permutation)\\n aval = ShapedArray(lazy_expr.shape, operand.dtype)\\n return xla.DeviceArray(aval, None, lazy_expr, operand.device_buffer)\\n else:\\n return xla.apply_primitive(transpose_p, operand, permutation=permutation)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_transpose_shape_rule\",\n \"text\": \"jax/lax/lax.py::_transpose_shape_rule\\ndef _transpose_shape_rule(operand, permutation):\\n if not isinstance(permutation, (tuple, list, onp.ndarray)):\\n msg = \\\"transpose permutation must be a tuple/list/ndarray, got {}.\\\"\\n raise TypeError(msg.format(type(permutation)))\\n if tuple(sorted(permutation)) != tuple(range(operand.ndim)):\\n msg = (\\\"transpose permutation isn't a permutation of operand dimensions, \\\"\\n \\\"got permutation {} for operand shape {}.\\\")\\n raise TypeError(msg.format(permutation, operand.shape))\\n return tuple(onp.take(operand.shape, permutation))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_transpose_batch_rule\",\n \"text\": \"jax/lax/lax.py::_transpose_batch_rule\\ndef _transpose_batch_rule(batched_args, batch_dims, permutation):\\n operand, = batched_args\\n bdim, = batch_dims\\n perm = (bdim,) + tuple(i if i < bdim else i+1 for i in permutation)\\n return transpose(operand, perm), 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_shape_rule\",\n \"text\": \"jax/lax/lax.py::_select_shape_rule\\ndef _select_shape_rule(pred, on_true, on_false):\\n if on_true.shape != on_false.shape:\\n msg = \\\"select on_true and on_false must have the same shape, got {} and {}.\\\"\\n raise TypeError(msg.format(on_true.shape, on_false.shape))\\n if pred.shape and pred.shape != on_true.shape:\\n msg = (\\\"select pred must be scalar or have the same shape as on_true and \\\"\\n \\\"on_false, got pred shape {} for on_true and on_false of shape {}.\\\")\\n raise TypeError(msg.format(pred.shape, on_true.shape))\\n return on_true.shape\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_select_dtype_rule\\ndef _select_dtype_rule(pred, on_true, on_false):\\n _check_same_dtypes(\\\"select\\\", False, on_true.dtype, on_false.dtype)\\n if not dtypes.issubdtype(pred.dtype, onp.bool_):\\n msg = \\\"select pred must be boolean type, got {}.\\\"\\n raise TypeError(msg.format(pred.dtype))\\n return on_true.dtype\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_select_transpose_rule\\ndef _select_transpose_rule(t, pred, on_true, on_false):\\n assert pred is not ad.undefined_primal\\n if t is ad_util.zero:\\n return [None,\\n ad_util.zero if on_true is ad.undefined_primal else None,\\n ad_util.zero if on_false is ad.undefined_primal else None]\\n else:\\n zeros = full_like(t, 0)\\n return [None,\\n select(pred, t, zeros) if on_true is ad.undefined_primal else None,\\n select(pred, zeros, t) if on_false is ad.undefined_primal else None]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_batch_rule\",\n \"text\": \"jax/lax/lax.py::_select_batch_rule\\ndef _select_batch_rule(batched_args, batch_dims, **unused_kwargs):\\n pred, on_true, on_false, = batched_args\\n pred_bdim, ot_bdim, of_bdim = batch_dims\\n size = next(x.shape[i] for x, i in zip(batched_args, batch_dims)\\n if i is not None)\\n\\n # avoid transposes and some broadcasts in special cases\\n if pred_bdim == ot_bdim == of_bdim:\\n if onp.shape(pred) == onp.shape(on_true):\\n return select(pred, on_true, on_false), pred_bdim\\n else:\\n # vmapped function had a scalar pred with nonscalar args\\n assert onp.ndim(pred) == 1\\n pred = broadcast_in_dim(pred, on_true.shape, [pred_bdim])\\n return select(pred, on_true, on_false), pred_bdim\\n elif onp.ndim(pred) == 0 and ot_bdim is not None and of_bdim is not None:\\n if ot_bdim == of_bdim:\\n return select(pred, on_true, on_false), ot_bdim\\n elif onp.shape(on_true) == onp.shape(on_false):\\n on_false = batching.moveaxis(on_false, of_bdim, ot_bdim)\\n return select(pred, on_true, on_false), ot_bdim\\n\\n pred = batching.bdim_at_front(pred, pred_bdim, size) if onp.shape(pred) else pred\\n if not onp.shape(on_true) == onp.shape(on_false) == ():\\n on_true = batching.bdim_at_front(on_true, ot_bdim, size)\\n on_false = batching.bdim_at_front(on_false, of_bdim, size)\\n assert onp.shape(on_true) == onp.shape(on_false)\\n if 0 < onp.ndim(pred) < onp.ndim(on_true):\\n # vmapped function had a scalar pred with nonscalar args\\n assert onp.ndim(pred) == 1\\n pred = broadcast_in_dim(pred, on_true.shape, [0])\\n if onp.ndim(pred) > onp.ndim(on_true):\\n assert onp.ndim(on_true) == 0\\n on_true = broadcast(on_true, pred.shape)\\n on_false = broadcast(on_false, pred.shape)\\n return select(pred, on_true, on_false), 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_slice_shape_rule\",\n \"text\": \"jax/lax/lax.py::_slice_shape_rule\\ndef _slice_shape_rule(operand, start_indices, limit_indices, strides,\\n operand_shape):\\n _check_shapelike(\\\"slice\\\", \\\"start_indices\\\", start_indices)\\n _check_shapelike(\\\"slice\\\", \\\"limit_indices\\\", limit_indices)\\n if operand.ndim != len(start_indices):\\n msg = (\\\"slice start_indices must have length equal to the number of \\\"\\n \\\"dimensions of the operand, got indices {} for operand shape {}.\\\")\\n raise TypeError(msg.format(start_indices, operand.shape))\\n if len(start_indices) != len(limit_indices):\\n msg = (\\\"slice limit_indices must have the same length as start_indices, \\\"\\n \\\"got start_inidices {} and limit_indices {}.\\\")\\n raise TypeError(msg.format(start_indices, limit_indices))\\n if not onp.all(onp.less_equal(limit_indices, operand.shape)):\\n msg = (\\\"slice limit_indices must be less than or equal to operand shape, \\\"\\n \\\"got limit_indices {} for operand shape {}.\\\")\\n raise TypeError(msg.format(limit_indices, operand.shape))\\n if not onp.all(onp.greater_equal(start_indices, 0)):\\n msg = (\\\"slice start_indices must be greater than or equal to zero, \\\"\\n \\\"got start_indices of {}.\\\")\\n raise TypeError(msg.format(start_indices))\\n if not onp.all(onp.greater_equal(limit_indices, start_indices)):\\n msg = (\\\"slice limit_indices must be greater than or equal to start_indices,\\\"\\n \\\" got start_indices {} and limit_indices {}.\\\")\\n raise TypeError(msg.format(start_indices, limit_indices))\\n if strides is None:\\n strides = onp.ones(operand.ndim, onp.int32)\\n else:\\n _check_shapelike(\\\"slice\\\", \\\"strides\\\", strides)\\n if len(strides) != operand.ndim:\\n msg = (\\\"slice strides must have length equal to the number of dimensions \\\"\\n \\\"of the operand, got strides {} for operand shape {}.\\\")\\n raise TypeError(msg.format(strides, operand.shape))\\n if not onp.all(onp.greater(strides, 0)):\\n msg = \\\"slice strides must be positive, got {}\\\"\\n raise TypeError(m\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_slice_translation_rule\",\n \"text\": \"jax/lax/lax.py::_slice_translation_rule\\ndef _slice_translation_rule(c, operand, start_indices, limit_indices, strides,\\n operand_shape):\\n return c.Slice(operand, start_indices, limit_indices, strides)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_slice_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_slice_transpose_rule\\ndef _slice_transpose_rule(t, start_indices, limit_indices, strides,\\n operand_shape):\\n if strides is None or onp.all(onp.equal(strides, 1)):\\n pads = zip(start_indices, onp.subtract(operand_shape, limit_indices),\\n (0,) * len(start_indices))\\n else:\\n real_limits = onp.add(onp.add(start_indices, 1),\\n onp.multiply(onp.subtract(t.shape, 1), strides))\\n pads = zip(start_indices, onp.subtract(operand_shape, real_limits),\\n onp.subtract(strides, 1))\\n result = pad(t, _const(t, 0), pads)\\n assert result.shape == operand_shape\\n return [result]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_slice_batching_rule\",\n \"text\": \"jax/lax/lax.py::_slice_batching_rule\\ndef _slice_batching_rule(batched_args, batch_dims, start_indices, limit_indices,\\n strides, **unused_kwargs):\\n operand, = batched_args\\n bdim, = batch_dims\\n\\n new_start_indices = list(start_indices)\\n new_start_indices.insert(bdim, 0)\\n\\n new_limit_indices = list(limit_indices)\\n new_limit_indices.insert(bdim, operand.shape[bdim])\\n\\n if strides is None:\\n new_strides = None\\n else:\\n new_strides = list(strides)\\n new_strides.insert(bdim, 1)\\n\\n out = slice(operand, new_start_indices, new_limit_indices, new_strides)\\n return out, bdim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_slice_shape_rule\",\n \"text\": \"jax/lax/lax.py::_dynamic_slice_shape_rule\\ndef _dynamic_slice_shape_rule(operand, *start_indices, **kwargs):\\n slice_sizes = kwargs[\\\"slice_sizes\\\"]\\n if operand.ndim != len(start_indices):\\n msg = (\\\"dynamic_slice start_indices must have length equal to the number \\\"\\n \\\"of dimensions of the operand, got indices {} for operand shape {}.\\\")\\n raise TypeError(msg.format(start_indices, operand.shape))\\n if len(start_indices) != len(slice_sizes):\\n msg = (\\\"dynamic_slice slice_sizes must have the same length as \\\"\\n \\\"start_indices, got start_inidices length {} and slice_sizes {}.\\\")\\n raise TypeError(msg.format(len(start_indices), slice_sizes))\\n if not onp.all(onp.less_equal(slice_sizes, operand.shape)):\\n msg = (\\\"slice slice_sizes must be less than or equal to operand shape, \\\"\\n \\\"got slice_sizes {} for operand shape {}.\\\")\\n raise TypeError(msg.format(slice_sizes, operand.shape))\\n if not onp.all(onp.greater_equal(slice_sizes, 0)):\\n msg = (\\\"slice slice_sizes must be greater than or equal to zero, \\\"\\n \\\"got slice_sizes of {}.\\\")\\n raise TypeError(msg.format(slice_sizes))\\n return tuple(slice_sizes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_slice_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_dynamic_slice_dtype_rule\\ndef _dynamic_slice_dtype_rule(operand, *start_indices, **kw):\\n if any(i.dtype != start_indices[0].dtype or\\n not dtypes.issubdtype(i.dtype, onp.integer) for i in start_indices):\\n msg = (\\\"index arguments to dynamic_slice must be integers of the same \\\"\\n \\\"type, got: {}\\\")\\n raise TypeError(msg.format(\\\", \\\".join(i.dtype.name for i in start_indices)))\\n return operand.dtype\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_slice_translation_rule\",\n \"text\": \"jax/lax/lax.py::_dynamic_slice_translation_rule\\ndef _dynamic_slice_translation_rule(c, operand, *start_indices, **kwargs):\\n slice_sizes = kwargs[\\\"slice_sizes\\\"]\\n return c.DynamicSlice(operand, start_indices, slice_sizes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_slice_jvp\",\n \"text\": \"jax/lax/lax.py::_dynamic_slice_jvp\\ndef _dynamic_slice_jvp(primals, tangents, slice_sizes, operand_shape):\\n tangent_out = ad_util.zero\\n if tangents[0] is not ad_util.zero:\\n tangent_out = dynamic_slice(tangents[0], primals[1:], slice_sizes)\\n return dynamic_slice(primals[0], primals[1:], slice_sizes), tangent_out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_slice_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_dynamic_slice_transpose_rule\\ndef _dynamic_slice_transpose_rule(t, operand, *start_indices, **kwargs):\\n operand_shape = kwargs[\\\"operand_shape\\\"]\\n assert operand is ad.undefined_primal\\n assert all(s is not ad.undefined_primal for s in start_indices)\\n zeros = full(operand_shape, tie_in(t, _zero(t)))\\n return ([dynamic_update_slice(zeros, t, start_indices)] +\\n [None] * len(start_indices))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_batch_dynamic_slice_indices\",\n \"text\": \"jax/lax/lax.py::_batch_dynamic_slice_indices\\ndef _batch_dynamic_slice_indices(indices, bdims):\\n size = next((x.shape[i] for x, i in zip(indices, bdims) if i is not None), -1)\\n if size < 0:\\n return concatenate([reshape(i, [1]) for i in indices], 0), None\\n indices = concatenate(\\n [broadcast_in_dim(x, (size, 1),\\n broadcast_dimensions=((0,) if i is not None else ()))\\n for x, i in zip(indices, bdims)],\\n dimension=1)\\n return indices, 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_slice_batching_rule\",\n \"text\": \"jax/lax/lax.py::_dynamic_slice_batching_rule\\ndef _dynamic_slice_batching_rule(batched_args, batch_dims, slice_sizes,\\n operand_shape):\\n # A dynamic slice is a special case of gather; we can delegate to the gather\\n # batching rule.\\n # TODO(phawkins): consider removing dynamic_slice entirely and using gather\\n # always.\\n dims = tuple(range(len(operand_shape)))\\n dnums = GatherDimensionNumbers(offset_dims=dims, collapsed_slice_dims=(),\\n start_index_map=dims)\\n index, index_bdim = _batch_dynamic_slice_indices(batched_args[1:],\\n batch_dims[1:])\\n return _gather_batching_rule(\\n [batched_args[0], index], [batch_dims[0], index_bdim], dnums, slice_sizes,\\n operand_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_update_slice_shape_rule\",\n \"text\": \"jax/lax/lax.py::_dynamic_update_slice_shape_rule\\ndef _dynamic_update_slice_shape_rule(operand, update, *start_indices, **kwargs):\\n if operand.ndim != update.ndim:\\n msg = (\\\"dynamic_update_slice update must have the same rank as operand, \\\"\\n \\\"got update shape {} for operand shape {}.\\\")\\n raise TypeError(msg.format(update.shape, operand.shape))\\n if operand.ndim != len(start_indices):\\n msg = (\\\"dynamic_update_slice start_indices must have length equal to the \\\"\\n \\\"rank of operand, got indices {} for operand shape {}.\\\")\\n raise TypeError(msg.format(start_indices, operand.shape))\\n if not onp.all(onp.less_equal(update.shape, operand.shape)):\\n msg = (\\\"dynamic_update_slice update shape must be smaller than operand \\\"\\n \\\"shape, got update shape {} for operand shape {}.\\\")\\n raise TypeError(msg.format(update.shape, operand.shape))\\n return operand.shape\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_update_slice_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_dynamic_update_slice_dtype_rule\\ndef _dynamic_update_slice_dtype_rule(operand, update, *start_indices, **kwargs):\\n _check_same_dtypes(\\\"dynamic_update_slice\\\", False, operand.dtype, update.dtype)\\n if any(i.dtype != start_indices[0].dtype or\\n not dtypes.issubdtype(i.dtype, onp.integer) for i in start_indices):\\n msg = (\\\"index arguments to dynamic_update_slice must be integers of the \\\"\\n \\\"same type, got {}\\\")\\n raise TypeError(msg.format(\\\", \\\".join(i.dtype.name for i in start_indices)))\\n return operand.dtype\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_update_slice_jvp\",\n \"text\": \"jax/lax/lax.py::_dynamic_update_slice_jvp\\ndef _dynamic_update_slice_jvp(primals, tangents, update_shape):\\n operand, update = primals[:2]\\n start_indices = primals[2:]\\n g_operand, g_update = tangents[:2]\\n val_out = dynamic_update_slice(operand, update, start_indices)\\n if g_operand is ad_util.zero and g_update is ad_util.zero:\\n tangent_out = ad_util.zero\\n else:\\n g_operand = ad.instantiate_zeros(operand, g_operand)\\n g_update = ad.instantiate_zeros(update, g_update)\\n tangent_out = dynamic_update_slice(g_operand, g_update, start_indices)\\n return val_out, tangent_out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_update_slice_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_dynamic_update_slice_transpose_rule\\ndef _dynamic_update_slice_transpose_rule(t, operand, update, *start_indices,\\n **kwargs):\\n update_shape = kwargs[\\\"update_shape\\\"]\\n assert all(x is not ad.undefined_primal for x in start_indices)\\n dus = dynamic_update_slice\\n ds = dynamic_slice\\n zeros = _zeros(t, shape=update_shape)\\n operand_t = dus(t, zeros, start_indices) if operand is ad.undefined_primal else None\\n update_t = ds(t, start_indices, update_shape) if update is ad.undefined_primal else None\\n return [operand_t, update_t] + [None] * len(start_indices)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_update_slice_translation_rule\",\n \"text\": \"jax/lax/lax.py::_dynamic_update_slice_translation_rule\\ndef _dynamic_update_slice_translation_rule(c, operand, update, *start_indices,\\n **kwargs):\\n return c.DynamicUpdateSlice(operand, update, start_indices)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_update_slice_batching_rule\",\n \"text\": \"jax/lax/lax.py::_dynamic_update_slice_batching_rule\\ndef _dynamic_update_slice_batching_rule(batched_args, batch_dims, update_shape):\\n # A dynamic update slice is a special case of scatter; we can delegate to the\\n # scatter batching rule.\\n # TODO(phawkins): consider removing dynamic_update_slice entirely and using\\n # scatter always.\\n operand, update = batched_args[:2]\\n operand_bdims, update_bdims = batch_dims[:2]\\n dims = tuple(range(len(update_shape)))\\n dnums = ScatterDimensionNumbers(update_window_dims=dims,\\n inserted_window_dims=(),\\n scatter_dims_to_operand_dims=dims)\\n index, index_bdim = _batch_dynamic_slice_indices(batched_args[2:],\\n batch_dims[2:])\\n return _scatter_batching_rule(\\n scatter,\\n (operand, index, update), (operand_bdims, index_bdim, update_bdims),\\n None, None, dnums, update_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_gather_dimensions_proto\",\n \"text\": \"jax/lax/lax.py::_gather_dimensions_proto\\ndef _gather_dimensions_proto(indices_shape, dimension_numbers):\\n assert type(dimension_numbers) is GatherDimensionNumbers\\n proto = xla_client.GatherDimensionNumbers()\\n proto.offset_dims.extend(dimension_numbers.offset_dims)\\n proto.collapsed_slice_dims.extend(dimension_numbers.collapsed_slice_dims)\\n proto.start_index_map.extend(dimension_numbers.start_index_map)\\n assert indices_shape.rank() > 0\\n proto.index_vector_dim = indices_shape.rank() - 1\\n return proto\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_gather_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_gather_dtype_rule\\ndef _gather_dtype_rule(operand, start_indices, **kwargs):\\n if not dtypes.issubdtype(start_indices.dtype, onp.integer):\\n raise ValueError(\\\"start_indices must have an integer type\\\")\\n return dtypes.canonicalize_dtype(operand.dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_gather_shape_rule\",\n \"text\": \"jax/lax/lax.py::_gather_shape_rule\\ndef _gather_shape_rule(operand, start_indices, dimension_numbers, slice_sizes,\\n operand_shape):\\n assert operand.shape == operand_shape\\n if len(operand_shape) != len(slice_sizes):\\n msg = (\\\"slice_sizes must have rank equal to the gather operand; \\\"\\n \\\"operand.shape={}, slice_sizes={}\\\".format(operand_shape, slice_sizes))\\n raise ValueError(msg)\\n result_rank = len(dimension_numbers.offset_dims) + start_indices.ndim - 1\\n start_indices_shape = iter(start_indices.shape[:-1])\\n slice_sizes = iter(onp.delete(slice_sizes, dimension_numbers.collapsed_slice_dims))\\n return tuple(next(slice_sizes) if i in dimension_numbers.offset_dims\\n else next(start_indices_shape) for i in range(result_rank))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_gather_translation_rule\",\n \"text\": \"jax/lax/lax.py::_gather_translation_rule\\ndef _gather_translation_rule(c, operand, start_indices, dimension_numbers,\\n slice_sizes, operand_shape):\\n indices_shape = c.GetShape(start_indices)\\n return c.Gather(\\n operand, start_indices,\\n _gather_dimensions_proto(indices_shape, dimension_numbers), slice_sizes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_gather_jvp_rule\",\n \"text\": \"jax/lax/lax.py::_gather_jvp_rule\\ndef _gather_jvp_rule(g, operand, start_indices, dimension_numbers, slice_sizes,\\n operand_shape):\\n return gather(g, start_indices, dimension_numbers, slice_sizes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_gather_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_gather_transpose_rule\\ndef _gather_transpose_rule(t, operand, start_indices, dimension_numbers,\\n slice_sizes, operand_shape):\\n assert operand is ad.undefined_primal\\n if t is ad_util.zero:\\n return [ad_util.zero, ad_util.zero]\\n zeros = full(operand_shape, tie_in(t, _zero(t)))\\n scatter_dnums = ScatterDimensionNumbers(\\n update_window_dims=dimension_numbers.offset_dims,\\n inserted_window_dims=dimension_numbers.collapsed_slice_dims,\\n scatter_dims_to_operand_dims=dimension_numbers.start_index_map)\\n return [scatter_add(zeros, start_indices, t, scatter_dnums), ad_util.zero]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_gather_batching_rule\",\n \"text\": \"jax/lax/lax.py::_gather_batching_rule\\ndef _gather_batching_rule(batched_args, batch_dims, dimension_numbers,\\n slice_sizes, operand_shape):\\n operand, start_indices = batched_args\\n operand_bdim, start_indices_bdim = batch_dims\\n\\n if operand_bdim is not None and start_indices_bdim is None:\\n operand = batching.moveaxis(operand, operand_bdim, 0)\\n slice_sizes = (operand.shape[0],) + slice_sizes\\n offset_dims = (0,) + tuple(onp.add(1, dimension_numbers.offset_dims))\\n collapsed_slice_dims = tuple(onp.add(1, dimension_numbers.collapsed_slice_dims))\\n start_index_map = tuple(onp.add(1, dimension_numbers.start_index_map))\\n dnums = GatherDimensionNumbers(\\n offset_dims=offset_dims,\\n collapsed_slice_dims=collapsed_slice_dims,\\n start_index_map=start_index_map)\\n return gather(operand, start_indices, dimension_numbers=dnums,\\n slice_sizes=slice_sizes), 0\\n\\n elif operand_bdim is None and start_indices_bdim is not None:\\n start_indices = batching.moveaxis(start_indices, start_indices_bdim, 0)\\n offset_dims = tuple(onp.add(1, dimension_numbers.offset_dims))\\n dnums = GatherDimensionNumbers(\\n offset_dims=offset_dims,\\n collapsed_slice_dims=dimension_numbers.collapsed_slice_dims,\\n start_index_map=dimension_numbers.start_index_map)\\n return gather(operand, start_indices, dimension_numbers=dnums,\\n slice_sizes=slice_sizes), 0\\n\\n else:\\n # move our batch dimensions to the front to preserve sanity\\n operand = batching.moveaxis(operand, operand_bdim, 0)\\n start_indices = batching.moveaxis(start_indices, start_indices_bdim, 0)\\n\\n # Example: user code had start_indices shape (3, 4, 5), and we have to deal\\n # with start_indices shape (7, 3, 4, 5). We transform that to a\\n # start_indices of shape (7, 3, 4, 6) where we concatenated an iota that\\n # counts along our batch dimension to the front of the ndindex.\\n count_shape = list(start_indices.shape)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_scatter_dimensions_proto\",\n \"text\": \"jax/lax/lax.py::_scatter_dimensions_proto\\ndef _scatter_dimensions_proto(indices_shape, dimension_numbers):\\n assert type(dimension_numbers) is ScatterDimensionNumbers\\n proto = xla_client.ScatterDimensionNumbers()\\n proto.update_window_dims.extend(dimension_numbers.update_window_dims)\\n proto.inserted_window_dims.extend(dimension_numbers.inserted_window_dims)\\n proto.scatter_dims_to_operand_dims.extend(\\n dimension_numbers.scatter_dims_to_operand_dims)\\n assert indices_shape.rank() > 0\\n proto.index_vector_dim = indices_shape.rank() - 1\\n return proto\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_scatter_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_scatter_dtype_rule\\ndef _scatter_dtype_rule(operand, scatter_indices, updates, **kwargs):\\n if not dtypes.issubdtype(scatter_indices.dtype, onp.integer):\\n raise ValueError(\\\"scatter_indices must have an integer type\\\")\\n _check_same_dtypes(\\\"scatter\\\", False, operand.dtype, updates.dtype)\\n return dtypes.canonicalize_dtype(operand.dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_scatter_shape_rule\",\n \"text\": \"jax/lax/lax.py::_scatter_shape_rule\\ndef _scatter_shape_rule(operand, scatter_indices, updates, **kwargs):\\n return operand.shape\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_scatter_translation_rule\",\n \"text\": \"jax/lax/lax.py::_scatter_translation_rule\\ndef _scatter_translation_rule(c, operand, scatter_indices, updates,\\n update_jaxpr, update_consts, dimension_numbers,\\n updates_shape):\\n dtype = c.GetShape(operand).numpy_dtype()\\n init_value = c.Constant(onp.array(0, dtype))\\n update_computation = _reduction_computation(\\n c, update_jaxpr, update_consts, init_value)\\n indices_shape = c.GetShape(scatter_indices)\\n return c.Scatter(operand, scatter_indices, updates, update_computation,\\n _scatter_dimensions_proto(indices_shape, dimension_numbers))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_scatter_add_jvp\",\n \"text\": \"jax/lax/lax.py::_scatter_add_jvp\\ndef _scatter_add_jvp(primals, tangents, update_jaxpr, update_consts,\\n dimension_numbers, updates_shape):\\n operand, scatter_indices, updates = primals\\n g_operand, g_scatter_indices, g_updates = tangents\\n val_out = scatter_add_p.bind(\\n operand, scatter_indices, updates, update_jaxpr=update_jaxpr,\\n update_consts=update_consts, dimension_numbers=dimension_numbers,\\n updates_shape=updates_shape)\\n if g_operand is ad_util.zero and g_updates is ad_util.zero:\\n tangent_out = ad_util.zero\\n else:\\n g_operand = ad.instantiate_zeros(operand, g_operand)\\n g_updates = ad.instantiate_zeros(updates, g_updates)\\n tangent_out = scatter_add_p.bind(\\n g_operand, scatter_indices, g_updates, update_jaxpr=update_jaxpr,\\n update_consts=update_consts, dimension_numbers=dimension_numbers,\\n updates_shape=updates_shape)\\n return val_out, tangent_out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_scatter_add_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_scatter_add_transpose_rule\\ndef _scatter_add_transpose_rule(t, operand, scatter_indices, updates,\\n update_jaxpr, update_consts, dimension_numbers,\\n updates_shape):\\n assert scatter_indices is not ad.undefined_primal\\n if t is ad_util.zero:\\n return [ad_util.zero, None, ad_util.zero]\\n\\n operand_t = update_t = None\\n if operand is ad.undefined_primal:\\n operand_t = t\\n\\n if updates is ad.undefined_primal:\\n gather_dnums = GatherDimensionNumbers(\\n offset_dims=dimension_numbers.update_window_dims,\\n collapsed_slice_dims=dimension_numbers.inserted_window_dims,\\n start_index_map=dimension_numbers.scatter_dims_to_operand_dims)\\n slice_sizes = []\\n pos = 0\\n for i in range(len(t.shape)):\\n if i in dimension_numbers.inserted_window_dims:\\n slice_sizes.append(1)\\n else:\\n slice_sizes.append(updates_shape[dimension_numbers.update_window_dims[pos]])\\n pos += 1\\n update_t = gather(t, scatter_indices, dimension_numbers=gather_dnums,\\n slice_sizes=slice_sizes)\\n return [operand_t, None, update_t]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_scatter_batching_rule\",\n \"text\": \"jax/lax/lax.py::_scatter_batching_rule\\ndef _scatter_batching_rule(\\n scatter_op, batched_args, batch_dims, update_jaxpr, update_consts,\\n dimension_numbers, updates_shape):\\n operand, scatter_indices, updates = batched_args\\n operand_bdim, scatter_indices_bdim, updates_bdim = batch_dims\\n del update_jaxpr, update_consts, updates_shape # Unused.\\n\\n # move the operand batch dim to the front if it is not None, otherwise create\\n # it at the front (so that we can scatter into it)\\n size = next(x.shape[ax] for x, ax in zip(batched_args, batch_dims)\\n if ax is not None)\\n operand = batching.bdim_at_front(operand, operand_bdim, size)\\n operand_bdim = 0\\n\\n updates = batching.bdim_at_front(updates, updates_bdim, size)\\n\\n if scatter_indices_bdim is None:\\n inserted_window_dims = tuple(onp.add(1, dimension_numbers.inserted_window_dims))\\n update_window_dims = (0,) + tuple(onp.add(1, dimension_numbers.update_window_dims))\\n scatter_dims_to_operand_dims = tuple(onp.add(1, dimension_numbers.scatter_dims_to_operand_dims))\\n dnums = ScatterDimensionNumbers(\\n update_window_dims=update_window_dims,\\n inserted_window_dims=inserted_window_dims,\\n scatter_dims_to_operand_dims=scatter_dims_to_operand_dims)\\n return scatter_op(operand, scatter_indices, updates, dnums), 0\\n\\n\\n # see the third case in _gather_batching_rule for comparison and comments\\n scatter_indices = batching.bdim_at_front(\\n scatter_indices, scatter_indices_bdim, size)\\n\\n count_shape = list(scatter_indices.shape)\\n count_shape[-1] = 1\\n counts = broadcasted_iota(scatter_indices.dtype, tuple(count_shape), 0)\\n scatter_indices = concatenate([counts, scatter_indices],\\n len(count_shape) - 1)\\n\\n update_window_dims = tuple(onp.add(1, dimension_numbers.update_window_dims))\\n inserted_window_dims = (0,) + tuple(onp.add(1, dimension_numbers.inserted_window_dims))\\n scatter_dims_to_operand_dims = (0,) + tuple(onp.add(1, dimension_numbers.scatter_dims_to_ope\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_scatter_jvp\",\n \"text\": \"jax/lax/lax.py::_scatter_jvp\\ndef _scatter_jvp(primals, tangents, update_jaxpr, update_consts,\\n dimension_numbers, updates_shape):\\n operand, scatter_indices, updates = primals\\n g_operand, g_scatter_indices, g_updates = tangents\\n dnums = dimension_numbers\\n\\n if g_operand is ad_util.zero and g_updates is ad_util.zero:\\n val_out = scatter_p.bind(\\n operand, scatter_indices, updates, update_jaxpr=update_jaxpr,\\n update_consts=update_consts, dimension_numbers=dnums,\\n updates_shape=updates_shape)\\n tangent_out = ad_util.zero\\n return val_out, tangent_out\\n\\n g_operand = ad.instantiate_zeros(operand, g_operand)\\n g_updates = ad.instantiate_zeros(updates, g_updates)\\n\\n # If there are overlapping indices in the scatter, it is unspecified which\\n # update \\\"wins\\\". So we use the following perhaps surprising scheme:\\n # a) attach a positive ID to each update in updates, forming (value, id) pairs\\n # (using a new array dimension because scatter doesn't actually support\\n # pairs).\\n # b) perform the scatter, yielding (value, id) updates, which we split apart.\\n # c) perform the inverse gather on the ids (similar to\\n # _scatter_add_transpose), and use it to build a mask for the tangent of\\n # `updates`.\\n # d) perform a scatter-add on the masked JVP values. A benefit of using\\n # scatter-add here is that we don't need a `scatter` transpose rule.\\n\\n # a) add unique positive IDs (iotas) to the updates, and zeros to the operand.\\n operand_shape = operand.shape\\n updates_shape = updates.shape\\n updates_dtype = _dtype(updates)\\n\\n new_operand = reshape(operand, (1,) + operand_shape)\\n new_operand = pad(new_operand, _zero(operand),\\n ((0, 1, 0),) + tuple((0, 0, 0) for _ in operand_shape))\\n\\n ids_shape = onp.array(updates_shape)\\n ids_shape[dnums.update_window_dims,] = 1\\n num_ids = onp.prod(ids_shape)\\n update_ids = add(reshape(iota(updates_dtype, num_ids), ids_shape),\\n _ones(updates))\\n\\n # TODO(phaw\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_shape_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_shape_rule\\ndef _reduce_shape_rule(operand, init_value, computation, jaxpr, consts, dimensions):\\n return tuple(onp.delete(operand.shape, dimensions))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_translation_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_translation_rule\\ndef _reduce_translation_rule(c, operand, init_value, computation, jaxpr, consts, dimensions):\\n xla_computation = _reduction_computation(c, jaxpr, consts, init_value)\\n return c.Reduce(operand, init_value, xla_computation, dimensions)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_batch_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_batch_rule\\ndef _reduce_batch_rule(batched_args, batch_dims, computation, jaxpr, consts, dimensions):\\n operand, init_value = batched_args\\n operand_bdim, init_value_bdim = batch_dims\\n if init_value_bdim is None:\\n assert operand_bdim is not None\\n new_dimensions = [d + bool(d >= operand_bdim) for d in dimensions]\\n new_operand_bdim = operand_bdim - int(onp.sum(onp.less(dimensions, operand_bdim)))\\n return reduce(operand, init_value, computation, new_dimensions), new_operand_bdim\\n else:\\n raise NotImplementedError # loop and stack\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduction_computation\",\n \"text\": \"jax/lax/lax.py::_reduction_computation\\ndef _reduction_computation(c, jaxpr, consts, init_value):\\n shape = c.GetShape(init_value)\\n axis_env = xla.AxisEnv() # no parallel primitives inside reductions\\n subc = xla_bridge.make_computation_builder(\\\"reduction_computation\\\")\\n consts = [subc.ParameterWithShape(const) for const in consts]\\n args = [subc.ParameterWithShape(shape), subc.ParameterWithShape(shape)]\\n out, = xla.jaxpr_subcomp(subc, jaxpr, None, axis_env, consts, (), *args)\\n return subc.Build(out)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_masking_defreducer\",\n \"text\": \"jax/lax/lax.py::_masking_defreducer\\ndef _masking_defreducer(prim, identity):\\n masking.shape_rules[prim] = _reducer_polymorphic_shape_rule\\n masking.masking_rules[prim] = partial(_reducer_masking_rule, prim, identity)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reducer_polymorphic_shape_rule\",\n \"text\": \"jax/lax/lax.py::_reducer_polymorphic_shape_rule\\ndef _reducer_polymorphic_shape_rule(shape_exprs, axes, **unused_params):\\n shape_expr, = shape_exprs\\n return ShapeExpr([d for i, d in enumerate(shape_expr) if i not in axes])\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reducer_masking_rule\",\n \"text\": \"jax/lax/lax.py::_reducer_masking_rule\\ndef _reducer_masking_rule(prim, identity, padded_vals, logical_shapes,\\n axes, input_shape):\\n del input_shape # Unused.\\n (padded_val,), (logical_shape,) = padded_vals, logical_shapes\\n padded_shape = masking.padded_shape_as_value(padded_val.shape)\\n masks = [broadcasted_iota(onp.int32, padded_shape, i) < d\\n for i, d in enumerate(logical_shape) if i in axes]\\n mask = _reduce(operator.and_, masks)\\n masked_val = select(mask, padded_val, identity(padded_shape, padded_val.dtype))\\n return prim.bind(masked_val, axes=axes, input_shape=padded_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_number_dtype_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_number_dtype_rule\\ndef _reduce_number_dtype_rule(name, operand, *args, **kw):\\n if not dtypes.issubdtype(operand.dtype, onp.number):\\n raise TypeError(\\\"{} does not accept dtype {}. Accepted dtypes are subtypes \\\"\\n \\\"of number.\\\".format(name, onp.dtype(operand.dtype).name))\\n return dtypes.canonicalize_dtype(operand.dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_sum_shape_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_sum_shape_rule\\ndef _reduce_sum_shape_rule(operand, axes, input_shape):\\n assert operand.shape == input_shape, ('{} != {}'\\n .format(operand.shape, input_shape))\\n return tuple(onp.delete(operand.shape, axes))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_sum_translation_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_sum_translation_rule\\ndef _reduce_sum_translation_rule(c, operand, axes, input_shape):\\n dtype = c.GetShape(operand).numpy_dtype()\\n scalar = ShapedArray((), dtype)\\n return c.Reduce(operand, c.Constant(onp.array(0, dtype)),\\n xla.primitive_subcomputation(add_p, scalar, scalar),\\n axes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_sum_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_sum_transpose_rule\\ndef _reduce_sum_transpose_rule(cotangent, input_shape, axes):\\n broadcast_dimensions = tuple(onp.delete(onp.arange(len(input_shape)), axes))\\n result = broadcast_in_dim(cotangent, input_shape, broadcast_dimensions)\\n assert result.shape == input_shape\\n return [result]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_prod_shape_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_prod_shape_rule\\ndef _reduce_prod_shape_rule(operand, axes):\\n return tuple(onp.delete(operand.shape, axes))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_prod_translation_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_prod_translation_rule\\ndef _reduce_prod_translation_rule(c, operand, axes):\\n dtype = c.GetShape(operand).numpy_dtype()\\n scalar = ShapedArray((), dtype)\\n return c.Reduce(operand, c.Constant(onp.array(1, dtype)),\\n xla.primitive_subcomputation(mul_p, scalar, scalar), axes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_prod_jvp_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_prod_jvp_rule\\ndef _reduce_prod_jvp_rule(tangent, operand, axes):\\n input_shape = onp.array(operand.shape)\\n\\n n = onp.prod(input_shape[list(axes)])\\n non_axes = onp.delete(onp.arange(len(input_shape)), axes)\\n\\n # Move the reduced axes to the front, and flatten them to 1D.\\n permutation = axes + tuple(non_axes)\\n new_shape = (n,) + tuple(input_shape[non_axes])\\n operand = reshape(operand, new_shape, permutation)\\n tangent = reshape(tangent, new_shape, permutation)\\n\\n one = _const(operand, 1)\\n window_dims = [n] + [1] * len(non_axes)\\n window_strides = [1] * (len(non_axes) + 1)\\n\\n # Form the partial products of all elements to the left and right of each\\n # element.\\n left_padding = [(n, -1, 0)] + [(0, 0, 0)] * len(non_axes)\\n right_padding = [(-1, n, 0)] + [(0, 0, 0)] * len(non_axes)\\n left_products = _reduce_window_prod(pad(operand, one, left_padding),\\n window_dims, window_strides,\\n xla_client.PaddingType.VALID)\\n right_products = _reduce_window_prod(pad(operand, one, right_padding),\\n window_dims, window_strides,\\n xla_client.PaddingType.VALID)\\n\\n # Multiply partial products with the tangents and sum.\\n return _reduce_sum(mul(tangent, mul(left_products, right_products)), (0,))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_chooser_shape_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_chooser_shape_rule\\ndef _reduce_chooser_shape_rule(operand, axes):\\n return tuple(onp.delete(operand.shape, axes))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_chooser_translation_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_chooser_translation_rule\\ndef _reduce_chooser_translation_rule(prim, identity, c, operand, axes):\\n dtype = c.GetShape(operand).numpy_dtype()\\n scalar = ShapedArray((), dtype)\\n return c.Reduce(operand, c.Constant(identity(dtype)),\\n xla.primitive_subcomputation(prim, scalar, scalar), axes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_chooser_jvp_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_chooser_jvp_rule\\ndef _reduce_chooser_jvp_rule(g, ans, operand, axes):\\n # TODO(mattjj): an alternative is to use variadic reduce to compute the chosen\\n # locations in a single pass (rather than comparing equality) and use a\\n # gather, and/or even push along the chosen elements of g (b/112040122)\\n shape = [1 if i in axes else d for i, d in enumerate(operand.shape)]\\n location_indicators = convert_element_type(\\n _eq_meet(operand, reshape(ans, shape)), g.dtype)\\n counts = _reduce_sum(location_indicators, axes)\\n return div(_reduce_sum(mul(g, location_indicators), axes), counts)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_logical_shape_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_logical_shape_rule\\ndef _reduce_logical_shape_rule(operand, axes):\\n if operand.dtype != onp.bool_:\\n msg = \\\"logical reduction requires operand dtype bool, got {}.\\\"\\n raise TypeError(msg.format(operand.dtype))\\n return tuple(onp.delete(operand.shape, axes))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_logical_translation_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_logical_translation_rule\\ndef _reduce_logical_translation_rule(prim, identity, c, operand, axes):\\n scalar = ShapedArray((), onp.bool_)\\n return c.Reduce(operand, c.Constant(identity(onp.bool_)),\\n xla.primitive_subcomputation(prim, scalar, scalar), axes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_shape_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_window_shape_rule\\ndef _reduce_window_shape_rule(operand, init_value, jaxpr, consts,\\n window_dimensions, window_strides, padding):\\n if operand.dtype != init_value.dtype:\\n msg = (\\\"reduce_window got inconsistent dtypes for operand and init_value: \\\"\\n \\\" got operand dtype {} and init_value dtype {}.\\\")\\n raise TypeError(msg.format(operand.dtype, init_value.dtype))\\n return _common_reduce_window_shape_rule(operand, window_dimensions,\\n window_strides, padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_translation_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_window_translation_rule\\ndef _reduce_window_translation_rule(c, operand, init_value, jaxpr, consts,\\n window_dimensions, window_strides, padding):\\n xla_computation = _reduction_computation(c, jaxpr, consts, init_value)\\n return c.ReduceWindow(operand, init_value, xla_computation, window_dimensions,\\n window_strides, padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_generic_reduce_window_batch_rule\",\n \"text\": \"jax/lax/lax.py::_generic_reduce_window_batch_rule\\ndef _generic_reduce_window_batch_rule(\\n batched_args, batch_dims, jaxpr, consts, window_dimensions, window_strides,\\n padding):\\n operand, init = batched_args\\n bdim, init_bdim = batch_dims\\n if init_bdim is not None:\\n raise NotImplementedError(\\\"reduce_window batching is not implemented for \\\"\\n \\\"initial values\\\")\\n\\n def reduce_window(x, window_dimensions, window_strides, padding):\\n return reduce_window_p.bind(\\n x, init, jaxpr=jaxpr, consts=consts, window_dimensions=window_dimensions,\\n window_strides=window_strides, padding=padding)\\n return _reduce_window_batch_rule(reduce_window, (operand,), (bdim,),\\n window_dimensions, window_strides, padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_generic_reduce_window_batch_rule.reduce_window\",\n \"text\": \"jax/lax/lax.py::_generic_reduce_window_batch_rule.reduce_window\\n def reduce_window(x, window_dimensions, window_strides, padding):\\n return reduce_window_p.bind(\\n x, init, jaxpr=jaxpr, consts=consts, window_dimensions=window_dimensions,\\n window_strides=window_strides, padding=padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_sum_shape_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_window_sum_shape_rule\\ndef _reduce_window_sum_shape_rule(operand, window_dimensions, window_strides,\\n padding, input_shape):\\n return _common_reduce_window_shape_rule(operand, window_dimensions,\\n window_strides, padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_sum_translation_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_window_sum_translation_rule\\ndef _reduce_window_sum_translation_rule(c, operand, window_dimensions,\\n window_strides, padding, input_shape):\\n dtype = c.GetShape(operand).numpy_dtype()\\n scalar = ShapedArray((), dtype)\\n return c.ReduceWindow(operand, c.Constant(onp.array(0, dtype)),\\n xla.primitive_subcomputation(add_p, scalar, scalar),\\n window_dimensions, window_strides, padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_sum_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_window_sum_transpose_rule\\ndef _reduce_window_sum_transpose_rule(cotangent, window_dimensions,\\n window_strides, padding, input_shape):\\n in_pads = padtype_to_pads(input_shape, window_dimensions, window_strides,\\n padding)\\n ones = [1] * len(input_shape)\\n pads = _conv_general_vjp_lhs_padding(\\n input_shape, window_dimensions, window_strides, cotangent.shape, in_pads,\\n ones, ones)\\n padding_config = [(lo, hi, stride - 1)\\n for (lo, hi), stride in zip(pads, window_strides)]\\n pad_cotangent = pad(cotangent, _zero(cotangent), padding_config)\\n result = _reduce_window_sum(pad_cotangent, window_dimensions, ones,\\n xla_client.PaddingType.VALID)\\n assert result.shape == input_shape\\n return [result]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_batch_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_window_batch_rule\\ndef _reduce_window_batch_rule(\\n reduce_window, batched_args, bdims, window_dimensions, window_strides,\\n padding, input_shape=None):\\n operand, = batched_args\\n bdim, = bdims\\n\\n if bdim is not None:\\n window_dimensions = \\\\\\n window_dimensions[:bdim] + (1,) + window_dimensions[bdim:]\\n window_strides = window_strides[:bdim] + (1,) + window_strides[bdim:]\\n\\n operand = reduce_window(\\n operand, window_dimensions, window_strides, padding)\\n\\n return operand, bdim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_chooser_translation_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_window_chooser_translation_rule\\ndef _reduce_window_chooser_translation_rule(\\n prim, identity, c, operand, window_dimensions, window_strides, padding):\\n dtype = c.GetShape(operand).numpy_dtype()\\n scalar = ShapedArray((), dtype)\\n return c.ReduceWindow(operand, c.Constant(identity(dtype)),\\n xla.primitive_subcomputation(prim, scalar, scalar),\\n window_dimensions, window_strides, padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_reduce_window_chooser_jvp_rule\",\n \"text\": \"jax/lax/lax.py::_reduce_window_chooser_jvp_rule\\ndef _reduce_window_chooser_jvp_rule(prim, g, operand, window_dimensions,\\n window_strides, padding):\\n assert prim is max_p or prim is min_p\\n select_prim = ge_p if prim is max_p else le_p\\n return _select_and_gather_add(g, operand, select_prim, window_dimensions,\\n window_strides, padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_common_reduce_window_shape_rule\",\n \"text\": \"jax/lax/lax.py::_common_reduce_window_shape_rule\\ndef _common_reduce_window_shape_rule(operand, window_dimensions, window_strides,\\n padding):\\n _check_shapelike(\\\"reduce_window\\\", \\\"window_dimensions\\\", window_dimensions)\\n _check_shapelike(\\\"reduce_window\\\", \\\"window_strides\\\", window_strides)\\n if operand.ndim != len(window_dimensions):\\n msg = (\\\"reduce_window got the wrong number of window_dimensions for \\\"\\n \\\"operand: got operand shape {} with window_dimensions {}.\\\")\\n raise TypeError(msg.format(operand.shape, window_dimensions))\\n if len(window_strides) != len(window_dimensions):\\n msg = (\\\"reduce_window got inconsistent window_strides and \\\"\\n \\\"window_dimensions: got window_strides {} and window_dimensions {}.\\\")\\n raise TypeError(msg.format(window_strides, window_dimensions))\\n\\n return reduce_window_shape_tuple(operand.shape, window_dimensions,\\n window_strides, padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::reduce_window_shape_tuple\",\n \"text\": \"jax/lax/lax.py::reduce_window_shape_tuple\\ndef reduce_window_shape_tuple(operand_shape, window_dimensions, window_strides,\\n padding):\\n pads = padtype_to_pads(operand_shape, window_dimensions, window_strides, padding)\\n operand_padded = onp.add(operand_shape, onp.add(*zip(*pads)))\\n t = onp.floor_divide(\\n onp.subtract(operand_padded, window_dimensions), window_strides) + 1\\n return tuple(t)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_scatter_shape_rule\",\n \"text\": \"jax/lax/lax.py::_select_and_scatter_shape_rule\\ndef _select_and_scatter_shape_rule(\\n operand, source, init_value, select_jaxpr, select_consts, scatter_jaxpr,\\n scatter_consts, window_dimensions, window_strides, padding):\\n _check_shapelike(\\\"select_and_scatter\\\", \\\"window_dimensions\\\", window_dimensions)\\n _check_shapelike(\\\"select_and_scatter\\\", \\\"window_strides\\\", window_strides)\\n if len(window_dimensions) != len(window_strides):\\n msg = (\\\"select_and_scatter got inconsistent window_strides and \\\"\\n \\\"window_dimensions: got window_strides {} and window_dimensions {}.\\\")\\n raise TypeError(msg.format(window_strides, window_dimensions))\\n return operand.shape\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_scatter_translation\",\n \"text\": \"jax/lax/lax.py::_select_and_scatter_translation\\ndef _select_and_scatter_translation(\\n c, operand, source, init_value, select_jaxpr, select_consts, scatter_jaxpr,\\n scatter_consts, window_dimensions, window_strides, padding):\\n select = _reduction_computation(c, select_jaxpr, select_consts, init_value)\\n scatter = _reduction_computation(c, scatter_jaxpr, scatter_consts, init_value)\\n return c.SelectAndScatter(operand, select, window_dimensions, window_strides,\\n padding, source, init_value, scatter)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_scatter_add_shape_rule\",\n \"text\": \"jax/lax/lax.py::_select_and_scatter_add_shape_rule\\ndef _select_and_scatter_add_shape_rule(\\n source, operand, select_prim, window_dimensions, window_strides, padding):\\n return operand.shape\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_scatter_add_translation\",\n \"text\": \"jax/lax/lax.py::_select_and_scatter_add_translation\\ndef _select_and_scatter_add_translation(\\n c, source, operand, select_prim, window_dimensions, window_strides,\\n padding):\\n dtype = c.GetShape(operand).numpy_dtype()\\n scalar = ShapedArray((), dtype)\\n select = xla.primitive_subcomputation(select_prim, scalar, scalar)\\n scatter = xla.primitive_subcomputation(add_p, scalar, scalar)\\n zero = c.Constant(onp.array(0, dtype))\\n return c.SelectAndScatter(operand, select, window_dimensions, window_strides,\\n padding, source, zero, scatter)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_scatter_add_jvp\",\n \"text\": \"jax/lax/lax.py::_select_and_scatter_add_jvp\\ndef _select_and_scatter_add_jvp(\\n primals, tangents, select_prim, window_dimensions, window_strides,\\n padding):\\n source, operand = primals\\n g_source, g_operand = tangents\\n val_out = _select_and_scatter_add(\\n source, operand, select_prim, window_dimensions, window_strides,\\n padding)\\n del g_operand\\n if g_source is ad_util.zero:\\n tangent_out = ad_util.zero\\n else:\\n tangent_out = _select_and_scatter_add(\\n g_source, operand, select_prim, window_dimensions,\\n window_strides, padding)\\n return val_out, tangent_out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_scatter_add_transpose\",\n \"text\": \"jax/lax/lax.py::_select_and_scatter_add_transpose\\ndef _select_and_scatter_add_transpose(\\n t, source, operand, select_prim, window_dimensions, window_strides,\\n padding):\\n assert source is ad.undefined_primal and operand is not ad.undefined_primal\\n source_t = _select_and_gather_add(t, operand, select_prim, window_dimensions,\\n window_strides, padding)\\n return [source_t, None]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_scatter_add_batch_rule\",\n \"text\": \"jax/lax/lax.py::_select_and_scatter_add_batch_rule\\ndef _select_and_scatter_add_batch_rule(batched_args, batch_dims, **kwargs):\\n source, operand = batched_args\\n s_bdims, o_bdims = batch_dims\\n\\n if s_bdims is not None and o_bdims is not None:\\n #TODO(#212): use a map construct instead of unrolling.\\n source = batching.moveaxis(source, s_bdims, 0)\\n operand = batching.moveaxis(operand, o_bdims, 0)\\n outputs = [\\n _select_and_scatter_add(s, o, **kwargs) for s, o in zip(source, operand)]\\n outputs = [reshape(out, (1,) + out.shape) for out in outputs]\\n outputs = concatenate(outputs, 0)\\n return outputs, 0\\n elif s_bdims is not None:\\n #TODO(#212): use a map construct instead of unrolling.\\n source = batching.moveaxis(source, s_bdims, 0)\\n outputs = [\\n _select_and_scatter_add(s, operand, **kwargs) for s in source]\\n outputs = [reshape(out, (1,) + out.shape) for out in outputs]\\n outputs = concatenate(outputs, 0)\\n return outputs, 0\\n elif o_bdims is not None:\\n #TODO(#212): use a map construct instead of unrolling.\\n operand = batching.moveaxis(operand, o_bdims, 0)\\n outputs = [\\n _select_and_scatter_add(source, o, **kwargs) for o in operand]\\n outputs = [reshape(out, (1,) + out.shape) for out in outputs]\\n outputs = concatenate(outputs, 0)\\n return outputs, 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_gather_add_shape_rule\",\n \"text\": \"jax/lax/lax.py::_select_and_gather_add_shape_rule\\ndef _select_and_gather_add_shape_rule(\\n tangents, operand, select_prim, window_dimensions, window_strides, padding):\\n if tangents.shape != operand.shape:\\n msg = (\\\"select_and_gather_add tangents and operand shapes must match, \\\"\\n \\\"got {} and {}.\\\")\\n raise TypeError(msg.format(tangents.shape, operand.shape))\\n return _common_reduce_window_shape_rule(operand, window_dimensions,\\n window_strides, padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_gather_add_translation\",\n \"text\": \"jax/lax/lax.py::_select_and_gather_add_translation\\ndef _select_and_gather_add_translation(\\n c, tangents, operand, select_prim, window_dimensions, window_strides,\\n padding, max_bits=64):\\n shape = c.GetShape(operand)\\n dtype = shape.numpy_dtype()\\n etype = shape.xla_element_type()\\n nbits = dtypes.finfo(dtype).bits\\n\\n assert nbits <= max_bits\\n double_word_reduction = nbits * 2 <= max_bits\\n\\n const = lambda c, dtype, x: c.Constant(onp.array(x, dtype=dtype),\\n canonicalize_types=False)\\n\\n if double_word_reduction:\\n # TODO(b/73062247): XLA doesn't yet implement ReduceWindow on tuples, so\\n # we implement a pair-wise ReduceWindow by packing two k-bit values into\\n # 2k-bit unsigned integer using bit tricks.\\n word_dtype = _UINT_DTYPES[nbits]\\n double_word_dtype = _UINT_DTYPES[nbits * 2]\\n word_type = xla_client.dtype_to_etype(word_dtype)\\n double_word_type = xla_client.dtype_to_etype(double_word_dtype)\\n\\n # Packs two values into a tuple.\\n def pack(a, b):\\n a = c.BitcastConvertType(a, word_type)\\n b = c.BitcastConvertType(b, word_type)\\n a = c.ConvertElementType(a, double_word_type)\\n b = c.ConvertElementType(b, double_word_type)\\n a = c.ShiftLeft(a, const(c, double_word_dtype, nbits))\\n return c.Or(a, b)\\n\\n # Unpacks the first element of a tuple.\\n def fst(c, t):\\n st = c.ShiftRightLogical(t, const(c, double_word_dtype, nbits))\\n return c.BitcastConvertType(c.ConvertElementType(st, word_type), etype)\\n\\n # Unpacks the second element of a tuple.\\n def snd(t):\\n return c.BitcastConvertType(c.ConvertElementType(t, word_type), etype)\\n\\n else:\\n # The double-word trick above only works if we have a sufficiently large\\n # type. As an alternative, we can pack two half words into a single word,\\n # at the cost of precision.\\n # TODO(b/73062247): add support for tuple reductions and remove this case.\\n warnings.warn(\\\"Using reduced precision for gradient of reduce\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_gather_add_translation.reducer\",\n \"text\": \"jax/lax/lax.py::_select_and_gather_add_translation.reducer\\n def reducer():\\n c = xla_bridge.make_computation_builder(\\\"select_and_gather_pair_reducer\\\")\\n x = c.ParameterWithShape(\\n xla_client.Shape.array_shape(onp.dtype(double_word_dtype), ()))\\n y = c.ParameterWithShape(\\n xla_client.Shape.array_shape(onp.dtype(double_word_dtype), ()))\\n assert select_prim is ge_p or select_prim is le_p\\n which = c.Ge if select_prim is ge_p else c.Le\\n c.Select(which(fst(c, x), fst(c, y)), x, y)\\n return c.Build()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_gather_add_jvp\",\n \"text\": \"jax/lax/lax.py::_select_and_gather_add_jvp\\ndef _select_and_gather_add_jvp(\\n primals, tangents, select_prim, window_dimensions, window_strides,\\n padding):\\n source, operand = primals\\n g_source, g_operand = tangents\\n val_out = _select_and_gather_add(\\n source, operand, select_prim, window_dimensions, window_strides,\\n padding)\\n del g_operand\\n if g_source is ad_util.zero:\\n tangent_out = ad_util.zero\\n else:\\n tangent_out = _select_and_gather_add(\\n g_source, operand, select_prim, window_dimensions,\\n window_strides, padding)\\n return val_out, tangent_out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_gather_add_transpose\",\n \"text\": \"jax/lax/lax.py::_select_and_gather_add_transpose\\ndef _select_and_gather_add_transpose(\\n t, tangents, operand, select_prim, window_dimensions, window_strides,\\n padding):\\n assert tangents is ad.undefined_primal and operand is not ad.undefined_primal\\n result = _select_and_scatter_add(t, operand, select_prim, window_dimensions,\\n window_strides, padding)\\n return [result, None]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_select_and_gather_add_batching_rule\",\n \"text\": \"jax/lax/lax.py::_select_and_gather_add_batching_rule\\ndef _select_and_gather_add_batching_rule(\\n batched_args, batch_dims, select_prim, window_dimensions, window_strides,\\n padding):\\n t, x = batched_args\\n t_bdim, x_bdim = batch_dims\\n size = next(a.shape[bdim] for a, bdim in zip(batched_args, batch_dims)\\n if bdim is not None)\\n t = batching.bdim_at_front(t, t_bdim, size)\\n x = batching.bdim_at_front(x, x_bdim, size)\\n window_dimensions = (1,) + window_dimensions\\n window_strides = (1,) + window_strides\\n out = _select_and_gather_add(t, x, select_prim, window_dimensions,\\n window_strides, padding)\\n return (out, 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_sort_jvp_rule\",\n \"text\": \"jax/lax/lax.py::_sort_jvp_rule\\ndef _sort_jvp_rule(g, operand, dimension):\\n _, g_out = sort_key_val(operand, g, dimension)\\n return g_out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_sort_batch_rule\",\n \"text\": \"jax/lax/lax.py::_sort_batch_rule\\ndef _sort_batch_rule(batched_args, batch_dims, dimension):\\n operand, = batched_args\\n bdim, = batch_dims\\n dimension = dimension % (operand.ndim - 1)\\n new_dimension = dimension + (bdim <= dimension)\\n return sort(operand, dimension=new_dimension), bdim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_sort_key_val_abstract_eval\",\n \"text\": \"jax/lax/lax.py::_sort_key_val_abstract_eval\\ndef _sort_key_val_abstract_eval(keys, values, dimension):\\n return raise_to_shaped(keys), raise_to_shaped(values)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_sort_key_val_jvp\",\n \"text\": \"jax/lax/lax.py::_sort_key_val_jvp\\ndef _sort_key_val_jvp(primals, tangents, dimension):\\n # NOTE(mattjj): this re-sorts three times, but if we had a variadic\\n # sort_key_val, or if we could apply a fixed permutation efficiently, we could\\n # implement this jvp rule with a single sort. The apply_permutation primitive\\n # would make the jvp (and corresponding transpose rule) faster and easier.\\n # This would also be cleaner if we didn't get the sorted keys out.\\n # TODO(mattjj): make sort_key_val variadic, no sorted keys out by default\\n keys, values = primals\\n keys_tangents, values_tangents = tangents\\n\\n val_out = sort_key_val(keys, values, dimension)\\n\\n if keys_tangents is ad_util.zero:\\n keys_tangents_out = ad_util.zero\\n else:\\n keys_tangents_out = _sort_jvp_rule(keys_tangents, keys, dimension)\\n\\n if values_tangents is ad_util.zero:\\n values_tangents_out = ad_util.zero\\n else:\\n values_tangents_out = _sort_jvp_rule(values_tangents, keys, dimension)\\n\\n tangents_out = keys_tangents_out, values_tangents_out\\n return val_out, tangents_out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_sort_key_val_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_sort_key_val_transpose_rule\\ndef _sort_key_val_transpose_rule(t, keys, values, dimension):\\n t_keys, t_values = t\\n assert t_keys is ad_util.zero\\n iota = broadcasted_iota(onp.int32, keys.shape, dimension % keys.ndim)\\n _, perm = sort_key_val(keys, iota)\\n keys_result = ad_util.zero if keys is ad.undefined_primal else None\\n values_result = sort_key_val(perm, t_values)[1] if values is ad.undefined_primal else None\\n return [keys_result, values_result]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_sort_key_val_batch_rule\",\n \"text\": \"jax/lax/lax.py::_sort_key_val_batch_rule\\ndef _sort_key_val_batch_rule(batched_args, batch_dims, dimension):\\n keys, values = batched_args\\n keys_bdim, values_bdim = batch_dims\\n assert keys_bdim is not None or values_bdim is not None\\n if keys_bdim == values_bdim:\\n new_dimension = dimension + (keys_bdim <= dimension)\\n return sort_key_val(keys, values, new_dimension), (keys_bdim, keys_bdim)\\n elif keys_bdim is not None and values_bdim is not None:\\n keys_trans = batching.moveaxis(keys, keys_bdim, values_bdim)\\n new_dimension = dimension + (values_bdim <= dimension)\\n return sort_key_val(keys_trans, values, new_dimension), (values_bdim, values_bdim)\\n elif keys_bdim is None:\\n broadcast_dimensions = onp.delete(onp.arange(values.ndim), values_bdim)\\n new_keys = broadcast_in_dim(keys, values.shape, broadcast_dimensions)\\n new_dimension = dimension + (values_bdim <= dimension)\\n return sort_key_val(new_keys, values, new_dimension), (values_bdim, values_bdim)\\n elif values_bdim is None:\\n broadcast_dimensions = onp.delete(onp.arange(keys.ndim), keys_bdim)\\n new_values = broadcast_in_dim(values, keys.shape, broadcast_dimensions)\\n new_dimension = dimension + (keys_bdim <= dimension)\\n return sort_key_val(keys, new_values, new_dimension), (keys_bdim, keys_bdim)\\n else:\\n assert False # unreachable\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_tie_in_transpose_rule\",\n \"text\": \"jax/lax/lax.py::_tie_in_transpose_rule\\ndef _tie_in_transpose_rule(t):\\n return [ad_util.zero, t]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_tie_in_batch_rule\",\n \"text\": \"jax/lax/lax.py::_tie_in_batch_rule\\ndef _tie_in_batch_rule(batched_args, batch_dims):\\n y = tie_in(*batched_args)\\n _, bdim_y = batch_dims\\n return y, bdim_y\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_stop_gradient_jvp_rule\",\n \"text\": \"jax/lax/lax.py::_stop_gradient_jvp_rule\\ndef _stop_gradient_jvp_rule(primals, tangents):\\n # if we don't call stop_gradient here, we'd only peel off one autodiff tracer\\n x, = primals\\n return stop_gradient(x), ad_util.zero\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_stop_gradient_batch_rule\",\n \"text\": \"jax/lax/lax.py::_stop_gradient_batch_rule\\ndef _stop_gradient_batch_rule(batched_args, batch_dims):\\n x, = batched_args\\n dim, = batch_dims\\n return stop_gradient(x), dim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::create_token\",\n \"text\": \"jax/lax/lax.py::create_token\\ndef create_token(x):\\n \\\"\\\"\\\"Creates an XLA token value with no preconditions for sequencing effects.\\n\\n Experimental.\\n\\n Args:\\n x: a dummy argument used to tie the CreateToken operator into a trace. The\\n value of `x` is ignored.\\n \\\"\\\"\\\"\\n # x is a dummy argument used to tie the operator into a trace.\\n return create_token_p.bind(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::after_all\",\n \"text\": \"jax/lax/lax.py::after_all\\ndef after_all(*operands):\\n \\\"\\\"\\\"Merges one or more XLA token values. Experimental.\\n\\n Wraps the XLA AfterAll operator.\\\"\\\"\\\"\\n return after_all_p.bind(*operands)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_after_all_abstract_eval\",\n \"text\": \"jax/lax/lax.py::_after_all_abstract_eval\\ndef _after_all_abstract_eval(*operands):\\n if any(x is not abstract_token for x in operands):\\n raise TypeError(\\\"Arguments to after_all must be tokens\\\")\\n return abstract_token\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_after_all_translation_rule\",\n \"text\": \"jax/lax/lax.py::_after_all_translation_rule\\ndef _after_all_translation_rule(c, *operands):\\n return c.AfterAll(operands)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::infeed\",\n \"text\": \"jax/lax/lax.py::infeed\\ndef infeed(token, shape=None):\\n \\\"\\\"\\\"Consumes an infeed value of `shape` from the host. Experimental.\\n\\n `token` is used to sequence infeed and outfeed effects.\\n \\\"\\\"\\\"\\n flat_shapes, treedef = pytree.flatten(shape)\\n for shape in flat_shapes:\\n if not isinstance(shape, ShapedArray):\\n raise TypeError(\\\"shapes argument to infeed must be a pytree of \\\"\\n \\\"ShapedArray values, got {}\\\".format(shapes))\\n xs_and_token = infeed_p.bind(token, shapes=tuple(flat_shapes))\\n return (treedef.unflatten(xs_and_token[:-1]), xs_and_token[-1])\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_infeed_abstract_eval\",\n \"text\": \"jax/lax/lax.py::_infeed_abstract_eval\\ndef _infeed_abstract_eval(token, shapes=None):\\n if token is not abstract_token:\\n raise TypeError(\\\"First argument to infeed must be a token\\\")\\n return shapes + (abstract_token,)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_infeed_translation_rule\",\n \"text\": \"jax/lax/lax.py::_infeed_translation_rule\\ndef _infeed_translation_rule(c, token, shapes=None):\\n shape = tuple(map(xla.aval_to_xla_shape, shapes))\\n xs_and_token = c.Infeed(xla_client.Shape.tuple_shape(shape), token)\\n xs = c.GetTupleElement(xs_and_token, 0)\\n token = c.GetTupleElement(xs_and_token, 1)\\n outs = [c.GetTupleElement(xs, i) for i in range(len(shapes))] + [token]\\n return c.Tuple(*outs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::outfeed\",\n \"text\": \"jax/lax/lax.py::outfeed\\ndef outfeed(token, xs):\\n \\\"\\\"\\\"Outfeeds value `xs` to the host. Experimental.\\n\\n `token` is used to sequence infeed and outfeed effects.\\n \\\"\\\"\\\"\\n flat_xs, _ = pytree.flatten(xs)\\n return outfeed_p.bind(token, *flat_xs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_outfeed_abstract_eval\",\n \"text\": \"jax/lax/lax.py::_outfeed_abstract_eval\\ndef _outfeed_abstract_eval(token, *xs):\\n if token is not abstract_token:\\n raise TypeError(\\\"First argument to outfeed must be a token\\\")\\n return abstract_token\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_outfeed_translation_rule\",\n \"text\": \"jax/lax/lax.py::_outfeed_translation_rule\\ndef _outfeed_translation_rule(c, token, *xs):\\n return c.Outfeed(c.Tuple(*xs), token)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dilate_shape\",\n \"text\": \"jax/lax/lax.py::_dilate_shape\\ndef _dilate_shape(shape, dilation):\\n \\\"\\\"\\\"Utility function for computing the shape resulting from a dilation.\\\"\\\"\\\"\\n if not onp.all(onp.greater(dilation, 0)):\\n msg = \\\"All dilations must be positive, got {}.\\\"\\n raise TypeError(msg.format(dilation))\\n dilation = (1,) * (len(shape) - len(dilation)) + tuple(dilation)\\n return onp.where(shape == 0, 0,\\n onp.multiply(dilation, onp.subtract(shape, 1)) + 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::padtype_to_pads\",\n \"text\": \"jax/lax/lax.py::padtype_to_pads\\ndef padtype_to_pads(in_shape, window_shape, window_strides, padding):\\n \\\"\\\"\\\"Convert padding string to list of pairs of pad values.\\\"\\\"\\\"\\n PaddingType = xla_client.PaddingType\\n\\n if isinstance(padding, str):\\n mapping = {'VALID': PaddingType.VALID, 'SAME': PaddingType.SAME}\\n try:\\n padding = mapping[padding.upper()]\\n except KeyError:\\n msg = \\\"Unrecognized padding type: expected 'VALID' or 'SAME', got {}.\\\"\\n raise RuntimeError(msg.format(padding))\\n\\n if padding == PaddingType.SAME:\\n out_shape = onp.ceil(onp.true_divide(in_shape, window_strides)).astype(int)\\n pad_sizes = [_max((out_size - 1) * stride + window_shape - in_size, 0)\\n for out_size, stride, window_shape, in_size\\n in zip(out_shape, window_strides, window_shape, in_shape)]\\n return [(pad_size // 2, pad_size - pad_size // 2) for pad_size in pad_sizes]\\n elif padding == PaddingType.VALID:\\n return [(0, 0)] * len(in_shape)\\n else:\\n msg = \\\"Unknown padding type: {}.\\\"\\n raise TypeError(msg.format(padding))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_check_same_dtypes\",\n \"text\": \"jax/lax/lax.py::_check_same_dtypes\\ndef _check_same_dtypes(name, ignore_fp_precision, *ttypes):\\n \\\"\\\"\\\"Check that dtypes agree, possibly ignoring float precision.\\\"\\\"\\\"\\n # the `ignore_fp_precision` flag exists because the XLA shape inference logic\\n # allows mixed floating point precision, but the HLO verifier often rejects it\\n types = list(map(onp.dtype, ttypes)) # canonicalize\\n if ignore_fp_precision:\\n types = [\\n onp.floating if dtypes.issubdtype(dtype, onp.floating)\\n else onp.complexfloating if dtypes.issubdtype(dtype, onp.complexfloating)\\n else dtype for dtype in types]\\n if len({dtypes.canonicalize_dtype(t) for t in types}) != 1:\\n if ignore_fp_precision:\\n msg = (\\\"{} requires arguments to have same dtypes up to floating point \\\"\\n \\\"precision, got {}.\\\")\\n else:\\n msg = \\\"{} requires arguments to have the same dtypes, got {}.\\\"\\n raise TypeError(msg.format(name, \\\", \\\".join(map(str, types))))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_check_conv_shapes\",\n \"text\": \"jax/lax/lax.py::_check_conv_shapes\\ndef _check_conv_shapes(name, lhs_shape, rhs_shape, window_strides):\\n \\\"\\\"\\\"Check that conv shapes are valid and are consistent with window_strides.\\\"\\\"\\\"\\n if len(lhs_shape) != len(rhs_shape):\\n msg = \\\"Arguments to {} must have same rank, got {} and {}.\\\"\\n raise TypeError(msg.format(name, len(lhs_shape), len(rhs_shape)))\\n if len(lhs_shape) < 2:\\n msg = \\\"Arguments to {} must have rank at least 2, got {} and {}.\\\"\\n raise TypeError(msg.format(name, len(lhs_shape), len(rhs_shape)))\\n if lhs_shape[1] != rhs_shape[1]:\\n msg = \\\"Arguments to {} must agree on input feature size, got {} and {}.\\\"\\n raise TypeError(msg.format(name, lhs_shape[1], rhs_shape[1]))\\n _check_shapelike(name, \\\"window_strides\\\", window_strides)\\n if not onp.all(onp.greater(window_strides, 0)):\\n msg = \\\"All elements of window_strides must be positive, got {}.\\\"\\n raise TypeError(msg.format(window_strides))\\n if len(window_strides) != len(lhs_shape) - 2:\\n msg = \\\"{} window_strides has wrong length: expected {}, got {}.\\\"\\n expected_length = len(lhs_shape) - 2\\n raise TypeError(msg.format(name, expected_length, len(window_strides)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::conv_shape_tuple\",\n \"text\": \"jax/lax/lax.py::conv_shape_tuple\\ndef conv_shape_tuple(lhs_shape, rhs_shape, strides, pads):\\n \\\"\\\"\\\"Compute the shape tuple of a conv given input shapes in canonical order.\\\"\\\"\\\"\\n if isinstance(pads, str):\\n pads = padtype_to_pads(lhs_shape[2:], rhs_shape[2:], strides, pads)\\n if len(pads) != len(lhs_shape) - 2:\\n msg = \\\"Wrong number of explicit pads for convolution: expected {}, got {}.\\\"\\n raise TypeError(msg.format(len(lhs_shape) - 2, len(pads)))\\n\\n lhs_padded = onp.add(lhs_shape[2:], onp.add(*zip(*pads)))\\n out_space = onp.floor_divide(\\n onp.subtract(lhs_padded, rhs_shape[2:]), strides) + 1\\n out_space = onp.maximum(0, out_space)\\n out_shape = (lhs_shape[0], rhs_shape[0]) + tuple(out_space)\\n return tuple(out_shape)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/lax/lax.py::conv_general_shape_tuple\",\n \"text\": \"jax/lax/lax.py::conv_general_shape_tuple\\ndef conv_general_shape_tuple(lhs_shape, rhs_shape, window_strides, padding,\\n dimension_numbers):\\n lhs_perm, rhs_perm, out_perm = conv_general_permutations(dimension_numbers)\\n lhs_trans = onp.take(lhs_shape, lhs_perm)\\n rhs_trans = onp.take(rhs_shape, rhs_perm)\\n out_trans = conv_shape_tuple(lhs_trans, rhs_trans, window_strides, padding)\\n return tuple(onp.take(out_trans, onp.argsort(out_perm)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::conv_transpose_shape_tuple\",\n \"text\": \"jax/lax/lax.py::conv_transpose_shape_tuple\\ndef conv_transpose_shape_tuple(lhs_shape, rhs_shape, window_strides, padding,\\n dimension_numbers):\\n lhs_perm, rhs_perm, out_perm = conv_general_permutations(dimension_numbers)\\n lhs_trans = onp.take(lhs_shape, lhs_perm)\\n rhs_trans = onp.take(rhs_shape, rhs_perm)\\n if isinstance(padding, str):\\n padding = [_conv_transpose_padding(k, s, padding)\\n for k,s in zip(rhs_trans[2:], window_strides)]\\n padding = list(map(onp.sum, padding))\\n unpad_out_space = [(i-1) * s - k + 2\\n for i, k, s in zip(lhs_trans[2:],\\n rhs_trans[2:],\\n window_strides)]\\n out_space = onp.sum([unpad_out_space, padding], axis=0).tolist()\\n out_trans = tuple((lhs_trans[0], rhs_trans[0]) + tuple(out_space))\\n return tuple(onp.take(out_trans, onp.argsort(out_perm)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_check_shapelike\",\n \"text\": \"jax/lax/lax.py::_check_shapelike\\ndef _check_shapelike(fun_name, arg_name, obj):\\n \\\"\\\"\\\"Check that `obj` is a shape-like value (e.g. tuple of nonnegative ints).\\\"\\\"\\\"\\n if (type(obj) is masking.ShapeExpr\\n or type(obj) is tuple and any(type(d) is masking.Poly for d in obj)):\\n return obj\\n if not isinstance(obj, (tuple, list, onp.ndarray)):\\n msg = \\\"{} {} must be of type tuple/list/ndarray, got {}.\\\"\\n raise TypeError(msg.format(fun_name, arg_name, type(obj)))\\n # bool(obj) for an ndarray raises an error, so we check len\\n if not len(obj): # pylint: disable=g-explicit-length-test\\n return\\n obj_arr = onp.array(obj)\\n if obj_arr.ndim != 1:\\n msg = \\\"{} {} must be rank 1, got {}.\\\"\\n raise TypeError(msg.format(obj_arr.ndim))\\n if not dtypes.issubdtype(obj_arr.dtype, onp.integer):\\n msg = \\\"{} {} must have every element be an integer type, got {}.\\\"\\n raise TypeError(msg.format(fun_name, arg_name, tuple(map(type, obj))))\\n if not (obj_arr >= 0).all():\\n msg = \\\"{} {} must have every element be nonnegative, got {}.\\\"\\n raise TypeError(msg.format(fun_name, arg_name, obj))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_dynamic_slice_indices\",\n \"text\": \"jax/lax/lax.py::_dynamic_slice_indices\\ndef _dynamic_slice_indices(operand, start_indices):\\n if not isinstance(start_indices, (tuple, list)):\\n if start_indices.ndim != 1:\\n raise ValueError(\\\"Slice indices must be a 1D sequence, got {}\\\"\\n .format(start_indices.shape))\\n start_indices = [reshape(slice(start_indices, [i], [i+1]), ())\\n for i in range(operand.ndim)]\\n else:\\n start_indices = [onp.asarray(i, dtype=dtypes.int_) if isinstance(i, int)\\n else i for i in start_indices]\\n if len(start_indices) != operand.ndim:\\n msg = (\\\"Length of slice indices must match number of operand dimensions ({} \\\"\\n \\\"vs {})\\\")\\n raise ValueError(msg.format(len(start_indices, operand.shape)))\\n # map int over operand.shape to raise any dynamic-shape errors\\n return [select(lt(i, _const(i, 0)), add(i, _const(i, int(d))), i)\\n for i, d in zip(start_indices, operand.shape)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_const\",\n \"text\": \"jax/lax/lax.py::_const\\ndef _const(example, val):\\n if dtypes.is_python_scalar(example):\\n return dtypes.scalar_type_of(example)(val)\\n return onp.array(val, _dtype(example))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::ranges_like\",\n \"text\": \"jax/lax/lax.py::ranges_like\\ndef ranges_like(*xs):\\n start = 0\\n for x in xs:\\n x_len = len(x)\\n yield range(start, start + x_len)\\n start += x_len\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::remaining\",\n \"text\": \"jax/lax/lax.py::remaining\\ndef remaining(original, *removed_lists):\\n blacklist = set(itertools.chain(*removed_lists))\\n return [i for i in original if i not in blacklist]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_canonicalize_precision\",\n \"text\": \"jax/lax/lax.py::_canonicalize_precision\\ndef _canonicalize_precision(precision):\\n if precision is None:\\n return None\\n if isinstance(precision, Precision):\\n return precision\\n else:\\n msg = \\\"Precision argument must be None or a lax.Precision value; got {}\\\"\\n raise ValueError(msg.format(precision))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::conv_dimension_numbers\",\n \"text\": \"jax/lax/lax.py::conv_dimension_numbers\\ndef conv_dimension_numbers(lhs_shape, rhs_shape, dimension_numbers):\\n \\\"\\\"\\\"Converts convolution `dimension_numbers` to a `ConvDimensionNumbers`.\\n\\n Args:\\n lhs_shape: tuple of nonnegative integers, shape of the convolution input.\\n rhs_shape: tuple of nonnegative integers, shape of the convolution kernel.\\n dimension_numbers: None or a tuple/list of strings, following the\\n convolution dimension number specification format in xla_client.py.\\n\\n Returns:\\n A `ConvDimensionNumbers` object that represents `dimension_numbers` in the\\n canonical form used by lax functions.\\n \\\"\\\"\\\"\\n if len(lhs_shape) != len(rhs_shape):\\n msg = \\\"convolution requires lhs and rhs ndim to be equal, got {} and {}.\\\"\\n raise TypeError(msg.format(len(lhs_shape), len(rhs_shape)))\\n\\n if dimension_numbers is None:\\n iota = tuple(range(len(lhs_shape)))\\n return ConvDimensionNumbers(iota, iota, iota)\\n elif isinstance(dimension_numbers, (list, tuple)):\\n if len(dimension_numbers) != 3:\\n msg = \\\"convolution dimension_numbers list/tuple must be length 3, got {}.\\\"\\n raise TypeError(msg.format(len(dimension_numbers)))\\n if not all(isinstance(elt, str) for elt in dimension_numbers):\\n msg = \\\"convolution dimension_numbers elements must be strings, got {}.\\\"\\n raise TypeError(msg.format(tuple(map(type, dimension_numbers))))\\n msg = (\\\"convolution dimension_numbers[{}] must have len equal to the ndim \\\"\\n \\\"of lhs and rhs, got {} for lhs and rhs shapes {} and {}.\\\")\\n for i, elt in enumerate(dimension_numbers):\\n if len(elt) != len(lhs_shape):\\n raise TypeError(msg.format(i, len(elt), lhs_shape, rhs_shape))\\n\\n lhs_spec, rhs_spec, out_spec = conv_general_permutations(dimension_numbers)\\n return ConvDimensionNumbers(lhs_spec, rhs_spec, out_spec)\\n else:\\n msg = \\\"convolution dimension_numbers must be tuple/list or None, got {}.\\\"\\n raise TypeError(msg.format(type(dimension_numbers)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::conv_general_permutations\",\n \"text\": \"jax/lax/lax.py::conv_general_permutations\\ndef conv_general_permutations(dimension_numbers):\\n \\\"\\\"\\\"Utility for convolution dimension permutations relative to Conv HLO.\\\"\\\"\\\"\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n lhs_char, rhs_char, out_char = charpairs = (\\\"N\\\", \\\"C\\\"), (\\\"O\\\", \\\"I\\\"), (\\\"N\\\", \\\"C\\\")\\n for i, (a, b) in enumerate(charpairs):\\n if not dimension_numbers[i].count(a) == dimension_numbers[i].count(b) == 1:\\n msg = (\\\"convolution dimension_numbers[{}] must contain the characters \\\"\\n \\\"'{}' and '{}' exatly once, got {}.\\\")\\n raise TypeError(msg.format(i, a, b, dimension_numbers[i]))\\n if len(dimension_numbers[i]) != len(set(dimension_numbers[i])):\\n msg = (\\\"convolution dimension_numbers[{}] cannot have duplicate \\\"\\n \\\"characters, got {}.\\\")\\n raise TypeError(msg.format(i, dimension_numbers[i]))\\n if not (set(lhs_spec) - set(lhs_char) == set(rhs_spec) - set(rhs_char) ==\\n set(out_spec) - set(out_char)):\\n msg = (\\\"convolution dimension_numbers elements must each have the same \\\"\\n \\\"set of spatial characters, got {}.\\\")\\n raise TypeError(msg.format(dimension_numbers))\\n\\n def getperm(spec, charpair):\\n spatial = (i for i, c in enumerate(spec) if c not in charpair)\\n if spec is not rhs_spec:\\n spatial = sorted(spatial, key=lambda i: rhs_spec.index(spec[i]))\\n return (spec.index(charpair[0]), spec.index(charpair[1])) + tuple(spatial)\\n\\n lhs_perm, rhs_perm, out_perm = map(getperm, dimension_numbers, charpairs)\\n return lhs_perm, rhs_perm, out_perm\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::conv_general_permutations.getperm\",\n \"text\": \"jax/lax/lax.py::conv_general_permutations.getperm\\n def getperm(spec, charpair):\\n spatial = (i for i, c in enumerate(spec) if c not in charpair)\\n if spec is not rhs_spec:\\n spatial = sorted(spatial, key=lambda i: rhs_spec.index(spec[i]))\\n return (spec.index(charpair[0]), spec.index(charpair[1])) + tuple(spatial)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_conv_general_proto\",\n \"text\": \"jax/lax/lax.py::_conv_general_proto\\ndef _conv_general_proto(dimension_numbers):\\n assert type(dimension_numbers) is ConvDimensionNumbers\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n proto = xla_client.ConvolutionDimensionNumbers()\\n proto.input_batch_dimension = lhs_spec[0]\\n proto.input_feature_dimension = lhs_spec[1]\\n proto.output_batch_dimension = out_spec[0]\\n proto.output_feature_dimension = out_spec[1]\\n proto.kernel_output_feature_dimension = rhs_spec[0]\\n proto.kernel_input_feature_dimension = rhs_spec[1]\\n proto.input_spatial_dimensions.extend(lhs_spec[2:])\\n proto.kernel_spatial_dimensions.extend(rhs_spec[2:])\\n proto.output_spatial_dimensions.extend(out_spec[2:])\\n return proto\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_conv_general_vjp_lhs_padding\",\n \"text\": \"jax/lax/lax.py::_conv_general_vjp_lhs_padding\\ndef _conv_general_vjp_lhs_padding(\\n in_shape, window_dimensions, window_strides, out_shape, padding,\\n lhs_dilation, rhs_dilation):\\n lhs_dilated_shape = _dilate_shape(in_shape, lhs_dilation)\\n rhs_dilated_shape = _dilate_shape(window_dimensions, rhs_dilation)\\n out_dilated_shape = _dilate_shape(out_shape, window_strides)\\n pad_before = onp.subtract(rhs_dilated_shape, [lo for lo, _ in padding]) - 1\\n pad_after = (onp.add(lhs_dilated_shape, rhs_dilated_shape) - 1\\n - out_dilated_shape - pad_before)\\n return zip(pad_before, pad_after)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_conv_general_vjp_rhs_padding\",\n \"text\": \"jax/lax/lax.py::_conv_general_vjp_rhs_padding\\ndef _conv_general_vjp_rhs_padding(\\n in_shape, window_dimensions, window_strides, out_shape, padding,\\n lhs_dilation, rhs_dilation):\\n lhs_dilated_shape = _dilate_shape(in_shape, lhs_dilation)\\n rhs_dilated_shape = _dilate_shape(window_dimensions, rhs_dilation)\\n out_dilated_shape = _dilate_shape(out_shape, window_strides)\\n total_in_pad = out_dilated_shape + rhs_dilated_shape - lhs_dilated_shape - 1\\n return [(pad[0], tot - pad[0]) for pad, tot in zip(padding, total_in_pad)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_balanced_eq\",\n \"text\": \"jax/lax/lax.py::_balanced_eq\\ndef _balanced_eq(x, z, y):\\n return div(select(_eq_meet(x, z), _ones(z), _zeros(z)),\\n select(_eq_meet(y, z), _twos(z), _ones(z)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_eq_meet\",\n \"text\": \"jax/lax/lax.py::_eq_meet\\ndef _eq_meet(a, b):\\n a_dtype, b_dtype = _dtype(a), _dtype(b)\\n if a_dtype != b_dtype:\\n higher_dtype = dtypes.promote_types(a_dtype, b_dtype)\\n if higher_dtype == a_dtype:\\n a = convert_element_type(a, b_dtype)\\n else:\\n b = convert_element_type(b, a_dtype)\\n return eq(a, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_abstractify\",\n \"text\": \"jax/lax/lax.py::_abstractify\\ndef _abstractify(x):\\n return raise_to_shaped(core.get_aval(x))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax.py::_check_user_dtype_supported\",\n \"text\": \"jax/lax/lax.py::_check_user_dtype_supported\\ndef _check_user_dtype_supported(dtype, fun_name=None):\\n if dtype is not None and onp.dtype(dtype) != dtypes.canonicalize_dtype(dtype):\\n msg = (\\\"Explicitly requested dtype {} {} is not available, \\\"\\n \\\"and will be truncated to dtype {}. To enable more dtypes, set the \\\"\\n \\\"jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell \\\"\\n \\\"environment variable. \\\"\\n \\\"See https://github.com/google/jax#current-gotchas for more.\\\")\\n fun_name = \\\"requested in {}\\\".format(fun_name) if fun_name else \\\"\\\"\\n truncated_dtype = dtypes.canonicalize_dtype(dtype).name\\n warnings.warn(msg.format(dtype, fun_name , truncated_dtype))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::psum\",\n \"text\": \"jax/lax/lax_parallel.py::psum\\ndef psum(x, axis_name):\\n \\\"\\\"\\\"Compute an all-reduce sum on ``x`` over the pmapped axis ``axis_name``.\\n\\n Args:\\n x: array with a mapped axis named ``axis_name``.\\n axis_name: hashable Python object used to name a pmapped axis (see the\\n ``pmap`` docstring for more details).\\n\\n Returns:\\n An array with the same shape as ``x`` representing the result of an\\n all-reduce sum along the axis ``axis_name``.\\n\\n For example, with 4 XLA devices available:\\n\\n >>> x = np.arange(4)\\n >>> y = jax.pmap(lambda x: jax.lax.psum(x, 'i'), axis_name='i')(x)\\n >>> print(y)\\n [6 6 6 6]\\n >>> y = jax.pmap(lambda x: x / jax.lax.psum(x, 'i'), axis_name='i')(x)\\n >>> print(y)\\n [ 0. 0.16666667 0.33333334 0.5 ]\\n \\\"\\\"\\\"\\n return psum_p.bind(x, axis_name=axis_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::pmax\",\n \"text\": \"jax/lax/lax_parallel.py::pmax\\ndef pmax(x, axis_name):\\n \\\"\\\"\\\"Compute an all-reduce max on ``x`` over the pmapped axis ``axis_name``.\\n\\n Args:\\n x: array with a mapped axis named ``axis_name``.\\n axis_name: hashable Python object used to name a pmapped axis (see the\\n ``pmap`` docstring for more details).\\n\\n Returns:\\n An array with the same shape as ``x`` representing the result of an\\n all-reduce max along the axis ``axis_name``.\\n \\\"\\\"\\\"\\n return pmax_p.bind(x, axis_name=axis_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::pmin\",\n \"text\": \"jax/lax/lax_parallel.py::pmin\\ndef pmin(x, axis_name):\\n \\\"\\\"\\\"Compute an all-reduce min on ``x`` over the pmapped axis ``axis_name``.\\n\\n Args:\\n x: array with a mapped axis named ``axis_name``.\\n axis_name: hashable Python object used to name a pmapped axis (see the\\n ``pmap`` docstring for more details).\\n\\n Returns:\\n An array with the same shape as ``x`` representing the result of an\\n all-reduce min along the axis ``axis_name``.\\n \\\"\\\"\\\"\\n return pmin_p.bind(x, axis_name=axis_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::ppermute\",\n \"text\": \"jax/lax/lax_parallel.py::ppermute\\ndef ppermute(x, axis_name, perm):\\n \\\"\\\"\\\"Perform a collective permutation according to the permutation ``perm``.\\n\\n This function is an analog of the CollectivePermute XLA HLO.\\n\\n Args:\\n x: array with a mapped axis named ``axis_name``.\\n axis_name: hashable Python object used to name a pmapped axis (see the\\n ``pmap`` docstring for more details).\\n perm: list of pairs of ints, representing (source_index, destination_index)\\n pairs that encode how the mapped axis named ``axis_name`` should be\\n shuffled. The integer values are treated as indices into the mapped axis\\n ``axis_name``. Any two pairs should not have the same source index or the\\n same destination index. For each index of the axis ``axis_name`` that does\\n not correspond to a destination index in ``perm``, the corresponding\\n values in the result are filled with zeros of the appropriate type.\\n\\n Returns:\\n An array with the same shape as ``x`` with slices along the axis\\n ``axis_name`` gathered from ``x`` according to the permutation ``perm``.\\n \\\"\\\"\\\"\\n return ppermute_p.bind(x, axis_name=axis_name, perm=tuple(perm))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::pswapaxes\",\n \"text\": \"jax/lax/lax_parallel.py::pswapaxes\\ndef pswapaxes(x, axis_name, axis):\\n \\\"\\\"\\\"Swap the pmapped axis ``axis_name`` with the unmapped axis ``axis``.\\n\\n The mapped axis size must be equal to the size of the unmapped axis; that is,\\n we must have ``lax.psum(1, axis_name) == x.shape[axis]``.\\n\\n This function is a special case of ``all_to_all`` where the pmapped axis of\\n the input is placed at the position ``axis`` in the output. That is, it is\\n equivalent to ``all_to_all(x, axis_name, axis, axis)``.\\n\\n Args:\\n x: array with a mapped axis named ``axis_name``.\\n axis_name: hashable Python object used to name a pmapped axis (see the\\n ``pmap`` docstring for more details).\\n axis: int indicating the unmapped axis of ``x`` to map with the name\\n ``axis_name``.\\n\\n Returns:\\n An array with shape ``np.insert(np.delete(x.shape, axis), axis, axis_size)``\\n where ``axis_size`` is the size of the mapped axis named ``axis_name`` in\\n the input ``x``.\\n \\\"\\\"\\\"\\n return all_to_all(x, axis_name, axis, axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::all_to_all\",\n \"text\": \"jax/lax/lax_parallel.py::all_to_all\\ndef all_to_all(x, axis_name, split_axis, concat_axis):\\n \\\"\\\"\\\"Materialize the mapped axis and map a different axis.\\n\\n In the output, the input mapped axis ``axis_name`` is materialized at the\\n logical axis position ``concat_axis``, and the input unmapped axis at position\\n ``split_axis`` is mapped with the name ``axis_name``.\\n\\n The input mapped axis size must be equal to the size of the axis to be mapped;\\n that is, we must have ``lax.psum(1, axis_name) == x.shape[split_axis]``.\\n\\n Args:\\n x: array with a mapped axis named ``axis_name``.\\n axis_name: hashable Python object used to name a pmapped axis (see the\\n ``pmap`` docstring for more details).\\n split_axis: int indicating the unmapped axis of ``x`` to map with the name\\n ``axis_name``.\\n concat_axis: int indicating the position in the output to materialize the\\n mapped axis of the input with the name ``axis_name``.\\n\\n Returns:\\n An array with shape given by the expression::\\n np.insert(np.delete(x.shape, split_axis), concat_axis, axis_size)\\n\\n where ``axis_size`` is the size of the mapped axis named ``axis_name`` in\\n the input ``x``, i.e. ``axis_size = lax.psum(1, axis_name)``.\\n \\\"\\\"\\\"\\n if psum(1, axis_name) != x.shape[split_axis]:\\n msg = (\\\"all_to_all requires the size of the mapped axis axis_name to equal \\\"\\n \\\"x.shape[split_axis], but they are {} and {} respectively.\\\")\\n raise ValueError(msg.format(psum(1, axis_name), x.shape[split_axis]))\\n return all_to_all_p.bind(x, split_axis=split_axis, concat_axis=concat_axis,\\n axis_name=axis_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::standard_pmap_primitive\",\n \"text\": \"jax/lax/lax_parallel.py::standard_pmap_primitive\\ndef standard_pmap_primitive(name):\\n prim = core.Primitive(name)\\n prim.def_impl(partial(pxla.apply_parallel_primitive, prim))\\n prim.def_abstract_eval(lambda x, *args, **params: x)\\n return prim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_allreduce_split_axis_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_allreduce_split_axis_rule\\ndef _allreduce_split_axis_rule(prim, reducer, vals, which_mapped, axis_name):\\n assert tuple(which_mapped) == (True,)\\n x, = vals\\n return prim.bind(reducer(x, [0]), axis_name=axis_name), False\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_allreduce_translation_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_allreduce_translation_rule\\ndef _allreduce_translation_rule(prim, c, val, replica_groups):\\n dtype = c.GetShape(val).numpy_dtype()\\n scalar = ShapedArray((), dtype)\\n computation = xla.primitive_subcomputation(prim, scalar, scalar)\\n return c.AllReduce(val, computation, replica_groups=replica_groups)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_psum_translation_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_psum_translation_rule\\ndef _psum_translation_rule(c, val, replica_groups):\\n psum = partial(_allreduce_translation_rule, lax.add_p, c,\\n replica_groups=replica_groups)\\n dtype = c.GetShape(val).numpy_dtype()\\n if dtypes.issubdtype(dtype, onp.complexfloating):\\n return c.Complex(psum(c.Real(val)), psum(c.Imag(val)))\\n else:\\n return psum(val)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_ppermute_translation_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_ppermute_translation_rule\\ndef _ppermute_translation_rule(c, x, replica_groups, perm):\\n group_size = len(replica_groups[0])\\n srcs, dsts = unzip2((src % group_size, dst % group_size) for src, dst in perm)\\n if not (len(srcs) == len(set(srcs)) and len(dsts) == len(set(dsts))):\\n msg = \\\"ppermute sources and destinations must be unique, got {}.\\\"\\n raise ValueError(msg.format(perm))\\n\\n full_perm = []\\n for grp in replica_groups:\\n grp = list(sorted(grp))\\n full_perm.extend((grp[src], grp[dst]) for src, dst in perm)\\n return c.CollectivePermute(x, full_perm)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_ppermute_transpose_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_ppermute_transpose_rule\\ndef _ppermute_transpose_rule(t, perm, axis_name):\\n srcs, dsts = unzip2(perm)\\n inverse_perm = list(zip(dsts, srcs))\\n return [ppermute(t, axis_name=axis_name, perm=inverse_perm)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_all_to_all_translation_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_all_to_all_translation_rule\\ndef _all_to_all_translation_rule(c, x, split_axis, concat_axis, replica_groups):\\n return c.AllToAll(x, split_axis, concat_axis, replica_groups)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_all_to_all_split_axis_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_all_to_all_split_axis_rule\\ndef _all_to_all_split_axis_rule(vals, which_mapped, split_axis, concat_axis,\\n axis_name):\\n assert tuple(which_mapped) == (True,)\\n x, = vals\\n # perform the communication to swap the hardware-mapped axes\\n stacked = all_to_all_p.bind(x, split_axis=split_axis + 1, concat_axis=0,\\n axis_name=axis_name)\\n # transpose the newly mapped axis to the front, newly unmapped to concat_axis\\n out = _moveaxis(split_axis + 1, 0, stacked)\\n out = _moveaxis(1, concat_axis + 1, out)\\n return out, True\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_moveaxis\",\n \"text\": \"jax/lax/lax_parallel.py::_moveaxis\\ndef _moveaxis(src, dst, x):\\n perm = [i for i in range(x.ndim) if i != src]\\n perm.insert(dst, src)\\n return lax.transpose(x, perm)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_drop\",\n \"text\": \"jax/lax/lax_parallel.py::_drop\\ndef _drop(x, dim, axis_name):\\n return lax.dynamic_index_in_dim(x, axis_index(axis_name), dim, False)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_allgather\",\n \"text\": \"jax/lax/lax_parallel.py::_allgather\\ndef _allgather(x, dim, size, axis_name):\\n shape = list(x.shape)\\n shape.insert(dim, size)\\n out = lax.full(shape, lax._const(x, 0))\\n out = lax.dynamic_update_index_in_dim(out, x, axis_index(axis_name), dim)\\n return psum(out, axis_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_broadcasting_papply\",\n \"text\": \"jax/lax/lax_parallel.py::_broadcasting_papply\\ndef _broadcasting_papply(prim, name, size, vals, axes, **params):\\n x, y = vals\\n xdim, ydim = axes\\n\\n if xdim is None:\\n if x.shape:\\n if x.shape[ydim] == 1:\\n x = x.reshape(onp.delete(x.shape, ydim))\\n else:\\n x = _drop(x, ydim, name)\\n return prim.bind(x, y, **params), ydim\\n elif ydim is None:\\n if y.shape:\\n if y.shape[xdim] == 1:\\n y = y.reshape(onp.delete(y.shape, xdim))\\n else:\\n y = _drop(y, xdim, name)\\n return prim.bind(x, y, **params), xdim\\n elif xdim == ydim:\\n return prim.bind(x, y, **params), xdim\\n else:\\n x_tosplit = ydim - int(xdim <= ydim)\\n y_tosplit = xdim - int(ydim <= xdim)\\n if y.shape[y_tosplit] == 1:\\n y = _allgather(y, ydim, size, name)\\n y = y.reshape(onp.delete(y.shape, xdim))\\n return prim.bind(x, y, **params), ydim\\n elif x.shape[x_tosplit] == 1:\\n x = _allgather(x, xdim, size, name)\\n x = x.reshape(onp.delete(x.shape, ydim))\\n return prim.bind(x, y, **params), ydim\\n else:\\n x = all_to_all(x, name, x_tosplit, xdim)\\n return prim.bind(x, y, **params), ydim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_defbroadcasting\",\n \"text\": \"jax/lax/lax_parallel.py::_defbroadcasting\\ndef _defbroadcasting(prim):\\n parallel.papply_primitive_rules[prim] = partial(_broadcasting_papply, prim)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_vectorized_papply\",\n \"text\": \"jax/lax/lax_parallel.py::_vectorized_papply\\ndef _vectorized_papply(prim, name, size, vals, axes, **params):\\n assert all(axes[0] == a for a in axes[1:])\\n return prim.bind(*vals, **params), axes[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_defvectorized\",\n \"text\": \"jax/lax/lax_parallel.py::_defvectorized\\ndef _defvectorized(prim):\\n parallel.papply_primitive_rules[prim] = partial(_vectorized_papply, prim)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_reducer_papply\",\n \"text\": \"jax/lax/lax_parallel.py::_reducer_papply\\ndef _reducer_papply(prim, cprim, name, size, vals, papply_axes, axes, **kwargs):\\n operand, = vals\\n papply_axis, = papply_axes\\n\\n other_axes = [i for i in axes if i != papply_axis]\\n other_axes = [i - 1 if i > papply_axis else i for i in other_axes]\\n\\n if other_axes:\\n if 'input_shape' in kwargs: # special to the reduce-sum family\\n s = kwargs['input_shape']\\n kwargs['input_shape'] = s[:papply_axis] + s[papply_axis + 1:]\\n result = prim.bind(operand, axes=tuple(other_axes), **kwargs)\\n else:\\n result = operand\\n\\n if not axes or papply_axis in axes:\\n return cprim.bind(result, axis_name=name), None\\n else:\\n new_papply_axis = papply_axis - onp.sum(onp.less(other_axes, papply_axis))\\n return result, new_papply_axis\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_defreducer\",\n \"text\": \"jax/lax/lax_parallel.py::_defreducer\\ndef _defreducer(prim, collective_prim):\\n parallel.papply_primitive_rules[prim] = partial(_reducer_papply, prim, collective_prim)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_identity_papply\",\n \"text\": \"jax/lax/lax_parallel.py::_identity_papply\\ndef _identity_papply(prim, argnum, name, size, vals, axes, **params):\\n return prim.bind(*vals, **params), axes[argnum]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_defidentity\",\n \"text\": \"jax/lax/lax_parallel.py::_defidentity\\ndef _defidentity(prim, argnum=0):\\n parallel.papply_primitive_rules[prim] = partial(_identity_papply, prim, argnum)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_dot_general_papply_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_dot_general_papply_rule\\ndef _dot_general_papply_rule(name, size, vals, dims, dimension_numbers,\\n precision):\\n x, y = vals\\n xdim, ydim = dims\\n\\n (lhs_contract, rhs_contract), (lhs_batch, rhs_batch) = dimension_numbers\\n\\n if lhs_batch or rhs_batch:\\n raise NotImplementedError(\\n ('papply of dot_general with batch dimensions: '\\n 'xdim={}, ydim={}, dimension_numbers={}').format(\\n xdim, ydim, dimension_numbers))\\n\\n def adjust_dims(dims, thresh):\\n return tuple(i - 1 if i > thresh else i for i in dims if i != thresh)\\n\\n def sub_dims(xdim, ydim, xcontract, ycontract, xbatch, ybatch):\\n if xdim is not None:\\n xbatch = adjust_dims(xbatch, xdim)\\n xcontract = adjust_dims(xcontract, xdim)\\n if ydim is not None:\\n ybatch = adjust_dims(ybatch, ydim)\\n ycontract = adjust_dims(ycontract, ydim)\\n return ((xcontract, ycontract), (xbatch, ybatch))\\n\\n def cases(x, y, xdim, ydim, xc, yc, xb, yb):\\n # Consider three states in which an operand may be\\n # 1: split, contracting\\n # 2: split, not contracting\\n # 3: not split\\n #\\n # We will handle the following cases, marked by corresponding letter\\n # symbols:\\n #\\n # |1 2 3|y\\n # -+-----+-\\n # 1|a b c\\n # 2|d e f\\n # 3|g h i\\n # -+\\n # x|\\n #\\n # Case i is already covered and we can assume that it is excluded at the\\n # outset, since a papply rule is not invoked when no operands are split.\\n\\n if xdim in xc:\\n # cases a, b, c\\n if ydim in yc:\\n # case a: both operands are split and contracting\\n # TODO(frostig): Might the following work?\\n # z = lax.dot_general(\\n # x, y, sub_dims(xdim, ydim, xc, yc, xb, yb), precision)\\n # return True, (psum(z, name), None)\\n return False, 'both operands split and contracting'\\n elif ydim is not None:\\n # case b: x split and contracting, y split but not contracting\\n # TODO(frostig): Might th\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_dot_general_papply_rule.adjust_dims\",\n \"text\": \"jax/lax/lax_parallel.py::_dot_general_papply_rule.adjust_dims\\n def adjust_dims(dims, thresh):\\n return tuple(i - 1 if i > thresh else i for i in dims if i != thresh)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_dot_general_papply_rule.sub_dims\",\n \"text\": \"jax/lax/lax_parallel.py::_dot_general_papply_rule.sub_dims\\n def sub_dims(xdim, ydim, xcontract, ycontract, xbatch, ybatch):\\n if xdim is not None:\\n xbatch = adjust_dims(xbatch, xdim)\\n xcontract = adjust_dims(xcontract, xdim)\\n if ydim is not None:\\n ybatch = adjust_dims(ybatch, ydim)\\n ycontract = adjust_dims(ycontract, ydim)\\n return ((xcontract, ycontract), (xbatch, ybatch))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_dot_general_papply_rule.cases\",\n \"text\": \"jax/lax/lax_parallel.py::_dot_general_papply_rule.cases\\n def cases(x, y, xdim, ydim, xc, yc, xb, yb):\\n # Consider three states in which an operand may be\\n # 1: split, contracting\\n # 2: split, not contracting\\n # 3: not split\\n #\\n # We will handle the following cases, marked by corresponding letter\\n # symbols:\\n #\\n # |1 2 3|y\\n # -+-----+-\\n # 1|a b c\\n # 2|d e f\\n # 3|g h i\\n # -+\\n # x|\\n #\\n # Case i is already covered and we can assume that it is excluded at the\\n # outset, since a papply rule is not invoked when no operands are split.\\n\\n if xdim in xc:\\n # cases a, b, c\\n if ydim in yc:\\n # case a: both operands are split and contracting\\n # TODO(frostig): Might the following work?\\n # z = lax.dot_general(\\n # x, y, sub_dims(xdim, ydim, xc, yc, xb, yb), precision)\\n # return True, (psum(z, name), None)\\n return False, 'both operands split and contracting'\\n elif ydim is not None:\\n # case b: x split and contracting, y split but not contracting\\n # TODO(frostig): Might the following work?\\n # new_ydim = yc[xc.index(xdim)]\\n # y = all_to_all(y, name, new_ydim, ydim)\\n # z = lax.dot_general(\\n # x, y, sub_dims(xdim, new_ydim, xc, yc, xb, yb), precision)\\n # return True, (psum(z, name), None)\\n return False, 'rhs split but not contracting, lhs split and contracting'\\n else:\\n # case c: x split and contracting, y not split\\n assert ydim is None\\n return False, 'one operand split and contracting, other is not split'\\n elif xdim is not None:\\n # cases d, e, f\\n if ydim in yc:\\n # case d: x split but not contracting, y split and contracting\\n # TODO(frostig): Might the following work?\\n # new_xdim = xc[yc.index(ydim)]\\n # x = all_to_all(x, name, new_xdim, xdim)\\n # z = lax.dot_general(\\n # x, y, sub_dims(new_xdim, ydim, xc, yc, xb, yb), precision\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_reshape_papply_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_reshape_papply_rule\\ndef _reshape_papply_rule(name, size, vals, axes, new_sizes, dimensions,\\n old_sizes):\\n operand, = vals\\n axis, = axes\\n\\n def filter_ones(xs):\\n return filter(lambda x: x != 1, xs)\\n\\n def find_new_axis(old_axis, old_sizes, new_sizes):\\n left = onp.prod(old_sizes[:old_axis])\\n size = old_sizes[old_axis]\\n prod = 1\\n for i, cur_sz in enumerate(new_sizes):\\n if prod == left and cur_sz == size:\\n return i\\n prod = prod * cur_sz\\n return None\\n\\n if dimensions is None:\\n new_axis = find_new_axis(axis, old_sizes, new_sizes)\\n if new_axis is not None:\\n new_sizes_ = new_sizes[:new_axis] + new_sizes[new_axis + 1:]\\n return lax.reshape(operand, new_sizes_, dimensions=dimensions), new_axis\\n else:\\n raise NotImplementedError(\\n 'papply of reshape that would change hidden dimension size')\\n else:\\n raise NotImplementedError('papply of reshape with `dimensions`')\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_reshape_papply_rule.filter_ones\",\n \"text\": \"jax/lax/lax_parallel.py::_reshape_papply_rule.filter_ones\\n def filter_ones(xs):\\n return filter(lambda x: x != 1, xs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_reshape_papply_rule.find_new_axis\",\n \"text\": \"jax/lax/lax_parallel.py::_reshape_papply_rule.find_new_axis\\n def find_new_axis(old_axis, old_sizes, new_sizes):\\n left = onp.prod(old_sizes[:old_axis])\\n size = old_sizes[old_axis]\\n prod = 1\\n for i, cur_sz in enumerate(new_sizes):\\n if prod == left and cur_sz == size:\\n return i\\n prod = prod * cur_sz\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_transpose_papply_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_transpose_papply_rule\\ndef _transpose_papply_rule(name, size, vals, dims, permutation):\\n x, = vals\\n xdim, = dims\\n local_perm = [i if i < xdim else i - 1 for i in permutation if i != xdim]\\n return lax.transpose(x, local_perm), permutation.index(xdim)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_select_papply_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_select_papply_rule\\ndef _select_papply_rule(name, size, vals, dims):\\n dimset = {d for d in dims if d is not None}\\n if len(dimset) != 1:\\n raise NotImplementedError(\\n 'papply of select with operands split along different dimensions')\\n dim, = dimset\\n def drop(x, d):\\n return _drop(x, dim, name) if d is None else x\\n return lax.select_p.bind(*map(drop, vals, dims)), dim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_select_papply_rule.drop\",\n \"text\": \"jax/lax/lax_parallel.py::_select_papply_rule.drop\\n def drop(x, d):\\n return _drop(x, dim, name) if d is None else x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_add_jaxvals_papply_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_add_jaxvals_papply_rule\\ndef _add_jaxvals_papply_rule(name, size, vals, dims):\\n x, y = vals\\n xdim, ydim = dims\\n if xdim == ydim:\\n out_dim = xdim\\n elif ydim is None:\\n y = lax.psplit_like(y, x, name)\\n out_dim = xdim\\n else:\\n x = lax.psplit_like(x, y, name)\\n out_dim = ydim\\n return ad_util.add_jaxvals_p.bind(x, y), out_dim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_convert_element_type_papply_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_convert_element_type_papply_rule\\ndef _convert_element_type_papply_rule(\\n name, size, vals, dims, new_dtype, **params):\\n operand, = vals\\n dim, = dims\\n return lax.convert_element_type(operand, new_dtype), dim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_conv_general_dilated_papply_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_conv_general_dilated_papply_rule\\ndef _conv_general_dilated_papply_rule(\\n name, size, vals, dims, window_strides, padding, lhs_dilation, rhs_dilation,\\n dimension_numbers, feature_group_count, precision, **unused_kwargs):\\n lhs, rhs = vals\\n lhs_dim, rhs_dim = dims\\n lhs_spec_batch_dim = dimension_numbers.lhs_spec[0]\\n if rhs_dim is None and lhs_dim == lhs_spec_batch_dim:\\n lhs = lax.reshape(lhs, tuple(onp.insert(lhs.shape, lhs_dim, 1)))\\n out = lax.conv_general_dilated(\\n lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,\\n dimension_numbers, feature_group_count, precision)\\n return out, lhs_dim\\n else:\\n raise NotImplementedError(\\n \\\"splitting a convolution along anything but input batch dimension\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_broadcast_in_dim_papply_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_broadcast_in_dim_papply_rule\\ndef _broadcast_in_dim_papply_rule(name, size, vals, dims, shape,\\n broadcast_dimensions):\\n operand, = vals\\n dim, = dims\\n out_dim = broadcast_dimensions[dim]\\n if shape[out_dim] != shape[dim]:\\n raise ValueError(\\n \\\"broadcast_in_dim changes hidden dimension size: {} to {}\\\".format(\\n shape[dim], shape[out_dim]))\\n sub_bdims = tuple(onp.delete(broadcast_dimensions, dim))\\n sub_shape = tuple(onp.delete(shape, out_dim))\\n return lax.broadcast_in_dim(operand, sub_shape, sub_bdims), out_dim\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax/lax_parallel.py::_pad_papply_rule\",\n \"text\": \"jax/lax/lax_parallel.py::_pad_papply_rule\\ndef _pad_papply_rule(name, size, vals, dims, padding_config):\\n operand, padding_value = vals\\n operand_dim, padding_value_dim = dims\\n assert padding_value_dim is None\\n padding_config = list(padding_config)\\n if padding_config[operand_dim] == (0, 0, 0):\\n padded = lax.pad(\\n operand,\\n padding_value,\\n padding_config[:operand_dim] + padding_config[operand_dim + 1:])\\n return padded, operand_dim\\n else:\\n raise NotImplementedError(\\n 'pad changes size of hidden dimension {} with config {}'.format(\\n operand_dim, padding_config))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::conj\",\n \"text\": \"jax/lax_reference.py::conj\\ndef conj(x):\\n return onp.conj(x) + onp.complex64(0)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::complex\",\n \"text\": \"jax/lax_reference.py::complex\\ndef complex(x, y):\\n return x + onp.complex64(1j) * y\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::div\",\n \"text\": \"jax/lax_reference.py::div\\ndef div(lhs, rhs):\\n if dtypes.issubdtype(dtypes.result_type(lhs), onp.integer):\\n quotient = onp.floor_divide(lhs, rhs)\\n select = onp.logical_and(onp.sign(lhs) != onp.sign(rhs),\\n onp.remainder(lhs, rhs) != 0)\\n return onp.where(select, quotient + 1, quotient)\\n else:\\n return onp.divide(lhs, rhs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::rem\",\n \"text\": \"jax/lax_reference.py::rem\\ndef rem(lhs, rhs):\\n return onp.sign(lhs) * onp.remainder(onp.abs(lhs), onp.abs(rhs))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::convert_element_type\",\n \"text\": \"jax/lax_reference.py::convert_element_type\\ndef convert_element_type(operand, dtype):\\n return onp.asarray(operand, dtype=dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::bitcast_convert_type\",\n \"text\": \"jax/lax_reference.py::bitcast_convert_type\\ndef bitcast_convert_type(operand, dtype):\\n return onp.asarray(operand).view(dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::clamp\",\n \"text\": \"jax/lax_reference.py::clamp\\ndef clamp(min, operand, max):\\n return onp.clip(operand, onp.clip(min, None, max), max)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::concatenate\",\n \"text\": \"jax/lax_reference.py::concatenate\\ndef concatenate(operands, dimension):\\n return onp.concatenate(operands, axis=dimension)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::conv\",\n \"text\": \"jax/lax_reference.py::conv\\ndef conv(lhs, rhs, window_strides, padding):\\n pads = padtype_to_pads(lhs.shape[2:], rhs.shape[2:], window_strides, padding)\\n return _conv(lhs, rhs, window_strides, pads)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::conv_with_general_padding\",\n \"text\": \"jax/lax_reference.py::conv_with_general_padding\\ndef conv_with_general_padding(\\n lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation):\\n return _conv(_dilate(lhs, lhs_dilation), _dilate(rhs, rhs_dilation),\\n window_strides, padding)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::conv_general_dilated\",\n \"text\": \"jax/lax_reference.py::conv_general_dilated\\ndef conv_general_dilated(lhs, rhs, window_strides, padding, lhs_dilation,\\n rhs_dilation, dimension_numbers):\\n lhs_perm, rhs_perm, out_perm = _conv_general_permutations(dimension_numbers)\\n if isinstance(padding, str):\\n padding = padtype_to_pads(onp.take(lhs.shape, lhs_perm)[2:],\\n onp.take(rhs.shape, rhs_perm)[2:],\\n window_strides, padding)\\n trans_lhs = transpose(lhs, lhs_perm)\\n trans_rhs = transpose(rhs, rhs_perm)\\n out = conv_with_general_padding(trans_lhs, trans_rhs, window_strides, padding,\\n lhs_dilation, rhs_dilation)\\n return transpose(out, onp.argsort(out_perm))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::dot_general\",\n \"text\": \"jax/lax_reference.py::dot_general\\ndef dot_general(lhs, rhs, dimension_numbers):\\n (lhs_contracting, rhs_contracting), (lhs_batch, rhs_batch) = dimension_numbers\\n new_id = itertools.count()\\n lhs_axis_ids = [next(new_id) for _ in lhs.shape]\\n rhs_axis_ids = [next(new_id) for _ in rhs.shape]\\n lhs_out_axis_ids = lhs_axis_ids[:]\\n rhs_out_axis_ids = rhs_axis_ids[:]\\n\\n for lhs_axis, rhs_axis in zip(lhs_contracting, rhs_contracting):\\n shared_id = next(new_id)\\n lhs_axis_ids[lhs_axis] = shared_id\\n rhs_axis_ids[rhs_axis] = shared_id\\n lhs_out_axis_ids[lhs_axis] = None\\n rhs_out_axis_ids[rhs_axis] = None\\n\\n batch_ids = []\\n for lhs_axis, rhs_axis in zip(lhs_batch, rhs_batch):\\n shared_id = next(new_id)\\n lhs_axis_ids[lhs_axis] = shared_id\\n rhs_axis_ids[rhs_axis] = shared_id\\n lhs_out_axis_ids[lhs_axis] = None\\n rhs_out_axis_ids[rhs_axis] = None\\n batch_ids.append(shared_id)\\n\\n not_none = lambda x: x is not None\\n out_axis_ids = filter(not_none,\\n batch_ids + lhs_out_axis_ids + rhs_out_axis_ids)\\n assert lhs.dtype == rhs.dtype\\n dtype = onp.float32 if lhs.dtype == dtypes.bfloat16 else None\\n out = onp.einsum(lhs, lhs_axis_ids, rhs, rhs_axis_ids, out_axis_ids,\\n dtype=dtype)\\n return out.astype(dtypes.bfloat16) if lhs.dtype == dtypes.bfloat16 else out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::broadcast\",\n \"text\": \"jax/lax_reference.py::broadcast\\ndef broadcast(operand, sizes):\\n return onp.broadcast_to(operand, sizes + onp.shape(operand))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::broadcast_in_dim\",\n \"text\": \"jax/lax_reference.py::broadcast_in_dim\\ndef broadcast_in_dim(operand, shape, broadcast_dimensions):\\n inshape = tuple(1 if i not in broadcast_dimensions else d\\n for i, d in enumerate(shape))\\n return onp.broadcast_to(onp.reshape(operand, inshape), shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::reshape\",\n \"text\": \"jax/lax_reference.py::reshape\\ndef reshape(operand, new_sizes, dimensions=None):\\n if dimensions is None:\\n dimensions = range(len(onp.shape(operand)))\\n return onp.reshape(onp.transpose(operand, dimensions), new_sizes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::pad\",\n \"text\": \"jax/lax_reference.py::pad\\ndef pad(operand, padding_value, padding_config):\\n lo, hi, interior = zip(*padding_config)\\n outshape = onp.add(onp.add(onp.add(lo, hi), operand.shape),\\n onp.multiply(interior, onp.subtract(operand.shape, 1)))\\n out = onp.full(outshape, padding_value, operand.dtype)\\n lhs_slices = tuple(_slice(l if l > 0 else 0, -h if h > 0 else None, step)\\n for l, h, step in zip(lo, hi, onp.add(1, interior)))\\n rhs_slices = tuple(_slice(l if l < 0 else 0, -h if h < 0 else None)\\n for l, h in zip(lo, hi))\\n out[lhs_slices] = operand[rhs_slices]\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::rev\",\n \"text\": \"jax/lax_reference.py::rev\\ndef rev(operand, dimensions):\\n dimensions = frozenset(dimensions)\\n indexer = (_slice(None, None, -1) if d in dimensions else _slice(None)\\n for d in range(onp.ndim(operand)))\\n return operand[tuple(indexer)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::slice\",\n \"text\": \"jax/lax_reference.py::slice\\ndef slice(operand, start_indices, limit_indices, strides=None): # pylint: disable=redefined-builtin\\n if strides is None:\\n strides = onp.ones(len(start_indices)).astype(int)\\n slices = tuple(_map(_slice, start_indices, limit_indices, strides))\\n return operand[slices]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::dynamic_slice\",\n \"text\": \"jax/lax_reference.py::dynamic_slice\\ndef dynamic_slice(operand, start_indices, slice_sizes):\\n out = onp.zeros(slice_sizes, dtype=operand.dtype)\\n idx = tuple(_slice(start, start+size)\\n for start, size in zip(start_indices, slice_sizes))\\n section = operand[idx]\\n out[tuple(_slice(None, stop) for stop in section.shape)] = section\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::dynamic_update_slice\",\n \"text\": \"jax/lax_reference.py::dynamic_update_slice\\ndef dynamic_update_slice(operand, update, start_indices):\\n slices = tuple(_map(_slice, start_indices, onp.add(start_indices, update.shape)))\\n updated_operand = onp.copy(operand)\\n updated_operand[slices] = update\\n return updated_operand\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::reduce\",\n \"text\": \"jax/lax_reference.py::reduce\\ndef reduce(operand, init_value, computation, dimensions): # pylint: disable=redefined-builtin\\n reducer = _make_reducer(computation, init_value)\\n return reducer(operand, tuple(dimensions)).astype(onp.asarray(operand).dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::reduce_window\",\n \"text\": \"jax/lax_reference.py::reduce_window\\ndef reduce_window(operand, init_value, computation, window_dimensions,\\n window_strides, padding):\\n op, dims, strides = operand, window_dimensions, window_strides\\n pads = padtype_to_pads(op.shape, dims, strides, padding)\\n view = _conv_view(op.reshape((1, 1) + op.shape), (1, 1) + dims, strides, pads,\\n pad_value=init_value)[0]\\n view = view.reshape(view.shape[1:1+len(dims)] + (-1,))\\n reducer = _make_reducer(computation, init_value)\\n return reducer(view, axis=-1)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::sort_key_val\",\n \"text\": \"jax/lax_reference.py::sort_key_val\\ndef sort_key_val(keys, values, dimension=-1):\\n idxs = list(onp.ix_(*[onp.arange(d) for d in keys.shape]))\\n idxs[dimension] = onp.argsort(keys, axis=dimension)\\n return keys[idxs], values[idxs]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::_conv\",\n \"text\": \"jax/lax_reference.py::_conv\\ndef _conv(lhs, rhs, window_strides, pads):\\n view, view_axes, rhs_axes, out_axes = _conv_view(\\n lhs, rhs.shape, window_strides, pads, 0.)\\n return opt_einsum.contract(\\n view, view_axes, rhs, rhs_axes, out_axes, use_blas=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::padtype_to_pads\",\n \"text\": \"jax/lax_reference.py::padtype_to_pads\\ndef padtype_to_pads(in_shape, filter_shape, window_strides, padding):\\n if padding.upper() == 'SAME':\\n out_shape = onp.ceil(onp.true_divide(in_shape, window_strides)).astype(int)\\n pad_sizes = [_max((out_size - 1) * stride + filter_size - in_size, 0)\\n for out_size, stride, filter_size, in_size\\n in zip(out_shape, window_strides, filter_shape, in_shape)]\\n return [(pad_size // 2, pad_size - pad_size // 2) for pad_size in pad_sizes]\\n else:\\n return [(0, 0)] * len(in_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::_conv_view\",\n \"text\": \"jax/lax_reference.py::_conv_view\\ndef _conv_view(lhs, rhs_shape, window_strides, pads, pad_value):\\n \\\"\\\"\\\"Compute the view (and its axes) of a convolution or window reduction.\\\"\\\"\\\"\\n if (_min(lhs.ndim, len(rhs_shape)) < 2 or lhs.ndim != len(rhs_shape)\\n or lhs.shape[1] != rhs_shape[1]):\\n raise ValueError('Dimension mismatch')\\n if len(window_strides) != len(rhs_shape) - 2:\\n raise ValueError('Wrong number of strides for spatial dimensions')\\n if len(pads) != len(rhs_shape) - 2:\\n raise ValueError('Wrong number of pads for spatial dimensions')\\n\\n lhs = _pad(lhs, [(0, 0)] * 2 + list(pads), pad_value)\\n in_shape = lhs.shape[2:]\\n filter_shape = rhs_shape[2:]\\n dim = len(filter_shape) # number of 'spatial' dimensions in convolution\\n\\n out_strides = onp.multiply(window_strides, lhs.strides[2:])\\n view_strides = lhs.strides[:1] + tuple(out_strides) + lhs.strides[1:]\\n\\n out_shape = onp.floor_divide(\\n onp.subtract(in_shape, filter_shape), window_strides) + 1\\n view_shape = lhs.shape[:1] + tuple(out_shape) + rhs_shape[1:]\\n\\n view = onp.lib.stride_tricks.as_strided(lhs, view_shape, view_strides)\\n\\n view_axes = list(range(view.ndim))\\n sum_axes = view_axes[-dim-1:]\\n rhs_axes = [view.ndim] + sum_axes\\n out_axes = [0, view.ndim] + list(range(1, dim+1))\\n\\n return view, view_axes, rhs_axes, out_axes\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::_pad\",\n \"text\": \"jax/lax_reference.py::_pad\\ndef _pad(arr, pads, pad_value):\\n out = onp.pad(arr, onp.maximum(0, pads), mode='constant',\\n constant_values=pad_value).astype(arr.dtype)\\n slices = tuple(_slice(abs(lo) if lo < 0 else 0, hi % dim if hi < 0 else None)\\n for (lo, hi), dim in zip(pads, onp.shape(arr)))\\n return out[slices]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::_dilate\",\n \"text\": \"jax/lax_reference.py::_dilate\\ndef _dilate(operand, factors):\\n # this logic is like lax.pad, but with two leading dimensions, no edge\\n # padding, and factors are at least 1 (interior padding is at least 0)\\n outspace = onp.add(operand.shape[2:],\\n onp.multiply(onp.subtract(factors, 1),\\n onp.subtract(operand.shape[2:], 1)))\\n out = onp.zeros(operand.shape[:2] + tuple(outspace), operand.dtype)\\n lhs_slices = tuple(_slice(None, None, step) for step in factors)\\n out[(_slice(None),) * 2 + lhs_slices] = operand\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::_conv_general_permutations\",\n \"text\": \"jax/lax_reference.py::_conv_general_permutations\\ndef _conv_general_permutations(dimension_numbers):\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n rhs_perm = ((rhs_spec.index('O'), rhs_spec.index('I'))\\n + tuple(i for i, c in enumerate(rhs_spec) if c not in {'O', 'I'}))\\n lhs_perm = ((lhs_spec.index('N'), lhs_spec.index('C'))\\n + tuple(sorted((i for i, c in enumerate(lhs_spec)\\n if c not in {'N', 'C'}),\\n key=lambda i: rhs_spec.index(lhs_spec[i]))))\\n out_perm = ((out_spec.index('N'), out_spec.index('C'))\\n + tuple(sorted((i for i, c in enumerate(out_spec)\\n if c not in {'N', 'C'}),\\n key=lambda i: rhs_spec.index(out_spec[i]))))\\n return lhs_perm, rhs_perm, out_perm\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::_make_reducer\",\n \"text\": \"jax/lax_reference.py::_make_reducer\\ndef _make_reducer(py_binop, init_val):\\n \\\"\\\"\\\"Make a reducer function given a Python binop and an initial value.\\\"\\\"\\\"\\n # It's tempting to use onp.ufunc.reduce (even with a ufunc generated by\\n # onp.frompyfunc(py_binop)), but this may not agree with custom init_val.\\n # We make an attempt to uncover an underlying numpy ufunc (which might be\\n # wrapped by autograd or lax) and check its identity against init_val.\\n monoid_record = _monoids.get(getattr(py_binop, '__name__'))\\n if monoid_record:\\n reducer, monoid_identity = monoid_record\\n if init_val == monoid_identity(dtypes.result_type(init_val)):\\n return reducer\\n return _reducer_from_pyfunc(py_binop, init_val)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::_get_max_identity\",\n \"text\": \"jax/lax_reference.py::_get_max_identity\\ndef _get_max_identity(dt):\\n return -onp.inf if dtypes.issubdtype(dt, onp.floating) else onp.iinfo(dt).min\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::_get_min_identity\",\n \"text\": \"jax/lax_reference.py::_get_min_identity\\ndef _get_min_identity(dt):\\n return onp.inf if dtypes.issubdtype(dt, onp.floating) else onp.iinfo(dt).max\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::_identity_getter\",\n \"text\": \"jax/lax_reference.py::_identity_getter\\ndef _identity_getter(op):\\n return lambda dtype: onp.asarray(op.identity, dtype=dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::_reducer_from_pyfunc\",\n \"text\": \"jax/lax_reference.py::_reducer_from_pyfunc\\ndef _reducer_from_pyfunc(py_binop, init_val):\\n def reducer(operand, axis=0):\\n axis = range(onp.ndim(operand)) if axis is None else axis\\n result = onp.full(onp.delete(onp.shape(operand), axis), init_val,\\n dtype=onp.asarray(operand).dtype)\\n for idx, _ in onp.ndenumerate(operand):\\n out_idx = tuple(onp.delete(idx, axis))\\n result[out_idx] = py_binop(result[out_idx], operand[idx])\\n return result\\n return reducer\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lax_reference.py::_reducer_from_pyfunc.reducer\",\n \"text\": \"jax/lax_reference.py::_reducer_from_pyfunc.reducer\\n def reducer(operand, axis=0):\\n axis = range(onp.ndim(operand)) if axis is None else axis\\n result = onp.full(onp.delete(onp.shape(operand), axis), init_val,\\n dtype=onp.asarray(operand).dtype)\\n for idx, _ in onp.ndenumerate(operand):\\n out_idx = tuple(onp.delete(idx, axis))\\n result[out_idx] = py_binop(result[out_idx], operand[idx])\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::get_compile_options\",\n \"text\": \"jax/lib/xla_bridge.py::get_compile_options\\ndef get_compile_options(num_replicas=None, device_assignment=None):\\n \\\"\\\"\\\"Returns the compile options to use, as derived from flag values.\\n\\n Args:\\n num_replicas: Optional int indicating the number of replicas for which to\\n compile (default inherited from xla_client.CompileOptions).\\n device_assignment: Optional tuple of integers indicating the assignment of\\n logical replicas to physical devices (default inherited from\\n xla_client.CompileOptions). Must be consistent with `num_replicas`.\\n \\\"\\\"\\\"\\n compile_options = None\\n if num_replicas is not None:\\n compile_options = compile_options or xla_client.CompileOptions()\\n compile_options.num_replicas = num_replicas\\n if device_assignment is not None:\\n logging.vlog(2, \\\"get_compile_options: num_replicas=%s device_assignment=%s\\\",\\n num_replicas, device_assignment)\\n # NOTE(mattjj): xla_client.DeviceAssignment.create expects a 2D ndarray\\n # indexed by replica number and computation per replica, respectively, while\\n # here we currently assume only one computation per replica, hence the\\n # second axis is always trivial.\\n if num_replicas is not None and num_replicas != len(device_assignment):\\n msg = \\\"device_assignment does not match num_replicas: {} vs {}.\\\"\\n raise ValueError(msg.format(device_assignment, num_replicas))\\n compile_options = compile_options or xla_client.CompileOptions()\\n device_assignment = onp.array(device_assignment)\\n if device_assignment.ndim == 1:\\n device_assignment = device_assignment[:, None]\\n device_assignment = xla_client.DeviceAssignment.create(device_assignment)\\n assert num_replicas is None or device_assignment.replica_count() == num_replicas\\n compile_options.device_assignment = device_assignment\\n return compile_options\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::register_backend\",\n \"text\": \"jax/lib/xla_bridge.py::register_backend\\ndef register_backend(name, factory):\\n _backends[name] = factory\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_get_local_backend\",\n \"text\": \"jax/lib/xla_bridge.py::_get_local_backend\\ndef _get_local_backend(platform=None):\\n if not platform:\\n platform = FLAGS.jax_platform_name\\n\\n # Canonicalize platform names.\\n cpu = 'cpu'\\n gpu = 'gpu'\\n if platform == 'Host':\\n platform = cpu\\n elif platform == 'CUDA':\\n platform = gpu\\n elif platform == '':\\n platform = None\\n\\n backend = xla_client.get_local_backend(platform)\\n if backend is None:\\n raise RuntimeError(\\\"No local XLA backends found.\\\")\\n\\n if backend.platform == cpu and platform != cpu:\\n warnings.warn('No GPU/TPU found, falling back to CPU.')\\n\\n return backend\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_get_tpu_driver_backend\",\n \"text\": \"jax/lib/xla_bridge.py::_get_tpu_driver_backend\\ndef _get_tpu_driver_backend(platform):\\n del platform\\n global _tpu_backend\\n if _tpu_backend is None:\\n backend_target = FLAGS.jax_backend_target\\n if backend_target is None:\\n raise ValueError('When using TPU Driver as the backend, you must specify '\\n '--jax_backend_target=<hostname>:8470.')\\n _tpu_backend = tpu_client.TpuBackend.create(worker=backend_target)\\n return _tpu_backend\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::get_backend\",\n \"text\": \"jax/lib/xla_bridge.py::get_backend\\n@util.memoize\\ndef get_backend(platform=None):\\n # TODO(mattjj,skyewm): remove this input polymorphism after we clean up how\\n # 'backend' values are handled\\n if isinstance(platform, xla_client.Backend):\\n return platform\\n\\n with _backend_lock:\\n backend = _backends.get(FLAGS.jax_xla_backend)\\n if backend is None:\\n msg = 'Unknown jax_xla_backend value \\\"{}\\\".'\\n raise ValueError(msg.format(FLAGS.jax_xla_backend))\\n return backend(platform)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::get_device_backend\",\n \"text\": \"jax/lib/xla_bridge.py::get_device_backend\\ndef get_device_backend(device=None):\\n \\\"\\\"\\\"Returns the Backend associated with `device`, or the default Backend.\\\"\\\"\\\"\\n platform = device.platform if device else None\\n return get_backend(platform)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::device_count\",\n \"text\": \"jax/lib/xla_bridge.py::device_count\\ndef device_count(backend=None):\\n \\\"\\\"\\\"Returns the total number of devices.\\n\\n On most platforms, this is the same as ``local_device_count()``. However, on\\n multi-host platforms, this will return the total number of devices across all\\n hosts.\\n\\n Args:\\n backend: This is an experimental feature and the API is likely to change.\\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\\n\\n Returns:\\n Number of devices.\\n \\\"\\\"\\\"\\n return int(get_backend(backend).device_count())\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::local_device_count\",\n \"text\": \"jax/lib/xla_bridge.py::local_device_count\\ndef local_device_count(backend=None):\\n \\\"\\\"\\\"Returns the number of devices on this host.\\\"\\\"\\\"\\n return int(get_backend(backend).local_device_count())\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::devices\",\n \"text\": \"jax/lib/xla_bridge.py::devices\\ndef devices(backend=None):\\n \\\"\\\"\\\"Returns a list of all devices.\\n\\n Each device is represented by a subclass of Device (e.g. CpuDevice,\\n GpuDevice). The length of the returned list is equal to\\n ``device_count()``. Local devices can be identified by comparing\\n ``Device.host_id`` to ``host_id()``.\\n\\n Args:\\n backend: This is an experimental feature and the API is likely to change.\\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\\n\\n Returns:\\n List of Device subclasses.\\n \\\"\\\"\\\"\\n return get_backend(backend).devices()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::local_devices\",\n \"text\": \"jax/lib/xla_bridge.py::local_devices\\ndef local_devices(host_id=None, backend=None):\\n \\\"\\\"\\\"Returns a list of devices local to a given host (this host by default).\\\"\\\"\\\"\\n if host_id is None:\\n host_id = get_backend(backend).host_id()\\n return [d for d in devices(backend) if d.host_id == host_id]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::host_id\",\n \"text\": \"jax/lib/xla_bridge.py::host_id\\ndef host_id(backend=None):\\n \\\"\\\"\\\"Returns the integer host ID of this host.\\n\\n On most platforms, this will always be 0. This will vary on multi-host\\n platforms though.\\n\\n Args:\\n backend: This is an experimental feature and the API is likely to change.\\n Optional, a string representing the xla backend. 'cpu', 'gpu', or 'tpu'.\\n\\n Returns:\\n Integer host ID.\\n \\\"\\\"\\\"\\n return get_backend(backend).host_id()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::host_ids\",\n \"text\": \"jax/lib/xla_bridge.py::host_ids\\ndef host_ids(backend=None):\\n \\\"\\\"\\\"Returns a list of all host IDs.\\\"\\\"\\\"\\n return list(set(d.host_id for d in devices(backend)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::host_count\",\n \"text\": \"jax/lib/xla_bridge.py::host_count\\ndef host_count(backend=None):\\n \\\"\\\"\\\"Returns the number of hosts.\\\"\\\"\\\"\\n return len(host_ids(backend))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::dtype_to_etype\",\n \"text\": \"jax/lib/xla_bridge.py::dtype_to_etype\\n@util.memoize\\ndef dtype_to_etype(dtype):\\n \\\"\\\"\\\"Convert from dtype to canonical etype (reading FLAGS.jax_enable_x64).\\\"\\\"\\\"\\n return xla_client.dtype_to_etype(dtypes.canonicalize_dtype(dtype))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::supported_numpy_dtypes\",\n \"text\": \"jax/lib/xla_bridge.py::supported_numpy_dtypes\\n@util.memoize\\ndef supported_numpy_dtypes():\\n return {dtypes.canonicalize_dtype(dtype)\\n for dtype in xla_client.XLA_ELEMENT_TYPE_TO_DTYPE.values()}\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::normalize_to_xla_dtypes\",\n \"text\": \"jax/lib/xla_bridge.py::normalize_to_xla_dtypes\\ndef normalize_to_xla_dtypes(val):\\n \\\"\\\"\\\"Normalize dtypes in a value.\\\"\\\"\\\"\\n if hasattr(val, '__array__') or onp.isscalar(val):\\n return onp.asarray(val,\\n dtype=dtypes.canonicalize_dtype(dtypes.result_type(val)))\\n elif isinstance(val, (tuple, list)):\\n return tuple(normalize_to_xla_dtypes(x) for x in val)\\n raise TypeError('Can\\\\'t convert to XLA: {}'.format(val))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.Build\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.Build\\n def Build(self, *args, **kwargs):\\n return super(_JaxComputationBuilder, self).Build(\\n *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.NumpyArrayConstant\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.NumpyArrayConstant\\n def NumpyArrayConstant(self, value, canonicalize_types=True):\\n if canonicalize_types:\\n value = normalize_to_xla_dtypes(value)\\n return super(_JaxComputationBuilder, self).Constant(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.ConstantLike\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.ConstantLike\\n def ConstantLike(self, example_value, value, canonicalize_types=True):\\n example_value = onp.asarray(example_value)\\n return self.Constant(onp.array(value, dtype=example_value.dtype))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.Constant\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.Constant\\n def Constant(self, py_val, canonicalize_types=True):\\n \\\"\\\"\\\"Translate constant `py_val` to a constant for this ComputationBuilder.\\n\\n Args:\\n py_val: a Python value to be translated to a constant.\\n\\n Returns:\\n A representation of the constant, either a ComputationDataHandle or None\\n \\\"\\\"\\\"\\n py_type = type(py_val)\\n if py_type in _constant_handlers:\\n return _constant_handlers[py_type](self, py_val, canonicalize_types)\\n else:\\n raise TypeError(\\\"No constant handler for type: {}\\\".format(py_type))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.CrossReplicaSum\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.CrossReplicaSum\\n def CrossReplicaSum(self, operand, replica_groups):\\n \\\"\\\"\\\"Workaround for CrossReplicaSum not being implemented on some backends.\\\"\\\"\\\"\\n if len(replica_groups[0]) == 1:\\n return operand\\n else:\\n return super(_JaxComputationBuilder, self).CrossReplicaSum(\\n operand, replica_groups)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.AllToAll\",\n \"text\": \"jax/lib/xla_bridge.py::_JaxComputationBuilder.AllToAll\\n def AllToAll(self, operand, split_axis, concat_axis, replica_groups):\\n \\\"\\\"\\\"Workaround for AllToAll not being implemented on some backends.\\\"\\\"\\\"\\n if len(replica_groups[0]) == 1:\\n return operand\\n else:\\n return super(_JaxComputationBuilder, self).AllToAll(\\n operand, split_axis, concat_axis, replica_groups)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::make_computation_builder\",\n \"text\": \"jax/lib/xla_bridge.py::make_computation_builder\\ndef make_computation_builder(name):\\n return _JaxComputationBuilder(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::register_constant_handler\",\n \"text\": \"jax/lib/xla_bridge.py::register_constant_handler\\ndef register_constant_handler(type_, handler_fun):\\n _constant_handlers[type_] = handler_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_ndarray_constant_handler\",\n \"text\": \"jax/lib/xla_bridge.py::_ndarray_constant_handler\\ndef _ndarray_constant_handler(c, val, canonicalize_types=True):\\n \\\"\\\"\\\"Constant handler for ndarray literals, handling zero-size strides.\\n\\n This function essentially calls c.NumpyArrayConstant(val) except it has\\n special handling of arrays with any strides of size zero: for those, it\\n generates appropriate calls to NumpyArrayConstant, Broadcast, and Transpose\\n to avoid staging in large literals that might arise from np.zeros or np.ones\\n or the output of lax.broadcast (which uses onp.broadcast_to which in turn\\n uses size-zero strides).\\n\\n Args:\\n c: XLA client ComputationBuilder.\\n val: an ndarray.\\n\\n Returns:\\n An XLA ComputationDataHandle / XlaOp representing the constant ndarray\\n staged into the XLA Computation.\\n \\\"\\\"\\\"\\n # TODO(mattjj): revise this to use c.BroadcastInDim rather than Transpose\\n if onp.any(onp.equal(0, val.strides)) and val.size > 0:\\n zero_stride_axes, = onp.where(onp.equal(0, val.strides))\\n other_axes, = onp.where(onp.not_equal(0, val.strides))\\n collapsed_val = val[tuple(0 if ax in zero_stride_axes else slice(None)\\n for ax in range(val.ndim))]\\n xla_val = c.Broadcast(\\n c.NumpyArrayConstant(collapsed_val, canonicalize_types),\\n onp.take(val.shape, zero_stride_axes))\\n permutation = onp.argsort(tuple(zero_stride_axes) + tuple(other_axes))\\n return c.Transpose(xla_val, permutation)\\n else:\\n return c.NumpyArrayConstant(val, canonicalize_types)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_scalar_constant_handler\",\n \"text\": \"jax/lib/xla_bridge.py::_scalar_constant_handler\\ndef _scalar_constant_handler(c, val, canonicalize_types=True):\\n return c.NumpyArrayConstant(val, canonicalize_types)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/lib/xla_bridge.py::_python_scalar_handler\",\n \"text\": \"jax/lib/xla_bridge.py::_python_scalar_handler\\ndef _python_scalar_handler(dtype, c, val, canonicalize_dtypes=True):\\n return c.NumpyArrayConstant(dtype.type(val))\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":79,"cells":{"instance_id":{"kind":"string","value":"numpy__numpy-3244"},"repo":{"kind":"string","value":"numpy/numpy"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"2to3 run `zip` fixer\nzip is now an iterator. This is simiar to the filter and map changers.\n\n"},"gold_files":{"kind":"list like","value":["doc/sphinxext/numpydoc/compiler_unparse.py","numpy/lib/_iotools.py","numpy/lib/npyio.py","numpy/ma/mrecords.py","tools/py3tool.py"],"string":"[\n \"doc/sphinxext/numpydoc/compiler_unparse.py\",\n \"numpy/lib/_iotools.py\",\n \"numpy/lib/npyio.py\",\n \"numpy/ma/mrecords.py\",\n \"tools/py3tool.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["doc/sphinxext/numpydoc/compiler_unparse.py::<module>","doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AugAssign","doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitand","doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitor","numpy/lib/_iotools.py::easy_dtype","numpy/lib/npyio.py::genfromtxt","numpy/ma/mrecords.py::fromarrays","tools/py3tool.py::<module>"],"string":"[\n \"doc/sphinxext/numpydoc/compiler_unparse.py::<module>\",\n \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AugAssign\",\n \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitand\",\n \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitor\",\n \"numpy/lib/_iotools.py::easy_dtype\",\n \"numpy/lib/npyio.py::genfromtxt\",\n \"numpy/ma/mrecords.py::fromarrays\",\n \"tools/py3tool.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py","text":"doc/sphinxext/numpydoc/compiler_unparse.py\ndef unparse(ast, single_line_functions=False)\nclass UnparseCompilerAst # Methods in this class recursively traverse an AST and\n def __init__(self, tree, file=sys.stdout, single_line_functions=False) # Unparser(tree, file=sys.stdout) -> None.\n def _fill(self, text='') # Indent a piece of text, according to the current indentation level\n def _write(self, text) # Append a piece of text to the current line.\n def _enter(self) # Print ':', and increase the indentation.\n def _leave(self) # Decrease the indentation level.\n def _dispatch(self, tree) # _dispatcher function, _dispatching tree type T to method _T.\n def _Add(self, t)\n def _And(self, t)\n def _AssAttr(self, t) # Handle assigning an attribute of an object\n def _Assign(self, t) # Expression Assignment such as \"a = 1\".\n def _AssName(self, t) # Name on left hand side of expression.\n def _AssTuple(self, t) # Tuple on left hand side of an expression.\n def _AugAssign(self, t) # +=,-=,*=,/=,**=, etc. operations\n def _Bitand(self, t) # Bit and operation.\n def _Bitor(self, t) # Bit or operation\n def _CallFunc(self, t) # Function call.\n def _Compare(self, t)\n def _Const(self, t) # A constant value such as an integer value, 3, or a string, \"hello\".\n def _Decorators(self, t) # Handle function decorators (eg. @has_units)\n def _Dict(self, t)\n def _Discard(self, t) # Node for when return value is ignored such as in \"foo(a)\".\n def _Div(self, t)\n def _Ellipsis(self, t)\n def _From(self, t) # Handle \"from xyz import foo, bar as baz\".\n def _Function(self, t) # Handle function definitions\n def _Getattr(self, t) # Handle getting an attribute of an object\n def _If(self, t)\n def _IfExp(self, t)\n def _Import(self, t) # Handle \"import xyz.foo\".\n def _Keyword(self, t) # Keyword value assignment within function calls and definitions.\n def _List(self, t)\n def _Module(self, t)\n def _Mul(self, t)\n def _Name(self, t)\n def _NoneType(self, t)\n def _Not(self, t)\n def _Or(self, t)\n def _Pass(self, t)\n def _Printnl(self, t)\n def _Power(self, t)\n def _Return(self, t)\n def _Slice(self, t)\n def _Sliceobj(self, t)\n def _Stmt(self, tree)\n def _Sub(self, t)\n def _Subscript(self, t)\n def _TryExcept(self, t)\n def _Tuple(self, t)\n def _UnaryAdd(self, t)\n def _UnarySub(self, t)\n def _With(self, t)\n def _int(self, t)\n def __binary_op(self, t, symbol)\n def _float(self, t)\n def _str(self, t)\n def _tuple(self, t)","label":1},{"cid":"numpy/lib/_iotools.py","text":"numpy/lib/_iotools.py\ndef _is_string_like(obj) # Check whether obj behaves like a string.\ndef _is_bytes_like(obj) # Check whether obj behaves like a bytes object.\ndef _to_filehandle(fname, flag='r', return_opened=False) # Returns the filehandle corresponding to a string or a file.\ndef has_nested_fields(ndtype) # Returns whether one or several fields of a dtype are nested.\ndef flatten_dtype(ndtype, flatten_base=False) # Unpack a structured data-type by collapsing nested fields and/or fields\nclass LineSplitter # Object to split a string at a given delimiter or at given places.\n def autostrip(self, method) # Wrapper to strip each member of the output of `method`.\n def __init__(self, delimiter=None, comments=asbytes('#'), autostrip=True)\n def _delimited_splitter(self, line)\n def _fixedwidth_splitter(self, line)\n def _variablewidth_splitter(self, line)\n def __call__(self, line)\nclass NameValidator # Object to validate a list of strings to use as field names.\n def __init__(self, excludelist=None, deletechars=None, case_sensitive=None, replace_space='_')\n def validate(self, names, defaultfmt='f%i', nbfields=None) # Validate a list of strings to use as field names for a structured array.\n def __call__(self, names, defaultfmt='f%i', nbfields=None)\ndef str2bool(value) # Tries to transform a string supposed to represent a boolean to a boolean.\nclass ConverterError # Exception raised when an error occurs in a converter for string values.\nclass ConverterLockError # Exception raised when an attempt is made to upgrade a locked converter.\nclass ConversionWarning # Warning issued when a string converter has a problem.\nclass StringConverter # Factory class for function transforming a string into another object (int,\n def _getdtype(cls, val) # Returns the dtype of the input variable.\n def _getsubdtype(cls, val) # Returns the type of the dtype of the input variable.\n def _dtypeortype(cls, dtype) # Returns dtype for datetime64 and type of dtype otherwise.\n def upgrade_mapper(cls, func, default=None) # Upgrade the mapper of a StringConverter by adding a new function and its\n def __init__(self, dtype_or_func=None, default=None, missing_values=None, locked=False)\n def _loose_call(self, value)\n def _strict_call(self, value)\n def __call__(self, value)\n def upgrade(self, value) # Try to find the best converter for a given string, and return the result.\n def iterupgrade(self, value)\n def update(self, func, default=None, testing_value=None, missing_values=asbytes(''), locked=False) # Set StringConverter attributes directly.\ndef easy_dtype(ndtype, names=None, defaultfmt='f%i', **validationargs) # Convenience function to create a `np.dtype` object.","label":1},{"cid":"numpy/lib/arrayterator.py","text":"numpy/lib/arrayterator.py\nclass Arrayterator # Buffered iterator for big arrays.\n def __init__(self, var, buf_size=None)\n def __getattr__(self, attr)\n def __getitem__(self, index) # Return a new arrayterator.\n def __array__(self) # Return corresponding data.\n def flat(self) # A 1-D flat iterator for Arrayterator objects.\n def shape(self) # The shape of the array to be iterated over.\n def __iter__(self)","label":0},{"cid":"numpy/lib/npyio.py","text":"numpy/lib/npyio.py\ndef seek_gzip_factory(f) # Use this factory to produce the class so that we can do a lazy\n class GzipFile\n def seek(self, offset, whence=0)\n def tell(self)\nclass BagObj # BagObj(obj)\n def __init__(self, obj)\n def __getattribute__(self, key)\ndef zipfile_factory(*args, **kwargs)\nclass NpzFile # NpzFile(fid)\n def __init__(self, fid, own_fid=False)\n def __enter__(self)\n def __exit__(self, exc_type, exc_value, traceback)\n def close(self) # Close the file.\n def __del__(self)\n def __getitem__(self, key)\n def __iter__(self)\n def items(self) # Return a list of tuples, with each tuple (filename, array in file).\n def iteritems(self) # Generator that returns tuples (filename, array in file).\n def keys(self) # Return files in the archive with a \".npy\" extension.\n def iterkeys(self) # Return an iterator over the files in the archive.\n def __contains__(self, key)\ndef load(file, mmap_mode=None) # Load an array(s) or pickled objects from .npy, .npz, or pickled files.\ndef save(file, arr) # Save an array to a binary file in NumPy ``.npy`` format.\ndef savez(file, *args, **kwds) # Save several arrays into a single file in uncompressed ``.npz`` format.\ndef savez_compressed(file, *args, **kwds) # Save several arrays into a single file in compressed ``.npz`` format.\ndef _savez(file, args, kwds, compress)\ndef _getconv(dtype)\ndef loadtxt(fname, dtype=float, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0) # Load data from a text file.\n def flatten_dtype(dt) # Unpack a structured data-type, and produce re-packing info.\n def pack_items(items, packing) # Pack items into nested lists based on re-packing info.\n def split_line(line) # Chop off comments, strip, and split at delimiter.\ndef savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\\n', header='', footer='', comments='# ') # Save an array to a text file.\ndef fromregex(file, regexp, dtype) # Construct an array from a text file, using regular expression parsing.\ndef genfromtxt(fname, dtype=float, comments='#', delimiter=None, skiprows=0, skip_header=0, skip_footer=0, converters=None, missing='', missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True) # Load data from a text file, with missing values handled as specified.\ndef ndfromtxt(fname, **kwargs) # Load ASCII data stored in a file and return it as a single array.\ndef mafromtxt(fname, **kwargs) # Load ASCII data stored in a text file and return a masked array.\ndef recfromtxt(fname, **kwargs) # Load ASCII data from a file and return it in a record array.\ndef recfromcsv(fname, **kwargs) # Load ASCII data stored in a comma-separated file.","label":1},{"cid":"numpy/ma/mrecords.py","text":"numpy/ma/mrecords.py\ndef _getformats(data) # Returns the formats of each array of arraylist as a comma-separated string.\ndef _checknames(descr, names=None) # Checks that the field names of the descriptor ``descr`` are not some\ndef _get_fieldmask(self)\nclass MaskedRecords # *IVariables*:\n def __new__(cls, shape, dtype=None, buf=None, offset=0, strides=None, formats=None, names=None, titles=None, byteorder=None, aligned=False, mask=nomask, hard_mask=False, fill_value=None, keep_mask=True, copy=False, **options)\n def __array_finalize__(self, obj)\n def _getdata(self) # Returns the data as a recarray.\n def _getfieldmask(self) # Alias to mask\n def __len__(self) # Returns the length\n def __getattribute__(self, attr)\n def __setattr__(self, attr, val) # Sets the attribute attr to the value val.\n def __getitem__(self, indx) # Returns all the fields sharing the same fieldname base.\n def __setitem__(self, indx, value) # Sets the given record to value.\n def __str__(self) # Calculates the string representation.\n def __repr__(self) # Calculates the repr representation.\n def view(self, dtype=None, type=None) # Returns a view of the mrecarray.\n def harden_mask(self) # Forces the mask to hard\n def soften_mask(self) # Forces the mask to soft\n def copy(self) # Returns a copy of the masked record.\n def tolist(self, fill_value=None) # Copy the data portion of the array to a hierarchical python\n def __getstate__(self) # Return the internal state of the masked array, for pickling purposes.\n def __setstate__(self, state) # Restore the internal state of the masked array, for pickling purposes.\n def __reduce__(self) # Return a 3-tuple for pickling a MaskedArray.\ndef _mrreconstruct(subtype, baseclass, baseshape, basetype) # Internal function that builds a new MaskedArray from the\ndef fromarrays(arraylist, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None, fill_value=None) # Creates a mrecarray from a (flat) list of masked arrays.\ndef fromrecords(reclist, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None, fill_value=None, mask=nomask) # Creates a MaskedRecords from a list of records.\ndef _guessvartypes(arr) # Tries to guess the dtypes of the str_ ndarray `arr`, by testing element-wise\ndef openfile(fname) # Opens the file handle of file `fname`\ndef fromtextfile(fname, delimitor=None, commentchar='#', missingchar='', varnames=None, vartypes=None) # Creates a mrecarray from data stored in the file `filename`.\ndef addfield(mrecord, newfield, newfieldname=None) # Adds a new field to the masked record array, using `newfield` as data","label":1},{"cid":"setup.py","text":"setup.py\ndef git_version()\n def _minimal_ext_cmd(cmd)\ndef write_version_py(filename='numpy/version.py')\ndef configuration(parent_package='', top_path=None)\ndef setup_package()","label":0},{"cid":"tools/py3tool.py","text":"tools/py3tool.py\ndef main()\n def getenv()\ndef walk_sync(dir1, dir2, _seen=None)\ndef sync_2to3(src, dst, patchfile=None, clean=False)","label":1}],"string":"[\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py\\ndef unparse(ast, single_line_functions=False)\\nclass UnparseCompilerAst # Methods in this class recursively traverse an AST and\\n def __init__(self, tree, file=sys.stdout, single_line_functions=False) # Unparser(tree, file=sys.stdout) -> None.\\n def _fill(self, text='') # Indent a piece of text, according to the current indentation level\\n def _write(self, text) # Append a piece of text to the current line.\\n def _enter(self) # Print ':', and increase the indentation.\\n def _leave(self) # Decrease the indentation level.\\n def _dispatch(self, tree) # _dispatcher function, _dispatching tree type T to method _T.\\n def _Add(self, t)\\n def _And(self, t)\\n def _AssAttr(self, t) # Handle assigning an attribute of an object\\n def _Assign(self, t) # Expression Assignment such as \\\"a = 1\\\".\\n def _AssName(self, t) # Name on left hand side of expression.\\n def _AssTuple(self, t) # Tuple on left hand side of an expression.\\n def _AugAssign(self, t) # +=,-=,*=,/=,**=, etc. operations\\n def _Bitand(self, t) # Bit and operation.\\n def _Bitor(self, t) # Bit or operation\\n def _CallFunc(self, t) # Function call.\\n def _Compare(self, t)\\n def _Const(self, t) # A constant value such as an integer value, 3, or a string, \\\"hello\\\".\\n def _Decorators(self, t) # Handle function decorators (eg. @has_units)\\n def _Dict(self, t)\\n def _Discard(self, t) # Node for when return value is ignored such as in \\\"foo(a)\\\".\\n def _Div(self, t)\\n def _Ellipsis(self, t)\\n def _From(self, t) # Handle \\\"from xyz import foo, bar as baz\\\".\\n def _Function(self, t) # Handle function definitions\\n def _Getattr(self, t) # Handle getting an attribute of an object\\n def _If(self, t)\\n def _IfExp(self, t)\\n def _Import(self, t) # Handle \\\"import xyz.foo\\\".\\n def _Keyword(self, t) # Keyword value assignment within function calls and definitions.\\n def _List(self, t)\\n def _Module(self, t)\\n def _Mul(self, t)\\n def _Name(self, t)\\n def _NoneType(self, t)\\n def _Not(self, t)\\n def _Or(self, t)\\n def _Pass(self, t)\\n def _Printnl(self, t)\\n def _Power(self, t)\\n def _Return(self, t)\\n def _Slice(self, t)\\n def _Sliceobj(self, t)\\n def _Stmt(self, tree)\\n def _Sub(self, t)\\n def _Subscript(self, t)\\n def _TryExcept(self, t)\\n def _Tuple(self, t)\\n def _UnaryAdd(self, t)\\n def _UnarySub(self, t)\\n def _With(self, t)\\n def _int(self, t)\\n def __binary_op(self, t, symbol)\\n def _float(self, t)\\n def _str(self, t)\\n def _tuple(self, t)\",\n \"label\": 1\n },\n {\n \"cid\": \"numpy/lib/_iotools.py\",\n \"text\": \"numpy/lib/_iotools.py\\ndef _is_string_like(obj) # Check whether obj behaves like a string.\\ndef _is_bytes_like(obj) # Check whether obj behaves like a bytes object.\\ndef _to_filehandle(fname, flag='r', return_opened=False) # Returns the filehandle corresponding to a string or a file.\\ndef has_nested_fields(ndtype) # Returns whether one or several fields of a dtype are nested.\\ndef flatten_dtype(ndtype, flatten_base=False) # Unpack a structured data-type by collapsing nested fields and/or fields\\nclass LineSplitter # Object to split a string at a given delimiter or at given places.\\n def autostrip(self, method) # Wrapper to strip each member of the output of `method`.\\n def __init__(self, delimiter=None, comments=asbytes('#'), autostrip=True)\\n def _delimited_splitter(self, line)\\n def _fixedwidth_splitter(self, line)\\n def _variablewidth_splitter(self, line)\\n def __call__(self, line)\\nclass NameValidator # Object to validate a list of strings to use as field names.\\n def __init__(self, excludelist=None, deletechars=None, case_sensitive=None, replace_space='_')\\n def validate(self, names, defaultfmt='f%i', nbfields=None) # Validate a list of strings to use as field names for a structured array.\\n def __call__(self, names, defaultfmt='f%i', nbfields=None)\\ndef str2bool(value) # Tries to transform a string supposed to represent a boolean to a boolean.\\nclass ConverterError # Exception raised when an error occurs in a converter for string values.\\nclass ConverterLockError # Exception raised when an attempt is made to upgrade a locked converter.\\nclass ConversionWarning # Warning issued when a string converter has a problem.\\nclass StringConverter # Factory class for function transforming a string into another object (int,\\n def _getdtype(cls, val) # Returns the dtype of the input variable.\\n def _getsubdtype(cls, val) # Returns the type of the dtype of the input variable.\\n def _dtypeortype(cls, dtype) # Returns dtype for datetime64 and type of dtype otherwise.\\n def upgrade_mapper(cls, func, default=None) # Upgrade the mapper of a StringConverter by adding a new function and its\\n def __init__(self, dtype_or_func=None, default=None, missing_values=None, locked=False)\\n def _loose_call(self, value)\\n def _strict_call(self, value)\\n def __call__(self, value)\\n def upgrade(self, value) # Try to find the best converter for a given string, and return the result.\\n def iterupgrade(self, value)\\n def update(self, func, default=None, testing_value=None, missing_values=asbytes(''), locked=False) # Set StringConverter attributes directly.\\ndef easy_dtype(ndtype, names=None, defaultfmt='f%i', **validationargs) # Convenience function to create a `np.dtype` object.\",\n \"label\": 1\n },\n {\n \"cid\": \"numpy/lib/arrayterator.py\",\n \"text\": \"numpy/lib/arrayterator.py\\nclass Arrayterator # Buffered iterator for big arrays.\\n def __init__(self, var, buf_size=None)\\n def __getattr__(self, attr)\\n def __getitem__(self, index) # Return a new arrayterator.\\n def __array__(self) # Return corresponding data.\\n def flat(self) # A 1-D flat iterator for Arrayterator objects.\\n def shape(self) # The shape of the array to be iterated over.\\n def __iter__(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py\",\n \"text\": \"numpy/lib/npyio.py\\ndef seek_gzip_factory(f) # Use this factory to produce the class so that we can do a lazy\\n class GzipFile\\n def seek(self, offset, whence=0)\\n def tell(self)\\nclass BagObj # BagObj(obj)\\n def __init__(self, obj)\\n def __getattribute__(self, key)\\ndef zipfile_factory(*args, **kwargs)\\nclass NpzFile # NpzFile(fid)\\n def __init__(self, fid, own_fid=False)\\n def __enter__(self)\\n def __exit__(self, exc_type, exc_value, traceback)\\n def close(self) # Close the file.\\n def __del__(self)\\n def __getitem__(self, key)\\n def __iter__(self)\\n def items(self) # Return a list of tuples, with each tuple (filename, array in file).\\n def iteritems(self) # Generator that returns tuples (filename, array in file).\\n def keys(self) # Return files in the archive with a \\\".npy\\\" extension.\\n def iterkeys(self) # Return an iterator over the files in the archive.\\n def __contains__(self, key)\\ndef load(file, mmap_mode=None) # Load an array(s) or pickled objects from .npy, .npz, or pickled files.\\ndef save(file, arr) # Save an array to a binary file in NumPy ``.npy`` format.\\ndef savez(file, *args, **kwds) # Save several arrays into a single file in uncompressed ``.npz`` format.\\ndef savez_compressed(file, *args, **kwds) # Save several arrays into a single file in compressed ``.npz`` format.\\ndef _savez(file, args, kwds, compress)\\ndef _getconv(dtype)\\ndef loadtxt(fname, dtype=float, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0) # Load data from a text file.\\n def flatten_dtype(dt) # Unpack a structured data-type, and produce re-packing info.\\n def pack_items(items, packing) # Pack items into nested lists based on re-packing info.\\n def split_line(line) # Chop off comments, strip, and split at delimiter.\\ndef savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\\\\n', header='', footer='', comments='# ') # Save an array to a text file.\\ndef fromregex(file, regexp, dtype) # Construct an array from a text file, using regular expression parsing.\\ndef genfromtxt(fname, dtype=float, comments='#', delimiter=None, skiprows=0, skip_header=0, skip_footer=0, converters=None, missing='', missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True) # Load data from a text file, with missing values handled as specified.\\ndef ndfromtxt(fname, **kwargs) # Load ASCII data stored in a file and return it as a single array.\\ndef mafromtxt(fname, **kwargs) # Load ASCII data stored in a text file and return a masked array.\\ndef recfromtxt(fname, **kwargs) # Load ASCII data from a file and return it in a record array.\\ndef recfromcsv(fname, **kwargs) # Load ASCII data stored in a comma-separated file.\",\n \"label\": 1\n },\n {\n \"cid\": \"numpy/ma/mrecords.py\",\n \"text\": \"numpy/ma/mrecords.py\\ndef _getformats(data) # Returns the formats of each array of arraylist as a comma-separated string.\\ndef _checknames(descr, names=None) # Checks that the field names of the descriptor ``descr`` are not some\\ndef _get_fieldmask(self)\\nclass MaskedRecords # *IVariables*:\\n def __new__(cls, shape, dtype=None, buf=None, offset=0, strides=None, formats=None, names=None, titles=None, byteorder=None, aligned=False, mask=nomask, hard_mask=False, fill_value=None, keep_mask=True, copy=False, **options)\\n def __array_finalize__(self, obj)\\n def _getdata(self) # Returns the data as a recarray.\\n def _getfieldmask(self) # Alias to mask\\n def __len__(self) # Returns the length\\n def __getattribute__(self, attr)\\n def __setattr__(self, attr, val) # Sets the attribute attr to the value val.\\n def __getitem__(self, indx) # Returns all the fields sharing the same fieldname base.\\n def __setitem__(self, indx, value) # Sets the given record to value.\\n def __str__(self) # Calculates the string representation.\\n def __repr__(self) # Calculates the repr representation.\\n def view(self, dtype=None, type=None) # Returns a view of the mrecarray.\\n def harden_mask(self) # Forces the mask to hard\\n def soften_mask(self) # Forces the mask to soft\\n def copy(self) # Returns a copy of the masked record.\\n def tolist(self, fill_value=None) # Copy the data portion of the array to a hierarchical python\\n def __getstate__(self) # Return the internal state of the masked array, for pickling purposes.\\n def __setstate__(self, state) # Restore the internal state of the masked array, for pickling purposes.\\n def __reduce__(self) # Return a 3-tuple for pickling a MaskedArray.\\ndef _mrreconstruct(subtype, baseclass, baseshape, basetype) # Internal function that builds a new MaskedArray from the\\ndef fromarrays(arraylist, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None, fill_value=None) # Creates a mrecarray from a (flat) list of masked arrays.\\ndef fromrecords(reclist, dtype=None, shape=None, formats=None, names=None, titles=None, aligned=False, byteorder=None, fill_value=None, mask=nomask) # Creates a MaskedRecords from a list of records.\\ndef _guessvartypes(arr) # Tries to guess the dtypes of the str_ ndarray `arr`, by testing element-wise\\ndef openfile(fname) # Opens the file handle of file `fname`\\ndef fromtextfile(fname, delimitor=None, commentchar='#', missingchar='', varnames=None, vartypes=None) # Creates a mrecarray from data stored in the file `filename`.\\ndef addfield(mrecord, newfield, newfieldname=None) # Adds a new field to the masked record array, using `newfield` as data\",\n \"label\": 1\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\\ndef git_version()\\n def _minimal_ext_cmd(cmd)\\ndef write_version_py(filename='numpy/version.py')\\ndef configuration(parent_package='', top_path=None)\\ndef setup_package()\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/py3tool.py\",\n \"text\": \"tools/py3tool.py\\ndef main()\\n def getenv()\\ndef walk_sync(dir1, dir2, _seen=None)\\ndef sync_2to3(src, dst, patchfile=None, clean=False)\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::unparse","text":"doc/sphinxext/numpydoc/compiler_unparse.py::unparse\ndef unparse(ast, single_line_functions=False):\n s = StringIO()\n UnparseCompilerAst(ast, s, single_line_functions)\n return s.getvalue().lstrip()","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst.__init__","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst.__init__\n def __init__(self, tree, file = sys.stdout, single_line_functions=False):\n \"\"\" Unparser(tree, file=sys.stdout) -> None.\n\n Print the source for tree to file.\n \"\"\"\n self.f = file\n self._single_func = single_line_functions\n self._do_indent = True\n self._indent = 0\n self._dispatch(tree)\n self._write(\"\\n\")\n self.f.flush()","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._fill","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._fill\n def _fill(self, text = \"\"):\n \"Indent a piece of text, according to the current indentation level\"\n if self._do_indent:\n self._write(\"\\n\"+\" \"*self._indent + text)\n else:\n self._write(text)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._write","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._write\n def _write(self, text):\n \"Append a piece of text to the current line.\"\n self.f.write(text)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._enter","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._enter\n def _enter(self):\n \"Print ':', and increase the indentation.\"\n self._write(\": \")\n self._indent += 1","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._leave","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._leave\n def _leave(self):\n \"Decrease the indentation level.\"\n self._indent -= 1","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._dispatch","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._dispatch\n def _dispatch(self, tree):\n \"_dispatcher function, _dispatching tree type T to method _T.\"\n if isinstance(tree, list):\n for t in tree:\n self._dispatch(t)\n return\n meth = getattr(self, \"_\"+tree.__class__.__name__)\n if tree.__class__.__name__ == 'NoneType' and not self._do_indent:\n return\n meth(tree)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Add","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Add\n def _Add(self, t):\n self.__binary_op(t, '+')","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._And","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._And\n def _And(self, t):\n self._write(\" (\")\n for i, node in enumerate(t.nodes):\n self._dispatch(node)\n if i != len(t.nodes)-1:\n self._write(\") and (\")\n self._write(\")\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssAttr","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssAttr\n def _AssAttr(self, t):\n \"\"\" Handle assigning an attribute of an object\n \"\"\"\n self._dispatch(t.expr)\n self._write('.'+t.attrname)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Assign","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Assign\n def _Assign(self, t):\n \"\"\" Expression Assignment such as \"a = 1\".\n\n This only handles assignment in expressions. Keyword assignment\n is handled separately.\n \"\"\"\n self._fill()\n for target in t.nodes:\n self._dispatch(target)\n self._write(\" = \")\n self._dispatch(t.expr)\n if not self._do_indent:\n self._write('; ')","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssName","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssName\n def _AssName(self, t):\n \"\"\" Name on left hand side of expression.\n\n Treat just like a name on the right side of an expression.\n \"\"\"\n self._Name(t)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssTuple","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssTuple\n def _AssTuple(self, t):\n \"\"\" Tuple on left hand side of an expression.\n \"\"\"\n\n # _write each elements, separated by a comma.\n for element in t.nodes[:-1]:\n self._dispatch(element)\n self._write(\", \")\n\n # Handle the last one without writing comma\n last_element = t.nodes[-1]\n self._dispatch(last_element)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AugAssign","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AugAssign\n def _AugAssign(self, t):\n \"\"\" +=,-=,*=,/=,**=, etc. operations\n \"\"\"\n \n self._fill()\n self._dispatch(t.node)\n self._write(' '+t.op+' ')\n self._dispatch(t.expr)\n if not self._do_indent:\n self._write(';')","label":1},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitand","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitand\n def _Bitand(self, t):\n \"\"\" Bit and operation.\n \"\"\"\n \n for i, node in enumerate(t.nodes):\n self._write(\"(\")\n self._dispatch(node)\n self._write(\")\")\n if i != len(t.nodes)-1:\n self._write(\" & \")","label":1},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitor","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitor\n def _Bitor(self, t):\n \"\"\" Bit or operation\n \"\"\"\n \n for i, node in enumerate(t.nodes):\n self._write(\"(\")\n self._dispatch(node)\n self._write(\")\")\n if i != len(t.nodes)-1:\n self._write(\" | \")","label":1},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._CallFunc","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._CallFunc\n def _CallFunc(self, t):\n \"\"\" Function call.\n \"\"\"\n self._dispatch(t.node)\n self._write(\"(\")\n comma = False\n for e in t.args:\n if comma: self._write(\", \")\n else: comma = True\n self._dispatch(e)\n if t.star_args:\n if comma: self._write(\", \")\n else: comma = True\n self._write(\"*\")\n self._dispatch(t.star_args)\n if t.dstar_args:\n if comma: self._write(\", \")\n else: comma = True\n self._write(\"**\")\n self._dispatch(t.dstar_args)\n self._write(\")\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Compare","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Compare\n def _Compare(self, t):\n self._dispatch(t.expr)\n for op, expr in t.ops:\n self._write(\" \" + op + \" \")\n self._dispatch(expr)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Const","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Const\n def _Const(self, t):\n \"\"\" A constant value such as an integer value, 3, or a string, \"hello\".\n \"\"\"\n self._dispatch(t.value)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Decorators","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Decorators\n def _Decorators(self, t):\n \"\"\" Handle function decorators (eg. @has_units)\n \"\"\"\n for node in t.nodes:\n self._dispatch(node)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Dict","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Dict\n def _Dict(self, t):\n self._write(\"{\")\n for i, (k, v) in enumerate(t.items):\n self._dispatch(k)\n self._write(\": \")\n self._dispatch(v)\n if i < len(t.items)-1:\n self._write(\", \")\n self._write(\"}\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Discard","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Discard\n def _Discard(self, t):\n \"\"\" Node for when return value is ignored such as in \"foo(a)\".\n \"\"\"\n self._fill()\n self._dispatch(t.expr)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Div","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Div\n def _Div(self, t):\n self.__binary_op(t, '/')","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Ellipsis","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Ellipsis\n def _Ellipsis(self, t):\n self._write(\"...\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._From","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._From\n def _From(self, t):\n \"\"\" Handle \"from xyz import foo, bar as baz\".\n \"\"\"\n # fixme: Are From and ImportFrom handled differently?\n self._fill(\"from \")\n self._write(t.modname)\n self._write(\" import \")\n for i, (name,asname) in enumerate(t.names):\n if i != 0:\n self._write(\", \")\n self._write(name)\n if asname is not None:\n self._write(\" as \"+asname)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Function","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Function\n def _Function(self, t):\n \"\"\" Handle function definitions\n \"\"\"\n if t.decorators is not None:\n self._fill(\"@\")\n self._dispatch(t.decorators)\n self._fill(\"def \"+t.name + \"(\")\n defaults = [None] * (len(t.argnames) - len(t.defaults)) + list(t.defaults)\n for i, arg in enumerate(zip(t.argnames, defaults)):\n self._write(arg[0])\n if arg[1] is not None:\n self._write('=')\n self._dispatch(arg[1])\n if i < len(t.argnames)-1:\n self._write(', ')\n self._write(\")\")\n if self._single_func:\n self._do_indent = False\n self._enter()\n self._dispatch(t.code)\n self._leave()\n self._do_indent = True","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Getattr","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Getattr\n def _Getattr(self, t):\n \"\"\" Handle getting an attribute of an object\n \"\"\"\n if isinstance(t.expr, (Div, Mul, Sub, Add)):\n self._write('(')\n self._dispatch(t.expr)\n self._write(')')\n else:\n self._dispatch(t.expr)\n \n self._write('.'+t.attrname)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._If","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._If\n def _If(self, t):\n self._fill()\n \n for i, (compare,code) in enumerate(t.tests):\n if i == 0:\n self._write(\"if \")\n else:\n self._write(\"elif \")\n self._dispatch(compare)\n self._enter()\n self._fill()\n self._dispatch(code)\n self._leave()\n self._write(\"\\n\")\n\n if t.else_ is not None:\n self._write(\"else\")\n self._enter()\n self._fill()\n self._dispatch(t.else_)\n self._leave()\n self._write(\"\\n\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._IfExp","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._IfExp\n def _IfExp(self, t):\n self._dispatch(t.then)\n self._write(\" if \")\n self._dispatch(t.test)\n\n if t.else_ is not None:\n self._write(\" else (\")\n self._dispatch(t.else_)\n self._write(\")\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Import","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Import\n def _Import(self, t):\n \"\"\" Handle \"import xyz.foo\".\n \"\"\"\n self._fill(\"import \")\n \n for i, (name,asname) in enumerate(t.names):\n if i != 0:\n self._write(\", \")\n self._write(name)\n if asname is not None:\n self._write(\" as \"+asname)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Keyword","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Keyword\n def _Keyword(self, t):\n \"\"\" Keyword value assignment within function calls and definitions.\n \"\"\"\n self._write(t.name)\n self._write(\"=\")\n self._dispatch(t.expr)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._List","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._List\n def _List(self, t):\n self._write(\"[\")\n for i,node in enumerate(t.nodes):\n self._dispatch(node)\n if i < len(t.nodes)-1:\n self._write(\", \")\n self._write(\"]\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Module","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Module\n def _Module(self, t):\n if t.doc is not None:\n self._dispatch(t.doc)\n self._dispatch(t.node)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Mul","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Mul\n def _Mul(self, t):\n self.__binary_op(t, '*')","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Name","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Name\n def _Name(self, t):\n self._write(t.name)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._NoneType","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._NoneType\n def _NoneType(self, t):\n self._write(\"None\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Not","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Not\n def _Not(self, t):\n self._write('not (')\n self._dispatch(t.expr)\n self._write(')')","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Or","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Or\n def _Or(self, t):\n self._write(\" (\")\n for i, node in enumerate(t.nodes):\n self._dispatch(node)\n if i != len(t.nodes)-1:\n self._write(\") or (\")\n self._write(\")\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Pass","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Pass\n def _Pass(self, t):\n self._write(\"pass\\n\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Printnl","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Printnl\n def _Printnl(self, t):\n self._fill(\"print \")\n if t.dest:\n self._write(\">> \")\n self._dispatch(t.dest)\n self._write(\", \")\n comma = False\n for node in t.nodes:\n if comma: self._write(', ')\n else: comma = True\n self._dispatch(node)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Power","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Power\n def _Power(self, t):\n self.__binary_op(t, '**')","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Return","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Return\n def _Return(self, t):\n self._fill(\"return \")\n if t.value:\n if isinstance(t.value, Tuple):\n text = ', '.join([ name.name for name in t.value.asList() ])\n self._write(text)\n else:\n self._dispatch(t.value)\n if not self._do_indent:\n self._write('; ')","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Slice","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Slice\n def _Slice(self, t):\n self._dispatch(t.expr)\n self._write(\"[\")\n if t.lower:\n self._dispatch(t.lower)\n self._write(\":\")\n if t.upper:\n self._dispatch(t.upper)\n #if t.step:\n # self._write(\":\")\n # self._dispatch(t.step)\n self._write(\"]\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Sliceobj","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Sliceobj\n def _Sliceobj(self, t):\n for i, node in enumerate(t.nodes):\n if i != 0:\n self._write(\":\")\n if not (isinstance(node, Const) and node.value is None):\n self._dispatch(node)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Stmt","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Stmt\n def _Stmt(self, tree):\n for node in tree.nodes:\n self._dispatch(node)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Sub","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Sub\n def _Sub(self, t):\n self.__binary_op(t, '-')","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Subscript","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Subscript\n def _Subscript(self, t):\n self._dispatch(t.expr)\n self._write(\"[\")\n for i, value in enumerate(t.subs):\n if i != 0:\n self._write(\",\")\n self._dispatch(value)\n self._write(\"]\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._TryExcept","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._TryExcept\n def _TryExcept(self, t):\n self._fill(\"try\")\n self._enter()\n self._dispatch(t.body)\n self._leave()\n\n for handler in t.handlers:\n self._fill('except ')\n self._dispatch(handler[0])\n if handler[1] is not None:\n self._write(', ')\n self._dispatch(handler[1])\n self._enter()\n self._dispatch(handler[2])\n self._leave()\n \n if t.else_:\n self._fill(\"else\")\n self._enter()\n self._dispatch(t.else_)\n self._leave()","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Tuple","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Tuple\n def _Tuple(self, t):\n\n if not t.nodes:\n # Empty tuple.\n self._write(\"()\")\n else:\n self._write(\"(\")\n\n # _write each elements, separated by a comma.\n for element in t.nodes[:-1]:\n self._dispatch(element)\n self._write(\", \")\n\n # Handle the last one without writing comma\n last_element = t.nodes[-1]\n self._dispatch(last_element)\n\n self._write(\")\")","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._UnaryAdd","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._UnaryAdd\n def _UnaryAdd(self, t):\n self._write(\"+\")\n self._dispatch(t.expr)","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._UnarySub","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._UnarySub\n def _UnarySub(self, t):\n self._write(\"-\")\n self._dispatch(t.expr) ","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._With","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._With\n def _With(self, t):\n self._fill('with ')\n self._dispatch(t.expr)\n if t.vars:\n self._write(' as ')\n self._dispatch(t.vars.name)\n self._enter()\n self._dispatch(t.body)\n self._leave()\n self._write('\\n')","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._int","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._int\n def _int(self, t):\n self._write(repr(t))","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst.__binary_op","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst.__binary_op\n def __binary_op(self, t, symbol):\n # Check if parenthesis are needed on left side and then dispatch\n has_paren = False\n left_class = str(t.left.__class__)\n if (left_class in op_precedence.keys() and\n op_precedence[left_class] < op_precedence[str(t.__class__)]):\n has_paren = True\n if has_paren:\n self._write('(')\n self._dispatch(t.left)\n if has_paren:\n self._write(')')\n # Write the appropriate symbol for operator\n self._write(symbol)\n # Check if parenthesis are needed on the right side and then dispatch\n has_paren = False\n right_class = str(t.right.__class__)\n if (right_class in op_precedence.keys() and\n op_precedence[right_class] < op_precedence[str(t.__class__)]):\n has_paren = True\n if has_paren:\n self._write('(')\n self._dispatch(t.right)\n if has_paren:\n self._write(')')","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._float","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._float\n def _float(self, t):\n # if t is 0.1, str(t)->'0.1' while repr(t)->'0.1000000000001'\n # We prefer str here.\n self._write(str(t))","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._str","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._str\n def _str(self, t):\n self._write(repr(t))","label":0},{"cid":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._tuple","text":"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._tuple\n def _tuple(self, t):\n self._write(str(t))","label":0},{"cid":"numpy/lib/_iotools.py::_is_string_like","text":"numpy/lib/_iotools.py::_is_string_like\ndef _is_string_like(obj):\n \"\"\"\n Check whether obj behaves like a string.\n \"\"\"\n try:\n obj + ''\n except (TypeError, ValueError):\n return False\n return True","label":0},{"cid":"numpy/lib/_iotools.py::_is_bytes_like","text":"numpy/lib/_iotools.py::_is_bytes_like\ndef _is_bytes_like(obj):\n \"\"\"\n Check whether obj behaves like a bytes object.\n \"\"\"\n try:\n obj + asbytes('')\n except (TypeError, ValueError):\n return False\n return True","label":0},{"cid":"numpy/lib/_iotools.py::_to_filehandle","text":"numpy/lib/_iotools.py::_to_filehandle\ndef _to_filehandle(fname, flag='r', return_opened=False):\n \"\"\"\n Returns the filehandle corresponding to a string or a file.\n If the string ends in '.gz', the file is automatically unzipped.\n\n Parameters\n ----------\n fname : string, filehandle\n Name of the file whose filehandle must be returned.\n flag : string, optional\n Flag indicating the status of the file ('r' for read, 'w' for write).\n return_opened : boolean, optional\n Whether to return the opening status of the file.\n \"\"\"\n if _is_string_like(fname):\n if fname.endswith('.gz'):\n import gzip\n fhd = gzip.open(fname, flag)\n elif fname.endswith('.bz2'):\n import bz2\n fhd = bz2.BZ2File(fname)\n else:\n fhd = file(fname, flag)\n opened = True\n elif hasattr(fname, 'seek'):\n fhd = fname\n opened = False\n else:\n raise ValueError('fname must be a string or file handle')\n if return_opened:\n return fhd, opened\n return fhd","label":0},{"cid":"numpy/lib/_iotools.py::has_nested_fields","text":"numpy/lib/_iotools.py::has_nested_fields\ndef has_nested_fields(ndtype):\n \"\"\"\n Returns whether one or several fields of a dtype are nested.\n\n Parameters\n ----------\n ndtype : dtype\n Data-type of a structured array.\n\n Raises\n ------\n AttributeError\n If `ndtype` does not have a `names` attribute.\n\n Examples\n --------\n >>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])\n >>> np.lib._iotools.has_nested_fields(dt)\n False\n\n \"\"\"\n for name in ndtype.names or ():\n if ndtype[name].names:\n return True\n return False","label":0},{"cid":"numpy/lib/_iotools.py::flatten_dtype","text":"numpy/lib/_iotools.py::flatten_dtype\ndef flatten_dtype(ndtype, flatten_base=False):\n \"\"\"\n Unpack a structured data-type by collapsing nested fields and/or fields\n with a shape.\n\n Note that the field names are lost.\n\n Parameters\n ----------\n ndtype : dtype\n The datatype to collapse\n flatten_base : {False, True}, optional\n Whether to transform a field with a shape into several fields or not.\n\n Examples\n --------\n >>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),\n ... ('block', int, (2, 3))])\n >>> np.lib._iotools.flatten_dtype(dt)\n [dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32')]\n >>> np.lib._iotools.flatten_dtype(dt, flatten_base=True)\n [dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32'),\n dtype('int32'), dtype('int32'), dtype('int32'), dtype('int32'),\n dtype('int32')]\n\n \"\"\"\n names = ndtype.names\n if names is None:\n if flatten_base:\n return [ndtype.base] * int(np.prod(ndtype.shape))\n return [ndtype.base]\n else:\n types = []\n for field in names:\n info = ndtype.fields[field]\n flat_dt = flatten_dtype(info[0], flatten_base)\n types.extend(flat_dt)\n return types","label":0},{"cid":"numpy/lib/_iotools.py::LineSplitter.autostrip","text":"numpy/lib/_iotools.py::LineSplitter.autostrip\n def autostrip(self, method):\n \"\"\"\n Wrapper to strip each member of the output of `method`.\n\n Parameters\n ----------\n method : function\n Function that takes a single argument and returns a sequence of\n strings.\n\n Returns\n -------\n wrapped : function\n The result of wrapping `method`. `wrapped` takes a single input\n argument and returns a list of strings that are stripped of\n white-space.\n\n \"\"\"\n return lambda input: [_.strip() for _ in method(input)]","label":0},{"cid":"numpy/lib/_iotools.py::LineSplitter.__init__","text":"numpy/lib/_iotools.py::LineSplitter.__init__\n def __init__(self, delimiter=None, comments=asbytes('#'), autostrip=True):\n self.comments = comments\n # Delimiter is a character\n if isinstance(delimiter, unicode):\n delimiter = delimiter.encode('ascii')\n if (delimiter is None) or _is_bytes_like(delimiter):\n delimiter = delimiter or None\n _handyman = self._delimited_splitter\n # Delimiter is a list of field widths\n elif hasattr(delimiter, '__iter__'):\n _handyman = self._variablewidth_splitter\n idx = np.cumsum([0] + list(delimiter))\n delimiter = [slice(i, j) for (i, j) in zip(idx[:-1], idx[1:])]\n # Delimiter is a single integer\n elif int(delimiter):\n (_handyman, delimiter) = (self._fixedwidth_splitter, int(delimiter))\n else:\n (_handyman, delimiter) = (self._delimited_splitter, None)\n self.delimiter = delimiter\n if autostrip:\n self._handyman = self.autostrip(_handyman)\n else:\n self._handyman = _handyman","label":0},{"cid":"numpy/lib/_iotools.py::LineSplitter._delimited_splitter","text":"numpy/lib/_iotools.py::LineSplitter._delimited_splitter\n def _delimited_splitter(self, line):\n if self.comments is not None:\n line = line.split(self.comments)[0]\n line = line.strip(asbytes(\" \\r\\n\"))\n if not line:\n return []\n return line.split(self.delimiter)","label":0},{"cid":"numpy/lib/_iotools.py::LineSplitter._fixedwidth_splitter","text":"numpy/lib/_iotools.py::LineSplitter._fixedwidth_splitter\n def _fixedwidth_splitter(self, line):\n if self.comments is not None:\n line = line.split(self.comments)[0]\n line = line.strip(asbytes(\"\\r\\n\"))\n if not line:\n return []\n fixed = self.delimiter\n slices = [slice(i, i + fixed) for i in range(0, len(line), fixed)]\n return [line[s] for s in slices]","label":0},{"cid":"numpy/lib/_iotools.py::LineSplitter._variablewidth_splitter","text":"numpy/lib/_iotools.py::LineSplitter._variablewidth_splitter\n def _variablewidth_splitter(self, line):\n if self.comments is not None:\n line = line.split(self.comments)[0]\n if not line:\n return []\n slices = self.delimiter\n return [line[s] for s in slices]","label":0},{"cid":"numpy/lib/_iotools.py::LineSplitter.__call__","text":"numpy/lib/_iotools.py::LineSplitter.__call__\n def __call__(self, line):\n return self._handyman(line)","label":0},{"cid":"numpy/lib/_iotools.py::NameValidator.__init__","text":"numpy/lib/_iotools.py::NameValidator.__init__\n def __init__(self, excludelist=None, deletechars=None,\n case_sensitive=None, replace_space='_'):\n # Process the exclusion list ..\n if excludelist is None:\n excludelist = []\n excludelist.extend(self.defaultexcludelist)\n self.excludelist = excludelist\n # Process the list of characters to delete\n if deletechars is None:\n delete = self.defaultdeletechars\n else:\n delete = set(deletechars)\n delete.add('\"')\n self.deletechars = delete\n # Process the case option .....\n if (case_sensitive is None) or (case_sensitive is True):\n self.case_converter = lambda x: x\n elif (case_sensitive is False) or ('u' in case_sensitive):\n self.case_converter = lambda x: x.upper()\n elif 'l' in case_sensitive:\n self.case_converter = lambda x: x.lower()\n else:\n self.case_converter = lambda x: x\n #\n self.replace_space = replace_space","label":0},{"cid":"numpy/lib/_iotools.py::NameValidator.validate","text":"numpy/lib/_iotools.py::NameValidator.validate\n def validate(self, names, defaultfmt=\"f%i\", nbfields=None):\n \"\"\"\n Validate a list of strings to use as field names for a structured array.\n\n Parameters\n ----------\n names : sequence of str\n Strings to be validated.\n defaultfmt : str, optional\n Default format string, used if validating a given string reduces its\n length to zero.\n nboutput : integer, optional\n Final number of validated names, used to expand or shrink the initial\n list of names.\n\n Returns\n -------\n validatednames : list of str\n The list of validated field names.\n\n Notes\n -----\n A `NameValidator` instance can be called directly, which is the same as\n calling `validate`. For examples, see `NameValidator`.\n\n \"\"\"\n # Initial checks ..............\n if (names is None):\n if (nbfields is None):\n return None\n names = []\n if isinstance(names, basestring):\n names = [names, ]\n if nbfields is not None:\n nbnames = len(names)\n if (nbnames < nbfields):\n names = list(names) + [''] * (nbfields - nbnames)\n elif (nbnames > nbfields):\n names = names[:nbfields]\n # Set some shortcuts ...........\n deletechars = self.deletechars\n excludelist = self.excludelist\n case_converter = self.case_converter\n replace_space = self.replace_space\n # Initializes some variables ...\n validatednames = []\n seen = dict()\n nbempty = 0\n #\n for item in names:\n item = case_converter(item).strip()\n if replace_space:\n item = item.replace(' ', replace_space)\n item = ''.join([c for c in item if c not in deletechars])\n if item == '':\n item = defaultfmt % ","label":0},{"cid":"numpy/lib/_iotools.py::NameValidator.__call__","text":"numpy/lib/_iotools.py::NameValidator.__call__\n def __call__(self, names, defaultfmt=\"f%i\", nbfields=None):\n return self.validate(names, defaultfmt=defaultfmt, nbfields=nbfields)","label":0},{"cid":"numpy/lib/_iotools.py::str2bool","text":"numpy/lib/_iotools.py::str2bool\ndef str2bool(value):\n \"\"\"\n Tries to transform a string supposed to represent a boolean to a boolean.\n\n Parameters\n ----------\n value : str\n The string that is transformed to a boolean.\n\n Returns\n -------\n boolval : bool\n The boolean representation of `value`.\n\n Raises\n ------\n ValueError\n If the string is not 'True' or 'False' (case independent)\n\n Examples\n --------\n >>> np.lib._iotools.str2bool('TRUE')\n True\n >>> np.lib._iotools.str2bool('false')\n False\n\n \"\"\"\n value = value.upper()\n if value == asbytes('TRUE'):\n return True\n elif value == asbytes('FALSE'):\n return False\n else:\n raise ValueError(\"Invalid boolean\")","label":0},{"cid":"numpy/lib/_iotools.py::StringConverter._getdtype","text":"numpy/lib/_iotools.py::StringConverter._getdtype\n @classmethod\n def _getdtype(cls, val):\n \"\"\"Returns the dtype of the input variable.\"\"\"\n return np.array(val).dtype","label":0},{"cid":"numpy/lib/_iotools.py::StringConverter._getsubdtype","text":"numpy/lib/_iotools.py::StringConverter._getsubdtype\n @classmethod\n def _getsubdtype(cls, val):\n \"\"\"Returns the type of the dtype of the input variable.\"\"\"\n return np.array(val).dtype.type","label":0},{"cid":"numpy/lib/_iotools.py::StringConverter._dtypeortype","text":"numpy/lib/_iotools.py::StringConverter._dtypeortype\n @classmethod\n def _dtypeortype(cls, dtype):\n \"\"\"Returns dtype for datetime64 and type of dtype otherwise.\"\"\"\n if dtype.type == np.datetime64:\n return dtype\n return dtype.type","label":0},{"cid":"numpy/lib/_iotools.py::StringConverter.upgrade_mapper","text":"numpy/lib/_iotools.py::StringConverter.upgrade_mapper\n @classmethod\n def upgrade_mapper(cls, func, default=None):\n \"\"\"\n Upgrade the mapper of a StringConverter by adding a new function and its\n corresponding default.\n\n The input function (or sequence of functions) and its associated default\n value (if any) is inserted in penultimate position of the mapper.\n The corresponding type is estimated from the dtype of the default value.\n\n Parameters\n ----------\n func : var\n Function, or sequence of functions\n\n Examples\n --------\n >>> import dateutil.parser\n >>> import datetime\n >>> dateparser = datetustil.parser.parse\n >>> defaultdate = datetime.date(2000, 1, 1)\n >>> StringConverter.upgrade_mapper(dateparser, default=defaultdate)\n \"\"\"\n # Func is a single functions\n if hasattr(func, '__call__'):\n cls._mapper.insert(-1, (cls._getsubdtype(default), func, default))\n return\n elif hasattr(func, '__iter__'):\n if isinstance(func[0], (tuple, list)):\n for _ in func:\n cls._mapper.insert(-1, _)\n return\n if default is None:\n default = [None] * len(func)\n else:\n default = list(default)\n default.append([None] * (len(func) - len(default)))\n for (fct, dft) in zip(func, default):\n cls._mapper.insert(-1, (cls._getsubdtype(dft), fct, dft))","label":0},{"cid":"numpy/lib/_iotools.py::StringConverter.__init__","text":"numpy/lib/_iotools.py::StringConverter.__init__\n def __init__(self, dtype_or_func=None, default=None, missing_values=None,\n locked=False):\n # Convert unicode (for Py3)\n if isinstance(missing_values, unicode):\n missing_values = asbytes(missing_values)\n elif isinstance(missing_values, (list, tuple)):\n missing_values = asbytes_nested(missing_values)\n # Defines a lock for upgrade\n self._locked = bool(locked)\n # No input dtype: minimal initialization\n if dtype_or_func is None:\n self.func = str2bool\n self._status = 0\n self.default = default or False\n dtype = np.dtype('bool')\n else:\n # Is the input a np.dtype ?\n try:\n self.func = None\n dtype = np.dtype(dtype_or_func)\n except TypeError:\n # dtype_or_func must be a function, then\n if not hasattr(dtype_or_func, '__call__'):\n errmsg = \"The input argument `dtype` is neither a function\"\\\n \" or a dtype (got '%s' instead)\"\n raise TypeError(errmsg % type(dtype_or_func))\n # Set the function\n self.func = dtype_or_func\n # If we don't have a default, try to guess it or set it to None\n if default is None:\n try:\n default = self.func(asbytes('0'))\n except ValueError:\n default = None\n dtype = self._getdtype(default)\n # Set the status according to the dtype\n _status = -1\n for (i, (deftype, func, default_def)) in enumerate(self._mapper):\n if np.issubdtype(dtype.type, deftype):\n _status = i\n if default is None:\n self.default = default_def\n else:\n ","label":0},{"cid":"numpy/lib/_iotools.py::StringConverter._loose_call","text":"numpy/lib/_iotools.py::StringConverter._loose_call\n def _loose_call(self, value):\n try:\n return self.func(value)\n except ValueError:\n return self.default","label":0},{"cid":"numpy/lib/_iotools.py::StringConverter._strict_call","text":"numpy/lib/_iotools.py::StringConverter._strict_call\n def _strict_call(self, value):\n try:\n return self.func(value)\n except ValueError:\n if value.strip() in self.missing_values:\n if not self._status:\n self._checked = False\n return self.default\n raise ValueError(\"Cannot convert string '%s'\" % value)","label":0},{"cid":"numpy/lib/_iotools.py::StringConverter.__call__","text":"numpy/lib/_iotools.py::StringConverter.__call__\n def __call__(self, value):\n return self._callingfunction(value)","label":0},{"cid":"numpy/lib/_iotools.py::StringConverter.upgrade","text":"numpy/lib/_iotools.py::StringConverter.upgrade\n def upgrade(self, value):\n \"\"\"\n Try to find the best converter for a given string, and return the result.\n\n The supplied string `value` is converted by testing different\n converters in order. First the `func` method of the `StringConverter`\n instance is tried, if this fails other available converters are tried.\n The order in which these other converters are tried is determined by the\n `_status` attribute of the instance.\n\n Parameters\n ----------\n value : str\n The string to convert.\n\n Returns\n -------\n out : any\n The result of converting `value` with the appropriate converter.\n\n \"\"\"\n self._checked = True\n try:\n self._strict_call(value)\n except ValueError:\n # Raise an exception if we locked the converter...\n if self._locked:\n errmsg = \"Converter is locked and cannot be upgraded\"\n raise ConverterLockError(errmsg)\n _statusmax = len(self._mapper)\n # Complains if we try to upgrade by the maximum\n _status = self._status\n if _status == _statusmax:\n errmsg = \"Could not find a valid conversion function\"\n raise ConverterError(errmsg)\n elif _status < _statusmax - 1:\n _status += 1\n (self.type, self.func, default) = self._mapper[_status]\n self._status = _status\n if self._initial_default is not None:\n self.default = self._initial_default\n else:\n self.default = default\n self.upgrade(value)","label":0},{"cid":"numpy/lib/_iotools.py::StringConverter.iterupgrade","text":"numpy/lib/_iotools.py::StringConverter.iterupgrade\n def iterupgrade(self, value):\n self._checked = True\n if not hasattr(value, '__iter__'):\n value = (value,)\n _strict_call = self._strict_call\n try:\n for _m in value:\n _strict_call(_m)\n except ValueError:\n # Raise an exception if we locked the converter...\n if self._locked:\n errmsg = \"Converter is locked and cannot be upgraded\"\n raise ConverterLockError(errmsg)\n _statusmax = len(self._mapper)\n # Complains if we try to upgrade by the maximum\n _status = self._status\n if _status == _statusmax:\n raise ConverterError(\"Could not find a valid conversion function\")\n elif _status < _statusmax - 1:\n _status += 1\n (self.type, self.func, default) = self._mapper[_status]\n if self._initial_default is not None:\n self.default = self._initial_default\n else:\n self.default = default\n self._status = _status\n self.iterupgrade(value)","label":0},{"cid":"numpy/lib/_iotools.py::StringConverter.update","text":"numpy/lib/_iotools.py::StringConverter.update\n def update(self, func, default=None, testing_value=None,\n missing_values=asbytes(''), locked=False):\n \"\"\"\n Set StringConverter attributes directly.\n\n Parameters\n ----------\n func : function\n Conversion function.\n default : any, optional\n Value to return by default, that is, when the string to be converted\n is flagged as missing. If not given, `StringConverter` tries to supply\n a reasonable default value.\n testing_value : str, optional\n A string representing a standard input value of the converter.\n This string is used to help defining a reasonable default value.\n missing_values : sequence of str, optional\n Sequence of strings indicating a missing value.\n locked : bool, optional\n Whether the StringConverter should be locked to prevent automatic\n upgrade or not. Default is False.\n\n Notes\n -----\n `update` takes the same parameters as the constructor of `StringConverter`,\n except that `func` does not accept a `dtype` whereas `dtype_or_func` in\n the constructor does.\n\n \"\"\"\n self.func = func\n self._locked = locked\n # Don't reset the default to None if we can avoid it\n if default is not None:\n self.default = default\n self.type = self._dtypeortype(self._getdtype(default))\n else:\n try:\n tester = func(testing_value or asbytes('1'))\n except (TypeError, ValueError):\n tester = None\n self.type = self._dtypeortype(self._getdtype(tester))\n # Add the missing values to the existing set\n if missing_values is not None:\n if _is_bytes_like(missing_values):\n self.missing_values.add(missing_values)\n elif hasattr(missing_values, '__iter__'):\n fo","label":0},{"cid":"numpy/lib/_iotools.py::easy_dtype","text":"numpy/lib/_iotools.py::easy_dtype\ndef easy_dtype(ndtype, names=None, defaultfmt=\"f%i\", **validationargs):\n \"\"\"\n Convenience function to create a `np.dtype` object.\n\n The function processes the input `dtype` and matches it with the given\n names.\n\n Parameters\n ----------\n ndtype : var\n Definition of the dtype. Can be any string or dictionary\n recognized by the `np.dtype` function, or a sequence of types.\n names : str or sequence, optional\n Sequence of strings to use as field names for a structured dtype.\n For convenience, `names` can be a string of a comma-separated list of\n names.\n defaultfmt : str, optional\n Format string used to define missing names, such as ``\"f%i\"``\n (default) or ``\"fields_%02i\"``.\n validationargs : optional\n A series of optional arguments used to initialize a `NameValidator`.\n\n Examples\n --------\n >>> np.lib._iotools.easy_dtype(float)\n dtype('float64')\n >>> np.lib._iotools.easy_dtype(\"i4, f8\")\n dtype([('f0', '<i4'), ('f1', '<f8')])\n >>> np.lib._iotools.easy_dtype(\"i4, f8\", defaultfmt=\"field_%03i\")\n dtype([('field_000', '<i4'), ('field_001', '<f8')])\n\n >>> np.lib._iotools.easy_dtype((int, float, float), names=\"a,b,c\")\n dtype([('a', '<i8'), ('b', '<f8'), ('c', '<f8')])\n >>> np.lib._iotools.easy_dtype(float, names=\"a,b,c\")\n dtype([('a', '<f8'), ('b', '<f8'), ('c', '<f8')])\n\n \"\"\"\n try:\n ndtype = np.dtype(ndtype)\n except TypeError:\n validate = NameValidator(**validationargs)\n nbfields = len(ndtype)\n if names is None:\n names = [''] * len(ndtype)\n elif isinstance(names, basestring):\n names = names.split(\",\")\n names = validate(names, nbfields=nbfields, defaultfmt=defaultfmt)\n ndtype = np.dtype(dict(formats=ndtype, names=names))\n else:\n nbtypes = len(ndtype)\n # Explicit names\n if names is not None:\n validate = NameValidat","label":1},{"cid":"numpy/lib/arrayterator.py::Arrayterator.__init__","text":"numpy/lib/arrayterator.py::Arrayterator.__init__\n def __init__(self, var, buf_size=None):\n self.var = var\n self.buf_size = buf_size\n\n self.start = [0 for dim in var.shape]\n self.stop = [dim for dim in var.shape]\n self.step = [1 for dim in var.shape]","label":0},{"cid":"numpy/lib/arrayterator.py::Arrayterator.__getattr__","text":"numpy/lib/arrayterator.py::Arrayterator.__getattr__\n def __getattr__(self, attr):\n return getattr(self.var, attr)","label":0},{"cid":"numpy/lib/arrayterator.py::Arrayterator.__getitem__","text":"numpy/lib/arrayterator.py::Arrayterator.__getitem__\n def __getitem__(self, index):\n \"\"\"\n Return a new arrayterator.\n\n \"\"\"\n # Fix index, handling ellipsis and incomplete slices.\n if not isinstance(index, tuple): index = (index,)\n fixed = []\n length, dims = len(index), len(self.shape)\n for slice_ in index:\n if slice_ is Ellipsis:\n fixed.extend([slice(None)] * (dims-length+1))\n length = len(fixed)\n elif isinstance(slice_, (int, long)):\n fixed.append(slice(slice_, slice_+1, 1))\n else:\n fixed.append(slice_)\n index = tuple(fixed)\n if len(index) < dims:\n index += (slice(None),) * (dims-len(index))\n\n # Return a new arrayterator object.\n out = self.__class__(self.var, self.buf_size)\n for i, (start, stop, step, slice_) in enumerate(\n zip(self.start, self.stop, self.step, index)):\n out.start[i] = start + (slice_.start or 0)\n out.step[i] = step * (slice_.step or 1)\n out.stop[i] = start + (slice_.stop or stop-start)\n out.stop[i] = min(stop, out.stop[i])\n return out","label":0},{"cid":"numpy/lib/arrayterator.py::Arrayterator.__array__","text":"numpy/lib/arrayterator.py::Arrayterator.__array__\n def __array__(self):\n \"\"\"\n Return corresponding data.\n\n \"\"\"\n slice_ = tuple(slice(*t) for t in zip(\n self.start, self.stop, self.step))\n return self.var[slice_]","label":0},{"cid":"numpy/lib/arrayterator.py::Arrayterator.flat","text":"numpy/lib/arrayterator.py::Arrayterator.flat\n @property\n def flat(self):\n \"\"\"\n A 1-D flat iterator for Arrayterator objects.\n\n This iterator returns elements of the array to be iterated over in\n `Arrayterator` one by one. It is similar to `flatiter`.\n\n See Also\n --------\n `Arrayterator`\n flatiter\n\n Examples\n --------\n >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)\n >>> a_itor = np.lib.arrayterator.Arrayterator(a, 2)\n\n >>> for subarr in a_itor.flat:\n ... if not subarr:\n ... print subarr, type(subarr)\n ...\n 0 <type 'numpy.int32'>\n\n \"\"\"\n for block in self:\n for value in block.flat:\n yield value","label":0},{"cid":"numpy/lib/arrayterator.py::Arrayterator.shape","text":"numpy/lib/arrayterator.py::Arrayterator.shape\n @property\n def shape(self):\n \"\"\"\n The shape of the array to be iterated over.\n\n For an example, see `Arrayterator`.\n\n \"\"\"\n return tuple(((stop-start-1)//step+1) for start, stop, step in\n zip(self.start, self.stop, self.step))","label":0},{"cid":"numpy/lib/arrayterator.py::Arrayterator.__iter__","text":"numpy/lib/arrayterator.py::Arrayterator.__iter__\n def __iter__(self):\n # Skip arrays with degenerate dimensions\n if [dim for dim in self.shape if dim <= 0]: raise StopIteration\n\n start = self.start[:]\n stop = self.stop[:]\n step = self.step[:]\n ndims = len(self.var.shape)\n\n while 1:\n count = self.buf_size or reduce(mul, self.shape)\n\n # iterate over each dimension, looking for the\n # running dimension (ie, the dimension along which\n # the blocks will be built from)\n rundim = 0\n for i in range(ndims-1, -1, -1):\n # if count is zero we ran out of elements to read\n # along higher dimensions, so we read only a single position\n if count == 0:\n stop[i] = start[i]+1\n elif count <= self.shape[i]: # limit along this dimension\n stop[i] = start[i] + count*step[i]\n rundim = i\n else:\n stop[i] = self.stop[i] # read everything along this\n # dimension\n stop[i] = min(self.stop[i], stop[i])\n count = count//self.shape[i]\n\n # yield a block\n slice_ = tuple(slice(*t) for t in zip(start, stop, step))\n yield self.var[slice_]\n\n # Update start position, taking care of overflow to\n # other dimensions\n start[rundim] = stop[rundim] # start where we stopped\n for i in range(ndims-1, 0, -1):\n if start[i] >= self.stop[i]:\n start[i] = self.start[i]\n start[i-1] += self.step[i-1]\n if start[0] >= self.stop[0]:\n raise StopIteration","label":0},{"cid":"numpy/lib/npyio.py::seek_gzip_factory","text":"numpy/lib/npyio.py::seek_gzip_factory\ndef seek_gzip_factory(f):\n \"\"\"Use this factory to produce the class so that we can do a lazy\n import on gzip.\n\n \"\"\"\n import gzip\n\n class GzipFile(gzip.GzipFile):\n\n def seek(self, offset, whence=0):\n # figure out new position (we can only seek forwards)\n if whence == 1:\n offset = self.offset + offset\n\n if whence not in [0, 1]:\n raise IOError(\"Illegal argument\")\n\n if offset < self.offset:\n # for negative seek, rewind and do positive seek\n self.rewind()\n count = offset - self.offset\n for i in range(count // 1024):\n self.read(1024)\n self.read(count % 1024)\n\n def tell(self):\n return self.offset\n\n\n if isinstance(f, str):\n f = GzipFile(f)\n elif isinstance(f, gzip.GzipFile):\n # cast to our GzipFile if its already a gzip.GzipFile\n\n try:\n name = f.name\n except AttributeError:\n # Backward compatibility for <= 2.5\n name = f.filename\n mode = f.mode\n\n f = GzipFile(fileobj=f.fileobj, filename=name)\n f.mode = mode\n\n return f","label":0},{"cid":"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.seek","text":"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.seek\n def seek(self, offset, whence=0):\n # figure out new position (we can only seek forwards)\n if whence == 1:\n offset = self.offset + offset\n\n if whence not in [0, 1]:\n raise IOError(\"Illegal argument\")\n\n if offset < self.offset:\n # for negative seek, rewind and do positive seek\n self.rewind()\n count = offset - self.offset\n for i in range(count // 1024):\n self.read(1024)\n self.read(count % 1024)","label":0},{"cid":"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.tell","text":"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.tell\n def tell(self):\n return self.offset","label":0},{"cid":"numpy/lib/npyio.py::BagObj.__init__","text":"numpy/lib/npyio.py::BagObj.__init__\n def __init__(self, obj):\n # Use weakref to make NpzFile objects collectable by refcount\n self._obj = weakref.proxy(obj)","label":0},{"cid":"numpy/lib/npyio.py::BagObj.__getattribute__","text":"numpy/lib/npyio.py::BagObj.__getattribute__\n def __getattribute__(self, key):\n try:\n return object.__getattribute__(self, '_obj')[key]\n except KeyError:\n raise AttributeError(key)","label":0},{"cid":"numpy/lib/npyio.py::zipfile_factory","text":"numpy/lib/npyio.py::zipfile_factory\ndef zipfile_factory(*args, **kwargs):\n import zipfile\n if sys.version_info >= (2, 5):\n kwargs['allowZip64'] = True\n return zipfile.ZipFile(*args, **kwargs)","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__init__","text":"numpy/lib/npyio.py::NpzFile.__init__\n def __init__(self, fid, own_fid=False):\n # Import is postponed to here since zipfile depends on gzip, an optional\n # component of the so-called standard library.\n _zip = zipfile_factory(fid)\n self._files = _zip.namelist()\n self.files = []\n for x in self._files:\n if x.endswith('.npy'):\n self.files.append(x[:-4])\n else:\n self.files.append(x)\n self.zip = _zip\n self.f = BagObj(self)\n if own_fid:\n self.fid = fid\n else:\n self.fid = None","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__enter__","text":"numpy/lib/npyio.py::NpzFile.__enter__\n def __enter__(self):\n return self","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__exit__","text":"numpy/lib/npyio.py::NpzFile.__exit__\n def __exit__(self, exc_type, exc_value, traceback):\n self.close()","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.close","text":"numpy/lib/npyio.py::NpzFile.close\n def close(self):\n \"\"\"\n Close the file.\n\n \"\"\"\n if self.zip is not None:\n self.zip.close()\n self.zip = None\n if self.fid is not None:\n self.fid.close()\n self.fid = None\n self.f = None # break reference cycle","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__del__","text":"numpy/lib/npyio.py::NpzFile.__del__\n def __del__(self):\n self.close()","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__getitem__","text":"numpy/lib/npyio.py::NpzFile.__getitem__\n def __getitem__(self, key):\n # FIXME: This seems like it will copy strings around\n # more than is strictly necessary. The zipfile\n # will read the string and then\n # the format.read_array will copy the string\n # to another place in memory.\n # It would be better if the zipfile could read\n # (or at least uncompress) the data\n # directly into the array memory.\n member = 0\n if key in self._files:\n member = 1\n elif key in self.files:\n member = 1\n key += '.npy'\n if member:\n bytes = self.zip.read(key)\n if bytes.startswith(format.MAGIC_PREFIX):\n value = BytesIO(bytes)\n return format.read_array(value)\n else:\n return bytes\n else:\n raise KeyError(\"%s is not a file in the archive\" % key)","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__iter__","text":"numpy/lib/npyio.py::NpzFile.__iter__\n def __iter__(self):\n return iter(self.files)","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.items","text":"numpy/lib/npyio.py::NpzFile.items\n def items(self):\n \"\"\"\n Return a list of tuples, with each tuple (filename, array in file).\n\n \"\"\"\n return [(f, self[f]) for f in self.files]","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.iteritems","text":"numpy/lib/npyio.py::NpzFile.iteritems\n def iteritems(self):\n \"\"\"Generator that returns tuples (filename, array in file).\"\"\"\n for f in self.files:\n yield (f, self[f])","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.keys","text":"numpy/lib/npyio.py::NpzFile.keys\n def keys(self):\n \"\"\"Return files in the archive with a \".npy\" extension.\"\"\"\n return self.files","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.iterkeys","text":"numpy/lib/npyio.py::NpzFile.iterkeys\n def iterkeys(self):\n \"\"\"Return an iterator over the files in the archive.\"\"\"\n return self.__iter__()","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__contains__","text":"numpy/lib/npyio.py::NpzFile.__contains__\n def __contains__(self, key):\n return self.files.__contains__(key)","label":0},{"cid":"numpy/lib/npyio.py::load","text":"numpy/lib/npyio.py::load\ndef load(file, mmap_mode=None):\n \"\"\"\n Load an array(s) or pickled objects from .npy, .npz, or pickled files.\n\n Parameters\n ----------\n file : file-like object or string\n The file to read. It must support ``seek()`` and ``read()`` methods.\n If the filename extension is ``.gz``, the file is first decompressed.\n mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional\n If not None, then memory-map the file, using the given mode\n (see `numpy.memmap` for a detailed description of the modes).\n A memory-mapped array is kept on disk. However, it can be accessed\n and sliced like any ndarray. Memory mapping is especially useful for\n accessing small fragments of large files without reading the entire\n file into memory.\n\n Returns\n -------\n result : array, tuple, dict, etc.\n Data stored in the file. For '.npz' files, the returned instance of\n NpzFile class must be closed to avoid leaking file descriptors.\n\n Raises\n ------\n IOError\n If the input file does not exist or cannot be read.\n\n See Also\n --------\n save, savez, loadtxt\n memmap : Create a memory-map to an array stored in a file on disk.\n\n Notes\n -----\n - If the file contains pickle data, then whatever object is stored\n in the pickle is returned.\n - If the file is a ``.npy`` file, then a single array is returned.\n - If the file is a ``.npz`` file, then a dictionary-like object is\n returned, containing ``{filename: array}`` key-value pairs, one for\n each file in the archive.\n - If the file is a ``.npz`` file, the returned value supports the context\n manager protocol in a similar fashion to the open function::\n\n with load('foo.npz') as data:\n a = data['a']\n\n The underlyling file descriptor is closed when exiting the 'with' block.\n\n Examples\n --------\n Store data to disk, and load it again:\n\n >>> np.save('/tmp/12","label":0},{"cid":"numpy/lib/npyio.py::save","text":"numpy/lib/npyio.py::save\ndef save(file, arr):\n \"\"\"\n Save an array to a binary file in NumPy ``.npy`` format.\n\n Parameters\n ----------\n file : file or str\n File or filename to which the data is saved. If file is a file-object,\n then the filename is unchanged. If file is a string, a ``.npy``\n extension will be appended to the file name if it does not already\n have one.\n arr : array_like\n Array data to be saved.\n\n See Also\n --------\n savez : Save several arrays into a ``.npz`` archive\n savetxt, load\n\n Notes\n -----\n For a description of the ``.npy`` format, see `format`.\n\n Examples\n --------\n >>> from tempfile import TemporaryFile\n >>> outfile = TemporaryFile()\n\n >>> x = np.arange(10)\n >>> np.save(outfile, x)\n\n >>> outfile.seek(0) # Only needed here to simulate closing & reopening file\n >>> np.load(outfile)\n array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n\n \"\"\"\n own_fid = False\n if isinstance(file, basestring):\n if not file.endswith('.npy'):\n file = file + '.npy'\n fid = open(file, \"wb\")\n own_fid = True\n else:\n fid = file\n\n try:\n arr = np.asanyarray(arr)\n format.write_array(fid, arr)\n finally:\n if own_fid:\n fid.close()","label":0},{"cid":"numpy/lib/npyio.py::savez","text":"numpy/lib/npyio.py::savez\ndef savez(file, *args, **kwds):\n \"\"\"\n Save several arrays into a single file in uncompressed ``.npz`` format.\n\n If arguments are passed in with no keywords, the corresponding variable\n names, in the .npz file, are 'arr_0', 'arr_1', etc. If keyword arguments\n are given, the corresponding variable names, in the ``.npz`` file will\n match the keyword names.\n\n Parameters\n ----------\n file : str or file\n Either the file name (string) or an open file (file-like object)\n where the data will be saved. If file is a string, the ``.npz``\n extension will be appended to the file name if it is not already there.\n args : Arguments, optional\n Arrays to save to the file. Since it is not possible for Python to\n know the names of the arrays outside `savez`, the arrays will be saved\n with names \"arr_0\", \"arr_1\", and so on. These arguments can be any\n expression.\n kwds : Keyword arguments, optional\n Arrays to save to the file. Arrays will be saved in the file with the\n keyword names.\n\n Returns\n -------\n None\n\n See Also\n --------\n save : Save a single array to a binary file in NumPy format.\n savetxt : Save an array to a file as plain text.\n savez_compressed : Save several arrays into a compressed .npz file format\n\n Notes\n -----\n The ``.npz`` file format is a zipped archive of files named after the\n variables they contain. The archive is not compressed and each file\n in the archive contains one variable in ``.npy`` format. For a\n description of the ``.npy`` format, see `format`.\n\n When opening the saved ``.npz`` file with `load` a `NpzFile` object is\n returned. This is a dictionary-like object which can be queried for\n its list of arrays (with the ``.files`` attribute), and for the arrays\n themselves.\n\n Examples\n --------\n >>> from tempfile import TemporaryFile\n >>> outfile = TemporaryFile()\n >>> x = ","label":0},{"cid":"numpy/lib/npyio.py::savez_compressed","text":"numpy/lib/npyio.py::savez_compressed\ndef savez_compressed(file, *args, **kwds):\n \"\"\"\n Save several arrays into a single file in compressed ``.npz`` format.\n\n If keyword arguments are given, then filenames are taken from the keywords.\n If arguments are passed in with no keywords, then stored file names are\n arr_0, arr_1, etc.\n\n Parameters\n ----------\n file : str\n File name of .npz file.\n args : Arguments\n Function arguments.\n kwds : Keyword arguments\n Keywords.\n\n See Also\n --------\n numpy.savez : Save several arrays into an uncompressed .npz file format\n\n \"\"\"\n _savez(file, args, kwds, True)","label":0},{"cid":"numpy/lib/npyio.py::_savez","text":"numpy/lib/npyio.py::_savez\ndef _savez(file, args, kwds, compress):\n # Import is postponed to here since zipfile depends on gzip, an optional\n # component of the so-called standard library.\n import zipfile\n # Import deferred for startup time improvement\n import tempfile\n\n if isinstance(file, basestring):\n if not file.endswith('.npz'):\n file = file + '.npz'\n\n namedict = kwds\n for i, val in enumerate(args):\n key = 'arr_%d' % i\n if key in namedict.keys():\n raise ValueError(\"Cannot use un-named variables and keyword %s\" % key)\n namedict[key] = val\n\n if compress:\n compression = zipfile.ZIP_DEFLATED\n else:\n compression = zipfile.ZIP_STORED\n\n zip = zipfile_factory(file, mode=\"w\", compression=compression)\n\n # Stage arrays in a temporary file on disk, before writing to zip.\n fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')\n os.close(fd)\n try:\n for key, val in namedict.items():\n fname = key + '.npy'\n fid = open(tmpfile, 'wb')\n try:\n format.write_array(fid, np.asanyarray(val))\n fid.close()\n fid = None\n zip.write(tmpfile, arcname=fname)\n finally:\n if fid:\n fid.close()\n finally:\n os.remove(tmpfile)\n\n zip.close()","label":0},{"cid":"numpy/lib/npyio.py::_getconv","text":"numpy/lib/npyio.py::_getconv\ndef _getconv(dtype):\n typ = dtype.type\n if issubclass(typ, np.bool_):\n return lambda x: bool(int(x))\n if issubclass(typ, np.uint64):\n return np.uint64\n if issubclass(typ, np.int64):\n return np.int64\n if issubclass(typ, np.integer):\n return lambda x: int(float(x))\n elif issubclass(typ, np.floating):\n return float\n elif issubclass(typ, np.complex):\n return complex\n elif issubclass(typ, np.bytes_):\n return bytes\n else:\n return str","label":0},{"cid":"numpy/lib/npyio.py::loadtxt","text":"numpy/lib/npyio.py::loadtxt\ndef loadtxt(fname, dtype=float, comments='#', delimiter=None,\n converters=None, skiprows=0, usecols=None, unpack=False,\n ndmin=0):\n \"\"\"\n Load data from a text file.\n\n Each row in the text file must have the same number of values.\n\n Parameters\n ----------\n fname : file or str\n File, filename, or generator to read. If the filename extension is\n ``.gz`` or ``.bz2``, the file is first decompressed. Note that\n generators should return byte strings for Python 3k.\n dtype : data-type, optional\n Data-type of the resulting array; default: float. If this is a\n record data-type, the resulting array will be 1-dimensional, and\n each row will be interpreted as an element of the array. In this\n case, the number of columns used must match the number of fields in\n the data-type.\n comments : str, optional\n The character used to indicate the start of a comment;\n default: '#'.\n delimiter : str, optional\n The string used to separate values. By default, this is any\n whitespace.\n converters : dict, optional\n A dictionary mapping column number to a function that will convert\n that column to a float. E.g., if column 0 is a date string:\n ``converters = {0: datestr2num}``. Converters can also be used to\n provide a default value for missing data (but see also `genfromtxt`):\n ``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None.\n skiprows : int, optional\n Skip the first `skiprows` lines; default: 0.\n usecols : sequence, optional\n Which columns to read, with 0 being the first. For example,\n ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.\n The default, None, results in all columns being read.\n unpack : bool, optional\n If True, the returned array is transposed, so that arguments may be\n unpacked using ``x, y, z =","label":0},{"cid":"numpy/lib/npyio.py::loadtxt.flatten_dtype","text":"numpy/lib/npyio.py::loadtxt.flatten_dtype\n def flatten_dtype(dt):\n \"\"\"Unpack a structured data-type, and produce re-packing info.\"\"\"\n if dt.names is None:\n # If the dtype is flattened, return.\n # If the dtype has a shape, the dtype occurs\n # in the list more than once.\n shape = dt.shape\n if len(shape) == 0:\n return ([dt.base], None)\n else:\n packing = [(shape[-1], list)]\n if len(shape) > 1:\n for dim in dt.shape[-2::-1]:\n packing = [(dim*packing[0][0], packing*dim)]\n return ([dt.base] * int(np.prod(dt.shape)), packing)\n else:\n types = []\n packing = []\n for field in dt.names:\n tp, bytes = dt.fields[field]\n flat_dt, flat_packing = flatten_dtype(tp)\n types.extend(flat_dt)\n # Avoid extra nesting for subarrays\n if len(tp.shape) > 0:\n packing.extend(flat_packing)\n else:\n packing.append((len(flat_dt), flat_packing))\n return (types, packing)","label":0},{"cid":"numpy/lib/npyio.py::loadtxt.pack_items","text":"numpy/lib/npyio.py::loadtxt.pack_items\n def pack_items(items, packing):\n \"\"\"Pack items into nested lists based on re-packing info.\"\"\"\n if packing == None:\n return items[0]\n elif packing is tuple:\n return tuple(items)\n elif packing is list:\n return list(items)\n else:\n start = 0\n ret = []\n for length, subpacking in packing:\n ret.append(pack_items(items[start:start+length], subpacking))\n start += length\n return tuple(ret)","label":0},{"cid":"numpy/lib/npyio.py::loadtxt.split_line","text":"numpy/lib/npyio.py::loadtxt.split_line\n def split_line(line):\n \"\"\"Chop off comments, strip, and split at delimiter.\"\"\"\n line = asbytes(line).split(comments)[0].strip(asbytes('\\r\\n'))\n if line:\n return line.split(delimiter)\n else:\n return []","label":0},{"cid":"numpy/lib/npyio.py::savetxt","text":"numpy/lib/npyio.py::savetxt\ndef savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\\n', header='',\n footer='', comments='# '):\n \"\"\"\n Save an array to a text file.\n\n Parameters\n ----------\n fname : filename or file handle\n If the filename ends in ``.gz``, the file is automatically saved in\n compressed gzip format. `loadtxt` understands gzipped files\n transparently.\n X : array_like\n Data to be saved to a text file.\n fmt : str or sequence of strs, optional\n A single format (%10.5f), a sequence of formats, or a\n multi-format string, e.g. 'Iteration %d -- %10.5f', in which\n case `delimiter` is ignored. For complex `X`, the legal options\n for `fmt` are:\n a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted\n like `' (%s+%sj)' % (fmt, fmt)`\n b) a full string specifying every real and imaginary part, e.g.\n `' %.4e %+.4j %.4e %+.4j %.4e %+.4j'` for 3 columns\n c) a list of specifiers, one per column - in this case, the real\n and imaginary part must have separate specifiers,\n e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns\n delimiter : str, optional\n Character separating columns.\n newline : str, optional\n .. versionadded:: 1.5.0\n header : str, optional\n String that will be written at the beginning of the file.\n .. versionadded:: 1.7.0\n footer : str, optional\n String that will be written at the end of the file.\n .. versionadded:: 1.7.0\n comments : str, optional\n String that will be prepended to the ``header`` and ``footer`` strings,\n to mark them as comments. Default: '# ', as expected by e.g.\n ``numpy.loadtxt``.\n .. versionadded:: 1.7.0\n\n Character separating lines.\n\n See Also\n --------\n save : Save an array to a binary file in NumPy ``.npy`` format\n savez : Save several ar","label":0},{"cid":"numpy/lib/npyio.py::fromregex","text":"numpy/lib/npyio.py::fromregex\ndef fromregex(file, regexp, dtype):\n \"\"\"\n Construct an array from a text file, using regular expression parsing.\n\n The returned array is always a structured array, and is constructed from\n all matches of the regular expression in the file. Groups in the regular\n expression are converted to fields of the structured array.\n\n Parameters\n ----------\n file : str or file\n File name or file object to read.\n regexp : str or regexp\n Regular expression used to parse the file.\n Groups in the regular expression correspond to fields in the dtype.\n dtype : dtype or list of dtypes\n Dtype for the structured array.\n\n Returns\n -------\n output : ndarray\n The output array, containing the part of the content of `file` that\n was matched by `regexp`. `output` is always a structured array.\n\n Raises\n ------\n TypeError\n When `dtype` is not a valid dtype for a structured array.\n\n See Also\n --------\n fromstring, loadtxt\n\n Notes\n -----\n Dtypes for structured arrays can be specified in several forms, but all\n forms specify at least the data type and field name. For details see\n `doc.structured_arrays`.\n\n Examples\n --------\n >>> f = open('test.dat', 'w')\n >>> f.write(\"1312 foo\\\\n1534 bar\\\\n444 qux\")\n >>> f.close()\n\n >>> regexp = r\"(\\\\d+)\\\\s+(...)\" # match [digits, whitespace, anything]\n >>> output = np.fromregex('test.dat', regexp,\n ... [('num', np.int64), ('key', 'S3')])\n >>> output\n array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],\n dtype=[('num', '<i8'), ('key', '|S3')])\n >>> output['num']\n array([1312, 1534, 444], dtype=int64)\n\n \"\"\"\n own_fh = False\n if not hasattr(file, \"read\"):\n file = open(file, 'rb')\n own_fh = True\n\n try:\n if not hasattr(regexp, 'match'):\n regexp = re.compile(asbytes(regexp))\n if not isinstance(dtyp","label":0},{"cid":"numpy/lib/npyio.py::genfromtxt","text":"numpy/lib/npyio.py::genfromtxt\ndef genfromtxt(fname, dtype=float, comments='#', delimiter=None,\n skiprows=0, skip_header=0, skip_footer=0, converters=None,\n missing='', missing_values=None, filling_values=None,\n usecols=None, names=None,\n excludelist=None, deletechars=None, replace_space='_',\n autostrip=False, case_sensitive=True, defaultfmt=\"f%i\",\n unpack=None, usemask=False, loose=True, invalid_raise=True):\n \"\"\"\n Load data from a text file, with missing values handled as specified.\n\n Each line past the first `skip_header` lines is split at the `delimiter`\n character, and characters following the `comments` character are discarded.\n\n Parameters\n ----------\n fname : file or str\n File, filename, or generator to read. If the filename extension is\n `.gz` or `.bz2`, the file is first decompressed. Note that\n generators must return byte strings in Python 3k.\n dtype : dtype, optional\n Data type of the resulting array.\n If None, the dtypes will be determined by the contents of each\n column, individually.\n comments : str, optional\n The character used to indicate the start of a comment.\n All the characters occurring on a line after a comment are discarded\n delimiter : str, int, or sequence, optional\n The string used to separate values. By default, any consecutive\n whitespaces act as delimiter. An integer or sequence of integers\n can also be provided as width(s) of each field.\n skip_header : int, optional\n The numbers of lines to skip at the beginning of the file.\n skip_footer : int, optional\n The numbers of lines to skip at the end of the file\n converters : variable, optional\n The set of functions that convert the data of a column to a value.\n The converters can also be used to provide a default value\n for missing data: ``converters = {3: la","label":1},{"cid":"numpy/lib/npyio.py::ndfromtxt","text":"numpy/lib/npyio.py::ndfromtxt\ndef ndfromtxt(fname, **kwargs):\n \"\"\"\n Load ASCII data stored in a file and return it as a single array.\n\n Parameters\n ----------\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\n\n See Also\n --------\n numpy.genfromtxt : generic function.\n\n \"\"\"\n kwargs['usemask'] = False\n return genfromtxt(fname, **kwargs)","label":0},{"cid":"numpy/lib/npyio.py::mafromtxt","text":"numpy/lib/npyio.py::mafromtxt\ndef mafromtxt(fname, **kwargs):\n \"\"\"\n Load ASCII data stored in a text file and return a masked array.\n\n Parameters\n ----------\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\n\n See Also\n --------\n numpy.genfromtxt : generic function to load ASCII data.\n\n \"\"\"\n kwargs['usemask'] = True\n return genfromtxt(fname, **kwargs)","label":0},{"cid":"numpy/lib/npyio.py::recfromtxt","text":"numpy/lib/npyio.py::recfromtxt\ndef recfromtxt(fname, **kwargs):\n \"\"\"\n Load ASCII data from a file and return it in a record array.\n\n If ``usemask=False`` a standard `recarray` is returned,\n if ``usemask=True`` a MaskedRecords array is returned.\n\n Parameters\n ----------\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\n\n See Also\n --------\n numpy.genfromtxt : generic function\n\n Notes\n -----\n By default, `dtype` is None, which means that the data-type of the output\n array will be determined from the data.\n\n \"\"\"\n kwargs.update(dtype=kwargs.get('dtype', None))\n usemask = kwargs.get('usemask', False)\n output = genfromtxt(fname, **kwargs)\n if usemask:\n from numpy.ma.mrecords import MaskedRecords\n output = output.view(MaskedRecords)\n else:\n output = output.view(np.recarray)\n return output","label":0},{"cid":"numpy/lib/npyio.py::recfromcsv","text":"numpy/lib/npyio.py::recfromcsv\ndef recfromcsv(fname, **kwargs):\n \"\"\"\n Load ASCII data stored in a comma-separated file.\n\n The returned array is a record array (if ``usemask=False``, see\n `recarray`) or a masked record array (if ``usemask=True``,\n see `ma.mrecords.MaskedRecords`).\n\n Parameters\n ----------\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\n\n See Also\n --------\n numpy.genfromtxt : generic function to load ASCII data.\n\n \"\"\"\n case_sensitive = kwargs.get('case_sensitive', \"lower\") or \"lower\"\n names = kwargs.get('names', True)\n if names is None:\n names = True\n kwargs.update(dtype=kwargs.get('update', None),\n delimiter=kwargs.get('delimiter', \",\") or \",\",\n names=names,\n case_sensitive=case_sensitive)\n usemask = kwargs.get(\"usemask\", False)\n output = genfromtxt(fname, **kwargs)\n if usemask:\n from numpy.ma.mrecords import MaskedRecords\n output = output.view(MaskedRecords)\n else:\n output = output.view(np.recarray)\n return output","label":0},{"cid":"numpy/ma/mrecords.py::_getformats","text":"numpy/ma/mrecords.py::_getformats\ndef _getformats(data):\n \"Returns the formats of each array of arraylist as a comma-separated string.\"\n if hasattr(data, 'dtype'):\n return \",\".join([desc[1] for desc in data.dtype.descr])\n\n formats = ''\n for obj in data:\n obj = np.asarray(obj)\n formats += _typestr[obj.dtype.type]\n if issubclass(obj.dtype.type, ntypes.flexible):\n formats += repr(obj.itemsize)\n formats += ','\n return formats[:-1]","label":0},{"cid":"numpy/ma/mrecords.py::_checknames","text":"numpy/ma/mrecords.py::_checknames\ndef _checknames(descr, names=None):\n \"\"\"Checks that the field names of the descriptor ``descr`` are not some\nreserved keywords. If this is the case, a default 'f%i' is substituted.\nIf the argument `names` is not None, updates the field names to valid names.\n \"\"\"\n ndescr = len(descr)\n default_names = ['f%i' % i for i in range(ndescr)]\n if names is None:\n new_names = default_names\n else:\n if isinstance(names, (tuple, list)):\n new_names = names\n elif isinstance(names, str):\n new_names = names.split(',')\n else:\n raise NameError(\"illegal input names %s\" % repr(names))\n nnames = len(new_names)\n if nnames < ndescr:\n new_names += default_names[nnames:]\n ndescr = []\n for (n, d, t) in zip(new_names, default_names, descr.descr):\n if n in reserved_fields:\n if t[0] in reserved_fields:\n ndescr.append((d, t[1]))\n else:\n ndescr.append(t)\n else:\n ndescr.append((n, t[1]))\n return np.dtype(ndescr)","label":0},{"cid":"numpy/ma/mrecords.py::_get_fieldmask","text":"numpy/ma/mrecords.py::_get_fieldmask\ndef _get_fieldmask(self):\n mdescr = [(n, '|b1') for n in self.dtype.names]\n fdmask = np.empty(self.shape, dtype=mdescr)\n fdmask.flat = tuple([False] * len(mdescr))\n return fdmask","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__new__","text":"numpy/ma/mrecords.py::MaskedRecords.__new__\n def __new__(cls, shape, dtype=None, buf=None, offset=0, strides=None,\n formats=None, names=None, titles=None,\n byteorder=None, aligned=False,\n mask=nomask, hard_mask=False, fill_value=None, keep_mask=True,\n copy=False,\n **options):\n #\n self = recarray.__new__(cls, shape, dtype=dtype, buf=buf, offset=offset,\n strides=strides, formats=formats, names=names,\n titles=titles, byteorder=byteorder,\n aligned=aligned,)\n #\n mdtype = ma.make_mask_descr(self.dtype)\n if mask is nomask or not np.size(mask):\n if not keep_mask:\n self._mask = tuple([False] * len(mdtype))\n else:\n mask = np.array(mask, copy=copy)\n if mask.shape != self.shape:\n (nd, nm) = (self.size, mask.size)\n if nm == 1:\n mask = np.resize(mask, self.shape)\n elif nm == nd:\n mask = np.reshape(mask, self.shape)\n else:\n msg = \"Mask and data not compatible: data size is %i, \" + \\\n \"mask size is %i.\"\n raise MAError(msg % (nd, nm))\n copy = True\n if not keep_mask:\n self.__setmask__(mask)\n self._sharedmask = True\n else:\n if mask.dtype == mdtype:\n _mask = mask\n else:\n _mask = np.array([tuple([m] * len(mdtype)) for m in mask],\n dtype=mdtype)\n self._mask = _mask\n return self","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__array_finalize__","text":"numpy/ma/mrecords.py::MaskedRecords.__array_finalize__\n def __array_finalize__(self, obj):\n # Make sure we have a _fieldmask by default ..\n _mask = getattr(obj, '_mask', None)\n if _mask is None:\n objmask = getattr(obj, '_mask', nomask)\n _dtype = ndarray.__getattribute__(self, 'dtype')\n if objmask is nomask:\n _mask = ma.make_mask_none(self.shape, dtype=_dtype)\n else:\n mdescr = ma.make_mask_descr(_dtype)\n _mask = narray([tuple([m] * len(mdescr)) for m in objmask],\n dtype=mdescr).view(recarray)\n # Update some of the attributes\n _dict = self.__dict__\n _dict.update(_mask=_mask)\n self._update_from(obj)\n if _dict['_baseclass'] == ndarray:\n _dict['_baseclass'] = recarray\n return","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords._getdata","text":"numpy/ma/mrecords.py::MaskedRecords._getdata\n def _getdata(self):\n \"Returns the data as a recarray.\"\n return ndarray.view(self, recarray)","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords._getfieldmask","text":"numpy/ma/mrecords.py::MaskedRecords._getfieldmask\n def _getfieldmask(self):\n \"Alias to mask\"\n return self._mask","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__len__","text":"numpy/ma/mrecords.py::MaskedRecords.__len__\n def __len__(self):\n \"Returns the length\"\n # We have more than one record\n if self.ndim:\n return len(self._data)\n # We have only one record: return the nb of fields\n return len(self.dtype)","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__getattribute__","text":"numpy/ma/mrecords.py::MaskedRecords.__getattribute__\n def __getattribute__(self, attr):\n try:\n return object.__getattribute__(self, attr)\n except AttributeError: # attr must be a fieldname\n pass\n fielddict = ndarray.__getattribute__(self, 'dtype').fields\n try:\n res = fielddict[attr][:2]\n except (TypeError, KeyError):\n raise AttributeError(\"record array has no attribute %s\" % attr)\n # So far, so good...\n _localdict = ndarray.__getattribute__(self, '__dict__')\n _data = ndarray.view(self, _localdict['_baseclass'])\n obj = _data.getfield(*res)\n if obj.dtype.fields:\n raise NotImplementedError(\"MaskedRecords is currently limited to\"\\\n \"simple records...\")\n # Get some special attributes\n # Reset the object's mask\n hasmasked = False\n _mask = _localdict.get('_mask', None)\n if _mask is not None:\n try:\n _mask = _mask[attr]\n except IndexError:\n # Couldn't find a mask: use the default (nomask)\n pass\n hasmasked = _mask.view((np.bool, (len(_mask.dtype) or 1))).any()\n if (obj.shape or hasmasked):\n obj = obj.view(MaskedArray)\n obj._baseclass = ndarray\n obj._isfield = True\n obj._mask = _mask\n # Reset the field values\n _fill_value = _localdict.get('_fill_value', None)\n if _fill_value is not None:\n try:\n obj._fill_value = _fill_value[attr]\n except ValueError:\n obj._fill_value = None\n else:\n obj = obj.item()\n return obj","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__setattr__","text":"numpy/ma/mrecords.py::MaskedRecords.__setattr__\n def __setattr__(self, attr, val):\n \"Sets the attribute attr to the value val.\"\n # Should we call __setmask__ first ?\n if attr in ['mask', 'fieldmask']:\n self.__setmask__(val)\n return\n # Create a shortcut (so that we don't have to call getattr all the time)\n _localdict = object.__getattribute__(self, '__dict__')\n # Check whether we're creating a new field\n newattr = attr not in _localdict\n try:\n # Is attr a generic attribute ?\n ret = object.__setattr__(self, attr, val)\n except:\n # Not a generic attribute: exit if it's not a valid field\n fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}\n optinfo = ndarray.__getattribute__(self, '_optinfo') or {}\n if not (attr in fielddict or attr in optinfo):\n exctype, value = sys.exc_info()[:2]\n raise exctype(value)\n else:\n # Get the list of names ......\n fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}\n # Check the attribute\n if attr not in fielddict:\n return ret\n if newattr: # We just added this one\n try: # or this setattr worked on an internal\n # attribute.\n object.__delattr__(self, attr)\n except:\n return ret\n # Let's try to set the field\n try:\n res = fielddict[attr][:2]\n except (TypeError, KeyError):\n raise AttributeError(\"record array has no attribute %s\" % attr)\n #\n if val is masked:\n _fill_value = _localdict['_fill_value']\n if _fill_value is not None:\n dval = _localdict['_fill_value'][attr]\n else:\n dval = val\n mval = True\n else:\n ","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__getitem__","text":"numpy/ma/mrecords.py::MaskedRecords.__getitem__\n def __getitem__(self, indx):\n \"\"\"Returns all the fields sharing the same fieldname base.\nThe fieldname base is either `_data` or `_mask`.\"\"\"\n _localdict = self.__dict__\n _mask = ndarray.__getattribute__(self, '_mask')\n _data = ndarray.view(self, _localdict['_baseclass'])\n # We want a field ........\n if isinstance(indx, basestring):\n #!!!: Make sure _sharedmask is True to propagate back to _fieldmask\n #!!!: Don't use _set_mask, there are some copies being made...\n #!!!: ...that break propagation\n #!!!: Don't force the mask to nomask, that wrecks easy masking\n obj = _data[indx].view(MaskedArray)\n obj._mask = _mask[indx]\n obj._sharedmask = True\n fval = _localdict['_fill_value']\n if fval is not None:\n obj._fill_value = fval[indx]\n # Force to masked if the mask is True\n if not obj.ndim and obj._mask:\n return masked\n return obj\n # We want some elements ..\n # First, the data ........\n obj = np.array(_data[indx], copy=False).view(mrecarray)\n obj._mask = np.array(_mask[indx], copy=False).view(recarray)\n return obj","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__setitem__","text":"numpy/ma/mrecords.py::MaskedRecords.__setitem__\n def __setitem__(self, indx, value):\n \"Sets the given record to value.\"\n MaskedArray.__setitem__(self, indx, value)\n if isinstance(indx, basestring):\n self._mask[indx] = ma.getmaskarray(value)","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__str__","text":"numpy/ma/mrecords.py::MaskedRecords.__str__\n def __str__(self):\n \"Calculates the string representation.\"\n if self.size > 1:\n mstr = [\"(%s)\" % \",\".join([str(i) for i in s])\n for s in zip(*[getattr(self, f) for f in self.dtype.names])]\n return \"[%s]\" % \", \".join(mstr)\n else:\n mstr = [\"%s\" % \",\".join([str(i) for i in s])\n for s in zip([getattr(self, f) for f in self.dtype.names])]\n return \"(%s)\" % \", \".join(mstr)","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__repr__","text":"numpy/ma/mrecords.py::MaskedRecords.__repr__\n def __repr__(self):\n \"Calculates the repr representation.\"\n _names = self.dtype.names\n fmt = \"%%%is : %%s\" % (max([len(n) for n in _names]) + 4,)\n reprstr = [fmt % (f, getattr(self, f)) for f in self.dtype.names]\n reprstr.insert(0, 'masked_records(')\n reprstr.extend([fmt % (' fill_value', self.fill_value),\n ' )'])\n return str(\"\\n\".join(reprstr))","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.view","text":"numpy/ma/mrecords.py::MaskedRecords.view\n def view(self, dtype=None, type=None):\n \"\"\"Returns a view of the mrecarray.\"\"\"\n # OK, basic copy-paste from MaskedArray.view...\n if dtype is None:\n if type is None:\n output = ndarray.view(self)\n else:\n output = ndarray.view(self, type)\n # Here again...\n elif type is None:\n try:\n if issubclass(dtype, ndarray):\n output = ndarray.view(self, dtype)\n dtype = None\n else:\n output = ndarray.view(self, dtype)\n # OK, there's the change\n except TypeError:\n dtype = np.dtype(dtype)\n # we need to revert to MaskedArray, but keeping the possibility\n # ...of subclasses (eg, TimeSeriesRecords), so we'll force a type\n # ...set to the first parent\n if dtype.fields is None:\n basetype = self.__class__.__bases__[0]\n output = self.__array__().view(dtype, basetype)\n output._update_from(self)\n else:\n output = ndarray.view(self, dtype)\n output._fill_value = None\n else:\n output = ndarray.view(self, dtype, type)\n # Update the mask, just like in MaskedArray.view\n if (getattr(output, '_mask', nomask) is not nomask):\n mdtype = ma.make_mask_descr(output.dtype)\n output._mask = self._mask.view(mdtype, ndarray)\n output._mask.shape = output.shape\n return output","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.harden_mask","text":"numpy/ma/mrecords.py::MaskedRecords.harden_mask\n def harden_mask(self):\n \"Forces the mask to hard\"\n self._hardmask = True","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.soften_mask","text":"numpy/ma/mrecords.py::MaskedRecords.soften_mask\n def soften_mask(self):\n \"Forces the mask to soft\"\n self._hardmask = False","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.copy","text":"numpy/ma/mrecords.py::MaskedRecords.copy\n def copy(self):\n \"\"\"Returns a copy of the masked record.\"\"\"\n _localdict = self.__dict__\n copied = self._data.copy().view(type(self))\n copied._mask = self._mask.copy()\n return copied","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.tolist","text":"numpy/ma/mrecords.py::MaskedRecords.tolist\n def tolist(self, fill_value=None):\n \"\"\"Copy the data portion of the array to a hierarchical python\n list and returns that list.\n\n Data items are converted to the nearest compatible Python\n type. Masked values are converted to fill_value. If\n fill_value is None, the corresponding entries in the output\n list will be ``None``.\n\n \"\"\"\n if fill_value is not None:\n return self.filled(fill_value).tolist()\n result = narray(self.filled().tolist(), dtype=object)\n mask = narray(self._mask.tolist())\n result[mask] = None\n return result.tolist()","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__getstate__","text":"numpy/ma/mrecords.py::MaskedRecords.__getstate__\n def __getstate__(self):\n \"\"\"Return the internal state of the masked array, for pickling purposes.\n\n \"\"\"\n state = (1,\n self.shape,\n self.dtype,\n self.flags.fnc,\n self._data.tostring(),\n self._mask.tostring(),\n self._fill_value,\n )\n return state","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__setstate__","text":"numpy/ma/mrecords.py::MaskedRecords.__setstate__\n def __setstate__(self, state):\n \"\"\"Restore the internal state of the masked array, for pickling purposes.\n ``state`` is typically the output of the ``__getstate__`` output, and is a\n 5-tuple:\n\n - class name\n - a tuple giving the shape of the data\n - a typecode for the data\n - a binary string for the data\n - a binary string for the mask.\n\n \"\"\"\n (ver, shp, typ, isf, raw, msk, flv) = state\n ndarray.__setstate__(self, (shp, typ, isf, raw))\n mdtype = dtype([(k, bool_) for (k, _) in self.dtype.descr])\n self.__dict__['_mask'].__setstate__((shp, mdtype, isf, msk))\n self.fill_value = flv","label":0},{"cid":"numpy/ma/mrecords.py::MaskedRecords.__reduce__","text":"numpy/ma/mrecords.py::MaskedRecords.__reduce__\n def __reduce__(self):\n \"\"\"Return a 3-tuple for pickling a MaskedArray.\n\n \"\"\"\n return (_mrreconstruct,\n (self.__class__, self._baseclass, (0,), 'b',),\n self.__getstate__())","label":0},{"cid":"numpy/ma/mrecords.py::_mrreconstruct","text":"numpy/ma/mrecords.py::_mrreconstruct\ndef _mrreconstruct(subtype, baseclass, baseshape, basetype,):\n \"\"\"Internal function that builds a new MaskedArray from the\n information stored in a pickle.\n\n \"\"\"\n _data = ndarray.__new__(baseclass, baseshape, basetype).view(subtype)\n# _data._mask = ndarray.__new__(ndarray, baseshape, 'b1')\n# return _data\n _mask = ndarray.__new__(ndarray, baseshape, 'b1')\n return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,)","label":0},{"cid":"numpy/ma/mrecords.py::fromarrays","text":"numpy/ma/mrecords.py::fromarrays\ndef fromarrays(arraylist, dtype=None, shape=None, formats=None,\n names=None, titles=None, aligned=False, byteorder=None,\n fill_value=None):\n \"\"\"Creates a mrecarray from a (flat) list of masked arrays.\n\n Parameters\n ----------\n arraylist : sequence\n A list of (masked) arrays. Each element of the sequence is first converted\n to a masked array if needed. If a 2D array is passed as argument, it is\n processed line by line\n dtype : {None, dtype}, optional\n Data type descriptor.\n shape : {None, integer}, optional\n Number of records. If None, shape is defined from the shape of the\n first array in the list.\n formats : {None, sequence}, optional\n Sequence of formats for each individual field. If None, the formats will\n be autodetected by inspecting the fields and selecting the highest dtype\n possible.\n names : {None, sequence}, optional\n Sequence of the names of each field.\n fill_value : {None, sequence}, optional\n Sequence of data to be used as filling values.\n\n Notes\n -----\n Lists of tuples should be preferred over lists of lists for faster processing.\n \"\"\"\n datalist = [getdata(x) for x in arraylist]\n masklist = [np.atleast_1d(getmaskarray(x)) for x in arraylist]\n _array = recfromarrays(datalist,\n dtype=dtype, shape=shape, formats=formats,\n names=names, titles=titles, aligned=aligned,\n byteorder=byteorder).view(mrecarray)\n _array._mask.flat = zip(*masklist)\n if fill_value is not None:\n _array.fill_value = fill_value\n return _array","label":1},{"cid":"numpy/ma/mrecords.py::fromrecords","text":"numpy/ma/mrecords.py::fromrecords\ndef fromrecords(reclist, dtype=None, shape=None, formats=None, names=None,\n titles=None, aligned=False, byteorder=None,\n fill_value=None, mask=nomask):\n \"\"\"Creates a MaskedRecords from a list of records.\n\n Parameters\n ----------\n reclist : sequence\n A list of records. Each element of the sequence is first converted\n to a masked array if needed. If a 2D array is passed as argument, it is\n processed line by line\n dtype : {None, dtype}, optional\n Data type descriptor.\n shape : {None,int}, optional\n Number of records. If None, ``shape`` is defined from the shape of the\n first array in the list.\n formats : {None, sequence}, optional\n Sequence of formats for each individual field. If None, the formats will\n be autodetected by inspecting the fields and selecting the highest dtype\n possible.\n names : {None, sequence}, optional\n Sequence of the names of each field.\n fill_value : {None, sequence}, optional\n Sequence of data to be used as filling values.\n mask : {nomask, sequence}, optional.\n External mask to apply on the data.\n\n Notes\n -----\n Lists of tuples should be preferred over lists of lists for faster processing.\n \"\"\"\n # Grab the initial _fieldmask, if needed:\n _mask = getattr(reclist, '_mask', None)\n # Get the list of records.....\n try:\n nfields = len(reclist[0])\n except TypeError:\n nfields = len(reclist[0].dtype)\n if isinstance(reclist, ndarray):\n # Make sure we don't have some hidden mask\n if isinstance(reclist, MaskedArray):\n reclist = reclist.filled().view(ndarray)\n # Grab the initial dtype, just in case\n if dtype is None:\n dtype = reclist.dtype\n reclist = reclist.tolist()\n mrec = recfromrecords(reclist, dtype=dtype, shape=shape, formats=formats,\n names=names, ti","label":0},{"cid":"numpy/ma/mrecords.py::_guessvartypes","text":"numpy/ma/mrecords.py::_guessvartypes\ndef _guessvartypes(arr):\n \"\"\"Tries to guess the dtypes of the str_ ndarray `arr`, by testing element-wise\nconversion. Returns a list of dtypes.\nThe array is first converted to ndarray. If the array is 2D, the test is performed\non the first line. An exception is raised if the file is 3D or more.\n \"\"\"\n vartypes = []\n arr = np.asarray(arr)\n if len(arr.shape) == 2 :\n arr = arr[0]\n elif len(arr.shape) > 2:\n raise ValueError(\"The array should be 2D at most!\")\n # Start the conversion loop .......\n for f in arr:\n try:\n int(f)\n except ValueError:\n try:\n float(f)\n except ValueError:\n try:\n val = complex(f)\n except ValueError:\n vartypes.append(arr.dtype)\n else:\n vartypes.append(np.dtype(complex))\n else:\n vartypes.append(np.dtype(float))\n else:\n vartypes.append(np.dtype(int))\n return vartypes","label":0},{"cid":"numpy/ma/mrecords.py::openfile","text":"numpy/ma/mrecords.py::openfile\ndef openfile(fname):\n \"Opens the file handle of file `fname`\"\n # A file handle ...................\n if hasattr(fname, 'readline'):\n return fname\n # Try to open the file and guess its type\n try:\n f = open(fname)\n except IOError:\n raise IOError(\"No such file: '%s'\" % fname)\n if f.readline()[:2] != \"\\\\x\":\n f.seek(0, 0)\n return f\n f.close()\n raise NotImplementedError(\"Wow, binary file\")","label":0},{"cid":"numpy/ma/mrecords.py::fromtextfile","text":"numpy/ma/mrecords.py::fromtextfile\ndef fromtextfile(fname, delimitor=None, commentchar='#', missingchar='',\n varnames=None, vartypes=None):\n \"\"\"Creates a mrecarray from data stored in the file `filename`.\n\n Parameters\n ----------\n filename : {file name/handle}\n Handle of an opened file.\n delimitor : {None, string}, optional\n Alphanumeric character used to separate columns in the file.\n If None, any (group of) white spacestring(s) will be used.\n commentchar : {'#', string}, optional\n Alphanumeric character used to mark the start of a comment.\n missingchar : {'', string}, optional\n String indicating missing data, and used to create the masks.\n varnames : {None, sequence}, optional\n Sequence of the variable names. If None, a list will be created from\n the first non empty line of the file.\n vartypes : {None, sequence}, optional\n Sequence of the variables dtypes. If None, it will be estimated from\n the first non-commented line.\n\n\n Ultra simple: the varnames are in the header, one line\"\"\"\n # Try to open the file ......................\n f = openfile(fname)\n\n # Get the first non-empty line as the varnames\n while True:\n line = f.readline()\n firstline = line[:line.find(commentchar)].strip()\n _varnames = firstline.split(delimitor)\n if len(_varnames) > 1:\n break\n if varnames is None:\n varnames = _varnames\n\n # Get the data ..............................\n _variables = masked_array([line.strip().split(delimitor) for line in f\n if line[0] != commentchar and len(line) > 1])\n (_, nfields) = _variables.shape\n f.close()\n\n # Try to guess the dtype ....................\n if vartypes is None:\n vartypes = _guessvartypes(_variables[0])\n else:\n vartypes = [np.dtype(v) for v in vartypes]\n if len(vartypes) != nfields:\n msg = \"Attempting to %i ","label":0},{"cid":"numpy/ma/mrecords.py::addfield","text":"numpy/ma/mrecords.py::addfield\ndef addfield(mrecord, newfield, newfieldname=None):\n \"\"\"Adds a new field to the masked record array, using `newfield` as data\nand `newfieldname` as name. If `newfieldname` is None, the new field name is\nset to 'fi', where `i` is the number of existing fields.\n \"\"\"\n _data = mrecord._data\n _mask = mrecord._mask\n if newfieldname is None or newfieldname in reserved_fields:\n newfieldname = 'f%i' % len(_data.dtype)\n newfield = ma.array(newfield)\n # Get the new data ............\n # Create a new empty recarray\n newdtype = np.dtype(_data.dtype.descr + [(newfieldname, newfield.dtype)])\n newdata = recarray(_data.shape, newdtype)\n # Add the exisintg field\n [newdata.setfield(_data.getfield(*f), *f)\n for f in _data.dtype.fields.values()]\n # Add the new field\n newdata.setfield(newfield._data, *newdata.dtype.fields[newfieldname])\n newdata = newdata.view(MaskedRecords)\n # Get the new mask .............\n # Create a new empty recarray\n newmdtype = np.dtype([(n, bool_) for n in newdtype.names])\n newmask = recarray(_data.shape, newmdtype)\n # Add the old masks\n [newmask.setfield(_mask.getfield(*f), *f)\n for f in _mask.dtype.fields.values()]\n # Add the mask of the new field\n newmask.setfield(getmaskarray(newfield),\n *newmask.dtype.fields[newfieldname])\n newdata._mask = newmask\n return newdata","label":0},{"cid":"setup.py::git_version","text":"setup.py::git_version\ndef git_version():\n def _minimal_ext_cmd(cmd):\n # construct minimal environment\n env = {}\n for k in ['SYSTEMROOT', 'PATH']:\n v = os.environ.get(k)\n if v is not None:\n env[k] = v\n # LANGUAGE is used on win32\n env['LANGUAGE'] = 'C'\n env['LANG'] = 'C'\n env['LC_ALL'] = 'C'\n out = subprocess.Popen(cmd, stdout = subprocess.PIPE, env=env).communicate()[0]\n return out\n\n try:\n out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])\n GIT_REVISION = out.strip().decode('ascii')\n except OSError:\n GIT_REVISION = \"Unknown\"\n\n return GIT_REVISION","label":0},{"cid":"setup.py::git_version._minimal_ext_cmd","text":"setup.py::git_version._minimal_ext_cmd\n def _minimal_ext_cmd(cmd):\n # construct minimal environment\n env = {}\n for k in ['SYSTEMROOT', 'PATH']:\n v = os.environ.get(k)\n if v is not None:\n env[k] = v\n # LANGUAGE is used on win32\n env['LANGUAGE'] = 'C'\n env['LANG'] = 'C'\n env['LC_ALL'] = 'C'\n out = subprocess.Popen(cmd, stdout = subprocess.PIPE, env=env).communicate()[0]\n return out","label":0},{"cid":"setup.py::write_version_py","text":"setup.py::write_version_py\ndef write_version_py(filename='numpy/version.py'):\n cnt = \"\"\"\n# THIS FILE IS GENERATED FROM NUMPY SETUP.PY\nshort_version = '%(version)s'\nversion = '%(version)s'\nfull_version = '%(full_version)s'\ngit_revision = '%(git_revision)s'\nrelease = %(isrelease)s\n\nif not release:\n version = full_version\n\"\"\"\n # Adding the git rev number needs to be done inside write_version_py(),\n # otherwise the import of numpy.version messes up the build under Python 3.\n FULLVERSION = VERSION\n if os.path.exists('.git'):\n GIT_REVISION = git_version()\n elif os.path.exists('numpy/version.py'):\n # must be a source distribution, use existing version file\n try:\n from numpy.version import git_revision as GIT_REVISION\n except ImportError:\n raise ImportError(\"Unable to import git_revision. Try removing \" \\\n \"numpy/version.py and the build directory \" \\\n \"before building.\")\n else:\n GIT_REVISION = \"Unknown\"\n\n if not ISRELEASED:\n FULLVERSION += '.dev-' + GIT_REVISION[:7]\n\n a = open(filename, 'w')\n try:\n a.write(cnt % {'version': VERSION,\n 'full_version' : FULLVERSION,\n 'git_revision' : GIT_REVISION,\n 'isrelease': str(ISRELEASED)})\n finally:\n a.close()","label":0},{"cid":"setup.py::configuration","text":"setup.py::configuration\ndef configuration(parent_package='',top_path=None):\n from numpy.distutils.misc_util import Configuration\n\n config = Configuration(None, parent_package, top_path)\n config.set_options(ignore_setup_xxx_py=True,\n assume_default_configuration=True,\n delegate_options_to_subpackages=True,\n quiet=True)\n\n config.add_subpackage('numpy')\n\n config.get_version('numpy/version.py') # sets config.version\n\n return config","label":0},{"cid":"setup.py::setup_package","text":"setup.py::setup_package\ndef setup_package():\n\n # Perform 2to3 if needed\n local_path = os.path.dirname(os.path.abspath(sys.argv[0]))\n src_path = local_path\n\n if sys.version_info[0] == 3:\n src_path = os.path.join(local_path, 'build', 'py3k')\n sys.path.insert(0, os.path.join(local_path, 'tools'))\n import py3tool\n print(\"Converting to Python3 via 2to3...\")\n py3tool.sync_2to3('numpy', os.path.join(src_path, 'numpy'))\n\n site_cfg = os.path.join(local_path, 'site.cfg')\n if os.path.isfile(site_cfg):\n shutil.copy(site_cfg, src_path)\n\n # Ugly hack to make pip work with Python 3, see #1857.\n # Explanation: pip messes with __file__ which interacts badly with the\n # change in directory due to the 2to3 conversion. Therefore we restore\n # __file__ to what it would have been otherwise.\n global __file__\n __file__ = os.path.join(os.curdir, os.path.basename(__file__))\n if '--egg-base' in sys.argv:\n # Change pip-egg-info entry to absolute path, so pip can find it\n # after changing directory.\n idx = sys.argv.index('--egg-base')\n if sys.argv[idx + 1] == 'pip-egg-info':\n sys.argv[idx + 1] = os.path.join(local_path, 'pip-egg-info')\n\n old_path = os.getcwd()\n os.chdir(src_path)\n sys.path.insert(0, src_path)\n\n # Rewrite the version file everytime\n write_version_py()\n\n # Run build\n from numpy.distutils.core import setup\n\n try:\n setup(\n name=NAME,\n maintainer=MAINTAINER,\n maintainer_email=MAINTAINER_EMAIL,\n description=DESCRIPTION,\n long_description=LONG_DESCRIPTION,\n url=URL,\n download_url=DOWNLOAD_URL,\n license=LICENSE,\n classifiers=CLASSIFIERS,\n author=AUTHOR,\n author_email=AUTHOR_EMAIL,\n platforms=PLATFORMS,\n configuration=configuration )\n ","label":0},{"cid":"tools/py3tool.py::main","text":"tools/py3tool.py::main\ndef main():\n p = OptionParser(usage=__doc__.strip())\n p.add_option(\"--clean\", \"-c\", action=\"store_true\",\n help=\"clean source directory\")\n options, args = p.parse_args()\n\n if not args:\n p.error('no submodules given')\n else:\n dirs = ['numpy/%s' % x for x in map(os.path.basename, args)]\n\n # Prepare\n if not os.path.isdir(TEMP):\n os.makedirs(TEMP)\n\n # Set up dummy files (for building only submodules)\n dummy_files = {\n '__init__.py': 'from numpy.version import version as __version__',\n 'version.py': 'version = \"1.4.0.dev\"'\n }\n\n for fn, content in dummy_files.items():\n fn = os.path.join(TEMP, 'numpy', fn)\n if not os.path.isfile(fn):\n try:\n os.makedirs(os.path.dirname(fn))\n except OSError:\n pass\n f = open(fn, 'wb+')\n f.write(content.encode('ascii'))\n f.close()\n\n # Environment\n pp = [os.path.abspath(TEMP)]\n def getenv():\n env = dict(os.environ)\n env.update({'PYTHONPATH': ':'.join(pp)})\n return env\n\n # Copy\n for d in dirs:\n src = os.path.join(BASE, d)\n dst = os.path.join(TEMP, d)\n\n # Run 2to3\n sync_2to3(dst=dst,\n src=src,\n patchfile=os.path.join(TEMP, os.path.basename(d) + '.patch'),\n clean=options.clean)\n\n # Run setup.py, falling back to Pdb post-mortem on exceptions\n setup_py = os.path.join(dst, 'setup.py')\n if os.path.isfile(setup_py):\n code = \"\"\"\\\nimport pdb, sys, traceback\np = pdb.Pdb()\ntry:\n import __main__\n __main__.__dict__.update({\n \"__name__\": \"__main__\", \"__file__\": \"setup.py\",\n \"__builtins__\": __builtins__})\n fp = open(\"setup.py\", \"rb\")\n try:\n exec(compile(fp.read(), \"setup.py\", 'exec'))\n finally:\n fp.close()\nexcept SystemExit:\n raise\nexcept:\n traceback.print_exc()\n ","label":0},{"cid":"tools/py3tool.py::main.getenv","text":"tools/py3tool.py::main.getenv\n def getenv():\n env = dict(os.environ)\n env.update({'PYTHONPATH': ':'.join(pp)})\n return env","label":0},{"cid":"tools/py3tool.py::walk_sync","text":"tools/py3tool.py::walk_sync\ndef walk_sync(dir1, dir2, _seen=None):\n if _seen is None:\n seen = {}\n else:\n seen = _seen\n\n if not dir1.endswith(os.path.sep):\n dir1 = dir1 + os.path.sep\n\n # Walk through stuff (which we haven't yet gone through) in dir1\n for root, dirs, files in os.walk(dir1):\n sub = root[len(dir1):]\n if sub in seen:\n dirs = [x for x in dirs if x not in seen[sub][0]]\n files = [x for x in files if x not in seen[sub][1]]\n seen[sub][0].extend(dirs)\n seen[sub][1].extend(files)\n else:\n seen[sub] = (dirs, files)\n if not dirs and not files:\n continue\n yield os.path.join(dir1, sub), os.path.join(dir2, sub), dirs, files\n\n if _seen is None:\n # Walk through stuff (which we haven't yet gone through) in dir2\n for root2, root1, dirs, files in walk_sync(dir2, dir1, _seen=seen):\n yield root1, root2, dirs, files","label":0},{"cid":"tools/py3tool.py::sync_2to3","text":"tools/py3tool.py::sync_2to3\ndef sync_2to3(src, dst, patchfile=None, clean=False):\n import lib2to3.main\n from io import StringIO\n\n to_convert = []\n\n for src_dir, dst_dir, dirs, files in walk_sync(src, dst):\n for fn in dirs + files:\n src_fn = os.path.join(src_dir, fn)\n dst_fn = os.path.join(dst_dir, fn)\n\n # skip temporary etc. files\n if fn.startswith('.#') or fn.endswith('~'):\n continue\n\n # remove non-existing\n if os.path.exists(dst_fn) and not os.path.exists(src_fn):\n if clean:\n if os.path.isdir(dst_fn):\n shutil.rmtree(dst_fn)\n else:\n os.unlink(dst_fn)\n continue\n\n # make directories\n if os.path.isdir(src_fn):\n if not os.path.isdir(dst_fn):\n os.makedirs(dst_fn)\n continue\n\n dst_dir = os.path.dirname(dst_fn)\n if os.path.isfile(dst_fn) and not os.path.isdir(dst_dir):\n os.makedirs(dst_dir)\n\n # don't replace up-to-date files\n try:\n if os.path.isfile(dst_fn) and \\\n os.stat(dst_fn).st_mtime >= os.stat(src_fn).st_mtime:\n continue\n except OSError:\n pass\n\n # copy file\n shutil.copyfile(src_fn, dst_fn)\n\n # add .py files to 2to3 list\n if dst_fn.endswith('.py'):\n to_convert.append((src_fn, dst_fn))\n\n # run 2to3\n flag_sets = {}\n for fn, dst_fn in to_convert:\n flag = ''\n for pat, opt in EXTRA_2TO3_FLAGS.items():\n if fnmatch.fnmatch(fn, pat):\n flag = opt\n break\n flag_sets.setdefault(flag, []).append(dst_fn)\n\n if patchfile:\n p = open(patchfile, 'wb+')\n else:\n p = open(os.devnull, 'wb')\n\n for flags, filenames in flag_s","label":0}],"string":"[\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::unparse\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::unparse\\ndef unparse(ast, single_line_functions=False):\\n s = StringIO()\\n UnparseCompilerAst(ast, s, single_line_functions)\\n return s.getvalue().lstrip()\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst.__init__\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst.__init__\\n def __init__(self, tree, file = sys.stdout, single_line_functions=False):\\n \\\"\\\"\\\" Unparser(tree, file=sys.stdout) -> None.\\n\\n Print the source for tree to file.\\n \\\"\\\"\\\"\\n self.f = file\\n self._single_func = single_line_functions\\n self._do_indent = True\\n self._indent = 0\\n self._dispatch(tree)\\n self._write(\\\"\\\\n\\\")\\n self.f.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._fill\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._fill\\n def _fill(self, text = \\\"\\\"):\\n \\\"Indent a piece of text, according to the current indentation level\\\"\\n if self._do_indent:\\n self._write(\\\"\\\\n\\\"+\\\" \\\"*self._indent + text)\\n else:\\n self._write(text)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._write\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._write\\n def _write(self, text):\\n \\\"Append a piece of text to the current line.\\\"\\n self.f.write(text)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._enter\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._enter\\n def _enter(self):\\n \\\"Print ':', and increase the indentation.\\\"\\n self._write(\\\": \\\")\\n self._indent += 1\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._leave\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._leave\\n def _leave(self):\\n \\\"Decrease the indentation level.\\\"\\n self._indent -= 1\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._dispatch\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._dispatch\\n def _dispatch(self, tree):\\n \\\"_dispatcher function, _dispatching tree type T to method _T.\\\"\\n if isinstance(tree, list):\\n for t in tree:\\n self._dispatch(t)\\n return\\n meth = getattr(self, \\\"_\\\"+tree.__class__.__name__)\\n if tree.__class__.__name__ == 'NoneType' and not self._do_indent:\\n return\\n meth(tree)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Add\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Add\\n def _Add(self, t):\\n self.__binary_op(t, '+')\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._And\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._And\\n def _And(self, t):\\n self._write(\\\" (\\\")\\n for i, node in enumerate(t.nodes):\\n self._dispatch(node)\\n if i != len(t.nodes)-1:\\n self._write(\\\") and (\\\")\\n self._write(\\\")\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssAttr\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssAttr\\n def _AssAttr(self, t):\\n \\\"\\\"\\\" Handle assigning an attribute of an object\\n \\\"\\\"\\\"\\n self._dispatch(t.expr)\\n self._write('.'+t.attrname)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Assign\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Assign\\n def _Assign(self, t):\\n \\\"\\\"\\\" Expression Assignment such as \\\"a = 1\\\".\\n\\n This only handles assignment in expressions. Keyword assignment\\n is handled separately.\\n \\\"\\\"\\\"\\n self._fill()\\n for target in t.nodes:\\n self._dispatch(target)\\n self._write(\\\" = \\\")\\n self._dispatch(t.expr)\\n if not self._do_indent:\\n self._write('; ')\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssName\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssName\\n def _AssName(self, t):\\n \\\"\\\"\\\" Name on left hand side of expression.\\n\\n Treat just like a name on the right side of an expression.\\n \\\"\\\"\\\"\\n self._Name(t)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssTuple\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AssTuple\\n def _AssTuple(self, t):\\n \\\"\\\"\\\" Tuple on left hand side of an expression.\\n \\\"\\\"\\\"\\n\\n # _write each elements, separated by a comma.\\n for element in t.nodes[:-1]:\\n self._dispatch(element)\\n self._write(\\\", \\\")\\n\\n # Handle the last one without writing comma\\n last_element = t.nodes[-1]\\n self._dispatch(last_element)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AugAssign\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._AugAssign\\n def _AugAssign(self, t):\\n \\\"\\\"\\\" +=,-=,*=,/=,**=, etc. operations\\n \\\"\\\"\\\"\\n \\n self._fill()\\n self._dispatch(t.node)\\n self._write(' '+t.op+' ')\\n self._dispatch(t.expr)\\n if not self._do_indent:\\n self._write(';')\",\n \"label\": 1\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitand\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitand\\n def _Bitand(self, t):\\n \\\"\\\"\\\" Bit and operation.\\n \\\"\\\"\\\"\\n \\n for i, node in enumerate(t.nodes):\\n self._write(\\\"(\\\")\\n self._dispatch(node)\\n self._write(\\\")\\\")\\n if i != len(t.nodes)-1:\\n self._write(\\\" & \\\")\",\n \"label\": 1\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitor\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Bitor\\n def _Bitor(self, t):\\n \\\"\\\"\\\" Bit or operation\\n \\\"\\\"\\\"\\n \\n for i, node in enumerate(t.nodes):\\n self._write(\\\"(\\\")\\n self._dispatch(node)\\n self._write(\\\")\\\")\\n if i != len(t.nodes)-1:\\n self._write(\\\" | \\\")\",\n \"label\": 1\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._CallFunc\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._CallFunc\\n def _CallFunc(self, t):\\n \\\"\\\"\\\" Function call.\\n \\\"\\\"\\\"\\n self._dispatch(t.node)\\n self._write(\\\"(\\\")\\n comma = False\\n for e in t.args:\\n if comma: self._write(\\\", \\\")\\n else: comma = True\\n self._dispatch(e)\\n if t.star_args:\\n if comma: self._write(\\\", \\\")\\n else: comma = True\\n self._write(\\\"*\\\")\\n self._dispatch(t.star_args)\\n if t.dstar_args:\\n if comma: self._write(\\\", \\\")\\n else: comma = True\\n self._write(\\\"**\\\")\\n self._dispatch(t.dstar_args)\\n self._write(\\\")\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Compare\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Compare\\n def _Compare(self, t):\\n self._dispatch(t.expr)\\n for op, expr in t.ops:\\n self._write(\\\" \\\" + op + \\\" \\\")\\n self._dispatch(expr)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Const\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Const\\n def _Const(self, t):\\n \\\"\\\"\\\" A constant value such as an integer value, 3, or a string, \\\"hello\\\".\\n \\\"\\\"\\\"\\n self._dispatch(t.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Decorators\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Decorators\\n def _Decorators(self, t):\\n \\\"\\\"\\\" Handle function decorators (eg. @has_units)\\n \\\"\\\"\\\"\\n for node in t.nodes:\\n self._dispatch(node)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Dict\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Dict\\n def _Dict(self, t):\\n self._write(\\\"{\\\")\\n for i, (k, v) in enumerate(t.items):\\n self._dispatch(k)\\n self._write(\\\": \\\")\\n self._dispatch(v)\\n if i < len(t.items)-1:\\n self._write(\\\", \\\")\\n self._write(\\\"}\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Discard\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Discard\\n def _Discard(self, t):\\n \\\"\\\"\\\" Node for when return value is ignored such as in \\\"foo(a)\\\".\\n \\\"\\\"\\\"\\n self._fill()\\n self._dispatch(t.expr)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Div\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Div\\n def _Div(self, t):\\n self.__binary_op(t, '/')\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Ellipsis\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Ellipsis\\n def _Ellipsis(self, t):\\n self._write(\\\"...\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._From\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._From\\n def _From(self, t):\\n \\\"\\\"\\\" Handle \\\"from xyz import foo, bar as baz\\\".\\n \\\"\\\"\\\"\\n # fixme: Are From and ImportFrom handled differently?\\n self._fill(\\\"from \\\")\\n self._write(t.modname)\\n self._write(\\\" import \\\")\\n for i, (name,asname) in enumerate(t.names):\\n if i != 0:\\n self._write(\\\", \\\")\\n self._write(name)\\n if asname is not None:\\n self._write(\\\" as \\\"+asname)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Function\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Function\\n def _Function(self, t):\\n \\\"\\\"\\\" Handle function definitions\\n \\\"\\\"\\\"\\n if t.decorators is not None:\\n self._fill(\\\"@\\\")\\n self._dispatch(t.decorators)\\n self._fill(\\\"def \\\"+t.name + \\\"(\\\")\\n defaults = [None] * (len(t.argnames) - len(t.defaults)) + list(t.defaults)\\n for i, arg in enumerate(zip(t.argnames, defaults)):\\n self._write(arg[0])\\n if arg[1] is not None:\\n self._write('=')\\n self._dispatch(arg[1])\\n if i < len(t.argnames)-1:\\n self._write(', ')\\n self._write(\\\")\\\")\\n if self._single_func:\\n self._do_indent = False\\n self._enter()\\n self._dispatch(t.code)\\n self._leave()\\n self._do_indent = True\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Getattr\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Getattr\\n def _Getattr(self, t):\\n \\\"\\\"\\\" Handle getting an attribute of an object\\n \\\"\\\"\\\"\\n if isinstance(t.expr, (Div, Mul, Sub, Add)):\\n self._write('(')\\n self._dispatch(t.expr)\\n self._write(')')\\n else:\\n self._dispatch(t.expr)\\n \\n self._write('.'+t.attrname)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._If\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._If\\n def _If(self, t):\\n self._fill()\\n \\n for i, (compare,code) in enumerate(t.tests):\\n if i == 0:\\n self._write(\\\"if \\\")\\n else:\\n self._write(\\\"elif \\\")\\n self._dispatch(compare)\\n self._enter()\\n self._fill()\\n self._dispatch(code)\\n self._leave()\\n self._write(\\\"\\\\n\\\")\\n\\n if t.else_ is not None:\\n self._write(\\\"else\\\")\\n self._enter()\\n self._fill()\\n self._dispatch(t.else_)\\n self._leave()\\n self._write(\\\"\\\\n\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._IfExp\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._IfExp\\n def _IfExp(self, t):\\n self._dispatch(t.then)\\n self._write(\\\" if \\\")\\n self._dispatch(t.test)\\n\\n if t.else_ is not None:\\n self._write(\\\" else (\\\")\\n self._dispatch(t.else_)\\n self._write(\\\")\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Import\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Import\\n def _Import(self, t):\\n \\\"\\\"\\\" Handle \\\"import xyz.foo\\\".\\n \\\"\\\"\\\"\\n self._fill(\\\"import \\\")\\n \\n for i, (name,asname) in enumerate(t.names):\\n if i != 0:\\n self._write(\\\", \\\")\\n self._write(name)\\n if asname is not None:\\n self._write(\\\" as \\\"+asname)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Keyword\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Keyword\\n def _Keyword(self, t):\\n \\\"\\\"\\\" Keyword value assignment within function calls and definitions.\\n \\\"\\\"\\\"\\n self._write(t.name)\\n self._write(\\\"=\\\")\\n self._dispatch(t.expr)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._List\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._List\\n def _List(self, t):\\n self._write(\\\"[\\\")\\n for i,node in enumerate(t.nodes):\\n self._dispatch(node)\\n if i < len(t.nodes)-1:\\n self._write(\\\", \\\")\\n self._write(\\\"]\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Module\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Module\\n def _Module(self, t):\\n if t.doc is not None:\\n self._dispatch(t.doc)\\n self._dispatch(t.node)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Mul\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Mul\\n def _Mul(self, t):\\n self.__binary_op(t, '*')\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Name\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Name\\n def _Name(self, t):\\n self._write(t.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._NoneType\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._NoneType\\n def _NoneType(self, t):\\n self._write(\\\"None\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Not\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Not\\n def _Not(self, t):\\n self._write('not (')\\n self._dispatch(t.expr)\\n self._write(')')\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Or\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Or\\n def _Or(self, t):\\n self._write(\\\" (\\\")\\n for i, node in enumerate(t.nodes):\\n self._dispatch(node)\\n if i != len(t.nodes)-1:\\n self._write(\\\") or (\\\")\\n self._write(\\\")\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Pass\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Pass\\n def _Pass(self, t):\\n self._write(\\\"pass\\\\n\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Printnl\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Printnl\\n def _Printnl(self, t):\\n self._fill(\\\"print \\\")\\n if t.dest:\\n self._write(\\\">> \\\")\\n self._dispatch(t.dest)\\n self._write(\\\", \\\")\\n comma = False\\n for node in t.nodes:\\n if comma: self._write(', ')\\n else: comma = True\\n self._dispatch(node)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Power\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Power\\n def _Power(self, t):\\n self.__binary_op(t, '**')\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Return\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Return\\n def _Return(self, t):\\n self._fill(\\\"return \\\")\\n if t.value:\\n if isinstance(t.value, Tuple):\\n text = ', '.join([ name.name for name in t.value.asList() ])\\n self._write(text)\\n else:\\n self._dispatch(t.value)\\n if not self._do_indent:\\n self._write('; ')\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Slice\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Slice\\n def _Slice(self, t):\\n self._dispatch(t.expr)\\n self._write(\\\"[\\\")\\n if t.lower:\\n self._dispatch(t.lower)\\n self._write(\\\":\\\")\\n if t.upper:\\n self._dispatch(t.upper)\\n #if t.step:\\n # self._write(\\\":\\\")\\n # self._dispatch(t.step)\\n self._write(\\\"]\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Sliceobj\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Sliceobj\\n def _Sliceobj(self, t):\\n for i, node in enumerate(t.nodes):\\n if i != 0:\\n self._write(\\\":\\\")\\n if not (isinstance(node, Const) and node.value is None):\\n self._dispatch(node)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Stmt\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Stmt\\n def _Stmt(self, tree):\\n for node in tree.nodes:\\n self._dispatch(node)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Sub\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Sub\\n def _Sub(self, t):\\n self.__binary_op(t, '-')\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Subscript\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Subscript\\n def _Subscript(self, t):\\n self._dispatch(t.expr)\\n self._write(\\\"[\\\")\\n for i, value in enumerate(t.subs):\\n if i != 0:\\n self._write(\\\",\\\")\\n self._dispatch(value)\\n self._write(\\\"]\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._TryExcept\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._TryExcept\\n def _TryExcept(self, t):\\n self._fill(\\\"try\\\")\\n self._enter()\\n self._dispatch(t.body)\\n self._leave()\\n\\n for handler in t.handlers:\\n self._fill('except ')\\n self._dispatch(handler[0])\\n if handler[1] is not None:\\n self._write(', ')\\n self._dispatch(handler[1])\\n self._enter()\\n self._dispatch(handler[2])\\n self._leave()\\n \\n if t.else_:\\n self._fill(\\\"else\\\")\\n self._enter()\\n self._dispatch(t.else_)\\n self._leave()\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Tuple\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._Tuple\\n def _Tuple(self, t):\\n\\n if not t.nodes:\\n # Empty tuple.\\n self._write(\\\"()\\\")\\n else:\\n self._write(\\\"(\\\")\\n\\n # _write each elements, separated by a comma.\\n for element in t.nodes[:-1]:\\n self._dispatch(element)\\n self._write(\\\", \\\")\\n\\n # Handle the last one without writing comma\\n last_element = t.nodes[-1]\\n self._dispatch(last_element)\\n\\n self._write(\\\")\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._UnaryAdd\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._UnaryAdd\\n def _UnaryAdd(self, t):\\n self._write(\\\"+\\\")\\n self._dispatch(t.expr)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._UnarySub\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._UnarySub\\n def _UnarySub(self, t):\\n self._write(\\\"-\\\")\\n self._dispatch(t.expr) \",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._With\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._With\\n def _With(self, t):\\n self._fill('with ')\\n self._dispatch(t.expr)\\n if t.vars:\\n self._write(' as ')\\n self._dispatch(t.vars.name)\\n self._enter()\\n self._dispatch(t.body)\\n self._leave()\\n self._write('\\\\n')\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._int\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._int\\n def _int(self, t):\\n self._write(repr(t))\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst.__binary_op\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst.__binary_op\\n def __binary_op(self, t, symbol):\\n # Check if parenthesis are needed on left side and then dispatch\\n has_paren = False\\n left_class = str(t.left.__class__)\\n if (left_class in op_precedence.keys() and\\n op_precedence[left_class] < op_precedence[str(t.__class__)]):\\n has_paren = True\\n if has_paren:\\n self._write('(')\\n self._dispatch(t.left)\\n if has_paren:\\n self._write(')')\\n # Write the appropriate symbol for operator\\n self._write(symbol)\\n # Check if parenthesis are needed on the right side and then dispatch\\n has_paren = False\\n right_class = str(t.right.__class__)\\n if (right_class in op_precedence.keys() and\\n op_precedence[right_class] < op_precedence[str(t.__class__)]):\\n has_paren = True\\n if has_paren:\\n self._write('(')\\n self._dispatch(t.right)\\n if has_paren:\\n self._write(')')\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._float\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._float\\n def _float(self, t):\\n # if t is 0.1, str(t)->'0.1' while repr(t)->'0.1000000000001'\\n # We prefer str here.\\n self._write(str(t))\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._str\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._str\\n def _str(self, t):\\n self._write(repr(t))\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._tuple\",\n \"text\": \"doc/sphinxext/numpydoc/compiler_unparse.py::UnparseCompilerAst._tuple\\n def _tuple(self, t):\\n self._write(str(t))\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::_is_string_like\",\n \"text\": \"numpy/lib/_iotools.py::_is_string_like\\ndef _is_string_like(obj):\\n \\\"\\\"\\\"\\n Check whether obj behaves like a string.\\n \\\"\\\"\\\"\\n try:\\n obj + ''\\n except (TypeError, ValueError):\\n return False\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::_is_bytes_like\",\n \"text\": \"numpy/lib/_iotools.py::_is_bytes_like\\ndef _is_bytes_like(obj):\\n \\\"\\\"\\\"\\n Check whether obj behaves like a bytes object.\\n \\\"\\\"\\\"\\n try:\\n obj + asbytes('')\\n except (TypeError, ValueError):\\n return False\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::_to_filehandle\",\n \"text\": \"numpy/lib/_iotools.py::_to_filehandle\\ndef _to_filehandle(fname, flag='r', return_opened=False):\\n \\\"\\\"\\\"\\n Returns the filehandle corresponding to a string or a file.\\n If the string ends in '.gz', the file is automatically unzipped.\\n\\n Parameters\\n ----------\\n fname : string, filehandle\\n Name of the file whose filehandle must be returned.\\n flag : string, optional\\n Flag indicating the status of the file ('r' for read, 'w' for write).\\n return_opened : boolean, optional\\n Whether to return the opening status of the file.\\n \\\"\\\"\\\"\\n if _is_string_like(fname):\\n if fname.endswith('.gz'):\\n import gzip\\n fhd = gzip.open(fname, flag)\\n elif fname.endswith('.bz2'):\\n import bz2\\n fhd = bz2.BZ2File(fname)\\n else:\\n fhd = file(fname, flag)\\n opened = True\\n elif hasattr(fname, 'seek'):\\n fhd = fname\\n opened = False\\n else:\\n raise ValueError('fname must be a string or file handle')\\n if return_opened:\\n return fhd, opened\\n return fhd\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::has_nested_fields\",\n \"text\": \"numpy/lib/_iotools.py::has_nested_fields\\ndef has_nested_fields(ndtype):\\n \\\"\\\"\\\"\\n Returns whether one or several fields of a dtype are nested.\\n\\n Parameters\\n ----------\\n ndtype : dtype\\n Data-type of a structured array.\\n\\n Raises\\n ------\\n AttributeError\\n If `ndtype` does not have a `names` attribute.\\n\\n Examples\\n --------\\n >>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])\\n >>> np.lib._iotools.has_nested_fields(dt)\\n False\\n\\n \\\"\\\"\\\"\\n for name in ndtype.names or ():\\n if ndtype[name].names:\\n return True\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::flatten_dtype\",\n \"text\": \"numpy/lib/_iotools.py::flatten_dtype\\ndef flatten_dtype(ndtype, flatten_base=False):\\n \\\"\\\"\\\"\\n Unpack a structured data-type by collapsing nested fields and/or fields\\n with a shape.\\n\\n Note that the field names are lost.\\n\\n Parameters\\n ----------\\n ndtype : dtype\\n The datatype to collapse\\n flatten_base : {False, True}, optional\\n Whether to transform a field with a shape into several fields or not.\\n\\n Examples\\n --------\\n >>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),\\n ... ('block', int, (2, 3))])\\n >>> np.lib._iotools.flatten_dtype(dt)\\n [dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32')]\\n >>> np.lib._iotools.flatten_dtype(dt, flatten_base=True)\\n [dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32'),\\n dtype('int32'), dtype('int32'), dtype('int32'), dtype('int32'),\\n dtype('int32')]\\n\\n \\\"\\\"\\\"\\n names = ndtype.names\\n if names is None:\\n if flatten_base:\\n return [ndtype.base] * int(np.prod(ndtype.shape))\\n return [ndtype.base]\\n else:\\n types = []\\n for field in names:\\n info = ndtype.fields[field]\\n flat_dt = flatten_dtype(info[0], flatten_base)\\n types.extend(flat_dt)\\n return types\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::LineSplitter.autostrip\",\n \"text\": \"numpy/lib/_iotools.py::LineSplitter.autostrip\\n def autostrip(self, method):\\n \\\"\\\"\\\"\\n Wrapper to strip each member of the output of `method`.\\n\\n Parameters\\n ----------\\n method : function\\n Function that takes a single argument and returns a sequence of\\n strings.\\n\\n Returns\\n -------\\n wrapped : function\\n The result of wrapping `method`. `wrapped` takes a single input\\n argument and returns a list of strings that are stripped of\\n white-space.\\n\\n \\\"\\\"\\\"\\n return lambda input: [_.strip() for _ in method(input)]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::LineSplitter.__init__\",\n \"text\": \"numpy/lib/_iotools.py::LineSplitter.__init__\\n def __init__(self, delimiter=None, comments=asbytes('#'), autostrip=True):\\n self.comments = comments\\n # Delimiter is a character\\n if isinstance(delimiter, unicode):\\n delimiter = delimiter.encode('ascii')\\n if (delimiter is None) or _is_bytes_like(delimiter):\\n delimiter = delimiter or None\\n _handyman = self._delimited_splitter\\n # Delimiter is a list of field widths\\n elif hasattr(delimiter, '__iter__'):\\n _handyman = self._variablewidth_splitter\\n idx = np.cumsum([0] + list(delimiter))\\n delimiter = [slice(i, j) for (i, j) in zip(idx[:-1], idx[1:])]\\n # Delimiter is a single integer\\n elif int(delimiter):\\n (_handyman, delimiter) = (self._fixedwidth_splitter, int(delimiter))\\n else:\\n (_handyman, delimiter) = (self._delimited_splitter, None)\\n self.delimiter = delimiter\\n if autostrip:\\n self._handyman = self.autostrip(_handyman)\\n else:\\n self._handyman = _handyman\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::LineSplitter._delimited_splitter\",\n \"text\": \"numpy/lib/_iotools.py::LineSplitter._delimited_splitter\\n def _delimited_splitter(self, line):\\n if self.comments is not None:\\n line = line.split(self.comments)[0]\\n line = line.strip(asbytes(\\\" \\\\r\\\\n\\\"))\\n if not line:\\n return []\\n return line.split(self.delimiter)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::LineSplitter._fixedwidth_splitter\",\n \"text\": \"numpy/lib/_iotools.py::LineSplitter._fixedwidth_splitter\\n def _fixedwidth_splitter(self, line):\\n if self.comments is not None:\\n line = line.split(self.comments)[0]\\n line = line.strip(asbytes(\\\"\\\\r\\\\n\\\"))\\n if not line:\\n return []\\n fixed = self.delimiter\\n slices = [slice(i, i + fixed) for i in range(0, len(line), fixed)]\\n return [line[s] for s in slices]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::LineSplitter._variablewidth_splitter\",\n \"text\": \"numpy/lib/_iotools.py::LineSplitter._variablewidth_splitter\\n def _variablewidth_splitter(self, line):\\n if self.comments is not None:\\n line = line.split(self.comments)[0]\\n if not line:\\n return []\\n slices = self.delimiter\\n return [line[s] for s in slices]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::LineSplitter.__call__\",\n \"text\": \"numpy/lib/_iotools.py::LineSplitter.__call__\\n def __call__(self, line):\\n return self._handyman(line)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::NameValidator.__init__\",\n \"text\": \"numpy/lib/_iotools.py::NameValidator.__init__\\n def __init__(self, excludelist=None, deletechars=None,\\n case_sensitive=None, replace_space='_'):\\n # Process the exclusion list ..\\n if excludelist is None:\\n excludelist = []\\n excludelist.extend(self.defaultexcludelist)\\n self.excludelist = excludelist\\n # Process the list of characters to delete\\n if deletechars is None:\\n delete = self.defaultdeletechars\\n else:\\n delete = set(deletechars)\\n delete.add('\\\"')\\n self.deletechars = delete\\n # Process the case option .....\\n if (case_sensitive is None) or (case_sensitive is True):\\n self.case_converter = lambda x: x\\n elif (case_sensitive is False) or ('u' in case_sensitive):\\n self.case_converter = lambda x: x.upper()\\n elif 'l' in case_sensitive:\\n self.case_converter = lambda x: x.lower()\\n else:\\n self.case_converter = lambda x: x\\n #\\n self.replace_space = replace_space\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::NameValidator.validate\",\n \"text\": \"numpy/lib/_iotools.py::NameValidator.validate\\n def validate(self, names, defaultfmt=\\\"f%i\\\", nbfields=None):\\n \\\"\\\"\\\"\\n Validate a list of strings to use as field names for a structured array.\\n\\n Parameters\\n ----------\\n names : sequence of str\\n Strings to be validated.\\n defaultfmt : str, optional\\n Default format string, used if validating a given string reduces its\\n length to zero.\\n nboutput : integer, optional\\n Final number of validated names, used to expand or shrink the initial\\n list of names.\\n\\n Returns\\n -------\\n validatednames : list of str\\n The list of validated field names.\\n\\n Notes\\n -----\\n A `NameValidator` instance can be called directly, which is the same as\\n calling `validate`. For examples, see `NameValidator`.\\n\\n \\\"\\\"\\\"\\n # Initial checks ..............\\n if (names is None):\\n if (nbfields is None):\\n return None\\n names = []\\n if isinstance(names, basestring):\\n names = [names, ]\\n if nbfields is not None:\\n nbnames = len(names)\\n if (nbnames < nbfields):\\n names = list(names) + [''] * (nbfields - nbnames)\\n elif (nbnames > nbfields):\\n names = names[:nbfields]\\n # Set some shortcuts ...........\\n deletechars = self.deletechars\\n excludelist = self.excludelist\\n case_converter = self.case_converter\\n replace_space = self.replace_space\\n # Initializes some variables ...\\n validatednames = []\\n seen = dict()\\n nbempty = 0\\n #\\n for item in names:\\n item = case_converter(item).strip()\\n if replace_space:\\n item = item.replace(' ', replace_space)\\n item = ''.join([c for c in item if c not in deletechars])\\n if item == '':\\n item = defaultfmt % \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::NameValidator.__call__\",\n \"text\": \"numpy/lib/_iotools.py::NameValidator.__call__\\n def __call__(self, names, defaultfmt=\\\"f%i\\\", nbfields=None):\\n return self.validate(names, defaultfmt=defaultfmt, nbfields=nbfields)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::str2bool\",\n \"text\": \"numpy/lib/_iotools.py::str2bool\\ndef str2bool(value):\\n \\\"\\\"\\\"\\n Tries to transform a string supposed to represent a boolean to a boolean.\\n\\n Parameters\\n ----------\\n value : str\\n The string that is transformed to a boolean.\\n\\n Returns\\n -------\\n boolval : bool\\n The boolean representation of `value`.\\n\\n Raises\\n ------\\n ValueError\\n If the string is not 'True' or 'False' (case independent)\\n\\n Examples\\n --------\\n >>> np.lib._iotools.str2bool('TRUE')\\n True\\n >>> np.lib._iotools.str2bool('false')\\n False\\n\\n \\\"\\\"\\\"\\n value = value.upper()\\n if value == asbytes('TRUE'):\\n return True\\n elif value == asbytes('FALSE'):\\n return False\\n else:\\n raise ValueError(\\\"Invalid boolean\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::StringConverter._getdtype\",\n \"text\": \"numpy/lib/_iotools.py::StringConverter._getdtype\\n @classmethod\\n def _getdtype(cls, val):\\n \\\"\\\"\\\"Returns the dtype of the input variable.\\\"\\\"\\\"\\n return np.array(val).dtype\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::StringConverter._getsubdtype\",\n \"text\": \"numpy/lib/_iotools.py::StringConverter._getsubdtype\\n @classmethod\\n def _getsubdtype(cls, val):\\n \\\"\\\"\\\"Returns the type of the dtype of the input variable.\\\"\\\"\\\"\\n return np.array(val).dtype.type\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::StringConverter._dtypeortype\",\n \"text\": \"numpy/lib/_iotools.py::StringConverter._dtypeortype\\n @classmethod\\n def _dtypeortype(cls, dtype):\\n \\\"\\\"\\\"Returns dtype for datetime64 and type of dtype otherwise.\\\"\\\"\\\"\\n if dtype.type == np.datetime64:\\n return dtype\\n return dtype.type\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::StringConverter.upgrade_mapper\",\n \"text\": \"numpy/lib/_iotools.py::StringConverter.upgrade_mapper\\n @classmethod\\n def upgrade_mapper(cls, func, default=None):\\n \\\"\\\"\\\"\\n Upgrade the mapper of a StringConverter by adding a new function and its\\n corresponding default.\\n\\n The input function (or sequence of functions) and its associated default\\n value (if any) is inserted in penultimate position of the mapper.\\n The corresponding type is estimated from the dtype of the default value.\\n\\n Parameters\\n ----------\\n func : var\\n Function, or sequence of functions\\n\\n Examples\\n --------\\n >>> import dateutil.parser\\n >>> import datetime\\n >>> dateparser = datetustil.parser.parse\\n >>> defaultdate = datetime.date(2000, 1, 1)\\n >>> StringConverter.upgrade_mapper(dateparser, default=defaultdate)\\n \\\"\\\"\\\"\\n # Func is a single functions\\n if hasattr(func, '__call__'):\\n cls._mapper.insert(-1, (cls._getsubdtype(default), func, default))\\n return\\n elif hasattr(func, '__iter__'):\\n if isinstance(func[0], (tuple, list)):\\n for _ in func:\\n cls._mapper.insert(-1, _)\\n return\\n if default is None:\\n default = [None] * len(func)\\n else:\\n default = list(default)\\n default.append([None] * (len(func) - len(default)))\\n for (fct, dft) in zip(func, default):\\n cls._mapper.insert(-1, (cls._getsubdtype(dft), fct, dft))\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::StringConverter.__init__\",\n \"text\": \"numpy/lib/_iotools.py::StringConverter.__init__\\n def __init__(self, dtype_or_func=None, default=None, missing_values=None,\\n locked=False):\\n # Convert unicode (for Py3)\\n if isinstance(missing_values, unicode):\\n missing_values = asbytes(missing_values)\\n elif isinstance(missing_values, (list, tuple)):\\n missing_values = asbytes_nested(missing_values)\\n # Defines a lock for upgrade\\n self._locked = bool(locked)\\n # No input dtype: minimal initialization\\n if dtype_or_func is None:\\n self.func = str2bool\\n self._status = 0\\n self.default = default or False\\n dtype = np.dtype('bool')\\n else:\\n # Is the input a np.dtype ?\\n try:\\n self.func = None\\n dtype = np.dtype(dtype_or_func)\\n except TypeError:\\n # dtype_or_func must be a function, then\\n if not hasattr(dtype_or_func, '__call__'):\\n errmsg = \\\"The input argument `dtype` is neither a function\\\"\\\\\\n \\\" or a dtype (got '%s' instead)\\\"\\n raise TypeError(errmsg % type(dtype_or_func))\\n # Set the function\\n self.func = dtype_or_func\\n # If we don't have a default, try to guess it or set it to None\\n if default is None:\\n try:\\n default = self.func(asbytes('0'))\\n except ValueError:\\n default = None\\n dtype = self._getdtype(default)\\n # Set the status according to the dtype\\n _status = -1\\n for (i, (deftype, func, default_def)) in enumerate(self._mapper):\\n if np.issubdtype(dtype.type, deftype):\\n _status = i\\n if default is None:\\n self.default = default_def\\n else:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::StringConverter._loose_call\",\n \"text\": \"numpy/lib/_iotools.py::StringConverter._loose_call\\n def _loose_call(self, value):\\n try:\\n return self.func(value)\\n except ValueError:\\n return self.default\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::StringConverter._strict_call\",\n \"text\": \"numpy/lib/_iotools.py::StringConverter._strict_call\\n def _strict_call(self, value):\\n try:\\n return self.func(value)\\n except ValueError:\\n if value.strip() in self.missing_values:\\n if not self._status:\\n self._checked = False\\n return self.default\\n raise ValueError(\\\"Cannot convert string '%s'\\\" % value)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::StringConverter.__call__\",\n \"text\": \"numpy/lib/_iotools.py::StringConverter.__call__\\n def __call__(self, value):\\n return self._callingfunction(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::StringConverter.upgrade\",\n \"text\": \"numpy/lib/_iotools.py::StringConverter.upgrade\\n def upgrade(self, value):\\n \\\"\\\"\\\"\\n Try to find the best converter for a given string, and return the result.\\n\\n The supplied string `value` is converted by testing different\\n converters in order. First the `func` method of the `StringConverter`\\n instance is tried, if this fails other available converters are tried.\\n The order in which these other converters are tried is determined by the\\n `_status` attribute of the instance.\\n\\n Parameters\\n ----------\\n value : str\\n The string to convert.\\n\\n Returns\\n -------\\n out : any\\n The result of converting `value` with the appropriate converter.\\n\\n \\\"\\\"\\\"\\n self._checked = True\\n try:\\n self._strict_call(value)\\n except ValueError:\\n # Raise an exception if we locked the converter...\\n if self._locked:\\n errmsg = \\\"Converter is locked and cannot be upgraded\\\"\\n raise ConverterLockError(errmsg)\\n _statusmax = len(self._mapper)\\n # Complains if we try to upgrade by the maximum\\n _status = self._status\\n if _status == _statusmax:\\n errmsg = \\\"Could not find a valid conversion function\\\"\\n raise ConverterError(errmsg)\\n elif _status < _statusmax - 1:\\n _status += 1\\n (self.type, self.func, default) = self._mapper[_status]\\n self._status = _status\\n if self._initial_default is not None:\\n self.default = self._initial_default\\n else:\\n self.default = default\\n self.upgrade(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::StringConverter.iterupgrade\",\n \"text\": \"numpy/lib/_iotools.py::StringConverter.iterupgrade\\n def iterupgrade(self, value):\\n self._checked = True\\n if not hasattr(value, '__iter__'):\\n value = (value,)\\n _strict_call = self._strict_call\\n try:\\n for _m in value:\\n _strict_call(_m)\\n except ValueError:\\n # Raise an exception if we locked the converter...\\n if self._locked:\\n errmsg = \\\"Converter is locked and cannot be upgraded\\\"\\n raise ConverterLockError(errmsg)\\n _statusmax = len(self._mapper)\\n # Complains if we try to upgrade by the maximum\\n _status = self._status\\n if _status == _statusmax:\\n raise ConverterError(\\\"Could not find a valid conversion function\\\")\\n elif _status < _statusmax - 1:\\n _status += 1\\n (self.type, self.func, default) = self._mapper[_status]\\n if self._initial_default is not None:\\n self.default = self._initial_default\\n else:\\n self.default = default\\n self._status = _status\\n self.iterupgrade(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::StringConverter.update\",\n \"text\": \"numpy/lib/_iotools.py::StringConverter.update\\n def update(self, func, default=None, testing_value=None,\\n missing_values=asbytes(''), locked=False):\\n \\\"\\\"\\\"\\n Set StringConverter attributes directly.\\n\\n Parameters\\n ----------\\n func : function\\n Conversion function.\\n default : any, optional\\n Value to return by default, that is, when the string to be converted\\n is flagged as missing. If not given, `StringConverter` tries to supply\\n a reasonable default value.\\n testing_value : str, optional\\n A string representing a standard input value of the converter.\\n This string is used to help defining a reasonable default value.\\n missing_values : sequence of str, optional\\n Sequence of strings indicating a missing value.\\n locked : bool, optional\\n Whether the StringConverter should be locked to prevent automatic\\n upgrade or not. Default is False.\\n\\n Notes\\n -----\\n `update` takes the same parameters as the constructor of `StringConverter`,\\n except that `func` does not accept a `dtype` whereas `dtype_or_func` in\\n the constructor does.\\n\\n \\\"\\\"\\\"\\n self.func = func\\n self._locked = locked\\n # Don't reset the default to None if we can avoid it\\n if default is not None:\\n self.default = default\\n self.type = self._dtypeortype(self._getdtype(default))\\n else:\\n try:\\n tester = func(testing_value or asbytes('1'))\\n except (TypeError, ValueError):\\n tester = None\\n self.type = self._dtypeortype(self._getdtype(tester))\\n # Add the missing values to the existing set\\n if missing_values is not None:\\n if _is_bytes_like(missing_values):\\n self.missing_values.add(missing_values)\\n elif hasattr(missing_values, '__iter__'):\\n fo\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/_iotools.py::easy_dtype\",\n \"text\": \"numpy/lib/_iotools.py::easy_dtype\\ndef easy_dtype(ndtype, names=None, defaultfmt=\\\"f%i\\\", **validationargs):\\n \\\"\\\"\\\"\\n Convenience function to create a `np.dtype` object.\\n\\n The function processes the input `dtype` and matches it with the given\\n names.\\n\\n Parameters\\n ----------\\n ndtype : var\\n Definition of the dtype. Can be any string or dictionary\\n recognized by the `np.dtype` function, or a sequence of types.\\n names : str or sequence, optional\\n Sequence of strings to use as field names for a structured dtype.\\n For convenience, `names` can be a string of a comma-separated list of\\n names.\\n defaultfmt : str, optional\\n Format string used to define missing names, such as ``\\\"f%i\\\"``\\n (default) or ``\\\"fields_%02i\\\"``.\\n validationargs : optional\\n A series of optional arguments used to initialize a `NameValidator`.\\n\\n Examples\\n --------\\n >>> np.lib._iotools.easy_dtype(float)\\n dtype('float64')\\n >>> np.lib._iotools.easy_dtype(\\\"i4, f8\\\")\\n dtype([('f0', '<i4'), ('f1', '<f8')])\\n >>> np.lib._iotools.easy_dtype(\\\"i4, f8\\\", defaultfmt=\\\"field_%03i\\\")\\n dtype([('field_000', '<i4'), ('field_001', '<f8')])\\n\\n >>> np.lib._iotools.easy_dtype((int, float, float), names=\\\"a,b,c\\\")\\n dtype([('a', '<i8'), ('b', '<f8'), ('c', '<f8')])\\n >>> np.lib._iotools.easy_dtype(float, names=\\\"a,b,c\\\")\\n dtype([('a', '<f8'), ('b', '<f8'), ('c', '<f8')])\\n\\n \\\"\\\"\\\"\\n try:\\n ndtype = np.dtype(ndtype)\\n except TypeError:\\n validate = NameValidator(**validationargs)\\n nbfields = len(ndtype)\\n if names is None:\\n names = [''] * len(ndtype)\\n elif isinstance(names, basestring):\\n names = names.split(\\\",\\\")\\n names = validate(names, nbfields=nbfields, defaultfmt=defaultfmt)\\n ndtype = np.dtype(dict(formats=ndtype, names=names))\\n else:\\n nbtypes = len(ndtype)\\n # Explicit names\\n if names is not None:\\n validate = NameValidat\",\n \"label\": 1\n },\n {\n \"cid\": \"numpy/lib/arrayterator.py::Arrayterator.__init__\",\n \"text\": \"numpy/lib/arrayterator.py::Arrayterator.__init__\\n def __init__(self, var, buf_size=None):\\n self.var = var\\n self.buf_size = buf_size\\n\\n self.start = [0 for dim in var.shape]\\n self.stop = [dim for dim in var.shape]\\n self.step = [1 for dim in var.shape]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/arrayterator.py::Arrayterator.__getattr__\",\n \"text\": \"numpy/lib/arrayterator.py::Arrayterator.__getattr__\\n def __getattr__(self, attr):\\n return getattr(self.var, attr)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/arrayterator.py::Arrayterator.__getitem__\",\n \"text\": \"numpy/lib/arrayterator.py::Arrayterator.__getitem__\\n def __getitem__(self, index):\\n \\\"\\\"\\\"\\n Return a new arrayterator.\\n\\n \\\"\\\"\\\"\\n # Fix index, handling ellipsis and incomplete slices.\\n if not isinstance(index, tuple): index = (index,)\\n fixed = []\\n length, dims = len(index), len(self.shape)\\n for slice_ in index:\\n if slice_ is Ellipsis:\\n fixed.extend([slice(None)] * (dims-length+1))\\n length = len(fixed)\\n elif isinstance(slice_, (int, long)):\\n fixed.append(slice(slice_, slice_+1, 1))\\n else:\\n fixed.append(slice_)\\n index = tuple(fixed)\\n if len(index) < dims:\\n index += (slice(None),) * (dims-len(index))\\n\\n # Return a new arrayterator object.\\n out = self.__class__(self.var, self.buf_size)\\n for i, (start, stop, step, slice_) in enumerate(\\n zip(self.start, self.stop, self.step, index)):\\n out.start[i] = start + (slice_.start or 0)\\n out.step[i] = step * (slice_.step or 1)\\n out.stop[i] = start + (slice_.stop or stop-start)\\n out.stop[i] = min(stop, out.stop[i])\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/arrayterator.py::Arrayterator.__array__\",\n \"text\": \"numpy/lib/arrayterator.py::Arrayterator.__array__\\n def __array__(self):\\n \\\"\\\"\\\"\\n Return corresponding data.\\n\\n \\\"\\\"\\\"\\n slice_ = tuple(slice(*t) for t in zip(\\n self.start, self.stop, self.step))\\n return self.var[slice_]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/arrayterator.py::Arrayterator.flat\",\n \"text\": \"numpy/lib/arrayterator.py::Arrayterator.flat\\n @property\\n def flat(self):\\n \\\"\\\"\\\"\\n A 1-D flat iterator for Arrayterator objects.\\n\\n This iterator returns elements of the array to be iterated over in\\n `Arrayterator` one by one. It is similar to `flatiter`.\\n\\n See Also\\n --------\\n `Arrayterator`\\n flatiter\\n\\n Examples\\n --------\\n >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)\\n >>> a_itor = np.lib.arrayterator.Arrayterator(a, 2)\\n\\n >>> for subarr in a_itor.flat:\\n ... if not subarr:\\n ... print subarr, type(subarr)\\n ...\\n 0 <type 'numpy.int32'>\\n\\n \\\"\\\"\\\"\\n for block in self:\\n for value in block.flat:\\n yield value\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/arrayterator.py::Arrayterator.shape\",\n \"text\": \"numpy/lib/arrayterator.py::Arrayterator.shape\\n @property\\n def shape(self):\\n \\\"\\\"\\\"\\n The shape of the array to be iterated over.\\n\\n For an example, see `Arrayterator`.\\n\\n \\\"\\\"\\\"\\n return tuple(((stop-start-1)//step+1) for start, stop, step in\\n zip(self.start, self.stop, self.step))\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/arrayterator.py::Arrayterator.__iter__\",\n \"text\": \"numpy/lib/arrayterator.py::Arrayterator.__iter__\\n def __iter__(self):\\n # Skip arrays with degenerate dimensions\\n if [dim for dim in self.shape if dim <= 0]: raise StopIteration\\n\\n start = self.start[:]\\n stop = self.stop[:]\\n step = self.step[:]\\n ndims = len(self.var.shape)\\n\\n while 1:\\n count = self.buf_size or reduce(mul, self.shape)\\n\\n # iterate over each dimension, looking for the\\n # running dimension (ie, the dimension along which\\n # the blocks will be built from)\\n rundim = 0\\n for i in range(ndims-1, -1, -1):\\n # if count is zero we ran out of elements to read\\n # along higher dimensions, so we read only a single position\\n if count == 0:\\n stop[i] = start[i]+1\\n elif count <= self.shape[i]: # limit along this dimension\\n stop[i] = start[i] + count*step[i]\\n rundim = i\\n else:\\n stop[i] = self.stop[i] # read everything along this\\n # dimension\\n stop[i] = min(self.stop[i], stop[i])\\n count = count//self.shape[i]\\n\\n # yield a block\\n slice_ = tuple(slice(*t) for t in zip(start, stop, step))\\n yield self.var[slice_]\\n\\n # Update start position, taking care of overflow to\\n # other dimensions\\n start[rundim] = stop[rundim] # start where we stopped\\n for i in range(ndims-1, 0, -1):\\n if start[i] >= self.stop[i]:\\n start[i] = self.start[i]\\n start[i-1] += self.step[i-1]\\n if start[0] >= self.stop[0]:\\n raise StopIteration\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::seek_gzip_factory\",\n \"text\": \"numpy/lib/npyio.py::seek_gzip_factory\\ndef seek_gzip_factory(f):\\n \\\"\\\"\\\"Use this factory to produce the class so that we can do a lazy\\n import on gzip.\\n\\n \\\"\\\"\\\"\\n import gzip\\n\\n class GzipFile(gzip.GzipFile):\\n\\n def seek(self, offset, whence=0):\\n # figure out new position (we can only seek forwards)\\n if whence == 1:\\n offset = self.offset + offset\\n\\n if whence not in [0, 1]:\\n raise IOError(\\\"Illegal argument\\\")\\n\\n if offset < self.offset:\\n # for negative seek, rewind and do positive seek\\n self.rewind()\\n count = offset - self.offset\\n for i in range(count // 1024):\\n self.read(1024)\\n self.read(count % 1024)\\n\\n def tell(self):\\n return self.offset\\n\\n\\n if isinstance(f, str):\\n f = GzipFile(f)\\n elif isinstance(f, gzip.GzipFile):\\n # cast to our GzipFile if its already a gzip.GzipFile\\n\\n try:\\n name = f.name\\n except AttributeError:\\n # Backward compatibility for <= 2.5\\n name = f.filename\\n mode = f.mode\\n\\n f = GzipFile(fileobj=f.fileobj, filename=name)\\n f.mode = mode\\n\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.seek\",\n \"text\": \"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.seek\\n def seek(self, offset, whence=0):\\n # figure out new position (we can only seek forwards)\\n if whence == 1:\\n offset = self.offset + offset\\n\\n if whence not in [0, 1]:\\n raise IOError(\\\"Illegal argument\\\")\\n\\n if offset < self.offset:\\n # for negative seek, rewind and do positive seek\\n self.rewind()\\n count = offset - self.offset\\n for i in range(count // 1024):\\n self.read(1024)\\n self.read(count % 1024)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.tell\",\n \"text\": \"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.tell\\n def tell(self):\\n return self.offset\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::BagObj.__init__\",\n \"text\": \"numpy/lib/npyio.py::BagObj.__init__\\n def __init__(self, obj):\\n # Use weakref to make NpzFile objects collectable by refcount\\n self._obj = weakref.proxy(obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::BagObj.__getattribute__\",\n \"text\": \"numpy/lib/npyio.py::BagObj.__getattribute__\\n def __getattribute__(self, key):\\n try:\\n return object.__getattribute__(self, '_obj')[key]\\n except KeyError:\\n raise AttributeError(key)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::zipfile_factory\",\n \"text\": \"numpy/lib/npyio.py::zipfile_factory\\ndef zipfile_factory(*args, **kwargs):\\n import zipfile\\n if sys.version_info >= (2, 5):\\n kwargs['allowZip64'] = True\\n return zipfile.ZipFile(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__init__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__init__\\n def __init__(self, fid, own_fid=False):\\n # Import is postponed to here since zipfile depends on gzip, an optional\\n # component of the so-called standard library.\\n _zip = zipfile_factory(fid)\\n self._files = _zip.namelist()\\n self.files = []\\n for x in self._files:\\n if x.endswith('.npy'):\\n self.files.append(x[:-4])\\n else:\\n self.files.append(x)\\n self.zip = _zip\\n self.f = BagObj(self)\\n if own_fid:\\n self.fid = fid\\n else:\\n self.fid = None\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__enter__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__enter__\\n def __enter__(self):\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__exit__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__exit__\\n def __exit__(self, exc_type, exc_value, traceback):\\n self.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.close\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.close\\n def close(self):\\n \\\"\\\"\\\"\\n Close the file.\\n\\n \\\"\\\"\\\"\\n if self.zip is not None:\\n self.zip.close()\\n self.zip = None\\n if self.fid is not None:\\n self.fid.close()\\n self.fid = None\\n self.f = None # break reference cycle\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__del__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__del__\\n def __del__(self):\\n self.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__getitem__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__getitem__\\n def __getitem__(self, key):\\n # FIXME: This seems like it will copy strings around\\n # more than is strictly necessary. The zipfile\\n # will read the string and then\\n # the format.read_array will copy the string\\n # to another place in memory.\\n # It would be better if the zipfile could read\\n # (or at least uncompress) the data\\n # directly into the array memory.\\n member = 0\\n if key in self._files:\\n member = 1\\n elif key in self.files:\\n member = 1\\n key += '.npy'\\n if member:\\n bytes = self.zip.read(key)\\n if bytes.startswith(format.MAGIC_PREFIX):\\n value = BytesIO(bytes)\\n return format.read_array(value)\\n else:\\n return bytes\\n else:\\n raise KeyError(\\\"%s is not a file in the archive\\\" % key)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__iter__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__iter__\\n def __iter__(self):\\n return iter(self.files)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.items\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.items\\n def items(self):\\n \\\"\\\"\\\"\\n Return a list of tuples, with each tuple (filename, array in file).\\n\\n \\\"\\\"\\\"\\n return [(f, self[f]) for f in self.files]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.iteritems\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.iteritems\\n def iteritems(self):\\n \\\"\\\"\\\"Generator that returns tuples (filename, array in file).\\\"\\\"\\\"\\n for f in self.files:\\n yield (f, self[f])\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.keys\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.keys\\n def keys(self):\\n \\\"\\\"\\\"Return files in the archive with a \\\".npy\\\" extension.\\\"\\\"\\\"\\n return self.files\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.iterkeys\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.iterkeys\\n def iterkeys(self):\\n \\\"\\\"\\\"Return an iterator over the files in the archive.\\\"\\\"\\\"\\n return self.__iter__()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__contains__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__contains__\\n def __contains__(self, key):\\n return self.files.__contains__(key)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::load\",\n \"text\": \"numpy/lib/npyio.py::load\\ndef load(file, mmap_mode=None):\\n \\\"\\\"\\\"\\n Load an array(s) or pickled objects from .npy, .npz, or pickled files.\\n\\n Parameters\\n ----------\\n file : file-like object or string\\n The file to read. It must support ``seek()`` and ``read()`` methods.\\n If the filename extension is ``.gz``, the file is first decompressed.\\n mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional\\n If not None, then memory-map the file, using the given mode\\n (see `numpy.memmap` for a detailed description of the modes).\\n A memory-mapped array is kept on disk. However, it can be accessed\\n and sliced like any ndarray. Memory mapping is especially useful for\\n accessing small fragments of large files without reading the entire\\n file into memory.\\n\\n Returns\\n -------\\n result : array, tuple, dict, etc.\\n Data stored in the file. For '.npz' files, the returned instance of\\n NpzFile class must be closed to avoid leaking file descriptors.\\n\\n Raises\\n ------\\n IOError\\n If the input file does not exist or cannot be read.\\n\\n See Also\\n --------\\n save, savez, loadtxt\\n memmap : Create a memory-map to an array stored in a file on disk.\\n\\n Notes\\n -----\\n - If the file contains pickle data, then whatever object is stored\\n in the pickle is returned.\\n - If the file is a ``.npy`` file, then a single array is returned.\\n - If the file is a ``.npz`` file, then a dictionary-like object is\\n returned, containing ``{filename: array}`` key-value pairs, one for\\n each file in the archive.\\n - If the file is a ``.npz`` file, the returned value supports the context\\n manager protocol in a similar fashion to the open function::\\n\\n with load('foo.npz') as data:\\n a = data['a']\\n\\n The underlyling file descriptor is closed when exiting the 'with' block.\\n\\n Examples\\n --------\\n Store data to disk, and load it again:\\n\\n >>> np.save('/tmp/12\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::save\",\n \"text\": \"numpy/lib/npyio.py::save\\ndef save(file, arr):\\n \\\"\\\"\\\"\\n Save an array to a binary file in NumPy ``.npy`` format.\\n\\n Parameters\\n ----------\\n file : file or str\\n File or filename to which the data is saved. If file is a file-object,\\n then the filename is unchanged. If file is a string, a ``.npy``\\n extension will be appended to the file name if it does not already\\n have one.\\n arr : array_like\\n Array data to be saved.\\n\\n See Also\\n --------\\n savez : Save several arrays into a ``.npz`` archive\\n savetxt, load\\n\\n Notes\\n -----\\n For a description of the ``.npy`` format, see `format`.\\n\\n Examples\\n --------\\n >>> from tempfile import TemporaryFile\\n >>> outfile = TemporaryFile()\\n\\n >>> x = np.arange(10)\\n >>> np.save(outfile, x)\\n\\n >>> outfile.seek(0) # Only needed here to simulate closing & reopening file\\n >>> np.load(outfile)\\n array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\\n\\n \\\"\\\"\\\"\\n own_fid = False\\n if isinstance(file, basestring):\\n if not file.endswith('.npy'):\\n file = file + '.npy'\\n fid = open(file, \\\"wb\\\")\\n own_fid = True\\n else:\\n fid = file\\n\\n try:\\n arr = np.asanyarray(arr)\\n format.write_array(fid, arr)\\n finally:\\n if own_fid:\\n fid.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::savez\",\n \"text\": \"numpy/lib/npyio.py::savez\\ndef savez(file, *args, **kwds):\\n \\\"\\\"\\\"\\n Save several arrays into a single file in uncompressed ``.npz`` format.\\n\\n If arguments are passed in with no keywords, the corresponding variable\\n names, in the .npz file, are 'arr_0', 'arr_1', etc. If keyword arguments\\n are given, the corresponding variable names, in the ``.npz`` file will\\n match the keyword names.\\n\\n Parameters\\n ----------\\n file : str or file\\n Either the file name (string) or an open file (file-like object)\\n where the data will be saved. If file is a string, the ``.npz``\\n extension will be appended to the file name if it is not already there.\\n args : Arguments, optional\\n Arrays to save to the file. Since it is not possible for Python to\\n know the names of the arrays outside `savez`, the arrays will be saved\\n with names \\\"arr_0\\\", \\\"arr_1\\\", and so on. These arguments can be any\\n expression.\\n kwds : Keyword arguments, optional\\n Arrays to save to the file. Arrays will be saved in the file with the\\n keyword names.\\n\\n Returns\\n -------\\n None\\n\\n See Also\\n --------\\n save : Save a single array to a binary file in NumPy format.\\n savetxt : Save an array to a file as plain text.\\n savez_compressed : Save several arrays into a compressed .npz file format\\n\\n Notes\\n -----\\n The ``.npz`` file format is a zipped archive of files named after the\\n variables they contain. The archive is not compressed and each file\\n in the archive contains one variable in ``.npy`` format. For a\\n description of the ``.npy`` format, see `format`.\\n\\n When opening the saved ``.npz`` file with `load` a `NpzFile` object is\\n returned. This is a dictionary-like object which can be queried for\\n its list of arrays (with the ``.files`` attribute), and for the arrays\\n themselves.\\n\\n Examples\\n --------\\n >>> from tempfile import TemporaryFile\\n >>> outfile = TemporaryFile()\\n >>> x = \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::savez_compressed\",\n \"text\": \"numpy/lib/npyio.py::savez_compressed\\ndef savez_compressed(file, *args, **kwds):\\n \\\"\\\"\\\"\\n Save several arrays into a single file in compressed ``.npz`` format.\\n\\n If keyword arguments are given, then filenames are taken from the keywords.\\n If arguments are passed in with no keywords, then stored file names are\\n arr_0, arr_1, etc.\\n\\n Parameters\\n ----------\\n file : str\\n File name of .npz file.\\n args : Arguments\\n Function arguments.\\n kwds : Keyword arguments\\n Keywords.\\n\\n See Also\\n --------\\n numpy.savez : Save several arrays into an uncompressed .npz file format\\n\\n \\\"\\\"\\\"\\n _savez(file, args, kwds, True)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::_savez\",\n \"text\": \"numpy/lib/npyio.py::_savez\\ndef _savez(file, args, kwds, compress):\\n # Import is postponed to here since zipfile depends on gzip, an optional\\n # component of the so-called standard library.\\n import zipfile\\n # Import deferred for startup time improvement\\n import tempfile\\n\\n if isinstance(file, basestring):\\n if not file.endswith('.npz'):\\n file = file + '.npz'\\n\\n namedict = kwds\\n for i, val in enumerate(args):\\n key = 'arr_%d' % i\\n if key in namedict.keys():\\n raise ValueError(\\\"Cannot use un-named variables and keyword %s\\\" % key)\\n namedict[key] = val\\n\\n if compress:\\n compression = zipfile.ZIP_DEFLATED\\n else:\\n compression = zipfile.ZIP_STORED\\n\\n zip = zipfile_factory(file, mode=\\\"w\\\", compression=compression)\\n\\n # Stage arrays in a temporary file on disk, before writing to zip.\\n fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')\\n os.close(fd)\\n try:\\n for key, val in namedict.items():\\n fname = key + '.npy'\\n fid = open(tmpfile, 'wb')\\n try:\\n format.write_array(fid, np.asanyarray(val))\\n fid.close()\\n fid = None\\n zip.write(tmpfile, arcname=fname)\\n finally:\\n if fid:\\n fid.close()\\n finally:\\n os.remove(tmpfile)\\n\\n zip.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::_getconv\",\n \"text\": \"numpy/lib/npyio.py::_getconv\\ndef _getconv(dtype):\\n typ = dtype.type\\n if issubclass(typ, np.bool_):\\n return lambda x: bool(int(x))\\n if issubclass(typ, np.uint64):\\n return np.uint64\\n if issubclass(typ, np.int64):\\n return np.int64\\n if issubclass(typ, np.integer):\\n return lambda x: int(float(x))\\n elif issubclass(typ, np.floating):\\n return float\\n elif issubclass(typ, np.complex):\\n return complex\\n elif issubclass(typ, np.bytes_):\\n return bytes\\n else:\\n return str\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::loadtxt\",\n \"text\": \"numpy/lib/npyio.py::loadtxt\\ndef loadtxt(fname, dtype=float, comments='#', delimiter=None,\\n converters=None, skiprows=0, usecols=None, unpack=False,\\n ndmin=0):\\n \\\"\\\"\\\"\\n Load data from a text file.\\n\\n Each row in the text file must have the same number of values.\\n\\n Parameters\\n ----------\\n fname : file or str\\n File, filename, or generator to read. If the filename extension is\\n ``.gz`` or ``.bz2``, the file is first decompressed. Note that\\n generators should return byte strings for Python 3k.\\n dtype : data-type, optional\\n Data-type of the resulting array; default: float. If this is a\\n record data-type, the resulting array will be 1-dimensional, and\\n each row will be interpreted as an element of the array. In this\\n case, the number of columns used must match the number of fields in\\n the data-type.\\n comments : str, optional\\n The character used to indicate the start of a comment;\\n default: '#'.\\n delimiter : str, optional\\n The string used to separate values. By default, this is any\\n whitespace.\\n converters : dict, optional\\n A dictionary mapping column number to a function that will convert\\n that column to a float. E.g., if column 0 is a date string:\\n ``converters = {0: datestr2num}``. Converters can also be used to\\n provide a default value for missing data (but see also `genfromtxt`):\\n ``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None.\\n skiprows : int, optional\\n Skip the first `skiprows` lines; default: 0.\\n usecols : sequence, optional\\n Which columns to read, with 0 being the first. For example,\\n ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.\\n The default, None, results in all columns being read.\\n unpack : bool, optional\\n If True, the returned array is transposed, so that arguments may be\\n unpacked using ``x, y, z =\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::loadtxt.flatten_dtype\",\n \"text\": \"numpy/lib/npyio.py::loadtxt.flatten_dtype\\n def flatten_dtype(dt):\\n \\\"\\\"\\\"Unpack a structured data-type, and produce re-packing info.\\\"\\\"\\\"\\n if dt.names is None:\\n # If the dtype is flattened, return.\\n # If the dtype has a shape, the dtype occurs\\n # in the list more than once.\\n shape = dt.shape\\n if len(shape) == 0:\\n return ([dt.base], None)\\n else:\\n packing = [(shape[-1], list)]\\n if len(shape) > 1:\\n for dim in dt.shape[-2::-1]:\\n packing = [(dim*packing[0][0], packing*dim)]\\n return ([dt.base] * int(np.prod(dt.shape)), packing)\\n else:\\n types = []\\n packing = []\\n for field in dt.names:\\n tp, bytes = dt.fields[field]\\n flat_dt, flat_packing = flatten_dtype(tp)\\n types.extend(flat_dt)\\n # Avoid extra nesting for subarrays\\n if len(tp.shape) > 0:\\n packing.extend(flat_packing)\\n else:\\n packing.append((len(flat_dt), flat_packing))\\n return (types, packing)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::loadtxt.pack_items\",\n \"text\": \"numpy/lib/npyio.py::loadtxt.pack_items\\n def pack_items(items, packing):\\n \\\"\\\"\\\"Pack items into nested lists based on re-packing info.\\\"\\\"\\\"\\n if packing == None:\\n return items[0]\\n elif packing is tuple:\\n return tuple(items)\\n elif packing is list:\\n return list(items)\\n else:\\n start = 0\\n ret = []\\n for length, subpacking in packing:\\n ret.append(pack_items(items[start:start+length], subpacking))\\n start += length\\n return tuple(ret)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::loadtxt.split_line\",\n \"text\": \"numpy/lib/npyio.py::loadtxt.split_line\\n def split_line(line):\\n \\\"\\\"\\\"Chop off comments, strip, and split at delimiter.\\\"\\\"\\\"\\n line = asbytes(line).split(comments)[0].strip(asbytes('\\\\r\\\\n'))\\n if line:\\n return line.split(delimiter)\\n else:\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::savetxt\",\n \"text\": \"numpy/lib/npyio.py::savetxt\\ndef savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\\\\n', header='',\\n footer='', comments='# '):\\n \\\"\\\"\\\"\\n Save an array to a text file.\\n\\n Parameters\\n ----------\\n fname : filename or file handle\\n If the filename ends in ``.gz``, the file is automatically saved in\\n compressed gzip format. `loadtxt` understands gzipped files\\n transparently.\\n X : array_like\\n Data to be saved to a text file.\\n fmt : str or sequence of strs, optional\\n A single format (%10.5f), a sequence of formats, or a\\n multi-format string, e.g. 'Iteration %d -- %10.5f', in which\\n case `delimiter` is ignored. For complex `X`, the legal options\\n for `fmt` are:\\n a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted\\n like `' (%s+%sj)' % (fmt, fmt)`\\n b) a full string specifying every real and imaginary part, e.g.\\n `' %.4e %+.4j %.4e %+.4j %.4e %+.4j'` for 3 columns\\n c) a list of specifiers, one per column - in this case, the real\\n and imaginary part must have separate specifiers,\\n e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns\\n delimiter : str, optional\\n Character separating columns.\\n newline : str, optional\\n .. versionadded:: 1.5.0\\n header : str, optional\\n String that will be written at the beginning of the file.\\n .. versionadded:: 1.7.0\\n footer : str, optional\\n String that will be written at the end of the file.\\n .. versionadded:: 1.7.0\\n comments : str, optional\\n String that will be prepended to the ``header`` and ``footer`` strings,\\n to mark them as comments. Default: '# ', as expected by e.g.\\n ``numpy.loadtxt``.\\n .. versionadded:: 1.7.0\\n\\n Character separating lines.\\n\\n See Also\\n --------\\n save : Save an array to a binary file in NumPy ``.npy`` format\\n savez : Save several ar\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::fromregex\",\n \"text\": \"numpy/lib/npyio.py::fromregex\\ndef fromregex(file, regexp, dtype):\\n \\\"\\\"\\\"\\n Construct an array from a text file, using regular expression parsing.\\n\\n The returned array is always a structured array, and is constructed from\\n all matches of the regular expression in the file. Groups in the regular\\n expression are converted to fields of the structured array.\\n\\n Parameters\\n ----------\\n file : str or file\\n File name or file object to read.\\n regexp : str or regexp\\n Regular expression used to parse the file.\\n Groups in the regular expression correspond to fields in the dtype.\\n dtype : dtype or list of dtypes\\n Dtype for the structured array.\\n\\n Returns\\n -------\\n output : ndarray\\n The output array, containing the part of the content of `file` that\\n was matched by `regexp`. `output` is always a structured array.\\n\\n Raises\\n ------\\n TypeError\\n When `dtype` is not a valid dtype for a structured array.\\n\\n See Also\\n --------\\n fromstring, loadtxt\\n\\n Notes\\n -----\\n Dtypes for structured arrays can be specified in several forms, but all\\n forms specify at least the data type and field name. For details see\\n `doc.structured_arrays`.\\n\\n Examples\\n --------\\n >>> f = open('test.dat', 'w')\\n >>> f.write(\\\"1312 foo\\\\\\\\n1534 bar\\\\\\\\n444 qux\\\")\\n >>> f.close()\\n\\n >>> regexp = r\\\"(\\\\\\\\d+)\\\\\\\\s+(...)\\\" # match [digits, whitespace, anything]\\n >>> output = np.fromregex('test.dat', regexp,\\n ... [('num', np.int64), ('key', 'S3')])\\n >>> output\\n array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],\\n dtype=[('num', '<i8'), ('key', '|S3')])\\n >>> output['num']\\n array([1312, 1534, 444], dtype=int64)\\n\\n \\\"\\\"\\\"\\n own_fh = False\\n if not hasattr(file, \\\"read\\\"):\\n file = open(file, 'rb')\\n own_fh = True\\n\\n try:\\n if not hasattr(regexp, 'match'):\\n regexp = re.compile(asbytes(regexp))\\n if not isinstance(dtyp\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::genfromtxt\",\n \"text\": \"numpy/lib/npyio.py::genfromtxt\\ndef genfromtxt(fname, dtype=float, comments='#', delimiter=None,\\n skiprows=0, skip_header=0, skip_footer=0, converters=None,\\n missing='', missing_values=None, filling_values=None,\\n usecols=None, names=None,\\n excludelist=None, deletechars=None, replace_space='_',\\n autostrip=False, case_sensitive=True, defaultfmt=\\\"f%i\\\",\\n unpack=None, usemask=False, loose=True, invalid_raise=True):\\n \\\"\\\"\\\"\\n Load data from a text file, with missing values handled as specified.\\n\\n Each line past the first `skip_header` lines is split at the `delimiter`\\n character, and characters following the `comments` character are discarded.\\n\\n Parameters\\n ----------\\n fname : file or str\\n File, filename, or generator to read. If the filename extension is\\n `.gz` or `.bz2`, the file is first decompressed. Note that\\n generators must return byte strings in Python 3k.\\n dtype : dtype, optional\\n Data type of the resulting array.\\n If None, the dtypes will be determined by the contents of each\\n column, individually.\\n comments : str, optional\\n The character used to indicate the start of a comment.\\n All the characters occurring on a line after a comment are discarded\\n delimiter : str, int, or sequence, optional\\n The string used to separate values. By default, any consecutive\\n whitespaces act as delimiter. An integer or sequence of integers\\n can also be provided as width(s) of each field.\\n skip_header : int, optional\\n The numbers of lines to skip at the beginning of the file.\\n skip_footer : int, optional\\n The numbers of lines to skip at the end of the file\\n converters : variable, optional\\n The set of functions that convert the data of a column to a value.\\n The converters can also be used to provide a default value\\n for missing data: ``converters = {3: la\",\n \"label\": 1\n },\n {\n \"cid\": \"numpy/lib/npyio.py::ndfromtxt\",\n \"text\": \"numpy/lib/npyio.py::ndfromtxt\\ndef ndfromtxt(fname, **kwargs):\\n \\\"\\\"\\\"\\n Load ASCII data stored in a file and return it as a single array.\\n\\n Parameters\\n ----------\\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\\n\\n See Also\\n --------\\n numpy.genfromtxt : generic function.\\n\\n \\\"\\\"\\\"\\n kwargs['usemask'] = False\\n return genfromtxt(fname, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::mafromtxt\",\n \"text\": \"numpy/lib/npyio.py::mafromtxt\\ndef mafromtxt(fname, **kwargs):\\n \\\"\\\"\\\"\\n Load ASCII data stored in a text file and return a masked array.\\n\\n Parameters\\n ----------\\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\\n\\n See Also\\n --------\\n numpy.genfromtxt : generic function to load ASCII data.\\n\\n \\\"\\\"\\\"\\n kwargs['usemask'] = True\\n return genfromtxt(fname, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::recfromtxt\",\n \"text\": \"numpy/lib/npyio.py::recfromtxt\\ndef recfromtxt(fname, **kwargs):\\n \\\"\\\"\\\"\\n Load ASCII data from a file and return it in a record array.\\n\\n If ``usemask=False`` a standard `recarray` is returned,\\n if ``usemask=True`` a MaskedRecords array is returned.\\n\\n Parameters\\n ----------\\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\\n\\n See Also\\n --------\\n numpy.genfromtxt : generic function\\n\\n Notes\\n -----\\n By default, `dtype` is None, which means that the data-type of the output\\n array will be determined from the data.\\n\\n \\\"\\\"\\\"\\n kwargs.update(dtype=kwargs.get('dtype', None))\\n usemask = kwargs.get('usemask', False)\\n output = genfromtxt(fname, **kwargs)\\n if usemask:\\n from numpy.ma.mrecords import MaskedRecords\\n output = output.view(MaskedRecords)\\n else:\\n output = output.view(np.recarray)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::recfromcsv\",\n \"text\": \"numpy/lib/npyio.py::recfromcsv\\ndef recfromcsv(fname, **kwargs):\\n \\\"\\\"\\\"\\n Load ASCII data stored in a comma-separated file.\\n\\n The returned array is a record array (if ``usemask=False``, see\\n `recarray`) or a masked record array (if ``usemask=True``,\\n see `ma.mrecords.MaskedRecords`).\\n\\n Parameters\\n ----------\\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\\n\\n See Also\\n --------\\n numpy.genfromtxt : generic function to load ASCII data.\\n\\n \\\"\\\"\\\"\\n case_sensitive = kwargs.get('case_sensitive', \\\"lower\\\") or \\\"lower\\\"\\n names = kwargs.get('names', True)\\n if names is None:\\n names = True\\n kwargs.update(dtype=kwargs.get('update', None),\\n delimiter=kwargs.get('delimiter', \\\",\\\") or \\\",\\\",\\n names=names,\\n case_sensitive=case_sensitive)\\n usemask = kwargs.get(\\\"usemask\\\", False)\\n output = genfromtxt(fname, **kwargs)\\n if usemask:\\n from numpy.ma.mrecords import MaskedRecords\\n output = output.view(MaskedRecords)\\n else:\\n output = output.view(np.recarray)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::_getformats\",\n \"text\": \"numpy/ma/mrecords.py::_getformats\\ndef _getformats(data):\\n \\\"Returns the formats of each array of arraylist as a comma-separated string.\\\"\\n if hasattr(data, 'dtype'):\\n return \\\",\\\".join([desc[1] for desc in data.dtype.descr])\\n\\n formats = ''\\n for obj in data:\\n obj = np.asarray(obj)\\n formats += _typestr[obj.dtype.type]\\n if issubclass(obj.dtype.type, ntypes.flexible):\\n formats += repr(obj.itemsize)\\n formats += ','\\n return formats[:-1]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::_checknames\",\n \"text\": \"numpy/ma/mrecords.py::_checknames\\ndef _checknames(descr, names=None):\\n \\\"\\\"\\\"Checks that the field names of the descriptor ``descr`` are not some\\nreserved keywords. If this is the case, a default 'f%i' is substituted.\\nIf the argument `names` is not None, updates the field names to valid names.\\n \\\"\\\"\\\"\\n ndescr = len(descr)\\n default_names = ['f%i' % i for i in range(ndescr)]\\n if names is None:\\n new_names = default_names\\n else:\\n if isinstance(names, (tuple, list)):\\n new_names = names\\n elif isinstance(names, str):\\n new_names = names.split(',')\\n else:\\n raise NameError(\\\"illegal input names %s\\\" % repr(names))\\n nnames = len(new_names)\\n if nnames < ndescr:\\n new_names += default_names[nnames:]\\n ndescr = []\\n for (n, d, t) in zip(new_names, default_names, descr.descr):\\n if n in reserved_fields:\\n if t[0] in reserved_fields:\\n ndescr.append((d, t[1]))\\n else:\\n ndescr.append(t)\\n else:\\n ndescr.append((n, t[1]))\\n return np.dtype(ndescr)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::_get_fieldmask\",\n \"text\": \"numpy/ma/mrecords.py::_get_fieldmask\\ndef _get_fieldmask(self):\\n mdescr = [(n, '|b1') for n in self.dtype.names]\\n fdmask = np.empty(self.shape, dtype=mdescr)\\n fdmask.flat = tuple([False] * len(mdescr))\\n return fdmask\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__new__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__new__\\n def __new__(cls, shape, dtype=None, buf=None, offset=0, strides=None,\\n formats=None, names=None, titles=None,\\n byteorder=None, aligned=False,\\n mask=nomask, hard_mask=False, fill_value=None, keep_mask=True,\\n copy=False,\\n **options):\\n #\\n self = recarray.__new__(cls, shape, dtype=dtype, buf=buf, offset=offset,\\n strides=strides, formats=formats, names=names,\\n titles=titles, byteorder=byteorder,\\n aligned=aligned,)\\n #\\n mdtype = ma.make_mask_descr(self.dtype)\\n if mask is nomask or not np.size(mask):\\n if not keep_mask:\\n self._mask = tuple([False] * len(mdtype))\\n else:\\n mask = np.array(mask, copy=copy)\\n if mask.shape != self.shape:\\n (nd, nm) = (self.size, mask.size)\\n if nm == 1:\\n mask = np.resize(mask, self.shape)\\n elif nm == nd:\\n mask = np.reshape(mask, self.shape)\\n else:\\n msg = \\\"Mask and data not compatible: data size is %i, \\\" + \\\\\\n \\\"mask size is %i.\\\"\\n raise MAError(msg % (nd, nm))\\n copy = True\\n if not keep_mask:\\n self.__setmask__(mask)\\n self._sharedmask = True\\n else:\\n if mask.dtype == mdtype:\\n _mask = mask\\n else:\\n _mask = np.array([tuple([m] * len(mdtype)) for m in mask],\\n dtype=mdtype)\\n self._mask = _mask\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__array_finalize__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__array_finalize__\\n def __array_finalize__(self, obj):\\n # Make sure we have a _fieldmask by default ..\\n _mask = getattr(obj, '_mask', None)\\n if _mask is None:\\n objmask = getattr(obj, '_mask', nomask)\\n _dtype = ndarray.__getattribute__(self, 'dtype')\\n if objmask is nomask:\\n _mask = ma.make_mask_none(self.shape, dtype=_dtype)\\n else:\\n mdescr = ma.make_mask_descr(_dtype)\\n _mask = narray([tuple([m] * len(mdescr)) for m in objmask],\\n dtype=mdescr).view(recarray)\\n # Update some of the attributes\\n _dict = self.__dict__\\n _dict.update(_mask=_mask)\\n self._update_from(obj)\\n if _dict['_baseclass'] == ndarray:\\n _dict['_baseclass'] = recarray\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords._getdata\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords._getdata\\n def _getdata(self):\\n \\\"Returns the data as a recarray.\\\"\\n return ndarray.view(self, recarray)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords._getfieldmask\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords._getfieldmask\\n def _getfieldmask(self):\\n \\\"Alias to mask\\\"\\n return self._mask\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__len__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__len__\\n def __len__(self):\\n \\\"Returns the length\\\"\\n # We have more than one record\\n if self.ndim:\\n return len(self._data)\\n # We have only one record: return the nb of fields\\n return len(self.dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__getattribute__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__getattribute__\\n def __getattribute__(self, attr):\\n try:\\n return object.__getattribute__(self, attr)\\n except AttributeError: # attr must be a fieldname\\n pass\\n fielddict = ndarray.__getattribute__(self, 'dtype').fields\\n try:\\n res = fielddict[attr][:2]\\n except (TypeError, KeyError):\\n raise AttributeError(\\\"record array has no attribute %s\\\" % attr)\\n # So far, so good...\\n _localdict = ndarray.__getattribute__(self, '__dict__')\\n _data = ndarray.view(self, _localdict['_baseclass'])\\n obj = _data.getfield(*res)\\n if obj.dtype.fields:\\n raise NotImplementedError(\\\"MaskedRecords is currently limited to\\\"\\\\\\n \\\"simple records...\\\")\\n # Get some special attributes\\n # Reset the object's mask\\n hasmasked = False\\n _mask = _localdict.get('_mask', None)\\n if _mask is not None:\\n try:\\n _mask = _mask[attr]\\n except IndexError:\\n # Couldn't find a mask: use the default (nomask)\\n pass\\n hasmasked = _mask.view((np.bool, (len(_mask.dtype) or 1))).any()\\n if (obj.shape or hasmasked):\\n obj = obj.view(MaskedArray)\\n obj._baseclass = ndarray\\n obj._isfield = True\\n obj._mask = _mask\\n # Reset the field values\\n _fill_value = _localdict.get('_fill_value', None)\\n if _fill_value is not None:\\n try:\\n obj._fill_value = _fill_value[attr]\\n except ValueError:\\n obj._fill_value = None\\n else:\\n obj = obj.item()\\n return obj\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__setattr__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__setattr__\\n def __setattr__(self, attr, val):\\n \\\"Sets the attribute attr to the value val.\\\"\\n # Should we call __setmask__ first ?\\n if attr in ['mask', 'fieldmask']:\\n self.__setmask__(val)\\n return\\n # Create a shortcut (so that we don't have to call getattr all the time)\\n _localdict = object.__getattribute__(self, '__dict__')\\n # Check whether we're creating a new field\\n newattr = attr not in _localdict\\n try:\\n # Is attr a generic attribute ?\\n ret = object.__setattr__(self, attr, val)\\n except:\\n # Not a generic attribute: exit if it's not a valid field\\n fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}\\n optinfo = ndarray.__getattribute__(self, '_optinfo') or {}\\n if not (attr in fielddict or attr in optinfo):\\n exctype, value = sys.exc_info()[:2]\\n raise exctype(value)\\n else:\\n # Get the list of names ......\\n fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}\\n # Check the attribute\\n if attr not in fielddict:\\n return ret\\n if newattr: # We just added this one\\n try: # or this setattr worked on an internal\\n # attribute.\\n object.__delattr__(self, attr)\\n except:\\n return ret\\n # Let's try to set the field\\n try:\\n res = fielddict[attr][:2]\\n except (TypeError, KeyError):\\n raise AttributeError(\\\"record array has no attribute %s\\\" % attr)\\n #\\n if val is masked:\\n _fill_value = _localdict['_fill_value']\\n if _fill_value is not None:\\n dval = _localdict['_fill_value'][attr]\\n else:\\n dval = val\\n mval = True\\n else:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__getitem__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__getitem__\\n def __getitem__(self, indx):\\n \\\"\\\"\\\"Returns all the fields sharing the same fieldname base.\\nThe fieldname base is either `_data` or `_mask`.\\\"\\\"\\\"\\n _localdict = self.__dict__\\n _mask = ndarray.__getattribute__(self, '_mask')\\n _data = ndarray.view(self, _localdict['_baseclass'])\\n # We want a field ........\\n if isinstance(indx, basestring):\\n #!!!: Make sure _sharedmask is True to propagate back to _fieldmask\\n #!!!: Don't use _set_mask, there are some copies being made...\\n #!!!: ...that break propagation\\n #!!!: Don't force the mask to nomask, that wrecks easy masking\\n obj = _data[indx].view(MaskedArray)\\n obj._mask = _mask[indx]\\n obj._sharedmask = True\\n fval = _localdict['_fill_value']\\n if fval is not None:\\n obj._fill_value = fval[indx]\\n # Force to masked if the mask is True\\n if not obj.ndim and obj._mask:\\n return masked\\n return obj\\n # We want some elements ..\\n # First, the data ........\\n obj = np.array(_data[indx], copy=False).view(mrecarray)\\n obj._mask = np.array(_mask[indx], copy=False).view(recarray)\\n return obj\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__setitem__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__setitem__\\n def __setitem__(self, indx, value):\\n \\\"Sets the given record to value.\\\"\\n MaskedArray.__setitem__(self, indx, value)\\n if isinstance(indx, basestring):\\n self._mask[indx] = ma.getmaskarray(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__str__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__str__\\n def __str__(self):\\n \\\"Calculates the string representation.\\\"\\n if self.size > 1:\\n mstr = [\\\"(%s)\\\" % \\\",\\\".join([str(i) for i in s])\\n for s in zip(*[getattr(self, f) for f in self.dtype.names])]\\n return \\\"[%s]\\\" % \\\", \\\".join(mstr)\\n else:\\n mstr = [\\\"%s\\\" % \\\",\\\".join([str(i) for i in s])\\n for s in zip([getattr(self, f) for f in self.dtype.names])]\\n return \\\"(%s)\\\" % \\\", \\\".join(mstr)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__repr__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__repr__\\n def __repr__(self):\\n \\\"Calculates the repr representation.\\\"\\n _names = self.dtype.names\\n fmt = \\\"%%%is : %%s\\\" % (max([len(n) for n in _names]) + 4,)\\n reprstr = [fmt % (f, getattr(self, f)) for f in self.dtype.names]\\n reprstr.insert(0, 'masked_records(')\\n reprstr.extend([fmt % (' fill_value', self.fill_value),\\n ' )'])\\n return str(\\\"\\\\n\\\".join(reprstr))\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.view\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.view\\n def view(self, dtype=None, type=None):\\n \\\"\\\"\\\"Returns a view of the mrecarray.\\\"\\\"\\\"\\n # OK, basic copy-paste from MaskedArray.view...\\n if dtype is None:\\n if type is None:\\n output = ndarray.view(self)\\n else:\\n output = ndarray.view(self, type)\\n # Here again...\\n elif type is None:\\n try:\\n if issubclass(dtype, ndarray):\\n output = ndarray.view(self, dtype)\\n dtype = None\\n else:\\n output = ndarray.view(self, dtype)\\n # OK, there's the change\\n except TypeError:\\n dtype = np.dtype(dtype)\\n # we need to revert to MaskedArray, but keeping the possibility\\n # ...of subclasses (eg, TimeSeriesRecords), so we'll force a type\\n # ...set to the first parent\\n if dtype.fields is None:\\n basetype = self.__class__.__bases__[0]\\n output = self.__array__().view(dtype, basetype)\\n output._update_from(self)\\n else:\\n output = ndarray.view(self, dtype)\\n output._fill_value = None\\n else:\\n output = ndarray.view(self, dtype, type)\\n # Update the mask, just like in MaskedArray.view\\n if (getattr(output, '_mask', nomask) is not nomask):\\n mdtype = ma.make_mask_descr(output.dtype)\\n output._mask = self._mask.view(mdtype, ndarray)\\n output._mask.shape = output.shape\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.harden_mask\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.harden_mask\\n def harden_mask(self):\\n \\\"Forces the mask to hard\\\"\\n self._hardmask = True\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.soften_mask\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.soften_mask\\n def soften_mask(self):\\n \\\"Forces the mask to soft\\\"\\n self._hardmask = False\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.copy\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.copy\\n def copy(self):\\n \\\"\\\"\\\"Returns a copy of the masked record.\\\"\\\"\\\"\\n _localdict = self.__dict__\\n copied = self._data.copy().view(type(self))\\n copied._mask = self._mask.copy()\\n return copied\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.tolist\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.tolist\\n def tolist(self, fill_value=None):\\n \\\"\\\"\\\"Copy the data portion of the array to a hierarchical python\\n list and returns that list.\\n\\n Data items are converted to the nearest compatible Python\\n type. Masked values are converted to fill_value. If\\n fill_value is None, the corresponding entries in the output\\n list will be ``None``.\\n\\n \\\"\\\"\\\"\\n if fill_value is not None:\\n return self.filled(fill_value).tolist()\\n result = narray(self.filled().tolist(), dtype=object)\\n mask = narray(self._mask.tolist())\\n result[mask] = None\\n return result.tolist()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__getstate__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__getstate__\\n def __getstate__(self):\\n \\\"\\\"\\\"Return the internal state of the masked array, for pickling purposes.\\n\\n \\\"\\\"\\\"\\n state = (1,\\n self.shape,\\n self.dtype,\\n self.flags.fnc,\\n self._data.tostring(),\\n self._mask.tostring(),\\n self._fill_value,\\n )\\n return state\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__setstate__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__setstate__\\n def __setstate__(self, state):\\n \\\"\\\"\\\"Restore the internal state of the masked array, for pickling purposes.\\n ``state`` is typically the output of the ``__getstate__`` output, and is a\\n 5-tuple:\\n\\n - class name\\n - a tuple giving the shape of the data\\n - a typecode for the data\\n - a binary string for the data\\n - a binary string for the mask.\\n\\n \\\"\\\"\\\"\\n (ver, shp, typ, isf, raw, msk, flv) = state\\n ndarray.__setstate__(self, (shp, typ, isf, raw))\\n mdtype = dtype([(k, bool_) for (k, _) in self.dtype.descr])\\n self.__dict__['_mask'].__setstate__((shp, mdtype, isf, msk))\\n self.fill_value = flv\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::MaskedRecords.__reduce__\",\n \"text\": \"numpy/ma/mrecords.py::MaskedRecords.__reduce__\\n def __reduce__(self):\\n \\\"\\\"\\\"Return a 3-tuple for pickling a MaskedArray.\\n\\n \\\"\\\"\\\"\\n return (_mrreconstruct,\\n (self.__class__, self._baseclass, (0,), 'b',),\\n self.__getstate__())\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::_mrreconstruct\",\n \"text\": \"numpy/ma/mrecords.py::_mrreconstruct\\ndef _mrreconstruct(subtype, baseclass, baseshape, basetype,):\\n \\\"\\\"\\\"Internal function that builds a new MaskedArray from the\\n information stored in a pickle.\\n\\n \\\"\\\"\\\"\\n _data = ndarray.__new__(baseclass, baseshape, basetype).view(subtype)\\n# _data._mask = ndarray.__new__(ndarray, baseshape, 'b1')\\n# return _data\\n _mask = ndarray.__new__(ndarray, baseshape, 'b1')\\n return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::fromarrays\",\n \"text\": \"numpy/ma/mrecords.py::fromarrays\\ndef fromarrays(arraylist, dtype=None, shape=None, formats=None,\\n names=None, titles=None, aligned=False, byteorder=None,\\n fill_value=None):\\n \\\"\\\"\\\"Creates a mrecarray from a (flat) list of masked arrays.\\n\\n Parameters\\n ----------\\n arraylist : sequence\\n A list of (masked) arrays. Each element of the sequence is first converted\\n to a masked array if needed. If a 2D array is passed as argument, it is\\n processed line by line\\n dtype : {None, dtype}, optional\\n Data type descriptor.\\n shape : {None, integer}, optional\\n Number of records. If None, shape is defined from the shape of the\\n first array in the list.\\n formats : {None, sequence}, optional\\n Sequence of formats for each individual field. If None, the formats will\\n be autodetected by inspecting the fields and selecting the highest dtype\\n possible.\\n names : {None, sequence}, optional\\n Sequence of the names of each field.\\n fill_value : {None, sequence}, optional\\n Sequence of data to be used as filling values.\\n\\n Notes\\n -----\\n Lists of tuples should be preferred over lists of lists for faster processing.\\n \\\"\\\"\\\"\\n datalist = [getdata(x) for x in arraylist]\\n masklist = [np.atleast_1d(getmaskarray(x)) for x in arraylist]\\n _array = recfromarrays(datalist,\\n dtype=dtype, shape=shape, formats=formats,\\n names=names, titles=titles, aligned=aligned,\\n byteorder=byteorder).view(mrecarray)\\n _array._mask.flat = zip(*masklist)\\n if fill_value is not None:\\n _array.fill_value = fill_value\\n return _array\",\n \"label\": 1\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::fromrecords\",\n \"text\": \"numpy/ma/mrecords.py::fromrecords\\ndef fromrecords(reclist, dtype=None, shape=None, formats=None, names=None,\\n titles=None, aligned=False, byteorder=None,\\n fill_value=None, mask=nomask):\\n \\\"\\\"\\\"Creates a MaskedRecords from a list of records.\\n\\n Parameters\\n ----------\\n reclist : sequence\\n A list of records. Each element of the sequence is first converted\\n to a masked array if needed. If a 2D array is passed as argument, it is\\n processed line by line\\n dtype : {None, dtype}, optional\\n Data type descriptor.\\n shape : {None,int}, optional\\n Number of records. If None, ``shape`` is defined from the shape of the\\n first array in the list.\\n formats : {None, sequence}, optional\\n Sequence of formats for each individual field. If None, the formats will\\n be autodetected by inspecting the fields and selecting the highest dtype\\n possible.\\n names : {None, sequence}, optional\\n Sequence of the names of each field.\\n fill_value : {None, sequence}, optional\\n Sequence of data to be used as filling values.\\n mask : {nomask, sequence}, optional.\\n External mask to apply on the data.\\n\\n Notes\\n -----\\n Lists of tuples should be preferred over lists of lists for faster processing.\\n \\\"\\\"\\\"\\n # Grab the initial _fieldmask, if needed:\\n _mask = getattr(reclist, '_mask', None)\\n # Get the list of records.....\\n try:\\n nfields = len(reclist[0])\\n except TypeError:\\n nfields = len(reclist[0].dtype)\\n if isinstance(reclist, ndarray):\\n # Make sure we don't have some hidden mask\\n if isinstance(reclist, MaskedArray):\\n reclist = reclist.filled().view(ndarray)\\n # Grab the initial dtype, just in case\\n if dtype is None:\\n dtype = reclist.dtype\\n reclist = reclist.tolist()\\n mrec = recfromrecords(reclist, dtype=dtype, shape=shape, formats=formats,\\n names=names, ti\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::_guessvartypes\",\n \"text\": \"numpy/ma/mrecords.py::_guessvartypes\\ndef _guessvartypes(arr):\\n \\\"\\\"\\\"Tries to guess the dtypes of the str_ ndarray `arr`, by testing element-wise\\nconversion. Returns a list of dtypes.\\nThe array is first converted to ndarray. If the array is 2D, the test is performed\\non the first line. An exception is raised if the file is 3D or more.\\n \\\"\\\"\\\"\\n vartypes = []\\n arr = np.asarray(arr)\\n if len(arr.shape) == 2 :\\n arr = arr[0]\\n elif len(arr.shape) > 2:\\n raise ValueError(\\\"The array should be 2D at most!\\\")\\n # Start the conversion loop .......\\n for f in arr:\\n try:\\n int(f)\\n except ValueError:\\n try:\\n float(f)\\n except ValueError:\\n try:\\n val = complex(f)\\n except ValueError:\\n vartypes.append(arr.dtype)\\n else:\\n vartypes.append(np.dtype(complex))\\n else:\\n vartypes.append(np.dtype(float))\\n else:\\n vartypes.append(np.dtype(int))\\n return vartypes\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::openfile\",\n \"text\": \"numpy/ma/mrecords.py::openfile\\ndef openfile(fname):\\n \\\"Opens the file handle of file `fname`\\\"\\n # A file handle ...................\\n if hasattr(fname, 'readline'):\\n return fname\\n # Try to open the file and guess its type\\n try:\\n f = open(fname)\\n except IOError:\\n raise IOError(\\\"No such file: '%s'\\\" % fname)\\n if f.readline()[:2] != \\\"\\\\\\\\x\\\":\\n f.seek(0, 0)\\n return f\\n f.close()\\n raise NotImplementedError(\\\"Wow, binary file\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::fromtextfile\",\n \"text\": \"numpy/ma/mrecords.py::fromtextfile\\ndef fromtextfile(fname, delimitor=None, commentchar='#', missingchar='',\\n varnames=None, vartypes=None):\\n \\\"\\\"\\\"Creates a mrecarray from data stored in the file `filename`.\\n\\n Parameters\\n ----------\\n filename : {file name/handle}\\n Handle of an opened file.\\n delimitor : {None, string}, optional\\n Alphanumeric character used to separate columns in the file.\\n If None, any (group of) white spacestring(s) will be used.\\n commentchar : {'#', string}, optional\\n Alphanumeric character used to mark the start of a comment.\\n missingchar : {'', string}, optional\\n String indicating missing data, and used to create the masks.\\n varnames : {None, sequence}, optional\\n Sequence of the variable names. If None, a list will be created from\\n the first non empty line of the file.\\n vartypes : {None, sequence}, optional\\n Sequence of the variables dtypes. If None, it will be estimated from\\n the first non-commented line.\\n\\n\\n Ultra simple: the varnames are in the header, one line\\\"\\\"\\\"\\n # Try to open the file ......................\\n f = openfile(fname)\\n\\n # Get the first non-empty line as the varnames\\n while True:\\n line = f.readline()\\n firstline = line[:line.find(commentchar)].strip()\\n _varnames = firstline.split(delimitor)\\n if len(_varnames) > 1:\\n break\\n if varnames is None:\\n varnames = _varnames\\n\\n # Get the data ..............................\\n _variables = masked_array([line.strip().split(delimitor) for line in f\\n if line[0] != commentchar and len(line) > 1])\\n (_, nfields) = _variables.shape\\n f.close()\\n\\n # Try to guess the dtype ....................\\n if vartypes is None:\\n vartypes = _guessvartypes(_variables[0])\\n else:\\n vartypes = [np.dtype(v) for v in vartypes]\\n if len(vartypes) != nfields:\\n msg = \\\"Attempting to %i \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/mrecords.py::addfield\",\n \"text\": \"numpy/ma/mrecords.py::addfield\\ndef addfield(mrecord, newfield, newfieldname=None):\\n \\\"\\\"\\\"Adds a new field to the masked record array, using `newfield` as data\\nand `newfieldname` as name. If `newfieldname` is None, the new field name is\\nset to 'fi', where `i` is the number of existing fields.\\n \\\"\\\"\\\"\\n _data = mrecord._data\\n _mask = mrecord._mask\\n if newfieldname is None or newfieldname in reserved_fields:\\n newfieldname = 'f%i' % len(_data.dtype)\\n newfield = ma.array(newfield)\\n # Get the new data ............\\n # Create a new empty recarray\\n newdtype = np.dtype(_data.dtype.descr + [(newfieldname, newfield.dtype)])\\n newdata = recarray(_data.shape, newdtype)\\n # Add the exisintg field\\n [newdata.setfield(_data.getfield(*f), *f)\\n for f in _data.dtype.fields.values()]\\n # Add the new field\\n newdata.setfield(newfield._data, *newdata.dtype.fields[newfieldname])\\n newdata = newdata.view(MaskedRecords)\\n # Get the new mask .............\\n # Create a new empty recarray\\n newmdtype = np.dtype([(n, bool_) for n in newdtype.names])\\n newmask = recarray(_data.shape, newmdtype)\\n # Add the old masks\\n [newmask.setfield(_mask.getfield(*f), *f)\\n for f in _mask.dtype.fields.values()]\\n # Add the mask of the new field\\n newmask.setfield(getmaskarray(newfield),\\n *newmask.dtype.fields[newfieldname])\\n newdata._mask = newmask\\n return newdata\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::git_version\",\n \"text\": \"setup.py::git_version\\ndef git_version():\\n def _minimal_ext_cmd(cmd):\\n # construct minimal environment\\n env = {}\\n for k in ['SYSTEMROOT', 'PATH']:\\n v = os.environ.get(k)\\n if v is not None:\\n env[k] = v\\n # LANGUAGE is used on win32\\n env['LANGUAGE'] = 'C'\\n env['LANG'] = 'C'\\n env['LC_ALL'] = 'C'\\n out = subprocess.Popen(cmd, stdout = subprocess.PIPE, env=env).communicate()[0]\\n return out\\n\\n try:\\n out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])\\n GIT_REVISION = out.strip().decode('ascii')\\n except OSError:\\n GIT_REVISION = \\\"Unknown\\\"\\n\\n return GIT_REVISION\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::git_version._minimal_ext_cmd\",\n \"text\": \"setup.py::git_version._minimal_ext_cmd\\n def _minimal_ext_cmd(cmd):\\n # construct minimal environment\\n env = {}\\n for k in ['SYSTEMROOT', 'PATH']:\\n v = os.environ.get(k)\\n if v is not None:\\n env[k] = v\\n # LANGUAGE is used on win32\\n env['LANGUAGE'] = 'C'\\n env['LANG'] = 'C'\\n env['LC_ALL'] = 'C'\\n out = subprocess.Popen(cmd, stdout = subprocess.PIPE, env=env).communicate()[0]\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::write_version_py\",\n \"text\": \"setup.py::write_version_py\\ndef write_version_py(filename='numpy/version.py'):\\n cnt = \\\"\\\"\\\"\\n# THIS FILE IS GENERATED FROM NUMPY SETUP.PY\\nshort_version = '%(version)s'\\nversion = '%(version)s'\\nfull_version = '%(full_version)s'\\ngit_revision = '%(git_revision)s'\\nrelease = %(isrelease)s\\n\\nif not release:\\n version = full_version\\n\\\"\\\"\\\"\\n # Adding the git rev number needs to be done inside write_version_py(),\\n # otherwise the import of numpy.version messes up the build under Python 3.\\n FULLVERSION = VERSION\\n if os.path.exists('.git'):\\n GIT_REVISION = git_version()\\n elif os.path.exists('numpy/version.py'):\\n # must be a source distribution, use existing version file\\n try:\\n from numpy.version import git_revision as GIT_REVISION\\n except ImportError:\\n raise ImportError(\\\"Unable to import git_revision. Try removing \\\" \\\\\\n \\\"numpy/version.py and the build directory \\\" \\\\\\n \\\"before building.\\\")\\n else:\\n GIT_REVISION = \\\"Unknown\\\"\\n\\n if not ISRELEASED:\\n FULLVERSION += '.dev-' + GIT_REVISION[:7]\\n\\n a = open(filename, 'w')\\n try:\\n a.write(cnt % {'version': VERSION,\\n 'full_version' : FULLVERSION,\\n 'git_revision' : GIT_REVISION,\\n 'isrelease': str(ISRELEASED)})\\n finally:\\n a.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::configuration\",\n \"text\": \"setup.py::configuration\\ndef configuration(parent_package='',top_path=None):\\n from numpy.distutils.misc_util import Configuration\\n\\n config = Configuration(None, parent_package, top_path)\\n config.set_options(ignore_setup_xxx_py=True,\\n assume_default_configuration=True,\\n delegate_options_to_subpackages=True,\\n quiet=True)\\n\\n config.add_subpackage('numpy')\\n\\n config.get_version('numpy/version.py') # sets config.version\\n\\n return config\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::setup_package\",\n \"text\": \"setup.py::setup_package\\ndef setup_package():\\n\\n # Perform 2to3 if needed\\n local_path = os.path.dirname(os.path.abspath(sys.argv[0]))\\n src_path = local_path\\n\\n if sys.version_info[0] == 3:\\n src_path = os.path.join(local_path, 'build', 'py3k')\\n sys.path.insert(0, os.path.join(local_path, 'tools'))\\n import py3tool\\n print(\\\"Converting to Python3 via 2to3...\\\")\\n py3tool.sync_2to3('numpy', os.path.join(src_path, 'numpy'))\\n\\n site_cfg = os.path.join(local_path, 'site.cfg')\\n if os.path.isfile(site_cfg):\\n shutil.copy(site_cfg, src_path)\\n\\n # Ugly hack to make pip work with Python 3, see #1857.\\n # Explanation: pip messes with __file__ which interacts badly with the\\n # change in directory due to the 2to3 conversion. Therefore we restore\\n # __file__ to what it would have been otherwise.\\n global __file__\\n __file__ = os.path.join(os.curdir, os.path.basename(__file__))\\n if '--egg-base' in sys.argv:\\n # Change pip-egg-info entry to absolute path, so pip can find it\\n # after changing directory.\\n idx = sys.argv.index('--egg-base')\\n if sys.argv[idx + 1] == 'pip-egg-info':\\n sys.argv[idx + 1] = os.path.join(local_path, 'pip-egg-info')\\n\\n old_path = os.getcwd()\\n os.chdir(src_path)\\n sys.path.insert(0, src_path)\\n\\n # Rewrite the version file everytime\\n write_version_py()\\n\\n # Run build\\n from numpy.distutils.core import setup\\n\\n try:\\n setup(\\n name=NAME,\\n maintainer=MAINTAINER,\\n maintainer_email=MAINTAINER_EMAIL,\\n description=DESCRIPTION,\\n long_description=LONG_DESCRIPTION,\\n url=URL,\\n download_url=DOWNLOAD_URL,\\n license=LICENSE,\\n classifiers=CLASSIFIERS,\\n author=AUTHOR,\\n author_email=AUTHOR_EMAIL,\\n platforms=PLATFORMS,\\n configuration=configuration )\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"tools/py3tool.py::main\",\n \"text\": \"tools/py3tool.py::main\\ndef main():\\n p = OptionParser(usage=__doc__.strip())\\n p.add_option(\\\"--clean\\\", \\\"-c\\\", action=\\\"store_true\\\",\\n help=\\\"clean source directory\\\")\\n options, args = p.parse_args()\\n\\n if not args:\\n p.error('no submodules given')\\n else:\\n dirs = ['numpy/%s' % x for x in map(os.path.basename, args)]\\n\\n # Prepare\\n if not os.path.isdir(TEMP):\\n os.makedirs(TEMP)\\n\\n # Set up dummy files (for building only submodules)\\n dummy_files = {\\n '__init__.py': 'from numpy.version import version as __version__',\\n 'version.py': 'version = \\\"1.4.0.dev\\\"'\\n }\\n\\n for fn, content in dummy_files.items():\\n fn = os.path.join(TEMP, 'numpy', fn)\\n if not os.path.isfile(fn):\\n try:\\n os.makedirs(os.path.dirname(fn))\\n except OSError:\\n pass\\n f = open(fn, 'wb+')\\n f.write(content.encode('ascii'))\\n f.close()\\n\\n # Environment\\n pp = [os.path.abspath(TEMP)]\\n def getenv():\\n env = dict(os.environ)\\n env.update({'PYTHONPATH': ':'.join(pp)})\\n return env\\n\\n # Copy\\n for d in dirs:\\n src = os.path.join(BASE, d)\\n dst = os.path.join(TEMP, d)\\n\\n # Run 2to3\\n sync_2to3(dst=dst,\\n src=src,\\n patchfile=os.path.join(TEMP, os.path.basename(d) + '.patch'),\\n clean=options.clean)\\n\\n # Run setup.py, falling back to Pdb post-mortem on exceptions\\n setup_py = os.path.join(dst, 'setup.py')\\n if os.path.isfile(setup_py):\\n code = \\\"\\\"\\\"\\\\\\nimport pdb, sys, traceback\\np = pdb.Pdb()\\ntry:\\n import __main__\\n __main__.__dict__.update({\\n \\\"__name__\\\": \\\"__main__\\\", \\\"__file__\\\": \\\"setup.py\\\",\\n \\\"__builtins__\\\": __builtins__})\\n fp = open(\\\"setup.py\\\", \\\"rb\\\")\\n try:\\n exec(compile(fp.read(), \\\"setup.py\\\", 'exec'))\\n finally:\\n fp.close()\\nexcept SystemExit:\\n raise\\nexcept:\\n traceback.print_exc()\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"tools/py3tool.py::main.getenv\",\n \"text\": \"tools/py3tool.py::main.getenv\\n def getenv():\\n env = dict(os.environ)\\n env.update({'PYTHONPATH': ':'.join(pp)})\\n return env\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/py3tool.py::walk_sync\",\n \"text\": \"tools/py3tool.py::walk_sync\\ndef walk_sync(dir1, dir2, _seen=None):\\n if _seen is None:\\n seen = {}\\n else:\\n seen = _seen\\n\\n if not dir1.endswith(os.path.sep):\\n dir1 = dir1 + os.path.sep\\n\\n # Walk through stuff (which we haven't yet gone through) in dir1\\n for root, dirs, files in os.walk(dir1):\\n sub = root[len(dir1):]\\n if sub in seen:\\n dirs = [x for x in dirs if x not in seen[sub][0]]\\n files = [x for x in files if x not in seen[sub][1]]\\n seen[sub][0].extend(dirs)\\n seen[sub][1].extend(files)\\n else:\\n seen[sub] = (dirs, files)\\n if not dirs and not files:\\n continue\\n yield os.path.join(dir1, sub), os.path.join(dir2, sub), dirs, files\\n\\n if _seen is None:\\n # Walk through stuff (which we haven't yet gone through) in dir2\\n for root2, root1, dirs, files in walk_sync(dir2, dir1, _seen=seen):\\n yield root1, root2, dirs, files\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/py3tool.py::sync_2to3\",\n \"text\": \"tools/py3tool.py::sync_2to3\\ndef sync_2to3(src, dst, patchfile=None, clean=False):\\n import lib2to3.main\\n from io import StringIO\\n\\n to_convert = []\\n\\n for src_dir, dst_dir, dirs, files in walk_sync(src, dst):\\n for fn in dirs + files:\\n src_fn = os.path.join(src_dir, fn)\\n dst_fn = os.path.join(dst_dir, fn)\\n\\n # skip temporary etc. files\\n if fn.startswith('.#') or fn.endswith('~'):\\n continue\\n\\n # remove non-existing\\n if os.path.exists(dst_fn) and not os.path.exists(src_fn):\\n if clean:\\n if os.path.isdir(dst_fn):\\n shutil.rmtree(dst_fn)\\n else:\\n os.unlink(dst_fn)\\n continue\\n\\n # make directories\\n if os.path.isdir(src_fn):\\n if not os.path.isdir(dst_fn):\\n os.makedirs(dst_fn)\\n continue\\n\\n dst_dir = os.path.dirname(dst_fn)\\n if os.path.isfile(dst_fn) and not os.path.isdir(dst_dir):\\n os.makedirs(dst_dir)\\n\\n # don't replace up-to-date files\\n try:\\n if os.path.isfile(dst_fn) and \\\\\\n os.stat(dst_fn).st_mtime >= os.stat(src_fn).st_mtime:\\n continue\\n except OSError:\\n pass\\n\\n # copy file\\n shutil.copyfile(src_fn, dst_fn)\\n\\n # add .py files to 2to3 list\\n if dst_fn.endswith('.py'):\\n to_convert.append((src_fn, dst_fn))\\n\\n # run 2to3\\n flag_sets = {}\\n for fn, dst_fn in to_convert:\\n flag = ''\\n for pat, opt in EXTRA_2TO3_FLAGS.items():\\n if fnmatch.fnmatch(fn, pat):\\n flag = opt\\n break\\n flag_sets.setdefault(flag, []).append(dst_fn)\\n\\n if patchfile:\\n p = open(patchfile, 'wb+')\\n else:\\n p = open(os.devnull, 'wb')\\n\\n for flags, filenames in flag_s\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":80,"cells":{"instance_id":{"kind":"string","value":"numpy__numpy-6543"},"repo":{"kind":"string","value":"numpy/numpy"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"BUG: ma: Several functions in the ma module return None when given a scalar.\nHere's an example, using `ma.atleast_1d`:\n\n```\nIn [4]: np.__version__\nOut[4]: '1.8.0.dev-31a5501'\n```\n\nWhen given a scalar, the regular version of `atleast_1d` returns an array:\n\n```\nIn [5]: x = np.atleast_1d(1.0)\n\nIn [6]: x\nOut[6]: array([ 1.])\n```\n\n`ma.atleast_1d` returns `None`:\n\n```\nIn [7]: x = np.ma.atleast_1d(1.0)\n\nIn [8]: print x\nNone\n```\n\n`ma.atleast_1d` and several other functions in the `ma` module are defined using the class `_fromnxfunction` (defined in numpy/ma/extras.py). In the `__call__` method of this class, in the case where `len(args) == 1`, the code handles the cases where the single argument is an ndarray, a tuple or list. If the single argument is not one of these, the code falls through to the end without returning anything.\n\nThe non-masked versions of these accept a scalar, so the masked version should too:\n\n```\natleast_1d\natleast_2d\natleast_3d\ndiagflat\n```\n\n"},"gold_files":{"kind":"list like","value":["numpy/ma/extras.py"],"string":"[\n \"numpy/ma/extras.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["numpy/ma/extras.py::_fromnxfunction.__call__"],"string":"[\n \"numpy/ma/extras.py::_fromnxfunction.__call__\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"numpy/ma/extras.py","text":"numpy/ma/extras.py\ndef issequence(seq) # Is seq a sequence (ndarray, list or tuple)?\ndef count_masked(arr, axis=None) # Count the number of masked elements along the given axis.\ndef masked_all(shape, dtype=float) # Empty masked array with all elements masked.\ndef masked_all_like(arr) # Empty masked array with the properties of an existing array.\nclass _fromnxfunction # Defines a wrapper to adapt NumPy functions to masked arrays.\n def __init__(self, funcname)\n def getdoc(self) # Retrieve the docstring and signature from the function.\n def __call__(self, *args, **params)\ndef flatten_inplace(seq) # Flatten a sequence in place.\ndef apply_along_axis(func1d, axis, arr, *args, **kwargs) # (This docstring should be overwritten)\ndef apply_over_axes(func, a, axes) # (This docstring will be overwritten)\ndef average(a, axis=None, weights=None, returned=False) # Return the weighted average of array over the given axis.\ndef median(a, axis=None, out=None, overwrite_input=False) # Compute the median along the specified axis.\ndef compress_nd(x, axis=None) # Supress slices from multiple dimensions which contain masked values.\ndef compress_rowcols(x, axis=None) # Suppress the rows and/or columns of a 2-D array that contain\ndef compress_rows(a) # Suppress whole rows of a 2-D array that contain masked values.\ndef compress_cols(a) # Suppress whole columns of a 2-D array that contain masked values.\ndef mask_rowcols(a, axis=None) # Mask rows and/or columns of a 2D array that contain masked values.\ndef mask_rows(a, axis=None) # Mask rows of a 2D array that contain masked values.\ndef mask_cols(a, axis=None) # Mask columns of a 2D array that contain masked values.\ndef dot(a, b, strict=False) # Return the dot product of two arrays.\ndef ediff1d(arr, to_end=None, to_begin=None) # Compute the differences between consecutive elements of an array.\ndef unique(ar1, return_index=False, return_inverse=False) # Finds the unique elements of an array.\ndef intersect1d(ar1, ar2, assume_unique=False) # Returns the unique elements common to both arrays.\ndef setxor1d(ar1, ar2, assume_unique=False) # Set exclusive-or of 1-D arrays with unique elements.\ndef in1d(ar1, ar2, assume_unique=False, invert=False) # Test whether each element of an array is also present in a second\ndef union1d(ar1, ar2) # Union of two arrays.\ndef setdiff1d(ar1, ar2, assume_unique=False) # Set difference of 1D arrays with unique elements.\ndef _covhelper(x, y=None, rowvar=True, allow_masked=True) # Private function for the computation of covariance and correlation\ndef cov(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None) # Estimate the covariance matrix.\ndef corrcoef(x, y=None, rowvar=True, bias=np._NoValue, allow_masked=True, ddof=np._NoValue) # Return Pearson product-moment correlation coefficients.\nclass MAxisConcatenator # Translate slice objects to concatenation along an axis.\n def __init__(self, axis=0)\n def __getitem__(self, key)\nclass mr_class # Trans","label":1}],"string":"[\n {\n \"cid\": \"numpy/ma/extras.py\",\n \"text\": \"numpy/ma/extras.py\\ndef issequence(seq) # Is seq a sequence (ndarray, list or tuple)?\\ndef count_masked(arr, axis=None) # Count the number of masked elements along the given axis.\\ndef masked_all(shape, dtype=float) # Empty masked array with all elements masked.\\ndef masked_all_like(arr) # Empty masked array with the properties of an existing array.\\nclass _fromnxfunction # Defines a wrapper to adapt NumPy functions to masked arrays.\\n def __init__(self, funcname)\\n def getdoc(self) # Retrieve the docstring and signature from the function.\\n def __call__(self, *args, **params)\\ndef flatten_inplace(seq) # Flatten a sequence in place.\\ndef apply_along_axis(func1d, axis, arr, *args, **kwargs) # (This docstring should be overwritten)\\ndef apply_over_axes(func, a, axes) # (This docstring will be overwritten)\\ndef average(a, axis=None, weights=None, returned=False) # Return the weighted average of array over the given axis.\\ndef median(a, axis=None, out=None, overwrite_input=False) # Compute the median along the specified axis.\\ndef compress_nd(x, axis=None) # Supress slices from multiple dimensions which contain masked values.\\ndef compress_rowcols(x, axis=None) # Suppress the rows and/or columns of a 2-D array that contain\\ndef compress_rows(a) # Suppress whole rows of a 2-D array that contain masked values.\\ndef compress_cols(a) # Suppress whole columns of a 2-D array that contain masked values.\\ndef mask_rowcols(a, axis=None) # Mask rows and/or columns of a 2D array that contain masked values.\\ndef mask_rows(a, axis=None) # Mask rows of a 2D array that contain masked values.\\ndef mask_cols(a, axis=None) # Mask columns of a 2D array that contain masked values.\\ndef dot(a, b, strict=False) # Return the dot product of two arrays.\\ndef ediff1d(arr, to_end=None, to_begin=None) # Compute the differences between consecutive elements of an array.\\ndef unique(ar1, return_index=False, return_inverse=False) # Finds the unique elements of an array.\\ndef intersect1d(ar1, ar2, assume_unique=False) # Returns the unique elements common to both arrays.\\ndef setxor1d(ar1, ar2, assume_unique=False) # Set exclusive-or of 1-D arrays with unique elements.\\ndef in1d(ar1, ar2, assume_unique=False, invert=False) # Test whether each element of an array is also present in a second\\ndef union1d(ar1, ar2) # Union of two arrays.\\ndef setdiff1d(ar1, ar2, assume_unique=False) # Set difference of 1D arrays with unique elements.\\ndef _covhelper(x, y=None, rowvar=True, allow_masked=True) # Private function for the computation of covariance and correlation\\ndef cov(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None) # Estimate the covariance matrix.\\ndef corrcoef(x, y=None, rowvar=True, bias=np._NoValue, allow_masked=True, ddof=np._NoValue) # Return Pearson product-moment correlation coefficients.\\nclass MAxisConcatenator # Translate slice objects to concatenation along an axis.\\n def __init__(self, axis=0)\\n def __getitem__(self, key)\\nclass mr_class # Trans\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"numpy/ma/extras.py::issequence","text":"numpy/ma/extras.py::issequence\ndef issequence(seq):\n \"\"\"\n Is seq a sequence (ndarray, list or tuple)?\n\n \"\"\"\n if isinstance(seq, (ndarray, tuple, list)):\n return True\n return False","label":0},{"cid":"numpy/ma/extras.py::count_masked","text":"numpy/ma/extras.py::count_masked\ndef count_masked(arr, axis=None):\n \"\"\"\n Count the number of masked elements along the given axis.\n\n Parameters\n ----------\n arr : array_like\n An array with (possibly) masked elements.\n axis : int, optional\n Axis along which to count. If None (default), a flattened\n version of the array is used.\n\n Returns\n -------\n count : int, ndarray\n The total number of masked elements (axis=None) or the number\n of masked elements along each slice of the given axis.\n\n See Also\n --------\n MaskedArray.count : Count non-masked elements.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.arange(9).reshape((3,3))\n >>> a = ma.array(a)\n >>> a[1, 0] = ma.masked\n >>> a[1, 2] = ma.masked\n >>> a[2, 1] = ma.masked\n >>> a\n masked_array(data =\n [[0 1 2]\n [-- 4 --]\n [6 -- 8]],\n mask =\n [[False False False]\n [ True False True]\n [False True False]],\n fill_value=999999)\n >>> ma.count_masked(a)\n 3\n\n When the `axis` keyword is used an array is returned.\n\n >>> ma.count_masked(a, axis=0)\n array([1, 1, 1])\n >>> ma.count_masked(a, axis=1)\n array([0, 2, 1])\n\n \"\"\"\n m = getmaskarray(arr)\n return m.sum(axis)","label":0},{"cid":"numpy/ma/extras.py::masked_all","text":"numpy/ma/extras.py::masked_all\ndef masked_all(shape, dtype=float):\n \"\"\"\n Empty masked array with all elements masked.\n\n Return an empty masked array of the given shape and dtype, where all the\n data are masked.\n\n Parameters\n ----------\n shape : tuple\n Shape of the required MaskedArray.\n dtype : dtype, optional\n Data type of the output.\n\n Returns\n -------\n a : MaskedArray\n A masked array with all data masked.\n\n See Also\n --------\n masked_all_like : Empty masked array modelled on an existing array.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> ma.masked_all((3, 3))\n masked_array(data =\n [[-- -- --]\n [-- -- --]\n [-- -- --]],\n mask =\n [[ True True True]\n [ True True True]\n [ True True True]],\n fill_value=1e+20)\n\n The `dtype` parameter defines the underlying data type.\n\n >>> a = ma.masked_all((3, 3))\n >>> a.dtype\n dtype('float64')\n >>> a = ma.masked_all((3, 3), dtype=np.int32)\n >>> a.dtype\n dtype('int32')\n\n \"\"\"\n a = masked_array(np.empty(shape, dtype),\n mask=np.ones(shape, make_mask_descr(dtype)))\n return a","label":0},{"cid":"numpy/ma/extras.py::masked_all_like","text":"numpy/ma/extras.py::masked_all_like\ndef masked_all_like(arr):\n \"\"\"\n Empty masked array with the properties of an existing array.\n\n Return an empty masked array of the same shape and dtype as\n the array `arr`, where all the data are masked.\n\n Parameters\n ----------\n arr : ndarray\n An array describing the shape and dtype of the required MaskedArray.\n\n Returns\n -------\n a : MaskedArray\n A masked array with all data masked.\n\n Raises\n ------\n AttributeError\n If `arr` doesn't have a shape attribute (i.e. not an ndarray)\n\n See Also\n --------\n masked_all : Empty masked array with all elements masked.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> arr = np.zeros((2, 3), dtype=np.float32)\n >>> arr\n array([[ 0., 0., 0.],\n [ 0., 0., 0.]], dtype=float32)\n >>> ma.masked_all_like(arr)\n masked_array(data =\n [[-- -- --]\n [-- -- --]],\n mask =\n [[ True True True]\n [ True True True]],\n fill_value=1e+20)\n\n The dtype of the masked array matches the dtype of `arr`.\n\n >>> arr.dtype\n dtype('float32')\n >>> ma.masked_all_like(arr).dtype\n dtype('float32')\n\n \"\"\"\n a = np.empty_like(arr).view(MaskedArray)\n a._mask = np.ones(a.shape, dtype=make_mask_descr(a.dtype))\n return a","label":0},{"cid":"numpy/ma/extras.py::_fromnxfunction.__init__","text":"numpy/ma/extras.py::_fromnxfunction.__init__\n def __init__(self, funcname):\n self.__name__ = funcname\n self.__doc__ = self.getdoc()","label":0},{"cid":"numpy/ma/extras.py::_fromnxfunction.getdoc","text":"numpy/ma/extras.py::_fromnxfunction.getdoc\n def getdoc(self):\n \"\"\"\n Retrieve the docstring and signature from the function.\n\n The ``__doc__`` attribute of the function is used as the docstring for\n the new masked array version of the function. A note on application\n of the function to the mask is appended.\n\n .. warning::\n If the function docstring already contained a Notes section, the\n new docstring will have two Notes sections instead of appending a note\n to the existing section.\n\n Parameters\n ----------\n None\n\n \"\"\"\n npfunc = getattr(np, self.__name__, None)\n doc = getattr(npfunc, '__doc__', None)\n if doc:\n sig = self.__name__ + ma.get_object_signature(npfunc)\n locdoc = \"Notes\\n-----\\nThe function is applied to both the _data\"\\\n \" and the _mask, if any.\"\n return '\\n'.join((sig, doc, locdoc))\n return","label":0},{"cid":"numpy/ma/extras.py::_fromnxfunction.__call__","text":"numpy/ma/extras.py::_fromnxfunction.__call__\n def __call__(self, *args, **params):\n func = getattr(np, self.__name__)\n if len(args) == 1:\n x = args[0]\n if isinstance(x, ndarray):\n _d = func(x.__array__(), **params)\n _m = func(getmaskarray(x), **params)\n return masked_array(_d, mask=_m)\n elif isinstance(x, tuple) or isinstance(x, list):\n _d = func(tuple([np.asarray(a) for a in x]), **params)\n _m = func(tuple([getmaskarray(a) for a in x]), **params)\n return masked_array(_d, mask=_m)\n else:\n arrays = []\n args = list(args)\n while len(args) > 0 and issequence(args[0]):\n arrays.append(args.pop(0))\n res = []\n for x in arrays:\n _d = func(np.asarray(x), *args, **params)\n _m = func(getmaskarray(x), *args, **params)\n res.append(masked_array(_d, mask=_m))\n return res","label":1},{"cid":"numpy/ma/extras.py::flatten_inplace","text":"numpy/ma/extras.py::flatten_inplace\ndef flatten_inplace(seq):\n \"\"\"Flatten a sequence in place.\"\"\"\n k = 0\n while (k != len(seq)):\n while hasattr(seq[k], '__iter__'):\n seq[k:(k + 1)] = seq[k]\n k += 1\n return seq","label":0},{"cid":"numpy/ma/extras.py::apply_along_axis","text":"numpy/ma/extras.py::apply_along_axis\ndef apply_along_axis(func1d, axis, arr, *args, **kwargs):\n \"\"\"\n (This docstring should be overwritten)\n \"\"\"\n arr = array(arr, copy=False, subok=True)\n nd = arr.ndim\n if axis < 0:\n axis += nd\n if (axis >= nd):\n raise ValueError(\"axis must be less than arr.ndim; axis=%d, rank=%d.\"\n % (axis, nd))\n ind = [0] * (nd - 1)\n i = np.zeros(nd, 'O')\n indlist = list(range(nd))\n indlist.remove(axis)\n i[axis] = slice(None, None)\n outshape = np.asarray(arr.shape).take(indlist)\n i.put(indlist, ind)\n j = i.copy()\n res = func1d(arr[tuple(i.tolist())], *args, **kwargs)\n # if res is a number, then we have a smaller output array\n asscalar = np.isscalar(res)\n if not asscalar:\n try:\n len(res)\n except TypeError:\n asscalar = True\n # Note: we shouldn't set the dtype of the output from the first result\n # so we force the type to object, and build a list of dtypes. We'll\n # just take the largest, to avoid some downcasting\n dtypes = []\n if asscalar:\n dtypes.append(np.asarray(res).dtype)\n outarr = zeros(outshape, object)\n outarr[tuple(ind)] = res\n Ntot = np.product(outshape)\n k = 1\n while k < Ntot:\n # increment the index\n ind[-1] += 1\n n = -1\n while (ind[n] >= outshape[n]) and (n > (1 - nd)):\n ind[n - 1] += 1\n ind[n] = 0\n n -= 1\n i.put(indlist, ind)\n res = func1d(arr[tuple(i.tolist())], *args, **kwargs)\n outarr[tuple(ind)] = res\n dtypes.append(asarray(res).dtype)\n k += 1\n else:\n res = array(res, copy=False, subok=True)\n j = i.copy()\n j[axis] = ([slice(None, None)] * res.ndim)\n j.put(indlist, ind)\n Ntot = np.product(outshape)\n holdshape = outshape\n outshape = list(arr.shape)\n outshape[axi","label":0},{"cid":"numpy/ma/extras.py::apply_over_axes","text":"numpy/ma/extras.py::apply_over_axes\ndef apply_over_axes(func, a, axes):\n \"\"\"\n (This docstring will be overwritten)\n \"\"\"\n val = asarray(a)\n N = a.ndim\n if array(axes).ndim == 0:\n axes = (axes,)\n for axis in axes:\n if axis < 0:\n axis = N + axis\n args = (val, axis)\n res = func(*args)\n if res.ndim == val.ndim:\n val = res\n else:\n res = ma.expand_dims(res, axis)\n if res.ndim == val.ndim:\n val = res\n else:\n raise ValueError(\"function is not returning \"\n \"an array of the correct shape\")\n return val","label":0},{"cid":"numpy/ma/extras.py::average","text":"numpy/ma/extras.py::average\ndef average(a, axis=None, weights=None, returned=False):\n \"\"\"\n Return the weighted average of array over the given axis.\n\n Parameters\n ----------\n a : array_like\n Data to be averaged.\n Masked entries are not taken into account in the computation.\n axis : int, optional\n Axis along which the average is computed. The default is to compute\n the average of the flattened array.\n weights : array_like, optional\n The importance that each element has in the computation of the average.\n The weights array can either be 1-D (in which case its length must be\n the size of `a` along the given axis) or of the same shape as `a`.\n If ``weights=None``, then all data in `a` are assumed to have a\n weight equal to one. If `weights` is complex, the imaginary parts\n are ignored.\n returned : bool, optional\n Flag indicating whether a tuple ``(result, sum of weights)``\n should be returned as output (True), or just the result (False).\n Default is False.\n\n Returns\n -------\n average, [sum_of_weights] : (tuple of) scalar or MaskedArray\n The average along the specified axis. When returned is `True`,\n return a tuple with the average as the first element and the sum\n of the weights as the second element. The return type is `np.float64`\n if `a` is of integer type and floats smaller than `float64`, or the\n input data-type, otherwise. If returned, `sum_of_weights` is always\n `float64`.\n\n Examples\n --------\n >>> a = np.ma.array([1., 2., 3., 4.], mask=[False, False, True, True])\n >>> np.ma.average(a, weights=[3, 1, 0, 0])\n 1.25\n\n >>> x = np.ma.arange(6.).reshape(3, 2)\n >>> print x\n [[ 0. 1.]\n [ 2. 3.]\n [ 4. 5.]]\n >>> avg, sumweights = np.ma.average(x, axis=0, weights=[1, 2, 3],\n ... returned=True)\n >>> print avg\n [2.66666666667 3.66666","label":0},{"cid":"numpy/ma/extras.py::median","text":"numpy/ma/extras.py::median\ndef median(a, axis=None, out=None, overwrite_input=False):\n \"\"\"\n Compute the median along the specified axis.\n\n Returns the median of the array elements.\n\n Parameters\n ----------\n a : array_like\n Input array or object that can be converted to an array.\n axis : int, optional\n Axis along which the medians are computed. The default (None) is\n to compute the median along a flattened version of the array.\n out : ndarray, optional\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output\n but the type will be cast if necessary.\n overwrite_input : bool, optional\n If True, then allow use of memory of input array (a) for\n calculations. The input array will be modified by the call to\n median. This will save memory when you do not need to preserve\n the contents of the input array. Treat the input as undefined,\n but it will probably be fully or partially sorted. Default is\n False. Note that, if `overwrite_input` is True, and the input\n is not already an `ndarray`, an error will be raised.\n\n Returns\n -------\n median : ndarray\n A new array holding the result is returned unless out is\n specified, in which case a reference to out is returned.\n Return data-type is `float64` for integers and floats smaller than\n `float64`, or the input data-type, otherwise.\n\n See Also\n --------\n mean\n\n Notes\n -----\n Given a vector ``V`` with ``N`` non masked values, the median of ``V``\n is the middle value of a sorted copy of ``V`` (``Vs``) - i.e.\n ``Vs[(N-1)/2]``, when ``N`` is odd, or ``{Vs[N/2 - 1] + Vs[N/2]}/2``\n when ``N`` is even.\n\n Examples\n --------\n >>> x = np.ma.array(np.arange(8), mask=[0]*4 + [1]*4)\n >>> np.ma.median(x)\n 1.5\n\n >>> x = np.ma.array(np.arange(10).reshape(2, 5), mask=[0]*6 + [1]*4)\n ","label":0},{"cid":"numpy/ma/extras.py::compress_nd","text":"numpy/ma/extras.py::compress_nd\ndef compress_nd(x, axis=None):\n \"\"\"Supress slices from multiple dimensions which contain masked values.\n\n Parameters\n ----------\n x : array_like, MaskedArray\n The array to operate on. If not a MaskedArray instance (or if no array\n elements are masked, `x` is interpreted as a MaskedArray with `mask`\n set to `nomask`.\n axis : tuple of ints or int, optional\n Which dimensions to supress slices from can be configured with this\n parameter.\n - If axis is a tuple of ints, those are the axes to supress slices from.\n - If axis is an int, then that is the only axis to supress slices from.\n - If axis is None, all axis are selected.\n\n Returns\n -------\n compress_array : ndarray\n The compressed array.\n \"\"\"\n x = asarray(x)\n m = getmask(x)\n # Set axis to tuple of ints\n if isinstance(axis, int):\n axis = (axis,)\n elif axis is None:\n axis = tuple(range(x.ndim))\n elif not isinstance(axis, tuple):\n raise ValueError('Invalid type for axis argument')\n # Check axis input\n axis = [ax + x.ndim if ax < 0 else ax for ax in axis]\n if not all(0 <= ax < x.ndim for ax in axis):\n raise ValueError(\"'axis' entry is out of bounds\")\n if len(axis) != len(set(axis)):\n raise ValueError(\"duplicate value in 'axis'\")\n # Nothing is masked: return x\n if m is nomask or not m.any():\n return x._data\n # All is masked: return empty\n if m.all():\n return nxarray([])\n # Filter elements through boolean indexing\n data = x._data\n for ax in axis:\n axes = tuple(list(range(ax)) + list(range(ax + 1, x.ndim)))\n data = data[(slice(None),)*ax + (~m.any(axis=axes),)]\n return data","label":0},{"cid":"numpy/ma/extras.py::compress_rowcols","text":"numpy/ma/extras.py::compress_rowcols\ndef compress_rowcols(x, axis=None):\n \"\"\"\n Suppress the rows and/or columns of a 2-D array that contain\n masked values.\n\n The suppression behavior is selected with the `axis` parameter.\n\n - If axis is None, both rows and columns are suppressed.\n - If axis is 0, only rows are suppressed.\n - If axis is 1 or -1, only columns are suppressed.\n\n Parameters\n ----------\n x : array_like, MaskedArray\n The array to operate on. If not a MaskedArray instance (or if no array\n elements are masked), `x` is interpreted as a MaskedArray with\n `mask` set to `nomask`. Must be a 2D array.\n axis : int, optional\n Axis along which to perform the operation. Default is None.\n\n Returns\n -------\n compressed_array : ndarray\n The compressed array.\n\n Examples\n --------\n >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],\n ... [1, 0, 0],\n ... [0, 0, 0]])\n >>> x\n masked_array(data =\n [[-- 1 2]\n [-- 4 5]\n [6 7 8]],\n mask =\n [[ True False False]\n [ True False False]\n [False False False]],\n fill_value = 999999)\n\n >>> np.ma.compress_rowcols(x)\n array([[7, 8]])\n >>> np.ma.compress_rowcols(x, 0)\n array([[6, 7, 8]])\n >>> np.ma.compress_rowcols(x, 1)\n array([[1, 2],\n [4, 5],\n [7, 8]])\n\n \"\"\"\n if asarray(x).ndim != 2:\n raise NotImplementedError(\"compress_rowcols works for 2D arrays only.\")\n return compress_nd(x, axis=axis)","label":0},{"cid":"numpy/ma/extras.py::compress_rows","text":"numpy/ma/extras.py::compress_rows\ndef compress_rows(a):\n \"\"\"\n Suppress whole rows of a 2-D array that contain masked values.\n\n This is equivalent to ``np.ma.compress_rowcols(a, 0)``, see\n `extras.compress_rowcols` for details.\n\n See Also\n --------\n extras.compress_rowcols\n\n \"\"\"\n a = asarray(a)\n if a.ndim != 2:\n raise NotImplementedError(\"compress_rows works for 2D arrays only.\")\n return compress_rowcols(a, 0)","label":0},{"cid":"numpy/ma/extras.py::compress_cols","text":"numpy/ma/extras.py::compress_cols\ndef compress_cols(a):\n \"\"\"\n Suppress whole columns of a 2-D array that contain masked values.\n\n This is equivalent to ``np.ma.compress_rowcols(a, 1)``, see\n `extras.compress_rowcols` for details.\n\n See Also\n --------\n extras.compress_rowcols\n\n \"\"\"\n a = asarray(a)\n if a.ndim != 2:\n raise NotImplementedError(\"compress_cols works for 2D arrays only.\")\n return compress_rowcols(a, 1)","label":0},{"cid":"numpy/ma/extras.py::mask_rowcols","text":"numpy/ma/extras.py::mask_rowcols\ndef mask_rowcols(a, axis=None):\n \"\"\"\n Mask rows and/or columns of a 2D array that contain masked values.\n\n Mask whole rows and/or columns of a 2D array that contain\n masked values. The masking behavior is selected using the\n `axis` parameter.\n\n - If `axis` is None, rows *and* columns are masked.\n - If `axis` is 0, only rows are masked.\n - If `axis` is 1 or -1, only columns are masked.\n\n Parameters\n ----------\n a : array_like, MaskedArray\n The array to mask. If not a MaskedArray instance (or if no array\n elements are masked). The result is a MaskedArray with `mask` set\n to `nomask` (False). Must be a 2D array.\n axis : int, optional\n Axis along which to perform the operation. If None, applies to a\n flattened version of the array.\n\n Returns\n -------\n a : MaskedArray\n A modified version of the input array, masked depending on the value\n of the `axis` parameter.\n\n Raises\n ------\n NotImplementedError\n If input array `a` is not 2D.\n\n See Also\n --------\n mask_rows : Mask rows of a 2D array that contain masked values.\n mask_cols : Mask cols of a 2D array that contain masked values.\n masked_where : Mask where a condition is met.\n\n Notes\n -----\n The input array's mask is modified by this function.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.zeros((3, 3), dtype=np.int)\n >>> a[1, 1] = 1\n >>> a\n array([[0, 0, 0],\n [0, 1, 0],\n [0, 0, 0]])\n >>> a = ma.masked_equal(a, 1)\n >>> a\n masked_array(data =\n [[0 0 0]\n [0 -- 0]\n [0 0 0]],\n mask =\n [[False False False]\n [False True False]\n [False False False]],\n fill_value=999999)\n >>> ma.mask_rowcols(a)\n masked_array(data =\n [[0 -- 0]\n [-- -- --]\n [0 -- 0]],\n mask =\n [[False True False]\n [ True True True]\n [False True Fa","label":0},{"cid":"numpy/ma/extras.py::mask_rows","text":"numpy/ma/extras.py::mask_rows\ndef mask_rows(a, axis=None):\n \"\"\"\n Mask rows of a 2D array that contain masked values.\n\n This function is a shortcut to ``mask_rowcols`` with `axis` equal to 0.\n\n See Also\n --------\n mask_rowcols : Mask rows and/or columns of a 2D array.\n masked_where : Mask where a condition is met.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.zeros((3, 3), dtype=np.int)\n >>> a[1, 1] = 1\n >>> a\n array([[0, 0, 0],\n [0, 1, 0],\n [0, 0, 0]])\n >>> a = ma.masked_equal(a, 1)\n >>> a\n masked_array(data =\n [[0 0 0]\n [0 -- 0]\n [0 0 0]],\n mask =\n [[False False False]\n [False True False]\n [False False False]],\n fill_value=999999)\n >>> ma.mask_rows(a)\n masked_array(data =\n [[0 0 0]\n [-- -- --]\n [0 0 0]],\n mask =\n [[False False False]\n [ True True True]\n [False False False]],\n fill_value=999999)\n\n \"\"\"\n return mask_rowcols(a, 0)","label":0},{"cid":"numpy/ma/extras.py::mask_cols","text":"numpy/ma/extras.py::mask_cols\ndef mask_cols(a, axis=None):\n \"\"\"\n Mask columns of a 2D array that contain masked values.\n\n This function is a shortcut to ``mask_rowcols`` with `axis` equal to 1.\n\n See Also\n --------\n mask_rowcols : Mask rows and/or columns of a 2D array.\n masked_where : Mask where a condition is met.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.zeros((3, 3), dtype=np.int)\n >>> a[1, 1] = 1\n >>> a\n array([[0, 0, 0],\n [0, 1, 0],\n [0, 0, 0]])\n >>> a = ma.masked_equal(a, 1)\n >>> a\n masked_array(data =\n [[0 0 0]\n [0 -- 0]\n [0 0 0]],\n mask =\n [[False False False]\n [False True False]\n [False False False]],\n fill_value=999999)\n >>> ma.mask_cols(a)\n masked_array(data =\n [[0 -- 0]\n [0 -- 0]\n [0 -- 0]],\n mask =\n [[False True False]\n [False True False]\n [False True False]],\n fill_value=999999)\n\n \"\"\"\n return mask_rowcols(a, 1)","label":0},{"cid":"numpy/ma/extras.py::dot","text":"numpy/ma/extras.py::dot\ndef dot(a, b, strict=False):\n \"\"\"\n Return the dot product of two arrays.\n\n .. note::\n Works only with 2-D arrays at the moment.\n\n This function is the equivalent of `numpy.dot` that takes masked values\n into account, see `numpy.dot` for details.\n\n Parameters\n ----------\n a, b : ndarray\n Inputs arrays.\n strict : bool, optional\n Whether masked data are propagated (True) or set to 0 (False) for the\n computation. Default is False.\n Propagating the mask means that if a masked value appears in a row or\n column, the whole row or column is considered masked.\n\n See Also\n --------\n numpy.dot : Equivalent function for ndarrays.\n\n Examples\n --------\n >>> a = ma.array([[1, 2, 3], [4, 5, 6]], mask=[[1, 0, 0], [0, 0, 0]])\n >>> b = ma.array([[1, 2], [3, 4], [5, 6]], mask=[[1, 0], [0, 0], [0, 0]])\n >>> np.ma.dot(a, b)\n masked_array(data =\n [[21 26]\n [45 64]],\n mask =\n [[False False]\n [False False]],\n fill_value = 999999)\n >>> np.ma.dot(a, b, strict=True)\n masked_array(data =\n [[-- --]\n [-- 64]],\n mask =\n [[ True True]\n [ True False]],\n fill_value = 999999)\n\n \"\"\"\n #!!!: Works only with 2D arrays. There should be a way to get it to run with higher dimension\n if strict and (a.ndim == 2) and (b.ndim == 2):\n a = mask_rows(a)\n b = mask_cols(b)\n return a.dot(b)","label":0},{"cid":"numpy/ma/extras.py::ediff1d","text":"numpy/ma/extras.py::ediff1d\ndef ediff1d(arr, to_end=None, to_begin=None):\n \"\"\"\n Compute the differences between consecutive elements of an array.\n\n This function is the equivalent of `numpy.ediff1d` that takes masked\n values into account, see `numpy.ediff1d` for details.\n\n See Also\n --------\n numpy.ediff1d : Equivalent function for ndarrays.\n\n \"\"\"\n arr = ma.asanyarray(arr).flat\n ed = arr[1:] - arr[:-1]\n arrays = [ed]\n #\n if to_begin is not None:\n arrays.insert(0, to_begin)\n if to_end is not None:\n arrays.append(to_end)\n #\n if len(arrays) != 1:\n # We'll save ourselves a copy of a potentially large array in the common\n # case where neither to_begin or to_end was given.\n ed = hstack(arrays)\n #\n return ed","label":0},{"cid":"numpy/ma/extras.py::unique","text":"numpy/ma/extras.py::unique\ndef unique(ar1, return_index=False, return_inverse=False):\n \"\"\"\n Finds the unique elements of an array.\n\n Masked values are considered the same element (masked). The output array\n is always a masked array. See `numpy.unique` for more details.\n\n See Also\n --------\n numpy.unique : Equivalent function for ndarrays.\n\n \"\"\"\n output = np.unique(ar1,\n return_index=return_index,\n return_inverse=return_inverse)\n if isinstance(output, tuple):\n output = list(output)\n output[0] = output[0].view(MaskedArray)\n output = tuple(output)\n else:\n output = output.view(MaskedArray)\n return output","label":0},{"cid":"numpy/ma/extras.py::intersect1d","text":"numpy/ma/extras.py::intersect1d\ndef intersect1d(ar1, ar2, assume_unique=False):\n \"\"\"\n Returns the unique elements common to both arrays.\n\n Masked values are considered equal one to the other.\n The output is always a masked array.\n\n See `numpy.intersect1d` for more details.\n\n See Also\n --------\n numpy.intersect1d : Equivalent function for ndarrays.\n\n Examples\n --------\n >>> x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])\n >>> y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])\n >>> intersect1d(x, y)\n masked_array(data = [1 3 --],\n mask = [False False True],\n fill_value = 999999)\n\n \"\"\"\n if assume_unique:\n aux = ma.concatenate((ar1, ar2))\n else:\n # Might be faster than unique( intersect1d( ar1, ar2 ) )?\n aux = ma.concatenate((unique(ar1), unique(ar2)))\n aux.sort()\n return aux[:-1][aux[1:] == aux[:-1]]","label":0},{"cid":"numpy/ma/extras.py::setxor1d","text":"numpy/ma/extras.py::setxor1d\ndef setxor1d(ar1, ar2, assume_unique=False):\n \"\"\"\n Set exclusive-or of 1-D arrays with unique elements.\n\n The output is always a masked array. See `numpy.setxor1d` for more details.\n\n See Also\n --------\n numpy.setxor1d : Equivalent function for ndarrays.\n\n \"\"\"\n if not assume_unique:\n ar1 = unique(ar1)\n ar2 = unique(ar2)\n\n aux = ma.concatenate((ar1, ar2))\n if aux.size == 0:\n return aux\n aux.sort()\n auxf = aux.filled()\n# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0\n flag = ma.concatenate(([True], (auxf[1:] != auxf[:-1]), [True]))\n# flag2 = ediff1d( flag ) == 0\n flag2 = (flag[1:] == flag[:-1])\n return aux[flag2]","label":0},{"cid":"numpy/ma/extras.py::in1d","text":"numpy/ma/extras.py::in1d\ndef in1d(ar1, ar2, assume_unique=False, invert=False):\n \"\"\"\n Test whether each element of an array is also present in a second\n array.\n\n The output is always a masked array. See `numpy.in1d` for more details.\n\n See Also\n --------\n numpy.in1d : Equivalent function for ndarrays.\n\n Notes\n -----\n .. versionadded:: 1.4.0\n\n \"\"\"\n if not assume_unique:\n ar1, rev_idx = unique(ar1, return_inverse=True)\n ar2 = unique(ar2)\n\n ar = ma.concatenate((ar1, ar2))\n # We need this to be a stable sort, so always use 'mergesort'\n # here. The values from the first array should always come before\n # the values from the second array.\n order = ar.argsort(kind='mergesort')\n sar = ar[order]\n if invert:\n bool_ar = (sar[1:] != sar[:-1])\n else:\n bool_ar = (sar[1:] == sar[:-1])\n flag = ma.concatenate((bool_ar, [invert]))\n indx = order.argsort(kind='mergesort')[:len(ar1)]\n\n if assume_unique:\n return flag[indx]\n else:\n return flag[indx][rev_idx]","label":0},{"cid":"numpy/ma/extras.py::union1d","text":"numpy/ma/extras.py::union1d\ndef union1d(ar1, ar2):\n \"\"\"\n Union of two arrays.\n\n The output is always a masked array. See `numpy.union1d` for more details.\n\n See also\n --------\n numpy.union1d : Equivalent function for ndarrays.\n\n \"\"\"\n return unique(ma.concatenate((ar1, ar2)))","label":0},{"cid":"numpy/ma/extras.py::setdiff1d","text":"numpy/ma/extras.py::setdiff1d\ndef setdiff1d(ar1, ar2, assume_unique=False):\n \"\"\"\n Set difference of 1D arrays with unique elements.\n\n The output is always a masked array. See `numpy.setdiff1d` for more\n details.\n\n See Also\n --------\n numpy.setdiff1d : Equivalent function for ndarrays.\n\n Examples\n --------\n >>> x = np.ma.array([1, 2, 3, 4], mask=[0, 1, 0, 1])\n >>> np.ma.setdiff1d(x, [1, 2])\n masked_array(data = [3 --],\n mask = [False True],\n fill_value = 999999)\n\n \"\"\"\n if assume_unique:\n ar1 = ma.asarray(ar1).ravel()\n else:\n ar1 = unique(ar1)\n ar2 = unique(ar2)\n return ar1[in1d(ar1, ar2, assume_unique=True, invert=True)]","label":0},{"cid":"numpy/ma/extras.py::_covhelper","text":"numpy/ma/extras.py::_covhelper\ndef _covhelper(x, y=None, rowvar=True, allow_masked=True):\n \"\"\"\n Private function for the computation of covariance and correlation\n coefficients.\n\n \"\"\"\n x = ma.array(x, ndmin=2, copy=True, dtype=float)\n xmask = ma.getmaskarray(x)\n # Quick exit if we can't process masked data\n if not allow_masked and xmask.any():\n raise ValueError(\"Cannot process masked data.\")\n #\n if x.shape[0] == 1:\n rowvar = True\n # Make sure that rowvar is either 0 or 1\n rowvar = int(bool(rowvar))\n axis = 1 - rowvar\n if rowvar:\n tup = (slice(None), None)\n else:\n tup = (None, slice(None))\n #\n if y is None:\n xnotmask = np.logical_not(xmask).astype(int)\n else:\n y = array(y, copy=False, ndmin=2, dtype=float)\n ymask = ma.getmaskarray(y)\n if not allow_masked and ymask.any():\n raise ValueError(\"Cannot process masked data.\")\n if xmask.any() or ymask.any():\n if y.shape == x.shape:\n # Define some common mask\n common_mask = np.logical_or(xmask, ymask)\n if common_mask is not nomask:\n x.unshare_mask()\n y.unshare_mask()\n xmask = x._mask = y._mask = ymask = common_mask\n x = ma.concatenate((x, y), axis)\n xnotmask = np.logical_not(np.concatenate((xmask, ymask), axis)).astype(int)\n x -= x.mean(axis=rowvar)[tup]\n return (x, xnotmask, rowvar)","label":0},{"cid":"numpy/ma/extras.py::cov","text":"numpy/ma/extras.py::cov\ndef cov(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None):\n \"\"\"\n Estimate the covariance matrix.\n\n Except for the handling of missing data this function does the same as\n `numpy.cov`. For more details and examples, see `numpy.cov`.\n\n By default, masked values are recognized as such. If `x` and `y` have the\n same shape, a common mask is allocated: if ``x[i,j]`` is masked, then\n ``y[i,j]`` will also be masked.\n Setting `allow_masked` to False will raise an exception if values are\n missing in either of the input arrays.\n\n Parameters\n ----------\n x : array_like\n A 1-D or 2-D array containing multiple variables and observations.\n Each row of `x` represents a variable, and each column a single\n observation of all those variables. Also see `rowvar` below.\n y : array_like, optional\n An additional set of variables and observations. `y` has the same\n form as `x`.\n rowvar : bool, optional\n If `rowvar` is True (default), then each row represents a\n variable, with observations in the columns. Otherwise, the relationship\n is transposed: each column represents a variable, while the rows\n contain observations.\n bias : bool, optional\n Default normalization (False) is by ``(N-1)``, where ``N`` is the\n number of observations given (unbiased estimate). If `bias` is True,\n then normalization is by ``N``. This keyword can be overridden by\n the keyword ``ddof`` in numpy versions >= 1.5.\n allow_masked : bool, optional\n If True, masked values are propagated pair-wise: if a value is masked\n in `x`, the corresponding value is masked in `y`.\n If False, raises a `ValueError` exception when some values are missing.\n ddof : {None, int}, optional\n If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is\n the number of observations; this overrides the value implied by\n ","label":0},{"cid":"numpy/ma/extras.py::corrcoef","text":"numpy/ma/extras.py::corrcoef\ndef corrcoef(x, y=None, rowvar=True, bias=np._NoValue, allow_masked=True,\n ddof=np._NoValue):\n \"\"\"\n Return Pearson product-moment correlation coefficients.\n\n Except for the handling of missing data this function does the same as\n `numpy.corrcoef`. For more details and examples, see `numpy.corrcoef`.\n\n Parameters\n ----------\n x : array_like\n A 1-D or 2-D array containing multiple variables and observations.\n Each row of `x` represents a variable, and each column a single\n observation of all those variables. Also see `rowvar` below.\n y : array_like, optional\n An additional set of variables and observations. `y` has the same\n shape as `x`.\n rowvar : bool, optional\n If `rowvar` is True (default), then each row represents a\n variable, with observations in the columns. Otherwise, the relationship\n is transposed: each column represents a variable, while the rows\n contain observations.\n bias : _NoValue, optional\n Has no affect, do not use.\n\n .. deprecated:: 1.10.0\n allow_masked : bool, optional\n If True, masked values are propagated pair-wise: if a value is masked\n in `x`, the corresponding value is masked in `y`.\n If False, raises an exception. Because `bias` is deprecated, this\n argument needs to be treated as keyword only to avoid a warning.\n ddof : _NoValue, optional\n Has no affect, do not use.\n\n .. deprecated:: 1.10.0\n\n See Also\n --------\n numpy.corrcoef : Equivalent function in top-level NumPy module.\n cov : Estimate the covariance matrix.\n\n Notes\n -----\n This function accepts but discards arguments `bias` and `ddof`. This is\n for backwards compatibility with previous versions of this function. These\n arguments had no effect on the return values of the function and can be\n safely ignored in this and previous versions of numpy.\n \"\"\"\n msg ","label":0},{"cid":"numpy/ma/extras.py::MAxisConcatenator.__init__","text":"numpy/ma/extras.py::MAxisConcatenator.__init__\n def __init__(self, axis=0):\n AxisConcatenator.__init__(self, axis, matrix=False)","label":0},{"cid":"numpy/ma/extras.py::MAxisConcatenator.__getitem__","text":"numpy/ma/extras.py::MAxisConcatenator.__getitem__\n def __getitem__(self, key):\n if isinstance(key, str):\n raise MAError(\"Unavailable for masked array.\")\n if not isinstance(key, tuple):\n key = (key,)\n objs = []\n scalars = []\n final_dtypedescr = None\n for k in range(len(key)):\n scalar = False\n if isinstance(key[k], slice):\n step = key[k].step\n start = key[k].start\n stop = key[k].stop\n if start is None:\n start = 0\n if step is None:\n step = 1\n if isinstance(step, complex):\n size = int(abs(step))\n newobj = np.linspace(start, stop, num=size)\n else:\n newobj = np.arange(start, stop, step)\n elif isinstance(key[k], str):\n if (key[k] in 'rc'):\n self.matrix = True\n self.col = (key[k] == 'c')\n continue\n try:\n self.axis = int(key[k])\n continue\n except (ValueError, TypeError):\n raise ValueError(\"Unknown special directive\")\n elif type(key[k]) in np.ScalarType:\n newobj = asarray([key[k]])\n scalars.append(k)\n scalar = True\n else:\n newobj = key[k]\n objs.append(newobj)\n if isinstance(newobj, ndarray) and not scalar:\n if final_dtypedescr is None:\n final_dtypedescr = newobj.dtype\n elif newobj.dtype > final_dtypedescr:\n final_dtypedescr = newobj.dtype\n if final_dtypedescr is not None:\n for k in scalars:\n objs[k] = objs[k].astype(final_dtypedescr)\n res = concatenate(tuple(objs), axis=self.axis)\n return self._retval(res)","label":0},{"cid":"numpy/ma/extras.py::mr_class.__init__","text":"numpy/ma/extras.py::mr_class.__init__\n def __init__(self):\n MAxisConcatenator.__init__(self, 0)","label":0},{"cid":"numpy/ma/extras.py::flatnotmasked_edges","text":"numpy/ma/extras.py::flatnotmasked_edges\ndef flatnotmasked_edges(a):\n \"\"\"\n Find the indices of the first and last unmasked values.\n\n Expects a 1-D `MaskedArray`, returns None if all values are masked.\n\n Parameters\n ----------\n a : array_like\n Input 1-D `MaskedArray`\n\n Returns\n -------\n edges : ndarray or None\n The indices of first and last non-masked value in the array.\n Returns None if all values are masked.\n\n See Also\n --------\n flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges,\n clump_masked, clump_unmasked\n\n Notes\n -----\n Only accepts 1-D arrays.\n\n Examples\n --------\n >>> a = np.ma.arange(10)\n >>> flatnotmasked_edges(a)\n [0,-1]\n\n >>> mask = (a < 3) | (a > 8) | (a == 5)\n >>> a[mask] = np.ma.masked\n >>> np.array(a[~a.mask])\n array([3, 4, 6, 7, 8])\n\n >>> flatnotmasked_edges(a)\n array([3, 8])\n\n >>> a[:] = np.ma.masked\n >>> print flatnotmasked_edges(ma)\n None\n\n \"\"\"\n m = getmask(a)\n if m is nomask or not np.any(m):\n return np.array([0, a.size - 1])\n unmasked = np.flatnonzero(~m)\n if len(unmasked) > 0:\n return unmasked[[0, -1]]\n else:\n return None","label":0},{"cid":"numpy/ma/extras.py::notmasked_edges","text":"numpy/ma/extras.py::notmasked_edges\ndef notmasked_edges(a, axis=None):\n \"\"\"\n Find the indices of the first and last unmasked values along an axis.\n\n If all values are masked, return None. Otherwise, return a list\n of two tuples, corresponding to the indices of the first and last\n unmasked values respectively.\n\n Parameters\n ----------\n a : array_like\n The input array.\n axis : int, optional\n Axis along which to perform the operation.\n If None (default), applies to a flattened version of the array.\n\n Returns\n -------\n edges : ndarray or list\n An array of start and end indexes if there are any masked data in\n the array. If there are no masked data in the array, `edges` is a\n list of the first and last index.\n\n See Also\n --------\n flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous,\n clump_masked, clump_unmasked\n\n Examples\n --------\n >>> a = np.arange(9).reshape((3, 3))\n >>> m = np.zeros_like(a)\n >>> m[1:, 1:] = 1\n\n >>> am = np.ma.array(a, mask=m)\n >>> np.array(am[~am.mask])\n array([0, 1, 2, 3, 6])\n\n >>> np.ma.notmasked_edges(ma)\n array([0, 6])\n\n \"\"\"\n a = asarray(a)\n if axis is None or a.ndim == 1:\n return flatnotmasked_edges(a)\n m = getmaskarray(a)\n idx = array(np.indices(a.shape), mask=np.asarray([m] * a.ndim))\n return [tuple([idx[i].min(axis).compressed() for i in range(a.ndim)]),\n tuple([idx[i].max(axis).compressed() for i in range(a.ndim)]), ]","label":0},{"cid":"numpy/ma/extras.py::flatnotmasked_contiguous","text":"numpy/ma/extras.py::flatnotmasked_contiguous\ndef flatnotmasked_contiguous(a):\n \"\"\"\n Find contiguous unmasked data in a masked array along the given axis.\n\n Parameters\n ----------\n a : narray\n The input array.\n\n Returns\n -------\n slice_list : list\n A sorted sequence of slices (start index, end index).\n\n See Also\n --------\n flatnotmasked_edges, notmasked_contiguous, notmasked_edges,\n clump_masked, clump_unmasked\n\n Notes\n -----\n Only accepts 2-D arrays at most.\n\n Examples\n --------\n >>> a = np.ma.arange(10)\n >>> np.ma.flatnotmasked_contiguous(a)\n slice(0, 10, None)\n\n >>> mask = (a < 3) | (a > 8) | (a == 5)\n >>> a[mask] = np.ma.masked\n >>> np.array(a[~a.mask])\n array([3, 4, 6, 7, 8])\n\n >>> np.ma.flatnotmasked_contiguous(a)\n [slice(3, 5, None), slice(6, 9, None)]\n >>> a[:] = np.ma.masked\n >>> print np.ma.flatnotmasked_edges(a)\n None\n\n \"\"\"\n m = getmask(a)\n if m is nomask:\n return slice(0, a.size, None)\n i = 0\n result = []\n for (k, g) in itertools.groupby(m.ravel()):\n n = len(list(g))\n if not k:\n result.append(slice(i, i + n))\n i += n\n return result or None","label":0},{"cid":"numpy/ma/extras.py::notmasked_contiguous","text":"numpy/ma/extras.py::notmasked_contiguous\ndef notmasked_contiguous(a, axis=None):\n \"\"\"\n Find contiguous unmasked data in a masked array along the given axis.\n\n Parameters\n ----------\n a : array_like\n The input array.\n axis : int, optional\n Axis along which to perform the operation.\n If None (default), applies to a flattened version of the array.\n\n Returns\n -------\n endpoints : list\n A list of slices (start and end indexes) of unmasked indexes\n in the array.\n\n See Also\n --------\n flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges,\n clump_masked, clump_unmasked\n\n Notes\n -----\n Only accepts 2-D arrays at most.\n\n Examples\n --------\n >>> a = np.arange(9).reshape((3, 3))\n >>> mask = np.zeros_like(a)\n >>> mask[1:, 1:] = 1\n\n >>> ma = np.ma.array(a, mask=mask)\n >>> np.array(ma[~ma.mask])\n array([0, 1, 2, 3, 6])\n\n >>> np.ma.notmasked_contiguous(ma)\n [slice(0, 4, None), slice(6, 7, None)]\n\n \"\"\"\n a = asarray(a)\n nd = a.ndim\n if nd > 2:\n raise NotImplementedError(\"Currently limited to atmost 2D array.\")\n if axis is None or nd == 1:\n return flatnotmasked_contiguous(a)\n #\n result = []\n #\n other = (axis + 1) % 2\n idx = [0, 0]\n idx[axis] = slice(None, None)\n #\n for i in range(a.shape[other]):\n idx[other] = i\n result.append(flatnotmasked_contiguous(a[idx]) or None)\n return result","label":0},{"cid":"numpy/ma/extras.py::_ezclump","text":"numpy/ma/extras.py::_ezclump\ndef _ezclump(mask):\n \"\"\"\n Finds the clumps (groups of data with the same values) for a 1D bool array.\n\n Returns a series of slices.\n \"\"\"\n #def clump_masked(a):\n if mask.ndim > 1:\n mask = mask.ravel()\n idx = (mask[1:] ^ mask[:-1]).nonzero()\n idx = idx[0] + 1\n slices = [slice(left, right)\n for (left, right) in zip(itertools.chain([0], idx),\n itertools.chain(idx, [len(mask)]),)]\n return slices","label":0},{"cid":"numpy/ma/extras.py::clump_unmasked","text":"numpy/ma/extras.py::clump_unmasked\ndef clump_unmasked(a):\n \"\"\"\n Return list of slices corresponding to the unmasked clumps of a 1-D array.\n (A \"clump\" is defined as a contiguous region of the array).\n\n Parameters\n ----------\n a : ndarray\n A one-dimensional masked array.\n\n Returns\n -------\n slices : list of slice\n The list of slices, one for each continuous region of unmasked\n elements in `a`.\n\n Notes\n -----\n .. versionadded:: 1.4.0\n\n See Also\n --------\n flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges,\n notmasked_contiguous, clump_masked\n\n Examples\n --------\n >>> a = np.ma.masked_array(np.arange(10))\n >>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked\n >>> np.ma.clump_unmasked(a)\n [slice(3, 6, None), slice(7, 8, None)]\n\n \"\"\"\n mask = getattr(a, '_mask', nomask)\n if mask is nomask:\n return [slice(0, a.size)]\n slices = _ezclump(mask)\n if a[0] is masked:\n result = slices[1::2]\n else:\n result = slices[::2]\n return result","label":0},{"cid":"numpy/ma/extras.py::clump_masked","text":"numpy/ma/extras.py::clump_masked\ndef clump_masked(a):\n \"\"\"\n Returns a list of slices corresponding to the masked clumps of a 1-D array.\n (A \"clump\" is defined as a contiguous region of the array).\n\n Parameters\n ----------\n a : ndarray\n A one-dimensional masked array.\n\n Returns\n -------\n slices : list of slice\n The list of slices, one for each continuous region of masked elements\n in `a`.\n\n Notes\n -----\n .. versionadded:: 1.4.0\n\n See Also\n --------\n flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges,\n notmasked_contiguous, clump_unmasked\n\n Examples\n --------\n >>> a = np.ma.masked_array(np.arange(10))\n >>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked\n >>> np.ma.clump_masked(a)\n [slice(0, 3, None), slice(6, 7, None), slice(8, 10, None)]\n\n \"\"\"\n mask = ma.getmask(a)\n if mask is nomask:\n return []\n slices = _ezclump(mask)\n if len(slices):\n if a[0] is masked:\n slices = slices[::2]\n else:\n slices = slices[1::2]\n return slices","label":0},{"cid":"numpy/ma/extras.py::vander","text":"numpy/ma/extras.py::vander\ndef vander(x, n=None):\n \"\"\"\n Masked values in the input array result in rows of zeros.\n\n \"\"\"\n _vander = np.vander(x, n)\n m = getmask(x)\n if m is not nomask:\n _vander[m] = 0\n return _vander","label":0},{"cid":"numpy/ma/extras.py::polyfit","text":"numpy/ma/extras.py::polyfit\ndef polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):\n \"\"\"\n Any masked values in x is propagated in y, and vice-versa.\n\n \"\"\"\n x = asarray(x)\n y = asarray(y)\n\n m = getmask(x)\n if y.ndim == 1:\n m = mask_or(m, getmask(y))\n elif y.ndim == 2:\n my = getmask(mask_rows(y))\n if my is not nomask:\n m = mask_or(m, my[:, 0])\n else:\n raise TypeError(\"Expected a 1D or 2D array for y!\")\n\n if w is not None:\n w = asarray(w)\n if w.ndim != 1:\n raise TypeError(\"expected a 1-d array for weights\")\n if w.shape[0] != y.shape[0]:\n raise TypeError(\"expected w and y to have the same length\")\n m = mask_or(m, getmask(w))\n\n if m is not nomask:\n not_m = ~m\n if w is not None:\n w = w[not_m]\n return np.polyfit(x[not_m], y[not_m], deg, rcond, full, w, cov)\n else:\n return np.polyfit(x, y, deg, rcond, full, w, cov)","label":0}],"string":"[\n {\n \"cid\": \"numpy/ma/extras.py::issequence\",\n \"text\": \"numpy/ma/extras.py::issequence\\ndef issequence(seq):\\n \\\"\\\"\\\"\\n Is seq a sequence (ndarray, list or tuple)?\\n\\n \\\"\\\"\\\"\\n if isinstance(seq, (ndarray, tuple, list)):\\n return True\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::count_masked\",\n \"text\": \"numpy/ma/extras.py::count_masked\\ndef count_masked(arr, axis=None):\\n \\\"\\\"\\\"\\n Count the number of masked elements along the given axis.\\n\\n Parameters\\n ----------\\n arr : array_like\\n An array with (possibly) masked elements.\\n axis : int, optional\\n Axis along which to count. If None (default), a flattened\\n version of the array is used.\\n\\n Returns\\n -------\\n count : int, ndarray\\n The total number of masked elements (axis=None) or the number\\n of masked elements along each slice of the given axis.\\n\\n See Also\\n --------\\n MaskedArray.count : Count non-masked elements.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.arange(9).reshape((3,3))\\n >>> a = ma.array(a)\\n >>> a[1, 0] = ma.masked\\n >>> a[1, 2] = ma.masked\\n >>> a[2, 1] = ma.masked\\n >>> a\\n masked_array(data =\\n [[0 1 2]\\n [-- 4 --]\\n [6 -- 8]],\\n mask =\\n [[False False False]\\n [ True False True]\\n [False True False]],\\n fill_value=999999)\\n >>> ma.count_masked(a)\\n 3\\n\\n When the `axis` keyword is used an array is returned.\\n\\n >>> ma.count_masked(a, axis=0)\\n array([1, 1, 1])\\n >>> ma.count_masked(a, axis=1)\\n array([0, 2, 1])\\n\\n \\\"\\\"\\\"\\n m = getmaskarray(arr)\\n return m.sum(axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::masked_all\",\n \"text\": \"numpy/ma/extras.py::masked_all\\ndef masked_all(shape, dtype=float):\\n \\\"\\\"\\\"\\n Empty masked array with all elements masked.\\n\\n Return an empty masked array of the given shape and dtype, where all the\\n data are masked.\\n\\n Parameters\\n ----------\\n shape : tuple\\n Shape of the required MaskedArray.\\n dtype : dtype, optional\\n Data type of the output.\\n\\n Returns\\n -------\\n a : MaskedArray\\n A masked array with all data masked.\\n\\n See Also\\n --------\\n masked_all_like : Empty masked array modelled on an existing array.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> ma.masked_all((3, 3))\\n masked_array(data =\\n [[-- -- --]\\n [-- -- --]\\n [-- -- --]],\\n mask =\\n [[ True True True]\\n [ True True True]\\n [ True True True]],\\n fill_value=1e+20)\\n\\n The `dtype` parameter defines the underlying data type.\\n\\n >>> a = ma.masked_all((3, 3))\\n >>> a.dtype\\n dtype('float64')\\n >>> a = ma.masked_all((3, 3), dtype=np.int32)\\n >>> a.dtype\\n dtype('int32')\\n\\n \\\"\\\"\\\"\\n a = masked_array(np.empty(shape, dtype),\\n mask=np.ones(shape, make_mask_descr(dtype)))\\n return a\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::masked_all_like\",\n \"text\": \"numpy/ma/extras.py::masked_all_like\\ndef masked_all_like(arr):\\n \\\"\\\"\\\"\\n Empty masked array with the properties of an existing array.\\n\\n Return an empty masked array of the same shape and dtype as\\n the array `arr`, where all the data are masked.\\n\\n Parameters\\n ----------\\n arr : ndarray\\n An array describing the shape and dtype of the required MaskedArray.\\n\\n Returns\\n -------\\n a : MaskedArray\\n A masked array with all data masked.\\n\\n Raises\\n ------\\n AttributeError\\n If `arr` doesn't have a shape attribute (i.e. not an ndarray)\\n\\n See Also\\n --------\\n masked_all : Empty masked array with all elements masked.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> arr = np.zeros((2, 3), dtype=np.float32)\\n >>> arr\\n array([[ 0., 0., 0.],\\n [ 0., 0., 0.]], dtype=float32)\\n >>> ma.masked_all_like(arr)\\n masked_array(data =\\n [[-- -- --]\\n [-- -- --]],\\n mask =\\n [[ True True True]\\n [ True True True]],\\n fill_value=1e+20)\\n\\n The dtype of the masked array matches the dtype of `arr`.\\n\\n >>> arr.dtype\\n dtype('float32')\\n >>> ma.masked_all_like(arr).dtype\\n dtype('float32')\\n\\n \\\"\\\"\\\"\\n a = np.empty_like(arr).view(MaskedArray)\\n a._mask = np.ones(a.shape, dtype=make_mask_descr(a.dtype))\\n return a\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::_fromnxfunction.__init__\",\n \"text\": \"numpy/ma/extras.py::_fromnxfunction.__init__\\n def __init__(self, funcname):\\n self.__name__ = funcname\\n self.__doc__ = self.getdoc()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::_fromnxfunction.getdoc\",\n \"text\": \"numpy/ma/extras.py::_fromnxfunction.getdoc\\n def getdoc(self):\\n \\\"\\\"\\\"\\n Retrieve the docstring and signature from the function.\\n\\n The ``__doc__`` attribute of the function is used as the docstring for\\n the new masked array version of the function. A note on application\\n of the function to the mask is appended.\\n\\n .. warning::\\n If the function docstring already contained a Notes section, the\\n new docstring will have two Notes sections instead of appending a note\\n to the existing section.\\n\\n Parameters\\n ----------\\n None\\n\\n \\\"\\\"\\\"\\n npfunc = getattr(np, self.__name__, None)\\n doc = getattr(npfunc, '__doc__', None)\\n if doc:\\n sig = self.__name__ + ma.get_object_signature(npfunc)\\n locdoc = \\\"Notes\\\\n-----\\\\nThe function is applied to both the _data\\\"\\\\\\n \\\" and the _mask, if any.\\\"\\n return '\\\\n'.join((sig, doc, locdoc))\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::_fromnxfunction.__call__\",\n \"text\": \"numpy/ma/extras.py::_fromnxfunction.__call__\\n def __call__(self, *args, **params):\\n func = getattr(np, self.__name__)\\n if len(args) == 1:\\n x = args[0]\\n if isinstance(x, ndarray):\\n _d = func(x.__array__(), **params)\\n _m = func(getmaskarray(x), **params)\\n return masked_array(_d, mask=_m)\\n elif isinstance(x, tuple) or isinstance(x, list):\\n _d = func(tuple([np.asarray(a) for a in x]), **params)\\n _m = func(tuple([getmaskarray(a) for a in x]), **params)\\n return masked_array(_d, mask=_m)\\n else:\\n arrays = []\\n args = list(args)\\n while len(args) > 0 and issequence(args[0]):\\n arrays.append(args.pop(0))\\n res = []\\n for x in arrays:\\n _d = func(np.asarray(x), *args, **params)\\n _m = func(getmaskarray(x), *args, **params)\\n res.append(masked_array(_d, mask=_m))\\n return res\",\n \"label\": 1\n },\n {\n \"cid\": \"numpy/ma/extras.py::flatten_inplace\",\n \"text\": \"numpy/ma/extras.py::flatten_inplace\\ndef flatten_inplace(seq):\\n \\\"\\\"\\\"Flatten a sequence in place.\\\"\\\"\\\"\\n k = 0\\n while (k != len(seq)):\\n while hasattr(seq[k], '__iter__'):\\n seq[k:(k + 1)] = seq[k]\\n k += 1\\n return seq\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::apply_along_axis\",\n \"text\": \"numpy/ma/extras.py::apply_along_axis\\ndef apply_along_axis(func1d, axis, arr, *args, **kwargs):\\n \\\"\\\"\\\"\\n (This docstring should be overwritten)\\n \\\"\\\"\\\"\\n arr = array(arr, copy=False, subok=True)\\n nd = arr.ndim\\n if axis < 0:\\n axis += nd\\n if (axis >= nd):\\n raise ValueError(\\\"axis must be less than arr.ndim; axis=%d, rank=%d.\\\"\\n % (axis, nd))\\n ind = [0] * (nd - 1)\\n i = np.zeros(nd, 'O')\\n indlist = list(range(nd))\\n indlist.remove(axis)\\n i[axis] = slice(None, None)\\n outshape = np.asarray(arr.shape).take(indlist)\\n i.put(indlist, ind)\\n j = i.copy()\\n res = func1d(arr[tuple(i.tolist())], *args, **kwargs)\\n # if res is a number, then we have a smaller output array\\n asscalar = np.isscalar(res)\\n if not asscalar:\\n try:\\n len(res)\\n except TypeError:\\n asscalar = True\\n # Note: we shouldn't set the dtype of the output from the first result\\n # so we force the type to object, and build a list of dtypes. We'll\\n # just take the largest, to avoid some downcasting\\n dtypes = []\\n if asscalar:\\n dtypes.append(np.asarray(res).dtype)\\n outarr = zeros(outshape, object)\\n outarr[tuple(ind)] = res\\n Ntot = np.product(outshape)\\n k = 1\\n while k < Ntot:\\n # increment the index\\n ind[-1] += 1\\n n = -1\\n while (ind[n] >= outshape[n]) and (n > (1 - nd)):\\n ind[n - 1] += 1\\n ind[n] = 0\\n n -= 1\\n i.put(indlist, ind)\\n res = func1d(arr[tuple(i.tolist())], *args, **kwargs)\\n outarr[tuple(ind)] = res\\n dtypes.append(asarray(res).dtype)\\n k += 1\\n else:\\n res = array(res, copy=False, subok=True)\\n j = i.copy()\\n j[axis] = ([slice(None, None)] * res.ndim)\\n j.put(indlist, ind)\\n Ntot = np.product(outshape)\\n holdshape = outshape\\n outshape = list(arr.shape)\\n outshape[axi\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::apply_over_axes\",\n \"text\": \"numpy/ma/extras.py::apply_over_axes\\ndef apply_over_axes(func, a, axes):\\n \\\"\\\"\\\"\\n (This docstring will be overwritten)\\n \\\"\\\"\\\"\\n val = asarray(a)\\n N = a.ndim\\n if array(axes).ndim == 0:\\n axes = (axes,)\\n for axis in axes:\\n if axis < 0:\\n axis = N + axis\\n args = (val, axis)\\n res = func(*args)\\n if res.ndim == val.ndim:\\n val = res\\n else:\\n res = ma.expand_dims(res, axis)\\n if res.ndim == val.ndim:\\n val = res\\n else:\\n raise ValueError(\\\"function is not returning \\\"\\n \\\"an array of the correct shape\\\")\\n return val\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::average\",\n \"text\": \"numpy/ma/extras.py::average\\ndef average(a, axis=None, weights=None, returned=False):\\n \\\"\\\"\\\"\\n Return the weighted average of array over the given axis.\\n\\n Parameters\\n ----------\\n a : array_like\\n Data to be averaged.\\n Masked entries are not taken into account in the computation.\\n axis : int, optional\\n Axis along which the average is computed. The default is to compute\\n the average of the flattened array.\\n weights : array_like, optional\\n The importance that each element has in the computation of the average.\\n The weights array can either be 1-D (in which case its length must be\\n the size of `a` along the given axis) or of the same shape as `a`.\\n If ``weights=None``, then all data in `a` are assumed to have a\\n weight equal to one. If `weights` is complex, the imaginary parts\\n are ignored.\\n returned : bool, optional\\n Flag indicating whether a tuple ``(result, sum of weights)``\\n should be returned as output (True), or just the result (False).\\n Default is False.\\n\\n Returns\\n -------\\n average, [sum_of_weights] : (tuple of) scalar or MaskedArray\\n The average along the specified axis. When returned is `True`,\\n return a tuple with the average as the first element and the sum\\n of the weights as the second element. The return type is `np.float64`\\n if `a` is of integer type and floats smaller than `float64`, or the\\n input data-type, otherwise. If returned, `sum_of_weights` is always\\n `float64`.\\n\\n Examples\\n --------\\n >>> a = np.ma.array([1., 2., 3., 4.], mask=[False, False, True, True])\\n >>> np.ma.average(a, weights=[3, 1, 0, 0])\\n 1.25\\n\\n >>> x = np.ma.arange(6.).reshape(3, 2)\\n >>> print x\\n [[ 0. 1.]\\n [ 2. 3.]\\n [ 4. 5.]]\\n >>> avg, sumweights = np.ma.average(x, axis=0, weights=[1, 2, 3],\\n ... returned=True)\\n >>> print avg\\n [2.66666666667 3.66666\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::median\",\n \"text\": \"numpy/ma/extras.py::median\\ndef median(a, axis=None, out=None, overwrite_input=False):\\n \\\"\\\"\\\"\\n Compute the median along the specified axis.\\n\\n Returns the median of the array elements.\\n\\n Parameters\\n ----------\\n a : array_like\\n Input array or object that can be converted to an array.\\n axis : int, optional\\n Axis along which the medians are computed. The default (None) is\\n to compute the median along a flattened version of the array.\\n out : ndarray, optional\\n Alternative output array in which to place the result. It must\\n have the same shape and buffer length as the expected output\\n but the type will be cast if necessary.\\n overwrite_input : bool, optional\\n If True, then allow use of memory of input array (a) for\\n calculations. The input array will be modified by the call to\\n median. This will save memory when you do not need to preserve\\n the contents of the input array. Treat the input as undefined,\\n but it will probably be fully or partially sorted. Default is\\n False. Note that, if `overwrite_input` is True, and the input\\n is not already an `ndarray`, an error will be raised.\\n\\n Returns\\n -------\\n median : ndarray\\n A new array holding the result is returned unless out is\\n specified, in which case a reference to out is returned.\\n Return data-type is `float64` for integers and floats smaller than\\n `float64`, or the input data-type, otherwise.\\n\\n See Also\\n --------\\n mean\\n\\n Notes\\n -----\\n Given a vector ``V`` with ``N`` non masked values, the median of ``V``\\n is the middle value of a sorted copy of ``V`` (``Vs``) - i.e.\\n ``Vs[(N-1)/2]``, when ``N`` is odd, or ``{Vs[N/2 - 1] + Vs[N/2]}/2``\\n when ``N`` is even.\\n\\n Examples\\n --------\\n >>> x = np.ma.array(np.arange(8), mask=[0]*4 + [1]*4)\\n >>> np.ma.median(x)\\n 1.5\\n\\n >>> x = np.ma.array(np.arange(10).reshape(2, 5), mask=[0]*6 + [1]*4)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::compress_nd\",\n \"text\": \"numpy/ma/extras.py::compress_nd\\ndef compress_nd(x, axis=None):\\n \\\"\\\"\\\"Supress slices from multiple dimensions which contain masked values.\\n\\n Parameters\\n ----------\\n x : array_like, MaskedArray\\n The array to operate on. If not a MaskedArray instance (or if no array\\n elements are masked, `x` is interpreted as a MaskedArray with `mask`\\n set to `nomask`.\\n axis : tuple of ints or int, optional\\n Which dimensions to supress slices from can be configured with this\\n parameter.\\n - If axis is a tuple of ints, those are the axes to supress slices from.\\n - If axis is an int, then that is the only axis to supress slices from.\\n - If axis is None, all axis are selected.\\n\\n Returns\\n -------\\n compress_array : ndarray\\n The compressed array.\\n \\\"\\\"\\\"\\n x = asarray(x)\\n m = getmask(x)\\n # Set axis to tuple of ints\\n if isinstance(axis, int):\\n axis = (axis,)\\n elif axis is None:\\n axis = tuple(range(x.ndim))\\n elif not isinstance(axis, tuple):\\n raise ValueError('Invalid type for axis argument')\\n # Check axis input\\n axis = [ax + x.ndim if ax < 0 else ax for ax in axis]\\n if not all(0 <= ax < x.ndim for ax in axis):\\n raise ValueError(\\\"'axis' entry is out of bounds\\\")\\n if len(axis) != len(set(axis)):\\n raise ValueError(\\\"duplicate value in 'axis'\\\")\\n # Nothing is masked: return x\\n if m is nomask or not m.any():\\n return x._data\\n # All is masked: return empty\\n if m.all():\\n return nxarray([])\\n # Filter elements through boolean indexing\\n data = x._data\\n for ax in axis:\\n axes = tuple(list(range(ax)) + list(range(ax + 1, x.ndim)))\\n data = data[(slice(None),)*ax + (~m.any(axis=axes),)]\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::compress_rowcols\",\n \"text\": \"numpy/ma/extras.py::compress_rowcols\\ndef compress_rowcols(x, axis=None):\\n \\\"\\\"\\\"\\n Suppress the rows and/or columns of a 2-D array that contain\\n masked values.\\n\\n The suppression behavior is selected with the `axis` parameter.\\n\\n - If axis is None, both rows and columns are suppressed.\\n - If axis is 0, only rows are suppressed.\\n - If axis is 1 or -1, only columns are suppressed.\\n\\n Parameters\\n ----------\\n x : array_like, MaskedArray\\n The array to operate on. If not a MaskedArray instance (or if no array\\n elements are masked), `x` is interpreted as a MaskedArray with\\n `mask` set to `nomask`. Must be a 2D array.\\n axis : int, optional\\n Axis along which to perform the operation. Default is None.\\n\\n Returns\\n -------\\n compressed_array : ndarray\\n The compressed array.\\n\\n Examples\\n --------\\n >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],\\n ... [1, 0, 0],\\n ... [0, 0, 0]])\\n >>> x\\n masked_array(data =\\n [[-- 1 2]\\n [-- 4 5]\\n [6 7 8]],\\n mask =\\n [[ True False False]\\n [ True False False]\\n [False False False]],\\n fill_value = 999999)\\n\\n >>> np.ma.compress_rowcols(x)\\n array([[7, 8]])\\n >>> np.ma.compress_rowcols(x, 0)\\n array([[6, 7, 8]])\\n >>> np.ma.compress_rowcols(x, 1)\\n array([[1, 2],\\n [4, 5],\\n [7, 8]])\\n\\n \\\"\\\"\\\"\\n if asarray(x).ndim != 2:\\n raise NotImplementedError(\\\"compress_rowcols works for 2D arrays only.\\\")\\n return compress_nd(x, axis=axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::compress_rows\",\n \"text\": \"numpy/ma/extras.py::compress_rows\\ndef compress_rows(a):\\n \\\"\\\"\\\"\\n Suppress whole rows of a 2-D array that contain masked values.\\n\\n This is equivalent to ``np.ma.compress_rowcols(a, 0)``, see\\n `extras.compress_rowcols` for details.\\n\\n See Also\\n --------\\n extras.compress_rowcols\\n\\n \\\"\\\"\\\"\\n a = asarray(a)\\n if a.ndim != 2:\\n raise NotImplementedError(\\\"compress_rows works for 2D arrays only.\\\")\\n return compress_rowcols(a, 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::compress_cols\",\n \"text\": \"numpy/ma/extras.py::compress_cols\\ndef compress_cols(a):\\n \\\"\\\"\\\"\\n Suppress whole columns of a 2-D array that contain masked values.\\n\\n This is equivalent to ``np.ma.compress_rowcols(a, 1)``, see\\n `extras.compress_rowcols` for details.\\n\\n See Also\\n --------\\n extras.compress_rowcols\\n\\n \\\"\\\"\\\"\\n a = asarray(a)\\n if a.ndim != 2:\\n raise NotImplementedError(\\\"compress_cols works for 2D arrays only.\\\")\\n return compress_rowcols(a, 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::mask_rowcols\",\n \"text\": \"numpy/ma/extras.py::mask_rowcols\\ndef mask_rowcols(a, axis=None):\\n \\\"\\\"\\\"\\n Mask rows and/or columns of a 2D array that contain masked values.\\n\\n Mask whole rows and/or columns of a 2D array that contain\\n masked values. The masking behavior is selected using the\\n `axis` parameter.\\n\\n - If `axis` is None, rows *and* columns are masked.\\n - If `axis` is 0, only rows are masked.\\n - If `axis` is 1 or -1, only columns are masked.\\n\\n Parameters\\n ----------\\n a : array_like, MaskedArray\\n The array to mask. If not a MaskedArray instance (or if no array\\n elements are masked). The result is a MaskedArray with `mask` set\\n to `nomask` (False). Must be a 2D array.\\n axis : int, optional\\n Axis along which to perform the operation. If None, applies to a\\n flattened version of the array.\\n\\n Returns\\n -------\\n a : MaskedArray\\n A modified version of the input array, masked depending on the value\\n of the `axis` parameter.\\n\\n Raises\\n ------\\n NotImplementedError\\n If input array `a` is not 2D.\\n\\n See Also\\n --------\\n mask_rows : Mask rows of a 2D array that contain masked values.\\n mask_cols : Mask cols of a 2D array that contain masked values.\\n masked_where : Mask where a condition is met.\\n\\n Notes\\n -----\\n The input array's mask is modified by this function.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.zeros((3, 3), dtype=np.int)\\n >>> a[1, 1] = 1\\n >>> a\\n array([[0, 0, 0],\\n [0, 1, 0],\\n [0, 0, 0]])\\n >>> a = ma.masked_equal(a, 1)\\n >>> a\\n masked_array(data =\\n [[0 0 0]\\n [0 -- 0]\\n [0 0 0]],\\n mask =\\n [[False False False]\\n [False True False]\\n [False False False]],\\n fill_value=999999)\\n >>> ma.mask_rowcols(a)\\n masked_array(data =\\n [[0 -- 0]\\n [-- -- --]\\n [0 -- 0]],\\n mask =\\n [[False True False]\\n [ True True True]\\n [False True Fa\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::mask_rows\",\n \"text\": \"numpy/ma/extras.py::mask_rows\\ndef mask_rows(a, axis=None):\\n \\\"\\\"\\\"\\n Mask rows of a 2D array that contain masked values.\\n\\n This function is a shortcut to ``mask_rowcols`` with `axis` equal to 0.\\n\\n See Also\\n --------\\n mask_rowcols : Mask rows and/or columns of a 2D array.\\n masked_where : Mask where a condition is met.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.zeros((3, 3), dtype=np.int)\\n >>> a[1, 1] = 1\\n >>> a\\n array([[0, 0, 0],\\n [0, 1, 0],\\n [0, 0, 0]])\\n >>> a = ma.masked_equal(a, 1)\\n >>> a\\n masked_array(data =\\n [[0 0 0]\\n [0 -- 0]\\n [0 0 0]],\\n mask =\\n [[False False False]\\n [False True False]\\n [False False False]],\\n fill_value=999999)\\n >>> ma.mask_rows(a)\\n masked_array(data =\\n [[0 0 0]\\n [-- -- --]\\n [0 0 0]],\\n mask =\\n [[False False False]\\n [ True True True]\\n [False False False]],\\n fill_value=999999)\\n\\n \\\"\\\"\\\"\\n return mask_rowcols(a, 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::mask_cols\",\n \"text\": \"numpy/ma/extras.py::mask_cols\\ndef mask_cols(a, axis=None):\\n \\\"\\\"\\\"\\n Mask columns of a 2D array that contain masked values.\\n\\n This function is a shortcut to ``mask_rowcols`` with `axis` equal to 1.\\n\\n See Also\\n --------\\n mask_rowcols : Mask rows and/or columns of a 2D array.\\n masked_where : Mask where a condition is met.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.zeros((3, 3), dtype=np.int)\\n >>> a[1, 1] = 1\\n >>> a\\n array([[0, 0, 0],\\n [0, 1, 0],\\n [0, 0, 0]])\\n >>> a = ma.masked_equal(a, 1)\\n >>> a\\n masked_array(data =\\n [[0 0 0]\\n [0 -- 0]\\n [0 0 0]],\\n mask =\\n [[False False False]\\n [False True False]\\n [False False False]],\\n fill_value=999999)\\n >>> ma.mask_cols(a)\\n masked_array(data =\\n [[0 -- 0]\\n [0 -- 0]\\n [0 -- 0]],\\n mask =\\n [[False True False]\\n [False True False]\\n [False True False]],\\n fill_value=999999)\\n\\n \\\"\\\"\\\"\\n return mask_rowcols(a, 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::dot\",\n \"text\": \"numpy/ma/extras.py::dot\\ndef dot(a, b, strict=False):\\n \\\"\\\"\\\"\\n Return the dot product of two arrays.\\n\\n .. note::\\n Works only with 2-D arrays at the moment.\\n\\n This function is the equivalent of `numpy.dot` that takes masked values\\n into account, see `numpy.dot` for details.\\n\\n Parameters\\n ----------\\n a, b : ndarray\\n Inputs arrays.\\n strict : bool, optional\\n Whether masked data are propagated (True) or set to 0 (False) for the\\n computation. Default is False.\\n Propagating the mask means that if a masked value appears in a row or\\n column, the whole row or column is considered masked.\\n\\n See Also\\n --------\\n numpy.dot : Equivalent function for ndarrays.\\n\\n Examples\\n --------\\n >>> a = ma.array([[1, 2, 3], [4, 5, 6]], mask=[[1, 0, 0], [0, 0, 0]])\\n >>> b = ma.array([[1, 2], [3, 4], [5, 6]], mask=[[1, 0], [0, 0], [0, 0]])\\n >>> np.ma.dot(a, b)\\n masked_array(data =\\n [[21 26]\\n [45 64]],\\n mask =\\n [[False False]\\n [False False]],\\n fill_value = 999999)\\n >>> np.ma.dot(a, b, strict=True)\\n masked_array(data =\\n [[-- --]\\n [-- 64]],\\n mask =\\n [[ True True]\\n [ True False]],\\n fill_value = 999999)\\n\\n \\\"\\\"\\\"\\n #!!!: Works only with 2D arrays. There should be a way to get it to run with higher dimension\\n if strict and (a.ndim == 2) and (b.ndim == 2):\\n a = mask_rows(a)\\n b = mask_cols(b)\\n return a.dot(b)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::ediff1d\",\n \"text\": \"numpy/ma/extras.py::ediff1d\\ndef ediff1d(arr, to_end=None, to_begin=None):\\n \\\"\\\"\\\"\\n Compute the differences between consecutive elements of an array.\\n\\n This function is the equivalent of `numpy.ediff1d` that takes masked\\n values into account, see `numpy.ediff1d` for details.\\n\\n See Also\\n --------\\n numpy.ediff1d : Equivalent function for ndarrays.\\n\\n \\\"\\\"\\\"\\n arr = ma.asanyarray(arr).flat\\n ed = arr[1:] - arr[:-1]\\n arrays = [ed]\\n #\\n if to_begin is not None:\\n arrays.insert(0, to_begin)\\n if to_end is not None:\\n arrays.append(to_end)\\n #\\n if len(arrays) != 1:\\n # We'll save ourselves a copy of a potentially large array in the common\\n # case where neither to_begin or to_end was given.\\n ed = hstack(arrays)\\n #\\n return ed\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::unique\",\n \"text\": \"numpy/ma/extras.py::unique\\ndef unique(ar1, return_index=False, return_inverse=False):\\n \\\"\\\"\\\"\\n Finds the unique elements of an array.\\n\\n Masked values are considered the same element (masked). The output array\\n is always a masked array. See `numpy.unique` for more details.\\n\\n See Also\\n --------\\n numpy.unique : Equivalent function for ndarrays.\\n\\n \\\"\\\"\\\"\\n output = np.unique(ar1,\\n return_index=return_index,\\n return_inverse=return_inverse)\\n if isinstance(output, tuple):\\n output = list(output)\\n output[0] = output[0].view(MaskedArray)\\n output = tuple(output)\\n else:\\n output = output.view(MaskedArray)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::intersect1d\",\n \"text\": \"numpy/ma/extras.py::intersect1d\\ndef intersect1d(ar1, ar2, assume_unique=False):\\n \\\"\\\"\\\"\\n Returns the unique elements common to both arrays.\\n\\n Masked values are considered equal one to the other.\\n The output is always a masked array.\\n\\n See `numpy.intersect1d` for more details.\\n\\n See Also\\n --------\\n numpy.intersect1d : Equivalent function for ndarrays.\\n\\n Examples\\n --------\\n >>> x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])\\n >>> y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])\\n >>> intersect1d(x, y)\\n masked_array(data = [1 3 --],\\n mask = [False False True],\\n fill_value = 999999)\\n\\n \\\"\\\"\\\"\\n if assume_unique:\\n aux = ma.concatenate((ar1, ar2))\\n else:\\n # Might be faster than unique( intersect1d( ar1, ar2 ) )?\\n aux = ma.concatenate((unique(ar1), unique(ar2)))\\n aux.sort()\\n return aux[:-1][aux[1:] == aux[:-1]]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::setxor1d\",\n \"text\": \"numpy/ma/extras.py::setxor1d\\ndef setxor1d(ar1, ar2, assume_unique=False):\\n \\\"\\\"\\\"\\n Set exclusive-or of 1-D arrays with unique elements.\\n\\n The output is always a masked array. See `numpy.setxor1d` for more details.\\n\\n See Also\\n --------\\n numpy.setxor1d : Equivalent function for ndarrays.\\n\\n \\\"\\\"\\\"\\n if not assume_unique:\\n ar1 = unique(ar1)\\n ar2 = unique(ar2)\\n\\n aux = ma.concatenate((ar1, ar2))\\n if aux.size == 0:\\n return aux\\n aux.sort()\\n auxf = aux.filled()\\n# flag = ediff1d( aux, to_end = 1, to_begin = 1 ) == 0\\n flag = ma.concatenate(([True], (auxf[1:] != auxf[:-1]), [True]))\\n# flag2 = ediff1d( flag ) == 0\\n flag2 = (flag[1:] == flag[:-1])\\n return aux[flag2]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::in1d\",\n \"text\": \"numpy/ma/extras.py::in1d\\ndef in1d(ar1, ar2, assume_unique=False, invert=False):\\n \\\"\\\"\\\"\\n Test whether each element of an array is also present in a second\\n array.\\n\\n The output is always a masked array. See `numpy.in1d` for more details.\\n\\n See Also\\n --------\\n numpy.in1d : Equivalent function for ndarrays.\\n\\n Notes\\n -----\\n .. versionadded:: 1.4.0\\n\\n \\\"\\\"\\\"\\n if not assume_unique:\\n ar1, rev_idx = unique(ar1, return_inverse=True)\\n ar2 = unique(ar2)\\n\\n ar = ma.concatenate((ar1, ar2))\\n # We need this to be a stable sort, so always use 'mergesort'\\n # here. The values from the first array should always come before\\n # the values from the second array.\\n order = ar.argsort(kind='mergesort')\\n sar = ar[order]\\n if invert:\\n bool_ar = (sar[1:] != sar[:-1])\\n else:\\n bool_ar = (sar[1:] == sar[:-1])\\n flag = ma.concatenate((bool_ar, [invert]))\\n indx = order.argsort(kind='mergesort')[:len(ar1)]\\n\\n if assume_unique:\\n return flag[indx]\\n else:\\n return flag[indx][rev_idx]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::union1d\",\n \"text\": \"numpy/ma/extras.py::union1d\\ndef union1d(ar1, ar2):\\n \\\"\\\"\\\"\\n Union of two arrays.\\n\\n The output is always a masked array. See `numpy.union1d` for more details.\\n\\n See also\\n --------\\n numpy.union1d : Equivalent function for ndarrays.\\n\\n \\\"\\\"\\\"\\n return unique(ma.concatenate((ar1, ar2)))\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::setdiff1d\",\n \"text\": \"numpy/ma/extras.py::setdiff1d\\ndef setdiff1d(ar1, ar2, assume_unique=False):\\n \\\"\\\"\\\"\\n Set difference of 1D arrays with unique elements.\\n\\n The output is always a masked array. See `numpy.setdiff1d` for more\\n details.\\n\\n See Also\\n --------\\n numpy.setdiff1d : Equivalent function for ndarrays.\\n\\n Examples\\n --------\\n >>> x = np.ma.array([1, 2, 3, 4], mask=[0, 1, 0, 1])\\n >>> np.ma.setdiff1d(x, [1, 2])\\n masked_array(data = [3 --],\\n mask = [False True],\\n fill_value = 999999)\\n\\n \\\"\\\"\\\"\\n if assume_unique:\\n ar1 = ma.asarray(ar1).ravel()\\n else:\\n ar1 = unique(ar1)\\n ar2 = unique(ar2)\\n return ar1[in1d(ar1, ar2, assume_unique=True, invert=True)]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::_covhelper\",\n \"text\": \"numpy/ma/extras.py::_covhelper\\ndef _covhelper(x, y=None, rowvar=True, allow_masked=True):\\n \\\"\\\"\\\"\\n Private function for the computation of covariance and correlation\\n coefficients.\\n\\n \\\"\\\"\\\"\\n x = ma.array(x, ndmin=2, copy=True, dtype=float)\\n xmask = ma.getmaskarray(x)\\n # Quick exit if we can't process masked data\\n if not allow_masked and xmask.any():\\n raise ValueError(\\\"Cannot process masked data.\\\")\\n #\\n if x.shape[0] == 1:\\n rowvar = True\\n # Make sure that rowvar is either 0 or 1\\n rowvar = int(bool(rowvar))\\n axis = 1 - rowvar\\n if rowvar:\\n tup = (slice(None), None)\\n else:\\n tup = (None, slice(None))\\n #\\n if y is None:\\n xnotmask = np.logical_not(xmask).astype(int)\\n else:\\n y = array(y, copy=False, ndmin=2, dtype=float)\\n ymask = ma.getmaskarray(y)\\n if not allow_masked and ymask.any():\\n raise ValueError(\\\"Cannot process masked data.\\\")\\n if xmask.any() or ymask.any():\\n if y.shape == x.shape:\\n # Define some common mask\\n common_mask = np.logical_or(xmask, ymask)\\n if common_mask is not nomask:\\n x.unshare_mask()\\n y.unshare_mask()\\n xmask = x._mask = y._mask = ymask = common_mask\\n x = ma.concatenate((x, y), axis)\\n xnotmask = np.logical_not(np.concatenate((xmask, ymask), axis)).astype(int)\\n x -= x.mean(axis=rowvar)[tup]\\n return (x, xnotmask, rowvar)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::cov\",\n \"text\": \"numpy/ma/extras.py::cov\\ndef cov(x, y=None, rowvar=True, bias=False, allow_masked=True, ddof=None):\\n \\\"\\\"\\\"\\n Estimate the covariance matrix.\\n\\n Except for the handling of missing data this function does the same as\\n `numpy.cov`. For more details and examples, see `numpy.cov`.\\n\\n By default, masked values are recognized as such. If `x` and `y` have the\\n same shape, a common mask is allocated: if ``x[i,j]`` is masked, then\\n ``y[i,j]`` will also be masked.\\n Setting `allow_masked` to False will raise an exception if values are\\n missing in either of the input arrays.\\n\\n Parameters\\n ----------\\n x : array_like\\n A 1-D or 2-D array containing multiple variables and observations.\\n Each row of `x` represents a variable, and each column a single\\n observation of all those variables. Also see `rowvar` below.\\n y : array_like, optional\\n An additional set of variables and observations. `y` has the same\\n form as `x`.\\n rowvar : bool, optional\\n If `rowvar` is True (default), then each row represents a\\n variable, with observations in the columns. Otherwise, the relationship\\n is transposed: each column represents a variable, while the rows\\n contain observations.\\n bias : bool, optional\\n Default normalization (False) is by ``(N-1)``, where ``N`` is the\\n number of observations given (unbiased estimate). If `bias` is True,\\n then normalization is by ``N``. This keyword can be overridden by\\n the keyword ``ddof`` in numpy versions >= 1.5.\\n allow_masked : bool, optional\\n If True, masked values are propagated pair-wise: if a value is masked\\n in `x`, the corresponding value is masked in `y`.\\n If False, raises a `ValueError` exception when some values are missing.\\n ddof : {None, int}, optional\\n If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is\\n the number of observations; this overrides the value implied by\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::corrcoef\",\n \"text\": \"numpy/ma/extras.py::corrcoef\\ndef corrcoef(x, y=None, rowvar=True, bias=np._NoValue, allow_masked=True,\\n ddof=np._NoValue):\\n \\\"\\\"\\\"\\n Return Pearson product-moment correlation coefficients.\\n\\n Except for the handling of missing data this function does the same as\\n `numpy.corrcoef`. For more details and examples, see `numpy.corrcoef`.\\n\\n Parameters\\n ----------\\n x : array_like\\n A 1-D or 2-D array containing multiple variables and observations.\\n Each row of `x` represents a variable, and each column a single\\n observation of all those variables. Also see `rowvar` below.\\n y : array_like, optional\\n An additional set of variables and observations. `y` has the same\\n shape as `x`.\\n rowvar : bool, optional\\n If `rowvar` is True (default), then each row represents a\\n variable, with observations in the columns. Otherwise, the relationship\\n is transposed: each column represents a variable, while the rows\\n contain observations.\\n bias : _NoValue, optional\\n Has no affect, do not use.\\n\\n .. deprecated:: 1.10.0\\n allow_masked : bool, optional\\n If True, masked values are propagated pair-wise: if a value is masked\\n in `x`, the corresponding value is masked in `y`.\\n If False, raises an exception. Because `bias` is deprecated, this\\n argument needs to be treated as keyword only to avoid a warning.\\n ddof : _NoValue, optional\\n Has no affect, do not use.\\n\\n .. deprecated:: 1.10.0\\n\\n See Also\\n --------\\n numpy.corrcoef : Equivalent function in top-level NumPy module.\\n cov : Estimate the covariance matrix.\\n\\n Notes\\n -----\\n This function accepts but discards arguments `bias` and `ddof`. This is\\n for backwards compatibility with previous versions of this function. These\\n arguments had no effect on the return values of the function and can be\\n safely ignored in this and previous versions of numpy.\\n \\\"\\\"\\\"\\n msg \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::MAxisConcatenator.__init__\",\n \"text\": \"numpy/ma/extras.py::MAxisConcatenator.__init__\\n def __init__(self, axis=0):\\n AxisConcatenator.__init__(self, axis, matrix=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::MAxisConcatenator.__getitem__\",\n \"text\": \"numpy/ma/extras.py::MAxisConcatenator.__getitem__\\n def __getitem__(self, key):\\n if isinstance(key, str):\\n raise MAError(\\\"Unavailable for masked array.\\\")\\n if not isinstance(key, tuple):\\n key = (key,)\\n objs = []\\n scalars = []\\n final_dtypedescr = None\\n for k in range(len(key)):\\n scalar = False\\n if isinstance(key[k], slice):\\n step = key[k].step\\n start = key[k].start\\n stop = key[k].stop\\n if start is None:\\n start = 0\\n if step is None:\\n step = 1\\n if isinstance(step, complex):\\n size = int(abs(step))\\n newobj = np.linspace(start, stop, num=size)\\n else:\\n newobj = np.arange(start, stop, step)\\n elif isinstance(key[k], str):\\n if (key[k] in 'rc'):\\n self.matrix = True\\n self.col = (key[k] == 'c')\\n continue\\n try:\\n self.axis = int(key[k])\\n continue\\n except (ValueError, TypeError):\\n raise ValueError(\\\"Unknown special directive\\\")\\n elif type(key[k]) in np.ScalarType:\\n newobj = asarray([key[k]])\\n scalars.append(k)\\n scalar = True\\n else:\\n newobj = key[k]\\n objs.append(newobj)\\n if isinstance(newobj, ndarray) and not scalar:\\n if final_dtypedescr is None:\\n final_dtypedescr = newobj.dtype\\n elif newobj.dtype > final_dtypedescr:\\n final_dtypedescr = newobj.dtype\\n if final_dtypedescr is not None:\\n for k in scalars:\\n objs[k] = objs[k].astype(final_dtypedescr)\\n res = concatenate(tuple(objs), axis=self.axis)\\n return self._retval(res)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::mr_class.__init__\",\n \"text\": \"numpy/ma/extras.py::mr_class.__init__\\n def __init__(self):\\n MAxisConcatenator.__init__(self, 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::flatnotmasked_edges\",\n \"text\": \"numpy/ma/extras.py::flatnotmasked_edges\\ndef flatnotmasked_edges(a):\\n \\\"\\\"\\\"\\n Find the indices of the first and last unmasked values.\\n\\n Expects a 1-D `MaskedArray`, returns None if all values are masked.\\n\\n Parameters\\n ----------\\n a : array_like\\n Input 1-D `MaskedArray`\\n\\n Returns\\n -------\\n edges : ndarray or None\\n The indices of first and last non-masked value in the array.\\n Returns None if all values are masked.\\n\\n See Also\\n --------\\n flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges,\\n clump_masked, clump_unmasked\\n\\n Notes\\n -----\\n Only accepts 1-D arrays.\\n\\n Examples\\n --------\\n >>> a = np.ma.arange(10)\\n >>> flatnotmasked_edges(a)\\n [0,-1]\\n\\n >>> mask = (a < 3) | (a > 8) | (a == 5)\\n >>> a[mask] = np.ma.masked\\n >>> np.array(a[~a.mask])\\n array([3, 4, 6, 7, 8])\\n\\n >>> flatnotmasked_edges(a)\\n array([3, 8])\\n\\n >>> a[:] = np.ma.masked\\n >>> print flatnotmasked_edges(ma)\\n None\\n\\n \\\"\\\"\\\"\\n m = getmask(a)\\n if m is nomask or not np.any(m):\\n return np.array([0, a.size - 1])\\n unmasked = np.flatnonzero(~m)\\n if len(unmasked) > 0:\\n return unmasked[[0, -1]]\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::notmasked_edges\",\n \"text\": \"numpy/ma/extras.py::notmasked_edges\\ndef notmasked_edges(a, axis=None):\\n \\\"\\\"\\\"\\n Find the indices of the first and last unmasked values along an axis.\\n\\n If all values are masked, return None. Otherwise, return a list\\n of two tuples, corresponding to the indices of the first and last\\n unmasked values respectively.\\n\\n Parameters\\n ----------\\n a : array_like\\n The input array.\\n axis : int, optional\\n Axis along which to perform the operation.\\n If None (default), applies to a flattened version of the array.\\n\\n Returns\\n -------\\n edges : ndarray or list\\n An array of start and end indexes if there are any masked data in\\n the array. If there are no masked data in the array, `edges` is a\\n list of the first and last index.\\n\\n See Also\\n --------\\n flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous,\\n clump_masked, clump_unmasked\\n\\n Examples\\n --------\\n >>> a = np.arange(9).reshape((3, 3))\\n >>> m = np.zeros_like(a)\\n >>> m[1:, 1:] = 1\\n\\n >>> am = np.ma.array(a, mask=m)\\n >>> np.array(am[~am.mask])\\n array([0, 1, 2, 3, 6])\\n\\n >>> np.ma.notmasked_edges(ma)\\n array([0, 6])\\n\\n \\\"\\\"\\\"\\n a = asarray(a)\\n if axis is None or a.ndim == 1:\\n return flatnotmasked_edges(a)\\n m = getmaskarray(a)\\n idx = array(np.indices(a.shape), mask=np.asarray([m] * a.ndim))\\n return [tuple([idx[i].min(axis).compressed() for i in range(a.ndim)]),\\n tuple([idx[i].max(axis).compressed() for i in range(a.ndim)]), ]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::flatnotmasked_contiguous\",\n \"text\": \"numpy/ma/extras.py::flatnotmasked_contiguous\\ndef flatnotmasked_contiguous(a):\\n \\\"\\\"\\\"\\n Find contiguous unmasked data in a masked array along the given axis.\\n\\n Parameters\\n ----------\\n a : narray\\n The input array.\\n\\n Returns\\n -------\\n slice_list : list\\n A sorted sequence of slices (start index, end index).\\n\\n See Also\\n --------\\n flatnotmasked_edges, notmasked_contiguous, notmasked_edges,\\n clump_masked, clump_unmasked\\n\\n Notes\\n -----\\n Only accepts 2-D arrays at most.\\n\\n Examples\\n --------\\n >>> a = np.ma.arange(10)\\n >>> np.ma.flatnotmasked_contiguous(a)\\n slice(0, 10, None)\\n\\n >>> mask = (a < 3) | (a > 8) | (a == 5)\\n >>> a[mask] = np.ma.masked\\n >>> np.array(a[~a.mask])\\n array([3, 4, 6, 7, 8])\\n\\n >>> np.ma.flatnotmasked_contiguous(a)\\n [slice(3, 5, None), slice(6, 9, None)]\\n >>> a[:] = np.ma.masked\\n >>> print np.ma.flatnotmasked_edges(a)\\n None\\n\\n \\\"\\\"\\\"\\n m = getmask(a)\\n if m is nomask:\\n return slice(0, a.size, None)\\n i = 0\\n result = []\\n for (k, g) in itertools.groupby(m.ravel()):\\n n = len(list(g))\\n if not k:\\n result.append(slice(i, i + n))\\n i += n\\n return result or None\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::notmasked_contiguous\",\n \"text\": \"numpy/ma/extras.py::notmasked_contiguous\\ndef notmasked_contiguous(a, axis=None):\\n \\\"\\\"\\\"\\n Find contiguous unmasked data in a masked array along the given axis.\\n\\n Parameters\\n ----------\\n a : array_like\\n The input array.\\n axis : int, optional\\n Axis along which to perform the operation.\\n If None (default), applies to a flattened version of the array.\\n\\n Returns\\n -------\\n endpoints : list\\n A list of slices (start and end indexes) of unmasked indexes\\n in the array.\\n\\n See Also\\n --------\\n flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges,\\n clump_masked, clump_unmasked\\n\\n Notes\\n -----\\n Only accepts 2-D arrays at most.\\n\\n Examples\\n --------\\n >>> a = np.arange(9).reshape((3, 3))\\n >>> mask = np.zeros_like(a)\\n >>> mask[1:, 1:] = 1\\n\\n >>> ma = np.ma.array(a, mask=mask)\\n >>> np.array(ma[~ma.mask])\\n array([0, 1, 2, 3, 6])\\n\\n >>> np.ma.notmasked_contiguous(ma)\\n [slice(0, 4, None), slice(6, 7, None)]\\n\\n \\\"\\\"\\\"\\n a = asarray(a)\\n nd = a.ndim\\n if nd > 2:\\n raise NotImplementedError(\\\"Currently limited to atmost 2D array.\\\")\\n if axis is None or nd == 1:\\n return flatnotmasked_contiguous(a)\\n #\\n result = []\\n #\\n other = (axis + 1) % 2\\n idx = [0, 0]\\n idx[axis] = slice(None, None)\\n #\\n for i in range(a.shape[other]):\\n idx[other] = i\\n result.append(flatnotmasked_contiguous(a[idx]) or None)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::_ezclump\",\n \"text\": \"numpy/ma/extras.py::_ezclump\\ndef _ezclump(mask):\\n \\\"\\\"\\\"\\n Finds the clumps (groups of data with the same values) for a 1D bool array.\\n\\n Returns a series of slices.\\n \\\"\\\"\\\"\\n #def clump_masked(a):\\n if mask.ndim > 1:\\n mask = mask.ravel()\\n idx = (mask[1:] ^ mask[:-1]).nonzero()\\n idx = idx[0] + 1\\n slices = [slice(left, right)\\n for (left, right) in zip(itertools.chain([0], idx),\\n itertools.chain(idx, [len(mask)]),)]\\n return slices\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::clump_unmasked\",\n \"text\": \"numpy/ma/extras.py::clump_unmasked\\ndef clump_unmasked(a):\\n \\\"\\\"\\\"\\n Return list of slices corresponding to the unmasked clumps of a 1-D array.\\n (A \\\"clump\\\" is defined as a contiguous region of the array).\\n\\n Parameters\\n ----------\\n a : ndarray\\n A one-dimensional masked array.\\n\\n Returns\\n -------\\n slices : list of slice\\n The list of slices, one for each continuous region of unmasked\\n elements in `a`.\\n\\n Notes\\n -----\\n .. versionadded:: 1.4.0\\n\\n See Also\\n --------\\n flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges,\\n notmasked_contiguous, clump_masked\\n\\n Examples\\n --------\\n >>> a = np.ma.masked_array(np.arange(10))\\n >>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked\\n >>> np.ma.clump_unmasked(a)\\n [slice(3, 6, None), slice(7, 8, None)]\\n\\n \\\"\\\"\\\"\\n mask = getattr(a, '_mask', nomask)\\n if mask is nomask:\\n return [slice(0, a.size)]\\n slices = _ezclump(mask)\\n if a[0] is masked:\\n result = slices[1::2]\\n else:\\n result = slices[::2]\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::clump_masked\",\n \"text\": \"numpy/ma/extras.py::clump_masked\\ndef clump_masked(a):\\n \\\"\\\"\\\"\\n Returns a list of slices corresponding to the masked clumps of a 1-D array.\\n (A \\\"clump\\\" is defined as a contiguous region of the array).\\n\\n Parameters\\n ----------\\n a : ndarray\\n A one-dimensional masked array.\\n\\n Returns\\n -------\\n slices : list of slice\\n The list of slices, one for each continuous region of masked elements\\n in `a`.\\n\\n Notes\\n -----\\n .. versionadded:: 1.4.0\\n\\n See Also\\n --------\\n flatnotmasked_edges, flatnotmasked_contiguous, notmasked_edges,\\n notmasked_contiguous, clump_unmasked\\n\\n Examples\\n --------\\n >>> a = np.ma.masked_array(np.arange(10))\\n >>> a[[0, 1, 2, 6, 8, 9]] = np.ma.masked\\n >>> np.ma.clump_masked(a)\\n [slice(0, 3, None), slice(6, 7, None), slice(8, 10, None)]\\n\\n \\\"\\\"\\\"\\n mask = ma.getmask(a)\\n if mask is nomask:\\n return []\\n slices = _ezclump(mask)\\n if len(slices):\\n if a[0] is masked:\\n slices = slices[::2]\\n else:\\n slices = slices[1::2]\\n return slices\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::vander\",\n \"text\": \"numpy/ma/extras.py::vander\\ndef vander(x, n=None):\\n \\\"\\\"\\\"\\n Masked values in the input array result in rows of zeros.\\n\\n \\\"\\\"\\\"\\n _vander = np.vander(x, n)\\n m = getmask(x)\\n if m is not nomask:\\n _vander[m] = 0\\n return _vander\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/extras.py::polyfit\",\n \"text\": \"numpy/ma/extras.py::polyfit\\ndef polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):\\n \\\"\\\"\\\"\\n Any masked values in x is propagated in y, and vice-versa.\\n\\n \\\"\\\"\\\"\\n x = asarray(x)\\n y = asarray(y)\\n\\n m = getmask(x)\\n if y.ndim == 1:\\n m = mask_or(m, getmask(y))\\n elif y.ndim == 2:\\n my = getmask(mask_rows(y))\\n if my is not nomask:\\n m = mask_or(m, my[:, 0])\\n else:\\n raise TypeError(\\\"Expected a 1D or 2D array for y!\\\")\\n\\n if w is not None:\\n w = asarray(w)\\n if w.ndim != 1:\\n raise TypeError(\\\"expected a 1-d array for weights\\\")\\n if w.shape[0] != y.shape[0]:\\n raise TypeError(\\\"expected w and y to have the same length\\\")\\n m = mask_or(m, getmask(w))\\n\\n if m is not nomask:\\n not_m = ~m\\n if w is not None:\\n w = w[not_m]\\n return np.polyfit(x[not_m], y[not_m], deg, rcond, full, w, cov)\\n else:\\n return np.polyfit(x, y, deg, rcond, full, w, cov)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":81,"cells":{"instance_id":{"kind":"string","value":"conan-io__conan-4317"},"repo":{"kind":"string","value":"conan-io/conan"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Hard to debug error ERROR: Expecting value: line 1 column 1 (char 0)\nHi, first of all i want to congratulate you guys on this great project that is conan.\r\n\r\nI performed some conan copy to rename the owner of the package on custom packages that some have sources other get the sources from git and so on, they all are different but now i'm getting this anoying error when i try to do a conan install on some packages:\r\n```\r\nERROR: Expecting value: line 1 column 1 (char 0)\r\n```\r\nThe problem is that it is very hard to know what package is creating this error.\r\n\r\nI kind understand that the problem was that the conan copy does not copy sources or it seems... something like that i can't really explain, but doing the conan create again on the package seems to fix this... (when i find the package with error... but i got many and is hard to find the one in error or ones)\r\n\r\nIt will be nice to add the package name on the error to easily find the package that is creating the problem (many package dependencies create this hard to find package in error)\n"},"gold_files":{"kind":"list like","value":["conans/client/rest/rest_client_common.py","conans/server/rest/multipart_encoder.py","conans/server/rest/server.py","conans/server/service/mime.py"],"string":"[\n \"conans/client/rest/rest_client_common.py\",\n \"conans/server/rest/multipart_encoder.py\",\n \"conans/server/rest/server.py\",\n \"conans/server/service/mime.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["conans/client/rest/rest_client_common.py::RestCommonMethods.get_json","conans/server/rest/multipart_encoder.py::<module>","conans/server/rest/server.py::<module>","conans/server/service/mime.py::get_mime_type"],"string":"[\n \"conans/client/rest/rest_client_common.py::RestCommonMethods.get_json\",\n \"conans/server/rest/multipart_encoder.py::<module>\",\n \"conans/server/rest/server.py::<module>\",\n \"conans/server/service/mime.py::get_mime_type\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"conans/client/command.py","text":"conans/client/command.py\nclass Extender # Allows to use the same flag several times in a command and creates a list with t\n def __call__(self, parser, namespace, values, option_strings=None)\nclass OnceArgument # Allows to declare a parameter that can have only one value, by default argparse \n def __call__(self, parser, namespace, values, option_string=None)\nclass Command # A single command of the conan application, with all the first level commands. Ma\n def __init__(self, conan_api, cache, user_io, outputer)\n def help(self, *args) # Show help of a specific command.\n def new(self, *args) # Creates a new package recipe template with a 'conanfile.py' and optionally,\n def inspect(self, *args) # Displays conanfile attributes, like name, version, options\n def test(self, *args) # Test a package consuming it from a conanfile.py with a test() method.\n def create(self, *args) # Builds a binary package for a recipe (conanfile.py).\n def download(self, *args) # Downloads recipe and binaries to the local cache, without using settings.\n def install(self, *args) # Installs the requirements specified in a recipe (conanfile.py or conanfile.txt).\n def config(self, *args) # Manages Conan configuration.\n def info(self, *args) # Gets information about the dependency graph of a recipe.\n def source(self, *args) # Calls your local conanfile.py 'source()' method.\n def build(self, *args) # Calls your local conanfile.py 'build()' method.\n def package(self, *args) # Calls your local conanfile.py 'package()' method.\n def imports(self, *args) # Calls your local conanfile.py or conanfile.txt 'imports' method.\n def export_pkg(self, *args) # Exports a recipe, then creates a package from local source and build folders.\n def export(self, *args) # Copies the recipe (conanfile.py & associated files) to your local cache.\n def remove(self, *args) # Removes packages or binaries matching pattern from local cache or remote.\n def copy(self, *args) # Copies conan recipes and packages to another user/channel.\n def user(self, *args) # Authenticates against a remote with user/pass, caching the auth token.\n def search(self, *args) # Searches package recipes and binaries in the local cache or in a remote.\n def upload(self, *args) # Uploads a recipe and binary packages to a remote.\n def remote(self, *args) # Manages the remote list and the package recipes associated to a remote.\n def profile(self, *args) # Lists profiles in the '.conan/profiles' folder, or shows profile details.\n def get(self, *args) # Gets a file or list a directory of a given reference or package.\n def alias(self, *args) # Creates and exports an 'alias package recipe'.\n def link(self, *args) # Links a conan reference (e.g lib/1.0@conan/stable) with a local folder path.\n def _show_help(self) # Prints a summary of all commands\n def check_all_commands_listed() # Keep updated the main directory, raise if don't","label":0},{"cid":"conans/client/recorder/action_recorder.py","text":"conans/client/recorder/action_recorder.py\nclass Action\n def __new__(cls, the_type, doc=None)\nclass ActionRecorder\n def __init__(self)\n def add_recipe_being_developed(self, ref)\n def _add_recipe_action(self, ref, action)\n def _add_package_action(self, pref, action)\n def recipe_exported(self, ref)\n def recipe_fetched_from_cache(self, ref)\n def recipe_downloaded(self, ref, remote_name)\n def recipe_install_error(self, ref, error_type, description, remote_name)\n def package_exported(self, pref)\n def package_built(self, pref)\n def package_fetched_from_cache(self, pref)\n def package_downloaded(self, pref, remote_name)\n def package_install_error(self, pref, error_type, description, remote_name=None)\n def package_cpp_info(self, pref, cpp_info)\n def install_errored(self)\n def _get_installed_packages(self, ref)\n def in_development_recipe(self, ref)\n def get_info(self)\n def get_install_info(self)\n def get_doc_for_ref(the_ref, the_actions)","label":0},{"cid":"conans/client/rest/rest_client_common.py","text":"conans/client/rest/rest_client_common.py\nclass JWTAuth # Attaches JWT Authentication to the given Request object.\n def __init__(self, token)\n def __call__(self, request)\ndef _base_error(error_code)\ndef get_exception_from_error(error_code)\ndef handle_return_deserializer(deserializer=None) # Decorator for rest api methods.\n def handle_return(method)\n def inner(*argc, **argv)\nclass RestCommonMethods\n def __init__(self, remote_url, token, custom_headers, output, requester, verify_ssl, put_headers=None)\n def auth(self)\n def users_router(self)\n def search_router(self)\n def base_router(self)\n def authenticate(self, user, password) # Sends user + password to get a token\n def check_credentials(self) # If token is not valid will raise AuthenticationException.\n def server_info(self) # Get information about the server: status, version, type and capabilities\n def get_json(self, url, data=None)\n def upload_recipe(self, ref, the_files, retry, retry_wait, policy, remote_manifest) # the_files: dict with relative_path: content\n def upload_package(self, pref, the_files, retry, retry_wait, policy) # basedir: Base directory with the files to upload (for read the files in disk)\n def search(self, pattern=None, ignorecase=True) # the_files: dict with relative_path: content\n def search_packages(self, reference, query)\n def remove_conanfile(self, ref) # Remove a recipe and packages\n def _post_json(self, url, payload)","label":1},{"cid":"conans/model/conan_file.py","text":"conans/model/conan_file.py\ndef create_options(conanfile)\ndef create_requirements(conanfile)\ndef create_settings(conanfile, settings)\ndef _env_and_python(conanfile)\ndef get_env_context_manager(conanfile, without_python=False)\nclass ConanFile # The base class for all package recipes\n def __init__(self, output, runner, display_name='', user=None, channel=None)\n def initialize(self, settings, env)\n def env(self) # Apply the self.deps_env_info into a copy of self._conan_env_values (will priorit\n def channel(self)\n def user(self)\n def collect_libs(self, folder=None)\n def build_policy_missing(self)\n def build_policy_always(self)\n def source(self)\n def system_requirements(self) # this method can be overwritten to implement logic for system package\n def config_options(self) # modify options, probably conditioned to some settings. This call is executed\n def configure(self) # modify settings, probably conditioned to some options. This call is executed\n def build(self) # build your project calling the desired build tools as done in the command line.\n def package(self) # package the needed files from source and build folders.\n def package_info(self) # define cpp_build_info, flags, etc\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True, ignore_errors=False, run_environment=False)\n def _run()\n def package_id(self) # modify the conans info, typically to narrow values\n def test(self) # test the generated executable.\n def __repr__(self)","label":0},{"cid":"conans/server/rest/multipart_encoder.py","text":"conans/server/rest/multipart_encoder.py\ndef get_response_chunk(response, basepath, filepaths, boundary=DEFAULT_BOUNDARY) # File is a list of filepaths\ndef _headers_for_file(filename)\ndef read_file_chunked(filepath)\ndef read_in_chunks(file_object, chunk_size=1024) # Lazy function (generator) to read a file piece by piece.","label":1},{"cid":"conans/server/rest/server.py","text":"conans/server/rest/server.py\nclass ConanServer # Server class. Instances api_v1 application and run it.\n def __init__(self, run_port, credentials_manager, updown_auth_manager, authorizer, authenticator, server_store, server_capabilities)\n def run(self, **kwargs)","label":1},{"cid":"conans/server/service/mime.py","text":"conans/server/service/mime.py\ndef get_mime_type(filepath)","label":1}],"string":"[\n {\n \"cid\": \"conans/client/command.py\",\n \"text\": \"conans/client/command.py\\nclass Extender # Allows to use the same flag several times in a command and creates a list with t\\n def __call__(self, parser, namespace, values, option_strings=None)\\nclass OnceArgument # Allows to declare a parameter that can have only one value, by default argparse \\n def __call__(self, parser, namespace, values, option_string=None)\\nclass Command # A single command of the conan application, with all the first level commands. Ma\\n def __init__(self, conan_api, cache, user_io, outputer)\\n def help(self, *args) # Show help of a specific command.\\n def new(self, *args) # Creates a new package recipe template with a 'conanfile.py' and optionally,\\n def inspect(self, *args) # Displays conanfile attributes, like name, version, options\\n def test(self, *args) # Test a package consuming it from a conanfile.py with a test() method.\\n def create(self, *args) # Builds a binary package for a recipe (conanfile.py).\\n def download(self, *args) # Downloads recipe and binaries to the local cache, without using settings.\\n def install(self, *args) # Installs the requirements specified in a recipe (conanfile.py or conanfile.txt).\\n def config(self, *args) # Manages Conan configuration.\\n def info(self, *args) # Gets information about the dependency graph of a recipe.\\n def source(self, *args) # Calls your local conanfile.py 'source()' method.\\n def build(self, *args) # Calls your local conanfile.py 'build()' method.\\n def package(self, *args) # Calls your local conanfile.py 'package()' method.\\n def imports(self, *args) # Calls your local conanfile.py or conanfile.txt 'imports' method.\\n def export_pkg(self, *args) # Exports a recipe, then creates a package from local source and build folders.\\n def export(self, *args) # Copies the recipe (conanfile.py & associated files) to your local cache.\\n def remove(self, *args) # Removes packages or binaries matching pattern from local cache or remote.\\n def copy(self, *args) # Copies conan recipes and packages to another user/channel.\\n def user(self, *args) # Authenticates against a remote with user/pass, caching the auth token.\\n def search(self, *args) # Searches package recipes and binaries in the local cache or in a remote.\\n def upload(self, *args) # Uploads a recipe and binary packages to a remote.\\n def remote(self, *args) # Manages the remote list and the package recipes associated to a remote.\\n def profile(self, *args) # Lists profiles in the '.conan/profiles' folder, or shows profile details.\\n def get(self, *args) # Gets a file or list a directory of a given reference or package.\\n def alias(self, *args) # Creates and exports an 'alias package recipe'.\\n def link(self, *args) # Links a conan reference (e.g lib/1.0@conan/stable) with a local folder path.\\n def _show_help(self) # Prints a summary of all commands\\n def check_all_commands_listed() # Keep updated the main directory, raise if don't\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py\",\n \"text\": \"conans/client/recorder/action_recorder.py\\nclass Action\\n def __new__(cls, the_type, doc=None)\\nclass ActionRecorder\\n def __init__(self)\\n def add_recipe_being_developed(self, ref)\\n def _add_recipe_action(self, ref, action)\\n def _add_package_action(self, pref, action)\\n def recipe_exported(self, ref)\\n def recipe_fetched_from_cache(self, ref)\\n def recipe_downloaded(self, ref, remote_name)\\n def recipe_install_error(self, ref, error_type, description, remote_name)\\n def package_exported(self, pref)\\n def package_built(self, pref)\\n def package_fetched_from_cache(self, pref)\\n def package_downloaded(self, pref, remote_name)\\n def package_install_error(self, pref, error_type, description, remote_name=None)\\n def package_cpp_info(self, pref, cpp_info)\\n def install_errored(self)\\n def _get_installed_packages(self, ref)\\n def in_development_recipe(self, ref)\\n def get_info(self)\\n def get_install_info(self)\\n def get_doc_for_ref(the_ref, the_actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py\",\n \"text\": \"conans/client/rest/rest_client_common.py\\nclass JWTAuth # Attaches JWT Authentication to the given Request object.\\n def __init__(self, token)\\n def __call__(self, request)\\ndef _base_error(error_code)\\ndef get_exception_from_error(error_code)\\ndef handle_return_deserializer(deserializer=None) # Decorator for rest api methods.\\n def handle_return(method)\\n def inner(*argc, **argv)\\nclass RestCommonMethods\\n def __init__(self, remote_url, token, custom_headers, output, requester, verify_ssl, put_headers=None)\\n def auth(self)\\n def users_router(self)\\n def search_router(self)\\n def base_router(self)\\n def authenticate(self, user, password) # Sends user + password to get a token\\n def check_credentials(self) # If token is not valid will raise AuthenticationException.\\n def server_info(self) # Get information about the server: status, version, type and capabilities\\n def get_json(self, url, data=None)\\n def upload_recipe(self, ref, the_files, retry, retry_wait, policy, remote_manifest) # the_files: dict with relative_path: content\\n def upload_package(self, pref, the_files, retry, retry_wait, policy) # basedir: Base directory with the files to upload (for read the files in disk)\\n def search(self, pattern=None, ignorecase=True) # the_files: dict with relative_path: content\\n def search_packages(self, reference, query)\\n def remove_conanfile(self, ref) # Remove a recipe and packages\\n def _post_json(self, url, payload)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/model/conan_file.py\",\n \"text\": \"conans/model/conan_file.py\\ndef create_options(conanfile)\\ndef create_requirements(conanfile)\\ndef create_settings(conanfile, settings)\\ndef _env_and_python(conanfile)\\ndef get_env_context_manager(conanfile, without_python=False)\\nclass ConanFile # The base class for all package recipes\\n def __init__(self, output, runner, display_name='', user=None, channel=None)\\n def initialize(self, settings, env)\\n def env(self) # Apply the self.deps_env_info into a copy of self._conan_env_values (will priorit\\n def channel(self)\\n def user(self)\\n def collect_libs(self, folder=None)\\n def build_policy_missing(self)\\n def build_policy_always(self)\\n def source(self)\\n def system_requirements(self) # this method can be overwritten to implement logic for system package\\n def config_options(self) # modify options, probably conditioned to some settings. This call is executed\\n def configure(self) # modify settings, probably conditioned to some options. This call is executed\\n def build(self) # build your project calling the desired build tools as done in the command line.\\n def package(self) # package the needed files from source and build folders.\\n def package_info(self) # define cpp_build_info, flags, etc\\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True, ignore_errors=False, run_environment=False)\\n def _run()\\n def package_id(self) # modify the conans info, typically to narrow values\\n def test(self) # test the generated executable.\\n def __repr__(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/server/rest/multipart_encoder.py\",\n \"text\": \"conans/server/rest/multipart_encoder.py\\ndef get_response_chunk(response, basepath, filepaths, boundary=DEFAULT_BOUNDARY) # File is a list of filepaths\\ndef _headers_for_file(filename)\\ndef read_file_chunked(filepath)\\ndef read_in_chunks(file_object, chunk_size=1024) # Lazy function (generator) to read a file piece by piece.\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/server/rest/server.py\",\n \"text\": \"conans/server/rest/server.py\\nclass ConanServer # Server class. Instances api_v1 application and run it.\\n def __init__(self, run_port, credentials_manager, updown_auth_manager, authorizer, authenticator, server_store, server_capabilities)\\n def run(self, **kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/server/service/mime.py\",\n \"text\": \"conans/server/service/mime.py\\ndef get_mime_type(filepath)\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"conans/client/command.py::Extender.__call__","text":"conans/client/command.py::Extender.__call__\n def __call__(self, parser, namespace, values, option_strings=None): # @UnusedVariable\n # Need None here incase `argparse.SUPPRESS` was supplied for `dest`\n dest = getattr(namespace, self.dest, None)\n if not hasattr(dest, 'extend') or dest == self.default:\n dest = []\n setattr(namespace, self.dest, dest)\n # if default isn't set to None, this method might be called\n # with the default as `values` for other arguments which\n # share this destination.\n parser.set_defaults(**{self.dest: None})\n\n if isinstance(values, str):\n dest.append(values)\n elif values:\n try:\n dest.extend(values)\n except ValueError:\n dest.append(values)","label":0},{"cid":"conans/client/command.py::OnceArgument.__call__","text":"conans/client/command.py::OnceArgument.__call__\n def __call__(self, parser, namespace, values, option_string=None):\n if getattr(namespace, self.dest) is not None and self.default is None:\n msg = '{o} can only be specified once'.format(o=option_string)\n raise argparse.ArgumentError(None, msg)\n setattr(namespace, self.dest, values)","label":0},{"cid":"conans/client/command.py::Command.__init__","text":"conans/client/command.py::Command.__init__\n def __init__(self, conan_api, cache, user_io, outputer):\n assert isinstance(conan_api, Conan)\n self._conan = conan_api\n self._cache = cache\n self._user_io = user_io\n self._outputer = outputer","label":0},{"cid":"conans/client/command.py::Command.help","text":"conans/client/command.py::Command.help\n def help(self, *args):\n \"\"\"Show help of a specific command.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.help.__doc__, prog=\"conan help\")\n parser.add_argument(\"command\", help='command', nargs=\"?\")\n args = parser.parse_args(*args)\n if not args.command:\n self._show_help()\n return\n try:\n commands = self._commands()\n method = commands[args.command]\n self._warn_python2()\n method([\"--help\"])\n except KeyError:\n raise ConanException(\"Unknown command '%s'\" % args.command)","label":0},{"cid":"conans/client/command.py::Command.new","text":"conans/client/command.py::Command.new\n def new(self, *args):\n \"\"\"Creates a new package recipe template with a 'conanfile.py' and optionally,\n 'test_package' testing files.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.new.__doc__, prog=\"conan new\")\n parser.add_argument(\"name\", help='Package name, e.g.: \"Poco/1.7.3\" or complete reference'\n ' for CI scripts: \"Poco/1.7.3@conan/stable\"')\n parser.add_argument(\"-t\", \"--test\", action='store_true', default=False,\n help='Create test_package skeleton to test package')\n parser.add_argument(\"-i\", \"--header\", action='store_true', default=False,\n help='Create a headers only package template')\n parser.add_argument(\"-c\", \"--pure-c\", action='store_true', default=False,\n help='Create a C language package only package, '\n 'deleting \"self.settings.compiler.libcxx\" setting '\n 'in the configure method')\n parser.add_argument(\"-s\", \"--sources\", action='store_true', default=False,\n help='Create a package with embedded sources in \"src\" folder, '\n 'using \"exports_sources\" instead of retrieving external code with '\n 'the \"source()\" method')\n parser.add_argument(\"-b\", \"--bare\", action='store_true', default=False,\n help='Create the minimum package recipe, without build() method. '\n 'Useful in combination with \"export-pkg\" command')\n parser.add_argument(\"-cis\", \"--ci-shared\", action='store_true',\n default=False,\n help='Package will have a \"shared\" option to be used in CI')\n parser.add_argument(\"-cilg\", \"--ci-travis-gcc\", action='store_true',\n defau","label":0},{"cid":"conans/client/command.py::Command.inspect","text":"conans/client/command.py::Command.inspect\n def inspect(self, *args):\n \"\"\"Displays conanfile attributes, like name, version, options\n Works both locally, in local cache and remote\n \"\"\"\n parser = argparse.ArgumentParser(description=self.inspect.__doc__, prog=\"conan inspect\")\n parser.add_argument(\"path_or_reference\", help=\"Path to a folder containing a recipe\"\n \" (conanfile.py) or to a recipe file. e.g., \"\n \"./my_project/conanfile.py. It could also be a reference\")\n parser.add_argument(\"-a\", \"--attribute\", help='The attribute to be displayed, e.g \"name\"',\n nargs=\"?\", action=Extender)\n parser.add_argument(\"-r\", \"--remote\", help='look in the specified remote server',\n action=OnceArgument)\n parser.add_argument(\"-j\", \"--json\", default=None, action=OnceArgument,\n help='json output file')\n\n args = parser.parse_args(*args)\n result = self._conan.inspect(args.path_or_reference, args.attribute, args.remote)\n Printer(self._user_io.out).print_inspect(result)\n if args.json:\n json_output = json.dumps(result)\n if not os.path.isabs(args.json):\n json_output_file = os.path.join(get_cwd(), args.json)\n else:\n json_output_file = args.json\n save(json_output_file, json_output)","label":0},{"cid":"conans/client/command.py::Command.test","text":"conans/client/command.py::Command.test\n def test(self, *args):\n \"\"\"Test a package consuming it from a conanfile.py with a test() method.\n\n This command installs the conanfile dependencies (including the tested\n package), calls a 'conan build' to build test apps and finally executes\n the test() method. The testing recipe does not require name or version,\n neither definition of package() or package_info() methods. The package\n to be tested must exist in the local cache or in any configured remote.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.test.__doc__, prog=\"conan test\")\n parser.add_argument(\"path\", help='Path to the \"testing\" folder containing a conanfile.py or'\n ' to a recipe file with test() method'\n 'e.g. conan test_package/conanfile.py pkg/version@user/channel')\n parser.add_argument(\"reference\",\n help='pkg/version@user/channel of the package to be tested')\n parser.add_argument(\"-tbf\", \"--test-build-folder\", action=OnceArgument,\n help=\"Working directory of the build process.\")\n\n _add_common_install_arguments(parser, build_help=_help_build_policies)\n args = parser.parse_args(*args)\n self._warn_python2()\n return self._conan.test(args.path, args.reference, args.profile, args.settings,\n args.options, args.env, args.remote, args.update,\n build_modes=args.build, test_build_folder=args.test_build_folder)","label":0},{"cid":"conans/client/command.py::Command.create","text":"conans/client/command.py::Command.create\n def create(self, *args):\n \"\"\"Builds a binary package for a recipe (conanfile.py).\n\n Uses the specified configuration in a profile or in -s settings, -o\n options etc. If a 'test_package' folder (the name can be configured\n with -tf) is found, the command will run the consumer project to ensure\n that the package has been created correctly. Check 'conan test' command\n to know more about 'test_folder' project.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.create.__doc__, prog=\"conan create\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"reference\",\n help='user/channel or pkg/version@user/channel (if name and version not'\n ' declared in conanfile.py) where the package will be created')\n parser.add_argument(\"-j\", \"--json\", default=None, action=OnceArgument,\n help='json file path where the install information will be written to')\n parser.add_argument('-k', '-ks', '--keep-source', default=False, action='store_true',\n help=_KEEP_SOURCE_HELP)\n parser.add_argument('-kb', '--keep-build', default=False, action='store_true',\n help='Do not remove the build folder in local cache. Implies --keep-source. '\n 'Use this for testing purposes only')\n parser.add_argument(\"-ne\", \"--not-export\", default=False, action='store_true',\n help='Do not export the conanfile.py')\n parser.add_argument(\"-tbf\", \"--test-build-folder\", action=OnceArgument,\n help='Working directory for the build of the test project.')\n parser.add_argument(\"-tf\", \"--test-folder\", action=OnceArgument,\n help='Alternative test folder name. By default it is \"test_package\". '\n ","label":0},{"cid":"conans/client/command.py::Command.download","text":"conans/client/command.py::Command.download\n def download(self, *args):\n \"\"\"Downloads recipe and binaries to the local cache, without using settings.\n\n It works specifying the recipe reference and package ID to be\n installed. Not transitive, requirements of the specified reference will\n NOT be retrieved. Useful together with 'conan copy' to automate the\n promotion of packages to a different user/channel. Only if a reference\n is specified, it will download all packages from the specified remote.\n If no remote is specified, it will use the default remote.\n \"\"\"\n\n parser = argparse.ArgumentParser(description=self.download.__doc__, prog=\"conan download\")\n parser.add_argument(\"reference\",\n help='pkg/version@user/channel')\n parser.add_argument(\"-p\", \"--package\", nargs=1, action=Extender,\n help='Force install specified package ID (ignore settings/options)')\n parser.add_argument(\"-r\", \"--remote\", help='look in the specified remote server',\n action=OnceArgument)\n parser.add_argument(\"-re\", \"--recipe\", help='Downloads only the recipe', default=False,\n action=\"store_true\")\n\n args = parser.parse_args(*args)\n\n self._warn_python2()\n return self._conan.download(reference=args.reference, package=args.package,\n remote_name=args.remote, recipe=args.recipe)","label":0},{"cid":"conans/client/command.py::Command.install","text":"conans/client/command.py::Command.install\n def install(self, *args):\n \"\"\"Installs the requirements specified in a recipe (conanfile.py or conanfile.txt).\n\n It can also be used to install a concrete package specifying a\n reference. If any requirement is not found in the local cache, it will\n retrieve the recipe from a remote, looking for it sequentially in the\n configured remotes. When the recipes have been downloaded it will try\n to download a binary package matching the specified settings, only from\n the remote from which the recipe was retrieved. If no binary package is\n found, it can be build from sources using the '--build' option. When\n the package is installed, Conan will write the files for the specified\n generators.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.install.__doc__, prog=\"conan install\")\n parser.add_argument(\"path_or_reference\", help=\"Path to a folder containing a recipe\"\n \" (conanfile.py or conanfile.txt) or to a recipe file. e.g., \"\n \"./my_project/conanfile.txt. It could also be a reference\")\n parser.add_argument(\"-g\", \"--generator\", nargs=1, action=Extender,\n help='Generators to use')\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help='Use this directory as the directory where to put the generator'\n 'files. e.g., conaninfo/conanbuildinfo.txt')\n\n _add_manifests_arguments(parser)\n\n parser.add_argument(\"--no-imports\", action='store_true', default=False,\n help='Install specified packages but avoid running imports')\n parser.add_argument(\"-j\", \"--json\", default=None, action=OnceArgument,\n help='Path to a json file where the install information will be '\n 'written')\n\n ","label":0},{"cid":"conans/client/command.py::Command.config","text":"conans/client/command.py::Command.config\n def config(self, *args):\n \"\"\"Manages Conan configuration.\n\n Used to edit conan.conf, or install config files.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.config.__doc__, prog=\"conan config\")\n\n subparsers = parser.add_subparsers(dest='subcommand', help='sub-command help')\n rm_subparser = subparsers.add_parser('rm', help='Remove an existing config element')\n set_subparser = subparsers.add_parser('set', help='Set a value for a configuration item')\n get_subparser = subparsers.add_parser('get', help='Get the value of configuration item')\n install_subparser = subparsers.add_parser('install', help='install a full configuration '\n 'from a local or remote zip file')\n rm_subparser.add_argument(\"item\", help=\"Item to remove\")\n get_subparser.add_argument(\"item\", nargs=\"?\", help=\"Item to print\")\n set_subparser.add_argument(\"item\", help=\"'item=value' to set\")\n install_subparser.add_argument(\"item\", nargs=\"?\", help=\"Configuration file or directory to use\")\n\n install_subparser.add_argument(\"--verify-ssl\", nargs=\"?\", default=\"True\",\n help='Verify SSL connection when downloading file')\n install_subparser.add_argument(\"--type\", \"-t\", choices=[\"git\"],\n help='Type of remote config')\n install_subparser.add_argument(\"--args\", \"-a\",\n help='String with extra arguments for \"git clone\"')\n\n args = parser.parse_args(*args)\n\n if args.subcommand == \"set\":\n try:\n key, value = args.item.split(\"=\", 1)\n except ValueError:\n if \"hooks.\" in args.item:\n key, value = args.item.split(\"=\", 1)[0], None\n else:\n raise ConanException(\"Please specify 'key","label":0},{"cid":"conans/client/command.py::Command.info","text":"conans/client/command.py::Command.info\n def info(self, *args):\n \"\"\"Gets information about the dependency graph of a recipe.\n\n It can be used with a recipe or a reference for any existing package in\n your local cache.\n \"\"\"\n\n info_only_options = [\"id\", \"build_id\", \"remote\", \"url\", \"license\", \"requires\", \"update\",\n \"required\", \"date\", \"author\", \"None\"]\n path_only_options = [\"export_folder\", \"build_folder\", \"package_folder\", \"source_folder\"]\n str_path_only_options = \", \".join(['\"%s\"' % field for field in path_only_options])\n str_only_options = \", \".join(['\"%s\"' % field for field in info_only_options])\n\n parser = argparse.ArgumentParser(description=self.info.__doc__, prog=\"conan info\")\n parser.add_argument(\"path_or_reference\", help=\"Path to a folder containing a recipe\"\n \" (conanfile.py or conanfile.txt) or to a recipe file. e.g., \"\n \"./my_project/conanfile.txt. It could also be a reference\")\n parser.add_argument(\"--paths\", action='store_true', default=False,\n help='Show package paths in local cache')\n parser.add_argument(\"-bo\", \"--build-order\",\n help='given a modified reference, return an ordered list to build (CI)',\n nargs=1, action=Extender)\n parser.add_argument(\"-g\", \"--graph\", action=OnceArgument,\n help='Creates file with project dependencies graph. It will generate '\n 'a DOT or HTML file depending on the filename extension')\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help=\"local folder containing the conaninfo.txt and conanbuildinfo.txt \"\n \"files (from a previous conan install execution). Defaulted to \"\n \"current folder, unless --profile, -s or -o","label":0},{"cid":"conans/client/command.py::Command.source","text":"conans/client/command.py::Command.source\n def source(self, *args):\n \"\"\" Calls your local conanfile.py 'source()' method.\n\n Usually downloads and uncompresses the package sources.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.source.__doc__, prog=\"conan source\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"-sf\", \"--source-folder\", action=OnceArgument,\n help='Destination directory. Defaulted to current directory')\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help=_INSTALL_FOLDER_HELP + \" Optional, source method will run without \"\n \"the information retrieved from the conaninfo.txt and \"\n \"conanbuildinfo.txt, only required when using conditional source() \"\n \"based on settings, options, env_info and user_info\")\n args = parser.parse_args(*args)\n\n try:\n if \"@\" in args.path and ConanFileReference.loads(args.path):\n raise ArgumentError(None,\n \"'conan source' doesn't accept a reference anymore. \"\n \"If you were using it as a concurrency workaround, \"\n \"you can call 'conan install' simultaneously from several \"\n \"different processes, the concurrency is now natively supported\"\n \". The path parameter should be a folder containing a \"\n \"conanfile.py file.\")\n except ConanException:\n pass\n\n self._warn_python2()\n return self._conan.source(args.path, args.source_folder, args.install_folder)","label":0},{"cid":"conans/client/command.py::Command.build","text":"conans/client/command.py::Command.build\n def build(self, *args):\n \"\"\"Calls your local conanfile.py 'build()' method.\n\n The recipe will be built in the local directory specified by\n --build-folder, reading the sources from --source-folder. If you are\n using a build helper, like CMake(), the --package-folder will be\n configured as destination folder for the install step.\n \"\"\"\n\n parser = argparse.ArgumentParser(description=self.build.__doc__, prog=\"conan build\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"-b\", \"--build\", default=None, action=\"store_true\",\n help=\"Execute the build step (variable should_build=True). When \"\n \"specified, configure/install/test won't run unless \"\n \"--configure/--install/--test specified\")\n parser.add_argument(\"-bf\", \"--build-folder\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\n parser.add_argument(\"-c\", \"--configure\", default=None, action=\"store_true\",\n help=\"Execute the configuration step (variable should_configure=True). \"\n \"When specified, build/install/test won't run unless \"\n \"--build/--install/--test specified\")\n parser.add_argument(\"-i\", \"--install\", default=None, action=\"store_true\",\n help=\"Execute the install step (variable should_install=True). When \"\n \"specified, configure/build/test won't run unless \"\n \"--configure/--build/--test specified\")\n parser.add_argument(\"-t\", \"--test\", default=None, action=\"store_true\",\n help=\"Execute the test step (variable should_test=True). When \"\n \"specified, configure/build/install won't run unless \"\n \"--configure/--build/--install specified\")\n parser.add_","label":0},{"cid":"conans/client/command.py::Command.package","text":"conans/client/command.py::Command.package\n def package(self, *args):\n \"\"\" Calls your local conanfile.py 'package()' method.\n\n This command works in the user space and it will copy artifacts from\n the --build-folder and --source-folder folder to the --package-folder\n one. It won't create a new package in the local cache, if you want to\n do it, use 'conan create' or 'conan export-pkg' after a 'conan build'\n command.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.package.__doc__, prog=\"conan package\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"-bf\", \"--build-folder\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help=_INSTALL_FOLDER_HELP)\n parser.add_argument(\"-pf\", \"--package-folder\", action=OnceArgument,\n help=\"folder to install the package. Defaulted to the \"\n \"'{build_folder}/package' folder. A relative path can be specified\"\n \" (relative to the current directory). Also an absolute path\"\n \" is allowed.\")\n parser.add_argument(\"-sf\", \"--source-folder\", action=OnceArgument, help=_SOURCE_FOLDER_HELP)\n args = parser.parse_args(*args)\n try:\n if \"@\" in args.path and ConanFileReference.loads(args.path):\n raise ArgumentError(None,\n \"'conan package' doesn't accept a reference anymore. \"\n \"The path parameter should be a conanfile.py or a folder \"\n \"containing one. If you were using the 'conan package' \"\n \"command for development purposes we recommend to use \"\n \"the local development commands: 'conan build' + \"\n ","label":0},{"cid":"conans/client/command.py::Command.imports","text":"conans/client/command.py::Command.imports\n def imports(self, *args):\n \"\"\" Calls your local conanfile.py or conanfile.txt 'imports' method.\n\n It requires to have been previously installed and have a\n conanbuildinfo.txt generated file in the --install-folder (defaulted to\n current directory).\n \"\"\"\n parser = argparse.ArgumentParser(description=self.imports.__doc__, prog=\"conan imports\")\n parser.add_argument(\"path\",\n help=_PATH_HELP + \" With --undo option, this parameter is the folder \"\n \"containing the conan_imports_manifest.txt file generated in a previous \"\n \"execution. e.g.: conan imports ./imported_files --undo \")\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help=_INSTALL_FOLDER_HELP)\n parser.add_argument(\"-imf\", \"--import-folder\", action=OnceArgument,\n help=\"Directory to copy the artifacts to. By default it will be the\"\n \" current directory\")\n parser.add_argument(\"-u\", \"--undo\", default=False, action=\"store_true\",\n help=\"Undo imports. Remove imported files\")\n args = parser.parse_args(*args)\n\n if args.undo:\n return self._conan.imports_undo(args.path)\n\n try:\n if \"@\" in args.path and ConanFileReference.loads(args.path):\n raise ArgumentError(None, \"Parameter 'path' cannot be a reference. Use a folder \"\n \"containing a conanfile.py or conanfile.txt file.\")\n except ConanException:\n pass\n self._warn_python2()\n return self._conan.imports(args.path, args.import_folder, args.install_folder)","label":0},{"cid":"conans/client/command.py::Command.export_pkg","text":"conans/client/command.py::Command.export_pkg\n def export_pkg(self, *args):\n \"\"\"Exports a recipe, then creates a package from local source and build folders.\n\n The package is created by calling the package() method applied to the\n local folders '--source-folder' and '--build-folder' It's created in\n the local cache for the specified 'reference' and for the specified\n '--settings', '--options' and or '--profile'.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.export_pkg.__doc__,\n prog=\"conan export-pkg\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"reference\", help=\"user/channel or pkg/version@user/channel \"\n \"(if name and version are not declared in the \"\n \"conanfile.py)\")\n parser.add_argument(\"-bf\", \"--build-folder\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\n parser.add_argument(\"-e\", \"--env\", nargs=1, action=Extender,\n help='Environment variables that will be set during the package build, '\n '-e CXX=/usr/bin/clang++')\n parser.add_argument('-f', '--force', default=False, action='store_true',\n help='Overwrite existing package if existing')\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help=_INSTALL_FOLDER_HELP + \" If these files are found in the specified\"\n \" folder and any of '-e', '-o', '-pr' or '-s' arguments are used, it \"\n \"will raise an error.\")\n parser.add_argument(\"-o\", \"--options\", nargs=1, action=Extender,\n help='Define options values, e.g., -o pkg:with_qt=true')\n parser.add_argument(\"-pr\", \"--profile\", action=OnceArgument,\n help='Profile for this ","label":0},{"cid":"conans/client/command.py::Command.export","text":"conans/client/command.py::Command.export\n def export(self, *args):\n \"\"\"Copies the recipe (conanfile.py & associated files) to your local cache.\n\n Use the 'reference' param to specify a user and channel where to export\n it. Once the recipe is in the local cache it can be shared, reused and\n to any remote with the 'conan upload' command.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.export.__doc__, prog=\"conan export\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"reference\", help=\"user/channel, or Pkg/version@user/channel (if name \"\n \"and version are not declared in the conanfile.py\")\n parser.add_argument('-k', '-ks', '--keep-source', default=False, action='store_true',\n help=_KEEP_SOURCE_HELP)\n\n args = parser.parse_args(*args)\n self._warn_python2()\n name, version, user, channel = get_reference_fields(args.reference)\n return self._conan.export(path=args.path,\n name=name, version=version, user=user, channel=channel,\n keep_source=args.keep_source)","label":0},{"cid":"conans/client/command.py::Command.remove","text":"conans/client/command.py::Command.remove\n def remove(self, *args):\n \"\"\"Removes packages or binaries matching pattern from local cache or remote.\n\n It can also be used to remove temporary source or build folders in the\n local conan cache. If no remote is specified, the removal will be done\n by default in the local conan cache.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.remove.__doc__, prog=\"conan remove\")\n parser.add_argument('pattern_or_reference', nargs=\"?\", help=_PATTERN_OR_REFERENCE_HELP)\n parser.add_argument('-b', '--builds', nargs=\"*\", action=Extender,\n help=(\"By default, remove all the build folders or select one, \"\n \"specifying the package ID\"))\n parser.add_argument('-f', '--force', default=False, action='store_true',\n help='Remove without requesting a confirmation')\n parser.add_argument(\"-o\", \"--outdated\", default=False, action=\"store_true\",\n help=\"Remove only outdated from recipe packages. \" \\\n \"This flag can only be used with a reference\")\n parser.add_argument('-p', '--packages', nargs=\"*\", action=Extender,\n help=\"Select package to remove specifying the package ID\")\n parser.add_argument('-q', '--query', default=None, action=OnceArgument, help=_QUERY_HELP)\n parser.add_argument('-r', '--remote', action=OnceArgument,\n help='Will remove from the specified remote')\n parser.add_argument('-s', '--src', default=False, action=\"store_true\",\n help='Remove source folders')\n parser.add_argument(\"-l\", \"--locks\", default=False, action=\"store_true\",\n help=\"Remove locks\")\n args = parser.parse_args(*args)\n\n self._warn_python2()\n\n # NOTE: returns the expanded pattern (if a pattern was g","label":0},{"cid":"conans/client/command.py::Command.copy","text":"conans/client/command.py::Command.copy\n def copy(self, *args):\n \"\"\"Copies conan recipes and packages to another user/channel.\n\n Useful to promote packages (e.g. from \"beta\" to \"stable\") or transfer\n them from one user to another.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.copy.__doc__, prog=\"conan copy\")\n parser.add_argument(\"reference\", default=\"\",\n help='package reference. e.g., MyPackage/1.2@user/channel')\n parser.add_argument(\"user_channel\", default=\"\",\n help='Destination user/channel. e.g., lasote/testing')\n parser.add_argument(\"-p\", \"--package\", nargs=1, action=Extender,\n help='copy specified package ID')\n parser.add_argument(\"--all\", action='store_true', default=False,\n help='Copy all packages from the specified package recipe')\n parser.add_argument(\"--force\", action='store_true', default=False,\n help='Override destination packages and the package recipe')\n args = parser.parse_args(*args)\n\n if args.all and args.package:\n raise ConanException(\"Cannot specify both --all and --package\")\n\n self._warn_python2()\n\n return self._conan.copy(reference=args.reference, user_channel=args.user_channel,\n force=args.force, packages=args.package or args.all)","label":0},{"cid":"conans/client/command.py::Command.user","text":"conans/client/command.py::Command.user\n def user(self, *args):\n \"\"\"Authenticates against a remote with user/pass, caching the auth token.\n\n Useful to avoid the user and password being requested later. e.g. while\n you're uploading a package. You can have one user for each remote.\n Changing the user, or introducing the password is only necessary to\n perform changes in remote packages.\n \"\"\"\n # FIXME: Difficult and confusing CLI. Better with:\n # - conan user clean -> clean users\n # - conan user list ('remote') -> list users (of a remote)\n # - conan user auth 'remote' ('user') ('password') -> login a remote (w/o user or pass)\n # - conan user set 'user' 'remote' -> set user for a remote (not login) necessary??\n parser = argparse.ArgumentParser(description=self.user.__doc__, prog=\"conan user\")\n parser.add_argument(\"name\", nargs='?', default=None,\n help='Username you want to use. If no name is provided it will show the'\n ' current user')\n parser.add_argument('-c', '--clean', default=False, action='store_true',\n help='Remove user and tokens for all remotes')\n parser.add_argument(\"-p\", \"--password\", nargs='?', const=\"\", type=str, action=OnceArgument,\n help='User password. Use double quotes if password with spacing, '\n 'and escape quotes if existing. If empty, the password is '\n 'requested interactively (not exposed)')\n parser.add_argument(\"-r\", \"--remote\", help='Use the specified remote server',\n action=OnceArgument)\n parser.add_argument(\"-j\", \"--json\", default=None, action=OnceArgument,\n help='json file path where the user list will be written to')\n args = parser.parse_args(*args)\n\n if args.clean and any((args.name, ar","label":0},{"cid":"conans/client/command.py::Command.search","text":"conans/client/command.py::Command.search\n def search(self, *args):\n \"\"\"Searches package recipes and binaries in the local cache or in a remote.\n\n If you provide a pattern, then it will search for existing package\n recipes matching it. If a full reference is provided\n (pkg/0.1@user/channel) then the existing binary packages for that\n reference will be displayed. If no remote is specified, the search\n will be done in the local cache. Search is case sensitive, exact case\n has to be used. For case insensitive file systems, like Windows, case\n sensitive search can be forced with '--case-sensitive'.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.search.__doc__, prog=\"conan search\")\n parser.add_argument('pattern_or_reference', nargs='?', help=_PATTERN_OR_REFERENCE_HELP)\n parser.add_argument('-o', '--outdated', default=False, action='store_true',\n help=\"Show only outdated from recipe packages. \" \\\n \"This flag can only be used with a reference\")\n parser.add_argument('-q', '--query', default=None, action=OnceArgument, help=_QUERY_HELP)\n parser.add_argument('-r', '--remote', action=OnceArgument,\n help=\"Remote to search in. '-r all' searches all remotes\")\n parser.add_argument('--case-sensitive', default=False, action='store_true',\n help='Make a case-sensitive search. Use it to guarantee case-sensitive '\n 'search in Windows or other case-insensitive file systems')\n parser.add_argument('--raw', default=False, action='store_true',\n help='Print just the list of recipes')\n parser.add_argument('--table', action=OnceArgument,\n help=\"Outputs html file with a table of binaries. Only valid for a \"\n \"reference search\")\n parser.add_ar","label":0},{"cid":"conans/client/command.py::Command.upload","text":"conans/client/command.py::Command.upload\n def upload(self, *args):\n \"\"\"Uploads a recipe and binary packages to a remote.\n\n If no remote is specified, the first configured remote (by default conan-center, use\n 'conan remote list' to list the remotes) will be used.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.upload.__doc__,\n prog=\"conan upload\")\n parser.add_argument('pattern_or_reference', help=_PATTERN_OR_REFERENCE_HELP)\n parser.add_argument(\"-p\", \"--package\", default=None, action=OnceArgument,\n help='package ID to upload')\n parser.add_argument('-q', '--query', default=None, action=OnceArgument,\n help=\"Only upload packages matching a specific query. \" + _QUERY_HELP)\n parser.add_argument(\"-r\", \"--remote\", action=OnceArgument,\n help='upload to this specific remote')\n parser.add_argument(\"--all\", action='store_true', default=False,\n help='Upload both package recipe and packages')\n parser.add_argument(\"--skip-upload\", action='store_true', default=False,\n help='Do not upload anything, just run the checks and the compression')\n parser.add_argument(\"--force\", action='store_true', default=False,\n help='Do not check conan recipe date, override remote with local')\n parser.add_argument(\"--check\", action='store_true', default=False,\n help='Perform an integrity check, using the manifests, before upload')\n parser.add_argument('-c', '--confirm', default=False, action='store_true',\n help='Upload all matching recipes without confirmation')\n parser.add_argument('--retry', default=2, type=int, action=OnceArgument,\n help=\"In case of fail retries to upload again the specified times. \"\n ","label":0},{"cid":"conans/client/command.py::Command.remote","text":"conans/client/command.py::Command.remote\n def remote(self, *args):\n \"\"\"Manages the remote list and the package recipes associated to a remote.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.remote.__doc__, prog=\"conan remote\")\n subparsers = parser.add_subparsers(dest='subcommand', help='sub-command help')\n\n # create the parser for the \"a\" command\n parser_list = subparsers.add_parser('list', help='List current remotes')\n parser_list.add_argument(\"-raw\", \"--raw\", action='store_true', default=False,\n help='Raw format. Valid for \"remotes.txt\" file for \"conan config install\"')\n parser_add = subparsers.add_parser('add', help='Add a remote')\n parser_add.add_argument('remote', help='Name of the remote')\n parser_add.add_argument('url', help='URL of the remote')\n parser_add.add_argument('verify_ssl', nargs=\"?\", default=\"True\",\n help='Verify SSL certificated. Default True')\n parser_add.add_argument(\"-i\", \"--insert\", nargs=\"?\", const=0, type=int, action=OnceArgument,\n help=\"insert remote at specific index\")\n parser_add.add_argument(\"-f\", \"--force\", default=False, action='store_true',\n help=\"Force addition, will update if existing\")\n parser_rm = subparsers.add_parser('remove', help='Remove a remote')\n parser_rm.add_argument('remote', help='Name of the remote')\n parser_upd = subparsers.add_parser('update', help='Update the remote url')\n parser_upd.add_argument('remote', help='Name of the remote')\n\n parser_upd.add_argument('url', help='URL')\n parser_upd.add_argument('verify_ssl', nargs=\"?\", default=\"True\",\n help='Verify SSL certificated. Default True')\n parser_upd.add_argument(\"-i\", \"--insert\", nargs=\"?\", const=0, type=int, action=OnceArgument,\n he","label":0},{"cid":"conans/client/command.py::Command.profile","text":"conans/client/command.py::Command.profile\n def profile(self, *args):\n \"\"\" Lists profiles in the '.conan/profiles' folder, or shows profile details.\n The 'list' subcommand will always use the default user 'conan/profiles' folder. But the\n 'show' subcommand is able to resolve absolute and relative paths, as well as to map names to\n '.conan/profiles' folder, in the same way as the '--profile' install argument.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.profile.__doc__, prog=\"conan profile\")\n subparsers = parser.add_subparsers(dest='subcommand')\n\n # create the parser for the \"profile\" command\n subparsers.add_parser('list', help='List current profiles')\n parser_show = subparsers.add_parser('show', help='Show the values defined for a profile')\n parser_show.add_argument('profile', help=\"name of the profile in the '.conan/profiles' \"\n \"folder or path to a profile file\")\n\n parser_new = subparsers.add_parser('new', help='Creates a new empty profile')\n parser_new.add_argument('profile', help=\"Name for the profile in the '.conan/profiles' \"\n \"folder or path and name for a profile file\")\n parser_new.add_argument(\"--detect\", action='store_true', default=False,\n help='Autodetect settings and fill [settings] section')\n\n parser_update = subparsers.add_parser('update', help='Update a profile with desired value')\n parser_update.add_argument('item',\n help=\"'item=value' to update. e.g., settings.compiler=gcc\")\n parser_update.add_argument('profile', help=\"Name of the profile in the '.conan/profiles' \"\n \"folder or path to a profile file\")\n\n parser_get = subparsers.add_parser('get', help='Get a profile key')\n parser_get.add_argument('ite","label":0},{"cid":"conans/client/command.py::Command.get","text":"conans/client/command.py::Command.get\n def get(self, *args):\n \"\"\" Gets a file or list a directory of a given reference or package.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.get.__doc__,\n prog=\"conan get\")\n parser.add_argument('reference', help='package recipe reference')\n parser.add_argument('path',\n help='Path to the file or directory. If not specified will get the '\n 'conanfile if only a reference is specified and a conaninfo.txt '\n 'file contents if the package is also specified',\n default=None, nargs=\"?\")\n parser.add_argument(\"-p\", \"--package\", default=None, help='Package ID',\n action=OnceArgument)\n parser.add_argument(\"-r\", \"--remote\", action=OnceArgument,\n help='Get from this specific remote')\n parser.add_argument(\"-raw\", \"--raw\", action='store_true', default=False,\n help='Do not decorate the text')\n args = parser.parse_args(*args)\n\n ret, path = self._conan.get_path(args.reference, args.package, args.path, args.remote)\n if isinstance(ret, list):\n self._outputer.print_dir_list(ret, path, args.raw)\n else:\n self._outputer.print_file_contents(ret, path, args.raw)\n\n return","label":0},{"cid":"conans/client/command.py::Command.alias","text":"conans/client/command.py::Command.alias\n def alias(self, *args):\n \"\"\"Creates and exports an 'alias package recipe'.\n\n An \"alias\" package is a symbolic name (reference) for another package\n (target). When some package depends on an alias, the target one will be\n retrieved and used instead, so the alias reference, the symbolic name,\n does not appear in the final dependency graph.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.alias.__doc__,\n prog=\"conan alias\")\n parser.add_argument('reference', help='Alias reference. e.g.: mylib/1.X@user/channel')\n parser.add_argument('target', help='Target reference. e.g.: mylib/1.12@user/channel')\n args = parser.parse_args(*args)\n\n self._warn_python2()\n\n self._conan.export_alias(args.reference, args.target)","label":0},{"cid":"conans/client/command.py::Command.link","text":"conans/client/command.py::Command.link\n def link(self, *args):\n \"\"\" Links a conan reference (e.g lib/1.0@conan/stable) with a local folder path.\n\n \"\"\"\n parser = argparse.ArgumentParser(description=self.link.__doc__,\n prog=\"conan link\")\n parser.add_argument('target', help='Path to the package folder in the user workspace',\n nargs='?',)\n parser.add_argument('reference', help='Reference to link. e.g.: mylib/1.X@user/channel')\n parser.add_argument(\"--remove\", action='store_true', default=False,\n help='Remove linked reference (target not required)')\n\n args = parser.parse_args(*args)\n self._warn_python2()\n\n # Args sanity check\n if args.remove and args.target:\n raise ConanException(\"Do not provide the 'target' argument for removal\")\n\n if not args.remove and not args.target:\n raise ConanException(\"Argument 'target' is required to link a reference\")\n\n if not args.remove:\n self._conan.link(args.target, args.reference, cwd=os.getcwd())\n self._outputer.writeln(\"Reference '{}' linked to directory \"\n \"'{}'\".format(args.reference, os.path.dirname(args.target)))\n else:\n ret = self._conan.unlink(args.reference)\n if ret:\n self._outputer.writeln(\"Removed linkage for reference '{}'\".format(args.reference))\n else:\n self._user_io.out.warn(\"Reference '{}' was not installed \"\n \"as editable\".format(args.reference))","label":0},{"cid":"conans/client/command.py::Command._show_help","text":"conans/client/command.py::Command._show_help\n def _show_help(self):\n \"\"\"Prints a summary of all commands\n \"\"\"\n grps = [(\"Consumer commands\", (\"install\", \"config\", \"get\", \"info\", \"search\")),\n (\"Creator commands\", (\"new\", \"create\", \"upload\", \"export\", \"export-pkg\", \"test\")),\n (\"Package development commands\", (\"source\", \"build\", \"package\", \"link\")),\n (\"Misc commands\", (\"profile\", \"remote\", \"user\", \"imports\", \"copy\", \"remove\",\n \"alias\", \"download\", \"inspect\", \"help\"))]\n\n def check_all_commands_listed():\n \"\"\"Keep updated the main directory, raise if don't\"\"\"\n all_commands = self._commands()\n all_in_grps = [command for _, command_list in grps for command in command_list]\n if set(all_in_grps) != set(all_commands):\n diff = set(all_commands) - set(all_in_grps)\n raise Exception(\"Some command is missing in the main help: %s\" % \",\".join(diff))\n return all_commands\n\n commands = check_all_commands_listed()\n max_len = max((len(c) for c in commands)) + 1\n fmt = ' %-{}s'.format(max_len)\n\n for group_name, comm_names in grps:\n self._user_io.out.writeln(group_name, Color.BRIGHT_MAGENTA)\n for name in comm_names:\n # future-proof way to ensure tabular formatting\n self._user_io.out.write(fmt % name, Color.GREEN)\n self._user_io.out.writeln(commands[name].__doc__.split('\\n', 1)[0].strip())\n\n self._user_io.out.writeln(\"\")\n self._user_io.out.writeln('Conan commands. Type \"conan <command> -h\" for help',\n Color.BRIGHT_YELLOW)","label":0},{"cid":"conans/client/command.py::Command._show_help.check_all_commands_listed","text":"conans/client/command.py::Command._show_help.check_all_commands_listed\n def check_all_commands_listed():\n \"\"\"Keep updated the main directory, raise if don't\"\"\"\n all_commands = self._commands()\n all_in_grps = [command for _, command_list in grps for command in command_list]\n if set(all_in_grps) != set(all_commands):\n diff = set(all_commands) - set(all_in_grps)\n raise Exception(\"Some command is missing in the main help: %s\" % \",\".join(diff))\n return all_commands","label":0},{"cid":"conans/client/command.py::Command._commands","text":"conans/client/command.py::Command._commands\n def _commands(self):\n \"\"\" returns a list of available commands\n \"\"\"\n result = {}\n for m in inspect.getmembers(self, predicate=inspect.ismethod):\n method_name = m[0]\n if not method_name.startswith('_'):\n if \"export_pkg\" == method_name:\n method_name = \"export-pkg\"\n method = m[1]\n if method.__doc__ and not method.__doc__.startswith('HIDDEN'):\n result[method_name] = method\n return result","label":0},{"cid":"conans/client/command.py::Command._warn_python2","text":"conans/client/command.py::Command._warn_python2\n def _warn_python2(self):\n if six.PY2:\n self._user_io.out.writeln(\"\")\n self._user_io.out.writeln(\"Python 2 will soon be deprecated. It is strongly \"\n \"recommended to use Python 3 with Conan:\",\n front=Color.BRIGHT_YELLOW)\n self._user_io.out.writeln(\"https://docs.conan.io/en/latest/installation.html\"\n \"#python-2-deprecation-notice\", front=Color.BRIGHT_YELLOW)\n self._user_io.out.writeln(\"\")","label":0},{"cid":"conans/client/command.py::Command._check_query_parameter_and_get_reference","text":"conans/client/command.py::Command._check_query_parameter_and_get_reference\n @staticmethod\n def _check_query_parameter_and_get_reference(pattern, query):\n ref = None\n if pattern:\n try:\n ref = ConanFileReference.loads(pattern)\n except ConanException:\n if query is not None:\n raise ConanException(\"-q parameter only allowed with a valid recipe \"\n \"reference as search pattern. e.g. conan search \"\n \"MyPackage/1.2@user/channel -q \\\"os=Windows\\\"\")\n return ref","label":0},{"cid":"conans/client/command.py::Command.run","text":"conans/client/command.py::Command.run\n def run(self, *args):\n \"\"\"HIDDEN: entry point for executing commands, dispatcher to class\n methods\n \"\"\"\n ret_code = SUCCESS\n try:\n try:\n command = args[0][0]\n commands = self._commands()\n method = commands[command]\n except KeyError as exc:\n if command in [\"-v\", \"--version\"]:\n self._user_io.out.success(\"Conan version %s\" % client_version)\n return False\n self._warn_python2()\n self._show_help()\n if command in [\"-h\", \"--help\"]:\n return False\n raise ConanException(\"Unknown command %s\" % str(exc))\n except IndexError: # No parameters\n self._show_help()\n return False\n method(args[0][1:])\n except KeyboardInterrupt as exc:\n logger.error(exc)\n ret_code = SUCCESS\n except SystemExit as exc:\n if exc.code != 0:\n logger.error(exc)\n self._user_io.out.error(\"Exiting with code: %d\" % exc.code)\n ret_code = exc.code\n except ConanInvalidConfiguration as exc:\n ret_code = ERROR_INVALID_CONFIGURATION\n msg = exception_message_safe(exc)\n self._user_io.out.error(msg)\n except ConanException as exc:\n ret_code = ERROR_GENERAL\n msg = exception_message_safe(exc)\n self._user_io.out.error(msg)\n except Exception as exc:\n import traceback\n print(traceback.format_exc())\n ret_code = ERROR_GENERAL\n msg = exception_message_safe(exc)\n self._user_io.out.error(msg)\n\n return ret_code","label":0},{"cid":"conans/client/command.py::get_reference_fields","text":"conans/client/command.py::get_reference_fields\ndef get_reference_fields(arg_reference):\n \"\"\"\n :param arg_reference: String with a complete reference, or only user/channel\n :return: name, version, user and channel, in a tuple\n \"\"\"\n\n if not arg_reference:\n return None, None, None, None\n\n try:\n name_version, user_channel = arg_reference.split(\"@\")\n name_version = name_version.split(\"/\")\n try:\n name, version = name_version\n except ValueError:\n name, version = None, name_version[0]\n user, channel = user_channel.split(\"/\")\n except ValueError:\n name, version = None, None\n try:\n user, channel = arg_reference.split(\"/\")\n except ValueError:\n raise ConanException(\"Invalid parameter '%s', specify the full reference or \"\n \"user/channel\" % arg_reference)\n\n return name, version, user, channel","label":0},{"cid":"conans/client/command.py::_add_manifests_arguments","text":"conans/client/command.py::_add_manifests_arguments\ndef _add_manifests_arguments(parser):\n parser.add_argument(\"-m\", \"--manifests\", const=default_manifest_folder, nargs=\"?\",\n help='Install dependencies manifests in folder for later verify.'\n ' Default folder is .conan_manifests, but can be changed',\n action=OnceArgument)\n parser.add_argument(\"-mi\", \"--manifests-interactive\", const=default_manifest_folder,\n nargs=\"?\",\n help='Install dependencies manifests in folder for later verify, '\n 'asking user for confirmation. '\n 'Default folder is .conan_manifests, but can be changed',\n action=OnceArgument)\n parser.add_argument(\"-v\", \"--verify\", const=default_manifest_folder, nargs=\"?\",\n help='Verify dependencies manifests against stored ones',\n action=OnceArgument)","label":0},{"cid":"conans/client/command.py::_add_common_install_arguments","text":"conans/client/command.py::_add_common_install_arguments\ndef _add_common_install_arguments(parser, build_help):\n if build_help:\n parser.add_argument(\"-b\", \"--build\", action=Extender, nargs=\"?\", help=build_help)\n\n parser.add_argument(\"-e\", \"--env\", nargs=1, action=Extender,\n help='Environment variables that will be set during the package build, '\n '-e CXX=/usr/bin/clang++')\n parser.add_argument(\"-o\", \"--options\", nargs=1, action=Extender,\n help='Define options values, e.g., -o Pkg:with_qt=true')\n parser.add_argument(\"-pr\", \"--profile\", default=None, action=OnceArgument,\n help='Apply the specified profile to the install command')\n parser.add_argument(\"-r\", \"--remote\", action=OnceArgument,\n help='Look in the specified remote server')\n parser.add_argument(\"-s\", \"--settings\", nargs=1, action=Extender,\n help='Settings to build the package, overwriting the defaults. e.g., '\n '-s compiler=gcc')\n parser.add_argument(\"-u\", \"--update\", action='store_true', default=False,\n help=\"Check updates exist from upstream remotes\")","label":0},{"cid":"conans/client/command.py::main","text":"conans/client/command.py::main\ndef main(args):\n \"\"\" main entry point of the conan application, using a Command to\n parse parameters\n\n Exit codes for conan command:\n\n 0: Success (done)\n 1: General ConanException error (done)\n 2: Migration error\n 3: Ctrl+C\n 4: Ctrl+Break\n 5: SIGTERM\n 6: Invalid configuration (done)\n \"\"\"\n try:\n conan_api, cache, user_io = Conan.factory()\n except ConanException: # Error migrating\n sys.exit(ERROR_MIGRATION)\n\n outputer = CommandOutputer(user_io, cache)\n command = Command(conan_api, cache, user_io, outputer)\n current_dir = get_cwd()\n try:\n import signal\n\n def ctrl_c_handler(_, __):\n print('You pressed Ctrl+C!')\n sys.exit(USER_CTRL_C)\n\n def sigterm_handler(_, __):\n print('Received SIGTERM!')\n sys.exit(ERROR_SIGTERM)\n\n def ctrl_break_handler(_, __):\n print('You pressed Ctrl+Break!')\n sys.exit(USER_CTRL_BREAK)\n\n signal.signal(signal.SIGINT, ctrl_c_handler)\n signal.signal(signal.SIGTERM, sigterm_handler)\n\n if sys.platform == 'win32':\n signal.signal(signal.SIGBREAK, ctrl_break_handler)\n error = command.run(args)\n finally:\n os.chdir(current_dir)\n sys.exit(error)","label":0},{"cid":"conans/client/recorder/action_recorder.py::Action.__new__","text":"conans/client/recorder/action_recorder.py::Action.__new__\n def __new__(cls, the_type, doc=None):\n doc = doc or {}\n the_time = datetime.utcnow()\n return super(cls, Action).__new__(cls, the_type, doc, the_time)","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.__init__","text":"conans/client/recorder/action_recorder.py::ActionRecorder.__init__\n def __init__(self):\n self.error = False\n self._inst_recipes_actions = OrderedDict()\n self._inst_packages_actions = OrderedDict()\n self._inst_recipes_develop = set() # Recipes being created (to set dependency=False)\n self._inst_packages_info = defaultdict(dict)","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.add_recipe_being_developed","text":"conans/client/recorder/action_recorder.py::ActionRecorder.add_recipe_being_developed\n def add_recipe_being_developed(self, ref):\n assert(isinstance(ref, ConanFileReference))\n self._inst_recipes_develop.add(ref)","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder._add_recipe_action","text":"conans/client/recorder/action_recorder.py::ActionRecorder._add_recipe_action\n def _add_recipe_action(self, ref, action):\n assert(isinstance(ref, ConanFileReference))\n ref = ref.copy_clear_rev()\n if ref not in self._inst_recipes_actions:\n self._inst_recipes_actions[ref] = []\n self._inst_recipes_actions[ref].append(action)","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder._add_package_action","text":"conans/client/recorder/action_recorder.py::ActionRecorder._add_package_action\n def _add_package_action(self, pref, action):\n assert(isinstance(pref, PackageReference))\n pref = pref.copy_clear_rev()\n if pref not in self._inst_packages_actions:\n self._inst_packages_actions[pref] = []\n self._inst_packages_actions[pref].append(action)","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_exported","text":"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_exported\n def recipe_exported(self, ref):\n self._add_recipe_action(ref, Action(INSTALL_EXPORTED))","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_fetched_from_cache","text":"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_fetched_from_cache\n def recipe_fetched_from_cache(self, ref):\n self._add_recipe_action(ref, Action(INSTALL_CACHE))","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_downloaded","text":"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_downloaded\n def recipe_downloaded(self, ref, remote_name):\n self._add_recipe_action(ref, Action(INSTALL_DOWNLOADED, {\"remote\": remote_name}))","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_install_error","text":"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_install_error\n def recipe_install_error(self, ref, error_type, description, remote_name):\n doc = {\"type\": error_type, \"description\": description, \"remote\": remote_name}\n self._add_recipe_action(ref, Action(INSTALL_ERROR, doc))","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.package_exported","text":"conans/client/recorder/action_recorder.py::ActionRecorder.package_exported\n def package_exported(self, pref):\n self._add_package_action(pref, Action(INSTALL_EXPORTED))","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.package_built","text":"conans/client/recorder/action_recorder.py::ActionRecorder.package_built\n def package_built(self, pref):\n self._add_package_action(pref, Action(INSTALL_BUILT))","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.package_fetched_from_cache","text":"conans/client/recorder/action_recorder.py::ActionRecorder.package_fetched_from_cache\n def package_fetched_from_cache(self, pref):\n self._add_package_action(pref, Action(INSTALL_CACHE))","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.package_downloaded","text":"conans/client/recorder/action_recorder.py::ActionRecorder.package_downloaded\n def package_downloaded(self, pref, remote_name):\n self._add_package_action(pref, Action(INSTALL_DOWNLOADED, {\"remote\": remote_name}))","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.package_install_error","text":"conans/client/recorder/action_recorder.py::ActionRecorder.package_install_error\n def package_install_error(self, pref, error_type, description, remote_name=None):\n assert(isinstance(pref, PackageReference))\n pref = pref.copy_clear_rev()\n if pref not in self._inst_packages_actions:\n self._inst_packages_actions[pref] = []\n doc = {\"type\": error_type, \"description\": description, \"remote\": remote_name}\n self._inst_packages_actions[pref].append(Action(INSTALL_ERROR, doc))","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.package_cpp_info","text":"conans/client/recorder/action_recorder.py::ActionRecorder.package_cpp_info\n def package_cpp_info(self, pref, cpp_info):\n assert isinstance(pref, PackageReference)\n pref = pref.copy_clear_rev()\n # assert isinstance(cpp_info, CppInfo)\n doc = {}\n for it, value in vars(cpp_info).items():\n if it.startswith(\"_\") or not value:\n continue\n doc[it] = value\n self._inst_packages_info[pref]['cpp_info'] = doc","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.install_errored","text":"conans/client/recorder/action_recorder.py::ActionRecorder.install_errored\n @property\n def install_errored(self):\n all_values = list(self._inst_recipes_actions.values()) + list(self._inst_packages_actions.values())\n for acts in all_values:\n for act in acts:\n if act.type == INSTALL_ERROR:\n return True\n return False","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder._get_installed_packages","text":"conans/client/recorder/action_recorder.py::ActionRecorder._get_installed_packages\n def _get_installed_packages(self, ref):\n assert(isinstance(ref, ConanFileReference))\n ret = []\n for _pref, _package_actions in self._inst_packages_actions.items():\n # Could be a download and then an access to cache, we want the first one\n _package_action = _package_actions[0]\n if _pref.ref == ref:\n ret.append((_pref, _package_action))\n return ret","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.in_development_recipe","text":"conans/client/recorder/action_recorder.py::ActionRecorder.in_development_recipe\n def in_development_recipe(self, ref):\n return ref in self._inst_recipes_develop","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.get_info","text":"conans/client/recorder/action_recorder.py::ActionRecorder.get_info\n def get_info(self):\n return self.get_install_info()","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.get_install_info","text":"conans/client/recorder/action_recorder.py::ActionRecorder.get_install_info\n def get_install_info(self):\n ret = {\"error\": self.install_errored or self.error,\n \"installed\": []}\n\n def get_doc_for_ref(the_ref, the_actions):\n errors = [action.doc for action in the_actions if action.type == INSTALL_ERROR]\n error = None if not errors else errors[0]\n remotes = [action.doc.get(\"remote\") for action in the_actions\n if action.doc.get(\"remote\", None) is not None]\n remote = None if not remotes else remotes[0]\n action_types = [action.type for action in the_actions]\n time = the_actions[0].time\n\n doc = {\"id\": str(the_ref),\n \"downloaded\": INSTALL_DOWNLOADED in action_types,\n \"exported\": INSTALL_EXPORTED in action_types,\n \"error\": error,\n \"remote\": remote,\n \"time\": time}\n if isinstance(the_ref, ConanFileReference):\n doc[\"dependency\"] = not self.in_development_recipe(the_ref.copy_clear_rev())\n doc[\"name\"] = the_ref.name\n doc[\"version\"] = the_ref.version\n doc[\"user\"] = the_ref.user\n doc[\"channel\"] = the_ref.channel\n if the_ref.revision:\n doc[\"revision\"] = the_ref.revision\n else:\n doc[\"built\"] = INSTALL_BUILT in action_types\n\n if doc[\"remote\"] is None and error:\n doc[\"remote\"] = error.get(\"remote\", None)\n return doc\n\n for ref, actions in self._inst_recipes_actions.items():\n tmp = {\"recipe\": get_doc_for_ref(ref, actions),\n \"packages\": []}\n\n packages = self._get_installed_packages(ref)\n for pref, p_action in packages:\n p_doc = get_doc_for_ref(pref.id, [p_action])\n package_data = self._inst_packages_info.get(","label":0},{"cid":"conans/client/recorder/action_recorder.py::ActionRecorder.get_install_info.get_doc_for_ref","text":"conans/client/recorder/action_recorder.py::ActionRecorder.get_install_info.get_doc_for_ref\n def get_doc_for_ref(the_ref, the_actions):\n errors = [action.doc for action in the_actions if action.type == INSTALL_ERROR]\n error = None if not errors else errors[0]\n remotes = [action.doc.get(\"remote\") for action in the_actions\n if action.doc.get(\"remote\", None) is not None]\n remote = None if not remotes else remotes[0]\n action_types = [action.type for action in the_actions]\n time = the_actions[0].time\n\n doc = {\"id\": str(the_ref),\n \"downloaded\": INSTALL_DOWNLOADED in action_types,\n \"exported\": INSTALL_EXPORTED in action_types,\n \"error\": error,\n \"remote\": remote,\n \"time\": time}\n if isinstance(the_ref, ConanFileReference):\n doc[\"dependency\"] = not self.in_development_recipe(the_ref.copy_clear_rev())\n doc[\"name\"] = the_ref.name\n doc[\"version\"] = the_ref.version\n doc[\"user\"] = the_ref.user\n doc[\"channel\"] = the_ref.channel\n if the_ref.revision:\n doc[\"revision\"] = the_ref.revision\n else:\n doc[\"built\"] = INSTALL_BUILT in action_types\n\n if doc[\"remote\"] is None and error:\n doc[\"remote\"] = error.get(\"remote\", None)\n return doc","label":0},{"cid":"conans/client/rest/rest_client_common.py::JWTAuth.__init__","text":"conans/client/rest/rest_client_common.py::JWTAuth.__init__\n def __init__(self, token):\n self.token = token","label":0},{"cid":"conans/client/rest/rest_client_common.py::JWTAuth.__call__","text":"conans/client/rest/rest_client_common.py::JWTAuth.__call__\n def __call__(self, request):\n if self.token:\n request.headers['Authorization'] = \"Bearer %s\" % str(self.token)\n return request","label":0},{"cid":"conans/client/rest/rest_client_common.py::_base_error","text":"conans/client/rest/rest_client_common.py::_base_error\ndef _base_error(error_code):\n return int(str(error_code)[0] + \"00\")","label":0},{"cid":"conans/client/rest/rest_client_common.py::get_exception_from_error","text":"conans/client/rest/rest_client_common.py::get_exception_from_error\ndef get_exception_from_error(error_code):\n try:\n tmp = {value: key for key, value in EXCEPTION_CODE_MAPPING.items()}\n if error_code in tmp:\n logger.debug(\"REST ERROR: %s\" % str(tmp[error_code]))\n return tmp[error_code]\n else:\n logger.debug(\"REST ERROR: %s\" % str(_base_error(error_code)))\n return tmp[_base_error(error_code)]\n except KeyError:\n return None","label":0},{"cid":"conans/client/rest/rest_client_common.py::handle_return_deserializer","text":"conans/client/rest/rest_client_common.py::handle_return_deserializer\ndef handle_return_deserializer(deserializer=None):\n \"\"\"Decorator for rest api methods.\n Map exceptions and http return codes and deserialize if needed.\n\n deserializer: Function for deserialize values\"\"\"\n def handle_return(method):\n def inner(*argc, **argv):\n ret = method(*argc, **argv)\n if ret.status_code != 200:\n ret.charset = \"utf-8\" # To be able to access ret.text (ret.content are bytes)\n text = ret.text if ret.status_code != 404 else \"404 Not found\"\n raise get_exception_from_error(ret.status_code)(text)\n return deserializer(ret.content) if deserializer else decode_text(ret.content)\n return inner\n return handle_return","label":0},{"cid":"conans/client/rest/rest_client_common.py::handle_return_deserializer.handle_return","text":"conans/client/rest/rest_client_common.py::handle_return_deserializer.handle_return\n def handle_return(method):\n def inner(*argc, **argv):\n ret = method(*argc, **argv)\n if ret.status_code != 200:\n ret.charset = \"utf-8\" # To be able to access ret.text (ret.content are bytes)\n text = ret.text if ret.status_code != 404 else \"404 Not found\"\n raise get_exception_from_error(ret.status_code)(text)\n return deserializer(ret.content) if deserializer else decode_text(ret.content)\n return inner","label":0},{"cid":"conans/client/rest/rest_client_common.py::handle_return_deserializer.handle_return.inner","text":"conans/client/rest/rest_client_common.py::handle_return_deserializer.handle_return.inner\n def inner(*argc, **argv):\n ret = method(*argc, **argv)\n if ret.status_code != 200:\n ret.charset = \"utf-8\" # To be able to access ret.text (ret.content are bytes)\n text = ret.text if ret.status_code != 404 else \"404 Not found\"\n raise get_exception_from_error(ret.status_code)(text)\n return deserializer(ret.content) if deserializer else decode_text(ret.content)","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.__init__","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.__init__\n def __init__(self, remote_url, token, custom_headers, output, requester, verify_ssl,\n put_headers=None):\n\n self.token = token\n self.remote_url = remote_url\n self.custom_headers = custom_headers\n self._output = output\n self.requester = requester\n self.verify_ssl = verify_ssl\n self._put_headers = put_headers","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.auth","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.auth\n @property\n def auth(self):\n return JWTAuth(self.token)","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.users_router","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.users_router\n @property\n def users_router(self):\n return ClientUsersRouterBuilder(self.remote_api_url)","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.search_router","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.search_router\n @property\n def search_router(self):\n return ClientSearchRouterBuilder(self.remote_api_url)","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.base_router","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.base_router\n @property\n def base_router(self):\n return ClientBaseRouterBuilder(self.remote_api_url)","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.authenticate","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.authenticate\n @handle_return_deserializer()\n def authenticate(self, user, password):\n \"\"\"Sends user + password to get a token\"\"\"\n auth = HTTPBasicAuth(user, password)\n url = self.users_router.common_authenticate()\n logger.debug(\"REST: Authenticate: %s\" % url)\n ret = self.requester.get(url, auth=auth, headers=self.custom_headers,\n verify=self.verify_ssl)\n if ret.status_code == 401:\n raise AuthenticationException(\"Wrong user or password\")\n # Cannot check content-type=text/html, conan server is doing it wrong\n if not ret.ok or \"html>\" in str(ret.content):\n raise ConanException(\"%s\\n\\nInvalid server response, check remote URL and \"\n \"try again\" % str(ret.content))\n return ret","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.check_credentials","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.check_credentials\n @handle_return_deserializer()\n def check_credentials(self):\n \"\"\"If token is not valid will raise AuthenticationException.\n User will be asked for new user/pass\"\"\"\n url = self.users_router.common_check_credentials()\n logger.debug(\"REST: Check credentials: %s\" % url)\n ret = self.requester.get(url, auth=self.auth, headers=self.custom_headers,\n verify=self.verify_ssl)\n return ret","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.server_info","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.server_info\n def server_info(self):\n \"\"\"Get information about the server: status, version, type and capabilities\"\"\"\n url = self.base_router.ping()\n logger.debug(\"REST: ping: %s\" % url)\n\n ret = self.requester.get(url, auth=self.auth, headers=self.custom_headers,\n verify=self.verify_ssl)\n if ret.status_code == 404:\n raise NotFoundException(\"Not implemented endpoint\")\n\n version_check = ret.headers.get('X-Conan-Client-Version-Check', None)\n server_version = ret.headers.get('X-Conan-Server-Version', None)\n server_capabilities = ret.headers.get('X-Conan-Server-Capabilities', \"\")\n server_capabilities = [cap.strip() for cap in server_capabilities.split(\",\") if cap]\n\n return version_check, server_version, server_capabilities","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.get_json","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.get_json\n def get_json(self, url, data=None):\n headers = self.custom_headers\n if data: # POST request\n headers.update({'Content-type': 'application/json',\n 'Accept': 'text/plain',\n 'Accept': 'application/json'})\n logger.debug(\"REST: post: %s\" % url)\n response = self.requester.post(url, auth=self.auth, headers=headers,\n verify=self.verify_ssl,\n stream=True,\n data=json.dumps(data))\n else:\n logger.debug(\"REST: get: %s\" % url)\n response = self.requester.get(url, auth=self.auth, headers=headers,\n verify=self.verify_ssl,\n stream=True)\n\n if response.status_code != 200: # Error message is text\n response.charset = \"utf-8\" # To be able to access ret.text (ret.content are bytes)\n raise get_exception_from_error(response.status_code)(response.text)\n\n result = json.loads(decode_text(response.content))\n if not isinstance(result, dict):\n raise ConanException(\"Unexpected server response %s\" % result)\n return result","label":1},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.upload_recipe","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.upload_recipe\n def upload_recipe(self, ref, the_files, retry, retry_wait, policy, remote_manifest):\n \"\"\"\n the_files: dict with relative_path: content\n \"\"\"\n self.check_credentials()\n\n revisions_enabled = get_env(\"CONAN_CLIENT_REVISIONS_ENABLED\", False)\n if not revisions_enabled and policy in (UPLOAD_POLICY_NO_OVERWRITE,\n UPLOAD_POLICY_NO_OVERWRITE_RECIPE):\n # Check if the latest revision is not the one we are uploading, with the compatibility\n # mode this is supposed to fail if someone tries to upload a different recipe\n latest_ref = ref.copy_clear_rev()\n latest_snapshot, ref_latest_snapshot, _ = self._get_recipe_snapshot(latest_ref)\n server_with_revisions = ref_latest_snapshot.revision != DEFAULT_REVISION_V1\n if latest_snapshot and server_with_revisions and \\\n ref_latest_snapshot.revision != ref.revision:\n raise ConanException(\"Local recipe is different from the remote recipe. \"\n \"Forbidden overwrite\")\n\n # Get the remote snapshot\n remote_snapshot, ref_snapshot, rev_time = self._get_recipe_snapshot(ref)\n\n if remote_snapshot and policy != UPLOAD_POLICY_FORCE:\n remote_manifest = remote_manifest or self.get_conan_manifest(ref_snapshot)\n local_manifest = FileTreeManifest.loads(load(the_files[\"conanmanifest.txt\"]))\n\n if remote_manifest == local_manifest:\n return False, rev_time\n\n if policy in (UPLOAD_POLICY_NO_OVERWRITE, UPLOAD_POLICY_NO_OVERWRITE_RECIPE):\n raise ConanException(\"Local recipe is different from the remote recipe. \"\n \"Forbidden overwrite\")\n\n files_to_upload = {filename.replace(\"\\\\\", \"/\"): path\n for filename, path i","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.upload_package","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.upload_package\n def upload_package(self, pref, the_files, retry, retry_wait, policy):\n \"\"\"\n basedir: Base directory with the files to upload (for read the files in disk)\n relative_files: relative paths to upload\n \"\"\"\n self.check_credentials()\n\n revisions_enabled = get_env(\"CONAN_CLIENT_REVISIONS_ENABLED\", False)\n if not revisions_enabled and policy == UPLOAD_POLICY_NO_OVERWRITE:\n # Check if the latest revision is not the one we are uploading, with the compatibility\n # mode this is supposed to fail if someone tries to upload a different recipe\n latest_pref = PackageReference(pref.ref, pref.id)\n latest_snapshot, ref_latest_snapshot, _ = self._get_package_snapshot(latest_pref)\n server_with_revisions = ref_latest_snapshot.revision != DEFAULT_REVISION_V1\n if latest_snapshot and server_with_revisions and \\\n ref_latest_snapshot.revision != pref.revision:\n raise ConanException(\"Local package is different from the remote package. \"\n \"Forbidden overwrite\")\n t1 = time.time()\n # Get the remote snapshot\n pref = pref\n remote_snapshot, pref_snapshot, rev_time = self._get_package_snapshot(pref)\n\n if remote_snapshot:\n remote_manifest = self.get_package_manifest(pref_snapshot)\n local_manifest = FileTreeManifest.loads(load(the_files[\"conanmanifest.txt\"]))\n\n if remote_manifest == local_manifest:\n return False, pref_snapshot, rev_time\n\n if policy == UPLOAD_POLICY_NO_OVERWRITE:\n raise ConanException(\"Local package is different from the remote package. \"\n \"Forbidden overwrite\")\n\n files_to_upload = the_files\n deleted = set(remote_snapshot).difference(the_files)\n if files_to_upload:\n ","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.search","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.search\n def search(self, pattern=None, ignorecase=True):\n \"\"\"\n the_files: dict with relative_path: content\n \"\"\"\n url = self.search_router.search(pattern, ignorecase)\n response = self.get_json(url)[\"results\"]\n return [ConanFileReference.loads(reference) for reference in response]","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.search_packages","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.search_packages\n def search_packages(self, reference, query):\n\n if not query:\n url = self.search_router.search_packages(reference)\n package_infos = self.get_json(url)\n return package_infos\n\n # Read capabilities\n try:\n _, _, capabilities = self.server_info()\n except NotFoundException:\n capabilities = []\n\n if COMPLEX_SEARCH_CAPABILITY in capabilities:\n url = self.search_router.search_packages(reference, query)\n package_infos = self.get_json(url)\n return package_infos\n else:\n url = self.search_router.search_packages(reference)\n package_infos = self.get_json(url)\n return filter_packages(query, package_infos)","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods.remove_conanfile","text":"conans/client/rest/rest_client_common.py::RestCommonMethods.remove_conanfile\n @handle_return_deserializer()\n def remove_conanfile(self, ref):\n \"\"\" Remove a recipe and packages \"\"\"\n self.check_credentials()\n url = self.conans_router.remove_recipe(ref)\n logger.debug(\"REST: remove: %s\" % url)\n response = self.requester.delete(url,\n auth=self.auth,\n headers=self.custom_headers,\n verify=self.verify_ssl)\n return response","label":0},{"cid":"conans/client/rest/rest_client_common.py::RestCommonMethods._post_json","text":"conans/client/rest/rest_client_common.py::RestCommonMethods._post_json\n def _post_json(self, url, payload):\n logger.debug(\"REST: post: %s\" % url)\n response = self.requester.post(url,\n auth=self.auth,\n headers=self.custom_headers,\n verify=self.verify_ssl,\n json=payload)\n return response","label":0},{"cid":"conans/model/conan_file.py::create_options","text":"conans/model/conan_file.py::create_options\ndef create_options(conanfile):\n try:\n package_options = PackageOptions(getattr(conanfile, \"options\", None))\n options = Options(package_options)\n\n default_options = getattr(conanfile, \"default_options\", None)\n if default_options:\n if isinstance(default_options, (list, tuple, dict)):\n default_values = OptionsValues(default_options)\n elif isinstance(default_options, str):\n default_values = OptionsValues.loads(default_options)\n else:\n raise ConanException(\"Please define your default_options as list, \"\n \"multiline string or dictionary\")\n options.values = default_values\n return options\n except Exception as e:\n raise ConanException(\"Error while initializing options. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::create_requirements","text":"conans/model/conan_file.py::create_requirements\ndef create_requirements(conanfile):\n try:\n # Actual requirements of this package\n if not hasattr(conanfile, \"requires\"):\n return Requirements()\n else:\n if not conanfile.requires:\n return Requirements()\n if isinstance(conanfile.requires, tuple):\n return Requirements(*conanfile.requires)\n else:\n return Requirements(conanfile.requires, )\n except Exception as e:\n raise ConanException(\"Error while initializing requirements. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::create_settings","text":"conans/model/conan_file.py::create_settings\ndef create_settings(conanfile, settings):\n try:\n defined_settings = getattr(conanfile, \"settings\", None)\n if isinstance(defined_settings, str):\n defined_settings = [defined_settings]\n current = defined_settings or {}\n settings.constraint(current)\n return settings\n except Exception as e:\n raise ConanException(\"Error while initializing settings. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::_env_and_python","text":"conans/model/conan_file.py::_env_and_python\n@contextmanager\ndef _env_and_python(conanfile):\n with environment_append(conanfile.env):\n with pythonpath(conanfile):\n yield","label":0},{"cid":"conans/model/conan_file.py::get_env_context_manager","text":"conans/model/conan_file.py::get_env_context_manager\ndef get_env_context_manager(conanfile, without_python=False):\n if not conanfile.apply_env:\n return no_op()\n if without_python:\n return environment_append(conanfile.env)\n return _env_and_python(conanfile)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.__init__","text":"conans/model/conan_file.py::ConanFile.__init__\n def __init__(self, output, runner, display_name=\"\", user=None, channel=None):\n # an output stream (writeln, info, warn error)\n self.output = ScopedOutput(display_name, output)\n self.display_name = display_name\n # something that can run commands, as os.sytem\n self._conan_runner = runner\n self._conan_user = user\n self._conan_channel = channel","label":0},{"cid":"conans/model/conan_file.py::ConanFile.initialize","text":"conans/model/conan_file.py::ConanFile.initialize\n def initialize(self, settings, env):\n if isinstance(self.generators, str):\n self.generators = [self.generators]\n # User defined options\n self.options = create_options(self)\n self.requires = create_requirements(self)\n self.settings = create_settings(self, settings)\n try:\n if self.settings.os_build and self.settings.os:\n self.output.writeln(\"*\"*60, front=Color.BRIGHT_RED)\n self.output.writeln(\" This package defines both 'os' and 'os_build' \",\n front=Color.BRIGHT_RED)\n self.output.writeln(\" Please use 'os' for libraries and 'os_build'\",\n front=Color.BRIGHT_RED)\n self.output.writeln(\" only for build-requires used for cross-building\",\n front=Color.BRIGHT_RED)\n self.output.writeln(\"*\"*60, front=Color.BRIGHT_RED)\n except ConanException:\n pass\n\n # needed variables to pack the project\n self.cpp_info = None # Will be initialized at processing time\n self.deps_cpp_info = DepsCppInfo()\n\n # environment variables declared in the package_info\n self.env_info = None # Will be initialized at processing time\n self.deps_env_info = DepsEnvInfo()\n\n # user declared variables\n self.user_info = None\n # Keys are the package names, and the values a dict with the vars\n self.deps_user_info = DepsUserInfo()\n\n # user specified env variables\n self._conan_env_values = env.copy() # user specified -e","label":0},{"cid":"conans/model/conan_file.py::ConanFile.env","text":"conans/model/conan_file.py::ConanFile.env\n @property\n def env(self):\n \"\"\"Apply the self.deps_env_info into a copy of self._conan_env_values (will prioritize the\n self._conan_env_values, user specified from profiles or -e first, then inherited)\"\"\"\n # Cannot be lazy cached, because it's called in configure node, and we still don't have\n # the deps_env_info objects available\n tmp_env_values = self._conan_env_values.copy()\n tmp_env_values.update(self.deps_env_info)\n\n ret, multiple = tmp_env_values.env_dicts(self.name)\n ret.update(multiple)\n return ret","label":0},{"cid":"conans/model/conan_file.py::ConanFile.channel","text":"conans/model/conan_file.py::ConanFile.channel\n @property\n def channel(self):\n if not self._conan_channel:\n self._conan_channel = os.getenv(\"CONAN_CHANNEL\") or self.default_channel\n if not self._conan_channel:\n raise ConanException(\"CONAN_CHANNEL environment variable not defined, \"\n \"but self.channel is used in conanfile\")\n return self._conan_channel","label":0},{"cid":"conans/model/conan_file.py::ConanFile.user","text":"conans/model/conan_file.py::ConanFile.user\n @property\n def user(self):\n if not self._conan_user:\n self._conan_user = os.getenv(\"CONAN_USERNAME\") or self.default_user\n if not self._conan_user:\n raise ConanException(\"CONAN_USERNAME environment variable not defined, \"\n \"but self.user is used in conanfile\")\n return self._conan_user","label":0},{"cid":"conans/model/conan_file.py::ConanFile.collect_libs","text":"conans/model/conan_file.py::ConanFile.collect_libs\n def collect_libs(self, folder=None):\n self.output.warn(\"'self.collect_libs' is deprecated, \"\n \"use 'tools.collect_libs(self)' instead\")\n return tools.collect_libs(self, folder=folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_policy_missing","text":"conans/model/conan_file.py::ConanFile.build_policy_missing\n @property\n def build_policy_missing(self):\n return self.build_policy == \"missing\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_policy_always","text":"conans/model/conan_file.py::ConanFile.build_policy_always\n @property\n def build_policy_always(self):\n return self.build_policy == \"always\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.source","text":"conans/model/conan_file.py::ConanFile.source\n def source(self):\n pass","label":0},{"cid":"conans/model/conan_file.py::ConanFile.system_requirements","text":"conans/model/conan_file.py::ConanFile.system_requirements\n def system_requirements(self):\n \"\"\" this method can be overwritten to implement logic for system package\n managers, as apt-get\n\n You can define self.global_system_requirements = True, if you want the installation\n to be for all packages (not depending on settings/options/requirements)\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.config_options","text":"conans/model/conan_file.py::ConanFile.config_options\n def config_options(self):\n \"\"\" modify options, probably conditioned to some settings. This call is executed\n before config_settings. E.g.\n if self.settings.os == \"Windows\":\n del self.options.shared # shared/static not supported in win\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.configure","text":"conans/model/conan_file.py::ConanFile.configure\n def configure(self):\n \"\"\" modify settings, probably conditioned to some options. This call is executed\n after config_options. E.g.\n if self.options.header_only:\n self.settings.clear()\n This is also the place for conditional requirements\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build","text":"conans/model/conan_file.py::ConanFile.build\n def build(self):\n \"\"\" build your project calling the desired build tools as done in the command line.\n E.g. self.run(\"cmake --build .\") Or use the provided build helpers. E.g. cmake.build()\n \"\"\"\n self.output.warn(\"This conanfile has no build step\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package","text":"conans/model/conan_file.py::ConanFile.package\n def package(self):\n \"\"\" package the needed files from source and build folders.\n E.g. self.copy(\"*.h\", src=\"src/includes\", dst=\"includes\")\n \"\"\"\n self.output.warn(\"This conanfile has no package step\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_info","text":"conans/model/conan_file.py::ConanFile.package_info\n def package_info(self):\n \"\"\" define cpp_build_info, flags, etc\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.run","text":"conans/model/conan_file.py::ConanFile.run\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True,\n ignore_errors=False, run_environment=False):\n def _run():\n if not win_bash:\n return self._conan_runner(command, output, os.path.abspath(RUN_LOG_NAME), cwd)\n # FIXME: run in windows bash is not using output\n return tools.run_in_windows_bash(self, bashcmd=command, cwd=cwd, subsystem=subsystem,\n msys_mingw=msys_mingw)\n if run_environment:\n with tools.run_environment(self):\n if OSInfo().is_macos:\n command = 'DYLD_LIBRARY_PATH=\"%s\" %s' % (os.environ.get('DYLD_LIBRARY_PATH', ''),\n command)\n retcode = _run()\n else:\n retcode = _run()\n\n if not ignore_errors and retcode != 0:\n raise ConanException(\"Error %d while executing %s\" % (retcode, command))\n\n return retcode","label":0},{"cid":"conans/model/conan_file.py::ConanFile.run._run","text":"conans/model/conan_file.py::ConanFile.run._run\n def _run():\n if not win_bash:\n return self._conan_runner(command, output, os.path.abspath(RUN_LOG_NAME), cwd)\n # FIXME: run in windows bash is not using output\n return tools.run_in_windows_bash(self, bashcmd=command, cwd=cwd, subsystem=subsystem,\n msys_mingw=msys_mingw)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_id","text":"conans/model/conan_file.py::ConanFile.package_id\n def package_id(self):\n \"\"\" modify the conans info, typically to narrow values\n eg.: conaninfo.package_references = []\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.test","text":"conans/model/conan_file.py::ConanFile.test\n def test(self):\n \"\"\" test the generated executable.\n E.g. self.run(\"./example\")\n \"\"\"\n raise ConanException(\"You need to create a method 'test' in your test/conanfile.py\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.__repr__","text":"conans/model/conan_file.py::ConanFile.__repr__\n def __repr__(self):\n return self.display_name","label":0},{"cid":"conans/server/rest/multipart_encoder.py::get_response_chunk","text":"conans/server/rest/multipart_encoder.py::get_response_chunk\ndef get_response_chunk(response, basepath, filepaths, boundary=DEFAULT_BOUNDARY):\n \"\"\"\n File is a list of filepaths\n \"\"\"\n response.add_header(\"Content-Type\", \"multipart/mixed;charset=utf-8;boundary=%s\" % boundary)\n\n for filepath in filepaths:\n yield \"--%s\\n\" % boundary + _headers_for_file(filepath)\n for data in iter(read_file_chunked(os.path.join(basepath, filepath))):\n yield data.encode(\"hex\")\n\n yield (\"--%s\\n--%s\\n\" % (boundary, boundary))","label":0},{"cid":"conans/server/rest/multipart_encoder.py::_headers_for_file","text":"conans/server/rest/multipart_encoder.py::_headers_for_file\ndef _headers_for_file(filename):\n return '''Content-type: application/octet-stream;charset=hex\nContent-transfer-encoding: binary\nContent-disposition: attachment; filename=\"%s\"\n\n''' % filename","label":0},{"cid":"conans/server/rest/multipart_encoder.py::read_file_chunked","text":"conans/server/rest/multipart_encoder.py::read_file_chunked\ndef read_file_chunked(filepath):\n thefile = open(filepath, \"rb\")\n for data in iter(read_in_chunks(thefile)):\n yield data","label":0},{"cid":"conans/server/rest/multipart_encoder.py::read_in_chunks","text":"conans/server/rest/multipart_encoder.py::read_in_chunks\ndef read_in_chunks(file_object, chunk_size=1024):\n \"\"\"Lazy function (generator) to read a file piece by piece.\n Default chunk size: 1k.\n Warn: Python file.read is returning \\n instead of EOF,\n we read two chunks before return to know if EOF is reached\n so used memory is chunk_size*2\"\"\"\n while True:\n buf1 = file_object.read(chunk_size)\n if not buf1:\n break\n\n# buf2 = file_object.read(chunk_size)\n# if not buf2: # Last buf1 has and ending \\n that is a EOF\n# buf1 = buf1[:-1] if buf1[-1] == \"\\n\" else buf1\n# yield buf1\n# else:\n yield buf1","label":0},{"cid":"conans/server/rest/server.py::ConanServer.__init__","text":"conans/server/rest/server.py::ConanServer.__init__\n def __init__(self, run_port, credentials_manager,\n updown_auth_manager, authorizer, authenticator,\n server_store, server_capabilities):\n\n self.run_port = run_port\n\n server_capabilities = server_capabilities or []\n self.root_app = bottle.Bottle()\n\n self.api_v1 = ApiV1(credentials_manager, updown_auth_manager,\n server_capabilities)\n self.api_v1.authorizer = authorizer\n self.api_v1.authenticator = authenticator\n self.api_v1.server_store = server_store\n self.api_v1.setup()\n\n self.root_app.mount(\"/v1/\", self.api_v1)\n\n self.api_v2 = ApiV2(credentials_manager, server_capabilities)\n self.api_v2.authorizer = authorizer\n self.api_v2.authenticator = authenticator\n self.api_v2.server_store = server_store\n self.api_v2.setup()\n self.root_app.mount(\"/v2/\", self.api_v2)","label":0},{"cid":"conans/server/rest/server.py::ConanServer.run","text":"conans/server/rest/server.py::ConanServer.run\n def run(self, **kwargs):\n port = kwargs.pop(\"port\", self.run_port)\n debug_set = kwargs.pop(\"debug\", False)\n host = kwargs.pop(\"host\", \"localhost\")\n bottle.Bottle.run(self.root_app, host=host,\n port=port, debug=debug_set, reloader=False)","label":0},{"cid":"conans/server/service/mime.py::get_mime_type","text":"conans/server/service/mime.py::get_mime_type\ndef get_mime_type(filepath):\n if filepath.endswith(\".tgz\"):\n mimetype = \"x-gzip\"\n elif filepath.endswith(\".txz\"):\n mimetype = \"x-xz\"\n else:\n mimetype = \"auto\"\n\n return mimetype","label":1}],"string":"[\n {\n \"cid\": \"conans/client/command.py::Extender.__call__\",\n \"text\": \"conans/client/command.py::Extender.__call__\\n def __call__(self, parser, namespace, values, option_strings=None): # @UnusedVariable\\n # Need None here incase `argparse.SUPPRESS` was supplied for `dest`\\n dest = getattr(namespace, self.dest, None)\\n if not hasattr(dest, 'extend') or dest == self.default:\\n dest = []\\n setattr(namespace, self.dest, dest)\\n # if default isn't set to None, this method might be called\\n # with the default as `values` for other arguments which\\n # share this destination.\\n parser.set_defaults(**{self.dest: None})\\n\\n if isinstance(values, str):\\n dest.append(values)\\n elif values:\\n try:\\n dest.extend(values)\\n except ValueError:\\n dest.append(values)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::OnceArgument.__call__\",\n \"text\": \"conans/client/command.py::OnceArgument.__call__\\n def __call__(self, parser, namespace, values, option_string=None):\\n if getattr(namespace, self.dest) is not None and self.default is None:\\n msg = '{o} can only be specified once'.format(o=option_string)\\n raise argparse.ArgumentError(None, msg)\\n setattr(namespace, self.dest, values)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.__init__\",\n \"text\": \"conans/client/command.py::Command.__init__\\n def __init__(self, conan_api, cache, user_io, outputer):\\n assert isinstance(conan_api, Conan)\\n self._conan = conan_api\\n self._cache = cache\\n self._user_io = user_io\\n self._outputer = outputer\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.help\",\n \"text\": \"conans/client/command.py::Command.help\\n def help(self, *args):\\n \\\"\\\"\\\"Show help of a specific command.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.help.__doc__, prog=\\\"conan help\\\")\\n parser.add_argument(\\\"command\\\", help='command', nargs=\\\"?\\\")\\n args = parser.parse_args(*args)\\n if not args.command:\\n self._show_help()\\n return\\n try:\\n commands = self._commands()\\n method = commands[args.command]\\n self._warn_python2()\\n method([\\\"--help\\\"])\\n except KeyError:\\n raise ConanException(\\\"Unknown command '%s'\\\" % args.command)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.new\",\n \"text\": \"conans/client/command.py::Command.new\\n def new(self, *args):\\n \\\"\\\"\\\"Creates a new package recipe template with a 'conanfile.py' and optionally,\\n 'test_package' testing files.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.new.__doc__, prog=\\\"conan new\\\")\\n parser.add_argument(\\\"name\\\", help='Package name, e.g.: \\\"Poco/1.7.3\\\" or complete reference'\\n ' for CI scripts: \\\"Poco/1.7.3@conan/stable\\\"')\\n parser.add_argument(\\\"-t\\\", \\\"--test\\\", action='store_true', default=False,\\n help='Create test_package skeleton to test package')\\n parser.add_argument(\\\"-i\\\", \\\"--header\\\", action='store_true', default=False,\\n help='Create a headers only package template')\\n parser.add_argument(\\\"-c\\\", \\\"--pure-c\\\", action='store_true', default=False,\\n help='Create a C language package only package, '\\n 'deleting \\\"self.settings.compiler.libcxx\\\" setting '\\n 'in the configure method')\\n parser.add_argument(\\\"-s\\\", \\\"--sources\\\", action='store_true', default=False,\\n help='Create a package with embedded sources in \\\"src\\\" folder, '\\n 'using \\\"exports_sources\\\" instead of retrieving external code with '\\n 'the \\\"source()\\\" method')\\n parser.add_argument(\\\"-b\\\", \\\"--bare\\\", action='store_true', default=False,\\n help='Create the minimum package recipe, without build() method. '\\n 'Useful in combination with \\\"export-pkg\\\" command')\\n parser.add_argument(\\\"-cis\\\", \\\"--ci-shared\\\", action='store_true',\\n default=False,\\n help='Package will have a \\\"shared\\\" option to be used in CI')\\n parser.add_argument(\\\"-cilg\\\", \\\"--ci-travis-gcc\\\", action='store_true',\\n defau\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.inspect\",\n \"text\": \"conans/client/command.py::Command.inspect\\n def inspect(self, *args):\\n \\\"\\\"\\\"Displays conanfile attributes, like name, version, options\\n Works both locally, in local cache and remote\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.inspect.__doc__, prog=\\\"conan inspect\\\")\\n parser.add_argument(\\\"path_or_reference\\\", help=\\\"Path to a folder containing a recipe\\\"\\n \\\" (conanfile.py) or to a recipe file. e.g., \\\"\\n \\\"./my_project/conanfile.py. It could also be a reference\\\")\\n parser.add_argument(\\\"-a\\\", \\\"--attribute\\\", help='The attribute to be displayed, e.g \\\"name\\\"',\\n nargs=\\\"?\\\", action=Extender)\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", help='look in the specified remote server',\\n action=OnceArgument)\\n parser.add_argument(\\\"-j\\\", \\\"--json\\\", default=None, action=OnceArgument,\\n help='json output file')\\n\\n args = parser.parse_args(*args)\\n result = self._conan.inspect(args.path_or_reference, args.attribute, args.remote)\\n Printer(self._user_io.out).print_inspect(result)\\n if args.json:\\n json_output = json.dumps(result)\\n if not os.path.isabs(args.json):\\n json_output_file = os.path.join(get_cwd(), args.json)\\n else:\\n json_output_file = args.json\\n save(json_output_file, json_output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.test\",\n \"text\": \"conans/client/command.py::Command.test\\n def test(self, *args):\\n \\\"\\\"\\\"Test a package consuming it from a conanfile.py with a test() method.\\n\\n This command installs the conanfile dependencies (including the tested\\n package), calls a 'conan build' to build test apps and finally executes\\n the test() method. The testing recipe does not require name or version,\\n neither definition of package() or package_info() methods. The package\\n to be tested must exist in the local cache or in any configured remote.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.test.__doc__, prog=\\\"conan test\\\")\\n parser.add_argument(\\\"path\\\", help='Path to the \\\"testing\\\" folder containing a conanfile.py or'\\n ' to a recipe file with test() method'\\n 'e.g. conan test_package/conanfile.py pkg/version@user/channel')\\n parser.add_argument(\\\"reference\\\",\\n help='pkg/version@user/channel of the package to be tested')\\n parser.add_argument(\\\"-tbf\\\", \\\"--test-build-folder\\\", action=OnceArgument,\\n help=\\\"Working directory of the build process.\\\")\\n\\n _add_common_install_arguments(parser, build_help=_help_build_policies)\\n args = parser.parse_args(*args)\\n self._warn_python2()\\n return self._conan.test(args.path, args.reference, args.profile, args.settings,\\n args.options, args.env, args.remote, args.update,\\n build_modes=args.build, test_build_folder=args.test_build_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.create\",\n \"text\": \"conans/client/command.py::Command.create\\n def create(self, *args):\\n \\\"\\\"\\\"Builds a binary package for a recipe (conanfile.py).\\n\\n Uses the specified configuration in a profile or in -s settings, -o\\n options etc. If a 'test_package' folder (the name can be configured\\n with -tf) is found, the command will run the consumer project to ensure\\n that the package has been created correctly. Check 'conan test' command\\n to know more about 'test_folder' project.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.create.__doc__, prog=\\\"conan create\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"reference\\\",\\n help='user/channel or pkg/version@user/channel (if name and version not'\\n ' declared in conanfile.py) where the package will be created')\\n parser.add_argument(\\\"-j\\\", \\\"--json\\\", default=None, action=OnceArgument,\\n help='json file path where the install information will be written to')\\n parser.add_argument('-k', '-ks', '--keep-source', default=False, action='store_true',\\n help=_KEEP_SOURCE_HELP)\\n parser.add_argument('-kb', '--keep-build', default=False, action='store_true',\\n help='Do not remove the build folder in local cache. Implies --keep-source. '\\n 'Use this for testing purposes only')\\n parser.add_argument(\\\"-ne\\\", \\\"--not-export\\\", default=False, action='store_true',\\n help='Do not export the conanfile.py')\\n parser.add_argument(\\\"-tbf\\\", \\\"--test-build-folder\\\", action=OnceArgument,\\n help='Working directory for the build of the test project.')\\n parser.add_argument(\\\"-tf\\\", \\\"--test-folder\\\", action=OnceArgument,\\n help='Alternative test folder name. By default it is \\\"test_package\\\". '\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.download\",\n \"text\": \"conans/client/command.py::Command.download\\n def download(self, *args):\\n \\\"\\\"\\\"Downloads recipe and binaries to the local cache, without using settings.\\n\\n It works specifying the recipe reference and package ID to be\\n installed. Not transitive, requirements of the specified reference will\\n NOT be retrieved. Useful together with 'conan copy' to automate the\\n promotion of packages to a different user/channel. Only if a reference\\n is specified, it will download all packages from the specified remote.\\n If no remote is specified, it will use the default remote.\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(description=self.download.__doc__, prog=\\\"conan download\\\")\\n parser.add_argument(\\\"reference\\\",\\n help='pkg/version@user/channel')\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", nargs=1, action=Extender,\\n help='Force install specified package ID (ignore settings/options)')\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", help='look in the specified remote server',\\n action=OnceArgument)\\n parser.add_argument(\\\"-re\\\", \\\"--recipe\\\", help='Downloads only the recipe', default=False,\\n action=\\\"store_true\\\")\\n\\n args = parser.parse_args(*args)\\n\\n self._warn_python2()\\n return self._conan.download(reference=args.reference, package=args.package,\\n remote_name=args.remote, recipe=args.recipe)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.install\",\n \"text\": \"conans/client/command.py::Command.install\\n def install(self, *args):\\n \\\"\\\"\\\"Installs the requirements specified in a recipe (conanfile.py or conanfile.txt).\\n\\n It can also be used to install a concrete package specifying a\\n reference. If any requirement is not found in the local cache, it will\\n retrieve the recipe from a remote, looking for it sequentially in the\\n configured remotes. When the recipes have been downloaded it will try\\n to download a binary package matching the specified settings, only from\\n the remote from which the recipe was retrieved. If no binary package is\\n found, it can be build from sources using the '--build' option. When\\n the package is installed, Conan will write the files for the specified\\n generators.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.install.__doc__, prog=\\\"conan install\\\")\\n parser.add_argument(\\\"path_or_reference\\\", help=\\\"Path to a folder containing a recipe\\\"\\n \\\" (conanfile.py or conanfile.txt) or to a recipe file. e.g., \\\"\\n \\\"./my_project/conanfile.txt. It could also be a reference\\\")\\n parser.add_argument(\\\"-g\\\", \\\"--generator\\\", nargs=1, action=Extender,\\n help='Generators to use')\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help='Use this directory as the directory where to put the generator'\\n 'files. e.g., conaninfo/conanbuildinfo.txt')\\n\\n _add_manifests_arguments(parser)\\n\\n parser.add_argument(\\\"--no-imports\\\", action='store_true', default=False,\\n help='Install specified packages but avoid running imports')\\n parser.add_argument(\\\"-j\\\", \\\"--json\\\", default=None, action=OnceArgument,\\n help='Path to a json file where the install information will be '\\n 'written')\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.config\",\n \"text\": \"conans/client/command.py::Command.config\\n def config(self, *args):\\n \\\"\\\"\\\"Manages Conan configuration.\\n\\n Used to edit conan.conf, or install config files.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.config.__doc__, prog=\\\"conan config\\\")\\n\\n subparsers = parser.add_subparsers(dest='subcommand', help='sub-command help')\\n rm_subparser = subparsers.add_parser('rm', help='Remove an existing config element')\\n set_subparser = subparsers.add_parser('set', help='Set a value for a configuration item')\\n get_subparser = subparsers.add_parser('get', help='Get the value of configuration item')\\n install_subparser = subparsers.add_parser('install', help='install a full configuration '\\n 'from a local or remote zip file')\\n rm_subparser.add_argument(\\\"item\\\", help=\\\"Item to remove\\\")\\n get_subparser.add_argument(\\\"item\\\", nargs=\\\"?\\\", help=\\\"Item to print\\\")\\n set_subparser.add_argument(\\\"item\\\", help=\\\"'item=value' to set\\\")\\n install_subparser.add_argument(\\\"item\\\", nargs=\\\"?\\\", help=\\\"Configuration file or directory to use\\\")\\n\\n install_subparser.add_argument(\\\"--verify-ssl\\\", nargs=\\\"?\\\", default=\\\"True\\\",\\n help='Verify SSL connection when downloading file')\\n install_subparser.add_argument(\\\"--type\\\", \\\"-t\\\", choices=[\\\"git\\\"],\\n help='Type of remote config')\\n install_subparser.add_argument(\\\"--args\\\", \\\"-a\\\",\\n help='String with extra arguments for \\\"git clone\\\"')\\n\\n args = parser.parse_args(*args)\\n\\n if args.subcommand == \\\"set\\\":\\n try:\\n key, value = args.item.split(\\\"=\\\", 1)\\n except ValueError:\\n if \\\"hooks.\\\" in args.item:\\n key, value = args.item.split(\\\"=\\\", 1)[0], None\\n else:\\n raise ConanException(\\\"Please specify 'key\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.info\",\n \"text\": \"conans/client/command.py::Command.info\\n def info(self, *args):\\n \\\"\\\"\\\"Gets information about the dependency graph of a recipe.\\n\\n It can be used with a recipe or a reference for any existing package in\\n your local cache.\\n \\\"\\\"\\\"\\n\\n info_only_options = [\\\"id\\\", \\\"build_id\\\", \\\"remote\\\", \\\"url\\\", \\\"license\\\", \\\"requires\\\", \\\"update\\\",\\n \\\"required\\\", \\\"date\\\", \\\"author\\\", \\\"None\\\"]\\n path_only_options = [\\\"export_folder\\\", \\\"build_folder\\\", \\\"package_folder\\\", \\\"source_folder\\\"]\\n str_path_only_options = \\\", \\\".join(['\\\"%s\\\"' % field for field in path_only_options])\\n str_only_options = \\\", \\\".join(['\\\"%s\\\"' % field for field in info_only_options])\\n\\n parser = argparse.ArgumentParser(description=self.info.__doc__, prog=\\\"conan info\\\")\\n parser.add_argument(\\\"path_or_reference\\\", help=\\\"Path to a folder containing a recipe\\\"\\n \\\" (conanfile.py or conanfile.txt) or to a recipe file. e.g., \\\"\\n \\\"./my_project/conanfile.txt. It could also be a reference\\\")\\n parser.add_argument(\\\"--paths\\\", action='store_true', default=False,\\n help='Show package paths in local cache')\\n parser.add_argument(\\\"-bo\\\", \\\"--build-order\\\",\\n help='given a modified reference, return an ordered list to build (CI)',\\n nargs=1, action=Extender)\\n parser.add_argument(\\\"-g\\\", \\\"--graph\\\", action=OnceArgument,\\n help='Creates file with project dependencies graph. It will generate '\\n 'a DOT or HTML file depending on the filename extension')\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=\\\"local folder containing the conaninfo.txt and conanbuildinfo.txt \\\"\\n \\\"files (from a previous conan install execution). Defaulted to \\\"\\n \\\"current folder, unless --profile, -s or -o\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.source\",\n \"text\": \"conans/client/command.py::Command.source\\n def source(self, *args):\\n \\\"\\\"\\\" Calls your local conanfile.py 'source()' method.\\n\\n Usually downloads and uncompresses the package sources.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.source.__doc__, prog=\\\"conan source\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"-sf\\\", \\\"--source-folder\\\", action=OnceArgument,\\n help='Destination directory. Defaulted to current directory')\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=_INSTALL_FOLDER_HELP + \\\" Optional, source method will run without \\\"\\n \\\"the information retrieved from the conaninfo.txt and \\\"\\n \\\"conanbuildinfo.txt, only required when using conditional source() \\\"\\n \\\"based on settings, options, env_info and user_info\\\")\\n args = parser.parse_args(*args)\\n\\n try:\\n if \\\"@\\\" in args.path and ConanFileReference.loads(args.path):\\n raise ArgumentError(None,\\n \\\"'conan source' doesn't accept a reference anymore. \\\"\\n \\\"If you were using it as a concurrency workaround, \\\"\\n \\\"you can call 'conan install' simultaneously from several \\\"\\n \\\"different processes, the concurrency is now natively supported\\\"\\n \\\". The path parameter should be a folder containing a \\\"\\n \\\"conanfile.py file.\\\")\\n except ConanException:\\n pass\\n\\n self._warn_python2()\\n return self._conan.source(args.path, args.source_folder, args.install_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.build\",\n \"text\": \"conans/client/command.py::Command.build\\n def build(self, *args):\\n \\\"\\\"\\\"Calls your local conanfile.py 'build()' method.\\n\\n The recipe will be built in the local directory specified by\\n --build-folder, reading the sources from --source-folder. If you are\\n using a build helper, like CMake(), the --package-folder will be\\n configured as destination folder for the install step.\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(description=self.build.__doc__, prog=\\\"conan build\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"-b\\\", \\\"--build\\\", default=None, action=\\\"store_true\\\",\\n help=\\\"Execute the build step (variable should_build=True). When \\\"\\n \\\"specified, configure/install/test won't run unless \\\"\\n \\\"--configure/--install/--test specified\\\")\\n parser.add_argument(\\\"-bf\\\", \\\"--build-folder\\\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\\n parser.add_argument(\\\"-c\\\", \\\"--configure\\\", default=None, action=\\\"store_true\\\",\\n help=\\\"Execute the configuration step (variable should_configure=True). \\\"\\n \\\"When specified, build/install/test won't run unless \\\"\\n \\\"--build/--install/--test specified\\\")\\n parser.add_argument(\\\"-i\\\", \\\"--install\\\", default=None, action=\\\"store_true\\\",\\n help=\\\"Execute the install step (variable should_install=True). When \\\"\\n \\\"specified, configure/build/test won't run unless \\\"\\n \\\"--configure/--build/--test specified\\\")\\n parser.add_argument(\\\"-t\\\", \\\"--test\\\", default=None, action=\\\"store_true\\\",\\n help=\\\"Execute the test step (variable should_test=True). When \\\"\\n \\\"specified, configure/build/install won't run unless \\\"\\n \\\"--configure/--build/--install specified\\\")\\n parser.add_\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.package\",\n \"text\": \"conans/client/command.py::Command.package\\n def package(self, *args):\\n \\\"\\\"\\\" Calls your local conanfile.py 'package()' method.\\n\\n This command works in the user space and it will copy artifacts from\\n the --build-folder and --source-folder folder to the --package-folder\\n one. It won't create a new package in the local cache, if you want to\\n do it, use 'conan create' or 'conan export-pkg' after a 'conan build'\\n command.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.package.__doc__, prog=\\\"conan package\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"-bf\\\", \\\"--build-folder\\\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=_INSTALL_FOLDER_HELP)\\n parser.add_argument(\\\"-pf\\\", \\\"--package-folder\\\", action=OnceArgument,\\n help=\\\"folder to install the package. Defaulted to the \\\"\\n \\\"'{build_folder}/package' folder. A relative path can be specified\\\"\\n \\\" (relative to the current directory). Also an absolute path\\\"\\n \\\" is allowed.\\\")\\n parser.add_argument(\\\"-sf\\\", \\\"--source-folder\\\", action=OnceArgument, help=_SOURCE_FOLDER_HELP)\\n args = parser.parse_args(*args)\\n try:\\n if \\\"@\\\" in args.path and ConanFileReference.loads(args.path):\\n raise ArgumentError(None,\\n \\\"'conan package' doesn't accept a reference anymore. \\\"\\n \\\"The path parameter should be a conanfile.py or a folder \\\"\\n \\\"containing one. If you were using the 'conan package' \\\"\\n \\\"command for development purposes we recommend to use \\\"\\n \\\"the local development commands: 'conan build' + \\\"\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.imports\",\n \"text\": \"conans/client/command.py::Command.imports\\n def imports(self, *args):\\n \\\"\\\"\\\" Calls your local conanfile.py or conanfile.txt 'imports' method.\\n\\n It requires to have been previously installed and have a\\n conanbuildinfo.txt generated file in the --install-folder (defaulted to\\n current directory).\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.imports.__doc__, prog=\\\"conan imports\\\")\\n parser.add_argument(\\\"path\\\",\\n help=_PATH_HELP + \\\" With --undo option, this parameter is the folder \\\"\\n \\\"containing the conan_imports_manifest.txt file generated in a previous \\\"\\n \\\"execution. e.g.: conan imports ./imported_files --undo \\\")\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=_INSTALL_FOLDER_HELP)\\n parser.add_argument(\\\"-imf\\\", \\\"--import-folder\\\", action=OnceArgument,\\n help=\\\"Directory to copy the artifacts to. By default it will be the\\\"\\n \\\" current directory\\\")\\n parser.add_argument(\\\"-u\\\", \\\"--undo\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Undo imports. Remove imported files\\\")\\n args = parser.parse_args(*args)\\n\\n if args.undo:\\n return self._conan.imports_undo(args.path)\\n\\n try:\\n if \\\"@\\\" in args.path and ConanFileReference.loads(args.path):\\n raise ArgumentError(None, \\\"Parameter 'path' cannot be a reference. Use a folder \\\"\\n \\\"containing a conanfile.py or conanfile.txt file.\\\")\\n except ConanException:\\n pass\\n self._warn_python2()\\n return self._conan.imports(args.path, args.import_folder, args.install_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.export_pkg\",\n \"text\": \"conans/client/command.py::Command.export_pkg\\n def export_pkg(self, *args):\\n \\\"\\\"\\\"Exports a recipe, then creates a package from local source and build folders.\\n\\n The package is created by calling the package() method applied to the\\n local folders '--source-folder' and '--build-folder' It's created in\\n the local cache for the specified 'reference' and for the specified\\n '--settings', '--options' and or '--profile'.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.export_pkg.__doc__,\\n prog=\\\"conan export-pkg\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"reference\\\", help=\\\"user/channel or pkg/version@user/channel \\\"\\n \\\"(if name and version are not declared in the \\\"\\n \\\"conanfile.py)\\\")\\n parser.add_argument(\\\"-bf\\\", \\\"--build-folder\\\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\\n parser.add_argument(\\\"-e\\\", \\\"--env\\\", nargs=1, action=Extender,\\n help='Environment variables that will be set during the package build, '\\n '-e CXX=/usr/bin/clang++')\\n parser.add_argument('-f', '--force', default=False, action='store_true',\\n help='Overwrite existing package if existing')\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=_INSTALL_FOLDER_HELP + \\\" If these files are found in the specified\\\"\\n \\\" folder and any of '-e', '-o', '-pr' or '-s' arguments are used, it \\\"\\n \\\"will raise an error.\\\")\\n parser.add_argument(\\\"-o\\\", \\\"--options\\\", nargs=1, action=Extender,\\n help='Define options values, e.g., -o pkg:with_qt=true')\\n parser.add_argument(\\\"-pr\\\", \\\"--profile\\\", action=OnceArgument,\\n help='Profile for this \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.export\",\n \"text\": \"conans/client/command.py::Command.export\\n def export(self, *args):\\n \\\"\\\"\\\"Copies the recipe (conanfile.py & associated files) to your local cache.\\n\\n Use the 'reference' param to specify a user and channel where to export\\n it. Once the recipe is in the local cache it can be shared, reused and\\n to any remote with the 'conan upload' command.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.export.__doc__, prog=\\\"conan export\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"reference\\\", help=\\\"user/channel, or Pkg/version@user/channel (if name \\\"\\n \\\"and version are not declared in the conanfile.py\\\")\\n parser.add_argument('-k', '-ks', '--keep-source', default=False, action='store_true',\\n help=_KEEP_SOURCE_HELP)\\n\\n args = parser.parse_args(*args)\\n self._warn_python2()\\n name, version, user, channel = get_reference_fields(args.reference)\\n return self._conan.export(path=args.path,\\n name=name, version=version, user=user, channel=channel,\\n keep_source=args.keep_source)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.remove\",\n \"text\": \"conans/client/command.py::Command.remove\\n def remove(self, *args):\\n \\\"\\\"\\\"Removes packages or binaries matching pattern from local cache or remote.\\n\\n It can also be used to remove temporary source or build folders in the\\n local conan cache. If no remote is specified, the removal will be done\\n by default in the local conan cache.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.remove.__doc__, prog=\\\"conan remove\\\")\\n parser.add_argument('pattern_or_reference', nargs=\\\"?\\\", help=_PATTERN_OR_REFERENCE_HELP)\\n parser.add_argument('-b', '--builds', nargs=\\\"*\\\", action=Extender,\\n help=(\\\"By default, remove all the build folders or select one, \\\"\\n \\\"specifying the package ID\\\"))\\n parser.add_argument('-f', '--force', default=False, action='store_true',\\n help='Remove without requesting a confirmation')\\n parser.add_argument(\\\"-o\\\", \\\"--outdated\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Remove only outdated from recipe packages. \\\" \\\\\\n \\\"This flag can only be used with a reference\\\")\\n parser.add_argument('-p', '--packages', nargs=\\\"*\\\", action=Extender,\\n help=\\\"Select package to remove specifying the package ID\\\")\\n parser.add_argument('-q', '--query', default=None, action=OnceArgument, help=_QUERY_HELP)\\n parser.add_argument('-r', '--remote', action=OnceArgument,\\n help='Will remove from the specified remote')\\n parser.add_argument('-s', '--src', default=False, action=\\\"store_true\\\",\\n help='Remove source folders')\\n parser.add_argument(\\\"-l\\\", \\\"--locks\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Remove locks\\\")\\n args = parser.parse_args(*args)\\n\\n self._warn_python2()\\n\\n # NOTE: returns the expanded pattern (if a pattern was g\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.copy\",\n \"text\": \"conans/client/command.py::Command.copy\\n def copy(self, *args):\\n \\\"\\\"\\\"Copies conan recipes and packages to another user/channel.\\n\\n Useful to promote packages (e.g. from \\\"beta\\\" to \\\"stable\\\") or transfer\\n them from one user to another.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.copy.__doc__, prog=\\\"conan copy\\\")\\n parser.add_argument(\\\"reference\\\", default=\\\"\\\",\\n help='package reference. e.g., MyPackage/1.2@user/channel')\\n parser.add_argument(\\\"user_channel\\\", default=\\\"\\\",\\n help='Destination user/channel. e.g., lasote/testing')\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", nargs=1, action=Extender,\\n help='copy specified package ID')\\n parser.add_argument(\\\"--all\\\", action='store_true', default=False,\\n help='Copy all packages from the specified package recipe')\\n parser.add_argument(\\\"--force\\\", action='store_true', default=False,\\n help='Override destination packages and the package recipe')\\n args = parser.parse_args(*args)\\n\\n if args.all and args.package:\\n raise ConanException(\\\"Cannot specify both --all and --package\\\")\\n\\n self._warn_python2()\\n\\n return self._conan.copy(reference=args.reference, user_channel=args.user_channel,\\n force=args.force, packages=args.package or args.all)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.user\",\n \"text\": \"conans/client/command.py::Command.user\\n def user(self, *args):\\n \\\"\\\"\\\"Authenticates against a remote with user/pass, caching the auth token.\\n\\n Useful to avoid the user and password being requested later. e.g. while\\n you're uploading a package. You can have one user for each remote.\\n Changing the user, or introducing the password is only necessary to\\n perform changes in remote packages.\\n \\\"\\\"\\\"\\n # FIXME: Difficult and confusing CLI. Better with:\\n # - conan user clean -> clean users\\n # - conan user list ('remote') -> list users (of a remote)\\n # - conan user auth 'remote' ('user') ('password') -> login a remote (w/o user or pass)\\n # - conan user set 'user' 'remote' -> set user for a remote (not login) necessary??\\n parser = argparse.ArgumentParser(description=self.user.__doc__, prog=\\\"conan user\\\")\\n parser.add_argument(\\\"name\\\", nargs='?', default=None,\\n help='Username you want to use. If no name is provided it will show the'\\n ' current user')\\n parser.add_argument('-c', '--clean', default=False, action='store_true',\\n help='Remove user and tokens for all remotes')\\n parser.add_argument(\\\"-p\\\", \\\"--password\\\", nargs='?', const=\\\"\\\", type=str, action=OnceArgument,\\n help='User password. Use double quotes if password with spacing, '\\n 'and escape quotes if existing. If empty, the password is '\\n 'requested interactively (not exposed)')\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", help='Use the specified remote server',\\n action=OnceArgument)\\n parser.add_argument(\\\"-j\\\", \\\"--json\\\", default=None, action=OnceArgument,\\n help='json file path where the user list will be written to')\\n args = parser.parse_args(*args)\\n\\n if args.clean and any((args.name, ar\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.search\",\n \"text\": \"conans/client/command.py::Command.search\\n def search(self, *args):\\n \\\"\\\"\\\"Searches package recipes and binaries in the local cache or in a remote.\\n\\n If you provide a pattern, then it will search for existing package\\n recipes matching it. If a full reference is provided\\n (pkg/0.1@user/channel) then the existing binary packages for that\\n reference will be displayed. If no remote is specified, the search\\n will be done in the local cache. Search is case sensitive, exact case\\n has to be used. For case insensitive file systems, like Windows, case\\n sensitive search can be forced with '--case-sensitive'.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.search.__doc__, prog=\\\"conan search\\\")\\n parser.add_argument('pattern_or_reference', nargs='?', help=_PATTERN_OR_REFERENCE_HELP)\\n parser.add_argument('-o', '--outdated', default=False, action='store_true',\\n help=\\\"Show only outdated from recipe packages. \\\" \\\\\\n \\\"This flag can only be used with a reference\\\")\\n parser.add_argument('-q', '--query', default=None, action=OnceArgument, help=_QUERY_HELP)\\n parser.add_argument('-r', '--remote', action=OnceArgument,\\n help=\\\"Remote to search in. '-r all' searches all remotes\\\")\\n parser.add_argument('--case-sensitive', default=False, action='store_true',\\n help='Make a case-sensitive search. Use it to guarantee case-sensitive '\\n 'search in Windows or other case-insensitive file systems')\\n parser.add_argument('--raw', default=False, action='store_true',\\n help='Print just the list of recipes')\\n parser.add_argument('--table', action=OnceArgument,\\n help=\\\"Outputs html file with a table of binaries. Only valid for a \\\"\\n \\\"reference search\\\")\\n parser.add_ar\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.upload\",\n \"text\": \"conans/client/command.py::Command.upload\\n def upload(self, *args):\\n \\\"\\\"\\\"Uploads a recipe and binary packages to a remote.\\n\\n If no remote is specified, the first configured remote (by default conan-center, use\\n 'conan remote list' to list the remotes) will be used.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.upload.__doc__,\\n prog=\\\"conan upload\\\")\\n parser.add_argument('pattern_or_reference', help=_PATTERN_OR_REFERENCE_HELP)\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", default=None, action=OnceArgument,\\n help='package ID to upload')\\n parser.add_argument('-q', '--query', default=None, action=OnceArgument,\\n help=\\\"Only upload packages matching a specific query. \\\" + _QUERY_HELP)\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", action=OnceArgument,\\n help='upload to this specific remote')\\n parser.add_argument(\\\"--all\\\", action='store_true', default=False,\\n help='Upload both package recipe and packages')\\n parser.add_argument(\\\"--skip-upload\\\", action='store_true', default=False,\\n help='Do not upload anything, just run the checks and the compression')\\n parser.add_argument(\\\"--force\\\", action='store_true', default=False,\\n help='Do not check conan recipe date, override remote with local')\\n parser.add_argument(\\\"--check\\\", action='store_true', default=False,\\n help='Perform an integrity check, using the manifests, before upload')\\n parser.add_argument('-c', '--confirm', default=False, action='store_true',\\n help='Upload all matching recipes without confirmation')\\n parser.add_argument('--retry', default=2, type=int, action=OnceArgument,\\n help=\\\"In case of fail retries to upload again the specified times. \\\"\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.remote\",\n \"text\": \"conans/client/command.py::Command.remote\\n def remote(self, *args):\\n \\\"\\\"\\\"Manages the remote list and the package recipes associated to a remote.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.remote.__doc__, prog=\\\"conan remote\\\")\\n subparsers = parser.add_subparsers(dest='subcommand', help='sub-command help')\\n\\n # create the parser for the \\\"a\\\" command\\n parser_list = subparsers.add_parser('list', help='List current remotes')\\n parser_list.add_argument(\\\"-raw\\\", \\\"--raw\\\", action='store_true', default=False,\\n help='Raw format. Valid for \\\"remotes.txt\\\" file for \\\"conan config install\\\"')\\n parser_add = subparsers.add_parser('add', help='Add a remote')\\n parser_add.add_argument('remote', help='Name of the remote')\\n parser_add.add_argument('url', help='URL of the remote')\\n parser_add.add_argument('verify_ssl', nargs=\\\"?\\\", default=\\\"True\\\",\\n help='Verify SSL certificated. Default True')\\n parser_add.add_argument(\\\"-i\\\", \\\"--insert\\\", nargs=\\\"?\\\", const=0, type=int, action=OnceArgument,\\n help=\\\"insert remote at specific index\\\")\\n parser_add.add_argument(\\\"-f\\\", \\\"--force\\\", default=False, action='store_true',\\n help=\\\"Force addition, will update if existing\\\")\\n parser_rm = subparsers.add_parser('remove', help='Remove a remote')\\n parser_rm.add_argument('remote', help='Name of the remote')\\n parser_upd = subparsers.add_parser('update', help='Update the remote url')\\n parser_upd.add_argument('remote', help='Name of the remote')\\n\\n parser_upd.add_argument('url', help='URL')\\n parser_upd.add_argument('verify_ssl', nargs=\\\"?\\\", default=\\\"True\\\",\\n help='Verify SSL certificated. Default True')\\n parser_upd.add_argument(\\\"-i\\\", \\\"--insert\\\", nargs=\\\"?\\\", const=0, type=int, action=OnceArgument,\\n he\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.profile\",\n \"text\": \"conans/client/command.py::Command.profile\\n def profile(self, *args):\\n \\\"\\\"\\\" Lists profiles in the '.conan/profiles' folder, or shows profile details.\\n The 'list' subcommand will always use the default user 'conan/profiles' folder. But the\\n 'show' subcommand is able to resolve absolute and relative paths, as well as to map names to\\n '.conan/profiles' folder, in the same way as the '--profile' install argument.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.profile.__doc__, prog=\\\"conan profile\\\")\\n subparsers = parser.add_subparsers(dest='subcommand')\\n\\n # create the parser for the \\\"profile\\\" command\\n subparsers.add_parser('list', help='List current profiles')\\n parser_show = subparsers.add_parser('show', help='Show the values defined for a profile')\\n parser_show.add_argument('profile', help=\\\"name of the profile in the '.conan/profiles' \\\"\\n \\\"folder or path to a profile file\\\")\\n\\n parser_new = subparsers.add_parser('new', help='Creates a new empty profile')\\n parser_new.add_argument('profile', help=\\\"Name for the profile in the '.conan/profiles' \\\"\\n \\\"folder or path and name for a profile file\\\")\\n parser_new.add_argument(\\\"--detect\\\", action='store_true', default=False,\\n help='Autodetect settings and fill [settings] section')\\n\\n parser_update = subparsers.add_parser('update', help='Update a profile with desired value')\\n parser_update.add_argument('item',\\n help=\\\"'item=value' to update. e.g., settings.compiler=gcc\\\")\\n parser_update.add_argument('profile', help=\\\"Name of the profile in the '.conan/profiles' \\\"\\n \\\"folder or path to a profile file\\\")\\n\\n parser_get = subparsers.add_parser('get', help='Get a profile key')\\n parser_get.add_argument('ite\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.get\",\n \"text\": \"conans/client/command.py::Command.get\\n def get(self, *args):\\n \\\"\\\"\\\" Gets a file or list a directory of a given reference or package.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.get.__doc__,\\n prog=\\\"conan get\\\")\\n parser.add_argument('reference', help='package recipe reference')\\n parser.add_argument('path',\\n help='Path to the file or directory. If not specified will get the '\\n 'conanfile if only a reference is specified and a conaninfo.txt '\\n 'file contents if the package is also specified',\\n default=None, nargs=\\\"?\\\")\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", default=None, help='Package ID',\\n action=OnceArgument)\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", action=OnceArgument,\\n help='Get from this specific remote')\\n parser.add_argument(\\\"-raw\\\", \\\"--raw\\\", action='store_true', default=False,\\n help='Do not decorate the text')\\n args = parser.parse_args(*args)\\n\\n ret, path = self._conan.get_path(args.reference, args.package, args.path, args.remote)\\n if isinstance(ret, list):\\n self._outputer.print_dir_list(ret, path, args.raw)\\n else:\\n self._outputer.print_file_contents(ret, path, args.raw)\\n\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.alias\",\n \"text\": \"conans/client/command.py::Command.alias\\n def alias(self, *args):\\n \\\"\\\"\\\"Creates and exports an 'alias package recipe'.\\n\\n An \\\"alias\\\" package is a symbolic name (reference) for another package\\n (target). When some package depends on an alias, the target one will be\\n retrieved and used instead, so the alias reference, the symbolic name,\\n does not appear in the final dependency graph.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.alias.__doc__,\\n prog=\\\"conan alias\\\")\\n parser.add_argument('reference', help='Alias reference. e.g.: mylib/1.X@user/channel')\\n parser.add_argument('target', help='Target reference. e.g.: mylib/1.12@user/channel')\\n args = parser.parse_args(*args)\\n\\n self._warn_python2()\\n\\n self._conan.export_alias(args.reference, args.target)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.link\",\n \"text\": \"conans/client/command.py::Command.link\\n def link(self, *args):\\n \\\"\\\"\\\" Links a conan reference (e.g lib/1.0@conan/stable) with a local folder path.\\n\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.link.__doc__,\\n prog=\\\"conan link\\\")\\n parser.add_argument('target', help='Path to the package folder in the user workspace',\\n nargs='?',)\\n parser.add_argument('reference', help='Reference to link. e.g.: mylib/1.X@user/channel')\\n parser.add_argument(\\\"--remove\\\", action='store_true', default=False,\\n help='Remove linked reference (target not required)')\\n\\n args = parser.parse_args(*args)\\n self._warn_python2()\\n\\n # Args sanity check\\n if args.remove and args.target:\\n raise ConanException(\\\"Do not provide the 'target' argument for removal\\\")\\n\\n if not args.remove and not args.target:\\n raise ConanException(\\\"Argument 'target' is required to link a reference\\\")\\n\\n if not args.remove:\\n self._conan.link(args.target, args.reference, cwd=os.getcwd())\\n self._outputer.writeln(\\\"Reference '{}' linked to directory \\\"\\n \\\"'{}'\\\".format(args.reference, os.path.dirname(args.target)))\\n else:\\n ret = self._conan.unlink(args.reference)\\n if ret:\\n self._outputer.writeln(\\\"Removed linkage for reference '{}'\\\".format(args.reference))\\n else:\\n self._user_io.out.warn(\\\"Reference '{}' was not installed \\\"\\n \\\"as editable\\\".format(args.reference))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._show_help\",\n \"text\": \"conans/client/command.py::Command._show_help\\n def _show_help(self):\\n \\\"\\\"\\\"Prints a summary of all commands\\n \\\"\\\"\\\"\\n grps = [(\\\"Consumer commands\\\", (\\\"install\\\", \\\"config\\\", \\\"get\\\", \\\"info\\\", \\\"search\\\")),\\n (\\\"Creator commands\\\", (\\\"new\\\", \\\"create\\\", \\\"upload\\\", \\\"export\\\", \\\"export-pkg\\\", \\\"test\\\")),\\n (\\\"Package development commands\\\", (\\\"source\\\", \\\"build\\\", \\\"package\\\", \\\"link\\\")),\\n (\\\"Misc commands\\\", (\\\"profile\\\", \\\"remote\\\", \\\"user\\\", \\\"imports\\\", \\\"copy\\\", \\\"remove\\\",\\n \\\"alias\\\", \\\"download\\\", \\\"inspect\\\", \\\"help\\\"))]\\n\\n def check_all_commands_listed():\\n \\\"\\\"\\\"Keep updated the main directory, raise if don't\\\"\\\"\\\"\\n all_commands = self._commands()\\n all_in_grps = [command for _, command_list in grps for command in command_list]\\n if set(all_in_grps) != set(all_commands):\\n diff = set(all_commands) - set(all_in_grps)\\n raise Exception(\\\"Some command is missing in the main help: %s\\\" % \\\",\\\".join(diff))\\n return all_commands\\n\\n commands = check_all_commands_listed()\\n max_len = max((len(c) for c in commands)) + 1\\n fmt = ' %-{}s'.format(max_len)\\n\\n for group_name, comm_names in grps:\\n self._user_io.out.writeln(group_name, Color.BRIGHT_MAGENTA)\\n for name in comm_names:\\n # future-proof way to ensure tabular formatting\\n self._user_io.out.write(fmt % name, Color.GREEN)\\n self._user_io.out.writeln(commands[name].__doc__.split('\\\\n', 1)[0].strip())\\n\\n self._user_io.out.writeln(\\\"\\\")\\n self._user_io.out.writeln('Conan commands. Type \\\"conan <command> -h\\\" for help',\\n Color.BRIGHT_YELLOW)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._show_help.check_all_commands_listed\",\n \"text\": \"conans/client/command.py::Command._show_help.check_all_commands_listed\\n def check_all_commands_listed():\\n \\\"\\\"\\\"Keep updated the main directory, raise if don't\\\"\\\"\\\"\\n all_commands = self._commands()\\n all_in_grps = [command for _, command_list in grps for command in command_list]\\n if set(all_in_grps) != set(all_commands):\\n diff = set(all_commands) - set(all_in_grps)\\n raise Exception(\\\"Some command is missing in the main help: %s\\\" % \\\",\\\".join(diff))\\n return all_commands\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._commands\",\n \"text\": \"conans/client/command.py::Command._commands\\n def _commands(self):\\n \\\"\\\"\\\" returns a list of available commands\\n \\\"\\\"\\\"\\n result = {}\\n for m in inspect.getmembers(self, predicate=inspect.ismethod):\\n method_name = m[0]\\n if not method_name.startswith('_'):\\n if \\\"export_pkg\\\" == method_name:\\n method_name = \\\"export-pkg\\\"\\n method = m[1]\\n if method.__doc__ and not method.__doc__.startswith('HIDDEN'):\\n result[method_name] = method\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._warn_python2\",\n \"text\": \"conans/client/command.py::Command._warn_python2\\n def _warn_python2(self):\\n if six.PY2:\\n self._user_io.out.writeln(\\\"\\\")\\n self._user_io.out.writeln(\\\"Python 2 will soon be deprecated. It is strongly \\\"\\n \\\"recommended to use Python 3 with Conan:\\\",\\n front=Color.BRIGHT_YELLOW)\\n self._user_io.out.writeln(\\\"https://docs.conan.io/en/latest/installation.html\\\"\\n \\\"#python-2-deprecation-notice\\\", front=Color.BRIGHT_YELLOW)\\n self._user_io.out.writeln(\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._check_query_parameter_and_get_reference\",\n \"text\": \"conans/client/command.py::Command._check_query_parameter_and_get_reference\\n @staticmethod\\n def _check_query_parameter_and_get_reference(pattern, query):\\n ref = None\\n if pattern:\\n try:\\n ref = ConanFileReference.loads(pattern)\\n except ConanException:\\n if query is not None:\\n raise ConanException(\\\"-q parameter only allowed with a valid recipe \\\"\\n \\\"reference as search pattern. e.g. conan search \\\"\\n \\\"MyPackage/1.2@user/channel -q \\\\\\\"os=Windows\\\\\\\"\\\")\\n return ref\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.run\",\n \"text\": \"conans/client/command.py::Command.run\\n def run(self, *args):\\n \\\"\\\"\\\"HIDDEN: entry point for executing commands, dispatcher to class\\n methods\\n \\\"\\\"\\\"\\n ret_code = SUCCESS\\n try:\\n try:\\n command = args[0][0]\\n commands = self._commands()\\n method = commands[command]\\n except KeyError as exc:\\n if command in [\\\"-v\\\", \\\"--version\\\"]:\\n self._user_io.out.success(\\\"Conan version %s\\\" % client_version)\\n return False\\n self._warn_python2()\\n self._show_help()\\n if command in [\\\"-h\\\", \\\"--help\\\"]:\\n return False\\n raise ConanException(\\\"Unknown command %s\\\" % str(exc))\\n except IndexError: # No parameters\\n self._show_help()\\n return False\\n method(args[0][1:])\\n except KeyboardInterrupt as exc:\\n logger.error(exc)\\n ret_code = SUCCESS\\n except SystemExit as exc:\\n if exc.code != 0:\\n logger.error(exc)\\n self._user_io.out.error(\\\"Exiting with code: %d\\\" % exc.code)\\n ret_code = exc.code\\n except ConanInvalidConfiguration as exc:\\n ret_code = ERROR_INVALID_CONFIGURATION\\n msg = exception_message_safe(exc)\\n self._user_io.out.error(msg)\\n except ConanException as exc:\\n ret_code = ERROR_GENERAL\\n msg = exception_message_safe(exc)\\n self._user_io.out.error(msg)\\n except Exception as exc:\\n import traceback\\n print(traceback.format_exc())\\n ret_code = ERROR_GENERAL\\n msg = exception_message_safe(exc)\\n self._user_io.out.error(msg)\\n\\n return ret_code\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::get_reference_fields\",\n \"text\": \"conans/client/command.py::get_reference_fields\\ndef get_reference_fields(arg_reference):\\n \\\"\\\"\\\"\\n :param arg_reference: String with a complete reference, or only user/channel\\n :return: name, version, user and channel, in a tuple\\n \\\"\\\"\\\"\\n\\n if not arg_reference:\\n return None, None, None, None\\n\\n try:\\n name_version, user_channel = arg_reference.split(\\\"@\\\")\\n name_version = name_version.split(\\\"/\\\")\\n try:\\n name, version = name_version\\n except ValueError:\\n name, version = None, name_version[0]\\n user, channel = user_channel.split(\\\"/\\\")\\n except ValueError:\\n name, version = None, None\\n try:\\n user, channel = arg_reference.split(\\\"/\\\")\\n except ValueError:\\n raise ConanException(\\\"Invalid parameter '%s', specify the full reference or \\\"\\n \\\"user/channel\\\" % arg_reference)\\n\\n return name, version, user, channel\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::_add_manifests_arguments\",\n \"text\": \"conans/client/command.py::_add_manifests_arguments\\ndef _add_manifests_arguments(parser):\\n parser.add_argument(\\\"-m\\\", \\\"--manifests\\\", const=default_manifest_folder, nargs=\\\"?\\\",\\n help='Install dependencies manifests in folder for later verify.'\\n ' Default folder is .conan_manifests, but can be changed',\\n action=OnceArgument)\\n parser.add_argument(\\\"-mi\\\", \\\"--manifests-interactive\\\", const=default_manifest_folder,\\n nargs=\\\"?\\\",\\n help='Install dependencies manifests in folder for later verify, '\\n 'asking user for confirmation. '\\n 'Default folder is .conan_manifests, but can be changed',\\n action=OnceArgument)\\n parser.add_argument(\\\"-v\\\", \\\"--verify\\\", const=default_manifest_folder, nargs=\\\"?\\\",\\n help='Verify dependencies manifests against stored ones',\\n action=OnceArgument)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::_add_common_install_arguments\",\n \"text\": \"conans/client/command.py::_add_common_install_arguments\\ndef _add_common_install_arguments(parser, build_help):\\n if build_help:\\n parser.add_argument(\\\"-b\\\", \\\"--build\\\", action=Extender, nargs=\\\"?\\\", help=build_help)\\n\\n parser.add_argument(\\\"-e\\\", \\\"--env\\\", nargs=1, action=Extender,\\n help='Environment variables that will be set during the package build, '\\n '-e CXX=/usr/bin/clang++')\\n parser.add_argument(\\\"-o\\\", \\\"--options\\\", nargs=1, action=Extender,\\n help='Define options values, e.g., -o Pkg:with_qt=true')\\n parser.add_argument(\\\"-pr\\\", \\\"--profile\\\", default=None, action=OnceArgument,\\n help='Apply the specified profile to the install command')\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", action=OnceArgument,\\n help='Look in the specified remote server')\\n parser.add_argument(\\\"-s\\\", \\\"--settings\\\", nargs=1, action=Extender,\\n help='Settings to build the package, overwriting the defaults. e.g., '\\n '-s compiler=gcc')\\n parser.add_argument(\\\"-u\\\", \\\"--update\\\", action='store_true', default=False,\\n help=\\\"Check updates exist from upstream remotes\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::main\",\n \"text\": \"conans/client/command.py::main\\ndef main(args):\\n \\\"\\\"\\\" main entry point of the conan application, using a Command to\\n parse parameters\\n\\n Exit codes for conan command:\\n\\n 0: Success (done)\\n 1: General ConanException error (done)\\n 2: Migration error\\n 3: Ctrl+C\\n 4: Ctrl+Break\\n 5: SIGTERM\\n 6: Invalid configuration (done)\\n \\\"\\\"\\\"\\n try:\\n conan_api, cache, user_io = Conan.factory()\\n except ConanException: # Error migrating\\n sys.exit(ERROR_MIGRATION)\\n\\n outputer = CommandOutputer(user_io, cache)\\n command = Command(conan_api, cache, user_io, outputer)\\n current_dir = get_cwd()\\n try:\\n import signal\\n\\n def ctrl_c_handler(_, __):\\n print('You pressed Ctrl+C!')\\n sys.exit(USER_CTRL_C)\\n\\n def sigterm_handler(_, __):\\n print('Received SIGTERM!')\\n sys.exit(ERROR_SIGTERM)\\n\\n def ctrl_break_handler(_, __):\\n print('You pressed Ctrl+Break!')\\n sys.exit(USER_CTRL_BREAK)\\n\\n signal.signal(signal.SIGINT, ctrl_c_handler)\\n signal.signal(signal.SIGTERM, sigterm_handler)\\n\\n if sys.platform == 'win32':\\n signal.signal(signal.SIGBREAK, ctrl_break_handler)\\n error = command.run(args)\\n finally:\\n os.chdir(current_dir)\\n sys.exit(error)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::Action.__new__\",\n \"text\": \"conans/client/recorder/action_recorder.py::Action.__new__\\n def __new__(cls, the_type, doc=None):\\n doc = doc or {}\\n the_time = datetime.utcnow()\\n return super(cls, Action).__new__(cls, the_type, doc, the_time)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.__init__\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.__init__\\n def __init__(self):\\n self.error = False\\n self._inst_recipes_actions = OrderedDict()\\n self._inst_packages_actions = OrderedDict()\\n self._inst_recipes_develop = set() # Recipes being created (to set dependency=False)\\n self._inst_packages_info = defaultdict(dict)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.add_recipe_being_developed\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.add_recipe_being_developed\\n def add_recipe_being_developed(self, ref):\\n assert(isinstance(ref, ConanFileReference))\\n self._inst_recipes_develop.add(ref)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder._add_recipe_action\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder._add_recipe_action\\n def _add_recipe_action(self, ref, action):\\n assert(isinstance(ref, ConanFileReference))\\n ref = ref.copy_clear_rev()\\n if ref not in self._inst_recipes_actions:\\n self._inst_recipes_actions[ref] = []\\n self._inst_recipes_actions[ref].append(action)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder._add_package_action\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder._add_package_action\\n def _add_package_action(self, pref, action):\\n assert(isinstance(pref, PackageReference))\\n pref = pref.copy_clear_rev()\\n if pref not in self._inst_packages_actions:\\n self._inst_packages_actions[pref] = []\\n self._inst_packages_actions[pref].append(action)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_exported\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_exported\\n def recipe_exported(self, ref):\\n self._add_recipe_action(ref, Action(INSTALL_EXPORTED))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_fetched_from_cache\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_fetched_from_cache\\n def recipe_fetched_from_cache(self, ref):\\n self._add_recipe_action(ref, Action(INSTALL_CACHE))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_downloaded\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_downloaded\\n def recipe_downloaded(self, ref, remote_name):\\n self._add_recipe_action(ref, Action(INSTALL_DOWNLOADED, {\\\"remote\\\": remote_name}))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_install_error\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.recipe_install_error\\n def recipe_install_error(self, ref, error_type, description, remote_name):\\n doc = {\\\"type\\\": error_type, \\\"description\\\": description, \\\"remote\\\": remote_name}\\n self._add_recipe_action(ref, Action(INSTALL_ERROR, doc))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_exported\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_exported\\n def package_exported(self, pref):\\n self._add_package_action(pref, Action(INSTALL_EXPORTED))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_built\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_built\\n def package_built(self, pref):\\n self._add_package_action(pref, Action(INSTALL_BUILT))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_fetched_from_cache\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_fetched_from_cache\\n def package_fetched_from_cache(self, pref):\\n self._add_package_action(pref, Action(INSTALL_CACHE))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_downloaded\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_downloaded\\n def package_downloaded(self, pref, remote_name):\\n self._add_package_action(pref, Action(INSTALL_DOWNLOADED, {\\\"remote\\\": remote_name}))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_install_error\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_install_error\\n def package_install_error(self, pref, error_type, description, remote_name=None):\\n assert(isinstance(pref, PackageReference))\\n pref = pref.copy_clear_rev()\\n if pref not in self._inst_packages_actions:\\n self._inst_packages_actions[pref] = []\\n doc = {\\\"type\\\": error_type, \\\"description\\\": description, \\\"remote\\\": remote_name}\\n self._inst_packages_actions[pref].append(Action(INSTALL_ERROR, doc))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_cpp_info\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.package_cpp_info\\n def package_cpp_info(self, pref, cpp_info):\\n assert isinstance(pref, PackageReference)\\n pref = pref.copy_clear_rev()\\n # assert isinstance(cpp_info, CppInfo)\\n doc = {}\\n for it, value in vars(cpp_info).items():\\n if it.startswith(\\\"_\\\") or not value:\\n continue\\n doc[it] = value\\n self._inst_packages_info[pref]['cpp_info'] = doc\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.install_errored\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.install_errored\\n @property\\n def install_errored(self):\\n all_values = list(self._inst_recipes_actions.values()) + list(self._inst_packages_actions.values())\\n for acts in all_values:\\n for act in acts:\\n if act.type == INSTALL_ERROR:\\n return True\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder._get_installed_packages\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder._get_installed_packages\\n def _get_installed_packages(self, ref):\\n assert(isinstance(ref, ConanFileReference))\\n ret = []\\n for _pref, _package_actions in self._inst_packages_actions.items():\\n # Could be a download and then an access to cache, we want the first one\\n _package_action = _package_actions[0]\\n if _pref.ref == ref:\\n ret.append((_pref, _package_action))\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.in_development_recipe\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.in_development_recipe\\n def in_development_recipe(self, ref):\\n return ref in self._inst_recipes_develop\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.get_info\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.get_info\\n def get_info(self):\\n return self.get_install_info()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.get_install_info\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.get_install_info\\n def get_install_info(self):\\n ret = {\\\"error\\\": self.install_errored or self.error,\\n \\\"installed\\\": []}\\n\\n def get_doc_for_ref(the_ref, the_actions):\\n errors = [action.doc for action in the_actions if action.type == INSTALL_ERROR]\\n error = None if not errors else errors[0]\\n remotes = [action.doc.get(\\\"remote\\\") for action in the_actions\\n if action.doc.get(\\\"remote\\\", None) is not None]\\n remote = None if not remotes else remotes[0]\\n action_types = [action.type for action in the_actions]\\n time = the_actions[0].time\\n\\n doc = {\\\"id\\\": str(the_ref),\\n \\\"downloaded\\\": INSTALL_DOWNLOADED in action_types,\\n \\\"exported\\\": INSTALL_EXPORTED in action_types,\\n \\\"error\\\": error,\\n \\\"remote\\\": remote,\\n \\\"time\\\": time}\\n if isinstance(the_ref, ConanFileReference):\\n doc[\\\"dependency\\\"] = not self.in_development_recipe(the_ref.copy_clear_rev())\\n doc[\\\"name\\\"] = the_ref.name\\n doc[\\\"version\\\"] = the_ref.version\\n doc[\\\"user\\\"] = the_ref.user\\n doc[\\\"channel\\\"] = the_ref.channel\\n if the_ref.revision:\\n doc[\\\"revision\\\"] = the_ref.revision\\n else:\\n doc[\\\"built\\\"] = INSTALL_BUILT in action_types\\n\\n if doc[\\\"remote\\\"] is None and error:\\n doc[\\\"remote\\\"] = error.get(\\\"remote\\\", None)\\n return doc\\n\\n for ref, actions in self._inst_recipes_actions.items():\\n tmp = {\\\"recipe\\\": get_doc_for_ref(ref, actions),\\n \\\"packages\\\": []}\\n\\n packages = self._get_installed_packages(ref)\\n for pref, p_action in packages:\\n p_doc = get_doc_for_ref(pref.id, [p_action])\\n package_data = self._inst_packages_info.get(\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/recorder/action_recorder.py::ActionRecorder.get_install_info.get_doc_for_ref\",\n \"text\": \"conans/client/recorder/action_recorder.py::ActionRecorder.get_install_info.get_doc_for_ref\\n def get_doc_for_ref(the_ref, the_actions):\\n errors = [action.doc for action in the_actions if action.type == INSTALL_ERROR]\\n error = None if not errors else errors[0]\\n remotes = [action.doc.get(\\\"remote\\\") for action in the_actions\\n if action.doc.get(\\\"remote\\\", None) is not None]\\n remote = None if not remotes else remotes[0]\\n action_types = [action.type for action in the_actions]\\n time = the_actions[0].time\\n\\n doc = {\\\"id\\\": str(the_ref),\\n \\\"downloaded\\\": INSTALL_DOWNLOADED in action_types,\\n \\\"exported\\\": INSTALL_EXPORTED in action_types,\\n \\\"error\\\": error,\\n \\\"remote\\\": remote,\\n \\\"time\\\": time}\\n if isinstance(the_ref, ConanFileReference):\\n doc[\\\"dependency\\\"] = not self.in_development_recipe(the_ref.copy_clear_rev())\\n doc[\\\"name\\\"] = the_ref.name\\n doc[\\\"version\\\"] = the_ref.version\\n doc[\\\"user\\\"] = the_ref.user\\n doc[\\\"channel\\\"] = the_ref.channel\\n if the_ref.revision:\\n doc[\\\"revision\\\"] = the_ref.revision\\n else:\\n doc[\\\"built\\\"] = INSTALL_BUILT in action_types\\n\\n if doc[\\\"remote\\\"] is None and error:\\n doc[\\\"remote\\\"] = error.get(\\\"remote\\\", None)\\n return doc\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::JWTAuth.__init__\",\n \"text\": \"conans/client/rest/rest_client_common.py::JWTAuth.__init__\\n def __init__(self, token):\\n self.token = token\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::JWTAuth.__call__\",\n \"text\": \"conans/client/rest/rest_client_common.py::JWTAuth.__call__\\n def __call__(self, request):\\n if self.token:\\n request.headers['Authorization'] = \\\"Bearer %s\\\" % str(self.token)\\n return request\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::_base_error\",\n \"text\": \"conans/client/rest/rest_client_common.py::_base_error\\ndef _base_error(error_code):\\n return int(str(error_code)[0] + \\\"00\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::get_exception_from_error\",\n \"text\": \"conans/client/rest/rest_client_common.py::get_exception_from_error\\ndef get_exception_from_error(error_code):\\n try:\\n tmp = {value: key for key, value in EXCEPTION_CODE_MAPPING.items()}\\n if error_code in tmp:\\n logger.debug(\\\"REST ERROR: %s\\\" % str(tmp[error_code]))\\n return tmp[error_code]\\n else:\\n logger.debug(\\\"REST ERROR: %s\\\" % str(_base_error(error_code)))\\n return tmp[_base_error(error_code)]\\n except KeyError:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::handle_return_deserializer\",\n \"text\": \"conans/client/rest/rest_client_common.py::handle_return_deserializer\\ndef handle_return_deserializer(deserializer=None):\\n \\\"\\\"\\\"Decorator for rest api methods.\\n Map exceptions and http return codes and deserialize if needed.\\n\\n deserializer: Function for deserialize values\\\"\\\"\\\"\\n def handle_return(method):\\n def inner(*argc, **argv):\\n ret = method(*argc, **argv)\\n if ret.status_code != 200:\\n ret.charset = \\\"utf-8\\\" # To be able to access ret.text (ret.content are bytes)\\n text = ret.text if ret.status_code != 404 else \\\"404 Not found\\\"\\n raise get_exception_from_error(ret.status_code)(text)\\n return deserializer(ret.content) if deserializer else decode_text(ret.content)\\n return inner\\n return handle_return\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::handle_return_deserializer.handle_return\",\n \"text\": \"conans/client/rest/rest_client_common.py::handle_return_deserializer.handle_return\\n def handle_return(method):\\n def inner(*argc, **argv):\\n ret = method(*argc, **argv)\\n if ret.status_code != 200:\\n ret.charset = \\\"utf-8\\\" # To be able to access ret.text (ret.content are bytes)\\n text = ret.text if ret.status_code != 404 else \\\"404 Not found\\\"\\n raise get_exception_from_error(ret.status_code)(text)\\n return deserializer(ret.content) if deserializer else decode_text(ret.content)\\n return inner\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::handle_return_deserializer.handle_return.inner\",\n \"text\": \"conans/client/rest/rest_client_common.py::handle_return_deserializer.handle_return.inner\\n def inner(*argc, **argv):\\n ret = method(*argc, **argv)\\n if ret.status_code != 200:\\n ret.charset = \\\"utf-8\\\" # To be able to access ret.text (ret.content are bytes)\\n text = ret.text if ret.status_code != 404 else \\\"404 Not found\\\"\\n raise get_exception_from_error(ret.status_code)(text)\\n return deserializer(ret.content) if deserializer else decode_text(ret.content)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.__init__\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.__init__\\n def __init__(self, remote_url, token, custom_headers, output, requester, verify_ssl,\\n put_headers=None):\\n\\n self.token = token\\n self.remote_url = remote_url\\n self.custom_headers = custom_headers\\n self._output = output\\n self.requester = requester\\n self.verify_ssl = verify_ssl\\n self._put_headers = put_headers\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.auth\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.auth\\n @property\\n def auth(self):\\n return JWTAuth(self.token)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.users_router\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.users_router\\n @property\\n def users_router(self):\\n return ClientUsersRouterBuilder(self.remote_api_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.search_router\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.search_router\\n @property\\n def search_router(self):\\n return ClientSearchRouterBuilder(self.remote_api_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.base_router\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.base_router\\n @property\\n def base_router(self):\\n return ClientBaseRouterBuilder(self.remote_api_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.authenticate\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.authenticate\\n @handle_return_deserializer()\\n def authenticate(self, user, password):\\n \\\"\\\"\\\"Sends user + password to get a token\\\"\\\"\\\"\\n auth = HTTPBasicAuth(user, password)\\n url = self.users_router.common_authenticate()\\n logger.debug(\\\"REST: Authenticate: %s\\\" % url)\\n ret = self.requester.get(url, auth=auth, headers=self.custom_headers,\\n verify=self.verify_ssl)\\n if ret.status_code == 401:\\n raise AuthenticationException(\\\"Wrong user or password\\\")\\n # Cannot check content-type=text/html, conan server is doing it wrong\\n if not ret.ok or \\\"html>\\\" in str(ret.content):\\n raise ConanException(\\\"%s\\\\n\\\\nInvalid server response, check remote URL and \\\"\\n \\\"try again\\\" % str(ret.content))\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.check_credentials\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.check_credentials\\n @handle_return_deserializer()\\n def check_credentials(self):\\n \\\"\\\"\\\"If token is not valid will raise AuthenticationException.\\n User will be asked for new user/pass\\\"\\\"\\\"\\n url = self.users_router.common_check_credentials()\\n logger.debug(\\\"REST: Check credentials: %s\\\" % url)\\n ret = self.requester.get(url, auth=self.auth, headers=self.custom_headers,\\n verify=self.verify_ssl)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.server_info\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.server_info\\n def server_info(self):\\n \\\"\\\"\\\"Get information about the server: status, version, type and capabilities\\\"\\\"\\\"\\n url = self.base_router.ping()\\n logger.debug(\\\"REST: ping: %s\\\" % url)\\n\\n ret = self.requester.get(url, auth=self.auth, headers=self.custom_headers,\\n verify=self.verify_ssl)\\n if ret.status_code == 404:\\n raise NotFoundException(\\\"Not implemented endpoint\\\")\\n\\n version_check = ret.headers.get('X-Conan-Client-Version-Check', None)\\n server_version = ret.headers.get('X-Conan-Server-Version', None)\\n server_capabilities = ret.headers.get('X-Conan-Server-Capabilities', \\\"\\\")\\n server_capabilities = [cap.strip() for cap in server_capabilities.split(\\\",\\\") if cap]\\n\\n return version_check, server_version, server_capabilities\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.get_json\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.get_json\\n def get_json(self, url, data=None):\\n headers = self.custom_headers\\n if data: # POST request\\n headers.update({'Content-type': 'application/json',\\n 'Accept': 'text/plain',\\n 'Accept': 'application/json'})\\n logger.debug(\\\"REST: post: %s\\\" % url)\\n response = self.requester.post(url, auth=self.auth, headers=headers,\\n verify=self.verify_ssl,\\n stream=True,\\n data=json.dumps(data))\\n else:\\n logger.debug(\\\"REST: get: %s\\\" % url)\\n response = self.requester.get(url, auth=self.auth, headers=headers,\\n verify=self.verify_ssl,\\n stream=True)\\n\\n if response.status_code != 200: # Error message is text\\n response.charset = \\\"utf-8\\\" # To be able to access ret.text (ret.content are bytes)\\n raise get_exception_from_error(response.status_code)(response.text)\\n\\n result = json.loads(decode_text(response.content))\\n if not isinstance(result, dict):\\n raise ConanException(\\\"Unexpected server response %s\\\" % result)\\n return result\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.upload_recipe\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.upload_recipe\\n def upload_recipe(self, ref, the_files, retry, retry_wait, policy, remote_manifest):\\n \\\"\\\"\\\"\\n the_files: dict with relative_path: content\\n \\\"\\\"\\\"\\n self.check_credentials()\\n\\n revisions_enabled = get_env(\\\"CONAN_CLIENT_REVISIONS_ENABLED\\\", False)\\n if not revisions_enabled and policy in (UPLOAD_POLICY_NO_OVERWRITE,\\n UPLOAD_POLICY_NO_OVERWRITE_RECIPE):\\n # Check if the latest revision is not the one we are uploading, with the compatibility\\n # mode this is supposed to fail if someone tries to upload a different recipe\\n latest_ref = ref.copy_clear_rev()\\n latest_snapshot, ref_latest_snapshot, _ = self._get_recipe_snapshot(latest_ref)\\n server_with_revisions = ref_latest_snapshot.revision != DEFAULT_REVISION_V1\\n if latest_snapshot and server_with_revisions and \\\\\\n ref_latest_snapshot.revision != ref.revision:\\n raise ConanException(\\\"Local recipe is different from the remote recipe. \\\"\\n \\\"Forbidden overwrite\\\")\\n\\n # Get the remote snapshot\\n remote_snapshot, ref_snapshot, rev_time = self._get_recipe_snapshot(ref)\\n\\n if remote_snapshot and policy != UPLOAD_POLICY_FORCE:\\n remote_manifest = remote_manifest or self.get_conan_manifest(ref_snapshot)\\n local_manifest = FileTreeManifest.loads(load(the_files[\\\"conanmanifest.txt\\\"]))\\n\\n if remote_manifest == local_manifest:\\n return False, rev_time\\n\\n if policy in (UPLOAD_POLICY_NO_OVERWRITE, UPLOAD_POLICY_NO_OVERWRITE_RECIPE):\\n raise ConanException(\\\"Local recipe is different from the remote recipe. \\\"\\n \\\"Forbidden overwrite\\\")\\n\\n files_to_upload = {filename.replace(\\\"\\\\\\\\\\\", \\\"/\\\"): path\\n for filename, path i\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.upload_package\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.upload_package\\n def upload_package(self, pref, the_files, retry, retry_wait, policy):\\n \\\"\\\"\\\"\\n basedir: Base directory with the files to upload (for read the files in disk)\\n relative_files: relative paths to upload\\n \\\"\\\"\\\"\\n self.check_credentials()\\n\\n revisions_enabled = get_env(\\\"CONAN_CLIENT_REVISIONS_ENABLED\\\", False)\\n if not revisions_enabled and policy == UPLOAD_POLICY_NO_OVERWRITE:\\n # Check if the latest revision is not the one we are uploading, with the compatibility\\n # mode this is supposed to fail if someone tries to upload a different recipe\\n latest_pref = PackageReference(pref.ref, pref.id)\\n latest_snapshot, ref_latest_snapshot, _ = self._get_package_snapshot(latest_pref)\\n server_with_revisions = ref_latest_snapshot.revision != DEFAULT_REVISION_V1\\n if latest_snapshot and server_with_revisions and \\\\\\n ref_latest_snapshot.revision != pref.revision:\\n raise ConanException(\\\"Local package is different from the remote package. \\\"\\n \\\"Forbidden overwrite\\\")\\n t1 = time.time()\\n # Get the remote snapshot\\n pref = pref\\n remote_snapshot, pref_snapshot, rev_time = self._get_package_snapshot(pref)\\n\\n if remote_snapshot:\\n remote_manifest = self.get_package_manifest(pref_snapshot)\\n local_manifest = FileTreeManifest.loads(load(the_files[\\\"conanmanifest.txt\\\"]))\\n\\n if remote_manifest == local_manifest:\\n return False, pref_snapshot, rev_time\\n\\n if policy == UPLOAD_POLICY_NO_OVERWRITE:\\n raise ConanException(\\\"Local package is different from the remote package. \\\"\\n \\\"Forbidden overwrite\\\")\\n\\n files_to_upload = the_files\\n deleted = set(remote_snapshot).difference(the_files)\\n if files_to_upload:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.search\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.search\\n def search(self, pattern=None, ignorecase=True):\\n \\\"\\\"\\\"\\n the_files: dict with relative_path: content\\n \\\"\\\"\\\"\\n url = self.search_router.search(pattern, ignorecase)\\n response = self.get_json(url)[\\\"results\\\"]\\n return [ConanFileReference.loads(reference) for reference in response]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.search_packages\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.search_packages\\n def search_packages(self, reference, query):\\n\\n if not query:\\n url = self.search_router.search_packages(reference)\\n package_infos = self.get_json(url)\\n return package_infos\\n\\n # Read capabilities\\n try:\\n _, _, capabilities = self.server_info()\\n except NotFoundException:\\n capabilities = []\\n\\n if COMPLEX_SEARCH_CAPABILITY in capabilities:\\n url = self.search_router.search_packages(reference, query)\\n package_infos = self.get_json(url)\\n return package_infos\\n else:\\n url = self.search_router.search_packages(reference)\\n package_infos = self.get_json(url)\\n return filter_packages(query, package_infos)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.remove_conanfile\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods.remove_conanfile\\n @handle_return_deserializer()\\n def remove_conanfile(self, ref):\\n \\\"\\\"\\\" Remove a recipe and packages \\\"\\\"\\\"\\n self.check_credentials()\\n url = self.conans_router.remove_recipe(ref)\\n logger.debug(\\\"REST: remove: %s\\\" % url)\\n response = self.requester.delete(url,\\n auth=self.auth,\\n headers=self.custom_headers,\\n verify=self.verify_ssl)\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/rest/rest_client_common.py::RestCommonMethods._post_json\",\n \"text\": \"conans/client/rest/rest_client_common.py::RestCommonMethods._post_json\\n def _post_json(self, url, payload):\\n logger.debug(\\\"REST: post: %s\\\" % url)\\n response = self.requester.post(url,\\n auth=self.auth,\\n headers=self.custom_headers,\\n verify=self.verify_ssl,\\n json=payload)\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_options\",\n \"text\": \"conans/model/conan_file.py::create_options\\ndef create_options(conanfile):\\n try:\\n package_options = PackageOptions(getattr(conanfile, \\\"options\\\", None))\\n options = Options(package_options)\\n\\n default_options = getattr(conanfile, \\\"default_options\\\", None)\\n if default_options:\\n if isinstance(default_options, (list, tuple, dict)):\\n default_values = OptionsValues(default_options)\\n elif isinstance(default_options, str):\\n default_values = OptionsValues.loads(default_options)\\n else:\\n raise ConanException(\\\"Please define your default_options as list, \\\"\\n \\\"multiline string or dictionary\\\")\\n options.values = default_values\\n return options\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing options. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_requirements\",\n \"text\": \"conans/model/conan_file.py::create_requirements\\ndef create_requirements(conanfile):\\n try:\\n # Actual requirements of this package\\n if not hasattr(conanfile, \\\"requires\\\"):\\n return Requirements()\\n else:\\n if not conanfile.requires:\\n return Requirements()\\n if isinstance(conanfile.requires, tuple):\\n return Requirements(*conanfile.requires)\\n else:\\n return Requirements(conanfile.requires, )\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing requirements. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_settings\",\n \"text\": \"conans/model/conan_file.py::create_settings\\ndef create_settings(conanfile, settings):\\n try:\\n defined_settings = getattr(conanfile, \\\"settings\\\", None)\\n if isinstance(defined_settings, str):\\n defined_settings = [defined_settings]\\n current = defined_settings or {}\\n settings.constraint(current)\\n return settings\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing settings. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::_env_and_python\",\n \"text\": \"conans/model/conan_file.py::_env_and_python\\n@contextmanager\\ndef _env_and_python(conanfile):\\n with environment_append(conanfile.env):\\n with pythonpath(conanfile):\\n yield\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::get_env_context_manager\",\n \"text\": \"conans/model/conan_file.py::get_env_context_manager\\ndef get_env_context_manager(conanfile, without_python=False):\\n if not conanfile.apply_env:\\n return no_op()\\n if without_python:\\n return environment_append(conanfile.env)\\n return _env_and_python(conanfile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.__init__\",\n \"text\": \"conans/model/conan_file.py::ConanFile.__init__\\n def __init__(self, output, runner, display_name=\\\"\\\", user=None, channel=None):\\n # an output stream (writeln, info, warn error)\\n self.output = ScopedOutput(display_name, output)\\n self.display_name = display_name\\n # something that can run commands, as os.sytem\\n self._conan_runner = runner\\n self._conan_user = user\\n self._conan_channel = channel\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.initialize\",\n \"text\": \"conans/model/conan_file.py::ConanFile.initialize\\n def initialize(self, settings, env):\\n if isinstance(self.generators, str):\\n self.generators = [self.generators]\\n # User defined options\\n self.options = create_options(self)\\n self.requires = create_requirements(self)\\n self.settings = create_settings(self, settings)\\n try:\\n if self.settings.os_build and self.settings.os:\\n self.output.writeln(\\\"*\\\"*60, front=Color.BRIGHT_RED)\\n self.output.writeln(\\\" This package defines both 'os' and 'os_build' \\\",\\n front=Color.BRIGHT_RED)\\n self.output.writeln(\\\" Please use 'os' for libraries and 'os_build'\\\",\\n front=Color.BRIGHT_RED)\\n self.output.writeln(\\\" only for build-requires used for cross-building\\\",\\n front=Color.BRIGHT_RED)\\n self.output.writeln(\\\"*\\\"*60, front=Color.BRIGHT_RED)\\n except ConanException:\\n pass\\n\\n # needed variables to pack the project\\n self.cpp_info = None # Will be initialized at processing time\\n self.deps_cpp_info = DepsCppInfo()\\n\\n # environment variables declared in the package_info\\n self.env_info = None # Will be initialized at processing time\\n self.deps_env_info = DepsEnvInfo()\\n\\n # user declared variables\\n self.user_info = None\\n # Keys are the package names, and the values a dict with the vars\\n self.deps_user_info = DepsUserInfo()\\n\\n # user specified env variables\\n self._conan_env_values = env.copy() # user specified -e\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.env\",\n \"text\": \"conans/model/conan_file.py::ConanFile.env\\n @property\\n def env(self):\\n \\\"\\\"\\\"Apply the self.deps_env_info into a copy of self._conan_env_values (will prioritize the\\n self._conan_env_values, user specified from profiles or -e first, then inherited)\\\"\\\"\\\"\\n # Cannot be lazy cached, because it's called in configure node, and we still don't have\\n # the deps_env_info objects available\\n tmp_env_values = self._conan_env_values.copy()\\n tmp_env_values.update(self.deps_env_info)\\n\\n ret, multiple = tmp_env_values.env_dicts(self.name)\\n ret.update(multiple)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.channel\",\n \"text\": \"conans/model/conan_file.py::ConanFile.channel\\n @property\\n def channel(self):\\n if not self._conan_channel:\\n self._conan_channel = os.getenv(\\\"CONAN_CHANNEL\\\") or self.default_channel\\n if not self._conan_channel:\\n raise ConanException(\\\"CONAN_CHANNEL environment variable not defined, \\\"\\n \\\"but self.channel is used in conanfile\\\")\\n return self._conan_channel\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.user\",\n \"text\": \"conans/model/conan_file.py::ConanFile.user\\n @property\\n def user(self):\\n if not self._conan_user:\\n self._conan_user = os.getenv(\\\"CONAN_USERNAME\\\") or self.default_user\\n if not self._conan_user:\\n raise ConanException(\\\"CONAN_USERNAME environment variable not defined, \\\"\\n \\\"but self.user is used in conanfile\\\")\\n return self._conan_user\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.collect_libs\",\n \"text\": \"conans/model/conan_file.py::ConanFile.collect_libs\\n def collect_libs(self, folder=None):\\n self.output.warn(\\\"'self.collect_libs' is deprecated, \\\"\\n \\\"use 'tools.collect_libs(self)' instead\\\")\\n return tools.collect_libs(self, folder=folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_policy_missing\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_policy_missing\\n @property\\n def build_policy_missing(self):\\n return self.build_policy == \\\"missing\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_policy_always\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_policy_always\\n @property\\n def build_policy_always(self):\\n return self.build_policy == \\\"always\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.source\",\n \"text\": \"conans/model/conan_file.py::ConanFile.source\\n def source(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.system_requirements\",\n \"text\": \"conans/model/conan_file.py::ConanFile.system_requirements\\n def system_requirements(self):\\n \\\"\\\"\\\" this method can be overwritten to implement logic for system package\\n managers, as apt-get\\n\\n You can define self.global_system_requirements = True, if you want the installation\\n to be for all packages (not depending on settings/options/requirements)\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.config_options\",\n \"text\": \"conans/model/conan_file.py::ConanFile.config_options\\n def config_options(self):\\n \\\"\\\"\\\" modify options, probably conditioned to some settings. This call is executed\\n before config_settings. E.g.\\n if self.settings.os == \\\"Windows\\\":\\n del self.options.shared # shared/static not supported in win\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.configure\",\n \"text\": \"conans/model/conan_file.py::ConanFile.configure\\n def configure(self):\\n \\\"\\\"\\\" modify settings, probably conditioned to some options. This call is executed\\n after config_options. E.g.\\n if self.options.header_only:\\n self.settings.clear()\\n This is also the place for conditional requirements\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build\\n def build(self):\\n \\\"\\\"\\\" build your project calling the desired build tools as done in the command line.\\n E.g. self.run(\\\"cmake --build .\\\") Or use the provided build helpers. E.g. cmake.build()\\n \\\"\\\"\\\"\\n self.output.warn(\\\"This conanfile has no build step\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package\\n def package(self):\\n \\\"\\\"\\\" package the needed files from source and build folders.\\n E.g. self.copy(\\\"*.h\\\", src=\\\"src/includes\\\", dst=\\\"includes\\\")\\n \\\"\\\"\\\"\\n self.output.warn(\\\"This conanfile has no package step\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_info\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_info\\n def package_info(self):\\n \\\"\\\"\\\" define cpp_build_info, flags, etc\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.run\",\n \"text\": \"conans/model/conan_file.py::ConanFile.run\\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True,\\n ignore_errors=False, run_environment=False):\\n def _run():\\n if not win_bash:\\n return self._conan_runner(command, output, os.path.abspath(RUN_LOG_NAME), cwd)\\n # FIXME: run in windows bash is not using output\\n return tools.run_in_windows_bash(self, bashcmd=command, cwd=cwd, subsystem=subsystem,\\n msys_mingw=msys_mingw)\\n if run_environment:\\n with tools.run_environment(self):\\n if OSInfo().is_macos:\\n command = 'DYLD_LIBRARY_PATH=\\\"%s\\\" %s' % (os.environ.get('DYLD_LIBRARY_PATH', ''),\\n command)\\n retcode = _run()\\n else:\\n retcode = _run()\\n\\n if not ignore_errors and retcode != 0:\\n raise ConanException(\\\"Error %d while executing %s\\\" % (retcode, command))\\n\\n return retcode\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.run._run\",\n \"text\": \"conans/model/conan_file.py::ConanFile.run._run\\n def _run():\\n if not win_bash:\\n return self._conan_runner(command, output, os.path.abspath(RUN_LOG_NAME), cwd)\\n # FIXME: run in windows bash is not using output\\n return tools.run_in_windows_bash(self, bashcmd=command, cwd=cwd, subsystem=subsystem,\\n msys_mingw=msys_mingw)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_id\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_id\\n def package_id(self):\\n \\\"\\\"\\\" modify the conans info, typically to narrow values\\n eg.: conaninfo.package_references = []\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.test\",\n \"text\": \"conans/model/conan_file.py::ConanFile.test\\n def test(self):\\n \\\"\\\"\\\" test the generated executable.\\n E.g. self.run(\\\"./example\\\")\\n \\\"\\\"\\\"\\n raise ConanException(\\\"You need to create a method 'test' in your test/conanfile.py\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.__repr__\",\n \"text\": \"conans/model/conan_file.py::ConanFile.__repr__\\n def __repr__(self):\\n return self.display_name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/server/rest/multipart_encoder.py::get_response_chunk\",\n \"text\": \"conans/server/rest/multipart_encoder.py::get_response_chunk\\ndef get_response_chunk(response, basepath, filepaths, boundary=DEFAULT_BOUNDARY):\\n \\\"\\\"\\\"\\n File is a list of filepaths\\n \\\"\\\"\\\"\\n response.add_header(\\\"Content-Type\\\", \\\"multipart/mixed;charset=utf-8;boundary=%s\\\" % boundary)\\n\\n for filepath in filepaths:\\n yield \\\"--%s\\\\n\\\" % boundary + _headers_for_file(filepath)\\n for data in iter(read_file_chunked(os.path.join(basepath, filepath))):\\n yield data.encode(\\\"hex\\\")\\n\\n yield (\\\"--%s\\\\n--%s\\\\n\\\" % (boundary, boundary))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/server/rest/multipart_encoder.py::_headers_for_file\",\n \"text\": \"conans/server/rest/multipart_encoder.py::_headers_for_file\\ndef _headers_for_file(filename):\\n return '''Content-type: application/octet-stream;charset=hex\\nContent-transfer-encoding: binary\\nContent-disposition: attachment; filename=\\\"%s\\\"\\n\\n''' % filename\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/server/rest/multipart_encoder.py::read_file_chunked\",\n \"text\": \"conans/server/rest/multipart_encoder.py::read_file_chunked\\ndef read_file_chunked(filepath):\\n thefile = open(filepath, \\\"rb\\\")\\n for data in iter(read_in_chunks(thefile)):\\n yield data\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/server/rest/multipart_encoder.py::read_in_chunks\",\n \"text\": \"conans/server/rest/multipart_encoder.py::read_in_chunks\\ndef read_in_chunks(file_object, chunk_size=1024):\\n \\\"\\\"\\\"Lazy function (generator) to read a file piece by piece.\\n Default chunk size: 1k.\\n Warn: Python file.read is returning \\\\n instead of EOF,\\n we read two chunks before return to know if EOF is reached\\n so used memory is chunk_size*2\\\"\\\"\\\"\\n while True:\\n buf1 = file_object.read(chunk_size)\\n if not buf1:\\n break\\n\\n# buf2 = file_object.read(chunk_size)\\n# if not buf2: # Last buf1 has and ending \\\\n that is a EOF\\n# buf1 = buf1[:-1] if buf1[-1] == \\\"\\\\n\\\" else buf1\\n# yield buf1\\n# else:\\n yield buf1\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/server/rest/server.py::ConanServer.__init__\",\n \"text\": \"conans/server/rest/server.py::ConanServer.__init__\\n def __init__(self, run_port, credentials_manager,\\n updown_auth_manager, authorizer, authenticator,\\n server_store, server_capabilities):\\n\\n self.run_port = run_port\\n\\n server_capabilities = server_capabilities or []\\n self.root_app = bottle.Bottle()\\n\\n self.api_v1 = ApiV1(credentials_manager, updown_auth_manager,\\n server_capabilities)\\n self.api_v1.authorizer = authorizer\\n self.api_v1.authenticator = authenticator\\n self.api_v1.server_store = server_store\\n self.api_v1.setup()\\n\\n self.root_app.mount(\\\"/v1/\\\", self.api_v1)\\n\\n self.api_v2 = ApiV2(credentials_manager, server_capabilities)\\n self.api_v2.authorizer = authorizer\\n self.api_v2.authenticator = authenticator\\n self.api_v2.server_store = server_store\\n self.api_v2.setup()\\n self.root_app.mount(\\\"/v2/\\\", self.api_v2)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/server/rest/server.py::ConanServer.run\",\n \"text\": \"conans/server/rest/server.py::ConanServer.run\\n def run(self, **kwargs):\\n port = kwargs.pop(\\\"port\\\", self.run_port)\\n debug_set = kwargs.pop(\\\"debug\\\", False)\\n host = kwargs.pop(\\\"host\\\", \\\"localhost\\\")\\n bottle.Bottle.run(self.root_app, host=host,\\n port=port, debug=debug_set, reloader=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/server/service/mime.py::get_mime_type\",\n \"text\": \"conans/server/service/mime.py::get_mime_type\\ndef get_mime_type(filepath):\\n if filepath.endswith(\\\".tgz\\\"):\\n mimetype = \\\"x-gzip\\\"\\n elif filepath.endswith(\\\".txz\\\"):\\n mimetype = \\\"x-xz\\\"\\n else:\\n mimetype = \\\"auto\\\"\\n\\n return mimetype\",\n \"label\": 1\n }\n]","isConversational":false}}},{"rowIdx":82,"cells":{"instance_id":{"kind":"string","value":"pantsbuild__pants-4686"},"repo":{"kind":"string","value":"pantsbuild/pants"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Support invalidation generically via an option\nCurrently, manual invalidation is only supported via `./pants invalidated`. This is problematic for a few reasons:\r\n1. \"invalidate\" is not a verb in pants user-space, it's only one we use internally in the codebase\r\n2. The `invalidate` goal acts globally, invalidating all targets across all tasks\r\n\r\nAs @benjyw suggested [here](https://github.com/pantsbuild/pants/pull/4660#pullrequestreview-43801196), a global recursive `--force` flag might take the place of the `invalidate` goal and also allow more targeted invalidation at the task level when placed to the right of a fully qualified goal name on the command line. To-boot, `--force` likely makes more intuitive sense in pants user-space; ie: force this command to run instead of skipping things.\n"},"gold_files":{"kind":"list like","value":["contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py","src/python/pants/cache/cache_setup.py","src/python/pants/core_tasks/invalidate.py","src/python/pants/invalidation/build_invalidator.py","src/python/pants/invalidation/cache_manager.py","src/python/pants/task/task.py"],"string":"[\n \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py\",\n \"src/python/pants/cache/cache_setup.py\",\n \"src/python/pants/core_tasks/invalidate.py\",\n \"src/python/pants/invalidation/build_invalidator.py\",\n \"src/python/pants/invalidation/cache_manager.py\",\n \"src/python/pants/task/task.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.execute","contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.register_options","src/python/pants/cache/cache_setup.py::<module>","src/python/pants/cache/cache_setup.py::CacheFactory.read_cache_available","src/python/pants/cache/cache_setup.py::CacheFactory.write_cache_available","src/python/pants/cache/cache_setup.py::CacheSetup.register_options","src/python/pants/core_tasks/invalidate.py::<module>","src/python/pants/core_tasks/invalidate.py::Invalidate.execute","src/python/pants/invalidation/build_invalidator.py::<module>","src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.__init__","src/python/pants/invalidation/cache_manager.py::<module>","src/python/pants/task/task.py::TaskBase.__init__","src/python/pants/task/task.py::TaskBase.invalidate","src/python/pants/task/task.py::TaskBase.invalidated","src/python/pants/task/task.py::TaskBase.subsystem_dependencies"],"string":"[\n \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.execute\",\n \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.register_options\",\n \"src/python/pants/cache/cache_setup.py::<module>\",\n \"src/python/pants/cache/cache_setup.py::CacheFactory.read_cache_available\",\n \"src/python/pants/cache/cache_setup.py::CacheFactory.write_cache_available\",\n \"src/python/pants/cache/cache_setup.py::CacheSetup.register_options\",\n \"src/python/pants/core_tasks/invalidate.py::<module>\",\n \"src/python/pants/core_tasks/invalidate.py::Invalidate.execute\",\n \"src/python/pants/invalidation/build_invalidator.py::<module>\",\n \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.__init__\",\n \"src/python/pants/invalidation/cache_manager.py::<module>\",\n \"src/python/pants/task/task.py::TaskBase.__init__\",\n \"src/python/pants/task/task.py::TaskBase.invalidate\",\n \"src/python/pants/task/task.py::TaskBase.invalidated\",\n \"src/python/pants/task/task.py::TaskBase.subsystem_dependencies\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py","text":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py\nclass IndexJava\n def implementation_version(cls)\n def product_types(cls)\n def prepare(cls, options, round_manager)\n def register_options(cls, register)\n def execute(self)\n def entries_file(_vt)","label":1},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py","text":"src/python/pants/backend/jvm/tasks/jar_publish.py\nclass PushDb\n def load(path) # Loads a pushdb maintained in a properties file at the given path.\n class Entry\n def __init__(self, sem_ver, named_ver, named_is_latest, sha, fingerprint) # Records the most recent push/release of an artifact.\n def version(self)\n def with_sem_ver(self, sem_ver) # Returns a clone of this entry with the given sem_ver marked as the latest.\n def with_named_ver(self, named_ver) # Returns a clone of this entry with the given name_ver marked as the latest.\n def with_sha_and_fingerprint(self, sha, fingerprint) # Returns a clone of this entry with the given sha and fingerprint.\n def __repr__(self)\n def __init__(self, props=None)\n def get_entry(self, target) # Given an internal target, return a PushDb.Entry, which might contain defaults.\n def set_entry(self, target, pushdb_entry)\n def _accessors_for_target(self, target)\n def key(prefix)\n def getter(prefix, default=None)\n def setter(prefix, value)\n def dump(self, path) # Saves the pushdb as a properties file to the given path.\nclass PomWriter\n def __init__(self, get_db, tag)\n def write(self, target, path)\n def _as_versioned_jar(self, internal_target) # Fetches the jar representation of the given target, and applies the latest pushd\n def _internaldep(self, jar_dependency, target)\n def _jardep(self, jar)\ndef coordinate(org, name, rev=None)\ndef jar_coordinate(jar, rev=None)\ndef pushdb_coordinate(jar, entry)\ndef target_internal_dependencies(target) # Returns internal Jarable dependencies that were \"directly\" declared.\nclass JarPublish # Publish jars to a maven repository.\n class Publication # Represents an artifact publication.\n class DuplicateArtifactError # An artifact was defined by two different targets.\n def register_options(cls, register)\n def prepare(cls, options, round_manager)\n def __init__(self, *args, **kwargs)\n def parse_jarcoordinate(coordinate)\n def confirm_push(self, coord, version) # Ask the user if a push should be done for a particular version of a\n def _copy_artifact(self, tgt, jar, version, typename, suffix='', extension='jar', artifact_ext='', override_name=None) # Copy the products for a target into the artifact path for the jar/version\n def _ivy_jvm_options(self, repo) # Get the JVM options for ivy authentication, if needed.\n def publish(self, publications, jar, entry, repo, published) # Run ivy to publish a jar. ivyxml_path is the path to the ivy file; published\n def execute(self)\n def get_db(tgt)\n def get_pushdb(tgt)\n def fingerprint_internal(tgt)\n def stage_artifacts(tgt, jar, version, tag, changelog)\n def artifact_path(self, jar, version, name=None, suffix='', extension='jar', artifact_ext='')\n def check_for_duplicate_artifacts(self, targets)\n def duplication_message(artifact)\n def check_targets(self, targets)\n ","label":0},{"cid":"src/python/pants/cache/cache_setup.py","text":"src/python/pants/cache/cache_setup.py\nclass EmptyCacheSpecError\nclass LocalCacheSpecRequiredError\nclass CacheSpecFormatError\nclass InvalidCacheSpecError\nclass RemoteCacheSpecRequiredError\nclass TooManyCacheSpecsError\nclass CacheSetup\n def register_options(cls, register)\n def create_cache_factory_for_task(cls, task, pinger=None, resolver=None)\nclass CacheFactory\n def __init__(self, options, log, stable_name, pinger=None, resolver=None) # Create a cache factory from settings.\n def read_cache_available(self)\n def write_cache_available(self)\n def overwrite(self)\n def get_read_cache(self) # Returns the read cache for this setup, creating it if necessary.\n def get_write_cache(self) # Returns the write cache for this setup, creating it if necessary.\n def _sanitize_cache_spec(self, spec)\n def _resolve(self, spec) # Attempt resolving cache URIs when a remote spec is provided.\n def is_local(string_spec)\n def is_remote(string_spec)\n def _baseurl(self, url)\n def get_available_urls(self, urls) # Return reachable urls sorted by their ping times.\n def _do_create_artifact_cache(self, spec, action) # Returns an artifact cache for the specified spec.\n def create_local_cache(parent_path)\n def create_remote_cache(remote_spec, local_cache)","label":1},{"cid":"src/python/pants/core_tasks/invalidate.py","text":"src/python/pants/core_tasks/invalidate.py\nclass Invalidate # Invalidate the entire build.\n def execute(self)","label":1},{"cid":"src/python/pants/invalidation/build_invalidator.py","text":"src/python/pants/invalidation/build_invalidator.py\nclass CacheKeyGenerator # Generates cache keys for versions of target sets.\n def combine_cache_keys(cache_keys) # Returns a cache key for a list of target sets that already have cache keys.\n def __init__(self, *base_fingerprint_inputs) # :base_fingerprint_inputs: Information to be included in the fingerprint for all \n def key_for_target(self, target, transitive=False, fingerprint_strategy=None) # Get a key representing the given target and its sources.\nclass BuildInvalidator # Invalidates build targets based on the SHA1 hash of source files and other input\n def __init__(self, root)\n def previous_key(self, cache_key) # If there was a previous successful build for the given key, return the previous \n def needs_update(self, cache_key) # Check if the given cached item is invalid.\n def update(self, cache_key) # Makes cache_key the valid version of the corresponding target set.\n def force_invalidate_all(self) # Force-invalidates all cached items.\n def force_invalidate(self, cache_key) # Force-invalidate the cached item.\n def _sha_file(self, cache_key)\n def _sha_file_by_id(self, id)\n def _write_sha(self, cache_key)\n def _read_sha(self, cache_key)\n def _read_sha_by_id(self, id)","label":1},{"cid":"src/python/pants/invalidation/cache_manager.py","text":"src/python/pants/invalidation/cache_manager.py\n# coding=utf-8\n# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom __future__ import (absolute_import, division, generators, nested_scopes, print_function,\n unicode_literals, with_statement)\n\nimport os\nimport shutil\nimport sys\nfrom hashlib import sha1\n\nfrom pants.build_graph.build_graph import sort_targets\nfrom pants.build_graph.target import Target\nfrom pants.invalidation.build_invalidator import BuildInvalidator, CacheKeyGenerator\nfrom pants.util.dirutil import relative_symlink, safe_delete, safe_mkdir, safe_rmtree\nfrom pants.util.memo import memoized_method\n\n\nclass VersionedTargetSet(object):\n \"\"\"Represents a list of targets, a corresponding CacheKey, and a flag determining whether the\n list of targets is currently valid.\n\n When invalidating a single target, this can be used to represent that target as a singleton.\n When checking the artifact cache, this can also be used to represent a list of targets that are\n built together into a single artifact.\n \"\"\"\n\n class IllegalResultsDir(Exception):\n \"\"\"Indicate a problem interacting with a versioned target results directory.\"\"\"\n\n @staticmethod\n def from_versioned_targets(versioned_targets):\n \"\"\"\n :API: public\n \"\"\"\n first_target = versioned_targets[0]\n cache_manager = first_target._cache_manager\n\n # Quick sanity check; all the versioned targets should have the same cache manager.\n # TODO(ryan): the way VersionedTargets store their own links to a single CacheManager instance\n # feels hacky; see if there's a cleaner way for callers to handle awareness of the CacheManager.\n for versioned_target in versioned_targets:\n if versioned_target._cache_manager != cache_manager:\n raise ValueError(\"Attempting to combine versioned targets {} and {} with different\"\n \" CacheManager instances: {} and {}\".format(first_target, versioned_target,\n cache_manager,\n versioned_target._cache_manager))\n return VersionedTargetSet(cache_manager, versioned_targets)\n\n def __init__(self, cache_manager, versioned_targets):\n self._cache_manager = cache_manager\n self.versioned_targets = versioned_targets\n self.targets = [vt.target for vt in versioned_targets]\n\n # The following line is a no-op if cache_key was set in the VersionedTarget __init__ method.\n self.cache_key = CacheKeyGenerator.combine_cache_keys([vt.cache_key\n for vt in versioned_targets])\n # NB: previous_cache_key may be None on the first build of a target.\n self.previous_cache_key = cache_manager.previous_key(self.cache_key)\n self.valid = self.previous_cache_key == self.cache_key\n\n if cache_manager.invalidation_report:\n cache_mana","label":1},{"cid":"src/python/pants/option/global_options.py","text":"src/python/pants/option/global_options.py\nclass GlobalOptionsRegistrar\n def register_bootstrap_options(cls, register) # Register bootstrap options.\n def register_options(cls, register) # Register options not tied to any particular task or subsystem.","label":0},{"cid":"src/python/pants/option/options.py","text":"src/python/pants/option/options.py\nclass Options # The outward-facing API for interacting with options.\n class OptionTrackerRequiredError # Options requires an OptionTracker instance.\n def complete_scopes(cls, scope_infos) # Expand a set of scopes to include all enclosing scopes.\n def create(cls, env, config, known_scope_infos, args=None, bootstrap_option_values=None, option_tracker=None) # Create an Options instance.\n def __init__(self, goals, scope_to_flags, target_specs, passthru, passthru_owner, help_request, parser_hierarchy, values_by_scope, bootstrap_option_values, known_scope_to_info, option_tracker) # The low-level constructor for an Options instance.\n def tracker(self)\n def help_request(self) # :API: public\n def target_specs(self) # The targets to operate on.\n def goals(self) # The requested goals, in the order specified on the cmd line.\n def known_scope_to_info(self)\n def scope_to_flags(self)\n def drop_flag_values(self) # Returns a copy of these options that ignores values specified via flags.\n def is_known_scope(self, scope) # Whether the given scope is known by this instance.\n def passthru_args_for_scope(self, scope)\n def register(self, scope, *args, **kwargs) # Register an option in the given scope.\n def registration_function_for_optionable(self, optionable_class) # Returns a function for registering options on the given scope.\n def register(*args, **kwargs)\n def get_parser(self, scope) # Returns the parser for the given scope, so code can register on it directly.\n def walk_parsers(self, callback)\n def for_scope(self, scope, inherit_from_enclosing_scope=True) # Return the option values for the given scope.\n def get_fingerprintable_for_scope(self, scope) # Returns a list of fingerprintable (option type, option value) pairs for the give\n def __getitem__(self, scope)\n def bootstrap_option_values(self) # Return the option values for bootstrap options.\n def for_global_scope(self) # Return the option values for the global scope.","label":0},{"cid":"src/python/pants/task/task.py","text":"src/python/pants/task/task.py\nclass TaskBase # Defines a lifecycle that prepares a task for execution and provides the base mac\n def implementation_version(cls) # :API: public\n def implementation_version_str(cls)\n def stable_name(cls) # The stable name of this task type.\n def _compute_stable_name(cls)\n def subsystem_dependencies(cls)\n def product_types(cls) # The list of products this Task produces. Set the product type(s) for this\n def known_scope_infos(cls) # Yields ScopeInfo for all known scopes for this task, in no particular order.\n def supports_passthru_args(cls) # Subclasses may override to indicate that they can use passthru args.\n def _scoped_options(cls, options)\n def get_alternate_target_roots(cls, options, address_mapper, build_graph)\n def alternate_target_roots(cls, options, address_mapper, build_graph) # Allows a Task to propose alternate target roots from those specified on the CLI.\n def invoke_prepare(cls, options, round_manager)\n def prepare(cls, options, round_manager) # Prepares a task for execution.\n def __init__(self, context, workdir) # Subclass __init__ methods, if defined, *must* follow this idiom:\n def get_options(self) # Returns the option values for this task's scope.\n def get_passthru_args(self) # :API: public\n def workdir(self) # A scratch-space for this task that will be deleted by `clean-all`.\n def _options_fingerprint(self, scope)\n def fingerprint(self) # Returns a fingerprint for the identity of the task.\n def artifact_cache_reads_enabled(self)\n def artifact_cache_writes_enabled(self)\n def invalidate(self) # Invalidates all targets for this task.\n def create_target_dirs(self) # Whether to create a results_dir per VersionedTarget in the workdir of the Task.\n def cache_target_dirs(self) # Whether to cache files in VersionedTarget's results_dir after exiting an invalid\n def incremental(self) # Whether this Task implements incremental building of individual targets.\n def cache_incremental(self) # For incremental tasks, indicates whether the results of incremental builds shoul\n def invalidated(self, targets, invalidate_dependents=False, silent=False, fingerprint_strategy=None, topological_order=False) # Checks targets for invalidation, first checking the artifact cache.\n def maybe_write_artifact(self, vt)\n def _launch_background_workdir_cleanup(self, vts)\n def _cleanup_workdir_stale_builds(self, vts)\n def _should_cache_target_dir(self, vt) # Return true if the given vt should be written to a cache (if configured).\n def _maybe_create_results_dirs(self, vts) # If `cache_target_dirs`, create results_dirs for the given versioned targets.\n def check_artifact_cache_for(self, invalidation_check) # Decides which VTS to check the artifact cache for.\n def check_artifact_cache(self, vts) # Checks the artifact cache for the specified list of VersionedTargetSets.\n def do_check_artifact_cache(self, vts, pos","label":1}],"string":"[\n {\n \"cid\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py\",\n \"text\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py\\nclass IndexJava\\n def implementation_version(cls)\\n def product_types(cls)\\n def prepare(cls, options, round_manager)\\n def register_options(cls, register)\\n def execute(self)\\n def entries_file(_vt)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py\\nclass PushDb\\n def load(path) # Loads a pushdb maintained in a properties file at the given path.\\n class Entry\\n def __init__(self, sem_ver, named_ver, named_is_latest, sha, fingerprint) # Records the most recent push/release of an artifact.\\n def version(self)\\n def with_sem_ver(self, sem_ver) # Returns a clone of this entry with the given sem_ver marked as the latest.\\n def with_named_ver(self, named_ver) # Returns a clone of this entry with the given name_ver marked as the latest.\\n def with_sha_and_fingerprint(self, sha, fingerprint) # Returns a clone of this entry with the given sha and fingerprint.\\n def __repr__(self)\\n def __init__(self, props=None)\\n def get_entry(self, target) # Given an internal target, return a PushDb.Entry, which might contain defaults.\\n def set_entry(self, target, pushdb_entry)\\n def _accessors_for_target(self, target)\\n def key(prefix)\\n def getter(prefix, default=None)\\n def setter(prefix, value)\\n def dump(self, path) # Saves the pushdb as a properties file to the given path.\\nclass PomWriter\\n def __init__(self, get_db, tag)\\n def write(self, target, path)\\n def _as_versioned_jar(self, internal_target) # Fetches the jar representation of the given target, and applies the latest pushd\\n def _internaldep(self, jar_dependency, target)\\n def _jardep(self, jar)\\ndef coordinate(org, name, rev=None)\\ndef jar_coordinate(jar, rev=None)\\ndef pushdb_coordinate(jar, entry)\\ndef target_internal_dependencies(target) # Returns internal Jarable dependencies that were \\\"directly\\\" declared.\\nclass JarPublish # Publish jars to a maven repository.\\n class Publication # Represents an artifact publication.\\n class DuplicateArtifactError # An artifact was defined by two different targets.\\n def register_options(cls, register)\\n def prepare(cls, options, round_manager)\\n def __init__(self, *args, **kwargs)\\n def parse_jarcoordinate(coordinate)\\n def confirm_push(self, coord, version) # Ask the user if a push should be done for a particular version of a\\n def _copy_artifact(self, tgt, jar, version, typename, suffix='', extension='jar', artifact_ext='', override_name=None) # Copy the products for a target into the artifact path for the jar/version\\n def _ivy_jvm_options(self, repo) # Get the JVM options for ivy authentication, if needed.\\n def publish(self, publications, jar, entry, repo, published) # Run ivy to publish a jar. ivyxml_path is the path to the ivy file; published\\n def execute(self)\\n def get_db(tgt)\\n def get_pushdb(tgt)\\n def fingerprint_internal(tgt)\\n def stage_artifacts(tgt, jar, version, tag, changelog)\\n def artifact_path(self, jar, version, name=None, suffix='', extension='jar', artifact_ext='')\\n def check_for_duplicate_artifacts(self, targets)\\n def duplication_message(artifact)\\n def check_targets(self, targets)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py\",\n \"text\": \"src/python/pants/cache/cache_setup.py\\nclass EmptyCacheSpecError\\nclass LocalCacheSpecRequiredError\\nclass CacheSpecFormatError\\nclass InvalidCacheSpecError\\nclass RemoteCacheSpecRequiredError\\nclass TooManyCacheSpecsError\\nclass CacheSetup\\n def register_options(cls, register)\\n def create_cache_factory_for_task(cls, task, pinger=None, resolver=None)\\nclass CacheFactory\\n def __init__(self, options, log, stable_name, pinger=None, resolver=None) # Create a cache factory from settings.\\n def read_cache_available(self)\\n def write_cache_available(self)\\n def overwrite(self)\\n def get_read_cache(self) # Returns the read cache for this setup, creating it if necessary.\\n def get_write_cache(self) # Returns the write cache for this setup, creating it if necessary.\\n def _sanitize_cache_spec(self, spec)\\n def _resolve(self, spec) # Attempt resolving cache URIs when a remote spec is provided.\\n def is_local(string_spec)\\n def is_remote(string_spec)\\n def _baseurl(self, url)\\n def get_available_urls(self, urls) # Return reachable urls sorted by their ping times.\\n def _do_create_artifact_cache(self, spec, action) # Returns an artifact cache for the specified spec.\\n def create_local_cache(parent_path)\\n def create_remote_cache(remote_spec, local_cache)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/core_tasks/invalidate.py\",\n \"text\": \"src/python/pants/core_tasks/invalidate.py\\nclass Invalidate # Invalidate the entire build.\\n def execute(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py\\nclass CacheKeyGenerator # Generates cache keys for versions of target sets.\\n def combine_cache_keys(cache_keys) # Returns a cache key for a list of target sets that already have cache keys.\\n def __init__(self, *base_fingerprint_inputs) # :base_fingerprint_inputs: Information to be included in the fingerprint for all \\n def key_for_target(self, target, transitive=False, fingerprint_strategy=None) # Get a key representing the given target and its sources.\\nclass BuildInvalidator # Invalidates build targets based on the SHA1 hash of source files and other input\\n def __init__(self, root)\\n def previous_key(self, cache_key) # If there was a previous successful build for the given key, return the previous \\n def needs_update(self, cache_key) # Check if the given cached item is invalid.\\n def update(self, cache_key) # Makes cache_key the valid version of the corresponding target set.\\n def force_invalidate_all(self) # Force-invalidates all cached items.\\n def force_invalidate(self, cache_key) # Force-invalidate the cached item.\\n def _sha_file(self, cache_key)\\n def _sha_file_by_id(self, id)\\n def _write_sha(self, cache_key)\\n def _read_sha(self, cache_key)\\n def _read_sha_by_id(self, id)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/invalidation/cache_manager.py\",\n \"text\": \"src/python/pants/invalidation/cache_manager.py\\n# coding=utf-8\\n# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).\\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\\n\\nfrom __future__ import (absolute_import, division, generators, nested_scopes, print_function,\\n unicode_literals, with_statement)\\n\\nimport os\\nimport shutil\\nimport sys\\nfrom hashlib import sha1\\n\\nfrom pants.build_graph.build_graph import sort_targets\\nfrom pants.build_graph.target import Target\\nfrom pants.invalidation.build_invalidator import BuildInvalidator, CacheKeyGenerator\\nfrom pants.util.dirutil import relative_symlink, safe_delete, safe_mkdir, safe_rmtree\\nfrom pants.util.memo import memoized_method\\n\\n\\nclass VersionedTargetSet(object):\\n \\\"\\\"\\\"Represents a list of targets, a corresponding CacheKey, and a flag determining whether the\\n list of targets is currently valid.\\n\\n When invalidating a single target, this can be used to represent that target as a singleton.\\n When checking the artifact cache, this can also be used to represent a list of targets that are\\n built together into a single artifact.\\n \\\"\\\"\\\"\\n\\n class IllegalResultsDir(Exception):\\n \\\"\\\"\\\"Indicate a problem interacting with a versioned target results directory.\\\"\\\"\\\"\\n\\n @staticmethod\\n def from_versioned_targets(versioned_targets):\\n \\\"\\\"\\\"\\n :API: public\\n \\\"\\\"\\\"\\n first_target = versioned_targets[0]\\n cache_manager = first_target._cache_manager\\n\\n # Quick sanity check; all the versioned targets should have the same cache manager.\\n # TODO(ryan): the way VersionedTargets store their own links to a single CacheManager instance\\n # feels hacky; see if there's a cleaner way for callers to handle awareness of the CacheManager.\\n for versioned_target in versioned_targets:\\n if versioned_target._cache_manager != cache_manager:\\n raise ValueError(\\\"Attempting to combine versioned targets {} and {} with different\\\"\\n \\\" CacheManager instances: {} and {}\\\".format(first_target, versioned_target,\\n cache_manager,\\n versioned_target._cache_manager))\\n return VersionedTargetSet(cache_manager, versioned_targets)\\n\\n def __init__(self, cache_manager, versioned_targets):\\n self._cache_manager = cache_manager\\n self.versioned_targets = versioned_targets\\n self.targets = [vt.target for vt in versioned_targets]\\n\\n # The following line is a no-op if cache_key was set in the VersionedTarget __init__ method.\\n self.cache_key = CacheKeyGenerator.combine_cache_keys([vt.cache_key\\n for vt in versioned_targets])\\n # NB: previous_cache_key may be None on the first build of a target.\\n self.previous_cache_key = cache_manager.previous_key(self.cache_key)\\n self.valid = self.previous_cache_key == self.cache_key\\n\\n if cache_manager.invalidation_report:\\n cache_mana\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py\",\n \"text\": \"src/python/pants/option/global_options.py\\nclass GlobalOptionsRegistrar\\n def register_bootstrap_options(cls, register) # Register bootstrap options.\\n def register_options(cls, register) # Register options not tied to any particular task or subsystem.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py\",\n \"text\": \"src/python/pants/option/options.py\\nclass Options # The outward-facing API for interacting with options.\\n class OptionTrackerRequiredError # Options requires an OptionTracker instance.\\n def complete_scopes(cls, scope_infos) # Expand a set of scopes to include all enclosing scopes.\\n def create(cls, env, config, known_scope_infos, args=None, bootstrap_option_values=None, option_tracker=None) # Create an Options instance.\\n def __init__(self, goals, scope_to_flags, target_specs, passthru, passthru_owner, help_request, parser_hierarchy, values_by_scope, bootstrap_option_values, known_scope_to_info, option_tracker) # The low-level constructor for an Options instance.\\n def tracker(self)\\n def help_request(self) # :API: public\\n def target_specs(self) # The targets to operate on.\\n def goals(self) # The requested goals, in the order specified on the cmd line.\\n def known_scope_to_info(self)\\n def scope_to_flags(self)\\n def drop_flag_values(self) # Returns a copy of these options that ignores values specified via flags.\\n def is_known_scope(self, scope) # Whether the given scope is known by this instance.\\n def passthru_args_for_scope(self, scope)\\n def register(self, scope, *args, **kwargs) # Register an option in the given scope.\\n def registration_function_for_optionable(self, optionable_class) # Returns a function for registering options on the given scope.\\n def register(*args, **kwargs)\\n def get_parser(self, scope) # Returns the parser for the given scope, so code can register on it directly.\\n def walk_parsers(self, callback)\\n def for_scope(self, scope, inherit_from_enclosing_scope=True) # Return the option values for the given scope.\\n def get_fingerprintable_for_scope(self, scope) # Returns a list of fingerprintable (option type, option value) pairs for the give\\n def __getitem__(self, scope)\\n def bootstrap_option_values(self) # Return the option values for bootstrap options.\\n def for_global_scope(self) # Return the option values for the global scope.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py\",\n \"text\": \"src/python/pants/task/task.py\\nclass TaskBase # Defines a lifecycle that prepares a task for execution and provides the base mac\\n def implementation_version(cls) # :API: public\\n def implementation_version_str(cls)\\n def stable_name(cls) # The stable name of this task type.\\n def _compute_stable_name(cls)\\n def subsystem_dependencies(cls)\\n def product_types(cls) # The list of products this Task produces. Set the product type(s) for this\\n def known_scope_infos(cls) # Yields ScopeInfo for all known scopes for this task, in no particular order.\\n def supports_passthru_args(cls) # Subclasses may override to indicate that they can use passthru args.\\n def _scoped_options(cls, options)\\n def get_alternate_target_roots(cls, options, address_mapper, build_graph)\\n def alternate_target_roots(cls, options, address_mapper, build_graph) # Allows a Task to propose alternate target roots from those specified on the CLI.\\n def invoke_prepare(cls, options, round_manager)\\n def prepare(cls, options, round_manager) # Prepares a task for execution.\\n def __init__(self, context, workdir) # Subclass __init__ methods, if defined, *must* follow this idiom:\\n def get_options(self) # Returns the option values for this task's scope.\\n def get_passthru_args(self) # :API: public\\n def workdir(self) # A scratch-space for this task that will be deleted by `clean-all`.\\n def _options_fingerprint(self, scope)\\n def fingerprint(self) # Returns a fingerprint for the identity of the task.\\n def artifact_cache_reads_enabled(self)\\n def artifact_cache_writes_enabled(self)\\n def invalidate(self) # Invalidates all targets for this task.\\n def create_target_dirs(self) # Whether to create a results_dir per VersionedTarget in the workdir of the Task.\\n def cache_target_dirs(self) # Whether to cache files in VersionedTarget's results_dir after exiting an invalid\\n def incremental(self) # Whether this Task implements incremental building of individual targets.\\n def cache_incremental(self) # For incremental tasks, indicates whether the results of incremental builds shoul\\n def invalidated(self, targets, invalidate_dependents=False, silent=False, fingerprint_strategy=None, topological_order=False) # Checks targets for invalidation, first checking the artifact cache.\\n def maybe_write_artifact(self, vt)\\n def _launch_background_workdir_cleanup(self, vts)\\n def _cleanup_workdir_stale_builds(self, vts)\\n def _should_cache_target_dir(self, vt) # Return true if the given vt should be written to a cache (if configured).\\n def _maybe_create_results_dirs(self, vts) # If `cache_target_dirs`, create results_dirs for the given versioned targets.\\n def check_artifact_cache_for(self, invalidation_check) # Decides which VTS to check the artifact cache for.\\n def check_artifact_cache(self, vts) # Checks the artifact cache for the specified list of VersionedTargetSets.\\n def do_check_artifact_cache(self, vts, pos\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.implementation_version","text":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.implementation_version\n @classmethod\n def implementation_version(cls):\n # Bump this version to invalidate all past artifacts generated by this task.\n return super(IndexJava, cls).implementation_version() + [('IndexJava', 6), ]","label":0},{"cid":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.product_types","text":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.product_types\n @classmethod\n def product_types(cls):\n return ['kythe_entries_files']","label":0},{"cid":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.prepare","text":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.prepare\n @classmethod\n def prepare(cls, options, round_manager):\n super(IndexJava, cls).prepare(options, round_manager)\n round_manager.require_data('kindex_files')","label":0},{"cid":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.register_options","text":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.register_options\n @classmethod\n def register_options(cls, register):\n super(IndexJava, cls).register_options(register)\n register('--force', type=bool, fingerprint=True,\n help='Re-index all targets, even if they are valid.')\n cls.register_jvm_tool(register,\n 'kythe-indexer',\n main=cls._KYTHE_INDEXER_MAIN)","label":1},{"cid":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.execute","text":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.execute\n def execute(self):\n def entries_file(_vt):\n return os.path.join(_vt.results_dir, 'index.entries')\n\n indexable_targets = IndexableJavaTargets.get(self.context)\n\n with self.invalidated(indexable_targets, invalidate_dependents=True) as invalidation_check:\n kindex_files = self.context.products.get_data('kindex_files')\n vts_to_index = (invalidation_check.all_vts if self.get_options().force\n else invalidation_check.invalid_vts)\n\n indexer_cp = self.tool_classpath('kythe-indexer')\n # Kythe jars embed a copy of Java 9's com.sun.tools.javac and javax.tools, for use on JDK8.\n # We must put these jars on the bootclasspath, ahead of any others, to ensure that we load\n # the Java 9 versions, and not the runtime's versions.\n jvm_options = ['-Xbootclasspath/p:{}'.format(':'.join(indexer_cp))]\n jvm_options.extend(self.get_options().jvm_options)\n\n for vt in vts_to_index:\n self.context.log.info('Kythe indexing {}'.format(vt.target.address.spec))\n kindex_file = kindex_files.get(vt.target)\n if not kindex_file:\n raise TaskError('No .kindex file found for {}'.format(vt.target.address.spec))\n args = [kindex_file, '--out', entries_file(vt)]\n result = self.runjava(classpath=indexer_cp, main=self._KYTHE_INDEXER_MAIN,\n jvm_options=jvm_options,\n args=args, workunit_name='kythe-index',\n workunit_labels=[WorkUnitLabel.COMPILER])\n if result != 0:\n raise TaskError('java {main} ... exited non-zero ({result})'.format(\n main=self._KYTHE_INDEXER_MAIN, result=result))\n\n for vt in invalidation_check.all_vts:\n self.context.products.get_data('kythe_entries_files', dict)[vt.target] = entries_file(vt)","label":1},{"cid":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.execute.entries_file","text":"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.execute.entries_file\n def entries_file(_vt):\n return os.path.join(_vt.results_dir, 'index.entries')","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.load","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.load\n @staticmethod\n def load(path):\n \"\"\"Loads a pushdb maintained in a properties file at the given path.\"\"\"\n with open(path, 'r') as props:\n properties = Properties.load(props)\n return PushDb(properties)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.__init__","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.__init__\n def __init__(self, sem_ver, named_ver, named_is_latest, sha, fingerprint):\n \"\"\"Records the most recent push/release of an artifact.\n\n :param Semver sem_ver: The last semantically versioned release (or Semver(0.0.0))\n :param Namedver named_ver: The last named release of this entry (or None)\n :param boolean named_is_latest: True if named_ver is the latest, false if sem_ver is\n :param string sha: The last Git SHA (or None)\n :param string fingerprint: A unique hash for the most recent version of the target.\n \"\"\"\n self.sem_ver = sem_ver\n self.named_ver = named_ver\n self.named_is_latest = named_is_latest\n self.sha = sha\n self.fingerprint = fingerprint","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.version","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.version\n def version(self):\n if self.named_is_latest:\n return self.named_ver\n else:\n return self.sem_ver","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_sem_ver","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_sem_ver\n def with_sem_ver(self, sem_ver):\n \"\"\"Returns a clone of this entry with the given sem_ver marked as the latest.\"\"\"\n return PushDb.Entry(sem_ver, self.named_ver, False, self.sha, self.fingerprint)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_named_ver","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_named_ver\n def with_named_ver(self, named_ver):\n \"\"\"Returns a clone of this entry with the given name_ver marked as the latest.\"\"\"\n return PushDb.Entry(self.sem_ver, named_ver, True, self.sha, self.fingerprint)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_sha_and_fingerprint","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_sha_and_fingerprint\n def with_sha_and_fingerprint(self, sha, fingerprint):\n \"\"\"Returns a clone of this entry with the given sha and fingerprint.\"\"\"\n return PushDb.Entry(self.sem_ver, self.named_ver, self.named_is_latest, sha, fingerprint)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.__repr__","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.__repr__\n def __repr__(self):\n return '<{}, {}, {}, {}, {}, {}>'.format(\n self.__class__.__name__, self.sem_ver, self.named_ver, self.named_is_latest,\n self.sha, self.fingerprint)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.__init__","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.__init__\n def __init__(self, props=None):\n self._props = props or OrderedDict()","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.get_entry","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.get_entry\n def get_entry(self, target):\n \"\"\"Given an internal target, return a PushDb.Entry, which might contain defaults.\"\"\"\n db_get, _ = self._accessors_for_target(target)\n\n major = int(db_get('revision.major', '0'))\n minor = int(db_get('revision.minor', '0'))\n patch = int(db_get('revision.patch', '0'))\n snapshot = str(db_get('revision.snapshot', 'false')).lower() == 'true'\n named_version = db_get('revision.named_version', None)\n named_is_latest = str(db_get('revision.named_is_latest', 'false')).lower() == 'true'\n sha = db_get('revision.sha', None)\n fingerprint = db_get('revision.fingerprint', None)\n sem_ver = Semver(major, minor, patch, snapshot=snapshot)\n named_ver = Namedver(named_version) if named_version else None\n return self.Entry(sem_ver, named_ver, named_is_latest, sha, fingerprint)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.set_entry","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.set_entry\n def set_entry(self, target, pushdb_entry):\n pe = pushdb_entry\n _, db_set = self._accessors_for_target(target)\n db_set('revision.major', pe.sem_ver.major)\n db_set('revision.minor', pe.sem_ver.minor)\n db_set('revision.patch', pe.sem_ver.patch)\n db_set('revision.snapshot', str(pe.sem_ver.snapshot).lower())\n if pe.named_ver:\n db_set('revision.named_version', pe.named_ver.version())\n db_set('revision.named_is_latest', str(pe.named_is_latest).lower())\n db_set('revision.sha', pe.sha)\n db_set('revision.fingerprint', pe.fingerprint)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target\n def _accessors_for_target(self, target):\n jar_dep, exported = target.get_artifact_info()\n if not exported:\n raise ValueError\n\n def key(prefix):\n return '{}.{}%{}'.format(prefix, jar_dep.org, jar_dep.name)\n\n def getter(prefix, default=None):\n return self._props.get(key(prefix), default)\n\n def setter(prefix, value):\n self._props[key(prefix)] = value\n\n return getter, setter","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.key","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.key\n def key(prefix):\n return '{}.{}%{}'.format(prefix, jar_dep.org, jar_dep.name)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.getter","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.getter\n def getter(prefix, default=None):\n return self._props.get(key(prefix), default)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.setter","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.setter\n def setter(prefix, value):\n self._props[key(prefix)] = value","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.dump","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.dump\n def dump(self, path):\n \"\"\"Saves the pushdb as a properties file to the given path.\"\"\"\n with open(path, 'w') as props:\n Properties.dump(self._props, props)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter.__init__","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter.__init__\n def __init__(self, get_db, tag):\n self._get_db = get_db\n self._tag = tag","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter.write","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter.write\n def write(self, target, path):\n dependencies = OrderedDict()\n for internal_dep in target_internal_dependencies(target):\n jar = self._as_versioned_jar(internal_dep)\n key = (jar.org, jar.name)\n dependencies[key] = self._internaldep(jar, internal_dep)\n\n for jar in target.jar_dependencies:\n jardep = self._jardep(jar)\n if jardep:\n key = (jar.org, jar.name, jar.classifier)\n dependencies[key] = jardep\n\n target_jar = self._internaldep(self._as_versioned_jar(target), target)\n if target_jar:\n target_jar = target_jar.extend(dependencies=dependencies.values())\n\n template_relpath = os.path.join(_TEMPLATES_RELPATH, 'pom.xml.mustache')\n template_text = pkgutil.get_data(__name__, template_relpath)\n generator = Generator(template_text, project=target_jar)\n with safe_open(path, 'w') as output:\n generator.write(output)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._as_versioned_jar","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._as_versioned_jar\n def _as_versioned_jar(self, internal_target):\n \"\"\"Fetches the jar representation of the given target, and applies the latest pushdb version.\"\"\"\n jar, _ = internal_target.get_artifact_info()\n pushdb_entry = self._get_db(internal_target).get_entry(internal_target)\n return jar.copy(rev=pushdb_entry.version().version())","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._internaldep","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._internaldep\n def _internaldep(self, jar_dependency, target):\n template_data = self._jardep(jar_dependency)\n if isinstance(target.provides.publication_metadata, OSSRHPublicationMetadata):\n pom = target.provides.publication_metadata\n\n # Forming the project name from the coordinates like this is acceptable as a fallback when\n # the user supplies no project name.\n # See: http://central.sonatype.org/pages/requirements.html#project-name-description-and-url\n name = pom.name or '{}:{}'.format(jar_dependency.org, jar_dependency.name)\n\n template_data = template_data.extend(name=name,\n description=pom.description,\n url=pom.url,\n licenses=pom.licenses,\n scm=pom.scm.tagged(self._tag),\n developers=pom.developers)\n return template_data","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._jardep","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._jardep\n def _jardep(self, jar):\n return TemplateData(\n classifier=jar.classifier,\n artifact_id=jar.name,\n group_id=jar.org,\n version=jar.rev,\n scope='compile',\n excludes=[TemplateData(org=exclude.org, name=exclude.name)\n for exclude in jar.excludes if exclude.name])","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::coordinate","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::coordinate\ndef coordinate(org, name, rev=None):\n return '{}#{};{}'.format(org, name, rev) if rev else '{}#{}'.format(org, name)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::jar_coordinate","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::jar_coordinate\ndef jar_coordinate(jar, rev=None):\n return coordinate(jar.org, jar.name, rev or jar.rev)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::pushdb_coordinate","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::pushdb_coordinate\ndef pushdb_coordinate(jar, entry):\n return jar_coordinate(jar, rev=entry.version().version())","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::target_internal_dependencies","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::target_internal_dependencies\ndef target_internal_dependencies(target):\n \"\"\"Returns internal Jarable dependencies that were \"directly\" declared.\n\n Directly declared deps are those that are explicitly listed in the definition of a\n target, rather than being depended on transitively. But in order to walk through\n aggregator targets such as `target`, `dependencies`, or `jar_library`, this recursively\n descends the dep graph and stops at Jarable instances.\"\"\"\n for dep in target.dependencies:\n if isinstance(dep, Jarable):\n yield dep\n else:\n for childdep in target_internal_dependencies(dep):\n yield childdep","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.register_options","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.register_options\n @classmethod\n def register_options(cls, register):\n super(JarPublish, cls).register_options(register)\n\n # TODO(John Sirois): Support a preview mode that outputs a file with entries like:\n # artifact id:\n # revision:\n # publish: (true|false)\n # changelog:\n #\n # Allow re-running this goal with the file as input to support forcing an arbitrary set of\n # revisions and supply of hand edited changelogs.\n\n register('--dryrun', default=True, type=bool,\n help='Run through a push without actually pushing artifacts, editing publish dbs or '\n 'otherwise writing data')\n register('--commit', default=True, type=bool,\n help='Commit the push db. Turn off for local testing.')\n register('--local', metavar='<PATH>',\n help='Publish jars to a maven repository on the local filesystem at this path.')\n register('--local-snapshot', default=True, type=bool,\n help='If --local is specified, publishes jars with -SNAPSHOT revision suffixes.')\n register('--named-snapshot', default=None,\n help='Publish all artifacts with the given snapshot name, replacing their version. '\n 'This is not Semantic Versioning compatible, but is easier to consume in cases '\n 'where many artifacts must align.')\n register('--transitive', default=True, type=bool,\n help='Publish the specified targets and all their internal dependencies transitively.')\n register('--force', type=bool,\n help='Force pushing jars even if there have been no changes since the last push.')\n register('--override', type=list,\n help='Specifies a published jar revision override in the form: '\n '([org]#[name]|[target spec])=[new revision] '\n 'For example, to specify 2 overrides: '\n '--override=com.foo.bar#baz=0.1.2 --override=s","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.prepare","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.prepare\n @classmethod\n def prepare(cls, options, round_manager):\n super(JarPublish, cls).prepare(options, round_manager)\n round_manager.require('jars')\n round_manager.require('javadoc')\n round_manager.require('scaladoc')","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.__init__","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.__init__\n def __init__(self, *args, **kwargs):\n super(JarPublish, self).__init__(*args, **kwargs)\n self.cachedir = os.path.join(self.workdir, 'cache')\n\n self._jvm_options = self.get_options().jvm_options\n\n self.scm = get_scm()\n self.log = self.context.log\n\n if self.get_options().local:\n local_repo = dict(\n resolver='publish_local',\n path=os.path.abspath(os.path.expanduser(self.get_options().local)),\n confs=['default'],\n auth=None\n )\n self.repos = defaultdict(lambda: local_repo)\n self.commit = False\n self.local_snapshot = self.get_options().local_snapshot\n else:\n self.repos = self.get_options().repos\n if not self.repos:\n raise TaskError(\n \"This repo is not configured to publish externally! Please configure per\\n\"\n \"http://pantsbuild.org/publish.html#authenticating-to-the-artifact-repository,\\n\"\n \"by setting --publish-jar-repos=<dict> or re-run with '--publish-jar-local=<dir>'.\")\n for repo, data in self.repos.items():\n auth = data.get('auth')\n if auth:\n credentials = next(iter(self.context.resolve(auth)))\n user = credentials.username(data['resolver'])\n password = credentials.password(data['resolver'])\n self.context.log.debug('Found auth for repo={} user={}'.format(repo, user))\n self.repos[repo]['username'] = user\n self.repos[repo]['password'] = password\n self.commit = self.get_options().commit\n self.push_postscript = self.get_options().push_postscript or ''\n self.local_snapshot = False\n\n self.named_snapshot = self.get_options().named_snapshot\n if self.named_snapshot:\n self.named_snapshot = Namedver.parse(self.named_snapshot)\n\n self.dryrun = self.get_options().dryrun\n self.transitive = self.get_options().transitive\n self.force = self.get_options().force\n self.publish_changelog = sel","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.__init__.parse_jarcoordinate","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.__init__.parse_jarcoordinate\n def parse_jarcoordinate(coordinate):\n components = coordinate.split('#', 1)\n if len(components) == 2:\n org, name = components\n return org, name\n else:\n spec = components[0]\n address = Address.parse(spec)\n try:\n self.context.build_graph.inject_address_closure(address)\n target = self.context.build_graph.get_target(address)\n if not target:\n siblings = self.context.address_mapper.addresses_in_spec_path(address.spec_path)\n prompt = 'did you mean' if len(siblings) == 1 else 'maybe you meant one of these'\n raise TaskError('{} => {}?:\\n {}'.format(address, prompt,\n '\\n '.join(str(a) for a in siblings)))\n if not target.is_exported:\n raise TaskError('{} is not an exported target'.format(coordinate))\n return target.provides.org, target.provides.name\n except (BuildFile.BuildFileError,\n BuildFileParser.BuildFileParserError,\n AddressLookupError) as e:\n raise TaskError('{message}\\n Problem identifying target at {spec}'\n .format(message=e, spec=spec))","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.confirm_push","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.confirm_push\n def confirm_push(self, coord, version):\n \"\"\"Ask the user if a push should be done for a particular version of a\n particular coordinate. Return True if the push should be done\"\"\"\n if not self.get_options().prompt:\n return True\n try:\n isatty = os.isatty(sys.stdin.fileno())\n except ValueError:\n # In tests, sys.stdin might not have a fileno\n isatty = False\n if not isatty:\n return True\n push = raw_input('\\nPublish {} with revision {} ? [y|N] '.format(\n coord, version\n ))\n print('\\n')\n return push.strip().lower() == 'y'","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish._copy_artifact","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish._copy_artifact\n def _copy_artifact(self, tgt, jar, version, typename, suffix='', extension='jar',\n artifact_ext='', override_name=None):\n \"\"\"Copy the products for a target into the artifact path for the jar/version\"\"\"\n genmap = self.context.products.get(typename)\n product_mapping = genmap.get(tgt)\n if product_mapping is None:\n raise ValueError(\"No product mapping in {} for {}. \"\n \"You may need to run some other task first\".format(typename, tgt))\n for basedir, jars in product_mapping.items():\n for artifact in jars:\n path = self.artifact_path(jar, version, name=override_name, suffix=suffix,\n extension=extension, artifact_ext=artifact_ext)\n safe_mkdir(os.path.dirname(path))\n shutil.copy(os.path.join(basedir, artifact), path)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish._ivy_jvm_options","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish._ivy_jvm_options\n def _ivy_jvm_options(self, repo):\n \"\"\"Get the JVM options for ivy authentication, if needed.\"\"\"\n # Get authentication for the publish repo if needed.\n if not repo.get('auth'):\n # No need to copy here, as this list isn't modified by the caller.\n return self._jvm_options\n\n # Create a copy of the options, so that the modification is appropriately transient.\n jvm_options = copy(self._jvm_options)\n user = repo.get('username')\n password = repo.get('password')\n if user and password:\n jvm_options.append('-Dlogin={}'.format(user))\n jvm_options.append('-Dpassword={}'.format(password))\n else:\n raise TaskError('Unable to publish to {}. {}'\n .format(repo.get('resolver'), repo.get('help', '')))\n return jvm_options","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.publish","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.publish\n def publish(self, publications, jar, entry, repo, published):\n \"\"\"Run ivy to publish a jar. ivyxml_path is the path to the ivy file; published\n is a list of jars published so far (including this one). entry is a pushdb entry.\"\"\"\n\n try:\n ivy = Bootstrapper.default_ivy()\n except Bootstrapper.Error as e:\n raise TaskError('Failed to push {0}! {1}'.format(pushdb_coordinate(jar, entry), e))\n\n path = repo.get('path')\n ivysettings = self.generate_ivysettings(ivy, published, publish_local=path)\n\n version = entry.version().version()\n ivyxml = self.generate_ivy(jar, version, publications)\n\n resolver = repo['resolver']\n args = [\n '-settings', ivysettings,\n '-ivy', ivyxml,\n\n # Without this setting, the ivy.xml is delivered to the CWD, littering the workspace. We\n # don't need the ivy.xml, so just give it path under the workdir we won't use.\n '-deliverto', ivyxml + '.unused',\n\n '-publish', resolver,\n '-publishpattern', '{}/[organisation]/[module]/'\n '[artifact]-[revision](-[classifier]).[ext]'.format(self.workdir),\n '-revision', version,\n '-m2compatible',\n ]\n\n # TODO(John Sirois): global logging options should be hidden behind some sort of log manager\n # that we can:\n # a.) obtain a handle to (dependency injection or manual plumbing)\n # b.) query for log detail, ie: `if log_manager.is_verbose:`\n if self.get_options().level == 'debug':\n args.append('-verbose')\n\n if self.local_snapshot:\n args.append('-overwrite')\n\n try:\n jvm_options = self._ivy_jvm_options(repo)\n ivy.execute(jvm_options=jvm_options, args=args,\n workunit_factory=self.context.new_workunit, workunit_name='ivy-publish')\n except Ivy.Error as e:\n raise TaskError('Failed to push {0}! {1}'.format(pushdb_coordinate(jar, entry), e))","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute\n def execute(self):\n self.check_clean_master(commit=(not self.dryrun and self.commit))\n\n exported_targets = self.exported_targets()\n self.check_targets(exported_targets)\n\n pushdbs = {}\n\n def get_db(tgt):\n # TODO(tdesai) Handle resource type in get_db.\n if tgt.provides is None:\n raise TaskError('trying to publish target {!r} which does not provide an artifact'.format(tgt))\n dbfile = tgt.provides.repo.push_db(tgt)\n result = pushdbs.get(dbfile)\n if not result:\n # Create an empty pushdb if no dbfile exists.\n if (os.path.exists(dbfile)):\n db = PushDb.load(dbfile)\n else:\n safe_mkdir(os.path.dirname(dbfile))\n db = PushDb()\n try:\n repo = self.repos[tgt.provides.repo.name]\n except KeyError:\n raise TaskError('Repository {0} has no entry in the --repos option.'.format(\n tgt.provides.repo.name))\n result = (db, dbfile, repo)\n pushdbs[dbfile] = result\n return result\n\n def get_pushdb(tgt):\n return get_db(tgt)[0]\n\n def fingerprint_internal(tgt):\n pushdb = get_pushdb(tgt)\n entry = pushdb.get_entry(tgt)\n return entry.fingerprint or '0.0.0'\n\n def stage_artifacts(tgt, jar, version, tag, changelog):\n publications = OrderedSet()\n\n # TODO Remove this once we fix https://github.com/pantsbuild/pants/issues/1229\n if (not self.context.products.get('jars').has(tgt) and\n not self.get_options().individual_plugins):\n raise TaskError('Expected to find a primary artifact for {} but there was no jar for it.'\n .format(tgt.address.reference()))\n\n # TODO Remove this guard once we fix https://github.com/pantsbuild/pants/issues/1229, there\n # should always be a primary artifact.\n if self.context.products.get('jars').has(tgt):\n self._copy_artifact(tgt, jar, version, typename='ja","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.get_db","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.get_db\n def get_db(tgt):\n # TODO(tdesai) Handle resource type in get_db.\n if tgt.provides is None:\n raise TaskError('trying to publish target {!r} which does not provide an artifact'.format(tgt))\n dbfile = tgt.provides.repo.push_db(tgt)\n result = pushdbs.get(dbfile)\n if not result:\n # Create an empty pushdb if no dbfile exists.\n if (os.path.exists(dbfile)):\n db = PushDb.load(dbfile)\n else:\n safe_mkdir(os.path.dirname(dbfile))\n db = PushDb()\n try:\n repo = self.repos[tgt.provides.repo.name]\n except KeyError:\n raise TaskError('Repository {0} has no entry in the --repos option.'.format(\n tgt.provides.repo.name))\n result = (db, dbfile, repo)\n pushdbs[dbfile] = result\n return result","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.get_pushdb","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.get_pushdb\n def get_pushdb(tgt):\n return get_db(tgt)[0]","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.fingerprint_internal","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.fingerprint_internal\n def fingerprint_internal(tgt):\n pushdb = get_pushdb(tgt)\n entry = pushdb.get_entry(tgt)\n return entry.fingerprint or '0.0.0'","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.stage_artifacts","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.stage_artifacts\n def stage_artifacts(tgt, jar, version, tag, changelog):\n publications = OrderedSet()\n\n # TODO Remove this once we fix https://github.com/pantsbuild/pants/issues/1229\n if (not self.context.products.get('jars').has(tgt) and\n not self.get_options().individual_plugins):\n raise TaskError('Expected to find a primary artifact for {} but there was no jar for it.'\n .format(tgt.address.reference()))\n\n # TODO Remove this guard once we fix https://github.com/pantsbuild/pants/issues/1229, there\n # should always be a primary artifact.\n if self.context.products.get('jars').has(tgt):\n self._copy_artifact(tgt, jar, version, typename='jars')\n publications.add(self.Publication(name=jar.name, classifier=None, ext='jar'))\n\n self.create_source_jar(tgt, jar, version)\n publications.add(self.Publication(name=jar.name, classifier='sources', ext='jar'))\n\n # don't request docs unless they are available for all transitive targets\n # TODO: doc products should be checked by an independent jar'ing task, and\n # conditionally enabled; see https://github.com/pantsbuild/pants/issues/568\n doc_jar = self.create_doc_jar(tgt, jar, version)\n if doc_jar:\n publications.add(self.Publication(name=jar.name, classifier='javadoc', ext='jar'))\n\n if self.publish_changelog:\n changelog_path = self.artifact_path(jar, version, suffix='-CHANGELOG', extension='txt')\n with safe_open(changelog_path, 'wb') as changelog_file:\n changelog_file.write(changelog.encode('utf-8'))\n publications.add(self.Publication(name=jar.name, classifier='CHANGELOG', ext='txt'))\n\n # Process any extra jars that might have been previously generated for this target, or a\n # target that it was derived from.\n for extra_product, extra_config in (self.get_options().p","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.artifact_path","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.artifact_path\n def artifact_path(self, jar, version, name=None, suffix='', extension='jar', artifact_ext=''):\n return os.path.join(self.workdir, jar.org, jar.name + artifact_ext,\n '{}{}-{}{}.{}'.format((name or jar.name),\n artifact_ext if name != 'ivy' else '',\n version,\n suffix,\n extension))","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.check_for_duplicate_artifacts","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.check_for_duplicate_artifacts\n def check_for_duplicate_artifacts(self, targets):\n targets_by_artifact = defaultdict(list)\n duplicates = set()\n for target in targets:\n artifact = target.provides\n if artifact in targets_by_artifact:\n duplicates.add(artifact)\n targets_by_artifact[artifact].append(target)\n\n def duplication_message(artifact):\n specs = sorted('\\n {}'.format(t.address.spec) for t in targets_by_artifact[artifact])\n return '\\n {artifact} is defined by:{specs}'.format(artifact=artifact, specs=''.join(specs))\n\n if duplicates:\n raise self.DuplicateArtifactError('Multiple targets define the same artifacts!\\n{}'.format(\n '\\n'.join(duplication_message(artifact) for artifact in duplicates)))","label":0},{"cid":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.check_for_duplicate_artifacts.duplication_message","text":"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.check_for_duplicate_artifacts.duplication_message\n def duplication_message(artifact):\n specs = sorted('\\n {}'.format(t.address.spec) for t in targets_by_artifact[artifact])\n return '\\n {artifact} is defined by:{specs}'.format(artifact=artifact, specs=''.join(specs))","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheSetup.register_options","text":"src/python/pants/cache/cache_setup.py::CacheSetup.register_options\n @classmethod\n def register_options(cls, register):\n super(CacheSetup, cls).register_options(register)\n default_cache = [os.path.join(get_buildroot(), '.cache')]\n register('--read', type=bool, default=True,\n help='Read build artifacts from cache, if available.')\n register('--write', type=bool, default=True,\n help='Write build artifacts to cache, if available.')\n register('--overwrite', advanced=True, type=bool,\n help='If writing build artifacts to cache, overwrite existing artifacts '\n 'instead of skipping them.')\n register('--resolver', advanced=True, choices=['none', 'rest'], default='none',\n help='Select which resolver strategy to use for discovering URIs that access '\n 'artifact caches. none: use URIs from static config options, i.e. '\n '--read-from, --write-to. rest: look up URIs by querying a RESTful '\n 'URL, which is a remote address from --read-from, --write-to.')\n register('--read-from', advanced=True, type=list, default=default_cache,\n help='The URIs of artifact caches to read directly from. Each entry is a URL of '\n 'a RESTful cache, a path of a filesystem cache, or a pipe-separated list of '\n 'alternate caches to choose from. This list is also used as input to '\n 'the resolver. When resolver is \\'none\\' list is used as is.')\n register('--write-to', advanced=True, type=list, default=default_cache,\n help='The URIs of artifact caches to write directly to. Each entry is a URL of'\n 'a RESTful cache, a path of a filesystem cache, or a pipe-separated list of '\n 'alternate caches to choose from. This list is also used as input to '\n 'the resolver. When resolver is \\'none\\' list is used as is.')\n register('--compression-level', adv","label":1},{"cid":"src/python/pants/cache/cache_setup.py::CacheSetup.create_cache_factory_for_task","text":"src/python/pants/cache/cache_setup.py::CacheSetup.create_cache_factory_for_task\n @classmethod\n def create_cache_factory_for_task(cls, task, pinger=None, resolver=None):\n return CacheFactory(cls.scoped_instance(task).get_options(),\n task.context.log, task.stable_name(), pinger=pinger, resolver=resolver)","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory.__init__","text":"src/python/pants/cache/cache_setup.py::CacheFactory.__init__\n def __init__(self, options, log, stable_name, pinger=None, resolver=None):\n \"\"\"Create a cache factory from settings.\n\n :param options: Task's scoped options.\n :param log: Task's context log.\n :param stable_name: Task's stable name.\n :param pinger: Pinger to choose the best remote artifact cache URL.\n :param resolver: Resolver to look up remote artifact cache URLs.\n :return: cache factory.\n \"\"\"\n self._options = options\n self._log = log\n self._stable_name = stable_name\n\n # Created on-demand.\n self._read_cache = None\n self._write_cache = None\n\n # Protects local filesystem setup, and assignment to the references above.\n self._cache_setup_lock = threading.Lock()\n\n # Caches are supposed to be close, and we don't want to waste time pinging on no-op builds.\n # So we ping twice with a short timeout.\n # TODO: Make lazy.\n self._pinger = pinger or Pinger(timeout=self._options.pinger_timeout,\n tries=self._options.pinger_tries)\n\n # resolver is also close but failing to resolve might have broader impact than\n # single ping failure, therefore use a higher timeout with more retries.\n if resolver:\n self._resolver = resolver\n elif self._options.resolver == 'rest':\n self._resolver = RESTfulResolver(timeout=1.0, tries=3)\n else:\n self._resolver = NoopResolver()","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory.read_cache_available","text":"src/python/pants/cache/cache_setup.py::CacheFactory.read_cache_available\n def read_cache_available(self):\n return self._options.read and bool(self._options.read_from) and self.get_read_cache()","label":1},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory.write_cache_available","text":"src/python/pants/cache/cache_setup.py::CacheFactory.write_cache_available\n def write_cache_available(self):\n return self._options.write and bool(self._options.write_to) and self.get_write_cache()","label":1},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory.overwrite","text":"src/python/pants/cache/cache_setup.py::CacheFactory.overwrite\n def overwrite(self):\n return self._options.overwrite","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory.get_read_cache","text":"src/python/pants/cache/cache_setup.py::CacheFactory.get_read_cache\n def get_read_cache(self):\n \"\"\"Returns the read cache for this setup, creating it if necessary.\n\n Returns None if no read cache is configured.\n \"\"\"\n if self._options.read_from and not self._read_cache:\n cache_spec = self._resolve(self._sanitize_cache_spec(self._options.read_from))\n if cache_spec:\n with self._cache_setup_lock:\n self._read_cache = self._do_create_artifact_cache(cache_spec, 'will read from')\n return self._read_cache","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory.get_write_cache","text":"src/python/pants/cache/cache_setup.py::CacheFactory.get_write_cache\n def get_write_cache(self):\n \"\"\"Returns the write cache for this setup, creating it if necessary.\n\n Returns None if no write cache is configured.\n \"\"\"\n if self._options.write_to and not self._write_cache:\n cache_spec = self._resolve(self._sanitize_cache_spec(self._options.write_to))\n if cache_spec:\n with self._cache_setup_lock:\n self._write_cache = self._do_create_artifact_cache(cache_spec, 'will write to')\n return self._write_cache","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory._sanitize_cache_spec","text":"src/python/pants/cache/cache_setup.py::CacheFactory._sanitize_cache_spec\n def _sanitize_cache_spec(self, spec):\n if not isinstance(spec, (list, tuple)):\n raise InvalidCacheSpecError('Invalid artifact cache spec type: {0} ({1})'.format(\n type(spec), spec))\n\n if not spec:\n raise EmptyCacheSpecError()\n\n if len(spec) > 2:\n raise TooManyCacheSpecsError('Too many artifact cache specs: ({0})'.format(spec))\n\n local_specs = [s for s in spec if self.is_local(s)]\n remote_specs = [s for s in spec if self.is_remote(s)]\n\n if not local_specs and not remote_specs:\n raise CacheSpecFormatError('Invalid cache spec: {0}, must be either local or remote'\n .format(spec))\n\n if len(spec) == 2:\n if not local_specs:\n raise LocalCacheSpecRequiredError('One of two cache specs must be a local cache path.')\n if not remote_specs:\n raise RemoteCacheSpecRequiredError('One of two cache specs must be a remote spec.')\n\n local_spec = local_specs[0] if len(local_specs) > 0 else None\n remote_spec = remote_specs[0] if len(remote_specs) > 0 else None\n\n return CacheSpec(local=local_spec, remote=remote_spec)","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory._resolve","text":"src/python/pants/cache/cache_setup.py::CacheFactory._resolve\n def _resolve(self, spec):\n \"\"\"Attempt resolving cache URIs when a remote spec is provided. \"\"\"\n if not spec.remote:\n return spec\n\n try:\n resolved_urls = self._resolver.resolve(spec.remote)\n if resolved_urls:\n # keep the bar separated list of URLs convention\n return CacheSpec(local=spec.local, remote='|'.join(resolved_urls))\n # no-op\n return spec\n except Resolver.ResolverError as e:\n self._log.warn('Error while resolving from {0}: {1}'.format(spec.remote, str(e)))\n # If for some reason resolver fails we continue to use local cache\n if spec.local:\n return CacheSpec(local=spec.local, remote=None)\n # resolver fails but there is no local cache\n return None","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory.is_local","text":"src/python/pants/cache/cache_setup.py::CacheFactory.is_local\n @staticmethod\n def is_local(string_spec):\n return string_spec.startswith('/') or string_spec.startswith('~')","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory.is_remote","text":"src/python/pants/cache/cache_setup.py::CacheFactory.is_remote\n @staticmethod\n def is_remote(string_spec):\n # both artifact cache and resolver use REST, add new protocols here once they are supported\n return string_spec.startswith('http://') or string_spec.startswith('https://')","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory._baseurl","text":"src/python/pants/cache/cache_setup.py::CacheFactory._baseurl\n def _baseurl(self, url):\n parsed_url = urlparse.urlparse(url)\n return '{scheme}://{netloc}'.format(scheme=parsed_url.scheme, netloc=parsed_url.netloc)","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory.get_available_urls","text":"src/python/pants/cache/cache_setup.py::CacheFactory.get_available_urls\n def get_available_urls(self, urls):\n \"\"\"Return reachable urls sorted by their ping times.\"\"\"\n baseurl_to_urls = {self._baseurl(url): url for url in urls}\n pingtimes = self._pinger.pings(baseurl_to_urls.keys()) # List of pairs (host, time in ms).\n self._log.debug('Artifact cache server ping times: {}'\n .format(', '.join(['{}: {:.6f} secs'.format(*p) for p in pingtimes])))\n\n sorted_pingtimes = sorted(pingtimes, key=lambda x: x[1])\n available_urls = [baseurl_to_urls[baseurl] for baseurl, pingtime in sorted_pingtimes\n if pingtime < Pinger.UNREACHABLE]\n self._log.debug('Available cache servers: {0}'.format(available_urls))\n\n return available_urls","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache","text":"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache\n def _do_create_artifact_cache(self, spec, action):\n \"\"\"Returns an artifact cache for the specified spec.\n\n spec can be:\n - a path to a file-based cache root.\n - a URL of a RESTful cache root.\n - a bar-separated list of URLs, where we'll pick the one with the best ping times.\n - A list or tuple of two specs, local, then remote, each as described above\n \"\"\"\n compression = self._options.compression_level\n if compression not in range(1, 10):\n raise ValueError('compression_level must be an integer 1-9: {}'.format(compression))\n\n artifact_root = self._options.pants_workdir\n\n def create_local_cache(parent_path):\n path = os.path.join(parent_path, self._stable_name)\n self._log.debug('{0} {1} local artifact cache at {2}'\n .format(self._stable_name, action, path))\n return LocalArtifactCache(artifact_root, path, compression,\n self._options.max_entries_per_target,\n permissions=self._options.write_permissions,\n dereference=self._options.dereference_symlinks)\n\n def create_remote_cache(remote_spec, local_cache):\n urls = self.get_available_urls(remote_spec.split('|'))\n\n if len(urls) > 0:\n best_url_selector = BestUrlSelector(['{}/{}'.format(url.rstrip('/'), self._stable_name)\n for url in urls])\n local_cache = local_cache or TempLocalArtifactCache(artifact_root, compression)\n return RESTfulArtifactCache(artifact_root, best_url_selector, local_cache)\n\n local_cache = create_local_cache(spec.local) if spec.local else None\n remote_cache = create_remote_cache(spec.remote, local_cache) if spec.remote else None\n if remote_cache:\n return remote_cache\n return local_cache","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache.create_local_cache","text":"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache.create_local_cache\n def create_local_cache(parent_path):\n path = os.path.join(parent_path, self._stable_name)\n self._log.debug('{0} {1} local artifact cache at {2}'\n .format(self._stable_name, action, path))\n return LocalArtifactCache(artifact_root, path, compression,\n self._options.max_entries_per_target,\n permissions=self._options.write_permissions,\n dereference=self._options.dereference_symlinks)","label":0},{"cid":"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache.create_remote_cache","text":"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache.create_remote_cache\n def create_remote_cache(remote_spec, local_cache):\n urls = self.get_available_urls(remote_spec.split('|'))\n\n if len(urls) > 0:\n best_url_selector = BestUrlSelector(['{}/{}'.format(url.rstrip('/'), self._stable_name)\n for url in urls])\n local_cache = local_cache or TempLocalArtifactCache(artifact_root, compression)\n return RESTfulArtifactCache(artifact_root, best_url_selector, local_cache)","label":0},{"cid":"src/python/pants/core_tasks/invalidate.py::Invalidate.execute","text":"src/python/pants/core_tasks/invalidate.py::Invalidate.execute\n def execute(self):\n build_invalidator_dir = os.path.join(self.get_options().pants_workdir, 'build_invalidator')\n safe_rmtree(build_invalidator_dir)","label":1},{"cid":"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.combine_cache_keys","text":"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.combine_cache_keys\n @staticmethod\n def combine_cache_keys(cache_keys):\n \"\"\"Returns a cache key for a list of target sets that already have cache keys.\n\n This operation is 'idempotent' in the sense that if cache_keys contains a single key\n then that key is returned.\n\n Note that this operation is commutative but not associative. We use the term 'combine' rather\n than 'merge' or 'union' to remind the user of this. Associativity is not a necessary property,\n in practice.\n \"\"\"\n if len(cache_keys) == 1:\n return cache_keys[0]\n else:\n combined_id = Target.maybe_readable_combine_ids(cache_key.id for cache_key in cache_keys)\n combined_hash = hash_all(sorted(cache_key.hash for cache_key in cache_keys))\n return CacheKey(combined_id, combined_hash)","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.__init__","text":"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.__init__\n def __init__(self, *base_fingerprint_inputs):\n \"\"\"\n :base_fingerprint_inputs: Information to be included in the fingerprint for all cache keys\n generated by this CacheKeyGenerator.\n \"\"\"\n hasher = hashlib.sha1()\n hasher.update(GLOBAL_CACHE_KEY_GEN_VERSION)\n for base_fingerprint_input in base_fingerprint_inputs:\n hasher.update(base_fingerprint_input)\n self._base_hasher = hasher","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.key_for_target","text":"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.key_for_target\n def key_for_target(self, target, transitive=False, fingerprint_strategy=None):\n \"\"\"Get a key representing the given target and its sources.\n\n A key for a set of targets can be created by calling combine_cache_keys()\n on the target's individual cache keys.\n\n :target: The target to create a CacheKey for.\n :transitive: Whether or not to include a fingerprint of all of :target:'s dependencies.\n :fingerprint_strategy: A FingerprintStrategy instance, which can do per task, finer grained\n fingerprinting of a given Target.\n \"\"\"\n\n hasher = self._base_hasher.copy()\n key_suffix = hasher.hexdigest()[:12]\n if transitive:\n target_key = target.transitive_invalidation_hash(fingerprint_strategy)\n else:\n target_key = target.invalidation_hash(fingerprint_strategy)\n if target_key is not None:\n full_key = '{target_key}_{key_suffix}'.format(target_key=target_key, key_suffix=key_suffix)\n return CacheKey(target.id, full_key)\n else:\n return None","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.__init__","text":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.__init__\n def __init__(self, root):\n self._root = os.path.join(root, GLOBAL_CACHE_KEY_GEN_VERSION)\n safe_mkdir(self._root)","label":1},{"cid":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.previous_key","text":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.previous_key\n def previous_key(self, cache_key):\n \"\"\"If there was a previous successful build for the given key, return the previous key.\n\n :param cache_key: A CacheKey object (as returned by CacheKeyGenerator.key_for().\n :returns: The previous cache_key, or None if there was not a previous build.\n \"\"\"\n previous_hash = self._read_sha(cache_key)\n if not previous_hash:\n return None\n return CacheKey(cache_key.id, previous_hash)","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.needs_update","text":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.needs_update\n def needs_update(self, cache_key):\n \"\"\"Check if the given cached item is invalid.\n\n :param cache_key: A CacheKey object (as returned by CacheKeyGenerator.key_for().\n :returns: True if the cached version of the item is out of date.\n \"\"\"\n return self._read_sha(cache_key) != cache_key.hash","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.update","text":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.update\n def update(self, cache_key):\n \"\"\"Makes cache_key the valid version of the corresponding target set.\n\n :param cache_key: A CacheKey object (typically returned by CacheKeyGenerator.key_for()).\n \"\"\"\n self._write_sha(cache_key)","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.force_invalidate_all","text":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.force_invalidate_all\n def force_invalidate_all(self):\n \"\"\"Force-invalidates all cached items.\"\"\"\n safe_mkdir(self._root, clean=True)","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.force_invalidate","text":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.force_invalidate\n def force_invalidate(self, cache_key):\n \"\"\"Force-invalidate the cached item.\"\"\"\n try:\n os.unlink(self._sha_file(cache_key))\n except OSError as e:\n if e.errno != errno.ENOENT:\n raise","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._sha_file","text":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._sha_file\n def _sha_file(self, cache_key):\n return self._sha_file_by_id(cache_key.id)","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._sha_file_by_id","text":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._sha_file_by_id\n def _sha_file_by_id(self, id):\n return os.path.join(self._root, safe_filename(id, extension='.hash'))","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._write_sha","text":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._write_sha\n def _write_sha(self, cache_key):\n with open(self._sha_file(cache_key), 'w') as fd:\n fd.write(cache_key.hash)","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._read_sha","text":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._read_sha\n def _read_sha(self, cache_key):\n return self._read_sha_by_id(cache_key.id)","label":0},{"cid":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._read_sha_by_id","text":"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._read_sha_by_id\n def _read_sha_by_id(self, id):\n try:\n with open(self._sha_file_by_id(id), 'rb') as fd:\n return fd.read().strip()\n except IOError as e:\n if e.errno != errno.ENOENT:\n raise\n return None # File doesn't exist.","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options","text":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\n @classmethod\n def register_bootstrap_options(cls, register):\n \"\"\"Register bootstrap options.\n\n \"Bootstrap options\" are a small set of options whose values are useful when registering other\n options. Therefore we must bootstrap them early, before other options are registered, let\n alone parsed.\n\n Bootstrap option values can be interpolated into the config file, and can be referenced\n programatically in registration code, e.g., as register.bootstrap.pants_workdir.\n\n Note that regular code can also access these options as normal global-scope options. Their\n status as \"bootstrap options\" is only pertinent during option registration.\n \"\"\"\n buildroot = get_buildroot()\n\n # Although logging supports the WARN level, its not documented and could conceivably be yanked.\n # Since pants has supported 'warn' since inception, leave the 'warn' choice as-is but explicitly\n # setup a 'WARN' logging level name that maps to 'WARNING'.\n logging.addLevelName(logging.WARNING, 'WARN')\n register('-l', '--level', choices=['debug', 'info', 'warn'], default='info', recursive=True,\n help='Set the logging level.')\n register('-q', '--quiet', type=bool, recursive=True,\n help='Squelches most console output.')\n # Not really needed in bootstrap options, but putting it here means it displays right\n # after -l and -q in help output, which is conveniently contextual.\n register('--colors', type=bool, default=sys.stdout.isatty(), recursive=True,\n help='Set whether log messages are displayed in color.')\n\n # Pants code uses this only to verify that we are of the requested version. However\n # setup scripts, runner scripts, IDE plugins, etc., may grep this out of pants.ini\n # and use it to select the right version.\n # Note that to print the version of the pants instance you're running, use -v, -V or --version.\n ","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options","text":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\n @classmethod\n def register_options(cls, register):\n \"\"\"Register options not tied to any particular task or subsystem.\"\"\"\n # The bootstrap options need to be registered on the post-bootstrap Options instance, so it\n # won't choke on them on the command line, and also so we can access their values as regular\n # global-scope options, for convenience.\n cls.register_bootstrap_options(register)\n\n register('-x', '--time', type=bool,\n help='Output a timing report at the end of the run.')\n register('-e', '--explain', type=bool,\n help='Explain the execution of goals.')\n register('--tag', type=list, metavar='[+-]tag1,tag2,...',\n help=\"Include only targets with these tags (optional '+' prefix) or without these \"\n \"tags ('-' prefix). Useful with ::, to find subsets of targets \"\n \"(e.g., integration tests.)\")\n\n register('-t', '--timeout', advanced=True, type=int, metavar='<seconds>',\n help='Number of seconds to wait for http connections.')\n # TODO: After moving to the new options system these abstraction leaks can go away.\n register('-k', '--kill-nailguns', advanced=True, type=bool,\n help='Kill nailguns before exiting')\n register('-i', '--interpreter', advanced=True, default=[], type=list,\n metavar='<requirement>',\n removal_version='1.5.0.dev0',\n removal_hint='Use --interpreter-constraints in scope python-setup instead.',\n help=\"Constrain what Python interpreters to use. Uses Requirement format from \"\n \"pkg_resources, e.g. 'CPython>=2.6,<3' or 'PyPy'. By default, no constraints \"\n \"are used. Multiple constraints may be added. They will be ORed together.\")\n register('--exclude-target-regexp', advanced=True, type=list, default=[],\n metavar='<regexp>',\n help='","label":0},{"cid":"src/python/pants/option/options.py::Options.complete_scopes","text":"src/python/pants/option/options.py::Options.complete_scopes\n @classmethod\n def complete_scopes(cls, scope_infos):\n \"\"\"Expand a set of scopes to include all enclosing scopes.\n\n E.g., if the set contains `foo.bar.baz`, ensure that it also contains `foo.bar` and `foo`.\n\n Also adds any deprecated scopes.\n \"\"\"\n ret = {GlobalOptionsRegistrar.get_scope_info()}\n original_scopes = set()\n for si in scope_infos:\n ret.add(si)\n original_scopes.add(si.scope)\n if si.deprecated_scope:\n ret.add(ScopeInfo(si.deprecated_scope, si.category, si.optionable_cls))\n original_scopes.add(si.deprecated_scope)\n\n # TODO: Once scope name validation is enforced (so there can be no dots in scope name\n # components) we can replace this line with `for si in scope_infos:`, because it will\n # not be possible for a deprecated_scope to introduce any new intermediate scopes.\n for si in copy.copy(ret):\n scope = si.scope\n while scope != '':\n if scope not in original_scopes:\n ret.add(ScopeInfo(scope, ScopeInfo.INTERMEDIATE))\n scope = enclosing_scope(scope)\n return ret","label":0},{"cid":"src/python/pants/option/options.py::Options.create","text":"src/python/pants/option/options.py::Options.create\n @classmethod\n def create(cls, env, config, known_scope_infos, args=None, bootstrap_option_values=None,\n option_tracker=None):\n \"\"\"Create an Options instance.\n\n :param env: a dict of environment variables.\n :param :class:`pants.option.config.Config` config: data from a config file.\n :param known_scope_infos: ScopeInfos for all scopes that may be encountered.\n :param args: a list of cmd-line args; defaults to `sys.argv` if None is supplied.\n :param bootstrap_option_values: An optional namespace containing the values of bootstrap\n options. We can use these values when registering other options.\n :param :class:`pants.option.option_tracker.OptionTracker` option_tracker: option tracker\n instance to record how option values were assigned.\n \"\"\"\n # We need parsers for all the intermediate scopes, so inherited option values\n # can propagate through them.\n complete_known_scope_infos = cls.complete_scopes(known_scope_infos)\n splitter = ArgSplitter(complete_known_scope_infos)\n args = sys.argv if args is None else args\n goals, scope_to_flags, target_specs, passthru, passthru_owner = splitter.split_args(args)\n\n if not option_tracker:\n raise cls.OptionTrackerRequiredError()\n\n if bootstrap_option_values:\n target_spec_files = bootstrap_option_values.target_spec_files\n if target_spec_files:\n for spec in target_spec_files:\n with open(spec) as f:\n target_specs.extend(filter(None, [line.strip() for line in f]))\n\n help_request = splitter.help_request\n\n parser_hierarchy = ParserHierarchy(env, config, complete_known_scope_infos, option_tracker)\n values_by_scope = {} # Arg values, parsed per-scope on demand.\n bootstrap_option_values = bootstrap_option_values\n known_scope_to_info = {s.scope: s for s in complete_known_scope_infos}\n return cls(goals, scope_to_flags, target_specs, passthru, passthru_owne","label":0},{"cid":"src/python/pants/option/options.py::Options.__init__","text":"src/python/pants/option/options.py::Options.__init__\n def __init__(self, goals, scope_to_flags, target_specs, passthru, passthru_owner, help_request,\n parser_hierarchy, values_by_scope, bootstrap_option_values, known_scope_to_info,\n option_tracker):\n \"\"\"The low-level constructor for an Options instance.\n\n Dependees should use `Options.create` instead.\n \"\"\"\n self._goals = goals\n self._scope_to_flags = scope_to_flags\n self._target_specs = target_specs\n self._passthru = passthru\n self._passthru_owner = passthru_owner\n self._help_request = help_request\n self._parser_hierarchy = parser_hierarchy\n self._values_by_scope = values_by_scope\n self._bootstrap_option_values = bootstrap_option_values\n self._known_scope_to_info = known_scope_to_info\n self._option_tracker = option_tracker","label":0},{"cid":"src/python/pants/option/options.py::Options.tracker","text":"src/python/pants/option/options.py::Options.tracker\n @property\n def tracker(self):\n return self._option_tracker","label":0},{"cid":"src/python/pants/option/options.py::Options.help_request","text":"src/python/pants/option/options.py::Options.help_request\n @property\n def help_request(self):\n \"\"\"\n :API: public\n \"\"\"\n return self._help_request","label":0},{"cid":"src/python/pants/option/options.py::Options.target_specs","text":"src/python/pants/option/options.py::Options.target_specs\n @property\n def target_specs(self):\n \"\"\"The targets to operate on.\n\n :API: public\n \"\"\"\n return self._target_specs","label":0},{"cid":"src/python/pants/option/options.py::Options.goals","text":"src/python/pants/option/options.py::Options.goals\n @property\n def goals(self):\n \"\"\"The requested goals, in the order specified on the cmd line.\n\n :API: public\n \"\"\"\n return self._goals","label":0},{"cid":"src/python/pants/option/options.py::Options.known_scope_to_info","text":"src/python/pants/option/options.py::Options.known_scope_to_info\n @property\n def known_scope_to_info(self):\n return self._known_scope_to_info","label":0},{"cid":"src/python/pants/option/options.py::Options.scope_to_flags","text":"src/python/pants/option/options.py::Options.scope_to_flags\n @property\n def scope_to_flags(self):\n return self._scope_to_flags","label":0},{"cid":"src/python/pants/option/options.py::Options.drop_flag_values","text":"src/python/pants/option/options.py::Options.drop_flag_values\n def drop_flag_values(self):\n \"\"\"Returns a copy of these options that ignores values specified via flags.\n\n Any pre-cached option values are cleared and only option values that come from option defaults,\n the config or the environment are used.\n \"\"\"\n # An empty scope_to_flags to force all values to come via the config -> env hierarchy alone\n # and empty values in case we already cached some from flags.\n no_flags = {}\n no_values = {}\n return Options(self._goals,\n no_flags,\n self._target_specs,\n self._passthru,\n self._passthru_owner,\n self._help_request,\n self._parser_hierarchy,\n no_values,\n self._bootstrap_option_values,\n self._known_scope_to_info,\n self._option_tracker)","label":0},{"cid":"src/python/pants/option/options.py::Options.is_known_scope","text":"src/python/pants/option/options.py::Options.is_known_scope\n def is_known_scope(self, scope):\n \"\"\"Whether the given scope is known by this instance.\n\n :API: public\n \"\"\"\n return scope in self._known_scope_to_info","label":0},{"cid":"src/python/pants/option/options.py::Options.passthru_args_for_scope","text":"src/python/pants/option/options.py::Options.passthru_args_for_scope\n def passthru_args_for_scope(self, scope):\n # Passthru args \"belong\" to the last scope mentioned on the command-line.\n\n # Note: If that last scope is a goal, we allow all tasks in that goal to access the passthru\n # args. This is to allow the more intuitive\n # pants run <target> -- <passthru args>\n # instead of requiring\n # pants run.py <target> -- <passthru args>.\n #\n # However note that in the case where multiple tasks run in the same goal, e.g.,\n # pants test <target> -- <passthru args>\n # Then, e.g., both junit and pytest will get the passthru args even though the user probably\n # only intended them to go to one of them. If the wrong one is not a no-op then the error will\n # be unpredictable. However this is not a common case, and can be circumvented with an\n # explicit test.pytest or test.junit scope.\n if (scope and self._passthru_owner and scope.startswith(self._passthru_owner) and\n (len(scope) == len(self._passthru_owner) or scope[len(self._passthru_owner)] == '.')):\n return self._passthru\n else:\n return []","label":0},{"cid":"src/python/pants/option/options.py::Options.register","text":"src/python/pants/option/options.py::Options.register\n def register(self, scope, *args, **kwargs):\n \"\"\"Register an option in the given scope.\"\"\"\n self.get_parser(scope).register(*args, **kwargs)\n deprecated_scope = self.known_scope_to_info[scope].deprecated_scope\n if deprecated_scope:\n self.get_parser(deprecated_scope).register(*args, **kwargs)","label":0},{"cid":"src/python/pants/option/options.py::Options.registration_function_for_optionable","text":"src/python/pants/option/options.py::Options.registration_function_for_optionable\n def registration_function_for_optionable(self, optionable_class):\n \"\"\"Returns a function for registering options on the given scope.\"\"\"\n # TODO(benjy): Make this an instance of a class that implements __call__, so we can\n # docstring it, and so it's less weird than attatching properties to a function.\n def register(*args, **kwargs):\n kwargs['registering_class'] = optionable_class\n self.register(optionable_class.options_scope, *args, **kwargs)\n # Clients can access the bootstrap option values as register.bootstrap.\n register.bootstrap = self.bootstrap_option_values()\n # Clients can access the scope as register.scope.\n register.scope = optionable_class.options_scope\n return register","label":0},{"cid":"src/python/pants/option/options.py::Options.registration_function_for_optionable.register","text":"src/python/pants/option/options.py::Options.registration_function_for_optionable.register\n def register(*args, **kwargs):\n kwargs['registering_class'] = optionable_class\n self.register(optionable_class.options_scope, *args, **kwargs)","label":0},{"cid":"src/python/pants/option/options.py::Options.get_parser","text":"src/python/pants/option/options.py::Options.get_parser\n def get_parser(self, scope):\n \"\"\"Returns the parser for the given scope, so code can register on it directly.\"\"\"\n return self._parser_hierarchy.get_parser_by_scope(scope)","label":0},{"cid":"src/python/pants/option/options.py::Options.walk_parsers","text":"src/python/pants/option/options.py::Options.walk_parsers\n def walk_parsers(self, callback):\n self._parser_hierarchy.walk(callback)","label":0},{"cid":"src/python/pants/option/options.py::Options.for_scope","text":"src/python/pants/option/options.py::Options.for_scope\n def for_scope(self, scope, inherit_from_enclosing_scope=True):\n \"\"\"Return the option values for the given scope.\n\n Values are attributes of the returned object, e.g., options.foo.\n Computed lazily per scope.\n\n :API: public\n \"\"\"\n # Short-circuit, if already computed.\n if scope in self._values_by_scope:\n return self._values_by_scope[scope]\n\n # First get enclosing scope's option values, if any.\n if scope == GLOBAL_SCOPE or not inherit_from_enclosing_scope:\n values = OptionValueContainer()\n else:\n values = copy.copy(self.for_scope(enclosing_scope(scope)))\n\n # Now add our values.\n flags_in_scope = self._scope_to_flags.get(scope, [])\n self._parser_hierarchy.get_parser_by_scope(scope).parse_args(flags_in_scope, values)\n\n # If we're the new name of a deprecated scope, also get values from that scope.\n deprecated_scope = self.known_scope_to_info[scope].deprecated_scope\n # Note that deprecated_scope and scope share the same Optionable class, so deprecated_scope's\n # Optionable has a deprecated_options_scope equal to deprecated_scope. Therefore we must\n # check that scope != deprecated_scope to prevent infinite recursion.\n if deprecated_scope is not None and scope != deprecated_scope:\n # Do the deprecation check only on keys that were explicitly set on the deprecated scope\n # (and not on its enclosing scopes).\n explicit_keys = self.for_scope(deprecated_scope,\n inherit_from_enclosing_scope=False).get_explicit_keys()\n if explicit_keys:\n warn_or_error(self.known_scope_to_info[scope].deprecated_scope_removal_version,\n 'scope {}'.format(deprecated_scope),\n 'Use scope {} instead (options: {})'.format(scope, ', '.join(explicit_keys)))\n # Update our values with those of the deprecated scope (now including values inherited\n # from its enclosin","label":0},{"cid":"src/python/pants/option/options.py::Options.get_fingerprintable_for_scope","text":"src/python/pants/option/options.py::Options.get_fingerprintable_for_scope\n def get_fingerprintable_for_scope(self, scope):\n \"\"\"Returns a list of fingerprintable (option type, option value) pairs for the given scope.\n\n Fingerprintable options are options registered via a \"fingerprint=True\" kwarg.\n\n :API: public\n \"\"\"\n pairs = []\n # Note that we iterate over options registered at `scope` and at all enclosing scopes, since\n # option-using code can read those values indirectly via its own OptionValueContainer, so\n # they can affect that code's output.\n registration_scope = scope\n while registration_scope is not None:\n parser = self._parser_hierarchy.get_parser_by_scope(registration_scope)\n # Sort the arguments, so that the fingerprint is consistent.\n for (_, kwargs) in sorted(parser.option_registrations_iter()):\n if kwargs.get('recursive') and not kwargs.get('recursive_root'):\n continue # We only need to fprint recursive options once.\n if kwargs.get('fingerprint') is not True:\n continue\n # Note that we read the value from scope, even if the registration was on an enclosing\n # scope, to get the right value for recursive options (and because this mirrors what\n # option-using code does).\n val = self.for_scope(scope)[kwargs['dest']]\n # If we have a list then we delegate to the fingerprinting implementation of the members.\n if is_list_option(kwargs):\n val_type = kwargs.get('member_type', str)\n else:\n val_type = kwargs.get('type', str)\n pairs.append((val_type, val))\n registration_scope = (None if registration_scope == ''\n else enclosing_scope(registration_scope))\n return pairs","label":0},{"cid":"src/python/pants/option/options.py::Options.__getitem__","text":"src/python/pants/option/options.py::Options.__getitem__\n def __getitem__(self, scope):\n # TODO(John Sirois): Mainly supports use of dict<str, dict<str, str>> for mock options in tests,\n # Consider killing if tests consolidate on using TestOptions instead of the raw dicts.\n return self.for_scope(scope)","label":0},{"cid":"src/python/pants/option/options.py::Options.bootstrap_option_values","text":"src/python/pants/option/options.py::Options.bootstrap_option_values\n def bootstrap_option_values(self):\n \"\"\"Return the option values for bootstrap options.\n\n General code can also access these values in the global scope. But option registration code\n cannot, hence this special-casing of this small set of options.\n \"\"\"\n return self._bootstrap_option_values","label":0},{"cid":"src/python/pants/option/options.py::Options.for_global_scope","text":"src/python/pants/option/options.py::Options.for_global_scope\n def for_global_scope(self):\n \"\"\"Return the option values for the global scope.\n\n :API: public\n \"\"\"\n return self.for_scope(GLOBAL_SCOPE)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.implementation_version","text":"src/python/pants/task/task.py::TaskBase.implementation_version\n @classmethod\n def implementation_version(cls):\n \"\"\"\n :API: public\n \"\"\"\n return [('TaskBase', 2)]","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.implementation_version_str","text":"src/python/pants/task/task.py::TaskBase.implementation_version_str\n @classmethod\n @memoized_method\n def implementation_version_str(cls):\n return '.'.join(['_'.join(map(str, x)) for x in cls.implementation_version()])","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.stable_name","text":"src/python/pants/task/task.py::TaskBase.stable_name\n @classmethod\n def stable_name(cls):\n \"\"\"The stable name of this task type.\n\n We synthesize subclasses of the task types at runtime, and these synthesized subclasses\n may have random names (e.g., in tests), so this gives us a stable name to use across runs,\n e.g., in artifact cache references.\n \"\"\"\n return cls._stable_name or cls._compute_stable_name()","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._compute_stable_name","text":"src/python/pants/task/task.py::TaskBase._compute_stable_name\n @classmethod\n def _compute_stable_name(cls):\n return '{}_{}'.format(cls.__module__, cls.__name__).replace('.', '_')","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.subsystem_dependencies","text":"src/python/pants/task/task.py::TaskBase.subsystem_dependencies\n @classmethod\n def subsystem_dependencies(cls):\n return super(TaskBase, cls).subsystem_dependencies() + (CacheSetup.scoped(cls),)","label":1},{"cid":"src/python/pants/task/task.py::TaskBase.product_types","text":"src/python/pants/task/task.py::TaskBase.product_types\n @classmethod\n def product_types(cls):\n \"\"\"The list of products this Task produces. Set the product type(s) for this\n task i.e. the product type(s) this task creates e.g ['classes'].\n\n By default, each task is considered as creating a unique product type(s).\n Subclasses that create products, should override this to specify their unique product type(s).\n\n :API: public\n \"\"\"\n return []","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.known_scope_infos","text":"src/python/pants/task/task.py::TaskBase.known_scope_infos\n @classmethod\n def known_scope_infos(cls):\n \"\"\"Yields ScopeInfo for all known scopes for this task, in no particular order.\"\"\"\n # The task's own scope.\n yield cls.get_scope_info()\n # The scopes of any task-specific subsystems it uses.\n for dep in cls.subsystem_dependencies_iter():\n if not dep.is_global():\n yield dep.subsystem_cls.get_scope_info(subscope=dep.scope)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.supports_passthru_args","text":"src/python/pants/task/task.py::TaskBase.supports_passthru_args\n @classmethod\n def supports_passthru_args(cls):\n \"\"\"Subclasses may override to indicate that they can use passthru args.\n\n :API: public\n \"\"\"\n return False","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._scoped_options","text":"src/python/pants/task/task.py::TaskBase._scoped_options\n @classmethod\n def _scoped_options(cls, options):\n return options[cls.options_scope]","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.get_alternate_target_roots","text":"src/python/pants/task/task.py::TaskBase.get_alternate_target_roots\n @classmethod\n def get_alternate_target_roots(cls, options, address_mapper, build_graph):\n # Subclasses should not generally need to override this method.\n return cls.alternate_target_roots(cls._scoped_options(options), address_mapper, build_graph)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.alternate_target_roots","text":"src/python/pants/task/task.py::TaskBase.alternate_target_roots\n @classmethod\n def alternate_target_roots(cls, options, address_mapper, build_graph):\n \"\"\"Allows a Task to propose alternate target roots from those specified on the CLI.\n\n At most 1 unique proposal is allowed amongst all tasks involved in the run. If more than 1\n unique list of target roots is proposed an error is raised during task scheduling.\n\n :API: public\n\n :returns list: The new target roots to use or None to accept the CLI specified target roots.\n \"\"\"","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.invoke_prepare","text":"src/python/pants/task/task.py::TaskBase.invoke_prepare\n @classmethod\n def invoke_prepare(cls, options, round_manager):\n # Subclasses should not generally need to override this method.\n return cls.prepare(cls._scoped_options(options), round_manager)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.prepare","text":"src/python/pants/task/task.py::TaskBase.prepare\n @classmethod\n def prepare(cls, options, round_manager):\n \"\"\"Prepares a task for execution.\n\n Called before execution and prior to any tasks that may be (indirectly) depended upon.\n\n Typically a task that requires products from other goals would register interest in those\n products here and then retrieve the requested product mappings when executed.\n\n :API: public\n \"\"\"","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.__init__","text":"src/python/pants/task/task.py::TaskBase.__init__\n def __init__(self, context, workdir):\n \"\"\"Subclass __init__ methods, if defined, *must* follow this idiom:\n\n class MyTask(Task):\n def __init__(self, *args, **kwargs):\n super(MyTask, self).__init__(*args, **kwargs)\n ...\n\n This allows us to change Task.__init__()'s arguments without\n changing every subclass. If the subclass does not need its own\n initialization, this method can (and should) be omitted entirely.\n\n :API: public\n \"\"\"\n super(TaskBase, self).__init__()\n self.context = context\n self._workdir = workdir\n\n self._cache_key_errors = set()\n\n self._build_invalidator_dir = os.path.join(\n self.context.options.for_global_scope().pants_workdir,\n 'build_invalidator',\n self.stable_name())\n\n self._cache_factory = CacheSetup.create_cache_factory_for_task(self)\n\n self._options_fingerprinter = OptionsFingerprinter(self.context.build_graph)","label":1},{"cid":"src/python/pants/task/task.py::TaskBase.get_options","text":"src/python/pants/task/task.py::TaskBase.get_options\n def get_options(self):\n \"\"\"Returns the option values for this task's scope.\n\n :API: public\n \"\"\"\n return self.context.options.for_scope(self.options_scope)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.get_passthru_args","text":"src/python/pants/task/task.py::TaskBase.get_passthru_args\n def get_passthru_args(self):\n \"\"\"\n :API: public\n \"\"\"\n if not self.supports_passthru_args():\n raise TaskError('{0} Does not support passthru args.'.format(self.stable_name()))\n else:\n return self.context.options.passthru_args_for_scope(self.options_scope)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.workdir","text":"src/python/pants/task/task.py::TaskBase.workdir\n @memoized_property\n def workdir(self):\n \"\"\"A scratch-space for this task that will be deleted by `clean-all`.\n\n It's guaranteed that no other task has been given this workdir path to use and that the workdir\n exists.\n\n :API: public\n \"\"\"\n safe_mkdir(self._workdir)\n return self._workdir","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._options_fingerprint","text":"src/python/pants/task/task.py::TaskBase._options_fingerprint\n def _options_fingerprint(self, scope):\n pairs = self.context.options.get_fingerprintable_for_scope(scope)\n hasher = sha1()\n for (option_type, option_val) in pairs:\n fp = self._options_fingerprinter.fingerprint(option_type, option_val)\n if fp is not None:\n hasher.update(fp)\n return hasher.hexdigest()","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.fingerprint","text":"src/python/pants/task/task.py::TaskBase.fingerprint\n @memoized_property\n def fingerprint(self):\n \"\"\"Returns a fingerprint for the identity of the task.\n\n A task fingerprint is composed of the options the task is currently running under.\n Useful for invalidating unchanging targets being executed beneath changing task\n options that affect outputted artifacts.\n\n A task's fingerprint is only valid afer the task has been fully initialized.\n \"\"\"\n hasher = sha1()\n hasher.update(self._options_fingerprint(self.options_scope))\n hasher.update(self.implementation_version_str())\n # TODO: this is not recursive, but should be: see #2739\n for dep in self.subsystem_dependencies_iter():\n hasher.update(self._options_fingerprint(dep.options_scope()))\n return str(hasher.hexdigest())","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.artifact_cache_reads_enabled","text":"src/python/pants/task/task.py::TaskBase.artifact_cache_reads_enabled\n def artifact_cache_reads_enabled(self):\n return self._cache_factory.read_cache_available()","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.artifact_cache_writes_enabled","text":"src/python/pants/task/task.py::TaskBase.artifact_cache_writes_enabled\n def artifact_cache_writes_enabled(self):\n return self._cache_factory.write_cache_available()","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.invalidate","text":"src/python/pants/task/task.py::TaskBase.invalidate\n def invalidate(self):\n \"\"\"Invalidates all targets for this task.\"\"\"\n BuildInvalidator(self._build_invalidator_dir).force_invalidate_all()","label":1},{"cid":"src/python/pants/task/task.py::TaskBase.create_target_dirs","text":"src/python/pants/task/task.py::TaskBase.create_target_dirs\n @property\n def create_target_dirs(self):\n \"\"\"Whether to create a results_dir per VersionedTarget in the workdir of the Task.\n\n This defaults to the value of `self.cache_target_dirs` (as caching them requires\n creating them), but may be overridden independently to create the dirs without caching\n them.\n\n :API: public\n \"\"\"\n return self.cache_target_dirs or False","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.cache_target_dirs","text":"src/python/pants/task/task.py::TaskBase.cache_target_dirs\n @property\n def cache_target_dirs(self):\n \"\"\"Whether to cache files in VersionedTarget's results_dir after exiting an invalidated block.\n\n Subclasses may override this method to return True if they wish to use this style\n of \"automated\" caching, where each VersionedTarget is given an associated results directory,\n which will automatically be uploaded to the cache. Tasks should place the output files\n for each VersionedTarget in said results directory. It is highly suggested to follow this\n schema for caching, rather than manually making updates to the artifact cache.\n\n :API: public\n \"\"\"\n return False","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.incremental","text":"src/python/pants/task/task.py::TaskBase.incremental\n @property\n def incremental(self):\n \"\"\"Whether this Task implements incremental building of individual targets.\n\n Incremental tasks with `cache_target_dirs` set will have the results_dir of the previous build\n for a target cloned into the results_dir for the current build (where possible). This\n copy-on-write behaviour allows for immutability of the results_dir once a target has been\n marked valid.\n\n :API: public\n \"\"\"\n return False","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.cache_incremental","text":"src/python/pants/task/task.py::TaskBase.cache_incremental\n @property\n def cache_incremental(self):\n \"\"\"For incremental tasks, indicates whether the results of incremental builds should be cached.\n\n Deterministic per-target incremental compilation is a relatively difficult thing to implement,\n so this property provides an escape hatch to avoid caching things in that riskier case.\n\n :API: public\n \"\"\"\n return False","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.invalidated","text":"src/python/pants/task/task.py::TaskBase.invalidated\n @contextmanager\n def invalidated(self,\n targets,\n invalidate_dependents=False,\n silent=False,\n fingerprint_strategy=None,\n topological_order=False):\n \"\"\"Checks targets for invalidation, first checking the artifact cache.\n\n Subclasses call this to figure out what to work on.\n\n :API: public\n\n :param targets: The targets to check for changes.\n :param invalidate_dependents: If True then any targets depending on changed targets are\n invalidated.\n :param silent: If true, suppress logging information about target invalidation.\n :param fingerprint_strategy: A FingerprintStrategy instance, which can do per task,\n finer grained fingerprinting of a given Target.\n :param topological_order: Whether to invalidate in dependency order.\n\n If no exceptions are thrown by work in the block, the build cache is updated for the targets.\n Note: the artifact cache is not updated. That must be done manually.\n\n :returns: Yields an InvalidationCheck object reflecting the targets.\n :rtype: InvalidationCheck\n \"\"\"\n\n cache_key_generator = CacheKeyGenerator(\n self.context.options.for_global_scope().cache_key_gen_version,\n self.fingerprint)\n cache_manager = InvalidationCacheManager(self.workdir,\n cache_key_generator,\n self._build_invalidator_dir,\n invalidate_dependents,\n fingerprint_strategy=fingerprint_strategy,\n invalidation_report=self.context.invalidation_report,\n task_name=type(self).__name__,\n task_version=self.implementation_version_str","label":1},{"cid":"src/python/pants/task/task.py::TaskBase.maybe_write_artifact","text":"src/python/pants/task/task.py::TaskBase.maybe_write_artifact\n def maybe_write_artifact(self, vt):\n if self._should_cache_target_dir(vt):\n self.update_artifact_cache([(vt, [vt.current_results_dir])])","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._launch_background_workdir_cleanup","text":"src/python/pants/task/task.py::TaskBase._launch_background_workdir_cleanup\n def _launch_background_workdir_cleanup(self, vts):\n workdir_build_cleanup_job = Work(self._cleanup_workdir_stale_builds, [(vts,)], 'workdir_build_cleanup')\n self.context.submit_background_work_chain([workdir_build_cleanup_job])","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._cleanup_workdir_stale_builds","text":"src/python/pants/task/task.py::TaskBase._cleanup_workdir_stale_builds\n def _cleanup_workdir_stale_builds(self, vts):\n # workdir_max_build_entries has been assured of not None before invoking this method.\n max_entries_per_target = max(2, self.context.options.for_global_scope().workdir_max_build_entries)\n for vt in vts:\n live_dirs = list(vt.live_dirs())\n if not live_dirs:\n continue\n root_dir = os.path.dirname(vt.results_dir)\n safe_rm_oldest_items_in_dir(root_dir, max_entries_per_target, excludes=live_dirs)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._should_cache_target_dir","text":"src/python/pants/task/task.py::TaskBase._should_cache_target_dir\n def _should_cache_target_dir(self, vt):\n \"\"\"Return true if the given vt should be written to a cache (if configured).\"\"\"\n return (\n self.cache_target_dirs and\n not vt.target.has_label('no_cache') and\n (not vt.is_incremental or self.cache_incremental) and\n self.artifact_cache_writes_enabled()\n )","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._maybe_create_results_dirs","text":"src/python/pants/task/task.py::TaskBase._maybe_create_results_dirs\n def _maybe_create_results_dirs(self, vts):\n \"\"\"If `cache_target_dirs`, create results_dirs for the given versioned targets.\"\"\"\n if self.create_target_dirs:\n for vt in vts:\n vt.create_results_dir()","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.check_artifact_cache_for","text":"src/python/pants/task/task.py::TaskBase.check_artifact_cache_for\n def check_artifact_cache_for(self, invalidation_check):\n \"\"\"Decides which VTS to check the artifact cache for.\n\n By default we check for each invalid target. Can be overridden, e.g., to\n instead check only for a single artifact for the entire target set.\n \"\"\"\n return invalidation_check.invalid_vts","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.check_artifact_cache","text":"src/python/pants/task/task.py::TaskBase.check_artifact_cache\n def check_artifact_cache(self, vts):\n \"\"\"Checks the artifact cache for the specified list of VersionedTargetSets.\n\n Returns a tuple (cached, uncached, uncached_causes) of VersionedTargets that were\n satisfied/unsatisfied from the cache. Uncached VTS are also attached with their\n causes for the miss: `False` indicates a legit miss while `UnreadableArtifact`\n is due to either local or remote cache failures.\n \"\"\"\n return self.do_check_artifact_cache(vts)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.do_check_artifact_cache","text":"src/python/pants/task/task.py::TaskBase.do_check_artifact_cache\n def do_check_artifact_cache(self, vts, post_process_cached_vts=None):\n \"\"\"Checks the artifact cache for the specified list of VersionedTargetSets.\n\n Returns a pair (cached, uncached) of VersionedTargets that were\n satisfied/unsatisfied from the cache.\n \"\"\"\n if not vts:\n return [], [], []\n\n read_cache = self._cache_factory.get_read_cache()\n items = [(read_cache, vt.cache_key, vt.current_results_dir if self.cache_target_dirs else None)\n for vt in vts]\n res = self.context.subproc_map(call_use_cached_files, items)\n\n cached_vts = []\n uncached_vts = []\n uncached_causes = []\n\n # Note that while the input vts may represent multiple targets (for tasks that overrride\n # check_artifact_cache_for), the ones we return must represent single targets.\n # Once flattened, cached/uncached vts are in separate lists. Each uncached vts is paired\n # with why it is missed for stat reporting purpose.\n for vt, was_in_cache in zip(vts, res):\n if was_in_cache:\n cached_vts.extend(vt.versioned_targets)\n else:\n uncached_vts.extend(vt.versioned_targets)\n uncached_causes.extend(repeat(was_in_cache, len(vt.versioned_targets)))\n if isinstance(was_in_cache, UnreadableArtifact):\n self._cache_key_errors.update(was_in_cache.key)\n\n if post_process_cached_vts:\n post_process_cached_vts(cached_vts)\n for vt in cached_vts:\n vt.update()\n return cached_vts, uncached_vts, uncached_causes","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.update_artifact_cache","text":"src/python/pants/task/task.py::TaskBase.update_artifact_cache\n def update_artifact_cache(self, vts_artifactfiles_pairs):\n \"\"\"Write to the artifact cache, if we're configured to.\n\n vts_artifactfiles_pairs - a list of pairs (vts, artifactfiles) where\n - vts is single VersionedTargetSet.\n - artifactfiles is a list of absolute paths to artifacts for the VersionedTargetSet.\n \"\"\"\n update_artifact_cache_work = self._get_update_artifact_cache_work(vts_artifactfiles_pairs)\n if update_artifact_cache_work:\n self.context.submit_background_work_chain([update_artifact_cache_work],\n parent_workunit_name='cache')","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._get_update_artifact_cache_work","text":"src/python/pants/task/task.py::TaskBase._get_update_artifact_cache_work\n def _get_update_artifact_cache_work(self, vts_artifactfiles_pairs):\n \"\"\"Create a Work instance to update an artifact cache, if we're configured to.\n\n vts_artifactfiles_pairs - a list of pairs (vts, artifactfiles) where\n - vts is single VersionedTargetSet.\n - artifactfiles is a list of paths to artifacts for the VersionedTargetSet.\n \"\"\"\n cache = self._cache_factory.get_write_cache()\n if cache:\n if len(vts_artifactfiles_pairs) == 0:\n return None\n # Do some reporting.\n targets = set()\n for vts, _ in vts_artifactfiles_pairs:\n targets.update(vts.targets)\n\n self._report_targets(\n 'Caching artifacts for ',\n list(targets),\n '.',\n logger=self.context.log.debug,\n )\n\n always_overwrite = self._cache_factory.overwrite()\n\n # Cache the artifacts.\n args_tuples = []\n for vts, artifactfiles in vts_artifactfiles_pairs:\n overwrite = always_overwrite or vts.cache_key in self._cache_key_errors\n args_tuples.append((cache, vts.cache_key, artifactfiles, overwrite))\n\n return Work(lambda x: self.context.subproc_map(call_insert, x), [(args_tuples,)], 'insert')\n else:\n return None","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._report_targets","text":"src/python/pants/task/task.py::TaskBase._report_targets\n def _report_targets(self, prefix, targets, suffix, logger=None):\n logger = logger or self.context.log.info\n logger(\n prefix,\n items_to_report_element([t.address.reference() for t in targets], 'target'),\n suffix,\n )","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.require_single_root_target","text":"src/python/pants/task/task.py::TaskBase.require_single_root_target\n def require_single_root_target(self):\n \"\"\"If a single target was specified on the cmd line, returns that target.\n\n Otherwise throws TaskError.\n\n :API: public\n \"\"\"\n target_roots = self.context.target_roots\n if len(target_roots) == 0:\n raise TaskError('No target specified.')\n elif len(target_roots) > 1:\n raise TaskError('Multiple targets specified: {}'\n .format(', '.join([repr(t) for t in target_roots])))\n return target_roots[0]","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.determine_target_roots","text":"src/python/pants/task/task.py::TaskBase.determine_target_roots\n def determine_target_roots(self, goal_name, predicate=None):\n \"\"\"Helper for tasks that scan for default target roots.\n\n :param string goal_name: The goal name to use for any warning emissions.\n :param callable predicate: The predicate to pass to `context.scan().targets(predicate=X)`.\n \"\"\"\n deprecated_conditional(\n lambda: not self.context.target_roots,\n '1.5.0.dev0',\n '`./pants {0}` (with no explicit targets) will soon become an error. Please specify '\n 'one or more explicit target specs (e.g. `./pants {0} ::`).'.format(goal_name))\n if not self.context.target_roots and not self.get_options().enable_v2_engine:\n # For the v1 path, continue the behavior of e.g. `./pants list` implies `./pants list ::`.\n return self.context.scan().targets(predicate=predicate)\n\n # For the v2 path, e.g. `./pants list` is a functional no-op. This matches the v2 mode behavior\n # of e.g. `./pants --changed-parent=HEAD list` (w/ no changes) returning an empty result.\n return self.context.target_roots","label":0},{"cid":"src/python/pants/task/task.py::Task.__init__","text":"src/python/pants/task/task.py::Task.__init__\n def __init__(self, context, workdir):\n \"\"\"\n Add pass-thru Task Constructor for public API visibility.\n\n :API: public\n \"\"\"\n super(Task, self).__init__(context, workdir)","label":0},{"cid":"src/python/pants/task/task.py::Task.execute","text":"src/python/pants/task/task.py::Task.execute\n @abstractmethod\n def execute(self):\n \"\"\"Executes this task.\n\n :API: public\n \"\"\"","label":0}],"string":"[\n {\n \"cid\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.implementation_version\",\n \"text\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.implementation_version\\n @classmethod\\n def implementation_version(cls):\\n # Bump this version to invalidate all past artifacts generated by this task.\\n return super(IndexJava, cls).implementation_version() + [('IndexJava', 6), ]\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.product_types\",\n \"text\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.product_types\\n @classmethod\\n def product_types(cls):\\n return ['kythe_entries_files']\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.prepare\",\n \"text\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.prepare\\n @classmethod\\n def prepare(cls, options, round_manager):\\n super(IndexJava, cls).prepare(options, round_manager)\\n round_manager.require_data('kindex_files')\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.register_options\",\n \"text\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.register_options\\n @classmethod\\n def register_options(cls, register):\\n super(IndexJava, cls).register_options(register)\\n register('--force', type=bool, fingerprint=True,\\n help='Re-index all targets, even if they are valid.')\\n cls.register_jvm_tool(register,\\n 'kythe-indexer',\\n main=cls._KYTHE_INDEXER_MAIN)\",\n \"label\": 1\n },\n {\n \"cid\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.execute\",\n \"text\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.execute\\n def execute(self):\\n def entries_file(_vt):\\n return os.path.join(_vt.results_dir, 'index.entries')\\n\\n indexable_targets = IndexableJavaTargets.get(self.context)\\n\\n with self.invalidated(indexable_targets, invalidate_dependents=True) as invalidation_check:\\n kindex_files = self.context.products.get_data('kindex_files')\\n vts_to_index = (invalidation_check.all_vts if self.get_options().force\\n else invalidation_check.invalid_vts)\\n\\n indexer_cp = self.tool_classpath('kythe-indexer')\\n # Kythe jars embed a copy of Java 9's com.sun.tools.javac and javax.tools, for use on JDK8.\\n # We must put these jars on the bootclasspath, ahead of any others, to ensure that we load\\n # the Java 9 versions, and not the runtime's versions.\\n jvm_options = ['-Xbootclasspath/p:{}'.format(':'.join(indexer_cp))]\\n jvm_options.extend(self.get_options().jvm_options)\\n\\n for vt in vts_to_index:\\n self.context.log.info('Kythe indexing {}'.format(vt.target.address.spec))\\n kindex_file = kindex_files.get(vt.target)\\n if not kindex_file:\\n raise TaskError('No .kindex file found for {}'.format(vt.target.address.spec))\\n args = [kindex_file, '--out', entries_file(vt)]\\n result = self.runjava(classpath=indexer_cp, main=self._KYTHE_INDEXER_MAIN,\\n jvm_options=jvm_options,\\n args=args, workunit_name='kythe-index',\\n workunit_labels=[WorkUnitLabel.COMPILER])\\n if result != 0:\\n raise TaskError('java {main} ... exited non-zero ({result})'.format(\\n main=self._KYTHE_INDEXER_MAIN, result=result))\\n\\n for vt in invalidation_check.all_vts:\\n self.context.products.get_data('kythe_entries_files', dict)[vt.target] = entries_file(vt)\",\n \"label\": 1\n },\n {\n \"cid\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.execute.entries_file\",\n \"text\": \"contrib/kythe/src/python/pants/contrib/kythe/tasks/index_java.py::IndexJava.execute.entries_file\\n def entries_file(_vt):\\n return os.path.join(_vt.results_dir, 'index.entries')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.load\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.load\\n @staticmethod\\n def load(path):\\n \\\"\\\"\\\"Loads a pushdb maintained in a properties file at the given path.\\\"\\\"\\\"\\n with open(path, 'r') as props:\\n properties = Properties.load(props)\\n return PushDb(properties)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.__init__\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.__init__\\n def __init__(self, sem_ver, named_ver, named_is_latest, sha, fingerprint):\\n \\\"\\\"\\\"Records the most recent push/release of an artifact.\\n\\n :param Semver sem_ver: The last semantically versioned release (or Semver(0.0.0))\\n :param Namedver named_ver: The last named release of this entry (or None)\\n :param boolean named_is_latest: True if named_ver is the latest, false if sem_ver is\\n :param string sha: The last Git SHA (or None)\\n :param string fingerprint: A unique hash for the most recent version of the target.\\n \\\"\\\"\\\"\\n self.sem_ver = sem_ver\\n self.named_ver = named_ver\\n self.named_is_latest = named_is_latest\\n self.sha = sha\\n self.fingerprint = fingerprint\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.version\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.version\\n def version(self):\\n if self.named_is_latest:\\n return self.named_ver\\n else:\\n return self.sem_ver\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_sem_ver\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_sem_ver\\n def with_sem_ver(self, sem_ver):\\n \\\"\\\"\\\"Returns a clone of this entry with the given sem_ver marked as the latest.\\\"\\\"\\\"\\n return PushDb.Entry(sem_ver, self.named_ver, False, self.sha, self.fingerprint)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_named_ver\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_named_ver\\n def with_named_ver(self, named_ver):\\n \\\"\\\"\\\"Returns a clone of this entry with the given name_ver marked as the latest.\\\"\\\"\\\"\\n return PushDb.Entry(self.sem_ver, named_ver, True, self.sha, self.fingerprint)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_sha_and_fingerprint\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.with_sha_and_fingerprint\\n def with_sha_and_fingerprint(self, sha, fingerprint):\\n \\\"\\\"\\\"Returns a clone of this entry with the given sha and fingerprint.\\\"\\\"\\\"\\n return PushDb.Entry(self.sem_ver, self.named_ver, self.named_is_latest, sha, fingerprint)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.__repr__\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.Entry.__repr__\\n def __repr__(self):\\n return '<{}, {}, {}, {}, {}, {}>'.format(\\n self.__class__.__name__, self.sem_ver, self.named_ver, self.named_is_latest,\\n self.sha, self.fingerprint)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.__init__\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.__init__\\n def __init__(self, props=None):\\n self._props = props or OrderedDict()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.get_entry\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.get_entry\\n def get_entry(self, target):\\n \\\"\\\"\\\"Given an internal target, return a PushDb.Entry, which might contain defaults.\\\"\\\"\\\"\\n db_get, _ = self._accessors_for_target(target)\\n\\n major = int(db_get('revision.major', '0'))\\n minor = int(db_get('revision.minor', '0'))\\n patch = int(db_get('revision.patch', '0'))\\n snapshot = str(db_get('revision.snapshot', 'false')).lower() == 'true'\\n named_version = db_get('revision.named_version', None)\\n named_is_latest = str(db_get('revision.named_is_latest', 'false')).lower() == 'true'\\n sha = db_get('revision.sha', None)\\n fingerprint = db_get('revision.fingerprint', None)\\n sem_ver = Semver(major, minor, patch, snapshot=snapshot)\\n named_ver = Namedver(named_version) if named_version else None\\n return self.Entry(sem_ver, named_ver, named_is_latest, sha, fingerprint)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.set_entry\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.set_entry\\n def set_entry(self, target, pushdb_entry):\\n pe = pushdb_entry\\n _, db_set = self._accessors_for_target(target)\\n db_set('revision.major', pe.sem_ver.major)\\n db_set('revision.minor', pe.sem_ver.minor)\\n db_set('revision.patch', pe.sem_ver.patch)\\n db_set('revision.snapshot', str(pe.sem_ver.snapshot).lower())\\n if pe.named_ver:\\n db_set('revision.named_version', pe.named_ver.version())\\n db_set('revision.named_is_latest', str(pe.named_is_latest).lower())\\n db_set('revision.sha', pe.sha)\\n db_set('revision.fingerprint', pe.fingerprint)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target\\n def _accessors_for_target(self, target):\\n jar_dep, exported = target.get_artifact_info()\\n if not exported:\\n raise ValueError\\n\\n def key(prefix):\\n return '{}.{}%{}'.format(prefix, jar_dep.org, jar_dep.name)\\n\\n def getter(prefix, default=None):\\n return self._props.get(key(prefix), default)\\n\\n def setter(prefix, value):\\n self._props[key(prefix)] = value\\n\\n return getter, setter\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.key\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.key\\n def key(prefix):\\n return '{}.{}%{}'.format(prefix, jar_dep.org, jar_dep.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.getter\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.getter\\n def getter(prefix, default=None):\\n return self._props.get(key(prefix), default)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.setter\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb._accessors_for_target.setter\\n def setter(prefix, value):\\n self._props[key(prefix)] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.dump\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PushDb.dump\\n def dump(self, path):\\n \\\"\\\"\\\"Saves the pushdb as a properties file to the given path.\\\"\\\"\\\"\\n with open(path, 'w') as props:\\n Properties.dump(self._props, props)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter.__init__\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter.__init__\\n def __init__(self, get_db, tag):\\n self._get_db = get_db\\n self._tag = tag\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter.write\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter.write\\n def write(self, target, path):\\n dependencies = OrderedDict()\\n for internal_dep in target_internal_dependencies(target):\\n jar = self._as_versioned_jar(internal_dep)\\n key = (jar.org, jar.name)\\n dependencies[key] = self._internaldep(jar, internal_dep)\\n\\n for jar in target.jar_dependencies:\\n jardep = self._jardep(jar)\\n if jardep:\\n key = (jar.org, jar.name, jar.classifier)\\n dependencies[key] = jardep\\n\\n target_jar = self._internaldep(self._as_versioned_jar(target), target)\\n if target_jar:\\n target_jar = target_jar.extend(dependencies=dependencies.values())\\n\\n template_relpath = os.path.join(_TEMPLATES_RELPATH, 'pom.xml.mustache')\\n template_text = pkgutil.get_data(__name__, template_relpath)\\n generator = Generator(template_text, project=target_jar)\\n with safe_open(path, 'w') as output:\\n generator.write(output)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._as_versioned_jar\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._as_versioned_jar\\n def _as_versioned_jar(self, internal_target):\\n \\\"\\\"\\\"Fetches the jar representation of the given target, and applies the latest pushdb version.\\\"\\\"\\\"\\n jar, _ = internal_target.get_artifact_info()\\n pushdb_entry = self._get_db(internal_target).get_entry(internal_target)\\n return jar.copy(rev=pushdb_entry.version().version())\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._internaldep\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._internaldep\\n def _internaldep(self, jar_dependency, target):\\n template_data = self._jardep(jar_dependency)\\n if isinstance(target.provides.publication_metadata, OSSRHPublicationMetadata):\\n pom = target.provides.publication_metadata\\n\\n # Forming the project name from the coordinates like this is acceptable as a fallback when\\n # the user supplies no project name.\\n # See: http://central.sonatype.org/pages/requirements.html#project-name-description-and-url\\n name = pom.name or '{}:{}'.format(jar_dependency.org, jar_dependency.name)\\n\\n template_data = template_data.extend(name=name,\\n description=pom.description,\\n url=pom.url,\\n licenses=pom.licenses,\\n scm=pom.scm.tagged(self._tag),\\n developers=pom.developers)\\n return template_data\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._jardep\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::PomWriter._jardep\\n def _jardep(self, jar):\\n return TemplateData(\\n classifier=jar.classifier,\\n artifact_id=jar.name,\\n group_id=jar.org,\\n version=jar.rev,\\n scope='compile',\\n excludes=[TemplateData(org=exclude.org, name=exclude.name)\\n for exclude in jar.excludes if exclude.name])\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::coordinate\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::coordinate\\ndef coordinate(org, name, rev=None):\\n return '{}#{};{}'.format(org, name, rev) if rev else '{}#{}'.format(org, name)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::jar_coordinate\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::jar_coordinate\\ndef jar_coordinate(jar, rev=None):\\n return coordinate(jar.org, jar.name, rev or jar.rev)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::pushdb_coordinate\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::pushdb_coordinate\\ndef pushdb_coordinate(jar, entry):\\n return jar_coordinate(jar, rev=entry.version().version())\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::target_internal_dependencies\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::target_internal_dependencies\\ndef target_internal_dependencies(target):\\n \\\"\\\"\\\"Returns internal Jarable dependencies that were \\\"directly\\\" declared.\\n\\n Directly declared deps are those that are explicitly listed in the definition of a\\n target, rather than being depended on transitively. But in order to walk through\\n aggregator targets such as `target`, `dependencies`, or `jar_library`, this recursively\\n descends the dep graph and stops at Jarable instances.\\\"\\\"\\\"\\n for dep in target.dependencies:\\n if isinstance(dep, Jarable):\\n yield dep\\n else:\\n for childdep in target_internal_dependencies(dep):\\n yield childdep\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.register_options\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.register_options\\n @classmethod\\n def register_options(cls, register):\\n super(JarPublish, cls).register_options(register)\\n\\n # TODO(John Sirois): Support a preview mode that outputs a file with entries like:\\n # artifact id:\\n # revision:\\n # publish: (true|false)\\n # changelog:\\n #\\n # Allow re-running this goal with the file as input to support forcing an arbitrary set of\\n # revisions and supply of hand edited changelogs.\\n\\n register('--dryrun', default=True, type=bool,\\n help='Run through a push without actually pushing artifacts, editing publish dbs or '\\n 'otherwise writing data')\\n register('--commit', default=True, type=bool,\\n help='Commit the push db. Turn off for local testing.')\\n register('--local', metavar='<PATH>',\\n help='Publish jars to a maven repository on the local filesystem at this path.')\\n register('--local-snapshot', default=True, type=bool,\\n help='If --local is specified, publishes jars with -SNAPSHOT revision suffixes.')\\n register('--named-snapshot', default=None,\\n help='Publish all artifacts with the given snapshot name, replacing their version. '\\n 'This is not Semantic Versioning compatible, but is easier to consume in cases '\\n 'where many artifacts must align.')\\n register('--transitive', default=True, type=bool,\\n help='Publish the specified targets and all their internal dependencies transitively.')\\n register('--force', type=bool,\\n help='Force pushing jars even if there have been no changes since the last push.')\\n register('--override', type=list,\\n help='Specifies a published jar revision override in the form: '\\n '([org]#[name]|[target spec])=[new revision] '\\n 'For example, to specify 2 overrides: '\\n '--override=com.foo.bar#baz=0.1.2 --override=s\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.prepare\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.prepare\\n @classmethod\\n def prepare(cls, options, round_manager):\\n super(JarPublish, cls).prepare(options, round_manager)\\n round_manager.require('jars')\\n round_manager.require('javadoc')\\n round_manager.require('scaladoc')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.__init__\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.__init__\\n def __init__(self, *args, **kwargs):\\n super(JarPublish, self).__init__(*args, **kwargs)\\n self.cachedir = os.path.join(self.workdir, 'cache')\\n\\n self._jvm_options = self.get_options().jvm_options\\n\\n self.scm = get_scm()\\n self.log = self.context.log\\n\\n if self.get_options().local:\\n local_repo = dict(\\n resolver='publish_local',\\n path=os.path.abspath(os.path.expanduser(self.get_options().local)),\\n confs=['default'],\\n auth=None\\n )\\n self.repos = defaultdict(lambda: local_repo)\\n self.commit = False\\n self.local_snapshot = self.get_options().local_snapshot\\n else:\\n self.repos = self.get_options().repos\\n if not self.repos:\\n raise TaskError(\\n \\\"This repo is not configured to publish externally! Please configure per\\\\n\\\"\\n \\\"http://pantsbuild.org/publish.html#authenticating-to-the-artifact-repository,\\\\n\\\"\\n \\\"by setting --publish-jar-repos=<dict> or re-run with '--publish-jar-local=<dir>'.\\\")\\n for repo, data in self.repos.items():\\n auth = data.get('auth')\\n if auth:\\n credentials = next(iter(self.context.resolve(auth)))\\n user = credentials.username(data['resolver'])\\n password = credentials.password(data['resolver'])\\n self.context.log.debug('Found auth for repo={} user={}'.format(repo, user))\\n self.repos[repo]['username'] = user\\n self.repos[repo]['password'] = password\\n self.commit = self.get_options().commit\\n self.push_postscript = self.get_options().push_postscript or ''\\n self.local_snapshot = False\\n\\n self.named_snapshot = self.get_options().named_snapshot\\n if self.named_snapshot:\\n self.named_snapshot = Namedver.parse(self.named_snapshot)\\n\\n self.dryrun = self.get_options().dryrun\\n self.transitive = self.get_options().transitive\\n self.force = self.get_options().force\\n self.publish_changelog = sel\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.__init__.parse_jarcoordinate\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.__init__.parse_jarcoordinate\\n def parse_jarcoordinate(coordinate):\\n components = coordinate.split('#', 1)\\n if len(components) == 2:\\n org, name = components\\n return org, name\\n else:\\n spec = components[0]\\n address = Address.parse(spec)\\n try:\\n self.context.build_graph.inject_address_closure(address)\\n target = self.context.build_graph.get_target(address)\\n if not target:\\n siblings = self.context.address_mapper.addresses_in_spec_path(address.spec_path)\\n prompt = 'did you mean' if len(siblings) == 1 else 'maybe you meant one of these'\\n raise TaskError('{} => {}?:\\\\n {}'.format(address, prompt,\\n '\\\\n '.join(str(a) for a in siblings)))\\n if not target.is_exported:\\n raise TaskError('{} is not an exported target'.format(coordinate))\\n return target.provides.org, target.provides.name\\n except (BuildFile.BuildFileError,\\n BuildFileParser.BuildFileParserError,\\n AddressLookupError) as e:\\n raise TaskError('{message}\\\\n Problem identifying target at {spec}'\\n .format(message=e, spec=spec))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.confirm_push\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.confirm_push\\n def confirm_push(self, coord, version):\\n \\\"\\\"\\\"Ask the user if a push should be done for a particular version of a\\n particular coordinate. Return True if the push should be done\\\"\\\"\\\"\\n if not self.get_options().prompt:\\n return True\\n try:\\n isatty = os.isatty(sys.stdin.fileno())\\n except ValueError:\\n # In tests, sys.stdin might not have a fileno\\n isatty = False\\n if not isatty:\\n return True\\n push = raw_input('\\\\nPublish {} with revision {} ? [y|N] '.format(\\n coord, version\\n ))\\n print('\\\\n')\\n return push.strip().lower() == 'y'\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish._copy_artifact\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish._copy_artifact\\n def _copy_artifact(self, tgt, jar, version, typename, suffix='', extension='jar',\\n artifact_ext='', override_name=None):\\n \\\"\\\"\\\"Copy the products for a target into the artifact path for the jar/version\\\"\\\"\\\"\\n genmap = self.context.products.get(typename)\\n product_mapping = genmap.get(tgt)\\n if product_mapping is None:\\n raise ValueError(\\\"No product mapping in {} for {}. \\\"\\n \\\"You may need to run some other task first\\\".format(typename, tgt))\\n for basedir, jars in product_mapping.items():\\n for artifact in jars:\\n path = self.artifact_path(jar, version, name=override_name, suffix=suffix,\\n extension=extension, artifact_ext=artifact_ext)\\n safe_mkdir(os.path.dirname(path))\\n shutil.copy(os.path.join(basedir, artifact), path)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish._ivy_jvm_options\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish._ivy_jvm_options\\n def _ivy_jvm_options(self, repo):\\n \\\"\\\"\\\"Get the JVM options for ivy authentication, if needed.\\\"\\\"\\\"\\n # Get authentication for the publish repo if needed.\\n if not repo.get('auth'):\\n # No need to copy here, as this list isn't modified by the caller.\\n return self._jvm_options\\n\\n # Create a copy of the options, so that the modification is appropriately transient.\\n jvm_options = copy(self._jvm_options)\\n user = repo.get('username')\\n password = repo.get('password')\\n if user and password:\\n jvm_options.append('-Dlogin={}'.format(user))\\n jvm_options.append('-Dpassword={}'.format(password))\\n else:\\n raise TaskError('Unable to publish to {}. {}'\\n .format(repo.get('resolver'), repo.get('help', '')))\\n return jvm_options\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.publish\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.publish\\n def publish(self, publications, jar, entry, repo, published):\\n \\\"\\\"\\\"Run ivy to publish a jar. ivyxml_path is the path to the ivy file; published\\n is a list of jars published so far (including this one). entry is a pushdb entry.\\\"\\\"\\\"\\n\\n try:\\n ivy = Bootstrapper.default_ivy()\\n except Bootstrapper.Error as e:\\n raise TaskError('Failed to push {0}! {1}'.format(pushdb_coordinate(jar, entry), e))\\n\\n path = repo.get('path')\\n ivysettings = self.generate_ivysettings(ivy, published, publish_local=path)\\n\\n version = entry.version().version()\\n ivyxml = self.generate_ivy(jar, version, publications)\\n\\n resolver = repo['resolver']\\n args = [\\n '-settings', ivysettings,\\n '-ivy', ivyxml,\\n\\n # Without this setting, the ivy.xml is delivered to the CWD, littering the workspace. We\\n # don't need the ivy.xml, so just give it path under the workdir we won't use.\\n '-deliverto', ivyxml + '.unused',\\n\\n '-publish', resolver,\\n '-publishpattern', '{}/[organisation]/[module]/'\\n '[artifact]-[revision](-[classifier]).[ext]'.format(self.workdir),\\n '-revision', version,\\n '-m2compatible',\\n ]\\n\\n # TODO(John Sirois): global logging options should be hidden behind some sort of log manager\\n # that we can:\\n # a.) obtain a handle to (dependency injection or manual plumbing)\\n # b.) query for log detail, ie: `if log_manager.is_verbose:`\\n if self.get_options().level == 'debug':\\n args.append('-verbose')\\n\\n if self.local_snapshot:\\n args.append('-overwrite')\\n\\n try:\\n jvm_options = self._ivy_jvm_options(repo)\\n ivy.execute(jvm_options=jvm_options, args=args,\\n workunit_factory=self.context.new_workunit, workunit_name='ivy-publish')\\n except Ivy.Error as e:\\n raise TaskError('Failed to push {0}! {1}'.format(pushdb_coordinate(jar, entry), e))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute\\n def execute(self):\\n self.check_clean_master(commit=(not self.dryrun and self.commit))\\n\\n exported_targets = self.exported_targets()\\n self.check_targets(exported_targets)\\n\\n pushdbs = {}\\n\\n def get_db(tgt):\\n # TODO(tdesai) Handle resource type in get_db.\\n if tgt.provides is None:\\n raise TaskError('trying to publish target {!r} which does not provide an artifact'.format(tgt))\\n dbfile = tgt.provides.repo.push_db(tgt)\\n result = pushdbs.get(dbfile)\\n if not result:\\n # Create an empty pushdb if no dbfile exists.\\n if (os.path.exists(dbfile)):\\n db = PushDb.load(dbfile)\\n else:\\n safe_mkdir(os.path.dirname(dbfile))\\n db = PushDb()\\n try:\\n repo = self.repos[tgt.provides.repo.name]\\n except KeyError:\\n raise TaskError('Repository {0} has no entry in the --repos option.'.format(\\n tgt.provides.repo.name))\\n result = (db, dbfile, repo)\\n pushdbs[dbfile] = result\\n return result\\n\\n def get_pushdb(tgt):\\n return get_db(tgt)[0]\\n\\n def fingerprint_internal(tgt):\\n pushdb = get_pushdb(tgt)\\n entry = pushdb.get_entry(tgt)\\n return entry.fingerprint or '0.0.0'\\n\\n def stage_artifacts(tgt, jar, version, tag, changelog):\\n publications = OrderedSet()\\n\\n # TODO Remove this once we fix https://github.com/pantsbuild/pants/issues/1229\\n if (not self.context.products.get('jars').has(tgt) and\\n not self.get_options().individual_plugins):\\n raise TaskError('Expected to find a primary artifact for {} but there was no jar for it.'\\n .format(tgt.address.reference()))\\n\\n # TODO Remove this guard once we fix https://github.com/pantsbuild/pants/issues/1229, there\\n # should always be a primary artifact.\\n if self.context.products.get('jars').has(tgt):\\n self._copy_artifact(tgt, jar, version, typename='ja\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.get_db\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.get_db\\n def get_db(tgt):\\n # TODO(tdesai) Handle resource type in get_db.\\n if tgt.provides is None:\\n raise TaskError('trying to publish target {!r} which does not provide an artifact'.format(tgt))\\n dbfile = tgt.provides.repo.push_db(tgt)\\n result = pushdbs.get(dbfile)\\n if not result:\\n # Create an empty pushdb if no dbfile exists.\\n if (os.path.exists(dbfile)):\\n db = PushDb.load(dbfile)\\n else:\\n safe_mkdir(os.path.dirname(dbfile))\\n db = PushDb()\\n try:\\n repo = self.repos[tgt.provides.repo.name]\\n except KeyError:\\n raise TaskError('Repository {0} has no entry in the --repos option.'.format(\\n tgt.provides.repo.name))\\n result = (db, dbfile, repo)\\n pushdbs[dbfile] = result\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.get_pushdb\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.get_pushdb\\n def get_pushdb(tgt):\\n return get_db(tgt)[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.fingerprint_internal\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.fingerprint_internal\\n def fingerprint_internal(tgt):\\n pushdb = get_pushdb(tgt)\\n entry = pushdb.get_entry(tgt)\\n return entry.fingerprint or '0.0.0'\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.stage_artifacts\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.execute.stage_artifacts\\n def stage_artifacts(tgt, jar, version, tag, changelog):\\n publications = OrderedSet()\\n\\n # TODO Remove this once we fix https://github.com/pantsbuild/pants/issues/1229\\n if (not self.context.products.get('jars').has(tgt) and\\n not self.get_options().individual_plugins):\\n raise TaskError('Expected to find a primary artifact for {} but there was no jar for it.'\\n .format(tgt.address.reference()))\\n\\n # TODO Remove this guard once we fix https://github.com/pantsbuild/pants/issues/1229, there\\n # should always be a primary artifact.\\n if self.context.products.get('jars').has(tgt):\\n self._copy_artifact(tgt, jar, version, typename='jars')\\n publications.add(self.Publication(name=jar.name, classifier=None, ext='jar'))\\n\\n self.create_source_jar(tgt, jar, version)\\n publications.add(self.Publication(name=jar.name, classifier='sources', ext='jar'))\\n\\n # don't request docs unless they are available for all transitive targets\\n # TODO: doc products should be checked by an independent jar'ing task, and\\n # conditionally enabled; see https://github.com/pantsbuild/pants/issues/568\\n doc_jar = self.create_doc_jar(tgt, jar, version)\\n if doc_jar:\\n publications.add(self.Publication(name=jar.name, classifier='javadoc', ext='jar'))\\n\\n if self.publish_changelog:\\n changelog_path = self.artifact_path(jar, version, suffix='-CHANGELOG', extension='txt')\\n with safe_open(changelog_path, 'wb') as changelog_file:\\n changelog_file.write(changelog.encode('utf-8'))\\n publications.add(self.Publication(name=jar.name, classifier='CHANGELOG', ext='txt'))\\n\\n # Process any extra jars that might have been previously generated for this target, or a\\n # target that it was derived from.\\n for extra_product, extra_config in (self.get_options().p\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.artifact_path\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.artifact_path\\n def artifact_path(self, jar, version, name=None, suffix='', extension='jar', artifact_ext=''):\\n return os.path.join(self.workdir, jar.org, jar.name + artifact_ext,\\n '{}{}-{}{}.{}'.format((name or jar.name),\\n artifact_ext if name != 'ivy' else '',\\n version,\\n suffix,\\n extension))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.check_for_duplicate_artifacts\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.check_for_duplicate_artifacts\\n def check_for_duplicate_artifacts(self, targets):\\n targets_by_artifact = defaultdict(list)\\n duplicates = set()\\n for target in targets:\\n artifact = target.provides\\n if artifact in targets_by_artifact:\\n duplicates.add(artifact)\\n targets_by_artifact[artifact].append(target)\\n\\n def duplication_message(artifact):\\n specs = sorted('\\\\n {}'.format(t.address.spec) for t in targets_by_artifact[artifact])\\n return '\\\\n {artifact} is defined by:{specs}'.format(artifact=artifact, specs=''.join(specs))\\n\\n if duplicates:\\n raise self.DuplicateArtifactError('Multiple targets define the same artifacts!\\\\n{}'.format(\\n '\\\\n'.join(duplication_message(artifact) for artifact in duplicates)))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.check_for_duplicate_artifacts.duplication_message\",\n \"text\": \"src/python/pants/backend/jvm/tasks/jar_publish.py::JarPublish.check_for_duplicate_artifacts.duplication_message\\n def duplication_message(artifact):\\n specs = sorted('\\\\n {}'.format(t.address.spec) for t in targets_by_artifact[artifact])\\n return '\\\\n {artifact} is defined by:{specs}'.format(artifact=artifact, specs=''.join(specs))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheSetup.register_options\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheSetup.register_options\\n @classmethod\\n def register_options(cls, register):\\n super(CacheSetup, cls).register_options(register)\\n default_cache = [os.path.join(get_buildroot(), '.cache')]\\n register('--read', type=bool, default=True,\\n help='Read build artifacts from cache, if available.')\\n register('--write', type=bool, default=True,\\n help='Write build artifacts to cache, if available.')\\n register('--overwrite', advanced=True, type=bool,\\n help='If writing build artifacts to cache, overwrite existing artifacts '\\n 'instead of skipping them.')\\n register('--resolver', advanced=True, choices=['none', 'rest'], default='none',\\n help='Select which resolver strategy to use for discovering URIs that access '\\n 'artifact caches. none: use URIs from static config options, i.e. '\\n '--read-from, --write-to. rest: look up URIs by querying a RESTful '\\n 'URL, which is a remote address from --read-from, --write-to.')\\n register('--read-from', advanced=True, type=list, default=default_cache,\\n help='The URIs of artifact caches to read directly from. Each entry is a URL of '\\n 'a RESTful cache, a path of a filesystem cache, or a pipe-separated list of '\\n 'alternate caches to choose from. This list is also used as input to '\\n 'the resolver. When resolver is \\\\'none\\\\' list is used as is.')\\n register('--write-to', advanced=True, type=list, default=default_cache,\\n help='The URIs of artifact caches to write directly to. Each entry is a URL of'\\n 'a RESTful cache, a path of a filesystem cache, or a pipe-separated list of '\\n 'alternate caches to choose from. This list is also used as input to '\\n 'the resolver. When resolver is \\\\'none\\\\' list is used as is.')\\n register('--compression-level', adv\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheSetup.create_cache_factory_for_task\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheSetup.create_cache_factory_for_task\\n @classmethod\\n def create_cache_factory_for_task(cls, task, pinger=None, resolver=None):\\n return CacheFactory(cls.scoped_instance(task).get_options(),\\n task.context.log, task.stable_name(), pinger=pinger, resolver=resolver)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory.__init__\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory.__init__\\n def __init__(self, options, log, stable_name, pinger=None, resolver=None):\\n \\\"\\\"\\\"Create a cache factory from settings.\\n\\n :param options: Task's scoped options.\\n :param log: Task's context log.\\n :param stable_name: Task's stable name.\\n :param pinger: Pinger to choose the best remote artifact cache URL.\\n :param resolver: Resolver to look up remote artifact cache URLs.\\n :return: cache factory.\\n \\\"\\\"\\\"\\n self._options = options\\n self._log = log\\n self._stable_name = stable_name\\n\\n # Created on-demand.\\n self._read_cache = None\\n self._write_cache = None\\n\\n # Protects local filesystem setup, and assignment to the references above.\\n self._cache_setup_lock = threading.Lock()\\n\\n # Caches are supposed to be close, and we don't want to waste time pinging on no-op builds.\\n # So we ping twice with a short timeout.\\n # TODO: Make lazy.\\n self._pinger = pinger or Pinger(timeout=self._options.pinger_timeout,\\n tries=self._options.pinger_tries)\\n\\n # resolver is also close but failing to resolve might have broader impact than\\n # single ping failure, therefore use a higher timeout with more retries.\\n if resolver:\\n self._resolver = resolver\\n elif self._options.resolver == 'rest':\\n self._resolver = RESTfulResolver(timeout=1.0, tries=3)\\n else:\\n self._resolver = NoopResolver()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory.read_cache_available\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory.read_cache_available\\n def read_cache_available(self):\\n return self._options.read and bool(self._options.read_from) and self.get_read_cache()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory.write_cache_available\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory.write_cache_available\\n def write_cache_available(self):\\n return self._options.write and bool(self._options.write_to) and self.get_write_cache()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory.overwrite\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory.overwrite\\n def overwrite(self):\\n return self._options.overwrite\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory.get_read_cache\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory.get_read_cache\\n def get_read_cache(self):\\n \\\"\\\"\\\"Returns the read cache for this setup, creating it if necessary.\\n\\n Returns None if no read cache is configured.\\n \\\"\\\"\\\"\\n if self._options.read_from and not self._read_cache:\\n cache_spec = self._resolve(self._sanitize_cache_spec(self._options.read_from))\\n if cache_spec:\\n with self._cache_setup_lock:\\n self._read_cache = self._do_create_artifact_cache(cache_spec, 'will read from')\\n return self._read_cache\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory.get_write_cache\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory.get_write_cache\\n def get_write_cache(self):\\n \\\"\\\"\\\"Returns the write cache for this setup, creating it if necessary.\\n\\n Returns None if no write cache is configured.\\n \\\"\\\"\\\"\\n if self._options.write_to and not self._write_cache:\\n cache_spec = self._resolve(self._sanitize_cache_spec(self._options.write_to))\\n if cache_spec:\\n with self._cache_setup_lock:\\n self._write_cache = self._do_create_artifact_cache(cache_spec, 'will write to')\\n return self._write_cache\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory._sanitize_cache_spec\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory._sanitize_cache_spec\\n def _sanitize_cache_spec(self, spec):\\n if not isinstance(spec, (list, tuple)):\\n raise InvalidCacheSpecError('Invalid artifact cache spec type: {0} ({1})'.format(\\n type(spec), spec))\\n\\n if not spec:\\n raise EmptyCacheSpecError()\\n\\n if len(spec) > 2:\\n raise TooManyCacheSpecsError('Too many artifact cache specs: ({0})'.format(spec))\\n\\n local_specs = [s for s in spec if self.is_local(s)]\\n remote_specs = [s for s in spec if self.is_remote(s)]\\n\\n if not local_specs and not remote_specs:\\n raise CacheSpecFormatError('Invalid cache spec: {0}, must be either local or remote'\\n .format(spec))\\n\\n if len(spec) == 2:\\n if not local_specs:\\n raise LocalCacheSpecRequiredError('One of two cache specs must be a local cache path.')\\n if not remote_specs:\\n raise RemoteCacheSpecRequiredError('One of two cache specs must be a remote spec.')\\n\\n local_spec = local_specs[0] if len(local_specs) > 0 else None\\n remote_spec = remote_specs[0] if len(remote_specs) > 0 else None\\n\\n return CacheSpec(local=local_spec, remote=remote_spec)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory._resolve\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory._resolve\\n def _resolve(self, spec):\\n \\\"\\\"\\\"Attempt resolving cache URIs when a remote spec is provided. \\\"\\\"\\\"\\n if not spec.remote:\\n return spec\\n\\n try:\\n resolved_urls = self._resolver.resolve(spec.remote)\\n if resolved_urls:\\n # keep the bar separated list of URLs convention\\n return CacheSpec(local=spec.local, remote='|'.join(resolved_urls))\\n # no-op\\n return spec\\n except Resolver.ResolverError as e:\\n self._log.warn('Error while resolving from {0}: {1}'.format(spec.remote, str(e)))\\n # If for some reason resolver fails we continue to use local cache\\n if spec.local:\\n return CacheSpec(local=spec.local, remote=None)\\n # resolver fails but there is no local cache\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory.is_local\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory.is_local\\n @staticmethod\\n def is_local(string_spec):\\n return string_spec.startswith('/') or string_spec.startswith('~')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory.is_remote\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory.is_remote\\n @staticmethod\\n def is_remote(string_spec):\\n # both artifact cache and resolver use REST, add new protocols here once they are supported\\n return string_spec.startswith('http://') or string_spec.startswith('https://')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory._baseurl\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory._baseurl\\n def _baseurl(self, url):\\n parsed_url = urlparse.urlparse(url)\\n return '{scheme}://{netloc}'.format(scheme=parsed_url.scheme, netloc=parsed_url.netloc)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory.get_available_urls\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory.get_available_urls\\n def get_available_urls(self, urls):\\n \\\"\\\"\\\"Return reachable urls sorted by their ping times.\\\"\\\"\\\"\\n baseurl_to_urls = {self._baseurl(url): url for url in urls}\\n pingtimes = self._pinger.pings(baseurl_to_urls.keys()) # List of pairs (host, time in ms).\\n self._log.debug('Artifact cache server ping times: {}'\\n .format(', '.join(['{}: {:.6f} secs'.format(*p) for p in pingtimes])))\\n\\n sorted_pingtimes = sorted(pingtimes, key=lambda x: x[1])\\n available_urls = [baseurl_to_urls[baseurl] for baseurl, pingtime in sorted_pingtimes\\n if pingtime < Pinger.UNREACHABLE]\\n self._log.debug('Available cache servers: {0}'.format(available_urls))\\n\\n return available_urls\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache\\n def _do_create_artifact_cache(self, spec, action):\\n \\\"\\\"\\\"Returns an artifact cache for the specified spec.\\n\\n spec can be:\\n - a path to a file-based cache root.\\n - a URL of a RESTful cache root.\\n - a bar-separated list of URLs, where we'll pick the one with the best ping times.\\n - A list or tuple of two specs, local, then remote, each as described above\\n \\\"\\\"\\\"\\n compression = self._options.compression_level\\n if compression not in range(1, 10):\\n raise ValueError('compression_level must be an integer 1-9: {}'.format(compression))\\n\\n artifact_root = self._options.pants_workdir\\n\\n def create_local_cache(parent_path):\\n path = os.path.join(parent_path, self._stable_name)\\n self._log.debug('{0} {1} local artifact cache at {2}'\\n .format(self._stable_name, action, path))\\n return LocalArtifactCache(artifact_root, path, compression,\\n self._options.max_entries_per_target,\\n permissions=self._options.write_permissions,\\n dereference=self._options.dereference_symlinks)\\n\\n def create_remote_cache(remote_spec, local_cache):\\n urls = self.get_available_urls(remote_spec.split('|'))\\n\\n if len(urls) > 0:\\n best_url_selector = BestUrlSelector(['{}/{}'.format(url.rstrip('/'), self._stable_name)\\n for url in urls])\\n local_cache = local_cache or TempLocalArtifactCache(artifact_root, compression)\\n return RESTfulArtifactCache(artifact_root, best_url_selector, local_cache)\\n\\n local_cache = create_local_cache(spec.local) if spec.local else None\\n remote_cache = create_remote_cache(spec.remote, local_cache) if spec.remote else None\\n if remote_cache:\\n return remote_cache\\n return local_cache\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache.create_local_cache\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache.create_local_cache\\n def create_local_cache(parent_path):\\n path = os.path.join(parent_path, self._stable_name)\\n self._log.debug('{0} {1} local artifact cache at {2}'\\n .format(self._stable_name, action, path))\\n return LocalArtifactCache(artifact_root, path, compression,\\n self._options.max_entries_per_target,\\n permissions=self._options.write_permissions,\\n dereference=self._options.dereference_symlinks)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache.create_remote_cache\",\n \"text\": \"src/python/pants/cache/cache_setup.py::CacheFactory._do_create_artifact_cache.create_remote_cache\\n def create_remote_cache(remote_spec, local_cache):\\n urls = self.get_available_urls(remote_spec.split('|'))\\n\\n if len(urls) > 0:\\n best_url_selector = BestUrlSelector(['{}/{}'.format(url.rstrip('/'), self._stable_name)\\n for url in urls])\\n local_cache = local_cache or TempLocalArtifactCache(artifact_root, compression)\\n return RESTfulArtifactCache(artifact_root, best_url_selector, local_cache)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/core_tasks/invalidate.py::Invalidate.execute\",\n \"text\": \"src/python/pants/core_tasks/invalidate.py::Invalidate.execute\\n def execute(self):\\n build_invalidator_dir = os.path.join(self.get_options().pants_workdir, 'build_invalidator')\\n safe_rmtree(build_invalidator_dir)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.combine_cache_keys\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.combine_cache_keys\\n @staticmethod\\n def combine_cache_keys(cache_keys):\\n \\\"\\\"\\\"Returns a cache key for a list of target sets that already have cache keys.\\n\\n This operation is 'idempotent' in the sense that if cache_keys contains a single key\\n then that key is returned.\\n\\n Note that this operation is commutative but not associative. We use the term 'combine' rather\\n than 'merge' or 'union' to remind the user of this. Associativity is not a necessary property,\\n in practice.\\n \\\"\\\"\\\"\\n if len(cache_keys) == 1:\\n return cache_keys[0]\\n else:\\n combined_id = Target.maybe_readable_combine_ids(cache_key.id for cache_key in cache_keys)\\n combined_hash = hash_all(sorted(cache_key.hash for cache_key in cache_keys))\\n return CacheKey(combined_id, combined_hash)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.__init__\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.__init__\\n def __init__(self, *base_fingerprint_inputs):\\n \\\"\\\"\\\"\\n :base_fingerprint_inputs: Information to be included in the fingerprint for all cache keys\\n generated by this CacheKeyGenerator.\\n \\\"\\\"\\\"\\n hasher = hashlib.sha1()\\n hasher.update(GLOBAL_CACHE_KEY_GEN_VERSION)\\n for base_fingerprint_input in base_fingerprint_inputs:\\n hasher.update(base_fingerprint_input)\\n self._base_hasher = hasher\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.key_for_target\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::CacheKeyGenerator.key_for_target\\n def key_for_target(self, target, transitive=False, fingerprint_strategy=None):\\n \\\"\\\"\\\"Get a key representing the given target and its sources.\\n\\n A key for a set of targets can be created by calling combine_cache_keys()\\n on the target's individual cache keys.\\n\\n :target: The target to create a CacheKey for.\\n :transitive: Whether or not to include a fingerprint of all of :target:'s dependencies.\\n :fingerprint_strategy: A FingerprintStrategy instance, which can do per task, finer grained\\n fingerprinting of a given Target.\\n \\\"\\\"\\\"\\n\\n hasher = self._base_hasher.copy()\\n key_suffix = hasher.hexdigest()[:12]\\n if transitive:\\n target_key = target.transitive_invalidation_hash(fingerprint_strategy)\\n else:\\n target_key = target.invalidation_hash(fingerprint_strategy)\\n if target_key is not None:\\n full_key = '{target_key}_{key_suffix}'.format(target_key=target_key, key_suffix=key_suffix)\\n return CacheKey(target.id, full_key)\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.__init__\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.__init__\\n def __init__(self, root):\\n self._root = os.path.join(root, GLOBAL_CACHE_KEY_GEN_VERSION)\\n safe_mkdir(self._root)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.previous_key\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.previous_key\\n def previous_key(self, cache_key):\\n \\\"\\\"\\\"If there was a previous successful build for the given key, return the previous key.\\n\\n :param cache_key: A CacheKey object (as returned by CacheKeyGenerator.key_for().\\n :returns: The previous cache_key, or None if there was not a previous build.\\n \\\"\\\"\\\"\\n previous_hash = self._read_sha(cache_key)\\n if not previous_hash:\\n return None\\n return CacheKey(cache_key.id, previous_hash)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.needs_update\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.needs_update\\n def needs_update(self, cache_key):\\n \\\"\\\"\\\"Check if the given cached item is invalid.\\n\\n :param cache_key: A CacheKey object (as returned by CacheKeyGenerator.key_for().\\n :returns: True if the cached version of the item is out of date.\\n \\\"\\\"\\\"\\n return self._read_sha(cache_key) != cache_key.hash\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.update\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.update\\n def update(self, cache_key):\\n \\\"\\\"\\\"Makes cache_key the valid version of the corresponding target set.\\n\\n :param cache_key: A CacheKey object (typically returned by CacheKeyGenerator.key_for()).\\n \\\"\\\"\\\"\\n self._write_sha(cache_key)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.force_invalidate_all\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.force_invalidate_all\\n def force_invalidate_all(self):\\n \\\"\\\"\\\"Force-invalidates all cached items.\\\"\\\"\\\"\\n safe_mkdir(self._root, clean=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.force_invalidate\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator.force_invalidate\\n def force_invalidate(self, cache_key):\\n \\\"\\\"\\\"Force-invalidate the cached item.\\\"\\\"\\\"\\n try:\\n os.unlink(self._sha_file(cache_key))\\n except OSError as e:\\n if e.errno != errno.ENOENT:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._sha_file\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._sha_file\\n def _sha_file(self, cache_key):\\n return self._sha_file_by_id(cache_key.id)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._sha_file_by_id\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._sha_file_by_id\\n def _sha_file_by_id(self, id):\\n return os.path.join(self._root, safe_filename(id, extension='.hash'))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._write_sha\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._write_sha\\n def _write_sha(self, cache_key):\\n with open(self._sha_file(cache_key), 'w') as fd:\\n fd.write(cache_key.hash)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._read_sha\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._read_sha\\n def _read_sha(self, cache_key):\\n return self._read_sha_by_id(cache_key.id)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._read_sha_by_id\",\n \"text\": \"src/python/pants/invalidation/build_invalidator.py::BuildInvalidator._read_sha_by_id\\n def _read_sha_by_id(self, id):\\n try:\\n with open(self._sha_file_by_id(id), 'rb') as fd:\\n return fd.read().strip()\\n except IOError as e:\\n if e.errno != errno.ENOENT:\\n raise\\n return None # File doesn't exist.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\\n @classmethod\\n def register_bootstrap_options(cls, register):\\n \\\"\\\"\\\"Register bootstrap options.\\n\\n \\\"Bootstrap options\\\" are a small set of options whose values are useful when registering other\\n options. Therefore we must bootstrap them early, before other options are registered, let\\n alone parsed.\\n\\n Bootstrap option values can be interpolated into the config file, and can be referenced\\n programatically in registration code, e.g., as register.bootstrap.pants_workdir.\\n\\n Note that regular code can also access these options as normal global-scope options. Their\\n status as \\\"bootstrap options\\\" is only pertinent during option registration.\\n \\\"\\\"\\\"\\n buildroot = get_buildroot()\\n\\n # Although logging supports the WARN level, its not documented and could conceivably be yanked.\\n # Since pants has supported 'warn' since inception, leave the 'warn' choice as-is but explicitly\\n # setup a 'WARN' logging level name that maps to 'WARNING'.\\n logging.addLevelName(logging.WARNING, 'WARN')\\n register('-l', '--level', choices=['debug', 'info', 'warn'], default='info', recursive=True,\\n help='Set the logging level.')\\n register('-q', '--quiet', type=bool, recursive=True,\\n help='Squelches most console output.')\\n # Not really needed in bootstrap options, but putting it here means it displays right\\n # after -l and -q in help output, which is conveniently contextual.\\n register('--colors', type=bool, default=sys.stdout.isatty(), recursive=True,\\n help='Set whether log messages are displayed in color.')\\n\\n # Pants code uses this only to verify that we are of the requested version. However\\n # setup scripts, runner scripts, IDE plugins, etc., may grep this out of pants.ini\\n # and use it to select the right version.\\n # Note that to print the version of the pants instance you're running, use -v, -V or --version.\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\\n @classmethod\\n def register_options(cls, register):\\n \\\"\\\"\\\"Register options not tied to any particular task or subsystem.\\\"\\\"\\\"\\n # The bootstrap options need to be registered on the post-bootstrap Options instance, so it\\n # won't choke on them on the command line, and also so we can access their values as regular\\n # global-scope options, for convenience.\\n cls.register_bootstrap_options(register)\\n\\n register('-x', '--time', type=bool,\\n help='Output a timing report at the end of the run.')\\n register('-e', '--explain', type=bool,\\n help='Explain the execution of goals.')\\n register('--tag', type=list, metavar='[+-]tag1,tag2,...',\\n help=\\\"Include only targets with these tags (optional '+' prefix) or without these \\\"\\n \\\"tags ('-' prefix). Useful with ::, to find subsets of targets \\\"\\n \\\"(e.g., integration tests.)\\\")\\n\\n register('-t', '--timeout', advanced=True, type=int, metavar='<seconds>',\\n help='Number of seconds to wait for http connections.')\\n # TODO: After moving to the new options system these abstraction leaks can go away.\\n register('-k', '--kill-nailguns', advanced=True, type=bool,\\n help='Kill nailguns before exiting')\\n register('-i', '--interpreter', advanced=True, default=[], type=list,\\n metavar='<requirement>',\\n removal_version='1.5.0.dev0',\\n removal_hint='Use --interpreter-constraints in scope python-setup instead.',\\n help=\\\"Constrain what Python interpreters to use. Uses Requirement format from \\\"\\n \\\"pkg_resources, e.g. 'CPython>=2.6,<3' or 'PyPy'. By default, no constraints \\\"\\n \\\"are used. Multiple constraints may be added. They will be ORed together.\\\")\\n register('--exclude-target-regexp', advanced=True, type=list, default=[],\\n metavar='<regexp>',\\n help='\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.complete_scopes\",\n \"text\": \"src/python/pants/option/options.py::Options.complete_scopes\\n @classmethod\\n def complete_scopes(cls, scope_infos):\\n \\\"\\\"\\\"Expand a set of scopes to include all enclosing scopes.\\n\\n E.g., if the set contains `foo.bar.baz`, ensure that it also contains `foo.bar` and `foo`.\\n\\n Also adds any deprecated scopes.\\n \\\"\\\"\\\"\\n ret = {GlobalOptionsRegistrar.get_scope_info()}\\n original_scopes = set()\\n for si in scope_infos:\\n ret.add(si)\\n original_scopes.add(si.scope)\\n if si.deprecated_scope:\\n ret.add(ScopeInfo(si.deprecated_scope, si.category, si.optionable_cls))\\n original_scopes.add(si.deprecated_scope)\\n\\n # TODO: Once scope name validation is enforced (so there can be no dots in scope name\\n # components) we can replace this line with `for si in scope_infos:`, because it will\\n # not be possible for a deprecated_scope to introduce any new intermediate scopes.\\n for si in copy.copy(ret):\\n scope = si.scope\\n while scope != '':\\n if scope not in original_scopes:\\n ret.add(ScopeInfo(scope, ScopeInfo.INTERMEDIATE))\\n scope = enclosing_scope(scope)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.create\",\n \"text\": \"src/python/pants/option/options.py::Options.create\\n @classmethod\\n def create(cls, env, config, known_scope_infos, args=None, bootstrap_option_values=None,\\n option_tracker=None):\\n \\\"\\\"\\\"Create an Options instance.\\n\\n :param env: a dict of environment variables.\\n :param :class:`pants.option.config.Config` config: data from a config file.\\n :param known_scope_infos: ScopeInfos for all scopes that may be encountered.\\n :param args: a list of cmd-line args; defaults to `sys.argv` if None is supplied.\\n :param bootstrap_option_values: An optional namespace containing the values of bootstrap\\n options. We can use these values when registering other options.\\n :param :class:`pants.option.option_tracker.OptionTracker` option_tracker: option tracker\\n instance to record how option values were assigned.\\n \\\"\\\"\\\"\\n # We need parsers for all the intermediate scopes, so inherited option values\\n # can propagate through them.\\n complete_known_scope_infos = cls.complete_scopes(known_scope_infos)\\n splitter = ArgSplitter(complete_known_scope_infos)\\n args = sys.argv if args is None else args\\n goals, scope_to_flags, target_specs, passthru, passthru_owner = splitter.split_args(args)\\n\\n if not option_tracker:\\n raise cls.OptionTrackerRequiredError()\\n\\n if bootstrap_option_values:\\n target_spec_files = bootstrap_option_values.target_spec_files\\n if target_spec_files:\\n for spec in target_spec_files:\\n with open(spec) as f:\\n target_specs.extend(filter(None, [line.strip() for line in f]))\\n\\n help_request = splitter.help_request\\n\\n parser_hierarchy = ParserHierarchy(env, config, complete_known_scope_infos, option_tracker)\\n values_by_scope = {} # Arg values, parsed per-scope on demand.\\n bootstrap_option_values = bootstrap_option_values\\n known_scope_to_info = {s.scope: s for s in complete_known_scope_infos}\\n return cls(goals, scope_to_flags, target_specs, passthru, passthru_owne\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.__init__\",\n \"text\": \"src/python/pants/option/options.py::Options.__init__\\n def __init__(self, goals, scope_to_flags, target_specs, passthru, passthru_owner, help_request,\\n parser_hierarchy, values_by_scope, bootstrap_option_values, known_scope_to_info,\\n option_tracker):\\n \\\"\\\"\\\"The low-level constructor for an Options instance.\\n\\n Dependees should use `Options.create` instead.\\n \\\"\\\"\\\"\\n self._goals = goals\\n self._scope_to_flags = scope_to_flags\\n self._target_specs = target_specs\\n self._passthru = passthru\\n self._passthru_owner = passthru_owner\\n self._help_request = help_request\\n self._parser_hierarchy = parser_hierarchy\\n self._values_by_scope = values_by_scope\\n self._bootstrap_option_values = bootstrap_option_values\\n self._known_scope_to_info = known_scope_to_info\\n self._option_tracker = option_tracker\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.tracker\",\n \"text\": \"src/python/pants/option/options.py::Options.tracker\\n @property\\n def tracker(self):\\n return self._option_tracker\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.help_request\",\n \"text\": \"src/python/pants/option/options.py::Options.help_request\\n @property\\n def help_request(self):\\n \\\"\\\"\\\"\\n :API: public\\n \\\"\\\"\\\"\\n return self._help_request\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.target_specs\",\n \"text\": \"src/python/pants/option/options.py::Options.target_specs\\n @property\\n def target_specs(self):\\n \\\"\\\"\\\"The targets to operate on.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return self._target_specs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.goals\",\n \"text\": \"src/python/pants/option/options.py::Options.goals\\n @property\\n def goals(self):\\n \\\"\\\"\\\"The requested goals, in the order specified on the cmd line.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return self._goals\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.known_scope_to_info\",\n \"text\": \"src/python/pants/option/options.py::Options.known_scope_to_info\\n @property\\n def known_scope_to_info(self):\\n return self._known_scope_to_info\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.scope_to_flags\",\n \"text\": \"src/python/pants/option/options.py::Options.scope_to_flags\\n @property\\n def scope_to_flags(self):\\n return self._scope_to_flags\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.drop_flag_values\",\n \"text\": \"src/python/pants/option/options.py::Options.drop_flag_values\\n def drop_flag_values(self):\\n \\\"\\\"\\\"Returns a copy of these options that ignores values specified via flags.\\n\\n Any pre-cached option values are cleared and only option values that come from option defaults,\\n the config or the environment are used.\\n \\\"\\\"\\\"\\n # An empty scope_to_flags to force all values to come via the config -> env hierarchy alone\\n # and empty values in case we already cached some from flags.\\n no_flags = {}\\n no_values = {}\\n return Options(self._goals,\\n no_flags,\\n self._target_specs,\\n self._passthru,\\n self._passthru_owner,\\n self._help_request,\\n self._parser_hierarchy,\\n no_values,\\n self._bootstrap_option_values,\\n self._known_scope_to_info,\\n self._option_tracker)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.is_known_scope\",\n \"text\": \"src/python/pants/option/options.py::Options.is_known_scope\\n def is_known_scope(self, scope):\\n \\\"\\\"\\\"Whether the given scope is known by this instance.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return scope in self._known_scope_to_info\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.passthru_args_for_scope\",\n \"text\": \"src/python/pants/option/options.py::Options.passthru_args_for_scope\\n def passthru_args_for_scope(self, scope):\\n # Passthru args \\\"belong\\\" to the last scope mentioned on the command-line.\\n\\n # Note: If that last scope is a goal, we allow all tasks in that goal to access the passthru\\n # args. This is to allow the more intuitive\\n # pants run <target> -- <passthru args>\\n # instead of requiring\\n # pants run.py <target> -- <passthru args>.\\n #\\n # However note that in the case where multiple tasks run in the same goal, e.g.,\\n # pants test <target> -- <passthru args>\\n # Then, e.g., both junit and pytest will get the passthru args even though the user probably\\n # only intended them to go to one of them. If the wrong one is not a no-op then the error will\\n # be unpredictable. However this is not a common case, and can be circumvented with an\\n # explicit test.pytest or test.junit scope.\\n if (scope and self._passthru_owner and scope.startswith(self._passthru_owner) and\\n (len(scope) == len(self._passthru_owner) or scope[len(self._passthru_owner)] == '.')):\\n return self._passthru\\n else:\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.register\",\n \"text\": \"src/python/pants/option/options.py::Options.register\\n def register(self, scope, *args, **kwargs):\\n \\\"\\\"\\\"Register an option in the given scope.\\\"\\\"\\\"\\n self.get_parser(scope).register(*args, **kwargs)\\n deprecated_scope = self.known_scope_to_info[scope].deprecated_scope\\n if deprecated_scope:\\n self.get_parser(deprecated_scope).register(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.registration_function_for_optionable\",\n \"text\": \"src/python/pants/option/options.py::Options.registration_function_for_optionable\\n def registration_function_for_optionable(self, optionable_class):\\n \\\"\\\"\\\"Returns a function for registering options on the given scope.\\\"\\\"\\\"\\n # TODO(benjy): Make this an instance of a class that implements __call__, so we can\\n # docstring it, and so it's less weird than attatching properties to a function.\\n def register(*args, **kwargs):\\n kwargs['registering_class'] = optionable_class\\n self.register(optionable_class.options_scope, *args, **kwargs)\\n # Clients can access the bootstrap option values as register.bootstrap.\\n register.bootstrap = self.bootstrap_option_values()\\n # Clients can access the scope as register.scope.\\n register.scope = optionable_class.options_scope\\n return register\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.registration_function_for_optionable.register\",\n \"text\": \"src/python/pants/option/options.py::Options.registration_function_for_optionable.register\\n def register(*args, **kwargs):\\n kwargs['registering_class'] = optionable_class\\n self.register(optionable_class.options_scope, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.get_parser\",\n \"text\": \"src/python/pants/option/options.py::Options.get_parser\\n def get_parser(self, scope):\\n \\\"\\\"\\\"Returns the parser for the given scope, so code can register on it directly.\\\"\\\"\\\"\\n return self._parser_hierarchy.get_parser_by_scope(scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.walk_parsers\",\n \"text\": \"src/python/pants/option/options.py::Options.walk_parsers\\n def walk_parsers(self, callback):\\n self._parser_hierarchy.walk(callback)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.for_scope\",\n \"text\": \"src/python/pants/option/options.py::Options.for_scope\\n def for_scope(self, scope, inherit_from_enclosing_scope=True):\\n \\\"\\\"\\\"Return the option values for the given scope.\\n\\n Values are attributes of the returned object, e.g., options.foo.\\n Computed lazily per scope.\\n\\n :API: public\\n \\\"\\\"\\\"\\n # Short-circuit, if already computed.\\n if scope in self._values_by_scope:\\n return self._values_by_scope[scope]\\n\\n # First get enclosing scope's option values, if any.\\n if scope == GLOBAL_SCOPE or not inherit_from_enclosing_scope:\\n values = OptionValueContainer()\\n else:\\n values = copy.copy(self.for_scope(enclosing_scope(scope)))\\n\\n # Now add our values.\\n flags_in_scope = self._scope_to_flags.get(scope, [])\\n self._parser_hierarchy.get_parser_by_scope(scope).parse_args(flags_in_scope, values)\\n\\n # If we're the new name of a deprecated scope, also get values from that scope.\\n deprecated_scope = self.known_scope_to_info[scope].deprecated_scope\\n # Note that deprecated_scope and scope share the same Optionable class, so deprecated_scope's\\n # Optionable has a deprecated_options_scope equal to deprecated_scope. Therefore we must\\n # check that scope != deprecated_scope to prevent infinite recursion.\\n if deprecated_scope is not None and scope != deprecated_scope:\\n # Do the deprecation check only on keys that were explicitly set on the deprecated scope\\n # (and not on its enclosing scopes).\\n explicit_keys = self.for_scope(deprecated_scope,\\n inherit_from_enclosing_scope=False).get_explicit_keys()\\n if explicit_keys:\\n warn_or_error(self.known_scope_to_info[scope].deprecated_scope_removal_version,\\n 'scope {}'.format(deprecated_scope),\\n 'Use scope {} instead (options: {})'.format(scope, ', '.join(explicit_keys)))\\n # Update our values with those of the deprecated scope (now including values inherited\\n # from its enclosin\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.get_fingerprintable_for_scope\",\n \"text\": \"src/python/pants/option/options.py::Options.get_fingerprintable_for_scope\\n def get_fingerprintable_for_scope(self, scope):\\n \\\"\\\"\\\"Returns a list of fingerprintable (option type, option value) pairs for the given scope.\\n\\n Fingerprintable options are options registered via a \\\"fingerprint=True\\\" kwarg.\\n\\n :API: public\\n \\\"\\\"\\\"\\n pairs = []\\n # Note that we iterate over options registered at `scope` and at all enclosing scopes, since\\n # option-using code can read those values indirectly via its own OptionValueContainer, so\\n # they can affect that code's output.\\n registration_scope = scope\\n while registration_scope is not None:\\n parser = self._parser_hierarchy.get_parser_by_scope(registration_scope)\\n # Sort the arguments, so that the fingerprint is consistent.\\n for (_, kwargs) in sorted(parser.option_registrations_iter()):\\n if kwargs.get('recursive') and not kwargs.get('recursive_root'):\\n continue # We only need to fprint recursive options once.\\n if kwargs.get('fingerprint') is not True:\\n continue\\n # Note that we read the value from scope, even if the registration was on an enclosing\\n # scope, to get the right value for recursive options (and because this mirrors what\\n # option-using code does).\\n val = self.for_scope(scope)[kwargs['dest']]\\n # If we have a list then we delegate to the fingerprinting implementation of the members.\\n if is_list_option(kwargs):\\n val_type = kwargs.get('member_type', str)\\n else:\\n val_type = kwargs.get('type', str)\\n pairs.append((val_type, val))\\n registration_scope = (None if registration_scope == ''\\n else enclosing_scope(registration_scope))\\n return pairs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.__getitem__\",\n \"text\": \"src/python/pants/option/options.py::Options.__getitem__\\n def __getitem__(self, scope):\\n # TODO(John Sirois): Mainly supports use of dict<str, dict<str, str>> for mock options in tests,\\n # Consider killing if tests consolidate on using TestOptions instead of the raw dicts.\\n return self.for_scope(scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.bootstrap_option_values\",\n \"text\": \"src/python/pants/option/options.py::Options.bootstrap_option_values\\n def bootstrap_option_values(self):\\n \\\"\\\"\\\"Return the option values for bootstrap options.\\n\\n General code can also access these values in the global scope. But option registration code\\n cannot, hence this special-casing of this small set of options.\\n \\\"\\\"\\\"\\n return self._bootstrap_option_values\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.for_global_scope\",\n \"text\": \"src/python/pants/option/options.py::Options.for_global_scope\\n def for_global_scope(self):\\n \\\"\\\"\\\"Return the option values for the global scope.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return self.for_scope(GLOBAL_SCOPE)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.implementation_version\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.implementation_version\\n @classmethod\\n def implementation_version(cls):\\n \\\"\\\"\\\"\\n :API: public\\n \\\"\\\"\\\"\\n return [('TaskBase', 2)]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.implementation_version_str\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.implementation_version_str\\n @classmethod\\n @memoized_method\\n def implementation_version_str(cls):\\n return '.'.join(['_'.join(map(str, x)) for x in cls.implementation_version()])\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.stable_name\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.stable_name\\n @classmethod\\n def stable_name(cls):\\n \\\"\\\"\\\"The stable name of this task type.\\n\\n We synthesize subclasses of the task types at runtime, and these synthesized subclasses\\n may have random names (e.g., in tests), so this gives us a stable name to use across runs,\\n e.g., in artifact cache references.\\n \\\"\\\"\\\"\\n return cls._stable_name or cls._compute_stable_name()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._compute_stable_name\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._compute_stable_name\\n @classmethod\\n def _compute_stable_name(cls):\\n return '{}_{}'.format(cls.__module__, cls.__name__).replace('.', '_')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.subsystem_dependencies\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.subsystem_dependencies\\n @classmethod\\n def subsystem_dependencies(cls):\\n return super(TaskBase, cls).subsystem_dependencies() + (CacheSetup.scoped(cls),)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.product_types\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.product_types\\n @classmethod\\n def product_types(cls):\\n \\\"\\\"\\\"The list of products this Task produces. Set the product type(s) for this\\n task i.e. the product type(s) this task creates e.g ['classes'].\\n\\n By default, each task is considered as creating a unique product type(s).\\n Subclasses that create products, should override this to specify their unique product type(s).\\n\\n :API: public\\n \\\"\\\"\\\"\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.known_scope_infos\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.known_scope_infos\\n @classmethod\\n def known_scope_infos(cls):\\n \\\"\\\"\\\"Yields ScopeInfo for all known scopes for this task, in no particular order.\\\"\\\"\\\"\\n # The task's own scope.\\n yield cls.get_scope_info()\\n # The scopes of any task-specific subsystems it uses.\\n for dep in cls.subsystem_dependencies_iter():\\n if not dep.is_global():\\n yield dep.subsystem_cls.get_scope_info(subscope=dep.scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.supports_passthru_args\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.supports_passthru_args\\n @classmethod\\n def supports_passthru_args(cls):\\n \\\"\\\"\\\"Subclasses may override to indicate that they can use passthru args.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._scoped_options\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._scoped_options\\n @classmethod\\n def _scoped_options(cls, options):\\n return options[cls.options_scope]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.get_alternate_target_roots\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.get_alternate_target_roots\\n @classmethod\\n def get_alternate_target_roots(cls, options, address_mapper, build_graph):\\n # Subclasses should not generally need to override this method.\\n return cls.alternate_target_roots(cls._scoped_options(options), address_mapper, build_graph)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.alternate_target_roots\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.alternate_target_roots\\n @classmethod\\n def alternate_target_roots(cls, options, address_mapper, build_graph):\\n \\\"\\\"\\\"Allows a Task to propose alternate target roots from those specified on the CLI.\\n\\n At most 1 unique proposal is allowed amongst all tasks involved in the run. If more than 1\\n unique list of target roots is proposed an error is raised during task scheduling.\\n\\n :API: public\\n\\n :returns list: The new target roots to use or None to accept the CLI specified target roots.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.invoke_prepare\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.invoke_prepare\\n @classmethod\\n def invoke_prepare(cls, options, round_manager):\\n # Subclasses should not generally need to override this method.\\n return cls.prepare(cls._scoped_options(options), round_manager)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.prepare\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.prepare\\n @classmethod\\n def prepare(cls, options, round_manager):\\n \\\"\\\"\\\"Prepares a task for execution.\\n\\n Called before execution and prior to any tasks that may be (indirectly) depended upon.\\n\\n Typically a task that requires products from other goals would register interest in those\\n products here and then retrieve the requested product mappings when executed.\\n\\n :API: public\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.__init__\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.__init__\\n def __init__(self, context, workdir):\\n \\\"\\\"\\\"Subclass __init__ methods, if defined, *must* follow this idiom:\\n\\n class MyTask(Task):\\n def __init__(self, *args, **kwargs):\\n super(MyTask, self).__init__(*args, **kwargs)\\n ...\\n\\n This allows us to change Task.__init__()'s arguments without\\n changing every subclass. If the subclass does not need its own\\n initialization, this method can (and should) be omitted entirely.\\n\\n :API: public\\n \\\"\\\"\\\"\\n super(TaskBase, self).__init__()\\n self.context = context\\n self._workdir = workdir\\n\\n self._cache_key_errors = set()\\n\\n self._build_invalidator_dir = os.path.join(\\n self.context.options.for_global_scope().pants_workdir,\\n 'build_invalidator',\\n self.stable_name())\\n\\n self._cache_factory = CacheSetup.create_cache_factory_for_task(self)\\n\\n self._options_fingerprinter = OptionsFingerprinter(self.context.build_graph)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.get_options\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.get_options\\n def get_options(self):\\n \\\"\\\"\\\"Returns the option values for this task's scope.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return self.context.options.for_scope(self.options_scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.get_passthru_args\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.get_passthru_args\\n def get_passthru_args(self):\\n \\\"\\\"\\\"\\n :API: public\\n \\\"\\\"\\\"\\n if not self.supports_passthru_args():\\n raise TaskError('{0} Does not support passthru args.'.format(self.stable_name()))\\n else:\\n return self.context.options.passthru_args_for_scope(self.options_scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.workdir\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.workdir\\n @memoized_property\\n def workdir(self):\\n \\\"\\\"\\\"A scratch-space for this task that will be deleted by `clean-all`.\\n\\n It's guaranteed that no other task has been given this workdir path to use and that the workdir\\n exists.\\n\\n :API: public\\n \\\"\\\"\\\"\\n safe_mkdir(self._workdir)\\n return self._workdir\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._options_fingerprint\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._options_fingerprint\\n def _options_fingerprint(self, scope):\\n pairs = self.context.options.get_fingerprintable_for_scope(scope)\\n hasher = sha1()\\n for (option_type, option_val) in pairs:\\n fp = self._options_fingerprinter.fingerprint(option_type, option_val)\\n if fp is not None:\\n hasher.update(fp)\\n return hasher.hexdigest()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.fingerprint\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.fingerprint\\n @memoized_property\\n def fingerprint(self):\\n \\\"\\\"\\\"Returns a fingerprint for the identity of the task.\\n\\n A task fingerprint is composed of the options the task is currently running under.\\n Useful for invalidating unchanging targets being executed beneath changing task\\n options that affect outputted artifacts.\\n\\n A task's fingerprint is only valid afer the task has been fully initialized.\\n \\\"\\\"\\\"\\n hasher = sha1()\\n hasher.update(self._options_fingerprint(self.options_scope))\\n hasher.update(self.implementation_version_str())\\n # TODO: this is not recursive, but should be: see #2739\\n for dep in self.subsystem_dependencies_iter():\\n hasher.update(self._options_fingerprint(dep.options_scope()))\\n return str(hasher.hexdigest())\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.artifact_cache_reads_enabled\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.artifact_cache_reads_enabled\\n def artifact_cache_reads_enabled(self):\\n return self._cache_factory.read_cache_available()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.artifact_cache_writes_enabled\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.artifact_cache_writes_enabled\\n def artifact_cache_writes_enabled(self):\\n return self._cache_factory.write_cache_available()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.invalidate\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.invalidate\\n def invalidate(self):\\n \\\"\\\"\\\"Invalidates all targets for this task.\\\"\\\"\\\"\\n BuildInvalidator(self._build_invalidator_dir).force_invalidate_all()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.create_target_dirs\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.create_target_dirs\\n @property\\n def create_target_dirs(self):\\n \\\"\\\"\\\"Whether to create a results_dir per VersionedTarget in the workdir of the Task.\\n\\n This defaults to the value of `self.cache_target_dirs` (as caching them requires\\n creating them), but may be overridden independently to create the dirs without caching\\n them.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return self.cache_target_dirs or False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.cache_target_dirs\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.cache_target_dirs\\n @property\\n def cache_target_dirs(self):\\n \\\"\\\"\\\"Whether to cache files in VersionedTarget's results_dir after exiting an invalidated block.\\n\\n Subclasses may override this method to return True if they wish to use this style\\n of \\\"automated\\\" caching, where each VersionedTarget is given an associated results directory,\\n which will automatically be uploaded to the cache. Tasks should place the output files\\n for each VersionedTarget in said results directory. It is highly suggested to follow this\\n schema for caching, rather than manually making updates to the artifact cache.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.incremental\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.incremental\\n @property\\n def incremental(self):\\n \\\"\\\"\\\"Whether this Task implements incremental building of individual targets.\\n\\n Incremental tasks with `cache_target_dirs` set will have the results_dir of the previous build\\n for a target cloned into the results_dir for the current build (where possible). This\\n copy-on-write behaviour allows for immutability of the results_dir once a target has been\\n marked valid.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.cache_incremental\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.cache_incremental\\n @property\\n def cache_incremental(self):\\n \\\"\\\"\\\"For incremental tasks, indicates whether the results of incremental builds should be cached.\\n\\n Deterministic per-target incremental compilation is a relatively difficult thing to implement,\\n so this property provides an escape hatch to avoid caching things in that riskier case.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.invalidated\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.invalidated\\n @contextmanager\\n def invalidated(self,\\n targets,\\n invalidate_dependents=False,\\n silent=False,\\n fingerprint_strategy=None,\\n topological_order=False):\\n \\\"\\\"\\\"Checks targets for invalidation, first checking the artifact cache.\\n\\n Subclasses call this to figure out what to work on.\\n\\n :API: public\\n\\n :param targets: The targets to check for changes.\\n :param invalidate_dependents: If True then any targets depending on changed targets are\\n invalidated.\\n :param silent: If true, suppress logging information about target invalidation.\\n :param fingerprint_strategy: A FingerprintStrategy instance, which can do per task,\\n finer grained fingerprinting of a given Target.\\n :param topological_order: Whether to invalidate in dependency order.\\n\\n If no exceptions are thrown by work in the block, the build cache is updated for the targets.\\n Note: the artifact cache is not updated. That must be done manually.\\n\\n :returns: Yields an InvalidationCheck object reflecting the targets.\\n :rtype: InvalidationCheck\\n \\\"\\\"\\\"\\n\\n cache_key_generator = CacheKeyGenerator(\\n self.context.options.for_global_scope().cache_key_gen_version,\\n self.fingerprint)\\n cache_manager = InvalidationCacheManager(self.workdir,\\n cache_key_generator,\\n self._build_invalidator_dir,\\n invalidate_dependents,\\n fingerprint_strategy=fingerprint_strategy,\\n invalidation_report=self.context.invalidation_report,\\n task_name=type(self).__name__,\\n task_version=self.implementation_version_str\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.maybe_write_artifact\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.maybe_write_artifact\\n def maybe_write_artifact(self, vt):\\n if self._should_cache_target_dir(vt):\\n self.update_artifact_cache([(vt, [vt.current_results_dir])])\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._launch_background_workdir_cleanup\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._launch_background_workdir_cleanup\\n def _launch_background_workdir_cleanup(self, vts):\\n workdir_build_cleanup_job = Work(self._cleanup_workdir_stale_builds, [(vts,)], 'workdir_build_cleanup')\\n self.context.submit_background_work_chain([workdir_build_cleanup_job])\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._cleanup_workdir_stale_builds\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._cleanup_workdir_stale_builds\\n def _cleanup_workdir_stale_builds(self, vts):\\n # workdir_max_build_entries has been assured of not None before invoking this method.\\n max_entries_per_target = max(2, self.context.options.for_global_scope().workdir_max_build_entries)\\n for vt in vts:\\n live_dirs = list(vt.live_dirs())\\n if not live_dirs:\\n continue\\n root_dir = os.path.dirname(vt.results_dir)\\n safe_rm_oldest_items_in_dir(root_dir, max_entries_per_target, excludes=live_dirs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._should_cache_target_dir\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._should_cache_target_dir\\n def _should_cache_target_dir(self, vt):\\n \\\"\\\"\\\"Return true if the given vt should be written to a cache (if configured).\\\"\\\"\\\"\\n return (\\n self.cache_target_dirs and\\n not vt.target.has_label('no_cache') and\\n (not vt.is_incremental or self.cache_incremental) and\\n self.artifact_cache_writes_enabled()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._maybe_create_results_dirs\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._maybe_create_results_dirs\\n def _maybe_create_results_dirs(self, vts):\\n \\\"\\\"\\\"If `cache_target_dirs`, create results_dirs for the given versioned targets.\\\"\\\"\\\"\\n if self.create_target_dirs:\\n for vt in vts:\\n vt.create_results_dir()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.check_artifact_cache_for\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.check_artifact_cache_for\\n def check_artifact_cache_for(self, invalidation_check):\\n \\\"\\\"\\\"Decides which VTS to check the artifact cache for.\\n\\n By default we check for each invalid target. Can be overridden, e.g., to\\n instead check only for a single artifact for the entire target set.\\n \\\"\\\"\\\"\\n return invalidation_check.invalid_vts\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.check_artifact_cache\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.check_artifact_cache\\n def check_artifact_cache(self, vts):\\n \\\"\\\"\\\"Checks the artifact cache for the specified list of VersionedTargetSets.\\n\\n Returns a tuple (cached, uncached, uncached_causes) of VersionedTargets that were\\n satisfied/unsatisfied from the cache. Uncached VTS are also attached with their\\n causes for the miss: `False` indicates a legit miss while `UnreadableArtifact`\\n is due to either local or remote cache failures.\\n \\\"\\\"\\\"\\n return self.do_check_artifact_cache(vts)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.do_check_artifact_cache\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.do_check_artifact_cache\\n def do_check_artifact_cache(self, vts, post_process_cached_vts=None):\\n \\\"\\\"\\\"Checks the artifact cache for the specified list of VersionedTargetSets.\\n\\n Returns a pair (cached, uncached) of VersionedTargets that were\\n satisfied/unsatisfied from the cache.\\n \\\"\\\"\\\"\\n if not vts:\\n return [], [], []\\n\\n read_cache = self._cache_factory.get_read_cache()\\n items = [(read_cache, vt.cache_key, vt.current_results_dir if self.cache_target_dirs else None)\\n for vt in vts]\\n res = self.context.subproc_map(call_use_cached_files, items)\\n\\n cached_vts = []\\n uncached_vts = []\\n uncached_causes = []\\n\\n # Note that while the input vts may represent multiple targets (for tasks that overrride\\n # check_artifact_cache_for), the ones we return must represent single targets.\\n # Once flattened, cached/uncached vts are in separate lists. Each uncached vts is paired\\n # with why it is missed for stat reporting purpose.\\n for vt, was_in_cache in zip(vts, res):\\n if was_in_cache:\\n cached_vts.extend(vt.versioned_targets)\\n else:\\n uncached_vts.extend(vt.versioned_targets)\\n uncached_causes.extend(repeat(was_in_cache, len(vt.versioned_targets)))\\n if isinstance(was_in_cache, UnreadableArtifact):\\n self._cache_key_errors.update(was_in_cache.key)\\n\\n if post_process_cached_vts:\\n post_process_cached_vts(cached_vts)\\n for vt in cached_vts:\\n vt.update()\\n return cached_vts, uncached_vts, uncached_causes\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.update_artifact_cache\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.update_artifact_cache\\n def update_artifact_cache(self, vts_artifactfiles_pairs):\\n \\\"\\\"\\\"Write to the artifact cache, if we're configured to.\\n\\n vts_artifactfiles_pairs - a list of pairs (vts, artifactfiles) where\\n - vts is single VersionedTargetSet.\\n - artifactfiles is a list of absolute paths to artifacts for the VersionedTargetSet.\\n \\\"\\\"\\\"\\n update_artifact_cache_work = self._get_update_artifact_cache_work(vts_artifactfiles_pairs)\\n if update_artifact_cache_work:\\n self.context.submit_background_work_chain([update_artifact_cache_work],\\n parent_workunit_name='cache')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._get_update_artifact_cache_work\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._get_update_artifact_cache_work\\n def _get_update_artifact_cache_work(self, vts_artifactfiles_pairs):\\n \\\"\\\"\\\"Create a Work instance to update an artifact cache, if we're configured to.\\n\\n vts_artifactfiles_pairs - a list of pairs (vts, artifactfiles) where\\n - vts is single VersionedTargetSet.\\n - artifactfiles is a list of paths to artifacts for the VersionedTargetSet.\\n \\\"\\\"\\\"\\n cache = self._cache_factory.get_write_cache()\\n if cache:\\n if len(vts_artifactfiles_pairs) == 0:\\n return None\\n # Do some reporting.\\n targets = set()\\n for vts, _ in vts_artifactfiles_pairs:\\n targets.update(vts.targets)\\n\\n self._report_targets(\\n 'Caching artifacts for ',\\n list(targets),\\n '.',\\n logger=self.context.log.debug,\\n )\\n\\n always_overwrite = self._cache_factory.overwrite()\\n\\n # Cache the artifacts.\\n args_tuples = []\\n for vts, artifactfiles in vts_artifactfiles_pairs:\\n overwrite = always_overwrite or vts.cache_key in self._cache_key_errors\\n args_tuples.append((cache, vts.cache_key, artifactfiles, overwrite))\\n\\n return Work(lambda x: self.context.subproc_map(call_insert, x), [(args_tuples,)], 'insert')\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._report_targets\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._report_targets\\n def _report_targets(self, prefix, targets, suffix, logger=None):\\n logger = logger or self.context.log.info\\n logger(\\n prefix,\\n items_to_report_element([t.address.reference() for t in targets], 'target'),\\n suffix,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.require_single_root_target\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.require_single_root_target\\n def require_single_root_target(self):\\n \\\"\\\"\\\"If a single target was specified on the cmd line, returns that target.\\n\\n Otherwise throws TaskError.\\n\\n :API: public\\n \\\"\\\"\\\"\\n target_roots = self.context.target_roots\\n if len(target_roots) == 0:\\n raise TaskError('No target specified.')\\n elif len(target_roots) > 1:\\n raise TaskError('Multiple targets specified: {}'\\n .format(', '.join([repr(t) for t in target_roots])))\\n return target_roots[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.determine_target_roots\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.determine_target_roots\\n def determine_target_roots(self, goal_name, predicate=None):\\n \\\"\\\"\\\"Helper for tasks that scan for default target roots.\\n\\n :param string goal_name: The goal name to use for any warning emissions.\\n :param callable predicate: The predicate to pass to `context.scan().targets(predicate=X)`.\\n \\\"\\\"\\\"\\n deprecated_conditional(\\n lambda: not self.context.target_roots,\\n '1.5.0.dev0',\\n '`./pants {0}` (with no explicit targets) will soon become an error. Please specify '\\n 'one or more explicit target specs (e.g. `./pants {0} ::`).'.format(goal_name))\\n if not self.context.target_roots and not self.get_options().enable_v2_engine:\\n # For the v1 path, continue the behavior of e.g. `./pants list` implies `./pants list ::`.\\n return self.context.scan().targets(predicate=predicate)\\n\\n # For the v2 path, e.g. `./pants list` is a functional no-op. This matches the v2 mode behavior\\n # of e.g. `./pants --changed-parent=HEAD list` (w/ no changes) returning an empty result.\\n return self.context.target_roots\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::Task.__init__\",\n \"text\": \"src/python/pants/task/task.py::Task.__init__\\n def __init__(self, context, workdir):\\n \\\"\\\"\\\"\\n Add pass-thru Task Constructor for public API visibility.\\n\\n :API: public\\n \\\"\\\"\\\"\\n super(Task, self).__init__(context, workdir)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::Task.execute\",\n \"text\": \"src/python/pants/task/task.py::Task.execute\\n @abstractmethod\\n def execute(self):\\n \\\"\\\"\\\"Executes this task.\\n\\n :API: public\\n \\\"\\\"\\\"\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":83,"cells":{"instance_id":{"kind":"string","value":"googleapis__google-cloud-python-2390"},"repo":{"kind":"string","value":"googleapis/google-cloud-python"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Make sure our 2 years old license headers are in line with\nCame up because of #2370. Related to #2371.\n\nTo paraphrase @rimey: The Apache license header given in our internal instructions differs from the one used in this project.\n\n/cc @rimey @jgeewax \n\n"},"gold_files":{"kind":"list like","value":["docs/bigquery_snippets.py","docs/conf.py","docs/pubsub_snippets.py","google/__init__.py","google/cloud/__init__.py","google/cloud/_helpers.py","google/cloud/bigquery/__init__.py","google/cloud/bigquery/_helpers.py","google/cloud/bigquery/client.py","google/cloud/bigquery/connection.py","google/cloud/bigquery/dataset.py","google/cloud/bigquery/job.py","google/cloud/bigquery/query.py","google/cloud/bigquery/schema.py","google/cloud/bigquery/table.py","google/cloud/bigtable/__init__.py","google/cloud/bigtable/_generated/__init__.py","google/cloud/bigtable/client.py","google/cloud/bigtable/cluster.py","google/cloud/bigtable/column_family.py","google/cloud/bigtable/instance.py","google/cloud/bigtable/row.py","google/cloud/bigtable/row_data.py","google/cloud/bigtable/row_filters.py","google/cloud/bigtable/table.py","google/cloud/client.py","google/cloud/connection.py","google/cloud/credentials.py","google/cloud/datastore/__init__.py","google/cloud/datastore/_generated/__init__.py","google/cloud/datastore/batch.py","google/cloud/datastore/client.py","google/cloud/datastore/connection.py","google/cloud/datastore/entity.py","google/cloud/datastore/helpers.py","google/cloud/datastore/key.py","google/cloud/datastore/query.py","google/cloud/datastore/transaction.py","google/cloud/dns/__init__.py","google/cloud/dns/changes.py","google/cloud/dns/client.py","google/cloud/dns/connection.py","google/cloud/dns/resource_record_set.py","google/cloud/dns/zone.py","google/cloud/environment_vars.py","google/cloud/error_reporting/__init__.py","google/cloud/error_reporting/client.py","google/cloud/exceptions.py","google/cloud/iterator.py","google/cloud/language/__init__.py","google/cloud/language/client.py","google/cloud/language/connection.py","google/cloud/language/document.py","google/cloud/language/entity.py","google/cloud/language/sentiment.py","google/cloud/language/syntax.py","google/cloud/logging/__init__.py","google/cloud/logging/_gax.py","google/cloud/logging/client.py","google/cloud/logging/connection.py","google/cloud/logging/entries.py","google/cloud/logging/handlers/__init__.py","google/cloud/logging/handlers/handlers.py","google/cloud/logging/handlers/transports/__init__.py","google/cloud/logging/handlers/transports/background_thread.py","google/cloud/logging/handlers/transports/base.py","google/cloud/logging/handlers/transports/sync.py","google/cloud/logging/logger.py","google/cloud/logging/metric.py","google/cloud/logging/sink.py","google/cloud/monitoring/__init__.py","google/cloud/monitoring/_dataframe.py","google/cloud/monitoring/client.py","google/cloud/monitoring/connection.py","google/cloud/monitoring/group.py","google/cloud/monitoring/label.py","google/cloud/monitoring/metric.py","google/cloud/monitoring/query.py","google/cloud/monitoring/resource.py","google/cloud/monitoring/timeseries.py","google/cloud/operation.py","google/cloud/pubsub/__init__.py","google/cloud/pubsub/_gax.py","google/cloud/pubsub/_helpers.py","google/cloud/pubsub/client.py","google/cloud/pubsub/connection.py","google/cloud/pubsub/iam.py","google/cloud/pubsub/message.py","google/cloud/pubsub/subscription.py","google/cloud/pubsub/topic.py","google/cloud/resource_manager/__init__.py","google/cloud/resource_manager/client.py","google/cloud/resource_manager/connection.py","google/cloud/resource_manager/project.py","google/cloud/speech/__init__.py","google/cloud/speech/client.py","google/cloud/speech/connection.py","google/cloud/storage/__init__.py","google/cloud/storage/_helpers.py","google/cloud/storage/acl.py","google/cloud/storage/batch.py","google/cloud/storage/blob.py","google/cloud/storage/bucket.py","google/cloud/storage/client.py","google/cloud/storage/connection.py","google/cloud/streaming/__init__.py","google/cloud/streaming/buffered_stream.py","google/cloud/streaming/exceptions.py","google/cloud/streaming/http_wrapper.py","google/cloud/streaming/stream_slice.py","google/cloud/streaming/transfer.py","google/cloud/streaming/util.py","google/cloud/translate/__init__.py","google/cloud/translate/client.py","google/cloud/translate/connection.py","google/cloud/vision/__init__.py","google/cloud/vision/client.py","google/cloud/vision/color.py","google/cloud/vision/connection.py","google/cloud/vision/entity.py","google/cloud/vision/face.py","google/cloud/vision/feature.py","google/cloud/vision/geometry.py","google/cloud/vision/image.py","google/cloud/vision/likelihood.py","google/cloud/vision/safe.py","scripts/generate_json_docs.py","scripts/get_version.py","scripts/make_datastore_grpc.py","scripts/make_operations_grpc.py","scripts/pycodestyle_on_repo.py","scripts/rewrite_imports.py","scripts/run_pylint.py","scripts/verify_included_modules.py","setup.py"],"string":"[\n \"docs/bigquery_snippets.py\",\n \"docs/conf.py\",\n \"docs/pubsub_snippets.py\",\n \"google/__init__.py\",\n \"google/cloud/__init__.py\",\n \"google/cloud/_helpers.py\",\n \"google/cloud/bigquery/__init__.py\",\n \"google/cloud/bigquery/_helpers.py\",\n \"google/cloud/bigquery/client.py\",\n \"google/cloud/bigquery/connection.py\",\n \"google/cloud/bigquery/dataset.py\",\n \"google/cloud/bigquery/job.py\",\n \"google/cloud/bigquery/query.py\",\n \"google/cloud/bigquery/schema.py\",\n \"google/cloud/bigquery/table.py\",\n \"google/cloud/bigtable/__init__.py\",\n \"google/cloud/bigtable/_generated/__init__.py\",\n \"google/cloud/bigtable/client.py\",\n \"google/cloud/bigtable/cluster.py\",\n \"google/cloud/bigtable/column_family.py\",\n \"google/cloud/bigtable/instance.py\",\n \"google/cloud/bigtable/row.py\",\n \"google/cloud/bigtable/row_data.py\",\n \"google/cloud/bigtable/row_filters.py\",\n \"google/cloud/bigtable/table.py\",\n \"google/cloud/client.py\",\n \"google/cloud/connection.py\",\n \"google/cloud/credentials.py\",\n \"google/cloud/datastore/__init__.py\",\n \"google/cloud/datastore/_generated/__init__.py\",\n \"google/cloud/datastore/batch.py\",\n \"google/cloud/datastore/client.py\",\n \"google/cloud/datastore/connection.py\",\n \"google/cloud/datastore/entity.py\",\n \"google/cloud/datastore/helpers.py\",\n \"google/cloud/datastore/key.py\",\n \"google/cloud/datastore/query.py\",\n \"google/cloud/datastore/transaction.py\",\n \"google/cloud/dns/__init__.py\",\n \"google/cloud/dns/changes.py\",\n \"google/cloud/dns/client.py\",\n \"google/cloud/dns/connection.py\",\n \"google/cloud/dns/resource_record_set.py\",\n \"google/cloud/dns/zone.py\",\n \"google/cloud/environment_vars.py\",\n \"google/cloud/error_reporting/__init__.py\",\n \"google/cloud/error_reporting/client.py\",\n \"google/cloud/exceptions.py\",\n \"google/cloud/iterator.py\",\n \"google/cloud/language/__init__.py\",\n \"google/cloud/language/client.py\",\n \"google/cloud/language/connection.py\",\n \"google/cloud/language/document.py\",\n \"google/cloud/language/entity.py\",\n \"google/cloud/language/sentiment.py\",\n \"google/cloud/language/syntax.py\",\n \"google/cloud/logging/__init__.py\",\n \"google/cloud/logging/_gax.py\",\n \"google/cloud/logging/client.py\",\n \"google/cloud/logging/connection.py\",\n \"google/cloud/logging/entries.py\",\n \"google/cloud/logging/handlers/__init__.py\",\n \"google/cloud/logging/handlers/handlers.py\",\n \"google/cloud/logging/handlers/transports/__init__.py\",\n \"google/cloud/logging/handlers/transports/background_thread.py\",\n \"google/cloud/logging/handlers/transports/base.py\",\n \"google/cloud/logging/handlers/transports/sync.py\",\n \"google/cloud/logging/logger.py\",\n \"google/cloud/logging/metric.py\",\n \"google/cloud/logging/sink.py\",\n \"google/cloud/monitoring/__init__.py\",\n \"google/cloud/monitoring/_dataframe.py\",\n \"google/cloud/monitoring/client.py\",\n \"google/cloud/monitoring/connection.py\",\n \"google/cloud/monitoring/group.py\",\n \"google/cloud/monitoring/label.py\",\n \"google/cloud/monitoring/metric.py\",\n \"google/cloud/monitoring/query.py\",\n \"google/cloud/monitoring/resource.py\",\n \"google/cloud/monitoring/timeseries.py\",\n \"google/cloud/operation.py\",\n \"google/cloud/pubsub/__init__.py\",\n \"google/cloud/pubsub/_gax.py\",\n \"google/cloud/pubsub/_helpers.py\",\n \"google/cloud/pubsub/client.py\",\n \"google/cloud/pubsub/connection.py\",\n \"google/cloud/pubsub/iam.py\",\n \"google/cloud/pubsub/message.py\",\n \"google/cloud/pubsub/subscription.py\",\n \"google/cloud/pubsub/topic.py\",\n \"google/cloud/resource_manager/__init__.py\",\n \"google/cloud/resource_manager/client.py\",\n \"google/cloud/resource_manager/connection.py\",\n \"google/cloud/resource_manager/project.py\",\n \"google/cloud/speech/__init__.py\",\n \"google/cloud/speech/client.py\",\n \"google/cloud/speech/connection.py\",\n \"google/cloud/storage/__init__.py\",\n \"google/cloud/storage/_helpers.py\",\n \"google/cloud/storage/acl.py\",\n \"google/cloud/storage/batch.py\",\n \"google/cloud/storage/blob.py\",\n \"google/cloud/storage/bucket.py\",\n \"google/cloud/storage/client.py\",\n \"google/cloud/storage/connection.py\",\n \"google/cloud/streaming/__init__.py\",\n \"google/cloud/streaming/buffered_stream.py\",\n \"google/cloud/streaming/exceptions.py\",\n \"google/cloud/streaming/http_wrapper.py\",\n \"google/cloud/streaming/stream_slice.py\",\n \"google/cloud/streaming/transfer.py\",\n \"google/cloud/streaming/util.py\",\n \"google/cloud/translate/__init__.py\",\n \"google/cloud/translate/client.py\",\n \"google/cloud/translate/connection.py\",\n \"google/cloud/vision/__init__.py\",\n \"google/cloud/vision/client.py\",\n \"google/cloud/vision/color.py\",\n \"google/cloud/vision/connection.py\",\n \"google/cloud/vision/entity.py\",\n \"google/cloud/vision/face.py\",\n \"google/cloud/vision/feature.py\",\n \"google/cloud/vision/geometry.py\",\n \"google/cloud/vision/image.py\",\n \"google/cloud/vision/likelihood.py\",\n \"google/cloud/vision/safe.py\",\n \"scripts/generate_json_docs.py\",\n \"scripts/get_version.py\",\n \"scripts/make_datastore_grpc.py\",\n \"scripts/make_operations_grpc.py\",\n \"scripts/pycodestyle_on_repo.py\",\n \"scripts/rewrite_imports.py\",\n \"scripts/run_pylint.py\",\n \"scripts/verify_included_modules.py\",\n \"setup.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["docs/bigquery_snippets.py::<module>","docs/conf.py::<module>","docs/pubsub_snippets.py::<module>","google/__init__.py::<module>","google/cloud/__init__.py::<module>","google/cloud/_helpers.py::<module>","google/cloud/bigquery/__init__.py::<module>","google/cloud/bigquery/_helpers.py::<module>","google/cloud/bigquery/client.py::<module>","google/cloud/bigquery/connection.py::<module>","google/cloud/bigquery/dataset.py::<module>","google/cloud/bigquery/job.py::<module>","google/cloud/bigquery/query.py::<module>","google/cloud/bigquery/schema.py::<module>","google/cloud/bigquery/table.py::<module>","google/cloud/bigtable/__init__.py::<module>","google/cloud/bigtable/_generated/__init__.py::<module>","google/cloud/bigtable/client.py::<module>","google/cloud/bigtable/cluster.py::<module>","google/cloud/bigtable/column_family.py::<module>","google/cloud/bigtable/instance.py::<module>","google/cloud/bigtable/row.py::<module>","google/cloud/bigtable/row_data.py::<module>","google/cloud/bigtable/row_filters.py::<module>","google/cloud/bigtable/table.py::<module>","google/cloud/client.py::<module>","google/cloud/connection.py::<module>","google/cloud/credentials.py::<module>","google/cloud/datastore/__init__.py::<module>","google/cloud/datastore/_generated/__init__.py::<module>","google/cloud/datastore/batch.py::<module>","google/cloud/datastore/client.py::<module>","google/cloud/datastore/connection.py::<module>","google/cloud/datastore/entity.py::<module>","google/cloud/datastore/helpers.py::<module>","google/cloud/datastore/key.py::<module>","google/cloud/datastore/query.py::<module>","google/cloud/datastore/transaction.py::<module>","google/cloud/dns/__init__.py::<module>","google/cloud/dns/changes.py::<module>","google/cloud/dns/client.py::<module>","google/cloud/dns/connection.py::<module>","google/cloud/dns/resource_record_set.py::<module>","google/cloud/dns/zone.py::<module>","google/cloud/environment_vars.py::<module>","google/cloud/error_reporting/__init__.py::<module>","google/cloud/error_reporting/client.py::<module>","google/cloud/exceptions.py::<module>","google/cloud/iterator.py::<module>","google/cloud/language/__init__.py::<module>","google/cloud/language/client.py::<module>","google/cloud/language/connection.py::<module>","google/cloud/language/document.py::<module>","google/cloud/language/entity.py::<module>","google/cloud/language/sentiment.py::<module>","google/cloud/language/syntax.py::<module>","google/cloud/logging/__init__.py::<module>","google/cloud/logging/_gax.py::<module>","google/cloud/logging/client.py::<module>","google/cloud/logging/connection.py::<module>","google/cloud/logging/entries.py::<module>","google/cloud/logging/handlers/__init__.py::<module>","google/cloud/logging/handlers/handlers.py::<module>","google/cloud/logging/handlers/transports/__init__.py::<module>","google/cloud/logging/handlers/transports/background_thread.py::<module>","google/cloud/logging/handlers/transports/base.py::<module>","google/cloud/logging/handlers/transports/sync.py::<module>","google/cloud/logging/logger.py::<module>","google/cloud/logging/metric.py::<module>","google/cloud/logging/sink.py::<module>","google/cloud/monitoring/__init__.py::<module>","google/cloud/monitoring/_dataframe.py::<module>","google/cloud/monitoring/client.py::<module>","google/cloud/monitoring/connection.py::<module>","google/cloud/monitoring/group.py::<module>","google/cloud/monitoring/label.py::<module>","google/cloud/monitoring/metric.py::<module>","google/cloud/monitoring/query.py::<module>","google/cloud/monitoring/resource.py::<module>","google/cloud/monitoring/timeseries.py::<module>","google/cloud/operation.py::<module>","google/cloud/pubsub/__init__.py::<module>","google/cloud/pubsub/_gax.py::<module>","google/cloud/pubsub/_helpers.py::<module>","google/cloud/pubsub/client.py::<module>","google/cloud/pubsub/connection.py::<module>","google/cloud/pubsub/iam.py::<module>","google/cloud/pubsub/message.py::<module>","google/cloud/pubsub/subscription.py::<module>","google/cloud/pubsub/topic.py::<module>","google/cloud/resource_manager/__init__.py::<module>","google/cloud/resource_manager/client.py::<module>","google/cloud/resource_manager/connection.py::<module>","google/cloud/resource_manager/project.py::<module>","google/cloud/speech/__init__.py::<module>","google/cloud/speech/client.py::<module>","google/cloud/speech/connection.py::<module>","google/cloud/storage/__init__.py::<module>","google/cloud/storage/_helpers.py::<module>","google/cloud/storage/acl.py::<module>","google/cloud/storage/batch.py::<module>","google/cloud/storage/blob.py::<module>","google/cloud/storage/bucket.py::<module>","google/cloud/storage/client.py::<module>","google/cloud/storage/connection.py::<module>","google/cloud/streaming/__init__.py::<module>","google/cloud/streaming/buffered_stream.py::<module>","google/cloud/streaming/exceptions.py::<module>","google/cloud/streaming/http_wrapper.py::<module>","google/cloud/streaming/stream_slice.py::<module>","google/cloud/streaming/transfer.py::<module>","google/cloud/streaming/util.py::<module>","google/cloud/translate/__init__.py::<module>","google/cloud/translate/client.py::<module>","google/cloud/translate/connection.py::<module>","google/cloud/vision/__init__.py::<module>","google/cloud/vision/client.py::<module>","google/cloud/vision/color.py::<module>","google/cloud/vision/connection.py::<module>","google/cloud/vision/entity.py::<module>","google/cloud/vision/face.py::<module>","google/cloud/vision/feature.py::<module>","google/cloud/vision/geometry.py::<module>","google/cloud/vision/image.py::<module>","google/cloud/vision/likelihood.py::<module>","google/cloud/vision/safe.py::<module>","scripts/generate_json_docs.py::<module>","scripts/get_version.py::<module>","scripts/make_datastore_grpc.py::<module>","scripts/make_operations_grpc.py::<module>","scripts/pycodestyle_on_repo.py::<module>","scripts/rewrite_imports.py::<module>","scripts/run_pylint.py::<module>","scripts/verify_included_modules.py::<module>","setup.py::<module>"],"string":"[\n \"docs/bigquery_snippets.py::<module>\",\n \"docs/conf.py::<module>\",\n \"docs/pubsub_snippets.py::<module>\",\n \"google/__init__.py::<module>\",\n \"google/cloud/__init__.py::<module>\",\n \"google/cloud/_helpers.py::<module>\",\n \"google/cloud/bigquery/__init__.py::<module>\",\n \"google/cloud/bigquery/_helpers.py::<module>\",\n \"google/cloud/bigquery/client.py::<module>\",\n \"google/cloud/bigquery/connection.py::<module>\",\n \"google/cloud/bigquery/dataset.py::<module>\",\n \"google/cloud/bigquery/job.py::<module>\",\n \"google/cloud/bigquery/query.py::<module>\",\n \"google/cloud/bigquery/schema.py::<module>\",\n \"google/cloud/bigquery/table.py::<module>\",\n \"google/cloud/bigtable/__init__.py::<module>\",\n \"google/cloud/bigtable/_generated/__init__.py::<module>\",\n \"google/cloud/bigtable/client.py::<module>\",\n \"google/cloud/bigtable/cluster.py::<module>\",\n \"google/cloud/bigtable/column_family.py::<module>\",\n \"google/cloud/bigtable/instance.py::<module>\",\n \"google/cloud/bigtable/row.py::<module>\",\n \"google/cloud/bigtable/row_data.py::<module>\",\n \"google/cloud/bigtable/row_filters.py::<module>\",\n \"google/cloud/bigtable/table.py::<module>\",\n \"google/cloud/client.py::<module>\",\n \"google/cloud/connection.py::<module>\",\n \"google/cloud/credentials.py::<module>\",\n \"google/cloud/datastore/__init__.py::<module>\",\n \"google/cloud/datastore/_generated/__init__.py::<module>\",\n \"google/cloud/datastore/batch.py::<module>\",\n \"google/cloud/datastore/client.py::<module>\",\n \"google/cloud/datastore/connection.py::<module>\",\n \"google/cloud/datastore/entity.py::<module>\",\n \"google/cloud/datastore/helpers.py::<module>\",\n \"google/cloud/datastore/key.py::<module>\",\n \"google/cloud/datastore/query.py::<module>\",\n \"google/cloud/datastore/transaction.py::<module>\",\n \"google/cloud/dns/__init__.py::<module>\",\n \"google/cloud/dns/changes.py::<module>\",\n \"google/cloud/dns/client.py::<module>\",\n \"google/cloud/dns/connection.py::<module>\",\n \"google/cloud/dns/resource_record_set.py::<module>\",\n \"google/cloud/dns/zone.py::<module>\",\n \"google/cloud/environment_vars.py::<module>\",\n \"google/cloud/error_reporting/__init__.py::<module>\",\n \"google/cloud/error_reporting/client.py::<module>\",\n \"google/cloud/exceptions.py::<module>\",\n \"google/cloud/iterator.py::<module>\",\n \"google/cloud/language/__init__.py::<module>\",\n \"google/cloud/language/client.py::<module>\",\n \"google/cloud/language/connection.py::<module>\",\n \"google/cloud/language/document.py::<module>\",\n \"google/cloud/language/entity.py::<module>\",\n \"google/cloud/language/sentiment.py::<module>\",\n \"google/cloud/language/syntax.py::<module>\",\n \"google/cloud/logging/__init__.py::<module>\",\n \"google/cloud/logging/_gax.py::<module>\",\n \"google/cloud/logging/client.py::<module>\",\n \"google/cloud/logging/connection.py::<module>\",\n \"google/cloud/logging/entries.py::<module>\",\n \"google/cloud/logging/handlers/__init__.py::<module>\",\n \"google/cloud/logging/handlers/handlers.py::<module>\",\n \"google/cloud/logging/handlers/transports/__init__.py::<module>\",\n \"google/cloud/logging/handlers/transports/background_thread.py::<module>\",\n \"google/cloud/logging/handlers/transports/base.py::<module>\",\n \"google/cloud/logging/handlers/transports/sync.py::<module>\",\n \"google/cloud/logging/logger.py::<module>\",\n \"google/cloud/logging/metric.py::<module>\",\n \"google/cloud/logging/sink.py::<module>\",\n \"google/cloud/monitoring/__init__.py::<module>\",\n \"google/cloud/monitoring/_dataframe.py::<module>\",\n \"google/cloud/monitoring/client.py::<module>\",\n \"google/cloud/monitoring/connection.py::<module>\",\n \"google/cloud/monitoring/group.py::<module>\",\n \"google/cloud/monitoring/label.py::<module>\",\n \"google/cloud/monitoring/metric.py::<module>\",\n \"google/cloud/monitoring/query.py::<module>\",\n \"google/cloud/monitoring/resource.py::<module>\",\n \"google/cloud/monitoring/timeseries.py::<module>\",\n \"google/cloud/operation.py::<module>\",\n \"google/cloud/pubsub/__init__.py::<module>\",\n \"google/cloud/pubsub/_gax.py::<module>\",\n \"google/cloud/pubsub/_helpers.py::<module>\",\n \"google/cloud/pubsub/client.py::<module>\",\n \"google/cloud/pubsub/connection.py::<module>\",\n \"google/cloud/pubsub/iam.py::<module>\",\n \"google/cloud/pubsub/message.py::<module>\",\n \"google/cloud/pubsub/subscription.py::<module>\",\n \"google/cloud/pubsub/topic.py::<module>\",\n \"google/cloud/resource_manager/__init__.py::<module>\",\n \"google/cloud/resource_manager/client.py::<module>\",\n \"google/cloud/resource_manager/connection.py::<module>\",\n \"google/cloud/resource_manager/project.py::<module>\",\n \"google/cloud/speech/__init__.py::<module>\",\n \"google/cloud/speech/client.py::<module>\",\n \"google/cloud/speech/connection.py::<module>\",\n \"google/cloud/storage/__init__.py::<module>\",\n \"google/cloud/storage/_helpers.py::<module>\",\n \"google/cloud/storage/acl.py::<module>\",\n \"google/cloud/storage/batch.py::<module>\",\n \"google/cloud/storage/blob.py::<module>\",\n \"google/cloud/storage/bucket.py::<module>\",\n \"google/cloud/storage/client.py::<module>\",\n \"google/cloud/storage/connection.py::<module>\",\n \"google/cloud/streaming/__init__.py::<module>\",\n \"google/cloud/streaming/buffered_stream.py::<module>\",\n \"google/cloud/streaming/exceptions.py::<module>\",\n \"google/cloud/streaming/http_wrapper.py::<module>\",\n \"google/cloud/streaming/stream_slice.py::<module>\",\n \"google/cloud/streaming/transfer.py::<module>\",\n \"google/cloud/streaming/util.py::<module>\",\n \"google/cloud/translate/__init__.py::<module>\",\n \"google/cloud/translate/client.py::<module>\",\n \"google/cloud/translate/connection.py::<module>\",\n \"google/cloud/vision/__init__.py::<module>\",\n \"google/cloud/vision/client.py::<module>\",\n \"google/cloud/vision/color.py::<module>\",\n \"google/cloud/vision/connection.py::<module>\",\n \"google/cloud/vision/entity.py::<module>\",\n \"google/cloud/vision/face.py::<module>\",\n \"google/cloud/vision/feature.py::<module>\",\n \"google/cloud/vision/geometry.py::<module>\",\n \"google/cloud/vision/image.py::<module>\",\n \"google/cloud/vision/likelihood.py::<module>\",\n \"google/cloud/vision/safe.py::<module>\",\n \"scripts/generate_json_docs.py::<module>\",\n \"scripts/get_version.py::<module>\",\n \"scripts/make_datastore_grpc.py::<module>\",\n \"scripts/make_operations_grpc.py::<module>\",\n \"scripts/pycodestyle_on_repo.py::<module>\",\n \"scripts/rewrite_imports.py::<module>\",\n \"scripts/run_pylint.py::<module>\",\n \"scripts/verify_included_modules.py::<module>\",\n \"setup.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/bigquery_snippets.py","text":"docs/bigquery_snippets.py\ndef snippet(func) # Mark ``func`` as a snippet example function.\ndef _millis()\nclass _CloseOnDelete\n def __init__(self, wrapped)\n def delete(self)\ndef client_list_datasets(client, _) # List datasets for a project.\n def do_something_with(_)\ndef dataset_create(client, to_delete) # Create a dataset.\ndef dataset_exists(client, to_delete) # Test existence of a dataset.\ndef dataset_reload(client, to_delete) # Reload a dataset's metadata.\ndef dataset_patch(client, to_delete) # Patch a dataset's metadata.\ndef dataset_update(client, to_delete) # Update a dataset's metadata.\ndef dataset_delete(client, _) # Delete a dataset.\ndef dataset_list_tables(client, to_delete) # List tables within a dataset.\ndef table_create(client, to_delete) # Create a table.\ndef table_exists(client, to_delete) # Test existence of a table.\ndef table_reload(client, to_delete) # Reload a table's metadata.\ndef table_patch(client, to_delete) # Patch a table's metadata.\ndef table_update(client, to_delete) # Update a table's metadata.\ndef _warm_up_inserted_table_data(table)\ndef table_insert_fetch_data(client, to_delete) # Insert / fetch table data.\n def do_something(row)\ndef table_upload_from_file(client, to_delete) # Upload table data from a CSV file.\ndef table_delete(client, _) # Delete a table.\ndef client_list_jobs(client, _) # List jobs for a project.\n def do_something_with(_)\ndef client_run_sync_query(client, _) # Run a synchronous query.\ndef client_run_sync_query_paged(client, _) # Run a synchronous query with paged results.\n def do_something_with(rows)\ndef client_run_sync_query_timeout(client, _) # Run a synchronous query w/ timeout\n def do_something_with(rows)\ndef _find_examples()\ndef main()","label":1},{"cid":"docs/conf.py","text":"docs/conf.py","label":1},{"cid":"docs/pubsub_snippets.py","text":"docs/pubsub_snippets.py\ndef snippet(func) # Mark ``func`` as a snippet example function.\ndef _millis()\ndef client_list_topics(client, to_delete) # List topics for a project.\n def do_something_with(sub)\ndef client_list_subscriptions(client, to_delete) # List all subscriptions for a project.\n def do_something_with(sub)\ndef topic_create(client, to_delete) # Create a topic.\ndef topic_exists(client, to_delete) # Test existence of a topic.\ndef topic_delete(client, to_delete) # Delete a topic.\ndef topic_iam_policy(client, to_delete) # Fetch / set a topic's IAM policy.\ndef topic_check_iam_permissions(client, to_delete) # Check topic IAM permissions.\ndef topic_publish_messages(client, to_delete) # Publish messages to a topic.\ndef topic_subscription(client, to_delete) # Create subscriptions to a topic.\n def do_something_with(sub)\ndef topic_subscription_push(client, to_delete) # Create subscriptions to a topic.\ndef subscription_lifecycle(client, to_delete) # Test lifecycle of a subscription.\ndef subscription_pull(client, to_delete) # Pull messges from a subscribed topic.\n def do_something_with(message)\n class ApplicationException\n def log_exception(_)\ndef subscription_pull_w_autoack(client, to_delete) # Pull messges from a topic, auto-acknowldging them\n def do_something_with(message)\ndef subscription_iam_policy(client, to_delete) # Fetch / set a subscription's IAM policy.\ndef subscription_check_iam_permissions(client, to_delete) # Check subscription IAM permissions.\ndef _line_no(func)\ndef _find_examples()\ndef _name_and_doc(func)\ndef main()","label":1},{"cid":"google/__init__.py","text":"google/__init__.py","label":1},{"cid":"google/cloud/__init__.py","text":"google/cloud/__init__.py","label":1},{"cid":"google/cloud/_helpers.py","text":"google/cloud/_helpers.py\nclass _LocalStack # Manage a thread-local LIFO stack of resources.\n def __init__(self)\n def __iter__(self) # Iterate the stack in LIFO order.\n def push(self, resource) # Push a resource onto our stack.\n def pop(self) # Pop a resource from our stack.\n def top(self) # Get the top-most resource\nclass _UTC # Basic UTC implementation.\n def dst(self, dt) # Daylight savings time offset.\n def fromutc(self, dt) # Convert a timestamp from (naive) UTC to this timezone.\n def tzname(self, dt) # Get the name of this timezone.\n def utcoffset(self, dt) # UTC offset of this timezone.\n def __repr__(self)\n def __str__(self)\ndef _ensure_tuple_or_list(arg_name, tuple_or_list) # Ensures an input is a tuple or list.\ndef _app_engine_id() # Gets the App Engine application ID if it can be inferred.\ndef _file_project_id() # Gets the project ID from the credentials file if one is available.\ndef _get_nix_config_path() # Get the ``gcloud`` CLI config path on *nix systems.\ndef _get_windows_config_path() # Get the ``gcloud`` CLI config path on Windows systems.\ndef _default_service_project_id() # Retrieves the project ID from the gcloud command line tool.\ndef _compute_engine_id() # Gets the Compute Engine project ID if it can be inferred.\ndef _get_production_project() # Gets the production project if it can be inferred.\ndef _determine_default_project(project=None) # Determine default project ID explicitly or implicitly as fall-back.\ndef _millis(when) # Convert a zone-aware datetime to integer milliseconds.\ndef _datetime_from_microseconds(value) # Convert timestamp to datetime, assuming UTC.\ndef _microseconds_from_datetime(value) # Convert non-none datetime to microseconds.\ndef _millis_from_datetime(value) # Convert non-none datetime to timestamp, assuming UTC.\ndef _date_from_iso8601_date(value) # Convert a ISO8601 date string to native datetime date\ndef _rfc3339_to_datetime(dt_str) # Convert a microsecond-precision timetamp to a native datetime.\ndef _rfc3339_nanos_to_datetime(dt_str) # Convert a nanosecond-precision timestamp to a native datetime.\ndef _datetime_to_rfc3339(value, ignore_zone=True) # Convert a timestamp to a string.\ndef _to_bytes(value, encoding='ascii') # Converts a string value to bytes, if necessary.\ndef _bytes_to_unicode(value) # Converts bytes to a unicode value, if necessary.\ndef _pb_timestamp_to_datetime(timestamp_pb) # Convert a Timestamp protobuf to a datetime object.\ndef _pb_timestamp_to_rfc3339(timestamp_pb) # Convert a Timestamp protobuf to an RFC 3339 string.\ndef _datetime_to_pb_timestamp(when) # Convert a datetime object to a Timestamp protobuf.\ndef _name_from_project_path(path, project, template) # Validate a URI path and get the leaf object's name.\nclass MetadataPlugin # Callable class to transform metadata for gRPC requests.\n def __init__(self, credentials)\n def __call__(self, unused_context, callback) # Adds authorization header to request metadata.","label":1},{"cid":"google/cloud/bigquery/__init__.py","text":"google/cloud/bigquery/__init__.py","label":1},{"cid":"google/cloud/bigquery/_helpers.py","text":"google/cloud/bigquery/_helpers.py\ndef _not_null(value, field) # Check whether 'value' should be coerced to 'field' type.\ndef _int_from_json(value, field) # Coerce 'value' to an int, if set or not nullable.\ndef _float_from_json(value, field) # Coerce 'value' to a float, if set or not nullable.\ndef _bool_from_json(value, field) # Coerce 'value' to a bool, if set or not nullable.\ndef _datetime_from_json(value, field) # Coerce 'value' to a datetime, if set or not nullable.\ndef _date_from_json(value, field) # Coerce 'value' to a datetime date, if set or not nullable\ndef _record_from_json(value, field) # Coerce 'value' to a mapping, if set or not nullable.\ndef _string_from_json(value, _) # NOOP string -> string coercion\ndef _rows_from_json(rows, schema) # Convert JSON row data to rows w/ appropriate types.\nclass _ConfigurationProperty # Base property implementation.\n def __init__(self, name)\n def __get__(self, instance, owner) # Descriptor protocal: accesstor\n def _validate(self, value) # Subclasses override to impose validation policy.\n def __set__(self, instance, value) # Descriptor protocal: mutator\n def __delete__(self, instance) # Descriptor protocal: deleter\nclass _TypedProperty # Property implementation: validates based on value type.\n def __init__(self, name, property_type)\n def _validate(self, value) # Ensure that 'value' is of the appropriate type.\nclass _EnumProperty # Pseudo-enumeration class.\n def _validate(self, value) # Check that ``value`` is one of the allowed values.\nclass UDFResource # Describe a single user-defined function (UDF) resource.\n def __init__(self, udf_type, value)\n def __eq__(self, other)\nclass UDFResourcesProperty # Custom property type, holding :class:`UDFResource` instances.\n def __get__(self, instance, owner) # Descriptor protocol: accessor\n def __set__(self, instance, value) # Descriptor protocol: mutator\ndef _build_udf_resources(resources) # :type resources: sequence of :class:`UDFResource`","label":1},{"cid":"google/cloud/bigquery/client.py","text":"google/cloud/bigquery/client.py\nclass Project # Wrapper for resource describing a BigQuery project.\n def __init__(self, project_id, numeric_id, friendly_name)\n def from_api_repr(cls, resource) # Factory: construct an instance from a resource dict.\nclass Client # Client to bundle configuration needed for API requests.\n def list_projects(self, max_results=None, page_token=None) # List projects for the project associated with this client.\n def list_datasets(self, include_all=False, max_results=None, page_token=None) # List datasets for the project associated with this client.\n def dataset(self, dataset_name) # Construct a dataset bound to this client.\n def job_from_resource(self, resource) # Detect correct job type from resource and instantiate.\n def list_jobs(self, max_results=None, page_token=None, all_users=None, state_filter=None) # List jobs for the project associated with this client.\n def load_table_from_storage(self, job_name, destination, *source_uris) # Construct a job for loading data into a table from CloudStorage.\n def copy_table(self, job_name, destination, *sources) # Construct a job for copying one or more tables into another table.\n def extract_table_to_storage(self, job_name, source, *destination_uris) # Construct a job for extracting a table into Cloud Storage files.\n def run_async_query(self, job_name, query) # Construct a job for running a SQL query asynchronously.\n def run_sync_query(self, query) # Run a SQL query synchronously.","label":1},{"cid":"google/cloud/bigquery/connection.py","text":"google/cloud/bigquery/connection.py\nclass Connection # A connection to Google Cloud BigQuery via the JSON REST API.","label":1},{"cid":"google/cloud/bigquery/dataset.py","text":"google/cloud/bigquery/dataset.py\nclass AccessGrant # Represent grant of an access role to an entity.\n def __init__(self, role, entity_type, entity_id)\n def __eq__(self, other)\n def __repr__(self)\nclass Dataset # Datasets are containers for tables.\n def __init__(self, name, client, access_grants=())\n def project(self) # Project bound to the dataset.\n def path(self) # URL path for the dataset's APIs.\n def access_grants(self) # Dataset's access grants.\n def access_grants(self, value) # Update dataset's access grants\n def created(self) # Datetime at which the dataset was created.\n def dataset_id(self) # ID for the dataset resource.\n def etag(self) # ETag for the dataset resource.\n def modified(self) # Datetime at which the dataset was last modified.\n def self_link(self) # URL for the dataset resource.\n def default_table_expiration_ms(self) # Default expiration time for tables in the dataset.\n def default_table_expiration_ms(self, value) # Update default expiration time for tables in the dataset.\n def description(self) # Description of the dataset.\n def description(self, value) # Update description of the dataset.\n def friendly_name(self) # Title of the dataset.\n def friendly_name(self, value) # Update title of the dataset.\n def location(self) # Location in which the dataset is hosted.\n def location(self, value) # Update location in which the dataset is hosted.\n def from_api_repr(cls, resource, client) # Factory: construct a dataset given its API representation\n def _require_client(self, client) # Check client or verify over-ride.\n def _parse_access_grants(access) # Parse a resource fragment into a set of access grants.\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\n def _build_access_resource(self) # Generate a resource fragment for dataset's access grants.\n def _build_resource(self) # Generate a resource for ``create`` or ``update``.\n def create(self, client=None) # API call: create the dataset via a PUT request.\n def exists(self, client=None) # API call: test for the existence of the dataset via a GET request\n def reload(self, client=None) # API call: refresh dataset properties via a GET request.\n def patch(self, client=None, **kw) # API call: update individual dataset properties via a PATCH request.\n def update(self, client=None) # API call: update dataset properties via a PUT request.\n def delete(self, client=None) # API call: delete the dataset via a DELETE request.\n def list_tables(self, max_results=None, page_token=None) # List tables for the project associated with this client.\n def table(self, name, schema=()) # Construct a table bound to this dataset.","label":1},{"cid":"google/cloud/bigquery/job.py","text":"google/cloud/bigquery/job.py\nclass Compression # Pseudo-enum for ``compression`` properties.\nclass CreateDisposition # Pseudo-enum for ``create_disposition`` properties.\nclass DestinationFormat # Pseudo-enum for ``destination_format`` properties.\nclass Encoding # Pseudo-enum for ``encoding`` properties.\nclass QueryPriority # Pseudo-enum for ``QueryJob.priority`` property.\nclass SourceFormat # Pseudo-enum for ``source_format`` properties.\nclass WriteDisposition # Pseudo-enum for ``write_disposition`` properties.\nclass _BaseJob # Base class for jobs.\n def __init__(self, client)\n def project(self) # Project bound to the job.\n def _require_client(self, client) # Check client or verify over-ride.\nclass _AsyncJob # Base class for asynchronous jobs.\n def __init__(self, name, client)\n def job_type(self) # Type of job\n def path(self) # URL path for the job's APIs.\n def etag(self) # ETag for the job resource.\n def self_link(self) # URL for the job resource.\n def user_email(self) # E-mail address of user who submitted the job.\n def created(self) # Datetime at which the job was created.\n def started(self) # Datetime at which the job was started.\n def ended(self) # Datetime at which the job finished.\n def error_result(self) # Error information about the job as a whole.\n def errors(self) # Information about individual errors generated by the job.\n def state(self) # Status of the job.\n def _scrub_local_properties(self, cleaned) # Helper: handle subclass properties in cleaned.\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\n def _get_resource_config(cls, resource) # Helper for :meth:`from_api_repr`\n def begin(self, client=None) # API call: begin the job via a POST request\n def exists(self, client=None) # API call: test for the existence of the job via a GET request\n def reload(self, client=None) # API call: refresh job properties via a GET request\n def cancel(self, client=None) # API call: cancel job via a POST request\nclass _LoadConfiguration # User-settable configuration options for load jobs.\nclass LoadTableFromStorageJob # Asynchronous job for loading data into a table from CloudStorage.\n def __init__(self, name, destination, source_uris, client, schema=())\n def schema(self) # Table's schema.\n def schema(self, value) # Update table's schema\n def input_file_bytes(self) # Count of bytes loaded from source files.\n def input_files(self) # Count of source files.\n def output_bytes(self) # Count of bytes saved to destination table.\n def output_rows(self) # Count of rows saved to destination table.\n def _populate_config_resource(self, configuration) # Helper for _build_resource: copy config properties to resource\n def _build_resource(self) # Generate a resource for :meth:`begin`.\n def _scrub_local_properties(self, cleaned) # Helper: handle subclass properties in cleaned.\n ","label":1},{"cid":"google/cloud/bigquery/query.py","text":"google/cloud/bigquery/query.py\nclass _SyncQueryConfiguration # User-settable configuration options for synchronous query jobs.\nclass QueryResults # Synchronous job: query tables.\n def __init__(self, query, client, udf_resources=())\n def from_query_job(cls, job) # Factory: construct from an existing job.\n def project(self) # Project bound to the job.\n def _require_client(self, client) # Check client or verify over-ride.\n def cache_hit(self) # Query results served from cache.\n def complete(self) # Server completed query.\n def errors(self) # Errors generated by the query.\n def name(self) # Job name, generated by the back-end.\n def job(self) # Job instance used to run the query.\n def page_token(self) # Token for fetching next bach of results.\n def total_rows(self) # Total number of rows returned by the query.\n def total_bytes_processed(self) # Total number of bytes processed by the query.\n def rows(self) # Query results.\n def schema(self) # Schema for query results.\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\n def _build_resource(self) # Generate a resource for :meth:`begin`.\n def run(self, client=None) # API call: run the query via a POST request\n def fetch_data(self, max_results=None, page_token=None, start_index=None, timeout_ms=None, client=None) # API call: fetch a page of query result data via a GET request","label":1},{"cid":"google/cloud/bigquery/schema.py","text":"google/cloud/bigquery/schema.py\nclass SchemaField # Describe a single field within a table schema.\n def __init__(self, name, field_type, mode='NULLABLE', description=None, fields=None)\n def __eq__(self, other)","label":1},{"cid":"google/cloud/bigquery/table.py","text":"google/cloud/bigquery/table.py\nclass Table # Tables represent a set of rows whose values correspond to a schema.\n def __init__(self, name, dataset, schema=())\n def project(self) # Project bound to the table.\n def dataset_name(self) # Name of dataset containing the table.\n def path(self) # URL path for the table's APIs.\n def schema(self) # Table's schema.\n def schema(self, value) # Update table's schema\n def created(self) # Datetime at which the table was created.\n def etag(self) # ETag for the table resource.\n def modified(self) # Datetime at which the table was last modified.\n def num_bytes(self) # The size of the table in bytes.\n def num_rows(self) # The number of rows in the table.\n def self_link(self) # URL for the table resource.\n def table_id(self) # ID for the table resource.\n def table_type(self) # The type of the table.\n def partitioning_type(self) # Time partitioning of the table.\n def partitioning_type(self, value) # Update the partitioning type of the table\n def partition_expiration(self) # Expiration time in ms for a partition\n def partition_expiration(self, value) # Update the experation time in ms for a partition\n def description(self) # Description of the table.\n def description(self, value) # Update description of the table.\n def expires(self) # Datetime at which the table will be removed.\n def expires(self, value) # Update datetime at which the table will be removed.\n def friendly_name(self) # Title of the table.\n def friendly_name(self, value) # Update title of the table.\n def location(self) # Location in which the table is hosted.\n def location(self, value) # Update location in which the table is hosted.\n def view_query(self) # SQL query defining the table as a view.\n def view_query(self, value) # Update SQL query defining the table as a view.\n def view_query(self) # Delete SQL query defining the table as a view.\n def list_partitions(self, client=None) # List the partitions in a table.\n def from_api_repr(cls, resource, dataset) # Factory: construct a table given its API representation\n def _require_client(self, client) # Check client or verify over-ride.\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\n def _build_resource(self) # Generate a resource for ``create`` or ``update``.\n def create(self, client=None) # API call: create the dataset via a PUT request\n def exists(self, client=None) # API call: test for the existence of the table via a GET request\n def reload(self, client=None) # API call: refresh table properties via a GET request\n def patch(self, client=None, friendly_name=_MARKER, description=_MARKER, location=_MARKER, expires=_MARKER, view_query=_MARKER, schema=_MARKER) # API call: update individual table properties via a PATCH request\n def update(self, client=None) # API call: update table properties via","label":1},{"cid":"google/cloud/bigtable/__init__.py","text":"google/cloud/bigtable/__init__.py","label":1},{"cid":"google/cloud/bigtable/_generated/__init__.py","text":"google/cloud/bigtable/_generated/__init__.py","label":1},{"cid":"google/cloud/bigtable/client.py","text":"google/cloud/bigtable/client.py\ndef _make_data_stub(client) # Creates gRPC stub to make requests to the Data API.\ndef _make_instance_stub(client) # Creates gRPC stub to make requests to the Instance Admin API.\ndef _make_operations_stub(client) # Creates gRPC stub to make requests to the Operations API.\ndef _make_table_stub(client) # Creates gRPC stub to make requests to the Table Admin API.\nclass Client # Client for interacting with Google Cloud Bigtable API.\n def __init__(self, project=None, credentials=None, read_only=False, admin=False, user_agent=DEFAULT_USER_AGENT)\n def copy(self) # Make a copy of this client.\n def credentials(self) # Getter for client's credentials.\n def project_name(self) # Project name to be used with Instance Admin API.\n def _instance_stub(self) # Getter for the gRPC stub used for the Instance Admin API.\n def _operations_stub(self) # Getter for the gRPC stub used for the Operations API.\n def _table_stub(self) # Getter for the gRPC stub used for the Table Admin API.\n def instance(self, instance_id, location=_EXISTING_INSTANCE_LOCATION_ID, display_name=None, serve_nodes=DEFAULT_SERVE_NODES) # Factory to create a instance associated with this client.\n def list_instances(self) # List instances owned by the project.","label":1},{"cid":"google/cloud/bigtable/cluster.py","text":"google/cloud/bigtable/cluster.py\ndef _prepare_create_request(cluster) # Creates a protobuf request for a CreateCluster request.\nclass Cluster # Representation of a Google Cloud Bigtable Cluster.\n def __init__(self, cluster_id, instance, serve_nodes=DEFAULT_SERVE_NODES)\n def _update_from_pb(self, cluster_pb) # Refresh self from the server-provided protobuf.\n def from_pb(cls, cluster_pb, instance) # Creates a cluster instance from a protobuf.\n def copy(self) # Make a copy of this cluster.\n def name(self) # Cluster name used in requests.\n def __eq__(self, other)\n def __ne__(self, other)\n def reload(self) # Reload the metadata for this cluster.\n def create(self) # Create this cluster.\n def update(self) # Update this cluster.\n def delete(self) # Delete this cluster.","label":1},{"cid":"google/cloud/bigtable/column_family.py","text":"google/cloud/bigtable/column_family.py\ndef _timedelta_to_duration_pb(timedelta_val) # Convert a Python timedelta object to a duration protobuf.\ndef _duration_pb_to_timedelta(duration_pb) # Convert a duration protobuf to a Python timedelta object.\nclass GarbageCollectionRule # Garbage collection rule for column families within a table.\n def __ne__(self, other)\nclass MaxVersionsGCRule # Garbage collection limiting the number of versions of a cell.\n def __init__(self, max_num_versions)\n def __eq__(self, other)\n def to_pb(self) # Converts the garbage collection rule to a protobuf.\nclass MaxAgeGCRule # Garbage collection limiting the age of a cell.\n def __init__(self, max_age)\n def __eq__(self, other)\n def to_pb(self) # Converts the garbage collection rule to a protobuf.\nclass GCRuleUnion # Union of garbage collection rules.\n def __init__(self, rules)\n def __eq__(self, other)\n def to_pb(self) # Converts the union into a single GC rule as a protobuf.\nclass GCRuleIntersection # Intersection of garbage collection rules.\n def __init__(self, rules)\n def __eq__(self, other)\n def to_pb(self) # Converts the intersection into a single GC rule as a protobuf.\nclass ColumnFamily # Representation of a Google Cloud Bigtable Column Family.\n def __init__(self, column_family_id, table, gc_rule=None)\n def name(self) # Column family name used in requests.\n def __eq__(self, other)\n def __ne__(self, other)\n def to_pb(self) # Converts the column family to a protobuf.\n def create(self) # Create this column family.\n def update(self) # Update this column family.\n def delete(self) # Delete this column family.\ndef _gc_rule_from_pb(gc_rule_pb) # Convert a protobuf GC rule to a native object.","label":1},{"cid":"google/cloud/bigtable/instance.py","text":"google/cloud/bigtable/instance.py\ndef _prepare_create_request(instance) # Creates a protobuf request for a CreateInstance request.\nclass Instance # Representation of a Google Cloud Bigtable Instance.\n def __init__(self, instance_id, client, location_id=_EXISTING_INSTANCE_LOCATION_ID, display_name=None, serve_nodes=DEFAULT_SERVE_NODES)\n def _update_from_pb(self, instance_pb) # Refresh self from the server-provided protobuf.\n def from_pb(cls, instance_pb, client) # Creates a instance instance from a protobuf.\n def copy(self) # Make a copy of this instance.\n def name(self) # Instance name used in requests.\n def __eq__(self, other)\n def __ne__(self, other)\n def reload(self) # Reload the metadata for this instance.\n def create(self) # Create this instance.\n def update(self) # Update this instance.\n def delete(self) # Delete this instance.\n def cluster(self, cluster_id, serve_nodes=3) # Factory to create a cluster associated with this client.\n def list_clusters(self) # Lists clusters in this instance.\n def table(self, table_id) # Factory to create a table associated with this instance.\n def list_tables(self) # List the tables in this instance.","label":1},{"cid":"google/cloud/bigtable/row.py","text":"google/cloud/bigtable/row.py\nclass Row # Base representation of a Google Cloud Bigtable Row.\n def __init__(self, row_key, table)\nclass _SetDeleteRow # Row helper for setting or deleting cell values.\n def _get_mutations(self, state) # Gets the list of mutations for a given state.\n def _set_cell(self, column_family_id, column, value, timestamp=None, state=None) # Helper for :meth:`set_cell`\n def _delete(self, state=None) # Helper for :meth:`delete`\n def _delete_cells(self, column_family_id, columns, time_range=None, state=None) # Helper for :meth:`delete_cell` and :meth:`delete_cells`.\nclass DirectRow # Google Cloud Bigtable Row for sending \"direct\" mutations.\n def __init__(self, row_key, table)\n def _get_mutations(self, state) # Gets the list of mutations for a given state.\n def set_cell(self, column_family_id, column, value, timestamp=None) # Sets a value in this row.\n def delete(self) # Deletes this row from the table.\n def delete_cell(self, column_family_id, column, time_range=None) # Deletes cell in this row.\n def delete_cells(self, column_family_id, columns, time_range=None) # Deletes cells in this row.\n def commit(self) # Makes a ``MutateRow`` API request.\n def clear(self) # Removes all currently accumulated mutations on the current row.\nclass ConditionalRow # Google Cloud Bigtable Row for sending mutations conditionally.\n def __init__(self, row_key, table, filter_)\n def _get_mutations(self, state) # Gets the list of mutations for a given state.\n def commit(self) # Makes a ``CheckAndMutateRow`` API request.\n def set_cell(self, column_family_id, column, value, timestamp=None, state=True) # Sets a value in this row.\n def delete(self, state=True) # Deletes this row from the table.\n def delete_cell(self, column_family_id, column, time_range=None, state=True) # Deletes cell in this row.\n def delete_cells(self, column_family_id, columns, time_range=None, state=True) # Deletes cells in this row.\n def clear(self) # Removes all currently accumulated mutations on the current row.\nclass AppendRow # Google Cloud Bigtable Row for sending append mutations.\n def __init__(self, row_key, table)\n def clear(self) # Removes all currently accumulated modifications on current row.\n def append_cell_value(self, column_family_id, column, value) # Appends a value to an existing cell.\n def increment_cell_value(self, column_family_id, column, int_value) # Increments a value in an existing cell.\n def commit(self) # Makes a ``ReadModifyWriteRow`` API request.\ndef _parse_rmw_row_response(row_response) # Parses the response to a ``ReadModifyWriteRow`` request.\ndef _parse_family_pb(family_pb) # Parses a Family protobuf into a dictionary.","label":1},{"cid":"google/cloud/bigtable/row_data.py","text":"google/cloud/bigtable/row_data.py\nclass Cell # Representation of a Google Cloud Bigtable Cell.\n def __init__(self, value, timestamp, labels=())\n def from_pb(cls, cell_pb) # Create a new cell from a Cell protobuf.\n def __eq__(self, other)\n def __ne__(self, other)\nclass PartialCellData # Representation of partial cell in a Google Cloud Bigtable Table.\n def __init__(self, row_key, family_name, qualifier, timestamp_micros, labels=(), value=b'')\n def append_value(self, value) # Append bytes from a new chunk to value.\nclass PartialRowData # Representation of partial row in a Google Cloud Bigtable Table.\n def __init__(self, row_key)\n def __eq__(self, other)\n def __ne__(self, other)\n def to_dict(self) # Convert the cells to a dictionary.\n def cells(self) # Property returning all the cells accumulated on this partial row.\n def row_key(self) # Getter for the current (partial) row's key.\nclass InvalidReadRowsResponse # Exception raised to to invalid response data from back-end.\nclass InvalidChunk # Exception raised to to invalid chunk data from back-end.\nclass PartialRowsData # Convenience wrapper for consuming a ``ReadRows`` streaming response.\n def __init__(self, response_iterator)\n def __eq__(self, other)\n def __ne__(self, other)\n def state(self) # State machine state.\n def rows(self) # Property returning all rows accumulated from the stream.\n def cancel(self) # Cancels the iterator, closing the stream.\n def consume_next(self) # Consume the next ``ReadRowsResponse`` from the stream.\n def consume_all(self, max_loops=None) # Consume the streamed responses until there are no more.\n def _validate_chunk_status(chunk) # Helper for :meth:`_validate_chunk_row_in_progress`, etc.\n def _validate_chunk_new_row(self, chunk) # Helper for :meth:`_validate_chunk`.\n def _same_as_previous(self, chunk) # Helper for :meth:`_validate_chunk_row_in_progress`\n def _validate_chunk_row_in_progress(self, chunk) # Helper for :meth:`_validate_chunk`\n def _validate_chunk_cell_in_progress(self, chunk) # Helper for :meth:`_validate_chunk`\n def _validate_chunk(self, chunk) # Helper for :meth:`consume_next`.\n def _save_current_cell(self) # Helper for :meth:`consume_next`.\n def _copy_from_current(self, chunk) # Helper for :meth:`consume_next`.\n def _copy_from_previous(self, cell) # Helper for :meth:`consume_next`.\n def _save_current_row(self) # Helper for :meth:`consume_next`.\ndef _raise_if(predicate, *args) # Helper for validation methods.","label":1},{"cid":"google/cloud/bigtable/row_filters.py","text":"google/cloud/bigtable/row_filters.py\nclass RowFilter # Basic filter to apply to cells in a row.\n def __ne__(self, other)\nclass _BoolFilter # Row filter that uses a boolean flag.\n def __init__(self, flag)\n def __eq__(self, other)\nclass SinkFilter # Advanced row filter to skip parent filters.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass PassAllFilter # Row filter equivalent to not filtering at all.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass BlockAllFilter # Row filter that doesn't match any cells.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass _RegexFilter # Row filter that uses a regular expression.\n def __init__(self, regex)\n def __eq__(self, other)\nclass RowKeyRegexFilter # Row filter for a row key regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass RowSampleFilter # Matches all cells from a row with probability p.\n def __init__(self, sample)\n def __eq__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass FamilyNameRegexFilter # Row filter for a family name regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ColumnQualifierRegexFilter # Row filter for a column qualifier regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass TimestampRange # Range of time with inclusive lower and exclusive upper bounds.\n def __init__(self, start=None, end=None)\n def __eq__(self, other)\n def __ne__(self, other)\n def to_pb(self) # Converts the :class:`TimestampRange` to a protobuf.\nclass TimestampRangeFilter # Row filter that limits cells to a range of time.\n def __init__(self, range_)\n def __eq__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ColumnRangeFilter # A row filter to restrict to a range of columns.\n def __init__(self, column_family_id, start_column=None, end_column=None, inclusive_start=None, inclusive_end=None)\n def __eq__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ValueRegexFilter # Row filter for a value regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ValueRangeFilter # A range of values to restrict to in a row filter.\n def __init__(self, start_value=None, end_value=None, inclusive_start=None, inclusive_end=None)\n def __eq__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass _CellCountFilter # Row filter that uses an integer count of cells.\n def __init__(self, num_cells)\n def __eq__(self, other)\nclass CellsRowOffsetFilter # Row filter to skip cells in a row.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass CellsRowLimitFilter # Row filter to limit cells in a row.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass CellsColumnLimitFilter # Row filter to limit cells in a column.\n def to_pb(self) # Co","label":1},{"cid":"google/cloud/bigtable/table.py","text":"google/cloud/bigtable/table.py\nclass Table # Representation of a Google Cloud Bigtable Table.\n def __init__(self, table_id, instance)\n def name(self) # Table name used in requests.\n def column_family(self, column_family_id, gc_rule=None) # Factory to create a column family associated with this table.\n def row(self, row_key, filter_=None, append=False) # Factory to create a row associated with this table.\n def __eq__(self, other)\n def __ne__(self, other)\n def create(self, initial_split_keys=None, column_families=()) # Creates this table.\n def delete(self) # Delete this table.\n def list_column_families(self) # List the column families owned by this table.\n def read_row(self, row_key, filter_=None) # Read a single row from this table.\n def read_rows(self, start_key=None, end_key=None, limit=None, filter_=None) # Read rows from this table.\n def sample_row_keys(self) # Read a sample of row keys in the table.\ndef _create_row_request(table_name, row_key=None, start_key=None, end_key=None, filter_=None, limit=None) # Creates a request to read rows in a table.","label":1},{"cid":"google/cloud/client.py","text":"google/cloud/client.py\nclass _ClientFactoryMixin # Mixin to allow factories that create credentials.\n def from_service_account_json(cls, json_credentials_path, *args, **kwargs) # Factory to retrieve JSON credentials while creating client.\n def from_service_account_p12(cls, client_email, private_key_path, *args, **kwargs) # Factory to retrieve P12 credentials while creating client.\nclass Client # Client to bundle configuration needed for API requests.\n def __init__(self, credentials=None, http=None)\nclass _ClientProjectMixin # Mixin to allow setting the project on the client.\n def __init__(self, project=None)\n def _determine_default(project) # Helper: use default project detection.\nclass JSONClient # Client to for Google JSON-based API.\n def __init__(self, project=None, credentials=None, http=None)","label":1},{"cid":"google/cloud/connection.py","text":"google/cloud/connection.py\nclass Connection # A generic connection to Google Cloud Platform.\n def __init__(self, credentials=None, http=None)\n def credentials(self) # Getter for current credentials.\n def http(self) # A getter for the HTTP transport used in talking to the API.\n def _create_scoped_credentials(credentials, scope) # Create a scoped set of credentials if it is required.\nclass JSONConnection # A connection to a Google JSON-based API.\n def build_api_url(cls, path, query_params=None, api_base_url=None, api_version=None) # Construct an API url given a few components, some optional.\n def _make_request(self, method, url, data=None, content_type=None, headers=None, target_object=None) # A low level method to send a request to the API.\n def _do_request(self, method, url, headers, data, target_object) # Low-level helper: perform the actual API request over HTTP.\n def api_request(self, method, path, query_params=None, data=None, content_type=None, api_base_url=None, api_version=None, expect_json=True, _target_object=None) # Make a request over the HTTP transport to the API.","label":1},{"cid":"google/cloud/credentials.py","text":"google/cloud/credentials.py\ndef get_credentials() # Gets credentials implicitly from the current environment.\ndef _get_signed_query_params(credentials, expiration, string_to_sign) # Gets query parameters for creating a signed URL.\ndef _get_expiration_seconds(expiration) # Convert 'expiration' to a number of seconds in the future.\ndef generate_signed_url(credentials, resource, expiration, api_access_endpoint='', method='GET', content_md5=None, content_type=None, response_type=None, response_disposition=None, generation=None) # Generate signed URL to provide query-string auth'n to a resource.","label":1},{"cid":"google/cloud/datastore/__init__.py","text":"google/cloud/datastore/__init__.py","label":1},{"cid":"google/cloud/datastore/_generated/__init__.py","text":"google/cloud/datastore/_generated/__init__.py","label":1},{"cid":"google/cloud/datastore/batch.py","text":"google/cloud/datastore/batch.py\nclass Batch # An abstraction representing a collected group of updates / deletes.\n def __init__(self, client)\n def current(self) # Return the topmost batch / transaction, or None.\n def project(self) # Getter for project in which the batch will run.\n def namespace(self) # Getter for namespace in which the batch will run.\n def connection(self) # Getter for connection over which the batch will run.\n def _add_partial_key_entity_pb(self) # Adds a new mutation for an entity with a partial key.\n def _add_complete_key_entity_pb(self) # Adds a new mutation for an entity with a completed key.\n def _add_delete_key_pb(self) # Adds a new mutation for a key to be deleted.\n def mutations(self) # Getter for the changes accumulated by this batch.\n def put(self, entity) # Remember an entity's state to be saved during :meth:`commit`.\n def delete(self, key) # Remember a key to be deleted during :meth:`commit`.\n def begin(self) # Begins a batch.\n def _commit(self) # Commits the batch.\n def commit(self) # Commits the batch.\n def rollback(self) # Rolls back the current batch.\n def __enter__(self)\n def __exit__(self, exc_type, exc_val, exc_tb)\ndef _assign_entity_to_pb(entity_pb, entity) # Copy ``entity`` into ``entity_pb``.","label":1},{"cid":"google/cloud/datastore/client.py","text":"google/cloud/datastore/client.py\ndef _get_gcd_project() # Gets the GCD application ID if it can be inferred.\ndef _determine_default_project(project=None) # Determine default project explicitly or implicitly as fall-back.\ndef _extended_lookup(connection, project, key_pbs, missing=None, deferred=None, eventual=False, transaction_id=None) # Repeat lookup until all keys found (unless stop requested).\nclass Client # Convenience wrapper for invoking APIs/factories w/ a project.\n def __init__(self, project=None, namespace=None, credentials=None, http=None)\n def _determine_default(project) # Helper: override default project detection.\n def _push_batch(self, batch) # Push a batch/transaction onto our stack.\n def _pop_batch(self) # Pop a batch/transaction from our stack.\n def current_batch(self) # Currently-active batch.\n def current_transaction(self) # Currently-active transaction.\n def get(self, key, missing=None, deferred=None, transaction=None) # Retrieve an entity from a single key (if it exists).\n def get_multi(self, keys, missing=None, deferred=None, transaction=None) # Retrieve entities, along with their attributes.\n def put(self, entity) # Save an entity in the Cloud Datastore.\n def put_multi(self, entities) # Save entities in the Cloud Datastore.\n def delete(self, key) # Delete the key in the Cloud Datastore.\n def delete_multi(self, keys) # Delete keys from the Cloud Datastore.\n def allocate_ids(self, incomplete_key, num_ids) # Allocate a list of IDs from a partial key.\n def key(self, *path_args, **kwargs) # Proxy to :class:`google.cloud.datastore.key.Key`.\n def batch(self) # Proxy to :class:`google.cloud.datastore.batch.Batch`.\n def transaction(self) # Proxy to :class:`google.cloud.datastore.transaction.Transaction`.\n def query(self, **kwargs) # Proxy to :class:`google.cloud.datastore.query.Query`.","label":1},{"cid":"google/cloud/datastore/connection.py","text":"google/cloud/datastore/connection.py\nclass _DatastoreAPIOverHttp # Helper mapping datastore API methods.\n def __init__(self, connection)\n def _request(self, project, method, data) # Make a request over the Http transport to the Cloud Datastore API.\n def _rpc(self, project, method, request_pb, response_pb_cls) # Make a protobuf RPC request.\n def lookup(self, project, request_pb) # Perform a ``lookup`` request.\n def run_query(self, project, request_pb) # Perform a ``runQuery`` request.\n def begin_transaction(self, project, request_pb) # Perform a ``beginTransaction`` request.\n def commit(self, project, request_pb) # Perform a ``commit`` request.\n def rollback(self, project, request_pb) # Perform a ``rollback`` request.\n def allocate_ids(self, project, request_pb) # Perform an ``allocateIds`` request.\nclass _DatastoreAPIOverGRPC # Helper mapping datastore API methods.\n def __init__(self, connection, secure)\n def lookup(self, project, request_pb) # Perform a ``lookup`` request.\n def run_query(self, project, request_pb) # Perform a ``runQuery`` request.\n def begin_transaction(self, project, request_pb) # Perform a ``beginTransaction`` request.\n def commit(self, project, request_pb) # Perform a ``commit`` request.\n def rollback(self, project, request_pb) # Perform a ``rollback`` request.\n def allocate_ids(self, project, request_pb) # Perform an ``allocateIds`` request.\nclass Connection # A connection to the Google Cloud Datastore via the Protobuf API.\n def __init__(self, credentials=None, http=None)\n def build_api_url(self, project, method, base_url=None, api_version=None) # Construct the URL for a particular API call.\n def lookup(self, project, key_pbs, eventual=False, transaction_id=None) # Lookup keys from a project in the Cloud Datastore.\n def run_query(self, project, query_pb, namespace=None, eventual=False, transaction_id=None) # Run a query on the Cloud Datastore.\n def begin_transaction(self, project) # Begin a transaction.\n def commit(self, project, request, transaction_id) # Commit mutations in context of current transaction (if any).\n def rollback(self, project, transaction_id) # Rollback the connection's existing transaction.\n def allocate_ids(self, project, key_pbs) # Obtain backend-generated IDs for a set of keys.\ndef _set_read_options(request, eventual, transaction_id) # Validate rules for read options, and assign to the request.\ndef _add_keys_to_request(request_field_pb, key_pbs) # Add protobuf keys to a request object.\ndef _parse_commit_response(commit_response_pb) # Extract response data from a commit response.","label":1},{"cid":"google/cloud/datastore/entity.py","text":"google/cloud/datastore/entity.py\nclass Entity # Entities are akin to rows in a relational database\n def __init__(self, key=None, exclude_from_indexes=())\n def __eq__(self, other) # Compare two entities for equality.\n def __ne__(self, other) # Compare two entities for inequality.\n def kind(self) # Get the kind of the current entity.\n def exclude_from_indexes(self) # Names of fields which are *not* to be indexed for this entity.\n def __repr__(self)","label":1},{"cid":"google/cloud/datastore/helpers.py","text":"google/cloud/datastore/helpers.py\ndef _get_meaning(value_pb, is_list=False) # Get the meaning from a protobuf value.\ndef _new_value_pb(entity_pb, name) # Add (by name) a new ``Value`` protobuf to an entity protobuf.\ndef _property_tuples(entity_pb) # Iterator of name, ``Value`` tuples from entity properties.\ndef entity_from_protobuf(pb) # Factory method for creating an entity based on a protobuf.\ndef _set_pb_meaning_from_entity(entity, name, value, value_pb, is_list=False) # Add meaning information (from an entity) to a protobuf.\ndef entity_to_protobuf(entity) # Converts an entity into a protobuf.\ndef key_from_protobuf(pb) # Factory method for creating a key based on a protobuf.\ndef _pb_attr_value(val) # Given a value, return the protobuf attribute name and proper value.\ndef _get_value_from_value_pb(value_pb) # Given a protobuf for a Value, get the correct value.\ndef _set_protobuf_value(value_pb, val) # Assign 'val' to the correct subfield of 'value_pb'.\nclass GeoPoint # Simple container for a geo point value.\n def __init__(self, latitude, longitude)\n def to_protobuf(self) # Convert the current object to protobuf.\n def __eq__(self, other) # Compare two geo points for equality.\n def __ne__(self, other) # Compare two geo points for inequality.","label":1},{"cid":"google/cloud/datastore/key.py","text":"google/cloud/datastore/key.py\nclass Key # An immutable representation of a datastore Key.\n def __init__(self, *path_args, **kwargs)\n def __eq__(self, other) # Compare two keys for equality.\n def __ne__(self, other) # Compare two keys for inequality.\n def __hash__(self) # Hash a keys for use in a dictionary lookp.\n def _parse_path(path_args) # Parses positional arguments into key path with kinds and IDs.\n def _combine_args(self) # Sets protected data by combining raw data set from the constructor.\n def _clone(self) # Duplicates the Key.\n def completed_key(self, id_or_name) # Creates new key from existing partial key by adding final ID/name.\n def to_protobuf(self) # Return a protobuf corresponding to the key.\n def is_partial(self) # Boolean indicating if the key has an ID (or name).\n def namespace(self) # Namespace getter.\n def path(self) # Path getter.\n def flat_path(self) # Getter for the key path as a tuple.\n def kind(self) # Kind getter. Based on the last element of path.\n def id(self) # ID getter. Based on the last element of path.\n def name(self) # Name getter. Based on the last element of path.\n def id_or_name(self) # Getter. Based on the last element of path.\n def project(self) # Project getter.\n def _make_parent(self) # Creates a parent key for the current path.\n def parent(self) # The parent of the current key.\n def __repr__(self)\ndef _validate_project(project, parent) # Ensure the project is set appropriately.","label":1},{"cid":"google/cloud/datastore/query.py","text":"google/cloud/datastore/query.py\nclass Query # A Query against the Cloud Datastore.\n def __init__(self, client, kind=None, project=None, namespace=None, ancestor=None, filters=(), projection=(), order=(), distinct_on=())\n def project(self) # Get the project for this Query.\n def namespace(self) # This query's namespace\n def namespace(self, value) # Update the query's namespace.\n def kind(self) # Get the Kind of the Query.\n def kind(self, value) # Update the Kind of the Query.\n def ancestor(self) # The ancestor key for the query.\n def ancestor(self, value) # Set the ancestor for the query\n def ancestor(self) # Remove the ancestor for the query.\n def filters(self) # Filters set on the query.\n def add_filter(self, property_name, operator, value) # Filter the query based on a property name, operator and a value.\n def projection(self) # Fields names returned by the query.\n def projection(self, projection) # Set the fields returned the query.\n def keys_only(self) # Set the projection to include only keys.\n def key_filter(self, key, operator='=') # Filter on a key.\n def order(self) # Names of fields used to sort query results.\n def order(self, value) # Set the fields used to sort query results.\n def distinct_on(self) # Names of fields used to group query results.\n def distinct_on(self, value) # Set fields used to group query results.\n def fetch(self, limit=None, offset=0, start_cursor=None, end_cursor=None, client=None) # Execute the Query; return an iterator for the matching entities.\nclass Iterator # Represent the state of a given execution of a Query.\n def __init__(self, query, client, limit=None, offset=None, start_cursor=None, end_cursor=None)\n def next_page(self) # Fetch a single \"page\" of query results.\n def __iter__(self) # Generator yielding all results matching our query.\ndef _pb_from_query(query) # Convert a Query instance to the corresponding protobuf.","label":1},{"cid":"google/cloud/datastore/transaction.py","text":"google/cloud/datastore/transaction.py\nclass Transaction # An abstraction representing datastore Transactions.\n def __init__(self, client)\n def id(self) # Getter for the transaction ID.\n def current(self) # Return the topmost transaction.\n def begin(self) # Begins a transaction.\n def rollback(self) # Rolls back the current transaction.\n def commit(self) # Commits the transaction.","label":1},{"cid":"google/cloud/dns/__init__.py","text":"google/cloud/dns/__init__.py","label":1},{"cid":"google/cloud/dns/changes.py","text":"google/cloud/dns/changes.py\nclass Changes # Changes are bundled additions / deletions of DNS resource records.\n def __init__(self, zone)\n def from_api_repr(cls, resource, zone) # Factory: construct a change set given its API representation\n def _set_properties(self, resource) # Helper method for :meth:`from_api_repr`, :meth:`create`, etc.\n def path(self) # URL path for change set APIs.\n def name(self) # Name of the change set.\n def name(self, value) # Update name of the change set.\n def status(self) # Status of the change set.\n def started(self) # Time when the change set was started.\n def additions(self) # Resource record sets to be added to the zone.\n def deletions(self) # Resource record sets to be deleted from the zone.\n def add_record_set(self, record_set) # Append a record set to the 'additions' for the change set.\n def delete_record_set(self, record_set) # Append a record set to the 'deletions' for the change set.\n def _require_client(self, client) # Check client or verify over-ride.\n def _build_resource(self) # Generate a resource for ``create``.\n def create(self, client=None) # API call: create the change set via a POST request.\n def exists(self, client=None) # API call: test for the existence of the change set via a GET request.\n def reload(self, client=None) # API call: refresh zone properties via a GET request.","label":1},{"cid":"google/cloud/dns/client.py","text":"google/cloud/dns/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def quotas(self) # Return DNS quotas for the project associated with this client.\n def list_zones(self, max_results=None, page_token=None) # List zones for the project associated with this client.\n def zone(self, name, dns_name=None, description=None) # Construct a zone bound to this client.","label":1},{"cid":"google/cloud/dns/connection.py","text":"google/cloud/dns/connection.py\nclass Connection # A connection to Google Cloud DNS via the JSON REST API.","label":1},{"cid":"google/cloud/dns/resource_record_set.py","text":"google/cloud/dns/resource_record_set.py\nclass ResourceRecordSet # ResourceRecordSets are DNS resource records.\n def __init__(self, name, record_type, ttl, rrdatas, zone)\n def from_api_repr(cls, resource, zone) # Factory: construct a record set given its API representation","label":1},{"cid":"google/cloud/dns/zone.py","text":"google/cloud/dns/zone.py\nclass ManagedZone # ManagedZones are containers for DNS resource records.\n def __init__(self, name, dns_name=None, client=None, description=None)\n def from_api_repr(cls, resource, client) # Factory: construct a zone given its API representation\n def project(self) # Project bound to the zone.\n def path(self) # URL path for the zone's APIs.\n def created(self) # Datetime at which the zone was created.\n def name_servers(self) # Datetime at which the zone was created.\n def zone_id(self) # ID for the zone resource.\n def description(self) # Description of the zone.\n def description(self, value) # Update description of the zone.\n def name_server_set(self) # Named set of DNS name servers that all host the same ManagedZones.\n def name_server_set(self, value) # Update named set of DNS name servers.\n def resource_record_set(self, name, record_type, ttl, rrdatas) # Construct a resource record set bound to this zone.\n def changes(self) # Construct a change set bound to this zone.\n def _require_client(self, client) # Check client or verify over-ride.\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\n def _build_resource(self) # Generate a resource for ``create`` or ``update``.\n def create(self, client=None) # API call: create the zone via a PUT request\n def exists(self, client=None) # API call: test for the existence of the zone via a GET request\n def reload(self, client=None) # API call: refresh zone properties via a GET request\n def delete(self, client=None) # API call: delete the zone via a DELETE request\n def list_resource_record_sets(self, max_results=None, page_token=None, client=None) # List resource record sets for this zone.\n def list_changes(self, max_results=None, page_token=None, client=None) # List change sets for this zone.","label":1},{"cid":"google/cloud/environment_vars.py","text":"google/cloud/environment_vars.py","label":1},{"cid":"google/cloud/error_reporting/__init__.py","text":"google/cloud/error_reporting/__init__.py","label":1},{"cid":"google/cloud/error_reporting/client.py","text":"google/cloud/error_reporting/client.py\nclass HTTPContext # HTTPContext defines an object that captures the parameter for the\n def __init__(self, method=None, url=None, user_agent=None, referrer=None, response_status_code=None, remote_ip=None)\nclass Client # Error Reporting client. Currently Error Reporting is done by creating\n def __init__(self, project=None, credentials=None, http=None, service=None, version=None)\n def _send_error_report(self, message, report_location=None, http_context=None, user=None) # Makes the call to the Error Reporting API via the log stream.\n def report(self, message, http_context=None, user=None) # Reports a message to Stackdriver Error Reporting\n def report_exception(self, http_context=None, user=None) # Reports the details of the latest exceptions to Stackdriver Error","label":1},{"cid":"google/cloud/exceptions.py","text":"google/cloud/exceptions.py\nclass GoogleCloudError # Base error class for Google Cloud errors (abstract).\n def __init__(self, message, errors=())\n def __str__(self)\n def errors(self) # Detailed error information.\nclass Redirection # Base for 3xx responses\nclass MovedPermanently # Exception mapping a '301 Moved Permanently' response.\nclass NotModified # Exception mapping a '304 Not Modified' response.\nclass TemporaryRedirect # Exception mapping a '307 Temporary Redirect' response.\nclass ResumeIncomplete # Exception mapping a '308 Resume Incomplete' response.\nclass ClientError # Base for 4xx responses\nclass BadRequest # Exception mapping a '400 Bad Request' response.\nclass Unauthorized # Exception mapping a '401 Unauthorized' response.\nclass Forbidden # Exception mapping a '403 Forbidden' response.\nclass NotFound # Exception mapping a '404 Not Found' response.\nclass MethodNotAllowed # Exception mapping a '405 Method Not Allowed' response.\nclass Conflict # Exception mapping a '409 Conflict' response.\nclass LengthRequired # Exception mapping a '411 Length Required' response.\nclass PreconditionFailed # Exception mapping a '412 Precondition Failed' response.\nclass RequestRangeNotSatisfiable # Exception mapping a '416 Request Range Not Satisfiable' response.\nclass TooManyRequests # Exception mapping a '429 Too Many Requests' response.\nclass ServerError # Base for 5xx responses: (abstract)\nclass InternalServerError # Exception mapping a '500 Internal Server Error' response.\nclass MethodNotImplemented # Exception mapping a '501 Not Implemented' response.\nclass BadGateway # Exception mapping a '502 Bad Gateway' response.\nclass ServiceUnavailable # Exception mapping a '503 Service Unavailable' response.\ndef make_exception(response, content, error_info=None, use_json=True) # Factory: create exception based on HTTP response code.\ndef _walk_subclasses(klass) # Recursively walk subclass tree.","label":1},{"cid":"google/cloud/iterator.py","text":"google/cloud/iterator.py\nclass Iterator # A generic class for iterating through Cloud JSON APIs list responses.\n def __init__(self, client, path, extra_params=None)\n def __iter__(self) # Iterate through the list of items.\n def has_next_page(self) # Determines whether or not this iterator has more pages.\n def get_query_params(self) # Getter for query parameters for the next request.\n def get_next_page_response(self) # Requests the next page from the path provided.\n def reset(self) # Resets the iterator to the beginning.\n def get_items_from_response(self, response) # Factory method called while iterating. This should be overridden.\nclass MethodIterator # Method-based iterator iterating through Cloud JSON APIs list responses.\n def __init__(self, method, page_token=None, page_size=None, max_calls=None, **kw)\n def __iter__(self)","label":1},{"cid":"google/cloud/language/__init__.py","text":"google/cloud/language/__init__.py","label":1},{"cid":"google/cloud/language/client.py","text":"google/cloud/language/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def document_from_text(self, content, **kwargs) # Create a plain text document bound to this client.\n def document_from_html(self, content, **kwargs) # Create an HTML document bound to this client.\n def document_from_url(self, gcs_url, doc_type=Document.PLAIN_TEXT, **kwargs) # Create a Cloud Storage document bound to this client.","label":1},{"cid":"google/cloud/language/connection.py","text":"google/cloud/language/connection.py\nclass Connection # A connection to Google Cloud Natural Language JSON REST API.","label":1},{"cid":"google/cloud/language/document.py","text":"google/cloud/language/document.py\nclass Encoding # Document text encoding types.\nclass Document # Document to send to Google Cloud Natural Language API.\n def __init__(self, client, content=None, gcs_url=None, doc_type=PLAIN_TEXT, language=DEFAULT_LANGUAGE, encoding=Encoding.UTF8)\n def _to_dict(self) # Helper to convert the current document into a dictionary.\n def analyze_entities(self) # Analyze the entities in the current document.\n def analyze_sentiment(self) # Analyze the sentiment in the current document.\n def annotate_text(self, include_syntax=True, include_entities=True, include_sentiment=True) # Advanced natural language API: document syntax and other features.","label":1},{"cid":"google/cloud/language/entity.py","text":"google/cloud/language/entity.py\nclass EntityType # List of possible entity types.\nclass Entity # A Google Cloud Natural Language API entity.\n def __init__(self, name, entity_type, metadata, salience, mentions)\n def from_api_repr(cls, payload) # Convert an Entity from the JSON API into an :class:`Entity`.","label":1},{"cid":"google/cloud/language/sentiment.py","text":"google/cloud/language/sentiment.py\nclass Sentiment # A Google Cloud Natural Language API sentiment object.\n def __init__(self, polarity, magnitude)\n def from_api_repr(cls, payload) # Convert a Sentiment from the JSON API into a :class:`Sentiment`.","label":1},{"cid":"google/cloud/language/syntax.py","text":"google/cloud/language/syntax.py\nclass PartOfSpeech # Part of speech of a :class:`Token`.\n def reverse(cls, tag) # Reverses the API's enum name for the one on this class.\nclass Token # A Google Cloud Natural Language API token object.\n def __init__(self, text_content, text_begin, part_of_speech, edge_index, edge_label, lemma)\n def from_api_repr(cls, payload) # Convert a token from the JSON API into a :class:`Sentiment`.\nclass Sentence # A Google Cloud Natural Language API sentence object.\n def __init__(self, content, begin)\n def from_api_repr(cls, payload) # Convert a sentence from the JSON API into a :class:`Sentiment`.","label":1},{"cid":"google/cloud/logging/__init__.py","text":"google/cloud/logging/__init__.py","label":1},{"cid":"google/cloud/logging/_gax.py","text":"google/cloud/logging/_gax.py\nclass _LoggingAPI # Helper mapping logging-related APIs.\n def __init__(self, gax_api)\n def list_entries(self, projects, filter_='', order_by='', page_size=0, page_token=None) # Return a page of log entry resources.\n def write_entries(self, entries, logger_name=None, resource=None, labels=None) # API call: log an entry resource via a POST request\n def logger_delete(self, project, logger_name) # API call: delete all entries in a logger via a DELETE request\nclass _SinksAPI # Helper mapping sink-related APIs.\n def __init__(self, gax_api)\n def list_sinks(self, project, page_size=0, page_token=None) # List sinks for the project associated with this client.\n def sink_create(self, project, sink_name, filter_, destination) # API call: create a sink resource.\n def sink_get(self, project, sink_name) # API call: retrieve a sink resource.\n def sink_update(self, project, sink_name, filter_, destination) # API call: update a sink resource.\n def sink_delete(self, project, sink_name) # API call: delete a sink resource.\nclass _MetricsAPI # Helper mapping sink-related APIs.\n def __init__(self, gax_api)\n def list_metrics(self, project, page_size=0, page_token=None) # List metrics for the project associated with this client.\n def metric_create(self, project, metric_name, filter_, description) # API call: create a metric resource.\n def metric_get(self, project, metric_name) # API call: retrieve a metric resource.\n def metric_update(self, project, metric_name, filter_, description) # API call: update a metric resource.\n def metric_delete(self, project, metric_name) # API call: delete a metric resource.\ndef _mon_resource_pb_to_mapping(resource_pb) # Helper for :func:_log_entry_pb_to_mapping`.\ndef _value_pb_to_value(value_pb) # Helper for :func:`_log_entry_pb_to_mapping`.\ndef _struct_pb_to_mapping(struct_pb) # Helper for :func:`_log_entry_pb_to_mapping`.\ndef _log_entry_pb_to_mapping(entry_pb) # Helper for :meth:`list_entries`, et aliae\ndef _http_request_mapping_to_pb(info, request) # Helper for _log_entry_mapping_to_pb\ndef _log_operation_mapping_to_pb(info, operation) # Helper for _log_entry_mapping_to_pb\ndef _log_entry_mapping_to_pb(mapping) # Helper for :meth:`write_entries`, et aliae\ndef _log_sink_pb_to_mapping(sink_pb) # Helper for :meth:`list_sinks`, et aliae\ndef _log_metric_pb_to_mapping(metric_pb) # Helper for :meth:`list_metrics`, et aliae","label":1},{"cid":"google/cloud/logging/client.py","text":"google/cloud/logging/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def logging_api(self) # Helper for logging-related API calls.\n def sinks_api(self) # Helper for log sink-related API calls.\n def metrics_api(self) # Helper for log metric-related API calls.\n def logger(self, name) # Creates a logger bound to the current client.\n def _entry_from_resource(self, resource, loggers) # Detect correct entry type from resource and instantiate.\n def list_entries(self, projects=None, filter_=None, order_by=None, page_size=None, page_token=None) # Return a page of log entries.\n def sink(self, name, filter_=None, destination=None) # Creates a sink bound to the current client.\n def list_sinks(self, page_size=None, page_token=None) # List sinks for the project associated with this client.\n def metric(self, name, filter_=None, description='') # Creates a metric bound to the current client.\n def list_metrics(self, page_size=None, page_token=None) # List metrics for the project associated with this client.","label":1},{"cid":"google/cloud/logging/connection.py","text":"google/cloud/logging/connection.py\nclass Connection # A connection to Google Stackdriver Logging via the JSON REST API.\nclass _LoggingAPI # Helper mapping logging-related APIs.\n def __init__(self, connection)\n def list_entries(self, projects, filter_=None, order_by=None, page_size=None, page_token=None) # Return a page of log entry resources.\n def write_entries(self, entries, logger_name=None, resource=None, labels=None) # API call: log an entry resource via a POST request\n def logger_delete(self, project, logger_name) # API call: delete all entries in a logger via a DELETE request\nclass _SinksAPI # Helper mapping sink-related APIs.\n def __init__(self, connection)\n def list_sinks(self, project, page_size=None, page_token=None) # List sinks for the project associated with this client.\n def sink_create(self, project, sink_name, filter_, destination) # API call: create a sink resource.\n def sink_get(self, project, sink_name) # API call: retrieve a sink resource.\n def sink_update(self, project, sink_name, filter_, destination) # API call: update a sink resource.\n def sink_delete(self, project, sink_name) # API call: delete a sink resource.\nclass _MetricsAPI # Helper mapping sink-related APIs.\n def __init__(self, connection)\n def list_metrics(self, project, page_size=None, page_token=None) # List metrics for the project associated with this client.\n def metric_create(self, project, metric_name, filter_, description=None) # API call: create a metric resource.\n def metric_get(self, project, metric_name) # API call: retrieve a metric resource.\n def metric_update(self, project, metric_name, filter_, description) # API call: update a metric resource.\n def metric_delete(self, project, metric_name) # API call: delete a metric resource.","label":1},{"cid":"google/cloud/logging/entries.py","text":"google/cloud/logging/entries.py\ndef logger_name_from_path(path) # Validate a logger URI path and get the logger name.\nclass _BaseEntry # Base class for TextEntry, StructEntry.\n def __init__(self, payload, logger, insert_id=None, timestamp=None, labels=None, severity=None, http_request=None)\n def from_api_repr(cls, resource, client, loggers=None) # Factory: construct an entry given its API representation\nclass TextEntry # Entry created with ``textPayload``.\nclass StructEntry # Entry created with ``jsonPayload``.\nclass ProtobufEntry # Entry created with ``protoPayload``.\n def parse_message(self, message) # Parse payload into a protobuf message.","label":1},{"cid":"google/cloud/logging/handlers/__init__.py","text":"google/cloud/logging/handlers/__init__.py","label":1},{"cid":"google/cloud/logging/handlers/handlers.py","text":"google/cloud/logging/handlers/handlers.py\nclass CloudLoggingHandler # Python standard ``logging`` handler.\n def __init__(self, client, name=DEFAULT_LOGGER_NAME, transport=BackgroundThreadTransport)\n def emit(self, record) # Actually log the specified logging record.\ndef setup_logging(handler, excluded_loggers=EXCLUDE_LOGGER_DEFAULTS) # Attach the ``CloudLogging`` handler to the Python root logger","label":1},{"cid":"google/cloud/logging/handlers/transports/__init__.py","text":"google/cloud/logging/handlers/transports/__init__.py","label":1},{"cid":"google/cloud/logging/handlers/transports/background_thread.py","text":"google/cloud/logging/handlers/transports/background_thread.py\nclass _Worker # A threaded worker that writes batches of log entries\n def __init__(self, logger)\n def _run(self) # The entry point for the worker thread.\n def _start(self) # Called by this class's constructor\n def _stop(self) # Signals the worker thread to shut down\n def enqueue(self, record, message) # Queues up a log entry to be written by the background thread.\nclass BackgroundThreadTransport # Aysnchronous transport that uses a background thread.\n def __init__(self, client, name)\n def send(self, record, message) # Overrides Transport.send().","label":1},{"cid":"google/cloud/logging/handlers/transports/base.py","text":"google/cloud/logging/handlers/transports/base.py\nclass Transport # Base class for Google Cloud Logging handler transports.\n def send(self, record, message) # Transport send to be implemented by subclasses.","label":1},{"cid":"google/cloud/logging/handlers/transports/sync.py","text":"google/cloud/logging/handlers/transports/sync.py\nclass SyncTransport # Basic sychronous transport.\n def __init__(self, client, name)\n def send(self, record, message) # Overrides transport.send().","label":1},{"cid":"google/cloud/logging/logger.py","text":"google/cloud/logging/logger.py\nclass Logger # Loggers represent named targets for log entries.\n def __init__(self, name, client, labels=None)\n def client(self) # Clent bound to the logger.\n def project(self) # Project bound to the logger.\n def full_name(self) # Fully-qualified name used in logging APIs\n def path(self) # URI path for use in logging APIs\n def _require_client(self, client) # Check client or verify over-ride.\n def batch(self, client=None) # Return a batch to use as a context manager.\n def _make_entry_resource(self, text=None, info=None, message=None, labels=None, insert_id=None, severity=None, http_request=None) # Return a log entry resource of the appropriate type.\n def log_text(self, text, client=None, labels=None, insert_id=None, severity=None, http_request=None) # API call: log a text message via a POST request\n def log_struct(self, info, client=None, labels=None, insert_id=None, severity=None, http_request=None) # API call: log a structured message via a POST request\n def log_proto(self, message, client=None, labels=None, insert_id=None, severity=None, http_request=None) # API call: log a protobuf message via a POST request\n def delete(self, client=None) # API call: delete all entries in a logger via a DELETE request\n def list_entries(self, projects=None, filter_=None, order_by=None, page_size=None, page_token=None) # Return a page of log entries.\nclass Batch # Context manager: collect entries to log via a single API call.\n def __init__(self, logger, client)\n def __enter__(self)\n def __exit__(self, exc_type, exc_val, exc_tb)\n def log_text(self, text, labels=None, insert_id=None, severity=None, http_request=None) # Add a text entry to be logged during :meth:`commit`.\n def log_struct(self, info, labels=None, insert_id=None, severity=None, http_request=None) # Add a struct entry to be logged during :meth:`commit`.\n def log_proto(self, message, labels=None, insert_id=None, severity=None, http_request=None) # Add a protobuf entry to be logged during :meth:`commit`.\n def commit(self, client=None) # Send saved log entries as a single API call.","label":1},{"cid":"google/cloud/logging/metric.py","text":"google/cloud/logging/metric.py\nclass Metric # Metrics represent named filters for log entries.\n def __init__(self, name, filter_=None, client=None, description='')\n def client(self) # Clent bound to the logger.\n def project(self) # Project bound to the logger.\n def full_name(self) # Fully-qualified name used in metric APIs\n def path(self) # URL path for the metric's APIs\n def from_api_repr(cls, resource, client) # Factory: construct a metric given its API representation\n def _require_client(self, client) # Check client or verify over-ride.\n def create(self, client=None) # API call: create the metric via a PUT request\n def exists(self, client=None) # API call: test for the existence of the metric via a GET request\n def reload(self, client=None) # API call: sync local metric configuration via a GET request\n def update(self, client=None) # API call: update metric configuration via a PUT request\n def delete(self, client=None) # API call: delete a metric via a DELETE request","label":1},{"cid":"google/cloud/logging/sink.py","text":"google/cloud/logging/sink.py\nclass Sink # Sinks represent filtered exports for log entries.\n def __init__(self, name, filter_=None, destination=None, client=None)\n def client(self) # Clent bound to the sink.\n def project(self) # Project bound to the sink.\n def full_name(self) # Fully-qualified name used in sink APIs\n def path(self) # URL path for the sink's APIs\n def from_api_repr(cls, resource, client) # Factory: construct a sink given its API representation\n def _require_client(self, client) # Check client or verify over-ride.\n def create(self, client=None) # API call: create the sink via a PUT request\n def exists(self, client=None) # API call: test for the existence of the sink via a GET request\n def reload(self, client=None) # API call: sync local sink configuration via a GET request\n def update(self, client=None) # API call: update sink configuration via a PUT request\n def delete(self, client=None) # API call: delete a sink via a DELETE request","label":1},{"cid":"google/cloud/monitoring/__init__.py","text":"google/cloud/monitoring/__init__.py","label":1},{"cid":"google/cloud/monitoring/_dataframe.py","text":"google/cloud/monitoring/_dataframe.py\ndef _build_dataframe(time_series_iterable, label=None, labels=None) # Build a :mod:`pandas` dataframe out of time series.\ndef _sorted_resource_labels(labels) # Sort label names, putting well-known resource labels first.","label":1},{"cid":"google/cloud/monitoring/client.py","text":"google/cloud/monitoring/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def query(self, metric_type=Query.DEFAULT_METRIC_TYPE, end_time=None, days=0, hours=0, minutes=0) # Construct a query object for retrieving metric data.\n def metric_descriptor(self, type_, metric_kind=MetricKind.METRIC_KIND_UNSPECIFIED, value_type=ValueType.VALUE_TYPE_UNSPECIFIED, labels=(), unit='', description='', display_name='') # Construct a metric descriptor object.\n def metric(type_, labels) # Factory for constructing metric objects.\n def resource(type_, labels) # Factory for constructing monitored resource objects.\n def time_series(metric, resource, value, end_time=None, start_time=None) # Construct a time series object for a single data point.\n def fetch_metric_descriptor(self, metric_type) # Look up a metric descriptor by type.\n def list_metric_descriptors(self, filter_string=None, type_prefix=None) # List all metric descriptors for the project.\n def fetch_resource_descriptor(self, resource_type) # Look up a monitored resource descriptor by type.\n def list_resource_descriptors(self, filter_string=None) # List all monitored resource descriptors for the project.\n def group(self, group_id=None, display_name=None, parent_id=None, filter_string=None, is_cluster=False) # Factory constructor for group object.\n def fetch_group(self, group_id) # Fetch a group from the API based on it's ID.\n def list_groups(self) # List all groups for the project.\n def write_time_series(self, timeseries_list) # Write a list of time series objects to the API.\n def write_point(self, metric, resource, value, end_time=None, start_time=None) # Write a single point for a metric to the API.","label":1},{"cid":"google/cloud/monitoring/connection.py","text":"google/cloud/monitoring/connection.py\nclass Connection # A connection to Google Stackdriver Monitoring via the JSON REST API.","label":1},{"cid":"google/cloud/monitoring/group.py","text":"google/cloud/monitoring/group.py\ndef _group_id_from_name(path, project=None) # Validate a group URI path and get the group ID.\ndef _group_name_from_id(project, group_id) # Build the group name given the project and group ID.\nclass Group # A dynamic collection of monitored resources.\n def __init__(self, client, group_id=None, display_name=None, parent_id=None, filter_string=None, is_cluster=False)\n def id(self) # Returns the group ID.\n def name(self) # Returns the fully qualified name of the group.\n def parent_name(self) # Returns the fully qualified name of the parent group.\n def path(self) # URL path to this group.\n def create(self) # Create a new group based on this object via a ``POST`` request.\n def exists(self) # Test for the existence of the group via a ``GET`` request.\n def reload(self) # Sync local group information via a ``GET`` request.\n def update(self) # Update the group via a ``PUT`` request.\n def delete(self) # Delete the group via a ``DELETE`` request.\n def fetch_parent(self) # Returns the parent group of this group via a ``GET`` request.\n def list_children(self) # Lists all children of this group via a ``GET`` request.\n def list_ancestors(self) # Lists all ancestors of this group via a ``GET`` request.\n def list_descendants(self) # Lists all descendants of this group via a ``GET`` request.\n def list_members(self, filter_string=None, end_time=None, start_time=None) # Lists all members of this group via a ``GET`` request.\n def _fetch(cls, client, group_id) # Fetch a group from the API based on it's ID.\n def _list(cls, client, children_of_group=None, ancestors_of_group=None, descendants_of_group=None) # Lists all groups in the project.\n def _from_dict(cls, client, info) # Constructs a Group instance from the parsed JSON representation.\n def _set_properties_from_dict(self, info) # Update the group properties from its API representation.\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON wire format.\n def __repr__(self)","label":1},{"cid":"google/cloud/monitoring/label.py","text":"google/cloud/monitoring/label.py\nclass LabelValueType # Allowed values for the `type of a label`_.\nclass LabelDescriptor # Schema specification and documentation for a single label.\n def __init__(self, key, value_type=LabelValueType.STRING, description='')\n def _from_dict(cls, info) # Construct a label descriptor from the parsed JSON representation.\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON wire format.\n def __eq__(self, other)\n def __ne__(self, other)\n def __repr__(self)","label":1},{"cid":"google/cloud/monitoring/metric.py","text":"google/cloud/monitoring/metric.py\nclass MetricKind # Choices for the `kind of measurement`_.\nclass ValueType # Choices for the `metric value type`_.\nclass MetricDescriptor # Specification of a metric type and its schema.\n def __init__(self, client, type_, metric_kind=MetricKind.METRIC_KIND_UNSPECIFIED, value_type=ValueType.VALUE_TYPE_UNSPECIFIED, labels=(), unit='', description='', display_name='', name=None)\n def create(self) # Create a new metric descriptor based on this object.\n def delete(self) # Delete the metric descriptor identified by this object.\n def _fetch(cls, client, metric_type) # Look up a metric descriptor by type.\n def _list(cls, client, filter_string=None, type_prefix=None) # List all metric descriptors for the project.\n def _from_dict(cls, client, info) # Construct a metric descriptor from the parsed JSON representation.\n def _init_from_dict(self, info) # Initialize attributes from the parsed JSON representation.\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON wire format.\n def __repr__(self)\nclass Metric # A specific metric identified by specifying values for all labels.\n def _from_dict(cls, info) # Construct a metric object from the parsed JSON representation.\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON format.","label":1},{"cid":"google/cloud/monitoring/query.py","text":"google/cloud/monitoring/query.py\nclass Aligner # Allowed values for the `supported aligners`_.\nclass Reducer # Allowed values for the `supported reducers`_.\nclass Query # Query object for retrieving metric data.\n def __init__(self, client, metric_type=DEFAULT_METRIC_TYPE, end_time=None, days=0, hours=0, minutes=0)\n def __iter__(self)\n def metric_type(self) # The metric type name.\n def filter(self) # The filter string.\n def select_interval(self, end_time, start_time=None) # Copy the query and set the query time interval.\n def select_group(self, group_id) # Copy the query and add filtering by group.\n def select_projects(self, *args) # Copy the query and add filtering by monitored projects.\n def select_resources(self, *args, **kwargs) # Copy the query and add filtering by resource labels.\n def select_metrics(self, *args, **kwargs) # Copy the query and add filtering by metric labels.\n def align(self, per_series_aligner, seconds=0, minutes=0, hours=0) # Copy the query and add temporal alignment.\n def reduce(self, cross_series_reducer, *group_by_fields) # Copy the query and add cross-series reduction.\n def iter(self, headers_only=False, page_size=None) # Yield all time series objects selected by the query.\n def _iter_fragments(self, headers_only=False, page_size=None) # Yield all time series fragments selected by the query.\n def _build_query_params(self, headers_only=False, page_size=None, page_token=None) # Yield key-value pairs for the URL query string.\n def as_dataframe(self, label=None, labels=None) # Return all the selected time series as a :mod:`pandas` dataframe.\n def copy(self) # Copy the query object.\nclass _Filter # Helper for assembling a filter string.\n def __init__(self, metric_type)\n def select_resources(self, *args, **kwargs) # Select by resource labels.\n def select_metrics(self, *args, **kwargs) # Select by metric labels.\n def __str__(self)\ndef _build_label_filter(category, *args, **kwargs) # Construct a filter string to filter on metric or resource labels.","label":1},{"cid":"google/cloud/monitoring/resource.py","text":"google/cloud/monitoring/resource.py\nclass ResourceDescriptor # Specification of a monitored resource type and its schema.\n def __init__(self, name, type_, display_name, description, labels)\n def _fetch(cls, client, resource_type) # Look up a monitored resource descriptor by type.\n def _list(cls, client, filter_string=None) # List all monitored resource descriptors for the project.\n def _from_dict(cls, info) # Construct a resource descriptor from the parsed JSON representation.\n def __repr__(self)\nclass Resource # A monitored resource identified by specifying values for all labels.\n def _from_dict(cls, info) # Construct a resource object from the parsed JSON representation.\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON format.","label":1},{"cid":"google/cloud/monitoring/timeseries.py","text":"google/cloud/monitoring/timeseries.py\nclass TimeSeries # A single time series of metric values.\n def labels(self) # A single dictionary with values for all the labels.\n def header(self, points=None) # Copy everything but the point data.\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON wire format.\n def _from_dict(cls, info) # Construct a time series from the parsed JSON representation.\n def __repr__(self) # Return a representation string with the points elided.\ndef _make_typed_value(value) # Create a dict representing a TypedValue API object.\nclass Point # A single point in a time series.\n def _from_dict(cls, info) # Construct a Point from the parsed JSON representation.\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON wire format.","label":1},{"cid":"google/cloud/operation.py","text":"google/cloud/operation.py\ndef _compute_type_url(klass, prefix=_GOOGLE_APIS_PREFIX) # Compute a type URL for a klass.\ndef _register_type_url(type_url, klass) # Register a klass as the factory for a given type URL.\nclass Operation # Representation of a Google API Long-Running Operation.\n def __init__(self, name, client, pb_metadata=None, **kw)\n def from_pb(cls, op_pb, client, **kw) # Factory: construct an instance from a protobuf.\n def complete(self) # Has the operation already completed?\n def poll(self) # Check if the operation has finished.","label":1},{"cid":"google/cloud/pubsub/__init__.py","text":"google/cloud/pubsub/__init__.py","label":1},{"cid":"google/cloud/pubsub/_gax.py","text":"google/cloud/pubsub/_gax.py\nclass _PublisherAPI # Helper mapping publisher-related APIs.\n def __init__(self, gax_api)\n def list_topics(self, project, page_size=0, page_token=None) # List topics for the project associated with this API.\n def topic_create(self, topic_path) # API call: create a topic\n def topic_get(self, topic_path) # API call: retrieve a topic\n def topic_delete(self, topic_path) # API call: delete a topic\n def topic_publish(self, topic_path, messages) # API call: publish one or more messages to a topic\n def topic_list_subscriptions(self, topic_path, page_size=0, page_token=None) # API call: list subscriptions bound to a topic\nclass _SubscriberAPI # Helper mapping subscriber-related APIs.\n def __init__(self, gax_api)\n def list_subscriptions(self, project, page_size=0, page_token=None) # List subscriptions for the project associated with this API.\n def subscription_create(self, subscription_path, topic_path, ack_deadline=None, push_endpoint=None) # API call: create a subscription\n def subscription_get(self, subscription_path) # API call: retrieve a subscription\n def subscription_delete(self, subscription_path) # API call: delete a subscription\n def subscription_modify_push_config(self, subscription_path, push_endpoint) # API call: update push config of a subscription\n def subscription_pull(self, subscription_path, return_immediately=False, max_messages=1) # API call: retrieve messages for a subscription\n def subscription_acknowledge(self, subscription_path, ack_ids) # API call: acknowledge retrieved messages\n def subscription_modify_ack_deadline(self, subscription_path, ack_ids, ack_deadline) # API call: update ack deadline for retrieved messages\ndef _message_pb_from_mapping(message) # Helper for :meth:`_PublisherAPI.topic_publish`.\ndef _subscription_pb_to_mapping(sub_pb) # Helper for :meth:`list_subscriptions`, et aliae\ndef _message_pb_to_mapping(message_pb) # Helper for :meth:`pull`, et aliae\ndef _received_message_pb_to_mapping(received_message_pb) # Helper for :meth:`pull`, et aliae\ndef make_gax_publisher_api(connection) # Create an instance of the GAX Publisher API.\ndef make_gax_subscriber_api(connection) # Create an instance of the GAX Subscriber API.","label":1},{"cid":"google/cloud/pubsub/_helpers.py","text":"google/cloud/pubsub/_helpers.py\ndef topic_name_from_path(path, project) # Validate a topic URI path and get the topic name.\ndef subscription_name_from_path(path, project) # Validate a subscription URI path and get the subscription name.","label":1},{"cid":"google/cloud/pubsub/client.py","text":"google/cloud/pubsub/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def publisher_api(self) # Helper for publisher-related API calls.\n def subscriber_api(self) # Helper for subscriber-related API calls.\n def iam_policy_api(self) # Helper for IAM policy-related API calls.\n def list_topics(self, page_size=None, page_token=None) # List topics for the project associated with this client.\n def list_subscriptions(self, page_size=None, page_token=None) # List subscriptions for the project associated with this client.\n def topic(self, name, timestamp_messages=False) # Creates a topic bound to the current client.","label":1},{"cid":"google/cloud/pubsub/connection.py","text":"google/cloud/pubsub/connection.py\nclass Connection # A connection to Google Cloud Pub/Sub via the JSON REST API.\n def __init__(self, credentials=None, http=None)\n def build_api_url(self, path, query_params=None, api_base_url=None, api_version=None) # Construct an API url given a few components, some optional.\nclass _PublisherAPI # Helper mapping publisher-related APIs.\n def __init__(self, connection)\n def list_topics(self, project, page_size=None, page_token=None) # API call: list topics for a given project\n def topic_create(self, topic_path) # API call: create a topic\n def topic_get(self, topic_path) # API call: retrieve a topic\n def topic_delete(self, topic_path) # API call: delete a topic\n def topic_publish(self, topic_path, messages) # API call: publish one or more messages to a topic\n def topic_list_subscriptions(self, topic_path, page_size=None, page_token=None) # API call: list subscriptions bound to a topic\nclass _SubscriberAPI # Helper mapping subscriber-related APIs.\n def __init__(self, connection)\n def list_subscriptions(self, project, page_size=None, page_token=None) # API call: list subscriptions for a given project\n def subscription_create(self, subscription_path, topic_path, ack_deadline=None, push_endpoint=None) # API call: create a subscription\n def subscription_get(self, subscription_path) # API call: retrieve a subscription\n def subscription_delete(self, subscription_path) # API call: delete a subscription\n def subscription_modify_push_config(self, subscription_path, push_endpoint) # API call: update push config of a subscription\n def subscription_pull(self, subscription_path, return_immediately=False, max_messages=1) # API call: retrieve messages for a subscription\n def subscription_acknowledge(self, subscription_path, ack_ids) # API call: acknowledge retrieved messages\n def subscription_modify_ack_deadline(self, subscription_path, ack_ids, ack_deadline) # API call: update ack deadline for retrieved messages\nclass _IAMPolicyAPI # Helper mapping IAM policy-related APIs.\n def __init__(self, connection)\n def get_iam_policy(self, target_path) # API call: fetch the IAM policy for the target\n def set_iam_policy(self, target_path, policy) # API call: update the IAM policy for the target\n def test_iam_permissions(self, target_path, permissions) # API call: test permissions","label":1},{"cid":"google/cloud/pubsub/iam.py","text":"google/cloud/pubsub/iam.py\nclass Policy # Combined IAM Policy / Bindings.\n def __init__(self, etag=None, version=None)\n def user(email) # Factory method for a user member.\n def service_account(email) # Factory method for a service account member.\n def group(email) # Factory method for a group member.\n def domain(domain) # Factory method for a domain member.\n def all_users() # Factory method for a member representing all users.\n def authenticated_users() # Factory method for a member representing all authenticated users.\n def from_api_repr(cls, resource) # Create a policy from the resource returned from the API.\n def to_api_repr(self) # Construct a Policy resource.","label":1},{"cid":"google/cloud/pubsub/message.py","text":"google/cloud/pubsub/message.py\nclass Message # Messages can be published to a topic and received by subscribers.\n def __init__(self, data, message_id, attributes=None)\n def attributes(self) # Lazily-constructed attribute dictionary.\n def timestamp(self) # Return sortable timestamp from attributes, if passed.\n def service_timestamp(self) # Return server-set timestamp.\n def from_api_repr(cls, api_repr) # Factory: construct message from API representation.","label":1},{"cid":"google/cloud/pubsub/subscription.py","text":"google/cloud/pubsub/subscription.py\nclass Subscription # Subscriptions receive messages published to their topics.\n def __init__(self, name, topic=None, ack_deadline=None, push_endpoint=None, client=None)\n def from_api_repr(cls, resource, client, topics=None) # Factory: construct a topic given its API representation\n def project(self) # Project bound to the subscription.\n def full_name(self) # Fully-qualified name used in subscription APIs\n def path(self) # URL path for the subscription's APIs\n def auto_ack(self, return_immediately=False, max_messages=1, client=None) # :class:`AutoAck` factory\n def _require_client(self, client) # Check client or verify over-ride.\n def create(self, client=None) # API call: create the subscription via a PUT request\n def exists(self, client=None) # API call: test existence of the subscription via a GET request\n def reload(self, client=None) # API call: sync local subscription configuration via a GET request\n def delete(self, client=None) # API call: delete the subscription via a DELETE request.\n def modify_push_configuration(self, push_endpoint, client=None) # API call: update the push endpoint for the subscription.\n def pull(self, return_immediately=False, max_messages=1, client=None) # API call: retrieve messages for the subscription.\n def acknowledge(self, ack_ids, client=None) # API call: acknowledge retrieved messages for the subscription.\n def modify_ack_deadline(self, ack_ids, ack_deadline, client=None) # API call: update acknowledgement deadline for a retrieved message.\n def get_iam_policy(self, client=None) # Fetch the IAM policy for the subscription.\n def set_iam_policy(self, policy, client=None) # Update the IAM policy for the subscription.\n def check_iam_permissions(self, permissions, client=None) # Verify permissions allowed for the current user.\nclass AutoAck # Wrapper for :meth:`Subscription.pull` results.\n def __init__(self, subscription, return_immediately=False, max_messages=1, client=None)\n def __enter__(self)\n def __exit__(self, exc_type, exc_val, exc_tb)","label":1},{"cid":"google/cloud/pubsub/topic.py","text":"google/cloud/pubsub/topic.py\nclass Topic # Topics are targets to which messages can be published.\n def __init__(self, name, client, timestamp_messages=False)\n def subscription(self, name, ack_deadline=None, push_endpoint=None) # Creates a subscription bound to the current topic.\n def from_api_repr(cls, resource, client) # Factory: construct a topic given its API representation\n def project(self) # Project bound to the topic.\n def full_name(self) # Fully-qualified name used in topic / subscription APIs\n def _require_client(self, client) # Check client or verify over-ride.\n def create(self, client=None) # API call: create the topic via a PUT request\n def exists(self, client=None) # API call: test for the existence of the topic via a GET request\n def delete(self, client=None) # API call: delete the topic via a DELETE request\n def _timestamp_message(self, attrs) # Add a timestamp to ``attrs``, if the topic is so configured.\n def publish(self, message, client=None, **attrs) # API call: publish a message to a topic via a POST request\n def batch(self, client=None) # Return a batch to use as a context manager.\n def list_subscriptions(self, page_size=None, page_token=None, client=None) # List subscriptions for the project associated with this client.\n def get_iam_policy(self, client=None) # Fetch the IAM policy for the topic.\n def set_iam_policy(self, policy, client=None) # Update the IAM policy for the topic.\n def check_iam_permissions(self, permissions, client=None) # Verify permissions allowed for the current user.\nclass Batch # Context manager: collect messages to publish via a single API call.\n def __init__(self, topic, client)\n def __enter__(self)\n def __exit__(self, exc_type, exc_val, exc_tb)\n def __iter__(self)\n def publish(self, message, **attrs) # Emulate publishing a message, but save it.\n def commit(self, client=None) # Send saved messages as a single API call.","label":1},{"cid":"google/cloud/resource_manager/__init__.py","text":"google/cloud/resource_manager/__init__.py","label":1},{"cid":"google/cloud/resource_manager/client.py","text":"google/cloud/resource_manager/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def new_project(self, project_id, name=None, labels=None) # Create a project bound to the current client.\n def fetch_project(self, project_id) # Fetch an existing project and it's relevant metadata by ID.\n def list_projects(self, filter_params=None, page_size=None) # List the projects visible to this client.\nclass _ProjectIterator # An iterator over a list of Project resources.\n def __init__(self, client, extra_params=None)\n def get_items_from_response(self, response) # Yield projects from response.","label":1},{"cid":"google/cloud/resource_manager/connection.py","text":"google/cloud/resource_manager/connection.py\nclass Connection # A connection to Google Cloud Resource Manager via the JSON REST API.","label":1},{"cid":"google/cloud/resource_manager/project.py","text":"google/cloud/resource_manager/project.py\nclass Project # Projects are containers for your work on Google Cloud Platform.\n def __init__(self, project_id, client, name=None, labels=None)\n def __repr__(self)\n def from_api_repr(cls, resource, client) # Factory: construct a project given its API representation.\n def set_properties_from_api_repr(self, resource) # Update specific properties from its API representation.\n def full_name(self) # Fully-qualified name (ie, ``'projects/purple-spaceship-123'``).\n def path(self) # URL for the project (ie, ``'/projects/purple-spaceship-123'``).\n def _require_client(self, client) # Check client or verify over-ride.\n def create(self, client=None) # API call: create the project via a ``POST`` request.\n def reload(self, client=None) # API call: reload the project via a ``GET`` request.\n def exists(self, client=None) # API call: test the existence of a project via a ``GET`` request.\n def update(self, client=None) # API call: update the project via a ``PUT`` request.\n def delete(self, client=None, reload_data=False) # API call: delete the project via a ``DELETE`` request.\n def undelete(self, client=None, reload_data=False) # API call: undelete the project via a ``POST`` request.","label":1},{"cid":"google/cloud/speech/__init__.py","text":"google/cloud/speech/__init__.py","label":1},{"cid":"google/cloud/speech/client.py","text":"google/cloud/speech/client.py\nclass Encoding # Audio encoding types.\nclass Client # Client to bundle configuration needed for API requests.\n def sync_recognize(self, content, source_uri, encoding, sample_rate, language_code=None, max_alternatives=None, profanity_filter=None, speech_context=None) # Synchronous Speech Recognition.","label":1},{"cid":"google/cloud/speech/connection.py","text":"google/cloud/speech/connection.py\nclass Connection # A connection to Google Cloud Speech JSON REST API.","label":1},{"cid":"google/cloud/storage/__init__.py","text":"google/cloud/storage/__init__.py","label":1},{"cid":"google/cloud/storage/_helpers.py","text":"google/cloud/storage/_helpers.py\nclass _PropertyMixin # Abstract mixin for cloud storage classes with associated propertties.\n def __init__(self, name=None)\n def path(self) # Abstract getter for the object path.\n def client(self) # Abstract getter for the object client.\n def _require_client(self, client) # Check client or verify over-ride.\n def reload(self, client=None) # Reload properties from Cloud Storage.\n def _patch_property(self, name, value) # Update field of this object's properties.\n def _set_properties(self, value) # Set the properties for the current object.\n def patch(self, client=None) # Sends all changed properties in a PATCH request.\ndef _scalar_property(fieldname) # Create a property descriptor around the :class:`_PropertyMixin` helpers.\n def _getter(self) # Scalar property getter.\n def _setter(self, value) # Scalar property setter.\ndef _write_buffer_to_hash(buffer_object, hash_obj, digest_block_size=8192) # Read blocks from a buffer and update a hash with them.\ndef _base64_md5hash(buffer_object) # Get MD5 hash of bytes (as base64).","label":1},{"cid":"google/cloud/storage/acl.py","text":"google/cloud/storage/acl.py\nclass _ACLEntity # Class representing a set of roles for an entity.\n def __init__(self, entity_type, identifier=None)\n def __str__(self)\n def __repr__(self)\n def get_roles(self) # Get the list of roles permitted by this entity.\n def grant(self, role) # Add a role to the entity.\n def revoke(self, role) # Remove a role from the entity.\n def grant_read(self) # Grant read access to the current entity.\n def grant_write(self) # Grant write access to the current entity.\n def grant_owner(self) # Grant owner access to the current entity.\n def revoke_read(self) # Revoke read access from the current entity.\n def revoke_write(self) # Revoke write access from the current entity.\n def revoke_owner(self) # Revoke owner access from the current entity.\nclass ACL # Container class representing a list of access controls.\n def __init__(self)\n def _ensure_loaded(self) # Load if not already loaded.\n def reset(self) # Remove all entities from the ACL, and clear the ``loaded`` flag.\n def __iter__(self)\n def entity_from_dict(self, entity_dict) # Build an _ACLEntity object from a dictionary of data.\n def has_entity(self, entity) # Returns whether or not this ACL has any entries for an entity.\n def get_entity(self, entity, default=None) # Gets an entity object from the ACL.\n def add_entity(self, entity) # Add an entity to the ACL.\n def entity(self, entity_type, identifier=None) # Factory method for creating an Entity.\n def user(self, identifier) # Factory method for a user Entity.\n def group(self, identifier) # Factory method for a group Entity.\n def domain(self, domain) # Factory method for a domain Entity.\n def all(self) # Factory method for an Entity representing all users.\n def all_authenticated(self) # Factory method for an Entity representing all authenticated users.\n def get_entities(self) # Get a list of all Entity objects.\n def client(self) # Abstract getter for the object client.\n def _require_client(self, client) # Check client or verify over-ride.\n def reload(self, client=None) # Reload the ACL data from Cloud Storage.\n def _save(self, acl, predefined, client) # Helper for :meth:`save` and :meth:`save_predefined`.\n def save(self, acl=None, client=None) # Save this ACL for the current bucket.\n def save_predefined(self, predefined, client=None) # Save this ACL for the current bucket using a predefined ACL.\n def clear(self, client=None) # Remove all ACL entries.\nclass BucketACL # An ACL specifically for a bucket.\n def __init__(self, bucket)\n def client(self) # The client bound to this ACL's bucket.\n def reload_path(self) # Compute the path for GET API requests for this ACL.\n def save_path(self) # Compute the path for PATCH API requests for this ACL.\nclass DefaultObjectACL # A class representing the default object ACL for a bucket.\nclass ObjectACL # An ACL specifically for a Cloud Storage ","label":1},{"cid":"google/cloud/storage/batch.py","text":"google/cloud/storage/batch.py\nclass MIMEApplicationHTTP # MIME type for ``application/http``.\n def __init__(self, method, uri, headers, body)\nclass NoContent # Emulate an HTTP '204 No Content' response.\nclass _FutureDict # Class to hold a future value for a deferred request.\n def get(key, default=None) # Stand-in for dict.get.\n def __getitem__(self, key) # Stand-in for dict[key].\n def __setitem__(self, key, value) # Stand-in for dict[key] = value.\nclass Batch # Proxy an underlying connection, batching up change operations.\n def __init__(self, client)\n def _do_request(self, method, url, headers, data, target_object) # Override Connection: defer actual HTTP request.\n def _prepare_batch_request(self) # Prepares headers and body for a batch request.\n def _finish_futures(self, responses) # Apply all the batch responses to the futures created.\n def finish(self) # Submit a single `multipart/mixed` request with deferred requests.\n def current(self) # Return the topmost batch, or None.\n def __enter__(self)\n def __exit__(self, exc_type, exc_val, exc_tb)\ndef _generate_faux_mime_message(parser, response, content) # Convert response, content -> (multipart) email.message.\ndef _unpack_batch_response(response, content) # Convert response, content -> [(headers, payload)].","label":1},{"cid":"google/cloud/storage/blob.py","text":"google/cloud/storage/blob.py\nclass Blob # A wrapper around Cloud Storage's concept of an ``Object``.\n def __init__(self, name, bucket, chunk_size=None)\n def chunk_size(self) # Get the blob's default chunk size.\n def chunk_size(self, value) # Set the blob's default chunk size.\n def path_helper(bucket_path, blob_name) # Relative URL path for a blob.\n def acl(self) # Create our ACL on demand.\n def __repr__(self)\n def path(self) # Getter property for the URL path to this Blob.\n def client(self) # The client bound to this blob.\n def public_url(self) # The public URL for this blob's object.\n def generate_signed_url(self, expiration, method='GET', content_type=None, generation=None, response_disposition=None, response_type=None, client=None, credentials=None) # Generates a signed URL for this blob.\n def exists(self, client=None) # Determines whether or not this blob exists.\n def delete(self, client=None) # Deletes a blob from Cloud Storage.\n def download_to_file(self, file_obj, encryption_key=None, client=None) # Download the contents of this blob into a file-like object.\n def download_to_filename(self, filename, encryption_key=None, client=None) # Download the contents of this blob into a named file.\n def download_as_string(self, encryption_key=None, client=None) # Download the contents of this blob as a string.\n def _check_response_error(request, http_response) # Helper for :meth:`upload_from_file`.\n def upload_from_file(self, file_obj, rewind=False, size=None, encryption_key=None, content_type=None, num_retries=6, client=None) # Upload the contents of this blob from a file-like object.\n def upload_from_filename(self, filename, content_type=None, encryption_key=None, client=None) # Upload this blob's contents from the content of a named file.\n def upload_from_string(self, data, content_type='text/plain', encryption_key=None, client=None) # Upload contents of this blob from the provided string.\n def make_public(self, client=None) # Make this blob public giving all users read access.\n def component_count(self) # Number of underlying components that make up this object.\n def etag(self) # Retrieve the ETag for the object.\n def generation(self) # Retrieve the generation for the object.\n def id(self) # Retrieve the ID for the object.\n def media_link(self) # Retrieve the media download URI for the object.\n def metadata(self) # Retrieve arbitrary/application specific metadata for the object.\n def metadata(self, value) # Update arbitrary/application specific metadata for the object.\n def metageneration(self) # Retrieve the metageneration for the object.\n def owner(self) # Retrieve info about the owner of the object.\n def self_link(self) # Retrieve the URI for the object.\n def size(self) # Size of the object, in bytes.\n def storage_class(self) # Retrieve the storage class for the object.\n def time_deleted(self) # Retrieve the timest","label":1},{"cid":"google/cloud/storage/bucket.py","text":"google/cloud/storage/bucket.py\nclass _BlobIterator # An iterator listing blobs in a bucket\n def __init__(self, bucket, extra_params=None, client=None)\n def get_items_from_response(self, response) # Yield :class:`.storage.blob.Blob` items from response.\nclass Bucket # A class representing a Bucket on Cloud Storage.\n def __init__(self, client, name=None)\n def __repr__(self)\n def client(self) # The client bound to this bucket.\n def blob(self, blob_name, chunk_size=None) # Factory constructor for blob object.\n def exists(self, client=None) # Determines whether or not this bucket exists.\n def create(self, client=None) # Creates current bucket.\n def acl(self) # Create our ACL on demand.\n def default_object_acl(self) # Create our defaultObjectACL on demand.\n def path_helper(bucket_name) # Relative URL path for a bucket.\n def path(self) # The URL path to this bucket.\n def get_blob(self, blob_name, client=None) # Get a blob object by name.\n def list_blobs(self, max_results=None, page_token=None, prefix=None, delimiter=None, versions=None, projection='noAcl', fields=None, client=None) # Return an iterator used to find blobs in the bucket.\n def delete(self, force=False, client=None) # Delete this bucket.\n def delete_blob(self, blob_name, client=None) # Deletes a blob from the current bucket.\n def delete_blobs(self, blobs, on_error=None, client=None) # Deletes a list of blobs from the current bucket.\n def copy_blob(self, blob, destination_bucket, new_name=None, client=None) # Copy the given blob to the given bucket, optionally with a new name.\n def rename_blob(self, blob, new_name, client=None) # Rename the given blob using copy and delete operations.\n def cors(self) # Retrieve CORS policies configured for this bucket.\n def cors(self, entries) # Set CORS policies configured for this bucket.\n def etag(self) # Retrieve the ETag for the bucket.\n def id(self) # Retrieve the ID for the bucket.\n def lifecycle_rules(self) # Lifecycle rules configured for this bucket.\n def lifecycle_rules(self, rules)\n def get_logging(self) # Return info about access logging for this bucket.\n def enable_logging(self, bucket_name, object_prefix='') # Enable access logging for this bucket.\n def disable_logging(self) # Disable access logging for this bucket.\n def metageneration(self) # Retrieve the metageneration for the bucket.\n def owner(self) # Retrieve info about the owner of the bucket.\n def project_number(self) # Retrieve the number of the project to which the bucket is assigned.\n def self_link(self) # Retrieve the URI for the bucket.\n def storage_class(self) # Retrieve the storage class for the bucket.\n def storage_class(self, value) # Set the storage class for the bucket.\n def time_created(self) # Retrieve the timestamp at which the bucket was created.\n def versioning_enabled(self) # Is versioning enabled for this bucket?\n def versioni","label":1},{"cid":"google/cloud/storage/client.py","text":"google/cloud/storage/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def __init__(self, project=None, credentials=None, http=None)\n def connection(self) # Get connection or batch on the client.\n def connection(self, value) # Set connection on the client.\n def _push_batch(self, batch) # Push a batch onto our stack.\n def _pop_batch(self) # Pop a batch from our stack.\n def current_batch(self) # Currently-active batch.\n def bucket(self, bucket_name) # Factory constructor for bucket object.\n def batch(self) # Factory constructor for batch object.\n def get_bucket(self, bucket_name) # Get a bucket by name.\n def lookup_bucket(self, bucket_name) # Get a bucket by name, returning None if not found.\n def create_bucket(self, bucket_name) # Create a new bucket.\n def list_buckets(self, max_results=None, page_token=None, prefix=None, projection='noAcl', fields=None) # Get all buckets in the project associated to the client.\nclass _BucketIterator # An iterator listing all buckets.\n def __init__(self, client, extra_params=None)\n def get_items_from_response(self, response) # Factory method which yields :class:`.Bucket` items from a response.","label":1},{"cid":"google/cloud/storage/connection.py","text":"google/cloud/storage/connection.py\nclass Connection # A connection to Google Cloud Storage via the JSON REST API.","label":1},{"cid":"google/cloud/streaming/__init__.py","text":"google/cloud/streaming/__init__.py","label":1},{"cid":"google/cloud/streaming/buffered_stream.py","text":"google/cloud/streaming/buffered_stream.py\nclass BufferedStream # Buffers a stream, reading ahead to determine if we're at the end.\n def __init__(self, stream, start, size)\n def __repr__(self)\n def __len__(self)\n def stream_exhausted(self) # Does the stream have bytes remaining beyond the buffer\n def stream_end_position(self) # Point to which stream was read into the buffer\n def _bytes_remaining(self) # Bytes remaining to be read from the buffer\n def read(self, size=None) # Read bytes from the buffer.","label":1},{"cid":"google/cloud/streaming/exceptions.py","text":"google/cloud/streaming/exceptions.py\nclass Error # Base class for all exceptions.\nclass CommunicationError # Any communication error talking to an API server.\nclass HttpError # Error making a request. Soon to be HttpError.\n def __init__(self, response, content, url)\n def __str__(self)\n def status_code(self) # Status code for the response.\n def from_response(cls, http_response) # Factory: construct an exception from a response.\nclass TransferError # Errors related to transfers.\nclass TransferRetryError # Retryable errors related to transfers.\nclass TransferInvalidError # The given transfer is invalid.\nclass RequestError # The request was not successful.\nclass RetryAfterError # The response contained a retry-after header.\n def __init__(self, response, content, url, retry_after)\n def from_response(cls, http_response) # Factory: construct an exception from a response.\nclass BadStatusCodeError # The request completed but returned a bad status code.","label":1},{"cid":"google/cloud/streaming/http_wrapper.py","text":"google/cloud/streaming/http_wrapper.py\ndef _httplib2_debug_level(http_request, level, http=None) # Temporarily change the value of httplib2.debuglevel, if necessary.\nclass Request # Encapsulates the data for an HTTP request.\n def __init__(self, url='', http_method='GET', headers=None, body='')\n def loggable_body(self) # Request body for logging purposes\n def loggable_body(self, value) # Update request body for logging purposes\n def body(self) # Request body\n def body(self, value) # Update the request body\ndef _process_content_range(content_range) # Convert a 'Content-Range' header into a length for the response.\nclass Response # Encapsulates data for an HTTP response.\n def __len__(self)\n def length(self) # Length of this response.\n def status_code(self) # HTTP status code\n def retry_after(self) # Retry interval (if set).\n def is_redirect(self) # Does this response contain a redirect\ndef _check_response(response) # Validate a response\ndef _reset_http_connections(http) # Rebuild all http connections in the httplib2.Http instance.\ndef _make_api_request_no_retry(http, http_request, redirections=_REDIRECTIONS) # Send an HTTP request via the given http instance.\ndef make_api_request(http, http_request, retries=7, redirections=_REDIRECTIONS) # Send an HTTP request via the given http, performing error/retry handling.","label":1},{"cid":"google/cloud/streaming/stream_slice.py","text":"google/cloud/streaming/stream_slice.py\nclass StreamSlice # Provides a slice-like object for streams.\n def __init__(self, stream, max_bytes)\n def __repr__(self)\n def __len__(self)\n def __nonzero__(self)\n def length(self) # Maximum number of bytes to return in the slice.\n def read(self, size=None) # Read bytes from the slice.","label":1},{"cid":"google/cloud/streaming/transfer.py","text":"google/cloud/streaming/transfer.py\nclass _Transfer # Generic bits common to Uploads and Downloads.\n def __init__(self, stream, close_stream=False, chunksize=_DEFAULT_CHUNKSIZE, auto_transfer=True, http=None, num_retries=5)\n def __repr__(self)\n def close_stream(self) # Should this instance close the stream when deleted.\n def http(self) # Http instance used to perform requests.\n def bytes_http(self) # Http instance used to perform binary requests.\n def bytes_http(self, value) # Update Http instance used to perform binary requests.\n def num_retries(self) # How many retries should the transfer attempt\n def num_retries(self, value) # Update how many retries should the transfer attempt\n def stream(self) # Stream to/from which data is downloaded/uploaded.\n def url(self) # URL to / from which data is downloaded/uploaded.\n def _initialize(self, http, url) # Initialize this download by setting :attr:`http` and :attr`url`.\n def initialized(self) # Has the instance been initialized\n def _ensure_initialized(self) # Helper: assert that the instance is initialized.\n def _ensure_uninitialized(self) # Helper: assert that the instance is not initialized.\n def __del__(self)\nclass Download # Represent a single download.\n def __init__(self, stream, **kwds)\n def from_file(cls, filename, overwrite=False, auto_transfer=True, **kwds) # Create a new download object from a filename.\n def from_stream(cls, stream, auto_transfer=True, total_size=None, **kwds) # Create a new Download object from a stream.\n def progress(self) # Number of bytes have been downloaded.\n def total_size(self) # Total number of bytes to be downloaded.\n def encoding(self) # 'Content-Encoding' used to transfer the file\n def __repr__(self)\n def configure_request(self, http_request, url_builder) # Update http_request/url_builder with download-appropriate values.\n def _set_total(self, info) # Update 'total_size' based on data from a response.\n def initialize_download(self, http_request, http) # Initialize this download.\n def _normalize_start_end(self, start, end=None) # Validate / fix up byte range.\n def _set_range_header(request, start, end=None) # Update the 'Range' header in a request to match a byte range.\n def _compute_end_byte(self, start, end=None, use_chunks=True) # Compute the last byte to fetch for this request.\n def _get_chunk(self, start, end) # Retrieve a chunk of the file.\n def _process_response(self, response) # Update attribtes and writing stream, based on response.\n def get_range(self, start, end=None, use_chunks=True) # Retrieve a given byte range from this download, inclusive.\n def stream_file(self, use_chunks=True) # Stream the entire download.\nclass Upload # Represent a single Upload.\n def __init__(self, stream, mime_type, total_size=None, http=None, close_stream=False, auto_transfer=True, **kwds)\n def from_file(cls, filename, mime_type=None, auto_transf","label":1},{"cid":"google/cloud/streaming/util.py","text":"google/cloud/streaming/util.py\ndef calculate_wait_for_retry(retry_attempt) # Calculate the amount of time to wait before a retry attempt.\ndef acceptable_mime_type(accept_patterns, mime_type) # Check that ``mime_type`` matches one of ``accept_patterns``.\n def _match(pattern, mime_type) # Return True iff mime_type is acceptable for pattern.","label":1},{"cid":"google/cloud/translate/__init__.py","text":"google/cloud/translate/__init__.py","label":1},{"cid":"google/cloud/translate/client.py","text":"google/cloud/translate/client.py\nclass Client # Client to bundle configuration needed for API requests.\n def __init__(self, api_key, http=None, target_language=ENGLISH_ISO_639)\n def get_languages(self, target_language=None) # Get list of supported languages for translation.\n def detect_language(self, values) # Detect the language of a string or list of strings.\n def translate(self, values, target_language=None, format_=None, source_language=None, customization_ids=()) # Translate a string or list of strings.","label":1},{"cid":"google/cloud/translate/connection.py","text":"google/cloud/translate/connection.py\nclass Connection # A connection to Google Cloud Translate via the JSON REST API.","label":1},{"cid":"google/cloud/vision/__init__.py","text":"google/cloud/vision/__init__.py","label":1},{"cid":"google/cloud/vision/client.py","text":"google/cloud/vision/client.py\nclass VisionRequest # Request container with image and features information to annotate.\n def __init__(self, image, features)\n def as_dict(self) # Dictionary representation of Image.\n def features(self) # List of Feature objects.\n def image(self) # Image object containing image content.\nclass Client # Client to bundle configuration needed for API requests.\n def annotate(self, image, features) # Annotate an image to discover it's attributes.\n def image(self, content=None, source_uri=None) # Get instance of Image using current client.","label":1},{"cid":"google/cloud/vision/color.py","text":"google/cloud/vision/color.py\nclass ImagePropertiesAnnotation # Representation of image properties\n def __init__(self, colors)\n def from_api_repr(cls, response) # Factory: construct ``ImagePropertiesAnnotation`` from a response.\n def colors(self) # Colors in an image.\nclass Color # Representation of RGBA color information.\n def __init__(self, red, green, blue, alpha)\n def from_api_repr(cls, response) # Factory: construct a ``Color`` from a Vision API response.\n def red(self) # Red component of the color.\n def green(self) # Green component of the color.\n def blue(self) # Blue component of the color.\n def alpha(self) # Alpha transparency level.\nclass ColorInformation # Representation of color information from API response.\n def __init__(self, color, score, pixel_fraction)\n def from_api_repr(cls, response) # Factory: construct ``ColorInformation`` for a color found.\n def color(self) # RGB components of the color.\n def score(self) # Image-specific score for this color. Value in range [0, 1].\n def pixel_fraction(self) # Stores the fraction of pixels the color occupies in the image.","label":1},{"cid":"google/cloud/vision/connection.py","text":"google/cloud/vision/connection.py\nclass Connection # A connection to Google Cloud Vision via the JSON REST API.","label":1},{"cid":"google/cloud/vision/entity.py","text":"google/cloud/vision/entity.py\nclass EntityAnnotation # Representation of an entity returned from the Vision API.\n def __init__(self, bounds, description, locale, locations, mid, score)\n def from_api_repr(cls, response) # Factory: construct entity from Vision API response.\n def bounds(self) # Bounding polygon of detected image feature.\n def description(self) # Description of feature detected in image.\n def locale(self) # The language code for text discovered in an image.\n def locations(self) # Location coordinates landmarks detected.\n def mid(self) # MID of feature detected in image.\n def score(self) # Overall score of the result. Range [0, 1].","label":1},{"cid":"google/cloud/vision/face.py","text":"google/cloud/vision/face.py\nclass Angles # Angles representing the positions of a face.\n def __init__(self, roll, pan, tilt)\n def from_api_repr(cls, response) # Factory: construct the angles from an Vision API response.\n def roll(self) # Roll angle of face.\n def pan(self) # Pan angle of face.\n def tilt(self) # Tilt angle of face.\nclass Bounds # The bounding polygon of the entire face.\nclass Emotions # Emotions displayed by the face detected in an image.\n def __init__(self, joy_likelihood, sorrow_likelihood, surprise_likelihood, anger_likelihood)\n def from_api_repr(cls, response) # Factory: construct `Emotions` from Vision API response.\n def joy_likelihood(self) # Likelihood of joy in detected face.\n def sorrow_likelihood(self) # Likelihood of sorrow in detected face.\n def surprise_likelihood(self) # Likelihood of surprise in detected face.\n def anger_likelihood(self) # Likelihood of anger in detected face.\nclass Face # Representation of a face found by the Vision API\n def __init__(self, angles, bounds, detection_confidence, emotions, fd_bounds, headwear_likelihood, image_properties, landmarks, landmarking_confidence)\n def from_api_repr(cls, response) # Factory: construct an instance of a Face from an API response\n def angles(self) # Accessor to the pan, tilt and roll angles of a Face.\n def bounds(self) # Accessor to the bounding poly information of the detected face.\n def detection_confidence(self) # Face detection confidence score determined by the Vision API.\n def emotions(self) # Accessor to the possible emotions expressed in the detected face.\n def fd_bounds(self) # Accessor to the skin area bounding poly of the detected face.\n def headwear_likelihood(self) # Headwear likelihood.\n def image_properties(self) # Image properties from imaged used in face detection.\n def landmarks(self) # Accessor to the facial landmarks detected in a face.\n def landmarking_confidence(self) # Landmarking confidence score determinged by the Vision API.\nclass FaceImageProperties # A representation of the image properties from face detection.\n def __init__(self, blurred_likelihood, underexposed_likelihood)\n def from_api_repr(cls, response) # Factory: construct image properties from image.\n def blurred_likelihood(self) # Likelihood of the image being blurred.\n def underexposed_likelihood(self) # Likelihood that the image used for detection was underexposed.\nclass FaceLandmarkTypes # A representation of the face detection landmark types.\nclass FDBounds # The bounding polygon of just the skin portion of the face.\nclass Landmark # A face-specific landmark (for example, a face feature, left eye).\n def __init__(self, position, landmark_type)\n def from_api_repr(cls, response_landmark) # Factory: construct an instance of a Landmark from a response.\n def position(self) # Landmark position on face.\n def landmark_type(self) # Landmark type of facial feature","label":1},{"cid":"google/cloud/vision/feature.py","text":"google/cloud/vision/feature.py\nclass FeatureTypes # Feature Types to indication which annotations to perform.\nclass Feature # Feature object specifying the annotation type and maximum results.\n def __init__(self, feature_type, max_results=1)\n def as_dict(self) # Generate dictionary for Feature request format.\n def feature_type(self) # \"Feature type string.\n def max_results(self) # Maximum number of results for feature type.","label":1},{"cid":"google/cloud/vision/geometry.py","text":"google/cloud/vision/geometry.py\nclass BoundsBase # Base class for handling bounds with vertices.\n def __init__(self, vertices)\n def from_api_repr(cls, response_vertices) # Factory: construct BoundsBase instance from Vision API response.\n def vertices(self) # List of vertices.\nclass Bounds # A polygon boundry of the detected feature.\nclass FDBounds # The bounding polygon of just the skin portion of the face.\nclass LocationInformation # Representation of location information returned by the Vision API.\n def __init__(self, latitude, longitude)\n def from_api_repr(cls, response) # Factory: construct location information from Vision API response.\n def latitude(self) # Latitude coordinate.\n def longitude(self) # Longitude coordinate.\nclass Position # A 3D position in the image.\n def __init__(self, x_coordinate, y_coordinate, z_coordinate)\n def from_api_repr(cls, response_position) # Factory: construct 3D position from API response.\n def x_coordinate(self) # X position coordinate.\n def y_coordinate(self) # Y position coordinate.\n def z_coordinate(self) # Z position coordinate.\nclass Vertex # A vertex represents a 2D point in the image.\n def __init__(self, x_coordinate, y_coordinate)\n def x_coordinate(self) # X position coordinate.\n def y_coordinate(self) # Y position coordinate.","label":1},{"cid":"google/cloud/vision/image.py","text":"google/cloud/vision/image.py\nclass Image # Image representation containing information to be annotate.\n def __init__(self, client, content=None, source_uri=None)\n def as_dict(self) # Generate dictionary structure for request.\n def content(self) # Base64 encoded image content.\n def source(self) # Google Cloud Storage URI.\n def _detect_annotation(self, feature) # Generic method for detecting a single annotation.\n def detect_faces(self, limit=10) # Detect faces in image.\n def detect_labels(self, limit=10) # Detect labels that describe objects in an image.\n def detect_landmarks(self, limit=10) # Detect landmarks in an image.\n def detect_logos(self, limit=10) # Detect logos in an image.\n def detect_properties(self, limit=10) # Detect the color properties of an image.\n def detect_safe_search(self, limit=10) # Retreive safe search properties from an image.\n def detect_text(self, limit=10) # Detect text in an image.","label":1},{"cid":"google/cloud/vision/likelihood.py","text":"google/cloud/vision/likelihood.py\nclass Likelihood # A representation of likelihood to give stable results across upgrades.","label":1},{"cid":"google/cloud/vision/safe.py","text":"google/cloud/vision/safe.py\nclass SafeSearchAnnotation # Representation of a SafeSearchAnnotation.\n def __init__(self, adult_likelihood, spoof_likelihood, medical_likelihood, violence_likelihood)\n def from_api_repr(cls, response) # Factory: construct SafeSearchAnnotation from Vision API response.\n def adult(self) # Represents the adult contents likelihood for the image.\n def spoof(self) # The likelihood that an obvious modification was made to the image.\n def medical(self) # Likelihood this is a medical image.\n def violence(self) # Likeliehood that this image contains violence.","label":1},{"cid":"scripts/generate_json_docs.py","text":"scripts/generate_json_docs.py\n# Copyright 2016 Google Inc. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport cgi\nimport doctest\nimport inspect\nimport json\nimport os\nimport shutil\nimport types\n\nimport pdoc\nfrom parinx.parser import parse_docstring\nfrom parinx.errors import MethodParsingException\nimport six\n\nfrom verify_included_modules import get_public_modules\n\n\n_DOCSTRING_TEST_PARSER = doctest.DocTestParser()\n\n\nclass Module(object):\n\n def __init__(self, module_id, name, description=None,\n examples=None, methods=None, source=None):\n self.module_id = module_id\n self.name = name\n self.description = description\n self.examples = examples or []\n self.methods = methods\n self.source = source\n\n @classmethod\n def from_module_name(cls, name, base_path):\n module = pdoc.Module(pdoc.import_module(name), allsubmodules=True)\n methods = module.functions() + module.variables()\n examples = []\n\n if '__init__' in name:\n snippets = get_snippet_examples(name.split('.')[1],\n os.path.join(base_path, 'docs'))\n examples.extend(snippets)\n\n source_path = clean_source_path(module)\n\n return cls(module_id=name,\n name=name.split('.')[-1].title(),\n description=module.docstring,\n examples=examples or [],\n methods=[Method.from_pdoc(m) for m in methods],\n source=source_path)\n\n def to_dict(self):\n return {'id': self.module_id,\n 'name': self.name,\n 'description': format_sphinx_doc(self.description),\n 'examples': self.examples,\n 'methods': [m.to_dict() for m in self.methods],\n 'source': self.source}\n\n\nclass Klass(object):\n def __init__(self, module_id, name, refname=None, description=None,\n examples=None, methods=None, source=None):\n self.module_id = module_id\n self.name = name\n self.refname = refname\n self.description = description\n self.examples = examples or []\n self.methods = methods\n self.source = source\n\n @classmethod\n def from_class_name(cls, module, kls):\n methods = kls.methods()\n\n examples = []\n source_path = clean_source_path(module)\n\n return cls(module_id=kls.name,\n name=kls.name.spl","label":1},{"cid":"scripts/get_version.py","text":"scripts/get_version.py","label":1},{"cid":"scripts/make_datastore_grpc.py","text":"scripts/make_datastore_grpc.py\ndef get_pb2_contents_with_grpc() # Get pb2 lines generated by protoc with gRPC plugin.\ndef get_pb2_contents_without_grpc() # Get pb2 lines generated by protoc without gRPC plugin.\ndef get_pb2_grpc_only() # Get pb2 lines that are only in gRPC.\ndef get_pb2_message_types() # Get message types defined in datastore pb2 file.\ndef main() # Write gRPC-only lines to custom module.","label":1},{"cid":"scripts/make_operations_grpc.py","text":"scripts/make_operations_grpc.py\ndef get_pb2_contents_with_grpc() # Get pb2 lines generated by protoc with gRPC plugin.\ndef get_pb2_contents_without_grpc() # Get pb2 lines generated by protoc without gRPC plugin.\ndef get_pb2_grpc_only() # Get pb2 lines that are only in gRPC.\ndef main() # Write gRPC-only lines to custom module.","label":1},{"cid":"scripts/pycodestyle_on_repo.py","text":"scripts/pycodestyle_on_repo.py\ndef main() # Run pycodestyle on all Python files in the repository.","label":1},{"cid":"scripts/rewrite_imports.py","text":"scripts/rewrite_imports.py\ndef transform_old_to_new(line, old_module, new_module, ignore_import_from=False) # Transforms from an old module to a new one.\ndef transform_line(line) # Transforms an import line in a PB2 module.\ndef rewrite_file(filename) # Rewrites a given PB2 modules.\ndef main() # Rewrites all PB2 files.","label":1},{"cid":"scripts/run_pylint.py","text":"scripts/run_pylint.py\ndef read_config(filename) # Reads pylintrc config onto native ConfigParser object.\ndef make_test_rc(base_rc_filename, additions_dict, replacements_dict, target_filename) # Combines a base rc and test additions into single file.\ndef valid_filename(filename) # Checks if a file is a Python file and is not ignored.\ndef is_production_filename(filename) # Checks if the file contains production code.\ndef get_files_for_linting(allow_limited=True) # Gets a list of files in the repository.\ndef get_python_files(all_files=None) # Gets a list of all Python files in the repository that need linting.\ndef lint_fileset(filenames, rcfile, description) # Lints a group of files using a given rcfile.\ndef main() # Script entry point. Lints both sets of files.","label":1},{"cid":"scripts/verify_included_modules.py","text":"scripts/verify_included_modules.py\nclass SphinxApp # Mock app to interact with Sphinx helpers.\ndef is_valid_module(filename) # Determines if a filename is a valid Python module.\ndef get_public_modules(path, base_package=None) # Get list of all public modules relative to a path.\ndef main(build_root='_build') # Main script to verify modules included.\ndef get_parser() # Get simple ``argparse`` parser to determine package.","label":1},{"cid":"setup.py","text":"setup.py","label":1}],"string":"[\n {\n \"cid\": \"docs/bigquery_snippets.py\",\n \"text\": \"docs/bigquery_snippets.py\\ndef snippet(func) # Mark ``func`` as a snippet example function.\\ndef _millis()\\nclass _CloseOnDelete\\n def __init__(self, wrapped)\\n def delete(self)\\ndef client_list_datasets(client, _) # List datasets for a project.\\n def do_something_with(_)\\ndef dataset_create(client, to_delete) # Create a dataset.\\ndef dataset_exists(client, to_delete) # Test existence of a dataset.\\ndef dataset_reload(client, to_delete) # Reload a dataset's metadata.\\ndef dataset_patch(client, to_delete) # Patch a dataset's metadata.\\ndef dataset_update(client, to_delete) # Update a dataset's metadata.\\ndef dataset_delete(client, _) # Delete a dataset.\\ndef dataset_list_tables(client, to_delete) # List tables within a dataset.\\ndef table_create(client, to_delete) # Create a table.\\ndef table_exists(client, to_delete) # Test existence of a table.\\ndef table_reload(client, to_delete) # Reload a table's metadata.\\ndef table_patch(client, to_delete) # Patch a table's metadata.\\ndef table_update(client, to_delete) # Update a table's metadata.\\ndef _warm_up_inserted_table_data(table)\\ndef table_insert_fetch_data(client, to_delete) # Insert / fetch table data.\\n def do_something(row)\\ndef table_upload_from_file(client, to_delete) # Upload table data from a CSV file.\\ndef table_delete(client, _) # Delete a table.\\ndef client_list_jobs(client, _) # List jobs for a project.\\n def do_something_with(_)\\ndef client_run_sync_query(client, _) # Run a synchronous query.\\ndef client_run_sync_query_paged(client, _) # Run a synchronous query with paged results.\\n def do_something_with(rows)\\ndef client_run_sync_query_timeout(client, _) # Run a synchronous query w/ timeout\\n def do_something_with(rows)\\ndef _find_examples()\\ndef main()\",\n \"label\": 1\n },\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\",\n \"label\": 1\n },\n {\n \"cid\": \"docs/pubsub_snippets.py\",\n \"text\": \"docs/pubsub_snippets.py\\ndef snippet(func) # Mark ``func`` as a snippet example function.\\ndef _millis()\\ndef client_list_topics(client, to_delete) # List topics for a project.\\n def do_something_with(sub)\\ndef client_list_subscriptions(client, to_delete) # List all subscriptions for a project.\\n def do_something_with(sub)\\ndef topic_create(client, to_delete) # Create a topic.\\ndef topic_exists(client, to_delete) # Test existence of a topic.\\ndef topic_delete(client, to_delete) # Delete a topic.\\ndef topic_iam_policy(client, to_delete) # Fetch / set a topic's IAM policy.\\ndef topic_check_iam_permissions(client, to_delete) # Check topic IAM permissions.\\ndef topic_publish_messages(client, to_delete) # Publish messages to a topic.\\ndef topic_subscription(client, to_delete) # Create subscriptions to a topic.\\n def do_something_with(sub)\\ndef topic_subscription_push(client, to_delete) # Create subscriptions to a topic.\\ndef subscription_lifecycle(client, to_delete) # Test lifecycle of a subscription.\\ndef subscription_pull(client, to_delete) # Pull messges from a subscribed topic.\\n def do_something_with(message)\\n class ApplicationException\\n def log_exception(_)\\ndef subscription_pull_w_autoack(client, to_delete) # Pull messges from a topic, auto-acknowldging them\\n def do_something_with(message)\\ndef subscription_iam_policy(client, to_delete) # Fetch / set a subscription's IAM policy.\\ndef subscription_check_iam_permissions(client, to_delete) # Check subscription IAM permissions.\\ndef _line_no(func)\\ndef _find_examples()\\ndef _name_and_doc(func)\\ndef main()\",\n \"label\": 1\n },\n {\n \"cid\": \"google/__init__.py\",\n \"text\": \"google/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/__init__.py\",\n \"text\": \"google/cloud/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/_helpers.py\",\n \"text\": \"google/cloud/_helpers.py\\nclass _LocalStack # Manage a thread-local LIFO stack of resources.\\n def __init__(self)\\n def __iter__(self) # Iterate the stack in LIFO order.\\n def push(self, resource) # Push a resource onto our stack.\\n def pop(self) # Pop a resource from our stack.\\n def top(self) # Get the top-most resource\\nclass _UTC # Basic UTC implementation.\\n def dst(self, dt) # Daylight savings time offset.\\n def fromutc(self, dt) # Convert a timestamp from (naive) UTC to this timezone.\\n def tzname(self, dt) # Get the name of this timezone.\\n def utcoffset(self, dt) # UTC offset of this timezone.\\n def __repr__(self)\\n def __str__(self)\\ndef _ensure_tuple_or_list(arg_name, tuple_or_list) # Ensures an input is a tuple or list.\\ndef _app_engine_id() # Gets the App Engine application ID if it can be inferred.\\ndef _file_project_id() # Gets the project ID from the credentials file if one is available.\\ndef _get_nix_config_path() # Get the ``gcloud`` CLI config path on *nix systems.\\ndef _get_windows_config_path() # Get the ``gcloud`` CLI config path on Windows systems.\\ndef _default_service_project_id() # Retrieves the project ID from the gcloud command line tool.\\ndef _compute_engine_id() # Gets the Compute Engine project ID if it can be inferred.\\ndef _get_production_project() # Gets the production project if it can be inferred.\\ndef _determine_default_project(project=None) # Determine default project ID explicitly or implicitly as fall-back.\\ndef _millis(when) # Convert a zone-aware datetime to integer milliseconds.\\ndef _datetime_from_microseconds(value) # Convert timestamp to datetime, assuming UTC.\\ndef _microseconds_from_datetime(value) # Convert non-none datetime to microseconds.\\ndef _millis_from_datetime(value) # Convert non-none datetime to timestamp, assuming UTC.\\ndef _date_from_iso8601_date(value) # Convert a ISO8601 date string to native datetime date\\ndef _rfc3339_to_datetime(dt_str) # Convert a microsecond-precision timetamp to a native datetime.\\ndef _rfc3339_nanos_to_datetime(dt_str) # Convert a nanosecond-precision timestamp to a native datetime.\\ndef _datetime_to_rfc3339(value, ignore_zone=True) # Convert a timestamp to a string.\\ndef _to_bytes(value, encoding='ascii') # Converts a string value to bytes, if necessary.\\ndef _bytes_to_unicode(value) # Converts bytes to a unicode value, if necessary.\\ndef _pb_timestamp_to_datetime(timestamp_pb) # Convert a Timestamp protobuf to a datetime object.\\ndef _pb_timestamp_to_rfc3339(timestamp_pb) # Convert a Timestamp protobuf to an RFC 3339 string.\\ndef _datetime_to_pb_timestamp(when) # Convert a datetime object to a Timestamp protobuf.\\ndef _name_from_project_path(path, project, template) # Validate a URI path and get the leaf object's name.\\nclass MetadataPlugin # Callable class to transform metadata for gRPC requests.\\n def __init__(self, credentials)\\n def __call__(self, unused_context, callback) # Adds authorization header to request metadata.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigquery/__init__.py\",\n \"text\": \"google/cloud/bigquery/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py\",\n \"text\": \"google/cloud/bigquery/_helpers.py\\ndef _not_null(value, field) # Check whether 'value' should be coerced to 'field' type.\\ndef _int_from_json(value, field) # Coerce 'value' to an int, if set or not nullable.\\ndef _float_from_json(value, field) # Coerce 'value' to a float, if set or not nullable.\\ndef _bool_from_json(value, field) # Coerce 'value' to a bool, if set or not nullable.\\ndef _datetime_from_json(value, field) # Coerce 'value' to a datetime, if set or not nullable.\\ndef _date_from_json(value, field) # Coerce 'value' to a datetime date, if set or not nullable\\ndef _record_from_json(value, field) # Coerce 'value' to a mapping, if set or not nullable.\\ndef _string_from_json(value, _) # NOOP string -> string coercion\\ndef _rows_from_json(rows, schema) # Convert JSON row data to rows w/ appropriate types.\\nclass _ConfigurationProperty # Base property implementation.\\n def __init__(self, name)\\n def __get__(self, instance, owner) # Descriptor protocal: accesstor\\n def _validate(self, value) # Subclasses override to impose validation policy.\\n def __set__(self, instance, value) # Descriptor protocal: mutator\\n def __delete__(self, instance) # Descriptor protocal: deleter\\nclass _TypedProperty # Property implementation: validates based on value type.\\n def __init__(self, name, property_type)\\n def _validate(self, value) # Ensure that 'value' is of the appropriate type.\\nclass _EnumProperty # Pseudo-enumeration class.\\n def _validate(self, value) # Check that ``value`` is one of the allowed values.\\nclass UDFResource # Describe a single user-defined function (UDF) resource.\\n def __init__(self, udf_type, value)\\n def __eq__(self, other)\\nclass UDFResourcesProperty # Custom property type, holding :class:`UDFResource` instances.\\n def __get__(self, instance, owner) # Descriptor protocol: accessor\\n def __set__(self, instance, value) # Descriptor protocol: mutator\\ndef _build_udf_resources(resources) # :type resources: sequence of :class:`UDFResource`\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py\",\n \"text\": \"google/cloud/bigquery/client.py\\nclass Project # Wrapper for resource describing a BigQuery project.\\n def __init__(self, project_id, numeric_id, friendly_name)\\n def from_api_repr(cls, resource) # Factory: construct an instance from a resource dict.\\nclass Client # Client to bundle configuration needed for API requests.\\n def list_projects(self, max_results=None, page_token=None) # List projects for the project associated with this client.\\n def list_datasets(self, include_all=False, max_results=None, page_token=None) # List datasets for the project associated with this client.\\n def dataset(self, dataset_name) # Construct a dataset bound to this client.\\n def job_from_resource(self, resource) # Detect correct job type from resource and instantiate.\\n def list_jobs(self, max_results=None, page_token=None, all_users=None, state_filter=None) # List jobs for the project associated with this client.\\n def load_table_from_storage(self, job_name, destination, *source_uris) # Construct a job for loading data into a table from CloudStorage.\\n def copy_table(self, job_name, destination, *sources) # Construct a job for copying one or more tables into another table.\\n def extract_table_to_storage(self, job_name, source, *destination_uris) # Construct a job for extracting a table into Cloud Storage files.\\n def run_async_query(self, job_name, query) # Construct a job for running a SQL query asynchronously.\\n def run_sync_query(self, query) # Run a SQL query synchronously.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigquery/connection.py\",\n \"text\": \"google/cloud/bigquery/connection.py\\nclass Connection # A connection to Google Cloud BigQuery via the JSON REST API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py\",\n \"text\": \"google/cloud/bigquery/dataset.py\\nclass AccessGrant # Represent grant of an access role to an entity.\\n def __init__(self, role, entity_type, entity_id)\\n def __eq__(self, other)\\n def __repr__(self)\\nclass Dataset # Datasets are containers for tables.\\n def __init__(self, name, client, access_grants=())\\n def project(self) # Project bound to the dataset.\\n def path(self) # URL path for the dataset's APIs.\\n def access_grants(self) # Dataset's access grants.\\n def access_grants(self, value) # Update dataset's access grants\\n def created(self) # Datetime at which the dataset was created.\\n def dataset_id(self) # ID for the dataset resource.\\n def etag(self) # ETag for the dataset resource.\\n def modified(self) # Datetime at which the dataset was last modified.\\n def self_link(self) # URL for the dataset resource.\\n def default_table_expiration_ms(self) # Default expiration time for tables in the dataset.\\n def default_table_expiration_ms(self, value) # Update default expiration time for tables in the dataset.\\n def description(self) # Description of the dataset.\\n def description(self, value) # Update description of the dataset.\\n def friendly_name(self) # Title of the dataset.\\n def friendly_name(self, value) # Update title of the dataset.\\n def location(self) # Location in which the dataset is hosted.\\n def location(self, value) # Update location in which the dataset is hosted.\\n def from_api_repr(cls, resource, client) # Factory: construct a dataset given its API representation\\n def _require_client(self, client) # Check client or verify over-ride.\\n def _parse_access_grants(access) # Parse a resource fragment into a set of access grants.\\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\\n def _build_access_resource(self) # Generate a resource fragment for dataset's access grants.\\n def _build_resource(self) # Generate a resource for ``create`` or ``update``.\\n def create(self, client=None) # API call: create the dataset via a PUT request.\\n def exists(self, client=None) # API call: test for the existence of the dataset via a GET request\\n def reload(self, client=None) # API call: refresh dataset properties via a GET request.\\n def patch(self, client=None, **kw) # API call: update individual dataset properties via a PATCH request.\\n def update(self, client=None) # API call: update dataset properties via a PUT request.\\n def delete(self, client=None) # API call: delete the dataset via a DELETE request.\\n def list_tables(self, max_results=None, page_token=None) # List tables for the project associated with this client.\\n def table(self, name, schema=()) # Construct a table bound to this dataset.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py\",\n \"text\": \"google/cloud/bigquery/job.py\\nclass Compression # Pseudo-enum for ``compression`` properties.\\nclass CreateDisposition # Pseudo-enum for ``create_disposition`` properties.\\nclass DestinationFormat # Pseudo-enum for ``destination_format`` properties.\\nclass Encoding # Pseudo-enum for ``encoding`` properties.\\nclass QueryPriority # Pseudo-enum for ``QueryJob.priority`` property.\\nclass SourceFormat # Pseudo-enum for ``source_format`` properties.\\nclass WriteDisposition # Pseudo-enum for ``write_disposition`` properties.\\nclass _BaseJob # Base class for jobs.\\n def __init__(self, client)\\n def project(self) # Project bound to the job.\\n def _require_client(self, client) # Check client or verify over-ride.\\nclass _AsyncJob # Base class for asynchronous jobs.\\n def __init__(self, name, client)\\n def job_type(self) # Type of job\\n def path(self) # URL path for the job's APIs.\\n def etag(self) # ETag for the job resource.\\n def self_link(self) # URL for the job resource.\\n def user_email(self) # E-mail address of user who submitted the job.\\n def created(self) # Datetime at which the job was created.\\n def started(self) # Datetime at which the job was started.\\n def ended(self) # Datetime at which the job finished.\\n def error_result(self) # Error information about the job as a whole.\\n def errors(self) # Information about individual errors generated by the job.\\n def state(self) # Status of the job.\\n def _scrub_local_properties(self, cleaned) # Helper: handle subclass properties in cleaned.\\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\\n def _get_resource_config(cls, resource) # Helper for :meth:`from_api_repr`\\n def begin(self, client=None) # API call: begin the job via a POST request\\n def exists(self, client=None) # API call: test for the existence of the job via a GET request\\n def reload(self, client=None) # API call: refresh job properties via a GET request\\n def cancel(self, client=None) # API call: cancel job via a POST request\\nclass _LoadConfiguration # User-settable configuration options for load jobs.\\nclass LoadTableFromStorageJob # Asynchronous job for loading data into a table from CloudStorage.\\n def __init__(self, name, destination, source_uris, client, schema=())\\n def schema(self) # Table's schema.\\n def schema(self, value) # Update table's schema\\n def input_file_bytes(self) # Count of bytes loaded from source files.\\n def input_files(self) # Count of source files.\\n def output_bytes(self) # Count of bytes saved to destination table.\\n def output_rows(self) # Count of rows saved to destination table.\\n def _populate_config_resource(self, configuration) # Helper for _build_resource: copy config properties to resource\\n def _build_resource(self) # Generate a resource for :meth:`begin`.\\n def _scrub_local_properties(self, cleaned) # Helper: handle subclass properties in cleaned.\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py\",\n \"text\": \"google/cloud/bigquery/query.py\\nclass _SyncQueryConfiguration # User-settable configuration options for synchronous query jobs.\\nclass QueryResults # Synchronous job: query tables.\\n def __init__(self, query, client, udf_resources=())\\n def from_query_job(cls, job) # Factory: construct from an existing job.\\n def project(self) # Project bound to the job.\\n def _require_client(self, client) # Check client or verify over-ride.\\n def cache_hit(self) # Query results served from cache.\\n def complete(self) # Server completed query.\\n def errors(self) # Errors generated by the query.\\n def name(self) # Job name, generated by the back-end.\\n def job(self) # Job instance used to run the query.\\n def page_token(self) # Token for fetching next bach of results.\\n def total_rows(self) # Total number of rows returned by the query.\\n def total_bytes_processed(self) # Total number of bytes processed by the query.\\n def rows(self) # Query results.\\n def schema(self) # Schema for query results.\\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\\n def _build_resource(self) # Generate a resource for :meth:`begin`.\\n def run(self, client=None) # API call: run the query via a POST request\\n def fetch_data(self, max_results=None, page_token=None, start_index=None, timeout_ms=None, client=None) # API call: fetch a page of query result data via a GET request\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigquery/schema.py\",\n \"text\": \"google/cloud/bigquery/schema.py\\nclass SchemaField # Describe a single field within a table schema.\\n def __init__(self, name, field_type, mode='NULLABLE', description=None, fields=None)\\n def __eq__(self, other)\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py\",\n \"text\": \"google/cloud/bigquery/table.py\\nclass Table # Tables represent a set of rows whose values correspond to a schema.\\n def __init__(self, name, dataset, schema=())\\n def project(self) # Project bound to the table.\\n def dataset_name(self) # Name of dataset containing the table.\\n def path(self) # URL path for the table's APIs.\\n def schema(self) # Table's schema.\\n def schema(self, value) # Update table's schema\\n def created(self) # Datetime at which the table was created.\\n def etag(self) # ETag for the table resource.\\n def modified(self) # Datetime at which the table was last modified.\\n def num_bytes(self) # The size of the table in bytes.\\n def num_rows(self) # The number of rows in the table.\\n def self_link(self) # URL for the table resource.\\n def table_id(self) # ID for the table resource.\\n def table_type(self) # The type of the table.\\n def partitioning_type(self) # Time partitioning of the table.\\n def partitioning_type(self, value) # Update the partitioning type of the table\\n def partition_expiration(self) # Expiration time in ms for a partition\\n def partition_expiration(self, value) # Update the experation time in ms for a partition\\n def description(self) # Description of the table.\\n def description(self, value) # Update description of the table.\\n def expires(self) # Datetime at which the table will be removed.\\n def expires(self, value) # Update datetime at which the table will be removed.\\n def friendly_name(self) # Title of the table.\\n def friendly_name(self, value) # Update title of the table.\\n def location(self) # Location in which the table is hosted.\\n def location(self, value) # Update location in which the table is hosted.\\n def view_query(self) # SQL query defining the table as a view.\\n def view_query(self, value) # Update SQL query defining the table as a view.\\n def view_query(self) # Delete SQL query defining the table as a view.\\n def list_partitions(self, client=None) # List the partitions in a table.\\n def from_api_repr(cls, resource, dataset) # Factory: construct a table given its API representation\\n def _require_client(self, client) # Check client or verify over-ride.\\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\\n def _build_resource(self) # Generate a resource for ``create`` or ``update``.\\n def create(self, client=None) # API call: create the dataset via a PUT request\\n def exists(self, client=None) # API call: test for the existence of the table via a GET request\\n def reload(self, client=None) # API call: refresh table properties via a GET request\\n def patch(self, client=None, friendly_name=_MARKER, description=_MARKER, location=_MARKER, expires=_MARKER, view_query=_MARKER, schema=_MARKER) # API call: update individual table properties via a PATCH request\\n def update(self, client=None) # API call: update table properties via\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigtable/__init__.py\",\n \"text\": \"google/cloud/bigtable/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigtable/_generated/__init__.py\",\n \"text\": \"google/cloud/bigtable/_generated/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py\",\n \"text\": \"google/cloud/bigtable/client.py\\ndef _make_data_stub(client) # Creates gRPC stub to make requests to the Data API.\\ndef _make_instance_stub(client) # Creates gRPC stub to make requests to the Instance Admin API.\\ndef _make_operations_stub(client) # Creates gRPC stub to make requests to the Operations API.\\ndef _make_table_stub(client) # Creates gRPC stub to make requests to the Table Admin API.\\nclass Client # Client for interacting with Google Cloud Bigtable API.\\n def __init__(self, project=None, credentials=None, read_only=False, admin=False, user_agent=DEFAULT_USER_AGENT)\\n def copy(self) # Make a copy of this client.\\n def credentials(self) # Getter for client's credentials.\\n def project_name(self) # Project name to be used with Instance Admin API.\\n def _instance_stub(self) # Getter for the gRPC stub used for the Instance Admin API.\\n def _operations_stub(self) # Getter for the gRPC stub used for the Operations API.\\n def _table_stub(self) # Getter for the gRPC stub used for the Table Admin API.\\n def instance(self, instance_id, location=_EXISTING_INSTANCE_LOCATION_ID, display_name=None, serve_nodes=DEFAULT_SERVE_NODES) # Factory to create a instance associated with this client.\\n def list_instances(self) # List instances owned by the project.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py\",\n \"text\": \"google/cloud/bigtable/cluster.py\\ndef _prepare_create_request(cluster) # Creates a protobuf request for a CreateCluster request.\\nclass Cluster # Representation of a Google Cloud Bigtable Cluster.\\n def __init__(self, cluster_id, instance, serve_nodes=DEFAULT_SERVE_NODES)\\n def _update_from_pb(self, cluster_pb) # Refresh self from the server-provided protobuf.\\n def from_pb(cls, cluster_pb, instance) # Creates a cluster instance from a protobuf.\\n def copy(self) # Make a copy of this cluster.\\n def name(self) # Cluster name used in requests.\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def reload(self) # Reload the metadata for this cluster.\\n def create(self) # Create this cluster.\\n def update(self) # Update this cluster.\\n def delete(self) # Delete this cluster.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py\",\n \"text\": \"google/cloud/bigtable/column_family.py\\ndef _timedelta_to_duration_pb(timedelta_val) # Convert a Python timedelta object to a duration protobuf.\\ndef _duration_pb_to_timedelta(duration_pb) # Convert a duration protobuf to a Python timedelta object.\\nclass GarbageCollectionRule # Garbage collection rule for column families within a table.\\n def __ne__(self, other)\\nclass MaxVersionsGCRule # Garbage collection limiting the number of versions of a cell.\\n def __init__(self, max_num_versions)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the garbage collection rule to a protobuf.\\nclass MaxAgeGCRule # Garbage collection limiting the age of a cell.\\n def __init__(self, max_age)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the garbage collection rule to a protobuf.\\nclass GCRuleUnion # Union of garbage collection rules.\\n def __init__(self, rules)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the union into a single GC rule as a protobuf.\\nclass GCRuleIntersection # Intersection of garbage collection rules.\\n def __init__(self, rules)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the intersection into a single GC rule as a protobuf.\\nclass ColumnFamily # Representation of a Google Cloud Bigtable Column Family.\\n def __init__(self, column_family_id, table, gc_rule=None)\\n def name(self) # Column family name used in requests.\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def to_pb(self) # Converts the column family to a protobuf.\\n def create(self) # Create this column family.\\n def update(self) # Update this column family.\\n def delete(self) # Delete this column family.\\ndef _gc_rule_from_pb(gc_rule_pb) # Convert a protobuf GC rule to a native object.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py\",\n \"text\": \"google/cloud/bigtable/instance.py\\ndef _prepare_create_request(instance) # Creates a protobuf request for a CreateInstance request.\\nclass Instance # Representation of a Google Cloud Bigtable Instance.\\n def __init__(self, instance_id, client, location_id=_EXISTING_INSTANCE_LOCATION_ID, display_name=None, serve_nodes=DEFAULT_SERVE_NODES)\\n def _update_from_pb(self, instance_pb) # Refresh self from the server-provided protobuf.\\n def from_pb(cls, instance_pb, client) # Creates a instance instance from a protobuf.\\n def copy(self) # Make a copy of this instance.\\n def name(self) # Instance name used in requests.\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def reload(self) # Reload the metadata for this instance.\\n def create(self) # Create this instance.\\n def update(self) # Update this instance.\\n def delete(self) # Delete this instance.\\n def cluster(self, cluster_id, serve_nodes=3) # Factory to create a cluster associated with this client.\\n def list_clusters(self) # Lists clusters in this instance.\\n def table(self, table_id) # Factory to create a table associated with this instance.\\n def list_tables(self) # List the tables in this instance.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py\",\n \"text\": \"google/cloud/bigtable/row.py\\nclass Row # Base representation of a Google Cloud Bigtable Row.\\n def __init__(self, row_key, table)\\nclass _SetDeleteRow # Row helper for setting or deleting cell values.\\n def _get_mutations(self, state) # Gets the list of mutations for a given state.\\n def _set_cell(self, column_family_id, column, value, timestamp=None, state=None) # Helper for :meth:`set_cell`\\n def _delete(self, state=None) # Helper for :meth:`delete`\\n def _delete_cells(self, column_family_id, columns, time_range=None, state=None) # Helper for :meth:`delete_cell` and :meth:`delete_cells`.\\nclass DirectRow # Google Cloud Bigtable Row for sending \\\"direct\\\" mutations.\\n def __init__(self, row_key, table)\\n def _get_mutations(self, state) # Gets the list of mutations for a given state.\\n def set_cell(self, column_family_id, column, value, timestamp=None) # Sets a value in this row.\\n def delete(self) # Deletes this row from the table.\\n def delete_cell(self, column_family_id, column, time_range=None) # Deletes cell in this row.\\n def delete_cells(self, column_family_id, columns, time_range=None) # Deletes cells in this row.\\n def commit(self) # Makes a ``MutateRow`` API request.\\n def clear(self) # Removes all currently accumulated mutations on the current row.\\nclass ConditionalRow # Google Cloud Bigtable Row for sending mutations conditionally.\\n def __init__(self, row_key, table, filter_)\\n def _get_mutations(self, state) # Gets the list of mutations for a given state.\\n def commit(self) # Makes a ``CheckAndMutateRow`` API request.\\n def set_cell(self, column_family_id, column, value, timestamp=None, state=True) # Sets a value in this row.\\n def delete(self, state=True) # Deletes this row from the table.\\n def delete_cell(self, column_family_id, column, time_range=None, state=True) # Deletes cell in this row.\\n def delete_cells(self, column_family_id, columns, time_range=None, state=True) # Deletes cells in this row.\\n def clear(self) # Removes all currently accumulated mutations on the current row.\\nclass AppendRow # Google Cloud Bigtable Row for sending append mutations.\\n def __init__(self, row_key, table)\\n def clear(self) # Removes all currently accumulated modifications on current row.\\n def append_cell_value(self, column_family_id, column, value) # Appends a value to an existing cell.\\n def increment_cell_value(self, column_family_id, column, int_value) # Increments a value in an existing cell.\\n def commit(self) # Makes a ``ReadModifyWriteRow`` API request.\\ndef _parse_rmw_row_response(row_response) # Parses the response to a ``ReadModifyWriteRow`` request.\\ndef _parse_family_pb(family_pb) # Parses a Family protobuf into a dictionary.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py\",\n \"text\": \"google/cloud/bigtable/row_data.py\\nclass Cell # Representation of a Google Cloud Bigtable Cell.\\n def __init__(self, value, timestamp, labels=())\\n def from_pb(cls, cell_pb) # Create a new cell from a Cell protobuf.\\n def __eq__(self, other)\\n def __ne__(self, other)\\nclass PartialCellData # Representation of partial cell in a Google Cloud Bigtable Table.\\n def __init__(self, row_key, family_name, qualifier, timestamp_micros, labels=(), value=b'')\\n def append_value(self, value) # Append bytes from a new chunk to value.\\nclass PartialRowData # Representation of partial row in a Google Cloud Bigtable Table.\\n def __init__(self, row_key)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def to_dict(self) # Convert the cells to a dictionary.\\n def cells(self) # Property returning all the cells accumulated on this partial row.\\n def row_key(self) # Getter for the current (partial) row's key.\\nclass InvalidReadRowsResponse # Exception raised to to invalid response data from back-end.\\nclass InvalidChunk # Exception raised to to invalid chunk data from back-end.\\nclass PartialRowsData # Convenience wrapper for consuming a ``ReadRows`` streaming response.\\n def __init__(self, response_iterator)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def state(self) # State machine state.\\n def rows(self) # Property returning all rows accumulated from the stream.\\n def cancel(self) # Cancels the iterator, closing the stream.\\n def consume_next(self) # Consume the next ``ReadRowsResponse`` from the stream.\\n def consume_all(self, max_loops=None) # Consume the streamed responses until there are no more.\\n def _validate_chunk_status(chunk) # Helper for :meth:`_validate_chunk_row_in_progress`, etc.\\n def _validate_chunk_new_row(self, chunk) # Helper for :meth:`_validate_chunk`.\\n def _same_as_previous(self, chunk) # Helper for :meth:`_validate_chunk_row_in_progress`\\n def _validate_chunk_row_in_progress(self, chunk) # Helper for :meth:`_validate_chunk`\\n def _validate_chunk_cell_in_progress(self, chunk) # Helper for :meth:`_validate_chunk`\\n def _validate_chunk(self, chunk) # Helper for :meth:`consume_next`.\\n def _save_current_cell(self) # Helper for :meth:`consume_next`.\\n def _copy_from_current(self, chunk) # Helper for :meth:`consume_next`.\\n def _copy_from_previous(self, cell) # Helper for :meth:`consume_next`.\\n def _save_current_row(self) # Helper for :meth:`consume_next`.\\ndef _raise_if(predicate, *args) # Helper for validation methods.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py\",\n \"text\": \"google/cloud/bigtable/row_filters.py\\nclass RowFilter # Basic filter to apply to cells in a row.\\n def __ne__(self, other)\\nclass _BoolFilter # Row filter that uses a boolean flag.\\n def __init__(self, flag)\\n def __eq__(self, other)\\nclass SinkFilter # Advanced row filter to skip parent filters.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass PassAllFilter # Row filter equivalent to not filtering at all.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass BlockAllFilter # Row filter that doesn't match any cells.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass _RegexFilter # Row filter that uses a regular expression.\\n def __init__(self, regex)\\n def __eq__(self, other)\\nclass RowKeyRegexFilter # Row filter for a row key regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass RowSampleFilter # Matches all cells from a row with probability p.\\n def __init__(self, sample)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass FamilyNameRegexFilter # Row filter for a family name regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ColumnQualifierRegexFilter # Row filter for a column qualifier regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass TimestampRange # Range of time with inclusive lower and exclusive upper bounds.\\n def __init__(self, start=None, end=None)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def to_pb(self) # Converts the :class:`TimestampRange` to a protobuf.\\nclass TimestampRangeFilter # Row filter that limits cells to a range of time.\\n def __init__(self, range_)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ColumnRangeFilter # A row filter to restrict to a range of columns.\\n def __init__(self, column_family_id, start_column=None, end_column=None, inclusive_start=None, inclusive_end=None)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ValueRegexFilter # Row filter for a value regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ValueRangeFilter # A range of values to restrict to in a row filter.\\n def __init__(self, start_value=None, end_value=None, inclusive_start=None, inclusive_end=None)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass _CellCountFilter # Row filter that uses an integer count of cells.\\n def __init__(self, num_cells)\\n def __eq__(self, other)\\nclass CellsRowOffsetFilter # Row filter to skip cells in a row.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass CellsRowLimitFilter # Row filter to limit cells in a row.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass CellsColumnLimitFilter # Row filter to limit cells in a column.\\n def to_pb(self) # Co\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py\",\n \"text\": \"google/cloud/bigtable/table.py\\nclass Table # Representation of a Google Cloud Bigtable Table.\\n def __init__(self, table_id, instance)\\n def name(self) # Table name used in requests.\\n def column_family(self, column_family_id, gc_rule=None) # Factory to create a column family associated with this table.\\n def row(self, row_key, filter_=None, append=False) # Factory to create a row associated with this table.\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def create(self, initial_split_keys=None, column_families=()) # Creates this table.\\n def delete(self) # Delete this table.\\n def list_column_families(self) # List the column families owned by this table.\\n def read_row(self, row_key, filter_=None) # Read a single row from this table.\\n def read_rows(self, start_key=None, end_key=None, limit=None, filter_=None) # Read rows from this table.\\n def sample_row_keys(self) # Read a sample of row keys in the table.\\ndef _create_row_request(table_name, row_key=None, start_key=None, end_key=None, filter_=None, limit=None) # Creates a request to read rows in a table.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/client.py\",\n \"text\": \"google/cloud/client.py\\nclass _ClientFactoryMixin # Mixin to allow factories that create credentials.\\n def from_service_account_json(cls, json_credentials_path, *args, **kwargs) # Factory to retrieve JSON credentials while creating client.\\n def from_service_account_p12(cls, client_email, private_key_path, *args, **kwargs) # Factory to retrieve P12 credentials while creating client.\\nclass Client # Client to bundle configuration needed for API requests.\\n def __init__(self, credentials=None, http=None)\\nclass _ClientProjectMixin # Mixin to allow setting the project on the client.\\n def __init__(self, project=None)\\n def _determine_default(project) # Helper: use default project detection.\\nclass JSONClient # Client to for Google JSON-based API.\\n def __init__(self, project=None, credentials=None, http=None)\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/connection.py\",\n \"text\": \"google/cloud/connection.py\\nclass Connection # A generic connection to Google Cloud Platform.\\n def __init__(self, credentials=None, http=None)\\n def credentials(self) # Getter for current credentials.\\n def http(self) # A getter for the HTTP transport used in talking to the API.\\n def _create_scoped_credentials(credentials, scope) # Create a scoped set of credentials if it is required.\\nclass JSONConnection # A connection to a Google JSON-based API.\\n def build_api_url(cls, path, query_params=None, api_base_url=None, api_version=None) # Construct an API url given a few components, some optional.\\n def _make_request(self, method, url, data=None, content_type=None, headers=None, target_object=None) # A low level method to send a request to the API.\\n def _do_request(self, method, url, headers, data, target_object) # Low-level helper: perform the actual API request over HTTP.\\n def api_request(self, method, path, query_params=None, data=None, content_type=None, api_base_url=None, api_version=None, expect_json=True, _target_object=None) # Make a request over the HTTP transport to the API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/credentials.py\",\n \"text\": \"google/cloud/credentials.py\\ndef get_credentials() # Gets credentials implicitly from the current environment.\\ndef _get_signed_query_params(credentials, expiration, string_to_sign) # Gets query parameters for creating a signed URL.\\ndef _get_expiration_seconds(expiration) # Convert 'expiration' to a number of seconds in the future.\\ndef generate_signed_url(credentials, resource, expiration, api_access_endpoint='', method='GET', content_md5=None, content_type=None, response_type=None, response_disposition=None, generation=None) # Generate signed URL to provide query-string auth'n to a resource.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/datastore/__init__.py\",\n \"text\": \"google/cloud/datastore/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/datastore/_generated/__init__.py\",\n \"text\": \"google/cloud/datastore/_generated/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py\",\n \"text\": \"google/cloud/datastore/batch.py\\nclass Batch # An abstraction representing a collected group of updates / deletes.\\n def __init__(self, client)\\n def current(self) # Return the topmost batch / transaction, or None.\\n def project(self) # Getter for project in which the batch will run.\\n def namespace(self) # Getter for namespace in which the batch will run.\\n def connection(self) # Getter for connection over which the batch will run.\\n def _add_partial_key_entity_pb(self) # Adds a new mutation for an entity with a partial key.\\n def _add_complete_key_entity_pb(self) # Adds a new mutation for an entity with a completed key.\\n def _add_delete_key_pb(self) # Adds a new mutation for a key to be deleted.\\n def mutations(self) # Getter for the changes accumulated by this batch.\\n def put(self, entity) # Remember an entity's state to be saved during :meth:`commit`.\\n def delete(self, key) # Remember a key to be deleted during :meth:`commit`.\\n def begin(self) # Begins a batch.\\n def _commit(self) # Commits the batch.\\n def commit(self) # Commits the batch.\\n def rollback(self) # Rolls back the current batch.\\n def __enter__(self)\\n def __exit__(self, exc_type, exc_val, exc_tb)\\ndef _assign_entity_to_pb(entity_pb, entity) # Copy ``entity`` into ``entity_pb``.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/datastore/client.py\",\n \"text\": \"google/cloud/datastore/client.py\\ndef _get_gcd_project() # Gets the GCD application ID if it can be inferred.\\ndef _determine_default_project(project=None) # Determine default project explicitly or implicitly as fall-back.\\ndef _extended_lookup(connection, project, key_pbs, missing=None, deferred=None, eventual=False, transaction_id=None) # Repeat lookup until all keys found (unless stop requested).\\nclass Client # Convenience wrapper for invoking APIs/factories w/ a project.\\n def __init__(self, project=None, namespace=None, credentials=None, http=None)\\n def _determine_default(project) # Helper: override default project detection.\\n def _push_batch(self, batch) # Push a batch/transaction onto our stack.\\n def _pop_batch(self) # Pop a batch/transaction from our stack.\\n def current_batch(self) # Currently-active batch.\\n def current_transaction(self) # Currently-active transaction.\\n def get(self, key, missing=None, deferred=None, transaction=None) # Retrieve an entity from a single key (if it exists).\\n def get_multi(self, keys, missing=None, deferred=None, transaction=None) # Retrieve entities, along with their attributes.\\n def put(self, entity) # Save an entity in the Cloud Datastore.\\n def put_multi(self, entities) # Save entities in the Cloud Datastore.\\n def delete(self, key) # Delete the key in the Cloud Datastore.\\n def delete_multi(self, keys) # Delete keys from the Cloud Datastore.\\n def allocate_ids(self, incomplete_key, num_ids) # Allocate a list of IDs from a partial key.\\n def key(self, *path_args, **kwargs) # Proxy to :class:`google.cloud.datastore.key.Key`.\\n def batch(self) # Proxy to :class:`google.cloud.datastore.batch.Batch`.\\n def transaction(self) # Proxy to :class:`google.cloud.datastore.transaction.Transaction`.\\n def query(self, **kwargs) # Proxy to :class:`google.cloud.datastore.query.Query`.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py\",\n \"text\": \"google/cloud/datastore/connection.py\\nclass _DatastoreAPIOverHttp # Helper mapping datastore API methods.\\n def __init__(self, connection)\\n def _request(self, project, method, data) # Make a request over the Http transport to the Cloud Datastore API.\\n def _rpc(self, project, method, request_pb, response_pb_cls) # Make a protobuf RPC request.\\n def lookup(self, project, request_pb) # Perform a ``lookup`` request.\\n def run_query(self, project, request_pb) # Perform a ``runQuery`` request.\\n def begin_transaction(self, project, request_pb) # Perform a ``beginTransaction`` request.\\n def commit(self, project, request_pb) # Perform a ``commit`` request.\\n def rollback(self, project, request_pb) # Perform a ``rollback`` request.\\n def allocate_ids(self, project, request_pb) # Perform an ``allocateIds`` request.\\nclass _DatastoreAPIOverGRPC # Helper mapping datastore API methods.\\n def __init__(self, connection, secure)\\n def lookup(self, project, request_pb) # Perform a ``lookup`` request.\\n def run_query(self, project, request_pb) # Perform a ``runQuery`` request.\\n def begin_transaction(self, project, request_pb) # Perform a ``beginTransaction`` request.\\n def commit(self, project, request_pb) # Perform a ``commit`` request.\\n def rollback(self, project, request_pb) # Perform a ``rollback`` request.\\n def allocate_ids(self, project, request_pb) # Perform an ``allocateIds`` request.\\nclass Connection # A connection to the Google Cloud Datastore via the Protobuf API.\\n def __init__(self, credentials=None, http=None)\\n def build_api_url(self, project, method, base_url=None, api_version=None) # Construct the URL for a particular API call.\\n def lookup(self, project, key_pbs, eventual=False, transaction_id=None) # Lookup keys from a project in the Cloud Datastore.\\n def run_query(self, project, query_pb, namespace=None, eventual=False, transaction_id=None) # Run a query on the Cloud Datastore.\\n def begin_transaction(self, project) # Begin a transaction.\\n def commit(self, project, request, transaction_id) # Commit mutations in context of current transaction (if any).\\n def rollback(self, project, transaction_id) # Rollback the connection's existing transaction.\\n def allocate_ids(self, project, key_pbs) # Obtain backend-generated IDs for a set of keys.\\ndef _set_read_options(request, eventual, transaction_id) # Validate rules for read options, and assign to the request.\\ndef _add_keys_to_request(request_field_pb, key_pbs) # Add protobuf keys to a request object.\\ndef _parse_commit_response(commit_response_pb) # Extract response data from a commit response.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/datastore/entity.py\",\n \"text\": \"google/cloud/datastore/entity.py\\nclass Entity # Entities are akin to rows in a relational database\\n def __init__(self, key=None, exclude_from_indexes=())\\n def __eq__(self, other) # Compare two entities for equality.\\n def __ne__(self, other) # Compare two entities for inequality.\\n def kind(self) # Get the kind of the current entity.\\n def exclude_from_indexes(self) # Names of fields which are *not* to be indexed for this entity.\\n def __repr__(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py\",\n \"text\": \"google/cloud/datastore/helpers.py\\ndef _get_meaning(value_pb, is_list=False) # Get the meaning from a protobuf value.\\ndef _new_value_pb(entity_pb, name) # Add (by name) a new ``Value`` protobuf to an entity protobuf.\\ndef _property_tuples(entity_pb) # Iterator of name, ``Value`` tuples from entity properties.\\ndef entity_from_protobuf(pb) # Factory method for creating an entity based on a protobuf.\\ndef _set_pb_meaning_from_entity(entity, name, value, value_pb, is_list=False) # Add meaning information (from an entity) to a protobuf.\\ndef entity_to_protobuf(entity) # Converts an entity into a protobuf.\\ndef key_from_protobuf(pb) # Factory method for creating a key based on a protobuf.\\ndef _pb_attr_value(val) # Given a value, return the protobuf attribute name and proper value.\\ndef _get_value_from_value_pb(value_pb) # Given a protobuf for a Value, get the correct value.\\ndef _set_protobuf_value(value_pb, val) # Assign 'val' to the correct subfield of 'value_pb'.\\nclass GeoPoint # Simple container for a geo point value.\\n def __init__(self, latitude, longitude)\\n def to_protobuf(self) # Convert the current object to protobuf.\\n def __eq__(self, other) # Compare two geo points for equality.\\n def __ne__(self, other) # Compare two geo points for inequality.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/datastore/key.py\",\n \"text\": \"google/cloud/datastore/key.py\\nclass Key # An immutable representation of a datastore Key.\\n def __init__(self, *path_args, **kwargs)\\n def __eq__(self, other) # Compare two keys for equality.\\n def __ne__(self, other) # Compare two keys for inequality.\\n def __hash__(self) # Hash a keys for use in a dictionary lookp.\\n def _parse_path(path_args) # Parses positional arguments into key path with kinds and IDs.\\n def _combine_args(self) # Sets protected data by combining raw data set from the constructor.\\n def _clone(self) # Duplicates the Key.\\n def completed_key(self, id_or_name) # Creates new key from existing partial key by adding final ID/name.\\n def to_protobuf(self) # Return a protobuf corresponding to the key.\\n def is_partial(self) # Boolean indicating if the key has an ID (or name).\\n def namespace(self) # Namespace getter.\\n def path(self) # Path getter.\\n def flat_path(self) # Getter for the key path as a tuple.\\n def kind(self) # Kind getter. Based on the last element of path.\\n def id(self) # ID getter. Based on the last element of path.\\n def name(self) # Name getter. Based on the last element of path.\\n def id_or_name(self) # Getter. Based on the last element of path.\\n def project(self) # Project getter.\\n def _make_parent(self) # Creates a parent key for the current path.\\n def parent(self) # The parent of the current key.\\n def __repr__(self)\\ndef _validate_project(project, parent) # Ensure the project is set appropriately.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/datastore/query.py\",\n \"text\": \"google/cloud/datastore/query.py\\nclass Query # A Query against the Cloud Datastore.\\n def __init__(self, client, kind=None, project=None, namespace=None, ancestor=None, filters=(), projection=(), order=(), distinct_on=())\\n def project(self) # Get the project for this Query.\\n def namespace(self) # This query's namespace\\n def namespace(self, value) # Update the query's namespace.\\n def kind(self) # Get the Kind of the Query.\\n def kind(self, value) # Update the Kind of the Query.\\n def ancestor(self) # The ancestor key for the query.\\n def ancestor(self, value) # Set the ancestor for the query\\n def ancestor(self) # Remove the ancestor for the query.\\n def filters(self) # Filters set on the query.\\n def add_filter(self, property_name, operator, value) # Filter the query based on a property name, operator and a value.\\n def projection(self) # Fields names returned by the query.\\n def projection(self, projection) # Set the fields returned the query.\\n def keys_only(self) # Set the projection to include only keys.\\n def key_filter(self, key, operator='=') # Filter on a key.\\n def order(self) # Names of fields used to sort query results.\\n def order(self, value) # Set the fields used to sort query results.\\n def distinct_on(self) # Names of fields used to group query results.\\n def distinct_on(self, value) # Set fields used to group query results.\\n def fetch(self, limit=None, offset=0, start_cursor=None, end_cursor=None, client=None) # Execute the Query; return an iterator for the matching entities.\\nclass Iterator # Represent the state of a given execution of a Query.\\n def __init__(self, query, client, limit=None, offset=None, start_cursor=None, end_cursor=None)\\n def next_page(self) # Fetch a single \\\"page\\\" of query results.\\n def __iter__(self) # Generator yielding all results matching our query.\\ndef _pb_from_query(query) # Convert a Query instance to the corresponding protobuf.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/datastore/transaction.py\",\n \"text\": \"google/cloud/datastore/transaction.py\\nclass Transaction # An abstraction representing datastore Transactions.\\n def __init__(self, client)\\n def id(self) # Getter for the transaction ID.\\n def current(self) # Return the topmost transaction.\\n def begin(self) # Begins a transaction.\\n def rollback(self) # Rolls back the current transaction.\\n def commit(self) # Commits the transaction.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/dns/__init__.py\",\n \"text\": \"google/cloud/dns/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/dns/changes.py\",\n \"text\": \"google/cloud/dns/changes.py\\nclass Changes # Changes are bundled additions / deletions of DNS resource records.\\n def __init__(self, zone)\\n def from_api_repr(cls, resource, zone) # Factory: construct a change set given its API representation\\n def _set_properties(self, resource) # Helper method for :meth:`from_api_repr`, :meth:`create`, etc.\\n def path(self) # URL path for change set APIs.\\n def name(self) # Name of the change set.\\n def name(self, value) # Update name of the change set.\\n def status(self) # Status of the change set.\\n def started(self) # Time when the change set was started.\\n def additions(self) # Resource record sets to be added to the zone.\\n def deletions(self) # Resource record sets to be deleted from the zone.\\n def add_record_set(self, record_set) # Append a record set to the 'additions' for the change set.\\n def delete_record_set(self, record_set) # Append a record set to the 'deletions' for the change set.\\n def _require_client(self, client) # Check client or verify over-ride.\\n def _build_resource(self) # Generate a resource for ``create``.\\n def create(self, client=None) # API call: create the change set via a POST request.\\n def exists(self, client=None) # API call: test for the existence of the change set via a GET request.\\n def reload(self, client=None) # API call: refresh zone properties via a GET request.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/dns/client.py\",\n \"text\": \"google/cloud/dns/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def quotas(self) # Return DNS quotas for the project associated with this client.\\n def list_zones(self, max_results=None, page_token=None) # List zones for the project associated with this client.\\n def zone(self, name, dns_name=None, description=None) # Construct a zone bound to this client.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/dns/connection.py\",\n \"text\": \"google/cloud/dns/connection.py\\nclass Connection # A connection to Google Cloud DNS via the JSON REST API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/dns/resource_record_set.py\",\n \"text\": \"google/cloud/dns/resource_record_set.py\\nclass ResourceRecordSet # ResourceRecordSets are DNS resource records.\\n def __init__(self, name, record_type, ttl, rrdatas, zone)\\n def from_api_repr(cls, resource, zone) # Factory: construct a record set given its API representation\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/dns/zone.py\",\n \"text\": \"google/cloud/dns/zone.py\\nclass ManagedZone # ManagedZones are containers for DNS resource records.\\n def __init__(self, name, dns_name=None, client=None, description=None)\\n def from_api_repr(cls, resource, client) # Factory: construct a zone given its API representation\\n def project(self) # Project bound to the zone.\\n def path(self) # URL path for the zone's APIs.\\n def created(self) # Datetime at which the zone was created.\\n def name_servers(self) # Datetime at which the zone was created.\\n def zone_id(self) # ID for the zone resource.\\n def description(self) # Description of the zone.\\n def description(self, value) # Update description of the zone.\\n def name_server_set(self) # Named set of DNS name servers that all host the same ManagedZones.\\n def name_server_set(self, value) # Update named set of DNS name servers.\\n def resource_record_set(self, name, record_type, ttl, rrdatas) # Construct a resource record set bound to this zone.\\n def changes(self) # Construct a change set bound to this zone.\\n def _require_client(self, client) # Check client or verify over-ride.\\n def _set_properties(self, api_response) # Update properties from resource in body of ``api_response``\\n def _build_resource(self) # Generate a resource for ``create`` or ``update``.\\n def create(self, client=None) # API call: create the zone via a PUT request\\n def exists(self, client=None) # API call: test for the existence of the zone via a GET request\\n def reload(self, client=None) # API call: refresh zone properties via a GET request\\n def delete(self, client=None) # API call: delete the zone via a DELETE request\\n def list_resource_record_sets(self, max_results=None, page_token=None, client=None) # List resource record sets for this zone.\\n def list_changes(self, max_results=None, page_token=None, client=None) # List change sets for this zone.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/environment_vars.py\",\n \"text\": \"google/cloud/environment_vars.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/error_reporting/__init__.py\",\n \"text\": \"google/cloud/error_reporting/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/error_reporting/client.py\",\n \"text\": \"google/cloud/error_reporting/client.py\\nclass HTTPContext # HTTPContext defines an object that captures the parameter for the\\n def __init__(self, method=None, url=None, user_agent=None, referrer=None, response_status_code=None, remote_ip=None)\\nclass Client # Error Reporting client. Currently Error Reporting is done by creating\\n def __init__(self, project=None, credentials=None, http=None, service=None, version=None)\\n def _send_error_report(self, message, report_location=None, http_context=None, user=None) # Makes the call to the Error Reporting API via the log stream.\\n def report(self, message, http_context=None, user=None) # Reports a message to Stackdriver Error Reporting\\n def report_exception(self, http_context=None, user=None) # Reports the details of the latest exceptions to Stackdriver Error\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/exceptions.py\",\n \"text\": \"google/cloud/exceptions.py\\nclass GoogleCloudError # Base error class for Google Cloud errors (abstract).\\n def __init__(self, message, errors=())\\n def __str__(self)\\n def errors(self) # Detailed error information.\\nclass Redirection # Base for 3xx responses\\nclass MovedPermanently # Exception mapping a '301 Moved Permanently' response.\\nclass NotModified # Exception mapping a '304 Not Modified' response.\\nclass TemporaryRedirect # Exception mapping a '307 Temporary Redirect' response.\\nclass ResumeIncomplete # Exception mapping a '308 Resume Incomplete' response.\\nclass ClientError # Base for 4xx responses\\nclass BadRequest # Exception mapping a '400 Bad Request' response.\\nclass Unauthorized # Exception mapping a '401 Unauthorized' response.\\nclass Forbidden # Exception mapping a '403 Forbidden' response.\\nclass NotFound # Exception mapping a '404 Not Found' response.\\nclass MethodNotAllowed # Exception mapping a '405 Method Not Allowed' response.\\nclass Conflict # Exception mapping a '409 Conflict' response.\\nclass LengthRequired # Exception mapping a '411 Length Required' response.\\nclass PreconditionFailed # Exception mapping a '412 Precondition Failed' response.\\nclass RequestRangeNotSatisfiable # Exception mapping a '416 Request Range Not Satisfiable' response.\\nclass TooManyRequests # Exception mapping a '429 Too Many Requests' response.\\nclass ServerError # Base for 5xx responses: (abstract)\\nclass InternalServerError # Exception mapping a '500 Internal Server Error' response.\\nclass MethodNotImplemented # Exception mapping a '501 Not Implemented' response.\\nclass BadGateway # Exception mapping a '502 Bad Gateway' response.\\nclass ServiceUnavailable # Exception mapping a '503 Service Unavailable' response.\\ndef make_exception(response, content, error_info=None, use_json=True) # Factory: create exception based on HTTP response code.\\ndef _walk_subclasses(klass) # Recursively walk subclass tree.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/iterator.py\",\n \"text\": \"google/cloud/iterator.py\\nclass Iterator # A generic class for iterating through Cloud JSON APIs list responses.\\n def __init__(self, client, path, extra_params=None)\\n def __iter__(self) # Iterate through the list of items.\\n def has_next_page(self) # Determines whether or not this iterator has more pages.\\n def get_query_params(self) # Getter for query parameters for the next request.\\n def get_next_page_response(self) # Requests the next page from the path provided.\\n def reset(self) # Resets the iterator to the beginning.\\n def get_items_from_response(self, response) # Factory method called while iterating. This should be overridden.\\nclass MethodIterator # Method-based iterator iterating through Cloud JSON APIs list responses.\\n def __init__(self, method, page_token=None, page_size=None, max_calls=None, **kw)\\n def __iter__(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/language/__init__.py\",\n \"text\": \"google/cloud/language/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/language/client.py\",\n \"text\": \"google/cloud/language/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def document_from_text(self, content, **kwargs) # Create a plain text document bound to this client.\\n def document_from_html(self, content, **kwargs) # Create an HTML document bound to this client.\\n def document_from_url(self, gcs_url, doc_type=Document.PLAIN_TEXT, **kwargs) # Create a Cloud Storage document bound to this client.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/language/connection.py\",\n \"text\": \"google/cloud/language/connection.py\\nclass Connection # A connection to Google Cloud Natural Language JSON REST API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/language/document.py\",\n \"text\": \"google/cloud/language/document.py\\nclass Encoding # Document text encoding types.\\nclass Document # Document to send to Google Cloud Natural Language API.\\n def __init__(self, client, content=None, gcs_url=None, doc_type=PLAIN_TEXT, language=DEFAULT_LANGUAGE, encoding=Encoding.UTF8)\\n def _to_dict(self) # Helper to convert the current document into a dictionary.\\n def analyze_entities(self) # Analyze the entities in the current document.\\n def analyze_sentiment(self) # Analyze the sentiment in the current document.\\n def annotate_text(self, include_syntax=True, include_entities=True, include_sentiment=True) # Advanced natural language API: document syntax and other features.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/language/entity.py\",\n \"text\": \"google/cloud/language/entity.py\\nclass EntityType # List of possible entity types.\\nclass Entity # A Google Cloud Natural Language API entity.\\n def __init__(self, name, entity_type, metadata, salience, mentions)\\n def from_api_repr(cls, payload) # Convert an Entity from the JSON API into an :class:`Entity`.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/language/sentiment.py\",\n \"text\": \"google/cloud/language/sentiment.py\\nclass Sentiment # A Google Cloud Natural Language API sentiment object.\\n def __init__(self, polarity, magnitude)\\n def from_api_repr(cls, payload) # Convert a Sentiment from the JSON API into a :class:`Sentiment`.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/language/syntax.py\",\n \"text\": \"google/cloud/language/syntax.py\\nclass PartOfSpeech # Part of speech of a :class:`Token`.\\n def reverse(cls, tag) # Reverses the API's enum name for the one on this class.\\nclass Token # A Google Cloud Natural Language API token object.\\n def __init__(self, text_content, text_begin, part_of_speech, edge_index, edge_label, lemma)\\n def from_api_repr(cls, payload) # Convert a token from the JSON API into a :class:`Sentiment`.\\nclass Sentence # A Google Cloud Natural Language API sentence object.\\n def __init__(self, content, begin)\\n def from_api_repr(cls, payload) # Convert a sentence from the JSON API into a :class:`Sentiment`.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/__init__.py\",\n \"text\": \"google/cloud/logging/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py\",\n \"text\": \"google/cloud/logging/_gax.py\\nclass _LoggingAPI # Helper mapping logging-related APIs.\\n def __init__(self, gax_api)\\n def list_entries(self, projects, filter_='', order_by='', page_size=0, page_token=None) # Return a page of log entry resources.\\n def write_entries(self, entries, logger_name=None, resource=None, labels=None) # API call: log an entry resource via a POST request\\n def logger_delete(self, project, logger_name) # API call: delete all entries in a logger via a DELETE request\\nclass _SinksAPI # Helper mapping sink-related APIs.\\n def __init__(self, gax_api)\\n def list_sinks(self, project, page_size=0, page_token=None) # List sinks for the project associated with this client.\\n def sink_create(self, project, sink_name, filter_, destination) # API call: create a sink resource.\\n def sink_get(self, project, sink_name) # API call: retrieve a sink resource.\\n def sink_update(self, project, sink_name, filter_, destination) # API call: update a sink resource.\\n def sink_delete(self, project, sink_name) # API call: delete a sink resource.\\nclass _MetricsAPI # Helper mapping sink-related APIs.\\n def __init__(self, gax_api)\\n def list_metrics(self, project, page_size=0, page_token=None) # List metrics for the project associated with this client.\\n def metric_create(self, project, metric_name, filter_, description) # API call: create a metric resource.\\n def metric_get(self, project, metric_name) # API call: retrieve a metric resource.\\n def metric_update(self, project, metric_name, filter_, description) # API call: update a metric resource.\\n def metric_delete(self, project, metric_name) # API call: delete a metric resource.\\ndef _mon_resource_pb_to_mapping(resource_pb) # Helper for :func:_log_entry_pb_to_mapping`.\\ndef _value_pb_to_value(value_pb) # Helper for :func:`_log_entry_pb_to_mapping`.\\ndef _struct_pb_to_mapping(struct_pb) # Helper for :func:`_log_entry_pb_to_mapping`.\\ndef _log_entry_pb_to_mapping(entry_pb) # Helper for :meth:`list_entries`, et aliae\\ndef _http_request_mapping_to_pb(info, request) # Helper for _log_entry_mapping_to_pb\\ndef _log_operation_mapping_to_pb(info, operation) # Helper for _log_entry_mapping_to_pb\\ndef _log_entry_mapping_to_pb(mapping) # Helper for :meth:`write_entries`, et aliae\\ndef _log_sink_pb_to_mapping(sink_pb) # Helper for :meth:`list_sinks`, et aliae\\ndef _log_metric_pb_to_mapping(metric_pb) # Helper for :meth:`list_metrics`, et aliae\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/client.py\",\n \"text\": \"google/cloud/logging/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def logging_api(self) # Helper for logging-related API calls.\\n def sinks_api(self) # Helper for log sink-related API calls.\\n def metrics_api(self) # Helper for log metric-related API calls.\\n def logger(self, name) # Creates a logger bound to the current client.\\n def _entry_from_resource(self, resource, loggers) # Detect correct entry type from resource and instantiate.\\n def list_entries(self, projects=None, filter_=None, order_by=None, page_size=None, page_token=None) # Return a page of log entries.\\n def sink(self, name, filter_=None, destination=None) # Creates a sink bound to the current client.\\n def list_sinks(self, page_size=None, page_token=None) # List sinks for the project associated with this client.\\n def metric(self, name, filter_=None, description='') # Creates a metric bound to the current client.\\n def list_metrics(self, page_size=None, page_token=None) # List metrics for the project associated with this client.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/connection.py\",\n \"text\": \"google/cloud/logging/connection.py\\nclass Connection # A connection to Google Stackdriver Logging via the JSON REST API.\\nclass _LoggingAPI # Helper mapping logging-related APIs.\\n def __init__(self, connection)\\n def list_entries(self, projects, filter_=None, order_by=None, page_size=None, page_token=None) # Return a page of log entry resources.\\n def write_entries(self, entries, logger_name=None, resource=None, labels=None) # API call: log an entry resource via a POST request\\n def logger_delete(self, project, logger_name) # API call: delete all entries in a logger via a DELETE request\\nclass _SinksAPI # Helper mapping sink-related APIs.\\n def __init__(self, connection)\\n def list_sinks(self, project, page_size=None, page_token=None) # List sinks for the project associated with this client.\\n def sink_create(self, project, sink_name, filter_, destination) # API call: create a sink resource.\\n def sink_get(self, project, sink_name) # API call: retrieve a sink resource.\\n def sink_update(self, project, sink_name, filter_, destination) # API call: update a sink resource.\\n def sink_delete(self, project, sink_name) # API call: delete a sink resource.\\nclass _MetricsAPI # Helper mapping sink-related APIs.\\n def __init__(self, connection)\\n def list_metrics(self, project, page_size=None, page_token=None) # List metrics for the project associated with this client.\\n def metric_create(self, project, metric_name, filter_, description=None) # API call: create a metric resource.\\n def metric_get(self, project, metric_name) # API call: retrieve a metric resource.\\n def metric_update(self, project, metric_name, filter_, description) # API call: update a metric resource.\\n def metric_delete(self, project, metric_name) # API call: delete a metric resource.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/entries.py\",\n \"text\": \"google/cloud/logging/entries.py\\ndef logger_name_from_path(path) # Validate a logger URI path and get the logger name.\\nclass _BaseEntry # Base class for TextEntry, StructEntry.\\n def __init__(self, payload, logger, insert_id=None, timestamp=None, labels=None, severity=None, http_request=None)\\n def from_api_repr(cls, resource, client, loggers=None) # Factory: construct an entry given its API representation\\nclass TextEntry # Entry created with ``textPayload``.\\nclass StructEntry # Entry created with ``jsonPayload``.\\nclass ProtobufEntry # Entry created with ``protoPayload``.\\n def parse_message(self, message) # Parse payload into a protobuf message.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/handlers/__init__.py\",\n \"text\": \"google/cloud/logging/handlers/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/handlers/handlers.py\",\n \"text\": \"google/cloud/logging/handlers/handlers.py\\nclass CloudLoggingHandler # Python standard ``logging`` handler.\\n def __init__(self, client, name=DEFAULT_LOGGER_NAME, transport=BackgroundThreadTransport)\\n def emit(self, record) # Actually log the specified logging record.\\ndef setup_logging(handler, excluded_loggers=EXCLUDE_LOGGER_DEFAULTS) # Attach the ``CloudLogging`` handler to the Python root logger\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/__init__.py\",\n \"text\": \"google/cloud/logging/handlers/transports/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/background_thread.py\",\n \"text\": \"google/cloud/logging/handlers/transports/background_thread.py\\nclass _Worker # A threaded worker that writes batches of log entries\\n def __init__(self, logger)\\n def _run(self) # The entry point for the worker thread.\\n def _start(self) # Called by this class's constructor\\n def _stop(self) # Signals the worker thread to shut down\\n def enqueue(self, record, message) # Queues up a log entry to be written by the background thread.\\nclass BackgroundThreadTransport # Aysnchronous transport that uses a background thread.\\n def __init__(self, client, name)\\n def send(self, record, message) # Overrides Transport.send().\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/base.py\",\n \"text\": \"google/cloud/logging/handlers/transports/base.py\\nclass Transport # Base class for Google Cloud Logging handler transports.\\n def send(self, record, message) # Transport send to be implemented by subclasses.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/sync.py\",\n \"text\": \"google/cloud/logging/handlers/transports/sync.py\\nclass SyncTransport # Basic sychronous transport.\\n def __init__(self, client, name)\\n def send(self, record, message) # Overrides transport.send().\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/logger.py\",\n \"text\": \"google/cloud/logging/logger.py\\nclass Logger # Loggers represent named targets for log entries.\\n def __init__(self, name, client, labels=None)\\n def client(self) # Clent bound to the logger.\\n def project(self) # Project bound to the logger.\\n def full_name(self) # Fully-qualified name used in logging APIs\\n def path(self) # URI path for use in logging APIs\\n def _require_client(self, client) # Check client or verify over-ride.\\n def batch(self, client=None) # Return a batch to use as a context manager.\\n def _make_entry_resource(self, text=None, info=None, message=None, labels=None, insert_id=None, severity=None, http_request=None) # Return a log entry resource of the appropriate type.\\n def log_text(self, text, client=None, labels=None, insert_id=None, severity=None, http_request=None) # API call: log a text message via a POST request\\n def log_struct(self, info, client=None, labels=None, insert_id=None, severity=None, http_request=None) # API call: log a structured message via a POST request\\n def log_proto(self, message, client=None, labels=None, insert_id=None, severity=None, http_request=None) # API call: log a protobuf message via a POST request\\n def delete(self, client=None) # API call: delete all entries in a logger via a DELETE request\\n def list_entries(self, projects=None, filter_=None, order_by=None, page_size=None, page_token=None) # Return a page of log entries.\\nclass Batch # Context manager: collect entries to log via a single API call.\\n def __init__(self, logger, client)\\n def __enter__(self)\\n def __exit__(self, exc_type, exc_val, exc_tb)\\n def log_text(self, text, labels=None, insert_id=None, severity=None, http_request=None) # Add a text entry to be logged during :meth:`commit`.\\n def log_struct(self, info, labels=None, insert_id=None, severity=None, http_request=None) # Add a struct entry to be logged during :meth:`commit`.\\n def log_proto(self, message, labels=None, insert_id=None, severity=None, http_request=None) # Add a protobuf entry to be logged during :meth:`commit`.\\n def commit(self, client=None) # Send saved log entries as a single API call.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/metric.py\",\n \"text\": \"google/cloud/logging/metric.py\\nclass Metric # Metrics represent named filters for log entries.\\n def __init__(self, name, filter_=None, client=None, description='')\\n def client(self) # Clent bound to the logger.\\n def project(self) # Project bound to the logger.\\n def full_name(self) # Fully-qualified name used in metric APIs\\n def path(self) # URL path for the metric's APIs\\n def from_api_repr(cls, resource, client) # Factory: construct a metric given its API representation\\n def _require_client(self, client) # Check client or verify over-ride.\\n def create(self, client=None) # API call: create the metric via a PUT request\\n def exists(self, client=None) # API call: test for the existence of the metric via a GET request\\n def reload(self, client=None) # API call: sync local metric configuration via a GET request\\n def update(self, client=None) # API call: update metric configuration via a PUT request\\n def delete(self, client=None) # API call: delete a metric via a DELETE request\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/logging/sink.py\",\n \"text\": \"google/cloud/logging/sink.py\\nclass Sink # Sinks represent filtered exports for log entries.\\n def __init__(self, name, filter_=None, destination=None, client=None)\\n def client(self) # Clent bound to the sink.\\n def project(self) # Project bound to the sink.\\n def full_name(self) # Fully-qualified name used in sink APIs\\n def path(self) # URL path for the sink's APIs\\n def from_api_repr(cls, resource, client) # Factory: construct a sink given its API representation\\n def _require_client(self, client) # Check client or verify over-ride.\\n def create(self, client=None) # API call: create the sink via a PUT request\\n def exists(self, client=None) # API call: test for the existence of the sink via a GET request\\n def reload(self, client=None) # API call: sync local sink configuration via a GET request\\n def update(self, client=None) # API call: update sink configuration via a PUT request\\n def delete(self, client=None) # API call: delete a sink via a DELETE request\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/monitoring/__init__.py\",\n \"text\": \"google/cloud/monitoring/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/monitoring/_dataframe.py\",\n \"text\": \"google/cloud/monitoring/_dataframe.py\\ndef _build_dataframe(time_series_iterable, label=None, labels=None) # Build a :mod:`pandas` dataframe out of time series.\\ndef _sorted_resource_labels(labels) # Sort label names, putting well-known resource labels first.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py\",\n \"text\": \"google/cloud/monitoring/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def query(self, metric_type=Query.DEFAULT_METRIC_TYPE, end_time=None, days=0, hours=0, minutes=0) # Construct a query object for retrieving metric data.\\n def metric_descriptor(self, type_, metric_kind=MetricKind.METRIC_KIND_UNSPECIFIED, value_type=ValueType.VALUE_TYPE_UNSPECIFIED, labels=(), unit='', description='', display_name='') # Construct a metric descriptor object.\\n def metric(type_, labels) # Factory for constructing metric objects.\\n def resource(type_, labels) # Factory for constructing monitored resource objects.\\n def time_series(metric, resource, value, end_time=None, start_time=None) # Construct a time series object for a single data point.\\n def fetch_metric_descriptor(self, metric_type) # Look up a metric descriptor by type.\\n def list_metric_descriptors(self, filter_string=None, type_prefix=None) # List all metric descriptors for the project.\\n def fetch_resource_descriptor(self, resource_type) # Look up a monitored resource descriptor by type.\\n def list_resource_descriptors(self, filter_string=None) # List all monitored resource descriptors for the project.\\n def group(self, group_id=None, display_name=None, parent_id=None, filter_string=None, is_cluster=False) # Factory constructor for group object.\\n def fetch_group(self, group_id) # Fetch a group from the API based on it's ID.\\n def list_groups(self) # List all groups for the project.\\n def write_time_series(self, timeseries_list) # Write a list of time series objects to the API.\\n def write_point(self, metric, resource, value, end_time=None, start_time=None) # Write a single point for a metric to the API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/monitoring/connection.py\",\n \"text\": \"google/cloud/monitoring/connection.py\\nclass Connection # A connection to Google Stackdriver Monitoring via the JSON REST API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py\",\n \"text\": \"google/cloud/monitoring/group.py\\ndef _group_id_from_name(path, project=None) # Validate a group URI path and get the group ID.\\ndef _group_name_from_id(project, group_id) # Build the group name given the project and group ID.\\nclass Group # A dynamic collection of monitored resources.\\n def __init__(self, client, group_id=None, display_name=None, parent_id=None, filter_string=None, is_cluster=False)\\n def id(self) # Returns the group ID.\\n def name(self) # Returns the fully qualified name of the group.\\n def parent_name(self) # Returns the fully qualified name of the parent group.\\n def path(self) # URL path to this group.\\n def create(self) # Create a new group based on this object via a ``POST`` request.\\n def exists(self) # Test for the existence of the group via a ``GET`` request.\\n def reload(self) # Sync local group information via a ``GET`` request.\\n def update(self) # Update the group via a ``PUT`` request.\\n def delete(self) # Delete the group via a ``DELETE`` request.\\n def fetch_parent(self) # Returns the parent group of this group via a ``GET`` request.\\n def list_children(self) # Lists all children of this group via a ``GET`` request.\\n def list_ancestors(self) # Lists all ancestors of this group via a ``GET`` request.\\n def list_descendants(self) # Lists all descendants of this group via a ``GET`` request.\\n def list_members(self, filter_string=None, end_time=None, start_time=None) # Lists all members of this group via a ``GET`` request.\\n def _fetch(cls, client, group_id) # Fetch a group from the API based on it's ID.\\n def _list(cls, client, children_of_group=None, ancestors_of_group=None, descendants_of_group=None) # Lists all groups in the project.\\n def _from_dict(cls, client, info) # Constructs a Group instance from the parsed JSON representation.\\n def _set_properties_from_dict(self, info) # Update the group properties from its API representation.\\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON wire format.\\n def __repr__(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/monitoring/label.py\",\n \"text\": \"google/cloud/monitoring/label.py\\nclass LabelValueType # Allowed values for the `type of a label`_.\\nclass LabelDescriptor # Schema specification and documentation for a single label.\\n def __init__(self, key, value_type=LabelValueType.STRING, description='')\\n def _from_dict(cls, info) # Construct a label descriptor from the parsed JSON representation.\\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON wire format.\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def __repr__(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py\",\n \"text\": \"google/cloud/monitoring/metric.py\\nclass MetricKind # Choices for the `kind of measurement`_.\\nclass ValueType # Choices for the `metric value type`_.\\nclass MetricDescriptor # Specification of a metric type and its schema.\\n def __init__(self, client, type_, metric_kind=MetricKind.METRIC_KIND_UNSPECIFIED, value_type=ValueType.VALUE_TYPE_UNSPECIFIED, labels=(), unit='', description='', display_name='', name=None)\\n def create(self) # Create a new metric descriptor based on this object.\\n def delete(self) # Delete the metric descriptor identified by this object.\\n def _fetch(cls, client, metric_type) # Look up a metric descriptor by type.\\n def _list(cls, client, filter_string=None, type_prefix=None) # List all metric descriptors for the project.\\n def _from_dict(cls, client, info) # Construct a metric descriptor from the parsed JSON representation.\\n def _init_from_dict(self, info) # Initialize attributes from the parsed JSON representation.\\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON wire format.\\n def __repr__(self)\\nclass Metric # A specific metric identified by specifying values for all labels.\\n def _from_dict(cls, info) # Construct a metric object from the parsed JSON representation.\\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON format.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py\",\n \"text\": \"google/cloud/monitoring/query.py\\nclass Aligner # Allowed values for the `supported aligners`_.\\nclass Reducer # Allowed values for the `supported reducers`_.\\nclass Query # Query object for retrieving metric data.\\n def __init__(self, client, metric_type=DEFAULT_METRIC_TYPE, end_time=None, days=0, hours=0, minutes=0)\\n def __iter__(self)\\n def metric_type(self) # The metric type name.\\n def filter(self) # The filter string.\\n def select_interval(self, end_time, start_time=None) # Copy the query and set the query time interval.\\n def select_group(self, group_id) # Copy the query and add filtering by group.\\n def select_projects(self, *args) # Copy the query and add filtering by monitored projects.\\n def select_resources(self, *args, **kwargs) # Copy the query and add filtering by resource labels.\\n def select_metrics(self, *args, **kwargs) # Copy the query and add filtering by metric labels.\\n def align(self, per_series_aligner, seconds=0, minutes=0, hours=0) # Copy the query and add temporal alignment.\\n def reduce(self, cross_series_reducer, *group_by_fields) # Copy the query and add cross-series reduction.\\n def iter(self, headers_only=False, page_size=None) # Yield all time series objects selected by the query.\\n def _iter_fragments(self, headers_only=False, page_size=None) # Yield all time series fragments selected by the query.\\n def _build_query_params(self, headers_only=False, page_size=None, page_token=None) # Yield key-value pairs for the URL query string.\\n def as_dataframe(self, label=None, labels=None) # Return all the selected time series as a :mod:`pandas` dataframe.\\n def copy(self) # Copy the query object.\\nclass _Filter # Helper for assembling a filter string.\\n def __init__(self, metric_type)\\n def select_resources(self, *args, **kwargs) # Select by resource labels.\\n def select_metrics(self, *args, **kwargs) # Select by metric labels.\\n def __str__(self)\\ndef _build_label_filter(category, *args, **kwargs) # Construct a filter string to filter on metric or resource labels.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/monitoring/resource.py\",\n \"text\": \"google/cloud/monitoring/resource.py\\nclass ResourceDescriptor # Specification of a monitored resource type and its schema.\\n def __init__(self, name, type_, display_name, description, labels)\\n def _fetch(cls, client, resource_type) # Look up a monitored resource descriptor by type.\\n def _list(cls, client, filter_string=None) # List all monitored resource descriptors for the project.\\n def _from_dict(cls, info) # Construct a resource descriptor from the parsed JSON representation.\\n def __repr__(self)\\nclass Resource # A monitored resource identified by specifying values for all labels.\\n def _from_dict(cls, info) # Construct a resource object from the parsed JSON representation.\\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON format.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/monitoring/timeseries.py\",\n \"text\": \"google/cloud/monitoring/timeseries.py\\nclass TimeSeries # A single time series of metric values.\\n def labels(self) # A single dictionary with values for all the labels.\\n def header(self, points=None) # Copy everything but the point data.\\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON wire format.\\n def _from_dict(cls, info) # Construct a time series from the parsed JSON representation.\\n def __repr__(self) # Return a representation string with the points elided.\\ndef _make_typed_value(value) # Create a dict representing a TypedValue API object.\\nclass Point # A single point in a time series.\\n def _from_dict(cls, info) # Construct a Point from the parsed JSON representation.\\n def _to_dict(self) # Build a dictionary ready to be serialized to the JSON wire format.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/operation.py\",\n \"text\": \"google/cloud/operation.py\\ndef _compute_type_url(klass, prefix=_GOOGLE_APIS_PREFIX) # Compute a type URL for a klass.\\ndef _register_type_url(type_url, klass) # Register a klass as the factory for a given type URL.\\nclass Operation # Representation of a Google API Long-Running Operation.\\n def __init__(self, name, client, pb_metadata=None, **kw)\\n def from_pb(cls, op_pb, client, **kw) # Factory: construct an instance from a protobuf.\\n def complete(self) # Has the operation already completed?\\n def poll(self) # Check if the operation has finished.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/pubsub/__init__.py\",\n \"text\": \"google/cloud/pubsub/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py\",\n \"text\": \"google/cloud/pubsub/_gax.py\\nclass _PublisherAPI # Helper mapping publisher-related APIs.\\n def __init__(self, gax_api)\\n def list_topics(self, project, page_size=0, page_token=None) # List topics for the project associated with this API.\\n def topic_create(self, topic_path) # API call: create a topic\\n def topic_get(self, topic_path) # API call: retrieve a topic\\n def topic_delete(self, topic_path) # API call: delete a topic\\n def topic_publish(self, topic_path, messages) # API call: publish one or more messages to a topic\\n def topic_list_subscriptions(self, topic_path, page_size=0, page_token=None) # API call: list subscriptions bound to a topic\\nclass _SubscriberAPI # Helper mapping subscriber-related APIs.\\n def __init__(self, gax_api)\\n def list_subscriptions(self, project, page_size=0, page_token=None) # List subscriptions for the project associated with this API.\\n def subscription_create(self, subscription_path, topic_path, ack_deadline=None, push_endpoint=None) # API call: create a subscription\\n def subscription_get(self, subscription_path) # API call: retrieve a subscription\\n def subscription_delete(self, subscription_path) # API call: delete a subscription\\n def subscription_modify_push_config(self, subscription_path, push_endpoint) # API call: update push config of a subscription\\n def subscription_pull(self, subscription_path, return_immediately=False, max_messages=1) # API call: retrieve messages for a subscription\\n def subscription_acknowledge(self, subscription_path, ack_ids) # API call: acknowledge retrieved messages\\n def subscription_modify_ack_deadline(self, subscription_path, ack_ids, ack_deadline) # API call: update ack deadline for retrieved messages\\ndef _message_pb_from_mapping(message) # Helper for :meth:`_PublisherAPI.topic_publish`.\\ndef _subscription_pb_to_mapping(sub_pb) # Helper for :meth:`list_subscriptions`, et aliae\\ndef _message_pb_to_mapping(message_pb) # Helper for :meth:`pull`, et aliae\\ndef _received_message_pb_to_mapping(received_message_pb) # Helper for :meth:`pull`, et aliae\\ndef make_gax_publisher_api(connection) # Create an instance of the GAX Publisher API.\\ndef make_gax_subscriber_api(connection) # Create an instance of the GAX Subscriber API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/pubsub/_helpers.py\",\n \"text\": \"google/cloud/pubsub/_helpers.py\\ndef topic_name_from_path(path, project) # Validate a topic URI path and get the topic name.\\ndef subscription_name_from_path(path, project) # Validate a subscription URI path and get the subscription name.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/pubsub/client.py\",\n \"text\": \"google/cloud/pubsub/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def publisher_api(self) # Helper for publisher-related API calls.\\n def subscriber_api(self) # Helper for subscriber-related API calls.\\n def iam_policy_api(self) # Helper for IAM policy-related API calls.\\n def list_topics(self, page_size=None, page_token=None) # List topics for the project associated with this client.\\n def list_subscriptions(self, page_size=None, page_token=None) # List subscriptions for the project associated with this client.\\n def topic(self, name, timestamp_messages=False) # Creates a topic bound to the current client.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py\",\n \"text\": \"google/cloud/pubsub/connection.py\\nclass Connection # A connection to Google Cloud Pub/Sub via the JSON REST API.\\n def __init__(self, credentials=None, http=None)\\n def build_api_url(self, path, query_params=None, api_base_url=None, api_version=None) # Construct an API url given a few components, some optional.\\nclass _PublisherAPI # Helper mapping publisher-related APIs.\\n def __init__(self, connection)\\n def list_topics(self, project, page_size=None, page_token=None) # API call: list topics for a given project\\n def topic_create(self, topic_path) # API call: create a topic\\n def topic_get(self, topic_path) # API call: retrieve a topic\\n def topic_delete(self, topic_path) # API call: delete a topic\\n def topic_publish(self, topic_path, messages) # API call: publish one or more messages to a topic\\n def topic_list_subscriptions(self, topic_path, page_size=None, page_token=None) # API call: list subscriptions bound to a topic\\nclass _SubscriberAPI # Helper mapping subscriber-related APIs.\\n def __init__(self, connection)\\n def list_subscriptions(self, project, page_size=None, page_token=None) # API call: list subscriptions for a given project\\n def subscription_create(self, subscription_path, topic_path, ack_deadline=None, push_endpoint=None) # API call: create a subscription\\n def subscription_get(self, subscription_path) # API call: retrieve a subscription\\n def subscription_delete(self, subscription_path) # API call: delete a subscription\\n def subscription_modify_push_config(self, subscription_path, push_endpoint) # API call: update push config of a subscription\\n def subscription_pull(self, subscription_path, return_immediately=False, max_messages=1) # API call: retrieve messages for a subscription\\n def subscription_acknowledge(self, subscription_path, ack_ids) # API call: acknowledge retrieved messages\\n def subscription_modify_ack_deadline(self, subscription_path, ack_ids, ack_deadline) # API call: update ack deadline for retrieved messages\\nclass _IAMPolicyAPI # Helper mapping IAM policy-related APIs.\\n def __init__(self, connection)\\n def get_iam_policy(self, target_path) # API call: fetch the IAM policy for the target\\n def set_iam_policy(self, target_path, policy) # API call: update the IAM policy for the target\\n def test_iam_permissions(self, target_path, permissions) # API call: test permissions\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/pubsub/iam.py\",\n \"text\": \"google/cloud/pubsub/iam.py\\nclass Policy # Combined IAM Policy / Bindings.\\n def __init__(self, etag=None, version=None)\\n def user(email) # Factory method for a user member.\\n def service_account(email) # Factory method for a service account member.\\n def group(email) # Factory method for a group member.\\n def domain(domain) # Factory method for a domain member.\\n def all_users() # Factory method for a member representing all users.\\n def authenticated_users() # Factory method for a member representing all authenticated users.\\n def from_api_repr(cls, resource) # Create a policy from the resource returned from the API.\\n def to_api_repr(self) # Construct a Policy resource.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/pubsub/message.py\",\n \"text\": \"google/cloud/pubsub/message.py\\nclass Message # Messages can be published to a topic and received by subscribers.\\n def __init__(self, data, message_id, attributes=None)\\n def attributes(self) # Lazily-constructed attribute dictionary.\\n def timestamp(self) # Return sortable timestamp from attributes, if passed.\\n def service_timestamp(self) # Return server-set timestamp.\\n def from_api_repr(cls, api_repr) # Factory: construct message from API representation.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py\",\n \"text\": \"google/cloud/pubsub/subscription.py\\nclass Subscription # Subscriptions receive messages published to their topics.\\n def __init__(self, name, topic=None, ack_deadline=None, push_endpoint=None, client=None)\\n def from_api_repr(cls, resource, client, topics=None) # Factory: construct a topic given its API representation\\n def project(self) # Project bound to the subscription.\\n def full_name(self) # Fully-qualified name used in subscription APIs\\n def path(self) # URL path for the subscription's APIs\\n def auto_ack(self, return_immediately=False, max_messages=1, client=None) # :class:`AutoAck` factory\\n def _require_client(self, client) # Check client or verify over-ride.\\n def create(self, client=None) # API call: create the subscription via a PUT request\\n def exists(self, client=None) # API call: test existence of the subscription via a GET request\\n def reload(self, client=None) # API call: sync local subscription configuration via a GET request\\n def delete(self, client=None) # API call: delete the subscription via a DELETE request.\\n def modify_push_configuration(self, push_endpoint, client=None) # API call: update the push endpoint for the subscription.\\n def pull(self, return_immediately=False, max_messages=1, client=None) # API call: retrieve messages for the subscription.\\n def acknowledge(self, ack_ids, client=None) # API call: acknowledge retrieved messages for the subscription.\\n def modify_ack_deadline(self, ack_ids, ack_deadline, client=None) # API call: update acknowledgement deadline for a retrieved message.\\n def get_iam_policy(self, client=None) # Fetch the IAM policy for the subscription.\\n def set_iam_policy(self, policy, client=None) # Update the IAM policy for the subscription.\\n def check_iam_permissions(self, permissions, client=None) # Verify permissions allowed for the current user.\\nclass AutoAck # Wrapper for :meth:`Subscription.pull` results.\\n def __init__(self, subscription, return_immediately=False, max_messages=1, client=None)\\n def __enter__(self)\\n def __exit__(self, exc_type, exc_val, exc_tb)\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py\",\n \"text\": \"google/cloud/pubsub/topic.py\\nclass Topic # Topics are targets to which messages can be published.\\n def __init__(self, name, client, timestamp_messages=False)\\n def subscription(self, name, ack_deadline=None, push_endpoint=None) # Creates a subscription bound to the current topic.\\n def from_api_repr(cls, resource, client) # Factory: construct a topic given its API representation\\n def project(self) # Project bound to the topic.\\n def full_name(self) # Fully-qualified name used in topic / subscription APIs\\n def _require_client(self, client) # Check client or verify over-ride.\\n def create(self, client=None) # API call: create the topic via a PUT request\\n def exists(self, client=None) # API call: test for the existence of the topic via a GET request\\n def delete(self, client=None) # API call: delete the topic via a DELETE request\\n def _timestamp_message(self, attrs) # Add a timestamp to ``attrs``, if the topic is so configured.\\n def publish(self, message, client=None, **attrs) # API call: publish a message to a topic via a POST request\\n def batch(self, client=None) # Return a batch to use as a context manager.\\n def list_subscriptions(self, page_size=None, page_token=None, client=None) # List subscriptions for the project associated with this client.\\n def get_iam_policy(self, client=None) # Fetch the IAM policy for the topic.\\n def set_iam_policy(self, policy, client=None) # Update the IAM policy for the topic.\\n def check_iam_permissions(self, permissions, client=None) # Verify permissions allowed for the current user.\\nclass Batch # Context manager: collect messages to publish via a single API call.\\n def __init__(self, topic, client)\\n def __enter__(self)\\n def __exit__(self, exc_type, exc_val, exc_tb)\\n def __iter__(self)\\n def publish(self, message, **attrs) # Emulate publishing a message, but save it.\\n def commit(self, client=None) # Send saved messages as a single API call.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/resource_manager/__init__.py\",\n \"text\": \"google/cloud/resource_manager/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/resource_manager/client.py\",\n \"text\": \"google/cloud/resource_manager/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def new_project(self, project_id, name=None, labels=None) # Create a project bound to the current client.\\n def fetch_project(self, project_id) # Fetch an existing project and it's relevant metadata by ID.\\n def list_projects(self, filter_params=None, page_size=None) # List the projects visible to this client.\\nclass _ProjectIterator # An iterator over a list of Project resources.\\n def __init__(self, client, extra_params=None)\\n def get_items_from_response(self, response) # Yield projects from response.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/resource_manager/connection.py\",\n \"text\": \"google/cloud/resource_manager/connection.py\\nclass Connection # A connection to Google Cloud Resource Manager via the JSON REST API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py\",\n \"text\": \"google/cloud/resource_manager/project.py\\nclass Project # Projects are containers for your work on Google Cloud Platform.\\n def __init__(self, project_id, client, name=None, labels=None)\\n def __repr__(self)\\n def from_api_repr(cls, resource, client) # Factory: construct a project given its API representation.\\n def set_properties_from_api_repr(self, resource) # Update specific properties from its API representation.\\n def full_name(self) # Fully-qualified name (ie, ``'projects/purple-spaceship-123'``).\\n def path(self) # URL for the project (ie, ``'/projects/purple-spaceship-123'``).\\n def _require_client(self, client) # Check client or verify over-ride.\\n def create(self, client=None) # API call: create the project via a ``POST`` request.\\n def reload(self, client=None) # API call: reload the project via a ``GET`` request.\\n def exists(self, client=None) # API call: test the existence of a project via a ``GET`` request.\\n def update(self, client=None) # API call: update the project via a ``PUT`` request.\\n def delete(self, client=None, reload_data=False) # API call: delete the project via a ``DELETE`` request.\\n def undelete(self, client=None, reload_data=False) # API call: undelete the project via a ``POST`` request.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/speech/__init__.py\",\n \"text\": \"google/cloud/speech/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/speech/client.py\",\n \"text\": \"google/cloud/speech/client.py\\nclass Encoding # Audio encoding types.\\nclass Client # Client to bundle configuration needed for API requests.\\n def sync_recognize(self, content, source_uri, encoding, sample_rate, language_code=None, max_alternatives=None, profanity_filter=None, speech_context=None) # Synchronous Speech Recognition.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/speech/connection.py\",\n \"text\": \"google/cloud/speech/connection.py\\nclass Connection # A connection to Google Cloud Speech JSON REST API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/storage/__init__.py\",\n \"text\": \"google/cloud/storage/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py\",\n \"text\": \"google/cloud/storage/_helpers.py\\nclass _PropertyMixin # Abstract mixin for cloud storage classes with associated propertties.\\n def __init__(self, name=None)\\n def path(self) # Abstract getter for the object path.\\n def client(self) # Abstract getter for the object client.\\n def _require_client(self, client) # Check client or verify over-ride.\\n def reload(self, client=None) # Reload properties from Cloud Storage.\\n def _patch_property(self, name, value) # Update field of this object's properties.\\n def _set_properties(self, value) # Set the properties for the current object.\\n def patch(self, client=None) # Sends all changed properties in a PATCH request.\\ndef _scalar_property(fieldname) # Create a property descriptor around the :class:`_PropertyMixin` helpers.\\n def _getter(self) # Scalar property getter.\\n def _setter(self, value) # Scalar property setter.\\ndef _write_buffer_to_hash(buffer_object, hash_obj, digest_block_size=8192) # Read blocks from a buffer and update a hash with them.\\ndef _base64_md5hash(buffer_object) # Get MD5 hash of bytes (as base64).\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/storage/acl.py\",\n \"text\": \"google/cloud/storage/acl.py\\nclass _ACLEntity # Class representing a set of roles for an entity.\\n def __init__(self, entity_type, identifier=None)\\n def __str__(self)\\n def __repr__(self)\\n def get_roles(self) # Get the list of roles permitted by this entity.\\n def grant(self, role) # Add a role to the entity.\\n def revoke(self, role) # Remove a role from the entity.\\n def grant_read(self) # Grant read access to the current entity.\\n def grant_write(self) # Grant write access to the current entity.\\n def grant_owner(self) # Grant owner access to the current entity.\\n def revoke_read(self) # Revoke read access from the current entity.\\n def revoke_write(self) # Revoke write access from the current entity.\\n def revoke_owner(self) # Revoke owner access from the current entity.\\nclass ACL # Container class representing a list of access controls.\\n def __init__(self)\\n def _ensure_loaded(self) # Load if not already loaded.\\n def reset(self) # Remove all entities from the ACL, and clear the ``loaded`` flag.\\n def __iter__(self)\\n def entity_from_dict(self, entity_dict) # Build an _ACLEntity object from a dictionary of data.\\n def has_entity(self, entity) # Returns whether or not this ACL has any entries for an entity.\\n def get_entity(self, entity, default=None) # Gets an entity object from the ACL.\\n def add_entity(self, entity) # Add an entity to the ACL.\\n def entity(self, entity_type, identifier=None) # Factory method for creating an Entity.\\n def user(self, identifier) # Factory method for a user Entity.\\n def group(self, identifier) # Factory method for a group Entity.\\n def domain(self, domain) # Factory method for a domain Entity.\\n def all(self) # Factory method for an Entity representing all users.\\n def all_authenticated(self) # Factory method for an Entity representing all authenticated users.\\n def get_entities(self) # Get a list of all Entity objects.\\n def client(self) # Abstract getter for the object client.\\n def _require_client(self, client) # Check client or verify over-ride.\\n def reload(self, client=None) # Reload the ACL data from Cloud Storage.\\n def _save(self, acl, predefined, client) # Helper for :meth:`save` and :meth:`save_predefined`.\\n def save(self, acl=None, client=None) # Save this ACL for the current bucket.\\n def save_predefined(self, predefined, client=None) # Save this ACL for the current bucket using a predefined ACL.\\n def clear(self, client=None) # Remove all ACL entries.\\nclass BucketACL # An ACL specifically for a bucket.\\n def __init__(self, bucket)\\n def client(self) # The client bound to this ACL's bucket.\\n def reload_path(self) # Compute the path for GET API requests for this ACL.\\n def save_path(self) # Compute the path for PATCH API requests for this ACL.\\nclass DefaultObjectACL # A class representing the default object ACL for a bucket.\\nclass ObjectACL # An ACL specifically for a Cloud Storage \",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/storage/batch.py\",\n \"text\": \"google/cloud/storage/batch.py\\nclass MIMEApplicationHTTP # MIME type for ``application/http``.\\n def __init__(self, method, uri, headers, body)\\nclass NoContent # Emulate an HTTP '204 No Content' response.\\nclass _FutureDict # Class to hold a future value for a deferred request.\\n def get(key, default=None) # Stand-in for dict.get.\\n def __getitem__(self, key) # Stand-in for dict[key].\\n def __setitem__(self, key, value) # Stand-in for dict[key] = value.\\nclass Batch # Proxy an underlying connection, batching up change operations.\\n def __init__(self, client)\\n def _do_request(self, method, url, headers, data, target_object) # Override Connection: defer actual HTTP request.\\n def _prepare_batch_request(self) # Prepares headers and body for a batch request.\\n def _finish_futures(self, responses) # Apply all the batch responses to the futures created.\\n def finish(self) # Submit a single `multipart/mixed` request with deferred requests.\\n def current(self) # Return the topmost batch, or None.\\n def __enter__(self)\\n def __exit__(self, exc_type, exc_val, exc_tb)\\ndef _generate_faux_mime_message(parser, response, content) # Convert response, content -> (multipart) email.message.\\ndef _unpack_batch_response(response, content) # Convert response, content -> [(headers, payload)].\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/storage/blob.py\",\n \"text\": \"google/cloud/storage/blob.py\\nclass Blob # A wrapper around Cloud Storage's concept of an ``Object``.\\n def __init__(self, name, bucket, chunk_size=None)\\n def chunk_size(self) # Get the blob's default chunk size.\\n def chunk_size(self, value) # Set the blob's default chunk size.\\n def path_helper(bucket_path, blob_name) # Relative URL path for a blob.\\n def acl(self) # Create our ACL on demand.\\n def __repr__(self)\\n def path(self) # Getter property for the URL path to this Blob.\\n def client(self) # The client bound to this blob.\\n def public_url(self) # The public URL for this blob's object.\\n def generate_signed_url(self, expiration, method='GET', content_type=None, generation=None, response_disposition=None, response_type=None, client=None, credentials=None) # Generates a signed URL for this blob.\\n def exists(self, client=None) # Determines whether or not this blob exists.\\n def delete(self, client=None) # Deletes a blob from Cloud Storage.\\n def download_to_file(self, file_obj, encryption_key=None, client=None) # Download the contents of this blob into a file-like object.\\n def download_to_filename(self, filename, encryption_key=None, client=None) # Download the contents of this blob into a named file.\\n def download_as_string(self, encryption_key=None, client=None) # Download the contents of this blob as a string.\\n def _check_response_error(request, http_response) # Helper for :meth:`upload_from_file`.\\n def upload_from_file(self, file_obj, rewind=False, size=None, encryption_key=None, content_type=None, num_retries=6, client=None) # Upload the contents of this blob from a file-like object.\\n def upload_from_filename(self, filename, content_type=None, encryption_key=None, client=None) # Upload this blob's contents from the content of a named file.\\n def upload_from_string(self, data, content_type='text/plain', encryption_key=None, client=None) # Upload contents of this blob from the provided string.\\n def make_public(self, client=None) # Make this blob public giving all users read access.\\n def component_count(self) # Number of underlying components that make up this object.\\n def etag(self) # Retrieve the ETag for the object.\\n def generation(self) # Retrieve the generation for the object.\\n def id(self) # Retrieve the ID for the object.\\n def media_link(self) # Retrieve the media download URI for the object.\\n def metadata(self) # Retrieve arbitrary/application specific metadata for the object.\\n def metadata(self, value) # Update arbitrary/application specific metadata for the object.\\n def metageneration(self) # Retrieve the metageneration for the object.\\n def owner(self) # Retrieve info about the owner of the object.\\n def self_link(self) # Retrieve the URI for the object.\\n def size(self) # Size of the object, in bytes.\\n def storage_class(self) # Retrieve the storage class for the object.\\n def time_deleted(self) # Retrieve the timest\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py\",\n \"text\": \"google/cloud/storage/bucket.py\\nclass _BlobIterator # An iterator listing blobs in a bucket\\n def __init__(self, bucket, extra_params=None, client=None)\\n def get_items_from_response(self, response) # Yield :class:`.storage.blob.Blob` items from response.\\nclass Bucket # A class representing a Bucket on Cloud Storage.\\n def __init__(self, client, name=None)\\n def __repr__(self)\\n def client(self) # The client bound to this bucket.\\n def blob(self, blob_name, chunk_size=None) # Factory constructor for blob object.\\n def exists(self, client=None) # Determines whether or not this bucket exists.\\n def create(self, client=None) # Creates current bucket.\\n def acl(self) # Create our ACL on demand.\\n def default_object_acl(self) # Create our defaultObjectACL on demand.\\n def path_helper(bucket_name) # Relative URL path for a bucket.\\n def path(self) # The URL path to this bucket.\\n def get_blob(self, blob_name, client=None) # Get a blob object by name.\\n def list_blobs(self, max_results=None, page_token=None, prefix=None, delimiter=None, versions=None, projection='noAcl', fields=None, client=None) # Return an iterator used to find blobs in the bucket.\\n def delete(self, force=False, client=None) # Delete this bucket.\\n def delete_blob(self, blob_name, client=None) # Deletes a blob from the current bucket.\\n def delete_blobs(self, blobs, on_error=None, client=None) # Deletes a list of blobs from the current bucket.\\n def copy_blob(self, blob, destination_bucket, new_name=None, client=None) # Copy the given blob to the given bucket, optionally with a new name.\\n def rename_blob(self, blob, new_name, client=None) # Rename the given blob using copy and delete operations.\\n def cors(self) # Retrieve CORS policies configured for this bucket.\\n def cors(self, entries) # Set CORS policies configured for this bucket.\\n def etag(self) # Retrieve the ETag for the bucket.\\n def id(self) # Retrieve the ID for the bucket.\\n def lifecycle_rules(self) # Lifecycle rules configured for this bucket.\\n def lifecycle_rules(self, rules)\\n def get_logging(self) # Return info about access logging for this bucket.\\n def enable_logging(self, bucket_name, object_prefix='') # Enable access logging for this bucket.\\n def disable_logging(self) # Disable access logging for this bucket.\\n def metageneration(self) # Retrieve the metageneration for the bucket.\\n def owner(self) # Retrieve info about the owner of the bucket.\\n def project_number(self) # Retrieve the number of the project to which the bucket is assigned.\\n def self_link(self) # Retrieve the URI for the bucket.\\n def storage_class(self) # Retrieve the storage class for the bucket.\\n def storage_class(self, value) # Set the storage class for the bucket.\\n def time_created(self) # Retrieve the timestamp at which the bucket was created.\\n def versioning_enabled(self) # Is versioning enabled for this bucket?\\n def versioni\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/storage/client.py\",\n \"text\": \"google/cloud/storage/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def __init__(self, project=None, credentials=None, http=None)\\n def connection(self) # Get connection or batch on the client.\\n def connection(self, value) # Set connection on the client.\\n def _push_batch(self, batch) # Push a batch onto our stack.\\n def _pop_batch(self) # Pop a batch from our stack.\\n def current_batch(self) # Currently-active batch.\\n def bucket(self, bucket_name) # Factory constructor for bucket object.\\n def batch(self) # Factory constructor for batch object.\\n def get_bucket(self, bucket_name) # Get a bucket by name.\\n def lookup_bucket(self, bucket_name) # Get a bucket by name, returning None if not found.\\n def create_bucket(self, bucket_name) # Create a new bucket.\\n def list_buckets(self, max_results=None, page_token=None, prefix=None, projection='noAcl', fields=None) # Get all buckets in the project associated to the client.\\nclass _BucketIterator # An iterator listing all buckets.\\n def __init__(self, client, extra_params=None)\\n def get_items_from_response(self, response) # Factory method which yields :class:`.Bucket` items from a response.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/storage/connection.py\",\n \"text\": \"google/cloud/storage/connection.py\\nclass Connection # A connection to Google Cloud Storage via the JSON REST API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/streaming/__init__.py\",\n \"text\": \"google/cloud/streaming/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/streaming/buffered_stream.py\",\n \"text\": \"google/cloud/streaming/buffered_stream.py\\nclass BufferedStream # Buffers a stream, reading ahead to determine if we're at the end.\\n def __init__(self, stream, start, size)\\n def __repr__(self)\\n def __len__(self)\\n def stream_exhausted(self) # Does the stream have bytes remaining beyond the buffer\\n def stream_end_position(self) # Point to which stream was read into the buffer\\n def _bytes_remaining(self) # Bytes remaining to be read from the buffer\\n def read(self, size=None) # Read bytes from the buffer.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/streaming/exceptions.py\",\n \"text\": \"google/cloud/streaming/exceptions.py\\nclass Error # Base class for all exceptions.\\nclass CommunicationError # Any communication error talking to an API server.\\nclass HttpError # Error making a request. Soon to be HttpError.\\n def __init__(self, response, content, url)\\n def __str__(self)\\n def status_code(self) # Status code for the response.\\n def from_response(cls, http_response) # Factory: construct an exception from a response.\\nclass TransferError # Errors related to transfers.\\nclass TransferRetryError # Retryable errors related to transfers.\\nclass TransferInvalidError # The given transfer is invalid.\\nclass RequestError # The request was not successful.\\nclass RetryAfterError # The response contained a retry-after header.\\n def __init__(self, response, content, url, retry_after)\\n def from_response(cls, http_response) # Factory: construct an exception from a response.\\nclass BadStatusCodeError # The request completed but returned a bad status code.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py\",\n \"text\": \"google/cloud/streaming/http_wrapper.py\\ndef _httplib2_debug_level(http_request, level, http=None) # Temporarily change the value of httplib2.debuglevel, if necessary.\\nclass Request # Encapsulates the data for an HTTP request.\\n def __init__(self, url='', http_method='GET', headers=None, body='')\\n def loggable_body(self) # Request body for logging purposes\\n def loggable_body(self, value) # Update request body for logging purposes\\n def body(self) # Request body\\n def body(self, value) # Update the request body\\ndef _process_content_range(content_range) # Convert a 'Content-Range' header into a length for the response.\\nclass Response # Encapsulates data for an HTTP response.\\n def __len__(self)\\n def length(self) # Length of this response.\\n def status_code(self) # HTTP status code\\n def retry_after(self) # Retry interval (if set).\\n def is_redirect(self) # Does this response contain a redirect\\ndef _check_response(response) # Validate a response\\ndef _reset_http_connections(http) # Rebuild all http connections in the httplib2.Http instance.\\ndef _make_api_request_no_retry(http, http_request, redirections=_REDIRECTIONS) # Send an HTTP request via the given http instance.\\ndef make_api_request(http, http_request, retries=7, redirections=_REDIRECTIONS) # Send an HTTP request via the given http, performing error/retry handling.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/streaming/stream_slice.py\",\n \"text\": \"google/cloud/streaming/stream_slice.py\\nclass StreamSlice # Provides a slice-like object for streams.\\n def __init__(self, stream, max_bytes)\\n def __repr__(self)\\n def __len__(self)\\n def __nonzero__(self)\\n def length(self) # Maximum number of bytes to return in the slice.\\n def read(self, size=None) # Read bytes from the slice.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py\",\n \"text\": \"google/cloud/streaming/transfer.py\\nclass _Transfer # Generic bits common to Uploads and Downloads.\\n def __init__(self, stream, close_stream=False, chunksize=_DEFAULT_CHUNKSIZE, auto_transfer=True, http=None, num_retries=5)\\n def __repr__(self)\\n def close_stream(self) # Should this instance close the stream when deleted.\\n def http(self) # Http instance used to perform requests.\\n def bytes_http(self) # Http instance used to perform binary requests.\\n def bytes_http(self, value) # Update Http instance used to perform binary requests.\\n def num_retries(self) # How many retries should the transfer attempt\\n def num_retries(self, value) # Update how many retries should the transfer attempt\\n def stream(self) # Stream to/from which data is downloaded/uploaded.\\n def url(self) # URL to / from which data is downloaded/uploaded.\\n def _initialize(self, http, url) # Initialize this download by setting :attr:`http` and :attr`url`.\\n def initialized(self) # Has the instance been initialized\\n def _ensure_initialized(self) # Helper: assert that the instance is initialized.\\n def _ensure_uninitialized(self) # Helper: assert that the instance is not initialized.\\n def __del__(self)\\nclass Download # Represent a single download.\\n def __init__(self, stream, **kwds)\\n def from_file(cls, filename, overwrite=False, auto_transfer=True, **kwds) # Create a new download object from a filename.\\n def from_stream(cls, stream, auto_transfer=True, total_size=None, **kwds) # Create a new Download object from a stream.\\n def progress(self) # Number of bytes have been downloaded.\\n def total_size(self) # Total number of bytes to be downloaded.\\n def encoding(self) # 'Content-Encoding' used to transfer the file\\n def __repr__(self)\\n def configure_request(self, http_request, url_builder) # Update http_request/url_builder with download-appropriate values.\\n def _set_total(self, info) # Update 'total_size' based on data from a response.\\n def initialize_download(self, http_request, http) # Initialize this download.\\n def _normalize_start_end(self, start, end=None) # Validate / fix up byte range.\\n def _set_range_header(request, start, end=None) # Update the 'Range' header in a request to match a byte range.\\n def _compute_end_byte(self, start, end=None, use_chunks=True) # Compute the last byte to fetch for this request.\\n def _get_chunk(self, start, end) # Retrieve a chunk of the file.\\n def _process_response(self, response) # Update attribtes and writing stream, based on response.\\n def get_range(self, start, end=None, use_chunks=True) # Retrieve a given byte range from this download, inclusive.\\n def stream_file(self, use_chunks=True) # Stream the entire download.\\nclass Upload # Represent a single Upload.\\n def __init__(self, stream, mime_type, total_size=None, http=None, close_stream=False, auto_transfer=True, **kwds)\\n def from_file(cls, filename, mime_type=None, auto_transf\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/streaming/util.py\",\n \"text\": \"google/cloud/streaming/util.py\\ndef calculate_wait_for_retry(retry_attempt) # Calculate the amount of time to wait before a retry attempt.\\ndef acceptable_mime_type(accept_patterns, mime_type) # Check that ``mime_type`` matches one of ``accept_patterns``.\\n def _match(pattern, mime_type) # Return True iff mime_type is acceptable for pattern.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/translate/__init__.py\",\n \"text\": \"google/cloud/translate/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/translate/client.py\",\n \"text\": \"google/cloud/translate/client.py\\nclass Client # Client to bundle configuration needed for API requests.\\n def __init__(self, api_key, http=None, target_language=ENGLISH_ISO_639)\\n def get_languages(self, target_language=None) # Get list of supported languages for translation.\\n def detect_language(self, values) # Detect the language of a string or list of strings.\\n def translate(self, values, target_language=None, format_=None, source_language=None, customization_ids=()) # Translate a string or list of strings.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/translate/connection.py\",\n \"text\": \"google/cloud/translate/connection.py\\nclass Connection # A connection to Google Cloud Translate via the JSON REST API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/vision/__init__.py\",\n \"text\": \"google/cloud/vision/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/vision/client.py\",\n \"text\": \"google/cloud/vision/client.py\\nclass VisionRequest # Request container with image and features information to annotate.\\n def __init__(self, image, features)\\n def as_dict(self) # Dictionary representation of Image.\\n def features(self) # List of Feature objects.\\n def image(self) # Image object containing image content.\\nclass Client # Client to bundle configuration needed for API requests.\\n def annotate(self, image, features) # Annotate an image to discover it's attributes.\\n def image(self, content=None, source_uri=None) # Get instance of Image using current client.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/vision/color.py\",\n \"text\": \"google/cloud/vision/color.py\\nclass ImagePropertiesAnnotation # Representation of image properties\\n def __init__(self, colors)\\n def from_api_repr(cls, response) # Factory: construct ``ImagePropertiesAnnotation`` from a response.\\n def colors(self) # Colors in an image.\\nclass Color # Representation of RGBA color information.\\n def __init__(self, red, green, blue, alpha)\\n def from_api_repr(cls, response) # Factory: construct a ``Color`` from a Vision API response.\\n def red(self) # Red component of the color.\\n def green(self) # Green component of the color.\\n def blue(self) # Blue component of the color.\\n def alpha(self) # Alpha transparency level.\\nclass ColorInformation # Representation of color information from API response.\\n def __init__(self, color, score, pixel_fraction)\\n def from_api_repr(cls, response) # Factory: construct ``ColorInformation`` for a color found.\\n def color(self) # RGB components of the color.\\n def score(self) # Image-specific score for this color. Value in range [0, 1].\\n def pixel_fraction(self) # Stores the fraction of pixels the color occupies in the image.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/vision/connection.py\",\n \"text\": \"google/cloud/vision/connection.py\\nclass Connection # A connection to Google Cloud Vision via the JSON REST API.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/vision/entity.py\",\n \"text\": \"google/cloud/vision/entity.py\\nclass EntityAnnotation # Representation of an entity returned from the Vision API.\\n def __init__(self, bounds, description, locale, locations, mid, score)\\n def from_api_repr(cls, response) # Factory: construct entity from Vision API response.\\n def bounds(self) # Bounding polygon of detected image feature.\\n def description(self) # Description of feature detected in image.\\n def locale(self) # The language code for text discovered in an image.\\n def locations(self) # Location coordinates landmarks detected.\\n def mid(self) # MID of feature detected in image.\\n def score(self) # Overall score of the result. Range [0, 1].\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/vision/face.py\",\n \"text\": \"google/cloud/vision/face.py\\nclass Angles # Angles representing the positions of a face.\\n def __init__(self, roll, pan, tilt)\\n def from_api_repr(cls, response) # Factory: construct the angles from an Vision API response.\\n def roll(self) # Roll angle of face.\\n def pan(self) # Pan angle of face.\\n def tilt(self) # Tilt angle of face.\\nclass Bounds # The bounding polygon of the entire face.\\nclass Emotions # Emotions displayed by the face detected in an image.\\n def __init__(self, joy_likelihood, sorrow_likelihood, surprise_likelihood, anger_likelihood)\\n def from_api_repr(cls, response) # Factory: construct `Emotions` from Vision API response.\\n def joy_likelihood(self) # Likelihood of joy in detected face.\\n def sorrow_likelihood(self) # Likelihood of sorrow in detected face.\\n def surprise_likelihood(self) # Likelihood of surprise in detected face.\\n def anger_likelihood(self) # Likelihood of anger in detected face.\\nclass Face # Representation of a face found by the Vision API\\n def __init__(self, angles, bounds, detection_confidence, emotions, fd_bounds, headwear_likelihood, image_properties, landmarks, landmarking_confidence)\\n def from_api_repr(cls, response) # Factory: construct an instance of a Face from an API response\\n def angles(self) # Accessor to the pan, tilt and roll angles of a Face.\\n def bounds(self) # Accessor to the bounding poly information of the detected face.\\n def detection_confidence(self) # Face detection confidence score determined by the Vision API.\\n def emotions(self) # Accessor to the possible emotions expressed in the detected face.\\n def fd_bounds(self) # Accessor to the skin area bounding poly of the detected face.\\n def headwear_likelihood(self) # Headwear likelihood.\\n def image_properties(self) # Image properties from imaged used in face detection.\\n def landmarks(self) # Accessor to the facial landmarks detected in a face.\\n def landmarking_confidence(self) # Landmarking confidence score determinged by the Vision API.\\nclass FaceImageProperties # A representation of the image properties from face detection.\\n def __init__(self, blurred_likelihood, underexposed_likelihood)\\n def from_api_repr(cls, response) # Factory: construct image properties from image.\\n def blurred_likelihood(self) # Likelihood of the image being blurred.\\n def underexposed_likelihood(self) # Likelihood that the image used for detection was underexposed.\\nclass FaceLandmarkTypes # A representation of the face detection landmark types.\\nclass FDBounds # The bounding polygon of just the skin portion of the face.\\nclass Landmark # A face-specific landmark (for example, a face feature, left eye).\\n def __init__(self, position, landmark_type)\\n def from_api_repr(cls, response_landmark) # Factory: construct an instance of a Landmark from a response.\\n def position(self) # Landmark position on face.\\n def landmark_type(self) # Landmark type of facial feature\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/vision/feature.py\",\n \"text\": \"google/cloud/vision/feature.py\\nclass FeatureTypes # Feature Types to indication which annotations to perform.\\nclass Feature # Feature object specifying the annotation type and maximum results.\\n def __init__(self, feature_type, max_results=1)\\n def as_dict(self) # Generate dictionary for Feature request format.\\n def feature_type(self) # \\\"Feature type string.\\n def max_results(self) # Maximum number of results for feature type.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py\",\n \"text\": \"google/cloud/vision/geometry.py\\nclass BoundsBase # Base class for handling bounds with vertices.\\n def __init__(self, vertices)\\n def from_api_repr(cls, response_vertices) # Factory: construct BoundsBase instance from Vision API response.\\n def vertices(self) # List of vertices.\\nclass Bounds # A polygon boundry of the detected feature.\\nclass FDBounds # The bounding polygon of just the skin portion of the face.\\nclass LocationInformation # Representation of location information returned by the Vision API.\\n def __init__(self, latitude, longitude)\\n def from_api_repr(cls, response) # Factory: construct location information from Vision API response.\\n def latitude(self) # Latitude coordinate.\\n def longitude(self) # Longitude coordinate.\\nclass Position # A 3D position in the image.\\n def __init__(self, x_coordinate, y_coordinate, z_coordinate)\\n def from_api_repr(cls, response_position) # Factory: construct 3D position from API response.\\n def x_coordinate(self) # X position coordinate.\\n def y_coordinate(self) # Y position coordinate.\\n def z_coordinate(self) # Z position coordinate.\\nclass Vertex # A vertex represents a 2D point in the image.\\n def __init__(self, x_coordinate, y_coordinate)\\n def x_coordinate(self) # X position coordinate.\\n def y_coordinate(self) # Y position coordinate.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/vision/image.py\",\n \"text\": \"google/cloud/vision/image.py\\nclass Image # Image representation containing information to be annotate.\\n def __init__(self, client, content=None, source_uri=None)\\n def as_dict(self) # Generate dictionary structure for request.\\n def content(self) # Base64 encoded image content.\\n def source(self) # Google Cloud Storage URI.\\n def _detect_annotation(self, feature) # Generic method for detecting a single annotation.\\n def detect_faces(self, limit=10) # Detect faces in image.\\n def detect_labels(self, limit=10) # Detect labels that describe objects in an image.\\n def detect_landmarks(self, limit=10) # Detect landmarks in an image.\\n def detect_logos(self, limit=10) # Detect logos in an image.\\n def detect_properties(self, limit=10) # Detect the color properties of an image.\\n def detect_safe_search(self, limit=10) # Retreive safe search properties from an image.\\n def detect_text(self, limit=10) # Detect text in an image.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/vision/likelihood.py\",\n \"text\": \"google/cloud/vision/likelihood.py\\nclass Likelihood # A representation of likelihood to give stable results across upgrades.\",\n \"label\": 1\n },\n {\n \"cid\": \"google/cloud/vision/safe.py\",\n \"text\": \"google/cloud/vision/safe.py\\nclass SafeSearchAnnotation # Representation of a SafeSearchAnnotation.\\n def __init__(self, adult_likelihood, spoof_likelihood, medical_likelihood, violence_likelihood)\\n def from_api_repr(cls, response) # Factory: construct SafeSearchAnnotation from Vision API response.\\n def adult(self) # Represents the adult contents likelihood for the image.\\n def spoof(self) # The likelihood that an obvious modification was made to the image.\\n def medical(self) # Likelihood this is a medical image.\\n def violence(self) # Likeliehood that this image contains violence.\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/generate_json_docs.py\",\n \"text\": \"scripts/generate_json_docs.py\\n# Copyright 2016 Google Inc. All rights reserved.\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\n\\nimport argparse\\nimport cgi\\nimport doctest\\nimport inspect\\nimport json\\nimport os\\nimport shutil\\nimport types\\n\\nimport pdoc\\nfrom parinx.parser import parse_docstring\\nfrom parinx.errors import MethodParsingException\\nimport six\\n\\nfrom verify_included_modules import get_public_modules\\n\\n\\n_DOCSTRING_TEST_PARSER = doctest.DocTestParser()\\n\\n\\nclass Module(object):\\n\\n def __init__(self, module_id, name, description=None,\\n examples=None, methods=None, source=None):\\n self.module_id = module_id\\n self.name = name\\n self.description = description\\n self.examples = examples or []\\n self.methods = methods\\n self.source = source\\n\\n @classmethod\\n def from_module_name(cls, name, base_path):\\n module = pdoc.Module(pdoc.import_module(name), allsubmodules=True)\\n methods = module.functions() + module.variables()\\n examples = []\\n\\n if '__init__' in name:\\n snippets = get_snippet_examples(name.split('.')[1],\\n os.path.join(base_path, 'docs'))\\n examples.extend(snippets)\\n\\n source_path = clean_source_path(module)\\n\\n return cls(module_id=name,\\n name=name.split('.')[-1].title(),\\n description=module.docstring,\\n examples=examples or [],\\n methods=[Method.from_pdoc(m) for m in methods],\\n source=source_path)\\n\\n def to_dict(self):\\n return {'id': self.module_id,\\n 'name': self.name,\\n 'description': format_sphinx_doc(self.description),\\n 'examples': self.examples,\\n 'methods': [m.to_dict() for m in self.methods],\\n 'source': self.source}\\n\\n\\nclass Klass(object):\\n def __init__(self, module_id, name, refname=None, description=None,\\n examples=None, methods=None, source=None):\\n self.module_id = module_id\\n self.name = name\\n self.refname = refname\\n self.description = description\\n self.examples = examples or []\\n self.methods = methods\\n self.source = source\\n\\n @classmethod\\n def from_class_name(cls, module, kls):\\n methods = kls.methods()\\n\\n examples = []\\n source_path = clean_source_path(module)\\n\\n return cls(module_id=kls.name,\\n name=kls.name.spl\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/get_version.py\",\n \"text\": \"scripts/get_version.py\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/make_datastore_grpc.py\",\n \"text\": \"scripts/make_datastore_grpc.py\\ndef get_pb2_contents_with_grpc() # Get pb2 lines generated by protoc with gRPC plugin.\\ndef get_pb2_contents_without_grpc() # Get pb2 lines generated by protoc without gRPC plugin.\\ndef get_pb2_grpc_only() # Get pb2 lines that are only in gRPC.\\ndef get_pb2_message_types() # Get message types defined in datastore pb2 file.\\ndef main() # Write gRPC-only lines to custom module.\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/make_operations_grpc.py\",\n \"text\": \"scripts/make_operations_grpc.py\\ndef get_pb2_contents_with_grpc() # Get pb2 lines generated by protoc with gRPC plugin.\\ndef get_pb2_contents_without_grpc() # Get pb2 lines generated by protoc without gRPC plugin.\\ndef get_pb2_grpc_only() # Get pb2 lines that are only in gRPC.\\ndef main() # Write gRPC-only lines to custom module.\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/pycodestyle_on_repo.py\",\n \"text\": \"scripts/pycodestyle_on_repo.py\\ndef main() # Run pycodestyle on all Python files in the repository.\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/rewrite_imports.py\",\n \"text\": \"scripts/rewrite_imports.py\\ndef transform_old_to_new(line, old_module, new_module, ignore_import_from=False) # Transforms from an old module to a new one.\\ndef transform_line(line) # Transforms an import line in a PB2 module.\\ndef rewrite_file(filename) # Rewrites a given PB2 modules.\\ndef main() # Rewrites all PB2 files.\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/run_pylint.py\",\n \"text\": \"scripts/run_pylint.py\\ndef read_config(filename) # Reads pylintrc config onto native ConfigParser object.\\ndef make_test_rc(base_rc_filename, additions_dict, replacements_dict, target_filename) # Combines a base rc and test additions into single file.\\ndef valid_filename(filename) # Checks if a file is a Python file and is not ignored.\\ndef is_production_filename(filename) # Checks if the file contains production code.\\ndef get_files_for_linting(allow_limited=True) # Gets a list of files in the repository.\\ndef get_python_files(all_files=None) # Gets a list of all Python files in the repository that need linting.\\ndef lint_fileset(filenames, rcfile, description) # Lints a group of files using a given rcfile.\\ndef main() # Script entry point. Lints both sets of files.\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/verify_included_modules.py\",\n \"text\": \"scripts/verify_included_modules.py\\nclass SphinxApp # Mock app to interact with Sphinx helpers.\\ndef is_valid_module(filename) # Determines if a filename is a valid Python module.\\ndef get_public_modules(path, base_package=None) # Get list of all public modules relative to a path.\\ndef main(build_root='_build') # Main script to verify modules included.\\ndef get_parser() # Get simple ``argparse`` parser to determine package.\",\n \"label\": 1\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"docs/bigquery_snippets.py::snippet","text":"docs/bigquery_snippets.py::snippet\ndef snippet(func):\n \"\"\"Mark ``func`` as a snippet example function.\"\"\"\n func._snippet = True\n return func","label":0},{"cid":"docs/bigquery_snippets.py::_millis","text":"docs/bigquery_snippets.py::_millis\ndef _millis():\n return time.time() * 1000","label":0},{"cid":"docs/bigquery_snippets.py::_CloseOnDelete.__init__","text":"docs/bigquery_snippets.py::_CloseOnDelete.__init__\n def __init__(self, wrapped):\n self._wrapped = wrapped","label":0},{"cid":"docs/bigquery_snippets.py::_CloseOnDelete.delete","text":"docs/bigquery_snippets.py::_CloseOnDelete.delete\n def delete(self):\n self._wrapped.close()","label":0},{"cid":"docs/bigquery_snippets.py::client_list_datasets","text":"docs/bigquery_snippets.py::client_list_datasets\n@snippet\ndef client_list_datasets(client, _):\n \"\"\"List datasets for a project.\"\"\"\n\n def do_something_with(_):\n pass\n\n # [START client_list_datasets]\n datasets, token = client.list_datasets() # API request\n while True:\n for dataset in datasets:\n do_something_with(dataset)\n if token is None:\n break\n datasets, token = client.list_datasets(page_token=token) # API request","label":0},{"cid":"docs/bigquery_snippets.py::client_list_datasets.do_something_with","text":"docs/bigquery_snippets.py::client_list_datasets.do_something_with\n def do_something_with(_):\n pass","label":0},{"cid":"docs/bigquery_snippets.py::dataset_create","text":"docs/bigquery_snippets.py::dataset_create\n@snippet\ndef dataset_create(client, to_delete):\n \"\"\"Create a dataset.\"\"\"\n DATASET_NAME = 'dataset_create_%d' % (_millis(),)\n\n # [START dataset_create]\n dataset = client.dataset(DATASET_NAME)\n dataset.create() # API request\n # [END dataset_create]\n\n to_delete.append(dataset)","label":0},{"cid":"docs/bigquery_snippets.py::dataset_exists","text":"docs/bigquery_snippets.py::dataset_exists\n@snippet\ndef dataset_exists(client, to_delete):\n \"\"\"Test existence of a dataset.\"\"\"\n DATASET_NAME = 'dataset_exists_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n to_delete.append(dataset)\n\n # [START dataset_exists]\n assert not dataset.exists() # API request\n dataset.create() # API request\n assert dataset.exists() # API request","label":0},{"cid":"docs/bigquery_snippets.py::dataset_reload","text":"docs/bigquery_snippets.py::dataset_reload\n@snippet\ndef dataset_reload(client, to_delete):\n \"\"\"Reload a dataset's metadata.\"\"\"\n DATASET_NAME = 'dataset_reload_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.description = ORIGINAL_DESCRIPTION\n dataset.create()\n to_delete.append(dataset)\n\n # [START dataset_reload]\n assert dataset.description == ORIGINAL_DESCRIPTION\n dataset.description = LOCALLY_CHANGED_DESCRIPTION\n assert dataset.description == LOCALLY_CHANGED_DESCRIPTION\n dataset.reload() # API request\n assert dataset.description == ORIGINAL_DESCRIPTION","label":0},{"cid":"docs/bigquery_snippets.py::dataset_patch","text":"docs/bigquery_snippets.py::dataset_patch\n@snippet\ndef dataset_patch(client, to_delete):\n \"\"\"Patch a dataset's metadata.\"\"\"\n DATASET_NAME = 'dataset_patch_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.description = ORIGINAL_DESCRIPTION\n dataset.create()\n to_delete.append(dataset)\n\n # [START dataset_patch]\n ONE_DAY_MS = 24 * 60 * 60 * 1000\n assert dataset.description == ORIGINAL_DESCRIPTION\n dataset.patch(\n description=PATCHED_DESCRIPTION,\n default_table_expiration_ms=ONE_DAY_MS\n ) # API request\n assert dataset.description == PATCHED_DESCRIPTION\n assert dataset.default_table_expiration_ms == ONE_DAY_MS","label":0},{"cid":"docs/bigquery_snippets.py::dataset_update","text":"docs/bigquery_snippets.py::dataset_update\n@snippet\ndef dataset_update(client, to_delete):\n \"\"\"Update a dataset's metadata.\"\"\"\n DATASET_NAME = 'dataset_update_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.description = ORIGINAL_DESCRIPTION\n dataset.create()\n to_delete.append(dataset)\n dataset.reload()\n\n # [START dataset_update]\n from google.cloud.bigquery import AccessGrant\n assert dataset.description == ORIGINAL_DESCRIPTION\n assert dataset.default_table_expiration_ms is None\n grant = AccessGrant(\n role='READER', entity_type='domain', entity_id='example.com')\n assert grant not in dataset.access_grants\n ONE_DAY_MS = 24 * 60 * 60 * 1000\n dataset.description = UPDATED_DESCRIPTION\n dataset.default_table_expiration_ms = ONE_DAY_MS\n grants = list(dataset.access_grants)\n grants.append(grant)\n dataset.access_grants = grants\n dataset.update() # API request\n assert dataset.description == UPDATED_DESCRIPTION\n assert dataset.default_table_expiration_ms == ONE_DAY_MS\n assert grant in dataset.access_grants","label":0},{"cid":"docs/bigquery_snippets.py::dataset_delete","text":"docs/bigquery_snippets.py::dataset_delete\n@snippet\ndef dataset_delete(client, _):\n \"\"\"Delete a dataset.\"\"\"\n DATASET_NAME = 'dataset_delete_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.create()\n\n # [START dataset_delete]\n assert dataset.exists() # API request\n dataset.delete()\n assert not dataset.exists() # API request","label":0},{"cid":"docs/bigquery_snippets.py::dataset_list_tables","text":"docs/bigquery_snippets.py::dataset_list_tables\n@snippet\ndef dataset_list_tables(client, to_delete):\n \"\"\"List tables within a dataset.\"\"\"\n DATASET_NAME = 'dataset_list_tables_dataset_%d' % (_millis(),)\n TABLE_NAME = 'dataset_list_tables_table_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.create()\n to_delete.append(dataset)\n\n # [START dataset_list_tables]\n tables, token = dataset.list_tables() # API request\n assert len(tables) == 0\n assert token is None\n table = dataset.table(TABLE_NAME)\n table.view_query = QUERY\n table.create() # API request\n tables, token = dataset.list_tables() # API request\n assert len(tables) == 1\n assert tables[0].name == TABLE_NAME\n # [END dataset_list_tables]\n to_delete.insert(0, table)","label":0},{"cid":"docs/bigquery_snippets.py::table_create","text":"docs/bigquery_snippets.py::table_create\n@snippet\ndef table_create(client, to_delete):\n \"\"\"Create a table.\"\"\"\n DATASET_NAME = 'table_create_dataset_%d' % (_millis(),)\n TABLE_NAME = 'table_create_table_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.create()\n to_delete.append(dataset)\n\n # [START table_create]\n table = dataset.table(TABLE_NAME, SCHEMA)\n table.create() # API request\n # [END table_create]\n\n to_delete.insert(0, table)","label":0},{"cid":"docs/bigquery_snippets.py::table_exists","text":"docs/bigquery_snippets.py::table_exists\n@snippet\ndef table_exists(client, to_delete):\n \"\"\"Test existence of a table.\"\"\"\n DATASET_NAME = 'table_exists_dataset_%d' % (_millis(),)\n TABLE_NAME = 'table_exists_table_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.create()\n to_delete.append(dataset)\n\n # [START table_exists]\n table = dataset.table(TABLE_NAME, SCHEMA)\n assert not table.exists() # API request\n table.create() # API request\n assert table.exists() # API request\n # [END table_exists]\n\n to_delete.insert(0, table)","label":0},{"cid":"docs/bigquery_snippets.py::table_reload","text":"docs/bigquery_snippets.py::table_reload\n@snippet\ndef table_reload(client, to_delete):\n \"\"\"Reload a table's metadata.\"\"\"\n DATASET_NAME = 'table_reload_dataset_%d' % (_millis(),)\n TABLE_NAME = 'table_reload_table_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.create()\n to_delete.append(dataset)\n\n table = dataset.table(TABLE_NAME, SCHEMA)\n table.friendly_name = ORIGINAL_FRIENDLY_NAME\n table.description = ORIGINAL_DESCRIPTION\n table.create()\n to_delete.insert(0, table)\n\n # [START table_reload]\n assert table.friendly_name == ORIGINAL_FRIENDLY_NAME\n assert table.description == ORIGINAL_DESCRIPTION\n table.friendly_name = LOCALLY_CHANGED_FRIENDLY_NAME\n table.description = LOCALLY_CHANGED_DESCRIPTION\n table.reload() # API request\n assert table.friendly_name == ORIGINAL_FRIENDLY_NAME\n assert table.description == ORIGINAL_DESCRIPTION","label":0},{"cid":"docs/bigquery_snippets.py::table_patch","text":"docs/bigquery_snippets.py::table_patch\n@snippet\ndef table_patch(client, to_delete):\n \"\"\"Patch a table's metadata.\"\"\"\n DATASET_NAME = 'table_patch_dataset_%d' % (_millis(),)\n TABLE_NAME = 'table_patch_table_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.description = ORIGINAL_DESCRIPTION\n dataset.create()\n to_delete.append(dataset)\n\n table = dataset.table(TABLE_NAME, SCHEMA)\n table.friendly_name = ORIGINAL_FRIENDLY_NAME\n table.description = ORIGINAL_DESCRIPTION\n table.create()\n to_delete.insert(0, table)\n\n # [START table_patch]\n assert table.friendly_name == ORIGINAL_FRIENDLY_NAME\n assert table.description == ORIGINAL_DESCRIPTION\n table.patch(\n friendly_name=PATCHED_FRIENDLY_NAME,\n description=PATCHED_DESCRIPTION,\n ) # API request\n assert table.friendly_name == PATCHED_FRIENDLY_NAME\n assert table.description == PATCHED_DESCRIPTION","label":0},{"cid":"docs/bigquery_snippets.py::table_update","text":"docs/bigquery_snippets.py::table_update\n@snippet\ndef table_update(client, to_delete):\n \"\"\"Update a table's metadata.\"\"\"\n DATASET_NAME = 'table_update_dataset_%d' % (_millis(),)\n TABLE_NAME = 'table_update_table_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.description = ORIGINAL_DESCRIPTION\n dataset.create()\n to_delete.append(dataset)\n\n table = dataset.table(TABLE_NAME, SCHEMA)\n table.friendly_name = ORIGINAL_FRIENDLY_NAME\n table.description = ORIGINAL_DESCRIPTION\n table.create()\n to_delete.insert(0, table)\n\n # [START table_update]\n assert table.friendly_name == ORIGINAL_FRIENDLY_NAME\n assert table.description == ORIGINAL_DESCRIPTION\n NEW_SCHEMA = table.schema[:]\n NEW_SCHEMA.append(SchemaField('phone', 'string'))\n table.friendly_name = UPDATED_FRIENDLY_NAME\n table.description = UPDATED_DESCRIPTION\n table.schema = NEW_SCHEMA\n table.update() # API request\n assert table.friendly_name == UPDATED_FRIENDLY_NAME\n assert table.description == UPDATED_DESCRIPTION\n assert table.schema == NEW_SCHEMA","label":0},{"cid":"docs/bigquery_snippets.py::_warm_up_inserted_table_data","text":"docs/bigquery_snippets.py::_warm_up_inserted_table_data\ndef _warm_up_inserted_table_data(table):\n # Allow for 90 seconds of \"warm up\" before rows visible. See:\n # https://cloud.google.com/bigquery/streaming-data-into-bigquery#dataavailability\n rows = ()\n counter = 18\n\n while len(rows) == 0 and counter > 0:\n counter -= 1\n rows, _, _ = table.fetch_data()\n if len(rows) == 0:\n time.sleep(5)","label":0},{"cid":"docs/bigquery_snippets.py::table_insert_fetch_data","text":"docs/bigquery_snippets.py::table_insert_fetch_data\n@snippet\ndef table_insert_fetch_data(client, to_delete):\n \"\"\"Insert / fetch table data.\"\"\"\n DATASET_NAME = 'table_insert_fetch_data_dataset_%d' % (_millis(),)\n TABLE_NAME = 'table_insert_fetch_data_table_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.create()\n to_delete.append(dataset)\n\n table = dataset.table(TABLE_NAME, SCHEMA)\n table.create()\n to_delete.insert(0, table)\n\n # [START table_insert_data]\n ROWS_TO_INSERT = [\n (u'Phred Phlyntstone', 32),\n (u'Wylma Phlyntstone', 29),\n ]\n\n table.insert_data(ROWS_TO_INSERT)\n # [END table_insert_data]\n\n _warm_up_inserted_table_data(table)\n\n found_rows = []\n\n def do_something(row):\n found_rows.append(row)\n\n # [START table_fetch_data]\n rows, _, token = table.fetch_data()\n while True:\n for row in rows:\n do_something(row)\n if token is None:\n break\n rows, _, token = table.fetch_data(page_token=token)\n # [END table_fetch_data]\n\n assert len(found_rows) == len(ROWS_TO_INSERT)\n by_age = operator.itemgetter(1)\n found_rows = reversed(sorted(found_rows, key=by_age))\n for found, to_insert in zip(found_rows, ROWS_TO_INSERT):\n assert found == to_insert","label":0},{"cid":"docs/bigquery_snippets.py::table_insert_fetch_data.do_something","text":"docs/bigquery_snippets.py::table_insert_fetch_data.do_something\n def do_something(row):\n found_rows.append(row)","label":0},{"cid":"docs/bigquery_snippets.py::table_upload_from_file","text":"docs/bigquery_snippets.py::table_upload_from_file\n@snippet\ndef table_upload_from_file(client, to_delete):\n \"\"\"Upload table data from a CSV file.\"\"\"\n import csv\n import tempfile\n DATASET_NAME = 'table_upload_from_file_dataset_%d' % (_millis(),)\n TABLE_NAME = 'table_upload_from_file_table_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.create()\n to_delete.append(dataset)\n\n table = dataset.table(TABLE_NAME, SCHEMA)\n table.create()\n to_delete.insert(0, table)\n\n csv_file = tempfile.NamedTemporaryFile(suffix='.csv')\n to_delete.append(_CloseOnDelete(csv_file))\n\n # [START table_upload_from_file]\n writer = csv.writer(csv_file)\n writer.writerow((b'full_name', b'age'))\n writer.writerow((b'Phred Phlyntstone', b'32'))\n writer.writerow((b'Wylma Phlyntstone', b'29'))\n csv_file.flush()\n\n with open(csv_file.name, 'rb') as readable:\n table.upload_from_file(\n readable, source_format='CSV', skip_leading_rows=1)\n # [END table_upload_from_file]\n\n _warm_up_inserted_table_data(table)\n\n rows, total, token = table.fetch_data()\n\n assert len(rows) == total == 2\n assert token is None\n assert rows[0] == (u'Phred Phlyntstone', 32)\n assert rows[1] == (u'Wylma Phlyntstone', 29)","label":0},{"cid":"docs/bigquery_snippets.py::table_delete","text":"docs/bigquery_snippets.py::table_delete\n@snippet\ndef table_delete(client, _):\n \"\"\"Delete a table.\"\"\"\n DATASET_NAME = 'table_delete_dataset_%d' % (_millis(),)\n TABLE_NAME = 'table_create_table_%d' % (_millis(),)\n dataset = client.dataset(DATASET_NAME)\n dataset.create()\n\n table = dataset.table(TABLE_NAME, SCHEMA)\n table.create()\n\n # [START table_delete]\n assert table.exists() # API request\n table.delete() # API request\n assert not table.exists() # API request","label":0},{"cid":"docs/bigquery_snippets.py::client_list_jobs","text":"docs/bigquery_snippets.py::client_list_jobs\n@snippet\ndef client_list_jobs(client, _):\n \"\"\"List jobs for a project.\"\"\"\n\n def do_something_with(_):\n pass\n\n # [START client_list_jobs]\n jobs, token = client.list_jobs() # API request\n while True:\n for job in jobs:\n do_something_with(job)\n if token is None:\n break\n jobs, token = client.list_jobs(page_token=token) # API request","label":0},{"cid":"docs/bigquery_snippets.py::client_list_jobs.do_something_with","text":"docs/bigquery_snippets.py::client_list_jobs.do_something_with\n def do_something_with(_):\n pass","label":0},{"cid":"docs/bigquery_snippets.py::client_run_sync_query","text":"docs/bigquery_snippets.py::client_run_sync_query\n@snippet\ndef client_run_sync_query(client, _):\n \"\"\"Run a synchronous query.\"\"\"\n LIMIT = 100\n LIMITED = '%s LIMIT %d' % (QUERY, LIMIT)\n TIMEOUT_MS = 1000\n\n # [START client_run_sync_query]\n query = client.run_sync_query(LIMITED)\n query.timeout_ms = TIMEOUT_MS\n query.run() # API request\n\n assert query.complete\n assert len(query.rows) == LIMIT\n assert [field.name for field in query.schema] == ['name']","label":0},{"cid":"docs/bigquery_snippets.py::client_run_sync_query_paged","text":"docs/bigquery_snippets.py::client_run_sync_query_paged\n@snippet\ndef client_run_sync_query_paged(client, _):\n \"\"\"Run a synchronous query with paged results.\"\"\"\n TIMEOUT_MS = 1000\n PAGE_SIZE = 100\n LIMIT = 1000\n LIMITED = '%s LIMIT %d' % (QUERY, LIMIT)\n\n all_rows = []\n\n def do_something_with(rows):\n all_rows.extend(rows)\n\n # [START client_run_sync_query_paged]\n query = client.run_sync_query(LIMITED)\n query.timeout_ms = TIMEOUT_MS\n query.max_results = PAGE_SIZE\n query.run() # API request\n\n assert query.complete\n assert query.page_token is not None\n assert len(query.rows) == PAGE_SIZE\n assert [field.name for field in query.schema] == ['name']\n\n rows = query.rows\n token = query.page_token\n\n while True:\n do_something_with(rows)\n if token is None:\n break\n rows, total_count, token = query.fetch_data(\n page_token=token) # API request\n # [END client_run_sync_query_paged]\n\n assert total_count == LIMIT\n assert len(all_rows) == LIMIT","label":0},{"cid":"docs/bigquery_snippets.py::client_run_sync_query_paged.do_something_with","text":"docs/bigquery_snippets.py::client_run_sync_query_paged.do_something_with\n def do_something_with(rows):\n all_rows.extend(rows)","label":0},{"cid":"docs/bigquery_snippets.py::client_run_sync_query_timeout","text":"docs/bigquery_snippets.py::client_run_sync_query_timeout\n@snippet\ndef client_run_sync_query_timeout(client, _):\n \"\"\"Run a synchronous query w/ timeout\"\"\"\n TIMEOUT_MS = 10\n\n all_rows = []\n\n def do_something_with(rows):\n all_rows.extend(rows)\n\n # [START client_run_sync_query_timeout]\n query = client.run_sync_query(QUERY)\n query.timeout_ms = TIMEOUT_MS\n query.use_query_cache = False\n query.run() # API request\n\n assert not query.complete\n\n job = query.job\n job.reload() # API rquest\n retry_count = 0\n\n while retry_count < 10 and job.state != u'DONE':\n time.sleep(1.5**retry_count) # exponential backoff\n retry_count += 1\n job.reload() # API request\n\n assert job.state == u'DONE'\n\n rows, total_count, token = query.fetch_data() # API request\n while True:\n do_something_with(rows)\n if token is None:\n break\n rows, total_count, token = query.fetch_data(\n page_token=token) # API request\n # [END client_run_sync_query_timeout]\n\n assert len(all_rows) == total_count","label":0},{"cid":"docs/bigquery_snippets.py::client_run_sync_query_timeout.do_something_with","text":"docs/bigquery_snippets.py::client_run_sync_query_timeout.do_something_with\n def do_something_with(rows):\n all_rows.extend(rows)","label":0},{"cid":"docs/bigquery_snippets.py::_find_examples","text":"docs/bigquery_snippets.py::_find_examples\ndef _find_examples():\n funcs = [obj for obj in globals().values()\n if getattr(obj, '_snippet', False)]\n for func in sorted(funcs, key=lambda f: f.func_code.co_firstlineno):\n yield func","label":0},{"cid":"docs/bigquery_snippets.py::main","text":"docs/bigquery_snippets.py::main\ndef main():\n client = Client()\n for example in _find_examples():\n to_delete = []\n print('%-30s: %s' % (\n example.func_name, example.func_doc))\n try:\n example(client, to_delete)\n except AssertionError as e:\n print(' FAIL: %s' % (e,))\n except Exception as e: # pylint: disable=broad-except\n print(' ERROR: %r' % (e,))\n for item in to_delete:\n item.delete()","label":0},{"cid":"docs/pubsub_snippets.py::snippet","text":"docs/pubsub_snippets.py::snippet\ndef snippet(func):\n \"\"\"Mark ``func`` as a snippet example function.\"\"\"\n func._snippet = True\n return func","label":0},{"cid":"docs/pubsub_snippets.py::_millis","text":"docs/pubsub_snippets.py::_millis\ndef _millis():\n return time.time() * 1000","label":0},{"cid":"docs/pubsub_snippets.py::client_list_topics","text":"docs/pubsub_snippets.py::client_list_topics\n@snippet\ndef client_list_topics(client, to_delete): # pylint: disable=unused-argument\n \"\"\"List topics for a project.\"\"\"\n\n def do_something_with(sub): # pylint: disable=unused-argument\n pass\n\n # [START client_list_topics]\n topics, token = client.list_topics() # API request\n while True:\n for topic in topics:\n do_something_with(topic)\n if token is None:\n break\n topics, token = client.list_topics(page_token=token) # API request","label":0},{"cid":"docs/pubsub_snippets.py::client_list_topics.do_something_with","text":"docs/pubsub_snippets.py::client_list_topics.do_something_with\n def do_something_with(sub): # pylint: disable=unused-argument\n pass","label":0},{"cid":"docs/pubsub_snippets.py::client_list_subscriptions","text":"docs/pubsub_snippets.py::client_list_subscriptions\n@snippet\ndef client_list_subscriptions(client,\n to_delete): # pylint: disable=unused-argument\n \"\"\"List all subscriptions for a project.\"\"\"\n\n def do_something_with(sub): # pylint: disable=unused-argument\n pass\n\n # [START client_list_subscriptions]\n subscriptions, token = client.list_subscriptions() # API request\n while True:\n for subscription in subscriptions:\n do_something_with(subscription)\n if token is None:\n break\n subscriptions, token = client.list_subscriptions(\n page_token=token) # API request","label":0},{"cid":"docs/pubsub_snippets.py::client_list_subscriptions.do_something_with","text":"docs/pubsub_snippets.py::client_list_subscriptions.do_something_with\n def do_something_with(sub): # pylint: disable=unused-argument\n pass","label":0},{"cid":"docs/pubsub_snippets.py::topic_create","text":"docs/pubsub_snippets.py::topic_create\n@snippet\ndef topic_create(client, to_delete):\n \"\"\"Create a topic.\"\"\"\n TOPIC_NAME = 'topic_create-%d' % (_millis(),)\n\n # [START topic_create]\n topic = client.topic(TOPIC_NAME)\n topic.create() # API request\n # [END topic_create]\n\n to_delete.append(topic)","label":0},{"cid":"docs/pubsub_snippets.py::topic_exists","text":"docs/pubsub_snippets.py::topic_exists\n@snippet\ndef topic_exists(client, to_delete):\n \"\"\"Test existence of a topic.\"\"\"\n TOPIC_NAME = 'topic_exists-%d' % (_millis(),)\n topic = client.topic(TOPIC_NAME)\n to_delete.append(topic)\n\n # [START topic_exists]\n assert not topic.exists() # API request\n topic.create() # API request\n assert topic.exists() # API request","label":0},{"cid":"docs/pubsub_snippets.py::topic_delete","text":"docs/pubsub_snippets.py::topic_delete\n@snippet\ndef topic_delete(client, to_delete): # pylint: disable=unused-argument\n \"\"\"Delete a topic.\"\"\"\n TOPIC_NAME = 'topic_delete-%d' % (_millis(),)\n topic = client.topic(TOPIC_NAME)\n topic.create() # API request\n\n # [START topic_delete]\n assert topic.exists() # API request\n topic.delete()\n assert not topic.exists() # API request","label":0},{"cid":"docs/pubsub_snippets.py::topic_iam_policy","text":"docs/pubsub_snippets.py::topic_iam_policy\n@snippet\ndef topic_iam_policy(client, to_delete):\n \"\"\"Fetch / set a topic's IAM policy.\"\"\"\n TOPIC_NAME = 'topic_iam_policy-%d' % (_millis(),)\n topic = client.topic(TOPIC_NAME)\n topic.create()\n to_delete.append(topic)\n\n # [START topic_get_iam_policy]\n policy = topic.get_iam_policy() # API request\n # [END topic_get_iam_policy]\n\n assert len(policy.viewers) == 0\n assert len(policy.editors) == 0\n assert len(policy.owners) == 0\n\n # [START topic_set_iam_policy]\n ALL_USERS = policy.all_users()\n policy.viewers.add(ALL_USERS)\n LOGS_GROUP = policy.group('cloud-logs@google.com')\n policy.editors.add(LOGS_GROUP)\n new_policy = topic.set_iam_policy(policy) # API request\n # [END topic_set_iam_policy]\n\n assert ALL_USERS in new_policy.viewers\n assert LOGS_GROUP in new_policy.editors","label":0},{"cid":"docs/pubsub_snippets.py::topic_check_iam_permissions","text":"docs/pubsub_snippets.py::topic_check_iam_permissions\ndef topic_check_iam_permissions(client, to_delete):\n \"\"\"Check topic IAM permissions.\"\"\"\n TOPIC_NAME = 'topic_check_iam_permissions-%d' % (_millis(),)\n topic = client.topic(TOPIC_NAME)\n topic.create()\n to_delete.append(topic)\n\n # [START topic_check_iam_permissions]\n from google.cloud.pubsub.iam import OWNER_ROLE, EDITOR_ROLE, VIEWER_ROLE\n TO_CHECK = [OWNER_ROLE, EDITOR_ROLE, VIEWER_ROLE]\n ALLOWED = topic.check_iam_permissions(TO_CHECK)\n assert set(ALLOWED) == set(TO_CHECK)","label":0},{"cid":"docs/pubsub_snippets.py::topic_publish_messages","text":"docs/pubsub_snippets.py::topic_publish_messages\n@snippet\ndef topic_publish_messages(client, to_delete):\n \"\"\"Publish messages to a topic.\"\"\"\n TOPIC_NAME = 'topic_publish_messages-%d' % (_millis(),)\n topic = client.topic(TOPIC_NAME)\n topic.create()\n to_delete.append(topic)\n\n # [START topic_publish_simple_message]\n topic.publish(b'This is the message payload') # API request\n # [END topic_publish_simple_message]\n\n # [START topic_publish_message_with_attrs]\n topic.publish(b'Another message payload', extra='EXTRA') # API request","label":0},{"cid":"docs/pubsub_snippets.py::topic_subscription","text":"docs/pubsub_snippets.py::topic_subscription\n@snippet\ndef topic_subscription(client, to_delete):\n \"\"\"Create subscriptions to a topic.\"\"\"\n TOPIC_NAME = 'topic_subscription-%d' % (_millis(),)\n SUB_DEFAULTS = 'topic_subscription-defaults-%d' % (_millis(),)\n SUB_ACK90 = 'topic_subscription-ack90-%d' % (_millis(),)\n topic = client.topic(TOPIC_NAME)\n topic.create()\n to_delete.append(topic)\n\n # [START topic_subscription_defaults]\n sub_defaults = topic.subscription(SUB_DEFAULTS)\n # [END topic_subscription_defaults]\n\n sub_defaults.create() # API request\n to_delete.append(sub_defaults)\n expected_names = set()\n expected_names.add(sub_defaults.full_name)\n\n # [START topic_subscription_ack90]\n sub_ack90 = topic.subscription(SUB_ACK90, ack_deadline=90)\n # [END topic_subscription_ack90]\n\n sub_ack90.create() # API request\n to_delete.append(sub_ack90)\n expected_names.add(sub_ack90.full_name)\n\n sub_names = set()\n\n def do_something_with(sub):\n sub_names.add(sub.full_name)\n\n # [START topic_list_subscriptions]\n subscriptions, token = topic.list_subscriptions() # API request\n while True:\n for subscription in subscriptions:\n do_something_with(subscription)\n if token is None:\n break\n subscriptions, token = topic.list_subscriptions(\n page_token=token) # API request\n # [END topic_list_subscriptions]\n\n assert sub_names.issuperset(expected_names)","label":0},{"cid":"docs/pubsub_snippets.py::topic_subscription.do_something_with","text":"docs/pubsub_snippets.py::topic_subscription.do_something_with\n def do_something_with(sub):\n sub_names.add(sub.full_name)","label":0},{"cid":"docs/pubsub_snippets.py::topic_subscription_push","text":"docs/pubsub_snippets.py::topic_subscription_push\ndef topic_subscription_push(client, to_delete):\n \"\"\"Create subscriptions to a topic.\"\"\"\n TOPIC_NAME = 'topic_subscription_push-%d' % (_millis(),)\n SUB_PUSH = 'topic_subscription_push-sub-%d' % (_millis(),)\n PUSH_URL = 'https://api.example.com/push-endpoint'\n topic = client.topic(TOPIC_NAME)\n topic.create()\n to_delete.append(topic)\n\n # [START topic_subscription_push]\n subscription = topic.subscription(SUB_PUSH, push_endpoint=PUSH_URL)\n subscription.create() # API request\n # [END topic_subscription_push]\n\n # [START subscription_push_pull]\n subscription.modify_push_configuration(push_endpoint=None) # API request\n # [END subscription_push_pull]\n\n # [START subscription_pull_push]\n subscription.modify_push_configuration(\n push_endpoint=PUSH_URL) # API request","label":0},{"cid":"docs/pubsub_snippets.py::subscription_lifecycle","text":"docs/pubsub_snippets.py::subscription_lifecycle\n@snippet\ndef subscription_lifecycle(client, to_delete):\n \"\"\"Test lifecycle of a subscription.\"\"\"\n TOPIC_NAME = 'subscription_lifecycle-%d' % (_millis(),)\n SUB_NAME = 'subscription_lifecycle-defaults-%d' % (_millis(),)\n topic = client.topic(TOPIC_NAME)\n topic.create()\n to_delete.append(topic)\n\n # [START subscription_create]\n subscription = topic.subscription(SUB_NAME)\n subscription.create() # API request\n # [END subscription_create]\n\n # [START subscription_exists]\n assert subscription.exists() # API request\n # [END subscription_exists]\n\n # [START subscription_reload]\n subscription.reload() # API request\n # [END subscription_reload]\n\n # [START subscription_delete]\n subscription.delete() # API request","label":0},{"cid":"docs/pubsub_snippets.py::subscription_pull","text":"docs/pubsub_snippets.py::subscription_pull\n@snippet\ndef subscription_pull(client, to_delete):\n \"\"\"Pull messges from a subscribed topic.\"\"\"\n TOPIC_NAME = 'subscription_pull-%d' % (_millis(),)\n SUB_NAME = 'subscription_pull-defaults-%d' % (_millis(),)\n PAYLOAD1 = b'PAYLOAD1'\n PAYLOAD2 = b'PAYLOAD2'\n EXTRA = 'EXTRA'\n topic = client.topic(TOPIC_NAME)\n topic.create()\n to_delete.append(topic)\n\n subscription = topic.subscription(SUB_NAME)\n subscription.create()\n to_delete.append(subscription)\n\n # [START subscription_pull_return_immediately]\n pulled = subscription.pull(return_immediately=True)\n # [END subscription_pull_return_immediately]\n assert len(pulled) == 0, \"unexpected message\"\n\n topic.publish(PAYLOAD1)\n topic.publish(PAYLOAD2, extra=EXTRA)\n\n time.sleep(1) # eventually-consistent\n\n # [START subscription_pull]\n pulled = subscription.pull(max_messages=2)\n # [END subscription_pull]\n\n assert len(pulled) == 2, \"eventual consistency\"\n\n # [START subscription_modify_ack_deadline]\n for ack_id, _ in pulled:\n subscription.modify_ack_deadline(ack_id, 90) # API request\n # [END subscription_modify_ack_deadline]\n\n payloads = []\n extras = []\n\n def do_something_with(message): # pylint: disable=unused-argument\n payloads.append(message.data)\n if message.attributes:\n extras.append(message.attributes)\n\n class ApplicationException(Exception):\n pass\n\n def log_exception(_):\n pass\n\n # [START subscription_acknowledge]\n for ack_id, message in pulled:\n try:\n do_something_with(message)\n except ApplicationException as e:\n log_exception(e)\n else:\n subscription.acknowledge([ack_id])\n # [END subscription_acknowledge]\n\n assert set(payloads) == set([PAYLOAD1, PAYLOAD2]), 'payloads: %s' % (\n (payloads,))\n assert extras == [{'extra': EXTRA}], 'extras: %s' % (\n (extras,))","label":0},{"cid":"docs/pubsub_snippets.py::subscription_pull.do_something_with","text":"docs/pubsub_snippets.py::subscription_pull.do_something_with\n def do_something_with(message): # pylint: disable=unused-argument\n payloads.append(message.data)\n if message.attributes:\n extras.append(message.attributes)","label":0},{"cid":"docs/pubsub_snippets.py::subscription_pull.log_exception","text":"docs/pubsub_snippets.py::subscription_pull.log_exception\n def log_exception(_):\n pass","label":0},{"cid":"docs/pubsub_snippets.py::subscription_pull_w_autoack","text":"docs/pubsub_snippets.py::subscription_pull_w_autoack\n@snippet\ndef subscription_pull_w_autoack(client, to_delete):\n \"\"\"Pull messges from a topic, auto-acknowldging them\"\"\"\n TOPIC_NAME = 'subscription_pull_autoack-%d' % (_millis(),)\n SUB_NAME = 'subscription_pull_autoack-defaults-%d' % (_millis(),)\n PAYLOAD1 = b'PAYLOAD1'\n PAYLOAD2 = b'PAYLOAD2'\n EXTRA = 'EXTRA'\n topic = client.topic(TOPIC_NAME)\n topic.create()\n to_delete.append(topic)\n\n subscription = topic.subscription(SUB_NAME)\n subscription.create()\n to_delete.append(subscription)\n\n # [START topic_batch]\n with topic.batch() as batch:\n batch.publish(PAYLOAD1)\n batch.publish(PAYLOAD2, extra=EXTRA)\n # [END topic_batch]\n\n time.sleep(1) # eventually-consistent\n\n payloads = []\n extras = []\n\n def do_something_with(message): # pylint: disable=unused-argument\n payloads.append(message.data)\n if message.attributes:\n extras.append(message.attributes)\n\n # [START subscription_pull_autoack]\n from google.cloud.pubsub.subscription import AutoAck\n with AutoAck(subscription, max_messages=10) as ack:\n for ack_id, message in list(ack.items()):\n try:\n do_something_with(message)\n except Exception: # pylint: disable=broad-except\n del ack[ack_id]\n # [END subscription_pull_autoack]\n\n assert set(payloads) == set(PAYLOAD1, PAYLOAD1), \"eventual consistency\"\n assert extras == [{'extra': EXTRA}], \"eventual consistency\"","label":0},{"cid":"docs/pubsub_snippets.py::subscription_pull_w_autoack.do_something_with","text":"docs/pubsub_snippets.py::subscription_pull_w_autoack.do_something_with\n def do_something_with(message): # pylint: disable=unused-argument\n payloads.append(message.data)\n if message.attributes:\n extras.append(message.attributes)","label":0},{"cid":"docs/pubsub_snippets.py::subscription_iam_policy","text":"docs/pubsub_snippets.py::subscription_iam_policy\n@snippet\ndef subscription_iam_policy(client, to_delete):\n \"\"\"Fetch / set a subscription's IAM policy.\"\"\"\n TOPIC_NAME = 'subscription_iam_policy-%d' % (_millis(),)\n SUB_NAME = 'subscription_iam_policy-defaults-%d' % (_millis(),)\n topic = client.topic(TOPIC_NAME)\n topic.create()\n to_delete.append(topic)\n\n subscription = topic.subscription(SUB_NAME)\n subscription.create()\n to_delete.append(subscription)\n\n # [START subscription_get_iam_policy]\n policy = subscription.get_iam_policy() # API request\n # [END subscription_get_iam_policy]\n\n assert len(policy.viewers) == 0\n assert len(policy.editors) == 0\n assert len(policy.owners) == 0\n\n # [START subscription_set_iam_policy]\n ALL_USERS = policy.all_users()\n policy.viewers.add(ALL_USERS)\n LOGS_GROUP = policy.group('cloud-logs@google.com')\n policy.editors.add(LOGS_GROUP)\n new_policy = subscription.set_iam_policy(policy) # API request\n # [END subscription_set_iam_policy]\n\n assert ALL_USERS in new_policy.viewers\n assert LOGS_GROUP in new_policy.editors","label":0},{"cid":"docs/pubsub_snippets.py::subscription_check_iam_permissions","text":"docs/pubsub_snippets.py::subscription_check_iam_permissions\ndef subscription_check_iam_permissions(client, to_delete):\n \"\"\"Check subscription IAM permissions.\"\"\"\n TOPIC_NAME = 'subscription_check_iam_permissions-%d' % (_millis(),)\n SUB_NAME = 'subscription_check_iam_permissions-defaults-%d' % (_millis(),)\n topic = client.topic(TOPIC_NAME)\n topic.create()\n to_delete.append(topic)\n\n subscription = topic.subscription(SUB_NAME)\n subscription.create()\n to_delete.append(subscription)\n\n # [START subscription_check_iam_permissions]\n from google.cloud.pubsub.iam import OWNER_ROLE, EDITOR_ROLE, VIEWER_ROLE\n TO_CHECK = [OWNER_ROLE, EDITOR_ROLE, VIEWER_ROLE]\n ALLOWED = subscription.check_iam_permissions(TO_CHECK)\n assert set(ALLOWED) == set(TO_CHECK)","label":0},{"cid":"docs/pubsub_snippets.py::_line_no","text":"docs/pubsub_snippets.py::_line_no\ndef _line_no(func):\n code = getattr(func, '__code__', None) or getattr(func, 'func_code')\n return code.co_firstlineno","label":0},{"cid":"docs/pubsub_snippets.py::_find_examples","text":"docs/pubsub_snippets.py::_find_examples\ndef _find_examples():\n funcs = [obj for obj in globals().values()\n if getattr(obj, '_snippet', False)]\n for func in sorted(funcs, key=_line_no):\n yield func","label":0},{"cid":"docs/pubsub_snippets.py::_name_and_doc","text":"docs/pubsub_snippets.py::_name_and_doc\ndef _name_and_doc(func):\n return func.__name__, func.__doc__","label":0},{"cid":"docs/pubsub_snippets.py::main","text":"docs/pubsub_snippets.py::main\ndef main():\n client = Client()\n for example in _find_examples():\n to_delete = []\n print('%-25s: %s' % _name_and_doc(example))\n try:\n example(client, to_delete)\n except AssertionError as e:\n print(' FAIL: %s' % (e,))\n except Exception as e: # pylint: disable=broad-except\n print(' ERROR: %r' % (e,))\n for item in to_delete:\n item.delete()","label":0},{"cid":"google/cloud/_helpers.py::_LocalStack.__init__","text":"google/cloud/_helpers.py::_LocalStack.__init__\n def __init__(self):\n super(_LocalStack, self).__init__()\n self._stack = []","label":0},{"cid":"google/cloud/_helpers.py::_LocalStack.__iter__","text":"google/cloud/_helpers.py::_LocalStack.__iter__\n def __iter__(self):\n \"\"\"Iterate the stack in LIFO order.\n \"\"\"\n return iter(reversed(self._stack))","label":0},{"cid":"google/cloud/_helpers.py::_LocalStack.push","text":"google/cloud/_helpers.py::_LocalStack.push\n def push(self, resource):\n \"\"\"Push a resource onto our stack.\n \"\"\"\n self._stack.append(resource)","label":0},{"cid":"google/cloud/_helpers.py::_LocalStack.pop","text":"google/cloud/_helpers.py::_LocalStack.pop\n def pop(self):\n \"\"\"Pop a resource from our stack.\n\n :rtype: object\n :returns: the top-most resource, after removing it.\n :raises IndexError: if the stack is empty.\n \"\"\"\n return self._stack.pop()","label":0},{"cid":"google/cloud/_helpers.py::_LocalStack.top","text":"google/cloud/_helpers.py::_LocalStack.top\n @property\n def top(self):\n \"\"\"Get the top-most resource\n\n :rtype: object\n :returns: the top-most item, or None if the stack is empty.\n \"\"\"\n if len(self._stack) > 0:\n return self._stack[-1]","label":0},{"cid":"google/cloud/_helpers.py::_UTC.dst","text":"google/cloud/_helpers.py::_UTC.dst\n def dst(self, dt): # pylint: disable=unused-argument\n \"\"\"Daylight savings time offset.\"\"\"\n return self._dst","label":0},{"cid":"google/cloud/_helpers.py::_UTC.fromutc","text":"google/cloud/_helpers.py::_UTC.fromutc\n def fromutc(self, dt):\n \"\"\"Convert a timestamp from (naive) UTC to this timezone.\"\"\"\n if dt.tzinfo is None:\n return dt.replace(tzinfo=self)\n return super(_UTC, self).fromutc(dt)","label":0},{"cid":"google/cloud/_helpers.py::_UTC.tzname","text":"google/cloud/_helpers.py::_UTC.tzname\n def tzname(self, dt): # pylint: disable=unused-argument\n \"\"\"Get the name of this timezone.\"\"\"\n return self._tzname","label":0},{"cid":"google/cloud/_helpers.py::_UTC.utcoffset","text":"google/cloud/_helpers.py::_UTC.utcoffset\n def utcoffset(self, dt): # pylint: disable=unused-argument\n \"\"\"UTC offset of this timezone.\"\"\"\n return self._utcoffset","label":0},{"cid":"google/cloud/_helpers.py::_UTC.__repr__","text":"google/cloud/_helpers.py::_UTC.__repr__\n def __repr__(self):\n return '<%s>' % (self._tzname,)","label":0},{"cid":"google/cloud/_helpers.py::_UTC.__str__","text":"google/cloud/_helpers.py::_UTC.__str__\n def __str__(self):\n return self._tzname","label":0},{"cid":"google/cloud/_helpers.py::_ensure_tuple_or_list","text":"google/cloud/_helpers.py::_ensure_tuple_or_list\ndef _ensure_tuple_or_list(arg_name, tuple_or_list):\n \"\"\"Ensures an input is a tuple or list.\n\n This effectively reduces the iterable types allowed to a very short\n whitelist: list and tuple.\n\n :type arg_name: str\n :param arg_name: Name of argument to use in error message.\n\n :type tuple_or_list: sequence of str\n :param tuple_or_list: Sequence to be verified.\n\n :rtype: list of str\n :returns: The ``tuple_or_list`` passed in cast to a ``list``.\n :raises TypeError: if the ``tuple_or_list`` is not a tuple or list.\n \"\"\"\n if not isinstance(tuple_or_list, (tuple, list)):\n raise TypeError('Expected %s to be a tuple or list. '\n 'Received %r' % (arg_name, tuple_or_list))\n return list(tuple_or_list)","label":0},{"cid":"google/cloud/_helpers.py::_app_engine_id","text":"google/cloud/_helpers.py::_app_engine_id\ndef _app_engine_id():\n \"\"\"Gets the App Engine application ID if it can be inferred.\n\n :rtype: str or ``NoneType``\n :returns: App Engine application ID if running in App Engine,\n else ``None``.\n \"\"\"\n if app_identity is None:\n return None\n\n return app_identity.get_application_id()","label":0},{"cid":"google/cloud/_helpers.py::_file_project_id","text":"google/cloud/_helpers.py::_file_project_id\ndef _file_project_id():\n \"\"\"Gets the project ID from the credentials file if one is available.\n\n :rtype: str or ``NoneType``\n :returns: Project ID from JSON credentials file if value exists,\n else ``None``.\n \"\"\"\n credentials_file_path = os.getenv(CREDENTIALS)\n if credentials_file_path:\n with open(credentials_file_path, 'rb') as credentials_file:\n credentials_json = credentials_file.read()\n credentials = json.loads(credentials_json.decode('utf-8'))\n return credentials.get('project_id')","label":0},{"cid":"google/cloud/_helpers.py::_get_nix_config_path","text":"google/cloud/_helpers.py::_get_nix_config_path\ndef _get_nix_config_path():\n \"\"\"Get the ``gcloud`` CLI config path on *nix systems.\n\n :rtype: str\n :returns: The filename on a *nix system containing the CLI\n config file.\n \"\"\"\n return os.path.join(_USER_ROOT, '.config', _GCLOUD_CONFIG_FILE)","label":0},{"cid":"google/cloud/_helpers.py::_get_windows_config_path","text":"google/cloud/_helpers.py::_get_windows_config_path\ndef _get_windows_config_path():\n \"\"\"Get the ``gcloud`` CLI config path on Windows systems.\n\n :rtype: str\n :returns: The filename on a Windows system containing the CLI\n config file.\n \"\"\"\n appdata_dir = os.getenv('APPDATA', '')\n return os.path.join(appdata_dir, _GCLOUD_CONFIG_FILE)","label":0},{"cid":"google/cloud/_helpers.py::_default_service_project_id","text":"google/cloud/_helpers.py::_default_service_project_id\ndef _default_service_project_id():\n \"\"\"Retrieves the project ID from the gcloud command line tool.\n\n This assumes the ``.config`` directory is stored\n - in ~/.config on *nix systems\n - in the %APPDATA% directory on Windows systems\n\n Additionally, the ${HOME} / \"~\" directory may not be present on Google\n App Engine, so this may be conditionally ignored.\n\n Files that cannot be opened with configparser are silently ignored; this is\n designed so that you can specify a list of potential configuration file\n locations.\n\n :rtype: str or ``NoneType``\n :returns: Project-ID from default configuration file else ``None``\n \"\"\"\n search_paths = []\n if _USER_ROOT is not None:\n search_paths.append(_get_nix_config_path())\n\n if os.name == 'nt':\n search_paths.append(_get_windows_config_path())\n\n config = configparser.RawConfigParser()\n config.read(search_paths)\n\n if config.has_section(_GCLOUD_CONFIG_SECTION):\n return config.get(_GCLOUD_CONFIG_SECTION, _GCLOUD_CONFIG_KEY)","label":0},{"cid":"google/cloud/_helpers.py::_compute_engine_id","text":"google/cloud/_helpers.py::_compute_engine_id\ndef _compute_engine_id():\n \"\"\"Gets the Compute Engine project ID if it can be inferred.\n\n Uses 169.254.169.254 for the metadata server to avoid request\n latency from DNS lookup.\n\n See https://cloud.google.com/compute/docs/metadata#metadataserver\n for information about this IP address. (This IP is also used for\n Amazon EC2 instances, so the metadata flavor is crucial.)\n\n See https://github.com/google/oauth2client/issues/93 for context about\n DNS latency.\n\n :rtype: str or ``NoneType``\n :returns: Compute Engine project ID if the metadata service is available,\n else ``None``.\n \"\"\"\n host = '169.254.169.254'\n uri_path = '/computeMetadata/v1/project/project-id'\n headers = {'Metadata-Flavor': 'Google'}\n connection = http_client.HTTPConnection(host, timeout=0.1)\n\n try:\n connection.request('GET', uri_path, headers=headers)\n response = connection.getresponse()\n if response.status == 200:\n return response.read()\n except socket.error: # socket.timeout or socket.error(64, 'Host is down')\n pass\n finally:\n connection.close()","label":0},{"cid":"google/cloud/_helpers.py::_get_production_project","text":"google/cloud/_helpers.py::_get_production_project\ndef _get_production_project():\n \"\"\"Gets the production project if it can be inferred.\"\"\"\n return os.getenv(PROJECT)","label":0},{"cid":"google/cloud/_helpers.py::_determine_default_project","text":"google/cloud/_helpers.py::_determine_default_project\ndef _determine_default_project(project=None):\n \"\"\"Determine default project ID explicitly or implicitly as fall-back.\n\n In implicit case, supports three environments. In order of precedence, the\n implicit environments are:\n\n * GOOGLE_CLOUD_PROJECT environment variable\n * GOOGLE_APPLICATION_CREDENTIALS JSON file\n * Get default service project from\n ``$ gcloud beta auth application-default login``\n * Google App Engine application ID\n * Google Compute Engine project ID (from metadata server)\n\n :type project: str\n :param project: Optional. The project name to use as default.\n\n :rtype: str or ``NoneType``\n :returns: Default project if it can be determined.\n \"\"\"\n if project is None:\n project = _get_production_project()\n\n if project is None:\n project = _file_project_id()\n\n if project is None:\n project = _default_service_project_id()\n\n if project is None:\n project = _app_engine_id()\n\n if project is None:\n project = _compute_engine_id()\n\n return project","label":0},{"cid":"google/cloud/_helpers.py::_millis","text":"google/cloud/_helpers.py::_millis\ndef _millis(when):\n \"\"\"Convert a zone-aware datetime to integer milliseconds.\n\n :type when: :class:`datetime.datetime`\n :param when: the datetime to convert\n\n :rtype: int\n :returns: milliseconds since epoch for ``when``\n \"\"\"\n micros = _microseconds_from_datetime(when)\n return micros // 1000","label":0},{"cid":"google/cloud/_helpers.py::_datetime_from_microseconds","text":"google/cloud/_helpers.py::_datetime_from_microseconds\ndef _datetime_from_microseconds(value):\n \"\"\"Convert timestamp to datetime, assuming UTC.\n\n :type value: float\n :param value: The timestamp to convert\n\n :rtype: :class:`datetime.datetime`\n :returns: The datetime object created from the value.\n \"\"\"\n return _EPOCH + datetime.timedelta(microseconds=value)","label":0},{"cid":"google/cloud/_helpers.py::_microseconds_from_datetime","text":"google/cloud/_helpers.py::_microseconds_from_datetime\ndef _microseconds_from_datetime(value):\n \"\"\"Convert non-none datetime to microseconds.\n\n :type value: :class:`datetime.datetime`\n :param value: The timestamp to convert.\n\n :rtype: int\n :returns: The timestamp, in microseconds.\n \"\"\"\n if not value.tzinfo:\n value = value.replace(tzinfo=UTC)\n # Regardless of what timezone is on the value, convert it to UTC.\n value = value.astimezone(UTC)\n # Convert the datetime to a microsecond timestamp.\n return int(calendar.timegm(value.timetuple()) * 1e6) + value.microsecond","label":0},{"cid":"google/cloud/_helpers.py::_millis_from_datetime","text":"google/cloud/_helpers.py::_millis_from_datetime\ndef _millis_from_datetime(value):\n \"\"\"Convert non-none datetime to timestamp, assuming UTC.\n\n :type value: :class:`datetime.datetime`, or None\n :param value: the timestamp\n\n :rtype: int, or ``NoneType``\n :returns: the timestamp, in milliseconds, or None\n \"\"\"\n if value is not None:\n return _millis(value)","label":0},{"cid":"google/cloud/_helpers.py::_date_from_iso8601_date","text":"google/cloud/_helpers.py::_date_from_iso8601_date\ndef _date_from_iso8601_date(value):\n \"\"\"Convert a ISO8601 date string to native datetime date\n\n :type value: str\n :param value: The date string to convert\n\n :rtype: :class:`datetime.date`\n :returns: A datetime date object created from the string\n\n \"\"\"\n return datetime.datetime.strptime(value, '%Y-%m-%d').date()","label":0},{"cid":"google/cloud/_helpers.py::_rfc3339_to_datetime","text":"google/cloud/_helpers.py::_rfc3339_to_datetime\ndef _rfc3339_to_datetime(dt_str):\n \"\"\"Convert a microsecond-precision timetamp to a native datetime.\n\n :type dt_str: str\n :param dt_str: The string to convert.\n\n :rtype: :class:`datetime.datetime`\n :returns: The datetime object created from the string.\n \"\"\"\n return datetime.datetime.strptime(\n dt_str, _RFC3339_MICROS).replace(tzinfo=UTC)","label":0},{"cid":"google/cloud/_helpers.py::_rfc3339_nanos_to_datetime","text":"google/cloud/_helpers.py::_rfc3339_nanos_to_datetime\ndef _rfc3339_nanos_to_datetime(dt_str):\n \"\"\"Convert a nanosecond-precision timestamp to a native datetime.\n\n .. note::\n\n Python datetimes do not support nanosecond precision; this function\n therefore truncates such values to microseconds.\n\n :type dt_str: str\n :param dt_str: The string to convert.\n\n :rtype: :class:`datetime.datetime`\n :returns: The datetime object created from the string.\n :raises ValueError: If the timestamp does not match the RFC 3339\n regular expression.\n \"\"\"\n with_nanos = _RFC3339_NANOS.match(dt_str)\n if with_nanos is None:\n raise ValueError(\n 'Timestamp: %r, does not match pattern: %r' % (\n dt_str, _RFC3339_NANOS.pattern))\n bare_seconds = datetime.datetime.strptime(\n with_nanos.group('no_fraction'), _RFC3339_NO_FRACTION)\n fraction = with_nanos.group('nanos')\n scale = 9 - len(fraction)\n nanos = int(fraction) * (10 ** scale)\n micros = nanos // 1000\n return bare_seconds.replace(microsecond=micros, tzinfo=UTC)","label":0},{"cid":"google/cloud/_helpers.py::_datetime_to_rfc3339","text":"google/cloud/_helpers.py::_datetime_to_rfc3339\ndef _datetime_to_rfc3339(value, ignore_zone=True):\n \"\"\"Convert a timestamp to a string.\n\n :type value: :class:`datetime.datetime`\n :param value: The datetime object to be converted to a string.\n\n :type ignore_zone: boolean\n :param ignore_zone: If True, then the timezone (if any) of the datetime\n object is ignored.\n\n :rtype: str\n :returns: The string representing the datetime stamp.\n \"\"\"\n if not ignore_zone and value.tzinfo is not None:\n # Convert to UTC and remove the time zone info.\n value = value.replace(tzinfo=None) - value.utcoffset()\n\n return value.strftime(_RFC3339_MICROS)","label":0},{"cid":"google/cloud/_helpers.py::_to_bytes","text":"google/cloud/_helpers.py::_to_bytes\ndef _to_bytes(value, encoding='ascii'):\n \"\"\"Converts a string value to bytes, if necessary.\n\n Unfortunately, ``six.b`` is insufficient for this task since in\n Python2 it does not modify ``unicode`` objects.\n\n :type value: str / bytes or unicode\n :param value: The string/bytes value to be converted.\n\n :type encoding: str\n :param encoding: The encoding to use to convert unicode to bytes. Defaults\n to \"ascii\", which will not allow any characters from\n ordinals larger than 127. Other useful values are\n \"latin-1\", which which will only allows byte ordinals\n (up to 255) and \"utf-8\", which will encode any unicode\n that needs to be.\n\n :rtype: str / bytes\n :returns: The original value converted to bytes (if unicode) or as passed\n in if it started out as bytes.\n :raises TypeError: if the value could not be converted to bytes.\n \"\"\"\n result = (value.encode(encoding)\n if isinstance(value, six.text_type) else value)\n if isinstance(result, six.binary_type):\n return result\n else:\n raise TypeError('%r could not be converted to bytes' % (value,))","label":0},{"cid":"google/cloud/_helpers.py::_bytes_to_unicode","text":"google/cloud/_helpers.py::_bytes_to_unicode\ndef _bytes_to_unicode(value):\n \"\"\"Converts bytes to a unicode value, if necessary.\n\n :type value: bytes\n :param value: bytes value to attempt string conversion on.\n\n :rtype: str\n :returns: The original value converted to unicode (if bytes) or as passed\n in if it started out as unicode.\n\n :raises ValueError: if the value could not be converted to unicode.\n \"\"\"\n result = (value.decode('utf-8')\n if isinstance(value, six.binary_type) else value)\n if isinstance(result, six.text_type):\n return result\n else:\n raise ValueError('%r could not be converted to unicode' % (value,))","label":0},{"cid":"google/cloud/_helpers.py::_pb_timestamp_to_datetime","text":"google/cloud/_helpers.py::_pb_timestamp_to_datetime\ndef _pb_timestamp_to_datetime(timestamp_pb):\n \"\"\"Convert a Timestamp protobuf to a datetime object.\n\n :type timestamp_pb: :class:`google.protobuf.timestamp_pb2.Timestamp`\n :param timestamp_pb: A Google returned timestamp protobuf.\n\n :rtype: :class:`datetime.datetime`\n :returns: A UTC datetime object converted from a protobuf timestamp.\n \"\"\"\n return (\n _EPOCH +\n datetime.timedelta(\n seconds=timestamp_pb.seconds,\n microseconds=(timestamp_pb.nanos / 1000.0),\n )\n )","label":0},{"cid":"google/cloud/_helpers.py::_pb_timestamp_to_rfc3339","text":"google/cloud/_helpers.py::_pb_timestamp_to_rfc3339\ndef _pb_timestamp_to_rfc3339(timestamp_pb):\n \"\"\"Convert a Timestamp protobuf to an RFC 3339 string.\n\n :type timestamp_pb: :class:`google.protobuf.timestamp_pb2.Timestamp`\n :param timestamp_pb: A Google returned timestamp protobuf.\n\n :rtype: string\n :returns: An RFC 3339 formatted timestamp string.\n \"\"\"\n timestamp = _pb_timestamp_to_datetime(timestamp_pb)\n return _datetime_to_rfc3339(timestamp)","label":0},{"cid":"google/cloud/_helpers.py::_datetime_to_pb_timestamp","text":"google/cloud/_helpers.py::_datetime_to_pb_timestamp\ndef _datetime_to_pb_timestamp(when):\n \"\"\"Convert a datetime object to a Timestamp protobuf.\n\n :type when: :class:`datetime.datetime`\n :param when: the datetime to convert\n\n :rtype: :class:`google.protobuf.timestamp_pb2.Timestamp`\n :returns: A timestamp protobuf corresponding to the object.\n \"\"\"\n ms_value = _microseconds_from_datetime(when)\n seconds, micros = divmod(ms_value, 10**6)\n nanos = micros * 10**3\n return timestamp_pb2.Timestamp(seconds=seconds, nanos=nanos)","label":0},{"cid":"google/cloud/_helpers.py::_name_from_project_path","text":"google/cloud/_helpers.py::_name_from_project_path\ndef _name_from_project_path(path, project, template):\n \"\"\"Validate a URI path and get the leaf object's name.\n\n :type path: str\n :param path: URI path containing the name.\n\n :type project: str or NoneType\n :param project: The project associated with the request. It is\n included for validation purposes. If passed as None,\n disables validation.\n\n :type template: str\n :param template: Template regex describing the expected form of the path.\n The regex must have two named groups, 'project' and\n 'name'.\n\n :rtype: str\n :returns: Name parsed from ``path``.\n :raises ValueError: if the ``path`` is ill-formed or if the project from\n the ``path`` does not agree with the ``project``\n passed in.\n \"\"\"\n if isinstance(template, str):\n template = re.compile(template)\n\n match = template.match(path)\n\n if not match:\n raise ValueError('path \"%s\" did not match expected pattern \"%s\"' % (\n path, template.pattern,))\n\n if project is not None:\n found_project = match.group('project')\n if found_project != project:\n raise ValueError(\n 'Project from client (%s) should agree with '\n 'project from resource(%s).' % (project, found_project))\n\n return match.group('name')","label":0},{"cid":"google/cloud/_helpers.py::MetadataPlugin.__init__","text":"google/cloud/_helpers.py::MetadataPlugin.__init__\n def __init__(self, credentials):\n self._credentials = credentials","label":0},{"cid":"google/cloud/_helpers.py::MetadataPlugin.__call__","text":"google/cloud/_helpers.py::MetadataPlugin.__call__\n def __call__(self, unused_context, callback):\n \"\"\"Adds authorization header to request metadata.\n\n :type unused_context: object\n :param unused_context: A gRPC context which is not needed\n to modify headers.\n\n :type callback: callable\n :param callback: A callback which will use the headers.\n \"\"\"\n access_token = self._credentials.get_access_token().access_token\n headers = [\n ('authorization', 'Bearer ' + access_token),\n ]\n callback(headers, None)","label":0},{"cid":"google/cloud/_helpers.py::make_secure_stub","text":"google/cloud/_helpers.py::make_secure_stub\ndef make_secure_stub(credentials, user_agent, stub_class, host):\n \"\"\"Makes a secure stub for an RPC service.\n\n Uses / depends on gRPC.\n\n :type credentials: :class:`oauth2client.client.OAuth2Credentials`\n :param credentials: The OAuth2 Credentials to use for creating\n access tokens.\n\n :type user_agent: str\n :param user_agent: (Optional) The user agent to be used with API requests.\n\n :type stub_class: type\n :param stub_class: A gRPC stub type for a given service.\n\n :type host: str\n :param host: The host for the service.\n\n :rtype: object, instance of ``stub_class``\n :returns: The stub object used to make gRPC requests to a given API.\n \"\"\"\n # ssl_channel_credentials() loads root certificates from\n # `grpc/_adapter/credentials/roots.pem`.\n transport_creds = grpc.ssl_channel_credentials()\n custom_metadata_plugin = MetadataPlugin(credentials)\n auth_creds = grpc.metadata_call_credentials(\n custom_metadata_plugin, name='google_creds')\n channel_creds = grpc.composite_channel_credentials(\n transport_creds, auth_creds)\n target = '%s:%d' % (host, http_client.HTTPS_PORT)\n channel_args = (\n ('grpc.primary_user_agent', user_agent),\n )\n channel = grpc.secure_channel(target, channel_creds,\n options=channel_args)\n return stub_class(channel)","label":0},{"cid":"google/cloud/_helpers.py::make_insecure_stub","text":"google/cloud/_helpers.py::make_insecure_stub\ndef make_insecure_stub(stub_class, host, port=None):\n \"\"\"Makes an insecure stub for an RPC service.\n\n Uses / depends on gRPC.\n\n :type stub_class: type\n :param stub_class: A gRPC stub type for a given service.\n\n :type host: str\n :param host: The host for the service. May also include the port\n if ``port`` is unspecified.\n\n :type port: int\n :param port: (Optional) The port for the service.\n\n :rtype: object, instance of ``stub_class``\n :returns: The stub object used to make gRPC requests to a given API.\n \"\"\"\n if port is None:\n target = host\n else:\n # NOTE: This assumes port != http_client.HTTPS_PORT:\n target = '%s:%d' % (host, port)\n channel = grpc.insecure_channel(target)\n return stub_class(channel)","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_not_null","text":"google/cloud/bigquery/_helpers.py::_not_null\ndef _not_null(value, field):\n \"\"\"Check whether 'value' should be coerced to 'field' type.\"\"\"\n return value is not None or field.mode != 'NULLABLE'","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_int_from_json","text":"google/cloud/bigquery/_helpers.py::_int_from_json\ndef _int_from_json(value, field):\n \"\"\"Coerce 'value' to an int, if set or not nullable.\"\"\"\n if _not_null(value, field):\n return int(value)","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_float_from_json","text":"google/cloud/bigquery/_helpers.py::_float_from_json\ndef _float_from_json(value, field):\n \"\"\"Coerce 'value' to a float, if set or not nullable.\"\"\"\n if _not_null(value, field):\n return float(value)","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_bool_from_json","text":"google/cloud/bigquery/_helpers.py::_bool_from_json\ndef _bool_from_json(value, field):\n \"\"\"Coerce 'value' to a bool, if set or not nullable.\"\"\"\n if _not_null(value, field):\n return value.lower() in ['t', 'true', '1']","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_datetime_from_json","text":"google/cloud/bigquery/_helpers.py::_datetime_from_json\ndef _datetime_from_json(value, field):\n \"\"\"Coerce 'value' to a datetime, if set or not nullable.\"\"\"\n if _not_null(value, field):\n # value will be a float in seconds, to microsecond precision, in UTC.\n return _datetime_from_microseconds(1e6 * float(value))","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_date_from_json","text":"google/cloud/bigquery/_helpers.py::_date_from_json\ndef _date_from_json(value, field):\n \"\"\"Coerce 'value' to a datetime date, if set or not nullable\"\"\"\n if _not_null(value, field):\n return _date_from_iso8601_date(value)","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_record_from_json","text":"google/cloud/bigquery/_helpers.py::_record_from_json\ndef _record_from_json(value, field):\n \"\"\"Coerce 'value' to a mapping, if set or not nullable.\"\"\"\n if _not_null(value, field):\n record = {}\n for subfield, cell in zip(field.fields, value['f']):\n converter = _CELLDATA_FROM_JSON[subfield.field_type]\n if field.mode == 'REPEATED':\n value = [converter(item, subfield) for item in cell['v']]\n else:\n value = converter(cell['v'], subfield)\n record[subfield.name] = value\n return record","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_string_from_json","text":"google/cloud/bigquery/_helpers.py::_string_from_json\ndef _string_from_json(value, _):\n \"\"\"NOOP string -> string coercion\"\"\"\n return value","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_rows_from_json","text":"google/cloud/bigquery/_helpers.py::_rows_from_json\ndef _rows_from_json(rows, schema):\n \"\"\"Convert JSON row data to rows w/ appropriate types.\"\"\"\n rows_data = []\n for row in rows:\n row_data = []\n for field, cell in zip(schema, row['f']):\n converter = _CELLDATA_FROM_JSON[field.field_type]\n if field.mode == 'REPEATED':\n row_data.append([converter(item, field)\n for item in cell['v']])\n else:\n row_data.append(converter(cell['v'], field))\n rows_data.append(tuple(row_data))\n return rows_data","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__init__","text":"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__init__\n def __init__(self, name):\n self.name = name\n self._backing_name = '_%s' % (self.name,)","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__get__","text":"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__get__\n def __get__(self, instance, owner):\n \"\"\"Descriptor protocal: accesstor\"\"\"\n if instance is None:\n return self\n return getattr(instance._configuration, self._backing_name)","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_ConfigurationProperty._validate","text":"google/cloud/bigquery/_helpers.py::_ConfigurationProperty._validate\n def _validate(self, value):\n \"\"\"Subclasses override to impose validation policy.\"\"\"\n pass","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__set__","text":"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__set__\n def __set__(self, instance, value):\n \"\"\"Descriptor protocal: mutator\"\"\"\n self._validate(value)\n setattr(instance._configuration, self._backing_name, value)","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__delete__","text":"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__delete__\n def __delete__(self, instance):\n \"\"\"Descriptor protocal: deleter\"\"\"\n delattr(instance._configuration, self._backing_name)","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_TypedProperty.__init__","text":"google/cloud/bigquery/_helpers.py::_TypedProperty.__init__\n def __init__(self, name, property_type):\n super(_TypedProperty, self).__init__(name)\n self.property_type = property_type","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_TypedProperty._validate","text":"google/cloud/bigquery/_helpers.py::_TypedProperty._validate\n def _validate(self, value):\n \"\"\"Ensure that 'value' is of the appropriate type.\n\n :raises: ValueError on a type mismatch.\n \"\"\"\n if not isinstance(value, self.property_type):\n raise ValueError('Required type: %s' % (self.property_type,))","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_EnumProperty._validate","text":"google/cloud/bigquery/_helpers.py::_EnumProperty._validate\n def _validate(self, value):\n \"\"\"Check that ``value`` is one of the allowed values.\n\n :raises: ValueError if value is not allowed.\n \"\"\"\n if value not in self.ALLOWED:\n raise ValueError('Pass one of: %s' ', '.join(self.ALLOWED))","label":0},{"cid":"google/cloud/bigquery/_helpers.py::UDFResource.__init__","text":"google/cloud/bigquery/_helpers.py::UDFResource.__init__\n def __init__(self, udf_type, value):\n self.udf_type = udf_type\n self.value = value","label":0},{"cid":"google/cloud/bigquery/_helpers.py::UDFResource.__eq__","text":"google/cloud/bigquery/_helpers.py::UDFResource.__eq__\n def __eq__(self, other):\n return(\n self.udf_type == other.udf_type and\n self.value == other.value)","label":0},{"cid":"google/cloud/bigquery/_helpers.py::UDFResourcesProperty.__get__","text":"google/cloud/bigquery/_helpers.py::UDFResourcesProperty.__get__\n def __get__(self, instance, owner):\n \"\"\"Descriptor protocol: accessor\"\"\"\n if instance is None:\n return self\n return list(instance._udf_resources)","label":0},{"cid":"google/cloud/bigquery/_helpers.py::UDFResourcesProperty.__set__","text":"google/cloud/bigquery/_helpers.py::UDFResourcesProperty.__set__\n def __set__(self, instance, value):\n \"\"\"Descriptor protocol: mutator\"\"\"\n if not all(isinstance(u, UDFResource) for u in value):\n raise ValueError(\"udf items must be UDFResource\")\n instance._udf_resources = tuple(value)","label":0},{"cid":"google/cloud/bigquery/_helpers.py::_build_udf_resources","text":"google/cloud/bigquery/_helpers.py::_build_udf_resources\ndef _build_udf_resources(resources):\n \"\"\"\n :type resources: sequence of :class:`UDFResource`\n :param resources: fields to be appended.\n\n :rtype: mapping\n :returns: a mapping describing userDefinedFunctionResources for the query.\n \"\"\"\n udfs = []\n for resource in resources:\n udf = {resource.udf_type: resource.value}\n udfs.append(udf)\n return udfs","label":0},{"cid":"google/cloud/bigquery/client.py::Project.__init__","text":"google/cloud/bigquery/client.py::Project.__init__\n def __init__(self, project_id, numeric_id, friendly_name):\n self.project_id = project_id\n self.numeric_id = numeric_id\n self.friendly_name = friendly_name","label":0},{"cid":"google/cloud/bigquery/client.py::Project.from_api_repr","text":"google/cloud/bigquery/client.py::Project.from_api_repr\n @classmethod\n def from_api_repr(cls, resource):\n \"\"\"Factory: construct an instance from a resource dict.\"\"\"\n return cls(\n resource['id'], resource['numericId'], resource['friendlyName'])","label":0},{"cid":"google/cloud/bigquery/client.py::Client.list_projects","text":"google/cloud/bigquery/client.py::Client.list_projects\n def list_projects(self, max_results=None, page_token=None):\n \"\"\"List projects for the project associated with this client.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/projects/list\n\n :type max_results: int\n :param max_results: maximum number of projects to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of projects. If\n not passed, the API will return the first page of\n projects.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`~google.cloud.bigquery.client.Project`,\n plus a \"next page token\" string: if the token is not None,\n indicates that more projects can be retrieved with another\n call (pass that value as ``page_token``).\n \"\"\"\n params = {}\n\n if max_results is not None:\n params['maxResults'] = max_results\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects'\n resp = self.connection.api_request(method='GET', path=path,\n query_params=params)\n projects = [Project.from_api_repr(resource)\n for resource in resp.get('projects', ())]\n return projects, resp.get('nextPageToken')","label":0},{"cid":"google/cloud/bigquery/client.py::Client.list_datasets","text":"google/cloud/bigquery/client.py::Client.list_datasets\n def list_datasets(self, include_all=False, max_results=None,\n page_token=None):\n \"\"\"List datasets for the project associated with this client.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/list\n\n :type include_all: boolean\n :param include_all: True if results include hidden datasets.\n\n :type max_results: int\n :param max_results: maximum number of datasets to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of datasets. If\n not passed, the API will return the first page of\n datasets.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`~google.cloud.bigquery.dataset.Dataset`,\n plus a \"next page token\" string: if the token is not None,\n indicates that more datasets can be retrieved with another\n call (pass that value as ``page_token``).\n \"\"\"\n params = {}\n\n if include_all:\n params['all'] = True\n\n if max_results is not None:\n params['maxResults'] = max_results\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/datasets' % (self.project,)\n resp = self.connection.api_request(method='GET', path=path,\n query_params=params)\n datasets = [Dataset.from_api_repr(resource, self)\n for resource in resp.get('datasets', ())]\n return datasets, resp.get('nextPageToken')","label":0},{"cid":"google/cloud/bigquery/client.py::Client.dataset","text":"google/cloud/bigquery/client.py::Client.dataset\n def dataset(self, dataset_name):\n \"\"\"Construct a dataset bound to this client.\n\n :type dataset_name: str\n :param dataset_name: Name of the dataset.\n\n :rtype: :class:`google.cloud.bigquery.dataset.Dataset`\n :returns: a new ``Dataset`` instance\n \"\"\"\n return Dataset(dataset_name, client=self)","label":0},{"cid":"google/cloud/bigquery/client.py::Client.job_from_resource","text":"google/cloud/bigquery/client.py::Client.job_from_resource\n def job_from_resource(self, resource):\n \"\"\"Detect correct job type from resource and instantiate.\n\n :type resource: dict\n :param resource: one job resource from API response\n\n :rtype: One of:\n :class:`google.cloud.bigquery.job.LoadTableFromStorageJob`,\n :class:`google.cloud.bigquery.job.CopyJob`,\n :class:`google.cloud.bigquery.job.ExtractTableToStorageJob`,\n :class:`google.cloud.bigquery.job.QueryJob`,\n :class:`google.cloud.bigquery.job.RunSyncQueryJob`\n :returns: the job instance, constructed via the resource\n \"\"\"\n config = resource['configuration']\n if 'load' in config:\n return LoadTableFromStorageJob.from_api_repr(resource, self)\n elif 'copy' in config:\n return CopyJob.from_api_repr(resource, self)\n elif 'extract' in config:\n return ExtractTableToStorageJob.from_api_repr(resource, self)\n elif 'query' in config:\n return QueryJob.from_api_repr(resource, self)\n raise ValueError('Cannot parse job resource')","label":0},{"cid":"google/cloud/bigquery/client.py::Client.list_jobs","text":"google/cloud/bigquery/client.py::Client.list_jobs\n def list_jobs(self, max_results=None, page_token=None, all_users=None,\n state_filter=None):\n \"\"\"List jobs for the project associated with this client.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/list\n\n :type max_results: int\n :param max_results: maximum number of jobs to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of jobs. If\n not passed, the API will return the first page of\n jobs.\n\n :type all_users: boolean\n :param all_users: if true, include jobs owned by all users in the\n project.\n\n :type state_filter: str\n :param state_filter: if passed, include only jobs matching the given\n state. One of\n\n * ``\"done\"``\n * ``\"pending\"``\n * ``\"running\"``\n\n :rtype: tuple, (list, str)\n :returns: list of job instances, plus a \"next page token\" string:\n if the token is not ``None``, indicates that more jobs can be\n retrieved with another call, passing that value as\n ``page_token``).\n \"\"\"\n params = {'projection': 'full'}\n\n if max_results is not None:\n params['maxResults'] = max_results\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n if all_users is not None:\n params['allUsers'] = all_users\n\n if state_filter is not None:\n params['stateFilter'] = state_filter\n\n path = '/projects/%s/jobs' % (self.project,)\n resp = self.connection.api_request(method='GET', path=path,\n query_params=params)\n ","label":0},{"cid":"google/cloud/bigquery/client.py::Client.load_table_from_storage","text":"google/cloud/bigquery/client.py::Client.load_table_from_storage\n def load_table_from_storage(self, job_name, destination, *source_uris):\n \"\"\"Construct a job for loading data into a table from CloudStorage.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.load\n\n :type job_name: str\n :param job_name: Name of the job.\n\n :type destination: :class:`google.cloud.bigquery.table.Table`\n :param destination: Table into which data is to be loaded.\n\n :type source_uris: sequence of string\n :param source_uris: URIs of data files to be loaded; in format\n ``gs://<bucket_name>/<object_name_or_glob>``.\n\n :rtype: :class:`google.cloud.bigquery.job.LoadTableFromStorageJob`\n :returns: a new ``LoadTableFromStorageJob`` instance\n \"\"\"\n return LoadTableFromStorageJob(job_name, destination, source_uris,\n client=self)","label":0},{"cid":"google/cloud/bigquery/client.py::Client.copy_table","text":"google/cloud/bigquery/client.py::Client.copy_table\n def copy_table(self, job_name, destination, *sources):\n \"\"\"Construct a job for copying one or more tables into another table.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.copy\n\n :type job_name: str\n :param job_name: Name of the job.\n\n :type destination: :class:`google.cloud.bigquery.table.Table`\n :param destination: Table into which data is to be copied.\n\n :type sources: sequence of :class:`google.cloud.bigquery.table.Table`\n :param sources: tables to be copied.\n\n :rtype: :class:`google.cloud.bigquery.job.CopyJob`\n :returns: a new ``CopyJob`` instance\n \"\"\"\n return CopyJob(job_name, destination, sources, client=self)","label":0},{"cid":"google/cloud/bigquery/client.py::Client.extract_table_to_storage","text":"google/cloud/bigquery/client.py::Client.extract_table_to_storage\n def extract_table_to_storage(self, job_name, source, *destination_uris):\n \"\"\"Construct a job for extracting a table into Cloud Storage files.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.extract\n\n :type job_name: str\n :param job_name: Name of the job.\n\n :type source: :class:`google.cloud.bigquery.table.Table`\n :param source: table to be extracted.\n\n :type destination_uris: sequence of string\n :param destination_uris: URIs of CloudStorage file(s) into which\n table data is to be extracted; in format\n ``gs://<bucket_name>/<object_name_or_glob>``.\n\n :rtype: :class:`google.cloud.bigquery.job.ExtractTableToStorageJob`\n :returns: a new ``ExtractTableToStorageJob`` instance\n \"\"\"\n return ExtractTableToStorageJob(job_name, source, destination_uris,\n client=self)","label":0},{"cid":"google/cloud/bigquery/client.py::Client.run_async_query","text":"google/cloud/bigquery/client.py::Client.run_async_query\n def run_async_query(self, job_name, query):\n \"\"\"Construct a job for running a SQL query asynchronously.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.query\n\n :type job_name: str\n :param job_name: Name of the job.\n\n :type query: str\n :param query: SQL query to be executed\n\n :rtype: :class:`google.cloud.bigquery.job.QueryJob`\n :returns: a new ``QueryJob`` instance\n \"\"\"\n return QueryJob(job_name, query, client=self)","label":0},{"cid":"google/cloud/bigquery/client.py::Client.run_sync_query","text":"google/cloud/bigquery/client.py::Client.run_sync_query\n def run_sync_query(self, query):\n \"\"\"Run a SQL query synchronously.\n\n :type query: str\n :param query: SQL query to be executed\n\n :rtype: :class:`google.cloud.bigquery.query.QueryResults`\n :returns: a new ``QueryResults`` instance\n \"\"\"\n return QueryResults(query, client=self)","label":0},{"cid":"google/cloud/bigquery/dataset.py::AccessGrant.__init__","text":"google/cloud/bigquery/dataset.py::AccessGrant.__init__\n def __init__(self, role, entity_type, entity_id):\n if entity_type not in self.ENTITY_TYPES:\n message = 'Entity type %r not among: %s' % (\n entity_type, ', '.join(self.ENTITY_TYPES))\n raise ValueError(message)\n if entity_type == 'view':\n if role is not None:\n raise ValueError('Role must be None for a view. Received '\n 'role: %r' % (role,))\n else:\n if role is None:\n raise ValueError('Role must be set for entity '\n 'type %r' % (entity_type,))\n\n self.role = role\n self.entity_type = entity_type\n self.entity_id = entity_id","label":0},{"cid":"google/cloud/bigquery/dataset.py::AccessGrant.__eq__","text":"google/cloud/bigquery/dataset.py::AccessGrant.__eq__\n def __eq__(self, other):\n return (\n self.role == other.role and\n self.entity_type == other.entity_type and\n self.entity_id == other.entity_id)","label":0},{"cid":"google/cloud/bigquery/dataset.py::AccessGrant.__repr__","text":"google/cloud/bigquery/dataset.py::AccessGrant.__repr__\n def __repr__(self):\n return '<AccessGrant: role=%s, %s=%s>' % (\n self.role, self.entity_type, self.entity_id)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.__init__","text":"google/cloud/bigquery/dataset.py::Dataset.__init__\n def __init__(self, name, client, access_grants=()):\n self.name = name\n self._client = client\n self._properties = {}\n # Let the @property do validation.\n self.access_grants = access_grants","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.project","text":"google/cloud/bigquery/dataset.py::Dataset.project\n @property\n def project(self):\n \"\"\"Project bound to the dataset.\n\n :rtype: string\n :returns: the project (derived from the client).\n \"\"\"\n return self._client.project","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.path","text":"google/cloud/bigquery/dataset.py::Dataset.path\n @property\n def path(self):\n \"\"\"URL path for the dataset's APIs.\n\n :rtype: string\n :returns: the path based on project and dataste name.\n \"\"\"\n return '/projects/%s/datasets/%s' % (self.project, self.name)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.access_grants","text":"google/cloud/bigquery/dataset.py::Dataset.access_grants\n @property\n def access_grants(self):\n \"\"\"Dataset's access grants.\n\n :rtype: list of :class:`AccessGrant`\n :returns: roles granted to entities for this dataset\n \"\"\"\n return list(self._access_grants)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.access_grants","text":"google/cloud/bigquery/dataset.py::Dataset.access_grants\n @access_grants.setter\n def access_grants(self, value):\n \"\"\"Update dataset's access grants\n\n :type value: list of :class:`AccessGrant`\n :param value: roles granted to entities for this dataset\n\n :raises: TypeError if 'value' is not a sequence, or ValueError if\n any item in the sequence is not an AccessGrant\n \"\"\"\n if not all(isinstance(field, AccessGrant) for field in value):\n raise ValueError('Values must be AccessGrant instances')\n self._access_grants = tuple(value)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.created","text":"google/cloud/bigquery/dataset.py::Dataset.created\n @property\n def created(self):\n \"\"\"Datetime at which the dataset was created.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the creation time (None until set from the server).\n \"\"\"\n creation_time = self._properties.get('creationTime')\n if creation_time is not None:\n # creation_time will be in milliseconds.\n return _datetime_from_microseconds(1000.0 * creation_time)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.dataset_id","text":"google/cloud/bigquery/dataset.py::Dataset.dataset_id\n @property\n def dataset_id(self):\n \"\"\"ID for the dataset resource.\n\n :rtype: string, or ``NoneType``\n :returns: the ID (None until set from the server).\n \"\"\"\n return self._properties.get('id')","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.etag","text":"google/cloud/bigquery/dataset.py::Dataset.etag\n @property\n def etag(self):\n \"\"\"ETag for the dataset resource.\n\n :rtype: string, or ``NoneType``\n :returns: the ETag (None until set from the server).\n \"\"\"\n return self._properties.get('etag')","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.modified","text":"google/cloud/bigquery/dataset.py::Dataset.modified\n @property\n def modified(self):\n \"\"\"Datetime at which the dataset was last modified.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the modification time (None until set from the server).\n \"\"\"\n modified_time = self._properties.get('lastModifiedTime')\n if modified_time is not None:\n # modified_time will be in milliseconds.\n return _datetime_from_microseconds(1000.0 * modified_time)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.self_link","text":"google/cloud/bigquery/dataset.py::Dataset.self_link\n @property\n def self_link(self):\n \"\"\"URL for the dataset resource.\n\n :rtype: string, or ``NoneType``\n :returns: the URL (None until set from the server).\n \"\"\"\n return self._properties.get('selfLink')","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.default_table_expiration_ms","text":"google/cloud/bigquery/dataset.py::Dataset.default_table_expiration_ms\n @property\n def default_table_expiration_ms(self):\n \"\"\"Default expiration time for tables in the dataset.\n\n :rtype: integer, or ``NoneType``\n :returns: The time in milliseconds, or None (the default).\n \"\"\"\n return self._properties.get('defaultTableExpirationMs')","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.default_table_expiration_ms","text":"google/cloud/bigquery/dataset.py::Dataset.default_table_expiration_ms\n @default_table_expiration_ms.setter\n def default_table_expiration_ms(self, value):\n \"\"\"Update default expiration time for tables in the dataset.\n\n :type value: integer, or ``NoneType``\n :param value: new default time, in milliseconds\n\n :raises: ValueError for invalid value types.\n \"\"\"\n if not isinstance(value, six.integer_types) and value is not None:\n raise ValueError(\"Pass an integer, or None\")\n self._properties['defaultTableExpirationMs'] = value","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.description","text":"google/cloud/bigquery/dataset.py::Dataset.description\n @property\n def description(self):\n \"\"\"Description of the dataset.\n\n :rtype: string, or ``NoneType``\n :returns: The description as set by the user, or None (the default).\n \"\"\"\n return self._properties.get('description')","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.description","text":"google/cloud/bigquery/dataset.py::Dataset.description\n @description.setter\n def description(self, value):\n \"\"\"Update description of the dataset.\n\n :type value: string, or ``NoneType``\n :param value: new description\n\n :raises: ValueError for invalid value types.\n \"\"\"\n if not isinstance(value, six.string_types) and value is not None:\n raise ValueError(\"Pass a string, or None\")\n self._properties['description'] = value","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.friendly_name","text":"google/cloud/bigquery/dataset.py::Dataset.friendly_name\n @property\n def friendly_name(self):\n \"\"\"Title of the dataset.\n\n :rtype: string, or ``NoneType``\n :returns: The name as set by the user, or None (the default).\n \"\"\"\n return self._properties.get('friendlyName')","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.friendly_name","text":"google/cloud/bigquery/dataset.py::Dataset.friendly_name\n @friendly_name.setter\n def friendly_name(self, value):\n \"\"\"Update title of the dataset.\n\n :type value: string, or ``NoneType``\n :param value: new title\n\n :raises: ValueError for invalid value types.\n \"\"\"\n if not isinstance(value, six.string_types) and value is not None:\n raise ValueError(\"Pass a string, or None\")\n self._properties['friendlyName'] = value","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.location","text":"google/cloud/bigquery/dataset.py::Dataset.location\n @property\n def location(self):\n \"\"\"Location in which the dataset is hosted.\n\n :rtype: string, or ``NoneType``\n :returns: The location as set by the user, or None (the default).\n \"\"\"\n return self._properties.get('location')","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.location","text":"google/cloud/bigquery/dataset.py::Dataset.location\n @location.setter\n def location(self, value):\n \"\"\"Update location in which the dataset is hosted.\n\n :type value: string, or ``NoneType``\n :param value: new location\n\n :raises: ValueError for invalid value types.\n \"\"\"\n if not isinstance(value, six.string_types) and value is not None:\n raise ValueError(\"Pass a string, or None\")\n self._properties['location'] = value","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.from_api_repr","text":"google/cloud/bigquery/dataset.py::Dataset.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client):\n \"\"\"Factory: construct a dataset given its API representation\n\n :type resource: dict\n :param resource: dataset resource representation returned from the API\n\n :type client: :class:`google.cloud.bigquery.client.Client`\n :param client: Client which holds credentials and project\n configuration for the dataset.\n\n :rtype: :class:`google.cloud.bigquery.dataset.Dataset`\n :returns: Dataset parsed from ``resource``.\n \"\"\"\n if ('datasetReference' not in resource or\n 'datasetId' not in resource['datasetReference']):\n raise KeyError('Resource lacks required identity information:'\n '[\"datasetReference\"][\"datasetId\"]')\n name = resource['datasetReference']['datasetId']\n dataset = cls(name, client=client)\n dataset._set_properties(resource)\n return dataset","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset._require_client","text":"google/cloud/bigquery/dataset.py::Dataset._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: :class:`google.cloud.bigquery.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._client\n return client","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset._parse_access_grants","text":"google/cloud/bigquery/dataset.py::Dataset._parse_access_grants\n @staticmethod\n def _parse_access_grants(access):\n \"\"\"Parse a resource fragment into a set of access grants.\n\n ``role`` augments the entity type and present **unless** the entity\n type is ``view``.\n\n :type access: list of mappings\n :param access: each mapping represents a single access grant.\n\n :rtype: list of :class:`AccessGrant`\n :returns: a list of parsed grants.\n :raises: :class:`ValueError` if a grant in ``access`` has more keys\n than ``role`` and one additional key.\n \"\"\"\n result = []\n for grant in access:\n grant = grant.copy()\n role = grant.pop('role', None)\n entity_type, entity_id = grant.popitem()\n if len(grant) != 0:\n raise ValueError('Grant has unexpected keys remaining.', grant)\n result.append(\n AccessGrant(role, entity_type, entity_id))\n return result","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset._set_properties","text":"google/cloud/bigquery/dataset.py::Dataset._set_properties\n def _set_properties(self, api_response):\n \"\"\"Update properties from resource in body of ``api_response``\n\n :type api_response: httplib2.Response\n :param api_response: response returned from an API call.\n \"\"\"\n self._properties.clear()\n cleaned = api_response.copy()\n access = cleaned.pop('access', ())\n self.access_grants = self._parse_access_grants(access)\n if 'creationTime' in cleaned:\n cleaned['creationTime'] = float(cleaned['creationTime'])\n if 'lastModifiedTime' in cleaned:\n cleaned['lastModifiedTime'] = float(cleaned['lastModifiedTime'])\n if 'defaultTableExpirationMs' in cleaned:\n cleaned['defaultTableExpirationMs'] = int(\n cleaned['defaultTableExpirationMs'])\n self._properties.update(cleaned)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset._build_access_resource","text":"google/cloud/bigquery/dataset.py::Dataset._build_access_resource\n def _build_access_resource(self):\n \"\"\"Generate a resource fragment for dataset's access grants.\"\"\"\n result = []\n for grant in self.access_grants:\n info = {grant.entity_type: grant.entity_id}\n if grant.role is not None:\n info['role'] = grant.role\n result.append(info)\n return result","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset._build_resource","text":"google/cloud/bigquery/dataset.py::Dataset._build_resource\n def _build_resource(self):\n \"\"\"Generate a resource for ``create`` or ``update``.\"\"\"\n resource = {\n 'datasetReference': {\n 'projectId': self.project, 'datasetId': self.name},\n }\n if self.default_table_expiration_ms is not None:\n value = self.default_table_expiration_ms\n resource['defaultTableExpirationMs'] = value\n\n if self.description is not None:\n resource['description'] = self.description\n\n if self.friendly_name is not None:\n resource['friendlyName'] = self.friendly_name\n\n if self.location is not None:\n resource['location'] = self.location\n\n if len(self.access_grants) > 0:\n resource['access'] = self._build_access_resource()\n\n return resource","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.create","text":"google/cloud/bigquery/dataset.py::Dataset.create\n def create(self, client=None):\n \"\"\"API call: create the dataset via a PUT request.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/tables/insert\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n path = '/projects/%s/datasets' % (self.project,)\n api_response = client.connection.api_request(\n method='POST', path=path, data=self._build_resource())\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.exists","text":"google/cloud/bigquery/dataset.py::Dataset.exists\n def exists(self, client=None):\n \"\"\"API call: test for the existence of the dataset via a GET request\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/get\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: bool\n :returns: Boolean indicating existence of the dataset.\n \"\"\"\n client = self._require_client(client)\n\n try:\n client.connection.api_request(method='GET', path=self.path,\n query_params={'fields': 'id'})\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.reload","text":"google/cloud/bigquery/dataset.py::Dataset.reload\n def reload(self, client=None):\n \"\"\"API call: refresh dataset properties via a GET request.\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/get\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n\n api_response = client.connection.api_request(\n method='GET', path=self.path)\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.patch","text":"google/cloud/bigquery/dataset.py::Dataset.patch\n def patch(self, client=None, **kw):\n \"\"\"API call: update individual dataset properties via a PATCH request.\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/patch\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :type kw: ``dict``\n :param kw: properties to be patched.\n\n :raises: ValueError for invalid value types.\n \"\"\"\n client = self._require_client(client)\n\n partial = {}\n\n if 'default_table_expiration_ms' in kw:\n value = kw['default_table_expiration_ms']\n if not isinstance(value, six.integer_types) and value is not None:\n raise ValueError(\"Pass an integer, or None\")\n partial['defaultTableExpirationMs'] = value\n\n if 'description' in kw:\n partial['description'] = kw['description']\n\n if 'friendly_name' in kw:\n partial['friendlyName'] = kw['friendly_name']\n\n if 'location' in kw:\n partial['location'] = kw['location']\n\n api_response = client.connection.api_request(\n method='PATCH', path=self.path, data=partial)\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.update","text":"google/cloud/bigquery/dataset.py::Dataset.update\n def update(self, client=None):\n \"\"\"API call: update dataset properties via a PUT request.\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/update\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n api_response = client.connection.api_request(\n method='PUT', path=self.path, data=self._build_resource())\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.delete","text":"google/cloud/bigquery/dataset.py::Dataset.delete\n def delete(self, client=None):\n \"\"\"API call: delete the dataset via a DELETE request.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/tables/delete\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n client.connection.api_request(method='DELETE', path=self.path)","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.list_tables","text":"google/cloud/bigquery/dataset.py::Dataset.list_tables\n def list_tables(self, max_results=None, page_token=None):\n \"\"\"List tables for the project associated with this client.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/tables/list\n\n :type max_results: int\n :param max_results: maximum number of tables to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of datasets. If\n not passed, the API will return the first page of\n datasets.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`google.cloud.bigquery.table.Table`, plus a\n \"next page token\" string: if not ``None``, indicates that\n more tables can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n params = {}\n\n if max_results is not None:\n params['maxResults'] = max_results\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/datasets/%s/tables' % (self.project, self.name)\n connection = self._client.connection\n resp = connection.api_request(method='GET', path=path,\n query_params=params)\n tables = [Table.from_api_repr(resource, self)\n for resource in resp.get('tables', ())]\n return tables, resp.get('nextPageToken')","label":0},{"cid":"google/cloud/bigquery/dataset.py::Dataset.table","text":"google/cloud/bigquery/dataset.py::Dataset.table\n def table(self, name, schema=()):\n \"\"\"Construct a table bound to this dataset.\n\n :type name: string\n :param name: Name of the table.\n\n :type schema: list of :class:`google.cloud.bigquery.table.SchemaField`\n :param schema: The table's schema\n\n :rtype: :class:`google.cloud.bigquery.table.Table`\n :returns: a new ``Table`` instance\n \"\"\"\n return Table(name, dataset=self, schema=schema)","label":0},{"cid":"google/cloud/bigquery/job.py::_BaseJob.__init__","text":"google/cloud/bigquery/job.py::_BaseJob.__init__\n def __init__(self, client):\n self._client = client\n self._properties = {}","label":0},{"cid":"google/cloud/bigquery/job.py::_BaseJob.project","text":"google/cloud/bigquery/job.py::_BaseJob.project\n @property\n def project(self):\n \"\"\"Project bound to the job.\n\n :rtype: string\n :returns: the project (derived from the client).\n \"\"\"\n return self._client.project","label":0},{"cid":"google/cloud/bigquery/job.py::_BaseJob._require_client","text":"google/cloud/bigquery/job.py::_BaseJob._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: :class:`google.cloud.bigquery.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._client\n return client","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.__init__","text":"google/cloud/bigquery/job.py::_AsyncJob.__init__\n def __init__(self, name, client):\n super(_AsyncJob, self).__init__(client)\n self.name = name","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.job_type","text":"google/cloud/bigquery/job.py::_AsyncJob.job_type\n @property\n def job_type(self):\n \"\"\"Type of job\n\n :rtype: string\n :returns: one of 'load', 'copy', 'extract', 'query'\n \"\"\"\n return self._JOB_TYPE","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.path","text":"google/cloud/bigquery/job.py::_AsyncJob.path\n @property\n def path(self):\n \"\"\"URL path for the job's APIs.\n\n :rtype: string\n :returns: the path based on project and job name.\n \"\"\"\n return '/projects/%s/jobs/%s' % (self.project, self.name)","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.etag","text":"google/cloud/bigquery/job.py::_AsyncJob.etag\n @property\n def etag(self):\n \"\"\"ETag for the job resource.\n\n :rtype: string, or ``NoneType``\n :returns: the ETag (None until set from the server).\n \"\"\"\n return self._properties.get('etag')","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.self_link","text":"google/cloud/bigquery/job.py::_AsyncJob.self_link\n @property\n def self_link(self):\n \"\"\"URL for the job resource.\n\n :rtype: string, or ``NoneType``\n :returns: the URL (None until set from the server).\n \"\"\"\n return self._properties.get('selfLink')","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.user_email","text":"google/cloud/bigquery/job.py::_AsyncJob.user_email\n @property\n def user_email(self):\n \"\"\"E-mail address of user who submitted the job.\n\n :rtype: string, or ``NoneType``\n :returns: the URL (None until set from the server).\n \"\"\"\n return self._properties.get('user_email')","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.created","text":"google/cloud/bigquery/job.py::_AsyncJob.created\n @property\n def created(self):\n \"\"\"Datetime at which the job was created.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the creation time (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n millis = statistics.get('creationTime')\n if millis is not None:\n return _datetime_from_microseconds(millis * 1000.0)","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.started","text":"google/cloud/bigquery/job.py::_AsyncJob.started\n @property\n def started(self):\n \"\"\"Datetime at which the job was started.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the start time (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n millis = statistics.get('startTime')\n if millis is not None:\n return _datetime_from_microseconds(millis * 1000.0)","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.ended","text":"google/cloud/bigquery/job.py::_AsyncJob.ended\n @property\n def ended(self):\n \"\"\"Datetime at which the job finished.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the end time (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n millis = statistics.get('endTime')\n if millis is not None:\n return _datetime_from_microseconds(millis * 1000.0)","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.error_result","text":"google/cloud/bigquery/job.py::_AsyncJob.error_result\n @property\n def error_result(self):\n \"\"\"Error information about the job as a whole.\n\n :rtype: mapping, or ``NoneType``\n :returns: the error information (None until set from the server).\n \"\"\"\n status = self._properties.get('status')\n if status is not None:\n return status.get('errorResult')","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.errors","text":"google/cloud/bigquery/job.py::_AsyncJob.errors\n @property\n def errors(self):\n \"\"\"Information about individual errors generated by the job.\n\n :rtype: list of mappings, or ``NoneType``\n :returns: the error information (None until set from the server).\n \"\"\"\n status = self._properties.get('status')\n if status is not None:\n return status.get('errors')","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.state","text":"google/cloud/bigquery/job.py::_AsyncJob.state\n @property\n def state(self):\n \"\"\"Status of the job.\n\n :rtype: string, or ``NoneType``\n :returns: the state (None until set from the server).\n \"\"\"\n status = self._properties.get('status')\n if status is not None:\n return status.get('state')","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob._scrub_local_properties","text":"google/cloud/bigquery/job.py::_AsyncJob._scrub_local_properties\n def _scrub_local_properties(self, cleaned):\n \"\"\"Helper: handle subclass properties in cleaned.\"\"\"\n pass","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob._set_properties","text":"google/cloud/bigquery/job.py::_AsyncJob._set_properties\n def _set_properties(self, api_response):\n \"\"\"Update properties from resource in body of ``api_response``\n\n :type api_response: httplib2.Response\n :param api_response: response returned from an API call\n \"\"\"\n cleaned = api_response.copy()\n self._scrub_local_properties(cleaned)\n\n statistics = cleaned.get('statistics', {})\n if 'creationTime' in statistics:\n statistics['creationTime'] = float(statistics['creationTime'])\n if 'startTime' in statistics:\n statistics['startTime'] = float(statistics['startTime'])\n if 'endTime' in statistics:\n statistics['endTime'] = float(statistics['endTime'])\n\n self._properties.clear()\n self._properties.update(cleaned)","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob._get_resource_config","text":"google/cloud/bigquery/job.py::_AsyncJob._get_resource_config\n @classmethod\n def _get_resource_config(cls, resource):\n \"\"\"Helper for :meth:`from_api_repr`\n\n :type resource: dict\n :param resource: resource for the job\n\n :rtype: dict\n :returns: tuple (string, dict), where the first element is the\n job name and the second contains job-specific configuration.\n :raises: :class:`KeyError` if the resource has no identifier, or\n is missing the appropriate configuration.\n \"\"\"\n if ('jobReference' not in resource or\n 'jobId' not in resource['jobReference']):\n raise KeyError('Resource lacks required identity information: '\n '[\"jobReference\"][\"jobId\"]')\n name = resource['jobReference']['jobId']\n if ('configuration' not in resource or\n cls._JOB_TYPE not in resource['configuration']):\n raise KeyError('Resource lacks required configuration: '\n '[\"configuration\"][\"%s\"]' % cls._JOB_TYPE)\n config = resource['configuration'][cls._JOB_TYPE]\n return name, config","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.begin","text":"google/cloud/bigquery/job.py::_AsyncJob.begin\n def begin(self, client=None):\n \"\"\"API call: begin the job via a POST request\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/insert\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :raises: :exc:`ValueError` if the job has already begin.\n \"\"\"\n if self.state is not None:\n raise ValueError(\"Job already begun.\")\n\n client = self._require_client(client)\n path = '/projects/%s/jobs' % (self.project,)\n api_response = client.connection.api_request(\n method='POST', path=path, data=self._build_resource())\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.exists","text":"google/cloud/bigquery/job.py::_AsyncJob.exists\n def exists(self, client=None):\n \"\"\"API call: test for the existence of the job via a GET request\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/get\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: bool\n :returns: Boolean indicating existence of the job.\n \"\"\"\n client = self._require_client(client)\n\n try:\n client.connection.api_request(method='GET', path=self.path,\n query_params={'fields': 'id'})\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.reload","text":"google/cloud/bigquery/job.py::_AsyncJob.reload\n def reload(self, client=None):\n \"\"\"API call: refresh job properties via a GET request\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/get\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n\n api_response = client.connection.api_request(\n method='GET', path=self.path)\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/bigquery/job.py::_AsyncJob.cancel","text":"google/cloud/bigquery/job.py::_AsyncJob.cancel\n def cancel(self, client=None):\n \"\"\"API call: cancel job via a POST request\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/cancel\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n\n api_response = client.connection.api_request(\n method='POST', path='%s/cancel' % (self.path,))\n self._set_properties(api_response['job'])","label":0},{"cid":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.__init__","text":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.__init__\n def __init__(self, name, destination, source_uris, client, schema=()):\n super(LoadTableFromStorageJob, self).__init__(name, client)\n self.destination = destination\n self.source_uris = source_uris\n # Let the @property do validation.\n self.schema = schema\n self._configuration = _LoadConfiguration()","label":0},{"cid":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.schema","text":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.schema\n @property\n def schema(self):\n \"\"\"Table's schema.\n\n :rtype: list of :class:`SchemaField`\n :returns: fields describing the schema\n \"\"\"\n return list(self._schema)","label":0},{"cid":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.schema","text":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.schema\n @schema.setter\n def schema(self, value):\n \"\"\"Update table's schema\n\n :type value: list of :class:`SchemaField`\n :param value: fields describing the schema\n\n :raises: TypeError if 'value' is not a sequence, or ValueError if\n any item in the sequence is not a SchemaField\n \"\"\"\n if not all(isinstance(field, SchemaField) for field in value):\n raise ValueError('Schema items must be fields')\n self._schema = tuple(value)","label":0},{"cid":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.input_file_bytes","text":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.input_file_bytes\n @property\n def input_file_bytes(self):\n \"\"\"Count of bytes loaded from source files.\n\n :rtype: integer, or ``NoneType``\n :returns: the count (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n return int(statistics['load']['inputFileBytes'])","label":0},{"cid":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.input_files","text":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.input_files\n @property\n def input_files(self):\n \"\"\"Count of source files.\n\n :rtype: integer, or ``NoneType``\n :returns: the count (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n return int(statistics['load']['inputFiles'])","label":0},{"cid":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.output_bytes","text":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.output_bytes\n @property\n def output_bytes(self):\n \"\"\"Count of bytes saved to destination table.\n\n :rtype: integer, or ``NoneType``\n :returns: the count (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n return int(statistics['load']['outputBytes'])","label":0},{"cid":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.output_rows","text":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.output_rows\n @property\n def output_rows(self):\n \"\"\"Count of rows saved to destination table.\n\n :rtype: integer, or ``NoneType``\n :returns: the count (None until set from the server).\n \"\"\"\n statistics = self._properties.get('statistics')\n if statistics is not None:\n return int(statistics['load']['outputRows'])","label":0},{"cid":"google/cloud/bigquery/job.py::LoadTableFromStorageJob._populate_config_resource","text":"google/cloud/bigquery/job.py::LoadTableFromStorageJob._populate_config_resource\n def _populate_config_resource(self, configuration):\n \"\"\"Helper for _build_resource: copy config properties to resource\"\"\"\n if self.allow_jagged_rows is not None:\n configuration['allowJaggedRows'] = self.allow_jagged_rows\n if self.allow_quoted_newlines is not None:\n configuration['allowQuotedNewlines'] = self.allow_quoted_newlines\n if self.create_disposition is not None:\n configuration['createDisposition'] = self.create_disposition\n if self.encoding is not None:\n configuration['encoding'] = self.encoding\n if self.field_delimiter is not None:\n configuration['fieldDelimiter'] = self.field_delimiter\n if self.ignore_unknown_values is not None:\n configuration['ignoreUnknownValues'] = self.ignore_unknown_values\n if self.max_bad_records is not None:\n configuration['maxBadRecords'] = self.max_bad_records\n if self.quote_character is not None:\n configuration['quote'] = self.quote_character\n if self.skip_leading_rows is not None:\n configuration['skipLeadingRows'] = self.skip_leading_rows\n if self.source_format is not None:\n configuration['sourceFormat'] = self.source_format\n if self.write_disposition is not None:\n configuration['writeDisposition'] = self.write_disposition","label":0},{"cid":"google/cloud/bigquery/job.py::LoadTableFromStorageJob._build_resource","text":"google/cloud/bigquery/job.py::LoadTableFromStorageJob._build_resource\n def _build_resource(self):\n \"\"\"Generate a resource for :meth:`begin`.\"\"\"\n resource = {\n 'jobReference': {\n 'projectId': self.project,\n 'jobId': self.name,\n },\n 'configuration': {\n self._JOB_TYPE: {\n 'sourceUris': self.source_uris,\n 'destinationTable': {\n 'projectId': self.destination.project,\n 'datasetId': self.destination.dataset_name,\n 'tableId': self.destination.name,\n },\n },\n },\n }\n configuration = resource['configuration'][self._JOB_TYPE]\n self._populate_config_resource(configuration)\n\n if len(self.schema) > 0:\n configuration['schema'] = {\n 'fields': _build_schema_resource(self.schema)}\n\n return resource","label":0},{"cid":"google/cloud/bigquery/job.py::LoadTableFromStorageJob._scrub_local_properties","text":"google/cloud/bigquery/job.py::LoadTableFromStorageJob._scrub_local_properties\n def _scrub_local_properties(self, cleaned):\n \"\"\"Helper: handle subclass properties in cleaned.\"\"\"\n schema = cleaned.pop('schema', {'fields': ()})\n self.schema = _parse_schema_resource(schema)","label":0},{"cid":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.from_api_repr","text":"google/cloud/bigquery/job.py::LoadTableFromStorageJob.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client):\n \"\"\"Factory: construct a job given its API representation\n\n .. note:\n\n This method assumes that the project found in the resource matches\n the client's project.\n\n :type resource: dict\n :param resource: dataset job representation returned from the API\n\n :type client: :class:`google.cloud.bigquery.client.Client`\n :param client: Client which holds credentials and project\n configuration for the dataset.\n\n :rtype: :class:`google.cloud.bigquery.job.LoadTableFromStorageJob`\n :returns: Job parsed from ``resource``.\n \"\"\"\n name, config = cls._get_resource_config(resource)\n dest_config = config['destinationTable']\n dataset = Dataset(dest_config['datasetId'], client)\n destination = Table(dest_config['tableId'], dataset)\n source_urls = config.get('sourceUris', ())\n job = cls(name, destination, source_urls, client=client)\n job._set_properties(resource)\n return job","label":0},{"cid":"google/cloud/bigquery/job.py::CopyJob.__init__","text":"google/cloud/bigquery/job.py::CopyJob.__init__\n def __init__(self, name, destination, sources, client):\n super(CopyJob, self).__init__(name, client)\n self.destination = destination\n self.sources = sources\n self._configuration = _CopyConfiguration()","label":0},{"cid":"google/cloud/bigquery/job.py::CopyJob._populate_config_resource","text":"google/cloud/bigquery/job.py::CopyJob._populate_config_resource\n def _populate_config_resource(self, configuration):\n \"\"\"Helper for _build_resource: copy config properties to resource\"\"\"\n if self.create_disposition is not None:\n configuration['createDisposition'] = self.create_disposition\n if self.write_disposition is not None:\n configuration['writeDisposition'] = self.write_disposition","label":0},{"cid":"google/cloud/bigquery/job.py::CopyJob._build_resource","text":"google/cloud/bigquery/job.py::CopyJob._build_resource\n def _build_resource(self):\n \"\"\"Generate a resource for :meth:`begin`.\"\"\"\n\n source_refs = [{\n 'projectId': table.project,\n 'datasetId': table.dataset_name,\n 'tableId': table.name,\n } for table in self.sources]\n\n resource = {\n 'jobReference': {\n 'projectId': self.project,\n 'jobId': self.name,\n },\n 'configuration': {\n self._JOB_TYPE: {\n 'sourceTables': source_refs,\n 'destinationTable': {\n 'projectId': self.destination.project,\n 'datasetId': self.destination.dataset_name,\n 'tableId': self.destination.name,\n },\n },\n },\n }\n configuration = resource['configuration'][self._JOB_TYPE]\n self._populate_config_resource(configuration)\n\n return resource","label":0},{"cid":"google/cloud/bigquery/job.py::CopyJob.from_api_repr","text":"google/cloud/bigquery/job.py::CopyJob.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client):\n \"\"\"Factory: construct a job given its API representation\n\n .. note:\n\n This method assumes that the project found in the resource matches\n the client's project.\n\n :type resource: dict\n :param resource: dataset job representation returned from the API\n\n :type client: :class:`google.cloud.bigquery.client.Client`\n :param client: Client which holds credentials and project\n configuration for the dataset.\n\n :rtype: :class:`google.cloud.bigquery.job.CopyJob`\n :returns: Job parsed from ``resource``.\n \"\"\"\n name, config = cls._get_resource_config(resource)\n dest_config = config['destinationTable']\n dataset = Dataset(dest_config['datasetId'], client)\n destination = Table(dest_config['tableId'], dataset)\n sources = []\n for source_config in config['sourceTables']:\n dataset = Dataset(source_config['datasetId'], client)\n sources.append(Table(source_config['tableId'], dataset))\n job = cls(name, destination, sources, client=client)\n job._set_properties(resource)\n return job","label":0},{"cid":"google/cloud/bigquery/job.py::ExtractTableToStorageJob.__init__","text":"google/cloud/bigquery/job.py::ExtractTableToStorageJob.__init__\n def __init__(self, name, source, destination_uris, client):\n super(ExtractTableToStorageJob, self).__init__(name, client)\n self.source = source\n self.destination_uris = destination_uris\n self._configuration = _ExtractConfiguration()","label":0},{"cid":"google/cloud/bigquery/job.py::ExtractTableToStorageJob._populate_config_resource","text":"google/cloud/bigquery/job.py::ExtractTableToStorageJob._populate_config_resource\n def _populate_config_resource(self, configuration):\n \"\"\"Helper for _build_resource: copy config properties to resource\"\"\"\n if self.compression is not None:\n configuration['compression'] = self.compression\n if self.destination_format is not None:\n configuration['destinationFormat'] = self.destination_format\n if self.field_delimiter is not None:\n configuration['fieldDelimiter'] = self.field_delimiter\n if self.print_header is not None:\n configuration['printHeader'] = self.print_header","label":0},{"cid":"google/cloud/bigquery/job.py::ExtractTableToStorageJob._build_resource","text":"google/cloud/bigquery/job.py::ExtractTableToStorageJob._build_resource\n def _build_resource(self):\n \"\"\"Generate a resource for :meth:`begin`.\"\"\"\n\n source_ref = {\n 'projectId': self.source.project,\n 'datasetId': self.source.dataset_name,\n 'tableId': self.source.name,\n }\n\n resource = {\n 'jobReference': {\n 'projectId': self.project,\n 'jobId': self.name,\n },\n 'configuration': {\n self._JOB_TYPE: {\n 'sourceTable': source_ref,\n 'destinationUris': self.destination_uris,\n },\n },\n }\n configuration = resource['configuration'][self._JOB_TYPE]\n self._populate_config_resource(configuration)\n\n return resource","label":0},{"cid":"google/cloud/bigquery/job.py::ExtractTableToStorageJob.from_api_repr","text":"google/cloud/bigquery/job.py::ExtractTableToStorageJob.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client):\n \"\"\"Factory: construct a job given its API representation\n\n .. note:\n\n This method assumes that the project found in the resource matches\n the client's project.\n\n :type resource: dict\n :param resource: dataset job representation returned from the API\n\n :type client: :class:`google.cloud.bigquery.client.Client`\n :param client: Client which holds credentials and project\n configuration for the dataset.\n\n :rtype: :class:`google.cloud.bigquery.job.ExtractTableToStorageJob`\n :returns: Job parsed from ``resource``.\n \"\"\"\n name, config = cls._get_resource_config(resource)\n source_config = config['sourceTable']\n dataset = Dataset(source_config['datasetId'], client)\n source = Table(source_config['tableId'], dataset)\n destination_uris = config['destinationUris']\n job = cls(name, source, destination_uris, client=client)\n job._set_properties(resource)\n return job","label":0},{"cid":"google/cloud/bigquery/job.py::QueryJob.__init__","text":"google/cloud/bigquery/job.py::QueryJob.__init__\n def __init__(self, name, query, client, udf_resources=()):\n super(QueryJob, self).__init__(name, client)\n self.query = query\n self.udf_resources = udf_resources\n self._configuration = _AsyncQueryConfiguration()","label":0},{"cid":"google/cloud/bigquery/job.py::QueryJob._destination_table_resource","text":"google/cloud/bigquery/job.py::QueryJob._destination_table_resource\n def _destination_table_resource(self):\n \"\"\"Create a JSON resource for the destination table.\n\n Helper for :meth:`_populate_config_resource` and\n :meth:`_scrub_local_properties`\n \"\"\"\n if self.destination is not None:\n return {\n 'projectId': self.destination.project,\n 'datasetId': self.destination.dataset_name,\n 'tableId': self.destination.name,\n }","label":0},{"cid":"google/cloud/bigquery/job.py::QueryJob._populate_config_resource_booleans","text":"google/cloud/bigquery/job.py::QueryJob._populate_config_resource_booleans\n def _populate_config_resource_booleans(self, configuration):\n \"\"\"Helper for _populate_config_resource.\"\"\"\n if self.allow_large_results is not None:\n configuration['allowLargeResults'] = self.allow_large_results\n if self.flatten_results is not None:\n configuration['flattenResults'] = self.flatten_results\n if self.use_query_cache is not None:\n configuration['useQueryCache'] = self.use_query_cache\n if self.use_legacy_sql is not None:\n configuration['useLegacySql'] = self.use_legacy_sql\n if self.dry_run is not None:\n configuration['dryRun'] = self.dry_run","label":0},{"cid":"google/cloud/bigquery/job.py::QueryJob._populate_config_resource","text":"google/cloud/bigquery/job.py::QueryJob._populate_config_resource\n def _populate_config_resource(self, configuration):\n \"\"\"Helper for _build_resource: copy config properties to resource\"\"\"\n self._populate_config_resource_booleans(configuration)\n\n if self.create_disposition is not None:\n configuration['createDisposition'] = self.create_disposition\n if self.default_dataset is not None:\n configuration['defaultDataset'] = {\n 'projectId': self.default_dataset.project,\n 'datasetId': self.default_dataset.name,\n }\n if self.destination is not None:\n table_res = self._destination_table_resource()\n configuration['destinationTable'] = table_res\n if self.priority is not None:\n configuration['priority'] = self.priority\n if self.write_disposition is not None:\n configuration['writeDisposition'] = self.write_disposition\n if self.maximum_billing_tier is not None:\n configuration['maximumBillingTier'] = self.maximum_billing_tier\n if self.maximum_bytes_billed is not None:\n configuration['maximumBytesBilled'] = self.maximum_bytes_billed\n if len(self._udf_resources) > 0:\n configuration[self._UDF_KEY] = _build_udf_resources(\n self._udf_resources)","label":0},{"cid":"google/cloud/bigquery/job.py::QueryJob._build_resource","text":"google/cloud/bigquery/job.py::QueryJob._build_resource\n def _build_resource(self):\n \"\"\"Generate a resource for :meth:`begin`.\"\"\"\n\n resource = {\n 'jobReference': {\n 'projectId': self.project,\n 'jobId': self.name,\n },\n 'configuration': {\n self._JOB_TYPE: {\n 'query': self.query,\n },\n },\n }\n configuration = resource['configuration'][self._JOB_TYPE]\n self._populate_config_resource(configuration)\n\n return resource","label":0},{"cid":"google/cloud/bigquery/job.py::QueryJob._scrub_local_properties","text":"google/cloud/bigquery/job.py::QueryJob._scrub_local_properties\n def _scrub_local_properties(self, cleaned):\n \"\"\"Helper: handle subclass properties in cleaned.\n\n .. note:\n\n This method assumes that the project found in the resource matches\n the client's project.\n \"\"\"\n configuration = cleaned['configuration']['query']\n dest_remote = configuration.get('destinationTable')\n\n if dest_remote is None:\n if self.destination is not None:\n del self.destination\n else:\n dest_local = self._destination_table_resource()\n if dest_remote != dest_local:\n dataset = self._client.dataset(dest_remote['datasetId'])\n self.destination = dataset.table(dest_remote['tableId'])","label":0},{"cid":"google/cloud/bigquery/job.py::QueryJob.from_api_repr","text":"google/cloud/bigquery/job.py::QueryJob.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client):\n \"\"\"Factory: construct a job given its API representation\n\n :type resource: dict\n :param resource: dataset job representation returned from the API\n\n :type client: :class:`google.cloud.bigquery.client.Client`\n :param client: Client which holds credentials and project\n configuration for the dataset.\n\n :rtype: :class:`google.cloud.bigquery.job.RunAsyncQueryJob`\n :returns: Job parsed from ``resource``.\n \"\"\"\n name, config = cls._get_resource_config(resource)\n query = config['query']\n job = cls(name, query, client=client)\n job._set_properties(resource)\n return job","label":0},{"cid":"google/cloud/bigquery/job.py::QueryJob.results","text":"google/cloud/bigquery/job.py::QueryJob.results\n def results(self):\n \"\"\"Construct a QueryResults instance, bound to this job.\n\n :rtype: :class:`~google.cloud.bigquery.query.QueryResults`\n :returns: results instance\n \"\"\"\n from google.cloud.bigquery.query import QueryResults\n return QueryResults.from_query_job(self)","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.__init__","text":"google/cloud/bigquery/query.py::QueryResults.__init__\n def __init__(self, query, client, udf_resources=()):\n self._client = client\n self._properties = {}\n self.query = query\n self._configuration = _SyncQueryConfiguration()\n self.udf_resources = udf_resources\n self._job = None","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.from_query_job","text":"google/cloud/bigquery/query.py::QueryResults.from_query_job\n @classmethod\n def from_query_job(cls, job):\n \"\"\"Factory: construct from an existing job.\n\n :type job: :class:`~google.cloud.bigquery.job.QueryJob`\n :param job: existing job\n\n :rtype: :class:`QueryResults`\n :returns: the instance, bound to the job\n \"\"\"\n instance = cls(job.query, job._client, job.udf_resources)\n instance._job = job\n job_ref = instance._properties.setdefault('jobReference', {})\n job_ref['jobId'] = job.name\n if job.default_dataset is not None:\n instance.default_dataset = job.default_dataset\n if job.use_query_cache is not None:\n instance.use_query_cache = job.use_query_cache\n if job.use_legacy_sql is not None:\n instance.use_legacy_sql = job.use_legacy_sql\n return instance","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.project","text":"google/cloud/bigquery/query.py::QueryResults.project\n @property\n def project(self):\n \"\"\"Project bound to the job.\n\n :rtype: string\n :returns: the project (derived from the client).\n \"\"\"\n return self._client.project","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults._require_client","text":"google/cloud/bigquery/query.py::QueryResults._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: :class:`google.cloud.bigquery.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._client\n return client","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.cache_hit","text":"google/cloud/bigquery/query.py::QueryResults.cache_hit\n @property\n def cache_hit(self):\n \"\"\"Query results served from cache.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#cacheHit\n\n :rtype: boolean or ``NoneType``\n :returns: True if the query results were served from cache (None\n until set by the server).\n \"\"\"\n return self._properties.get('cacheHit')","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.complete","text":"google/cloud/bigquery/query.py::QueryResults.complete\n @property\n def complete(self):\n \"\"\"Server completed query.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#jobComplete\n\n :rtype: boolean or ``NoneType``\n :returns: True if the query completed on the server (None\n until set by the server).\n \"\"\"\n return self._properties.get('jobComplete')","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.errors","text":"google/cloud/bigquery/query.py::QueryResults.errors\n @property\n def errors(self):\n \"\"\"Errors generated by the query.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#errors\n\n :rtype: list of mapping, or ``NoneType``\n :returns: Mappings describing errors generated on the server (None\n until set by the server).\n \"\"\"\n return self._properties.get('errors')","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.name","text":"google/cloud/bigquery/query.py::QueryResults.name\n @property\n def name(self):\n \"\"\"Job name, generated by the back-end.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#jobReference\n\n :rtype: list of mapping, or ``NoneType``\n :returns: Mappings describing errors generated on the server (None\n until set by the server).\n \"\"\"\n return self._properties.get('jobReference', {}).get('jobId')","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.job","text":"google/cloud/bigquery/query.py::QueryResults.job\n @property\n def job(self):\n \"\"\"Job instance used to run the query.\n\n :rtype: :class:`google.cloud.bigquery.job.QueryJob`, or ``NoneType``\n :returns: Job instance used to run the query (None until\n ``jobReference`` property is set by the server).\n \"\"\"\n if self._job is None:\n job_ref = self._properties.get('jobReference')\n if job_ref is not None:\n self._job = QueryJob(job_ref['jobId'], self.query,\n self._client)\n return self._job","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.page_token","text":"google/cloud/bigquery/query.py::QueryResults.page_token\n @property\n def page_token(self):\n \"\"\"Token for fetching next bach of results.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#pageToken\n\n :rtype: string, or ``NoneType``\n :returns: Token generated on the server (None until set by the server).\n \"\"\"\n return self._properties.get('pageToken')","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.total_rows","text":"google/cloud/bigquery/query.py::QueryResults.total_rows\n @property\n def total_rows(self):\n \"\"\"Total number of rows returned by the query.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#totalRows\n\n :rtype: integer, or ``NoneType``\n :returns: Count generated on the server (None until set by the server).\n \"\"\"\n return self._properties.get('totalRows')","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.total_bytes_processed","text":"google/cloud/bigquery/query.py::QueryResults.total_bytes_processed\n @property\n def total_bytes_processed(self):\n \"\"\"Total number of bytes processed by the query.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#totalBytesProcessed\n\n :rtype: integer, or ``NoneType``\n :returns: Count generated on the server (None until set by the server).\n \"\"\"\n return self._properties.get('totalBytesProcessed')","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.rows","text":"google/cloud/bigquery/query.py::QueryResults.rows\n @property\n def rows(self):\n \"\"\"Query results.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#rows\n\n :rtype: list of tuples of row values, or ``NoneType``\n :returns: fields describing the schema (None until set by the server).\n \"\"\"\n return _rows_from_json(self._properties.get('rows', ()), self.schema)","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.schema","text":"google/cloud/bigquery/query.py::QueryResults.schema\n @property\n def schema(self):\n \"\"\"Schema for query results.\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#schema\n\n :rtype: list of :class:`SchemaField`, or ``NoneType``\n :returns: fields describing the schema (None until set by the server).\n \"\"\"\n return _parse_schema_resource(self._properties.get('schema', {}))","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults._set_properties","text":"google/cloud/bigquery/query.py::QueryResults._set_properties\n def _set_properties(self, api_response):\n \"\"\"Update properties from resource in body of ``api_response``\n\n :type api_response: httplib2.Response\n :param api_response: response returned from an API call\n \"\"\"\n self._properties.clear()\n self._properties.update(api_response)","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults._build_resource","text":"google/cloud/bigquery/query.py::QueryResults._build_resource\n def _build_resource(self):\n \"\"\"Generate a resource for :meth:`begin`.\"\"\"\n resource = {'query': self.query}\n\n if self.default_dataset is not None:\n resource['defaultDataset'] = {\n 'projectId': self.project,\n 'datasetId': self.default_dataset.name,\n }\n\n if self.max_results is not None:\n resource['maxResults'] = self.max_results\n\n if self.preserve_nulls is not None:\n resource['preserveNulls'] = self.preserve_nulls\n\n if self.timeout_ms is not None:\n resource['timeoutMs'] = self.timeout_ms\n\n if self.use_query_cache is not None:\n resource['useQueryCache'] = self.use_query_cache\n\n if self.use_legacy_sql is not None:\n resource['useLegacySql'] = self.use_legacy_sql\n\n if self.dry_run is not None:\n resource['dryRun'] = self.dry_run\n\n if len(self._udf_resources) > 0:\n resource[self._UDF_KEY] = _build_udf_resources(self._udf_resources)\n\n return resource","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.run","text":"google/cloud/bigquery/query.py::QueryResults.run\n def run(self, client=None):\n \"\"\"API call: run the query via a POST request\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n if self._job is not None:\n raise ValueError(\"Query job is already running.\")\n\n client = self._require_client(client)\n path = '/projects/%s/queries' % (self.project,)\n api_response = client.connection.api_request(\n method='POST', path=path, data=self._build_resource())\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/bigquery/query.py::QueryResults.fetch_data","text":"google/cloud/bigquery/query.py::QueryResults.fetch_data\n def fetch_data(self, max_results=None, page_token=None, start_index=None,\n timeout_ms=None, client=None):\n \"\"\"API call: fetch a page of query result data via a GET request\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/getQueryResults\n\n :type max_results: integer or ``NoneType``\n :param max_results: maximum number of rows to return.\n\n :type page_token: string or ``NoneType``\n :param page_token: token representing a cursor into the table's rows.\n\n :type start_index: integer or ``NoneType``\n :param start_index: zero-based index of starting row\n\n :type timeout_ms: integer or ``NoneType``\n :param timeout_ms: timeout, in milliseconds, to wait for query to\n complete\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: tuple\n :returns: ``(row_data, total_rows, page_token)``, where ``row_data``\n is a list of tuples, one per result row, containing only\n the values; ``total_rows`` is a count of the total number\n of rows in the table; and ``page_token`` is an opaque\n string which can be used to fetch the next batch of rows\n (``None`` if no further batches can be fetched).\n :raises: ValueError if the query has not yet been executed.\n \"\"\"\n if self.name is None:\n raise ValueError(\"Query not yet executed: call 'run()'\")\n\n client = self._require_client(client)\n params = {}\n\n if max_results is not None:\n params['maxResults'] = max_results\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n ","label":0},{"cid":"google/cloud/bigquery/schema.py::SchemaField.__init__","text":"google/cloud/bigquery/schema.py::SchemaField.__init__\n def __init__(self, name, field_type, mode='NULLABLE', description=None,\n fields=None):\n self.name = name\n self.field_type = field_type\n self.mode = mode\n self.description = description\n self.fields = fields","label":0},{"cid":"google/cloud/bigquery/schema.py::SchemaField.__eq__","text":"google/cloud/bigquery/schema.py::SchemaField.__eq__\n def __eq__(self, other):\n return (\n self.name == other.name and\n self.field_type.lower() == other.field_type.lower() and\n self.mode == other.mode and\n self.description == other.description and\n self.fields == other.fields)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.__init__","text":"google/cloud/bigquery/table.py::Table.__init__\n def __init__(self, name, dataset, schema=()):\n self.name = name\n self._dataset = dataset\n self._properties = {}\n # Let the @property do validation.\n self.schema = schema","label":0},{"cid":"google/cloud/bigquery/table.py::Table.project","text":"google/cloud/bigquery/table.py::Table.project\n @property\n def project(self):\n \"\"\"Project bound to the table.\n\n :rtype: str\n :returns: the project (derived from the dataset).\n \"\"\"\n return self._dataset.project","label":0},{"cid":"google/cloud/bigquery/table.py::Table.dataset_name","text":"google/cloud/bigquery/table.py::Table.dataset_name\n @property\n def dataset_name(self):\n \"\"\"Name of dataset containing the table.\n\n :rtype: str\n :returns: the ID (derived from the dataset).\n \"\"\"\n return self._dataset.name","label":0},{"cid":"google/cloud/bigquery/table.py::Table.path","text":"google/cloud/bigquery/table.py::Table.path\n @property\n def path(self):\n \"\"\"URL path for the table's APIs.\n\n :rtype: str\n :returns: the path based on project and dataste name.\n \"\"\"\n return '%s/tables/%s' % (self._dataset.path, self.name)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.schema","text":"google/cloud/bigquery/table.py::Table.schema\n @property\n def schema(self):\n \"\"\"Table's schema.\n\n :rtype: list of :class:`SchemaField`\n :returns: fields describing the schema\n \"\"\"\n return list(self._schema)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.schema","text":"google/cloud/bigquery/table.py::Table.schema\n @schema.setter\n def schema(self, value):\n \"\"\"Update table's schema\n\n :type value: list of :class:`SchemaField`\n :param value: fields describing the schema\n\n :raises: TypeError if 'value' is not a sequence, or ValueError if\n any item in the sequence is not a SchemaField\n \"\"\"\n if not all(isinstance(field, SchemaField) for field in value):\n raise ValueError('Schema items must be fields')\n self._schema = tuple(value)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.created","text":"google/cloud/bigquery/table.py::Table.created\n @property\n def created(self):\n \"\"\"Datetime at which the table was created.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the creation time (None until set from the server).\n \"\"\"\n creation_time = self._properties.get('creationTime')\n if creation_time is not None:\n # creation_time will be in milliseconds.\n return _datetime_from_microseconds(1000.0 * creation_time)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.etag","text":"google/cloud/bigquery/table.py::Table.etag\n @property\n def etag(self):\n \"\"\"ETag for the table resource.\n\n :rtype: str, or ``NoneType``\n :returns: the ETag (None until set from the server).\n \"\"\"\n return self._properties.get('etag')","label":0},{"cid":"google/cloud/bigquery/table.py::Table.modified","text":"google/cloud/bigquery/table.py::Table.modified\n @property\n def modified(self):\n \"\"\"Datetime at which the table was last modified.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the modification time (None until set from the server).\n \"\"\"\n modified_time = self._properties.get('lastModifiedTime')\n if modified_time is not None:\n # modified_time will be in milliseconds.\n return _datetime_from_microseconds(1000.0 * modified_time)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.num_bytes","text":"google/cloud/bigquery/table.py::Table.num_bytes\n @property\n def num_bytes(self):\n \"\"\"The size of the table in bytes.\n\n :rtype: integer, or ``NoneType``\n :returns: the byte count (None until set from the server).\n \"\"\"\n num_bytes_as_str = self._properties.get('numBytes')\n if num_bytes_as_str is not None:\n return int(num_bytes_as_str)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.num_rows","text":"google/cloud/bigquery/table.py::Table.num_rows\n @property\n def num_rows(self):\n \"\"\"The number of rows in the table.\n\n :rtype: integer, or ``NoneType``\n :returns: the row count (None until set from the server).\n \"\"\"\n num_rows_as_str = self._properties.get('numRows')\n if num_rows_as_str is not None:\n return int(num_rows_as_str)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.self_link","text":"google/cloud/bigquery/table.py::Table.self_link\n @property\n def self_link(self):\n \"\"\"URL for the table resource.\n\n :rtype: str, or ``NoneType``\n :returns: the URL (None until set from the server).\n \"\"\"\n return self._properties.get('selfLink')","label":0},{"cid":"google/cloud/bigquery/table.py::Table.table_id","text":"google/cloud/bigquery/table.py::Table.table_id\n @property\n def table_id(self):\n \"\"\"ID for the table resource.\n\n :rtype: str, or ``NoneType``\n :returns: the ID (None until set from the server).\n \"\"\"\n return self._properties.get('id')","label":0},{"cid":"google/cloud/bigquery/table.py::Table.table_type","text":"google/cloud/bigquery/table.py::Table.table_type\n @property\n def table_type(self):\n \"\"\"The type of the table.\n\n Possible values are \"TABLE\" or \"VIEW\".\n\n :rtype: str, or ``NoneType``\n :returns: the URL (None until set from the server).\n \"\"\"\n return self._properties.get('type')","label":0},{"cid":"google/cloud/bigquery/table.py::Table.partitioning_type","text":"google/cloud/bigquery/table.py::Table.partitioning_type\n @property\n def partitioning_type(self):\n \"\"\"Time partitioning of the table.\n :rtype: str, or ``NoneType``\n :returns: Returns type if the table is partitioned, None otherwise.\n \"\"\"\n return self._properties.get('timePartitioning', {}).get('type')","label":0},{"cid":"google/cloud/bigquery/table.py::Table.partitioning_type","text":"google/cloud/bigquery/table.py::Table.partitioning_type\n @partitioning_type.setter\n def partitioning_type(self, value):\n \"\"\"Update the partitioning type of the table\n\n :type value: str\n :param value: partitioning type only \"DAY\" is currently supported\n \"\"\"\n if value not in ('DAY', None):\n raise ValueError(\"value must be one of ['DAY', None]\")\n\n if value is None:\n self._properties.pop('timePartitioning', None)\n else:\n time_part = self._properties.setdefault('timePartitioning', {})\n time_part['type'] = value.upper()","label":0},{"cid":"google/cloud/bigquery/table.py::Table.partition_expiration","text":"google/cloud/bigquery/table.py::Table.partition_expiration\n @property\n def partition_expiration(self):\n \"\"\"Expiration time in ms for a partition\n :rtype: int, or ``NoneType``\n :returns: Returns the time in ms for partition expiration\n \"\"\"\n return self._properties.get('timePartitioning', {}).get('expirationMs')","label":0},{"cid":"google/cloud/bigquery/table.py::Table.partition_expiration","text":"google/cloud/bigquery/table.py::Table.partition_expiration\n @partition_expiration.setter\n def partition_expiration(self, value):\n \"\"\"Update the experation time in ms for a partition\n\n :type value: int\n :param value: partition experiation time in ms\n \"\"\"\n if not isinstance(value, (int, type(None))):\n raise ValueError(\n \"must be an integer representing millisseconds or None\")\n\n if value is None:\n if 'timePartitioning' in self._properties:\n self._properties['timePartitioning'].pop('expirationMs')\n else:\n try:\n self._properties['timePartitioning']['expirationMs'] = value\n except KeyError:\n self._properties['timePartitioning'] = {'type': 'DAY'}\n self._properties['timePartitioning']['expirationMs'] = value","label":0},{"cid":"google/cloud/bigquery/table.py::Table.description","text":"google/cloud/bigquery/table.py::Table.description\n @property\n def description(self):\n \"\"\"Description of the table.\n\n :rtype: str, or ``NoneType``\n :returns: The description as set by the user, or None (the default).\n \"\"\"\n return self._properties.get('description')","label":0},{"cid":"google/cloud/bigquery/table.py::Table.description","text":"google/cloud/bigquery/table.py::Table.description\n @description.setter\n def description(self, value):\n \"\"\"Update description of the table.\n\n :type value: str, or ``NoneType``\n :param value: new description\n\n :raises: ValueError for invalid value types.\n \"\"\"\n if not isinstance(value, six.string_types) and value is not None:\n raise ValueError(\"Pass a string, or None\")\n self._properties['description'] = value","label":0},{"cid":"google/cloud/bigquery/table.py::Table.expires","text":"google/cloud/bigquery/table.py::Table.expires\n @property\n def expires(self):\n \"\"\"Datetime at which the table will be removed.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the expiration time, or None\n \"\"\"\n expiration_time = self._properties.get('expirationTime')\n if expiration_time is not None:\n # expiration_time will be in milliseconds.\n return _datetime_from_microseconds(1000.0 * expiration_time)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.expires","text":"google/cloud/bigquery/table.py::Table.expires\n @expires.setter\n def expires(self, value):\n \"\"\"Update datetime at which the table will be removed.\n\n :type value: ``datetime.datetime``, or ``NoneType``\n :param value: the new expiration time, or None\n \"\"\"\n if not isinstance(value, datetime.datetime) and value is not None:\n raise ValueError(\"Pass a datetime, or None\")\n self._properties['expirationTime'] = _millis_from_datetime(value)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.friendly_name","text":"google/cloud/bigquery/table.py::Table.friendly_name\n @property\n def friendly_name(self):\n \"\"\"Title of the table.\n\n :rtype: str, or ``NoneType``\n :returns: The name as set by the user, or None (the default).\n \"\"\"\n return self._properties.get('friendlyName')","label":0},{"cid":"google/cloud/bigquery/table.py::Table.friendly_name","text":"google/cloud/bigquery/table.py::Table.friendly_name\n @friendly_name.setter\n def friendly_name(self, value):\n \"\"\"Update title of the table.\n\n :type value: str, or ``NoneType``\n :param value: new title\n\n :raises: ValueError for invalid value types.\n \"\"\"\n if not isinstance(value, six.string_types) and value is not None:\n raise ValueError(\"Pass a string, or None\")\n self._properties['friendlyName'] = value","label":0},{"cid":"google/cloud/bigquery/table.py::Table.location","text":"google/cloud/bigquery/table.py::Table.location\n @property\n def location(self):\n \"\"\"Location in which the table is hosted.\n\n :rtype: str, or ``NoneType``\n :returns: The location as set by the user, or None (the default).\n \"\"\"\n return self._properties.get('location')","label":0},{"cid":"google/cloud/bigquery/table.py::Table.location","text":"google/cloud/bigquery/table.py::Table.location\n @location.setter\n def location(self, value):\n \"\"\"Update location in which the table is hosted.\n\n :type value: str, or ``NoneType``\n :param value: new location\n\n :raises: ValueError for invalid value types.\n \"\"\"\n if not isinstance(value, six.string_types) and value is not None:\n raise ValueError(\"Pass a string, or None\")\n self._properties['location'] = value","label":0},{"cid":"google/cloud/bigquery/table.py::Table.view_query","text":"google/cloud/bigquery/table.py::Table.view_query\n @property\n def view_query(self):\n \"\"\"SQL query defining the table as a view.\n\n :rtype: str, or ``NoneType``\n :returns: The query as set by the user, or None (the default).\n \"\"\"\n view = self._properties.get('view')\n if view is not None:\n return view.get('query')","label":0},{"cid":"google/cloud/bigquery/table.py::Table.view_query","text":"google/cloud/bigquery/table.py::Table.view_query\n @view_query.setter\n def view_query(self, value):\n \"\"\"Update SQL query defining the table as a view.\n\n :type value: str\n :param value: new query\n\n :raises: ValueError for invalid value types.\n \"\"\"\n if not isinstance(value, six.string_types):\n raise ValueError(\"Pass a string\")\n self._properties['view'] = {'query': value}","label":0},{"cid":"google/cloud/bigquery/table.py::Table.view_query","text":"google/cloud/bigquery/table.py::Table.view_query\n @view_query.deleter\n def view_query(self):\n \"\"\"Delete SQL query defining the table as a view.\"\"\"\n self._properties.pop('view', None)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.list_partitions","text":"google/cloud/bigquery/table.py::Table.list_partitions\n def list_partitions(self, client=None):\n \"\"\"List the partitions in a table.\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: list\n :returns: a list of time partitions\n \"\"\"\n query = self._require_client(client).run_sync_query(\n 'SELECT partition_id from [%s.%s$__PARTITIONS_SUMMARY__]' %\n (self.dataset_name, self.name))\n query.run()\n return [row[0] for row in query.rows]","label":0},{"cid":"google/cloud/bigquery/table.py::Table.from_api_repr","text":"google/cloud/bigquery/table.py::Table.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, dataset):\n \"\"\"Factory: construct a table given its API representation\n\n :type resource: dict\n :param resource: table resource representation returned from the API\n\n :type dataset: :class:`google.cloud.bigquery.dataset.Dataset`\n :param dataset: The dataset containing the table.\n\n :rtype: :class:`google.cloud.bigquery.table.Table`\n :returns: Table parsed from ``resource``.\n \"\"\"\n if ('tableReference' not in resource or\n 'tableId' not in resource['tableReference']):\n raise KeyError('Resource lacks required identity information:'\n '[\"tableReference\"][\"tableId\"]')\n table_name = resource['tableReference']['tableId']\n table = cls(table_name, dataset=dataset)\n table._set_properties(resource)\n return table","label":0},{"cid":"google/cloud/bigquery/table.py::Table._require_client","text":"google/cloud/bigquery/table.py::Table._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: :class:`google.cloud.bigquery.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._dataset._client\n return client","label":0},{"cid":"google/cloud/bigquery/table.py::Table._set_properties","text":"google/cloud/bigquery/table.py::Table._set_properties\n def _set_properties(self, api_response):\n \"\"\"Update properties from resource in body of ``api_response``\n\n :type api_response: httplib2.Response\n :param api_response: response returned from an API call\n \"\"\"\n self._properties.clear()\n cleaned = api_response.copy()\n schema = cleaned.pop('schema', {'fields': ()})\n self.schema = _parse_schema_resource(schema)\n if 'creationTime' in cleaned:\n cleaned['creationTime'] = float(cleaned['creationTime'])\n if 'lastModifiedTime' in cleaned:\n cleaned['lastModifiedTime'] = float(cleaned['lastModifiedTime'])\n if 'expirationTime' in cleaned:\n cleaned['expirationTime'] = float(cleaned['expirationTime'])\n self._properties.update(cleaned)","label":0},{"cid":"google/cloud/bigquery/table.py::Table._build_resource","text":"google/cloud/bigquery/table.py::Table._build_resource\n def _build_resource(self):\n \"\"\"Generate a resource for ``create`` or ``update``.\"\"\"\n resource = {\n 'tableReference': {\n 'projectId': self._dataset.project,\n 'datasetId': self._dataset.name,\n 'tableId': self.name},\n }\n if self.description is not None:\n resource['description'] = self.description\n\n if self.expires is not None:\n value = _millis_from_datetime(self.expires)\n resource['expirationTime'] = value\n\n if self.friendly_name is not None:\n resource['friendlyName'] = self.friendly_name\n\n if self.location is not None:\n resource['location'] = self.location\n\n if self.partitioning_type is not None:\n resource['timePartitioning'] = self._properties['timePartitioning']\n\n if self.view_query is not None:\n view = resource['view'] = {}\n view['query'] = self.view_query\n elif self._schema:\n resource['schema'] = {\n 'fields': _build_schema_resource(self._schema)\n }\n else:\n raise ValueError(\"Set either 'view_query' or 'schema'.\")\n\n return resource","label":0},{"cid":"google/cloud/bigquery/table.py::Table.create","text":"google/cloud/bigquery/table.py::Table.create\n def create(self, client=None):\n \"\"\"API call: create the dataset via a PUT request\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/tables/insert\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n path = '/projects/%s/datasets/%s/tables' % (\n self._dataset.project, self._dataset.name)\n api_response = client.connection.api_request(\n method='POST', path=path, data=self._build_resource())\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.exists","text":"google/cloud/bigquery/table.py::Table.exists\n def exists(self, client=None):\n \"\"\"API call: test for the existence of the table via a GET request\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/tables/get\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: bool\n :returns: Boolean indicating existence of the table.\n \"\"\"\n client = self._require_client(client)\n\n try:\n client.connection.api_request(method='GET', path=self.path,\n query_params={'fields': 'id'})\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"google/cloud/bigquery/table.py::Table.reload","text":"google/cloud/bigquery/table.py::Table.reload\n def reload(self, client=None):\n \"\"\"API call: refresh table properties via a GET request\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/tables/get\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n\n api_response = client.connection.api_request(\n method='GET', path=self.path)\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.patch","text":"google/cloud/bigquery/table.py::Table.patch\n def patch(self,\n client=None,\n friendly_name=_MARKER,\n description=_MARKER,\n location=_MARKER,\n expires=_MARKER,\n view_query=_MARKER,\n schema=_MARKER):\n \"\"\"API call: update individual table properties via a PATCH request\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/tables/patch\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :type friendly_name: str or ``NoneType``\n :param friendly_name: point in time at which the table expires.\n\n :type description: str or ``NoneType``\n :param description: point in time at which the table expires.\n\n :type location: str or ``NoneType``\n :param location: point in time at which the table expires.\n\n :type expires: :class:`datetime.datetime` or ``NoneType``\n :param expires: point in time at which the table expires.\n\n :type view_query: str\n :param view_query: SQL query defining the table as a view\n\n :type schema: list of :class:`SchemaField`\n :param schema: fields describing the schema\n\n :raises: ValueError for invalid value types.\n \"\"\"\n client = self._require_client(client)\n\n partial = {}\n\n if expires is not _MARKER:\n if (not isinstance(expires, datetime.datetime) and\n expires is not None):\n raise ValueError(\"Pass a datetime, or None\")\n partial['expirationTime'] = _millis_from_datetime(expires)\n\n if description is not _MARKER:\n partial['description'] = description\n\n if friendly_name is not _MARKER:\n partial['friendlyName'] = friendly_name\n\n if location i","label":0},{"cid":"google/cloud/bigquery/table.py::Table.update","text":"google/cloud/bigquery/table.py::Table.update\n def update(self, client=None):\n \"\"\"API call: update table properties via a PUT request\n\n See\n https://cloud.google.com/bigquery/docs/reference/v2/tables/update\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n api_response = client.connection.api_request(\n method='PUT', path=self.path, data=self._build_resource())\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.delete","text":"google/cloud/bigquery/table.py::Table.delete\n def delete(self, client=None):\n \"\"\"API call: delete the table via a DELETE request\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/tables/delete\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n \"\"\"\n client = self._require_client(client)\n client.connection.api_request(method='DELETE', path=self.path)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.fetch_data","text":"google/cloud/bigquery/table.py::Table.fetch_data\n def fetch_data(self, max_results=None, page_token=None, client=None):\n \"\"\"API call: fetch the table data via a GET request\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/tabledata/list\n\n .. note::\n\n This method assumes that its instance's ``schema`` attribute is\n up-to-date with the schema as defined on the back-end: if the\n two schemas are not identical, the values returned may be\n incomplete. To ensure that the local copy of the schema is\n up-to-date, call the table's ``reload`` method.\n\n :type max_results: integer or ``NoneType``\n :param max_results: maximum number of rows to return.\n\n :type page_token: str or ``NoneType``\n :param page_token: token representing a cursor into the table's rows.\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: tuple\n :returns: ``(row_data, total_rows, page_token)``, where ``row_data``\n is a list of tuples, one per result row, containing only\n the values; ``total_rows`` is a count of the total number\n of rows in the table; and ``page_token`` is an opaque\n string which can be used to fetch the next batch of rows\n (``None`` if no further batches can be fetched).\n \"\"\"\n client = self._require_client(client)\n params = {}\n\n if max_results is not None:\n params['maxResults'] = max_results\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n response = client.connection.api_request(method='GET',\n path='%s/data' % self.path,\n ","label":0},{"cid":"google/cloud/bigquery/table.py::Table.insert_data","text":"google/cloud/bigquery/table.py::Table.insert_data\n def insert_data(self,\n rows,\n row_ids=None,\n skip_invalid_rows=None,\n ignore_unknown_values=None,\n template_suffix=None,\n client=None):\n \"\"\"API call: insert table data via a POST request\n\n See:\n https://cloud.google.com/bigquery/docs/reference/v2/tabledata/insertAll\n\n :type rows: list of tuples\n :param rows: Row data to be inserted. Each tuple should contain data\n for each schema field on the current table and in the\n same order as the schema fields.\n\n :type row_ids: list of string\n :param row_ids: Unique ids, one per row being inserted. If not\n passed, no de-duplication occurs.\n\n :type skip_invalid_rows: boolean or ``NoneType``\n :param skip_invalid_rows: skip rows w/ invalid data?\n\n :type ignore_unknown_values: boolean or ``NoneType``\n :param ignore_unknown_values: ignore columns beyond schema?\n\n :type template_suffix: str or ``NoneType``\n :param template_suffix: treat ``name`` as a template table and provide\n a suffix. BigQuery will create the table\n ``<name> + <template_suffix>`` based on the\n schema of the template table. See:\n https://cloud.google.com/bigquery/streaming-data-into-bigquery#template-tables\n\n :type client: :class:`~google.cloud.bigquery.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current dataset.\n\n :rtype: list of mappings\n :returns: One mapping per row with insert errors: the \"index\" key\n identifies the row, and the \"errors\" key contains ","label":0},{"cid":"google/cloud/bigquery/table.py::Table._check_response_error","text":"google/cloud/bigquery/table.py::Table._check_response_error\n @staticmethod\n def _check_response_error(request, http_response):\n \"\"\"Helper for :meth:`upload_from_file`.\"\"\"\n info = http_response.info\n status = int(info['status'])\n if not 200 <= status < 300:\n faux_response = httplib2.Response({'status': status})\n raise make_exception(faux_response, http_response.content,\n error_info=request.url)","label":0},{"cid":"google/cloud/bigquery/table.py::Table.upload_from_file","text":"google/cloud/bigquery/table.py::Table.upload_from_file\n def upload_from_file(self,\n file_obj,\n source_format,\n rewind=False,\n size=None,\n num_retries=6,\n allow_jagged_rows=None,\n allow_quoted_newlines=None,\n create_disposition=None,\n encoding=None,\n field_delimiter=None,\n ignore_unknown_values=None,\n max_bad_records=None,\n quote_character=None,\n skip_leading_rows=None,\n write_disposition=None,\n client=None):\n \"\"\"Upload the contents of this table from a file-like object.\n\n The content type of the upload will either be\n - The value passed in to the function (if any)\n - ``text/csv``.\n\n :type file_obj: file\n :param file_obj: A file handle opened in binary mode for reading.\n\n :type source_format: str\n :param source_format: one of 'CSV' or 'NEWLINE_DELIMITED_JSON'.\n job configuration option; see\n :meth:`google.cloud.bigquery.job.LoadJob`\n\n :type rewind: boolean\n :param rewind: If True, seek to the beginning of the file handle before\n writing the file to Cloud Storage.\n\n :type size: int\n :param size: The number of bytes to read from the file handle.\n If not provided, we'll try to guess the size using\n :func:`os.fstat`. (If the file handle is not from the\n filesystem this won't be possible.)\n\n :type num_retries: integer\n :param num_retries: Number of upload retries. Defaults to 6.\n\n :type allow_jagged_rows: boolean\n :param allow_jagged_rows: job c","label":0},{"cid":"google/cloud/bigquery/table.py::_configure_job_metadata","text":"google/cloud/bigquery/table.py::_configure_job_metadata\ndef _configure_job_metadata(metadata, # pylint: disable=too-many-arguments\n allow_jagged_rows,\n allow_quoted_newlines,\n create_disposition,\n encoding,\n field_delimiter,\n ignore_unknown_values,\n max_bad_records,\n quote_character,\n skip_leading_rows,\n write_disposition):\n \"\"\"Helper for :meth:`Table.upload_from_file`.\"\"\"\n load_config = metadata['configuration']['load']\n\n if allow_jagged_rows is not None:\n load_config['allowJaggedRows'] = allow_jagged_rows\n\n if allow_quoted_newlines is not None:\n load_config['allowQuotedNewlines'] = allow_quoted_newlines\n\n if create_disposition is not None:\n load_config['createDisposition'] = create_disposition\n\n if encoding is not None:\n load_config['encoding'] = encoding\n\n if field_delimiter is not None:\n load_config['fieldDelimiter'] = field_delimiter\n\n if ignore_unknown_values is not None:\n load_config['ignoreUnknownValues'] = ignore_unknown_values\n\n if max_bad_records is not None:\n load_config['maxBadRecords'] = max_bad_records\n\n if quote_character is not None:\n load_config['quote'] = quote_character\n\n if skip_leading_rows is not None:\n load_config['skipLeadingRows'] = skip_leading_rows\n\n if write_disposition is not None:\n load_config['writeDisposition'] = write_disposition","label":0},{"cid":"google/cloud/bigquery/table.py::_parse_schema_resource","text":"google/cloud/bigquery/table.py::_parse_schema_resource\ndef _parse_schema_resource(info):\n \"\"\"Parse a resource fragment into a schema field.\n\n :type info: mapping\n :param info: should contain a \"fields\" key to be parsed\n\n :rtype: list of :class:`SchemaField`, or ``NoneType``\n :returns: a list of parsed fields, or ``None`` if no \"fields\" key is\n present in ``info``.\n \"\"\"\n if 'fields' not in info:\n return None\n\n schema = []\n for r_field in info['fields']:\n name = r_field['name']\n field_type = r_field['type']\n mode = r_field.get('mode', 'NULLABLE')\n description = r_field.get('description')\n sub_fields = _parse_schema_resource(r_field)\n schema.append(\n SchemaField(name, field_type, mode, description, sub_fields))\n return schema","label":0},{"cid":"google/cloud/bigquery/table.py::_build_schema_resource","text":"google/cloud/bigquery/table.py::_build_schema_resource\ndef _build_schema_resource(fields):\n \"\"\"Generate a resource fragment for a schema.\n\n :type fields: sequence of :class:`SchemaField`\n :param fields: schema to be dumped\n\n :rtype: mapping\n :returns: a mapping describing the schema of the supplied fields.\n \"\"\"\n infos = []\n for field in fields:\n info = {'name': field.name,\n 'type': field.field_type,\n 'mode': field.mode}\n if field.description is not None:\n info['description'] = field.description\n if field.fields is not None:\n info['fields'] = _build_schema_resource(field.fields)\n infos.append(info)\n return infos","label":0},{"cid":"google/cloud/bigquery/table.py::_UrlBuilder.__init__","text":"google/cloud/bigquery/table.py::_UrlBuilder.__init__\n def __init__(self):\n self.query_params = {}\n self._relative_path = ''","label":0},{"cid":"google/cloud/bigtable/client.py::_make_data_stub","text":"google/cloud/bigtable/client.py::_make_data_stub\ndef _make_data_stub(client):\n \"\"\"Creates gRPC stub to make requests to the Data API.\n\n :type client: :class:`Client`\n :param client: The client that will hold the stub.\n\n :rtype: :class:`._generated.bigtable_pb2.BigtableStub`\n :returns: A gRPC stub object.\n \"\"\"\n if client.emulator_host is None:\n return make_secure_stub(client.credentials, client.user_agent,\n bigtable_pb2.BigtableStub, DATA_API_HOST)\n else:\n return make_insecure_stub(bigtable_pb2.BigtableStub,\n client.emulator_host)","label":0},{"cid":"google/cloud/bigtable/client.py::_make_instance_stub","text":"google/cloud/bigtable/client.py::_make_instance_stub\ndef _make_instance_stub(client):\n \"\"\"Creates gRPC stub to make requests to the Instance Admin API.\n\n :type client: :class:`Client`\n :param client: The client that will hold the stub.\n\n :rtype: :class:`.bigtable_instance_admin_pb2.BigtableInstanceAdminStub`\n :returns: A gRPC stub object.\n \"\"\"\n if client.emulator_host is None:\n return make_secure_stub(\n client.credentials, client.user_agent,\n bigtable_instance_admin_pb2.BigtableInstanceAdminStub,\n INSTANCE_ADMIN_HOST)\n else:\n return make_insecure_stub(\n bigtable_instance_admin_pb2.BigtableInstanceAdminStub,\n client.emulator_host)","label":0},{"cid":"google/cloud/bigtable/client.py::_make_operations_stub","text":"google/cloud/bigtable/client.py::_make_operations_stub\ndef _make_operations_stub(client):\n \"\"\"Creates gRPC stub to make requests to the Operations API.\n\n These are for long-running operations of the Instance Admin API,\n hence the host and port matching.\n\n :type client: :class:`Client`\n :param client: The client that will hold the stub.\n\n :rtype: :class:`._generated.operations_grpc_pb2.OperationsStub`\n :returns: A gRPC stub object.\n \"\"\"\n if client.emulator_host is None:\n return make_secure_stub(client.credentials, client.user_agent,\n operations_grpc_pb2.OperationsStub,\n OPERATIONS_API_HOST)\n else:\n return make_insecure_stub(operations_grpc_pb2.OperationsStub,\n client.emulator_host)","label":0},{"cid":"google/cloud/bigtable/client.py::_make_table_stub","text":"google/cloud/bigtable/client.py::_make_table_stub\ndef _make_table_stub(client):\n \"\"\"Creates gRPC stub to make requests to the Table Admin API.\n\n :type client: :class:`Client`\n :param client: The client that will hold the stub.\n\n :rtype: :class:`.bigtable_instance_admin_pb2.BigtableTableAdminStub`\n :returns: A gRPC stub object.\n \"\"\"\n if client.emulator_host is None:\n return make_secure_stub(\n client.credentials, client.user_agent,\n bigtable_table_admin_pb2.BigtableTableAdminStub,\n TABLE_ADMIN_HOST)\n else:\n return make_insecure_stub(\n bigtable_table_admin_pb2.BigtableTableAdminStub,\n client.emulator_host)","label":0},{"cid":"google/cloud/bigtable/client.py::Client.__init__","text":"google/cloud/bigtable/client.py::Client.__init__\n def __init__(self, project=None, credentials=None,\n read_only=False, admin=False, user_agent=DEFAULT_USER_AGENT):\n _ClientProjectMixin.__init__(self, project=project)\n if credentials is None:\n credentials = get_credentials()\n\n if read_only and admin:\n raise ValueError('A read-only client cannot also perform'\n 'administrative actions.')\n\n scopes = []\n if read_only:\n scopes.append(READ_ONLY_SCOPE)\n else:\n scopes.append(DATA_SCOPE)\n\n if admin:\n scopes.append(ADMIN_SCOPE)\n\n self._admin = bool(admin)\n try:\n credentials = credentials.create_scoped(scopes)\n except AttributeError:\n pass\n self._credentials = credentials\n self.user_agent = user_agent\n self.emulator_host = os.getenv(BIGTABLE_EMULATOR)\n\n # Create gRPC stubs for making requests.\n self._data_stub = _make_data_stub(self)\n if self._admin:\n self._instance_stub_internal = _make_instance_stub(self)\n self._operations_stub_internal = _make_operations_stub(self)\n self._table_stub_internal = _make_table_stub(self)","label":0},{"cid":"google/cloud/bigtable/client.py::Client.copy","text":"google/cloud/bigtable/client.py::Client.copy\n def copy(self):\n \"\"\"Make a copy of this client.\n\n Copies the local data stored as simple types but does not copy the\n current state of any open connections with the Cloud Bigtable API.\n\n :rtype: :class:`.Client`\n :returns: A copy of the current client.\n \"\"\"\n credentials = self._credentials\n copied_creds = credentials.create_scoped(credentials.scopes)\n return self.__class__(\n self.project,\n copied_creds,\n READ_ONLY_SCOPE in copied_creds.scopes,\n self._admin,\n self.user_agent,\n )","label":0},{"cid":"google/cloud/bigtable/client.py::Client.credentials","text":"google/cloud/bigtable/client.py::Client.credentials\n @property\n def credentials(self):\n \"\"\"Getter for client's credentials.\n\n :rtype:\n :class:`OAuth2Credentials <oauth2client.client.OAuth2Credentials>`\n :returns: The credentials stored on the client.\n \"\"\"\n return self._credentials","label":0},{"cid":"google/cloud/bigtable/client.py::Client.project_name","text":"google/cloud/bigtable/client.py::Client.project_name\n @property\n def project_name(self):\n \"\"\"Project name to be used with Instance Admin API.\n\n .. note::\n\n This property will not change if ``project`` does not, but the\n return value is not cached.\n\n The project name is of the form\n\n ``\"projects/{project}\"``\n\n :rtype: str\n :returns: The project name to be used with the Cloud Bigtable Admin\n API RPC service.\n \"\"\"\n return 'projects/' + self.project","label":0},{"cid":"google/cloud/bigtable/client.py::Client._instance_stub","text":"google/cloud/bigtable/client.py::Client._instance_stub\n @property\n def _instance_stub(self):\n \"\"\"Getter for the gRPC stub used for the Instance Admin API.\n\n :rtype: :class:`.bigtable_instance_admin_pb2.BigtableInstanceAdminStub`\n :returns: A gRPC stub object.\n :raises: :class:`ValueError <exceptions.ValueError>` if the current\n client is not an admin client or if it has not been\n :meth:`start`-ed.\n \"\"\"\n if not self._admin:\n raise ValueError('Client is not an admin client.')\n return self._instance_stub_internal","label":0},{"cid":"google/cloud/bigtable/client.py::Client._operations_stub","text":"google/cloud/bigtable/client.py::Client._operations_stub\n @property\n def _operations_stub(self):\n \"\"\"Getter for the gRPC stub used for the Operations API.\n\n :rtype: :class:`._generated.operations_grpc_pb2.OperationsStub`\n :returns: A gRPC stub object.\n :raises: :class:`ValueError <exceptions.ValueError>` if the current\n client is not an admin client or if it has not been\n :meth:`start`-ed.\n \"\"\"\n if not self._admin:\n raise ValueError('Client is not an admin client.')\n return self._operations_stub_internal","label":0},{"cid":"google/cloud/bigtable/client.py::Client._table_stub","text":"google/cloud/bigtable/client.py::Client._table_stub\n @property\n def _table_stub(self):\n \"\"\"Getter for the gRPC stub used for the Table Admin API.\n\n :rtype: :class:`.bigtable_instance_admin_pb2.BigtableTableAdminStub`\n :returns: A gRPC stub object.\n :raises: :class:`ValueError <exceptions.ValueError>` if the current\n client is not an admin client or if it has not been\n :meth:`start`-ed.\n \"\"\"\n if not self._admin:\n raise ValueError('Client is not an admin client.')\n return self._table_stub_internal","label":0},{"cid":"google/cloud/bigtable/client.py::Client.instance","text":"google/cloud/bigtable/client.py::Client.instance\n def instance(self, instance_id, location=_EXISTING_INSTANCE_LOCATION_ID,\n display_name=None, serve_nodes=DEFAULT_SERVE_NODES):\n \"\"\"Factory to create a instance associated with this client.\n\n :type instance_id: str\n :param instance_id: The ID of the instance.\n\n :type location: string\n :param location: location name, in form\n ``projects/<project>/locations/<location>``; used to\n set up the instance's cluster.\n\n :type display_name: str\n :param display_name: (Optional) The display name for the instance in\n the Cloud Console UI. (Must be between 4 and 30\n characters.) If this value is not set in the\n constructor, will fall back to the instance ID.\n\n :type serve_nodes: int\n :param serve_nodes: (Optional) The number of nodes in the instance's\n cluster; used to set up the instance's cluster.\n\n :rtype: :class:`.Instance`\n :returns: an instance owned by this client.\n \"\"\"\n return Instance(instance_id, self, location,\n display_name=display_name, serve_nodes=serve_nodes)","label":0},{"cid":"google/cloud/bigtable/client.py::Client.list_instances","text":"google/cloud/bigtable/client.py::Client.list_instances\n def list_instances(self):\n \"\"\"List instances owned by the project.\n\n :rtype: tuple\n :returns: A pair of results, the first is a list of\n :class:`.Instance` objects returned and the second is a\n list of strings (the failed locations in the request).\n \"\"\"\n request_pb = bigtable_instance_admin_pb2.ListInstancesRequest(\n parent=self.project_name)\n\n response = self._instance_stub.ListInstances(request_pb)\n\n instances = [Instance.from_pb(instance_pb, self)\n for instance_pb in response.instances]\n return instances, response.failed_locations","label":0},{"cid":"google/cloud/bigtable/cluster.py::_prepare_create_request","text":"google/cloud/bigtable/cluster.py::_prepare_create_request\ndef _prepare_create_request(cluster):\n \"\"\"Creates a protobuf request for a CreateCluster request.\n\n :type cluster: :class:`Cluster`\n :param cluster: The cluster to be created.\n\n :rtype: :class:`.messages_v2_pb2.CreateClusterRequest`\n :returns: The CreateCluster request object containing the cluster info.\n \"\"\"\n return messages_v2_pb2.CreateClusterRequest(\n parent=cluster._instance.name,\n cluster_id=cluster.cluster_id,\n cluster=data_v2_pb2.Cluster(\n serve_nodes=cluster.serve_nodes,\n ),\n )","label":0},{"cid":"google/cloud/bigtable/cluster.py::Cluster.__init__","text":"google/cloud/bigtable/cluster.py::Cluster.__init__\n def __init__(self, cluster_id, instance,\n serve_nodes=DEFAULT_SERVE_NODES):\n self.cluster_id = cluster_id\n self._instance = instance\n self.serve_nodes = serve_nodes\n self.location = None","label":0},{"cid":"google/cloud/bigtable/cluster.py::Cluster._update_from_pb","text":"google/cloud/bigtable/cluster.py::Cluster._update_from_pb\n def _update_from_pb(self, cluster_pb):\n \"\"\"Refresh self from the server-provided protobuf.\n\n Helper for :meth:`from_pb` and :meth:`reload`.\n \"\"\"\n if not cluster_pb.serve_nodes: # Simple field (int32)\n raise ValueError('Cluster protobuf does not contain serve_nodes')\n self.serve_nodes = cluster_pb.serve_nodes\n self.location = cluster_pb.location","label":0},{"cid":"google/cloud/bigtable/cluster.py::Cluster.from_pb","text":"google/cloud/bigtable/cluster.py::Cluster.from_pb\n @classmethod\n def from_pb(cls, cluster_pb, instance):\n \"\"\"Creates a cluster instance from a protobuf.\n\n :type cluster_pb: :class:`instance_pb2.Cluster`\n :param cluster_pb: A cluster protobuf object.\n\n :type instance: :class:`.instance.Instance>`\n :param instance: The instance that owns the cluster.\n\n :rtype: :class:`Cluster`\n :returns: The cluster parsed from the protobuf response.\n :raises:\n :class:`ValueError <exceptions.ValueError>` if the cluster\n name does not match\n ``projects/{project}/instances/{instance}/clusters/{cluster_id}``\n or if the parsed project ID does not match the project ID\n on the client.\n \"\"\"\n match = _CLUSTER_NAME_RE.match(cluster_pb.name)\n if match is None:\n raise ValueError('Cluster protobuf name was not in the '\n 'expected format.', cluster_pb.name)\n if match.group('project') != instance._client.project:\n raise ValueError('Project ID on cluster does not match the '\n 'project ID on the client')\n if match.group('instance') != instance.instance_id:\n raise ValueError('Instance ID on cluster does not match the '\n 'instance ID on the client')\n\n result = cls(match.group('cluster_id'), instance)\n result._update_from_pb(cluster_pb)\n return result","label":0},{"cid":"google/cloud/bigtable/cluster.py::Cluster.copy","text":"google/cloud/bigtable/cluster.py::Cluster.copy\n def copy(self):\n \"\"\"Make a copy of this cluster.\n\n Copies the local data stored as simple types and copies the client\n attached to this instance.\n\n :rtype: :class:`.Cluster`\n :returns: A copy of the current cluster.\n \"\"\"\n new_instance = self._instance.copy()\n return self.__class__(self.cluster_id, new_instance,\n serve_nodes=self.serve_nodes)","label":0},{"cid":"google/cloud/bigtable/cluster.py::Cluster.name","text":"google/cloud/bigtable/cluster.py::Cluster.name\n @property\n def name(self):\n \"\"\"Cluster name used in requests.\n\n .. note::\n This property will not change if ``_instance`` and ``cluster_id``\n do not, but the return value is not cached.\n\n The cluster name is of the form\n\n ``\"projects/{project}/instances/{instance}/clusters/{cluster_id}\"``\n\n :rtype: str\n :returns: The cluster name.\n \"\"\"\n return self._instance.name + '/clusters/' + self.cluster_id","label":0},{"cid":"google/cloud/bigtable/cluster.py::Cluster.__eq__","text":"google/cloud/bigtable/cluster.py::Cluster.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n # NOTE: This does not compare the configuration values, such as\n # the serve_nodes. Instead, it only compares\n # identifying values instance, cluster ID and client. This is\n # intentional, since the same cluster can be in different states\n # if not synchronized. Clusters with similar instance/cluster\n # settings but different clients can't be used in the same way.\n return (other.cluster_id == self.cluster_id and\n other._instance == self._instance)","label":0},{"cid":"google/cloud/bigtable/cluster.py::Cluster.__ne__","text":"google/cloud/bigtable/cluster.py::Cluster.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/bigtable/cluster.py::Cluster.reload","text":"google/cloud/bigtable/cluster.py::Cluster.reload\n def reload(self):\n \"\"\"Reload the metadata for this cluster.\"\"\"\n request_pb = messages_v2_pb2.GetClusterRequest(name=self.name)\n # We expect a `._generated.instance_pb2.Cluster`.\n cluster_pb = self._instance._client._instance_stub.GetCluster(\n request_pb)\n\n # NOTE: _update_from_pb does not check that the project, instance and\n # cluster ID on the response match the request.\n self._update_from_pb(cluster_pb)","label":0},{"cid":"google/cloud/bigtable/cluster.py::Cluster.create","text":"google/cloud/bigtable/cluster.py::Cluster.create\n def create(self):\n \"\"\"Create this cluster.\n\n .. note::\n\n Uses the ``project``, ``instance`` and ``cluster_id`` on the\n current :class:`Cluster` in addition to the ``serve_nodes``.\n To change them before creating, reset the values via\n\n .. code:: python\n\n cluster.serve_nodes = 8\n cluster.cluster_id = 'i-changed-my-mind'\n\n before calling :meth:`create`.\n\n :rtype: :class:`Operation`\n :returns: The long-running operation corresponding to the\n create operation.\n \"\"\"\n request_pb = _prepare_create_request(self)\n # We expect a `google.longrunning.operations_pb2.Operation`.\n client = self._instance._client\n operation_pb = client._instance_stub.CreateCluster(request_pb)\n\n operation = Operation.from_pb(operation_pb, client)\n operation.target = self\n operation.metadata['request_type'] = 'CreateCluster'\n return operation","label":0},{"cid":"google/cloud/bigtable/cluster.py::Cluster.update","text":"google/cloud/bigtable/cluster.py::Cluster.update\n def update(self):\n \"\"\"Update this cluster.\n\n .. note::\n\n Updates the ``serve_nodes``. If you'd like to\n change them before updating, reset the values via\n\n .. code:: python\n\n cluster.serve_nodes = 8\n\n before calling :meth:`update`.\n\n :rtype: :class:`Operation`\n :returns: The long-running operation corresponding to the\n update operation.\n \"\"\"\n request_pb = data_v2_pb2.Cluster(\n name=self.name,\n serve_nodes=self.serve_nodes,\n )\n # We expect a `google.longrunning.operations_pb2.Operation`.\n client = self._instance._client\n operation_pb = client._instance_stub.UpdateCluster(request_pb)\n\n operation = Operation.from_pb(operation_pb, client)\n operation.target = self\n operation.metadata['request_type'] = 'UpdateCluster'\n return operation","label":0},{"cid":"google/cloud/bigtable/cluster.py::Cluster.delete","text":"google/cloud/bigtable/cluster.py::Cluster.delete\n def delete(self):\n \"\"\"Delete this cluster.\n\n Marks a cluster and all of its tables for permanent deletion in 7 days.\n\n Immediately upon completion of the request:\n\n * Billing will cease for all of the cluster's reserved resources.\n * The cluster's ``delete_time`` field will be set 7 days in the future.\n\n Soon afterward:\n\n * All tables within the cluster will become unavailable.\n\n At the cluster's ``delete_time``:\n\n * The cluster and **all of its tables** will immediately and\n irrevocably disappear from the API, and their data will be\n permanently deleted.\n \"\"\"\n request_pb = messages_v2_pb2.DeleteClusterRequest(name=self.name)\n # We expect a `google.protobuf.empty_pb2.Empty`\n self._instance._client._instance_stub.DeleteCluster(request_pb)","label":0},{"cid":"google/cloud/bigtable/column_family.py::_timedelta_to_duration_pb","text":"google/cloud/bigtable/column_family.py::_timedelta_to_duration_pb\ndef _timedelta_to_duration_pb(timedelta_val):\n \"\"\"Convert a Python timedelta object to a duration protobuf.\n\n .. note::\n\n The Python timedelta has a granularity of microseconds while\n the protobuf duration type has a duration of nanoseconds.\n\n :type timedelta_val: :class:`datetime.timedelta`\n :param timedelta_val: A timedelta object.\n\n :rtype: :class:`google.protobuf.duration_pb2.Duration`\n :returns: A duration object equivalent to the time delta.\n \"\"\"\n seconds_decimal = timedelta_val.total_seconds()\n # Truncate the parts other than the integer.\n seconds = int(seconds_decimal)\n if seconds_decimal < 0:\n signed_micros = timedelta_val.microseconds - 10**6\n else:\n signed_micros = timedelta_val.microseconds\n # Convert nanoseconds to microseconds.\n nanos = 1000 * signed_micros\n return duration_pb2.Duration(seconds=seconds, nanos=nanos)","label":0},{"cid":"google/cloud/bigtable/column_family.py::_duration_pb_to_timedelta","text":"google/cloud/bigtable/column_family.py::_duration_pb_to_timedelta\ndef _duration_pb_to_timedelta(duration_pb):\n \"\"\"Convert a duration protobuf to a Python timedelta object.\n\n .. note::\n\n The Python timedelta has a granularity of microseconds while\n the protobuf duration type has a duration of nanoseconds.\n\n :type duration_pb: :class:`google.protobuf.duration_pb2.Duration`\n :param duration_pb: A protobuf duration object.\n\n :rtype: :class:`datetime.timedelta`\n :returns: The converted timedelta object.\n \"\"\"\n return datetime.timedelta(\n seconds=duration_pb.seconds,\n microseconds=(duration_pb.nanos / 1000.0),\n )","label":0},{"cid":"google/cloud/bigtable/column_family.py::GarbageCollectionRule.__ne__","text":"google/cloud/bigtable/column_family.py::GarbageCollectionRule.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.__init__","text":"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.__init__\n def __init__(self, max_num_versions):\n self.max_num_versions = max_num_versions","label":0},{"cid":"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.__eq__","text":"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.max_num_versions == self.max_num_versions","label":0},{"cid":"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.to_pb","text":"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.to_pb\n def to_pb(self):\n \"\"\"Converts the garbage collection rule to a protobuf.\n\n :rtype: :class:`.table_v2_pb2.GcRule`\n :returns: The converted current object.\n \"\"\"\n return table_v2_pb2.GcRule(max_num_versions=self.max_num_versions)","label":0},{"cid":"google/cloud/bigtable/column_family.py::MaxAgeGCRule.__init__","text":"google/cloud/bigtable/column_family.py::MaxAgeGCRule.__init__\n def __init__(self, max_age):\n self.max_age = max_age","label":0},{"cid":"google/cloud/bigtable/column_family.py::MaxAgeGCRule.__eq__","text":"google/cloud/bigtable/column_family.py::MaxAgeGCRule.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.max_age == self.max_age","label":0},{"cid":"google/cloud/bigtable/column_family.py::MaxAgeGCRule.to_pb","text":"google/cloud/bigtable/column_family.py::MaxAgeGCRule.to_pb\n def to_pb(self):\n \"\"\"Converts the garbage collection rule to a protobuf.\n\n :rtype: :class:`.table_v2_pb2.GcRule`\n :returns: The converted current object.\n \"\"\"\n max_age = _timedelta_to_duration_pb(self.max_age)\n return table_v2_pb2.GcRule(max_age=max_age)","label":0},{"cid":"google/cloud/bigtable/column_family.py::GCRuleUnion.__init__","text":"google/cloud/bigtable/column_family.py::GCRuleUnion.__init__\n def __init__(self, rules):\n self.rules = rules","label":0},{"cid":"google/cloud/bigtable/column_family.py::GCRuleUnion.__eq__","text":"google/cloud/bigtable/column_family.py::GCRuleUnion.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.rules == self.rules","label":0},{"cid":"google/cloud/bigtable/column_family.py::GCRuleUnion.to_pb","text":"google/cloud/bigtable/column_family.py::GCRuleUnion.to_pb\n def to_pb(self):\n \"\"\"Converts the union into a single GC rule as a protobuf.\n\n :rtype: :class:`.table_v2_pb2.GcRule`\n :returns: The converted current object.\n \"\"\"\n union = table_v2_pb2.GcRule.Union(\n rules=[rule.to_pb() for rule in self.rules])\n return table_v2_pb2.GcRule(union=union)","label":0},{"cid":"google/cloud/bigtable/column_family.py::GCRuleIntersection.__init__","text":"google/cloud/bigtable/column_family.py::GCRuleIntersection.__init__\n def __init__(self, rules):\n self.rules = rules","label":0},{"cid":"google/cloud/bigtable/column_family.py::GCRuleIntersection.__eq__","text":"google/cloud/bigtable/column_family.py::GCRuleIntersection.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.rules == self.rules","label":0},{"cid":"google/cloud/bigtable/column_family.py::GCRuleIntersection.to_pb","text":"google/cloud/bigtable/column_family.py::GCRuleIntersection.to_pb\n def to_pb(self):\n \"\"\"Converts the intersection into a single GC rule as a protobuf.\n\n :rtype: :class:`.table_v2_pb2.GcRule`\n :returns: The converted current object.\n \"\"\"\n intersection = table_v2_pb2.GcRule.Intersection(\n rules=[rule.to_pb() for rule in self.rules])\n return table_v2_pb2.GcRule(intersection=intersection)","label":0},{"cid":"google/cloud/bigtable/column_family.py::ColumnFamily.__init__","text":"google/cloud/bigtable/column_family.py::ColumnFamily.__init__\n def __init__(self, column_family_id, table, gc_rule=None):\n self.column_family_id = column_family_id\n self._table = table\n self.gc_rule = gc_rule","label":0},{"cid":"google/cloud/bigtable/column_family.py::ColumnFamily.name","text":"google/cloud/bigtable/column_family.py::ColumnFamily.name\n @property\n def name(self):\n \"\"\"Column family name used in requests.\n\n .. note::\n\n This property will not change if ``column_family_id`` does not, but\n the return value is not cached.\n\n The table name is of the form\n\n ``\"projects/../zones/../clusters/../tables/../columnFamilies/..\"``\n\n :rtype: str\n :returns: The column family name.\n \"\"\"\n return self._table.name + '/columnFamilies/' + self.column_family_id","label":0},{"cid":"google/cloud/bigtable/column_family.py::ColumnFamily.__eq__","text":"google/cloud/bigtable/column_family.py::ColumnFamily.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.column_family_id == self.column_family_id and\n other._table == self._table and\n other.gc_rule == self.gc_rule)","label":0},{"cid":"google/cloud/bigtable/column_family.py::ColumnFamily.__ne__","text":"google/cloud/bigtable/column_family.py::ColumnFamily.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/bigtable/column_family.py::ColumnFamily.to_pb","text":"google/cloud/bigtable/column_family.py::ColumnFamily.to_pb\n def to_pb(self):\n \"\"\"Converts the column family to a protobuf.\n\n :rtype: :class:`.table_v2_pb2.ColumnFamily`\n :returns: The converted current object.\n \"\"\"\n if self.gc_rule is None:\n return table_v2_pb2.ColumnFamily()\n else:\n return table_v2_pb2.ColumnFamily(gc_rule=self.gc_rule.to_pb())","label":0},{"cid":"google/cloud/bigtable/column_family.py::ColumnFamily.create","text":"google/cloud/bigtable/column_family.py::ColumnFamily.create\n def create(self):\n \"\"\"Create this column family.\"\"\"\n column_family = self.to_pb()\n request_pb = table_admin_v2_pb2.ModifyColumnFamiliesRequest(\n name=self._table.name)\n request_pb.modifications.add(\n id=self.column_family_id,\n create=column_family,\n )\n client = self._table._instance._client\n # We expect a `.table_v2_pb2.ColumnFamily`. We ignore it since the only\n # data it contains are the GC rule and the column family ID already\n # stored on this instance.\n client._table_stub.ModifyColumnFamilies(request_pb)","label":0},{"cid":"google/cloud/bigtable/column_family.py::ColumnFamily.update","text":"google/cloud/bigtable/column_family.py::ColumnFamily.update\n def update(self):\n \"\"\"Update this column family.\n\n .. note::\n\n Only the GC rule can be updated. By changing the column family ID,\n you will simply be referring to a different column family.\n \"\"\"\n column_family = self.to_pb()\n request_pb = table_admin_v2_pb2.ModifyColumnFamiliesRequest(\n name=self._table.name)\n request_pb.modifications.add(\n id=self.column_family_id,\n update=column_family)\n client = self._table._instance._client\n # We expect a `.table_v2_pb2.ColumnFamily`. We ignore it since the only\n # data it contains are the GC rule and the column family ID already\n # stored on this instance.\n client._table_stub.ModifyColumnFamilies(request_pb)","label":0},{"cid":"google/cloud/bigtable/column_family.py::ColumnFamily.delete","text":"google/cloud/bigtable/column_family.py::ColumnFamily.delete\n def delete(self):\n \"\"\"Delete this column family.\"\"\"\n request_pb = table_admin_v2_pb2.ModifyColumnFamiliesRequest(\n name=self._table.name)\n request_pb.modifications.add(\n id=self.column_family_id,\n drop=True)\n client = self._table._instance._client\n # We expect a `google.protobuf.empty_pb2.Empty`\n client._table_stub.ModifyColumnFamilies(request_pb)","label":0},{"cid":"google/cloud/bigtable/column_family.py::_gc_rule_from_pb","text":"google/cloud/bigtable/column_family.py::_gc_rule_from_pb\ndef _gc_rule_from_pb(gc_rule_pb):\n \"\"\"Convert a protobuf GC rule to a native object.\n\n :type gc_rule_pb: :class:`.table_v2_pb2.GcRule`\n :param gc_rule_pb: The GC rule to convert.\n\n :rtype: :class:`GarbageCollectionRule` or :data:`NoneType <types.NoneType>`\n :returns: An instance of one of the native rules defined\n in :module:`column_family` or :data:`None` if no values were\n set on the protobuf passed in.\n :raises: :class:`ValueError <exceptions.ValueError>` if the rule name\n is unexpected.\n \"\"\"\n rule_name = gc_rule_pb.WhichOneof('rule')\n if rule_name is None:\n return None\n\n if rule_name == 'max_num_versions':\n return MaxVersionsGCRule(gc_rule_pb.max_num_versions)\n elif rule_name == 'max_age':\n max_age = _duration_pb_to_timedelta(gc_rule_pb.max_age)\n return MaxAgeGCRule(max_age)\n elif rule_name == 'union':\n return GCRuleUnion([_gc_rule_from_pb(rule)\n for rule in gc_rule_pb.union.rules])\n elif rule_name == 'intersection':\n rules = [_gc_rule_from_pb(rule)\n for rule in gc_rule_pb.intersection.rules]\n return GCRuleIntersection(rules)\n else:\n raise ValueError('Unexpected rule name', rule_name)","label":0},{"cid":"google/cloud/bigtable/instance.py::_prepare_create_request","text":"google/cloud/bigtable/instance.py::_prepare_create_request\ndef _prepare_create_request(instance):\n \"\"\"Creates a protobuf request for a CreateInstance request.\n\n :type instance: :class:`Instance`\n :param instance: The instance to be created.\n\n :rtype: :class:`.messages_v2_pb2.CreateInstanceRequest`\n :returns: The CreateInstance request object containing the instance info.\n \"\"\"\n parent_name = ('projects/' + instance._client.project)\n message = messages_v2_pb2.CreateInstanceRequest(\n parent=parent_name,\n instance_id=instance.instance_id,\n instance=data_v2_pb2.Instance(\n display_name=instance.display_name,\n ),\n )\n cluster = message.clusters[instance.instance_id]\n cluster.name = instance.name + '/clusters/' + instance.instance_id\n cluster.location = (\n parent_name + '/locations/' + instance._cluster_location_id)\n cluster.serve_nodes = instance._cluster_serve_nodes\n return message","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.__init__","text":"google/cloud/bigtable/instance.py::Instance.__init__\n def __init__(self, instance_id, client,\n location_id=_EXISTING_INSTANCE_LOCATION_ID,\n display_name=None,\n serve_nodes=DEFAULT_SERVE_NODES):\n self.instance_id = instance_id\n self.display_name = display_name or instance_id\n self._cluster_location_id = location_id\n self._cluster_serve_nodes = serve_nodes\n self._client = client","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance._update_from_pb","text":"google/cloud/bigtable/instance.py::Instance._update_from_pb\n def _update_from_pb(self, instance_pb):\n \"\"\"Refresh self from the server-provided protobuf.\n\n Helper for :meth:`from_pb` and :meth:`reload`.\n \"\"\"\n if not instance_pb.display_name: # Simple field (string)\n raise ValueError('Instance protobuf does not contain display_name')\n self.display_name = instance_pb.display_name","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.from_pb","text":"google/cloud/bigtable/instance.py::Instance.from_pb\n @classmethod\n def from_pb(cls, instance_pb, client):\n \"\"\"Creates a instance instance from a protobuf.\n\n :type instance_pb: :class:`instance_pb2.Instance`\n :param instance_pb: A instance protobuf object.\n\n :type client: :class:`Client <google.cloud.bigtable.client.Client>`\n :param client: The client that owns the instance.\n\n :rtype: :class:`Instance`\n :returns: The instance parsed from the protobuf response.\n :raises: :class:`ValueError <exceptions.ValueError>` if the instance\n name does not match\n ``projects/{project}/instances/{instance_id}``\n or if the parsed project ID does not match the project ID\n on the client.\n \"\"\"\n match = _INSTANCE_NAME_RE.match(instance_pb.name)\n if match is None:\n raise ValueError('Instance protobuf name was not in the '\n 'expected format.', instance_pb.name)\n if match.group('project') != client.project:\n raise ValueError('Project ID on instance does not match the '\n 'project ID on the client')\n instance_id = match.group('instance_id')\n\n result = cls(instance_id, client, _EXISTING_INSTANCE_LOCATION_ID)\n result._update_from_pb(instance_pb)\n return result","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.copy","text":"google/cloud/bigtable/instance.py::Instance.copy\n def copy(self):\n \"\"\"Make a copy of this instance.\n\n Copies the local data stored as simple types and copies the client\n attached to this instance.\n\n :rtype: :class:`.Instance`\n :returns: A copy of the current instance.\n \"\"\"\n new_client = self._client.copy()\n return self.__class__(self.instance_id, new_client,\n self._cluster_location_id,\n display_name=self.display_name)","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.name","text":"google/cloud/bigtable/instance.py::Instance.name\n @property\n def name(self):\n \"\"\"Instance name used in requests.\n\n .. note::\n This property will not change if ``instance_id`` does not,\n but the return value is not cached.\n\n The instance name is of the form\n\n ``\"projects/{project}/instances/{instance_id}\"``\n\n :rtype: str\n :returns: The instance name.\n \"\"\"\n return self._client.project_name + '/instances/' + self.instance_id","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.__eq__","text":"google/cloud/bigtable/instance.py::Instance.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n # NOTE: This does not compare the configuration values, such as\n # the display_name. Instead, it only compares\n # identifying values instance ID and client. This is\n # intentional, since the same instance can be in different states\n # if not synchronized. Instances with similar instance\n # settings but different clients can't be used in the same way.\n return (other.instance_id == self.instance_id and\n other._client == self._client)","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.__ne__","text":"google/cloud/bigtable/instance.py::Instance.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.reload","text":"google/cloud/bigtable/instance.py::Instance.reload\n def reload(self):\n \"\"\"Reload the metadata for this instance.\"\"\"\n request_pb = messages_v2_pb2.GetInstanceRequest(name=self.name)\n # We expect `data_v2_pb2.Instance`.\n instance_pb = self._client._instance_stub.GetInstance(request_pb)\n\n # NOTE: _update_from_pb does not check that the project and\n # instance ID on the response match the request.\n self._update_from_pb(instance_pb)","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.create","text":"google/cloud/bigtable/instance.py::Instance.create\n def create(self):\n \"\"\"Create this instance.\n\n .. note::\n\n Uses the ``project`` and ``instance_id`` on the current\n :class:`Instance` in addition to the ``display_name``.\n To change them before creating, reset the values via\n\n .. code:: python\n\n instance.display_name = 'New display name'\n instance.instance_id = 'i-changed-my-mind'\n\n before calling :meth:`create`.\n\n :rtype: :class:`Operation`\n :returns: The long-running operation corresponding to the\n create operation.\n \"\"\"\n request_pb = _prepare_create_request(self)\n # We expect a `google.longrunning.operations_pb2.Operation`.\n operation_pb = self._client._instance_stub.CreateInstance(request_pb)\n\n operation = Operation.from_pb(operation_pb, self._client)\n operation.target = self\n operation.metadata['request_type'] = 'CreateInstance'\n return operation","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.update","text":"google/cloud/bigtable/instance.py::Instance.update\n def update(self):\n \"\"\"Update this instance.\n\n .. note::\n\n Updates the ``display_name``. To change that value before\n updating, reset its values via\n\n .. code:: python\n\n instance.display_name = 'New display name'\n\n before calling :meth:`update`.\n \"\"\"\n request_pb = data_v2_pb2.Instance(\n name=self.name,\n display_name=self.display_name,\n )\n # Ignore the expected `data_v2_pb2.Instance`.\n self._client._instance_stub.UpdateInstance(request_pb)","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.delete","text":"google/cloud/bigtable/instance.py::Instance.delete\n def delete(self):\n \"\"\"Delete this instance.\n\n Marks a instance and all of its tables for permanent deletion\n in 7 days.\n\n Immediately upon completion of the request:\n\n * Billing will cease for all of the instance's reserved resources.\n * The instance's ``delete_time`` field will be set 7 days in\n the future.\n\n Soon afterward:\n\n * All tables within the instance will become unavailable.\n\n At the instance's ``delete_time``:\n\n * The instance and **all of its tables** will immediately and\n irrevocably disappear from the API, and their data will be\n permanently deleted.\n \"\"\"\n request_pb = messages_v2_pb2.DeleteInstanceRequest(name=self.name)\n # We expect a `google.protobuf.empty_pb2.Empty`\n self._client._instance_stub.DeleteInstance(request_pb)","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.cluster","text":"google/cloud/bigtable/instance.py::Instance.cluster\n def cluster(self, cluster_id, serve_nodes=3):\n \"\"\"Factory to create a cluster associated with this client.\n\n :type cluster_id: str\n :param cluster_id: The ID of the cluster.\n\n :type serve_nodes: int\n :param serve_nodes: (Optional) The number of nodes in the cluster.\n Defaults to 3.\n\n :rtype: :class:`.Cluster`\n :returns: The cluster owned by this client.\n \"\"\"\n return Cluster(cluster_id, self, serve_nodes=serve_nodes)","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.list_clusters","text":"google/cloud/bigtable/instance.py::Instance.list_clusters\n def list_clusters(self):\n \"\"\"Lists clusters in this instance.\n\n :rtype: tuple\n :returns: A pair of results, the first is a list of :class:`.Cluster` s\n returned and the second is a list of strings (the failed\n locations in the request).\n \"\"\"\n request_pb = messages_v2_pb2.ListClustersRequest(parent=self.name)\n # We expect a `.cluster_messages_v1_pb2.ListClustersResponse`\n list_clusters_response = self._client._instance_stub.ListClusters(\n request_pb)\n\n failed_locations = [\n location for location in list_clusters_response.failed_locations]\n clusters = [Cluster.from_pb(cluster_pb, self)\n for cluster_pb in list_clusters_response.clusters]\n return clusters, failed_locations","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.table","text":"google/cloud/bigtable/instance.py::Instance.table\n def table(self, table_id):\n \"\"\"Factory to create a table associated with this instance.\n\n :type table_id: str\n :param table_id: The ID of the table.\n\n :rtype: :class:`Table <google.cloud.bigtable.table.Table>`\n :returns: The table owned by this instance.\n \"\"\"\n return Table(table_id, self)","label":0},{"cid":"google/cloud/bigtable/instance.py::Instance.list_tables","text":"google/cloud/bigtable/instance.py::Instance.list_tables\n def list_tables(self):\n \"\"\"List the tables in this instance.\n\n :rtype: list of :class:`Table <google.cloud.bigtable.table.Table>`\n :returns: The list of tables owned by the instance.\n :raises: :class:`ValueError <exceptions.ValueError>` if one of the\n returned tables has a name that is not of the expected format.\n \"\"\"\n request_pb = table_messages_v2_pb2.ListTablesRequest(parent=self.name)\n # We expect a `table_messages_v2_pb2.ListTablesResponse`\n table_list_pb = self._client._table_stub.ListTables(request_pb)\n\n result = []\n for table_pb in table_list_pb.tables:\n table_prefix = self.name + '/tables/'\n if not table_pb.name.startswith(table_prefix):\n raise ValueError('Table name %s not of expected format' % (\n table_pb.name,))\n table_id = table_pb.name[len(table_prefix):]\n result.append(self.table(table_id))\n\n return result","label":0},{"cid":"google/cloud/bigtable/row.py::Row.__init__","text":"google/cloud/bigtable/row.py::Row.__init__\n def __init__(self, row_key, table):\n self._row_key = _to_bytes(row_key)\n self._table = table","label":0},{"cid":"google/cloud/bigtable/row.py::_SetDeleteRow._get_mutations","text":"google/cloud/bigtable/row.py::_SetDeleteRow._get_mutations\n def _get_mutations(self, state):\n \"\"\"Gets the list of mutations for a given state.\n\n This method intended to be implemented by subclasses.\n\n ``state`` may not need to be used by all subclasses.\n\n :type state: bool\n :param state: The state that the mutation should be\n applied in.\n\n :raises: :class:`NotImplementedError <exceptions.NotImplementedError>`\n always.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"google/cloud/bigtable/row.py::_SetDeleteRow._set_cell","text":"google/cloud/bigtable/row.py::_SetDeleteRow._set_cell\n def _set_cell(self, column_family_id, column, value, timestamp=None,\n state=None):\n \"\"\"Helper for :meth:`set_cell`\n\n Adds a mutation to set the value in a specific cell.\n\n ``state`` is unused by :class:`DirectRow` but is used by\n subclasses.\n\n :type column_family_id: str\n :param column_family_id: The column family that contains the column.\n Must be of the form\n ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type column: bytes\n :param column: The column within the column family where the cell\n is located.\n\n :type value: bytes or :class:`int`\n :param value: The value to set in the cell. If an integer is used,\n will be interpreted as a 64-bit big-endian signed\n integer (8 bytes).\n\n :type timestamp: :class:`datetime.datetime`\n :param timestamp: (Optional) The timestamp of the operation.\n\n :type state: bool\n :param state: (Optional) The state that is passed along to\n :meth:`_get_mutations`.\n \"\"\"\n column = _to_bytes(column)\n if isinstance(value, six.integer_types):\n value = _PACK_I64(value)\n value = _to_bytes(value)\n if timestamp is None:\n # Use -1 for current Bigtable server time.\n timestamp_micros = -1\n else:\n timestamp_micros = _microseconds_from_datetime(timestamp)\n # Truncate to millisecond granularity.\n timestamp_micros -= (timestamp_micros % 1000)\n\n mutation_val = data_v2_pb2.Mutation.SetCell(\n family_name=column_family_id,\n column_qualifier=column,\n timestamp_micros=timestamp_micros,\n value=value,\n )\n mutation_pb = data_v2_pb2.Mutation(set_cell=mutation_val)\n self._get_mutations(sta","label":0},{"cid":"google/cloud/bigtable/row.py::_SetDeleteRow._delete","text":"google/cloud/bigtable/row.py::_SetDeleteRow._delete\n def _delete(self, state=None):\n \"\"\"Helper for :meth:`delete`\n\n Adds a delete mutation (for the entire row) to the accumulated\n mutations.\n\n ``state`` is unused by :class:`DirectRow` but is used by\n subclasses.\n\n :type state: bool\n :param state: (Optional) The state that is passed along to\n :meth:`_get_mutations`.\n \"\"\"\n mutation_val = data_v2_pb2.Mutation.DeleteFromRow()\n mutation_pb = data_v2_pb2.Mutation(delete_from_row=mutation_val)\n self._get_mutations(state).append(mutation_pb)","label":0},{"cid":"google/cloud/bigtable/row.py::_SetDeleteRow._delete_cells","text":"google/cloud/bigtable/row.py::_SetDeleteRow._delete_cells\n def _delete_cells(self, column_family_id, columns, time_range=None,\n state=None):\n \"\"\"Helper for :meth:`delete_cell` and :meth:`delete_cells`.\n\n ``state`` is unused by :class:`DirectRow` but is used by\n subclasses.\n\n :type column_family_id: str\n :param column_family_id: The column family that contains the column\n or columns with cells being deleted. Must be\n of the form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type columns: :class:`list` of :class:`str` /\n :func:`unicode <unicode>`, or :class:`object`\n :param columns: The columns within the column family that will have\n cells deleted. If :attr:`ALL_COLUMNS` is used then\n the entire column family will be deleted from the row.\n\n :type time_range: :class:`TimestampRange`\n :param time_range: (Optional) The range of time within which cells\n should be deleted.\n\n :type state: bool\n :param state: (Optional) The state that is passed along to\n :meth:`_get_mutations`.\n \"\"\"\n mutations_list = self._get_mutations(state)\n if columns is self.ALL_COLUMNS:\n mutation_val = data_v2_pb2.Mutation.DeleteFromFamily(\n family_name=column_family_id,\n )\n mutation_pb = data_v2_pb2.Mutation(delete_from_family=mutation_val)\n mutations_list.append(mutation_pb)\n else:\n delete_kwargs = {}\n if time_range is not None:\n delete_kwargs['time_range'] = time_range.to_pb()\n\n to_append = []\n for column in columns:\n column = _to_bytes(column)\n # time_range will never change if present, but the rest of\n # delete_kwargs will\n delet","label":0},{"cid":"google/cloud/bigtable/row.py::DirectRow.__init__","text":"google/cloud/bigtable/row.py::DirectRow.__init__\n def __init__(self, row_key, table):\n super(DirectRow, self).__init__(row_key, table)\n self._pb_mutations = []","label":0},{"cid":"google/cloud/bigtable/row.py::DirectRow._get_mutations","text":"google/cloud/bigtable/row.py::DirectRow._get_mutations\n def _get_mutations(self, state): # pylint: disable=unused-argument\n \"\"\"Gets the list of mutations for a given state.\n\n ``state`` is unused by :class:`DirectRow` but is used by\n subclasses.\n\n :type state: bool\n :param state: The state that the mutation should be\n applied in.\n\n :rtype: list\n :returns: The list to add new mutations to (for the current state).\n \"\"\"\n return self._pb_mutations","label":0},{"cid":"google/cloud/bigtable/row.py::DirectRow.set_cell","text":"google/cloud/bigtable/row.py::DirectRow.set_cell\n def set_cell(self, column_family_id, column, value, timestamp=None):\n \"\"\"Sets a value in this row.\n\n The cell is determined by the ``row_key`` of this :class:`DirectRow`\n and the ``column``. The ``column`` must be in an existing\n :class:`.ColumnFamily` (as determined by ``column_family_id``).\n\n .. note::\n\n This method adds a mutation to the accumulated mutations on this\n row, but does not make an API request. To actually\n send an API request (with the mutations) to the Google Cloud\n Bigtable API, call :meth:`commit`.\n\n :type column_family_id: str\n :param column_family_id: The column family that contains the column.\n Must be of the form\n ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type column: bytes\n :param column: The column within the column family where the cell\n is located.\n\n :type value: bytes or :class:`int`\n :param value: The value to set in the cell. If an integer is used,\n will be interpreted as a 64-bit big-endian signed\n integer (8 bytes).\n\n :type timestamp: :class:`datetime.datetime`\n :param timestamp: (Optional) The timestamp of the operation.\n \"\"\"\n self._set_cell(column_family_id, column, value, timestamp=timestamp,\n state=None)","label":0},{"cid":"google/cloud/bigtable/row.py::DirectRow.delete","text":"google/cloud/bigtable/row.py::DirectRow.delete\n def delete(self):\n \"\"\"Deletes this row from the table.\n\n .. note::\n\n This method adds a mutation to the accumulated mutations on this\n row, but does not make an API request. To actually\n send an API request (with the mutations) to the Google Cloud\n Bigtable API, call :meth:`commit`.\n \"\"\"\n self._delete(state=None)","label":0},{"cid":"google/cloud/bigtable/row.py::DirectRow.delete_cell","text":"google/cloud/bigtable/row.py::DirectRow.delete_cell\n def delete_cell(self, column_family_id, column, time_range=None):\n \"\"\"Deletes cell in this row.\n\n .. note::\n\n This method adds a mutation to the accumulated mutations on this\n row, but does not make an API request. To actually\n send an API request (with the mutations) to the Google Cloud\n Bigtable API, call :meth:`commit`.\n\n :type column_family_id: str\n :param column_family_id: The column family that contains the column\n or columns with cells being deleted. Must be\n of the form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type column: bytes\n :param column: The column within the column family that will have a\n cell deleted.\n\n :type time_range: :class:`TimestampRange`\n :param time_range: (Optional) The range of time within which cells\n should be deleted.\n \"\"\"\n self._delete_cells(column_family_id, [column], time_range=time_range,\n state=None)","label":0},{"cid":"google/cloud/bigtable/row.py::DirectRow.delete_cells","text":"google/cloud/bigtable/row.py::DirectRow.delete_cells\n def delete_cells(self, column_family_id, columns, time_range=None):\n \"\"\"Deletes cells in this row.\n\n .. note::\n\n This method adds a mutation to the accumulated mutations on this\n row, but does not make an API request. To actually\n send an API request (with the mutations) to the Google Cloud\n Bigtable API, call :meth:`commit`.\n\n :type column_family_id: str\n :param column_family_id: The column family that contains the column\n or columns with cells being deleted. Must be\n of the form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type columns: :class:`list` of :class:`str` /\n :func:`unicode <unicode>`, or :class:`object`\n :param columns: The columns within the column family that will have\n cells deleted. If :attr:`ALL_COLUMNS` is used then\n the entire column family will be deleted from the row.\n\n :type time_range: :class:`TimestampRange`\n :param time_range: (Optional) The range of time within which cells\n should be deleted.\n \"\"\"\n self._delete_cells(column_family_id, columns, time_range=time_range,\n state=None)","label":0},{"cid":"google/cloud/bigtable/row.py::DirectRow.commit","text":"google/cloud/bigtable/row.py::DirectRow.commit\n def commit(self):\n \"\"\"Makes a ``MutateRow`` API request.\n\n If no mutations have been created in the row, no request is made.\n\n Mutations are applied atomically and in order, meaning that earlier\n mutations can be masked / negated by later ones. Cells already present\n in the row are left unchanged unless explicitly changed by a mutation.\n\n After committing the accumulated mutations, resets the local\n mutations to an empty list.\n\n :raises: :class:`ValueError <exceptions.ValueError>` if the number of\n mutations exceeds the :data:`MAX_MUTATIONS`.\n \"\"\"\n mutations_list = self._get_mutations(None)\n num_mutations = len(mutations_list)\n if num_mutations == 0:\n return\n if num_mutations > MAX_MUTATIONS:\n raise ValueError('%d total mutations exceed the maximum allowable '\n '%d.' % (num_mutations, MAX_MUTATIONS))\n request_pb = messages_v2_pb2.MutateRowRequest(\n table_name=self._table.name,\n row_key=self._row_key,\n mutations=mutations_list,\n )\n # We expect a `google.protobuf.empty_pb2.Empty`\n client = self._table._instance._client\n client._data_stub.MutateRow(request_pb)\n self.clear()","label":0},{"cid":"google/cloud/bigtable/row.py::DirectRow.clear","text":"google/cloud/bigtable/row.py::DirectRow.clear\n def clear(self):\n \"\"\"Removes all currently accumulated mutations on the current row.\"\"\"\n del self._pb_mutations[:]","label":0},{"cid":"google/cloud/bigtable/row.py::ConditionalRow.__init__","text":"google/cloud/bigtable/row.py::ConditionalRow.__init__\n def __init__(self, row_key, table, filter_):\n super(ConditionalRow, self).__init__(row_key, table)\n self._filter = filter_\n self._true_pb_mutations = []\n self._false_pb_mutations = []","label":0},{"cid":"google/cloud/bigtable/row.py::ConditionalRow._get_mutations","text":"google/cloud/bigtable/row.py::ConditionalRow._get_mutations\n def _get_mutations(self, state):\n \"\"\"Gets the list of mutations for a given state.\n\n Over-ridden so that the state can be used in:\n\n * :meth:`set_cell`\n * :meth:`delete`\n * :meth:`delete_cell`\n * :meth:`delete_cells`\n\n :type state: bool\n :param state: The state that the mutation should be\n applied in.\n\n :rtype: list\n :returns: The list to add new mutations to (for the current state).\n \"\"\"\n if state:\n return self._true_pb_mutations\n else:\n return self._false_pb_mutations","label":0},{"cid":"google/cloud/bigtable/row.py::ConditionalRow.commit","text":"google/cloud/bigtable/row.py::ConditionalRow.commit\n def commit(self):\n \"\"\"Makes a ``CheckAndMutateRow`` API request.\n\n If no mutations have been created in the row, no request is made.\n\n The mutations will be applied conditionally, based on whether the\n filter matches any cells in the :class:`ConditionalRow` or not. (Each\n method which adds a mutation has a ``state`` parameter for this\n purpose.)\n\n Mutations are applied atomically and in order, meaning that earlier\n mutations can be masked / negated by later ones. Cells already present\n in the row are left unchanged unless explicitly changed by a mutation.\n\n After committing the accumulated mutations, resets the local\n mutations.\n\n :rtype: bool\n :returns: Flag indicating if the filter was matched (which also\n indicates which set of mutations were applied by the server).\n :raises: :class:`ValueError <exceptions.ValueError>` if the number of\n mutations exceeds the :data:`MAX_MUTATIONS`.\n \"\"\"\n true_mutations = self._get_mutations(state=True)\n false_mutations = self._get_mutations(state=False)\n num_true_mutations = len(true_mutations)\n num_false_mutations = len(false_mutations)\n if num_true_mutations == 0 and num_false_mutations == 0:\n return\n if (num_true_mutations > MAX_MUTATIONS or\n num_false_mutations > MAX_MUTATIONS):\n raise ValueError(\n 'Exceed the maximum allowable mutations (%d). Had %s true '\n 'mutations and %d false mutations.' % (\n MAX_MUTATIONS, num_true_mutations, num_false_mutations))\n\n request_pb = messages_v2_pb2.CheckAndMutateRowRequest(\n table_name=self._table.name,\n row_key=self._row_key,\n predicate_filter=self._filter.to_pb(),\n true_mutations=true_mutations,\n false_mutations","label":0},{"cid":"google/cloud/bigtable/row.py::ConditionalRow.set_cell","text":"google/cloud/bigtable/row.py::ConditionalRow.set_cell\n def set_cell(self, column_family_id, column, value, timestamp=None,\n state=True):\n \"\"\"Sets a value in this row.\n\n The cell is determined by the ``row_key`` of this\n :class:`ConditionalRow` and the ``column``. The ``column`` must be in\n an existing :class:`.ColumnFamily` (as determined by\n ``column_family_id``).\n\n .. note::\n\n This method adds a mutation to the accumulated mutations on this\n row, but does not make an API request. To actually\n send an API request (with the mutations) to the Google Cloud\n Bigtable API, call :meth:`commit`.\n\n :type column_family_id: str\n :param column_family_id: The column family that contains the column.\n Must be of the form\n ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type column: bytes\n :param column: The column within the column family where the cell\n is located.\n\n :type value: bytes or :class:`int`\n :param value: The value to set in the cell. If an integer is used,\n will be interpreted as a 64-bit big-endian signed\n integer (8 bytes).\n\n :type timestamp: :class:`datetime.datetime`\n :param timestamp: (Optional) The timestamp of the operation.\n\n :type state: bool\n :param state: (Optional) The state that the mutation should be\n applied in. Defaults to :data:`True`.\n \"\"\"\n self._set_cell(column_family_id, column, value, timestamp=timestamp,\n state=state)","label":0},{"cid":"google/cloud/bigtable/row.py::ConditionalRow.delete","text":"google/cloud/bigtable/row.py::ConditionalRow.delete\n def delete(self, state=True):\n \"\"\"Deletes this row from the table.\n\n .. note::\n\n This method adds a mutation to the accumulated mutations on this\n row, but does not make an API request. To actually\n send an API request (with the mutations) to the Google Cloud\n Bigtable API, call :meth:`commit`.\n\n :type state: bool\n :param state: (Optional) The state that the mutation should be\n applied in. Defaults to :data:`True`.\n \"\"\"\n self._delete(state=state)","label":0},{"cid":"google/cloud/bigtable/row.py::ConditionalRow.delete_cell","text":"google/cloud/bigtable/row.py::ConditionalRow.delete_cell\n def delete_cell(self, column_family_id, column, time_range=None,\n state=True):\n \"\"\"Deletes cell in this row.\n\n .. note::\n\n This method adds a mutation to the accumulated mutations on this\n row, but does not make an API request. To actually\n send an API request (with the mutations) to the Google Cloud\n Bigtable API, call :meth:`commit`.\n\n :type column_family_id: str\n :param column_family_id: The column family that contains the column\n or columns with cells being deleted. Must be\n of the form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type column: bytes\n :param column: The column within the column family that will have a\n cell deleted.\n\n :type time_range: :class:`TimestampRange`\n :param time_range: (Optional) The range of time within which cells\n should be deleted.\n\n :type state: bool\n :param state: (Optional) The state that the mutation should be\n applied in. Defaults to :data:`True`.\n \"\"\"\n self._delete_cells(column_family_id, [column], time_range=time_range,\n state=state)","label":0},{"cid":"google/cloud/bigtable/row.py::ConditionalRow.delete_cells","text":"google/cloud/bigtable/row.py::ConditionalRow.delete_cells\n def delete_cells(self, column_family_id, columns, time_range=None,\n state=True):\n \"\"\"Deletes cells in this row.\n\n .. note::\n\n This method adds a mutation to the accumulated mutations on this\n row, but does not make an API request. To actually\n send an API request (with the mutations) to the Google Cloud\n Bigtable API, call :meth:`commit`.\n\n :type column_family_id: str\n :param column_family_id: The column family that contains the column\n or columns with cells being deleted. Must be\n of the form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type columns: :class:`list` of :class:`str` /\n :func:`unicode <unicode>`, or :class:`object`\n :param columns: The columns within the column family that will have\n cells deleted. If :attr:`ALL_COLUMNS` is used then the\n entire column family will be deleted from the row.\n\n :type time_range: :class:`TimestampRange`\n :param time_range: (Optional) The range of time within which cells\n should be deleted.\n\n :type state: bool\n :param state: (Optional) The state that the mutation should be\n applied in. Defaults to :data:`True`.\n \"\"\"\n self._delete_cells(column_family_id, columns, time_range=time_range,\n state=state)","label":0},{"cid":"google/cloud/bigtable/row.py::ConditionalRow.clear","text":"google/cloud/bigtable/row.py::ConditionalRow.clear\n def clear(self):\n \"\"\"Removes all currently accumulated mutations on the current row.\"\"\"\n del self._true_pb_mutations[:]\n del self._false_pb_mutations[:]","label":0},{"cid":"google/cloud/bigtable/row.py::AppendRow.__init__","text":"google/cloud/bigtable/row.py::AppendRow.__init__\n def __init__(self, row_key, table):\n super(AppendRow, self).__init__(row_key, table)\n self._rule_pb_list = []","label":0},{"cid":"google/cloud/bigtable/row.py::AppendRow.clear","text":"google/cloud/bigtable/row.py::AppendRow.clear\n def clear(self):\n \"\"\"Removes all currently accumulated modifications on current row.\"\"\"\n del self._rule_pb_list[:]","label":0},{"cid":"google/cloud/bigtable/row.py::AppendRow.append_cell_value","text":"google/cloud/bigtable/row.py::AppendRow.append_cell_value\n def append_cell_value(self, column_family_id, column, value):\n \"\"\"Appends a value to an existing cell.\n\n .. note::\n\n This method adds a read-modify rule protobuf to the accumulated\n read-modify rules on this row, but does not make an API\n request. To actually send an API request (with the rules) to the\n Google Cloud Bigtable API, call :meth:`commit`.\n\n :type column_family_id: str\n :param column_family_id: The column family that contains the column.\n Must be of the form\n ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type column: bytes\n :param column: The column within the column family where the cell\n is located.\n\n :type value: bytes\n :param value: The value to append to the existing value in the cell. If\n the targeted cell is unset, it will be treated as\n containing the empty string.\n \"\"\"\n column = _to_bytes(column)\n value = _to_bytes(value)\n rule_pb = data_v2_pb2.ReadModifyWriteRule(\n family_name=column_family_id,\n column_qualifier=column,\n append_value=value)\n self._rule_pb_list.append(rule_pb)","label":0},{"cid":"google/cloud/bigtable/row.py::AppendRow.increment_cell_value","text":"google/cloud/bigtable/row.py::AppendRow.increment_cell_value\n def increment_cell_value(self, column_family_id, column, int_value):\n \"\"\"Increments a value in an existing cell.\n\n Assumes the value in the cell is stored as a 64 bit integer\n serialized to bytes.\n\n .. note::\n\n This method adds a read-modify rule protobuf to the accumulated\n read-modify rules on this row, but does not make an API\n request. To actually send an API request (with the rules) to the\n Google Cloud Bigtable API, call :meth:`commit`.\n\n :type column_family_id: str\n :param column_family_id: The column family that contains the column.\n Must be of the form\n ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type column: bytes\n :param column: The column within the column family where the cell\n is located.\n\n :type int_value: int\n :param int_value: The value to increment the existing value in the cell\n by. If the targeted cell is unset, it will be treated\n as containing a zero. Otherwise, the targeted cell\n must contain an 8-byte value (interpreted as a 64-bit\n big-endian signed integer), or the entire request\n will fail.\n \"\"\"\n column = _to_bytes(column)\n rule_pb = data_v2_pb2.ReadModifyWriteRule(\n family_name=column_family_id,\n column_qualifier=column,\n increment_amount=int_value)\n self._rule_pb_list.append(rule_pb)","label":0},{"cid":"google/cloud/bigtable/row.py::AppendRow.commit","text":"google/cloud/bigtable/row.py::AppendRow.commit\n def commit(self):\n \"\"\"Makes a ``ReadModifyWriteRow`` API request.\n\n This commits modifications made by :meth:`append_cell_value` and\n :meth:`increment_cell_value`. If no modifications were made, makes\n no API request and just returns ``{}``.\n\n Modifies a row atomically, reading the latest existing\n timestamp / value from the specified columns and writing a new value by\n appending / incrementing. The new cell created uses either the current\n server time or the highest timestamp of a cell in that column (if it\n exceeds the server time).\n\n After committing the accumulated mutations, resets the local mutations.\n\n .. code:: python\n\n >>> append_row.commit()\n {\n u'col-fam-id': {\n b'col-name1': [\n (b'cell-val', datetime.datetime(...)),\n (b'cell-val-newer', datetime.datetime(...)),\n ],\n b'col-name2': [\n (b'altcol-cell-val', datetime.datetime(...)),\n ],\n },\n u'col-fam-id2': {\n b'col-name3-but-other-fam': [\n (b'foo', datetime.datetime(...)),\n ],\n },\n }\n\n :rtype: dict\n :returns: The new contents of all modified cells. Returned as a\n dictionary of column families, each of which holds a\n dictionary of columns. Each column contains a list of cells\n modified. Each cell is represented with a two-tuple with the\n value (in bytes) and the timestamp for the cell.\n :raises: :class:`ValueError <exceptions.ValueError>` if the number of\n mutations exceeds the :data:`MAX_MUTATIONS`.\n \"\"\"\n num_mutations = len(self._rule_pb_list)\n if num_mutations == ","label":0},{"cid":"google/cloud/bigtable/row.py::_parse_rmw_row_response","text":"google/cloud/bigtable/row.py::_parse_rmw_row_response\ndef _parse_rmw_row_response(row_response):\n \"\"\"Parses the response to a ``ReadModifyWriteRow`` request.\n\n :type row_response: :class:`.data_v2_pb2.Row`\n :param row_response: The response row (with only modified cells) from a\n ``ReadModifyWriteRow`` request.\n\n :rtype: dict\n :returns: The new contents of all modified cells. Returned as a\n dictionary of column families, each of which holds a\n dictionary of columns. Each column contains a list of cells\n modified. Each cell is represented with a two-tuple with the\n value (in bytes) and the timestamp for the cell. For example:\n\n .. code:: python\n\n {\n u'col-fam-id': {\n b'col-name1': [\n (b'cell-val', datetime.datetime(...)),\n (b'cell-val-newer', datetime.datetime(...)),\n ],\n b'col-name2': [\n (b'altcol-cell-val', datetime.datetime(...)),\n ],\n },\n u'col-fam-id2': {\n b'col-name3-but-other-fam': [\n (b'foo', datetime.datetime(...)),\n ],\n },\n }\n \"\"\"\n result = {}\n for column_family in row_response.row.families:\n column_family_id, curr_family = _parse_family_pb(column_family)\n result[column_family_id] = curr_family\n return result","label":0},{"cid":"google/cloud/bigtable/row.py::_parse_family_pb","text":"google/cloud/bigtable/row.py::_parse_family_pb\ndef _parse_family_pb(family_pb):\n \"\"\"Parses a Family protobuf into a dictionary.\n\n :type family_pb: :class:`._generated.data_pb2.Family`\n :param family_pb: A protobuf\n\n :rtype: tuple\n :returns: A string and dictionary. The string is the name of the\n column family and the dictionary has column names (within the\n family) as keys and cell lists as values. Each cell is\n represented with a two-tuple with the value (in bytes) and the\n timestamp for the cell. For example:\n\n .. code:: python\n\n {\n b'col-name1': [\n (b'cell-val', datetime.datetime(...)),\n (b'cell-val-newer', datetime.datetime(...)),\n ],\n b'col-name2': [\n (b'altcol-cell-val', datetime.datetime(...)),\n ],\n }\n \"\"\"\n result = {}\n for column in family_pb.columns:\n result[column.qualifier] = cells = []\n for cell in column.cells:\n val_pair = (\n cell.value,\n _datetime_from_microseconds(cell.timestamp_micros),\n )\n cells.append(val_pair)\n\n return family_pb.name, result","label":0},{"cid":"google/cloud/bigtable/row_data.py::Cell.__init__","text":"google/cloud/bigtable/row_data.py::Cell.__init__\n def __init__(self, value, timestamp, labels=()):\n self.value = value\n self.timestamp = timestamp\n self.labels = list(labels)","label":0},{"cid":"google/cloud/bigtable/row_data.py::Cell.from_pb","text":"google/cloud/bigtable/row_data.py::Cell.from_pb\n @classmethod\n def from_pb(cls, cell_pb):\n \"\"\"Create a new cell from a Cell protobuf.\n\n :type cell_pb: :class:`._generated.data_pb2.Cell`\n :param cell_pb: The protobuf to convert.\n\n :rtype: :class:`Cell`\n :returns: The cell corresponding to the protobuf.\n \"\"\"\n timestamp = _datetime_from_microseconds(cell_pb.timestamp_micros)\n if cell_pb.labels:\n return cls(cell_pb.value, timestamp, labels=cell_pb.labels)\n else:\n return cls(cell_pb.value, timestamp)","label":0},{"cid":"google/cloud/bigtable/row_data.py::Cell.__eq__","text":"google/cloud/bigtable/row_data.py::Cell.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.value == self.value and\n other.timestamp == self.timestamp and\n other.labels == self.labels)","label":0},{"cid":"google/cloud/bigtable/row_data.py::Cell.__ne__","text":"google/cloud/bigtable/row_data.py::Cell.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialCellData.__init__","text":"google/cloud/bigtable/row_data.py::PartialCellData.__init__\n def __init__(self, row_key, family_name, qualifier, timestamp_micros,\n labels=(), value=b''):\n self.row_key = row_key\n self.family_name = family_name\n self.qualifier = qualifier\n self.timestamp_micros = timestamp_micros\n self.labels = labels\n self.value = value","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialCellData.append_value","text":"google/cloud/bigtable/row_data.py::PartialCellData.append_value\n def append_value(self, value):\n \"\"\"Append bytes from a new chunk to value.\n\n :type value: bytes\n :param value: bytes to append\n \"\"\"\n self.value += value","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowData.__init__","text":"google/cloud/bigtable/row_data.py::PartialRowData.__init__\n def __init__(self, row_key):\n self._row_key = row_key\n self._cells = {}","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowData.__eq__","text":"google/cloud/bigtable/row_data.py::PartialRowData.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other._row_key == self._row_key and\n other._cells == self._cells)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowData.__ne__","text":"google/cloud/bigtable/row_data.py::PartialRowData.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowData.to_dict","text":"google/cloud/bigtable/row_data.py::PartialRowData.to_dict\n def to_dict(self):\n \"\"\"Convert the cells to a dictionary.\n\n This is intended to be used with HappyBase, so the column family and\n column qualiers are combined (with ``:``).\n\n :rtype: dict\n :returns: Dictionary containing all the data in the cells of this row.\n \"\"\"\n result = {}\n for column_family_id, columns in six.iteritems(self._cells):\n for column_qual, cells in six.iteritems(columns):\n key = (_to_bytes(column_family_id) + b':' +\n _to_bytes(column_qual))\n result[key] = cells\n return result","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowData.cells","text":"google/cloud/bigtable/row_data.py::PartialRowData.cells\n @property\n def cells(self):\n \"\"\"Property returning all the cells accumulated on this partial row.\n\n :rtype: dict\n :returns: Dictionary of the :class:`Cell` objects accumulated. This\n dictionary has two-levels of keys (first for column families\n and second for column names/qualifiers within a family). For\n a given column, a list of :class:`Cell` objects is stored.\n \"\"\"\n return copy.deepcopy(self._cells)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowData.row_key","text":"google/cloud/bigtable/row_data.py::PartialRowData.row_key\n @property\n def row_key(self):\n \"\"\"Getter for the current (partial) row's key.\n\n :rtype: bytes\n :returns: The current (partial) row's key.\n \"\"\"\n return self._row_key","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData.__init__","text":"google/cloud/bigtable/row_data.py::PartialRowsData.__init__\n def __init__(self, response_iterator):\n self._response_iterator = response_iterator\n # Fully-processed rows, keyed by `row_key`\n self._rows = {}\n # Counter for responses pulled from iterator\n self._counter = 0\n # Maybe cached from previous response\n self._last_scanned_row_key = None\n # In-progress row, unset until first response, after commit/reset\n self._row = None\n # Last complete row, unset until first commit\n self._previous_row = None\n # In-progress cell, unset until first response, after completion\n self._cell = None\n # Last complete cell, unset until first completion, after new row\n self._previous_cell = None","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData.__eq__","text":"google/cloud/bigtable/row_data.py::PartialRowsData.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other._response_iterator == self._response_iterator","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData.__ne__","text":"google/cloud/bigtable/row_data.py::PartialRowsData.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData.state","text":"google/cloud/bigtable/row_data.py::PartialRowsData.state\n @property\n def state(self):\n \"\"\"State machine state.\n\n :rtype: str\n :returns: name of state corresponding to currrent row / chunk\n processing.\n \"\"\"\n if self._last_scanned_row_key is None:\n return self.START\n if self._row is None:\n assert self._cell is None\n assert self._previous_cell is None\n return self.NEW_ROW\n if self._cell is not None:\n return self.CELL_IN_PROGRESS\n if self._previous_cell is not None:\n return self.ROW_IN_PROGRESS\n return self.NEW_ROW # row added, no chunk yet processed","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData.rows","text":"google/cloud/bigtable/row_data.py::PartialRowsData.rows\n @property\n def rows(self):\n \"\"\"Property returning all rows accumulated from the stream.\n\n :rtype: dict\n :returns: row_key -> :class:`PartialRowData`.\n \"\"\"\n # NOTE: To avoid duplicating large objects, this is just the\n # mutable private data.\n return self._rows","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData.cancel","text":"google/cloud/bigtable/row_data.py::PartialRowsData.cancel\n def cancel(self):\n \"\"\"Cancels the iterator, closing the stream.\"\"\"\n self._response_iterator.cancel()","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData.consume_next","text":"google/cloud/bigtable/row_data.py::PartialRowsData.consume_next\n def consume_next(self):\n \"\"\"Consume the next ``ReadRowsResponse`` from the stream.\n\n Parse the response and its chunks into a new/existing row in\n :attr:`_rows`\n \"\"\"\n response = six.next(self._response_iterator)\n self._counter += 1\n\n if self._last_scanned_row_key is None: # first response\n if response.last_scanned_row_key:\n raise InvalidReadRowsResponse()\n\n self._last_scanned_row_key = response.last_scanned_row_key\n\n row = self._row\n cell = self._cell\n\n for chunk in response.chunks:\n\n self._validate_chunk(chunk)\n\n if chunk.reset_row:\n row = self._row = None\n cell = self._cell = self._previous_cell = None\n continue\n\n if row is None:\n row = self._row = PartialRowData(chunk.row_key)\n\n if cell is None:\n cell = self._cell = PartialCellData(\n chunk.row_key,\n chunk.family_name.value,\n chunk.qualifier.value,\n chunk.timestamp_micros,\n chunk.labels,\n chunk.value)\n self._copy_from_previous(cell)\n else:\n cell.append_value(chunk.value)\n\n if chunk.commit_row:\n self._save_current_row()\n row = cell = None\n continue\n\n if chunk.value_size == 0:\n self._save_current_cell()\n cell = None","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData.consume_all","text":"google/cloud/bigtable/row_data.py::PartialRowsData.consume_all\n def consume_all(self, max_loops=None):\n \"\"\"Consume the streamed responses until there are no more.\n\n This simply calls :meth:`consume_next` until there are no\n more to consume.\n\n :type max_loops: int\n :param max_loops: (Optional) Maximum number of times to try to consume\n an additional ``ReadRowsResponse``. You can use this\n to avoid long wait times.\n \"\"\"\n curr_loop = 0\n if max_loops is None:\n max_loops = float('inf')\n while curr_loop < max_loops:\n curr_loop += 1\n try:\n self.consume_next()\n except StopIteration:\n break","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_status","text":"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_status\n @staticmethod\n def _validate_chunk_status(chunk):\n \"\"\"Helper for :meth:`_validate_chunk_row_in_progress`, etc.\"\"\"\n # No reseet with other keys\n if chunk.reset_row:\n _raise_if(chunk.row_key)\n _raise_if(chunk.HasField('family_name'))\n _raise_if(chunk.HasField('qualifier'))\n _raise_if(chunk.timestamp_micros)\n _raise_if(chunk.labels)\n _raise_if(chunk.value_size)\n _raise_if(chunk.value)\n # No commit with value size\n _raise_if(chunk.commit_row and chunk.value_size > 0)\n # No negative value_size (inferred as a general constraint).\n _raise_if(chunk.value_size < 0)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_new_row","text":"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_new_row\n def _validate_chunk_new_row(self, chunk):\n \"\"\"Helper for :meth:`_validate_chunk`.\"\"\"\n assert self.state == self.NEW_ROW\n _raise_if(chunk.reset_row)\n _raise_if(not chunk.row_key)\n _raise_if(not chunk.family_name)\n _raise_if(not chunk.qualifier)\n # This constraint is not enforced in the Go example.\n _raise_if(chunk.value_size > 0 and chunk.commit_row is not False)\n # This constraint is from the Go example, not the spec.\n _raise_if(self._previous_row is not None and\n chunk.row_key <= self._previous_row.row_key)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData._same_as_previous","text":"google/cloud/bigtable/row_data.py::PartialRowsData._same_as_previous\n def _same_as_previous(self, chunk):\n \"\"\"Helper for :meth:`_validate_chunk_row_in_progress`\"\"\"\n previous = self._previous_cell\n return (chunk.row_key == previous.row_key and\n chunk.family_name == previous.family_name and\n chunk.qualifier == previous.qualifier and\n chunk.labels == previous.labels)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_row_in_progress","text":"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_row_in_progress\n def _validate_chunk_row_in_progress(self, chunk):\n \"\"\"Helper for :meth:`_validate_chunk`\"\"\"\n assert self.state == self.ROW_IN_PROGRESS\n self._validate_chunk_status(chunk)\n if not chunk.HasField('commit_row') and not chunk.reset_row:\n _raise_if(not chunk.timestamp_micros or not chunk.value)\n _raise_if(chunk.row_key and\n chunk.row_key != self._row.row_key)\n _raise_if(chunk.HasField('family_name') and\n not chunk.HasField('qualifier'))\n previous = self._previous_cell\n _raise_if(self._same_as_previous(chunk) and\n chunk.timestamp_micros <= previous.timestamp_micros)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_cell_in_progress","text":"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_cell_in_progress\n def _validate_chunk_cell_in_progress(self, chunk):\n \"\"\"Helper for :meth:`_validate_chunk`\"\"\"\n assert self.state == self.CELL_IN_PROGRESS\n self._validate_chunk_status(chunk)\n self._copy_from_current(chunk)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk","text":"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk\n def _validate_chunk(self, chunk):\n \"\"\"Helper for :meth:`consume_next`.\"\"\"\n if self.state == self.NEW_ROW:\n self._validate_chunk_new_row(chunk)\n if self.state == self.ROW_IN_PROGRESS:\n self._validate_chunk_row_in_progress(chunk)\n if self.state == self.CELL_IN_PROGRESS:\n self._validate_chunk_cell_in_progress(chunk)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData._save_current_cell","text":"google/cloud/bigtable/row_data.py::PartialRowsData._save_current_cell\n def _save_current_cell(self):\n \"\"\"Helper for :meth:`consume_next`.\"\"\"\n row, cell = self._row, self._cell\n family = row._cells.setdefault(cell.family_name, {})\n qualified = family.setdefault(cell.qualifier, [])\n complete = Cell.from_pb(self._cell)\n qualified.append(complete)\n self._cell, self._previous_cell = None, cell","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData._copy_from_current","text":"google/cloud/bigtable/row_data.py::PartialRowsData._copy_from_current\n def _copy_from_current(self, chunk):\n \"\"\"Helper for :meth:`consume_next`.\"\"\"\n current = self._cell\n if current is not None:\n if not chunk.row_key:\n chunk.row_key = current.row_key\n if not chunk.HasField('family_name'):\n chunk.family_name.value = current.family_name\n if not chunk.HasField('qualifier'):\n chunk.qualifier.value = current.qualifier\n if not chunk.timestamp_micros:\n chunk.timestamp_micros = current.timestamp_micros\n if not chunk.labels:\n chunk.labels.extend(current.labels)","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData._copy_from_previous","text":"google/cloud/bigtable/row_data.py::PartialRowsData._copy_from_previous\n def _copy_from_previous(self, cell):\n \"\"\"Helper for :meth:`consume_next`.\"\"\"\n previous = self._previous_cell\n if previous is not None:\n if not cell.row_key:\n cell.row_key = previous.row_key\n if not cell.family_name:\n cell.family_name = previous.family_name\n if not cell.qualifier:\n cell.qualifier = previous.qualifier","label":0},{"cid":"google/cloud/bigtable/row_data.py::PartialRowsData._save_current_row","text":"google/cloud/bigtable/row_data.py::PartialRowsData._save_current_row\n def _save_current_row(self):\n \"\"\"Helper for :meth:`consume_next`.\"\"\"\n if self._cell:\n self._save_current_cell()\n self._rows[self._row.row_key] = self._row\n self._row, self._previous_row = None, self._row\n self._previous_cell = None","label":0},{"cid":"google/cloud/bigtable/row_data.py::_raise_if","text":"google/cloud/bigtable/row_data.py::_raise_if\ndef _raise_if(predicate, *args):\n \"\"\"Helper for validation methods.\"\"\"\n if predicate:\n raise InvalidChunk(*args)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::RowFilter.__ne__","text":"google/cloud/bigtable/row_filters.py::RowFilter.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::_BoolFilter.__init__","text":"google/cloud/bigtable/row_filters.py::_BoolFilter.__init__\n def __init__(self, flag):\n self.flag = flag","label":0},{"cid":"google/cloud/bigtable/row_filters.py::_BoolFilter.__eq__","text":"google/cloud/bigtable/row_filters.py::_BoolFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.flag == self.flag","label":0},{"cid":"google/cloud/bigtable/row_filters.py::SinkFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::SinkFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(sink=self.flag)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::PassAllFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::PassAllFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(pass_all_filter=self.flag)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::BlockAllFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::BlockAllFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(block_all_filter=self.flag)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::_RegexFilter.__init__","text":"google/cloud/bigtable/row_filters.py::_RegexFilter.__init__\n def __init__(self, regex):\n self.regex = _to_bytes(regex)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::_RegexFilter.__eq__","text":"google/cloud/bigtable/row_filters.py::_RegexFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.regex == self.regex","label":0},{"cid":"google/cloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(row_key_regex_filter=self.regex)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::RowSampleFilter.__init__","text":"google/cloud/bigtable/row_filters.py::RowSampleFilter.__init__\n def __init__(self, sample):\n self.sample = sample","label":0},{"cid":"google/cloud/bigtable/row_filters.py::RowSampleFilter.__eq__","text":"google/cloud/bigtable/row_filters.py::RowSampleFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.sample == self.sample","label":0},{"cid":"google/cloud/bigtable/row_filters.py::RowSampleFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::RowSampleFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(row_sample_filter=self.sample)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(family_name_regex_filter=self.regex)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(column_qualifier_regex_filter=self.regex)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::TimestampRange.__init__","text":"google/cloud/bigtable/row_filters.py::TimestampRange.__init__\n def __init__(self, start=None, end=None):\n self.start = start\n self.end = end","label":0},{"cid":"google/cloud/bigtable/row_filters.py::TimestampRange.__eq__","text":"google/cloud/bigtable/row_filters.py::TimestampRange.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.start == self.start and\n other.end == self.end)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::TimestampRange.__ne__","text":"google/cloud/bigtable/row_filters.py::TimestampRange.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::TimestampRange.to_pb","text":"google/cloud/bigtable/row_filters.py::TimestampRange.to_pb\n def to_pb(self):\n \"\"\"Converts the :class:`TimestampRange` to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.TimestampRange`\n :returns: The converted current object.\n \"\"\"\n timestamp_range_kwargs = {}\n if self.start is not None:\n timestamp_range_kwargs['start_timestamp_micros'] = (\n _microseconds_from_datetime(self.start))\n if self.end is not None:\n timestamp_range_kwargs['end_timestamp_micros'] = (\n _microseconds_from_datetime(self.end))\n return data_v2_pb2.TimestampRange(**timestamp_range_kwargs)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__init__","text":"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__init__\n def __init__(self, range_):\n self.range_ = range_","label":0},{"cid":"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__","text":"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.range_ == self.range_","label":0},{"cid":"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n First converts the ``range_`` on the current object to a protobuf and\n then uses it in the ``timestamp_range_filter`` field.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(\n timestamp_range_filter=self.range_.to_pb())","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__init__","text":"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__init__\n def __init__(self, column_family_id, start_column=None, end_column=None,\n inclusive_start=None, inclusive_end=None):\n self.column_family_id = column_family_id\n\n if inclusive_start is None:\n inclusive_start = True\n elif start_column is None:\n raise ValueError('Inclusive start was specified but no '\n 'start column was given.')\n self.start_column = start_column\n self.inclusive_start = inclusive_start\n\n if inclusive_end is None:\n inclusive_end = True\n elif end_column is None:\n raise ValueError('Inclusive end was specified but no '\n 'end column was given.')\n self.end_column = end_column\n self.inclusive_end = inclusive_end","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__","text":"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.column_family_id == self.column_family_id and\n other.start_column == self.start_column and\n other.end_column == self.end_column and\n other.inclusive_start == self.inclusive_start and\n other.inclusive_end == self.inclusive_end)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n First converts to a :class:`.data_v2_pb2.ColumnRange` and then uses it\n in the ``column_range_filter`` field.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n column_range_kwargs = {'family_name': self.column_family_id}\n if self.start_column is not None:\n if self.inclusive_start:\n key = 'start_qualifier_closed'\n else:\n key = 'start_qualifier_open'\n column_range_kwargs[key] = _to_bytes(self.start_column)\n if self.end_column is not None:\n if self.inclusive_end:\n key = 'end_qualifier_closed'\n else:\n key = 'end_qualifier_open'\n column_range_kwargs[key] = _to_bytes(self.end_column)\n\n column_range = data_v2_pb2.ColumnRange(**column_range_kwargs)\n return data_v2_pb2.RowFilter(column_range_filter=column_range)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ValueRegexFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::ValueRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(value_regex_filter=self.regex)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ValueRangeFilter.__init__","text":"google/cloud/bigtable/row_filters.py::ValueRangeFilter.__init__\n def __init__(self, start_value=None, end_value=None,\n inclusive_start=None, inclusive_end=None):\n if inclusive_start is None:\n inclusive_start = True\n elif start_value is None:\n raise ValueError('Inclusive start was specified but no '\n 'start value was given.')\n self.start_value = start_value\n self.inclusive_start = inclusive_start\n\n if inclusive_end is None:\n inclusive_end = True\n elif end_value is None:\n raise ValueError('Inclusive end was specified but no '\n 'end value was given.')\n self.end_value = end_value\n self.inclusive_end = inclusive_end","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ValueRangeFilter.__eq__","text":"google/cloud/bigtable/row_filters.py::ValueRangeFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.start_value == self.start_value and\n other.end_value == self.end_value and\n other.inclusive_start == self.inclusive_start and\n other.inclusive_end == self.inclusive_end)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ValueRangeFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::ValueRangeFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n First converts to a :class:`.data_v2_pb2.ValueRange` and then uses\n it to create a row filter protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n value_range_kwargs = {}\n if self.start_value is not None:\n if self.inclusive_start:\n key = 'start_value_closed'\n else:\n key = 'start_value_open'\n value_range_kwargs[key] = _to_bytes(self.start_value)\n if self.end_value is not None:\n if self.inclusive_end:\n key = 'end_value_closed'\n else:\n key = 'end_value_open'\n value_range_kwargs[key] = _to_bytes(self.end_value)\n\n value_range = data_v2_pb2.ValueRange(**value_range_kwargs)\n return data_v2_pb2.RowFilter(value_range_filter=value_range)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::_CellCountFilter.__init__","text":"google/cloud/bigtable/row_filters.py::_CellCountFilter.__init__\n def __init__(self, num_cells):\n self.num_cells = num_cells","label":0},{"cid":"google/cloud/bigtable/row_filters.py::_CellCountFilter.__eq__","text":"google/cloud/bigtable/row_filters.py::_CellCountFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.num_cells == self.num_cells","label":0},{"cid":"google/cloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(\n cells_per_row_offset_filter=self.num_cells)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(cells_per_row_limit_filter=self.num_cells)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(\n cells_per_column_limit_filter=self.num_cells)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(strip_value_transformer=self.flag)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.__init__","text":"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.__init__\n def __init__(self, label):\n self.label = label","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.__eq__","text":"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.label == self.label","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(apply_label_transformer=self.label)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::_FilterCombination.__init__","text":"google/cloud/bigtable/row_filters.py::_FilterCombination.__init__\n def __init__(self, filters=None):\n if filters is None:\n filters = []\n self.filters = filters","label":0},{"cid":"google/cloud/bigtable/row_filters.py::_FilterCombination.__eq__","text":"google/cloud/bigtable/row_filters.py::_FilterCombination.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.filters == self.filters","label":0},{"cid":"google/cloud/bigtable/row_filters.py::RowFilterChain.to_pb","text":"google/cloud/bigtable/row_filters.py::RowFilterChain.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n chain = data_v2_pb2.RowFilter.Chain(\n filters=[row_filter.to_pb() for row_filter in self.filters])\n return data_v2_pb2.RowFilter(chain=chain)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::RowFilterUnion.to_pb","text":"google/cloud/bigtable/row_filters.py::RowFilterUnion.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n interleave = data_v2_pb2.RowFilter.Interleave(\n filters=[row_filter.to_pb() for row_filter in self.filters])\n return data_v2_pb2.RowFilter(interleave=interleave)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.__init__","text":"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.__init__\n def __init__(self, base_filter, true_filter=None, false_filter=None):\n self.base_filter = base_filter\n self.true_filter = true_filter\n self.false_filter = false_filter","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.__eq__","text":"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.base_filter == self.base_filter and\n other.true_filter == self.true_filter and\n other.false_filter == self.false_filter)","label":0},{"cid":"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.to_pb","text":"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n condition_kwargs = {'predicate_filter': self.base_filter.to_pb()}\n if self.true_filter is not None:\n condition_kwargs['true_filter'] = self.true_filter.to_pb()\n if self.false_filter is not None:\n condition_kwargs['false_filter'] = self.false_filter.to_pb()\n condition = data_v2_pb2.RowFilter.Condition(**condition_kwargs)\n return data_v2_pb2.RowFilter(condition=condition)","label":0},{"cid":"google/cloud/bigtable/table.py::Table.__init__","text":"google/cloud/bigtable/table.py::Table.__init__\n def __init__(self, table_id, instance):\n self.table_id = table_id\n self._instance = instance","label":0},{"cid":"google/cloud/bigtable/table.py::Table.name","text":"google/cloud/bigtable/table.py::Table.name\n @property\n def name(self):\n \"\"\"Table name used in requests.\n\n .. note::\n\n This property will not change if ``table_id`` does not, but the\n return value is not cached.\n\n The table name is of the form\n\n ``\"projects/../instances/../tables/{table_id}\"``\n\n :rtype: str\n :returns: The table name.\n \"\"\"\n return self._instance.name + '/tables/' + self.table_id","label":0},{"cid":"google/cloud/bigtable/table.py::Table.column_family","text":"google/cloud/bigtable/table.py::Table.column_family\n def column_family(self, column_family_id, gc_rule=None):\n \"\"\"Factory to create a column family associated with this table.\n\n :type column_family_id: str\n :param column_family_id: The ID of the column family. Must be of the\n form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type gc_rule: :class:`.GarbageCollectionRule`\n :param gc_rule: (Optional) The garbage collection settings for this\n column family.\n\n :rtype: :class:`.ColumnFamily`\n :returns: A column family owned by this table.\n \"\"\"\n return ColumnFamily(column_family_id, self, gc_rule=gc_rule)","label":0},{"cid":"google/cloud/bigtable/table.py::Table.row","text":"google/cloud/bigtable/table.py::Table.row\n def row(self, row_key, filter_=None, append=False):\n \"\"\"Factory to create a row associated with this table.\n\n .. warning::\n\n At most one of ``filter_`` and ``append`` can be used in a\n :class:`Row`.\n\n :type row_key: bytes\n :param row_key: The key for the row being created.\n\n :type filter_: :class:`.RowFilter`\n :param filter_: (Optional) Filter to be used for conditional mutations.\n See :class:`.DirectRow` for more details.\n\n :type append: bool\n :param append: (Optional) Flag to determine if the row should be used\n for append mutations.\n\n :rtype: :class:`.DirectRow`\n :returns: A row owned by this table.\n :raises: :class:`ValueError <exceptions.ValueError>` if both\n ``filter_`` and ``append`` are used.\n \"\"\"\n if append and filter_ is not None:\n raise ValueError('At most one of filter_ and append can be set')\n if append:\n return AppendRow(row_key, self)\n elif filter_ is not None:\n return ConditionalRow(row_key, self, filter_=filter_)\n else:\n return DirectRow(row_key, self)","label":0},{"cid":"google/cloud/bigtable/table.py::Table.__eq__","text":"google/cloud/bigtable/table.py::Table.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.table_id == self.table_id and\n other._instance == self._instance)","label":0},{"cid":"google/cloud/bigtable/table.py::Table.__ne__","text":"google/cloud/bigtable/table.py::Table.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/bigtable/table.py::Table.create","text":"google/cloud/bigtable/table.py::Table.create\n def create(self, initial_split_keys=None, column_families=()):\n \"\"\"Creates this table.\n\n .. note::\n\n A create request returns a\n :class:`._generated.table_pb2.Table` but we don't use\n this response.\n\n :type initial_split_keys: list\n :param initial_split_keys: (Optional) List of row keys that will be\n used to initially split the table into\n several tablets (Tablets are similar to\n HBase regions). Given two split keys,\n ``\"s1\"`` and ``\"s2\"``, three tablets will be\n created, spanning the key ranges:\n ``[, s1)``, ``[s1, s2)``, ``[s2, )``.\n\n :type column_families: list\n :param column_families: (Optional) List or other iterable of\n :class:`.ColumnFamily` instances.\n \"\"\"\n if initial_split_keys is not None:\n split_pb = table_admin_messages_v2_pb2.CreateTableRequest.Split\n initial_split_keys = [\n split_pb(key=key) for key in initial_split_keys]\n\n table_pb = None\n if column_families:\n table_pb = table_v2_pb2.Table()\n for col_fam in column_families:\n curr_id = col_fam.column_family_id\n table_pb.column_families[curr_id].MergeFrom(col_fam.to_pb())\n\n request_pb = table_admin_messages_v2_pb2.CreateTableRequest(\n initial_splits=initial_split_keys or [],\n parent=self._instance.name,\n table_id=self.table_id,\n table=table_pb,\n )\n client = self._instance._client\n # We expect a `._generated.table_pb2.Table`\n client._table_stub.CreateTable(request_pb)","label":0},{"cid":"google/cloud/bigtable/table.py::Table.delete","text":"google/cloud/bigtable/table.py::Table.delete\n def delete(self):\n \"\"\"Delete this table.\"\"\"\n request_pb = table_admin_messages_v2_pb2.DeleteTableRequest(\n name=self.name)\n client = self._instance._client\n # We expect a `google.protobuf.empty_pb2.Empty`\n client._table_stub.DeleteTable(request_pb)","label":0},{"cid":"google/cloud/bigtable/table.py::Table.list_column_families","text":"google/cloud/bigtable/table.py::Table.list_column_families\n def list_column_families(self):\n \"\"\"List the column families owned by this table.\n\n :rtype: dict\n :returns: Dictionary of column families attached to this table. Keys\n are strings (column family names) and values are\n :class:`.ColumnFamily` instances.\n :raises: :class:`ValueError <exceptions.ValueError>` if the column\n family name from the response does not agree with the computed\n name from the column family ID.\n \"\"\"\n request_pb = table_admin_messages_v2_pb2.GetTableRequest(\n name=self.name)\n client = self._instance._client\n # We expect a `._generated.table_pb2.Table`\n table_pb = client._table_stub.GetTable(request_pb)\n\n result = {}\n for column_family_id, value_pb in table_pb.column_families.items():\n gc_rule = _gc_rule_from_pb(value_pb.gc_rule)\n column_family = self.column_family(column_family_id,\n gc_rule=gc_rule)\n result[column_family_id] = column_family\n return result","label":0},{"cid":"google/cloud/bigtable/table.py::Table.read_row","text":"google/cloud/bigtable/table.py::Table.read_row\n def read_row(self, row_key, filter_=None):\n \"\"\"Read a single row from this table.\n\n :type row_key: bytes\n :param row_key: The key of the row to read from.\n\n :type filter_: :class:`.RowFilter`\n :param filter_: (Optional) The filter to apply to the contents of the\n row. If unset, returns the entire row.\n\n :rtype: :class:`.PartialRowData`, :data:`NoneType <types.NoneType>`\n :returns: The contents of the row if any chunks were returned in\n the response, otherwise :data:`None`.\n :raises: :class:`ValueError <exceptions.ValueError>` if a commit row\n chunk is never encountered.\n \"\"\"\n request_pb = _create_row_request(self.name, row_key=row_key,\n filter_=filter_)\n client = self._instance._client\n response_iterator = client._data_stub.ReadRows(request_pb)\n rows_data = PartialRowsData(response_iterator)\n rows_data.consume_all()\n if rows_data.state not in (rows_data.NEW_ROW, rows_data.START):\n raise ValueError('The row remains partial / is not committed.')\n\n if len(rows_data.rows) == 0:\n return None\n\n return rows_data.rows[row_key]","label":0},{"cid":"google/cloud/bigtable/table.py::Table.read_rows","text":"google/cloud/bigtable/table.py::Table.read_rows\n def read_rows(self, start_key=None, end_key=None, limit=None,\n filter_=None):\n \"\"\"Read rows from this table.\n\n :type start_key: bytes\n :param start_key: (Optional) The beginning of a range of row keys to\n read from. The range will include ``start_key``. If\n left empty, will be interpreted as the empty string.\n\n :type end_key: bytes\n :param end_key: (Optional) The end of a range of row keys to read from.\n The range will not include ``end_key``. If left empty,\n will be interpreted as an infinite string.\n\n :type limit: int\n :param limit: (Optional) The read will terminate after committing to N\n rows' worth of results. The default (zero) is to return\n all results.\n\n :type filter_: :class:`.RowFilter`\n :param filter_: (Optional) The filter to apply to the contents of the\n specified row(s). If unset, reads every column in\n each row.\n\n :rtype: :class:`.PartialRowsData`\n :returns: A :class:`.PartialRowsData` convenience wrapper for consuming\n the streamed results.\n \"\"\"\n request_pb = _create_row_request(\n self.name, start_key=start_key, end_key=end_key, filter_=filter_,\n limit=limit)\n client = self._instance._client\n response_iterator = client._data_stub.ReadRows(request_pb)\n # We expect an iterator of `data_messages_v2_pb2.ReadRowsResponse`\n return PartialRowsData(response_iterator)","label":0},{"cid":"google/cloud/bigtable/table.py::Table.sample_row_keys","text":"google/cloud/bigtable/table.py::Table.sample_row_keys\n def sample_row_keys(self):\n \"\"\"Read a sample of row keys in the table.\n\n The returned row keys will delimit contiguous sections of the table of\n approximately equal size, which can be used to break up the data for\n distributed tasks like mapreduces.\n\n The elements in the iterator are a SampleRowKeys response and they have\n the properties ``offset_bytes`` and ``row_key``. They occur in sorted\n order. The table might have contents before the first row key in the\n list and after the last one, but a key containing the empty string\n indicates \"end of table\" and will be the last response given, if\n present.\n\n .. note::\n\n Row keys in this list may not have ever been written to or read\n from, and users should therefore not make any assumptions about the\n row key structure that are specific to their use case.\n\n The ``offset_bytes`` field on a response indicates the approximate\n total storage space used by all rows in the table which precede\n ``row_key``. Buffering the contents of all rows between two subsequent\n samples would require space roughly equal to the difference in their\n ``offset_bytes`` fields.\n\n :rtype: :class:`~google.cloud.exceptions.GrpcRendezvous`\n :returns: A cancel-able iterator. Can be consumed by calling ``next()``\n or by casting to a :class:`list` and can be cancelled by\n calling ``cancel()``.\n \"\"\"\n request_pb = data_messages_v2_pb2.SampleRowKeysRequest(\n table_name=self.name)\n client = self._instance._client\n response_iterator = client._data_stub.SampleRowKeys(request_pb)\n return response_iterator","label":0},{"cid":"google/cloud/bigtable/table.py::_create_row_request","text":"google/cloud/bigtable/table.py::_create_row_request\ndef _create_row_request(table_name, row_key=None, start_key=None, end_key=None,\n filter_=None, limit=None):\n \"\"\"Creates a request to read rows in a table.\n\n :type table_name: str\n :param table_name: The name of the table to read from.\n\n :type row_key: bytes\n :param row_key: (Optional) The key of a specific row to read from.\n\n :type start_key: bytes\n :param start_key: (Optional) The beginning of a range of row keys to\n read from. The range will include ``start_key``. If\n left empty, will be interpreted as the empty string.\n\n :type end_key: bytes\n :param end_key: (Optional) The end of a range of row keys to read from.\n The range will not include ``end_key``. If left empty,\n will be interpreted as an infinite string.\n\n :type filter_: :class:`.RowFilter`\n :param filter_: (Optional) The filter to apply to the contents of the\n specified row(s). If unset, reads the entire table.\n\n :type limit: int\n :param limit: (Optional) The read will terminate after committing to N\n rows' worth of results. The default (zero) is to return\n all results.\n\n :rtype: :class:`data_messages_v2_pb2.ReadRowsRequest`\n :returns: The ``ReadRowsRequest`` protobuf corresponding to the inputs.\n :raises: :class:`ValueError <exceptions.ValueError>` if both\n ``row_key`` and one of ``start_key`` and ``end_key`` are set\n \"\"\"\n request_kwargs = {'table_name': table_name}\n if (row_key is not None and\n (start_key is not None or end_key is not None)):\n raise ValueError('Row key and row range cannot be '\n 'set simultaneously')\n range_kwargs = {}\n if start_key is not None or end_key is not None:\n if start_key is not None:\n range_kwargs['start_key_closed'] = _to_bytes(start_key)","label":0},{"cid":"google/cloud/client.py::_ClientFactoryMixin.from_service_account_json","text":"google/cloud/client.py::_ClientFactoryMixin.from_service_account_json\n @classmethod\n def from_service_account_json(cls, json_credentials_path, *args, **kwargs):\n \"\"\"Factory to retrieve JSON credentials while creating client.\n\n :type json_credentials_path: string\n :param json_credentials_path: The path to a private key file (this file\n was given to you when you created the\n service account). This file must contain\n a JSON object with a private key and\n other credentials information (downloaded\n from the Google APIs console).\n\n :type args: tuple\n :param args: Remaining positional arguments to pass to constructor.\n\n :type kwargs: dict\n :param kwargs: Remaining keyword arguments to pass to constructor.\n\n :rtype: :class:`google.cloud.pubsub.client.Client`\n :returns: The client created with the retrieved JSON credentials.\n :raises: :class:`TypeError` if there is a conflict with the kwargs\n and the credentials created by the factory.\n \"\"\"\n if 'credentials' in kwargs:\n raise TypeError('credentials must not be in keyword arguments')\n credentials = ServiceAccountCredentials.from_json_keyfile_name(\n json_credentials_path)\n kwargs['credentials'] = credentials\n return cls(*args, **kwargs)","label":0},{"cid":"google/cloud/client.py::_ClientFactoryMixin.from_service_account_p12","text":"google/cloud/client.py::_ClientFactoryMixin.from_service_account_p12\n @classmethod\n def from_service_account_p12(cls, client_email, private_key_path,\n *args, **kwargs):\n \"\"\"Factory to retrieve P12 credentials while creating client.\n\n .. note::\n Unless you have an explicit reason to use a PKCS12 key for your\n service account, we recommend using a JSON key.\n\n :type client_email: string\n :param client_email: The e-mail attached to the service account.\n\n :type private_key_path: string\n :param private_key_path: The path to a private key file (this file was\n given to you when you created the service\n account). This file must be in P12 format.\n\n :type args: tuple\n :param args: Remaining positional arguments to pass to constructor.\n\n :type kwargs: dict\n :param kwargs: Remaining keyword arguments to pass to constructor.\n\n :rtype: :class:`google.cloud.client.Client`\n :returns: The client created with the retrieved P12 credentials.\n :raises: :class:`TypeError` if there is a conflict with the kwargs\n and the credentials created by the factory.\n \"\"\"\n if 'credentials' in kwargs:\n raise TypeError('credentials must not be in keyword arguments')\n credentials = ServiceAccountCredentials.from_p12_keyfile(\n client_email, private_key_path)\n kwargs['credentials'] = credentials\n return cls(*args, **kwargs)","label":0},{"cid":"google/cloud/client.py::Client.__init__","text":"google/cloud/client.py::Client.__init__\n def __init__(self, credentials=None, http=None):\n if credentials is None and http is None:\n credentials = get_credentials()\n self.connection = self._connection_class(\n credentials=credentials, http=http)","label":0},{"cid":"google/cloud/client.py::_ClientProjectMixin.__init__","text":"google/cloud/client.py::_ClientProjectMixin.__init__\n def __init__(self, project=None):\n project = self._determine_default(project)\n if project is None:\n raise EnvironmentError('Project was not passed and could not be '\n 'determined from the environment.')\n if isinstance(project, six.binary_type):\n project = project.decode('utf-8')\n if not isinstance(project, six.string_types):\n raise ValueError('Project must be a string.')\n self.project = project","label":0},{"cid":"google/cloud/client.py::_ClientProjectMixin._determine_default","text":"google/cloud/client.py::_ClientProjectMixin._determine_default\n @staticmethod\n def _determine_default(project):\n \"\"\"Helper: use default project detection.\"\"\"\n return _determine_default_project(project)","label":0},{"cid":"google/cloud/client.py::JSONClient.__init__","text":"google/cloud/client.py::JSONClient.__init__\n def __init__(self, project=None, credentials=None, http=None):\n _ClientProjectMixin.__init__(self, project=project)\n Client.__init__(self, credentials=credentials, http=http)","label":0},{"cid":"google/cloud/connection.py::Connection.__init__","text":"google/cloud/connection.py::Connection.__init__\n def __init__(self, credentials=None, http=None):\n self._http = http\n self._credentials = self._create_scoped_credentials(\n credentials, self.SCOPE)","label":0},{"cid":"google/cloud/connection.py::Connection.credentials","text":"google/cloud/connection.py::Connection.credentials\n @property\n def credentials(self):\n \"\"\"Getter for current credentials.\n\n :rtype: :class:`oauth2client.client.OAuth2Credentials` or\n :class:`NoneType`\n :returns: The credentials object associated with this connection.\n \"\"\"\n return self._credentials","label":0},{"cid":"google/cloud/connection.py::Connection.http","text":"google/cloud/connection.py::Connection.http\n @property\n def http(self):\n \"\"\"A getter for the HTTP transport used in talking to the API.\n\n :rtype: :class:`httplib2.Http`\n :returns: A Http object used to transport data.\n \"\"\"\n if self._http is None:\n self._http = httplib2.Http()\n if self._credentials:\n self._http = self._credentials.authorize(self._http)\n return self._http","label":0},{"cid":"google/cloud/connection.py::Connection._create_scoped_credentials","text":"google/cloud/connection.py::Connection._create_scoped_credentials\n @staticmethod\n def _create_scoped_credentials(credentials, scope):\n \"\"\"Create a scoped set of credentials if it is required.\n\n :type credentials: :class:`oauth2client.client.OAuth2Credentials` or\n :class:`NoneType`\n :param credentials: The OAuth2 Credentials to add a scope to.\n\n :type scope: list of URLs\n :param scope: the effective service auth scopes for the connection.\n\n :rtype: :class:`oauth2client.client.OAuth2Credentials` or\n :class:`NoneType`\n :returns: A new credentials object that has a scope added (if needed).\n \"\"\"\n if credentials:\n try:\n if credentials.create_scoped_required():\n credentials = credentials.create_scoped(scope)\n except AttributeError:\n pass\n return credentials","label":0},{"cid":"google/cloud/connection.py::JSONConnection.build_api_url","text":"google/cloud/connection.py::JSONConnection.build_api_url\n @classmethod\n def build_api_url(cls, path, query_params=None,\n api_base_url=None, api_version=None):\n \"\"\"Construct an API url given a few components, some optional.\n\n Typically, you shouldn't need to use this method.\n\n :type path: string\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\n\n :type query_params: dict or list\n :param query_params: A dictionary of keys and values (or list of\n key-value pairs) to insert into the query\n string of the URL.\n\n :type api_base_url: string\n :param api_base_url: The base URL for the API endpoint.\n Typically you won't have to provide this.\n\n :type api_version: string\n :param api_version: The version of the API to call.\n Typically you shouldn't provide this and instead\n use the default for the library.\n\n :rtype: string\n :returns: The URL assembled from the pieces provided.\n \"\"\"\n url = cls.API_URL_TEMPLATE.format(\n api_base_url=(api_base_url or cls.API_BASE_URL),\n api_version=(api_version or cls.API_VERSION),\n path=path)\n\n query_params = query_params or {}\n if query_params:\n url += '?' + urlencode(query_params)\n\n return url","label":0},{"cid":"google/cloud/connection.py::JSONConnection._make_request","text":"google/cloud/connection.py::JSONConnection._make_request\n def _make_request(self, method, url, data=None, content_type=None,\n headers=None, target_object=None):\n \"\"\"A low level method to send a request to the API.\n\n Typically, you shouldn't need to use this method.\n\n :type method: string\n :param method: The HTTP method to use in the request.\n\n :type url: string\n :param url: The URL to send the request to.\n\n :type data: string\n :param data: The data to send as the body of the request.\n\n :type content_type: string\n :param content_type: The proper MIME type of the data provided.\n\n :type headers: dict\n :param headers: A dictionary of HTTP headers to send with the request.\n\n :type target_object: object or :class:`NoneType`\n :param target_object: Argument to be used by library callers.\n This can allow custom behavior, for example, to\n defer an HTTP request and complete initialization\n of the object at a later time.\n\n :rtype: tuple of ``response`` (a dictionary of sorts)\n and ``content`` (a string).\n :returns: The HTTP response object and the content of the response,\n returned by :meth:`_do_request`.\n \"\"\"\n headers = headers or {}\n headers['Accept-Encoding'] = 'gzip'\n\n if data:\n content_length = len(str(data))\n else:\n content_length = 0\n\n # NOTE: str is intended, bytes are sufficient for headers.\n headers['Content-Length'] = str(content_length)\n\n if content_type:\n headers['Content-Type'] = content_type\n\n headers['User-Agent'] = self.USER_AGENT\n\n return self._do_request(method, url, headers, data, target_object)","label":0},{"cid":"google/cloud/connection.py::JSONConnection._do_request","text":"google/cloud/connection.py::JSONConnection._do_request\n def _do_request(self, method, url, headers, data,\n target_object): # pylint: disable=unused-argument\n \"\"\"Low-level helper: perform the actual API request over HTTP.\n\n Allows batch context managers to override and defer a request.\n\n :type method: string\n :param method: The HTTP method to use in the request.\n\n :type url: string\n :param url: The URL to send the request to.\n\n :type headers: dict\n :param headers: A dictionary of HTTP headers to send with the request.\n\n :type data: string\n :param data: The data to send as the body of the request.\n\n :type target_object: object or :class:`NoneType`\n :param target_object: Unused ``target_object`` here but may be used\n by a superclass.\n\n :rtype: tuple of ``response`` (a dictionary of sorts)\n and ``content`` (a string).\n :returns: The HTTP response object and the content of the response.\n \"\"\"\n return self.http.request(uri=url, method=method, headers=headers,\n body=data)","label":0},{"cid":"google/cloud/connection.py::JSONConnection.api_request","text":"google/cloud/connection.py::JSONConnection.api_request\n def api_request(self, method, path, query_params=None,\n data=None, content_type=None,\n api_base_url=None, api_version=None,\n expect_json=True, _target_object=None):\n \"\"\"Make a request over the HTTP transport to the API.\n\n You shouldn't need to use this method, but if you plan to\n interact with the API using these primitives, this is the\n correct one to use.\n\n :type method: string\n :param method: The HTTP method name (ie, ``GET``, ``POST``, etc).\n Required.\n\n :type path: string\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\n Required.\n\n :type query_params: dict or list\n :param query_params: A dictionary of keys and values (or list of\n key-value pairs) to insert into the query\n string of the URL.\n\n :type data: string\n :param data: The data to send as the body of the request. Default is\n the empty string.\n\n :type content_type: string\n :param content_type: The proper MIME type of the data provided. Default\n is None.\n\n :type api_base_url: string\n :param api_base_url: The base URL for the API endpoint.\n Typically you won't have to provide this.\n Default is the standard API base URL.\n\n :type api_version: string\n :param api_version: The version of the API to call. Typically\n you shouldn't provide this and instead use\n the default for the library. Default is the\n latest API version supported by\n google-cloud-python.\n\n :type expect_json: bool\n :param expect_json: If True, this method will try to par","label":0},{"cid":"google/cloud/credentials.py::get_credentials","text":"google/cloud/credentials.py::get_credentials\ndef get_credentials():\n \"\"\"Gets credentials implicitly from the current environment.\n\n .. note::\n\n You should not need to use this function directly. Instead, use a\n helper method which uses this method under the hood.\n\n Checks environment in order of precedence:\n\n * Google App Engine (production and testing)\n * Environment variable :envvar:`GOOGLE_APPLICATION_CREDENTIALS` pointing to\n a file with stored credentials information.\n * Stored \"well known\" file associated with ``gcloud`` command line tool.\n * Google Compute Engine production environment.\n\n The file referred to in :envvar:`GOOGLE_APPLICATION_CREDENTIALS` is\n expected to contain information about credentials that are ready to use.\n This means either service account information or user account information\n with a ready-to-use refresh token:\n\n .. code:: json\n\n {\n 'type': 'authorized_user',\n 'client_id': '...',\n 'client_secret': '...',\n 'refresh_token': '...'\n }\n\n or\n\n .. code:: json\n\n {\n 'type': 'service_account',\n 'project_id': '...',\n 'private_key_id': '...',\n 'private_key': '...',\n 'client_email': '...',\n 'client_id': '...',\n 'auth_uri': '...',\n 'token_uri': '...',\n 'auth_provider_x509_cert_url': '...',\n 'client_x509_cert_url': '...'\n }\n\n The second of these is simply a JSON key downloaded from the Google APIs\n console. The first is a close cousin of the \"client secrets\" JSON file\n used by :mod:`oauth2client.clientsecrets` but differs in formatting.\n\n :rtype: :class:`oauth2client.client.GoogleCredentials`,\n :class:`oauth2client.contrib.appengine.AppAssertionCredentials`,\n :class:`oauth2client.contrib.gce.AppAssertionCredentials`,\n :class:`oauth2client.service_account.ServiceAccountCredentials`\n :returns: A new ","label":0},{"cid":"google/cloud/credentials.py::_get_signed_query_params","text":"google/cloud/credentials.py::_get_signed_query_params\ndef _get_signed_query_params(credentials, expiration, string_to_sign):\n \"\"\"Gets query parameters for creating a signed URL.\n\n :type credentials: :class:`oauth2client.client.AssertionCredentials`\n :param credentials: The credentials used to create a private key\n for signing text.\n\n :type expiration: int or long\n :param expiration: When the signed URL should expire.\n\n :type string_to_sign: string\n :param string_to_sign: The string to be signed by the credentials.\n\n :raises AttributeError: If :meth: sign_blob is unavailable.\n\n :rtype: dict\n :returns: Query parameters matching the signing credentials with a\n signed payload.\n \"\"\"\n if not hasattr(credentials, 'sign_blob'):\n auth_uri = ('http://google-cloud-python.readthedocs.io/en/latest/'\n 'google-cloud-auth.html#setting-up-a-service-account')\n raise AttributeError('you need a private key to sign credentials.'\n 'the credentials you are currently using %s '\n 'just contains a token. see %s for more '\n 'details.' % (type(credentials), auth_uri))\n\n _, signature_bytes = credentials.sign_blob(string_to_sign)\n signature = base64.b64encode(signature_bytes)\n service_account_name = credentials.service_account_email\n return {\n 'GoogleAccessId': service_account_name,\n 'Expires': str(expiration),\n 'Signature': signature,\n }","label":0},{"cid":"google/cloud/credentials.py::_get_expiration_seconds","text":"google/cloud/credentials.py::_get_expiration_seconds\ndef _get_expiration_seconds(expiration):\n \"\"\"Convert 'expiration' to a number of seconds in the future.\n\n :type expiration: int, long, datetime.datetime, datetime.timedelta\n :param expiration: When the signed URL should expire.\n\n :raises TypeError: When expiration is not an integer.\n\n :rtype: int\n :returns: a timestamp as an absolute number of seconds.\n \"\"\"\n # If it's a timedelta, add it to `now` in UTC.\n if isinstance(expiration, datetime.timedelta):\n now = _NOW().replace(tzinfo=UTC)\n expiration = now + expiration\n\n # If it's a datetime, convert to a timestamp.\n if isinstance(expiration, datetime.datetime):\n micros = _microseconds_from_datetime(expiration)\n expiration = micros // 10**6\n\n if not isinstance(expiration, six.integer_types):\n raise TypeError('Expected an integer timestamp, datetime, or '\n 'timedelta. Got %s' % type(expiration))\n return expiration","label":0},{"cid":"google/cloud/credentials.py::generate_signed_url","text":"google/cloud/credentials.py::generate_signed_url\ndef generate_signed_url(credentials, resource, expiration,\n api_access_endpoint='',\n method='GET', content_md5=None,\n content_type=None, response_type=None,\n response_disposition=None, generation=None):\n \"\"\"Generate signed URL to provide query-string auth'n to a resource.\n\n .. note::\n\n Assumes ``credentials`` implements a ``sign_blob()`` method that takes\n bytes to sign and returns a pair of the key ID (unused here) and the\n signed bytes (this is abstract in the base class\n :class:`oauth2client.client.AssertionCredentials`). Also assumes\n ``credentials`` has a ``service_account_email`` property which\n identifies the credentials.\n\n .. note::\n\n If you are on Google Compute Engine, you can't generate a signed URL.\n Follow `Issue 922`_ for updates on this. If you'd like to be able to\n generate a signed URL from GCE, you can use a standard service account\n from a JSON file rather than a GCE service account.\n\n See headers `reference`_ for more details on optional arguments.\n\n .. _Issue 922: https://github.com/GoogleCloudPlatform/\\\n google-cloud-python/issues/922\n .. _reference: https://cloud.google.com/storage/docs/reference-headers\n\n :type credentials: :class:`oauth2client.appengine.AppAssertionCredentials`\n :param credentials: Credentials object with an associated private key to\n sign text.\n\n :type resource: string\n :param resource: A pointer to a specific resource\n (typically, ``/bucket-name/path/to/blob.txt``).\n\n :type expiration: :class:`int`, :class:`long`, :class:`datetime.datetime`,\n :class:`datetime.timedelta`\n :param expiration: When the signed URL should expire.\n\n :type api_access_endpoint: str\n :param api_access_endpoint: Optional ","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.__init__","text":"google/cloud/datastore/batch.py::Batch.__init__\n def __init__(self, client):\n self._client = client\n self._commit_request = _datastore_pb2.CommitRequest()\n self._partial_key_entities = []\n self._status = self._INITIAL","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.current","text":"google/cloud/datastore/batch.py::Batch.current\n def current(self):\n \"\"\"Return the topmost batch / transaction, or None.\"\"\"\n return self._client.current_batch","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.project","text":"google/cloud/datastore/batch.py::Batch.project\n @property\n def project(self):\n \"\"\"Getter for project in which the batch will run.\n\n :rtype: :class:`str`\n :returns: The project in which the batch will run.\n \"\"\"\n return self._client.project","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.namespace","text":"google/cloud/datastore/batch.py::Batch.namespace\n @property\n def namespace(self):\n \"\"\"Getter for namespace in which the batch will run.\n\n :rtype: :class:`str`\n :returns: The namespace in which the batch will run.\n \"\"\"\n return self._client.namespace","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.connection","text":"google/cloud/datastore/batch.py::Batch.connection\n @property\n def connection(self):\n \"\"\"Getter for connection over which the batch will run.\n\n :rtype: :class:`google.cloud.datastore.connection.Connection`\n :returns: The connection over which the batch will run.\n \"\"\"\n return self._client.connection","label":0},{"cid":"google/cloud/datastore/batch.py::Batch._add_partial_key_entity_pb","text":"google/cloud/datastore/batch.py::Batch._add_partial_key_entity_pb\n def _add_partial_key_entity_pb(self):\n \"\"\"Adds a new mutation for an entity with a partial key.\n\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Entity`\n :returns: The newly created entity protobuf that will be\n updated and sent with a commit.\n \"\"\"\n new_mutation = self.mutations.add()\n return new_mutation.insert","label":0},{"cid":"google/cloud/datastore/batch.py::Batch._add_complete_key_entity_pb","text":"google/cloud/datastore/batch.py::Batch._add_complete_key_entity_pb\n def _add_complete_key_entity_pb(self):\n \"\"\"Adds a new mutation for an entity with a completed key.\n\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Entity`\n :returns: The newly created entity protobuf that will be\n updated and sent with a commit.\n \"\"\"\n # We use ``upsert`` for entities with completed keys, rather than\n # ``insert`` or ``update``, in order not to create race conditions\n # based on prior existence / removal of the entity.\n new_mutation = self.mutations.add()\n return new_mutation.upsert","label":0},{"cid":"google/cloud/datastore/batch.py::Batch._add_delete_key_pb","text":"google/cloud/datastore/batch.py::Batch._add_delete_key_pb\n def _add_delete_key_pb(self):\n \"\"\"Adds a new mutation for a key to be deleted.\n\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Key`\n :returns: The newly created key protobuf that will be\n deleted when sent with a commit.\n \"\"\"\n new_mutation = self.mutations.add()\n return new_mutation.delete","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.mutations","text":"google/cloud/datastore/batch.py::Batch.mutations\n @property\n def mutations(self):\n \"\"\"Getter for the changes accumulated by this batch.\n\n Every batch is committed with a single commit request containing all\n the work to be done as mutations. Inside a batch, calling :meth:`put`\n with an entity, or :meth:`delete` with a key, builds up the request by\n adding a new mutation. This getter returns the protobuf that has been\n built-up so far.\n\n :rtype: iterable\n :returns: The list of :class:`._generated.datastore_pb2.Mutation`\n protobufs to be sent in the commit request.\n \"\"\"\n return self._commit_request.mutations","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.put","text":"google/cloud/datastore/batch.py::Batch.put\n def put(self, entity):\n \"\"\"Remember an entity's state to be saved during :meth:`commit`.\n\n .. note::\n Any existing properties for the entity will be replaced by those\n currently set on this instance. Already-stored properties which do\n not correspond to keys set on this instance will be removed from\n the datastore.\n\n .. note::\n Property values which are \"text\" ('unicode' in Python2, 'str' in\n Python3) map to 'string_value' in the datastore; values which are\n \"bytes\" ('str' in Python2, 'bytes' in Python3) map to 'blob_value'.\n\n When an entity has a partial key, calling :meth:`commit` sends it as\n an ``insert`` mutation and the key is completed. On return,\n the key for the ``entity`` passed in is updated to match the key ID\n assigned by the server.\n\n :type entity: :class:`google.cloud.datastore.entity.Entity`\n :param entity: the entity to be saved.\n\n :raises: :class:`~exceptions.ValueError` if the batch is not in\n progress, if entity has no key assigned, or if the key's\n ``project`` does not match ours.\n \"\"\"\n if self._status != self._IN_PROGRESS:\n raise ValueError('Batch must be in progress to put()')\n\n if entity.key is None:\n raise ValueError(\"Entity must have a key\")\n\n if self.project != entity.key.project:\n raise ValueError(\"Key must be from same project as batch\")\n\n if entity.key.is_partial:\n entity_pb = self._add_partial_key_entity_pb()\n self._partial_key_entities.append(entity)\n else:\n entity_pb = self._add_complete_key_entity_pb()\n\n _assign_entity_to_pb(entity_pb, entity)","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.delete","text":"google/cloud/datastore/batch.py::Batch.delete\n def delete(self, key):\n \"\"\"Remember a key to be deleted during :meth:`commit`.\n\n :type key: :class:`google.cloud.datastore.key.Key`\n :param key: the key to be deleted.\n\n :raises: :class:`~exceptions.ValueError` if the batch is not in\n progress, if key is not complete, or if the key's\n ``project`` does not match ours.\n \"\"\"\n if self._status != self._IN_PROGRESS:\n raise ValueError('Batch must be in progress to delete()')\n\n if key.is_partial:\n raise ValueError(\"Key must be complete\")\n\n if self.project != key.project:\n raise ValueError(\"Key must be from same project as batch\")\n\n key_pb = key.to_protobuf()\n self._add_delete_key_pb().CopyFrom(key_pb)","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.begin","text":"google/cloud/datastore/batch.py::Batch.begin\n def begin(self):\n \"\"\"Begins a batch.\n\n This method is called automatically when entering a with\n statement, however it can be called explicitly if you don't want\n to use a context manager.\n\n Overridden by :class:`google.cloud.datastore.transaction.Transaction`.\n\n :raises: :class:`ValueError` if the batch has already begun.\n \"\"\"\n if self._status != self._INITIAL:\n raise ValueError('Batch already started previously.')\n self._status = self._IN_PROGRESS","label":0},{"cid":"google/cloud/datastore/batch.py::Batch._commit","text":"google/cloud/datastore/batch.py::Batch._commit\n def _commit(self):\n \"\"\"Commits the batch.\n\n This is called by :meth:`commit`.\n \"\"\"\n # NOTE: ``self._commit_request`` will be modified.\n _, updated_keys = self.connection.commit(\n self.project, self._commit_request, self._id)\n # If the back-end returns without error, we are guaranteed that\n # :meth:`Connection.commit` will return keys that match (length and\n # order) directly ``_partial_key_entities``.\n for new_key_pb, entity in zip(updated_keys,\n self._partial_key_entities):\n new_id = new_key_pb.path[-1].id\n entity.key = entity.key.completed_key(new_id)","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.commit","text":"google/cloud/datastore/batch.py::Batch.commit\n def commit(self):\n \"\"\"Commits the batch.\n\n This is called automatically upon exiting a with statement,\n however it can be called explicitly if you don't want to use a\n context manager.\n\n :raises: :class:`~exceptions.ValueError` if the batch is not\n in progress.\n \"\"\"\n if self._status != self._IN_PROGRESS:\n raise ValueError('Batch must be in progress to commit()')\n\n try:\n self._commit()\n finally:\n self._status = self._FINISHED","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.rollback","text":"google/cloud/datastore/batch.py::Batch.rollback\n def rollback(self):\n \"\"\"Rolls back the current batch.\n\n Marks the batch as aborted (can't be used again).\n\n Overridden by :class:`google.cloud.datastore.transaction.Transaction`.\n\n :raises: :class:`~exceptions.ValueError` if the batch is not\n in progress.\n \"\"\"\n if self._status != self._IN_PROGRESS:\n raise ValueError('Batch must be in progress to rollback()')\n\n self._status = self._ABORTED","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.__enter__","text":"google/cloud/datastore/batch.py::Batch.__enter__\n def __enter__(self):\n self.begin()\n # NOTE: We make sure begin() succeeds before pushing onto the stack.\n self._client._push_batch(self)\n return self","label":0},{"cid":"google/cloud/datastore/batch.py::Batch.__exit__","text":"google/cloud/datastore/batch.py::Batch.__exit__\n def __exit__(self, exc_type, exc_val, exc_tb):\n try:\n if exc_type is None:\n self.commit()\n else:\n self.rollback()\n finally:\n self._client._pop_batch()","label":0},{"cid":"google/cloud/datastore/batch.py::_assign_entity_to_pb","text":"google/cloud/datastore/batch.py::_assign_entity_to_pb\ndef _assign_entity_to_pb(entity_pb, entity):\n \"\"\"Copy ``entity`` into ``entity_pb``.\n\n Helper method for ``Batch.put``.\n\n :type entity_pb: :class:`._generated.entity_pb2.Entity`\n :param entity_pb: The entity owned by a mutation.\n\n :type entity: :class:`google.cloud.datastore.entity.Entity`\n :param entity: The entity being updated within the batch / transaction.\n \"\"\"\n bare_entity_pb = helpers.entity_to_protobuf(entity)\n bare_entity_pb.key.CopyFrom(bare_entity_pb.key)\n entity_pb.CopyFrom(bare_entity_pb)","label":0},{"cid":"google/cloud/datastore/client.py::_get_gcd_project","text":"google/cloud/datastore/client.py::_get_gcd_project\ndef _get_gcd_project():\n \"\"\"Gets the GCD application ID if it can be inferred.\"\"\"\n return os.getenv(GCD_DATASET)","label":0},{"cid":"google/cloud/datastore/client.py::_determine_default_project","text":"google/cloud/datastore/client.py::_determine_default_project\ndef _determine_default_project(project=None):\n \"\"\"Determine default project explicitly or implicitly as fall-back.\n\n In implicit case, supports four environments. In order of precedence, the\n implicit environments are:\n\n * DATASTORE_DATASET environment variable (for ``gcd`` / emulator testing)\n * GOOGLE_CLOUD_PROJECT environment variable\n * Google App Engine application ID\n * Google Compute Engine project ID (from metadata server)\n\n :type project: string\n :param project: Optional. The project to use as default.\n\n :rtype: string or ``NoneType``\n :returns: Default project if it can be determined.\n \"\"\"\n if project is None:\n project = _get_gcd_project()\n\n if project is None:\n project = _base_default_project(project=project)\n\n return project","label":0},{"cid":"google/cloud/datastore/client.py::_extended_lookup","text":"google/cloud/datastore/client.py::_extended_lookup\ndef _extended_lookup(connection, project, key_pbs,\n missing=None, deferred=None,\n eventual=False, transaction_id=None):\n \"\"\"Repeat lookup until all keys found (unless stop requested).\n\n Helper function for :meth:`Client.get_multi`.\n\n :type connection: :class:`google.cloud.datastore.connection.Connection`\n :param connection: The connection used to connect to datastore.\n\n :type project: string\n :param project: The project to make the request for.\n\n :type key_pbs: list of :class:`._generated.entity_pb2.Key`\n :param key_pbs: The keys to retrieve from the datastore.\n\n :type missing: list\n :param missing: (Optional) If a list is passed, the key-only entity\n protobufs returned by the backend as \"missing\" will be\n copied into it.\n\n :type deferred: list\n :param deferred: (Optional) If a list is passed, the key protobufs returned\n by the backend as \"deferred\" will be copied into it.\n\n :type eventual: bool\n :param eventual: If False (the default), request ``STRONG`` read\n consistency. If True, request ``EVENTUAL`` read\n consistency.\n\n :type transaction_id: string\n :param transaction_id: If passed, make the request in the scope of\n the given transaction. Incompatible with\n ``eventual==True``.\n\n :rtype: list of :class:`._generated.entity_pb2.Entity`\n :returns: The requested entities.\n :raises: :class:`ValueError` if missing / deferred are not null or\n empty list.\n \"\"\"\n if missing is not None and missing != []:\n raise ValueError('missing must be None or an empty list')\n\n if deferred is not None and deferred != []:\n raise ValueError('deferred must be None or an empty list')\n\n results = []\n\n loop_num = 0\n while loop_num < _MAX_LOOPS: # loop ag","label":0},{"cid":"google/cloud/datastore/client.py::Client.__init__","text":"google/cloud/datastore/client.py::Client.__init__\n def __init__(self, project=None, namespace=None,\n credentials=None, http=None):\n _ClientProjectMixin.__init__(self, project=project)\n self.namespace = namespace\n self._batch_stack = _LocalStack()\n super(Client, self).__init__(credentials, http)","label":0},{"cid":"google/cloud/datastore/client.py::Client._determine_default","text":"google/cloud/datastore/client.py::Client._determine_default\n @staticmethod\n def _determine_default(project):\n \"\"\"Helper: override default project detection.\"\"\"\n return _determine_default_project(project)","label":0},{"cid":"google/cloud/datastore/client.py::Client._push_batch","text":"google/cloud/datastore/client.py::Client._push_batch\n def _push_batch(self, batch):\n \"\"\"Push a batch/transaction onto our stack.\n\n \"Protected\", intended for use by batch / transaction context mgrs.\n\n :type batch: :class:`google.cloud.datastore.batch.Batch`, or an object\n implementing its API.\n :param batch: newly-active batch/transaction.\n \"\"\"\n self._batch_stack.push(batch)","label":0},{"cid":"google/cloud/datastore/client.py::Client._pop_batch","text":"google/cloud/datastore/client.py::Client._pop_batch\n def _pop_batch(self):\n \"\"\"Pop a batch/transaction from our stack.\n\n \"Protected\", intended for use by batch / transaction context mgrs.\n\n :raises: IndexError if the stack is empty.\n :rtype: :class:`google.cloud.datastore.batch.Batch`, or an object\n implementing its API.\n :returns: the top-most batch/transaction, after removing it.\n \"\"\"\n return self._batch_stack.pop()","label":0},{"cid":"google/cloud/datastore/client.py::Client.current_batch","text":"google/cloud/datastore/client.py::Client.current_batch\n @property\n def current_batch(self):\n \"\"\"Currently-active batch.\n\n :rtype: :class:`google.cloud.datastore.batch.Batch`, or an object\n implementing its API, or ``NoneType`` (if no batch is active).\n :returns: The batch/transaction at the top of the batch stack.\n \"\"\"\n return self._batch_stack.top","label":0},{"cid":"google/cloud/datastore/client.py::Client.current_transaction","text":"google/cloud/datastore/client.py::Client.current_transaction\n @property\n def current_transaction(self):\n \"\"\"Currently-active transaction.\n\n :rtype: :class:`google.cloud.datastore.transaction.Transaction`, or an\n object implementing its API, or ``NoneType`` (if no transaction\n is active).\n :returns: The transaction at the top of the batch stack.\n \"\"\"\n transaction = self.current_batch\n if isinstance(transaction, Transaction):\n return transaction","label":0},{"cid":"google/cloud/datastore/client.py::Client.get","text":"google/cloud/datastore/client.py::Client.get\n def get(self, key, missing=None, deferred=None, transaction=None):\n \"\"\"Retrieve an entity from a single key (if it exists).\n\n .. note::\n\n This is just a thin wrapper over :meth:`get_multi`.\n The backend API does not make a distinction between a single key or\n multiple keys in a lookup request.\n\n :type key: :class:`google.cloud.datastore.key.Key`\n :param key: The key to be retrieved from the datastore.\n\n :type missing: list\n :param missing: (Optional) If a list is passed, the key-only entities\n returned by the backend as \"missing\" will be copied\n into it.\n\n :type deferred: list\n :param deferred: (Optional) If a list is passed, the keys returned\n by the backend as \"deferred\" will be copied into it.\n\n :type transaction: :class:`~.transaction.Transaction`\n :param transaction: (Optional) Transaction to use for read consistency.\n If not passed, uses current transaction, if set.\n\n :rtype: :class:`google.cloud.datastore.entity.Entity` or ``NoneType``\n :returns: The requested entity if it exists.\n \"\"\"\n entities = self.get_multi(keys=[key], missing=missing,\n deferred=deferred, transaction=transaction)\n if entities:\n return entities[0]","label":0},{"cid":"google/cloud/datastore/client.py::Client.get_multi","text":"google/cloud/datastore/client.py::Client.get_multi\n def get_multi(self, keys, missing=None, deferred=None, transaction=None):\n \"\"\"Retrieve entities, along with their attributes.\n\n :type keys: list of :class:`google.cloud.datastore.key.Key`\n :param keys: The keys to be retrieved from the datastore.\n\n :type missing: list\n :param missing: (Optional) If a list is passed, the key-only entities\n returned by the backend as \"missing\" will be copied\n into it. If the list is not empty, an error will occur.\n\n :type deferred: list\n :param deferred: (Optional) If a list is passed, the keys returned\n by the backend as \"deferred\" will be copied into it.\n If the list is not empty, an error will occur.\n\n :type transaction: :class:`~.transaction.Transaction`\n :param transaction: (Optional) Transaction to use for read consistency.\n If not passed, uses current transaction, if set.\n\n :rtype: list of :class:`google.cloud.datastore.entity.Entity`\n :returns: The requested entities.\n :raises: :class:`ValueError` if one or more of ``keys`` has a project\n which does not match our project.\n \"\"\"\n if not keys:\n return []\n\n ids = set(key.project for key in keys)\n for current_id in ids:\n if current_id != self.project:\n raise ValueError('Keys do not match project')\n\n if transaction is None:\n transaction = self.current_transaction\n\n entity_pbs = _extended_lookup(\n connection=self.connection,\n project=self.project,\n key_pbs=[k.to_protobuf() for k in keys],\n missing=missing,\n deferred=deferred,\n transaction_id=transaction and transaction.id,\n )\n\n if missing is not None:\n missing[:] = [\n he","label":0},{"cid":"google/cloud/datastore/client.py::Client.put","text":"google/cloud/datastore/client.py::Client.put\n def put(self, entity):\n \"\"\"Save an entity in the Cloud Datastore.\n\n .. note::\n\n This is just a thin wrapper over :meth:`put_multi`.\n The backend API does not make a distinction between a single\n entity or multiple entities in a commit request.\n\n :type entity: :class:`google.cloud.datastore.entity.Entity`\n :param entity: The entity to be saved to the datastore.\n \"\"\"\n self.put_multi(entities=[entity])","label":0},{"cid":"google/cloud/datastore/client.py::Client.put_multi","text":"google/cloud/datastore/client.py::Client.put_multi\n def put_multi(self, entities):\n \"\"\"Save entities in the Cloud Datastore.\n\n :type entities: list of :class:`google.cloud.datastore.entity.Entity`\n :param entities: The entities to be saved to the datastore.\n\n :raises: :class:`ValueError` if ``entities`` is a single entity.\n \"\"\"\n if isinstance(entities, Entity):\n raise ValueError(\"Pass a sequence of entities\")\n\n if not entities:\n return\n\n current = self.current_batch\n in_batch = current is not None\n\n if not in_batch:\n current = self.batch()\n current.begin()\n\n for entity in entities:\n current.put(entity)\n\n if not in_batch:\n current.commit()","label":0},{"cid":"google/cloud/datastore/client.py::Client.delete","text":"google/cloud/datastore/client.py::Client.delete\n def delete(self, key):\n \"\"\"Delete the key in the Cloud Datastore.\n\n .. note::\n\n This is just a thin wrapper over :meth:`delete_multi`.\n The backend API does not make a distinction between a single key or\n multiple keys in a commit request.\n\n :type key: :class:`google.cloud.datastore.key.Key`\n :param key: The key to be deleted from the datastore.\n \"\"\"\n self.delete_multi(keys=[key])","label":0},{"cid":"google/cloud/datastore/client.py::Client.delete_multi","text":"google/cloud/datastore/client.py::Client.delete_multi\n def delete_multi(self, keys):\n \"\"\"Delete keys from the Cloud Datastore.\n\n :type keys: list of :class:`google.cloud.datastore.key.Key`\n :param keys: The keys to be deleted from the Datastore.\n \"\"\"\n if not keys:\n return\n\n # We allow partial keys to attempt a delete, the backend will fail.\n current = self.current_batch\n in_batch = current is not None\n\n if not in_batch:\n current = self.batch()\n current.begin()\n\n for key in keys:\n current.delete(key)\n\n if not in_batch:\n current.commit()","label":0},{"cid":"google/cloud/datastore/client.py::Client.allocate_ids","text":"google/cloud/datastore/client.py::Client.allocate_ids\n def allocate_ids(self, incomplete_key, num_ids):\n \"\"\"Allocate a list of IDs from a partial key.\n\n :type incomplete_key: :class:`google.cloud.datastore.key.Key`\n :param incomplete_key: Partial key to use as base for allocated IDs.\n\n :type num_ids: int\n :param num_ids: The number of IDs to allocate.\n\n :rtype: list of :class:`google.cloud.datastore.key.Key`\n :returns: The (complete) keys allocated with ``incomplete_key`` as\n root.\n :raises: :class:`ValueError` if ``incomplete_key`` is not a\n partial key.\n \"\"\"\n if not incomplete_key.is_partial:\n raise ValueError(('Key is not partial.', incomplete_key))\n\n incomplete_key_pb = incomplete_key.to_protobuf()\n incomplete_key_pbs = [incomplete_key_pb] * num_ids\n\n conn = self.connection\n allocated_key_pbs = conn.allocate_ids(incomplete_key.project,\n incomplete_key_pbs)\n allocated_ids = [allocated_key_pb.path[-1].id\n for allocated_key_pb in allocated_key_pbs]\n return [incomplete_key.completed_key(allocated_id)\n for allocated_id in allocated_ids]","label":0},{"cid":"google/cloud/datastore/client.py::Client.key","text":"google/cloud/datastore/client.py::Client.key\n def key(self, *path_args, **kwargs):\n \"\"\"Proxy to :class:`google.cloud.datastore.key.Key`.\n\n Passes our ``project``.\n \"\"\"\n if 'project' in kwargs:\n raise TypeError('Cannot pass project')\n kwargs['project'] = self.project\n if 'namespace' not in kwargs:\n kwargs['namespace'] = self.namespace\n return Key(*path_args, **kwargs)","label":0},{"cid":"google/cloud/datastore/client.py::Client.batch","text":"google/cloud/datastore/client.py::Client.batch\n def batch(self):\n \"\"\"Proxy to :class:`google.cloud.datastore.batch.Batch`.\"\"\"\n return Batch(self)","label":0},{"cid":"google/cloud/datastore/client.py::Client.transaction","text":"google/cloud/datastore/client.py::Client.transaction\n def transaction(self):\n \"\"\"Proxy to :class:`google.cloud.datastore.transaction.Transaction`.\"\"\"\n return Transaction(self)","label":0},{"cid":"google/cloud/datastore/client.py::Client.query","text":"google/cloud/datastore/client.py::Client.query\n def query(self, **kwargs):\n \"\"\"Proxy to :class:`google.cloud.datastore.query.Query`.\n\n Passes our ``project``.\n\n Using query to search a datastore::\n\n >>> from google.cloud import datastore\n >>> client = datastore.Client()\n >>> query = client.query(kind='MyKind')\n >>> query.add_filter('property', '=', 'val')\n\n Using the query iterator's\n :meth:`~google.cloud.datastore.query.Iterator.next_page` method:\n\n >>> query_iter = query.fetch()\n >>> entities, more_results, cursor = query_iter.next_page()\n >>> entities\n [<list of Entity unmarshalled from protobuf>]\n >>> more_results\n <boolean of more results>\n >>> cursor\n <string containing cursor where fetch stopped>\n\n Under the hood this is doing:\n\n >>> connection.run_query('project', query.to_protobuf())\n [<list of Entity Protobufs>], cursor, more_results, skipped_results\n\n :type kwargs: dict\n :param kwargs: Parameters for initializing and instance of\n :class:`google.cloud.datastore.query.Query`.\n\n :rtype: :class:`google.cloud.datastore.query.Query`\n :returns: An instance of :class:`google.cloud.datastore.query.Query`\n \"\"\"\n if 'client' in kwargs:\n raise TypeError('Cannot pass client')\n if 'project' in kwargs:\n raise TypeError('Cannot pass project')\n kwargs['project'] = self.project\n if 'namespace' not in kwargs:\n kwargs['namespace'] = self.namespace\n return Query(self, **kwargs)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.__init__","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.__init__\n def __init__(self, connection):\n self.connection = connection","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp._request","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp._request\n def _request(self, project, method, data):\n \"\"\"Make a request over the Http transport to the Cloud Datastore API.\n\n :type project: string\n :param project: The project to make the request for.\n\n :type method: string\n :param method: The API call method name (ie, ``runQuery``,\n ``lookup``, etc)\n\n :type data: string\n :param data: The data to send with the API call.\n Typically this is a serialized Protobuf string.\n\n :rtype: string\n :returns: The string response content from the API call.\n :raises: :class:`google.cloud.exceptions.GoogleCloudError` if the\n response code is not 200 OK.\n \"\"\"\n headers = {\n 'Content-Type': 'application/x-protobuf',\n 'Content-Length': str(len(data)),\n 'User-Agent': self.connection.USER_AGENT,\n }\n headers, content = self.connection.http.request(\n uri=self.connection.build_api_url(project=project, method=method),\n method='POST', headers=headers, body=data)\n\n status = headers['status']\n if status != '200':\n error_status = status_pb2.Status.FromString(content)\n raise make_exception(headers, error_status.message, use_json=False)\n\n return content","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp._rpc","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp._rpc\n def _rpc(self, project, method, request_pb, response_pb_cls):\n \"\"\"Make a protobuf RPC request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type method: string\n :param method: The name of the method to invoke.\n\n :type request_pb: :class:`google.protobuf.message.Message` instance\n :param request_pb: the protobuf instance representing the request.\n\n :type response_pb_cls: A :class:`google.protobuf.message.Message`\n subclass.\n :param response_pb_cls: The class used to unmarshall the response\n protobuf.\n\n :rtype: :class:`google.protobuf.message.Message`\n :returns: The RPC message parsed from the response.\n \"\"\"\n response = self._request(project=project, method=method,\n data=request_pb.SerializeToString())\n return response_pb_cls.FromString(response)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.lookup","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.lookup\n def lookup(self, project, request_pb):\n \"\"\"Perform a ``lookup`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb: :class:`._generated.datastore_pb2.LookupRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.LookupResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n return self._rpc(project, 'lookup', request_pb,\n _datastore_pb2.LookupResponse)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.run_query","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.run_query\n def run_query(self, project, request_pb):\n \"\"\"Perform a ``runQuery`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb: :class:`._generated.datastore_pb2.RunQueryRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.RunQueryResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n return self._rpc(project, 'runQuery', request_pb,\n _datastore_pb2.RunQueryResponse)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.begin_transaction","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.begin_transaction\n def begin_transaction(self, project, request_pb):\n \"\"\"Perform a ``beginTransaction`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb:\n :class:`._generated.datastore_pb2.BeginTransactionRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.BeginTransactionResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n return self._rpc(project, 'beginTransaction', request_pb,\n _datastore_pb2.BeginTransactionResponse)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.commit","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.commit\n def commit(self, project, request_pb):\n \"\"\"Perform a ``commit`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb: :class:`._generated.datastore_pb2.CommitRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.CommitResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n return self._rpc(project, 'commit', request_pb,\n _datastore_pb2.CommitResponse)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.rollback","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.rollback\n def rollback(self, project, request_pb):\n \"\"\"Perform a ``rollback`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb: :class:`._generated.datastore_pb2.RollbackRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.RollbackResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n return self._rpc(project, 'rollback', request_pb,\n _datastore_pb2.RollbackResponse)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.allocate_ids","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.allocate_ids\n def allocate_ids(self, project, request_pb):\n \"\"\"Perform an ``allocateIds`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb: :class:`._generated.datastore_pb2.AllocateIdsRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.AllocateIdsResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n return self._rpc(project, 'allocateIds', request_pb,\n _datastore_pb2.AllocateIdsResponse)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.__init__","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.__init__\n def __init__(self, connection, secure):\n if secure:\n self._stub = make_secure_stub(connection.credentials,\n connection.USER_AGENT,\n datastore_grpc_pb2.DatastoreStub,\n connection.host)\n else:\n self._stub = make_insecure_stub(datastore_grpc_pb2.DatastoreStub,\n connection.host)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.lookup","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.lookup\n def lookup(self, project, request_pb):\n \"\"\"Perform a ``lookup`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb: :class:`._generated.datastore_pb2.LookupRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.LookupResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n request_pb.project_id = project\n return self._stub.Lookup(request_pb)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.run_query","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.run_query\n def run_query(self, project, request_pb):\n \"\"\"Perform a ``runQuery`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb: :class:`._generated.datastore_pb2.RunQueryRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.RunQueryResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n request_pb.project_id = project\n return self._stub.RunQuery(request_pb)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.begin_transaction","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.begin_transaction\n def begin_transaction(self, project, request_pb):\n \"\"\"Perform a ``beginTransaction`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb:\n :class:`._generated.datastore_pb2.BeginTransactionRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.BeginTransactionResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n request_pb.project_id = project\n return self._stub.BeginTransaction(request_pb)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.commit","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.commit\n def commit(self, project, request_pb):\n \"\"\"Perform a ``commit`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb: :class:`._generated.datastore_pb2.CommitRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.CommitResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n request_pb.project_id = project\n try:\n return self._stub.Commit(request_pb)\n except GrpcRendezvous as exc:\n if exc.code() == StatusCode.ABORTED:\n raise Conflict(exc.details())\n raise","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.rollback","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.rollback\n def rollback(self, project, request_pb):\n \"\"\"Perform a ``rollback`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb: :class:`._generated.datastore_pb2.RollbackRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.RollbackResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n request_pb.project_id = project\n return self._stub.Rollback(request_pb)","label":0},{"cid":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.allocate_ids","text":"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.allocate_ids\n def allocate_ids(self, project, request_pb):\n \"\"\"Perform an ``allocateIds`` request.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type request_pb: :class:`._generated.datastore_pb2.AllocateIdsRequest`\n :param request_pb: The request protobuf object.\n\n :rtype: :class:`._generated.datastore_pb2.AllocateIdsResponse`\n :returns: The returned protobuf response object.\n \"\"\"\n request_pb.project_id = project\n return self._stub.AllocateIds(request_pb)","label":0},{"cid":"google/cloud/datastore/connection.py::Connection.__init__","text":"google/cloud/datastore/connection.py::Connection.__init__\n def __init__(self, credentials=None, http=None):\n super(Connection, self).__init__(credentials=credentials, http=http)\n try:\n self.host = os.environ[GCD_HOST]\n self.api_base_url = 'http://' + self.host\n secure = False\n except KeyError:\n self.host = DATASTORE_API_HOST\n self.api_base_url = self.__class__.API_BASE_URL\n secure = True\n if _USE_GRPC:\n self._datastore_api = _DatastoreAPIOverGRPC(self, secure=secure)\n else:\n self._datastore_api = _DatastoreAPIOverHttp(self)","label":0},{"cid":"google/cloud/datastore/connection.py::Connection.build_api_url","text":"google/cloud/datastore/connection.py::Connection.build_api_url\n def build_api_url(self, project, method, base_url=None,\n api_version=None):\n \"\"\"Construct the URL for a particular API call.\n\n This method is used internally to come up with the URL to use when\n making RPCs to the Cloud Datastore API.\n\n :type project: string\n :param project: The project to connect to. This is\n usually your project name in the cloud console.\n\n :type method: string\n :param method: The API method to call (e.g. 'runQuery', 'lookup').\n\n :type base_url: string\n :param base_url: The base URL where the API lives.\n You shouldn't have to provide this.\n\n :type api_version: string\n :param api_version: The version of the API to connect to.\n You shouldn't have to provide this.\n\n :rtype: str\n :returns: The API URL created.\n \"\"\"\n return self.API_URL_TEMPLATE.format(\n api_base=(base_url or self.api_base_url),\n api_version=(api_version or self.API_VERSION),\n project=project, method=method)","label":0},{"cid":"google/cloud/datastore/connection.py::Connection.lookup","text":"google/cloud/datastore/connection.py::Connection.lookup\n def lookup(self, project, key_pbs,\n eventual=False, transaction_id=None):\n \"\"\"Lookup keys from a project in the Cloud Datastore.\n\n Maps the ``DatastoreService.Lookup`` protobuf RPC.\n\n This uses mostly protobufs\n (:class:`google.cloud.datastore._generated.entity_pb2.Key` as input\n and :class:`google.cloud.datastore._generated.entity_pb2.Entity`\n as output). It is used under the hood in\n :meth:`Client.get() <.datastore.client.Client.get>`:\n\n >>> from google.cloud import datastore\n >>> client = datastore.Client(project='project')\n >>> key = client.key('MyKind', 1234)\n >>> client.get(key)\n [<Entity object>]\n\n Using a :class:`Connection` directly:\n\n >>> connection.lookup('project', [key.to_protobuf()])\n [<Entity protobuf>]\n\n :type project: string\n :param project: The project to look up the keys in.\n\n :type key_pbs: list of\n :class:`google.cloud.datastore._generated.entity_pb2.Key`\n :param key_pbs: The keys to retrieve from the datastore.\n\n :type eventual: bool\n :param eventual: If False (the default), request ``STRONG`` read\n consistency. If True, request ``EVENTUAL`` read\n consistency.\n\n :type transaction_id: string\n :param transaction_id: If passed, make the request in the scope of\n the given transaction. Incompatible with\n ``eventual==True``.\n\n :rtype: tuple\n :returns: A triple of (``results``, ``missing``, ``deferred``) where\n both ``results`` and ``missing`` are lists of\n :class:`google.cloud.datastore._generated.entity_pb2.Entity`\n and ``deferred`` is a list of\n :class:`google.cloud.datastore._generated.entity_pb2.Key`.\n ","label":0},{"cid":"google/cloud/datastore/connection.py::Connection.run_query","text":"google/cloud/datastore/connection.py::Connection.run_query\n def run_query(self, project, query_pb, namespace=None,\n eventual=False, transaction_id=None):\n \"\"\"Run a query on the Cloud Datastore.\n\n Maps the ``DatastoreService.RunQuery`` protobuf RPC.\n\n Given a Query protobuf, sends a ``runQuery`` request to the\n Cloud Datastore API and returns a list of entity protobufs\n matching the query.\n\n You typically wouldn't use this method directly, in favor of the\n :meth:`google.cloud.datastore.query.Query.fetch` method.\n\n Under the hood, the :class:`google.cloud.datastore.query.Query` class\n uses this method to fetch data.\n\n :type project: string\n :param project: The project over which to run the query.\n\n :type query_pb: :class:`.datastore._generated.query_pb2.Query`\n :param query_pb: The Protobuf representing the query to run.\n\n :type namespace: string\n :param namespace: The namespace over which to run the query.\n\n :type eventual: bool\n :param eventual: If False (the default), request ``STRONG`` read\n consistency. If True, request ``EVENTUAL`` read\n consistency.\n\n :type transaction_id: string\n :param transaction_id: If passed, make the request in the scope of\n the given transaction. Incompatible with\n ``eventual==True``.\n\n :rtype: tuple\n :returns: Four-tuple containing the entities returned,\n the end cursor of the query, a ``more_results``\n enum and a count of the number of skipped results.\n \"\"\"\n request = _datastore_pb2.RunQueryRequest()\n _set_read_options(request, eventual, transaction_id)\n\n if namespace:\n request.partition_id.namespace_id = namespace\n\n request.query.CopyFrom(query_pb)\n response = self._datastore_a","label":0},{"cid":"google/cloud/datastore/connection.py::Connection.begin_transaction","text":"google/cloud/datastore/connection.py::Connection.begin_transaction\n def begin_transaction(self, project):\n \"\"\"Begin a transaction.\n\n Maps the ``DatastoreService.BeginTransaction`` protobuf RPC.\n\n :type project: string\n :param project: The project to which the transaction applies.\n\n :rtype: bytes\n :returns: The serialized transaction that was begun.\n \"\"\"\n request = _datastore_pb2.BeginTransactionRequest()\n response = self._datastore_api.begin_transaction(project, request)\n return response.transaction","label":0},{"cid":"google/cloud/datastore/connection.py::Connection.commit","text":"google/cloud/datastore/connection.py::Connection.commit\n def commit(self, project, request, transaction_id):\n \"\"\"Commit mutations in context of current transaction (if any).\n\n Maps the ``DatastoreService.Commit`` protobuf RPC.\n\n :type project: string\n :param project: The project to which the transaction applies.\n\n :type request: :class:`._generated.datastore_pb2.CommitRequest`\n :param request: The protobuf with the mutations being committed.\n\n :type transaction_id: string or None\n :param transaction_id: The transaction ID returned from\n :meth:`begin_transaction`. Non-transactional\n batches must pass ``None``.\n\n .. note::\n\n This method will mutate ``request`` before using it.\n\n :rtype: tuple\n :returns: The pair of the number of index updates and a list of\n :class:`._generated.entity_pb2.Key` for each incomplete key\n that was completed in the commit.\n \"\"\"\n if transaction_id:\n request.mode = _datastore_pb2.CommitRequest.TRANSACTIONAL\n request.transaction = transaction_id\n else:\n request.mode = _datastore_pb2.CommitRequest.NON_TRANSACTIONAL\n\n response = self._datastore_api.commit(project, request)\n return _parse_commit_response(response)","label":0},{"cid":"google/cloud/datastore/connection.py::Connection.rollback","text":"google/cloud/datastore/connection.py::Connection.rollback\n def rollback(self, project, transaction_id):\n \"\"\"Rollback the connection's existing transaction.\n\n Maps the ``DatastoreService.Rollback`` protobuf RPC.\n\n :type project: string\n :param project: The project to which the transaction belongs.\n\n :type transaction_id: string\n :param transaction_id: The transaction ID returned from\n :meth:`begin_transaction`.\n \"\"\"\n request = _datastore_pb2.RollbackRequest()\n request.transaction = transaction_id\n # Nothing to do with this response, so just execute the method.\n self._datastore_api.rollback(project, request)","label":0},{"cid":"google/cloud/datastore/connection.py::Connection.allocate_ids","text":"google/cloud/datastore/connection.py::Connection.allocate_ids\n def allocate_ids(self, project, key_pbs):\n \"\"\"Obtain backend-generated IDs for a set of keys.\n\n Maps the ``DatastoreService.AllocateIds`` protobuf RPC.\n\n :type project: string\n :param project: The project to which the transaction belongs.\n\n :type key_pbs: list of\n :class:`google.cloud.datastore._generated.entity_pb2.Key`\n :param key_pbs: The keys for which the backend should allocate IDs.\n\n :rtype: list of :class:`.datastore._generated.entity_pb2.Key`\n :returns: An equal number of keys, with IDs filled in by the backend.\n \"\"\"\n request = _datastore_pb2.AllocateIdsRequest()\n _add_keys_to_request(request.keys, key_pbs)\n # Nothing to do with this response, so just execute the method.\n response = self._datastore_api.allocate_ids(project, request)\n return list(response.keys)","label":0},{"cid":"google/cloud/datastore/connection.py::_set_read_options","text":"google/cloud/datastore/connection.py::_set_read_options\ndef _set_read_options(request, eventual, transaction_id):\n \"\"\"Validate rules for read options, and assign to the request.\n\n Helper method for ``lookup()`` and ``run_query``.\n\n :raises: :class:`ValueError` if ``eventual`` is ``True`` and the\n ``transaction_id`` is not ``None``.\n \"\"\"\n if eventual and (transaction_id is not None):\n raise ValueError('eventual must be False when in a transaction')\n\n opts = request.read_options\n if eventual:\n opts.read_consistency = _datastore_pb2.ReadOptions.EVENTUAL\n elif transaction_id:\n opts.transaction = transaction_id","label":0},{"cid":"google/cloud/datastore/connection.py::_add_keys_to_request","text":"google/cloud/datastore/connection.py::_add_keys_to_request\ndef _add_keys_to_request(request_field_pb, key_pbs):\n \"\"\"Add protobuf keys to a request object.\n\n :type request_field_pb: `RepeatedCompositeFieldContainer`\n :param request_field_pb: A repeated proto field that contains keys.\n\n :type key_pbs: list of :class:`.datastore._generated.entity_pb2.Key`\n :param key_pbs: The keys to add to a request.\n \"\"\"\n for key_pb in key_pbs:\n request_field_pb.add().CopyFrom(key_pb)","label":0},{"cid":"google/cloud/datastore/connection.py::_parse_commit_response","text":"google/cloud/datastore/connection.py::_parse_commit_response\ndef _parse_commit_response(commit_response_pb):\n \"\"\"Extract response data from a commit response.\n\n :type commit_response_pb: :class:`._generated.datastore_pb2.CommitResponse`\n :param commit_response_pb: The protobuf response from a commit request.\n\n :rtype: tuple\n :returns: The pair of the number of index updates and a list of\n :class:`._generated.entity_pb2.Key` for each incomplete key\n that was completed in the commit.\n \"\"\"\n mut_results = commit_response_pb.mutation_results\n index_updates = commit_response_pb.index_updates\n completed_keys = [mut_result.key for mut_result in mut_results\n if mut_result.HasField('key')] # Message field (Key)\n return index_updates, completed_keys","label":0},{"cid":"google/cloud/datastore/entity.py::Entity.__init__","text":"google/cloud/datastore/entity.py::Entity.__init__\n def __init__(self, key=None, exclude_from_indexes=()):\n super(Entity, self).__init__()\n self.key = key\n self._exclude_from_indexes = set(_ensure_tuple_or_list(\n 'exclude_from_indexes', exclude_from_indexes))\n # NOTE: This will be populated when parsing a protobuf in\n # google.cloud.datastore.helpers.entity_from_protobuf.\n self._meanings = {}","label":0},{"cid":"google/cloud/datastore/entity.py::Entity.__eq__","text":"google/cloud/datastore/entity.py::Entity.__eq__\n def __eq__(self, other):\n \"\"\"Compare two entities for equality.\n\n Entities compare equal if their keys compare equal and their\n properties compare equal.\n\n :rtype: boolean\n :returns: True if the entities compare equal, else False.\n \"\"\"\n if not isinstance(other, Entity):\n return False\n\n return (self.key == other.key and\n self._exclude_from_indexes == other._exclude_from_indexes and\n self._meanings == other._meanings and\n super(Entity, self).__eq__(other))","label":0},{"cid":"google/cloud/datastore/entity.py::Entity.__ne__","text":"google/cloud/datastore/entity.py::Entity.__ne__\n def __ne__(self, other):\n \"\"\"Compare two entities for inequality.\n\n Entities compare equal if their keys compare equal and their\n properties compare equal.\n\n :rtype: boolean\n :returns: False if the entities compare equal, else True.\n \"\"\"\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/datastore/entity.py::Entity.kind","text":"google/cloud/datastore/entity.py::Entity.kind\n @property\n def kind(self):\n \"\"\"Get the kind of the current entity.\n\n .. note::\n This relies entirely on the :class:`google.cloud.datastore.key.Key`\n set on the entity. That means that we're not storing the kind\n of the entity at all, just the properties and a pointer to a\n Key which knows its Kind.\n \"\"\"\n if self.key:\n return self.key.kind","label":0},{"cid":"google/cloud/datastore/entity.py::Entity.exclude_from_indexes","text":"google/cloud/datastore/entity.py::Entity.exclude_from_indexes\n @property\n def exclude_from_indexes(self):\n \"\"\"Names of fields which are *not* to be indexed for this entity.\n\n :rtype: sequence of field names\n :returns: The set of fields excluded from indexes.\n \"\"\"\n return frozenset(self._exclude_from_indexes)","label":0},{"cid":"google/cloud/datastore/entity.py::Entity.__repr__","text":"google/cloud/datastore/entity.py::Entity.__repr__\n def __repr__(self):\n if self.key:\n return '<Entity%s %s>' % (self.key.path,\n super(Entity, self).__repr__())\n else:\n return '<Entity %s>' % (super(Entity, self).__repr__())","label":0},{"cid":"google/cloud/datastore/helpers.py::_get_meaning","text":"google/cloud/datastore/helpers.py::_get_meaning\ndef _get_meaning(value_pb, is_list=False):\n \"\"\"Get the meaning from a protobuf value.\n\n :type value_pb: :class:`google.cloud.datastore._generated.entity_pb2.Value`\n :param value_pb: The protobuf value to be checked for an\n associated meaning.\n\n :type is_list: bool\n :param is_list: Boolean indicating if the ``value_pb`` contains\n a list value.\n\n :rtype: int\n :returns: The meaning for the ``value_pb`` if one is set, else\n :data:`None`. For a list value, if there are disagreeing\n means it just returns a list of meanings. If all the\n list meanings agree, it just condenses them.\n \"\"\"\n meaning = None\n if is_list:\n # An empty list will have no values, hence no shared meaning\n # set among them.\n if len(value_pb.array_value.values) == 0:\n return None\n\n # We check among all the meanings, some of which may be None,\n # the rest which may be enum/int values.\n all_meanings = [_get_meaning(sub_value_pb)\n for sub_value_pb in value_pb.array_value.values]\n unique_meanings = set(all_meanings)\n if len(unique_meanings) == 1:\n # If there is a unique meaning, we preserve it.\n meaning = unique_meanings.pop()\n else: # We know len(value_pb.array_value.values) > 0.\n # If the meaning is not unique, just return all of them.\n meaning = all_meanings\n elif value_pb.meaning: # Simple field (int32).\n meaning = value_pb.meaning\n\n return meaning","label":0},{"cid":"google/cloud/datastore/helpers.py::_new_value_pb","text":"google/cloud/datastore/helpers.py::_new_value_pb\ndef _new_value_pb(entity_pb, name):\n \"\"\"Add (by name) a new ``Value`` protobuf to an entity protobuf.\n\n :type entity_pb: :class:`.datastore._generated.entity_pb2.Entity`\n :param entity_pb: An entity protobuf to add a new property to.\n\n :type name: string\n :param name: The name of the new property.\n\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Value`\n :returns: The new ``Value`` protobuf that was added to the entity.\n \"\"\"\n return entity_pb.properties.get_or_create(name)","label":0},{"cid":"google/cloud/datastore/helpers.py::_property_tuples","text":"google/cloud/datastore/helpers.py::_property_tuples\ndef _property_tuples(entity_pb):\n \"\"\"Iterator of name, ``Value`` tuples from entity properties.\n\n :type entity_pb: :class:`.datastore._generated.entity_pb2.Entity`\n :param entity_pb: An entity protobuf to add a new property to.\n\n :rtype: :class:`generator`\n :returns: An iterator that yields tuples of a name and ``Value``\n corresponding to properties on the entity.\n \"\"\"\n return six.iteritems(entity_pb.properties)","label":0},{"cid":"google/cloud/datastore/helpers.py::entity_from_protobuf","text":"google/cloud/datastore/helpers.py::entity_from_protobuf\ndef entity_from_protobuf(pb):\n \"\"\"Factory method for creating an entity based on a protobuf.\n\n The protobuf should be one returned from the Cloud Datastore\n Protobuf API.\n\n :type pb: :class:`google.cloud.datastore._generated.entity_pb2.Entity`\n :param pb: The Protobuf representing the entity.\n\n :rtype: :class:`google.cloud.datastore.entity.Entity`\n :returns: The entity derived from the protobuf.\n \"\"\"\n key = None\n if pb.HasField('key'): # Message field (Key)\n key = key_from_protobuf(pb.key)\n\n entity_props = {}\n entity_meanings = {}\n exclude_from_indexes = []\n\n for prop_name, value_pb in _property_tuples(pb):\n value = _get_value_from_value_pb(value_pb)\n entity_props[prop_name] = value\n\n # Check if the property has an associated meaning.\n is_list = isinstance(value, list)\n meaning = _get_meaning(value_pb, is_list=is_list)\n if meaning is not None:\n entity_meanings[prop_name] = (meaning, value)\n\n # Check if ``value_pb`` was excluded from index. Lists need to be\n # special-cased and we require all ``exclude_from_indexes`` values\n # in a list agree.\n if is_list:\n exclude_values = set(value_pb.exclude_from_indexes\n for value_pb in value_pb.array_value.values)\n if len(exclude_values) != 1:\n raise ValueError('For an array_value, subvalues must either '\n 'all be indexed or all excluded from '\n 'indexes.')\n\n if exclude_values.pop():\n exclude_from_indexes.append(prop_name)\n else:\n if value_pb.exclude_from_indexes:\n exclude_from_indexes.append(prop_name)\n\n entity = Entity(key=key, exclude_from_indexes=exclude_from_indexes)\n entity.update(entity_props)\n entity._meanings.update(entity_meanings)\n re","label":0},{"cid":"google/cloud/datastore/helpers.py::_set_pb_meaning_from_entity","text":"google/cloud/datastore/helpers.py::_set_pb_meaning_from_entity\ndef _set_pb_meaning_from_entity(entity, name, value, value_pb,\n is_list=False):\n \"\"\"Add meaning information (from an entity) to a protobuf.\n\n :type entity: :class:`google.cloud.datastore.entity.Entity`\n :param entity: The entity to be turned into a protobuf.\n\n :type name: string\n :param name: The name of the property.\n\n :type value: object\n :param value: The current value stored as property ``name``.\n\n :type value_pb: :class:`google.cloud.datastore._generated.entity_pb2.Value`\n :param value_pb: The protobuf value to add meaning / meanings to.\n\n :type is_list: bool\n :param is_list: (Optional) Boolean indicating if the ``value`` is\n a list value.\n \"\"\"\n if name not in entity._meanings:\n return\n\n meaning, orig_value = entity._meanings[name]\n # Only add the meaning back to the protobuf if the value is\n # unchanged from when it was originally read from the API.\n if orig_value is not value:\n return\n\n # For lists, we set meaning on each sub-element.\n if is_list:\n if not isinstance(meaning, list):\n meaning = itertools.repeat(meaning)\n val_iter = six.moves.zip(value_pb.array_value.values,\n meaning)\n for sub_value_pb, sub_meaning in val_iter:\n if sub_meaning is not None:\n sub_value_pb.meaning = sub_meaning\n else:\n value_pb.meaning = meaning","label":0},{"cid":"google/cloud/datastore/helpers.py::entity_to_protobuf","text":"google/cloud/datastore/helpers.py::entity_to_protobuf\ndef entity_to_protobuf(entity):\n \"\"\"Converts an entity into a protobuf.\n\n :type entity: :class:`google.cloud.datastore.entity.Entity`\n :param entity: The entity to be turned into a protobuf.\n\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Entity`\n :returns: The protobuf representing the entity.\n \"\"\"\n entity_pb = _entity_pb2.Entity()\n if entity.key is not None:\n key_pb = entity.key.to_protobuf()\n entity_pb.key.CopyFrom(key_pb)\n\n for name, value in entity.items():\n value_is_list = isinstance(value, list)\n if value_is_list and len(value) == 0:\n continue\n\n value_pb = _new_value_pb(entity_pb, name)\n # Set the appropriate value.\n _set_protobuf_value(value_pb, value)\n\n # Add index information to protobuf.\n if name in entity.exclude_from_indexes:\n if not value_is_list:\n value_pb.exclude_from_indexes = True\n\n for sub_value in value_pb.array_value.values:\n sub_value.exclude_from_indexes = True\n\n # Add meaning information to protobuf.\n _set_pb_meaning_from_entity(entity, name, value, value_pb,\n is_list=value_is_list)\n\n return entity_pb","label":0},{"cid":"google/cloud/datastore/helpers.py::key_from_protobuf","text":"google/cloud/datastore/helpers.py::key_from_protobuf\ndef key_from_protobuf(pb):\n \"\"\"Factory method for creating a key based on a protobuf.\n\n The protobuf should be one returned from the Cloud Datastore\n Protobuf API.\n\n :type pb: :class:`google.cloud.datastore._generated.entity_pb2.Key`\n :param pb: The Protobuf representing the key.\n\n :rtype: :class:`google.cloud.datastore.key.Key`\n :returns: a new `Key` instance\n \"\"\"\n path_args = []\n for element in pb.path:\n path_args.append(element.kind)\n if element.id: # Simple field (int64)\n path_args.append(element.id)\n # This is safe: we expect proto objects returned will only have\n # one of `name` or `id` set.\n if element.name: # Simple field (string)\n path_args.append(element.name)\n\n project = None\n if pb.partition_id.project_id: # Simple field (string)\n project = pb.partition_id.project_id\n namespace = None\n if pb.partition_id.namespace_id: # Simple field (string)\n namespace = pb.partition_id.namespace_id\n\n return Key(*path_args, namespace=namespace, project=project)","label":0},{"cid":"google/cloud/datastore/helpers.py::_pb_attr_value","text":"google/cloud/datastore/helpers.py::_pb_attr_value\ndef _pb_attr_value(val):\n \"\"\"Given a value, return the protobuf attribute name and proper value.\n\n The Protobuf API uses different attribute names based on value types\n rather than inferring the type. This function simply determines the\n proper attribute name based on the type of the value provided and\n returns the attribute name as well as a properly formatted value.\n\n Certain value types need to be coerced into a different type (such\n as a `datetime.datetime` into an integer timestamp, or a\n `google.cloud.datastore.key.Key` into a Protobuf representation. This\n function handles that for you.\n\n .. note::\n Values which are \"text\" ('unicode' in Python2, 'str' in Python3) map\n to 'string_value' in the datastore; values which are \"bytes\"\n ('str' in Python2, 'bytes' in Python3) map to 'blob_value'.\n\n For example:\n\n >>> _pb_attr_value(1234)\n ('integer_value', 1234)\n >>> _pb_attr_value('my_string')\n ('string_value', 'my_string')\n\n :type val: `datetime.datetime`, :class:`google.cloud.datastore.key.Key`,\n bool, float, integer, string\n :param val: The value to be scrutinized.\n\n :rtype: tuple\n :returns: A tuple of the attribute name and proper value type.\n \"\"\"\n\n if isinstance(val, datetime.datetime):\n name = 'timestamp'\n value = _datetime_to_pb_timestamp(val)\n elif isinstance(val, Key):\n name, value = 'key', val.to_protobuf()\n elif isinstance(val, bool):\n name, value = 'boolean', val\n elif isinstance(val, float):\n name, value = 'double', val\n elif isinstance(val, six.integer_types):\n name, value = 'integer', val\n elif isinstance(val, six.text_type):\n name, value = 'string', val\n elif isinstance(val, (bytes, str)):\n name, value = 'blob', val\n elif isinstance(val, Entity):\n name, value = 'entity', val\n elif isinstance(val, list):\n name, val","label":0},{"cid":"google/cloud/datastore/helpers.py::_get_value_from_value_pb","text":"google/cloud/datastore/helpers.py::_get_value_from_value_pb\ndef _get_value_from_value_pb(value_pb):\n \"\"\"Given a protobuf for a Value, get the correct value.\n\n The Cloud Datastore Protobuf API returns a Property Protobuf which\n has one value set and the rest blank. This function retrieves the\n the one value provided.\n\n Some work is done to coerce the return value into a more useful type\n (particularly in the case of a timestamp value, or a key value).\n\n :type value_pb: :class:`google.cloud.datastore._generated.entity_pb2.Value`\n :param value_pb: The Value Protobuf.\n\n :rtype: object\n :returns: The value provided by the Protobuf.\n :raises: :class:`ValueError <exceptions.ValueError>` if no value type\n has been set.\n \"\"\"\n value_type = value_pb.WhichOneof('value_type')\n\n if value_type == 'timestamp_value':\n result = _pb_timestamp_to_datetime(value_pb.timestamp_value)\n\n elif value_type == 'key_value':\n result = key_from_protobuf(value_pb.key_value)\n\n elif value_type == 'boolean_value':\n result = value_pb.boolean_value\n\n elif value_type == 'double_value':\n result = value_pb.double_value\n\n elif value_type == 'integer_value':\n result = value_pb.integer_value\n\n elif value_type == 'string_value':\n result = value_pb.string_value\n\n elif value_type == 'blob_value':\n result = value_pb.blob_value\n\n elif value_type == 'entity_value':\n result = entity_from_protobuf(value_pb.entity_value)\n\n elif value_type == 'array_value':\n result = [_get_value_from_value_pb(value)\n for value in value_pb.array_value.values]\n\n elif value_type == 'geo_point_value':\n result = GeoPoint(value_pb.geo_point_value.latitude,\n value_pb.geo_point_value.longitude)\n\n elif value_type == 'null_value':\n result = None\n\n else:\n raise ValueError('Value protobuf did not have any value set')\n\n return result","label":0},{"cid":"google/cloud/datastore/helpers.py::_set_protobuf_value","text":"google/cloud/datastore/helpers.py::_set_protobuf_value\ndef _set_protobuf_value(value_pb, val):\n \"\"\"Assign 'val' to the correct subfield of 'value_pb'.\n\n The Protobuf API uses different attribute names based on value types\n rather than inferring the type.\n\n Some value types (entities, keys, lists) cannot be directly\n assigned; this function handles them correctly.\n\n :type value_pb: :class:`google.cloud.datastore._generated.entity_pb2.Value`\n :param value_pb: The value protobuf to which the value is being assigned.\n\n :type val: :class:`datetime.datetime`, boolean, float, integer, string,\n :class:`google.cloud.datastore.key.Key`,\n :class:`google.cloud.datastore.entity.Entity`\n :param val: The value to be assigned.\n \"\"\"\n attr, val = _pb_attr_value(val)\n if attr == 'key_value':\n value_pb.key_value.CopyFrom(val)\n elif attr == 'timestamp_value':\n value_pb.timestamp_value.CopyFrom(val)\n elif attr == 'entity_value':\n entity_pb = entity_to_protobuf(val)\n value_pb.entity_value.CopyFrom(entity_pb)\n elif attr == 'array_value':\n l_pb = value_pb.array_value.values\n for item in val:\n i_pb = l_pb.add()\n _set_protobuf_value(i_pb, item)\n elif attr == 'geo_point_value':\n value_pb.geo_point_value.CopyFrom(val)\n else: # scalar, just assign\n setattr(value_pb, attr, val)","label":0},{"cid":"google/cloud/datastore/helpers.py::GeoPoint.__init__","text":"google/cloud/datastore/helpers.py::GeoPoint.__init__\n def __init__(self, latitude, longitude):\n self.latitude = latitude\n self.longitude = longitude","label":0},{"cid":"google/cloud/datastore/helpers.py::GeoPoint.to_protobuf","text":"google/cloud/datastore/helpers.py::GeoPoint.to_protobuf\n def to_protobuf(self):\n \"\"\"Convert the current object to protobuf.\n\n :rtype: :class:`google.type.latlng_pb2.LatLng`.\n :returns: The current point as a protobuf.\n \"\"\"\n return latlng_pb2.LatLng(latitude=self.latitude,\n longitude=self.longitude)","label":0},{"cid":"google/cloud/datastore/helpers.py::GeoPoint.__eq__","text":"google/cloud/datastore/helpers.py::GeoPoint.__eq__\n def __eq__(self, other):\n \"\"\"Compare two geo points for equality.\n\n :rtype: boolean\n :returns: True if the points compare equal, else False.\n \"\"\"\n if not isinstance(other, GeoPoint):\n return False\n\n return (self.latitude == other.latitude and\n self.longitude == other.longitude)","label":0},{"cid":"google/cloud/datastore/helpers.py::GeoPoint.__ne__","text":"google/cloud/datastore/helpers.py::GeoPoint.__ne__\n def __ne__(self, other):\n \"\"\"Compare two geo points for inequality.\n\n :rtype: boolean\n :returns: False if the points compare equal, else True.\n \"\"\"\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/datastore/key.py::Key.__init__","text":"google/cloud/datastore/key.py::Key.__init__\n def __init__(self, *path_args, **kwargs):\n self._flat_path = path_args\n parent = self._parent = kwargs.get('parent')\n self._namespace = kwargs.get('namespace')\n project = kwargs.get('project')\n self._project = _validate_project(project, parent)\n # _flat_path, _parent, _namespace and _project must be set before\n # _combine_args() is called.\n self._path = self._combine_args()","label":0},{"cid":"google/cloud/datastore/key.py::Key.__eq__","text":"google/cloud/datastore/key.py::Key.__eq__\n def __eq__(self, other):\n \"\"\"Compare two keys for equality.\n\n Incomplete keys never compare equal to any other key.\n\n Completed keys compare equal if they have the same path, project,\n and namespace.\n\n :rtype: bool\n :returns: True if the keys compare equal, else False.\n \"\"\"\n if not isinstance(other, Key):\n return False\n\n if self.is_partial or other.is_partial:\n return False\n\n return (self.flat_path == other.flat_path and\n self.project == other.project and\n self.namespace == other.namespace)","label":0},{"cid":"google/cloud/datastore/key.py::Key.__ne__","text":"google/cloud/datastore/key.py::Key.__ne__\n def __ne__(self, other):\n \"\"\"Compare two keys for inequality.\n\n Incomplete keys never compare equal to any other key.\n\n Completed keys compare equal if they have the same path, project,\n and namespace.\n\n :rtype: bool\n :returns: False if the keys compare equal, else True.\n \"\"\"\n return not self.__eq__(other)","label":0},{"cid":"google/cloud/datastore/key.py::Key.__hash__","text":"google/cloud/datastore/key.py::Key.__hash__\n def __hash__(self):\n \"\"\"Hash a keys for use in a dictionary lookp.\n\n :rtype: integer\n :returns: a hash of the key's state.\n \"\"\"\n return (hash(self.flat_path) +\n hash(self.project) +\n hash(self.namespace))","label":0},{"cid":"google/cloud/datastore/key.py::Key._parse_path","text":"google/cloud/datastore/key.py::Key._parse_path\n @staticmethod\n def _parse_path(path_args):\n \"\"\"Parses positional arguments into key path with kinds and IDs.\n\n :type path_args: tuple\n :param path_args: A tuple from positional arguments. Should be\n alternating list of kinds (string) and ID/name\n parts (int or string).\n\n :rtype: :class:`list` of :class:`dict`\n :returns: A list of key parts with kind and ID or name set.\n :raises: :class:`ValueError` if there are no ``path_args``, if one of\n the kinds is not a string or if one of the IDs/names is not\n a string or an integer.\n \"\"\"\n if len(path_args) == 0:\n raise ValueError('Key path must not be empty.')\n\n kind_list = path_args[::2]\n id_or_name_list = path_args[1::2]\n # Dummy sentinel value to pad incomplete key to even length path.\n partial_ending = object()\n if len(path_args) % 2 == 1:\n id_or_name_list += (partial_ending,)\n\n result = []\n for kind, id_or_name in zip(kind_list, id_or_name_list):\n curr_key_part = {}\n if isinstance(kind, six.string_types):\n curr_key_part['kind'] = kind\n else:\n raise ValueError(kind, 'Kind was not a string.')\n\n if isinstance(id_or_name, six.string_types):\n curr_key_part['name'] = id_or_name\n elif isinstance(id_or_name, six.integer_types):\n curr_key_part['id'] = id_or_name\n elif id_or_name is not partial_ending:\n raise ValueError(id_or_name,\n 'ID/name was not a string or integer.')\n\n result.append(curr_key_part)\n\n return result","label":0},{"cid":"google/cloud/datastore/key.py::Key._combine_args","text":"google/cloud/datastore/key.py::Key._combine_args\n def _combine_args(self):\n \"\"\"Sets protected data by combining raw data set from the constructor.\n\n If a ``_parent`` is set, updates the ``_flat_path`` and sets the\n ``_namespace`` and ``_project`` if not already set.\n\n :rtype: :class:`list` of :class:`dict`\n :returns: A list of key parts with kind and ID or name set.\n :raises: :class:`ValueError` if the parent key is not complete.\n \"\"\"\n child_path = self._parse_path(self._flat_path)\n\n if self._parent is not None:\n if self._parent.is_partial:\n raise ValueError('Parent key must be complete.')\n\n # We know that _parent.path() will return a copy.\n child_path = self._parent.path + child_path\n self._flat_path = self._parent.flat_path + self._flat_path\n if (self._namespace is not None and\n self._namespace != self._parent.namespace):\n raise ValueError('Child namespace must agree with parent\\'s.')\n self._namespace = self._parent.namespace\n if (self._project is not None and\n self._project != self._parent.project):\n raise ValueError('Child project must agree with parent\\'s.')\n self._project = self._parent.project\n\n return child_path","label":0},{"cid":"google/cloud/datastore/key.py::Key._clone","text":"google/cloud/datastore/key.py::Key._clone\n def _clone(self):\n \"\"\"Duplicates the Key.\n\n Most attributes are simple types, so don't require copying. Other\n attributes like ``parent`` are long-lived and so we re-use them.\n\n :rtype: :class:`google.cloud.datastore.key.Key`\n :returns: A new ``Key`` instance with the same data as the current one.\n \"\"\"\n cloned_self = self.__class__(*self.flat_path,\n project=self.project,\n namespace=self.namespace)\n # If the current parent has already been set, we re-use\n # the same instance\n cloned_self._parent = self._parent\n return cloned_self","label":0},{"cid":"google/cloud/datastore/key.py::Key.completed_key","text":"google/cloud/datastore/key.py::Key.completed_key\n def completed_key(self, id_or_name):\n \"\"\"Creates new key from existing partial key by adding final ID/name.\n\n :type id_or_name: string or integer\n :param id_or_name: ID or name to be added to the key.\n\n :rtype: :class:`google.cloud.datastore.key.Key`\n :returns: A new ``Key`` instance with the same data as the current one\n and an extra ID or name added.\n :raises: :class:`ValueError` if the current key is not partial or if\n ``id_or_name`` is not a string or integer.\n \"\"\"\n if not self.is_partial:\n raise ValueError('Only a partial key can be completed.')\n\n if isinstance(id_or_name, six.string_types):\n id_or_name_key = 'name'\n elif isinstance(id_or_name, six.integer_types):\n id_or_name_key = 'id'\n else:\n raise ValueError(id_or_name,\n 'ID/name was not a string or integer.')\n\n new_key = self._clone()\n new_key._path[-1][id_or_name_key] = id_or_name\n new_key._flat_path += (id_or_name,)\n return new_key","label":0},{"cid":"google/cloud/datastore/key.py::Key.to_protobuf","text":"google/cloud/datastore/key.py::Key.to_protobuf\n def to_protobuf(self):\n \"\"\"Return a protobuf corresponding to the key.\n\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Key`\n :returns: The protobuf representing the key.\n \"\"\"\n key = _entity_pb2.Key()\n key.partition_id.project_id = self.project\n\n if self.namespace:\n key.partition_id.namespace_id = self.namespace\n\n for item in self.path:\n element = key.path.add()\n if 'kind' in item:\n element.kind = item['kind']\n if 'id' in item:\n element.id = item['id']\n if 'name' in item:\n element.name = item['name']\n\n return key","label":0},{"cid":"google/cloud/datastore/key.py::Key.is_partial","text":"google/cloud/datastore/key.py::Key.is_partial\n @property\n def is_partial(self):\n \"\"\"Boolean indicating if the key has an ID (or name).\n\n :rtype: bool\n :returns: ``True`` if the last element of the key's path does not have\n an ``id`` or a ``name``.\n \"\"\"\n return self.id_or_name is None","label":0},{"cid":"google/cloud/datastore/key.py::Key.namespace","text":"google/cloud/datastore/key.py::Key.namespace\n @property\n def namespace(self):\n \"\"\"Namespace getter.\n\n :rtype: string\n :returns: The namespace of the current key.\n \"\"\"\n return self._namespace","label":0},{"cid":"google/cloud/datastore/key.py::Key.path","text":"google/cloud/datastore/key.py::Key.path\n @property\n def path(self):\n \"\"\"Path getter.\n\n Returns a copy so that the key remains immutable.\n\n :rtype: :class:`list` of :class:`dict`\n :returns: The (key) path of the current key.\n \"\"\"\n return copy.deepcopy(self._path)","label":0},{"cid":"google/cloud/datastore/key.py::Key.flat_path","text":"google/cloud/datastore/key.py::Key.flat_path\n @property\n def flat_path(self):\n \"\"\"Getter for the key path as a tuple.\n\n :rtype: tuple of string and integer\n :returns: The tuple of elements in the path.\n \"\"\"\n return self._flat_path","label":0},{"cid":"google/cloud/datastore/key.py::Key.kind","text":"google/cloud/datastore/key.py::Key.kind\n @property\n def kind(self):\n \"\"\"Kind getter. Based on the last element of path.\n\n :rtype: string\n :returns: The kind of the current key.\n \"\"\"\n return self.path[-1]['kind']","label":0},{"cid":"google/cloud/datastore/key.py::Key.id","text":"google/cloud/datastore/key.py::Key.id\n @property\n def id(self):\n \"\"\"ID getter. Based on the last element of path.\n\n :rtype: integer\n :returns: The (integer) ID of the key.\n \"\"\"\n return self.path[-1].get('id')","label":0},{"cid":"google/cloud/datastore/key.py::Key.name","text":"google/cloud/datastore/key.py::Key.name\n @property\n def name(self):\n \"\"\"Name getter. Based on the last element of path.\n\n :rtype: string\n :returns: The (string) name of the key.\n \"\"\"\n return self.path[-1].get('name')","label":0},{"cid":"google/cloud/datastore/key.py::Key.id_or_name","text":"google/cloud/datastore/key.py::Key.id_or_name\n @property\n def id_or_name(self):\n \"\"\"Getter. Based on the last element of path.\n\n :rtype: integer (if ``id``) or string (if ``name``)\n :returns: The last element of the key's path if it is either an ``id``\n or a ``name``.\n \"\"\"\n return self.id or self.name","label":0},{"cid":"google/cloud/datastore/key.py::Key.project","text":"google/cloud/datastore/key.py::Key.project\n @property\n def project(self):\n \"\"\"Project getter.\n\n :rtype: string\n :returns: The key's project.\n \"\"\"\n return self._project","label":0},{"cid":"google/cloud/datastore/key.py::Key._make_parent","text":"google/cloud/datastore/key.py::Key._make_parent\n def _make_parent(self):\n \"\"\"Creates a parent key for the current path.\n\n Extracts all but the last element in the key path and creates a new\n key, while still matching the namespace and the project.\n\n :rtype: :class:`google.cloud.datastore.key.Key` or :class:`NoneType`\n :returns: A new ``Key`` instance, whose path consists of all but the\n last element of current path. If the current key has only\n one path element, returns ``None``.\n \"\"\"\n if self.is_partial:\n parent_args = self.flat_path[:-1]\n else:\n parent_args = self.flat_path[:-2]\n if parent_args:\n return self.__class__(*parent_args, project=self.project,\n namespace=self.namespace)","label":0},{"cid":"google/cloud/datastore/key.py::Key.parent","text":"google/cloud/datastore/key.py::Key.parent\n @property\n def parent(self):\n \"\"\"The parent of the current key.\n\n :rtype: :class:`google.cloud.datastore.key.Key` or :class:`NoneType`\n :returns: A new ``Key`` instance, whose path consists of all but the\n last element of current path. If the current key has only\n one path element, returns ``None``.\n \"\"\"\n if self._parent is None:\n self._parent = self._make_parent()\n\n return self._parent","label":0},{"cid":"google/cloud/datastore/key.py::Key.__repr__","text":"google/cloud/datastore/key.py::Key.__repr__\n def __repr__(self):\n return '<Key%s, project=%s>' % (self.path, self.project)","label":0},{"cid":"google/cloud/datastore/key.py::_validate_project","text":"google/cloud/datastore/key.py::_validate_project\ndef _validate_project(project, parent):\n \"\"\"Ensure the project is set appropriately.\n\n If ``parent`` is passed, skip the test (it will be checked / fixed up\n later).\n\n If ``project`` is unset, attempt to infer the project from the environment.\n\n :type project: string\n :param project: A project.\n\n :type parent: :class:`google.cloud.datastore.key.Key` or ``NoneType``\n :param parent: The parent of the key or ``None``.\n\n :rtype: string\n :returns: The ``project`` passed in, or implied from the environment.\n :raises: :class:`ValueError` if ``project`` is ``None`` and no project\n can be inferred from the parent.\n \"\"\"\n if parent is None:\n if project is None:\n raise ValueError(\"A Key must have a project set.\")\n\n return project","label":0},{"cid":"google/cloud/datastore/query.py::Query.__init__","text":"google/cloud/datastore/query.py::Query.__init__\n def __init__(self,\n client,\n kind=None,\n project=None,\n namespace=None,\n ancestor=None,\n filters=(),\n projection=(),\n order=(),\n distinct_on=()):\n\n self._client = client\n self._kind = kind\n self._project = project or client.project\n self._namespace = namespace or client.namespace\n self._ancestor = ancestor\n self._filters = []\n # Verify filters passed in.\n for property_name, operator, value in filters:\n self.add_filter(property_name, operator, value)\n self._projection = _ensure_tuple_or_list('projection', projection)\n self._order = _ensure_tuple_or_list('order', order)\n self._distinct_on = _ensure_tuple_or_list('distinct_on', distinct_on)","label":0},{"cid":"google/cloud/datastore/query.py::Query.project","text":"google/cloud/datastore/query.py::Query.project\n @property\n def project(self):\n \"\"\"Get the project for this Query.\n\n :rtype: str\n :returns: The project for the query.\n \"\"\"\n return self._project or self._client.project","label":0},{"cid":"google/cloud/datastore/query.py::Query.namespace","text":"google/cloud/datastore/query.py::Query.namespace\n @property\n def namespace(self):\n \"\"\"This query's namespace\n\n :rtype: string or None\n :returns: the namespace assigned to this query\n \"\"\"\n return self._namespace or self._client.namespace","label":0},{"cid":"google/cloud/datastore/query.py::Query.namespace","text":"google/cloud/datastore/query.py::Query.namespace\n @namespace.setter\n def namespace(self, value):\n \"\"\"Update the query's namespace.\n\n :type value: string\n \"\"\"\n if not isinstance(value, str):\n raise ValueError(\"Namespace must be a string\")\n self._namespace = value","label":0},{"cid":"google/cloud/datastore/query.py::Query.kind","text":"google/cloud/datastore/query.py::Query.kind\n @property\n def kind(self):\n \"\"\"Get the Kind of the Query.\n\n :rtype: string\n :returns: The kind for the query.\n \"\"\"\n return self._kind","label":0},{"cid":"google/cloud/datastore/query.py::Query.kind","text":"google/cloud/datastore/query.py::Query.kind\n @kind.setter\n def kind(self, value):\n \"\"\"Update the Kind of the Query.\n\n :type value: string\n :param value: updated kind for the query.\n\n .. note::\n\n The protobuf specification allows for ``kind`` to be repeated,\n but the current implementation returns an error if more than\n one value is passed. If the back-end changes in the future to\n allow multiple values, this method will be updated to allow passing\n either a string or a sequence of strings.\n \"\"\"\n if not isinstance(value, str):\n raise TypeError(\"Kind must be a string\")\n self._kind = value","label":0},{"cid":"google/cloud/datastore/query.py::Query.ancestor","text":"google/cloud/datastore/query.py::Query.ancestor\n @property\n def ancestor(self):\n \"\"\"The ancestor key for the query.\n\n :rtype: Key or None\n :returns: The ancestor for the query.\n \"\"\"\n return self._ancestor","label":0},{"cid":"google/cloud/datastore/query.py::Query.ancestor","text":"google/cloud/datastore/query.py::Query.ancestor\n @ancestor.setter\n def ancestor(self, value):\n \"\"\"Set the ancestor for the query\n\n :type value: Key\n :param value: the new ancestor key\n \"\"\"\n if not isinstance(value, Key):\n raise TypeError(\"Ancestor must be a Key\")\n self._ancestor = value","label":0},{"cid":"google/cloud/datastore/query.py::Query.ancestor","text":"google/cloud/datastore/query.py::Query.ancestor\n @ancestor.deleter\n def ancestor(self):\n \"\"\"Remove the ancestor for the query.\"\"\"\n self._ancestor = None","label":0},{"cid":"google/cloud/datastore/query.py::Query.filters","text":"google/cloud/datastore/query.py::Query.filters\n @property\n def filters(self):\n \"\"\"Filters set on the query.\n\n :rtype: sequence of (property_name, operator, value) tuples.\n :returns: The filters set on the query.\n \"\"\"\n return self._filters[:]","label":0},{"cid":"google/cloud/datastore/query.py::Query.add_filter","text":"google/cloud/datastore/query.py::Query.add_filter\n def add_filter(self, property_name, operator, value):\n \"\"\"Filter the query based on a property name, operator and a value.\n\n Expressions take the form of::\n\n .add_filter('<property>', '<operator>', <value>)\n\n where property is a property stored on the entity in the datastore\n and operator is one of ``OPERATORS``\n (ie, ``=``, ``<``, ``<=``, ``>``, ``>=``)::\n\n >>> from google.cloud import datastore\n >>> client = datastore.Client()\n >>> query = client.query(kind='Person')\n >>> query.add_filter('name', '=', 'James')\n >>> query.add_filter('age', '>', 50)\n\n :type property_name: string\n :param property_name: A property name.\n\n :type operator: string\n :param operator: One of ``=``, ``<``, ``<=``, ``>``, ``>=``.\n\n :type value: :class:`int`, :class:`str`, :class:`bool`,\n :class:`float`, :class:`NoneType`,\n :class:`datetime.datetime`,\n :class:`google.cloud.datastore.key.Key`\n :param value: The value to filter on.\n\n :raises: :class:`ValueError` if ``operation`` is not one of the\n specified values, or if a filter names ``'__key__'`` but\n passes an invalid value (a key is required).\n \"\"\"\n if self.OPERATORS.get(operator) is None:\n error_message = 'Invalid expression: \"%s\"' % (operator,)\n choices_message = 'Please use one of: =, <, <=, >, >=.'\n raise ValueError(error_message, choices_message)\n\n if property_name == '__key__' and not isinstance(value, Key):\n raise ValueError('Invalid key: \"%s\"' % value)\n\n self._filters.append((property_name, operator, value))","label":0},{"cid":"google/cloud/datastore/query.py::Query.projection","text":"google/cloud/datastore/query.py::Query.projection\n @property\n def projection(self):\n \"\"\"Fields names returned by the query.\n\n :rtype: sequence of string\n :returns: Names of fields in query results.\n \"\"\"\n return self._projection[:]","label":0},{"cid":"google/cloud/datastore/query.py::Query.projection","text":"google/cloud/datastore/query.py::Query.projection\n @projection.setter\n def projection(self, projection):\n \"\"\"Set the fields returned the query.\n\n :type projection: string or sequence of strings\n :param projection: Each value is a string giving the name of a\n property to be included in the projection query.\n \"\"\"\n if isinstance(projection, str):\n projection = [projection]\n self._projection[:] = projection","label":0},{"cid":"google/cloud/datastore/query.py::Query.keys_only","text":"google/cloud/datastore/query.py::Query.keys_only\n def keys_only(self):\n \"\"\"Set the projection to include only keys.\"\"\"\n self._projection[:] = ['__key__']","label":0},{"cid":"google/cloud/datastore/query.py::Query.key_filter","text":"google/cloud/datastore/query.py::Query.key_filter\n def key_filter(self, key, operator='='):\n \"\"\"Filter on a key.\n\n :type key: :class:`google.cloud.datastore.key.Key`\n :param key: The key to filter on.\n\n :type operator: string\n :param operator: (Optional) One of ``=``, ``<``, ``<=``, ``>``, ``>=``.\n Defaults to ``=``.\n \"\"\"\n self.add_filter('__key__', operator, key)","label":0},{"cid":"google/cloud/datastore/query.py::Query.order","text":"google/cloud/datastore/query.py::Query.order\n @property\n def order(self):\n \"\"\"Names of fields used to sort query results.\n\n :rtype: sequence of string\n :returns: The order(s) set on the query.\n \"\"\"\n return self._order[:]","label":0},{"cid":"google/cloud/datastore/query.py::Query.order","text":"google/cloud/datastore/query.py::Query.order\n @order.setter\n def order(self, value):\n \"\"\"Set the fields used to sort query results.\n\n Sort fields will be applied in the order specified.\n\n :type value: string or sequence of strings\n :param value: Each value is a string giving the name of the\n property on which to sort, optionally preceded by a\n hyphen (-) to specify descending order.\n Omitting the hyphen implies ascending order.\n \"\"\"\n if isinstance(value, str):\n value = [value]\n self._order[:] = value","label":0},{"cid":"google/cloud/datastore/query.py::Query.distinct_on","text":"google/cloud/datastore/query.py::Query.distinct_on\n @property\n def distinct_on(self):\n \"\"\"Names of fields used to group query results.\n\n :rtype: sequence of string\n :returns: The \"distinct on\" fields set on the query.\n \"\"\"\n return self._distinct_on[:]","label":0},{"cid":"google/cloud/datastore/query.py::Query.distinct_on","text":"google/cloud/datastore/query.py::Query.distinct_on\n @distinct_on.setter\n def distinct_on(self, value):\n \"\"\"Set fields used to group query results.\n\n :type value: string or sequence of strings\n :param value: Each value is a string giving the name of a\n property to use to group results together.\n \"\"\"\n if isinstance(value, str):\n value = [value]\n self._distinct_on[:] = value","label":0},{"cid":"google/cloud/datastore/query.py::Query.fetch","text":"google/cloud/datastore/query.py::Query.fetch\n def fetch(self, limit=None, offset=0, start_cursor=None, end_cursor=None,\n client=None):\n \"\"\"Execute the Query; return an iterator for the matching entities.\n\n For example::\n\n >>> from google.cloud import datastore\n >>> client = datastore.Client()\n >>> query = client.query(kind='Person')\n >>> query.add_filter('name', '=', 'Sally')\n >>> list(query.fetch())\n [<Entity object>, <Entity object>, ...]\n >>> list(query.fetch(1))\n [<Entity object>]\n\n :type limit: integer or None\n :param limit: An optional limit passed through to the iterator.\n\n :type offset: integer\n :param offset: An optional offset passed through to the iterator.\n\n :type start_cursor: bytes\n :param start_cursor: An optional cursor passed through to the iterator.\n\n :type end_cursor: bytes\n :param end_cursor: An optional cursor passed through to the iterator.\n\n :type client: :class:`google.cloud.datastore.client.Client`\n :param client: client used to connect to datastore.\n If not supplied, uses the query's value.\n\n :rtype: :class:`Iterator`\n :returns: The iterator for the query.\n :raises: ValueError if ``connection`` is not passed and no implicit\n default has been set.\n \"\"\"\n if client is None:\n client = self._client\n\n return Iterator(\n self, client, limit, offset, start_cursor, end_cursor)","label":0},{"cid":"google/cloud/datastore/query.py::Iterator.__init__","text":"google/cloud/datastore/query.py::Iterator.__init__\n def __init__(self, query, client, limit=None, offset=None,\n start_cursor=None, end_cursor=None):\n self._query = query\n self._client = client\n self._limit = limit\n self._offset = offset\n self._start_cursor = start_cursor\n self._end_cursor = end_cursor\n self._page = self._more_results = None\n self._skipped_results = None","label":0},{"cid":"google/cloud/datastore/query.py::Iterator.next_page","text":"google/cloud/datastore/query.py::Iterator.next_page\n def next_page(self):\n \"\"\"Fetch a single \"page\" of query results.\n\n Low-level API for fine control: the more convenient API is\n to iterate on the current Iterator.\n\n :rtype: tuple, (entities, more_results, cursor)\n :returns: The next page of results.\n \"\"\"\n pb = _pb_from_query(self._query)\n\n start_cursor = self._start_cursor\n if start_cursor is not None:\n pb.start_cursor = base64.urlsafe_b64decode(start_cursor)\n\n end_cursor = self._end_cursor\n if end_cursor is not None:\n pb.end_cursor = base64.urlsafe_b64decode(end_cursor)\n\n if self._limit is not None:\n pb.limit.value = self._limit\n\n if self._offset is not None:\n pb.offset = self._offset\n\n transaction = self._client.current_transaction\n\n query_results = self._client.connection.run_query(\n query_pb=pb,\n project=self._query.project,\n namespace=self._query.namespace,\n transaction_id=transaction and transaction.id,\n )\n (entity_pbs, cursor_as_bytes,\n more_results_enum, self._skipped_results) = query_results\n\n if cursor_as_bytes == b'':\n self._start_cursor = None\n else:\n self._start_cursor = base64.urlsafe_b64encode(cursor_as_bytes)\n self._end_cursor = None\n\n if more_results_enum == self._NOT_FINISHED:\n self._more_results = True\n elif more_results_enum in self._FINISHED:\n self._more_results = False\n else:\n raise ValueError('Unexpected value returned for `more_results`.')\n\n self._page = [\n helpers.entity_from_protobuf(entity)\n for entity in entity_pbs]\n return self._page, self._more_results, self._start_cursor","label":0},{"cid":"google/cloud/datastore/query.py::Iterator.__iter__","text":"google/cloud/datastore/query.py::Iterator.__iter__\n def __iter__(self):\n \"\"\"Generator yielding all results matching our query.\n\n :rtype: sequence of :class:`google.cloud.datastore.entity.Entity`\n \"\"\"\n while True:\n self.next_page()\n for entity in self._page:\n yield entity\n if not self._more_results:\n break\n num_results = len(self._page)\n if self._limit is not None:\n self._limit -= num_results\n if self._offset is not None and self._skipped_results is not None:\n # NOTE: The offset goes down relative to the location\n # because we are updating the cursor each time.\n self._offset -= self._skipped_results","label":0},{"cid":"google/cloud/datastore/query.py::_pb_from_query","text":"google/cloud/datastore/query.py::_pb_from_query\ndef _pb_from_query(query):\n \"\"\"Convert a Query instance to the corresponding protobuf.\n\n :type query: :class:`Query`\n :param query: The source query.\n\n :rtype: :class:`google.cloud.datastore._generated.query_pb2.Query`\n :returns: A protobuf that can be sent to the protobuf API. N.b. that\n it does not contain \"in-flight\" fields for ongoing query\n executions (cursors, offset, limit).\n \"\"\"\n pb = _query_pb2.Query()\n\n for projection_name in query.projection:\n pb.projection.add().property.name = projection_name\n\n if query.kind:\n pb.kind.add().name = query.kind\n\n composite_filter = pb.filter.composite_filter\n composite_filter.op = _query_pb2.CompositeFilter.AND\n\n if query.ancestor:\n ancestor_pb = query.ancestor.to_protobuf()\n\n # Filter on __key__ HAS_ANCESTOR == ancestor.\n ancestor_filter = composite_filter.filters.add().property_filter\n ancestor_filter.property.name = '__key__'\n ancestor_filter.op = _query_pb2.PropertyFilter.HAS_ANCESTOR\n ancestor_filter.value.key_value.CopyFrom(ancestor_pb)\n\n for property_name, operator, value in query.filters:\n pb_op_enum = query.OPERATORS.get(operator)\n\n # Add the specific filter\n property_filter = composite_filter.filters.add().property_filter\n property_filter.property.name = property_name\n property_filter.op = pb_op_enum\n\n # Set the value to filter on based on the type.\n if property_name == '__key__':\n key_pb = value.to_protobuf()\n property_filter.value.key_value.CopyFrom(key_pb)\n else:\n helpers._set_protobuf_value(property_filter.value, value)\n\n if not composite_filter.filters:\n pb.ClearField('filter')\n\n for prop in query.order:\n property_order = pb.order.add()\n\n if prop.startswith('-'):\n property_order.property.name = prop[1:]\n p","label":0},{"cid":"google/cloud/datastore/transaction.py::Transaction.__init__","text":"google/cloud/datastore/transaction.py::Transaction.__init__\n def __init__(self, client):\n super(Transaction, self).__init__(client)\n self._id = None","label":0},{"cid":"google/cloud/datastore/transaction.py::Transaction.id","text":"google/cloud/datastore/transaction.py::Transaction.id\n @property\n def id(self):\n \"\"\"Getter for the transaction ID.\n\n :rtype: string\n :returns: The ID of the current transaction.\n \"\"\"\n return self._id","label":0},{"cid":"google/cloud/datastore/transaction.py::Transaction.current","text":"google/cloud/datastore/transaction.py::Transaction.current\n def current(self):\n \"\"\"Return the topmost transaction.\n\n .. note::\n\n If the topmost element on the stack is not a transaction,\n returns None.\n\n :rtype: :class:`google.cloud.datastore.transaction.Transaction` or None\n :returns: The current transaction (if any are active).\n \"\"\"\n top = super(Transaction, self).current()\n if isinstance(top, Transaction):\n return top","label":0},{"cid":"google/cloud/datastore/transaction.py::Transaction.begin","text":"google/cloud/datastore/transaction.py::Transaction.begin\n def begin(self):\n \"\"\"Begins a transaction.\n\n This method is called automatically when entering a with\n statement, however it can be called explicitly if you don't want\n to use a context manager.\n\n :raises: :class:`~exceptions.ValueError` if the transaction has\n already begun.\n \"\"\"\n super(Transaction, self).begin()\n try:\n self._id = self.connection.begin_transaction(self.project)\n except:\n self._status = self._ABORTED\n raise","label":0},{"cid":"google/cloud/datastore/transaction.py::Transaction.rollback","text":"google/cloud/datastore/transaction.py::Transaction.rollback\n def rollback(self):\n \"\"\"Rolls back the current transaction.\n\n This method has necessary side-effects:\n\n - Sets the current connection's transaction reference to None.\n - Sets the current transaction's ID to None.\n \"\"\"\n try:\n self.connection.rollback(self.project, self._id)\n finally:\n super(Transaction, self).rollback()\n # Clear our own ID in case this gets accidentally reused.\n self._id = None","label":0},{"cid":"google/cloud/datastore/transaction.py::Transaction.commit","text":"google/cloud/datastore/transaction.py::Transaction.commit\n def commit(self):\n \"\"\"Commits the transaction.\n\n This is called automatically upon exiting a with statement,\n however it can be called explicitly if you don't want to use a\n context manager.\n\n This method has necessary side-effects:\n\n - Sets the current transaction's ID to None.\n \"\"\"\n try:\n super(Transaction, self).commit()\n finally:\n # Clear our own ID in case this gets accidentally reused.\n self._id = None","label":0},{"cid":"google/cloud/dns/changes.py::Changes.__init__","text":"google/cloud/dns/changes.py::Changes.__init__\n def __init__(self, zone):\n self.zone = zone\n self._properties = {}\n self._additions = self._deletions = ()","label":0},{"cid":"google/cloud/dns/changes.py::Changes.from_api_repr","text":"google/cloud/dns/changes.py::Changes.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, zone):\n \"\"\"Factory: construct a change set given its API representation\n\n :type resource: dict\n :param resource: change set representation returned from the API.\n\n :type zone: :class:`google.cloud.dns.zone.ManagedZone`\n :param zone: A zone which holds zero or more change sets.\n\n :rtype: :class:`google.cloud.dns.changes.Changes`\n :returns: RRS parsed from ``resource``.\n \"\"\"\n changes = cls(zone=zone)\n changes._set_properties(resource)\n return changes","label":0},{"cid":"google/cloud/dns/changes.py::Changes._set_properties","text":"google/cloud/dns/changes.py::Changes._set_properties\n def _set_properties(self, resource):\n \"\"\"Helper method for :meth:`from_api_repr`, :meth:`create`, etc.\n\n :type resource: dict\n :param resource: change set representation returned from the API.\n \"\"\"\n resource = resource.copy()\n self._additions = tuple([\n ResourceRecordSet.from_api_repr(added_res, self.zone)\n for added_res in resource.pop('additions', ())])\n self._deletions = tuple([\n ResourceRecordSet.from_api_repr(added_res, self.zone)\n for added_res in resource.pop('deletions', ())])\n self._properties = resource","label":0},{"cid":"google/cloud/dns/changes.py::Changes.path","text":"google/cloud/dns/changes.py::Changes.path\n @property\n def path(self):\n \"\"\"URL path for change set APIs.\n\n :rtype: string\n :returns: the path based on project, zone, and change set names.\n \"\"\"\n return '/projects/%s/managedZones/%s/changes/%s' % (\n self.zone.project, self.zone.name, self.name)","label":0},{"cid":"google/cloud/dns/changes.py::Changes.name","text":"google/cloud/dns/changes.py::Changes.name\n @property\n def name(self):\n \"\"\"Name of the change set.\n\n :rtype: string or ``NoneType``\n :returns: Name, as set by the back-end, or None.\n \"\"\"\n return self._properties.get('id')","label":0},{"cid":"google/cloud/dns/changes.py::Changes.name","text":"google/cloud/dns/changes.py::Changes.name\n @name.setter\n def name(self, value):\n \"\"\"Update name of the change set.\n\n :type value: string\n :param value: New name for the changeset.\n \"\"\"\n if not isinstance(value, six.string_types):\n raise ValueError(\"Pass a string\")\n self._properties['id'] = value","label":0},{"cid":"google/cloud/dns/changes.py::Changes.status","text":"google/cloud/dns/changes.py::Changes.status\n @property\n def status(self):\n \"\"\"Status of the change set.\n\n :rtype: string or ``NoneType``\n :returns: Status, as set by the back-end, or None.\n \"\"\"\n return self._properties.get('status')","label":0},{"cid":"google/cloud/dns/changes.py::Changes.started","text":"google/cloud/dns/changes.py::Changes.started\n @property\n def started(self):\n \"\"\"Time when the change set was started.\n\n :rtype: ``datetime.datetime`` or ``NoneType``\n :returns: Time, as set by the back-end, or None.\n \"\"\"\n stamp = self._properties.get('startTime')\n if stamp is not None:\n return _rfc3339_to_datetime(stamp)","label":0},{"cid":"google/cloud/dns/changes.py::Changes.additions","text":"google/cloud/dns/changes.py::Changes.additions\n @property\n def additions(self):\n \"\"\"Resource record sets to be added to the zone.\n\n :rtype: sequence of\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`.\n :returns: record sets appended via :meth:`add_record_set`.\n \"\"\"\n return self._additions","label":0},{"cid":"google/cloud/dns/changes.py::Changes.deletions","text":"google/cloud/dns/changes.py::Changes.deletions\n @property\n def deletions(self):\n \"\"\"Resource record sets to be deleted from the zone.\n\n :rtype: sequence of\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`.\n :returns: record sets appended via :meth:`delete_record_set`.\n \"\"\"\n return self._deletions","label":0},{"cid":"google/cloud/dns/changes.py::Changes.add_record_set","text":"google/cloud/dns/changes.py::Changes.add_record_set\n def add_record_set(self, record_set):\n \"\"\"Append a record set to the 'additions' for the change set.\n\n :type record_set:\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`\n :param record_set: the record set to append.\n\n :raises: ``ValueError`` if ``record_set`` is not of the required type.\n \"\"\"\n if not isinstance(record_set, ResourceRecordSet):\n raise ValueError(\"Pass a ResourceRecordSet\")\n self._additions += (record_set,)","label":0},{"cid":"google/cloud/dns/changes.py::Changes.delete_record_set","text":"google/cloud/dns/changes.py::Changes.delete_record_set\n def delete_record_set(self, record_set):\n \"\"\"Append a record set to the 'deletions' for the change set.\n\n :type record_set:\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`\n :param record_set: the record set to append.\n\n :raises: ``ValueError`` if ``record_set`` is not of the required type.\n \"\"\"\n if not isinstance(record_set, ResourceRecordSet):\n raise ValueError(\"Pass a ResourceRecordSet\")\n self._deletions += (record_set,)","label":0},{"cid":"google/cloud/dns/changes.py::Changes._require_client","text":"google/cloud/dns/changes.py::Changes._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current zone.\n\n :rtype: :class:`google.cloud.dns.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self.zone._client\n return client","label":0},{"cid":"google/cloud/dns/changes.py::Changes._build_resource","text":"google/cloud/dns/changes.py::Changes._build_resource\n def _build_resource(self):\n \"\"\"Generate a resource for ``create``.\"\"\"\n additions = [{\n 'name': added.name,\n 'type': added.record_type,\n 'ttl': str(added.ttl),\n 'rrdatas': added.rrdatas,\n } for added in self.additions]\n\n deletions = [{\n 'name': deleted.name,\n 'type': deleted.record_type,\n 'ttl': str(deleted.ttl),\n 'rrdatas': deleted.rrdatas,\n } for deleted in self.deletions]\n\n return {\n 'additions': additions,\n 'deletions': deletions,\n }","label":0},{"cid":"google/cloud/dns/changes.py::Changes.create","text":"google/cloud/dns/changes.py::Changes.create\n def create(self, client=None):\n \"\"\"API call: create the change set via a POST request.\n\n See:\n https://cloud.google.com/dns/api/v1/changes/create\n\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current zone.\n \"\"\"\n if len(self.additions) == 0 and len(self.deletions) == 0:\n raise ValueError(\"No record sets added or deleted\")\n client = self._require_client(client)\n path = '/projects/%s/managedZones/%s/changes' % (\n self.zone.project, self.zone.name)\n api_response = client.connection.api_request(\n method='POST', path=path, data=self._build_resource())\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/dns/changes.py::Changes.exists","text":"google/cloud/dns/changes.py::Changes.exists\n def exists(self, client=None):\n \"\"\"API call: test for the existence of the change set via a GET request.\n\n See\n https://cloud.google.com/dns/api/v1/changes/get\n\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current zone.\n\n :rtype: bool\n :returns: Boolean indicating existence of the changes.\n \"\"\"\n client = self._require_client(client)\n try:\n client.connection.api_request(method='GET', path=self.path,\n query_params={'fields': 'id'})\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"google/cloud/dns/changes.py::Changes.reload","text":"google/cloud/dns/changes.py::Changes.reload\n def reload(self, client=None):\n \"\"\"API call: refresh zone properties via a GET request.\n\n See\n https://cloud.google.com/dns/api/v1/changes/get\n\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current zone.\n \"\"\"\n client = self._require_client(client)\n\n api_response = client.connection.api_request(\n method='GET', path=self.path)\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/dns/client.py::Client.quotas","text":"google/cloud/dns/client.py::Client.quotas\n def quotas(self):\n \"\"\"Return DNS quotas for the project associated with this client.\n\n See:\n https://cloud.google.com/dns/api/v1/projects/get\n\n :rtype: mapping\n :returns: keys for the mapping correspond to those of the ``quota``\n sub-mapping of the project resource.\n \"\"\"\n path = '/projects/%s' % (self.project,)\n resp = self.connection.api_request(method='GET', path=path)\n\n return {key: int(value)\n for key, value in resp['quota'].items()\n if key != 'kind'}","label":0},{"cid":"google/cloud/dns/client.py::Client.list_zones","text":"google/cloud/dns/client.py::Client.list_zones\n def list_zones(self, max_results=None, page_token=None):\n \"\"\"List zones for the project associated with this client.\n\n See:\n https://cloud.google.com/dns/api/v1/managedZones/list\n\n :type max_results: int\n :param max_results: maximum number of zones to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of zones. If\n not passed, the API will return the first page of\n zones.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`google.cloud.dns.zone.ManagedZone`, plus a\n \"next page token\" string: if the token is not None,\n indicates that more zones can be retrieved with another\n call (pass that value as ``page_token``).\n \"\"\"\n params = {}\n\n if max_results is not None:\n params['maxResults'] = max_results\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/managedZones' % (self.project,)\n resp = self.connection.api_request(method='GET', path=path,\n query_params=params)\n zones = [ManagedZone.from_api_repr(resource, self)\n for resource in resp['managedZones']]\n return zones, resp.get('nextPageToken')","label":0},{"cid":"google/cloud/dns/client.py::Client.zone","text":"google/cloud/dns/client.py::Client.zone\n def zone(self, name, dns_name=None, description=None):\n \"\"\"Construct a zone bound to this client.\n\n :type name: string\n :param name: Name of the zone.\n\n :type dns_name: string or :class:`NoneType`\n :param dns_name: DNS name of the zone. If not passed, then calls\n to :meth:`zone.create` will fail.\n\n :type description: string or :class:`NoneType`\n :param description: the description for the zone. If not passed,\n defaults to the value of 'dns_name'.\n\n :rtype: :class:`google.cloud.dns.zone.ManagedZone`\n :returns: a new ``ManagedZone`` instance.\n \"\"\"\n return ManagedZone(name, dns_name, client=self,\n description=description)","label":0},{"cid":"google/cloud/dns/resource_record_set.py::ResourceRecordSet.__init__","text":"google/cloud/dns/resource_record_set.py::ResourceRecordSet.__init__\n def __init__(self, name, record_type, ttl, rrdatas, zone):\n self.name = name\n self.record_type = record_type\n self.ttl = ttl\n self.rrdatas = rrdatas\n self.zone = zone","label":0},{"cid":"google/cloud/dns/resource_record_set.py::ResourceRecordSet.from_api_repr","text":"google/cloud/dns/resource_record_set.py::ResourceRecordSet.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, zone):\n \"\"\"Factory: construct a record set given its API representation\n\n :type resource: dict\n :param resource: record sets representation returned from the API\n\n :type zone: :class:`google.cloud.dns.zone.ManagedZone`\n :param zone: A zone which holds one or more record sets.\n\n :rtype: :class:`google.cloud.dns.zone.ResourceRecordSet`\n :returns: RRS parsed from ``resource``.\n \"\"\"\n name = resource['name']\n record_type = resource['type']\n ttl = int(resource['ttl'])\n rrdatas = resource['rrdatas']\n return cls(name, record_type, ttl, rrdatas, zone=zone)","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.__init__","text":"google/cloud/dns/zone.py::ManagedZone.__init__\n def __init__(self, name, dns_name=None, client=None, description=None):\n self.name = name\n self.dns_name = dns_name\n self._client = client\n self._properties = {}\n if description is None:\n description = dns_name\n self.description = description","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.from_api_repr","text":"google/cloud/dns/zone.py::ManagedZone.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client):\n \"\"\"Factory: construct a zone given its API representation\n\n :type resource: dict\n :param resource: zone resource representation returned from the API\n\n :type client: :class:`google.cloud.dns.client.Client`\n :param client: Client which holds credentials and project\n configuration for the zone.\n\n :rtype: :class:`google.cloud.dns.zone.ManagedZone`\n :returns: Zone parsed from ``resource``.\n \"\"\"\n name = resource.get('name')\n dns_name = resource.get('dnsName')\n if name is None or dns_name is None:\n raise KeyError('Resource lacks required identity information:'\n '[\"name\"][\"dnsName\"]')\n zone = cls(name, dns_name, client=client)\n zone._set_properties(resource)\n return zone","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.project","text":"google/cloud/dns/zone.py::ManagedZone.project\n @property\n def project(self):\n \"\"\"Project bound to the zone.\n\n :rtype: string\n :returns: the project (derived from the client).\n \"\"\"\n return self._client.project","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.path","text":"google/cloud/dns/zone.py::ManagedZone.path\n @property\n def path(self):\n \"\"\"URL path for the zone's APIs.\n\n :rtype: string\n :returns: the path based on project and dataste name.\n \"\"\"\n return '/projects/%s/managedZones/%s' % (self.project, self.name)","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.created","text":"google/cloud/dns/zone.py::ManagedZone.created\n @property\n def created(self):\n \"\"\"Datetime at which the zone was created.\n\n :rtype: ``datetime.datetime``, or ``NoneType``\n :returns: the creation time (None until set from the server).\n \"\"\"\n return self._properties.get('creationTime')","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.name_servers","text":"google/cloud/dns/zone.py::ManagedZone.name_servers\n @property\n def name_servers(self):\n \"\"\"Datetime at which the zone was created.\n\n :rtype: list of strings, or ``NoneType``.\n :returns: the assigned name servers (None until set from the server).\n \"\"\"\n return self._properties.get('nameServers')","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.zone_id","text":"google/cloud/dns/zone.py::ManagedZone.zone_id\n @property\n def zone_id(self):\n \"\"\"ID for the zone resource.\n\n :rtype: string, or ``NoneType``\n :returns: the ID (None until set from the server).\n \"\"\"\n return self._properties.get('id')","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.description","text":"google/cloud/dns/zone.py::ManagedZone.description\n @property\n def description(self):\n \"\"\"Description of the zone.\n\n :rtype: string, or ``NoneType``\n :returns: The description as set by the user, or None (the default).\n \"\"\"\n return self._properties.get('description')","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.description","text":"google/cloud/dns/zone.py::ManagedZone.description\n @description.setter\n def description(self, value):\n \"\"\"Update description of the zone.\n\n :type value: string, or ``NoneType``\n :param value: new description\n\n :raises: ValueError for invalid value types.\n \"\"\"\n if not isinstance(value, six.string_types) and value is not None:\n raise ValueError(\"Pass a string, or None\")\n self._properties['description'] = value","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.name_server_set","text":"google/cloud/dns/zone.py::ManagedZone.name_server_set\n @property\n def name_server_set(self):\n \"\"\"Named set of DNS name servers that all host the same ManagedZones.\n\n Most users will leave this blank.\n\n See:\n https://cloud.google.com/dns/api/v1/managedZones#nameServerSet\n\n :rtype: string, or ``NoneType``\n :returns: The name as set by the user, or None (the default).\n \"\"\"\n return self._properties.get('nameServerSet')","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.name_server_set","text":"google/cloud/dns/zone.py::ManagedZone.name_server_set\n @name_server_set.setter\n def name_server_set(self, value):\n \"\"\"Update named set of DNS name servers.\n\n :type value: string, or ``NoneType``\n :param value: new title\n\n :raises: ValueError for invalid value types.\n \"\"\"\n if not isinstance(value, six.string_types) and value is not None:\n raise ValueError(\"Pass a string, or None\")\n self._properties['nameServerSet'] = value","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.resource_record_set","text":"google/cloud/dns/zone.py::ManagedZone.resource_record_set\n def resource_record_set(self, name, record_type, ttl, rrdatas):\n \"\"\"Construct a resource record set bound to this zone.\n\n :type name: string\n :param name: Name of the record set.\n\n :type record_type: string\n :param record_type: RR type\n\n :type ttl: integer\n :param ttl: TTL for the RR, in seconds\n\n :type rrdatas: list of string\n :param rrdatas: resource data for the RR\n\n :rtype: :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`\n :returns: a new ``ResourceRecordSet`` instance\n \"\"\"\n return ResourceRecordSet(name, record_type, ttl, rrdatas, zone=self)","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.changes","text":"google/cloud/dns/zone.py::ManagedZone.changes\n def changes(self):\n \"\"\"Construct a change set bound to this zone.\n\n :rtype: :class:`google.cloud.dns.changes.Changes`\n :returns: a new ``Changes`` instance\n \"\"\"\n return Changes(zone=self)","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone._require_client","text":"google/cloud/dns/zone.py::ManagedZone._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current zone.\n\n :rtype: :class:`google.cloud.dns.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._client\n return client","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone._set_properties","text":"google/cloud/dns/zone.py::ManagedZone._set_properties\n def _set_properties(self, api_response):\n \"\"\"Update properties from resource in body of ``api_response``\n\n :type api_response: httplib2.Response\n :param api_response: response returned from an API call\n \"\"\"\n self._properties.clear()\n cleaned = api_response.copy()\n self.dns_name = cleaned.pop('dnsName', None)\n if 'creationTime' in cleaned:\n cleaned['creationTime'] = _rfc3339_to_datetime(\n cleaned['creationTime'])\n self._properties.update(cleaned)","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone._build_resource","text":"google/cloud/dns/zone.py::ManagedZone._build_resource\n def _build_resource(self):\n \"\"\"Generate a resource for ``create`` or ``update``.\"\"\"\n resource = {\n 'name': self.name,\n }\n\n if self.dns_name is not None:\n resource['dnsName'] = self.dns_name\n\n if self.description is not None:\n resource['description'] = self.description\n\n if self.name_server_set is not None:\n resource['nameServerSet'] = self.name_server_set\n\n return resource","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.create","text":"google/cloud/dns/zone.py::ManagedZone.create\n def create(self, client=None):\n \"\"\"API call: create the zone via a PUT request\n\n See:\n https://cloud.google.com/dns/api/v1/managedZones/create\n\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current zone.\n \"\"\"\n client = self._require_client(client)\n path = '/projects/%s/managedZones' % (self.project,)\n api_response = client.connection.api_request(\n method='POST', path=path, data=self._build_resource())\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.exists","text":"google/cloud/dns/zone.py::ManagedZone.exists\n def exists(self, client=None):\n \"\"\"API call: test for the existence of the zone via a GET request\n\n See\n https://cloud.google.com/dns/api/v1/managedZones/get\n\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current zone.\n\n :rtype: bool\n :returns: Boolean indicating existence of the managed zone.\n \"\"\"\n client = self._require_client(client)\n\n try:\n client.connection.api_request(method='GET', path=self.path,\n query_params={'fields': 'id'})\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.reload","text":"google/cloud/dns/zone.py::ManagedZone.reload\n def reload(self, client=None):\n \"\"\"API call: refresh zone properties via a GET request\n\n See\n https://cloud.google.com/dns/api/v1/managedZones/get\n\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current zone.\n \"\"\"\n client = self._require_client(client)\n\n api_response = client.connection.api_request(\n method='GET', path=self.path)\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.delete","text":"google/cloud/dns/zone.py::ManagedZone.delete\n def delete(self, client=None):\n \"\"\"API call: delete the zone via a DELETE request\n\n See:\n https://cloud.google.com/dns/api/v1/managedZones/delete\n\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current zone.\n \"\"\"\n client = self._require_client(client)\n client.connection.api_request(method='DELETE', path=self.path)","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.list_resource_record_sets","text":"google/cloud/dns/zone.py::ManagedZone.list_resource_record_sets\n def list_resource_record_sets(self, max_results=None, page_token=None,\n client=None):\n \"\"\"List resource record sets for this zone.\n\n See:\n https://cloud.google.com/dns/api/v1/resourceRecordSets/list\n\n :type max_results: int\n :param max_results: maximum number of zones to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of zones. If\n not passed, the API will return the first page of\n zones.\n\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current zone.\n\n :rtype: tuple, (list, str)\n :returns: list of\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`,\n plus a \"next page token\" string: if the token is not None,\n indicates that more zones can be retrieved with another\n call (pass that value as ``page_token``).\n \"\"\"\n params = {}\n\n if max_results is not None:\n params['maxResults'] = max_results\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/managedZones/%s/rrsets' % (\n self.project, self.name)\n client = self._require_client(client)\n conn = client.connection\n resp = conn.api_request(method='GET', path=path, query_params=params)\n zones = [ResourceRecordSet.from_api_repr(resource, self)\n for resource in resp['rrsets']]\n return zones, resp.get('nextPageToken')","label":0},{"cid":"google/cloud/dns/zone.py::ManagedZone.list_changes","text":"google/cloud/dns/zone.py::ManagedZone.list_changes\n def list_changes(self, max_results=None, page_token=None, client=None):\n \"\"\"List change sets for this zone.\n\n See:\n https://cloud.google.com/dns/api/v1/resourceRecordSets/list\n\n :type max_results: int\n :param max_results: maximum number of zones to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of zones. If\n not passed, the API will return the first page of\n zones.\n\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current zone.\n\n :rtype: tuple, (list, str)\n :returns: list of\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`,\n plus a \"next page token\" string: if the token is not None,\n indicates that more zones can be retrieved with another\n call (pass that value as ``page_token``).\n \"\"\"\n params = {}\n\n if max_results is not None:\n params['maxResults'] = max_results\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/managedZones/%s/changes' % (\n self.project, self.name)\n client = self._require_client(client)\n conn = client.connection\n resp = conn.api_request(method='GET', path=path, query_params=params)\n zones = [Changes.from_api_repr(resource, self)\n for resource in resp['changes']]\n return zones, resp.get('nextPageToken')","label":0},{"cid":"google/cloud/error_reporting/client.py::HTTPContext.__init__","text":"google/cloud/error_reporting/client.py::HTTPContext.__init__\n def __init__(self, method=None, url=None,\n user_agent=None, referrer=None,\n response_status_code=None, remote_ip=None):\n self.method = method\n self.url = url\n # intentionally camel case for mapping to JSON API expects\n # pylint: disable=invalid-name\n self.userAgent = user_agent\n self.referrer = referrer\n self.responseStatusCode = response_status_code\n self.remoteIp = remote_ip","label":0},{"cid":"google/cloud/error_reporting/client.py::Client.__init__","text":"google/cloud/error_reporting/client.py::Client.__init__\n def __init__(self, project=None,\n credentials=None,\n http=None,\n service=None,\n version=None):\n self.logging_client = google.cloud.logging.client.Client(\n project, credentials, http)\n self.service = service if service else self.DEFAULT_SERVICE\n self.version = version","label":0},{"cid":"google/cloud/error_reporting/client.py::Client._send_error_report","text":"google/cloud/error_reporting/client.py::Client._send_error_report\n def _send_error_report(self, message,\n report_location=None, http_context=None, user=None):\n \"\"\"Makes the call to the Error Reporting API via the log stream.\n\n This is the lower-level interface to build the payload, generally\n users will use either report() or report_exception() to automatically\n gather the parameters for this method.\n\n Currently this method sends the Error Report by formatting a structured\n log message according to\n\n https://cloud.google.com/error-reporting/docs/formatting-error-messages\n\n :type message: string\n :param message: The stack trace that was reported or logged by the\n service.\n\n :type report_location: dict\n :param report_location: The location in the source code where the\n decision was made to report the error, usually the place\n where it was logged. For a logged exception this would be the\n source line where the exception is logged, usually close to\n the place where it was caught.\n\n This should be a Python dict that contains the keys 'filePath',\n 'lineNumber', and 'functionName'\n\n :type http_context: :class`google.cloud.error_reporting.HTTPContext`\n :param http_context: The HTTP request which was processed when the\n error was triggered.\n\n :type user: string\n :param user: The user who caused or was affected by the crash. This can\n be a user ID, an email address, or an arbitrary token that\n uniquely identifies the user. When sending an error\n report, leave this field empty if the user was not\n logged in. In this case the Error Reporting system will\n use other data, such as remote IP address,\n t","label":0},{"cid":"google/cloud/error_reporting/client.py::Client.report","text":"google/cloud/error_reporting/client.py::Client.report\n def report(self, message, http_context=None, user=None):\n \"\"\" Reports a message to Stackdriver Error Reporting\n https://cloud.google.com/error-reporting/docs/formatting-error-messages\n\n :type message: str\n :param message: A user-supplied message to report\n\n\n :type http_context: :class`google.cloud.error_reporting.HTTPContext`\n :param http_context: The HTTP request which was processed when the\n error was triggered.\n\n :type user: string\n :param user: The user who caused or was affected by the crash. This\n can be a user ID, an email address, or an arbitrary\n token that uniquely identifies the user. When sending\n an error report, leave this field empty if the user\n was not logged in. In this case the Error Reporting\n system will use other data, such as remote IP address,\n to distinguish affected users.\n\n Example::\n >>> client.report(\"Something went wrong!\")\n \"\"\"\n stack = traceback.extract_stack()\n last_call = stack[-2]\n file_path = last_call[0]\n line_number = last_call[1]\n function_name = last_call[2]\n report_location = {\n 'filePath': file_path,\n 'lineNumber': line_number,\n 'functionName': function_name\n }\n\n self._send_error_report(message,\n http_context=http_context,\n user=user,\n report_location=report_location)","label":0},{"cid":"google/cloud/error_reporting/client.py::Client.report_exception","text":"google/cloud/error_reporting/client.py::Client.report_exception\n def report_exception(self, http_context=None, user=None):\n \"\"\" Reports the details of the latest exceptions to Stackdriver Error\n Reporting.\n\n :type http_context: :class`google.cloud.error_reporting.HTTPContext`\n :param http_context: The HTTP request which was processed when the\n error was triggered.\n\n :type user: string\n :param user: The user who caused or was affected by the crash. This\n can be a user ID, an email address, or an arbitrary\n token that uniquely identifies the user. When sending an\n error report, leave this field empty if the user was\n not logged in. In this case the Error Reporting system\n will use other data, such as remote IP address,\n to distinguish affected users.\n\n Example::\n\n >>> try:\n >>> raise NameError\n >>> except Exception:\n >>> client.report_exception()\n \"\"\"\n self._send_error_report(traceback.format_exc(),\n http_context=http_context,\n user=user)","label":0},{"cid":"google/cloud/exceptions.py::GoogleCloudError.__init__","text":"google/cloud/exceptions.py::GoogleCloudError.__init__\n def __init__(self, message, errors=()):\n super(GoogleCloudError, self).__init__(message)\n self.message = message\n self._errors = errors","label":0},{"cid":"google/cloud/exceptions.py::GoogleCloudError.__str__","text":"google/cloud/exceptions.py::GoogleCloudError.__str__\n def __str__(self):\n result = u'%d %s' % (self.code, self.message)\n if six.PY2:\n result = _to_bytes(result, 'utf-8')\n return result","label":0},{"cid":"google/cloud/exceptions.py::GoogleCloudError.errors","text":"google/cloud/exceptions.py::GoogleCloudError.errors\n @property\n def errors(self):\n \"\"\"Detailed error information.\n\n :rtype: list(dict)\n :returns: a list of mappings describing each error.\n \"\"\"\n return [copy.deepcopy(error) for error in self._errors]","label":0},{"cid":"google/cloud/exceptions.py::make_exception","text":"google/cloud/exceptions.py::make_exception\ndef make_exception(response, content, error_info=None, use_json=True):\n \"\"\"Factory: create exception based on HTTP response code.\n\n :type response: :class:`httplib2.Response` or other HTTP response object\n :param response: A response object that defines a status code as the\n status attribute.\n\n :type content: string or dictionary\n :param content: The body of the HTTP error response.\n\n :type error_info: string\n :param error_info: Optional string giving extra information about the\n failed request.\n\n :type use_json: bool\n :param use_json: Flag indicating if ``content`` is expected to be JSON.\n\n :rtype: instance of :class:`GoogleCloudError`, or a concrete subclass.\n :returns: Exception specific to the error response.\n \"\"\"\n if isinstance(content, six.binary_type):\n content = content.decode('utf-8')\n\n if isinstance(content, six.string_types):\n payload = None\n if use_json:\n try:\n payload = json.loads(content)\n except ValueError:\n # Expected JSON but received something else.\n pass\n if payload is None:\n payload = {'error': {'message': content}}\n else:\n payload = content\n\n message = payload.get('error', {}).get('message', '')\n errors = payload.get('error', {}).get('errors', ())\n\n if error_info is not None:\n message += ' (%s)' % (error_info,)\n\n try:\n klass = _HTTP_CODE_TO_EXCEPTION[response.status]\n except KeyError:\n error = GoogleCloudError(message, errors)\n error.code = response.status\n else:\n error = klass(message, errors)\n return error","label":0},{"cid":"google/cloud/exceptions.py::_walk_subclasses","text":"google/cloud/exceptions.py::_walk_subclasses\ndef _walk_subclasses(klass):\n \"\"\"Recursively walk subclass tree.\"\"\"\n for sub in klass.__subclasses__():\n yield sub\n for subsub in _walk_subclasses(sub):\n yield subsub","label":0},{"cid":"google/cloud/iterator.py::Iterator.__init__","text":"google/cloud/iterator.py::Iterator.__init__\n def __init__(self, client, path, extra_params=None):\n self.client = client\n self.path = path\n self.page_number = 0\n self.next_page_token = None\n self.extra_params = extra_params or {}\n reserved_in_use = self.RESERVED_PARAMS.intersection(\n self.extra_params)\n if reserved_in_use:\n raise ValueError(('Using a reserved parameter',\n reserved_in_use))","label":0},{"cid":"google/cloud/iterator.py::Iterator.__iter__","text":"google/cloud/iterator.py::Iterator.__iter__\n def __iter__(self):\n \"\"\"Iterate through the list of items.\"\"\"\n while self.has_next_page():\n response = self.get_next_page_response()\n for item in self.get_items_from_response(response):\n yield item","label":0},{"cid":"google/cloud/iterator.py::Iterator.has_next_page","text":"google/cloud/iterator.py::Iterator.has_next_page\n def has_next_page(self):\n \"\"\"Determines whether or not this iterator has more pages.\n\n :rtype: boolean\n :returns: Whether the iterator has more pages or not.\n \"\"\"\n if self.page_number == 0:\n return True\n\n return self.next_page_token is not None","label":0},{"cid":"google/cloud/iterator.py::Iterator.get_query_params","text":"google/cloud/iterator.py::Iterator.get_query_params\n def get_query_params(self):\n \"\"\"Getter for query parameters for the next request.\n\n :rtype: dict\n :returns: A dictionary of query parameters.\n \"\"\"\n result = ({self.PAGE_TOKEN: self.next_page_token}\n if self.next_page_token else {})\n result.update(self.extra_params)\n return result","label":0},{"cid":"google/cloud/iterator.py::Iterator.get_next_page_response","text":"google/cloud/iterator.py::Iterator.get_next_page_response\n def get_next_page_response(self):\n \"\"\"Requests the next page from the path provided.\n\n :rtype: dict\n :returns: The parsed JSON response of the next page's contents.\n \"\"\"\n if not self.has_next_page():\n raise RuntimeError('No more pages. Try resetting the iterator.')\n\n response = self.client.connection.api_request(\n method='GET', path=self.path, query_params=self.get_query_params())\n\n self.page_number += 1\n self.next_page_token = response.get('nextPageToken')\n\n return response","label":0},{"cid":"google/cloud/iterator.py::Iterator.reset","text":"google/cloud/iterator.py::Iterator.reset\n def reset(self):\n \"\"\"Resets the iterator to the beginning.\"\"\"\n self.page_number = 0\n self.next_page_token = None","label":0},{"cid":"google/cloud/iterator.py::Iterator.get_items_from_response","text":"google/cloud/iterator.py::Iterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Factory method called while iterating. This should be overridden.\n\n This method should be overridden by a subclass. It should\n accept the API response of a request for the next page of items,\n and return a list (or other iterable) of items.\n\n Typically this method will construct a Bucket or a Blob from the\n page of results in the response.\n\n :type response: dict\n :param response: The response of asking for the next page of items.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"google/cloud/iterator.py::MethodIterator.__init__","text":"google/cloud/iterator.py::MethodIterator.__init__\n def __init__(self, method, page_token=None, page_size=None,\n max_calls=None, **kw):\n self._method = method\n self._token = page_token\n self._page_size = page_size\n self._kw = kw\n self._max_calls = max_calls\n self._page_num = 0","label":0},{"cid":"google/cloud/iterator.py::MethodIterator.__iter__","text":"google/cloud/iterator.py::MethodIterator.__iter__\n def __iter__(self):\n while self._max_calls is None or self._page_num < self._max_calls:\n items, new_token = self._method(\n page_token=self._token, page_size=self._page_size, **self._kw)\n for item in items:\n yield item\n if new_token is None:\n return\n self._page_num += 1\n self._token = new_token","label":0},{"cid":"google/cloud/language/client.py::Client.document_from_text","text":"google/cloud/language/client.py::Client.document_from_text\n def document_from_text(self, content, **kwargs):\n \"\"\"Create a plain text document bound to this client.\n\n :type content: str\n :param content: The document plain text content.\n\n :type kwargs: dict\n :param kwargs: Remaining keyword arguments to be passed along to the\n :class:`.Document` constructor.\n\n :rtype: :class:`.Document`\n :returns: A plain-text document bound to this client.\n :raises: :class:`~exceptions.TypeError` if ``doc_type`` is passed as a\n keyword argument.\n \"\"\"\n if 'doc_type' in kwargs:\n raise TypeError('Cannot pass doc_type')\n return Document(self, content=content,\n doc_type=Document.PLAIN_TEXT, **kwargs)","label":0},{"cid":"google/cloud/language/client.py::Client.document_from_html","text":"google/cloud/language/client.py::Client.document_from_html\n def document_from_html(self, content, **kwargs):\n \"\"\"Create an HTML document bound to this client.\n\n :type content: str\n :param content: The document HTML text content.\n\n :type kwargs: dict\n :param kwargs: Remaining keyword arguments to be passed along to the\n :class:`.Document` constructor.\n\n :rtype: :class:`.Document`\n :returns: An HTML document bound to this client.\n :raises: :class:`~exceptions.TypeError` if ``doc_type`` is passed as a\n keyword argument.\n \"\"\"\n if 'doc_type' in kwargs:\n raise TypeError('Cannot pass doc_type')\n return Document(self, content=content,\n doc_type=Document.HTML, **kwargs)","label":0},{"cid":"google/cloud/language/client.py::Client.document_from_url","text":"google/cloud/language/client.py::Client.document_from_url\n def document_from_url(self, gcs_url,\n doc_type=Document.PLAIN_TEXT, **kwargs):\n \"\"\"Create a Cloud Storage document bound to this client.\n\n :type gcs_url: str\n :param gcs_url: The URL of the Google Cloud Storage object\n holding the content. Of the form\n ``gs://{bucket}/{blob-name}``.\n\n :type doc_type: str\n :param doc_type: (Optional) The type of text in the document.\n Defaults to plain text. Can also be specified\n as HTML via :attr:`~.Document.HTML`.\n\n :type kwargs: dict\n :param kwargs: Remaining keyword arguments to be passed along to the\n :class:`.Document` constructor.\n\n :rtype: :class:`.Document`\n :returns: A document bound to this client.\n \"\"\"\n return Document(self, gcs_url=gcs_url, doc_type=doc_type, **kwargs)","label":0},{"cid":"google/cloud/language/document.py::Document.__init__","text":"google/cloud/language/document.py::Document.__init__\n def __init__(self, client, content=None, gcs_url=None, doc_type=PLAIN_TEXT,\n language=DEFAULT_LANGUAGE, encoding=Encoding.UTF8):\n if content is not None and gcs_url is not None:\n raise ValueError('A Document cannot contain both local text and '\n 'a link to text in a Google Cloud Storage object')\n if content is None and gcs_url is None:\n raise ValueError('A Document must contain either local text or a '\n 'link to text in a Google Cloud Storage object')\n self.client = client\n self.content = content\n self.gcs_url = gcs_url\n self.doc_type = doc_type\n self.language = language\n self.encoding = encoding","label":0},{"cid":"google/cloud/language/document.py::Document._to_dict","text":"google/cloud/language/document.py::Document._to_dict\n def _to_dict(self):\n \"\"\"Helper to convert the current document into a dictionary.\n\n To be used when constructing requests.\n\n :rtype: dict\n :returns: The Document value as a JSON dictionary.\n \"\"\"\n info = {\n 'type': self.doc_type,\n 'language': self.language,\n }\n if self.content is not None:\n info['content'] = self.content\n elif self.gcs_url is not None:\n info['gcsContentUri'] = self.gcs_url\n return info","label":0},{"cid":"google/cloud/language/document.py::Document.analyze_entities","text":"google/cloud/language/document.py::Document.analyze_entities\n def analyze_entities(self):\n \"\"\"Analyze the entities in the current document.\n\n Finds named entities (currently finds proper names as of August 2016)\n in the text, entity types, salience, mentions for each entity, and\n other properties.\n\n .. _analyzeEntities: https://cloud.google.com/natural-language/\\\n reference/rest/v1beta1/documents/analyzeEntities\n\n See `analyzeEntities`_.\n\n :rtype: list\n :returns: A list of :class:`~.language.entity.Entity` returned from\n the API.\n \"\"\"\n data = {\n 'document': self._to_dict(),\n 'encodingType': self.encoding,\n }\n api_response = self.client.connection.api_request(\n method='POST', path='analyzeEntities', data=data)\n return [Entity.from_api_repr(entity)\n for entity in api_response['entities']]","label":0},{"cid":"google/cloud/language/document.py::Document.analyze_sentiment","text":"google/cloud/language/document.py::Document.analyze_sentiment\n def analyze_sentiment(self):\n \"\"\"Analyze the sentiment in the current document.\n\n .. _analyzeSentiment: https://cloud.google.com/natural-language/\\\n reference/rest/v1beta1/documents/analyzeSentiment\n\n See `analyzeSentiment`_.\n\n :rtype: :class:`.Sentiment`\n :returns: The sentiment of the current document.\n \"\"\"\n data = {'document': self._to_dict()}\n api_response = self.client.connection.api_request(\n method='POST', path='analyzeSentiment', data=data)\n return Sentiment.from_api_repr(api_response['documentSentiment'])","label":0},{"cid":"google/cloud/language/document.py::Document.annotate_text","text":"google/cloud/language/document.py::Document.annotate_text\n def annotate_text(self, include_syntax=True, include_entities=True,\n include_sentiment=True):\n \"\"\"Advanced natural language API: document syntax and other features.\n\n Includes the full functionality of :meth:`analyze_entities` and\n :meth:`analyze_sentiment`, enabled by the flags\n ``include_entities`` and ``include_sentiment`` respectively.\n\n In addition ``include_syntax`` adds a new feature that analyzes\n the document for semantic and syntacticinformation.\n\n .. note::\n\n This API is intended for users who are familiar with machine\n learning and need in-depth text features to build upon.\n\n .. _annotateText: https://cloud.google.com/natural-language/\\\n reference/rest/v1beta1/documents/annotateText\n\n See `annotateText`_.\n\n :type include_syntax: bool\n :param include_syntax: (Optional) Flag to enable syntax analysis\n of the current document.\n\n :type include_entities: bool\n :param include_entities: (Optional) Flag to enable entity extraction\n from the current document.\n\n :type include_sentiment: bool\n :param include_sentiment: (Optional) Flag to enable sentiment\n analysis of the current document.\n\n :rtype: :class:`Annotations`\n :returns: A tuple of each of the four values returned from the API:\n sentences, tokens, sentiment and entities.\n \"\"\"\n features = {}\n if include_syntax:\n features['extractSyntax'] = True\n if include_entities:\n features['extractEntities'] = True\n if include_sentiment:\n features['extractDocumentSentiment'] = True\n\n data = {\n 'document': self._to_dict(),\n 'features': features,\n 'encodingType':","label":0},{"cid":"google/cloud/language/entity.py::Entity.__init__","text":"google/cloud/language/entity.py::Entity.__init__\n def __init__(self, name, entity_type, metadata, salience, mentions):\n self.name = name\n self.entity_type = entity_type\n self.wikipedia_url = metadata.pop('wikipedia_url', None)\n self.metadata = metadata\n self.salience = salience\n self.mentions = mentions","label":0},{"cid":"google/cloud/language/entity.py::Entity.from_api_repr","text":"google/cloud/language/entity.py::Entity.from_api_repr\n @classmethod\n def from_api_repr(cls, payload):\n \"\"\"Convert an Entity from the JSON API into an :class:`Entity`.\n\n :param payload: dict\n :type payload: The value from the backend.\n\n :rtype: :class:`Entity`\n :returns: The entity parsed from the API representation.\n \"\"\"\n name = payload['name']\n entity_type = payload['type']\n metadata = payload['metadata']\n salience = payload['salience']\n mentions = [value['text']['content']\n for value in payload['mentions']]\n return cls(name, entity_type, metadata, salience, mentions)","label":0},{"cid":"google/cloud/language/sentiment.py::Sentiment.__init__","text":"google/cloud/language/sentiment.py::Sentiment.__init__\n def __init__(self, polarity, magnitude):\n self.polarity = polarity\n self.magnitude = magnitude","label":0},{"cid":"google/cloud/language/sentiment.py::Sentiment.from_api_repr","text":"google/cloud/language/sentiment.py::Sentiment.from_api_repr\n @classmethod\n def from_api_repr(cls, payload):\n \"\"\"Convert a Sentiment from the JSON API into a :class:`Sentiment`.\n\n :param payload: dict\n :type payload: The value from the backend.\n\n :rtype: :class:`Sentiment`\n :returns: The sentiment parsed from the API representation.\n \"\"\"\n polarity = payload['polarity']\n magnitude = payload['magnitude']\n return cls(polarity, magnitude)","label":0},{"cid":"google/cloud/language/syntax.py::PartOfSpeech.reverse","text":"google/cloud/language/syntax.py::PartOfSpeech.reverse\n @classmethod\n def reverse(cls, tag):\n \"\"\"Reverses the API's enum name for the one on this class.\n\n For example::\n\n >>> PartOfSpeech.OTHER\n 'X'\n >>> PartOfSpeech.reverse('X')\n 'OTHER'\n\n :rtype: str\n :returns: The attribute name corresponding to the API part of\n speech enum.\n \"\"\"\n return cls._REVERSE_MAP[tag]","label":0},{"cid":"google/cloud/language/syntax.py::Token.__init__","text":"google/cloud/language/syntax.py::Token.__init__\n def __init__(self, text_content, text_begin, part_of_speech,\n edge_index, edge_label, lemma):\n self.text_content = text_content\n self.text_begin = text_begin\n self.part_of_speech = part_of_speech\n self.edge_index = edge_index\n self.edge_label = edge_label\n self.lemma = lemma","label":0},{"cid":"google/cloud/language/syntax.py::Token.from_api_repr","text":"google/cloud/language/syntax.py::Token.from_api_repr\n @classmethod\n def from_api_repr(cls, payload):\n \"\"\"Convert a token from the JSON API into a :class:`Sentiment`.\n\n :param payload: dict\n :type payload: The value from the backend.\n\n :rtype: :class:`Token`\n :returns: The token parsed from the API representation.\n \"\"\"\n text_span = payload['text']\n text_content = text_span['content']\n text_begin = text_span['beginOffset']\n part_of_speech = payload['partOfSpeech']['tag']\n edge = payload['dependencyEdge']\n edge_index = edge['headTokenIndex']\n edge_label = edge['label']\n lemma = payload['lemma']\n return cls(text_content, text_begin, part_of_speech,\n edge_index, edge_label, lemma)","label":0},{"cid":"google/cloud/language/syntax.py::Sentence.__init__","text":"google/cloud/language/syntax.py::Sentence.__init__\n def __init__(self, content, begin):\n self.content = content\n self.begin = begin","label":0},{"cid":"google/cloud/language/syntax.py::Sentence.from_api_repr","text":"google/cloud/language/syntax.py::Sentence.from_api_repr\n @classmethod\n def from_api_repr(cls, payload):\n \"\"\"Convert a sentence from the JSON API into a :class:`Sentiment`.\n\n :param payload: dict\n :type payload: The value from the backend.\n\n :rtype: :class:`Sentence`\n :returns: The sentence parsed from the API representation.\n \"\"\"\n text_span = payload['text']\n return cls(text_span['content'], text_span['beginOffset'])","label":0},{"cid":"google/cloud/logging/_gax.py::_LoggingAPI.__init__","text":"google/cloud/logging/_gax.py::_LoggingAPI.__init__\n def __init__(self, gax_api):\n self._gax_api = gax_api","label":0},{"cid":"google/cloud/logging/_gax.py::_LoggingAPI.list_entries","text":"google/cloud/logging/_gax.py::_LoggingAPI.list_entries\n def list_entries(self, projects, filter_='', order_by='',\n page_size=0, page_token=None):\n \"\"\"Return a page of log entry resources.\n\n :type projects: list of strings\n :param projects: project IDs to include. If not passed,\n defaults to the project bound to the API's client.\n\n :type filter_: str\n :param filter_: a filter expression. See:\n https://cloud.google.com/logging/docs/view/advanced_filters\n\n :type order_by: str\n :param order_by: One of :data:`~google.cloud.logging.ASCENDING`\n or :data:`~google.cloud.logging.DESCENDING`.\n\n :type page_size: int\n :param page_size: maximum number of entries to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of entries. If not\n passed, the API will return the first page of\n entries.\n\n :rtype: tuple, (list, str)\n :returns: list of mappings, plus a \"next page token\" string:\n if not None, indicates that more entries can be retrieved\n with another call (pass that value as ``page_token``).\n \"\"\"\n if page_token is None:\n page_token = INITIAL_PAGE\n options = CallOptions(page_token=page_token)\n page_iter = self._gax_api.list_log_entries(\n projects, filter_, order_by, page_size, options)\n entries = [_log_entry_pb_to_mapping(entry_pb)\n for entry_pb in page_iter.next()]\n token = page_iter.page_token or None\n return entries, token","label":0},{"cid":"google/cloud/logging/_gax.py::_LoggingAPI.write_entries","text":"google/cloud/logging/_gax.py::_LoggingAPI.write_entries\n def write_entries(self, entries, logger_name=None, resource=None,\n labels=None):\n \"\"\"API call: log an entry resource via a POST request\n\n :type entries: sequence of mapping\n :param entries: the log entry resources to log.\n\n :type logger_name: string\n :param logger_name: name of default logger to which to log the entries;\n individual entries may override.\n\n :type resource: mapping\n :param resource: default resource to associate with entries;\n individual entries may override.\n\n :type labels: mapping\n :param labels: default labels to associate with entries;\n individual entries may override.\n \"\"\"\n options = None\n partial_success = False\n entry_pbs = [_log_entry_mapping_to_pb(entry) for entry in entries]\n self._gax_api.write_log_entries(entry_pbs, logger_name, resource,\n labels, partial_success, options)","label":0},{"cid":"google/cloud/logging/_gax.py::_LoggingAPI.logger_delete","text":"google/cloud/logging/_gax.py::_LoggingAPI.logger_delete\n def logger_delete(self, project, logger_name):\n \"\"\"API call: delete all entries in a logger via a DELETE request\n\n :type project: string\n :param project: ID of project containing the log entries to delete\n\n :type logger_name: string\n :param logger_name: name of logger containing the log entries to delete\n \"\"\"\n options = None\n path = 'projects/%s/logs/%s' % (project, logger_name)\n try:\n self._gax_api.delete_log(path, options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(path)\n raise","label":0},{"cid":"google/cloud/logging/_gax.py::_SinksAPI.__init__","text":"google/cloud/logging/_gax.py::_SinksAPI.__init__\n def __init__(self, gax_api):\n self._gax_api = gax_api","label":0},{"cid":"google/cloud/logging/_gax.py::_SinksAPI.list_sinks","text":"google/cloud/logging/_gax.py::_SinksAPI.list_sinks\n def list_sinks(self, project, page_size=0, page_token=None):\n \"\"\"List sinks for the project associated with this client.\n\n :type project: string\n :param project: ID of the project whose sinks are to be listed.\n\n :type page_size: int\n :param page_size: maximum number of sinks to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of sinks. If not\n passed, the API will return the first page of\n sinks.\n\n :rtype: tuple, (list, str)\n :returns: list of mappings, plus a \"next page token\" string:\n if not None, indicates that more sinks can be retrieved\n with another call (pass that value as ``page_token``).\n \"\"\"\n if page_token is None:\n page_token = INITIAL_PAGE\n options = CallOptions(page_token=page_token)\n path = 'projects/%s' % (project,)\n page_iter = self._gax_api.list_sinks(path, page_size, options)\n sinks = [_log_sink_pb_to_mapping(log_sink_pb)\n for log_sink_pb in page_iter.next()]\n token = page_iter.page_token or None\n return sinks, token","label":0},{"cid":"google/cloud/logging/_gax.py::_SinksAPI.sink_create","text":"google/cloud/logging/_gax.py::_SinksAPI.sink_create\n def sink_create(self, project, sink_name, filter_, destination):\n \"\"\"API call: create a sink resource.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/create\n\n :type project: string\n :param project: ID of the project in which to create the sink.\n\n :type sink_name: string\n :param sink_name: the name of the sink\n\n :type filter_: string\n :param filter_: the advanced logs filter expression defining the\n entries exported by the sink.\n\n :type destination: string\n :param destination: destination URI for the entries exported by\n the sink.\n \"\"\"\n options = None\n parent = 'projects/%s' % (project,)\n sink_pb = LogSink(name=sink_name, filter=filter_,\n destination=destination)\n try:\n self._gax_api.create_sink(parent, sink_pb, options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.FAILED_PRECONDITION:\n path = 'projects/%s/sinks/%s' % (project, sink_name)\n raise Conflict(path)\n raise","label":0},{"cid":"google/cloud/logging/_gax.py::_SinksAPI.sink_get","text":"google/cloud/logging/_gax.py::_SinksAPI.sink_get\n def sink_get(self, project, sink_name):\n \"\"\"API call: retrieve a sink resource.\n\n :type project: string\n :param project: ID of the project containing the sink.\n\n :type sink_name: string\n :param sink_name: the name of the sink\n\n :rtype: dict\n :returns: The sink object returned from the API (converted from a\n protobuf to a dictionary).\n \"\"\"\n options = None\n path = 'projects/%s/sinks/%s' % (project, sink_name)\n try:\n sink_pb = self._gax_api.get_sink(path, options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(path)\n raise\n return _log_sink_pb_to_mapping(sink_pb)","label":0},{"cid":"google/cloud/logging/_gax.py::_SinksAPI.sink_update","text":"google/cloud/logging/_gax.py::_SinksAPI.sink_update\n def sink_update(self, project, sink_name, filter_, destination):\n \"\"\"API call: update a sink resource.\n\n :type project: string\n :param project: ID of the project containing the sink.\n\n :type sink_name: string\n :param sink_name: the name of the sink\n\n :type filter_: string\n :param filter_: the advanced logs filter expression defining the\n entries exported by the sink.\n\n :type destination: string\n :param destination: destination URI for the entries exported by\n the sink.\n\n :rtype: dict\n :returns: The sink object returned from the API (converted from a\n protobuf to a dictionary).\n \"\"\"\n options = None\n path = 'projects/%s/sinks/%s' % (project, sink_name)\n sink_pb = LogSink(name=path, filter=filter_, destination=destination)\n try:\n self._gax_api.update_sink(path, sink_pb, options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(path)\n raise\n return _log_sink_pb_to_mapping(sink_pb)","label":0},{"cid":"google/cloud/logging/_gax.py::_SinksAPI.sink_delete","text":"google/cloud/logging/_gax.py::_SinksAPI.sink_delete\n def sink_delete(self, project, sink_name):\n \"\"\"API call: delete a sink resource.\n\n :type project: string\n :param project: ID of the project containing the sink.\n\n :type sink_name: string\n :param sink_name: the name of the sink\n \"\"\"\n options = None\n path = 'projects/%s/sinks/%s' % (project, sink_name)\n try:\n self._gax_api.delete_sink(path, options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(path)\n raise","label":0},{"cid":"google/cloud/logging/_gax.py::_MetricsAPI.__init__","text":"google/cloud/logging/_gax.py::_MetricsAPI.__init__\n def __init__(self, gax_api):\n self._gax_api = gax_api","label":0},{"cid":"google/cloud/logging/_gax.py::_MetricsAPI.list_metrics","text":"google/cloud/logging/_gax.py::_MetricsAPI.list_metrics\n def list_metrics(self, project, page_size=0, page_token=None):\n \"\"\"List metrics for the project associated with this client.\n\n :type project: string\n :param project: ID of the project whose metrics are to be listed.\n\n :type page_size: int\n :param page_size: maximum number of metrics to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of metrics. If not\n passed, the API will return the first page of\n metrics.\n\n :rtype: tuple, (list, str)\n :returns: list of mappings, plus a \"next page token\" string:\n if not None, indicates that more metrics can be retrieved\n with another call (pass that value as ``page_token``).\n \"\"\"\n if page_token is None:\n page_token = INITIAL_PAGE\n options = CallOptions(page_token=page_token)\n path = 'projects/%s' % (project,)\n page_iter = self._gax_api.list_log_metrics(path, page_size, options)\n metrics = [_log_metric_pb_to_mapping(log_metric_pb)\n for log_metric_pb in page_iter.next()]\n token = page_iter.page_token or None\n return metrics, token","label":0},{"cid":"google/cloud/logging/_gax.py::_MetricsAPI.metric_create","text":"google/cloud/logging/_gax.py::_MetricsAPI.metric_create\n def metric_create(self, project, metric_name, filter_, description):\n \"\"\"API call: create a metric resource.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/create\n\n :type project: string\n :param project: ID of the project in which to create the metric.\n\n :type metric_name: string\n :param metric_name: the name of the metric\n\n :type filter_: string\n :param filter_: the advanced logs filter expression defining the\n entries exported by the metric.\n\n :type description: string\n :param description: description of the metric.\n \"\"\"\n options = None\n parent = 'projects/%s' % (project,)\n metric_pb = LogMetric(name=metric_name, filter=filter_,\n description=description)\n try:\n self._gax_api.create_log_metric(parent, metric_pb, options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.FAILED_PRECONDITION:\n path = 'projects/%s/metrics/%s' % (project, metric_name)\n raise Conflict(path)\n raise","label":0},{"cid":"google/cloud/logging/_gax.py::_MetricsAPI.metric_get","text":"google/cloud/logging/_gax.py::_MetricsAPI.metric_get\n def metric_get(self, project, metric_name):\n \"\"\"API call: retrieve a metric resource.\n\n :type project: string\n :param project: ID of the project containing the metric.\n\n :type metric_name: string\n :param metric_name: the name of the metric\n\n :rtype: dict\n :returns: The metric object returned from the API (converted from a\n protobuf to a dictionary).\n \"\"\"\n options = None\n path = 'projects/%s/metrics/%s' % (project, metric_name)\n try:\n metric_pb = self._gax_api.get_log_metric(path, options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(path)\n raise\n return _log_metric_pb_to_mapping(metric_pb)","label":0},{"cid":"google/cloud/logging/_gax.py::_MetricsAPI.metric_update","text":"google/cloud/logging/_gax.py::_MetricsAPI.metric_update\n def metric_update(self, project, metric_name, filter_, description):\n \"\"\"API call: update a metric resource.\n\n :type project: string\n :param project: ID of the project containing the metric.\n\n :type metric_name: string\n :param metric_name: the name of the metric\n\n :type filter_: string\n :param filter_: the advanced logs filter expression defining the\n entries exported by the metric.\n\n :type description: string\n :param description: description of the metric.\n\n :rtype: dict\n :returns: The metric object returned from the API (converted from a\n protobuf to a dictionary).\n \"\"\"\n options = None\n path = 'projects/%s/metrics/%s' % (project, metric_name)\n metric_pb = LogMetric(name=path, filter=filter_,\n description=description)\n try:\n self._gax_api.update_log_metric(path, metric_pb, options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(path)\n raise\n return _log_metric_pb_to_mapping(metric_pb)","label":0},{"cid":"google/cloud/logging/_gax.py::_MetricsAPI.metric_delete","text":"google/cloud/logging/_gax.py::_MetricsAPI.metric_delete\n def metric_delete(self, project, metric_name):\n \"\"\"API call: delete a metric resource.\n\n :type project: string\n :param project: ID of the project containing the metric.\n\n :type metric_name: string\n :param metric_name: the name of the metric\n \"\"\"\n options = None\n path = 'projects/%s/metrics/%s' % (project, metric_name)\n try:\n self._gax_api.delete_log_metric(path, options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(path)\n raise","label":0},{"cid":"google/cloud/logging/_gax.py::_mon_resource_pb_to_mapping","text":"google/cloud/logging/_gax.py::_mon_resource_pb_to_mapping\ndef _mon_resource_pb_to_mapping(resource_pb):\n \"\"\"Helper for :func:_log_entry_pb_to_mapping`.\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n mapping = {\n 'type': resource_pb.type,\n }\n if resource_pb.labels:\n mapping['labels'] = resource_pb.labels\n return mapping","label":0},{"cid":"google/cloud/logging/_gax.py::_value_pb_to_value","text":"google/cloud/logging/_gax.py::_value_pb_to_value\ndef _value_pb_to_value(value_pb):\n \"\"\"Helper for :func:`_log_entry_pb_to_mapping`.\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n kind = value_pb.WhichOneof('kind')\n\n if kind is None:\n result = None\n\n elif kind == 'string_value':\n result = value_pb.string_value\n\n elif kind == 'bool_value':\n result = value_pb.bool_value\n\n elif kind == 'number_value':\n result = value_pb.number_value\n\n elif kind == 'list_value':\n result = [_value_pb_to_value(element)\n for element in value_pb.list_value.values]\n\n elif kind == 'struct_value':\n result = _struct_pb_to_mapping(value_pb.struct_value)\n\n else:\n raise ValueError('Value protobuf had unknown kind: %s' % (kind,))\n\n return result","label":0},{"cid":"google/cloud/logging/_gax.py::_struct_pb_to_mapping","text":"google/cloud/logging/_gax.py::_struct_pb_to_mapping\ndef _struct_pb_to_mapping(struct_pb):\n \"\"\"Helper for :func:`_log_entry_pb_to_mapping`.\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n return {key: _value_pb_to_value(struct_pb.fields[key])\n for key in struct_pb.fields}","label":0},{"cid":"google/cloud/logging/_gax.py::_log_entry_pb_to_mapping","text":"google/cloud/logging/_gax.py::_log_entry_pb_to_mapping\ndef _log_entry_pb_to_mapping(entry_pb):\n \"\"\"Helper for :meth:`list_entries`, et aliae\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n mapping = {\n 'logName': entry_pb.log_name,\n 'resource': _mon_resource_pb_to_mapping(entry_pb.resource),\n 'severity': LogSeverity.Name(entry_pb.severity),\n 'insertId': entry_pb.insert_id,\n 'timestamp': _pb_timestamp_to_rfc3339(entry_pb.timestamp),\n 'labels': entry_pb.labels,\n }\n if entry_pb.HasField('text_payload'):\n mapping['textPayload'] = entry_pb.text_payload\n\n if entry_pb.HasField('json_payload'):\n mapping['jsonPayload'] = _struct_pb_to_mapping(entry_pb.json_payload)\n\n if entry_pb.HasField('proto_payload'):\n mapping['protoPayload'] = entry_pb.proto_payload\n\n if entry_pb.http_request:\n request = entry_pb.http_request\n mapping['httpRequest'] = {\n 'requestMethod': request.request_method,\n 'requestUrl': request.request_url,\n 'status': request.status,\n 'referer': request.referer,\n 'userAgent': request.user_agent,\n 'cacheHit': request.cache_hit,\n 'requestSize': request.request_size,\n 'responseSize': request.response_size,\n 'remoteIp': request.remote_ip,\n }\n\n if entry_pb.operation:\n operation = entry_pb.operation\n mapping['operation'] = {\n 'producer': operation.producer,\n 'id': operation.id,\n 'first': operation.first,\n 'last': operation.last,\n }\n\n return mapping","label":0},{"cid":"google/cloud/logging/_gax.py::_http_request_mapping_to_pb","text":"google/cloud/logging/_gax.py::_http_request_mapping_to_pb\ndef _http_request_mapping_to_pb(info, request):\n \"\"\"Helper for _log_entry_mapping_to_pb\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n optional_request_keys = {\n 'requestMethod': 'request_method',\n 'requestUrl': 'request_url',\n 'status': 'status',\n 'referer': 'referer',\n 'userAgent': 'user_agent',\n 'cacheHit': 'cache_hit',\n 'requestSize': 'request_size',\n 'responseSize': 'response_size',\n 'remoteIp': 'remote_ip',\n }\n for key, pb_name in optional_request_keys.items():\n if key in info:\n setattr(request, pb_name, info[key])","label":0},{"cid":"google/cloud/logging/_gax.py::_log_operation_mapping_to_pb","text":"google/cloud/logging/_gax.py::_log_operation_mapping_to_pb\ndef _log_operation_mapping_to_pb(info, operation):\n \"\"\"Helper for _log_entry_mapping_to_pb\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n operation.producer = info['producer']\n operation.id = info['id']\n\n if 'first' in info:\n operation.first = info['first']\n\n if 'last' in info:\n operation.last = info['last']","label":0},{"cid":"google/cloud/logging/_gax.py::_log_entry_mapping_to_pb","text":"google/cloud/logging/_gax.py::_log_entry_mapping_to_pb\ndef _log_entry_mapping_to_pb(mapping):\n \"\"\"Helper for :meth:`write_entries`, et aliae\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n # pylint: disable=too-many-branches\n entry_pb = LogEntry()\n\n optional_scalar_keys = {\n 'logName': 'log_name',\n 'insertId': 'insert_id',\n 'textPayload': 'text_payload',\n }\n\n for key, pb_name in optional_scalar_keys.items():\n if key in mapping:\n setattr(entry_pb, pb_name, mapping[key])\n\n if 'resource' in mapping:\n entry_pb.resource.type = mapping['resource']['type']\n\n if 'severity' in mapping:\n severity = mapping['severity']\n if isinstance(severity, str):\n severity = LogSeverity.Value(severity)\n entry_pb.severity = severity\n\n if 'timestamp' in mapping:\n timestamp = _datetime_to_pb_timestamp(mapping['timestamp'])\n entry_pb.timestamp.CopyFrom(timestamp)\n\n if 'labels' in mapping:\n for key, value in mapping['labels'].items():\n entry_pb.labels[key] = value\n\n if 'jsonPayload' in mapping:\n for key, value in mapping['jsonPayload'].items():\n entry_pb.json_payload[key] = value\n\n if 'protoPayload' in mapping:\n Parse(json.dumps(mapping['protoPayload']), entry_pb.proto_payload)\n\n if 'httpRequest' in mapping:\n _http_request_mapping_to_pb(\n mapping['httpRequest'], entry_pb.http_request)\n\n if 'operation' in mapping:\n _log_operation_mapping_to_pb(\n mapping['operation'], entry_pb.operation)\n\n return entry_pb","label":0},{"cid":"google/cloud/logging/_gax.py::_log_sink_pb_to_mapping","text":"google/cloud/logging/_gax.py::_log_sink_pb_to_mapping\ndef _log_sink_pb_to_mapping(sink_pb):\n \"\"\"Helper for :meth:`list_sinks`, et aliae\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n return {\n 'name': sink_pb.name,\n 'destination': sink_pb.destination,\n 'filter': sink_pb.filter,\n }","label":0},{"cid":"google/cloud/logging/_gax.py::_log_metric_pb_to_mapping","text":"google/cloud/logging/_gax.py::_log_metric_pb_to_mapping\ndef _log_metric_pb_to_mapping(metric_pb):\n \"\"\"Helper for :meth:`list_metrics`, et aliae\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n return {\n 'name': metric_pb.name,\n 'description': metric_pb.description,\n 'filter': metric_pb.filter,\n }","label":0},{"cid":"google/cloud/logging/client.py::Client.logging_api","text":"google/cloud/logging/client.py::Client.logging_api\n @property\n def logging_api(self):\n \"\"\"Helper for logging-related API calls.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.logs\n \"\"\"\n if self._logging_api is None:\n if _USE_GAX:\n generated = GeneratedLoggingAPI()\n self._logging_api = GAXLoggingAPI(generated)\n else:\n self._logging_api = JSONLoggingAPI(self.connection)\n return self._logging_api","label":0},{"cid":"google/cloud/logging/client.py::Client.sinks_api","text":"google/cloud/logging/client.py::Client.sinks_api\n @property\n def sinks_api(self):\n \"\"\"Helper for log sink-related API calls.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks\n \"\"\"\n if self._sinks_api is None:\n if _USE_GAX:\n generated = GeneratedSinksAPI()\n self._sinks_api = GAXSinksAPI(generated)\n else:\n self._sinks_api = JSONSinksAPI(self.connection)\n return self._sinks_api","label":0},{"cid":"google/cloud/logging/client.py::Client.metrics_api","text":"google/cloud/logging/client.py::Client.metrics_api\n @property\n def metrics_api(self):\n \"\"\"Helper for log metric-related API calls.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics\n \"\"\"\n if self._metrics_api is None:\n if _USE_GAX:\n generated = GeneratedMetricsAPI()\n self._metrics_api = GAXMetricsAPI(generated)\n else:\n self._metrics_api = JSONMetricsAPI(self.connection)\n return self._metrics_api","label":0},{"cid":"google/cloud/logging/client.py::Client.logger","text":"google/cloud/logging/client.py::Client.logger\n def logger(self, name):\n \"\"\"Creates a logger bound to the current client.\n\n :type name: str\n :param name: the name of the logger to be constructed.\n\n :rtype: :class:`google.cloud.logging.logger.Logger`\n :returns: Logger created with the current client.\n \"\"\"\n return Logger(name, client=self)","label":0},{"cid":"google/cloud/logging/client.py::Client._entry_from_resource","text":"google/cloud/logging/client.py::Client._entry_from_resource\n def _entry_from_resource(self, resource, loggers):\n \"\"\"Detect correct entry type from resource and instantiate.\n\n :type resource: dict\n :param resource: one entry resource from API response\n\n :type loggers: dict or None\n :param loggers: A mapping of logger fullnames -> loggers. If not\n passed, the entry will have a newly-created logger.\n\n :rtype: One of:\n :class:`google.cloud.logging.entries.TextEntry`,\n :class:`google.cloud.logging.entries.StructEntry`,\n :class:`google.cloud.logging.entries.ProtobufEntry`\n :returns: the entry instance, constructed via the resource\n \"\"\"\n if 'textPayload' in resource:\n return TextEntry.from_api_repr(resource, self, loggers)\n elif 'jsonPayload' in resource:\n return StructEntry.from_api_repr(resource, self, loggers)\n elif 'protoPayload' in resource:\n return ProtobufEntry.from_api_repr(resource, self, loggers)\n raise ValueError('Cannot parse log entry resource')","label":0},{"cid":"google/cloud/logging/client.py::Client.list_entries","text":"google/cloud/logging/client.py::Client.list_entries\n def list_entries(self, projects=None, filter_=None, order_by=None,\n page_size=None, page_token=None):\n \"\"\"Return a page of log entries.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/list\n\n :type projects: list of strings\n :param projects: project IDs to include. If not passed,\n defaults to the project bound to the client.\n\n :type filter_: str\n :param filter_: a filter expression. See:\n https://cloud.google.com/logging/docs/view/advanced_filters\n\n :type order_by: str\n :param order_by: One of :data:`~google.cloud.logging.ASCENDING`\n or :data:`~google.cloud.logging.DESCENDING`.\n\n :type page_size: int\n :param page_size: maximum number of entries to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of entries. If not\n passed, the API will return the first page of\n entries.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`google.cloud.logging.entry.TextEntry`, plus a\n \"next page token\" string: if not None, indicates that\n more entries can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n if projects is None:\n projects = [self.project]\n\n resources, token = self.logging_api.list_entries(\n projects=projects, filter_=filter_, order_by=order_by,\n page_size=page_size, page_token=page_token)\n loggers = {}\n entries = [self._entry_from_resource(resource, loggers)\n for resource in resources]\n return entries, token","label":0},{"cid":"google/cloud/logging/client.py::Client.sink","text":"google/cloud/logging/client.py::Client.sink\n def sink(self, name, filter_=None, destination=None):\n \"\"\"Creates a sink bound to the current client.\n\n :type name: str\n :param name: the name of the sink to be constructed.\n\n :type filter_: str\n :param filter_: (optional) the advanced logs filter expression\n defining the entries exported by the sink. If not\n passed, the instance should already exist, to be\n refreshed via :meth:`Sink.reload`.\n\n :type destination: str\n :param destination: destination URI for the entries exported by\n the sink. If not passed, the instance should\n already exist, to be refreshed via\n :meth:`Sink.reload`.\n\n :rtype: :class:`google.cloud.logging.sink.Sink`\n :returns: Sink created with the current client.\n \"\"\"\n return Sink(name, filter_, destination, client=self)","label":0},{"cid":"google/cloud/logging/client.py::Client.list_sinks","text":"google/cloud/logging/client.py::Client.list_sinks\n def list_sinks(self, page_size=None, page_token=None):\n \"\"\"List sinks for the project associated with this client.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/list\n\n :type page_size: int\n :param page_size: maximum number of sinks to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of sinks. If not\n passed, the API will return the first page of\n sinks.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`google.cloud.logging.sink.Sink`, plus a\n \"next page token\" string: if not None, indicates that\n more sinks can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n resources, token = self.sinks_api.list_sinks(\n self.project, page_size, page_token)\n sinks = [Sink.from_api_repr(resource, self)\n for resource in resources]\n return sinks, token","label":0},{"cid":"google/cloud/logging/client.py::Client.metric","text":"google/cloud/logging/client.py::Client.metric\n def metric(self, name, filter_=None, description=''):\n \"\"\"Creates a metric bound to the current client.\n\n :type name: str\n :param name: the name of the metric to be constructed.\n\n :type filter_: str\n :param filter_: the advanced logs filter expression defining the\n entries tracked by the metric. If not\n passed, the instance should already exist, to be\n refreshed via :meth:`Metric.reload`.\n\n :type description: str\n :param description: the description of the metric to be constructed.\n If not passed, the instance should already exist,\n to be refreshed via :meth:`Metric.reload`.\n\n :rtype: :class:`google.cloud.logging.metric.Metric`\n :returns: Metric created with the current client.\n \"\"\"\n return Metric(name, filter_, client=self, description=description)","label":0},{"cid":"google/cloud/logging/client.py::Client.list_metrics","text":"google/cloud/logging/client.py::Client.list_metrics\n def list_metrics(self, page_size=None, page_token=None):\n \"\"\"List metrics for the project associated with this client.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/list\n\n :type page_size: int\n :param page_size: maximum number of metrics to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of metrics. If not\n passed, the API will return the first page of\n metrics.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`google.cloud.logging.metric.Metric`, plus a\n \"next page token\" string: if not None, indicates that\n more metrics can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n resources, token = self.metrics_api.list_metrics(\n self.project, page_size, page_token)\n metrics = [Metric.from_api_repr(resource, self)\n for resource in resources]\n return metrics, token","label":0},{"cid":"google/cloud/logging/connection.py::_LoggingAPI.__init__","text":"google/cloud/logging/connection.py::_LoggingAPI.__init__\n def __init__(self, connection):\n self._connection = connection","label":0},{"cid":"google/cloud/logging/connection.py::_LoggingAPI.list_entries","text":"google/cloud/logging/connection.py::_LoggingAPI.list_entries\n def list_entries(self, projects, filter_=None, order_by=None,\n page_size=None, page_token=None):\n \"\"\"Return a page of log entry resources.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/list\n\n :type projects: list of strings\n :param projects: project IDs to include. If not passed,\n defaults to the project bound to the client.\n\n :type filter_: str\n :param filter_: a filter expression. See:\n https://cloud.google.com/logging/docs/view/advanced_filters\n\n :type order_by: str\n :param order_by: One of :data:`~google.cloud.logging.ASCENDING`\n or :data:`~google.cloud.logging.DESCENDING`.\n\n :type page_size: int\n :param page_size: maximum number of entries to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of entries. If not\n passed, the API will return the first page of\n entries.\n\n :rtype: tuple, (list, str)\n :returns: list of mappings, plus a \"next page token\" string:\n if not None, indicates that more entries can be retrieved\n with another call (pass that value as ``page_token``).\n \"\"\"\n params = {'projectIds': projects}\n\n if filter_ is not None:\n params['filter'] = filter_\n\n if order_by is not None:\n params['orderBy'] = order_by\n\n if page_size is not None:\n params['pageSize'] = page_size\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n resp = self._connection.api_request(\n method='POST', path='/entries:list', data=params)\n\n return resp.get('entries', ()), res","label":0},{"cid":"google/cloud/logging/connection.py::_LoggingAPI.write_entries","text":"google/cloud/logging/connection.py::_LoggingAPI.write_entries\n def write_entries(self, entries, logger_name=None, resource=None,\n labels=None):\n \"\"\"API call: log an entry resource via a POST request\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/write\n\n :type entries: sequence of mapping\n :param entries: the log entry resources to log.\n\n :type logger_name: string\n :param logger_name: name of default logger to which to log the entries;\n individual entries may override.\n\n :type resource: mapping\n :param resource: default resource to associate with entries;\n individual entries may override.\n\n :type labels: mapping\n :param labels: default labels to associate with entries;\n individual entries may override.\n \"\"\"\n data = {'entries': list(entries)}\n\n if logger_name is not None:\n data['logName'] = logger_name\n\n if resource is not None:\n data['resource'] = resource\n\n if labels is not None:\n data['labels'] = labels\n\n self._connection.api_request(method='POST', path='/entries:write',\n data=data)","label":0},{"cid":"google/cloud/logging/connection.py::_LoggingAPI.logger_delete","text":"google/cloud/logging/connection.py::_LoggingAPI.logger_delete\n def logger_delete(self, project, logger_name):\n \"\"\"API call: delete all entries in a logger via a DELETE request\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.logs/delete\n\n :type project: string\n :param project: ID of project containing the log entries to delete\n\n :type logger_name: string\n :param logger_name: name of logger containing the log entries to delete\n \"\"\"\n path = '/projects/%s/logs/%s' % (project, logger_name)\n self._connection.api_request(method='DELETE', path=path)","label":0},{"cid":"google/cloud/logging/connection.py::_SinksAPI.__init__","text":"google/cloud/logging/connection.py::_SinksAPI.__init__\n def __init__(self, connection):\n self._connection = connection","label":0},{"cid":"google/cloud/logging/connection.py::_SinksAPI.list_sinks","text":"google/cloud/logging/connection.py::_SinksAPI.list_sinks\n def list_sinks(self, project, page_size=None, page_token=None):\n \"\"\"List sinks for the project associated with this client.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/list\n\n :type project: string\n :param project: ID of the project whose sinks are to be listed.\n\n :type page_size: int\n :param page_size: maximum number of sinks to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of sinks. If not\n passed, the API will return the first page of\n sinks.\n\n :rtype: tuple, (list, str)\n :returns: list of mappings, plus a \"next page token\" string:\n if not None, indicates that more sinks can be retrieved\n with another call (pass that value as ``page_token``).\n \"\"\"\n params = {}\n\n if page_size is not None:\n params['pageSize'] = page_size\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/sinks' % (project,)\n resp = self._connection.api_request(\n method='GET', path=path, query_params=params)\n sinks = resp.get('sinks', ())\n return sinks, resp.get('nextPageToken')","label":0},{"cid":"google/cloud/logging/connection.py::_SinksAPI.sink_create","text":"google/cloud/logging/connection.py::_SinksAPI.sink_create\n def sink_create(self, project, sink_name, filter_, destination):\n \"\"\"API call: create a sink resource.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/create\n\n :type project: string\n :param project: ID of the project in which to create the sink.\n\n :type sink_name: string\n :param sink_name: the name of the sink\n\n :type filter_: string\n :param filter_: the advanced logs filter expression defining the\n entries exported by the sink.\n\n :type destination: string\n :param destination: destination URI for the entries exported by\n the sink.\n \"\"\"\n target = '/projects/%s/sinks' % (project,)\n data = {\n 'name': sink_name,\n 'filter': filter_,\n 'destination': destination,\n }\n self._connection.api_request(method='POST', path=target, data=data)","label":0},{"cid":"google/cloud/logging/connection.py::_SinksAPI.sink_get","text":"google/cloud/logging/connection.py::_SinksAPI.sink_get\n def sink_get(self, project, sink_name):\n \"\"\"API call: retrieve a sink resource.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/get\n\n :type project: string\n :param project: ID of the project containing the sink.\n\n :type sink_name: string\n :param sink_name: the name of the sink\n\n :rtype: dict\n :returns: The JSON sink object returned from the API.\n \"\"\"\n target = '/projects/%s/sinks/%s' % (project, sink_name)\n return self._connection.api_request(method='GET', path=target)","label":0},{"cid":"google/cloud/logging/connection.py::_SinksAPI.sink_update","text":"google/cloud/logging/connection.py::_SinksAPI.sink_update\n def sink_update(self, project, sink_name, filter_, destination):\n \"\"\"API call: update a sink resource.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/update\n\n :type project: string\n :param project: ID of the project containing the sink.\n\n :type sink_name: string\n :param sink_name: the name of the sink\n\n :type filter_: string\n :param filter_: the advanced logs filter expression defining the\n entries exported by the sink.\n\n :type destination: string\n :param destination: destination URI for the entries exported by\n the sink.\n \"\"\"\n target = '/projects/%s/sinks/%s' % (project, sink_name)\n data = {\n 'name': sink_name,\n 'filter': filter_,\n 'destination': destination,\n }\n self._connection.api_request(method='PUT', path=target, data=data)","label":0},{"cid":"google/cloud/logging/connection.py::_SinksAPI.sink_delete","text":"google/cloud/logging/connection.py::_SinksAPI.sink_delete\n def sink_delete(self, project, sink_name):\n \"\"\"API call: delete a sink resource.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/delete\n\n :type project: string\n :param project: ID of the project containing the sink.\n\n :type sink_name: string\n :param sink_name: the name of the sink\n \"\"\"\n target = '/projects/%s/sinks/%s' % (project, sink_name)\n self._connection.api_request(method='DELETE', path=target)","label":0},{"cid":"google/cloud/logging/connection.py::_MetricsAPI.__init__","text":"google/cloud/logging/connection.py::_MetricsAPI.__init__\n def __init__(self, connection):\n self._connection = connection","label":0},{"cid":"google/cloud/logging/connection.py::_MetricsAPI.list_metrics","text":"google/cloud/logging/connection.py::_MetricsAPI.list_metrics\n def list_metrics(self, project, page_size=None, page_token=None):\n \"\"\"List metrics for the project associated with this client.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/list\n\n :type project: string\n :param project: ID of the project whose metrics are to be listed.\n\n :type page_size: int\n :param page_size: maximum number of metrics to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: str\n :param page_token: opaque marker for the next \"page\" of metrics. If not\n passed, the API will return the first page of\n metrics.\n\n :rtype: tuple, (list, str)\n :returns: list of mappings, plus a \"next page token\" string:\n if not None, indicates that more metrics can be retrieved\n with another call (pass that value as ``page_token``).\n \"\"\"\n params = {}\n\n if page_size is not None:\n params['pageSize'] = page_size\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/metrics' % (project,)\n resp = self._connection.api_request(\n method='GET', path=path, query_params=params)\n metrics = resp.get('metrics', ())\n return metrics, resp.get('nextPageToken')","label":0},{"cid":"google/cloud/logging/connection.py::_MetricsAPI.metric_create","text":"google/cloud/logging/connection.py::_MetricsAPI.metric_create\n def metric_create(self, project, metric_name, filter_, description=None):\n \"\"\"API call: create a metric resource.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/create\n\n :type project: string\n :param project: ID of the project in which to create the metric.\n\n :type metric_name: string\n :param metric_name: the name of the metric\n\n :type filter_: string\n :param filter_: the advanced logs filter expression defining the\n entries exported by the metric.\n\n :type description: string\n :param description: description of the metric.\n \"\"\"\n target = '/projects/%s/metrics' % (project,)\n data = {\n 'name': metric_name,\n 'filter': filter_,\n 'description': description,\n }\n self._connection.api_request(method='POST', path=target, data=data)","label":0},{"cid":"google/cloud/logging/connection.py::_MetricsAPI.metric_get","text":"google/cloud/logging/connection.py::_MetricsAPI.metric_get\n def metric_get(self, project, metric_name):\n \"\"\"API call: retrieve a metric resource.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/get\n\n :type project: string\n :param project: ID of the project containing the metric.\n\n :type metric_name: string\n :param metric_name: the name of the metric\n\n :rtype: dict\n :returns: The JSON metric object returned from the API.\n \"\"\"\n target = '/projects/%s/metrics/%s' % (project, metric_name)\n return self._connection.api_request(method='GET', path=target)","label":0},{"cid":"google/cloud/logging/connection.py::_MetricsAPI.metric_update","text":"google/cloud/logging/connection.py::_MetricsAPI.metric_update\n def metric_update(self, project, metric_name, filter_, description):\n \"\"\"API call: update a metric resource.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/update\n\n :type project: string\n :param project: ID of the project containing the metric.\n\n :type metric_name: string\n :param metric_name: the name of the metric\n\n :type filter_: string\n :param filter_: the advanced logs filter expression defining the\n entries exported by the metric.\n\n :type description: string\n :param description: description of the metric.\n \"\"\"\n target = '/projects/%s/metrics/%s' % (project, metric_name)\n data = {\n 'name': metric_name,\n 'filter': filter_,\n 'description': description,\n }\n self._connection.api_request(method='PUT', path=target, data=data)","label":0},{"cid":"google/cloud/logging/connection.py::_MetricsAPI.metric_delete","text":"google/cloud/logging/connection.py::_MetricsAPI.metric_delete\n def metric_delete(self, project, metric_name):\n \"\"\"API call: delete a metric resource.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/delete\n\n :type project: string\n :param project: ID of the project containing the metric.\n\n :type metric_name: string\n :param metric_name: the name of the metric.\n \"\"\"\n target = '/projects/%s/metrics/%s' % (project, metric_name)\n self._connection.api_request(method='DELETE', path=target)","label":0},{"cid":"google/cloud/logging/entries.py::logger_name_from_path","text":"google/cloud/logging/entries.py::logger_name_from_path\ndef logger_name_from_path(path):\n \"\"\"Validate a logger URI path and get the logger name.\n\n :type path: str\n :param path: URI path for a logger API request.\n\n :rtype: str\n :returns: Logger name parsed from ``path``.\n :raises: :class:`ValueError` if the ``path`` is ill-formed or if\n the project from the ``path`` does not agree with the\n ``project`` passed in.\n \"\"\"\n return _name_from_project_path(path, None, _LOGGER_TEMPLATE)","label":0},{"cid":"google/cloud/logging/entries.py::_BaseEntry.__init__","text":"google/cloud/logging/entries.py::_BaseEntry.__init__\n def __init__(self, payload, logger, insert_id=None, timestamp=None,\n labels=None, severity=None, http_request=None):\n self.payload = payload\n self.logger = logger\n self.insert_id = insert_id\n self.timestamp = timestamp\n self.labels = labels\n self.severity = severity\n self.http_request = http_request","label":0},{"cid":"google/cloud/logging/entries.py::_BaseEntry.from_api_repr","text":"google/cloud/logging/entries.py::_BaseEntry.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client, loggers=None):\n \"\"\"Factory: construct an entry given its API representation\n\n :type resource: dict\n :param resource: text entry resource representation returned from\n the API\n\n :type client: :class:`google.cloud.logging.client.Client`\n :param client: Client which holds credentials and project\n configuration.\n\n :type loggers: dict or None\n :param loggers: A mapping of logger fullnames -> loggers. If not\n passed, the entry will have a newly-created logger.\n\n :rtype: :class:`google.cloud.logging.entries.TextEntry`\n :returns: Text entry parsed from ``resource``.\n \"\"\"\n if loggers is None:\n loggers = {}\n logger_fullname = resource['logName']\n logger = loggers.get(logger_fullname)\n if logger is None:\n logger_name = logger_name_from_path(logger_fullname)\n logger = loggers[logger_fullname] = client.logger(logger_name)\n payload = resource[cls._PAYLOAD_KEY]\n insert_id = resource.get('insertId')\n timestamp = resource.get('timestamp')\n if timestamp is not None:\n timestamp = _rfc3339_nanos_to_datetime(timestamp)\n labels = resource.get('labels')\n severity = resource.get('severity')\n http_request = resource.get('httpRequest')\n return cls(payload, logger, insert_id=insert_id, timestamp=timestamp,\n labels=labels, severity=severity, http_request=http_request)","label":0},{"cid":"google/cloud/logging/entries.py::ProtobufEntry.parse_message","text":"google/cloud/logging/entries.py::ProtobufEntry.parse_message\n def parse_message(self, message):\n \"\"\"Parse payload into a protobuf message.\n\n Mutates the passed-in ``message`` in place.\n\n :type message: Protobuf message\n :param message: the message to be logged\n \"\"\"\n Parse(json.dumps(self.payload), message)","label":0},{"cid":"google/cloud/logging/handlers/handlers.py::CloudLoggingHandler.__init__","text":"google/cloud/logging/handlers/handlers.py::CloudLoggingHandler.__init__\n def __init__(self, client,\n name=DEFAULT_LOGGER_NAME,\n transport=BackgroundThreadTransport):\n super(CloudLoggingHandler, self).__init__()\n self.name = name\n self.client = client\n self.transport = transport(client, name)","label":0},{"cid":"google/cloud/logging/handlers/handlers.py::CloudLoggingHandler.emit","text":"google/cloud/logging/handlers/handlers.py::CloudLoggingHandler.emit\n def emit(self, record):\n \"\"\"Actually log the specified logging record.\n\n Overrides the default emit behavior of ``StreamHandler``.\n\n See: https://docs.python.org/2/library/logging.html#handler-objects\n\n :type record: :class:`logging.LogRecord`\n :param record: The record to be logged.\n \"\"\"\n message = super(CloudLoggingHandler, self).format(record)\n self.transport.send(record, message)","label":0},{"cid":"google/cloud/logging/handlers/handlers.py::setup_logging","text":"google/cloud/logging/handlers/handlers.py::setup_logging\ndef setup_logging(handler, excluded_loggers=EXCLUDE_LOGGER_DEFAULTS):\n \"\"\"Attach the ``CloudLogging`` handler to the Python root logger\n\n Excludes loggers that this library itself uses to avoid\n infinite recursion.\n\n :type handler: :class:`logging.handler`\n :param handler: the handler to attach to the global handler\n\n :type excluded_loggers: tuple\n :param excluded_loggers: The loggers to not attach the handler to. This\n will always include the loggers in the path of\n the logging client itself.\n\n Example:\n\n .. doctest::\n\n import logging\n import google.cloud.logging\n from google.cloud.logging.handlers import CloudLoggingHandler\n\n client = google.cloud.logging.Client()\n handler = CloudLoggingHandler(client)\n google.cloud.logging.setup_logging(handler)\n logging.getLogger().setLevel(logging.DEBUG)\n\n logging.error('bad news') # API call\n\n \"\"\"\n all_excluded_loggers = set(excluded_loggers + EXCLUDE_LOGGER_DEFAULTS)\n logger = logging.getLogger()\n logger.addHandler(handler)\n logger.addHandler(logging.StreamHandler())\n for logger_name in all_excluded_loggers:\n logger = logging.getLogger(logger_name)\n logger.propagate = False\n logger.addHandler(logging.StreamHandler())","label":0},{"cid":"google/cloud/logging/handlers/transports/background_thread.py::_Worker.__init__","text":"google/cloud/logging/handlers/transports/background_thread.py::_Worker.__init__\n def __init__(self, logger):\n self.started = False\n self.stopping = False\n self.stopped = False\n\n # _entries_condition is used to signal from the main thread whether\n # there are any waiting queued logger entries to be written\n self._entries_condition = threading.Condition()\n\n # _stop_condition is used to signal from the worker thread to the\n # main thread that it's finished its last entries\n self._stop_condition = threading.Condition()\n\n # This object continually reuses the same :class:`Batch` object to\n # write multiple entries at the same time.\n self.logger = logger\n self.batch = self.logger.batch()\n\n self._thread = None\n\n # Number in seconds of how long to wait for worker to send remaining\n self._stop_timeout = 5\n\n self._start()","label":0},{"cid":"google/cloud/logging/handlers/transports/background_thread.py::_Worker._run","text":"google/cloud/logging/handlers/transports/background_thread.py::_Worker._run\n def _run(self):\n \"\"\"The entry point for the worker thread.\n\n Loops until ``stopping`` is set to :data:`True`, and commits batch\n entries written during :meth:`enqueue`.\n \"\"\"\n try:\n self._entries_condition.acquire()\n self.started = True\n while not self.stopping:\n if len(self.batch.entries) == 0:\n # branch coverage of this code extremely flaky\n self._entries_condition.wait() # pragma: NO COVER\n\n if len(self.batch.entries) > 0:\n self.batch.commit()\n finally:\n self._entries_condition.release()\n\n # main thread may be waiting for worker thread to finish writing its\n # final entries. here we signal that it's done.\n self._stop_condition.acquire()\n self._stop_condition.notify()\n self._stop_condition.release()","label":0},{"cid":"google/cloud/logging/handlers/transports/background_thread.py::_Worker._start","text":"google/cloud/logging/handlers/transports/background_thread.py::_Worker._start\n def _start(self):\n \"\"\"Called by this class's constructor\n\n This method is responsible for starting the thread and registering\n the exit handlers.\n \"\"\"\n try:\n self._entries_condition.acquire()\n self._thread = threading.Thread(\n target=self._run,\n name='google.cloud.logging.handlers.transport.Worker')\n self._thread.setDaemon(True)\n self._thread.start()\n finally:\n self._entries_condition.release()\n atexit.register(self._stop)","label":0},{"cid":"google/cloud/logging/handlers/transports/background_thread.py::_Worker._stop","text":"google/cloud/logging/handlers/transports/background_thread.py::_Worker._stop\n def _stop(self):\n \"\"\"Signals the worker thread to shut down\n\n Also waits for ``stop_timeout`` seconds for the worker to finish.\n\n This method is called by the ``atexit`` handler registered by\n :meth:`start`.\n \"\"\"\n if not self.started or self.stopping:\n return\n\n # lock the stop condition first so that the worker\n # thread can't notify it's finished before we wait\n self._stop_condition.acquire()\n\n # now notify the worker thread to shutdown\n self._entries_condition.acquire()\n self.stopping = True\n self._entries_condition.notify()\n self._entries_condition.release()\n\n # now wait for it to signal it's finished\n self._stop_condition.wait(self._stop_timeout)\n self._stop_condition.release()\n self.stopped = True","label":0},{"cid":"google/cloud/logging/handlers/transports/background_thread.py::_Worker.enqueue","text":"google/cloud/logging/handlers/transports/background_thread.py::_Worker.enqueue\n def enqueue(self, record, message):\n \"\"\"Queues up a log entry to be written by the background thread.\"\"\"\n try:\n self._entries_condition.acquire()\n if self.stopping:\n return\n info = {'message': message, 'python_logger': record.name}\n self.batch.log_struct(info, severity=record.levelname)\n self._entries_condition.notify()\n finally:\n self._entries_condition.release()","label":0},{"cid":"google/cloud/logging/handlers/transports/background_thread.py::BackgroundThreadTransport.__init__","text":"google/cloud/logging/handlers/transports/background_thread.py::BackgroundThreadTransport.__init__\n def __init__(self, client, name):\n http = copy.deepcopy(client.connection.http)\n http = client.connection.credentials.authorize(http)\n self.client = Client(client.project,\n client.connection.credentials,\n http)\n logger = self.client.logger(name)\n self.worker = _Worker(logger)","label":0},{"cid":"google/cloud/logging/handlers/transports/background_thread.py::BackgroundThreadTransport.send","text":"google/cloud/logging/handlers/transports/background_thread.py::BackgroundThreadTransport.send\n def send(self, record, message):\n \"\"\"Overrides Transport.send().\n\n :type record: :class:`logging.LogRecord`\n :param record: Python log record that the handler was called with.\n\n :type message: str\n :param message: The message from the ``LogRecord`` after being\n formatted by the associated log formatters.\n \"\"\"\n self.worker.enqueue(record, message)","label":0},{"cid":"google/cloud/logging/handlers/transports/base.py::Transport.send","text":"google/cloud/logging/handlers/transports/base.py::Transport.send\n def send(self, record, message):\n \"\"\"Transport send to be implemented by subclasses.\n\n :type record: :class:`logging.LogRecord`\n :param record: Python log record that the handler was called with.\n\n :type message: str\n :param message: The message from the ``LogRecord`` after being\n formatted by the associated log formatters.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"google/cloud/logging/handlers/transports/sync.py::SyncTransport.__init__","text":"google/cloud/logging/handlers/transports/sync.py::SyncTransport.__init__\n def __init__(self, client, name):\n self.logger = client.logger(name)","label":0},{"cid":"google/cloud/logging/handlers/transports/sync.py::SyncTransport.send","text":"google/cloud/logging/handlers/transports/sync.py::SyncTransport.send\n def send(self, record, message):\n \"\"\"Overrides transport.send().\n\n :type record: :class:`logging.LogRecord`\n :param record: Python log record that the handler was called with.\n\n :type message: str\n :param message: The message from the ``LogRecord`` after being\n formatted by the associated log formatters.\n \"\"\"\n info = {'message': message, 'python_logger': record.name}\n self.logger.log_struct(info, severity=record.levelname)","label":0},{"cid":"google/cloud/logging/logger.py::Logger.__init__","text":"google/cloud/logging/logger.py::Logger.__init__\n def __init__(self, name, client, labels=None):\n self.name = name\n self._client = client\n self.labels = labels","label":0},{"cid":"google/cloud/logging/logger.py::Logger.client","text":"google/cloud/logging/logger.py::Logger.client\n @property\n def client(self):\n \"\"\"Clent bound to the logger.\"\"\"\n return self._client","label":0},{"cid":"google/cloud/logging/logger.py::Logger.project","text":"google/cloud/logging/logger.py::Logger.project\n @property\n def project(self):\n \"\"\"Project bound to the logger.\"\"\"\n return self._client.project","label":0},{"cid":"google/cloud/logging/logger.py::Logger.full_name","text":"google/cloud/logging/logger.py::Logger.full_name\n @property\n def full_name(self):\n \"\"\"Fully-qualified name used in logging APIs\"\"\"\n return 'projects/%s/logs/%s' % (self.project, self.name)","label":0},{"cid":"google/cloud/logging/logger.py::Logger.path","text":"google/cloud/logging/logger.py::Logger.path\n @property\n def path(self):\n \"\"\"URI path for use in logging APIs\"\"\"\n return '/%s' % (self.full_name,)","label":0},{"cid":"google/cloud/logging/logger.py::Logger._require_client","text":"google/cloud/logging/logger.py::Logger._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current logger.\n\n :rtype: :class:`google.cloud.logging.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._client\n return client","label":0},{"cid":"google/cloud/logging/logger.py::Logger.batch","text":"google/cloud/logging/logger.py::Logger.batch\n def batch(self, client=None):\n \"\"\"Return a batch to use as a context manager.\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current topic.\n\n :rtype: :class:`Batch`\n :returns: A batch to use as a context manager.\n \"\"\"\n client = self._require_client(client)\n return Batch(self, client)","label":0},{"cid":"google/cloud/logging/logger.py::Logger._make_entry_resource","text":"google/cloud/logging/logger.py::Logger._make_entry_resource\n def _make_entry_resource(self, text=None, info=None, message=None,\n labels=None, insert_id=None, severity=None,\n http_request=None):\n \"\"\"Return a log entry resource of the appropriate type.\n\n Helper for :meth:`log_text`, :meth:`log_struct`, and :meth:`log_proto`.\n\n Only one of ``text``, ``info``, or ``message`` should be passed.\n\n :type text: string or :class:`NoneType`\n :param text: text payload\n\n :type info: dict or :class:`NoneType`\n :param info: struct payload\n\n :type message: Protobuf message or :class:`NoneType`\n :param message: protobuf payload\n\n :type labels: dict or :class:`NoneType`\n :param labels: labels passed in to calling method.\n\n :type insert_id: string or :class:`NoneType`\n :param insert_id: (optional) unique ID for log entry.\n\n :type severity: string or :class:`NoneType`\n :param severity: (optional) severity of event being logged.\n\n :type http_request: dict or :class:`NoneType`\n :param http_request: (optional) info about HTTP request associated with\n the entry\n\n :rtype: dict\n :returns: The JSON resource created.\n \"\"\"\n resource = {\n 'logName': self.full_name,\n 'resource': {'type': 'global'},\n }\n\n if text is not None:\n resource['textPayload'] = text\n\n if info is not None:\n resource['jsonPayload'] = info\n\n if message is not None:\n as_json_str = MessageToJson(message)\n as_json = json.loads(as_json_str)\n resource['protoPayload'] = as_json\n\n if labels is None:\n labels = self.labels\n\n if labels is not None:\n resource['labels'] = labels\n\n if insert_id is not None:\n resource['insertId'] = insert_id\n\n if s","label":0},{"cid":"google/cloud/logging/logger.py::Logger.log_text","text":"google/cloud/logging/logger.py::Logger.log_text\n def log_text(self, text, client=None, labels=None, insert_id=None,\n severity=None, http_request=None):\n \"\"\"API call: log a text message via a POST request\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/write\n\n :type text: text\n :param text: the log message.\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current logger.\n\n :type labels: dict or :class:`NoneType`\n :param labels: (optional) mapping of labels for the entry.\n\n :type insert_id: string or :class:`NoneType`\n :param insert_id: (optional) unique ID for log entry.\n\n :type severity: string or :class:`NoneType`\n :param severity: (optional) severity of event being logged.\n\n :type http_request: dict or :class:`NoneType`\n :param http_request: (optional) info about HTTP request associated with\n the entry\n \"\"\"\n client = self._require_client(client)\n entry_resource = self._make_entry_resource(\n text=text, labels=labels, insert_id=insert_id, severity=severity,\n http_request=http_request)\n client.logging_api.write_entries([entry_resource])","label":0},{"cid":"google/cloud/logging/logger.py::Logger.log_struct","text":"google/cloud/logging/logger.py::Logger.log_struct\n def log_struct(self, info, client=None, labels=None, insert_id=None,\n severity=None, http_request=None):\n \"\"\"API call: log a structured message via a POST request\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/write\n\n :type info: dict\n :param info: the log entry information\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current logger.\n\n :type labels: dict or :class:`NoneType`\n :param labels: (optional) mapping of labels for the entry.\n\n :type insert_id: string or :class:`NoneType`\n :param insert_id: (optional) unique ID for log entry.\n\n :type severity: string or :class:`NoneType`\n :param severity: (optional) severity of event being logged.\n\n :type http_request: dict or :class:`NoneType`\n :param http_request: (optional) info about HTTP request associated with\n the entry.\n \"\"\"\n client = self._require_client(client)\n entry_resource = self._make_entry_resource(\n info=info, labels=labels, insert_id=insert_id, severity=severity,\n http_request=http_request)\n client.logging_api.write_entries([entry_resource])","label":0},{"cid":"google/cloud/logging/logger.py::Logger.log_proto","text":"google/cloud/logging/logger.py::Logger.log_proto\n def log_proto(self, message, client=None, labels=None, insert_id=None,\n severity=None, http_request=None):\n \"\"\"API call: log a protobuf message via a POST request\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/write\n\n :type message: Protobuf message\n :param message: the message to be logged\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current logger.\n\n :type labels: dict or :class:`NoneType`\n :param labels: (optional) mapping of labels for the entry.\n\n :type insert_id: string or :class:`NoneType`\n :param insert_id: (optional) unique ID for log entry.\n\n :type severity: string or :class:`NoneType`\n :param severity: (optional) severity of event being logged.\n\n :type http_request: dict or :class:`NoneType`\n :param http_request: (optional) info about HTTP request associated with\n the entry.\n \"\"\"\n client = self._require_client(client)\n entry_resource = self._make_entry_resource(\n message=message, labels=labels, insert_id=insert_id,\n severity=severity, http_request=http_request)\n client.logging_api.write_entries([entry_resource])","label":0},{"cid":"google/cloud/logging/logger.py::Logger.delete","text":"google/cloud/logging/logger.py::Logger.delete\n def delete(self, client=None):\n \"\"\"API call: delete all entries in a logger via a DELETE request\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.logs/delete\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current logger.\n \"\"\"\n client = self._require_client(client)\n client.logging_api.logger_delete(self.project, self.name)","label":0},{"cid":"google/cloud/logging/logger.py::Logger.list_entries","text":"google/cloud/logging/logger.py::Logger.list_entries\n def list_entries(self, projects=None, filter_=None, order_by=None,\n page_size=None, page_token=None):\n \"\"\"Return a page of log entries.\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/list\n\n :type projects: list of strings\n :param projects: project IDs to include. If not passed,\n defaults to the project bound to the client.\n\n :type filter_: string\n :param filter_: a filter expression. See:\n https://cloud.google.com/logging/docs/view/advanced_filters\n\n :type order_by: string\n :param order_by: One of :data:`~google.cloud.logging.ASCENDING`\n or :data:`~google.cloud.logging.DESCENDING`.\n\n :type page_size: int\n :param page_size: maximum number of entries to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of entries. If not\n passed, the API will return the first page of\n entries.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`google.cloud.logging.entry.TextEntry`, plus a\n \"next page token\" string: if not None, indicates that\n more entries can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n log_filter = 'logName=%s' % (self.full_name,)\n if filter_ is not None:\n filter_ = '%s AND %s' % (filter_, log_filter)\n else:\n filter_ = log_filter\n return self.client.list_entries(\n projects=projects, filter_=filter_, order_by=order_by,\n page_size=page_size, page_token=page_token)","label":0},{"cid":"google/cloud/logging/logger.py::Batch.__init__","text":"google/cloud/logging/logger.py::Batch.__init__\n def __init__(self, logger, client):\n self.logger = logger\n self.entries = []\n self.client = client","label":0},{"cid":"google/cloud/logging/logger.py::Batch.__enter__","text":"google/cloud/logging/logger.py::Batch.__enter__\n def __enter__(self):\n return self","label":0},{"cid":"google/cloud/logging/logger.py::Batch.__exit__","text":"google/cloud/logging/logger.py::Batch.__exit__\n def __exit__(self, exc_type, exc_val, exc_tb):\n if exc_type is None:\n self.commit()","label":0},{"cid":"google/cloud/logging/logger.py::Batch.log_text","text":"google/cloud/logging/logger.py::Batch.log_text\n def log_text(self, text, labels=None, insert_id=None, severity=None,\n http_request=None):\n \"\"\"Add a text entry to be logged during :meth:`commit`.\n\n :type text: string\n :param text: the text entry\n\n :type labels: dict or :class:`NoneType`\n :param labels: (optional) mapping of labels for the entry.\n\n :type insert_id: string or :class:`NoneType`\n :param insert_id: (optional) unique ID for log entry.\n\n :type severity: string or :class:`NoneType`\n :param severity: (optional) severity of event being logged.\n\n :type http_request: dict or :class:`NoneType`\n :param http_request: (optional) info about HTTP request associated with\n the entry.\n \"\"\"\n self.entries.append(\n ('text', text, labels, insert_id, severity, http_request))","label":0},{"cid":"google/cloud/logging/logger.py::Batch.log_struct","text":"google/cloud/logging/logger.py::Batch.log_struct\n def log_struct(self, info, labels=None, insert_id=None, severity=None,\n http_request=None):\n \"\"\"Add a struct entry to be logged during :meth:`commit`.\n\n :type info: dict\n :param info: the struct entry\n\n :type labels: dict or :class:`NoneType`\n :param labels: (optional) mapping of labels for the entry.\n\n :type insert_id: string or :class:`NoneType`\n :param insert_id: (optional) unique ID for log entry.\n\n :type severity: string or :class:`NoneType`\n :param severity: (optional) severity of event being logged.\n\n :type http_request: dict or :class:`NoneType`\n :param http_request: (optional) info about HTTP request associated with\n the entry.\n \"\"\"\n self.entries.append(\n ('struct', info, labels, insert_id, severity, http_request))","label":0},{"cid":"google/cloud/logging/logger.py::Batch.log_proto","text":"google/cloud/logging/logger.py::Batch.log_proto\n def log_proto(self, message, labels=None, insert_id=None, severity=None,\n http_request=None):\n \"\"\"Add a protobuf entry to be logged during :meth:`commit`.\n\n :type message: protobuf message\n :param message: the protobuf entry\n\n :type labels: dict or :class:`NoneType`\n :param labels: (optional) mapping of labels for the entry.\n\n :type insert_id: string or :class:`NoneType`\n :param insert_id: (optional) unique ID for log entry.\n\n :type severity: string or :class:`NoneType`\n :param severity: (optional) severity of event being logged.\n\n :type http_request: dict or :class:`NoneType`\n :param http_request: (optional) info about HTTP request associated with\n the entry.\n \"\"\"\n self.entries.append(\n ('proto', message, labels, insert_id, severity, http_request))","label":0},{"cid":"google/cloud/logging/logger.py::Batch.commit","text":"google/cloud/logging/logger.py::Batch.commit\n def commit(self, client=None):\n \"\"\"Send saved log entries as a single API call.\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current batch.\n \"\"\"\n if client is None:\n client = self.client\n\n kwargs = {\n 'logger_name': self.logger.full_name,\n 'resource': {'type': 'global'},\n }\n if self.logger.labels is not None:\n kwargs['labels'] = self.logger.labels\n\n entries = []\n for entry_type, entry, labels, iid, severity, http_req in self.entries:\n if entry_type == 'text':\n info = {'textPayload': entry}\n elif entry_type == 'struct':\n info = {'jsonPayload': entry}\n elif entry_type == 'proto':\n as_json_str = MessageToJson(entry)\n as_json = json.loads(as_json_str)\n info = {'protoPayload': as_json}\n else:\n raise ValueError('Unknown entry type: %s' % (entry_type,))\n if labels is not None:\n info['labels'] = labels\n if iid is not None:\n info['insertId'] = iid\n if severity is not None:\n info['severity'] = severity\n if http_req is not None:\n info['httpRequest'] = http_req\n entries.append(info)\n\n client.logging_api.write_entries(entries, **kwargs)\n del self.entries[:]","label":0},{"cid":"google/cloud/logging/metric.py::Metric.__init__","text":"google/cloud/logging/metric.py::Metric.__init__\n def __init__(self, name, filter_=None, client=None, description=''):\n self.name = name\n self._client = client\n self.filter_ = filter_\n self.description = description","label":0},{"cid":"google/cloud/logging/metric.py::Metric.client","text":"google/cloud/logging/metric.py::Metric.client\n @property\n def client(self):\n \"\"\"Clent bound to the logger.\"\"\"\n return self._client","label":0},{"cid":"google/cloud/logging/metric.py::Metric.project","text":"google/cloud/logging/metric.py::Metric.project\n @property\n def project(self):\n \"\"\"Project bound to the logger.\"\"\"\n return self._client.project","label":0},{"cid":"google/cloud/logging/metric.py::Metric.full_name","text":"google/cloud/logging/metric.py::Metric.full_name\n @property\n def full_name(self):\n \"\"\"Fully-qualified name used in metric APIs\"\"\"\n return 'projects/%s/metrics/%s' % (self.project, self.name)","label":0},{"cid":"google/cloud/logging/metric.py::Metric.path","text":"google/cloud/logging/metric.py::Metric.path\n @property\n def path(self):\n \"\"\"URL path for the metric's APIs\"\"\"\n return '/%s' % (self.full_name,)","label":0},{"cid":"google/cloud/logging/metric.py::Metric.from_api_repr","text":"google/cloud/logging/metric.py::Metric.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client):\n \"\"\"Factory: construct a metric given its API representation\n\n :type resource: dict\n :param resource: metric resource representation returned from the API\n\n :type client: :class:`google.cloud.logging.client.Client`\n :param client: Client which holds credentials and project\n configuration for the metric.\n\n :rtype: :class:`google.cloud.logging.metric.Metric`\n :returns: Metric parsed from ``resource``.\n \"\"\"\n metric_name = resource['name']\n filter_ = resource['filter']\n description = resource.get('description', '')\n return cls(metric_name, filter_, client=client,\n description=description)","label":0},{"cid":"google/cloud/logging/metric.py::Metric._require_client","text":"google/cloud/logging/metric.py::Metric._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current metric.\n\n :rtype: :class:`google.cloud.logging.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._client\n return client","label":0},{"cid":"google/cloud/logging/metric.py::Metric.create","text":"google/cloud/logging/metric.py::Metric.create\n def create(self, client=None):\n \"\"\"API call: create the metric via a PUT request\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/create\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current metric.\n \"\"\"\n client = self._require_client(client)\n client.metrics_api.metric_create(\n self.project, self.name, self.filter_, self.description)","label":0},{"cid":"google/cloud/logging/metric.py::Metric.exists","text":"google/cloud/logging/metric.py::Metric.exists\n def exists(self, client=None):\n \"\"\"API call: test for the existence of the metric via a GET request\n\n See\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/get\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current metric.\n\n :rtype: bool\n :returns: Boolean indicating existence of the metric.\n \"\"\"\n client = self._require_client(client)\n\n try:\n client.metrics_api.metric_get(self.project, self.name)\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"google/cloud/logging/metric.py::Metric.reload","text":"google/cloud/logging/metric.py::Metric.reload\n def reload(self, client=None):\n \"\"\"API call: sync local metric configuration via a GET request\n\n See\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/get\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current metric.\n \"\"\"\n client = self._require_client(client)\n data = client.metrics_api.metric_get(self.project, self.name)\n self.description = data.get('description', '')\n self.filter_ = data['filter']","label":0},{"cid":"google/cloud/logging/metric.py::Metric.update","text":"google/cloud/logging/metric.py::Metric.update\n def update(self, client=None):\n \"\"\"API call: update metric configuration via a PUT request\n\n See\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/update\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current metric.\n \"\"\"\n client = self._require_client(client)\n client.metrics_api.metric_update(\n self.project, self.name, self.filter_, self.description)","label":0},{"cid":"google/cloud/logging/metric.py::Metric.delete","text":"google/cloud/logging/metric.py::Metric.delete\n def delete(self, client=None):\n \"\"\"API call: delete a metric via a DELETE request\n\n See\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/delete\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current metric.\n \"\"\"\n client = self._require_client(client)\n client.metrics_api.metric_delete(self.project, self.name)","label":0},{"cid":"google/cloud/logging/sink.py::Sink.__init__","text":"google/cloud/logging/sink.py::Sink.__init__\n def __init__(self, name, filter_=None, destination=None, client=None):\n self.name = name\n self.filter_ = filter_\n self.destination = destination\n self._client = client","label":0},{"cid":"google/cloud/logging/sink.py::Sink.client","text":"google/cloud/logging/sink.py::Sink.client\n @property\n def client(self):\n \"\"\"Clent bound to the sink.\"\"\"\n return self._client","label":0},{"cid":"google/cloud/logging/sink.py::Sink.project","text":"google/cloud/logging/sink.py::Sink.project\n @property\n def project(self):\n \"\"\"Project bound to the sink.\"\"\"\n return self._client.project","label":0},{"cid":"google/cloud/logging/sink.py::Sink.full_name","text":"google/cloud/logging/sink.py::Sink.full_name\n @property\n def full_name(self):\n \"\"\"Fully-qualified name used in sink APIs\"\"\"\n return 'projects/%s/sinks/%s' % (self.project, self.name)","label":0},{"cid":"google/cloud/logging/sink.py::Sink.path","text":"google/cloud/logging/sink.py::Sink.path\n @property\n def path(self):\n \"\"\"URL path for the sink's APIs\"\"\"\n return '/%s' % (self.full_name)","label":0},{"cid":"google/cloud/logging/sink.py::Sink.from_api_repr","text":"google/cloud/logging/sink.py::Sink.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client):\n \"\"\"Factory: construct a sink given its API representation\n\n :type resource: dict\n :param resource: sink resource representation returned from the API\n\n :type client: :class:`google.cloud.logging.client.Client`\n :param client: Client which holds credentials and project\n configuration for the sink.\n\n :rtype: :class:`google.cloud.logging.sink.Sink`\n :returns: Sink parsed from ``resource``.\n :raises: :class:`ValueError` if ``client`` is not ``None`` and the\n project from the resource does not agree with the project\n from the client.\n \"\"\"\n sink_name = resource['name']\n filter_ = resource['filter']\n destination = resource['destination']\n return cls(sink_name, filter_, destination, client=client)","label":0},{"cid":"google/cloud/logging/sink.py::Sink._require_client","text":"google/cloud/logging/sink.py::Sink._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current sink.\n\n :rtype: :class:`google.cloud.logging.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._client\n return client","label":0},{"cid":"google/cloud/logging/sink.py::Sink.create","text":"google/cloud/logging/sink.py::Sink.create\n def create(self, client=None):\n \"\"\"API call: create the sink via a PUT request\n\n See:\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/create\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current sink.\n \"\"\"\n client = self._require_client(client)\n client.sinks_api.sink_create(\n self.project, self.name, self.filter_, self.destination)","label":0},{"cid":"google/cloud/logging/sink.py::Sink.exists","text":"google/cloud/logging/sink.py::Sink.exists\n def exists(self, client=None):\n \"\"\"API call: test for the existence of the sink via a GET request\n\n See\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/get\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current sink.\n\n :rtype: bool\n :returns: Boolean indicating existence of the sink.\n \"\"\"\n client = self._require_client(client)\n\n try:\n client.sinks_api.sink_get(self.project, self.name)\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"google/cloud/logging/sink.py::Sink.reload","text":"google/cloud/logging/sink.py::Sink.reload\n def reload(self, client=None):\n \"\"\"API call: sync local sink configuration via a GET request\n\n See\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/get\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current sink.\n \"\"\"\n client = self._require_client(client)\n data = client.sinks_api.sink_get(self.project, self.name)\n self.filter_ = data['filter']\n self.destination = data['destination']","label":0},{"cid":"google/cloud/logging/sink.py::Sink.update","text":"google/cloud/logging/sink.py::Sink.update\n def update(self, client=None):\n \"\"\"API call: update sink configuration via a PUT request\n\n See\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/update\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current sink.\n \"\"\"\n client = self._require_client(client)\n client.sinks_api.sink_update(\n self.project, self.name, self.filter_, self.destination)","label":0},{"cid":"google/cloud/logging/sink.py::Sink.delete","text":"google/cloud/logging/sink.py::Sink.delete\n def delete(self, client=None):\n \"\"\"API call: delete a sink via a DELETE request\n\n See\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/delete\n\n :type client: :class:`~google.cloud.logging.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current sink.\n \"\"\"\n client = self._require_client(client)\n client.sinks_api.sink_delete(self.project, self.name)","label":0},{"cid":"google/cloud/monitoring/_dataframe.py::_build_dataframe","text":"google/cloud/monitoring/_dataframe.py::_build_dataframe\ndef _build_dataframe(time_series_iterable,\n label=None, labels=None): # pragma: NO COVER\n \"\"\"Build a :mod:`pandas` dataframe out of time series.\n\n :type time_series_iterable:\n iterable over :class:`~google.cloud.monitoring.timeseries.TimeSeries`\n :param time_series_iterable:\n An iterable (e.g., a query object) yielding time series.\n\n :type label: string or None\n :param label:\n The label name to use for the dataframe header. This can be the name\n of a resource label or metric label (e.g., ``\"instance_name\"``), or\n the string ``\"resource_type\"``.\n\n :type labels: list of strings, or None\n :param labels:\n A list or tuple of label names to use for the dataframe header.\n If more than one label name is provided, the resulting dataframe\n will have a multi-level column header.\n\n Specifying neither ``label`` or ``labels`` results in a dataframe\n with a multi-level column header including the resource type and\n all available resource and metric labels.\n\n Specifying both ``label`` and ``labels`` is an error.\n\n :rtype: :class:`pandas.DataFrame`\n :returns: A dataframe where each column represents one time series.\n \"\"\"\n import pandas # pylint: disable=import-error\n\n if labels is not None:\n if label is not None:\n raise ValueError('Cannot specify both \"label\" and \"labels\".')\n elif not labels:\n raise ValueError('\"labels\" must be non-empty or None.')\n\n columns = []\n headers = []\n for time_series in time_series_iterable:\n pandas_series = pandas.Series(\n data=[point.value for point in time_series.points],\n index=[point.end_time for point in time_series.points],\n )\n columns.append(pandas_series)\n headers.append(time_series.header())\n\n # Implement a smart default of using all available labels.\n ","label":0},{"cid":"google/cloud/monitoring/_dataframe.py::_sorted_resource_labels","text":"google/cloud/monitoring/_dataframe.py::_sorted_resource_labels\ndef _sorted_resource_labels(labels):\n \"\"\"Sort label names, putting well-known resource labels first.\"\"\"\n head = [label for label in TOP_RESOURCE_LABELS if label in labels]\n tail = sorted(label for label in labels\n if label not in TOP_RESOURCE_LABELS)\n return head + tail","label":0},{"cid":"google/cloud/monitoring/client.py::Client.query","text":"google/cloud/monitoring/client.py::Client.query\n def query(self,\n metric_type=Query.DEFAULT_METRIC_TYPE,\n end_time=None,\n days=0, hours=0, minutes=0):\n \"\"\"Construct a query object for retrieving metric data.\n\n Example::\n\n >>> query = client.query(minutes=5)\n >>> print(query.as_dataframe()) # Requires pandas.\n\n :type metric_type: string\n :param metric_type: The metric type name. The default value is\n :data:`Query.DEFAULT_METRIC_TYPE\n <google.cloud.monitoring.query.Query.DEFAULT_METRIC_TYPE>`,\n but please note that this default value is provided only for\n demonstration purposes and is subject to change. See the\n `supported metrics`_.\n\n :type end_time: :class:`datetime.datetime` or None\n :param end_time: The end time (inclusive) of the time interval\n for which results should be returned, as a datetime object.\n The default is the start of the current minute.\n\n The start time (exclusive) is determined by combining the\n values of ``days``, ``hours``, and ``minutes``, and\n subtracting the resulting duration from the end time.\n\n It is also allowed to omit the end time and duration here,\n in which case\n :meth:`~google.cloud.monitoring.query.Query.select_interval`\n must be called before the query is executed.\n\n :type days: integer\n :param days: The number of days in the time interval.\n\n :type hours: integer\n :param hours: The number of hours in the time interval.\n\n :type minutes: integer\n :param minutes: The number of minutes in the time interval.\n\n :rtype: :class:`~google.cloud.monitoring.query.Query`\n :returns: The query object.\n\n :raises: :exc:`ValueError` if ``end_time`` is specified but\n ``days``, ``hours``, and ``minutes`` are all zero.\n ","label":0},{"cid":"google/cloud/monitoring/client.py::Client.metric_descriptor","text":"google/cloud/monitoring/client.py::Client.metric_descriptor\n def metric_descriptor(self, type_,\n metric_kind=MetricKind.METRIC_KIND_UNSPECIFIED,\n value_type=ValueType.VALUE_TYPE_UNSPECIFIED,\n labels=(), unit='', description='', display_name=''):\n \"\"\"Construct a metric descriptor object.\n\n Metric descriptors specify the schema for a particular metric type.\n\n This factory method is used most often in conjunction with the metric\n descriptor\n :meth:`~google.cloud.monitoring.metric.MetricDescriptor.create`\n method to define custom metrics::\n\n >>> descriptor = client.metric_descriptor(\n ... 'custom.googleapis.com/my_metric',\n ... metric_kind=MetricKind.GAUGE,\n ... value_type=ValueType.DOUBLE,\n ... description='This is a simple example of a custom metric.')\n >>> descriptor.create()\n\n Here is an example where the custom metric is parameterized by a\n metric label::\n\n >>> label = LabelDescriptor('response_code', LabelValueType.INT64,\n ... description='HTTP status code')\n >>> descriptor = client.metric_descriptor(\n ... 'custom.googleapis.com/my_app/response_count',\n ... metric_kind=MetricKind.CUMULATIVE,\n ... value_type=ValueType.INT64,\n ... labels=[label],\n ... description='Cumulative count of HTTP responses.')\n >>> descriptor.create()\n\n :type type_: string\n :param type_:\n The metric type including a DNS name prefix. For example:\n ``\"custom.googleapis.com/my_metric\"``\n\n :type metric_kind: string\n :param metric_kind:\n The kind of measurement. It must be one of\n :data:`MetricKind.GAUGE`, :data:`MetricKind.DELTA`,\n or :data:`MetricKind.CUMULATIV","label":0},{"cid":"google/cloud/monitoring/client.py::Client.metric","text":"google/cloud/monitoring/client.py::Client.metric\n @staticmethod\n def metric(type_, labels):\n \"\"\"Factory for constructing metric objects.\n\n :class:`~google.cloud.monitoring.metric.Metric` objects are typically\n created to write custom metric values. The type should match the\n metric type specified in the\n :class:`~google.cloud.monitoring.metric.MetricDescriptor` used to\n create the custom metric::\n\n >>> metric = client.metric('custom.googleapis.com/my_metric',\n ... labels={\n ... 'status': 'successful',\n ... })\n\n :type type_: string\n :param type_: The metric type name.\n\n :type labels: dict\n :param labels: A mapping from label names to values for all labels\n enumerated in the associated\n :class:`~google.cloud.monitoring.metric.MetricDescriptor`.\n\n :rtype: :class:`~google.cloud.monitoring.metric.Metric`\n :returns: The metric object.\n \"\"\"\n return Metric(type=type_, labels=labels)","label":0},{"cid":"google/cloud/monitoring/client.py::Client.resource","text":"google/cloud/monitoring/client.py::Client.resource\n @staticmethod\n def resource(type_, labels):\n \"\"\"Factory for constructing monitored resource objects.\n\n A monitored resource object (\n :class:`~google.cloud.monitoring.resource.Resource`) is\n typically used to create a\n :class:`~google.cloud.monitoring.timeseries.TimeSeries` object.\n\n For a list of possible monitored resource types and their associated\n labels, see:\n\n https://cloud.google.com/monitoring/api/resources\n\n :type type_: string\n :param type_: The monitored resource type name.\n\n :type labels: dict\n :param labels: A mapping from label names to values for all labels\n enumerated in the associated\n :class:`~google.cloud.monitoring.resource.ResourceDescriptor`,\n except that ``project_id`` can and should be omitted\n when writing time series data.\n\n :rtype: :class:`~google.cloud.monitoring.resource.Resource`\n :returns: A monitored resource object.\n \"\"\"\n return Resource(type_, labels)","label":0},{"cid":"google/cloud/monitoring/client.py::Client.time_series","text":"google/cloud/monitoring/client.py::Client.time_series\n @staticmethod\n def time_series(metric, resource, value,\n end_time=None, start_time=None):\n \"\"\"Construct a time series object for a single data point.\n\n .. note::\n\n While :class:`~google.cloud.monitoring.timeseries.TimeSeries`\n objects returned by the API typically have multiple data points,\n :class:`~google.cloud.monitoring.timeseries.TimeSeries` objects\n sent to the API must have at most one point.\n\n For example::\n\n >>> timeseries = client.time_series(metric, resource, 1.23,\n ... end_time=end)\n\n For more information, see:\n\n https://cloud.google.com/monitoring/api/ref_v3/rest/v3/TimeSeries\n\n :type metric: :class:`~google.cloud.monitoring.metric.Metric`\n :param metric: A :class:`~google.cloud.monitoring.metric.Metric`.\n\n :type resource: :class:`~google.cloud.monitoring.resource.Resource`\n :param resource: A :class:`~google.cloud.monitoring.resource.Resource`\n object.\n\n :type value: bool, int, string, or float\n :param value:\n The value of the data point to create for the\n :class:`~google.cloud.monitoring.timeseries.TimeSeries`.\n\n .. note::\n\n The Python type of the value will determine the\n :class:`~ValueType` sent to the API, which must match the value\n type specified in the metric descriptor. For example, a Python\n float will be sent to the API as a :data:`ValueType.DOUBLE`.\n\n :type end_time: :class:`~datetime.datetime`\n :param end_time:\n The end time for the point to be included in the time series.\n Assumed to be UTC if no time zone information is present.\n Defaults to the current time, as obtained by calling\n :meth:`datetime.datetime.utcnow`.\n\n ","label":0},{"cid":"google/cloud/monitoring/client.py::Client.fetch_metric_descriptor","text":"google/cloud/monitoring/client.py::Client.fetch_metric_descriptor\n def fetch_metric_descriptor(self, metric_type):\n \"\"\"Look up a metric descriptor by type.\n\n Example::\n\n >>> METRIC = 'compute.googleapis.com/instance/cpu/utilization'\n >>> print(client.fetch_metric_descriptor(METRIC))\n\n :type metric_type: string\n :param metric_type: The metric type name.\n\n :rtype: :class:`~google.cloud.monitoring.metric.MetricDescriptor`\n :returns: The metric descriptor instance.\n\n :raises: :class:`google.cloud.exceptions.NotFound` if the metric\n descriptor is not found.\n \"\"\"\n return MetricDescriptor._fetch(self, metric_type)","label":0},{"cid":"google/cloud/monitoring/client.py::Client.list_metric_descriptors","text":"google/cloud/monitoring/client.py::Client.list_metric_descriptors\n def list_metric_descriptors(self, filter_string=None, type_prefix=None):\n \"\"\"List all metric descriptors for the project.\n\n Examples::\n\n >>> for descriptor in client.list_metric_descriptors():\n ... print(descriptor.type)\n\n >>> for descriptor in client.list_metric_descriptors(\n ... type_prefix='custom.'):\n ... print(descriptor.type)\n\n :type filter_string: string or None\n :param filter_string:\n An optional filter expression describing the metric descriptors\n to be returned. See the `filter documentation`_.\n\n :type type_prefix: string or None\n :param type_prefix: An optional prefix constraining the selected\n metric types. This adds ``metric.type = starts_with(\"<prefix>\")``\n to the filter.\n\n :rtype:\n list of :class:`~google.cloud.monitoring.metric.MetricDescriptor`\n :returns: A list of metric descriptor instances.\n\n .. _filter documentation:\n https://cloud.google.com/monitoring/api/v3/filters\n \"\"\"\n return MetricDescriptor._list(self, filter_string,\n type_prefix=type_prefix)","label":0},{"cid":"google/cloud/monitoring/client.py::Client.fetch_resource_descriptor","text":"google/cloud/monitoring/client.py::Client.fetch_resource_descriptor\n def fetch_resource_descriptor(self, resource_type):\n \"\"\"Look up a monitored resource descriptor by type.\n\n Example::\n\n >>> print(client.fetch_resource_descriptor('gce_instance'))\n\n :type resource_type: string\n :param resource_type: The resource type name.\n\n :rtype: :class:`~google.cloud.monitoring.resource.ResourceDescriptor`\n :returns: The resource descriptor instance.\n\n :raises: :class:`google.cloud.exceptions.NotFound` if the resource\n descriptor is not found.\n \"\"\"\n return ResourceDescriptor._fetch(self, resource_type)","label":0},{"cid":"google/cloud/monitoring/client.py::Client.list_resource_descriptors","text":"google/cloud/monitoring/client.py::Client.list_resource_descriptors\n def list_resource_descriptors(self, filter_string=None):\n \"\"\"List all monitored resource descriptors for the project.\n\n Example::\n\n >>> for descriptor in client.list_resource_descriptors():\n ... print(descriptor.type)\n\n :type filter_string: string or None\n :param filter_string:\n An optional filter expression describing the resource descriptors\n to be returned. See the `filter documentation`_.\n\n :rtype: list of\n :class:`~google.cloud.monitoring.resource.ResourceDescriptor`\n :returns: A list of resource descriptor instances.\n\n .. _filter documentation:\n https://cloud.google.com/monitoring/api/v3/filters\n \"\"\"\n return ResourceDescriptor._list(self, filter_string)","label":0},{"cid":"google/cloud/monitoring/client.py::Client.group","text":"google/cloud/monitoring/client.py::Client.group\n def group(self, group_id=None, display_name=None, parent_id=None,\n filter_string=None, is_cluster=False):\n \"\"\"Factory constructor for group object.\n\n .. note::\n This will not make an HTTP request; it simply instantiates\n a group object owned by this client.\n\n :type group_id: string or None\n :param group_id: The ID of the group.\n\n :type display_name: string or None\n :param display_name:\n A user-assigned name for this group, used only for display\n purposes.\n\n :type parent_id: string or None\n :param parent_id:\n The ID of the group's parent, if it has one.\n\n :type filter_string: string or None\n :param filter_string:\n The filter string used to determine which monitored resources\n belong to this group.\n\n :type is_cluster: boolean\n :param is_cluster:\n If true, the members of this group are considered to be a cluster.\n The system can perform additional analysis on groups that are\n clusters.\n\n :rtype: :class:`Group`\n :returns: The group created with the passed-in arguments.\n\n :raises:\n :exc:`ValueError` if both ``group_id`` and ``name`` are specified.\n \"\"\"\n return Group(\n self,\n group_id=group_id,\n display_name=display_name,\n parent_id=parent_id,\n filter_string=filter_string,\n is_cluster=is_cluster,\n )","label":0},{"cid":"google/cloud/monitoring/client.py::Client.fetch_group","text":"google/cloud/monitoring/client.py::Client.fetch_group\n def fetch_group(self, group_id):\n \"\"\"Fetch a group from the API based on it's ID.\n\n Example::\n\n >>> try:\n >>> group = client.fetch_group('1234')\n >>> except google.cloud.exceptions.NotFound:\n >>> print('That group does not exist!')\n\n :type group_id: string\n :param group_id: The ID of the group.\n\n :rtype: :class:`~google.cloud.monitoring.group.Group`\n :returns: The group instance.\n\n :raises: :class:`google.cloud.exceptions.NotFound` if the group\n is not found.\n \"\"\"\n return Group._fetch(self, group_id)","label":0},{"cid":"google/cloud/monitoring/client.py::Client.list_groups","text":"google/cloud/monitoring/client.py::Client.list_groups\n def list_groups(self):\n \"\"\"List all groups for the project.\n\n Example::\n\n >>> for group in client.list_groups():\n ... print((group.display_name, group.name))\n\n :rtype: list of :class:`~google.cloud.monitoring.group.Group`\n :returns: A list of group instances.\n \"\"\"\n return Group._list(self)","label":0},{"cid":"google/cloud/monitoring/client.py::Client.write_time_series","text":"google/cloud/monitoring/client.py::Client.write_time_series\n def write_time_series(self, timeseries_list):\n \"\"\"Write a list of time series objects to the API.\n\n The recommended approach to creating time series objects is using\n the :meth:`~google.cloud.monitoring.client.Client.time_series` factory\n method.\n\n Example::\n\n >>> client.write_time_series([ts1, ts2])\n\n If you only need to write a single time series object, consider using\n the :meth:`~google.cloud.monitoring.client.Client.write_point` method\n instead.\n\n :type timeseries_list:\n list of :class:`~google.cloud.monitoring.timeseries.TimeSeries`\n :param timeseries_list:\n A list of time series object to be written\n to the API. Each time series must contain exactly one point.\n \"\"\"\n path = '/projects/{project}/timeSeries/'.format(\n project=self.project)\n timeseries_dict = [timeseries._to_dict()\n for timeseries in timeseries_list]\n self.connection.api_request(method='POST', path=path,\n data={'timeSeries': timeseries_dict})","label":0},{"cid":"google/cloud/monitoring/client.py::Client.write_point","text":"google/cloud/monitoring/client.py::Client.write_point\n def write_point(self, metric, resource, value,\n end_time=None,\n start_time=None):\n \"\"\"Write a single point for a metric to the API.\n\n This is a convenience method to write a single time series object to\n the API. To write multiple time series objects to the API as a batch\n operation, use the\n :meth:`~google.cloud.monitoring.client.Client.time_series`\n factory method to create time series objects and the\n :meth:`~google.cloud.monitoring.client.Client.write_time_series`\n method to write the objects.\n\n Example::\n\n >>> client.write_point(metric, resource, 3.14)\n\n :type metric: :class:`~google.cloud.monitoring.metric.Metric`\n :param metric: A :class:`~google.cloud.monitoring.metric.Metric`\n object.\n\n :type resource: :class:`~google.cloud.monitoring.resource.Resource`\n :param resource: A :class:`~google.cloud.monitoring.resource.Resource`\n object.\n\n :type value: bool, int, string, or float\n :param value:\n The value of the data point to create for the\n :class:`~google.cloud.monitoring.timeseries.TimeSeries`.\n\n .. note::\n\n The Python type of the value will determine the\n :class:`~ValueType` sent to the API, which must match the value\n type specified in the metric descriptor. For example, a Python\n float will be sent to the API as a :data:`ValueType.DOUBLE`.\n\n :type end_time: :class:`~datetime.datetime`\n :param end_time:\n The end time for the point to be included in the time series.\n Assumed to be UTC if no time zone information is present.\n Defaults to the current time, as obtained by calling\n :meth:`datetime.datetime.utcnow`.\n\n :type start_time: :class:`~datetime.datet","label":0},{"cid":"google/cloud/monitoring/group.py::_group_id_from_name","text":"google/cloud/monitoring/group.py::_group_id_from_name\ndef _group_id_from_name(path, project=None):\n \"\"\"Validate a group URI path and get the group ID.\n\n :type path: string\n :param path: URI path for a group API request.\n\n :type project: string or None\n :param project: The project associated with the request. It is\n included for validation purposes.\n\n :rtype: string\n :returns: Group ID parsed from ``path``.\n :raises: :class:`ValueError` if the ``path`` is ill-formed or if\n the project from the ``path`` does not agree with the\n ``project`` passed in.\n \"\"\"\n return _name_from_project_path(path, project, _GROUP_TEMPLATE)","label":0},{"cid":"google/cloud/monitoring/group.py::_group_name_from_id","text":"google/cloud/monitoring/group.py::_group_name_from_id\ndef _group_name_from_id(project, group_id):\n \"\"\"Build the group name given the project and group ID.\n\n :type project: string\n :param project: The project associated with the group.\n\n :type group_id: string\n :param group_id: The group ID.\n\n :rtype: string\n :returns: The fully qualified name of the group.\n \"\"\"\n return 'projects/{project}/groups/{group_id}'.format(\n project=project, group_id=group_id)","label":0},{"cid":"google/cloud/monitoring/group.py::Group.__init__","text":"google/cloud/monitoring/group.py::Group.__init__\n def __init__(self, client, group_id=None, display_name=None,\n parent_id=None, filter_string=None, is_cluster=False):\n self.client = client\n self._id = group_id\n self.display_name = display_name\n self.parent_id = parent_id\n self.filter = filter_string\n self.is_cluster = is_cluster\n\n if group_id:\n self._name = _group_name_from_id(client.project, group_id)\n else:\n self._name = None","label":0},{"cid":"google/cloud/monitoring/group.py::Group.id","text":"google/cloud/monitoring/group.py::Group.id\n @property\n def id(self):\n \"\"\"Returns the group ID.\n\n :rtype: str or None\n :returns: the ID of the group based on it's name.\n \"\"\"\n return self._id","label":0},{"cid":"google/cloud/monitoring/group.py::Group.name","text":"google/cloud/monitoring/group.py::Group.name\n @property\n def name(self):\n \"\"\"Returns the fully qualified name of the group.\n\n :rtype: str or None\n :returns:\n The fully qualified name of the group in the format\n \"projects/<project>/groups/<id>\".\n \"\"\"\n return self._name","label":0},{"cid":"google/cloud/monitoring/group.py::Group.parent_name","text":"google/cloud/monitoring/group.py::Group.parent_name\n @property\n def parent_name(self):\n \"\"\"Returns the fully qualified name of the parent group.\n\n :rtype: str or None\n :returns:\n The fully qualified name of the parent group.\n \"\"\"\n if not self.parent_id:\n return None\n return _group_name_from_id(self.client.project, self.parent_id)","label":0},{"cid":"google/cloud/monitoring/group.py::Group.path","text":"google/cloud/monitoring/group.py::Group.path\n @property\n def path(self):\n \"\"\"URL path to this group.\n\n :rtype: str\n :returns: the path based on project and group name.\n\n :raises: :exc:`ValueError` if :attr:`name` is not specified.\n \"\"\"\n if not self.id:\n raise ValueError('Cannot determine path without group ID.')\n return '/' + self.name","label":0},{"cid":"google/cloud/monitoring/group.py::Group.create","text":"google/cloud/monitoring/group.py::Group.create\n def create(self):\n \"\"\"Create a new group based on this object via a ``POST`` request.\n\n Example::\n\n >>> filter_string = 'resource.type = \"gce_instance\"'\n >>> group = client.group(\n ... display_name='My group',\n ... filter_string=filter_string,\n ... parent_id='5678',\n ... is_cluster=True)\n >>> group.create()\n\n The ``name`` attribute is ignored in preparing the creation request.\n All attributes are overwritten by the values received in the response\n (normally affecting only ``name``).\n \"\"\"\n path = '/projects/%s/groups/' % (self.client.project,)\n info = self.client.connection.api_request(\n method='POST', path=path, data=self._to_dict())\n self._set_properties_from_dict(info)","label":0},{"cid":"google/cloud/monitoring/group.py::Group.exists","text":"google/cloud/monitoring/group.py::Group.exists\n def exists(self):\n \"\"\"Test for the existence of the group via a ``GET`` request.\n\n :rtype: bool\n :returns: Boolean indicating existence of the group.\n \"\"\"\n try:\n self.client.connection.api_request(\n method='GET', path=self.path, query_params={'fields': 'name'})\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"google/cloud/monitoring/group.py::Group.reload","text":"google/cloud/monitoring/group.py::Group.reload\n def reload(self):\n \"\"\"Sync local group information via a ``GET`` request.\n\n .. warning::\n\n This will overwrite any local changes you've made and not saved\n via :meth:`update`.\n \"\"\"\n info = self.client.connection.api_request(method='GET', path=self.path)\n self._set_properties_from_dict(info)","label":0},{"cid":"google/cloud/monitoring/group.py::Group.update","text":"google/cloud/monitoring/group.py::Group.update\n def update(self):\n \"\"\"Update the group via a ``PUT`` request.\"\"\"\n info = self.client.connection.api_request(\n method='PUT', path=self.path, data=self._to_dict())\n self._set_properties_from_dict(info)","label":0},{"cid":"google/cloud/monitoring/group.py::Group.delete","text":"google/cloud/monitoring/group.py::Group.delete\n def delete(self):\n \"\"\"Delete the group via a ``DELETE`` request.\n\n Example::\n\n >>> group = client.group('1234')\n >>> group.delete()\n\n Only the ``client`` and ``name`` attributes are used.\n\n .. warning::\n\n This method will fail for groups that have one or more children\n groups.\n \"\"\"\n self.client.connection.api_request(method='DELETE', path=self.path)","label":0},{"cid":"google/cloud/monitoring/group.py::Group.fetch_parent","text":"google/cloud/monitoring/group.py::Group.fetch_parent\n def fetch_parent(self):\n \"\"\"Returns the parent group of this group via a ``GET`` request.\n\n :rtype: :class:`Group` or None\n :returns: The parent of the group.\n \"\"\"\n if not self.parent_id:\n return None\n return self._fetch(self.client, self.parent_id)","label":0},{"cid":"google/cloud/monitoring/group.py::Group.list_children","text":"google/cloud/monitoring/group.py::Group.list_children\n def list_children(self):\n \"\"\"Lists all children of this group via a ``GET`` request.\n\n Returns groups whose parent_name field contains the group name. If no\n groups have this parent, the results are empty.\n\n :rtype: list of :class:`~google.cloud.monitoring.group.Group`\n :returns: A list of group instances.\n \"\"\"\n return self._list(self.client, children_of_group=self.name)","label":0},{"cid":"google/cloud/monitoring/group.py::Group.list_ancestors","text":"google/cloud/monitoring/group.py::Group.list_ancestors\n def list_ancestors(self):\n \"\"\"Lists all ancestors of this group via a ``GET`` request.\n\n The groups are returned in order, starting with the immediate parent\n and ending with the most distant ancestor. If the specified group has\n no immediate parent, the results are empty.\n\n :rtype: list of :class:`~google.cloud.monitoring.group.Group`\n :returns: A list of group instances.\n \"\"\"\n return self._list(self.client, ancestors_of_group=self.name)","label":0},{"cid":"google/cloud/monitoring/group.py::Group.list_descendants","text":"google/cloud/monitoring/group.py::Group.list_descendants\n def list_descendants(self):\n \"\"\"Lists all descendants of this group via a ``GET`` request.\n\n This returns a superset of the results returned by the :meth:`children`\n method, and includes children-of-children, and so forth.\n\n :rtype: list of :class:`~google.cloud.monitoring.group.Group`\n :returns: A list of group instances.\n \"\"\"\n return self._list(self.client, descendants_of_group=self.name)","label":0},{"cid":"google/cloud/monitoring/group.py::Group.list_members","text":"google/cloud/monitoring/group.py::Group.list_members\n def list_members(self, filter_string=None, end_time=None, start_time=None):\n \"\"\"Lists all members of this group via a ``GET`` request.\n\n If no ``end_time`` is provided then the group membership over the last\n minute is returned.\n\n Example::\n\n >>> for member in group.list_members():\n ... print member\n\n List members that are Compute Engine VM instances::\n\n >>> filter_string = 'resource.type = \"gce_instance\"'\n >>> for member in group.list_members(filter_string=filter_string):\n ... print member\n\n List historical members that existed between 4 and 5 hours ago::\n\n >>> import datetime\n >>> t1 = datetime.datetime.utcnow() - datetime.timedelta(hours=4)\n >>> t0 = t1 - datetime.timedelta(hours=1)\n >>> for member in group.list_members(end_time=t1, start_time=t0):\n ... print member\n\n\n :type filter_string: string or None\n :param filter_string:\n An optional list filter describing the members to be returned. The\n filter may reference the type, labels, and metadata of monitored\n resources that comprise the group. See the `filter documentation`_.\n\n :type end_time: :class:`datetime.datetime` or None\n :param end_time:\n The end time (inclusive) of the time interval for which results\n should be returned, as a datetime object. If ``start_time`` is\n specified, then this must also be specified.\n\n :type start_time: :class:`datetime.datetime` or None\n :param start_time:\n The start time (exclusive) of the time interval for which results\n should be returned, as a datetime object.\n\n :rtype: list of :class:`~google.cloud.monitoring.resource.Resource`\n :returns: A list of resource instances.\n\n :raises:\n :exc:`ValueError` ","label":0},{"cid":"google/cloud/monitoring/group.py::Group._fetch","text":"google/cloud/monitoring/group.py::Group._fetch\n @classmethod\n def _fetch(cls, client, group_id):\n \"\"\"Fetch a group from the API based on it's ID.\n\n :type client: :class:`google.cloud.monitoring.client.Client`\n :param client: The client to use.\n\n :type group_id: string\n :param group_id: The group ID.\n\n :rtype: :class:`Group`\n :returns: The group instance.\n\n :raises: :class:`google.cloud.exceptions.NotFound` if the group\n is not found.\n \"\"\"\n new_group = cls(client, group_id)\n new_group.reload()\n return new_group","label":0},{"cid":"google/cloud/monitoring/group.py::Group._list","text":"google/cloud/monitoring/group.py::Group._list\n @classmethod\n def _list(cls, client, children_of_group=None, ancestors_of_group=None,\n descendants_of_group=None):\n \"\"\"Lists all groups in the project.\n\n :type client: :class:`google.cloud.monitoring.client.Client`\n :param client: The client to use.\n\n :type children_of_group: string or None\n :param children_of_group:\n Returns groups whose parent_name field contains the group name. If\n no groups have this parent, the results are empty.\n\n :type ancestors_of_group: string or None\n :param ancestors_of_group:\n Returns groups that are ancestors of the specified group. If the\n specified group has no immediate parent, the results are empty.\n\n :type descendants_of_group: string or None\n :param descendants_of_group:\n Returns the descendants of the specified group. This is a superset\n of the results returned by the children_of_group filter, and\n includes children-of-children, and so forth.\n\n :rtype: list of :class:`~google.cloud.monitoring.group.Group`\n :returns: A list of group instances.\n \"\"\"\n path = '/projects/%s/groups/' % (client.project,)\n groups = []\n page_token = None\n params = {}\n\n if children_of_group is not None:\n params['childrenOfGroup'] = children_of_group\n\n if ancestors_of_group is not None:\n params['ancestorsOfGroup'] = ancestors_of_group\n\n if descendants_of_group is not None:\n params['descendantsOfGroup'] = descendants_of_group\n\n while True:\n if page_token is not None:\n params['pageToken'] = page_token\n\n response = client.connection.api_request(\n method='GET', path=path, query_params=params.copy())\n for info in response.get('group', ()):\n groups.append(cls._from_dict(client","label":0},{"cid":"google/cloud/monitoring/group.py::Group._from_dict","text":"google/cloud/monitoring/group.py::Group._from_dict\n @classmethod\n def _from_dict(cls, client, info):\n \"\"\"Constructs a Group instance from the parsed JSON representation.\n\n :type client: :class:`google.cloud.monitoring.client.Client`\n :param client: A client to be included in the returned object.\n\n :type info: dict\n :param info:\n A ``dict`` parsed from the JSON wire-format representation.\n\n :rtype: :class:`Group`\n :returns: A group.\n \"\"\"\n group = cls(client)\n group._set_properties_from_dict(info)\n return group","label":0},{"cid":"google/cloud/monitoring/group.py::Group._set_properties_from_dict","text":"google/cloud/monitoring/group.py::Group._set_properties_from_dict\n def _set_properties_from_dict(self, info):\n \"\"\"Update the group properties from its API representation.\n\n :type info: dict\n :param info:\n A ``dict`` parsed from the JSON wire-format representation.\n \"\"\"\n self._name = info['name']\n self._id = _group_id_from_name(self._name)\n self.display_name = info['displayName']\n self.filter = info['filter']\n self.is_cluster = info.get('isCluster', False)\n\n parent_name = info.get('parentName')\n if parent_name is None:\n self.parent_id = None\n else:\n self.parent_id = _group_id_from_name(parent_name)","label":0},{"cid":"google/cloud/monitoring/group.py::Group._to_dict","text":"google/cloud/monitoring/group.py::Group._to_dict\n def _to_dict(self):\n \"\"\"Build a dictionary ready to be serialized to the JSON wire format.\n\n :rtype: dict\n :returns: A dictionary.\n \"\"\"\n info = {\n 'filter': self.filter,\n 'displayName': self.display_name,\n 'isCluster': self.is_cluster,\n }\n\n if self.name is not None:\n info['name'] = self.name\n\n parent_name = self.parent_name\n if parent_name is not None:\n info['parentName'] = parent_name\n\n return info","label":0},{"cid":"google/cloud/monitoring/group.py::Group.__repr__","text":"google/cloud/monitoring/group.py::Group.__repr__\n def __repr__(self):\n return '<Group: %s>' % (self.name,)","label":0},{"cid":"google/cloud/monitoring/label.py::LabelDescriptor.__init__","text":"google/cloud/monitoring/label.py::LabelDescriptor.__init__\n def __init__(self, key, value_type=LabelValueType.STRING, description=''):\n self.key = key\n self.value_type = value_type\n self.description = description","label":0},{"cid":"google/cloud/monitoring/label.py::LabelDescriptor._from_dict","text":"google/cloud/monitoring/label.py::LabelDescriptor._from_dict\n @classmethod\n def _from_dict(cls, info):\n \"\"\"Construct a label descriptor from the parsed JSON representation.\n\n :type info: dict\n :param info:\n A ``dict`` parsed from the JSON wire-format representation.\n\n :rtype: :class:`LabelDescriptor`\n :returns: A label descriptor.\n \"\"\"\n return cls(\n info['key'],\n info.get('valueType', LabelValueType.STRING),\n info.get('description', ''),\n )","label":0},{"cid":"google/cloud/monitoring/label.py::LabelDescriptor._to_dict","text":"google/cloud/monitoring/label.py::LabelDescriptor._to_dict\n def _to_dict(self):\n \"\"\"Build a dictionary ready to be serialized to the JSON wire format.\n\n :rtype: dict\n :returns: A dictionary.\n \"\"\"\n info = {\n 'key': self.key,\n 'valueType': self.value_type,\n }\n\n if self.description:\n info['description'] = self.description\n\n return info","label":0},{"cid":"google/cloud/monitoring/label.py::LabelDescriptor.__eq__","text":"google/cloud/monitoring/label.py::LabelDescriptor.__eq__\n def __eq__(self, other):\n return self.__dict__ == other.__dict__","label":0},{"cid":"google/cloud/monitoring/label.py::LabelDescriptor.__ne__","text":"google/cloud/monitoring/label.py::LabelDescriptor.__ne__\n def __ne__(self, other):\n return self.__dict__ != other.__dict__","label":0},{"cid":"google/cloud/monitoring/label.py::LabelDescriptor.__repr__","text":"google/cloud/monitoring/label.py::LabelDescriptor.__repr__\n def __repr__(self):\n return (\n 'LabelDescriptor(key={key!r}, value_type={value_type!r},'\n ' description={description!r})'\n ).format(**self.__dict__)","label":0},{"cid":"google/cloud/monitoring/metric.py::MetricDescriptor.__init__","text":"google/cloud/monitoring/metric.py::MetricDescriptor.__init__\n def __init__(self, client, type_,\n metric_kind=MetricKind.METRIC_KIND_UNSPECIFIED,\n value_type=ValueType.VALUE_TYPE_UNSPECIFIED,\n labels=(),\n unit='', description='', display_name='',\n name=None):\n self.client = client\n self.name = name\n self.type = type_\n self.labels = labels\n self.metric_kind = metric_kind\n self.value_type = value_type\n self.unit = unit\n self.description = description\n self.display_name = display_name","label":0},{"cid":"google/cloud/monitoring/metric.py::MetricDescriptor.create","text":"google/cloud/monitoring/metric.py::MetricDescriptor.create\n def create(self):\n \"\"\"Create a new metric descriptor based on this object.\n\n Example::\n\n >>> descriptor = client.metric_descriptor(\n ... 'custom.googleapis.com/my_metric',\n ... metric_kind=MetricKind.GAUGE,\n ... value_type=ValueType.DOUBLE,\n ... description='This is a simple example of a custom metric.')\n >>> descriptor.create()\n\n The metric kind must not be :data:`MetricKind.METRIC_KIND_UNSPECIFIED`,\n and the value type must not be\n :data:`ValueType.VALUE_TYPE_UNSPECIFIED`.\n\n The ``name`` attribute is ignored in preparing the creation request.\n All attributes are overwritten by the values received in the response\n (normally affecting only ``name``).\n \"\"\"\n path = '/projects/{project}/metricDescriptors/'.format(\n project=self.client.project)\n response = self.client.connection.api_request(method='POST', path=path,\n data=self._to_dict())\n self._init_from_dict(response)","label":0},{"cid":"google/cloud/monitoring/metric.py::MetricDescriptor.delete","text":"google/cloud/monitoring/metric.py::MetricDescriptor.delete\n def delete(self):\n \"\"\"Delete the metric descriptor identified by this object.\n\n Example::\n\n >>> descriptor = client.metric_descriptor(\n ... 'custom.googleapis.com/my_metric')\n >>> descriptor.delete()\n\n Only the ``client`` and ``type`` attributes are used.\n \"\"\"\n path = '/projects/{project}/metricDescriptors/{type}'.format(\n project=self.client.project,\n type=self.type)\n self.client.connection.api_request(method='DELETE', path=path)","label":0},{"cid":"google/cloud/monitoring/metric.py::MetricDescriptor._fetch","text":"google/cloud/monitoring/metric.py::MetricDescriptor._fetch\n @classmethod\n def _fetch(cls, client, metric_type):\n \"\"\"Look up a metric descriptor by type.\n\n :type client: :class:`google.cloud.monitoring.client.Client`\n :param client: The client to use.\n\n :type metric_type: string\n :param metric_type: The metric type name.\n\n :rtype: :class:`MetricDescriptor`\n :returns: The metric descriptor instance.\n\n :raises: :class:`google.cloud.exceptions.NotFound` if the metric\n descriptor is not found.\n \"\"\"\n path = '/projects/{project}/metricDescriptors/{type}'.format(\n project=client.project,\n type=metric_type)\n info = client.connection.api_request(method='GET', path=path)\n return cls._from_dict(client, info)","label":0},{"cid":"google/cloud/monitoring/metric.py::MetricDescriptor._list","text":"google/cloud/monitoring/metric.py::MetricDescriptor._list\n @classmethod\n def _list(cls, client, filter_string=None, type_prefix=None):\n \"\"\"List all metric descriptors for the project.\n\n :type client: :class:`google.cloud.monitoring.client.Client`\n :param client: The client to use.\n\n :type filter_string: string or None\n :param filter_string:\n An optional filter expression describing the metric descriptors\n to be returned. See the `filter documentation`_.\n\n :type type_prefix: string or None\n :param type_prefix: An optional prefix constraining the selected\n metric types. This adds ``metric.type = starts_with(\"<prefix>\")``\n to the filter.\n\n :rtype: list of :class:`MetricDescriptor`\n :returns: A list of metric descriptor instances.\n\n .. _filter documentation:\n https://cloud.google.com/monitoring/api/v3/filters\n \"\"\"\n path = '/projects/{project}/metricDescriptors/'.format(\n project=client.project)\n\n filters = []\n if filter_string is not None:\n filters.append(filter_string)\n\n if type_prefix is not None:\n filters.append('metric.type = starts_with(\"{prefix}\")'.format(\n prefix=type_prefix))\n\n descriptors = []\n page_token = None\n while True:\n params = {}\n\n if filters:\n params['filter'] = ' AND '.join(filters)\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n response = client.connection.api_request(\n method='GET', path=path, query_params=params)\n for info in response.get('metricDescriptors', ()):\n descriptors.append(cls._from_dict(client, info))\n\n page_token = response.get('nextPageToken')\n if not page_token:\n break\n\n return descriptors","label":0},{"cid":"google/cloud/monitoring/metric.py::MetricDescriptor._from_dict","text":"google/cloud/monitoring/metric.py::MetricDescriptor._from_dict\n @classmethod\n def _from_dict(cls, client, info):\n \"\"\"Construct a metric descriptor from the parsed JSON representation.\n\n :type client: :class:`google.cloud.monitoring.client.Client`\n :param client: A client to be included in the returned object.\n\n :type info: dict\n :param info:\n A ``dict`` parsed from the JSON wire-format representation.\n\n :rtype: :class:`MetricDescriptor`\n :returns: A metric descriptor.\n \"\"\"\n descriptor = cls(client, None)\n descriptor._init_from_dict(info)\n return descriptor","label":0},{"cid":"google/cloud/monitoring/metric.py::MetricDescriptor._init_from_dict","text":"google/cloud/monitoring/metric.py::MetricDescriptor._init_from_dict\n def _init_from_dict(self, info):\n \"\"\"Initialize attributes from the parsed JSON representation.\n\n :type info: dict\n :param info:\n A ``dict`` parsed from the JSON wire-format representation.\n \"\"\"\n self.name = info['name']\n self.type = info['type']\n self.labels = tuple(LabelDescriptor._from_dict(label)\n for label in info.get('labels', []))\n self.metric_kind = info['metricKind']\n self.value_type = info['valueType']\n self.unit = info.get('unit', '')\n self.description = info.get('description', '')\n self.display_name = info.get('displayName', '')","label":0},{"cid":"google/cloud/monitoring/metric.py::MetricDescriptor._to_dict","text":"google/cloud/monitoring/metric.py::MetricDescriptor._to_dict\n def _to_dict(self):\n \"\"\"Build a dictionary ready to be serialized to the JSON wire format.\n\n :rtype: dict\n :returns: A dictionary.\n \"\"\"\n info = {\n 'type': self.type,\n 'metricKind': self.metric_kind,\n 'valueType': self.value_type,\n }\n\n if self.labels:\n info['labels'] = [label._to_dict() for label in self.labels]\n if self.unit:\n info['unit'] = self.unit\n if self.description:\n info['description'] = self.description\n if self.display_name:\n info['displayName'] = self.display_name\n\n return info","label":0},{"cid":"google/cloud/monitoring/metric.py::MetricDescriptor.__repr__","text":"google/cloud/monitoring/metric.py::MetricDescriptor.__repr__\n def __repr__(self):\n return (\n '<MetricDescriptor:\\n'\n ' name={name!r},\\n'\n ' type={type!r},\\n'\n ' metric_kind={metric_kind!r}, value_type={value_type!r},\\n'\n ' labels={labels!r},\\n'\n ' display_name={display_name!r}, unit={unit!r},\\n'\n ' description={description!r}>'\n ).format(**self.__dict__)","label":0},{"cid":"google/cloud/monitoring/metric.py::Metric._from_dict","text":"google/cloud/monitoring/metric.py::Metric._from_dict\n @classmethod\n def _from_dict(cls, info):\n \"\"\"Construct a metric object from the parsed JSON representation.\n\n :type info: dict\n :param info:\n A ``dict`` parsed from the JSON wire-format representation.\n\n :rtype: :class:`Metric`\n :returns: A metric object.\n \"\"\"\n return cls(\n type=info['type'],\n labels=info.get('labels', {}),\n )","label":0},{"cid":"google/cloud/monitoring/metric.py::Metric._to_dict","text":"google/cloud/monitoring/metric.py::Metric._to_dict\n def _to_dict(self):\n \"\"\"Build a dictionary ready to be serialized to the JSON format.\n\n :rtype: dict\n :returns: A dict representation of the object that can be written to\n the API.\n \"\"\"\n return {\n 'type': self.type,\n 'labels': self.labels,\n }","label":0},{"cid":"google/cloud/monitoring/query.py::Query.__init__","text":"google/cloud/monitoring/query.py::Query.__init__\n def __init__(self, client,\n metric_type=DEFAULT_METRIC_TYPE,\n end_time=None, days=0, hours=0, minutes=0):\n start_time = None\n if days or hours or minutes:\n if end_time is None:\n end_time = _UTCNOW().replace(second=0, microsecond=0)\n start_time = end_time - datetime.timedelta(days=days,\n hours=hours,\n minutes=minutes)\n elif end_time is not None:\n raise ValueError('Non-zero duration required for time interval.')\n\n self._client = client\n self._end_time = end_time\n self._start_time = start_time\n self._filter = _Filter(metric_type)\n\n self._per_series_aligner = None\n self._alignment_period_seconds = None\n self._cross_series_reducer = None\n self._group_by_fields = ()","label":0},{"cid":"google/cloud/monitoring/query.py::Query.__iter__","text":"google/cloud/monitoring/query.py::Query.__iter__\n def __iter__(self):\n return self.iter()","label":0},{"cid":"google/cloud/monitoring/query.py::Query.metric_type","text":"google/cloud/monitoring/query.py::Query.metric_type\n @property\n def metric_type(self):\n \"\"\"The metric type name.\"\"\"\n return self._filter.metric_type","label":0},{"cid":"google/cloud/monitoring/query.py::Query.filter","text":"google/cloud/monitoring/query.py::Query.filter\n @property\n def filter(self):\n \"\"\"The filter string.\n\n This is constructed from the metric type, the resource type, and\n selectors for the group ID, monitored projects, resource labels,\n and metric labels.\n \"\"\"\n return str(self._filter)","label":0},{"cid":"google/cloud/monitoring/query.py::Query.select_interval","text":"google/cloud/monitoring/query.py::Query.select_interval\n def select_interval(self, end_time, start_time=None):\n \"\"\"Copy the query and set the query time interval.\n\n Example::\n\n import datetime\n\n now = datetime.datetime.utcnow()\n query = query.select_interval(\n end_time=now,\n start_time=now - datetime.timedelta(minutes=5))\n\n As a convenience, you can alternatively specify the end time and\n an interval duration when you create the query initially.\n\n :type end_time: :class:`datetime.datetime`\n :param end_time: The end time (inclusive) of the time interval\n for which results should be returned, as a datetime object.\n\n :type start_time: :class:`datetime.datetime` or None\n :param start_time: The start time (exclusive) of the time interval\n for which results should be returned, as a datetime object.\n If not specified, the interval is a point in time.\n\n :rtype: :class:`Query`\n :returns: The new query object.\n \"\"\"\n new_query = self.copy()\n new_query._end_time = end_time\n new_query._start_time = start_time\n return new_query","label":0},{"cid":"google/cloud/monitoring/query.py::Query.select_group","text":"google/cloud/monitoring/query.py::Query.select_group\n def select_group(self, group_id):\n \"\"\"Copy the query and add filtering by group.\n\n Example::\n\n query = query.select_group('1234567')\n\n :type group_id: string\n :param group_id: The ID of a group to filter by.\n\n :rtype: :class:`Query`\n :returns: The new query object.\n \"\"\"\n new_query = self.copy()\n new_query._filter.group_id = group_id\n return new_query","label":0},{"cid":"google/cloud/monitoring/query.py::Query.select_projects","text":"google/cloud/monitoring/query.py::Query.select_projects\n def select_projects(self, *args):\n \"\"\"Copy the query and add filtering by monitored projects.\n\n This is only useful if the target project represents a Stackdriver\n account containing the specified monitored projects.\n\n Examples::\n\n query = query.select_projects('project-1')\n query = query.select_projects('project-1', 'project-2')\n\n :type args: tuple\n :param args: Project IDs limiting the resources to be included\n in the query.\n\n :rtype: :class:`Query`\n :returns: The new query object.\n \"\"\"\n new_query = self.copy()\n new_query._filter.projects = args\n return new_query","label":0},{"cid":"google/cloud/monitoring/query.py::Query.select_resources","text":"google/cloud/monitoring/query.py::Query.select_resources\n def select_resources(self, *args, **kwargs):\n \"\"\"Copy the query and add filtering by resource labels.\n\n Examples::\n\n query = query.select_resources(zone='us-central1-a')\n query = query.select_resources(zone_prefix='europe-')\n query = query.select_resources(resource_type='gce_instance')\n\n A keyword argument ``<label>=<value>`` ordinarily generates a filter\n expression of the form::\n\n resource.label.<label> = \"<value>\"\n\n However, by adding ``\"_prefix\"`` or ``\"_suffix\"`` to the keyword,\n you can specify a partial match.\n\n ``<label>_prefix=<value>`` generates::\n\n resource.label.<label> = starts_with(\"<value>\")\n\n ``<label>_suffix=<value>`` generates::\n\n resource.label.<label> = ends_with(\"<value>\")\n\n As a special case, ``\"resource_type\"`` is treated as a special\n pseudo-label corresponding to the filter object ``resource.type``.\n For example, ``resource_type=<value>`` generates::\n\n resource.type = \"<value>\"\n\n See the `defined resource types`_.\n\n .. note::\n\n The label ``\"instance_name\"`` is a metric label,\n not a resource label. You would filter on it using\n ``select_metrics(instance_name=...)``.\n\n :type args: tuple\n :param args: Raw filter expression strings to include in the\n conjunction. If just one is provided and no keyword arguments\n are provided, it can be a disjunction.\n\n :type kwargs: dict\n :param kwargs: Label filters to include in the conjunction as\n described above.\n\n :rtype: :class:`Query`\n :returns: The new query object.\n\n .. _defined resource types:\n https://cloud.google.com/monitoring/api/v3/monitored-resources\n \"\"\"\n new_query = self.copy()\n new_query._filter.select_resources(*args, **kw","label":0},{"cid":"google/cloud/monitoring/query.py::Query.select_metrics","text":"google/cloud/monitoring/query.py::Query.select_metrics\n def select_metrics(self, *args, **kwargs):\n \"\"\"Copy the query and add filtering by metric labels.\n\n Examples::\n\n query = query.select_metrics(instance_name='myinstance')\n query = query.select_metrics(instance_name_prefix='mycluster-')\n\n A keyword argument ``<label>=<value>`` ordinarily generates a filter\n expression of the form::\n\n metric.label.<label> = \"<value>\"\n\n However, by adding ``\"_prefix\"`` or ``\"_suffix\"`` to the keyword,\n you can specify a partial match.\n\n ``<label>_prefix=<value>`` generates::\n\n metric.label.<label> = starts_with(\"<value>\")\n\n ``<label>_suffix=<value>`` generates::\n\n metric.label.<label> = ends_with(\"<value>\")\n\n If the label's value type is ``INT64``, a similar notation can be\n used to express inequalities:\n\n ``<label>_less=<value>`` generates::\n\n metric.label.<label> < <value>\n\n ``<label>_lessequal=<value>`` generates::\n\n metric.label.<label> <= <value>\n\n ``<label>_greater=<value>`` generates::\n\n metric.label.<label> > <value>\n\n ``<label>_greaterequal=<value>`` generates::\n\n metric.label.<label> >= <value>\n\n :type args: tuple\n :param args: Raw filter expression strings to include in the\n conjunction. If just one is provided and no keyword arguments\n are provided, it can be a disjunction.\n\n :type kwargs: dict\n :param kwargs: Label filters to include in the conjunction as\n described above.\n\n :rtype: :class:`Query`\n :returns: The new query object.\n \"\"\"\n new_query = self.copy()\n new_query._filter.select_metrics(*args, **kwargs)\n return new_query","label":0},{"cid":"google/cloud/monitoring/query.py::Query.align","text":"google/cloud/monitoring/query.py::Query.align\n def align(self, per_series_aligner, seconds=0, minutes=0, hours=0):\n \"\"\"Copy the query and add temporal alignment.\n\n If ``per_series_aligner`` is not :data:`Aligner.ALIGN_NONE`, each time\n series will contain data points only on the period boundaries.\n\n Example::\n\n query = query.align(Aligner.ALIGN_MEAN, minutes=5)\n\n It is also possible to specify the aligner as a literal string::\n\n query = query.align('ALIGN_MEAN', minutes=5)\n\n :type per_series_aligner: string\n :param per_series_aligner: The approach to be used to align\n individual time series. For example: :data:`Aligner.ALIGN_MEAN`.\n See :class:`Aligner` and the descriptions of the `supported\n aligners`_.\n\n :type seconds: integer\n :param seconds: The number of seconds in the alignment period.\n\n :type minutes: integer\n :param minutes: The number of minutes in the alignment period.\n\n :type hours: integer\n :param hours: The number of hours in the alignment period.\n\n :rtype: :class:`Query`\n :returns: The new query object.\n\n .. _supported aligners:\n https://cloud.google.com/monitoring/api/ref_v3/rest/v3/\\\n projects.timeSeries/list#Aligner\n \"\"\"\n new_query = self.copy()\n new_query._per_series_aligner = per_series_aligner\n new_query._alignment_period_seconds = seconds + 60 * (minutes +\n 60 * hours)\n return new_query","label":0},{"cid":"google/cloud/monitoring/query.py::Query.reduce","text":"google/cloud/monitoring/query.py::Query.reduce\n def reduce(self, cross_series_reducer, *group_by_fields):\n \"\"\"Copy the query and add cross-series reduction.\n\n Cross-series reduction combines time series by aggregating their\n data points.\n\n For example, you could request an aggregated time series for each\n combination of project and zone as follows::\n\n query = query.reduce(Reducer.REDUCE_MEAN,\n 'resource.project_id', 'resource.zone')\n\n :type cross_series_reducer: string\n :param cross_series_reducer:\n The approach to be used to combine time series. For example:\n :data:`Reducer.REDUCE_MEAN`. See :class:`Reducer` and the\n descriptions of the `supported reducers`_.\n\n :type group_by_fields: strings\n :param group_by_fields:\n Fields to be preserved by the reduction. For example, specifying\n just ``\"resource.zone\"`` will result in one time series per zone.\n The default is to aggregate all of the time series into just one.\n\n :rtype: :class:`Query`\n :returns: The new query object.\n\n .. _supported reducers:\n https://cloud.google.com/monitoring/api/ref_v3/rest/v3/\\\n projects.timeSeries/list#Reducer\n \"\"\"\n new_query = self.copy()\n new_query._cross_series_reducer = cross_series_reducer\n new_query._group_by_fields = group_by_fields\n return new_query","label":0},{"cid":"google/cloud/monitoring/query.py::Query.iter","text":"google/cloud/monitoring/query.py::Query.iter\n def iter(self, headers_only=False, page_size=None):\n \"\"\"Yield all time series objects selected by the query.\n\n The generator returned iterates over\n :class:`~google.cloud.monitoring.timeseries.TimeSeries` objects\n containing points ordered from oldest to newest.\n\n Note that the :class:`Query` object itself is an iterable, such that\n the following are equivalent::\n\n for timeseries in query:\n ...\n\n for timeseries in query.iter():\n ...\n\n :type headers_only: boolean\n :param headers_only:\n Whether to omit the point data from the time series objects.\n\n :type page_size: integer or None\n :param page_size:\n An optional positive number specifying the maximum number of\n points to return per page. This can be used to control how far\n the iterator reads ahead.\n\n :raises: :exc:`ValueError` if the query time interval has not been\n specified.\n \"\"\"\n # The following use of groupby() relies on equality comparison\n # of time series as (named) tuples.\n for timeseries, fragments in itertools.groupby(\n self._iter_fragments(headers_only, page_size),\n lambda fragment: fragment.header()):\n points = list(itertools.chain.from_iterable(\n fragment.points for fragment in fragments))\n points.reverse() # Order from oldest to newest.\n yield timeseries.header(points=points)","label":0},{"cid":"google/cloud/monitoring/query.py::Query._iter_fragments","text":"google/cloud/monitoring/query.py::Query._iter_fragments\n def _iter_fragments(self, headers_only=False, page_size=None):\n \"\"\"Yield all time series fragments selected by the query.\n\n There may be multiple fragments per time series. These will be\n contiguous.\n\n The parameters and return value are as for :meth:`Query.iter`.\n \"\"\"\n if self._end_time is None:\n raise ValueError('Query time interval not specified.')\n\n path = '/projects/{project}/timeSeries/'.format(\n project=self._client.project)\n\n page_token = None\n while True:\n params = list(self._build_query_params(\n headers_only=headers_only,\n page_size=page_size,\n page_token=page_token,\n ))\n response = self._client.connection.api_request(\n method='GET',\n path=path,\n query_params=params,\n )\n for info in response.get('timeSeries', ()):\n yield TimeSeries._from_dict(info)\n\n page_token = response.get('nextPageToken')\n if not page_token:\n break","label":0},{"cid":"google/cloud/monitoring/query.py::Query._build_query_params","text":"google/cloud/monitoring/query.py::Query._build_query_params\n def _build_query_params(self, headers_only=False,\n page_size=None, page_token=None):\n \"\"\"Yield key-value pairs for the URL query string.\n\n We use a series of key-value pairs (suitable for passing to\n ``urlencode``) instead of a ``dict`` to allow for repeated fields.\n\n :type headers_only: boolean\n :param headers_only:\n Whether to omit the point data from the\n :class:`~google.cloud.monitoring.timeseries.TimeSeries` objects.\n\n :type page_size: integer or None\n :param page_size: A limit on the number of points to return per page.\n\n :type page_token: string or None\n :param page_token: A token to continue the retrieval.\n \"\"\"\n yield 'filter', self.filter\n\n yield 'interval.endTime', _datetime_to_rfc3339(\n self._end_time, ignore_zone=False)\n\n if self._start_time is not None:\n yield 'interval.startTime', _datetime_to_rfc3339(\n self._start_time, ignore_zone=False)\n\n if self._per_series_aligner is not None:\n yield 'aggregation.perSeriesAligner', self._per_series_aligner\n\n if self._alignment_period_seconds is not None:\n alignment_period = '{period}s'.format(\n period=self._alignment_period_seconds)\n yield 'aggregation.alignmentPeriod', alignment_period\n\n if self._cross_series_reducer is not None:\n yield ('aggregation.crossSeriesReducer',\n self._cross_series_reducer)\n\n for field in self._group_by_fields:\n yield 'aggregation.groupByFields', field\n\n if headers_only:\n yield 'view', 'HEADERS'\n\n if page_size is not None:\n yield 'pageSize', page_size\n\n if page_token is not None:\n yield 'pageToken', page_token","label":0},{"cid":"google/cloud/monitoring/query.py::Query.as_dataframe","text":"google/cloud/monitoring/query.py::Query.as_dataframe\n def as_dataframe(self, label=None, labels=None):\n \"\"\"Return all the selected time series as a :mod:`pandas` dataframe.\n\n .. note::\n\n Use of this method requires that you have :mod:`pandas` installed.\n\n Examples::\n\n # Generate a dataframe with a multi-level column header including\n # the resource type and all available resource and metric labels.\n # This can be useful for seeing what labels are available.\n dataframe = query.as_dataframe()\n\n # Generate a dataframe using a particular label for the column\n # names.\n dataframe = query.as_dataframe(label='instance_name')\n\n # Generate a dataframe with a multi-level column header.\n dataframe = query.as_dataframe(labels=['zone', 'instance_name'])\n\n # Generate a dataframe with a multi-level column header, assuming\n # the metric is issued by more than one type of resource.\n dataframe = query.as_dataframe(\n labels=['resource_type', 'instance_id'])\n\n :type label: string or None\n :param label: The label name to use for the dataframe header.\n This can be the name of a resource label or metric label\n (e.g., ``\"instance_name\"``), or the string ``\"resource_type\"``.\n\n :type labels: list of strings, or None\n :param labels: A list or tuple of label names to use for the dataframe\n header. If more than one label name is provided, the resulting\n dataframe will have a multi-level column header. Providing values\n for both ``label`` and ``labels`` is an error.\n\n :rtype: :class:`pandas.DataFrame`\n :returns: A dataframe where each column represents one time series.\n \"\"\"\n return _build_dataframe(self, label, labels) # pragma: NO COVER","label":0},{"cid":"google/cloud/monitoring/query.py::Query.copy","text":"google/cloud/monitoring/query.py::Query.copy\n def copy(self):\n \"\"\"Copy the query object.\n\n :rtype: :class:`Query`\n :returns: The new query object.\n \"\"\"\n # Using copy.deepcopy() would be appropriate, except that we want\n # to copy self._client only as a reference.\n new_query = copy.copy(self)\n new_query._filter = copy.copy(self._filter)\n return new_query","label":0},{"cid":"google/cloud/monitoring/query.py::_Filter.__init__","text":"google/cloud/monitoring/query.py::_Filter.__init__\n def __init__(self, metric_type):\n self.metric_type = metric_type\n self.group_id = None\n self.projects = ()\n self.resource_label_filter = None\n self.metric_label_filter = None","label":0},{"cid":"google/cloud/monitoring/query.py::_Filter.select_resources","text":"google/cloud/monitoring/query.py::_Filter.select_resources\n def select_resources(self, *args, **kwargs):\n \"\"\"Select by resource labels.\n\n See :meth:`Query.select_resources`.\n \"\"\"\n self.resource_label_filter = _build_label_filter('resource',\n *args, **kwargs)","label":0},{"cid":"google/cloud/monitoring/query.py::_Filter.select_metrics","text":"google/cloud/monitoring/query.py::_Filter.select_metrics\n def select_metrics(self, *args, **kwargs):\n \"\"\"Select by metric labels.\n\n See :meth:`Query.select_metrics`.\n \"\"\"\n self.metric_label_filter = _build_label_filter('metric',\n *args, **kwargs)","label":0},{"cid":"google/cloud/monitoring/query.py::_Filter.__str__","text":"google/cloud/monitoring/query.py::_Filter.__str__\n def __str__(self):\n filters = ['metric.type = \"{type}\"'.format(type=self.metric_type)]\n if self.group_id is not None:\n filters.append('group.id = \"{id}\"'.format(id=self.group_id))\n if self.projects:\n filters.append(\n ' OR '.join('project = \"{project}\"'.format(project=project)\n for project in self.projects))\n if self.resource_label_filter:\n filters.append(self.resource_label_filter)\n if self.metric_label_filter:\n filters.append(self.metric_label_filter)\n\n # Parentheses are never actually required, because OR binds more\n # tightly than AND in the Monitoring API's filter syntax.\n return ' AND '.join(filters)","label":0},{"cid":"google/cloud/monitoring/query.py::_build_label_filter","text":"google/cloud/monitoring/query.py::_build_label_filter\ndef _build_label_filter(category, *args, **kwargs):\n \"\"\"Construct a filter string to filter on metric or resource labels.\"\"\"\n terms = list(args)\n for key, value in six.iteritems(kwargs):\n if value is None:\n continue\n\n suffix = None\n if key.endswith(('_prefix', '_suffix', '_greater', '_greaterequal',\n '_less', '_lessequal')):\n key, suffix = key.rsplit('_', 1)\n\n if category == 'resource' and key == 'resource_type':\n key = 'resource.type'\n else:\n key = '.'.join((category, 'label', key))\n\n if suffix == 'prefix':\n term = '{key} = starts_with(\"{value}\")'\n elif suffix == 'suffix':\n term = '{key} = ends_with(\"{value}\")'\n elif suffix == 'greater':\n term = '{key} > {value}'\n elif suffix == 'greaterequal':\n term = '{key} >= {value}'\n elif suffix == 'less':\n term = '{key} < {value}'\n elif suffix == 'lessequal':\n term = '{key} <= {value}'\n else:\n term = '{key} = \"{value}\"'\n\n terms.append(term.format(key=key, value=value))\n\n return ' AND '.join(sorted(terms))","label":0},{"cid":"google/cloud/monitoring/resource.py::ResourceDescriptor.__init__","text":"google/cloud/monitoring/resource.py::ResourceDescriptor.__init__\n def __init__(self, name, type_, display_name, description, labels):\n self.name = name\n self.type = type_\n self.display_name = display_name\n self.description = description\n self.labels = labels","label":0},{"cid":"google/cloud/monitoring/resource.py::ResourceDescriptor._fetch","text":"google/cloud/monitoring/resource.py::ResourceDescriptor._fetch\n @classmethod\n def _fetch(cls, client, resource_type):\n \"\"\"Look up a monitored resource descriptor by type.\n\n :type client: :class:`google.cloud.monitoring.client.Client`\n :param client: The client to use.\n\n :type resource_type: string\n :param resource_type: The resource type name.\n\n :rtype: :class:`ResourceDescriptor`\n :returns: The resource descriptor instance.\n\n :raises: :class:`google.cloud.exceptions.NotFound` if the resource\n descriptor is not found.\n \"\"\"\n path = ('/projects/{project}/monitoredResourceDescriptors/{type}'\n .format(project=client.project,\n type=resource_type))\n info = client.connection.api_request(method='GET', path=path)\n return cls._from_dict(info)","label":0},{"cid":"google/cloud/monitoring/resource.py::ResourceDescriptor._list","text":"google/cloud/monitoring/resource.py::ResourceDescriptor._list\n @classmethod\n def _list(cls, client, filter_string=None):\n \"\"\"List all monitored resource descriptors for the project.\n\n :type client: :class:`google.cloud.monitoring.client.Client`\n :param client: The client to use.\n\n :type filter_string: string or None\n :param filter_string:\n An optional filter expression describing the resource descriptors\n to be returned. See the `filter documentation`_.\n\n :rtype: list of :class:`ResourceDescriptor`\n :returns: A list of resource descriptor instances.\n\n .. _filter documentation:\n https://cloud.google.com/monitoring/api/v3/filters\n \"\"\"\n path = '/projects/{project}/monitoredResourceDescriptors/'.format(\n project=client.project)\n\n descriptors = []\n\n page_token = None\n while True:\n params = {}\n\n if filter_string is not None:\n params['filter'] = filter_string\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n response = client.connection.api_request(\n method='GET', path=path, query_params=params)\n for info in response.get('resourceDescriptors', ()):\n descriptors.append(cls._from_dict(info))\n\n page_token = response.get('nextPageToken')\n if not page_token:\n break\n\n return descriptors","label":0},{"cid":"google/cloud/monitoring/resource.py::ResourceDescriptor._from_dict","text":"google/cloud/monitoring/resource.py::ResourceDescriptor._from_dict\n @classmethod\n def _from_dict(cls, info):\n \"\"\"Construct a resource descriptor from the parsed JSON representation.\n\n :type info: dict\n :param info:\n A ``dict`` parsed from the JSON wire-format representation.\n\n :rtype: :class:`ResourceDescriptor`\n :returns: A resource descriptor.\n \"\"\"\n return cls(\n name=info['name'],\n type_=info['type'],\n display_name=info.get('displayName', ''),\n description=info.get('description', ''),\n labels=tuple(LabelDescriptor._from_dict(label)\n for label in info.get('labels', ())),\n )","label":0},{"cid":"google/cloud/monitoring/resource.py::ResourceDescriptor.__repr__","text":"google/cloud/monitoring/resource.py::ResourceDescriptor.__repr__\n def __repr__(self):\n return (\n '<ResourceDescriptor:\\n'\n ' name={name!r},\\n'\n ' type={type!r},\\n'\n ' labels={labels!r},\\n'\n ' display_name={display_name!r},\\n'\n ' description={description!r}>'\n ).format(**self.__dict__)","label":0},{"cid":"google/cloud/monitoring/resource.py::Resource._from_dict","text":"google/cloud/monitoring/resource.py::Resource._from_dict\n @classmethod\n def _from_dict(cls, info):\n \"\"\"Construct a resource object from the parsed JSON representation.\n\n :type info: dict\n :param info:\n A ``dict`` parsed from the JSON wire-format representation.\n\n :rtype: :class:`Resource`\n :returns: A resource object.\n \"\"\"\n return cls(\n type=info['type'],\n labels=info.get('labels', {}),\n )","label":0},{"cid":"google/cloud/monitoring/resource.py::Resource._to_dict","text":"google/cloud/monitoring/resource.py::Resource._to_dict\n def _to_dict(self):\n \"\"\"Build a dictionary ready to be serialized to the JSON format.\n\n :rtype: dict\n :returns: A dict representation of the object that can be written to\n the API.\n \"\"\"\n return {\n 'type': self.type,\n 'labels': self.labels,\n }","label":0},{"cid":"google/cloud/monitoring/timeseries.py::TimeSeries.labels","text":"google/cloud/monitoring/timeseries.py::TimeSeries.labels\n @property\n def labels(self):\n \"\"\"A single dictionary with values for all the labels.\n\n This combines ``resource.labels`` and ``metric.labels`` and also\n adds ``\"resource_type\"``.\n \"\"\"\n if self._labels is None:\n labels = {'resource_type': self.resource.type}\n labels.update(self.resource.labels)\n labels.update(self.metric.labels)\n self._labels = labels\n\n return self._labels","label":0},{"cid":"google/cloud/monitoring/timeseries.py::TimeSeries.header","text":"google/cloud/monitoring/timeseries.py::TimeSeries.header\n def header(self, points=None):\n \"\"\"Copy everything but the point data.\n\n :type points: list of :class:`Point`, or None\n :param points: An optional point list.\n\n :rtype: :class:`TimeSeries`\n :returns: The new time series object.\n \"\"\"\n points = list(points) if points else []\n return self._replace(points=points)","label":0},{"cid":"google/cloud/monitoring/timeseries.py::TimeSeries._to_dict","text":"google/cloud/monitoring/timeseries.py::TimeSeries._to_dict\n def _to_dict(self):\n \"\"\"Build a dictionary ready to be serialized to the JSON wire format.\n\n Since this method is used when writing to the API, it excludes\n output-only fields.\n\n :rtype: dict\n :returns: The dictionary representation of the time series object.\n \"\"\"\n info = {\n 'metric': self.metric._to_dict(),\n 'resource': self.resource._to_dict(),\n 'points': [point._to_dict() for point in self.points],\n }\n\n return info","label":0},{"cid":"google/cloud/monitoring/timeseries.py::TimeSeries._from_dict","text":"google/cloud/monitoring/timeseries.py::TimeSeries._from_dict\n @classmethod\n def _from_dict(cls, info):\n \"\"\"Construct a time series from the parsed JSON representation.\n\n :type info: dict\n :param info:\n A ``dict`` parsed from the JSON wire-format representation.\n\n :rtype: :class:`TimeSeries`\n :returns: A time series object.\n \"\"\"\n metric = Metric._from_dict(info['metric'])\n resource = Resource._from_dict(info['resource'])\n metric_kind = info['metricKind']\n value_type = info['valueType']\n points = [Point._from_dict(p) for p in info.get('points', ())]\n return cls(metric, resource, metric_kind, value_type, points)","label":0},{"cid":"google/cloud/monitoring/timeseries.py::TimeSeries.__repr__","text":"google/cloud/monitoring/timeseries.py::TimeSeries.__repr__\n def __repr__(self):\n \"\"\"Return a representation string with the points elided.\"\"\"\n return (\n '<TimeSeries with {num} points:\\n'\n ' metric={metric!r},\\n'\n ' resource={resource!r},\\n'\n ' metric_kind={kind!r}, value_type={type!r}>'\n ).format(\n num=len(self.points),\n metric=self.metric,\n resource=self.resource,\n kind=self.metric_kind,\n type=self.value_type,\n )","label":0},{"cid":"google/cloud/monitoring/timeseries.py::_make_typed_value","text":"google/cloud/monitoring/timeseries.py::_make_typed_value\ndef _make_typed_value(value):\n \"\"\"Create a dict representing a TypedValue API object.\n\n Typed values are objects with the value itself as the value, keyed by the\n type of the value. They are used when writing points to time series. This\n method returns the dict representation for the TypedValue.\n\n This method uses the Python type of the object to infer the correct\n type to send to the API. For example, a Python float will be sent to the\n API with \"doubleValue\" as its key.\n\n See: https://cloud.google.com/monitoring/api/ref_v3/rest/v3/TypedValue\n\n :type value: bool, int, float, str, or dict\n :param value: value to infer the typed value of.\n\n :rtype: dict\n :returns: A dict\n \"\"\"\n typed_value_map = {\n bool: 'boolValue',\n int: 'int64Value',\n float: 'doubleValue',\n str: 'stringValue',\n dict: 'distributionValue',\n }\n type_ = typed_value_map[type(value)]\n if type_ == 'int64Value':\n value = str(value)\n return {type_: value}","label":0},{"cid":"google/cloud/monitoring/timeseries.py::Point._from_dict","text":"google/cloud/monitoring/timeseries.py::Point._from_dict\n @classmethod\n def _from_dict(cls, info):\n \"\"\"Construct a Point from the parsed JSON representation.\n\n :type info: dict\n :param info:\n A ``dict`` parsed from the JSON wire-format representation.\n\n :rtype: :class:`Point`\n :returns: A point object.\n \"\"\"\n end_time = info['interval']['endTime']\n start_time = info['interval'].get('startTime')\n (value_type, value), = info['value'].items()\n if value_type == 'int64Value':\n value = int(value) # Convert from string.\n\n return cls(end_time, start_time, value)","label":0},{"cid":"google/cloud/monitoring/timeseries.py::Point._to_dict","text":"google/cloud/monitoring/timeseries.py::Point._to_dict\n def _to_dict(self):\n \"\"\"Build a dictionary ready to be serialized to the JSON wire format.\n\n This method serializes a point in JSON format to be written\n to the API.\n\n :rtype: dict\n :returns: The dictionary representation of the point object.\n \"\"\"\n info = {\n 'interval': {\n 'endTime': self.end_time,\n },\n 'value': _make_typed_value(self.value),\n }\n\n if self.start_time is not None:\n info['interval']['startTime'] = self.start_time\n\n return info","label":0},{"cid":"google/cloud/operation.py::_compute_type_url","text":"google/cloud/operation.py::_compute_type_url\ndef _compute_type_url(klass, prefix=_GOOGLE_APIS_PREFIX):\n \"\"\"Compute a type URL for a klass.\n\n :type klass: type\n :param klass: class to be used as a factory for the given type\n\n :type prefix: str\n :param prefix: URL prefix for the type\n\n :rtype: str\n :returns: the URL, prefixed as appropriate\n \"\"\"\n name = klass.DESCRIPTOR.full_name\n return '%s/%s' % (prefix, name)","label":0},{"cid":"google/cloud/operation.py::_register_type_url","text":"google/cloud/operation.py::_register_type_url\ndef _register_type_url(type_url, klass):\n \"\"\"Register a klass as the factory for a given type URL.\n\n :type type_url: str\n :param type_url: URL naming the type\n\n :type klass: type\n :param klass: class to be used as a factory for the given type\n\n :raises: ValueError if a registration already exists for the URL.\n \"\"\"\n if type_url in _TYPE_URL_MAP:\n if _TYPE_URL_MAP[type_url] is not klass:\n raise ValueError(\"Conflict: %s\" % (_TYPE_URL_MAP[type_url],))\n\n _TYPE_URL_MAP[type_url] = klass","label":0},{"cid":"google/cloud/operation.py::Operation.__init__","text":"google/cloud/operation.py::Operation.__init__\n def __init__(self, name, client, pb_metadata=None, **kw):\n self.name = name\n self.client = client\n self.pb_metadata = pb_metadata\n self.metadata = kw.copy()\n self._complete = False","label":0},{"cid":"google/cloud/operation.py::Operation.from_pb","text":"google/cloud/operation.py::Operation.from_pb\n @classmethod\n def from_pb(cls, op_pb, client, **kw):\n \"\"\"Factory: construct an instance from a protobuf.\n\n :type op_pb: :class:`google.longrunning.operations_pb2.Operation`\n :param op_pb: Protobuf to be parsed.\n\n :type client: object: must provide ``_operations_stub`` accessor.\n :param client: The client used to poll for the status of the operation.\n\n :type kw: dict\n :param kw: caller-assigned metadata about the operation\n\n :rtype: :class:`Operation`\n :returns: new instance, with attributes based on the protobuf.\n \"\"\"\n pb_metadata = None\n if op_pb.metadata.type_url:\n type_url = op_pb.metadata.type_url\n md_klass = _TYPE_URL_MAP.get(type_url)\n if md_klass:\n pb_metadata = md_klass.FromString(op_pb.metadata.value)\n return cls(op_pb.name, client, pb_metadata, **kw)","label":0},{"cid":"google/cloud/operation.py::Operation.complete","text":"google/cloud/operation.py::Operation.complete\n @property\n def complete(self):\n \"\"\"Has the operation already completed?\n\n :rtype: bool\n :returns: True if already completed, else false.\n \"\"\"\n return self._complete","label":0},{"cid":"google/cloud/operation.py::Operation.poll","text":"google/cloud/operation.py::Operation.poll\n def poll(self):\n \"\"\"Check if the operation has finished.\n\n :rtype: bool\n :returns: A boolean indicating if the current operation has completed.\n :raises: :class:`ValueError <exceptions.ValueError>` if the operation\n has already completed.\n \"\"\"\n if self.complete:\n raise ValueError('The operation has completed.')\n\n request_pb = operations_pb2.GetOperationRequest(name=self.name)\n # We expect a `google.longrunning.operations_pb2.Operation`.\n operation_pb = self.client._operations_stub.GetOperation(request_pb)\n\n if operation_pb.done:\n self._complete = True\n\n return self.complete","label":0},{"cid":"google/cloud/pubsub/_gax.py::_PublisherAPI.__init__","text":"google/cloud/pubsub/_gax.py::_PublisherAPI.__init__\n def __init__(self, gax_api):\n self._gax_api = gax_api","label":0},{"cid":"google/cloud/pubsub/_gax.py::_PublisherAPI.list_topics","text":"google/cloud/pubsub/_gax.py::_PublisherAPI.list_topics\n def list_topics(self, project, page_size=0, page_token=None):\n \"\"\"List topics for the project associated with this API.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/list\n\n :type project: string\n :param project: project ID\n\n :type page_size: int\n :param page_size: maximum number of topics to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of topics. If not\n passed, the API will return the first page of\n topics.\n\n :rtype: tuple, (list, str)\n :returns: list of ``Topic`` resource dicts, plus a\n \"next page token\" string: if not None, indicates that\n more topics can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n if page_token is None:\n page_token = INITIAL_PAGE\n options = CallOptions(page_token=page_token)\n path = 'projects/%s' % (project,)\n page_iter = self._gax_api.list_topics(\n path, page_size=page_size, options=options)\n topics = [{'name': topic_pb.name} for topic_pb in page_iter.next()]\n token = page_iter.page_token or None\n return topics, token","label":0},{"cid":"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_create","text":"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_create\n def topic_create(self, topic_path):\n \"\"\"API call: create a topic\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/create\n\n :type topic_path: string\n :param topic_path: fully-qualified path of the new topic, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n\n :rtype: dict\n :returns: ``Topic`` resource returned from the API.\n :raises: :exc:`google.cloud.exceptions.Conflict` if the topic already\n exists\n \"\"\"\n try:\n topic_pb = self._gax_api.create_topic(topic_path)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.FAILED_PRECONDITION:\n raise Conflict(topic_path)\n raise\n return {'name': topic_pb.name}","label":0},{"cid":"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_get","text":"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_get\n def topic_get(self, topic_path):\n \"\"\"API call: retrieve a topic\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/get\n\n :type topic_path: string\n :param topic_path: fully-qualified path of the topic, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n\n :rtype: dict\n :returns: ``Topic`` resource returned from the API.\n :raises: :exc:`google.cloud.exceptions.NotFound` if the topic does not\n exist\n \"\"\"\n try:\n topic_pb = self._gax_api.get_topic(topic_path)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(topic_path)\n raise\n return {'name': topic_pb.name}","label":0},{"cid":"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_delete","text":"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_delete\n def topic_delete(self, topic_path):\n \"\"\"API call: delete a topic\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/create\n\n :type topic_path: string\n :param topic_path: fully-qualified path of the new topic, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n \"\"\"\n try:\n self._gax_api.delete_topic(topic_path)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(topic_path)\n raise","label":0},{"cid":"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_publish","text":"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_publish\n def topic_publish(self, topic_path, messages):\n \"\"\"API call: publish one or more messages to a topic\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/publish\n\n :type topic_path: string\n :param topic_path: fully-qualified path of the topic, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n\n :type messages: list of dict\n :param messages: messages to be published.\n\n :rtype: list of string\n :returns: list of opaque IDs for published messages.\n :raises: :exc:`google.cloud.exceptions.NotFound` if the topic does not\n exist\n \"\"\"\n options = CallOptions(is_bundling=False)\n message_pbs = [_message_pb_from_mapping(message)\n for message in messages]\n try:\n result = self._gax_api.publish(topic_path, message_pbs,\n options=options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(topic_path)\n raise\n return result.message_ids","label":0},{"cid":"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_list_subscriptions","text":"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_list_subscriptions\n def topic_list_subscriptions(self, topic_path, page_size=0,\n page_token=None):\n \"\"\"API call: list subscriptions bound to a topic\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics.subscriptions/list\n\n :type topic_path: string\n :param topic_path: fully-qualified path of the topic, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n\n :type page_size: int\n :param page_size: maximum number of subscriptions to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of subscriptions.\n If not passed, the API will return the first page\n of subscriptions.\n\n :rtype: list of strings\n :returns: fully-qualified names of subscriptions for the supplied\n topic.\n :raises: :exc:`google.cloud.exceptions.NotFound` if the topic does not\n exist\n \"\"\"\n if page_token is None:\n page_token = INITIAL_PAGE\n options = CallOptions(page_token=page_token)\n try:\n page_iter = self._gax_api.list_topic_subscriptions(\n topic_path, page_size=page_size, options=options)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(topic_path)\n raise\n subs = page_iter.next()\n token = page_iter.page_token or None\n return subs, token","label":0},{"cid":"google/cloud/pubsub/_gax.py::_SubscriberAPI.__init__","text":"google/cloud/pubsub/_gax.py::_SubscriberAPI.__init__\n def __init__(self, gax_api):\n self._gax_api = gax_api","label":0},{"cid":"google/cloud/pubsub/_gax.py::_SubscriberAPI.list_subscriptions","text":"google/cloud/pubsub/_gax.py::_SubscriberAPI.list_subscriptions\n def list_subscriptions(self, project, page_size=0, page_token=None):\n \"\"\"List subscriptions for the project associated with this API.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/list\n\n :type project: string\n :param project: project ID\n\n :type page_size: int\n :param page_size: maximum number of subscriptions to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of subscriptions.\n If not passed, the API will return the first page\n of subscriptions.\n\n :rtype: tuple, (list, str)\n :returns: list of ``Subscription`` resource dicts, plus a\n \"next page token\" string: if not None, indicates that\n more topics can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n if page_token is None:\n page_token = INITIAL_PAGE\n options = CallOptions(page_token=page_token)\n path = 'projects/%s' % (project,)\n page_iter = self._gax_api.list_subscriptions(\n path, page_size=page_size, options=options)\n subscriptions = [_subscription_pb_to_mapping(sub_pb)\n for sub_pb in page_iter.next()]\n token = page_iter.page_token or None\n return subscriptions, token","label":0},{"cid":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_create","text":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_create\n def subscription_create(self, subscription_path, topic_path,\n ack_deadline=None, push_endpoint=None):\n \"\"\"API call: create a subscription\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/create\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the new\n subscription, in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :type topic_path: string\n :param topic_path: the fully-qualified path of the topic being\n subscribed, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n\n :type ack_deadline: int, or ``NoneType``\n :param ack_deadline: the deadline (in seconds) by which messages pulled\n from the back-end must be acknowledged.\n\n :type push_endpoint: string, or ``NoneType``\n :param push_endpoint: URL to which messages will be pushed by the\n back-end. If not set, the application must pull\n messages.\n\n :rtype: dict\n :returns: ``Subscription`` resource returned from the API.\n \"\"\"\n if push_endpoint is not None:\n push_config = PushConfig(push_endpoint=push_endpoint)\n else:\n push_config = None\n\n if ack_deadline is None:\n ack_deadline = 0\n\n try:\n sub_pb = self._gax_api.create_subscription(\n subscription_path, topic_path, push_config, ack_deadline)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.FAILED_PRECONDITION:\n raise Conflict(topic_path)\n raise\n return _subscription_pb_to_mapping(sub_pb)","label":0},{"cid":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_get","text":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_get\n def subscription_get(self, subscription_path):\n \"\"\"API call: retrieve a subscription\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/get\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the subscription,\n in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :rtype: dict\n :returns: ``Subscription`` resource returned from the API.\n \"\"\"\n try:\n sub_pb = self._gax_api.get_subscription(subscription_path)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(subscription_path)\n raise\n return _subscription_pb_to_mapping(sub_pb)","label":0},{"cid":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_delete","text":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_delete\n def subscription_delete(self, subscription_path):\n \"\"\"API call: delete a subscription\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/delete\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the subscription,\n in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n \"\"\"\n try:\n self._gax_api.delete_subscription(subscription_path)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(subscription_path)\n raise","label":0},{"cid":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_modify_push_config","text":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_modify_push_config\n def subscription_modify_push_config(self, subscription_path,\n push_endpoint):\n \"\"\"API call: update push config of a subscription\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the new\n subscription, in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :type push_endpoint: string, or ``NoneType``\n :param push_endpoint: URL to which messages will be pushed by the\n back-end. If not set, the application must pull\n messages.\n \"\"\"\n push_config = PushConfig(push_endpoint=push_endpoint)\n try:\n self._gax_api.modify_push_config(subscription_path, push_config)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(subscription_path)\n raise","label":0},{"cid":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_pull","text":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_pull\n def subscription_pull(self, subscription_path, return_immediately=False,\n max_messages=1):\n \"\"\"API call: retrieve messages for a subscription\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the new\n subscription, in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :type return_immediately: boolean\n :param return_immediately: if True, the back-end returns even if no\n messages are available; if False, the API\n call blocks until one or more messages are\n available.\n\n :type max_messages: int\n :param max_messages: the maximum number of messages to return.\n\n :rtype: list of dict\n :returns: the ``receivedMessages`` element of the response.\n \"\"\"\n try:\n response_pb = self._gax_api.pull(\n subscription_path, max_messages, return_immediately)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(subscription_path)\n raise\n return [_received_message_pb_to_mapping(rmpb)\n for rmpb in response_pb.received_messages]","label":0},{"cid":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_acknowledge","text":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_acknowledge\n def subscription_acknowledge(self, subscription_path, ack_ids):\n \"\"\"API call: acknowledge retrieved messages\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the new\n subscription, in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :type ack_ids: list of string\n :param ack_ids: ack IDs of messages being acknowledged\n \"\"\"\n try:\n self._gax_api.acknowledge(subscription_path, ack_ids)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(subscription_path)\n raise","label":0},{"cid":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_modify_ack_deadline","text":"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_modify_ack_deadline\n def subscription_modify_ack_deadline(self, subscription_path, ack_ids,\n ack_deadline):\n \"\"\"API call: update ack deadline for retrieved messages\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyAckDeadline\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the new\n subscription, in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :type ack_ids: list of string\n :param ack_ids: ack IDs of messages being acknowledged\n\n :type ack_deadline: int\n :param ack_deadline: the deadline (in seconds) by which messages pulled\n from the back-end must be acknowledged.\n \"\"\"\n try:\n self._gax_api.modify_ack_deadline(\n subscription_path, ack_ids, ack_deadline)\n except GaxError as exc:\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\n raise NotFound(subscription_path)\n raise","label":0},{"cid":"google/cloud/pubsub/_gax.py::_message_pb_from_mapping","text":"google/cloud/pubsub/_gax.py::_message_pb_from_mapping\ndef _message_pb_from_mapping(message):\n \"\"\"Helper for :meth:`_PublisherAPI.topic_publish`.\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n return PubsubMessage(data=_to_bytes(message['data']),\n attributes=message['attributes'])","label":0},{"cid":"google/cloud/pubsub/_gax.py::_subscription_pb_to_mapping","text":"google/cloud/pubsub/_gax.py::_subscription_pb_to_mapping\ndef _subscription_pb_to_mapping(sub_pb):\n \"\"\"Helper for :meth:`list_subscriptions`, et aliae\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n mapping = {\n 'name': sub_pb.name,\n 'topic': sub_pb.topic,\n 'ackDeadlineSeconds': sub_pb.ack_deadline_seconds,\n }\n if sub_pb.push_config.push_endpoint != '':\n mapping['pushConfig'] = {\n 'pushEndpoint': sub_pb.push_config.push_endpoint,\n }\n return mapping","label":0},{"cid":"google/cloud/pubsub/_gax.py::_message_pb_to_mapping","text":"google/cloud/pubsub/_gax.py::_message_pb_to_mapping\ndef _message_pb_to_mapping(message_pb):\n \"\"\"Helper for :meth:`pull`, et aliae\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n return {\n 'messageId': message_pb.message_id,\n 'data': message_pb.data,\n 'attributes': message_pb.attributes,\n 'publishTime': _pb_timestamp_to_rfc3339(message_pb.publish_time),\n }","label":0},{"cid":"google/cloud/pubsub/_gax.py::_received_message_pb_to_mapping","text":"google/cloud/pubsub/_gax.py::_received_message_pb_to_mapping\ndef _received_message_pb_to_mapping(received_message_pb):\n \"\"\"Helper for :meth:`pull`, et aliae\n\n Performs \"impedance matching\" between the protobuf attrs and the keys\n expected in the JSON API.\n \"\"\"\n return {\n 'ackId': received_message_pb.ack_id,\n 'message': _message_pb_to_mapping(\n received_message_pb.message),\n }","label":0},{"cid":"google/cloud/pubsub/_gax.py::make_gax_publisher_api","text":"google/cloud/pubsub/_gax.py::make_gax_publisher_api\ndef make_gax_publisher_api(connection):\n \"\"\"Create an instance of the GAX Publisher API.\n\n If the ``connection`` is intended for a local emulator, then\n an insecure ``channel`` is created pointing at the local\n Pub / Sub server.\n\n :type connection: :class:`~google.cloud.pubsub.connection.Connection`\n :param connection: The connection that holds configuration details.\n\n :rtype: :class:`~google.cloud.pubsub.v1.publisher_api.PublisherApi`\n :returns: A publisher API instance with the proper connection\n configuration.\n :rtype: :class:`~google.cloud.pubsub.v1.subscriber_api.SubscriberApi`\n \"\"\"\n channel = None\n if connection.in_emulator:\n channel = insecure_channel(connection.host)\n return PublisherApi(channel=channel)","label":0},{"cid":"google/cloud/pubsub/_gax.py::make_gax_subscriber_api","text":"google/cloud/pubsub/_gax.py::make_gax_subscriber_api\ndef make_gax_subscriber_api(connection):\n \"\"\"Create an instance of the GAX Subscriber API.\n\n If the ``connection`` is intended for a local emulator, then\n an insecure ``channel`` is created pointing at the local\n Pub / Sub server.\n\n :type connection: :class:`~google.cloud.pubsub.connection.Connection`\n :param connection: The connection that holds configuration details.\n\n :rtype: :class:`~google.cloud.pubsub.v1.subscriber_api.SubscriberApi`\n :returns: A subscriber API instance with the proper connection\n configuration.\n \"\"\"\n channel = None\n if connection.in_emulator:\n channel = insecure_channel(connection.host)\n return SubscriberApi(channel=channel)","label":0},{"cid":"google/cloud/pubsub/_helpers.py::topic_name_from_path","text":"google/cloud/pubsub/_helpers.py::topic_name_from_path\ndef topic_name_from_path(path, project):\n \"\"\"Validate a topic URI path and get the topic name.\n\n :type path: string\n :param path: URI path for a topic API request.\n\n :type project: string\n :param project: The project associated with the request. It is\n included for validation purposes.\n\n :rtype: string\n :returns: Topic name parsed from ``path``.\n :raises: :class:`ValueError` if the ``path`` is ill-formed or if\n the project from the ``path`` does not agree with the\n ``project`` passed in.\n \"\"\"\n return _name_from_project_path(path, project, _TOPIC_TEMPLATE)","label":0},{"cid":"google/cloud/pubsub/_helpers.py::subscription_name_from_path","text":"google/cloud/pubsub/_helpers.py::subscription_name_from_path\ndef subscription_name_from_path(path, project):\n \"\"\"Validate a subscription URI path and get the subscription name.\n\n :type path: string\n :param path: URI path for a subscription API request.\n\n :type project: string\n :param project: The project associated with the request. It is\n included for validation purposes.\n\n :rtype: string\n :returns: subscription name parsed from ``path``.\n :raises: :class:`ValueError` if the ``path`` is ill-formed or if\n the project from the ``path`` does not agree with the\n ``project`` passed in.\n \"\"\"\n return _name_from_project_path(path, project, _SUBSCRIPTION_TEMPLATE)","label":0},{"cid":"google/cloud/pubsub/client.py::Client.publisher_api","text":"google/cloud/pubsub/client.py::Client.publisher_api\n @property\n def publisher_api(self):\n \"\"\"Helper for publisher-related API calls.\"\"\"\n if self._publisher_api is None:\n if _USE_GAX:\n generated = make_gax_publisher_api(self.connection)\n self._publisher_api = GAXPublisherAPI(generated)\n else:\n self._publisher_api = JSONPublisherAPI(self.connection)\n return self._publisher_api","label":0},{"cid":"google/cloud/pubsub/client.py::Client.subscriber_api","text":"google/cloud/pubsub/client.py::Client.subscriber_api\n @property\n def subscriber_api(self):\n \"\"\"Helper for subscriber-related API calls.\"\"\"\n if self._subscriber_api is None:\n if _USE_GAX:\n generated = make_gax_subscriber_api(self.connection)\n self._subscriber_api = GAXSubscriberAPI(generated)\n else:\n self._subscriber_api = JSONSubscriberAPI(self.connection)\n return self._subscriber_api","label":0},{"cid":"google/cloud/pubsub/client.py::Client.iam_policy_api","text":"google/cloud/pubsub/client.py::Client.iam_policy_api\n @property\n def iam_policy_api(self):\n \"\"\"Helper for IAM policy-related API calls.\"\"\"\n if self._iam_policy_api is None:\n self._iam_policy_api = _IAMPolicyAPI(self.connection)\n return self._iam_policy_api","label":0},{"cid":"google/cloud/pubsub/client.py::Client.list_topics","text":"google/cloud/pubsub/client.py::Client.list_topics\n def list_topics(self, page_size=None, page_token=None):\n \"\"\"List topics for the project associated with this client.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/list\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START client_list_topics]\n :end-before: [END client_list_topics]\n\n :type page_size: int\n :param page_size: maximum number of topics to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of topics. If not\n passed, the API will return the first page of\n topics.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`google.cloud.pubsub.topic.Topic`, plus a\n \"next page token\" string: if not None, indicates that\n more topics can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n api = self.publisher_api\n resources, next_token = api.list_topics(\n self.project, page_size, page_token)\n topics = [Topic.from_api_repr(resource, self)\n for resource in resources]\n return topics, next_token","label":0},{"cid":"google/cloud/pubsub/client.py::Client.list_subscriptions","text":"google/cloud/pubsub/client.py::Client.list_subscriptions\n def list_subscriptions(self, page_size=None, page_token=None):\n \"\"\"List subscriptions for the project associated with this client.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/list\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START client_list_subscriptions]\n :end-before: [END client_list_subscriptions]\n\n :type page_size: int\n :param page_size: maximum number of topics to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of topics. If not\n passed, the API will return the first page of\n topics.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`~.pubsub.subscription.Subscription`,\n plus a \"next page token\" string: if not None, indicates that\n more topics can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n api = self.subscriber_api\n resources, next_token = api.list_subscriptions(\n self.project, page_size, page_token)\n topics = {}\n subscriptions = [Subscription.from_api_repr(resource, self,\n topics=topics)\n for resource in resources]\n return subscriptions, next_token","label":0},{"cid":"google/cloud/pubsub/client.py::Client.topic","text":"google/cloud/pubsub/client.py::Client.topic\n def topic(self, name, timestamp_messages=False):\n \"\"\"Creates a topic bound to the current client.\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START client_topic]\n :end-before: [END client_topic]\n\n :type name: string\n :param name: the name of the topic to be constructed.\n\n :type timestamp_messages: boolean\n :param timestamp_messages: To be passed to ``Topic`` constructor.\n\n :rtype: :class:`google.cloud.pubsub.topic.Topic`\n :returns: Topic created with the current client.\n \"\"\"\n return Topic(name, client=self, timestamp_messages=timestamp_messages)","label":0},{"cid":"google/cloud/pubsub/connection.py::Connection.__init__","text":"google/cloud/pubsub/connection.py::Connection.__init__\n def __init__(self, credentials=None, http=None):\n super(Connection, self).__init__(credentials=credentials, http=http)\n emulator_host = os.getenv(PUBSUB_EMULATOR)\n if emulator_host is None:\n self.host = self.__class__.API_BASE_URL\n self.api_base_url = self.__class__.API_BASE_URL\n self.in_emulator = False\n else:\n self.host = emulator_host\n self.api_base_url = 'http://' + emulator_host\n self.in_emulator = True","label":0},{"cid":"google/cloud/pubsub/connection.py::Connection.build_api_url","text":"google/cloud/pubsub/connection.py::Connection.build_api_url\n def build_api_url(self, path, query_params=None,\n api_base_url=None, api_version=None):\n \"\"\"Construct an API url given a few components, some optional.\n\n Typically, you shouldn't need to use this method.\n\n :type path: string\n :param path: The path to the resource.\n\n :type query_params: dict or list\n :param query_params: A dictionary of keys and values (or list of\n key-value pairs) to insert into the query\n string of the URL.\n\n :type api_base_url: string\n :param api_base_url: The base URL for the API endpoint.\n Typically you won't have to provide this.\n\n :type api_version: string\n :param api_version: The version of the API to call.\n Typically you shouldn't provide this and instead\n use the default for the library.\n\n :rtype: string\n :returns: The URL assembled from the pieces provided.\n \"\"\"\n if api_base_url is None:\n api_base_url = self.api_base_url\n return super(Connection, self.__class__).build_api_url(\n path, query_params=query_params,\n api_base_url=api_base_url, api_version=api_version)","label":0},{"cid":"google/cloud/pubsub/connection.py::_PublisherAPI.__init__","text":"google/cloud/pubsub/connection.py::_PublisherAPI.__init__\n def __init__(self, connection):\n self._connection = connection","label":0},{"cid":"google/cloud/pubsub/connection.py::_PublisherAPI.list_topics","text":"google/cloud/pubsub/connection.py::_PublisherAPI.list_topics\n def list_topics(self, project, page_size=None, page_token=None):\n \"\"\"API call: list topics for a given project\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/list\n\n :type project: string\n :param project: project ID\n\n :type page_size: int\n :param page_size: maximum number of topics to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of topics. If not\n passed, the API will return the first page of\n topics.\n\n :rtype: tuple, (list, str)\n :returns: list of ``Topic`` resource dicts, plus a\n \"next page token\" string: if not None, indicates that\n more topics can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n conn = self._connection\n params = {}\n\n if page_size is not None:\n params['pageSize'] = page_size\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/topics' % (project,)\n resp = conn.api_request(method='GET', path=path, query_params=params)\n return resp.get('topics', ()), resp.get('nextPageToken')","label":0},{"cid":"google/cloud/pubsub/connection.py::_PublisherAPI.topic_create","text":"google/cloud/pubsub/connection.py::_PublisherAPI.topic_create\n def topic_create(self, topic_path):\n \"\"\"API call: create a topic\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/create\n\n :type topic_path: string\n :param topic_path: the fully-qualified path of the new topic, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n\n :rtype: dict\n :returns: ``Topic`` resource returned from the API.\n \"\"\"\n conn = self._connection\n return conn.api_request(method='PUT', path='/%s' % (topic_path,))","label":0},{"cid":"google/cloud/pubsub/connection.py::_PublisherAPI.topic_get","text":"google/cloud/pubsub/connection.py::_PublisherAPI.topic_get\n def topic_get(self, topic_path):\n \"\"\"API call: retrieve a topic\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/get\n\n :type topic_path: string\n :param topic_path: the fully-qualified path of the topic, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n\n :rtype: dict\n :returns: ``Topic`` resource returned from the API.\n \"\"\"\n conn = self._connection\n return conn.api_request(method='GET', path='/%s' % (topic_path,))","label":0},{"cid":"google/cloud/pubsub/connection.py::_PublisherAPI.topic_delete","text":"google/cloud/pubsub/connection.py::_PublisherAPI.topic_delete\n def topic_delete(self, topic_path):\n \"\"\"API call: delete a topic\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/delete\n\n :type topic_path: string\n :param topic_path: the fully-qualified path of the topic, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n \"\"\"\n conn = self._connection\n conn.api_request(method='DELETE', path='/%s' % (topic_path,))","label":0},{"cid":"google/cloud/pubsub/connection.py::_PublisherAPI.topic_publish","text":"google/cloud/pubsub/connection.py::_PublisherAPI.topic_publish\n def topic_publish(self, topic_path, messages):\n \"\"\"API call: publish one or more messages to a topic\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/publish\n\n :type topic_path: string\n :param topic_path: the fully-qualified path of the topic, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n\n :type messages: list of dict\n :param messages: messages to be published.\n\n :rtype: list of string\n :returns: list of opaque IDs for published messages.\n \"\"\"\n conn = self._connection\n data = {'messages': messages}\n response = conn.api_request(\n method='POST', path='/%s:publish' % (topic_path,), data=data)\n return response['messageIds']","label":0},{"cid":"google/cloud/pubsub/connection.py::_PublisherAPI.topic_list_subscriptions","text":"google/cloud/pubsub/connection.py::_PublisherAPI.topic_list_subscriptions\n def topic_list_subscriptions(self, topic_path, page_size=None,\n page_token=None):\n \"\"\"API call: list subscriptions bound to a topic\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics.subscriptions/list\n\n :type topic_path: string\n :param topic_path: the fully-qualified path of the topic, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n\n :type page_size: int\n :param page_size: maximum number of subscriptions to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of topics. If not\n passed, the API will return the first page of\n topics.\n\n :rtype: list of strings\n :returns: fully-qualified names of subscriptions for the supplied\n topic.\n \"\"\"\n conn = self._connection\n params = {}\n\n if page_size is not None:\n params['pageSize'] = page_size\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/%s/subscriptions' % (topic_path,)\n resp = conn.api_request(method='GET', path=path, query_params=params)\n return resp.get('subscriptions', ()), resp.get('nextPageToken')","label":0},{"cid":"google/cloud/pubsub/connection.py::_SubscriberAPI.__init__","text":"google/cloud/pubsub/connection.py::_SubscriberAPI.__init__\n def __init__(self, connection):\n self._connection = connection","label":0},{"cid":"google/cloud/pubsub/connection.py::_SubscriberAPI.list_subscriptions","text":"google/cloud/pubsub/connection.py::_SubscriberAPI.list_subscriptions\n def list_subscriptions(self, project, page_size=None, page_token=None):\n \"\"\"API call: list subscriptions for a given project\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/list\n\n :type project: string\n :param project: project ID\n\n :type page_size: int\n :param page_size: maximum number of subscriptions to return, If not\n passed, defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of subscriptions.\n If not passed, the API will return the first page\n of subscriptions.\n\n :rtype: tuple, (list, str)\n :returns: list of ``Subscription`` resource dicts, plus a\n \"next page token\" string: if not None, indicates that\n more subscriptions can be retrieved with another call (pass\n that value as ``page_token``).\n \"\"\"\n conn = self._connection\n params = {}\n\n if page_size is not None:\n params['pageSize'] = page_size\n\n if page_token is not None:\n params['pageToken'] = page_token\n\n path = '/projects/%s/subscriptions' % (project,)\n resp = conn.api_request(method='GET', path=path, query_params=params)\n return resp.get('subscriptions', ()), resp.get('nextPageToken')","label":0},{"cid":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_create","text":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_create\n def subscription_create(self, subscription_path, topic_path,\n ack_deadline=None, push_endpoint=None):\n \"\"\"API call: create a subscription\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/create\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the new\n subscription, in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :type topic_path: string\n :param topic_path: the fully-qualified path of the topic being\n subscribed, in format\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\n\n :type ack_deadline: int, or ``NoneType``\n :param ack_deadline: the deadline (in seconds) by which messages pulled\n from the back-end must be acknowledged.\n\n :type push_endpoint: string, or ``NoneType``\n :param push_endpoint: URL to which messages will be pushed by the\n back-end. If not set, the application must pull\n messages.\n\n :rtype: dict\n :returns: ``Subscription`` resource returned from the API.\n \"\"\"\n conn = self._connection\n path = '/%s' % (subscription_path,)\n resource = {'topic': topic_path}\n\n if ack_deadline is not None:\n resource['ackDeadlineSeconds'] = ack_deadline\n\n if push_endpoint is not None:\n resource['pushConfig'] = {'pushEndpoint': push_endpoint}\n\n return conn.api_request(method='PUT', path=path, data=resource)","label":0},{"cid":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_get","text":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_get\n def subscription_get(self, subscription_path):\n \"\"\"API call: retrieve a subscription\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/get\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the subscription,\n in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :rtype: dict\n :returns: ``Subscription`` resource returned from the API.\n \"\"\"\n conn = self._connection\n path = '/%s' % (subscription_path,)\n return conn.api_request(method='GET', path=path)","label":0},{"cid":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_delete","text":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_delete\n def subscription_delete(self, subscription_path):\n \"\"\"API call: delete a subscription\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/delete\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the subscription,\n in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n \"\"\"\n conn = self._connection\n path = '/%s' % (subscription_path,)\n conn.api_request(method='DELETE', path=path)","label":0},{"cid":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_modify_push_config","text":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_modify_push_config\n def subscription_modify_push_config(self, subscription_path,\n push_endpoint):\n \"\"\"API call: update push config of a subscription\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the new\n subscription, in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :type push_endpoint: string, or ``NoneType``\n :param push_endpoint: URL to which messages will be pushed by the\n back-end. If not set, the application must pull\n messages.\n \"\"\"\n conn = self._connection\n path = '/%s:modifyPushConfig' % (subscription_path,)\n resource = {'pushConfig': {'pushEndpoint': push_endpoint}}\n conn.api_request(method='POST', path=path, data=resource)","label":0},{"cid":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_pull","text":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_pull\n def subscription_pull(self, subscription_path, return_immediately=False,\n max_messages=1):\n \"\"\"API call: retrieve messages for a subscription\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the new\n subscription, in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :type return_immediately: boolean\n :param return_immediately: if True, the back-end returns even if no\n messages are available; if False, the API\n call blocks until one or more messages are\n available.\n\n :type max_messages: int\n :param max_messages: the maximum number of messages to return.\n\n :rtype: list of dict\n :returns: the ``receivedMessages`` element of the response.\n \"\"\"\n conn = self._connection\n path = '/%s:pull' % (subscription_path,)\n data = {\n 'returnImmediately': return_immediately,\n 'maxMessages': max_messages,\n }\n response = conn.api_request(method='POST', path=path, data=data)\n return response.get('receivedMessages', ())","label":0},{"cid":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_acknowledge","text":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_acknowledge\n def subscription_acknowledge(self, subscription_path, ack_ids):\n \"\"\"API call: acknowledge retrieved messages\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the new\n subscription, in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :type ack_ids: list of string\n :param ack_ids: ack IDs of messages being acknowledged\n \"\"\"\n conn = self._connection\n path = '/%s:acknowledge' % (subscription_path,)\n data = {\n 'ackIds': ack_ids,\n }\n conn.api_request(method='POST', path=path, data=data)","label":0},{"cid":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_modify_ack_deadline","text":"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_modify_ack_deadline\n def subscription_modify_ack_deadline(self, subscription_path, ack_ids,\n ack_deadline):\n \"\"\"API call: update ack deadline for retrieved messages\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyAckDeadline\n\n :type subscription_path: string\n :param subscription_path: the fully-qualified path of the new\n subscription, in format\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\n\n :type ack_ids: list of string\n :param ack_ids: ack IDs of messages being acknowledged\n\n :type ack_deadline: int\n :param ack_deadline: the deadline (in seconds) by which messages pulled\n from the back-end must be acknowledged.\n \"\"\"\n conn = self._connection\n path = '/%s:modifyAckDeadline' % (subscription_path,)\n data = {\n 'ackIds': ack_ids,\n 'ackDeadlineSeconds': ack_deadline,\n }\n conn.api_request(method='POST', path=path, data=data)","label":0},{"cid":"google/cloud/pubsub/connection.py::_IAMPolicyAPI.__init__","text":"google/cloud/pubsub/connection.py::_IAMPolicyAPI.__init__\n def __init__(self, connection):\n self._connection = connection","label":0},{"cid":"google/cloud/pubsub/connection.py::_IAMPolicyAPI.get_iam_policy","text":"google/cloud/pubsub/connection.py::_IAMPolicyAPI.get_iam_policy\n def get_iam_policy(self, target_path):\n \"\"\"API call: fetch the IAM policy for the target\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/getIamPolicy\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/getIamPolicy\n\n :type target_path: string\n :param target_path: the path of the target object.\n\n :rtype: dict\n :returns: the resource returned by the ``getIamPolicy`` API request.\n \"\"\"\n conn = self._connection\n path = '/%s:getIamPolicy' % (target_path,)\n return conn.api_request(method='GET', path=path)","label":0},{"cid":"google/cloud/pubsub/connection.py::_IAMPolicyAPI.set_iam_policy","text":"google/cloud/pubsub/connection.py::_IAMPolicyAPI.set_iam_policy\n def set_iam_policy(self, target_path, policy):\n \"\"\"API call: update the IAM policy for the target\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/setIamPolicy\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/setIamPolicy\n\n :type target_path: string\n :param target_path: the path of the target object.\n\n :type policy: dict\n :param policy: the new policy resource.\n\n :rtype: dict\n :returns: the resource returned by the ``setIamPolicy`` API request.\n \"\"\"\n conn = self._connection\n wrapped = {'policy': policy}\n path = '/%s:setIamPolicy' % (target_path,)\n return conn.api_request(method='POST', path=path, data=wrapped)","label":0},{"cid":"google/cloud/pubsub/connection.py::_IAMPolicyAPI.test_iam_permissions","text":"google/cloud/pubsub/connection.py::_IAMPolicyAPI.test_iam_permissions\n def test_iam_permissions(self, target_path, permissions):\n \"\"\"API call: test permissions\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/testIamPermissions\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/testIamPermissions\n\n :type target_path: string\n :param target_path: the path of the target object.\n\n :type permissions: list of string\n :param permissions: the permissions to check\n\n :rtype: dict\n :returns: the resource returned by the ``getIamPolicy`` API request.\n \"\"\"\n conn = self._connection\n wrapped = {'permissions': permissions}\n path = '/%s:testIamPermissions' % (target_path,)\n resp = conn.api_request(method='POST', path=path, data=wrapped)\n return resp.get('permissions', [])","label":0},{"cid":"google/cloud/pubsub/iam.py::Policy.__init__","text":"google/cloud/pubsub/iam.py::Policy.__init__\n def __init__(self, etag=None, version=None):\n self.etag = etag\n self.version = version\n self.owners = set()\n self.editors = set()\n self.viewers = set()\n self.publishers = set()\n self.subscribers = set()","label":0},{"cid":"google/cloud/pubsub/iam.py::Policy.user","text":"google/cloud/pubsub/iam.py::Policy.user\n @staticmethod\n def user(email):\n \"\"\"Factory method for a user member.\n\n :type email: string\n :param email: E-mail for this particular user.\n\n :rtype: string\n :returns: A member string corresponding to the given user.\n \"\"\"\n return 'user:%s' % (email,)","label":0},{"cid":"google/cloud/pubsub/iam.py::Policy.service_account","text":"google/cloud/pubsub/iam.py::Policy.service_account\n @staticmethod\n def service_account(email):\n \"\"\"Factory method for a service account member.\n\n :type email: string\n :param email: E-mail for this particular service account.\n\n :rtype: string\n :returns: A member string corresponding to the given service account.\n \"\"\"\n return 'serviceAccount:%s' % (email,)","label":0},{"cid":"google/cloud/pubsub/iam.py::Policy.group","text":"google/cloud/pubsub/iam.py::Policy.group\n @staticmethod\n def group(email):\n \"\"\"Factory method for a group member.\n\n :type email: string\n :param email: An id or e-mail for this particular group.\n\n :rtype: string\n :returns: A member string corresponding to the given group.\n \"\"\"\n return 'group:%s' % (email,)","label":0},{"cid":"google/cloud/pubsub/iam.py::Policy.domain","text":"google/cloud/pubsub/iam.py::Policy.domain\n @staticmethod\n def domain(domain):\n \"\"\"Factory method for a domain member.\n\n :type domain: string\n :param domain: The domain for this member.\n\n :rtype: string\n :returns: A member string corresponding to the given domain.\n \"\"\"\n return 'domain:%s' % (domain,)","label":0},{"cid":"google/cloud/pubsub/iam.py::Policy.all_users","text":"google/cloud/pubsub/iam.py::Policy.all_users\n @staticmethod\n def all_users():\n \"\"\"Factory method for a member representing all users.\n\n :rtype: string\n :returns: A member string representing all users.\n \"\"\"\n return 'allUsers'","label":0},{"cid":"google/cloud/pubsub/iam.py::Policy.authenticated_users","text":"google/cloud/pubsub/iam.py::Policy.authenticated_users\n @staticmethod\n def authenticated_users():\n \"\"\"Factory method for a member representing all authenticated users.\n\n :rtype: string\n :returns: A member string representing all authenticated users.\n \"\"\"\n return 'allAuthenticatedUsers'","label":0},{"cid":"google/cloud/pubsub/iam.py::Policy.from_api_repr","text":"google/cloud/pubsub/iam.py::Policy.from_api_repr\n @classmethod\n def from_api_repr(cls, resource):\n \"\"\"Create a policy from the resource returned from the API.\n\n :type resource: dict\n :param resource: resource returned from the ``getIamPolicy`` API.\n\n :rtype: :class:`Policy`\n :returns: the parsed policy\n \"\"\"\n version = resource.get('version')\n etag = resource.get('etag')\n policy = cls(etag, version)\n for binding in resource.get('bindings', ()):\n role = binding['role']\n members = set(binding['members'])\n if role in (OWNER_ROLE, PUBSUB_ADMIN_ROLE):\n policy.owners |= members\n elif role in (EDITOR_ROLE, PUBSUB_EDITOR_ROLE):\n policy.editors |= members\n elif role in (VIEWER_ROLE, PUBSUB_VIEWER_ROLE):\n policy.viewers |= members\n elif role == PUBSUB_PUBLISHER_ROLE:\n policy.publishers |= members\n elif role == PUBSUB_SUBSCRIBER_ROLE:\n policy.subscribers |= members\n else:\n raise ValueError('Unknown role: %s' % (role,))\n return policy","label":0},{"cid":"google/cloud/pubsub/iam.py::Policy.to_api_repr","text":"google/cloud/pubsub/iam.py::Policy.to_api_repr\n def to_api_repr(self):\n \"\"\"Construct a Policy resource.\n\n :rtype: dict\n :returns: a resource to be passed to the ``setIamPolicy`` API.\n \"\"\"\n resource = {}\n\n if self.etag is not None:\n resource['etag'] = self.etag\n\n if self.version is not None:\n resource['version'] = self.version\n\n bindings = []\n\n if self.owners:\n bindings.append(\n {'role': PUBSUB_ADMIN_ROLE,\n 'members': sorted(self.owners)})\n\n if self.editors:\n bindings.append(\n {'role': PUBSUB_EDITOR_ROLE,\n 'members': sorted(self.editors)})\n\n if self.viewers:\n bindings.append(\n {'role': PUBSUB_VIEWER_ROLE,\n 'members': sorted(self.viewers)})\n\n if self.publishers:\n bindings.append(\n {'role': PUBSUB_PUBLISHER_ROLE,\n 'members': sorted(self.publishers)})\n\n if self.subscribers:\n bindings.append(\n {'role': PUBSUB_SUBSCRIBER_ROLE,\n 'members': sorted(self.subscribers)})\n\n if bindings:\n resource['bindings'] = bindings\n\n return resource","label":0},{"cid":"google/cloud/pubsub/message.py::Message.__init__","text":"google/cloud/pubsub/message.py::Message.__init__\n def __init__(self, data, message_id, attributes=None):\n self.data = data\n self.message_id = message_id\n self._attributes = attributes","label":0},{"cid":"google/cloud/pubsub/message.py::Message.attributes","text":"google/cloud/pubsub/message.py::Message.attributes\n @property\n def attributes(self):\n \"\"\"Lazily-constructed attribute dictionary.\"\"\"\n if self._attributes is None:\n self._attributes = {}\n return self._attributes","label":0},{"cid":"google/cloud/pubsub/message.py::Message.timestamp","text":"google/cloud/pubsub/message.py::Message.timestamp\n @property\n def timestamp(self):\n \"\"\"Return sortable timestamp from attributes, if passed.\n\n Allows sorting messages in publication order (assuming consistent\n clocks across all publishers).\n\n :rtype: :class:`datetime.datetime`\n :returns: timestamp (in UTC timezone) parsed from RFC 3339 timestamp\n :raises: ValueError if timestamp not in ``attributes``, or if it does\n not match the RFC 3339 format.\n \"\"\"\n stamp = self.attributes.get('timestamp')\n if stamp is None:\n raise ValueError('No timestamp')\n return _rfc3339_to_datetime(stamp)","label":0},{"cid":"google/cloud/pubsub/message.py::Message.service_timestamp","text":"google/cloud/pubsub/message.py::Message.service_timestamp\n @property\n def service_timestamp(self):\n \"\"\"Return server-set timestamp.\n\n :rtype: string\n :returns: timestamp (in UTC timezone) in RFC 3339 format\n \"\"\"\n return self._service_timestamp","label":0},{"cid":"google/cloud/pubsub/message.py::Message.from_api_repr","text":"google/cloud/pubsub/message.py::Message.from_api_repr\n @classmethod\n def from_api_repr(cls, api_repr):\n \"\"\"Factory: construct message from API representation.\n\n :type api_repr: dict or None\n :param api_repr: The API representation of the message\n\n :rtype: :class:`Message`\n :returns: The message created from the response.\n \"\"\"\n data = base64.b64decode(api_repr.get('data', b''))\n instance = cls(\n data=data, message_id=api_repr['messageId'],\n attributes=api_repr.get('attributes'))\n instance._service_timestamp = api_repr.get('publishTimestamp')\n return instance","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.__init__","text":"google/cloud/pubsub/subscription.py::Subscription.__init__\n def __init__(self, name, topic=None, ack_deadline=None, push_endpoint=None,\n client=None):\n\n if client is None and topic is None:\n raise TypeError(\"Pass only one of 'topic' or 'client'.\")\n\n if client is not None and topic is not None:\n raise TypeError(\"Pass only one of 'topic' or 'client'.\")\n\n self.name = name\n self.topic = topic\n self._client = client or topic._client\n self._project = self._client.project\n self.ack_deadline = ack_deadline\n self.push_endpoint = push_endpoint","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.from_api_repr","text":"google/cloud/pubsub/subscription.py::Subscription.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client, topics=None):\n \"\"\"Factory: construct a topic given its API representation\n\n :type resource: dict\n :param resource: topic resource representation returned from the API.\n\n :type client: :class:`google.cloud.pubsub.client.Client`\n :param client: Client which holds credentials and project\n configuration for a topic.\n\n :type topics: dict or None\n :param topics: A mapping of topic names -> topics. If not passed,\n the subscription will have a newly-created topic.\n\n :rtype: :class:`google.cloud.pubsub.subscription.Subscription`\n :returns: Subscription parsed from ``resource``.\n \"\"\"\n if topics is None:\n topics = {}\n topic_path = resource['topic']\n if topic_path == cls._DELETED_TOPIC_PATH:\n topic = None\n else:\n topic = topics.get(topic_path)\n if topic is None:\n # NOTE: This duplicates behavior from Topic.from_api_repr to\n # avoid an import cycle.\n topic_name = topic_name_from_path(topic_path, client.project)\n topic = topics[topic_path] = client.topic(topic_name)\n _, _, _, name = resource['name'].split('/')\n ack_deadline = resource.get('ackDeadlineSeconds')\n push_config = resource.get('pushConfig', {})\n push_endpoint = push_config.get('pushEndpoint')\n if topic is None:\n return cls(name, ack_deadline=ack_deadline,\n push_endpoint=push_endpoint, client=client)\n return cls(name, topic, ack_deadline, push_endpoint)","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.project","text":"google/cloud/pubsub/subscription.py::Subscription.project\n @property\n def project(self):\n \"\"\"Project bound to the subscription.\"\"\"\n return self._client.project","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.full_name","text":"google/cloud/pubsub/subscription.py::Subscription.full_name\n @property\n def full_name(self):\n \"\"\"Fully-qualified name used in subscription APIs\"\"\"\n return 'projects/%s/subscriptions/%s' % (self.project, self.name)","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.path","text":"google/cloud/pubsub/subscription.py::Subscription.path\n @property\n def path(self):\n \"\"\"URL path for the subscription's APIs\"\"\"\n return '/%s' % (self.full_name,)","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.auto_ack","text":"google/cloud/pubsub/subscription.py::Subscription.auto_ack\n def auto_ack(self, return_immediately=False, max_messages=1, client=None):\n \"\"\":class:`AutoAck` factory\n\n :type return_immediately: boolean\n :param return_immediately: passed through to :meth:`Subscription.pull`\n\n :type max_messages: int\n :param max_messages: passed through to :meth:`Subscription.pull`\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: passed through to :meth:`Subscription.pull` and\n :meth:`Subscription.acknowledge`.\n\n :rtype: :class:`AutoAck`\n :returns: the instance created for the given ``ack_id`` and ``message``\n \"\"\"\n return AutoAck(self, return_immediately, max_messages, client)","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription._require_client","text":"google/cloud/pubsub/subscription.py::Subscription._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the topic of the\n current subscription.\n\n :rtype: :class:`google.cloud.pubsub.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._client\n return client","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.create","text":"google/cloud/pubsub/subscription.py::Subscription.create\n def create(self, client=None):\n \"\"\"API call: create the subscription via a PUT request\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/create\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START subscription_create]\n :end-before: [END subscription_create]\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current subscription's topic.\n \"\"\"\n client = self._require_client(client)\n api = client.subscriber_api\n api.subscription_create(\n self.full_name, self.topic.full_name, self.ack_deadline,\n self.push_endpoint)","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.exists","text":"google/cloud/pubsub/subscription.py::Subscription.exists\n def exists(self, client=None):\n \"\"\"API call: test existence of the subscription via a GET request\n\n See\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/get\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START subscription_exists]\n :end-before: [END subscription_exists]\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current subscription's topic.\n\n :rtype: bool\n :returns: Boolean indicating existence of the subscription.\n \"\"\"\n client = self._require_client(client)\n api = client.subscriber_api\n try:\n api.subscription_get(self.full_name)\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.reload","text":"google/cloud/pubsub/subscription.py::Subscription.reload\n def reload(self, client=None):\n \"\"\"API call: sync local subscription configuration via a GET request\n\n See\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/get\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START subscription_reload]\n :end-before: [END subscription_reload]\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current subscription's topic.\n \"\"\"\n client = self._require_client(client)\n api = client.subscriber_api\n data = api.subscription_get(self.full_name)\n self.ack_deadline = data.get('ackDeadlineSeconds')\n push_config = data.get('pushConfig', {})\n self.push_endpoint = push_config.get('pushEndpoint')","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.delete","text":"google/cloud/pubsub/subscription.py::Subscription.delete\n def delete(self, client=None):\n \"\"\"API call: delete the subscription via a DELETE request.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/delete\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START subscription_delete]\n :end-before: [END subscription_delete]\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current subscription's topic.\n \"\"\"\n client = self._require_client(client)\n api = client.subscriber_api\n api.subscription_delete(self.full_name)","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.modify_push_configuration","text":"google/cloud/pubsub/subscription.py::Subscription.modify_push_configuration\n def modify_push_configuration(self, push_endpoint, client=None):\n \"\"\"API call: update the push endpoint for the subscription.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START subscription_push_pull]\n :end-before: [END subscription_push_pull]\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START subscription_pull_push]\n :end-before: [END subscription_pull_push]\n\n :type push_endpoint: string\n :param push_endpoint: URL to which messages will be pushed by the\n back-end. If None, the application must pull\n messages.\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current subscription's topic.\n \"\"\"\n client = self._require_client(client)\n api = client.subscriber_api\n api.subscription_modify_push_config(self.full_name, push_endpoint)\n self.push_endpoint = push_endpoint","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.pull","text":"google/cloud/pubsub/subscription.py::Subscription.pull\n def pull(self, return_immediately=False, max_messages=1, client=None):\n \"\"\"API call: retrieve messages for the subscription.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/pull\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START subscription_pull]\n :end-before: [END subscription_pull]\n\n :type return_immediately: boolean\n :param return_immediately: if True, the back-end returns even if no\n messages are available; if False, the API\n call blocks until one or more messages are\n available.\n\n :type max_messages: int\n :param max_messages: the maximum number of messages to return.\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current subscription's topic.\n\n :rtype: list of (ack_id, message) tuples\n :returns: sequence of tuples: ``ack_id`` is the ID to be used in a\n subsequent call to :meth:`acknowledge`, and ``message``\n is an instance of\n :class:`~google.cloud.pubsub.message.Message`.\n \"\"\"\n client = self._require_client(client)\n api = client.subscriber_api\n response = api.subscription_pull(\n self.full_name, return_immediately, max_messages)\n return [(info['ackId'], Message.from_api_repr(info['message']))\n for info in response]","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.acknowledge","text":"google/cloud/pubsub/subscription.py::Subscription.acknowledge\n def acknowledge(self, ack_ids, client=None):\n \"\"\"API call: acknowledge retrieved messages for the subscription.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/acknowledge\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START subscription_acknowledge]\n :end-before: [END subscription_acknowledge]\n\n :type ack_ids: list of string\n :param ack_ids: ack IDs of messages being acknowledged\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current subscription's topic.\n \"\"\"\n client = self._require_client(client)\n api = client.subscriber_api\n api.subscription_acknowledge(self.full_name, ack_ids)","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.modify_ack_deadline","text":"google/cloud/pubsub/subscription.py::Subscription.modify_ack_deadline\n def modify_ack_deadline(self, ack_ids, ack_deadline, client=None):\n \"\"\"API call: update acknowledgement deadline for a retrieved message.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyAckDeadline\n\n :type ack_ids: list of string\n :param ack_ids: ack IDs of messages being updated\n\n :type ack_deadline: int\n :param ack_deadline: new deadline for the message, in seconds\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current subscription's topic.\n \"\"\"\n client = self._require_client(client)\n api = client.subscriber_api\n api.subscription_modify_ack_deadline(\n self.full_name, ack_ids, ack_deadline)","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.get_iam_policy","text":"google/cloud/pubsub/subscription.py::Subscription.get_iam_policy\n def get_iam_policy(self, client=None):\n \"\"\"Fetch the IAM policy for the subscription.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/getIamPolicy\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START subscription_get_iam_policy]\n :end-before: [END subscription_get_iam_policy]\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current subscription's topic.\n\n :rtype: :class:`google.cloud.pubsub.iam.Policy`\n :returns: policy created from the resource returned by the\n ``getIamPolicy`` API request.\n \"\"\"\n client = self._require_client(client)\n api = client.iam_policy_api\n resp = api.get_iam_policy(self.full_name)\n return Policy.from_api_repr(resp)","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.set_iam_policy","text":"google/cloud/pubsub/subscription.py::Subscription.set_iam_policy\n def set_iam_policy(self, policy, client=None):\n \"\"\"Update the IAM policy for the subscription.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/setIamPolicy\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START subscription_set_iam_policy]\n :end-before: [END subscription_set_iam_policy]\n\n :type policy: :class:`google.cloud.pubsub.iam.Policy`\n :param policy: the new policy, typically fetched via\n :meth:`get_iam_policy` and updated in place.\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current subscription's topic.\n\n :rtype: :class:`google.cloud.pubsub.iam.Policy`\n :returns: updated policy created from the resource returned by the\n ``setIamPolicy`` API request.\n \"\"\"\n client = self._require_client(client)\n api = client.iam_policy_api\n resource = policy.to_api_repr()\n resp = api.set_iam_policy(self.full_name, resource)\n return Policy.from_api_repr(resp)","label":0},{"cid":"google/cloud/pubsub/subscription.py::Subscription.check_iam_permissions","text":"google/cloud/pubsub/subscription.py::Subscription.check_iam_permissions\n def check_iam_permissions(self, permissions, client=None):\n \"\"\"Verify permissions allowed for the current user.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/testIamPermissions\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START subscription_check_iam_permissions]\n :end-before: [END subscription_check_iam_permissions]\n\n :type permissions: list of string\n :param permissions: list of permissions to be tested\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current subscription's topic.\n\n :rtype: sequence of string\n :returns: subset of ``permissions`` allowed by current IAM policy.\n \"\"\"\n client = self._require_client(client)\n api = client.iam_policy_api\n return api.test_iam_permissions(\n self.full_name, list(permissions))","label":0},{"cid":"google/cloud/pubsub/subscription.py::AutoAck.__init__","text":"google/cloud/pubsub/subscription.py::AutoAck.__init__\n def __init__(self, subscription,\n return_immediately=False, max_messages=1, client=None):\n super(AutoAck, self).__init__()\n self._subscription = subscription\n self._return_immediately = return_immediately\n self._max_messages = max_messages\n self._client = client","label":0},{"cid":"google/cloud/pubsub/subscription.py::AutoAck.__enter__","text":"google/cloud/pubsub/subscription.py::AutoAck.__enter__\n def __enter__(self):\n items = self._subscription.pull(\n self._return_immediately, self._max_messages, self._client)\n self.update(items)\n return self","label":0},{"cid":"google/cloud/pubsub/subscription.py::AutoAck.__exit__","text":"google/cloud/pubsub/subscription.py::AutoAck.__exit__\n def __exit__(self, exc_type, exc_val, exc_tb):\n self._subscription.acknowledge(list(self), self._client)","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.__init__","text":"google/cloud/pubsub/topic.py::Topic.__init__\n def __init__(self, name, client, timestamp_messages=False):\n self.name = name\n self._client = client\n self.timestamp_messages = timestamp_messages","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.subscription","text":"google/cloud/pubsub/topic.py::Topic.subscription\n def subscription(self, name, ack_deadline=None, push_endpoint=None):\n \"\"\"Creates a subscription bound to the current topic.\n\n Example: pull-mode subcription, default paramter values\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_subscription_defaults]\n :end-before: [END topic_subscription_defaults]\n\n Example: pull-mode subcription, override ``ack_deadline`` default\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_subscription_ack90]\n :end-before: [END topic_subscription_ack90]\n\n Example: push-mode subcription\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_subscription_push]\n :end-before: [END topic_subscription_push]\n\n :type name: string\n :param name: the name of the subscription\n\n :type ack_deadline: int\n :param ack_deadline: the deadline (in seconds) by which messages pulled\n from the back-end must be acknowledged.\n\n :type push_endpoint: string\n :param push_endpoint: URL to which messages will be pushed by the\n back-end. If not set, the application must pull\n messages.\n\n :rtype: :class:`Subscription`\n :returns: The subscription created with the passed in arguments.\n \"\"\"\n return Subscription(name, self, ack_deadline=ack_deadline,\n push_endpoint=push_endpoint)","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.from_api_repr","text":"google/cloud/pubsub/topic.py::Topic.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client):\n \"\"\"Factory: construct a topic given its API representation\n\n :type resource: dict\n :param resource: topic resource representation returned from the API\n\n :type client: :class:`google.cloud.pubsub.client.Client`\n :param client: Client which holds credentials and project\n configuration for the topic.\n\n :rtype: :class:`google.cloud.pubsub.topic.Topic`\n :returns: Topic parsed from ``resource``.\n :raises: :class:`ValueError` if ``client`` is not ``None`` and the\n project from the resource does not agree with the project\n from the client.\n \"\"\"\n topic_name = topic_name_from_path(resource['name'], client.project)\n return cls(topic_name, client=client)","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.project","text":"google/cloud/pubsub/topic.py::Topic.project\n @property\n def project(self):\n \"\"\"Project bound to the topic.\"\"\"\n return self._client.project","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.full_name","text":"google/cloud/pubsub/topic.py::Topic.full_name\n @property\n def full_name(self):\n \"\"\"Fully-qualified name used in topic / subscription APIs\"\"\"\n return 'projects/%s/topics/%s' % (self.project, self.name)","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic._require_client","text":"google/cloud/pubsub/topic.py::Topic._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current topic.\n\n :rtype: :class:`google.cloud.pubsub.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._client\n return client","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.create","text":"google/cloud/pubsub/topic.py::Topic.create\n def create(self, client=None):\n \"\"\"API call: create the topic via a PUT request\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/create\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_create]\n :end-before: [END topic_create]\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current topic.\n \"\"\"\n client = self._require_client(client)\n api = client.publisher_api\n api.topic_create(topic_path=self.full_name)","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.exists","text":"google/cloud/pubsub/topic.py::Topic.exists\n def exists(self, client=None):\n \"\"\"API call: test for the existence of the topic via a GET request\n\n See\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/get\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_exists]\n :end-before: [END topic_exists]\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current topic.\n\n :rtype: bool\n :returns: Boolean indicating existence of the topic.\n \"\"\"\n client = self._require_client(client)\n api = client.publisher_api\n\n try:\n api.topic_get(topic_path=self.full_name)\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.delete","text":"google/cloud/pubsub/topic.py::Topic.delete\n def delete(self, client=None):\n \"\"\"API call: delete the topic via a DELETE request\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/delete\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_delete]\n :end-before: [END topic_delete]\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current topic.\n \"\"\"\n client = self._require_client(client)\n api = client.publisher_api\n api.topic_delete(topic_path=self.full_name)","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic._timestamp_message","text":"google/cloud/pubsub/topic.py::Topic._timestamp_message\n def _timestamp_message(self, attrs):\n \"\"\"Add a timestamp to ``attrs``, if the topic is so configured.\n\n If ``attrs`` already has the key, do nothing.\n\n Helper method for ``publish``/``Batch.publish``.\n \"\"\"\n if self.timestamp_messages and 'timestamp' not in attrs:\n attrs['timestamp'] = _datetime_to_rfc3339(_NOW())","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.publish","text":"google/cloud/pubsub/topic.py::Topic.publish\n def publish(self, message, client=None, **attrs):\n \"\"\"API call: publish a message to a topic via a POST request\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/publish\n\n Example without message attributes:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_publish_simple_message]\n :end-before: [END topic_publish_simple_message]\n\n With message attributes:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_publish_message_with_attrs]\n :end-before: [END topic_publish_message_with_attrs]\n\n :type message: bytes\n :param message: the message payload\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current topic.\n\n :type attrs: dict (string -> string)\n :param attrs: key-value pairs to send as message attributes\n\n :rtype: str\n :returns: message ID assigned by the server to the published message\n \"\"\"\n client = self._require_client(client)\n api = client.publisher_api\n\n self._timestamp_message(attrs)\n message_b = base64.b64encode(message).decode('ascii')\n message_data = {'data': message_b, 'attributes': attrs}\n message_ids = api.topic_publish(self.full_name, [message_data])\n return message_ids[0]","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.batch","text":"google/cloud/pubsub/topic.py::Topic.batch\n def batch(self, client=None):\n \"\"\"Return a batch to use as a context manager.\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_batch]\n :end-before: [END topic_batch]\n\n .. note::\n\n The only API request happens during the ``__exit__()`` of the topic\n used as a context manager, and only if the block exits without\n raising an exception.\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current topic.\n\n :rtype: :class:`Batch`\n :returns: A batch to use as a context manager.\n \"\"\"\n client = self._require_client(client)\n return Batch(self, client)","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.list_subscriptions","text":"google/cloud/pubsub/topic.py::Topic.list_subscriptions\n def list_subscriptions(self, page_size=None, page_token=None, client=None):\n \"\"\"List subscriptions for the project associated with this client.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics.subscriptions/list\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_list_subscriptions]\n :end-before: [END topic_list_subscriptions]\n\n :type page_size: int\n :param page_size: maximum number of topics to return, If not passed,\n defaults to a value set by the API.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of topics. If not\n passed, the API will return the first page of\n topics.\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current topic.\n\n :rtype: tuple, (list, str)\n :returns: list of :class:`~.pubsub.subscription.Subscription`,\n plus a \"next page token\" string: if not None, indicates that\n more topics can be retrieved with another call (pass that\n value as ``page_token``).\n \"\"\"\n client = self._require_client(client)\n api = client.publisher_api\n sub_paths, next_token = api.topic_list_subscriptions(\n self.full_name, page_size, page_token)\n subscriptions = []\n for sub_path in sub_paths:\n sub_name = subscription_name_from_path(sub_path, self.project)\n subscriptions.append(Subscription(sub_name, self))\n return subscriptions, next_token","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.get_iam_policy","text":"google/cloud/pubsub/topic.py::Topic.get_iam_policy\n def get_iam_policy(self, client=None):\n \"\"\"Fetch the IAM policy for the topic.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/getIamPolicy\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_get_iam_policy]\n :end-before: [END topic_get_iam_policy]\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current batch.\n\n :rtype: :class:`google.cloud.pubsub.iam.Policy`\n :returns: policy created from the resource returned by the\n ``getIamPolicy`` API request.\n \"\"\"\n client = self._require_client(client)\n api = client.iam_policy_api\n resp = api.get_iam_policy(self.full_name)\n return Policy.from_api_repr(resp)","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.set_iam_policy","text":"google/cloud/pubsub/topic.py::Topic.set_iam_policy\n def set_iam_policy(self, policy, client=None):\n \"\"\"Update the IAM policy for the topic.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/setIamPolicy\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_set_iam_policy]\n :end-before: [END topic_set_iam_policy]\n\n :type policy: :class:`google.cloud.pubsub.iam.Policy`\n :param policy: the new policy, typically fetched via\n :meth:`get_iam_policy` and updated in place.\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current batch.\n\n :rtype: :class:`google.cloud.pubsub.iam.Policy`\n :returns: updated policy created from the resource returned by the\n ``setIamPolicy`` API request.\n \"\"\"\n client = self._require_client(client)\n api = client.iam_policy_api\n resource = policy.to_api_repr()\n resp = api.set_iam_policy(self.full_name, resource)\n return Policy.from_api_repr(resp)","label":0},{"cid":"google/cloud/pubsub/topic.py::Topic.check_iam_permissions","text":"google/cloud/pubsub/topic.py::Topic.check_iam_permissions\n def check_iam_permissions(self, permissions, client=None):\n \"\"\"Verify permissions allowed for the current user.\n\n See:\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/testIamPermissions\n\n Example:\n\n .. literalinclude:: pubsub_snippets.py\n :start-after: [START topic_check_iam_permissions]\n :end-before: [END topic_check_iam_permissions]\n\n :type permissions: list of string\n :param permissions: list of permissions to be tested\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current batch.\n\n :rtype: sequence of string\n :returns: subset of ``permissions`` allowed by current IAM policy.\n \"\"\"\n client = self._require_client(client)\n api = client.iam_policy_api\n return api.test_iam_permissions(\n self.full_name, list(permissions))","label":0},{"cid":"google/cloud/pubsub/topic.py::Batch.__init__","text":"google/cloud/pubsub/topic.py::Batch.__init__\n def __init__(self, topic, client):\n self.topic = topic\n self.messages = []\n self.message_ids = []\n self.client = client","label":0},{"cid":"google/cloud/pubsub/topic.py::Batch.__enter__","text":"google/cloud/pubsub/topic.py::Batch.__enter__\n def __enter__(self):\n return self","label":0},{"cid":"google/cloud/pubsub/topic.py::Batch.__exit__","text":"google/cloud/pubsub/topic.py::Batch.__exit__\n def __exit__(self, exc_type, exc_val, exc_tb):\n if exc_type is None:\n self.commit()","label":0},{"cid":"google/cloud/pubsub/topic.py::Batch.__iter__","text":"google/cloud/pubsub/topic.py::Batch.__iter__\n def __iter__(self):\n return iter(self.message_ids)","label":0},{"cid":"google/cloud/pubsub/topic.py::Batch.publish","text":"google/cloud/pubsub/topic.py::Batch.publish\n def publish(self, message, **attrs):\n \"\"\"Emulate publishing a message, but save it.\n\n :type message: bytes\n :param message: the message payload\n\n :type attrs: dict (string -> string)\n :param attrs: key-value pairs to send as message attributes\n \"\"\"\n self.topic._timestamp_message(attrs)\n self.messages.append(\n {'data': base64.b64encode(message).decode('ascii'),\n 'attributes': attrs})","label":0},{"cid":"google/cloud/pubsub/topic.py::Batch.commit","text":"google/cloud/pubsub/topic.py::Batch.commit\n def commit(self, client=None):\n \"\"\"Send saved messages as a single API call.\n\n :type client: :class:`~google.cloud.pubsub.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current batch.\n \"\"\"\n if not self.messages:\n return\n\n if client is None:\n client = self.client\n api = client.publisher_api\n message_ids = api.topic_publish(self.topic.full_name, self.messages[:])\n self.message_ids.extend(message_ids)\n del self.messages[:]","label":0},{"cid":"google/cloud/resource_manager/client.py::Client.new_project","text":"google/cloud/resource_manager/client.py::Client.new_project\n def new_project(self, project_id, name=None, labels=None):\n \"\"\"Create a project bound to the current client.\n\n Use :meth:`Project.reload() \\\n <google.cloud.resource_manager.project.Project.reload>` to retrieve\n project metadata after creating a\n :class:`~google.cloud.resource_manager.project.Project` instance.\n\n .. note:\n\n This does not make an API call.\n\n :type project_id: str\n :param project_id: The ID for this project.\n\n :type name: string\n :param name: The display name of the project.\n\n :type labels: dict\n :param labels: A list of labels associated with the project.\n\n :rtype: :class:`~google.cloud.resource_manager.project.Project`\n :returns: A new instance of a\n :class:`~google.cloud.resource_manager.project.Project`\n **without** any metadata loaded.\n \"\"\"\n return Project(project_id=project_id,\n client=self, name=name, labels=labels)","label":0},{"cid":"google/cloud/resource_manager/client.py::Client.fetch_project","text":"google/cloud/resource_manager/client.py::Client.fetch_project\n def fetch_project(self, project_id):\n \"\"\"Fetch an existing project and it's relevant metadata by ID.\n\n .. note::\n\n If the project does not exist, this will raise a\n :class:`NotFound <google.cloud.exceptions.NotFound>` error.\n\n :type project_id: str\n :param project_id: The ID for this project.\n\n :rtype: :class:`~google.cloud.resource_manager.project.Project`\n :returns: A :class:`~google.cloud.resource_manager.project.Project`\n with metadata fetched from the API.\n \"\"\"\n project = self.new_project(project_id)\n project.reload()\n return project","label":0},{"cid":"google/cloud/resource_manager/client.py::Client.list_projects","text":"google/cloud/resource_manager/client.py::Client.list_projects\n def list_projects(self, filter_params=None, page_size=None):\n \"\"\"List the projects visible to this client.\n\n Example::\n\n >>> from google.cloud import resource_manager\n >>> client = resource_manager.Client()\n >>> for project in client.list_projects():\n ... print project.project_id\n\n List all projects with label ``'environment'`` set to ``'prod'``\n (filtering by labels)::\n\n >>> from google.cloud import resource_manager\n >>> client = resource_manager.Client()\n >>> env_filter = {'labels.environment': 'prod'}\n >>> for project in client.list_projects(env_filter):\n ... print project.project_id\n\n See:\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/list\n\n Complete filtering example::\n\n >>> project_filter = { # Return projects with...\n ... 'name': 'My Project', # name set to 'My Project'.\n ... 'id': 'my-project-id', # id set to 'my-project-id'.\n ... 'labels.stage': 'prod', # the label 'stage' set to 'prod'\n ... 'labels.color': '*' # a label 'color' set to anything.\n ... }\n >>> client.list_projects(project_filter)\n\n :type filter_params: dict\n :param filter_params: (Optional) A dictionary of filter options where\n each key is a property to filter on, and each\n value is the (case-insensitive) value to check\n (or the glob ``*`` to check for existence of the\n property). See the example above for more\n details.\n\n :type page_size: int\n :param page_size: (Optional) Maximum number of projects to return in a\n single page. If not passed, defaults to a ","label":0},{"cid":"google/cloud/resource_manager/client.py::_ProjectIterator.__init__","text":"google/cloud/resource_manager/client.py::_ProjectIterator.__init__\n def __init__(self, client, extra_params=None):\n super(_ProjectIterator, self).__init__(client=client, path='/projects',\n extra_params=extra_params)","label":0},{"cid":"google/cloud/resource_manager/client.py::_ProjectIterator.get_items_from_response","text":"google/cloud/resource_manager/client.py::_ProjectIterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Yield projects from response.\n\n :type response: dict\n :param response: The JSON API response for a page of projects.\n \"\"\"\n for resource in response.get('projects', []):\n item = Project.from_api_repr(resource, client=self.client)\n yield item","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.__init__","text":"google/cloud/resource_manager/project.py::Project.__init__\n def __init__(self, project_id, client, name=None, labels=None):\n self._client = client\n self.project_id = project_id\n self.name = name\n self.number = None\n self.labels = labels or {}\n self.status = None","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.__repr__","text":"google/cloud/resource_manager/project.py::Project.__repr__\n def __repr__(self):\n return '<Project: %r (%r)>' % (self.name, self.project_id)","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.from_api_repr","text":"google/cloud/resource_manager/project.py::Project.from_api_repr\n @classmethod\n def from_api_repr(cls, resource, client):\n \"\"\"Factory: construct a project given its API representation.\n\n :type resource: dict\n :param resource: project resource representation returned from the API\n\n :type client: :class:`google.cloud.resource_manager.client.Client`\n :param client: The Client used with this project.\n\n :rtype: :class:`google.cloud.resource_manager.project.Project`\n :returns: The project created.\n \"\"\"\n project = cls(project_id=resource['projectId'], client=client)\n project.set_properties_from_api_repr(resource)\n return project","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.set_properties_from_api_repr","text":"google/cloud/resource_manager/project.py::Project.set_properties_from_api_repr\n def set_properties_from_api_repr(self, resource):\n \"\"\"Update specific properties from its API representation.\"\"\"\n self.name = resource.get('name')\n self.number = resource['projectNumber']\n self.labels = resource.get('labels', {})\n self.status = resource['lifecycleState']","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.full_name","text":"google/cloud/resource_manager/project.py::Project.full_name\n @property\n def full_name(self):\n \"\"\"Fully-qualified name (ie, ``'projects/purple-spaceship-123'``).\"\"\"\n if not self.project_id:\n raise ValueError('Missing project ID.')\n return 'projects/%s' % (self.project_id)","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.path","text":"google/cloud/resource_manager/project.py::Project.path\n @property\n def path(self):\n \"\"\"URL for the project (ie, ``'/projects/purple-spaceship-123'``).\"\"\"\n return '/%s' % (self.full_name)","label":0},{"cid":"google/cloud/resource_manager/project.py::Project._require_client","text":"google/cloud/resource_manager/project.py::Project._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`google.cloud.resource_manager.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current project.\n\n :rtype: :class:`google.cloud.resource_manager.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self._client\n return client","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.create","text":"google/cloud/resource_manager/project.py::Project.create\n def create(self, client=None):\n \"\"\"API call: create the project via a ``POST`` request.\n\n See\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/create\n\n :type client: :class:`google.cloud.resource_manager.client.Client` or\n :data:`NoneType <types.NoneType>`\n :param client: the client to use. If not passed, falls back to\n the client stored on the current project.\n \"\"\"\n client = self._require_client(client)\n\n data = {\n 'projectId': self.project_id,\n 'name': self.name,\n 'labels': self.labels,\n }\n resp = client.connection.api_request(method='POST', path='/projects',\n data=data)\n self.set_properties_from_api_repr(resource=resp)","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.reload","text":"google/cloud/resource_manager/project.py::Project.reload\n def reload(self, client=None):\n \"\"\"API call: reload the project via a ``GET`` request.\n\n This method will reload the newest metadata for the project. If you've\n created a new :class:`Project` instance via\n :meth:`Client.new_project() \\\n <google.cloud.resource_manager.client.Client.new_project>`,\n this method will retrieve project metadata.\n\n .. warning::\n\n This will overwrite any local changes you've made and not saved\n via :meth:`update`.\n\n See\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/get\n\n :type client: :class:`google.cloud.resource_manager.client.Client` or\n :data:`NoneType <types.NoneType>`\n :param client: the client to use. If not passed, falls back to\n the client stored on the current project.\n \"\"\"\n client = self._require_client(client)\n\n # We assume the project exists. If it doesn't it will raise a NotFound\n # exception.\n resp = client.connection.api_request(method='GET', path=self.path)\n self.set_properties_from_api_repr(resource=resp)","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.exists","text":"google/cloud/resource_manager/project.py::Project.exists\n def exists(self, client=None):\n \"\"\"API call: test the existence of a project via a ``GET`` request.\n\n See\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/get\n\n :type client: :class:`google.cloud.resource_manager.client.Client` or\n :data:`NoneType <types.NoneType>`\n :param client: the client to use. If not passed, falls back to\n the client stored on the current project.\n\n :rtype: bool\n :returns: Boolean indicating existence of the project.\n \"\"\"\n client = self._require_client(client)\n\n try:\n # Note that we have to request the entire resource as the API\n # doesn't provide a way tocheck for existence only.\n client.connection.api_request(method='GET', path=self.path)\n except NotFound:\n return False\n else:\n return True","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.update","text":"google/cloud/resource_manager/project.py::Project.update\n def update(self, client=None):\n \"\"\"API call: update the project via a ``PUT`` request.\n\n See\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/update\n\n :type client: :class:`google.cloud.resource_manager.client.Client` or\n :data:`NoneType <types.NoneType>`\n :param client: the client to use. If not passed, falls back to\n the client stored on the current project.\n \"\"\"\n client = self._require_client(client)\n\n data = {'name': self.name, 'labels': self.labels}\n resp = client.connection.api_request(method='PUT', path=self.path,\n data=data)\n self.set_properties_from_api_repr(resp)","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.delete","text":"google/cloud/resource_manager/project.py::Project.delete\n def delete(self, client=None, reload_data=False):\n \"\"\"API call: delete the project via a ``DELETE`` request.\n\n See:\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/delete\n\n This actually changes the status (``lifecycleState``) from ``ACTIVE``\n to ``DELETE_REQUESTED``.\n Later (it's not specified when), the project will move into the\n ``DELETE_IN_PROGRESS`` state, which means the deleting has actually\n begun.\n\n :type client: :class:`google.cloud.resource_manager.client.Client` or\n :data:`NoneType <types.NoneType>`\n :param client: the client to use. If not passed, falls back to\n the client stored on the current project.\n\n :type reload_data: bool\n :param reload_data: Whether to reload the project with the latest\n state. If you want to get the updated status,\n you'll want this set to :data:`True` as the DELETE\n method doesn't send back the updated project.\n Default: :data:`False`.\n \"\"\"\n client = self._require_client(client)\n client.connection.api_request(method='DELETE', path=self.path)\n\n # If the reload flag is set, reload the project.\n if reload_data:\n self.reload()","label":0},{"cid":"google/cloud/resource_manager/project.py::Project.undelete","text":"google/cloud/resource_manager/project.py::Project.undelete\n def undelete(self, client=None, reload_data=False):\n \"\"\"API call: undelete the project via a ``POST`` request.\n\n See\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/undelete\n\n This actually changes the project status (``lifecycleState``) from\n ``DELETE_REQUESTED`` to ``ACTIVE``.\n If the project has already reached a status of ``DELETE_IN_PROGRESS``,\n this request will fail and the project cannot be restored.\n\n :type client: :class:`google.cloud.resource_manager.client.Client` or\n :data:`NoneType <types.NoneType>`\n :param client: the client to use. If not passed, falls back to\n the client stored on the current project.\n\n :type reload_data: bool\n :param reload_data: Whether to reload the project with the latest\n state. If you want to get the updated status,\n you'll want this set to :data:`True` as the DELETE\n method doesn't send back the updated project.\n Default: :data:`False`.\n \"\"\"\n client = self._require_client(client)\n client.connection.api_request(method='POST',\n path=self.path + ':undelete')\n\n # If the reload flag is set, reload the project.\n if reload_data:\n self.reload()","label":0},{"cid":"google/cloud/speech/client.py::Client.sync_recognize","text":"google/cloud/speech/client.py::Client.sync_recognize\n def sync_recognize(self, content, source_uri, encoding, sample_rate,\n language_code=None, max_alternatives=None,\n profanity_filter=None, speech_context=None):\n \"\"\"Synchronous Speech Recognition.\n\n .. _sync_recognize: https://cloud.google.com/speech/reference/\\\n rest/v1beta1/speech/syncrecognize\n\n See `sync_recognize`_.\n\n :type content: bytes\n :param content: Byte stream of audio.\n\n :type source_uri: str\n :param source_uri: URI that points to a file that contains audio\n data bytes as specified in RecognitionConfig.\n Currently, only Google Cloud Storage URIs are\n supported, which must be specified in the following\n format: ``gs://bucket_name/object_name``.\n\n :type encoding: str\n :param encoding: encoding of audio data sent in all RecognitionAudio\n messages, can be one of: :attr:`~.Encoding.LINEAR16`,\n :attr:`~.Encoding.FLAC`, :attr:`~.Encoding.MULAW`,\n :attr:`~.Encoding.AMR`, :attr:`~.Encoding.AMR_WB`\n\n :type sample_rate: int\n :param sample_rate: Sample rate in Hertz of the audio data sent in all\n requests. Valid values are: 8000-48000. For best\n results, set the sampling rate of the audio source\n to 16000 Hz. If that's not possible, use the\n native sample rate of the audio source (instead of\n re-sampling).\n\n :type language_code: str\n :param language_code: (Optional) The language of the supplied audio as\n BCP-47 language tag. Example: ``'en-GB'``.\n If omitted, defaults to ``'en-US'``.\n\n ","label":0},{"cid":"google/cloud/storage/_helpers.py::_PropertyMixin.__init__","text":"google/cloud/storage/_helpers.py::_PropertyMixin.__init__\n def __init__(self, name=None):\n self.name = name\n self._properties = {}\n self._changes = set()","label":0},{"cid":"google/cloud/storage/_helpers.py::_PropertyMixin.path","text":"google/cloud/storage/_helpers.py::_PropertyMixin.path\n @property\n def path(self):\n \"\"\"Abstract getter for the object path.\"\"\"\n raise NotImplementedError","label":0},{"cid":"google/cloud/storage/_helpers.py::_PropertyMixin.client","text":"google/cloud/storage/_helpers.py::_PropertyMixin.client\n @property\n def client(self):\n \"\"\"Abstract getter for the object client.\"\"\"\n raise NotImplementedError","label":0},{"cid":"google/cloud/storage/_helpers.py::_PropertyMixin._require_client","text":"google/cloud/storage/_helpers.py::_PropertyMixin._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current object.\n\n :rtype: :class:`google.cloud.storage.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self.client\n return client","label":0},{"cid":"google/cloud/storage/_helpers.py::_PropertyMixin.reload","text":"google/cloud/storage/_helpers.py::_PropertyMixin.reload\n def reload(self, client=None):\n \"\"\"Reload properties from Cloud Storage.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current object.\n \"\"\"\n client = self._require_client(client)\n # Pass only '?projection=noAcl' here because 'acl' and related\n # are handled via custom endpoints.\n query_params = {'projection': 'noAcl'}\n api_response = client.connection.api_request(\n method='GET', path=self.path, query_params=query_params,\n _target_object=self)\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/storage/_helpers.py::_PropertyMixin._patch_property","text":"google/cloud/storage/_helpers.py::_PropertyMixin._patch_property\n def _patch_property(self, name, value):\n \"\"\"Update field of this object's properties.\n\n This method will only update the field provided and will not\n touch the other fields.\n\n It **will not** reload the properties from the server. The behavior is\n local only and syncing occurs via :meth:`patch`.\n\n :type name: string\n :param name: The field name to update.\n\n :type value: object\n :param value: The value being updated.\n \"\"\"\n self._changes.add(name)\n self._properties[name] = value","label":0},{"cid":"google/cloud/storage/_helpers.py::_PropertyMixin._set_properties","text":"google/cloud/storage/_helpers.py::_PropertyMixin._set_properties\n def _set_properties(self, value):\n \"\"\"Set the properties for the current object.\n\n :type value: dict or :class:`google.cloud.storage.batch._FutureDict`\n :param value: The properties to be set.\n \"\"\"\n self._properties = value\n # If the values are reset, the changes must as well.\n self._changes = set()","label":0},{"cid":"google/cloud/storage/_helpers.py::_PropertyMixin.patch","text":"google/cloud/storage/_helpers.py::_PropertyMixin.patch\n def patch(self, client=None):\n \"\"\"Sends all changed properties in a PATCH request.\n\n Updates the ``_properties`` with the response from the backend.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current object.\n \"\"\"\n client = self._require_client(client)\n # Pass '?projection=full' here because 'PATCH' documented not\n # to work properly w/ 'noAcl'.\n update_properties = {key: self._properties[key]\n for key in self._changes}\n api_response = client.connection.api_request(\n method='PATCH', path=self.path, data=update_properties,\n query_params={'projection': 'full'}, _target_object=self)\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/storage/_helpers.py::_scalar_property","text":"google/cloud/storage/_helpers.py::_scalar_property\ndef _scalar_property(fieldname):\n \"\"\"Create a property descriptor around the :class:`_PropertyMixin` helpers.\n \"\"\"\n def _getter(self):\n \"\"\"Scalar property getter.\"\"\"\n return self._properties.get(fieldname)\n\n def _setter(self, value):\n \"\"\"Scalar property setter.\"\"\"\n self._patch_property(fieldname, value)\n\n return property(_getter, _setter)","label":0},{"cid":"google/cloud/storage/_helpers.py::_scalar_property._getter","text":"google/cloud/storage/_helpers.py::_scalar_property._getter\n def _getter(self):\n \"\"\"Scalar property getter.\"\"\"\n return self._properties.get(fieldname)","label":0},{"cid":"google/cloud/storage/_helpers.py::_scalar_property._setter","text":"google/cloud/storage/_helpers.py::_scalar_property._setter\n def _setter(self, value):\n \"\"\"Scalar property setter.\"\"\"\n self._patch_property(fieldname, value)","label":0},{"cid":"google/cloud/storage/_helpers.py::_write_buffer_to_hash","text":"google/cloud/storage/_helpers.py::_write_buffer_to_hash\ndef _write_buffer_to_hash(buffer_object, hash_obj, digest_block_size=8192):\n \"\"\"Read blocks from a buffer and update a hash with them.\n\n :type buffer_object: bytes buffer\n :param buffer_object: Buffer containing bytes used to update a hash object.\n\n :type hash_obj: object that implements update\n :param hash_obj: A hash object (MD5 or CRC32-C).\n\n :type digest_block_size: integer\n :param digest_block_size: The block size to write to the hash.\n Defaults to 8192.\n \"\"\"\n block = buffer_object.read(digest_block_size)\n\n while len(block) > 0:\n hash_obj.update(block)\n # Update the block for the next iteration.\n block = buffer_object.read(digest_block_size)","label":0},{"cid":"google/cloud/storage/_helpers.py::_base64_md5hash","text":"google/cloud/storage/_helpers.py::_base64_md5hash\ndef _base64_md5hash(buffer_object):\n \"\"\"Get MD5 hash of bytes (as base64).\n\n :type buffer_object: bytes buffer\n :param buffer_object: Buffer containing bytes used to compute an MD5\n hash (as base64).\n\n :rtype: str\n :returns: A base64 encoded digest of the MD5 hash.\n \"\"\"\n hash_obj = md5()\n _write_buffer_to_hash(buffer_object, hash_obj)\n digest_bytes = hash_obj.digest()\n return base64.b64encode(digest_bytes)","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.__init__","text":"google/cloud/storage/acl.py::_ACLEntity.__init__\n def __init__(self, entity_type, identifier=None):\n self.identifier = identifier\n self.roles = set([])\n self.type = entity_type","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.__str__","text":"google/cloud/storage/acl.py::_ACLEntity.__str__\n def __str__(self):\n if not self.identifier:\n return str(self.type)\n else:\n return '{acl.type}-{acl.identifier}'.format(acl=self)","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.__repr__","text":"google/cloud/storage/acl.py::_ACLEntity.__repr__\n def __repr__(self):\n return '<ACL Entity: {acl} ({roles})>'.format(\n acl=self, roles=', '.join(self.roles))","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.get_roles","text":"google/cloud/storage/acl.py::_ACLEntity.get_roles\n def get_roles(self):\n \"\"\"Get the list of roles permitted by this entity.\n\n :rtype: list of strings\n :returns: The list of roles associated with this entity.\n \"\"\"\n return self.roles","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.grant","text":"google/cloud/storage/acl.py::_ACLEntity.grant\n def grant(self, role):\n \"\"\"Add a role to the entity.\n\n :type role: string\n :param role: The role to add to the entity.\n \"\"\"\n self.roles.add(role)","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.revoke","text":"google/cloud/storage/acl.py::_ACLEntity.revoke\n def revoke(self, role):\n \"\"\"Remove a role from the entity.\n\n :type role: string\n :param role: The role to remove from the entity.\n \"\"\"\n if role in self.roles:\n self.roles.remove(role)","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.grant_read","text":"google/cloud/storage/acl.py::_ACLEntity.grant_read\n def grant_read(self):\n \"\"\"Grant read access to the current entity.\"\"\"\n self.grant(_ACLEntity.READER_ROLE)","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.grant_write","text":"google/cloud/storage/acl.py::_ACLEntity.grant_write\n def grant_write(self):\n \"\"\"Grant write access to the current entity.\"\"\"\n self.grant(_ACLEntity.WRITER_ROLE)","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.grant_owner","text":"google/cloud/storage/acl.py::_ACLEntity.grant_owner\n def grant_owner(self):\n \"\"\"Grant owner access to the current entity.\"\"\"\n self.grant(_ACLEntity.OWNER_ROLE)","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.revoke_read","text":"google/cloud/storage/acl.py::_ACLEntity.revoke_read\n def revoke_read(self):\n \"\"\"Revoke read access from the current entity.\"\"\"\n self.revoke(_ACLEntity.READER_ROLE)","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.revoke_write","text":"google/cloud/storage/acl.py::_ACLEntity.revoke_write\n def revoke_write(self):\n \"\"\"Revoke write access from the current entity.\"\"\"\n self.revoke(_ACLEntity.WRITER_ROLE)","label":0},{"cid":"google/cloud/storage/acl.py::_ACLEntity.revoke_owner","text":"google/cloud/storage/acl.py::_ACLEntity.revoke_owner\n def revoke_owner(self):\n \"\"\"Revoke owner access from the current entity.\"\"\"\n self.revoke(_ACLEntity.OWNER_ROLE)","label":0},{"cid":"google/cloud/storage/acl.py::ACL.__init__","text":"google/cloud/storage/acl.py::ACL.__init__\n def __init__(self):\n self.entities = {}","label":0},{"cid":"google/cloud/storage/acl.py::ACL._ensure_loaded","text":"google/cloud/storage/acl.py::ACL._ensure_loaded\n def _ensure_loaded(self):\n \"\"\"Load if not already loaded.\"\"\"\n if not self.loaded:\n self.reload()","label":0},{"cid":"google/cloud/storage/acl.py::ACL.reset","text":"google/cloud/storage/acl.py::ACL.reset\n def reset(self):\n \"\"\"Remove all entities from the ACL, and clear the ``loaded`` flag.\"\"\"\n self.entities.clear()\n self.loaded = False","label":0},{"cid":"google/cloud/storage/acl.py::ACL.__iter__","text":"google/cloud/storage/acl.py::ACL.__iter__\n def __iter__(self):\n self._ensure_loaded()\n\n for entity in self.entities.values():\n for role in entity.get_roles():\n if role:\n yield {'entity': str(entity), 'role': role}","label":0},{"cid":"google/cloud/storage/acl.py::ACL.entity_from_dict","text":"google/cloud/storage/acl.py::ACL.entity_from_dict\n def entity_from_dict(self, entity_dict):\n \"\"\"Build an _ACLEntity object from a dictionary of data.\n\n An entity is a mutable object that represents a list of roles\n belonging to either a user or group or the special types for all\n users and all authenticated users.\n\n :type entity_dict: dict\n :param entity_dict: Dictionary full of data from an ACL lookup.\n\n :rtype: :class:`_ACLEntity`\n :returns: An Entity constructed from the dictionary.\n \"\"\"\n entity = entity_dict['entity']\n role = entity_dict['role']\n\n if entity == 'allUsers':\n entity = self.all()\n\n elif entity == 'allAuthenticatedUsers':\n entity = self.all_authenticated()\n\n elif '-' in entity:\n entity_type, identifier = entity.split('-', 1)\n entity = self.entity(entity_type=entity_type,\n identifier=identifier)\n\n if not isinstance(entity, _ACLEntity):\n raise ValueError('Invalid dictionary: %s' % entity_dict)\n\n entity.grant(role)\n return entity","label":0},{"cid":"google/cloud/storage/acl.py::ACL.has_entity","text":"google/cloud/storage/acl.py::ACL.has_entity\n def has_entity(self, entity):\n \"\"\"Returns whether or not this ACL has any entries for an entity.\n\n :type entity: :class:`_ACLEntity`\n :param entity: The entity to check for existence in this ACL.\n\n :rtype: boolean\n :returns: True of the entity exists in the ACL.\n \"\"\"\n self._ensure_loaded()\n return str(entity) in self.entities","label":0},{"cid":"google/cloud/storage/acl.py::ACL.get_entity","text":"google/cloud/storage/acl.py::ACL.get_entity\n def get_entity(self, entity, default=None):\n \"\"\"Gets an entity object from the ACL.\n\n :type entity: :class:`_ACLEntity` or string\n :param entity: The entity to get lookup in the ACL.\n\n :type default: anything\n :param default: This value will be returned if the entity\n doesn't exist.\n\n :rtype: :class:`_ACLEntity`\n :returns: The corresponding entity or the value provided\n to ``default``.\n \"\"\"\n self._ensure_loaded()\n return self.entities.get(str(entity), default)","label":0},{"cid":"google/cloud/storage/acl.py::ACL.add_entity","text":"google/cloud/storage/acl.py::ACL.add_entity\n def add_entity(self, entity):\n \"\"\"Add an entity to the ACL.\n\n :type entity: :class:`_ACLEntity`\n :param entity: The entity to add to this ACL.\n \"\"\"\n self._ensure_loaded()\n self.entities[str(entity)] = entity","label":0},{"cid":"google/cloud/storage/acl.py::ACL.entity","text":"google/cloud/storage/acl.py::ACL.entity\n def entity(self, entity_type, identifier=None):\n \"\"\"Factory method for creating an Entity.\n\n If an entity with the same type and identifier already exists,\n this will return a reference to that entity. If not, it will\n create a new one and add it to the list of known entities for\n this ACL.\n\n :type entity_type: string\n :param entity_type: The type of entity to create\n (ie, ``user``, ``group``, etc)\n\n :type identifier: string\n :param identifier: The ID of the entity (if applicable).\n This can be either an ID or an e-mail address.\n\n :rtype: :class:`_ACLEntity`\n :returns: A new Entity or a reference to an existing identical entity.\n \"\"\"\n entity = _ACLEntity(entity_type=entity_type, identifier=identifier)\n if self.has_entity(entity):\n entity = self.get_entity(entity)\n else:\n self.add_entity(entity)\n return entity","label":0},{"cid":"google/cloud/storage/acl.py::ACL.user","text":"google/cloud/storage/acl.py::ACL.user\n def user(self, identifier):\n \"\"\"Factory method for a user Entity.\n\n :type identifier: string\n :param identifier: An id or e-mail for this particular user.\n\n :rtype: :class:`_ACLEntity`\n :returns: An Entity corresponding to this user.\n \"\"\"\n return self.entity('user', identifier=identifier)","label":0},{"cid":"google/cloud/storage/acl.py::ACL.group","text":"google/cloud/storage/acl.py::ACL.group\n def group(self, identifier):\n \"\"\"Factory method for a group Entity.\n\n :type identifier: string\n :param identifier: An id or e-mail for this particular group.\n\n :rtype: :class:`_ACLEntity`\n :returns: An Entity corresponding to this group.\n \"\"\"\n return self.entity('group', identifier=identifier)","label":0},{"cid":"google/cloud/storage/acl.py::ACL.domain","text":"google/cloud/storage/acl.py::ACL.domain\n def domain(self, domain):\n \"\"\"Factory method for a domain Entity.\n\n :type domain: string\n :param domain: The domain for this entity.\n\n :rtype: :class:`_ACLEntity`\n :returns: An entity corresponding to this domain.\n \"\"\"\n return self.entity('domain', identifier=domain)","label":0},{"cid":"google/cloud/storage/acl.py::ACL.all","text":"google/cloud/storage/acl.py::ACL.all\n def all(self):\n \"\"\"Factory method for an Entity representing all users.\n\n :rtype: :class:`_ACLEntity`\n :returns: An entity representing all users.\n \"\"\"\n return self.entity('allUsers')","label":0},{"cid":"google/cloud/storage/acl.py::ACL.all_authenticated","text":"google/cloud/storage/acl.py::ACL.all_authenticated\n def all_authenticated(self):\n \"\"\"Factory method for an Entity representing all authenticated users.\n\n :rtype: :class:`_ACLEntity`\n :returns: An entity representing all authenticated users.\n \"\"\"\n return self.entity('allAuthenticatedUsers')","label":0},{"cid":"google/cloud/storage/acl.py::ACL.get_entities","text":"google/cloud/storage/acl.py::ACL.get_entities\n def get_entities(self):\n \"\"\"Get a list of all Entity objects.\n\n :rtype: list of :class:`_ACLEntity` objects\n :returns: A list of all Entity objects.\n \"\"\"\n self._ensure_loaded()\n return list(self.entities.values())","label":0},{"cid":"google/cloud/storage/acl.py::ACL.client","text":"google/cloud/storage/acl.py::ACL.client\n @property\n def client(self):\n \"\"\"Abstract getter for the object client.\"\"\"\n raise NotImplementedError","label":0},{"cid":"google/cloud/storage/acl.py::ACL._require_client","text":"google/cloud/storage/acl.py::ACL._require_client\n def _require_client(self, client):\n \"\"\"Check client or verify over-ride.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: the client to use. If not passed, falls back to the\n ``client`` stored on the current ACL.\n\n :rtype: :class:`google.cloud.storage.client.Client`\n :returns: The client passed in or the currently bound client.\n \"\"\"\n if client is None:\n client = self.client\n return client","label":0},{"cid":"google/cloud/storage/acl.py::ACL.reload","text":"google/cloud/storage/acl.py::ACL.reload\n def reload(self, client=None):\n \"\"\"Reload the ACL data from Cloud Storage.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the ACL's parent.\n \"\"\"\n path = self.reload_path\n client = self._require_client(client)\n\n self.entities.clear()\n\n found = client.connection.api_request(method='GET', path=path)\n self.loaded = True\n for entry in found.get('items', ()):\n self.add_entity(self.entity_from_dict(entry))","label":0},{"cid":"google/cloud/storage/acl.py::ACL._save","text":"google/cloud/storage/acl.py::ACL._save\n def _save(self, acl, predefined, client):\n \"\"\"Helper for :meth:`save` and :meth:`save_predefined`.\n\n :type acl: :class:`google.cloud.storage.acl.ACL`, or a compatible list.\n :param acl: The ACL object to save. If left blank, this will save\n current entries.\n\n :type predefined: string or None\n :param predefined: An identifier for a predefined ACL. Must be one\n of the keys in :attr:`PREDEFINED_JSON_ACLS`\n If passed, `acl` must be None.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the ACL's parent.\n \"\"\"\n query_params = {'projection': 'full'}\n if predefined is not None:\n acl = []\n query_params[self._PREDEFINED_QUERY_PARAM] = predefined\n\n path = self.save_path\n client = self._require_client(client)\n result = client.connection.api_request(\n method='PATCH',\n path=path,\n data={self._URL_PATH_ELEM: list(acl)},\n query_params=query_params)\n self.entities.clear()\n for entry in result.get(self._URL_PATH_ELEM, ()):\n self.add_entity(self.entity_from_dict(entry))\n self.loaded = True","label":0},{"cid":"google/cloud/storage/acl.py::ACL.save","text":"google/cloud/storage/acl.py::ACL.save\n def save(self, acl=None, client=None):\n \"\"\"Save this ACL for the current bucket.\n\n :type acl: :class:`google.cloud.storage.acl.ACL`, or a compatible list.\n :param acl: The ACL object to save. If left blank, this will save\n current entries.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the ACL's parent.\n \"\"\"\n if acl is None:\n acl = self\n save_to_backend = acl.loaded\n else:\n save_to_backend = True\n\n if save_to_backend:\n self._save(acl, None, client)","label":0},{"cid":"google/cloud/storage/acl.py::ACL.save_predefined","text":"google/cloud/storage/acl.py::ACL.save_predefined\n def save_predefined(self, predefined, client=None):\n \"\"\"Save this ACL for the current bucket using a predefined ACL.\n\n :type predefined: string\n :param predefined: An identifier for a predefined ACL. Must be one\n of the keys in :attr:`PREDEFINED_JSON_ACLS`\n or :attr:`PREDEFINED_XML_ACLS` (which will be\n aliased to the corresponding JSON name).\n If passed, `acl` must be None.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the ACL's parent.\n \"\"\"\n predefined = self.PREDEFINED_XML_ACLS.get(predefined, predefined)\n\n if predefined not in self.PREDEFINED_JSON_ACLS:\n raise ValueError(\"Invalid predefined ACL: %s\" % (predefined,))\n\n self._save(None, predefined, client)","label":0},{"cid":"google/cloud/storage/acl.py::ACL.clear","text":"google/cloud/storage/acl.py::ACL.clear\n def clear(self, client=None):\n \"\"\"Remove all ACL entries.\n\n Note that this won't actually remove *ALL* the rules, but it\n will remove all the non-default rules. In short, you'll still\n have access to a bucket that you created even after you clear\n ACL rules with this method.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the ACL's parent.\n \"\"\"\n self.save([], client=client)","label":0},{"cid":"google/cloud/storage/acl.py::BucketACL.__init__","text":"google/cloud/storage/acl.py::BucketACL.__init__\n def __init__(self, bucket):\n super(BucketACL, self).__init__()\n self.bucket = bucket","label":0},{"cid":"google/cloud/storage/acl.py::BucketACL.client","text":"google/cloud/storage/acl.py::BucketACL.client\n @property\n def client(self):\n \"\"\"The client bound to this ACL's bucket.\"\"\"\n return self.bucket.client","label":0},{"cid":"google/cloud/storage/acl.py::BucketACL.reload_path","text":"google/cloud/storage/acl.py::BucketACL.reload_path\n @property\n def reload_path(self):\n \"\"\"Compute the path for GET API requests for this ACL.\"\"\"\n return '%s/%s' % (self.bucket.path, self._URL_PATH_ELEM)","label":0},{"cid":"google/cloud/storage/acl.py::BucketACL.save_path","text":"google/cloud/storage/acl.py::BucketACL.save_path\n @property\n def save_path(self):\n \"\"\"Compute the path for PATCH API requests for this ACL.\"\"\"\n return self.bucket.path","label":0},{"cid":"google/cloud/storage/acl.py::ObjectACL.__init__","text":"google/cloud/storage/acl.py::ObjectACL.__init__\n def __init__(self, blob):\n super(ObjectACL, self).__init__()\n self.blob = blob","label":0},{"cid":"google/cloud/storage/acl.py::ObjectACL.client","text":"google/cloud/storage/acl.py::ObjectACL.client\n @property\n def client(self):\n \"\"\"The client bound to this ACL's blob.\"\"\"\n return self.blob.client","label":0},{"cid":"google/cloud/storage/acl.py::ObjectACL.reload_path","text":"google/cloud/storage/acl.py::ObjectACL.reload_path\n @property\n def reload_path(self):\n \"\"\"Compute the path for GET API requests for this ACL.\"\"\"\n return '%s/acl' % self.blob.path","label":0},{"cid":"google/cloud/storage/acl.py::ObjectACL.save_path","text":"google/cloud/storage/acl.py::ObjectACL.save_path\n @property\n def save_path(self):\n \"\"\"Compute the path for PATCH API requests for this ACL.\"\"\"\n return self.blob.path","label":0},{"cid":"google/cloud/storage/batch.py::MIMEApplicationHTTP.__init__","text":"google/cloud/storage/batch.py::MIMEApplicationHTTP.__init__\n def __init__(self, method, uri, headers, body):\n if isinstance(body, dict):\n body = json.dumps(body)\n headers['Content-Type'] = 'application/json'\n headers['Content-Length'] = len(body)\n if body is None:\n body = ''\n lines = ['%s %s HTTP/1.1' % (method, uri)]\n lines.extend(['%s: %s' % (key, value)\n for key, value in sorted(headers.items())])\n lines.append('')\n lines.append(body)\n payload = '\\r\\n'.join(lines)\n if six.PY2:\n # email.message.Message is an old-style class, so we\n # cannot use 'super()'.\n MIMEApplication.__init__(self, payload, 'http', encode_noop)\n else: # pragma: NO COVER Python3\n super_init = super(MIMEApplicationHTTP, self).__init__\n super_init(payload, 'http', encode_noop)","label":0},{"cid":"google/cloud/storage/batch.py::_FutureDict.get","text":"google/cloud/storage/batch.py::_FutureDict.get\n @staticmethod\n def get(key, default=None):\n \"\"\"Stand-in for dict.get.\n\n :type key: object\n :param key: Hashable dictionary key.\n\n :type default: object\n :param default: Fallback value to dict.get.\n\n :raises: :class:`KeyError` always since the future is intended to fail\n as a dictionary.\n \"\"\"\n raise KeyError('Cannot get(%r, default=%r) on a future' % (\n key, default))","label":0},{"cid":"google/cloud/storage/batch.py::_FutureDict.__getitem__","text":"google/cloud/storage/batch.py::_FutureDict.__getitem__\n def __getitem__(self, key):\n \"\"\"Stand-in for dict[key].\n\n :type key: object\n :param key: Hashable dictionary key.\n\n :raises: :class:`KeyError` always since the future is intended to fail\n as a dictionary.\n \"\"\"\n raise KeyError('Cannot get item %r from a future' % (key,))","label":0},{"cid":"google/cloud/storage/batch.py::_FutureDict.__setitem__","text":"google/cloud/storage/batch.py::_FutureDict.__setitem__\n def __setitem__(self, key, value):\n \"\"\"Stand-in for dict[key] = value.\n\n :type key: object\n :param key: Hashable dictionary key.\n\n :type value: object\n :param value: Dictionary value.\n\n :raises: :class:`KeyError` always since the future is intended to fail\n as a dictionary.\n \"\"\"\n raise KeyError('Cannot set %r -> %r on a future' % (key, value))","label":0},{"cid":"google/cloud/storage/batch.py::Batch.__init__","text":"google/cloud/storage/batch.py::Batch.__init__\n def __init__(self, client):\n super(Batch, self).__init__()\n self._client = client\n self._requests = []\n self._target_objects = []","label":0},{"cid":"google/cloud/storage/batch.py::Batch._do_request","text":"google/cloud/storage/batch.py::Batch._do_request\n def _do_request(self, method, url, headers, data, target_object):\n \"\"\"Override Connection: defer actual HTTP request.\n\n Only allow up to ``_MAX_BATCH_SIZE`` requests to be deferred.\n\n :type method: str\n :param method: The HTTP method to use in the request.\n\n :type url: str\n :param url: The URL to send the request to.\n\n :type headers: dict\n :param headers: A dictionary of HTTP headers to send with the request.\n\n :type data: str\n :param data: The data to send as the body of the request.\n\n :type target_object: object or :class:`NoneType`\n :param target_object: This allows us to enable custom behavior in our\n batch connection. Here we defer an HTTP request\n and complete initialization of the object at a\n later time.\n\n :rtype: tuple of ``response`` (a dictionary of sorts)\n and ``content`` (a string).\n :returns: The HTTP response object and the content of the response.\n \"\"\"\n if len(self._requests) >= self._MAX_BATCH_SIZE:\n raise ValueError(\"Too many deferred requests (max %d)\" %\n self._MAX_BATCH_SIZE)\n self._requests.append((method, url, headers, data))\n result = _FutureDict()\n self._target_objects.append(target_object)\n if target_object is not None:\n target_object._properties = result\n return NoContent(), result","label":0},{"cid":"google/cloud/storage/batch.py::Batch._prepare_batch_request","text":"google/cloud/storage/batch.py::Batch._prepare_batch_request\n def _prepare_batch_request(self):\n \"\"\"Prepares headers and body for a batch request.\n\n :rtype: tuple (dict, str)\n :returns: The pair of headers and body of the batch request to be sent.\n :raises: :class:`ValueError` if no requests have been deferred.\n \"\"\"\n if len(self._requests) == 0:\n raise ValueError(\"No deferred requests\")\n\n multi = MIMEMultipart()\n\n for method, uri, headers, body in self._requests:\n subrequest = MIMEApplicationHTTP(method, uri, headers, body)\n multi.attach(subrequest)\n\n # The `email` package expects to deal with \"native\" strings\n if six.PY3: # pragma: NO COVER Python3\n buf = io.StringIO()\n else:\n buf = io.BytesIO()\n generator = Generator(buf, False, 0)\n generator.flatten(multi)\n payload = buf.getvalue()\n\n # Strip off redundant header text\n _, body = payload.split('\\n\\n', 1)\n return dict(multi._headers), body","label":0},{"cid":"google/cloud/storage/batch.py::Batch._finish_futures","text":"google/cloud/storage/batch.py::Batch._finish_futures\n def _finish_futures(self, responses):\n \"\"\"Apply all the batch responses to the futures created.\n\n :type responses: list of (headers, payload) tuples.\n :param responses: List of headers and payloads from each response in\n the batch.\n\n :raises: :class:`ValueError` if no requests have been deferred.\n \"\"\"\n # If a bad status occurs, we track it, but don't raise an exception\n # until all futures have been populated.\n exception_args = None\n\n if len(self._target_objects) != len(responses):\n raise ValueError('Expected a response for every request.')\n\n for target_object, sub_response in zip(self._target_objects,\n responses):\n resp_headers, sub_payload = sub_response\n if not 200 <= resp_headers.status < 300:\n exception_args = exception_args or (resp_headers,\n sub_payload)\n elif target_object is not None:\n target_object._properties = sub_payload\n\n if exception_args is not None:\n raise make_exception(*exception_args)","label":0},{"cid":"google/cloud/storage/batch.py::Batch.finish","text":"google/cloud/storage/batch.py::Batch.finish\n def finish(self):\n \"\"\"Submit a single `multipart/mixed` request with deferred requests.\n\n :rtype: list of tuples\n :returns: one ``(headers, payload)`` tuple per deferred request.\n \"\"\"\n headers, body = self._prepare_batch_request()\n\n url = '%s/batch' % self.API_BASE_URL\n\n # Use the private ``_connection`` rather than the public\n # ``.connection``, since the public connection may be this\n # current batch.\n response, content = self._client._connection._make_request(\n 'POST', url, data=body, headers=headers)\n responses = list(_unpack_batch_response(response, content))\n self._finish_futures(responses)\n return responses","label":0},{"cid":"google/cloud/storage/batch.py::Batch.current","text":"google/cloud/storage/batch.py::Batch.current\n def current(self):\n \"\"\"Return the topmost batch, or None.\"\"\"\n return self._client.current_batch","label":0},{"cid":"google/cloud/storage/batch.py::Batch.__enter__","text":"google/cloud/storage/batch.py::Batch.__enter__\n def __enter__(self):\n self._client._push_batch(self)\n return self","label":0},{"cid":"google/cloud/storage/batch.py::Batch.__exit__","text":"google/cloud/storage/batch.py::Batch.__exit__\n def __exit__(self, exc_type, exc_val, exc_tb):\n try:\n if exc_type is None:\n self.finish()\n finally:\n self._client._pop_batch()","label":0},{"cid":"google/cloud/storage/batch.py::_generate_faux_mime_message","text":"google/cloud/storage/batch.py::_generate_faux_mime_message\ndef _generate_faux_mime_message(parser, response, content):\n \"\"\"Convert response, content -> (multipart) email.message.\n\n Helper for _unpack_batch_response.\n \"\"\"\n # We coerce to bytes to get consistent concat across\n # Py2 and Py3. Percent formatting is insufficient since\n # it includes the b in Py3.\n if not isinstance(content, six.binary_type):\n content = content.encode('utf-8')\n content_type = response['content-type']\n if not isinstance(content_type, six.binary_type):\n content_type = content_type.encode('utf-8')\n faux_message = b''.join([\n b'Content-Type: ',\n content_type,\n b'\\nMIME-Version: 1.0\\n\\n',\n content,\n ])\n\n if six.PY2:\n return parser.parsestr(faux_message)\n else: # pragma: NO COVER Python3\n return parser.parsestr(faux_message.decode('utf-8'))","label":0},{"cid":"google/cloud/storage/batch.py::_unpack_batch_response","text":"google/cloud/storage/batch.py::_unpack_batch_response\ndef _unpack_batch_response(response, content):\n \"\"\"Convert response, content -> [(headers, payload)].\n\n Creates a generator of tuples of emulating the responses to\n :meth:`httplib2.Http.request` (a pair of headers and payload).\n\n :type response: :class:`httplib2.Response`\n :param response: HTTP response / headers from a request.\n\n :type content: str\n :param content: Response payload with a batch response.\n \"\"\"\n parser = Parser()\n message = _generate_faux_mime_message(parser, response, content)\n\n if not isinstance(message._payload, list):\n raise ValueError('Bad response: not multi-part')\n\n for subrequest in message._payload:\n status_line, rest = subrequest._payload.split('\\n', 1)\n _, status, _ = status_line.split(' ', 2)\n sub_message = parser.parsestr(rest)\n payload = sub_message._payload\n ctype = sub_message['Content-Type']\n msg_headers = dict(sub_message._headers)\n msg_headers['status'] = status\n headers = httplib2.Response(msg_headers)\n if ctype and ctype.startswith('application/json'):\n payload = json.loads(payload)\n yield headers, payload","label":0},{"cid":"google/cloud/storage/blob.py::Blob.__init__","text":"google/cloud/storage/blob.py::Blob.__init__\n def __init__(self, name, bucket, chunk_size=None):\n super(Blob, self).__init__(name=name)\n\n self.chunk_size = chunk_size # Check that setter accepts value.\n self.bucket = bucket\n self._acl = ObjectACL(self)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.chunk_size","text":"google/cloud/storage/blob.py::Blob.chunk_size\n @property\n def chunk_size(self):\n \"\"\"Get the blob's default chunk size.\n\n :rtype: integer or ``NoneType``\n :returns: The current blob's chunk size, if it is set.\n \"\"\"\n return self._chunk_size","label":0},{"cid":"google/cloud/storage/blob.py::Blob.chunk_size","text":"google/cloud/storage/blob.py::Blob.chunk_size\n @chunk_size.setter\n def chunk_size(self, value):\n \"\"\"Set the blob's default chunk size.\n\n :type value: integer or ``NoneType``\n :param value: The current blob's chunk size, if it is set.\n\n :raises: :class:`ValueError` if ``value`` is not ``None`` and is not a\n multiple of 256 KB.\n \"\"\"\n if value is not None and value % self._CHUNK_SIZE_MULTIPLE != 0:\n raise ValueError('Chunk size must be a multiple of %d.' % (\n self._CHUNK_SIZE_MULTIPLE,))\n self._chunk_size = value","label":0},{"cid":"google/cloud/storage/blob.py::Blob.path_helper","text":"google/cloud/storage/blob.py::Blob.path_helper\n @staticmethod\n def path_helper(bucket_path, blob_name):\n \"\"\"Relative URL path for a blob.\n\n :type bucket_path: string\n :param bucket_path: The URL path for a bucket.\n\n :type blob_name: string\n :param blob_name: The name of the blob.\n\n :rtype: string\n :returns: The relative URL path for ``blob_name``.\n \"\"\"\n return bucket_path + '/o/' + quote(blob_name, safe='')","label":0},{"cid":"google/cloud/storage/blob.py::Blob.acl","text":"google/cloud/storage/blob.py::Blob.acl\n @property\n def acl(self):\n \"\"\"Create our ACL on demand.\"\"\"\n return self._acl","label":0},{"cid":"google/cloud/storage/blob.py::Blob.__repr__","text":"google/cloud/storage/blob.py::Blob.__repr__\n def __repr__(self):\n if self.bucket:\n bucket_name = self.bucket.name\n else:\n bucket_name = None\n\n return '<Blob: %s, %s>' % (bucket_name, self.name)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.path","text":"google/cloud/storage/blob.py::Blob.path\n @property\n def path(self):\n \"\"\"Getter property for the URL path to this Blob.\n\n :rtype: string\n :returns: The URL path to this Blob.\n \"\"\"\n if not self.name:\n raise ValueError('Cannot determine path without a blob name.')\n\n return self.path_helper(self.bucket.path, self.name)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.client","text":"google/cloud/storage/blob.py::Blob.client\n @property\n def client(self):\n \"\"\"The client bound to this blob.\"\"\"\n return self.bucket.client","label":0},{"cid":"google/cloud/storage/blob.py::Blob.public_url","text":"google/cloud/storage/blob.py::Blob.public_url\n @property\n def public_url(self):\n \"\"\"The public URL for this blob's object.\n\n :rtype: `string`\n :returns: The public URL for this blob.\n \"\"\"\n return '{storage_base_url}/{bucket_name}/{quoted_name}'.format(\n storage_base_url='https://storage.googleapis.com',\n bucket_name=self.bucket.name,\n quoted_name=quote(self.name, safe=''))","label":0},{"cid":"google/cloud/storage/blob.py::Blob.generate_signed_url","text":"google/cloud/storage/blob.py::Blob.generate_signed_url\n def generate_signed_url(self, expiration, method='GET',\n content_type=None,\n generation=None, response_disposition=None,\n response_type=None, client=None, credentials=None):\n \"\"\"Generates a signed URL for this blob.\n\n .. note::\n\n If you are on Google Compute Engine, you can't generate a signed\n URL. Follow `Issue 922`_ for updates on this. If you'd like to\n be able to generate a signed URL from GCE, you can use a standard\n service account from a JSON file rather than a GCE service account.\n\n .. _Issue 922: https://github.com/GoogleCloudPlatform/\\\n google-cloud-python/issues/922\n\n If you have a blob that you want to allow access to for a set\n amount of time, you can use this method to generate a URL that\n is only valid within a certain time period.\n\n This is particularly useful if you don't want publicly\n accessible blobs, but don't want to require users to explicitly\n log in.\n\n :type expiration: int, long, datetime.datetime, datetime.timedelta\n :param expiration: When the signed URL should expire.\n\n :type method: str\n :param method: The HTTP verb that will be used when requesting the URL.\n\n :type content_type: str\n :param content_type: (Optional) The content type of the object\n referenced by ``resource``.\n\n :type generation: str\n :param generation: (Optional) A value that indicates which generation\n of the resource to fetch.\n\n :type response_disposition: str\n :param response_disposition: (Optional) Content disposition of\n responses to requests for the signed URL.\n For example, to enable the signed URL\n ","label":0},{"cid":"google/cloud/storage/blob.py::Blob.exists","text":"google/cloud/storage/blob.py::Blob.exists\n def exists(self, client=None):\n \"\"\"Determines whether or not this blob exists.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the blob's bucket.\n\n :rtype: boolean\n :returns: True if the blob exists in Cloud Storage.\n \"\"\"\n client = self._require_client(client)\n try:\n # We only need the status code (200 or not) so we seek to\n # minimize the returned payload.\n query_params = {'fields': 'name'}\n # We intentionally pass `_target_object=None` since fields=name\n # would limit the local properties.\n client.connection.api_request(method='GET', path=self.path,\n query_params=query_params,\n _target_object=None)\n # NOTE: This will not fail immediately in a batch. However, when\n # Batch.finish() is called, the resulting `NotFound` will be\n # raised.\n return True\n except NotFound:\n return False","label":0},{"cid":"google/cloud/storage/blob.py::Blob.delete","text":"google/cloud/storage/blob.py::Blob.delete\n def delete(self, client=None):\n \"\"\"Deletes a blob from Cloud Storage.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the blob's bucket.\n\n :rtype: :class:`Blob`\n :returns: The blob that was just deleted.\n :raises: :class:`google.cloud.exceptions.NotFound`\n (propagated from\n :meth:`google.cloud.storage.bucket.Bucket.delete_blob`).\n \"\"\"\n return self.bucket.delete_blob(self.name, client=client)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.download_to_file","text":"google/cloud/storage/blob.py::Blob.download_to_file\n def download_to_file(self, file_obj, encryption_key=None, client=None):\n \"\"\"Download the contents of this blob into a file-like object.\n\n .. note::\n\n If the server-set property, :attr:`media_link`, is not yet\n initialized, makes an additional API request to load it.\n\n Downloading a file that has been encrypted with a `customer-supplied`_\n encryption key::\n\n >>> from google.cloud import storage\n >>> from google.cloud.storage import Blob\n\n >>> client = storage.Client(project='my-project')\n >>> bucket = client.get_bucket('my-bucket')\n >>> encryption_key = 'aa426195405adee2c8081bb9e7e74b19'\n >>> blob = Blob('secure-data', bucket)\n >>> with open('/tmp/my-secure-file', 'wb') as file_obj:\n >>> blob.download_to_file(file_obj,\n ... encryption_key=encryption_key)\n\n The ``encryption_key`` should be a str or bytes with a length of at\n least 32.\n\n .. _customer-supplied: https://cloud.google.com/storage/docs/\\\n encryption#customer-supplied\n\n :type file_obj: file\n :param file_obj: A file handle to which to write the blob's data.\n\n :type encryption_key: str or bytes\n :param encryption_key: Optional 32 byte encryption key for\n customer-supplied encryption.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the blob's bucket.\n\n :raises: :class:`google.cloud.exceptions.NotFound`\n \"\"\"\n client = self._require_client(client)\n if self.media_link is None: # not yet loaded\n self.reload()\n\n download_url = self.media_link\n\n ","label":0},{"cid":"google/cloud/storage/blob.py::Blob.download_to_filename","text":"google/cloud/storage/blob.py::Blob.download_to_filename\n def download_to_filename(self, filename, encryption_key=None, client=None):\n \"\"\"Download the contents of this blob into a named file.\n\n :type filename: string\n :param filename: A filename to be passed to ``open``.\n\n :type encryption_key: str or bytes\n :param encryption_key: Optional 32 byte encryption key for\n customer-supplied encryption.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the blob's bucket.\n\n :raises: :class:`google.cloud.exceptions.NotFound`\n \"\"\"\n with open(filename, 'wb') as file_obj:\n self.download_to_file(file_obj, encryption_key=encryption_key,\n client=client)\n\n mtime = time.mktime(self.updated.timetuple())\n os.utime(file_obj.name, (mtime, mtime))","label":0},{"cid":"google/cloud/storage/blob.py::Blob.download_as_string","text":"google/cloud/storage/blob.py::Blob.download_as_string\n def download_as_string(self, encryption_key=None, client=None):\n \"\"\"Download the contents of this blob as a string.\n\n :type encryption_key: str or bytes\n :param encryption_key: Optional 32 byte encryption key for\n customer-supplied encryption.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the blob's bucket.\n\n :rtype: bytes\n :returns: The data stored in this blob.\n :raises: :class:`google.cloud.exceptions.NotFound`\n \"\"\"\n string_buffer = BytesIO()\n self.download_to_file(string_buffer, encryption_key=encryption_key,\n client=client)\n return string_buffer.getvalue()","label":0},{"cid":"google/cloud/storage/blob.py::Blob._check_response_error","text":"google/cloud/storage/blob.py::Blob._check_response_error\n @staticmethod\n def _check_response_error(request, http_response):\n \"\"\"Helper for :meth:`upload_from_file`.\"\"\"\n info = http_response.info\n status = int(info['status'])\n if not 200 <= status < 300:\n faux_response = httplib2.Response({'status': status})\n raise make_exception(faux_response, http_response.content,\n error_info=request.url)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.upload_from_file","text":"google/cloud/storage/blob.py::Blob.upload_from_file\n def upload_from_file(self, file_obj, rewind=False, size=None,\n encryption_key=None, content_type=None, num_retries=6,\n client=None):\n \"\"\"Upload the contents of this blob from a file-like object.\n\n The content type of the upload will either be\n - The value passed in to the function (if any)\n - The value stored on the current blob\n - The default value of 'application/octet-stream'\n\n .. note::\n The effect of uploading to an existing blob depends on the\n \"versioning\" and \"lifecycle\" policies defined on the blob's\n bucket. In the absence of those policies, upload will\n overwrite any existing contents.\n\n See the `object versioning\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\n API documents for details.\n\n Uploading a file with a `customer-supplied`_ encryption key::\n\n >>> from google.cloud import storage\n >>> from google.cloud.storage import Blob\n\n >>> client = storage.Client(project='my-project')\n >>> bucket = client.get_bucket('my-bucket')\n >>> encryption_key = 'aa426195405adee2c8081bb9e7e74b19'\n >>> blob = Blob('secure-data', bucket)\n >>> with open('my-file', 'rb') as my_file:\n >>> blob.upload_from_file(my_file,\n ... encryption_key=encryption_key)\n\n The ``encryption_key`` should be a str or bytes with a length of at\n least 32.\n\n .. _customer-supplied: https://cloud.google.com/storage/docs/\\\n encryption#customer-supplied\n\n :type file_obj: file\n :param file_obj: A file handle open for reading.\n\n :type rewind: boolean\n :param rewind: If True, seek to the be","label":0},{"cid":"google/cloud/storage/blob.py::Blob.upload_from_filename","text":"google/cloud/storage/blob.py::Blob.upload_from_filename\n def upload_from_filename(self, filename, content_type=None,\n encryption_key=None, client=None):\n \"\"\"Upload this blob's contents from the content of a named file.\n\n The content type of the upload will either be\n - The value passed in to the function (if any)\n - The value stored on the current blob\n - The value given by mimetypes.guess_type\n\n .. note::\n The effect of uploading to an existing blob depends on the\n \"versioning\" and \"lifecycle\" policies defined on the blob's\n bucket. In the absence of those policies, upload will\n overwrite any existing contents.\n\n See the `object versioning\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\n API documents for details.\n\n :type filename: string\n :param filename: The path to the file.\n\n :type content_type: string or ``NoneType``\n :param content_type: Optional type of content being uploaded.\n\n :type encryption_key: str or bytes\n :param encryption_key: Optional 32 byte encryption key for\n customer-supplied encryption.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the blob's bucket.\n \"\"\"\n content_type = content_type or self._properties.get('contentType')\n if content_type is None:\n content_type, _ = mimetypes.guess_type(filename)\n\n with open(filename, 'rb') as file_obj:\n self.upload_from_file(file_obj, content_type=content_type,\n encryption_key=encryption_key, client=client)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.upload_from_string","text":"google/cloud/storage/blob.py::Blob.upload_from_string\n def upload_from_string(self, data, content_type='text/plain',\n encryption_key=None, client=None):\n \"\"\"Upload contents of this blob from the provided string.\n\n .. note::\n The effect of uploading to an existing blob depends on the\n \"versioning\" and \"lifecycle\" policies defined on the blob's\n bucket. In the absence of those policies, upload will\n overwrite any existing contents.\n\n See the `object versioning\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\n API documents for details.\n\n :type data: bytes or text\n :param data: The data to store in this blob. If the value is\n text, it will be encoded as UTF-8.\n\n :type content_type: string\n :param content_type: Optional type of content being uploaded. Defaults\n to ``'text/plain'``.\n\n :type encryption_key: str or bytes\n :param encryption_key: Optional 32 byte encryption key for\n customer-supplied encryption.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the blob's bucket.\n \"\"\"\n if isinstance(data, six.text_type):\n data = data.encode('utf-8')\n string_buffer = BytesIO()\n string_buffer.write(data)\n self.upload_from_file(file_obj=string_buffer, rewind=True,\n size=len(data), content_type=content_type,\n encryption_key=encryption_key, client=client)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.make_public","text":"google/cloud/storage/blob.py::Blob.make_public\n def make_public(self, client=None):\n \"\"\"Make this blob public giving all users read access.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the blob's bucket.\n \"\"\"\n self.acl.all().grant_read()\n self.acl.save(client=client)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.component_count","text":"google/cloud/storage/blob.py::Blob.component_count\n @property\n def component_count(self):\n \"\"\"Number of underlying components that make up this object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer or ``NoneType``\n :returns: The component count (in case of a composed object) or\n ``None`` if the property is not set locally. This property\n will not be set on objects not created via ``compose``.\n \"\"\"\n component_count = self._properties.get('componentCount')\n if component_count is not None:\n return int(component_count)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.etag","text":"google/cloud/storage/blob.py::Blob.etag\n @property\n def etag(self):\n \"\"\"Retrieve the ETag for the object.\n\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\n https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string or ``NoneType``\n :returns: The blob etag or ``None`` if the property is not set locally.\n \"\"\"\n return self._properties.get('etag')","label":0},{"cid":"google/cloud/storage/blob.py::Blob.generation","text":"google/cloud/storage/blob.py::Blob.generation\n @property\n def generation(self):\n \"\"\"Retrieve the generation for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer or ``NoneType``\n :returns: The generation of the blob or ``None`` if the property\n is not set locally.\n \"\"\"\n generation = self._properties.get('generation')\n if generation is not None:\n return int(generation)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.id","text":"google/cloud/storage/blob.py::Blob.id\n @property\n def id(self):\n \"\"\"Retrieve the ID for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string or ``NoneType``\n :returns: The ID of the blob or ``None`` if the property is not\n set locally.\n \"\"\"\n return self._properties.get('id')","label":0},{"cid":"google/cloud/storage/blob.py::Blob.media_link","text":"google/cloud/storage/blob.py::Blob.media_link\n @property\n def media_link(self):\n \"\"\"Retrieve the media download URI for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string or ``NoneType``\n :returns: The media link for the blob or ``None`` if the property is\n not set locally.\n \"\"\"\n return self._properties.get('mediaLink')","label":0},{"cid":"google/cloud/storage/blob.py::Blob.metadata","text":"google/cloud/storage/blob.py::Blob.metadata\n @property\n def metadata(self):\n \"\"\"Retrieve arbitrary/application specific metadata for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: dict or ``NoneType``\n :returns: The metadata associated with the blob or ``None`` if the\n property is not set locally.\n \"\"\"\n return copy.deepcopy(self._properties.get('metadata'))","label":0},{"cid":"google/cloud/storage/blob.py::Blob.metadata","text":"google/cloud/storage/blob.py::Blob.metadata\n @metadata.setter\n def metadata(self, value):\n \"\"\"Update arbitrary/application specific metadata for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :type value: dict or ``NoneType``\n :param value: The blob metadata to set.\n \"\"\"\n self._patch_property('metadata', value)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.metageneration","text":"google/cloud/storage/blob.py::Blob.metageneration\n @property\n def metageneration(self):\n \"\"\"Retrieve the metageneration for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer or ``NoneType``\n :returns: The metageneration of the blob or ``None`` if the property\n is not set locally.\n \"\"\"\n metageneration = self._properties.get('metageneration')\n if metageneration is not None:\n return int(metageneration)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.owner","text":"google/cloud/storage/blob.py::Blob.owner\n @property\n def owner(self):\n \"\"\"Retrieve info about the owner of the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: dict or ``NoneType``\n :returns: Mapping of owner's role/ID. If the property is not set\n locally, returns ``None``.\n \"\"\"\n return copy.deepcopy(self._properties.get('owner'))","label":0},{"cid":"google/cloud/storage/blob.py::Blob.self_link","text":"google/cloud/storage/blob.py::Blob.self_link\n @property\n def self_link(self):\n \"\"\"Retrieve the URI for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string or ``NoneType``\n :returns: The self link for the blob or ``None`` if the property is\n not set locally.\n \"\"\"\n return self._properties.get('selfLink')","label":0},{"cid":"google/cloud/storage/blob.py::Blob.size","text":"google/cloud/storage/blob.py::Blob.size\n @property\n def size(self):\n \"\"\"Size of the object, in bytes.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer or ``NoneType``\n :returns: The size of the blob or ``None`` if the property\n is not set locally.\n \"\"\"\n size = self._properties.get('size')\n if size is not None:\n return int(size)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.storage_class","text":"google/cloud/storage/blob.py::Blob.storage_class\n @property\n def storage_class(self):\n \"\"\"Retrieve the storage class for the object.\n\n See: https://cloud.google.com/storage/docs/storage-classes\n https://cloud.google.com/storage/docs/nearline-storage\n https://cloud.google.com/storage/docs/durable-reduced-availability\n\n :rtype: string or ``NoneType``\n :returns: If set, one of \"STANDARD\", \"NEARLINE\", or\n \"DURABLE_REDUCED_AVAILABILITY\", else ``None``.\n \"\"\"\n return self._properties.get('storageClass')","label":0},{"cid":"google/cloud/storage/blob.py::Blob.time_deleted","text":"google/cloud/storage/blob.py::Blob.time_deleted\n @property\n def time_deleted(self):\n \"\"\"Retrieve the timestamp at which the object was deleted.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: :class:`datetime.datetime` or ``NoneType``\n :returns: Datetime object parsed from RFC3339 valid timestamp, or\n ``None`` if the property is not set locally. If the blob has\n not been deleted, this will never be set.\n \"\"\"\n value = self._properties.get('timeDeleted')\n if value is not None:\n return _rfc3339_to_datetime(value)","label":0},{"cid":"google/cloud/storage/blob.py::Blob.updated","text":"google/cloud/storage/blob.py::Blob.updated\n @property\n def updated(self):\n \"\"\"Retrieve the timestamp at which the object was updated.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: :class:`datetime.datetime` or ``NoneType``\n :returns: Datetime object parsed from RFC3339 valid timestamp, or\n ``None`` if the property is not set locally.\n \"\"\"\n value = self._properties.get('updated')\n if value is not None:\n return _rfc3339_to_datetime(value)","label":0},{"cid":"google/cloud/storage/blob.py::_UrlBuilder.__init__","text":"google/cloud/storage/blob.py::_UrlBuilder.__init__\n def __init__(self, bucket_name, object_name):\n self.query_params = {'name': object_name}\n self._bucket_name = bucket_name\n self._relative_path = ''","label":0},{"cid":"google/cloud/storage/blob.py::_set_encryption_headers","text":"google/cloud/storage/blob.py::_set_encryption_headers\ndef _set_encryption_headers(key, headers):\n \"\"\"Builds customer encryption key headers\n\n :type key: str or bytes\n :param key: 32 byte key to build request key and hash.\n\n :type headers: dict\n :param headers: dict of HTTP headers being sent in request.\n \"\"\"\n key = _to_bytes(key)\n sha256_key = hashlib.sha256(key).digest()\n key_hash = base64.b64encode(sha256_key).rstrip()\n encoded_key = base64.b64encode(key).rstrip()\n headers['X-Goog-Encryption-Algorithm'] = 'AES256'\n headers['X-Goog-Encryption-Key'] = _bytes_to_unicode(encoded_key)\n headers['X-Goog-Encryption-Key-Sha256'] = _bytes_to_unicode(key_hash)","label":0},{"cid":"google/cloud/storage/bucket.py::_BlobIterator.__init__","text":"google/cloud/storage/bucket.py::_BlobIterator.__init__\n def __init__(self, bucket, extra_params=None, client=None):\n if client is None:\n client = bucket.client\n self.bucket = bucket\n self.prefixes = set()\n self._current_prefixes = None\n super(_BlobIterator, self).__init__(\n client=client, path=bucket.path + '/o',\n extra_params=extra_params)","label":0},{"cid":"google/cloud/storage/bucket.py::_BlobIterator.get_items_from_response","text":"google/cloud/storage/bucket.py::_BlobIterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Yield :class:`.storage.blob.Blob` items from response.\n\n :type response: dict\n :param response: The JSON API response for a page of blobs.\n \"\"\"\n self._current_prefixes = tuple(response.get('prefixes', ()))\n self.prefixes.update(self._current_prefixes)\n for item in response.get('items', []):\n name = item.get('name')\n blob = Blob(name, bucket=self.bucket)\n blob._set_properties(item)\n yield blob","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.__init__","text":"google/cloud/storage/bucket.py::Bucket.__init__\n def __init__(self, client, name=None):\n super(Bucket, self).__init__(name=name)\n self._client = client\n self._acl = BucketACL(self)\n self._default_object_acl = DefaultObjectACL(self)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.__repr__","text":"google/cloud/storage/bucket.py::Bucket.__repr__\n def __repr__(self):\n return '<Bucket: %s>' % self.name","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.client","text":"google/cloud/storage/bucket.py::Bucket.client\n @property\n def client(self):\n \"\"\"The client bound to this bucket.\"\"\"\n return self._client","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.blob","text":"google/cloud/storage/bucket.py::Bucket.blob\n def blob(self, blob_name, chunk_size=None):\n \"\"\"Factory constructor for blob object.\n\n .. note::\n This will not make an HTTP request; it simply instantiates\n a blob object owned by this bucket.\n\n :type blob_name: string\n :param blob_name: The name of the blob to be instantiated.\n\n :type chunk_size: integer\n :param chunk_size: The size of a chunk of data whenever iterating\n (1 MB). This must be a multiple of 256 KB per the\n API specification.\n\n :rtype: :class:`google.cloud.storage.blob.Blob`\n :returns: The blob object created.\n \"\"\"\n return Blob(name=blob_name, bucket=self, chunk_size=chunk_size)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.exists","text":"google/cloud/storage/bucket.py::Bucket.exists\n def exists(self, client=None):\n \"\"\"Determines whether or not this bucket exists.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the current bucket.\n\n :rtype: boolean\n :returns: True if the bucket exists in Cloud Storage.\n \"\"\"\n client = self._require_client(client)\n try:\n # We only need the status code (200 or not) so we seek to\n # minimize the returned payload.\n query_params = {'fields': 'name'}\n # We intentionally pass `_target_object=None` since fields=name\n # would limit the local properties.\n client.connection.api_request(method='GET', path=self.path,\n query_params=query_params,\n _target_object=None)\n # NOTE: This will not fail immediately in a batch. However, when\n # Batch.finish() is called, the resulting `NotFound` will be\n # raised.\n return True\n except NotFound:\n return False","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.create","text":"google/cloud/storage/bucket.py::Bucket.create\n def create(self, client=None):\n \"\"\"Creates current bucket.\n\n If the bucket already exists, will raise\n :class:`google.cloud.exceptions.Conflict`.\n\n This implements \"storage.buckets.insert\".\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the current bucket.\n \"\"\"\n client = self._require_client(client)\n query_params = {'project': client.project}\n properties = {key: self._properties[key] for key in self._changes}\n properties['name'] = self.name\n api_response = client.connection.api_request(\n method='POST', path='/b', query_params=query_params,\n data=properties, _target_object=self)\n self._set_properties(api_response)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.acl","text":"google/cloud/storage/bucket.py::Bucket.acl\n @property\n def acl(self):\n \"\"\"Create our ACL on demand.\"\"\"\n return self._acl","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.default_object_acl","text":"google/cloud/storage/bucket.py::Bucket.default_object_acl\n @property\n def default_object_acl(self):\n \"\"\"Create our defaultObjectACL on demand.\"\"\"\n return self._default_object_acl","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.path_helper","text":"google/cloud/storage/bucket.py::Bucket.path_helper\n @staticmethod\n def path_helper(bucket_name):\n \"\"\"Relative URL path for a bucket.\n\n :type bucket_name: string\n :param bucket_name: The bucket name in the path.\n\n :rtype: string\n :returns: The relative URL path for ``bucket_name``.\n \"\"\"\n return '/b/' + bucket_name","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.path","text":"google/cloud/storage/bucket.py::Bucket.path\n @property\n def path(self):\n \"\"\"The URL path to this bucket.\"\"\"\n if not self.name:\n raise ValueError('Cannot determine path without bucket name.')\n\n return self.path_helper(self.name)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.get_blob","text":"google/cloud/storage/bucket.py::Bucket.get_blob\n def get_blob(self, blob_name, client=None):\n \"\"\"Get a blob object by name.\n\n This will return None if the blob doesn't exist::\n\n >>> from google.cloud import storage\n >>> client = storage.Client()\n >>> bucket = client.get_bucket('my-bucket')\n >>> print bucket.get_blob('/path/to/blob.txt')\n <Blob: my-bucket, /path/to/blob.txt>\n >>> print bucket.get_blob('/does-not-exist.txt')\n None\n\n :type blob_name: string\n :param blob_name: The name of the blob to retrieve.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the current bucket.\n\n :rtype: :class:`google.cloud.storage.blob.Blob` or None\n :returns: The blob object if it exists, otherwise None.\n \"\"\"\n client = self._require_client(client)\n blob = Blob(bucket=self, name=blob_name)\n try:\n response = client.connection.api_request(\n method='GET', path=blob.path, _target_object=blob)\n # NOTE: We assume response.get('name') matches `blob_name`.\n blob._set_properties(response)\n # NOTE: This will not fail immediately in a batch. However, when\n # Batch.finish() is called, the resulting `NotFound` will be\n # raised.\n return blob\n except NotFound:\n return None","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.list_blobs","text":"google/cloud/storage/bucket.py::Bucket.list_blobs\n def list_blobs(self, max_results=None, page_token=None, prefix=None,\n delimiter=None, versions=None,\n projection='noAcl', fields=None, client=None):\n \"\"\"Return an iterator used to find blobs in the bucket.\n\n :type max_results: integer or ``NoneType``\n :param max_results: maximum number of blobs to return.\n\n :type page_token: string\n :param page_token: opaque marker for the next \"page\" of blobs. If not\n passed, will return the first page of blobs.\n\n :type prefix: string or ``NoneType``\n :param prefix: optional prefix used to filter blobs.\n\n :type delimiter: string or ``NoneType``\n :param delimiter: optional delimter, used with ``prefix`` to\n emulate hierarchy.\n\n :type versions: boolean or ``NoneType``\n :param versions: whether object versions should be returned as\n separate blobs.\n\n :type projection: string or ``NoneType``\n :param projection: If used, must be 'full' or 'noAcl'. Defaults to\n 'noAcl'. Specifies the set of properties to return.\n\n :type fields: string or ``NoneType``\n :param fields: Selector specifying which fields to include in a\n partial response. Must be a list of fields. For example\n to get a partial response with just the next page token\n and the language of each blob returned:\n 'items/contentLanguage,nextPageToken'\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the current bucket.\n\n :rtype: :class:`_BlobIterator`.\n :returns: An iterator of blobs.\n \"\"\"\n extra_","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.delete","text":"google/cloud/storage/bucket.py::Bucket.delete\n def delete(self, force=False, client=None):\n \"\"\"Delete this bucket.\n\n The bucket **must** be empty in order to submit a delete request. If\n ``force=True`` is passed, this will first attempt to delete all the\n objects / blobs in the bucket (i.e. try to empty the bucket).\n\n If the bucket doesn't exist, this will raise\n :class:`google.cloud.exceptions.NotFound`. If the bucket is not empty\n (and ``force=False``), will raise\n :class:`google.cloud.exceptions.Conflict`.\n\n If ``force=True`` and the bucket contains more than 256 objects / blobs\n this will cowardly refuse to delete the objects (or the bucket). This\n is to prevent accidental bucket deletion and to prevent extremely long\n runtime of this method.\n\n :type force: boolean\n :param force: If True, empties the bucket's objects then deletes it.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the current bucket.\n\n :raises: :class:`ValueError` if ``force`` is ``True`` and the bucket\n contains more than 256 objects / blobs.\n \"\"\"\n client = self._require_client(client)\n if force:\n blobs = list(self.list_blobs(\n max_results=self._MAX_OBJECTS_FOR_ITERATION + 1,\n client=client))\n if len(blobs) > self._MAX_OBJECTS_FOR_ITERATION:\n message = (\n 'Refusing to delete bucket with more than '\n '%d objects. If you actually want to delete '\n 'this bucket, please delete the objects '\n 'yourself before calling Bucket.delete().'\n ) % (self._MAX_OBJECTS_FOR_ITERATION,)\n raise ValueError(message)\n\n","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.delete_blob","text":"google/cloud/storage/bucket.py::Bucket.delete_blob\n def delete_blob(self, blob_name, client=None):\n \"\"\"Deletes a blob from the current bucket.\n\n If the blob isn't found (backend 404), raises a\n :class:`google.cloud.exceptions.NotFound`.\n\n For example::\n\n >>> from google.cloud.exceptions import NotFound\n >>> from google.cloud import storage\n >>> client = storage.Client()\n >>> bucket = client.get_bucket('my-bucket')\n >>> print bucket.list_blobs()\n [<Blob: my-bucket, my-file.txt>]\n >>> bucket.delete_blob('my-file.txt')\n >>> try:\n ... bucket.delete_blob('doesnt-exist')\n ... except NotFound:\n ... pass\n\n :type blob_name: string\n :param blob_name: A blob name to delete.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the current bucket.\n\n :raises: :class:`google.cloud.exceptions.NotFound` (to suppress\n the exception, call ``delete_blobs``, passing a no-op\n ``on_error`` callback, e.g.::\n\n >>> bucket.delete_blobs([blob], on_error=lambda blob: None)\n \"\"\"\n client = self._require_client(client)\n blob_path = Blob.path_helper(self.path, blob_name)\n # We intentionally pass `_target_object=None` since a DELETE\n # request has no response value (whether in a standard request or\n # in a batch request).\n client.connection.api_request(method='DELETE', path=blob_path,\n _target_object=None)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.delete_blobs","text":"google/cloud/storage/bucket.py::Bucket.delete_blobs\n def delete_blobs(self, blobs, on_error=None, client=None):\n \"\"\"Deletes a list of blobs from the current bucket.\n\n Uses :func:`Bucket.delete_blob` to delete each individual blob.\n\n :type blobs: list of string or :class:`google.cloud.storage.blob.Blob`\n :param blobs: A list of blob names or Blob objects to delete.\n\n :type on_error: a callable taking (blob)\n :param on_error: If not ``None``, called once for each blob raising\n :class:`google.cloud.exceptions.NotFound`;\n otherwise, the exception is propagated.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the current bucket.\n\n :raises: :class:`google.cloud.exceptions.NotFound` (if\n `on_error` is not passed).\n \"\"\"\n for blob in blobs:\n try:\n blob_name = blob\n if not isinstance(blob_name, six.string_types):\n blob_name = blob.name\n self.delete_blob(blob_name, client=client)\n except NotFound:\n if on_error is not None:\n on_error(blob)\n else:\n raise","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.copy_blob","text":"google/cloud/storage/bucket.py::Bucket.copy_blob\n def copy_blob(self, blob, destination_bucket, new_name=None,\n client=None):\n \"\"\"Copy the given blob to the given bucket, optionally with a new name.\n\n :type blob: :class:`google.cloud.storage.blob.Blob`\n :param blob: The blob to be copied.\n\n :type destination_bucket: :class:`google.cloud.storage.bucket.Bucket`\n :param destination_bucket: The bucket into which the blob should be\n copied.\n\n :type new_name: string\n :param new_name: (optional) the new name for the copied file.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the current bucket.\n\n :rtype: :class:`google.cloud.storage.blob.Blob`\n :returns: The new Blob.\n \"\"\"\n client = self._require_client(client)\n if new_name is None:\n new_name = blob.name\n new_blob = Blob(bucket=destination_bucket, name=new_name)\n api_path = blob.path + '/copyTo' + new_blob.path\n copy_result = client.connection.api_request(\n method='POST', path=api_path, _target_object=new_blob)\n new_blob._set_properties(copy_result)\n return new_blob","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.rename_blob","text":"google/cloud/storage/bucket.py::Bucket.rename_blob\n def rename_blob(self, blob, new_name, client=None):\n \"\"\"Rename the given blob using copy and delete operations.\n\n Effectively, copies blob to the same bucket with a new name, then\n deletes the blob.\n\n .. warning::\n\n This method will first duplicate the data and then delete the\n old blob. This means that with very large objects renaming\n could be a very (temporarily) costly or a very slow operation.\n\n :type blob: :class:`google.cloud.storage.blob.Blob`\n :param blob: The blob to be renamed.\n\n :type new_name: string\n :param new_name: The new name for this blob.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the current bucket.\n\n :rtype: :class:`Blob`\n :returns: The newly-renamed blob.\n \"\"\"\n new_blob = self.copy_blob(blob, self, new_name, client=client)\n blob.delete(client=client)\n return new_blob","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.cors","text":"google/cloud/storage/bucket.py::Bucket.cors\n @property\n def cors(self):\n \"\"\"Retrieve CORS policies configured for this bucket.\n\n See: http://www.w3.org/TR/cors/ and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: list of dictionaries\n :returns: A sequence of mappings describing each CORS policy.\n \"\"\"\n return [copy.deepcopy(policy)\n for policy in self._properties.get('cors', ())]","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.cors","text":"google/cloud/storage/bucket.py::Bucket.cors\n @cors.setter\n def cors(self, entries):\n \"\"\"Set CORS policies configured for this bucket.\n\n See: http://www.w3.org/TR/cors/ and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :type entries: list of dictionaries\n :param entries: A sequence of mappings describing each CORS policy.\n \"\"\"\n self._patch_property('cors', entries)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.etag","text":"google/cloud/storage/bucket.py::Bucket.etag\n @property\n def etag(self):\n \"\"\"Retrieve the ETag for the bucket.\n\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string or ``NoneType``\n :returns: The bucket etag or ``None`` if the property is not\n set locally.\n \"\"\"\n return self._properties.get('etag')","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.id","text":"google/cloud/storage/bucket.py::Bucket.id\n @property\n def id(self):\n \"\"\"Retrieve the ID for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string or ``NoneType``\n :returns: The ID of the bucket or ``None`` if the property is not\n set locally.\n \"\"\"\n return self._properties.get('id')","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.lifecycle_rules","text":"google/cloud/storage/bucket.py::Bucket.lifecycle_rules\n @property\n def lifecycle_rules(self):\n \"\"\"Lifecycle rules configured for this bucket.\n\n See: https://cloud.google.com/storage/docs/lifecycle and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: list(dict)\n :returns: A sequence of mappings describing each lifecycle rule.\n \"\"\"\n info = self._properties.get('lifecycle', {})\n return [copy.deepcopy(rule) for rule in info.get('rule', ())]","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.lifecycle_rules","text":"google/cloud/storage/bucket.py::Bucket.lifecycle_rules\n @lifecycle_rules.setter\n def lifecycle_rules(self, rules):\n self._patch_property('lifecycle', {'rule': rules})","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.get_logging","text":"google/cloud/storage/bucket.py::Bucket.get_logging\n def get_logging(self):\n \"\"\"Return info about access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#status\n\n :rtype: dict or None\n :returns: a dict w/ keys, ``logBucket`` and ``logObjectPrefix``\n (if logging is enabled), or None (if not).\n \"\"\"\n info = self._properties.get('logging')\n return copy.deepcopy(info)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.enable_logging","text":"google/cloud/storage/bucket.py::Bucket.enable_logging\n def enable_logging(self, bucket_name, object_prefix=''):\n \"\"\"Enable access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#delivery\n\n :type bucket_name: string\n :param bucket_name: name of bucket in which to store access logs\n\n :type object_prefix: string\n :param object_prefix: prefix for access log filenames\n \"\"\"\n info = {'logBucket': bucket_name, 'logObjectPrefix': object_prefix}\n self._patch_property('logging', info)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.disable_logging","text":"google/cloud/storage/bucket.py::Bucket.disable_logging\n def disable_logging(self):\n \"\"\"Disable access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#disabling\n \"\"\"\n self._patch_property('logging', None)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.metageneration","text":"google/cloud/storage/bucket.py::Bucket.metageneration\n @property\n def metageneration(self):\n \"\"\"Retrieve the metageneration for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: integer or ``NoneType``\n :returns: The metageneration of the bucket or ``None`` if the property\n is not set locally.\n \"\"\"\n metageneration = self._properties.get('metageneration')\n if metageneration is not None:\n return int(metageneration)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.owner","text":"google/cloud/storage/bucket.py::Bucket.owner\n @property\n def owner(self):\n \"\"\"Retrieve info about the owner of the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: dict or ``NoneType``\n :returns: Mapping of owner's role/ID. If the property is not set\n locally, returns ``None``.\n \"\"\"\n return copy.deepcopy(self._properties.get('owner'))","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.project_number","text":"google/cloud/storage/bucket.py::Bucket.project_number\n @property\n def project_number(self):\n \"\"\"Retrieve the number of the project to which the bucket is assigned.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: integer or ``NoneType``\n :returns: The project number that owns the bucket or ``None`` if the\n property is not set locally.\n \"\"\"\n project_number = self._properties.get('projectNumber')\n if project_number is not None:\n return int(project_number)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.self_link","text":"google/cloud/storage/bucket.py::Bucket.self_link\n @property\n def self_link(self):\n \"\"\"Retrieve the URI for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string or ``NoneType``\n :returns: The self link for the bucket or ``None`` if the property is\n not set locally.\n \"\"\"\n return self._properties.get('selfLink')","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.storage_class","text":"google/cloud/storage/bucket.py::Bucket.storage_class\n @property\n def storage_class(self):\n \"\"\"Retrieve the storage class for the bucket.\n\n See: https://cloud.google.com/storage/docs/storage-classes\n https://cloud.google.com/storage/docs/nearline-storage\n https://cloud.google.com/storage/docs/durable-reduced-availability\n\n :rtype: string or ``NoneType``\n :returns: If set, one of \"STANDARD\", \"NEARLINE\", or\n \"DURABLE_REDUCED_AVAILABILITY\", else ``None``.\n \"\"\"\n return self._properties.get('storageClass')","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.storage_class","text":"google/cloud/storage/bucket.py::Bucket.storage_class\n @storage_class.setter\n def storage_class(self, value):\n \"\"\"Set the storage class for the bucket.\n\n See: https://cloud.google.com/storage/docs/storage-classes\n https://cloud.google.com/storage/docs/nearline-storage\n https://cloud.google.com/storage/docs/durable-reduced-availability\n\n :type value: string\n :param value: one of \"STANDARD\", \"NEARLINE\", or\n \"DURABLE_REDUCED_AVAILABILITY\"\n \"\"\"\n if value not in self._STORAGE_CLASSES:\n raise ValueError('Invalid storage class: %s' % (value,))\n self._patch_property('storageClass', value)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.time_created","text":"google/cloud/storage/bucket.py::Bucket.time_created\n @property\n def time_created(self):\n \"\"\"Retrieve the timestamp at which the bucket was created.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: :class:`datetime.datetime` or ``NoneType``\n :returns: Datetime object parsed from RFC3339 valid timestamp, or\n ``None`` if the property is not set locally.\n \"\"\"\n value = self._properties.get('timeCreated')\n if value is not None:\n return _rfc3339_to_datetime(value)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.versioning_enabled","text":"google/cloud/storage/bucket.py::Bucket.versioning_enabled\n @property\n def versioning_enabled(self):\n \"\"\"Is versioning enabled for this bucket?\n\n See: https://cloud.google.com/storage/docs/object-versioning for\n details.\n\n :rtype: boolean\n :returns: True if enabled, else False.\n \"\"\"\n versioning = self._properties.get('versioning', {})\n return versioning.get('enabled', False)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.versioning_enabled","text":"google/cloud/storage/bucket.py::Bucket.versioning_enabled\n @versioning_enabled.setter\n def versioning_enabled(self, value):\n \"\"\"Enable versioning for this bucket.\n\n See: https://cloud.google.com/storage/docs/object-versioning for\n details.\n\n :type value: convertible to boolean\n :param value: should versioning be anabled for the bucket?\n \"\"\"\n self._patch_property('versioning', {'enabled': bool(value)})","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.configure_website","text":"google/cloud/storage/bucket.py::Bucket.configure_website\n def configure_website(self, main_page_suffix=None, not_found_page=None):\n \"\"\"Configure website-related properties.\n\n See: https://developers.google.com/storage/docs/website-configuration\n\n .. note::\n This (apparently) only works\n if your bucket name is a domain name\n (and to do that, you need to get approved somehow...).\n\n If you want this bucket to host a website, just provide the name\n of an index page and a page to use when a blob isn't found::\n\n >>> from google.cloud import storage\n >>> client = storage.Client()\n >>> bucket = client.get_bucket(bucket_name)\n >>> bucket.configure_website('index.html', '404.html')\n\n You probably should also make the whole bucket public::\n\n >>> bucket.make_public(recursive=True, future=True)\n\n This says: \"Make the bucket public, and all the stuff already in\n the bucket, and anything else I add to the bucket. Just make it\n all public.\"\n\n :type main_page_suffix: string\n :param main_page_suffix: The page to use as the main page\n of a directory.\n Typically something like index.html.\n\n :type not_found_page: string\n :param not_found_page: The file to use when a page isn't found.\n \"\"\"\n data = {\n 'mainPageSuffix': main_page_suffix,\n 'notFoundPage': not_found_page,\n }\n self._patch_property('website', data)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.disable_website","text":"google/cloud/storage/bucket.py::Bucket.disable_website\n def disable_website(self):\n \"\"\"Disable the website configuration for this bucket.\n\n This is really just a shortcut for setting the website-related\n attributes to ``None``.\n \"\"\"\n return self.configure_website(None, None)","label":0},{"cid":"google/cloud/storage/bucket.py::Bucket.make_public","text":"google/cloud/storage/bucket.py::Bucket.make_public\n def make_public(self, recursive=False, future=False, client=None):\n \"\"\"Make a bucket public.\n\n If ``recursive=True`` and the bucket contains more than 256\n objects / blobs this will cowardly refuse to make the objects public.\n This is to prevent extremely long runtime of this method.\n\n :type recursive: boolean\n :param recursive: If True, this will make all blobs inside the bucket\n public as well.\n\n :type future: boolean\n :param future: If True, this will make all objects created in the\n future public as well.\n\n :type client: :class:`~google.cloud.storage.client.Client` or\n ``NoneType``\n :param client: Optional. The client to use. If not passed, falls back\n to the ``client`` stored on the current bucket.\n \"\"\"\n self.acl.all().grant_read()\n self.acl.save(client=client)\n\n if future:\n doa = self.default_object_acl\n if not doa.loaded:\n doa.reload(client=client)\n doa.all().grant_read()\n doa.save(client=client)\n\n if recursive:\n blobs = list(self.list_blobs(\n projection='full',\n max_results=self._MAX_OBJECTS_FOR_ITERATION + 1,\n client=client))\n if len(blobs) > self._MAX_OBJECTS_FOR_ITERATION:\n message = (\n 'Refusing to make public recursively with more than '\n '%d objects. If you actually want to make every object '\n 'in this bucket public, please do it on the objects '\n 'yourself.'\n ) % (self._MAX_OBJECTS_FOR_ITERATION,)\n raise ValueError(message)\n\n for blob in blobs:\n blob.acl.all().grant_read()\n blob.acl.save(client=client)","label":0},{"cid":"google/cloud/storage/client.py::Client.__init__","text":"google/cloud/storage/client.py::Client.__init__\n def __init__(self, project=None, credentials=None, http=None):\n self._connection = None\n super(Client, self).__init__(project=project, credentials=credentials,\n http=http)\n self._batch_stack = _LocalStack()","label":0},{"cid":"google/cloud/storage/client.py::Client.connection","text":"google/cloud/storage/client.py::Client.connection\n @property\n def connection(self):\n \"\"\"Get connection or batch on the client.\n\n :rtype: :class:`google.cloud.storage.connection.Connection`\n :returns: The connection set on the client, or the batch\n if one is set.\n \"\"\"\n if self.current_batch is not None:\n return self.current_batch\n else:\n return self._connection","label":0},{"cid":"google/cloud/storage/client.py::Client.connection","text":"google/cloud/storage/client.py::Client.connection\n @connection.setter\n def connection(self, value):\n \"\"\"Set connection on the client.\n\n Intended to be used by constructor (since the base class calls)\n self.connection = connection\n Will raise if the connection is set more than once.\n\n :type value: :class:`google.cloud.storage.connection.Connection`\n :param value: The connection set on the client.\n\n :raises: :class:`ValueError` if connection has already been set.\n \"\"\"\n if self._connection is not None:\n raise ValueError('Connection already set on client')\n self._connection = value","label":0},{"cid":"google/cloud/storage/client.py::Client._push_batch","text":"google/cloud/storage/client.py::Client._push_batch\n def _push_batch(self, batch):\n \"\"\"Push a batch onto our stack.\n\n \"Protected\", intended for use by batch context mgrs.\n\n :type batch: :class:`google.cloud.storage.batch.Batch`\n :param batch: newly-active batch\n \"\"\"\n self._batch_stack.push(batch)","label":0},{"cid":"google/cloud/storage/client.py::Client._pop_batch","text":"google/cloud/storage/client.py::Client._pop_batch\n def _pop_batch(self):\n \"\"\"Pop a batch from our stack.\n\n \"Protected\", intended for use by batch context mgrs.\n\n :raises: IndexError if the stack is empty.\n :rtype: :class:`google.cloud.storage.batch.Batch`\n :returns: the top-most batch/transaction, after removing it.\n \"\"\"\n return self._batch_stack.pop()","label":0},{"cid":"google/cloud/storage/client.py::Client.current_batch","text":"google/cloud/storage/client.py::Client.current_batch\n @property\n def current_batch(self):\n \"\"\"Currently-active batch.\n\n :rtype: :class:`google.cloud.storage.batch.Batch` or ``NoneType`` (if\n no batch is active).\n :returns: The batch at the top of the batch stack.\n \"\"\"\n return self._batch_stack.top","label":0},{"cid":"google/cloud/storage/client.py::Client.bucket","text":"google/cloud/storage/client.py::Client.bucket\n def bucket(self, bucket_name):\n \"\"\"Factory constructor for bucket object.\n\n .. note::\n This will not make an HTTP request; it simply instantiates\n a bucket object owned by this client.\n\n :type bucket_name: string\n :param bucket_name: The name of the bucket to be instantiated.\n\n :rtype: :class:`google.cloud.storage.bucket.Bucket`\n :returns: The bucket object created.\n \"\"\"\n return Bucket(client=self, name=bucket_name)","label":0},{"cid":"google/cloud/storage/client.py::Client.batch","text":"google/cloud/storage/client.py::Client.batch\n def batch(self):\n \"\"\"Factory constructor for batch object.\n\n .. note::\n This will not make an HTTP request; it simply instantiates\n a batch object owned by this client.\n\n :rtype: :class:`google.cloud.storage.batch.Batch`\n :returns: The batch object created.\n \"\"\"\n return Batch(client=self)","label":0},{"cid":"google/cloud/storage/client.py::Client.get_bucket","text":"google/cloud/storage/client.py::Client.get_bucket\n def get_bucket(self, bucket_name):\n \"\"\"Get a bucket by name.\n\n If the bucket isn't found, this will raise a\n :class:`google.cloud.storage.exceptions.NotFound`.\n\n For example::\n\n >>> try:\n >>> bucket = client.get_bucket('my-bucket')\n >>> except google.cloud.exceptions.NotFound:\n >>> print 'Sorry, that bucket does not exist!'\n\n This implements \"storage.buckets.get\".\n\n :type bucket_name: string\n :param bucket_name: The name of the bucket to get.\n\n :rtype: :class:`google.cloud.storage.bucket.Bucket`\n :returns: The bucket matching the name provided.\n :raises: :class:`google.cloud.exceptions.NotFound`\n \"\"\"\n bucket = Bucket(self, name=bucket_name)\n bucket.reload(client=self)\n return bucket","label":0},{"cid":"google/cloud/storage/client.py::Client.lookup_bucket","text":"google/cloud/storage/client.py::Client.lookup_bucket\n def lookup_bucket(self, bucket_name):\n \"\"\"Get a bucket by name, returning None if not found.\n\n You can use this if you would rather check for a None value\n than catching an exception::\n\n >>> bucket = client.lookup_bucket('doesnt-exist')\n >>> print bucket\n None\n >>> bucket = client.lookup_bucket('my-bucket')\n >>> print bucket\n <Bucket: my-bucket>\n\n :type bucket_name: string\n :param bucket_name: The name of the bucket to get.\n\n :rtype: :class:`google.cloud.storage.bucket.Bucket`\n :returns: The bucket matching the name provided or None if not found.\n \"\"\"\n try:\n return self.get_bucket(bucket_name)\n except NotFound:\n return None","label":0},{"cid":"google/cloud/storage/client.py::Client.create_bucket","text":"google/cloud/storage/client.py::Client.create_bucket\n def create_bucket(self, bucket_name):\n \"\"\"Create a new bucket.\n\n For example::\n\n >>> bucket = client.create_bucket('my-bucket')\n >>> print bucket\n <Bucket: my-bucket>\n\n This implements \"storage.buckets.insert\".\n\n If the bucket already exists, will raise\n :class:`google.cloud.exceptions.Conflict`.\n\n :type bucket_name: string\n :param bucket_name: The bucket name to create.\n\n :rtype: :class:`google.cloud.storage.bucket.Bucket`\n :returns: The newly created bucket.\n \"\"\"\n bucket = Bucket(self, name=bucket_name)\n bucket.create(client=self)\n return bucket","label":0},{"cid":"google/cloud/storage/client.py::Client.list_buckets","text":"google/cloud/storage/client.py::Client.list_buckets\n def list_buckets(self, max_results=None, page_token=None, prefix=None,\n projection='noAcl', fields=None):\n \"\"\"Get all buckets in the project associated to the client.\n\n This will not populate the list of blobs available in each\n bucket.\n\n >>> for bucket in client.list_buckets():\n >>> print bucket\n\n This implements \"storage.buckets.list\".\n\n :type max_results: integer or ``NoneType``\n :param max_results: Optional. Maximum number of buckets to return.\n\n :type page_token: string or ``NoneType``\n :param page_token: Optional. Opaque marker for the next \"page\" of\n buckets. If not passed, will return the first page\n of buckets.\n\n :type prefix: string or ``NoneType``\n :param prefix: Optional. Filter results to buckets whose names begin\n with this prefix.\n\n :type projection: string or ``NoneType``\n :param projection: If used, must be 'full' or 'noAcl'. Defaults to\n 'noAcl'. Specifies the set of properties to return.\n\n :type fields: string or ``NoneType``\n :param fields: Selector specifying which fields to include in a\n partial response. Must be a list of fields. For example\n to get a partial response with just the next page token\n and the language of each bucket returned:\n 'items/id,nextPageToken'\n\n :rtype: iterable of :class:`google.cloud.storage.bucket.Bucket`\n objects.\n :returns: All buckets belonging to this project.\n \"\"\"\n extra_params = {'project': self.project}\n\n if max_results is not None:\n extra_params['maxResults'] = max_results\n\n if prefix is not None:\n extra_params['prefix'] = prefix\n\n extra_params['projec","label":0},{"cid":"google/cloud/storage/client.py::_BucketIterator.__init__","text":"google/cloud/storage/client.py::_BucketIterator.__init__\n def __init__(self, client, extra_params=None):\n super(_BucketIterator, self).__init__(client=client, path='/b',\n extra_params=extra_params)","label":0},{"cid":"google/cloud/storage/client.py::_BucketIterator.get_items_from_response","text":"google/cloud/storage/client.py::_BucketIterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Factory method which yields :class:`.Bucket` items from a response.\n\n :type response: dict\n :param response: The JSON API response for a page of buckets.\n \"\"\"\n for item in response.get('items', []):\n name = item.get('name')\n bucket = Bucket(self.client, name)\n bucket._set_properties(item)\n yield bucket","label":0},{"cid":"google/cloud/streaming/buffered_stream.py::BufferedStream.__init__","text":"google/cloud/streaming/buffered_stream.py::BufferedStream.__init__\n def __init__(self, stream, start, size):\n self._stream = stream\n self._start_pos = start\n self._buffer_pos = 0\n\n if not hasattr(self._stream, 'closed') or not self._stream.closed:\n self._buffered_data = self._stream.read(size)\n else:\n self._buffered_data = b''\n\n self._stream_at_end = len(self._buffered_data) < size\n self._end_pos = self._start_pos + len(self._buffered_data)","label":0},{"cid":"google/cloud/streaming/buffered_stream.py::BufferedStream.__repr__","text":"google/cloud/streaming/buffered_stream.py::BufferedStream.__repr__\n def __repr__(self):\n return ('Buffered stream %s from position %s-%s with %s '\n 'bytes remaining' % (self._stream, self._start_pos,\n self._end_pos, self._bytes_remaining))","label":0},{"cid":"google/cloud/streaming/buffered_stream.py::BufferedStream.__len__","text":"google/cloud/streaming/buffered_stream.py::BufferedStream.__len__\n def __len__(self):\n return len(self._buffered_data)","label":0},{"cid":"google/cloud/streaming/buffered_stream.py::BufferedStream.stream_exhausted","text":"google/cloud/streaming/buffered_stream.py::BufferedStream.stream_exhausted\n @property\n def stream_exhausted(self):\n \"\"\"Does the stream have bytes remaining beyond the buffer\n\n :rtype: boolean\n :returns: Boolean indicating if the stream is exhausted.\n \"\"\"\n return self._stream_at_end","label":0},{"cid":"google/cloud/streaming/buffered_stream.py::BufferedStream.stream_end_position","text":"google/cloud/streaming/buffered_stream.py::BufferedStream.stream_end_position\n @property\n def stream_end_position(self):\n \"\"\"Point to which stream was read into the buffer\n\n :rtype: integer\n :returns: The end-position of the stream.\n \"\"\"\n return self._end_pos","label":0},{"cid":"google/cloud/streaming/buffered_stream.py::BufferedStream._bytes_remaining","text":"google/cloud/streaming/buffered_stream.py::BufferedStream._bytes_remaining\n @property\n def _bytes_remaining(self):\n \"\"\"Bytes remaining to be read from the buffer\n\n :rtype: integer\n :returns: The number of bytes remaining.\n \"\"\"\n return len(self._buffered_data) - self._buffer_pos","label":0},{"cid":"google/cloud/streaming/buffered_stream.py::BufferedStream.read","text":"google/cloud/streaming/buffered_stream.py::BufferedStream.read\n def read(self, size=None):\n \"\"\"Read bytes from the buffer.\n\n :type size: integer or None\n :param size: How many bytes to read (defaults to all remaining bytes).\n\n :rtype: str\n :returns: The data read from the stream.\n \"\"\"\n if size is None or size < 0:\n raise ValueError(\n 'Illegal read of size %s requested on BufferedStream. '\n 'Wrapped stream %s is at position %s-%s, '\n '%s bytes remaining.' %\n (size, self._stream, self._start_pos, self._end_pos,\n self._bytes_remaining))\n\n if not self._bytes_remaining:\n return b''\n\n size = min(size, self._bytes_remaining)\n data = self._buffered_data[self._buffer_pos:self._buffer_pos + size]\n self._buffer_pos += size\n return data","label":0},{"cid":"google/cloud/streaming/exceptions.py::HttpError.__init__","text":"google/cloud/streaming/exceptions.py::HttpError.__init__\n def __init__(self, response, content, url):\n super(HttpError, self).__init__()\n self.response = response\n self.content = content\n self.url = url","label":0},{"cid":"google/cloud/streaming/exceptions.py::HttpError.__str__","text":"google/cloud/streaming/exceptions.py::HttpError.__str__\n def __str__(self):\n content = self.content.decode('ascii', 'replace')\n return 'HttpError accessing <%s>: response: <%s>, content <%s>' % (\n self.url, self.response, content)","label":0},{"cid":"google/cloud/streaming/exceptions.py::HttpError.status_code","text":"google/cloud/streaming/exceptions.py::HttpError.status_code\n @property\n def status_code(self):\n \"\"\"Status code for the response.\n\n :rtype: integer\n :returns: the code\n \"\"\"\n return int(self.response['status'])","label":0},{"cid":"google/cloud/streaming/exceptions.py::HttpError.from_response","text":"google/cloud/streaming/exceptions.py::HttpError.from_response\n @classmethod\n def from_response(cls, http_response):\n \"\"\"Factory: construct an exception from a response.\n\n :type http_response: :class:`~.http_wrapper.Response`\n :param http_response: the response which returned the error\n\n :rtype: :class:`HttpError`\n :returns: The error created from the response.\n \"\"\"\n return cls(http_response.info, http_response.content,\n http_response.request_url)","label":0},{"cid":"google/cloud/streaming/exceptions.py::RetryAfterError.__init__","text":"google/cloud/streaming/exceptions.py::RetryAfterError.__init__\n def __init__(self, response, content, url, retry_after):\n super(RetryAfterError, self).__init__(response, content, url)\n self.retry_after = int(retry_after)","label":0},{"cid":"google/cloud/streaming/exceptions.py::RetryAfterError.from_response","text":"google/cloud/streaming/exceptions.py::RetryAfterError.from_response\n @classmethod\n def from_response(cls, http_response):\n \"\"\"Factory: construct an exception from a response.\n\n :type http_response: :class:`~.http_wrapper.Response`\n :param http_response: the response which returned the error.\n\n :rtype: :class:`RetryAfterError`\n :returns: The error created from the response.\n \"\"\"\n return cls(http_response.info, http_response.content,\n http_response.request_url, http_response.retry_after)","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::_httplib2_debug_level","text":"google/cloud/streaming/http_wrapper.py::_httplib2_debug_level\n@contextlib.contextmanager\ndef _httplib2_debug_level(http_request, level, http=None):\n \"\"\"Temporarily change the value of httplib2.debuglevel, if necessary.\n\n If http_request has a `loggable_body` distinct from `body`, then we\n need to prevent httplib2 from logging the full body. This sets\n httplib2.debuglevel for the duration of the `with` block; however,\n that alone won't change the value of existing HTTP connections. If\n an httplib2.Http object is provided, we'll also change the level on\n any cached connections attached to it.\n\n :type http_request: :class:`Request`\n :param http_request: the request to be logged.\n\n :type level: integer\n :param level: the debuglevel for logging.\n\n :type http: :class:`httplib2.Http`, or ``None``\n :param http: the instance on whose connections to set the debuglevel.\n \"\"\"\n if http_request.loggable_body is None:\n yield\n return\n old_level = httplib2.debuglevel\n http_levels = {}\n httplib2.debuglevel = level\n if http is not None:\n for connection_key, connection in http.connections.items():\n # httplib2 stores two kinds of values in this dict, connection\n # classes and instances. Since the connection types are all\n # old-style classes, we can't easily distinguish by connection\n # type -- so instead we use the key pattern.\n if ':' not in connection_key:\n continue\n http_levels[connection_key] = connection.debuglevel\n connection.set_debuglevel(level)\n yield\n httplib2.debuglevel = old_level\n if http is not None:\n for connection_key, old_level in http_levels.items():\n http.connections[connection_key].set_debuglevel(old_level)","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::Request.__init__","text":"google/cloud/streaming/http_wrapper.py::Request.__init__\n def __init__(self, url='', http_method='GET', headers=None, body=''):\n self.url = url\n self.http_method = http_method\n self.headers = headers or {}\n self._body = None\n self._loggable_body = None\n self.body = body","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::Request.loggable_body","text":"google/cloud/streaming/http_wrapper.py::Request.loggable_body\n @property\n def loggable_body(self):\n \"\"\"Request body for logging purposes\n\n :rtype: str\n :returns: The body to be logged.\n \"\"\"\n return self._loggable_body","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::Request.loggable_body","text":"google/cloud/streaming/http_wrapper.py::Request.loggable_body\n @loggable_body.setter\n def loggable_body(self, value):\n \"\"\"Update request body for logging purposes\n\n :type value: str\n :param value: updated body\n\n :raises: :exc:`RequestError` if the request does not have a body.\n \"\"\"\n if self.body is None:\n raise RequestError(\n 'Cannot set loggable body on request with no body')\n self._loggable_body = value","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::Request.body","text":"google/cloud/streaming/http_wrapper.py::Request.body\n @property\n def body(self):\n \"\"\"Request body\n\n :rtype: str\n :returns: The body of the request.\n \"\"\"\n return self._body","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::Request.body","text":"google/cloud/streaming/http_wrapper.py::Request.body\n @body.setter\n def body(self, value):\n \"\"\"Update the request body\n\n Handles logging and length measurement.\n\n :type value: str\n :param value: updated body\n \"\"\"\n self._body = value\n if value is not None:\n # Avoid calling len() which cannot exceed 4GiB in 32-bit python.\n body_length = getattr(\n self._body, 'length', None) or len(self._body)\n self.headers['content-length'] = str(body_length)\n else:\n self.headers.pop('content-length', None)\n # This line ensures we don't try to print large requests.\n if not isinstance(value, (type(None), six.string_types)):\n self.loggable_body = '<media body>'","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::_process_content_range","text":"google/cloud/streaming/http_wrapper.py::_process_content_range\ndef _process_content_range(content_range):\n \"\"\"Convert a 'Content-Range' header into a length for the response.\n\n Helper for :meth:`Response.length`.\n\n :type content_range: str\n :param content_range: the header value being parsed.\n\n :rtype: integer\n :returns: the length of the response chunk.\n \"\"\"\n _, _, range_spec = content_range.partition(' ')\n byte_range, _, _ = range_spec.partition('/')\n start, _, end = byte_range.partition('-')\n return int(end) - int(start) + 1","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::Response.__len__","text":"google/cloud/streaming/http_wrapper.py::Response.__len__\n def __len__(self):\n return self.length","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::Response.length","text":"google/cloud/streaming/http_wrapper.py::Response.length\n @property\n def length(self):\n \"\"\"Length of this response.\n\n Exposed as an attribute since using ``len()`` directly can fail\n for responses larger than ``sys.maxint``.\n\n :rtype: integer or long\n :returns: The length of the response.\n \"\"\"\n if 'content-encoding' in self.info and 'content-range' in self.info:\n # httplib2 rewrites content-length in the case of a compressed\n # transfer; we can't trust the content-length header in that\n # case, but we *can* trust content-range, if it's present.\n return _process_content_range(self.info['content-range'])\n elif 'content-length' in self.info:\n return int(self.info.get('content-length'))\n elif 'content-range' in self.info:\n return _process_content_range(self.info['content-range'])\n return len(self.content)","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::Response.status_code","text":"google/cloud/streaming/http_wrapper.py::Response.status_code\n @property\n def status_code(self):\n \"\"\"HTTP status code\n\n :rtype: integer\n :returns: The response status code.\n \"\"\"\n return int(self.info['status'])","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::Response.retry_after","text":"google/cloud/streaming/http_wrapper.py::Response.retry_after\n @property\n def retry_after(self):\n \"\"\"Retry interval (if set).\n\n :rtype: integer\n :returns: interval in seconds\n \"\"\"\n if 'retry-after' in self.info:\n return int(self.info['retry-after'])","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::Response.is_redirect","text":"google/cloud/streaming/http_wrapper.py::Response.is_redirect\n @property\n def is_redirect(self):\n \"\"\"Does this response contain a redirect\n\n :rtype: boolean\n :returns: True if the status code indicates a redirect and the\n 'location' header is present.\n \"\"\"\n return (self.status_code in _REDIRECT_STATUS_CODES and\n 'location' in self.info)","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::_check_response","text":"google/cloud/streaming/http_wrapper.py::_check_response\ndef _check_response(response):\n \"\"\"Validate a response\n\n :type response: :class:`Response`\n :param response: the response to validate\n\n :raises: :exc:`google.cloud.streaming.exceptions.RequestError` if response\n is None, :exc:`~.exceptions.BadStatusCodeError` if response status\n code indicates an error, or :exc:`~.exceptions.RetryAfterError`\n if response indicates a retry interval.\n \"\"\"\n if response is None:\n # Caller shouldn't call us if the response is None, but handle anyway.\n raise RequestError(\n 'Request did not return a response.')\n elif (response.status_code >= 500 or\n response.status_code == TOO_MANY_REQUESTS):\n raise BadStatusCodeError.from_response(response)\n elif response.retry_after:\n raise RetryAfterError.from_response(response)","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::_reset_http_connections","text":"google/cloud/streaming/http_wrapper.py::_reset_http_connections\ndef _reset_http_connections(http):\n \"\"\"Rebuild all http connections in the httplib2.Http instance.\n\n httplib2 overloads the map in http.connections to contain two different\n types of values:\n { scheme string: connection class } and\n { scheme + authority string : actual http connection }\n Here we remove all of the entries for actual connections so that on the\n next request httplib2 will rebuild them from the connection types.\n\n :type http: :class:`httplib2.Http`\n :param http: the instance whose connections are to be rebuilt\n \"\"\"\n if getattr(http, 'connections', None):\n for conn_key in list(http.connections.keys()):\n if ':' in conn_key:\n del http.connections[conn_key]","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::_make_api_request_no_retry","text":"google/cloud/streaming/http_wrapper.py::_make_api_request_no_retry\ndef _make_api_request_no_retry(http, http_request, redirections=_REDIRECTIONS):\n \"\"\"Send an HTTP request via the given http instance.\n\n This wrapper exists to handle translation between the plain httplib2\n request/response types and the Request and Response types above.\n\n :type http: :class:`httplib2.Http`\n :param http: an instance which impelements the `Http` API.\n\n :type http_request: :class:`Request`\n :param http_request: the request to send.\n\n :type redirections: integer\n :param redirections: Number of redirects to follow.\n\n :rtype: :class:`Response`\n :returns: an object representing the server's response\n\n :raises: :exc:`google.cloud.streaming.exceptions.RequestError` if no\n response could be parsed.\n \"\"\"\n connection_type = None\n # Handle overrides for connection types. This is used if the caller\n # wants control over the underlying connection for managing callbacks\n # or hash digestion.\n if getattr(http, 'connections', None):\n url_scheme = parse.urlsplit(http_request.url).scheme\n if url_scheme and url_scheme in http.connections:\n connection_type = http.connections[url_scheme]\n\n # Custom printing only at debuglevel 4\n new_debuglevel = 4 if httplib2.debuglevel == 4 else 0\n with _httplib2_debug_level(http_request, new_debuglevel, http=http):\n info, content = http.request(\n str(http_request.url), method=str(http_request.http_method),\n body=http_request.body, headers=http_request.headers,\n redirections=redirections, connection_type=connection_type)\n\n if info is None:\n raise RequestError()\n\n response = Response(info, content, http_request.url)\n _check_response(response)\n return response","label":0},{"cid":"google/cloud/streaming/http_wrapper.py::make_api_request","text":"google/cloud/streaming/http_wrapper.py::make_api_request\ndef make_api_request(http, http_request, retries=7,\n redirections=_REDIRECTIONS):\n \"\"\"Send an HTTP request via the given http, performing error/retry handling.\n\n :type http: :class:`httplib2.Http`\n :param http: an instance which implements the `Http` API.\n\n :type http_request: :class:`Request`\n :param http_request: the request to send.\n\n :type retries: integer\n :param retries: Number of retries to attempt on retryable\n responses (such as 429 or 5XX).\n\n :type redirections: integer\n :param redirections: Number of redirects to follow.\n\n :rtype: :class:`Response`\n :returns: an object representing the server's response.\n\n :raises: :exc:`google.cloud.streaming.exceptions.RequestError` if no\n response could be parsed.\n \"\"\"\n retry = 0\n while True:\n try:\n return _make_api_request_no_retry(http, http_request,\n redirections=redirections)\n except _RETRYABLE_EXCEPTIONS as exc:\n retry += 1\n if retry >= retries:\n raise\n retry_after = getattr(exc, 'retry_after', None)\n if retry_after is None:\n retry_after = calculate_wait_for_retry(retry)\n\n _reset_http_connections(http)\n logging.debug('Retrying request to url %s after exception %s',\n http_request.url, type(exc).__name__)\n time.sleep(retry_after)","label":0},{"cid":"google/cloud/streaming/stream_slice.py::StreamSlice.__init__","text":"google/cloud/streaming/stream_slice.py::StreamSlice.__init__\n def __init__(self, stream, max_bytes):\n self._stream = stream\n self._remaining_bytes = max_bytes\n self._max_bytes = max_bytes","label":0},{"cid":"google/cloud/streaming/stream_slice.py::StreamSlice.__repr__","text":"google/cloud/streaming/stream_slice.py::StreamSlice.__repr__\n def __repr__(self):\n return 'Slice of stream %s with %s/%s bytes not yet read' % (\n self._stream, self._remaining_bytes, self._max_bytes)","label":0},{"cid":"google/cloud/streaming/stream_slice.py::StreamSlice.__len__","text":"google/cloud/streaming/stream_slice.py::StreamSlice.__len__\n def __len__(self):\n return self._max_bytes","label":0},{"cid":"google/cloud/streaming/stream_slice.py::StreamSlice.__nonzero__","text":"google/cloud/streaming/stream_slice.py::StreamSlice.__nonzero__\n def __nonzero__(self):\n # For 32-bit python2.x, len() cannot exceed a 32-bit number; avoid\n # accidental len() calls from httplib in the form of \"if this_object:\".\n return bool(self._max_bytes)","label":0},{"cid":"google/cloud/streaming/stream_slice.py::StreamSlice.length","text":"google/cloud/streaming/stream_slice.py::StreamSlice.length\n @property\n def length(self):\n \"\"\"Maximum number of bytes to return in the slice.\n\n .. note::\n\n For 32-bit python2.x, len() cannot exceed a 32-bit number.\n\n :rtype: integer\n :returns: The max \"length\" of the stream.\n \"\"\"\n return self._max_bytes","label":0},{"cid":"google/cloud/streaming/stream_slice.py::StreamSlice.read","text":"google/cloud/streaming/stream_slice.py::StreamSlice.read\n def read(self, size=None):\n \"\"\"Read bytes from the slice.\n\n Compared to other streams, there is one case where we may\n unexpectedly raise an exception on read: if the underlying stream\n is exhausted (i.e. returns no bytes on read), and the size of this\n slice indicates we should still be able to read more bytes, we\n raise :exc:`IncompleteRead`.\n\n :type size: integer or None\n :param size: If provided, read no more than size bytes from the stream.\n\n :rtype: bytes\n :returns: bytes read from this slice.\n\n :raises: :exc:`IncompleteRead`\n \"\"\"\n if size is not None:\n read_size = min(size, self._remaining_bytes)\n else:\n read_size = self._remaining_bytes\n data = self._stream.read(read_size)\n if read_size > 0 and not data:\n raise http_client.IncompleteRead(\n self._max_bytes - self._remaining_bytes, self._max_bytes)\n self._remaining_bytes -= len(data)\n return data","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.__init__","text":"google/cloud/streaming/transfer.py::_Transfer.__init__\n def __init__(self, stream, close_stream=False,\n chunksize=_DEFAULT_CHUNKSIZE, auto_transfer=True,\n http=None, num_retries=5):\n self._bytes_http = None\n self._close_stream = close_stream\n self._http = http\n self._stream = stream\n self._url = None\n\n # Let the @property do validation.\n self.num_retries = num_retries\n\n self.auto_transfer = auto_transfer\n self.chunksize = chunksize","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.__repr__","text":"google/cloud/streaming/transfer.py::_Transfer.__repr__\n def __repr__(self):\n return str(self)","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.close_stream","text":"google/cloud/streaming/transfer.py::_Transfer.close_stream\n @property\n def close_stream(self):\n \"\"\"Should this instance close the stream when deleted.\n\n :rtype: boolean\n :returns: Boolean indicated if the stream should be closed.\n \"\"\"\n return self._close_stream","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.http","text":"google/cloud/streaming/transfer.py::_Transfer.http\n @property\n def http(self):\n \"\"\"Http instance used to perform requests.\n\n :rtype: :class:`httplib2.Http` (or workalike)\n :returns: The HTTP object used for requests.\n \"\"\"\n return self._http","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.bytes_http","text":"google/cloud/streaming/transfer.py::_Transfer.bytes_http\n @property\n def bytes_http(self):\n \"\"\"Http instance used to perform binary requests.\n\n Defaults to :attr:`http`.\n\n :rtype: :class:`httplib2.Http` (or workalike)\n :returns: The HTTP object used for binary requests.\n \"\"\"\n return self._bytes_http or self.http","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.bytes_http","text":"google/cloud/streaming/transfer.py::_Transfer.bytes_http\n @bytes_http.setter\n def bytes_http(self, value):\n \"\"\"Update Http instance used to perform binary requests.\n\n :type value: :class:`httplib2.Http` (or workalike)\n :param value: new instance\n \"\"\"\n self._bytes_http = value","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.num_retries","text":"google/cloud/streaming/transfer.py::_Transfer.num_retries\n @property\n def num_retries(self):\n \"\"\"How many retries should the transfer attempt\n\n :rtype: integer\n :returns: The number of retries allowed.\n \"\"\"\n return self._num_retries","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.num_retries","text":"google/cloud/streaming/transfer.py::_Transfer.num_retries\n @num_retries.setter\n def num_retries(self, value):\n \"\"\"Update how many retries should the transfer attempt\n\n :type value: integer\n \"\"\"\n if not isinstance(value, six.integer_types):\n raise ValueError(\"num_retries: pass an integer\")\n\n if value < 0:\n raise ValueError(\n 'Cannot have negative value for num_retries')\n self._num_retries = value","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.stream","text":"google/cloud/streaming/transfer.py::_Transfer.stream\n @property\n def stream(self):\n \"\"\"Stream to/from which data is downloaded/uploaded.\n\n :rtype: file-like object\n :returns: The stream that sends/receives data.\n \"\"\"\n return self._stream","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.url","text":"google/cloud/streaming/transfer.py::_Transfer.url\n @property\n def url(self):\n \"\"\"URL to / from which data is downloaded/uploaded.\n\n :rtype: string\n :returns: The URL where data is sent/received.\n \"\"\"\n return self._url","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer._initialize","text":"google/cloud/streaming/transfer.py::_Transfer._initialize\n def _initialize(self, http, url):\n \"\"\"Initialize this download by setting :attr:`http` and :attr`url`.\n\n Allow the user to be able to pre-initialize :attr:`http` by setting\n the value in the constructor; in that case, we ignore the provided\n http.\n\n :type http: :class:`httplib2.Http` (or a worklike) or None.\n :param http: the Http instance to use to make requests.\n\n :type url: string\n :param url: The url for this transfer.\n \"\"\"\n self._ensure_uninitialized()\n if self.http is None:\n self._http = http or httplib2.Http()\n self._url = url","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.initialized","text":"google/cloud/streaming/transfer.py::_Transfer.initialized\n @property\n def initialized(self):\n \"\"\"Has the instance been initialized\n\n :rtype: boolean\n :returns: Boolean indicating if the current transfer\n has been initialized.\n \"\"\"\n return self.url is not None and self.http is not None","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer._ensure_initialized","text":"google/cloud/streaming/transfer.py::_Transfer._ensure_initialized\n def _ensure_initialized(self):\n \"\"\"Helper: assert that the instance is initialized.\n\n :raises: :exc:`google.cloud.streaming.exceptions.TransferInvalidError`\n if the instance is not initialized.\n \"\"\"\n if not self.initialized:\n raise TransferInvalidError(\n 'Cannot use uninitialized %s', type(self).__name__)","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer._ensure_uninitialized","text":"google/cloud/streaming/transfer.py::_Transfer._ensure_uninitialized\n def _ensure_uninitialized(self):\n \"\"\"Helper: assert that the instance is not initialized.\n\n :raises: :exc:`google.cloud.streaming.exceptions.TransferInvalidError`\n if the instance is already initialized.\n \"\"\"\n if self.initialized:\n raise TransferInvalidError(\n 'Cannot re-initialize %s', type(self).__name__)","label":0},{"cid":"google/cloud/streaming/transfer.py::_Transfer.__del__","text":"google/cloud/streaming/transfer.py::_Transfer.__del__\n def __del__(self):\n if self._close_stream:\n self._stream.close()","label":0},{"cid":"google/cloud/streaming/transfer.py::Download.__init__","text":"google/cloud/streaming/transfer.py::Download.__init__\n def __init__(self, stream, **kwds):\n total_size = kwds.pop('total_size', None)\n super(Download, self).__init__(stream, **kwds)\n self._initial_response = None\n self._progress = 0\n self._total_size = total_size\n self._encoding = None","label":0},{"cid":"google/cloud/streaming/transfer.py::Download.from_file","text":"google/cloud/streaming/transfer.py::Download.from_file\n @classmethod\n def from_file(cls, filename, overwrite=False, auto_transfer=True, **kwds):\n \"\"\"Create a new download object from a filename.\n\n :type filename: string\n :param filename: path/filename for the target file\n\n :type overwrite: boolean\n :param overwrite: should an existing file be overwritten\n\n :type auto_transfer: boolean\n :param auto_transfer: should the transfer be started immediately\n\n :type kwds: dict\n :param kwds: keyword arguments: passed\n through to :meth:`_Transfer.__init__()`.\n\n :rtype: :class:`Download`\n :returns: The download initiated from the file passed.\n \"\"\"\n path = os.path.expanduser(filename)\n if os.path.exists(path) and not overwrite:\n raise ValueError(\n 'File %s exists and overwrite not specified' % path)\n return cls(open(path, 'wb'), close_stream=True,\n auto_transfer=auto_transfer, **kwds)","label":0},{"cid":"google/cloud/streaming/transfer.py::Download.from_stream","text":"google/cloud/streaming/transfer.py::Download.from_stream\n @classmethod\n def from_stream(cls, stream, auto_transfer=True, total_size=None, **kwds):\n \"\"\"Create a new Download object from a stream.\n\n :type stream: writable file-like object\n :param stream: the target file\n\n :type total_size: integer or None\n :param total_size: total size of the file to be downloaded\n\n :type auto_transfer: boolean\n :param auto_transfer: should the transfer be started immediately\n\n :type kwds: dict\n :param kwds: keyword arguments: passed\n through to :meth:`_Transfer.__init__()`.\n\n :rtype: :class:`Download`\n :returns: The download initiated from the stream passed.\n \"\"\"\n return cls(stream, auto_transfer=auto_transfer, total_size=total_size,\n **kwds)","label":0},{"cid":"google/cloud/streaming/transfer.py::Download.progress","text":"google/cloud/streaming/transfer.py::Download.progress\n @property\n def progress(self):\n \"\"\"Number of bytes have been downloaded.\n\n :rtype: integer >= 0\n :returns: The number of downloaded bytes.\n \"\"\"\n return self._progress","label":0},{"cid":"google/cloud/streaming/transfer.py::Download.total_size","text":"google/cloud/streaming/transfer.py::Download.total_size\n @property\n def total_size(self):\n \"\"\"Total number of bytes to be downloaded.\n\n :rtype: integer or None\n :returns: The total number of bytes to download.\n \"\"\"\n return self._total_size","label":0},{"cid":"google/cloud/streaming/transfer.py::Download.encoding","text":"google/cloud/streaming/transfer.py::Download.encoding\n @property\n def encoding(self):\n \"\"\"'Content-Encoding' used to transfer the file\n\n :rtype: string or None\n :returns: The encoding of the downloaded content.\n \"\"\"\n return self._encoding","label":0},{"cid":"google/cloud/streaming/transfer.py::Download.__repr__","text":"google/cloud/streaming/transfer.py::Download.__repr__\n def __repr__(self):\n if not self.initialized:\n return 'Download (uninitialized)'\n else:\n return 'Download with %d/%s bytes transferred from url %s' % (\n self.progress, self.total_size, self.url)","label":0},{"cid":"google/cloud/streaming/transfer.py::Download.configure_request","text":"google/cloud/streaming/transfer.py::Download.configure_request\n def configure_request(self, http_request, url_builder):\n \"\"\"Update http_request/url_builder with download-appropriate values.\n\n :type http_request: :class:`~.streaming.http_wrapper.Request`\n :param http_request: the request to be updated\n\n :type url_builder: instance with settable 'query_params' attribute.\n :param url_builder: transfer policy object to be updated\n \"\"\"\n url_builder.query_params['alt'] = 'media'\n http_request.headers['Range'] = 'bytes=0-%d' % (self.chunksize - 1,)","label":0},{"cid":"google/cloud/streaming/transfer.py::Download._set_total","text":"google/cloud/streaming/transfer.py::Download._set_total\n def _set_total(self, info):\n \"\"\"Update 'total_size' based on data from a response.\n\n :type info: mapping\n :param info: response headers\n \"\"\"\n if 'content-range' in info:\n _, _, total = info['content-range'].rpartition('/')\n if total != '*':\n self._total_size = int(total)\n # Note \"total_size is None\" means we don't know it; if no size\n # info was returned on our initial range request, that means we\n # have a 0-byte file. (That last statement has been verified\n # empirically, but is not clearly documented anywhere.)\n if self.total_size is None:\n self._total_size = 0","label":0},{"cid":"google/cloud/streaming/transfer.py::Download.initialize_download","text":"google/cloud/streaming/transfer.py::Download.initialize_download\n def initialize_download(self, http_request, http):\n \"\"\"Initialize this download.\n\n If the instance has :attr:`auto_transfer` enabled, begins the\n download immediately.\n\n :type http_request: :class:`~.streaming.http_wrapper.Request`\n :param http_request: the request to use to initialize this download.\n\n :type http: :class:`httplib2.Http` (or workalike)\n :param http: Http instance for this request.\n \"\"\"\n self._ensure_uninitialized()\n url = http_request.url\n if self.auto_transfer:\n end_byte = self._compute_end_byte(0)\n self._set_range_header(http_request, 0, end_byte)\n response = make_api_request(\n self.bytes_http or http, http_request)\n if response.status_code not in self._ACCEPTABLE_STATUSES:\n raise HttpError.from_response(response)\n self._initial_response = response\n self._set_total(response.info)\n url = response.info.get('content-location', response.request_url)\n self._initialize(http, url)\n # Unless the user has requested otherwise, we want to just\n # go ahead and pump the bytes now.\n if self.auto_transfer:\n self.stream_file(use_chunks=True)","label":0},{"cid":"google/cloud/streaming/transfer.py::Download._normalize_start_end","text":"google/cloud/streaming/transfer.py::Download._normalize_start_end\n def _normalize_start_end(self, start, end=None):\n \"\"\"Validate / fix up byte range.\n\n :type start: integer\n :param start: start byte of the range: if negative, used as an\n offset from the end.\n\n :type end: integer\n :param end: end byte of the range.\n\n :rtype: tuple, (start, end)\n :returns: the normalized start, end pair.\n :raises: :exc:`google.cloud.streaming.exceptions.TransferInvalidError`\n for invalid combinations of start, end.\n \"\"\"\n if end is not None:\n if start < 0:\n raise TransferInvalidError(\n 'Cannot have end index with negative start index')\n elif start >= self.total_size:\n raise TransferInvalidError(\n 'Cannot have start index greater than total size')\n end = min(end, self.total_size - 1)\n if end < start:\n raise TransferInvalidError(\n 'Range requested with end[%s] < start[%s]' % (end, start))\n return start, end\n else:\n if start < 0:\n start = max(0, start + self.total_size)\n return start, self.total_size - 1","label":0},{"cid":"google/cloud/streaming/transfer.py::Download._set_range_header","text":"google/cloud/streaming/transfer.py::Download._set_range_header\n @staticmethod\n def _set_range_header(request, start, end=None):\n \"\"\"Update the 'Range' header in a request to match a byte range.\n\n :type request: :class:`google.cloud.streaming.http_wrapper.Request`\n :param request: the request to update\n\n :type start: integer\n :param start: start byte of the range: if negative, used as an\n offset from the end.\n\n :type end: integer\n :param end: end byte of the range.\n \"\"\"\n if start < 0:\n request.headers['range'] = 'bytes=%d' % start\n elif end is None:\n request.headers['range'] = 'bytes=%d-' % start\n else:\n request.headers['range'] = 'bytes=%d-%d' % (start, end)","label":0},{"cid":"google/cloud/streaming/transfer.py::Download._compute_end_byte","text":"google/cloud/streaming/transfer.py::Download._compute_end_byte\n def _compute_end_byte(self, start, end=None, use_chunks=True):\n \"\"\"Compute the last byte to fetch for this request.\n\n Based on the HTTP spec for Range and Content-Range.\n\n .. note::\n This is potentially confusing in several ways:\n - the value for the last byte is 0-based, eg \"fetch 10 bytes\n from the beginning\" would return 9 here.\n - if we have no information about size, and don't want to\n use the chunksize, we'll return None.\n\n :type start: integer\n :param start: start byte of the range.\n\n :type end: integer or None\n :param end: suggested last byte of the range.\n\n :type use_chunks: boolean\n :param use_chunks: If False, ignore :attr:`chunksize`.\n\n :rtype: str\n :returns: Last byte to use in a 'Range' header, or None.\n \"\"\"\n end_byte = end\n\n if start < 0 and not self.total_size:\n return end_byte\n\n if use_chunks:\n alternate = start + self.chunksize - 1\n if end_byte is not None:\n end_byte = min(end_byte, alternate)\n else:\n end_byte = alternate\n\n if self.total_size:\n alternate = self.total_size - 1\n if end_byte is not None:\n end_byte = min(end_byte, alternate)\n else:\n end_byte = alternate\n\n return end_byte","label":0},{"cid":"google/cloud/streaming/transfer.py::Download._get_chunk","text":"google/cloud/streaming/transfer.py::Download._get_chunk\n def _get_chunk(self, start, end):\n \"\"\"Retrieve a chunk of the file.\n\n :type start: integer\n :param start: start byte of the range.\n\n :type end: integer or None\n :param end: end byte of the range.\n\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\n :returns: response from the chunk request.\n \"\"\"\n self._ensure_initialized()\n request = Request(url=self.url)\n self._set_range_header(request, start, end=end)\n return make_api_request(\n self.bytes_http, request, retries=self.num_retries)","label":0},{"cid":"google/cloud/streaming/transfer.py::Download._process_response","text":"google/cloud/streaming/transfer.py::Download._process_response\n def _process_response(self, response):\n \"\"\"Update attribtes and writing stream, based on response.\n\n :type response: :class:`google.cloud.streaming.http_wrapper.Response`\n :param response: response from a download request.\n\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\n :returns: the response\n :raises: :exc:`google.cloud.streaming.exceptions.HttpError` for\n missing / unauthorized responses;\n :exc:`google.cloud.streaming.exceptions.TransferRetryError`\n for other error responses.\n \"\"\"\n if response.status_code not in self._ACCEPTABLE_STATUSES:\n # We distinguish errors that mean we made a mistake in setting\n # up the transfer versus something we should attempt again.\n if response.status_code in (http_client.FORBIDDEN,\n http_client.NOT_FOUND):\n raise HttpError.from_response(response)\n else:\n raise TransferRetryError(response.content)\n if response.status_code in (http_client.OK,\n http_client.PARTIAL_CONTENT):\n self.stream.write(response.content)\n self._progress += response.length\n if response.info and 'content-encoding' in response.info:\n self._encoding = response.info['content-encoding']\n elif response.status_code == http_client.NO_CONTENT:\n # It's important to write something to the stream for the case\n # of a 0-byte download to a file, as otherwise python won't\n # create the file.\n self.stream.write('')\n return response","label":0},{"cid":"google/cloud/streaming/transfer.py::Download.get_range","text":"google/cloud/streaming/transfer.py::Download.get_range\n def get_range(self, start, end=None, use_chunks=True):\n \"\"\"Retrieve a given byte range from this download, inclusive.\n\n Writes retrieved bytes into :attr:`stream`.\n\n Range must be of one of these three forms:\n * 0 <= start, end = None: Fetch from start to the end of the file.\n * 0 <= start <= end: Fetch the bytes from start to end.\n * start < 0, end = None: Fetch the last -start bytes of the file.\n\n (These variations correspond to those described in the HTTP 1.1\n protocol for range headers in RFC 2616, sec. 14.35.1.)\n\n :type start: integer\n :param start: Where to start fetching bytes. (See above.)\n\n :type end: integer or ``None``\n :param end: Where to stop fetching bytes. (See above.)\n\n :type use_chunks: boolean\n :param use_chunks: If False, ignore :attr:`chunksize`\n and fetch this range in a single request.\n If True, streams via chunks.\n\n :raises: :exc:`google.cloud.streaming.exceptions.TransferRetryError`\n if a request returns an empty response.\n \"\"\"\n self._ensure_initialized()\n progress_end_normalized = False\n if self.total_size is not None:\n progress, end_byte = self._normalize_start_end(start, end)\n progress_end_normalized = True\n else:\n progress = start\n end_byte = end\n while (not progress_end_normalized or end_byte is None or\n progress <= end_byte):\n end_byte = self._compute_end_byte(progress, end=end_byte,\n use_chunks=use_chunks)\n response = self._get_chunk(progress, end_byte)\n if not progress_end_normalized:\n self._set_total(response.info)\n progress, end_byte = self._normalize_start_end(start, end)\n progress_en","label":0},{"cid":"google/cloud/streaming/transfer.py::Download.stream_file","text":"google/cloud/streaming/transfer.py::Download.stream_file\n def stream_file(self, use_chunks=True):\n \"\"\"Stream the entire download.\n\n Writes retrieved bytes into :attr:`stream`.\n\n :type use_chunks: boolean\n :param use_chunks: If False, ignore :attr:`chunksize`\n and stream this download in a single request.\n If True, streams via chunks.\n \"\"\"\n self._ensure_initialized()\n while True:\n if self._initial_response is not None:\n response = self._initial_response\n self._initial_response = None\n else:\n end_byte = self._compute_end_byte(self.progress,\n use_chunks=use_chunks)\n response = self._get_chunk(self.progress, end_byte)\n if self.total_size is None:\n self._set_total(response.info)\n response = self._process_response(response)\n if (response.status_code == http_client.OK or\n self.progress >= self.total_size):\n break","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.__init__","text":"google/cloud/streaming/transfer.py::Upload.__init__\n def __init__(self, stream, mime_type, total_size=None, http=None,\n close_stream=False, auto_transfer=True,\n **kwds):\n super(Upload, self).__init__(\n stream, close_stream=close_stream, auto_transfer=auto_transfer,\n http=http, **kwds)\n self._final_response = None\n self._server_chunk_granularity = None\n self._complete = False\n self._mime_type = mime_type\n self._progress = 0\n self._strategy = None\n self._total_size = total_size","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.from_file","text":"google/cloud/streaming/transfer.py::Upload.from_file\n @classmethod\n def from_file(cls, filename, mime_type=None, auto_transfer=True, **kwds):\n \"\"\"Create a new Upload object from a filename.\n\n :type filename: string\n :param filename: path/filename to the file being uploaded\n\n :type mime_type: string\n :param mime_type: MIMEtype of the file being uploaded\n\n :type auto_transfer: boolean or None\n :param auto_transfer: should the transfer be started immediately\n\n :type kwds: dict\n :param kwds: keyword arguments: passed\n through to :meth:`_Transfer.__init__()`.\n\n :rtype: :class:`Upload`\n :returns: The upload initiated from the file passed.\n \"\"\"\n path = os.path.expanduser(filename)\n if not mime_type:\n mime_type, _ = mimetypes.guess_type(path)\n if mime_type is None:\n raise ValueError(\n 'Could not guess mime type for %s' % path)\n size = os.stat(path).st_size\n return cls(open(path, 'rb'), mime_type, total_size=size,\n close_stream=True, auto_transfer=auto_transfer, **kwds)","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.from_stream","text":"google/cloud/streaming/transfer.py::Upload.from_stream\n @classmethod\n def from_stream(cls, stream, mime_type,\n total_size=None, auto_transfer=True, **kwds):\n \"\"\"Create a new Upload object from a stream.\n\n :type stream: writable file-like object\n :param stream: the target file\n\n :type mime_type: string\n :param mime_type: MIMEtype of the file being uploaded\n\n :type total_size: integer or None\n :param total_size: Size of the file being uploaded\n\n :type auto_transfer: boolean or None\n :param auto_transfer: should the transfer be started immediately\n\n :type kwds: dict\n :param kwds: keyword arguments: passed\n through to :meth:`_Transfer.__init__()`.\n\n :rtype: :class:`Upload`\n :returns: The upload initiated from the stream passed.\n \"\"\"\n if mime_type is None:\n raise ValueError(\n 'No mime_type specified for stream')\n return cls(stream, mime_type, total_size=total_size,\n close_stream=False, auto_transfer=auto_transfer, **kwds)","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.complete","text":"google/cloud/streaming/transfer.py::Upload.complete\n @property\n def complete(self):\n \"\"\"Has the entire stream been uploaded.\n\n :rtype: boolean\n :returns: Boolean indicated if the upload is complete.\n \"\"\"\n return self._complete","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.mime_type","text":"google/cloud/streaming/transfer.py::Upload.mime_type\n @property\n def mime_type(self):\n \"\"\"MIMEtype of the file being uploaded.\n\n :rtype: string\n :returns: The mime-type of the upload.\n \"\"\"\n return self._mime_type","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.progress","text":"google/cloud/streaming/transfer.py::Upload.progress\n @property\n def progress(self):\n \"\"\"Bytes uploaded so far\n\n :rtype: integer\n :returns: The amount uploaded so far.\n \"\"\"\n return self._progress","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.strategy","text":"google/cloud/streaming/transfer.py::Upload.strategy\n @property\n def strategy(self):\n \"\"\"Upload strategy to use\n\n :rtype: string or None\n :returns: The strategy used to upload the data.\n \"\"\"\n return self._strategy","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.strategy","text":"google/cloud/streaming/transfer.py::Upload.strategy\n @strategy.setter\n def strategy(self, value):\n \"\"\"Update upload strategy to use\n\n :type value: string (one of :data:`SIMPLE_UPLOAD` or\n :data:`RESUMABLE_UPLOAD`)\n\n :raises: :exc:`ValueError` if value is not one of the two allowed\n strings.\n \"\"\"\n if value not in (SIMPLE_UPLOAD, RESUMABLE_UPLOAD):\n raise ValueError((\n 'Invalid value \"%s\" for upload strategy, must be one of '\n '\"simple\" or \"resumable\".') % value)\n self._strategy = value","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.total_size","text":"google/cloud/streaming/transfer.py::Upload.total_size\n @property\n def total_size(self):\n \"\"\"Total size of the stream to be uploaded.\n\n :rtype: integer or None\n :returns: The total size to be uploaded.\n \"\"\"\n return self._total_size","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.total_size","text":"google/cloud/streaming/transfer.py::Upload.total_size\n @total_size.setter\n def total_size(self, value):\n \"\"\"Update total size of the stream to be uploaded.\n\n :type value: integer or None\n :param value: the size\n \"\"\"\n self._ensure_uninitialized()\n self._total_size = value","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.__repr__","text":"google/cloud/streaming/transfer.py::Upload.__repr__\n def __repr__(self):\n if not self.initialized:\n return 'Upload (uninitialized)'\n else:\n return 'Upload with %d/%s bytes transferred for url %s' % (\n self.progress, self.total_size or '???', self.url)","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload._set_default_strategy","text":"google/cloud/streaming/transfer.py::Upload._set_default_strategy\n def _set_default_strategy(self, upload_config, http_request):\n \"\"\"Determine and set the default upload strategy for this upload.\n\n We generally prefer simple or multipart, unless we're forced to\n use resumable. This happens when any of (1) the upload is too\n large, (2) the simple endpoint doesn't support multipart requests\n and we have metadata, or (3) there is no simple upload endpoint.\n\n :type upload_config: instance w/ ``max_size`` and ``accept``\n attributes\n :param upload_config: Configuration for the upload endpoint.\n\n :type http_request: :class:`~.streaming.http_wrapper.Request`\n :param http_request: The associated http request.\n \"\"\"\n if upload_config.resumable_path is None:\n self.strategy = SIMPLE_UPLOAD\n if self.strategy is not None:\n return\n strategy = SIMPLE_UPLOAD\n if (self.total_size is not None and\n self.total_size > RESUMABLE_UPLOAD_THRESHOLD):\n strategy = RESUMABLE_UPLOAD\n if http_request.body and not upload_config.simple_multipart:\n strategy = RESUMABLE_UPLOAD\n if not upload_config.simple_path:\n strategy = RESUMABLE_UPLOAD\n self.strategy = strategy","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.configure_request","text":"google/cloud/streaming/transfer.py::Upload.configure_request\n def configure_request(self, upload_config, http_request, url_builder):\n \"\"\"Configure the request and url for this upload.\n\n :type upload_config: instance w/ ``max_size`` and ``accept``\n attributes\n :param upload_config: transfer policy object to be queried\n\n :type http_request: :class:`~.streaming.http_wrapper.Request`\n :param http_request: the request to be updated\n\n :type url_builder: instance with settable 'relative_path' and\n 'query_params' attributes.\n :param url_builder: transfer policy object to be updated\n\n :raises: :exc:`ValueError` if the requested upload is too big,\n or does not have an acceptable MIME type.\n \"\"\"\n # Validate total_size vs. max_size\n if (self.total_size and upload_config.max_size and\n self.total_size > upload_config.max_size):\n raise ValueError(\n 'Upload too big: %s larger than max size %s' % (\n self.total_size, upload_config.max_size))\n # Validate mime type\n if not acceptable_mime_type(upload_config.accept, self.mime_type):\n raise ValueError(\n 'MIME type %s does not match any accepted MIME ranges %s' % (\n self.mime_type, upload_config.accept))\n\n self._set_default_strategy(upload_config, http_request)\n if self.strategy == SIMPLE_UPLOAD:\n url_builder.relative_path = upload_config.simple_path\n if http_request.body:\n url_builder.query_params['uploadType'] = 'multipart'\n self._configure_multipart_request(http_request)\n else:\n url_builder.query_params['uploadType'] = 'media'\n self._configure_media_request(http_request)\n else:\n url_builder.relative_path = upload_config.resumable_path\n u","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload._configure_media_request","text":"google/cloud/streaming/transfer.py::Upload._configure_media_request\n def _configure_media_request(self, http_request):\n \"\"\"Helper for 'configure_request': set up simple request.\"\"\"\n http_request.headers['content-type'] = self.mime_type\n http_request.body = self.stream.read()\n http_request.loggable_body = '<media body>'","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload._configure_multipart_request","text":"google/cloud/streaming/transfer.py::Upload._configure_multipart_request\n def _configure_multipart_request(self, http_request):\n \"\"\"Helper for 'configure_request': set up multipart request.\"\"\"\n # This is a multipart/related upload.\n msg_root = mime_multipart.MIMEMultipart('related')\n # msg_root should not write out its own headers\n setattr(msg_root, '_write_headers', lambda self: None)\n\n # attach the body as one part\n msg = mime_nonmultipart.MIMENonMultipart(\n *http_request.headers['content-type'].split('/'))\n msg.set_payload(http_request.body)\n msg_root.attach(msg)\n\n # attach the media as the second part\n msg = mime_nonmultipart.MIMENonMultipart(*self.mime_type.split('/'))\n msg['Content-Transfer-Encoding'] = 'binary'\n msg.set_payload(self.stream.read())\n msg_root.attach(msg)\n\n # NOTE: generate multipart message as bytes, not text\n stream = six.BytesIO()\n if six.PY3: # pragma: NO COVER Python3\n generator_class = email_generator.BytesGenerator\n else:\n generator_class = email_generator.Generator\n generator = generator_class(stream, mangle_from_=False)\n generator.flatten(msg_root, unixfrom=False)\n http_request.body = stream.getvalue()\n\n multipart_boundary = msg_root.get_boundary()\n http_request.headers['content-type'] = (\n 'multipart/related; boundary=\"%s\"' % multipart_boundary)\n\n boundary_bytes = _to_bytes(multipart_boundary)\n body_components = http_request.body.split(boundary_bytes)\n headers, _, _ = body_components[-2].partition(b'\\n\\n')\n body_components[-2] = b'\\n\\n'.join([headers, b'<media body>\\n\\n--'])\n http_request.loggable_body = boundary_bytes.join(body_components)","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload._configure_resumable_request","text":"google/cloud/streaming/transfer.py::Upload._configure_resumable_request\n def _configure_resumable_request(self, http_request):\n \"\"\"Helper for 'configure_request': set up resumable request.\"\"\"\n http_request.headers['X-Upload-Content-Type'] = self.mime_type\n if self.total_size is not None:\n http_request.headers[\n 'X-Upload-Content-Length'] = str(self.total_size)","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.refresh_upload_state","text":"google/cloud/streaming/transfer.py::Upload.refresh_upload_state\n def refresh_upload_state(self):\n \"\"\"Refresh the state of a resumable upload via query to the back-end.\n \"\"\"\n if self.strategy != RESUMABLE_UPLOAD:\n return\n self._ensure_initialized()\n # NOTE: Per RFC 2616[1]/7231[2], a 'PUT' request is inappropriate\n # here: it is intended to be used to replace the entire\n # resource, not to query for a status.\n #\n # If the back-end doesn't provide a way to query for this state\n # via a 'GET' request, somebody should be spanked.\n #\n # The violation is documented[3].\n #\n # [1] http://www.w3.org/Protocols/rfc2616/rfc2616-sec9.html#sec9.6\n # [2] http://tools.ietf.org/html/rfc7231#section-4.3.4\n # [3]\n # https://cloud.google.com/storage/docs/json_api/v1/how-tos/upload#resume-upload\n refresh_request = Request(\n url=self.url, http_method='PUT',\n headers={'Content-Range': 'bytes */*'})\n refresh_response = make_api_request(\n self.http, refresh_request, redirections=0,\n retries=self.num_retries)\n range_header = self._get_range_header(refresh_response)\n if refresh_response.status_code in (http_client.OK,\n http_client.CREATED):\n self._complete = True\n self._progress = self.total_size\n self.stream.seek(self.progress)\n # If we're finished, the refresh response will contain the metadata\n # originally requested. Cache it so it can be returned in\n # StreamInChunks.\n self._final_response = refresh_response\n elif refresh_response.status_code == RESUME_INCOMPLETE:\n if range_header is None:\n self._progress = 0\n else:\n self._progress = self._last_byte(range_header) + 1\n ","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload._get_range_header","text":"google/cloud/streaming/transfer.py::Upload._get_range_header\n @staticmethod\n def _get_range_header(response):\n \"\"\"Return a 'Range' header from a response.\n\n :type response: :class:`google.cloud.streaming.http_wrapper.Response`\n :param response: response to be queried\n\n :rtype: string\n :returns: The header used to determine the bytes range.\n \"\"\"\n # NOTE: Per RFC 2616[1]/7233[2][3], 'Range' is a request header,\n # not a response header. If the back-end is actually setting\n # 'Range' on responses, somebody should be spanked: it should\n # be sending 'Content-Range' (including the # '/<length>'\n # trailer).\n #\n # The violation is documented[4].\n #\n # [1] http://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html\n # [2] http://tools.ietf.org/html/rfc7233#section-3.1\n # [3] http://tools.ietf.org/html/rfc7233#section-4.2\n # [4]\n # https://cloud.google.com/storage/docs/json_api/v1/how-tos/upload#chunking\n return response.info.get('Range', response.info.get('range'))","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.initialize_upload","text":"google/cloud/streaming/transfer.py::Upload.initialize_upload\n def initialize_upload(self, http_request, http):\n \"\"\"Initialize this upload from the given http_request.\n\n :type http_request: :class:`~.streaming.http_wrapper.Request`\n :param http_request: the request to be used\n\n :type http: :class:`httplib2.Http` (or workalike)\n :param http: Http instance for this request.\n\n :raises: :exc:`ValueError` if the instance has not been configured\n with a strategy.\n :rtype: :class:`~google.cloud.streaming.http_wrapper.Response`\n :returns: The response if the upload is resumable and auto transfer\n is not used.\n \"\"\"\n if self.strategy is None:\n raise ValueError(\n 'No upload strategy set; did you call configure_request?')\n if self.strategy != RESUMABLE_UPLOAD:\n return\n self._ensure_uninitialized()\n http_response = make_api_request(http, http_request,\n retries=self.num_retries)\n if http_response.status_code != http_client.OK:\n raise HttpError.from_response(http_response)\n\n granularity = http_response.info.get('X-Goog-Upload-Chunk-Granularity')\n if granularity is not None:\n granularity = int(granularity)\n self._server_chunk_granularity = granularity\n url = http_response.info['location']\n self._initialize(http, url)\n\n # Unless the user has requested otherwise, we want to just\n # go ahead and pump the bytes now.\n if self.auto_transfer:\n return self.stream_file(use_chunks=True)\n else:\n return http_response","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload._last_byte","text":"google/cloud/streaming/transfer.py::Upload._last_byte\n @staticmethod\n def _last_byte(range_header):\n \"\"\"Parse the last byte from a 'Range' header.\n\n :type range_header: string\n :param range_header: 'Range' header value per RFC 2616/7233\n\n :rtype: int\n :returns: The last byte from a range header.\n \"\"\"\n _, _, end = range_header.partition('-')\n return int(end)","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload._validate_chunksize","text":"google/cloud/streaming/transfer.py::Upload._validate_chunksize\n def _validate_chunksize(self, chunksize=None):\n \"\"\"Validate chunksize against server-specified granularity.\n\n Helper for :meth:`stream_file`.\n\n :type chunksize: integer or None\n :param chunksize: the chunk size to be tested.\n\n :raises: :exc:`ValueError` if ``chunksize`` is not a multiple\n of the server-specified granulariy.\n \"\"\"\n if self._server_chunk_granularity is None:\n return\n chunksize = chunksize or self.chunksize\n if chunksize % self._server_chunk_granularity:\n raise ValueError(\n 'Server requires chunksize to be a multiple of %d',\n self._server_chunk_granularity)","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload.stream_file","text":"google/cloud/streaming/transfer.py::Upload.stream_file\n def stream_file(self, use_chunks=True):\n \"\"\"Upload the stream.\n\n :type use_chunks: boolean\n :param use_chunks: If False, send the stream in a single request.\n Otherwise, send it in chunks.\n\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\n :returns: The response for the final request made.\n \"\"\"\n if self.strategy != RESUMABLE_UPLOAD:\n raise ValueError(\n 'Cannot stream non-resumable upload')\n # final_response is set if we resumed an already-completed upload.\n response = self._final_response\n send_func = self._send_chunk if use_chunks else self._send_media_body\n if use_chunks:\n self._validate_chunksize(self.chunksize)\n self._ensure_initialized()\n while not self.complete:\n response = send_func(self.stream.tell())\n if response.status_code in (http_client.OK, http_client.CREATED):\n self._complete = True\n break\n self._progress = self._last_byte(response.info['range'])\n if self.progress + 1 != self.stream.tell():\n raise CommunicationError(\n 'Failed to transfer all bytes in chunk, upload paused at '\n 'byte %d' % self.progress)\n if self.complete and hasattr(self.stream, 'seek'):\n if not hasattr(self.stream, 'seekable') or self.stream.seekable():\n current_pos = self.stream.tell()\n self.stream.seek(0, os.SEEK_END)\n end_pos = self.stream.tell()\n self.stream.seek(current_pos)\n if current_pos != end_pos:\n raise TransferInvalidError(\n 'Upload complete with %s '\n 'additional bytes left in stream' %\n (int(end_pos) - int(current_pos)))\n return response","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload._send_media_request","text":"google/cloud/streaming/transfer.py::Upload._send_media_request\n def _send_media_request(self, request, end):\n \"\"\"Peform API upload request.\n\n Helper for _send_media_body & _send_chunk:\n\n :type request: :class:`google.cloud.streaming.http_wrapper.Request`\n :param request: the request to upload\n\n :type end: integer\n :param end: end byte of the to be uploaded\n\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\n :returns: the response\n :raises: :exc:`~.streaming.exceptions.HttpError` if the status\n code from the response indicates an error.\n \"\"\"\n response = make_api_request(\n self.bytes_http, request, retries=self.num_retries)\n if response.status_code not in (http_client.OK, http_client.CREATED,\n RESUME_INCOMPLETE):\n # We want to reset our state to wherever the server left us\n # before this failed request, and then raise.\n self.refresh_upload_state()\n raise HttpError.from_response(response)\n if response.status_code == RESUME_INCOMPLETE:\n last_byte = self._last_byte(\n self._get_range_header(response))\n if last_byte + 1 != end:\n self.stream.seek(last_byte)\n return response","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload._send_media_body","text":"google/cloud/streaming/transfer.py::Upload._send_media_body\n def _send_media_body(self, start):\n \"\"\"Send the entire stream in a single request.\n\n Helper for :meth:`stream_file`:\n\n :type start: integer\n :param start: start byte of the range.\n\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\n :returns: The response from the media upload request.\n \"\"\"\n self._ensure_initialized()\n if self.total_size is None:\n raise TransferInvalidError(\n 'Total size must be known for SendMediaBody')\n body_stream = StreamSlice(self.stream, self.total_size - start)\n\n request = Request(url=self.url, http_method='PUT', body=body_stream)\n request.headers['Content-Type'] = self.mime_type\n if start == self.total_size:\n # End of an upload with 0 bytes left to send; just finalize.\n range_string = 'bytes */%s' % self.total_size\n else:\n range_string = 'bytes %s-%s/%s' % (start, self.total_size - 1,\n self.total_size)\n\n request.headers['Content-Range'] = range_string\n\n return self._send_media_request(request, self.total_size)","label":0},{"cid":"google/cloud/streaming/transfer.py::Upload._send_chunk","text":"google/cloud/streaming/transfer.py::Upload._send_chunk\n def _send_chunk(self, start):\n \"\"\"Send a chunk of the stream.\n\n Helper for :meth:`stream_file`:\n\n :type start: integer\n :param start: start byte of the range.\n\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\n :returns: The response from the chunked upload request.\n \"\"\"\n self._ensure_initialized()\n no_log_body = self.total_size is None\n if self.total_size is None:\n # For the streaming resumable case, we need to detect when\n # we're at the end of the stream.\n body_stream = BufferedStream(\n self.stream, start, self.chunksize)\n end = body_stream.stream_end_position\n if body_stream.stream_exhausted:\n self._total_size = end\n # Here, change body_stream from a stream to a string object,\n # which means reading a chunk into memory. This works around\n # https://code.google.com/p/httplib2/issues/detail?id=176 which can\n # cause httplib2 to skip bytes on 401's for file objects.\n body_stream = body_stream.read(self.chunksize)\n else:\n end = min(start + self.chunksize, self.total_size)\n body_stream = StreamSlice(self.stream, end - start)\n request = Request(url=self.url, http_method='PUT', body=body_stream)\n request.headers['Content-Type'] = self.mime_type\n if no_log_body:\n # Disable logging of streaming body.\n request.loggable_body = '<media body>'\n if self.total_size is None:\n # Streaming resumable upload case, unknown total size.\n range_string = 'bytes %s-%s/*' % (start, end - 1)\n elif end == start:\n # End of an upload with 0 bytes left to send; just finalize.\n range_string = 'bytes */%s' % self.total_size\n else:\n # Normal resumable upload case with known","label":0},{"cid":"google/cloud/streaming/util.py::calculate_wait_for_retry","text":"google/cloud/streaming/util.py::calculate_wait_for_retry\ndef calculate_wait_for_retry(retry_attempt):\n \"\"\"Calculate the amount of time to wait before a retry attempt.\n\n Wait time grows exponentially with the number of attempts. A\n random amount of jitter is added to spread out retry attempts from\n different clients.\n\n :type retry_attempt: integer\n :param retry_attempt: Retry attempt counter.\n\n :rtype: integer\n :returns: Number of seconds to wait before retrying request.\n \"\"\"\n wait_time = 2 ** retry_attempt\n max_jitter = wait_time / 4.0\n wait_time += random.uniform(-max_jitter, max_jitter)\n return max(1, min(wait_time, _MAX_RETRY_WAIT))","label":0},{"cid":"google/cloud/streaming/util.py::acceptable_mime_type","text":"google/cloud/streaming/util.py::acceptable_mime_type\ndef acceptable_mime_type(accept_patterns, mime_type):\n \"\"\"Check that ``mime_type`` matches one of ``accept_patterns``.\n\n Note that this function assumes that all patterns in accept_patterns\n will be simple types of the form \"type/subtype\", where one or both\n of these can be \"*\". We do not support parameters (i.e. \"; q=\") in\n patterns.\n\n :type accept_patterns: list of string\n :param accept_patterns: acceptable MIME types.\n\n :type mime_type: string\n :param mime_type: the MIME being checked\n\n :rtype: boolean\n :returns: True if the supplied MIME type matches at least one of the\n patterns, else False.\n \"\"\"\n if '/' not in mime_type:\n raise ValueError(\n 'Invalid MIME type: \"%s\"' % mime_type)\n unsupported_patterns = [p for p in accept_patterns if ';' in p]\n if unsupported_patterns:\n raise ValueError(\n 'MIME patterns with parameter unsupported: \"%s\"' % ', '.join(\n unsupported_patterns))\n\n def _match(pattern, mime_type):\n \"\"\"Return True iff mime_type is acceptable for pattern.\"\"\"\n return all(accept in ('*', provided) for accept, provided\n in zip(pattern.split('/'), mime_type.split('/')))\n\n return any(_match(pattern, mime_type) for pattern in accept_patterns)","label":0},{"cid":"google/cloud/streaming/util.py::acceptable_mime_type._match","text":"google/cloud/streaming/util.py::acceptable_mime_type._match\n def _match(pattern, mime_type):\n \"\"\"Return True iff mime_type is acceptable for pattern.\"\"\"\n return all(accept in ('*', provided) for accept, provided\n in zip(pattern.split('/'), mime_type.split('/')))","label":0},{"cid":"google/cloud/translate/client.py::Client.__init__","text":"google/cloud/translate/client.py::Client.__init__\n def __init__(self, api_key, http=None, target_language=ENGLISH_ISO_639):\n self.api_key = api_key\n if http is None:\n http = httplib2.Http()\n self.connection = Connection(http=http)\n self.target_language = target_language","label":0},{"cid":"google/cloud/translate/client.py::Client.get_languages","text":"google/cloud/translate/client.py::Client.get_languages\n def get_languages(self, target_language=None):\n \"\"\"Get list of supported languages for translation.\n\n Response\n\n See: https://cloud.google.com/translate/v2/\\\n discovering-supported-languages-with-rest\n\n :type target_language: str\n :param target_language: (Optional) The language used to localize\n returned language names. Defaults to the\n target language on the current client.\n\n :rtype: list\n :returns: List of dictionaries. Each dictionary contains a supported\n ISO 639-1 language code (using the dictionary key\n ``language``). If ``target_language`` is passed, each\n dictionary will also contain the name of each supported\n language (localized to the target language).\n \"\"\"\n query_params = {'key': self.api_key}\n if target_language is None:\n target_language = self.target_language\n if target_language is not None:\n query_params['target'] = target_language\n response = self.connection.api_request(\n method='GET', path='/languages', query_params=query_params)\n return response.get('data', {}).get('languages', ())","label":0},{"cid":"google/cloud/translate/client.py::Client.detect_language","text":"google/cloud/translate/client.py::Client.detect_language\n def detect_language(self, values):\n \"\"\"Detect the language of a string or list of strings.\n\n See: https://cloud.google.com/translate/v2/\\\n detecting-language-with-rest\n\n :type values: str or list\n :param values: String or list of strings that will have\n language detected.\n\n :rtype: str or list\n :returns: A list of dictionaries for each queried value. Each\n dictionary typically contains three keys\n\n * ``confidence``: The confidence in language detection, a\n float between 0 and 1.\n * ``input``: The corresponding input value.\n * ``language``: The detected language (as an ISO 639-1\n language code).\n\n though the key ``confidence`` may not always be present.\n\n If only a single value is passed, then only a single\n dictionary will be returned.\n :raises: :class:`ValueError <exceptions.ValueError>` if the number of\n detections is not equal to the number of values.\n :class:`ValueError <exceptions.ValueError>` if a value\n produces a list of detections with 0 or multiple results\n in it.\n \"\"\"\n single_value = False\n if isinstance(values, six.string_types):\n single_value = True\n values = [values]\n\n query_params = [('key', self.api_key)]\n query_params.extend(('q', _to_bytes(value, 'utf-8'))\n for value in values)\n response = self.connection.api_request(\n method='GET', path='/detect', query_params=query_params)\n detections = response.get('data', {}).get('detections', ())\n\n if len(values) != len(detections):\n raise ValueError('Expected same number of values and detections',\n value","label":0},{"cid":"google/cloud/translate/client.py::Client.translate","text":"google/cloud/translate/client.py::Client.translate\n def translate(self, values, target_language=None, format_=None,\n source_language=None, customization_ids=()):\n \"\"\"Translate a string or list of strings.\n\n See: https://cloud.google.com/translate/v2/\\\n translating-text-with-rest\n\n :type values: str or list\n :param values: String or list of strings to translate.\n\n :type target_language: str\n :param target_language: The language to translate results into. This\n is required by the API and defaults to\n the target language of the current instance.\n\n :type format_: str\n :param format_: (Optional) One of ``text`` or ``html``, to specify\n if the input text is plain text or HTML.\n\n :type source_language: str\n :param source_language: (Optional) The language of the text to\n be translated.\n\n :type customization_ids: str or list\n :param customization_ids: (Optional) ID or list of customization IDs\n for translation. Sets the ``cid`` parameter\n in the query.\n\n :rtype: str or list list\n :returns: A list of dictionaries for each queried value. Each\n dictionary typically contains three keys (though not\n all will be present in all cases)\n\n * ``detectedSourceLanguage``: The detected language (as an\n ISO 639-1 language code) of the text.\n * ``translatedText``: The translation of the text into the\n target language.\n * ``input``: The corresponding input value.\n\n If only a single value is passed, then only a single\n dictionary will be returned.\n :raises: :class:`ValueError <exceptions.ValueError>` if the number of\n ","label":0},{"cid":"google/cloud/vision/client.py::VisionRequest.__init__","text":"google/cloud/vision/client.py::VisionRequest.__init__\n def __init__(self, image, features):\n self._features = []\n self._image = image\n\n if isinstance(features, list):\n self._features.extend(features)\n elif isinstance(features, Feature):\n self._features.append(features)\n else:\n raise TypeError('Feature or list of Feature classes are required.')","label":0},{"cid":"google/cloud/vision/client.py::VisionRequest.as_dict","text":"google/cloud/vision/client.py::VisionRequest.as_dict\n def as_dict(self):\n \"\"\"Dictionary representation of Image.\"\"\"\n return {\n 'image': self.image.as_dict(),\n 'features': [feature.as_dict() for feature in self.features]\n }","label":0},{"cid":"google/cloud/vision/client.py::VisionRequest.features","text":"google/cloud/vision/client.py::VisionRequest.features\n @property\n def features(self):\n \"\"\"List of Feature objects.\"\"\"\n return self._features","label":0},{"cid":"google/cloud/vision/client.py::VisionRequest.image","text":"google/cloud/vision/client.py::VisionRequest.image\n @property\n def image(self):\n \"\"\"Image object containing image content.\"\"\"\n return self._image","label":0},{"cid":"google/cloud/vision/client.py::Client.annotate","text":"google/cloud/vision/client.py::Client.annotate\n def annotate(self, image, features):\n \"\"\"Annotate an image to discover it's attributes.\n\n :type image: str\n :param image: A string which can be a URL, a Google Cloud Storage path,\n or a byte stream of the image.\n\n :type features: list of :class:`~google.cloud.vision.feature.Feature`\n :param features: The type of detection that the Vision API should\n use to determine image attributes. Pricing is\n based on the number of Feature Types.\n\n See: https://cloud.google.com/vision/docs/pricing\n :rtype: dict\n :returns: List of annotations.\n \"\"\"\n request = VisionRequest(image, features)\n\n data = {'requests': [request.as_dict()]}\n response = self.connection.api_request(method='POST',\n path='/images:annotate',\n data=data)\n\n return response['responses'][0]","label":0},{"cid":"google/cloud/vision/client.py::Client.image","text":"google/cloud/vision/client.py::Client.image\n def image(self, content=None, source_uri=None):\n \"\"\"Get instance of Image using current client.\n\n :type content: bytes\n :param content: Byte stream of an image.\n\n :type source_uri: str\n :param source_uri: Google Cloud Storage URI of image.\n\n :rtype: :class:`~google.cloud.vision.image.Image`\n :returns: Image instance with the current client attached.\n \"\"\"\n return Image(client=self, content=content, source_uri=source_uri)","label":0},{"cid":"google/cloud/vision/color.py::ImagePropertiesAnnotation.__init__","text":"google/cloud/vision/color.py::ImagePropertiesAnnotation.__init__\n def __init__(self, colors):\n self._colors = colors","label":0},{"cid":"google/cloud/vision/color.py::ImagePropertiesAnnotation.from_api_repr","text":"google/cloud/vision/color.py::ImagePropertiesAnnotation.from_api_repr\n @classmethod\n def from_api_repr(cls, response):\n \"\"\"Factory: construct ``ImagePropertiesAnnotation`` from a response.\n\n :type response: dict\n :param response: Dictionary response from Vision API with image\n properties data.\n\n :rtype: :class:`~google.cloud.vision.color.ImagePropertiesAnnotation`.\n :returns: Populated instance of ``ImagePropertiesAnnotation``.\n \"\"\"\n colors = [ColorInformation.from_api_repr(color) for color in\n response['dominantColors']['colors']]\n return cls(colors)","label":0},{"cid":"google/cloud/vision/color.py::ImagePropertiesAnnotation.colors","text":"google/cloud/vision/color.py::ImagePropertiesAnnotation.colors\n @property\n def colors(self):\n \"\"\"Colors in an image.\n\n :rtype: list of :class:`~google.cloud.vision.color.ColorInformation`\n :returns: Populated list of ``ColorInformation``.\n \"\"\"\n return self._colors","label":0},{"cid":"google/cloud/vision/color.py::Color.__init__","text":"google/cloud/vision/color.py::Color.__init__\n def __init__(self, red, green, blue, alpha):\n self._red = red\n self._green = green\n self._blue = blue\n self._alpha = alpha","label":0},{"cid":"google/cloud/vision/color.py::Color.from_api_repr","text":"google/cloud/vision/color.py::Color.from_api_repr\n @classmethod\n def from_api_repr(cls, response):\n \"\"\"Factory: construct a ``Color`` from a Vision API response.\n\n :type response: dict\n :param response: Color from API Response.\n\n :rtype: :class:`~google.cloud.vision.color.Color`\n :returns: Instance of :class:`~google.cloud.vision.color.Color`.\n \"\"\"\n red = response['red']\n green = response['green']\n blue = response['blue']\n alpha = response.get('alpha')\n\n return cls(red, green, blue, alpha)","label":0},{"cid":"google/cloud/vision/color.py::Color.red","text":"google/cloud/vision/color.py::Color.red\n @property\n def red(self):\n \"\"\"Red component of the color.\n\n :rtype: int\n :returns: Red RGB value.\n \"\"\"\n return self._red","label":0},{"cid":"google/cloud/vision/color.py::Color.green","text":"google/cloud/vision/color.py::Color.green\n @property\n def green(self):\n \"\"\"Green component of the color.\n\n :rtype: int\n :returns: Green RGB value.\n \"\"\"\n return self._green","label":0},{"cid":"google/cloud/vision/color.py::Color.blue","text":"google/cloud/vision/color.py::Color.blue\n @property\n def blue(self):\n \"\"\"Blue component of the color.\n\n :rtype: int\n :returns: Blue RGB value.\n \"\"\"\n return self._blue","label":0},{"cid":"google/cloud/vision/color.py::Color.alpha","text":"google/cloud/vision/color.py::Color.alpha\n @property\n def alpha(self):\n \"\"\"Alpha transparency level.\n\n :rtype: float\n :returns: Alpha transparency level.\n \"\"\"\n return self._alpha","label":0},{"cid":"google/cloud/vision/color.py::ColorInformation.__init__","text":"google/cloud/vision/color.py::ColorInformation.__init__\n def __init__(self, color, score, pixel_fraction):\n self._color = color\n self._score = score\n self._pixel_fraction = pixel_fraction","label":0},{"cid":"google/cloud/vision/color.py::ColorInformation.from_api_repr","text":"google/cloud/vision/color.py::ColorInformation.from_api_repr\n @classmethod\n def from_api_repr(cls, response):\n \"\"\"Factory: construct ``ColorInformation`` for a color found.\n\n :type response: dict\n :param response: Color data with extra meta information.\n\n :rtype: :class:`~google.cloud.vision.color.ColorInformation`\n :returns: Instance of ``ColorInformation``.\n \"\"\"\n color = Color.from_api_repr(response['color'])\n score = response['score']\n pixel_fraction = response['pixelFraction']\n\n return cls(color, score, pixel_fraction)","label":0},{"cid":"google/cloud/vision/color.py::ColorInformation.color","text":"google/cloud/vision/color.py::ColorInformation.color\n @property\n def color(self):\n \"\"\"RGB components of the color.\n\n :rtype: :class:`~google.vision.color.Color`\n :returns: Instance of ``Color``.\n \"\"\"\n return self._color","label":0},{"cid":"google/cloud/vision/color.py::ColorInformation.score","text":"google/cloud/vision/color.py::ColorInformation.score\n @property\n def score(self):\n \"\"\"Image-specific score for this color. Value in range [0, 1].\n\n :rtype: float\n :returns: Image score for this color.\n \"\"\"\n return self._score","label":0},{"cid":"google/cloud/vision/color.py::ColorInformation.pixel_fraction","text":"google/cloud/vision/color.py::ColorInformation.pixel_fraction\n @property\n def pixel_fraction(self):\n \"\"\"Stores the fraction of pixels the color occupies in the image.\n\n :rtype: float\n :returns: Pixel fraction value in range [0, 1].\n \"\"\"\n return self._pixel_fraction","label":0},{"cid":"google/cloud/vision/entity.py::EntityAnnotation.__init__","text":"google/cloud/vision/entity.py::EntityAnnotation.__init__\n def __init__(self, bounds, description, locale, locations, mid, score):\n self._bounds = bounds\n self._description = description\n self._locale = locale\n self._locations = locations\n self._mid = mid\n self._score = score","label":0},{"cid":"google/cloud/vision/entity.py::EntityAnnotation.from_api_repr","text":"google/cloud/vision/entity.py::EntityAnnotation.from_api_repr\n @classmethod\n def from_api_repr(cls, response):\n \"\"\"Factory: construct entity from Vision API response.\n\n :type response: dict\n :param response: Dictionary response from Vision API with entity data.\n\n :rtype: :class:`~google.cloud.vision.entity.EntityAnnotation`\n :returns: Instance of ``EntityAnnotation``.\n \"\"\"\n bounds = Bounds.from_api_repr(response.get('boundingPoly'))\n description = response['description']\n locale = response.get('locale', None)\n locations = [LocationInformation.from_api_repr(location)\n for location in response.get('locations', [])]\n mid = response.get('mid', None)\n score = response.get('score', None)\n\n return cls(bounds, description, locale, locations, mid, score)","label":0},{"cid":"google/cloud/vision/entity.py::EntityAnnotation.bounds","text":"google/cloud/vision/entity.py::EntityAnnotation.bounds\n @property\n def bounds(self):\n \"\"\"Bounding polygon of detected image feature.\n\n :rtype: :class:`~google.cloud.vision.geometry.Bounds`\n :returns: Instance of ``Bounds`` with populated vertices.\n \"\"\"\n return self._bounds","label":0},{"cid":"google/cloud/vision/entity.py::EntityAnnotation.description","text":"google/cloud/vision/entity.py::EntityAnnotation.description\n @property\n def description(self):\n \"\"\"Description of feature detected in image.\n\n :rtype: str\n :returns: String description of feature detected in image.\n \"\"\"\n return self._description","label":0},{"cid":"google/cloud/vision/entity.py::EntityAnnotation.locale","text":"google/cloud/vision/entity.py::EntityAnnotation.locale\n @property\n def locale(self):\n \"\"\"The language code for text discovered in an image.\n\n :rtype: str\n :returns: String language code of text found in the image.\n \"\"\"\n return self._locale","label":0},{"cid":"google/cloud/vision/entity.py::EntityAnnotation.locations","text":"google/cloud/vision/entity.py::EntityAnnotation.locations\n @property\n def locations(self):\n \"\"\"Location coordinates landmarks detected.\n\n :rtype: :class:`~google.cloud.vision.geometry.LocationInformation`\n :returns: ``LocationInformation`` populated with latitude and longitude\n of object detected in an image.\n \"\"\"\n return self._locations","label":0},{"cid":"google/cloud/vision/entity.py::EntityAnnotation.mid","text":"google/cloud/vision/entity.py::EntityAnnotation.mid\n @property\n def mid(self):\n \"\"\"MID of feature detected in image.\n\n :rtype: str\n :returns: String MID of feature detected in image.\n \"\"\"\n return self._mid","label":0},{"cid":"google/cloud/vision/entity.py::EntityAnnotation.score","text":"google/cloud/vision/entity.py::EntityAnnotation.score\n @property\n def score(self):\n \"\"\"Overall score of the result. Range [0, 1].\n\n :rtype: float\n :returns: Overall score of the result. Range [0, 1].\n \"\"\"\n return self._score","label":0},{"cid":"google/cloud/vision/face.py::Angles.__init__","text":"google/cloud/vision/face.py::Angles.__init__\n def __init__(self, roll, pan, tilt):\n self._roll = roll\n self._pan = pan\n self._tilt = tilt","label":0},{"cid":"google/cloud/vision/face.py::Angles.from_api_repr","text":"google/cloud/vision/face.py::Angles.from_api_repr\n @classmethod\n def from_api_repr(cls, response):\n \"\"\"Factory: construct the angles from an Vision API response.\n\n :rtype: :class:`~google.cloud.vision.face.Angles`\n :returns: An `Angles` instance with data parsed from `response`.\n \"\"\"\n roll = response['rollAngle']\n pan = response['panAngle']\n tilt = response['tiltAngle']\n\n return cls(roll, pan, tilt)","label":0},{"cid":"google/cloud/vision/face.py::Angles.roll","text":"google/cloud/vision/face.py::Angles.roll\n @property\n def roll(self):\n \"\"\"Roll angle of face.\n\n :rtype: float\n :returns: Roll angle of face in degrees.\n \"\"\"\n return self._roll","label":0},{"cid":"google/cloud/vision/face.py::Angles.pan","text":"google/cloud/vision/face.py::Angles.pan\n @property\n def pan(self):\n \"\"\"Pan angle of face.\n\n :rtype: float\n :returns: Pan angle of face in degrees.\n \"\"\"\n return self._pan","label":0},{"cid":"google/cloud/vision/face.py::Angles.tilt","text":"google/cloud/vision/face.py::Angles.tilt\n @property\n def tilt(self):\n \"\"\"Tilt angle of face.\n\n :rtype: float\n :returns: Tilt angle of face in degrees.\n \"\"\"\n return self._tilt","label":0},{"cid":"google/cloud/vision/face.py::Emotions.__init__","text":"google/cloud/vision/face.py::Emotions.__init__\n def __init__(self, joy_likelihood, sorrow_likelihood,\n surprise_likelihood, anger_likelihood):\n self._joy_likelihood = joy_likelihood\n self._sorrow_likelihood = sorrow_likelihood\n self._surprise_likelihood = surprise_likelihood\n self._anger_likelihood = anger_likelihood","label":0},{"cid":"google/cloud/vision/face.py::Emotions.from_api_repr","text":"google/cloud/vision/face.py::Emotions.from_api_repr\n @classmethod\n def from_api_repr(cls, response):\n \"\"\"Factory: construct `Emotions` from Vision API response.\n\n :type response: dict\n :param response: Response dictionary representing a face.\n\n :rtype: :class:`~google.cloud.vision.face.Emotions`\n :returns: Populated instance of `Emotions`.\n \"\"\"\n joy_likelihood = getattr(Likelihood, response['joyLikelihood'])\n sorrow_likelihood = getattr(Likelihood, response['sorrowLikelihood'])\n surprise_likelihood = getattr(Likelihood,\n response['surpriseLikelihood'])\n anger_likelihood = getattr(Likelihood, response['angerLikelihood'])\n\n return cls(joy_likelihood, sorrow_likelihood, surprise_likelihood,\n anger_likelihood)","label":0},{"cid":"google/cloud/vision/face.py::Emotions.joy_likelihood","text":"google/cloud/vision/face.py::Emotions.joy_likelihood\n @property\n def joy_likelihood(self):\n \"\"\"Likelihood of joy in detected face.\n\n :rtype: str\n :returns: String derived from\n :class:`~google.cloud.vision.face.Likelihood`.\n \"\"\"\n return self._joy_likelihood","label":0},{"cid":"google/cloud/vision/face.py::Emotions.sorrow_likelihood","text":"google/cloud/vision/face.py::Emotions.sorrow_likelihood\n @property\n def sorrow_likelihood(self):\n \"\"\"Likelihood of sorrow in detected face.\n\n :rtype: str\n :returns: String derived from\n :class:`~google.cloud.vision.face.Likelihood`.\n \"\"\"\n return self._sorrow_likelihood","label":0},{"cid":"google/cloud/vision/face.py::Emotions.surprise_likelihood","text":"google/cloud/vision/face.py::Emotions.surprise_likelihood\n @property\n def surprise_likelihood(self):\n \"\"\"Likelihood of surprise in detected face.\n\n :rtype: str\n :returns: String derived from\n :class:`~google.cloud.vision.face.Likelihood`.\n \"\"\"\n return self._surprise_likelihood","label":0},{"cid":"google/cloud/vision/face.py::Emotions.anger_likelihood","text":"google/cloud/vision/face.py::Emotions.anger_likelihood\n @property\n def anger_likelihood(self):\n \"\"\"Likelihood of anger in detected face.\n\n :rtype: str\n :returns: String derived from\n :class:`~google.cloud.vision.face.Likelihood`.\n \"\"\"\n return self._anger_likelihood","label":0},{"cid":"google/cloud/vision/face.py::Face.__init__","text":"google/cloud/vision/face.py::Face.__init__\n def __init__(self, angles, bounds, detection_confidence, emotions,\n fd_bounds, headwear_likelihood, image_properties, landmarks,\n landmarking_confidence):\n self._angles = angles\n self._bounds = bounds\n self._detection_confidence = detection_confidence\n self._emotions = emotions\n self._fd_bounds = fd_bounds\n self._headwear_likelihood = headwear_likelihood\n self._landmarks = landmarks\n self._landmarking_confidence = landmarking_confidence\n self._image_properties = image_properties","label":0},{"cid":"google/cloud/vision/face.py::Face.from_api_repr","text":"google/cloud/vision/face.py::Face.from_api_repr\n @classmethod\n def from_api_repr(cls, response):\n \"\"\"Factory: construct an instance of a Face from an API response\n\n :type response: dict\n :param response: Face annotation dict returned from the Vision API.\n\n :rtype: :class:`~google.cloud.vision.face.Face`\n :returns: A instance of `Face` with data parsed from `response`.\n \"\"\"\n angles = Angles.from_api_repr(response)\n bounds = Bounds.from_api_repr(response['boundingPoly'])\n detection_confidence = response['detectionConfidence']\n emotions = Emotions.from_api_repr(response)\n fd_bounds = FDBounds.from_api_repr(response['fdBoundingPoly'])\n headwear_likelihood = getattr(Likelihood,\n response['headwearLikelihood'])\n image_properties = FaceImageProperties.from_api_repr(response)\n landmarks = Landmarks(response['landmarks'])\n landmarking_confidence = response['landmarkingConfidence']\n\n return cls(angles, bounds, detection_confidence, emotions, fd_bounds,\n headwear_likelihood, image_properties, landmarks,\n landmarking_confidence)","label":0},{"cid":"google/cloud/vision/face.py::Face.angles","text":"google/cloud/vision/face.py::Face.angles\n @property\n def angles(self):\n \"\"\"Accessor to the pan, tilt and roll angles of a Face.\n\n :rtype: :class:`~google.cloud.vision.face.Angles`\n :returns: Pan, tilt and roll angles of the detected face.\n \"\"\"\n\n return self._angles","label":0},{"cid":"google/cloud/vision/face.py::Face.bounds","text":"google/cloud/vision/face.py::Face.bounds\n @property\n def bounds(self):\n \"\"\"Accessor to the bounding poly information of the detected face.\n\n :rtype: :class:`~google.cloud.vision.face.Bounds`\n :returns: An instance of ``Bounds`` which has a list of vertices.\n \"\"\"\n return self._bounds","label":0},{"cid":"google/cloud/vision/face.py::Face.detection_confidence","text":"google/cloud/vision/face.py::Face.detection_confidence\n @property\n def detection_confidence(self):\n \"\"\"Face detection confidence score determined by the Vision API.\n\n :rtype: float\n :returns: Float representation of confidence ranging from 0 to 1.\n \"\"\"\n return self._detection_confidence","label":0},{"cid":"google/cloud/vision/face.py::Face.emotions","text":"google/cloud/vision/face.py::Face.emotions\n @property\n def emotions(self):\n \"\"\"Accessor to the possible emotions expressed in the detected face.\n\n :rtype: :class:`~google.cloud.vision.face.Emotions`\n :returns: An instance of ``Emotions`` with joy, sorrow, anger, surprise\n likelihood.\n \"\"\"\n return self._emotions","label":0},{"cid":"google/cloud/vision/face.py::Face.fd_bounds","text":"google/cloud/vision/face.py::Face.fd_bounds\n @property\n def fd_bounds(self):\n \"\"\"Accessor to the skin area bounding poly of the detected face.\n\n :rtype: :class:`~google.cloud.vision.image.FDBounds`\n :returns: An instance of ``FDBounds`` which has a list of vertices.\n \"\"\"\n return self._fd_bounds","label":0},{"cid":"google/cloud/vision/face.py::Face.headwear_likelihood","text":"google/cloud/vision/face.py::Face.headwear_likelihood\n @property\n def headwear_likelihood(self):\n \"\"\"Headwear likelihood.\n\n :rtype: :class:`~google.cloud.vision.face.Likelihood`\n :returns: String representing the likelihood based on\n :class:`~google.cloud.vision.face.Likelihood`\n \"\"\"\n return self._headwear_likelihood","label":0},{"cid":"google/cloud/vision/face.py::Face.image_properties","text":"google/cloud/vision/face.py::Face.image_properties\n @property\n def image_properties(self):\n \"\"\"Image properties from imaged used in face detection.\n\n :rtype: :class:`~google.cloud.vision.face.FaceImageProperties`\n :returns: ``FaceImageProperties`` object with image properties.\n \"\"\"\n return self._image_properties","label":0},{"cid":"google/cloud/vision/face.py::Face.landmarks","text":"google/cloud/vision/face.py::Face.landmarks\n @property\n def landmarks(self):\n \"\"\"Accessor to the facial landmarks detected in a face.\n\n :rtype: :class:`~google.cloud.vision.face.Landmarks`\n :returns: ``Landmarks`` object with facial landmarks as properies.\n \"\"\"\n return self._landmarks","label":0},{"cid":"google/cloud/vision/face.py::Face.landmarking_confidence","text":"google/cloud/vision/face.py::Face.landmarking_confidence\n @property\n def landmarking_confidence(self):\n \"\"\"Landmarking confidence score determinged by the Vision API.\n\n :rtype: float\n :returns: Float representing the confidence of the Vision API in\n determining the landmarks on a face.\n \"\"\"\n return self._landmarking_confidence","label":0},{"cid":"google/cloud/vision/face.py::FaceImageProperties.__init__","text":"google/cloud/vision/face.py::FaceImageProperties.__init__\n def __init__(self, blurred_likelihood, underexposed_likelihood):\n self._blurred_likelihood = blurred_likelihood\n self._underexposed_likelihood = underexposed_likelihood","label":0},{"cid":"google/cloud/vision/face.py::FaceImageProperties.from_api_repr","text":"google/cloud/vision/face.py::FaceImageProperties.from_api_repr\n @classmethod\n def from_api_repr(cls, response):\n \"\"\"Factory: construct image properties from image.\n\n :rtype: :class:`~google.cloud.vision.face.FaceImageProperties`\n :returns: Instance populated with image property data.\n \"\"\"\n blurred_likelihood = getattr(Likelihood,\n response['blurredLikelihood'])\n underexposed_likelihood = getattr(Likelihood,\n response['underExposedLikelihood'])\n\n return cls(blurred_likelihood, underexposed_likelihood)","label":0},{"cid":"google/cloud/vision/face.py::FaceImageProperties.blurred_likelihood","text":"google/cloud/vision/face.py::FaceImageProperties.blurred_likelihood\n @property\n def blurred_likelihood(self):\n \"\"\"Likelihood of the image being blurred.\n\n :rtype: str\n :returns: String representation derived from\n :class:`~google.cloud.vision.face.Position`.\n \"\"\"\n return self._blurred_likelihood","label":0},{"cid":"google/cloud/vision/face.py::FaceImageProperties.underexposed_likelihood","text":"google/cloud/vision/face.py::FaceImageProperties.underexposed_likelihood\n @property\n def underexposed_likelihood(self):\n \"\"\"Likelihood that the image used for detection was underexposed.\n\n :rtype: str\n :returns: String representation derived from\n :class:`~google.cloud.vision.face.Position`.\n \"\"\"\n return self._underexposed_likelihood","label":0},{"cid":"google/cloud/vision/face.py::Landmark.__init__","text":"google/cloud/vision/face.py::Landmark.__init__\n def __init__(self, position, landmark_type):\n self._position = position\n self._landmark_type = landmark_type","label":0},{"cid":"google/cloud/vision/face.py::Landmark.from_api_repr","text":"google/cloud/vision/face.py::Landmark.from_api_repr\n @classmethod\n def from_api_repr(cls, response_landmark):\n \"\"\"Factory: construct an instance of a Landmark from a response.\n\n :type response_landmark: dict\n :param response_landmark: Landmark representation from Vision API.\n\n :rtype: :class:`~google.cloud.vision.face.Landmark`\n :returns: Populated instance of `Landmark`.\n \"\"\"\n position = Position.from_api_repr(response_landmark['position'])\n landmark_type = getattr(FaceLandmarkTypes, response_landmark['type'])\n return cls(position, landmark_type)","label":0},{"cid":"google/cloud/vision/face.py::Landmark.position","text":"google/cloud/vision/face.py::Landmark.position\n @property\n def position(self):\n \"\"\"Landmark position on face.\n\n :rtype: :class:`~google.cloud.vision.face.Position`\n :returns: Instance of `Position` with landmark coordinates.\n \"\"\"\n return self._position","label":0},{"cid":"google/cloud/vision/face.py::Landmark.landmark_type","text":"google/cloud/vision/face.py::Landmark.landmark_type\n @property\n def landmark_type(self):\n \"\"\"Landmark type of facial feature.\n\n :rtype: str\n :returns: String representation of facial landmark type.\n \"\"\"\n return self._landmark_type","label":0},{"cid":"google/cloud/vision/face.py::Landmarks.__init__","text":"google/cloud/vision/face.py::Landmarks.__init__\n def __init__(self, landmarks):\n for landmark_response in landmarks:\n landmark = Landmark.from_api_repr(landmark_response)\n setattr(self, landmark.landmark_type.lower(), landmark)","label":0},{"cid":"google/cloud/vision/feature.py::Feature.__init__","text":"google/cloud/vision/feature.py::Feature.__init__\n def __init__(self, feature_type, max_results=1):\n try:\n self._feature_type = getattr(FeatureTypes, feature_type)\n except AttributeError:\n raise AttributeError('Feature type passed in cannot be found.')\n self._max_results = int(max_results)","label":0},{"cid":"google/cloud/vision/feature.py::Feature.as_dict","text":"google/cloud/vision/feature.py::Feature.as_dict\n def as_dict(self):\n \"\"\"Generate dictionary for Feature request format.\n\n :rtype: dict\n :returns: Dictionary representation of a\n :class:`~google.cloud.vision.feature.FeatureType`.\n \"\"\"\n return {\n 'type': self.feature_type,\n 'maxResults': self.max_results\n }","label":0},{"cid":"google/cloud/vision/feature.py::Feature.feature_type","text":"google/cloud/vision/feature.py::Feature.feature_type\n @property\n def feature_type(self):\n \"\"\"\"Feature type string.\n\n :rtype: :class:`~google.cloud.vision.feature.FeatureTypes`\n :returns: Instance of\n :class:`~google.cloud.vision.feature.FeatureTypes`\n \"\"\"\n return self._feature_type","label":0},{"cid":"google/cloud/vision/feature.py::Feature.max_results","text":"google/cloud/vision/feature.py::Feature.max_results\n @property\n def max_results(self):\n \"\"\"Maximum number of results for feature type.\n\n :rtype: int\n :returns: Maxium results to be returned.\n \"\"\"\n return self._max_results","label":0},{"cid":"google/cloud/vision/geometry.py::BoundsBase.__init__","text":"google/cloud/vision/geometry.py::BoundsBase.__init__\n def __init__(self, vertices):\n self._vertices = vertices","label":0},{"cid":"google/cloud/vision/geometry.py::BoundsBase.from_api_repr","text":"google/cloud/vision/geometry.py::BoundsBase.from_api_repr\n @classmethod\n def from_api_repr(cls, response_vertices):\n \"\"\"Factory: construct BoundsBase instance from Vision API response.\n\n :type response_vertices: dict\n :param response_vertices: List of vertices.\n\n :rtype: :class:`~google.cloud.vision.geometry.BoundsBase` or None\n :returns: Instance of BoundsBase with populated verticies or None.\n \"\"\"\n if not response_vertices:\n return None\n\n vertices = [Vertex(vertex.get('x', None), vertex.get('y', None)) for\n vertex in response_vertices.get('vertices', [])]\n return cls(vertices)","label":0},{"cid":"google/cloud/vision/geometry.py::BoundsBase.vertices","text":"google/cloud/vision/geometry.py::BoundsBase.vertices\n @property\n def vertices(self):\n \"\"\"List of vertices.\n\n :rtype: list of :class:`~google.cloud.vision.geometry.Vertex`\n :returns: List of populated vertices.\n \"\"\"\n return self._vertices","label":0},{"cid":"google/cloud/vision/geometry.py::LocationInformation.__init__","text":"google/cloud/vision/geometry.py::LocationInformation.__init__\n def __init__(self, latitude, longitude):\n self._latitude = latitude\n self._longitude = longitude","label":0},{"cid":"google/cloud/vision/geometry.py::LocationInformation.from_api_repr","text":"google/cloud/vision/geometry.py::LocationInformation.from_api_repr\n @classmethod\n def from_api_repr(cls, response):\n \"\"\"Factory: construct location information from Vision API response.\n\n :type response: dict\n :param response: Dictionary response of locations.\n\n :rtype: :class:`~google.cloud.vision.geometry.LocationInformation`\n :returns: ``LocationInformation`` with populated latitude and\n longitude.\n \"\"\"\n latitude = response['latLng']['latitude']\n longitude = response['latLng']['longitude']\n return cls(latitude, longitude)","label":0},{"cid":"google/cloud/vision/geometry.py::LocationInformation.latitude","text":"google/cloud/vision/geometry.py::LocationInformation.latitude\n @property\n def latitude(self):\n \"\"\"Latitude coordinate.\n\n :rtype: float\n :returns: Latitude coordinate of location.\n \"\"\"\n return self._latitude","label":0},{"cid":"google/cloud/vision/geometry.py::LocationInformation.longitude","text":"google/cloud/vision/geometry.py::LocationInformation.longitude\n @property\n def longitude(self):\n \"\"\"Longitude coordinate.\n\n :rtype: float\n :returns: Longitude coordinate of location.\n \"\"\"\n return self._longitude","label":0},{"cid":"google/cloud/vision/geometry.py::Position.__init__","text":"google/cloud/vision/geometry.py::Position.__init__\n def __init__(self, x_coordinate, y_coordinate, z_coordinate):\n self._x_coordinate = x_coordinate\n self._y_coordinate = y_coordinate\n self._z_coordinate = z_coordinate","label":0},{"cid":"google/cloud/vision/geometry.py::Position.from_api_repr","text":"google/cloud/vision/geometry.py::Position.from_api_repr\n @classmethod\n def from_api_repr(cls, response_position):\n \"\"\"Factory: construct 3D position from API response.\n\n :rtype: :class:`~google.cloud.vision.geometry.Position`\n :returns: `Position` constructed with 3D points from API response.\n \"\"\"\n x_coordinate = response_position['x']\n y_coordinate = response_position['y']\n z_coordinate = response_position['z']\n return cls(x_coordinate, y_coordinate, z_coordinate)","label":0},{"cid":"google/cloud/vision/geometry.py::Position.x_coordinate","text":"google/cloud/vision/geometry.py::Position.x_coordinate\n @property\n def x_coordinate(self):\n \"\"\"X position coordinate.\n\n :rtype: float\n :returns: X position coordinate.\n \"\"\"\n return self._x_coordinate","label":0},{"cid":"google/cloud/vision/geometry.py::Position.y_coordinate","text":"google/cloud/vision/geometry.py::Position.y_coordinate\n @property\n def y_coordinate(self):\n \"\"\"Y position coordinate.\n\n :rtype: float\n :returns: Y position coordinate.\n \"\"\"\n return self._y_coordinate","label":0},{"cid":"google/cloud/vision/geometry.py::Position.z_coordinate","text":"google/cloud/vision/geometry.py::Position.z_coordinate\n @property\n def z_coordinate(self):\n \"\"\"Z position coordinate.\n\n :rtype: float\n :returns: Z position coordinate.\n \"\"\"\n return self._z_coordinate","label":0},{"cid":"google/cloud/vision/geometry.py::Vertex.__init__","text":"google/cloud/vision/geometry.py::Vertex.__init__\n def __init__(self, x_coordinate, y_coordinate):\n self._x_coordinate = x_coordinate\n self._y_coordinate = y_coordinate","label":0},{"cid":"google/cloud/vision/geometry.py::Vertex.x_coordinate","text":"google/cloud/vision/geometry.py::Vertex.x_coordinate\n @property\n def x_coordinate(self):\n \"\"\"X position coordinate.\n\n :rtype: float\n :returns: X position coordinate.\n \"\"\"\n return self._x_coordinate","label":0},{"cid":"google/cloud/vision/geometry.py::Vertex.y_coordinate","text":"google/cloud/vision/geometry.py::Vertex.y_coordinate\n @property\n def y_coordinate(self):\n \"\"\"Y position coordinate.\n\n :rtype: float\n :returns: Y position coordinate.\n \"\"\"\n return self._y_coordinate","label":0},{"cid":"google/cloud/vision/image.py::Image.__init__","text":"google/cloud/vision/image.py::Image.__init__\n def __init__(self, client, content=None, source_uri=None):\n self.client = client\n self._content = None\n self._source = None\n\n if source_uri:\n self._source = source_uri\n else:\n self._content = b64encode(_to_bytes(content))","label":0},{"cid":"google/cloud/vision/image.py::Image.as_dict","text":"google/cloud/vision/image.py::Image.as_dict\n def as_dict(self):\n \"\"\"Generate dictionary structure for request.\n\n :rtype: dict\n :returns: Dictionary with source information for image.\n \"\"\"\n if self.content:\n return {\n 'content': self.content\n }\n else:\n return {\n 'source': {\n 'gcs_image_uri': self.source\n }\n }","label":0},{"cid":"google/cloud/vision/image.py::Image.content","text":"google/cloud/vision/image.py::Image.content\n @property\n def content(self):\n \"\"\"Base64 encoded image content.\n\n :rtype: str\n :returns: Base64 encoded image bytes.\n \"\"\"\n return self._content","label":0},{"cid":"google/cloud/vision/image.py::Image.source","text":"google/cloud/vision/image.py::Image.source\n @property\n def source(self):\n \"\"\"Google Cloud Storage URI.\n\n :rtype: str\n :returns: String of Google Cloud Storage URI.\n \"\"\"\n return self._source","label":0},{"cid":"google/cloud/vision/image.py::Image._detect_annotation","text":"google/cloud/vision/image.py::Image._detect_annotation\n def _detect_annotation(self, feature):\n \"\"\"Generic method for detecting a single annotation.\n\n :type feature: :class:`~google.cloud.vision.feature.Feature`\n :param feature: The ``Feature`` indication the type of annotation to\n perform.\n\n :rtype: list\n :returns: List of\n :class:`~google.cloud.vision.entity.EntityAnnotation`.\n \"\"\"\n reverse_types = {\n 'LABEL_DETECTION': 'labelAnnotations',\n 'LANDMARK_DETECTION': 'landmarkAnnotations',\n 'LOGO_DETECTION': 'logoAnnotations',\n 'TEXT_DETECTION': 'textAnnotations',\n }\n detected_objects = []\n result = self.client.annotate(self, [feature])\n for response in result[reverse_types[feature.feature_type]]:\n detected_object = EntityAnnotation.from_api_repr(response)\n detected_objects.append(detected_object)\n return detected_objects","label":0},{"cid":"google/cloud/vision/image.py::Image.detect_faces","text":"google/cloud/vision/image.py::Image.detect_faces\n def detect_faces(self, limit=10):\n \"\"\"Detect faces in image.\n\n :type limit: int\n :param limit: The number of faces to try and detect.\n\n :rtype: list\n :returns: List of :class:`~google.cloud.vision.face.Face`.\n \"\"\"\n faces = []\n face_detection_feature = Feature(FeatureTypes.FACE_DETECTION, limit)\n result = self.client.annotate(self, [face_detection_feature])\n for face_response in result['faceAnnotations']:\n face = Face.from_api_repr(face_response)\n faces.append(face)\n\n return faces","label":0},{"cid":"google/cloud/vision/image.py::Image.detect_labels","text":"google/cloud/vision/image.py::Image.detect_labels\n def detect_labels(self, limit=10):\n \"\"\"Detect labels that describe objects in an image.\n\n :type limit: int\n :param limit: The maximum number of labels to try and detect.\n\n :rtype: list\n :returns: List of :class:`~google.cloud.vision.entity.EntityAnnotation`\n \"\"\"\n feature = Feature(FeatureTypes.LABEL_DETECTION, limit)\n return self._detect_annotation(feature)","label":0},{"cid":"google/cloud/vision/image.py::Image.detect_landmarks","text":"google/cloud/vision/image.py::Image.detect_landmarks\n def detect_landmarks(self, limit=10):\n \"\"\"Detect landmarks in an image.\n\n :type limit: int\n :param limit: The maximum number of landmarks to find.\n\n :rtype: list\n :returns: List of\n :class:`~google.cloud.vision.entity.EntityAnnotation`.\n \"\"\"\n feature = Feature(FeatureTypes.LANDMARK_DETECTION, limit)\n return self._detect_annotation(feature)","label":0},{"cid":"google/cloud/vision/image.py::Image.detect_logos","text":"google/cloud/vision/image.py::Image.detect_logos\n def detect_logos(self, limit=10):\n \"\"\"Detect logos in an image.\n\n :type limit: int\n :param limit: The maximum number of logos to find.\n\n :rtype: list\n :returns: List of\n :class:`~google.cloud.vision.entity.EntityAnnotation`.\n \"\"\"\n feature = Feature(FeatureTypes.LOGO_DETECTION, limit)\n return self._detect_annotation(feature)","label":0},{"cid":"google/cloud/vision/image.py::Image.detect_properties","text":"google/cloud/vision/image.py::Image.detect_properties\n def detect_properties(self, limit=10):\n \"\"\"Detect the color properties of an image.\n\n :type limit: int\n :param limit: The maximum number of image properties to find.\n\n :rtype: list\n :returns: List of\n :class:`~google.cloud.vision.color.ImagePropertiesAnnotation`.\n \"\"\"\n feature = Feature(FeatureTypes.IMAGE_PROPERTIES, limit)\n result = self.client.annotate(self, [feature])\n response = result['imagePropertiesAnnotation']\n return ImagePropertiesAnnotation.from_api_repr(response)","label":0},{"cid":"google/cloud/vision/image.py::Image.detect_safe_search","text":"google/cloud/vision/image.py::Image.detect_safe_search\n def detect_safe_search(self, limit=10):\n \"\"\"Retreive safe search properties from an image.\n\n :type limit: int\n :param limit: The number of faces to try and detect.\n\n :rtype: list\n :returns: List of\n :class:`~google.cloud.vision.sage.SafeSearchAnnotation`.\n \"\"\"\n safe_detection_feature = Feature(FeatureTypes.SAFE_SEARCH_DETECTION,\n limit)\n result = self.client.annotate(self, [safe_detection_feature])\n safe_search_response = result['safeSearchAnnotation']\n return SafeSearchAnnotation.from_api_repr(safe_search_response)","label":0},{"cid":"google/cloud/vision/image.py::Image.detect_text","text":"google/cloud/vision/image.py::Image.detect_text\n def detect_text(self, limit=10):\n \"\"\"Detect text in an image.\n\n :type limit: int\n :param limit: The maximum instances of text to find.\n\n :rtype: list\n :returns: List of\n :class:`~google.cloud.vision.entity.EntityAnnotation`.\n \"\"\"\n feature = Feature(FeatureTypes.TEXT_DETECTION, limit)\n return self._detect_annotation(feature)","label":0},{"cid":"google/cloud/vision/safe.py::SafeSearchAnnotation.__init__","text":"google/cloud/vision/safe.py::SafeSearchAnnotation.__init__\n def __init__(self, adult_likelihood, spoof_likelihood, medical_likelihood,\n violence_likelihood):\n self._adult_likelihood = adult_likelihood\n self._spoof_likelihood = spoof_likelihood\n self._medical_likeliehood = medical_likelihood\n self._violence_likelihood = violence_likelihood","label":0},{"cid":"google/cloud/vision/safe.py::SafeSearchAnnotation.from_api_repr","text":"google/cloud/vision/safe.py::SafeSearchAnnotation.from_api_repr\n @classmethod\n def from_api_repr(cls, response):\n \"\"\"Factory: construct SafeSearchAnnotation from Vision API response.\n\n :type response: dict\n :param response: Dictionary response from Vision API with safe search\n data.\n\n :rtype: :class:`~google.cloud.vision.safe.SafeSearchAnnotation`\n :returns: Instance of ``SafeSearchAnnotation``.\n \"\"\"\n adult_likelihood = getattr(Likelihood, response['adult'])\n spoof_likelihood = getattr(Likelihood, response['spoof'])\n medical_likelihood = getattr(Likelihood, response['medical'])\n violence_likelihood = getattr(Likelihood, response['violence'])\n\n return cls(adult_likelihood, spoof_likelihood, medical_likelihood,\n violence_likelihood)","label":0},{"cid":"google/cloud/vision/safe.py::SafeSearchAnnotation.adult","text":"google/cloud/vision/safe.py::SafeSearchAnnotation.adult\n @property\n def adult(self):\n \"\"\"Represents the adult contents likelihood for the image.\n\n :rtype: :class:`~google.cloud.vision.likelihood.Likelihood`\n :returns: ``Likelihood`` of the image containing adult content.\n \"\"\"\n return self._adult_likelihood","label":0},{"cid":"google/cloud/vision/safe.py::SafeSearchAnnotation.spoof","text":"google/cloud/vision/safe.py::SafeSearchAnnotation.spoof\n @property\n def spoof(self):\n \"\"\"The likelihood that an obvious modification was made to the image.\n\n :rtype: :class:`~google.cloud.vision.likelihood.Likelihood`\n :returns: The ``Likelihood`` that an obvious modification was made to\n the image's canonical version to make it appear funny or\n offensive.\n \"\"\"\n return self._spoof_likelihood","label":0},{"cid":"google/cloud/vision/safe.py::SafeSearchAnnotation.medical","text":"google/cloud/vision/safe.py::SafeSearchAnnotation.medical\n @property\n def medical(self):\n \"\"\"Likelihood this is a medical image.\n\n :rtype: :class:`~google.cloud.vision.likelihood.Likelihood`\n :returns: The ``Likelihood`` that the image is medical in origin.\n \"\"\"\n return self._medical_likeliehood","label":0},{"cid":"google/cloud/vision/safe.py::SafeSearchAnnotation.violence","text":"google/cloud/vision/safe.py::SafeSearchAnnotation.violence\n @property\n def violence(self):\n \"\"\"Likeliehood that this image contains violence.\n\n :rtype: :class:`~google.cloud.vision.likelihood.Likelihood`\n :returns: The ``Likelihood`` that the image contains violence.\n \"\"\"\n return self._violence_likelihood","label":0},{"cid":"scripts/make_datastore_grpc.py::get_pb2_contents_with_grpc","text":"scripts/make_datastore_grpc.py::get_pb2_contents_with_grpc\ndef get_pb2_contents_with_grpc():\n \"\"\"Get pb2 lines generated by protoc with gRPC plugin.\n\n :rtype: list\n :returns: A list of lines in the generated file.\n \"\"\"\n temp_dir = tempfile.mkdtemp()\n generated_path = os.path.join(temp_dir, 'google', 'datastore',\n 'v1', 'datastore_pb2.py')\n try:\n return_code = subprocess.call([\n PYTHON_EXECUTABLE,\n '-m',\n 'grpc.tools.protoc',\n '--proto_path',\n PROTOS_DIR,\n '--python_out',\n temp_dir,\n '--grpc_python_out',\n temp_dir,\n PROTO_PATH,\n ])\n if return_code != 0:\n sys.exit(return_code)\n with open(generated_path, 'rb') as file_obj:\n return file_obj.readlines()\n finally:\n shutil.rmtree(temp_dir, ignore_errors=True)","label":0},{"cid":"scripts/make_datastore_grpc.py::get_pb2_contents_without_grpc","text":"scripts/make_datastore_grpc.py::get_pb2_contents_without_grpc\ndef get_pb2_contents_without_grpc():\n \"\"\"Get pb2 lines generated by protoc without gRPC plugin.\n\n :rtype: list\n :returns: A list of lines in the generated file.\n \"\"\"\n temp_dir = tempfile.mkdtemp()\n generated_path = os.path.join(temp_dir, 'google', 'datastore',\n 'v1', 'datastore_pb2.py')\n try:\n return_code = subprocess.call([\n PYTHON_EXECUTABLE,\n '-m',\n 'grpc.tools.protoc',\n '--proto_path',\n PROTOS_DIR,\n '--python_out',\n temp_dir,\n PROTO_PATH,\n ])\n if return_code != 0:\n sys.exit(return_code)\n with open(generated_path, 'rb') as file_obj:\n return file_obj.readlines()\n finally:\n shutil.rmtree(temp_dir, ignore_errors=True)","label":0},{"cid":"scripts/make_datastore_grpc.py::get_pb2_grpc_only","text":"scripts/make_datastore_grpc.py::get_pb2_grpc_only\ndef get_pb2_grpc_only():\n \"\"\"Get pb2 lines that are only in gRPC.\n\n :rtype: list\n :returns: A list of lines that are only in the pb2 file\n generated with the gRPC plugin.\n \"\"\"\n grpc_contents = get_pb2_contents_with_grpc()\n non_grpc_contents = get_pb2_contents_without_grpc()\n\n grpc_only_lines = []\n curr_non_grpc_line = 0\n for line in grpc_contents:\n if line == non_grpc_contents[curr_non_grpc_line]:\n curr_non_grpc_line += 1\n else:\n grpc_only_lines.append(line)\n\n return grpc_only_lines","label":0},{"cid":"scripts/make_datastore_grpc.py::get_pb2_message_types","text":"scripts/make_datastore_grpc.py::get_pb2_message_types\ndef get_pb2_message_types():\n \"\"\"Get message types defined in datastore pb2 file.\n\n :rtype: list\n :returns: A list of names that are defined as message types.\n \"\"\"\n non_grpc_contents = get_pb2_contents_without_grpc()\n result = []\n for line in non_grpc_contents:\n if MESSAGE_SNIPPET in line:\n name, _ = line.split(MESSAGE_SNIPPET)\n result.append(name)\n\n return sorted(result)","label":0},{"cid":"scripts/make_datastore_grpc.py::main","text":"scripts/make_datastore_grpc.py::main\ndef main():\n \"\"\"Write gRPC-only lines to custom module.\"\"\"\n grpc_only_lines = get_pb2_grpc_only()\n with open(GRPC_ONLY_FILE, 'wb') as file_obj:\n # First add imports for public objects in the original.\n file_obj.write('# BEGIN: Imports from datastore_pb2\\n')\n for name in get_pb2_message_types():\n import_line = IMPORT_TEMPLATE % (name,)\n file_obj.write(import_line)\n file_obj.write('# END: Imports from datastore_pb2\\n')\n file_obj.write(''.join(grpc_only_lines))","label":0},{"cid":"scripts/make_operations_grpc.py::get_pb2_contents_with_grpc","text":"scripts/make_operations_grpc.py::get_pb2_contents_with_grpc\ndef get_pb2_contents_with_grpc():\n \"\"\"Get pb2 lines generated by protoc with gRPC plugin.\n\n :rtype: list\n :returns: A list of lines in the generated file.\n \"\"\"\n temp_dir = tempfile.mkdtemp()\n generated_path = os.path.join(temp_dir, 'google', 'longrunning',\n 'operations_pb2.py')\n try:\n return_code = subprocess.call([\n PYTHON_EXECUTABLE,\n '-m',\n 'grpc.tools.protoc',\n '--proto_path',\n PROTOS_DIR,\n '--python_out',\n temp_dir,\n '--grpc_python_out',\n temp_dir,\n PROTO_PATH,\n ])\n if return_code != 0:\n sys.exit(return_code)\n with open(generated_path, 'rb') as file_obj:\n return file_obj.readlines()\n finally:\n shutil.rmtree(temp_dir, ignore_errors=True)","label":0},{"cid":"scripts/make_operations_grpc.py::get_pb2_contents_without_grpc","text":"scripts/make_operations_grpc.py::get_pb2_contents_without_grpc\ndef get_pb2_contents_without_grpc():\n \"\"\"Get pb2 lines generated by protoc without gRPC plugin.\n\n :rtype: list\n :returns: A list of lines in the generated file.\n \"\"\"\n temp_dir = tempfile.mkdtemp()\n generated_path = os.path.join(temp_dir, 'google', 'longrunning',\n 'operations_pb2.py')\n try:\n return_code = subprocess.call([\n PYTHON_EXECUTABLE,\n '-m',\n 'grpc.tools.protoc',\n '--proto_path',\n PROTOS_DIR,\n '--python_out',\n temp_dir,\n PROTO_PATH,\n ])\n if return_code != 0:\n sys.exit(return_code)\n with open(generated_path, 'rb') as file_obj:\n return file_obj.readlines()\n finally:\n shutil.rmtree(temp_dir, ignore_errors=True)","label":0},{"cid":"scripts/make_operations_grpc.py::get_pb2_grpc_only","text":"scripts/make_operations_grpc.py::get_pb2_grpc_only\ndef get_pb2_grpc_only():\n \"\"\"Get pb2 lines that are only in gRPC.\n\n :rtype: list\n :returns: A list of lines that are only in the pb2 file\n generated with the gRPC plugin.\n \"\"\"\n grpc_contents = get_pb2_contents_with_grpc()\n non_grpc_contents = get_pb2_contents_without_grpc()\n\n grpc_only_lines = []\n curr_non_grpc_line = 0\n for line in grpc_contents:\n if line == non_grpc_contents[curr_non_grpc_line]:\n curr_non_grpc_line += 1\n else:\n grpc_only_lines.append(line)\n\n return grpc_only_lines","label":0},{"cid":"scripts/make_operations_grpc.py::main","text":"scripts/make_operations_grpc.py::main\ndef main():\n \"\"\"Write gRPC-only lines to custom module.\"\"\"\n grpc_only_lines = get_pb2_grpc_only()\n with open(GRPC_ONLY_FILE, 'wb') as file_obj:\n file_obj.writelines(grpc_only_lines)","label":0},{"cid":"scripts/pycodestyle_on_repo.py::main","text":"scripts/pycodestyle_on_repo.py::main\ndef main():\n \"\"\"Run pycodestyle on all Python files in the repository.\"\"\"\n git_root = subprocess.check_output(\n ['git', 'rev-parse', '--show-toplevel']).strip()\n os.chdir(git_root)\n python_files = subprocess.check_output(['git', 'ls-files', '*py'])\n python_files = python_files.strip().split()\n\n pycodestyle_command = ['pycodestyle'] + python_files\n status_code = subprocess.call(pycodestyle_command)\n sys.exit(status_code)","label":0},{"cid":"scripts/rewrite_imports.py::transform_old_to_new","text":"scripts/rewrite_imports.py::transform_old_to_new\ndef transform_old_to_new(line, old_module, new_module,\n ignore_import_from=False):\n \"\"\"Transforms from an old module to a new one.\n\n First checks if a line starts with\n \"from {old_module} import ...\"\n then checks if the line contains\n \"import {old_module} ...\"\n then checks if the line starts with (ignoring whitespace)\n \"{old_module} ...\"\n and finally checks if the line contians\n \"'some-dict-key': {old_module} ...\"\n\n In any of these cases, \"{old_module}\" is replaced with \"{new_module}\".\n If none match, nothing is returned.\n\n :type line: str\n :param line: The line to be transformed.\n\n :type old_module: str\n :param old_module: The import to be re-written.\n\n :type new_module: str\n :param new_module: The new location of the re-written import.\n\n :type ignore_import_from: bool\n :param ignore_import_from: Flag to determine if the \"from * import\"\n statements should be ignored.\n\n :rtype: :class:`str` or :data:`NoneType <types.NoneType>`\n :returns: The transformed line if the old module was found, otherwise\n does nothing.\n \"\"\"\n if not ignore_import_from:\n import_from_statement = IMPORT_FROM_TEMPLATE % (old_module,)\n if line.startswith(import_from_statement):\n new_import_from_statement = IMPORT_FROM_TEMPLATE % (new_module,)\n # Only replace the first instance of the import statement.\n return line.replace(import_from_statement,\n new_import_from_statement, 1)\n\n # If the line doesn't start with a \"from * import *\" statement, it\n # may still contain a \"import * ...\" statement.\n import_statement = IMPORT_TEMPLATE % (old_module,)\n if import_statement in line:\n new_import_statement = IMPORT_TEMPLATE % (new_module,)\n # Only replace the first instance of the import statement.\n retu","label":0},{"cid":"scripts/rewrite_imports.py::transform_line","text":"scripts/rewrite_imports.py::transform_line\ndef transform_line(line):\n \"\"\"Transforms an import line in a PB2 module.\n\n If the line is not an import of one of the packages in ``REPLACEMENTS``,\n does nothing and returns the original. Otherwise it replaces the package\n matched with our local package.\n\n :type line: str\n :param line: The line to be transformed.\n\n :rtype: str\n :returns: The transformed line.\n \"\"\"\n # Work around https://github.com/grpc/grpc/issues/7101\n if line == 'import ':\n return ''\n\n for old_module, new_module in REPLACEMENTS.iteritems():\n result = transform_old_to_new(line, old_module, new_module)\n if result is not None:\n return result\n\n # If no matches, there is nothing to transform.\n return line","label":0},{"cid":"scripts/rewrite_imports.py::rewrite_file","text":"scripts/rewrite_imports.py::rewrite_file\ndef rewrite_file(filename):\n \"\"\"Rewrites a given PB2 modules.\n\n :type filename: str\n :param filename: The name of the file to be rewritten.\n \"\"\"\n with open(filename, 'rU') as file_obj:\n content_lines = file_obj.read().split('\\n')\n\n new_content = []\n for line in content_lines:\n new_content.append(transform_line(line))\n\n with open(filename, 'w') as file_obj:\n file_obj.write('\\n'.join(new_content))","label":0},{"cid":"scripts/rewrite_imports.py::main","text":"scripts/rewrite_imports.py::main\ndef main():\n \"\"\"Rewrites all PB2 files.\"\"\"\n for filename in sys.argv[1:]:\n rewrite_file(filename)","label":0},{"cid":"scripts/run_pylint.py::read_config","text":"scripts/run_pylint.py::read_config\ndef read_config(filename):\n \"\"\"Reads pylintrc config onto native ConfigParser object.\"\"\"\n config = ConfigParser.ConfigParser()\n with open(filename, 'r') as file_obj:\n config.readfp(file_obj)\n return config","label":0},{"cid":"scripts/run_pylint.py::make_test_rc","text":"scripts/run_pylint.py::make_test_rc\ndef make_test_rc(base_rc_filename, additions_dict,\n replacements_dict, target_filename):\n \"\"\"Combines a base rc and test additions into single file.\"\"\"\n main_cfg = read_config(base_rc_filename)\n\n # Create fresh config for test, which must extend production.\n test_cfg = ConfigParser.ConfigParser()\n test_cfg._sections = copy.deepcopy(main_cfg._sections)\n\n for section, opts in additions_dict.items():\n curr_section = test_cfg._sections.setdefault(\n section, test_cfg._dict())\n for opt, opt_val in opts.items():\n curr_val = curr_section.get(opt)\n if curr_val is None:\n raise KeyError('Expected to be adding to existing option.')\n curr_val = curr_val.rstrip(',')\n curr_section[opt] = '%s, %s' % (curr_val, opt_val)\n\n for section, opts in replacements_dict.items():\n curr_section = test_cfg._sections.setdefault(\n section, test_cfg._dict())\n for opt, opt_val in opts.items():\n curr_val = curr_section.get(opt)\n if curr_val is None:\n raise KeyError('Expected to be replacing existing option.')\n curr_section[opt] = '%s' % (opt_val,)\n\n with open(target_filename, 'w') as file_obj:\n test_cfg.write(file_obj)","label":0},{"cid":"scripts/run_pylint.py::valid_filename","text":"scripts/run_pylint.py::valid_filename\ndef valid_filename(filename):\n \"\"\"Checks if a file is a Python file and is not ignored.\"\"\"\n for directory in IGNORED_DIRECTORIES:\n if filename.startswith(directory):\n return False\n return (filename.endswith('.py') and\n filename not in IGNORED_FILES)","label":0},{"cid":"scripts/run_pylint.py::is_production_filename","text":"scripts/run_pylint.py::is_production_filename\ndef is_production_filename(filename):\n \"\"\"Checks if the file contains production code.\n\n :rtype: bool\n :returns: Boolean indicating production status.\n \"\"\"\n return 'test' not in filename and 'docs' not in filename","label":0},{"cid":"scripts/run_pylint.py::get_files_for_linting","text":"scripts/run_pylint.py::get_files_for_linting\ndef get_files_for_linting(allow_limited=True):\n \"\"\"Gets a list of files in the repository.\n\n By default, returns all files via ``git ls-files``. However, in some cases\n uses a specific commit or branch (a so-called diff base) to compare\n against for changed files. (This requires ``allow_limited=True``.)\n\n To speed up linting on Travis pull requests against master, we manually\n set the diff base to origin/master. We don't do this on non-pull requests\n since origin/master will be equivalent to the currently checked out code.\n One could potentially use ${TRAVIS_COMMIT_RANGE} to find a diff base but\n this value is not dependable.\n\n To allow faster local ``tox`` runs, the environment variables\n ``GOOGLE_CLOUD_REMOTE_FOR_LINT`` and ``GOOGLE_CLOUD_BRANCH_FOR_LINT`` can\n be set to specify a remote branch to diff against.\n\n :type allow_limited: bool\n :param allow_limited: Boolean indicating if a reduced set of files can\n be used.\n\n :rtype: pair\n :returns: Tuple of the diff base using the the list of filenames to be\n linted.\n \"\"\"\n diff_base = None\n if (os.getenv('TRAVIS_BRANCH') == 'master' and\n os.getenv('TRAVIS_PULL_REQUEST') != 'false'):\n # In the case of a pull request into master, we want to\n # diff against HEAD in master.\n diff_base = 'origin/master'\n elif os.getenv('TRAVIS') is None:\n # Only allow specified remote and branch in local dev.\n remote = os.getenv('GOOGLE_CLOUD_REMOTE_FOR_LINT')\n branch = os.getenv('GOOGLE_CLOUD_BRANCH_FOR_LINT')\n if remote is not None and branch is not None:\n diff_base = '%s/%s' % (remote, branch)\n\n if diff_base is not None and allow_limited:\n result = subprocess.check_output(['git', 'diff', '--name-only',\n diff_base])\n print('Using files changed relative to %s:' % (diff_ba","label":0},{"cid":"scripts/run_pylint.py::get_python_files","text":"scripts/run_pylint.py::get_python_files\ndef get_python_files(all_files=None):\n \"\"\"Gets a list of all Python files in the repository that need linting.\n\n Relies on :func:`get_files_for_linting()` to determine which files should\n be considered.\n\n NOTE: This requires ``git`` to be installed and requires that this\n is run within the ``git`` repository.\n\n :type all_files: list or ``NoneType``\n :param all_files: Optional list of files to be linted.\n\n :rtype: tuple\n :returns: A tuple containing two lists. The first list\n contains all production files, the next all test files.\n \"\"\"\n if all_files is None:\n all_files, diff_base = get_files_for_linting()\n\n library_files = []\n non_library_files = []\n for filename in all_files:\n if valid_filename(filename):\n if is_production_filename(filename):\n library_files.append(filename)\n else:\n non_library_files.append(filename)\n\n return library_files, non_library_files, diff_base","label":0},{"cid":"scripts/run_pylint.py::lint_fileset","text":"scripts/run_pylint.py::lint_fileset\ndef lint_fileset(filenames, rcfile, description):\n \"\"\"Lints a group of files using a given rcfile.\"\"\"\n # Only lint filenames that exist. For example, 'git diff --name-only'\n # could spit out deleted / renamed files. Another alternative could\n # be to use 'git diff --name-status' and filter out files with a\n # status of 'D'.\n filenames = [filename for filename in filenames\n if os.path.exists(filename)]\n if filenames:\n rc_flag = '--rcfile=%s' % (rcfile,)\n pylint_shell_command = ['pylint', rc_flag]\n errors = {} # filename -> status_code\n for filename in filenames:\n cmd = pylint_shell_command + [filename]\n status_code = subprocess.call(cmd)\n if status_code != 0:\n errors[filename] = status_code\n if errors:\n for filename, status_code in sorted(errors.items()):\n print('%-30s: %d' % (filename, status_code), file=sys.stderr)\n sys.exit(len(errors))\n else:\n print('Skipping %s, no files to lint.' % (description,))","label":0},{"cid":"scripts/run_pylint.py::main","text":"scripts/run_pylint.py::main\ndef main():\n \"\"\"Script entry point. Lints both sets of files.\"\"\"\n make_test_rc(PRODUCTION_RC, TEST_RC_ADDITIONS,\n TEST_RC_REPLACEMENTS, TEST_RC)\n library_files, non_library_files, diff_base = get_python_files()\n if diff_base:\n print('Checking only files which differ from base.')\n lint_fileset(library_files, PRODUCTION_RC, 'library code')\n lint_fileset(non_library_files, TEST_RC, 'test code')","label":0},{"cid":"scripts/verify_included_modules.py::is_valid_module","text":"scripts/verify_included_modules.py::is_valid_module\ndef is_valid_module(filename):\n \"\"\"Determines if a filename is a valid Python module.\n\n Assumes if is just the end of a path (i.e. does not contain\n ``os.path.sep``.\n\n :type filename: string\n :param filename: The name of a file.\n\n :rtype: bool\n :returns: Flag indicating if the filename is valid.\n \"\"\"\n if not filename.endswith('.py'):\n return False\n if filename == '__init__.py':\n return True\n for prefix in IGNORED_PREFIXES:\n if filename.startswith(prefix):\n return False\n return True","label":0},{"cid":"scripts/verify_included_modules.py::get_public_modules","text":"scripts/verify_included_modules.py::get_public_modules\ndef get_public_modules(path, base_package=None):\n \"\"\"Get list of all public modules relative to a path.\n\n :type path: string\n :param path: The path containing the python modules.\n\n :type base_package: string\n :param base_package: (Optional) A package to prepend in\n front of the path.\n\n :rtype: list\n :returns: List of all modules found.\n \"\"\"\n result = []\n for subdir, _, files in os.walk(path):\n # Skip folders that start with _.\n if any([part.startswith('_')\n for part in subdir.split(os.path.sep)]):\n continue\n _, rel_dir = subdir.split(path)\n rel_dir = rel_dir.lstrip(os.path.sep)\n for filename in files:\n if is_valid_module(filename):\n mod_name, _ = os.path.splitext(filename)\n rel_path = os.path.join(rel_dir, mod_name)\n if base_package is not None:\n rel_path = os.path.join(base_package, rel_path)\n # Turn into a Python module rather than a file path.\n result.append(rel_path.replace(os.path.sep, '.'))\n\n return result","label":0},{"cid":"scripts/verify_included_modules.py::main","text":"scripts/verify_included_modules.py::main\ndef main(build_root='_build'):\n \"\"\"Main script to verify modules included.\n\n :type build_root: str\n :param build_root: The root of the directory where docs are built into.\n Defaults to ``_build``.\n \"\"\"\n object_inventory_relpath = os.path.join(build_root, 'html', 'objects.inv')\n\n mock_uri = ''\n inventory = fetch_inventory(SphinxApp, mock_uri,\n object_inventory_relpath)\n sphinx_mods = set(inventory['py:module'].keys())\n\n library_dir = os.path.join(BASE_DIR, 'google', 'cloud')\n public_mods = get_public_modules(library_dir,\n base_package='google.cloud')\n public_mods = set(public_mods)\n\n if not sphinx_mods <= public_mods:\n unexpected_mods = sphinx_mods - public_mods\n message = ['Unexpected error. There were modules referenced by '\n 'Sphinx that are not among the public modules.']\n message.extend(['- %s' % (mod,) for mod in unexpected_mods])\n print('\\n'.join(message), file=sys.stderr)\n sys.exit(1)\n\n undocumented_mods = public_mods - sphinx_mods\n # Remove ignored modules.\n undocumented_mods -= IGNORED_MODULES\n if undocumented_mods:\n message_parts = ['Found undocumented public modules:']\n message_parts.extend(['- ' + mod_name\n for mod_name in sorted(undocumented_mods)])\n print('\\n'.join(message_parts), file=sys.stderr)\n sys.exit(1)","label":0},{"cid":"scripts/verify_included_modules.py::get_parser","text":"scripts/verify_included_modules.py::get_parser\ndef get_parser():\n \"\"\"Get simple ``argparse`` parser to determine package.\n\n :rtype: :class:`argparse.ArgumentParser`\n :returns: The parser for this script.\n \"\"\"\n description = ('Run check that all google-cloud '\n 'modules are included in docs.')\n parser = argparse.ArgumentParser(description=description)\n parser.add_argument('--build-root', dest='build_root',\n help='The root directory where docs are located.')\n return parser","label":0}],"string":"[\n {\n \"cid\": \"docs/bigquery_snippets.py::snippet\",\n \"text\": \"docs/bigquery_snippets.py::snippet\\ndef snippet(func):\\n \\\"\\\"\\\"Mark ``func`` as a snippet example function.\\\"\\\"\\\"\\n func._snippet = True\\n return func\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::_millis\",\n \"text\": \"docs/bigquery_snippets.py::_millis\\ndef _millis():\\n return time.time() * 1000\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::_CloseOnDelete.__init__\",\n \"text\": \"docs/bigquery_snippets.py::_CloseOnDelete.__init__\\n def __init__(self, wrapped):\\n self._wrapped = wrapped\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::_CloseOnDelete.delete\",\n \"text\": \"docs/bigquery_snippets.py::_CloseOnDelete.delete\\n def delete(self):\\n self._wrapped.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::client_list_datasets\",\n \"text\": \"docs/bigquery_snippets.py::client_list_datasets\\n@snippet\\ndef client_list_datasets(client, _):\\n \\\"\\\"\\\"List datasets for a project.\\\"\\\"\\\"\\n\\n def do_something_with(_):\\n pass\\n\\n # [START client_list_datasets]\\n datasets, token = client.list_datasets() # API request\\n while True:\\n for dataset in datasets:\\n do_something_with(dataset)\\n if token is None:\\n break\\n datasets, token = client.list_datasets(page_token=token) # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::client_list_datasets.do_something_with\",\n \"text\": \"docs/bigquery_snippets.py::client_list_datasets.do_something_with\\n def do_something_with(_):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::dataset_create\",\n \"text\": \"docs/bigquery_snippets.py::dataset_create\\n@snippet\\ndef dataset_create(client, to_delete):\\n \\\"\\\"\\\"Create a dataset.\\\"\\\"\\\"\\n DATASET_NAME = 'dataset_create_%d' % (_millis(),)\\n\\n # [START dataset_create]\\n dataset = client.dataset(DATASET_NAME)\\n dataset.create() # API request\\n # [END dataset_create]\\n\\n to_delete.append(dataset)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::dataset_exists\",\n \"text\": \"docs/bigquery_snippets.py::dataset_exists\\n@snippet\\ndef dataset_exists(client, to_delete):\\n \\\"\\\"\\\"Test existence of a dataset.\\\"\\\"\\\"\\n DATASET_NAME = 'dataset_exists_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n to_delete.append(dataset)\\n\\n # [START dataset_exists]\\n assert not dataset.exists() # API request\\n dataset.create() # API request\\n assert dataset.exists() # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::dataset_reload\",\n \"text\": \"docs/bigquery_snippets.py::dataset_reload\\n@snippet\\ndef dataset_reload(client, to_delete):\\n \\\"\\\"\\\"Reload a dataset's metadata.\\\"\\\"\\\"\\n DATASET_NAME = 'dataset_reload_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.description = ORIGINAL_DESCRIPTION\\n dataset.create()\\n to_delete.append(dataset)\\n\\n # [START dataset_reload]\\n assert dataset.description == ORIGINAL_DESCRIPTION\\n dataset.description = LOCALLY_CHANGED_DESCRIPTION\\n assert dataset.description == LOCALLY_CHANGED_DESCRIPTION\\n dataset.reload() # API request\\n assert dataset.description == ORIGINAL_DESCRIPTION\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::dataset_patch\",\n \"text\": \"docs/bigquery_snippets.py::dataset_patch\\n@snippet\\ndef dataset_patch(client, to_delete):\\n \\\"\\\"\\\"Patch a dataset's metadata.\\\"\\\"\\\"\\n DATASET_NAME = 'dataset_patch_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.description = ORIGINAL_DESCRIPTION\\n dataset.create()\\n to_delete.append(dataset)\\n\\n # [START dataset_patch]\\n ONE_DAY_MS = 24 * 60 * 60 * 1000\\n assert dataset.description == ORIGINAL_DESCRIPTION\\n dataset.patch(\\n description=PATCHED_DESCRIPTION,\\n default_table_expiration_ms=ONE_DAY_MS\\n ) # API request\\n assert dataset.description == PATCHED_DESCRIPTION\\n assert dataset.default_table_expiration_ms == ONE_DAY_MS\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::dataset_update\",\n \"text\": \"docs/bigquery_snippets.py::dataset_update\\n@snippet\\ndef dataset_update(client, to_delete):\\n \\\"\\\"\\\"Update a dataset's metadata.\\\"\\\"\\\"\\n DATASET_NAME = 'dataset_update_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.description = ORIGINAL_DESCRIPTION\\n dataset.create()\\n to_delete.append(dataset)\\n dataset.reload()\\n\\n # [START dataset_update]\\n from google.cloud.bigquery import AccessGrant\\n assert dataset.description == ORIGINAL_DESCRIPTION\\n assert dataset.default_table_expiration_ms is None\\n grant = AccessGrant(\\n role='READER', entity_type='domain', entity_id='example.com')\\n assert grant not in dataset.access_grants\\n ONE_DAY_MS = 24 * 60 * 60 * 1000\\n dataset.description = UPDATED_DESCRIPTION\\n dataset.default_table_expiration_ms = ONE_DAY_MS\\n grants = list(dataset.access_grants)\\n grants.append(grant)\\n dataset.access_grants = grants\\n dataset.update() # API request\\n assert dataset.description == UPDATED_DESCRIPTION\\n assert dataset.default_table_expiration_ms == ONE_DAY_MS\\n assert grant in dataset.access_grants\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::dataset_delete\",\n \"text\": \"docs/bigquery_snippets.py::dataset_delete\\n@snippet\\ndef dataset_delete(client, _):\\n \\\"\\\"\\\"Delete a dataset.\\\"\\\"\\\"\\n DATASET_NAME = 'dataset_delete_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.create()\\n\\n # [START dataset_delete]\\n assert dataset.exists() # API request\\n dataset.delete()\\n assert not dataset.exists() # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::dataset_list_tables\",\n \"text\": \"docs/bigquery_snippets.py::dataset_list_tables\\n@snippet\\ndef dataset_list_tables(client, to_delete):\\n \\\"\\\"\\\"List tables within a dataset.\\\"\\\"\\\"\\n DATASET_NAME = 'dataset_list_tables_dataset_%d' % (_millis(),)\\n TABLE_NAME = 'dataset_list_tables_table_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.create()\\n to_delete.append(dataset)\\n\\n # [START dataset_list_tables]\\n tables, token = dataset.list_tables() # API request\\n assert len(tables) == 0\\n assert token is None\\n table = dataset.table(TABLE_NAME)\\n table.view_query = QUERY\\n table.create() # API request\\n tables, token = dataset.list_tables() # API request\\n assert len(tables) == 1\\n assert tables[0].name == TABLE_NAME\\n # [END dataset_list_tables]\\n to_delete.insert(0, table)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::table_create\",\n \"text\": \"docs/bigquery_snippets.py::table_create\\n@snippet\\ndef table_create(client, to_delete):\\n \\\"\\\"\\\"Create a table.\\\"\\\"\\\"\\n DATASET_NAME = 'table_create_dataset_%d' % (_millis(),)\\n TABLE_NAME = 'table_create_table_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.create()\\n to_delete.append(dataset)\\n\\n # [START table_create]\\n table = dataset.table(TABLE_NAME, SCHEMA)\\n table.create() # API request\\n # [END table_create]\\n\\n to_delete.insert(0, table)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::table_exists\",\n \"text\": \"docs/bigquery_snippets.py::table_exists\\n@snippet\\ndef table_exists(client, to_delete):\\n \\\"\\\"\\\"Test existence of a table.\\\"\\\"\\\"\\n DATASET_NAME = 'table_exists_dataset_%d' % (_millis(),)\\n TABLE_NAME = 'table_exists_table_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.create()\\n to_delete.append(dataset)\\n\\n # [START table_exists]\\n table = dataset.table(TABLE_NAME, SCHEMA)\\n assert not table.exists() # API request\\n table.create() # API request\\n assert table.exists() # API request\\n # [END table_exists]\\n\\n to_delete.insert(0, table)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::table_reload\",\n \"text\": \"docs/bigquery_snippets.py::table_reload\\n@snippet\\ndef table_reload(client, to_delete):\\n \\\"\\\"\\\"Reload a table's metadata.\\\"\\\"\\\"\\n DATASET_NAME = 'table_reload_dataset_%d' % (_millis(),)\\n TABLE_NAME = 'table_reload_table_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.create()\\n to_delete.append(dataset)\\n\\n table = dataset.table(TABLE_NAME, SCHEMA)\\n table.friendly_name = ORIGINAL_FRIENDLY_NAME\\n table.description = ORIGINAL_DESCRIPTION\\n table.create()\\n to_delete.insert(0, table)\\n\\n # [START table_reload]\\n assert table.friendly_name == ORIGINAL_FRIENDLY_NAME\\n assert table.description == ORIGINAL_DESCRIPTION\\n table.friendly_name = LOCALLY_CHANGED_FRIENDLY_NAME\\n table.description = LOCALLY_CHANGED_DESCRIPTION\\n table.reload() # API request\\n assert table.friendly_name == ORIGINAL_FRIENDLY_NAME\\n assert table.description == ORIGINAL_DESCRIPTION\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::table_patch\",\n \"text\": \"docs/bigquery_snippets.py::table_patch\\n@snippet\\ndef table_patch(client, to_delete):\\n \\\"\\\"\\\"Patch a table's metadata.\\\"\\\"\\\"\\n DATASET_NAME = 'table_patch_dataset_%d' % (_millis(),)\\n TABLE_NAME = 'table_patch_table_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.description = ORIGINAL_DESCRIPTION\\n dataset.create()\\n to_delete.append(dataset)\\n\\n table = dataset.table(TABLE_NAME, SCHEMA)\\n table.friendly_name = ORIGINAL_FRIENDLY_NAME\\n table.description = ORIGINAL_DESCRIPTION\\n table.create()\\n to_delete.insert(0, table)\\n\\n # [START table_patch]\\n assert table.friendly_name == ORIGINAL_FRIENDLY_NAME\\n assert table.description == ORIGINAL_DESCRIPTION\\n table.patch(\\n friendly_name=PATCHED_FRIENDLY_NAME,\\n description=PATCHED_DESCRIPTION,\\n ) # API request\\n assert table.friendly_name == PATCHED_FRIENDLY_NAME\\n assert table.description == PATCHED_DESCRIPTION\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::table_update\",\n \"text\": \"docs/bigquery_snippets.py::table_update\\n@snippet\\ndef table_update(client, to_delete):\\n \\\"\\\"\\\"Update a table's metadata.\\\"\\\"\\\"\\n DATASET_NAME = 'table_update_dataset_%d' % (_millis(),)\\n TABLE_NAME = 'table_update_table_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.description = ORIGINAL_DESCRIPTION\\n dataset.create()\\n to_delete.append(dataset)\\n\\n table = dataset.table(TABLE_NAME, SCHEMA)\\n table.friendly_name = ORIGINAL_FRIENDLY_NAME\\n table.description = ORIGINAL_DESCRIPTION\\n table.create()\\n to_delete.insert(0, table)\\n\\n # [START table_update]\\n assert table.friendly_name == ORIGINAL_FRIENDLY_NAME\\n assert table.description == ORIGINAL_DESCRIPTION\\n NEW_SCHEMA = table.schema[:]\\n NEW_SCHEMA.append(SchemaField('phone', 'string'))\\n table.friendly_name = UPDATED_FRIENDLY_NAME\\n table.description = UPDATED_DESCRIPTION\\n table.schema = NEW_SCHEMA\\n table.update() # API request\\n assert table.friendly_name == UPDATED_FRIENDLY_NAME\\n assert table.description == UPDATED_DESCRIPTION\\n assert table.schema == NEW_SCHEMA\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::_warm_up_inserted_table_data\",\n \"text\": \"docs/bigquery_snippets.py::_warm_up_inserted_table_data\\ndef _warm_up_inserted_table_data(table):\\n # Allow for 90 seconds of \\\"warm up\\\" before rows visible. See:\\n # https://cloud.google.com/bigquery/streaming-data-into-bigquery#dataavailability\\n rows = ()\\n counter = 18\\n\\n while len(rows) == 0 and counter > 0:\\n counter -= 1\\n rows, _, _ = table.fetch_data()\\n if len(rows) == 0:\\n time.sleep(5)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::table_insert_fetch_data\",\n \"text\": \"docs/bigquery_snippets.py::table_insert_fetch_data\\n@snippet\\ndef table_insert_fetch_data(client, to_delete):\\n \\\"\\\"\\\"Insert / fetch table data.\\\"\\\"\\\"\\n DATASET_NAME = 'table_insert_fetch_data_dataset_%d' % (_millis(),)\\n TABLE_NAME = 'table_insert_fetch_data_table_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.create()\\n to_delete.append(dataset)\\n\\n table = dataset.table(TABLE_NAME, SCHEMA)\\n table.create()\\n to_delete.insert(0, table)\\n\\n # [START table_insert_data]\\n ROWS_TO_INSERT = [\\n (u'Phred Phlyntstone', 32),\\n (u'Wylma Phlyntstone', 29),\\n ]\\n\\n table.insert_data(ROWS_TO_INSERT)\\n # [END table_insert_data]\\n\\n _warm_up_inserted_table_data(table)\\n\\n found_rows = []\\n\\n def do_something(row):\\n found_rows.append(row)\\n\\n # [START table_fetch_data]\\n rows, _, token = table.fetch_data()\\n while True:\\n for row in rows:\\n do_something(row)\\n if token is None:\\n break\\n rows, _, token = table.fetch_data(page_token=token)\\n # [END table_fetch_data]\\n\\n assert len(found_rows) == len(ROWS_TO_INSERT)\\n by_age = operator.itemgetter(1)\\n found_rows = reversed(sorted(found_rows, key=by_age))\\n for found, to_insert in zip(found_rows, ROWS_TO_INSERT):\\n assert found == to_insert\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::table_insert_fetch_data.do_something\",\n \"text\": \"docs/bigquery_snippets.py::table_insert_fetch_data.do_something\\n def do_something(row):\\n found_rows.append(row)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::table_upload_from_file\",\n \"text\": \"docs/bigquery_snippets.py::table_upload_from_file\\n@snippet\\ndef table_upload_from_file(client, to_delete):\\n \\\"\\\"\\\"Upload table data from a CSV file.\\\"\\\"\\\"\\n import csv\\n import tempfile\\n DATASET_NAME = 'table_upload_from_file_dataset_%d' % (_millis(),)\\n TABLE_NAME = 'table_upload_from_file_table_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.create()\\n to_delete.append(dataset)\\n\\n table = dataset.table(TABLE_NAME, SCHEMA)\\n table.create()\\n to_delete.insert(0, table)\\n\\n csv_file = tempfile.NamedTemporaryFile(suffix='.csv')\\n to_delete.append(_CloseOnDelete(csv_file))\\n\\n # [START table_upload_from_file]\\n writer = csv.writer(csv_file)\\n writer.writerow((b'full_name', b'age'))\\n writer.writerow((b'Phred Phlyntstone', b'32'))\\n writer.writerow((b'Wylma Phlyntstone', b'29'))\\n csv_file.flush()\\n\\n with open(csv_file.name, 'rb') as readable:\\n table.upload_from_file(\\n readable, source_format='CSV', skip_leading_rows=1)\\n # [END table_upload_from_file]\\n\\n _warm_up_inserted_table_data(table)\\n\\n rows, total, token = table.fetch_data()\\n\\n assert len(rows) == total == 2\\n assert token is None\\n assert rows[0] == (u'Phred Phlyntstone', 32)\\n assert rows[1] == (u'Wylma Phlyntstone', 29)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::table_delete\",\n \"text\": \"docs/bigquery_snippets.py::table_delete\\n@snippet\\ndef table_delete(client, _):\\n \\\"\\\"\\\"Delete a table.\\\"\\\"\\\"\\n DATASET_NAME = 'table_delete_dataset_%d' % (_millis(),)\\n TABLE_NAME = 'table_create_table_%d' % (_millis(),)\\n dataset = client.dataset(DATASET_NAME)\\n dataset.create()\\n\\n table = dataset.table(TABLE_NAME, SCHEMA)\\n table.create()\\n\\n # [START table_delete]\\n assert table.exists() # API request\\n table.delete() # API request\\n assert not table.exists() # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::client_list_jobs\",\n \"text\": \"docs/bigquery_snippets.py::client_list_jobs\\n@snippet\\ndef client_list_jobs(client, _):\\n \\\"\\\"\\\"List jobs for a project.\\\"\\\"\\\"\\n\\n def do_something_with(_):\\n pass\\n\\n # [START client_list_jobs]\\n jobs, token = client.list_jobs() # API request\\n while True:\\n for job in jobs:\\n do_something_with(job)\\n if token is None:\\n break\\n jobs, token = client.list_jobs(page_token=token) # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::client_list_jobs.do_something_with\",\n \"text\": \"docs/bigquery_snippets.py::client_list_jobs.do_something_with\\n def do_something_with(_):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::client_run_sync_query\",\n \"text\": \"docs/bigquery_snippets.py::client_run_sync_query\\n@snippet\\ndef client_run_sync_query(client, _):\\n \\\"\\\"\\\"Run a synchronous query.\\\"\\\"\\\"\\n LIMIT = 100\\n LIMITED = '%s LIMIT %d' % (QUERY, LIMIT)\\n TIMEOUT_MS = 1000\\n\\n # [START client_run_sync_query]\\n query = client.run_sync_query(LIMITED)\\n query.timeout_ms = TIMEOUT_MS\\n query.run() # API request\\n\\n assert query.complete\\n assert len(query.rows) == LIMIT\\n assert [field.name for field in query.schema] == ['name']\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::client_run_sync_query_paged\",\n \"text\": \"docs/bigquery_snippets.py::client_run_sync_query_paged\\n@snippet\\ndef client_run_sync_query_paged(client, _):\\n \\\"\\\"\\\"Run a synchronous query with paged results.\\\"\\\"\\\"\\n TIMEOUT_MS = 1000\\n PAGE_SIZE = 100\\n LIMIT = 1000\\n LIMITED = '%s LIMIT %d' % (QUERY, LIMIT)\\n\\n all_rows = []\\n\\n def do_something_with(rows):\\n all_rows.extend(rows)\\n\\n # [START client_run_sync_query_paged]\\n query = client.run_sync_query(LIMITED)\\n query.timeout_ms = TIMEOUT_MS\\n query.max_results = PAGE_SIZE\\n query.run() # API request\\n\\n assert query.complete\\n assert query.page_token is not None\\n assert len(query.rows) == PAGE_SIZE\\n assert [field.name for field in query.schema] == ['name']\\n\\n rows = query.rows\\n token = query.page_token\\n\\n while True:\\n do_something_with(rows)\\n if token is None:\\n break\\n rows, total_count, token = query.fetch_data(\\n page_token=token) # API request\\n # [END client_run_sync_query_paged]\\n\\n assert total_count == LIMIT\\n assert len(all_rows) == LIMIT\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::client_run_sync_query_paged.do_something_with\",\n \"text\": \"docs/bigquery_snippets.py::client_run_sync_query_paged.do_something_with\\n def do_something_with(rows):\\n all_rows.extend(rows)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::client_run_sync_query_timeout\",\n \"text\": \"docs/bigquery_snippets.py::client_run_sync_query_timeout\\n@snippet\\ndef client_run_sync_query_timeout(client, _):\\n \\\"\\\"\\\"Run a synchronous query w/ timeout\\\"\\\"\\\"\\n TIMEOUT_MS = 10\\n\\n all_rows = []\\n\\n def do_something_with(rows):\\n all_rows.extend(rows)\\n\\n # [START client_run_sync_query_timeout]\\n query = client.run_sync_query(QUERY)\\n query.timeout_ms = TIMEOUT_MS\\n query.use_query_cache = False\\n query.run() # API request\\n\\n assert not query.complete\\n\\n job = query.job\\n job.reload() # API rquest\\n retry_count = 0\\n\\n while retry_count < 10 and job.state != u'DONE':\\n time.sleep(1.5**retry_count) # exponential backoff\\n retry_count += 1\\n job.reload() # API request\\n\\n assert job.state == u'DONE'\\n\\n rows, total_count, token = query.fetch_data() # API request\\n while True:\\n do_something_with(rows)\\n if token is None:\\n break\\n rows, total_count, token = query.fetch_data(\\n page_token=token) # API request\\n # [END client_run_sync_query_timeout]\\n\\n assert len(all_rows) == total_count\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::client_run_sync_query_timeout.do_something_with\",\n \"text\": \"docs/bigquery_snippets.py::client_run_sync_query_timeout.do_something_with\\n def do_something_with(rows):\\n all_rows.extend(rows)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::_find_examples\",\n \"text\": \"docs/bigquery_snippets.py::_find_examples\\ndef _find_examples():\\n funcs = [obj for obj in globals().values()\\n if getattr(obj, '_snippet', False)]\\n for func in sorted(funcs, key=lambda f: f.func_code.co_firstlineno):\\n yield func\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/bigquery_snippets.py::main\",\n \"text\": \"docs/bigquery_snippets.py::main\\ndef main():\\n client = Client()\\n for example in _find_examples():\\n to_delete = []\\n print('%-30s: %s' % (\\n example.func_name, example.func_doc))\\n try:\\n example(client, to_delete)\\n except AssertionError as e:\\n print(' FAIL: %s' % (e,))\\n except Exception as e: # pylint: disable=broad-except\\n print(' ERROR: %r' % (e,))\\n for item in to_delete:\\n item.delete()\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::snippet\",\n \"text\": \"docs/pubsub_snippets.py::snippet\\ndef snippet(func):\\n \\\"\\\"\\\"Mark ``func`` as a snippet example function.\\\"\\\"\\\"\\n func._snippet = True\\n return func\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::_millis\",\n \"text\": \"docs/pubsub_snippets.py::_millis\\ndef _millis():\\n return time.time() * 1000\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::client_list_topics\",\n \"text\": \"docs/pubsub_snippets.py::client_list_topics\\n@snippet\\ndef client_list_topics(client, to_delete): # pylint: disable=unused-argument\\n \\\"\\\"\\\"List topics for a project.\\\"\\\"\\\"\\n\\n def do_something_with(sub): # pylint: disable=unused-argument\\n pass\\n\\n # [START client_list_topics]\\n topics, token = client.list_topics() # API request\\n while True:\\n for topic in topics:\\n do_something_with(topic)\\n if token is None:\\n break\\n topics, token = client.list_topics(page_token=token) # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::client_list_topics.do_something_with\",\n \"text\": \"docs/pubsub_snippets.py::client_list_topics.do_something_with\\n def do_something_with(sub): # pylint: disable=unused-argument\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::client_list_subscriptions\",\n \"text\": \"docs/pubsub_snippets.py::client_list_subscriptions\\n@snippet\\ndef client_list_subscriptions(client,\\n to_delete): # pylint: disable=unused-argument\\n \\\"\\\"\\\"List all subscriptions for a project.\\\"\\\"\\\"\\n\\n def do_something_with(sub): # pylint: disable=unused-argument\\n pass\\n\\n # [START client_list_subscriptions]\\n subscriptions, token = client.list_subscriptions() # API request\\n while True:\\n for subscription in subscriptions:\\n do_something_with(subscription)\\n if token is None:\\n break\\n subscriptions, token = client.list_subscriptions(\\n page_token=token) # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::client_list_subscriptions.do_something_with\",\n \"text\": \"docs/pubsub_snippets.py::client_list_subscriptions.do_something_with\\n def do_something_with(sub): # pylint: disable=unused-argument\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::topic_create\",\n \"text\": \"docs/pubsub_snippets.py::topic_create\\n@snippet\\ndef topic_create(client, to_delete):\\n \\\"\\\"\\\"Create a topic.\\\"\\\"\\\"\\n TOPIC_NAME = 'topic_create-%d' % (_millis(),)\\n\\n # [START topic_create]\\n topic = client.topic(TOPIC_NAME)\\n topic.create() # API request\\n # [END topic_create]\\n\\n to_delete.append(topic)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::topic_exists\",\n \"text\": \"docs/pubsub_snippets.py::topic_exists\\n@snippet\\ndef topic_exists(client, to_delete):\\n \\\"\\\"\\\"Test existence of a topic.\\\"\\\"\\\"\\n TOPIC_NAME = 'topic_exists-%d' % (_millis(),)\\n topic = client.topic(TOPIC_NAME)\\n to_delete.append(topic)\\n\\n # [START topic_exists]\\n assert not topic.exists() # API request\\n topic.create() # API request\\n assert topic.exists() # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::topic_delete\",\n \"text\": \"docs/pubsub_snippets.py::topic_delete\\n@snippet\\ndef topic_delete(client, to_delete): # pylint: disable=unused-argument\\n \\\"\\\"\\\"Delete a topic.\\\"\\\"\\\"\\n TOPIC_NAME = 'topic_delete-%d' % (_millis(),)\\n topic = client.topic(TOPIC_NAME)\\n topic.create() # API request\\n\\n # [START topic_delete]\\n assert topic.exists() # API request\\n topic.delete()\\n assert not topic.exists() # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::topic_iam_policy\",\n \"text\": \"docs/pubsub_snippets.py::topic_iam_policy\\n@snippet\\ndef topic_iam_policy(client, to_delete):\\n \\\"\\\"\\\"Fetch / set a topic's IAM policy.\\\"\\\"\\\"\\n TOPIC_NAME = 'topic_iam_policy-%d' % (_millis(),)\\n topic = client.topic(TOPIC_NAME)\\n topic.create()\\n to_delete.append(topic)\\n\\n # [START topic_get_iam_policy]\\n policy = topic.get_iam_policy() # API request\\n # [END topic_get_iam_policy]\\n\\n assert len(policy.viewers) == 0\\n assert len(policy.editors) == 0\\n assert len(policy.owners) == 0\\n\\n # [START topic_set_iam_policy]\\n ALL_USERS = policy.all_users()\\n policy.viewers.add(ALL_USERS)\\n LOGS_GROUP = policy.group('cloud-logs@google.com')\\n policy.editors.add(LOGS_GROUP)\\n new_policy = topic.set_iam_policy(policy) # API request\\n # [END topic_set_iam_policy]\\n\\n assert ALL_USERS in new_policy.viewers\\n assert LOGS_GROUP in new_policy.editors\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::topic_check_iam_permissions\",\n \"text\": \"docs/pubsub_snippets.py::topic_check_iam_permissions\\ndef topic_check_iam_permissions(client, to_delete):\\n \\\"\\\"\\\"Check topic IAM permissions.\\\"\\\"\\\"\\n TOPIC_NAME = 'topic_check_iam_permissions-%d' % (_millis(),)\\n topic = client.topic(TOPIC_NAME)\\n topic.create()\\n to_delete.append(topic)\\n\\n # [START topic_check_iam_permissions]\\n from google.cloud.pubsub.iam import OWNER_ROLE, EDITOR_ROLE, VIEWER_ROLE\\n TO_CHECK = [OWNER_ROLE, EDITOR_ROLE, VIEWER_ROLE]\\n ALLOWED = topic.check_iam_permissions(TO_CHECK)\\n assert set(ALLOWED) == set(TO_CHECK)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::topic_publish_messages\",\n \"text\": \"docs/pubsub_snippets.py::topic_publish_messages\\n@snippet\\ndef topic_publish_messages(client, to_delete):\\n \\\"\\\"\\\"Publish messages to a topic.\\\"\\\"\\\"\\n TOPIC_NAME = 'topic_publish_messages-%d' % (_millis(),)\\n topic = client.topic(TOPIC_NAME)\\n topic.create()\\n to_delete.append(topic)\\n\\n # [START topic_publish_simple_message]\\n topic.publish(b'This is the message payload') # API request\\n # [END topic_publish_simple_message]\\n\\n # [START topic_publish_message_with_attrs]\\n topic.publish(b'Another message payload', extra='EXTRA') # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::topic_subscription\",\n \"text\": \"docs/pubsub_snippets.py::topic_subscription\\n@snippet\\ndef topic_subscription(client, to_delete):\\n \\\"\\\"\\\"Create subscriptions to a topic.\\\"\\\"\\\"\\n TOPIC_NAME = 'topic_subscription-%d' % (_millis(),)\\n SUB_DEFAULTS = 'topic_subscription-defaults-%d' % (_millis(),)\\n SUB_ACK90 = 'topic_subscription-ack90-%d' % (_millis(),)\\n topic = client.topic(TOPIC_NAME)\\n topic.create()\\n to_delete.append(topic)\\n\\n # [START topic_subscription_defaults]\\n sub_defaults = topic.subscription(SUB_DEFAULTS)\\n # [END topic_subscription_defaults]\\n\\n sub_defaults.create() # API request\\n to_delete.append(sub_defaults)\\n expected_names = set()\\n expected_names.add(sub_defaults.full_name)\\n\\n # [START topic_subscription_ack90]\\n sub_ack90 = topic.subscription(SUB_ACK90, ack_deadline=90)\\n # [END topic_subscription_ack90]\\n\\n sub_ack90.create() # API request\\n to_delete.append(sub_ack90)\\n expected_names.add(sub_ack90.full_name)\\n\\n sub_names = set()\\n\\n def do_something_with(sub):\\n sub_names.add(sub.full_name)\\n\\n # [START topic_list_subscriptions]\\n subscriptions, token = topic.list_subscriptions() # API request\\n while True:\\n for subscription in subscriptions:\\n do_something_with(subscription)\\n if token is None:\\n break\\n subscriptions, token = topic.list_subscriptions(\\n page_token=token) # API request\\n # [END topic_list_subscriptions]\\n\\n assert sub_names.issuperset(expected_names)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::topic_subscription.do_something_with\",\n \"text\": \"docs/pubsub_snippets.py::topic_subscription.do_something_with\\n def do_something_with(sub):\\n sub_names.add(sub.full_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::topic_subscription_push\",\n \"text\": \"docs/pubsub_snippets.py::topic_subscription_push\\ndef topic_subscription_push(client, to_delete):\\n \\\"\\\"\\\"Create subscriptions to a topic.\\\"\\\"\\\"\\n TOPIC_NAME = 'topic_subscription_push-%d' % (_millis(),)\\n SUB_PUSH = 'topic_subscription_push-sub-%d' % (_millis(),)\\n PUSH_URL = 'https://api.example.com/push-endpoint'\\n topic = client.topic(TOPIC_NAME)\\n topic.create()\\n to_delete.append(topic)\\n\\n # [START topic_subscription_push]\\n subscription = topic.subscription(SUB_PUSH, push_endpoint=PUSH_URL)\\n subscription.create() # API request\\n # [END topic_subscription_push]\\n\\n # [START subscription_push_pull]\\n subscription.modify_push_configuration(push_endpoint=None) # API request\\n # [END subscription_push_pull]\\n\\n # [START subscription_pull_push]\\n subscription.modify_push_configuration(\\n push_endpoint=PUSH_URL) # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::subscription_lifecycle\",\n \"text\": \"docs/pubsub_snippets.py::subscription_lifecycle\\n@snippet\\ndef subscription_lifecycle(client, to_delete):\\n \\\"\\\"\\\"Test lifecycle of a subscription.\\\"\\\"\\\"\\n TOPIC_NAME = 'subscription_lifecycle-%d' % (_millis(),)\\n SUB_NAME = 'subscription_lifecycle-defaults-%d' % (_millis(),)\\n topic = client.topic(TOPIC_NAME)\\n topic.create()\\n to_delete.append(topic)\\n\\n # [START subscription_create]\\n subscription = topic.subscription(SUB_NAME)\\n subscription.create() # API request\\n # [END subscription_create]\\n\\n # [START subscription_exists]\\n assert subscription.exists() # API request\\n # [END subscription_exists]\\n\\n # [START subscription_reload]\\n subscription.reload() # API request\\n # [END subscription_reload]\\n\\n # [START subscription_delete]\\n subscription.delete() # API request\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::subscription_pull\",\n \"text\": \"docs/pubsub_snippets.py::subscription_pull\\n@snippet\\ndef subscription_pull(client, to_delete):\\n \\\"\\\"\\\"Pull messges from a subscribed topic.\\\"\\\"\\\"\\n TOPIC_NAME = 'subscription_pull-%d' % (_millis(),)\\n SUB_NAME = 'subscription_pull-defaults-%d' % (_millis(),)\\n PAYLOAD1 = b'PAYLOAD1'\\n PAYLOAD2 = b'PAYLOAD2'\\n EXTRA = 'EXTRA'\\n topic = client.topic(TOPIC_NAME)\\n topic.create()\\n to_delete.append(topic)\\n\\n subscription = topic.subscription(SUB_NAME)\\n subscription.create()\\n to_delete.append(subscription)\\n\\n # [START subscription_pull_return_immediately]\\n pulled = subscription.pull(return_immediately=True)\\n # [END subscription_pull_return_immediately]\\n assert len(pulled) == 0, \\\"unexpected message\\\"\\n\\n topic.publish(PAYLOAD1)\\n topic.publish(PAYLOAD2, extra=EXTRA)\\n\\n time.sleep(1) # eventually-consistent\\n\\n # [START subscription_pull]\\n pulled = subscription.pull(max_messages=2)\\n # [END subscription_pull]\\n\\n assert len(pulled) == 2, \\\"eventual consistency\\\"\\n\\n # [START subscription_modify_ack_deadline]\\n for ack_id, _ in pulled:\\n subscription.modify_ack_deadline(ack_id, 90) # API request\\n # [END subscription_modify_ack_deadline]\\n\\n payloads = []\\n extras = []\\n\\n def do_something_with(message): # pylint: disable=unused-argument\\n payloads.append(message.data)\\n if message.attributes:\\n extras.append(message.attributes)\\n\\n class ApplicationException(Exception):\\n pass\\n\\n def log_exception(_):\\n pass\\n\\n # [START subscription_acknowledge]\\n for ack_id, message in pulled:\\n try:\\n do_something_with(message)\\n except ApplicationException as e:\\n log_exception(e)\\n else:\\n subscription.acknowledge([ack_id])\\n # [END subscription_acknowledge]\\n\\n assert set(payloads) == set([PAYLOAD1, PAYLOAD2]), 'payloads: %s' % (\\n (payloads,))\\n assert extras == [{'extra': EXTRA}], 'extras: %s' % (\\n (extras,))\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::subscription_pull.do_something_with\",\n \"text\": \"docs/pubsub_snippets.py::subscription_pull.do_something_with\\n def do_something_with(message): # pylint: disable=unused-argument\\n payloads.append(message.data)\\n if message.attributes:\\n extras.append(message.attributes)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::subscription_pull.log_exception\",\n \"text\": \"docs/pubsub_snippets.py::subscription_pull.log_exception\\n def log_exception(_):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::subscription_pull_w_autoack\",\n \"text\": \"docs/pubsub_snippets.py::subscription_pull_w_autoack\\n@snippet\\ndef subscription_pull_w_autoack(client, to_delete):\\n \\\"\\\"\\\"Pull messges from a topic, auto-acknowldging them\\\"\\\"\\\"\\n TOPIC_NAME = 'subscription_pull_autoack-%d' % (_millis(),)\\n SUB_NAME = 'subscription_pull_autoack-defaults-%d' % (_millis(),)\\n PAYLOAD1 = b'PAYLOAD1'\\n PAYLOAD2 = b'PAYLOAD2'\\n EXTRA = 'EXTRA'\\n topic = client.topic(TOPIC_NAME)\\n topic.create()\\n to_delete.append(topic)\\n\\n subscription = topic.subscription(SUB_NAME)\\n subscription.create()\\n to_delete.append(subscription)\\n\\n # [START topic_batch]\\n with topic.batch() as batch:\\n batch.publish(PAYLOAD1)\\n batch.publish(PAYLOAD2, extra=EXTRA)\\n # [END topic_batch]\\n\\n time.sleep(1) # eventually-consistent\\n\\n payloads = []\\n extras = []\\n\\n def do_something_with(message): # pylint: disable=unused-argument\\n payloads.append(message.data)\\n if message.attributes:\\n extras.append(message.attributes)\\n\\n # [START subscription_pull_autoack]\\n from google.cloud.pubsub.subscription import AutoAck\\n with AutoAck(subscription, max_messages=10) as ack:\\n for ack_id, message in list(ack.items()):\\n try:\\n do_something_with(message)\\n except Exception: # pylint: disable=broad-except\\n del ack[ack_id]\\n # [END subscription_pull_autoack]\\n\\n assert set(payloads) == set(PAYLOAD1, PAYLOAD1), \\\"eventual consistency\\\"\\n assert extras == [{'extra': EXTRA}], \\\"eventual consistency\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::subscription_pull_w_autoack.do_something_with\",\n \"text\": \"docs/pubsub_snippets.py::subscription_pull_w_autoack.do_something_with\\n def do_something_with(message): # pylint: disable=unused-argument\\n payloads.append(message.data)\\n if message.attributes:\\n extras.append(message.attributes)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::subscription_iam_policy\",\n \"text\": \"docs/pubsub_snippets.py::subscription_iam_policy\\n@snippet\\ndef subscription_iam_policy(client, to_delete):\\n \\\"\\\"\\\"Fetch / set a subscription's IAM policy.\\\"\\\"\\\"\\n TOPIC_NAME = 'subscription_iam_policy-%d' % (_millis(),)\\n SUB_NAME = 'subscription_iam_policy-defaults-%d' % (_millis(),)\\n topic = client.topic(TOPIC_NAME)\\n topic.create()\\n to_delete.append(topic)\\n\\n subscription = topic.subscription(SUB_NAME)\\n subscription.create()\\n to_delete.append(subscription)\\n\\n # [START subscription_get_iam_policy]\\n policy = subscription.get_iam_policy() # API request\\n # [END subscription_get_iam_policy]\\n\\n assert len(policy.viewers) == 0\\n assert len(policy.editors) == 0\\n assert len(policy.owners) == 0\\n\\n # [START subscription_set_iam_policy]\\n ALL_USERS = policy.all_users()\\n policy.viewers.add(ALL_USERS)\\n LOGS_GROUP = policy.group('cloud-logs@google.com')\\n policy.editors.add(LOGS_GROUP)\\n new_policy = subscription.set_iam_policy(policy) # API request\\n # [END subscription_set_iam_policy]\\n\\n assert ALL_USERS in new_policy.viewers\\n assert LOGS_GROUP in new_policy.editors\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::subscription_check_iam_permissions\",\n \"text\": \"docs/pubsub_snippets.py::subscription_check_iam_permissions\\ndef subscription_check_iam_permissions(client, to_delete):\\n \\\"\\\"\\\"Check subscription IAM permissions.\\\"\\\"\\\"\\n TOPIC_NAME = 'subscription_check_iam_permissions-%d' % (_millis(),)\\n SUB_NAME = 'subscription_check_iam_permissions-defaults-%d' % (_millis(),)\\n topic = client.topic(TOPIC_NAME)\\n topic.create()\\n to_delete.append(topic)\\n\\n subscription = topic.subscription(SUB_NAME)\\n subscription.create()\\n to_delete.append(subscription)\\n\\n # [START subscription_check_iam_permissions]\\n from google.cloud.pubsub.iam import OWNER_ROLE, EDITOR_ROLE, VIEWER_ROLE\\n TO_CHECK = [OWNER_ROLE, EDITOR_ROLE, VIEWER_ROLE]\\n ALLOWED = subscription.check_iam_permissions(TO_CHECK)\\n assert set(ALLOWED) == set(TO_CHECK)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::_line_no\",\n \"text\": \"docs/pubsub_snippets.py::_line_no\\ndef _line_no(func):\\n code = getattr(func, '__code__', None) or getattr(func, 'func_code')\\n return code.co_firstlineno\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::_find_examples\",\n \"text\": \"docs/pubsub_snippets.py::_find_examples\\ndef _find_examples():\\n funcs = [obj for obj in globals().values()\\n if getattr(obj, '_snippet', False)]\\n for func in sorted(funcs, key=_line_no):\\n yield func\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::_name_and_doc\",\n \"text\": \"docs/pubsub_snippets.py::_name_and_doc\\ndef _name_and_doc(func):\\n return func.__name__, func.__doc__\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/pubsub_snippets.py::main\",\n \"text\": \"docs/pubsub_snippets.py::main\\ndef main():\\n client = Client()\\n for example in _find_examples():\\n to_delete = []\\n print('%-25s: %s' % _name_and_doc(example))\\n try:\\n example(client, to_delete)\\n except AssertionError as e:\\n print(' FAIL: %s' % (e,))\\n except Exception as e: # pylint: disable=broad-except\\n print(' ERROR: %r' % (e,))\\n for item in to_delete:\\n item.delete()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_LocalStack.__init__\",\n \"text\": \"google/cloud/_helpers.py::_LocalStack.__init__\\n def __init__(self):\\n super(_LocalStack, self).__init__()\\n self._stack = []\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_LocalStack.__iter__\",\n \"text\": \"google/cloud/_helpers.py::_LocalStack.__iter__\\n def __iter__(self):\\n \\\"\\\"\\\"Iterate the stack in LIFO order.\\n \\\"\\\"\\\"\\n return iter(reversed(self._stack))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_LocalStack.push\",\n \"text\": \"google/cloud/_helpers.py::_LocalStack.push\\n def push(self, resource):\\n \\\"\\\"\\\"Push a resource onto our stack.\\n \\\"\\\"\\\"\\n self._stack.append(resource)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_LocalStack.pop\",\n \"text\": \"google/cloud/_helpers.py::_LocalStack.pop\\n def pop(self):\\n \\\"\\\"\\\"Pop a resource from our stack.\\n\\n :rtype: object\\n :returns: the top-most resource, after removing it.\\n :raises IndexError: if the stack is empty.\\n \\\"\\\"\\\"\\n return self._stack.pop()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_LocalStack.top\",\n \"text\": \"google/cloud/_helpers.py::_LocalStack.top\\n @property\\n def top(self):\\n \\\"\\\"\\\"Get the top-most resource\\n\\n :rtype: object\\n :returns: the top-most item, or None if the stack is empty.\\n \\\"\\\"\\\"\\n if len(self._stack) > 0:\\n return self._stack[-1]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_UTC.dst\",\n \"text\": \"google/cloud/_helpers.py::_UTC.dst\\n def dst(self, dt): # pylint: disable=unused-argument\\n \\\"\\\"\\\"Daylight savings time offset.\\\"\\\"\\\"\\n return self._dst\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_UTC.fromutc\",\n \"text\": \"google/cloud/_helpers.py::_UTC.fromutc\\n def fromutc(self, dt):\\n \\\"\\\"\\\"Convert a timestamp from (naive) UTC to this timezone.\\\"\\\"\\\"\\n if dt.tzinfo is None:\\n return dt.replace(tzinfo=self)\\n return super(_UTC, self).fromutc(dt)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_UTC.tzname\",\n \"text\": \"google/cloud/_helpers.py::_UTC.tzname\\n def tzname(self, dt): # pylint: disable=unused-argument\\n \\\"\\\"\\\"Get the name of this timezone.\\\"\\\"\\\"\\n return self._tzname\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_UTC.utcoffset\",\n \"text\": \"google/cloud/_helpers.py::_UTC.utcoffset\\n def utcoffset(self, dt): # pylint: disable=unused-argument\\n \\\"\\\"\\\"UTC offset of this timezone.\\\"\\\"\\\"\\n return self._utcoffset\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_UTC.__repr__\",\n \"text\": \"google/cloud/_helpers.py::_UTC.__repr__\\n def __repr__(self):\\n return '<%s>' % (self._tzname,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_UTC.__str__\",\n \"text\": \"google/cloud/_helpers.py::_UTC.__str__\\n def __str__(self):\\n return self._tzname\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_ensure_tuple_or_list\",\n \"text\": \"google/cloud/_helpers.py::_ensure_tuple_or_list\\ndef _ensure_tuple_or_list(arg_name, tuple_or_list):\\n \\\"\\\"\\\"Ensures an input is a tuple or list.\\n\\n This effectively reduces the iterable types allowed to a very short\\n whitelist: list and tuple.\\n\\n :type arg_name: str\\n :param arg_name: Name of argument to use in error message.\\n\\n :type tuple_or_list: sequence of str\\n :param tuple_or_list: Sequence to be verified.\\n\\n :rtype: list of str\\n :returns: The ``tuple_or_list`` passed in cast to a ``list``.\\n :raises TypeError: if the ``tuple_or_list`` is not a tuple or list.\\n \\\"\\\"\\\"\\n if not isinstance(tuple_or_list, (tuple, list)):\\n raise TypeError('Expected %s to be a tuple or list. '\\n 'Received %r' % (arg_name, tuple_or_list))\\n return list(tuple_or_list)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_app_engine_id\",\n \"text\": \"google/cloud/_helpers.py::_app_engine_id\\ndef _app_engine_id():\\n \\\"\\\"\\\"Gets the App Engine application ID if it can be inferred.\\n\\n :rtype: str or ``NoneType``\\n :returns: App Engine application ID if running in App Engine,\\n else ``None``.\\n \\\"\\\"\\\"\\n if app_identity is None:\\n return None\\n\\n return app_identity.get_application_id()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_file_project_id\",\n \"text\": \"google/cloud/_helpers.py::_file_project_id\\ndef _file_project_id():\\n \\\"\\\"\\\"Gets the project ID from the credentials file if one is available.\\n\\n :rtype: str or ``NoneType``\\n :returns: Project ID from JSON credentials file if value exists,\\n else ``None``.\\n \\\"\\\"\\\"\\n credentials_file_path = os.getenv(CREDENTIALS)\\n if credentials_file_path:\\n with open(credentials_file_path, 'rb') as credentials_file:\\n credentials_json = credentials_file.read()\\n credentials = json.loads(credentials_json.decode('utf-8'))\\n return credentials.get('project_id')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_get_nix_config_path\",\n \"text\": \"google/cloud/_helpers.py::_get_nix_config_path\\ndef _get_nix_config_path():\\n \\\"\\\"\\\"Get the ``gcloud`` CLI config path on *nix systems.\\n\\n :rtype: str\\n :returns: The filename on a *nix system containing the CLI\\n config file.\\n \\\"\\\"\\\"\\n return os.path.join(_USER_ROOT, '.config', _GCLOUD_CONFIG_FILE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_get_windows_config_path\",\n \"text\": \"google/cloud/_helpers.py::_get_windows_config_path\\ndef _get_windows_config_path():\\n \\\"\\\"\\\"Get the ``gcloud`` CLI config path on Windows systems.\\n\\n :rtype: str\\n :returns: The filename on a Windows system containing the CLI\\n config file.\\n \\\"\\\"\\\"\\n appdata_dir = os.getenv('APPDATA', '')\\n return os.path.join(appdata_dir, _GCLOUD_CONFIG_FILE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_default_service_project_id\",\n \"text\": \"google/cloud/_helpers.py::_default_service_project_id\\ndef _default_service_project_id():\\n \\\"\\\"\\\"Retrieves the project ID from the gcloud command line tool.\\n\\n This assumes the ``.config`` directory is stored\\n - in ~/.config on *nix systems\\n - in the %APPDATA% directory on Windows systems\\n\\n Additionally, the ${HOME} / \\\"~\\\" directory may not be present on Google\\n App Engine, so this may be conditionally ignored.\\n\\n Files that cannot be opened with configparser are silently ignored; this is\\n designed so that you can specify a list of potential configuration file\\n locations.\\n\\n :rtype: str or ``NoneType``\\n :returns: Project-ID from default configuration file else ``None``\\n \\\"\\\"\\\"\\n search_paths = []\\n if _USER_ROOT is not None:\\n search_paths.append(_get_nix_config_path())\\n\\n if os.name == 'nt':\\n search_paths.append(_get_windows_config_path())\\n\\n config = configparser.RawConfigParser()\\n config.read(search_paths)\\n\\n if config.has_section(_GCLOUD_CONFIG_SECTION):\\n return config.get(_GCLOUD_CONFIG_SECTION, _GCLOUD_CONFIG_KEY)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_compute_engine_id\",\n \"text\": \"google/cloud/_helpers.py::_compute_engine_id\\ndef _compute_engine_id():\\n \\\"\\\"\\\"Gets the Compute Engine project ID if it can be inferred.\\n\\n Uses 169.254.169.254 for the metadata server to avoid request\\n latency from DNS lookup.\\n\\n See https://cloud.google.com/compute/docs/metadata#metadataserver\\n for information about this IP address. (This IP is also used for\\n Amazon EC2 instances, so the metadata flavor is crucial.)\\n\\n See https://github.com/google/oauth2client/issues/93 for context about\\n DNS latency.\\n\\n :rtype: str or ``NoneType``\\n :returns: Compute Engine project ID if the metadata service is available,\\n else ``None``.\\n \\\"\\\"\\\"\\n host = '169.254.169.254'\\n uri_path = '/computeMetadata/v1/project/project-id'\\n headers = {'Metadata-Flavor': 'Google'}\\n connection = http_client.HTTPConnection(host, timeout=0.1)\\n\\n try:\\n connection.request('GET', uri_path, headers=headers)\\n response = connection.getresponse()\\n if response.status == 200:\\n return response.read()\\n except socket.error: # socket.timeout or socket.error(64, 'Host is down')\\n pass\\n finally:\\n connection.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_get_production_project\",\n \"text\": \"google/cloud/_helpers.py::_get_production_project\\ndef _get_production_project():\\n \\\"\\\"\\\"Gets the production project if it can be inferred.\\\"\\\"\\\"\\n return os.getenv(PROJECT)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_determine_default_project\",\n \"text\": \"google/cloud/_helpers.py::_determine_default_project\\ndef _determine_default_project(project=None):\\n \\\"\\\"\\\"Determine default project ID explicitly or implicitly as fall-back.\\n\\n In implicit case, supports three environments. In order of precedence, the\\n implicit environments are:\\n\\n * GOOGLE_CLOUD_PROJECT environment variable\\n * GOOGLE_APPLICATION_CREDENTIALS JSON file\\n * Get default service project from\\n ``$ gcloud beta auth application-default login``\\n * Google App Engine application ID\\n * Google Compute Engine project ID (from metadata server)\\n\\n :type project: str\\n :param project: Optional. The project name to use as default.\\n\\n :rtype: str or ``NoneType``\\n :returns: Default project if it can be determined.\\n \\\"\\\"\\\"\\n if project is None:\\n project = _get_production_project()\\n\\n if project is None:\\n project = _file_project_id()\\n\\n if project is None:\\n project = _default_service_project_id()\\n\\n if project is None:\\n project = _app_engine_id()\\n\\n if project is None:\\n project = _compute_engine_id()\\n\\n return project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_millis\",\n \"text\": \"google/cloud/_helpers.py::_millis\\ndef _millis(when):\\n \\\"\\\"\\\"Convert a zone-aware datetime to integer milliseconds.\\n\\n :type when: :class:`datetime.datetime`\\n :param when: the datetime to convert\\n\\n :rtype: int\\n :returns: milliseconds since epoch for ``when``\\n \\\"\\\"\\\"\\n micros = _microseconds_from_datetime(when)\\n return micros // 1000\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_datetime_from_microseconds\",\n \"text\": \"google/cloud/_helpers.py::_datetime_from_microseconds\\ndef _datetime_from_microseconds(value):\\n \\\"\\\"\\\"Convert timestamp to datetime, assuming UTC.\\n\\n :type value: float\\n :param value: The timestamp to convert\\n\\n :rtype: :class:`datetime.datetime`\\n :returns: The datetime object created from the value.\\n \\\"\\\"\\\"\\n return _EPOCH + datetime.timedelta(microseconds=value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_microseconds_from_datetime\",\n \"text\": \"google/cloud/_helpers.py::_microseconds_from_datetime\\ndef _microseconds_from_datetime(value):\\n \\\"\\\"\\\"Convert non-none datetime to microseconds.\\n\\n :type value: :class:`datetime.datetime`\\n :param value: The timestamp to convert.\\n\\n :rtype: int\\n :returns: The timestamp, in microseconds.\\n \\\"\\\"\\\"\\n if not value.tzinfo:\\n value = value.replace(tzinfo=UTC)\\n # Regardless of what timezone is on the value, convert it to UTC.\\n value = value.astimezone(UTC)\\n # Convert the datetime to a microsecond timestamp.\\n return int(calendar.timegm(value.timetuple()) * 1e6) + value.microsecond\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_millis_from_datetime\",\n \"text\": \"google/cloud/_helpers.py::_millis_from_datetime\\ndef _millis_from_datetime(value):\\n \\\"\\\"\\\"Convert non-none datetime to timestamp, assuming UTC.\\n\\n :type value: :class:`datetime.datetime`, or None\\n :param value: the timestamp\\n\\n :rtype: int, or ``NoneType``\\n :returns: the timestamp, in milliseconds, or None\\n \\\"\\\"\\\"\\n if value is not None:\\n return _millis(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_date_from_iso8601_date\",\n \"text\": \"google/cloud/_helpers.py::_date_from_iso8601_date\\ndef _date_from_iso8601_date(value):\\n \\\"\\\"\\\"Convert a ISO8601 date string to native datetime date\\n\\n :type value: str\\n :param value: The date string to convert\\n\\n :rtype: :class:`datetime.date`\\n :returns: A datetime date object created from the string\\n\\n \\\"\\\"\\\"\\n return datetime.datetime.strptime(value, '%Y-%m-%d').date()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_rfc3339_to_datetime\",\n \"text\": \"google/cloud/_helpers.py::_rfc3339_to_datetime\\ndef _rfc3339_to_datetime(dt_str):\\n \\\"\\\"\\\"Convert a microsecond-precision timetamp to a native datetime.\\n\\n :type dt_str: str\\n :param dt_str: The string to convert.\\n\\n :rtype: :class:`datetime.datetime`\\n :returns: The datetime object created from the string.\\n \\\"\\\"\\\"\\n return datetime.datetime.strptime(\\n dt_str, _RFC3339_MICROS).replace(tzinfo=UTC)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_rfc3339_nanos_to_datetime\",\n \"text\": \"google/cloud/_helpers.py::_rfc3339_nanos_to_datetime\\ndef _rfc3339_nanos_to_datetime(dt_str):\\n \\\"\\\"\\\"Convert a nanosecond-precision timestamp to a native datetime.\\n\\n .. note::\\n\\n Python datetimes do not support nanosecond precision; this function\\n therefore truncates such values to microseconds.\\n\\n :type dt_str: str\\n :param dt_str: The string to convert.\\n\\n :rtype: :class:`datetime.datetime`\\n :returns: The datetime object created from the string.\\n :raises ValueError: If the timestamp does not match the RFC 3339\\n regular expression.\\n \\\"\\\"\\\"\\n with_nanos = _RFC3339_NANOS.match(dt_str)\\n if with_nanos is None:\\n raise ValueError(\\n 'Timestamp: %r, does not match pattern: %r' % (\\n dt_str, _RFC3339_NANOS.pattern))\\n bare_seconds = datetime.datetime.strptime(\\n with_nanos.group('no_fraction'), _RFC3339_NO_FRACTION)\\n fraction = with_nanos.group('nanos')\\n scale = 9 - len(fraction)\\n nanos = int(fraction) * (10 ** scale)\\n micros = nanos // 1000\\n return bare_seconds.replace(microsecond=micros, tzinfo=UTC)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_datetime_to_rfc3339\",\n \"text\": \"google/cloud/_helpers.py::_datetime_to_rfc3339\\ndef _datetime_to_rfc3339(value, ignore_zone=True):\\n \\\"\\\"\\\"Convert a timestamp to a string.\\n\\n :type value: :class:`datetime.datetime`\\n :param value: The datetime object to be converted to a string.\\n\\n :type ignore_zone: boolean\\n :param ignore_zone: If True, then the timezone (if any) of the datetime\\n object is ignored.\\n\\n :rtype: str\\n :returns: The string representing the datetime stamp.\\n \\\"\\\"\\\"\\n if not ignore_zone and value.tzinfo is not None:\\n # Convert to UTC and remove the time zone info.\\n value = value.replace(tzinfo=None) - value.utcoffset()\\n\\n return value.strftime(_RFC3339_MICROS)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_to_bytes\",\n \"text\": \"google/cloud/_helpers.py::_to_bytes\\ndef _to_bytes(value, encoding='ascii'):\\n \\\"\\\"\\\"Converts a string value to bytes, if necessary.\\n\\n Unfortunately, ``six.b`` is insufficient for this task since in\\n Python2 it does not modify ``unicode`` objects.\\n\\n :type value: str / bytes or unicode\\n :param value: The string/bytes value to be converted.\\n\\n :type encoding: str\\n :param encoding: The encoding to use to convert unicode to bytes. Defaults\\n to \\\"ascii\\\", which will not allow any characters from\\n ordinals larger than 127. Other useful values are\\n \\\"latin-1\\\", which which will only allows byte ordinals\\n (up to 255) and \\\"utf-8\\\", which will encode any unicode\\n that needs to be.\\n\\n :rtype: str / bytes\\n :returns: The original value converted to bytes (if unicode) or as passed\\n in if it started out as bytes.\\n :raises TypeError: if the value could not be converted to bytes.\\n \\\"\\\"\\\"\\n result = (value.encode(encoding)\\n if isinstance(value, six.text_type) else value)\\n if isinstance(result, six.binary_type):\\n return result\\n else:\\n raise TypeError('%r could not be converted to bytes' % (value,))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_bytes_to_unicode\",\n \"text\": \"google/cloud/_helpers.py::_bytes_to_unicode\\ndef _bytes_to_unicode(value):\\n \\\"\\\"\\\"Converts bytes to a unicode value, if necessary.\\n\\n :type value: bytes\\n :param value: bytes value to attempt string conversion on.\\n\\n :rtype: str\\n :returns: The original value converted to unicode (if bytes) or as passed\\n in if it started out as unicode.\\n\\n :raises ValueError: if the value could not be converted to unicode.\\n \\\"\\\"\\\"\\n result = (value.decode('utf-8')\\n if isinstance(value, six.binary_type) else value)\\n if isinstance(result, six.text_type):\\n return result\\n else:\\n raise ValueError('%r could not be converted to unicode' % (value,))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_pb_timestamp_to_datetime\",\n \"text\": \"google/cloud/_helpers.py::_pb_timestamp_to_datetime\\ndef _pb_timestamp_to_datetime(timestamp_pb):\\n \\\"\\\"\\\"Convert a Timestamp protobuf to a datetime object.\\n\\n :type timestamp_pb: :class:`google.protobuf.timestamp_pb2.Timestamp`\\n :param timestamp_pb: A Google returned timestamp protobuf.\\n\\n :rtype: :class:`datetime.datetime`\\n :returns: A UTC datetime object converted from a protobuf timestamp.\\n \\\"\\\"\\\"\\n return (\\n _EPOCH +\\n datetime.timedelta(\\n seconds=timestamp_pb.seconds,\\n microseconds=(timestamp_pb.nanos / 1000.0),\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_pb_timestamp_to_rfc3339\",\n \"text\": \"google/cloud/_helpers.py::_pb_timestamp_to_rfc3339\\ndef _pb_timestamp_to_rfc3339(timestamp_pb):\\n \\\"\\\"\\\"Convert a Timestamp protobuf to an RFC 3339 string.\\n\\n :type timestamp_pb: :class:`google.protobuf.timestamp_pb2.Timestamp`\\n :param timestamp_pb: A Google returned timestamp protobuf.\\n\\n :rtype: string\\n :returns: An RFC 3339 formatted timestamp string.\\n \\\"\\\"\\\"\\n timestamp = _pb_timestamp_to_datetime(timestamp_pb)\\n return _datetime_to_rfc3339(timestamp)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_datetime_to_pb_timestamp\",\n \"text\": \"google/cloud/_helpers.py::_datetime_to_pb_timestamp\\ndef _datetime_to_pb_timestamp(when):\\n \\\"\\\"\\\"Convert a datetime object to a Timestamp protobuf.\\n\\n :type when: :class:`datetime.datetime`\\n :param when: the datetime to convert\\n\\n :rtype: :class:`google.protobuf.timestamp_pb2.Timestamp`\\n :returns: A timestamp protobuf corresponding to the object.\\n \\\"\\\"\\\"\\n ms_value = _microseconds_from_datetime(when)\\n seconds, micros = divmod(ms_value, 10**6)\\n nanos = micros * 10**3\\n return timestamp_pb2.Timestamp(seconds=seconds, nanos=nanos)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::_name_from_project_path\",\n \"text\": \"google/cloud/_helpers.py::_name_from_project_path\\ndef _name_from_project_path(path, project, template):\\n \\\"\\\"\\\"Validate a URI path and get the leaf object's name.\\n\\n :type path: str\\n :param path: URI path containing the name.\\n\\n :type project: str or NoneType\\n :param project: The project associated with the request. It is\\n included for validation purposes. If passed as None,\\n disables validation.\\n\\n :type template: str\\n :param template: Template regex describing the expected form of the path.\\n The regex must have two named groups, 'project' and\\n 'name'.\\n\\n :rtype: str\\n :returns: Name parsed from ``path``.\\n :raises ValueError: if the ``path`` is ill-formed or if the project from\\n the ``path`` does not agree with the ``project``\\n passed in.\\n \\\"\\\"\\\"\\n if isinstance(template, str):\\n template = re.compile(template)\\n\\n match = template.match(path)\\n\\n if not match:\\n raise ValueError('path \\\"%s\\\" did not match expected pattern \\\"%s\\\"' % (\\n path, template.pattern,))\\n\\n if project is not None:\\n found_project = match.group('project')\\n if found_project != project:\\n raise ValueError(\\n 'Project from client (%s) should agree with '\\n 'project from resource(%s).' % (project, found_project))\\n\\n return match.group('name')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::MetadataPlugin.__init__\",\n \"text\": \"google/cloud/_helpers.py::MetadataPlugin.__init__\\n def __init__(self, credentials):\\n self._credentials = credentials\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::MetadataPlugin.__call__\",\n \"text\": \"google/cloud/_helpers.py::MetadataPlugin.__call__\\n def __call__(self, unused_context, callback):\\n \\\"\\\"\\\"Adds authorization header to request metadata.\\n\\n :type unused_context: object\\n :param unused_context: A gRPC context which is not needed\\n to modify headers.\\n\\n :type callback: callable\\n :param callback: A callback which will use the headers.\\n \\\"\\\"\\\"\\n access_token = self._credentials.get_access_token().access_token\\n headers = [\\n ('authorization', 'Bearer ' + access_token),\\n ]\\n callback(headers, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::make_secure_stub\",\n \"text\": \"google/cloud/_helpers.py::make_secure_stub\\ndef make_secure_stub(credentials, user_agent, stub_class, host):\\n \\\"\\\"\\\"Makes a secure stub for an RPC service.\\n\\n Uses / depends on gRPC.\\n\\n :type credentials: :class:`oauth2client.client.OAuth2Credentials`\\n :param credentials: The OAuth2 Credentials to use for creating\\n access tokens.\\n\\n :type user_agent: str\\n :param user_agent: (Optional) The user agent to be used with API requests.\\n\\n :type stub_class: type\\n :param stub_class: A gRPC stub type for a given service.\\n\\n :type host: str\\n :param host: The host for the service.\\n\\n :rtype: object, instance of ``stub_class``\\n :returns: The stub object used to make gRPC requests to a given API.\\n \\\"\\\"\\\"\\n # ssl_channel_credentials() loads root certificates from\\n # `grpc/_adapter/credentials/roots.pem`.\\n transport_creds = grpc.ssl_channel_credentials()\\n custom_metadata_plugin = MetadataPlugin(credentials)\\n auth_creds = grpc.metadata_call_credentials(\\n custom_metadata_plugin, name='google_creds')\\n channel_creds = grpc.composite_channel_credentials(\\n transport_creds, auth_creds)\\n target = '%s:%d' % (host, http_client.HTTPS_PORT)\\n channel_args = (\\n ('grpc.primary_user_agent', user_agent),\\n )\\n channel = grpc.secure_channel(target, channel_creds,\\n options=channel_args)\\n return stub_class(channel)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/_helpers.py::make_insecure_stub\",\n \"text\": \"google/cloud/_helpers.py::make_insecure_stub\\ndef make_insecure_stub(stub_class, host, port=None):\\n \\\"\\\"\\\"Makes an insecure stub for an RPC service.\\n\\n Uses / depends on gRPC.\\n\\n :type stub_class: type\\n :param stub_class: A gRPC stub type for a given service.\\n\\n :type host: str\\n :param host: The host for the service. May also include the port\\n if ``port`` is unspecified.\\n\\n :type port: int\\n :param port: (Optional) The port for the service.\\n\\n :rtype: object, instance of ``stub_class``\\n :returns: The stub object used to make gRPC requests to a given API.\\n \\\"\\\"\\\"\\n if port is None:\\n target = host\\n else:\\n # NOTE: This assumes port != http_client.HTTPS_PORT:\\n target = '%s:%d' % (host, port)\\n channel = grpc.insecure_channel(target)\\n return stub_class(channel)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_not_null\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_not_null\\ndef _not_null(value, field):\\n \\\"\\\"\\\"Check whether 'value' should be coerced to 'field' type.\\\"\\\"\\\"\\n return value is not None or field.mode != 'NULLABLE'\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_int_from_json\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_int_from_json\\ndef _int_from_json(value, field):\\n \\\"\\\"\\\"Coerce 'value' to an int, if set or not nullable.\\\"\\\"\\\"\\n if _not_null(value, field):\\n return int(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_float_from_json\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_float_from_json\\ndef _float_from_json(value, field):\\n \\\"\\\"\\\"Coerce 'value' to a float, if set or not nullable.\\\"\\\"\\\"\\n if _not_null(value, field):\\n return float(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_bool_from_json\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_bool_from_json\\ndef _bool_from_json(value, field):\\n \\\"\\\"\\\"Coerce 'value' to a bool, if set or not nullable.\\\"\\\"\\\"\\n if _not_null(value, field):\\n return value.lower() in ['t', 'true', '1']\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_datetime_from_json\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_datetime_from_json\\ndef _datetime_from_json(value, field):\\n \\\"\\\"\\\"Coerce 'value' to a datetime, if set or not nullable.\\\"\\\"\\\"\\n if _not_null(value, field):\\n # value will be a float in seconds, to microsecond precision, in UTC.\\n return _datetime_from_microseconds(1e6 * float(value))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_date_from_json\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_date_from_json\\ndef _date_from_json(value, field):\\n \\\"\\\"\\\"Coerce 'value' to a datetime date, if set or not nullable\\\"\\\"\\\"\\n if _not_null(value, field):\\n return _date_from_iso8601_date(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_record_from_json\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_record_from_json\\ndef _record_from_json(value, field):\\n \\\"\\\"\\\"Coerce 'value' to a mapping, if set or not nullable.\\\"\\\"\\\"\\n if _not_null(value, field):\\n record = {}\\n for subfield, cell in zip(field.fields, value['f']):\\n converter = _CELLDATA_FROM_JSON[subfield.field_type]\\n if field.mode == 'REPEATED':\\n value = [converter(item, subfield) for item in cell['v']]\\n else:\\n value = converter(cell['v'], subfield)\\n record[subfield.name] = value\\n return record\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_string_from_json\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_string_from_json\\ndef _string_from_json(value, _):\\n \\\"\\\"\\\"NOOP string -> string coercion\\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_rows_from_json\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_rows_from_json\\ndef _rows_from_json(rows, schema):\\n \\\"\\\"\\\"Convert JSON row data to rows w/ appropriate types.\\\"\\\"\\\"\\n rows_data = []\\n for row in rows:\\n row_data = []\\n for field, cell in zip(schema, row['f']):\\n converter = _CELLDATA_FROM_JSON[field.field_type]\\n if field.mode == 'REPEATED':\\n row_data.append([converter(item, field)\\n for item in cell['v']])\\n else:\\n row_data.append(converter(cell['v'], field))\\n rows_data.append(tuple(row_data))\\n return rows_data\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__init__\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__init__\\n def __init__(self, name):\\n self.name = name\\n self._backing_name = '_%s' % (self.name,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__get__\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__get__\\n def __get__(self, instance, owner):\\n \\\"\\\"\\\"Descriptor protocal: accesstor\\\"\\\"\\\"\\n if instance is None:\\n return self\\n return getattr(instance._configuration, self._backing_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_ConfigurationProperty._validate\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_ConfigurationProperty._validate\\n def _validate(self, value):\\n \\\"\\\"\\\"Subclasses override to impose validation policy.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__set__\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__set__\\n def __set__(self, instance, value):\\n \\\"\\\"\\\"Descriptor protocal: mutator\\\"\\\"\\\"\\n self._validate(value)\\n setattr(instance._configuration, self._backing_name, value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__delete__\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_ConfigurationProperty.__delete__\\n def __delete__(self, instance):\\n \\\"\\\"\\\"Descriptor protocal: deleter\\\"\\\"\\\"\\n delattr(instance._configuration, self._backing_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_TypedProperty.__init__\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_TypedProperty.__init__\\n def __init__(self, name, property_type):\\n super(_TypedProperty, self).__init__(name)\\n self.property_type = property_type\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_TypedProperty._validate\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_TypedProperty._validate\\n def _validate(self, value):\\n \\\"\\\"\\\"Ensure that 'value' is of the appropriate type.\\n\\n :raises: ValueError on a type mismatch.\\n \\\"\\\"\\\"\\n if not isinstance(value, self.property_type):\\n raise ValueError('Required type: %s' % (self.property_type,))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_EnumProperty._validate\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_EnumProperty._validate\\n def _validate(self, value):\\n \\\"\\\"\\\"Check that ``value`` is one of the allowed values.\\n\\n :raises: ValueError if value is not allowed.\\n \\\"\\\"\\\"\\n if value not in self.ALLOWED:\\n raise ValueError('Pass one of: %s' ', '.join(self.ALLOWED))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::UDFResource.__init__\",\n \"text\": \"google/cloud/bigquery/_helpers.py::UDFResource.__init__\\n def __init__(self, udf_type, value):\\n self.udf_type = udf_type\\n self.value = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::UDFResource.__eq__\",\n \"text\": \"google/cloud/bigquery/_helpers.py::UDFResource.__eq__\\n def __eq__(self, other):\\n return(\\n self.udf_type == other.udf_type and\\n self.value == other.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::UDFResourcesProperty.__get__\",\n \"text\": \"google/cloud/bigquery/_helpers.py::UDFResourcesProperty.__get__\\n def __get__(self, instance, owner):\\n \\\"\\\"\\\"Descriptor protocol: accessor\\\"\\\"\\\"\\n if instance is None:\\n return self\\n return list(instance._udf_resources)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::UDFResourcesProperty.__set__\",\n \"text\": \"google/cloud/bigquery/_helpers.py::UDFResourcesProperty.__set__\\n def __set__(self, instance, value):\\n \\\"\\\"\\\"Descriptor protocol: mutator\\\"\\\"\\\"\\n if not all(isinstance(u, UDFResource) for u in value):\\n raise ValueError(\\\"udf items must be UDFResource\\\")\\n instance._udf_resources = tuple(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/_helpers.py::_build_udf_resources\",\n \"text\": \"google/cloud/bigquery/_helpers.py::_build_udf_resources\\ndef _build_udf_resources(resources):\\n \\\"\\\"\\\"\\n :type resources: sequence of :class:`UDFResource`\\n :param resources: fields to be appended.\\n\\n :rtype: mapping\\n :returns: a mapping describing userDefinedFunctionResources for the query.\\n \\\"\\\"\\\"\\n udfs = []\\n for resource in resources:\\n udf = {resource.udf_type: resource.value}\\n udfs.append(udf)\\n return udfs\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Project.__init__\",\n \"text\": \"google/cloud/bigquery/client.py::Project.__init__\\n def __init__(self, project_id, numeric_id, friendly_name):\\n self.project_id = project_id\\n self.numeric_id = numeric_id\\n self.friendly_name = friendly_name\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Project.from_api_repr\",\n \"text\": \"google/cloud/bigquery/client.py::Project.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource):\\n \\\"\\\"\\\"Factory: construct an instance from a resource dict.\\\"\\\"\\\"\\n return cls(\\n resource['id'], resource['numericId'], resource['friendlyName'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Client.list_projects\",\n \"text\": \"google/cloud/bigquery/client.py::Client.list_projects\\n def list_projects(self, max_results=None, page_token=None):\\n \\\"\\\"\\\"List projects for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/projects/list\\n\\n :type max_results: int\\n :param max_results: maximum number of projects to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of projects. If\\n not passed, the API will return the first page of\\n projects.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`~google.cloud.bigquery.client.Project`,\\n plus a \\\"next page token\\\" string: if the token is not None,\\n indicates that more projects can be retrieved with another\\n call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if max_results is not None:\\n params['maxResults'] = max_results\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects'\\n resp = self.connection.api_request(method='GET', path=path,\\n query_params=params)\\n projects = [Project.from_api_repr(resource)\\n for resource in resp.get('projects', ())]\\n return projects, resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Client.list_datasets\",\n \"text\": \"google/cloud/bigquery/client.py::Client.list_datasets\\n def list_datasets(self, include_all=False, max_results=None,\\n page_token=None):\\n \\\"\\\"\\\"List datasets for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/list\\n\\n :type include_all: boolean\\n :param include_all: True if results include hidden datasets.\\n\\n :type max_results: int\\n :param max_results: maximum number of datasets to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of datasets. If\\n not passed, the API will return the first page of\\n datasets.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`~google.cloud.bigquery.dataset.Dataset`,\\n plus a \\\"next page token\\\" string: if the token is not None,\\n indicates that more datasets can be retrieved with another\\n call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if include_all:\\n params['all'] = True\\n\\n if max_results is not None:\\n params['maxResults'] = max_results\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/datasets' % (self.project,)\\n resp = self.connection.api_request(method='GET', path=path,\\n query_params=params)\\n datasets = [Dataset.from_api_repr(resource, self)\\n for resource in resp.get('datasets', ())]\\n return datasets, resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Client.dataset\",\n \"text\": \"google/cloud/bigquery/client.py::Client.dataset\\n def dataset(self, dataset_name):\\n \\\"\\\"\\\"Construct a dataset bound to this client.\\n\\n :type dataset_name: str\\n :param dataset_name: Name of the dataset.\\n\\n :rtype: :class:`google.cloud.bigquery.dataset.Dataset`\\n :returns: a new ``Dataset`` instance\\n \\\"\\\"\\\"\\n return Dataset(dataset_name, client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Client.job_from_resource\",\n \"text\": \"google/cloud/bigquery/client.py::Client.job_from_resource\\n def job_from_resource(self, resource):\\n \\\"\\\"\\\"Detect correct job type from resource and instantiate.\\n\\n :type resource: dict\\n :param resource: one job resource from API response\\n\\n :rtype: One of:\\n :class:`google.cloud.bigquery.job.LoadTableFromStorageJob`,\\n :class:`google.cloud.bigquery.job.CopyJob`,\\n :class:`google.cloud.bigquery.job.ExtractTableToStorageJob`,\\n :class:`google.cloud.bigquery.job.QueryJob`,\\n :class:`google.cloud.bigquery.job.RunSyncQueryJob`\\n :returns: the job instance, constructed via the resource\\n \\\"\\\"\\\"\\n config = resource['configuration']\\n if 'load' in config:\\n return LoadTableFromStorageJob.from_api_repr(resource, self)\\n elif 'copy' in config:\\n return CopyJob.from_api_repr(resource, self)\\n elif 'extract' in config:\\n return ExtractTableToStorageJob.from_api_repr(resource, self)\\n elif 'query' in config:\\n return QueryJob.from_api_repr(resource, self)\\n raise ValueError('Cannot parse job resource')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Client.list_jobs\",\n \"text\": \"google/cloud/bigquery/client.py::Client.list_jobs\\n def list_jobs(self, max_results=None, page_token=None, all_users=None,\\n state_filter=None):\\n \\\"\\\"\\\"List jobs for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/list\\n\\n :type max_results: int\\n :param max_results: maximum number of jobs to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of jobs. If\\n not passed, the API will return the first page of\\n jobs.\\n\\n :type all_users: boolean\\n :param all_users: if true, include jobs owned by all users in the\\n project.\\n\\n :type state_filter: str\\n :param state_filter: if passed, include only jobs matching the given\\n state. One of\\n\\n * ``\\\"done\\\"``\\n * ``\\\"pending\\\"``\\n * ``\\\"running\\\"``\\n\\n :rtype: tuple, (list, str)\\n :returns: list of job instances, plus a \\\"next page token\\\" string:\\n if the token is not ``None``, indicates that more jobs can be\\n retrieved with another call, passing that value as\\n ``page_token``).\\n \\\"\\\"\\\"\\n params = {'projection': 'full'}\\n\\n if max_results is not None:\\n params['maxResults'] = max_results\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n if all_users is not None:\\n params['allUsers'] = all_users\\n\\n if state_filter is not None:\\n params['stateFilter'] = state_filter\\n\\n path = '/projects/%s/jobs' % (self.project,)\\n resp = self.connection.api_request(method='GET', path=path,\\n query_params=params)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Client.load_table_from_storage\",\n \"text\": \"google/cloud/bigquery/client.py::Client.load_table_from_storage\\n def load_table_from_storage(self, job_name, destination, *source_uris):\\n \\\"\\\"\\\"Construct a job for loading data into a table from CloudStorage.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.load\\n\\n :type job_name: str\\n :param job_name: Name of the job.\\n\\n :type destination: :class:`google.cloud.bigquery.table.Table`\\n :param destination: Table into which data is to be loaded.\\n\\n :type source_uris: sequence of string\\n :param source_uris: URIs of data files to be loaded; in format\\n ``gs://<bucket_name>/<object_name_or_glob>``.\\n\\n :rtype: :class:`google.cloud.bigquery.job.LoadTableFromStorageJob`\\n :returns: a new ``LoadTableFromStorageJob`` instance\\n \\\"\\\"\\\"\\n return LoadTableFromStorageJob(job_name, destination, source_uris,\\n client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Client.copy_table\",\n \"text\": \"google/cloud/bigquery/client.py::Client.copy_table\\n def copy_table(self, job_name, destination, *sources):\\n \\\"\\\"\\\"Construct a job for copying one or more tables into another table.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.copy\\n\\n :type job_name: str\\n :param job_name: Name of the job.\\n\\n :type destination: :class:`google.cloud.bigquery.table.Table`\\n :param destination: Table into which data is to be copied.\\n\\n :type sources: sequence of :class:`google.cloud.bigquery.table.Table`\\n :param sources: tables to be copied.\\n\\n :rtype: :class:`google.cloud.bigquery.job.CopyJob`\\n :returns: a new ``CopyJob`` instance\\n \\\"\\\"\\\"\\n return CopyJob(job_name, destination, sources, client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Client.extract_table_to_storage\",\n \"text\": \"google/cloud/bigquery/client.py::Client.extract_table_to_storage\\n def extract_table_to_storage(self, job_name, source, *destination_uris):\\n \\\"\\\"\\\"Construct a job for extracting a table into Cloud Storage files.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.extract\\n\\n :type job_name: str\\n :param job_name: Name of the job.\\n\\n :type source: :class:`google.cloud.bigquery.table.Table`\\n :param source: table to be extracted.\\n\\n :type destination_uris: sequence of string\\n :param destination_uris: URIs of CloudStorage file(s) into which\\n table data is to be extracted; in format\\n ``gs://<bucket_name>/<object_name_or_glob>``.\\n\\n :rtype: :class:`google.cloud.bigquery.job.ExtractTableToStorageJob`\\n :returns: a new ``ExtractTableToStorageJob`` instance\\n \\\"\\\"\\\"\\n return ExtractTableToStorageJob(job_name, source, destination_uris,\\n client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Client.run_async_query\",\n \"text\": \"google/cloud/bigquery/client.py::Client.run_async_query\\n def run_async_query(self, job_name, query):\\n \\\"\\\"\\\"Construct a job for running a SQL query asynchronously.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs#configuration.query\\n\\n :type job_name: str\\n :param job_name: Name of the job.\\n\\n :type query: str\\n :param query: SQL query to be executed\\n\\n :rtype: :class:`google.cloud.bigquery.job.QueryJob`\\n :returns: a new ``QueryJob`` instance\\n \\\"\\\"\\\"\\n return QueryJob(job_name, query, client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/client.py::Client.run_sync_query\",\n \"text\": \"google/cloud/bigquery/client.py::Client.run_sync_query\\n def run_sync_query(self, query):\\n \\\"\\\"\\\"Run a SQL query synchronously.\\n\\n :type query: str\\n :param query: SQL query to be executed\\n\\n :rtype: :class:`google.cloud.bigquery.query.QueryResults`\\n :returns: a new ``QueryResults`` instance\\n \\\"\\\"\\\"\\n return QueryResults(query, client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::AccessGrant.__init__\",\n \"text\": \"google/cloud/bigquery/dataset.py::AccessGrant.__init__\\n def __init__(self, role, entity_type, entity_id):\\n if entity_type not in self.ENTITY_TYPES:\\n message = 'Entity type %r not among: %s' % (\\n entity_type, ', '.join(self.ENTITY_TYPES))\\n raise ValueError(message)\\n if entity_type == 'view':\\n if role is not None:\\n raise ValueError('Role must be None for a view. Received '\\n 'role: %r' % (role,))\\n else:\\n if role is None:\\n raise ValueError('Role must be set for entity '\\n 'type %r' % (entity_type,))\\n\\n self.role = role\\n self.entity_type = entity_type\\n self.entity_id = entity_id\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::AccessGrant.__eq__\",\n \"text\": \"google/cloud/bigquery/dataset.py::AccessGrant.__eq__\\n def __eq__(self, other):\\n return (\\n self.role == other.role and\\n self.entity_type == other.entity_type and\\n self.entity_id == other.entity_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::AccessGrant.__repr__\",\n \"text\": \"google/cloud/bigquery/dataset.py::AccessGrant.__repr__\\n def __repr__(self):\\n return '<AccessGrant: role=%s, %s=%s>' % (\\n self.role, self.entity_type, self.entity_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.__init__\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.__init__\\n def __init__(self, name, client, access_grants=()):\\n self.name = name\\n self._client = client\\n self._properties = {}\\n # Let the @property do validation.\\n self.access_grants = access_grants\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.project\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project bound to the dataset.\\n\\n :rtype: string\\n :returns: the project (derived from the client).\\n \\\"\\\"\\\"\\n return self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.path\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URL path for the dataset's APIs.\\n\\n :rtype: string\\n :returns: the path based on project and dataste name.\\n \\\"\\\"\\\"\\n return '/projects/%s/datasets/%s' % (self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.access_grants\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.access_grants\\n @property\\n def access_grants(self):\\n \\\"\\\"\\\"Dataset's access grants.\\n\\n :rtype: list of :class:`AccessGrant`\\n :returns: roles granted to entities for this dataset\\n \\\"\\\"\\\"\\n return list(self._access_grants)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.access_grants\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.access_grants\\n @access_grants.setter\\n def access_grants(self, value):\\n \\\"\\\"\\\"Update dataset's access grants\\n\\n :type value: list of :class:`AccessGrant`\\n :param value: roles granted to entities for this dataset\\n\\n :raises: TypeError if 'value' is not a sequence, or ValueError if\\n any item in the sequence is not an AccessGrant\\n \\\"\\\"\\\"\\n if not all(isinstance(field, AccessGrant) for field in value):\\n raise ValueError('Values must be AccessGrant instances')\\n self._access_grants = tuple(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.created\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.created\\n @property\\n def created(self):\\n \\\"\\\"\\\"Datetime at which the dataset was created.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the creation time (None until set from the server).\\n \\\"\\\"\\\"\\n creation_time = self._properties.get('creationTime')\\n if creation_time is not None:\\n # creation_time will be in milliseconds.\\n return _datetime_from_microseconds(1000.0 * creation_time)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.dataset_id\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.dataset_id\\n @property\\n def dataset_id(self):\\n \\\"\\\"\\\"ID for the dataset resource.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the ID (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('id')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.etag\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.etag\\n @property\\n def etag(self):\\n \\\"\\\"\\\"ETag for the dataset resource.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the ETag (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('etag')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.modified\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.modified\\n @property\\n def modified(self):\\n \\\"\\\"\\\"Datetime at which the dataset was last modified.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the modification time (None until set from the server).\\n \\\"\\\"\\\"\\n modified_time = self._properties.get('lastModifiedTime')\\n if modified_time is not None:\\n # modified_time will be in milliseconds.\\n return _datetime_from_microseconds(1000.0 * modified_time)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.self_link\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.self_link\\n @property\\n def self_link(self):\\n \\\"\\\"\\\"URL for the dataset resource.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the URL (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('selfLink')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.default_table_expiration_ms\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.default_table_expiration_ms\\n @property\\n def default_table_expiration_ms(self):\\n \\\"\\\"\\\"Default expiration time for tables in the dataset.\\n\\n :rtype: integer, or ``NoneType``\\n :returns: The time in milliseconds, or None (the default).\\n \\\"\\\"\\\"\\n return self._properties.get('defaultTableExpirationMs')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.default_table_expiration_ms\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.default_table_expiration_ms\\n @default_table_expiration_ms.setter\\n def default_table_expiration_ms(self, value):\\n \\\"\\\"\\\"Update default expiration time for tables in the dataset.\\n\\n :type value: integer, or ``NoneType``\\n :param value: new default time, in milliseconds\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n if not isinstance(value, six.integer_types) and value is not None:\\n raise ValueError(\\\"Pass an integer, or None\\\")\\n self._properties['defaultTableExpirationMs'] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.description\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.description\\n @property\\n def description(self):\\n \\\"\\\"\\\"Description of the dataset.\\n\\n :rtype: string, or ``NoneType``\\n :returns: The description as set by the user, or None (the default).\\n \\\"\\\"\\\"\\n return self._properties.get('description')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.description\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.description\\n @description.setter\\n def description(self, value):\\n \\\"\\\"\\\"Update description of the dataset.\\n\\n :type value: string, or ``NoneType``\\n :param value: new description\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n if not isinstance(value, six.string_types) and value is not None:\\n raise ValueError(\\\"Pass a string, or None\\\")\\n self._properties['description'] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.friendly_name\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.friendly_name\\n @property\\n def friendly_name(self):\\n \\\"\\\"\\\"Title of the dataset.\\n\\n :rtype: string, or ``NoneType``\\n :returns: The name as set by the user, or None (the default).\\n \\\"\\\"\\\"\\n return self._properties.get('friendlyName')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.friendly_name\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.friendly_name\\n @friendly_name.setter\\n def friendly_name(self, value):\\n \\\"\\\"\\\"Update title of the dataset.\\n\\n :type value: string, or ``NoneType``\\n :param value: new title\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n if not isinstance(value, six.string_types) and value is not None:\\n raise ValueError(\\\"Pass a string, or None\\\")\\n self._properties['friendlyName'] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.location\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.location\\n @property\\n def location(self):\\n \\\"\\\"\\\"Location in which the dataset is hosted.\\n\\n :rtype: string, or ``NoneType``\\n :returns: The location as set by the user, or None (the default).\\n \\\"\\\"\\\"\\n return self._properties.get('location')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.location\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.location\\n @location.setter\\n def location(self, value):\\n \\\"\\\"\\\"Update location in which the dataset is hosted.\\n\\n :type value: string, or ``NoneType``\\n :param value: new location\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n if not isinstance(value, six.string_types) and value is not None:\\n raise ValueError(\\\"Pass a string, or None\\\")\\n self._properties['location'] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.from_api_repr\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client):\\n \\\"\\\"\\\"Factory: construct a dataset given its API representation\\n\\n :type resource: dict\\n :param resource: dataset resource representation returned from the API\\n\\n :type client: :class:`google.cloud.bigquery.client.Client`\\n :param client: Client which holds credentials and project\\n configuration for the dataset.\\n\\n :rtype: :class:`google.cloud.bigquery.dataset.Dataset`\\n :returns: Dataset parsed from ``resource``.\\n \\\"\\\"\\\"\\n if ('datasetReference' not in resource or\\n 'datasetId' not in resource['datasetReference']):\\n raise KeyError('Resource lacks required identity information:'\\n '[\\\"datasetReference\\\"][\\\"datasetId\\\"]')\\n name = resource['datasetReference']['datasetId']\\n dataset = cls(name, client=client)\\n dataset._set_properties(resource)\\n return dataset\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset._require_client\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: :class:`google.cloud.bigquery.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset._parse_access_grants\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset._parse_access_grants\\n @staticmethod\\n def _parse_access_grants(access):\\n \\\"\\\"\\\"Parse a resource fragment into a set of access grants.\\n\\n ``role`` augments the entity type and present **unless** the entity\\n type is ``view``.\\n\\n :type access: list of mappings\\n :param access: each mapping represents a single access grant.\\n\\n :rtype: list of :class:`AccessGrant`\\n :returns: a list of parsed grants.\\n :raises: :class:`ValueError` if a grant in ``access`` has more keys\\n than ``role`` and one additional key.\\n \\\"\\\"\\\"\\n result = []\\n for grant in access:\\n grant = grant.copy()\\n role = grant.pop('role', None)\\n entity_type, entity_id = grant.popitem()\\n if len(grant) != 0:\\n raise ValueError('Grant has unexpected keys remaining.', grant)\\n result.append(\\n AccessGrant(role, entity_type, entity_id))\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset._set_properties\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset._set_properties\\n def _set_properties(self, api_response):\\n \\\"\\\"\\\"Update properties from resource in body of ``api_response``\\n\\n :type api_response: httplib2.Response\\n :param api_response: response returned from an API call.\\n \\\"\\\"\\\"\\n self._properties.clear()\\n cleaned = api_response.copy()\\n access = cleaned.pop('access', ())\\n self.access_grants = self._parse_access_grants(access)\\n if 'creationTime' in cleaned:\\n cleaned['creationTime'] = float(cleaned['creationTime'])\\n if 'lastModifiedTime' in cleaned:\\n cleaned['lastModifiedTime'] = float(cleaned['lastModifiedTime'])\\n if 'defaultTableExpirationMs' in cleaned:\\n cleaned['defaultTableExpirationMs'] = int(\\n cleaned['defaultTableExpirationMs'])\\n self._properties.update(cleaned)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset._build_access_resource\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset._build_access_resource\\n def _build_access_resource(self):\\n \\\"\\\"\\\"Generate a resource fragment for dataset's access grants.\\\"\\\"\\\"\\n result = []\\n for grant in self.access_grants:\\n info = {grant.entity_type: grant.entity_id}\\n if grant.role is not None:\\n info['role'] = grant.role\\n result.append(info)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset._build_resource\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset._build_resource\\n def _build_resource(self):\\n \\\"\\\"\\\"Generate a resource for ``create`` or ``update``.\\\"\\\"\\\"\\n resource = {\\n 'datasetReference': {\\n 'projectId': self.project, 'datasetId': self.name},\\n }\\n if self.default_table_expiration_ms is not None:\\n value = self.default_table_expiration_ms\\n resource['defaultTableExpirationMs'] = value\\n\\n if self.description is not None:\\n resource['description'] = self.description\\n\\n if self.friendly_name is not None:\\n resource['friendlyName'] = self.friendly_name\\n\\n if self.location is not None:\\n resource['location'] = self.location\\n\\n if len(self.access_grants) > 0:\\n resource['access'] = self._build_access_resource()\\n\\n return resource\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.create\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.create\\n def create(self, client=None):\\n \\\"\\\"\\\"API call: create the dataset via a PUT request.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/tables/insert\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n path = '/projects/%s/datasets' % (self.project,)\\n api_response = client.connection.api_request(\\n method='POST', path=path, data=self._build_resource())\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.exists\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"API call: test for the existence of the dataset via a GET request\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/get\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: bool\\n :returns: Boolean indicating existence of the dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n try:\\n client.connection.api_request(method='GET', path=self.path,\\n query_params={'fields': 'id'})\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.reload\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"API call: refresh dataset properties via a GET request.\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/get\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n api_response = client.connection.api_request(\\n method='GET', path=self.path)\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.patch\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.patch\\n def patch(self, client=None, **kw):\\n \\\"\\\"\\\"API call: update individual dataset properties via a PATCH request.\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/patch\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :type kw: ``dict``\\n :param kw: properties to be patched.\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n partial = {}\\n\\n if 'default_table_expiration_ms' in kw:\\n value = kw['default_table_expiration_ms']\\n if not isinstance(value, six.integer_types) and value is not None:\\n raise ValueError(\\\"Pass an integer, or None\\\")\\n partial['defaultTableExpirationMs'] = value\\n\\n if 'description' in kw:\\n partial['description'] = kw['description']\\n\\n if 'friendly_name' in kw:\\n partial['friendlyName'] = kw['friendly_name']\\n\\n if 'location' in kw:\\n partial['location'] = kw['location']\\n\\n api_response = client.connection.api_request(\\n method='PATCH', path=self.path, data=partial)\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.update\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.update\\n def update(self, client=None):\\n \\\"\\\"\\\"API call: update dataset properties via a PUT request.\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/datasets/update\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api_response = client.connection.api_request(\\n method='PUT', path=self.path, data=self._build_resource())\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.delete\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.delete\\n def delete(self, client=None):\\n \\\"\\\"\\\"API call: delete the dataset via a DELETE request.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/tables/delete\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.connection.api_request(method='DELETE', path=self.path)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.list_tables\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.list_tables\\n def list_tables(self, max_results=None, page_token=None):\\n \\\"\\\"\\\"List tables for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/tables/list\\n\\n :type max_results: int\\n :param max_results: maximum number of tables to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of datasets. If\\n not passed, the API will return the first page of\\n datasets.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`google.cloud.bigquery.table.Table`, plus a\\n \\\"next page token\\\" string: if not ``None``, indicates that\\n more tables can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if max_results is not None:\\n params['maxResults'] = max_results\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/datasets/%s/tables' % (self.project, self.name)\\n connection = self._client.connection\\n resp = connection.api_request(method='GET', path=path,\\n query_params=params)\\n tables = [Table.from_api_repr(resource, self)\\n for resource in resp.get('tables', ())]\\n return tables, resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/dataset.py::Dataset.table\",\n \"text\": \"google/cloud/bigquery/dataset.py::Dataset.table\\n def table(self, name, schema=()):\\n \\\"\\\"\\\"Construct a table bound to this dataset.\\n\\n :type name: string\\n :param name: Name of the table.\\n\\n :type schema: list of :class:`google.cloud.bigquery.table.SchemaField`\\n :param schema: The table's schema\\n\\n :rtype: :class:`google.cloud.bigquery.table.Table`\\n :returns: a new ``Table`` instance\\n \\\"\\\"\\\"\\n return Table(name, dataset=self, schema=schema)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_BaseJob.__init__\",\n \"text\": \"google/cloud/bigquery/job.py::_BaseJob.__init__\\n def __init__(self, client):\\n self._client = client\\n self._properties = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_BaseJob.project\",\n \"text\": \"google/cloud/bigquery/job.py::_BaseJob.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project bound to the job.\\n\\n :rtype: string\\n :returns: the project (derived from the client).\\n \\\"\\\"\\\"\\n return self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_BaseJob._require_client\",\n \"text\": \"google/cloud/bigquery/job.py::_BaseJob._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: :class:`google.cloud.bigquery.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.__init__\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.__init__\\n def __init__(self, name, client):\\n super(_AsyncJob, self).__init__(client)\\n self.name = name\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.job_type\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.job_type\\n @property\\n def job_type(self):\\n \\\"\\\"\\\"Type of job\\n\\n :rtype: string\\n :returns: one of 'load', 'copy', 'extract', 'query'\\n \\\"\\\"\\\"\\n return self._JOB_TYPE\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.path\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URL path for the job's APIs.\\n\\n :rtype: string\\n :returns: the path based on project and job name.\\n \\\"\\\"\\\"\\n return '/projects/%s/jobs/%s' % (self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.etag\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.etag\\n @property\\n def etag(self):\\n \\\"\\\"\\\"ETag for the job resource.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the ETag (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('etag')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.self_link\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.self_link\\n @property\\n def self_link(self):\\n \\\"\\\"\\\"URL for the job resource.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the URL (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('selfLink')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.user_email\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.user_email\\n @property\\n def user_email(self):\\n \\\"\\\"\\\"E-mail address of user who submitted the job.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the URL (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('user_email')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.created\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.created\\n @property\\n def created(self):\\n \\\"\\\"\\\"Datetime at which the job was created.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the creation time (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n millis = statistics.get('creationTime')\\n if millis is not None:\\n return _datetime_from_microseconds(millis * 1000.0)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.started\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.started\\n @property\\n def started(self):\\n \\\"\\\"\\\"Datetime at which the job was started.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the start time (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n millis = statistics.get('startTime')\\n if millis is not None:\\n return _datetime_from_microseconds(millis * 1000.0)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.ended\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.ended\\n @property\\n def ended(self):\\n \\\"\\\"\\\"Datetime at which the job finished.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the end time (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n millis = statistics.get('endTime')\\n if millis is not None:\\n return _datetime_from_microseconds(millis * 1000.0)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.error_result\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.error_result\\n @property\\n def error_result(self):\\n \\\"\\\"\\\"Error information about the job as a whole.\\n\\n :rtype: mapping, or ``NoneType``\\n :returns: the error information (None until set from the server).\\n \\\"\\\"\\\"\\n status = self._properties.get('status')\\n if status is not None:\\n return status.get('errorResult')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.errors\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.errors\\n @property\\n def errors(self):\\n \\\"\\\"\\\"Information about individual errors generated by the job.\\n\\n :rtype: list of mappings, or ``NoneType``\\n :returns: the error information (None until set from the server).\\n \\\"\\\"\\\"\\n status = self._properties.get('status')\\n if status is not None:\\n return status.get('errors')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.state\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.state\\n @property\\n def state(self):\\n \\\"\\\"\\\"Status of the job.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the state (None until set from the server).\\n \\\"\\\"\\\"\\n status = self._properties.get('status')\\n if status is not None:\\n return status.get('state')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob._scrub_local_properties\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob._scrub_local_properties\\n def _scrub_local_properties(self, cleaned):\\n \\\"\\\"\\\"Helper: handle subclass properties in cleaned.\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob._set_properties\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob._set_properties\\n def _set_properties(self, api_response):\\n \\\"\\\"\\\"Update properties from resource in body of ``api_response``\\n\\n :type api_response: httplib2.Response\\n :param api_response: response returned from an API call\\n \\\"\\\"\\\"\\n cleaned = api_response.copy()\\n self._scrub_local_properties(cleaned)\\n\\n statistics = cleaned.get('statistics', {})\\n if 'creationTime' in statistics:\\n statistics['creationTime'] = float(statistics['creationTime'])\\n if 'startTime' in statistics:\\n statistics['startTime'] = float(statistics['startTime'])\\n if 'endTime' in statistics:\\n statistics['endTime'] = float(statistics['endTime'])\\n\\n self._properties.clear()\\n self._properties.update(cleaned)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob._get_resource_config\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob._get_resource_config\\n @classmethod\\n def _get_resource_config(cls, resource):\\n \\\"\\\"\\\"Helper for :meth:`from_api_repr`\\n\\n :type resource: dict\\n :param resource: resource for the job\\n\\n :rtype: dict\\n :returns: tuple (string, dict), where the first element is the\\n job name and the second contains job-specific configuration.\\n :raises: :class:`KeyError` if the resource has no identifier, or\\n is missing the appropriate configuration.\\n \\\"\\\"\\\"\\n if ('jobReference' not in resource or\\n 'jobId' not in resource['jobReference']):\\n raise KeyError('Resource lacks required identity information: '\\n '[\\\"jobReference\\\"][\\\"jobId\\\"]')\\n name = resource['jobReference']['jobId']\\n if ('configuration' not in resource or\\n cls._JOB_TYPE not in resource['configuration']):\\n raise KeyError('Resource lacks required configuration: '\\n '[\\\"configuration\\\"][\\\"%s\\\"]' % cls._JOB_TYPE)\\n config = resource['configuration'][cls._JOB_TYPE]\\n return name, config\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.begin\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.begin\\n def begin(self, client=None):\\n \\\"\\\"\\\"API call: begin the job via a POST request\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/insert\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :raises: :exc:`ValueError` if the job has already begin.\\n \\\"\\\"\\\"\\n if self.state is not None:\\n raise ValueError(\\\"Job already begun.\\\")\\n\\n client = self._require_client(client)\\n path = '/projects/%s/jobs' % (self.project,)\\n api_response = client.connection.api_request(\\n method='POST', path=path, data=self._build_resource())\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.exists\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"API call: test for the existence of the job via a GET request\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/get\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: bool\\n :returns: Boolean indicating existence of the job.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n try:\\n client.connection.api_request(method='GET', path=self.path,\\n query_params={'fields': 'id'})\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.reload\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"API call: refresh job properties via a GET request\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/get\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n api_response = client.connection.api_request(\\n method='GET', path=self.path)\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::_AsyncJob.cancel\",\n \"text\": \"google/cloud/bigquery/job.py::_AsyncJob.cancel\\n def cancel(self, client=None):\\n \\\"\\\"\\\"API call: cancel job via a POST request\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/cancel\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n api_response = client.connection.api_request(\\n method='POST', path='%s/cancel' % (self.path,))\\n self._set_properties(api_response['job'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.__init__\",\n \"text\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.__init__\\n def __init__(self, name, destination, source_uris, client, schema=()):\\n super(LoadTableFromStorageJob, self).__init__(name, client)\\n self.destination = destination\\n self.source_uris = source_uris\\n # Let the @property do validation.\\n self.schema = schema\\n self._configuration = _LoadConfiguration()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.schema\",\n \"text\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.schema\\n @property\\n def schema(self):\\n \\\"\\\"\\\"Table's schema.\\n\\n :rtype: list of :class:`SchemaField`\\n :returns: fields describing the schema\\n \\\"\\\"\\\"\\n return list(self._schema)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.schema\",\n \"text\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.schema\\n @schema.setter\\n def schema(self, value):\\n \\\"\\\"\\\"Update table's schema\\n\\n :type value: list of :class:`SchemaField`\\n :param value: fields describing the schema\\n\\n :raises: TypeError if 'value' is not a sequence, or ValueError if\\n any item in the sequence is not a SchemaField\\n \\\"\\\"\\\"\\n if not all(isinstance(field, SchemaField) for field in value):\\n raise ValueError('Schema items must be fields')\\n self._schema = tuple(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.input_file_bytes\",\n \"text\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.input_file_bytes\\n @property\\n def input_file_bytes(self):\\n \\\"\\\"\\\"Count of bytes loaded from source files.\\n\\n :rtype: integer, or ``NoneType``\\n :returns: the count (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n return int(statistics['load']['inputFileBytes'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.input_files\",\n \"text\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.input_files\\n @property\\n def input_files(self):\\n \\\"\\\"\\\"Count of source files.\\n\\n :rtype: integer, or ``NoneType``\\n :returns: the count (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n return int(statistics['load']['inputFiles'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.output_bytes\",\n \"text\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.output_bytes\\n @property\\n def output_bytes(self):\\n \\\"\\\"\\\"Count of bytes saved to destination table.\\n\\n :rtype: integer, or ``NoneType``\\n :returns: the count (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n return int(statistics['load']['outputBytes'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.output_rows\",\n \"text\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.output_rows\\n @property\\n def output_rows(self):\\n \\\"\\\"\\\"Count of rows saved to destination table.\\n\\n :rtype: integer, or ``NoneType``\\n :returns: the count (None until set from the server).\\n \\\"\\\"\\\"\\n statistics = self._properties.get('statistics')\\n if statistics is not None:\\n return int(statistics['load']['outputRows'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob._populate_config_resource\",\n \"text\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob._populate_config_resource\\n def _populate_config_resource(self, configuration):\\n \\\"\\\"\\\"Helper for _build_resource: copy config properties to resource\\\"\\\"\\\"\\n if self.allow_jagged_rows is not None:\\n configuration['allowJaggedRows'] = self.allow_jagged_rows\\n if self.allow_quoted_newlines is not None:\\n configuration['allowQuotedNewlines'] = self.allow_quoted_newlines\\n if self.create_disposition is not None:\\n configuration['createDisposition'] = self.create_disposition\\n if self.encoding is not None:\\n configuration['encoding'] = self.encoding\\n if self.field_delimiter is not None:\\n configuration['fieldDelimiter'] = self.field_delimiter\\n if self.ignore_unknown_values is not None:\\n configuration['ignoreUnknownValues'] = self.ignore_unknown_values\\n if self.max_bad_records is not None:\\n configuration['maxBadRecords'] = self.max_bad_records\\n if self.quote_character is not None:\\n configuration['quote'] = self.quote_character\\n if self.skip_leading_rows is not None:\\n configuration['skipLeadingRows'] = self.skip_leading_rows\\n if self.source_format is not None:\\n configuration['sourceFormat'] = self.source_format\\n if self.write_disposition is not None:\\n configuration['writeDisposition'] = self.write_disposition\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob._build_resource\",\n \"text\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob._build_resource\\n def _build_resource(self):\\n \\\"\\\"\\\"Generate a resource for :meth:`begin`.\\\"\\\"\\\"\\n resource = {\\n 'jobReference': {\\n 'projectId': self.project,\\n 'jobId': self.name,\\n },\\n 'configuration': {\\n self._JOB_TYPE: {\\n 'sourceUris': self.source_uris,\\n 'destinationTable': {\\n 'projectId': self.destination.project,\\n 'datasetId': self.destination.dataset_name,\\n 'tableId': self.destination.name,\\n },\\n },\\n },\\n }\\n configuration = resource['configuration'][self._JOB_TYPE]\\n self._populate_config_resource(configuration)\\n\\n if len(self.schema) > 0:\\n configuration['schema'] = {\\n 'fields': _build_schema_resource(self.schema)}\\n\\n return resource\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob._scrub_local_properties\",\n \"text\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob._scrub_local_properties\\n def _scrub_local_properties(self, cleaned):\\n \\\"\\\"\\\"Helper: handle subclass properties in cleaned.\\\"\\\"\\\"\\n schema = cleaned.pop('schema', {'fields': ()})\\n self.schema = _parse_schema_resource(schema)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.from_api_repr\",\n \"text\": \"google/cloud/bigquery/job.py::LoadTableFromStorageJob.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client):\\n \\\"\\\"\\\"Factory: construct a job given its API representation\\n\\n .. note:\\n\\n This method assumes that the project found in the resource matches\\n the client's project.\\n\\n :type resource: dict\\n :param resource: dataset job representation returned from the API\\n\\n :type client: :class:`google.cloud.bigquery.client.Client`\\n :param client: Client which holds credentials and project\\n configuration for the dataset.\\n\\n :rtype: :class:`google.cloud.bigquery.job.LoadTableFromStorageJob`\\n :returns: Job parsed from ``resource``.\\n \\\"\\\"\\\"\\n name, config = cls._get_resource_config(resource)\\n dest_config = config['destinationTable']\\n dataset = Dataset(dest_config['datasetId'], client)\\n destination = Table(dest_config['tableId'], dataset)\\n source_urls = config.get('sourceUris', ())\\n job = cls(name, destination, source_urls, client=client)\\n job._set_properties(resource)\\n return job\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::CopyJob.__init__\",\n \"text\": \"google/cloud/bigquery/job.py::CopyJob.__init__\\n def __init__(self, name, destination, sources, client):\\n super(CopyJob, self).__init__(name, client)\\n self.destination = destination\\n self.sources = sources\\n self._configuration = _CopyConfiguration()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::CopyJob._populate_config_resource\",\n \"text\": \"google/cloud/bigquery/job.py::CopyJob._populate_config_resource\\n def _populate_config_resource(self, configuration):\\n \\\"\\\"\\\"Helper for _build_resource: copy config properties to resource\\\"\\\"\\\"\\n if self.create_disposition is not None:\\n configuration['createDisposition'] = self.create_disposition\\n if self.write_disposition is not None:\\n configuration['writeDisposition'] = self.write_disposition\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::CopyJob._build_resource\",\n \"text\": \"google/cloud/bigquery/job.py::CopyJob._build_resource\\n def _build_resource(self):\\n \\\"\\\"\\\"Generate a resource for :meth:`begin`.\\\"\\\"\\\"\\n\\n source_refs = [{\\n 'projectId': table.project,\\n 'datasetId': table.dataset_name,\\n 'tableId': table.name,\\n } for table in self.sources]\\n\\n resource = {\\n 'jobReference': {\\n 'projectId': self.project,\\n 'jobId': self.name,\\n },\\n 'configuration': {\\n self._JOB_TYPE: {\\n 'sourceTables': source_refs,\\n 'destinationTable': {\\n 'projectId': self.destination.project,\\n 'datasetId': self.destination.dataset_name,\\n 'tableId': self.destination.name,\\n },\\n },\\n },\\n }\\n configuration = resource['configuration'][self._JOB_TYPE]\\n self._populate_config_resource(configuration)\\n\\n return resource\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::CopyJob.from_api_repr\",\n \"text\": \"google/cloud/bigquery/job.py::CopyJob.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client):\\n \\\"\\\"\\\"Factory: construct a job given its API representation\\n\\n .. note:\\n\\n This method assumes that the project found in the resource matches\\n the client's project.\\n\\n :type resource: dict\\n :param resource: dataset job representation returned from the API\\n\\n :type client: :class:`google.cloud.bigquery.client.Client`\\n :param client: Client which holds credentials and project\\n configuration for the dataset.\\n\\n :rtype: :class:`google.cloud.bigquery.job.CopyJob`\\n :returns: Job parsed from ``resource``.\\n \\\"\\\"\\\"\\n name, config = cls._get_resource_config(resource)\\n dest_config = config['destinationTable']\\n dataset = Dataset(dest_config['datasetId'], client)\\n destination = Table(dest_config['tableId'], dataset)\\n sources = []\\n for source_config in config['sourceTables']:\\n dataset = Dataset(source_config['datasetId'], client)\\n sources.append(Table(source_config['tableId'], dataset))\\n job = cls(name, destination, sources, client=client)\\n job._set_properties(resource)\\n return job\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::ExtractTableToStorageJob.__init__\",\n \"text\": \"google/cloud/bigquery/job.py::ExtractTableToStorageJob.__init__\\n def __init__(self, name, source, destination_uris, client):\\n super(ExtractTableToStorageJob, self).__init__(name, client)\\n self.source = source\\n self.destination_uris = destination_uris\\n self._configuration = _ExtractConfiguration()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::ExtractTableToStorageJob._populate_config_resource\",\n \"text\": \"google/cloud/bigquery/job.py::ExtractTableToStorageJob._populate_config_resource\\n def _populate_config_resource(self, configuration):\\n \\\"\\\"\\\"Helper for _build_resource: copy config properties to resource\\\"\\\"\\\"\\n if self.compression is not None:\\n configuration['compression'] = self.compression\\n if self.destination_format is not None:\\n configuration['destinationFormat'] = self.destination_format\\n if self.field_delimiter is not None:\\n configuration['fieldDelimiter'] = self.field_delimiter\\n if self.print_header is not None:\\n configuration['printHeader'] = self.print_header\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::ExtractTableToStorageJob._build_resource\",\n \"text\": \"google/cloud/bigquery/job.py::ExtractTableToStorageJob._build_resource\\n def _build_resource(self):\\n \\\"\\\"\\\"Generate a resource for :meth:`begin`.\\\"\\\"\\\"\\n\\n source_ref = {\\n 'projectId': self.source.project,\\n 'datasetId': self.source.dataset_name,\\n 'tableId': self.source.name,\\n }\\n\\n resource = {\\n 'jobReference': {\\n 'projectId': self.project,\\n 'jobId': self.name,\\n },\\n 'configuration': {\\n self._JOB_TYPE: {\\n 'sourceTable': source_ref,\\n 'destinationUris': self.destination_uris,\\n },\\n },\\n }\\n configuration = resource['configuration'][self._JOB_TYPE]\\n self._populate_config_resource(configuration)\\n\\n return resource\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::ExtractTableToStorageJob.from_api_repr\",\n \"text\": \"google/cloud/bigquery/job.py::ExtractTableToStorageJob.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client):\\n \\\"\\\"\\\"Factory: construct a job given its API representation\\n\\n .. note:\\n\\n This method assumes that the project found in the resource matches\\n the client's project.\\n\\n :type resource: dict\\n :param resource: dataset job representation returned from the API\\n\\n :type client: :class:`google.cloud.bigquery.client.Client`\\n :param client: Client which holds credentials and project\\n configuration for the dataset.\\n\\n :rtype: :class:`google.cloud.bigquery.job.ExtractTableToStorageJob`\\n :returns: Job parsed from ``resource``.\\n \\\"\\\"\\\"\\n name, config = cls._get_resource_config(resource)\\n source_config = config['sourceTable']\\n dataset = Dataset(source_config['datasetId'], client)\\n source = Table(source_config['tableId'], dataset)\\n destination_uris = config['destinationUris']\\n job = cls(name, source, destination_uris, client=client)\\n job._set_properties(resource)\\n return job\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::QueryJob.__init__\",\n \"text\": \"google/cloud/bigquery/job.py::QueryJob.__init__\\n def __init__(self, name, query, client, udf_resources=()):\\n super(QueryJob, self).__init__(name, client)\\n self.query = query\\n self.udf_resources = udf_resources\\n self._configuration = _AsyncQueryConfiguration()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::QueryJob._destination_table_resource\",\n \"text\": \"google/cloud/bigquery/job.py::QueryJob._destination_table_resource\\n def _destination_table_resource(self):\\n \\\"\\\"\\\"Create a JSON resource for the destination table.\\n\\n Helper for :meth:`_populate_config_resource` and\\n :meth:`_scrub_local_properties`\\n \\\"\\\"\\\"\\n if self.destination is not None:\\n return {\\n 'projectId': self.destination.project,\\n 'datasetId': self.destination.dataset_name,\\n 'tableId': self.destination.name,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::QueryJob._populate_config_resource_booleans\",\n \"text\": \"google/cloud/bigquery/job.py::QueryJob._populate_config_resource_booleans\\n def _populate_config_resource_booleans(self, configuration):\\n \\\"\\\"\\\"Helper for _populate_config_resource.\\\"\\\"\\\"\\n if self.allow_large_results is not None:\\n configuration['allowLargeResults'] = self.allow_large_results\\n if self.flatten_results is not None:\\n configuration['flattenResults'] = self.flatten_results\\n if self.use_query_cache is not None:\\n configuration['useQueryCache'] = self.use_query_cache\\n if self.use_legacy_sql is not None:\\n configuration['useLegacySql'] = self.use_legacy_sql\\n if self.dry_run is not None:\\n configuration['dryRun'] = self.dry_run\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::QueryJob._populate_config_resource\",\n \"text\": \"google/cloud/bigquery/job.py::QueryJob._populate_config_resource\\n def _populate_config_resource(self, configuration):\\n \\\"\\\"\\\"Helper for _build_resource: copy config properties to resource\\\"\\\"\\\"\\n self._populate_config_resource_booleans(configuration)\\n\\n if self.create_disposition is not None:\\n configuration['createDisposition'] = self.create_disposition\\n if self.default_dataset is not None:\\n configuration['defaultDataset'] = {\\n 'projectId': self.default_dataset.project,\\n 'datasetId': self.default_dataset.name,\\n }\\n if self.destination is not None:\\n table_res = self._destination_table_resource()\\n configuration['destinationTable'] = table_res\\n if self.priority is not None:\\n configuration['priority'] = self.priority\\n if self.write_disposition is not None:\\n configuration['writeDisposition'] = self.write_disposition\\n if self.maximum_billing_tier is not None:\\n configuration['maximumBillingTier'] = self.maximum_billing_tier\\n if self.maximum_bytes_billed is not None:\\n configuration['maximumBytesBilled'] = self.maximum_bytes_billed\\n if len(self._udf_resources) > 0:\\n configuration[self._UDF_KEY] = _build_udf_resources(\\n self._udf_resources)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::QueryJob._build_resource\",\n \"text\": \"google/cloud/bigquery/job.py::QueryJob._build_resource\\n def _build_resource(self):\\n \\\"\\\"\\\"Generate a resource for :meth:`begin`.\\\"\\\"\\\"\\n\\n resource = {\\n 'jobReference': {\\n 'projectId': self.project,\\n 'jobId': self.name,\\n },\\n 'configuration': {\\n self._JOB_TYPE: {\\n 'query': self.query,\\n },\\n },\\n }\\n configuration = resource['configuration'][self._JOB_TYPE]\\n self._populate_config_resource(configuration)\\n\\n return resource\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::QueryJob._scrub_local_properties\",\n \"text\": \"google/cloud/bigquery/job.py::QueryJob._scrub_local_properties\\n def _scrub_local_properties(self, cleaned):\\n \\\"\\\"\\\"Helper: handle subclass properties in cleaned.\\n\\n .. note:\\n\\n This method assumes that the project found in the resource matches\\n the client's project.\\n \\\"\\\"\\\"\\n configuration = cleaned['configuration']['query']\\n dest_remote = configuration.get('destinationTable')\\n\\n if dest_remote is None:\\n if self.destination is not None:\\n del self.destination\\n else:\\n dest_local = self._destination_table_resource()\\n if dest_remote != dest_local:\\n dataset = self._client.dataset(dest_remote['datasetId'])\\n self.destination = dataset.table(dest_remote['tableId'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::QueryJob.from_api_repr\",\n \"text\": \"google/cloud/bigquery/job.py::QueryJob.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client):\\n \\\"\\\"\\\"Factory: construct a job given its API representation\\n\\n :type resource: dict\\n :param resource: dataset job representation returned from the API\\n\\n :type client: :class:`google.cloud.bigquery.client.Client`\\n :param client: Client which holds credentials and project\\n configuration for the dataset.\\n\\n :rtype: :class:`google.cloud.bigquery.job.RunAsyncQueryJob`\\n :returns: Job parsed from ``resource``.\\n \\\"\\\"\\\"\\n name, config = cls._get_resource_config(resource)\\n query = config['query']\\n job = cls(name, query, client=client)\\n job._set_properties(resource)\\n return job\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/job.py::QueryJob.results\",\n \"text\": \"google/cloud/bigquery/job.py::QueryJob.results\\n def results(self):\\n \\\"\\\"\\\"Construct a QueryResults instance, bound to this job.\\n\\n :rtype: :class:`~google.cloud.bigquery.query.QueryResults`\\n :returns: results instance\\n \\\"\\\"\\\"\\n from google.cloud.bigquery.query import QueryResults\\n return QueryResults.from_query_job(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.__init__\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.__init__\\n def __init__(self, query, client, udf_resources=()):\\n self._client = client\\n self._properties = {}\\n self.query = query\\n self._configuration = _SyncQueryConfiguration()\\n self.udf_resources = udf_resources\\n self._job = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.from_query_job\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.from_query_job\\n @classmethod\\n def from_query_job(cls, job):\\n \\\"\\\"\\\"Factory: construct from an existing job.\\n\\n :type job: :class:`~google.cloud.bigquery.job.QueryJob`\\n :param job: existing job\\n\\n :rtype: :class:`QueryResults`\\n :returns: the instance, bound to the job\\n \\\"\\\"\\\"\\n instance = cls(job.query, job._client, job.udf_resources)\\n instance._job = job\\n job_ref = instance._properties.setdefault('jobReference', {})\\n job_ref['jobId'] = job.name\\n if job.default_dataset is not None:\\n instance.default_dataset = job.default_dataset\\n if job.use_query_cache is not None:\\n instance.use_query_cache = job.use_query_cache\\n if job.use_legacy_sql is not None:\\n instance.use_legacy_sql = job.use_legacy_sql\\n return instance\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.project\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project bound to the job.\\n\\n :rtype: string\\n :returns: the project (derived from the client).\\n \\\"\\\"\\\"\\n return self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults._require_client\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: :class:`google.cloud.bigquery.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.cache_hit\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.cache_hit\\n @property\\n def cache_hit(self):\\n \\\"\\\"\\\"Query results served from cache.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#cacheHit\\n\\n :rtype: boolean or ``NoneType``\\n :returns: True if the query results were served from cache (None\\n until set by the server).\\n \\\"\\\"\\\"\\n return self._properties.get('cacheHit')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.complete\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.complete\\n @property\\n def complete(self):\\n \\\"\\\"\\\"Server completed query.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#jobComplete\\n\\n :rtype: boolean or ``NoneType``\\n :returns: True if the query completed on the server (None\\n until set by the server).\\n \\\"\\\"\\\"\\n return self._properties.get('jobComplete')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.errors\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.errors\\n @property\\n def errors(self):\\n \\\"\\\"\\\"Errors generated by the query.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#errors\\n\\n :rtype: list of mapping, or ``NoneType``\\n :returns: Mappings describing errors generated on the server (None\\n until set by the server).\\n \\\"\\\"\\\"\\n return self._properties.get('errors')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.name\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Job name, generated by the back-end.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#jobReference\\n\\n :rtype: list of mapping, or ``NoneType``\\n :returns: Mappings describing errors generated on the server (None\\n until set by the server).\\n \\\"\\\"\\\"\\n return self._properties.get('jobReference', {}).get('jobId')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.job\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.job\\n @property\\n def job(self):\\n \\\"\\\"\\\"Job instance used to run the query.\\n\\n :rtype: :class:`google.cloud.bigquery.job.QueryJob`, or ``NoneType``\\n :returns: Job instance used to run the query (None until\\n ``jobReference`` property is set by the server).\\n \\\"\\\"\\\"\\n if self._job is None:\\n job_ref = self._properties.get('jobReference')\\n if job_ref is not None:\\n self._job = QueryJob(job_ref['jobId'], self.query,\\n self._client)\\n return self._job\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.page_token\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.page_token\\n @property\\n def page_token(self):\\n \\\"\\\"\\\"Token for fetching next bach of results.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#pageToken\\n\\n :rtype: string, or ``NoneType``\\n :returns: Token generated on the server (None until set by the server).\\n \\\"\\\"\\\"\\n return self._properties.get('pageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.total_rows\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.total_rows\\n @property\\n def total_rows(self):\\n \\\"\\\"\\\"Total number of rows returned by the query.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#totalRows\\n\\n :rtype: integer, or ``NoneType``\\n :returns: Count generated on the server (None until set by the server).\\n \\\"\\\"\\\"\\n return self._properties.get('totalRows')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.total_bytes_processed\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.total_bytes_processed\\n @property\\n def total_bytes_processed(self):\\n \\\"\\\"\\\"Total number of bytes processed by the query.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#totalBytesProcessed\\n\\n :rtype: integer, or ``NoneType``\\n :returns: Count generated on the server (None until set by the server).\\n \\\"\\\"\\\"\\n return self._properties.get('totalBytesProcessed')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.rows\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.rows\\n @property\\n def rows(self):\\n \\\"\\\"\\\"Query results.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#rows\\n\\n :rtype: list of tuples of row values, or ``NoneType``\\n :returns: fields describing the schema (None until set by the server).\\n \\\"\\\"\\\"\\n return _rows_from_json(self._properties.get('rows', ()), self.schema)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.schema\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.schema\\n @property\\n def schema(self):\\n \\\"\\\"\\\"Schema for query results.\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query#schema\\n\\n :rtype: list of :class:`SchemaField`, or ``NoneType``\\n :returns: fields describing the schema (None until set by the server).\\n \\\"\\\"\\\"\\n return _parse_schema_resource(self._properties.get('schema', {}))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults._set_properties\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults._set_properties\\n def _set_properties(self, api_response):\\n \\\"\\\"\\\"Update properties from resource in body of ``api_response``\\n\\n :type api_response: httplib2.Response\\n :param api_response: response returned from an API call\\n \\\"\\\"\\\"\\n self._properties.clear()\\n self._properties.update(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults._build_resource\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults._build_resource\\n def _build_resource(self):\\n \\\"\\\"\\\"Generate a resource for :meth:`begin`.\\\"\\\"\\\"\\n resource = {'query': self.query}\\n\\n if self.default_dataset is not None:\\n resource['defaultDataset'] = {\\n 'projectId': self.project,\\n 'datasetId': self.default_dataset.name,\\n }\\n\\n if self.max_results is not None:\\n resource['maxResults'] = self.max_results\\n\\n if self.preserve_nulls is not None:\\n resource['preserveNulls'] = self.preserve_nulls\\n\\n if self.timeout_ms is not None:\\n resource['timeoutMs'] = self.timeout_ms\\n\\n if self.use_query_cache is not None:\\n resource['useQueryCache'] = self.use_query_cache\\n\\n if self.use_legacy_sql is not None:\\n resource['useLegacySql'] = self.use_legacy_sql\\n\\n if self.dry_run is not None:\\n resource['dryRun'] = self.dry_run\\n\\n if len(self._udf_resources) > 0:\\n resource[self._UDF_KEY] = _build_udf_resources(self._udf_resources)\\n\\n return resource\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.run\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.run\\n def run(self, client=None):\\n \\\"\\\"\\\"API call: run the query via a POST request\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/query\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n if self._job is not None:\\n raise ValueError(\\\"Query job is already running.\\\")\\n\\n client = self._require_client(client)\\n path = '/projects/%s/queries' % (self.project,)\\n api_response = client.connection.api_request(\\n method='POST', path=path, data=self._build_resource())\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/query.py::QueryResults.fetch_data\",\n \"text\": \"google/cloud/bigquery/query.py::QueryResults.fetch_data\\n def fetch_data(self, max_results=None, page_token=None, start_index=None,\\n timeout_ms=None, client=None):\\n \\\"\\\"\\\"API call: fetch a page of query result data via a GET request\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/jobs/getQueryResults\\n\\n :type max_results: integer or ``NoneType``\\n :param max_results: maximum number of rows to return.\\n\\n :type page_token: string or ``NoneType``\\n :param page_token: token representing a cursor into the table's rows.\\n\\n :type start_index: integer or ``NoneType``\\n :param start_index: zero-based index of starting row\\n\\n :type timeout_ms: integer or ``NoneType``\\n :param timeout_ms: timeout, in milliseconds, to wait for query to\\n complete\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: tuple\\n :returns: ``(row_data, total_rows, page_token)``, where ``row_data``\\n is a list of tuples, one per result row, containing only\\n the values; ``total_rows`` is a count of the total number\\n of rows in the table; and ``page_token`` is an opaque\\n string which can be used to fetch the next batch of rows\\n (``None`` if no further batches can be fetched).\\n :raises: ValueError if the query has not yet been executed.\\n \\\"\\\"\\\"\\n if self.name is None:\\n raise ValueError(\\\"Query not yet executed: call 'run()'\\\")\\n\\n client = self._require_client(client)\\n params = {}\\n\\n if max_results is not None:\\n params['maxResults'] = max_results\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/schema.py::SchemaField.__init__\",\n \"text\": \"google/cloud/bigquery/schema.py::SchemaField.__init__\\n def __init__(self, name, field_type, mode='NULLABLE', description=None,\\n fields=None):\\n self.name = name\\n self.field_type = field_type\\n self.mode = mode\\n self.description = description\\n self.fields = fields\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/schema.py::SchemaField.__eq__\",\n \"text\": \"google/cloud/bigquery/schema.py::SchemaField.__eq__\\n def __eq__(self, other):\\n return (\\n self.name == other.name and\\n self.field_type.lower() == other.field_type.lower() and\\n self.mode == other.mode and\\n self.description == other.description and\\n self.fields == other.fields)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.__init__\",\n \"text\": \"google/cloud/bigquery/table.py::Table.__init__\\n def __init__(self, name, dataset, schema=()):\\n self.name = name\\n self._dataset = dataset\\n self._properties = {}\\n # Let the @property do validation.\\n self.schema = schema\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.project\",\n \"text\": \"google/cloud/bigquery/table.py::Table.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project bound to the table.\\n\\n :rtype: str\\n :returns: the project (derived from the dataset).\\n \\\"\\\"\\\"\\n return self._dataset.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.dataset_name\",\n \"text\": \"google/cloud/bigquery/table.py::Table.dataset_name\\n @property\\n def dataset_name(self):\\n \\\"\\\"\\\"Name of dataset containing the table.\\n\\n :rtype: str\\n :returns: the ID (derived from the dataset).\\n \\\"\\\"\\\"\\n return self._dataset.name\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.path\",\n \"text\": \"google/cloud/bigquery/table.py::Table.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URL path for the table's APIs.\\n\\n :rtype: str\\n :returns: the path based on project and dataste name.\\n \\\"\\\"\\\"\\n return '%s/tables/%s' % (self._dataset.path, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.schema\",\n \"text\": \"google/cloud/bigquery/table.py::Table.schema\\n @property\\n def schema(self):\\n \\\"\\\"\\\"Table's schema.\\n\\n :rtype: list of :class:`SchemaField`\\n :returns: fields describing the schema\\n \\\"\\\"\\\"\\n return list(self._schema)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.schema\",\n \"text\": \"google/cloud/bigquery/table.py::Table.schema\\n @schema.setter\\n def schema(self, value):\\n \\\"\\\"\\\"Update table's schema\\n\\n :type value: list of :class:`SchemaField`\\n :param value: fields describing the schema\\n\\n :raises: TypeError if 'value' is not a sequence, or ValueError if\\n any item in the sequence is not a SchemaField\\n \\\"\\\"\\\"\\n if not all(isinstance(field, SchemaField) for field in value):\\n raise ValueError('Schema items must be fields')\\n self._schema = tuple(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.created\",\n \"text\": \"google/cloud/bigquery/table.py::Table.created\\n @property\\n def created(self):\\n \\\"\\\"\\\"Datetime at which the table was created.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the creation time (None until set from the server).\\n \\\"\\\"\\\"\\n creation_time = self._properties.get('creationTime')\\n if creation_time is not None:\\n # creation_time will be in milliseconds.\\n return _datetime_from_microseconds(1000.0 * creation_time)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.etag\",\n \"text\": \"google/cloud/bigquery/table.py::Table.etag\\n @property\\n def etag(self):\\n \\\"\\\"\\\"ETag for the table resource.\\n\\n :rtype: str, or ``NoneType``\\n :returns: the ETag (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('etag')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.modified\",\n \"text\": \"google/cloud/bigquery/table.py::Table.modified\\n @property\\n def modified(self):\\n \\\"\\\"\\\"Datetime at which the table was last modified.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the modification time (None until set from the server).\\n \\\"\\\"\\\"\\n modified_time = self._properties.get('lastModifiedTime')\\n if modified_time is not None:\\n # modified_time will be in milliseconds.\\n return _datetime_from_microseconds(1000.0 * modified_time)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.num_bytes\",\n \"text\": \"google/cloud/bigquery/table.py::Table.num_bytes\\n @property\\n def num_bytes(self):\\n \\\"\\\"\\\"The size of the table in bytes.\\n\\n :rtype: integer, or ``NoneType``\\n :returns: the byte count (None until set from the server).\\n \\\"\\\"\\\"\\n num_bytes_as_str = self._properties.get('numBytes')\\n if num_bytes_as_str is not None:\\n return int(num_bytes_as_str)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.num_rows\",\n \"text\": \"google/cloud/bigquery/table.py::Table.num_rows\\n @property\\n def num_rows(self):\\n \\\"\\\"\\\"The number of rows in the table.\\n\\n :rtype: integer, or ``NoneType``\\n :returns: the row count (None until set from the server).\\n \\\"\\\"\\\"\\n num_rows_as_str = self._properties.get('numRows')\\n if num_rows_as_str is not None:\\n return int(num_rows_as_str)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.self_link\",\n \"text\": \"google/cloud/bigquery/table.py::Table.self_link\\n @property\\n def self_link(self):\\n \\\"\\\"\\\"URL for the table resource.\\n\\n :rtype: str, or ``NoneType``\\n :returns: the URL (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('selfLink')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.table_id\",\n \"text\": \"google/cloud/bigquery/table.py::Table.table_id\\n @property\\n def table_id(self):\\n \\\"\\\"\\\"ID for the table resource.\\n\\n :rtype: str, or ``NoneType``\\n :returns: the ID (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('id')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.table_type\",\n \"text\": \"google/cloud/bigquery/table.py::Table.table_type\\n @property\\n def table_type(self):\\n \\\"\\\"\\\"The type of the table.\\n\\n Possible values are \\\"TABLE\\\" or \\\"VIEW\\\".\\n\\n :rtype: str, or ``NoneType``\\n :returns: the URL (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('type')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.partitioning_type\",\n \"text\": \"google/cloud/bigquery/table.py::Table.partitioning_type\\n @property\\n def partitioning_type(self):\\n \\\"\\\"\\\"Time partitioning of the table.\\n :rtype: str, or ``NoneType``\\n :returns: Returns type if the table is partitioned, None otherwise.\\n \\\"\\\"\\\"\\n return self._properties.get('timePartitioning', {}).get('type')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.partitioning_type\",\n \"text\": \"google/cloud/bigquery/table.py::Table.partitioning_type\\n @partitioning_type.setter\\n def partitioning_type(self, value):\\n \\\"\\\"\\\"Update the partitioning type of the table\\n\\n :type value: str\\n :param value: partitioning type only \\\"DAY\\\" is currently supported\\n \\\"\\\"\\\"\\n if value not in ('DAY', None):\\n raise ValueError(\\\"value must be one of ['DAY', None]\\\")\\n\\n if value is None:\\n self._properties.pop('timePartitioning', None)\\n else:\\n time_part = self._properties.setdefault('timePartitioning', {})\\n time_part['type'] = value.upper()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.partition_expiration\",\n \"text\": \"google/cloud/bigquery/table.py::Table.partition_expiration\\n @property\\n def partition_expiration(self):\\n \\\"\\\"\\\"Expiration time in ms for a partition\\n :rtype: int, or ``NoneType``\\n :returns: Returns the time in ms for partition expiration\\n \\\"\\\"\\\"\\n return self._properties.get('timePartitioning', {}).get('expirationMs')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.partition_expiration\",\n \"text\": \"google/cloud/bigquery/table.py::Table.partition_expiration\\n @partition_expiration.setter\\n def partition_expiration(self, value):\\n \\\"\\\"\\\"Update the experation time in ms for a partition\\n\\n :type value: int\\n :param value: partition experiation time in ms\\n \\\"\\\"\\\"\\n if not isinstance(value, (int, type(None))):\\n raise ValueError(\\n \\\"must be an integer representing millisseconds or None\\\")\\n\\n if value is None:\\n if 'timePartitioning' in self._properties:\\n self._properties['timePartitioning'].pop('expirationMs')\\n else:\\n try:\\n self._properties['timePartitioning']['expirationMs'] = value\\n except KeyError:\\n self._properties['timePartitioning'] = {'type': 'DAY'}\\n self._properties['timePartitioning']['expirationMs'] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.description\",\n \"text\": \"google/cloud/bigquery/table.py::Table.description\\n @property\\n def description(self):\\n \\\"\\\"\\\"Description of the table.\\n\\n :rtype: str, or ``NoneType``\\n :returns: The description as set by the user, or None (the default).\\n \\\"\\\"\\\"\\n return self._properties.get('description')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.description\",\n \"text\": \"google/cloud/bigquery/table.py::Table.description\\n @description.setter\\n def description(self, value):\\n \\\"\\\"\\\"Update description of the table.\\n\\n :type value: str, or ``NoneType``\\n :param value: new description\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n if not isinstance(value, six.string_types) and value is not None:\\n raise ValueError(\\\"Pass a string, or None\\\")\\n self._properties['description'] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.expires\",\n \"text\": \"google/cloud/bigquery/table.py::Table.expires\\n @property\\n def expires(self):\\n \\\"\\\"\\\"Datetime at which the table will be removed.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the expiration time, or None\\n \\\"\\\"\\\"\\n expiration_time = self._properties.get('expirationTime')\\n if expiration_time is not None:\\n # expiration_time will be in milliseconds.\\n return _datetime_from_microseconds(1000.0 * expiration_time)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.expires\",\n \"text\": \"google/cloud/bigquery/table.py::Table.expires\\n @expires.setter\\n def expires(self, value):\\n \\\"\\\"\\\"Update datetime at which the table will be removed.\\n\\n :type value: ``datetime.datetime``, or ``NoneType``\\n :param value: the new expiration time, or None\\n \\\"\\\"\\\"\\n if not isinstance(value, datetime.datetime) and value is not None:\\n raise ValueError(\\\"Pass a datetime, or None\\\")\\n self._properties['expirationTime'] = _millis_from_datetime(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.friendly_name\",\n \"text\": \"google/cloud/bigquery/table.py::Table.friendly_name\\n @property\\n def friendly_name(self):\\n \\\"\\\"\\\"Title of the table.\\n\\n :rtype: str, or ``NoneType``\\n :returns: The name as set by the user, or None (the default).\\n \\\"\\\"\\\"\\n return self._properties.get('friendlyName')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.friendly_name\",\n \"text\": \"google/cloud/bigquery/table.py::Table.friendly_name\\n @friendly_name.setter\\n def friendly_name(self, value):\\n \\\"\\\"\\\"Update title of the table.\\n\\n :type value: str, or ``NoneType``\\n :param value: new title\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n if not isinstance(value, six.string_types) and value is not None:\\n raise ValueError(\\\"Pass a string, or None\\\")\\n self._properties['friendlyName'] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.location\",\n \"text\": \"google/cloud/bigquery/table.py::Table.location\\n @property\\n def location(self):\\n \\\"\\\"\\\"Location in which the table is hosted.\\n\\n :rtype: str, or ``NoneType``\\n :returns: The location as set by the user, or None (the default).\\n \\\"\\\"\\\"\\n return self._properties.get('location')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.location\",\n \"text\": \"google/cloud/bigquery/table.py::Table.location\\n @location.setter\\n def location(self, value):\\n \\\"\\\"\\\"Update location in which the table is hosted.\\n\\n :type value: str, or ``NoneType``\\n :param value: new location\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n if not isinstance(value, six.string_types) and value is not None:\\n raise ValueError(\\\"Pass a string, or None\\\")\\n self._properties['location'] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.view_query\",\n \"text\": \"google/cloud/bigquery/table.py::Table.view_query\\n @property\\n def view_query(self):\\n \\\"\\\"\\\"SQL query defining the table as a view.\\n\\n :rtype: str, or ``NoneType``\\n :returns: The query as set by the user, or None (the default).\\n \\\"\\\"\\\"\\n view = self._properties.get('view')\\n if view is not None:\\n return view.get('query')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.view_query\",\n \"text\": \"google/cloud/bigquery/table.py::Table.view_query\\n @view_query.setter\\n def view_query(self, value):\\n \\\"\\\"\\\"Update SQL query defining the table as a view.\\n\\n :type value: str\\n :param value: new query\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n if not isinstance(value, six.string_types):\\n raise ValueError(\\\"Pass a string\\\")\\n self._properties['view'] = {'query': value}\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.view_query\",\n \"text\": \"google/cloud/bigquery/table.py::Table.view_query\\n @view_query.deleter\\n def view_query(self):\\n \\\"\\\"\\\"Delete SQL query defining the table as a view.\\\"\\\"\\\"\\n self._properties.pop('view', None)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.list_partitions\",\n \"text\": \"google/cloud/bigquery/table.py::Table.list_partitions\\n def list_partitions(self, client=None):\\n \\\"\\\"\\\"List the partitions in a table.\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: list\\n :returns: a list of time partitions\\n \\\"\\\"\\\"\\n query = self._require_client(client).run_sync_query(\\n 'SELECT partition_id from [%s.%s$__PARTITIONS_SUMMARY__]' %\\n (self.dataset_name, self.name))\\n query.run()\\n return [row[0] for row in query.rows]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.from_api_repr\",\n \"text\": \"google/cloud/bigquery/table.py::Table.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, dataset):\\n \\\"\\\"\\\"Factory: construct a table given its API representation\\n\\n :type resource: dict\\n :param resource: table resource representation returned from the API\\n\\n :type dataset: :class:`google.cloud.bigquery.dataset.Dataset`\\n :param dataset: The dataset containing the table.\\n\\n :rtype: :class:`google.cloud.bigquery.table.Table`\\n :returns: Table parsed from ``resource``.\\n \\\"\\\"\\\"\\n if ('tableReference' not in resource or\\n 'tableId' not in resource['tableReference']):\\n raise KeyError('Resource lacks required identity information:'\\n '[\\\"tableReference\\\"][\\\"tableId\\\"]')\\n table_name = resource['tableReference']['tableId']\\n table = cls(table_name, dataset=dataset)\\n table._set_properties(resource)\\n return table\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table._require_client\",\n \"text\": \"google/cloud/bigquery/table.py::Table._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: :class:`google.cloud.bigquery.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._dataset._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table._set_properties\",\n \"text\": \"google/cloud/bigquery/table.py::Table._set_properties\\n def _set_properties(self, api_response):\\n \\\"\\\"\\\"Update properties from resource in body of ``api_response``\\n\\n :type api_response: httplib2.Response\\n :param api_response: response returned from an API call\\n \\\"\\\"\\\"\\n self._properties.clear()\\n cleaned = api_response.copy()\\n schema = cleaned.pop('schema', {'fields': ()})\\n self.schema = _parse_schema_resource(schema)\\n if 'creationTime' in cleaned:\\n cleaned['creationTime'] = float(cleaned['creationTime'])\\n if 'lastModifiedTime' in cleaned:\\n cleaned['lastModifiedTime'] = float(cleaned['lastModifiedTime'])\\n if 'expirationTime' in cleaned:\\n cleaned['expirationTime'] = float(cleaned['expirationTime'])\\n self._properties.update(cleaned)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table._build_resource\",\n \"text\": \"google/cloud/bigquery/table.py::Table._build_resource\\n def _build_resource(self):\\n \\\"\\\"\\\"Generate a resource for ``create`` or ``update``.\\\"\\\"\\\"\\n resource = {\\n 'tableReference': {\\n 'projectId': self._dataset.project,\\n 'datasetId': self._dataset.name,\\n 'tableId': self.name},\\n }\\n if self.description is not None:\\n resource['description'] = self.description\\n\\n if self.expires is not None:\\n value = _millis_from_datetime(self.expires)\\n resource['expirationTime'] = value\\n\\n if self.friendly_name is not None:\\n resource['friendlyName'] = self.friendly_name\\n\\n if self.location is not None:\\n resource['location'] = self.location\\n\\n if self.partitioning_type is not None:\\n resource['timePartitioning'] = self._properties['timePartitioning']\\n\\n if self.view_query is not None:\\n view = resource['view'] = {}\\n view['query'] = self.view_query\\n elif self._schema:\\n resource['schema'] = {\\n 'fields': _build_schema_resource(self._schema)\\n }\\n else:\\n raise ValueError(\\\"Set either 'view_query' or 'schema'.\\\")\\n\\n return resource\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.create\",\n \"text\": \"google/cloud/bigquery/table.py::Table.create\\n def create(self, client=None):\\n \\\"\\\"\\\"API call: create the dataset via a PUT request\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/tables/insert\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n path = '/projects/%s/datasets/%s/tables' % (\\n self._dataset.project, self._dataset.name)\\n api_response = client.connection.api_request(\\n method='POST', path=path, data=self._build_resource())\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.exists\",\n \"text\": \"google/cloud/bigquery/table.py::Table.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"API call: test for the existence of the table via a GET request\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/tables/get\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: bool\\n :returns: Boolean indicating existence of the table.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n try:\\n client.connection.api_request(method='GET', path=self.path,\\n query_params={'fields': 'id'})\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.reload\",\n \"text\": \"google/cloud/bigquery/table.py::Table.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"API call: refresh table properties via a GET request\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/tables/get\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n api_response = client.connection.api_request(\\n method='GET', path=self.path)\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.patch\",\n \"text\": \"google/cloud/bigquery/table.py::Table.patch\\n def patch(self,\\n client=None,\\n friendly_name=_MARKER,\\n description=_MARKER,\\n location=_MARKER,\\n expires=_MARKER,\\n view_query=_MARKER,\\n schema=_MARKER):\\n \\\"\\\"\\\"API call: update individual table properties via a PATCH request\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/tables/patch\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :type friendly_name: str or ``NoneType``\\n :param friendly_name: point in time at which the table expires.\\n\\n :type description: str or ``NoneType``\\n :param description: point in time at which the table expires.\\n\\n :type location: str or ``NoneType``\\n :param location: point in time at which the table expires.\\n\\n :type expires: :class:`datetime.datetime` or ``NoneType``\\n :param expires: point in time at which the table expires.\\n\\n :type view_query: str\\n :param view_query: SQL query defining the table as a view\\n\\n :type schema: list of :class:`SchemaField`\\n :param schema: fields describing the schema\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n partial = {}\\n\\n if expires is not _MARKER:\\n if (not isinstance(expires, datetime.datetime) and\\n expires is not None):\\n raise ValueError(\\\"Pass a datetime, or None\\\")\\n partial['expirationTime'] = _millis_from_datetime(expires)\\n\\n if description is not _MARKER:\\n partial['description'] = description\\n\\n if friendly_name is not _MARKER:\\n partial['friendlyName'] = friendly_name\\n\\n if location i\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.update\",\n \"text\": \"google/cloud/bigquery/table.py::Table.update\\n def update(self, client=None):\\n \\\"\\\"\\\"API call: update table properties via a PUT request\\n\\n See\\n https://cloud.google.com/bigquery/docs/reference/v2/tables/update\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api_response = client.connection.api_request(\\n method='PUT', path=self.path, data=self._build_resource())\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.delete\",\n \"text\": \"google/cloud/bigquery/table.py::Table.delete\\n def delete(self, client=None):\\n \\\"\\\"\\\"API call: delete the table via a DELETE request\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/tables/delete\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.connection.api_request(method='DELETE', path=self.path)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.fetch_data\",\n \"text\": \"google/cloud/bigquery/table.py::Table.fetch_data\\n def fetch_data(self, max_results=None, page_token=None, client=None):\\n \\\"\\\"\\\"API call: fetch the table data via a GET request\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/tabledata/list\\n\\n .. note::\\n\\n This method assumes that its instance's ``schema`` attribute is\\n up-to-date with the schema as defined on the back-end: if the\\n two schemas are not identical, the values returned may be\\n incomplete. To ensure that the local copy of the schema is\\n up-to-date, call the table's ``reload`` method.\\n\\n :type max_results: integer or ``NoneType``\\n :param max_results: maximum number of rows to return.\\n\\n :type page_token: str or ``NoneType``\\n :param page_token: token representing a cursor into the table's rows.\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: tuple\\n :returns: ``(row_data, total_rows, page_token)``, where ``row_data``\\n is a list of tuples, one per result row, containing only\\n the values; ``total_rows`` is a count of the total number\\n of rows in the table; and ``page_token`` is an opaque\\n string which can be used to fetch the next batch of rows\\n (``None`` if no further batches can be fetched).\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n params = {}\\n\\n if max_results is not None:\\n params['maxResults'] = max_results\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n response = client.connection.api_request(method='GET',\\n path='%s/data' % self.path,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.insert_data\",\n \"text\": \"google/cloud/bigquery/table.py::Table.insert_data\\n def insert_data(self,\\n rows,\\n row_ids=None,\\n skip_invalid_rows=None,\\n ignore_unknown_values=None,\\n template_suffix=None,\\n client=None):\\n \\\"\\\"\\\"API call: insert table data via a POST request\\n\\n See:\\n https://cloud.google.com/bigquery/docs/reference/v2/tabledata/insertAll\\n\\n :type rows: list of tuples\\n :param rows: Row data to be inserted. Each tuple should contain data\\n for each schema field on the current table and in the\\n same order as the schema fields.\\n\\n :type row_ids: list of string\\n :param row_ids: Unique ids, one per row being inserted. If not\\n passed, no de-duplication occurs.\\n\\n :type skip_invalid_rows: boolean or ``NoneType``\\n :param skip_invalid_rows: skip rows w/ invalid data?\\n\\n :type ignore_unknown_values: boolean or ``NoneType``\\n :param ignore_unknown_values: ignore columns beyond schema?\\n\\n :type template_suffix: str or ``NoneType``\\n :param template_suffix: treat ``name`` as a template table and provide\\n a suffix. BigQuery will create the table\\n ``<name> + <template_suffix>`` based on the\\n schema of the template table. See:\\n https://cloud.google.com/bigquery/streaming-data-into-bigquery#template-tables\\n\\n :type client: :class:`~google.cloud.bigquery.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current dataset.\\n\\n :rtype: list of mappings\\n :returns: One mapping per row with insert errors: the \\\"index\\\" key\\n identifies the row, and the \\\"errors\\\" key contains \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table._check_response_error\",\n \"text\": \"google/cloud/bigquery/table.py::Table._check_response_error\\n @staticmethod\\n def _check_response_error(request, http_response):\\n \\\"\\\"\\\"Helper for :meth:`upload_from_file`.\\\"\\\"\\\"\\n info = http_response.info\\n status = int(info['status'])\\n if not 200 <= status < 300:\\n faux_response = httplib2.Response({'status': status})\\n raise make_exception(faux_response, http_response.content,\\n error_info=request.url)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::Table.upload_from_file\",\n \"text\": \"google/cloud/bigquery/table.py::Table.upload_from_file\\n def upload_from_file(self,\\n file_obj,\\n source_format,\\n rewind=False,\\n size=None,\\n num_retries=6,\\n allow_jagged_rows=None,\\n allow_quoted_newlines=None,\\n create_disposition=None,\\n encoding=None,\\n field_delimiter=None,\\n ignore_unknown_values=None,\\n max_bad_records=None,\\n quote_character=None,\\n skip_leading_rows=None,\\n write_disposition=None,\\n client=None):\\n \\\"\\\"\\\"Upload the contents of this table from a file-like object.\\n\\n The content type of the upload will either be\\n - The value passed in to the function (if any)\\n - ``text/csv``.\\n\\n :type file_obj: file\\n :param file_obj: A file handle opened in binary mode for reading.\\n\\n :type source_format: str\\n :param source_format: one of 'CSV' or 'NEWLINE_DELIMITED_JSON'.\\n job configuration option; see\\n :meth:`google.cloud.bigquery.job.LoadJob`\\n\\n :type rewind: boolean\\n :param rewind: If True, seek to the beginning of the file handle before\\n writing the file to Cloud Storage.\\n\\n :type size: int\\n :param size: The number of bytes to read from the file handle.\\n If not provided, we'll try to guess the size using\\n :func:`os.fstat`. (If the file handle is not from the\\n filesystem this won't be possible.)\\n\\n :type num_retries: integer\\n :param num_retries: Number of upload retries. Defaults to 6.\\n\\n :type allow_jagged_rows: boolean\\n :param allow_jagged_rows: job c\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::_configure_job_metadata\",\n \"text\": \"google/cloud/bigquery/table.py::_configure_job_metadata\\ndef _configure_job_metadata(metadata, # pylint: disable=too-many-arguments\\n allow_jagged_rows,\\n allow_quoted_newlines,\\n create_disposition,\\n encoding,\\n field_delimiter,\\n ignore_unknown_values,\\n max_bad_records,\\n quote_character,\\n skip_leading_rows,\\n write_disposition):\\n \\\"\\\"\\\"Helper for :meth:`Table.upload_from_file`.\\\"\\\"\\\"\\n load_config = metadata['configuration']['load']\\n\\n if allow_jagged_rows is not None:\\n load_config['allowJaggedRows'] = allow_jagged_rows\\n\\n if allow_quoted_newlines is not None:\\n load_config['allowQuotedNewlines'] = allow_quoted_newlines\\n\\n if create_disposition is not None:\\n load_config['createDisposition'] = create_disposition\\n\\n if encoding is not None:\\n load_config['encoding'] = encoding\\n\\n if field_delimiter is not None:\\n load_config['fieldDelimiter'] = field_delimiter\\n\\n if ignore_unknown_values is not None:\\n load_config['ignoreUnknownValues'] = ignore_unknown_values\\n\\n if max_bad_records is not None:\\n load_config['maxBadRecords'] = max_bad_records\\n\\n if quote_character is not None:\\n load_config['quote'] = quote_character\\n\\n if skip_leading_rows is not None:\\n load_config['skipLeadingRows'] = skip_leading_rows\\n\\n if write_disposition is not None:\\n load_config['writeDisposition'] = write_disposition\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::_parse_schema_resource\",\n \"text\": \"google/cloud/bigquery/table.py::_parse_schema_resource\\ndef _parse_schema_resource(info):\\n \\\"\\\"\\\"Parse a resource fragment into a schema field.\\n\\n :type info: mapping\\n :param info: should contain a \\\"fields\\\" key to be parsed\\n\\n :rtype: list of :class:`SchemaField`, or ``NoneType``\\n :returns: a list of parsed fields, or ``None`` if no \\\"fields\\\" key is\\n present in ``info``.\\n \\\"\\\"\\\"\\n if 'fields' not in info:\\n return None\\n\\n schema = []\\n for r_field in info['fields']:\\n name = r_field['name']\\n field_type = r_field['type']\\n mode = r_field.get('mode', 'NULLABLE')\\n description = r_field.get('description')\\n sub_fields = _parse_schema_resource(r_field)\\n schema.append(\\n SchemaField(name, field_type, mode, description, sub_fields))\\n return schema\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::_build_schema_resource\",\n \"text\": \"google/cloud/bigquery/table.py::_build_schema_resource\\ndef _build_schema_resource(fields):\\n \\\"\\\"\\\"Generate a resource fragment for a schema.\\n\\n :type fields: sequence of :class:`SchemaField`\\n :param fields: schema to be dumped\\n\\n :rtype: mapping\\n :returns: a mapping describing the schema of the supplied fields.\\n \\\"\\\"\\\"\\n infos = []\\n for field in fields:\\n info = {'name': field.name,\\n 'type': field.field_type,\\n 'mode': field.mode}\\n if field.description is not None:\\n info['description'] = field.description\\n if field.fields is not None:\\n info['fields'] = _build_schema_resource(field.fields)\\n infos.append(info)\\n return infos\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigquery/table.py::_UrlBuilder.__init__\",\n \"text\": \"google/cloud/bigquery/table.py::_UrlBuilder.__init__\\n def __init__(self):\\n self.query_params = {}\\n self._relative_path = ''\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::_make_data_stub\",\n \"text\": \"google/cloud/bigtable/client.py::_make_data_stub\\ndef _make_data_stub(client):\\n \\\"\\\"\\\"Creates gRPC stub to make requests to the Data API.\\n\\n :type client: :class:`Client`\\n :param client: The client that will hold the stub.\\n\\n :rtype: :class:`._generated.bigtable_pb2.BigtableStub`\\n :returns: A gRPC stub object.\\n \\\"\\\"\\\"\\n if client.emulator_host is None:\\n return make_secure_stub(client.credentials, client.user_agent,\\n bigtable_pb2.BigtableStub, DATA_API_HOST)\\n else:\\n return make_insecure_stub(bigtable_pb2.BigtableStub,\\n client.emulator_host)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::_make_instance_stub\",\n \"text\": \"google/cloud/bigtable/client.py::_make_instance_stub\\ndef _make_instance_stub(client):\\n \\\"\\\"\\\"Creates gRPC stub to make requests to the Instance Admin API.\\n\\n :type client: :class:`Client`\\n :param client: The client that will hold the stub.\\n\\n :rtype: :class:`.bigtable_instance_admin_pb2.BigtableInstanceAdminStub`\\n :returns: A gRPC stub object.\\n \\\"\\\"\\\"\\n if client.emulator_host is None:\\n return make_secure_stub(\\n client.credentials, client.user_agent,\\n bigtable_instance_admin_pb2.BigtableInstanceAdminStub,\\n INSTANCE_ADMIN_HOST)\\n else:\\n return make_insecure_stub(\\n bigtable_instance_admin_pb2.BigtableInstanceAdminStub,\\n client.emulator_host)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::_make_operations_stub\",\n \"text\": \"google/cloud/bigtable/client.py::_make_operations_stub\\ndef _make_operations_stub(client):\\n \\\"\\\"\\\"Creates gRPC stub to make requests to the Operations API.\\n\\n These are for long-running operations of the Instance Admin API,\\n hence the host and port matching.\\n\\n :type client: :class:`Client`\\n :param client: The client that will hold the stub.\\n\\n :rtype: :class:`._generated.operations_grpc_pb2.OperationsStub`\\n :returns: A gRPC stub object.\\n \\\"\\\"\\\"\\n if client.emulator_host is None:\\n return make_secure_stub(client.credentials, client.user_agent,\\n operations_grpc_pb2.OperationsStub,\\n OPERATIONS_API_HOST)\\n else:\\n return make_insecure_stub(operations_grpc_pb2.OperationsStub,\\n client.emulator_host)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::_make_table_stub\",\n \"text\": \"google/cloud/bigtable/client.py::_make_table_stub\\ndef _make_table_stub(client):\\n \\\"\\\"\\\"Creates gRPC stub to make requests to the Table Admin API.\\n\\n :type client: :class:`Client`\\n :param client: The client that will hold the stub.\\n\\n :rtype: :class:`.bigtable_instance_admin_pb2.BigtableTableAdminStub`\\n :returns: A gRPC stub object.\\n \\\"\\\"\\\"\\n if client.emulator_host is None:\\n return make_secure_stub(\\n client.credentials, client.user_agent,\\n bigtable_table_admin_pb2.BigtableTableAdminStub,\\n TABLE_ADMIN_HOST)\\n else:\\n return make_insecure_stub(\\n bigtable_table_admin_pb2.BigtableTableAdminStub,\\n client.emulator_host)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::Client.__init__\",\n \"text\": \"google/cloud/bigtable/client.py::Client.__init__\\n def __init__(self, project=None, credentials=None,\\n read_only=False, admin=False, user_agent=DEFAULT_USER_AGENT):\\n _ClientProjectMixin.__init__(self, project=project)\\n if credentials is None:\\n credentials = get_credentials()\\n\\n if read_only and admin:\\n raise ValueError('A read-only client cannot also perform'\\n 'administrative actions.')\\n\\n scopes = []\\n if read_only:\\n scopes.append(READ_ONLY_SCOPE)\\n else:\\n scopes.append(DATA_SCOPE)\\n\\n if admin:\\n scopes.append(ADMIN_SCOPE)\\n\\n self._admin = bool(admin)\\n try:\\n credentials = credentials.create_scoped(scopes)\\n except AttributeError:\\n pass\\n self._credentials = credentials\\n self.user_agent = user_agent\\n self.emulator_host = os.getenv(BIGTABLE_EMULATOR)\\n\\n # Create gRPC stubs for making requests.\\n self._data_stub = _make_data_stub(self)\\n if self._admin:\\n self._instance_stub_internal = _make_instance_stub(self)\\n self._operations_stub_internal = _make_operations_stub(self)\\n self._table_stub_internal = _make_table_stub(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::Client.copy\",\n \"text\": \"google/cloud/bigtable/client.py::Client.copy\\n def copy(self):\\n \\\"\\\"\\\"Make a copy of this client.\\n\\n Copies the local data stored as simple types but does not copy the\\n current state of any open connections with the Cloud Bigtable API.\\n\\n :rtype: :class:`.Client`\\n :returns: A copy of the current client.\\n \\\"\\\"\\\"\\n credentials = self._credentials\\n copied_creds = credentials.create_scoped(credentials.scopes)\\n return self.__class__(\\n self.project,\\n copied_creds,\\n READ_ONLY_SCOPE in copied_creds.scopes,\\n self._admin,\\n self.user_agent,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::Client.credentials\",\n \"text\": \"google/cloud/bigtable/client.py::Client.credentials\\n @property\\n def credentials(self):\\n \\\"\\\"\\\"Getter for client's credentials.\\n\\n :rtype:\\n :class:`OAuth2Credentials <oauth2client.client.OAuth2Credentials>`\\n :returns: The credentials stored on the client.\\n \\\"\\\"\\\"\\n return self._credentials\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::Client.project_name\",\n \"text\": \"google/cloud/bigtable/client.py::Client.project_name\\n @property\\n def project_name(self):\\n \\\"\\\"\\\"Project name to be used with Instance Admin API.\\n\\n .. note::\\n\\n This property will not change if ``project`` does not, but the\\n return value is not cached.\\n\\n The project name is of the form\\n\\n ``\\\"projects/{project}\\\"``\\n\\n :rtype: str\\n :returns: The project name to be used with the Cloud Bigtable Admin\\n API RPC service.\\n \\\"\\\"\\\"\\n return 'projects/' + self.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::Client._instance_stub\",\n \"text\": \"google/cloud/bigtable/client.py::Client._instance_stub\\n @property\\n def _instance_stub(self):\\n \\\"\\\"\\\"Getter for the gRPC stub used for the Instance Admin API.\\n\\n :rtype: :class:`.bigtable_instance_admin_pb2.BigtableInstanceAdminStub`\\n :returns: A gRPC stub object.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the current\\n client is not an admin client or if it has not been\\n :meth:`start`-ed.\\n \\\"\\\"\\\"\\n if not self._admin:\\n raise ValueError('Client is not an admin client.')\\n return self._instance_stub_internal\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::Client._operations_stub\",\n \"text\": \"google/cloud/bigtable/client.py::Client._operations_stub\\n @property\\n def _operations_stub(self):\\n \\\"\\\"\\\"Getter for the gRPC stub used for the Operations API.\\n\\n :rtype: :class:`._generated.operations_grpc_pb2.OperationsStub`\\n :returns: A gRPC stub object.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the current\\n client is not an admin client or if it has not been\\n :meth:`start`-ed.\\n \\\"\\\"\\\"\\n if not self._admin:\\n raise ValueError('Client is not an admin client.')\\n return self._operations_stub_internal\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::Client._table_stub\",\n \"text\": \"google/cloud/bigtable/client.py::Client._table_stub\\n @property\\n def _table_stub(self):\\n \\\"\\\"\\\"Getter for the gRPC stub used for the Table Admin API.\\n\\n :rtype: :class:`.bigtable_instance_admin_pb2.BigtableTableAdminStub`\\n :returns: A gRPC stub object.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the current\\n client is not an admin client or if it has not been\\n :meth:`start`-ed.\\n \\\"\\\"\\\"\\n if not self._admin:\\n raise ValueError('Client is not an admin client.')\\n return self._table_stub_internal\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::Client.instance\",\n \"text\": \"google/cloud/bigtable/client.py::Client.instance\\n def instance(self, instance_id, location=_EXISTING_INSTANCE_LOCATION_ID,\\n display_name=None, serve_nodes=DEFAULT_SERVE_NODES):\\n \\\"\\\"\\\"Factory to create a instance associated with this client.\\n\\n :type instance_id: str\\n :param instance_id: The ID of the instance.\\n\\n :type location: string\\n :param location: location name, in form\\n ``projects/<project>/locations/<location>``; used to\\n set up the instance's cluster.\\n\\n :type display_name: str\\n :param display_name: (Optional) The display name for the instance in\\n the Cloud Console UI. (Must be between 4 and 30\\n characters.) If this value is not set in the\\n constructor, will fall back to the instance ID.\\n\\n :type serve_nodes: int\\n :param serve_nodes: (Optional) The number of nodes in the instance's\\n cluster; used to set up the instance's cluster.\\n\\n :rtype: :class:`.Instance`\\n :returns: an instance owned by this client.\\n \\\"\\\"\\\"\\n return Instance(instance_id, self, location,\\n display_name=display_name, serve_nodes=serve_nodes)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/client.py::Client.list_instances\",\n \"text\": \"google/cloud/bigtable/client.py::Client.list_instances\\n def list_instances(self):\\n \\\"\\\"\\\"List instances owned by the project.\\n\\n :rtype: tuple\\n :returns: A pair of results, the first is a list of\\n :class:`.Instance` objects returned and the second is a\\n list of strings (the failed locations in the request).\\n \\\"\\\"\\\"\\n request_pb = bigtable_instance_admin_pb2.ListInstancesRequest(\\n parent=self.project_name)\\n\\n response = self._instance_stub.ListInstances(request_pb)\\n\\n instances = [Instance.from_pb(instance_pb, self)\\n for instance_pb in response.instances]\\n return instances, response.failed_locations\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::_prepare_create_request\",\n \"text\": \"google/cloud/bigtable/cluster.py::_prepare_create_request\\ndef _prepare_create_request(cluster):\\n \\\"\\\"\\\"Creates a protobuf request for a CreateCluster request.\\n\\n :type cluster: :class:`Cluster`\\n :param cluster: The cluster to be created.\\n\\n :rtype: :class:`.messages_v2_pb2.CreateClusterRequest`\\n :returns: The CreateCluster request object containing the cluster info.\\n \\\"\\\"\\\"\\n return messages_v2_pb2.CreateClusterRequest(\\n parent=cluster._instance.name,\\n cluster_id=cluster.cluster_id,\\n cluster=data_v2_pb2.Cluster(\\n serve_nodes=cluster.serve_nodes,\\n ),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::Cluster.__init__\",\n \"text\": \"google/cloud/bigtable/cluster.py::Cluster.__init__\\n def __init__(self, cluster_id, instance,\\n serve_nodes=DEFAULT_SERVE_NODES):\\n self.cluster_id = cluster_id\\n self._instance = instance\\n self.serve_nodes = serve_nodes\\n self.location = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::Cluster._update_from_pb\",\n \"text\": \"google/cloud/bigtable/cluster.py::Cluster._update_from_pb\\n def _update_from_pb(self, cluster_pb):\\n \\\"\\\"\\\"Refresh self from the server-provided protobuf.\\n\\n Helper for :meth:`from_pb` and :meth:`reload`.\\n \\\"\\\"\\\"\\n if not cluster_pb.serve_nodes: # Simple field (int32)\\n raise ValueError('Cluster protobuf does not contain serve_nodes')\\n self.serve_nodes = cluster_pb.serve_nodes\\n self.location = cluster_pb.location\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::Cluster.from_pb\",\n \"text\": \"google/cloud/bigtable/cluster.py::Cluster.from_pb\\n @classmethod\\n def from_pb(cls, cluster_pb, instance):\\n \\\"\\\"\\\"Creates a cluster instance from a protobuf.\\n\\n :type cluster_pb: :class:`instance_pb2.Cluster`\\n :param cluster_pb: A cluster protobuf object.\\n\\n :type instance: :class:`.instance.Instance>`\\n :param instance: The instance that owns the cluster.\\n\\n :rtype: :class:`Cluster`\\n :returns: The cluster parsed from the protobuf response.\\n :raises:\\n :class:`ValueError <exceptions.ValueError>` if the cluster\\n name does not match\\n ``projects/{project}/instances/{instance}/clusters/{cluster_id}``\\n or if the parsed project ID does not match the project ID\\n on the client.\\n \\\"\\\"\\\"\\n match = _CLUSTER_NAME_RE.match(cluster_pb.name)\\n if match is None:\\n raise ValueError('Cluster protobuf name was not in the '\\n 'expected format.', cluster_pb.name)\\n if match.group('project') != instance._client.project:\\n raise ValueError('Project ID on cluster does not match the '\\n 'project ID on the client')\\n if match.group('instance') != instance.instance_id:\\n raise ValueError('Instance ID on cluster does not match the '\\n 'instance ID on the client')\\n\\n result = cls(match.group('cluster_id'), instance)\\n result._update_from_pb(cluster_pb)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::Cluster.copy\",\n \"text\": \"google/cloud/bigtable/cluster.py::Cluster.copy\\n def copy(self):\\n \\\"\\\"\\\"Make a copy of this cluster.\\n\\n Copies the local data stored as simple types and copies the client\\n attached to this instance.\\n\\n :rtype: :class:`.Cluster`\\n :returns: A copy of the current cluster.\\n \\\"\\\"\\\"\\n new_instance = self._instance.copy()\\n return self.__class__(self.cluster_id, new_instance,\\n serve_nodes=self.serve_nodes)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::Cluster.name\",\n \"text\": \"google/cloud/bigtable/cluster.py::Cluster.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Cluster name used in requests.\\n\\n .. note::\\n This property will not change if ``_instance`` and ``cluster_id``\\n do not, but the return value is not cached.\\n\\n The cluster name is of the form\\n\\n ``\\\"projects/{project}/instances/{instance}/clusters/{cluster_id}\\\"``\\n\\n :rtype: str\\n :returns: The cluster name.\\n \\\"\\\"\\\"\\n return self._instance.name + '/clusters/' + self.cluster_id\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::Cluster.__eq__\",\n \"text\": \"google/cloud/bigtable/cluster.py::Cluster.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n # NOTE: This does not compare the configuration values, such as\\n # the serve_nodes. Instead, it only compares\\n # identifying values instance, cluster ID and client. This is\\n # intentional, since the same cluster can be in different states\\n # if not synchronized. Clusters with similar instance/cluster\\n # settings but different clients can't be used in the same way.\\n return (other.cluster_id == self.cluster_id and\\n other._instance == self._instance)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::Cluster.__ne__\",\n \"text\": \"google/cloud/bigtable/cluster.py::Cluster.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::Cluster.reload\",\n \"text\": \"google/cloud/bigtable/cluster.py::Cluster.reload\\n def reload(self):\\n \\\"\\\"\\\"Reload the metadata for this cluster.\\\"\\\"\\\"\\n request_pb = messages_v2_pb2.GetClusterRequest(name=self.name)\\n # We expect a `._generated.instance_pb2.Cluster`.\\n cluster_pb = self._instance._client._instance_stub.GetCluster(\\n request_pb)\\n\\n # NOTE: _update_from_pb does not check that the project, instance and\\n # cluster ID on the response match the request.\\n self._update_from_pb(cluster_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::Cluster.create\",\n \"text\": \"google/cloud/bigtable/cluster.py::Cluster.create\\n def create(self):\\n \\\"\\\"\\\"Create this cluster.\\n\\n .. note::\\n\\n Uses the ``project``, ``instance`` and ``cluster_id`` on the\\n current :class:`Cluster` in addition to the ``serve_nodes``.\\n To change them before creating, reset the values via\\n\\n .. code:: python\\n\\n cluster.serve_nodes = 8\\n cluster.cluster_id = 'i-changed-my-mind'\\n\\n before calling :meth:`create`.\\n\\n :rtype: :class:`Operation`\\n :returns: The long-running operation corresponding to the\\n create operation.\\n \\\"\\\"\\\"\\n request_pb = _prepare_create_request(self)\\n # We expect a `google.longrunning.operations_pb2.Operation`.\\n client = self._instance._client\\n operation_pb = client._instance_stub.CreateCluster(request_pb)\\n\\n operation = Operation.from_pb(operation_pb, client)\\n operation.target = self\\n operation.metadata['request_type'] = 'CreateCluster'\\n return operation\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::Cluster.update\",\n \"text\": \"google/cloud/bigtable/cluster.py::Cluster.update\\n def update(self):\\n \\\"\\\"\\\"Update this cluster.\\n\\n .. note::\\n\\n Updates the ``serve_nodes``. If you'd like to\\n change them before updating, reset the values via\\n\\n .. code:: python\\n\\n cluster.serve_nodes = 8\\n\\n before calling :meth:`update`.\\n\\n :rtype: :class:`Operation`\\n :returns: The long-running operation corresponding to the\\n update operation.\\n \\\"\\\"\\\"\\n request_pb = data_v2_pb2.Cluster(\\n name=self.name,\\n serve_nodes=self.serve_nodes,\\n )\\n # We expect a `google.longrunning.operations_pb2.Operation`.\\n client = self._instance._client\\n operation_pb = client._instance_stub.UpdateCluster(request_pb)\\n\\n operation = Operation.from_pb(operation_pb, client)\\n operation.target = self\\n operation.metadata['request_type'] = 'UpdateCluster'\\n return operation\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/cluster.py::Cluster.delete\",\n \"text\": \"google/cloud/bigtable/cluster.py::Cluster.delete\\n def delete(self):\\n \\\"\\\"\\\"Delete this cluster.\\n\\n Marks a cluster and all of its tables for permanent deletion in 7 days.\\n\\n Immediately upon completion of the request:\\n\\n * Billing will cease for all of the cluster's reserved resources.\\n * The cluster's ``delete_time`` field will be set 7 days in the future.\\n\\n Soon afterward:\\n\\n * All tables within the cluster will become unavailable.\\n\\n At the cluster's ``delete_time``:\\n\\n * The cluster and **all of its tables** will immediately and\\n irrevocably disappear from the API, and their data will be\\n permanently deleted.\\n \\\"\\\"\\\"\\n request_pb = messages_v2_pb2.DeleteClusterRequest(name=self.name)\\n # We expect a `google.protobuf.empty_pb2.Empty`\\n self._instance._client._instance_stub.DeleteCluster(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::_timedelta_to_duration_pb\",\n \"text\": \"google/cloud/bigtable/column_family.py::_timedelta_to_duration_pb\\ndef _timedelta_to_duration_pb(timedelta_val):\\n \\\"\\\"\\\"Convert a Python timedelta object to a duration protobuf.\\n\\n .. note::\\n\\n The Python timedelta has a granularity of microseconds while\\n the protobuf duration type has a duration of nanoseconds.\\n\\n :type timedelta_val: :class:`datetime.timedelta`\\n :param timedelta_val: A timedelta object.\\n\\n :rtype: :class:`google.protobuf.duration_pb2.Duration`\\n :returns: A duration object equivalent to the time delta.\\n \\\"\\\"\\\"\\n seconds_decimal = timedelta_val.total_seconds()\\n # Truncate the parts other than the integer.\\n seconds = int(seconds_decimal)\\n if seconds_decimal < 0:\\n signed_micros = timedelta_val.microseconds - 10**6\\n else:\\n signed_micros = timedelta_val.microseconds\\n # Convert nanoseconds to microseconds.\\n nanos = 1000 * signed_micros\\n return duration_pb2.Duration(seconds=seconds, nanos=nanos)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::_duration_pb_to_timedelta\",\n \"text\": \"google/cloud/bigtable/column_family.py::_duration_pb_to_timedelta\\ndef _duration_pb_to_timedelta(duration_pb):\\n \\\"\\\"\\\"Convert a duration protobuf to a Python timedelta object.\\n\\n .. note::\\n\\n The Python timedelta has a granularity of microseconds while\\n the protobuf duration type has a duration of nanoseconds.\\n\\n :type duration_pb: :class:`google.protobuf.duration_pb2.Duration`\\n :param duration_pb: A protobuf duration object.\\n\\n :rtype: :class:`datetime.timedelta`\\n :returns: The converted timedelta object.\\n \\\"\\\"\\\"\\n return datetime.timedelta(\\n seconds=duration_pb.seconds,\\n microseconds=(duration_pb.nanos / 1000.0),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::GarbageCollectionRule.__ne__\",\n \"text\": \"google/cloud/bigtable/column_family.py::GarbageCollectionRule.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.__init__\",\n \"text\": \"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.__init__\\n def __init__(self, max_num_versions):\\n self.max_num_versions = max_num_versions\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.__eq__\",\n \"text\": \"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.max_num_versions == self.max_num_versions\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.to_pb\",\n \"text\": \"google/cloud/bigtable/column_family.py::MaxVersionsGCRule.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the garbage collection rule to a protobuf.\\n\\n :rtype: :class:`.table_v2_pb2.GcRule`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return table_v2_pb2.GcRule(max_num_versions=self.max_num_versions)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::MaxAgeGCRule.__init__\",\n \"text\": \"google/cloud/bigtable/column_family.py::MaxAgeGCRule.__init__\\n def __init__(self, max_age):\\n self.max_age = max_age\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::MaxAgeGCRule.__eq__\",\n \"text\": \"google/cloud/bigtable/column_family.py::MaxAgeGCRule.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.max_age == self.max_age\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::MaxAgeGCRule.to_pb\",\n \"text\": \"google/cloud/bigtable/column_family.py::MaxAgeGCRule.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the garbage collection rule to a protobuf.\\n\\n :rtype: :class:`.table_v2_pb2.GcRule`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n max_age = _timedelta_to_duration_pb(self.max_age)\\n return table_v2_pb2.GcRule(max_age=max_age)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::GCRuleUnion.__init__\",\n \"text\": \"google/cloud/bigtable/column_family.py::GCRuleUnion.__init__\\n def __init__(self, rules):\\n self.rules = rules\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::GCRuleUnion.__eq__\",\n \"text\": \"google/cloud/bigtable/column_family.py::GCRuleUnion.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.rules == self.rules\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::GCRuleUnion.to_pb\",\n \"text\": \"google/cloud/bigtable/column_family.py::GCRuleUnion.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the union into a single GC rule as a protobuf.\\n\\n :rtype: :class:`.table_v2_pb2.GcRule`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n union = table_v2_pb2.GcRule.Union(\\n rules=[rule.to_pb() for rule in self.rules])\\n return table_v2_pb2.GcRule(union=union)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::GCRuleIntersection.__init__\",\n \"text\": \"google/cloud/bigtable/column_family.py::GCRuleIntersection.__init__\\n def __init__(self, rules):\\n self.rules = rules\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::GCRuleIntersection.__eq__\",\n \"text\": \"google/cloud/bigtable/column_family.py::GCRuleIntersection.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.rules == self.rules\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::GCRuleIntersection.to_pb\",\n \"text\": \"google/cloud/bigtable/column_family.py::GCRuleIntersection.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the intersection into a single GC rule as a protobuf.\\n\\n :rtype: :class:`.table_v2_pb2.GcRule`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n intersection = table_v2_pb2.GcRule.Intersection(\\n rules=[rule.to_pb() for rule in self.rules])\\n return table_v2_pb2.GcRule(intersection=intersection)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::ColumnFamily.__init__\",\n \"text\": \"google/cloud/bigtable/column_family.py::ColumnFamily.__init__\\n def __init__(self, column_family_id, table, gc_rule=None):\\n self.column_family_id = column_family_id\\n self._table = table\\n self.gc_rule = gc_rule\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::ColumnFamily.name\",\n \"text\": \"google/cloud/bigtable/column_family.py::ColumnFamily.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Column family name used in requests.\\n\\n .. note::\\n\\n This property will not change if ``column_family_id`` does not, but\\n the return value is not cached.\\n\\n The table name is of the form\\n\\n ``\\\"projects/../zones/../clusters/../tables/../columnFamilies/..\\\"``\\n\\n :rtype: str\\n :returns: The column family name.\\n \\\"\\\"\\\"\\n return self._table.name + '/columnFamilies/' + self.column_family_id\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::ColumnFamily.__eq__\",\n \"text\": \"google/cloud/bigtable/column_family.py::ColumnFamily.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.column_family_id == self.column_family_id and\\n other._table == self._table and\\n other.gc_rule == self.gc_rule)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::ColumnFamily.__ne__\",\n \"text\": \"google/cloud/bigtable/column_family.py::ColumnFamily.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::ColumnFamily.to_pb\",\n \"text\": \"google/cloud/bigtable/column_family.py::ColumnFamily.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the column family to a protobuf.\\n\\n :rtype: :class:`.table_v2_pb2.ColumnFamily`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n if self.gc_rule is None:\\n return table_v2_pb2.ColumnFamily()\\n else:\\n return table_v2_pb2.ColumnFamily(gc_rule=self.gc_rule.to_pb())\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::ColumnFamily.create\",\n \"text\": \"google/cloud/bigtable/column_family.py::ColumnFamily.create\\n def create(self):\\n \\\"\\\"\\\"Create this column family.\\\"\\\"\\\"\\n column_family = self.to_pb()\\n request_pb = table_admin_v2_pb2.ModifyColumnFamiliesRequest(\\n name=self._table.name)\\n request_pb.modifications.add(\\n id=self.column_family_id,\\n create=column_family,\\n )\\n client = self._table._instance._client\\n # We expect a `.table_v2_pb2.ColumnFamily`. We ignore it since the only\\n # data it contains are the GC rule and the column family ID already\\n # stored on this instance.\\n client._table_stub.ModifyColumnFamilies(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::ColumnFamily.update\",\n \"text\": \"google/cloud/bigtable/column_family.py::ColumnFamily.update\\n def update(self):\\n \\\"\\\"\\\"Update this column family.\\n\\n .. note::\\n\\n Only the GC rule can be updated. By changing the column family ID,\\n you will simply be referring to a different column family.\\n \\\"\\\"\\\"\\n column_family = self.to_pb()\\n request_pb = table_admin_v2_pb2.ModifyColumnFamiliesRequest(\\n name=self._table.name)\\n request_pb.modifications.add(\\n id=self.column_family_id,\\n update=column_family)\\n client = self._table._instance._client\\n # We expect a `.table_v2_pb2.ColumnFamily`. We ignore it since the only\\n # data it contains are the GC rule and the column family ID already\\n # stored on this instance.\\n client._table_stub.ModifyColumnFamilies(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::ColumnFamily.delete\",\n \"text\": \"google/cloud/bigtable/column_family.py::ColumnFamily.delete\\n def delete(self):\\n \\\"\\\"\\\"Delete this column family.\\\"\\\"\\\"\\n request_pb = table_admin_v2_pb2.ModifyColumnFamiliesRequest(\\n name=self._table.name)\\n request_pb.modifications.add(\\n id=self.column_family_id,\\n drop=True)\\n client = self._table._instance._client\\n # We expect a `google.protobuf.empty_pb2.Empty`\\n client._table_stub.ModifyColumnFamilies(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/column_family.py::_gc_rule_from_pb\",\n \"text\": \"google/cloud/bigtable/column_family.py::_gc_rule_from_pb\\ndef _gc_rule_from_pb(gc_rule_pb):\\n \\\"\\\"\\\"Convert a protobuf GC rule to a native object.\\n\\n :type gc_rule_pb: :class:`.table_v2_pb2.GcRule`\\n :param gc_rule_pb: The GC rule to convert.\\n\\n :rtype: :class:`GarbageCollectionRule` or :data:`NoneType <types.NoneType>`\\n :returns: An instance of one of the native rules defined\\n in :module:`column_family` or :data:`None` if no values were\\n set on the protobuf passed in.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the rule name\\n is unexpected.\\n \\\"\\\"\\\"\\n rule_name = gc_rule_pb.WhichOneof('rule')\\n if rule_name is None:\\n return None\\n\\n if rule_name == 'max_num_versions':\\n return MaxVersionsGCRule(gc_rule_pb.max_num_versions)\\n elif rule_name == 'max_age':\\n max_age = _duration_pb_to_timedelta(gc_rule_pb.max_age)\\n return MaxAgeGCRule(max_age)\\n elif rule_name == 'union':\\n return GCRuleUnion([_gc_rule_from_pb(rule)\\n for rule in gc_rule_pb.union.rules])\\n elif rule_name == 'intersection':\\n rules = [_gc_rule_from_pb(rule)\\n for rule in gc_rule_pb.intersection.rules]\\n return GCRuleIntersection(rules)\\n else:\\n raise ValueError('Unexpected rule name', rule_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::_prepare_create_request\",\n \"text\": \"google/cloud/bigtable/instance.py::_prepare_create_request\\ndef _prepare_create_request(instance):\\n \\\"\\\"\\\"Creates a protobuf request for a CreateInstance request.\\n\\n :type instance: :class:`Instance`\\n :param instance: The instance to be created.\\n\\n :rtype: :class:`.messages_v2_pb2.CreateInstanceRequest`\\n :returns: The CreateInstance request object containing the instance info.\\n \\\"\\\"\\\"\\n parent_name = ('projects/' + instance._client.project)\\n message = messages_v2_pb2.CreateInstanceRequest(\\n parent=parent_name,\\n instance_id=instance.instance_id,\\n instance=data_v2_pb2.Instance(\\n display_name=instance.display_name,\\n ),\\n )\\n cluster = message.clusters[instance.instance_id]\\n cluster.name = instance.name + '/clusters/' + instance.instance_id\\n cluster.location = (\\n parent_name + '/locations/' + instance._cluster_location_id)\\n cluster.serve_nodes = instance._cluster_serve_nodes\\n return message\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.__init__\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.__init__\\n def __init__(self, instance_id, client,\\n location_id=_EXISTING_INSTANCE_LOCATION_ID,\\n display_name=None,\\n serve_nodes=DEFAULT_SERVE_NODES):\\n self.instance_id = instance_id\\n self.display_name = display_name or instance_id\\n self._cluster_location_id = location_id\\n self._cluster_serve_nodes = serve_nodes\\n self._client = client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance._update_from_pb\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance._update_from_pb\\n def _update_from_pb(self, instance_pb):\\n \\\"\\\"\\\"Refresh self from the server-provided protobuf.\\n\\n Helper for :meth:`from_pb` and :meth:`reload`.\\n \\\"\\\"\\\"\\n if not instance_pb.display_name: # Simple field (string)\\n raise ValueError('Instance protobuf does not contain display_name')\\n self.display_name = instance_pb.display_name\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.from_pb\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.from_pb\\n @classmethod\\n def from_pb(cls, instance_pb, client):\\n \\\"\\\"\\\"Creates a instance instance from a protobuf.\\n\\n :type instance_pb: :class:`instance_pb2.Instance`\\n :param instance_pb: A instance protobuf object.\\n\\n :type client: :class:`Client <google.cloud.bigtable.client.Client>`\\n :param client: The client that owns the instance.\\n\\n :rtype: :class:`Instance`\\n :returns: The instance parsed from the protobuf response.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the instance\\n name does not match\\n ``projects/{project}/instances/{instance_id}``\\n or if the parsed project ID does not match the project ID\\n on the client.\\n \\\"\\\"\\\"\\n match = _INSTANCE_NAME_RE.match(instance_pb.name)\\n if match is None:\\n raise ValueError('Instance protobuf name was not in the '\\n 'expected format.', instance_pb.name)\\n if match.group('project') != client.project:\\n raise ValueError('Project ID on instance does not match the '\\n 'project ID on the client')\\n instance_id = match.group('instance_id')\\n\\n result = cls(instance_id, client, _EXISTING_INSTANCE_LOCATION_ID)\\n result._update_from_pb(instance_pb)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.copy\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.copy\\n def copy(self):\\n \\\"\\\"\\\"Make a copy of this instance.\\n\\n Copies the local data stored as simple types and copies the client\\n attached to this instance.\\n\\n :rtype: :class:`.Instance`\\n :returns: A copy of the current instance.\\n \\\"\\\"\\\"\\n new_client = self._client.copy()\\n return self.__class__(self.instance_id, new_client,\\n self._cluster_location_id,\\n display_name=self.display_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.name\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Instance name used in requests.\\n\\n .. note::\\n This property will not change if ``instance_id`` does not,\\n but the return value is not cached.\\n\\n The instance name is of the form\\n\\n ``\\\"projects/{project}/instances/{instance_id}\\\"``\\n\\n :rtype: str\\n :returns: The instance name.\\n \\\"\\\"\\\"\\n return self._client.project_name + '/instances/' + self.instance_id\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.__eq__\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n # NOTE: This does not compare the configuration values, such as\\n # the display_name. Instead, it only compares\\n # identifying values instance ID and client. This is\\n # intentional, since the same instance can be in different states\\n # if not synchronized. Instances with similar instance\\n # settings but different clients can't be used in the same way.\\n return (other.instance_id == self.instance_id and\\n other._client == self._client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.__ne__\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.reload\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.reload\\n def reload(self):\\n \\\"\\\"\\\"Reload the metadata for this instance.\\\"\\\"\\\"\\n request_pb = messages_v2_pb2.GetInstanceRequest(name=self.name)\\n # We expect `data_v2_pb2.Instance`.\\n instance_pb = self._client._instance_stub.GetInstance(request_pb)\\n\\n # NOTE: _update_from_pb does not check that the project and\\n # instance ID on the response match the request.\\n self._update_from_pb(instance_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.create\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.create\\n def create(self):\\n \\\"\\\"\\\"Create this instance.\\n\\n .. note::\\n\\n Uses the ``project`` and ``instance_id`` on the current\\n :class:`Instance` in addition to the ``display_name``.\\n To change them before creating, reset the values via\\n\\n .. code:: python\\n\\n instance.display_name = 'New display name'\\n instance.instance_id = 'i-changed-my-mind'\\n\\n before calling :meth:`create`.\\n\\n :rtype: :class:`Operation`\\n :returns: The long-running operation corresponding to the\\n create operation.\\n \\\"\\\"\\\"\\n request_pb = _prepare_create_request(self)\\n # We expect a `google.longrunning.operations_pb2.Operation`.\\n operation_pb = self._client._instance_stub.CreateInstance(request_pb)\\n\\n operation = Operation.from_pb(operation_pb, self._client)\\n operation.target = self\\n operation.metadata['request_type'] = 'CreateInstance'\\n return operation\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.update\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.update\\n def update(self):\\n \\\"\\\"\\\"Update this instance.\\n\\n .. note::\\n\\n Updates the ``display_name``. To change that value before\\n updating, reset its values via\\n\\n .. code:: python\\n\\n instance.display_name = 'New display name'\\n\\n before calling :meth:`update`.\\n \\\"\\\"\\\"\\n request_pb = data_v2_pb2.Instance(\\n name=self.name,\\n display_name=self.display_name,\\n )\\n # Ignore the expected `data_v2_pb2.Instance`.\\n self._client._instance_stub.UpdateInstance(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.delete\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.delete\\n def delete(self):\\n \\\"\\\"\\\"Delete this instance.\\n\\n Marks a instance and all of its tables for permanent deletion\\n in 7 days.\\n\\n Immediately upon completion of the request:\\n\\n * Billing will cease for all of the instance's reserved resources.\\n * The instance's ``delete_time`` field will be set 7 days in\\n the future.\\n\\n Soon afterward:\\n\\n * All tables within the instance will become unavailable.\\n\\n At the instance's ``delete_time``:\\n\\n * The instance and **all of its tables** will immediately and\\n irrevocably disappear from the API, and their data will be\\n permanently deleted.\\n \\\"\\\"\\\"\\n request_pb = messages_v2_pb2.DeleteInstanceRequest(name=self.name)\\n # We expect a `google.protobuf.empty_pb2.Empty`\\n self._client._instance_stub.DeleteInstance(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.cluster\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.cluster\\n def cluster(self, cluster_id, serve_nodes=3):\\n \\\"\\\"\\\"Factory to create a cluster associated with this client.\\n\\n :type cluster_id: str\\n :param cluster_id: The ID of the cluster.\\n\\n :type serve_nodes: int\\n :param serve_nodes: (Optional) The number of nodes in the cluster.\\n Defaults to 3.\\n\\n :rtype: :class:`.Cluster`\\n :returns: The cluster owned by this client.\\n \\\"\\\"\\\"\\n return Cluster(cluster_id, self, serve_nodes=serve_nodes)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.list_clusters\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.list_clusters\\n def list_clusters(self):\\n \\\"\\\"\\\"Lists clusters in this instance.\\n\\n :rtype: tuple\\n :returns: A pair of results, the first is a list of :class:`.Cluster` s\\n returned and the second is a list of strings (the failed\\n locations in the request).\\n \\\"\\\"\\\"\\n request_pb = messages_v2_pb2.ListClustersRequest(parent=self.name)\\n # We expect a `.cluster_messages_v1_pb2.ListClustersResponse`\\n list_clusters_response = self._client._instance_stub.ListClusters(\\n request_pb)\\n\\n failed_locations = [\\n location for location in list_clusters_response.failed_locations]\\n clusters = [Cluster.from_pb(cluster_pb, self)\\n for cluster_pb in list_clusters_response.clusters]\\n return clusters, failed_locations\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.table\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.table\\n def table(self, table_id):\\n \\\"\\\"\\\"Factory to create a table associated with this instance.\\n\\n :type table_id: str\\n :param table_id: The ID of the table.\\n\\n :rtype: :class:`Table <google.cloud.bigtable.table.Table>`\\n :returns: The table owned by this instance.\\n \\\"\\\"\\\"\\n return Table(table_id, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/instance.py::Instance.list_tables\",\n \"text\": \"google/cloud/bigtable/instance.py::Instance.list_tables\\n def list_tables(self):\\n \\\"\\\"\\\"List the tables in this instance.\\n\\n :rtype: list of :class:`Table <google.cloud.bigtable.table.Table>`\\n :returns: The list of tables owned by the instance.\\n :raises: :class:`ValueError <exceptions.ValueError>` if one of the\\n returned tables has a name that is not of the expected format.\\n \\\"\\\"\\\"\\n request_pb = table_messages_v2_pb2.ListTablesRequest(parent=self.name)\\n # We expect a `table_messages_v2_pb2.ListTablesResponse`\\n table_list_pb = self._client._table_stub.ListTables(request_pb)\\n\\n result = []\\n for table_pb in table_list_pb.tables:\\n table_prefix = self.name + '/tables/'\\n if not table_pb.name.startswith(table_prefix):\\n raise ValueError('Table name %s not of expected format' % (\\n table_pb.name,))\\n table_id = table_pb.name[len(table_prefix):]\\n result.append(self.table(table_id))\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::Row.__init__\",\n \"text\": \"google/cloud/bigtable/row.py::Row.__init__\\n def __init__(self, row_key, table):\\n self._row_key = _to_bytes(row_key)\\n self._table = table\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::_SetDeleteRow._get_mutations\",\n \"text\": \"google/cloud/bigtable/row.py::_SetDeleteRow._get_mutations\\n def _get_mutations(self, state):\\n \\\"\\\"\\\"Gets the list of mutations for a given state.\\n\\n This method intended to be implemented by subclasses.\\n\\n ``state`` may not need to be used by all subclasses.\\n\\n :type state: bool\\n :param state: The state that the mutation should be\\n applied in.\\n\\n :raises: :class:`NotImplementedError <exceptions.NotImplementedError>`\\n always.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::_SetDeleteRow._set_cell\",\n \"text\": \"google/cloud/bigtable/row.py::_SetDeleteRow._set_cell\\n def _set_cell(self, column_family_id, column, value, timestamp=None,\\n state=None):\\n \\\"\\\"\\\"Helper for :meth:`set_cell`\\n\\n Adds a mutation to set the value in a specific cell.\\n\\n ``state`` is unused by :class:`DirectRow` but is used by\\n subclasses.\\n\\n :type column_family_id: str\\n :param column_family_id: The column family that contains the column.\\n Must be of the form\\n ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type column: bytes\\n :param column: The column within the column family where the cell\\n is located.\\n\\n :type value: bytes or :class:`int`\\n :param value: The value to set in the cell. If an integer is used,\\n will be interpreted as a 64-bit big-endian signed\\n integer (8 bytes).\\n\\n :type timestamp: :class:`datetime.datetime`\\n :param timestamp: (Optional) The timestamp of the operation.\\n\\n :type state: bool\\n :param state: (Optional) The state that is passed along to\\n :meth:`_get_mutations`.\\n \\\"\\\"\\\"\\n column = _to_bytes(column)\\n if isinstance(value, six.integer_types):\\n value = _PACK_I64(value)\\n value = _to_bytes(value)\\n if timestamp is None:\\n # Use -1 for current Bigtable server time.\\n timestamp_micros = -1\\n else:\\n timestamp_micros = _microseconds_from_datetime(timestamp)\\n # Truncate to millisecond granularity.\\n timestamp_micros -= (timestamp_micros % 1000)\\n\\n mutation_val = data_v2_pb2.Mutation.SetCell(\\n family_name=column_family_id,\\n column_qualifier=column,\\n timestamp_micros=timestamp_micros,\\n value=value,\\n )\\n mutation_pb = data_v2_pb2.Mutation(set_cell=mutation_val)\\n self._get_mutations(sta\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::_SetDeleteRow._delete\",\n \"text\": \"google/cloud/bigtable/row.py::_SetDeleteRow._delete\\n def _delete(self, state=None):\\n \\\"\\\"\\\"Helper for :meth:`delete`\\n\\n Adds a delete mutation (for the entire row) to the accumulated\\n mutations.\\n\\n ``state`` is unused by :class:`DirectRow` but is used by\\n subclasses.\\n\\n :type state: bool\\n :param state: (Optional) The state that is passed along to\\n :meth:`_get_mutations`.\\n \\\"\\\"\\\"\\n mutation_val = data_v2_pb2.Mutation.DeleteFromRow()\\n mutation_pb = data_v2_pb2.Mutation(delete_from_row=mutation_val)\\n self._get_mutations(state).append(mutation_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::_SetDeleteRow._delete_cells\",\n \"text\": \"google/cloud/bigtable/row.py::_SetDeleteRow._delete_cells\\n def _delete_cells(self, column_family_id, columns, time_range=None,\\n state=None):\\n \\\"\\\"\\\"Helper for :meth:`delete_cell` and :meth:`delete_cells`.\\n\\n ``state`` is unused by :class:`DirectRow` but is used by\\n subclasses.\\n\\n :type column_family_id: str\\n :param column_family_id: The column family that contains the column\\n or columns with cells being deleted. Must be\\n of the form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type columns: :class:`list` of :class:`str` /\\n :func:`unicode <unicode>`, or :class:`object`\\n :param columns: The columns within the column family that will have\\n cells deleted. If :attr:`ALL_COLUMNS` is used then\\n the entire column family will be deleted from the row.\\n\\n :type time_range: :class:`TimestampRange`\\n :param time_range: (Optional) The range of time within which cells\\n should be deleted.\\n\\n :type state: bool\\n :param state: (Optional) The state that is passed along to\\n :meth:`_get_mutations`.\\n \\\"\\\"\\\"\\n mutations_list = self._get_mutations(state)\\n if columns is self.ALL_COLUMNS:\\n mutation_val = data_v2_pb2.Mutation.DeleteFromFamily(\\n family_name=column_family_id,\\n )\\n mutation_pb = data_v2_pb2.Mutation(delete_from_family=mutation_val)\\n mutations_list.append(mutation_pb)\\n else:\\n delete_kwargs = {}\\n if time_range is not None:\\n delete_kwargs['time_range'] = time_range.to_pb()\\n\\n to_append = []\\n for column in columns:\\n column = _to_bytes(column)\\n # time_range will never change if present, but the rest of\\n # delete_kwargs will\\n delet\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::DirectRow.__init__\",\n \"text\": \"google/cloud/bigtable/row.py::DirectRow.__init__\\n def __init__(self, row_key, table):\\n super(DirectRow, self).__init__(row_key, table)\\n self._pb_mutations = []\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::DirectRow._get_mutations\",\n \"text\": \"google/cloud/bigtable/row.py::DirectRow._get_mutations\\n def _get_mutations(self, state): # pylint: disable=unused-argument\\n \\\"\\\"\\\"Gets the list of mutations for a given state.\\n\\n ``state`` is unused by :class:`DirectRow` but is used by\\n subclasses.\\n\\n :type state: bool\\n :param state: The state that the mutation should be\\n applied in.\\n\\n :rtype: list\\n :returns: The list to add new mutations to (for the current state).\\n \\\"\\\"\\\"\\n return self._pb_mutations\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::DirectRow.set_cell\",\n \"text\": \"google/cloud/bigtable/row.py::DirectRow.set_cell\\n def set_cell(self, column_family_id, column, value, timestamp=None):\\n \\\"\\\"\\\"Sets a value in this row.\\n\\n The cell is determined by the ``row_key`` of this :class:`DirectRow`\\n and the ``column``. The ``column`` must be in an existing\\n :class:`.ColumnFamily` (as determined by ``column_family_id``).\\n\\n .. note::\\n\\n This method adds a mutation to the accumulated mutations on this\\n row, but does not make an API request. To actually\\n send an API request (with the mutations) to the Google Cloud\\n Bigtable API, call :meth:`commit`.\\n\\n :type column_family_id: str\\n :param column_family_id: The column family that contains the column.\\n Must be of the form\\n ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type column: bytes\\n :param column: The column within the column family where the cell\\n is located.\\n\\n :type value: bytes or :class:`int`\\n :param value: The value to set in the cell. If an integer is used,\\n will be interpreted as a 64-bit big-endian signed\\n integer (8 bytes).\\n\\n :type timestamp: :class:`datetime.datetime`\\n :param timestamp: (Optional) The timestamp of the operation.\\n \\\"\\\"\\\"\\n self._set_cell(column_family_id, column, value, timestamp=timestamp,\\n state=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::DirectRow.delete\",\n \"text\": \"google/cloud/bigtable/row.py::DirectRow.delete\\n def delete(self):\\n \\\"\\\"\\\"Deletes this row from the table.\\n\\n .. note::\\n\\n This method adds a mutation to the accumulated mutations on this\\n row, but does not make an API request. To actually\\n send an API request (with the mutations) to the Google Cloud\\n Bigtable API, call :meth:`commit`.\\n \\\"\\\"\\\"\\n self._delete(state=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::DirectRow.delete_cell\",\n \"text\": \"google/cloud/bigtable/row.py::DirectRow.delete_cell\\n def delete_cell(self, column_family_id, column, time_range=None):\\n \\\"\\\"\\\"Deletes cell in this row.\\n\\n .. note::\\n\\n This method adds a mutation to the accumulated mutations on this\\n row, but does not make an API request. To actually\\n send an API request (with the mutations) to the Google Cloud\\n Bigtable API, call :meth:`commit`.\\n\\n :type column_family_id: str\\n :param column_family_id: The column family that contains the column\\n or columns with cells being deleted. Must be\\n of the form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type column: bytes\\n :param column: The column within the column family that will have a\\n cell deleted.\\n\\n :type time_range: :class:`TimestampRange`\\n :param time_range: (Optional) The range of time within which cells\\n should be deleted.\\n \\\"\\\"\\\"\\n self._delete_cells(column_family_id, [column], time_range=time_range,\\n state=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::DirectRow.delete_cells\",\n \"text\": \"google/cloud/bigtable/row.py::DirectRow.delete_cells\\n def delete_cells(self, column_family_id, columns, time_range=None):\\n \\\"\\\"\\\"Deletes cells in this row.\\n\\n .. note::\\n\\n This method adds a mutation to the accumulated mutations on this\\n row, but does not make an API request. To actually\\n send an API request (with the mutations) to the Google Cloud\\n Bigtable API, call :meth:`commit`.\\n\\n :type column_family_id: str\\n :param column_family_id: The column family that contains the column\\n or columns with cells being deleted. Must be\\n of the form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type columns: :class:`list` of :class:`str` /\\n :func:`unicode <unicode>`, or :class:`object`\\n :param columns: The columns within the column family that will have\\n cells deleted. If :attr:`ALL_COLUMNS` is used then\\n the entire column family will be deleted from the row.\\n\\n :type time_range: :class:`TimestampRange`\\n :param time_range: (Optional) The range of time within which cells\\n should be deleted.\\n \\\"\\\"\\\"\\n self._delete_cells(column_family_id, columns, time_range=time_range,\\n state=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::DirectRow.commit\",\n \"text\": \"google/cloud/bigtable/row.py::DirectRow.commit\\n def commit(self):\\n \\\"\\\"\\\"Makes a ``MutateRow`` API request.\\n\\n If no mutations have been created in the row, no request is made.\\n\\n Mutations are applied atomically and in order, meaning that earlier\\n mutations can be masked / negated by later ones. Cells already present\\n in the row are left unchanged unless explicitly changed by a mutation.\\n\\n After committing the accumulated mutations, resets the local\\n mutations to an empty list.\\n\\n :raises: :class:`ValueError <exceptions.ValueError>` if the number of\\n mutations exceeds the :data:`MAX_MUTATIONS`.\\n \\\"\\\"\\\"\\n mutations_list = self._get_mutations(None)\\n num_mutations = len(mutations_list)\\n if num_mutations == 0:\\n return\\n if num_mutations > MAX_MUTATIONS:\\n raise ValueError('%d total mutations exceed the maximum allowable '\\n '%d.' % (num_mutations, MAX_MUTATIONS))\\n request_pb = messages_v2_pb2.MutateRowRequest(\\n table_name=self._table.name,\\n row_key=self._row_key,\\n mutations=mutations_list,\\n )\\n # We expect a `google.protobuf.empty_pb2.Empty`\\n client = self._table._instance._client\\n client._data_stub.MutateRow(request_pb)\\n self.clear()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::DirectRow.clear\",\n \"text\": \"google/cloud/bigtable/row.py::DirectRow.clear\\n def clear(self):\\n \\\"\\\"\\\"Removes all currently accumulated mutations on the current row.\\\"\\\"\\\"\\n del self._pb_mutations[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::ConditionalRow.__init__\",\n \"text\": \"google/cloud/bigtable/row.py::ConditionalRow.__init__\\n def __init__(self, row_key, table, filter_):\\n super(ConditionalRow, self).__init__(row_key, table)\\n self._filter = filter_\\n self._true_pb_mutations = []\\n self._false_pb_mutations = []\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::ConditionalRow._get_mutations\",\n \"text\": \"google/cloud/bigtable/row.py::ConditionalRow._get_mutations\\n def _get_mutations(self, state):\\n \\\"\\\"\\\"Gets the list of mutations for a given state.\\n\\n Over-ridden so that the state can be used in:\\n\\n * :meth:`set_cell`\\n * :meth:`delete`\\n * :meth:`delete_cell`\\n * :meth:`delete_cells`\\n\\n :type state: bool\\n :param state: The state that the mutation should be\\n applied in.\\n\\n :rtype: list\\n :returns: The list to add new mutations to (for the current state).\\n \\\"\\\"\\\"\\n if state:\\n return self._true_pb_mutations\\n else:\\n return self._false_pb_mutations\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::ConditionalRow.commit\",\n \"text\": \"google/cloud/bigtable/row.py::ConditionalRow.commit\\n def commit(self):\\n \\\"\\\"\\\"Makes a ``CheckAndMutateRow`` API request.\\n\\n If no mutations have been created in the row, no request is made.\\n\\n The mutations will be applied conditionally, based on whether the\\n filter matches any cells in the :class:`ConditionalRow` or not. (Each\\n method which adds a mutation has a ``state`` parameter for this\\n purpose.)\\n\\n Mutations are applied atomically and in order, meaning that earlier\\n mutations can be masked / negated by later ones. Cells already present\\n in the row are left unchanged unless explicitly changed by a mutation.\\n\\n After committing the accumulated mutations, resets the local\\n mutations.\\n\\n :rtype: bool\\n :returns: Flag indicating if the filter was matched (which also\\n indicates which set of mutations were applied by the server).\\n :raises: :class:`ValueError <exceptions.ValueError>` if the number of\\n mutations exceeds the :data:`MAX_MUTATIONS`.\\n \\\"\\\"\\\"\\n true_mutations = self._get_mutations(state=True)\\n false_mutations = self._get_mutations(state=False)\\n num_true_mutations = len(true_mutations)\\n num_false_mutations = len(false_mutations)\\n if num_true_mutations == 0 and num_false_mutations == 0:\\n return\\n if (num_true_mutations > MAX_MUTATIONS or\\n num_false_mutations > MAX_MUTATIONS):\\n raise ValueError(\\n 'Exceed the maximum allowable mutations (%d). Had %s true '\\n 'mutations and %d false mutations.' % (\\n MAX_MUTATIONS, num_true_mutations, num_false_mutations))\\n\\n request_pb = messages_v2_pb2.CheckAndMutateRowRequest(\\n table_name=self._table.name,\\n row_key=self._row_key,\\n predicate_filter=self._filter.to_pb(),\\n true_mutations=true_mutations,\\n false_mutations\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::ConditionalRow.set_cell\",\n \"text\": \"google/cloud/bigtable/row.py::ConditionalRow.set_cell\\n def set_cell(self, column_family_id, column, value, timestamp=None,\\n state=True):\\n \\\"\\\"\\\"Sets a value in this row.\\n\\n The cell is determined by the ``row_key`` of this\\n :class:`ConditionalRow` and the ``column``. The ``column`` must be in\\n an existing :class:`.ColumnFamily` (as determined by\\n ``column_family_id``).\\n\\n .. note::\\n\\n This method adds a mutation to the accumulated mutations on this\\n row, but does not make an API request. To actually\\n send an API request (with the mutations) to the Google Cloud\\n Bigtable API, call :meth:`commit`.\\n\\n :type column_family_id: str\\n :param column_family_id: The column family that contains the column.\\n Must be of the form\\n ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type column: bytes\\n :param column: The column within the column family where the cell\\n is located.\\n\\n :type value: bytes or :class:`int`\\n :param value: The value to set in the cell. If an integer is used,\\n will be interpreted as a 64-bit big-endian signed\\n integer (8 bytes).\\n\\n :type timestamp: :class:`datetime.datetime`\\n :param timestamp: (Optional) The timestamp of the operation.\\n\\n :type state: bool\\n :param state: (Optional) The state that the mutation should be\\n applied in. Defaults to :data:`True`.\\n \\\"\\\"\\\"\\n self._set_cell(column_family_id, column, value, timestamp=timestamp,\\n state=state)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::ConditionalRow.delete\",\n \"text\": \"google/cloud/bigtable/row.py::ConditionalRow.delete\\n def delete(self, state=True):\\n \\\"\\\"\\\"Deletes this row from the table.\\n\\n .. note::\\n\\n This method adds a mutation to the accumulated mutations on this\\n row, but does not make an API request. To actually\\n send an API request (with the mutations) to the Google Cloud\\n Bigtable API, call :meth:`commit`.\\n\\n :type state: bool\\n :param state: (Optional) The state that the mutation should be\\n applied in. Defaults to :data:`True`.\\n \\\"\\\"\\\"\\n self._delete(state=state)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::ConditionalRow.delete_cell\",\n \"text\": \"google/cloud/bigtable/row.py::ConditionalRow.delete_cell\\n def delete_cell(self, column_family_id, column, time_range=None,\\n state=True):\\n \\\"\\\"\\\"Deletes cell in this row.\\n\\n .. note::\\n\\n This method adds a mutation to the accumulated mutations on this\\n row, but does not make an API request. To actually\\n send an API request (with the mutations) to the Google Cloud\\n Bigtable API, call :meth:`commit`.\\n\\n :type column_family_id: str\\n :param column_family_id: The column family that contains the column\\n or columns with cells being deleted. Must be\\n of the form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type column: bytes\\n :param column: The column within the column family that will have a\\n cell deleted.\\n\\n :type time_range: :class:`TimestampRange`\\n :param time_range: (Optional) The range of time within which cells\\n should be deleted.\\n\\n :type state: bool\\n :param state: (Optional) The state that the mutation should be\\n applied in. Defaults to :data:`True`.\\n \\\"\\\"\\\"\\n self._delete_cells(column_family_id, [column], time_range=time_range,\\n state=state)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::ConditionalRow.delete_cells\",\n \"text\": \"google/cloud/bigtable/row.py::ConditionalRow.delete_cells\\n def delete_cells(self, column_family_id, columns, time_range=None,\\n state=True):\\n \\\"\\\"\\\"Deletes cells in this row.\\n\\n .. note::\\n\\n This method adds a mutation to the accumulated mutations on this\\n row, but does not make an API request. To actually\\n send an API request (with the mutations) to the Google Cloud\\n Bigtable API, call :meth:`commit`.\\n\\n :type column_family_id: str\\n :param column_family_id: The column family that contains the column\\n or columns with cells being deleted. Must be\\n of the form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type columns: :class:`list` of :class:`str` /\\n :func:`unicode <unicode>`, or :class:`object`\\n :param columns: The columns within the column family that will have\\n cells deleted. If :attr:`ALL_COLUMNS` is used then the\\n entire column family will be deleted from the row.\\n\\n :type time_range: :class:`TimestampRange`\\n :param time_range: (Optional) The range of time within which cells\\n should be deleted.\\n\\n :type state: bool\\n :param state: (Optional) The state that the mutation should be\\n applied in. Defaults to :data:`True`.\\n \\\"\\\"\\\"\\n self._delete_cells(column_family_id, columns, time_range=time_range,\\n state=state)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::ConditionalRow.clear\",\n \"text\": \"google/cloud/bigtable/row.py::ConditionalRow.clear\\n def clear(self):\\n \\\"\\\"\\\"Removes all currently accumulated mutations on the current row.\\\"\\\"\\\"\\n del self._true_pb_mutations[:]\\n del self._false_pb_mutations[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::AppendRow.__init__\",\n \"text\": \"google/cloud/bigtable/row.py::AppendRow.__init__\\n def __init__(self, row_key, table):\\n super(AppendRow, self).__init__(row_key, table)\\n self._rule_pb_list = []\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::AppendRow.clear\",\n \"text\": \"google/cloud/bigtable/row.py::AppendRow.clear\\n def clear(self):\\n \\\"\\\"\\\"Removes all currently accumulated modifications on current row.\\\"\\\"\\\"\\n del self._rule_pb_list[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::AppendRow.append_cell_value\",\n \"text\": \"google/cloud/bigtable/row.py::AppendRow.append_cell_value\\n def append_cell_value(self, column_family_id, column, value):\\n \\\"\\\"\\\"Appends a value to an existing cell.\\n\\n .. note::\\n\\n This method adds a read-modify rule protobuf to the accumulated\\n read-modify rules on this row, but does not make an API\\n request. To actually send an API request (with the rules) to the\\n Google Cloud Bigtable API, call :meth:`commit`.\\n\\n :type column_family_id: str\\n :param column_family_id: The column family that contains the column.\\n Must be of the form\\n ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type column: bytes\\n :param column: The column within the column family where the cell\\n is located.\\n\\n :type value: bytes\\n :param value: The value to append to the existing value in the cell. If\\n the targeted cell is unset, it will be treated as\\n containing the empty string.\\n \\\"\\\"\\\"\\n column = _to_bytes(column)\\n value = _to_bytes(value)\\n rule_pb = data_v2_pb2.ReadModifyWriteRule(\\n family_name=column_family_id,\\n column_qualifier=column,\\n append_value=value)\\n self._rule_pb_list.append(rule_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::AppendRow.increment_cell_value\",\n \"text\": \"google/cloud/bigtable/row.py::AppendRow.increment_cell_value\\n def increment_cell_value(self, column_family_id, column, int_value):\\n \\\"\\\"\\\"Increments a value in an existing cell.\\n\\n Assumes the value in the cell is stored as a 64 bit integer\\n serialized to bytes.\\n\\n .. note::\\n\\n This method adds a read-modify rule protobuf to the accumulated\\n read-modify rules on this row, but does not make an API\\n request. To actually send an API request (with the rules) to the\\n Google Cloud Bigtable API, call :meth:`commit`.\\n\\n :type column_family_id: str\\n :param column_family_id: The column family that contains the column.\\n Must be of the form\\n ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type column: bytes\\n :param column: The column within the column family where the cell\\n is located.\\n\\n :type int_value: int\\n :param int_value: The value to increment the existing value in the cell\\n by. If the targeted cell is unset, it will be treated\\n as containing a zero. Otherwise, the targeted cell\\n must contain an 8-byte value (interpreted as a 64-bit\\n big-endian signed integer), or the entire request\\n will fail.\\n \\\"\\\"\\\"\\n column = _to_bytes(column)\\n rule_pb = data_v2_pb2.ReadModifyWriteRule(\\n family_name=column_family_id,\\n column_qualifier=column,\\n increment_amount=int_value)\\n self._rule_pb_list.append(rule_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::AppendRow.commit\",\n \"text\": \"google/cloud/bigtable/row.py::AppendRow.commit\\n def commit(self):\\n \\\"\\\"\\\"Makes a ``ReadModifyWriteRow`` API request.\\n\\n This commits modifications made by :meth:`append_cell_value` and\\n :meth:`increment_cell_value`. If no modifications were made, makes\\n no API request and just returns ``{}``.\\n\\n Modifies a row atomically, reading the latest existing\\n timestamp / value from the specified columns and writing a new value by\\n appending / incrementing. The new cell created uses either the current\\n server time or the highest timestamp of a cell in that column (if it\\n exceeds the server time).\\n\\n After committing the accumulated mutations, resets the local mutations.\\n\\n .. code:: python\\n\\n >>> append_row.commit()\\n {\\n u'col-fam-id': {\\n b'col-name1': [\\n (b'cell-val', datetime.datetime(...)),\\n (b'cell-val-newer', datetime.datetime(...)),\\n ],\\n b'col-name2': [\\n (b'altcol-cell-val', datetime.datetime(...)),\\n ],\\n },\\n u'col-fam-id2': {\\n b'col-name3-but-other-fam': [\\n (b'foo', datetime.datetime(...)),\\n ],\\n },\\n }\\n\\n :rtype: dict\\n :returns: The new contents of all modified cells. Returned as a\\n dictionary of column families, each of which holds a\\n dictionary of columns. Each column contains a list of cells\\n modified. Each cell is represented with a two-tuple with the\\n value (in bytes) and the timestamp for the cell.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the number of\\n mutations exceeds the :data:`MAX_MUTATIONS`.\\n \\\"\\\"\\\"\\n num_mutations = len(self._rule_pb_list)\\n if num_mutations == \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::_parse_rmw_row_response\",\n \"text\": \"google/cloud/bigtable/row.py::_parse_rmw_row_response\\ndef _parse_rmw_row_response(row_response):\\n \\\"\\\"\\\"Parses the response to a ``ReadModifyWriteRow`` request.\\n\\n :type row_response: :class:`.data_v2_pb2.Row`\\n :param row_response: The response row (with only modified cells) from a\\n ``ReadModifyWriteRow`` request.\\n\\n :rtype: dict\\n :returns: The new contents of all modified cells. Returned as a\\n dictionary of column families, each of which holds a\\n dictionary of columns. Each column contains a list of cells\\n modified. Each cell is represented with a two-tuple with the\\n value (in bytes) and the timestamp for the cell. For example:\\n\\n .. code:: python\\n\\n {\\n u'col-fam-id': {\\n b'col-name1': [\\n (b'cell-val', datetime.datetime(...)),\\n (b'cell-val-newer', datetime.datetime(...)),\\n ],\\n b'col-name2': [\\n (b'altcol-cell-val', datetime.datetime(...)),\\n ],\\n },\\n u'col-fam-id2': {\\n b'col-name3-but-other-fam': [\\n (b'foo', datetime.datetime(...)),\\n ],\\n },\\n }\\n \\\"\\\"\\\"\\n result = {}\\n for column_family in row_response.row.families:\\n column_family_id, curr_family = _parse_family_pb(column_family)\\n result[column_family_id] = curr_family\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row.py::_parse_family_pb\",\n \"text\": \"google/cloud/bigtable/row.py::_parse_family_pb\\ndef _parse_family_pb(family_pb):\\n \\\"\\\"\\\"Parses a Family protobuf into a dictionary.\\n\\n :type family_pb: :class:`._generated.data_pb2.Family`\\n :param family_pb: A protobuf\\n\\n :rtype: tuple\\n :returns: A string and dictionary. The string is the name of the\\n column family and the dictionary has column names (within the\\n family) as keys and cell lists as values. Each cell is\\n represented with a two-tuple with the value (in bytes) and the\\n timestamp for the cell. For example:\\n\\n .. code:: python\\n\\n {\\n b'col-name1': [\\n (b'cell-val', datetime.datetime(...)),\\n (b'cell-val-newer', datetime.datetime(...)),\\n ],\\n b'col-name2': [\\n (b'altcol-cell-val', datetime.datetime(...)),\\n ],\\n }\\n \\\"\\\"\\\"\\n result = {}\\n for column in family_pb.columns:\\n result[column.qualifier] = cells = []\\n for cell in column.cells:\\n val_pair = (\\n cell.value,\\n _datetime_from_microseconds(cell.timestamp_micros),\\n )\\n cells.append(val_pair)\\n\\n return family_pb.name, result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::Cell.__init__\",\n \"text\": \"google/cloud/bigtable/row_data.py::Cell.__init__\\n def __init__(self, value, timestamp, labels=()):\\n self.value = value\\n self.timestamp = timestamp\\n self.labels = list(labels)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::Cell.from_pb\",\n \"text\": \"google/cloud/bigtable/row_data.py::Cell.from_pb\\n @classmethod\\n def from_pb(cls, cell_pb):\\n \\\"\\\"\\\"Create a new cell from a Cell protobuf.\\n\\n :type cell_pb: :class:`._generated.data_pb2.Cell`\\n :param cell_pb: The protobuf to convert.\\n\\n :rtype: :class:`Cell`\\n :returns: The cell corresponding to the protobuf.\\n \\\"\\\"\\\"\\n timestamp = _datetime_from_microseconds(cell_pb.timestamp_micros)\\n if cell_pb.labels:\\n return cls(cell_pb.value, timestamp, labels=cell_pb.labels)\\n else:\\n return cls(cell_pb.value, timestamp)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::Cell.__eq__\",\n \"text\": \"google/cloud/bigtable/row_data.py::Cell.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.value == self.value and\\n other.timestamp == self.timestamp and\\n other.labels == self.labels)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::Cell.__ne__\",\n \"text\": \"google/cloud/bigtable/row_data.py::Cell.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialCellData.__init__\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialCellData.__init__\\n def __init__(self, row_key, family_name, qualifier, timestamp_micros,\\n labels=(), value=b''):\\n self.row_key = row_key\\n self.family_name = family_name\\n self.qualifier = qualifier\\n self.timestamp_micros = timestamp_micros\\n self.labels = labels\\n self.value = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialCellData.append_value\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialCellData.append_value\\n def append_value(self, value):\\n \\\"\\\"\\\"Append bytes from a new chunk to value.\\n\\n :type value: bytes\\n :param value: bytes to append\\n \\\"\\\"\\\"\\n self.value += value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowData.__init__\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowData.__init__\\n def __init__(self, row_key):\\n self._row_key = row_key\\n self._cells = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowData.__eq__\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowData.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other._row_key == self._row_key and\\n other._cells == self._cells)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowData.__ne__\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowData.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowData.to_dict\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowData.to_dict\\n def to_dict(self):\\n \\\"\\\"\\\"Convert the cells to a dictionary.\\n\\n This is intended to be used with HappyBase, so the column family and\\n column qualiers are combined (with ``:``).\\n\\n :rtype: dict\\n :returns: Dictionary containing all the data in the cells of this row.\\n \\\"\\\"\\\"\\n result = {}\\n for column_family_id, columns in six.iteritems(self._cells):\\n for column_qual, cells in six.iteritems(columns):\\n key = (_to_bytes(column_family_id) + b':' +\\n _to_bytes(column_qual))\\n result[key] = cells\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowData.cells\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowData.cells\\n @property\\n def cells(self):\\n \\\"\\\"\\\"Property returning all the cells accumulated on this partial row.\\n\\n :rtype: dict\\n :returns: Dictionary of the :class:`Cell` objects accumulated. This\\n dictionary has two-levels of keys (first for column families\\n and second for column names/qualifiers within a family). For\\n a given column, a list of :class:`Cell` objects is stored.\\n \\\"\\\"\\\"\\n return copy.deepcopy(self._cells)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowData.row_key\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowData.row_key\\n @property\\n def row_key(self):\\n \\\"\\\"\\\"Getter for the current (partial) row's key.\\n\\n :rtype: bytes\\n :returns: The current (partial) row's key.\\n \\\"\\\"\\\"\\n return self._row_key\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData.__init__\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData.__init__\\n def __init__(self, response_iterator):\\n self._response_iterator = response_iterator\\n # Fully-processed rows, keyed by `row_key`\\n self._rows = {}\\n # Counter for responses pulled from iterator\\n self._counter = 0\\n # Maybe cached from previous response\\n self._last_scanned_row_key = None\\n # In-progress row, unset until first response, after commit/reset\\n self._row = None\\n # Last complete row, unset until first commit\\n self._previous_row = None\\n # In-progress cell, unset until first response, after completion\\n self._cell = None\\n # Last complete cell, unset until first completion, after new row\\n self._previous_cell = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData.__eq__\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other._response_iterator == self._response_iterator\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData.__ne__\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData.state\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData.state\\n @property\\n def state(self):\\n \\\"\\\"\\\"State machine state.\\n\\n :rtype: str\\n :returns: name of state corresponding to currrent row / chunk\\n processing.\\n \\\"\\\"\\\"\\n if self._last_scanned_row_key is None:\\n return self.START\\n if self._row is None:\\n assert self._cell is None\\n assert self._previous_cell is None\\n return self.NEW_ROW\\n if self._cell is not None:\\n return self.CELL_IN_PROGRESS\\n if self._previous_cell is not None:\\n return self.ROW_IN_PROGRESS\\n return self.NEW_ROW # row added, no chunk yet processed\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData.rows\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData.rows\\n @property\\n def rows(self):\\n \\\"\\\"\\\"Property returning all rows accumulated from the stream.\\n\\n :rtype: dict\\n :returns: row_key -> :class:`PartialRowData`.\\n \\\"\\\"\\\"\\n # NOTE: To avoid duplicating large objects, this is just the\\n # mutable private data.\\n return self._rows\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData.cancel\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData.cancel\\n def cancel(self):\\n \\\"\\\"\\\"Cancels the iterator, closing the stream.\\\"\\\"\\\"\\n self._response_iterator.cancel()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData.consume_next\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData.consume_next\\n def consume_next(self):\\n \\\"\\\"\\\"Consume the next ``ReadRowsResponse`` from the stream.\\n\\n Parse the response and its chunks into a new/existing row in\\n :attr:`_rows`\\n \\\"\\\"\\\"\\n response = six.next(self._response_iterator)\\n self._counter += 1\\n\\n if self._last_scanned_row_key is None: # first response\\n if response.last_scanned_row_key:\\n raise InvalidReadRowsResponse()\\n\\n self._last_scanned_row_key = response.last_scanned_row_key\\n\\n row = self._row\\n cell = self._cell\\n\\n for chunk in response.chunks:\\n\\n self._validate_chunk(chunk)\\n\\n if chunk.reset_row:\\n row = self._row = None\\n cell = self._cell = self._previous_cell = None\\n continue\\n\\n if row is None:\\n row = self._row = PartialRowData(chunk.row_key)\\n\\n if cell is None:\\n cell = self._cell = PartialCellData(\\n chunk.row_key,\\n chunk.family_name.value,\\n chunk.qualifier.value,\\n chunk.timestamp_micros,\\n chunk.labels,\\n chunk.value)\\n self._copy_from_previous(cell)\\n else:\\n cell.append_value(chunk.value)\\n\\n if chunk.commit_row:\\n self._save_current_row()\\n row = cell = None\\n continue\\n\\n if chunk.value_size == 0:\\n self._save_current_cell()\\n cell = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData.consume_all\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData.consume_all\\n def consume_all(self, max_loops=None):\\n \\\"\\\"\\\"Consume the streamed responses until there are no more.\\n\\n This simply calls :meth:`consume_next` until there are no\\n more to consume.\\n\\n :type max_loops: int\\n :param max_loops: (Optional) Maximum number of times to try to consume\\n an additional ``ReadRowsResponse``. You can use this\\n to avoid long wait times.\\n \\\"\\\"\\\"\\n curr_loop = 0\\n if max_loops is None:\\n max_loops = float('inf')\\n while curr_loop < max_loops:\\n curr_loop += 1\\n try:\\n self.consume_next()\\n except StopIteration:\\n break\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_status\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_status\\n @staticmethod\\n def _validate_chunk_status(chunk):\\n \\\"\\\"\\\"Helper for :meth:`_validate_chunk_row_in_progress`, etc.\\\"\\\"\\\"\\n # No reseet with other keys\\n if chunk.reset_row:\\n _raise_if(chunk.row_key)\\n _raise_if(chunk.HasField('family_name'))\\n _raise_if(chunk.HasField('qualifier'))\\n _raise_if(chunk.timestamp_micros)\\n _raise_if(chunk.labels)\\n _raise_if(chunk.value_size)\\n _raise_if(chunk.value)\\n # No commit with value size\\n _raise_if(chunk.commit_row and chunk.value_size > 0)\\n # No negative value_size (inferred as a general constraint).\\n _raise_if(chunk.value_size < 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_new_row\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_new_row\\n def _validate_chunk_new_row(self, chunk):\\n \\\"\\\"\\\"Helper for :meth:`_validate_chunk`.\\\"\\\"\\\"\\n assert self.state == self.NEW_ROW\\n _raise_if(chunk.reset_row)\\n _raise_if(not chunk.row_key)\\n _raise_if(not chunk.family_name)\\n _raise_if(not chunk.qualifier)\\n # This constraint is not enforced in the Go example.\\n _raise_if(chunk.value_size > 0 and chunk.commit_row is not False)\\n # This constraint is from the Go example, not the spec.\\n _raise_if(self._previous_row is not None and\\n chunk.row_key <= self._previous_row.row_key)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData._same_as_previous\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData._same_as_previous\\n def _same_as_previous(self, chunk):\\n \\\"\\\"\\\"Helper for :meth:`_validate_chunk_row_in_progress`\\\"\\\"\\\"\\n previous = self._previous_cell\\n return (chunk.row_key == previous.row_key and\\n chunk.family_name == previous.family_name and\\n chunk.qualifier == previous.qualifier and\\n chunk.labels == previous.labels)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_row_in_progress\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_row_in_progress\\n def _validate_chunk_row_in_progress(self, chunk):\\n \\\"\\\"\\\"Helper for :meth:`_validate_chunk`\\\"\\\"\\\"\\n assert self.state == self.ROW_IN_PROGRESS\\n self._validate_chunk_status(chunk)\\n if not chunk.HasField('commit_row') and not chunk.reset_row:\\n _raise_if(not chunk.timestamp_micros or not chunk.value)\\n _raise_if(chunk.row_key and\\n chunk.row_key != self._row.row_key)\\n _raise_if(chunk.HasField('family_name') and\\n not chunk.HasField('qualifier'))\\n previous = self._previous_cell\\n _raise_if(self._same_as_previous(chunk) and\\n chunk.timestamp_micros <= previous.timestamp_micros)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_cell_in_progress\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk_cell_in_progress\\n def _validate_chunk_cell_in_progress(self, chunk):\\n \\\"\\\"\\\"Helper for :meth:`_validate_chunk`\\\"\\\"\\\"\\n assert self.state == self.CELL_IN_PROGRESS\\n self._validate_chunk_status(chunk)\\n self._copy_from_current(chunk)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData._validate_chunk\\n def _validate_chunk(self, chunk):\\n \\\"\\\"\\\"Helper for :meth:`consume_next`.\\\"\\\"\\\"\\n if self.state == self.NEW_ROW:\\n self._validate_chunk_new_row(chunk)\\n if self.state == self.ROW_IN_PROGRESS:\\n self._validate_chunk_row_in_progress(chunk)\\n if self.state == self.CELL_IN_PROGRESS:\\n self._validate_chunk_cell_in_progress(chunk)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData._save_current_cell\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData._save_current_cell\\n def _save_current_cell(self):\\n \\\"\\\"\\\"Helper for :meth:`consume_next`.\\\"\\\"\\\"\\n row, cell = self._row, self._cell\\n family = row._cells.setdefault(cell.family_name, {})\\n qualified = family.setdefault(cell.qualifier, [])\\n complete = Cell.from_pb(self._cell)\\n qualified.append(complete)\\n self._cell, self._previous_cell = None, cell\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData._copy_from_current\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData._copy_from_current\\n def _copy_from_current(self, chunk):\\n \\\"\\\"\\\"Helper for :meth:`consume_next`.\\\"\\\"\\\"\\n current = self._cell\\n if current is not None:\\n if not chunk.row_key:\\n chunk.row_key = current.row_key\\n if not chunk.HasField('family_name'):\\n chunk.family_name.value = current.family_name\\n if not chunk.HasField('qualifier'):\\n chunk.qualifier.value = current.qualifier\\n if not chunk.timestamp_micros:\\n chunk.timestamp_micros = current.timestamp_micros\\n if not chunk.labels:\\n chunk.labels.extend(current.labels)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData._copy_from_previous\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData._copy_from_previous\\n def _copy_from_previous(self, cell):\\n \\\"\\\"\\\"Helper for :meth:`consume_next`.\\\"\\\"\\\"\\n previous = self._previous_cell\\n if previous is not None:\\n if not cell.row_key:\\n cell.row_key = previous.row_key\\n if not cell.family_name:\\n cell.family_name = previous.family_name\\n if not cell.qualifier:\\n cell.qualifier = previous.qualifier\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::PartialRowsData._save_current_row\",\n \"text\": \"google/cloud/bigtable/row_data.py::PartialRowsData._save_current_row\\n def _save_current_row(self):\\n \\\"\\\"\\\"Helper for :meth:`consume_next`.\\\"\\\"\\\"\\n if self._cell:\\n self._save_current_cell()\\n self._rows[self._row.row_key] = self._row\\n self._row, self._previous_row = None, self._row\\n self._previous_cell = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_data.py::_raise_if\",\n \"text\": \"google/cloud/bigtable/row_data.py::_raise_if\\ndef _raise_if(predicate, *args):\\n \\\"\\\"\\\"Helper for validation methods.\\\"\\\"\\\"\\n if predicate:\\n raise InvalidChunk(*args)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::RowFilter.__ne__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::RowFilter.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::_BoolFilter.__init__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::_BoolFilter.__init__\\n def __init__(self, flag):\\n self.flag = flag\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::_BoolFilter.__eq__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::_BoolFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.flag == self.flag\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::SinkFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::SinkFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(sink=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::PassAllFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::PassAllFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(pass_all_filter=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::BlockAllFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::BlockAllFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(block_all_filter=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::_RegexFilter.__init__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::_RegexFilter.__init__\\n def __init__(self, regex):\\n self.regex = _to_bytes(regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::_RegexFilter.__eq__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::_RegexFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.regex == self.regex\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(row_key_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::RowSampleFilter.__init__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::RowSampleFilter.__init__\\n def __init__(self, sample):\\n self.sample = sample\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::RowSampleFilter.__eq__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::RowSampleFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.sample == self.sample\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::RowSampleFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::RowSampleFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(row_sample_filter=self.sample)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(family_name_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(column_qualifier_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::TimestampRange.__init__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::TimestampRange.__init__\\n def __init__(self, start=None, end=None):\\n self.start = start\\n self.end = end\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::TimestampRange.__eq__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::TimestampRange.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.start == self.start and\\n other.end == self.end)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::TimestampRange.__ne__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::TimestampRange.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::TimestampRange.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::TimestampRange.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the :class:`TimestampRange` to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.TimestampRange`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n timestamp_range_kwargs = {}\\n if self.start is not None:\\n timestamp_range_kwargs['start_timestamp_micros'] = (\\n _microseconds_from_datetime(self.start))\\n if self.end is not None:\\n timestamp_range_kwargs['end_timestamp_micros'] = (\\n _microseconds_from_datetime(self.end))\\n return data_v2_pb2.TimestampRange(**timestamp_range_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__init__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__init__\\n def __init__(self, range_):\\n self.range_ = range_\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.range_ == self.range_\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n First converts the ``range_`` on the current object to a protobuf and\\n then uses it in the ``timestamp_range_filter`` field.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(\\n timestamp_range_filter=self.range_.to_pb())\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__init__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__init__\\n def __init__(self, column_family_id, start_column=None, end_column=None,\\n inclusive_start=None, inclusive_end=None):\\n self.column_family_id = column_family_id\\n\\n if inclusive_start is None:\\n inclusive_start = True\\n elif start_column is None:\\n raise ValueError('Inclusive start was specified but no '\\n 'start column was given.')\\n self.start_column = start_column\\n self.inclusive_start = inclusive_start\\n\\n if inclusive_end is None:\\n inclusive_end = True\\n elif end_column is None:\\n raise ValueError('Inclusive end was specified but no '\\n 'end column was given.')\\n self.end_column = end_column\\n self.inclusive_end = inclusive_end\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.column_family_id == self.column_family_id and\\n other.start_column == self.start_column and\\n other.end_column == self.end_column and\\n other.inclusive_start == self.inclusive_start and\\n other.inclusive_end == self.inclusive_end)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n First converts to a :class:`.data_v2_pb2.ColumnRange` and then uses it\\n in the ``column_range_filter`` field.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n column_range_kwargs = {'family_name': self.column_family_id}\\n if self.start_column is not None:\\n if self.inclusive_start:\\n key = 'start_qualifier_closed'\\n else:\\n key = 'start_qualifier_open'\\n column_range_kwargs[key] = _to_bytes(self.start_column)\\n if self.end_column is not None:\\n if self.inclusive_end:\\n key = 'end_qualifier_closed'\\n else:\\n key = 'end_qualifier_open'\\n column_range_kwargs[key] = _to_bytes(self.end_column)\\n\\n column_range = data_v2_pb2.ColumnRange(**column_range_kwargs)\\n return data_v2_pb2.RowFilter(column_range_filter=column_range)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ValueRegexFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ValueRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(value_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ValueRangeFilter.__init__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ValueRangeFilter.__init__\\n def __init__(self, start_value=None, end_value=None,\\n inclusive_start=None, inclusive_end=None):\\n if inclusive_start is None:\\n inclusive_start = True\\n elif start_value is None:\\n raise ValueError('Inclusive start was specified but no '\\n 'start value was given.')\\n self.start_value = start_value\\n self.inclusive_start = inclusive_start\\n\\n if inclusive_end is None:\\n inclusive_end = True\\n elif end_value is None:\\n raise ValueError('Inclusive end was specified but no '\\n 'end value was given.')\\n self.end_value = end_value\\n self.inclusive_end = inclusive_end\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ValueRangeFilter.__eq__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ValueRangeFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.start_value == self.start_value and\\n other.end_value == self.end_value and\\n other.inclusive_start == self.inclusive_start and\\n other.inclusive_end == self.inclusive_end)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ValueRangeFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ValueRangeFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n First converts to a :class:`.data_v2_pb2.ValueRange` and then uses\\n it to create a row filter protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n value_range_kwargs = {}\\n if self.start_value is not None:\\n if self.inclusive_start:\\n key = 'start_value_closed'\\n else:\\n key = 'start_value_open'\\n value_range_kwargs[key] = _to_bytes(self.start_value)\\n if self.end_value is not None:\\n if self.inclusive_end:\\n key = 'end_value_closed'\\n else:\\n key = 'end_value_open'\\n value_range_kwargs[key] = _to_bytes(self.end_value)\\n\\n value_range = data_v2_pb2.ValueRange(**value_range_kwargs)\\n return data_v2_pb2.RowFilter(value_range_filter=value_range)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::_CellCountFilter.__init__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::_CellCountFilter.__init__\\n def __init__(self, num_cells):\\n self.num_cells = num_cells\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::_CellCountFilter.__eq__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::_CellCountFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.num_cells == self.num_cells\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(\\n cells_per_row_offset_filter=self.num_cells)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(cells_per_row_limit_filter=self.num_cells)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(\\n cells_per_column_limit_filter=self.num_cells)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(strip_value_transformer=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.__init__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.__init__\\n def __init__(self, label):\\n self.label = label\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.__eq__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.label == self.label\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ApplyLabelFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(apply_label_transformer=self.label)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::_FilterCombination.__init__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::_FilterCombination.__init__\\n def __init__(self, filters=None):\\n if filters is None:\\n filters = []\\n self.filters = filters\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::_FilterCombination.__eq__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::_FilterCombination.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.filters == self.filters\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::RowFilterChain.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::RowFilterChain.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n chain = data_v2_pb2.RowFilter.Chain(\\n filters=[row_filter.to_pb() for row_filter in self.filters])\\n return data_v2_pb2.RowFilter(chain=chain)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::RowFilterUnion.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::RowFilterUnion.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n interleave = data_v2_pb2.RowFilter.Interleave(\\n filters=[row_filter.to_pb() for row_filter in self.filters])\\n return data_v2_pb2.RowFilter(interleave=interleave)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.__init__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.__init__\\n def __init__(self, base_filter, true_filter=None, false_filter=None):\\n self.base_filter = base_filter\\n self.true_filter = true_filter\\n self.false_filter = false_filter\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.__eq__\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.base_filter == self.base_filter and\\n other.true_filter == self.true_filter and\\n other.false_filter == self.false_filter)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.to_pb\",\n \"text\": \"google/cloud/bigtable/row_filters.py::ConditionalRowFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n condition_kwargs = {'predicate_filter': self.base_filter.to_pb()}\\n if self.true_filter is not None:\\n condition_kwargs['true_filter'] = self.true_filter.to_pb()\\n if self.false_filter is not None:\\n condition_kwargs['false_filter'] = self.false_filter.to_pb()\\n condition = data_v2_pb2.RowFilter.Condition(**condition_kwargs)\\n return data_v2_pb2.RowFilter(condition=condition)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.__init__\",\n \"text\": \"google/cloud/bigtable/table.py::Table.__init__\\n def __init__(self, table_id, instance):\\n self.table_id = table_id\\n self._instance = instance\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.name\",\n \"text\": \"google/cloud/bigtable/table.py::Table.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Table name used in requests.\\n\\n .. note::\\n\\n This property will not change if ``table_id`` does not, but the\\n return value is not cached.\\n\\n The table name is of the form\\n\\n ``\\\"projects/../instances/../tables/{table_id}\\\"``\\n\\n :rtype: str\\n :returns: The table name.\\n \\\"\\\"\\\"\\n return self._instance.name + '/tables/' + self.table_id\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.column_family\",\n \"text\": \"google/cloud/bigtable/table.py::Table.column_family\\n def column_family(self, column_family_id, gc_rule=None):\\n \\\"\\\"\\\"Factory to create a column family associated with this table.\\n\\n :type column_family_id: str\\n :param column_family_id: The ID of the column family. Must be of the\\n form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type gc_rule: :class:`.GarbageCollectionRule`\\n :param gc_rule: (Optional) The garbage collection settings for this\\n column family.\\n\\n :rtype: :class:`.ColumnFamily`\\n :returns: A column family owned by this table.\\n \\\"\\\"\\\"\\n return ColumnFamily(column_family_id, self, gc_rule=gc_rule)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.row\",\n \"text\": \"google/cloud/bigtable/table.py::Table.row\\n def row(self, row_key, filter_=None, append=False):\\n \\\"\\\"\\\"Factory to create a row associated with this table.\\n\\n .. warning::\\n\\n At most one of ``filter_`` and ``append`` can be used in a\\n :class:`Row`.\\n\\n :type row_key: bytes\\n :param row_key: The key for the row being created.\\n\\n :type filter_: :class:`.RowFilter`\\n :param filter_: (Optional) Filter to be used for conditional mutations.\\n See :class:`.DirectRow` for more details.\\n\\n :type append: bool\\n :param append: (Optional) Flag to determine if the row should be used\\n for append mutations.\\n\\n :rtype: :class:`.DirectRow`\\n :returns: A row owned by this table.\\n :raises: :class:`ValueError <exceptions.ValueError>` if both\\n ``filter_`` and ``append`` are used.\\n \\\"\\\"\\\"\\n if append and filter_ is not None:\\n raise ValueError('At most one of filter_ and append can be set')\\n if append:\\n return AppendRow(row_key, self)\\n elif filter_ is not None:\\n return ConditionalRow(row_key, self, filter_=filter_)\\n else:\\n return DirectRow(row_key, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.__eq__\",\n \"text\": \"google/cloud/bigtable/table.py::Table.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.table_id == self.table_id and\\n other._instance == self._instance)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.__ne__\",\n \"text\": \"google/cloud/bigtable/table.py::Table.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.create\",\n \"text\": \"google/cloud/bigtable/table.py::Table.create\\n def create(self, initial_split_keys=None, column_families=()):\\n \\\"\\\"\\\"Creates this table.\\n\\n .. note::\\n\\n A create request returns a\\n :class:`._generated.table_pb2.Table` but we don't use\\n this response.\\n\\n :type initial_split_keys: list\\n :param initial_split_keys: (Optional) List of row keys that will be\\n used to initially split the table into\\n several tablets (Tablets are similar to\\n HBase regions). Given two split keys,\\n ``\\\"s1\\\"`` and ``\\\"s2\\\"``, three tablets will be\\n created, spanning the key ranges:\\n ``[, s1)``, ``[s1, s2)``, ``[s2, )``.\\n\\n :type column_families: list\\n :param column_families: (Optional) List or other iterable of\\n :class:`.ColumnFamily` instances.\\n \\\"\\\"\\\"\\n if initial_split_keys is not None:\\n split_pb = table_admin_messages_v2_pb2.CreateTableRequest.Split\\n initial_split_keys = [\\n split_pb(key=key) for key in initial_split_keys]\\n\\n table_pb = None\\n if column_families:\\n table_pb = table_v2_pb2.Table()\\n for col_fam in column_families:\\n curr_id = col_fam.column_family_id\\n table_pb.column_families[curr_id].MergeFrom(col_fam.to_pb())\\n\\n request_pb = table_admin_messages_v2_pb2.CreateTableRequest(\\n initial_splits=initial_split_keys or [],\\n parent=self._instance.name,\\n table_id=self.table_id,\\n table=table_pb,\\n )\\n client = self._instance._client\\n # We expect a `._generated.table_pb2.Table`\\n client._table_stub.CreateTable(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.delete\",\n \"text\": \"google/cloud/bigtable/table.py::Table.delete\\n def delete(self):\\n \\\"\\\"\\\"Delete this table.\\\"\\\"\\\"\\n request_pb = table_admin_messages_v2_pb2.DeleteTableRequest(\\n name=self.name)\\n client = self._instance._client\\n # We expect a `google.protobuf.empty_pb2.Empty`\\n client._table_stub.DeleteTable(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.list_column_families\",\n \"text\": \"google/cloud/bigtable/table.py::Table.list_column_families\\n def list_column_families(self):\\n \\\"\\\"\\\"List the column families owned by this table.\\n\\n :rtype: dict\\n :returns: Dictionary of column families attached to this table. Keys\\n are strings (column family names) and values are\\n :class:`.ColumnFamily` instances.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the column\\n family name from the response does not agree with the computed\\n name from the column family ID.\\n \\\"\\\"\\\"\\n request_pb = table_admin_messages_v2_pb2.GetTableRequest(\\n name=self.name)\\n client = self._instance._client\\n # We expect a `._generated.table_pb2.Table`\\n table_pb = client._table_stub.GetTable(request_pb)\\n\\n result = {}\\n for column_family_id, value_pb in table_pb.column_families.items():\\n gc_rule = _gc_rule_from_pb(value_pb.gc_rule)\\n column_family = self.column_family(column_family_id,\\n gc_rule=gc_rule)\\n result[column_family_id] = column_family\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.read_row\",\n \"text\": \"google/cloud/bigtable/table.py::Table.read_row\\n def read_row(self, row_key, filter_=None):\\n \\\"\\\"\\\"Read a single row from this table.\\n\\n :type row_key: bytes\\n :param row_key: The key of the row to read from.\\n\\n :type filter_: :class:`.RowFilter`\\n :param filter_: (Optional) The filter to apply to the contents of the\\n row. If unset, returns the entire row.\\n\\n :rtype: :class:`.PartialRowData`, :data:`NoneType <types.NoneType>`\\n :returns: The contents of the row if any chunks were returned in\\n the response, otherwise :data:`None`.\\n :raises: :class:`ValueError <exceptions.ValueError>` if a commit row\\n chunk is never encountered.\\n \\\"\\\"\\\"\\n request_pb = _create_row_request(self.name, row_key=row_key,\\n filter_=filter_)\\n client = self._instance._client\\n response_iterator = client._data_stub.ReadRows(request_pb)\\n rows_data = PartialRowsData(response_iterator)\\n rows_data.consume_all()\\n if rows_data.state not in (rows_data.NEW_ROW, rows_data.START):\\n raise ValueError('The row remains partial / is not committed.')\\n\\n if len(rows_data.rows) == 0:\\n return None\\n\\n return rows_data.rows[row_key]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.read_rows\",\n \"text\": \"google/cloud/bigtable/table.py::Table.read_rows\\n def read_rows(self, start_key=None, end_key=None, limit=None,\\n filter_=None):\\n \\\"\\\"\\\"Read rows from this table.\\n\\n :type start_key: bytes\\n :param start_key: (Optional) The beginning of a range of row keys to\\n read from. The range will include ``start_key``. If\\n left empty, will be interpreted as the empty string.\\n\\n :type end_key: bytes\\n :param end_key: (Optional) The end of a range of row keys to read from.\\n The range will not include ``end_key``. If left empty,\\n will be interpreted as an infinite string.\\n\\n :type limit: int\\n :param limit: (Optional) The read will terminate after committing to N\\n rows' worth of results. The default (zero) is to return\\n all results.\\n\\n :type filter_: :class:`.RowFilter`\\n :param filter_: (Optional) The filter to apply to the contents of the\\n specified row(s). If unset, reads every column in\\n each row.\\n\\n :rtype: :class:`.PartialRowsData`\\n :returns: A :class:`.PartialRowsData` convenience wrapper for consuming\\n the streamed results.\\n \\\"\\\"\\\"\\n request_pb = _create_row_request(\\n self.name, start_key=start_key, end_key=end_key, filter_=filter_,\\n limit=limit)\\n client = self._instance._client\\n response_iterator = client._data_stub.ReadRows(request_pb)\\n # We expect an iterator of `data_messages_v2_pb2.ReadRowsResponse`\\n return PartialRowsData(response_iterator)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::Table.sample_row_keys\",\n \"text\": \"google/cloud/bigtable/table.py::Table.sample_row_keys\\n def sample_row_keys(self):\\n \\\"\\\"\\\"Read a sample of row keys in the table.\\n\\n The returned row keys will delimit contiguous sections of the table of\\n approximately equal size, which can be used to break up the data for\\n distributed tasks like mapreduces.\\n\\n The elements in the iterator are a SampleRowKeys response and they have\\n the properties ``offset_bytes`` and ``row_key``. They occur in sorted\\n order. The table might have contents before the first row key in the\\n list and after the last one, but a key containing the empty string\\n indicates \\\"end of table\\\" and will be the last response given, if\\n present.\\n\\n .. note::\\n\\n Row keys in this list may not have ever been written to or read\\n from, and users should therefore not make any assumptions about the\\n row key structure that are specific to their use case.\\n\\n The ``offset_bytes`` field on a response indicates the approximate\\n total storage space used by all rows in the table which precede\\n ``row_key``. Buffering the contents of all rows between two subsequent\\n samples would require space roughly equal to the difference in their\\n ``offset_bytes`` fields.\\n\\n :rtype: :class:`~google.cloud.exceptions.GrpcRendezvous`\\n :returns: A cancel-able iterator. Can be consumed by calling ``next()``\\n or by casting to a :class:`list` and can be cancelled by\\n calling ``cancel()``.\\n \\\"\\\"\\\"\\n request_pb = data_messages_v2_pb2.SampleRowKeysRequest(\\n table_name=self.name)\\n client = self._instance._client\\n response_iterator = client._data_stub.SampleRowKeys(request_pb)\\n return response_iterator\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/bigtable/table.py::_create_row_request\",\n \"text\": \"google/cloud/bigtable/table.py::_create_row_request\\ndef _create_row_request(table_name, row_key=None, start_key=None, end_key=None,\\n filter_=None, limit=None):\\n \\\"\\\"\\\"Creates a request to read rows in a table.\\n\\n :type table_name: str\\n :param table_name: The name of the table to read from.\\n\\n :type row_key: bytes\\n :param row_key: (Optional) The key of a specific row to read from.\\n\\n :type start_key: bytes\\n :param start_key: (Optional) The beginning of a range of row keys to\\n read from. The range will include ``start_key``. If\\n left empty, will be interpreted as the empty string.\\n\\n :type end_key: bytes\\n :param end_key: (Optional) The end of a range of row keys to read from.\\n The range will not include ``end_key``. If left empty,\\n will be interpreted as an infinite string.\\n\\n :type filter_: :class:`.RowFilter`\\n :param filter_: (Optional) The filter to apply to the contents of the\\n specified row(s). If unset, reads the entire table.\\n\\n :type limit: int\\n :param limit: (Optional) The read will terminate after committing to N\\n rows' worth of results. The default (zero) is to return\\n all results.\\n\\n :rtype: :class:`data_messages_v2_pb2.ReadRowsRequest`\\n :returns: The ``ReadRowsRequest`` protobuf corresponding to the inputs.\\n :raises: :class:`ValueError <exceptions.ValueError>` if both\\n ``row_key`` and one of ``start_key`` and ``end_key`` are set\\n \\\"\\\"\\\"\\n request_kwargs = {'table_name': table_name}\\n if (row_key is not None and\\n (start_key is not None or end_key is not None)):\\n raise ValueError('Row key and row range cannot be '\\n 'set simultaneously')\\n range_kwargs = {}\\n if start_key is not None or end_key is not None:\\n if start_key is not None:\\n range_kwargs['start_key_closed'] = _to_bytes(start_key)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/client.py::_ClientFactoryMixin.from_service_account_json\",\n \"text\": \"google/cloud/client.py::_ClientFactoryMixin.from_service_account_json\\n @classmethod\\n def from_service_account_json(cls, json_credentials_path, *args, **kwargs):\\n \\\"\\\"\\\"Factory to retrieve JSON credentials while creating client.\\n\\n :type json_credentials_path: string\\n :param json_credentials_path: The path to a private key file (this file\\n was given to you when you created the\\n service account). This file must contain\\n a JSON object with a private key and\\n other credentials information (downloaded\\n from the Google APIs console).\\n\\n :type args: tuple\\n :param args: Remaining positional arguments to pass to constructor.\\n\\n :type kwargs: dict\\n :param kwargs: Remaining keyword arguments to pass to constructor.\\n\\n :rtype: :class:`google.cloud.pubsub.client.Client`\\n :returns: The client created with the retrieved JSON credentials.\\n :raises: :class:`TypeError` if there is a conflict with the kwargs\\n and the credentials created by the factory.\\n \\\"\\\"\\\"\\n if 'credentials' in kwargs:\\n raise TypeError('credentials must not be in keyword arguments')\\n credentials = ServiceAccountCredentials.from_json_keyfile_name(\\n json_credentials_path)\\n kwargs['credentials'] = credentials\\n return cls(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/client.py::_ClientFactoryMixin.from_service_account_p12\",\n \"text\": \"google/cloud/client.py::_ClientFactoryMixin.from_service_account_p12\\n @classmethod\\n def from_service_account_p12(cls, client_email, private_key_path,\\n *args, **kwargs):\\n \\\"\\\"\\\"Factory to retrieve P12 credentials while creating client.\\n\\n .. note::\\n Unless you have an explicit reason to use a PKCS12 key for your\\n service account, we recommend using a JSON key.\\n\\n :type client_email: string\\n :param client_email: The e-mail attached to the service account.\\n\\n :type private_key_path: string\\n :param private_key_path: The path to a private key file (this file was\\n given to you when you created the service\\n account). This file must be in P12 format.\\n\\n :type args: tuple\\n :param args: Remaining positional arguments to pass to constructor.\\n\\n :type kwargs: dict\\n :param kwargs: Remaining keyword arguments to pass to constructor.\\n\\n :rtype: :class:`google.cloud.client.Client`\\n :returns: The client created with the retrieved P12 credentials.\\n :raises: :class:`TypeError` if there is a conflict with the kwargs\\n and the credentials created by the factory.\\n \\\"\\\"\\\"\\n if 'credentials' in kwargs:\\n raise TypeError('credentials must not be in keyword arguments')\\n credentials = ServiceAccountCredentials.from_p12_keyfile(\\n client_email, private_key_path)\\n kwargs['credentials'] = credentials\\n return cls(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/client.py::Client.__init__\",\n \"text\": \"google/cloud/client.py::Client.__init__\\n def __init__(self, credentials=None, http=None):\\n if credentials is None and http is None:\\n credentials = get_credentials()\\n self.connection = self._connection_class(\\n credentials=credentials, http=http)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/client.py::_ClientProjectMixin.__init__\",\n \"text\": \"google/cloud/client.py::_ClientProjectMixin.__init__\\n def __init__(self, project=None):\\n project = self._determine_default(project)\\n if project is None:\\n raise EnvironmentError('Project was not passed and could not be '\\n 'determined from the environment.')\\n if isinstance(project, six.binary_type):\\n project = project.decode('utf-8')\\n if not isinstance(project, six.string_types):\\n raise ValueError('Project must be a string.')\\n self.project = project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/client.py::_ClientProjectMixin._determine_default\",\n \"text\": \"google/cloud/client.py::_ClientProjectMixin._determine_default\\n @staticmethod\\n def _determine_default(project):\\n \\\"\\\"\\\"Helper: use default project detection.\\\"\\\"\\\"\\n return _determine_default_project(project)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/client.py::JSONClient.__init__\",\n \"text\": \"google/cloud/client.py::JSONClient.__init__\\n def __init__(self, project=None, credentials=None, http=None):\\n _ClientProjectMixin.__init__(self, project=project)\\n Client.__init__(self, credentials=credentials, http=http)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/connection.py::Connection.__init__\",\n \"text\": \"google/cloud/connection.py::Connection.__init__\\n def __init__(self, credentials=None, http=None):\\n self._http = http\\n self._credentials = self._create_scoped_credentials(\\n credentials, self.SCOPE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/connection.py::Connection.credentials\",\n \"text\": \"google/cloud/connection.py::Connection.credentials\\n @property\\n def credentials(self):\\n \\\"\\\"\\\"Getter for current credentials.\\n\\n :rtype: :class:`oauth2client.client.OAuth2Credentials` or\\n :class:`NoneType`\\n :returns: The credentials object associated with this connection.\\n \\\"\\\"\\\"\\n return self._credentials\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/connection.py::Connection.http\",\n \"text\": \"google/cloud/connection.py::Connection.http\\n @property\\n def http(self):\\n \\\"\\\"\\\"A getter for the HTTP transport used in talking to the API.\\n\\n :rtype: :class:`httplib2.Http`\\n :returns: A Http object used to transport data.\\n \\\"\\\"\\\"\\n if self._http is None:\\n self._http = httplib2.Http()\\n if self._credentials:\\n self._http = self._credentials.authorize(self._http)\\n return self._http\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/connection.py::Connection._create_scoped_credentials\",\n \"text\": \"google/cloud/connection.py::Connection._create_scoped_credentials\\n @staticmethod\\n def _create_scoped_credentials(credentials, scope):\\n \\\"\\\"\\\"Create a scoped set of credentials if it is required.\\n\\n :type credentials: :class:`oauth2client.client.OAuth2Credentials` or\\n :class:`NoneType`\\n :param credentials: The OAuth2 Credentials to add a scope to.\\n\\n :type scope: list of URLs\\n :param scope: the effective service auth scopes for the connection.\\n\\n :rtype: :class:`oauth2client.client.OAuth2Credentials` or\\n :class:`NoneType`\\n :returns: A new credentials object that has a scope added (if needed).\\n \\\"\\\"\\\"\\n if credentials:\\n try:\\n if credentials.create_scoped_required():\\n credentials = credentials.create_scoped(scope)\\n except AttributeError:\\n pass\\n return credentials\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/connection.py::JSONConnection.build_api_url\",\n \"text\": \"google/cloud/connection.py::JSONConnection.build_api_url\\n @classmethod\\n def build_api_url(cls, path, query_params=None,\\n api_base_url=None, api_version=None):\\n \\\"\\\"\\\"Construct an API url given a few components, some optional.\\n\\n Typically, you shouldn't need to use this method.\\n\\n :type path: string\\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\\n\\n :type query_params: dict or list\\n :param query_params: A dictionary of keys and values (or list of\\n key-value pairs) to insert into the query\\n string of the URL.\\n\\n :type api_base_url: string\\n :param api_base_url: The base URL for the API endpoint.\\n Typically you won't have to provide this.\\n\\n :type api_version: string\\n :param api_version: The version of the API to call.\\n Typically you shouldn't provide this and instead\\n use the default for the library.\\n\\n :rtype: string\\n :returns: The URL assembled from the pieces provided.\\n \\\"\\\"\\\"\\n url = cls.API_URL_TEMPLATE.format(\\n api_base_url=(api_base_url or cls.API_BASE_URL),\\n api_version=(api_version or cls.API_VERSION),\\n path=path)\\n\\n query_params = query_params or {}\\n if query_params:\\n url += '?' + urlencode(query_params)\\n\\n return url\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/connection.py::JSONConnection._make_request\",\n \"text\": \"google/cloud/connection.py::JSONConnection._make_request\\n def _make_request(self, method, url, data=None, content_type=None,\\n headers=None, target_object=None):\\n \\\"\\\"\\\"A low level method to send a request to the API.\\n\\n Typically, you shouldn't need to use this method.\\n\\n :type method: string\\n :param method: The HTTP method to use in the request.\\n\\n :type url: string\\n :param url: The URL to send the request to.\\n\\n :type data: string\\n :param data: The data to send as the body of the request.\\n\\n :type content_type: string\\n :param content_type: The proper MIME type of the data provided.\\n\\n :type headers: dict\\n :param headers: A dictionary of HTTP headers to send with the request.\\n\\n :type target_object: object or :class:`NoneType`\\n :param target_object: Argument to be used by library callers.\\n This can allow custom behavior, for example, to\\n defer an HTTP request and complete initialization\\n of the object at a later time.\\n\\n :rtype: tuple of ``response`` (a dictionary of sorts)\\n and ``content`` (a string).\\n :returns: The HTTP response object and the content of the response,\\n returned by :meth:`_do_request`.\\n \\\"\\\"\\\"\\n headers = headers or {}\\n headers['Accept-Encoding'] = 'gzip'\\n\\n if data:\\n content_length = len(str(data))\\n else:\\n content_length = 0\\n\\n # NOTE: str is intended, bytes are sufficient for headers.\\n headers['Content-Length'] = str(content_length)\\n\\n if content_type:\\n headers['Content-Type'] = content_type\\n\\n headers['User-Agent'] = self.USER_AGENT\\n\\n return self._do_request(method, url, headers, data, target_object)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/connection.py::JSONConnection._do_request\",\n \"text\": \"google/cloud/connection.py::JSONConnection._do_request\\n def _do_request(self, method, url, headers, data,\\n target_object): # pylint: disable=unused-argument\\n \\\"\\\"\\\"Low-level helper: perform the actual API request over HTTP.\\n\\n Allows batch context managers to override and defer a request.\\n\\n :type method: string\\n :param method: The HTTP method to use in the request.\\n\\n :type url: string\\n :param url: The URL to send the request to.\\n\\n :type headers: dict\\n :param headers: A dictionary of HTTP headers to send with the request.\\n\\n :type data: string\\n :param data: The data to send as the body of the request.\\n\\n :type target_object: object or :class:`NoneType`\\n :param target_object: Unused ``target_object`` here but may be used\\n by a superclass.\\n\\n :rtype: tuple of ``response`` (a dictionary of sorts)\\n and ``content`` (a string).\\n :returns: The HTTP response object and the content of the response.\\n \\\"\\\"\\\"\\n return self.http.request(uri=url, method=method, headers=headers,\\n body=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/connection.py::JSONConnection.api_request\",\n \"text\": \"google/cloud/connection.py::JSONConnection.api_request\\n def api_request(self, method, path, query_params=None,\\n data=None, content_type=None,\\n api_base_url=None, api_version=None,\\n expect_json=True, _target_object=None):\\n \\\"\\\"\\\"Make a request over the HTTP transport to the API.\\n\\n You shouldn't need to use this method, but if you plan to\\n interact with the API using these primitives, this is the\\n correct one to use.\\n\\n :type method: string\\n :param method: The HTTP method name (ie, ``GET``, ``POST``, etc).\\n Required.\\n\\n :type path: string\\n :param path: The path to the resource (ie, ``'/b/bucket-name'``).\\n Required.\\n\\n :type query_params: dict or list\\n :param query_params: A dictionary of keys and values (or list of\\n key-value pairs) to insert into the query\\n string of the URL.\\n\\n :type data: string\\n :param data: The data to send as the body of the request. Default is\\n the empty string.\\n\\n :type content_type: string\\n :param content_type: The proper MIME type of the data provided. Default\\n is None.\\n\\n :type api_base_url: string\\n :param api_base_url: The base URL for the API endpoint.\\n Typically you won't have to provide this.\\n Default is the standard API base URL.\\n\\n :type api_version: string\\n :param api_version: The version of the API to call. Typically\\n you shouldn't provide this and instead use\\n the default for the library. Default is the\\n latest API version supported by\\n google-cloud-python.\\n\\n :type expect_json: bool\\n :param expect_json: If True, this method will try to par\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/credentials.py::get_credentials\",\n \"text\": \"google/cloud/credentials.py::get_credentials\\ndef get_credentials():\\n \\\"\\\"\\\"Gets credentials implicitly from the current environment.\\n\\n .. note::\\n\\n You should not need to use this function directly. Instead, use a\\n helper method which uses this method under the hood.\\n\\n Checks environment in order of precedence:\\n\\n * Google App Engine (production and testing)\\n * Environment variable :envvar:`GOOGLE_APPLICATION_CREDENTIALS` pointing to\\n a file with stored credentials information.\\n * Stored \\\"well known\\\" file associated with ``gcloud`` command line tool.\\n * Google Compute Engine production environment.\\n\\n The file referred to in :envvar:`GOOGLE_APPLICATION_CREDENTIALS` is\\n expected to contain information about credentials that are ready to use.\\n This means either service account information or user account information\\n with a ready-to-use refresh token:\\n\\n .. code:: json\\n\\n {\\n 'type': 'authorized_user',\\n 'client_id': '...',\\n 'client_secret': '...',\\n 'refresh_token': '...'\\n }\\n\\n or\\n\\n .. code:: json\\n\\n {\\n 'type': 'service_account',\\n 'project_id': '...',\\n 'private_key_id': '...',\\n 'private_key': '...',\\n 'client_email': '...',\\n 'client_id': '...',\\n 'auth_uri': '...',\\n 'token_uri': '...',\\n 'auth_provider_x509_cert_url': '...',\\n 'client_x509_cert_url': '...'\\n }\\n\\n The second of these is simply a JSON key downloaded from the Google APIs\\n console. The first is a close cousin of the \\\"client secrets\\\" JSON file\\n used by :mod:`oauth2client.clientsecrets` but differs in formatting.\\n\\n :rtype: :class:`oauth2client.client.GoogleCredentials`,\\n :class:`oauth2client.contrib.appengine.AppAssertionCredentials`,\\n :class:`oauth2client.contrib.gce.AppAssertionCredentials`,\\n :class:`oauth2client.service_account.ServiceAccountCredentials`\\n :returns: A new \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/credentials.py::_get_signed_query_params\",\n \"text\": \"google/cloud/credentials.py::_get_signed_query_params\\ndef _get_signed_query_params(credentials, expiration, string_to_sign):\\n \\\"\\\"\\\"Gets query parameters for creating a signed URL.\\n\\n :type credentials: :class:`oauth2client.client.AssertionCredentials`\\n :param credentials: The credentials used to create a private key\\n for signing text.\\n\\n :type expiration: int or long\\n :param expiration: When the signed URL should expire.\\n\\n :type string_to_sign: string\\n :param string_to_sign: The string to be signed by the credentials.\\n\\n :raises AttributeError: If :meth: sign_blob is unavailable.\\n\\n :rtype: dict\\n :returns: Query parameters matching the signing credentials with a\\n signed payload.\\n \\\"\\\"\\\"\\n if not hasattr(credentials, 'sign_blob'):\\n auth_uri = ('http://google-cloud-python.readthedocs.io/en/latest/'\\n 'google-cloud-auth.html#setting-up-a-service-account')\\n raise AttributeError('you need a private key to sign credentials.'\\n 'the credentials you are currently using %s '\\n 'just contains a token. see %s for more '\\n 'details.' % (type(credentials), auth_uri))\\n\\n _, signature_bytes = credentials.sign_blob(string_to_sign)\\n signature = base64.b64encode(signature_bytes)\\n service_account_name = credentials.service_account_email\\n return {\\n 'GoogleAccessId': service_account_name,\\n 'Expires': str(expiration),\\n 'Signature': signature,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/credentials.py::_get_expiration_seconds\",\n \"text\": \"google/cloud/credentials.py::_get_expiration_seconds\\ndef _get_expiration_seconds(expiration):\\n \\\"\\\"\\\"Convert 'expiration' to a number of seconds in the future.\\n\\n :type expiration: int, long, datetime.datetime, datetime.timedelta\\n :param expiration: When the signed URL should expire.\\n\\n :raises TypeError: When expiration is not an integer.\\n\\n :rtype: int\\n :returns: a timestamp as an absolute number of seconds.\\n \\\"\\\"\\\"\\n # If it's a timedelta, add it to `now` in UTC.\\n if isinstance(expiration, datetime.timedelta):\\n now = _NOW().replace(tzinfo=UTC)\\n expiration = now + expiration\\n\\n # If it's a datetime, convert to a timestamp.\\n if isinstance(expiration, datetime.datetime):\\n micros = _microseconds_from_datetime(expiration)\\n expiration = micros // 10**6\\n\\n if not isinstance(expiration, six.integer_types):\\n raise TypeError('Expected an integer timestamp, datetime, or '\\n 'timedelta. Got %s' % type(expiration))\\n return expiration\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/credentials.py::generate_signed_url\",\n \"text\": \"google/cloud/credentials.py::generate_signed_url\\ndef generate_signed_url(credentials, resource, expiration,\\n api_access_endpoint='',\\n method='GET', content_md5=None,\\n content_type=None, response_type=None,\\n response_disposition=None, generation=None):\\n \\\"\\\"\\\"Generate signed URL to provide query-string auth'n to a resource.\\n\\n .. note::\\n\\n Assumes ``credentials`` implements a ``sign_blob()`` method that takes\\n bytes to sign and returns a pair of the key ID (unused here) and the\\n signed bytes (this is abstract in the base class\\n :class:`oauth2client.client.AssertionCredentials`). Also assumes\\n ``credentials`` has a ``service_account_email`` property which\\n identifies the credentials.\\n\\n .. note::\\n\\n If you are on Google Compute Engine, you can't generate a signed URL.\\n Follow `Issue 922`_ for updates on this. If you'd like to be able to\\n generate a signed URL from GCE, you can use a standard service account\\n from a JSON file rather than a GCE service account.\\n\\n See headers `reference`_ for more details on optional arguments.\\n\\n .. _Issue 922: https://github.com/GoogleCloudPlatform/\\\\\\n google-cloud-python/issues/922\\n .. _reference: https://cloud.google.com/storage/docs/reference-headers\\n\\n :type credentials: :class:`oauth2client.appengine.AppAssertionCredentials`\\n :param credentials: Credentials object with an associated private key to\\n sign text.\\n\\n :type resource: string\\n :param resource: A pointer to a specific resource\\n (typically, ``/bucket-name/path/to/blob.txt``).\\n\\n :type expiration: :class:`int`, :class:`long`, :class:`datetime.datetime`,\\n :class:`datetime.timedelta`\\n :param expiration: When the signed URL should expire.\\n\\n :type api_access_endpoint: str\\n :param api_access_endpoint: Optional \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.__init__\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.__init__\\n def __init__(self, client):\\n self._client = client\\n self._commit_request = _datastore_pb2.CommitRequest()\\n self._partial_key_entities = []\\n self._status = self._INITIAL\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.current\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.current\\n def current(self):\\n \\\"\\\"\\\"Return the topmost batch / transaction, or None.\\\"\\\"\\\"\\n return self._client.current_batch\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.project\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Getter for project in which the batch will run.\\n\\n :rtype: :class:`str`\\n :returns: The project in which the batch will run.\\n \\\"\\\"\\\"\\n return self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.namespace\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.namespace\\n @property\\n def namespace(self):\\n \\\"\\\"\\\"Getter for namespace in which the batch will run.\\n\\n :rtype: :class:`str`\\n :returns: The namespace in which the batch will run.\\n \\\"\\\"\\\"\\n return self._client.namespace\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.connection\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.connection\\n @property\\n def connection(self):\\n \\\"\\\"\\\"Getter for connection over which the batch will run.\\n\\n :rtype: :class:`google.cloud.datastore.connection.Connection`\\n :returns: The connection over which the batch will run.\\n \\\"\\\"\\\"\\n return self._client.connection\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch._add_partial_key_entity_pb\",\n \"text\": \"google/cloud/datastore/batch.py::Batch._add_partial_key_entity_pb\\n def _add_partial_key_entity_pb(self):\\n \\\"\\\"\\\"Adds a new mutation for an entity with a partial key.\\n\\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Entity`\\n :returns: The newly created entity protobuf that will be\\n updated and sent with a commit.\\n \\\"\\\"\\\"\\n new_mutation = self.mutations.add()\\n return new_mutation.insert\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch._add_complete_key_entity_pb\",\n \"text\": \"google/cloud/datastore/batch.py::Batch._add_complete_key_entity_pb\\n def _add_complete_key_entity_pb(self):\\n \\\"\\\"\\\"Adds a new mutation for an entity with a completed key.\\n\\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Entity`\\n :returns: The newly created entity protobuf that will be\\n updated and sent with a commit.\\n \\\"\\\"\\\"\\n # We use ``upsert`` for entities with completed keys, rather than\\n # ``insert`` or ``update``, in order not to create race conditions\\n # based on prior existence / removal of the entity.\\n new_mutation = self.mutations.add()\\n return new_mutation.upsert\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch._add_delete_key_pb\",\n \"text\": \"google/cloud/datastore/batch.py::Batch._add_delete_key_pb\\n def _add_delete_key_pb(self):\\n \\\"\\\"\\\"Adds a new mutation for a key to be deleted.\\n\\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Key`\\n :returns: The newly created key protobuf that will be\\n deleted when sent with a commit.\\n \\\"\\\"\\\"\\n new_mutation = self.mutations.add()\\n return new_mutation.delete\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.mutations\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.mutations\\n @property\\n def mutations(self):\\n \\\"\\\"\\\"Getter for the changes accumulated by this batch.\\n\\n Every batch is committed with a single commit request containing all\\n the work to be done as mutations. Inside a batch, calling :meth:`put`\\n with an entity, or :meth:`delete` with a key, builds up the request by\\n adding a new mutation. This getter returns the protobuf that has been\\n built-up so far.\\n\\n :rtype: iterable\\n :returns: The list of :class:`._generated.datastore_pb2.Mutation`\\n protobufs to be sent in the commit request.\\n \\\"\\\"\\\"\\n return self._commit_request.mutations\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.put\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.put\\n def put(self, entity):\\n \\\"\\\"\\\"Remember an entity's state to be saved during :meth:`commit`.\\n\\n .. note::\\n Any existing properties for the entity will be replaced by those\\n currently set on this instance. Already-stored properties which do\\n not correspond to keys set on this instance will be removed from\\n the datastore.\\n\\n .. note::\\n Property values which are \\\"text\\\" ('unicode' in Python2, 'str' in\\n Python3) map to 'string_value' in the datastore; values which are\\n \\\"bytes\\\" ('str' in Python2, 'bytes' in Python3) map to 'blob_value'.\\n\\n When an entity has a partial key, calling :meth:`commit` sends it as\\n an ``insert`` mutation and the key is completed. On return,\\n the key for the ``entity`` passed in is updated to match the key ID\\n assigned by the server.\\n\\n :type entity: :class:`google.cloud.datastore.entity.Entity`\\n :param entity: the entity to be saved.\\n\\n :raises: :class:`~exceptions.ValueError` if the batch is not in\\n progress, if entity has no key assigned, or if the key's\\n ``project`` does not match ours.\\n \\\"\\\"\\\"\\n if self._status != self._IN_PROGRESS:\\n raise ValueError('Batch must be in progress to put()')\\n\\n if entity.key is None:\\n raise ValueError(\\\"Entity must have a key\\\")\\n\\n if self.project != entity.key.project:\\n raise ValueError(\\\"Key must be from same project as batch\\\")\\n\\n if entity.key.is_partial:\\n entity_pb = self._add_partial_key_entity_pb()\\n self._partial_key_entities.append(entity)\\n else:\\n entity_pb = self._add_complete_key_entity_pb()\\n\\n _assign_entity_to_pb(entity_pb, entity)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.delete\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.delete\\n def delete(self, key):\\n \\\"\\\"\\\"Remember a key to be deleted during :meth:`commit`.\\n\\n :type key: :class:`google.cloud.datastore.key.Key`\\n :param key: the key to be deleted.\\n\\n :raises: :class:`~exceptions.ValueError` if the batch is not in\\n progress, if key is not complete, or if the key's\\n ``project`` does not match ours.\\n \\\"\\\"\\\"\\n if self._status != self._IN_PROGRESS:\\n raise ValueError('Batch must be in progress to delete()')\\n\\n if key.is_partial:\\n raise ValueError(\\\"Key must be complete\\\")\\n\\n if self.project != key.project:\\n raise ValueError(\\\"Key must be from same project as batch\\\")\\n\\n key_pb = key.to_protobuf()\\n self._add_delete_key_pb().CopyFrom(key_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.begin\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.begin\\n def begin(self):\\n \\\"\\\"\\\"Begins a batch.\\n\\n This method is called automatically when entering a with\\n statement, however it can be called explicitly if you don't want\\n to use a context manager.\\n\\n Overridden by :class:`google.cloud.datastore.transaction.Transaction`.\\n\\n :raises: :class:`ValueError` if the batch has already begun.\\n \\\"\\\"\\\"\\n if self._status != self._INITIAL:\\n raise ValueError('Batch already started previously.')\\n self._status = self._IN_PROGRESS\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch._commit\",\n \"text\": \"google/cloud/datastore/batch.py::Batch._commit\\n def _commit(self):\\n \\\"\\\"\\\"Commits the batch.\\n\\n This is called by :meth:`commit`.\\n \\\"\\\"\\\"\\n # NOTE: ``self._commit_request`` will be modified.\\n _, updated_keys = self.connection.commit(\\n self.project, self._commit_request, self._id)\\n # If the back-end returns without error, we are guaranteed that\\n # :meth:`Connection.commit` will return keys that match (length and\\n # order) directly ``_partial_key_entities``.\\n for new_key_pb, entity in zip(updated_keys,\\n self._partial_key_entities):\\n new_id = new_key_pb.path[-1].id\\n entity.key = entity.key.completed_key(new_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.commit\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.commit\\n def commit(self):\\n \\\"\\\"\\\"Commits the batch.\\n\\n This is called automatically upon exiting a with statement,\\n however it can be called explicitly if you don't want to use a\\n context manager.\\n\\n :raises: :class:`~exceptions.ValueError` if the batch is not\\n in progress.\\n \\\"\\\"\\\"\\n if self._status != self._IN_PROGRESS:\\n raise ValueError('Batch must be in progress to commit()')\\n\\n try:\\n self._commit()\\n finally:\\n self._status = self._FINISHED\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.rollback\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.rollback\\n def rollback(self):\\n \\\"\\\"\\\"Rolls back the current batch.\\n\\n Marks the batch as aborted (can't be used again).\\n\\n Overridden by :class:`google.cloud.datastore.transaction.Transaction`.\\n\\n :raises: :class:`~exceptions.ValueError` if the batch is not\\n in progress.\\n \\\"\\\"\\\"\\n if self._status != self._IN_PROGRESS:\\n raise ValueError('Batch must be in progress to rollback()')\\n\\n self._status = self._ABORTED\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.__enter__\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.__enter__\\n def __enter__(self):\\n self.begin()\\n # NOTE: We make sure begin() succeeds before pushing onto the stack.\\n self._client._push_batch(self)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::Batch.__exit__\",\n \"text\": \"google/cloud/datastore/batch.py::Batch.__exit__\\n def __exit__(self, exc_type, exc_val, exc_tb):\\n try:\\n if exc_type is None:\\n self.commit()\\n else:\\n self.rollback()\\n finally:\\n self._client._pop_batch()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/batch.py::_assign_entity_to_pb\",\n \"text\": \"google/cloud/datastore/batch.py::_assign_entity_to_pb\\ndef _assign_entity_to_pb(entity_pb, entity):\\n \\\"\\\"\\\"Copy ``entity`` into ``entity_pb``.\\n\\n Helper method for ``Batch.put``.\\n\\n :type entity_pb: :class:`._generated.entity_pb2.Entity`\\n :param entity_pb: The entity owned by a mutation.\\n\\n :type entity: :class:`google.cloud.datastore.entity.Entity`\\n :param entity: The entity being updated within the batch / transaction.\\n \\\"\\\"\\\"\\n bare_entity_pb = helpers.entity_to_protobuf(entity)\\n bare_entity_pb.key.CopyFrom(bare_entity_pb.key)\\n entity_pb.CopyFrom(bare_entity_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::_get_gcd_project\",\n \"text\": \"google/cloud/datastore/client.py::_get_gcd_project\\ndef _get_gcd_project():\\n \\\"\\\"\\\"Gets the GCD application ID if it can be inferred.\\\"\\\"\\\"\\n return os.getenv(GCD_DATASET)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::_determine_default_project\",\n \"text\": \"google/cloud/datastore/client.py::_determine_default_project\\ndef _determine_default_project(project=None):\\n \\\"\\\"\\\"Determine default project explicitly or implicitly as fall-back.\\n\\n In implicit case, supports four environments. In order of precedence, the\\n implicit environments are:\\n\\n * DATASTORE_DATASET environment variable (for ``gcd`` / emulator testing)\\n * GOOGLE_CLOUD_PROJECT environment variable\\n * Google App Engine application ID\\n * Google Compute Engine project ID (from metadata server)\\n\\n :type project: string\\n :param project: Optional. The project to use as default.\\n\\n :rtype: string or ``NoneType``\\n :returns: Default project if it can be determined.\\n \\\"\\\"\\\"\\n if project is None:\\n project = _get_gcd_project()\\n\\n if project is None:\\n project = _base_default_project(project=project)\\n\\n return project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::_extended_lookup\",\n \"text\": \"google/cloud/datastore/client.py::_extended_lookup\\ndef _extended_lookup(connection, project, key_pbs,\\n missing=None, deferred=None,\\n eventual=False, transaction_id=None):\\n \\\"\\\"\\\"Repeat lookup until all keys found (unless stop requested).\\n\\n Helper function for :meth:`Client.get_multi`.\\n\\n :type connection: :class:`google.cloud.datastore.connection.Connection`\\n :param connection: The connection used to connect to datastore.\\n\\n :type project: string\\n :param project: The project to make the request for.\\n\\n :type key_pbs: list of :class:`._generated.entity_pb2.Key`\\n :param key_pbs: The keys to retrieve from the datastore.\\n\\n :type missing: list\\n :param missing: (Optional) If a list is passed, the key-only entity\\n protobufs returned by the backend as \\\"missing\\\" will be\\n copied into it.\\n\\n :type deferred: list\\n :param deferred: (Optional) If a list is passed, the key protobufs returned\\n by the backend as \\\"deferred\\\" will be copied into it.\\n\\n :type eventual: bool\\n :param eventual: If False (the default), request ``STRONG`` read\\n consistency. If True, request ``EVENTUAL`` read\\n consistency.\\n\\n :type transaction_id: string\\n :param transaction_id: If passed, make the request in the scope of\\n the given transaction. Incompatible with\\n ``eventual==True``.\\n\\n :rtype: list of :class:`._generated.entity_pb2.Entity`\\n :returns: The requested entities.\\n :raises: :class:`ValueError` if missing / deferred are not null or\\n empty list.\\n \\\"\\\"\\\"\\n if missing is not None and missing != []:\\n raise ValueError('missing must be None or an empty list')\\n\\n if deferred is not None and deferred != []:\\n raise ValueError('deferred must be None or an empty list')\\n\\n results = []\\n\\n loop_num = 0\\n while loop_num < _MAX_LOOPS: # loop ag\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.__init__\",\n \"text\": \"google/cloud/datastore/client.py::Client.__init__\\n def __init__(self, project=None, namespace=None,\\n credentials=None, http=None):\\n _ClientProjectMixin.__init__(self, project=project)\\n self.namespace = namespace\\n self._batch_stack = _LocalStack()\\n super(Client, self).__init__(credentials, http)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client._determine_default\",\n \"text\": \"google/cloud/datastore/client.py::Client._determine_default\\n @staticmethod\\n def _determine_default(project):\\n \\\"\\\"\\\"Helper: override default project detection.\\\"\\\"\\\"\\n return _determine_default_project(project)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client._push_batch\",\n \"text\": \"google/cloud/datastore/client.py::Client._push_batch\\n def _push_batch(self, batch):\\n \\\"\\\"\\\"Push a batch/transaction onto our stack.\\n\\n \\\"Protected\\\", intended for use by batch / transaction context mgrs.\\n\\n :type batch: :class:`google.cloud.datastore.batch.Batch`, or an object\\n implementing its API.\\n :param batch: newly-active batch/transaction.\\n \\\"\\\"\\\"\\n self._batch_stack.push(batch)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client._pop_batch\",\n \"text\": \"google/cloud/datastore/client.py::Client._pop_batch\\n def _pop_batch(self):\\n \\\"\\\"\\\"Pop a batch/transaction from our stack.\\n\\n \\\"Protected\\\", intended for use by batch / transaction context mgrs.\\n\\n :raises: IndexError if the stack is empty.\\n :rtype: :class:`google.cloud.datastore.batch.Batch`, or an object\\n implementing its API.\\n :returns: the top-most batch/transaction, after removing it.\\n \\\"\\\"\\\"\\n return self._batch_stack.pop()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.current_batch\",\n \"text\": \"google/cloud/datastore/client.py::Client.current_batch\\n @property\\n def current_batch(self):\\n \\\"\\\"\\\"Currently-active batch.\\n\\n :rtype: :class:`google.cloud.datastore.batch.Batch`, or an object\\n implementing its API, or ``NoneType`` (if no batch is active).\\n :returns: The batch/transaction at the top of the batch stack.\\n \\\"\\\"\\\"\\n return self._batch_stack.top\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.current_transaction\",\n \"text\": \"google/cloud/datastore/client.py::Client.current_transaction\\n @property\\n def current_transaction(self):\\n \\\"\\\"\\\"Currently-active transaction.\\n\\n :rtype: :class:`google.cloud.datastore.transaction.Transaction`, or an\\n object implementing its API, or ``NoneType`` (if no transaction\\n is active).\\n :returns: The transaction at the top of the batch stack.\\n \\\"\\\"\\\"\\n transaction = self.current_batch\\n if isinstance(transaction, Transaction):\\n return transaction\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.get\",\n \"text\": \"google/cloud/datastore/client.py::Client.get\\n def get(self, key, missing=None, deferred=None, transaction=None):\\n \\\"\\\"\\\"Retrieve an entity from a single key (if it exists).\\n\\n .. note::\\n\\n This is just a thin wrapper over :meth:`get_multi`.\\n The backend API does not make a distinction between a single key or\\n multiple keys in a lookup request.\\n\\n :type key: :class:`google.cloud.datastore.key.Key`\\n :param key: The key to be retrieved from the datastore.\\n\\n :type missing: list\\n :param missing: (Optional) If a list is passed, the key-only entities\\n returned by the backend as \\\"missing\\\" will be copied\\n into it.\\n\\n :type deferred: list\\n :param deferred: (Optional) If a list is passed, the keys returned\\n by the backend as \\\"deferred\\\" will be copied into it.\\n\\n :type transaction: :class:`~.transaction.Transaction`\\n :param transaction: (Optional) Transaction to use for read consistency.\\n If not passed, uses current transaction, if set.\\n\\n :rtype: :class:`google.cloud.datastore.entity.Entity` or ``NoneType``\\n :returns: The requested entity if it exists.\\n \\\"\\\"\\\"\\n entities = self.get_multi(keys=[key], missing=missing,\\n deferred=deferred, transaction=transaction)\\n if entities:\\n return entities[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.get_multi\",\n \"text\": \"google/cloud/datastore/client.py::Client.get_multi\\n def get_multi(self, keys, missing=None, deferred=None, transaction=None):\\n \\\"\\\"\\\"Retrieve entities, along with their attributes.\\n\\n :type keys: list of :class:`google.cloud.datastore.key.Key`\\n :param keys: The keys to be retrieved from the datastore.\\n\\n :type missing: list\\n :param missing: (Optional) If a list is passed, the key-only entities\\n returned by the backend as \\\"missing\\\" will be copied\\n into it. If the list is not empty, an error will occur.\\n\\n :type deferred: list\\n :param deferred: (Optional) If a list is passed, the keys returned\\n by the backend as \\\"deferred\\\" will be copied into it.\\n If the list is not empty, an error will occur.\\n\\n :type transaction: :class:`~.transaction.Transaction`\\n :param transaction: (Optional) Transaction to use for read consistency.\\n If not passed, uses current transaction, if set.\\n\\n :rtype: list of :class:`google.cloud.datastore.entity.Entity`\\n :returns: The requested entities.\\n :raises: :class:`ValueError` if one or more of ``keys`` has a project\\n which does not match our project.\\n \\\"\\\"\\\"\\n if not keys:\\n return []\\n\\n ids = set(key.project for key in keys)\\n for current_id in ids:\\n if current_id != self.project:\\n raise ValueError('Keys do not match project')\\n\\n if transaction is None:\\n transaction = self.current_transaction\\n\\n entity_pbs = _extended_lookup(\\n connection=self.connection,\\n project=self.project,\\n key_pbs=[k.to_protobuf() for k in keys],\\n missing=missing,\\n deferred=deferred,\\n transaction_id=transaction and transaction.id,\\n )\\n\\n if missing is not None:\\n missing[:] = [\\n he\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.put\",\n \"text\": \"google/cloud/datastore/client.py::Client.put\\n def put(self, entity):\\n \\\"\\\"\\\"Save an entity in the Cloud Datastore.\\n\\n .. note::\\n\\n This is just a thin wrapper over :meth:`put_multi`.\\n The backend API does not make a distinction between a single\\n entity or multiple entities in a commit request.\\n\\n :type entity: :class:`google.cloud.datastore.entity.Entity`\\n :param entity: The entity to be saved to the datastore.\\n \\\"\\\"\\\"\\n self.put_multi(entities=[entity])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.put_multi\",\n \"text\": \"google/cloud/datastore/client.py::Client.put_multi\\n def put_multi(self, entities):\\n \\\"\\\"\\\"Save entities in the Cloud Datastore.\\n\\n :type entities: list of :class:`google.cloud.datastore.entity.Entity`\\n :param entities: The entities to be saved to the datastore.\\n\\n :raises: :class:`ValueError` if ``entities`` is a single entity.\\n \\\"\\\"\\\"\\n if isinstance(entities, Entity):\\n raise ValueError(\\\"Pass a sequence of entities\\\")\\n\\n if not entities:\\n return\\n\\n current = self.current_batch\\n in_batch = current is not None\\n\\n if not in_batch:\\n current = self.batch()\\n current.begin()\\n\\n for entity in entities:\\n current.put(entity)\\n\\n if not in_batch:\\n current.commit()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.delete\",\n \"text\": \"google/cloud/datastore/client.py::Client.delete\\n def delete(self, key):\\n \\\"\\\"\\\"Delete the key in the Cloud Datastore.\\n\\n .. note::\\n\\n This is just a thin wrapper over :meth:`delete_multi`.\\n The backend API does not make a distinction between a single key or\\n multiple keys in a commit request.\\n\\n :type key: :class:`google.cloud.datastore.key.Key`\\n :param key: The key to be deleted from the datastore.\\n \\\"\\\"\\\"\\n self.delete_multi(keys=[key])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.delete_multi\",\n \"text\": \"google/cloud/datastore/client.py::Client.delete_multi\\n def delete_multi(self, keys):\\n \\\"\\\"\\\"Delete keys from the Cloud Datastore.\\n\\n :type keys: list of :class:`google.cloud.datastore.key.Key`\\n :param keys: The keys to be deleted from the Datastore.\\n \\\"\\\"\\\"\\n if not keys:\\n return\\n\\n # We allow partial keys to attempt a delete, the backend will fail.\\n current = self.current_batch\\n in_batch = current is not None\\n\\n if not in_batch:\\n current = self.batch()\\n current.begin()\\n\\n for key in keys:\\n current.delete(key)\\n\\n if not in_batch:\\n current.commit()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.allocate_ids\",\n \"text\": \"google/cloud/datastore/client.py::Client.allocate_ids\\n def allocate_ids(self, incomplete_key, num_ids):\\n \\\"\\\"\\\"Allocate a list of IDs from a partial key.\\n\\n :type incomplete_key: :class:`google.cloud.datastore.key.Key`\\n :param incomplete_key: Partial key to use as base for allocated IDs.\\n\\n :type num_ids: int\\n :param num_ids: The number of IDs to allocate.\\n\\n :rtype: list of :class:`google.cloud.datastore.key.Key`\\n :returns: The (complete) keys allocated with ``incomplete_key`` as\\n root.\\n :raises: :class:`ValueError` if ``incomplete_key`` is not a\\n partial key.\\n \\\"\\\"\\\"\\n if not incomplete_key.is_partial:\\n raise ValueError(('Key is not partial.', incomplete_key))\\n\\n incomplete_key_pb = incomplete_key.to_protobuf()\\n incomplete_key_pbs = [incomplete_key_pb] * num_ids\\n\\n conn = self.connection\\n allocated_key_pbs = conn.allocate_ids(incomplete_key.project,\\n incomplete_key_pbs)\\n allocated_ids = [allocated_key_pb.path[-1].id\\n for allocated_key_pb in allocated_key_pbs]\\n return [incomplete_key.completed_key(allocated_id)\\n for allocated_id in allocated_ids]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.key\",\n \"text\": \"google/cloud/datastore/client.py::Client.key\\n def key(self, *path_args, **kwargs):\\n \\\"\\\"\\\"Proxy to :class:`google.cloud.datastore.key.Key`.\\n\\n Passes our ``project``.\\n \\\"\\\"\\\"\\n if 'project' in kwargs:\\n raise TypeError('Cannot pass project')\\n kwargs['project'] = self.project\\n if 'namespace' not in kwargs:\\n kwargs['namespace'] = self.namespace\\n return Key(*path_args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.batch\",\n \"text\": \"google/cloud/datastore/client.py::Client.batch\\n def batch(self):\\n \\\"\\\"\\\"Proxy to :class:`google.cloud.datastore.batch.Batch`.\\\"\\\"\\\"\\n return Batch(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.transaction\",\n \"text\": \"google/cloud/datastore/client.py::Client.transaction\\n def transaction(self):\\n \\\"\\\"\\\"Proxy to :class:`google.cloud.datastore.transaction.Transaction`.\\\"\\\"\\\"\\n return Transaction(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/client.py::Client.query\",\n \"text\": \"google/cloud/datastore/client.py::Client.query\\n def query(self, **kwargs):\\n \\\"\\\"\\\"Proxy to :class:`google.cloud.datastore.query.Query`.\\n\\n Passes our ``project``.\\n\\n Using query to search a datastore::\\n\\n >>> from google.cloud import datastore\\n >>> client = datastore.Client()\\n >>> query = client.query(kind='MyKind')\\n >>> query.add_filter('property', '=', 'val')\\n\\n Using the query iterator's\\n :meth:`~google.cloud.datastore.query.Iterator.next_page` method:\\n\\n >>> query_iter = query.fetch()\\n >>> entities, more_results, cursor = query_iter.next_page()\\n >>> entities\\n [<list of Entity unmarshalled from protobuf>]\\n >>> more_results\\n <boolean of more results>\\n >>> cursor\\n <string containing cursor where fetch stopped>\\n\\n Under the hood this is doing:\\n\\n >>> connection.run_query('project', query.to_protobuf())\\n [<list of Entity Protobufs>], cursor, more_results, skipped_results\\n\\n :type kwargs: dict\\n :param kwargs: Parameters for initializing and instance of\\n :class:`google.cloud.datastore.query.Query`.\\n\\n :rtype: :class:`google.cloud.datastore.query.Query`\\n :returns: An instance of :class:`google.cloud.datastore.query.Query`\\n \\\"\\\"\\\"\\n if 'client' in kwargs:\\n raise TypeError('Cannot pass client')\\n if 'project' in kwargs:\\n raise TypeError('Cannot pass project')\\n kwargs['project'] = self.project\\n if 'namespace' not in kwargs:\\n kwargs['namespace'] = self.namespace\\n return Query(self, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.__init__\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.__init__\\n def __init__(self, connection):\\n self.connection = connection\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp._request\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp._request\\n def _request(self, project, method, data):\\n \\\"\\\"\\\"Make a request over the Http transport to the Cloud Datastore API.\\n\\n :type project: string\\n :param project: The project to make the request for.\\n\\n :type method: string\\n :param method: The API call method name (ie, ``runQuery``,\\n ``lookup``, etc)\\n\\n :type data: string\\n :param data: The data to send with the API call.\\n Typically this is a serialized Protobuf string.\\n\\n :rtype: string\\n :returns: The string response content from the API call.\\n :raises: :class:`google.cloud.exceptions.GoogleCloudError` if the\\n response code is not 200 OK.\\n \\\"\\\"\\\"\\n headers = {\\n 'Content-Type': 'application/x-protobuf',\\n 'Content-Length': str(len(data)),\\n 'User-Agent': self.connection.USER_AGENT,\\n }\\n headers, content = self.connection.http.request(\\n uri=self.connection.build_api_url(project=project, method=method),\\n method='POST', headers=headers, body=data)\\n\\n status = headers['status']\\n if status != '200':\\n error_status = status_pb2.Status.FromString(content)\\n raise make_exception(headers, error_status.message, use_json=False)\\n\\n return content\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp._rpc\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp._rpc\\n def _rpc(self, project, method, request_pb, response_pb_cls):\\n \\\"\\\"\\\"Make a protobuf RPC request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type method: string\\n :param method: The name of the method to invoke.\\n\\n :type request_pb: :class:`google.protobuf.message.Message` instance\\n :param request_pb: the protobuf instance representing the request.\\n\\n :type response_pb_cls: A :class:`google.protobuf.message.Message`\\n subclass.\\n :param response_pb_cls: The class used to unmarshall the response\\n protobuf.\\n\\n :rtype: :class:`google.protobuf.message.Message`\\n :returns: The RPC message parsed from the response.\\n \\\"\\\"\\\"\\n response = self._request(project=project, method=method,\\n data=request_pb.SerializeToString())\\n return response_pb_cls.FromString(response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.lookup\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.lookup\\n def lookup(self, project, request_pb):\\n \\\"\\\"\\\"Perform a ``lookup`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb: :class:`._generated.datastore_pb2.LookupRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.LookupResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n return self._rpc(project, 'lookup', request_pb,\\n _datastore_pb2.LookupResponse)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.run_query\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.run_query\\n def run_query(self, project, request_pb):\\n \\\"\\\"\\\"Perform a ``runQuery`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb: :class:`._generated.datastore_pb2.RunQueryRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.RunQueryResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n return self._rpc(project, 'runQuery', request_pb,\\n _datastore_pb2.RunQueryResponse)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.begin_transaction\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.begin_transaction\\n def begin_transaction(self, project, request_pb):\\n \\\"\\\"\\\"Perform a ``beginTransaction`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb:\\n :class:`._generated.datastore_pb2.BeginTransactionRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.BeginTransactionResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n return self._rpc(project, 'beginTransaction', request_pb,\\n _datastore_pb2.BeginTransactionResponse)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.commit\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.commit\\n def commit(self, project, request_pb):\\n \\\"\\\"\\\"Perform a ``commit`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb: :class:`._generated.datastore_pb2.CommitRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.CommitResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n return self._rpc(project, 'commit', request_pb,\\n _datastore_pb2.CommitResponse)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.rollback\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.rollback\\n def rollback(self, project, request_pb):\\n \\\"\\\"\\\"Perform a ``rollback`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb: :class:`._generated.datastore_pb2.RollbackRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.RollbackResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n return self._rpc(project, 'rollback', request_pb,\\n _datastore_pb2.RollbackResponse)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.allocate_ids\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverHttp.allocate_ids\\n def allocate_ids(self, project, request_pb):\\n \\\"\\\"\\\"Perform an ``allocateIds`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb: :class:`._generated.datastore_pb2.AllocateIdsRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.AllocateIdsResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n return self._rpc(project, 'allocateIds', request_pb,\\n _datastore_pb2.AllocateIdsResponse)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.__init__\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.__init__\\n def __init__(self, connection, secure):\\n if secure:\\n self._stub = make_secure_stub(connection.credentials,\\n connection.USER_AGENT,\\n datastore_grpc_pb2.DatastoreStub,\\n connection.host)\\n else:\\n self._stub = make_insecure_stub(datastore_grpc_pb2.DatastoreStub,\\n connection.host)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.lookup\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.lookup\\n def lookup(self, project, request_pb):\\n \\\"\\\"\\\"Perform a ``lookup`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb: :class:`._generated.datastore_pb2.LookupRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.LookupResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n request_pb.project_id = project\\n return self._stub.Lookup(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.run_query\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.run_query\\n def run_query(self, project, request_pb):\\n \\\"\\\"\\\"Perform a ``runQuery`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb: :class:`._generated.datastore_pb2.RunQueryRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.RunQueryResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n request_pb.project_id = project\\n return self._stub.RunQuery(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.begin_transaction\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.begin_transaction\\n def begin_transaction(self, project, request_pb):\\n \\\"\\\"\\\"Perform a ``beginTransaction`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb:\\n :class:`._generated.datastore_pb2.BeginTransactionRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.BeginTransactionResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n request_pb.project_id = project\\n return self._stub.BeginTransaction(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.commit\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.commit\\n def commit(self, project, request_pb):\\n \\\"\\\"\\\"Perform a ``commit`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb: :class:`._generated.datastore_pb2.CommitRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.CommitResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n request_pb.project_id = project\\n try:\\n return self._stub.Commit(request_pb)\\n except GrpcRendezvous as exc:\\n if exc.code() == StatusCode.ABORTED:\\n raise Conflict(exc.details())\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.rollback\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.rollback\\n def rollback(self, project, request_pb):\\n \\\"\\\"\\\"Perform a ``rollback`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb: :class:`._generated.datastore_pb2.RollbackRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.RollbackResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n request_pb.project_id = project\\n return self._stub.Rollback(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.allocate_ids\",\n \"text\": \"google/cloud/datastore/connection.py::_DatastoreAPIOverGRPC.allocate_ids\\n def allocate_ids(self, project, request_pb):\\n \\\"\\\"\\\"Perform an ``allocateIds`` request.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type request_pb: :class:`._generated.datastore_pb2.AllocateIdsRequest`\\n :param request_pb: The request protobuf object.\\n\\n :rtype: :class:`._generated.datastore_pb2.AllocateIdsResponse`\\n :returns: The returned protobuf response object.\\n \\\"\\\"\\\"\\n request_pb.project_id = project\\n return self._stub.AllocateIds(request_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::Connection.__init__\",\n \"text\": \"google/cloud/datastore/connection.py::Connection.__init__\\n def __init__(self, credentials=None, http=None):\\n super(Connection, self).__init__(credentials=credentials, http=http)\\n try:\\n self.host = os.environ[GCD_HOST]\\n self.api_base_url = 'http://' + self.host\\n secure = False\\n except KeyError:\\n self.host = DATASTORE_API_HOST\\n self.api_base_url = self.__class__.API_BASE_URL\\n secure = True\\n if _USE_GRPC:\\n self._datastore_api = _DatastoreAPIOverGRPC(self, secure=secure)\\n else:\\n self._datastore_api = _DatastoreAPIOverHttp(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::Connection.build_api_url\",\n \"text\": \"google/cloud/datastore/connection.py::Connection.build_api_url\\n def build_api_url(self, project, method, base_url=None,\\n api_version=None):\\n \\\"\\\"\\\"Construct the URL for a particular API call.\\n\\n This method is used internally to come up with the URL to use when\\n making RPCs to the Cloud Datastore API.\\n\\n :type project: string\\n :param project: The project to connect to. This is\\n usually your project name in the cloud console.\\n\\n :type method: string\\n :param method: The API method to call (e.g. 'runQuery', 'lookup').\\n\\n :type base_url: string\\n :param base_url: The base URL where the API lives.\\n You shouldn't have to provide this.\\n\\n :type api_version: string\\n :param api_version: The version of the API to connect to.\\n You shouldn't have to provide this.\\n\\n :rtype: str\\n :returns: The API URL created.\\n \\\"\\\"\\\"\\n return self.API_URL_TEMPLATE.format(\\n api_base=(base_url or self.api_base_url),\\n api_version=(api_version or self.API_VERSION),\\n project=project, method=method)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::Connection.lookup\",\n \"text\": \"google/cloud/datastore/connection.py::Connection.lookup\\n def lookup(self, project, key_pbs,\\n eventual=False, transaction_id=None):\\n \\\"\\\"\\\"Lookup keys from a project in the Cloud Datastore.\\n\\n Maps the ``DatastoreService.Lookup`` protobuf RPC.\\n\\n This uses mostly protobufs\\n (:class:`google.cloud.datastore._generated.entity_pb2.Key` as input\\n and :class:`google.cloud.datastore._generated.entity_pb2.Entity`\\n as output). It is used under the hood in\\n :meth:`Client.get() <.datastore.client.Client.get>`:\\n\\n >>> from google.cloud import datastore\\n >>> client = datastore.Client(project='project')\\n >>> key = client.key('MyKind', 1234)\\n >>> client.get(key)\\n [<Entity object>]\\n\\n Using a :class:`Connection` directly:\\n\\n >>> connection.lookup('project', [key.to_protobuf()])\\n [<Entity protobuf>]\\n\\n :type project: string\\n :param project: The project to look up the keys in.\\n\\n :type key_pbs: list of\\n :class:`google.cloud.datastore._generated.entity_pb2.Key`\\n :param key_pbs: The keys to retrieve from the datastore.\\n\\n :type eventual: bool\\n :param eventual: If False (the default), request ``STRONG`` read\\n consistency. If True, request ``EVENTUAL`` read\\n consistency.\\n\\n :type transaction_id: string\\n :param transaction_id: If passed, make the request in the scope of\\n the given transaction. Incompatible with\\n ``eventual==True``.\\n\\n :rtype: tuple\\n :returns: A triple of (``results``, ``missing``, ``deferred``) where\\n both ``results`` and ``missing`` are lists of\\n :class:`google.cloud.datastore._generated.entity_pb2.Entity`\\n and ``deferred`` is a list of\\n :class:`google.cloud.datastore._generated.entity_pb2.Key`.\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::Connection.run_query\",\n \"text\": \"google/cloud/datastore/connection.py::Connection.run_query\\n def run_query(self, project, query_pb, namespace=None,\\n eventual=False, transaction_id=None):\\n \\\"\\\"\\\"Run a query on the Cloud Datastore.\\n\\n Maps the ``DatastoreService.RunQuery`` protobuf RPC.\\n\\n Given a Query protobuf, sends a ``runQuery`` request to the\\n Cloud Datastore API and returns a list of entity protobufs\\n matching the query.\\n\\n You typically wouldn't use this method directly, in favor of the\\n :meth:`google.cloud.datastore.query.Query.fetch` method.\\n\\n Under the hood, the :class:`google.cloud.datastore.query.Query` class\\n uses this method to fetch data.\\n\\n :type project: string\\n :param project: The project over which to run the query.\\n\\n :type query_pb: :class:`.datastore._generated.query_pb2.Query`\\n :param query_pb: The Protobuf representing the query to run.\\n\\n :type namespace: string\\n :param namespace: The namespace over which to run the query.\\n\\n :type eventual: bool\\n :param eventual: If False (the default), request ``STRONG`` read\\n consistency. If True, request ``EVENTUAL`` read\\n consistency.\\n\\n :type transaction_id: string\\n :param transaction_id: If passed, make the request in the scope of\\n the given transaction. Incompatible with\\n ``eventual==True``.\\n\\n :rtype: tuple\\n :returns: Four-tuple containing the entities returned,\\n the end cursor of the query, a ``more_results``\\n enum and a count of the number of skipped results.\\n \\\"\\\"\\\"\\n request = _datastore_pb2.RunQueryRequest()\\n _set_read_options(request, eventual, transaction_id)\\n\\n if namespace:\\n request.partition_id.namespace_id = namespace\\n\\n request.query.CopyFrom(query_pb)\\n response = self._datastore_a\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::Connection.begin_transaction\",\n \"text\": \"google/cloud/datastore/connection.py::Connection.begin_transaction\\n def begin_transaction(self, project):\\n \\\"\\\"\\\"Begin a transaction.\\n\\n Maps the ``DatastoreService.BeginTransaction`` protobuf RPC.\\n\\n :type project: string\\n :param project: The project to which the transaction applies.\\n\\n :rtype: bytes\\n :returns: The serialized transaction that was begun.\\n \\\"\\\"\\\"\\n request = _datastore_pb2.BeginTransactionRequest()\\n response = self._datastore_api.begin_transaction(project, request)\\n return response.transaction\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::Connection.commit\",\n \"text\": \"google/cloud/datastore/connection.py::Connection.commit\\n def commit(self, project, request, transaction_id):\\n \\\"\\\"\\\"Commit mutations in context of current transaction (if any).\\n\\n Maps the ``DatastoreService.Commit`` protobuf RPC.\\n\\n :type project: string\\n :param project: The project to which the transaction applies.\\n\\n :type request: :class:`._generated.datastore_pb2.CommitRequest`\\n :param request: The protobuf with the mutations being committed.\\n\\n :type transaction_id: string or None\\n :param transaction_id: The transaction ID returned from\\n :meth:`begin_transaction`. Non-transactional\\n batches must pass ``None``.\\n\\n .. note::\\n\\n This method will mutate ``request`` before using it.\\n\\n :rtype: tuple\\n :returns: The pair of the number of index updates and a list of\\n :class:`._generated.entity_pb2.Key` for each incomplete key\\n that was completed in the commit.\\n \\\"\\\"\\\"\\n if transaction_id:\\n request.mode = _datastore_pb2.CommitRequest.TRANSACTIONAL\\n request.transaction = transaction_id\\n else:\\n request.mode = _datastore_pb2.CommitRequest.NON_TRANSACTIONAL\\n\\n response = self._datastore_api.commit(project, request)\\n return _parse_commit_response(response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::Connection.rollback\",\n \"text\": \"google/cloud/datastore/connection.py::Connection.rollback\\n def rollback(self, project, transaction_id):\\n \\\"\\\"\\\"Rollback the connection's existing transaction.\\n\\n Maps the ``DatastoreService.Rollback`` protobuf RPC.\\n\\n :type project: string\\n :param project: The project to which the transaction belongs.\\n\\n :type transaction_id: string\\n :param transaction_id: The transaction ID returned from\\n :meth:`begin_transaction`.\\n \\\"\\\"\\\"\\n request = _datastore_pb2.RollbackRequest()\\n request.transaction = transaction_id\\n # Nothing to do with this response, so just execute the method.\\n self._datastore_api.rollback(project, request)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::Connection.allocate_ids\",\n \"text\": \"google/cloud/datastore/connection.py::Connection.allocate_ids\\n def allocate_ids(self, project, key_pbs):\\n \\\"\\\"\\\"Obtain backend-generated IDs for a set of keys.\\n\\n Maps the ``DatastoreService.AllocateIds`` protobuf RPC.\\n\\n :type project: string\\n :param project: The project to which the transaction belongs.\\n\\n :type key_pbs: list of\\n :class:`google.cloud.datastore._generated.entity_pb2.Key`\\n :param key_pbs: The keys for which the backend should allocate IDs.\\n\\n :rtype: list of :class:`.datastore._generated.entity_pb2.Key`\\n :returns: An equal number of keys, with IDs filled in by the backend.\\n \\\"\\\"\\\"\\n request = _datastore_pb2.AllocateIdsRequest()\\n _add_keys_to_request(request.keys, key_pbs)\\n # Nothing to do with this response, so just execute the method.\\n response = self._datastore_api.allocate_ids(project, request)\\n return list(response.keys)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_set_read_options\",\n \"text\": \"google/cloud/datastore/connection.py::_set_read_options\\ndef _set_read_options(request, eventual, transaction_id):\\n \\\"\\\"\\\"Validate rules for read options, and assign to the request.\\n\\n Helper method for ``lookup()`` and ``run_query``.\\n\\n :raises: :class:`ValueError` if ``eventual`` is ``True`` and the\\n ``transaction_id`` is not ``None``.\\n \\\"\\\"\\\"\\n if eventual and (transaction_id is not None):\\n raise ValueError('eventual must be False when in a transaction')\\n\\n opts = request.read_options\\n if eventual:\\n opts.read_consistency = _datastore_pb2.ReadOptions.EVENTUAL\\n elif transaction_id:\\n opts.transaction = transaction_id\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_add_keys_to_request\",\n \"text\": \"google/cloud/datastore/connection.py::_add_keys_to_request\\ndef _add_keys_to_request(request_field_pb, key_pbs):\\n \\\"\\\"\\\"Add protobuf keys to a request object.\\n\\n :type request_field_pb: `RepeatedCompositeFieldContainer`\\n :param request_field_pb: A repeated proto field that contains keys.\\n\\n :type key_pbs: list of :class:`.datastore._generated.entity_pb2.Key`\\n :param key_pbs: The keys to add to a request.\\n \\\"\\\"\\\"\\n for key_pb in key_pbs:\\n request_field_pb.add().CopyFrom(key_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/connection.py::_parse_commit_response\",\n \"text\": \"google/cloud/datastore/connection.py::_parse_commit_response\\ndef _parse_commit_response(commit_response_pb):\\n \\\"\\\"\\\"Extract response data from a commit response.\\n\\n :type commit_response_pb: :class:`._generated.datastore_pb2.CommitResponse`\\n :param commit_response_pb: The protobuf response from a commit request.\\n\\n :rtype: tuple\\n :returns: The pair of the number of index updates and a list of\\n :class:`._generated.entity_pb2.Key` for each incomplete key\\n that was completed in the commit.\\n \\\"\\\"\\\"\\n mut_results = commit_response_pb.mutation_results\\n index_updates = commit_response_pb.index_updates\\n completed_keys = [mut_result.key for mut_result in mut_results\\n if mut_result.HasField('key')] # Message field (Key)\\n return index_updates, completed_keys\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/entity.py::Entity.__init__\",\n \"text\": \"google/cloud/datastore/entity.py::Entity.__init__\\n def __init__(self, key=None, exclude_from_indexes=()):\\n super(Entity, self).__init__()\\n self.key = key\\n self._exclude_from_indexes = set(_ensure_tuple_or_list(\\n 'exclude_from_indexes', exclude_from_indexes))\\n # NOTE: This will be populated when parsing a protobuf in\\n # google.cloud.datastore.helpers.entity_from_protobuf.\\n self._meanings = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/entity.py::Entity.__eq__\",\n \"text\": \"google/cloud/datastore/entity.py::Entity.__eq__\\n def __eq__(self, other):\\n \\\"\\\"\\\"Compare two entities for equality.\\n\\n Entities compare equal if their keys compare equal and their\\n properties compare equal.\\n\\n :rtype: boolean\\n :returns: True if the entities compare equal, else False.\\n \\\"\\\"\\\"\\n if not isinstance(other, Entity):\\n return False\\n\\n return (self.key == other.key and\\n self._exclude_from_indexes == other._exclude_from_indexes and\\n self._meanings == other._meanings and\\n super(Entity, self).__eq__(other))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/entity.py::Entity.__ne__\",\n \"text\": \"google/cloud/datastore/entity.py::Entity.__ne__\\n def __ne__(self, other):\\n \\\"\\\"\\\"Compare two entities for inequality.\\n\\n Entities compare equal if their keys compare equal and their\\n properties compare equal.\\n\\n :rtype: boolean\\n :returns: False if the entities compare equal, else True.\\n \\\"\\\"\\\"\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/entity.py::Entity.kind\",\n \"text\": \"google/cloud/datastore/entity.py::Entity.kind\\n @property\\n def kind(self):\\n \\\"\\\"\\\"Get the kind of the current entity.\\n\\n .. note::\\n This relies entirely on the :class:`google.cloud.datastore.key.Key`\\n set on the entity. That means that we're not storing the kind\\n of the entity at all, just the properties and a pointer to a\\n Key which knows its Kind.\\n \\\"\\\"\\\"\\n if self.key:\\n return self.key.kind\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/entity.py::Entity.exclude_from_indexes\",\n \"text\": \"google/cloud/datastore/entity.py::Entity.exclude_from_indexes\\n @property\\n def exclude_from_indexes(self):\\n \\\"\\\"\\\"Names of fields which are *not* to be indexed for this entity.\\n\\n :rtype: sequence of field names\\n :returns: The set of fields excluded from indexes.\\n \\\"\\\"\\\"\\n return frozenset(self._exclude_from_indexes)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/entity.py::Entity.__repr__\",\n \"text\": \"google/cloud/datastore/entity.py::Entity.__repr__\\n def __repr__(self):\\n if self.key:\\n return '<Entity%s %s>' % (self.key.path,\\n super(Entity, self).__repr__())\\n else:\\n return '<Entity %s>' % (super(Entity, self).__repr__())\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::_get_meaning\",\n \"text\": \"google/cloud/datastore/helpers.py::_get_meaning\\ndef _get_meaning(value_pb, is_list=False):\\n \\\"\\\"\\\"Get the meaning from a protobuf value.\\n\\n :type value_pb: :class:`google.cloud.datastore._generated.entity_pb2.Value`\\n :param value_pb: The protobuf value to be checked for an\\n associated meaning.\\n\\n :type is_list: bool\\n :param is_list: Boolean indicating if the ``value_pb`` contains\\n a list value.\\n\\n :rtype: int\\n :returns: The meaning for the ``value_pb`` if one is set, else\\n :data:`None`. For a list value, if there are disagreeing\\n means it just returns a list of meanings. If all the\\n list meanings agree, it just condenses them.\\n \\\"\\\"\\\"\\n meaning = None\\n if is_list:\\n # An empty list will have no values, hence no shared meaning\\n # set among them.\\n if len(value_pb.array_value.values) == 0:\\n return None\\n\\n # We check among all the meanings, some of which may be None,\\n # the rest which may be enum/int values.\\n all_meanings = [_get_meaning(sub_value_pb)\\n for sub_value_pb in value_pb.array_value.values]\\n unique_meanings = set(all_meanings)\\n if len(unique_meanings) == 1:\\n # If there is a unique meaning, we preserve it.\\n meaning = unique_meanings.pop()\\n else: # We know len(value_pb.array_value.values) > 0.\\n # If the meaning is not unique, just return all of them.\\n meaning = all_meanings\\n elif value_pb.meaning: # Simple field (int32).\\n meaning = value_pb.meaning\\n\\n return meaning\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::_new_value_pb\",\n \"text\": \"google/cloud/datastore/helpers.py::_new_value_pb\\ndef _new_value_pb(entity_pb, name):\\n \\\"\\\"\\\"Add (by name) a new ``Value`` protobuf to an entity protobuf.\\n\\n :type entity_pb: :class:`.datastore._generated.entity_pb2.Entity`\\n :param entity_pb: An entity protobuf to add a new property to.\\n\\n :type name: string\\n :param name: The name of the new property.\\n\\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Value`\\n :returns: The new ``Value`` protobuf that was added to the entity.\\n \\\"\\\"\\\"\\n return entity_pb.properties.get_or_create(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::_property_tuples\",\n \"text\": \"google/cloud/datastore/helpers.py::_property_tuples\\ndef _property_tuples(entity_pb):\\n \\\"\\\"\\\"Iterator of name, ``Value`` tuples from entity properties.\\n\\n :type entity_pb: :class:`.datastore._generated.entity_pb2.Entity`\\n :param entity_pb: An entity protobuf to add a new property to.\\n\\n :rtype: :class:`generator`\\n :returns: An iterator that yields tuples of a name and ``Value``\\n corresponding to properties on the entity.\\n \\\"\\\"\\\"\\n return six.iteritems(entity_pb.properties)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::entity_from_protobuf\",\n \"text\": \"google/cloud/datastore/helpers.py::entity_from_protobuf\\ndef entity_from_protobuf(pb):\\n \\\"\\\"\\\"Factory method for creating an entity based on a protobuf.\\n\\n The protobuf should be one returned from the Cloud Datastore\\n Protobuf API.\\n\\n :type pb: :class:`google.cloud.datastore._generated.entity_pb2.Entity`\\n :param pb: The Protobuf representing the entity.\\n\\n :rtype: :class:`google.cloud.datastore.entity.Entity`\\n :returns: The entity derived from the protobuf.\\n \\\"\\\"\\\"\\n key = None\\n if pb.HasField('key'): # Message field (Key)\\n key = key_from_protobuf(pb.key)\\n\\n entity_props = {}\\n entity_meanings = {}\\n exclude_from_indexes = []\\n\\n for prop_name, value_pb in _property_tuples(pb):\\n value = _get_value_from_value_pb(value_pb)\\n entity_props[prop_name] = value\\n\\n # Check if the property has an associated meaning.\\n is_list = isinstance(value, list)\\n meaning = _get_meaning(value_pb, is_list=is_list)\\n if meaning is not None:\\n entity_meanings[prop_name] = (meaning, value)\\n\\n # Check if ``value_pb`` was excluded from index. Lists need to be\\n # special-cased and we require all ``exclude_from_indexes`` values\\n # in a list agree.\\n if is_list:\\n exclude_values = set(value_pb.exclude_from_indexes\\n for value_pb in value_pb.array_value.values)\\n if len(exclude_values) != 1:\\n raise ValueError('For an array_value, subvalues must either '\\n 'all be indexed or all excluded from '\\n 'indexes.')\\n\\n if exclude_values.pop():\\n exclude_from_indexes.append(prop_name)\\n else:\\n if value_pb.exclude_from_indexes:\\n exclude_from_indexes.append(prop_name)\\n\\n entity = Entity(key=key, exclude_from_indexes=exclude_from_indexes)\\n entity.update(entity_props)\\n entity._meanings.update(entity_meanings)\\n re\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::_set_pb_meaning_from_entity\",\n \"text\": \"google/cloud/datastore/helpers.py::_set_pb_meaning_from_entity\\ndef _set_pb_meaning_from_entity(entity, name, value, value_pb,\\n is_list=False):\\n \\\"\\\"\\\"Add meaning information (from an entity) to a protobuf.\\n\\n :type entity: :class:`google.cloud.datastore.entity.Entity`\\n :param entity: The entity to be turned into a protobuf.\\n\\n :type name: string\\n :param name: The name of the property.\\n\\n :type value: object\\n :param value: The current value stored as property ``name``.\\n\\n :type value_pb: :class:`google.cloud.datastore._generated.entity_pb2.Value`\\n :param value_pb: The protobuf value to add meaning / meanings to.\\n\\n :type is_list: bool\\n :param is_list: (Optional) Boolean indicating if the ``value`` is\\n a list value.\\n \\\"\\\"\\\"\\n if name not in entity._meanings:\\n return\\n\\n meaning, orig_value = entity._meanings[name]\\n # Only add the meaning back to the protobuf if the value is\\n # unchanged from when it was originally read from the API.\\n if orig_value is not value:\\n return\\n\\n # For lists, we set meaning on each sub-element.\\n if is_list:\\n if not isinstance(meaning, list):\\n meaning = itertools.repeat(meaning)\\n val_iter = six.moves.zip(value_pb.array_value.values,\\n meaning)\\n for sub_value_pb, sub_meaning in val_iter:\\n if sub_meaning is not None:\\n sub_value_pb.meaning = sub_meaning\\n else:\\n value_pb.meaning = meaning\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::entity_to_protobuf\",\n \"text\": \"google/cloud/datastore/helpers.py::entity_to_protobuf\\ndef entity_to_protobuf(entity):\\n \\\"\\\"\\\"Converts an entity into a protobuf.\\n\\n :type entity: :class:`google.cloud.datastore.entity.Entity`\\n :param entity: The entity to be turned into a protobuf.\\n\\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Entity`\\n :returns: The protobuf representing the entity.\\n \\\"\\\"\\\"\\n entity_pb = _entity_pb2.Entity()\\n if entity.key is not None:\\n key_pb = entity.key.to_protobuf()\\n entity_pb.key.CopyFrom(key_pb)\\n\\n for name, value in entity.items():\\n value_is_list = isinstance(value, list)\\n if value_is_list and len(value) == 0:\\n continue\\n\\n value_pb = _new_value_pb(entity_pb, name)\\n # Set the appropriate value.\\n _set_protobuf_value(value_pb, value)\\n\\n # Add index information to protobuf.\\n if name in entity.exclude_from_indexes:\\n if not value_is_list:\\n value_pb.exclude_from_indexes = True\\n\\n for sub_value in value_pb.array_value.values:\\n sub_value.exclude_from_indexes = True\\n\\n # Add meaning information to protobuf.\\n _set_pb_meaning_from_entity(entity, name, value, value_pb,\\n is_list=value_is_list)\\n\\n return entity_pb\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::key_from_protobuf\",\n \"text\": \"google/cloud/datastore/helpers.py::key_from_protobuf\\ndef key_from_protobuf(pb):\\n \\\"\\\"\\\"Factory method for creating a key based on a protobuf.\\n\\n The protobuf should be one returned from the Cloud Datastore\\n Protobuf API.\\n\\n :type pb: :class:`google.cloud.datastore._generated.entity_pb2.Key`\\n :param pb: The Protobuf representing the key.\\n\\n :rtype: :class:`google.cloud.datastore.key.Key`\\n :returns: a new `Key` instance\\n \\\"\\\"\\\"\\n path_args = []\\n for element in pb.path:\\n path_args.append(element.kind)\\n if element.id: # Simple field (int64)\\n path_args.append(element.id)\\n # This is safe: we expect proto objects returned will only have\\n # one of `name` or `id` set.\\n if element.name: # Simple field (string)\\n path_args.append(element.name)\\n\\n project = None\\n if pb.partition_id.project_id: # Simple field (string)\\n project = pb.partition_id.project_id\\n namespace = None\\n if pb.partition_id.namespace_id: # Simple field (string)\\n namespace = pb.partition_id.namespace_id\\n\\n return Key(*path_args, namespace=namespace, project=project)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::_pb_attr_value\",\n \"text\": \"google/cloud/datastore/helpers.py::_pb_attr_value\\ndef _pb_attr_value(val):\\n \\\"\\\"\\\"Given a value, return the protobuf attribute name and proper value.\\n\\n The Protobuf API uses different attribute names based on value types\\n rather than inferring the type. This function simply determines the\\n proper attribute name based on the type of the value provided and\\n returns the attribute name as well as a properly formatted value.\\n\\n Certain value types need to be coerced into a different type (such\\n as a `datetime.datetime` into an integer timestamp, or a\\n `google.cloud.datastore.key.Key` into a Protobuf representation. This\\n function handles that for you.\\n\\n .. note::\\n Values which are \\\"text\\\" ('unicode' in Python2, 'str' in Python3) map\\n to 'string_value' in the datastore; values which are \\\"bytes\\\"\\n ('str' in Python2, 'bytes' in Python3) map to 'blob_value'.\\n\\n For example:\\n\\n >>> _pb_attr_value(1234)\\n ('integer_value', 1234)\\n >>> _pb_attr_value('my_string')\\n ('string_value', 'my_string')\\n\\n :type val: `datetime.datetime`, :class:`google.cloud.datastore.key.Key`,\\n bool, float, integer, string\\n :param val: The value to be scrutinized.\\n\\n :rtype: tuple\\n :returns: A tuple of the attribute name and proper value type.\\n \\\"\\\"\\\"\\n\\n if isinstance(val, datetime.datetime):\\n name = 'timestamp'\\n value = _datetime_to_pb_timestamp(val)\\n elif isinstance(val, Key):\\n name, value = 'key', val.to_protobuf()\\n elif isinstance(val, bool):\\n name, value = 'boolean', val\\n elif isinstance(val, float):\\n name, value = 'double', val\\n elif isinstance(val, six.integer_types):\\n name, value = 'integer', val\\n elif isinstance(val, six.text_type):\\n name, value = 'string', val\\n elif isinstance(val, (bytes, str)):\\n name, value = 'blob', val\\n elif isinstance(val, Entity):\\n name, value = 'entity', val\\n elif isinstance(val, list):\\n name, val\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::_get_value_from_value_pb\",\n \"text\": \"google/cloud/datastore/helpers.py::_get_value_from_value_pb\\ndef _get_value_from_value_pb(value_pb):\\n \\\"\\\"\\\"Given a protobuf for a Value, get the correct value.\\n\\n The Cloud Datastore Protobuf API returns a Property Protobuf which\\n has one value set and the rest blank. This function retrieves the\\n the one value provided.\\n\\n Some work is done to coerce the return value into a more useful type\\n (particularly in the case of a timestamp value, or a key value).\\n\\n :type value_pb: :class:`google.cloud.datastore._generated.entity_pb2.Value`\\n :param value_pb: The Value Protobuf.\\n\\n :rtype: object\\n :returns: The value provided by the Protobuf.\\n :raises: :class:`ValueError <exceptions.ValueError>` if no value type\\n has been set.\\n \\\"\\\"\\\"\\n value_type = value_pb.WhichOneof('value_type')\\n\\n if value_type == 'timestamp_value':\\n result = _pb_timestamp_to_datetime(value_pb.timestamp_value)\\n\\n elif value_type == 'key_value':\\n result = key_from_protobuf(value_pb.key_value)\\n\\n elif value_type == 'boolean_value':\\n result = value_pb.boolean_value\\n\\n elif value_type == 'double_value':\\n result = value_pb.double_value\\n\\n elif value_type == 'integer_value':\\n result = value_pb.integer_value\\n\\n elif value_type == 'string_value':\\n result = value_pb.string_value\\n\\n elif value_type == 'blob_value':\\n result = value_pb.blob_value\\n\\n elif value_type == 'entity_value':\\n result = entity_from_protobuf(value_pb.entity_value)\\n\\n elif value_type == 'array_value':\\n result = [_get_value_from_value_pb(value)\\n for value in value_pb.array_value.values]\\n\\n elif value_type == 'geo_point_value':\\n result = GeoPoint(value_pb.geo_point_value.latitude,\\n value_pb.geo_point_value.longitude)\\n\\n elif value_type == 'null_value':\\n result = None\\n\\n else:\\n raise ValueError('Value protobuf did not have any value set')\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::_set_protobuf_value\",\n \"text\": \"google/cloud/datastore/helpers.py::_set_protobuf_value\\ndef _set_protobuf_value(value_pb, val):\\n \\\"\\\"\\\"Assign 'val' to the correct subfield of 'value_pb'.\\n\\n The Protobuf API uses different attribute names based on value types\\n rather than inferring the type.\\n\\n Some value types (entities, keys, lists) cannot be directly\\n assigned; this function handles them correctly.\\n\\n :type value_pb: :class:`google.cloud.datastore._generated.entity_pb2.Value`\\n :param value_pb: The value protobuf to which the value is being assigned.\\n\\n :type val: :class:`datetime.datetime`, boolean, float, integer, string,\\n :class:`google.cloud.datastore.key.Key`,\\n :class:`google.cloud.datastore.entity.Entity`\\n :param val: The value to be assigned.\\n \\\"\\\"\\\"\\n attr, val = _pb_attr_value(val)\\n if attr == 'key_value':\\n value_pb.key_value.CopyFrom(val)\\n elif attr == 'timestamp_value':\\n value_pb.timestamp_value.CopyFrom(val)\\n elif attr == 'entity_value':\\n entity_pb = entity_to_protobuf(val)\\n value_pb.entity_value.CopyFrom(entity_pb)\\n elif attr == 'array_value':\\n l_pb = value_pb.array_value.values\\n for item in val:\\n i_pb = l_pb.add()\\n _set_protobuf_value(i_pb, item)\\n elif attr == 'geo_point_value':\\n value_pb.geo_point_value.CopyFrom(val)\\n else: # scalar, just assign\\n setattr(value_pb, attr, val)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::GeoPoint.__init__\",\n \"text\": \"google/cloud/datastore/helpers.py::GeoPoint.__init__\\n def __init__(self, latitude, longitude):\\n self.latitude = latitude\\n self.longitude = longitude\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::GeoPoint.to_protobuf\",\n \"text\": \"google/cloud/datastore/helpers.py::GeoPoint.to_protobuf\\n def to_protobuf(self):\\n \\\"\\\"\\\"Convert the current object to protobuf.\\n\\n :rtype: :class:`google.type.latlng_pb2.LatLng`.\\n :returns: The current point as a protobuf.\\n \\\"\\\"\\\"\\n return latlng_pb2.LatLng(latitude=self.latitude,\\n longitude=self.longitude)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::GeoPoint.__eq__\",\n \"text\": \"google/cloud/datastore/helpers.py::GeoPoint.__eq__\\n def __eq__(self, other):\\n \\\"\\\"\\\"Compare two geo points for equality.\\n\\n :rtype: boolean\\n :returns: True if the points compare equal, else False.\\n \\\"\\\"\\\"\\n if not isinstance(other, GeoPoint):\\n return False\\n\\n return (self.latitude == other.latitude and\\n self.longitude == other.longitude)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/helpers.py::GeoPoint.__ne__\",\n \"text\": \"google/cloud/datastore/helpers.py::GeoPoint.__ne__\\n def __ne__(self, other):\\n \\\"\\\"\\\"Compare two geo points for inequality.\\n\\n :rtype: boolean\\n :returns: False if the points compare equal, else True.\\n \\\"\\\"\\\"\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.__init__\",\n \"text\": \"google/cloud/datastore/key.py::Key.__init__\\n def __init__(self, *path_args, **kwargs):\\n self._flat_path = path_args\\n parent = self._parent = kwargs.get('parent')\\n self._namespace = kwargs.get('namespace')\\n project = kwargs.get('project')\\n self._project = _validate_project(project, parent)\\n # _flat_path, _parent, _namespace and _project must be set before\\n # _combine_args() is called.\\n self._path = self._combine_args()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.__eq__\",\n \"text\": \"google/cloud/datastore/key.py::Key.__eq__\\n def __eq__(self, other):\\n \\\"\\\"\\\"Compare two keys for equality.\\n\\n Incomplete keys never compare equal to any other key.\\n\\n Completed keys compare equal if they have the same path, project,\\n and namespace.\\n\\n :rtype: bool\\n :returns: True if the keys compare equal, else False.\\n \\\"\\\"\\\"\\n if not isinstance(other, Key):\\n return False\\n\\n if self.is_partial or other.is_partial:\\n return False\\n\\n return (self.flat_path == other.flat_path and\\n self.project == other.project and\\n self.namespace == other.namespace)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.__ne__\",\n \"text\": \"google/cloud/datastore/key.py::Key.__ne__\\n def __ne__(self, other):\\n \\\"\\\"\\\"Compare two keys for inequality.\\n\\n Incomplete keys never compare equal to any other key.\\n\\n Completed keys compare equal if they have the same path, project,\\n and namespace.\\n\\n :rtype: bool\\n :returns: False if the keys compare equal, else True.\\n \\\"\\\"\\\"\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.__hash__\",\n \"text\": \"google/cloud/datastore/key.py::Key.__hash__\\n def __hash__(self):\\n \\\"\\\"\\\"Hash a keys for use in a dictionary lookp.\\n\\n :rtype: integer\\n :returns: a hash of the key's state.\\n \\\"\\\"\\\"\\n return (hash(self.flat_path) +\\n hash(self.project) +\\n hash(self.namespace))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key._parse_path\",\n \"text\": \"google/cloud/datastore/key.py::Key._parse_path\\n @staticmethod\\n def _parse_path(path_args):\\n \\\"\\\"\\\"Parses positional arguments into key path with kinds and IDs.\\n\\n :type path_args: tuple\\n :param path_args: A tuple from positional arguments. Should be\\n alternating list of kinds (string) and ID/name\\n parts (int or string).\\n\\n :rtype: :class:`list` of :class:`dict`\\n :returns: A list of key parts with kind and ID or name set.\\n :raises: :class:`ValueError` if there are no ``path_args``, if one of\\n the kinds is not a string or if one of the IDs/names is not\\n a string or an integer.\\n \\\"\\\"\\\"\\n if len(path_args) == 0:\\n raise ValueError('Key path must not be empty.')\\n\\n kind_list = path_args[::2]\\n id_or_name_list = path_args[1::2]\\n # Dummy sentinel value to pad incomplete key to even length path.\\n partial_ending = object()\\n if len(path_args) % 2 == 1:\\n id_or_name_list += (partial_ending,)\\n\\n result = []\\n for kind, id_or_name in zip(kind_list, id_or_name_list):\\n curr_key_part = {}\\n if isinstance(kind, six.string_types):\\n curr_key_part['kind'] = kind\\n else:\\n raise ValueError(kind, 'Kind was not a string.')\\n\\n if isinstance(id_or_name, six.string_types):\\n curr_key_part['name'] = id_or_name\\n elif isinstance(id_or_name, six.integer_types):\\n curr_key_part['id'] = id_or_name\\n elif id_or_name is not partial_ending:\\n raise ValueError(id_or_name,\\n 'ID/name was not a string or integer.')\\n\\n result.append(curr_key_part)\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key._combine_args\",\n \"text\": \"google/cloud/datastore/key.py::Key._combine_args\\n def _combine_args(self):\\n \\\"\\\"\\\"Sets protected data by combining raw data set from the constructor.\\n\\n If a ``_parent`` is set, updates the ``_flat_path`` and sets the\\n ``_namespace`` and ``_project`` if not already set.\\n\\n :rtype: :class:`list` of :class:`dict`\\n :returns: A list of key parts with kind and ID or name set.\\n :raises: :class:`ValueError` if the parent key is not complete.\\n \\\"\\\"\\\"\\n child_path = self._parse_path(self._flat_path)\\n\\n if self._parent is not None:\\n if self._parent.is_partial:\\n raise ValueError('Parent key must be complete.')\\n\\n # We know that _parent.path() will return a copy.\\n child_path = self._parent.path + child_path\\n self._flat_path = self._parent.flat_path + self._flat_path\\n if (self._namespace is not None and\\n self._namespace != self._parent.namespace):\\n raise ValueError('Child namespace must agree with parent\\\\'s.')\\n self._namespace = self._parent.namespace\\n if (self._project is not None and\\n self._project != self._parent.project):\\n raise ValueError('Child project must agree with parent\\\\'s.')\\n self._project = self._parent.project\\n\\n return child_path\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key._clone\",\n \"text\": \"google/cloud/datastore/key.py::Key._clone\\n def _clone(self):\\n \\\"\\\"\\\"Duplicates the Key.\\n\\n Most attributes are simple types, so don't require copying. Other\\n attributes like ``parent`` are long-lived and so we re-use them.\\n\\n :rtype: :class:`google.cloud.datastore.key.Key`\\n :returns: A new ``Key`` instance with the same data as the current one.\\n \\\"\\\"\\\"\\n cloned_self = self.__class__(*self.flat_path,\\n project=self.project,\\n namespace=self.namespace)\\n # If the current parent has already been set, we re-use\\n # the same instance\\n cloned_self._parent = self._parent\\n return cloned_self\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.completed_key\",\n \"text\": \"google/cloud/datastore/key.py::Key.completed_key\\n def completed_key(self, id_or_name):\\n \\\"\\\"\\\"Creates new key from existing partial key by adding final ID/name.\\n\\n :type id_or_name: string or integer\\n :param id_or_name: ID or name to be added to the key.\\n\\n :rtype: :class:`google.cloud.datastore.key.Key`\\n :returns: A new ``Key`` instance with the same data as the current one\\n and an extra ID or name added.\\n :raises: :class:`ValueError` if the current key is not partial or if\\n ``id_or_name`` is not a string or integer.\\n \\\"\\\"\\\"\\n if not self.is_partial:\\n raise ValueError('Only a partial key can be completed.')\\n\\n if isinstance(id_or_name, six.string_types):\\n id_or_name_key = 'name'\\n elif isinstance(id_or_name, six.integer_types):\\n id_or_name_key = 'id'\\n else:\\n raise ValueError(id_or_name,\\n 'ID/name was not a string or integer.')\\n\\n new_key = self._clone()\\n new_key._path[-1][id_or_name_key] = id_or_name\\n new_key._flat_path += (id_or_name,)\\n return new_key\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.to_protobuf\",\n \"text\": \"google/cloud/datastore/key.py::Key.to_protobuf\\n def to_protobuf(self):\\n \\\"\\\"\\\"Return a protobuf corresponding to the key.\\n\\n :rtype: :class:`google.cloud.datastore._generated.entity_pb2.Key`\\n :returns: The protobuf representing the key.\\n \\\"\\\"\\\"\\n key = _entity_pb2.Key()\\n key.partition_id.project_id = self.project\\n\\n if self.namespace:\\n key.partition_id.namespace_id = self.namespace\\n\\n for item in self.path:\\n element = key.path.add()\\n if 'kind' in item:\\n element.kind = item['kind']\\n if 'id' in item:\\n element.id = item['id']\\n if 'name' in item:\\n element.name = item['name']\\n\\n return key\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.is_partial\",\n \"text\": \"google/cloud/datastore/key.py::Key.is_partial\\n @property\\n def is_partial(self):\\n \\\"\\\"\\\"Boolean indicating if the key has an ID (or name).\\n\\n :rtype: bool\\n :returns: ``True`` if the last element of the key's path does not have\\n an ``id`` or a ``name``.\\n \\\"\\\"\\\"\\n return self.id_or_name is None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.namespace\",\n \"text\": \"google/cloud/datastore/key.py::Key.namespace\\n @property\\n def namespace(self):\\n \\\"\\\"\\\"Namespace getter.\\n\\n :rtype: string\\n :returns: The namespace of the current key.\\n \\\"\\\"\\\"\\n return self._namespace\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.path\",\n \"text\": \"google/cloud/datastore/key.py::Key.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"Path getter.\\n\\n Returns a copy so that the key remains immutable.\\n\\n :rtype: :class:`list` of :class:`dict`\\n :returns: The (key) path of the current key.\\n \\\"\\\"\\\"\\n return copy.deepcopy(self._path)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.flat_path\",\n \"text\": \"google/cloud/datastore/key.py::Key.flat_path\\n @property\\n def flat_path(self):\\n \\\"\\\"\\\"Getter for the key path as a tuple.\\n\\n :rtype: tuple of string and integer\\n :returns: The tuple of elements in the path.\\n \\\"\\\"\\\"\\n return self._flat_path\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.kind\",\n \"text\": \"google/cloud/datastore/key.py::Key.kind\\n @property\\n def kind(self):\\n \\\"\\\"\\\"Kind getter. Based on the last element of path.\\n\\n :rtype: string\\n :returns: The kind of the current key.\\n \\\"\\\"\\\"\\n return self.path[-1]['kind']\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.id\",\n \"text\": \"google/cloud/datastore/key.py::Key.id\\n @property\\n def id(self):\\n \\\"\\\"\\\"ID getter. Based on the last element of path.\\n\\n :rtype: integer\\n :returns: The (integer) ID of the key.\\n \\\"\\\"\\\"\\n return self.path[-1].get('id')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.name\",\n \"text\": \"google/cloud/datastore/key.py::Key.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Name getter. Based on the last element of path.\\n\\n :rtype: string\\n :returns: The (string) name of the key.\\n \\\"\\\"\\\"\\n return self.path[-1].get('name')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.id_or_name\",\n \"text\": \"google/cloud/datastore/key.py::Key.id_or_name\\n @property\\n def id_or_name(self):\\n \\\"\\\"\\\"Getter. Based on the last element of path.\\n\\n :rtype: integer (if ``id``) or string (if ``name``)\\n :returns: The last element of the key's path if it is either an ``id``\\n or a ``name``.\\n \\\"\\\"\\\"\\n return self.id or self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.project\",\n \"text\": \"google/cloud/datastore/key.py::Key.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project getter.\\n\\n :rtype: string\\n :returns: The key's project.\\n \\\"\\\"\\\"\\n return self._project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key._make_parent\",\n \"text\": \"google/cloud/datastore/key.py::Key._make_parent\\n def _make_parent(self):\\n \\\"\\\"\\\"Creates a parent key for the current path.\\n\\n Extracts all but the last element in the key path and creates a new\\n key, while still matching the namespace and the project.\\n\\n :rtype: :class:`google.cloud.datastore.key.Key` or :class:`NoneType`\\n :returns: A new ``Key`` instance, whose path consists of all but the\\n last element of current path. If the current key has only\\n one path element, returns ``None``.\\n \\\"\\\"\\\"\\n if self.is_partial:\\n parent_args = self.flat_path[:-1]\\n else:\\n parent_args = self.flat_path[:-2]\\n if parent_args:\\n return self.__class__(*parent_args, project=self.project,\\n namespace=self.namespace)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.parent\",\n \"text\": \"google/cloud/datastore/key.py::Key.parent\\n @property\\n def parent(self):\\n \\\"\\\"\\\"The parent of the current key.\\n\\n :rtype: :class:`google.cloud.datastore.key.Key` or :class:`NoneType`\\n :returns: A new ``Key`` instance, whose path consists of all but the\\n last element of current path. If the current key has only\\n one path element, returns ``None``.\\n \\\"\\\"\\\"\\n if self._parent is None:\\n self._parent = self._make_parent()\\n\\n return self._parent\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::Key.__repr__\",\n \"text\": \"google/cloud/datastore/key.py::Key.__repr__\\n def __repr__(self):\\n return '<Key%s, project=%s>' % (self.path, self.project)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/key.py::_validate_project\",\n \"text\": \"google/cloud/datastore/key.py::_validate_project\\ndef _validate_project(project, parent):\\n \\\"\\\"\\\"Ensure the project is set appropriately.\\n\\n If ``parent`` is passed, skip the test (it will be checked / fixed up\\n later).\\n\\n If ``project`` is unset, attempt to infer the project from the environment.\\n\\n :type project: string\\n :param project: A project.\\n\\n :type parent: :class:`google.cloud.datastore.key.Key` or ``NoneType``\\n :param parent: The parent of the key or ``None``.\\n\\n :rtype: string\\n :returns: The ``project`` passed in, or implied from the environment.\\n :raises: :class:`ValueError` if ``project`` is ``None`` and no project\\n can be inferred from the parent.\\n \\\"\\\"\\\"\\n if parent is None:\\n if project is None:\\n raise ValueError(\\\"A Key must have a project set.\\\")\\n\\n return project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.__init__\",\n \"text\": \"google/cloud/datastore/query.py::Query.__init__\\n def __init__(self,\\n client,\\n kind=None,\\n project=None,\\n namespace=None,\\n ancestor=None,\\n filters=(),\\n projection=(),\\n order=(),\\n distinct_on=()):\\n\\n self._client = client\\n self._kind = kind\\n self._project = project or client.project\\n self._namespace = namespace or client.namespace\\n self._ancestor = ancestor\\n self._filters = []\\n # Verify filters passed in.\\n for property_name, operator, value in filters:\\n self.add_filter(property_name, operator, value)\\n self._projection = _ensure_tuple_or_list('projection', projection)\\n self._order = _ensure_tuple_or_list('order', order)\\n self._distinct_on = _ensure_tuple_or_list('distinct_on', distinct_on)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.project\",\n \"text\": \"google/cloud/datastore/query.py::Query.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Get the project for this Query.\\n\\n :rtype: str\\n :returns: The project for the query.\\n \\\"\\\"\\\"\\n return self._project or self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.namespace\",\n \"text\": \"google/cloud/datastore/query.py::Query.namespace\\n @property\\n def namespace(self):\\n \\\"\\\"\\\"This query's namespace\\n\\n :rtype: string or None\\n :returns: the namespace assigned to this query\\n \\\"\\\"\\\"\\n return self._namespace or self._client.namespace\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.namespace\",\n \"text\": \"google/cloud/datastore/query.py::Query.namespace\\n @namespace.setter\\n def namespace(self, value):\\n \\\"\\\"\\\"Update the query's namespace.\\n\\n :type value: string\\n \\\"\\\"\\\"\\n if not isinstance(value, str):\\n raise ValueError(\\\"Namespace must be a string\\\")\\n self._namespace = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.kind\",\n \"text\": \"google/cloud/datastore/query.py::Query.kind\\n @property\\n def kind(self):\\n \\\"\\\"\\\"Get the Kind of the Query.\\n\\n :rtype: string\\n :returns: The kind for the query.\\n \\\"\\\"\\\"\\n return self._kind\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.kind\",\n \"text\": \"google/cloud/datastore/query.py::Query.kind\\n @kind.setter\\n def kind(self, value):\\n \\\"\\\"\\\"Update the Kind of the Query.\\n\\n :type value: string\\n :param value: updated kind for the query.\\n\\n .. note::\\n\\n The protobuf specification allows for ``kind`` to be repeated,\\n but the current implementation returns an error if more than\\n one value is passed. If the back-end changes in the future to\\n allow multiple values, this method will be updated to allow passing\\n either a string or a sequence of strings.\\n \\\"\\\"\\\"\\n if not isinstance(value, str):\\n raise TypeError(\\\"Kind must be a string\\\")\\n self._kind = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.ancestor\",\n \"text\": \"google/cloud/datastore/query.py::Query.ancestor\\n @property\\n def ancestor(self):\\n \\\"\\\"\\\"The ancestor key for the query.\\n\\n :rtype: Key or None\\n :returns: The ancestor for the query.\\n \\\"\\\"\\\"\\n return self._ancestor\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.ancestor\",\n \"text\": \"google/cloud/datastore/query.py::Query.ancestor\\n @ancestor.setter\\n def ancestor(self, value):\\n \\\"\\\"\\\"Set the ancestor for the query\\n\\n :type value: Key\\n :param value: the new ancestor key\\n \\\"\\\"\\\"\\n if not isinstance(value, Key):\\n raise TypeError(\\\"Ancestor must be a Key\\\")\\n self._ancestor = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.ancestor\",\n \"text\": \"google/cloud/datastore/query.py::Query.ancestor\\n @ancestor.deleter\\n def ancestor(self):\\n \\\"\\\"\\\"Remove the ancestor for the query.\\\"\\\"\\\"\\n self._ancestor = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.filters\",\n \"text\": \"google/cloud/datastore/query.py::Query.filters\\n @property\\n def filters(self):\\n \\\"\\\"\\\"Filters set on the query.\\n\\n :rtype: sequence of (property_name, operator, value) tuples.\\n :returns: The filters set on the query.\\n \\\"\\\"\\\"\\n return self._filters[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.add_filter\",\n \"text\": \"google/cloud/datastore/query.py::Query.add_filter\\n def add_filter(self, property_name, operator, value):\\n \\\"\\\"\\\"Filter the query based on a property name, operator and a value.\\n\\n Expressions take the form of::\\n\\n .add_filter('<property>', '<operator>', <value>)\\n\\n where property is a property stored on the entity in the datastore\\n and operator is one of ``OPERATORS``\\n (ie, ``=``, ``<``, ``<=``, ``>``, ``>=``)::\\n\\n >>> from google.cloud import datastore\\n >>> client = datastore.Client()\\n >>> query = client.query(kind='Person')\\n >>> query.add_filter('name', '=', 'James')\\n >>> query.add_filter('age', '>', 50)\\n\\n :type property_name: string\\n :param property_name: A property name.\\n\\n :type operator: string\\n :param operator: One of ``=``, ``<``, ``<=``, ``>``, ``>=``.\\n\\n :type value: :class:`int`, :class:`str`, :class:`bool`,\\n :class:`float`, :class:`NoneType`,\\n :class:`datetime.datetime`,\\n :class:`google.cloud.datastore.key.Key`\\n :param value: The value to filter on.\\n\\n :raises: :class:`ValueError` if ``operation`` is not one of the\\n specified values, or if a filter names ``'__key__'`` but\\n passes an invalid value (a key is required).\\n \\\"\\\"\\\"\\n if self.OPERATORS.get(operator) is None:\\n error_message = 'Invalid expression: \\\"%s\\\"' % (operator,)\\n choices_message = 'Please use one of: =, <, <=, >, >=.'\\n raise ValueError(error_message, choices_message)\\n\\n if property_name == '__key__' and not isinstance(value, Key):\\n raise ValueError('Invalid key: \\\"%s\\\"' % value)\\n\\n self._filters.append((property_name, operator, value))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.projection\",\n \"text\": \"google/cloud/datastore/query.py::Query.projection\\n @property\\n def projection(self):\\n \\\"\\\"\\\"Fields names returned by the query.\\n\\n :rtype: sequence of string\\n :returns: Names of fields in query results.\\n \\\"\\\"\\\"\\n return self._projection[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.projection\",\n \"text\": \"google/cloud/datastore/query.py::Query.projection\\n @projection.setter\\n def projection(self, projection):\\n \\\"\\\"\\\"Set the fields returned the query.\\n\\n :type projection: string or sequence of strings\\n :param projection: Each value is a string giving the name of a\\n property to be included in the projection query.\\n \\\"\\\"\\\"\\n if isinstance(projection, str):\\n projection = [projection]\\n self._projection[:] = projection\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.keys_only\",\n \"text\": \"google/cloud/datastore/query.py::Query.keys_only\\n def keys_only(self):\\n \\\"\\\"\\\"Set the projection to include only keys.\\\"\\\"\\\"\\n self._projection[:] = ['__key__']\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.key_filter\",\n \"text\": \"google/cloud/datastore/query.py::Query.key_filter\\n def key_filter(self, key, operator='='):\\n \\\"\\\"\\\"Filter on a key.\\n\\n :type key: :class:`google.cloud.datastore.key.Key`\\n :param key: The key to filter on.\\n\\n :type operator: string\\n :param operator: (Optional) One of ``=``, ``<``, ``<=``, ``>``, ``>=``.\\n Defaults to ``=``.\\n \\\"\\\"\\\"\\n self.add_filter('__key__', operator, key)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.order\",\n \"text\": \"google/cloud/datastore/query.py::Query.order\\n @property\\n def order(self):\\n \\\"\\\"\\\"Names of fields used to sort query results.\\n\\n :rtype: sequence of string\\n :returns: The order(s) set on the query.\\n \\\"\\\"\\\"\\n return self._order[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.order\",\n \"text\": \"google/cloud/datastore/query.py::Query.order\\n @order.setter\\n def order(self, value):\\n \\\"\\\"\\\"Set the fields used to sort query results.\\n\\n Sort fields will be applied in the order specified.\\n\\n :type value: string or sequence of strings\\n :param value: Each value is a string giving the name of the\\n property on which to sort, optionally preceded by a\\n hyphen (-) to specify descending order.\\n Omitting the hyphen implies ascending order.\\n \\\"\\\"\\\"\\n if isinstance(value, str):\\n value = [value]\\n self._order[:] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.distinct_on\",\n \"text\": \"google/cloud/datastore/query.py::Query.distinct_on\\n @property\\n def distinct_on(self):\\n \\\"\\\"\\\"Names of fields used to group query results.\\n\\n :rtype: sequence of string\\n :returns: The \\\"distinct on\\\" fields set on the query.\\n \\\"\\\"\\\"\\n return self._distinct_on[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.distinct_on\",\n \"text\": \"google/cloud/datastore/query.py::Query.distinct_on\\n @distinct_on.setter\\n def distinct_on(self, value):\\n \\\"\\\"\\\"Set fields used to group query results.\\n\\n :type value: string or sequence of strings\\n :param value: Each value is a string giving the name of a\\n property to use to group results together.\\n \\\"\\\"\\\"\\n if isinstance(value, str):\\n value = [value]\\n self._distinct_on[:] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Query.fetch\",\n \"text\": \"google/cloud/datastore/query.py::Query.fetch\\n def fetch(self, limit=None, offset=0, start_cursor=None, end_cursor=None,\\n client=None):\\n \\\"\\\"\\\"Execute the Query; return an iterator for the matching entities.\\n\\n For example::\\n\\n >>> from google.cloud import datastore\\n >>> client = datastore.Client()\\n >>> query = client.query(kind='Person')\\n >>> query.add_filter('name', '=', 'Sally')\\n >>> list(query.fetch())\\n [<Entity object>, <Entity object>, ...]\\n >>> list(query.fetch(1))\\n [<Entity object>]\\n\\n :type limit: integer or None\\n :param limit: An optional limit passed through to the iterator.\\n\\n :type offset: integer\\n :param offset: An optional offset passed through to the iterator.\\n\\n :type start_cursor: bytes\\n :param start_cursor: An optional cursor passed through to the iterator.\\n\\n :type end_cursor: bytes\\n :param end_cursor: An optional cursor passed through to the iterator.\\n\\n :type client: :class:`google.cloud.datastore.client.Client`\\n :param client: client used to connect to datastore.\\n If not supplied, uses the query's value.\\n\\n :rtype: :class:`Iterator`\\n :returns: The iterator for the query.\\n :raises: ValueError if ``connection`` is not passed and no implicit\\n default has been set.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n\\n return Iterator(\\n self, client, limit, offset, start_cursor, end_cursor)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Iterator.__init__\",\n \"text\": \"google/cloud/datastore/query.py::Iterator.__init__\\n def __init__(self, query, client, limit=None, offset=None,\\n start_cursor=None, end_cursor=None):\\n self._query = query\\n self._client = client\\n self._limit = limit\\n self._offset = offset\\n self._start_cursor = start_cursor\\n self._end_cursor = end_cursor\\n self._page = self._more_results = None\\n self._skipped_results = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Iterator.next_page\",\n \"text\": \"google/cloud/datastore/query.py::Iterator.next_page\\n def next_page(self):\\n \\\"\\\"\\\"Fetch a single \\\"page\\\" of query results.\\n\\n Low-level API for fine control: the more convenient API is\\n to iterate on the current Iterator.\\n\\n :rtype: tuple, (entities, more_results, cursor)\\n :returns: The next page of results.\\n \\\"\\\"\\\"\\n pb = _pb_from_query(self._query)\\n\\n start_cursor = self._start_cursor\\n if start_cursor is not None:\\n pb.start_cursor = base64.urlsafe_b64decode(start_cursor)\\n\\n end_cursor = self._end_cursor\\n if end_cursor is not None:\\n pb.end_cursor = base64.urlsafe_b64decode(end_cursor)\\n\\n if self._limit is not None:\\n pb.limit.value = self._limit\\n\\n if self._offset is not None:\\n pb.offset = self._offset\\n\\n transaction = self._client.current_transaction\\n\\n query_results = self._client.connection.run_query(\\n query_pb=pb,\\n project=self._query.project,\\n namespace=self._query.namespace,\\n transaction_id=transaction and transaction.id,\\n )\\n (entity_pbs, cursor_as_bytes,\\n more_results_enum, self._skipped_results) = query_results\\n\\n if cursor_as_bytes == b'':\\n self._start_cursor = None\\n else:\\n self._start_cursor = base64.urlsafe_b64encode(cursor_as_bytes)\\n self._end_cursor = None\\n\\n if more_results_enum == self._NOT_FINISHED:\\n self._more_results = True\\n elif more_results_enum in self._FINISHED:\\n self._more_results = False\\n else:\\n raise ValueError('Unexpected value returned for `more_results`.')\\n\\n self._page = [\\n helpers.entity_from_protobuf(entity)\\n for entity in entity_pbs]\\n return self._page, self._more_results, self._start_cursor\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::Iterator.__iter__\",\n \"text\": \"google/cloud/datastore/query.py::Iterator.__iter__\\n def __iter__(self):\\n \\\"\\\"\\\"Generator yielding all results matching our query.\\n\\n :rtype: sequence of :class:`google.cloud.datastore.entity.Entity`\\n \\\"\\\"\\\"\\n while True:\\n self.next_page()\\n for entity in self._page:\\n yield entity\\n if not self._more_results:\\n break\\n num_results = len(self._page)\\n if self._limit is not None:\\n self._limit -= num_results\\n if self._offset is not None and self._skipped_results is not None:\\n # NOTE: The offset goes down relative to the location\\n # because we are updating the cursor each time.\\n self._offset -= self._skipped_results\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/query.py::_pb_from_query\",\n \"text\": \"google/cloud/datastore/query.py::_pb_from_query\\ndef _pb_from_query(query):\\n \\\"\\\"\\\"Convert a Query instance to the corresponding protobuf.\\n\\n :type query: :class:`Query`\\n :param query: The source query.\\n\\n :rtype: :class:`google.cloud.datastore._generated.query_pb2.Query`\\n :returns: A protobuf that can be sent to the protobuf API. N.b. that\\n it does not contain \\\"in-flight\\\" fields for ongoing query\\n executions (cursors, offset, limit).\\n \\\"\\\"\\\"\\n pb = _query_pb2.Query()\\n\\n for projection_name in query.projection:\\n pb.projection.add().property.name = projection_name\\n\\n if query.kind:\\n pb.kind.add().name = query.kind\\n\\n composite_filter = pb.filter.composite_filter\\n composite_filter.op = _query_pb2.CompositeFilter.AND\\n\\n if query.ancestor:\\n ancestor_pb = query.ancestor.to_protobuf()\\n\\n # Filter on __key__ HAS_ANCESTOR == ancestor.\\n ancestor_filter = composite_filter.filters.add().property_filter\\n ancestor_filter.property.name = '__key__'\\n ancestor_filter.op = _query_pb2.PropertyFilter.HAS_ANCESTOR\\n ancestor_filter.value.key_value.CopyFrom(ancestor_pb)\\n\\n for property_name, operator, value in query.filters:\\n pb_op_enum = query.OPERATORS.get(operator)\\n\\n # Add the specific filter\\n property_filter = composite_filter.filters.add().property_filter\\n property_filter.property.name = property_name\\n property_filter.op = pb_op_enum\\n\\n # Set the value to filter on based on the type.\\n if property_name == '__key__':\\n key_pb = value.to_protobuf()\\n property_filter.value.key_value.CopyFrom(key_pb)\\n else:\\n helpers._set_protobuf_value(property_filter.value, value)\\n\\n if not composite_filter.filters:\\n pb.ClearField('filter')\\n\\n for prop in query.order:\\n property_order = pb.order.add()\\n\\n if prop.startswith('-'):\\n property_order.property.name = prop[1:]\\n p\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/transaction.py::Transaction.__init__\",\n \"text\": \"google/cloud/datastore/transaction.py::Transaction.__init__\\n def __init__(self, client):\\n super(Transaction, self).__init__(client)\\n self._id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/transaction.py::Transaction.id\",\n \"text\": \"google/cloud/datastore/transaction.py::Transaction.id\\n @property\\n def id(self):\\n \\\"\\\"\\\"Getter for the transaction ID.\\n\\n :rtype: string\\n :returns: The ID of the current transaction.\\n \\\"\\\"\\\"\\n return self._id\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/transaction.py::Transaction.current\",\n \"text\": \"google/cloud/datastore/transaction.py::Transaction.current\\n def current(self):\\n \\\"\\\"\\\"Return the topmost transaction.\\n\\n .. note::\\n\\n If the topmost element on the stack is not a transaction,\\n returns None.\\n\\n :rtype: :class:`google.cloud.datastore.transaction.Transaction` or None\\n :returns: The current transaction (if any are active).\\n \\\"\\\"\\\"\\n top = super(Transaction, self).current()\\n if isinstance(top, Transaction):\\n return top\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/transaction.py::Transaction.begin\",\n \"text\": \"google/cloud/datastore/transaction.py::Transaction.begin\\n def begin(self):\\n \\\"\\\"\\\"Begins a transaction.\\n\\n This method is called automatically when entering a with\\n statement, however it can be called explicitly if you don't want\\n to use a context manager.\\n\\n :raises: :class:`~exceptions.ValueError` if the transaction has\\n already begun.\\n \\\"\\\"\\\"\\n super(Transaction, self).begin()\\n try:\\n self._id = self.connection.begin_transaction(self.project)\\n except:\\n self._status = self._ABORTED\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/transaction.py::Transaction.rollback\",\n \"text\": \"google/cloud/datastore/transaction.py::Transaction.rollback\\n def rollback(self):\\n \\\"\\\"\\\"Rolls back the current transaction.\\n\\n This method has necessary side-effects:\\n\\n - Sets the current connection's transaction reference to None.\\n - Sets the current transaction's ID to None.\\n \\\"\\\"\\\"\\n try:\\n self.connection.rollback(self.project, self._id)\\n finally:\\n super(Transaction, self).rollback()\\n # Clear our own ID in case this gets accidentally reused.\\n self._id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/datastore/transaction.py::Transaction.commit\",\n \"text\": \"google/cloud/datastore/transaction.py::Transaction.commit\\n def commit(self):\\n \\\"\\\"\\\"Commits the transaction.\\n\\n This is called automatically upon exiting a with statement,\\n however it can be called explicitly if you don't want to use a\\n context manager.\\n\\n This method has necessary side-effects:\\n\\n - Sets the current transaction's ID to None.\\n \\\"\\\"\\\"\\n try:\\n super(Transaction, self).commit()\\n finally:\\n # Clear our own ID in case this gets accidentally reused.\\n self._id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.__init__\",\n \"text\": \"google/cloud/dns/changes.py::Changes.__init__\\n def __init__(self, zone):\\n self.zone = zone\\n self._properties = {}\\n self._additions = self._deletions = ()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.from_api_repr\",\n \"text\": \"google/cloud/dns/changes.py::Changes.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, zone):\\n \\\"\\\"\\\"Factory: construct a change set given its API representation\\n\\n :type resource: dict\\n :param resource: change set representation returned from the API.\\n\\n :type zone: :class:`google.cloud.dns.zone.ManagedZone`\\n :param zone: A zone which holds zero or more change sets.\\n\\n :rtype: :class:`google.cloud.dns.changes.Changes`\\n :returns: RRS parsed from ``resource``.\\n \\\"\\\"\\\"\\n changes = cls(zone=zone)\\n changes._set_properties(resource)\\n return changes\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes._set_properties\",\n \"text\": \"google/cloud/dns/changes.py::Changes._set_properties\\n def _set_properties(self, resource):\\n \\\"\\\"\\\"Helper method for :meth:`from_api_repr`, :meth:`create`, etc.\\n\\n :type resource: dict\\n :param resource: change set representation returned from the API.\\n \\\"\\\"\\\"\\n resource = resource.copy()\\n self._additions = tuple([\\n ResourceRecordSet.from_api_repr(added_res, self.zone)\\n for added_res in resource.pop('additions', ())])\\n self._deletions = tuple([\\n ResourceRecordSet.from_api_repr(added_res, self.zone)\\n for added_res in resource.pop('deletions', ())])\\n self._properties = resource\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.path\",\n \"text\": \"google/cloud/dns/changes.py::Changes.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URL path for change set APIs.\\n\\n :rtype: string\\n :returns: the path based on project, zone, and change set names.\\n \\\"\\\"\\\"\\n return '/projects/%s/managedZones/%s/changes/%s' % (\\n self.zone.project, self.zone.name, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.name\",\n \"text\": \"google/cloud/dns/changes.py::Changes.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Name of the change set.\\n\\n :rtype: string or ``NoneType``\\n :returns: Name, as set by the back-end, or None.\\n \\\"\\\"\\\"\\n return self._properties.get('id')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.name\",\n \"text\": \"google/cloud/dns/changes.py::Changes.name\\n @name.setter\\n def name(self, value):\\n \\\"\\\"\\\"Update name of the change set.\\n\\n :type value: string\\n :param value: New name for the changeset.\\n \\\"\\\"\\\"\\n if not isinstance(value, six.string_types):\\n raise ValueError(\\\"Pass a string\\\")\\n self._properties['id'] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.status\",\n \"text\": \"google/cloud/dns/changes.py::Changes.status\\n @property\\n def status(self):\\n \\\"\\\"\\\"Status of the change set.\\n\\n :rtype: string or ``NoneType``\\n :returns: Status, as set by the back-end, or None.\\n \\\"\\\"\\\"\\n return self._properties.get('status')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.started\",\n \"text\": \"google/cloud/dns/changes.py::Changes.started\\n @property\\n def started(self):\\n \\\"\\\"\\\"Time when the change set was started.\\n\\n :rtype: ``datetime.datetime`` or ``NoneType``\\n :returns: Time, as set by the back-end, or None.\\n \\\"\\\"\\\"\\n stamp = self._properties.get('startTime')\\n if stamp is not None:\\n return _rfc3339_to_datetime(stamp)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.additions\",\n \"text\": \"google/cloud/dns/changes.py::Changes.additions\\n @property\\n def additions(self):\\n \\\"\\\"\\\"Resource record sets to be added to the zone.\\n\\n :rtype: sequence of\\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`.\\n :returns: record sets appended via :meth:`add_record_set`.\\n \\\"\\\"\\\"\\n return self._additions\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.deletions\",\n \"text\": \"google/cloud/dns/changes.py::Changes.deletions\\n @property\\n def deletions(self):\\n \\\"\\\"\\\"Resource record sets to be deleted from the zone.\\n\\n :rtype: sequence of\\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`.\\n :returns: record sets appended via :meth:`delete_record_set`.\\n \\\"\\\"\\\"\\n return self._deletions\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.add_record_set\",\n \"text\": \"google/cloud/dns/changes.py::Changes.add_record_set\\n def add_record_set(self, record_set):\\n \\\"\\\"\\\"Append a record set to the 'additions' for the change set.\\n\\n :type record_set:\\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`\\n :param record_set: the record set to append.\\n\\n :raises: ``ValueError`` if ``record_set`` is not of the required type.\\n \\\"\\\"\\\"\\n if not isinstance(record_set, ResourceRecordSet):\\n raise ValueError(\\\"Pass a ResourceRecordSet\\\")\\n self._additions += (record_set,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.delete_record_set\",\n \"text\": \"google/cloud/dns/changes.py::Changes.delete_record_set\\n def delete_record_set(self, record_set):\\n \\\"\\\"\\\"Append a record set to the 'deletions' for the change set.\\n\\n :type record_set:\\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`\\n :param record_set: the record set to append.\\n\\n :raises: ``ValueError`` if ``record_set`` is not of the required type.\\n \\\"\\\"\\\"\\n if not isinstance(record_set, ResourceRecordSet):\\n raise ValueError(\\\"Pass a ResourceRecordSet\\\")\\n self._deletions += (record_set,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes._require_client\",\n \"text\": \"google/cloud/dns/changes.py::Changes._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current zone.\\n\\n :rtype: :class:`google.cloud.dns.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self.zone._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes._build_resource\",\n \"text\": \"google/cloud/dns/changes.py::Changes._build_resource\\n def _build_resource(self):\\n \\\"\\\"\\\"Generate a resource for ``create``.\\\"\\\"\\\"\\n additions = [{\\n 'name': added.name,\\n 'type': added.record_type,\\n 'ttl': str(added.ttl),\\n 'rrdatas': added.rrdatas,\\n } for added in self.additions]\\n\\n deletions = [{\\n 'name': deleted.name,\\n 'type': deleted.record_type,\\n 'ttl': str(deleted.ttl),\\n 'rrdatas': deleted.rrdatas,\\n } for deleted in self.deletions]\\n\\n return {\\n 'additions': additions,\\n 'deletions': deletions,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.create\",\n \"text\": \"google/cloud/dns/changes.py::Changes.create\\n def create(self, client=None):\\n \\\"\\\"\\\"API call: create the change set via a POST request.\\n\\n See:\\n https://cloud.google.com/dns/api/v1/changes/create\\n\\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current zone.\\n \\\"\\\"\\\"\\n if len(self.additions) == 0 and len(self.deletions) == 0:\\n raise ValueError(\\\"No record sets added or deleted\\\")\\n client = self._require_client(client)\\n path = '/projects/%s/managedZones/%s/changes' % (\\n self.zone.project, self.zone.name)\\n api_response = client.connection.api_request(\\n method='POST', path=path, data=self._build_resource())\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.exists\",\n \"text\": \"google/cloud/dns/changes.py::Changes.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"API call: test for the existence of the change set via a GET request.\\n\\n See\\n https://cloud.google.com/dns/api/v1/changes/get\\n\\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current zone.\\n\\n :rtype: bool\\n :returns: Boolean indicating existence of the changes.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n try:\\n client.connection.api_request(method='GET', path=self.path,\\n query_params={'fields': 'id'})\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/changes.py::Changes.reload\",\n \"text\": \"google/cloud/dns/changes.py::Changes.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"API call: refresh zone properties via a GET request.\\n\\n See\\n https://cloud.google.com/dns/api/v1/changes/get\\n\\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current zone.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n api_response = client.connection.api_request(\\n method='GET', path=self.path)\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/client.py::Client.quotas\",\n \"text\": \"google/cloud/dns/client.py::Client.quotas\\n def quotas(self):\\n \\\"\\\"\\\"Return DNS quotas for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/dns/api/v1/projects/get\\n\\n :rtype: mapping\\n :returns: keys for the mapping correspond to those of the ``quota``\\n sub-mapping of the project resource.\\n \\\"\\\"\\\"\\n path = '/projects/%s' % (self.project,)\\n resp = self.connection.api_request(method='GET', path=path)\\n\\n return {key: int(value)\\n for key, value in resp['quota'].items()\\n if key != 'kind'}\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/client.py::Client.list_zones\",\n \"text\": \"google/cloud/dns/client.py::Client.list_zones\\n def list_zones(self, max_results=None, page_token=None):\\n \\\"\\\"\\\"List zones for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/dns/api/v1/managedZones/list\\n\\n :type max_results: int\\n :param max_results: maximum number of zones to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of zones. If\\n not passed, the API will return the first page of\\n zones.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`google.cloud.dns.zone.ManagedZone`, plus a\\n \\\"next page token\\\" string: if the token is not None,\\n indicates that more zones can be retrieved with another\\n call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if max_results is not None:\\n params['maxResults'] = max_results\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/managedZones' % (self.project,)\\n resp = self.connection.api_request(method='GET', path=path,\\n query_params=params)\\n zones = [ManagedZone.from_api_repr(resource, self)\\n for resource in resp['managedZones']]\\n return zones, resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/client.py::Client.zone\",\n \"text\": \"google/cloud/dns/client.py::Client.zone\\n def zone(self, name, dns_name=None, description=None):\\n \\\"\\\"\\\"Construct a zone bound to this client.\\n\\n :type name: string\\n :param name: Name of the zone.\\n\\n :type dns_name: string or :class:`NoneType`\\n :param dns_name: DNS name of the zone. If not passed, then calls\\n to :meth:`zone.create` will fail.\\n\\n :type description: string or :class:`NoneType`\\n :param description: the description for the zone. If not passed,\\n defaults to the value of 'dns_name'.\\n\\n :rtype: :class:`google.cloud.dns.zone.ManagedZone`\\n :returns: a new ``ManagedZone`` instance.\\n \\\"\\\"\\\"\\n return ManagedZone(name, dns_name, client=self,\\n description=description)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/resource_record_set.py::ResourceRecordSet.__init__\",\n \"text\": \"google/cloud/dns/resource_record_set.py::ResourceRecordSet.__init__\\n def __init__(self, name, record_type, ttl, rrdatas, zone):\\n self.name = name\\n self.record_type = record_type\\n self.ttl = ttl\\n self.rrdatas = rrdatas\\n self.zone = zone\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/resource_record_set.py::ResourceRecordSet.from_api_repr\",\n \"text\": \"google/cloud/dns/resource_record_set.py::ResourceRecordSet.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, zone):\\n \\\"\\\"\\\"Factory: construct a record set given its API representation\\n\\n :type resource: dict\\n :param resource: record sets representation returned from the API\\n\\n :type zone: :class:`google.cloud.dns.zone.ManagedZone`\\n :param zone: A zone which holds one or more record sets.\\n\\n :rtype: :class:`google.cloud.dns.zone.ResourceRecordSet`\\n :returns: RRS parsed from ``resource``.\\n \\\"\\\"\\\"\\n name = resource['name']\\n record_type = resource['type']\\n ttl = int(resource['ttl'])\\n rrdatas = resource['rrdatas']\\n return cls(name, record_type, ttl, rrdatas, zone=zone)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.__init__\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.__init__\\n def __init__(self, name, dns_name=None, client=None, description=None):\\n self.name = name\\n self.dns_name = dns_name\\n self._client = client\\n self._properties = {}\\n if description is None:\\n description = dns_name\\n self.description = description\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.from_api_repr\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client):\\n \\\"\\\"\\\"Factory: construct a zone given its API representation\\n\\n :type resource: dict\\n :param resource: zone resource representation returned from the API\\n\\n :type client: :class:`google.cloud.dns.client.Client`\\n :param client: Client which holds credentials and project\\n configuration for the zone.\\n\\n :rtype: :class:`google.cloud.dns.zone.ManagedZone`\\n :returns: Zone parsed from ``resource``.\\n \\\"\\\"\\\"\\n name = resource.get('name')\\n dns_name = resource.get('dnsName')\\n if name is None or dns_name is None:\\n raise KeyError('Resource lacks required identity information:'\\n '[\\\"name\\\"][\\\"dnsName\\\"]')\\n zone = cls(name, dns_name, client=client)\\n zone._set_properties(resource)\\n return zone\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.project\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project bound to the zone.\\n\\n :rtype: string\\n :returns: the project (derived from the client).\\n \\\"\\\"\\\"\\n return self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.path\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URL path for the zone's APIs.\\n\\n :rtype: string\\n :returns: the path based on project and dataste name.\\n \\\"\\\"\\\"\\n return '/projects/%s/managedZones/%s' % (self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.created\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.created\\n @property\\n def created(self):\\n \\\"\\\"\\\"Datetime at which the zone was created.\\n\\n :rtype: ``datetime.datetime``, or ``NoneType``\\n :returns: the creation time (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('creationTime')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.name_servers\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.name_servers\\n @property\\n def name_servers(self):\\n \\\"\\\"\\\"Datetime at which the zone was created.\\n\\n :rtype: list of strings, or ``NoneType``.\\n :returns: the assigned name servers (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('nameServers')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.zone_id\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.zone_id\\n @property\\n def zone_id(self):\\n \\\"\\\"\\\"ID for the zone resource.\\n\\n :rtype: string, or ``NoneType``\\n :returns: the ID (None until set from the server).\\n \\\"\\\"\\\"\\n return self._properties.get('id')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.description\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.description\\n @property\\n def description(self):\\n \\\"\\\"\\\"Description of the zone.\\n\\n :rtype: string, or ``NoneType``\\n :returns: The description as set by the user, or None (the default).\\n \\\"\\\"\\\"\\n return self._properties.get('description')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.description\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.description\\n @description.setter\\n def description(self, value):\\n \\\"\\\"\\\"Update description of the zone.\\n\\n :type value: string, or ``NoneType``\\n :param value: new description\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n if not isinstance(value, six.string_types) and value is not None:\\n raise ValueError(\\\"Pass a string, or None\\\")\\n self._properties['description'] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.name_server_set\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.name_server_set\\n @property\\n def name_server_set(self):\\n \\\"\\\"\\\"Named set of DNS name servers that all host the same ManagedZones.\\n\\n Most users will leave this blank.\\n\\n See:\\n https://cloud.google.com/dns/api/v1/managedZones#nameServerSet\\n\\n :rtype: string, or ``NoneType``\\n :returns: The name as set by the user, or None (the default).\\n \\\"\\\"\\\"\\n return self._properties.get('nameServerSet')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.name_server_set\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.name_server_set\\n @name_server_set.setter\\n def name_server_set(self, value):\\n \\\"\\\"\\\"Update named set of DNS name servers.\\n\\n :type value: string, or ``NoneType``\\n :param value: new title\\n\\n :raises: ValueError for invalid value types.\\n \\\"\\\"\\\"\\n if not isinstance(value, six.string_types) and value is not None:\\n raise ValueError(\\\"Pass a string, or None\\\")\\n self._properties['nameServerSet'] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.resource_record_set\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.resource_record_set\\n def resource_record_set(self, name, record_type, ttl, rrdatas):\\n \\\"\\\"\\\"Construct a resource record set bound to this zone.\\n\\n :type name: string\\n :param name: Name of the record set.\\n\\n :type record_type: string\\n :param record_type: RR type\\n\\n :type ttl: integer\\n :param ttl: TTL for the RR, in seconds\\n\\n :type rrdatas: list of string\\n :param rrdatas: resource data for the RR\\n\\n :rtype: :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`\\n :returns: a new ``ResourceRecordSet`` instance\\n \\\"\\\"\\\"\\n return ResourceRecordSet(name, record_type, ttl, rrdatas, zone=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.changes\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.changes\\n def changes(self):\\n \\\"\\\"\\\"Construct a change set bound to this zone.\\n\\n :rtype: :class:`google.cloud.dns.changes.Changes`\\n :returns: a new ``Changes`` instance\\n \\\"\\\"\\\"\\n return Changes(zone=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone._require_client\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current zone.\\n\\n :rtype: :class:`google.cloud.dns.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone._set_properties\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone._set_properties\\n def _set_properties(self, api_response):\\n \\\"\\\"\\\"Update properties from resource in body of ``api_response``\\n\\n :type api_response: httplib2.Response\\n :param api_response: response returned from an API call\\n \\\"\\\"\\\"\\n self._properties.clear()\\n cleaned = api_response.copy()\\n self.dns_name = cleaned.pop('dnsName', None)\\n if 'creationTime' in cleaned:\\n cleaned['creationTime'] = _rfc3339_to_datetime(\\n cleaned['creationTime'])\\n self._properties.update(cleaned)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone._build_resource\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone._build_resource\\n def _build_resource(self):\\n \\\"\\\"\\\"Generate a resource for ``create`` or ``update``.\\\"\\\"\\\"\\n resource = {\\n 'name': self.name,\\n }\\n\\n if self.dns_name is not None:\\n resource['dnsName'] = self.dns_name\\n\\n if self.description is not None:\\n resource['description'] = self.description\\n\\n if self.name_server_set is not None:\\n resource['nameServerSet'] = self.name_server_set\\n\\n return resource\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.create\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.create\\n def create(self, client=None):\\n \\\"\\\"\\\"API call: create the zone via a PUT request\\n\\n See:\\n https://cloud.google.com/dns/api/v1/managedZones/create\\n\\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current zone.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n path = '/projects/%s/managedZones' % (self.project,)\\n api_response = client.connection.api_request(\\n method='POST', path=path, data=self._build_resource())\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.exists\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"API call: test for the existence of the zone via a GET request\\n\\n See\\n https://cloud.google.com/dns/api/v1/managedZones/get\\n\\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current zone.\\n\\n :rtype: bool\\n :returns: Boolean indicating existence of the managed zone.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n try:\\n client.connection.api_request(method='GET', path=self.path,\\n query_params={'fields': 'id'})\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.reload\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"API call: refresh zone properties via a GET request\\n\\n See\\n https://cloud.google.com/dns/api/v1/managedZones/get\\n\\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current zone.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n api_response = client.connection.api_request(\\n method='GET', path=self.path)\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.delete\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.delete\\n def delete(self, client=None):\\n \\\"\\\"\\\"API call: delete the zone via a DELETE request\\n\\n See:\\n https://cloud.google.com/dns/api/v1/managedZones/delete\\n\\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current zone.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.connection.api_request(method='DELETE', path=self.path)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.list_resource_record_sets\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.list_resource_record_sets\\n def list_resource_record_sets(self, max_results=None, page_token=None,\\n client=None):\\n \\\"\\\"\\\"List resource record sets for this zone.\\n\\n See:\\n https://cloud.google.com/dns/api/v1/resourceRecordSets/list\\n\\n :type max_results: int\\n :param max_results: maximum number of zones to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of zones. If\\n not passed, the API will return the first page of\\n zones.\\n\\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current zone.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of\\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`,\\n plus a \\\"next page token\\\" string: if the token is not None,\\n indicates that more zones can be retrieved with another\\n call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if max_results is not None:\\n params['maxResults'] = max_results\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/managedZones/%s/rrsets' % (\\n self.project, self.name)\\n client = self._require_client(client)\\n conn = client.connection\\n resp = conn.api_request(method='GET', path=path, query_params=params)\\n zones = [ResourceRecordSet.from_api_repr(resource, self)\\n for resource in resp['rrsets']]\\n return zones, resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/dns/zone.py::ManagedZone.list_changes\",\n \"text\": \"google/cloud/dns/zone.py::ManagedZone.list_changes\\n def list_changes(self, max_results=None, page_token=None, client=None):\\n \\\"\\\"\\\"List change sets for this zone.\\n\\n See:\\n https://cloud.google.com/dns/api/v1/resourceRecordSets/list\\n\\n :type max_results: int\\n :param max_results: maximum number of zones to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of zones. If\\n not passed, the API will return the first page of\\n zones.\\n\\n :type client: :class:`google.cloud.dns.client.Client` or ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current zone.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of\\n :class:`google.cloud.dns.resource_record_set.ResourceRecordSet`,\\n plus a \\\"next page token\\\" string: if the token is not None,\\n indicates that more zones can be retrieved with another\\n call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if max_results is not None:\\n params['maxResults'] = max_results\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/managedZones/%s/changes' % (\\n self.project, self.name)\\n client = self._require_client(client)\\n conn = client.connection\\n resp = conn.api_request(method='GET', path=path, query_params=params)\\n zones = [Changes.from_api_repr(resource, self)\\n for resource in resp['changes']]\\n return zones, resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/error_reporting/client.py::HTTPContext.__init__\",\n \"text\": \"google/cloud/error_reporting/client.py::HTTPContext.__init__\\n def __init__(self, method=None, url=None,\\n user_agent=None, referrer=None,\\n response_status_code=None, remote_ip=None):\\n self.method = method\\n self.url = url\\n # intentionally camel case for mapping to JSON API expects\\n # pylint: disable=invalid-name\\n self.userAgent = user_agent\\n self.referrer = referrer\\n self.responseStatusCode = response_status_code\\n self.remoteIp = remote_ip\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/error_reporting/client.py::Client.__init__\",\n \"text\": \"google/cloud/error_reporting/client.py::Client.__init__\\n def __init__(self, project=None,\\n credentials=None,\\n http=None,\\n service=None,\\n version=None):\\n self.logging_client = google.cloud.logging.client.Client(\\n project, credentials, http)\\n self.service = service if service else self.DEFAULT_SERVICE\\n self.version = version\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/error_reporting/client.py::Client._send_error_report\",\n \"text\": \"google/cloud/error_reporting/client.py::Client._send_error_report\\n def _send_error_report(self, message,\\n report_location=None, http_context=None, user=None):\\n \\\"\\\"\\\"Makes the call to the Error Reporting API via the log stream.\\n\\n This is the lower-level interface to build the payload, generally\\n users will use either report() or report_exception() to automatically\\n gather the parameters for this method.\\n\\n Currently this method sends the Error Report by formatting a structured\\n log message according to\\n\\n https://cloud.google.com/error-reporting/docs/formatting-error-messages\\n\\n :type message: string\\n :param message: The stack trace that was reported or logged by the\\n service.\\n\\n :type report_location: dict\\n :param report_location: The location in the source code where the\\n decision was made to report the error, usually the place\\n where it was logged. For a logged exception this would be the\\n source line where the exception is logged, usually close to\\n the place where it was caught.\\n\\n This should be a Python dict that contains the keys 'filePath',\\n 'lineNumber', and 'functionName'\\n\\n :type http_context: :class`google.cloud.error_reporting.HTTPContext`\\n :param http_context: The HTTP request which was processed when the\\n error was triggered.\\n\\n :type user: string\\n :param user: The user who caused or was affected by the crash. This can\\n be a user ID, an email address, or an arbitrary token that\\n uniquely identifies the user. When sending an error\\n report, leave this field empty if the user was not\\n logged in. In this case the Error Reporting system will\\n use other data, such as remote IP address,\\n t\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/error_reporting/client.py::Client.report\",\n \"text\": \"google/cloud/error_reporting/client.py::Client.report\\n def report(self, message, http_context=None, user=None):\\n \\\"\\\"\\\" Reports a message to Stackdriver Error Reporting\\n https://cloud.google.com/error-reporting/docs/formatting-error-messages\\n\\n :type message: str\\n :param message: A user-supplied message to report\\n\\n\\n :type http_context: :class`google.cloud.error_reporting.HTTPContext`\\n :param http_context: The HTTP request which was processed when the\\n error was triggered.\\n\\n :type user: string\\n :param user: The user who caused or was affected by the crash. This\\n can be a user ID, an email address, or an arbitrary\\n token that uniquely identifies the user. When sending\\n an error report, leave this field empty if the user\\n was not logged in. In this case the Error Reporting\\n system will use other data, such as remote IP address,\\n to distinguish affected users.\\n\\n Example::\\n >>> client.report(\\\"Something went wrong!\\\")\\n \\\"\\\"\\\"\\n stack = traceback.extract_stack()\\n last_call = stack[-2]\\n file_path = last_call[0]\\n line_number = last_call[1]\\n function_name = last_call[2]\\n report_location = {\\n 'filePath': file_path,\\n 'lineNumber': line_number,\\n 'functionName': function_name\\n }\\n\\n self._send_error_report(message,\\n http_context=http_context,\\n user=user,\\n report_location=report_location)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/error_reporting/client.py::Client.report_exception\",\n \"text\": \"google/cloud/error_reporting/client.py::Client.report_exception\\n def report_exception(self, http_context=None, user=None):\\n \\\"\\\"\\\" Reports the details of the latest exceptions to Stackdriver Error\\n Reporting.\\n\\n :type http_context: :class`google.cloud.error_reporting.HTTPContext`\\n :param http_context: The HTTP request which was processed when the\\n error was triggered.\\n\\n :type user: string\\n :param user: The user who caused or was affected by the crash. This\\n can be a user ID, an email address, or an arbitrary\\n token that uniquely identifies the user. When sending an\\n error report, leave this field empty if the user was\\n not logged in. In this case the Error Reporting system\\n will use other data, such as remote IP address,\\n to distinguish affected users.\\n\\n Example::\\n\\n >>> try:\\n >>> raise NameError\\n >>> except Exception:\\n >>> client.report_exception()\\n \\\"\\\"\\\"\\n self._send_error_report(traceback.format_exc(),\\n http_context=http_context,\\n user=user)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/exceptions.py::GoogleCloudError.__init__\",\n \"text\": \"google/cloud/exceptions.py::GoogleCloudError.__init__\\n def __init__(self, message, errors=()):\\n super(GoogleCloudError, self).__init__(message)\\n self.message = message\\n self._errors = errors\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/exceptions.py::GoogleCloudError.__str__\",\n \"text\": \"google/cloud/exceptions.py::GoogleCloudError.__str__\\n def __str__(self):\\n result = u'%d %s' % (self.code, self.message)\\n if six.PY2:\\n result = _to_bytes(result, 'utf-8')\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/exceptions.py::GoogleCloudError.errors\",\n \"text\": \"google/cloud/exceptions.py::GoogleCloudError.errors\\n @property\\n def errors(self):\\n \\\"\\\"\\\"Detailed error information.\\n\\n :rtype: list(dict)\\n :returns: a list of mappings describing each error.\\n \\\"\\\"\\\"\\n return [copy.deepcopy(error) for error in self._errors]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/exceptions.py::make_exception\",\n \"text\": \"google/cloud/exceptions.py::make_exception\\ndef make_exception(response, content, error_info=None, use_json=True):\\n \\\"\\\"\\\"Factory: create exception based on HTTP response code.\\n\\n :type response: :class:`httplib2.Response` or other HTTP response object\\n :param response: A response object that defines a status code as the\\n status attribute.\\n\\n :type content: string or dictionary\\n :param content: The body of the HTTP error response.\\n\\n :type error_info: string\\n :param error_info: Optional string giving extra information about the\\n failed request.\\n\\n :type use_json: bool\\n :param use_json: Flag indicating if ``content`` is expected to be JSON.\\n\\n :rtype: instance of :class:`GoogleCloudError`, or a concrete subclass.\\n :returns: Exception specific to the error response.\\n \\\"\\\"\\\"\\n if isinstance(content, six.binary_type):\\n content = content.decode('utf-8')\\n\\n if isinstance(content, six.string_types):\\n payload = None\\n if use_json:\\n try:\\n payload = json.loads(content)\\n except ValueError:\\n # Expected JSON but received something else.\\n pass\\n if payload is None:\\n payload = {'error': {'message': content}}\\n else:\\n payload = content\\n\\n message = payload.get('error', {}).get('message', '')\\n errors = payload.get('error', {}).get('errors', ())\\n\\n if error_info is not None:\\n message += ' (%s)' % (error_info,)\\n\\n try:\\n klass = _HTTP_CODE_TO_EXCEPTION[response.status]\\n except KeyError:\\n error = GoogleCloudError(message, errors)\\n error.code = response.status\\n else:\\n error = klass(message, errors)\\n return error\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/exceptions.py::_walk_subclasses\",\n \"text\": \"google/cloud/exceptions.py::_walk_subclasses\\ndef _walk_subclasses(klass):\\n \\\"\\\"\\\"Recursively walk subclass tree.\\\"\\\"\\\"\\n for sub in klass.__subclasses__():\\n yield sub\\n for subsub in _walk_subclasses(sub):\\n yield subsub\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/iterator.py::Iterator.__init__\",\n \"text\": \"google/cloud/iterator.py::Iterator.__init__\\n def __init__(self, client, path, extra_params=None):\\n self.client = client\\n self.path = path\\n self.page_number = 0\\n self.next_page_token = None\\n self.extra_params = extra_params or {}\\n reserved_in_use = self.RESERVED_PARAMS.intersection(\\n self.extra_params)\\n if reserved_in_use:\\n raise ValueError(('Using a reserved parameter',\\n reserved_in_use))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/iterator.py::Iterator.__iter__\",\n \"text\": \"google/cloud/iterator.py::Iterator.__iter__\\n def __iter__(self):\\n \\\"\\\"\\\"Iterate through the list of items.\\\"\\\"\\\"\\n while self.has_next_page():\\n response = self.get_next_page_response()\\n for item in self.get_items_from_response(response):\\n yield item\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/iterator.py::Iterator.has_next_page\",\n \"text\": \"google/cloud/iterator.py::Iterator.has_next_page\\n def has_next_page(self):\\n \\\"\\\"\\\"Determines whether or not this iterator has more pages.\\n\\n :rtype: boolean\\n :returns: Whether the iterator has more pages or not.\\n \\\"\\\"\\\"\\n if self.page_number == 0:\\n return True\\n\\n return self.next_page_token is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/iterator.py::Iterator.get_query_params\",\n \"text\": \"google/cloud/iterator.py::Iterator.get_query_params\\n def get_query_params(self):\\n \\\"\\\"\\\"Getter for query parameters for the next request.\\n\\n :rtype: dict\\n :returns: A dictionary of query parameters.\\n \\\"\\\"\\\"\\n result = ({self.PAGE_TOKEN: self.next_page_token}\\n if self.next_page_token else {})\\n result.update(self.extra_params)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/iterator.py::Iterator.get_next_page_response\",\n \"text\": \"google/cloud/iterator.py::Iterator.get_next_page_response\\n def get_next_page_response(self):\\n \\\"\\\"\\\"Requests the next page from the path provided.\\n\\n :rtype: dict\\n :returns: The parsed JSON response of the next page's contents.\\n \\\"\\\"\\\"\\n if not self.has_next_page():\\n raise RuntimeError('No more pages. Try resetting the iterator.')\\n\\n response = self.client.connection.api_request(\\n method='GET', path=self.path, query_params=self.get_query_params())\\n\\n self.page_number += 1\\n self.next_page_token = response.get('nextPageToken')\\n\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/iterator.py::Iterator.reset\",\n \"text\": \"google/cloud/iterator.py::Iterator.reset\\n def reset(self):\\n \\\"\\\"\\\"Resets the iterator to the beginning.\\\"\\\"\\\"\\n self.page_number = 0\\n self.next_page_token = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/iterator.py::Iterator.get_items_from_response\",\n \"text\": \"google/cloud/iterator.py::Iterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Factory method called while iterating. This should be overridden.\\n\\n This method should be overridden by a subclass. It should\\n accept the API response of a request for the next page of items,\\n and return a list (or other iterable) of items.\\n\\n Typically this method will construct a Bucket or a Blob from the\\n page of results in the response.\\n\\n :type response: dict\\n :param response: The response of asking for the next page of items.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/iterator.py::MethodIterator.__init__\",\n \"text\": \"google/cloud/iterator.py::MethodIterator.__init__\\n def __init__(self, method, page_token=None, page_size=None,\\n max_calls=None, **kw):\\n self._method = method\\n self._token = page_token\\n self._page_size = page_size\\n self._kw = kw\\n self._max_calls = max_calls\\n self._page_num = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/iterator.py::MethodIterator.__iter__\",\n \"text\": \"google/cloud/iterator.py::MethodIterator.__iter__\\n def __iter__(self):\\n while self._max_calls is None or self._page_num < self._max_calls:\\n items, new_token = self._method(\\n page_token=self._token, page_size=self._page_size, **self._kw)\\n for item in items:\\n yield item\\n if new_token is None:\\n return\\n self._page_num += 1\\n self._token = new_token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/client.py::Client.document_from_text\",\n \"text\": \"google/cloud/language/client.py::Client.document_from_text\\n def document_from_text(self, content, **kwargs):\\n \\\"\\\"\\\"Create a plain text document bound to this client.\\n\\n :type content: str\\n :param content: The document plain text content.\\n\\n :type kwargs: dict\\n :param kwargs: Remaining keyword arguments to be passed along to the\\n :class:`.Document` constructor.\\n\\n :rtype: :class:`.Document`\\n :returns: A plain-text document bound to this client.\\n :raises: :class:`~exceptions.TypeError` if ``doc_type`` is passed as a\\n keyword argument.\\n \\\"\\\"\\\"\\n if 'doc_type' in kwargs:\\n raise TypeError('Cannot pass doc_type')\\n return Document(self, content=content,\\n doc_type=Document.PLAIN_TEXT, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/client.py::Client.document_from_html\",\n \"text\": \"google/cloud/language/client.py::Client.document_from_html\\n def document_from_html(self, content, **kwargs):\\n \\\"\\\"\\\"Create an HTML document bound to this client.\\n\\n :type content: str\\n :param content: The document HTML text content.\\n\\n :type kwargs: dict\\n :param kwargs: Remaining keyword arguments to be passed along to the\\n :class:`.Document` constructor.\\n\\n :rtype: :class:`.Document`\\n :returns: An HTML document bound to this client.\\n :raises: :class:`~exceptions.TypeError` if ``doc_type`` is passed as a\\n keyword argument.\\n \\\"\\\"\\\"\\n if 'doc_type' in kwargs:\\n raise TypeError('Cannot pass doc_type')\\n return Document(self, content=content,\\n doc_type=Document.HTML, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/client.py::Client.document_from_url\",\n \"text\": \"google/cloud/language/client.py::Client.document_from_url\\n def document_from_url(self, gcs_url,\\n doc_type=Document.PLAIN_TEXT, **kwargs):\\n \\\"\\\"\\\"Create a Cloud Storage document bound to this client.\\n\\n :type gcs_url: str\\n :param gcs_url: The URL of the Google Cloud Storage object\\n holding the content. Of the form\\n ``gs://{bucket}/{blob-name}``.\\n\\n :type doc_type: str\\n :param doc_type: (Optional) The type of text in the document.\\n Defaults to plain text. Can also be specified\\n as HTML via :attr:`~.Document.HTML`.\\n\\n :type kwargs: dict\\n :param kwargs: Remaining keyword arguments to be passed along to the\\n :class:`.Document` constructor.\\n\\n :rtype: :class:`.Document`\\n :returns: A document bound to this client.\\n \\\"\\\"\\\"\\n return Document(self, gcs_url=gcs_url, doc_type=doc_type, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/document.py::Document.__init__\",\n \"text\": \"google/cloud/language/document.py::Document.__init__\\n def __init__(self, client, content=None, gcs_url=None, doc_type=PLAIN_TEXT,\\n language=DEFAULT_LANGUAGE, encoding=Encoding.UTF8):\\n if content is not None and gcs_url is not None:\\n raise ValueError('A Document cannot contain both local text and '\\n 'a link to text in a Google Cloud Storage object')\\n if content is None and gcs_url is None:\\n raise ValueError('A Document must contain either local text or a '\\n 'link to text in a Google Cloud Storage object')\\n self.client = client\\n self.content = content\\n self.gcs_url = gcs_url\\n self.doc_type = doc_type\\n self.language = language\\n self.encoding = encoding\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/document.py::Document._to_dict\",\n \"text\": \"google/cloud/language/document.py::Document._to_dict\\n def _to_dict(self):\\n \\\"\\\"\\\"Helper to convert the current document into a dictionary.\\n\\n To be used when constructing requests.\\n\\n :rtype: dict\\n :returns: The Document value as a JSON dictionary.\\n \\\"\\\"\\\"\\n info = {\\n 'type': self.doc_type,\\n 'language': self.language,\\n }\\n if self.content is not None:\\n info['content'] = self.content\\n elif self.gcs_url is not None:\\n info['gcsContentUri'] = self.gcs_url\\n return info\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/document.py::Document.analyze_entities\",\n \"text\": \"google/cloud/language/document.py::Document.analyze_entities\\n def analyze_entities(self):\\n \\\"\\\"\\\"Analyze the entities in the current document.\\n\\n Finds named entities (currently finds proper names as of August 2016)\\n in the text, entity types, salience, mentions for each entity, and\\n other properties.\\n\\n .. _analyzeEntities: https://cloud.google.com/natural-language/\\\\\\n reference/rest/v1beta1/documents/analyzeEntities\\n\\n See `analyzeEntities`_.\\n\\n :rtype: list\\n :returns: A list of :class:`~.language.entity.Entity` returned from\\n the API.\\n \\\"\\\"\\\"\\n data = {\\n 'document': self._to_dict(),\\n 'encodingType': self.encoding,\\n }\\n api_response = self.client.connection.api_request(\\n method='POST', path='analyzeEntities', data=data)\\n return [Entity.from_api_repr(entity)\\n for entity in api_response['entities']]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/document.py::Document.analyze_sentiment\",\n \"text\": \"google/cloud/language/document.py::Document.analyze_sentiment\\n def analyze_sentiment(self):\\n \\\"\\\"\\\"Analyze the sentiment in the current document.\\n\\n .. _analyzeSentiment: https://cloud.google.com/natural-language/\\\\\\n reference/rest/v1beta1/documents/analyzeSentiment\\n\\n See `analyzeSentiment`_.\\n\\n :rtype: :class:`.Sentiment`\\n :returns: The sentiment of the current document.\\n \\\"\\\"\\\"\\n data = {'document': self._to_dict()}\\n api_response = self.client.connection.api_request(\\n method='POST', path='analyzeSentiment', data=data)\\n return Sentiment.from_api_repr(api_response['documentSentiment'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/document.py::Document.annotate_text\",\n \"text\": \"google/cloud/language/document.py::Document.annotate_text\\n def annotate_text(self, include_syntax=True, include_entities=True,\\n include_sentiment=True):\\n \\\"\\\"\\\"Advanced natural language API: document syntax and other features.\\n\\n Includes the full functionality of :meth:`analyze_entities` and\\n :meth:`analyze_sentiment`, enabled by the flags\\n ``include_entities`` and ``include_sentiment`` respectively.\\n\\n In addition ``include_syntax`` adds a new feature that analyzes\\n the document for semantic and syntacticinformation.\\n\\n .. note::\\n\\n This API is intended for users who are familiar with machine\\n learning and need in-depth text features to build upon.\\n\\n .. _annotateText: https://cloud.google.com/natural-language/\\\\\\n reference/rest/v1beta1/documents/annotateText\\n\\n See `annotateText`_.\\n\\n :type include_syntax: bool\\n :param include_syntax: (Optional) Flag to enable syntax analysis\\n of the current document.\\n\\n :type include_entities: bool\\n :param include_entities: (Optional) Flag to enable entity extraction\\n from the current document.\\n\\n :type include_sentiment: bool\\n :param include_sentiment: (Optional) Flag to enable sentiment\\n analysis of the current document.\\n\\n :rtype: :class:`Annotations`\\n :returns: A tuple of each of the four values returned from the API:\\n sentences, tokens, sentiment and entities.\\n \\\"\\\"\\\"\\n features = {}\\n if include_syntax:\\n features['extractSyntax'] = True\\n if include_entities:\\n features['extractEntities'] = True\\n if include_sentiment:\\n features['extractDocumentSentiment'] = True\\n\\n data = {\\n 'document': self._to_dict(),\\n 'features': features,\\n 'encodingType':\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/entity.py::Entity.__init__\",\n \"text\": \"google/cloud/language/entity.py::Entity.__init__\\n def __init__(self, name, entity_type, metadata, salience, mentions):\\n self.name = name\\n self.entity_type = entity_type\\n self.wikipedia_url = metadata.pop('wikipedia_url', None)\\n self.metadata = metadata\\n self.salience = salience\\n self.mentions = mentions\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/entity.py::Entity.from_api_repr\",\n \"text\": \"google/cloud/language/entity.py::Entity.from_api_repr\\n @classmethod\\n def from_api_repr(cls, payload):\\n \\\"\\\"\\\"Convert an Entity from the JSON API into an :class:`Entity`.\\n\\n :param payload: dict\\n :type payload: The value from the backend.\\n\\n :rtype: :class:`Entity`\\n :returns: The entity parsed from the API representation.\\n \\\"\\\"\\\"\\n name = payload['name']\\n entity_type = payload['type']\\n metadata = payload['metadata']\\n salience = payload['salience']\\n mentions = [value['text']['content']\\n for value in payload['mentions']]\\n return cls(name, entity_type, metadata, salience, mentions)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/sentiment.py::Sentiment.__init__\",\n \"text\": \"google/cloud/language/sentiment.py::Sentiment.__init__\\n def __init__(self, polarity, magnitude):\\n self.polarity = polarity\\n self.magnitude = magnitude\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/sentiment.py::Sentiment.from_api_repr\",\n \"text\": \"google/cloud/language/sentiment.py::Sentiment.from_api_repr\\n @classmethod\\n def from_api_repr(cls, payload):\\n \\\"\\\"\\\"Convert a Sentiment from the JSON API into a :class:`Sentiment`.\\n\\n :param payload: dict\\n :type payload: The value from the backend.\\n\\n :rtype: :class:`Sentiment`\\n :returns: The sentiment parsed from the API representation.\\n \\\"\\\"\\\"\\n polarity = payload['polarity']\\n magnitude = payload['magnitude']\\n return cls(polarity, magnitude)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/syntax.py::PartOfSpeech.reverse\",\n \"text\": \"google/cloud/language/syntax.py::PartOfSpeech.reverse\\n @classmethod\\n def reverse(cls, tag):\\n \\\"\\\"\\\"Reverses the API's enum name for the one on this class.\\n\\n For example::\\n\\n >>> PartOfSpeech.OTHER\\n 'X'\\n >>> PartOfSpeech.reverse('X')\\n 'OTHER'\\n\\n :rtype: str\\n :returns: The attribute name corresponding to the API part of\\n speech enum.\\n \\\"\\\"\\\"\\n return cls._REVERSE_MAP[tag]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/syntax.py::Token.__init__\",\n \"text\": \"google/cloud/language/syntax.py::Token.__init__\\n def __init__(self, text_content, text_begin, part_of_speech,\\n edge_index, edge_label, lemma):\\n self.text_content = text_content\\n self.text_begin = text_begin\\n self.part_of_speech = part_of_speech\\n self.edge_index = edge_index\\n self.edge_label = edge_label\\n self.lemma = lemma\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/syntax.py::Token.from_api_repr\",\n \"text\": \"google/cloud/language/syntax.py::Token.from_api_repr\\n @classmethod\\n def from_api_repr(cls, payload):\\n \\\"\\\"\\\"Convert a token from the JSON API into a :class:`Sentiment`.\\n\\n :param payload: dict\\n :type payload: The value from the backend.\\n\\n :rtype: :class:`Token`\\n :returns: The token parsed from the API representation.\\n \\\"\\\"\\\"\\n text_span = payload['text']\\n text_content = text_span['content']\\n text_begin = text_span['beginOffset']\\n part_of_speech = payload['partOfSpeech']['tag']\\n edge = payload['dependencyEdge']\\n edge_index = edge['headTokenIndex']\\n edge_label = edge['label']\\n lemma = payload['lemma']\\n return cls(text_content, text_begin, part_of_speech,\\n edge_index, edge_label, lemma)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/syntax.py::Sentence.__init__\",\n \"text\": \"google/cloud/language/syntax.py::Sentence.__init__\\n def __init__(self, content, begin):\\n self.content = content\\n self.begin = begin\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/language/syntax.py::Sentence.from_api_repr\",\n \"text\": \"google/cloud/language/syntax.py::Sentence.from_api_repr\\n @classmethod\\n def from_api_repr(cls, payload):\\n \\\"\\\"\\\"Convert a sentence from the JSON API into a :class:`Sentiment`.\\n\\n :param payload: dict\\n :type payload: The value from the backend.\\n\\n :rtype: :class:`Sentence`\\n :returns: The sentence parsed from the API representation.\\n \\\"\\\"\\\"\\n text_span = payload['text']\\n return cls(text_span['content'], text_span['beginOffset'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_LoggingAPI.__init__\",\n \"text\": \"google/cloud/logging/_gax.py::_LoggingAPI.__init__\\n def __init__(self, gax_api):\\n self._gax_api = gax_api\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_LoggingAPI.list_entries\",\n \"text\": \"google/cloud/logging/_gax.py::_LoggingAPI.list_entries\\n def list_entries(self, projects, filter_='', order_by='',\\n page_size=0, page_token=None):\\n \\\"\\\"\\\"Return a page of log entry resources.\\n\\n :type projects: list of strings\\n :param projects: project IDs to include. If not passed,\\n defaults to the project bound to the API's client.\\n\\n :type filter_: str\\n :param filter_: a filter expression. See:\\n https://cloud.google.com/logging/docs/view/advanced_filters\\n\\n :type order_by: str\\n :param order_by: One of :data:`~google.cloud.logging.ASCENDING`\\n or :data:`~google.cloud.logging.DESCENDING`.\\n\\n :type page_size: int\\n :param page_size: maximum number of entries to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of entries. If not\\n passed, the API will return the first page of\\n entries.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of mappings, plus a \\\"next page token\\\" string:\\n if not None, indicates that more entries can be retrieved\\n with another call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n if page_token is None:\\n page_token = INITIAL_PAGE\\n options = CallOptions(page_token=page_token)\\n page_iter = self._gax_api.list_log_entries(\\n projects, filter_, order_by, page_size, options)\\n entries = [_log_entry_pb_to_mapping(entry_pb)\\n for entry_pb in page_iter.next()]\\n token = page_iter.page_token or None\\n return entries, token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_LoggingAPI.write_entries\",\n \"text\": \"google/cloud/logging/_gax.py::_LoggingAPI.write_entries\\n def write_entries(self, entries, logger_name=None, resource=None,\\n labels=None):\\n \\\"\\\"\\\"API call: log an entry resource via a POST request\\n\\n :type entries: sequence of mapping\\n :param entries: the log entry resources to log.\\n\\n :type logger_name: string\\n :param logger_name: name of default logger to which to log the entries;\\n individual entries may override.\\n\\n :type resource: mapping\\n :param resource: default resource to associate with entries;\\n individual entries may override.\\n\\n :type labels: mapping\\n :param labels: default labels to associate with entries;\\n individual entries may override.\\n \\\"\\\"\\\"\\n options = None\\n partial_success = False\\n entry_pbs = [_log_entry_mapping_to_pb(entry) for entry in entries]\\n self._gax_api.write_log_entries(entry_pbs, logger_name, resource,\\n labels, partial_success, options)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_LoggingAPI.logger_delete\",\n \"text\": \"google/cloud/logging/_gax.py::_LoggingAPI.logger_delete\\n def logger_delete(self, project, logger_name):\\n \\\"\\\"\\\"API call: delete all entries in a logger via a DELETE request\\n\\n :type project: string\\n :param project: ID of project containing the log entries to delete\\n\\n :type logger_name: string\\n :param logger_name: name of logger containing the log entries to delete\\n \\\"\\\"\\\"\\n options = None\\n path = 'projects/%s/logs/%s' % (project, logger_name)\\n try:\\n self._gax_api.delete_log(path, options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(path)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_SinksAPI.__init__\",\n \"text\": \"google/cloud/logging/_gax.py::_SinksAPI.__init__\\n def __init__(self, gax_api):\\n self._gax_api = gax_api\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_SinksAPI.list_sinks\",\n \"text\": \"google/cloud/logging/_gax.py::_SinksAPI.list_sinks\\n def list_sinks(self, project, page_size=0, page_token=None):\\n \\\"\\\"\\\"List sinks for the project associated with this client.\\n\\n :type project: string\\n :param project: ID of the project whose sinks are to be listed.\\n\\n :type page_size: int\\n :param page_size: maximum number of sinks to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of sinks. If not\\n passed, the API will return the first page of\\n sinks.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of mappings, plus a \\\"next page token\\\" string:\\n if not None, indicates that more sinks can be retrieved\\n with another call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n if page_token is None:\\n page_token = INITIAL_PAGE\\n options = CallOptions(page_token=page_token)\\n path = 'projects/%s' % (project,)\\n page_iter = self._gax_api.list_sinks(path, page_size, options)\\n sinks = [_log_sink_pb_to_mapping(log_sink_pb)\\n for log_sink_pb in page_iter.next()]\\n token = page_iter.page_token or None\\n return sinks, token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_SinksAPI.sink_create\",\n \"text\": \"google/cloud/logging/_gax.py::_SinksAPI.sink_create\\n def sink_create(self, project, sink_name, filter_, destination):\\n \\\"\\\"\\\"API call: create a sink resource.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/create\\n\\n :type project: string\\n :param project: ID of the project in which to create the sink.\\n\\n :type sink_name: string\\n :param sink_name: the name of the sink\\n\\n :type filter_: string\\n :param filter_: the advanced logs filter expression defining the\\n entries exported by the sink.\\n\\n :type destination: string\\n :param destination: destination URI for the entries exported by\\n the sink.\\n \\\"\\\"\\\"\\n options = None\\n parent = 'projects/%s' % (project,)\\n sink_pb = LogSink(name=sink_name, filter=filter_,\\n destination=destination)\\n try:\\n self._gax_api.create_sink(parent, sink_pb, options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.FAILED_PRECONDITION:\\n path = 'projects/%s/sinks/%s' % (project, sink_name)\\n raise Conflict(path)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_SinksAPI.sink_get\",\n \"text\": \"google/cloud/logging/_gax.py::_SinksAPI.sink_get\\n def sink_get(self, project, sink_name):\\n \\\"\\\"\\\"API call: retrieve a sink resource.\\n\\n :type project: string\\n :param project: ID of the project containing the sink.\\n\\n :type sink_name: string\\n :param sink_name: the name of the sink\\n\\n :rtype: dict\\n :returns: The sink object returned from the API (converted from a\\n protobuf to a dictionary).\\n \\\"\\\"\\\"\\n options = None\\n path = 'projects/%s/sinks/%s' % (project, sink_name)\\n try:\\n sink_pb = self._gax_api.get_sink(path, options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(path)\\n raise\\n return _log_sink_pb_to_mapping(sink_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_SinksAPI.sink_update\",\n \"text\": \"google/cloud/logging/_gax.py::_SinksAPI.sink_update\\n def sink_update(self, project, sink_name, filter_, destination):\\n \\\"\\\"\\\"API call: update a sink resource.\\n\\n :type project: string\\n :param project: ID of the project containing the sink.\\n\\n :type sink_name: string\\n :param sink_name: the name of the sink\\n\\n :type filter_: string\\n :param filter_: the advanced logs filter expression defining the\\n entries exported by the sink.\\n\\n :type destination: string\\n :param destination: destination URI for the entries exported by\\n the sink.\\n\\n :rtype: dict\\n :returns: The sink object returned from the API (converted from a\\n protobuf to a dictionary).\\n \\\"\\\"\\\"\\n options = None\\n path = 'projects/%s/sinks/%s' % (project, sink_name)\\n sink_pb = LogSink(name=path, filter=filter_, destination=destination)\\n try:\\n self._gax_api.update_sink(path, sink_pb, options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(path)\\n raise\\n return _log_sink_pb_to_mapping(sink_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_SinksAPI.sink_delete\",\n \"text\": \"google/cloud/logging/_gax.py::_SinksAPI.sink_delete\\n def sink_delete(self, project, sink_name):\\n \\\"\\\"\\\"API call: delete a sink resource.\\n\\n :type project: string\\n :param project: ID of the project containing the sink.\\n\\n :type sink_name: string\\n :param sink_name: the name of the sink\\n \\\"\\\"\\\"\\n options = None\\n path = 'projects/%s/sinks/%s' % (project, sink_name)\\n try:\\n self._gax_api.delete_sink(path, options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(path)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_MetricsAPI.__init__\",\n \"text\": \"google/cloud/logging/_gax.py::_MetricsAPI.__init__\\n def __init__(self, gax_api):\\n self._gax_api = gax_api\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_MetricsAPI.list_metrics\",\n \"text\": \"google/cloud/logging/_gax.py::_MetricsAPI.list_metrics\\n def list_metrics(self, project, page_size=0, page_token=None):\\n \\\"\\\"\\\"List metrics for the project associated with this client.\\n\\n :type project: string\\n :param project: ID of the project whose metrics are to be listed.\\n\\n :type page_size: int\\n :param page_size: maximum number of metrics to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of metrics. If not\\n passed, the API will return the first page of\\n metrics.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of mappings, plus a \\\"next page token\\\" string:\\n if not None, indicates that more metrics can be retrieved\\n with another call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n if page_token is None:\\n page_token = INITIAL_PAGE\\n options = CallOptions(page_token=page_token)\\n path = 'projects/%s' % (project,)\\n page_iter = self._gax_api.list_log_metrics(path, page_size, options)\\n metrics = [_log_metric_pb_to_mapping(log_metric_pb)\\n for log_metric_pb in page_iter.next()]\\n token = page_iter.page_token or None\\n return metrics, token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_MetricsAPI.metric_create\",\n \"text\": \"google/cloud/logging/_gax.py::_MetricsAPI.metric_create\\n def metric_create(self, project, metric_name, filter_, description):\\n \\\"\\\"\\\"API call: create a metric resource.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/create\\n\\n :type project: string\\n :param project: ID of the project in which to create the metric.\\n\\n :type metric_name: string\\n :param metric_name: the name of the metric\\n\\n :type filter_: string\\n :param filter_: the advanced logs filter expression defining the\\n entries exported by the metric.\\n\\n :type description: string\\n :param description: description of the metric.\\n \\\"\\\"\\\"\\n options = None\\n parent = 'projects/%s' % (project,)\\n metric_pb = LogMetric(name=metric_name, filter=filter_,\\n description=description)\\n try:\\n self._gax_api.create_log_metric(parent, metric_pb, options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.FAILED_PRECONDITION:\\n path = 'projects/%s/metrics/%s' % (project, metric_name)\\n raise Conflict(path)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_MetricsAPI.metric_get\",\n \"text\": \"google/cloud/logging/_gax.py::_MetricsAPI.metric_get\\n def metric_get(self, project, metric_name):\\n \\\"\\\"\\\"API call: retrieve a metric resource.\\n\\n :type project: string\\n :param project: ID of the project containing the metric.\\n\\n :type metric_name: string\\n :param metric_name: the name of the metric\\n\\n :rtype: dict\\n :returns: The metric object returned from the API (converted from a\\n protobuf to a dictionary).\\n \\\"\\\"\\\"\\n options = None\\n path = 'projects/%s/metrics/%s' % (project, metric_name)\\n try:\\n metric_pb = self._gax_api.get_log_metric(path, options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(path)\\n raise\\n return _log_metric_pb_to_mapping(metric_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_MetricsAPI.metric_update\",\n \"text\": \"google/cloud/logging/_gax.py::_MetricsAPI.metric_update\\n def metric_update(self, project, metric_name, filter_, description):\\n \\\"\\\"\\\"API call: update a metric resource.\\n\\n :type project: string\\n :param project: ID of the project containing the metric.\\n\\n :type metric_name: string\\n :param metric_name: the name of the metric\\n\\n :type filter_: string\\n :param filter_: the advanced logs filter expression defining the\\n entries exported by the metric.\\n\\n :type description: string\\n :param description: description of the metric.\\n\\n :rtype: dict\\n :returns: The metric object returned from the API (converted from a\\n protobuf to a dictionary).\\n \\\"\\\"\\\"\\n options = None\\n path = 'projects/%s/metrics/%s' % (project, metric_name)\\n metric_pb = LogMetric(name=path, filter=filter_,\\n description=description)\\n try:\\n self._gax_api.update_log_metric(path, metric_pb, options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(path)\\n raise\\n return _log_metric_pb_to_mapping(metric_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_MetricsAPI.metric_delete\",\n \"text\": \"google/cloud/logging/_gax.py::_MetricsAPI.metric_delete\\n def metric_delete(self, project, metric_name):\\n \\\"\\\"\\\"API call: delete a metric resource.\\n\\n :type project: string\\n :param project: ID of the project containing the metric.\\n\\n :type metric_name: string\\n :param metric_name: the name of the metric\\n \\\"\\\"\\\"\\n options = None\\n path = 'projects/%s/metrics/%s' % (project, metric_name)\\n try:\\n self._gax_api.delete_log_metric(path, options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(path)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_mon_resource_pb_to_mapping\",\n \"text\": \"google/cloud/logging/_gax.py::_mon_resource_pb_to_mapping\\ndef _mon_resource_pb_to_mapping(resource_pb):\\n \\\"\\\"\\\"Helper for :func:_log_entry_pb_to_mapping`.\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n mapping = {\\n 'type': resource_pb.type,\\n }\\n if resource_pb.labels:\\n mapping['labels'] = resource_pb.labels\\n return mapping\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_value_pb_to_value\",\n \"text\": \"google/cloud/logging/_gax.py::_value_pb_to_value\\ndef _value_pb_to_value(value_pb):\\n \\\"\\\"\\\"Helper for :func:`_log_entry_pb_to_mapping`.\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n kind = value_pb.WhichOneof('kind')\\n\\n if kind is None:\\n result = None\\n\\n elif kind == 'string_value':\\n result = value_pb.string_value\\n\\n elif kind == 'bool_value':\\n result = value_pb.bool_value\\n\\n elif kind == 'number_value':\\n result = value_pb.number_value\\n\\n elif kind == 'list_value':\\n result = [_value_pb_to_value(element)\\n for element in value_pb.list_value.values]\\n\\n elif kind == 'struct_value':\\n result = _struct_pb_to_mapping(value_pb.struct_value)\\n\\n else:\\n raise ValueError('Value protobuf had unknown kind: %s' % (kind,))\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_struct_pb_to_mapping\",\n \"text\": \"google/cloud/logging/_gax.py::_struct_pb_to_mapping\\ndef _struct_pb_to_mapping(struct_pb):\\n \\\"\\\"\\\"Helper for :func:`_log_entry_pb_to_mapping`.\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n return {key: _value_pb_to_value(struct_pb.fields[key])\\n for key in struct_pb.fields}\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_log_entry_pb_to_mapping\",\n \"text\": \"google/cloud/logging/_gax.py::_log_entry_pb_to_mapping\\ndef _log_entry_pb_to_mapping(entry_pb):\\n \\\"\\\"\\\"Helper for :meth:`list_entries`, et aliae\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n mapping = {\\n 'logName': entry_pb.log_name,\\n 'resource': _mon_resource_pb_to_mapping(entry_pb.resource),\\n 'severity': LogSeverity.Name(entry_pb.severity),\\n 'insertId': entry_pb.insert_id,\\n 'timestamp': _pb_timestamp_to_rfc3339(entry_pb.timestamp),\\n 'labels': entry_pb.labels,\\n }\\n if entry_pb.HasField('text_payload'):\\n mapping['textPayload'] = entry_pb.text_payload\\n\\n if entry_pb.HasField('json_payload'):\\n mapping['jsonPayload'] = _struct_pb_to_mapping(entry_pb.json_payload)\\n\\n if entry_pb.HasField('proto_payload'):\\n mapping['protoPayload'] = entry_pb.proto_payload\\n\\n if entry_pb.http_request:\\n request = entry_pb.http_request\\n mapping['httpRequest'] = {\\n 'requestMethod': request.request_method,\\n 'requestUrl': request.request_url,\\n 'status': request.status,\\n 'referer': request.referer,\\n 'userAgent': request.user_agent,\\n 'cacheHit': request.cache_hit,\\n 'requestSize': request.request_size,\\n 'responseSize': request.response_size,\\n 'remoteIp': request.remote_ip,\\n }\\n\\n if entry_pb.operation:\\n operation = entry_pb.operation\\n mapping['operation'] = {\\n 'producer': operation.producer,\\n 'id': operation.id,\\n 'first': operation.first,\\n 'last': operation.last,\\n }\\n\\n return mapping\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_http_request_mapping_to_pb\",\n \"text\": \"google/cloud/logging/_gax.py::_http_request_mapping_to_pb\\ndef _http_request_mapping_to_pb(info, request):\\n \\\"\\\"\\\"Helper for _log_entry_mapping_to_pb\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n optional_request_keys = {\\n 'requestMethod': 'request_method',\\n 'requestUrl': 'request_url',\\n 'status': 'status',\\n 'referer': 'referer',\\n 'userAgent': 'user_agent',\\n 'cacheHit': 'cache_hit',\\n 'requestSize': 'request_size',\\n 'responseSize': 'response_size',\\n 'remoteIp': 'remote_ip',\\n }\\n for key, pb_name in optional_request_keys.items():\\n if key in info:\\n setattr(request, pb_name, info[key])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_log_operation_mapping_to_pb\",\n \"text\": \"google/cloud/logging/_gax.py::_log_operation_mapping_to_pb\\ndef _log_operation_mapping_to_pb(info, operation):\\n \\\"\\\"\\\"Helper for _log_entry_mapping_to_pb\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n operation.producer = info['producer']\\n operation.id = info['id']\\n\\n if 'first' in info:\\n operation.first = info['first']\\n\\n if 'last' in info:\\n operation.last = info['last']\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_log_entry_mapping_to_pb\",\n \"text\": \"google/cloud/logging/_gax.py::_log_entry_mapping_to_pb\\ndef _log_entry_mapping_to_pb(mapping):\\n \\\"\\\"\\\"Helper for :meth:`write_entries`, et aliae\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n # pylint: disable=too-many-branches\\n entry_pb = LogEntry()\\n\\n optional_scalar_keys = {\\n 'logName': 'log_name',\\n 'insertId': 'insert_id',\\n 'textPayload': 'text_payload',\\n }\\n\\n for key, pb_name in optional_scalar_keys.items():\\n if key in mapping:\\n setattr(entry_pb, pb_name, mapping[key])\\n\\n if 'resource' in mapping:\\n entry_pb.resource.type = mapping['resource']['type']\\n\\n if 'severity' in mapping:\\n severity = mapping['severity']\\n if isinstance(severity, str):\\n severity = LogSeverity.Value(severity)\\n entry_pb.severity = severity\\n\\n if 'timestamp' in mapping:\\n timestamp = _datetime_to_pb_timestamp(mapping['timestamp'])\\n entry_pb.timestamp.CopyFrom(timestamp)\\n\\n if 'labels' in mapping:\\n for key, value in mapping['labels'].items():\\n entry_pb.labels[key] = value\\n\\n if 'jsonPayload' in mapping:\\n for key, value in mapping['jsonPayload'].items():\\n entry_pb.json_payload[key] = value\\n\\n if 'protoPayload' in mapping:\\n Parse(json.dumps(mapping['protoPayload']), entry_pb.proto_payload)\\n\\n if 'httpRequest' in mapping:\\n _http_request_mapping_to_pb(\\n mapping['httpRequest'], entry_pb.http_request)\\n\\n if 'operation' in mapping:\\n _log_operation_mapping_to_pb(\\n mapping['operation'], entry_pb.operation)\\n\\n return entry_pb\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_log_sink_pb_to_mapping\",\n \"text\": \"google/cloud/logging/_gax.py::_log_sink_pb_to_mapping\\ndef _log_sink_pb_to_mapping(sink_pb):\\n \\\"\\\"\\\"Helper for :meth:`list_sinks`, et aliae\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n return {\\n 'name': sink_pb.name,\\n 'destination': sink_pb.destination,\\n 'filter': sink_pb.filter,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/_gax.py::_log_metric_pb_to_mapping\",\n \"text\": \"google/cloud/logging/_gax.py::_log_metric_pb_to_mapping\\ndef _log_metric_pb_to_mapping(metric_pb):\\n \\\"\\\"\\\"Helper for :meth:`list_metrics`, et aliae\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n return {\\n 'name': metric_pb.name,\\n 'description': metric_pb.description,\\n 'filter': metric_pb.filter,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/client.py::Client.logging_api\",\n \"text\": \"google/cloud/logging/client.py::Client.logging_api\\n @property\\n def logging_api(self):\\n \\\"\\\"\\\"Helper for logging-related API calls.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.logs\\n \\\"\\\"\\\"\\n if self._logging_api is None:\\n if _USE_GAX:\\n generated = GeneratedLoggingAPI()\\n self._logging_api = GAXLoggingAPI(generated)\\n else:\\n self._logging_api = JSONLoggingAPI(self.connection)\\n return self._logging_api\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/client.py::Client.sinks_api\",\n \"text\": \"google/cloud/logging/client.py::Client.sinks_api\\n @property\\n def sinks_api(self):\\n \\\"\\\"\\\"Helper for log sink-related API calls.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks\\n \\\"\\\"\\\"\\n if self._sinks_api is None:\\n if _USE_GAX:\\n generated = GeneratedSinksAPI()\\n self._sinks_api = GAXSinksAPI(generated)\\n else:\\n self._sinks_api = JSONSinksAPI(self.connection)\\n return self._sinks_api\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/client.py::Client.metrics_api\",\n \"text\": \"google/cloud/logging/client.py::Client.metrics_api\\n @property\\n def metrics_api(self):\\n \\\"\\\"\\\"Helper for log metric-related API calls.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics\\n \\\"\\\"\\\"\\n if self._metrics_api is None:\\n if _USE_GAX:\\n generated = GeneratedMetricsAPI()\\n self._metrics_api = GAXMetricsAPI(generated)\\n else:\\n self._metrics_api = JSONMetricsAPI(self.connection)\\n return self._metrics_api\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/client.py::Client.logger\",\n \"text\": \"google/cloud/logging/client.py::Client.logger\\n def logger(self, name):\\n \\\"\\\"\\\"Creates a logger bound to the current client.\\n\\n :type name: str\\n :param name: the name of the logger to be constructed.\\n\\n :rtype: :class:`google.cloud.logging.logger.Logger`\\n :returns: Logger created with the current client.\\n \\\"\\\"\\\"\\n return Logger(name, client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/client.py::Client._entry_from_resource\",\n \"text\": \"google/cloud/logging/client.py::Client._entry_from_resource\\n def _entry_from_resource(self, resource, loggers):\\n \\\"\\\"\\\"Detect correct entry type from resource and instantiate.\\n\\n :type resource: dict\\n :param resource: one entry resource from API response\\n\\n :type loggers: dict or None\\n :param loggers: A mapping of logger fullnames -> loggers. If not\\n passed, the entry will have a newly-created logger.\\n\\n :rtype: One of:\\n :class:`google.cloud.logging.entries.TextEntry`,\\n :class:`google.cloud.logging.entries.StructEntry`,\\n :class:`google.cloud.logging.entries.ProtobufEntry`\\n :returns: the entry instance, constructed via the resource\\n \\\"\\\"\\\"\\n if 'textPayload' in resource:\\n return TextEntry.from_api_repr(resource, self, loggers)\\n elif 'jsonPayload' in resource:\\n return StructEntry.from_api_repr(resource, self, loggers)\\n elif 'protoPayload' in resource:\\n return ProtobufEntry.from_api_repr(resource, self, loggers)\\n raise ValueError('Cannot parse log entry resource')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/client.py::Client.list_entries\",\n \"text\": \"google/cloud/logging/client.py::Client.list_entries\\n def list_entries(self, projects=None, filter_=None, order_by=None,\\n page_size=None, page_token=None):\\n \\\"\\\"\\\"Return a page of log entries.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/list\\n\\n :type projects: list of strings\\n :param projects: project IDs to include. If not passed,\\n defaults to the project bound to the client.\\n\\n :type filter_: str\\n :param filter_: a filter expression. See:\\n https://cloud.google.com/logging/docs/view/advanced_filters\\n\\n :type order_by: str\\n :param order_by: One of :data:`~google.cloud.logging.ASCENDING`\\n or :data:`~google.cloud.logging.DESCENDING`.\\n\\n :type page_size: int\\n :param page_size: maximum number of entries to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of entries. If not\\n passed, the API will return the first page of\\n entries.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`google.cloud.logging.entry.TextEntry`, plus a\\n \\\"next page token\\\" string: if not None, indicates that\\n more entries can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n if projects is None:\\n projects = [self.project]\\n\\n resources, token = self.logging_api.list_entries(\\n projects=projects, filter_=filter_, order_by=order_by,\\n page_size=page_size, page_token=page_token)\\n loggers = {}\\n entries = [self._entry_from_resource(resource, loggers)\\n for resource in resources]\\n return entries, token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/client.py::Client.sink\",\n \"text\": \"google/cloud/logging/client.py::Client.sink\\n def sink(self, name, filter_=None, destination=None):\\n \\\"\\\"\\\"Creates a sink bound to the current client.\\n\\n :type name: str\\n :param name: the name of the sink to be constructed.\\n\\n :type filter_: str\\n :param filter_: (optional) the advanced logs filter expression\\n defining the entries exported by the sink. If not\\n passed, the instance should already exist, to be\\n refreshed via :meth:`Sink.reload`.\\n\\n :type destination: str\\n :param destination: destination URI for the entries exported by\\n the sink. If not passed, the instance should\\n already exist, to be refreshed via\\n :meth:`Sink.reload`.\\n\\n :rtype: :class:`google.cloud.logging.sink.Sink`\\n :returns: Sink created with the current client.\\n \\\"\\\"\\\"\\n return Sink(name, filter_, destination, client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/client.py::Client.list_sinks\",\n \"text\": \"google/cloud/logging/client.py::Client.list_sinks\\n def list_sinks(self, page_size=None, page_token=None):\\n \\\"\\\"\\\"List sinks for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/list\\n\\n :type page_size: int\\n :param page_size: maximum number of sinks to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of sinks. If not\\n passed, the API will return the first page of\\n sinks.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`google.cloud.logging.sink.Sink`, plus a\\n \\\"next page token\\\" string: if not None, indicates that\\n more sinks can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n resources, token = self.sinks_api.list_sinks(\\n self.project, page_size, page_token)\\n sinks = [Sink.from_api_repr(resource, self)\\n for resource in resources]\\n return sinks, token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/client.py::Client.metric\",\n \"text\": \"google/cloud/logging/client.py::Client.metric\\n def metric(self, name, filter_=None, description=''):\\n \\\"\\\"\\\"Creates a metric bound to the current client.\\n\\n :type name: str\\n :param name: the name of the metric to be constructed.\\n\\n :type filter_: str\\n :param filter_: the advanced logs filter expression defining the\\n entries tracked by the metric. If not\\n passed, the instance should already exist, to be\\n refreshed via :meth:`Metric.reload`.\\n\\n :type description: str\\n :param description: the description of the metric to be constructed.\\n If not passed, the instance should already exist,\\n to be refreshed via :meth:`Metric.reload`.\\n\\n :rtype: :class:`google.cloud.logging.metric.Metric`\\n :returns: Metric created with the current client.\\n \\\"\\\"\\\"\\n return Metric(name, filter_, client=self, description=description)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/client.py::Client.list_metrics\",\n \"text\": \"google/cloud/logging/client.py::Client.list_metrics\\n def list_metrics(self, page_size=None, page_token=None):\\n \\\"\\\"\\\"List metrics for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/list\\n\\n :type page_size: int\\n :param page_size: maximum number of metrics to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of metrics. If not\\n passed, the API will return the first page of\\n metrics.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`google.cloud.logging.metric.Metric`, plus a\\n \\\"next page token\\\" string: if not None, indicates that\\n more metrics can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n resources, token = self.metrics_api.list_metrics(\\n self.project, page_size, page_token)\\n metrics = [Metric.from_api_repr(resource, self)\\n for resource in resources]\\n return metrics, token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_LoggingAPI.__init__\",\n \"text\": \"google/cloud/logging/connection.py::_LoggingAPI.__init__\\n def __init__(self, connection):\\n self._connection = connection\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_LoggingAPI.list_entries\",\n \"text\": \"google/cloud/logging/connection.py::_LoggingAPI.list_entries\\n def list_entries(self, projects, filter_=None, order_by=None,\\n page_size=None, page_token=None):\\n \\\"\\\"\\\"Return a page of log entry resources.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/list\\n\\n :type projects: list of strings\\n :param projects: project IDs to include. If not passed,\\n defaults to the project bound to the client.\\n\\n :type filter_: str\\n :param filter_: a filter expression. See:\\n https://cloud.google.com/logging/docs/view/advanced_filters\\n\\n :type order_by: str\\n :param order_by: One of :data:`~google.cloud.logging.ASCENDING`\\n or :data:`~google.cloud.logging.DESCENDING`.\\n\\n :type page_size: int\\n :param page_size: maximum number of entries to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of entries. If not\\n passed, the API will return the first page of\\n entries.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of mappings, plus a \\\"next page token\\\" string:\\n if not None, indicates that more entries can be retrieved\\n with another call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {'projectIds': projects}\\n\\n if filter_ is not None:\\n params['filter'] = filter_\\n\\n if order_by is not None:\\n params['orderBy'] = order_by\\n\\n if page_size is not None:\\n params['pageSize'] = page_size\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n resp = self._connection.api_request(\\n method='POST', path='/entries:list', data=params)\\n\\n return resp.get('entries', ()), res\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_LoggingAPI.write_entries\",\n \"text\": \"google/cloud/logging/connection.py::_LoggingAPI.write_entries\\n def write_entries(self, entries, logger_name=None, resource=None,\\n labels=None):\\n \\\"\\\"\\\"API call: log an entry resource via a POST request\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/write\\n\\n :type entries: sequence of mapping\\n :param entries: the log entry resources to log.\\n\\n :type logger_name: string\\n :param logger_name: name of default logger to which to log the entries;\\n individual entries may override.\\n\\n :type resource: mapping\\n :param resource: default resource to associate with entries;\\n individual entries may override.\\n\\n :type labels: mapping\\n :param labels: default labels to associate with entries;\\n individual entries may override.\\n \\\"\\\"\\\"\\n data = {'entries': list(entries)}\\n\\n if logger_name is not None:\\n data['logName'] = logger_name\\n\\n if resource is not None:\\n data['resource'] = resource\\n\\n if labels is not None:\\n data['labels'] = labels\\n\\n self._connection.api_request(method='POST', path='/entries:write',\\n data=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_LoggingAPI.logger_delete\",\n \"text\": \"google/cloud/logging/connection.py::_LoggingAPI.logger_delete\\n def logger_delete(self, project, logger_name):\\n \\\"\\\"\\\"API call: delete all entries in a logger via a DELETE request\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.logs/delete\\n\\n :type project: string\\n :param project: ID of project containing the log entries to delete\\n\\n :type logger_name: string\\n :param logger_name: name of logger containing the log entries to delete\\n \\\"\\\"\\\"\\n path = '/projects/%s/logs/%s' % (project, logger_name)\\n self._connection.api_request(method='DELETE', path=path)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_SinksAPI.__init__\",\n \"text\": \"google/cloud/logging/connection.py::_SinksAPI.__init__\\n def __init__(self, connection):\\n self._connection = connection\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_SinksAPI.list_sinks\",\n \"text\": \"google/cloud/logging/connection.py::_SinksAPI.list_sinks\\n def list_sinks(self, project, page_size=None, page_token=None):\\n \\\"\\\"\\\"List sinks for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/list\\n\\n :type project: string\\n :param project: ID of the project whose sinks are to be listed.\\n\\n :type page_size: int\\n :param page_size: maximum number of sinks to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of sinks. If not\\n passed, the API will return the first page of\\n sinks.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of mappings, plus a \\\"next page token\\\" string:\\n if not None, indicates that more sinks can be retrieved\\n with another call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if page_size is not None:\\n params['pageSize'] = page_size\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/sinks' % (project,)\\n resp = self._connection.api_request(\\n method='GET', path=path, query_params=params)\\n sinks = resp.get('sinks', ())\\n return sinks, resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_SinksAPI.sink_create\",\n \"text\": \"google/cloud/logging/connection.py::_SinksAPI.sink_create\\n def sink_create(self, project, sink_name, filter_, destination):\\n \\\"\\\"\\\"API call: create a sink resource.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/create\\n\\n :type project: string\\n :param project: ID of the project in which to create the sink.\\n\\n :type sink_name: string\\n :param sink_name: the name of the sink\\n\\n :type filter_: string\\n :param filter_: the advanced logs filter expression defining the\\n entries exported by the sink.\\n\\n :type destination: string\\n :param destination: destination URI for the entries exported by\\n the sink.\\n \\\"\\\"\\\"\\n target = '/projects/%s/sinks' % (project,)\\n data = {\\n 'name': sink_name,\\n 'filter': filter_,\\n 'destination': destination,\\n }\\n self._connection.api_request(method='POST', path=target, data=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_SinksAPI.sink_get\",\n \"text\": \"google/cloud/logging/connection.py::_SinksAPI.sink_get\\n def sink_get(self, project, sink_name):\\n \\\"\\\"\\\"API call: retrieve a sink resource.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/get\\n\\n :type project: string\\n :param project: ID of the project containing the sink.\\n\\n :type sink_name: string\\n :param sink_name: the name of the sink\\n\\n :rtype: dict\\n :returns: The JSON sink object returned from the API.\\n \\\"\\\"\\\"\\n target = '/projects/%s/sinks/%s' % (project, sink_name)\\n return self._connection.api_request(method='GET', path=target)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_SinksAPI.sink_update\",\n \"text\": \"google/cloud/logging/connection.py::_SinksAPI.sink_update\\n def sink_update(self, project, sink_name, filter_, destination):\\n \\\"\\\"\\\"API call: update a sink resource.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/update\\n\\n :type project: string\\n :param project: ID of the project containing the sink.\\n\\n :type sink_name: string\\n :param sink_name: the name of the sink\\n\\n :type filter_: string\\n :param filter_: the advanced logs filter expression defining the\\n entries exported by the sink.\\n\\n :type destination: string\\n :param destination: destination URI for the entries exported by\\n the sink.\\n \\\"\\\"\\\"\\n target = '/projects/%s/sinks/%s' % (project, sink_name)\\n data = {\\n 'name': sink_name,\\n 'filter': filter_,\\n 'destination': destination,\\n }\\n self._connection.api_request(method='PUT', path=target, data=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_SinksAPI.sink_delete\",\n \"text\": \"google/cloud/logging/connection.py::_SinksAPI.sink_delete\\n def sink_delete(self, project, sink_name):\\n \\\"\\\"\\\"API call: delete a sink resource.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/delete\\n\\n :type project: string\\n :param project: ID of the project containing the sink.\\n\\n :type sink_name: string\\n :param sink_name: the name of the sink\\n \\\"\\\"\\\"\\n target = '/projects/%s/sinks/%s' % (project, sink_name)\\n self._connection.api_request(method='DELETE', path=target)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_MetricsAPI.__init__\",\n \"text\": \"google/cloud/logging/connection.py::_MetricsAPI.__init__\\n def __init__(self, connection):\\n self._connection = connection\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_MetricsAPI.list_metrics\",\n \"text\": \"google/cloud/logging/connection.py::_MetricsAPI.list_metrics\\n def list_metrics(self, project, page_size=None, page_token=None):\\n \\\"\\\"\\\"List metrics for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/list\\n\\n :type project: string\\n :param project: ID of the project whose metrics are to be listed.\\n\\n :type page_size: int\\n :param page_size: maximum number of metrics to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: str\\n :param page_token: opaque marker for the next \\\"page\\\" of metrics. If not\\n passed, the API will return the first page of\\n metrics.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of mappings, plus a \\\"next page token\\\" string:\\n if not None, indicates that more metrics can be retrieved\\n with another call (pass that value as ``page_token``).\\n \\\"\\\"\\\"\\n params = {}\\n\\n if page_size is not None:\\n params['pageSize'] = page_size\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/metrics' % (project,)\\n resp = self._connection.api_request(\\n method='GET', path=path, query_params=params)\\n metrics = resp.get('metrics', ())\\n return metrics, resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_MetricsAPI.metric_create\",\n \"text\": \"google/cloud/logging/connection.py::_MetricsAPI.metric_create\\n def metric_create(self, project, metric_name, filter_, description=None):\\n \\\"\\\"\\\"API call: create a metric resource.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/create\\n\\n :type project: string\\n :param project: ID of the project in which to create the metric.\\n\\n :type metric_name: string\\n :param metric_name: the name of the metric\\n\\n :type filter_: string\\n :param filter_: the advanced logs filter expression defining the\\n entries exported by the metric.\\n\\n :type description: string\\n :param description: description of the metric.\\n \\\"\\\"\\\"\\n target = '/projects/%s/metrics' % (project,)\\n data = {\\n 'name': metric_name,\\n 'filter': filter_,\\n 'description': description,\\n }\\n self._connection.api_request(method='POST', path=target, data=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_MetricsAPI.metric_get\",\n \"text\": \"google/cloud/logging/connection.py::_MetricsAPI.metric_get\\n def metric_get(self, project, metric_name):\\n \\\"\\\"\\\"API call: retrieve a metric resource.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/get\\n\\n :type project: string\\n :param project: ID of the project containing the metric.\\n\\n :type metric_name: string\\n :param metric_name: the name of the metric\\n\\n :rtype: dict\\n :returns: The JSON metric object returned from the API.\\n \\\"\\\"\\\"\\n target = '/projects/%s/metrics/%s' % (project, metric_name)\\n return self._connection.api_request(method='GET', path=target)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_MetricsAPI.metric_update\",\n \"text\": \"google/cloud/logging/connection.py::_MetricsAPI.metric_update\\n def metric_update(self, project, metric_name, filter_, description):\\n \\\"\\\"\\\"API call: update a metric resource.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/update\\n\\n :type project: string\\n :param project: ID of the project containing the metric.\\n\\n :type metric_name: string\\n :param metric_name: the name of the metric\\n\\n :type filter_: string\\n :param filter_: the advanced logs filter expression defining the\\n entries exported by the metric.\\n\\n :type description: string\\n :param description: description of the metric.\\n \\\"\\\"\\\"\\n target = '/projects/%s/metrics/%s' % (project, metric_name)\\n data = {\\n 'name': metric_name,\\n 'filter': filter_,\\n 'description': description,\\n }\\n self._connection.api_request(method='PUT', path=target, data=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/connection.py::_MetricsAPI.metric_delete\",\n \"text\": \"google/cloud/logging/connection.py::_MetricsAPI.metric_delete\\n def metric_delete(self, project, metric_name):\\n \\\"\\\"\\\"API call: delete a metric resource.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/delete\\n\\n :type project: string\\n :param project: ID of the project containing the metric.\\n\\n :type metric_name: string\\n :param metric_name: the name of the metric.\\n \\\"\\\"\\\"\\n target = '/projects/%s/metrics/%s' % (project, metric_name)\\n self._connection.api_request(method='DELETE', path=target)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/entries.py::logger_name_from_path\",\n \"text\": \"google/cloud/logging/entries.py::logger_name_from_path\\ndef logger_name_from_path(path):\\n \\\"\\\"\\\"Validate a logger URI path and get the logger name.\\n\\n :type path: str\\n :param path: URI path for a logger API request.\\n\\n :rtype: str\\n :returns: Logger name parsed from ``path``.\\n :raises: :class:`ValueError` if the ``path`` is ill-formed or if\\n the project from the ``path`` does not agree with the\\n ``project`` passed in.\\n \\\"\\\"\\\"\\n return _name_from_project_path(path, None, _LOGGER_TEMPLATE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/entries.py::_BaseEntry.__init__\",\n \"text\": \"google/cloud/logging/entries.py::_BaseEntry.__init__\\n def __init__(self, payload, logger, insert_id=None, timestamp=None,\\n labels=None, severity=None, http_request=None):\\n self.payload = payload\\n self.logger = logger\\n self.insert_id = insert_id\\n self.timestamp = timestamp\\n self.labels = labels\\n self.severity = severity\\n self.http_request = http_request\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/entries.py::_BaseEntry.from_api_repr\",\n \"text\": \"google/cloud/logging/entries.py::_BaseEntry.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client, loggers=None):\\n \\\"\\\"\\\"Factory: construct an entry given its API representation\\n\\n :type resource: dict\\n :param resource: text entry resource representation returned from\\n the API\\n\\n :type client: :class:`google.cloud.logging.client.Client`\\n :param client: Client which holds credentials and project\\n configuration.\\n\\n :type loggers: dict or None\\n :param loggers: A mapping of logger fullnames -> loggers. If not\\n passed, the entry will have a newly-created logger.\\n\\n :rtype: :class:`google.cloud.logging.entries.TextEntry`\\n :returns: Text entry parsed from ``resource``.\\n \\\"\\\"\\\"\\n if loggers is None:\\n loggers = {}\\n logger_fullname = resource['logName']\\n logger = loggers.get(logger_fullname)\\n if logger is None:\\n logger_name = logger_name_from_path(logger_fullname)\\n logger = loggers[logger_fullname] = client.logger(logger_name)\\n payload = resource[cls._PAYLOAD_KEY]\\n insert_id = resource.get('insertId')\\n timestamp = resource.get('timestamp')\\n if timestamp is not None:\\n timestamp = _rfc3339_nanos_to_datetime(timestamp)\\n labels = resource.get('labels')\\n severity = resource.get('severity')\\n http_request = resource.get('httpRequest')\\n return cls(payload, logger, insert_id=insert_id, timestamp=timestamp,\\n labels=labels, severity=severity, http_request=http_request)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/entries.py::ProtobufEntry.parse_message\",\n \"text\": \"google/cloud/logging/entries.py::ProtobufEntry.parse_message\\n def parse_message(self, message):\\n \\\"\\\"\\\"Parse payload into a protobuf message.\\n\\n Mutates the passed-in ``message`` in place.\\n\\n :type message: Protobuf message\\n :param message: the message to be logged\\n \\\"\\\"\\\"\\n Parse(json.dumps(self.payload), message)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/handlers.py::CloudLoggingHandler.__init__\",\n \"text\": \"google/cloud/logging/handlers/handlers.py::CloudLoggingHandler.__init__\\n def __init__(self, client,\\n name=DEFAULT_LOGGER_NAME,\\n transport=BackgroundThreadTransport):\\n super(CloudLoggingHandler, self).__init__()\\n self.name = name\\n self.client = client\\n self.transport = transport(client, name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/handlers.py::CloudLoggingHandler.emit\",\n \"text\": \"google/cloud/logging/handlers/handlers.py::CloudLoggingHandler.emit\\n def emit(self, record):\\n \\\"\\\"\\\"Actually log the specified logging record.\\n\\n Overrides the default emit behavior of ``StreamHandler``.\\n\\n See: https://docs.python.org/2/library/logging.html#handler-objects\\n\\n :type record: :class:`logging.LogRecord`\\n :param record: The record to be logged.\\n \\\"\\\"\\\"\\n message = super(CloudLoggingHandler, self).format(record)\\n self.transport.send(record, message)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/handlers.py::setup_logging\",\n \"text\": \"google/cloud/logging/handlers/handlers.py::setup_logging\\ndef setup_logging(handler, excluded_loggers=EXCLUDE_LOGGER_DEFAULTS):\\n \\\"\\\"\\\"Attach the ``CloudLogging`` handler to the Python root logger\\n\\n Excludes loggers that this library itself uses to avoid\\n infinite recursion.\\n\\n :type handler: :class:`logging.handler`\\n :param handler: the handler to attach to the global handler\\n\\n :type excluded_loggers: tuple\\n :param excluded_loggers: The loggers to not attach the handler to. This\\n will always include the loggers in the path of\\n the logging client itself.\\n\\n Example:\\n\\n .. doctest::\\n\\n import logging\\n import google.cloud.logging\\n from google.cloud.logging.handlers import CloudLoggingHandler\\n\\n client = google.cloud.logging.Client()\\n handler = CloudLoggingHandler(client)\\n google.cloud.logging.setup_logging(handler)\\n logging.getLogger().setLevel(logging.DEBUG)\\n\\n logging.error('bad news') # API call\\n\\n \\\"\\\"\\\"\\n all_excluded_loggers = set(excluded_loggers + EXCLUDE_LOGGER_DEFAULTS)\\n logger = logging.getLogger()\\n logger.addHandler(handler)\\n logger.addHandler(logging.StreamHandler())\\n for logger_name in all_excluded_loggers:\\n logger = logging.getLogger(logger_name)\\n logger.propagate = False\\n logger.addHandler(logging.StreamHandler())\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/background_thread.py::_Worker.__init__\",\n \"text\": \"google/cloud/logging/handlers/transports/background_thread.py::_Worker.__init__\\n def __init__(self, logger):\\n self.started = False\\n self.stopping = False\\n self.stopped = False\\n\\n # _entries_condition is used to signal from the main thread whether\\n # there are any waiting queued logger entries to be written\\n self._entries_condition = threading.Condition()\\n\\n # _stop_condition is used to signal from the worker thread to the\\n # main thread that it's finished its last entries\\n self._stop_condition = threading.Condition()\\n\\n # This object continually reuses the same :class:`Batch` object to\\n # write multiple entries at the same time.\\n self.logger = logger\\n self.batch = self.logger.batch()\\n\\n self._thread = None\\n\\n # Number in seconds of how long to wait for worker to send remaining\\n self._stop_timeout = 5\\n\\n self._start()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/background_thread.py::_Worker._run\",\n \"text\": \"google/cloud/logging/handlers/transports/background_thread.py::_Worker._run\\n def _run(self):\\n \\\"\\\"\\\"The entry point for the worker thread.\\n\\n Loops until ``stopping`` is set to :data:`True`, and commits batch\\n entries written during :meth:`enqueue`.\\n \\\"\\\"\\\"\\n try:\\n self._entries_condition.acquire()\\n self.started = True\\n while not self.stopping:\\n if len(self.batch.entries) == 0:\\n # branch coverage of this code extremely flaky\\n self._entries_condition.wait() # pragma: NO COVER\\n\\n if len(self.batch.entries) > 0:\\n self.batch.commit()\\n finally:\\n self._entries_condition.release()\\n\\n # main thread may be waiting for worker thread to finish writing its\\n # final entries. here we signal that it's done.\\n self._stop_condition.acquire()\\n self._stop_condition.notify()\\n self._stop_condition.release()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/background_thread.py::_Worker._start\",\n \"text\": \"google/cloud/logging/handlers/transports/background_thread.py::_Worker._start\\n def _start(self):\\n \\\"\\\"\\\"Called by this class's constructor\\n\\n This method is responsible for starting the thread and registering\\n the exit handlers.\\n \\\"\\\"\\\"\\n try:\\n self._entries_condition.acquire()\\n self._thread = threading.Thread(\\n target=self._run,\\n name='google.cloud.logging.handlers.transport.Worker')\\n self._thread.setDaemon(True)\\n self._thread.start()\\n finally:\\n self._entries_condition.release()\\n atexit.register(self._stop)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/background_thread.py::_Worker._stop\",\n \"text\": \"google/cloud/logging/handlers/transports/background_thread.py::_Worker._stop\\n def _stop(self):\\n \\\"\\\"\\\"Signals the worker thread to shut down\\n\\n Also waits for ``stop_timeout`` seconds for the worker to finish.\\n\\n This method is called by the ``atexit`` handler registered by\\n :meth:`start`.\\n \\\"\\\"\\\"\\n if not self.started or self.stopping:\\n return\\n\\n # lock the stop condition first so that the worker\\n # thread can't notify it's finished before we wait\\n self._stop_condition.acquire()\\n\\n # now notify the worker thread to shutdown\\n self._entries_condition.acquire()\\n self.stopping = True\\n self._entries_condition.notify()\\n self._entries_condition.release()\\n\\n # now wait for it to signal it's finished\\n self._stop_condition.wait(self._stop_timeout)\\n self._stop_condition.release()\\n self.stopped = True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/background_thread.py::_Worker.enqueue\",\n \"text\": \"google/cloud/logging/handlers/transports/background_thread.py::_Worker.enqueue\\n def enqueue(self, record, message):\\n \\\"\\\"\\\"Queues up a log entry to be written by the background thread.\\\"\\\"\\\"\\n try:\\n self._entries_condition.acquire()\\n if self.stopping:\\n return\\n info = {'message': message, 'python_logger': record.name}\\n self.batch.log_struct(info, severity=record.levelname)\\n self._entries_condition.notify()\\n finally:\\n self._entries_condition.release()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/background_thread.py::BackgroundThreadTransport.__init__\",\n \"text\": \"google/cloud/logging/handlers/transports/background_thread.py::BackgroundThreadTransport.__init__\\n def __init__(self, client, name):\\n http = copy.deepcopy(client.connection.http)\\n http = client.connection.credentials.authorize(http)\\n self.client = Client(client.project,\\n client.connection.credentials,\\n http)\\n logger = self.client.logger(name)\\n self.worker = _Worker(logger)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/background_thread.py::BackgroundThreadTransport.send\",\n \"text\": \"google/cloud/logging/handlers/transports/background_thread.py::BackgroundThreadTransport.send\\n def send(self, record, message):\\n \\\"\\\"\\\"Overrides Transport.send().\\n\\n :type record: :class:`logging.LogRecord`\\n :param record: Python log record that the handler was called with.\\n\\n :type message: str\\n :param message: The message from the ``LogRecord`` after being\\n formatted by the associated log formatters.\\n \\\"\\\"\\\"\\n self.worker.enqueue(record, message)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/base.py::Transport.send\",\n \"text\": \"google/cloud/logging/handlers/transports/base.py::Transport.send\\n def send(self, record, message):\\n \\\"\\\"\\\"Transport send to be implemented by subclasses.\\n\\n :type record: :class:`logging.LogRecord`\\n :param record: Python log record that the handler was called with.\\n\\n :type message: str\\n :param message: The message from the ``LogRecord`` after being\\n formatted by the associated log formatters.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/sync.py::SyncTransport.__init__\",\n \"text\": \"google/cloud/logging/handlers/transports/sync.py::SyncTransport.__init__\\n def __init__(self, client, name):\\n self.logger = client.logger(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/handlers/transports/sync.py::SyncTransport.send\",\n \"text\": \"google/cloud/logging/handlers/transports/sync.py::SyncTransport.send\\n def send(self, record, message):\\n \\\"\\\"\\\"Overrides transport.send().\\n\\n :type record: :class:`logging.LogRecord`\\n :param record: Python log record that the handler was called with.\\n\\n :type message: str\\n :param message: The message from the ``LogRecord`` after being\\n formatted by the associated log formatters.\\n \\\"\\\"\\\"\\n info = {'message': message, 'python_logger': record.name}\\n self.logger.log_struct(info, severity=record.levelname)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger.__init__\",\n \"text\": \"google/cloud/logging/logger.py::Logger.__init__\\n def __init__(self, name, client, labels=None):\\n self.name = name\\n self._client = client\\n self.labels = labels\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger.client\",\n \"text\": \"google/cloud/logging/logger.py::Logger.client\\n @property\\n def client(self):\\n \\\"\\\"\\\"Clent bound to the logger.\\\"\\\"\\\"\\n return self._client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger.project\",\n \"text\": \"google/cloud/logging/logger.py::Logger.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project bound to the logger.\\\"\\\"\\\"\\n return self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger.full_name\",\n \"text\": \"google/cloud/logging/logger.py::Logger.full_name\\n @property\\n def full_name(self):\\n \\\"\\\"\\\"Fully-qualified name used in logging APIs\\\"\\\"\\\"\\n return 'projects/%s/logs/%s' % (self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger.path\",\n \"text\": \"google/cloud/logging/logger.py::Logger.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URI path for use in logging APIs\\\"\\\"\\\"\\n return '/%s' % (self.full_name,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger._require_client\",\n \"text\": \"google/cloud/logging/logger.py::Logger._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current logger.\\n\\n :rtype: :class:`google.cloud.logging.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger.batch\",\n \"text\": \"google/cloud/logging/logger.py::Logger.batch\\n def batch(self, client=None):\\n \\\"\\\"\\\"Return a batch to use as a context manager.\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current topic.\\n\\n :rtype: :class:`Batch`\\n :returns: A batch to use as a context manager.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n return Batch(self, client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger._make_entry_resource\",\n \"text\": \"google/cloud/logging/logger.py::Logger._make_entry_resource\\n def _make_entry_resource(self, text=None, info=None, message=None,\\n labels=None, insert_id=None, severity=None,\\n http_request=None):\\n \\\"\\\"\\\"Return a log entry resource of the appropriate type.\\n\\n Helper for :meth:`log_text`, :meth:`log_struct`, and :meth:`log_proto`.\\n\\n Only one of ``text``, ``info``, or ``message`` should be passed.\\n\\n :type text: string or :class:`NoneType`\\n :param text: text payload\\n\\n :type info: dict or :class:`NoneType`\\n :param info: struct payload\\n\\n :type message: Protobuf message or :class:`NoneType`\\n :param message: protobuf payload\\n\\n :type labels: dict or :class:`NoneType`\\n :param labels: labels passed in to calling method.\\n\\n :type insert_id: string or :class:`NoneType`\\n :param insert_id: (optional) unique ID for log entry.\\n\\n :type severity: string or :class:`NoneType`\\n :param severity: (optional) severity of event being logged.\\n\\n :type http_request: dict or :class:`NoneType`\\n :param http_request: (optional) info about HTTP request associated with\\n the entry\\n\\n :rtype: dict\\n :returns: The JSON resource created.\\n \\\"\\\"\\\"\\n resource = {\\n 'logName': self.full_name,\\n 'resource': {'type': 'global'},\\n }\\n\\n if text is not None:\\n resource['textPayload'] = text\\n\\n if info is not None:\\n resource['jsonPayload'] = info\\n\\n if message is not None:\\n as_json_str = MessageToJson(message)\\n as_json = json.loads(as_json_str)\\n resource['protoPayload'] = as_json\\n\\n if labels is None:\\n labels = self.labels\\n\\n if labels is not None:\\n resource['labels'] = labels\\n\\n if insert_id is not None:\\n resource['insertId'] = insert_id\\n\\n if s\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger.log_text\",\n \"text\": \"google/cloud/logging/logger.py::Logger.log_text\\n def log_text(self, text, client=None, labels=None, insert_id=None,\\n severity=None, http_request=None):\\n \\\"\\\"\\\"API call: log a text message via a POST request\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/write\\n\\n :type text: text\\n :param text: the log message.\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current logger.\\n\\n :type labels: dict or :class:`NoneType`\\n :param labels: (optional) mapping of labels for the entry.\\n\\n :type insert_id: string or :class:`NoneType`\\n :param insert_id: (optional) unique ID for log entry.\\n\\n :type severity: string or :class:`NoneType`\\n :param severity: (optional) severity of event being logged.\\n\\n :type http_request: dict or :class:`NoneType`\\n :param http_request: (optional) info about HTTP request associated with\\n the entry\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n entry_resource = self._make_entry_resource(\\n text=text, labels=labels, insert_id=insert_id, severity=severity,\\n http_request=http_request)\\n client.logging_api.write_entries([entry_resource])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger.log_struct\",\n \"text\": \"google/cloud/logging/logger.py::Logger.log_struct\\n def log_struct(self, info, client=None, labels=None, insert_id=None,\\n severity=None, http_request=None):\\n \\\"\\\"\\\"API call: log a structured message via a POST request\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/write\\n\\n :type info: dict\\n :param info: the log entry information\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current logger.\\n\\n :type labels: dict or :class:`NoneType`\\n :param labels: (optional) mapping of labels for the entry.\\n\\n :type insert_id: string or :class:`NoneType`\\n :param insert_id: (optional) unique ID for log entry.\\n\\n :type severity: string or :class:`NoneType`\\n :param severity: (optional) severity of event being logged.\\n\\n :type http_request: dict or :class:`NoneType`\\n :param http_request: (optional) info about HTTP request associated with\\n the entry.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n entry_resource = self._make_entry_resource(\\n info=info, labels=labels, insert_id=insert_id, severity=severity,\\n http_request=http_request)\\n client.logging_api.write_entries([entry_resource])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger.log_proto\",\n \"text\": \"google/cloud/logging/logger.py::Logger.log_proto\\n def log_proto(self, message, client=None, labels=None, insert_id=None,\\n severity=None, http_request=None):\\n \\\"\\\"\\\"API call: log a protobuf message via a POST request\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/write\\n\\n :type message: Protobuf message\\n :param message: the message to be logged\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current logger.\\n\\n :type labels: dict or :class:`NoneType`\\n :param labels: (optional) mapping of labels for the entry.\\n\\n :type insert_id: string or :class:`NoneType`\\n :param insert_id: (optional) unique ID for log entry.\\n\\n :type severity: string or :class:`NoneType`\\n :param severity: (optional) severity of event being logged.\\n\\n :type http_request: dict or :class:`NoneType`\\n :param http_request: (optional) info about HTTP request associated with\\n the entry.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n entry_resource = self._make_entry_resource(\\n message=message, labels=labels, insert_id=insert_id,\\n severity=severity, http_request=http_request)\\n client.logging_api.write_entries([entry_resource])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger.delete\",\n \"text\": \"google/cloud/logging/logger.py::Logger.delete\\n def delete(self, client=None):\\n \\\"\\\"\\\"API call: delete all entries in a logger via a DELETE request\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.logs/delete\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current logger.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.logging_api.logger_delete(self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Logger.list_entries\",\n \"text\": \"google/cloud/logging/logger.py::Logger.list_entries\\n def list_entries(self, projects=None, filter_=None, order_by=None,\\n page_size=None, page_token=None):\\n \\\"\\\"\\\"Return a page of log entries.\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/entries/list\\n\\n :type projects: list of strings\\n :param projects: project IDs to include. If not passed,\\n defaults to the project bound to the client.\\n\\n :type filter_: string\\n :param filter_: a filter expression. See:\\n https://cloud.google.com/logging/docs/view/advanced_filters\\n\\n :type order_by: string\\n :param order_by: One of :data:`~google.cloud.logging.ASCENDING`\\n or :data:`~google.cloud.logging.DESCENDING`.\\n\\n :type page_size: int\\n :param page_size: maximum number of entries to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of entries. If not\\n passed, the API will return the first page of\\n entries.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`google.cloud.logging.entry.TextEntry`, plus a\\n \\\"next page token\\\" string: if not None, indicates that\\n more entries can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n log_filter = 'logName=%s' % (self.full_name,)\\n if filter_ is not None:\\n filter_ = '%s AND %s' % (filter_, log_filter)\\n else:\\n filter_ = log_filter\\n return self.client.list_entries(\\n projects=projects, filter_=filter_, order_by=order_by,\\n page_size=page_size, page_token=page_token)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Batch.__init__\",\n \"text\": \"google/cloud/logging/logger.py::Batch.__init__\\n def __init__(self, logger, client):\\n self.logger = logger\\n self.entries = []\\n self.client = client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Batch.__enter__\",\n \"text\": \"google/cloud/logging/logger.py::Batch.__enter__\\n def __enter__(self):\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Batch.__exit__\",\n \"text\": \"google/cloud/logging/logger.py::Batch.__exit__\\n def __exit__(self, exc_type, exc_val, exc_tb):\\n if exc_type is None:\\n self.commit()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Batch.log_text\",\n \"text\": \"google/cloud/logging/logger.py::Batch.log_text\\n def log_text(self, text, labels=None, insert_id=None, severity=None,\\n http_request=None):\\n \\\"\\\"\\\"Add a text entry to be logged during :meth:`commit`.\\n\\n :type text: string\\n :param text: the text entry\\n\\n :type labels: dict or :class:`NoneType`\\n :param labels: (optional) mapping of labels for the entry.\\n\\n :type insert_id: string or :class:`NoneType`\\n :param insert_id: (optional) unique ID for log entry.\\n\\n :type severity: string or :class:`NoneType`\\n :param severity: (optional) severity of event being logged.\\n\\n :type http_request: dict or :class:`NoneType`\\n :param http_request: (optional) info about HTTP request associated with\\n the entry.\\n \\\"\\\"\\\"\\n self.entries.append(\\n ('text', text, labels, insert_id, severity, http_request))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Batch.log_struct\",\n \"text\": \"google/cloud/logging/logger.py::Batch.log_struct\\n def log_struct(self, info, labels=None, insert_id=None, severity=None,\\n http_request=None):\\n \\\"\\\"\\\"Add a struct entry to be logged during :meth:`commit`.\\n\\n :type info: dict\\n :param info: the struct entry\\n\\n :type labels: dict or :class:`NoneType`\\n :param labels: (optional) mapping of labels for the entry.\\n\\n :type insert_id: string or :class:`NoneType`\\n :param insert_id: (optional) unique ID for log entry.\\n\\n :type severity: string or :class:`NoneType`\\n :param severity: (optional) severity of event being logged.\\n\\n :type http_request: dict or :class:`NoneType`\\n :param http_request: (optional) info about HTTP request associated with\\n the entry.\\n \\\"\\\"\\\"\\n self.entries.append(\\n ('struct', info, labels, insert_id, severity, http_request))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Batch.log_proto\",\n \"text\": \"google/cloud/logging/logger.py::Batch.log_proto\\n def log_proto(self, message, labels=None, insert_id=None, severity=None,\\n http_request=None):\\n \\\"\\\"\\\"Add a protobuf entry to be logged during :meth:`commit`.\\n\\n :type message: protobuf message\\n :param message: the protobuf entry\\n\\n :type labels: dict or :class:`NoneType`\\n :param labels: (optional) mapping of labels for the entry.\\n\\n :type insert_id: string or :class:`NoneType`\\n :param insert_id: (optional) unique ID for log entry.\\n\\n :type severity: string or :class:`NoneType`\\n :param severity: (optional) severity of event being logged.\\n\\n :type http_request: dict or :class:`NoneType`\\n :param http_request: (optional) info about HTTP request associated with\\n the entry.\\n \\\"\\\"\\\"\\n self.entries.append(\\n ('proto', message, labels, insert_id, severity, http_request))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/logger.py::Batch.commit\",\n \"text\": \"google/cloud/logging/logger.py::Batch.commit\\n def commit(self, client=None):\\n \\\"\\\"\\\"Send saved log entries as a single API call.\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current batch.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self.client\\n\\n kwargs = {\\n 'logger_name': self.logger.full_name,\\n 'resource': {'type': 'global'},\\n }\\n if self.logger.labels is not None:\\n kwargs['labels'] = self.logger.labels\\n\\n entries = []\\n for entry_type, entry, labels, iid, severity, http_req in self.entries:\\n if entry_type == 'text':\\n info = {'textPayload': entry}\\n elif entry_type == 'struct':\\n info = {'jsonPayload': entry}\\n elif entry_type == 'proto':\\n as_json_str = MessageToJson(entry)\\n as_json = json.loads(as_json_str)\\n info = {'protoPayload': as_json}\\n else:\\n raise ValueError('Unknown entry type: %s' % (entry_type,))\\n if labels is not None:\\n info['labels'] = labels\\n if iid is not None:\\n info['insertId'] = iid\\n if severity is not None:\\n info['severity'] = severity\\n if http_req is not None:\\n info['httpRequest'] = http_req\\n entries.append(info)\\n\\n client.logging_api.write_entries(entries, **kwargs)\\n del self.entries[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric.__init__\",\n \"text\": \"google/cloud/logging/metric.py::Metric.__init__\\n def __init__(self, name, filter_=None, client=None, description=''):\\n self.name = name\\n self._client = client\\n self.filter_ = filter_\\n self.description = description\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric.client\",\n \"text\": \"google/cloud/logging/metric.py::Metric.client\\n @property\\n def client(self):\\n \\\"\\\"\\\"Clent bound to the logger.\\\"\\\"\\\"\\n return self._client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric.project\",\n \"text\": \"google/cloud/logging/metric.py::Metric.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project bound to the logger.\\\"\\\"\\\"\\n return self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric.full_name\",\n \"text\": \"google/cloud/logging/metric.py::Metric.full_name\\n @property\\n def full_name(self):\\n \\\"\\\"\\\"Fully-qualified name used in metric APIs\\\"\\\"\\\"\\n return 'projects/%s/metrics/%s' % (self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric.path\",\n \"text\": \"google/cloud/logging/metric.py::Metric.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URL path for the metric's APIs\\\"\\\"\\\"\\n return '/%s' % (self.full_name,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric.from_api_repr\",\n \"text\": \"google/cloud/logging/metric.py::Metric.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client):\\n \\\"\\\"\\\"Factory: construct a metric given its API representation\\n\\n :type resource: dict\\n :param resource: metric resource representation returned from the API\\n\\n :type client: :class:`google.cloud.logging.client.Client`\\n :param client: Client which holds credentials and project\\n configuration for the metric.\\n\\n :rtype: :class:`google.cloud.logging.metric.Metric`\\n :returns: Metric parsed from ``resource``.\\n \\\"\\\"\\\"\\n metric_name = resource['name']\\n filter_ = resource['filter']\\n description = resource.get('description', '')\\n return cls(metric_name, filter_, client=client,\\n description=description)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric._require_client\",\n \"text\": \"google/cloud/logging/metric.py::Metric._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current metric.\\n\\n :rtype: :class:`google.cloud.logging.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric.create\",\n \"text\": \"google/cloud/logging/metric.py::Metric.create\\n def create(self, client=None):\\n \\\"\\\"\\\"API call: create the metric via a PUT request\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/create\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current metric.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.metrics_api.metric_create(\\n self.project, self.name, self.filter_, self.description)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric.exists\",\n \"text\": \"google/cloud/logging/metric.py::Metric.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"API call: test for the existence of the metric via a GET request\\n\\n See\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/get\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current metric.\\n\\n :rtype: bool\\n :returns: Boolean indicating existence of the metric.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n try:\\n client.metrics_api.metric_get(self.project, self.name)\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric.reload\",\n \"text\": \"google/cloud/logging/metric.py::Metric.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"API call: sync local metric configuration via a GET request\\n\\n See\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/get\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current metric.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n data = client.metrics_api.metric_get(self.project, self.name)\\n self.description = data.get('description', '')\\n self.filter_ = data['filter']\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric.update\",\n \"text\": \"google/cloud/logging/metric.py::Metric.update\\n def update(self, client=None):\\n \\\"\\\"\\\"API call: update metric configuration via a PUT request\\n\\n See\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/update\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current metric.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.metrics_api.metric_update(\\n self.project, self.name, self.filter_, self.description)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/metric.py::Metric.delete\",\n \"text\": \"google/cloud/logging/metric.py::Metric.delete\\n def delete(self, client=None):\\n \\\"\\\"\\\"API call: delete a metric via a DELETE request\\n\\n See\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.metrics/delete\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current metric.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.metrics_api.metric_delete(self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink.__init__\",\n \"text\": \"google/cloud/logging/sink.py::Sink.__init__\\n def __init__(self, name, filter_=None, destination=None, client=None):\\n self.name = name\\n self.filter_ = filter_\\n self.destination = destination\\n self._client = client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink.client\",\n \"text\": \"google/cloud/logging/sink.py::Sink.client\\n @property\\n def client(self):\\n \\\"\\\"\\\"Clent bound to the sink.\\\"\\\"\\\"\\n return self._client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink.project\",\n \"text\": \"google/cloud/logging/sink.py::Sink.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project bound to the sink.\\\"\\\"\\\"\\n return self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink.full_name\",\n \"text\": \"google/cloud/logging/sink.py::Sink.full_name\\n @property\\n def full_name(self):\\n \\\"\\\"\\\"Fully-qualified name used in sink APIs\\\"\\\"\\\"\\n return 'projects/%s/sinks/%s' % (self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink.path\",\n \"text\": \"google/cloud/logging/sink.py::Sink.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URL path for the sink's APIs\\\"\\\"\\\"\\n return '/%s' % (self.full_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink.from_api_repr\",\n \"text\": \"google/cloud/logging/sink.py::Sink.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client):\\n \\\"\\\"\\\"Factory: construct a sink given its API representation\\n\\n :type resource: dict\\n :param resource: sink resource representation returned from the API\\n\\n :type client: :class:`google.cloud.logging.client.Client`\\n :param client: Client which holds credentials and project\\n configuration for the sink.\\n\\n :rtype: :class:`google.cloud.logging.sink.Sink`\\n :returns: Sink parsed from ``resource``.\\n :raises: :class:`ValueError` if ``client`` is not ``None`` and the\\n project from the resource does not agree with the project\\n from the client.\\n \\\"\\\"\\\"\\n sink_name = resource['name']\\n filter_ = resource['filter']\\n destination = resource['destination']\\n return cls(sink_name, filter_, destination, client=client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink._require_client\",\n \"text\": \"google/cloud/logging/sink.py::Sink._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current sink.\\n\\n :rtype: :class:`google.cloud.logging.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink.create\",\n \"text\": \"google/cloud/logging/sink.py::Sink.create\\n def create(self, client=None):\\n \\\"\\\"\\\"API call: create the sink via a PUT request\\n\\n See:\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/create\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current sink.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.sinks_api.sink_create(\\n self.project, self.name, self.filter_, self.destination)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink.exists\",\n \"text\": \"google/cloud/logging/sink.py::Sink.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"API call: test for the existence of the sink via a GET request\\n\\n See\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/get\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current sink.\\n\\n :rtype: bool\\n :returns: Boolean indicating existence of the sink.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n try:\\n client.sinks_api.sink_get(self.project, self.name)\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink.reload\",\n \"text\": \"google/cloud/logging/sink.py::Sink.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"API call: sync local sink configuration via a GET request\\n\\n See\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/get\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current sink.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n data = client.sinks_api.sink_get(self.project, self.name)\\n self.filter_ = data['filter']\\n self.destination = data['destination']\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink.update\",\n \"text\": \"google/cloud/logging/sink.py::Sink.update\\n def update(self, client=None):\\n \\\"\\\"\\\"API call: update sink configuration via a PUT request\\n\\n See\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/update\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current sink.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.sinks_api.sink_update(\\n self.project, self.name, self.filter_, self.destination)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/logging/sink.py::Sink.delete\",\n \"text\": \"google/cloud/logging/sink.py::Sink.delete\\n def delete(self, client=None):\\n \\\"\\\"\\\"API call: delete a sink via a DELETE request\\n\\n See\\n https://cloud.google.com/logging/docs/api/ref_v2beta1/rest/v2beta1/projects.sinks/delete\\n\\n :type client: :class:`~google.cloud.logging.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current sink.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.sinks_api.sink_delete(self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/_dataframe.py::_build_dataframe\",\n \"text\": \"google/cloud/monitoring/_dataframe.py::_build_dataframe\\ndef _build_dataframe(time_series_iterable,\\n label=None, labels=None): # pragma: NO COVER\\n \\\"\\\"\\\"Build a :mod:`pandas` dataframe out of time series.\\n\\n :type time_series_iterable:\\n iterable over :class:`~google.cloud.monitoring.timeseries.TimeSeries`\\n :param time_series_iterable:\\n An iterable (e.g., a query object) yielding time series.\\n\\n :type label: string or None\\n :param label:\\n The label name to use for the dataframe header. This can be the name\\n of a resource label or metric label (e.g., ``\\\"instance_name\\\"``), or\\n the string ``\\\"resource_type\\\"``.\\n\\n :type labels: list of strings, or None\\n :param labels:\\n A list or tuple of label names to use for the dataframe header.\\n If more than one label name is provided, the resulting dataframe\\n will have a multi-level column header.\\n\\n Specifying neither ``label`` or ``labels`` results in a dataframe\\n with a multi-level column header including the resource type and\\n all available resource and metric labels.\\n\\n Specifying both ``label`` and ``labels`` is an error.\\n\\n :rtype: :class:`pandas.DataFrame`\\n :returns: A dataframe where each column represents one time series.\\n \\\"\\\"\\\"\\n import pandas # pylint: disable=import-error\\n\\n if labels is not None:\\n if label is not None:\\n raise ValueError('Cannot specify both \\\"label\\\" and \\\"labels\\\".')\\n elif not labels:\\n raise ValueError('\\\"labels\\\" must be non-empty or None.')\\n\\n columns = []\\n headers = []\\n for time_series in time_series_iterable:\\n pandas_series = pandas.Series(\\n data=[point.value for point in time_series.points],\\n index=[point.end_time for point in time_series.points],\\n )\\n columns.append(pandas_series)\\n headers.append(time_series.header())\\n\\n # Implement a smart default of using all available labels.\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/_dataframe.py::_sorted_resource_labels\",\n \"text\": \"google/cloud/monitoring/_dataframe.py::_sorted_resource_labels\\ndef _sorted_resource_labels(labels):\\n \\\"\\\"\\\"Sort label names, putting well-known resource labels first.\\\"\\\"\\\"\\n head = [label for label in TOP_RESOURCE_LABELS if label in labels]\\n tail = sorted(label for label in labels\\n if label not in TOP_RESOURCE_LABELS)\\n return head + tail\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.query\",\n \"text\": \"google/cloud/monitoring/client.py::Client.query\\n def query(self,\\n metric_type=Query.DEFAULT_METRIC_TYPE,\\n end_time=None,\\n days=0, hours=0, minutes=0):\\n \\\"\\\"\\\"Construct a query object for retrieving metric data.\\n\\n Example::\\n\\n >>> query = client.query(minutes=5)\\n >>> print(query.as_dataframe()) # Requires pandas.\\n\\n :type metric_type: string\\n :param metric_type: The metric type name. The default value is\\n :data:`Query.DEFAULT_METRIC_TYPE\\n <google.cloud.monitoring.query.Query.DEFAULT_METRIC_TYPE>`,\\n but please note that this default value is provided only for\\n demonstration purposes and is subject to change. See the\\n `supported metrics`_.\\n\\n :type end_time: :class:`datetime.datetime` or None\\n :param end_time: The end time (inclusive) of the time interval\\n for which results should be returned, as a datetime object.\\n The default is the start of the current minute.\\n\\n The start time (exclusive) is determined by combining the\\n values of ``days``, ``hours``, and ``minutes``, and\\n subtracting the resulting duration from the end time.\\n\\n It is also allowed to omit the end time and duration here,\\n in which case\\n :meth:`~google.cloud.monitoring.query.Query.select_interval`\\n must be called before the query is executed.\\n\\n :type days: integer\\n :param days: The number of days in the time interval.\\n\\n :type hours: integer\\n :param hours: The number of hours in the time interval.\\n\\n :type minutes: integer\\n :param minutes: The number of minutes in the time interval.\\n\\n :rtype: :class:`~google.cloud.monitoring.query.Query`\\n :returns: The query object.\\n\\n :raises: :exc:`ValueError` if ``end_time`` is specified but\\n ``days``, ``hours``, and ``minutes`` are all zero.\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.metric_descriptor\",\n \"text\": \"google/cloud/monitoring/client.py::Client.metric_descriptor\\n def metric_descriptor(self, type_,\\n metric_kind=MetricKind.METRIC_KIND_UNSPECIFIED,\\n value_type=ValueType.VALUE_TYPE_UNSPECIFIED,\\n labels=(), unit='', description='', display_name=''):\\n \\\"\\\"\\\"Construct a metric descriptor object.\\n\\n Metric descriptors specify the schema for a particular metric type.\\n\\n This factory method is used most often in conjunction with the metric\\n descriptor\\n :meth:`~google.cloud.monitoring.metric.MetricDescriptor.create`\\n method to define custom metrics::\\n\\n >>> descriptor = client.metric_descriptor(\\n ... 'custom.googleapis.com/my_metric',\\n ... metric_kind=MetricKind.GAUGE,\\n ... value_type=ValueType.DOUBLE,\\n ... description='This is a simple example of a custom metric.')\\n >>> descriptor.create()\\n\\n Here is an example where the custom metric is parameterized by a\\n metric label::\\n\\n >>> label = LabelDescriptor('response_code', LabelValueType.INT64,\\n ... description='HTTP status code')\\n >>> descriptor = client.metric_descriptor(\\n ... 'custom.googleapis.com/my_app/response_count',\\n ... metric_kind=MetricKind.CUMULATIVE,\\n ... value_type=ValueType.INT64,\\n ... labels=[label],\\n ... description='Cumulative count of HTTP responses.')\\n >>> descriptor.create()\\n\\n :type type_: string\\n :param type_:\\n The metric type including a DNS name prefix. For example:\\n ``\\\"custom.googleapis.com/my_metric\\\"``\\n\\n :type metric_kind: string\\n :param metric_kind:\\n The kind of measurement. It must be one of\\n :data:`MetricKind.GAUGE`, :data:`MetricKind.DELTA`,\\n or :data:`MetricKind.CUMULATIV\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.metric\",\n \"text\": \"google/cloud/monitoring/client.py::Client.metric\\n @staticmethod\\n def metric(type_, labels):\\n \\\"\\\"\\\"Factory for constructing metric objects.\\n\\n :class:`~google.cloud.monitoring.metric.Metric` objects are typically\\n created to write custom metric values. The type should match the\\n metric type specified in the\\n :class:`~google.cloud.monitoring.metric.MetricDescriptor` used to\\n create the custom metric::\\n\\n >>> metric = client.metric('custom.googleapis.com/my_metric',\\n ... labels={\\n ... 'status': 'successful',\\n ... })\\n\\n :type type_: string\\n :param type_: The metric type name.\\n\\n :type labels: dict\\n :param labels: A mapping from label names to values for all labels\\n enumerated in the associated\\n :class:`~google.cloud.monitoring.metric.MetricDescriptor`.\\n\\n :rtype: :class:`~google.cloud.monitoring.metric.Metric`\\n :returns: The metric object.\\n \\\"\\\"\\\"\\n return Metric(type=type_, labels=labels)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.resource\",\n \"text\": \"google/cloud/monitoring/client.py::Client.resource\\n @staticmethod\\n def resource(type_, labels):\\n \\\"\\\"\\\"Factory for constructing monitored resource objects.\\n\\n A monitored resource object (\\n :class:`~google.cloud.monitoring.resource.Resource`) is\\n typically used to create a\\n :class:`~google.cloud.monitoring.timeseries.TimeSeries` object.\\n\\n For a list of possible monitored resource types and their associated\\n labels, see:\\n\\n https://cloud.google.com/monitoring/api/resources\\n\\n :type type_: string\\n :param type_: The monitored resource type name.\\n\\n :type labels: dict\\n :param labels: A mapping from label names to values for all labels\\n enumerated in the associated\\n :class:`~google.cloud.monitoring.resource.ResourceDescriptor`,\\n except that ``project_id`` can and should be omitted\\n when writing time series data.\\n\\n :rtype: :class:`~google.cloud.monitoring.resource.Resource`\\n :returns: A monitored resource object.\\n \\\"\\\"\\\"\\n return Resource(type_, labels)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.time_series\",\n \"text\": \"google/cloud/monitoring/client.py::Client.time_series\\n @staticmethod\\n def time_series(metric, resource, value,\\n end_time=None, start_time=None):\\n \\\"\\\"\\\"Construct a time series object for a single data point.\\n\\n .. note::\\n\\n While :class:`~google.cloud.monitoring.timeseries.TimeSeries`\\n objects returned by the API typically have multiple data points,\\n :class:`~google.cloud.monitoring.timeseries.TimeSeries` objects\\n sent to the API must have at most one point.\\n\\n For example::\\n\\n >>> timeseries = client.time_series(metric, resource, 1.23,\\n ... end_time=end)\\n\\n For more information, see:\\n\\n https://cloud.google.com/monitoring/api/ref_v3/rest/v3/TimeSeries\\n\\n :type metric: :class:`~google.cloud.monitoring.metric.Metric`\\n :param metric: A :class:`~google.cloud.monitoring.metric.Metric`.\\n\\n :type resource: :class:`~google.cloud.monitoring.resource.Resource`\\n :param resource: A :class:`~google.cloud.monitoring.resource.Resource`\\n object.\\n\\n :type value: bool, int, string, or float\\n :param value:\\n The value of the data point to create for the\\n :class:`~google.cloud.monitoring.timeseries.TimeSeries`.\\n\\n .. note::\\n\\n The Python type of the value will determine the\\n :class:`~ValueType` sent to the API, which must match the value\\n type specified in the metric descriptor. For example, a Python\\n float will be sent to the API as a :data:`ValueType.DOUBLE`.\\n\\n :type end_time: :class:`~datetime.datetime`\\n :param end_time:\\n The end time for the point to be included in the time series.\\n Assumed to be UTC if no time zone information is present.\\n Defaults to the current time, as obtained by calling\\n :meth:`datetime.datetime.utcnow`.\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.fetch_metric_descriptor\",\n \"text\": \"google/cloud/monitoring/client.py::Client.fetch_metric_descriptor\\n def fetch_metric_descriptor(self, metric_type):\\n \\\"\\\"\\\"Look up a metric descriptor by type.\\n\\n Example::\\n\\n >>> METRIC = 'compute.googleapis.com/instance/cpu/utilization'\\n >>> print(client.fetch_metric_descriptor(METRIC))\\n\\n :type metric_type: string\\n :param metric_type: The metric type name.\\n\\n :rtype: :class:`~google.cloud.monitoring.metric.MetricDescriptor`\\n :returns: The metric descriptor instance.\\n\\n :raises: :class:`google.cloud.exceptions.NotFound` if the metric\\n descriptor is not found.\\n \\\"\\\"\\\"\\n return MetricDescriptor._fetch(self, metric_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.list_metric_descriptors\",\n \"text\": \"google/cloud/monitoring/client.py::Client.list_metric_descriptors\\n def list_metric_descriptors(self, filter_string=None, type_prefix=None):\\n \\\"\\\"\\\"List all metric descriptors for the project.\\n\\n Examples::\\n\\n >>> for descriptor in client.list_metric_descriptors():\\n ... print(descriptor.type)\\n\\n >>> for descriptor in client.list_metric_descriptors(\\n ... type_prefix='custom.'):\\n ... print(descriptor.type)\\n\\n :type filter_string: string or None\\n :param filter_string:\\n An optional filter expression describing the metric descriptors\\n to be returned. See the `filter documentation`_.\\n\\n :type type_prefix: string or None\\n :param type_prefix: An optional prefix constraining the selected\\n metric types. This adds ``metric.type = starts_with(\\\"<prefix>\\\")``\\n to the filter.\\n\\n :rtype:\\n list of :class:`~google.cloud.monitoring.metric.MetricDescriptor`\\n :returns: A list of metric descriptor instances.\\n\\n .. _filter documentation:\\n https://cloud.google.com/monitoring/api/v3/filters\\n \\\"\\\"\\\"\\n return MetricDescriptor._list(self, filter_string,\\n type_prefix=type_prefix)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.fetch_resource_descriptor\",\n \"text\": \"google/cloud/monitoring/client.py::Client.fetch_resource_descriptor\\n def fetch_resource_descriptor(self, resource_type):\\n \\\"\\\"\\\"Look up a monitored resource descriptor by type.\\n\\n Example::\\n\\n >>> print(client.fetch_resource_descriptor('gce_instance'))\\n\\n :type resource_type: string\\n :param resource_type: The resource type name.\\n\\n :rtype: :class:`~google.cloud.monitoring.resource.ResourceDescriptor`\\n :returns: The resource descriptor instance.\\n\\n :raises: :class:`google.cloud.exceptions.NotFound` if the resource\\n descriptor is not found.\\n \\\"\\\"\\\"\\n return ResourceDescriptor._fetch(self, resource_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.list_resource_descriptors\",\n \"text\": \"google/cloud/monitoring/client.py::Client.list_resource_descriptors\\n def list_resource_descriptors(self, filter_string=None):\\n \\\"\\\"\\\"List all monitored resource descriptors for the project.\\n\\n Example::\\n\\n >>> for descriptor in client.list_resource_descriptors():\\n ... print(descriptor.type)\\n\\n :type filter_string: string or None\\n :param filter_string:\\n An optional filter expression describing the resource descriptors\\n to be returned. See the `filter documentation`_.\\n\\n :rtype: list of\\n :class:`~google.cloud.monitoring.resource.ResourceDescriptor`\\n :returns: A list of resource descriptor instances.\\n\\n .. _filter documentation:\\n https://cloud.google.com/monitoring/api/v3/filters\\n \\\"\\\"\\\"\\n return ResourceDescriptor._list(self, filter_string)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.group\",\n \"text\": \"google/cloud/monitoring/client.py::Client.group\\n def group(self, group_id=None, display_name=None, parent_id=None,\\n filter_string=None, is_cluster=False):\\n \\\"\\\"\\\"Factory constructor for group object.\\n\\n .. note::\\n This will not make an HTTP request; it simply instantiates\\n a group object owned by this client.\\n\\n :type group_id: string or None\\n :param group_id: The ID of the group.\\n\\n :type display_name: string or None\\n :param display_name:\\n A user-assigned name for this group, used only for display\\n purposes.\\n\\n :type parent_id: string or None\\n :param parent_id:\\n The ID of the group's parent, if it has one.\\n\\n :type filter_string: string or None\\n :param filter_string:\\n The filter string used to determine which monitored resources\\n belong to this group.\\n\\n :type is_cluster: boolean\\n :param is_cluster:\\n If true, the members of this group are considered to be a cluster.\\n The system can perform additional analysis on groups that are\\n clusters.\\n\\n :rtype: :class:`Group`\\n :returns: The group created with the passed-in arguments.\\n\\n :raises:\\n :exc:`ValueError` if both ``group_id`` and ``name`` are specified.\\n \\\"\\\"\\\"\\n return Group(\\n self,\\n group_id=group_id,\\n display_name=display_name,\\n parent_id=parent_id,\\n filter_string=filter_string,\\n is_cluster=is_cluster,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.fetch_group\",\n \"text\": \"google/cloud/monitoring/client.py::Client.fetch_group\\n def fetch_group(self, group_id):\\n \\\"\\\"\\\"Fetch a group from the API based on it's ID.\\n\\n Example::\\n\\n >>> try:\\n >>> group = client.fetch_group('1234')\\n >>> except google.cloud.exceptions.NotFound:\\n >>> print('That group does not exist!')\\n\\n :type group_id: string\\n :param group_id: The ID of the group.\\n\\n :rtype: :class:`~google.cloud.monitoring.group.Group`\\n :returns: The group instance.\\n\\n :raises: :class:`google.cloud.exceptions.NotFound` if the group\\n is not found.\\n \\\"\\\"\\\"\\n return Group._fetch(self, group_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.list_groups\",\n \"text\": \"google/cloud/monitoring/client.py::Client.list_groups\\n def list_groups(self):\\n \\\"\\\"\\\"List all groups for the project.\\n\\n Example::\\n\\n >>> for group in client.list_groups():\\n ... print((group.display_name, group.name))\\n\\n :rtype: list of :class:`~google.cloud.monitoring.group.Group`\\n :returns: A list of group instances.\\n \\\"\\\"\\\"\\n return Group._list(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.write_time_series\",\n \"text\": \"google/cloud/monitoring/client.py::Client.write_time_series\\n def write_time_series(self, timeseries_list):\\n \\\"\\\"\\\"Write a list of time series objects to the API.\\n\\n The recommended approach to creating time series objects is using\\n the :meth:`~google.cloud.monitoring.client.Client.time_series` factory\\n method.\\n\\n Example::\\n\\n >>> client.write_time_series([ts1, ts2])\\n\\n If you only need to write a single time series object, consider using\\n the :meth:`~google.cloud.monitoring.client.Client.write_point` method\\n instead.\\n\\n :type timeseries_list:\\n list of :class:`~google.cloud.monitoring.timeseries.TimeSeries`\\n :param timeseries_list:\\n A list of time series object to be written\\n to the API. Each time series must contain exactly one point.\\n \\\"\\\"\\\"\\n path = '/projects/{project}/timeSeries/'.format(\\n project=self.project)\\n timeseries_dict = [timeseries._to_dict()\\n for timeseries in timeseries_list]\\n self.connection.api_request(method='POST', path=path,\\n data={'timeSeries': timeseries_dict})\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/client.py::Client.write_point\",\n \"text\": \"google/cloud/monitoring/client.py::Client.write_point\\n def write_point(self, metric, resource, value,\\n end_time=None,\\n start_time=None):\\n \\\"\\\"\\\"Write a single point for a metric to the API.\\n\\n This is a convenience method to write a single time series object to\\n the API. To write multiple time series objects to the API as a batch\\n operation, use the\\n :meth:`~google.cloud.monitoring.client.Client.time_series`\\n factory method to create time series objects and the\\n :meth:`~google.cloud.monitoring.client.Client.write_time_series`\\n method to write the objects.\\n\\n Example::\\n\\n >>> client.write_point(metric, resource, 3.14)\\n\\n :type metric: :class:`~google.cloud.monitoring.metric.Metric`\\n :param metric: A :class:`~google.cloud.monitoring.metric.Metric`\\n object.\\n\\n :type resource: :class:`~google.cloud.monitoring.resource.Resource`\\n :param resource: A :class:`~google.cloud.monitoring.resource.Resource`\\n object.\\n\\n :type value: bool, int, string, or float\\n :param value:\\n The value of the data point to create for the\\n :class:`~google.cloud.monitoring.timeseries.TimeSeries`.\\n\\n .. note::\\n\\n The Python type of the value will determine the\\n :class:`~ValueType` sent to the API, which must match the value\\n type specified in the metric descriptor. For example, a Python\\n float will be sent to the API as a :data:`ValueType.DOUBLE`.\\n\\n :type end_time: :class:`~datetime.datetime`\\n :param end_time:\\n The end time for the point to be included in the time series.\\n Assumed to be UTC if no time zone information is present.\\n Defaults to the current time, as obtained by calling\\n :meth:`datetime.datetime.utcnow`.\\n\\n :type start_time: :class:`~datetime.datet\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::_group_id_from_name\",\n \"text\": \"google/cloud/monitoring/group.py::_group_id_from_name\\ndef _group_id_from_name(path, project=None):\\n \\\"\\\"\\\"Validate a group URI path and get the group ID.\\n\\n :type path: string\\n :param path: URI path for a group API request.\\n\\n :type project: string or None\\n :param project: The project associated with the request. It is\\n included for validation purposes.\\n\\n :rtype: string\\n :returns: Group ID parsed from ``path``.\\n :raises: :class:`ValueError` if the ``path`` is ill-formed or if\\n the project from the ``path`` does not agree with the\\n ``project`` passed in.\\n \\\"\\\"\\\"\\n return _name_from_project_path(path, project, _GROUP_TEMPLATE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::_group_name_from_id\",\n \"text\": \"google/cloud/monitoring/group.py::_group_name_from_id\\ndef _group_name_from_id(project, group_id):\\n \\\"\\\"\\\"Build the group name given the project and group ID.\\n\\n :type project: string\\n :param project: The project associated with the group.\\n\\n :type group_id: string\\n :param group_id: The group ID.\\n\\n :rtype: string\\n :returns: The fully qualified name of the group.\\n \\\"\\\"\\\"\\n return 'projects/{project}/groups/{group_id}'.format(\\n project=project, group_id=group_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.__init__\",\n \"text\": \"google/cloud/monitoring/group.py::Group.__init__\\n def __init__(self, client, group_id=None, display_name=None,\\n parent_id=None, filter_string=None, is_cluster=False):\\n self.client = client\\n self._id = group_id\\n self.display_name = display_name\\n self.parent_id = parent_id\\n self.filter = filter_string\\n self.is_cluster = is_cluster\\n\\n if group_id:\\n self._name = _group_name_from_id(client.project, group_id)\\n else:\\n self._name = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.id\",\n \"text\": \"google/cloud/monitoring/group.py::Group.id\\n @property\\n def id(self):\\n \\\"\\\"\\\"Returns the group ID.\\n\\n :rtype: str or None\\n :returns: the ID of the group based on it's name.\\n \\\"\\\"\\\"\\n return self._id\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.name\",\n \"text\": \"google/cloud/monitoring/group.py::Group.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Returns the fully qualified name of the group.\\n\\n :rtype: str or None\\n :returns:\\n The fully qualified name of the group in the format\\n \\\"projects/<project>/groups/<id>\\\".\\n \\\"\\\"\\\"\\n return self._name\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.parent_name\",\n \"text\": \"google/cloud/monitoring/group.py::Group.parent_name\\n @property\\n def parent_name(self):\\n \\\"\\\"\\\"Returns the fully qualified name of the parent group.\\n\\n :rtype: str or None\\n :returns:\\n The fully qualified name of the parent group.\\n \\\"\\\"\\\"\\n if not self.parent_id:\\n return None\\n return _group_name_from_id(self.client.project, self.parent_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.path\",\n \"text\": \"google/cloud/monitoring/group.py::Group.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URL path to this group.\\n\\n :rtype: str\\n :returns: the path based on project and group name.\\n\\n :raises: :exc:`ValueError` if :attr:`name` is not specified.\\n \\\"\\\"\\\"\\n if not self.id:\\n raise ValueError('Cannot determine path without group ID.')\\n return '/' + self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.create\",\n \"text\": \"google/cloud/monitoring/group.py::Group.create\\n def create(self):\\n \\\"\\\"\\\"Create a new group based on this object via a ``POST`` request.\\n\\n Example::\\n\\n >>> filter_string = 'resource.type = \\\"gce_instance\\\"'\\n >>> group = client.group(\\n ... display_name='My group',\\n ... filter_string=filter_string,\\n ... parent_id='5678',\\n ... is_cluster=True)\\n >>> group.create()\\n\\n The ``name`` attribute is ignored in preparing the creation request.\\n All attributes are overwritten by the values received in the response\\n (normally affecting only ``name``).\\n \\\"\\\"\\\"\\n path = '/projects/%s/groups/' % (self.client.project,)\\n info = self.client.connection.api_request(\\n method='POST', path=path, data=self._to_dict())\\n self._set_properties_from_dict(info)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.exists\",\n \"text\": \"google/cloud/monitoring/group.py::Group.exists\\n def exists(self):\\n \\\"\\\"\\\"Test for the existence of the group via a ``GET`` request.\\n\\n :rtype: bool\\n :returns: Boolean indicating existence of the group.\\n \\\"\\\"\\\"\\n try:\\n self.client.connection.api_request(\\n method='GET', path=self.path, query_params={'fields': 'name'})\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.reload\",\n \"text\": \"google/cloud/monitoring/group.py::Group.reload\\n def reload(self):\\n \\\"\\\"\\\"Sync local group information via a ``GET`` request.\\n\\n .. warning::\\n\\n This will overwrite any local changes you've made and not saved\\n via :meth:`update`.\\n \\\"\\\"\\\"\\n info = self.client.connection.api_request(method='GET', path=self.path)\\n self._set_properties_from_dict(info)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.update\",\n \"text\": \"google/cloud/monitoring/group.py::Group.update\\n def update(self):\\n \\\"\\\"\\\"Update the group via a ``PUT`` request.\\\"\\\"\\\"\\n info = self.client.connection.api_request(\\n method='PUT', path=self.path, data=self._to_dict())\\n self._set_properties_from_dict(info)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.delete\",\n \"text\": \"google/cloud/monitoring/group.py::Group.delete\\n def delete(self):\\n \\\"\\\"\\\"Delete the group via a ``DELETE`` request.\\n\\n Example::\\n\\n >>> group = client.group('1234')\\n >>> group.delete()\\n\\n Only the ``client`` and ``name`` attributes are used.\\n\\n .. warning::\\n\\n This method will fail for groups that have one or more children\\n groups.\\n \\\"\\\"\\\"\\n self.client.connection.api_request(method='DELETE', path=self.path)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.fetch_parent\",\n \"text\": \"google/cloud/monitoring/group.py::Group.fetch_parent\\n def fetch_parent(self):\\n \\\"\\\"\\\"Returns the parent group of this group via a ``GET`` request.\\n\\n :rtype: :class:`Group` or None\\n :returns: The parent of the group.\\n \\\"\\\"\\\"\\n if not self.parent_id:\\n return None\\n return self._fetch(self.client, self.parent_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.list_children\",\n \"text\": \"google/cloud/monitoring/group.py::Group.list_children\\n def list_children(self):\\n \\\"\\\"\\\"Lists all children of this group via a ``GET`` request.\\n\\n Returns groups whose parent_name field contains the group name. If no\\n groups have this parent, the results are empty.\\n\\n :rtype: list of :class:`~google.cloud.monitoring.group.Group`\\n :returns: A list of group instances.\\n \\\"\\\"\\\"\\n return self._list(self.client, children_of_group=self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.list_ancestors\",\n \"text\": \"google/cloud/monitoring/group.py::Group.list_ancestors\\n def list_ancestors(self):\\n \\\"\\\"\\\"Lists all ancestors of this group via a ``GET`` request.\\n\\n The groups are returned in order, starting with the immediate parent\\n and ending with the most distant ancestor. If the specified group has\\n no immediate parent, the results are empty.\\n\\n :rtype: list of :class:`~google.cloud.monitoring.group.Group`\\n :returns: A list of group instances.\\n \\\"\\\"\\\"\\n return self._list(self.client, ancestors_of_group=self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.list_descendants\",\n \"text\": \"google/cloud/monitoring/group.py::Group.list_descendants\\n def list_descendants(self):\\n \\\"\\\"\\\"Lists all descendants of this group via a ``GET`` request.\\n\\n This returns a superset of the results returned by the :meth:`children`\\n method, and includes children-of-children, and so forth.\\n\\n :rtype: list of :class:`~google.cloud.monitoring.group.Group`\\n :returns: A list of group instances.\\n \\\"\\\"\\\"\\n return self._list(self.client, descendants_of_group=self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.list_members\",\n \"text\": \"google/cloud/monitoring/group.py::Group.list_members\\n def list_members(self, filter_string=None, end_time=None, start_time=None):\\n \\\"\\\"\\\"Lists all members of this group via a ``GET`` request.\\n\\n If no ``end_time`` is provided then the group membership over the last\\n minute is returned.\\n\\n Example::\\n\\n >>> for member in group.list_members():\\n ... print member\\n\\n List members that are Compute Engine VM instances::\\n\\n >>> filter_string = 'resource.type = \\\"gce_instance\\\"'\\n >>> for member in group.list_members(filter_string=filter_string):\\n ... print member\\n\\n List historical members that existed between 4 and 5 hours ago::\\n\\n >>> import datetime\\n >>> t1 = datetime.datetime.utcnow() - datetime.timedelta(hours=4)\\n >>> t0 = t1 - datetime.timedelta(hours=1)\\n >>> for member in group.list_members(end_time=t1, start_time=t0):\\n ... print member\\n\\n\\n :type filter_string: string or None\\n :param filter_string:\\n An optional list filter describing the members to be returned. The\\n filter may reference the type, labels, and metadata of monitored\\n resources that comprise the group. See the `filter documentation`_.\\n\\n :type end_time: :class:`datetime.datetime` or None\\n :param end_time:\\n The end time (inclusive) of the time interval for which results\\n should be returned, as a datetime object. If ``start_time`` is\\n specified, then this must also be specified.\\n\\n :type start_time: :class:`datetime.datetime` or None\\n :param start_time:\\n The start time (exclusive) of the time interval for which results\\n should be returned, as a datetime object.\\n\\n :rtype: list of :class:`~google.cloud.monitoring.resource.Resource`\\n :returns: A list of resource instances.\\n\\n :raises:\\n :exc:`ValueError` \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group._fetch\",\n \"text\": \"google/cloud/monitoring/group.py::Group._fetch\\n @classmethod\\n def _fetch(cls, client, group_id):\\n \\\"\\\"\\\"Fetch a group from the API based on it's ID.\\n\\n :type client: :class:`google.cloud.monitoring.client.Client`\\n :param client: The client to use.\\n\\n :type group_id: string\\n :param group_id: The group ID.\\n\\n :rtype: :class:`Group`\\n :returns: The group instance.\\n\\n :raises: :class:`google.cloud.exceptions.NotFound` if the group\\n is not found.\\n \\\"\\\"\\\"\\n new_group = cls(client, group_id)\\n new_group.reload()\\n return new_group\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group._list\",\n \"text\": \"google/cloud/monitoring/group.py::Group._list\\n @classmethod\\n def _list(cls, client, children_of_group=None, ancestors_of_group=None,\\n descendants_of_group=None):\\n \\\"\\\"\\\"Lists all groups in the project.\\n\\n :type client: :class:`google.cloud.monitoring.client.Client`\\n :param client: The client to use.\\n\\n :type children_of_group: string or None\\n :param children_of_group:\\n Returns groups whose parent_name field contains the group name. If\\n no groups have this parent, the results are empty.\\n\\n :type ancestors_of_group: string or None\\n :param ancestors_of_group:\\n Returns groups that are ancestors of the specified group. If the\\n specified group has no immediate parent, the results are empty.\\n\\n :type descendants_of_group: string or None\\n :param descendants_of_group:\\n Returns the descendants of the specified group. This is a superset\\n of the results returned by the children_of_group filter, and\\n includes children-of-children, and so forth.\\n\\n :rtype: list of :class:`~google.cloud.monitoring.group.Group`\\n :returns: A list of group instances.\\n \\\"\\\"\\\"\\n path = '/projects/%s/groups/' % (client.project,)\\n groups = []\\n page_token = None\\n params = {}\\n\\n if children_of_group is not None:\\n params['childrenOfGroup'] = children_of_group\\n\\n if ancestors_of_group is not None:\\n params['ancestorsOfGroup'] = ancestors_of_group\\n\\n if descendants_of_group is not None:\\n params['descendantsOfGroup'] = descendants_of_group\\n\\n while True:\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n response = client.connection.api_request(\\n method='GET', path=path, query_params=params.copy())\\n for info in response.get('group', ()):\\n groups.append(cls._from_dict(client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group._from_dict\",\n \"text\": \"google/cloud/monitoring/group.py::Group._from_dict\\n @classmethod\\n def _from_dict(cls, client, info):\\n \\\"\\\"\\\"Constructs a Group instance from the parsed JSON representation.\\n\\n :type client: :class:`google.cloud.monitoring.client.Client`\\n :param client: A client to be included in the returned object.\\n\\n :type info: dict\\n :param info:\\n A ``dict`` parsed from the JSON wire-format representation.\\n\\n :rtype: :class:`Group`\\n :returns: A group.\\n \\\"\\\"\\\"\\n group = cls(client)\\n group._set_properties_from_dict(info)\\n return group\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group._set_properties_from_dict\",\n \"text\": \"google/cloud/monitoring/group.py::Group._set_properties_from_dict\\n def _set_properties_from_dict(self, info):\\n \\\"\\\"\\\"Update the group properties from its API representation.\\n\\n :type info: dict\\n :param info:\\n A ``dict`` parsed from the JSON wire-format representation.\\n \\\"\\\"\\\"\\n self._name = info['name']\\n self._id = _group_id_from_name(self._name)\\n self.display_name = info['displayName']\\n self.filter = info['filter']\\n self.is_cluster = info.get('isCluster', False)\\n\\n parent_name = info.get('parentName')\\n if parent_name is None:\\n self.parent_id = None\\n else:\\n self.parent_id = _group_id_from_name(parent_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group._to_dict\",\n \"text\": \"google/cloud/monitoring/group.py::Group._to_dict\\n def _to_dict(self):\\n \\\"\\\"\\\"Build a dictionary ready to be serialized to the JSON wire format.\\n\\n :rtype: dict\\n :returns: A dictionary.\\n \\\"\\\"\\\"\\n info = {\\n 'filter': self.filter,\\n 'displayName': self.display_name,\\n 'isCluster': self.is_cluster,\\n }\\n\\n if self.name is not None:\\n info['name'] = self.name\\n\\n parent_name = self.parent_name\\n if parent_name is not None:\\n info['parentName'] = parent_name\\n\\n return info\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/group.py::Group.__repr__\",\n \"text\": \"google/cloud/monitoring/group.py::Group.__repr__\\n def __repr__(self):\\n return '<Group: %s>' % (self.name,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/label.py::LabelDescriptor.__init__\",\n \"text\": \"google/cloud/monitoring/label.py::LabelDescriptor.__init__\\n def __init__(self, key, value_type=LabelValueType.STRING, description=''):\\n self.key = key\\n self.value_type = value_type\\n self.description = description\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/label.py::LabelDescriptor._from_dict\",\n \"text\": \"google/cloud/monitoring/label.py::LabelDescriptor._from_dict\\n @classmethod\\n def _from_dict(cls, info):\\n \\\"\\\"\\\"Construct a label descriptor from the parsed JSON representation.\\n\\n :type info: dict\\n :param info:\\n A ``dict`` parsed from the JSON wire-format representation.\\n\\n :rtype: :class:`LabelDescriptor`\\n :returns: A label descriptor.\\n \\\"\\\"\\\"\\n return cls(\\n info['key'],\\n info.get('valueType', LabelValueType.STRING),\\n info.get('description', ''),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/label.py::LabelDescriptor._to_dict\",\n \"text\": \"google/cloud/monitoring/label.py::LabelDescriptor._to_dict\\n def _to_dict(self):\\n \\\"\\\"\\\"Build a dictionary ready to be serialized to the JSON wire format.\\n\\n :rtype: dict\\n :returns: A dictionary.\\n \\\"\\\"\\\"\\n info = {\\n 'key': self.key,\\n 'valueType': self.value_type,\\n }\\n\\n if self.description:\\n info['description'] = self.description\\n\\n return info\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/label.py::LabelDescriptor.__eq__\",\n \"text\": \"google/cloud/monitoring/label.py::LabelDescriptor.__eq__\\n def __eq__(self, other):\\n return self.__dict__ == other.__dict__\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/label.py::LabelDescriptor.__ne__\",\n \"text\": \"google/cloud/monitoring/label.py::LabelDescriptor.__ne__\\n def __ne__(self, other):\\n return self.__dict__ != other.__dict__\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/label.py::LabelDescriptor.__repr__\",\n \"text\": \"google/cloud/monitoring/label.py::LabelDescriptor.__repr__\\n def __repr__(self):\\n return (\\n 'LabelDescriptor(key={key!r}, value_type={value_type!r},'\\n ' description={description!r})'\\n ).format(**self.__dict__)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py::MetricDescriptor.__init__\",\n \"text\": \"google/cloud/monitoring/metric.py::MetricDescriptor.__init__\\n def __init__(self, client, type_,\\n metric_kind=MetricKind.METRIC_KIND_UNSPECIFIED,\\n value_type=ValueType.VALUE_TYPE_UNSPECIFIED,\\n labels=(),\\n unit='', description='', display_name='',\\n name=None):\\n self.client = client\\n self.name = name\\n self.type = type_\\n self.labels = labels\\n self.metric_kind = metric_kind\\n self.value_type = value_type\\n self.unit = unit\\n self.description = description\\n self.display_name = display_name\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py::MetricDescriptor.create\",\n \"text\": \"google/cloud/monitoring/metric.py::MetricDescriptor.create\\n def create(self):\\n \\\"\\\"\\\"Create a new metric descriptor based on this object.\\n\\n Example::\\n\\n >>> descriptor = client.metric_descriptor(\\n ... 'custom.googleapis.com/my_metric',\\n ... metric_kind=MetricKind.GAUGE,\\n ... value_type=ValueType.DOUBLE,\\n ... description='This is a simple example of a custom metric.')\\n >>> descriptor.create()\\n\\n The metric kind must not be :data:`MetricKind.METRIC_KIND_UNSPECIFIED`,\\n and the value type must not be\\n :data:`ValueType.VALUE_TYPE_UNSPECIFIED`.\\n\\n The ``name`` attribute is ignored in preparing the creation request.\\n All attributes are overwritten by the values received in the response\\n (normally affecting only ``name``).\\n \\\"\\\"\\\"\\n path = '/projects/{project}/metricDescriptors/'.format(\\n project=self.client.project)\\n response = self.client.connection.api_request(method='POST', path=path,\\n data=self._to_dict())\\n self._init_from_dict(response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py::MetricDescriptor.delete\",\n \"text\": \"google/cloud/monitoring/metric.py::MetricDescriptor.delete\\n def delete(self):\\n \\\"\\\"\\\"Delete the metric descriptor identified by this object.\\n\\n Example::\\n\\n >>> descriptor = client.metric_descriptor(\\n ... 'custom.googleapis.com/my_metric')\\n >>> descriptor.delete()\\n\\n Only the ``client`` and ``type`` attributes are used.\\n \\\"\\\"\\\"\\n path = '/projects/{project}/metricDescriptors/{type}'.format(\\n project=self.client.project,\\n type=self.type)\\n self.client.connection.api_request(method='DELETE', path=path)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py::MetricDescriptor._fetch\",\n \"text\": \"google/cloud/monitoring/metric.py::MetricDescriptor._fetch\\n @classmethod\\n def _fetch(cls, client, metric_type):\\n \\\"\\\"\\\"Look up a metric descriptor by type.\\n\\n :type client: :class:`google.cloud.monitoring.client.Client`\\n :param client: The client to use.\\n\\n :type metric_type: string\\n :param metric_type: The metric type name.\\n\\n :rtype: :class:`MetricDescriptor`\\n :returns: The metric descriptor instance.\\n\\n :raises: :class:`google.cloud.exceptions.NotFound` if the metric\\n descriptor is not found.\\n \\\"\\\"\\\"\\n path = '/projects/{project}/metricDescriptors/{type}'.format(\\n project=client.project,\\n type=metric_type)\\n info = client.connection.api_request(method='GET', path=path)\\n return cls._from_dict(client, info)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py::MetricDescriptor._list\",\n \"text\": \"google/cloud/monitoring/metric.py::MetricDescriptor._list\\n @classmethod\\n def _list(cls, client, filter_string=None, type_prefix=None):\\n \\\"\\\"\\\"List all metric descriptors for the project.\\n\\n :type client: :class:`google.cloud.monitoring.client.Client`\\n :param client: The client to use.\\n\\n :type filter_string: string or None\\n :param filter_string:\\n An optional filter expression describing the metric descriptors\\n to be returned. See the `filter documentation`_.\\n\\n :type type_prefix: string or None\\n :param type_prefix: An optional prefix constraining the selected\\n metric types. This adds ``metric.type = starts_with(\\\"<prefix>\\\")``\\n to the filter.\\n\\n :rtype: list of :class:`MetricDescriptor`\\n :returns: A list of metric descriptor instances.\\n\\n .. _filter documentation:\\n https://cloud.google.com/monitoring/api/v3/filters\\n \\\"\\\"\\\"\\n path = '/projects/{project}/metricDescriptors/'.format(\\n project=client.project)\\n\\n filters = []\\n if filter_string is not None:\\n filters.append(filter_string)\\n\\n if type_prefix is not None:\\n filters.append('metric.type = starts_with(\\\"{prefix}\\\")'.format(\\n prefix=type_prefix))\\n\\n descriptors = []\\n page_token = None\\n while True:\\n params = {}\\n\\n if filters:\\n params['filter'] = ' AND '.join(filters)\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n response = client.connection.api_request(\\n method='GET', path=path, query_params=params)\\n for info in response.get('metricDescriptors', ()):\\n descriptors.append(cls._from_dict(client, info))\\n\\n page_token = response.get('nextPageToken')\\n if not page_token:\\n break\\n\\n return descriptors\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py::MetricDescriptor._from_dict\",\n \"text\": \"google/cloud/monitoring/metric.py::MetricDescriptor._from_dict\\n @classmethod\\n def _from_dict(cls, client, info):\\n \\\"\\\"\\\"Construct a metric descriptor from the parsed JSON representation.\\n\\n :type client: :class:`google.cloud.monitoring.client.Client`\\n :param client: A client to be included in the returned object.\\n\\n :type info: dict\\n :param info:\\n A ``dict`` parsed from the JSON wire-format representation.\\n\\n :rtype: :class:`MetricDescriptor`\\n :returns: A metric descriptor.\\n \\\"\\\"\\\"\\n descriptor = cls(client, None)\\n descriptor._init_from_dict(info)\\n return descriptor\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py::MetricDescriptor._init_from_dict\",\n \"text\": \"google/cloud/monitoring/metric.py::MetricDescriptor._init_from_dict\\n def _init_from_dict(self, info):\\n \\\"\\\"\\\"Initialize attributes from the parsed JSON representation.\\n\\n :type info: dict\\n :param info:\\n A ``dict`` parsed from the JSON wire-format representation.\\n \\\"\\\"\\\"\\n self.name = info['name']\\n self.type = info['type']\\n self.labels = tuple(LabelDescriptor._from_dict(label)\\n for label in info.get('labels', []))\\n self.metric_kind = info['metricKind']\\n self.value_type = info['valueType']\\n self.unit = info.get('unit', '')\\n self.description = info.get('description', '')\\n self.display_name = info.get('displayName', '')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py::MetricDescriptor._to_dict\",\n \"text\": \"google/cloud/monitoring/metric.py::MetricDescriptor._to_dict\\n def _to_dict(self):\\n \\\"\\\"\\\"Build a dictionary ready to be serialized to the JSON wire format.\\n\\n :rtype: dict\\n :returns: A dictionary.\\n \\\"\\\"\\\"\\n info = {\\n 'type': self.type,\\n 'metricKind': self.metric_kind,\\n 'valueType': self.value_type,\\n }\\n\\n if self.labels:\\n info['labels'] = [label._to_dict() for label in self.labels]\\n if self.unit:\\n info['unit'] = self.unit\\n if self.description:\\n info['description'] = self.description\\n if self.display_name:\\n info['displayName'] = self.display_name\\n\\n return info\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py::MetricDescriptor.__repr__\",\n \"text\": \"google/cloud/monitoring/metric.py::MetricDescriptor.__repr__\\n def __repr__(self):\\n return (\\n '<MetricDescriptor:\\\\n'\\n ' name={name!r},\\\\n'\\n ' type={type!r},\\\\n'\\n ' metric_kind={metric_kind!r}, value_type={value_type!r},\\\\n'\\n ' labels={labels!r},\\\\n'\\n ' display_name={display_name!r}, unit={unit!r},\\\\n'\\n ' description={description!r}>'\\n ).format(**self.__dict__)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py::Metric._from_dict\",\n \"text\": \"google/cloud/monitoring/metric.py::Metric._from_dict\\n @classmethod\\n def _from_dict(cls, info):\\n \\\"\\\"\\\"Construct a metric object from the parsed JSON representation.\\n\\n :type info: dict\\n :param info:\\n A ``dict`` parsed from the JSON wire-format representation.\\n\\n :rtype: :class:`Metric`\\n :returns: A metric object.\\n \\\"\\\"\\\"\\n return cls(\\n type=info['type'],\\n labels=info.get('labels', {}),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/metric.py::Metric._to_dict\",\n \"text\": \"google/cloud/monitoring/metric.py::Metric._to_dict\\n def _to_dict(self):\\n \\\"\\\"\\\"Build a dictionary ready to be serialized to the JSON format.\\n\\n :rtype: dict\\n :returns: A dict representation of the object that can be written to\\n the API.\\n \\\"\\\"\\\"\\n return {\\n 'type': self.type,\\n 'labels': self.labels,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.__init__\",\n \"text\": \"google/cloud/monitoring/query.py::Query.__init__\\n def __init__(self, client,\\n metric_type=DEFAULT_METRIC_TYPE,\\n end_time=None, days=0, hours=0, minutes=0):\\n start_time = None\\n if days or hours or minutes:\\n if end_time is None:\\n end_time = _UTCNOW().replace(second=0, microsecond=0)\\n start_time = end_time - datetime.timedelta(days=days,\\n hours=hours,\\n minutes=minutes)\\n elif end_time is not None:\\n raise ValueError('Non-zero duration required for time interval.')\\n\\n self._client = client\\n self._end_time = end_time\\n self._start_time = start_time\\n self._filter = _Filter(metric_type)\\n\\n self._per_series_aligner = None\\n self._alignment_period_seconds = None\\n self._cross_series_reducer = None\\n self._group_by_fields = ()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.__iter__\",\n \"text\": \"google/cloud/monitoring/query.py::Query.__iter__\\n def __iter__(self):\\n return self.iter()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.metric_type\",\n \"text\": \"google/cloud/monitoring/query.py::Query.metric_type\\n @property\\n def metric_type(self):\\n \\\"\\\"\\\"The metric type name.\\\"\\\"\\\"\\n return self._filter.metric_type\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.filter\",\n \"text\": \"google/cloud/monitoring/query.py::Query.filter\\n @property\\n def filter(self):\\n \\\"\\\"\\\"The filter string.\\n\\n This is constructed from the metric type, the resource type, and\\n selectors for the group ID, monitored projects, resource labels,\\n and metric labels.\\n \\\"\\\"\\\"\\n return str(self._filter)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.select_interval\",\n \"text\": \"google/cloud/monitoring/query.py::Query.select_interval\\n def select_interval(self, end_time, start_time=None):\\n \\\"\\\"\\\"Copy the query and set the query time interval.\\n\\n Example::\\n\\n import datetime\\n\\n now = datetime.datetime.utcnow()\\n query = query.select_interval(\\n end_time=now,\\n start_time=now - datetime.timedelta(minutes=5))\\n\\n As a convenience, you can alternatively specify the end time and\\n an interval duration when you create the query initially.\\n\\n :type end_time: :class:`datetime.datetime`\\n :param end_time: The end time (inclusive) of the time interval\\n for which results should be returned, as a datetime object.\\n\\n :type start_time: :class:`datetime.datetime` or None\\n :param start_time: The start time (exclusive) of the time interval\\n for which results should be returned, as a datetime object.\\n If not specified, the interval is a point in time.\\n\\n :rtype: :class:`Query`\\n :returns: The new query object.\\n \\\"\\\"\\\"\\n new_query = self.copy()\\n new_query._end_time = end_time\\n new_query._start_time = start_time\\n return new_query\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.select_group\",\n \"text\": \"google/cloud/monitoring/query.py::Query.select_group\\n def select_group(self, group_id):\\n \\\"\\\"\\\"Copy the query and add filtering by group.\\n\\n Example::\\n\\n query = query.select_group('1234567')\\n\\n :type group_id: string\\n :param group_id: The ID of a group to filter by.\\n\\n :rtype: :class:`Query`\\n :returns: The new query object.\\n \\\"\\\"\\\"\\n new_query = self.copy()\\n new_query._filter.group_id = group_id\\n return new_query\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.select_projects\",\n \"text\": \"google/cloud/monitoring/query.py::Query.select_projects\\n def select_projects(self, *args):\\n \\\"\\\"\\\"Copy the query and add filtering by monitored projects.\\n\\n This is only useful if the target project represents a Stackdriver\\n account containing the specified monitored projects.\\n\\n Examples::\\n\\n query = query.select_projects('project-1')\\n query = query.select_projects('project-1', 'project-2')\\n\\n :type args: tuple\\n :param args: Project IDs limiting the resources to be included\\n in the query.\\n\\n :rtype: :class:`Query`\\n :returns: The new query object.\\n \\\"\\\"\\\"\\n new_query = self.copy()\\n new_query._filter.projects = args\\n return new_query\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.select_resources\",\n \"text\": \"google/cloud/monitoring/query.py::Query.select_resources\\n def select_resources(self, *args, **kwargs):\\n \\\"\\\"\\\"Copy the query and add filtering by resource labels.\\n\\n Examples::\\n\\n query = query.select_resources(zone='us-central1-a')\\n query = query.select_resources(zone_prefix='europe-')\\n query = query.select_resources(resource_type='gce_instance')\\n\\n A keyword argument ``<label>=<value>`` ordinarily generates a filter\\n expression of the form::\\n\\n resource.label.<label> = \\\"<value>\\\"\\n\\n However, by adding ``\\\"_prefix\\\"`` or ``\\\"_suffix\\\"`` to the keyword,\\n you can specify a partial match.\\n\\n ``<label>_prefix=<value>`` generates::\\n\\n resource.label.<label> = starts_with(\\\"<value>\\\")\\n\\n ``<label>_suffix=<value>`` generates::\\n\\n resource.label.<label> = ends_with(\\\"<value>\\\")\\n\\n As a special case, ``\\\"resource_type\\\"`` is treated as a special\\n pseudo-label corresponding to the filter object ``resource.type``.\\n For example, ``resource_type=<value>`` generates::\\n\\n resource.type = \\\"<value>\\\"\\n\\n See the `defined resource types`_.\\n\\n .. note::\\n\\n The label ``\\\"instance_name\\\"`` is a metric label,\\n not a resource label. You would filter on it using\\n ``select_metrics(instance_name=...)``.\\n\\n :type args: tuple\\n :param args: Raw filter expression strings to include in the\\n conjunction. If just one is provided and no keyword arguments\\n are provided, it can be a disjunction.\\n\\n :type kwargs: dict\\n :param kwargs: Label filters to include in the conjunction as\\n described above.\\n\\n :rtype: :class:`Query`\\n :returns: The new query object.\\n\\n .. _defined resource types:\\n https://cloud.google.com/monitoring/api/v3/monitored-resources\\n \\\"\\\"\\\"\\n new_query = self.copy()\\n new_query._filter.select_resources(*args, **kw\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.select_metrics\",\n \"text\": \"google/cloud/monitoring/query.py::Query.select_metrics\\n def select_metrics(self, *args, **kwargs):\\n \\\"\\\"\\\"Copy the query and add filtering by metric labels.\\n\\n Examples::\\n\\n query = query.select_metrics(instance_name='myinstance')\\n query = query.select_metrics(instance_name_prefix='mycluster-')\\n\\n A keyword argument ``<label>=<value>`` ordinarily generates a filter\\n expression of the form::\\n\\n metric.label.<label> = \\\"<value>\\\"\\n\\n However, by adding ``\\\"_prefix\\\"`` or ``\\\"_suffix\\\"`` to the keyword,\\n you can specify a partial match.\\n\\n ``<label>_prefix=<value>`` generates::\\n\\n metric.label.<label> = starts_with(\\\"<value>\\\")\\n\\n ``<label>_suffix=<value>`` generates::\\n\\n metric.label.<label> = ends_with(\\\"<value>\\\")\\n\\n If the label's value type is ``INT64``, a similar notation can be\\n used to express inequalities:\\n\\n ``<label>_less=<value>`` generates::\\n\\n metric.label.<label> < <value>\\n\\n ``<label>_lessequal=<value>`` generates::\\n\\n metric.label.<label> <= <value>\\n\\n ``<label>_greater=<value>`` generates::\\n\\n metric.label.<label> > <value>\\n\\n ``<label>_greaterequal=<value>`` generates::\\n\\n metric.label.<label> >= <value>\\n\\n :type args: tuple\\n :param args: Raw filter expression strings to include in the\\n conjunction. If just one is provided and no keyword arguments\\n are provided, it can be a disjunction.\\n\\n :type kwargs: dict\\n :param kwargs: Label filters to include in the conjunction as\\n described above.\\n\\n :rtype: :class:`Query`\\n :returns: The new query object.\\n \\\"\\\"\\\"\\n new_query = self.copy()\\n new_query._filter.select_metrics(*args, **kwargs)\\n return new_query\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.align\",\n \"text\": \"google/cloud/monitoring/query.py::Query.align\\n def align(self, per_series_aligner, seconds=0, minutes=0, hours=0):\\n \\\"\\\"\\\"Copy the query and add temporal alignment.\\n\\n If ``per_series_aligner`` is not :data:`Aligner.ALIGN_NONE`, each time\\n series will contain data points only on the period boundaries.\\n\\n Example::\\n\\n query = query.align(Aligner.ALIGN_MEAN, minutes=5)\\n\\n It is also possible to specify the aligner as a literal string::\\n\\n query = query.align('ALIGN_MEAN', minutes=5)\\n\\n :type per_series_aligner: string\\n :param per_series_aligner: The approach to be used to align\\n individual time series. For example: :data:`Aligner.ALIGN_MEAN`.\\n See :class:`Aligner` and the descriptions of the `supported\\n aligners`_.\\n\\n :type seconds: integer\\n :param seconds: The number of seconds in the alignment period.\\n\\n :type minutes: integer\\n :param minutes: The number of minutes in the alignment period.\\n\\n :type hours: integer\\n :param hours: The number of hours in the alignment period.\\n\\n :rtype: :class:`Query`\\n :returns: The new query object.\\n\\n .. _supported aligners:\\n https://cloud.google.com/monitoring/api/ref_v3/rest/v3/\\\\\\n projects.timeSeries/list#Aligner\\n \\\"\\\"\\\"\\n new_query = self.copy()\\n new_query._per_series_aligner = per_series_aligner\\n new_query._alignment_period_seconds = seconds + 60 * (minutes +\\n 60 * hours)\\n return new_query\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.reduce\",\n \"text\": \"google/cloud/monitoring/query.py::Query.reduce\\n def reduce(self, cross_series_reducer, *group_by_fields):\\n \\\"\\\"\\\"Copy the query and add cross-series reduction.\\n\\n Cross-series reduction combines time series by aggregating their\\n data points.\\n\\n For example, you could request an aggregated time series for each\\n combination of project and zone as follows::\\n\\n query = query.reduce(Reducer.REDUCE_MEAN,\\n 'resource.project_id', 'resource.zone')\\n\\n :type cross_series_reducer: string\\n :param cross_series_reducer:\\n The approach to be used to combine time series. For example:\\n :data:`Reducer.REDUCE_MEAN`. See :class:`Reducer` and the\\n descriptions of the `supported reducers`_.\\n\\n :type group_by_fields: strings\\n :param group_by_fields:\\n Fields to be preserved by the reduction. For example, specifying\\n just ``\\\"resource.zone\\\"`` will result in one time series per zone.\\n The default is to aggregate all of the time series into just one.\\n\\n :rtype: :class:`Query`\\n :returns: The new query object.\\n\\n .. _supported reducers:\\n https://cloud.google.com/monitoring/api/ref_v3/rest/v3/\\\\\\n projects.timeSeries/list#Reducer\\n \\\"\\\"\\\"\\n new_query = self.copy()\\n new_query._cross_series_reducer = cross_series_reducer\\n new_query._group_by_fields = group_by_fields\\n return new_query\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.iter\",\n \"text\": \"google/cloud/monitoring/query.py::Query.iter\\n def iter(self, headers_only=False, page_size=None):\\n \\\"\\\"\\\"Yield all time series objects selected by the query.\\n\\n The generator returned iterates over\\n :class:`~google.cloud.monitoring.timeseries.TimeSeries` objects\\n containing points ordered from oldest to newest.\\n\\n Note that the :class:`Query` object itself is an iterable, such that\\n the following are equivalent::\\n\\n for timeseries in query:\\n ...\\n\\n for timeseries in query.iter():\\n ...\\n\\n :type headers_only: boolean\\n :param headers_only:\\n Whether to omit the point data from the time series objects.\\n\\n :type page_size: integer or None\\n :param page_size:\\n An optional positive number specifying the maximum number of\\n points to return per page. This can be used to control how far\\n the iterator reads ahead.\\n\\n :raises: :exc:`ValueError` if the query time interval has not been\\n specified.\\n \\\"\\\"\\\"\\n # The following use of groupby() relies on equality comparison\\n # of time series as (named) tuples.\\n for timeseries, fragments in itertools.groupby(\\n self._iter_fragments(headers_only, page_size),\\n lambda fragment: fragment.header()):\\n points = list(itertools.chain.from_iterable(\\n fragment.points for fragment in fragments))\\n points.reverse() # Order from oldest to newest.\\n yield timeseries.header(points=points)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query._iter_fragments\",\n \"text\": \"google/cloud/monitoring/query.py::Query._iter_fragments\\n def _iter_fragments(self, headers_only=False, page_size=None):\\n \\\"\\\"\\\"Yield all time series fragments selected by the query.\\n\\n There may be multiple fragments per time series. These will be\\n contiguous.\\n\\n The parameters and return value are as for :meth:`Query.iter`.\\n \\\"\\\"\\\"\\n if self._end_time is None:\\n raise ValueError('Query time interval not specified.')\\n\\n path = '/projects/{project}/timeSeries/'.format(\\n project=self._client.project)\\n\\n page_token = None\\n while True:\\n params = list(self._build_query_params(\\n headers_only=headers_only,\\n page_size=page_size,\\n page_token=page_token,\\n ))\\n response = self._client.connection.api_request(\\n method='GET',\\n path=path,\\n query_params=params,\\n )\\n for info in response.get('timeSeries', ()):\\n yield TimeSeries._from_dict(info)\\n\\n page_token = response.get('nextPageToken')\\n if not page_token:\\n break\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query._build_query_params\",\n \"text\": \"google/cloud/monitoring/query.py::Query._build_query_params\\n def _build_query_params(self, headers_only=False,\\n page_size=None, page_token=None):\\n \\\"\\\"\\\"Yield key-value pairs for the URL query string.\\n\\n We use a series of key-value pairs (suitable for passing to\\n ``urlencode``) instead of a ``dict`` to allow for repeated fields.\\n\\n :type headers_only: boolean\\n :param headers_only:\\n Whether to omit the point data from the\\n :class:`~google.cloud.monitoring.timeseries.TimeSeries` objects.\\n\\n :type page_size: integer or None\\n :param page_size: A limit on the number of points to return per page.\\n\\n :type page_token: string or None\\n :param page_token: A token to continue the retrieval.\\n \\\"\\\"\\\"\\n yield 'filter', self.filter\\n\\n yield 'interval.endTime', _datetime_to_rfc3339(\\n self._end_time, ignore_zone=False)\\n\\n if self._start_time is not None:\\n yield 'interval.startTime', _datetime_to_rfc3339(\\n self._start_time, ignore_zone=False)\\n\\n if self._per_series_aligner is not None:\\n yield 'aggregation.perSeriesAligner', self._per_series_aligner\\n\\n if self._alignment_period_seconds is not None:\\n alignment_period = '{period}s'.format(\\n period=self._alignment_period_seconds)\\n yield 'aggregation.alignmentPeriod', alignment_period\\n\\n if self._cross_series_reducer is not None:\\n yield ('aggregation.crossSeriesReducer',\\n self._cross_series_reducer)\\n\\n for field in self._group_by_fields:\\n yield 'aggregation.groupByFields', field\\n\\n if headers_only:\\n yield 'view', 'HEADERS'\\n\\n if page_size is not None:\\n yield 'pageSize', page_size\\n\\n if page_token is not None:\\n yield 'pageToken', page_token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.as_dataframe\",\n \"text\": \"google/cloud/monitoring/query.py::Query.as_dataframe\\n def as_dataframe(self, label=None, labels=None):\\n \\\"\\\"\\\"Return all the selected time series as a :mod:`pandas` dataframe.\\n\\n .. note::\\n\\n Use of this method requires that you have :mod:`pandas` installed.\\n\\n Examples::\\n\\n # Generate a dataframe with a multi-level column header including\\n # the resource type and all available resource and metric labels.\\n # This can be useful for seeing what labels are available.\\n dataframe = query.as_dataframe()\\n\\n # Generate a dataframe using a particular label for the column\\n # names.\\n dataframe = query.as_dataframe(label='instance_name')\\n\\n # Generate a dataframe with a multi-level column header.\\n dataframe = query.as_dataframe(labels=['zone', 'instance_name'])\\n\\n # Generate a dataframe with a multi-level column header, assuming\\n # the metric is issued by more than one type of resource.\\n dataframe = query.as_dataframe(\\n labels=['resource_type', 'instance_id'])\\n\\n :type label: string or None\\n :param label: The label name to use for the dataframe header.\\n This can be the name of a resource label or metric label\\n (e.g., ``\\\"instance_name\\\"``), or the string ``\\\"resource_type\\\"``.\\n\\n :type labels: list of strings, or None\\n :param labels: A list or tuple of label names to use for the dataframe\\n header. If more than one label name is provided, the resulting\\n dataframe will have a multi-level column header. Providing values\\n for both ``label`` and ``labels`` is an error.\\n\\n :rtype: :class:`pandas.DataFrame`\\n :returns: A dataframe where each column represents one time series.\\n \\\"\\\"\\\"\\n return _build_dataframe(self, label, labels) # pragma: NO COVER\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::Query.copy\",\n \"text\": \"google/cloud/monitoring/query.py::Query.copy\\n def copy(self):\\n \\\"\\\"\\\"Copy the query object.\\n\\n :rtype: :class:`Query`\\n :returns: The new query object.\\n \\\"\\\"\\\"\\n # Using copy.deepcopy() would be appropriate, except that we want\\n # to copy self._client only as a reference.\\n new_query = copy.copy(self)\\n new_query._filter = copy.copy(self._filter)\\n return new_query\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::_Filter.__init__\",\n \"text\": \"google/cloud/monitoring/query.py::_Filter.__init__\\n def __init__(self, metric_type):\\n self.metric_type = metric_type\\n self.group_id = None\\n self.projects = ()\\n self.resource_label_filter = None\\n self.metric_label_filter = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::_Filter.select_resources\",\n \"text\": \"google/cloud/monitoring/query.py::_Filter.select_resources\\n def select_resources(self, *args, **kwargs):\\n \\\"\\\"\\\"Select by resource labels.\\n\\n See :meth:`Query.select_resources`.\\n \\\"\\\"\\\"\\n self.resource_label_filter = _build_label_filter('resource',\\n *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::_Filter.select_metrics\",\n \"text\": \"google/cloud/monitoring/query.py::_Filter.select_metrics\\n def select_metrics(self, *args, **kwargs):\\n \\\"\\\"\\\"Select by metric labels.\\n\\n See :meth:`Query.select_metrics`.\\n \\\"\\\"\\\"\\n self.metric_label_filter = _build_label_filter('metric',\\n *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::_Filter.__str__\",\n \"text\": \"google/cloud/monitoring/query.py::_Filter.__str__\\n def __str__(self):\\n filters = ['metric.type = \\\"{type}\\\"'.format(type=self.metric_type)]\\n if self.group_id is not None:\\n filters.append('group.id = \\\"{id}\\\"'.format(id=self.group_id))\\n if self.projects:\\n filters.append(\\n ' OR '.join('project = \\\"{project}\\\"'.format(project=project)\\n for project in self.projects))\\n if self.resource_label_filter:\\n filters.append(self.resource_label_filter)\\n if self.metric_label_filter:\\n filters.append(self.metric_label_filter)\\n\\n # Parentheses are never actually required, because OR binds more\\n # tightly than AND in the Monitoring API's filter syntax.\\n return ' AND '.join(filters)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/query.py::_build_label_filter\",\n \"text\": \"google/cloud/monitoring/query.py::_build_label_filter\\ndef _build_label_filter(category, *args, **kwargs):\\n \\\"\\\"\\\"Construct a filter string to filter on metric or resource labels.\\\"\\\"\\\"\\n terms = list(args)\\n for key, value in six.iteritems(kwargs):\\n if value is None:\\n continue\\n\\n suffix = None\\n if key.endswith(('_prefix', '_suffix', '_greater', '_greaterequal',\\n '_less', '_lessequal')):\\n key, suffix = key.rsplit('_', 1)\\n\\n if category == 'resource' and key == 'resource_type':\\n key = 'resource.type'\\n else:\\n key = '.'.join((category, 'label', key))\\n\\n if suffix == 'prefix':\\n term = '{key} = starts_with(\\\"{value}\\\")'\\n elif suffix == 'suffix':\\n term = '{key} = ends_with(\\\"{value}\\\")'\\n elif suffix == 'greater':\\n term = '{key} > {value}'\\n elif suffix == 'greaterequal':\\n term = '{key} >= {value}'\\n elif suffix == 'less':\\n term = '{key} < {value}'\\n elif suffix == 'lessequal':\\n term = '{key} <= {value}'\\n else:\\n term = '{key} = \\\"{value}\\\"'\\n\\n terms.append(term.format(key=key, value=value))\\n\\n return ' AND '.join(sorted(terms))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/resource.py::ResourceDescriptor.__init__\",\n \"text\": \"google/cloud/monitoring/resource.py::ResourceDescriptor.__init__\\n def __init__(self, name, type_, display_name, description, labels):\\n self.name = name\\n self.type = type_\\n self.display_name = display_name\\n self.description = description\\n self.labels = labels\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/resource.py::ResourceDescriptor._fetch\",\n \"text\": \"google/cloud/monitoring/resource.py::ResourceDescriptor._fetch\\n @classmethod\\n def _fetch(cls, client, resource_type):\\n \\\"\\\"\\\"Look up a monitored resource descriptor by type.\\n\\n :type client: :class:`google.cloud.monitoring.client.Client`\\n :param client: The client to use.\\n\\n :type resource_type: string\\n :param resource_type: The resource type name.\\n\\n :rtype: :class:`ResourceDescriptor`\\n :returns: The resource descriptor instance.\\n\\n :raises: :class:`google.cloud.exceptions.NotFound` if the resource\\n descriptor is not found.\\n \\\"\\\"\\\"\\n path = ('/projects/{project}/monitoredResourceDescriptors/{type}'\\n .format(project=client.project,\\n type=resource_type))\\n info = client.connection.api_request(method='GET', path=path)\\n return cls._from_dict(info)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/resource.py::ResourceDescriptor._list\",\n \"text\": \"google/cloud/monitoring/resource.py::ResourceDescriptor._list\\n @classmethod\\n def _list(cls, client, filter_string=None):\\n \\\"\\\"\\\"List all monitored resource descriptors for the project.\\n\\n :type client: :class:`google.cloud.monitoring.client.Client`\\n :param client: The client to use.\\n\\n :type filter_string: string or None\\n :param filter_string:\\n An optional filter expression describing the resource descriptors\\n to be returned. See the `filter documentation`_.\\n\\n :rtype: list of :class:`ResourceDescriptor`\\n :returns: A list of resource descriptor instances.\\n\\n .. _filter documentation:\\n https://cloud.google.com/monitoring/api/v3/filters\\n \\\"\\\"\\\"\\n path = '/projects/{project}/monitoredResourceDescriptors/'.format(\\n project=client.project)\\n\\n descriptors = []\\n\\n page_token = None\\n while True:\\n params = {}\\n\\n if filter_string is not None:\\n params['filter'] = filter_string\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n response = client.connection.api_request(\\n method='GET', path=path, query_params=params)\\n for info in response.get('resourceDescriptors', ()):\\n descriptors.append(cls._from_dict(info))\\n\\n page_token = response.get('nextPageToken')\\n if not page_token:\\n break\\n\\n return descriptors\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/resource.py::ResourceDescriptor._from_dict\",\n \"text\": \"google/cloud/monitoring/resource.py::ResourceDescriptor._from_dict\\n @classmethod\\n def _from_dict(cls, info):\\n \\\"\\\"\\\"Construct a resource descriptor from the parsed JSON representation.\\n\\n :type info: dict\\n :param info:\\n A ``dict`` parsed from the JSON wire-format representation.\\n\\n :rtype: :class:`ResourceDescriptor`\\n :returns: A resource descriptor.\\n \\\"\\\"\\\"\\n return cls(\\n name=info['name'],\\n type_=info['type'],\\n display_name=info.get('displayName', ''),\\n description=info.get('description', ''),\\n labels=tuple(LabelDescriptor._from_dict(label)\\n for label in info.get('labels', ())),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/resource.py::ResourceDescriptor.__repr__\",\n \"text\": \"google/cloud/monitoring/resource.py::ResourceDescriptor.__repr__\\n def __repr__(self):\\n return (\\n '<ResourceDescriptor:\\\\n'\\n ' name={name!r},\\\\n'\\n ' type={type!r},\\\\n'\\n ' labels={labels!r},\\\\n'\\n ' display_name={display_name!r},\\\\n'\\n ' description={description!r}>'\\n ).format(**self.__dict__)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/resource.py::Resource._from_dict\",\n \"text\": \"google/cloud/monitoring/resource.py::Resource._from_dict\\n @classmethod\\n def _from_dict(cls, info):\\n \\\"\\\"\\\"Construct a resource object from the parsed JSON representation.\\n\\n :type info: dict\\n :param info:\\n A ``dict`` parsed from the JSON wire-format representation.\\n\\n :rtype: :class:`Resource`\\n :returns: A resource object.\\n \\\"\\\"\\\"\\n return cls(\\n type=info['type'],\\n labels=info.get('labels', {}),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/resource.py::Resource._to_dict\",\n \"text\": \"google/cloud/monitoring/resource.py::Resource._to_dict\\n def _to_dict(self):\\n \\\"\\\"\\\"Build a dictionary ready to be serialized to the JSON format.\\n\\n :rtype: dict\\n :returns: A dict representation of the object that can be written to\\n the API.\\n \\\"\\\"\\\"\\n return {\\n 'type': self.type,\\n 'labels': self.labels,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/timeseries.py::TimeSeries.labels\",\n \"text\": \"google/cloud/monitoring/timeseries.py::TimeSeries.labels\\n @property\\n def labels(self):\\n \\\"\\\"\\\"A single dictionary with values for all the labels.\\n\\n This combines ``resource.labels`` and ``metric.labels`` and also\\n adds ``\\\"resource_type\\\"``.\\n \\\"\\\"\\\"\\n if self._labels is None:\\n labels = {'resource_type': self.resource.type}\\n labels.update(self.resource.labels)\\n labels.update(self.metric.labels)\\n self._labels = labels\\n\\n return self._labels\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/timeseries.py::TimeSeries.header\",\n \"text\": \"google/cloud/monitoring/timeseries.py::TimeSeries.header\\n def header(self, points=None):\\n \\\"\\\"\\\"Copy everything but the point data.\\n\\n :type points: list of :class:`Point`, or None\\n :param points: An optional point list.\\n\\n :rtype: :class:`TimeSeries`\\n :returns: The new time series object.\\n \\\"\\\"\\\"\\n points = list(points) if points else []\\n return self._replace(points=points)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/timeseries.py::TimeSeries._to_dict\",\n \"text\": \"google/cloud/monitoring/timeseries.py::TimeSeries._to_dict\\n def _to_dict(self):\\n \\\"\\\"\\\"Build a dictionary ready to be serialized to the JSON wire format.\\n\\n Since this method is used when writing to the API, it excludes\\n output-only fields.\\n\\n :rtype: dict\\n :returns: The dictionary representation of the time series object.\\n \\\"\\\"\\\"\\n info = {\\n 'metric': self.metric._to_dict(),\\n 'resource': self.resource._to_dict(),\\n 'points': [point._to_dict() for point in self.points],\\n }\\n\\n return info\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/timeseries.py::TimeSeries._from_dict\",\n \"text\": \"google/cloud/monitoring/timeseries.py::TimeSeries._from_dict\\n @classmethod\\n def _from_dict(cls, info):\\n \\\"\\\"\\\"Construct a time series from the parsed JSON representation.\\n\\n :type info: dict\\n :param info:\\n A ``dict`` parsed from the JSON wire-format representation.\\n\\n :rtype: :class:`TimeSeries`\\n :returns: A time series object.\\n \\\"\\\"\\\"\\n metric = Metric._from_dict(info['metric'])\\n resource = Resource._from_dict(info['resource'])\\n metric_kind = info['metricKind']\\n value_type = info['valueType']\\n points = [Point._from_dict(p) for p in info.get('points', ())]\\n return cls(metric, resource, metric_kind, value_type, points)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/timeseries.py::TimeSeries.__repr__\",\n \"text\": \"google/cloud/monitoring/timeseries.py::TimeSeries.__repr__\\n def __repr__(self):\\n \\\"\\\"\\\"Return a representation string with the points elided.\\\"\\\"\\\"\\n return (\\n '<TimeSeries with {num} points:\\\\n'\\n ' metric={metric!r},\\\\n'\\n ' resource={resource!r},\\\\n'\\n ' metric_kind={kind!r}, value_type={type!r}>'\\n ).format(\\n num=len(self.points),\\n metric=self.metric,\\n resource=self.resource,\\n kind=self.metric_kind,\\n type=self.value_type,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/timeseries.py::_make_typed_value\",\n \"text\": \"google/cloud/monitoring/timeseries.py::_make_typed_value\\ndef _make_typed_value(value):\\n \\\"\\\"\\\"Create a dict representing a TypedValue API object.\\n\\n Typed values are objects with the value itself as the value, keyed by the\\n type of the value. They are used when writing points to time series. This\\n method returns the dict representation for the TypedValue.\\n\\n This method uses the Python type of the object to infer the correct\\n type to send to the API. For example, a Python float will be sent to the\\n API with \\\"doubleValue\\\" as its key.\\n\\n See: https://cloud.google.com/monitoring/api/ref_v3/rest/v3/TypedValue\\n\\n :type value: bool, int, float, str, or dict\\n :param value: value to infer the typed value of.\\n\\n :rtype: dict\\n :returns: A dict\\n \\\"\\\"\\\"\\n typed_value_map = {\\n bool: 'boolValue',\\n int: 'int64Value',\\n float: 'doubleValue',\\n str: 'stringValue',\\n dict: 'distributionValue',\\n }\\n type_ = typed_value_map[type(value)]\\n if type_ == 'int64Value':\\n value = str(value)\\n return {type_: value}\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/timeseries.py::Point._from_dict\",\n \"text\": \"google/cloud/monitoring/timeseries.py::Point._from_dict\\n @classmethod\\n def _from_dict(cls, info):\\n \\\"\\\"\\\"Construct a Point from the parsed JSON representation.\\n\\n :type info: dict\\n :param info:\\n A ``dict`` parsed from the JSON wire-format representation.\\n\\n :rtype: :class:`Point`\\n :returns: A point object.\\n \\\"\\\"\\\"\\n end_time = info['interval']['endTime']\\n start_time = info['interval'].get('startTime')\\n (value_type, value), = info['value'].items()\\n if value_type == 'int64Value':\\n value = int(value) # Convert from string.\\n\\n return cls(end_time, start_time, value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/monitoring/timeseries.py::Point._to_dict\",\n \"text\": \"google/cloud/monitoring/timeseries.py::Point._to_dict\\n def _to_dict(self):\\n \\\"\\\"\\\"Build a dictionary ready to be serialized to the JSON wire format.\\n\\n This method serializes a point in JSON format to be written\\n to the API.\\n\\n :rtype: dict\\n :returns: The dictionary representation of the point object.\\n \\\"\\\"\\\"\\n info = {\\n 'interval': {\\n 'endTime': self.end_time,\\n },\\n 'value': _make_typed_value(self.value),\\n }\\n\\n if self.start_time is not None:\\n info['interval']['startTime'] = self.start_time\\n\\n return info\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/operation.py::_compute_type_url\",\n \"text\": \"google/cloud/operation.py::_compute_type_url\\ndef _compute_type_url(klass, prefix=_GOOGLE_APIS_PREFIX):\\n \\\"\\\"\\\"Compute a type URL for a klass.\\n\\n :type klass: type\\n :param klass: class to be used as a factory for the given type\\n\\n :type prefix: str\\n :param prefix: URL prefix for the type\\n\\n :rtype: str\\n :returns: the URL, prefixed as appropriate\\n \\\"\\\"\\\"\\n name = klass.DESCRIPTOR.full_name\\n return '%s/%s' % (prefix, name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/operation.py::_register_type_url\",\n \"text\": \"google/cloud/operation.py::_register_type_url\\ndef _register_type_url(type_url, klass):\\n \\\"\\\"\\\"Register a klass as the factory for a given type URL.\\n\\n :type type_url: str\\n :param type_url: URL naming the type\\n\\n :type klass: type\\n :param klass: class to be used as a factory for the given type\\n\\n :raises: ValueError if a registration already exists for the URL.\\n \\\"\\\"\\\"\\n if type_url in _TYPE_URL_MAP:\\n if _TYPE_URL_MAP[type_url] is not klass:\\n raise ValueError(\\\"Conflict: %s\\\" % (_TYPE_URL_MAP[type_url],))\\n\\n _TYPE_URL_MAP[type_url] = klass\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/operation.py::Operation.__init__\",\n \"text\": \"google/cloud/operation.py::Operation.__init__\\n def __init__(self, name, client, pb_metadata=None, **kw):\\n self.name = name\\n self.client = client\\n self.pb_metadata = pb_metadata\\n self.metadata = kw.copy()\\n self._complete = False\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/operation.py::Operation.from_pb\",\n \"text\": \"google/cloud/operation.py::Operation.from_pb\\n @classmethod\\n def from_pb(cls, op_pb, client, **kw):\\n \\\"\\\"\\\"Factory: construct an instance from a protobuf.\\n\\n :type op_pb: :class:`google.longrunning.operations_pb2.Operation`\\n :param op_pb: Protobuf to be parsed.\\n\\n :type client: object: must provide ``_operations_stub`` accessor.\\n :param client: The client used to poll for the status of the operation.\\n\\n :type kw: dict\\n :param kw: caller-assigned metadata about the operation\\n\\n :rtype: :class:`Operation`\\n :returns: new instance, with attributes based on the protobuf.\\n \\\"\\\"\\\"\\n pb_metadata = None\\n if op_pb.metadata.type_url:\\n type_url = op_pb.metadata.type_url\\n md_klass = _TYPE_URL_MAP.get(type_url)\\n if md_klass:\\n pb_metadata = md_klass.FromString(op_pb.metadata.value)\\n return cls(op_pb.name, client, pb_metadata, **kw)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/operation.py::Operation.complete\",\n \"text\": \"google/cloud/operation.py::Operation.complete\\n @property\\n def complete(self):\\n \\\"\\\"\\\"Has the operation already completed?\\n\\n :rtype: bool\\n :returns: True if already completed, else false.\\n \\\"\\\"\\\"\\n return self._complete\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/operation.py::Operation.poll\",\n \"text\": \"google/cloud/operation.py::Operation.poll\\n def poll(self):\\n \\\"\\\"\\\"Check if the operation has finished.\\n\\n :rtype: bool\\n :returns: A boolean indicating if the current operation has completed.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the operation\\n has already completed.\\n \\\"\\\"\\\"\\n if self.complete:\\n raise ValueError('The operation has completed.')\\n\\n request_pb = operations_pb2.GetOperationRequest(name=self.name)\\n # We expect a `google.longrunning.operations_pb2.Operation`.\\n operation_pb = self.client._operations_stub.GetOperation(request_pb)\\n\\n if operation_pb.done:\\n self._complete = True\\n\\n return self.complete\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.__init__\",\n \"text\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.__init__\\n def __init__(self, gax_api):\\n self._gax_api = gax_api\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.list_topics\",\n \"text\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.list_topics\\n def list_topics(self, project, page_size=0, page_token=None):\\n \\\"\\\"\\\"List topics for the project associated with this API.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/list\\n\\n :type project: string\\n :param project: project ID\\n\\n :type page_size: int\\n :param page_size: maximum number of topics to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of topics. If not\\n passed, the API will return the first page of\\n topics.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of ``Topic`` resource dicts, plus a\\n \\\"next page token\\\" string: if not None, indicates that\\n more topics can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n if page_token is None:\\n page_token = INITIAL_PAGE\\n options = CallOptions(page_token=page_token)\\n path = 'projects/%s' % (project,)\\n page_iter = self._gax_api.list_topics(\\n path, page_size=page_size, options=options)\\n topics = [{'name': topic_pb.name} for topic_pb in page_iter.next()]\\n token = page_iter.page_token or None\\n return topics, token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_create\",\n \"text\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_create\\n def topic_create(self, topic_path):\\n \\\"\\\"\\\"API call: create a topic\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/create\\n\\n :type topic_path: string\\n :param topic_path: fully-qualified path of the new topic, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n\\n :rtype: dict\\n :returns: ``Topic`` resource returned from the API.\\n :raises: :exc:`google.cloud.exceptions.Conflict` if the topic already\\n exists\\n \\\"\\\"\\\"\\n try:\\n topic_pb = self._gax_api.create_topic(topic_path)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.FAILED_PRECONDITION:\\n raise Conflict(topic_path)\\n raise\\n return {'name': topic_pb.name}\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_get\",\n \"text\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_get\\n def topic_get(self, topic_path):\\n \\\"\\\"\\\"API call: retrieve a topic\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/get\\n\\n :type topic_path: string\\n :param topic_path: fully-qualified path of the topic, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n\\n :rtype: dict\\n :returns: ``Topic`` resource returned from the API.\\n :raises: :exc:`google.cloud.exceptions.NotFound` if the topic does not\\n exist\\n \\\"\\\"\\\"\\n try:\\n topic_pb = self._gax_api.get_topic(topic_path)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(topic_path)\\n raise\\n return {'name': topic_pb.name}\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_delete\",\n \"text\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_delete\\n def topic_delete(self, topic_path):\\n \\\"\\\"\\\"API call: delete a topic\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/create\\n\\n :type topic_path: string\\n :param topic_path: fully-qualified path of the new topic, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n \\\"\\\"\\\"\\n try:\\n self._gax_api.delete_topic(topic_path)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(topic_path)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_publish\",\n \"text\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_publish\\n def topic_publish(self, topic_path, messages):\\n \\\"\\\"\\\"API call: publish one or more messages to a topic\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/publish\\n\\n :type topic_path: string\\n :param topic_path: fully-qualified path of the topic, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n\\n :type messages: list of dict\\n :param messages: messages to be published.\\n\\n :rtype: list of string\\n :returns: list of opaque IDs for published messages.\\n :raises: :exc:`google.cloud.exceptions.NotFound` if the topic does not\\n exist\\n \\\"\\\"\\\"\\n options = CallOptions(is_bundling=False)\\n message_pbs = [_message_pb_from_mapping(message)\\n for message in messages]\\n try:\\n result = self._gax_api.publish(topic_path, message_pbs,\\n options=options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(topic_path)\\n raise\\n return result.message_ids\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_list_subscriptions\",\n \"text\": \"google/cloud/pubsub/_gax.py::_PublisherAPI.topic_list_subscriptions\\n def topic_list_subscriptions(self, topic_path, page_size=0,\\n page_token=None):\\n \\\"\\\"\\\"API call: list subscriptions bound to a topic\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics.subscriptions/list\\n\\n :type topic_path: string\\n :param topic_path: fully-qualified path of the topic, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n\\n :type page_size: int\\n :param page_size: maximum number of subscriptions to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of subscriptions.\\n If not passed, the API will return the first page\\n of subscriptions.\\n\\n :rtype: list of strings\\n :returns: fully-qualified names of subscriptions for the supplied\\n topic.\\n :raises: :exc:`google.cloud.exceptions.NotFound` if the topic does not\\n exist\\n \\\"\\\"\\\"\\n if page_token is None:\\n page_token = INITIAL_PAGE\\n options = CallOptions(page_token=page_token)\\n try:\\n page_iter = self._gax_api.list_topic_subscriptions(\\n topic_path, page_size=page_size, options=options)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(topic_path)\\n raise\\n subs = page_iter.next()\\n token = page_iter.page_token or None\\n return subs, token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.__init__\",\n \"text\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.__init__\\n def __init__(self, gax_api):\\n self._gax_api = gax_api\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.list_subscriptions\",\n \"text\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.list_subscriptions\\n def list_subscriptions(self, project, page_size=0, page_token=None):\\n \\\"\\\"\\\"List subscriptions for the project associated with this API.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/list\\n\\n :type project: string\\n :param project: project ID\\n\\n :type page_size: int\\n :param page_size: maximum number of subscriptions to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of subscriptions.\\n If not passed, the API will return the first page\\n of subscriptions.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of ``Subscription`` resource dicts, plus a\\n \\\"next page token\\\" string: if not None, indicates that\\n more topics can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n if page_token is None:\\n page_token = INITIAL_PAGE\\n options = CallOptions(page_token=page_token)\\n path = 'projects/%s' % (project,)\\n page_iter = self._gax_api.list_subscriptions(\\n path, page_size=page_size, options=options)\\n subscriptions = [_subscription_pb_to_mapping(sub_pb)\\n for sub_pb in page_iter.next()]\\n token = page_iter.page_token or None\\n return subscriptions, token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_create\",\n \"text\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_create\\n def subscription_create(self, subscription_path, topic_path,\\n ack_deadline=None, push_endpoint=None):\\n \\\"\\\"\\\"API call: create a subscription\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/create\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the new\\n subscription, in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :type topic_path: string\\n :param topic_path: the fully-qualified path of the topic being\\n subscribed, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n\\n :type ack_deadline: int, or ``NoneType``\\n :param ack_deadline: the deadline (in seconds) by which messages pulled\\n from the back-end must be acknowledged.\\n\\n :type push_endpoint: string, or ``NoneType``\\n :param push_endpoint: URL to which messages will be pushed by the\\n back-end. If not set, the application must pull\\n messages.\\n\\n :rtype: dict\\n :returns: ``Subscription`` resource returned from the API.\\n \\\"\\\"\\\"\\n if push_endpoint is not None:\\n push_config = PushConfig(push_endpoint=push_endpoint)\\n else:\\n push_config = None\\n\\n if ack_deadline is None:\\n ack_deadline = 0\\n\\n try:\\n sub_pb = self._gax_api.create_subscription(\\n subscription_path, topic_path, push_config, ack_deadline)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.FAILED_PRECONDITION:\\n raise Conflict(topic_path)\\n raise\\n return _subscription_pb_to_mapping(sub_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_get\",\n \"text\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_get\\n def subscription_get(self, subscription_path):\\n \\\"\\\"\\\"API call: retrieve a subscription\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/get\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the subscription,\\n in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :rtype: dict\\n :returns: ``Subscription`` resource returned from the API.\\n \\\"\\\"\\\"\\n try:\\n sub_pb = self._gax_api.get_subscription(subscription_path)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(subscription_path)\\n raise\\n return _subscription_pb_to_mapping(sub_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_delete\",\n \"text\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_delete\\n def subscription_delete(self, subscription_path):\\n \\\"\\\"\\\"API call: delete a subscription\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/delete\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the subscription,\\n in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n \\\"\\\"\\\"\\n try:\\n self._gax_api.delete_subscription(subscription_path)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(subscription_path)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_modify_push_config\",\n \"text\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_modify_push_config\\n def subscription_modify_push_config(self, subscription_path,\\n push_endpoint):\\n \\\"\\\"\\\"API call: update push config of a subscription\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the new\\n subscription, in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :type push_endpoint: string, or ``NoneType``\\n :param push_endpoint: URL to which messages will be pushed by the\\n back-end. If not set, the application must pull\\n messages.\\n \\\"\\\"\\\"\\n push_config = PushConfig(push_endpoint=push_endpoint)\\n try:\\n self._gax_api.modify_push_config(subscription_path, push_config)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(subscription_path)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_pull\",\n \"text\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_pull\\n def subscription_pull(self, subscription_path, return_immediately=False,\\n max_messages=1):\\n \\\"\\\"\\\"API call: retrieve messages for a subscription\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the new\\n subscription, in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :type return_immediately: boolean\\n :param return_immediately: if True, the back-end returns even if no\\n messages are available; if False, the API\\n call blocks until one or more messages are\\n available.\\n\\n :type max_messages: int\\n :param max_messages: the maximum number of messages to return.\\n\\n :rtype: list of dict\\n :returns: the ``receivedMessages`` element of the response.\\n \\\"\\\"\\\"\\n try:\\n response_pb = self._gax_api.pull(\\n subscription_path, max_messages, return_immediately)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(subscription_path)\\n raise\\n return [_received_message_pb_to_mapping(rmpb)\\n for rmpb in response_pb.received_messages]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_acknowledge\",\n \"text\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_acknowledge\\n def subscription_acknowledge(self, subscription_path, ack_ids):\\n \\\"\\\"\\\"API call: acknowledge retrieved messages\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the new\\n subscription, in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :type ack_ids: list of string\\n :param ack_ids: ack IDs of messages being acknowledged\\n \\\"\\\"\\\"\\n try:\\n self._gax_api.acknowledge(subscription_path, ack_ids)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(subscription_path)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_modify_ack_deadline\",\n \"text\": \"google/cloud/pubsub/_gax.py::_SubscriberAPI.subscription_modify_ack_deadline\\n def subscription_modify_ack_deadline(self, subscription_path, ack_ids,\\n ack_deadline):\\n \\\"\\\"\\\"API call: update ack deadline for retrieved messages\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyAckDeadline\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the new\\n subscription, in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :type ack_ids: list of string\\n :param ack_ids: ack IDs of messages being acknowledged\\n\\n :type ack_deadline: int\\n :param ack_deadline: the deadline (in seconds) by which messages pulled\\n from the back-end must be acknowledged.\\n \\\"\\\"\\\"\\n try:\\n self._gax_api.modify_ack_deadline(\\n subscription_path, ack_ids, ack_deadline)\\n except GaxError as exc:\\n if exc_to_code(exc.cause) == StatusCode.NOT_FOUND:\\n raise NotFound(subscription_path)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_message_pb_from_mapping\",\n \"text\": \"google/cloud/pubsub/_gax.py::_message_pb_from_mapping\\ndef _message_pb_from_mapping(message):\\n \\\"\\\"\\\"Helper for :meth:`_PublisherAPI.topic_publish`.\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n return PubsubMessage(data=_to_bytes(message['data']),\\n attributes=message['attributes'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_subscription_pb_to_mapping\",\n \"text\": \"google/cloud/pubsub/_gax.py::_subscription_pb_to_mapping\\ndef _subscription_pb_to_mapping(sub_pb):\\n \\\"\\\"\\\"Helper for :meth:`list_subscriptions`, et aliae\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n mapping = {\\n 'name': sub_pb.name,\\n 'topic': sub_pb.topic,\\n 'ackDeadlineSeconds': sub_pb.ack_deadline_seconds,\\n }\\n if sub_pb.push_config.push_endpoint != '':\\n mapping['pushConfig'] = {\\n 'pushEndpoint': sub_pb.push_config.push_endpoint,\\n }\\n return mapping\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_message_pb_to_mapping\",\n \"text\": \"google/cloud/pubsub/_gax.py::_message_pb_to_mapping\\ndef _message_pb_to_mapping(message_pb):\\n \\\"\\\"\\\"Helper for :meth:`pull`, et aliae\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n return {\\n 'messageId': message_pb.message_id,\\n 'data': message_pb.data,\\n 'attributes': message_pb.attributes,\\n 'publishTime': _pb_timestamp_to_rfc3339(message_pb.publish_time),\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::_received_message_pb_to_mapping\",\n \"text\": \"google/cloud/pubsub/_gax.py::_received_message_pb_to_mapping\\ndef _received_message_pb_to_mapping(received_message_pb):\\n \\\"\\\"\\\"Helper for :meth:`pull`, et aliae\\n\\n Performs \\\"impedance matching\\\" between the protobuf attrs and the keys\\n expected in the JSON API.\\n \\\"\\\"\\\"\\n return {\\n 'ackId': received_message_pb.ack_id,\\n 'message': _message_pb_to_mapping(\\n received_message_pb.message),\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::make_gax_publisher_api\",\n \"text\": \"google/cloud/pubsub/_gax.py::make_gax_publisher_api\\ndef make_gax_publisher_api(connection):\\n \\\"\\\"\\\"Create an instance of the GAX Publisher API.\\n\\n If the ``connection`` is intended for a local emulator, then\\n an insecure ``channel`` is created pointing at the local\\n Pub / Sub server.\\n\\n :type connection: :class:`~google.cloud.pubsub.connection.Connection`\\n :param connection: The connection that holds configuration details.\\n\\n :rtype: :class:`~google.cloud.pubsub.v1.publisher_api.PublisherApi`\\n :returns: A publisher API instance with the proper connection\\n configuration.\\n :rtype: :class:`~google.cloud.pubsub.v1.subscriber_api.SubscriberApi`\\n \\\"\\\"\\\"\\n channel = None\\n if connection.in_emulator:\\n channel = insecure_channel(connection.host)\\n return PublisherApi(channel=channel)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_gax.py::make_gax_subscriber_api\",\n \"text\": \"google/cloud/pubsub/_gax.py::make_gax_subscriber_api\\ndef make_gax_subscriber_api(connection):\\n \\\"\\\"\\\"Create an instance of the GAX Subscriber API.\\n\\n If the ``connection`` is intended for a local emulator, then\\n an insecure ``channel`` is created pointing at the local\\n Pub / Sub server.\\n\\n :type connection: :class:`~google.cloud.pubsub.connection.Connection`\\n :param connection: The connection that holds configuration details.\\n\\n :rtype: :class:`~google.cloud.pubsub.v1.subscriber_api.SubscriberApi`\\n :returns: A subscriber API instance with the proper connection\\n configuration.\\n \\\"\\\"\\\"\\n channel = None\\n if connection.in_emulator:\\n channel = insecure_channel(connection.host)\\n return SubscriberApi(channel=channel)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_helpers.py::topic_name_from_path\",\n \"text\": \"google/cloud/pubsub/_helpers.py::topic_name_from_path\\ndef topic_name_from_path(path, project):\\n \\\"\\\"\\\"Validate a topic URI path and get the topic name.\\n\\n :type path: string\\n :param path: URI path for a topic API request.\\n\\n :type project: string\\n :param project: The project associated with the request. It is\\n included for validation purposes.\\n\\n :rtype: string\\n :returns: Topic name parsed from ``path``.\\n :raises: :class:`ValueError` if the ``path`` is ill-formed or if\\n the project from the ``path`` does not agree with the\\n ``project`` passed in.\\n \\\"\\\"\\\"\\n return _name_from_project_path(path, project, _TOPIC_TEMPLATE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/_helpers.py::subscription_name_from_path\",\n \"text\": \"google/cloud/pubsub/_helpers.py::subscription_name_from_path\\ndef subscription_name_from_path(path, project):\\n \\\"\\\"\\\"Validate a subscription URI path and get the subscription name.\\n\\n :type path: string\\n :param path: URI path for a subscription API request.\\n\\n :type project: string\\n :param project: The project associated with the request. It is\\n included for validation purposes.\\n\\n :rtype: string\\n :returns: subscription name parsed from ``path``.\\n :raises: :class:`ValueError` if the ``path`` is ill-formed or if\\n the project from the ``path`` does not agree with the\\n ``project`` passed in.\\n \\\"\\\"\\\"\\n return _name_from_project_path(path, project, _SUBSCRIPTION_TEMPLATE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/client.py::Client.publisher_api\",\n \"text\": \"google/cloud/pubsub/client.py::Client.publisher_api\\n @property\\n def publisher_api(self):\\n \\\"\\\"\\\"Helper for publisher-related API calls.\\\"\\\"\\\"\\n if self._publisher_api is None:\\n if _USE_GAX:\\n generated = make_gax_publisher_api(self.connection)\\n self._publisher_api = GAXPublisherAPI(generated)\\n else:\\n self._publisher_api = JSONPublisherAPI(self.connection)\\n return self._publisher_api\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/client.py::Client.subscriber_api\",\n \"text\": \"google/cloud/pubsub/client.py::Client.subscriber_api\\n @property\\n def subscriber_api(self):\\n \\\"\\\"\\\"Helper for subscriber-related API calls.\\\"\\\"\\\"\\n if self._subscriber_api is None:\\n if _USE_GAX:\\n generated = make_gax_subscriber_api(self.connection)\\n self._subscriber_api = GAXSubscriberAPI(generated)\\n else:\\n self._subscriber_api = JSONSubscriberAPI(self.connection)\\n return self._subscriber_api\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/client.py::Client.iam_policy_api\",\n \"text\": \"google/cloud/pubsub/client.py::Client.iam_policy_api\\n @property\\n def iam_policy_api(self):\\n \\\"\\\"\\\"Helper for IAM policy-related API calls.\\\"\\\"\\\"\\n if self._iam_policy_api is None:\\n self._iam_policy_api = _IAMPolicyAPI(self.connection)\\n return self._iam_policy_api\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/client.py::Client.list_topics\",\n \"text\": \"google/cloud/pubsub/client.py::Client.list_topics\\n def list_topics(self, page_size=None, page_token=None):\\n \\\"\\\"\\\"List topics for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/list\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START client_list_topics]\\n :end-before: [END client_list_topics]\\n\\n :type page_size: int\\n :param page_size: maximum number of topics to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of topics. If not\\n passed, the API will return the first page of\\n topics.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`google.cloud.pubsub.topic.Topic`, plus a\\n \\\"next page token\\\" string: if not None, indicates that\\n more topics can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n api = self.publisher_api\\n resources, next_token = api.list_topics(\\n self.project, page_size, page_token)\\n topics = [Topic.from_api_repr(resource, self)\\n for resource in resources]\\n return topics, next_token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/client.py::Client.list_subscriptions\",\n \"text\": \"google/cloud/pubsub/client.py::Client.list_subscriptions\\n def list_subscriptions(self, page_size=None, page_token=None):\\n \\\"\\\"\\\"List subscriptions for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/list\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START client_list_subscriptions]\\n :end-before: [END client_list_subscriptions]\\n\\n :type page_size: int\\n :param page_size: maximum number of topics to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of topics. If not\\n passed, the API will return the first page of\\n topics.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`~.pubsub.subscription.Subscription`,\\n plus a \\\"next page token\\\" string: if not None, indicates that\\n more topics can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n api = self.subscriber_api\\n resources, next_token = api.list_subscriptions(\\n self.project, page_size, page_token)\\n topics = {}\\n subscriptions = [Subscription.from_api_repr(resource, self,\\n topics=topics)\\n for resource in resources]\\n return subscriptions, next_token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/client.py::Client.topic\",\n \"text\": \"google/cloud/pubsub/client.py::Client.topic\\n def topic(self, name, timestamp_messages=False):\\n \\\"\\\"\\\"Creates a topic bound to the current client.\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START client_topic]\\n :end-before: [END client_topic]\\n\\n :type name: string\\n :param name: the name of the topic to be constructed.\\n\\n :type timestamp_messages: boolean\\n :param timestamp_messages: To be passed to ``Topic`` constructor.\\n\\n :rtype: :class:`google.cloud.pubsub.topic.Topic`\\n :returns: Topic created with the current client.\\n \\\"\\\"\\\"\\n return Topic(name, client=self, timestamp_messages=timestamp_messages)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::Connection.__init__\",\n \"text\": \"google/cloud/pubsub/connection.py::Connection.__init__\\n def __init__(self, credentials=None, http=None):\\n super(Connection, self).__init__(credentials=credentials, http=http)\\n emulator_host = os.getenv(PUBSUB_EMULATOR)\\n if emulator_host is None:\\n self.host = self.__class__.API_BASE_URL\\n self.api_base_url = self.__class__.API_BASE_URL\\n self.in_emulator = False\\n else:\\n self.host = emulator_host\\n self.api_base_url = 'http://' + emulator_host\\n self.in_emulator = True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::Connection.build_api_url\",\n \"text\": \"google/cloud/pubsub/connection.py::Connection.build_api_url\\n def build_api_url(self, path, query_params=None,\\n api_base_url=None, api_version=None):\\n \\\"\\\"\\\"Construct an API url given a few components, some optional.\\n\\n Typically, you shouldn't need to use this method.\\n\\n :type path: string\\n :param path: The path to the resource.\\n\\n :type query_params: dict or list\\n :param query_params: A dictionary of keys and values (or list of\\n key-value pairs) to insert into the query\\n string of the URL.\\n\\n :type api_base_url: string\\n :param api_base_url: The base URL for the API endpoint.\\n Typically you won't have to provide this.\\n\\n :type api_version: string\\n :param api_version: The version of the API to call.\\n Typically you shouldn't provide this and instead\\n use the default for the library.\\n\\n :rtype: string\\n :returns: The URL assembled from the pieces provided.\\n \\\"\\\"\\\"\\n if api_base_url is None:\\n api_base_url = self.api_base_url\\n return super(Connection, self.__class__).build_api_url(\\n path, query_params=query_params,\\n api_base_url=api_base_url, api_version=api_version)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_PublisherAPI.__init__\",\n \"text\": \"google/cloud/pubsub/connection.py::_PublisherAPI.__init__\\n def __init__(self, connection):\\n self._connection = connection\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_PublisherAPI.list_topics\",\n \"text\": \"google/cloud/pubsub/connection.py::_PublisherAPI.list_topics\\n def list_topics(self, project, page_size=None, page_token=None):\\n \\\"\\\"\\\"API call: list topics for a given project\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/list\\n\\n :type project: string\\n :param project: project ID\\n\\n :type page_size: int\\n :param page_size: maximum number of topics to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of topics. If not\\n passed, the API will return the first page of\\n topics.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of ``Topic`` resource dicts, plus a\\n \\\"next page token\\\" string: if not None, indicates that\\n more topics can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n conn = self._connection\\n params = {}\\n\\n if page_size is not None:\\n params['pageSize'] = page_size\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/topics' % (project,)\\n resp = conn.api_request(method='GET', path=path, query_params=params)\\n return resp.get('topics', ()), resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_PublisherAPI.topic_create\",\n \"text\": \"google/cloud/pubsub/connection.py::_PublisherAPI.topic_create\\n def topic_create(self, topic_path):\\n \\\"\\\"\\\"API call: create a topic\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/create\\n\\n :type topic_path: string\\n :param topic_path: the fully-qualified path of the new topic, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n\\n :rtype: dict\\n :returns: ``Topic`` resource returned from the API.\\n \\\"\\\"\\\"\\n conn = self._connection\\n return conn.api_request(method='PUT', path='/%s' % (topic_path,))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_PublisherAPI.topic_get\",\n \"text\": \"google/cloud/pubsub/connection.py::_PublisherAPI.topic_get\\n def topic_get(self, topic_path):\\n \\\"\\\"\\\"API call: retrieve a topic\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/get\\n\\n :type topic_path: string\\n :param topic_path: the fully-qualified path of the topic, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n\\n :rtype: dict\\n :returns: ``Topic`` resource returned from the API.\\n \\\"\\\"\\\"\\n conn = self._connection\\n return conn.api_request(method='GET', path='/%s' % (topic_path,))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_PublisherAPI.topic_delete\",\n \"text\": \"google/cloud/pubsub/connection.py::_PublisherAPI.topic_delete\\n def topic_delete(self, topic_path):\\n \\\"\\\"\\\"API call: delete a topic\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/delete\\n\\n :type topic_path: string\\n :param topic_path: the fully-qualified path of the topic, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n \\\"\\\"\\\"\\n conn = self._connection\\n conn.api_request(method='DELETE', path='/%s' % (topic_path,))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_PublisherAPI.topic_publish\",\n \"text\": \"google/cloud/pubsub/connection.py::_PublisherAPI.topic_publish\\n def topic_publish(self, topic_path, messages):\\n \\\"\\\"\\\"API call: publish one or more messages to a topic\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/publish\\n\\n :type topic_path: string\\n :param topic_path: the fully-qualified path of the topic, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n\\n :type messages: list of dict\\n :param messages: messages to be published.\\n\\n :rtype: list of string\\n :returns: list of opaque IDs for published messages.\\n \\\"\\\"\\\"\\n conn = self._connection\\n data = {'messages': messages}\\n response = conn.api_request(\\n method='POST', path='/%s:publish' % (topic_path,), data=data)\\n return response['messageIds']\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_PublisherAPI.topic_list_subscriptions\",\n \"text\": \"google/cloud/pubsub/connection.py::_PublisherAPI.topic_list_subscriptions\\n def topic_list_subscriptions(self, topic_path, page_size=None,\\n page_token=None):\\n \\\"\\\"\\\"API call: list subscriptions bound to a topic\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics.subscriptions/list\\n\\n :type topic_path: string\\n :param topic_path: the fully-qualified path of the topic, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n\\n :type page_size: int\\n :param page_size: maximum number of subscriptions to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of topics. If not\\n passed, the API will return the first page of\\n topics.\\n\\n :rtype: list of strings\\n :returns: fully-qualified names of subscriptions for the supplied\\n topic.\\n \\\"\\\"\\\"\\n conn = self._connection\\n params = {}\\n\\n if page_size is not None:\\n params['pageSize'] = page_size\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/%s/subscriptions' % (topic_path,)\\n resp = conn.api_request(method='GET', path=path, query_params=params)\\n return resp.get('subscriptions', ()), resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.__init__\",\n \"text\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.__init__\\n def __init__(self, connection):\\n self._connection = connection\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.list_subscriptions\",\n \"text\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.list_subscriptions\\n def list_subscriptions(self, project, page_size=None, page_token=None):\\n \\\"\\\"\\\"API call: list subscriptions for a given project\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/list\\n\\n :type project: string\\n :param project: project ID\\n\\n :type page_size: int\\n :param page_size: maximum number of subscriptions to return, If not\\n passed, defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of subscriptions.\\n If not passed, the API will return the first page\\n of subscriptions.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of ``Subscription`` resource dicts, plus a\\n \\\"next page token\\\" string: if not None, indicates that\\n more subscriptions can be retrieved with another call (pass\\n that value as ``page_token``).\\n \\\"\\\"\\\"\\n conn = self._connection\\n params = {}\\n\\n if page_size is not None:\\n params['pageSize'] = page_size\\n\\n if page_token is not None:\\n params['pageToken'] = page_token\\n\\n path = '/projects/%s/subscriptions' % (project,)\\n resp = conn.api_request(method='GET', path=path, query_params=params)\\n return resp.get('subscriptions', ()), resp.get('nextPageToken')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_create\",\n \"text\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_create\\n def subscription_create(self, subscription_path, topic_path,\\n ack_deadline=None, push_endpoint=None):\\n \\\"\\\"\\\"API call: create a subscription\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/create\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the new\\n subscription, in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :type topic_path: string\\n :param topic_path: the fully-qualified path of the topic being\\n subscribed, in format\\n ``projects/<PROJECT>/topics/<TOPIC_NAME>``.\\n\\n :type ack_deadline: int, or ``NoneType``\\n :param ack_deadline: the deadline (in seconds) by which messages pulled\\n from the back-end must be acknowledged.\\n\\n :type push_endpoint: string, or ``NoneType``\\n :param push_endpoint: URL to which messages will be pushed by the\\n back-end. If not set, the application must pull\\n messages.\\n\\n :rtype: dict\\n :returns: ``Subscription`` resource returned from the API.\\n \\\"\\\"\\\"\\n conn = self._connection\\n path = '/%s' % (subscription_path,)\\n resource = {'topic': topic_path}\\n\\n if ack_deadline is not None:\\n resource['ackDeadlineSeconds'] = ack_deadline\\n\\n if push_endpoint is not None:\\n resource['pushConfig'] = {'pushEndpoint': push_endpoint}\\n\\n return conn.api_request(method='PUT', path=path, data=resource)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_get\",\n \"text\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_get\\n def subscription_get(self, subscription_path):\\n \\\"\\\"\\\"API call: retrieve a subscription\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/get\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the subscription,\\n in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :rtype: dict\\n :returns: ``Subscription`` resource returned from the API.\\n \\\"\\\"\\\"\\n conn = self._connection\\n path = '/%s' % (subscription_path,)\\n return conn.api_request(method='GET', path=path)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_delete\",\n \"text\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_delete\\n def subscription_delete(self, subscription_path):\\n \\\"\\\"\\\"API call: delete a subscription\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/delete\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the subscription,\\n in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n \\\"\\\"\\\"\\n conn = self._connection\\n path = '/%s' % (subscription_path,)\\n conn.api_request(method='DELETE', path=path)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_modify_push_config\",\n \"text\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_modify_push_config\\n def subscription_modify_push_config(self, subscription_path,\\n push_endpoint):\\n \\\"\\\"\\\"API call: update push config of a subscription\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the new\\n subscription, in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :type push_endpoint: string, or ``NoneType``\\n :param push_endpoint: URL to which messages will be pushed by the\\n back-end. If not set, the application must pull\\n messages.\\n \\\"\\\"\\\"\\n conn = self._connection\\n path = '/%s:modifyPushConfig' % (subscription_path,)\\n resource = {'pushConfig': {'pushEndpoint': push_endpoint}}\\n conn.api_request(method='POST', path=path, data=resource)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_pull\",\n \"text\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_pull\\n def subscription_pull(self, subscription_path, return_immediately=False,\\n max_messages=1):\\n \\\"\\\"\\\"API call: retrieve messages for a subscription\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the new\\n subscription, in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :type return_immediately: boolean\\n :param return_immediately: if True, the back-end returns even if no\\n messages are available; if False, the API\\n call blocks until one or more messages are\\n available.\\n\\n :type max_messages: int\\n :param max_messages: the maximum number of messages to return.\\n\\n :rtype: list of dict\\n :returns: the ``receivedMessages`` element of the response.\\n \\\"\\\"\\\"\\n conn = self._connection\\n path = '/%s:pull' % (subscription_path,)\\n data = {\\n 'returnImmediately': return_immediately,\\n 'maxMessages': max_messages,\\n }\\n response = conn.api_request(method='POST', path=path, data=data)\\n return response.get('receivedMessages', ())\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_acknowledge\",\n \"text\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_acknowledge\\n def subscription_acknowledge(self, subscription_path, ack_ids):\\n \\\"\\\"\\\"API call: acknowledge retrieved messages\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the new\\n subscription, in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :type ack_ids: list of string\\n :param ack_ids: ack IDs of messages being acknowledged\\n \\\"\\\"\\\"\\n conn = self._connection\\n path = '/%s:acknowledge' % (subscription_path,)\\n data = {\\n 'ackIds': ack_ids,\\n }\\n conn.api_request(method='POST', path=path, data=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_modify_ack_deadline\",\n \"text\": \"google/cloud/pubsub/connection.py::_SubscriberAPI.subscription_modify_ack_deadline\\n def subscription_modify_ack_deadline(self, subscription_path, ack_ids,\\n ack_deadline):\\n \\\"\\\"\\\"API call: update ack deadline for retrieved messages\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyAckDeadline\\n\\n :type subscription_path: string\\n :param subscription_path: the fully-qualified path of the new\\n subscription, in format\\n ``projects/<PROJECT>/subscriptions/<SUB_NAME>``.\\n\\n :type ack_ids: list of string\\n :param ack_ids: ack IDs of messages being acknowledged\\n\\n :type ack_deadline: int\\n :param ack_deadline: the deadline (in seconds) by which messages pulled\\n from the back-end must be acknowledged.\\n \\\"\\\"\\\"\\n conn = self._connection\\n path = '/%s:modifyAckDeadline' % (subscription_path,)\\n data = {\\n 'ackIds': ack_ids,\\n 'ackDeadlineSeconds': ack_deadline,\\n }\\n conn.api_request(method='POST', path=path, data=data)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_IAMPolicyAPI.__init__\",\n \"text\": \"google/cloud/pubsub/connection.py::_IAMPolicyAPI.__init__\\n def __init__(self, connection):\\n self._connection = connection\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_IAMPolicyAPI.get_iam_policy\",\n \"text\": \"google/cloud/pubsub/connection.py::_IAMPolicyAPI.get_iam_policy\\n def get_iam_policy(self, target_path):\\n \\\"\\\"\\\"API call: fetch the IAM policy for the target\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/getIamPolicy\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/getIamPolicy\\n\\n :type target_path: string\\n :param target_path: the path of the target object.\\n\\n :rtype: dict\\n :returns: the resource returned by the ``getIamPolicy`` API request.\\n \\\"\\\"\\\"\\n conn = self._connection\\n path = '/%s:getIamPolicy' % (target_path,)\\n return conn.api_request(method='GET', path=path)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_IAMPolicyAPI.set_iam_policy\",\n \"text\": \"google/cloud/pubsub/connection.py::_IAMPolicyAPI.set_iam_policy\\n def set_iam_policy(self, target_path, policy):\\n \\\"\\\"\\\"API call: update the IAM policy for the target\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/setIamPolicy\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/setIamPolicy\\n\\n :type target_path: string\\n :param target_path: the path of the target object.\\n\\n :type policy: dict\\n :param policy: the new policy resource.\\n\\n :rtype: dict\\n :returns: the resource returned by the ``setIamPolicy`` API request.\\n \\\"\\\"\\\"\\n conn = self._connection\\n wrapped = {'policy': policy}\\n path = '/%s:setIamPolicy' % (target_path,)\\n return conn.api_request(method='POST', path=path, data=wrapped)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/connection.py::_IAMPolicyAPI.test_iam_permissions\",\n \"text\": \"google/cloud/pubsub/connection.py::_IAMPolicyAPI.test_iam_permissions\\n def test_iam_permissions(self, target_path, permissions):\\n \\\"\\\"\\\"API call: test permissions\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/testIamPermissions\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/testIamPermissions\\n\\n :type target_path: string\\n :param target_path: the path of the target object.\\n\\n :type permissions: list of string\\n :param permissions: the permissions to check\\n\\n :rtype: dict\\n :returns: the resource returned by the ``getIamPolicy`` API request.\\n \\\"\\\"\\\"\\n conn = self._connection\\n wrapped = {'permissions': permissions}\\n path = '/%s:testIamPermissions' % (target_path,)\\n resp = conn.api_request(method='POST', path=path, data=wrapped)\\n return resp.get('permissions', [])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/iam.py::Policy.__init__\",\n \"text\": \"google/cloud/pubsub/iam.py::Policy.__init__\\n def __init__(self, etag=None, version=None):\\n self.etag = etag\\n self.version = version\\n self.owners = set()\\n self.editors = set()\\n self.viewers = set()\\n self.publishers = set()\\n self.subscribers = set()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/iam.py::Policy.user\",\n \"text\": \"google/cloud/pubsub/iam.py::Policy.user\\n @staticmethod\\n def user(email):\\n \\\"\\\"\\\"Factory method for a user member.\\n\\n :type email: string\\n :param email: E-mail for this particular user.\\n\\n :rtype: string\\n :returns: A member string corresponding to the given user.\\n \\\"\\\"\\\"\\n return 'user:%s' % (email,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/iam.py::Policy.service_account\",\n \"text\": \"google/cloud/pubsub/iam.py::Policy.service_account\\n @staticmethod\\n def service_account(email):\\n \\\"\\\"\\\"Factory method for a service account member.\\n\\n :type email: string\\n :param email: E-mail for this particular service account.\\n\\n :rtype: string\\n :returns: A member string corresponding to the given service account.\\n \\\"\\\"\\\"\\n return 'serviceAccount:%s' % (email,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/iam.py::Policy.group\",\n \"text\": \"google/cloud/pubsub/iam.py::Policy.group\\n @staticmethod\\n def group(email):\\n \\\"\\\"\\\"Factory method for a group member.\\n\\n :type email: string\\n :param email: An id or e-mail for this particular group.\\n\\n :rtype: string\\n :returns: A member string corresponding to the given group.\\n \\\"\\\"\\\"\\n return 'group:%s' % (email,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/iam.py::Policy.domain\",\n \"text\": \"google/cloud/pubsub/iam.py::Policy.domain\\n @staticmethod\\n def domain(domain):\\n \\\"\\\"\\\"Factory method for a domain member.\\n\\n :type domain: string\\n :param domain: The domain for this member.\\n\\n :rtype: string\\n :returns: A member string corresponding to the given domain.\\n \\\"\\\"\\\"\\n return 'domain:%s' % (domain,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/iam.py::Policy.all_users\",\n \"text\": \"google/cloud/pubsub/iam.py::Policy.all_users\\n @staticmethod\\n def all_users():\\n \\\"\\\"\\\"Factory method for a member representing all users.\\n\\n :rtype: string\\n :returns: A member string representing all users.\\n \\\"\\\"\\\"\\n return 'allUsers'\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/iam.py::Policy.authenticated_users\",\n \"text\": \"google/cloud/pubsub/iam.py::Policy.authenticated_users\\n @staticmethod\\n def authenticated_users():\\n \\\"\\\"\\\"Factory method for a member representing all authenticated users.\\n\\n :rtype: string\\n :returns: A member string representing all authenticated users.\\n \\\"\\\"\\\"\\n return 'allAuthenticatedUsers'\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/iam.py::Policy.from_api_repr\",\n \"text\": \"google/cloud/pubsub/iam.py::Policy.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource):\\n \\\"\\\"\\\"Create a policy from the resource returned from the API.\\n\\n :type resource: dict\\n :param resource: resource returned from the ``getIamPolicy`` API.\\n\\n :rtype: :class:`Policy`\\n :returns: the parsed policy\\n \\\"\\\"\\\"\\n version = resource.get('version')\\n etag = resource.get('etag')\\n policy = cls(etag, version)\\n for binding in resource.get('bindings', ()):\\n role = binding['role']\\n members = set(binding['members'])\\n if role in (OWNER_ROLE, PUBSUB_ADMIN_ROLE):\\n policy.owners |= members\\n elif role in (EDITOR_ROLE, PUBSUB_EDITOR_ROLE):\\n policy.editors |= members\\n elif role in (VIEWER_ROLE, PUBSUB_VIEWER_ROLE):\\n policy.viewers |= members\\n elif role == PUBSUB_PUBLISHER_ROLE:\\n policy.publishers |= members\\n elif role == PUBSUB_SUBSCRIBER_ROLE:\\n policy.subscribers |= members\\n else:\\n raise ValueError('Unknown role: %s' % (role,))\\n return policy\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/iam.py::Policy.to_api_repr\",\n \"text\": \"google/cloud/pubsub/iam.py::Policy.to_api_repr\\n def to_api_repr(self):\\n \\\"\\\"\\\"Construct a Policy resource.\\n\\n :rtype: dict\\n :returns: a resource to be passed to the ``setIamPolicy`` API.\\n \\\"\\\"\\\"\\n resource = {}\\n\\n if self.etag is not None:\\n resource['etag'] = self.etag\\n\\n if self.version is not None:\\n resource['version'] = self.version\\n\\n bindings = []\\n\\n if self.owners:\\n bindings.append(\\n {'role': PUBSUB_ADMIN_ROLE,\\n 'members': sorted(self.owners)})\\n\\n if self.editors:\\n bindings.append(\\n {'role': PUBSUB_EDITOR_ROLE,\\n 'members': sorted(self.editors)})\\n\\n if self.viewers:\\n bindings.append(\\n {'role': PUBSUB_VIEWER_ROLE,\\n 'members': sorted(self.viewers)})\\n\\n if self.publishers:\\n bindings.append(\\n {'role': PUBSUB_PUBLISHER_ROLE,\\n 'members': sorted(self.publishers)})\\n\\n if self.subscribers:\\n bindings.append(\\n {'role': PUBSUB_SUBSCRIBER_ROLE,\\n 'members': sorted(self.subscribers)})\\n\\n if bindings:\\n resource['bindings'] = bindings\\n\\n return resource\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/message.py::Message.__init__\",\n \"text\": \"google/cloud/pubsub/message.py::Message.__init__\\n def __init__(self, data, message_id, attributes=None):\\n self.data = data\\n self.message_id = message_id\\n self._attributes = attributes\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/message.py::Message.attributes\",\n \"text\": \"google/cloud/pubsub/message.py::Message.attributes\\n @property\\n def attributes(self):\\n \\\"\\\"\\\"Lazily-constructed attribute dictionary.\\\"\\\"\\\"\\n if self._attributes is None:\\n self._attributes = {}\\n return self._attributes\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/message.py::Message.timestamp\",\n \"text\": \"google/cloud/pubsub/message.py::Message.timestamp\\n @property\\n def timestamp(self):\\n \\\"\\\"\\\"Return sortable timestamp from attributes, if passed.\\n\\n Allows sorting messages in publication order (assuming consistent\\n clocks across all publishers).\\n\\n :rtype: :class:`datetime.datetime`\\n :returns: timestamp (in UTC timezone) parsed from RFC 3339 timestamp\\n :raises: ValueError if timestamp not in ``attributes``, or if it does\\n not match the RFC 3339 format.\\n \\\"\\\"\\\"\\n stamp = self.attributes.get('timestamp')\\n if stamp is None:\\n raise ValueError('No timestamp')\\n return _rfc3339_to_datetime(stamp)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/message.py::Message.service_timestamp\",\n \"text\": \"google/cloud/pubsub/message.py::Message.service_timestamp\\n @property\\n def service_timestamp(self):\\n \\\"\\\"\\\"Return server-set timestamp.\\n\\n :rtype: string\\n :returns: timestamp (in UTC timezone) in RFC 3339 format\\n \\\"\\\"\\\"\\n return self._service_timestamp\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/message.py::Message.from_api_repr\",\n \"text\": \"google/cloud/pubsub/message.py::Message.from_api_repr\\n @classmethod\\n def from_api_repr(cls, api_repr):\\n \\\"\\\"\\\"Factory: construct message from API representation.\\n\\n :type api_repr: dict or None\\n :param api_repr: The API representation of the message\\n\\n :rtype: :class:`Message`\\n :returns: The message created from the response.\\n \\\"\\\"\\\"\\n data = base64.b64decode(api_repr.get('data', b''))\\n instance = cls(\\n data=data, message_id=api_repr['messageId'],\\n attributes=api_repr.get('attributes'))\\n instance._service_timestamp = api_repr.get('publishTimestamp')\\n return instance\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.__init__\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.__init__\\n def __init__(self, name, topic=None, ack_deadline=None, push_endpoint=None,\\n client=None):\\n\\n if client is None and topic is None:\\n raise TypeError(\\\"Pass only one of 'topic' or 'client'.\\\")\\n\\n if client is not None and topic is not None:\\n raise TypeError(\\\"Pass only one of 'topic' or 'client'.\\\")\\n\\n self.name = name\\n self.topic = topic\\n self._client = client or topic._client\\n self._project = self._client.project\\n self.ack_deadline = ack_deadline\\n self.push_endpoint = push_endpoint\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.from_api_repr\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client, topics=None):\\n \\\"\\\"\\\"Factory: construct a topic given its API representation\\n\\n :type resource: dict\\n :param resource: topic resource representation returned from the API.\\n\\n :type client: :class:`google.cloud.pubsub.client.Client`\\n :param client: Client which holds credentials and project\\n configuration for a topic.\\n\\n :type topics: dict or None\\n :param topics: A mapping of topic names -> topics. If not passed,\\n the subscription will have a newly-created topic.\\n\\n :rtype: :class:`google.cloud.pubsub.subscription.Subscription`\\n :returns: Subscription parsed from ``resource``.\\n \\\"\\\"\\\"\\n if topics is None:\\n topics = {}\\n topic_path = resource['topic']\\n if topic_path == cls._DELETED_TOPIC_PATH:\\n topic = None\\n else:\\n topic = topics.get(topic_path)\\n if topic is None:\\n # NOTE: This duplicates behavior from Topic.from_api_repr to\\n # avoid an import cycle.\\n topic_name = topic_name_from_path(topic_path, client.project)\\n topic = topics[topic_path] = client.topic(topic_name)\\n _, _, _, name = resource['name'].split('/')\\n ack_deadline = resource.get('ackDeadlineSeconds')\\n push_config = resource.get('pushConfig', {})\\n push_endpoint = push_config.get('pushEndpoint')\\n if topic is None:\\n return cls(name, ack_deadline=ack_deadline,\\n push_endpoint=push_endpoint, client=client)\\n return cls(name, topic, ack_deadline, push_endpoint)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.project\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project bound to the subscription.\\\"\\\"\\\"\\n return self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.full_name\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.full_name\\n @property\\n def full_name(self):\\n \\\"\\\"\\\"Fully-qualified name used in subscription APIs\\\"\\\"\\\"\\n return 'projects/%s/subscriptions/%s' % (self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.path\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URL path for the subscription's APIs\\\"\\\"\\\"\\n return '/%s' % (self.full_name,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.auto_ack\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.auto_ack\\n def auto_ack(self, return_immediately=False, max_messages=1, client=None):\\n \\\"\\\"\\\":class:`AutoAck` factory\\n\\n :type return_immediately: boolean\\n :param return_immediately: passed through to :meth:`Subscription.pull`\\n\\n :type max_messages: int\\n :param max_messages: passed through to :meth:`Subscription.pull`\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: passed through to :meth:`Subscription.pull` and\\n :meth:`Subscription.acknowledge`.\\n\\n :rtype: :class:`AutoAck`\\n :returns: the instance created for the given ``ack_id`` and ``message``\\n \\\"\\\"\\\"\\n return AutoAck(self, return_immediately, max_messages, client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription._require_client\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the topic of the\\n current subscription.\\n\\n :rtype: :class:`google.cloud.pubsub.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.create\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.create\\n def create(self, client=None):\\n \\\"\\\"\\\"API call: create the subscription via a PUT request\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/create\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START subscription_create]\\n :end-before: [END subscription_create]\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current subscription's topic.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.subscriber_api\\n api.subscription_create(\\n self.full_name, self.topic.full_name, self.ack_deadline,\\n self.push_endpoint)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.exists\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"API call: test existence of the subscription via a GET request\\n\\n See\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/get\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START subscription_exists]\\n :end-before: [END subscription_exists]\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current subscription's topic.\\n\\n :rtype: bool\\n :returns: Boolean indicating existence of the subscription.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.subscriber_api\\n try:\\n api.subscription_get(self.full_name)\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.reload\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"API call: sync local subscription configuration via a GET request\\n\\n See\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/get\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START subscription_reload]\\n :end-before: [END subscription_reload]\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current subscription's topic.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.subscriber_api\\n data = api.subscription_get(self.full_name)\\n self.ack_deadline = data.get('ackDeadlineSeconds')\\n push_config = data.get('pushConfig', {})\\n self.push_endpoint = push_config.get('pushEndpoint')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.delete\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.delete\\n def delete(self, client=None):\\n \\\"\\\"\\\"API call: delete the subscription via a DELETE request.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/delete\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START subscription_delete]\\n :end-before: [END subscription_delete]\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current subscription's topic.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.subscriber_api\\n api.subscription_delete(self.full_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.modify_push_configuration\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.modify_push_configuration\\n def modify_push_configuration(self, push_endpoint, client=None):\\n \\\"\\\"\\\"API call: update the push endpoint for the subscription.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyPushConfig\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START subscription_push_pull]\\n :end-before: [END subscription_push_pull]\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START subscription_pull_push]\\n :end-before: [END subscription_pull_push]\\n\\n :type push_endpoint: string\\n :param push_endpoint: URL to which messages will be pushed by the\\n back-end. If None, the application must pull\\n messages.\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current subscription's topic.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.subscriber_api\\n api.subscription_modify_push_config(self.full_name, push_endpoint)\\n self.push_endpoint = push_endpoint\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.pull\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.pull\\n def pull(self, return_immediately=False, max_messages=1, client=None):\\n \\\"\\\"\\\"API call: retrieve messages for the subscription.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/pull\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START subscription_pull]\\n :end-before: [END subscription_pull]\\n\\n :type return_immediately: boolean\\n :param return_immediately: if True, the back-end returns even if no\\n messages are available; if False, the API\\n call blocks until one or more messages are\\n available.\\n\\n :type max_messages: int\\n :param max_messages: the maximum number of messages to return.\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current subscription's topic.\\n\\n :rtype: list of (ack_id, message) tuples\\n :returns: sequence of tuples: ``ack_id`` is the ID to be used in a\\n subsequent call to :meth:`acknowledge`, and ``message``\\n is an instance of\\n :class:`~google.cloud.pubsub.message.Message`.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.subscriber_api\\n response = api.subscription_pull(\\n self.full_name, return_immediately, max_messages)\\n return [(info['ackId'], Message.from_api_repr(info['message']))\\n for info in response]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.acknowledge\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.acknowledge\\n def acknowledge(self, ack_ids, client=None):\\n \\\"\\\"\\\"API call: acknowledge retrieved messages for the subscription.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/acknowledge\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START subscription_acknowledge]\\n :end-before: [END subscription_acknowledge]\\n\\n :type ack_ids: list of string\\n :param ack_ids: ack IDs of messages being acknowledged\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current subscription's topic.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.subscriber_api\\n api.subscription_acknowledge(self.full_name, ack_ids)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.modify_ack_deadline\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.modify_ack_deadline\\n def modify_ack_deadline(self, ack_ids, ack_deadline, client=None):\\n \\\"\\\"\\\"API call: update acknowledgement deadline for a retrieved message.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/modifyAckDeadline\\n\\n :type ack_ids: list of string\\n :param ack_ids: ack IDs of messages being updated\\n\\n :type ack_deadline: int\\n :param ack_deadline: new deadline for the message, in seconds\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current subscription's topic.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.subscriber_api\\n api.subscription_modify_ack_deadline(\\n self.full_name, ack_ids, ack_deadline)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.get_iam_policy\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.get_iam_policy\\n def get_iam_policy(self, client=None):\\n \\\"\\\"\\\"Fetch the IAM policy for the subscription.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/getIamPolicy\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START subscription_get_iam_policy]\\n :end-before: [END subscription_get_iam_policy]\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current subscription's topic.\\n\\n :rtype: :class:`google.cloud.pubsub.iam.Policy`\\n :returns: policy created from the resource returned by the\\n ``getIamPolicy`` API request.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.iam_policy_api\\n resp = api.get_iam_policy(self.full_name)\\n return Policy.from_api_repr(resp)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.set_iam_policy\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.set_iam_policy\\n def set_iam_policy(self, policy, client=None):\\n \\\"\\\"\\\"Update the IAM policy for the subscription.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/setIamPolicy\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START subscription_set_iam_policy]\\n :end-before: [END subscription_set_iam_policy]\\n\\n :type policy: :class:`google.cloud.pubsub.iam.Policy`\\n :param policy: the new policy, typically fetched via\\n :meth:`get_iam_policy` and updated in place.\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current subscription's topic.\\n\\n :rtype: :class:`google.cloud.pubsub.iam.Policy`\\n :returns: updated policy created from the resource returned by the\\n ``setIamPolicy`` API request.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.iam_policy_api\\n resource = policy.to_api_repr()\\n resp = api.set_iam_policy(self.full_name, resource)\\n return Policy.from_api_repr(resp)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::Subscription.check_iam_permissions\",\n \"text\": \"google/cloud/pubsub/subscription.py::Subscription.check_iam_permissions\\n def check_iam_permissions(self, permissions, client=None):\\n \\\"\\\"\\\"Verify permissions allowed for the current user.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.subscriptions/testIamPermissions\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START subscription_check_iam_permissions]\\n :end-before: [END subscription_check_iam_permissions]\\n\\n :type permissions: list of string\\n :param permissions: list of permissions to be tested\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current subscription's topic.\\n\\n :rtype: sequence of string\\n :returns: subset of ``permissions`` allowed by current IAM policy.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.iam_policy_api\\n return api.test_iam_permissions(\\n self.full_name, list(permissions))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::AutoAck.__init__\",\n \"text\": \"google/cloud/pubsub/subscription.py::AutoAck.__init__\\n def __init__(self, subscription,\\n return_immediately=False, max_messages=1, client=None):\\n super(AutoAck, self).__init__()\\n self._subscription = subscription\\n self._return_immediately = return_immediately\\n self._max_messages = max_messages\\n self._client = client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::AutoAck.__enter__\",\n \"text\": \"google/cloud/pubsub/subscription.py::AutoAck.__enter__\\n def __enter__(self):\\n items = self._subscription.pull(\\n self._return_immediately, self._max_messages, self._client)\\n self.update(items)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/subscription.py::AutoAck.__exit__\",\n \"text\": \"google/cloud/pubsub/subscription.py::AutoAck.__exit__\\n def __exit__(self, exc_type, exc_val, exc_tb):\\n self._subscription.acknowledge(list(self), self._client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.__init__\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.__init__\\n def __init__(self, name, client, timestamp_messages=False):\\n self.name = name\\n self._client = client\\n self.timestamp_messages = timestamp_messages\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.subscription\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.subscription\\n def subscription(self, name, ack_deadline=None, push_endpoint=None):\\n \\\"\\\"\\\"Creates a subscription bound to the current topic.\\n\\n Example: pull-mode subcription, default paramter values\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_subscription_defaults]\\n :end-before: [END topic_subscription_defaults]\\n\\n Example: pull-mode subcription, override ``ack_deadline`` default\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_subscription_ack90]\\n :end-before: [END topic_subscription_ack90]\\n\\n Example: push-mode subcription\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_subscription_push]\\n :end-before: [END topic_subscription_push]\\n\\n :type name: string\\n :param name: the name of the subscription\\n\\n :type ack_deadline: int\\n :param ack_deadline: the deadline (in seconds) by which messages pulled\\n from the back-end must be acknowledged.\\n\\n :type push_endpoint: string\\n :param push_endpoint: URL to which messages will be pushed by the\\n back-end. If not set, the application must pull\\n messages.\\n\\n :rtype: :class:`Subscription`\\n :returns: The subscription created with the passed in arguments.\\n \\\"\\\"\\\"\\n return Subscription(name, self, ack_deadline=ack_deadline,\\n push_endpoint=push_endpoint)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.from_api_repr\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client):\\n \\\"\\\"\\\"Factory: construct a topic given its API representation\\n\\n :type resource: dict\\n :param resource: topic resource representation returned from the API\\n\\n :type client: :class:`google.cloud.pubsub.client.Client`\\n :param client: Client which holds credentials and project\\n configuration for the topic.\\n\\n :rtype: :class:`google.cloud.pubsub.topic.Topic`\\n :returns: Topic parsed from ``resource``.\\n :raises: :class:`ValueError` if ``client`` is not ``None`` and the\\n project from the resource does not agree with the project\\n from the client.\\n \\\"\\\"\\\"\\n topic_name = topic_name_from_path(resource['name'], client.project)\\n return cls(topic_name, client=client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.project\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.project\\n @property\\n def project(self):\\n \\\"\\\"\\\"Project bound to the topic.\\\"\\\"\\\"\\n return self._client.project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.full_name\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.full_name\\n @property\\n def full_name(self):\\n \\\"\\\"\\\"Fully-qualified name used in topic / subscription APIs\\\"\\\"\\\"\\n return 'projects/%s/topics/%s' % (self.project, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic._require_client\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current topic.\\n\\n :rtype: :class:`google.cloud.pubsub.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.create\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.create\\n def create(self, client=None):\\n \\\"\\\"\\\"API call: create the topic via a PUT request\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/create\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_create]\\n :end-before: [END topic_create]\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current topic.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.publisher_api\\n api.topic_create(topic_path=self.full_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.exists\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"API call: test for the existence of the topic via a GET request\\n\\n See\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/get\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_exists]\\n :end-before: [END topic_exists]\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current topic.\\n\\n :rtype: bool\\n :returns: Boolean indicating existence of the topic.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.publisher_api\\n\\n try:\\n api.topic_get(topic_path=self.full_name)\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.delete\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.delete\\n def delete(self, client=None):\\n \\\"\\\"\\\"API call: delete the topic via a DELETE request\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/delete\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_delete]\\n :end-before: [END topic_delete]\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current topic.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.publisher_api\\n api.topic_delete(topic_path=self.full_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic._timestamp_message\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic._timestamp_message\\n def _timestamp_message(self, attrs):\\n \\\"\\\"\\\"Add a timestamp to ``attrs``, if the topic is so configured.\\n\\n If ``attrs`` already has the key, do nothing.\\n\\n Helper method for ``publish``/``Batch.publish``.\\n \\\"\\\"\\\"\\n if self.timestamp_messages and 'timestamp' not in attrs:\\n attrs['timestamp'] = _datetime_to_rfc3339(_NOW())\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.publish\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.publish\\n def publish(self, message, client=None, **attrs):\\n \\\"\\\"\\\"API call: publish a message to a topic via a POST request\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/publish\\n\\n Example without message attributes:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_publish_simple_message]\\n :end-before: [END topic_publish_simple_message]\\n\\n With message attributes:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_publish_message_with_attrs]\\n :end-before: [END topic_publish_message_with_attrs]\\n\\n :type message: bytes\\n :param message: the message payload\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current topic.\\n\\n :type attrs: dict (string -> string)\\n :param attrs: key-value pairs to send as message attributes\\n\\n :rtype: str\\n :returns: message ID assigned by the server to the published message\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.publisher_api\\n\\n self._timestamp_message(attrs)\\n message_b = base64.b64encode(message).decode('ascii')\\n message_data = {'data': message_b, 'attributes': attrs}\\n message_ids = api.topic_publish(self.full_name, [message_data])\\n return message_ids[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.batch\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.batch\\n def batch(self, client=None):\\n \\\"\\\"\\\"Return a batch to use as a context manager.\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_batch]\\n :end-before: [END topic_batch]\\n\\n .. note::\\n\\n The only API request happens during the ``__exit__()`` of the topic\\n used as a context manager, and only if the block exits without\\n raising an exception.\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current topic.\\n\\n :rtype: :class:`Batch`\\n :returns: A batch to use as a context manager.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n return Batch(self, client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.list_subscriptions\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.list_subscriptions\\n def list_subscriptions(self, page_size=None, page_token=None, client=None):\\n \\\"\\\"\\\"List subscriptions for the project associated with this client.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics.subscriptions/list\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_list_subscriptions]\\n :end-before: [END topic_list_subscriptions]\\n\\n :type page_size: int\\n :param page_size: maximum number of topics to return, If not passed,\\n defaults to a value set by the API.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of topics. If not\\n passed, the API will return the first page of\\n topics.\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current topic.\\n\\n :rtype: tuple, (list, str)\\n :returns: list of :class:`~.pubsub.subscription.Subscription`,\\n plus a \\\"next page token\\\" string: if not None, indicates that\\n more topics can be retrieved with another call (pass that\\n value as ``page_token``).\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.publisher_api\\n sub_paths, next_token = api.topic_list_subscriptions(\\n self.full_name, page_size, page_token)\\n subscriptions = []\\n for sub_path in sub_paths:\\n sub_name = subscription_name_from_path(sub_path, self.project)\\n subscriptions.append(Subscription(sub_name, self))\\n return subscriptions, next_token\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.get_iam_policy\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.get_iam_policy\\n def get_iam_policy(self, client=None):\\n \\\"\\\"\\\"Fetch the IAM policy for the topic.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/getIamPolicy\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_get_iam_policy]\\n :end-before: [END topic_get_iam_policy]\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current batch.\\n\\n :rtype: :class:`google.cloud.pubsub.iam.Policy`\\n :returns: policy created from the resource returned by the\\n ``getIamPolicy`` API request.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.iam_policy_api\\n resp = api.get_iam_policy(self.full_name)\\n return Policy.from_api_repr(resp)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.set_iam_policy\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.set_iam_policy\\n def set_iam_policy(self, policy, client=None):\\n \\\"\\\"\\\"Update the IAM policy for the topic.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/setIamPolicy\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_set_iam_policy]\\n :end-before: [END topic_set_iam_policy]\\n\\n :type policy: :class:`google.cloud.pubsub.iam.Policy`\\n :param policy: the new policy, typically fetched via\\n :meth:`get_iam_policy` and updated in place.\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current batch.\\n\\n :rtype: :class:`google.cloud.pubsub.iam.Policy`\\n :returns: updated policy created from the resource returned by the\\n ``setIamPolicy`` API request.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.iam_policy_api\\n resource = policy.to_api_repr()\\n resp = api.set_iam_policy(self.full_name, resource)\\n return Policy.from_api_repr(resp)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Topic.check_iam_permissions\",\n \"text\": \"google/cloud/pubsub/topic.py::Topic.check_iam_permissions\\n def check_iam_permissions(self, permissions, client=None):\\n \\\"\\\"\\\"Verify permissions allowed for the current user.\\n\\n See:\\n https://cloud.google.com/pubsub/reference/rest/v1/projects.topics/testIamPermissions\\n\\n Example:\\n\\n .. literalinclude:: pubsub_snippets.py\\n :start-after: [START topic_check_iam_permissions]\\n :end-before: [END topic_check_iam_permissions]\\n\\n :type permissions: list of string\\n :param permissions: list of permissions to be tested\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current batch.\\n\\n :rtype: sequence of string\\n :returns: subset of ``permissions`` allowed by current IAM policy.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n api = client.iam_policy_api\\n return api.test_iam_permissions(\\n self.full_name, list(permissions))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Batch.__init__\",\n \"text\": \"google/cloud/pubsub/topic.py::Batch.__init__\\n def __init__(self, topic, client):\\n self.topic = topic\\n self.messages = []\\n self.message_ids = []\\n self.client = client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Batch.__enter__\",\n \"text\": \"google/cloud/pubsub/topic.py::Batch.__enter__\\n def __enter__(self):\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Batch.__exit__\",\n \"text\": \"google/cloud/pubsub/topic.py::Batch.__exit__\\n def __exit__(self, exc_type, exc_val, exc_tb):\\n if exc_type is None:\\n self.commit()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Batch.__iter__\",\n \"text\": \"google/cloud/pubsub/topic.py::Batch.__iter__\\n def __iter__(self):\\n return iter(self.message_ids)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Batch.publish\",\n \"text\": \"google/cloud/pubsub/topic.py::Batch.publish\\n def publish(self, message, **attrs):\\n \\\"\\\"\\\"Emulate publishing a message, but save it.\\n\\n :type message: bytes\\n :param message: the message payload\\n\\n :type attrs: dict (string -> string)\\n :param attrs: key-value pairs to send as message attributes\\n \\\"\\\"\\\"\\n self.topic._timestamp_message(attrs)\\n self.messages.append(\\n {'data': base64.b64encode(message).decode('ascii'),\\n 'attributes': attrs})\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/pubsub/topic.py::Batch.commit\",\n \"text\": \"google/cloud/pubsub/topic.py::Batch.commit\\n def commit(self, client=None):\\n \\\"\\\"\\\"Send saved messages as a single API call.\\n\\n :type client: :class:`~google.cloud.pubsub.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current batch.\\n \\\"\\\"\\\"\\n if not self.messages:\\n return\\n\\n if client is None:\\n client = self.client\\n api = client.publisher_api\\n message_ids = api.topic_publish(self.topic.full_name, self.messages[:])\\n self.message_ids.extend(message_ids)\\n del self.messages[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/client.py::Client.new_project\",\n \"text\": \"google/cloud/resource_manager/client.py::Client.new_project\\n def new_project(self, project_id, name=None, labels=None):\\n \\\"\\\"\\\"Create a project bound to the current client.\\n\\n Use :meth:`Project.reload() \\\\\\n <google.cloud.resource_manager.project.Project.reload>` to retrieve\\n project metadata after creating a\\n :class:`~google.cloud.resource_manager.project.Project` instance.\\n\\n .. note:\\n\\n This does not make an API call.\\n\\n :type project_id: str\\n :param project_id: The ID for this project.\\n\\n :type name: string\\n :param name: The display name of the project.\\n\\n :type labels: dict\\n :param labels: A list of labels associated with the project.\\n\\n :rtype: :class:`~google.cloud.resource_manager.project.Project`\\n :returns: A new instance of a\\n :class:`~google.cloud.resource_manager.project.Project`\\n **without** any metadata loaded.\\n \\\"\\\"\\\"\\n return Project(project_id=project_id,\\n client=self, name=name, labels=labels)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/client.py::Client.fetch_project\",\n \"text\": \"google/cloud/resource_manager/client.py::Client.fetch_project\\n def fetch_project(self, project_id):\\n \\\"\\\"\\\"Fetch an existing project and it's relevant metadata by ID.\\n\\n .. note::\\n\\n If the project does not exist, this will raise a\\n :class:`NotFound <google.cloud.exceptions.NotFound>` error.\\n\\n :type project_id: str\\n :param project_id: The ID for this project.\\n\\n :rtype: :class:`~google.cloud.resource_manager.project.Project`\\n :returns: A :class:`~google.cloud.resource_manager.project.Project`\\n with metadata fetched from the API.\\n \\\"\\\"\\\"\\n project = self.new_project(project_id)\\n project.reload()\\n return project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/client.py::Client.list_projects\",\n \"text\": \"google/cloud/resource_manager/client.py::Client.list_projects\\n def list_projects(self, filter_params=None, page_size=None):\\n \\\"\\\"\\\"List the projects visible to this client.\\n\\n Example::\\n\\n >>> from google.cloud import resource_manager\\n >>> client = resource_manager.Client()\\n >>> for project in client.list_projects():\\n ... print project.project_id\\n\\n List all projects with label ``'environment'`` set to ``'prod'``\\n (filtering by labels)::\\n\\n >>> from google.cloud import resource_manager\\n >>> client = resource_manager.Client()\\n >>> env_filter = {'labels.environment': 'prod'}\\n >>> for project in client.list_projects(env_filter):\\n ... print project.project_id\\n\\n See:\\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/list\\n\\n Complete filtering example::\\n\\n >>> project_filter = { # Return projects with...\\n ... 'name': 'My Project', # name set to 'My Project'.\\n ... 'id': 'my-project-id', # id set to 'my-project-id'.\\n ... 'labels.stage': 'prod', # the label 'stage' set to 'prod'\\n ... 'labels.color': '*' # a label 'color' set to anything.\\n ... }\\n >>> client.list_projects(project_filter)\\n\\n :type filter_params: dict\\n :param filter_params: (Optional) A dictionary of filter options where\\n each key is a property to filter on, and each\\n value is the (case-insensitive) value to check\\n (or the glob ``*`` to check for existence of the\\n property). See the example above for more\\n details.\\n\\n :type page_size: int\\n :param page_size: (Optional) Maximum number of projects to return in a\\n single page. If not passed, defaults to a \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/client.py::_ProjectIterator.__init__\",\n \"text\": \"google/cloud/resource_manager/client.py::_ProjectIterator.__init__\\n def __init__(self, client, extra_params=None):\\n super(_ProjectIterator, self).__init__(client=client, path='/projects',\\n extra_params=extra_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/client.py::_ProjectIterator.get_items_from_response\",\n \"text\": \"google/cloud/resource_manager/client.py::_ProjectIterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Yield projects from response.\\n\\n :type response: dict\\n :param response: The JSON API response for a page of projects.\\n \\\"\\\"\\\"\\n for resource in response.get('projects', []):\\n item = Project.from_api_repr(resource, client=self.client)\\n yield item\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.__init__\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.__init__\\n def __init__(self, project_id, client, name=None, labels=None):\\n self._client = client\\n self.project_id = project_id\\n self.name = name\\n self.number = None\\n self.labels = labels or {}\\n self.status = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.__repr__\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.__repr__\\n def __repr__(self):\\n return '<Project: %r (%r)>' % (self.name, self.project_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.from_api_repr\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.from_api_repr\\n @classmethod\\n def from_api_repr(cls, resource, client):\\n \\\"\\\"\\\"Factory: construct a project given its API representation.\\n\\n :type resource: dict\\n :param resource: project resource representation returned from the API\\n\\n :type client: :class:`google.cloud.resource_manager.client.Client`\\n :param client: The Client used with this project.\\n\\n :rtype: :class:`google.cloud.resource_manager.project.Project`\\n :returns: The project created.\\n \\\"\\\"\\\"\\n project = cls(project_id=resource['projectId'], client=client)\\n project.set_properties_from_api_repr(resource)\\n return project\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.set_properties_from_api_repr\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.set_properties_from_api_repr\\n def set_properties_from_api_repr(self, resource):\\n \\\"\\\"\\\"Update specific properties from its API representation.\\\"\\\"\\\"\\n self.name = resource.get('name')\\n self.number = resource['projectNumber']\\n self.labels = resource.get('labels', {})\\n self.status = resource['lifecycleState']\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.full_name\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.full_name\\n @property\\n def full_name(self):\\n \\\"\\\"\\\"Fully-qualified name (ie, ``'projects/purple-spaceship-123'``).\\\"\\\"\\\"\\n if not self.project_id:\\n raise ValueError('Missing project ID.')\\n return 'projects/%s' % (self.project_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.path\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"URL for the project (ie, ``'/projects/purple-spaceship-123'``).\\\"\\\"\\\"\\n return '/%s' % (self.full_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project._require_client\",\n \"text\": \"google/cloud/resource_manager/project.py::Project._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`google.cloud.resource_manager.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current project.\\n\\n :rtype: :class:`google.cloud.resource_manager.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self._client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.create\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.create\\n def create(self, client=None):\\n \\\"\\\"\\\"API call: create the project via a ``POST`` request.\\n\\n See\\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/create\\n\\n :type client: :class:`google.cloud.resource_manager.client.Client` or\\n :data:`NoneType <types.NoneType>`\\n :param client: the client to use. If not passed, falls back to\\n the client stored on the current project.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n data = {\\n 'projectId': self.project_id,\\n 'name': self.name,\\n 'labels': self.labels,\\n }\\n resp = client.connection.api_request(method='POST', path='/projects',\\n data=data)\\n self.set_properties_from_api_repr(resource=resp)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.reload\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"API call: reload the project via a ``GET`` request.\\n\\n This method will reload the newest metadata for the project. If you've\\n created a new :class:`Project` instance via\\n :meth:`Client.new_project() \\\\\\n <google.cloud.resource_manager.client.Client.new_project>`,\\n this method will retrieve project metadata.\\n\\n .. warning::\\n\\n This will overwrite any local changes you've made and not saved\\n via :meth:`update`.\\n\\n See\\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/get\\n\\n :type client: :class:`google.cloud.resource_manager.client.Client` or\\n :data:`NoneType <types.NoneType>`\\n :param client: the client to use. If not passed, falls back to\\n the client stored on the current project.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n # We assume the project exists. If it doesn't it will raise a NotFound\\n # exception.\\n resp = client.connection.api_request(method='GET', path=self.path)\\n self.set_properties_from_api_repr(resource=resp)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.exists\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"API call: test the existence of a project via a ``GET`` request.\\n\\n See\\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/get\\n\\n :type client: :class:`google.cloud.resource_manager.client.Client` or\\n :data:`NoneType <types.NoneType>`\\n :param client: the client to use. If not passed, falls back to\\n the client stored on the current project.\\n\\n :rtype: bool\\n :returns: Boolean indicating existence of the project.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n try:\\n # Note that we have to request the entire resource as the API\\n # doesn't provide a way tocheck for existence only.\\n client.connection.api_request(method='GET', path=self.path)\\n except NotFound:\\n return False\\n else:\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.update\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.update\\n def update(self, client=None):\\n \\\"\\\"\\\"API call: update the project via a ``PUT`` request.\\n\\n See\\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/update\\n\\n :type client: :class:`google.cloud.resource_manager.client.Client` or\\n :data:`NoneType <types.NoneType>`\\n :param client: the client to use. If not passed, falls back to\\n the client stored on the current project.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n\\n data = {'name': self.name, 'labels': self.labels}\\n resp = client.connection.api_request(method='PUT', path=self.path,\\n data=data)\\n self.set_properties_from_api_repr(resp)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.delete\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.delete\\n def delete(self, client=None, reload_data=False):\\n \\\"\\\"\\\"API call: delete the project via a ``DELETE`` request.\\n\\n See:\\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/delete\\n\\n This actually changes the status (``lifecycleState``) from ``ACTIVE``\\n to ``DELETE_REQUESTED``.\\n Later (it's not specified when), the project will move into the\\n ``DELETE_IN_PROGRESS`` state, which means the deleting has actually\\n begun.\\n\\n :type client: :class:`google.cloud.resource_manager.client.Client` or\\n :data:`NoneType <types.NoneType>`\\n :param client: the client to use. If not passed, falls back to\\n the client stored on the current project.\\n\\n :type reload_data: bool\\n :param reload_data: Whether to reload the project with the latest\\n state. If you want to get the updated status,\\n you'll want this set to :data:`True` as the DELETE\\n method doesn't send back the updated project.\\n Default: :data:`False`.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.connection.api_request(method='DELETE', path=self.path)\\n\\n # If the reload flag is set, reload the project.\\n if reload_data:\\n self.reload()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/resource_manager/project.py::Project.undelete\",\n \"text\": \"google/cloud/resource_manager/project.py::Project.undelete\\n def undelete(self, client=None, reload_data=False):\\n \\\"\\\"\\\"API call: undelete the project via a ``POST`` request.\\n\\n See\\n https://cloud.google.com/resource-manager/reference/rest/v1beta1/projects/undelete\\n\\n This actually changes the project status (``lifecycleState``) from\\n ``DELETE_REQUESTED`` to ``ACTIVE``.\\n If the project has already reached a status of ``DELETE_IN_PROGRESS``,\\n this request will fail and the project cannot be restored.\\n\\n :type client: :class:`google.cloud.resource_manager.client.Client` or\\n :data:`NoneType <types.NoneType>`\\n :param client: the client to use. If not passed, falls back to\\n the client stored on the current project.\\n\\n :type reload_data: bool\\n :param reload_data: Whether to reload the project with the latest\\n state. If you want to get the updated status,\\n you'll want this set to :data:`True` as the DELETE\\n method doesn't send back the updated project.\\n Default: :data:`False`.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n client.connection.api_request(method='POST',\\n path=self.path + ':undelete')\\n\\n # If the reload flag is set, reload the project.\\n if reload_data:\\n self.reload()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/speech/client.py::Client.sync_recognize\",\n \"text\": \"google/cloud/speech/client.py::Client.sync_recognize\\n def sync_recognize(self, content, source_uri, encoding, sample_rate,\\n language_code=None, max_alternatives=None,\\n profanity_filter=None, speech_context=None):\\n \\\"\\\"\\\"Synchronous Speech Recognition.\\n\\n .. _sync_recognize: https://cloud.google.com/speech/reference/\\\\\\n rest/v1beta1/speech/syncrecognize\\n\\n See `sync_recognize`_.\\n\\n :type content: bytes\\n :param content: Byte stream of audio.\\n\\n :type source_uri: str\\n :param source_uri: URI that points to a file that contains audio\\n data bytes as specified in RecognitionConfig.\\n Currently, only Google Cloud Storage URIs are\\n supported, which must be specified in the following\\n format: ``gs://bucket_name/object_name``.\\n\\n :type encoding: str\\n :param encoding: encoding of audio data sent in all RecognitionAudio\\n messages, can be one of: :attr:`~.Encoding.LINEAR16`,\\n :attr:`~.Encoding.FLAC`, :attr:`~.Encoding.MULAW`,\\n :attr:`~.Encoding.AMR`, :attr:`~.Encoding.AMR_WB`\\n\\n :type sample_rate: int\\n :param sample_rate: Sample rate in Hertz of the audio data sent in all\\n requests. Valid values are: 8000-48000. For best\\n results, set the sampling rate of the audio source\\n to 16000 Hz. If that's not possible, use the\\n native sample rate of the audio source (instead of\\n re-sampling).\\n\\n :type language_code: str\\n :param language_code: (Optional) The language of the supplied audio as\\n BCP-47 language tag. Example: ``'en-GB'``.\\n If omitted, defaults to ``'en-US'``.\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_PropertyMixin.__init__\",\n \"text\": \"google/cloud/storage/_helpers.py::_PropertyMixin.__init__\\n def __init__(self, name=None):\\n self.name = name\\n self._properties = {}\\n self._changes = set()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_PropertyMixin.path\",\n \"text\": \"google/cloud/storage/_helpers.py::_PropertyMixin.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"Abstract getter for the object path.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_PropertyMixin.client\",\n \"text\": \"google/cloud/storage/_helpers.py::_PropertyMixin.client\\n @property\\n def client(self):\\n \\\"\\\"\\\"Abstract getter for the object client.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_PropertyMixin._require_client\",\n \"text\": \"google/cloud/storage/_helpers.py::_PropertyMixin._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current object.\\n\\n :rtype: :class:`google.cloud.storage.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self.client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_PropertyMixin.reload\",\n \"text\": \"google/cloud/storage/_helpers.py::_PropertyMixin.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"Reload properties from Cloud Storage.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current object.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n # Pass only '?projection=noAcl' here because 'acl' and related\\n # are handled via custom endpoints.\\n query_params = {'projection': 'noAcl'}\\n api_response = client.connection.api_request(\\n method='GET', path=self.path, query_params=query_params,\\n _target_object=self)\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_PropertyMixin._patch_property\",\n \"text\": \"google/cloud/storage/_helpers.py::_PropertyMixin._patch_property\\n def _patch_property(self, name, value):\\n \\\"\\\"\\\"Update field of this object's properties.\\n\\n This method will only update the field provided and will not\\n touch the other fields.\\n\\n It **will not** reload the properties from the server. The behavior is\\n local only and syncing occurs via :meth:`patch`.\\n\\n :type name: string\\n :param name: The field name to update.\\n\\n :type value: object\\n :param value: The value being updated.\\n \\\"\\\"\\\"\\n self._changes.add(name)\\n self._properties[name] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_PropertyMixin._set_properties\",\n \"text\": \"google/cloud/storage/_helpers.py::_PropertyMixin._set_properties\\n def _set_properties(self, value):\\n \\\"\\\"\\\"Set the properties for the current object.\\n\\n :type value: dict or :class:`google.cloud.storage.batch._FutureDict`\\n :param value: The properties to be set.\\n \\\"\\\"\\\"\\n self._properties = value\\n # If the values are reset, the changes must as well.\\n self._changes = set()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_PropertyMixin.patch\",\n \"text\": \"google/cloud/storage/_helpers.py::_PropertyMixin.patch\\n def patch(self, client=None):\\n \\\"\\\"\\\"Sends all changed properties in a PATCH request.\\n\\n Updates the ``_properties`` with the response from the backend.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current object.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n # Pass '?projection=full' here because 'PATCH' documented not\\n # to work properly w/ 'noAcl'.\\n update_properties = {key: self._properties[key]\\n for key in self._changes}\\n api_response = client.connection.api_request(\\n method='PATCH', path=self.path, data=update_properties,\\n query_params={'projection': 'full'}, _target_object=self)\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_scalar_property\",\n \"text\": \"google/cloud/storage/_helpers.py::_scalar_property\\ndef _scalar_property(fieldname):\\n \\\"\\\"\\\"Create a property descriptor around the :class:`_PropertyMixin` helpers.\\n \\\"\\\"\\\"\\n def _getter(self):\\n \\\"\\\"\\\"Scalar property getter.\\\"\\\"\\\"\\n return self._properties.get(fieldname)\\n\\n def _setter(self, value):\\n \\\"\\\"\\\"Scalar property setter.\\\"\\\"\\\"\\n self._patch_property(fieldname, value)\\n\\n return property(_getter, _setter)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_scalar_property._getter\",\n \"text\": \"google/cloud/storage/_helpers.py::_scalar_property._getter\\n def _getter(self):\\n \\\"\\\"\\\"Scalar property getter.\\\"\\\"\\\"\\n return self._properties.get(fieldname)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_scalar_property._setter\",\n \"text\": \"google/cloud/storage/_helpers.py::_scalar_property._setter\\n def _setter(self, value):\\n \\\"\\\"\\\"Scalar property setter.\\\"\\\"\\\"\\n self._patch_property(fieldname, value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_write_buffer_to_hash\",\n \"text\": \"google/cloud/storage/_helpers.py::_write_buffer_to_hash\\ndef _write_buffer_to_hash(buffer_object, hash_obj, digest_block_size=8192):\\n \\\"\\\"\\\"Read blocks from a buffer and update a hash with them.\\n\\n :type buffer_object: bytes buffer\\n :param buffer_object: Buffer containing bytes used to update a hash object.\\n\\n :type hash_obj: object that implements update\\n :param hash_obj: A hash object (MD5 or CRC32-C).\\n\\n :type digest_block_size: integer\\n :param digest_block_size: The block size to write to the hash.\\n Defaults to 8192.\\n \\\"\\\"\\\"\\n block = buffer_object.read(digest_block_size)\\n\\n while len(block) > 0:\\n hash_obj.update(block)\\n # Update the block for the next iteration.\\n block = buffer_object.read(digest_block_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/_helpers.py::_base64_md5hash\",\n \"text\": \"google/cloud/storage/_helpers.py::_base64_md5hash\\ndef _base64_md5hash(buffer_object):\\n \\\"\\\"\\\"Get MD5 hash of bytes (as base64).\\n\\n :type buffer_object: bytes buffer\\n :param buffer_object: Buffer containing bytes used to compute an MD5\\n hash (as base64).\\n\\n :rtype: str\\n :returns: A base64 encoded digest of the MD5 hash.\\n \\\"\\\"\\\"\\n hash_obj = md5()\\n _write_buffer_to_hash(buffer_object, hash_obj)\\n digest_bytes = hash_obj.digest()\\n return base64.b64encode(digest_bytes)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.__init__\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.__init__\\n def __init__(self, entity_type, identifier=None):\\n self.identifier = identifier\\n self.roles = set([])\\n self.type = entity_type\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.__str__\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.__str__\\n def __str__(self):\\n if not self.identifier:\\n return str(self.type)\\n else:\\n return '{acl.type}-{acl.identifier}'.format(acl=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.__repr__\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.__repr__\\n def __repr__(self):\\n return '<ACL Entity: {acl} ({roles})>'.format(\\n acl=self, roles=', '.join(self.roles))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.get_roles\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.get_roles\\n def get_roles(self):\\n \\\"\\\"\\\"Get the list of roles permitted by this entity.\\n\\n :rtype: list of strings\\n :returns: The list of roles associated with this entity.\\n \\\"\\\"\\\"\\n return self.roles\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.grant\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.grant\\n def grant(self, role):\\n \\\"\\\"\\\"Add a role to the entity.\\n\\n :type role: string\\n :param role: The role to add to the entity.\\n \\\"\\\"\\\"\\n self.roles.add(role)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.revoke\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.revoke\\n def revoke(self, role):\\n \\\"\\\"\\\"Remove a role from the entity.\\n\\n :type role: string\\n :param role: The role to remove from the entity.\\n \\\"\\\"\\\"\\n if role in self.roles:\\n self.roles.remove(role)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.grant_read\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.grant_read\\n def grant_read(self):\\n \\\"\\\"\\\"Grant read access to the current entity.\\\"\\\"\\\"\\n self.grant(_ACLEntity.READER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.grant_write\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.grant_write\\n def grant_write(self):\\n \\\"\\\"\\\"Grant write access to the current entity.\\\"\\\"\\\"\\n self.grant(_ACLEntity.WRITER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.grant_owner\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.grant_owner\\n def grant_owner(self):\\n \\\"\\\"\\\"Grant owner access to the current entity.\\\"\\\"\\\"\\n self.grant(_ACLEntity.OWNER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.revoke_read\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.revoke_read\\n def revoke_read(self):\\n \\\"\\\"\\\"Revoke read access from the current entity.\\\"\\\"\\\"\\n self.revoke(_ACLEntity.READER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.revoke_write\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.revoke_write\\n def revoke_write(self):\\n \\\"\\\"\\\"Revoke write access from the current entity.\\\"\\\"\\\"\\n self.revoke(_ACLEntity.WRITER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::_ACLEntity.revoke_owner\",\n \"text\": \"google/cloud/storage/acl.py::_ACLEntity.revoke_owner\\n def revoke_owner(self):\\n \\\"\\\"\\\"Revoke owner access from the current entity.\\\"\\\"\\\"\\n self.revoke(_ACLEntity.OWNER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.__init__\",\n \"text\": \"google/cloud/storage/acl.py::ACL.__init__\\n def __init__(self):\\n self.entities = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL._ensure_loaded\",\n \"text\": \"google/cloud/storage/acl.py::ACL._ensure_loaded\\n def _ensure_loaded(self):\\n \\\"\\\"\\\"Load if not already loaded.\\\"\\\"\\\"\\n if not self.loaded:\\n self.reload()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.reset\",\n \"text\": \"google/cloud/storage/acl.py::ACL.reset\\n def reset(self):\\n \\\"\\\"\\\"Remove all entities from the ACL, and clear the ``loaded`` flag.\\\"\\\"\\\"\\n self.entities.clear()\\n self.loaded = False\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.__iter__\",\n \"text\": \"google/cloud/storage/acl.py::ACL.__iter__\\n def __iter__(self):\\n self._ensure_loaded()\\n\\n for entity in self.entities.values():\\n for role in entity.get_roles():\\n if role:\\n yield {'entity': str(entity), 'role': role}\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.entity_from_dict\",\n \"text\": \"google/cloud/storage/acl.py::ACL.entity_from_dict\\n def entity_from_dict(self, entity_dict):\\n \\\"\\\"\\\"Build an _ACLEntity object from a dictionary of data.\\n\\n An entity is a mutable object that represents a list of roles\\n belonging to either a user or group or the special types for all\\n users and all authenticated users.\\n\\n :type entity_dict: dict\\n :param entity_dict: Dictionary full of data from an ACL lookup.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An Entity constructed from the dictionary.\\n \\\"\\\"\\\"\\n entity = entity_dict['entity']\\n role = entity_dict['role']\\n\\n if entity == 'allUsers':\\n entity = self.all()\\n\\n elif entity == 'allAuthenticatedUsers':\\n entity = self.all_authenticated()\\n\\n elif '-' in entity:\\n entity_type, identifier = entity.split('-', 1)\\n entity = self.entity(entity_type=entity_type,\\n identifier=identifier)\\n\\n if not isinstance(entity, _ACLEntity):\\n raise ValueError('Invalid dictionary: %s' % entity_dict)\\n\\n entity.grant(role)\\n return entity\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.has_entity\",\n \"text\": \"google/cloud/storage/acl.py::ACL.has_entity\\n def has_entity(self, entity):\\n \\\"\\\"\\\"Returns whether or not this ACL has any entries for an entity.\\n\\n :type entity: :class:`_ACLEntity`\\n :param entity: The entity to check for existence in this ACL.\\n\\n :rtype: boolean\\n :returns: True of the entity exists in the ACL.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n return str(entity) in self.entities\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.get_entity\",\n \"text\": \"google/cloud/storage/acl.py::ACL.get_entity\\n def get_entity(self, entity, default=None):\\n \\\"\\\"\\\"Gets an entity object from the ACL.\\n\\n :type entity: :class:`_ACLEntity` or string\\n :param entity: The entity to get lookup in the ACL.\\n\\n :type default: anything\\n :param default: This value will be returned if the entity\\n doesn't exist.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: The corresponding entity or the value provided\\n to ``default``.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n return self.entities.get(str(entity), default)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.add_entity\",\n \"text\": \"google/cloud/storage/acl.py::ACL.add_entity\\n def add_entity(self, entity):\\n \\\"\\\"\\\"Add an entity to the ACL.\\n\\n :type entity: :class:`_ACLEntity`\\n :param entity: The entity to add to this ACL.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n self.entities[str(entity)] = entity\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.entity\",\n \"text\": \"google/cloud/storage/acl.py::ACL.entity\\n def entity(self, entity_type, identifier=None):\\n \\\"\\\"\\\"Factory method for creating an Entity.\\n\\n If an entity with the same type and identifier already exists,\\n this will return a reference to that entity. If not, it will\\n create a new one and add it to the list of known entities for\\n this ACL.\\n\\n :type entity_type: string\\n :param entity_type: The type of entity to create\\n (ie, ``user``, ``group``, etc)\\n\\n :type identifier: string\\n :param identifier: The ID of the entity (if applicable).\\n This can be either an ID or an e-mail address.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: A new Entity or a reference to an existing identical entity.\\n \\\"\\\"\\\"\\n entity = _ACLEntity(entity_type=entity_type, identifier=identifier)\\n if self.has_entity(entity):\\n entity = self.get_entity(entity)\\n else:\\n self.add_entity(entity)\\n return entity\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.user\",\n \"text\": \"google/cloud/storage/acl.py::ACL.user\\n def user(self, identifier):\\n \\\"\\\"\\\"Factory method for a user Entity.\\n\\n :type identifier: string\\n :param identifier: An id or e-mail for this particular user.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An Entity corresponding to this user.\\n \\\"\\\"\\\"\\n return self.entity('user', identifier=identifier)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.group\",\n \"text\": \"google/cloud/storage/acl.py::ACL.group\\n def group(self, identifier):\\n \\\"\\\"\\\"Factory method for a group Entity.\\n\\n :type identifier: string\\n :param identifier: An id or e-mail for this particular group.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An Entity corresponding to this group.\\n \\\"\\\"\\\"\\n return self.entity('group', identifier=identifier)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.domain\",\n \"text\": \"google/cloud/storage/acl.py::ACL.domain\\n def domain(self, domain):\\n \\\"\\\"\\\"Factory method for a domain Entity.\\n\\n :type domain: string\\n :param domain: The domain for this entity.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An entity corresponding to this domain.\\n \\\"\\\"\\\"\\n return self.entity('domain', identifier=domain)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.all\",\n \"text\": \"google/cloud/storage/acl.py::ACL.all\\n def all(self):\\n \\\"\\\"\\\"Factory method for an Entity representing all users.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An entity representing all users.\\n \\\"\\\"\\\"\\n return self.entity('allUsers')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.all_authenticated\",\n \"text\": \"google/cloud/storage/acl.py::ACL.all_authenticated\\n def all_authenticated(self):\\n \\\"\\\"\\\"Factory method for an Entity representing all authenticated users.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An entity representing all authenticated users.\\n \\\"\\\"\\\"\\n return self.entity('allAuthenticatedUsers')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.get_entities\",\n \"text\": \"google/cloud/storage/acl.py::ACL.get_entities\\n def get_entities(self):\\n \\\"\\\"\\\"Get a list of all Entity objects.\\n\\n :rtype: list of :class:`_ACLEntity` objects\\n :returns: A list of all Entity objects.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n return list(self.entities.values())\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.client\",\n \"text\": \"google/cloud/storage/acl.py::ACL.client\\n @property\\n def client(self):\\n \\\"\\\"\\\"Abstract getter for the object client.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL._require_client\",\n \"text\": \"google/cloud/storage/acl.py::ACL._require_client\\n def _require_client(self, client):\\n \\\"\\\"\\\"Check client or verify over-ride.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: the client to use. If not passed, falls back to the\\n ``client`` stored on the current ACL.\\n\\n :rtype: :class:`google.cloud.storage.client.Client`\\n :returns: The client passed in or the currently bound client.\\n \\\"\\\"\\\"\\n if client is None:\\n client = self.client\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.reload\",\n \"text\": \"google/cloud/storage/acl.py::ACL.reload\\n def reload(self, client=None):\\n \\\"\\\"\\\"Reload the ACL data from Cloud Storage.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the ACL's parent.\\n \\\"\\\"\\\"\\n path = self.reload_path\\n client = self._require_client(client)\\n\\n self.entities.clear()\\n\\n found = client.connection.api_request(method='GET', path=path)\\n self.loaded = True\\n for entry in found.get('items', ()):\\n self.add_entity(self.entity_from_dict(entry))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL._save\",\n \"text\": \"google/cloud/storage/acl.py::ACL._save\\n def _save(self, acl, predefined, client):\\n \\\"\\\"\\\"Helper for :meth:`save` and :meth:`save_predefined`.\\n\\n :type acl: :class:`google.cloud.storage.acl.ACL`, or a compatible list.\\n :param acl: The ACL object to save. If left blank, this will save\\n current entries.\\n\\n :type predefined: string or None\\n :param predefined: An identifier for a predefined ACL. Must be one\\n of the keys in :attr:`PREDEFINED_JSON_ACLS`\\n If passed, `acl` must be None.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the ACL's parent.\\n \\\"\\\"\\\"\\n query_params = {'projection': 'full'}\\n if predefined is not None:\\n acl = []\\n query_params[self._PREDEFINED_QUERY_PARAM] = predefined\\n\\n path = self.save_path\\n client = self._require_client(client)\\n result = client.connection.api_request(\\n method='PATCH',\\n path=path,\\n data={self._URL_PATH_ELEM: list(acl)},\\n query_params=query_params)\\n self.entities.clear()\\n for entry in result.get(self._URL_PATH_ELEM, ()):\\n self.add_entity(self.entity_from_dict(entry))\\n self.loaded = True\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.save\",\n \"text\": \"google/cloud/storage/acl.py::ACL.save\\n def save(self, acl=None, client=None):\\n \\\"\\\"\\\"Save this ACL for the current bucket.\\n\\n :type acl: :class:`google.cloud.storage.acl.ACL`, or a compatible list.\\n :param acl: The ACL object to save. If left blank, this will save\\n current entries.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the ACL's parent.\\n \\\"\\\"\\\"\\n if acl is None:\\n acl = self\\n save_to_backend = acl.loaded\\n else:\\n save_to_backend = True\\n\\n if save_to_backend:\\n self._save(acl, None, client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.save_predefined\",\n \"text\": \"google/cloud/storage/acl.py::ACL.save_predefined\\n def save_predefined(self, predefined, client=None):\\n \\\"\\\"\\\"Save this ACL for the current bucket using a predefined ACL.\\n\\n :type predefined: string\\n :param predefined: An identifier for a predefined ACL. Must be one\\n of the keys in :attr:`PREDEFINED_JSON_ACLS`\\n or :attr:`PREDEFINED_XML_ACLS` (which will be\\n aliased to the corresponding JSON name).\\n If passed, `acl` must be None.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the ACL's parent.\\n \\\"\\\"\\\"\\n predefined = self.PREDEFINED_XML_ACLS.get(predefined, predefined)\\n\\n if predefined not in self.PREDEFINED_JSON_ACLS:\\n raise ValueError(\\\"Invalid predefined ACL: %s\\\" % (predefined,))\\n\\n self._save(None, predefined, client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ACL.clear\",\n \"text\": \"google/cloud/storage/acl.py::ACL.clear\\n def clear(self, client=None):\\n \\\"\\\"\\\"Remove all ACL entries.\\n\\n Note that this won't actually remove *ALL* the rules, but it\\n will remove all the non-default rules. In short, you'll still\\n have access to a bucket that you created even after you clear\\n ACL rules with this method.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the ACL's parent.\\n \\\"\\\"\\\"\\n self.save([], client=client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::BucketACL.__init__\",\n \"text\": \"google/cloud/storage/acl.py::BucketACL.__init__\\n def __init__(self, bucket):\\n super(BucketACL, self).__init__()\\n self.bucket = bucket\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::BucketACL.client\",\n \"text\": \"google/cloud/storage/acl.py::BucketACL.client\\n @property\\n def client(self):\\n \\\"\\\"\\\"The client bound to this ACL's bucket.\\\"\\\"\\\"\\n return self.bucket.client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::BucketACL.reload_path\",\n \"text\": \"google/cloud/storage/acl.py::BucketACL.reload_path\\n @property\\n def reload_path(self):\\n \\\"\\\"\\\"Compute the path for GET API requests for this ACL.\\\"\\\"\\\"\\n return '%s/%s' % (self.bucket.path, self._URL_PATH_ELEM)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::BucketACL.save_path\",\n \"text\": \"google/cloud/storage/acl.py::BucketACL.save_path\\n @property\\n def save_path(self):\\n \\\"\\\"\\\"Compute the path for PATCH API requests for this ACL.\\\"\\\"\\\"\\n return self.bucket.path\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ObjectACL.__init__\",\n \"text\": \"google/cloud/storage/acl.py::ObjectACL.__init__\\n def __init__(self, blob):\\n super(ObjectACL, self).__init__()\\n self.blob = blob\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ObjectACL.client\",\n \"text\": \"google/cloud/storage/acl.py::ObjectACL.client\\n @property\\n def client(self):\\n \\\"\\\"\\\"The client bound to this ACL's blob.\\\"\\\"\\\"\\n return self.blob.client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ObjectACL.reload_path\",\n \"text\": \"google/cloud/storage/acl.py::ObjectACL.reload_path\\n @property\\n def reload_path(self):\\n \\\"\\\"\\\"Compute the path for GET API requests for this ACL.\\\"\\\"\\\"\\n return '%s/acl' % self.blob.path\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/acl.py::ObjectACL.save_path\",\n \"text\": \"google/cloud/storage/acl.py::ObjectACL.save_path\\n @property\\n def save_path(self):\\n \\\"\\\"\\\"Compute the path for PATCH API requests for this ACL.\\\"\\\"\\\"\\n return self.blob.path\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::MIMEApplicationHTTP.__init__\",\n \"text\": \"google/cloud/storage/batch.py::MIMEApplicationHTTP.__init__\\n def __init__(self, method, uri, headers, body):\\n if isinstance(body, dict):\\n body = json.dumps(body)\\n headers['Content-Type'] = 'application/json'\\n headers['Content-Length'] = len(body)\\n if body is None:\\n body = ''\\n lines = ['%s %s HTTP/1.1' % (method, uri)]\\n lines.extend(['%s: %s' % (key, value)\\n for key, value in sorted(headers.items())])\\n lines.append('')\\n lines.append(body)\\n payload = '\\\\r\\\\n'.join(lines)\\n if six.PY2:\\n # email.message.Message is an old-style class, so we\\n # cannot use 'super()'.\\n MIMEApplication.__init__(self, payload, 'http', encode_noop)\\n else: # pragma: NO COVER Python3\\n super_init = super(MIMEApplicationHTTP, self).__init__\\n super_init(payload, 'http', encode_noop)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::_FutureDict.get\",\n \"text\": \"google/cloud/storage/batch.py::_FutureDict.get\\n @staticmethod\\n def get(key, default=None):\\n \\\"\\\"\\\"Stand-in for dict.get.\\n\\n :type key: object\\n :param key: Hashable dictionary key.\\n\\n :type default: object\\n :param default: Fallback value to dict.get.\\n\\n :raises: :class:`KeyError` always since the future is intended to fail\\n as a dictionary.\\n \\\"\\\"\\\"\\n raise KeyError('Cannot get(%r, default=%r) on a future' % (\\n key, default))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::_FutureDict.__getitem__\",\n \"text\": \"google/cloud/storage/batch.py::_FutureDict.__getitem__\\n def __getitem__(self, key):\\n \\\"\\\"\\\"Stand-in for dict[key].\\n\\n :type key: object\\n :param key: Hashable dictionary key.\\n\\n :raises: :class:`KeyError` always since the future is intended to fail\\n as a dictionary.\\n \\\"\\\"\\\"\\n raise KeyError('Cannot get item %r from a future' % (key,))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::_FutureDict.__setitem__\",\n \"text\": \"google/cloud/storage/batch.py::_FutureDict.__setitem__\\n def __setitem__(self, key, value):\\n \\\"\\\"\\\"Stand-in for dict[key] = value.\\n\\n :type key: object\\n :param key: Hashable dictionary key.\\n\\n :type value: object\\n :param value: Dictionary value.\\n\\n :raises: :class:`KeyError` always since the future is intended to fail\\n as a dictionary.\\n \\\"\\\"\\\"\\n raise KeyError('Cannot set %r -> %r on a future' % (key, value))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::Batch.__init__\",\n \"text\": \"google/cloud/storage/batch.py::Batch.__init__\\n def __init__(self, client):\\n super(Batch, self).__init__()\\n self._client = client\\n self._requests = []\\n self._target_objects = []\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::Batch._do_request\",\n \"text\": \"google/cloud/storage/batch.py::Batch._do_request\\n def _do_request(self, method, url, headers, data, target_object):\\n \\\"\\\"\\\"Override Connection: defer actual HTTP request.\\n\\n Only allow up to ``_MAX_BATCH_SIZE`` requests to be deferred.\\n\\n :type method: str\\n :param method: The HTTP method to use in the request.\\n\\n :type url: str\\n :param url: The URL to send the request to.\\n\\n :type headers: dict\\n :param headers: A dictionary of HTTP headers to send with the request.\\n\\n :type data: str\\n :param data: The data to send as the body of the request.\\n\\n :type target_object: object or :class:`NoneType`\\n :param target_object: This allows us to enable custom behavior in our\\n batch connection. Here we defer an HTTP request\\n and complete initialization of the object at a\\n later time.\\n\\n :rtype: tuple of ``response`` (a dictionary of sorts)\\n and ``content`` (a string).\\n :returns: The HTTP response object and the content of the response.\\n \\\"\\\"\\\"\\n if len(self._requests) >= self._MAX_BATCH_SIZE:\\n raise ValueError(\\\"Too many deferred requests (max %d)\\\" %\\n self._MAX_BATCH_SIZE)\\n self._requests.append((method, url, headers, data))\\n result = _FutureDict()\\n self._target_objects.append(target_object)\\n if target_object is not None:\\n target_object._properties = result\\n return NoContent(), result\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::Batch._prepare_batch_request\",\n \"text\": \"google/cloud/storage/batch.py::Batch._prepare_batch_request\\n def _prepare_batch_request(self):\\n \\\"\\\"\\\"Prepares headers and body for a batch request.\\n\\n :rtype: tuple (dict, str)\\n :returns: The pair of headers and body of the batch request to be sent.\\n :raises: :class:`ValueError` if no requests have been deferred.\\n \\\"\\\"\\\"\\n if len(self._requests) == 0:\\n raise ValueError(\\\"No deferred requests\\\")\\n\\n multi = MIMEMultipart()\\n\\n for method, uri, headers, body in self._requests:\\n subrequest = MIMEApplicationHTTP(method, uri, headers, body)\\n multi.attach(subrequest)\\n\\n # The `email` package expects to deal with \\\"native\\\" strings\\n if six.PY3: # pragma: NO COVER Python3\\n buf = io.StringIO()\\n else:\\n buf = io.BytesIO()\\n generator = Generator(buf, False, 0)\\n generator.flatten(multi)\\n payload = buf.getvalue()\\n\\n # Strip off redundant header text\\n _, body = payload.split('\\\\n\\\\n', 1)\\n return dict(multi._headers), body\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::Batch._finish_futures\",\n \"text\": \"google/cloud/storage/batch.py::Batch._finish_futures\\n def _finish_futures(self, responses):\\n \\\"\\\"\\\"Apply all the batch responses to the futures created.\\n\\n :type responses: list of (headers, payload) tuples.\\n :param responses: List of headers and payloads from each response in\\n the batch.\\n\\n :raises: :class:`ValueError` if no requests have been deferred.\\n \\\"\\\"\\\"\\n # If a bad status occurs, we track it, but don't raise an exception\\n # until all futures have been populated.\\n exception_args = None\\n\\n if len(self._target_objects) != len(responses):\\n raise ValueError('Expected a response for every request.')\\n\\n for target_object, sub_response in zip(self._target_objects,\\n responses):\\n resp_headers, sub_payload = sub_response\\n if not 200 <= resp_headers.status < 300:\\n exception_args = exception_args or (resp_headers,\\n sub_payload)\\n elif target_object is not None:\\n target_object._properties = sub_payload\\n\\n if exception_args is not None:\\n raise make_exception(*exception_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::Batch.finish\",\n \"text\": \"google/cloud/storage/batch.py::Batch.finish\\n def finish(self):\\n \\\"\\\"\\\"Submit a single `multipart/mixed` request with deferred requests.\\n\\n :rtype: list of tuples\\n :returns: one ``(headers, payload)`` tuple per deferred request.\\n \\\"\\\"\\\"\\n headers, body = self._prepare_batch_request()\\n\\n url = '%s/batch' % self.API_BASE_URL\\n\\n # Use the private ``_connection`` rather than the public\\n # ``.connection``, since the public connection may be this\\n # current batch.\\n response, content = self._client._connection._make_request(\\n 'POST', url, data=body, headers=headers)\\n responses = list(_unpack_batch_response(response, content))\\n self._finish_futures(responses)\\n return responses\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::Batch.current\",\n \"text\": \"google/cloud/storage/batch.py::Batch.current\\n def current(self):\\n \\\"\\\"\\\"Return the topmost batch, or None.\\\"\\\"\\\"\\n return self._client.current_batch\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::Batch.__enter__\",\n \"text\": \"google/cloud/storage/batch.py::Batch.__enter__\\n def __enter__(self):\\n self._client._push_batch(self)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::Batch.__exit__\",\n \"text\": \"google/cloud/storage/batch.py::Batch.__exit__\\n def __exit__(self, exc_type, exc_val, exc_tb):\\n try:\\n if exc_type is None:\\n self.finish()\\n finally:\\n self._client._pop_batch()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::_generate_faux_mime_message\",\n \"text\": \"google/cloud/storage/batch.py::_generate_faux_mime_message\\ndef _generate_faux_mime_message(parser, response, content):\\n \\\"\\\"\\\"Convert response, content -> (multipart) email.message.\\n\\n Helper for _unpack_batch_response.\\n \\\"\\\"\\\"\\n # We coerce to bytes to get consistent concat across\\n # Py2 and Py3. Percent formatting is insufficient since\\n # it includes the b in Py3.\\n if not isinstance(content, six.binary_type):\\n content = content.encode('utf-8')\\n content_type = response['content-type']\\n if not isinstance(content_type, six.binary_type):\\n content_type = content_type.encode('utf-8')\\n faux_message = b''.join([\\n b'Content-Type: ',\\n content_type,\\n b'\\\\nMIME-Version: 1.0\\\\n\\\\n',\\n content,\\n ])\\n\\n if six.PY2:\\n return parser.parsestr(faux_message)\\n else: # pragma: NO COVER Python3\\n return parser.parsestr(faux_message.decode('utf-8'))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/batch.py::_unpack_batch_response\",\n \"text\": \"google/cloud/storage/batch.py::_unpack_batch_response\\ndef _unpack_batch_response(response, content):\\n \\\"\\\"\\\"Convert response, content -> [(headers, payload)].\\n\\n Creates a generator of tuples of emulating the responses to\\n :meth:`httplib2.Http.request` (a pair of headers and payload).\\n\\n :type response: :class:`httplib2.Response`\\n :param response: HTTP response / headers from a request.\\n\\n :type content: str\\n :param content: Response payload with a batch response.\\n \\\"\\\"\\\"\\n parser = Parser()\\n message = _generate_faux_mime_message(parser, response, content)\\n\\n if not isinstance(message._payload, list):\\n raise ValueError('Bad response: not multi-part')\\n\\n for subrequest in message._payload:\\n status_line, rest = subrequest._payload.split('\\\\n', 1)\\n _, status, _ = status_line.split(' ', 2)\\n sub_message = parser.parsestr(rest)\\n payload = sub_message._payload\\n ctype = sub_message['Content-Type']\\n msg_headers = dict(sub_message._headers)\\n msg_headers['status'] = status\\n headers = httplib2.Response(msg_headers)\\n if ctype and ctype.startswith('application/json'):\\n payload = json.loads(payload)\\n yield headers, payload\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.__init__\",\n \"text\": \"google/cloud/storage/blob.py::Blob.__init__\\n def __init__(self, name, bucket, chunk_size=None):\\n super(Blob, self).__init__(name=name)\\n\\n self.chunk_size = chunk_size # Check that setter accepts value.\\n self.bucket = bucket\\n self._acl = ObjectACL(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.chunk_size\",\n \"text\": \"google/cloud/storage/blob.py::Blob.chunk_size\\n @property\\n def chunk_size(self):\\n \\\"\\\"\\\"Get the blob's default chunk size.\\n\\n :rtype: integer or ``NoneType``\\n :returns: The current blob's chunk size, if it is set.\\n \\\"\\\"\\\"\\n return self._chunk_size\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.chunk_size\",\n \"text\": \"google/cloud/storage/blob.py::Blob.chunk_size\\n @chunk_size.setter\\n def chunk_size(self, value):\\n \\\"\\\"\\\"Set the blob's default chunk size.\\n\\n :type value: integer or ``NoneType``\\n :param value: The current blob's chunk size, if it is set.\\n\\n :raises: :class:`ValueError` if ``value`` is not ``None`` and is not a\\n multiple of 256 KB.\\n \\\"\\\"\\\"\\n if value is not None and value % self._CHUNK_SIZE_MULTIPLE != 0:\\n raise ValueError('Chunk size must be a multiple of %d.' % (\\n self._CHUNK_SIZE_MULTIPLE,))\\n self._chunk_size = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.path_helper\",\n \"text\": \"google/cloud/storage/blob.py::Blob.path_helper\\n @staticmethod\\n def path_helper(bucket_path, blob_name):\\n \\\"\\\"\\\"Relative URL path for a blob.\\n\\n :type bucket_path: string\\n :param bucket_path: The URL path for a bucket.\\n\\n :type blob_name: string\\n :param blob_name: The name of the blob.\\n\\n :rtype: string\\n :returns: The relative URL path for ``blob_name``.\\n \\\"\\\"\\\"\\n return bucket_path + '/o/' + quote(blob_name, safe='')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.acl\",\n \"text\": \"google/cloud/storage/blob.py::Blob.acl\\n @property\\n def acl(self):\\n \\\"\\\"\\\"Create our ACL on demand.\\\"\\\"\\\"\\n return self._acl\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.__repr__\",\n \"text\": \"google/cloud/storage/blob.py::Blob.__repr__\\n def __repr__(self):\\n if self.bucket:\\n bucket_name = self.bucket.name\\n else:\\n bucket_name = None\\n\\n return '<Blob: %s, %s>' % (bucket_name, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.path\",\n \"text\": \"google/cloud/storage/blob.py::Blob.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"Getter property for the URL path to this Blob.\\n\\n :rtype: string\\n :returns: The URL path to this Blob.\\n \\\"\\\"\\\"\\n if not self.name:\\n raise ValueError('Cannot determine path without a blob name.')\\n\\n return self.path_helper(self.bucket.path, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.client\",\n \"text\": \"google/cloud/storage/blob.py::Blob.client\\n @property\\n def client(self):\\n \\\"\\\"\\\"The client bound to this blob.\\\"\\\"\\\"\\n return self.bucket.client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.public_url\",\n \"text\": \"google/cloud/storage/blob.py::Blob.public_url\\n @property\\n def public_url(self):\\n \\\"\\\"\\\"The public URL for this blob's object.\\n\\n :rtype: `string`\\n :returns: The public URL for this blob.\\n \\\"\\\"\\\"\\n return '{storage_base_url}/{bucket_name}/{quoted_name}'.format(\\n storage_base_url='https://storage.googleapis.com',\\n bucket_name=self.bucket.name,\\n quoted_name=quote(self.name, safe=''))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.generate_signed_url\",\n \"text\": \"google/cloud/storage/blob.py::Blob.generate_signed_url\\n def generate_signed_url(self, expiration, method='GET',\\n content_type=None,\\n generation=None, response_disposition=None,\\n response_type=None, client=None, credentials=None):\\n \\\"\\\"\\\"Generates a signed URL for this blob.\\n\\n .. note::\\n\\n If you are on Google Compute Engine, you can't generate a signed\\n URL. Follow `Issue 922`_ for updates on this. If you'd like to\\n be able to generate a signed URL from GCE, you can use a standard\\n service account from a JSON file rather than a GCE service account.\\n\\n .. _Issue 922: https://github.com/GoogleCloudPlatform/\\\\\\n google-cloud-python/issues/922\\n\\n If you have a blob that you want to allow access to for a set\\n amount of time, you can use this method to generate a URL that\\n is only valid within a certain time period.\\n\\n This is particularly useful if you don't want publicly\\n accessible blobs, but don't want to require users to explicitly\\n log in.\\n\\n :type expiration: int, long, datetime.datetime, datetime.timedelta\\n :param expiration: When the signed URL should expire.\\n\\n :type method: str\\n :param method: The HTTP verb that will be used when requesting the URL.\\n\\n :type content_type: str\\n :param content_type: (Optional) The content type of the object\\n referenced by ``resource``.\\n\\n :type generation: str\\n :param generation: (Optional) A value that indicates which generation\\n of the resource to fetch.\\n\\n :type response_disposition: str\\n :param response_disposition: (Optional) Content disposition of\\n responses to requests for the signed URL.\\n For example, to enable the signed URL\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.exists\",\n \"text\": \"google/cloud/storage/blob.py::Blob.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"Determines whether or not this blob exists.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the blob's bucket.\\n\\n :rtype: boolean\\n :returns: True if the blob exists in Cloud Storage.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n try:\\n # We only need the status code (200 or not) so we seek to\\n # minimize the returned payload.\\n query_params = {'fields': 'name'}\\n # We intentionally pass `_target_object=None` since fields=name\\n # would limit the local properties.\\n client.connection.api_request(method='GET', path=self.path,\\n query_params=query_params,\\n _target_object=None)\\n # NOTE: This will not fail immediately in a batch. However, when\\n # Batch.finish() is called, the resulting `NotFound` will be\\n # raised.\\n return True\\n except NotFound:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.delete\",\n \"text\": \"google/cloud/storage/blob.py::Blob.delete\\n def delete(self, client=None):\\n \\\"\\\"\\\"Deletes a blob from Cloud Storage.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the blob's bucket.\\n\\n :rtype: :class:`Blob`\\n :returns: The blob that was just deleted.\\n :raises: :class:`google.cloud.exceptions.NotFound`\\n (propagated from\\n :meth:`google.cloud.storage.bucket.Bucket.delete_blob`).\\n \\\"\\\"\\\"\\n return self.bucket.delete_blob(self.name, client=client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.download_to_file\",\n \"text\": \"google/cloud/storage/blob.py::Blob.download_to_file\\n def download_to_file(self, file_obj, encryption_key=None, client=None):\\n \\\"\\\"\\\"Download the contents of this blob into a file-like object.\\n\\n .. note::\\n\\n If the server-set property, :attr:`media_link`, is not yet\\n initialized, makes an additional API request to load it.\\n\\n Downloading a file that has been encrypted with a `customer-supplied`_\\n encryption key::\\n\\n >>> from google.cloud import storage\\n >>> from google.cloud.storage import Blob\\n\\n >>> client = storage.Client(project='my-project')\\n >>> bucket = client.get_bucket('my-bucket')\\n >>> encryption_key = 'aa426195405adee2c8081bb9e7e74b19'\\n >>> blob = Blob('secure-data', bucket)\\n >>> with open('/tmp/my-secure-file', 'wb') as file_obj:\\n >>> blob.download_to_file(file_obj,\\n ... encryption_key=encryption_key)\\n\\n The ``encryption_key`` should be a str or bytes with a length of at\\n least 32.\\n\\n .. _customer-supplied: https://cloud.google.com/storage/docs/\\\\\\n encryption#customer-supplied\\n\\n :type file_obj: file\\n :param file_obj: A file handle to which to write the blob's data.\\n\\n :type encryption_key: str or bytes\\n :param encryption_key: Optional 32 byte encryption key for\\n customer-supplied encryption.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the blob's bucket.\\n\\n :raises: :class:`google.cloud.exceptions.NotFound`\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n if self.media_link is None: # not yet loaded\\n self.reload()\\n\\n download_url = self.media_link\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.download_to_filename\",\n \"text\": \"google/cloud/storage/blob.py::Blob.download_to_filename\\n def download_to_filename(self, filename, encryption_key=None, client=None):\\n \\\"\\\"\\\"Download the contents of this blob into a named file.\\n\\n :type filename: string\\n :param filename: A filename to be passed to ``open``.\\n\\n :type encryption_key: str or bytes\\n :param encryption_key: Optional 32 byte encryption key for\\n customer-supplied encryption.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the blob's bucket.\\n\\n :raises: :class:`google.cloud.exceptions.NotFound`\\n \\\"\\\"\\\"\\n with open(filename, 'wb') as file_obj:\\n self.download_to_file(file_obj, encryption_key=encryption_key,\\n client=client)\\n\\n mtime = time.mktime(self.updated.timetuple())\\n os.utime(file_obj.name, (mtime, mtime))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.download_as_string\",\n \"text\": \"google/cloud/storage/blob.py::Blob.download_as_string\\n def download_as_string(self, encryption_key=None, client=None):\\n \\\"\\\"\\\"Download the contents of this blob as a string.\\n\\n :type encryption_key: str or bytes\\n :param encryption_key: Optional 32 byte encryption key for\\n customer-supplied encryption.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the blob's bucket.\\n\\n :rtype: bytes\\n :returns: The data stored in this blob.\\n :raises: :class:`google.cloud.exceptions.NotFound`\\n \\\"\\\"\\\"\\n string_buffer = BytesIO()\\n self.download_to_file(string_buffer, encryption_key=encryption_key,\\n client=client)\\n return string_buffer.getvalue()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob._check_response_error\",\n \"text\": \"google/cloud/storage/blob.py::Blob._check_response_error\\n @staticmethod\\n def _check_response_error(request, http_response):\\n \\\"\\\"\\\"Helper for :meth:`upload_from_file`.\\\"\\\"\\\"\\n info = http_response.info\\n status = int(info['status'])\\n if not 200 <= status < 300:\\n faux_response = httplib2.Response({'status': status})\\n raise make_exception(faux_response, http_response.content,\\n error_info=request.url)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.upload_from_file\",\n \"text\": \"google/cloud/storage/blob.py::Blob.upload_from_file\\n def upload_from_file(self, file_obj, rewind=False, size=None,\\n encryption_key=None, content_type=None, num_retries=6,\\n client=None):\\n \\\"\\\"\\\"Upload the contents of this blob from a file-like object.\\n\\n The content type of the upload will either be\\n - The value passed in to the function (if any)\\n - The value stored on the current blob\\n - The default value of 'application/octet-stream'\\n\\n .. note::\\n The effect of uploading to an existing blob depends on the\\n \\\"versioning\\\" and \\\"lifecycle\\\" policies defined on the blob's\\n bucket. In the absence of those policies, upload will\\n overwrite any existing contents.\\n\\n See the `object versioning\\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\\n API documents for details.\\n\\n Uploading a file with a `customer-supplied`_ encryption key::\\n\\n >>> from google.cloud import storage\\n >>> from google.cloud.storage import Blob\\n\\n >>> client = storage.Client(project='my-project')\\n >>> bucket = client.get_bucket('my-bucket')\\n >>> encryption_key = 'aa426195405adee2c8081bb9e7e74b19'\\n >>> blob = Blob('secure-data', bucket)\\n >>> with open('my-file', 'rb') as my_file:\\n >>> blob.upload_from_file(my_file,\\n ... encryption_key=encryption_key)\\n\\n The ``encryption_key`` should be a str or bytes with a length of at\\n least 32.\\n\\n .. _customer-supplied: https://cloud.google.com/storage/docs/\\\\\\n encryption#customer-supplied\\n\\n :type file_obj: file\\n :param file_obj: A file handle open for reading.\\n\\n :type rewind: boolean\\n :param rewind: If True, seek to the be\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.upload_from_filename\",\n \"text\": \"google/cloud/storage/blob.py::Blob.upload_from_filename\\n def upload_from_filename(self, filename, content_type=None,\\n encryption_key=None, client=None):\\n \\\"\\\"\\\"Upload this blob's contents from the content of a named file.\\n\\n The content type of the upload will either be\\n - The value passed in to the function (if any)\\n - The value stored on the current blob\\n - The value given by mimetypes.guess_type\\n\\n .. note::\\n The effect of uploading to an existing blob depends on the\\n \\\"versioning\\\" and \\\"lifecycle\\\" policies defined on the blob's\\n bucket. In the absence of those policies, upload will\\n overwrite any existing contents.\\n\\n See the `object versioning\\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\\n API documents for details.\\n\\n :type filename: string\\n :param filename: The path to the file.\\n\\n :type content_type: string or ``NoneType``\\n :param content_type: Optional type of content being uploaded.\\n\\n :type encryption_key: str or bytes\\n :param encryption_key: Optional 32 byte encryption key for\\n customer-supplied encryption.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the blob's bucket.\\n \\\"\\\"\\\"\\n content_type = content_type or self._properties.get('contentType')\\n if content_type is None:\\n content_type, _ = mimetypes.guess_type(filename)\\n\\n with open(filename, 'rb') as file_obj:\\n self.upload_from_file(file_obj, content_type=content_type,\\n encryption_key=encryption_key, client=client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.upload_from_string\",\n \"text\": \"google/cloud/storage/blob.py::Blob.upload_from_string\\n def upload_from_string(self, data, content_type='text/plain',\\n encryption_key=None, client=None):\\n \\\"\\\"\\\"Upload contents of this blob from the provided string.\\n\\n .. note::\\n The effect of uploading to an existing blob depends on the\\n \\\"versioning\\\" and \\\"lifecycle\\\" policies defined on the blob's\\n bucket. In the absence of those policies, upload will\\n overwrite any existing contents.\\n\\n See the `object versioning\\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\\n API documents for details.\\n\\n :type data: bytes or text\\n :param data: The data to store in this blob. If the value is\\n text, it will be encoded as UTF-8.\\n\\n :type content_type: string\\n :param content_type: Optional type of content being uploaded. Defaults\\n to ``'text/plain'``.\\n\\n :type encryption_key: str or bytes\\n :param encryption_key: Optional 32 byte encryption key for\\n customer-supplied encryption.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the blob's bucket.\\n \\\"\\\"\\\"\\n if isinstance(data, six.text_type):\\n data = data.encode('utf-8')\\n string_buffer = BytesIO()\\n string_buffer.write(data)\\n self.upload_from_file(file_obj=string_buffer, rewind=True,\\n size=len(data), content_type=content_type,\\n encryption_key=encryption_key, client=client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.make_public\",\n \"text\": \"google/cloud/storage/blob.py::Blob.make_public\\n def make_public(self, client=None):\\n \\\"\\\"\\\"Make this blob public giving all users read access.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the blob's bucket.\\n \\\"\\\"\\\"\\n self.acl.all().grant_read()\\n self.acl.save(client=client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.component_count\",\n \"text\": \"google/cloud/storage/blob.py::Blob.component_count\\n @property\\n def component_count(self):\\n \\\"\\\"\\\"Number of underlying components that make up this object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer or ``NoneType``\\n :returns: The component count (in case of a composed object) or\\n ``None`` if the property is not set locally. This property\\n will not be set on objects not created via ``compose``.\\n \\\"\\\"\\\"\\n component_count = self._properties.get('componentCount')\\n if component_count is not None:\\n return int(component_count)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.etag\",\n \"text\": \"google/cloud/storage/blob.py::Blob.etag\\n @property\\n def etag(self):\\n \\\"\\\"\\\"Retrieve the ETag for the object.\\n\\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\\n https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string or ``NoneType``\\n :returns: The blob etag or ``None`` if the property is not set locally.\\n \\\"\\\"\\\"\\n return self._properties.get('etag')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.generation\",\n \"text\": \"google/cloud/storage/blob.py::Blob.generation\\n @property\\n def generation(self):\\n \\\"\\\"\\\"Retrieve the generation for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer or ``NoneType``\\n :returns: The generation of the blob or ``None`` if the property\\n is not set locally.\\n \\\"\\\"\\\"\\n generation = self._properties.get('generation')\\n if generation is not None:\\n return int(generation)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.id\",\n \"text\": \"google/cloud/storage/blob.py::Blob.id\\n @property\\n def id(self):\\n \\\"\\\"\\\"Retrieve the ID for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string or ``NoneType``\\n :returns: The ID of the blob or ``None`` if the property is not\\n set locally.\\n \\\"\\\"\\\"\\n return self._properties.get('id')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.media_link\",\n \"text\": \"google/cloud/storage/blob.py::Blob.media_link\\n @property\\n def media_link(self):\\n \\\"\\\"\\\"Retrieve the media download URI for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string or ``NoneType``\\n :returns: The media link for the blob or ``None`` if the property is\\n not set locally.\\n \\\"\\\"\\\"\\n return self._properties.get('mediaLink')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.metadata\",\n \"text\": \"google/cloud/storage/blob.py::Blob.metadata\\n @property\\n def metadata(self):\\n \\\"\\\"\\\"Retrieve arbitrary/application specific metadata for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: dict or ``NoneType``\\n :returns: The metadata associated with the blob or ``None`` if the\\n property is not set locally.\\n \\\"\\\"\\\"\\n return copy.deepcopy(self._properties.get('metadata'))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.metadata\",\n \"text\": \"google/cloud/storage/blob.py::Blob.metadata\\n @metadata.setter\\n def metadata(self, value):\\n \\\"\\\"\\\"Update arbitrary/application specific metadata for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :type value: dict or ``NoneType``\\n :param value: The blob metadata to set.\\n \\\"\\\"\\\"\\n self._patch_property('metadata', value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.metageneration\",\n \"text\": \"google/cloud/storage/blob.py::Blob.metageneration\\n @property\\n def metageneration(self):\\n \\\"\\\"\\\"Retrieve the metageneration for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer or ``NoneType``\\n :returns: The metageneration of the blob or ``None`` if the property\\n is not set locally.\\n \\\"\\\"\\\"\\n metageneration = self._properties.get('metageneration')\\n if metageneration is not None:\\n return int(metageneration)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.owner\",\n \"text\": \"google/cloud/storage/blob.py::Blob.owner\\n @property\\n def owner(self):\\n \\\"\\\"\\\"Retrieve info about the owner of the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: dict or ``NoneType``\\n :returns: Mapping of owner's role/ID. If the property is not set\\n locally, returns ``None``.\\n \\\"\\\"\\\"\\n return copy.deepcopy(self._properties.get('owner'))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.self_link\",\n \"text\": \"google/cloud/storage/blob.py::Blob.self_link\\n @property\\n def self_link(self):\\n \\\"\\\"\\\"Retrieve the URI for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string or ``NoneType``\\n :returns: The self link for the blob or ``None`` if the property is\\n not set locally.\\n \\\"\\\"\\\"\\n return self._properties.get('selfLink')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.size\",\n \"text\": \"google/cloud/storage/blob.py::Blob.size\\n @property\\n def size(self):\\n \\\"\\\"\\\"Size of the object, in bytes.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer or ``NoneType``\\n :returns: The size of the blob or ``None`` if the property\\n is not set locally.\\n \\\"\\\"\\\"\\n size = self._properties.get('size')\\n if size is not None:\\n return int(size)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.storage_class\",\n \"text\": \"google/cloud/storage/blob.py::Blob.storage_class\\n @property\\n def storage_class(self):\\n \\\"\\\"\\\"Retrieve the storage class for the object.\\n\\n See: https://cloud.google.com/storage/docs/storage-classes\\n https://cloud.google.com/storage/docs/nearline-storage\\n https://cloud.google.com/storage/docs/durable-reduced-availability\\n\\n :rtype: string or ``NoneType``\\n :returns: If set, one of \\\"STANDARD\\\", \\\"NEARLINE\\\", or\\n \\\"DURABLE_REDUCED_AVAILABILITY\\\", else ``None``.\\n \\\"\\\"\\\"\\n return self._properties.get('storageClass')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.time_deleted\",\n \"text\": \"google/cloud/storage/blob.py::Blob.time_deleted\\n @property\\n def time_deleted(self):\\n \\\"\\\"\\\"Retrieve the timestamp at which the object was deleted.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: :class:`datetime.datetime` or ``NoneType``\\n :returns: Datetime object parsed from RFC3339 valid timestamp, or\\n ``None`` if the property is not set locally. If the blob has\\n not been deleted, this will never be set.\\n \\\"\\\"\\\"\\n value = self._properties.get('timeDeleted')\\n if value is not None:\\n return _rfc3339_to_datetime(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::Blob.updated\",\n \"text\": \"google/cloud/storage/blob.py::Blob.updated\\n @property\\n def updated(self):\\n \\\"\\\"\\\"Retrieve the timestamp at which the object was updated.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: :class:`datetime.datetime` or ``NoneType``\\n :returns: Datetime object parsed from RFC3339 valid timestamp, or\\n ``None`` if the property is not set locally.\\n \\\"\\\"\\\"\\n value = self._properties.get('updated')\\n if value is not None:\\n return _rfc3339_to_datetime(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::_UrlBuilder.__init__\",\n \"text\": \"google/cloud/storage/blob.py::_UrlBuilder.__init__\\n def __init__(self, bucket_name, object_name):\\n self.query_params = {'name': object_name}\\n self._bucket_name = bucket_name\\n self._relative_path = ''\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/blob.py::_set_encryption_headers\",\n \"text\": \"google/cloud/storage/blob.py::_set_encryption_headers\\ndef _set_encryption_headers(key, headers):\\n \\\"\\\"\\\"Builds customer encryption key headers\\n\\n :type key: str or bytes\\n :param key: 32 byte key to build request key and hash.\\n\\n :type headers: dict\\n :param headers: dict of HTTP headers being sent in request.\\n \\\"\\\"\\\"\\n key = _to_bytes(key)\\n sha256_key = hashlib.sha256(key).digest()\\n key_hash = base64.b64encode(sha256_key).rstrip()\\n encoded_key = base64.b64encode(key).rstrip()\\n headers['X-Goog-Encryption-Algorithm'] = 'AES256'\\n headers['X-Goog-Encryption-Key'] = _bytes_to_unicode(encoded_key)\\n headers['X-Goog-Encryption-Key-Sha256'] = _bytes_to_unicode(key_hash)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::_BlobIterator.__init__\",\n \"text\": \"google/cloud/storage/bucket.py::_BlobIterator.__init__\\n def __init__(self, bucket, extra_params=None, client=None):\\n if client is None:\\n client = bucket.client\\n self.bucket = bucket\\n self.prefixes = set()\\n self._current_prefixes = None\\n super(_BlobIterator, self).__init__(\\n client=client, path=bucket.path + '/o',\\n extra_params=extra_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::_BlobIterator.get_items_from_response\",\n \"text\": \"google/cloud/storage/bucket.py::_BlobIterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Yield :class:`.storage.blob.Blob` items from response.\\n\\n :type response: dict\\n :param response: The JSON API response for a page of blobs.\\n \\\"\\\"\\\"\\n self._current_prefixes = tuple(response.get('prefixes', ()))\\n self.prefixes.update(self._current_prefixes)\\n for item in response.get('items', []):\\n name = item.get('name')\\n blob = Blob(name, bucket=self.bucket)\\n blob._set_properties(item)\\n yield blob\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.__init__\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.__init__\\n def __init__(self, client, name=None):\\n super(Bucket, self).__init__(name=name)\\n self._client = client\\n self._acl = BucketACL(self)\\n self._default_object_acl = DefaultObjectACL(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.__repr__\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.__repr__\\n def __repr__(self):\\n return '<Bucket: %s>' % self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.client\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.client\\n @property\\n def client(self):\\n \\\"\\\"\\\"The client bound to this bucket.\\\"\\\"\\\"\\n return self._client\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.blob\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.blob\\n def blob(self, blob_name, chunk_size=None):\\n \\\"\\\"\\\"Factory constructor for blob object.\\n\\n .. note::\\n This will not make an HTTP request; it simply instantiates\\n a blob object owned by this bucket.\\n\\n :type blob_name: string\\n :param blob_name: The name of the blob to be instantiated.\\n\\n :type chunk_size: integer\\n :param chunk_size: The size of a chunk of data whenever iterating\\n (1 MB). This must be a multiple of 256 KB per the\\n API specification.\\n\\n :rtype: :class:`google.cloud.storage.blob.Blob`\\n :returns: The blob object created.\\n \\\"\\\"\\\"\\n return Blob(name=blob_name, bucket=self, chunk_size=chunk_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.exists\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.exists\\n def exists(self, client=None):\\n \\\"\\\"\\\"Determines whether or not this bucket exists.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the current bucket.\\n\\n :rtype: boolean\\n :returns: True if the bucket exists in Cloud Storage.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n try:\\n # We only need the status code (200 or not) so we seek to\\n # minimize the returned payload.\\n query_params = {'fields': 'name'}\\n # We intentionally pass `_target_object=None` since fields=name\\n # would limit the local properties.\\n client.connection.api_request(method='GET', path=self.path,\\n query_params=query_params,\\n _target_object=None)\\n # NOTE: This will not fail immediately in a batch. However, when\\n # Batch.finish() is called, the resulting `NotFound` will be\\n # raised.\\n return True\\n except NotFound:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.create\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.create\\n def create(self, client=None):\\n \\\"\\\"\\\"Creates current bucket.\\n\\n If the bucket already exists, will raise\\n :class:`google.cloud.exceptions.Conflict`.\\n\\n This implements \\\"storage.buckets.insert\\\".\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the current bucket.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n query_params = {'project': client.project}\\n properties = {key: self._properties[key] for key in self._changes}\\n properties['name'] = self.name\\n api_response = client.connection.api_request(\\n method='POST', path='/b', query_params=query_params,\\n data=properties, _target_object=self)\\n self._set_properties(api_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.acl\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.acl\\n @property\\n def acl(self):\\n \\\"\\\"\\\"Create our ACL on demand.\\\"\\\"\\\"\\n return self._acl\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.default_object_acl\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.default_object_acl\\n @property\\n def default_object_acl(self):\\n \\\"\\\"\\\"Create our defaultObjectACL on demand.\\\"\\\"\\\"\\n return self._default_object_acl\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.path_helper\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.path_helper\\n @staticmethod\\n def path_helper(bucket_name):\\n \\\"\\\"\\\"Relative URL path for a bucket.\\n\\n :type bucket_name: string\\n :param bucket_name: The bucket name in the path.\\n\\n :rtype: string\\n :returns: The relative URL path for ``bucket_name``.\\n \\\"\\\"\\\"\\n return '/b/' + bucket_name\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.path\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"The URL path to this bucket.\\\"\\\"\\\"\\n if not self.name:\\n raise ValueError('Cannot determine path without bucket name.')\\n\\n return self.path_helper(self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.get_blob\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.get_blob\\n def get_blob(self, blob_name, client=None):\\n \\\"\\\"\\\"Get a blob object by name.\\n\\n This will return None if the blob doesn't exist::\\n\\n >>> from google.cloud import storage\\n >>> client = storage.Client()\\n >>> bucket = client.get_bucket('my-bucket')\\n >>> print bucket.get_blob('/path/to/blob.txt')\\n <Blob: my-bucket, /path/to/blob.txt>\\n >>> print bucket.get_blob('/does-not-exist.txt')\\n None\\n\\n :type blob_name: string\\n :param blob_name: The name of the blob to retrieve.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the current bucket.\\n\\n :rtype: :class:`google.cloud.storage.blob.Blob` or None\\n :returns: The blob object if it exists, otherwise None.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n blob = Blob(bucket=self, name=blob_name)\\n try:\\n response = client.connection.api_request(\\n method='GET', path=blob.path, _target_object=blob)\\n # NOTE: We assume response.get('name') matches `blob_name`.\\n blob._set_properties(response)\\n # NOTE: This will not fail immediately in a batch. However, when\\n # Batch.finish() is called, the resulting `NotFound` will be\\n # raised.\\n return blob\\n except NotFound:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.list_blobs\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.list_blobs\\n def list_blobs(self, max_results=None, page_token=None, prefix=None,\\n delimiter=None, versions=None,\\n projection='noAcl', fields=None, client=None):\\n \\\"\\\"\\\"Return an iterator used to find blobs in the bucket.\\n\\n :type max_results: integer or ``NoneType``\\n :param max_results: maximum number of blobs to return.\\n\\n :type page_token: string\\n :param page_token: opaque marker for the next \\\"page\\\" of blobs. If not\\n passed, will return the first page of blobs.\\n\\n :type prefix: string or ``NoneType``\\n :param prefix: optional prefix used to filter blobs.\\n\\n :type delimiter: string or ``NoneType``\\n :param delimiter: optional delimter, used with ``prefix`` to\\n emulate hierarchy.\\n\\n :type versions: boolean or ``NoneType``\\n :param versions: whether object versions should be returned as\\n separate blobs.\\n\\n :type projection: string or ``NoneType``\\n :param projection: If used, must be 'full' or 'noAcl'. Defaults to\\n 'noAcl'. Specifies the set of properties to return.\\n\\n :type fields: string or ``NoneType``\\n :param fields: Selector specifying which fields to include in a\\n partial response. Must be a list of fields. For example\\n to get a partial response with just the next page token\\n and the language of each blob returned:\\n 'items/contentLanguage,nextPageToken'\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the current bucket.\\n\\n :rtype: :class:`_BlobIterator`.\\n :returns: An iterator of blobs.\\n \\\"\\\"\\\"\\n extra_\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.delete\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.delete\\n def delete(self, force=False, client=None):\\n \\\"\\\"\\\"Delete this bucket.\\n\\n The bucket **must** be empty in order to submit a delete request. If\\n ``force=True`` is passed, this will first attempt to delete all the\\n objects / blobs in the bucket (i.e. try to empty the bucket).\\n\\n If the bucket doesn't exist, this will raise\\n :class:`google.cloud.exceptions.NotFound`. If the bucket is not empty\\n (and ``force=False``), will raise\\n :class:`google.cloud.exceptions.Conflict`.\\n\\n If ``force=True`` and the bucket contains more than 256 objects / blobs\\n this will cowardly refuse to delete the objects (or the bucket). This\\n is to prevent accidental bucket deletion and to prevent extremely long\\n runtime of this method.\\n\\n :type force: boolean\\n :param force: If True, empties the bucket's objects then deletes it.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the current bucket.\\n\\n :raises: :class:`ValueError` if ``force`` is ``True`` and the bucket\\n contains more than 256 objects / blobs.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n if force:\\n blobs = list(self.list_blobs(\\n max_results=self._MAX_OBJECTS_FOR_ITERATION + 1,\\n client=client))\\n if len(blobs) > self._MAX_OBJECTS_FOR_ITERATION:\\n message = (\\n 'Refusing to delete bucket with more than '\\n '%d objects. If you actually want to delete '\\n 'this bucket, please delete the objects '\\n 'yourself before calling Bucket.delete().'\\n ) % (self._MAX_OBJECTS_FOR_ITERATION,)\\n raise ValueError(message)\\n\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.delete_blob\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.delete_blob\\n def delete_blob(self, blob_name, client=None):\\n \\\"\\\"\\\"Deletes a blob from the current bucket.\\n\\n If the blob isn't found (backend 404), raises a\\n :class:`google.cloud.exceptions.NotFound`.\\n\\n For example::\\n\\n >>> from google.cloud.exceptions import NotFound\\n >>> from google.cloud import storage\\n >>> client = storage.Client()\\n >>> bucket = client.get_bucket('my-bucket')\\n >>> print bucket.list_blobs()\\n [<Blob: my-bucket, my-file.txt>]\\n >>> bucket.delete_blob('my-file.txt')\\n >>> try:\\n ... bucket.delete_blob('doesnt-exist')\\n ... except NotFound:\\n ... pass\\n\\n :type blob_name: string\\n :param blob_name: A blob name to delete.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the current bucket.\\n\\n :raises: :class:`google.cloud.exceptions.NotFound` (to suppress\\n the exception, call ``delete_blobs``, passing a no-op\\n ``on_error`` callback, e.g.::\\n\\n >>> bucket.delete_blobs([blob], on_error=lambda blob: None)\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n blob_path = Blob.path_helper(self.path, blob_name)\\n # We intentionally pass `_target_object=None` since a DELETE\\n # request has no response value (whether in a standard request or\\n # in a batch request).\\n client.connection.api_request(method='DELETE', path=blob_path,\\n _target_object=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.delete_blobs\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.delete_blobs\\n def delete_blobs(self, blobs, on_error=None, client=None):\\n \\\"\\\"\\\"Deletes a list of blobs from the current bucket.\\n\\n Uses :func:`Bucket.delete_blob` to delete each individual blob.\\n\\n :type blobs: list of string or :class:`google.cloud.storage.blob.Blob`\\n :param blobs: A list of blob names or Blob objects to delete.\\n\\n :type on_error: a callable taking (blob)\\n :param on_error: If not ``None``, called once for each blob raising\\n :class:`google.cloud.exceptions.NotFound`;\\n otherwise, the exception is propagated.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the current bucket.\\n\\n :raises: :class:`google.cloud.exceptions.NotFound` (if\\n `on_error` is not passed).\\n \\\"\\\"\\\"\\n for blob in blobs:\\n try:\\n blob_name = blob\\n if not isinstance(blob_name, six.string_types):\\n blob_name = blob.name\\n self.delete_blob(blob_name, client=client)\\n except NotFound:\\n if on_error is not None:\\n on_error(blob)\\n else:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.copy_blob\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.copy_blob\\n def copy_blob(self, blob, destination_bucket, new_name=None,\\n client=None):\\n \\\"\\\"\\\"Copy the given blob to the given bucket, optionally with a new name.\\n\\n :type blob: :class:`google.cloud.storage.blob.Blob`\\n :param blob: The blob to be copied.\\n\\n :type destination_bucket: :class:`google.cloud.storage.bucket.Bucket`\\n :param destination_bucket: The bucket into which the blob should be\\n copied.\\n\\n :type new_name: string\\n :param new_name: (optional) the new name for the copied file.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the current bucket.\\n\\n :rtype: :class:`google.cloud.storage.blob.Blob`\\n :returns: The new Blob.\\n \\\"\\\"\\\"\\n client = self._require_client(client)\\n if new_name is None:\\n new_name = blob.name\\n new_blob = Blob(bucket=destination_bucket, name=new_name)\\n api_path = blob.path + '/copyTo' + new_blob.path\\n copy_result = client.connection.api_request(\\n method='POST', path=api_path, _target_object=new_blob)\\n new_blob._set_properties(copy_result)\\n return new_blob\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.rename_blob\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.rename_blob\\n def rename_blob(self, blob, new_name, client=None):\\n \\\"\\\"\\\"Rename the given blob using copy and delete operations.\\n\\n Effectively, copies blob to the same bucket with a new name, then\\n deletes the blob.\\n\\n .. warning::\\n\\n This method will first duplicate the data and then delete the\\n old blob. This means that with very large objects renaming\\n could be a very (temporarily) costly or a very slow operation.\\n\\n :type blob: :class:`google.cloud.storage.blob.Blob`\\n :param blob: The blob to be renamed.\\n\\n :type new_name: string\\n :param new_name: The new name for this blob.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the current bucket.\\n\\n :rtype: :class:`Blob`\\n :returns: The newly-renamed blob.\\n \\\"\\\"\\\"\\n new_blob = self.copy_blob(blob, self, new_name, client=client)\\n blob.delete(client=client)\\n return new_blob\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.cors\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.cors\\n @property\\n def cors(self):\\n \\\"\\\"\\\"Retrieve CORS policies configured for this bucket.\\n\\n See: http://www.w3.org/TR/cors/ and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: list of dictionaries\\n :returns: A sequence of mappings describing each CORS policy.\\n \\\"\\\"\\\"\\n return [copy.deepcopy(policy)\\n for policy in self._properties.get('cors', ())]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.cors\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.cors\\n @cors.setter\\n def cors(self, entries):\\n \\\"\\\"\\\"Set CORS policies configured for this bucket.\\n\\n See: http://www.w3.org/TR/cors/ and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :type entries: list of dictionaries\\n :param entries: A sequence of mappings describing each CORS policy.\\n \\\"\\\"\\\"\\n self._patch_property('cors', entries)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.etag\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.etag\\n @property\\n def etag(self):\\n \\\"\\\"\\\"Retrieve the ETag for the bucket.\\n\\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string or ``NoneType``\\n :returns: The bucket etag or ``None`` if the property is not\\n set locally.\\n \\\"\\\"\\\"\\n return self._properties.get('etag')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.id\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.id\\n @property\\n def id(self):\\n \\\"\\\"\\\"Retrieve the ID for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string or ``NoneType``\\n :returns: The ID of the bucket or ``None`` if the property is not\\n set locally.\\n \\\"\\\"\\\"\\n return self._properties.get('id')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.lifecycle_rules\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.lifecycle_rules\\n @property\\n def lifecycle_rules(self):\\n \\\"\\\"\\\"Lifecycle rules configured for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/lifecycle and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: list(dict)\\n :returns: A sequence of mappings describing each lifecycle rule.\\n \\\"\\\"\\\"\\n info = self._properties.get('lifecycle', {})\\n return [copy.deepcopy(rule) for rule in info.get('rule', ())]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.lifecycle_rules\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.lifecycle_rules\\n @lifecycle_rules.setter\\n def lifecycle_rules(self, rules):\\n self._patch_property('lifecycle', {'rule': rules})\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.get_logging\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.get_logging\\n def get_logging(self):\\n \\\"\\\"\\\"Return info about access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#status\\n\\n :rtype: dict or None\\n :returns: a dict w/ keys, ``logBucket`` and ``logObjectPrefix``\\n (if logging is enabled), or None (if not).\\n \\\"\\\"\\\"\\n info = self._properties.get('logging')\\n return copy.deepcopy(info)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.enable_logging\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.enable_logging\\n def enable_logging(self, bucket_name, object_prefix=''):\\n \\\"\\\"\\\"Enable access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#delivery\\n\\n :type bucket_name: string\\n :param bucket_name: name of bucket in which to store access logs\\n\\n :type object_prefix: string\\n :param object_prefix: prefix for access log filenames\\n \\\"\\\"\\\"\\n info = {'logBucket': bucket_name, 'logObjectPrefix': object_prefix}\\n self._patch_property('logging', info)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.disable_logging\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.disable_logging\\n def disable_logging(self):\\n \\\"\\\"\\\"Disable access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#disabling\\n \\\"\\\"\\\"\\n self._patch_property('logging', None)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.metageneration\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.metageneration\\n @property\\n def metageneration(self):\\n \\\"\\\"\\\"Retrieve the metageneration for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: integer or ``NoneType``\\n :returns: The metageneration of the bucket or ``None`` if the property\\n is not set locally.\\n \\\"\\\"\\\"\\n metageneration = self._properties.get('metageneration')\\n if metageneration is not None:\\n return int(metageneration)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.owner\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.owner\\n @property\\n def owner(self):\\n \\\"\\\"\\\"Retrieve info about the owner of the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: dict or ``NoneType``\\n :returns: Mapping of owner's role/ID. If the property is not set\\n locally, returns ``None``.\\n \\\"\\\"\\\"\\n return copy.deepcopy(self._properties.get('owner'))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.project_number\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.project_number\\n @property\\n def project_number(self):\\n \\\"\\\"\\\"Retrieve the number of the project to which the bucket is assigned.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: integer or ``NoneType``\\n :returns: The project number that owns the bucket or ``None`` if the\\n property is not set locally.\\n \\\"\\\"\\\"\\n project_number = self._properties.get('projectNumber')\\n if project_number is not None:\\n return int(project_number)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.self_link\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.self_link\\n @property\\n def self_link(self):\\n \\\"\\\"\\\"Retrieve the URI for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string or ``NoneType``\\n :returns: The self link for the bucket or ``None`` if the property is\\n not set locally.\\n \\\"\\\"\\\"\\n return self._properties.get('selfLink')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.storage_class\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.storage_class\\n @property\\n def storage_class(self):\\n \\\"\\\"\\\"Retrieve the storage class for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/storage-classes\\n https://cloud.google.com/storage/docs/nearline-storage\\n https://cloud.google.com/storage/docs/durable-reduced-availability\\n\\n :rtype: string or ``NoneType``\\n :returns: If set, one of \\\"STANDARD\\\", \\\"NEARLINE\\\", or\\n \\\"DURABLE_REDUCED_AVAILABILITY\\\", else ``None``.\\n \\\"\\\"\\\"\\n return self._properties.get('storageClass')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.storage_class\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.storage_class\\n @storage_class.setter\\n def storage_class(self, value):\\n \\\"\\\"\\\"Set the storage class for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/storage-classes\\n https://cloud.google.com/storage/docs/nearline-storage\\n https://cloud.google.com/storage/docs/durable-reduced-availability\\n\\n :type value: string\\n :param value: one of \\\"STANDARD\\\", \\\"NEARLINE\\\", or\\n \\\"DURABLE_REDUCED_AVAILABILITY\\\"\\n \\\"\\\"\\\"\\n if value not in self._STORAGE_CLASSES:\\n raise ValueError('Invalid storage class: %s' % (value,))\\n self._patch_property('storageClass', value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.time_created\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.time_created\\n @property\\n def time_created(self):\\n \\\"\\\"\\\"Retrieve the timestamp at which the bucket was created.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: :class:`datetime.datetime` or ``NoneType``\\n :returns: Datetime object parsed from RFC3339 valid timestamp, or\\n ``None`` if the property is not set locally.\\n \\\"\\\"\\\"\\n value = self._properties.get('timeCreated')\\n if value is not None:\\n return _rfc3339_to_datetime(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.versioning_enabled\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.versioning_enabled\\n @property\\n def versioning_enabled(self):\\n \\\"\\\"\\\"Is versioning enabled for this bucket?\\n\\n See: https://cloud.google.com/storage/docs/object-versioning for\\n details.\\n\\n :rtype: boolean\\n :returns: True if enabled, else False.\\n \\\"\\\"\\\"\\n versioning = self._properties.get('versioning', {})\\n return versioning.get('enabled', False)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.versioning_enabled\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.versioning_enabled\\n @versioning_enabled.setter\\n def versioning_enabled(self, value):\\n \\\"\\\"\\\"Enable versioning for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/object-versioning for\\n details.\\n\\n :type value: convertible to boolean\\n :param value: should versioning be anabled for the bucket?\\n \\\"\\\"\\\"\\n self._patch_property('versioning', {'enabled': bool(value)})\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.configure_website\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.configure_website\\n def configure_website(self, main_page_suffix=None, not_found_page=None):\\n \\\"\\\"\\\"Configure website-related properties.\\n\\n See: https://developers.google.com/storage/docs/website-configuration\\n\\n .. note::\\n This (apparently) only works\\n if your bucket name is a domain name\\n (and to do that, you need to get approved somehow...).\\n\\n If you want this bucket to host a website, just provide the name\\n of an index page and a page to use when a blob isn't found::\\n\\n >>> from google.cloud import storage\\n >>> client = storage.Client()\\n >>> bucket = client.get_bucket(bucket_name)\\n >>> bucket.configure_website('index.html', '404.html')\\n\\n You probably should also make the whole bucket public::\\n\\n >>> bucket.make_public(recursive=True, future=True)\\n\\n This says: \\\"Make the bucket public, and all the stuff already in\\n the bucket, and anything else I add to the bucket. Just make it\\n all public.\\\"\\n\\n :type main_page_suffix: string\\n :param main_page_suffix: The page to use as the main page\\n of a directory.\\n Typically something like index.html.\\n\\n :type not_found_page: string\\n :param not_found_page: The file to use when a page isn't found.\\n \\\"\\\"\\\"\\n data = {\\n 'mainPageSuffix': main_page_suffix,\\n 'notFoundPage': not_found_page,\\n }\\n self._patch_property('website', data)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.disable_website\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.disable_website\\n def disable_website(self):\\n \\\"\\\"\\\"Disable the website configuration for this bucket.\\n\\n This is really just a shortcut for setting the website-related\\n attributes to ``None``.\\n \\\"\\\"\\\"\\n return self.configure_website(None, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/bucket.py::Bucket.make_public\",\n \"text\": \"google/cloud/storage/bucket.py::Bucket.make_public\\n def make_public(self, recursive=False, future=False, client=None):\\n \\\"\\\"\\\"Make a bucket public.\\n\\n If ``recursive=True`` and the bucket contains more than 256\\n objects / blobs this will cowardly refuse to make the objects public.\\n This is to prevent extremely long runtime of this method.\\n\\n :type recursive: boolean\\n :param recursive: If True, this will make all blobs inside the bucket\\n public as well.\\n\\n :type future: boolean\\n :param future: If True, this will make all objects created in the\\n future public as well.\\n\\n :type client: :class:`~google.cloud.storage.client.Client` or\\n ``NoneType``\\n :param client: Optional. The client to use. If not passed, falls back\\n to the ``client`` stored on the current bucket.\\n \\\"\\\"\\\"\\n self.acl.all().grant_read()\\n self.acl.save(client=client)\\n\\n if future:\\n doa = self.default_object_acl\\n if not doa.loaded:\\n doa.reload(client=client)\\n doa.all().grant_read()\\n doa.save(client=client)\\n\\n if recursive:\\n blobs = list(self.list_blobs(\\n projection='full',\\n max_results=self._MAX_OBJECTS_FOR_ITERATION + 1,\\n client=client))\\n if len(blobs) > self._MAX_OBJECTS_FOR_ITERATION:\\n message = (\\n 'Refusing to make public recursively with more than '\\n '%d objects. If you actually want to make every object '\\n 'in this bucket public, please do it on the objects '\\n 'yourself.'\\n ) % (self._MAX_OBJECTS_FOR_ITERATION,)\\n raise ValueError(message)\\n\\n for blob in blobs:\\n blob.acl.all().grant_read()\\n blob.acl.save(client=client)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client.__init__\",\n \"text\": \"google/cloud/storage/client.py::Client.__init__\\n def __init__(self, project=None, credentials=None, http=None):\\n self._connection = None\\n super(Client, self).__init__(project=project, credentials=credentials,\\n http=http)\\n self._batch_stack = _LocalStack()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client.connection\",\n \"text\": \"google/cloud/storage/client.py::Client.connection\\n @property\\n def connection(self):\\n \\\"\\\"\\\"Get connection or batch on the client.\\n\\n :rtype: :class:`google.cloud.storage.connection.Connection`\\n :returns: The connection set on the client, or the batch\\n if one is set.\\n \\\"\\\"\\\"\\n if self.current_batch is not None:\\n return self.current_batch\\n else:\\n return self._connection\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client.connection\",\n \"text\": \"google/cloud/storage/client.py::Client.connection\\n @connection.setter\\n def connection(self, value):\\n \\\"\\\"\\\"Set connection on the client.\\n\\n Intended to be used by constructor (since the base class calls)\\n self.connection = connection\\n Will raise if the connection is set more than once.\\n\\n :type value: :class:`google.cloud.storage.connection.Connection`\\n :param value: The connection set on the client.\\n\\n :raises: :class:`ValueError` if connection has already been set.\\n \\\"\\\"\\\"\\n if self._connection is not None:\\n raise ValueError('Connection already set on client')\\n self._connection = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client._push_batch\",\n \"text\": \"google/cloud/storage/client.py::Client._push_batch\\n def _push_batch(self, batch):\\n \\\"\\\"\\\"Push a batch onto our stack.\\n\\n \\\"Protected\\\", intended for use by batch context mgrs.\\n\\n :type batch: :class:`google.cloud.storage.batch.Batch`\\n :param batch: newly-active batch\\n \\\"\\\"\\\"\\n self._batch_stack.push(batch)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client._pop_batch\",\n \"text\": \"google/cloud/storage/client.py::Client._pop_batch\\n def _pop_batch(self):\\n \\\"\\\"\\\"Pop a batch from our stack.\\n\\n \\\"Protected\\\", intended for use by batch context mgrs.\\n\\n :raises: IndexError if the stack is empty.\\n :rtype: :class:`google.cloud.storage.batch.Batch`\\n :returns: the top-most batch/transaction, after removing it.\\n \\\"\\\"\\\"\\n return self._batch_stack.pop()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client.current_batch\",\n \"text\": \"google/cloud/storage/client.py::Client.current_batch\\n @property\\n def current_batch(self):\\n \\\"\\\"\\\"Currently-active batch.\\n\\n :rtype: :class:`google.cloud.storage.batch.Batch` or ``NoneType`` (if\\n no batch is active).\\n :returns: The batch at the top of the batch stack.\\n \\\"\\\"\\\"\\n return self._batch_stack.top\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client.bucket\",\n \"text\": \"google/cloud/storage/client.py::Client.bucket\\n def bucket(self, bucket_name):\\n \\\"\\\"\\\"Factory constructor for bucket object.\\n\\n .. note::\\n This will not make an HTTP request; it simply instantiates\\n a bucket object owned by this client.\\n\\n :type bucket_name: string\\n :param bucket_name: The name of the bucket to be instantiated.\\n\\n :rtype: :class:`google.cloud.storage.bucket.Bucket`\\n :returns: The bucket object created.\\n \\\"\\\"\\\"\\n return Bucket(client=self, name=bucket_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client.batch\",\n \"text\": \"google/cloud/storage/client.py::Client.batch\\n def batch(self):\\n \\\"\\\"\\\"Factory constructor for batch object.\\n\\n .. note::\\n This will not make an HTTP request; it simply instantiates\\n a batch object owned by this client.\\n\\n :rtype: :class:`google.cloud.storage.batch.Batch`\\n :returns: The batch object created.\\n \\\"\\\"\\\"\\n return Batch(client=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client.get_bucket\",\n \"text\": \"google/cloud/storage/client.py::Client.get_bucket\\n def get_bucket(self, bucket_name):\\n \\\"\\\"\\\"Get a bucket by name.\\n\\n If the bucket isn't found, this will raise a\\n :class:`google.cloud.storage.exceptions.NotFound`.\\n\\n For example::\\n\\n >>> try:\\n >>> bucket = client.get_bucket('my-bucket')\\n >>> except google.cloud.exceptions.NotFound:\\n >>> print 'Sorry, that bucket does not exist!'\\n\\n This implements \\\"storage.buckets.get\\\".\\n\\n :type bucket_name: string\\n :param bucket_name: The name of the bucket to get.\\n\\n :rtype: :class:`google.cloud.storage.bucket.Bucket`\\n :returns: The bucket matching the name provided.\\n :raises: :class:`google.cloud.exceptions.NotFound`\\n \\\"\\\"\\\"\\n bucket = Bucket(self, name=bucket_name)\\n bucket.reload(client=self)\\n return bucket\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client.lookup_bucket\",\n \"text\": \"google/cloud/storage/client.py::Client.lookup_bucket\\n def lookup_bucket(self, bucket_name):\\n \\\"\\\"\\\"Get a bucket by name, returning None if not found.\\n\\n You can use this if you would rather check for a None value\\n than catching an exception::\\n\\n >>> bucket = client.lookup_bucket('doesnt-exist')\\n >>> print bucket\\n None\\n >>> bucket = client.lookup_bucket('my-bucket')\\n >>> print bucket\\n <Bucket: my-bucket>\\n\\n :type bucket_name: string\\n :param bucket_name: The name of the bucket to get.\\n\\n :rtype: :class:`google.cloud.storage.bucket.Bucket`\\n :returns: The bucket matching the name provided or None if not found.\\n \\\"\\\"\\\"\\n try:\\n return self.get_bucket(bucket_name)\\n except NotFound:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client.create_bucket\",\n \"text\": \"google/cloud/storage/client.py::Client.create_bucket\\n def create_bucket(self, bucket_name):\\n \\\"\\\"\\\"Create a new bucket.\\n\\n For example::\\n\\n >>> bucket = client.create_bucket('my-bucket')\\n >>> print bucket\\n <Bucket: my-bucket>\\n\\n This implements \\\"storage.buckets.insert\\\".\\n\\n If the bucket already exists, will raise\\n :class:`google.cloud.exceptions.Conflict`.\\n\\n :type bucket_name: string\\n :param bucket_name: The bucket name to create.\\n\\n :rtype: :class:`google.cloud.storage.bucket.Bucket`\\n :returns: The newly created bucket.\\n \\\"\\\"\\\"\\n bucket = Bucket(self, name=bucket_name)\\n bucket.create(client=self)\\n return bucket\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::Client.list_buckets\",\n \"text\": \"google/cloud/storage/client.py::Client.list_buckets\\n def list_buckets(self, max_results=None, page_token=None, prefix=None,\\n projection='noAcl', fields=None):\\n \\\"\\\"\\\"Get all buckets in the project associated to the client.\\n\\n This will not populate the list of blobs available in each\\n bucket.\\n\\n >>> for bucket in client.list_buckets():\\n >>> print bucket\\n\\n This implements \\\"storage.buckets.list\\\".\\n\\n :type max_results: integer or ``NoneType``\\n :param max_results: Optional. Maximum number of buckets to return.\\n\\n :type page_token: string or ``NoneType``\\n :param page_token: Optional. Opaque marker for the next \\\"page\\\" of\\n buckets. If not passed, will return the first page\\n of buckets.\\n\\n :type prefix: string or ``NoneType``\\n :param prefix: Optional. Filter results to buckets whose names begin\\n with this prefix.\\n\\n :type projection: string or ``NoneType``\\n :param projection: If used, must be 'full' or 'noAcl'. Defaults to\\n 'noAcl'. Specifies the set of properties to return.\\n\\n :type fields: string or ``NoneType``\\n :param fields: Selector specifying which fields to include in a\\n partial response. Must be a list of fields. For example\\n to get a partial response with just the next page token\\n and the language of each bucket returned:\\n 'items/id,nextPageToken'\\n\\n :rtype: iterable of :class:`google.cloud.storage.bucket.Bucket`\\n objects.\\n :returns: All buckets belonging to this project.\\n \\\"\\\"\\\"\\n extra_params = {'project': self.project}\\n\\n if max_results is not None:\\n extra_params['maxResults'] = max_results\\n\\n if prefix is not None:\\n extra_params['prefix'] = prefix\\n\\n extra_params['projec\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::_BucketIterator.__init__\",\n \"text\": \"google/cloud/storage/client.py::_BucketIterator.__init__\\n def __init__(self, client, extra_params=None):\\n super(_BucketIterator, self).__init__(client=client, path='/b',\\n extra_params=extra_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/storage/client.py::_BucketIterator.get_items_from_response\",\n \"text\": \"google/cloud/storage/client.py::_BucketIterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Factory method which yields :class:`.Bucket` items from a response.\\n\\n :type response: dict\\n :param response: The JSON API response for a page of buckets.\\n \\\"\\\"\\\"\\n for item in response.get('items', []):\\n name = item.get('name')\\n bucket = Bucket(self.client, name)\\n bucket._set_properties(item)\\n yield bucket\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.__init__\",\n \"text\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.__init__\\n def __init__(self, stream, start, size):\\n self._stream = stream\\n self._start_pos = start\\n self._buffer_pos = 0\\n\\n if not hasattr(self._stream, 'closed') or not self._stream.closed:\\n self._buffered_data = self._stream.read(size)\\n else:\\n self._buffered_data = b''\\n\\n self._stream_at_end = len(self._buffered_data) < size\\n self._end_pos = self._start_pos + len(self._buffered_data)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.__repr__\",\n \"text\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.__repr__\\n def __repr__(self):\\n return ('Buffered stream %s from position %s-%s with %s '\\n 'bytes remaining' % (self._stream, self._start_pos,\\n self._end_pos, self._bytes_remaining))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.__len__\",\n \"text\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.__len__\\n def __len__(self):\\n return len(self._buffered_data)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.stream_exhausted\",\n \"text\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.stream_exhausted\\n @property\\n def stream_exhausted(self):\\n \\\"\\\"\\\"Does the stream have bytes remaining beyond the buffer\\n\\n :rtype: boolean\\n :returns: Boolean indicating if the stream is exhausted.\\n \\\"\\\"\\\"\\n return self._stream_at_end\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.stream_end_position\",\n \"text\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.stream_end_position\\n @property\\n def stream_end_position(self):\\n \\\"\\\"\\\"Point to which stream was read into the buffer\\n\\n :rtype: integer\\n :returns: The end-position of the stream.\\n \\\"\\\"\\\"\\n return self._end_pos\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/buffered_stream.py::BufferedStream._bytes_remaining\",\n \"text\": \"google/cloud/streaming/buffered_stream.py::BufferedStream._bytes_remaining\\n @property\\n def _bytes_remaining(self):\\n \\\"\\\"\\\"Bytes remaining to be read from the buffer\\n\\n :rtype: integer\\n :returns: The number of bytes remaining.\\n \\\"\\\"\\\"\\n return len(self._buffered_data) - self._buffer_pos\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.read\",\n \"text\": \"google/cloud/streaming/buffered_stream.py::BufferedStream.read\\n def read(self, size=None):\\n \\\"\\\"\\\"Read bytes from the buffer.\\n\\n :type size: integer or None\\n :param size: How many bytes to read (defaults to all remaining bytes).\\n\\n :rtype: str\\n :returns: The data read from the stream.\\n \\\"\\\"\\\"\\n if size is None or size < 0:\\n raise ValueError(\\n 'Illegal read of size %s requested on BufferedStream. '\\n 'Wrapped stream %s is at position %s-%s, '\\n '%s bytes remaining.' %\\n (size, self._stream, self._start_pos, self._end_pos,\\n self._bytes_remaining))\\n\\n if not self._bytes_remaining:\\n return b''\\n\\n size = min(size, self._bytes_remaining)\\n data = self._buffered_data[self._buffer_pos:self._buffer_pos + size]\\n self._buffer_pos += size\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/exceptions.py::HttpError.__init__\",\n \"text\": \"google/cloud/streaming/exceptions.py::HttpError.__init__\\n def __init__(self, response, content, url):\\n super(HttpError, self).__init__()\\n self.response = response\\n self.content = content\\n self.url = url\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/exceptions.py::HttpError.__str__\",\n \"text\": \"google/cloud/streaming/exceptions.py::HttpError.__str__\\n def __str__(self):\\n content = self.content.decode('ascii', 'replace')\\n return 'HttpError accessing <%s>: response: <%s>, content <%s>' % (\\n self.url, self.response, content)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/exceptions.py::HttpError.status_code\",\n \"text\": \"google/cloud/streaming/exceptions.py::HttpError.status_code\\n @property\\n def status_code(self):\\n \\\"\\\"\\\"Status code for the response.\\n\\n :rtype: integer\\n :returns: the code\\n \\\"\\\"\\\"\\n return int(self.response['status'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/exceptions.py::HttpError.from_response\",\n \"text\": \"google/cloud/streaming/exceptions.py::HttpError.from_response\\n @classmethod\\n def from_response(cls, http_response):\\n \\\"\\\"\\\"Factory: construct an exception from a response.\\n\\n :type http_response: :class:`~.http_wrapper.Response`\\n :param http_response: the response which returned the error\\n\\n :rtype: :class:`HttpError`\\n :returns: The error created from the response.\\n \\\"\\\"\\\"\\n return cls(http_response.info, http_response.content,\\n http_response.request_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/exceptions.py::RetryAfterError.__init__\",\n \"text\": \"google/cloud/streaming/exceptions.py::RetryAfterError.__init__\\n def __init__(self, response, content, url, retry_after):\\n super(RetryAfterError, self).__init__(response, content, url)\\n self.retry_after = int(retry_after)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/exceptions.py::RetryAfterError.from_response\",\n \"text\": \"google/cloud/streaming/exceptions.py::RetryAfterError.from_response\\n @classmethod\\n def from_response(cls, http_response):\\n \\\"\\\"\\\"Factory: construct an exception from a response.\\n\\n :type http_response: :class:`~.http_wrapper.Response`\\n :param http_response: the response which returned the error.\\n\\n :rtype: :class:`RetryAfterError`\\n :returns: The error created from the response.\\n \\\"\\\"\\\"\\n return cls(http_response.info, http_response.content,\\n http_response.request_url, http_response.retry_after)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::_httplib2_debug_level\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::_httplib2_debug_level\\n@contextlib.contextmanager\\ndef _httplib2_debug_level(http_request, level, http=None):\\n \\\"\\\"\\\"Temporarily change the value of httplib2.debuglevel, if necessary.\\n\\n If http_request has a `loggable_body` distinct from `body`, then we\\n need to prevent httplib2 from logging the full body. This sets\\n httplib2.debuglevel for the duration of the `with` block; however,\\n that alone won't change the value of existing HTTP connections. If\\n an httplib2.Http object is provided, we'll also change the level on\\n any cached connections attached to it.\\n\\n :type http_request: :class:`Request`\\n :param http_request: the request to be logged.\\n\\n :type level: integer\\n :param level: the debuglevel for logging.\\n\\n :type http: :class:`httplib2.Http`, or ``None``\\n :param http: the instance on whose connections to set the debuglevel.\\n \\\"\\\"\\\"\\n if http_request.loggable_body is None:\\n yield\\n return\\n old_level = httplib2.debuglevel\\n http_levels = {}\\n httplib2.debuglevel = level\\n if http is not None:\\n for connection_key, connection in http.connections.items():\\n # httplib2 stores two kinds of values in this dict, connection\\n # classes and instances. Since the connection types are all\\n # old-style classes, we can't easily distinguish by connection\\n # type -- so instead we use the key pattern.\\n if ':' not in connection_key:\\n continue\\n http_levels[connection_key] = connection.debuglevel\\n connection.set_debuglevel(level)\\n yield\\n httplib2.debuglevel = old_level\\n if http is not None:\\n for connection_key, old_level in http_levels.items():\\n http.connections[connection_key].set_debuglevel(old_level)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::Request.__init__\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::Request.__init__\\n def __init__(self, url='', http_method='GET', headers=None, body=''):\\n self.url = url\\n self.http_method = http_method\\n self.headers = headers or {}\\n self._body = None\\n self._loggable_body = None\\n self.body = body\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::Request.loggable_body\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::Request.loggable_body\\n @property\\n def loggable_body(self):\\n \\\"\\\"\\\"Request body for logging purposes\\n\\n :rtype: str\\n :returns: The body to be logged.\\n \\\"\\\"\\\"\\n return self._loggable_body\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::Request.loggable_body\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::Request.loggable_body\\n @loggable_body.setter\\n def loggable_body(self, value):\\n \\\"\\\"\\\"Update request body for logging purposes\\n\\n :type value: str\\n :param value: updated body\\n\\n :raises: :exc:`RequestError` if the request does not have a body.\\n \\\"\\\"\\\"\\n if self.body is None:\\n raise RequestError(\\n 'Cannot set loggable body on request with no body')\\n self._loggable_body = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::Request.body\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::Request.body\\n @property\\n def body(self):\\n \\\"\\\"\\\"Request body\\n\\n :rtype: str\\n :returns: The body of the request.\\n \\\"\\\"\\\"\\n return self._body\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::Request.body\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::Request.body\\n @body.setter\\n def body(self, value):\\n \\\"\\\"\\\"Update the request body\\n\\n Handles logging and length measurement.\\n\\n :type value: str\\n :param value: updated body\\n \\\"\\\"\\\"\\n self._body = value\\n if value is not None:\\n # Avoid calling len() which cannot exceed 4GiB in 32-bit python.\\n body_length = getattr(\\n self._body, 'length', None) or len(self._body)\\n self.headers['content-length'] = str(body_length)\\n else:\\n self.headers.pop('content-length', None)\\n # This line ensures we don't try to print large requests.\\n if not isinstance(value, (type(None), six.string_types)):\\n self.loggable_body = '<media body>'\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::_process_content_range\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::_process_content_range\\ndef _process_content_range(content_range):\\n \\\"\\\"\\\"Convert a 'Content-Range' header into a length for the response.\\n\\n Helper for :meth:`Response.length`.\\n\\n :type content_range: str\\n :param content_range: the header value being parsed.\\n\\n :rtype: integer\\n :returns: the length of the response chunk.\\n \\\"\\\"\\\"\\n _, _, range_spec = content_range.partition(' ')\\n byte_range, _, _ = range_spec.partition('/')\\n start, _, end = byte_range.partition('-')\\n return int(end) - int(start) + 1\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::Response.__len__\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::Response.__len__\\n def __len__(self):\\n return self.length\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::Response.length\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::Response.length\\n @property\\n def length(self):\\n \\\"\\\"\\\"Length of this response.\\n\\n Exposed as an attribute since using ``len()`` directly can fail\\n for responses larger than ``sys.maxint``.\\n\\n :rtype: integer or long\\n :returns: The length of the response.\\n \\\"\\\"\\\"\\n if 'content-encoding' in self.info and 'content-range' in self.info:\\n # httplib2 rewrites content-length in the case of a compressed\\n # transfer; we can't trust the content-length header in that\\n # case, but we *can* trust content-range, if it's present.\\n return _process_content_range(self.info['content-range'])\\n elif 'content-length' in self.info:\\n return int(self.info.get('content-length'))\\n elif 'content-range' in self.info:\\n return _process_content_range(self.info['content-range'])\\n return len(self.content)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::Response.status_code\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::Response.status_code\\n @property\\n def status_code(self):\\n \\\"\\\"\\\"HTTP status code\\n\\n :rtype: integer\\n :returns: The response status code.\\n \\\"\\\"\\\"\\n return int(self.info['status'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::Response.retry_after\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::Response.retry_after\\n @property\\n def retry_after(self):\\n \\\"\\\"\\\"Retry interval (if set).\\n\\n :rtype: integer\\n :returns: interval in seconds\\n \\\"\\\"\\\"\\n if 'retry-after' in self.info:\\n return int(self.info['retry-after'])\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::Response.is_redirect\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::Response.is_redirect\\n @property\\n def is_redirect(self):\\n \\\"\\\"\\\"Does this response contain a redirect\\n\\n :rtype: boolean\\n :returns: True if the status code indicates a redirect and the\\n 'location' header is present.\\n \\\"\\\"\\\"\\n return (self.status_code in _REDIRECT_STATUS_CODES and\\n 'location' in self.info)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::_check_response\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::_check_response\\ndef _check_response(response):\\n \\\"\\\"\\\"Validate a response\\n\\n :type response: :class:`Response`\\n :param response: the response to validate\\n\\n :raises: :exc:`google.cloud.streaming.exceptions.RequestError` if response\\n is None, :exc:`~.exceptions.BadStatusCodeError` if response status\\n code indicates an error, or :exc:`~.exceptions.RetryAfterError`\\n if response indicates a retry interval.\\n \\\"\\\"\\\"\\n if response is None:\\n # Caller shouldn't call us if the response is None, but handle anyway.\\n raise RequestError(\\n 'Request did not return a response.')\\n elif (response.status_code >= 500 or\\n response.status_code == TOO_MANY_REQUESTS):\\n raise BadStatusCodeError.from_response(response)\\n elif response.retry_after:\\n raise RetryAfterError.from_response(response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::_reset_http_connections\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::_reset_http_connections\\ndef _reset_http_connections(http):\\n \\\"\\\"\\\"Rebuild all http connections in the httplib2.Http instance.\\n\\n httplib2 overloads the map in http.connections to contain two different\\n types of values:\\n { scheme string: connection class } and\\n { scheme + authority string : actual http connection }\\n Here we remove all of the entries for actual connections so that on the\\n next request httplib2 will rebuild them from the connection types.\\n\\n :type http: :class:`httplib2.Http`\\n :param http: the instance whose connections are to be rebuilt\\n \\\"\\\"\\\"\\n if getattr(http, 'connections', None):\\n for conn_key in list(http.connections.keys()):\\n if ':' in conn_key:\\n del http.connections[conn_key]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::_make_api_request_no_retry\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::_make_api_request_no_retry\\ndef _make_api_request_no_retry(http, http_request, redirections=_REDIRECTIONS):\\n \\\"\\\"\\\"Send an HTTP request via the given http instance.\\n\\n This wrapper exists to handle translation between the plain httplib2\\n request/response types and the Request and Response types above.\\n\\n :type http: :class:`httplib2.Http`\\n :param http: an instance which impelements the `Http` API.\\n\\n :type http_request: :class:`Request`\\n :param http_request: the request to send.\\n\\n :type redirections: integer\\n :param redirections: Number of redirects to follow.\\n\\n :rtype: :class:`Response`\\n :returns: an object representing the server's response\\n\\n :raises: :exc:`google.cloud.streaming.exceptions.RequestError` if no\\n response could be parsed.\\n \\\"\\\"\\\"\\n connection_type = None\\n # Handle overrides for connection types. This is used if the caller\\n # wants control over the underlying connection for managing callbacks\\n # or hash digestion.\\n if getattr(http, 'connections', None):\\n url_scheme = parse.urlsplit(http_request.url).scheme\\n if url_scheme and url_scheme in http.connections:\\n connection_type = http.connections[url_scheme]\\n\\n # Custom printing only at debuglevel 4\\n new_debuglevel = 4 if httplib2.debuglevel == 4 else 0\\n with _httplib2_debug_level(http_request, new_debuglevel, http=http):\\n info, content = http.request(\\n str(http_request.url), method=str(http_request.http_method),\\n body=http_request.body, headers=http_request.headers,\\n redirections=redirections, connection_type=connection_type)\\n\\n if info is None:\\n raise RequestError()\\n\\n response = Response(info, content, http_request.url)\\n _check_response(response)\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/http_wrapper.py::make_api_request\",\n \"text\": \"google/cloud/streaming/http_wrapper.py::make_api_request\\ndef make_api_request(http, http_request, retries=7,\\n redirections=_REDIRECTIONS):\\n \\\"\\\"\\\"Send an HTTP request via the given http, performing error/retry handling.\\n\\n :type http: :class:`httplib2.Http`\\n :param http: an instance which implements the `Http` API.\\n\\n :type http_request: :class:`Request`\\n :param http_request: the request to send.\\n\\n :type retries: integer\\n :param retries: Number of retries to attempt on retryable\\n responses (such as 429 or 5XX).\\n\\n :type redirections: integer\\n :param redirections: Number of redirects to follow.\\n\\n :rtype: :class:`Response`\\n :returns: an object representing the server's response.\\n\\n :raises: :exc:`google.cloud.streaming.exceptions.RequestError` if no\\n response could be parsed.\\n \\\"\\\"\\\"\\n retry = 0\\n while True:\\n try:\\n return _make_api_request_no_retry(http, http_request,\\n redirections=redirections)\\n except _RETRYABLE_EXCEPTIONS as exc:\\n retry += 1\\n if retry >= retries:\\n raise\\n retry_after = getattr(exc, 'retry_after', None)\\n if retry_after is None:\\n retry_after = calculate_wait_for_retry(retry)\\n\\n _reset_http_connections(http)\\n logging.debug('Retrying request to url %s after exception %s',\\n http_request.url, type(exc).__name__)\\n time.sleep(retry_after)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/stream_slice.py::StreamSlice.__init__\",\n \"text\": \"google/cloud/streaming/stream_slice.py::StreamSlice.__init__\\n def __init__(self, stream, max_bytes):\\n self._stream = stream\\n self._remaining_bytes = max_bytes\\n self._max_bytes = max_bytes\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/stream_slice.py::StreamSlice.__repr__\",\n \"text\": \"google/cloud/streaming/stream_slice.py::StreamSlice.__repr__\\n def __repr__(self):\\n return 'Slice of stream %s with %s/%s bytes not yet read' % (\\n self._stream, self._remaining_bytes, self._max_bytes)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/stream_slice.py::StreamSlice.__len__\",\n \"text\": \"google/cloud/streaming/stream_slice.py::StreamSlice.__len__\\n def __len__(self):\\n return self._max_bytes\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/stream_slice.py::StreamSlice.__nonzero__\",\n \"text\": \"google/cloud/streaming/stream_slice.py::StreamSlice.__nonzero__\\n def __nonzero__(self):\\n # For 32-bit python2.x, len() cannot exceed a 32-bit number; avoid\\n # accidental len() calls from httplib in the form of \\\"if this_object:\\\".\\n return bool(self._max_bytes)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/stream_slice.py::StreamSlice.length\",\n \"text\": \"google/cloud/streaming/stream_slice.py::StreamSlice.length\\n @property\\n def length(self):\\n \\\"\\\"\\\"Maximum number of bytes to return in the slice.\\n\\n .. note::\\n\\n For 32-bit python2.x, len() cannot exceed a 32-bit number.\\n\\n :rtype: integer\\n :returns: The max \\\"length\\\" of the stream.\\n \\\"\\\"\\\"\\n return self._max_bytes\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/stream_slice.py::StreamSlice.read\",\n \"text\": \"google/cloud/streaming/stream_slice.py::StreamSlice.read\\n def read(self, size=None):\\n \\\"\\\"\\\"Read bytes from the slice.\\n\\n Compared to other streams, there is one case where we may\\n unexpectedly raise an exception on read: if the underlying stream\\n is exhausted (i.e. returns no bytes on read), and the size of this\\n slice indicates we should still be able to read more bytes, we\\n raise :exc:`IncompleteRead`.\\n\\n :type size: integer or None\\n :param size: If provided, read no more than size bytes from the stream.\\n\\n :rtype: bytes\\n :returns: bytes read from this slice.\\n\\n :raises: :exc:`IncompleteRead`\\n \\\"\\\"\\\"\\n if size is not None:\\n read_size = min(size, self._remaining_bytes)\\n else:\\n read_size = self._remaining_bytes\\n data = self._stream.read(read_size)\\n if read_size > 0 and not data:\\n raise http_client.IncompleteRead(\\n self._max_bytes - self._remaining_bytes, self._max_bytes)\\n self._remaining_bytes -= len(data)\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.__init__\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.__init__\\n def __init__(self, stream, close_stream=False,\\n chunksize=_DEFAULT_CHUNKSIZE, auto_transfer=True,\\n http=None, num_retries=5):\\n self._bytes_http = None\\n self._close_stream = close_stream\\n self._http = http\\n self._stream = stream\\n self._url = None\\n\\n # Let the @property do validation.\\n self.num_retries = num_retries\\n\\n self.auto_transfer = auto_transfer\\n self.chunksize = chunksize\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.__repr__\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.__repr__\\n def __repr__(self):\\n return str(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.close_stream\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.close_stream\\n @property\\n def close_stream(self):\\n \\\"\\\"\\\"Should this instance close the stream when deleted.\\n\\n :rtype: boolean\\n :returns: Boolean indicated if the stream should be closed.\\n \\\"\\\"\\\"\\n return self._close_stream\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.http\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.http\\n @property\\n def http(self):\\n \\\"\\\"\\\"Http instance used to perform requests.\\n\\n :rtype: :class:`httplib2.Http` (or workalike)\\n :returns: The HTTP object used for requests.\\n \\\"\\\"\\\"\\n return self._http\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.bytes_http\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.bytes_http\\n @property\\n def bytes_http(self):\\n \\\"\\\"\\\"Http instance used to perform binary requests.\\n\\n Defaults to :attr:`http`.\\n\\n :rtype: :class:`httplib2.Http` (or workalike)\\n :returns: The HTTP object used for binary requests.\\n \\\"\\\"\\\"\\n return self._bytes_http or self.http\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.bytes_http\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.bytes_http\\n @bytes_http.setter\\n def bytes_http(self, value):\\n \\\"\\\"\\\"Update Http instance used to perform binary requests.\\n\\n :type value: :class:`httplib2.Http` (or workalike)\\n :param value: new instance\\n \\\"\\\"\\\"\\n self._bytes_http = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.num_retries\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.num_retries\\n @property\\n def num_retries(self):\\n \\\"\\\"\\\"How many retries should the transfer attempt\\n\\n :rtype: integer\\n :returns: The number of retries allowed.\\n \\\"\\\"\\\"\\n return self._num_retries\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.num_retries\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.num_retries\\n @num_retries.setter\\n def num_retries(self, value):\\n \\\"\\\"\\\"Update how many retries should the transfer attempt\\n\\n :type value: integer\\n \\\"\\\"\\\"\\n if not isinstance(value, six.integer_types):\\n raise ValueError(\\\"num_retries: pass an integer\\\")\\n\\n if value < 0:\\n raise ValueError(\\n 'Cannot have negative value for num_retries')\\n self._num_retries = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.stream\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.stream\\n @property\\n def stream(self):\\n \\\"\\\"\\\"Stream to/from which data is downloaded/uploaded.\\n\\n :rtype: file-like object\\n :returns: The stream that sends/receives data.\\n \\\"\\\"\\\"\\n return self._stream\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.url\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.url\\n @property\\n def url(self):\\n \\\"\\\"\\\"URL to / from which data is downloaded/uploaded.\\n\\n :rtype: string\\n :returns: The URL where data is sent/received.\\n \\\"\\\"\\\"\\n return self._url\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer._initialize\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer._initialize\\n def _initialize(self, http, url):\\n \\\"\\\"\\\"Initialize this download by setting :attr:`http` and :attr`url`.\\n\\n Allow the user to be able to pre-initialize :attr:`http` by setting\\n the value in the constructor; in that case, we ignore the provided\\n http.\\n\\n :type http: :class:`httplib2.Http` (or a worklike) or None.\\n :param http: the Http instance to use to make requests.\\n\\n :type url: string\\n :param url: The url for this transfer.\\n \\\"\\\"\\\"\\n self._ensure_uninitialized()\\n if self.http is None:\\n self._http = http or httplib2.Http()\\n self._url = url\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.initialized\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.initialized\\n @property\\n def initialized(self):\\n \\\"\\\"\\\"Has the instance been initialized\\n\\n :rtype: boolean\\n :returns: Boolean indicating if the current transfer\\n has been initialized.\\n \\\"\\\"\\\"\\n return self.url is not None and self.http is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer._ensure_initialized\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer._ensure_initialized\\n def _ensure_initialized(self):\\n \\\"\\\"\\\"Helper: assert that the instance is initialized.\\n\\n :raises: :exc:`google.cloud.streaming.exceptions.TransferInvalidError`\\n if the instance is not initialized.\\n \\\"\\\"\\\"\\n if not self.initialized:\\n raise TransferInvalidError(\\n 'Cannot use uninitialized %s', type(self).__name__)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer._ensure_uninitialized\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer._ensure_uninitialized\\n def _ensure_uninitialized(self):\\n \\\"\\\"\\\"Helper: assert that the instance is not initialized.\\n\\n :raises: :exc:`google.cloud.streaming.exceptions.TransferInvalidError`\\n if the instance is already initialized.\\n \\\"\\\"\\\"\\n if self.initialized:\\n raise TransferInvalidError(\\n 'Cannot re-initialize %s', type(self).__name__)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::_Transfer.__del__\",\n \"text\": \"google/cloud/streaming/transfer.py::_Transfer.__del__\\n def __del__(self):\\n if self._close_stream:\\n self._stream.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download.__init__\",\n \"text\": \"google/cloud/streaming/transfer.py::Download.__init__\\n def __init__(self, stream, **kwds):\\n total_size = kwds.pop('total_size', None)\\n super(Download, self).__init__(stream, **kwds)\\n self._initial_response = None\\n self._progress = 0\\n self._total_size = total_size\\n self._encoding = None\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download.from_file\",\n \"text\": \"google/cloud/streaming/transfer.py::Download.from_file\\n @classmethod\\n def from_file(cls, filename, overwrite=False, auto_transfer=True, **kwds):\\n \\\"\\\"\\\"Create a new download object from a filename.\\n\\n :type filename: string\\n :param filename: path/filename for the target file\\n\\n :type overwrite: boolean\\n :param overwrite: should an existing file be overwritten\\n\\n :type auto_transfer: boolean\\n :param auto_transfer: should the transfer be started immediately\\n\\n :type kwds: dict\\n :param kwds: keyword arguments: passed\\n through to :meth:`_Transfer.__init__()`.\\n\\n :rtype: :class:`Download`\\n :returns: The download initiated from the file passed.\\n \\\"\\\"\\\"\\n path = os.path.expanduser(filename)\\n if os.path.exists(path) and not overwrite:\\n raise ValueError(\\n 'File %s exists and overwrite not specified' % path)\\n return cls(open(path, 'wb'), close_stream=True,\\n auto_transfer=auto_transfer, **kwds)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download.from_stream\",\n \"text\": \"google/cloud/streaming/transfer.py::Download.from_stream\\n @classmethod\\n def from_stream(cls, stream, auto_transfer=True, total_size=None, **kwds):\\n \\\"\\\"\\\"Create a new Download object from a stream.\\n\\n :type stream: writable file-like object\\n :param stream: the target file\\n\\n :type total_size: integer or None\\n :param total_size: total size of the file to be downloaded\\n\\n :type auto_transfer: boolean\\n :param auto_transfer: should the transfer be started immediately\\n\\n :type kwds: dict\\n :param kwds: keyword arguments: passed\\n through to :meth:`_Transfer.__init__()`.\\n\\n :rtype: :class:`Download`\\n :returns: The download initiated from the stream passed.\\n \\\"\\\"\\\"\\n return cls(stream, auto_transfer=auto_transfer, total_size=total_size,\\n **kwds)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download.progress\",\n \"text\": \"google/cloud/streaming/transfer.py::Download.progress\\n @property\\n def progress(self):\\n \\\"\\\"\\\"Number of bytes have been downloaded.\\n\\n :rtype: integer >= 0\\n :returns: The number of downloaded bytes.\\n \\\"\\\"\\\"\\n return self._progress\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download.total_size\",\n \"text\": \"google/cloud/streaming/transfer.py::Download.total_size\\n @property\\n def total_size(self):\\n \\\"\\\"\\\"Total number of bytes to be downloaded.\\n\\n :rtype: integer or None\\n :returns: The total number of bytes to download.\\n \\\"\\\"\\\"\\n return self._total_size\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download.encoding\",\n \"text\": \"google/cloud/streaming/transfer.py::Download.encoding\\n @property\\n def encoding(self):\\n \\\"\\\"\\\"'Content-Encoding' used to transfer the file\\n\\n :rtype: string or None\\n :returns: The encoding of the downloaded content.\\n \\\"\\\"\\\"\\n return self._encoding\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download.__repr__\",\n \"text\": \"google/cloud/streaming/transfer.py::Download.__repr__\\n def __repr__(self):\\n if not self.initialized:\\n return 'Download (uninitialized)'\\n else:\\n return 'Download with %d/%s bytes transferred from url %s' % (\\n self.progress, self.total_size, self.url)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download.configure_request\",\n \"text\": \"google/cloud/streaming/transfer.py::Download.configure_request\\n def configure_request(self, http_request, url_builder):\\n \\\"\\\"\\\"Update http_request/url_builder with download-appropriate values.\\n\\n :type http_request: :class:`~.streaming.http_wrapper.Request`\\n :param http_request: the request to be updated\\n\\n :type url_builder: instance with settable 'query_params' attribute.\\n :param url_builder: transfer policy object to be updated\\n \\\"\\\"\\\"\\n url_builder.query_params['alt'] = 'media'\\n http_request.headers['Range'] = 'bytes=0-%d' % (self.chunksize - 1,)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download._set_total\",\n \"text\": \"google/cloud/streaming/transfer.py::Download._set_total\\n def _set_total(self, info):\\n \\\"\\\"\\\"Update 'total_size' based on data from a response.\\n\\n :type info: mapping\\n :param info: response headers\\n \\\"\\\"\\\"\\n if 'content-range' in info:\\n _, _, total = info['content-range'].rpartition('/')\\n if total != '*':\\n self._total_size = int(total)\\n # Note \\\"total_size is None\\\" means we don't know it; if no size\\n # info was returned on our initial range request, that means we\\n # have a 0-byte file. (That last statement has been verified\\n # empirically, but is not clearly documented anywhere.)\\n if self.total_size is None:\\n self._total_size = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download.initialize_download\",\n \"text\": \"google/cloud/streaming/transfer.py::Download.initialize_download\\n def initialize_download(self, http_request, http):\\n \\\"\\\"\\\"Initialize this download.\\n\\n If the instance has :attr:`auto_transfer` enabled, begins the\\n download immediately.\\n\\n :type http_request: :class:`~.streaming.http_wrapper.Request`\\n :param http_request: the request to use to initialize this download.\\n\\n :type http: :class:`httplib2.Http` (or workalike)\\n :param http: Http instance for this request.\\n \\\"\\\"\\\"\\n self._ensure_uninitialized()\\n url = http_request.url\\n if self.auto_transfer:\\n end_byte = self._compute_end_byte(0)\\n self._set_range_header(http_request, 0, end_byte)\\n response = make_api_request(\\n self.bytes_http or http, http_request)\\n if response.status_code not in self._ACCEPTABLE_STATUSES:\\n raise HttpError.from_response(response)\\n self._initial_response = response\\n self._set_total(response.info)\\n url = response.info.get('content-location', response.request_url)\\n self._initialize(http, url)\\n # Unless the user has requested otherwise, we want to just\\n # go ahead and pump the bytes now.\\n if self.auto_transfer:\\n self.stream_file(use_chunks=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download._normalize_start_end\",\n \"text\": \"google/cloud/streaming/transfer.py::Download._normalize_start_end\\n def _normalize_start_end(self, start, end=None):\\n \\\"\\\"\\\"Validate / fix up byte range.\\n\\n :type start: integer\\n :param start: start byte of the range: if negative, used as an\\n offset from the end.\\n\\n :type end: integer\\n :param end: end byte of the range.\\n\\n :rtype: tuple, (start, end)\\n :returns: the normalized start, end pair.\\n :raises: :exc:`google.cloud.streaming.exceptions.TransferInvalidError`\\n for invalid combinations of start, end.\\n \\\"\\\"\\\"\\n if end is not None:\\n if start < 0:\\n raise TransferInvalidError(\\n 'Cannot have end index with negative start index')\\n elif start >= self.total_size:\\n raise TransferInvalidError(\\n 'Cannot have start index greater than total size')\\n end = min(end, self.total_size - 1)\\n if end < start:\\n raise TransferInvalidError(\\n 'Range requested with end[%s] < start[%s]' % (end, start))\\n return start, end\\n else:\\n if start < 0:\\n start = max(0, start + self.total_size)\\n return start, self.total_size - 1\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download._set_range_header\",\n \"text\": \"google/cloud/streaming/transfer.py::Download._set_range_header\\n @staticmethod\\n def _set_range_header(request, start, end=None):\\n \\\"\\\"\\\"Update the 'Range' header in a request to match a byte range.\\n\\n :type request: :class:`google.cloud.streaming.http_wrapper.Request`\\n :param request: the request to update\\n\\n :type start: integer\\n :param start: start byte of the range: if negative, used as an\\n offset from the end.\\n\\n :type end: integer\\n :param end: end byte of the range.\\n \\\"\\\"\\\"\\n if start < 0:\\n request.headers['range'] = 'bytes=%d' % start\\n elif end is None:\\n request.headers['range'] = 'bytes=%d-' % start\\n else:\\n request.headers['range'] = 'bytes=%d-%d' % (start, end)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download._compute_end_byte\",\n \"text\": \"google/cloud/streaming/transfer.py::Download._compute_end_byte\\n def _compute_end_byte(self, start, end=None, use_chunks=True):\\n \\\"\\\"\\\"Compute the last byte to fetch for this request.\\n\\n Based on the HTTP spec for Range and Content-Range.\\n\\n .. note::\\n This is potentially confusing in several ways:\\n - the value for the last byte is 0-based, eg \\\"fetch 10 bytes\\n from the beginning\\\" would return 9 here.\\n - if we have no information about size, and don't want to\\n use the chunksize, we'll return None.\\n\\n :type start: integer\\n :param start: start byte of the range.\\n\\n :type end: integer or None\\n :param end: suggested last byte of the range.\\n\\n :type use_chunks: boolean\\n :param use_chunks: If False, ignore :attr:`chunksize`.\\n\\n :rtype: str\\n :returns: Last byte to use in a 'Range' header, or None.\\n \\\"\\\"\\\"\\n end_byte = end\\n\\n if start < 0 and not self.total_size:\\n return end_byte\\n\\n if use_chunks:\\n alternate = start + self.chunksize - 1\\n if end_byte is not None:\\n end_byte = min(end_byte, alternate)\\n else:\\n end_byte = alternate\\n\\n if self.total_size:\\n alternate = self.total_size - 1\\n if end_byte is not None:\\n end_byte = min(end_byte, alternate)\\n else:\\n end_byte = alternate\\n\\n return end_byte\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download._get_chunk\",\n \"text\": \"google/cloud/streaming/transfer.py::Download._get_chunk\\n def _get_chunk(self, start, end):\\n \\\"\\\"\\\"Retrieve a chunk of the file.\\n\\n :type start: integer\\n :param start: start byte of the range.\\n\\n :type end: integer or None\\n :param end: end byte of the range.\\n\\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\\n :returns: response from the chunk request.\\n \\\"\\\"\\\"\\n self._ensure_initialized()\\n request = Request(url=self.url)\\n self._set_range_header(request, start, end=end)\\n return make_api_request(\\n self.bytes_http, request, retries=self.num_retries)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download._process_response\",\n \"text\": \"google/cloud/streaming/transfer.py::Download._process_response\\n def _process_response(self, response):\\n \\\"\\\"\\\"Update attribtes and writing stream, based on response.\\n\\n :type response: :class:`google.cloud.streaming.http_wrapper.Response`\\n :param response: response from a download request.\\n\\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\\n :returns: the response\\n :raises: :exc:`google.cloud.streaming.exceptions.HttpError` for\\n missing / unauthorized responses;\\n :exc:`google.cloud.streaming.exceptions.TransferRetryError`\\n for other error responses.\\n \\\"\\\"\\\"\\n if response.status_code not in self._ACCEPTABLE_STATUSES:\\n # We distinguish errors that mean we made a mistake in setting\\n # up the transfer versus something we should attempt again.\\n if response.status_code in (http_client.FORBIDDEN,\\n http_client.NOT_FOUND):\\n raise HttpError.from_response(response)\\n else:\\n raise TransferRetryError(response.content)\\n if response.status_code in (http_client.OK,\\n http_client.PARTIAL_CONTENT):\\n self.stream.write(response.content)\\n self._progress += response.length\\n if response.info and 'content-encoding' in response.info:\\n self._encoding = response.info['content-encoding']\\n elif response.status_code == http_client.NO_CONTENT:\\n # It's important to write something to the stream for the case\\n # of a 0-byte download to a file, as otherwise python won't\\n # create the file.\\n self.stream.write('')\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download.get_range\",\n \"text\": \"google/cloud/streaming/transfer.py::Download.get_range\\n def get_range(self, start, end=None, use_chunks=True):\\n \\\"\\\"\\\"Retrieve a given byte range from this download, inclusive.\\n\\n Writes retrieved bytes into :attr:`stream`.\\n\\n Range must be of one of these three forms:\\n * 0 <= start, end = None: Fetch from start to the end of the file.\\n * 0 <= start <= end: Fetch the bytes from start to end.\\n * start < 0, end = None: Fetch the last -start bytes of the file.\\n\\n (These variations correspond to those described in the HTTP 1.1\\n protocol for range headers in RFC 2616, sec. 14.35.1.)\\n\\n :type start: integer\\n :param start: Where to start fetching bytes. (See above.)\\n\\n :type end: integer or ``None``\\n :param end: Where to stop fetching bytes. (See above.)\\n\\n :type use_chunks: boolean\\n :param use_chunks: If False, ignore :attr:`chunksize`\\n and fetch this range in a single request.\\n If True, streams via chunks.\\n\\n :raises: :exc:`google.cloud.streaming.exceptions.TransferRetryError`\\n if a request returns an empty response.\\n \\\"\\\"\\\"\\n self._ensure_initialized()\\n progress_end_normalized = False\\n if self.total_size is not None:\\n progress, end_byte = self._normalize_start_end(start, end)\\n progress_end_normalized = True\\n else:\\n progress = start\\n end_byte = end\\n while (not progress_end_normalized or end_byte is None or\\n progress <= end_byte):\\n end_byte = self._compute_end_byte(progress, end=end_byte,\\n use_chunks=use_chunks)\\n response = self._get_chunk(progress, end_byte)\\n if not progress_end_normalized:\\n self._set_total(response.info)\\n progress, end_byte = self._normalize_start_end(start, end)\\n progress_en\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Download.stream_file\",\n \"text\": \"google/cloud/streaming/transfer.py::Download.stream_file\\n def stream_file(self, use_chunks=True):\\n \\\"\\\"\\\"Stream the entire download.\\n\\n Writes retrieved bytes into :attr:`stream`.\\n\\n :type use_chunks: boolean\\n :param use_chunks: If False, ignore :attr:`chunksize`\\n and stream this download in a single request.\\n If True, streams via chunks.\\n \\\"\\\"\\\"\\n self._ensure_initialized()\\n while True:\\n if self._initial_response is not None:\\n response = self._initial_response\\n self._initial_response = None\\n else:\\n end_byte = self._compute_end_byte(self.progress,\\n use_chunks=use_chunks)\\n response = self._get_chunk(self.progress, end_byte)\\n if self.total_size is None:\\n self._set_total(response.info)\\n response = self._process_response(response)\\n if (response.status_code == http_client.OK or\\n self.progress >= self.total_size):\\n break\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.__init__\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.__init__\\n def __init__(self, stream, mime_type, total_size=None, http=None,\\n close_stream=False, auto_transfer=True,\\n **kwds):\\n super(Upload, self).__init__(\\n stream, close_stream=close_stream, auto_transfer=auto_transfer,\\n http=http, **kwds)\\n self._final_response = None\\n self._server_chunk_granularity = None\\n self._complete = False\\n self._mime_type = mime_type\\n self._progress = 0\\n self._strategy = None\\n self._total_size = total_size\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.from_file\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.from_file\\n @classmethod\\n def from_file(cls, filename, mime_type=None, auto_transfer=True, **kwds):\\n \\\"\\\"\\\"Create a new Upload object from a filename.\\n\\n :type filename: string\\n :param filename: path/filename to the file being uploaded\\n\\n :type mime_type: string\\n :param mime_type: MIMEtype of the file being uploaded\\n\\n :type auto_transfer: boolean or None\\n :param auto_transfer: should the transfer be started immediately\\n\\n :type kwds: dict\\n :param kwds: keyword arguments: passed\\n through to :meth:`_Transfer.__init__()`.\\n\\n :rtype: :class:`Upload`\\n :returns: The upload initiated from the file passed.\\n \\\"\\\"\\\"\\n path = os.path.expanduser(filename)\\n if not mime_type:\\n mime_type, _ = mimetypes.guess_type(path)\\n if mime_type is None:\\n raise ValueError(\\n 'Could not guess mime type for %s' % path)\\n size = os.stat(path).st_size\\n return cls(open(path, 'rb'), mime_type, total_size=size,\\n close_stream=True, auto_transfer=auto_transfer, **kwds)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.from_stream\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.from_stream\\n @classmethod\\n def from_stream(cls, stream, mime_type,\\n total_size=None, auto_transfer=True, **kwds):\\n \\\"\\\"\\\"Create a new Upload object from a stream.\\n\\n :type stream: writable file-like object\\n :param stream: the target file\\n\\n :type mime_type: string\\n :param mime_type: MIMEtype of the file being uploaded\\n\\n :type total_size: integer or None\\n :param total_size: Size of the file being uploaded\\n\\n :type auto_transfer: boolean or None\\n :param auto_transfer: should the transfer be started immediately\\n\\n :type kwds: dict\\n :param kwds: keyword arguments: passed\\n through to :meth:`_Transfer.__init__()`.\\n\\n :rtype: :class:`Upload`\\n :returns: The upload initiated from the stream passed.\\n \\\"\\\"\\\"\\n if mime_type is None:\\n raise ValueError(\\n 'No mime_type specified for stream')\\n return cls(stream, mime_type, total_size=total_size,\\n close_stream=False, auto_transfer=auto_transfer, **kwds)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.complete\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.complete\\n @property\\n def complete(self):\\n \\\"\\\"\\\"Has the entire stream been uploaded.\\n\\n :rtype: boolean\\n :returns: Boolean indicated if the upload is complete.\\n \\\"\\\"\\\"\\n return self._complete\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.mime_type\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.mime_type\\n @property\\n def mime_type(self):\\n \\\"\\\"\\\"MIMEtype of the file being uploaded.\\n\\n :rtype: string\\n :returns: The mime-type of the upload.\\n \\\"\\\"\\\"\\n return self._mime_type\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.progress\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.progress\\n @property\\n def progress(self):\\n \\\"\\\"\\\"Bytes uploaded so far\\n\\n :rtype: integer\\n :returns: The amount uploaded so far.\\n \\\"\\\"\\\"\\n return self._progress\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.strategy\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.strategy\\n @property\\n def strategy(self):\\n \\\"\\\"\\\"Upload strategy to use\\n\\n :rtype: string or None\\n :returns: The strategy used to upload the data.\\n \\\"\\\"\\\"\\n return self._strategy\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.strategy\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.strategy\\n @strategy.setter\\n def strategy(self, value):\\n \\\"\\\"\\\"Update upload strategy to use\\n\\n :type value: string (one of :data:`SIMPLE_UPLOAD` or\\n :data:`RESUMABLE_UPLOAD`)\\n\\n :raises: :exc:`ValueError` if value is not one of the two allowed\\n strings.\\n \\\"\\\"\\\"\\n if value not in (SIMPLE_UPLOAD, RESUMABLE_UPLOAD):\\n raise ValueError((\\n 'Invalid value \\\"%s\\\" for upload strategy, must be one of '\\n '\\\"simple\\\" or \\\"resumable\\\".') % value)\\n self._strategy = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.total_size\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.total_size\\n @property\\n def total_size(self):\\n \\\"\\\"\\\"Total size of the stream to be uploaded.\\n\\n :rtype: integer or None\\n :returns: The total size to be uploaded.\\n \\\"\\\"\\\"\\n return self._total_size\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.total_size\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.total_size\\n @total_size.setter\\n def total_size(self, value):\\n \\\"\\\"\\\"Update total size of the stream to be uploaded.\\n\\n :type value: integer or None\\n :param value: the size\\n \\\"\\\"\\\"\\n self._ensure_uninitialized()\\n self._total_size = value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.__repr__\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.__repr__\\n def __repr__(self):\\n if not self.initialized:\\n return 'Upload (uninitialized)'\\n else:\\n return 'Upload with %d/%s bytes transferred for url %s' % (\\n self.progress, self.total_size or '???', self.url)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload._set_default_strategy\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload._set_default_strategy\\n def _set_default_strategy(self, upload_config, http_request):\\n \\\"\\\"\\\"Determine and set the default upload strategy for this upload.\\n\\n We generally prefer simple or multipart, unless we're forced to\\n use resumable. This happens when any of (1) the upload is too\\n large, (2) the simple endpoint doesn't support multipart requests\\n and we have metadata, or (3) there is no simple upload endpoint.\\n\\n :type upload_config: instance w/ ``max_size`` and ``accept``\\n attributes\\n :param upload_config: Configuration for the upload endpoint.\\n\\n :type http_request: :class:`~.streaming.http_wrapper.Request`\\n :param http_request: The associated http request.\\n \\\"\\\"\\\"\\n if upload_config.resumable_path is None:\\n self.strategy = SIMPLE_UPLOAD\\n if self.strategy is not None:\\n return\\n strategy = SIMPLE_UPLOAD\\n if (self.total_size is not None and\\n self.total_size > RESUMABLE_UPLOAD_THRESHOLD):\\n strategy = RESUMABLE_UPLOAD\\n if http_request.body and not upload_config.simple_multipart:\\n strategy = RESUMABLE_UPLOAD\\n if not upload_config.simple_path:\\n strategy = RESUMABLE_UPLOAD\\n self.strategy = strategy\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.configure_request\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.configure_request\\n def configure_request(self, upload_config, http_request, url_builder):\\n \\\"\\\"\\\"Configure the request and url for this upload.\\n\\n :type upload_config: instance w/ ``max_size`` and ``accept``\\n attributes\\n :param upload_config: transfer policy object to be queried\\n\\n :type http_request: :class:`~.streaming.http_wrapper.Request`\\n :param http_request: the request to be updated\\n\\n :type url_builder: instance with settable 'relative_path' and\\n 'query_params' attributes.\\n :param url_builder: transfer policy object to be updated\\n\\n :raises: :exc:`ValueError` if the requested upload is too big,\\n or does not have an acceptable MIME type.\\n \\\"\\\"\\\"\\n # Validate total_size vs. max_size\\n if (self.total_size and upload_config.max_size and\\n self.total_size > upload_config.max_size):\\n raise ValueError(\\n 'Upload too big: %s larger than max size %s' % (\\n self.total_size, upload_config.max_size))\\n # Validate mime type\\n if not acceptable_mime_type(upload_config.accept, self.mime_type):\\n raise ValueError(\\n 'MIME type %s does not match any accepted MIME ranges %s' % (\\n self.mime_type, upload_config.accept))\\n\\n self._set_default_strategy(upload_config, http_request)\\n if self.strategy == SIMPLE_UPLOAD:\\n url_builder.relative_path = upload_config.simple_path\\n if http_request.body:\\n url_builder.query_params['uploadType'] = 'multipart'\\n self._configure_multipart_request(http_request)\\n else:\\n url_builder.query_params['uploadType'] = 'media'\\n self._configure_media_request(http_request)\\n else:\\n url_builder.relative_path = upload_config.resumable_path\\n u\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload._configure_media_request\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload._configure_media_request\\n def _configure_media_request(self, http_request):\\n \\\"\\\"\\\"Helper for 'configure_request': set up simple request.\\\"\\\"\\\"\\n http_request.headers['content-type'] = self.mime_type\\n http_request.body = self.stream.read()\\n http_request.loggable_body = '<media body>'\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload._configure_multipart_request\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload._configure_multipart_request\\n def _configure_multipart_request(self, http_request):\\n \\\"\\\"\\\"Helper for 'configure_request': set up multipart request.\\\"\\\"\\\"\\n # This is a multipart/related upload.\\n msg_root = mime_multipart.MIMEMultipart('related')\\n # msg_root should not write out its own headers\\n setattr(msg_root, '_write_headers', lambda self: None)\\n\\n # attach the body as one part\\n msg = mime_nonmultipart.MIMENonMultipart(\\n *http_request.headers['content-type'].split('/'))\\n msg.set_payload(http_request.body)\\n msg_root.attach(msg)\\n\\n # attach the media as the second part\\n msg = mime_nonmultipart.MIMENonMultipart(*self.mime_type.split('/'))\\n msg['Content-Transfer-Encoding'] = 'binary'\\n msg.set_payload(self.stream.read())\\n msg_root.attach(msg)\\n\\n # NOTE: generate multipart message as bytes, not text\\n stream = six.BytesIO()\\n if six.PY3: # pragma: NO COVER Python3\\n generator_class = email_generator.BytesGenerator\\n else:\\n generator_class = email_generator.Generator\\n generator = generator_class(stream, mangle_from_=False)\\n generator.flatten(msg_root, unixfrom=False)\\n http_request.body = stream.getvalue()\\n\\n multipart_boundary = msg_root.get_boundary()\\n http_request.headers['content-type'] = (\\n 'multipart/related; boundary=\\\"%s\\\"' % multipart_boundary)\\n\\n boundary_bytes = _to_bytes(multipart_boundary)\\n body_components = http_request.body.split(boundary_bytes)\\n headers, _, _ = body_components[-2].partition(b'\\\\n\\\\n')\\n body_components[-2] = b'\\\\n\\\\n'.join([headers, b'<media body>\\\\n\\\\n--'])\\n http_request.loggable_body = boundary_bytes.join(body_components)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload._configure_resumable_request\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload._configure_resumable_request\\n def _configure_resumable_request(self, http_request):\\n \\\"\\\"\\\"Helper for 'configure_request': set up resumable request.\\\"\\\"\\\"\\n http_request.headers['X-Upload-Content-Type'] = self.mime_type\\n if self.total_size is not None:\\n http_request.headers[\\n 'X-Upload-Content-Length'] = str(self.total_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.refresh_upload_state\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.refresh_upload_state\\n def refresh_upload_state(self):\\n \\\"\\\"\\\"Refresh the state of a resumable upload via query to the back-end.\\n \\\"\\\"\\\"\\n if self.strategy != RESUMABLE_UPLOAD:\\n return\\n self._ensure_initialized()\\n # NOTE: Per RFC 2616[1]/7231[2], a 'PUT' request is inappropriate\\n # here: it is intended to be used to replace the entire\\n # resource, not to query for a status.\\n #\\n # If the back-end doesn't provide a way to query for this state\\n # via a 'GET' request, somebody should be spanked.\\n #\\n # The violation is documented[3].\\n #\\n # [1] http://www.w3.org/Protocols/rfc2616/rfc2616-sec9.html#sec9.6\\n # [2] http://tools.ietf.org/html/rfc7231#section-4.3.4\\n # [3]\\n # https://cloud.google.com/storage/docs/json_api/v1/how-tos/upload#resume-upload\\n refresh_request = Request(\\n url=self.url, http_method='PUT',\\n headers={'Content-Range': 'bytes */*'})\\n refresh_response = make_api_request(\\n self.http, refresh_request, redirections=0,\\n retries=self.num_retries)\\n range_header = self._get_range_header(refresh_response)\\n if refresh_response.status_code in (http_client.OK,\\n http_client.CREATED):\\n self._complete = True\\n self._progress = self.total_size\\n self.stream.seek(self.progress)\\n # If we're finished, the refresh response will contain the metadata\\n # originally requested. Cache it so it can be returned in\\n # StreamInChunks.\\n self._final_response = refresh_response\\n elif refresh_response.status_code == RESUME_INCOMPLETE:\\n if range_header is None:\\n self._progress = 0\\n else:\\n self._progress = self._last_byte(range_header) + 1\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload._get_range_header\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload._get_range_header\\n @staticmethod\\n def _get_range_header(response):\\n \\\"\\\"\\\"Return a 'Range' header from a response.\\n\\n :type response: :class:`google.cloud.streaming.http_wrapper.Response`\\n :param response: response to be queried\\n\\n :rtype: string\\n :returns: The header used to determine the bytes range.\\n \\\"\\\"\\\"\\n # NOTE: Per RFC 2616[1]/7233[2][3], 'Range' is a request header,\\n # not a response header. If the back-end is actually setting\\n # 'Range' on responses, somebody should be spanked: it should\\n # be sending 'Content-Range' (including the # '/<length>'\\n # trailer).\\n #\\n # The violation is documented[4].\\n #\\n # [1] http://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html\\n # [2] http://tools.ietf.org/html/rfc7233#section-3.1\\n # [3] http://tools.ietf.org/html/rfc7233#section-4.2\\n # [4]\\n # https://cloud.google.com/storage/docs/json_api/v1/how-tos/upload#chunking\\n return response.info.get('Range', response.info.get('range'))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.initialize_upload\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.initialize_upload\\n def initialize_upload(self, http_request, http):\\n \\\"\\\"\\\"Initialize this upload from the given http_request.\\n\\n :type http_request: :class:`~.streaming.http_wrapper.Request`\\n :param http_request: the request to be used\\n\\n :type http: :class:`httplib2.Http` (or workalike)\\n :param http: Http instance for this request.\\n\\n :raises: :exc:`ValueError` if the instance has not been configured\\n with a strategy.\\n :rtype: :class:`~google.cloud.streaming.http_wrapper.Response`\\n :returns: The response if the upload is resumable and auto transfer\\n is not used.\\n \\\"\\\"\\\"\\n if self.strategy is None:\\n raise ValueError(\\n 'No upload strategy set; did you call configure_request?')\\n if self.strategy != RESUMABLE_UPLOAD:\\n return\\n self._ensure_uninitialized()\\n http_response = make_api_request(http, http_request,\\n retries=self.num_retries)\\n if http_response.status_code != http_client.OK:\\n raise HttpError.from_response(http_response)\\n\\n granularity = http_response.info.get('X-Goog-Upload-Chunk-Granularity')\\n if granularity is not None:\\n granularity = int(granularity)\\n self._server_chunk_granularity = granularity\\n url = http_response.info['location']\\n self._initialize(http, url)\\n\\n # Unless the user has requested otherwise, we want to just\\n # go ahead and pump the bytes now.\\n if self.auto_transfer:\\n return self.stream_file(use_chunks=True)\\n else:\\n return http_response\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload._last_byte\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload._last_byte\\n @staticmethod\\n def _last_byte(range_header):\\n \\\"\\\"\\\"Parse the last byte from a 'Range' header.\\n\\n :type range_header: string\\n :param range_header: 'Range' header value per RFC 2616/7233\\n\\n :rtype: int\\n :returns: The last byte from a range header.\\n \\\"\\\"\\\"\\n _, _, end = range_header.partition('-')\\n return int(end)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload._validate_chunksize\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload._validate_chunksize\\n def _validate_chunksize(self, chunksize=None):\\n \\\"\\\"\\\"Validate chunksize against server-specified granularity.\\n\\n Helper for :meth:`stream_file`.\\n\\n :type chunksize: integer or None\\n :param chunksize: the chunk size to be tested.\\n\\n :raises: :exc:`ValueError` if ``chunksize`` is not a multiple\\n of the server-specified granulariy.\\n \\\"\\\"\\\"\\n if self._server_chunk_granularity is None:\\n return\\n chunksize = chunksize or self.chunksize\\n if chunksize % self._server_chunk_granularity:\\n raise ValueError(\\n 'Server requires chunksize to be a multiple of %d',\\n self._server_chunk_granularity)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload.stream_file\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload.stream_file\\n def stream_file(self, use_chunks=True):\\n \\\"\\\"\\\"Upload the stream.\\n\\n :type use_chunks: boolean\\n :param use_chunks: If False, send the stream in a single request.\\n Otherwise, send it in chunks.\\n\\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\\n :returns: The response for the final request made.\\n \\\"\\\"\\\"\\n if self.strategy != RESUMABLE_UPLOAD:\\n raise ValueError(\\n 'Cannot stream non-resumable upload')\\n # final_response is set if we resumed an already-completed upload.\\n response = self._final_response\\n send_func = self._send_chunk if use_chunks else self._send_media_body\\n if use_chunks:\\n self._validate_chunksize(self.chunksize)\\n self._ensure_initialized()\\n while not self.complete:\\n response = send_func(self.stream.tell())\\n if response.status_code in (http_client.OK, http_client.CREATED):\\n self._complete = True\\n break\\n self._progress = self._last_byte(response.info['range'])\\n if self.progress + 1 != self.stream.tell():\\n raise CommunicationError(\\n 'Failed to transfer all bytes in chunk, upload paused at '\\n 'byte %d' % self.progress)\\n if self.complete and hasattr(self.stream, 'seek'):\\n if not hasattr(self.stream, 'seekable') or self.stream.seekable():\\n current_pos = self.stream.tell()\\n self.stream.seek(0, os.SEEK_END)\\n end_pos = self.stream.tell()\\n self.stream.seek(current_pos)\\n if current_pos != end_pos:\\n raise TransferInvalidError(\\n 'Upload complete with %s '\\n 'additional bytes left in stream' %\\n (int(end_pos) - int(current_pos)))\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload._send_media_request\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload._send_media_request\\n def _send_media_request(self, request, end):\\n \\\"\\\"\\\"Peform API upload request.\\n\\n Helper for _send_media_body & _send_chunk:\\n\\n :type request: :class:`google.cloud.streaming.http_wrapper.Request`\\n :param request: the request to upload\\n\\n :type end: integer\\n :param end: end byte of the to be uploaded\\n\\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\\n :returns: the response\\n :raises: :exc:`~.streaming.exceptions.HttpError` if the status\\n code from the response indicates an error.\\n \\\"\\\"\\\"\\n response = make_api_request(\\n self.bytes_http, request, retries=self.num_retries)\\n if response.status_code not in (http_client.OK, http_client.CREATED,\\n RESUME_INCOMPLETE):\\n # We want to reset our state to wherever the server left us\\n # before this failed request, and then raise.\\n self.refresh_upload_state()\\n raise HttpError.from_response(response)\\n if response.status_code == RESUME_INCOMPLETE:\\n last_byte = self._last_byte(\\n self._get_range_header(response))\\n if last_byte + 1 != end:\\n self.stream.seek(last_byte)\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload._send_media_body\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload._send_media_body\\n def _send_media_body(self, start):\\n \\\"\\\"\\\"Send the entire stream in a single request.\\n\\n Helper for :meth:`stream_file`:\\n\\n :type start: integer\\n :param start: start byte of the range.\\n\\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\\n :returns: The response from the media upload request.\\n \\\"\\\"\\\"\\n self._ensure_initialized()\\n if self.total_size is None:\\n raise TransferInvalidError(\\n 'Total size must be known for SendMediaBody')\\n body_stream = StreamSlice(self.stream, self.total_size - start)\\n\\n request = Request(url=self.url, http_method='PUT', body=body_stream)\\n request.headers['Content-Type'] = self.mime_type\\n if start == self.total_size:\\n # End of an upload with 0 bytes left to send; just finalize.\\n range_string = 'bytes */%s' % self.total_size\\n else:\\n range_string = 'bytes %s-%s/%s' % (start, self.total_size - 1,\\n self.total_size)\\n\\n request.headers['Content-Range'] = range_string\\n\\n return self._send_media_request(request, self.total_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/transfer.py::Upload._send_chunk\",\n \"text\": \"google/cloud/streaming/transfer.py::Upload._send_chunk\\n def _send_chunk(self, start):\\n \\\"\\\"\\\"Send a chunk of the stream.\\n\\n Helper for :meth:`stream_file`:\\n\\n :type start: integer\\n :param start: start byte of the range.\\n\\n :rtype: :class:`google.cloud.streaming.http_wrapper.Response`\\n :returns: The response from the chunked upload request.\\n \\\"\\\"\\\"\\n self._ensure_initialized()\\n no_log_body = self.total_size is None\\n if self.total_size is None:\\n # For the streaming resumable case, we need to detect when\\n # we're at the end of the stream.\\n body_stream = BufferedStream(\\n self.stream, start, self.chunksize)\\n end = body_stream.stream_end_position\\n if body_stream.stream_exhausted:\\n self._total_size = end\\n # Here, change body_stream from a stream to a string object,\\n # which means reading a chunk into memory. This works around\\n # https://code.google.com/p/httplib2/issues/detail?id=176 which can\\n # cause httplib2 to skip bytes on 401's for file objects.\\n body_stream = body_stream.read(self.chunksize)\\n else:\\n end = min(start + self.chunksize, self.total_size)\\n body_stream = StreamSlice(self.stream, end - start)\\n request = Request(url=self.url, http_method='PUT', body=body_stream)\\n request.headers['Content-Type'] = self.mime_type\\n if no_log_body:\\n # Disable logging of streaming body.\\n request.loggable_body = '<media body>'\\n if self.total_size is None:\\n # Streaming resumable upload case, unknown total size.\\n range_string = 'bytes %s-%s/*' % (start, end - 1)\\n elif end == start:\\n # End of an upload with 0 bytes left to send; just finalize.\\n range_string = 'bytes */%s' % self.total_size\\n else:\\n # Normal resumable upload case with known\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/util.py::calculate_wait_for_retry\",\n \"text\": \"google/cloud/streaming/util.py::calculate_wait_for_retry\\ndef calculate_wait_for_retry(retry_attempt):\\n \\\"\\\"\\\"Calculate the amount of time to wait before a retry attempt.\\n\\n Wait time grows exponentially with the number of attempts. A\\n random amount of jitter is added to spread out retry attempts from\\n different clients.\\n\\n :type retry_attempt: integer\\n :param retry_attempt: Retry attempt counter.\\n\\n :rtype: integer\\n :returns: Number of seconds to wait before retrying request.\\n \\\"\\\"\\\"\\n wait_time = 2 ** retry_attempt\\n max_jitter = wait_time / 4.0\\n wait_time += random.uniform(-max_jitter, max_jitter)\\n return max(1, min(wait_time, _MAX_RETRY_WAIT))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/util.py::acceptable_mime_type\",\n \"text\": \"google/cloud/streaming/util.py::acceptable_mime_type\\ndef acceptable_mime_type(accept_patterns, mime_type):\\n \\\"\\\"\\\"Check that ``mime_type`` matches one of ``accept_patterns``.\\n\\n Note that this function assumes that all patterns in accept_patterns\\n will be simple types of the form \\\"type/subtype\\\", where one or both\\n of these can be \\\"*\\\". We do not support parameters (i.e. \\\"; q=\\\") in\\n patterns.\\n\\n :type accept_patterns: list of string\\n :param accept_patterns: acceptable MIME types.\\n\\n :type mime_type: string\\n :param mime_type: the MIME being checked\\n\\n :rtype: boolean\\n :returns: True if the supplied MIME type matches at least one of the\\n patterns, else False.\\n \\\"\\\"\\\"\\n if '/' not in mime_type:\\n raise ValueError(\\n 'Invalid MIME type: \\\"%s\\\"' % mime_type)\\n unsupported_patterns = [p for p in accept_patterns if ';' in p]\\n if unsupported_patterns:\\n raise ValueError(\\n 'MIME patterns with parameter unsupported: \\\"%s\\\"' % ', '.join(\\n unsupported_patterns))\\n\\n def _match(pattern, mime_type):\\n \\\"\\\"\\\"Return True iff mime_type is acceptable for pattern.\\\"\\\"\\\"\\n return all(accept in ('*', provided) for accept, provided\\n in zip(pattern.split('/'), mime_type.split('/')))\\n\\n return any(_match(pattern, mime_type) for pattern in accept_patterns)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/streaming/util.py::acceptable_mime_type._match\",\n \"text\": \"google/cloud/streaming/util.py::acceptable_mime_type._match\\n def _match(pattern, mime_type):\\n \\\"\\\"\\\"Return True iff mime_type is acceptable for pattern.\\\"\\\"\\\"\\n return all(accept in ('*', provided) for accept, provided\\n in zip(pattern.split('/'), mime_type.split('/')))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/translate/client.py::Client.__init__\",\n \"text\": \"google/cloud/translate/client.py::Client.__init__\\n def __init__(self, api_key, http=None, target_language=ENGLISH_ISO_639):\\n self.api_key = api_key\\n if http is None:\\n http = httplib2.Http()\\n self.connection = Connection(http=http)\\n self.target_language = target_language\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/translate/client.py::Client.get_languages\",\n \"text\": \"google/cloud/translate/client.py::Client.get_languages\\n def get_languages(self, target_language=None):\\n \\\"\\\"\\\"Get list of supported languages for translation.\\n\\n Response\\n\\n See: https://cloud.google.com/translate/v2/\\\\\\n discovering-supported-languages-with-rest\\n\\n :type target_language: str\\n :param target_language: (Optional) The language used to localize\\n returned language names. Defaults to the\\n target language on the current client.\\n\\n :rtype: list\\n :returns: List of dictionaries. Each dictionary contains a supported\\n ISO 639-1 language code (using the dictionary key\\n ``language``). If ``target_language`` is passed, each\\n dictionary will also contain the name of each supported\\n language (localized to the target language).\\n \\\"\\\"\\\"\\n query_params = {'key': self.api_key}\\n if target_language is None:\\n target_language = self.target_language\\n if target_language is not None:\\n query_params['target'] = target_language\\n response = self.connection.api_request(\\n method='GET', path='/languages', query_params=query_params)\\n return response.get('data', {}).get('languages', ())\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/translate/client.py::Client.detect_language\",\n \"text\": \"google/cloud/translate/client.py::Client.detect_language\\n def detect_language(self, values):\\n \\\"\\\"\\\"Detect the language of a string or list of strings.\\n\\n See: https://cloud.google.com/translate/v2/\\\\\\n detecting-language-with-rest\\n\\n :type values: str or list\\n :param values: String or list of strings that will have\\n language detected.\\n\\n :rtype: str or list\\n :returns: A list of dictionaries for each queried value. Each\\n dictionary typically contains three keys\\n\\n * ``confidence``: The confidence in language detection, a\\n float between 0 and 1.\\n * ``input``: The corresponding input value.\\n * ``language``: The detected language (as an ISO 639-1\\n language code).\\n\\n though the key ``confidence`` may not always be present.\\n\\n If only a single value is passed, then only a single\\n dictionary will be returned.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the number of\\n detections is not equal to the number of values.\\n :class:`ValueError <exceptions.ValueError>` if a value\\n produces a list of detections with 0 or multiple results\\n in it.\\n \\\"\\\"\\\"\\n single_value = False\\n if isinstance(values, six.string_types):\\n single_value = True\\n values = [values]\\n\\n query_params = [('key', self.api_key)]\\n query_params.extend(('q', _to_bytes(value, 'utf-8'))\\n for value in values)\\n response = self.connection.api_request(\\n method='GET', path='/detect', query_params=query_params)\\n detections = response.get('data', {}).get('detections', ())\\n\\n if len(values) != len(detections):\\n raise ValueError('Expected same number of values and detections',\\n value\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/translate/client.py::Client.translate\",\n \"text\": \"google/cloud/translate/client.py::Client.translate\\n def translate(self, values, target_language=None, format_=None,\\n source_language=None, customization_ids=()):\\n \\\"\\\"\\\"Translate a string or list of strings.\\n\\n See: https://cloud.google.com/translate/v2/\\\\\\n translating-text-with-rest\\n\\n :type values: str or list\\n :param values: String or list of strings to translate.\\n\\n :type target_language: str\\n :param target_language: The language to translate results into. This\\n is required by the API and defaults to\\n the target language of the current instance.\\n\\n :type format_: str\\n :param format_: (Optional) One of ``text`` or ``html``, to specify\\n if the input text is plain text or HTML.\\n\\n :type source_language: str\\n :param source_language: (Optional) The language of the text to\\n be translated.\\n\\n :type customization_ids: str or list\\n :param customization_ids: (Optional) ID or list of customization IDs\\n for translation. Sets the ``cid`` parameter\\n in the query.\\n\\n :rtype: str or list list\\n :returns: A list of dictionaries for each queried value. Each\\n dictionary typically contains three keys (though not\\n all will be present in all cases)\\n\\n * ``detectedSourceLanguage``: The detected language (as an\\n ISO 639-1 language code) of the text.\\n * ``translatedText``: The translation of the text into the\\n target language.\\n * ``input``: The corresponding input value.\\n\\n If only a single value is passed, then only a single\\n dictionary will be returned.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the number of\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/client.py::VisionRequest.__init__\",\n \"text\": \"google/cloud/vision/client.py::VisionRequest.__init__\\n def __init__(self, image, features):\\n self._features = []\\n self._image = image\\n\\n if isinstance(features, list):\\n self._features.extend(features)\\n elif isinstance(features, Feature):\\n self._features.append(features)\\n else:\\n raise TypeError('Feature or list of Feature classes are required.')\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/client.py::VisionRequest.as_dict\",\n \"text\": \"google/cloud/vision/client.py::VisionRequest.as_dict\\n def as_dict(self):\\n \\\"\\\"\\\"Dictionary representation of Image.\\\"\\\"\\\"\\n return {\\n 'image': self.image.as_dict(),\\n 'features': [feature.as_dict() for feature in self.features]\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/client.py::VisionRequest.features\",\n \"text\": \"google/cloud/vision/client.py::VisionRequest.features\\n @property\\n def features(self):\\n \\\"\\\"\\\"List of Feature objects.\\\"\\\"\\\"\\n return self._features\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/client.py::VisionRequest.image\",\n \"text\": \"google/cloud/vision/client.py::VisionRequest.image\\n @property\\n def image(self):\\n \\\"\\\"\\\"Image object containing image content.\\\"\\\"\\\"\\n return self._image\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/client.py::Client.annotate\",\n \"text\": \"google/cloud/vision/client.py::Client.annotate\\n def annotate(self, image, features):\\n \\\"\\\"\\\"Annotate an image to discover it's attributes.\\n\\n :type image: str\\n :param image: A string which can be a URL, a Google Cloud Storage path,\\n or a byte stream of the image.\\n\\n :type features: list of :class:`~google.cloud.vision.feature.Feature`\\n :param features: The type of detection that the Vision API should\\n use to determine image attributes. Pricing is\\n based on the number of Feature Types.\\n\\n See: https://cloud.google.com/vision/docs/pricing\\n :rtype: dict\\n :returns: List of annotations.\\n \\\"\\\"\\\"\\n request = VisionRequest(image, features)\\n\\n data = {'requests': [request.as_dict()]}\\n response = self.connection.api_request(method='POST',\\n path='/images:annotate',\\n data=data)\\n\\n return response['responses'][0]\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/client.py::Client.image\",\n \"text\": \"google/cloud/vision/client.py::Client.image\\n def image(self, content=None, source_uri=None):\\n \\\"\\\"\\\"Get instance of Image using current client.\\n\\n :type content: bytes\\n :param content: Byte stream of an image.\\n\\n :type source_uri: str\\n :param source_uri: Google Cloud Storage URI of image.\\n\\n :rtype: :class:`~google.cloud.vision.image.Image`\\n :returns: Image instance with the current client attached.\\n \\\"\\\"\\\"\\n return Image(client=self, content=content, source_uri=source_uri)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::ImagePropertiesAnnotation.__init__\",\n \"text\": \"google/cloud/vision/color.py::ImagePropertiesAnnotation.__init__\\n def __init__(self, colors):\\n self._colors = colors\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::ImagePropertiesAnnotation.from_api_repr\",\n \"text\": \"google/cloud/vision/color.py::ImagePropertiesAnnotation.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response):\\n \\\"\\\"\\\"Factory: construct ``ImagePropertiesAnnotation`` from a response.\\n\\n :type response: dict\\n :param response: Dictionary response from Vision API with image\\n properties data.\\n\\n :rtype: :class:`~google.cloud.vision.color.ImagePropertiesAnnotation`.\\n :returns: Populated instance of ``ImagePropertiesAnnotation``.\\n \\\"\\\"\\\"\\n colors = [ColorInformation.from_api_repr(color) for color in\\n response['dominantColors']['colors']]\\n return cls(colors)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::ImagePropertiesAnnotation.colors\",\n \"text\": \"google/cloud/vision/color.py::ImagePropertiesAnnotation.colors\\n @property\\n def colors(self):\\n \\\"\\\"\\\"Colors in an image.\\n\\n :rtype: list of :class:`~google.cloud.vision.color.ColorInformation`\\n :returns: Populated list of ``ColorInformation``.\\n \\\"\\\"\\\"\\n return self._colors\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::Color.__init__\",\n \"text\": \"google/cloud/vision/color.py::Color.__init__\\n def __init__(self, red, green, blue, alpha):\\n self._red = red\\n self._green = green\\n self._blue = blue\\n self._alpha = alpha\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::Color.from_api_repr\",\n \"text\": \"google/cloud/vision/color.py::Color.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response):\\n \\\"\\\"\\\"Factory: construct a ``Color`` from a Vision API response.\\n\\n :type response: dict\\n :param response: Color from API Response.\\n\\n :rtype: :class:`~google.cloud.vision.color.Color`\\n :returns: Instance of :class:`~google.cloud.vision.color.Color`.\\n \\\"\\\"\\\"\\n red = response['red']\\n green = response['green']\\n blue = response['blue']\\n alpha = response.get('alpha')\\n\\n return cls(red, green, blue, alpha)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::Color.red\",\n \"text\": \"google/cloud/vision/color.py::Color.red\\n @property\\n def red(self):\\n \\\"\\\"\\\"Red component of the color.\\n\\n :rtype: int\\n :returns: Red RGB value.\\n \\\"\\\"\\\"\\n return self._red\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::Color.green\",\n \"text\": \"google/cloud/vision/color.py::Color.green\\n @property\\n def green(self):\\n \\\"\\\"\\\"Green component of the color.\\n\\n :rtype: int\\n :returns: Green RGB value.\\n \\\"\\\"\\\"\\n return self._green\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::Color.blue\",\n \"text\": \"google/cloud/vision/color.py::Color.blue\\n @property\\n def blue(self):\\n \\\"\\\"\\\"Blue component of the color.\\n\\n :rtype: int\\n :returns: Blue RGB value.\\n \\\"\\\"\\\"\\n return self._blue\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::Color.alpha\",\n \"text\": \"google/cloud/vision/color.py::Color.alpha\\n @property\\n def alpha(self):\\n \\\"\\\"\\\"Alpha transparency level.\\n\\n :rtype: float\\n :returns: Alpha transparency level.\\n \\\"\\\"\\\"\\n return self._alpha\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::ColorInformation.__init__\",\n \"text\": \"google/cloud/vision/color.py::ColorInformation.__init__\\n def __init__(self, color, score, pixel_fraction):\\n self._color = color\\n self._score = score\\n self._pixel_fraction = pixel_fraction\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::ColorInformation.from_api_repr\",\n \"text\": \"google/cloud/vision/color.py::ColorInformation.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response):\\n \\\"\\\"\\\"Factory: construct ``ColorInformation`` for a color found.\\n\\n :type response: dict\\n :param response: Color data with extra meta information.\\n\\n :rtype: :class:`~google.cloud.vision.color.ColorInformation`\\n :returns: Instance of ``ColorInformation``.\\n \\\"\\\"\\\"\\n color = Color.from_api_repr(response['color'])\\n score = response['score']\\n pixel_fraction = response['pixelFraction']\\n\\n return cls(color, score, pixel_fraction)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::ColorInformation.color\",\n \"text\": \"google/cloud/vision/color.py::ColorInformation.color\\n @property\\n def color(self):\\n \\\"\\\"\\\"RGB components of the color.\\n\\n :rtype: :class:`~google.vision.color.Color`\\n :returns: Instance of ``Color``.\\n \\\"\\\"\\\"\\n return self._color\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::ColorInformation.score\",\n \"text\": \"google/cloud/vision/color.py::ColorInformation.score\\n @property\\n def score(self):\\n \\\"\\\"\\\"Image-specific score for this color. Value in range [0, 1].\\n\\n :rtype: float\\n :returns: Image score for this color.\\n \\\"\\\"\\\"\\n return self._score\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/color.py::ColorInformation.pixel_fraction\",\n \"text\": \"google/cloud/vision/color.py::ColorInformation.pixel_fraction\\n @property\\n def pixel_fraction(self):\\n \\\"\\\"\\\"Stores the fraction of pixels the color occupies in the image.\\n\\n :rtype: float\\n :returns: Pixel fraction value in range [0, 1].\\n \\\"\\\"\\\"\\n return self._pixel_fraction\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/entity.py::EntityAnnotation.__init__\",\n \"text\": \"google/cloud/vision/entity.py::EntityAnnotation.__init__\\n def __init__(self, bounds, description, locale, locations, mid, score):\\n self._bounds = bounds\\n self._description = description\\n self._locale = locale\\n self._locations = locations\\n self._mid = mid\\n self._score = score\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/entity.py::EntityAnnotation.from_api_repr\",\n \"text\": \"google/cloud/vision/entity.py::EntityAnnotation.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response):\\n \\\"\\\"\\\"Factory: construct entity from Vision API response.\\n\\n :type response: dict\\n :param response: Dictionary response from Vision API with entity data.\\n\\n :rtype: :class:`~google.cloud.vision.entity.EntityAnnotation`\\n :returns: Instance of ``EntityAnnotation``.\\n \\\"\\\"\\\"\\n bounds = Bounds.from_api_repr(response.get('boundingPoly'))\\n description = response['description']\\n locale = response.get('locale', None)\\n locations = [LocationInformation.from_api_repr(location)\\n for location in response.get('locations', [])]\\n mid = response.get('mid', None)\\n score = response.get('score', None)\\n\\n return cls(bounds, description, locale, locations, mid, score)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/entity.py::EntityAnnotation.bounds\",\n \"text\": \"google/cloud/vision/entity.py::EntityAnnotation.bounds\\n @property\\n def bounds(self):\\n \\\"\\\"\\\"Bounding polygon of detected image feature.\\n\\n :rtype: :class:`~google.cloud.vision.geometry.Bounds`\\n :returns: Instance of ``Bounds`` with populated vertices.\\n \\\"\\\"\\\"\\n return self._bounds\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/entity.py::EntityAnnotation.description\",\n \"text\": \"google/cloud/vision/entity.py::EntityAnnotation.description\\n @property\\n def description(self):\\n \\\"\\\"\\\"Description of feature detected in image.\\n\\n :rtype: str\\n :returns: String description of feature detected in image.\\n \\\"\\\"\\\"\\n return self._description\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/entity.py::EntityAnnotation.locale\",\n \"text\": \"google/cloud/vision/entity.py::EntityAnnotation.locale\\n @property\\n def locale(self):\\n \\\"\\\"\\\"The language code for text discovered in an image.\\n\\n :rtype: str\\n :returns: String language code of text found in the image.\\n \\\"\\\"\\\"\\n return self._locale\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/entity.py::EntityAnnotation.locations\",\n \"text\": \"google/cloud/vision/entity.py::EntityAnnotation.locations\\n @property\\n def locations(self):\\n \\\"\\\"\\\"Location coordinates landmarks detected.\\n\\n :rtype: :class:`~google.cloud.vision.geometry.LocationInformation`\\n :returns: ``LocationInformation`` populated with latitude and longitude\\n of object detected in an image.\\n \\\"\\\"\\\"\\n return self._locations\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/entity.py::EntityAnnotation.mid\",\n \"text\": \"google/cloud/vision/entity.py::EntityAnnotation.mid\\n @property\\n def mid(self):\\n \\\"\\\"\\\"MID of feature detected in image.\\n\\n :rtype: str\\n :returns: String MID of feature detected in image.\\n \\\"\\\"\\\"\\n return self._mid\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/entity.py::EntityAnnotation.score\",\n \"text\": \"google/cloud/vision/entity.py::EntityAnnotation.score\\n @property\\n def score(self):\\n \\\"\\\"\\\"Overall score of the result. Range [0, 1].\\n\\n :rtype: float\\n :returns: Overall score of the result. Range [0, 1].\\n \\\"\\\"\\\"\\n return self._score\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Angles.__init__\",\n \"text\": \"google/cloud/vision/face.py::Angles.__init__\\n def __init__(self, roll, pan, tilt):\\n self._roll = roll\\n self._pan = pan\\n self._tilt = tilt\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Angles.from_api_repr\",\n \"text\": \"google/cloud/vision/face.py::Angles.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response):\\n \\\"\\\"\\\"Factory: construct the angles from an Vision API response.\\n\\n :rtype: :class:`~google.cloud.vision.face.Angles`\\n :returns: An `Angles` instance with data parsed from `response`.\\n \\\"\\\"\\\"\\n roll = response['rollAngle']\\n pan = response['panAngle']\\n tilt = response['tiltAngle']\\n\\n return cls(roll, pan, tilt)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Angles.roll\",\n \"text\": \"google/cloud/vision/face.py::Angles.roll\\n @property\\n def roll(self):\\n \\\"\\\"\\\"Roll angle of face.\\n\\n :rtype: float\\n :returns: Roll angle of face in degrees.\\n \\\"\\\"\\\"\\n return self._roll\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Angles.pan\",\n \"text\": \"google/cloud/vision/face.py::Angles.pan\\n @property\\n def pan(self):\\n \\\"\\\"\\\"Pan angle of face.\\n\\n :rtype: float\\n :returns: Pan angle of face in degrees.\\n \\\"\\\"\\\"\\n return self._pan\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Angles.tilt\",\n \"text\": \"google/cloud/vision/face.py::Angles.tilt\\n @property\\n def tilt(self):\\n \\\"\\\"\\\"Tilt angle of face.\\n\\n :rtype: float\\n :returns: Tilt angle of face in degrees.\\n \\\"\\\"\\\"\\n return self._tilt\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Emotions.__init__\",\n \"text\": \"google/cloud/vision/face.py::Emotions.__init__\\n def __init__(self, joy_likelihood, sorrow_likelihood,\\n surprise_likelihood, anger_likelihood):\\n self._joy_likelihood = joy_likelihood\\n self._sorrow_likelihood = sorrow_likelihood\\n self._surprise_likelihood = surprise_likelihood\\n self._anger_likelihood = anger_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Emotions.from_api_repr\",\n \"text\": \"google/cloud/vision/face.py::Emotions.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response):\\n \\\"\\\"\\\"Factory: construct `Emotions` from Vision API response.\\n\\n :type response: dict\\n :param response: Response dictionary representing a face.\\n\\n :rtype: :class:`~google.cloud.vision.face.Emotions`\\n :returns: Populated instance of `Emotions`.\\n \\\"\\\"\\\"\\n joy_likelihood = getattr(Likelihood, response['joyLikelihood'])\\n sorrow_likelihood = getattr(Likelihood, response['sorrowLikelihood'])\\n surprise_likelihood = getattr(Likelihood,\\n response['surpriseLikelihood'])\\n anger_likelihood = getattr(Likelihood, response['angerLikelihood'])\\n\\n return cls(joy_likelihood, sorrow_likelihood, surprise_likelihood,\\n anger_likelihood)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Emotions.joy_likelihood\",\n \"text\": \"google/cloud/vision/face.py::Emotions.joy_likelihood\\n @property\\n def joy_likelihood(self):\\n \\\"\\\"\\\"Likelihood of joy in detected face.\\n\\n :rtype: str\\n :returns: String derived from\\n :class:`~google.cloud.vision.face.Likelihood`.\\n \\\"\\\"\\\"\\n return self._joy_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Emotions.sorrow_likelihood\",\n \"text\": \"google/cloud/vision/face.py::Emotions.sorrow_likelihood\\n @property\\n def sorrow_likelihood(self):\\n \\\"\\\"\\\"Likelihood of sorrow in detected face.\\n\\n :rtype: str\\n :returns: String derived from\\n :class:`~google.cloud.vision.face.Likelihood`.\\n \\\"\\\"\\\"\\n return self._sorrow_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Emotions.surprise_likelihood\",\n \"text\": \"google/cloud/vision/face.py::Emotions.surprise_likelihood\\n @property\\n def surprise_likelihood(self):\\n \\\"\\\"\\\"Likelihood of surprise in detected face.\\n\\n :rtype: str\\n :returns: String derived from\\n :class:`~google.cloud.vision.face.Likelihood`.\\n \\\"\\\"\\\"\\n return self._surprise_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Emotions.anger_likelihood\",\n \"text\": \"google/cloud/vision/face.py::Emotions.anger_likelihood\\n @property\\n def anger_likelihood(self):\\n \\\"\\\"\\\"Likelihood of anger in detected face.\\n\\n :rtype: str\\n :returns: String derived from\\n :class:`~google.cloud.vision.face.Likelihood`.\\n \\\"\\\"\\\"\\n return self._anger_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Face.__init__\",\n \"text\": \"google/cloud/vision/face.py::Face.__init__\\n def __init__(self, angles, bounds, detection_confidence, emotions,\\n fd_bounds, headwear_likelihood, image_properties, landmarks,\\n landmarking_confidence):\\n self._angles = angles\\n self._bounds = bounds\\n self._detection_confidence = detection_confidence\\n self._emotions = emotions\\n self._fd_bounds = fd_bounds\\n self._headwear_likelihood = headwear_likelihood\\n self._landmarks = landmarks\\n self._landmarking_confidence = landmarking_confidence\\n self._image_properties = image_properties\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Face.from_api_repr\",\n \"text\": \"google/cloud/vision/face.py::Face.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response):\\n \\\"\\\"\\\"Factory: construct an instance of a Face from an API response\\n\\n :type response: dict\\n :param response: Face annotation dict returned from the Vision API.\\n\\n :rtype: :class:`~google.cloud.vision.face.Face`\\n :returns: A instance of `Face` with data parsed from `response`.\\n \\\"\\\"\\\"\\n angles = Angles.from_api_repr(response)\\n bounds = Bounds.from_api_repr(response['boundingPoly'])\\n detection_confidence = response['detectionConfidence']\\n emotions = Emotions.from_api_repr(response)\\n fd_bounds = FDBounds.from_api_repr(response['fdBoundingPoly'])\\n headwear_likelihood = getattr(Likelihood,\\n response['headwearLikelihood'])\\n image_properties = FaceImageProperties.from_api_repr(response)\\n landmarks = Landmarks(response['landmarks'])\\n landmarking_confidence = response['landmarkingConfidence']\\n\\n return cls(angles, bounds, detection_confidence, emotions, fd_bounds,\\n headwear_likelihood, image_properties, landmarks,\\n landmarking_confidence)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Face.angles\",\n \"text\": \"google/cloud/vision/face.py::Face.angles\\n @property\\n def angles(self):\\n \\\"\\\"\\\"Accessor to the pan, tilt and roll angles of a Face.\\n\\n :rtype: :class:`~google.cloud.vision.face.Angles`\\n :returns: Pan, tilt and roll angles of the detected face.\\n \\\"\\\"\\\"\\n\\n return self._angles\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Face.bounds\",\n \"text\": \"google/cloud/vision/face.py::Face.bounds\\n @property\\n def bounds(self):\\n \\\"\\\"\\\"Accessor to the bounding poly information of the detected face.\\n\\n :rtype: :class:`~google.cloud.vision.face.Bounds`\\n :returns: An instance of ``Bounds`` which has a list of vertices.\\n \\\"\\\"\\\"\\n return self._bounds\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Face.detection_confidence\",\n \"text\": \"google/cloud/vision/face.py::Face.detection_confidence\\n @property\\n def detection_confidence(self):\\n \\\"\\\"\\\"Face detection confidence score determined by the Vision API.\\n\\n :rtype: float\\n :returns: Float representation of confidence ranging from 0 to 1.\\n \\\"\\\"\\\"\\n return self._detection_confidence\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Face.emotions\",\n \"text\": \"google/cloud/vision/face.py::Face.emotions\\n @property\\n def emotions(self):\\n \\\"\\\"\\\"Accessor to the possible emotions expressed in the detected face.\\n\\n :rtype: :class:`~google.cloud.vision.face.Emotions`\\n :returns: An instance of ``Emotions`` with joy, sorrow, anger, surprise\\n likelihood.\\n \\\"\\\"\\\"\\n return self._emotions\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Face.fd_bounds\",\n \"text\": \"google/cloud/vision/face.py::Face.fd_bounds\\n @property\\n def fd_bounds(self):\\n \\\"\\\"\\\"Accessor to the skin area bounding poly of the detected face.\\n\\n :rtype: :class:`~google.cloud.vision.image.FDBounds`\\n :returns: An instance of ``FDBounds`` which has a list of vertices.\\n \\\"\\\"\\\"\\n return self._fd_bounds\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Face.headwear_likelihood\",\n \"text\": \"google/cloud/vision/face.py::Face.headwear_likelihood\\n @property\\n def headwear_likelihood(self):\\n \\\"\\\"\\\"Headwear likelihood.\\n\\n :rtype: :class:`~google.cloud.vision.face.Likelihood`\\n :returns: String representing the likelihood based on\\n :class:`~google.cloud.vision.face.Likelihood`\\n \\\"\\\"\\\"\\n return self._headwear_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Face.image_properties\",\n \"text\": \"google/cloud/vision/face.py::Face.image_properties\\n @property\\n def image_properties(self):\\n \\\"\\\"\\\"Image properties from imaged used in face detection.\\n\\n :rtype: :class:`~google.cloud.vision.face.FaceImageProperties`\\n :returns: ``FaceImageProperties`` object with image properties.\\n \\\"\\\"\\\"\\n return self._image_properties\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Face.landmarks\",\n \"text\": \"google/cloud/vision/face.py::Face.landmarks\\n @property\\n def landmarks(self):\\n \\\"\\\"\\\"Accessor to the facial landmarks detected in a face.\\n\\n :rtype: :class:`~google.cloud.vision.face.Landmarks`\\n :returns: ``Landmarks`` object with facial landmarks as properies.\\n \\\"\\\"\\\"\\n return self._landmarks\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Face.landmarking_confidence\",\n \"text\": \"google/cloud/vision/face.py::Face.landmarking_confidence\\n @property\\n def landmarking_confidence(self):\\n \\\"\\\"\\\"Landmarking confidence score determinged by the Vision API.\\n\\n :rtype: float\\n :returns: Float representing the confidence of the Vision API in\\n determining the landmarks on a face.\\n \\\"\\\"\\\"\\n return self._landmarking_confidence\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::FaceImageProperties.__init__\",\n \"text\": \"google/cloud/vision/face.py::FaceImageProperties.__init__\\n def __init__(self, blurred_likelihood, underexposed_likelihood):\\n self._blurred_likelihood = blurred_likelihood\\n self._underexposed_likelihood = underexposed_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::FaceImageProperties.from_api_repr\",\n \"text\": \"google/cloud/vision/face.py::FaceImageProperties.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response):\\n \\\"\\\"\\\"Factory: construct image properties from image.\\n\\n :rtype: :class:`~google.cloud.vision.face.FaceImageProperties`\\n :returns: Instance populated with image property data.\\n \\\"\\\"\\\"\\n blurred_likelihood = getattr(Likelihood,\\n response['blurredLikelihood'])\\n underexposed_likelihood = getattr(Likelihood,\\n response['underExposedLikelihood'])\\n\\n return cls(blurred_likelihood, underexposed_likelihood)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::FaceImageProperties.blurred_likelihood\",\n \"text\": \"google/cloud/vision/face.py::FaceImageProperties.blurred_likelihood\\n @property\\n def blurred_likelihood(self):\\n \\\"\\\"\\\"Likelihood of the image being blurred.\\n\\n :rtype: str\\n :returns: String representation derived from\\n :class:`~google.cloud.vision.face.Position`.\\n \\\"\\\"\\\"\\n return self._blurred_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::FaceImageProperties.underexposed_likelihood\",\n \"text\": \"google/cloud/vision/face.py::FaceImageProperties.underexposed_likelihood\\n @property\\n def underexposed_likelihood(self):\\n \\\"\\\"\\\"Likelihood that the image used for detection was underexposed.\\n\\n :rtype: str\\n :returns: String representation derived from\\n :class:`~google.cloud.vision.face.Position`.\\n \\\"\\\"\\\"\\n return self._underexposed_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Landmark.__init__\",\n \"text\": \"google/cloud/vision/face.py::Landmark.__init__\\n def __init__(self, position, landmark_type):\\n self._position = position\\n self._landmark_type = landmark_type\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Landmark.from_api_repr\",\n \"text\": \"google/cloud/vision/face.py::Landmark.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response_landmark):\\n \\\"\\\"\\\"Factory: construct an instance of a Landmark from a response.\\n\\n :type response_landmark: dict\\n :param response_landmark: Landmark representation from Vision API.\\n\\n :rtype: :class:`~google.cloud.vision.face.Landmark`\\n :returns: Populated instance of `Landmark`.\\n \\\"\\\"\\\"\\n position = Position.from_api_repr(response_landmark['position'])\\n landmark_type = getattr(FaceLandmarkTypes, response_landmark['type'])\\n return cls(position, landmark_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Landmark.position\",\n \"text\": \"google/cloud/vision/face.py::Landmark.position\\n @property\\n def position(self):\\n \\\"\\\"\\\"Landmark position on face.\\n\\n :rtype: :class:`~google.cloud.vision.face.Position`\\n :returns: Instance of `Position` with landmark coordinates.\\n \\\"\\\"\\\"\\n return self._position\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Landmark.landmark_type\",\n \"text\": \"google/cloud/vision/face.py::Landmark.landmark_type\\n @property\\n def landmark_type(self):\\n \\\"\\\"\\\"Landmark type of facial feature.\\n\\n :rtype: str\\n :returns: String representation of facial landmark type.\\n \\\"\\\"\\\"\\n return self._landmark_type\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/face.py::Landmarks.__init__\",\n \"text\": \"google/cloud/vision/face.py::Landmarks.__init__\\n def __init__(self, landmarks):\\n for landmark_response in landmarks:\\n landmark = Landmark.from_api_repr(landmark_response)\\n setattr(self, landmark.landmark_type.lower(), landmark)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/feature.py::Feature.__init__\",\n \"text\": \"google/cloud/vision/feature.py::Feature.__init__\\n def __init__(self, feature_type, max_results=1):\\n try:\\n self._feature_type = getattr(FeatureTypes, feature_type)\\n except AttributeError:\\n raise AttributeError('Feature type passed in cannot be found.')\\n self._max_results = int(max_results)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/feature.py::Feature.as_dict\",\n \"text\": \"google/cloud/vision/feature.py::Feature.as_dict\\n def as_dict(self):\\n \\\"\\\"\\\"Generate dictionary for Feature request format.\\n\\n :rtype: dict\\n :returns: Dictionary representation of a\\n :class:`~google.cloud.vision.feature.FeatureType`.\\n \\\"\\\"\\\"\\n return {\\n 'type': self.feature_type,\\n 'maxResults': self.max_results\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/feature.py::Feature.feature_type\",\n \"text\": \"google/cloud/vision/feature.py::Feature.feature_type\\n @property\\n def feature_type(self):\\n \\\"\\\"\\\"\\\"Feature type string.\\n\\n :rtype: :class:`~google.cloud.vision.feature.FeatureTypes`\\n :returns: Instance of\\n :class:`~google.cloud.vision.feature.FeatureTypes`\\n \\\"\\\"\\\"\\n return self._feature_type\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/feature.py::Feature.max_results\",\n \"text\": \"google/cloud/vision/feature.py::Feature.max_results\\n @property\\n def max_results(self):\\n \\\"\\\"\\\"Maximum number of results for feature type.\\n\\n :rtype: int\\n :returns: Maxium results to be returned.\\n \\\"\\\"\\\"\\n return self._max_results\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::BoundsBase.__init__\",\n \"text\": \"google/cloud/vision/geometry.py::BoundsBase.__init__\\n def __init__(self, vertices):\\n self._vertices = vertices\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::BoundsBase.from_api_repr\",\n \"text\": \"google/cloud/vision/geometry.py::BoundsBase.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response_vertices):\\n \\\"\\\"\\\"Factory: construct BoundsBase instance from Vision API response.\\n\\n :type response_vertices: dict\\n :param response_vertices: List of vertices.\\n\\n :rtype: :class:`~google.cloud.vision.geometry.BoundsBase` or None\\n :returns: Instance of BoundsBase with populated verticies or None.\\n \\\"\\\"\\\"\\n if not response_vertices:\\n return None\\n\\n vertices = [Vertex(vertex.get('x', None), vertex.get('y', None)) for\\n vertex in response_vertices.get('vertices', [])]\\n return cls(vertices)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::BoundsBase.vertices\",\n \"text\": \"google/cloud/vision/geometry.py::BoundsBase.vertices\\n @property\\n def vertices(self):\\n \\\"\\\"\\\"List of vertices.\\n\\n :rtype: list of :class:`~google.cloud.vision.geometry.Vertex`\\n :returns: List of populated vertices.\\n \\\"\\\"\\\"\\n return self._vertices\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::LocationInformation.__init__\",\n \"text\": \"google/cloud/vision/geometry.py::LocationInformation.__init__\\n def __init__(self, latitude, longitude):\\n self._latitude = latitude\\n self._longitude = longitude\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::LocationInformation.from_api_repr\",\n \"text\": \"google/cloud/vision/geometry.py::LocationInformation.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response):\\n \\\"\\\"\\\"Factory: construct location information from Vision API response.\\n\\n :type response: dict\\n :param response: Dictionary response of locations.\\n\\n :rtype: :class:`~google.cloud.vision.geometry.LocationInformation`\\n :returns: ``LocationInformation`` with populated latitude and\\n longitude.\\n \\\"\\\"\\\"\\n latitude = response['latLng']['latitude']\\n longitude = response['latLng']['longitude']\\n return cls(latitude, longitude)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::LocationInformation.latitude\",\n \"text\": \"google/cloud/vision/geometry.py::LocationInformation.latitude\\n @property\\n def latitude(self):\\n \\\"\\\"\\\"Latitude coordinate.\\n\\n :rtype: float\\n :returns: Latitude coordinate of location.\\n \\\"\\\"\\\"\\n return self._latitude\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::LocationInformation.longitude\",\n \"text\": \"google/cloud/vision/geometry.py::LocationInformation.longitude\\n @property\\n def longitude(self):\\n \\\"\\\"\\\"Longitude coordinate.\\n\\n :rtype: float\\n :returns: Longitude coordinate of location.\\n \\\"\\\"\\\"\\n return self._longitude\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::Position.__init__\",\n \"text\": \"google/cloud/vision/geometry.py::Position.__init__\\n def __init__(self, x_coordinate, y_coordinate, z_coordinate):\\n self._x_coordinate = x_coordinate\\n self._y_coordinate = y_coordinate\\n self._z_coordinate = z_coordinate\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::Position.from_api_repr\",\n \"text\": \"google/cloud/vision/geometry.py::Position.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response_position):\\n \\\"\\\"\\\"Factory: construct 3D position from API response.\\n\\n :rtype: :class:`~google.cloud.vision.geometry.Position`\\n :returns: `Position` constructed with 3D points from API response.\\n \\\"\\\"\\\"\\n x_coordinate = response_position['x']\\n y_coordinate = response_position['y']\\n z_coordinate = response_position['z']\\n return cls(x_coordinate, y_coordinate, z_coordinate)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::Position.x_coordinate\",\n \"text\": \"google/cloud/vision/geometry.py::Position.x_coordinate\\n @property\\n def x_coordinate(self):\\n \\\"\\\"\\\"X position coordinate.\\n\\n :rtype: float\\n :returns: X position coordinate.\\n \\\"\\\"\\\"\\n return self._x_coordinate\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::Position.y_coordinate\",\n \"text\": \"google/cloud/vision/geometry.py::Position.y_coordinate\\n @property\\n def y_coordinate(self):\\n \\\"\\\"\\\"Y position coordinate.\\n\\n :rtype: float\\n :returns: Y position coordinate.\\n \\\"\\\"\\\"\\n return self._y_coordinate\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::Position.z_coordinate\",\n \"text\": \"google/cloud/vision/geometry.py::Position.z_coordinate\\n @property\\n def z_coordinate(self):\\n \\\"\\\"\\\"Z position coordinate.\\n\\n :rtype: float\\n :returns: Z position coordinate.\\n \\\"\\\"\\\"\\n return self._z_coordinate\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::Vertex.__init__\",\n \"text\": \"google/cloud/vision/geometry.py::Vertex.__init__\\n def __init__(self, x_coordinate, y_coordinate):\\n self._x_coordinate = x_coordinate\\n self._y_coordinate = y_coordinate\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::Vertex.x_coordinate\",\n \"text\": \"google/cloud/vision/geometry.py::Vertex.x_coordinate\\n @property\\n def x_coordinate(self):\\n \\\"\\\"\\\"X position coordinate.\\n\\n :rtype: float\\n :returns: X position coordinate.\\n \\\"\\\"\\\"\\n return self._x_coordinate\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/geometry.py::Vertex.y_coordinate\",\n \"text\": \"google/cloud/vision/geometry.py::Vertex.y_coordinate\\n @property\\n def y_coordinate(self):\\n \\\"\\\"\\\"Y position coordinate.\\n\\n :rtype: float\\n :returns: Y position coordinate.\\n \\\"\\\"\\\"\\n return self._y_coordinate\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image.__init__\",\n \"text\": \"google/cloud/vision/image.py::Image.__init__\\n def __init__(self, client, content=None, source_uri=None):\\n self.client = client\\n self._content = None\\n self._source = None\\n\\n if source_uri:\\n self._source = source_uri\\n else:\\n self._content = b64encode(_to_bytes(content))\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image.as_dict\",\n \"text\": \"google/cloud/vision/image.py::Image.as_dict\\n def as_dict(self):\\n \\\"\\\"\\\"Generate dictionary structure for request.\\n\\n :rtype: dict\\n :returns: Dictionary with source information for image.\\n \\\"\\\"\\\"\\n if self.content:\\n return {\\n 'content': self.content\\n }\\n else:\\n return {\\n 'source': {\\n 'gcs_image_uri': self.source\\n }\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image.content\",\n \"text\": \"google/cloud/vision/image.py::Image.content\\n @property\\n def content(self):\\n \\\"\\\"\\\"Base64 encoded image content.\\n\\n :rtype: str\\n :returns: Base64 encoded image bytes.\\n \\\"\\\"\\\"\\n return self._content\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image.source\",\n \"text\": \"google/cloud/vision/image.py::Image.source\\n @property\\n def source(self):\\n \\\"\\\"\\\"Google Cloud Storage URI.\\n\\n :rtype: str\\n :returns: String of Google Cloud Storage URI.\\n \\\"\\\"\\\"\\n return self._source\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image._detect_annotation\",\n \"text\": \"google/cloud/vision/image.py::Image._detect_annotation\\n def _detect_annotation(self, feature):\\n \\\"\\\"\\\"Generic method for detecting a single annotation.\\n\\n :type feature: :class:`~google.cloud.vision.feature.Feature`\\n :param feature: The ``Feature`` indication the type of annotation to\\n perform.\\n\\n :rtype: list\\n :returns: List of\\n :class:`~google.cloud.vision.entity.EntityAnnotation`.\\n \\\"\\\"\\\"\\n reverse_types = {\\n 'LABEL_DETECTION': 'labelAnnotations',\\n 'LANDMARK_DETECTION': 'landmarkAnnotations',\\n 'LOGO_DETECTION': 'logoAnnotations',\\n 'TEXT_DETECTION': 'textAnnotations',\\n }\\n detected_objects = []\\n result = self.client.annotate(self, [feature])\\n for response in result[reverse_types[feature.feature_type]]:\\n detected_object = EntityAnnotation.from_api_repr(response)\\n detected_objects.append(detected_object)\\n return detected_objects\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image.detect_faces\",\n \"text\": \"google/cloud/vision/image.py::Image.detect_faces\\n def detect_faces(self, limit=10):\\n \\\"\\\"\\\"Detect faces in image.\\n\\n :type limit: int\\n :param limit: The number of faces to try and detect.\\n\\n :rtype: list\\n :returns: List of :class:`~google.cloud.vision.face.Face`.\\n \\\"\\\"\\\"\\n faces = []\\n face_detection_feature = Feature(FeatureTypes.FACE_DETECTION, limit)\\n result = self.client.annotate(self, [face_detection_feature])\\n for face_response in result['faceAnnotations']:\\n face = Face.from_api_repr(face_response)\\n faces.append(face)\\n\\n return faces\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image.detect_labels\",\n \"text\": \"google/cloud/vision/image.py::Image.detect_labels\\n def detect_labels(self, limit=10):\\n \\\"\\\"\\\"Detect labels that describe objects in an image.\\n\\n :type limit: int\\n :param limit: The maximum number of labels to try and detect.\\n\\n :rtype: list\\n :returns: List of :class:`~google.cloud.vision.entity.EntityAnnotation`\\n \\\"\\\"\\\"\\n feature = Feature(FeatureTypes.LABEL_DETECTION, limit)\\n return self._detect_annotation(feature)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image.detect_landmarks\",\n \"text\": \"google/cloud/vision/image.py::Image.detect_landmarks\\n def detect_landmarks(self, limit=10):\\n \\\"\\\"\\\"Detect landmarks in an image.\\n\\n :type limit: int\\n :param limit: The maximum number of landmarks to find.\\n\\n :rtype: list\\n :returns: List of\\n :class:`~google.cloud.vision.entity.EntityAnnotation`.\\n \\\"\\\"\\\"\\n feature = Feature(FeatureTypes.LANDMARK_DETECTION, limit)\\n return self._detect_annotation(feature)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image.detect_logos\",\n \"text\": \"google/cloud/vision/image.py::Image.detect_logos\\n def detect_logos(self, limit=10):\\n \\\"\\\"\\\"Detect logos in an image.\\n\\n :type limit: int\\n :param limit: The maximum number of logos to find.\\n\\n :rtype: list\\n :returns: List of\\n :class:`~google.cloud.vision.entity.EntityAnnotation`.\\n \\\"\\\"\\\"\\n feature = Feature(FeatureTypes.LOGO_DETECTION, limit)\\n return self._detect_annotation(feature)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image.detect_properties\",\n \"text\": \"google/cloud/vision/image.py::Image.detect_properties\\n def detect_properties(self, limit=10):\\n \\\"\\\"\\\"Detect the color properties of an image.\\n\\n :type limit: int\\n :param limit: The maximum number of image properties to find.\\n\\n :rtype: list\\n :returns: List of\\n :class:`~google.cloud.vision.color.ImagePropertiesAnnotation`.\\n \\\"\\\"\\\"\\n feature = Feature(FeatureTypes.IMAGE_PROPERTIES, limit)\\n result = self.client.annotate(self, [feature])\\n response = result['imagePropertiesAnnotation']\\n return ImagePropertiesAnnotation.from_api_repr(response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image.detect_safe_search\",\n \"text\": \"google/cloud/vision/image.py::Image.detect_safe_search\\n def detect_safe_search(self, limit=10):\\n \\\"\\\"\\\"Retreive safe search properties from an image.\\n\\n :type limit: int\\n :param limit: The number of faces to try and detect.\\n\\n :rtype: list\\n :returns: List of\\n :class:`~google.cloud.vision.sage.SafeSearchAnnotation`.\\n \\\"\\\"\\\"\\n safe_detection_feature = Feature(FeatureTypes.SAFE_SEARCH_DETECTION,\\n limit)\\n result = self.client.annotate(self, [safe_detection_feature])\\n safe_search_response = result['safeSearchAnnotation']\\n return SafeSearchAnnotation.from_api_repr(safe_search_response)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/image.py::Image.detect_text\",\n \"text\": \"google/cloud/vision/image.py::Image.detect_text\\n def detect_text(self, limit=10):\\n \\\"\\\"\\\"Detect text in an image.\\n\\n :type limit: int\\n :param limit: The maximum instances of text to find.\\n\\n :rtype: list\\n :returns: List of\\n :class:`~google.cloud.vision.entity.EntityAnnotation`.\\n \\\"\\\"\\\"\\n feature = Feature(FeatureTypes.TEXT_DETECTION, limit)\\n return self._detect_annotation(feature)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.__init__\",\n \"text\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.__init__\\n def __init__(self, adult_likelihood, spoof_likelihood, medical_likelihood,\\n violence_likelihood):\\n self._adult_likelihood = adult_likelihood\\n self._spoof_likelihood = spoof_likelihood\\n self._medical_likeliehood = medical_likelihood\\n self._violence_likelihood = violence_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.from_api_repr\",\n \"text\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.from_api_repr\\n @classmethod\\n def from_api_repr(cls, response):\\n \\\"\\\"\\\"Factory: construct SafeSearchAnnotation from Vision API response.\\n\\n :type response: dict\\n :param response: Dictionary response from Vision API with safe search\\n data.\\n\\n :rtype: :class:`~google.cloud.vision.safe.SafeSearchAnnotation`\\n :returns: Instance of ``SafeSearchAnnotation``.\\n \\\"\\\"\\\"\\n adult_likelihood = getattr(Likelihood, response['adult'])\\n spoof_likelihood = getattr(Likelihood, response['spoof'])\\n medical_likelihood = getattr(Likelihood, response['medical'])\\n violence_likelihood = getattr(Likelihood, response['violence'])\\n\\n return cls(adult_likelihood, spoof_likelihood, medical_likelihood,\\n violence_likelihood)\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.adult\",\n \"text\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.adult\\n @property\\n def adult(self):\\n \\\"\\\"\\\"Represents the adult contents likelihood for the image.\\n\\n :rtype: :class:`~google.cloud.vision.likelihood.Likelihood`\\n :returns: ``Likelihood`` of the image containing adult content.\\n \\\"\\\"\\\"\\n return self._adult_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.spoof\",\n \"text\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.spoof\\n @property\\n def spoof(self):\\n \\\"\\\"\\\"The likelihood that an obvious modification was made to the image.\\n\\n :rtype: :class:`~google.cloud.vision.likelihood.Likelihood`\\n :returns: The ``Likelihood`` that an obvious modification was made to\\n the image's canonical version to make it appear funny or\\n offensive.\\n \\\"\\\"\\\"\\n return self._spoof_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.medical\",\n \"text\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.medical\\n @property\\n def medical(self):\\n \\\"\\\"\\\"Likelihood this is a medical image.\\n\\n :rtype: :class:`~google.cloud.vision.likelihood.Likelihood`\\n :returns: The ``Likelihood`` that the image is medical in origin.\\n \\\"\\\"\\\"\\n return self._medical_likeliehood\",\n \"label\": 0\n },\n {\n \"cid\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.violence\",\n \"text\": \"google/cloud/vision/safe.py::SafeSearchAnnotation.violence\\n @property\\n def violence(self):\\n \\\"\\\"\\\"Likeliehood that this image contains violence.\\n\\n :rtype: :class:`~google.cloud.vision.likelihood.Likelihood`\\n :returns: The ``Likelihood`` that the image contains violence.\\n \\\"\\\"\\\"\\n return self._violence_likelihood\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_datastore_grpc.py::get_pb2_contents_with_grpc\",\n \"text\": \"scripts/make_datastore_grpc.py::get_pb2_contents_with_grpc\\ndef get_pb2_contents_with_grpc():\\n \\\"\\\"\\\"Get pb2 lines generated by protoc with gRPC plugin.\\n\\n :rtype: list\\n :returns: A list of lines in the generated file.\\n \\\"\\\"\\\"\\n temp_dir = tempfile.mkdtemp()\\n generated_path = os.path.join(temp_dir, 'google', 'datastore',\\n 'v1', 'datastore_pb2.py')\\n try:\\n return_code = subprocess.call([\\n PYTHON_EXECUTABLE,\\n '-m',\\n 'grpc.tools.protoc',\\n '--proto_path',\\n PROTOS_DIR,\\n '--python_out',\\n temp_dir,\\n '--grpc_python_out',\\n temp_dir,\\n PROTO_PATH,\\n ])\\n if return_code != 0:\\n sys.exit(return_code)\\n with open(generated_path, 'rb') as file_obj:\\n return file_obj.readlines()\\n finally:\\n shutil.rmtree(temp_dir, ignore_errors=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_datastore_grpc.py::get_pb2_contents_without_grpc\",\n \"text\": \"scripts/make_datastore_grpc.py::get_pb2_contents_without_grpc\\ndef get_pb2_contents_without_grpc():\\n \\\"\\\"\\\"Get pb2 lines generated by protoc without gRPC plugin.\\n\\n :rtype: list\\n :returns: A list of lines in the generated file.\\n \\\"\\\"\\\"\\n temp_dir = tempfile.mkdtemp()\\n generated_path = os.path.join(temp_dir, 'google', 'datastore',\\n 'v1', 'datastore_pb2.py')\\n try:\\n return_code = subprocess.call([\\n PYTHON_EXECUTABLE,\\n '-m',\\n 'grpc.tools.protoc',\\n '--proto_path',\\n PROTOS_DIR,\\n '--python_out',\\n temp_dir,\\n PROTO_PATH,\\n ])\\n if return_code != 0:\\n sys.exit(return_code)\\n with open(generated_path, 'rb') as file_obj:\\n return file_obj.readlines()\\n finally:\\n shutil.rmtree(temp_dir, ignore_errors=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_datastore_grpc.py::get_pb2_grpc_only\",\n \"text\": \"scripts/make_datastore_grpc.py::get_pb2_grpc_only\\ndef get_pb2_grpc_only():\\n \\\"\\\"\\\"Get pb2 lines that are only in gRPC.\\n\\n :rtype: list\\n :returns: A list of lines that are only in the pb2 file\\n generated with the gRPC plugin.\\n \\\"\\\"\\\"\\n grpc_contents = get_pb2_contents_with_grpc()\\n non_grpc_contents = get_pb2_contents_without_grpc()\\n\\n grpc_only_lines = []\\n curr_non_grpc_line = 0\\n for line in grpc_contents:\\n if line == non_grpc_contents[curr_non_grpc_line]:\\n curr_non_grpc_line += 1\\n else:\\n grpc_only_lines.append(line)\\n\\n return grpc_only_lines\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_datastore_grpc.py::get_pb2_message_types\",\n \"text\": \"scripts/make_datastore_grpc.py::get_pb2_message_types\\ndef get_pb2_message_types():\\n \\\"\\\"\\\"Get message types defined in datastore pb2 file.\\n\\n :rtype: list\\n :returns: A list of names that are defined as message types.\\n \\\"\\\"\\\"\\n non_grpc_contents = get_pb2_contents_without_grpc()\\n result = []\\n for line in non_grpc_contents:\\n if MESSAGE_SNIPPET in line:\\n name, _ = line.split(MESSAGE_SNIPPET)\\n result.append(name)\\n\\n return sorted(result)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_datastore_grpc.py::main\",\n \"text\": \"scripts/make_datastore_grpc.py::main\\ndef main():\\n \\\"\\\"\\\"Write gRPC-only lines to custom module.\\\"\\\"\\\"\\n grpc_only_lines = get_pb2_grpc_only()\\n with open(GRPC_ONLY_FILE, 'wb') as file_obj:\\n # First add imports for public objects in the original.\\n file_obj.write('# BEGIN: Imports from datastore_pb2\\\\n')\\n for name in get_pb2_message_types():\\n import_line = IMPORT_TEMPLATE % (name,)\\n file_obj.write(import_line)\\n file_obj.write('# END: Imports from datastore_pb2\\\\n')\\n file_obj.write(''.join(grpc_only_lines))\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_operations_grpc.py::get_pb2_contents_with_grpc\",\n \"text\": \"scripts/make_operations_grpc.py::get_pb2_contents_with_grpc\\ndef get_pb2_contents_with_grpc():\\n \\\"\\\"\\\"Get pb2 lines generated by protoc with gRPC plugin.\\n\\n :rtype: list\\n :returns: A list of lines in the generated file.\\n \\\"\\\"\\\"\\n temp_dir = tempfile.mkdtemp()\\n generated_path = os.path.join(temp_dir, 'google', 'longrunning',\\n 'operations_pb2.py')\\n try:\\n return_code = subprocess.call([\\n PYTHON_EXECUTABLE,\\n '-m',\\n 'grpc.tools.protoc',\\n '--proto_path',\\n PROTOS_DIR,\\n '--python_out',\\n temp_dir,\\n '--grpc_python_out',\\n temp_dir,\\n PROTO_PATH,\\n ])\\n if return_code != 0:\\n sys.exit(return_code)\\n with open(generated_path, 'rb') as file_obj:\\n return file_obj.readlines()\\n finally:\\n shutil.rmtree(temp_dir, ignore_errors=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_operations_grpc.py::get_pb2_contents_without_grpc\",\n \"text\": \"scripts/make_operations_grpc.py::get_pb2_contents_without_grpc\\ndef get_pb2_contents_without_grpc():\\n \\\"\\\"\\\"Get pb2 lines generated by protoc without gRPC plugin.\\n\\n :rtype: list\\n :returns: A list of lines in the generated file.\\n \\\"\\\"\\\"\\n temp_dir = tempfile.mkdtemp()\\n generated_path = os.path.join(temp_dir, 'google', 'longrunning',\\n 'operations_pb2.py')\\n try:\\n return_code = subprocess.call([\\n PYTHON_EXECUTABLE,\\n '-m',\\n 'grpc.tools.protoc',\\n '--proto_path',\\n PROTOS_DIR,\\n '--python_out',\\n temp_dir,\\n PROTO_PATH,\\n ])\\n if return_code != 0:\\n sys.exit(return_code)\\n with open(generated_path, 'rb') as file_obj:\\n return file_obj.readlines()\\n finally:\\n shutil.rmtree(temp_dir, ignore_errors=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_operations_grpc.py::get_pb2_grpc_only\",\n \"text\": \"scripts/make_operations_grpc.py::get_pb2_grpc_only\\ndef get_pb2_grpc_only():\\n \\\"\\\"\\\"Get pb2 lines that are only in gRPC.\\n\\n :rtype: list\\n :returns: A list of lines that are only in the pb2 file\\n generated with the gRPC plugin.\\n \\\"\\\"\\\"\\n grpc_contents = get_pb2_contents_with_grpc()\\n non_grpc_contents = get_pb2_contents_without_grpc()\\n\\n grpc_only_lines = []\\n curr_non_grpc_line = 0\\n for line in grpc_contents:\\n if line == non_grpc_contents[curr_non_grpc_line]:\\n curr_non_grpc_line += 1\\n else:\\n grpc_only_lines.append(line)\\n\\n return grpc_only_lines\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_operations_grpc.py::main\",\n \"text\": \"scripts/make_operations_grpc.py::main\\ndef main():\\n \\\"\\\"\\\"Write gRPC-only lines to custom module.\\\"\\\"\\\"\\n grpc_only_lines = get_pb2_grpc_only()\\n with open(GRPC_ONLY_FILE, 'wb') as file_obj:\\n file_obj.writelines(grpc_only_lines)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/pycodestyle_on_repo.py::main\",\n \"text\": \"scripts/pycodestyle_on_repo.py::main\\ndef main():\\n \\\"\\\"\\\"Run pycodestyle on all Python files in the repository.\\\"\\\"\\\"\\n git_root = subprocess.check_output(\\n ['git', 'rev-parse', '--show-toplevel']).strip()\\n os.chdir(git_root)\\n python_files = subprocess.check_output(['git', 'ls-files', '*py'])\\n python_files = python_files.strip().split()\\n\\n pycodestyle_command = ['pycodestyle'] + python_files\\n status_code = subprocess.call(pycodestyle_command)\\n sys.exit(status_code)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/rewrite_imports.py::transform_old_to_new\",\n \"text\": \"scripts/rewrite_imports.py::transform_old_to_new\\ndef transform_old_to_new(line, old_module, new_module,\\n ignore_import_from=False):\\n \\\"\\\"\\\"Transforms from an old module to a new one.\\n\\n First checks if a line starts with\\n \\\"from {old_module} import ...\\\"\\n then checks if the line contains\\n \\\"import {old_module} ...\\\"\\n then checks if the line starts with (ignoring whitespace)\\n \\\"{old_module} ...\\\"\\n and finally checks if the line contians\\n \\\"'some-dict-key': {old_module} ...\\\"\\n\\n In any of these cases, \\\"{old_module}\\\" is replaced with \\\"{new_module}\\\".\\n If none match, nothing is returned.\\n\\n :type line: str\\n :param line: The line to be transformed.\\n\\n :type old_module: str\\n :param old_module: The import to be re-written.\\n\\n :type new_module: str\\n :param new_module: The new location of the re-written import.\\n\\n :type ignore_import_from: bool\\n :param ignore_import_from: Flag to determine if the \\\"from * import\\\"\\n statements should be ignored.\\n\\n :rtype: :class:`str` or :data:`NoneType <types.NoneType>`\\n :returns: The transformed line if the old module was found, otherwise\\n does nothing.\\n \\\"\\\"\\\"\\n if not ignore_import_from:\\n import_from_statement = IMPORT_FROM_TEMPLATE % (old_module,)\\n if line.startswith(import_from_statement):\\n new_import_from_statement = IMPORT_FROM_TEMPLATE % (new_module,)\\n # Only replace the first instance of the import statement.\\n return line.replace(import_from_statement,\\n new_import_from_statement, 1)\\n\\n # If the line doesn't start with a \\\"from * import *\\\" statement, it\\n # may still contain a \\\"import * ...\\\" statement.\\n import_statement = IMPORT_TEMPLATE % (old_module,)\\n if import_statement in line:\\n new_import_statement = IMPORT_TEMPLATE % (new_module,)\\n # Only replace the first instance of the import statement.\\n retu\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/rewrite_imports.py::transform_line\",\n \"text\": \"scripts/rewrite_imports.py::transform_line\\ndef transform_line(line):\\n \\\"\\\"\\\"Transforms an import line in a PB2 module.\\n\\n If the line is not an import of one of the packages in ``REPLACEMENTS``,\\n does nothing and returns the original. Otherwise it replaces the package\\n matched with our local package.\\n\\n :type line: str\\n :param line: The line to be transformed.\\n\\n :rtype: str\\n :returns: The transformed line.\\n \\\"\\\"\\\"\\n # Work around https://github.com/grpc/grpc/issues/7101\\n if line == 'import ':\\n return ''\\n\\n for old_module, new_module in REPLACEMENTS.iteritems():\\n result = transform_old_to_new(line, old_module, new_module)\\n if result is not None:\\n return result\\n\\n # If no matches, there is nothing to transform.\\n return line\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/rewrite_imports.py::rewrite_file\",\n \"text\": \"scripts/rewrite_imports.py::rewrite_file\\ndef rewrite_file(filename):\\n \\\"\\\"\\\"Rewrites a given PB2 modules.\\n\\n :type filename: str\\n :param filename: The name of the file to be rewritten.\\n \\\"\\\"\\\"\\n with open(filename, 'rU') as file_obj:\\n content_lines = file_obj.read().split('\\\\n')\\n\\n new_content = []\\n for line in content_lines:\\n new_content.append(transform_line(line))\\n\\n with open(filename, 'w') as file_obj:\\n file_obj.write('\\\\n'.join(new_content))\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/rewrite_imports.py::main\",\n \"text\": \"scripts/rewrite_imports.py::main\\ndef main():\\n \\\"\\\"\\\"Rewrites all PB2 files.\\\"\\\"\\\"\\n for filename in sys.argv[1:]:\\n rewrite_file(filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::read_config\",\n \"text\": \"scripts/run_pylint.py::read_config\\ndef read_config(filename):\\n \\\"\\\"\\\"Reads pylintrc config onto native ConfigParser object.\\\"\\\"\\\"\\n config = ConfigParser.ConfigParser()\\n with open(filename, 'r') as file_obj:\\n config.readfp(file_obj)\\n return config\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::make_test_rc\",\n \"text\": \"scripts/run_pylint.py::make_test_rc\\ndef make_test_rc(base_rc_filename, additions_dict,\\n replacements_dict, target_filename):\\n \\\"\\\"\\\"Combines a base rc and test additions into single file.\\\"\\\"\\\"\\n main_cfg = read_config(base_rc_filename)\\n\\n # Create fresh config for test, which must extend production.\\n test_cfg = ConfigParser.ConfigParser()\\n test_cfg._sections = copy.deepcopy(main_cfg._sections)\\n\\n for section, opts in additions_dict.items():\\n curr_section = test_cfg._sections.setdefault(\\n section, test_cfg._dict())\\n for opt, opt_val in opts.items():\\n curr_val = curr_section.get(opt)\\n if curr_val is None:\\n raise KeyError('Expected to be adding to existing option.')\\n curr_val = curr_val.rstrip(',')\\n curr_section[opt] = '%s, %s' % (curr_val, opt_val)\\n\\n for section, opts in replacements_dict.items():\\n curr_section = test_cfg._sections.setdefault(\\n section, test_cfg._dict())\\n for opt, opt_val in opts.items():\\n curr_val = curr_section.get(opt)\\n if curr_val is None:\\n raise KeyError('Expected to be replacing existing option.')\\n curr_section[opt] = '%s' % (opt_val,)\\n\\n with open(target_filename, 'w') as file_obj:\\n test_cfg.write(file_obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::valid_filename\",\n \"text\": \"scripts/run_pylint.py::valid_filename\\ndef valid_filename(filename):\\n \\\"\\\"\\\"Checks if a file is a Python file and is not ignored.\\\"\\\"\\\"\\n for directory in IGNORED_DIRECTORIES:\\n if filename.startswith(directory):\\n return False\\n return (filename.endswith('.py') and\\n filename not in IGNORED_FILES)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::is_production_filename\",\n \"text\": \"scripts/run_pylint.py::is_production_filename\\ndef is_production_filename(filename):\\n \\\"\\\"\\\"Checks if the file contains production code.\\n\\n :rtype: bool\\n :returns: Boolean indicating production status.\\n \\\"\\\"\\\"\\n return 'test' not in filename and 'docs' not in filename\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::get_files_for_linting\",\n \"text\": \"scripts/run_pylint.py::get_files_for_linting\\ndef get_files_for_linting(allow_limited=True):\\n \\\"\\\"\\\"Gets a list of files in the repository.\\n\\n By default, returns all files via ``git ls-files``. However, in some cases\\n uses a specific commit or branch (a so-called diff base) to compare\\n against for changed files. (This requires ``allow_limited=True``.)\\n\\n To speed up linting on Travis pull requests against master, we manually\\n set the diff base to origin/master. We don't do this on non-pull requests\\n since origin/master will be equivalent to the currently checked out code.\\n One could potentially use ${TRAVIS_COMMIT_RANGE} to find a diff base but\\n this value is not dependable.\\n\\n To allow faster local ``tox`` runs, the environment variables\\n ``GOOGLE_CLOUD_REMOTE_FOR_LINT`` and ``GOOGLE_CLOUD_BRANCH_FOR_LINT`` can\\n be set to specify a remote branch to diff against.\\n\\n :type allow_limited: bool\\n :param allow_limited: Boolean indicating if a reduced set of files can\\n be used.\\n\\n :rtype: pair\\n :returns: Tuple of the diff base using the the list of filenames to be\\n linted.\\n \\\"\\\"\\\"\\n diff_base = None\\n if (os.getenv('TRAVIS_BRANCH') == 'master' and\\n os.getenv('TRAVIS_PULL_REQUEST') != 'false'):\\n # In the case of a pull request into master, we want to\\n # diff against HEAD in master.\\n diff_base = 'origin/master'\\n elif os.getenv('TRAVIS') is None:\\n # Only allow specified remote and branch in local dev.\\n remote = os.getenv('GOOGLE_CLOUD_REMOTE_FOR_LINT')\\n branch = os.getenv('GOOGLE_CLOUD_BRANCH_FOR_LINT')\\n if remote is not None and branch is not None:\\n diff_base = '%s/%s' % (remote, branch)\\n\\n if diff_base is not None and allow_limited:\\n result = subprocess.check_output(['git', 'diff', '--name-only',\\n diff_base])\\n print('Using files changed relative to %s:' % (diff_ba\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::get_python_files\",\n \"text\": \"scripts/run_pylint.py::get_python_files\\ndef get_python_files(all_files=None):\\n \\\"\\\"\\\"Gets a list of all Python files in the repository that need linting.\\n\\n Relies on :func:`get_files_for_linting()` to determine which files should\\n be considered.\\n\\n NOTE: This requires ``git`` to be installed and requires that this\\n is run within the ``git`` repository.\\n\\n :type all_files: list or ``NoneType``\\n :param all_files: Optional list of files to be linted.\\n\\n :rtype: tuple\\n :returns: A tuple containing two lists. The first list\\n contains all production files, the next all test files.\\n \\\"\\\"\\\"\\n if all_files is None:\\n all_files, diff_base = get_files_for_linting()\\n\\n library_files = []\\n non_library_files = []\\n for filename in all_files:\\n if valid_filename(filename):\\n if is_production_filename(filename):\\n library_files.append(filename)\\n else:\\n non_library_files.append(filename)\\n\\n return library_files, non_library_files, diff_base\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::lint_fileset\",\n \"text\": \"scripts/run_pylint.py::lint_fileset\\ndef lint_fileset(filenames, rcfile, description):\\n \\\"\\\"\\\"Lints a group of files using a given rcfile.\\\"\\\"\\\"\\n # Only lint filenames that exist. For example, 'git diff --name-only'\\n # could spit out deleted / renamed files. Another alternative could\\n # be to use 'git diff --name-status' and filter out files with a\\n # status of 'D'.\\n filenames = [filename for filename in filenames\\n if os.path.exists(filename)]\\n if filenames:\\n rc_flag = '--rcfile=%s' % (rcfile,)\\n pylint_shell_command = ['pylint', rc_flag]\\n errors = {} # filename -> status_code\\n for filename in filenames:\\n cmd = pylint_shell_command + [filename]\\n status_code = subprocess.call(cmd)\\n if status_code != 0:\\n errors[filename] = status_code\\n if errors:\\n for filename, status_code in sorted(errors.items()):\\n print('%-30s: %d' % (filename, status_code), file=sys.stderr)\\n sys.exit(len(errors))\\n else:\\n print('Skipping %s, no files to lint.' % (description,))\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::main\",\n \"text\": \"scripts/run_pylint.py::main\\ndef main():\\n \\\"\\\"\\\"Script entry point. Lints both sets of files.\\\"\\\"\\\"\\n make_test_rc(PRODUCTION_RC, TEST_RC_ADDITIONS,\\n TEST_RC_REPLACEMENTS, TEST_RC)\\n library_files, non_library_files, diff_base = get_python_files()\\n if diff_base:\\n print('Checking only files which differ from base.')\\n lint_fileset(library_files, PRODUCTION_RC, 'library code')\\n lint_fileset(non_library_files, TEST_RC, 'test code')\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/verify_included_modules.py::is_valid_module\",\n \"text\": \"scripts/verify_included_modules.py::is_valid_module\\ndef is_valid_module(filename):\\n \\\"\\\"\\\"Determines if a filename is a valid Python module.\\n\\n Assumes if is just the end of a path (i.e. does not contain\\n ``os.path.sep``.\\n\\n :type filename: string\\n :param filename: The name of a file.\\n\\n :rtype: bool\\n :returns: Flag indicating if the filename is valid.\\n \\\"\\\"\\\"\\n if not filename.endswith('.py'):\\n return False\\n if filename == '__init__.py':\\n return True\\n for prefix in IGNORED_PREFIXES:\\n if filename.startswith(prefix):\\n return False\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/verify_included_modules.py::get_public_modules\",\n \"text\": \"scripts/verify_included_modules.py::get_public_modules\\ndef get_public_modules(path, base_package=None):\\n \\\"\\\"\\\"Get list of all public modules relative to a path.\\n\\n :type path: string\\n :param path: The path containing the python modules.\\n\\n :type base_package: string\\n :param base_package: (Optional) A package to prepend in\\n front of the path.\\n\\n :rtype: list\\n :returns: List of all modules found.\\n \\\"\\\"\\\"\\n result = []\\n for subdir, _, files in os.walk(path):\\n # Skip folders that start with _.\\n if any([part.startswith('_')\\n for part in subdir.split(os.path.sep)]):\\n continue\\n _, rel_dir = subdir.split(path)\\n rel_dir = rel_dir.lstrip(os.path.sep)\\n for filename in files:\\n if is_valid_module(filename):\\n mod_name, _ = os.path.splitext(filename)\\n rel_path = os.path.join(rel_dir, mod_name)\\n if base_package is not None:\\n rel_path = os.path.join(base_package, rel_path)\\n # Turn into a Python module rather than a file path.\\n result.append(rel_path.replace(os.path.sep, '.'))\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/verify_included_modules.py::main\",\n \"text\": \"scripts/verify_included_modules.py::main\\ndef main(build_root='_build'):\\n \\\"\\\"\\\"Main script to verify modules included.\\n\\n :type build_root: str\\n :param build_root: The root of the directory where docs are built into.\\n Defaults to ``_build``.\\n \\\"\\\"\\\"\\n object_inventory_relpath = os.path.join(build_root, 'html', 'objects.inv')\\n\\n mock_uri = ''\\n inventory = fetch_inventory(SphinxApp, mock_uri,\\n object_inventory_relpath)\\n sphinx_mods = set(inventory['py:module'].keys())\\n\\n library_dir = os.path.join(BASE_DIR, 'google', 'cloud')\\n public_mods = get_public_modules(library_dir,\\n base_package='google.cloud')\\n public_mods = set(public_mods)\\n\\n if not sphinx_mods <= public_mods:\\n unexpected_mods = sphinx_mods - public_mods\\n message = ['Unexpected error. There were modules referenced by '\\n 'Sphinx that are not among the public modules.']\\n message.extend(['- %s' % (mod,) for mod in unexpected_mods])\\n print('\\\\n'.join(message), file=sys.stderr)\\n sys.exit(1)\\n\\n undocumented_mods = public_mods - sphinx_mods\\n # Remove ignored modules.\\n undocumented_mods -= IGNORED_MODULES\\n if undocumented_mods:\\n message_parts = ['Found undocumented public modules:']\\n message_parts.extend(['- ' + mod_name\\n for mod_name in sorted(undocumented_mods)])\\n print('\\\\n'.join(message_parts), file=sys.stderr)\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/verify_included_modules.py::get_parser\",\n \"text\": \"scripts/verify_included_modules.py::get_parser\\ndef get_parser():\\n \\\"\\\"\\\"Get simple ``argparse`` parser to determine package.\\n\\n :rtype: :class:`argparse.ArgumentParser`\\n :returns: The parser for this script.\\n \\\"\\\"\\\"\\n description = ('Run check that all google-cloud '\\n 'modules are included in docs.')\\n parser = argparse.ArgumentParser(description=description)\\n parser.add_argument('--build-root', dest='build_root',\\n help='The root directory where docs are located.')\\n return parser\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":84,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-1724"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Also update progress_bar in training_epoch_end\n## 🚀 Feature\r\n[PR 1357](https://github.com/PyTorchLightning/pytorch-lightning/pull/1357) implements training_epoch_end to log metrics. The comments in the issue suggest that it should behave like validation_epoch_end, but the PR only replicates the callbacks and metric logging. This feature would add updates to the progress bar as well.\r\n\r\n### Motivation\r\nMotivation is for the same usecase as having a progress bar. \r\nWhile tensorboard or any other logger is a great resource to have. It is painful to set up in environments like slurm where you can only submit jobs and possibly cannot forward ports or set up the logging.\r\n### Pitch\r\n\r\nThe changes should only entail adding an update to tqdm [here](https://github.com/PyTorchLightning/pytorch-lightning/blob/b2707c9b2ebeac03f19a3939df9432ac8859d894/pytorch_lightning/trainer/training_loop.py#L503)\r\n\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/core/lightning.py","pytorch_lightning/trainer/training_loop.py"],"string":"[\n \"pytorch_lightning/core/lightning.py\",\n \"pytorch_lightning/trainer/training_loop.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end","pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch"],"string":"[\n \"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\",\n \"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"pl_examples/__init__.py","text":"pl_examples/__init__.py","label":0},{"cid":"pytorch_lightning/callbacks/progress.py","text":"pytorch_lightning/callbacks/progress.py\nclass ProgressBarBase # The base class for progress bars in Lightning. It is a :class:`~pytorch_lightnin\n def __init__(self)\n def trainer(self)\n def train_batch_idx(self) # The current batch index being processed during training.\n def val_batch_idx(self) # The current batch index being processed during validation.\n def test_batch_idx(self) # The current batch index being processed during testing.\n def total_train_batches(self) # The total number of training batches during training, which may change from epoc\n def total_val_batches(self) # The total number of training batches during validation, which may change from ep\n def total_test_batches(self) # The total number of training batches during testing, which may change from epoch\n def disable(self) # You should provide a way to disable the progress bar.\n def enable(self) # You should provide a way to enable the progress bar.\n def on_init_end(self, trainer)\n def on_train_start(self, trainer, pl_module)\n def on_epoch_start(self, trainer, pl_module)\n def on_batch_end(self, trainer, pl_module)\n def on_validation_start(self, trainer, pl_module)\n def on_validation_batch_end(self, trainer, pl_module)\n def on_test_start(self, trainer, pl_module)\n def on_test_batch_end(self, trainer, pl_module)\nclass ProgressBar # This is the default progress bar used by Lightning. It prints to `stdout` using \n def __init__(self, refresh_rate: int=1, process_position: int=0)\n def __getstate__(self)\n def refresh_rate(self)\n def process_position(self)\n def is_enabled(self)\n def is_disabled(self)\n def disable(self)\n def enable(self)\n def init_sanity_tqdm(self) # Override this to customize the tqdm bar for the validation sanity run.\n def init_train_tqdm(self) # Override this to customize the tqdm bar for training.\n def init_validation_tqdm(self) # Override this to customize the tqdm bar for validation.\n def init_test_tqdm(self) # Override this to customize the tqdm bar for testing.\n def on_sanity_check_start(self, trainer, pl_module)\n def on_sanity_check_end(self, trainer, pl_module)\n def on_train_start(self, trainer, pl_module)\n def on_epoch_start(self, trainer, pl_module)\n def on_batch_end(self, trainer, pl_module)\n def on_validation_start(self, trainer, pl_module)\n def on_validation_batch_end(self, trainer, pl_module)\n def on_validation_end(self, trainer, pl_module)\n def on_train_end(self, trainer, pl_module)\n def on_test_start(self, trainer, pl_module)\n def on_test_batch_end(self, trainer, pl_module)\n def on_test_end(self, trainer, pl_module)\ndef convert_inf(x) # The tqdm doesn't support inf values. We have to convert it to None.","label":0},{"cid":"pytorch_lightning/core/lightning.py","text":"pytorch_lightning/core/lightning.py\nclass LightningModule\n def __init__(self, *args, **kwargs)\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once.\n def forward(self, *args, **kwargs) # Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to\n def training_step(self, *args, **kwargs) # Here you compute and return the training loss and some additional metrics for e.\n def training_end(self, *args, **kwargs) # Warnings:\n def training_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the training epoch with the outputs of all training steps.\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because :meth:`training_step`\n def validation_step(self, *args, **kwargs) # Operates on a single batch of data from the validation set.\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because :meth:`validation_step`\n def validation_end(self, outputs) # Warnings:\n def validation_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the validation epoch with the outputs of all validation ste\n def test_step(self, *args, **kwargs) # Operates on a single batch of data from the test set.\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because :meth:`test_step` will operate\n def test_end(self, outputs) # Warnings:\n def test_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of a test epoch with the output of all test steps.\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) # Override to init DDP in your own way or with your own wrapper.\n def _init_slurm_connection(self) # Sets up environemnt variables necessary for pytorch distributed communications\n def init_ddp_connection(self, proc_rank: int, world_size: int, is_slurm_managing_tasks: bool=True) # Override to define your custom way of setting up a distributed environment.\n def configure_apex(self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str) # Override to init AMP your own way.\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\n def optimizer_step(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int, second_order_closure: Optional[Callable]=None) # Override this method to adjust the default way the\n def tbptt_split_batch(self, batch: Tensor, split_size: int) # When using truncated backpropagation through time, each batch must be split alon\n def prepare_data(self) # Use this to download and prepare data.\n def train_dataloader(self) # Implement a PyTorch DataLoader for training.\n def tng_dataloader(self) # War","label":1},{"cid":"pytorch_lightning/trainer/training_loop.py","text":"pytorch_lightning/trainer/training_loop.py\nclass TrainerTrainLoopMixin\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\n def is_function_implemented(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def run_evaluation(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def clip_gradients(self) # Warning: this is just empty shell for code implemented in other class.\n def detect_nan_tensors(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def is_overriden(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def add_progress_bar_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def process_output(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_train_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_val_dataloader(self, model) # Warning: this is just empty shell for code implemented in other class.\n def has_arg(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def train(self)\n def run_training_epoch(self)\n def run_training_batch(self, batch, batch_idx)\n def _get_optimizers_iterable(self)\n def run_training_teardown(self)\n def training_forward(self, batch, batch_idx, opt_idx, hiddens) # Handle forward for each training case (distributed, single gpu, etc...)\n def update_learning_rates(self, interval: str) # Update learning rates.\n def call_checkpoint_callback(self)\n def call_early_stop_callback(self)\ndef _with_is_last(iterable) # Pass through values from the given iterable with an added boolean indicating if ","label":1}],"string":"[\n {\n \"cid\": \"pl_examples/__init__.py\",\n \"text\": \"pl_examples/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py\",\n \"text\": \"pytorch_lightning/callbacks/progress.py\\nclass ProgressBarBase # The base class for progress bars in Lightning. It is a :class:`~pytorch_lightnin\\n def __init__(self)\\n def trainer(self)\\n def train_batch_idx(self) # The current batch index being processed during training.\\n def val_batch_idx(self) # The current batch index being processed during validation.\\n def test_batch_idx(self) # The current batch index being processed during testing.\\n def total_train_batches(self) # The total number of training batches during training, which may change from epoc\\n def total_val_batches(self) # The total number of training batches during validation, which may change from ep\\n def total_test_batches(self) # The total number of training batches during testing, which may change from epoch\\n def disable(self) # You should provide a way to disable the progress bar.\\n def enable(self) # You should provide a way to enable the progress bar.\\n def on_init_end(self, trainer)\\n def on_train_start(self, trainer, pl_module)\\n def on_epoch_start(self, trainer, pl_module)\\n def on_batch_end(self, trainer, pl_module)\\n def on_validation_start(self, trainer, pl_module)\\n def on_validation_batch_end(self, trainer, pl_module)\\n def on_test_start(self, trainer, pl_module)\\n def on_test_batch_end(self, trainer, pl_module)\\nclass ProgressBar # This is the default progress bar used by Lightning. It prints to `stdout` using \\n def __init__(self, refresh_rate: int=1, process_position: int=0)\\n def __getstate__(self)\\n def refresh_rate(self)\\n def process_position(self)\\n def is_enabled(self)\\n def is_disabled(self)\\n def disable(self)\\n def enable(self)\\n def init_sanity_tqdm(self) # Override this to customize the tqdm bar for the validation sanity run.\\n def init_train_tqdm(self) # Override this to customize the tqdm bar for training.\\n def init_validation_tqdm(self) # Override this to customize the tqdm bar for validation.\\n def init_test_tqdm(self) # Override this to customize the tqdm bar for testing.\\n def on_sanity_check_start(self, trainer, pl_module)\\n def on_sanity_check_end(self, trainer, pl_module)\\n def on_train_start(self, trainer, pl_module)\\n def on_epoch_start(self, trainer, pl_module)\\n def on_batch_end(self, trainer, pl_module)\\n def on_validation_start(self, trainer, pl_module)\\n def on_validation_batch_end(self, trainer, pl_module)\\n def on_validation_end(self, trainer, pl_module)\\n def on_train_end(self, trainer, pl_module)\\n def on_test_start(self, trainer, pl_module)\\n def on_test_batch_end(self, trainer, pl_module)\\n def on_test_end(self, trainer, pl_module)\\ndef convert_inf(x) # The tqdm doesn't support inf values. We have to convert it to None.\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py\",\n \"text\": \"pytorch_lightning/core/lightning.py\\nclass LightningModule\\n def __init__(self, *args, **kwargs)\\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once.\\n def forward(self, *args, **kwargs) # Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to\\n def training_step(self, *args, **kwargs) # Here you compute and return the training loss and some additional metrics for e.\\n def training_end(self, *args, **kwargs) # Warnings:\\n def training_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the training epoch with the outputs of all training steps.\\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because :meth:`training_step`\\n def validation_step(self, *args, **kwargs) # Operates on a single batch of data from the validation set.\\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because :meth:`validation_step`\\n def validation_end(self, outputs) # Warnings:\\n def validation_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the validation epoch with the outputs of all validation ste\\n def test_step(self, *args, **kwargs) # Operates on a single batch of data from the test set.\\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because :meth:`test_step` will operate\\n def test_end(self, outputs) # Warnings:\\n def test_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of a test epoch with the output of all test steps.\\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) # Override to init DDP in your own way or with your own wrapper.\\n def _init_slurm_connection(self) # Sets up environemnt variables necessary for pytorch distributed communications\\n def init_ddp_connection(self, proc_rank: int, world_size: int, is_slurm_managing_tasks: bool=True) # Override to define your custom way of setting up a distributed environment.\\n def configure_apex(self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str) # Override to init AMP your own way.\\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\\n def optimizer_step(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int, second_order_closure: Optional[Callable]=None) # Override this method to adjust the default way the\\n def tbptt_split_batch(self, batch: Tensor, split_size: int) # When using truncated backpropagation through time, each batch must be split alon\\n def prepare_data(self) # Use this to download and prepare data.\\n def train_dataloader(self) # Implement a PyTorch DataLoader for training.\\n def tng_dataloader(self) # War\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_loop.py\",\n \"text\": \"pytorch_lightning/trainer/training_loop.py\\nclass TrainerTrainLoopMixin\\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\\n def is_function_implemented(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def run_evaluation(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def clip_gradients(self) # Warning: this is just empty shell for code implemented in other class.\\n def detect_nan_tensors(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def is_overriden(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def add_progress_bar_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def process_output(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_train_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_val_dataloader(self, model) # Warning: this is just empty shell for code implemented in other class.\\n def has_arg(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def train(self)\\n def run_training_epoch(self)\\n def run_training_batch(self, batch, batch_idx)\\n def _get_optimizers_iterable(self)\\n def run_training_teardown(self)\\n def training_forward(self, batch, batch_idx, opt_idx, hiddens) # Handle forward for each training case (distributed, single gpu, etc...)\\n def update_learning_rates(self, interval: str) # Update learning rates.\\n def call_checkpoint_callback(self)\\n def call_early_stop_callback(self)\\ndef _with_is_last(iterable) # Pass through values from the given iterable with an added boolean indicating if \",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.__init__","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.__init__\n def __init__(self):\n\n self._trainer = None\n self._train_batch_idx = 0\n self._val_batch_idx = 0\n self._test_batch_idx = 0","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.trainer","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.trainer\n @property\n def trainer(self):\n return self._trainer","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.train_batch_idx","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.train_batch_idx\n @property\n def train_batch_idx(self) -> int:\n \"\"\"\n The current batch index being processed during training.\n Use this to update your progress bar.\n \"\"\"\n return self._train_batch_idx","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.val_batch_idx","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.val_batch_idx\n @property\n def val_batch_idx(self) -> int:\n \"\"\"\n The current batch index being processed during validation.\n Use this to update your progress bar.\n \"\"\"\n return self._val_batch_idx","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.test_batch_idx","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.test_batch_idx\n @property\n def test_batch_idx(self) -> int:\n \"\"\"\n The current batch index being processed during testing.\n Use this to update your progress bar.\n \"\"\"\n return self._test_batch_idx","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_train_batches","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_train_batches\n @property\n def total_train_batches(self) -> int:\n \"\"\"\n The total number of training batches during training, which may change from epoch to epoch.\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\n training dataloader is of infinite size.\n \"\"\"\n total_train_batches = 1 if self.trainer.fast_dev_run else self.trainer.num_training_batches\n return total_train_batches","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_val_batches","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_val_batches\n @property\n def total_val_batches(self) -> int:\n \"\"\"\n The total number of training batches during validation, which may change from epoch to epoch.\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\n validation dataloader is of infinite size.\n \"\"\"\n trainer = self.trainer\n total_val_batches = 0\n if trainer.fast_dev_run:\n total_val_batches = len(trainer.val_dataloaders)\n elif not self.trainer.disable_validation:\n is_val_epoch = (trainer.current_epoch + 1) % trainer.check_val_every_n_epoch == 0\n total_val_batches = trainer.num_val_batches if is_val_epoch else 0\n return total_val_batches","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_test_batches","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_test_batches\n @property\n def total_test_batches(self) -> int:\n \"\"\"\n The total number of training batches during testing, which may change from epoch to epoch.\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\n test dataloader is of infinite size.\n \"\"\"\n if self.trainer.fast_dev_run:\n total_test_batches = len(self.trainer.test_dataloaders)\n else:\n total_test_batches = self.trainer.num_test_batches\n return total_test_batches","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.disable","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.disable\n def disable(self):\n \"\"\"\n You should provide a way to disable the progress bar.\n The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this to disable the\n output on processes that have a rank different from 0, e.g., in multi-node training.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.enable","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.enable\n def enable(self):\n \"\"\"\n You should provide a way to enable the progress bar.\n The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this in e.g. pre-training\n routines like the `learning rate finder <lr_finder.rst>`_ to temporarily enable and\n disable the main progress bar.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_init_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_init_end\n def on_init_end(self, trainer):\n self._trainer = trainer","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_train_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_train_start\n def on_train_start(self, trainer, pl_module):\n self._train_batch_idx = trainer.batch_idx","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_epoch_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_epoch_start\n def on_epoch_start(self, trainer, pl_module):\n self._train_batch_idx = 0","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_batch_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_batch_end\n def on_batch_end(self, trainer, pl_module):\n self._train_batch_idx += 1","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_start\n def on_validation_start(self, trainer, pl_module):\n self._val_batch_idx = 0","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_batch_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_batch_end\n def on_validation_batch_end(self, trainer, pl_module):\n self._val_batch_idx += 1","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_start\n def on_test_start(self, trainer, pl_module):\n self._test_batch_idx = 0","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_batch_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_batch_end\n def on_test_batch_end(self, trainer, pl_module):\n self._test_batch_idx += 1","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.__init__","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.__init__\n def __init__(self, refresh_rate: int = 1, process_position: int = 0):\n super().__init__()\n self._refresh_rate = refresh_rate\n self._process_position = process_position\n self._enabled = True\n self.main_progress_bar = None\n self.val_progress_bar = None\n self.test_progress_bar = None","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.__getstate__","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.__getstate__\n def __getstate__(self):\n # can't pickle the tqdm objects\n state = self.__dict__.copy()\n state['main_progress_bar'] = None\n state['val_progress_bar'] = None\n state['test_progress_bar'] = None\n return state","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.refresh_rate","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.refresh_rate\n @property\n def refresh_rate(self) -> int:\n return self._refresh_rate","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.process_position","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.process_position\n @property\n def process_position(self) -> int:\n return self._process_position","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.is_enabled","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.is_enabled\n @property\n def is_enabled(self) -> bool:\n return self._enabled and self.refresh_rate > 0","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.is_disabled","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.is_disabled\n @property\n def is_disabled(self) -> bool:\n return not self.is_enabled","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.disable","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.disable\n def disable(self) -> None:\n self._enabled = False","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.enable","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.enable\n def enable(self) -> None:\n self._enabled = True","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_sanity_tqdm","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_sanity_tqdm\n def init_sanity_tqdm(self) -> tqdm:\n \"\"\" Override this to customize the tqdm bar for the validation sanity run. \"\"\"\n bar = tqdm(\n desc='Validation sanity check',\n position=(2 * self.process_position),\n disable=self.is_disabled,\n leave=False,\n dynamic_ncols=True,\n file=sys.stdout,\n )\n return bar","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_train_tqdm","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_train_tqdm\n def init_train_tqdm(self) -> tqdm:\n \"\"\" Override this to customize the tqdm bar for training. \"\"\"\n bar = tqdm(\n desc='Training',\n initial=self.train_batch_idx,\n position=(2 * self.process_position),\n disable=self.is_disabled,\n leave=True,\n dynamic_ncols=True,\n file=sys.stdout,\n smoothing=0,\n )\n return bar","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_validation_tqdm","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_validation_tqdm\n def init_validation_tqdm(self) -> tqdm:\n \"\"\" Override this to customize the tqdm bar for validation. \"\"\"\n bar = tqdm(\n desc='Validating',\n position=(2 * self.process_position + 1),\n disable=self.is_disabled,\n leave=False,\n dynamic_ncols=True,\n file=sys.stdout\n )\n return bar","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_test_tqdm","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.init_test_tqdm\n def init_test_tqdm(self) -> tqdm:\n \"\"\" Override this to customize the tqdm bar for testing. \"\"\"\n bar = tqdm(\n desc='Testing',\n position=(2 * self.process_position),\n disable=self.is_disabled,\n leave=True,\n dynamic_ncols=True,\n file=sys.stdout\n )\n return bar","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_start\n def on_sanity_check_start(self, trainer, pl_module):\n super().on_sanity_check_start(trainer, pl_module)\n self.val_progress_bar = self.init_sanity_tqdm()\n self.val_progress_bar.total = trainer.num_sanity_val_steps * len(trainer.val_dataloaders)\n self.main_progress_bar = tqdm(disable=True) # dummy progress bar","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_sanity_check_end\n def on_sanity_check_end(self, trainer, pl_module):\n super().on_sanity_check_end(trainer, pl_module)\n self.main_progress_bar.close()\n self.val_progress_bar.close()","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_start\n def on_train_start(self, trainer, pl_module):\n super().on_train_start(trainer, pl_module)\n self.main_progress_bar = self.init_train_tqdm()","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_epoch_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_epoch_start\n def on_epoch_start(self, trainer, pl_module):\n super().on_epoch_start(trainer, pl_module)\n total_train_batches = self.total_train_batches\n total_val_batches = self.total_val_batches\n if total_train_batches != float('inf') and not trainer.fast_dev_run:\n # val can be checked multiple times per epoch\n val_checks_per_epoch = total_train_batches // trainer.val_check_batch\n total_val_batches = total_val_batches * val_checks_per_epoch\n total_batches = total_train_batches + total_val_batches\n if not self.main_progress_bar.disable:\n self.main_progress_bar.reset(convert_inf(total_batches))\n self.main_progress_bar.set_description(f'Epoch {trainer.current_epoch + 1}')","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_batch_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_batch_end\n def on_batch_end(self, trainer, pl_module):\n super().on_batch_end(trainer, pl_module)\n if self.is_enabled and self.train_batch_idx % self.refresh_rate == 0:\n self.main_progress_bar.update(self.refresh_rate)\n self.main_progress_bar.set_postfix(**trainer.progress_bar_dict)","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_start\n def on_validation_start(self, trainer, pl_module):\n super().on_validation_start(trainer, pl_module)\n self.val_progress_bar = self.init_validation_tqdm()\n self.val_progress_bar.total = convert_inf(self.total_val_batches)","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_batch_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_batch_end\n def on_validation_batch_end(self, trainer, pl_module):\n super().on_validation_batch_end(trainer, pl_module)\n if self.is_enabled and self.val_batch_idx % self.refresh_rate == 0:\n self.val_progress_bar.update(self.refresh_rate)\n self.main_progress_bar.update(self.refresh_rate)","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_validation_end\n def on_validation_end(self, trainer, pl_module):\n super().on_validation_end(trainer, pl_module)\n self.main_progress_bar.set_postfix(**trainer.progress_bar_dict)\n self.val_progress_bar.close()","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_end","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_train_end\n def on_train_end(self, trainer, pl_module):\n super().on_train_end(trainer, pl_module)\n self.main_progress_bar.close()","label":0},{"cid":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_start","text":"pytorch_lightning/callbacks/progress.py::ProgressBar.on_test_start\n def on_test_start(self, trainer, pl_module):\n super().on_test_start(trainer, pl_module)\n self.test_progress_bar = self.init_test_tqdm()\n self.test_progress_bar.total = convert_inf(self.total_test_batches)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.__init__","text":"pytorch_lightning/core/lightning.py::LightningModule.__init__\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n #: Current dtype\n self.dtype = torch.FloatTensor\n\n self.exp_save_path = None\n\n #: The current epoch\n self.current_epoch = 0\n\n #: Total training batches seen across all epochs\n self.global_step = 0\n\n self.loaded_optimizer_states_dict = {}\n\n #: Pointer to the trainer object\n self.trainer = None\n\n #: Pointer to the logger object\n self.logger = None\n self.example_input_array = None\n\n #: True if your model is currently running on GPUs.\n #: Useful to set flags around the LightningModule for different CPU vs GPU behavior.\n self.on_gpu = False\n\n #: True if using dp\n self.use_dp = False\n\n #: True if using ddp\n self.use_ddp = False\n\n #: True if using ddp2\n self.use_ddp2 = False\n\n #: True if using amp\n self.use_amp = False\n\n self.hparams = None","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.print","text":"pytorch_lightning/core/lightning.py::LightningModule.print\n def print(self, *args, **kwargs) -> None:\n r\"\"\"\n Prints only from process 0. Use this in any distributed mode to log only once.\n\n Args:\n *args: The thing to print. Will be passed to Python's built-in print function.\n **kwargs: Will be passed to Python's built-in print function.\n\n Example:\n\n .. code-block:: python\n\n def forward(self, x):\n self.print(x, 'in forward')\n\n \"\"\"\n if self.trainer.proc_rank == 0:\n print(*args, **kwargs)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.forward","text":"pytorch_lightning/core/lightning.py::LightningModule.forward\n @abstractmethod\n def forward(self, *args, **kwargs):\n r\"\"\"\n Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define\n the operations you want to use for prediction (i.e.: on a server or as a feature extractor).\n\n Normally you'd call ``self()`` from your :meth:`training_step` method.\n This makes it easy to write a complex system for training with the outputs\n you'd want in a prediction setting.\n\n Args:\n *args: Whatever you decide to pass into the forward method.\n **kwargs: Keyword arguments are also possible.\n\n Return:\n Predicted output\n\n Examples:\n .. code-block:: python\n\n # example if we were using this model as a feature extractor\n def forward(self, x):\n feature_maps = self.convnet(x)\n return feature_maps\n\n def training_step(self, batch, batch_idx):\n x, y = batch\n feature_maps = self(x)\n logits = self.classifier(feature_maps)\n\n # ...\n return loss\n\n # splitting it this way allows model to be used a feature extractor\n model = MyModelAbove()\n\n inputs = server.get_request()\n results = model(inputs)\n server.write_results(results)\n\n # -------------\n # This is in stark contrast to torch.nn.Module where normally you would have this:\n def forward(self, batch):\n x, y = batch\n feature_maps = self.convnet(x)\n logits = self.classifier(feature_maps)\n return logits\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step\n def training_step(self, *args, **kwargs) -> Union[\n int, Dict[str, Union[Tensor, Dict[str, Tensor]]]\n ]:\n r\"\"\"\n Here you compute and return the training loss and some additional metrics for e.g.\n the progress bar or logger.\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): Integer displaying index of this batch\n optimizer_idx (int): When using multiple optimizers, this argument will also be present.\n hiddens(:class:`~torch.Tensor`): Passed in if\n :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.\n\n Return:\n Dict with loss key and optional log or progress bar keys.\n When implementing :meth:`training_step`, return whatever you need in that step:\n\n - loss -> tensor scalar **REQUIRED**\n - progress_bar -> Dict for progress bar display. Must have only tensors\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n In this step you'd normally do the forward pass and calculate the loss for a batch.\n You can also do fancier things like multiple forward passes or something model specific.\n\n Examples:\n .. code-block:: python\n\n def training_step(self, batch, batch_idx):\n x, y, z = batch\n\n # implement your own\n out = self(x)\n loss = self.loss(out, x)\n\n logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)\n\n # if using TestTubeLogger or TensorBoardLogger you can nest scalars\n logger_logs = {'losses': logger_logs} # optional (MUST ALL","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_end\n def training_end(self, *args, **kwargs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`training_step_end` instead.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\n def training_epoch_end(\n self,\n outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"Called at the end of the training epoch with the outputs of all training steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n train_outs = []\n for train_batch in train_data:\n out = training_step(train_batch)\n train_outs.append(out)\n training_epoch_end(train_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`training_step`, or if there are\n multiple dataloaders, a list containing a list of outputs for each dataloader.\n\n Return:\n Dict or OrderedDict.\n May contain the following optional keys:\n\n - log (metrics to be added to the logger; only tensors)\n - any metric used in a callback (e.g. early stopping).\n\n Note:\n If this method is not overridden, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def training_epoch_end(self, outputs):\n train_acc_mean = 0\n for output in outputs:\n train_acc_mean += output['train_acc']\n\n train_acc_mean /= len(outputs)\n\n # log training accuracy at the end of an epoch\n results = {\n 'log': {'train_acc': train_acc_mean.item()}\n }\n return results\n\n With multiple dataloaders, ``outputs`` ","label":1},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\n def training_step_end(self, *args, **kwargs) -> Dict[\n str, Union[Tensor, Dict[str, Tensor]]\n ]:\n \"\"\"\n Use this when training with dp or ddp2 because :meth:`training_step`\n will operate on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]\n training_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in `training_step` for each batch part.\n\n Return:\n Dict with loss key and optional log or progress bar keys.\n\n - loss -> tensor scalar **REQUIRED**\n - progress_bar -> Dict for progress bar display. Must have only tensors\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n Examples:\n .. code-block:: python\n\n # WITHOUT training_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with training_step_end to do softmax over the full batch\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step\n def validation_step(self, *args, **kwargs) -> Dict[str, Tensor]:\n r\"\"\"\n Operates on a single batch of data from the validation set.\n In this step you'd might generate examples or calculate anything of interest like accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n val_outs = []\n for val_batch in val_data:\n out = validation_step(train_batch)\n val_outs.append(out)\n validation_epoch_end(val_outs)\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): The index of this batch\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple val datasets used)\n\n Return:\n Dict or OrderedDict - passed to :meth:`validation_epoch_end`.\n If you defined :meth:`validation_step_end` it will go to that first.\n\n .. code-block:: python\n\n # pseudocode of order\n out = validation_step()\n if defined('validation_step_end'):\n out = validation_step_end(out)\n out = validation_epoch_end(out)\n\n\n .. code-block:: python\n\n # if you have one val dataloader:\n def validation_step(self, batch, batch_idx)\n\n # if you have multiple val dataloaders:\n def validation_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single validation dataset\n def validation_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self(x)\n loss = self.los","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\n def validation_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\n \"\"\"\n Use this when validating with dp or ddp2 because :meth:`validation_step`\n will operate on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code.\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]\n validation_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in :meth:`validation_step`\n for each batch part.\n\n Return:\n Dict or OrderedDict - passed to the :meth:`validation_epoch_end` method.\n\n Examples:\n .. code-block:: python\n\n # WITHOUT validation_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with validation_step_end to do softmax over the full batch\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n return {'out': out}\n\n def validation_epoch_end(self, outputs):\n # this out is now the full size of the batch\n out = outputs['out']\n\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_end\n def validation_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead.\n Will be removed in 1.0.0.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\n def validation_epoch_end(\n self,\n outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"\n Called at the end of the validation epoch with the outputs of all validation steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n val_outs = []\n for val_batch in val_data:\n out = validation_step(val_batch)\n val_outs.append(out)\n validation_epoch_end(val_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`validation_step`, or if there\n are multiple dataloaders, a list containing a list of outputs for each dataloader.\n\n Return:\n Dict or OrderedDict.\n May have the following optional keys:\n\n - progress_bar (dict for progress bar display; only tensors)\n - log (dict of metrics to add to logger; only tensors).\n\n Note:\n If you didn't define a :meth:`validation_step`, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def validation_epoch_end(self, outputs):\n val_acc_mean = 0\n for output in outputs:\n val_acc_mean += output['val_acc']\n\n val_acc_mean /= len(outputs)\n tqdm_dict = {'val_acc': val_acc_mean.item()}\n\n # show val_acc in progress bar but only log val_loss\n results = {\n 'progress_bar': tqdm_dict,\n 'log': {'","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step\n def test_step(self, *args, **kwargs) -> Dict[str, Tensor]:\n r\"\"\"\n Operates on a single batch of data from the test set.\n In this step you'd normally generate examples or calculate anything of interest\n such as accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n test_outs = []\n for test_batch in test_data:\n out = test_step(test_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): The index of this batch.\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple test datasets used).\n\n Return:\n Dict or OrderedDict - passed to the :meth:`test_epoch_end` method.\n If you defined :meth:`test_step_end` it will go to that first.\n\n .. code-block:: python\n\n # if you have one test dataloader:\n def test_step(self, batch, batch_idx)\n\n # if you have multiple test dataloaders:\n def test_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single test dataset\n def test_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self(x)\n loss = self.loss(out, y)\n\n # log 6 example images\n # or generated text... or whatever\n sample_imgs = x[:6]\n grid = torchvision.utils.make_grid(sample_imgs)\n self.logger.experiment.add_image('example","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\n def test_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\n \"\"\"\n Use this when testing with dp or ddp2 because :meth:`test_step` will operate\n on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code.\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]\n test_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in :meth:`test_step` for each batch part.\n\n Return:\n Dict or OrderedDict - passed to the :meth:`test_epoch_end`.\n\n Examples:\n .. code-block:: python\n\n # WITHOUT test_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with test_step_end to do softmax over the full batch\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n return {'out': out}\n\n def test_step_end(self, outputs):\n # this out is now the full size of the batch\n out = outputs['out']\n\n # this softmax now uses the full batch size\n loss = nce_loss(loss)\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_end\n def test_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead.\n Will be removed in 1.0.0.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\n def test_epoch_end(\n self,\n outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"\n Called at the end of a test epoch with the output of all test steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n test_outs = []\n for test_batch in test_data:\n out = test_step(test_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`test_step_end`, or if there\n are multiple dataloaders, a list containing a list of outputs for each dataloader\n\n Return:\n Dict or OrderedDict: Dict has the following optional keys:\n\n - progress_bar -> Dict for progress bar display. Must have only tensors.\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc).\n\n Note:\n If you didn't define a :meth:`test_step`, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def test_epoch_end(self, outputs):\n test_acc_mean = 0\n for output in outputs:\n test_acc_mean += output['test_acc']\n\n test_acc_mean /= len(outputs)\n tqdm_dict = {'test_acc': test_acc_mean.item()}\n\n # show test_loss and test_acc in progress bar but only log test_loss\n results = {\n 'progress_bar': tqdm_dict,\n 'log': {","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\n def configure_ddp(\n self,\n model: 'LightningModule',\n device_ids: List[int]\n ) -> DistributedDataParallel:\n r\"\"\"\n Override to init DDP in your own way or with your own wrapper.\n The only requirements are that:\n\n 1. On a validation batch the call goes to ``model.validation_step``.\n 2. On a training batch the call goes to ``model.training_step``.\n 3. On a testing batch, the call goes to ``model.test_step``.+\n\n Args:\n model: the :class:`LightningModule` currently being optimized.\n device_ids: the list of GPU ids.\n\n Return:\n DDP wrapped model\n\n Examples:\n .. code-block:: python\n\n # default implementation used in Trainer\n def configure_ddp(self, model, device_ids):\n # Lightning DDP simply routes to test_step, val_step, etc...\n model = LightningDistributedDataParallel(\n model,\n device_ids=device_ids,\n find_unused_parameters=True\n )\n return model\n\n \"\"\"\n model = LightningDistributedDataParallel(\n model,\n device_ids=device_ids,\n find_unused_parameters=True\n )\n return model","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection","text":"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\n def _init_slurm_connection(self) -> None:\n \"\"\"\n Sets up environemnt variables necessary for pytorch distributed communications\n based on slurm environment.\n \"\"\"\n # use slurm job id for the port number\n # guarantees unique ports across jobs from same grid search\n try:\n # use the last 4 numbers in the job id as the id\n default_port = os.environ['SLURM_JOB_ID']\n default_port = default_port[-4:]\n\n # all ports should be in the 10k+ range\n default_port = int(default_port) + 15000\n\n except Exception:\n default_port = 12910\n\n # if user gave a port number, use that one instead\n try:\n default_port = os.environ['MASTER_PORT']\n except Exception:\n os.environ['MASTER_PORT'] = str(default_port)\n\n # figure out the root node addr\n try:\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\n except Exception:\n root_node = '127.0.0.1'\n\n root_node = self.trainer.resolve_root_node_address(root_node)\n os.environ['MASTER_ADDR'] = root_node","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection","text":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\n def init_ddp_connection(\n self,\n proc_rank: int,\n world_size: int,\n is_slurm_managing_tasks: bool = True\n ) -> None:\n \"\"\"\n Override to define your custom way of setting up a distributed environment.\n\n Lightning's implementation uses env:// init by default and sets the first node as root\n for SLURM managed cluster.\n\n Args:\n proc_rank: The current process rank within the node.\n world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).\n is_slurm_managing_tasks: is cluster managed by SLURM.\n\n \"\"\"\n if is_slurm_managing_tasks:\n self._init_slurm_connection()\n\n if 'MASTER_ADDR' not in os.environ:\n log.warning(\"MASTER_ADDR environment variable is not defined. Set as localhost\")\n os.environ['MASTER_ADDR'] = '127.0.0.1'\n log.debug(f\"MASTER_ADDR: {os.environ['MASTER_ADDR']}\")\n\n if 'MASTER_PORT' not in os.environ:\n log.warning(\"MASTER_PORT environment variable is not defined. Set as 12910\")\n os.environ['MASTER_PORT'] = '12910'\n log.debug(f\"MASTER_PORT: {os.environ['MASTER_PORT']}\")\n\n if 'WORLD_SIZE' in os.environ and os.environ['WORLD_SIZE'] != world_size:\n log.warning(\"WORLD_SIZE environment variable is not equal to the computed \"\n \"world size. Ignored.\")\n\n torch_backend = \"nccl\" if self.trainer.on_gpu else \"gloo\"\n torch_distrib.init_process_group(torch_backend, rank=proc_rank, world_size=world_size)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\n def configure_apex(\n self,\n amp: object,\n model: 'LightningModule',\n optimizers: List[Optimizer],\n amp_level: str\n ) -> Tuple['LightningModule', List[Optimizer]]:\n r\"\"\"\n Override to init AMP your own way.\n Must return a model and list of optimizers.\n\n Args:\n amp: pointer to amp library object.\n model: pointer to current :class:`LightningModule`.\n optimizers: list of optimizers passed in :meth:`configure_optimizers`.\n amp_level: AMP mode chosen ('O1', 'O2', etc...)\n\n Return:\n Apex wrapped model and optimizers\n\n Examples:\n .. code-block:: python\n\n # Default implementation used by Trainer.\n def configure_apex(self, amp, model, optimizers, amp_level):\n model, optimizers = amp.initialize(\n model, optimizers, opt_level=amp_level,\n )\n\n return model, optimizers\n \"\"\"\n model, optimizers = amp.initialize(\n model, optimizers, opt_level=amp_level,\n )\n\n return model, optimizers","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\n def configure_optimizers(self) -> Optional[Union[\n Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List]\n ]]:\n r\"\"\"\n Choose what optimizers and learning-rate schedulers to use in your optimization.\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\n\n Return:\n Any of these 6 options.\n\n - Single optimizer.\n - List or Tuple - List of optimizers.\n - Two lists - The first list has multiple optimizers, the second a list of LR schedulers.\n - Dictionary, with an 'optimizer' key and (optionally) a 'lr_scheduler' key.\n - Tuple of dictionaries as described, with an optional 'frequency' key.\n - None - Fit will run without any optimizer.\n\n Note:\n The 'frequency' value is an int corresponding to the number of sequential batches\n optimized with the specific optimizer. It should be given to none or to all of the optimizers.\n There is a difference between passing multiple optimizers in a list,\n and passing multiple optimizers in dictionaries with a frequency of 1:\n In the former case, all optimizers will operate on the given batch in each optimization step.\n In the latter, only one optimizer will operate on the given batch at every step.\n\n Examples:\n .. code-block:: python\n\n # most cases\n def configure_optimizers(self):\n opt = Adam(self.parameters(), lr=1e-3)\n return opt\n\n # multiple optimizer case (e.g.: GAN)\n def configure_optimizers(self):\n generator_opt = Adam(self.model_gen.parameters(), lr=0.01)\n disriminator_opt = Adam(self.model_disc.parameters(), lr=0.02)\n return generator_opt, disriminator_","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step","text":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\n def optimizer_step(\n self,\n epoch: int,\n batch_idx: int,\n optimizer: Optimizer,\n optimizer_idx: int,\n second_order_closure: Optional[Callable] = None,\n ) -> None:\n r\"\"\"\n Override this method to adjust the default way the\n :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.\n By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example\n once per optimizer.\n\n Args:\n epoch: Current epoch\n batch_idx: Index of current batch\n optimizer: A PyTorch optimizer\n optimizer_idx: If you used multiple optimizers this indexes into that list.\n second_order_closure: closure for second order methods\n\n Examples:\n .. code-block:: python\n\n # DEFAULT\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure=None):\n optimizer.step()\n optimizer.zero_grad()\n\n # Alternating schedule for optimizer steps (i.e.: GANs)\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure=None):\n # update generator opt every 2 steps\n if optimizer_idx == 0:\n if batch_idx % 2 == 0 :\n optimizer.step()\n optimizer.zero_grad()\n\n # update discriminator opt every 4 steps\n if optimizer_idx == 1:\n if batch_idx % 4 == 0 :\n optimizer.step()\n optimizer.zero_grad()\n\n # ...\n # add as many optimizers as you want\n\n\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch","text":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\n def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:\n r\"\"\"\n When using truncated backpropagation through time, each batch must be split along the\n time dimension. Lightning handles this by default, but for custom behavior override\n this function.\n\n Args:\n batch: Current batch\n split_size: The size of the split\n\n Return:\n List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated\n back propagation through time. The default implementation splits root level Tensors and\n Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.\n\n Examples:\n .. code-block:: python\n\n def tbptt_split_batch(self, batch, split_size):\n splits = []\n for t in range(0, time_dims[0], split_size):\n batch_split = []\n for i, x in enumerate(batch):\n if isinstance(x, torch.Tensor):\n split_x = x[:, t:t + split_size]\n elif isinstance(x, collections.Sequence):\n split_x = [None] * len(x)\n for batch_idx in range(len(x)):\n split_x[batch_idx] = x[batch_idx][t:t + split_size]\n\n batch_split.append(split_x)\n\n splits.append(batch_split)\n\n return splits\n\n Note:\n Called in the training loop after\n :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`\n if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.\n Each returned batch split is passed separately to :meth:`training_step`.\n\n \"\"\"\n time_dims = [len(x[0]) for x in batch if isinstance(x","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data","text":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\n def prepare_data(self) -> None:\n \"\"\"\n Use this to download and prepare data.\n In distributed (GPU, TPU), this will only be called once.\n This is called before requesting the dataloaders:\n\n .. code-block:: python\n\n model.prepare_data()\n model.train_dataloader()\n model.val_dataloader()\n model.test_dataloader()\n\n Examples:\n .. code-block:: python\n\n def prepare_data(self):\n download_imagenet()\n clean_imagenet()\n cache_imagenet()\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\n def train_dataloader(self) -> DataLoader:\n \"\"\"\n Implement a PyTorch DataLoader for training.\n\n Return:\n Single PyTorch :class:`~torch.utils.data.DataLoader`.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`train_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware.\n There is no need to set it yourself.\n\n Example:\n .. code-block:: python\n\n def train_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.hparams.batch_size,\n shuffle=True\n )\n return loader\n\n \"\"\"\n rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\n def tng_dataloader(self): # todo: remove in v1.0.0\n \"\"\"\n Warnings:\n Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.\n \"\"\"\n output = self.train_dataloader()\n rank_zero_warn(\"`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0.\"\n \" and this method will be removed in v1.0.0\", DeprecationWarning)\n return output","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\n def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\n r\"\"\"\n Implement one or multiple PyTorch DataLoaders for testing.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`train_dataloader`\n - :meth:`val_dataloader`\n - :meth:`test_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware.\n There is no need to set it yourself.\n\n Return:\n Single or multiple PyTorch DataLoaders.\n\n Example:\n .. code-block:: python\n\n def test_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.hparams.batch_size,\n shuffle=False\n )\n\n return loader\n\n Note:\n If you don't need a test dataset and a :meth:`test_step`, you don't need to implement\n this method.\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\n def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\n r\"\"\"\n Implement one or multiple PyTorch DataLoaders for validation.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`train_dataloader`\n - :meth:`val_dataloader`\n - :meth:`test_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware\n There is no need to set it yourself.\n\n Return:\n Single or multiple PyTorch DataLoaders.\n\n Examples:\n .. code-block:: python\n\n def val_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False,\n transform=transform, download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.hparams.batch_size,\n shuffle=False\n )\n\n return loader\n\n # can also return multiple dataloaders\n def val_dataloader(self):\n return [loader_a, loader_b, ..., loader_n]\n\n Note:\n If you don't need a validation dataset and a :meth:`validation_step`, you don't need to\n implement this method.\n\n Note:\n In the case where you return multiple validation dataloaders, the :meth:`valid","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.load_from_metrics","text":"pytorch_lightning/core/lightning.py::LightningModule.load_from_metrics\n @classmethod\n def load_from_metrics(cls, weights_path, tags_csv, map_location=None):\n r\"\"\"\n Warning:\n Deprecated in version 0.7.0. You should use :meth:`load_from_checkpoint` instead.\n Will be removed in v0.9.0.\n \"\"\"\n rank_zero_warn(\n \"`load_from_metrics` method has been unified with `load_from_checkpoint` in v0.7.0.\"\n \" The deprecated method will be removed in v0.9.0.\", DeprecationWarning\n )\n return cls.load_from_checkpoint(weights_path, tags_csv=tags_csv, map_location=map_location)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.load_from_checkpoint\n @classmethod\n def load_from_checkpoint(\n cls,\n checkpoint_path: str,\n map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,\n tags_csv: Optional[str] = None,\n *args, **kwargs\n ) -> 'LightningModule':\n r\"\"\"\n Primary way of loading a model from a checkpoint. When Lightning saves a checkpoint\n it stores the hyperparameters in the checkpoint if you initialized your :class:`LightningModule`\n with an argument called ``hparams`` which is a :class:`~argparse.Namespace`\n (output of :meth:`~argparse.ArgumentParser.parse_args` when parsing command line arguments).\n Any other arguments specified through \\*args and \\*\\*kwargs will be passed to the model.\n\n Example:\n .. code-block:: python\n\n from argparse import Namespace\n hparams = Namespace(**{'learning_rate': 0.1})\n\n model = MyModel(hparams)\n\n class MyModel(LightningModule):\n def __init__(self, hparams):\n self.learning_rate = hparams.learning_rate\n\n Args:\n checkpoint_path: Path to checkpoint.\n model_args: Any keyword args needed to init the model.\n map_location:\n If your checkpoint saved a GPU model and you now load on CPUs\n or a different number of GPUs, use this to map to the new setup.\n The behaviour is the same as in :func:`torch.load`.\n tags_csv: Optional path to a .csv file with two columns (key, value)\n as in this example::\n\n key,value\n drop_prob,0.2\n batch_size,32\n\n You most likely won't need this since Lightning will always save the hyperparameters\n to the checkpoint.\n However, if ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._load_model_state","text":"pytorch_lightning/core/lightning.py::LightningModule._load_model_state\n @classmethod\n def _load_model_state(cls, checkpoint: Dict[str, Any], *args, **kwargs) -> 'LightningModule':\n cls_takes_hparams = 'hparams' in inspect.signature(cls.__init__).parameters\n ckpt_hparams = checkpoint.get('hparams')\n\n if cls_takes_hparams:\n if ckpt_hparams is not None:\n is_namespace = checkpoint.get('hparams_type', 'namespace') == 'namespace'\n hparams = Namespace(**ckpt_hparams) if is_namespace else ckpt_hparams\n else:\n rank_zero_warn(\n f\"Checkpoint does not contain hyperparameters but {cls.__name__}'s __init__\"\n \" contains argument 'hparams'. Will pass in an empty Namespace instead.\"\n \" Did you forget to store your model hyperparameters in self.hparams?\"\n )\n hparams = Namespace()\n else: # The user's LightningModule does not define a hparams argument\n if ckpt_hparams is None:\n hparams = None\n else:\n raise MisconfigurationException(\n f\"Checkpoint contains hyperparameters but {cls.__name__}'s __init__ \"\n f\"is missing the argument 'hparams'. Are you loading the correct checkpoint?\"\n )\n\n # load the state_dict on the model automatically\n if hparams:\n kwargs.update(hparams=hparams)\n model = cls(*args, **kwargs)\n model.load_state_dict(checkpoint['state_dict'])\n\n # give model a chance to load something\n model.on_load_checkpoint(checkpoint)\n\n return model","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.summarize","text":"pytorch_lightning/core/lightning.py::LightningModule.summarize\n def summarize(self, mode: str) -> None:\n model_summary = ModelSummary(self, mode=mode)\n log.info('\\n' + model_summary.__str__())","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.freeze","text":"pytorch_lightning/core/lightning.py::LightningModule.freeze\n def freeze(self) -> None:\n r\"\"\"\n Freeze all params for inference.\n\n Example:\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.freeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = False\n\n self.eval()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze","text":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\n def unfreeze(self) -> None:\n \"\"\"\n Unfreeze all parameters for training.\n\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.unfreeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = True\n\n self.train()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n r\"\"\"\n Called by Lightning to restore your model.\n If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.\n\n Args:\n checkpoint: Loaded checkpoint\n\n\n Example:\n .. code-block:: python\n\n def on_load_checkpoint(self, checkpoint):\n # 99% of the time you don't need to implement this method\n self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']\n\n Note:\n Lightning auto-restores global step, epoch, and train state including amp scaling.\n There is no need for you to restore anything regarding training.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n r\"\"\"\n Called by Lightning when saving a checkpoint to give you a chance to store anything\n else you might want to save.\n\n Args:\n checkpoint: Checkpoint to be saved\n\n Example:\n .. code-block:: python\n\n\n def on_save_checkpoint(self, checkpoint):\n # 99% of use cases you don't need to implement this method\n checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object\n\n Note:\n Lightning saves all aspects of training (epoch, global step, etc...)\n including amp scaling.\n There is no need for you to store anything about training.\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict","text":"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\n def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:\n r\"\"\"\n Additional items to be displayed in the progress bar.\n\n Return:\n Dictionary with the items to be displayed in the progress bar.\n \"\"\"\n # call .item() only once but store elements without graphs\n running_train_loss = self.trainer.running_loss.mean()\n avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')\n tqdm_dict = {\n 'loss': '{:.3f}'.format(avg_training_loss)\n }\n\n if self.trainer.truncated_bptt_steps is not None:\n tqdm_dict['split_idx'] = self.trainer.split_idx\n\n if self.trainer.logger is not None and self.trainer.logger.version is not None:\n tqdm_dict['v_num'] = self.trainer.logger.version\n\n return tqdm_dict","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict","text":"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\n def get_tqdm_dict(self) -> Dict[str, Union[int, str]]:\n \"\"\"\n Additional items to be displayed in the progress bar.\n\n Return:\n Dictionary with the items to be displayed in the progress bar.\n\n Warning:\n Deprecated since v0.7.3.\n Use :meth:`get_progress_bar_dict` instead.\n \"\"\"\n rank_zero_warn(\"`get_tqdm_dict` was renamed to `get_progress_bar_dict` in v0.7.3\"\n \" and this method will be removed in v1.0.0\", DeprecationWarning)\n return self.get_progress_bar_dict()","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.get_model\n @abstractmethod\n def get_model(self):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_function_implemented\n @abstractmethod\n def is_function_implemented(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_evaluation\n @abstractmethod\n def run_evaluation(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_gpu\n @abstractmethod\n def transfer_batch_to_gpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.transfer_batch_to_tpu\n @abstractmethod\n def transfer_batch_to_tpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.clip_gradients\n @abstractmethod\n def clip_gradients(self):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.detect_nan_tensors","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.detect_nan_tensors\n @abstractmethod\n def detect_nan_tensors(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overriden","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.is_overriden\n @abstractmethod\n def is_overriden(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_progress_bar_metrics","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.add_progress_bar_metrics\n @abstractmethod\n def add_progress_bar_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.log_metrics\n @abstractmethod\n def log_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.process_output\n @abstractmethod\n def process_output(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_train_dataloader\n @abstractmethod\n def reset_train_dataloader(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.reset_val_dataloader\n @abstractmethod\n def reset_val_dataloader(self, model):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.has_arg\n @abstractmethod\n def has_arg(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.train\n def train(self):\n # get model\n model = self.get_model()\n\n # load data\n # if reload_dataloaders_every_epoch, this is moved to the epoch loop\n if not self.reload_dataloaders_every_epoch:\n self.reset_train_dataloader(model)\n self.reset_val_dataloader(model)\n\n # Train start events\n with self.profiler.profile('on_train_start'):\n # callbacks\n self.on_train_start()\n # initialize early stop callback\n if self.early_stop_callback is not None:\n self.early_stop_callback.on_train_start(self, self.get_model())\n # model hooks\n model.on_train_start()\n\n try:\n # run all epochs\n for epoch in range(self.current_epoch, self.max_epochs):\n # reset train dataloader\n if self.reload_dataloaders_every_epoch:\n self.reset_train_dataloader(model)\n # set seed for distributed sampler (enables shuffling for each epoch)\n if (self.use_ddp or self.use_horovod) \\\n and hasattr(self.train_dataloader.sampler, 'set_epoch'):\n self.train_dataloader.sampler.set_epoch(epoch)\n\n # update training progress in trainer and model\n model.current_epoch = epoch\n self.current_epoch = epoch\n\n # changing gradient according accumulation_scheduler\n self.accumulation_scheduler.on_epoch_start(self, self.get_model())\n\n # stores accumulated grad fractions per batch\n self.batch_loss_value = TensorRunningAccum(\n window_length=self.accumulate_grad_batches\n )\n\n # -----------------\n # RUN TNG EPOCH\n # -----------------\n self.run_training_epoch()\n\n # update","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_epoch\n def run_training_epoch(self):\n\n # get model\n model = self.get_model()\n\n # Epoch start events\n with self.profiler.profile('on_epoch_start'):\n # callbacks\n self.on_epoch_start()\n\n # model hooks\n if self.is_function_implemented('on_epoch_start'):\n model.on_epoch_start()\n\n # track local dataloader so TPU can wrap each epoch\n train_dataloader = self.train_dataloader\n\n # on TPU we have to wrap it under the ParallelLoader\n if self.use_tpu:\n device = xm.xla_device()\n train_dataloader = xla_pl.ParallelLoader(train_dataloader, [device])\n train_dataloader = train_dataloader.per_device_loader(device)\n\n # bookkeeping\n outputs = []\n\n # run epoch\n for batch_idx, (batch, is_last_batch) in self.profiler.profile_iterable(\n enumerate(_with_is_last(train_dataloader)), \"get_train_batch\"\n ):\n # stop epoch if we limited the number of training batches\n if batch_idx >= self.num_training_batches:\n break\n\n self.batch_idx = batch_idx\n\n model.global_step = self.global_step\n\n # ---------------\n # RUN TRAIN STEP\n # ---------------\n _outputs = self.run_training_batch(batch, batch_idx)\n batch_result, grad_norm_dic, batch_step_metrics, batch_output = _outputs\n\n # only track outputs when user implementes training_epoch_end\n # otherwise we will build up unecessary memory\n if self.is_overriden('training_epoch_end', model=self.get_model()):\n outputs.append(batch_output)\n\n # when returning -1 from train_step, we end epoch early\n early_stop_epoch = batch_result == -1\n\n # TODO: consolidate all actions that need to take place only after\n ","label":1},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_batch\n def run_training_batch(self, batch, batch_idx):\n # track grad norms\n grad_norm_dic = {}\n\n # track all metrics for callbacks\n all_callback_metrics = []\n\n # track metrics to log\n all_log_metrics = []\n\n if batch is None:\n return 0, grad_norm_dic, {}, {}\n\n # Batch start events\n with self.profiler.profile('on_batch_start'):\n # callbacks\n self.on_batch_start()\n # hooks\n if self.is_function_implemented('on_batch_start'):\n response = self.get_model().on_batch_start(batch)\n if response == -1:\n return -1, grad_norm_dic, {}, {}\n\n splits = [batch]\n if self.truncated_bptt_steps is not None:\n model_ref = self.get_model()\n with self.profiler.profile('tbptt_split_batch'):\n splits = model_ref.tbptt_split_batch(batch, self.truncated_bptt_steps)\n\n self.hiddens = None\n for split_idx, split_batch in enumerate(splits):\n self.split_idx = split_idx\n\n for opt_idx, optimizer in self._get_optimizers_iterable():\n # make sure only the gradients of the current optimizer's paramaters are calculated\n # in the training step to prevent dangling gradients in multiple-optimizer setup.\n if len(self.optimizers) > 1:\n for param in self.get_model().parameters():\n param.requires_grad = False\n for group in optimizer.param_groups:\n for param in group['params']:\n param.requires_grad = True\n\n # wrap the forward step in a closure so second order methods work\n def optimizer_closure():\n # forward pass\n with self.profiler.profile('model_forward'):\n ","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin._get_optimizers_iterable","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin._get_optimizers_iterable\n def _get_optimizers_iterable(self):\n if not self.optimizer_frequencies:\n # call training_step once per optimizer\n return list(enumerate(self.optimizers))\n\n optimizer_freq_cumsum = np.cumsum(self.optimizer_frequencies)\n optimizers_loop_length = optimizer_freq_cumsum[-1]\n current_place_in_loop = self.total_batch_idx % optimizers_loop_length\n\n # find optimzier index by looking for the first {item > current_place} in the cumsum list\n opt_idx = np.argmax(optimizer_freq_cumsum > current_place_in_loop)\n return [(opt_idx, self.optimizers[opt_idx])]","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.run_training_teardown\n def run_training_teardown(self):\n # Train end events\n with self.profiler.profile('on_train_end'):\n # callbacks\n self.on_train_end()\n # model hooks\n if self.is_function_implemented('on_train_end'):\n self.get_model().on_train_end()\n\n if self.logger is not None:\n self.logger.finalize(\"success\")\n\n # summarize profile results\n self.profiler.describe()","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.training_forward\n def training_forward(self, batch, batch_idx, opt_idx, hiddens):\n \"\"\"\n Handle forward for each training case (distributed, single gpu, etc...)\n :param batch:\n :param batch_idx:\n :return:\n \"\"\"\n # ---------------\n # FORWARD\n # ---------------\n # enable not needing to add opt_idx to training_step\n args = [batch, batch_idx]\n\n if len(self.optimizers) > 1:\n if self.has_arg('training_step', 'optimizer_idx'):\n args.append(opt_idx)\n else:\n num_opts = len(self.optimizers)\n raise ValueError(\n f'Your LightningModule defines {num_opts} optimizers but '\n f'training_step is missing the \"optimizer_idx\" argument.'\n )\n\n # pass hiddens if using tbptt\n if self.truncated_bptt_steps is not None:\n args.append(hiddens)\n\n # distributed forward\n if self.use_ddp or self.use_ddp2 or self.use_dp:\n output = self.model(*args)\n\n # Horovod\n elif self.use_horovod and self.on_gpu:\n batch = self.transfer_batch_to_gpu(batch, hvd.local_rank())\n args[0] = batch\n output = self.model.training_step(*args)\n\n # single GPU forward\n elif self.single_gpu:\n gpu_id = 0\n if isinstance(self.data_parallel_device_ids, list):\n gpu_id = self.data_parallel_device_ids[0]\n\n # Don't copy the batch since there is a single gpu that the batch could\n # be referenced from and if there are multiple optimizers the batch will\n # wind up copying it to the same device repeatedly.\n batch = self.transfer_batch_to_gpu(batch, gpu_id)\n args[0] = batch\n output = self.model.training_step(*args)\n\n # TPU support\n elif self.use_tpu:\n ","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.update_learning_rates\n def update_learning_rates(self, interval: str):\n \"\"\"Update learning rates.\n\n Args:\n interval: either 'epoch' or 'step'.\n \"\"\"\n if not self.lr_schedulers:\n return\n\n for lr_scheduler in self.lr_schedulers:\n current_idx = self.batch_idx if interval == 'step' else self.current_epoch\n current_idx += 1 # account for both batch and epoch starts from 0\n # Take step if call to update_learning_rates matches the interval key and\n # the current step modulo the schedulers frequency is zero\n if lr_scheduler['interval'] == interval and current_idx % lr_scheduler['frequency'] == 0:\n # If instance of ReduceLROnPlateau, we need to pass validation loss\n if lr_scheduler['reduce_on_plateau']:\n monitor_key = lr_scheduler['monitor']\n monitor_val = self.callback_metrics.get(monitor_key)\n if monitor_val is None:\n avail_metrics = ','.join(list(self.callback_metrics.keys()))\n raise MisconfigurationException(\n f'ReduceLROnPlateau conditioned on metric {monitor_key}'\n f' which is not available. Available metrics are: {avail_metrics}.'\n ' Condition can be set using `monitor` key in lr scheduler dict'\n )\n lr_scheduler['scheduler'].step(monitor_val)\n else:\n lr_scheduler['scheduler'].step()","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_checkpoint_callback","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_checkpoint_callback\n def call_checkpoint_callback(self):\n if self.checkpoint_callback is not None:\n self.checkpoint_callback.on_validation_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_early_stop_callback","text":"pytorch_lightning/trainer/training_loop.py::TrainerTrainLoopMixin.call_early_stop_callback\n def call_early_stop_callback(self):\n if self.early_stop_callback:\n self.early_stop_callback.on_epoch_end(self, self.get_model())","label":0},{"cid":"pytorch_lightning/trainer/training_loop.py::_with_is_last","text":"pytorch_lightning/trainer/training_loop.py::_with_is_last\ndef _with_is_last(iterable):\n \"\"\"Pass through values from the given iterable with an added boolean indicating if this is the last item.\n See `https://stackoverflow.com/a/1630350 <https://stackoverflow.com/a/1630350>`_\"\"\"\n it = iter(iterable)\n last = next(it)\n for val in it:\n # yield last and has next\n yield last, False\n last = val\n # yield last, no longer has next\n yield last, True","label":0}],"string":"[\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.__init__\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.__init__\\n def __init__(self):\\n\\n self._trainer = None\\n self._train_batch_idx = 0\\n self._val_batch_idx = 0\\n self._test_batch_idx = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.trainer\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.trainer\\n @property\\n def trainer(self):\\n return self._trainer\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.train_batch_idx\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.train_batch_idx\\n @property\\n def train_batch_idx(self) -> int:\\n \\\"\\\"\\\"\\n The current batch index being processed during training.\\n Use this to update your progress bar.\\n \\\"\\\"\\\"\\n return self._train_batch_idx\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.val_batch_idx\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.val_batch_idx\\n @property\\n def val_batch_idx(self) -> int:\\n \\\"\\\"\\\"\\n The current batch index being processed during validation.\\n Use this to update your progress bar.\\n \\\"\\\"\\\"\\n return self._val_batch_idx\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.test_batch_idx\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.test_batch_idx\\n @property\\n def test_batch_idx(self) -> int:\\n \\\"\\\"\\\"\\n The current batch index being processed during testing.\\n Use this to update your progress bar.\\n \\\"\\\"\\\"\\n return self._test_batch_idx\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_train_batches\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_train_batches\\n @property\\n def total_train_batches(self) -> int:\\n \\\"\\\"\\\"\\n The total number of training batches during training, which may change from epoch to epoch.\\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\\n training dataloader is of infinite size.\\n \\\"\\\"\\\"\\n total_train_batches = 1 if self.trainer.fast_dev_run else self.trainer.num_training_batches\\n return total_train_batches\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_val_batches\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_val_batches\\n @property\\n def total_val_batches(self) -> int:\\n \\\"\\\"\\\"\\n The total number of training batches during validation, which may change from epoch to epoch.\\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\\n validation dataloader is of infinite size.\\n \\\"\\\"\\\"\\n trainer = self.trainer\\n total_val_batches = 0\\n if trainer.fast_dev_run:\\n total_val_batches = len(trainer.val_dataloaders)\\n elif not self.trainer.disable_validation:\\n is_val_epoch = (trainer.current_epoch + 1) % trainer.check_val_every_n_epoch == 0\\n total_val_batches = trainer.num_val_batches if is_val_epoch else 0\\n return total_val_batches\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_test_batches\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.total_test_batches\\n @property\\n def total_test_batches(self) -> int:\\n \\\"\\\"\\\"\\n The total number of training batches during testing, which may change from epoch to epoch.\\n Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the\\n test dataloader is of infinite size.\\n \\\"\\\"\\\"\\n if self.trainer.fast_dev_run:\\n total_test_batches = len(self.trainer.test_dataloaders)\\n else:\\n total_test_batches = self.trainer.num_test_batches\\n return total_test_batches\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.disable\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.disable\\n def disable(self):\\n \\\"\\\"\\\"\\n You should provide a way to disable the progress bar.\\n The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this to disable the\\n output on processes that have a rank different from 0, e.g., in multi-node training.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.enable\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.enable\\n def enable(self):\\n \\\"\\\"\\\"\\n You should provide a way to enable the progress bar.\\n The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this in e.g. pre-training\\n routines like the `learning rate finder <lr_finder.rst>`_ to temporarily enable and\\n disable the main progress bar.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_init_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_init_end\\n def on_init_end(self, trainer):\\n self._trainer = trainer\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_train_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_train_start\\n def on_train_start(self, trainer, pl_module):\\n self._train_batch_idx = trainer.batch_idx\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_epoch_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_epoch_start\\n def on_epoch_start(self, trainer, pl_module):\\n self._train_batch_idx = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_batch_end\\n def on_batch_end(self, trainer, pl_module):\\n self._train_batch_idx += 1\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_start\\n def on_validation_start(self, trainer, pl_module):\\n self._val_batch_idx = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_validation_batch_end\\n def on_validation_batch_end(self, trainer, pl_module):\\n self._val_batch_idx += 1\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_start\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_start\\n def on_test_start(self, trainer, pl_module):\\n self._test_batch_idx = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBarBase.on_test_batch_end\\n def on_test_batch_end(self, trainer, pl_module):\\n self._test_batch_idx += 1\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.__init__\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.__init__\\n def __init__(self, refresh_rate: int = 1, process_position: int = 0):\\n super().__init__()\\n self._refresh_rate = refresh_rate\\n self._process_position = process_position\\n self._enabled = True\\n self.main_progress_bar = None\\n self.val_progress_bar = None\\n self.test_progress_bar = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.__getstate__\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.__getstate__\\n def __getstate__(self):\\n # can't pickle the tqdm objects\\n state = self.__dict__.copy()\\n state['main_progress_bar'] = None\\n state['val_progress_bar'] = None\\n state['test_progress_bar'] = None\\n return state\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.refresh_rate\",\n \"text\": \"pytorch_lightning/callbacks/progress.py::ProgressBar.refresh_rate\\n @property\\n def refresh_rate(self) -> int:\\n return self._refresh_rate\",\n \"label\": 0\n },\n {\n \"cid\": \"